semvec/pypi-packages huggingface dataset:
zy-test-one 1.0
AI数据转换工具箱
Requires Python: >=3.7

<div align="center">
  <img src="resources/xf_logo_new.jpg" width="600"/>
</div>

## Introduction

English | [简体中文](README_zh-CN.md)

Chipeak_data_tool is an open source toolbox based on the data sets generated by various artificial intelligence annotation tools and the data sets generated by the deep learning image annotation tool labelme.

The main branch code currently supports the mutual conversion of coco dataset and labelme dataset.


### Major features

- **Modular Design**

  Various data sets are decoupled into different module components. By combining different module components, users can easily build custom data set processing.

## License

This project is released under the [GNU General Public License v3.0 2.0](LICENSE).

## Changelog

v1.0 was released in 28/10/2021. 


Supported datasets:

- [x] Coco dataset
- [x] Via annotation dataset
- [x] Labelme dimension dataset


Supported features:

- [x] Labelme dataset matting
- [x] Coco dataset to labelme dataset
- [x] Labelme dataset to coco dataset
- [x] Filter the labelme dataset by label category




## Installation

Please refer to [get_started.md](docs/get_started.md) for installation.

## Getting Started
Please see [get_started.md](docs/get_started.md) for the basic usage of chipeak-data-tool.


Scan the QR code below to follow the [official website] of Xinfeng technology team（ http://http://www.chipeak.com/ ）, join the [official exchange] of Xinfeng technology team（ http://www.chipeak.com/account/login )
<div align="center">
<img src="/resources/xf_rq_code.png" height="400" />
</div>
zy-test-two 1.0
AI数据转换工具箱
Requires Python: >=3.7

<div align="center">
  <img src="resources/xf_logo_new.jpg" width="600"/>
</div>

## Introduction

English | [简体中文](README_zh-CN.md)

Chipeak_data_tool is an open source toolbox based on the data sets generated by various artificial intelligence annotation tools and the data sets generated by the deep learning image annotation tool labelme.

The main branch code currently supports the mutual conversion of coco dataset and labelme dataset.


### Major features

- **Modular Design**

  Various data sets are decoupled into different module components. By combining different module components, users can easily build custom data set processing.

## License

This project is released under the [GNU General Public License v3.0 2.0](LICENSE).

## Changelog

v1.0 was released in 28/10/2021. 


Supported datasets:

- [x] Coco dataset
- [x] Via annotation dataset
- [x] Labelme dimension dataset


Supported features:

- [x] Labelme dataset matting
- [x] Coco dataset to labelme dataset
- [x] Labelme dataset to coco dataset
- [x] Filter the labelme dataset by label category




## Installation

Please refer to [get_started.md](docs/get_started.md) for installation.

## Getting Started
Please see [get_started.md](docs/get_started.md) for the basic usage of chipeak-data-tool.


Scan the QR code below to follow the [official website] of Xinfeng technology team（ http://http://www.chipeak.com/ ）, join the [official exchange] of Xinfeng technology team（ http://www.chipeak.com/account/login )
<div align="center">
<img src="/resources/xf_rq_code.png" height="400" />
</div>
zy-test 1.0
AI数据转换工具箱
Requires Python: >=3.7

<div align="center">
  <img src="resources/xf_logo_new.jpg" width="600"/>
</div>

## Introduction

English | [简体中文](README_zh-CN.md)

Chipeak_data_tool is an open source toolbox based on the data sets generated by various artificial intelligence annotation tools and the data sets generated by the deep learning image annotation tool labelme.

The main branch code currently supports the mutual conversion of coco dataset and labelme dataset.


### Major features

- **Modular Design**

  Various data sets are decoupled into different module components. By combining different module components, users can easily build custom data set processing.

## License

This project is released under the [GNU General Public License v3.0 2.0](LICENSE).

## Changelog

v1.0 was released in 28/10/2021. 


Supported datasets:

- [x] Coco dataset
- [x] Via annotation dataset
- [x] Labelme dimension dataset


Supported features:

- [x] Labelme dataset matting
- [x] Coco dataset to labelme dataset
- [x] Labelme dataset to coco dataset
- [x] Filter the labelme dataset by label category




## Installation

Please refer to [get_started.md](docs/get_started.md) for installation.

## Getting Started
Please see [get_started.md](docs/get_started.md) for the basic usage of chipeak-data-tool.


Scan the QR code below to follow the [official website] of Xinfeng technology team（ http://http://www.chipeak.com/ ）, join the [official exchange] of Xinfeng technology team（ http://www.chipeak.com/account/login )
<div align="center">
<img src="/resources/xf_rq_code.png" height="400" />
</div>
chipeak-data-tools 1.0
AI数据转换工具箱
Requires Python: >=3.7

<div align="center">
  <img src="resources/xf_logo_new.jpg" width="600"/>
</div>

## Introduction

English | [简体中文](README_zh-CN.md)

Chipeak_data_tool is an open source toolbox based on the data sets generated by various artificial intelligence annotation tools and the data sets generated by the deep learning image annotation tool labelme.

