semvec/pypi-packages huggingface dataset:
oect-excel-processor 0.1.0
处理OECT性能测试后的Excel数据并转换为CSV格式
Requires Python: >=3.6

# OECT Excel Processor

用于处理OECT（有机电化学晶体管）性能测试后的Excel数据并转换为CSV格式的Python包。

## 功能特点

- 支持两种类型的工作表处理：
  - `transfer`类型：从第三行开始有字段名，共四列数据
  - `transient`类型：第三行前两列是字段名，数据按每两列一组排列，需要合并
- 可以处理包含多个工作表的Excel文件
- 自动将每个工作表转换为单独的CSV文件
- 支持自定义输出文件名前缀
- 自动去除transient类型工作表中的空行和不完整数据行
- 支持批量处理多个Excel文件，并按自然排序处理
- 支持多核并行处理，加速批量文件处理
- 提供命令行工具，方便直接使用

## 安装

### 从PyPI安装

```bash
pip install oect-excel-processor
```

### 从源码安装

```bash
git clone https://github.com/Durian-leader/oect-excel-processor.git
cd oect-excel-processor
pip install -e .
```

## 使用方法

### 作为Python库使用

#### 单个Excel文件处理

```python
from oect_excel_processor import ExcelProcessor

# 创建处理器实例
excel_file = "your_excel_file.xls"
sheet_types = ["transfer", "transient", "transfer", "transfer"]
output_prefix = "output"

processor = ExcelProcessor(excel_file, sheet_types, output_prefix)
saved_files = processor.process_and_save()

print("保存的CSV文件:")
for file in saved_files:
    print(f"- {file}")
```

#### 批量处理Excel文件

```python
from oect_excel_processor import BatchExcelProcessor

# 创建批处理器实例
batch_processor = BatchExcelProcessor(
    directory="your_directory",
    file_pattern="*.xls",
    sheet_types=["transfer", "transient"],
    output_prefix="batch_output"
)

# 使用多核处理加速
results = batch_processor.process_all_files(
    output_dir="csv_output",
    use_multiprocessing=True,  # 启用多进程处理
    max_workers=None  # 自动使用所有可用CPU核心
)

# 获取处理摘要
summary = batch_processor.get_processing_summary(results)
print(f"总Excel文件数: {summary['total_excel_files']}")
print(f"成功处理的文件数: {summary['successful_files']}")
print(f"生成的CSV文件总数: {summary['total_csv_files']}")
```

### 作为命令行工具使用

安装后，可以使用`oect-processor`命令行工具：

#### 处理单个文件

```bash
oect-processor single your_excel_file.xls --sheet-types transfer,transient --output-prefix output
```

#### 批量处理文件

```bash
oect-processor batch your_directory --pattern "*.xls" --sheet-types transfer,transient --output-prefix batch_output --output-dir csv_output --multiprocessing
```

## 工作表类型说明

### transfer类型

- 从第三行开始，第三行包含字段名
- 总共有四列数据
- 从第四行开始是实际数据

### transient类型

- 第三行的前两列是字段名
- 数据按每两列一组排列（AB, CD, EF等）
- 每组的数据应该连接在一起
- 从第四行开始是实际数据
- 自动去除空行和不完整的数据行

## 输出文件

输出的CSV文件将按照以下格式命名：

```
{output_prefix}-{sheet_index}-{sheet_type}.csv
```

例如，如果`output_prefix`设置为"data"，则输出文件将为：
- data-1-transfer.csv
- data-2-transient.csv
- 等等

对于批处理，输出文件名格式为：

```
{output_prefix}-{file_index}-{sheet_index}-{sheet_type}.csv
```

## 示例

包中提供了几个示例脚本，展示如何使用该包：

- `single_file_example.py`: 展示如何处理单个Excel文件
- `batch_processing_example.py`: 展示如何批量处理Excel文件
- `multiprocessing_comparison.py`: 展示多核处理与单核处理的性能对比

## 许可证

MIT
asgard-lab-DM-tools 0.0.10
Tools to interact with the Asgard BMC DMs in lab
Requires Python: >=3.9

# asgard-dm-tools
User tools to interact in the laboratory with the Asgard DMs

At least for now, a PyQt5 GUI designed to control one of the four deformable mirrors of the ASGARD instrument suite, assuming that the corresponding Asgard DM server is running and that at least two DM channels are set up. And a turbulence simulator prototype.

