semvec/pypi-packages huggingface dataset:
mlops-misis2025 0.0.1
A short description of the project.
Requires Python: ~=3.13.0

# mlops_misis2025

<a target="_blank" href="https://cookiecutter-data-science.drivendata.org/">
    <img src="https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter" />
</a>

A short description of the project.

## Project Organization

```
├── LICENSE            <- Open-source license if one is chosen
├── Makefile           <- Makefile with convenience commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default mkdocs project; see www.mkdocs.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── pyproject.toml     <- Project configuration file with package metadata for 
│                         sample_project and configuration for tools like black
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.cfg          <- Configuration file for flake8
│
└── sample_project   <- Source code for use in this project.
    │
    ├── __init__.py             <- Makes sample_project a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    └── plots.py                <- Code to create visualizations
```

--------
testcase-converter 0.4.0
Convert test cases between Excel and XMind formats with enhanced features
Requires Python: >=3.7

# Test Case Converter

Convert test cases between Excel and XMind formats.

## Installation
```bash
pip install testcase-converter
```

## New Features in v0.4.0

- Added functionality to split XMind files by specified row count
- Added case type field with default value "功能用例"
- Reordered XMind notes fields to match required sequence
- Enhanced compatibility for both old and new Excel formats

## Usage Examples

### As a CLI Tool:
```bash
# Excel to XMind
testcase-converter test_cases.xlsx

# XMind to Excel
testcase-converter test_cases.xmind

# 启用日志文件生成（使用简短参数 -l）
testcase-converter -l test_cases.xlsx

# 设置默认车型和优先级（使用简短参数 -v 和 -p）
testcase-converter -v "默认车型" -p "3" test_cases.xlsx

# 仅填充模式（使用简短参数 -f）
testcase-converter -f -v "OTA" -p "0" test_cases.xlsx

# 按指定行数分割生成多个XMind文件（使用简短参数 -r）
testcase-converter -r 100 test_cases.xlsx

# 组合使用所有参数
testcase-converter -l -f -v "OTA" -p "0" -r 50 test_cases.xlsx
```

### As a Python Library:
```python
from testcase_converter import TestCaseConverter, ConversionType

# Auto-detect conversion type
converter = TestCaseConverter("input.xlsx")
converter.convert()

# Explicitly specify conversion type with new options
converter = TestCaseConverter(
    "input.xmind", 
    ConversionType.XMIND_TO_EXCEL,
    enable_logging=True,              # 启用日志文件生成
    default_vehicle_type="默认车型",      # 设置默认车型
    default_priority="3",             # 设置默认优先级
    rows_per_xmind=100                # 每个XMind文件包含100行
)

# 仅填充模式
converter = TestCaseConverter(
    "input.xlsx",
    ConversionType.FILL_ONLY,         # 设置为仅填充模式
    default_vehicle_type="OTA",       # 设置默认车型
    default_priority="0"              # 设置默认优先级
)
converter.convert()
```

### 命令行参数说明

| 长参数名 | 短参数名 | 说明 |
|---------|---------|------|
| `--enable-logging` | `-l` | 启用日志文件生成 |
| `--default-vehicle-type` | `-v` | 默认车型，用于填充空的车型字段 |
| `--default-priority` | `-p` | 默认优先级，用于填充空的优先级字段 |
| `--fill-only` | `-f` | 仅填充模式，不转换文件格式 |
| `--rows-per-xmind` | `-r` | 每个XMind文件包含的行数 |
| `--debug` | 无 | 启用调试模式 |

### Format Details

The converter now automatically adds a case type field with the default value "功能用例" and reorders the fields in XMind notes.

For Excel files:
- New format includes 9 columns: Module, Case Name, Precondition, Steps, Expected Result, Remark, Case Type, Vehicle Type, Priority
- Backward compatibility maintained for 7-column and 8-column formats (missing fields will be added with default values)

For XMind files:
- Fields are now ordered as follows:
  【用例类型】功能用例
  【车型】OTA
  【优先级】1
  【前置条件】
  【执行步骤】
  【预期结果】
  【备注】
gemseo-jax 2.1.1
GEMSEO plugin for JAX (jit compilation, autodiff, XLA)
Requires Python: <3.14,>=3.10

<!--
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 Commons, PO Box 1866, Mountain View, CA 94042, USA.
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# gemseo-jax

[![PyPI - License](https://img.shields.io/pypi/l/gemseo-jax)](https://www.gnu.org/licenses/lgpl-3.0.en.html)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gemseo-jax)](https://pypi.org/project/gemseo-jax/)
[![PyPI](https://img.shields.io/pypi/v/gemseo-jax)](https://pypi.org/project/gemseo-jax/)
[![Codecov branch](https://img.shields.io/codecov/c/gitlab/gemseo:dev/gemseo-jax/develop)](https://app.codecov.io/gl/gemseo:dev/gemseo-jax)

## Overview

GEMSEO plugin for JAX (jit compilation, autodiff, XLA).

