semvec/pypi-packages huggingface dataset:
lucid-dream 0.1.2
A package for simulating and enhancing lucid dreaming experiences.

# Lucid Dream

The `lucid-dream` Python package provides a simple and interactive way to simulate and enhance the experience of lucid dreaming. It includes features such as meditation techniques, lucid dream initiation, and reality checks.

## Features

- **Enter and Exit Lucid Dream**: Control the state of your lucid dream.
- **Meditation Techniques**: Guide the user through meditation to enhance lucid dreaming.
- **Reality Checks**: Includes a simple reality check method to confirm lucid dreaming.
- **Lucid Dreaming Techniques**: Provides tips to improve the likelihood of having a lucid dream.

## Installation

You can install the package via pip:

```
pip install lucid-dream
```

## Usage

```python
from lucid_dream.lucid import LucidDream

# Create a LucidDream instance
lucid = LucidDream()

# Enter lucid dreaming state
print(lucid.enter_lucid_dream())
# Output: You are now lucid dreaming!

# Check if currently lucid dreaming
print(lucid.is_lucid())
# Output: True

# Set meditation time
print(lucid.set_meditation_time(10))
# Output: Meditation time set to 10 minutes.

# Start meditation
print(lucid.meditate())
# Output: Meditation complete. You are relaxed and focused.

# Use lucid dream technique for better results
print(lucid.lucid_dream_technique())
# Output: Instructions on how to enhance your lucid dreaming experience.

# Exit lucid dream state
print(lucid.exit_lucid_dream())
# Output: You have exited the lucid dream.
```

## Functions

- **`enter_lucid_dream()`**: Initiates the lucid dream.
- **`exit_lucid_dream()`**: Ends the lucid dream.
- **`is_lucid()`**: Checks if the user is currently lucid dreaming.
- **`set_meditation_time(minutes)`**: Sets the meditation duration (in minutes).
- **`meditate()`**: Guides the user through a meditation technique.
- **`lucid_dream_technique()`**: Provides a technique to enhance lucid dreaming.

## License

This package is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.



**Repository Views** ![Views](https://profile-counter.glitch.me/lucidream/count.svg)
pollux-application 0.2.2
Core application library of the POLLUX Digital Twin
Requires Python: <4.0,>=3.11

[![Test workflow status](https://github.com/POLLUX-Digital-Twin/pollux-application/actions/workflows/python_test.yaml/badge.svg)](https://github.com/POLLUX-Digital-Twin/pollux-application/actions/workflows/python_test.yaml)
[![Lint workflow status](https://github.com/POLLUX-Digital-Twin/pollux-application/actions/workflows/python_lint.yaml/badge.svg)](https://github.com/POLLUX-Digital-Twin/pollux-application/actions/workflows/python_lint.yaml)


# pollux-application

Application repository of the POLLUX Digital Twin


## Bug reports and discussions

If you think you found a bug, feel free to open an [issue](https://github.com/POLLUX-Digital-Twin/pollux-application/issues).
Focused suggestions and requests can also be opened as issues. Before opening a pull request, please start an issue or a discussion on the topic.

## Contribution

If you want to contribute to the package, please read our [CONTRIBUTING.md](https://github.com/POLLUX-Digital-Twin/pollux-application/blob/main/CONTRIBUTING.md) file.

## License

This content is released under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) License.


## Acknowledgments
Organization: TNO
srilanka 1.0.1
A comprehensive package for working with data about Sri Lanka, including provinces, districts, and cities.
Requires Python: >=3.6
Keywords: Sri Lanka, provinces, districts, cities, data, utility

# Srilanka

The `srilanka` package provides data and utility functions to work with information about Sri Lanka, including its provinces, districts, cities, and divisional secretariats.

