semvec/pypi-packages huggingface dataset:
ArtColorAnalyzer-Hue-Adjustment-Tool 0.1.0
A tool for overall and object-specific hue adjustment in images.
Requires Python: >=3.6

# ArtColorAnalyzer: Hue Adjustment Tool

ArtColorAnalyzer is a Python package that allows you to adjust the hue of images either overall or object-specifically. It leverages OpenCV, YOLOv3, NumPy, and scikit-image to provide a simple GUI for adjusting hues.

## Features

- **Overall Hue Adjustment**: Easily adjust the hue for entire images
- **Object-Specific Hue Adjustment**: With the object detection capability powered by YOLOv3, you can selectively adjust the hue of specific objects within an image. For instance, in scenes containing multiple elements like people, cars, and trees, you can modify the hue of just the car while leaving the other elements unaffected. Additionally, you can adjust the hue within selected grids. This feature proves beneficial for complex images where object detection is challenging, such as paintings or abstract art.

- See example.py if you want to try it too!

### Example of Object-Specific Hue Adjustment
Object-Specific Hue Adjustment (Cup):
![Adjusted Image](adjusted_image/object_adjusted_image.png)

### Example of Grid-Based -- Object-Specific Hue Adjustment
Grid-Based -- Object-Specific Hue Adjustment:
![Adjusted Image](adjusted_image/grid_adjusted_image.png)


## file structure
```sh
artcoloranalyzer/
├── artcoloranalyzer/
│   ├── __init__.py
│   ├── analyzer.py
│   ├── overall_hue_adjustment.py
│   ├── object_specific_hue_adjustment.py
│   └── yolov3/
│       ├── yolov3.cfg
│       ├── yolov3.weights
│       └── coco.names
├── example_image/
│   ├── 00_kaiga.JPG
│   ├── breakfast.png
├── example.py
├── README.md
├── YOLOv3_LICENSE
└── LICENSE
```


- please Download this file and place it under yolov3 as in the structure above


...

## Third-Party Licenses

This project includes code and data from the YOLOv3 project. The YOLOv3 project is licensed under the Apache License 2.0. For more information, see the `YOLOv3_LICENSE` file included in this repository.

...

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

The YOLOv3 components are licensed under their respective licenses - see the [YOLOv3_LICENSE](YOLOv3_LICENSE) file for details.


## Installation

You can install the package via pip:

```sh
pip install ArtColorAnalyzer
LexBuilder 1.3.0
Library for automatic construction of lexers
Requires Python: >=3.7
Keywords: lexbuilder python c++ lexer

# About LexBuilder:
LexBuilder is a library for automatically building a lexer in Python and C++. In the future, the library will be able to build lexers in major programming languages such as Golang, Java/Kotlin, etc.

## About Syntax:
In order for the library to generate a Lexer.py or Lexer.h file, you need to pass a list of tokens to the Builder class or use its .add_token() method. To declare a token, you need to import the Token() class from the Builder() class. You need to pass the token name and its value to the Token() class. After that, add all the tokens we created to a list and pass it as an argument to the PyBuilder() or CppBuilder class. By default, the lexer contains the tokens:
```python
INT_NUMBER = "INT_NUMBER"
FLOAT_NUMBER = "FLOAT_NUMBER"
PLUS = "PLUS"
MINUS = "MINUS"
STRING = "STRING"
MULTIPLY = "MULTIPLY"
DIVIDE = "DIVIDE"
VAR = "VAR"
ASSIGN = "ASSIGN"
EOF = "EOF"
```
### Example of creating a list of tokens:
```python
from LexBuilder.Builder import Token

PRINT = Token("PRINT", "print")
INPUT = Token("INPUT", "input")

tokens = [PRINT, INPUT]
```
#
## Python Example:
### Generate Lexer:
```python
from LexBuilder.Builder import PyBuilder, Token

PRINT = Token("PRINT", "print")
tokens = [PRINT]

lexer = PyBuilder(tokens)

lexer.add_token(Token("INPUT", "input"))
lexer.add_token(Token("LPAREN", "("))
lexer.add_token(Token("RPAREN", ")"))

lexer.build()
```

### Use Lexer:
```python
from Lexer import *


code = 'print("Hello, world!")'
lexer = Lexer(code)

token = lexer.get_next_token()
print(token)

while token.type != EOF:
    token = lexer.get_next_token()
    print(token)
```

```python
Token(PRINT, "print")
Token(LPAREN, "(")
Token(STRING, "Hello, world!")
Token(RPAREN, ")")
Token(EOF, None)
```
#
## C++ Example:
### Generate Lexer:
```python
from LexBuilder.Builder import CppBuilder, Token

