semvec/pypi-packages huggingface dataset:
pywst 1.0.1
WST and RWST analyses tools for astrophysical data.
Requires Python: >=3.6

# PyWST: WST and RWST for Astrophysics

**PyWST** is a public Python package designed for performing statistical analyses of two-dimensional data using the **Wavelet Scattering Transform (WST)** and the **Reduced Wavelet Scattering Transform (RWST)**.

The WST/RWST provides a comprehensive set of coefficients that efficiently describe the non-Gaussian features of your data.

Install PyWST and explore our [Jupyter notebook tutorial](examples/tutorial.ipynb) available in the [examples/](examples/) folder.

If you use this package, please cite the following paper:

B. Regaldo-Saint Blancard, F. Levrier, E. Allys, E. Bellomi, F. Boulanger, "Statistical description of dust polarized emission from the diffuse interstellar medium - A RWST approach", [*Astronomy \& Astrophysics*, 642, A217](https://doi.org/10.1051/0004-6361/202038044) (2020). ArXiv: [2007.08242](https://arxiv.org/abs/2007.08242)

*Note:* For GPU-accelerated WST computations, consider using [kymatio](https://github.com/kymatio/kymatio) (which served as an initial inspiration for parts of this code).

## Install/Uninstall

### Standard installation (from PyPI)

Run in a terminal:

```bash
pip install pywst
```

### Install from source

Clone the repository and run the following command from the main directory:

```bash
pip install .
```

### Uninstall

```bash
pip uninstall pywst
```

## Changelog

### 1.0

* Minor updates.

### 0.9

* First public version.
fennel-glue-ai 1.5.68
The modern realtime feature engineering platform
Requires Python: <4.0,>=3.9

# Welcome to Fennel

## The modern realtime feature engineering platform

---

Fennel is a modern realtime feature engineering platform written in Rust and this repo contains the Python client to talk to the server. 

Quick Links:
- [Documentation](https://docs.fennel.ai/)
- [Fennel Website](https://fennel.ai/)
- [Main Concepts Behind Fennel](https://docs.fennel.ai/concepts/introduction/)
- [Quickstart Tutorial](https://docs.fennel.ai/getting-started/quickstart/)
slgps 2.0.7
NN assisted chemistry reduction of mechanism based on Global Pathway Selection algorithm
Requires Python: >=3.8

# Simple steps to get started

1. Install basic libraries - `pip install matplotlib cantera tensorflow`
2. Install the SL-GPS library - `pip install slgps`
3. Test your installation by running any of the files in the [tests folder](tests/)
4. The trained neural network is stored as .h5 file which can be accessed and utilized to produced reduced mechanisms for any given composition, temperature and pressure.

For questions and discussions please join : https://discord.com/channels/1333609076726431798/1333610748424880128
Please feel free to ask any questions related to SL-GPS there. 

# SL-GPS
This repository contains the means to create a neural network architecture for dynamic chemistry reduction based on reduction results from Global Pathway Selection. The basic procedure is to first run adaptive GPS for 0D auto-ignition simulation so as to create a dataset. This dataset is later used for training the Artificial Neural Network (ANN). You can reach out to us at rmishra@tamu.edu (Rohit Mishra) or aaronnelson@tamu.edu (Aaron Nelson) for code issues, suggestions and/or pull requests. The step-by-step procedure to use this repository is as follows:

## About
This code was developed entirely in Python 3. Dependent packages include Cantera 2, Tensorflow 2, pandas, sklearn, numpy, pickle, and networkx. Code for GPS has been copied from https://github.com/golsun/GPS and modified to work in Python 3. 
## How to Cite
- Mishra, R., Nelson, A., Jarrahbashi, D., "Adaptive global pathway selection using artificial neural networks: A-priori study", **Combustion and Flame**, 244 (2022) 112279 [[link](https://doi.org/10.1016/j.combustflame.2022.112279)]
## Related Publications
- X. Gao, S. Yang, W. Sun, "A global pathway selection algorithm for the reduction of detailed chemical kinetic mechanisms", **Combustion and Flame**, 167 (2016) 238-247 [[link](https://doi.org/10.1016/j.combustflame.2016.02.007)]