A practitioner's utility for working with SDO data.
The results of the Master's thesis accompanying this source code can be found in ./docs/MG_Masters_Thesis_final.pdf
pip install -U sdo-cli
A small helper toolkit for downloading and working with SDO data complementing sunpy by giving illustrative examples how to solve tasks. The data is loaded from the Image Parameter dataset which is the result of [1].
TLDR;
How to use sdo-cli:
Usage: sdo-cli [OPTIONS] COMMAND [ARGS]...
CLI to manipulate and model SDO data.
Options:
--home DIRECTORY Changes the folder to operate on.
-v, --verbose Enables verbose mode.
--help Show this message and exit.
Commands:
data
events
goes
sood
Helpers for interacting with the Curated Image Parameter Dataset.
Usage: sdo-cli data [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
download Loads a set of SDO images between start and end from the Georgia
State University Data Lab API
patch Generates patches from a set of images
resize Generates a set of resized images
Helpers for downloading HEK events.
Usage: sdo-cli events [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
analyze Analyzes model outputs and compares it with events from HEK
get Loads events from HEK
list Lists local events from HEK
Helpers for downloading and interacting with GOES data.
Usage: sdo-cli goes [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
download Loads a the GOES X-Ray flux timeseries for a date range and stores
it partitioned by year and month in a CSV
get Gets a the GOES flux at a point in time
Helpers for training and using solar out-of-distribution models.
Usage: sdo-cli sood [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
ae
ce_vae
threshold
Helper for training and interacting with the Context-Encoding Variational Autoencoder inspired by Zimmerer et al. [2].
Usage: sdo-cli sood ce_vae [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
generate Generate a set of images with the CE-VAE model (requires a
pretrained model)
predict Predicts anomaly scores using a CE-VAE model (requires a
pretrained model)
reconstruct Reconstructs input images (requires a pretrained model)
train Trains a CE-VAE model
sdo-cli data download --path='./data/aia_171_2012' --start='2012-03-07T00:02:00' --end='2012-03-07T00:40:00' --freq='6min' --wavelength='171'
sdo-cli data resize --path='./data/aia_171_2012' --targetpath='./data/aia_171_2012_256' --wavelength='171' --size=256
sdo-cli data patch --path='./data/aia_171_2012_256' --targetpath='./data/aia_171_2012_256_patches' --wavelength='171' --size=32
pip install psycopg2-binary
docker-compose up
sdo-cli events get --start="2012-01-01T00:00:00" --end="2012-01-02T23:59:59" --event-type="AR"
# Please install either pyarrow or fastparquet
pip install pyarrow
sdo-cli goes download --start=2010-01-01T00:00:00 --end=2020-12-31T23:59:59 --output=./tmp/goes
Requires downloading the time series with sdo-cli goes download beforehand.
sdo-cli goes get --timestamp=2015-06-01T02:20:00 --cache-dir=./tmp/goes
The sood command implements a Solar Out-of-Distribution model based on the Context-encoding Variational Autoencoder (ceVAE) by Zimmerer et al. [2]. The model makes use of the model-internal latent representation deviations to end up with a more expressive reconstruction error and allows anomaly detection on both an image as well as a pixel-level.
A full Anomaly Detection pipeline can be examined in the example notebook notebooks/ce-vae__e2e-pipeline.ipynb. For this start jupyter:
make notebook
For training a model, make sure to download an appropriate dataset (either the Curated Image Parameter Dataset, the SDO ML v1 dataset or the SDO ML v2 dataset). It is encouraged to use the SDO ML v2 dataset as the code was last tested with this data. To configure hyperparameters, create an appropriate config file by either copying an existing file in ./config/ce-vae/ or creating a new one. Config options from a specific file will override the config options from a defaults.yaml file in the same directory. nohup is used for non-blocking long-running tasks. Check the nohup.out file for logs.
nohup sdo-cli sood ce_vae train --config-file="./config/ce-vae/run-fhnw-full-2-256.yaml" &
tail -f nohup.out
Running the predict command requires a pretrained model.
For sample-level scores, run:
nohup sdo-cli sood ce_vae predict --config-file="./config/ce-vae/run-fhnw-full-2-256-predict.yaml" --precit-mode="sample" &
For pixel-level scores, run:
sdo-cli sood ce_vae predict --config-file="./config/ce-vae/run-fhnw-full-2-256-predict.yaml" --precit-mode="pixel"
Running the generate command requires a pretrained model.
sdo-cli sood ce_vae generate --config-file="./config/ce-vae/run-fhnw-full-2-256-predict.yaml"
Setup Virtual Environment and install sdo-cli.
git clone [email protected]:i4Ds/sdo-cli.git
cd sdo-cli
make setup
make install
Add your pypi credentials to ~/.pypirc, increase the version number in setup.py and run:
make publish
- [1] Ahmadzadeh, Azim, Dustin J. Kempton, and Rafal A. Angryk. "A Curated Image Parameter Data Set from the Solar Dynamics Observatory Mission." The Astrophysical Journal Supplement Series 243.1 (2019): 18.
- [2] Zimmerer, David, et al. "Context-encoding variational autoencoder for unsupervised anomaly detection." arXiv preprint arXiv:1812.05941 (2018). Also refer to the Medical Out-of-Distribution Analysis Challenge Repository.