semvec/pypi-packages huggingface dataset:
synthetic-dataset 0.0.0.2
Generating accurate and safe synthetic datasets for tabular, classification, and time-series labeling tasks
Requires Python: >=3.8
Keywords: python,pandas,numpy,scikit-learn,scipy,matplotlib,seaborn


# Synthetic Data Generation for Tabular, Classification, and Time-Series Labels

This repository contains a Python-based framework for generating accurate and safe synthetic datasets for tabular, classification, and time-series labeling tasks. It is designed to help researchers, data scientists, and machine learning engineers create high-quality, realistic datasets for training and evaluating their models while ensuring privacy and compliance with data protection regulations.

## Features

1. **Tabular Data Generation**: Easily generate synthetic tabular datasets with customizable column types, distribution patterns, and correlations between variables.
2. **Classification Data Generation**: Create datasets for binary or multi-class classification tasks, controlling class imbalance and feature importance.
3. **Time-Series Data Generation**: Generate synthetic time-series datasets with user-defined seasonality, trend, and noise components.
4. **Data Privacy**: Ensure data privacy by using differential privacy techniques and limiting the degree of similarity between the original and synthetic datasets.
5. **Flexible and Extensible**: The framework is designed to be easily extended and adapted to a wide range of data generation tasks, with support for custom data generation modules and integration with other data generation tools.

## Installation

Clone the repository and install the required dependencies:

```bash
git clone https://github.com/syntheticdataset/synthetic-dataset.git
cd synthetic-dataset
pip install -r requirements.txt
```

## Usage
Refer to the provided examples and documentation for guidance on how to generate synthetic datasets for your specific use case.

from synthetic_data import TabularDataGenerator, ClassificationDataGenerator, TimeSeriesDataGenerator

```python
# Tabular data generation
tabular_gen = TabularDataGenerator(num_rows=1000)
tabular_data = tabular_gen.generate()

# Classification data generation
classification_gen = ClassificationDataGenerator(num_samples=1000, num_classes=3)
classification_data, labels = classification_gen.generate()

# Time-series data generation
time_series_gen = TimeSeriesDataGenerator(num_samples=1000, seasonal_period=12)
time_series_data = time_series_gen.generate()
```


## Contributing
Please read the CONTRIBUTING.md file for details on how to contribute to the project. We welcome pull requests, bug reports, and feature requests.

## License
This project is licensed under the MIT License - [Licence](https://github.com/syntheticdataset/synthetic-dataset/blob/main/LICENSE) see the  file for details.
advantage-air 0.4.4
API helper for Advantage Air's MyAir and e-zone API
Requires Python: >=3.6

Advantage Air API Wrapper

# Get

Returns the current state of all components.

> async_get()

# Set

Change attributes by sending the updated values to the relevant endpoint.

## aircon.async_update_ac(ac: str, data: dict)

Update values on the AC system

## aircon.async_update_zone(ac: str, zone: str, data: dict)

Update values on a specific zone

## lights.async_update_state(id: str, state: str|bool)

Update the state of a light. off|false = off, on|true = on

## lights.async_update_value(id: str, value: int)

Update the brightness of a light, and assumes 0 is off.

## things.async_update_value(id: str, value : int|bool)

Update the value of a thing. 0|false = off, 100|true = on

## \*.async_update(data: dict)

Directly update with data to the endpoint.

# Example

```{.python}
import asyncio
import aiohttp
from advantage_air import advantage_air


async def main():
    async with aiohttp.ClientSession() as session:
        aa = advantage_air("192.168.1.24", port=2025, session=session, retry=5)
        if data := await aa.async_get(1):
            print(data)
            aa.aircon.async_update_ac("ac1",{"state": "on"})
            await asyncio.gather(
                aa.aircon.async_update_zone("ac1","z01", {"value": 25}),
                aa.aircon.async_update_zone("ac1","z02", {"value": 50}),
            )

asyncio.run(main())
```