| Name | terratorch-surya JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Implementation of the Surya Foundation Model and Downstream Tasks for Heliophysics |
| upload_time | 2025-11-04 18:45:22 |
| maintainer | Surya Team |
| docs_url | None |
| author | Surya Team |
| requires_python | >=3.11 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. |
| keywords |
deep learning
forecasting
foundation model
heliophysics
pytorch
solar dynamics
solar wind
spectformer
transformer
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
This is an adapted version of the original Surya source code to work with [TerraTorch](https://github.com/IBM/terratorch)
# ☀️ Surya: Foundation Model for Heliophysics ☀️
[](https://huggingface.co/nasa-ibm-ai4science)
[](https://arxiv.org/abs/2508.14112)
[](https://opensource.org/licenses/Apache-2.0)
*The first foundation model for heliophysics trained on full-resolution Solar Dynamics Observatory data*
</div>
<p align="center">
<img src="https://i.imgur.com/waxVImv.png" alt="Oryx Prithvi-EO-2.0">
</p>
## 📖 Overview
**Surya** (Sanskrit for "Sun") is a 366M-parameter foundation model for heliophysics, trained on full-resolution multi-instrument SDO observations (AIA & HMI). It learns general-purpose solar representations through spatiotemporal transformers, enabling state-of-the-art performance in solar flare forecasting, active region segmentation, solar wind prediction, and EUV spectra modeling.
### Key Features
- **Multi-instrument Learning**: Trained on 13 channels from SDO's AIA (8 channels) and HMI (5 channels) instruments
- **Full Resolution**: Native 4096×4096 pixel resolution with 12-minute cadence
- **Novel Architecture**: Spatiotemporal transformer with spectral gating and long-short range attention
- **Zero-shot Capabilities**: Forecasts solar dynamics and flare events without additional training
- **Versatile Fine-tuning**: Parameter-efficient LoRA adaptation for diverse downstream tasks
### What Makes Surya Special?
Unlike traditional task-specific models, Surya learns physics-aware representations that generalize across multiple solar phenomena:
- **Solar Flare Forecasting**
- **Active Region Segmentation**
- **Solar Wind Prediction**
- **EUV Spectra Modeling**
## 🚀 Quick Start
### Prerequisites
- Python 3.11+
- CUDA-capable GPU (recommended)
- [uv package manager](https://docs.astral.sh/uv/) (recommended)
### 🛠️ Installation
1. **Clone the repository**
```bash
git clone https://github.com/NASA-IMPACT/Surya.git
cd Surya
```
2. **Install uv package manager (optional)**
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.bashrc
```
3. **Set up the environment**
```bash
uv sync
source .venv/bin/activate
```
### 🧪 Verify Installation
Run the end-to-end test to ensure everything is working:
```bash
python -m pytest -s -o log_cli=true tests/test_surya.py
```
This will:
- Download the pretrained model and test data
- Generate 2-hour ahead forecasts for 2014-01-07
- Create a validation visualization (`surya_model_validation.png`)
- Verify model inference
Expected output:
```
============================= test session starts ==============================
INFO test_surya:test_surya.py:188 GPU detected. Running the test on device 0.
INFO test_surya:test_surya.py:195 Surya FM: 366.19 M total parameters.
INFO test_surya:test_surya.py:199 Loaded weights.
INFO test_surya:test_surya.py:201 Starting inference run.
INFO test_surya:test_surya.py:215 Completed validation run. Local loss 0.31665.
PASSED [100%]
```
## 🎯 Downstream Applications
To download the Surya model and a sample dataset for downstream tasks, please follow these steps:
```bash
# Step 1: Run pytest to download the model and verify dependencies
python -m pytest -s -o log_cli=true tests/test_surya.py
# Step 2: Navigate to the downstream examples
cd downstream_examples/
# Step 3: Download the sample dataset
python download_data.py
```
### 1. Solar Flare Forecasting
Predict M-class and X-class solar flares up to 24 hours in advance.
```bash
cd downstream_examples/solar_flare_forcasting
python3 download_data.sh
torchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py
```
### 2. Active Region Segmentation
Segment solar active regions and polarity inversion lines from magnetograms.
```bash
cd downstream_examples/ar_segmentation
python3 download_data.sh
torchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py
```
### 3. Solar Wind Forecasting
Predict solar wind speed at L1 point with 4-day lead time.
