semvec/pypi-packages huggingface dataset:
WeatherSPy 1.2.1
a weather library
Requires Python: >=3.6

# 下载
```shell script
pip install WeatherSPy -i https://pypi.org/project/
```
# 使用
导入库:
```python
import WeatherSPy as ws
```
在命令行或终端输入:
```shell script
python -m WeatherSPy 北京
```
可以查看北京的天气信息。

在python中:
```python
import WeatherSPy as ws
beijing = ws.City('北京')
weather = ws.get_weather(beijing)
tomorrow = weather[1]  # 获取明日天气
```
oneNeuron-pypi-saquibquddus 0.0.7
It's a implementation of Perceptron and all_utils
Requires Python: >=3.7

# oneNeuron_pypi
oneNeuron_pypi

## Reference -
[Python package docs](https://packaging.python.org/tutorials/packaging-projects/)

[Github doc for gihub actions](https://docs.github.com/en/actions/guides/building-and-testing-python#publishing-to-package-registries)

## How to use
```python
from oneNeuron.perceptron import Perceptron

## get X and y and then use below commands
model = Perceptron(eta=eta, epochs=epochs)
model.fit(X, y)
```
validator-toolbox 1.0.7
Harmony ONE Validator Node Toolbox and Easy Setup
Requires Python: >=3.6

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/c023922143c04967bffb1a2469af938e)](https://app.codacy.com/gh/easy-node-one/validator-toolbox?utm_source=github.com&utm_medium=referral&utm_content=easy-node-one/validator-toolbox&utm_campaign=Badge_Grade_Settings)
[![Telegram Badge](https://img.shields.io/badge/chat-telegram-brightgreen?logo=telegram)](https://t.me/easynodesupport)
[![Discord Badge](https://img.shields.io/badge/chat-discord-brightgreen?logo=discord)](https://discord.gg/babnYCEZ7Q)
[![Stake Now Badge](https://img.shields.io/badge/stake-harmony-brightgreen)](https://bit.ly/easynode)


# Easy Node - Validator Toolbox
by [EasyNode.ONE](http://EasyNode.ONE "EasyNode.ONE")

This repository provides two functions in one application:
-   Harmony Validator Menu Driven Installer
-   Harmony Validator Management Interface Menu

[![Easy Node - Harmony Validator Operations Toolbox](http://img.youtube.com/vi/ydvMXFDrHwg/0.jpg)](http://www.youtube.com/watch?v=ydvMXFDrHwg "Easy Node - Harmony Validator Operations Toolbox")

# Quickstart Guide
#### Pre-Installation Requirements:
-   ✔️ A new Ubuntu 20.04LTS server
-   ✔️ Login as a user account with sudoless root access or run and install as root
	-   Digital Ocean Admins: Use the same guide as our validator-toolbox Application to setup "User Data" on your Digital Ocean Droplet.
	-   Other Providers Admins: Use the Any Other Provider Guide on how to edit your sudoers file or give access via groups.
-   ✔️ Volume Support
	-   Digital Ocean Auto Mounted Volumes will be detected and used for installation
	-   Any other setup: Mount a volume at ~/harmony before you start or in a folder in /mnt and we\'ll install there and symlink to ~/harmony

#### Installation - New & Existing servers:
After completing the pre-requisits pull the repository into your home directory.
```bash
git clone https://github.com/easy-node-one/validator-toolbox.git
```
Install requirements.
```bash
cd validator-toolbox
pip3 install -r requirements.txt
```
Run the installer from anywhere. You'll be presented with our menu to grab configuration details.
```bash
python3 ~/validator-toolbox/toolbox/start.py
```

# Full Installation Guide & Documentation
See the gitbook for validator-toolbox to [install our software here](https://validator-toolbox-guide.easynode.one/ "validator-toolbox gitbook guide").

# Community
Join our [Discord](https://discord.gg/babnYCEZ7Q "Discord") or [Telegram](https://t.me/easynodesupport "Telegram") servers for free comunity driven support.
trboxcmp 0.0.6
A collection of libraries to handle the XCMP protocol for Motorola radios
Requires Python: >=3.6

# trboxcmp

This is a library that interfaces with the MotoTRBO XCMP protocol. This allows control of a radio via remote.

