Ceres

Inspiration

Inspired by predictions-generating software like stock tickers, Ceres set out with a goal of exposing only relevant, aesthetically pleasing material to the user.

What it does

Ceres sanitizes and processes .csv files which contain information about the drilling of asteroids, attempting to isolate the most efficient bits for a given task, at a certain depth. Efficiency can be evaluated across multiple axes.

How we built it

Ceres is currently just a python library which sanitizes and interprets .csv files. A frontend portion of the application was planned which would pull information pushed from the python library to a webserver and render graphs with it.

Challenges we ran into

Most of our group was not familiar with developing apps of any sort, so beginning to grasp how electron and javascript worked was a big hurdle for us. On the more specific end of things, working with csv files in Python is easy initially but becomes a hassle due to the inability to iterate backwards or read things out of order or in advance, which necessitated a full refactor of our sanitation approach ones the issues became clear.

Accomplishments that we're proud of

We're quite happy with how the sanitation algorithm turned out, as it effectively patches over the large gaps in data left in the given datasets. By making these datasets fully usable Ceres is, at the very least, able to effectively complete a major component of the asteroid data analysis pipeline on its own.

What we learned

Though no app was developed with it, we learned a lot more about electron apps and web development / javascript applications. We also got good experience learning how to more effectively delegate responsibility amongst a team. We also improved our familiarity with working with n-dimensional arrays in numpy.

What's next for Ceres

With the sanitation algorithm functional and the Run object defined and functional, the next step for Ceres is pushing the already interpreted data to a database, and then pulling that data from the database using a web or desktop app, which then is able to present the data in a visually appealing manner using existing graphing libraries.