semvec/pypi-packages huggingface dataset:
MyColorsPy 0.1.3
Module for using color in win command line or terminal.

# MyColorsPy

<img src="https://img.shields.io/badge/MyColorsPy-0.1.3-violet"/> <img src="https://img.shields.io/badge/python-3.10+-blue"/>

## About:

MyColorsPy - python`s module for using color in win command line or terminal.

### Functional:

- Add color for text;
- Add style for text (works differently in different systems);
- Add user`s custom color for text (RGB format).

## Screenshots:

<img src="https://sun9-31.userapi.com/impg/tDyTXun_rzpDzIUwKNBXhH6Ya7xKf7tPAoSV1Q/gynp_Frjf-Y.jpg?size=975x114&quality=95&sign=32cd19b9d59acb26fd5b190a7f03e19d&type=album"/>

> example.py output.

## How to use:
```python
# import module
from MyColorsPy.MyColorsPy import Colors

# init Colors class
color = Colors()

# print red text
print(color.F_RED + "RED TEXT")
# clear formatting
print(color.S_CLEAR)
```
> Using red color in output

- If not use S_CLEAR subsequent lines will also be colored red.

```python
# import module
from MyColorsPy.MyColorsPy import Colors

# init Colors class
color = Colors()

# print custom color in rgb format (white)
print(color.custom(255, 255, 255, False) + "Hello world!")
# clear formatting
print(color.S_CLEAR)
```
> Using custom color in output. 

- You can use S_CLEAR in one line with output content to avoid a space along with clearing formatting.

#### custom(r, g, b, background)

Return custom rgb color in ascii format.

| Parameter | Type     | Description                       |
| :-------- | :------- | :-------------------------------- |
| `r`  | `int` | red |
| `g`  | `int` | green |
| `b`  | `int` | blue |
| `bacground`  | `bool` | If equal True return custom background color, else if equal False return custom foreground color |

## Preseted colors and styles

#### Colors

| Foreground | Bacground | Color | 
| :--------- | :-------- | :---- |
| `F_BLACK` | `B_BLACK` | Black |
| `F_BLUE` | `B_BLUE` | Blue |
| `F_WHITE` | `B_WHITE` |White |
| `F_PURPLE` | `B_PURPLE` | Purple |
| `F_YELLOW` | `B_YELLOW` | Yellow |
| `F_GREEN` | `B_GREEN` | Green |
| `F_AQUA` | `B_AQUA` | Aqua |
| `F_RED` | `B_RED` | Red |

#### Styles

| Value | Description |
| :---- | :---------- |
| `S_CLEAR` | Clear formatting |
| `S_BOLD` | Bold text |
| `S_PALE` | Pale text |
| `S_ITALIC` | Italic text |
| `S_UNDERLINE` | Underlined text |
| `S_FLASH` | Flashed text |
| `S_HIDE` | Hidden text |
| `S_CROSS` | Crossed text |
| `S_DOUBLEUL` | DoubleUL text |
| `S_FRAME` | Framed text |
| `S_SURROUND` | Surrounded text |
| `S_CROSSOUT` | Crossouted text |

> P.S: Depending on the system, styles may or may not work.

## Install:

```bash
pip install MyColorsPy
```
contigion-utils 1.0.5
A library for utility functions common across Contigion projects

# contigion-utils
A library for utility functions common across Contigion projects

[![Pylint](https://github.com/Contigion/utils/actions/workflows/pylint.yml/badge.svg?branch=main)](https://github.com/Contigion/utils/actions/workflows/pylint.yml)
[![Publish to PyPI](https://github.com/Contigion/utils/actions/workflows/publish.yml/badge.svg?branch=main)](https://github.com/Contigion/utils/actions/workflows/publish.yml)
[![Python Version](https://img.shields.io/pypi/pyversions/contigion-utils?style=flat)](https://pypi.org/project/contigion-utils/)
[![PyPi Version](https://img.shields.io/pypi/v/contigion-utils?style=flat)](https://pypi.org/project/contigion-utils/)

## Installation
To install the library, simply run:

`
pip install contigion-utils
`

## Usage
You can use any of the functions in this library by importing them. For example:


``` python
from contigion_utils.log import create_file, save_dataframe
from contigion_utils.print import print_info, print_error

# Create a new file
create_file('test_file', 'This is a test.')

# Print a message
print_info('This is an informational message.')

# Save a DataFrame
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
save_dataframe('data_file', df)
```

## Modules
The library has the following modules with these functions
### `contigion_utils.log`
  - create_file (filename, content)
  - write_to_file (filename, content)
  - save_dataframe (filename, dataframe)
  - save_plot (filename, plot)

### `contigion_utils.print`
  - print_info (text)
  - print_debug (text)
  - print_warning (text)
  - print_success (text)
  - print_error (text)


## Contributions

Contributions are welcome! 
If you find any issues or want to improve the library, feel free to submit an issue.
vistool 0.1.1
A Python package for data analysis, cleaning, and visualization.
Requires Python: >=3.8

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/KaysHaydock/VisTool.git/HEAD)

# VisTool: A Python Package for Visualising Health Datasets

## Overview
VisTool is a Python package designed to provide high-level visualisation tools for analysing health datasets. This toolkit enables users to visualise and interrogate the end users data with ease, facilitating better insights and decision-making.

