https://github.com/tcapelle/eclipse_pytorch

My take on ECLIPSE solar nowcasting DL paper
https://github.com/tcapelle/eclipse_pytorch

Last synced: about 1 month ago
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My take on ECLIPSE solar nowcasting DL paper

• Host: GitHub
• URL: https://github.com/tcapelle/eclipse_pytorch
• Owner: tcapelle
• License: apache-2.0
• Created: 2021-10-11T14:19:01.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2021-10-29T22:20:56.000Z (about 3 years ago)
• Last Synced: 2024-10-03T06:39:44.263Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 190 KB
• Stars: 11
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# Eclipse

> Implementing Paletta et al in Pytorch

Most of the codebase comes from [Fiery](https://github.com/wayveai/fiery)

![Image](nbs/images/eclipse_diagram.png)

## Install

```bash

pip install eclipse_pytorch

```

## How to use

```python

import torch

from eclipse_pytorch.model import Eclipse

```

```python

eclipse = Eclipse(horizon=5)

```

let's simulte some input images:

```python

images = [torch.rand(2, 3, 128, 128) for _ in range(4)]

```

```python

preds = eclipse(images)

```

you get a dict with forecasted masks and irradiances:

```python

len(preds['masks']), preds['masks'][0].shape, preds['irradiances'].shape

```

    (6, torch.Size([2, 4, 128, 128]), torch.Size([2, 6]))

## Citation

```latex

@article{paletta2021eclipse,

  title     = {{ECLIPSE} : Envisioning Cloud Induced Perturbations in Solar Energy},

  author    = {Quentin Paletta and Anthony Hu and Guillaume Arbod and Joan Lasenby},

  year      = {2021},

  eprinttype = {arXiv},

  eprint    = {2104.12419}

}

```

## Contribute

This repo is made with [nbdev](https://github.com/fastai/nbdev), please read the documentation to contribute