## Installation

> pip install asgard_lab_DM_tools -U

## Usage

Pip installs a GUI that can be called from the Linux CLI using the following command:

> lab-MDM-control &

that takes no argument and uses tabs to control from the same GUI the 4 Asgard DMs.
This replaces the previous single-DM GUI that will eventually be dropped out of the distribution:

> lab-DM-control 1&

to connect to the Asgard DM server #1.

The GUI is otherwise simple and should be self-explanatory to somebody who knows what it is designed for!

As well as a turbulence_simulation script also to be called from the Linux CLI

> turbulence-simulator
langgraph-gen 0.0.6
Genenerate code stubs for langgraph agents.
Requires Python: >=3.9

# langgraph-gen

langgraph-gen is a CLI tool that allows you to auto-generate a LangGraph stub from a
specification file.

## Usage

```shell
pip install langgraph-gen
```

## Basic Usage

```shell
# Generate Python code from a YAML spec
langgraph-gen spec.yml

# Generate TypeScript code from a YAML spec
langgraph-gen spec.yml --language typescript

# Generate with custom output paths
langgraph-gen spec.yml -o custom_output.py --implementation custom_impl.py
```

## Command Line Options

```
langgraph-gen [options] input

Required arguments:
  input                 Input YAML specification file

Optional arguments:
  -l, --language        Language to generate code for (python, typescript)
                        Default: python
  -o, --output          Output file path for the agent stub
  --implementation      Output file path for an implementation with function stubs for all nodes
  -V, --version         Show program's version number and exit
```

## Example Spec

```YAML
# A simple 2-step Retrieval-Augmented Generation workflow
name: RagWorkflow
nodes:
- name: retrieve
- name: generate
edges:
- from: __start__
  to: retrieve
- from: retrieve
  to: generate
- from: generate
  to: __end__
```

## Quick Start

Create an example specification file and generate the code:

```shell
# Create a simple RAG workflow specification
cat > rag_example.yml << 'EOF'
# A simple 2-step Retrieval-Augmented Generation workflow
name: RagWorkflow
nodes:
  - name: retrieve
  - name: generate
edges:
  - from: __start__
    to: retrieve
  - from: retrieve
    to: generate
  - from: generate
    to: __end__
EOF

# Generate Python code
langgraph-gen rag_example.yml

# This will create rag_example.py and rag_example_impl.py
```

## Examples

You can find examples of the LangGraph specification together with the generated LangGraph stubs in the [examples](./examples) directory.
readme-metrics 3.4.0
ReadMe API Metrics SDK

# ReadMe Metrics

<p align="center">
  <img src="https://user-images.githubusercontent.com/33762/182927634-2aebeb46-c215-4ac3-9e98-61f931e33583.png" />
</p>

<p align="center">
  Track usage of your API and troubleshoot issues faster.
</p>

<p align="center">
  <a href="https://pypi.org/project/readme-metrics/"><img src="https://img.shields.io/pypi/v/readme-metrics.svg?style=for-the-badge" alt="Latest release"></a>
  <a href="https://github.com/readmeio/metrics-sdks"><img src="https://img.shields.io/github/actions/workflow/status/readmeio/metrics-sdks/python.yml?branch=main&style=for-the-badge" alt="Build status"></a>
</p>

With [ReadMe's Metrics API](https://readme.com/metrics) your team can get deep insights into your API's usage. If you're a developer, it takes a few small steps to send your API logs to [ReadMe](http://readme.com). Here's an overview of how the integration works:

- You add the ReadMe middleware to your [Django](https://www.djangoproject.com/), [Flask](https://flask.palletsprojects.com/), or [WSGI](https://wsgi.readthedocs.io/) application.
- The middleware sends to ReadMe the response object that your application generates each time a user makes a request to your API. The entire response is sent, unless you allow or deny keys.
- ReadMe populates Metrics with this information, such as which endpoint is being called, response code, and error messages. It also identifies the customer who called your API, using whichever keys in the middleware you call out as containing relevant customer info.

```bash
pip install "readme-metrics[Django]" # Django applications
pip install "readme-metrics[Flask]" # Flask applications
pip install readme-metrics # WSGI
```

- [Django Integrations](https://docs.readme.com/docs/python-django-api-metrics)
- [Flask Integrations](https://docs.readme.com/docs/python-flask-api-metrics)
- [WSGI Integrations](https://docs.readme.com/docs/python-wsgi-api-metrics)

> 🚧 Any Issues?
>
> Integrations can be tricky! [Contact support](https://docs.readme.com/guides/docs/contact-support) if you have any questions/issues.
XRP-BASE 0.1.4
A Python package for XRPL transactions and installations.
Requires Python: >=3.6

# XRPL Toolkit

A powerful Python package for working with XRP transactions, address validation, conversions, and more. This toolkit provides functionalities like generating XRP addresses, checking balances, and processing transactions securely.