## Project Structure
The repository is organised as follows:  

1. **`vistool/`**  
   Contains the core Python modules and functions, split into four main areas:  
   - **`combine.py`**: Tools for merging and combining datasets.  
   - **`download.py`**: Utilities for downloading and preprocessing data.  
   - **`visualize.py`**: Functions to generate various visualisations.  
   - **`wrangle.py`**: Methods for cleaning and preparing datasets for analysis.  

2. **`binder/`**  
   Includes the `environment.yml` file, which can be used to recreate the Python software environment required to run VisTool.  

3. **`notebooks/`**  
   - **`example_usage.ipynb`**: Demonstrates how to interact with the package's functions and provides examples of analyses and visualisations.  
   - **`documentation.ipynb`**: This is an additional notebook serving as documentation, including a user guide and a tutorial for using the package.
   -  **`advanced_example_usage.ipynb`**: This is an additional/optional notebook to read if you wish to see a few more advanced way of interacting with the packages functions.
   -  **`interactive_usage_in_dev.ipynb`**: This is an additional notebook still in the development stages which could be extended, it is for interactive features, showcasing the next milestone of the package.
4. **`tests/`**  
   - **`test_combine.py`, `test_download.py`, `test_visualize.py`, `test_wrangle.py`** - Four Python files containing Pytest-based unit tests for the core modules.  
   - An Excel file, **`functional_testing.xlsx`**, documenting functional tests with input-output examples.  

5. **`Instructions_readme/`**  
   A separate file providing detailed instructions on installing and running the package.  

6. **`LICENSE`**  
   The project is licensed under the MIT Licence.  

7. **`pyproject.toml`**  
   Contains metadata and configurations for building the package.

**Access VisTool via [PyPi](https://pypi.org/project/vistool/)**
contigion-metatrader 1.0.5
A library for retrieving forex data in the Contigion projects

# contigion-metatrader
A library for retrieving forex data in the Contigion projects

[![Pylint](https://github.com/Contigion/metatrader/actions/workflows/pylint.yml/badge.svg?branch=main)](https://github.com/Contigion/metatrader/actions/workflows/pylint.yml)
[![Publish to PyPI](https://github.com/Contigion/metatrader/actions/workflows/publish.yml/badge.svg?branch=main)](https://github.com/Contigion/metatrader/actions/workflows/publish.yml)
[![Python Version](https://img.shields.io/pypi/pyversions/contigion-metatrader?style=flat)](https://pypi.org/project/contigion-metatrader/)
[![PyPi Version](https://img.shields.io/pypi/v/contigion-metatrader?style=flat)](https://pypi.org/project/contigion-metatrader/)

## Installation
To install the library, simply run:

`
pip install contigion-metatrader
`

## Usage
You can use any of the functions in this library by importing them. For example:


``` python

```

## Modules
The library has the following modules with these functions

### `
  - 



## Contributions

Contributions are welcome! 
If you find any issues or want to improve the library, feel free to submit an issue.
dizzyblog 0.2.0
A simple blogging CLI application.
Requires Python: >=3.6

# DizzyBlog

Welcome to DizzyBlog, a user-friendly CLI-based Python blog manager. DizzyBlog simplifies the process of managing your blog posts and comments directly from the command line.

## Features

- **Manage Posts**: Add, update, delete, and search for blog posts effortlessly.
- **Manage Comments**: Add, delete, and view comments on your selected posts.
- **Command-Line Interface**: Intuitive CLI for seamless blog management.

## Installation

Install DizzyBlog easily using pip:

```sh
pip install dizzyblog
```

## Usage

Once installed, you can start using DizzyBlog from your command line:

```sh
dizzyblog
```

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contributing

We welcome contributions! Feel free to open an issue or submit a pull request on GitHub.

## Contact

For any questions or feedback, please reach out to the project maintainer.
gattfuzz 0.0.14
A tool for fuzzing BLE GATT
Requires Python: >=3.6

![Gattfuzz logo](./GATTFuzzing.jpg)

# GattFuzzing
GattFuzzing 是一款针对BLE Gatt的模糊测试工具。

### 环境
Linux

### Requirements
Bleak、Scapy

### 安装与使用
#### 方法一

```
pip install -r requirements.txt
python main.py
```

#### 方法二

```
python setup.py install
gattfuzz 
```

#### 方法三

```
pip install gattfuzz
gattfuzz
```

#### 使用
```
gattfuzz -h
```
支持两个参数，-f 和 -m，-f为可选。 -m为目标的mac地址。-f 就是你抓取到的pcap包，会根据包提取去进行变异fuzz。如果不指定，则直接遍历目标设备gatt，并进行fuzzing 测试。


### 功能描述

#### 支持两种方式：

1. 基于抓到的数据包进行payload变异，具体变异规则待述；
2. 没有数据包的情况下支持随机数Fuzz。


#### 预期结果：

当检测到目标设备结束广播、断连会push error，可进一步人工核实目标设备状态，重放payload

### 可能遇到的一些问题

目前支持手机hci log抓的pcap包（需要自己转格式）和抓取的空中包两种pcap包，其他的包解析出可能欢迎提issue
tpc182tools 1.0.1
A collection of useful tools for the TPCs of 182.
Requires Python: >=3.10

# TPC 182 Tools

A general package for processing data generated from TPCs in CERN B182.

