semvec/pypi-packages huggingface dataset:
yadasm 0.1.1
Yet Another Disassembler
License: MIT


# yadasm

![](https://github.com/unlink2/yadasm/actions/workflows/build.yml/badge.svg)
![](https://github.com/unlink2/yadasm/actions/workflows/test.yml/badge.svg)

## Yet Another Disassembler

Yadasm is a dynamically configurable dissasembler tool.
It is aimed mainly at romhacking and reverse engeneering and intends to be a scriptable
and highly configurable tool that can be applied to any project as needed.

## Table of content

- [Installation](#Installation)
- [Usage](#Usage)
- [License](#License)
- [Contributing](#Contributing)
- [TODO](#TODO)

## Installation

To install yadasm as a pip package first make sure you Wheel is installed
```sh
pip install wheel
```

Then run
```sh
make
```

This will create a wheel package for you.

Lastly simply install it using

```sh
pip install ./dist/yadasm-0.1.0-py3-none-any.whl
```

## Usage

The yadasm command line is very simple:

```
usage: yadasm [-h] [--arch ARCH] [--file-start-offset FILE_START_OFFSET] [--file-end-offset FILE_END_OFFSET] [--start-addr START_ADDR] [--end-addr END_ADDR] [--label-postfix LABEL_POSTFIX] file

yadasm

positional arguments:
  file                  the input file

options:
  -h, --help            show this help message and exit
  --arch ARCH           The target cpu architecture dict_keys(['6502', '6502-byte', '65c02', '65c02-byte', '65c816', '65c816-byte', '65c816-emu', '65c816-emu-byte'])
  --file-start-offset FILE_START_OFFSET
                        Start parsing input file at offset
  --file-end-offset FILE_END_OFFSET
                        Stop parsing input file at offset
  --start-addr START_ADDR
                        Starting address
  --end-addr END_ADDR   End addres
  --label-postfix LABEL_POSTFIX
```

However yadasm can also be used as a library which allows precies configuration
of the dissassembly output.

```py
import yadasm
```

TODO further library documentation

## License

This program is distributed under the terms of the MIT License.

## Contributing

All contributions are welcome.
Both pull requests and issue reports are always appreciated.
Please make sure that all existing tests pass before submitting a pull request.
The code is formatted using [black](https://github.com/psf/black).
You can execute the entire code quality test suite using the `make test` script.

## TODO
- context needs to keep track of "seen" addresses/line numbers and add labels accordingly
summary-classification 0.0.1
Basic metrics for evaluating classification results
License: MIT
Keywords: summary,F1 score,accuracy,confusion matrix,FDR

A confusion matrix is a summary of classification problem prediction results. 
The number of correct and incorrect predictions is summarized using count values and divided by class.

The function takes two arrays of same length and returns a list of four metrics i.e., TN, FP, FN, and TP
By using confusion matrix we can calculate precision, recall, f1 score, FDR and accuracy.

CHANGE LOG

==============

0.0.1 (16/01/2022)
------------------
- First Release
xsensing-client 0.1.0
XSensing Unified AI Interface Client, UAIC
License: MIT
Requires Python: >=3.6.0

#### 本仓库是Xsensing统一AI接口客户端(Unified Artificial Intelligence Interface Client, UAIC)代码和使用说明。

本仓库提供客户端、API。

# 1 About UAIC

Xsensing的统一AI接口(Unified Artificial Intelligence Interface, UAII)分为服务端UAIS和客户端UAIC。

+ 服务端UAIS运行在GPU服务器上，统一调度不同算法模块、管理模型库，执行AI计算任务；
+ 客户端UAIC运行在应用服务器上，通过Socket与UAIS通讯，可远程查看UAIS运行情况；自定义处理流程、管道和模块；管理UAIS；获取UAIS的输出。
# 2 Getting Started
## 2.1 安装
### 源码安装
```python
git clone https://github.com/zhangzhengde0225/xsensing_client.git
cd xsensing_client
python setup.py install
```
### pip安装
```python
pip install xsensing_client
```

# 2.2 样例

Client Examples，下面是一个简单的客户端代码。

```python
import xsensing_client

uaic = xsensing_client.UAIC(url='http://127.0.0.1:5000')  # 连接到UAIS服务器

uaic.ps()  # 查看服务器流、管道和模块的状态

uaic.stop(stream=None, pipeline=None, module=None)  # 停止流，无参数时，停止全部；指定流时停止指定的流；指定管道或模块时，停止与之相关的所有流
uaic.get_cfg()  # 从服务器读取各流、管道、模块的默认配置文件，保存到本地。
uaic.configure(config_file='config.py')  # 上传服务端的流、管道和模块配置文件，更新服务端配置。
uaic.start(stream=None)  # 启动流，无参数是启动所有流；指定流时启动指定的流；启动流的同时调起所有相关管道和模块。

uaic.scan(pipeline=None)  # 窥视指定管道的输出。

```

# 3 详细文档

[客户端文档](https://github.com/zhangzhengde0225/xsensing_client/blob/master/docs/client_doc.md)
Pyodbc-Knight 4.0.0.34
DB API Module for ODBC
License: MIT