```bash
cd downstream_examples/solar_wind_forcasting
python3 download_data.sh
torchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py
```
### 4. EUV Spectra Modeling
Model extreme ultraviolet irradiance across 1343 spectral bands (5-35 nm).
```bash
cd downstream_examples/euv_spectra_prediction
python3 download_data.sh
torchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py
```
## 📥 Data and Model Access
### Pretrained Models
The Surya foundation model and datasets are available on HuggingFace 🤗 :
- **Model Repository**: [`nasa-ibm-ai4science/Surya-1.0`](https://huggingface.co/nasa-ibm-ai4science/Surya-1.0)
- **Dataset Repository**: [`nasa-ibm-ai4science/core-sdo`](https://huggingface.co/datasets/nasa-ibm-ai4science/core-sdo)
### SDO Data Download
For downstream applications, download the preprocessed SDO data:
```bash
cd downstream_examples
python download_data.py
```
This will:
1. Download data from HuggingFace repository
2. Extract and organize validation/test datasets
3. Generate CSV index files for each downstream task
4. Set up data in the expected directory structure
## 📊 Model Architecture
Surya employs a novel spatiotemporal transformer architecture optimized for solar dynamics:
### Core Components
1. **Spectral Gating Blocks** (2 layers)
- Frequency-domain filtering with learnable complex weights
- Adaptive re-weighting of spectral components
- Noise suppression and feature enhancement
2. **Long-Short Attention Blocks** (8 layers)
- **Local attention**: Fine-scale dependencies within spatial windows
- **Global attention**: Long-range correlations via dynamic projection
- Multi-scale representation learning
3. **Decoder Block**
- Lightweight projection back to physical domain
- Maintains spatial structure and channel relationships
### Training Strategy
- **Phase 1**: One-step ahead forecasting (160k steps, 128 GPUs)
- **Phase 2**: Autoregressive rollout tuning (2-5 hour horizons)
- **Objective**: Mean Squared Error with signum-log normalization
- **Data**: 2011-2019 SDO observations (~257TB processed)
### Data Processing Pipeline
Our preprocessing ensures ML-ready, physics-consistent data:
- **Temporal alignment**: 12-minute cadence across all instruments
- **Spatial registration**: Uniform 0.6"/pixel grid, solar north alignment
- **Calibration**: Instrument degradation correction, exposure normalization
- **Quality control**: Automated flagging and filtering
## 🏆 Performance Benchmarks
| Task | Metric | Surya | Baseline | Improvement |
|------|---------|-------|----------|-------------|
| Solar Flare Forecasting | TSS | **0.436** | 0.358 (AlexNet) | 22% |
| Active Region Segmentation | IoU | **0.768** | 0.688 (UNet) | 12% |
| Solar Wind Prediction | RMSE | **75.92** | 93.76 (ResNet50) | 19% |
| EUV Spectra Modeling | MAPE | **1.48%** | 1.68% (AlexNet) | 12% |
## 📄 Citation
If you use Surya in your research, please cite our paper:
```bibtex
@article{roy2025surya,
title={Surya: Foundation Model for Heliophysics},
author={Roy, Sujit and Schmude, Johannes and Lal, Rohit and Gaur, Vishal and Freitag, Marcus and Kuehnert, Julian and van Kessel, Theodore and Hegde, Dinesha V and Mu{\~n}oz-Jaramillo, Andr{\'e}s and Jakubik, Johannes and others},
journal={arXiv preprint arXiv:2508.14112},
year={2025}
}
```
## 📜 License
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.
