semvec/pypi-packages huggingface dataset:
asgi-logger 0.1.0
Middleware based uvicorn access logger! :tada:
License: MIT
Requires Python: >=3.8,<4.0

<h1 align="center">
    <strong>asgi-logger</strong>
</h1>
<p align="center">
    <a href="https://github.com/Kludex/asgi-logger" target="_blank">
        <img src="https://img.shields.io/github/last-commit/Kludex/asgi-logger" alt="Latest Commit">
    </a>
        <img src="https://img.shields.io/github/workflow/status/Kludex/asgi-logger/Test">
        <img src="https://img.shields.io/codecov/c/github/Kludex/asgi-logger">
    <br />
    <a href="https://pypi.org/project/asgi-logger" target="_blank">
        <img src="https://img.shields.io/pypi/v/asgi-logger" alt="Package version">
    </a>
    <img src="https://img.shields.io/pypi/pyversions/asgi-logger">
    <img src="https://img.shields.io/github/license/Kludex/asgi-logger">
</p>


## Installation

``` bash
pip install asgi-logger
```

## License

This project is licensed under the terms of the MIT license.
scmvae 1.0.0
a comprehensive single-cell multimodal analysis python package based on mixed variational autoencoder
License: MIT
Requires Python: >=3.6.5


The paired measurement of multiple modalities, known as multimodal analysis, is an exciting frontier for connecting single-cell genomics with epitopes and functions. Mapping of transcriptomes in single-cells and the integration with cell phenotypes enable a better understanding of cellular states. However, assembling these paired omics into a unified representation of the cellular state remains challenging with the unique technical characteristics of each measurement. In this work, we present a python package for single-cell multimodal analysis based on a mixing variational autoencoder that not only joins single-cell transcriptomes and epitopes but also enables simultaneous execution of independent modal analysis.