semvec/pypi-packages huggingface dataset:
burphttprequest 0.1.0
一个用于解析和发送HTTP请求的Python库，支持代理和保存响应
Requires Python: >=3.6

# BurpHTTPRequest

一个简单易用的Python HTTP请求处理库，支持从文件读取请求、设置代理、保存响应等功能。

## 安装

```bash
pip install burphttprequest
```

## 特性

- 从文件读取HTTP请求
- 支持设置HTTP代理
- 支持设置Cookie
- 自动处理响应编码
- 支持保存响应内容到文件
- 支持移除压缩编码

## 使用示例

```python
from burphttp import burphttp

# 创建实例
bq = burphttp()

# 从文件读取请求
bq.parse_request_from_file("request.http")

# 设置代理（可选）
bq.set_proxy("http://127.0.0.1:8080")

# 设置Cookie（可选）
bq.set_cookie("session=abc123; user=test")

# 移除压缩编码（可选）
bq.fixEncoding()

# 发送请求
bq.send_request()

# 保存响应体到文件
bq.save_response_body("response.txt")

# 打印响应信息
print(bq.response_status_code)  # 状态码
print(bq.response_headers)      # 响应头
print(bq.response_body)         # 响应体
```

## HTTP请求文件格式

请求文件格式示例：

```http
POST /api/test HTTP/1.1
Host: example.com
Content-Type: application/json

{"key": "value"}
```

## 许可证

MIT License
Box-n-Label-Augmentation-Module 0.1.0
Custom data augmentation module for images and bounding boxes
Requires Python: >=3.9.7

# Box n Label Augmentation Module
This Python package provides a customizable image augmentation toolset primarily aimed at generating augmented images and their corresponding labels for training deep learning models. It offers various augmentation techniques such as Salt and Pepper Noise, Brightness adjustment, Horizontal and Vertical Flipping, Histogram Equalization, and Rotation.

## Installation
Clone this repository to your local directory:
```bash
git clone https://github.com/MHosseinHashemi/Box-n-Label-Augmentation-Module.git
```

## Usage

```python
from Image_Custom_Augmentation import Image_Custom_Augmentation

#### Initialize Image Custom Augmentation object
My_data = Module.Image_Custom_Augmentation(
                                            SP_intensity=0.2,             # Salt and Pepper Intensity
                                            CWRO_Key=20,                  # CW Rotation Degree
                                            CCWRO_Key=20,                 # CCW Rotation Degree
                                            Br_intensity=True,            # Brightness Intensity
                                            H_Key = True,                 # Horizontal Flip
                                            V_Key = True,                 # Vertical Flip
                                            HE_Key= True,                 # Histogram Equalization
                                            GaussianBlur_KSize = 5,       # Gaussian Blur (Kernel Size, Kernel Size)
                                            Random_Translation = True,    # Random Translation (Shifting)
                                            Scaling_Range = (0.75, 1.25), # Random Scaling Range (Upscaling and Downscaling)
                                            Img_res=540                   # Image Resolution
)


#### Generate augmented data
My_data.Generate_Data(input_path="input_directory_path", output_path="output_directory_path")
```


<img width="1010" alt="Vis" src="https://github.com/user-attachments/assets/f21f386b-0782-473c-b5ad-edb6c6555ccc" />


## Notes

- **This module is currently under developement!**
- **The module initially designed to be a tool in data preprocessing for binary classification task, however we seek to enable it to work for multiclass cases**
- Input images must be in JPG format.
- The tool generates augmented labels for images with corresponding bounding box labels in YOLO format.
- The module exclusively handles augmentation for non-target samples, i.e., images without labels (also known as background samples), and generates augmented images accordingly.
- If the matching label file for each image sample is not present in the folder, the module treats them as background samples.

