semvec/pypi-packages huggingface dataset:
vnpy-wind 1.0.5
Wind datafeed for VeighNa quant trading framework.
License: MIT
Keywords: quant,quantitative,investment,trading,algotrading

# VeighNa框架的万得Wind数据服务接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-1.0.5-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.8|3.9|3.10|3.11|3.12-blue.svg" />
    <img src ="https://img.shields.io/github/license/vnpy/vnpy.svg?color=orange"/>
</p>

## 说明

基于万得WindPy接口开发，支持以下中国金融市场的K线数据：

* 期货：
  * CFFEX：中国金融期货交易所
  * SHFE：上海期货交易所
  * DCE：大连商品交易所
  * CZCE：郑州商品交易所
  * INE：国际能源交易中心
  * GFEX：广州期货交易所
* 股票：
  * SSE：上海证券交易所
  * SZSE：深圳证券交易所

注意：需要购买[Wind金融终端](https://www.wind.com.cn)，并安装好Python版本数据接口（WindPy）。


## 安装

安装环境推荐基于3.9.0版本以上的【[**VeighNa Studio**](https://www.vnpy.com)】。

直接使用pip命令：

```
pip install vnpy_wind
```


或者下载源代码后，解压后在cmd中运行：

```
pip install .
```
dbdesigner 3.2.0
DBDesigner
License: MIT

# DBDesigner

```
python setup.py sdist
twine upload dist/*

```

* create auth file ~/.pypirc
```
[distutils]
index-servers=pypi
 
[pypi]
repository = https://upload.pypi.org/legacy/
username = <username>
password = <password>
```


```
```
vnpy-sopt 3.7.0.0
SOPT gateway for vn.py quant trading framework.
License: MIT
Requires Python: >=3.8
Keywords: quant,quantitative,investment,trading,algotrading,ctp

# VeighNa框架的CTP期权（ETF）交易接口

<p align="center">
  <img src ="https://vnpy.oss-cn-shanghai.aliyuncs.com/vnpy-logo.png"/>
</p>

<p align="center">
    <img src ="https://img.shields.io/badge/version-3.7.0.0-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/platform-windows|linux-yellow.svg"/>
    <img src ="https://img.shields.io/badge/python-3.8|3.9|3.10|3.11|3.12-blue.svg" />
    <img src ="https://img.shields.io/github/license/vnpy/vnpy.svg?color=orange"/>
</p>

## 说明

基于CTP期权版的3.7.0接口封装开发，接口中自带的是【穿透式实盘环境】的dll文件。

## 安装

安装环境推荐基于3.9.0版本以上的【[**VeighNa Studio**](https://www.vnpy.com)】。

直接使用pip命令：

```
pip install vnpy_sopt
```


或者下载源代码后，解压后在cmd中运行：

```
pip install .
```

使用源代码安装时需要进行C++编译，因此在执行上述命令之前请确保已经安装了【Visual Studio（Windows）】或者【GCC（Linux）】编译器。


## 使用

以脚本方式启动（script/run.py）：

```
from vnpy.event import EventEngine
from vnpy.trader.engine import MainEngine
from vnpy.trader.ui import MainWindow, create_qapp

from vnpy_sopt import SoptGateway


def main():
    """主入口函数"""
    qapp = create_qapp()

    event_engine = EventEngine()
    main_engine = MainEngine(event_engine)
    main_engine.add_gateway(SoptGateway)
    
    main_window = MainWindow(main_engine, event_engine)
    main_window.showMaximized()

    qapp.exec()


if __name__ == "__main__":
    main()
```
webrtcvad123 2.0.11.dev0
Python interface to the Google WebRTC Voice Activity Detector (VAD)
License: MIT
Keywords: speechrecognition asr voiceactivitydetection vad webrtc

.. image:: https://travis-ci.org/wiseman/py-webrtcvad.svg?branch=master
    :target: https://travis-ci.org/wiseman/py-webrtcvad

py-webrtcvad
============

This is a python interface to the WebRTC Voice Activity Detector
(VAD).  It is compatible with Python 2 and Python 3.

A `VAD <https://en.wikipedia.org/wiki/Voice_activity_detection>`_
classifies a piece of audio data as being voiced or unvoiced. It can
be useful for telephony and speech recognition.

The VAD that Google developed for the `WebRTC <https://webrtc.org/>`_
project is reportedly one of the best available, being fast, modern
and free.

