semvec/pypi-packages huggingface dataset:
odd-even-lang-packs 1.2
Language packs for the Odd Even application
Requires Python: >=3.6

# Odd Even Language Packs

This repository contains language packs for the Odd Even application. These language packs provide translations for various languages to enhance the user experience.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)

## Installation

To install a language pack, follow these steps:
- Install the package using pip:
    ```sh
    pip install odd_even_lang_packs
    ```

## Example Usage

To use the `odd_even_lang_packs` package in your Python application, follow these steps:

1. Import the package in your Python code:
    ```python
    from odd_even_lang_packs import OddEvenLangPacks
    ```

2. Use the odd even language packs:
    ```python
    # Create the OddEvenLangPacks Object
    lang_pack = OddEvenLangPacks()

    # Change the language to Indonesia
    lang_pack.changeLanguage("ID")

    # Check odd or even and print the output in string text
    oddEvenText = lang_pack.check(4)

    print(oddEvenText)
    ```

## Contributing

We welcome contributions to improve and add new language packs. To contribute, follow these steps:

1. Fork the repository.
2. Create a new branch:
    ```sh
    git checkout -b feature/new-language-pack
    ```
3. Make your changes and commit them:
    ```sh
    git commit -m "Add new language pack for [Language]"
    ```
4. Push to the branch:
    ```sh
    git push origin feature/new-language-pack
    ```
5. Create a pull request with a detailed description of your changes.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
JSNMFuP 0.0.3
A unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorization.
Requires Python: >=3.8

# JSNMFup_py
JSNMFuP: A unsupervised method for the integrative analysis of single-cell multi-omics data based on non-negative matrix factorization.

This is the Python implementation of the JSNMFuP algorithm. Note that this implementation supports GPU acceleration.

## 1. Installation
You can use the following command to install JSNMFuP:
```
pip install JSNMFuP-py
```

## 2. Usage
The main class "JSNMFuP" needs to be initialized with three and more objects for the "RNA", "ATAC" data and the "the feature association matrix between 'RNA' and 'ATAC' data. The preprocessed data are stored in 'RNA.X', 'ATAC.X' and 'R'. And the real cell labels should be stored in 'RNA.obs['celltype']'. Note that the data preprocessing process is done via python. The maximum number of epochs for a run (i.e., the 'max_epochs' parameter) is set to 200 by default. therefore, it is very simple to initialize the JSNMFuP model with the following code:

```
from JSNMFuP.model import JSNMFuP
test_model = JSNMFuP(rna,atac,R)
```
After initializing, run the model is also quite easy: 
```
test_model.run()
```
The result is saved in 'test_model.result', which is a dict, and the major output of JSNMFuP, the complete graph S, can be get with easy access:
```
S = test_model.result['S']
```
'JSNMFuP' class also has other methods, you can use the 'help' or '?' command for more details explanations of the methods.
humatron-python-sdk 1.3.14
SDK library for Humatron developers
Requires Python: >=3.11
Keywords: Humatron python

```
 в–€в–€в•—  в–€в–€в•—в–€в–€в•—   в–€в–€в•—в–€в–€в–€в•—   в–€в–€в–€в•— в–€в–€в–€в–€в–€в•— в–€в–€в–€в–€в–€в–€в–€в–€в•—в–€в–€в–€в–€в–€в–€в•—  в–€в–€в–€в–€в–€в–€в•— в–€в–€в–€в•—   в–€в–€в•—
 в–€в–€в•‘  в–€в–€в•‘в–€в–€в•‘   в–€в–€в•‘в–€в–€в–€в–€в•— в–€в–€в–€в–€в•‘в–€в–€в•”в•ђв•ђв–€в–€в•—в•љв•ђв•ђв–€в–€в•”в•ђв•ђв•ќв–€в–€в•”в•ђв•ђв–€в–€в•—в–€в–€в•”в•ђв•ђв•ђв–€в–€в•—в–€в–€в–€в–€в•—  в–€в–€в•‘
 в–€в–€в–€в–€в–€в–€в–€в•‘в–€в–€в•‘   в–€в–€в•‘в–€в–€в•”в–€в–€в–€в–€в•”в–€в–€в•‘в–€в–€в–€в–€в–€в–€в–€в•‘   в–€в–€в•‘   в–€в–€в–€в–€в–€в–€в•”в•ќв–€в–€в•‘   в–€в–€в•‘в–€в–€в•”в–€в–€в•— в–€в–€в•‘
 в–€в–€в•”в•ђв•ђв–€в–€в•‘в–€в–€в•‘   в–€в–€в•‘в–€в–€в•‘в•љв–€в–€в•”в•ќв–€в–€в•‘в–€в–€в•”в•ђв•ђв–€в–€в•‘   в–€в–€в•‘   в–€в–€в•”в•ђв•ђв–€в–€в•—в–€в–€в•‘   в–€в–€в•‘в–€в–€в•‘в•љв–€в–€в•—в–€в–€в•‘
 в–€в–€в•‘  в–€в–€в•‘в•љв–€в–€в–€в–€в–€в–€в•”в•ќв–€в–€в•‘ в•љв•ђв•ќ в–€в–€в•‘в–€в–€в•‘  в–€в–€в•‘   в–€в–€в•‘   в–€в–€в•‘  в–€в–€в•‘в•љв–€в–€в–€в–€в–€в–€в•”в•ќв–€в–€в•‘ в•љв–€в–€в–€в–€в•‘
 в•љв•ђв•ќ  в•љв•ђв•ќ в•љв•ђв•ђв•ђв•ђв•ђв•ќ в•љв•ђв•ќ     в•љв•ђв•ќв•љв•ђв•ќ  в•љв•ђв•ќ   в•љв•ђв•ќ   в•љв•ђв•ќ  в•љв•ђв•ќ в•љв•ђв•ђв•ђв•ђв•ђв•ќ в•љв•ђв•ќ  в•љв•ђв•ђв•ђв•ќ

                   Copyright (C) 2024 Humatron, Inc.
                          All rights reserved.
```

# Humatron Python SDK Library

## Content

This project provides a Python SDK library designed for Humatron developers. The library includes:

- Worker developers section
- REST channel client developers section

### Worker developers section

- Request and Response Classes: Python classes for handling API interactions efficiently.
- Abstract Helper Classes: Simplify asynchronous logic processing.
- REST Utility Methods: Offer helpful functions for working with REST APIs.

### REST channel client developers section

- REST channel client synchronous and asynchronous implementations.

## Installation

```bash
pip install humatron-python-sdk
```

Depends on API section usage following libraries must be installed:

### Worker developers section

- `locked-dict`, version >= `2023.10.22`, mandatory.
    - `pip install locked-dict`.
- `flask`, version >= `3.0.3`, optional.
    - `pip install flask`.

### REST client developers section

- `requests`, version >= `2.32.3`, mandatory.
    - `pip install requests`.

## Usage

- Examples: Visit the examples section on the [Humatron website](https://humatron.ai) for practical use cases and
  demonstrations.
- Test Sections: Review the test sections in the documentation to understand how to implement and use the library's
  features.

## Library Release

- `rm -rf dist/*`
- `pip list --format=freeze > requirements.txt`
- `python setup.py sdist bdist_wheel`
- `twine upload --repository testpypi dist/*` or `twine upload --repository pypi dist/*`

## Documentation generation

- `./make-doc.sh <version>`

  Example: `./make-doc.sh 1.2.0`

<br />
<img src="https://humatron.ai/images/logo_64x64.png" alt="Humatron Logo">