semvec/pypi-packages huggingface dataset:
foxtail-blog 0.24.2
A simple blog for Django 
Requires Python: >=3.11

# foxtail-blog [![PyPI](https://img.shields.io/pypi/v/foxtail-blog)](https://pypi.org/project/foxtail-blog/)
A simple, reusable Django blog application. This application is designed for
internal use and the API should not be considered stable.

## License

This software is released under the MIT license.
```
Copyright (c) 2019-2024 Luke Rogers

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
foxtail-contact 0.6.0
A simple contact form for Django 
Requires Python: >=3.6

# foxtail-contact  [![PyPI](https://img.shields.io/pypi/v/foxtail-contact)](https://pypi.org/project/foxtail-contact/)
A simple, reusable Django contact form application. This application is designed for
internal use and the API should not be considered stable.

## License

This software is released under the MIT license.
```
Copyright (c) 2019-2024 Luke Rogers

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
shii-study 1.1.4
PYPI tutorial package creation written by Boran
Requires Python: >=3.8

hello! 반갑습니다! 이 파일은 shii 폴더의 README.md 파일입니다.
```import shii```
```shii.study(text, text, text)```
```shii.study_return_text(text)```
```shii.study_say(text)```
자세한 것은 https://github.com/boranloves/shii 를 참고 하세요.
CellChip 1.2
An algorithm framework employing multi-layered regional constraints to accurately assign cell locations, enhancing spatial accuracy and resilience to noise.
Requires Python: >=3.8.0

Mapping cell locations via multi-layer regionalization constraints
=========================================================================

Introduction
------------
Resolving spatial cell arrangement is crucial for understanding physiological and pathological processes. While scRNA-seq captures gene expression at single-cell resolution, it loses spatial context, and current spatial transcriptomics methods often compromise on throughput or resolution. Existing integration methods face challenges with accuracy and scalability due to noise from molecular diffusion, cell segmentation errors, and disproportionate cell-type representation. We present CellChIP, an algorithm framework employing multi-layered regional constraints to accurately assign cell locations, enhancing spatial accuracy and resilience to noise. Comparative analysis on benchmark datasets demonstrates CellChIP’s superior accuracy and scalability over existing methods. Applied to liver cancer tissue, CellChIP revealed spatial heterogeneity of cDC cells, a detail missed by deconvolution-based techniques, and achieved cell-cell interaction resolution beyond traditional mapping approaches. Additionally, CellChIP outperforms current techniques in computational efficiency and resource usage, particularly with large-scale stereo-seq data, underscoring its potential for broad application.

.. image:: ./overview.png
  :width: 1200
  :align: center
  :alt: Overview of CellChip

Installation
------------
CellChip can be installed either through GitHub or PyPI.

To install from GitHub:

.. code-block:: bash

    git clone https://github.com/caiquanyou/CellChip
    cd CellChip
    python setup.py install # or pip install .

Alternatively, install via PyPI using:

.. code-block:: bash

    pip install CellChip

Usage
-----
After installation, CellChip can be used in Python as follows:

.. code-block:: python

    import CellChip as cc

Contributing
------------
Contributions to CellChip are welcome. Please refer to the project's issues and pull requests for areas where you can help.

License
-------
(Include license information here if available)

Support and Contact
-------------------
For support or to contact the developers, please use the project's GitHub Issues page.