How to use it
-------------

0. Install the webrtcvad module::

    pip install webrtcvad

1. Create a ``Vad`` object::

    import webrtcvad
    vad = webrtcvad.Vad()

2. Optionally, set its aggressiveness mode, which is an integer
   between 0 and 3. 0 is the least aggressive about filtering out
   non-speech, 3 is the most aggressive. (You can also set the mode
   when you create the VAD, e.g. ``vad = webrtcvad.Vad(3)``)::

    vad.set_mode(1)

3. Give it a short segment ("frame") of audio. The WebRTC VAD only
   accepts 16-bit mono PCM audio, sampled at 8000, 16000, 32000 or 48000 Hz.
   A frame must be either 10, 20, or 30 ms in duration::

    # Run the VAD on 10 ms of silence. The result should be False.
    sample_rate = 16000
    frame_duration = 10  # ms
    frame = b'\x00\x00' * int(sample_rate * frame_duration / 1000)
    print 'Contains speech: %s' % (vad.is_speech(frame, sample_rate)


See `example.py
<https://github.com/wiseman/py-webrtcvad/blob/master/example.py>`_ for
a more detailed example that will process a .wav file, find the voiced
segments, and write each one as a separate .wav.


How to run unit tests
---------------------

To run unit tests::

    pip install -e ".[dev]"
    python setup.py test


History
-------

2.0.10

    Fixed memory leak. Thank you, `bond005
    <https://github.com/bond005>`_!

2.0.9

    Improved example code. Added WebRTC license.

2.0.8

    Fixed Windows compilation errors. Thank you, `xiongyihui
    <https://github.com/xiongyihui>`_!
cythonrgbasearch 0.10
RGBA color search with Cython
License: MIT
Keywords: cython,RGBA


# RGBA color search with Cython

## pip install cythonrgbasearch

### Tested against Windows / Python 3.11 / Anaconda


## Cython (and a C/C++ compiler) must be installed



```python
from cythonrgbasearch import find_rgba_colors, find_rgb_colors
import cv2
import numpy as np

data = cv2.imread(r"C:\Users\hansc\Downloads\pexels-alex-andrews-2295744.jpg")
# 4525 x 6623 x 3 picture https://www.pexels.com/pt-br/foto/foto-da-raposa-sentada-no-chao-2295744/
data = np.ascontiguousarray(np.dstack([data, np.full(data.shape[:2], 0, dtype=np.uint8)]))
colors1 = np.array(
    [(66, 71, 69), (62, 67, 65), (144, 155, 153), (52, 57, 55), (127, 138, 136), (53, 58, 56), (51, 56, 54),
     (32, 27, 18), (24, 17, 8), ], dtype=np.uint8)
colors1 = np.array([list((x.tolist())) + [0] for x in colors1], dtype=np.uint8)
ocol = np.array([[255, 255, 255, 0]], dtype=np.uint8)
r1 = find_rgba_colors(pic=data, colors=colors1, dummy_alpha=0)
r2 = find_rgba_colors(pic=data, colors=ocol, dummy_alpha=0)
r3 = find_rgb_colors(pic=data, colors=colors1)
r4 = find_rgb_colors(pic=data, colors=ocol)

# r1
# Out[4]:
# rec.array([(136, 138, 127, 0,    0,   38), ( 69,  71,  66, 0,    0, 4522),
#            ( 65,  67,  62, 0,    0, 4523), ...,
#            (  8,  17,  24, 0, 4524, 6620), (  8,  17,  24, 0, 4524, 6621),
#            (  8,  17,  24, 0, 4524, 6622)],
#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1'), ('x', '<i8'), ('y', '<i8')])
# r2
# Out[5]:
# rec.array([(255, 255, 255, 0,  568, 5021), (255, 255, 255, 0,  586, 3064),
#            (255, 255, 255, 0,  612, 2812), ...,
#            (255, 255, 255, 0, 3752,  805), (255, 255, 255, 0, 3752,  806),
#            (255, 255, 255, 0, 3775, 1291)],
#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1'), ('x', '<i8'), ('y', '<i8')])
# r3
# Out[6]:
# rec.array([(136, 138, 127,    0,   38), ( 69,  71,  66,    0, 4522),
#            ( 65,  67,  62,    0, 4523), ..., (  8,  17,  24, 4524, 6620),
#            (  8,  17,  24, 4524, 6621), (  8,  17,  24, 4524, 6622)],
#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('x', '<i8'), ('y', '<i8')])
# r4
# Out[7]:
# rec.array([(255, 255, 255,  568, 5021), (255, 255, 255,  586, 3064),
#            (255, 255, 255,  612, 2812), ..., (255, 255, 255, 3752,  805),
#            (255, 255, 255, 3752,  806), (255, 255, 255, 3775, 1291)],
#           dtype=[('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('x', '<i8'), ('y', '<i8')])


```