## Installation

```sh
pip install srilanka
```

## Usage

```python
from srilanka import SriLanka

# Access core data
info = SriLanka.sri_lanka_info
provinces = SriLanka.sri_lanka_provinces
districts = SriLanka.sri_lanka_districts
cities = SriLanka.sri_lanka_cities
divisional = SriLanka.sri_lanka_divisional


```

<!-- # Example usage
province = SriLanka.get_province_by_name("Western")
districts = SriLanka.get_districts_by_province_name("Western")
cities = SriLanka.get_cities_by_district_name("Colombo") -->

## Available Data

- `sri_lanka_info`: General information about Sri Lanka
- `sri_lanka_provinces`: List of provinces
- `sri_lanka_districts`: List of districts
- `sri_lanka_cities`: List of cities
- `sri_lanka_divisional`: List of divisional secretariats
<!-- 
## Methods

### Location Queries
- `get_province_by_name(name)`: Get province by name
- `get_district_by_name(name)`: Get district by name
- `get_divisional_by_name(name)`: Get divisional secretariat by name

### Hierarchical Queries
- `get_districts_by_province_name(name)`: Get districts in province
- `get_cities_by_district_name(name)`: Get cities in district
- `get_province_by_district_name(name)`: Get province of district
- `get_province_by_divisional_name(name)`: Get province of divisional secretariat

### ID-based Queries
- `get_province_by_id(id)`
- `get_district_by_id(id)`
- `get_divisional_by_id(id)`
- `get_districts_by_province_id(id)`
- `get_cities_by_district_id(id)`
- `get_cities_by_province_id(id)`

### Data Connections
- `connect_province_district()`
- `connect_district_divisional()`
- `connect_divisions_to_districts_and_provinces()` -->

## License

MIT License - see [LICENSE](LICENSE)


**Repository Views** ![Views](https://profile-counter.glitch.me/srilankap/count.svg)
pollux-model 0.2.1
Core model library of the POLLUX Digital Twin
Requires Python: <4.0,>=3.11

[![Test workflow status](https://github.com/pollux-Digital-Twin/pollux-model/actions/workflows/python_test.yaml/badge.svg?branch=main)](https://github.com/pollux-Digital-Twin/pollux-model/actions/workflows/python_test.yaml?query=branch%3Amain)
[![Lint workflow status](https://github.com/pollux-Digital-Twin/pollux-model/actions/workflows/python_lint.yaml/badge.svg?branch=main)](https://github.com/pollux-Digital-Twin/pollux-model/actions/workflows/python_lint.yaml?query=branch%3Amain)



# pollux-model

This repository is part of the 'POLLUX Digital Twin' project. 

Model library of the POLLUX Digital Twin


## Bug reports and discussions

If you think you found a bug, feel free to open an [issue](https://github.com/pollux-digital-twin/pollux-model/issues).
Focused suggestions and requests can also be opened as issues. Before opening a pull request, please start an issue or a discussion on the topic.

## Contribution

If you want to contribute to the package, please read our [CONTRIBUTING.md](https://github.com/pollux-digital-twin/pollux-model/blob/main/CONTRIBUTING.md) file.

## License

This content is released under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0.html) License.


## Acknowledgments
Organization: TNO
modulus-ml 0.1.0
A library for comparing and evaluating ML models
Requires Python: >=3.6

# Modulus-ML

**Modulus-ML** is a Python library for comparing and evaluating machine learning models. It simplifies the process of benchmarking multiple models by providing an easy-to-use interface for model comparison, performance evaluation, and summary generation.

## Features

- Add multiple machine learning models for comparison.
- Evaluate models on classification and regression tasks.
- Generate detailed summaries of model performance.
- Select the best model based on evaluation metrics.

## Installation

To install Modulus-ML, use pip:

```bash
pip install modulus-ml
```

## Usage

Here is a quick example of how to use Modulus-ML:

```python
from modulus_ml import ModelComparator
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification

# Create sample data
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)

# Initialize comparator
comparator = ModelComparator()

# Add models
comparator.add_model('logistic', LogisticRegression())
comparator.add_model('random_forest', RandomForestClassifier())

# Run comparison
results = comparator.compare(X, y)

# Print summary
print(comparator.summary())

# Get best model
best_model = comparator.get_best_model()
print(f"Best model: {best_model}")
```

## Requirements

- Python 3.6+
- scikit-learn
- numpy
- pandas
- matplotlib

## Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgements

Thanks to the open-source community for providing tools and inspiration for this library.