semvec/pypi-packages huggingface dataset:
SyntheticDataAlchemy 0.1.0
A package for generating synthetic data
Requires Python: >=3.6

# SyntheticDataAlchemy

SyntheticDataAlchemy is a Python package for generating synthetic data that preserves the statistical properties and relationships of the original dataset.

## Installation

You can install SyntheticDataAlchemy using pip:

```
pip install SyntheticDataAlchemy
```

## Usage

Here's a simple example of how to use SyntheticDataAlchemy:

```python
import pandas as pd
from SyntheticDataAlchemy import generate_synthetic_data

# Load your original data
original_df = pd.read_csv('your_data.csv')

# Generate synthetic data
synthetic_df = generate_synthetic_data(original_df, n_samples=1000)

# Save the synthetic data
synthetic_df.to_csv('synthetic_data.csv', index=False)
```

## Features

- Generates synthetic data for both numerical and categorical features
- Preserves statistical properties of the original data
- Maintains relationships between features
- Handles missing data

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License.
jnp3 0.5.2
一个 Python 语言的辅助工具集
Requires Python: >=3.10

# jnp3

一个 Python 语言的辅助工具集，用于记录个人感觉比较有用的辅助函数或者类。

> 名称中的 3 是指 python3，同时也是为了避免和已存在的包冲突。

## 安装

```sh
pip install jnp3
```

### 安装 pyside6 工具集

```shell
pip install jnp3[gui6]
```

### 安装 pyside2 工具集

pyside2 主要是为了兼容 MacOS 10.13

```shell
pip install jnp3[gui2]
```
mkite-catalysis 24.9.5
plugin to deal with surface- and catalysis-related operations in mkite
Requires Python: >=3.8
Keywords: workflow, materials-science

# mkite_catalysis

`mkite_catalysis` is a plugin to run catalysis-related recipes with mkite. 
As of now, the plugin has the following recipes implemented: surface cutting, supercell generation, and combinatorial adsorption, implemented with the help of [pymatgen](https://github.com/materialsproject/pymatgen).

## Documentation

General tutorial for `mkite` and its plugins are available in the [main documentation](https://mkite.org).
Complete API documentation is pending.

## Installation

To install `mkite_catalysis`, use pip:

```bash
pip install mkite_catalysis
```

Alternatively, for a development version, clone this repo and install it in editable form:

```bash
pip install -U git+https://github.com/mkite-group/mkite_catalysis
```

## Contributions

Contributions to the entire mkite suite are welcomed.
You can send a pull request or open an issue for this plugin or either of the packages in mkite.
When doing so, please adhere to the [Code of Conduct](CODE_OF_CONDUCT.md) in the mkite suite.

The mkite package was created by Daniel Schwalbe-Koda <dskoda@ucla.edu>.

### Citing mkite

If you use mkite in a publication, please cite the following paper:

```bibtex
@article{mkite2023,
    title = {mkite: A distributed computing platform for high-throughput materials simulations},
    author = {Schwalbe-Koda, Daniel},
    year = {2023},
    journal = {arXiv:2301.08841},
    doi = {10.48550/arXiv.2301.08841},
    url = {https://doi.org/10.48550/arXiv.2301.08841},
    arxiv={2301.08841},
}
```

## License

The mkite suite is distributed under the following license: Apache 2.0 WITH LLVM exception.

All new contributions must be made under this license.

SPDX: Apache-2.0, LLVM-exception

LLNL-CODE-848161