Metadata-Version: 2.1
Name: mag-net
Version: 0.0.3
Summary: MagNet is a large-scale dataset designed to enable researchers modeling magnetic core loss using machine learning to accelerate the design process of power electronics.
Home-page: https://github.com/seungjaeryanlee/MagNet
Author: Seungjae Ryan Lee
Author-email: seungjaeryanlee@gmail.com
License: UNKNOWN
Description: ![MagNet Logo](images/magnet_logo.jpg)
        
        ![PyPI](https://img.shields.io/pypi/v/mag-net?color=blue)
        ![wandb](https://img.shields.io/badge/wandb-metric-yellow)
        
        MagNet is a large-scale dataset designed to enable researchers modeling magnetic core loss using machine learning to accelerate the design process of power electronics. The dataset contains a large amount of voltage and current data of different magnetic components with different shapes of waveforms and different properties measured in the real world. Researchers may use these data as pairs of excitations and responses to build up dynamic magnetic models or calculate the core loss to derive static models.
        
        
        
        ## Installation
        
        The trained models are provided via the `mag-net` PyPI package.
        
        ```
        pip install mag-net
        ```
        
        
        
        ## How to Use
        
        ### Use Trained Model
        
        The `mag-net` package has **PyTorch** pretrained models that you can load and use.
        
        ```python
        import magnet
        
        magnet.models.pytorch.MiniLSTM(pretrained=True)
        ```
        
        If you want to train the model yourself and want the model without the pretrained weights, you can set `pretrained=False`.
        
        We will publish more trained models soon. Please look forward to it!
        
        ### Use Dataset
        
        The `mag-net` package supports **PyTorch** natively by providing a PyTorch dataset. You can get the dataset the following way:
        
        ```python
        import magnet
        
        dataset = magnet.PyTorchDataset(download_path="data/", download=True)
        ```
        
        With `download=True`, the data will automatically be downloaded if it does not exist yet locally. 
        
        We also support **TensorFlow** natively by providing a `tf.data` style dataset. You can get the dataset the following way:
        
        ```python
        import magnet
        
        dataset = magnet.TensorFlowDataset(download_path="data/", download=True)
        ```
        
        With `download=True`, the data will automatically be downloaded if it does not exist yet locally. 
        
        For other use cases, you must download the dataset manually. The following code will download the dataset to `data/` directory.
        
        ```python
        import magnet
        
        magnet.download_dataset(download_path="data/")
        ```
        
        
        ## How to Cite
        
        If you used MagNet, please cite us with the following BibTeX item.
        
        <!-- TODO: Update once COMPEL 2020 happens -->
        
        ```
        @INPROCEEDINGS{MagNet,
          author={H. {Li} and S. {Lee} and M. {Luo} and C. {Sullivan} and Y. {Chen} and M. {Chen}},
          booktitle={2020 Twenty-first IEEE Workshop on Control and Modeling for Power Electronics (COMPEL)}, 
          title={MagNet: A Machine Learning Framework for Magnetic Core Loss Modeling}, 
          year={2020}
        }
        ```
        
        ## Sponsors
        
        This work is sponsored by the ARPA-E DIFFERENTIATE Program.
        
        <img src="images/arpae.jpg" width=300>
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