!! NOTE !!

This is beta software. Not everything will work and it will be buggy. I am by no means a real developer, I just try stuff until it works.

## TODOs

Todos:

* Add brightness control and parsing
* Add channel, display text, etc parsing
* Add keypad button opcodes

## Basic Layout

Most of the basic data services can be set up as such:

    from trboxcmp import xcmp
    keys = [
        0x00000000,
        0x00000000,
        0x00000000,
        0x00000000
    ]
    delta = 0x00000000
    listener = xcmp.XCMP(keys, delta)
    listener.connect()
    listener.sendChUp()

The true keys and delta are property of Motorola Solutions and will not be included with this library.

## Documentation

    listener.close()

Closes the connection to the radio.

    listener.setChannel(chan)

Sets the radio to channel number <chan> in the current zone.

    listener.setZone(zone)

Sets the radio to zone number <zone>.

    listener.chUp()

Increments the current channel.

    listener.chDown()

Decrements the current channel.

    listener.enterRSSI()

Quick macro to enter the "hidden" RSSI menu on the radio (left, left, left, right, right, right).

    listener.pressButton(button)

Presses <button> on the radio. <button> can be any of those found in ButtonCodes:
    class ButtonCodes():
    STATUS_PRESSED = b'\x01'
    STATUS_RELEASED = b'\x00'
    LEFT = b'\x80'
    RIGHT = b'\x82'
    UP = b'\x87'
    DOWN = b'\x88'
    MENU = b'\x8b'
    BACK = b'\x81'
    OK = b'\x55'
    P1 = b'\xa0'
    P2 = b'\xa1'
    P3 = b'\xa2'
    P4 = b'\xa3'
yolov5-inference 0.8
code de inference yolov5
Requires Python: >=3.6

Package dung de inference model yolov5 voi custom object
# Install
```bash
pip install yolov5-inference==0.6
```

# Use
```bash
from yolov5_inference.infer import DetectObject
class_name = {0: 'text', 1: 'title', 2: 'list', 3: 'table', 4: 'figure'}
model = DetectObject(weights='../path/of/weights', classes=class_name)
img_path = '../path/of/img'
img = cv2.imread(img_path)
rs = model.detect(img)
# [x_min, y_min, x_max, y_max, cls]
```
BMI500caonia 2.0.0
BMI500 HW4
Requires Python: >=3.6
Keywords: example,pypi,package

# The Kmeans unsupervised clustering package

# Contents
This package is an example Kmeans' package for BMI500's project in Emory University. The package will automatically download the iris data collected from UCI. The dataset contains three classes of flowers, and the clustering algorithm is to seperate each group of the flowers. It does not necessary tell you what kind of flower is, but will tell you which flowers are in a same group.

# FAQ

## How to install?

In your command line, type "pip install BMI500caonia"

## How to use

python

from BMI500caonia import BMI500clustering

BMI500clustering.Kmeans_run(n, iteration, random_state)

(n is the number of clusters, iteration is the number of iteration, random_state is the number of random initializations)

## Running time and hardware requirement

The running time is 14 seconds in Titan'x 12 GB gpu and Intel Iris 16 GB cpu. 

## Future work

The function should be modified to be more flexible in the future, so the user can customize parameters.
Perceptron-pypi-SourabhSomdeve 0.0.2
An Implementation of one Neuron Perceptron
Requires Python: >=3.7

# Perceptron_pypi
OneNeuron | Perceptron package

This is only for showing how perceptron works you can try with and and or gate data.

# How to use it

```python
from OneNeuron.Perceptron import Perceptron

## get X and y and then use below commands
model = Perceptron(eta=eta, epochs=epochs)
model.fit(X, y)

```

# References
[Python PyPI docs](https://packaging.python.org/tutorials/packaging-projects/)

[Github Action docs](https://docs.github.com/en/actions/guides/building-and-testing-python#publishing-to-package-registries)