spacekit documentation¶
This is the documentation for spacekit,
the Astronomical Data Science and Machine Learning Toolkit
Overview¶
Spacekit is a python library designed to do the heavy lifting of machine learning in astronomy-related applications.
The modules contained in this package can be used to assist and streamline each step of a typical data science project:
Ingest/Extract import large datasets from a variety of file formats .csv, .hdf5, .fits, .json, .png (.asdf coming soon)
Scrub/Preprocess scrub and preprocess raw data to prepare it for use in a machine learning model
Modeling build, train and deploy custom machine learning models using classification, logistic regression estimation, computer vision and more
Analysis evaluate model performance and do exploratory data analysis (EDA) using interactive graphs and visualizations
Visualize deploy a web-based custom dashboard for your models and datasets via docker, a great way to summarize and share comparative model evaluations and data analysis visuals with others
Applications¶
The Skøpes module includes real-world machine learning applications used by the Hubble and James Webb Space Telescopes in data calibration pipelines. These mini-applications are an orchestration of functions and classes from other spacekit modules to run automated analysis, training, and inference in real-time on a local server or in the cloud (AWS).