## 🤝 Contributing
We welcome contributions to the Surya repository! Please see our contribution guidelines and feel free to:
- 🐛 Report bugs and issues
- 💡 Suggest new features or applications
- 🔧 Submit pull requests for improvements
- 📖 Improve documentation and examples
Raw data
{
"_id": null,
"home_page": null,
"name": "terratorch-surya",
"maintainer": "Surya Team",
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "deep learning, forecasting, foundation model, heliophysics, pytorch, solar dynamics, solar wind, spectformer, transformer",
"author": "Surya Team",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/94/ee/eab12458248e968d17653b7ddd29255a1243c2c8018689ca1b208633c569/terratorch_surya-0.1.0.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n\nThis is an adapted version of the original Surya source code to work with [TerraTorch](https://github.com/IBM/terratorch)\n# \u2600\ufe0f Surya: Foundation Model for Heliophysics \u2600\ufe0f\n\n[](https://huggingface.co/nasa-ibm-ai4science)\n[](https://arxiv.org/abs/2508.14112)\n[](https://opensource.org/licenses/Apache-2.0)\n \n*The first foundation model for heliophysics trained on full-resolution Solar Dynamics Observatory data*\n\n</div>\n\n<p align=\"center\">\n <img src=\"https://i.imgur.com/waxVImv.png\" alt=\"Oryx Prithvi-EO-2.0\">\n</p>\n\n\n\n## \ud83d\udcd6 Overview\n\n**Surya** (Sanskrit for \"Sun\") is a 366M-parameter foundation model for heliophysics, trained on full-resolution multi-instrument SDO observations (AIA & HMI). It learns general-purpose solar representations through spatiotemporal transformers, enabling state-of-the-art performance in solar flare forecasting, active region segmentation, solar wind prediction, and EUV spectra modeling.\n\n\n### Key Features\n\n- **Multi-instrument Learning**: Trained on 13 channels from SDO's AIA (8 channels) and HMI (5 channels) instruments\n- **Full Resolution**: Native 4096\u00d74096 pixel resolution with 12-minute cadence\n- **Novel Architecture**: Spatiotemporal transformer with spectral gating and long-short range attention\n- **Zero-shot Capabilities**: Forecasts solar dynamics and flare events without additional training\n- **Versatile Fine-tuning**: Parameter-efficient LoRA adaptation for diverse downstream tasks\n\n### What Makes Surya Special?\n\nUnlike traditional task-specific models, Surya learns physics-aware representations that generalize across multiple solar phenomena:\n\n- **Solar Flare Forecasting**\n- **Active Region Segmentation**\n- **Solar Wind Prediction**\n- **EUV Spectra Modeling**\n\n## \ud83d\ude80 Quick Start\n\n### Prerequisites\n\n- Python 3.11+\n- CUDA-capable GPU (recommended)\n- [uv package manager](https://docs.astral.sh/uv/) (recommended)\n\n### \ud83d\udee0\ufe0f Installation\n\n1. **Clone the repository**\n```bash\ngit clone https://github.com/NASA-IMPACT/Surya.git\ncd Surya\n```\n\n2. **Install uv package manager (optional)**\n```bash\ncurl -LsSf https://astral.sh/uv/install.sh | sh\nsource ~/.bashrc\n```\n\n3. **Set up the environment**\n```bash\nuv sync\nsource .venv/bin/activate\n```\n\n### \ud83e\uddea Verify Installation\n\nRun the end-to-end test to ensure everything is working:\n\n```bash\npython -m pytest -s -o log_cli=true tests/test_surya.py\n```\n\nThis will:\n- Download the pretrained model and test data\n- Generate 2-hour ahead forecasts for 2014-01-07\n- Create a validation visualization (`surya_model_validation.png`)\n- Verify model inference\n\nExpected output:\n```\n============================= test session starts ==============================\nINFO test_surya:test_surya.py:188 GPU detected. Running the test on device 0.\nINFO test_surya:test_surya.py:195 Surya FM: 366.19 M total parameters.\nINFO test_surya:test_surya.py:199 Loaded weights.\nINFO test_surya:test_surya.py:201 Starting inference run.\nINFO test_surya:test_surya.py:215 Completed validation run. Local loss 0.31665.\nPASSED [100%]\n```\n\n\n\n## \ud83c\udfaf Downstream Applications\n\nTo download the Surya model and a sample dataset for downstream tasks, please follow these steps:\n\n```bash\n# Step 1: Run pytest to download the model and verify dependencies\npython -m pytest -s -o log_cli=true tests/test_surya.py \n\n# Step 2: Navigate to the downstream examples\ncd downstream_examples/\n\n# Step 3: Download the sample dataset\npython download_data.py\n```\n\n\n### 1. Solar Flare Forecasting\n\nPredict M-class and X-class solar flares up to 24 hours in advance.\n\n```bash\ncd downstream_examples/solar_flare_forcasting\npython3 download_data.sh\ntorchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py\n```\n\n### 2. Active Region Segmentation\n\nSegment solar active regions and polarity inversion lines from magnetograms.\n\n```bash\ncd downstream_examples/ar_segmentation \npython3 download_data.sh\ntorchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py\n```\n\n### 3. Solar Wind Forecasting\n\nPredict solar wind speed at L1 point with 4-day lead time.