PRINT = Token("PRINT", "print")
tokens = [PRINT]

lexer = CppBuilder(tokens)

lexer.add_token(Token("INPUT", "input"))
lexer.add_token(Token("LPAREN", "("))
lexer.add_token(Token("RPAREN", ")"))

lexer.build()
```

### Use Lexer:
```cpp
#include "Lexer.h"

using namespace std;


int main() {
    string code = "5 + 5";
    Lexer lexer(code);
    Token current_token = lexer.get_next_token();
    cout << current_token << endl;
    while (current_token.type != EOF_TOKEN) {
        current_token = lexer.get_next_token();
        cout << current_token << endl;
    }
}

```

```python
Token(INT_NUMBER, 5)
Token(PLUS, +)
Token(INT_NUMBER, 5)
Token(EOF, )
```
cdmn 2.1.2
A package providing a (c)DMN solver and API
Requires Python: >=3.7

# cDMN

## Welcome to the cDMN solver's code repository.

cDMN stands for Constraint Decision and Model Notation.
It is an extension to the [DMN](https://www.omg.org/spec/DMN/About-DMN/) standard, managed by the Object Management Group (OMG).
cDMN combines the readability and straighforwardness of DMN and the expressiveness and flexibility of constraint reasoning.
For more specific details, please visit our [cDMN documentation](https://cdmn.readthedocs.io/en/latest/notation.html).

## Examples

Example implementations can also be found in the [cDMN documentation](https://cdmn.readthedocs.io/en/latest/examples.html).

## Installation and usage

The full installation and usage guide for the cDMN solver can be found [here](https://cdmn.readthedocs.io/en/latest/solver.html).
In short for Linux: after cloning this repo, install the Python dependencies.

```
git clone https://gitlab.com/EAVISE/cdmn/cdmn-solver
cd cdmn-solver
pip3 install -r requirements.txt
```

After this, you can run the solver. Example usage is as follows:

```
python3 -O solver.py Name_Of_XLSX.xlsx -n "Name_Of_Sheet" -o output_name.idp
```

## Reference

If you used cDMN in a publication or in other works, reference us as follows:

BibTeX:
```

@article{cDMN,
  title = {Tackling the {{DM}} Challenges with {{cDMN}}: {{A}} Tight Integration of {{DMN}} and Constraint Reasoning},
  author = {Vandevelde, Simon and Aerts, Bram and Vennekens, Joost},
  year = {2021},
  journal = {Theory and Practice of Logic Programming},
  pages = {1--24},
  publisher = {{Cambridge University Press}},
  doi = {10.1017/S1471068421000491}
}


```

or direct cite:

```
Vandevelde, S., Aerts, B., & Vennekens, J. (2021). Tackling the DM challenges with cDMN: A tight integration of DMN and constraint reasoning. Theory and Practice of Logic Programming, 1–24. https://doi.org/10.1017/S1471068421000491

```

# Contributors

We would also like to thank all contributors who have developed code or given suggestions to us, such as Marjolein Deryck, Pierre Carbonnelle, Jo Devriendt, and many more!
custom-viz-lib 1.0.1
A customizable data visualization library
Requires Python: >=3.6

# Custom Viz Lib

A customizable data visualization library

## Overview

Custom Viz Lib is a library for easily creating interactive and customizable data visualizations.

## Features

- Create interactive graphs and charts
- Customizable themes and styles
- Support for data storytelling

## Customization Details

### Chart Types
- `BarChart`: Bar Chart
- `LineChart`: Line Chart

### Themes
- `DarkTheme`: Dark Theme
- `LightTheme`: Light Theme

### Data Loading
- `load_csv(file_path)`: Function to load data from a CSV file

### Utilities
- `normalize_data(data)`: Function to normalize data to a range of 0 to 1

## Installation

```bash
pip install custom_viz_lib
```

## Usage
```python
from custom_viz_lib import chart, theme

# Load data
data = [...]

# Create a chart
bar_chart = chart.BarChart(data)
bar_chart.apply_theme(theme.DarkTheme())
bar_chart.show()
```

## Example 1: Using in Google Colab to create a bar chart
```python
!pip install custom_viz_lib

from custom_viz_lib.chart import BarChart
from custom_viz_lib.theme import DarkTheme

