semvec/pypi-packages huggingface dataset:
liferaft-python-lib 0.0.5
A shared library for reusable abstracted components
License: MIT
Requires Python: >=3.9

# Liferaft Python Library

`liferaft_python_lib` is a shared Python library providing reusable, abstracted components designed for integration across multiple services. This package standardizes and simplifies the development of distributed systems by offering commonly used abstractions such as queue adapters, clients, and models.

## Features

- **Abstracted Components**: Foundational building blocks for distributed systems.
- **Modular Design**: Easily integrate reusable components like queue adapters, clients, and models.
- **Future-Ready**: Upcoming iterations will include advanced abstractions such as Navigator models and shared utilities.

## Installation

Install the package via pip:

```bash
pip install liferaft_python_lib
cfmtoolbox 0.5.0
A plugin-based toolbox for working with cardinality-based feature models
License: MIT
Requires Python: <4.0,>=3.11

# CFM Toolbox

[![Versions][versions-image]][versions-url]
[![PyPI][pypi-image]][pypi-url]
[![License][license-image]][license-url]

[versions-image]: https://img.shields.io/pypi/pyversions/cfmtoolbox
[versions-url]: https://github.com/KIT-TVA/cfmtoolbox/blob/main/pyproject.toml
[pypi-image]: https://img.shields.io/pypi/v/cfmtoolbox
[pypi-url]: https://pypi.org/project/cfmtoolbox/
[license-image]: https://img.shields.io/pypi/l/cfmtoolbox
[license-url]: https://github.com/KIT-TVA/cfmtoolbox/blob/main/LICENSE

The CFM Toolbox is a plugin-based framework for working with cardinality-based feature models.
The framework ships with a set of core plugins, but can also easily be extended with third-party and custom plugins.

[Read the documentation](https://kit-tva.github.io/cfmtoolbox/) to learn more.

## Features

- **Easily extensible**: Designed to be easily extensible with third-party and custom plugins
- **Core plugins**: Core plugins provide essential functionality for working with CFMs
- **Command-line interface**: The included CLI allows for easy automation of tasks
- **Cross-platform**: Written in Python, the toolbox runs on all major operating systems
- **FeatureIDE import**: XML-based FeatureIDE feature models can easily be imported
- **UVL import and export**: Universal Variability Language files are supported out of the box
datasette-google-analytics 0.1.2
Datasette plugin that adds Google Analytics tracking code to your Datasette instance
License: MIT
Requires Python: >=3.9

# datasette-google-analytics

A [Datasette](https://datasette.io/) plugin that adds Google Analytics tracking code to your Datasette instance.

## Installation

Install this plugin in the same environment as Datasette:

```sh
pip install datasette-google-analytics
```

## Usage

Configure the plugin by adding a `metadata.json` file with your Google Analytics tracking ID:

```json
{
    "plugins": {
        "datasette-google-analytics": {
            "tracking_id": "G-XXXXXXXXXX"
        }
    }
}
```

Replace `G-XXXXXXXXXX` with your actual Google Analytics 4 tracking ID.

Then start Datasette with:

```sh
datasette --metadata metadata.json your-database.db
```

## Development

To set up this plugin locally:

```sh
cd datasette-google-analytics
python -m venv venv
source venv/bin/activate
pip install -e '.[test]'
```

## How This Works

1. The plugin creates a custom template that extends the default base template
2. It uses the `extra_template_vars` hook to pass the Google Analytics tracking ID to the template
3. It uses the `prepare_jinja2_environment` hook to modify the Jinjia2 environment by adding our template directories
4. The custom template adds the Google Analytics tracking code in the `extra_head` block

This approach follows Google's recommendations for placing the tracking code immediately after the `<head>` element.
random-allocation-fork 0.2.12
Tools for analyzing and comparing different sampling schemes in differential privacy
License: MIT
Requires Python: >=3.8
Keywords: differential-privacy, random-allocation, privacy-analysis

# Random Allocation for Differential Privacy

This package provides tools for analyzing and comparing different random allocation schemes in the context of differential privacy.

## Installation

You can install the package using pip:

```bash
pip install random-allocation
```

## Usage

Here's a simple example of how to use the package to run experiments:

```python
from random_allocation import run_experiment, PlotType
from random_allocation import ALLOCATION, ALLOCATION_ANALYTIC, ALLOCATION_RDP, ALLOCATION_DECOMPOSITION

# Define experiment parameters
params_dict = {
    'x_var': 'sigma',
    'y_var': 'epsilon',
    'sigma': [0.1, 0.2, 0.3, 0.4, 0.5],
    'n': 1000,
    'k': 10,
    'delta': 1e-5
}

# Define configuration
config_dict = {
    'title': 'Sigma vs Epsilon',
    'x name': 'Sigma',
    'y name': 'Epsilon'
}

# Define visualization configuration
visualization_config = {
    'log_x_axis': False,
    'log_y_axis': True
}

# Define methods to compare
methods = [ALLOCATION_ANALYTIC, ALLOCATION_RDP, ALLOCATION_DECOMPOSITION]

# Run the experiment
run_experiment(
    params_dict=params_dict,
    config_dict=config_dict,
    methods=methods,
    visualization_config=visualization_config,
    experiment_name='sigma_vs_epsilon',
    plot_type=PlotType.COMPARISON,
    save_data=True,
    save_plots=True
)
```

## Creating Custom Experiments

To create your own experiments:

1. Create a new Python file (e.g., `my_experiments.py`)
2. Import the necessary functions and constants from `random_allocation`
3. Define your experiment parameters, configuration, and methods
4. Call `run_experiment` with your settings

The package provides two types of plots:
- `PlotType.COMPARISON`: For comparing different methods
- `PlotType.COMBINED`: For showing combined results

## Available Methods

The package includes several methods for comparison:
- `ALLOCATION_ANALYTIC`: Our analytic method
- `ALLOCATION_RDP`: Our RDP-based method
- `ALLOCATION_DECOMPOSITION`: Our decomposition method

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Citation

If you use this code in your research, please cite:
```
@article{yourcitation,
  title={Your Paper Title},
  author={Your Name},
  journal={Journal Name},
  year={2024}
}
```