semvec/pypi-packages huggingface dataset:
Topsis-VishwasKhattar-102303170 1.0.1
A command-line implementation of TOPSIS

# TOPSIS Command Line Tool

This project is a Python implementation of the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method.  
TOPSIS is a popular multi-criteria decision-making technique used to rank alternatives based on their distance from an ideal best and an ideal worst solution.

This tool is implemented as a **command-line utility** and can be installed directly using `pip`.

---

## Why TOPSIS?

In many real-world problems, decisions depend on multiple criteria.  
For example:
- Choosing the best mobile phone based on price, battery, and camera
- Selecting a fund based on risk, return, and stability
- Ranking suppliers based on cost, quality, and delivery time

TOPSIS helps by:
- Normalizing different criteria
- Applying user-defined weights
- Ranking alternatives objectively

---

## Installation

You can install the package from PyPI using:

```bash
pip install Topsis-VishwasKhattar-102303170


How to Use (After Installation)
Once installed, the TOPSIS tool can be executed directly from the command line.

Command Format:
topsis <InputDataFile> <Weights> <Impacts> <OutputFile>


Input Details
1. Input CSV File

The input file must be a CSV file
The first column should contain names of alternatives
All remaining columns must contain numeric values only

Example input file (data.csv):
Phone,Price,Battery,Camera
A,20000,4000,48
B,25000,5000,64
C,18000,4500,50



2. Weights
Weights define the importance of each criterion.
"1,1,2"


Meaning:
Price â†’ weight 1
Battery â†’ weight 1
Camera â†’ weight 2 (more important)


3. Impacts
Impacts define whether a criterion is beneficial or non-beneficial.
+ â†’ Higher value is better
- â†’ Lower value is better

Example:
"- , + , +"

Meaning:
Price â†’ lower is better
Battery â†’ higher is better
Camera â†’ higher is better

Complete Example
topsis data.csv "1,1,2" "-,+,+" output.csv


This command:
Reads data from data.csv
Applies the given weights
Uses the specified impacts
Calculates TOPSIS scores
Ranks the alternatives
Saves the result in output.csv



Output File
The output is a CSV file containing:
All original input columns
A new column Topsis Score
A new column Rank


Example output format:
Phone,Price,Battery,Camera,Topsis Score,Rank
A,20000,4000,48,0.42,2
B,25000,5000,64,0.78,1
C,18000,4500,50,0.36,3


Higher Topsis Score means a better alternative
Rank 1 represents the best choice


Error Handling:
The program performs input validation and raises errors if:
Input file is missing
Criteria columns contain non-numeric values
Number of weights does not match criteria
Number of impacts does not match criteria
Invalid impact symbols are provided



Implementation Details:
Vector normalization is used
Weighted normalized decision matrix is computed
Euclidean distance is used for distance calculation
Ranking is based on relative closeness to the ideal solution
Topsis-RishabhSharma-102303286 1.0
TOPSIS implementation

# TOPSIS – Technique for Order Preference by Similarity to Ideal Solution

This Python package implements the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method.
It allows users to rank alternatives based on multiple criteria using a **command-line interface**.


---

## 📌 What is TOPSIS?

TOPSIS is a **Multi-Criteria Decision-Making (MCDM)** technique that identifies solutions from a finite set of alternatives based on:

- Closest distance to the **ideal best solution**
- Farthest distance from the **ideal worst solution**

## 🔧 Requirements

The package requires the following Python libraries:

- Python ≥ 3.7
- pandas
- numpy

These dependencies are installed automatically.

---

## 📥 Installation

Install the package from PyPI using:

```bash
pip install Topsis-RishabhSharma-102303286
genomemanagement 2.0.0
Genome Management Package
Requires Python: >=3.7

# Genome Management Package

This tool is copyright 2016 by Nattawet Sriwichai,
King Mongkut's University Technology Thonburi (Bioinformatics and Systems Biology Program), Thailand.
All rights reserved. See the licence text below.

# Installation

## Install from PyPI

    pip install genomemanagement

## Install from GitHub

    pip install git+https://github.com/evolu-tion/GenomeManagement.git

## Install from Source (Standard)

    git clone https://github.com/evolu-tion/GenomeManagement.git
    cd GenomeManagement
    pip install .

## Install for Development (Editable)

    pip install -e .

# Usage: Promoter of genes retrieving

Then run python script by used command line on windows or unix:

    promoter-retrieve \
    	--output <output_file.fa> \
    	--output_format <fasta/gff> \
    	--genome <genome.fa> \
    	--gff <genome.gff> \
    	--type <TLS/TSS> \
    	--upstream <bp> \
    	--downstream <bp> \
    	--all_gene <Y/N> \
    	--selected_gene_list <gene_list.txt, is optional if all_gene is N> \
    	--remove_n_gap <Y/N> \
    	--min_length <default is 100 bp>

# Usage: Get protein or gene sequences from genome

    proteins-retrieve \
    	--input <input_fasta_file> \
    	--list_of_interest <list_of_protein_id.txt> \
    	--output <output_file.fa>

# Usage: PlantPAN3

    plantpan3

# Usage: Get genome statistic

    get-genome-statistic \
    	--genome <genome.fa>

# License

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