semvec/pypi-packages huggingface dataset:
simplisafe-python 2024.1.0
A Python3, async interface to the SimpliSafe API
License: MIT
Requires Python: >=3.10,<4.0

# 🚨 simplisafe-python: A Python3, async interface to the SimpliSafe™ API

[![CI][ci-badge]][ci]
[![PyPI][pypi-badge]][pypi]
[![Version][version-badge]][version]
[![License][license-badge]][license]
[![Code Coverage][codecov-badge]][codecov]
[![Maintainability][maintainability-badge]][maintainability]

<a href="https://www.buymeacoffee.com/bachya1208P" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>

`simplisafe-python` (hereafter referred to as `simplipy`) is a Python3,
`asyncio`-driven interface to the unofficial [SimpliSafe™][simplisafe] API. With it,
users can get data on their system (including available sensors), set the system state,
and more.

# Documentation

You can find complete documentation [here][docs].

# Contributing

Thanks to all of [our contributors][contributors] so far!

1. [Check for open features/bugs][issues] or [initiate a discussion on one][new-issue].
2. [Fork the repository][fork].
3. (_optional, but highly recommended_) Create a virtual environment: `python3 -m venv .venv`
4. (_optional, but highly recommended_) Enter the virtual environment: `source ./.venv/bin/activate`
5. Install the dev environment: `script/setup`
6. Code your new feature or bug fix on a new branch.
7. Write tests that cover your new functionality.
8. Run tests and ensure 100% code coverage: `poetry run pytest --cov simplipy tests`
9. Update `README.md` with any new documentation.
10. Submit a pull request!

[ci-badge]: https://img.shields.io/github/actions/workflow/status/bachya/simplisafe-python/test.yml
[ci]: https://github.com/bachya/simplisafe-python/actions
[codecov-badge]: https://codecov.io/gh/bachya/simplisafe-python/branch/dev/graph/badge.svg
[codecov]: https://codecov.io/gh/bachya/simplisafe-python
[contributors]: https://github.com/bachya/simplisafe-python/graphs/contributors
[docs]: https://simplisafe-python.readthedocs.io
[fork]: https://github.com/bachya/simplisafe-python/fork
[issues]: https://github.com/bachya/simplisafe-python/issues
[license-badge]: https://img.shields.io/pypi/l/simplisafe-python.svg
[license]: https://github.com/bachya/simplisafe-python/blob/main/LICENSE
[maintainability-badge]: https://api.codeclimate.com/v1/badges/f46d8b1dcfde6a2f683d/maintainability
[maintainability]: https://codeclimate.com/github/bachya/simplisafe-python/maintainability
[new-issue]: https://github.com/bachya/simplisafe-python/issues/new
[pypi-badge]: https://img.shields.io/pypi/v/simplisafe-python.svg
[pypi]: https://pypi.python.org/pypi/simplisafe-python
[simplisafe]: https://simplisafe.com
[version-badge]: https://img.shields.io/pypi/pyversions/simplisafe-python.svg
[version]: https://pypi.python.org/pypi/simplisafe-python
Topsis-Samarjeet-102103145 1.1
Topsis-Samarjeet-102103145
License: MIT

Project Description

for: Assignment-1 (UCS654) submitted by: Samarjeet Singh Gandhi Roll no: 102103145 Group: 3COE5

Topsis-Samarjeet-102103145 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).

Installation

`pip install Topsis-Samarjeet-102103145`

How to use it?

Enter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and then the csv filename in which you want to store the result by .csv extension

`Topsis sample.csv "1,1,1,2,2" "+,+,-,+,+" result.csv`

Example

sample.csv

Fund Name P1 P2 P3 P4 P5

M1 0.68 0.46 4 52.7 14.46

M2 0.61 0.37 6.9 51.5 14.85

M3 0.61 0.37 4.4 53.5 14.72

M4 0.7, 0.49 6 54.5 15.42

M5 0.65 0.42 4.1 68.7 18.47

M6 0.62 0.38 3.6 36.4 10.25

M7 0.92 0.85 5.2 44.6 12.89

M8 0.81 0.66 3.7 35.9 10.27

weights vector = [ 1,1,1,2,2 ]

impacts vector = [ +,+,-,+,+ ]

input

`Topsis sample.csv "1,1,1,2,2" "+,+,-,+,+" result.csv`

output

result.csv

Fund Name,P1,P2,P3,P4,P5,Topsis Score,Rank

M1,0.68,0.46,4.0,52.7,14.46,0.5903064989592446,4.0

M2,0.61,0.37,6.9,51.5,14.85,0.5408709514358343,5.0

M3,0.61,0.37,4.4,53.5,14.72,0.5933191124418361,3.0

M4,0.7,0.49,6.0,54.5,15.42,0.4714313335411608,7.0

M5,0.65,0.42,4.1,68.7,18.47,0.3803866453639049,8.0

M6,0.62,0.38,3.6,36.4,10.25,0.9855303249104818,1.0

M7,0.92,0.85,5.2,44.6,12.89,0.5209148114910495,6.0

M8,0.81,0.66,3.7,35.9,10.27,0.7535236734320149,2.0

Other Notes

1. The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.

2. Make sure the csv does not contain categorical values

License

© 2024 Samarjeet Singh Gandhi

This repository is licensed under the MIT license.

See LICENSE for details.