semvec/pypi-packages huggingface dataset:
balderich 1.3
A Python library for the NSSCTF Balderich API
Requires Python: >=3.8

# balderich-py

[![Issues](https://img.shields.io/github/issues/NSSCTF/balderich-py)](https://github.com/cryptography-wiki/cryptography-wiki.github.io/issues)
[![Forks](https://img.shields.io/github/forks/NSSCTF/balderich-py)](https://github.com/cryptography-wiki/cryptography-wiki.github.io/network/members)
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![Size](https://img.shields.io/github/repo-size/NSSCTF/balderich-py)

* [NSSCTF](https://www.ctfer.vip/)平台API Python包，你可以使用此库来和Balderich后端进行快速交互。编写属于你的自动化bot。

## 安装

* 你可以直接使用pip来进行安装本包

    ```python
    pip install balderich
    ```

* **请注意**：你需要使用Python3.8及以上版本来安装本包。

## 使用

* 你可以通过以下方式来快速的创建一个客户端

    ```python
    import balderich

    # 直接传入字符串
    client = balderich.NSSClient(key='xxx',
                                 secret='xxxx')
    
    # 传入配置文件路径
    client = balderich.NSSClient(balderich.AuthConfig('config.json'))

    # 传入配置文件IO流
    client = balderich.NSSClient(balderich.AuthConfig(open('config.json', 'rb')))
    ```

    然后你便可以开始使用客户端下各模块API来进行交互，例如查询用户信息

    ```python
    data = client.user.get_user_info(1)
    print(data)
    ```
body-matrix 0.4.4
Package to Calculate Human Body Measurements
Requires Python: >=3.7

## Human Body Measurement via Video and Image
[Demo Video](https://www.youtube.com/watch?v=cunkg-qAOss)

### Install the Package
```
$ pip install body-matrix
```

### Package Usage

```

#### LOAD VIDEO, Keypoints and Segmentation Model

from body_matrix import load, infer, process, measure, draw, export

video_path = "/content/drive/MyDrive/Body_Matrix/Raw_Vids/vinmart_customers.mp4"
video_rotate = -90
device = "cuda"
font_path = "/content/drive/MyDrive/Body_Matrix/Roboto-Bold.ttf"

keypoints_model, keypoints_transform = load.keypoints_model(device)
segment_model, segment_transform = load.segment_model(device)

video, frame_counts, fps, sample_frame = load.video(
    video_path=video_path, 
    rotate_angle=video_rotate,
    frame_position=1
)

sample_frame


#### Measure and Visualize Every frame
from body_matrix import score
from torchvision.transforms.functional import to_pil_image

measure_frames = []
measures = []

for index, vid_frame in enumerate(video):
    frame = to_pil_image(vid_frame)
    frame = frame.rotate(video_rotate, expand=True)
    height, leg, hip, shoulder, markers = measure.find_real_measures(
        image_frame=frame,
        device=device,
        keypoints_model=keypoints_model,
        keypoints_transform=keypoints_transform,
        segment_model=segment_model,
        segment_transform=segment_transform
    )

    visualized_frame = draw.visualize_measures(
        height, leg, hip, shoulder, markers, 
        frame, font_path
    )

    measure_frames.append(visualized_frame)
    measures.append(height)


mean, median, minim, maxim = score.best_scores(
    measures,
    100, 
    200
)

best_score, frame_index = score.find_nearest(
    measures, 
    median
)

#### Export Instagram Video with Measures
export.generate_instagram_vid(
    vid_name="instameasures_hoangdo.mp4", 
    vid_width = sample_frame.width, 
    vid_height = sample_frame.height, 
    pil_images = measure_frames, 
    stop_index=frame_index, 
    fps=fps, 
    repeat_rate=2, 
    slow_motion_rate=1
)

```
![instameasures_hoangdo](https://user-images.githubusercontent.com/1859661/221099305-61ca2447-48e0-4c9c-9461-cd68988e5445.gif)
g36-ahu-faults 0.0.1
Package for fault detection and diagnosis of AHUs using G36 fault conditions
Requires Python: >=3.7

# g36-ahu-faults

[![Build Status](https://app.travis-ci.com/bbartling/g36-ahu-faults.svg?branch=develop)](https://app.travis-ci.com/bbartling/g36-ahu-faults)

## This project is under development to eventually be a Pypi package for Fault Detection Diagnostics (FDD) on HVAC system datasets with the Pandas computing library. 
See also (in development) for other HVAC system types:
* [g36-boiler-plant-faults](https://github.com/bbartling/g36-boiler-plant-faults)
* [g36-chiller-plant-faults](https://github.com/bbartling/g36-chiller-plant-faults)

###### Note - Fault equations expect a float between 0.0 and 1.0 for a control system analog output that is typically expressed in industry HVAC controls as a percentage between 0 and 100% of command. 
Examples of a analog output could a heating valve, air damper, or fan VFD speed. For sensor input data these can be either float or integer based. Boolean on or off data for control system 
binary commands the fault equation expects an integer of 0 for Off and 1 for On.

## Reference AHU fault equations here defined by ASHRAE Guideline 36:
https://github.com/bbartling/open-fdd/tree/master/air_handling_unit/images


## License
g36-ahu-faults is made available under the MIT license. Enjoy! 

## Development
You can follow the development of g36-ahu-faults on [GitHub](https://github.com/bbartling/open-fdd) via the `open-fdd` project for ASHRAE G36 Fault Detection Diagnostics.

## Contributing
To contribute to g36-ahu-faults please open an issue on the [GitHub issue tracker](https://github.com/bbartling/g36-ahu-faults/issues) and/or leave a Pull Request. Thanks!

## FDD Dataset Contributing
Any good anonymous HVAC FDD datasets in CSV format where lots of faults are found please contribute in a community effort:
https://github.com/bbartling/Data