# Sample data
data = [1, 2, 3, 4, 5]
chart = BarChart(data)
chart.apply_theme(DarkTheme())
chart.show()
```

## Example 2: Using in Google Colab to create a line chart
```python
!pip install custom_viz_lib

from custom_viz_lib.chart import LineChart
from custom_viz_lib.theme import LightTheme

# Sample data
data = [10, 20, 15, 25, 30]
chart = LineChart(data)
chart.apply_theme(LightTheme())
chart.show()
```

## Example 3: Using in Google Colab to load data from a CSV file and create a bar chart
```python
!pip install custom_viz_lib

import pandas as pd
from custom_viz_lib.chart import BarChart
from custom_viz_lib.theme import DarkTheme
from custom_viz_lib.data_loader import load_csv

# Path to CSV file
file_path = 'path/to/your/data.csv'

# Load data
data = load_csv(file_path)['column_name'].tolist()
chart = BarChart(data)
chart.apply_theme(DarkTheme())
chart.show()
```

## Example 4: Using in Google Colab to normalize data and create a line chart
```python
!pip install custom_viz_lib

from custom_viz_lib.chart import LineChart
from custom_viz_lib.theme import LightTheme
from custom_viz_lib.utils import normalize_data

# Sample data
data = [10, 20, 15, 25, 30]

# Normalize data
normalized_data = normalize_data(data)
chart = LineChart(normalized_data)
chart.apply_theme(LightTheme())

chart.show()
```

## License
This project is licensed under the MIT License.
medcamEditedbyFiras 0.1.0
An easy to use library that makes model predictions more interpretable for humans.
Requires Python: >=3.6

An easy to use library that makes model predictions more interpretable for humans. M3d-CAM allows the generation of attention maps with multiple methods like Guided Backpropagation, Grad-Cam, Guided Grad-Cam and Grad-Cam++.
pypi-wukunhuan 0.1.0
description
Requires Python: >=3

# PyPI Project

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Easier PyPI packages creation, publishment and more in shell. Welcome to any contribution. 

## Installation

```
pip install pypi-shell
```

## Usage

Explain how to use your project. Provide examples and code snippets if necessary.

## Contributing

Specify guidelines for contributing to your project. Include information on how to submit bug reports, feature requests, and pull requests.

## License

This project is licensed under the [MIT License](LICENSE).
textfile-compare-tool 0.1.0
A tool to compare and highlight differences between text files.
Requires Python: >=3.9

# text-compare-tool
### English
## Description
This is a tool for comparing two text files and highlighting the differences between them. It uses the `difflib` library to compute the differences and displays added and removed lines in different colors for easy visualization.

## Features
- Compare two text files line by line
- Highlight added lines in green
- Highlight removed lines in red

## Installation
You can install this tool using `pip`:
```
pip install textfile-compare-tool
```

## Usage
To use this tool, run the following command:
```
compare-textfiles --file1 <path_to_file1> --file2 <path_to_file2>
```

## Command Line Arguments
- `--file1`: Path to the first text file for comparison.
- `--file2`: Path to the second text file for comparison.

## Example
Suppose you have two files, file1.txt and file2.txt. You can compare them like this:
```
compare-textfiles --file1 file1.txt --file2 file2.txt
```

### 日本語
## 説明
これは2つのテキストファイルを比較し、その違いをハイライト表示するツールです。`difflib`ライブラリを使用して差分を計算し、追加された行と削除された行を異なる色で表示して、視覚的に分かりやすくします。

## 特徴
- 2つのテキストファイルを行ごとに比較
- 追加された行を緑色でハイライト
- 削除された行を赤色でハイライト

## インストール
このツールはpipを使用してインストールできます:
```
pip install textfile-compare-tool
```

## 使用方法
このツールを使用するには、以下のコマンドを実行します:
```
compare-textfiles --file1 <file1へのパス> --file2 <file2へのパス>
```

## コマンドライン引数
- `--file1`: 比較する最初のテキストファイルへのパスです。
- `--file2`: 比較する2番目のテキストファイルへのパスです。

## 例
例えば、file1.txtとfile2.txtという2つのファイルがあるとします。それらを次のように比較できます:
```
compare-textfiles --file1 file1.txt --file2 file2.txt
```
trade-lib 0.2.1
我的交易库
Requires Python: <4.0,>=3.7

# trade-lib
自己的交易常用库

