semvec/pypi-packages huggingface dataset:
aspose-barcode-for-python-via-java 26.1.0
Barcode generation and recognition component. It allows developers to quickly and easily add barcode creation and scanning functionality to their Python applications.
Requires Python: >=3.6
Keywords: aspose, barcode, java

# Barcode Generation & Recognition via Python

Aspose.BarCode for Python via Java is a robust and reliable barcode generation and recognition component, written in Python and Java. It allows developers to quickly and easily add barcode creation and scanning functionality to their Python applications.

## General Barcode Features

- Supports most established barcode standards and barcode specifications.
- Ability to read & export barcodes in multiple image formats including BMP, GIF, JPEG & PNG.  
- Provides full control over barcode images including background color, bar color, image quality, rotation angle, x-dimension, resolution and more.  
- Complete control over barcode captions including caption font, back color, fore color, alignment, and location.
- Support for checksum.
- Support for X-dimension & Y-dimension for 2D BarCodes.
- Support for Wide to Narrow Ratio for supported symbologies.
- Support for DataMatrix barcode with X12, EDIFACT & Base 256 encoding. 

## Barcode Recognition Features

- Can read most common 1D, 2D barcodes anywhere at any angle from an image.
- Specify an area in the image to scan the barcode 
- Get region information for the barcodes recognized in the image

## Barcode Imaging Features

- Manipulate the barcode's image borders, border color, style, margins, width, etc.
- Rotate barcode images to any degree.
- Set anti-aliasing for barcode images.
- Manage barcode image margins.
- Customize image resolution.
- Set size in inches or millimeters.
- Auto size barcode images.

## Barcode Symbologies

**Numeric Only:** EAN13, EAN8, UPCA, UPCE, ISBN, ISMN, ISSN, Interleaved2of5, Standard2of5, MSI, Code11, Codabar, Postnet, Planet, EAN14(SCC14), SSCC18, ITF14, IATA 2 of 5, DatabarOmniDirectional, DatabarStackedOmniDirectional, DatabarExpandedStacked, DatabarStacked, DatabarLimited, DatabarTruncated\
**Alpha-Numeric:** GS1Code128, Code128, Code39 Extended, Code39 Standard, Code93 Extended, Code93 Standard, Australia Post, Italian Post 25, Matrix 2 of 5, DatabarExpanded, PatchCode\
**2D Symbologies:** PDF417, DataMatrix, Aztec, QR, MicroQR, GS1DataMatrix, Code16K, CompactPDF417, Swiss QR (QR Bill)

## Read Barcodes From

**Images:** JPEG, TIFF, PNG, BMP

## Save Barcode Labels As

**Images:** JPEG, TIFF, PNG, BMP

[Product](https://products.aspose.com/barcode/python-java) | [Documentation](https://docs.aspose.com/barcode/pythonjava/) | [Blog](https://blog.aspose.com/category/barcode/) | [API Reference](https://apireference.aspose.com/barcode/java) | [Free Support](https://forum.aspose.com/c/barcode) | [Temporary License](https://purchase.aspose.com/temporary-license)
TOPSIS-Yash-102303701 0.0.3
TOPSIS implementation using Python

## TOPSIS-Yash-102303701

TOPSIS-Yash-102303701 is a Python package that implements the **TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method for multi-criteria decision-making (MCDM).

TOPSIS is widely used in decision analysis to rank alternatives based on their distance from an ideal best solution and an ideal worst solution. This package allows users to apply TOPSIS easily on tabular data stored in CSV files through a simple command-line interface.

The package validates inputs, handles common user errors, and generates a ranked output with TOPSIS scores, making it suitable for academic assignments as well as real-world decision analysis problems.

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## Features
- Command-line implementation of TOPSIS
- Supports multiple decision criteria
- Handles both beneficial (+) and non-beneficial (-) attributes
- Input validation for files, weights, and impacts
- Outputs TOPSIS score and rank for each alternative

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## Installation

Install the package directly from PyPI:

pip install TOPSIS-Yash-102303701

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## Usage

Run the TOPSIS analysis from the command line:

topsis input.csv "1,1,1,2,1" "+,+,-,+,+" output.csv

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## Input File Format
- The input file must be a CSV file
- The first column should contain the names of alternatives
- All remaining columns must contain numeric values only
- The number of weights and impacts must match the number of criteria columns

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## Output File
The output CSV file contains:
- All original input columns
- A new column named **Topsis Score**
- A new column named **Rank**, where rank 1 indicates the best alternative

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## Author
Yash Sharma  
Roll No: 102303701  

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## License
This project is developed for academic and educational purposes.