Research

Python Packages

Open-source Python packages I have made and manage. For more information about the projects I am currently involved in, check out my work on Github

centerline-width: A river and hydrology analysis tool. This package determines the centerline and width of rivers based on the latitude and longitude of the right and left bank
PYDAR: A Python package to access, download, view, and manipulate Cassini RADAR images in one place. PYDAR collects relevant flyby observation numbers/IDs for a feature, range of latitude/longitudes (or specific latitude/longitude), or a time range (or specific time) and displays the images
Star-Chart-Spherical-Projection: Generate astronomy star charts that corrects for distortions with stereographic projection. A Python package to generate circular astronomy star charts (past, present, and future) with spherical projection to correct for distortions with all IAU named stars accurate over 400,000 years with proper motion and precession of the equinoxes
iau-star-names Database of all named stars from IAU WGSN (International Astronomical Union – Working Group on Star Names) with data for the common name, right ascension, declination, magnitude, proper motion speed, proper motion angle, proper motion RA/DEC, and alternative names (updated monthly)
iNaturalist Push Notifications 🐛🌿🐌🍄: Receive live phone alerts when a friend posts a new observation to iNaturalist
IFE-Search: Automated identification of events and search for the source of Interplanetary Field Enhancements (IFEs) events. IFEs were first identified in associated with the passage of an asteroid with the Venusian orbit and are believed to be the result of charged dust interactions with the flowing solar wind. The evolution and geoeffectiveness of IFEs is still an area of active research, so fast and objective identification of IFEs at 1 AU is important. This script automatically identifies and generates graphs of potential events based on these criteria.
Hail Hydra – Named Entity Resolution: Characters and ideas in text are represented by names. A casual reader would have no trouble understanding that a passing reference to Mr. Holmes, Mr. Sherlock Holmes, Sherlock Holmes, and Holmes all trace back to the world’s most famous detective. Names are often shortened or rearranged with common abbreviation or elaborate titles. Each version of a character’s name can be understood as a single head on a multi-headed hydra, all tracing back to the same body. Raw text analysis requires more literary context about how English is structured and how words in a sentence interact to generate the most accurate named entities possible.
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