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jiwoncpark / aracle

Predictive model of solar magnetic flux emergence using deep learning
MIT License
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hmi instance-segmentation object-detection spaceweather

readme

====== aracle

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Prediction of solar magnetic flux emergence using deep neural nets. "Active region (AR) oracle"

Installation

  1. Virtual environments are strongly recommended, to prevent dependencies with conflicting versions. Create a conda virtual environment and activate it:

::

$conda create -n aracle python=3.6 -y $conda activate aracle

  1. Now do one of the following.

Option 2(a): clone the repo (please do this if you'd like to contribute to the development).

::

$git clone https://github.com/jiwoncpark/aracle.git $cd aracle $pip install -e . -r requirements.txt

Option 2(b): pip install the release version (only recommended if you do not plan to contribute to the development).

::

$pip install aracle

  1. (Optional) To run the notebooks, add the Jupyter kernel.

::

$python -m ipykernel install --user --name aracle --display-name "Python (aracle)"

How to train

  1. Generate the training toy data, e.g.

::

$python -m aracle.toy_data.generate_toy_data 5 224 ./my_data

  1. Run

::

$python -m aracle.train_faster_rcnn

You can visualize the training results by running

::

$tensorboard --logdir runs

Feedback and More

Suggestions are always welcome! If you encounter issues or areas for improvement, please message @jiwoncpark or make an issue <https://github.com/jiwoncpark/aracle/issues>_.