\n\n```bash\ncd downstream_examples/solar_wind_forcasting\npython3 download_data.sh\ntorchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py\n```\n\n### 4. EUV Spectra Modeling\n\nModel extreme ultraviolet irradiance across 1343 spectral bands (5-35 nm).\n\n```bash\ncd downstream_examples/euv_spectra_prediction\npython3 download_data.sh\ntorchrun --nnodes=1 --nproc_per_node=1 --standalone finetune.py\n```\n\n\n## \ud83d\udce5 Data and Model Access\n\n### Pretrained Models\n\nThe Surya foundation model and datasets are available on HuggingFace \ud83e\udd17 :\n\n- **Model Repository**: [`nasa-ibm-ai4science/Surya-1.0`](https://huggingface.co/nasa-ibm-ai4science/Surya-1.0)\n- **Dataset Repository**: [`nasa-ibm-ai4science/core-sdo`](https://huggingface.co/datasets/nasa-ibm-ai4science/core-sdo)\n\n### SDO Data Download\n\nFor downstream applications, download the preprocessed SDO data:\n\n```bash\ncd downstream_examples\npython download_data.py\n```\n\nThis will:\n1. Download data from HuggingFace repository\n2. Extract and organize validation/test datasets \n3. Generate CSV index files for each downstream task\n4. Set up data in the expected directory structure\n\n\n## \ud83d\udcca Model Architecture\n\nSurya employs a novel spatiotemporal transformer architecture optimized for solar dynamics:\n\n### Core Components\n\n1. **Spectral Gating Blocks** (2 layers)\n - Frequency-domain filtering with learnable complex weights\n - Adaptive re-weighting of spectral components\n - Noise suppression and feature enhancement\n\n2. **Long-Short Attention Blocks** (8 layers) \n - **Local attention**: Fine-scale dependencies within spatial windows\n - **Global attention**: Long-range correlations via dynamic projection\n - Multi-scale representation learning\n\n3. **Decoder Block**\n - Lightweight projection back to physical domain\n - Maintains spatial structure and channel relationships\n\n### Training Strategy\n\n- **Phase 1**: One-step ahead forecasting (160k steps, 128 GPUs)\n- **Phase 2**: Autoregressive rollout tuning (2-5 hour horizons)\n- **Objective**: Mean Squared Error with signum-log normalization\n- **Data**: 2011-2019 SDO observations (~257TB processed)\n\n\n### Data Processing Pipeline\n\nOur preprocessing ensures ML-ready, physics-consistent data:\n\n- **Temporal alignment**: 12-minute cadence across all instruments\n- **Spatial registration**: Uniform 0.6\"/pixel grid, solar north alignment\n- **Calibration**: Instrument degradation correction, exposure normalization\n- **Quality control**: Automated flagging and filtering\n\n## \ud83c\udfc6 Performance Benchmarks\n\n| Task | Metric | Surya | Baseline | Improvement |\n|------|---------|-------|----------|-------------|\n| Solar Flare Forecasting | TSS | **0.436** | 0.358 (AlexNet) | 22% |\n| Active Region Segmentation | IoU | **0.768** | 0.688 (UNet) | 12% | \n| Solar Wind Prediction | RMSE | **75.92** | 93.76 (ResNet50) | 19% |\n| EUV Spectra Modeling | MAPE | **1.48%** | 1.68% (AlexNet) | 12% |\n\n## \ud83d\udcc4 Citation\n\nIf you use Surya in your research, please cite our paper:\n\n```bibtex\n@article{roy2025surya,\n title={Surya: Foundation Model for Heliophysics},\n author={Roy, Sujit and Schmude, Johannes and Lal, Rohit and Gaur, Vishal and Freitag, Marcus and Kuehnert, Julian and van Kessel, Theodore and Hegde, Dinesha V and Mu{\\~n}oz-Jaramillo, Andr{\\'e}s and Jakubik, Johannes and others},\n journal={arXiv preprint arXiv:2508.14112},\n year={2025}\n}\n```\n\n## \ud83d\udcdc License\n\nThis project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions to the Surya repository! Please see our contribution guidelines and feel free to:\n\n- \ud83d\udc1b Report bugs and issues\n- \ud83d\udca1 Suggest new features or applications\n- \ud83d\udd27 Submit pull requests for improvements\n- \ud83d\udcd6 Improve documentation and examples\n",
"bugtrack_url": null,
"license": "Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n \n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n \n 1. Definitions.\n \n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n \n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n \n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n \n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n \n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n \n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n \n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n \n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. For the purposes\n of this License, Derivative Works shall not include works that remain\n separable from, or merely link (or bind by name) to the interfaces of,\n the Work and Derivative Works thereof.