semvec/pypi-packages huggingface dataset:
plot-plasmids 0.2.1
A tool to generate PCoA or NMDS plots for plasmids.

# plot_plasmids

[![PyPI version](https://badge.fury.io/py/plot-plasmids.svg)](https://badge.fury.io/py/plot-plasmids)
[![codecov](https://codecov.io/gh/jules-corp/plot_plasmids/branch/main/graph/badge.svg?token=placeholder)](https://codecov.io/gh/jules-corp/plot_plasmids)

A tool to generate PCoA or NMDS plots for plasmids, colored by carbapenemase genes and rep types.

## Installation

You can install `plot_plasmids` from PyPI:

```bash
pip install plot-plasmids
```

Or, for development, you can install it from this repository:

```bash
git clone https://github.com/example/plot_plasmids.git
cd plot_plasmids
pip install -e .
```

## Usage

`plot_plasmids` has two subcommands: `prep` and `plot`.

### `prep`

The `prep` command takes the raw output from `skani`, `mob_typer`, and `amrfinderplus` and prepares the files for the `plot` command.

```bash
plot_plasmids prep --skani <skani_matrix> --mob_dir <mob_dir> --amr_dir <amr_dir> --output_dir <out_dir>
```

#### Arguments

- `--skani`: Path to the `skani triangle` output file.
- `--mob_dir`: Directory containing the `mob_typer` output files.
- `--amr_dir`: Directory containing the `amrfinderplus` output files.
- `--output_dir`: Directory to save the processed files.

### `plot`

The `plot` command generates the PCoA or NMDS plot.

```bash
plot_plasmids plot -d <distance_matrix> -a <amr_results> -m <mob_results> -o <plot.png>
```

![Example plot](tests/expected_output/expected_plot_nmds.png)

#### Arguments

- `-d`, `--dist_matrix`: Path to the plasmid distance matrix (CSV or TSV format), as generated by the `prep` command.
- `-a`, `--amr`: Path to the AMRfinderPlus summary results file (TSV format), as generated by the `prep` command.
- `-m`, `--mob`: Path to the MOB-typer results file (TSV format), as generated by the `prep` command.
- `-o`, `--output`: Path for the output plot file (e.g., plot.png, plot.svg).
- `-p`, `--plot_type`: Type of ordination plot to generate (`pcoa` or `nmds`). Default is `pcoa`.
hotopic 0.1.0
A package for analyzing hot topics in text data
Requires Python: >=3.7

# Hot Topic Analyzer

Hot Topic Analyzer 是一个用于分析文本数据中热门话题的Python包。它使用先进的自然语言处理技术来识别和总结大量文本中的主要主题。

## 安装

你可以使用pip安装Hot Topic Analyzer：

```
pip install hot-topic-analyzer
```

## 使用方法

以下是一个基本的使用示例：

```python
from hotopic.hotopic import HoTopic
from hotopic.utils import Config

# 准备输入数据
input_data = [
    {"content_id": "1", "content": "这是第一篇关于黄金价格的文章..."},
    {"content_id": "2", "content": "这是第二篇关于黄金市场的文章..."},
    # ... 更多文章
]

# 创建配置
config = Config(
    min_content_length=10,
    max_content_length=1000,
    similarity_threshold=0.5,
    top_topics_count=10,
    max_keywords_per_topic=15,
    representative_docs_count=3,
    doc_preview_length=200,
    quality_weights={
        "coherence": 0.6,
        "distinctiveness": 0.2,
        "size_ratio": 0.2
    },
    seed=42,
    output_dir="../output"
)

# 初始化HotTopic
hot_topic = HoTopic(**config.__dict__)

# 运行热门话题分析
topic_metadata, topic_contents = hot_topic.run(input_data)

# 打印结果
for topic in topic_metadata:
    print(f"话题ID: {topic['topic_id']}")
    print(f"话题标题: {topic['topic_title']}")
    print(f"关键词: {', '.join(topic['topic_keywords'])}")
    print(f"文档数量: {topic['count']}")
    print(f"质量分数: {topic['quality_score']}")
    print("代表性文档:")
    for doc in topic['representative_docs']:
        print(f"  - {doc}")
    print("\n")
```

