semvec/pypi-packages huggingface dataset:
hw-oa 0.1.6
通过提示调用不同api，实现请假、考勤、外出等OA功能
Requires Python: >=3.10

hw-oa 
# 项目文档

## 本地运行项目
uvicorn hw_oa_server.main:app --reload
## 安装twine
uv pip install twine
## 发布项目
创建 ~/.pypirc，内容如下
```
[distutils]
index-servers =
    pypi

[pypi]
repository = https://upload.pypi.org/legacy/
username = __token__
password = **** #这个在pypi网站上获取个人的token
```
uv build # 构建，完成后会生成目录 dist，下面放着压缩包
cd /d/workspace/mcp/hwoa_mcp_server/hw_oa #项目所在路径
export PYTHONIOENCODING=utf-8  # 设置 Python 输出编码
twine upload dist/* # 配置 ～/.pypirc后不需要手动输入密码
## 测试
更新 pip install --upgrade  hw-oa
本地运行  uv run hw-oa
如果没问题说明发布成功
[uvicorn参考文档](https://www.uvicorn.org/deployment/)

## 项目描述
- 由cicd工具生成模板
dicom_image_tools 25.0.0
Python package for managing DICOM images from different modalities
Requires Python: >=3.11

[![ci](https://github.com/rvbCMTS/PySkinDose/actions/workflows/ci.yml/badge.svg)](https://github.com/BwKodex/dicomimagetools/actions/workflows/python-package.yml) [![codecov](https://codecov.io/gh/BwKodex/dicomimagetools/branch/master/graph/badge.svg?token=2O32UO12V6)](https://codecov.io/gh/BwKodex/dicomimagetools)

# Dicom Image Tools 
The dicom image tools package was created for giving a framework handling DICOM image data. Adding functionality often used for programmatic image analysis.

The package is still in early development and more features will be added.

# Install Dicom Image Tools

Install using pipenv by running:

``
$ pipenv install dicom-image-tools
``

Install using pip by running:

``
$ pip install dicom-image-tools
``

Only **Python 3.10+** is supported.

See project wiki for more detailed documentation

# Usage

When you've installed the package you import it as any other package using 
 
```python 
import dicom_image_tools 
``` 

## Importing DICOM images
There are two functions for importing DICOM images, ``import_dicom_file(file: pathlib.Path)`` and ``import_dicom_from_folder(folder: pathlib.Path, recursively: bool = True)``.

The latter function has an optional input argument for specifying if the folder given should be searched for DICOM files recursively, default = ``True`` 

Both will return the image/-s in ``DicomStudy`` objects, the ``import_dicom_from_folder`` function returns a dictionary with the _Study Instance UID_ as the _key_ and the corresponding ``DicomStudy`` object as _value_.

You can then add additional files to the ``DicomStudy`` object through the ``DicomStudy.add_file`` which takes the file path as a ``pathlib.Path`` object as input.

The ``DicomStudy.Series`` is a list of all series belonging to the study that has been imported. Each ``DicomStudy.Series`` item is an object containing the image/image volume and metadata for each image. The image/image volume is accessed through the ``ImageVolume`` attribute of the ``DicomStudy.Series`` item, and the metadata in the ``CompleteMetadata`` attribute.
moransi 1.0.1
Compute Moran's Index for images or adjacency matrix
Requires Python: >=3.8

# Moran's Index

[![License MIT](https://img.shields.io/github/license/GuignardLab/moransi?color=green)](https://github.com/GuignardLab/moransi/raw/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/moransi.svg?color=green)](https://pypi.org/project/moransi)
[![Python Version](https://img.shields.io/pypi/pyversions/moransi.svg?color=green)](https://python.org)
[![Copier](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/copier-org/copier/master/img/badge/badge-grayscale-inverted-border-orange.json)](https://github.com/copier-org/copier)

Compute Moran's Index for images or adjacency matrices

----------------------------------

## Installation

You can install `moransi` via [pip]:

```shell
pip install moransi
```

To install latest development version :

```shell
pip install git+https://github.com/GuignardLab/moransi.git
```

## Usage

You can use this code after installing it the following way:

```python
from moransi import morans_i_image

image = ... # opening an image
kernel = ... # A kernel for the weights of the neighbouring pixels

morans_i_image(image, kernel) # Returns the Moran's index
```

```python
from moransi import morans_i_adjacency_matrix

adjacency_matrix = ... # an adjacency matrix of size N by N.
                       # It is either binary or links weights
                       # adjacency_matrix[i, j] is >0 if there is an edge
                       # between i and j
metric = ... # An array of size N where metric[i] is the value of node `i`

morans_i_adjacency_matrix(image, metric) # Returns the Moran's index
```

## Contributing

Contributions are very welcome.

## License

Distributed under the terms of the [MIT] license,
"moransi" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
[tox]: https://tox.readthedocs.io/en/latest/

[file an issue]: https://github.com/GuignardLab/moransi/issues