semvec/pypi-packages huggingface dataset:
netas-zeep 4.0.0
A modern/fast Python SOAP client based on lxml / requests, netas version
License: MIT
Requires Python: >=3.6

========================
Zeep: Python SOAP client
========================

A fast and modern Python SOAP client

Highlights:
 * Compatible with Python 3.6, 3.7, 3.8 and PyPy
 * Build on top of lxml and requests
 * Support for Soap 1.1, Soap 1.2 and HTTP bindings
 * Support for WS-Addressing headers
 * Support for WSSE (UserNameToken / x.509 signing)
 * Support for asyncio using the httpx module
 * Experimental support for XOP messages


Please see for more information the documentation at
http://docs.python-zeep.org/




Installation
------------

.. code-block:: bash

    pip install zeep

Note that the latest version to support Python 2.7, 3.3, 3.4 and 3.5 is Zeep 3.4, install via `pip install zeep==3.4.0`


Usage
-----
.. code-block:: python

    from zeep import Client

    client = Client('tests/wsdl_files/example.rst')
    client.service.ping()


To quickly inspect a WSDL file use::

    python -m zeep <url-to-wsdl>


Please see the documentation at http://docs.python-zeep.org for more
information.


Support
=======

If you want to report a bug then please first read
http://docs.python-zeep.org/en/master/reporting_bugs.html

Please only report bugs and not support requests to the GitHub issue tracker.
rxbus 0.1.2
基于rxpy 实现的RxBus
License: MIT
Requires Python: >=3.6.0

# RxBus

#### 介绍
基于rxpy 实现的RxBus

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 码云特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  码云官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解码云上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目
5.  码云官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  码云封面人物是一档用来展示码云会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
PySild2QtScheduler 0.0.3
基于Pyside2 实现的markdwon edit
License: MIT
Requires Python: >=3.6.0

# PySild2QtScheduler

#### 介绍
{**以下是码云平台说明，您可以替换此简介**
码云是 OSCHINA 推出的基于 Git 的代码托管平台（同时支持 SVN）。专为开发者提供稳定、高效、安全的云端软件开发协作平台
无论是个人、团队、或是企业，都能够用码云实现代码托管、项目管理、协作开发。企业项目请看 [https://gitee.com/enterprises](https://gitee.com/enterprises)}

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 码云特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  码云官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解码云上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目
5.  码云官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  码云封面人物是一档用来展示码云会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
EasyMenusBar 0.3.5
基于Pyside2 实现的menusbar
License: MIT
Requires Python: >=3.6.0

# EasyMenusBar

#### 介绍
PySide2 MenusBar

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 码云特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  码云官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解码云上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目
5.  码云官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  码云封面人物是一档用来展示码云会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
pyviewercloud 0.2.1
Python bindings for viewercloud
License: MIT
Keywords: python

# pyviewercloud

pyviewercloud are the bindings to use viewercloud directly in python.

`Viewercloud` is a library and also a cli to read and display Pointcloud.
It was initially used to display [KITTI](http://www.cvlibs.net/datasets/kitti/) pointcloud.
However it was also tested on the [Lyft Level 5 Dataset](https://self-driving.lyft.com/level5/data/) pointcloud.

It will also be able to display the 3D annotations and the 3D BoundingBox computed by your favorite algorithm.

Viewercloud will open a openGL window to display the pointcloud. Press `q`to close.

![kitti-centroid](https://raw.githubusercontent.com/ThomAub/viewercloud/master/assets/kitti_with_centroid.png)

It can also take a screen shot of the current view to save as png. Press `s` take screenshot.

![lyft-color](https://raw.githubusercontent.com/ThomAub/viewercloud/master/assets/screenshot.png)

## Python Usage

You can install `pyviewercloud` the python bindings to `viewercloud` thanks to [Pyo3](https://github.com/PyO3/pyo3) and [Pyo3-numpy](https://github.com/PyO3/rust-numpy)

```bash
poetry add pyviewercloud
```

```bash
pip install pyviewercloud
```

```python
import numpy as np
import pyviewercloud as pyviewer

# Create a new viewer with a window size 1200x1800.
viewer = pyviewer.PointcloudViewer(1200, 1800, 15000)

# Load some pointcloud from the lyft perception dataset
# Currently only support pointcloud as numpy.ndarray Nx3 in np.float32
lyft_point_cloud_1 = np.fromfile("tests/data/lyft/host-a101_lidar0_1241893239502712366.bin", dtype=np.float32).reshape((-1, 5))[:,:3]
lyft_point_cloud_2= np.fromfile("tests/data/lyft/host-a101_lidar1_1241893239502712366.bin", dtype=np.float32).reshape((-1, 5))[:,:3]
lyft_point_cloud_3= np.fromfile("tests/data/lyft/host-a101_lidar2_1241893239502712366.bin", dtype=np.float32).reshape((-1, 5))[:,:3]

# Add them one by one to the viewer to have different color
viewer.add_pointcloud(lyft_point_cloud_1, [255, 0, 0])
viewer.add_pointcloud(lyft_point_cloud_2, [0, 0, 255])
viewer.add_pointcloud(lyft_point_cloud_3, [0, 255, 0])

# You can now display the window
viewer.show()
```

```python
import numpy as np
import pyviewercloud as pyviewer

# Create a new viewer with a window size 1200x1800.
viewer = pyviewer.PointcloudViewer(1200, 1800, 15000)

# Load some pointcloud from the kitti dataset
kitti_point_cloud = np.fromfile("tests/data/kitti/velodyne/000001.bin", dtype=np.float32).reshape((-1, 4))[:,:3]
viewer.add_pointcloud(kitti_point_cloud, [255, 255, 255])

# Add some centroids to have the same color.
# Currently only support centroids as numpy.ndarray Nx3 in np.float32
centroids = np.array([[-11.5,0,-0.8]]).astype(np.float32)
viewer.add_centroid(centroids, [255, 0, 0])
viewer.show()
```
fixturepy 0.5
Create random data to be used in tests
License: MIT
Keywords: UnitTest,Fixture

# Python data fixtures

[![Build Status](https://travis-ci.org/exentriquesolutions/fixturepy.svg?branch=master)](https://travis-ci.org/github/exentriquesolutions/fixturepy)

Create data fixtures to use them in  your tests

Sample usage:

    >>> from fixturepy import Fixture, Email

    >>> fixture = Fixture()

    >>> fixture(int) # create an integer
    20932

    >>> fixture(str) # create a string
    '63d0b4e450354948b69f6c3b4f9238f9'

    >>> fixture(Email) # create an email
    'ad34d31609344283bd7ab77922b75e8b@14a8ba0c73a64ffda645cdd4d776757e.com'