The power of [JAX](https://jax.readthedocs.io) (a Python library for high-performance array computing) to accelerate MDO.

JAX is heavily used for large-scale machine learning research, but many of its benefits can also be used to leverage
scientific computing as a whole. In the context of Multidisciplinary Optimization (MDO), we use JAX to avoid manual
implementation of derivatives of objective functions and constraints wrt optimization variables, which allows for using
gradient-based optimizers without an extra implementation cost.


## Installation

Install the latest version with `pip install gemseo-jax`.

See [pip](https://pip.pypa.io/en/stable/getting-started/) for more information.

## Bugs and questions

Please use the [gitlab issue tracker](https://gitlab.com/gemseo/dev/gemseo-jax/-/issues)
to submit bugs or questions.

## Contributing

See the [contributing section of GEMSEO](https://gemseo.readthedocs.io/en/stable/software/developing.html#dev).

## Contributors

- Ian Costa-Alves
- François Gallard
- Matthias De Lozzo
- Antoine DECHAUME
scviz 0.1.10b0
A package to visualize single cell proteomics (and more to come!) data
Requires Python: >=3.8

# scviz
The `scviz` package is a Python library designed for scientific visualization of single cell proteomics data, with plans to support more data types in the future.

## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install scviz. Currently, scviz is available on testPyPi:
```bash
pip install -i https://test.pypi.org/simple/ scviz
```
A local version of the package can also be installed by:
```bash
$ pip install -e \<insert folder path\>
```

## Usage
Please refer to docs (link) example jupyter notebook: Chapter 1.

## License
`scviz` was created by Marion Pang. It is licensed under the terms of the MIT license.
mcp-judge 0.1.4
An interactive application for testing MCP tools.
Requires Python: >=3.11

# 🧑‍⚖️ MCP Judge

An interactive interface for testing your MCP tools. This application helps you connect to an MCP server, list available tools, inspect their schemas, and run them with custom inputs, all from a clean, web-based UI.

## ✨ Features

* **Connect to any MCP Server:** Easily connect to a new MCP server by simply entering its URL.
* **Discover Tools:** View a list of all available tools and their descriptions.
* **Dynamic Input Forms:** The app automatically generates input forms based on the tool's `inputSchema`.
* **Live Tool Testing:** Run any tool with your specified inputs and see the output in real-time.

## 🆕 New Feature
* **Custom Headers Support:** You can now add custom request headers (like API tokens, authorization keys, or metadata) when connecting to an MCP server.
This makes it easy to test protected or authenticated MCP tools directly from the interface.

---

### 🚀 Installation

You can install `mcp-judge` directly from PyPI.

```bash
pip install mcp-judge
```

---

### 💻 How to Run

After installation, you can launch the application from your terminal with a single command:

```bash
mcp-judge
```
This will automatically start a local server and open the app in your default web browser.

---

### 🛠️ Usage

1.  **Enter Server URL:** On the left side of the screen, enter the URL of the MCP server you want to test and click **"Connect"**.
2.  **Optional Headers:** Click on “⚙️ Advanced Options (Custom Headers)” below the URL input if your MCP tools requires custom headers and header-based access is already configured in your MCP server.
    - Enter the header name (e.g., Authorization)
    - Enter the header value (e.g., Bearer your_api_token)
3.  **Select a Tool:** Once connected, a list of available tools will appear below. Choose the tool you want to test from the dropdown menu.
4.  **Provide Inputs:** The right side of the screen will show a form based on the selected tool's required inputs. Fill in the necessary details.
5.  **Run the Tool:** Click the **"Run Tool"** button to execute the tool. The output will be displayed in the **"Tool Result"** section below.

---

### 📄 Requirements

* Python 3.11+

---

## 🔌 Sample MCP Connections

### 1. Connected to Microsoft's Sample MCP

![Microsoft MCP](https://raw.githubusercontent.com/NilavoBoral/mcp-judge/main/screenshot_microsoft_mcp_v0.1.4.png)


### 2. Connected to Custom Test MCP

![Custom MCP](https://raw.githubusercontent.com/NilavoBoral/mcp-judge/main/screenshot_custom_mcp_v0.1.4.png)

---

## 🔗 Project Links

- 📂 [Source Code](https://github.com/NilavoBoral/mcp-judge)