\n \n \"Contribution\" shall mean any work of authorship, including\n the original version of the Work and any modifications or additions\n to that Work or Derivative Works thereof, that is intentionally\n submitted to Licensor for inclusion in the Work by the copyright owner\n or by an individual or Legal Entity authorized to submit on behalf of\n the copyright owner. For the purposes of this definition, \"submitted\"\n means any form of electronic, verbal, or written communication sent\n to the Licensor or its representatives, including but not limited to\n communication on electronic mailing lists, source code control systems,\n and issue tracking systems that are managed by, or on behalf of, the\n Licensor for the purpose of discussing and improving the Work, but\n excluding communication that is conspicuously marked or otherwise\n designated in writing by the copyright owner as \"Not a Contribution.\"\n \n \"Contributor\" shall mean Licensor and any individual or Legal Entity\n on behalf of whom a Contribution has been received by Licensor and\n subsequently incorporated within the Work.\n \n 2. Grant of Copyright License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n copyright license to reproduce, prepare Derivative Works of,\n publicly display, publicly perform, sublicense, and distribute the\n Work and such Derivative Works in Source or Object form.\n \n 3. Grant of Patent License. Subject to the terms and conditions of\n this License, each Contributor hereby grants to You a perpetual,\n worldwide, non-exclusive, no-charge, royalty-free, irrevocable\n (except as stated in this section) patent license to make, have made,\n use, offer to sell, sell, import, and otherwise transfer the Work,\n where such license applies only to those patent claims licensable\n by such Contributor that are necessarily infringed by their\n Contribution(s) alone or by combination of their Contribution(s)\n with the Work to which such Contribution(s) was submitted. If You\n institute patent litigation against any entity (including a\n cross-claim or counterclaim in a lawsuit) alleging that the Work\n or a Contribution incorporated within the Work constitutes direct\n or contributory patent infringement, then any patent licenses\n granted to You under this License for that Work shall terminate\n as of the date such litigation is filed.\n \n 4. Redistribution. You may reproduce and distribute copies of the\n Work or Derivative Works thereof in any medium, with or without\n modifications, and in Source or Object form, provided that You\n meet the following conditions:\n \n (a) You must give any other recipients of the Work or\n Derivative Works a copy of this License; and\n \n (b) You must cause any modified files to carry prominent notices\n stating that You changed the files; and\n \n (c) You must retain, in the Source form of any Derivative Works\n that You distribute, all copyright, patent, trademark, and\n attribution notices from the Source form of the Work,\n excluding those notices that do not pertain to any part of\n the Derivative Works; and\n \n (d) If the Work includes a \"NOTICE\" text file as part of its\n distribution, then any Derivative Works that You distribute must\n include a readable copy of the attribution notices contained\n within such NOTICE file, excluding those notices that do not\n pertain to any part of the Derivative Works, in at least one\n of the following places: within a NOTICE text file distributed\n as part of the Derivative Works; within the Source form or\n documentation, if provided along with the Derivative Works; or,\n within a display generated by the Derivative Works, if and\n wherever such third-party notices normally appear. The contents\n of the NOTICE file are for informational purposes only and\n do not modify the License. You may add Your own attribution\n notices within Derivative Works that You distribute, alongside\n or as an addendum to the NOTICE text from the Work, provided\n that such additional attribution notices cannot be construed\n as modifying the License.\n \n You may add Your own copyright statement to Your modifications and\n may provide additional or different license terms and conditions\n for use, reproduction, or distribution of Your modifications, or\n for any such Derivative Works as a whole, provided Your use,\n reproduction, and distribution of the Work otherwise complies with\n the conditions stated in this License.\n \n 5. Submission of Contributions. Unless You explicitly state otherwise,\n any Contribution intentionally submitted for inclusion in the Work\n by You to the Licensor shall be under the terms and conditions of\n this License, without any additional terms or conditions.\n Notwithstanding the above, nothing herein shall supersede or modify\n the terms of any separate license agreement you may have executed\n with Licensor regarding such Contributions.