semvec/pypi-packages huggingface dataset:
topsis-Eshita-102003522 0.1.2
Calculate Topsis score and save it in a csv file
License: MIT
Keywords: TOPSISSCORE,RANK,DATAFRAME

#Project description
##TOPSIS
What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making (MCDM) method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.

##How to Install this Package?
pip install Topsis-Eshita-102003522

##How to Run this Package?
topsis <inputFileName> <weights> <impacts> <resultFileName>

Eg. topsis /Users/Eshita/Desktop/102003522-data.csv "1,1,1,1,1" "+,+,-,+,+" /Users/Eshita/Desktop/result.csv

##Constraints Applied
Number of parameters should be correct i.e. 5.
Print error message if input file doesn't exist.
The impacts and weights should be comma separated.
Impacts should only have +ve or -ve symbols.
Number of columns in the input csv file should be more or equal to 3.
The 2nd to last columns should be in numeric data type.
Number of weights, impacts and columns should be equal.
Input File
Fund Name	P1	P2	P3	P4	P5
M1	0.75	0.56	6.3	51.1	14.68
M2	0.82	0.67	4.2	41.2	11.72
M3	0.89	0.79	6.5	40.2	12.1
M4	0.92	0.85	5.8	49.7	14.32
M6	0.72	0.52	5.3	61.1	16.91
M7	0.69	0.48	3.6	57.9	15.67
M8	0.92	0.85	5.7	31.2	9.67
Output File
Fund Name	P1	P2	P3	P4	P5	TOPSIS Score	Rank
M1	0.32	0.29	0.39	0.36	0.37	0.3655	8
M2	0.35	0.34	0.26	0.29	0.29	0.55	2
M3	0.38	0.41	0.41	0.28	0.30	0.48	5
M4	0.39	0.44	0.36	0.35	0.36	0.57	1
M5	0.33	0.30	0.41	0.39	0.39	0.39	7
M6	0.31	0.27	0.33	0.43	0.42	0.44	6
M7	0.29	0.25	0.22	0.41	0.39	0.50	4
M8	0.39	0.44	0.36	0.22	0.24	0.53	3
License
MIT

##Written By
Name : Eshita Arora
Roll No. : 102003522
napari-solarized 0.1.1
Solarized themes for napari
License: MIT
Requires Python: >=3.8

# napari-solarized

Solarized (-ish) themes for napari, based on [solarized](https://ethanschoonover.com/solarized/).

![solarized dark screenshot](https://raw.githubusercontent.com/aganders3/napari-solarized/main/screenshot_dark.png)
![solarized light screenshot](https://raw.githubusercontent.com/aganders3/napari-solarized/main/screenshot_light.png)

[![License MIT](https://img.shields.io/pypi/l/napari-solarized.svg?color=green)](https://github.com/aganders3/napari-solarized/raw/main/LICENSE)
[![PyPI](https://img.shields.io/pypi/v/napari-solarized.svg?color=green)](https://pypi.org/project/napari-solarized)
[![Python Version](https://img.shields.io/pypi/pyversions/napari-solarized.svg?color=green)](https://python.org)
[![tests](https://github.com/aganders3/napari-solarized/workflows/tests/badge.svg)](https://github.com/aganders3/napari-solarized/actions)
[![napari hub](https://img.shields.io/endpoint?url=https://api.napari-hub.org/shields/napari-solarized)](https://napari-hub.org/plugins/napari-solarized)

----------------------------------

This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookiecutter-napari-plugin] template.

## Installation

You can install `napari-solarized` via [pip]:

    pip install napari-solarized



To install latest development version :

    pip install git+https://github.com/aganders3/napari-solarized.git


## Contributing

Contributions are very welcome.

## License

Distributed under the terms of the [MIT] license,
"napari-solarized" is free and open source software

## Issues

If you encounter any problems, please [file an issue] along with a detailed description.

[napari]: https://github.com/napari/napari
[Cookiecutter]: https://github.com/audreyr/cookiecutter
[@napari]: https://github.com/napari
[MIT]: http://opensource.org/licenses/MIT
[BSD-3]: http://opensource.org/licenses/BSD-3-Clause
[GNU GPL v3.0]: http://www.gnu.org/licenses/gpl-3.0.txt
[GNU LGPL v3.0]: http://www.gnu.org/licenses/lgpl-3.0.txt
[Apache Software License 2.0]: http://www.apache.org/licenses/LICENSE-2.0
[Mozilla Public License 2.0]: https://www.mozilla.org/media/MPL/2.0/index.txt
[cookiecutter-napari-plugin]: https://github.com/napari/cookiecutter-napari-plugin

[file an issue]: https://github.com/aganders3/napari-solarized/issues

[napari]: https://github.com/napari/napari
[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
Topsis-Eshita-102003522 0.1.2
Calculate Topsis score and save it in a csv file
License: MIT
Keywords: TOPSISSCORE,RANK,DATAFRAME

#Project description
##TOPSIS
What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making (MCDM) method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.

##How to Install this Package?
pip install Topsis-Eshita-102003522

##How to Run this Package?
topsis <inputFileName> <weights> <impacts> <resultFileName>

Eg. topsis /Users/Eshita/Desktop/102003522-data.csv "1,1,1,1,1" "+,+,-,+,+" /Users/Eshita/Desktop/result.csv

##Constraints Applied
Number of parameters should be correct i.e. 5.
Print error message if input file doesn't exist.
The impacts and weights should be comma separated.
Impacts should only have +ve or -ve symbols.
Number of columns in the input csv file should be more or equal to 3.
The 2nd to last columns should be in numeric data type.
Number of weights, impacts and columns should be equal.
Input File
Fund Name	P1	P2	P3	P4	P5
M1	0.75	0.56	6.3	51.1	14.68
M2	0.82	0.67	4.2	41.2	11.72
M3	0.89	0.79	6.5	40.2	12.1
M4	0.92	0.85	5.8	49.7	14.32
M6	0.72	0.52	5.3	61.1	16.91
M7	0.69	0.48	3.6	57.9	15.67
M8	0.92	0.85	5.7	31.2	9.67
Output File
Fund Name	P1	P2	P3	P4	P5	TOPSIS Score	Rank
M1	0.32	0.29	0.39	0.36	0.37	0.3655	8
M2	0.35	0.34	0.26	0.29	0.29	0.55	2
M3	0.38	0.41	0.41	0.28	0.30	0.48	5
M4	0.39	0.44	0.36	0.35	0.36	0.57	1
M5	0.33	0.30	0.41	0.39	0.39	0.39	7
M6	0.31	0.27	0.33	0.43	0.42	0.44	6
M7	0.29	0.25	0.22	0.41	0.39	0.50	4
M8	0.39	0.44	0.36	0.22	0.24	0.53	3
License
MIT

##Written By
Name : Eshita Arora
Roll No. : 102003522