```
pip install trade-lib
```

## 使用

```python

import trade_lib

# load exchange config from ~/.config/exchange.yaml
config = trade_lib.get_exchange_config(exname)

# set_dingding
dingding_config = {'robot_id':'', 'secret':''}
trade_lib.set_dingding(dingding_config)
trade_lib.dinding_send('ding...')
```

## upload to pypi

```
python3 setup.py sdist bdist_wheel

twine upload dist/*
```


## api

1、 取 venus.io 上的借贷利率
```python
from trade_lib.venus import get_apy

supply_apy, borrow_apy = get_apy('BNB')
print(supply_apy, borrow_apy)

```


## howto install

```shell
pip install peotry
poetry build 
poetry publish





```
termpandas 0.2.3
Scrollable pandas dataframes in the terminal.

# termpandas
Scrollable Pandas and Polars DataFrames in the Terminal.

![tprint_demo.gif](https://raw.githubusercontent.com/juan-esteban-berger/termpandas/main/tprint_demo.gif)

## Installation

```bash
pip install termpandas
```

Note: This library is not compatible with Windows at the moment.

## Basic Usage

```python
import pandas as pd
from termpandas import tprint

df = pd.read_csv('titanic.csv')
tprint(df)
```

- Key Bindings:
    - `k` or `Up Arrow Key` to scroll up
    - `j` or `Down Arrow Key` to scroll down
    - `h` or `Left Arrow Key` to scroll left
    - `l` or `Right Arrow Key` to scroll right
    - `enter` to select a row
    - `q` to quit

## Parameters

- `num_rows`: Number of rows to display at a time. Default is 10.
```python
tprint(df, num_rows=5)
```

- `highlight`: Highlights the selected row. Default is `True`.
```python
tprint(df, highlight=True)
```

- `highlight_color`: Color of the selected row. Default is `Gray`.
```python
tprint(df, highlight_color='Gray')
```

- `masks`: Dictionary of masks to highlight rows. Default is `{}`. The keys are the colors and the values are the masks.
```python
mask_1 = df['Survived'] == 1
mask_2 = df['Sex'] == 'male'
tprint(df.head(20), masks={'Red': mask_1,'Blue': mask_2})
```

- `return_row`: Returns the selected row. Default is `False`. Press `enter` to select a row.
```python
result = tprint(df.head(20), return_row=True)
try:
    tprint(result)
except:
    print("Quit termpandas: No row selected")
```

## Polars Support

- Note: Masks are not currently supported for Polars DataFrames. Convert to pandas DataFrames to utilize masks functionality.

- Returning Selected Row is supported and works just like with Pandas DataFrames.

```python
import polars as pl
from termpandas import tprint

# DataFrame needs to be collected
df = pl.scan_csv('titanic.csv').collect()
tprint(df)
```

## Available Colors

- `Red`
- `Blue`
- `Magenta`
- `Gray`
pywikisource 0.0.5
Wikisource Dedicated Python API library

# pywikisource

This library contains the basic API operations like

* Number of Index Book
* Current page quality status
* Proofreader of page
* Validator of page

It is wikisource dedicated API library.

## Installation
* `pip install pywikisource`

## Demo 1
```python
from pywikisource import WikiSourceApi

# Create the Wikisource object
WS = WikiSourceApi('en')

# or
userAgent = "MyCoolTool/1.1 (https://example.org/MyCoolTool/; MyCoolTool@example.org) pywikisource/0.0.4"
WS = WikiSourceApi('en', userAgent)  # It is recommended to add user agent.

# Get page list
pageList = WS.createdPageList('Landon in Literary Gazette 1833.pdf')
for page in pageList:
    print(page)
    

# Get the number of pages of Index Book
print(WS.numpage('Landon in Literary Gazette 1833.pdf'))

# Get the proofreader of single page
print(WS.proofreader('Page:Landon_in_Literary_Gazette_1833.pdf/3'))


# Get the validator of single page
print(WS.validator('Page:Landon_in_Literary_Gazette_1833.pdf/3'))

```

## Demo 2 (Async bookStatus; Since pywikisource 0.0.4)
```python
from pywikisource import WikiSourceApi
import asyncio

userAgent = "MyCoolTool/1.1 (https://example.org/MyCoolTool/; MyCoolTool@example.org) pywikisource/0.0.4"
WS = WikiSourceApi('ta', userAgent)  # It is recommended to add user agent.

