semvec/pypi-packages huggingface dataset:
topsis-saanvi-102483080 2.0.0
TOPSIS implementation for multi-criteria decision making

Project Description

Submitted by: Saanvi Wadhwa
Roll No: 102483080
Group: 3C15

## Overview

topsis-saanvi-102483080 is a Python package designed to solve multi-criteria decision-making (MCDM) problems using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.
The package allows users to evaluate multiple alternatives based on several criteria and ranks them according to their relative closeness to the ideal solution.

## Key Features
Command-line based execution
Supports any number of criteria and alternatives
Accepts weights and impacts as comma-separated inputs
Handles both benefit (+) and cost (-) criteria
Produces a ranked output in CSV format

## Installation
The package can be installed using pip.
From TestPyPI:
pip install -i https://test.pypi.org/simple/ topsis-saanvi-102483080 --extra-index-url https://pypi.org/simple


## How to Use

Provide the input CSV file followed by the weights and impacts.
Syntax:
topsis <input.csv> <weights> <impacts> <output.csv>

Example:
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv

Note:
If weights or impacts contain spaces, enclose them within double quotes.

## Sample Input
Example Dataset
data.csv

The following dataset represents different investment funds evaluated across multiple parameters.

| Fund Name | P1   | P2   | P3  | P4   | P5    |
| --------- | ---- | ---- | --- | ---- | ----- |
| M1        | 0.84 | 0.71 | 6.7 | 42.1 | 12.59 |
| M2        | 0.91 | 0.83 | 7.0 | 31.7 | 10.11 |
| M3        | 0.79 | 0.62 | 4.8 | 46.7 | 13.23 |
| M4        | 0.78 | 0.61 | 6.4 | 42.4 | 12.55 |
| M5        | 0.94 | 0.88 | 3.6 | 62.2 | 16.91 |
| M6        | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 |
| M7        | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 |
| M8        | 0.93 | 0.86 | 3.4 | 37.0 | 10.55 |


## Output
The output file includes:
TOPSIS Score – numerical measure of preference
Rank – relative ranking of alternatives
Higher score indicates a better alternative.

| Fund Name | Topsis Score | Rank |
|----------|--------------|------|
| M6 | 0.653276 | 1 |
| M2 | 0.605985 | 2 |
| M1 | 0.603261 | 3 |
| M5 | 0.546243 | 4 |
| M4 | 0.532353 | 5 |
| M8 | 0.494089 | 6 |
| M3 | 0.453053 | 7 |
| M7 | 0.393907 | 8 |


## Assumptions
The first column contains alternative names.
Remaining columns must be numeric.
Number of weights must match number of criteria.
Impacts can only be + or -.

## License
This project is released under the MIT License and is intended for academic use.

## Author
Saanvi Wadhwa
mlhp 0.1.2
A work-in-progress finite element library.

## About

Mlhp is a C++ library with python bindings that implements multi-level _hp_- and other finite element methods efficiently also for dimensions > 3. The project is still very much work in progress and therefore does not have an extensive documentation.

## Submodules

The header-only libraries [pybind11](https://pybind11.readthedocs.io/en/stable/), [Catch2](https://github.com/catchorg/Catch2), and [vtu11](https://github.com/phmkopp/vtu11) are included as git submodules. To clone them resursively, use: 
```
git clone --recursive https://gitlab.com/hpfem/code/mlhp.git
```

## Getting started
- [Compiler and platform support](https://gitlab.com/hpfem/code/mlhp/-/wikis/Compiler%20and%20platform%20support) 
- [Setting up an executable that uses mlhp](https://gitlab.com/hpfem/code/mlhp/-/wikis/Setting%20up%20an%20executable%20that%20uses%20mlhp).
- [Core class structure](https://gitlab.com/hpfem/code/mlhp/-/wikis/Core%20class%20structure)
- [Doxygen documentation](https://hpfem/code.gitlab.io/mlhp)

## References and publications

If you use our code for your scientific research, please acknowledge this by referring to the following publication:

P. Kopp, E. Rank, V. M. Calo, S. Kollmannsberger, 2022: Efficient multi-level hp-finite elements in arbitrary dimensions, Computer Methods in Applied Mechanics and Engineering, Volume 401, Part B, 115575, DOI: [10.1016/j.cma.2022.115575](https://doi.org/10.1016/j.cma.2022.115575)

Here are some of the publications using this project:

- V. Holla, P. Kopp, J. Grünewald; P. Praegla, C. Meier, K. Wudy, S. Kollmannsberger, 2023: Laser beam shape optimization: Exploring alternative profiles to Gaussian-shaped laser beams in powder bed fusion of metals, 2023 International Solid Freeform Fabrication Symposium, 
DOI: [10.26153/TSW/50986](https://doi.org/10.26153/TSW/50986)
- V. Holla, P. Kopp, J. Grünewald, K. Wudy, S. Kollmannsberger, 2023: Laser beam shape optimization in powder bed fusion of metals, Additive Manufacturing, Volume 72, 103609, DOI: [10.1016/j.addma.2023.103609](https://doi.org/10.1016/j.addma.2023.103609)
- P. Kopp, V. M. Calo, E. Rank, S. Kollmannsberger, 2022: Space-time hp-finite elements for heat evolution in laser powder bed fusion additive manufacturing, Engineering with Computers, Volume 38, pages 4879–4893, DOI: [10.1007/s00366-022-01719-1](https://doi.org/10.1007/s00366-022-01719-1)
- S. Kollmannsberger; P. Kopp, 2021: On accurate time integration for temperature evolutions in additive manufacturing, GAMM-Mitteilungen, Volume 44, Issue 4, DOI: [10.1002/gamm.202100019](https://doi.org/10.1002/gamm.202100019)
topicminer-louati 0.1.2
Advanced topic modeling for Pandas with email auth, metrics, and Plotly visualizations.
Requires Python: >=3.7

# TopicMiner

An advanced topic modeling library for Pandas DataFrames with email authentication, detailed metrics, and interactive visualizations.

## Features
- **Email Authentication**: Secure token-based access using SMTP.
- **Advanced Topic Modeling**: Utilizes BERTopic for state-of-the-art topic discovery.
- **Detailed Metrics**: Generates tabulated reports using `tabulate`.
- **Interactive Visualizations**: Creates charts using `plotly`.
- **Usage Tracking**: Displays session duration and user details in the footer.

## Installation
```bash
pip install topicminer-louati
snoogle13-vault-secrets 0.1.9
Retrieve secrets from HashiCorp Vault using AppRole authentication.
Requires Python: >=3.7

# vault-secrets

Reusable Python package for downloading secrets from HashiCorp Vault using AppRole authentication.

## Installation

```sh
pip install .
```

## Usage

```python
from vault_secrets import VaultSecretsClient

client = VaultSecretsClient()
secret = client.get_secret("my/path")
```