semvec/pypi-packages huggingface dataset:
aurora-torch 0.0.9
Weather Swarm - Pytorch
License: MIT
Requires Python: <4.0,>=3.10
Keywords: artificial intelligence, deep learning, optimizers, Prompt Engineering

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# Aurora
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![Aurora](aurora.png)

Community and Open Source Implementation of the paper: "Aurora: A Foundation Model of the Atmosphere" in PyTorch: [Paper link](https://arxiv.org/abs/2405.13063)


## Install
```bash
pip3 install aurora-torch
```


## Example
```python
import torch
from aurora_torch.main import SwinTransformerUNet3D
from loguru import logger

# Test with random input tensor of shape (B, D, H, W, C)
B, D, H, W, C = 2, 16, 64, 64, 32
model = SwinTransformerUNet3D(input_dim=C, output_dim=C)
input_tensor = torch.rand(B, D, H, W, C)

# Forward pass through the model
output = model(input_tensor)
logger.info(f"Output shape: {output.shape}")

```


# License
MIT


#  Bibtex
```bibtex
@misc{bodnar2024aurora,
    title={Aurora: A Foundation Model of the Atmosphere}, 
    author={Cristian Bodnar and Wessel P. Bruinsma and Ana Lucic and Megan Stanley and Johannes Brandstetter and Patrick Garvan and Maik Riechert and Jonathan Weyn and Haiyu Dong and Anna Vaughan and Jayesh K. Gupta and Kit Tambiratnam and Alex Archibald and Elizabeth Heider and Max Welling and Richard E. Turner and Paris Perdikaris},
    year={2024},
    eprint={2405.13063},
    archivePrefix={arXiv},
    primaryClass={physics.ao-ph}
}
```

# References

- [Blog Release: Introducing Aurora: The first large-scale foundation model of the atmosphere](https://www.microsoft.com/en-us/research/blog/introducing-aurora-the-first-large-scale-foundation-model-of-the-atmosphere/)

- [Paper Link](https://arxiv.org/abs/2405.13063)

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otus-open-lesson 0.4.0
Python package code and GitHub actions examples for OTUS open lesson for Team-Lead learning course
License: MIT
Requires Python: >=3
Keywords: python, audio, package, text

# team-lead-ci-cd-open-lesson
Python package code and GitHub actions examples for OTUS open lesson for Team-Lead learning course

## In debian based linux

```commandline
sudo apt update
sudo apt install boxes
sudo apt install ffmpeg
```

## Install package

```commandline
pip install otus-open-lesson
```

## Usage

```python
from say_something import show_text, say


while True:
    text = input()
    if text:
        show_text(text)
        say(text)
    else:
        break
```
async-flow-control 0.1.1
Throttle tasks to spread execution across time and implement flow control, in an asyncio environment.
License: MIT
Keywords: throttle, throttling, rate control, asyncio, rate-limit, concurrency-limit


Throttle tasks to spread execution across time and implement flow control, in an asyncio environment.

This package provides Python classes that allow to control the execution of
coroutines across time, limiting by different criteria (e.g. task rate or concurrent
execution).

See package documentation at the [GitHub repository](https://github.com/paulovn/async-flow-control)