## 配置参数

你可以通过修改Config对象来自定义以下参数：

- min_content_length: 最小内容长度
- max_content_length: 最大内容长度
- similarity_threshold: 相似度阈值
- top_topics_count: 返回的热门话题数量
- max_keywords_per_topic: 每个话题的最大关键词数量
- representative_docs_count: 每个话题的代表性文档数量
- doc_preview_length: 文档预览长度
- quality_weights: 话题质量评分权重
- seed: 随机种子
- output_dir: 输出目录

## 许可证

本项目采用 MIT 许可证。详情请参见 [LICENSE](LICENSE) 文件。

## 贡献

欢迎贡献！请阅读 [CONTRIBUTING.md](CONTRIBUTING.md) 了解如何为这个项目做出贡献。

## 联系方式

如有任何问题或建议，请联系 [your.email@example.com](mailto:your.email@example.com)。
space-raccoon 1.0.0
Raccoon cleans modulation created by undersampling the PSF in the JWST/NIRSpec spectra.
Requires Python: >=3.10
Keywords: dolphin, lenstronomy

==========
🦝 raccoon
==========

|Read the Docs| |GitHub| |Codecov| |Black| |docformatter| |docstyle|


Raccoon cleans the "wiggles" (i.e., low-frequency sinusoidal artifacts) in the JWST-NIRSpec IFS (integral field spectroscopy) data. These wiggles are caused by resampling noise or aliasing artifacts. For a quick start or demonstration, see the `example notebook here`_.

.. _`example notebook here`: https://github.com/ajshajib/raccoon/blob/main/example/example_notebook.ipynb


Installation
------------

.. image:: https://img.shields.io/pypi/v/space-raccoon.svg
   :alt: PyPI - Version
   :target: https://pypi.org/project/space-raccoon/


You can install ``raccoon`` using ``pip``. Run the following command:

.. code-block:: bash

    pip install space-raccoon

Alternatively, you can install the latest development version from GitHub as:

.. code-block:: bash

    git clone https://github.com/ajshajib/raccoon.git
    cd raccoon
    pip install .

Features
--------

- **Wiggle cleaning**: The primary feature of the package is to clean wiggles from JWST-NIRSpec IFS data.
- **Visualization**: It provides tools to visualize the data before and after wiggle cleaning.
- **Documentation**: Comprehensive documentation is available to help users understand how to use the package effectively.
- |codecov| **tested**: The package includes unit tests to ensure the functionality works as expected.
- **User-Friendly**: The package aims to provide a user-friendly experience, with intuitive interfaces and clear documentation.

.. |Read the Docs| image:: https://readthedocs.org/projects/raccoon-docs/badge/?version=latest
    :target: https://raccoon-docs.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

.. |GitHub| image:: https://github.com/ajshajib/raccoon/actions/workflows/ci.yaml/badge.svg?branch=main
    :target: https://github.com/ajshajib/dolphin/actions/workflows/ci.yaml
    :alt: Build Status

.. |Codecov| image:: https://codecov.io/github/ajshajib/raccoon/graph/badge.svg?token=IZOMFPHA7W 
    :target: https://codecov.io/github/ajshajib/raccoon
    :alt: Code coverage

.. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black

.. |docstyle| image:: https://img.shields.io/badge/%20style-sphinx-0a507a.svg
    :target: https://www.sphinx-doc.org/en/master/usage/index.html

.. |docformatter| image:: https://img.shields.io/badge/%20formatter-docformatter-fedcba.svg
    :target: https://github.com/PyCQA/docformatter
aig 0.1
A simple example package
Requires Python: >=3.8

# aig

一个简单的 Python 示例包，用于演示 Python 项目打包。

## 安装

```bash
pip install aig
```

## 使用方法

```python
from aig.example import add_one, multiply_by_two

# 给数字加一
result = add_one(5)  # 返回 6

# 给数字乘以二
result = multiply_by_two(4)  # 返回 8
```

## 功能

- `add_one(number)`: 给指定数字加一
- `multiply_by_two(number)`: 给指定数字乘以二

## 开发

本项目使用标准的 Python 包管理工具进行构建和分发。

### 构建包

```bash
python -m build
```

### 安装开发版本

```bash
pip install -e .
```

## 许可证

MIT License
mseep-frankfurtermcp 0.3.2
A MCP server for currency exchange rates and currency conversion using the Frankfurter API.
Requires Python: >=3.6
Keywords: mseep, finance, mcp, currency-exchange-rates, currency-converter, frankfurter-api, model-context-protocol, mcp-server, fastmcp, model-context-protocol-server, mcp-composition

Package managed by MseeP.ai