/nasa_avionics_data_ml

Primary LanguageJupyter Notebook

NASA_avionics_data_ML

FYI:

  • The python and notebooks in this project were used as for research towards writing a paper ("A Gradient Descent Multi-Algorithm Grid Search Optimization of Deep Learning for Sensor Fusion") and are not stellar examples of proper software development, but they worked for me.
  • There may also be some files missing since i couldn't upload my entire 18GB folder that I used to perform this research.

Getting started

  • You need to download the NASA matlab files first. There is a powershell script download_687_1_flight_data.ps1 that provides a good example if your on a windows machine.
  • unzip the file/script
  • open the ETL.ipynb notebook to see how to convert the matlab (*.m) files into a metadata JSON file and a time-series flight data parquet file. This should run if everything is named correctly.

Once all the files you want to convert to parquet are done you can explore the data with the following files:

  • Altitude_visualization.ipynb
  • NASA_data_exploration.ipynb

The DTED data that is required for a few of the RNN functions can be downloaded from my NexusDE Google Drive:

Once DTED is downloaded you can visualize it with elevation_visualization.ipynb