# pyodbc

[![Windows Status](https://ci.appveyor.com/api/projects/status/github/mkleehammer/pyodbc?branch=master&svg=true&passingText=Windows%20build)](https://ci.appveyor.com/project/mkleehammer/pyodbc)
[![Ubuntu build](https://github.com/mkleehammer/pyodbc/actions/workflows/ubuntu_build.yml/badge.svg)](https://github.com/mkleehammer/pyodbc/actions/workflows/ubuntu_build.yml)
[![PyPI](https://img.shields.io/pypi/v/pyodbc?color=brightgreen)](https://pypi.org/project/pyodbc/)

pyodbc is an open source Python module that makes accessing ODBC databases simple.  It
implements the [DB API 2.0](https://www.python.org/dev/peps/pep-0249) specification but is
packed with even more Pythonic convenience.

The easiest way to install is to use pip:

    pip install pyodbc

Precompiled binary wheels are provided for most Python versions on Windows and macOS.  On other
operating systems this will build from source.  Note, pyodbc contains C++ extensions so you will
need a suitable C++ compiler on your computer to install pyodbc, for all operating systems.  See
the [docs](https://github.com/mkleehammer/pyodbc/wiki/Install) for details.

[Documentation](https://github.com/mkleehammer/pyodbc/wiki)

[Release Notes](https://github.com/mkleehammer/pyodbc/releases)
py-az-cli 0.1.8
Pythonic wrapper for az cli
License: MIT

# py-az-cli
Pythonic wrapper for az cli

Requires Python 3

## Install 
Note: We recommend first creating a virtual env and activating it
```
pip install py-az-cli
```

## Usage
```
% python3
>>> import pyaz
>>> result = pyaz.version()
>>> print(result)
{'azure-cli': '2.28.1', 'azure-cli-core': '2.28.1', 'azure-cli-telemetry': '1.0.6', 'extensions': {}}
```
FaceIn 1.0.1
Classroom visual sign in program
License: MIT
Keywords: face detection,face recognition

# Face In
Sign in system with face recognition
Based on code from https://github.com/serengil/deepface

Urs Utzinger, 2022

## Requisits
```
pip3 install deepface
pip3 install camera-util
```

Create folder database and place images of known faces into them. Supports .jpg files.  

Supports multiple images for same person, in database/First_Last/First_Last001.jpg

## Configuration

```
detector_backend        = "retinaface"  # opencv*, ssd, mtcnn, dlib, retinaface
freeze_time             = 5             # how will the results be displayed
frame_threshold         = 5             # how many frames required to focus on a face, 5*
minimum_face_width      = 180           # filter out small faces, depends on camera
```

```
model_name              = "ArcFace"     # VGG-Face*, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace or Ensemble
distance_metric         = "cosine"      # cosine*, euclidean, euclidean_l2
datafile                = "./database/bme210_2022Spring.dat"
db_path                 = "./database"  # Folder for .jpg files of known faces
```

## Performance

| Detector   | Example exection time in ms | Works w glasses | Works w obstruction | Works w facemask |
|------------|-----------------------------|-----------------|---------------------|------------------|
| Retinaface | 172-233                     | yes             | yes                 | yes              |
| OpenCV     | 40-56                       | no              | no                  | no               |
| SSD        | 19-30                       | yes             | yes                 | no               |
| MTCNN      | 873-1029                    | yes             | yes                 | no               |
| DLIB       | 346-352                     | yes             | no                  | no               |

| Model      | Example exection time in ms | Works w glasses | Works w obstruction | Works w facemask |
|------------|-----------------------------|-----------------|---------------------|------------------|
| ArcFace    |                             |                 |                     |                  |
| VGG-Face   |                             |                 |                     |                  |
| FaceNet    |                             |                 |                     |                  |
| OpenFace   |                             |                 |                     |                  |
| DeepID     |                             |                 |                     |                  |
spindex 0.0.7
Indexer under construction.
License: MIT
Requires Python: >=3.9

# dex

Library index:

- Data managed as ZIP files
  - If installing from a clone of the git repo, the files will be stored within the repo
  - If installing from PyPI, you must set the `DEX_SHELVES` environment variable with a path to
    locate the source files

## Key features

- Reliable metadata handling from ISBN numbers
- Robust modern OCR of book indexes from neural networks pretrained for document image analysis

## Requires

- Python 3.9+

## Installation

> _dex_ is available from [PyPI](https://pypi.org/project/spindex), and
> the code is on [GitHub](https://github.com/lmmx/dex)

In theory you can install as follows:

```sh
pip install spindex
```

In practice, the suggested installation is stored in `CONDA_SETUP.md`:

```
conda create -n dex "python=3.9"
conda activate dex
conda install -y pytorch torchvision cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt
```

For earlier versions of CUDA:
```
conda install -y "cudatoolkit<11.2" -c conda-forge
conda install -y pytorch torchvision -c pytorch
```

## Usage

```py
>>> import dex
>>> dex.load_library()
[INFO] Library contains 1 files
[[LibraryItem(metadata='📖: Strang (2019) Linear Algebra And Learning From Data',
archive_path=PosixPath('/home/louis/dev/dex/data/shelves/dex9780692196380.zip'))]]
```