semvec/pypi-packages huggingface dataset:
hangman-package 1.0.2
A package to play a custom hangman game
Requires Python: >=3.8

# Hangman Package

The Hangman package is a Python implementation of the classic game, Hangman. It allows users to play the game by guessing letters to complete a hidden word.

## Installation

The package can be installed using pip:

```bash
pip install hangman-package
```

## Usage

The package can be used in the following way:

```python
from hangman import Hangman

# Create a new instance of Hangman
hangman = Hangman()

# Start the game
hangman.play()
```

You can pass a list of strings to utilize custom words:
```python
from hangman import Hangman

words = ["banana", "orange", "apple", "grape", "strawberry", "lemon", "watermelon"]

# Create a new instance of Hangman with custom words
hangman = Hangman(words=words)

# Start the game
hangman.play()
```

When `play()` is called, the game will begin and the user will be prompted to guess letters to complete the hidden word. The game ends when the user either completes the word or runs out of guesses.

## Contributing

Contributions to the Hangman package are welcome! If you encounter a bug or have a feature request, please open an issue on the [GitHub repository](https://github.com/viniciusenari/hangman-package).

If you would like to contribute to the package, please fork the repository and submit a pull request with your changes.

## License

The Hangman package is open-source software licensed under the [MIT license](https://opensource.org/licenses/MIT).
DeepMap 1.1
The DeepMap is a deep learning based python package for genotype to phenotype mapping.
Requires Python: >=3.6

Deep Learning has rapidly evolved and is now routinely being used in prediction-based studies for crop improvement. In this study, an effort was made to develop the DeepMap, a unique deep learning-enabled python package using DNN for genomic prediction. It can be outstretched across crops such as maize, wheat, soybean etc. It utilizes epistatic interactions for data augmentation and outperforms the existing state-of-the-art machine/deep learning models such as Bayesian LASSO, GBLUP, DeepGS, and dualCNN. It is hosted on Python Package Index (PyPI) for ease of use to encourage reproducibility with four-line code execution that imports libraries, takes genotypic and phenotypic data, invokes model for training/testing data and stores result. The DeepMap can be further improve by adding environmental interactions, incorporating nascent architecture of deep learning such as GANs, autoencoders, transformers, and the development of a graphical user interface would increase the user community. Please contact, irrisah@gmail.com for more information.
tsmart 0.1.0
Library for accessing T-Smart thermostats
Requires Python: >=3.7

# Python library for T-Smart smart thermostats

This library provides access to [T-Smart smart thermostats](https://www.tsmart.co.uk/).

```py
from tsmart import TSmart
import asyncio

async def find_devices():
    devices = await TSmart.async_discover()
    print("Found %d devices" % len(devices))
    for device in devices:
        await device.async_get_status()
        print("Thermostat: %s" % device.name)
        print("Temperature: %.1f°C" % device.temperature)

asyncio.run(find_devices())
```