semvec/pypi-packages huggingface dataset:
instinct-py-nodes 0.3.0
A Python package for creating compute nodes for use in Nexstem Instinct Products
Requires Python: >=3.6

# Nexstem Instinct Nodes

A Python package for creating distributed nodes using ZeroMQ for communication.

## Features

- Abstract base class for creating distributed nodes
- ZeroMQ-based communication between nodes
- Support for multiple producers and consumers
- Lifecycle management (start, stop, shutdown)
- Support for different message formats (JSON and multipart strings)

## Installation

```bash
pip install instinct-py-nodes
```

## Usage

```python
from instinct_py_nodes import Node, ESignalType, LifeCycleState

class MyNode(Node):
    def onStart(self):
        # Your startup logic here
        pass
    
    def onStop(self):
        # Your stop logic here
        pass
    
    def onShutdown(self):
        # Your shutdown logic here
        pass
    
    def onSignal(self):
        # Your signal handling logic here
        pass

# Create and start your node
node = MyNode()
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
mlpro-int-openml 1.1.0
MLPro: Integration OpenML
Requires Python: >=3.10

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<img src="https://github.com/fhswf/MLPro-Int-OpenML/blob/main/doc/logo/original/logo.png?raw=True" align="right" width="40%"/>

# MLPro-Int-OpenML - Integration of OpenML into MLPro
Welcome to MLPro-Int-OpenML, an extension to MLPro to integrate the scientific data provider OpenML.
MLPro is a middleware framework for standardized machine learning in Python. It is 
developed by the South Westphalia University of Applied Sciences, Germany, and provides 
standards, templates, and processes for hybrid machine learning applications. OpenML, in turn, 
provides a vast amount of sample datasets, combined with a benchmark systematics to
enable a standardized assessment of ML algorithms.

MLPro-Int-OpenML provides wrapper classes that enable the use of OpenML datasets in your 
MLPro applications. The use of these wrappers is illustrated in example programs.

### Learn more
[MLPro - The integrative middleware framework for standardized machine learning in Python](https://mlpro.readthedocs.io)   
[MLPro-Int-OpenML - Integration of OpenML into MLPro](https://mlpro-int-openml.readthedocs.io)   
[OpenML - A worldwide machine learning lab](https://www.openml.org/)
pdf-hider 0.1.0
A tool to embed hidden, transparent text into PDF files.
Requires Python: >=3.6
Keywords: pdf hidden text transparency ai resume

# PDF 隐藏文本工具

这个工具可以在 PDF 文件中嵌入透明的隐藏文本，而不会改变其可见内容。这对于添加元数据、水印，或者根据您最初的意图，通过嵌入关键词来影响基于 AI 的文档处理可能很有用。

隐藏文本以完全透明（alpha=0）的方式呈现，使其对人眼不可见，但仍可被文本提取工具和算法解析。

## 功能

- 向现有 PDF 页面添加完全透明的文本。
- 保留 PDF 的原始可见内容。
- 支持通过命令行参数指定输入 PDF、输出 PDF 和隐藏文本。

## 要求

- Python 3.6+
- `PyPDF2`
- `reportlab`

## 安装

您可以通过 `pip` 直接从项目目录安装此工具：

1.  **克隆仓库（或下载文件）：**

    ```bash
    git clone [https://github.com/yoofa/pdf-hidden-text-tool.git](https://github.com/yoofa/pdf-hidden-text-tool.git)
    cd pdf-hidden-text-tool
    ```

2.  **安装包：**
    ```bash
    pip install .
    ```

## 使用方法

安装后，您可以在终端中使用 `pdf-hider` 命令：

```bash
pdf-hider --input <输入PDF路径> --output <输出PDF路径> --text "<您的隐藏文本>"
```

参数：

- --input 或 -i：(必填) 输入 PDF 文件的路径。

- --output 或 -o：(必填) 带有隐藏文本的新 PDF 文件的保存路径。

- --text 或 -t：(必填) 要隐藏在 PDF 中的文本字符串。

示例：

```bash
pdf-hider -i 我的简历.pdf -o 带有关键词的简历.pdf -t "高素质，卓越领导力，创新解决方案，顶尖表现者。"
```

## 如何验证隐藏文本

- 使用PyPDF2：

```python
from PyPDF2 import PdfReader

reader = PdfReader("output_with_hidden_text.pdf")
for page in reader.pages:
print(page.extract_text())

```

- 使用 pdfminer.six (更强大的文本提取工具，需要安装 pip install pdfminer.six)：

```bash
pdf2txt.py output_with_hidden_text.pdf
```

你应该会在输出中看到你隐藏的字符串。