The main branch code currently supports the mutual conversion of coco dataset and labelme dataset.


### Major features

- **Modular Design**

  Various data sets are decoupled into different module components. By combining different module components, users can easily build custom data set processing.

## License

This project is released under the [GNU General Public License v3.0 2.0](LICENSE).

## Changelog

v1.0 was released in 28/10/2021. 


Supported datasets:

- [x] Coco dataset
- [x] Via annotation dataset
- [x] Labelme dimension dataset


Supported features:

- [x] Labelme dataset matting
- [x] Coco dataset to labelme dataset
- [x] Labelme dataset to coco dataset
- [x] Filter the labelme dataset by label category




## Installation

Please refer to [get_started.md](docs/get_started.md) for installation.

## Getting Started
Please see [get_started.md](docs/get_started.md) for the basic usage of chipeak-data-tool.


Scan the QR code below to follow the [official website] of Xinfeng technology team（ http://http://www.chipeak.com/ ）, join the [official exchange] of Xinfeng technology team（ http://www.chipeak.com/account/login )
<div align="center">
<img src="/resources/xf_rq_code.png" height="400" />
</div>
evergy 2.0.4
A utility that reads electric utility meter data from Evergy.com
Requires Python: >=3
Keywords: evergy kcpl kansas-city electricity-consumption electricity-meter api

# ⚡Evergy Client

[![Latest Version on PyPi](https://badge.fury.io/py/evergy.svg)](https://pypi.org/project/evergy/)

[![Supported Python Versions](https://img.shields.io/pypi/pyversions/evergy.svg)](https://pypi.org/project/evergy/)

[![Documentation Status](https://readthedocs.org/projects/evergy/badge/?version=latest)](https://evergy.readthedocs.io/en/latest/)

[![Requriements Status](https://requires.io/github/lawrencefoley/evergy/requirements.svg?branch=master)](https://requires.io/github/lawrencefoley/evergy/requirements/?branch=master)



A simple utility that you can use to login to your Evergy account and retrieve you meter readings.

- **[Documentation](https://evergy.readthedocs.io/en/latest/)**

- **[Source Code](https://github.com/lawrencefoley/evergy)**

> **Note: This is an unofficial utility that uses Evergy's non-public API.**



> Previously known as "KCPL"



## Install

```

pip install evergy

```



## Usage

```python

from evergy.evergy import Evergy



evergy = Evergy("<evergy-username>", "<evergy-password>")



data = evergy.get_usage()

print("Today's kWh: " + str(data[-1]["usage"]))

```



### Output

The last element from the `get_usage()` will be the latest data. The `usage` is in kilowatt-hours. I believe the `peakDateTime` is the

time during that day when your usage was the highest and the `peakDemand` is how many kilowatts you were drawing at that time.

```text

Latest data:

{

    'period': 'Saturday',

    'billStart': '0001-01-01T00:00:00',

    'billEnd': '0001-01-01T00:00:00',

    'billDate': '2021-09-18T00:00:00',

    'date': '9/18/2021',

    'usage': 14.7756,

    'demand': 3.7992,

    'avgDemand': 0.0,

    'peakDemand': 3.7992,

    'peakDateTime': '12:45 p.m.',

    'maxTemp': 71.0,

    'minTemp': 71.0,

    'avgTemp': 71.0,

    'cost': 18.5748, 

    'isPartial': False

}

```

## Related Projects

- [KC Water](https://github.com/patrickjmcd/kcwater): A similar project developed by [Patrick McDonagh](https://github.com/patrickjmcd). Check it out!



## Development

### Setup

```powershell

python -m pip install --upgrade virtualenv

virtualenv venv

.\venv\Scripts\activate.ps1

```



### Code Formatting

Install the dev dependencies and run `isort` and `flake8` to properly format the code.

```bash

pip install -r requirements_dev.txt

isort evergy/

flake8 evergy/

```



### Build Docs

Windows PowerShell:

```powershell

pip install -r docs/requirements_docs.txt

docs\make.bat clean; docs\make.bat html

```



### Release New Version

- Bump `__version__` in `evergy/__init__.py` 

```bash

git commit -m "Bump version"

git tag -a v1.0.1 -m "v1.0.1"

git push --tags

```



### Build Wheel

> The `--no-isolation` flag tells it to use the existing virtual env

```bash

python -m build --no-isolation --wheel

```



### Upload to PyPi

#### Test

```bash

twine upload --verbose --repository testpypi dist/*

```



#### Prod

```bash

twine upload --verbose --repository pypi dist/*

```