  
## Dependencies

- OpenCV (`cv2`)
- NumPy (`numpy`)
- tqdm (`tqdm`)
- Matplotlib (`matplotlib`)
mlint 0.1.0
The ML Model Intercomparison package facilitates calculation of autmated verification statistics and other forms of evaluation on ML weather models.
Requires Python: >=3.8

# mlint 
*ML Model Intercomparison: Enables efficient evaluation of ML weather forecast models for use within the Met Office. It is enabling the comparison of ML models (including FastNet) and physics-based weather forecast models to make appropriate choices for different use cases.*

This library provides building blocks to run an intercomparison project from a workflow or a data pipeline.

## Installing mlint
You might need a python virtual environment to install mlint and its dependencies. You can create one either using conda or python:
### Conda
```bash
conda create -n mlint_env -c conda-forge -y python=3.11
conda activate mlint_env
```
### Python
```bash
python -m venv <path>/mlint_env  
source <path>/mlint_env/bin/activate
```
### Install mlint
```bash
make install
```

This will install mlint in the active virtual environment together with all the dependencies

### Installing the development environment
```bash
make depend  # if dependencies haven't been installed yet, e.g. make install
make dev     # only the first time
make test
```
tfdocs-cli 0.2.24
Terraform provider documentation viewer for the CLI
Requires Python: <3.14,>=3.11

# TFDocs
*Read Terraform provider documentation in the terminal*

TFDocs is a command-line tool that lets you view provider documentation from the terminal.

> WARNING:
>
> This application is still under active development and has bugs! If you decide to use it anyways and anything goes wrong, please open an issue on the github repo describing the bug and any steps to recreate (if you can, if not just a description of the issue will suffice).

## Usage
### Setup
Before you can view any documentation, you need to initialise the cache. First, navigate to a directory with terraform initialised and run the following command:
```
  tfdocs init
```
This will create a cache file called `.tfdocs.db` in the directory that the command was run. When TFDocs runs, it searches for a cache in the local directory. If you want to run tfdocs from another directory you can move the cache in the filesystem.
<!-- If you want to run tfdocs from another directory you can either pass the -c flag, or you can move the cache in the filesystem. -->


The top-level entrypoint for the interface is called `tfdocs`. Running this command will open the graphical view of the program. A number of flags can be passed to modify its behaviour:
### Global Arguments
- `--verbose`: Allows the user to increase verbosity
- `--serve-logs`: Sends any logs generated by the program while in the GUI to the local log server (accessed using the `watch-logs` subcommand)
- `--help`: View help information

#### Top-level Specific Arguments
- `--provider PROVIDER`: Open the GUI directly to a specified provider

### SubCommands
- `init`: Sub-command that must be run at least once in a working directory in order to generate the local cache
- `watch-logs`: View any logs sent by the program (the main program must be run with `--serve-logs`)

## Installation
### Install Script (trivial, not recommended for secure environments)
 Too busy to fuss with any of the more involved methods? Run this command in a Linux or Mac terminal to install the program:
`curl tfdocs.crease.sh/install | bash`

### PipX (simple, requires Python and PipX, works for any platform)
Tfdocs is published on PyPI under the name `tfdocs-cli`. You can add Tfdocs to a project by running `pip install tfdocs-cli`. If you want to install Tfdocs system-wide in a reliable fashion, it's recommended to use *pipx* instead of `pip install -U tfdocs-cli`. You can use *pipx* like so:
```bash
pipx install tfdocs-cli
```

### Manual Installation on Linux (any distro, no requirements)
This is the current primary method of installation for different platforms. In the future, different repositories for different platforms will be provided.
1. Download the AppImage file from the 'Releases' section on GitHub
  ```
    curl -O https://tfdocs.crease.sh/release/latest/download
  ```
2. Move the executable somewhere on your PATH, typically `/usr/bin/local`
  ```
    mv tfdocs.AppImage /usr/local/bin/tfdocs
  ```
3. Start a new terminal session, or source your shell's init script (i.e. `.bashrc`) again
  ```
    source ~/.bashrc
  ```