## Features

-   Generate and validate XRP addresses
-   Check XRP balance and transaction details
-   Convert XRP to USD
-   Simulate package installations for XRPL-related dependencies
-   Secure transaction processing

## Installation

To install the package, run:

```bash
pip install xrpl-toolkit
```

## Usage

### Command-Line Interface (CLI)

Once installed, you can use the toolkit via the command line:

```bash
xrpl-toolkit
```

It will present a menu with options to generate addresses, check balances, validate addresses, and more.

### Importing in Python

You can also import the package in your Python script:

```bash
from xrpl_toolkit.xrp_toolkit import xrp_toolkit

xrp_toolkit()
```

## Dependencies

The package relies on several key libraries for XRP-related functionalities:
-   xrpl-py – for interacting with the XRP Ledger
-   base58 – for encoding addresses
-   cryptography – for secure transactions
-   web3 – for blockchain integration
-   rich – for enhanced terminal output
mcp-python-sdk-aliyun 0.3.3
mcp python sdk aliyun



[//]: # (1. python3 -m venv myenv)

[//]: # (2. source myenv/bin/activate  激活虚拟环境)

[//]: # (3. pip3 install -r requirements.txt  安装依赖)

[//]: # (4. pyinstaller --onefile __main__.py --name mcp-server-aliyun-python-sdk)


# **说明**
mcp python sdk，用于适配阿里云服务注册中心开发的运行在客户端本地的mcp server。

整体方案：https://alidocs.dingtalk.com/i/nodes/gwva2dxOW4vRkd9DUqbedAnGJbkz3BRL

sdk使用方式：https://alidocs.dingtalk.com/i/nodes/gvNG4YZ7Jnxop15OCnEXnDqEW2LD0oRE

# 使用方式概览
两种使用方式。

## 方式一：直接使用源码

源码clone到本地后，创建python虚拟环境，从requirements.txt文件安装依赖，然后在支持cline的客户端启动客户端即可。

详情参照[sdk使用文档](https://alidocs.dingtalk.com/i/nodes/gvNG4YZ7Jnxop15OCnEXnDqEW2LD0oRE)

## 方式二：打包可执行文件

源码clone到本地后，创建python虚拟环境，从requirements.txt文件安装依赖，使用pyinstaller打包成可执行文件，然后在支持cline的客户端通过该可执行文件启动即可。

文档[sdk使用文档](https://alidocs.dingtalk.com/i/nodes/gvNG4YZ7Jnxop15OCnEXnDqEW2LD0oRE)中有已经打包好的可执行文件。
BluePyStaxx 0.2.0
Add your description here
Requires Python: >=3.13

# BluePyStaxx

A Python library for controlling BlueStacks using ADB (Android Debug Bridge) commands. This library provides a simple interface to interact with the BlueStacks emulator, enabling users to automate tasks and manage applications effectively.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Features](#features)
- [Commands](#commands)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)

## Installation

To install the library, use uv:

```bash
uv add BluePyStaxx
```

## Usage

To use the library, you need to have BlueStacks installed.
- Currently has only been tested on Windows.



### Basic Example

```python
from BluePyStaxx.controllers.bs_controller import BluestacksController

# Initialize the BlueStacks controller
bs = BluestacksController()

# Start an application
bs.open_app("com.example.app")

# Stop an application
bs.close_app("com.example.app")
```

## Features

- Start and stop applications on BlueStacks
- Send ADB commands directly
- Capture screenshots
- Manage installed applications
- Detect UI elements using image recognition
- Automate typing text into the emulator

## Commands

| Command           | Description                          |
|-------------------|--------------------------------------|
| `open_app(app)`  | Opens the specified application.    |
| `close_app(app)`   | Closes the specified application.     |
| `send_command(cmd)` | Sends a raw ADB command.          |
| `screenshot()`     | Captures a screenshot of the emulator. |
| `detect_ui_element(image_path)` | Detects a UI element using image recognition. |
| `type_text(text)` | Types text into the emulator. |

## Examples

### Opening an App

```python
bs.open_app("com.example.app")
```

### Sending a Custom ADB Command

```python
bs.send_command("adb shell input tap 100 200")
```

### Detecting UI Elements

The library can detect UI elements using image recognition. You can provide image paths and the library will locate them on the screen.