\n \n 6. Trademarks. This License does not grant permission to use the trade\n names, trademarks, service marks, or product names of the Licensor,\n except as required for reasonable and customary use in describing the\n origin of the Work and reproducing the content of the NOTICE file.\n \n 7. Disclaimer of Warranty. Unless required by applicable law or\n agreed to in writing, Licensor provides the Work (and each\n Contributor provides its Contributions) on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied, including, without limitation, any warranties or conditions\n of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\n PARTICULAR PURPOSE. You are solely responsible for determining the\n appropriateness of using or redistributing the Work and assume any\n risks associated with Your exercise of permissions under this License.\n \n 8. Limitation of Liability. In no event and under no legal theory,\n whether in tort (including negligence), contract, or otherwise,\n unless required by applicable law (such as deliberate and grossly\n negligent acts) or agreed to in writing, shall any Contributor be\n liable to You for damages, including any direct, indirect, special,\n incidental, or consequential damages of any character arising as a\n result of this License or out of the use or inability to use the\n Work (including but not limited to damages for loss of goodwill,\n work stoppage, computer failure or malfunction, or any and all\n other commercial damages or losses), even if such Contributor\n has been advised of the possibility of such damages.\n \n 9. Accepting Warranty or Additional Liability. While redistributing\n the Work or Derivative Works thereof, You may choose to offer,\n and charge a fee for, acceptance of support, warranty, indemnity,\n or other liability obligations and/or rights consistent with this\n License. However, in accepting such obligations, You may act only\n on Your own behalf and on Your sole responsibility, not on behalf\n of any other Contributor, and only if You agree to indemnify,\n defend, and hold each Contributor harmless for any liability\n incurred by, or claims asserted against, such Contributor by reason\n of your accepting any such warranty or additional liability.\n \n END OF TERMS AND CONDITIONS\n \n APPENDIX: How to apply the Apache License to your work.\n \n To apply the Apache License to your work, attach the following\n boilerplate notice, with the fields enclosed by brackets \"[]\"\n replaced with your own identifying information. (Don't include\n the brackets!) The text should be enclosed in the appropriate\n comment syntax for the file format. We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n \n Copyright [yyyy] [name of copyright owner]\n \n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n \n http://www.apache.org/licenses/LICENSE-2.0\n \n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.",
"summary": "Implementation of the Surya Foundation Model and Downstream Tasks for Heliophysics",
"version": "0.1.0",
"project_urls": {
"Issues": "https://github.com/NASA-IMPACT/Surya/issues",
"Repository": "https://github.com/NASA-IMPACT/Surya"
},
"split_keywords": [
"deep learning",
" forecasting",
" foundation model",
" heliophysics",
" pytorch",
" solar dynamics",
" solar wind",
" spectformer",
" transformer"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "0f6dcfb204cc2ab969323df5ef8f7221c37a173c900443888550bc34fd9b0b19",
"md5": "c167da5ac7ef4f0713ef4e6da0da816a",
"sha256": "07df8f4ca629a8d3a631ded5f795ef2765a6a8669af5b3830bba7b8df7c51c6f"
},
"downloads": -1,
"filename": "terratorch_surya-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "c167da5ac7ef4f0713ef4e6da0da816a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.11",
"size": 154678,
"upload_time": "2025-11-04T18:45:12",
"upload_time_iso_8601": "2025-11-04T18:45:12.071953Z",
"url": "https://files.pythonhosted.org/packages/0f/6d/cfb204cc2ab969323df5ef8f7221c37a173c900443888550bc34fd9b0b19/terratorch_surya-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "94eeeab12458248e968d17653b7ddd29255a1243c2c8018689ca1b208633c569",
"md5": "c5ff16ec5dc86958a014315ee902be32",
"sha256": "83726ef049a9dc704f2968c3ced3d01acb8f53825719a37a60537f51fd7f9959"
},
"downloads": -1,
"filename": "terratorch_surya-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "c5ff16ec5dc86958a014315ee902be32",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.11",
"size": 6381068,
"upload_time": "2025-11-04T18:45:22",
"upload_time_iso_8601": "2025-11-04T18:45:22.041935Z",
"url": "https://files.pythonhosted.org/packages/94/ee/eab12458248e968d17653b7ddd29255a1243c2c8018689ca1b208633c569/terratorch_surya-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-11-04 18:45:22",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "NASA-IMPACT",
"github_project": "Surya",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "terratorch-surya"
}
