semvec/pypi-packages huggingface dataset:
abstract-parallels 0.0.405
A Python package for managing clipboard, window info, services, and APIs with GUI interfaces
Requires Python: >=3.6

# Abstract Parallels (v0.0.1)

Abstract Parallels is a Python package that provides GUI-based tools for managing clipboard data, window information, systemd services, and APIs. It includes a dashboard for launching various components and a systemd service manager for remote service monitoring and control.

## Features
- **Clipboard Watcher**: Monitors clipboard changes and provides a GUI interface.
- **Window Info GUI**: Displays information about active windows.
- **Services Manager**: A PyQt5-based GUI for viewing and managing remote systemd services via SSH.
- **API GUI**: Interface for interacting with APIs (placeholder implementation).
- **Interactive Dashboard**: A bash-driven menu to launch different components.

## Installation

Install the package using pip:

```bash
pip install abstract_parallels
```

## Dependencies
- Python >= 3.6
- `flask`
- `paramiko`
- `PyQt5`
- Bash (for shell scripts)
- systemd (for service management)

## Usage

After installation, use the provided commands to launch components. These commands are available globally thanks to entry points.

### Launch the Dashboard
```bash
abstract-parallels-dashboard
```

This opens an interactive menu to select components like the Clipboard Watcher, Window Info GUI, or both.

### Manage Systemd Services
```bash
abstract-parallels-service-manager
```

This launches the PyQt5-based service viewer for remote systemd service management.

### Setup All Services
```bash
abstract-parallels-setup
```

This installs and starts all components as user systemd services.

### Available Commands
Run the setup script with different arguments:
```bash
abstract-parallels-setup            # Install and start all services
abstract-parallels-setup start      # Start all services
abstract-parallels-setup stop       # Stop all services
abstract-parallels-setup restart    # Restart all services
abstract-parallels-setup status     # Show service status
abstract-parallels-setup logs       # View service logs
```

## Modules
- `abstract_parallels.apis`: API GUI functionality.
- `abstract_parallels.clipit`: Clipboard monitoring tools.
- `abstract_parallels.services_mgr`: Systemd service management GUI.
- `abstract_parallels.window_mgr`: Window information GUI.

## Development
To contribute or modify the package:
1. Clone the repository (if available).
2. Install dependencies: `pip install -r requirements.txt`.
3. Build the package: `python -m build`.
4. Install locally: `pip install .`.

## License
MIT License. See [LICENSE](LICENSE) for details.
odspy 0.1.0
A library for parsing OpenDocument Spreadsheet (ODS) files.
Requires Python: >=3.13

# ODSPy

ODSPy is a Python library for parsing and working with OpenDocument Spreadsheet (ODS) files. It allows you to extract data from ODS files and manipulate it programmatically.

## Features

- Parse ODS files and extract sheet data.
- Access rows and cells with ease.
- Built-in support for XML parsing using `lxml`.

## Installation

You can install ODSPy using pip:

```bash
pip install odspy
```

## Usage

### Example: Reading an ODS File

```python
from odspy import ODSDocument

# Specify the path to the ODS file and the sheet name
path = "example.ods"
sheet = "Sheet1"

# Create an ODSDocument instance
document = ODSDocument(path, sheet)

# Access rows and cells
for row in document.rows:
    for cell in row:
        print(cell)
```

## Requirements

- Python 3.13 or higher
- `lxml` library

## Development

### Setting up the environment

1. Clone the repository.
2. Create a virtual environment:
   ```bash
   python3 -m venv env
   source env/bin/activate
   ```
3. Install dependencies:
   ```bash
   pip install -r requirements.txt
   ```

### Running Tests

Currently, there are no tests implemented.

## Distribution

To distribute the package, follow the instructions in the `distribute.sh` script.

## License

This project is licensed under the MIT License.
alibabacloud-ros-iact3 0.1.12
Iact3 is a tool that tests Terraform and ROS(Resource Orchestration Service) templates.
Requires Python: >=3.7

# IaC Template Testing Tool

Iact3(IaC Template Testing Tool) is a tool that tests [Alibaba Cloud ROS(Resource Orchestration Service)](https://www.alibabacloud.com/help/resource-orchestration-service) templates and  [Terraform](https://developer.hashicorp.com/terraform). It deploys your template in multiple Alibaba Cloud Regions and generates a report for each region via a simple configuration file.  

# Requirements
Python 3.7+

# Installation

`pip install alibabacloud-ros-iact3`

# Document

Fantastic documentation is available at:
[English](https://aliyun.github.io/alibabacloud-ros-tool-iact3/#/en) |
[中文版](https://aliyun.github.io/alibabacloud-ros-tool-iact3).
notorch 0.2.3a0
Neural networks and ML from scratch in pure Python

![text](https://pouch.jumpshare.com/preview/-1bim1AldefPqJAQ8tSYVMv4QCpJQ01cjvxm3Hsiqvj8El3FZhJFamBeqytuK5aClqfxZB9c_M6FK4pY8ecv7ePIslOnu4vxog90qafZ7dg)

# NoTorch 🕯

**A pure Python, educational machine learning library — built from scratch.**

**NoTorch** is a learning-focused machine learning and deep learning library that re-implements popular models using only core Python (and `pandas` for data handling). Inspired by the design of `scikit-learn` and `PyTorch`, NoTorch offers a simple and consistent API while prioritizing **readability**, **clarity**, and **educational value** over performance or production use.

This project is being developed as part of a personal journey to deeply understand how machine learning works — and to help others learn by example. Whether you're a student, a hobbyist, or just curious about what happens *under the hood* of your favorite ML libraries, NoTorch is here to make complex concepts transparent and approachable.

---

## 🔍 Project Goals

* ✅ Build core ML and DL models **from scratch** in pure Python
* ✅ Keep the code **readable**, **modular**, and **well-documented**
* ✅ Mimic the familiar APIs of `sklearn` and `torch` for a smoother learning curve
* ✅ Prioritize **educational clarity** over performance or abstraction
* ✅ Share the learning journey with the community

---

## ✅ Implemented Models

### Classical Machine Learning

* [x] **K-Nearest Neighbors (KNN)**
* [ ] **Decision Tree**
* [ ] **Random Forest**
* [ ] **Naive Bayes**
* [ ] **Linear Regression**
* [ ] **Logistic Regression**
* [ ] **Support Vector Machine (SVM)**

### Neural Networks (Coming Soon)

* [ ] **Basic Feedforward Neural Network**
* [ ] **Manual Backpropagation Engine**
* [ ] **Custom Autograd System (Experimental)**
* [ ] **Convolutional Neural Networks (CNNs)**
* [ ] **Recurrent Neural Networks (RNNs)**
* [ ] **Long Short-Term Memory (LSTM)**

---

## 🛠 Example Usage
### KNN Model
```python
from NoTorch.KNN import KNN

model = KNN(n_neighbors=1)
model.fit(X_train, y_train)

print(model.predict(X_test))
```

---

<!-- ## 📚 Documentation

Check out our full documentation, examples, and development notes at:
👉 [Project Docs](https://your-docs-url-here.com)

--- -->

## 🚧 This Project is Under Active Development

NoTorch is a work-in-progress, built step-by-step with the intent to **learn by building**. Contributions, ideas, and educational feedback are very welcome!