Python Library for NASA Earthdata APIs

SciQLop is an ergonomic and efficient application to browse and label in situ plasma measurements from multi-mission satellite data.

Generates 3D graphs of terrain elevation, heatmaps based on the regolith temperature, thermal imaging representations of the surfaces, shortest paths, and top views of the terrain.

Python library to download, stack, and manipulated atmospheric reanalyses.

Generalized data analysis workflow via a consistent easy to use interface.

process lander data file from raw data files

KD-NASA Telegram Bot: Your cosmic companion! Explore NASA's Astronomy Picture of the Day, Mars rover photos, details on nearest asteroids, and experience the sound of a black hole. Embark on a celestial journey with KD-NASA! 🚀🌌

Software pipeline for processing Cassini spacecraft images into wonderful movies.

Python client for ITSLIVE glacier ice velocities.

A python utility library for interacting with NASA JPL's PO.DAAC

Astronomy Picture of the Day (APOD)

Data extracts and analysis of the Music to Wake-up by project from the NASA History Division

Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.

This Python script collects real-time data on the number of people in space and the current location of the International Space Station (ISS). It displays useful information, such as people's names, craft, and the ISS's location, including latitude, longitude, and timestamp, on the console.

rgbmaker for #RADatHomeIndia is a python-based astronomy web app to communicate with different astronomical services to generate images and fetch data from Skyview, Vizier, NVAS, NED etc

Source codes for groundwater pumping prediction using integrated remote sensing datasets and machine learning

Open Notify API client for python

An async python wrapper for NASA open APIs.

Keeping track of code updates for Harvard/GMU Center for Graphical Analysis Project.

Groundwater pumping estimation in Arizona using remote sensing datasets and machine learning