This package provides:
* DUNE-DAQ HDF5 file reading (must be in a DUNE-DAQ environment),
  * Transcoding these files into a self-contained format,
* Reading `tpc182tools` HDF5 format files.

It does not provide analysis procedures or algorithms that one may want to apply to the data that was read. It is currently expected that the user understands the returned contents and may manipulate how they wish.

## File Formats
### DUNE-DAQ
The TPCs in 182 have so far been generating data files using the DUNE-DAQ system. This comes with a proprietary format that requires being in a DUNE-DAQ environment to be able to reasonably read these files. This package provides a bare-bones way of reading and reformatting (apply channel maps and other utilities) the data available in these files. These data files may be read by using `tpc182tools.readers.WIBEthFrameReader`.

### `tpc182tools` HDF5
To avoid the DUNE-DAQ environment requirement, this package also provides a centralized transcoder `tpc182tools.transcoders.HDF5Transcoder`. This reads and transcodes the data into an HDF5 format that does not require the DUNE-DAQ environment while preserving all the data from the `WIBEthFrame`s.

These data files can be read using `tpc182tools.readers.HDF5Reader` with many of the same methods as the `WIBEthFrameReader`. Since generating the transcoded files does not make use of any proprietary data format, these HDF5 files may be read by any file reader. If a user has access to a transcoded data file, they may use ROOT, C++, etc. with the appropriate h5 library to read the data in their preferred way.

These files **do not** feature the DUNE-DAQ trigger data (`TriggerPrimitive`, `TriggerActivity`, `TriggerCandidate`). These data structures are seen as niche and proprietary to the DUNE-DAQ environment, so these were left out (though, they may be added later).
dfhelper 0.0.6
dfhelper is a Python package that simplifies data preprocessing and visualization in Jupyter Notebooks.
Requires Python: >=3.9
Keywords: preprocessing visualization

# 🛠️ dfhelper

## What is this?

Dfhelper is a versatile Python package that streamlines data preprocessing and visualization in Jupyter Notebooks. 
With intuitive functions for cleaning and transforming pandas DataFrames and the ability to render them as interactive 
HTML tables, this toolkit is an essential addition to any data scientist's arsenal. Simplify your data analysis process 
and gain clearer insights with dfhelper.

## Quick installation
```
!pip install dfhelper
```

## Main functionality
1) Output date frames in HTML. This is extremely useful when working with multiple selections
2) Output in HTML df.info() multiple dataframes
3) Output the size of the date frames in html.
4) The ability to display dataframes vertically and horizontally
5) Functions for summarizing the date of Ephraim, created for conducting EDA

## Quick Guide
```python
import pandas as pd
from dfhelper.viz import df_view, df_info_view, df_shape_view
from dfhelper.scout import summarize_df, summarize_dfs


# viz
df1 = pd.DataFrame(
            {"A": [1, 0, 0, None],
             "B": [1, 1, 2, 2],
             "C": [None, None, None, None]}
        )

df2 = pd.DataFrame(
            {"A": [1, 5, 0, 10],
             "B": [1, 1, 2, 2],
             "C": [None, 4, 16, 101]}
        )

# Output of two dataframes
df_view(df1, df2)
# Output information about two dataframes
df_info_view(df1, df2)
# Output of the sizes of two dataframes
df_shape_view(df1, df2)

# scount
# Table of the main characteristics of the dataframe
summarize_df(df1)
# Displaying the main characteristics of dataframes
summarize_dfs(df1, df2)
```
ulauncher-generator 0.1.3
A Ulauncher extension generator
Requires Python: >=3.6

# ulauncher-extension-generator

This project is a tool to generate boilerplate code for Ulauncher extensions. It simplifies the process of creating new extensions by providing a basic structure and necessary files.

<a href="https://www.buymeacoffee.com/ubuntupunk" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>

## Features

- Generates a basic Ulauncher extension structure.
- Includes a `main.py` file with basic extension logic.
- Provides a `manifest.json` file for extension configuration.
- Creates a `versions.json` file for version information.
- Creates a `requirements.txt` file for package dependencies.
- Creates an `images` directory for extension icons.

## Usage

To use this tool, run the `ulauncher-generator` command, and follow the prompts to create a new extension.

```bash
ulauncher-extension-generator
```

## Contributing

Contributions are welcome! Please feel free to submit a pull request.

## References

- [Ulauncher Documentation](http://docs.ulauncher.io/en/stable/extensions/intro.html)

## License

[MIT License](LICENSE)