semvec/pypi-packages huggingface dataset:
topsis-manya-102203284 1.0.0
A Python package to implement TOPSIS for decision-making problems.
Requires Python: >=3.6

# Topsis-Manya-102203284

This is a Python package to implement the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)** for multi-criteria decision-making problems. 

TOPSIS is a useful method for ranking and selecting alternatives when multiple criteria are involved, such as decision-making problems in business, engineering, and more.

## Features

- **Flexible Input**: Accepts any CSV file with numeric data.
- **Customizable Weights**: Allows users to specify weights for each criterion.
- **Impact Specification**: Supports both beneficial (`+`) and non-beneficial (`-`) criteria.
- **Accurate Ranking**: Calculates TOPSIS scores and ranks the alternatives.
- **Error Handling**: Ensures robust handling of invalid inputs and edge cases.

---

## Installation

You can install this package via pip:
pip install topsis-manya-102203284


## Usage

```python
from topsis_manya.topsis import topsis

# Example usage
topsis('input.csv', [1, 1, 1, 2, 1], ['+', '+', '-', '+', '-'], 'output.csv')

## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
topsis-vardhan-102203268 1.0.3
A Python package to perform TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.6

# topsis_vardhan_102203268

`topsis_vardhan_102203268` is a Python package that implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria decision analysis.

## Installation

You can install the package using pip:

```bash
pip install topsis_vardhan_102203268
```

## Usage

### Input File Format

The input CSV file should have the following structure:

- The first column should contain the names of the models.
- The subsequent columns should contain the criteria values for each alternative.
- The first row should contain the headers for each column.

Example:

| model | Criteria 1 | Criteria 2 | Criteria 3 |
|-------------|------------|------------|------------|
| m1        | 250        | 16         | 12         |
| m2        | 200        | 14         | 8          |
| m3        | 300        | 18         | 15         |

### Command-line Usage

After installation, you can use the `topsis` command in the terminal:

```bash
topsis <input_file> <weights> <impacts> <output_file>
```

- `<input_file>`: Path to the input CSV file.
- `<weights>`: Comma-separated string of weights for the criteria.
- `<impacts>`: Comma-separated string of '+' or '-' indicating the desirability of the criteria.
- `<output_file>`: Path to the output CSV file to save the results.

## Example

```bash
topsis data.csv "0.5,0.3,0.2" "+,-,+" results.csv
```

This command will process the `data.csv` file using the specified weights and impacts and output the results to `results.csv`.

## NOTE

- Ensure that the input file has at least three columns: one for alternatives and at least two for criteria.
- All criteria columns should contain numeric values.
- The number of weights and impacts must match the number of criteria.
- Impacts should only be '+' or '-'.

## License

This project is licensed under the MIT License. See the LICENSE file for details.
topsis-testing-2025 0.3.0
TOPSIS Python Package
Requires Python: >=3.6

# TOPSIS Package

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for decision-making.

## Installation
```bash
pip install topsis

## To Run file
pyhton topsis.py <input_file_location> "weights" "impacts" <output_file_location.csv>
customisedLogs 2.0.3
A well maintained package to have coloured logs on the terminal. This would help isolate errors and success messages from each other by their colours.
Requires Python: >=3.6
Keywords: log, logs, pretty, prettified, color, colored, coloured logs, colored terminal, logger, tailwind

# customisedLogs v2.0.3

```pip install customisedlogs --upgrade```

###### <br>A well maintained program to have coloured logs on the terminal. This would help isolate errors and success messages from each other just by their colours.
###### The package contains all colours combinations from https://davidpiesse.github.io/tailwind-md-colours


<br>To install: 
```
pip install customisedlogs --upgrade
pip3 install customisedlogs --upgrade
python -m pip install customisedlogs --upgrade
python3 -m pip install customisedlogs --upgrade
```


#### <br><br>Using this program is as simple as:
```
from customisedLogs import CustomisedLogs
logger = CustomisedLogs(100)

logger.log(logger.Colors.teal, "teal")
logger.log(logger.Colors.red_100_accent, "red_100_accent")
logger.log(logger.Colors.blue_900, "blue_900")
logger.log(logger.Colors.grey_400, "grey_400")
logger.log(logger.Colors.deep_purple_700, "deep_purple_700")
logger.log(logger.Colors.black, "black")
logger.log(logger.Colors.white, "white")
```
And it would look like:<br>
![output-readme.png](https://raw.githubusercontent.com/BhaskarPanja93/customisedLogs/master/output-readme.png?raw=True)

### Future implementations:
* Let user change the default RGB values.
* Different formats. Compatible to render on HTML or any other stdout method.


###### <br>This project is always open to suggestions and feature requests.
streamlit-slb-video-image-gallery 1.11.0
Streamlit component that allows user to display image and video gallery
Requires Python: >=3.11

# streamlit_slb_video_image_gallery

Streamlit component that allows user to display image and video gallery

## Installation instructions

```sh
pip install streamlit_slb_video_image_gallery
```

## Release Notes

1.0.1

- Initial Release

1.0.2

- Add video viewing features

1.0.3

- Add image lazy loading 

1.0.4

- Fix card css 

1.0.5

- Add environment condition

1.0.6

- Fix css

1.0.7

- Fix css 2

1.0.8

- Fix css 3

1.0.9

- Fix css 4

1.10.0

- Fix css 5
topsispackage-102203280 0.3
A TOPSIS implementation package

#TOPSIS Package

Submitted by Bhumika Tandon 102203280

What is TOPSIS?
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method that helps in ranking and selecting from several alternatives based on their closeness to an ideal solution. It's particularly useful when you have to consider multiple, sometimes conflicting criteria.It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.
An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.

This package has been created based on Project 1 of course UCS654.
Bhumika Tandon 102203280 3C22

To install,run in cmd:

```bash
pip install -e .[dev]
```

After installation, in Command Prompt/Terminal in pwd/current dir:

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
topsispackage3280 0.3
A TOPSIS implementation package

#TOPSIS Package

Submitted by Bhumika Tandon 102203280

What is TOPSIS?
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method that helps in ranking and selecting from several alternatives based on their closeness to an ideal solution. It's particularly useful when you have to consider multiple, sometimes conflicting criteria.It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.
An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. In this Python package Vector Normalization has been implemented.

This package has been created based on Project 1 of course UCS654.
Bhumika Tandon 102203280 3C22

To install,run in cmd:

```bash
pip install -e .[dev]
```

After installation, in Command Prompt/Terminal in pwd/current dir:

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```