books = [
    "தமிழர் வரலாறு 1, பி. டி. சீனிவாச அய்யங்கார்.pdf",
    "தமிழர் வரலாறு 2, பி. டி. சீனிவாச அய்யங்கார்.pdf",
    "மாவீரர் மருதுபாண்டியர்.pdf",
    "சேரன் செங்குட்டுவன்.djvu"
]

for book in books:
    pageList = WS.createdPageList( book )

    # Default limit is 25 requests per second
    data = asyncio.run( WS.bookStatus( pageList, limit=50 ) )

    for title, status in data.items():
        print( title, " -->  ",  status["code"] )

```

## Author
* [Jay Prakash](https://meta.wikimedia.org/wiki/User:Jayprakash12345), Indic-TechCom

## Instruction for Maintainer
To deploy on PyPI
```bash
administrator@Jay-Prakash % python setup.py sdist bdist_wheel
administrator@Jay-Prakash % twine check dist/*
administrator@Jay-Prakash % twine upload dist/*
```

## Licence
This is Free Software, released under the MIT.
winfetch-gamerjamer43 1.0.0
a little replacement for neofetch that can be run on windows. i made this in like an hour lol
Requires Python: >=3.8

#  __      __.__        _____       __         .__     
# /  \    /  \__| _____/ ____\_____/  |_  ____ |  |__ 
# \   \/\/   /  |/    \   __\/ __ \   __\/ ___\|  |  \ 
#  \        /|  |   |  \  | \  ___/|  | \  \___|   Y  \
#   \__/\  / |__|___|  /__|  \___  >__|  \___  >___|  /
#        \/          \/          \/          \/     \/ 

i made this in like hour, supposed to be a funny neofetch replacement for windows. i prolly will not update this lol
pdfzipcracker 1.0
A tool to crack encrypted PDF and ZIP files.
Requires Python: >=3.6


pdfzipcracker
=============

pdfzipcracker is a Python package designed to help you crack passwords for encrypted ZIP and PDF files using brute-force techniques.

Features
--------

- Crack passwords for encrypted ZIP files.
- Crack passwords for encrypted PDF files.
- Flexible options to specify character sets and password lengths.

Installation
------------

You can install pdfzipcracker via pip:

.. code-block:: bash

    pip install pdfzipcracker

Usage
-----

To use pdfzipcracker, run the script and follow the on-screen instructions:

.. code-block:: bash

    pdfzipcracker

You will be prompted to choose between cracking a PDF or a ZIP file, and then to provide necessary details about the expected password characteristics.

Crack a ZIP Password
--------------------

.. code-block:: text

    Enter ZIP File Path: /path/to/yourfile.zip
    Is The Password Contains Numbers? (if don't know, enter 'yes') (yes / no): yes
    Is The Password Contains Characters? (if don't know, enter 'yes') (yes / no): yes
    Is The Password Contains Special Characters? (if don't know, enter 'yes') (yes / no): no
    Enter Exact Length Of Password If You know Else Enter 'n': n
    Enter Maximum Guessed Password Length (e.g., 6 numbers or 9 characters): 6

Crack a PDF Password
--------------------

.. code-block:: text

    Enter PDF File Path: /path/to/yourfile.pdf
    Is The Password Contains Numbers? (if don't know, enter 'yes') (yes / no): yes
    Is The Password Contains Characters? (if don't know, enter 'yes') (yes / no): yes
    Is The Password Contains Special Characters? (if don't know, enter 'yes') (yes / no): no
    Enter Exact Length Of Password If You know Else Enter 'n': n
    Enter Maximum Guessed Password Length (e.g., 6 numbers or 9 characters): 6

License
-------

This project is licensed under the MIT License. See the [LISENCE.txt](https://github.com/neuqs90/pdfzipcracker/blob/fd7829df3844ccdfe00f43a3f3f859116694b47f/LISENCE.txt) file for more details.

Contributing
------------

Contributions are welcome! Please fork the repository and submit a pull request.

Support
-------

If you have any questions or issues, please open an issue in the GitHub repository.

Acknowledgments
---------------

This package uses the following libraries:

- [pyzipper](https://github.com/danifus/pyzipper.git)
- [PyPDF2](https://pypi.org/project/PyPDF2/)