/magnet-notebooks

Primary LanguageJupyter Notebook

README

README for collaborative work between L. Stephey, M. Marchevsky, and M. Mustafa at Lawrence Berkeley National Lab, 2019-2020.

The goal of this effort is to use unsupervised learning techniques to better understand acoustic events during the superconducting magnet training process.

This repo contains:

sample-data

About 20 sample spectrograms in .csv format. Kindly provided by M. Marchevsky.

k-means

Some exploratory work into k-means clustering with data contained in summary files (not the raw spectrograms themselves). Summary files kindly provided by M. Marchevsky.

conv2d-autoencoder

Jupyter notebooks designed to build and train a 2D convolutional autoencoder to learn features in our unlabeled spectrograms. Several scripts to prepare the data, several scripts to build and train the network, and several scripts to plot and analyze the encoded data produced by the trained network.

To reproduce the workflow presented in a poster at the 2020 ai4science workshop, you can use the notebooks in the following order:

process_march_data.ipynb

data_prep.ipynb

process_post_quench_data.ipynb

post_quench_data_prep.ipynb

build_conv2d_autoencoder.ipynb

train_conv2d_autoencoder.ipynb

save_conv2d_autoencoder.ipynb

plot_conv2d_pca.ipynb

plot_conv2d_spectrograms.ipynb

Disclaimer

These scripts and notebooks will not work "out of the box" since filepaths are hardcoded for NERSC's corigpu system and file locations may change, but hopefully the general ideas and techniques we used are clear and may be helpful to others.