### Typing Text

You can also automate typing text into the emulator using the `type_text` command.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
yamlio 0.1.5
Library for interacting with YAML files.
Requires Python: >=3.12

## Donations

[Buy Me A Coffee](https://www.buymeacoffee.com/KingKairos)  
[GitHub Sponsors](https://github.com/sponsors/melvinquick)

## Purpose

This is a library I made to interact with YAML files in the way I need to for my own projects. If you'd like to use it, you can install it with the command below.

## Install/Uninstall

Install: `pip install yamlio`  
Uninstall: `pip uninstall yamlio`

## Useful Information

[Releases](https://pypi.org/project/yamlio)
converter-kunnap 0.0.1
WGS84 <-> L-Est97 converter
Requires Python: >=3.8

# WGS84 to L-Est97 Converter

## Description
A program that converts coordinates between the WGS84 (EPSG:4326) and L-Est97 (EPSG:3301) coordinate systems. The program takes latitude and longitude in WGS84 format and converts them to L-Est97 coordinates, and vice versa.

## Requirements
- Python 3.x
- pyproj library for Python

## Installing pyproj
1. Install `pyproj` using pip:
   ```sh
   python -m pip install pyproj
   ```
   OR If this fails, and you are using Homebrew (macOS/Linux), install `proj`:
   ```sh
   brew install proj
   ```
whl2conda 25.3.0
Build conda packages directly from pure python wheels
Requires Python: >=3.9
Keywords: build, conda, wheel

# whl2conda 

[![pypi version](https://img.shields.io/pypi/v/whl2conda.svg)](https://pypi.org/project/whl2conda/)
[![conda version](https://img.shields.io/conda/vn/conda-forge/whl2conda)](https://anaconda.org/conda-forge/whl2conda)
[![documentation](https://img.shields.io/badge/docs-mkdocs%20material-blue.svg?style=flat)](https://zuzukin.github.io/whl2conda/)  
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/whl2conda)
![GitHub](https://img.shields.io/github/license/analog-cbarber/whl2conda)  
[![CI](https://github.com/zuzukin/whl2conda/actions/workflows/python-package-conda.yml/badge.svg)](https://github.com/zuzukin/whl2conda/actions/workflows/python-package-conda.yml) [![codecov](https://codecov.io/gh/zuzukin/whl2conda/graph/badge.svg?token=097C3MBNIX)](https://codecov.io/gh/zuzukin/whl2conda)
![GitHub issues](https://img.shields.io/github/issues/analog-cbarber/whl2conda)


**Generate conda packages directly from pure python wheels**

*whl2conda* is a command line utility to build and test conda packages
generated directly from pure python wheels.

* **Performance**: because it does not need to create conda environments
    in order to build, this is *much* faster than solutions involving *conda-build*.

* **Dependency renaming**: renames pypi package dependencies to their 
    corresponding conda name. Automatically renames packages from known
    list collected from conda-forge and supports user-specified rename
    patterns as well.

* **Multiple package formats**: can generate both V1 ('.tar.bz2') and V2 ('.conda')
    conda package formats. Can also generate a unpacked directory tree for debugging
    or additional user customization.

* **Project configuration**: *whl2conda* project-specific options can be read from
    project's `pyproject.toml` file.

* **Test install support**: supports installing conda package into a conda
    environment for testing prior to deployment.

* **Hides pypi dependencies**: rewrites the original pip/pypi dependencies in the
    installed dist-info to avoid [compatibility issues](https://zuzukin.github.io/whl2conda/latest/guide/renaming.html#hide-pip).


## Installation

With pip:

```bash
pip install whl2conda
```

With conda (upcoming):

```bash
conda install -c conda-forge whl2conda
```

## Quick usage

Generate a conda package in same directory as wheel file:

```bash
whl2conda build dist/mypackage-1.2.3-py3-none-any.whl
```

Add default tool options to `pyproject.toml`

```bash
whl2conda config --generate-pyproject pyproject.toml
```
Build both wheel and conda package for project:

```bash
whl2conda build --build-wheel my-project-root
```

Create python 3.10 test environment for generated conda package:

```bash
whl2conda install dist/mypackage-1.2.3.-py_0.conda --create -n testenv \
  --extra pytest python=3.10
```