semvec/pypi-packages huggingface dataset:
srvCloud-keycloak 1.0.0
Lib for interacting with keycloak from srvCloud
Requires Python: >=3.6
Keywords: files speedfiles

# srvCloud-keycloak Library #

## What is this? ##
The module allows you to work with keycloak

### Using ###


Using the library is as simple and convenient as possible:

Let's import it first:
First, import everything from the library (use the `from srvCloud-keycloak import Keycloack` construct).

Examples of all operations:

Init class:

    keycloak = Keycloack(keycloak_host, keycloak_port, realm_name, client_id, client_secret)


Init session:

    cookies = keycloak.auth('user', 'password')

Refresh token:

    cookies = keycloak.refresh_token(cookies['access_token'], cookies['refresh_token'])

Close session:

    keycloak.logout(cookies['access_token'], cookies['refresh_token'])

Get introspect:

    introspect = keycloak.introspect(cookies['access_token'])

## Developer ##
Viktor Podlevski

My site: [github](https://github.com/VITca64rus)
sgia 0.1.1
Sparse Grid Indexing Algorithm
Requires Python: >=3.6

# Installation
The following instructions are for installing the SGIA package on a Linux/Windows/MacOS system. The following vector indexing algorithm is implemented in Python 3.6.8. The package is available on PyPI and can be installed using pip. The package is also available on GitHub.

## Requirements
    numpy
    scipy

## Installation
    pip install sgia

## Source
    https://pypi.org/project/sgia/

# Usage
```python
from sgia import SGIA

# Create a SGIA object
sgia = SGIA(dimensions=2)
sgia.insert([1.0, 2.0], "Data A")
sgia.insert([3.0, 4.0], "Data B")
sgia.insert([5.0, 6.0], "Data C")

# Display the index
sgia.display()

# Search for nearest neighbors to a query vector
query = [2.0, 3.0]
k_neighbors = sgia.search(query, k=3)

print("Nearest neighbors to query:", k_neighbors)

```
grading_tools 0.21.3
Tools for evaluating student submissions.
Requires Python: >=3.11,<3.13

# Grading Tools

[![build](https://github.com/worldquant-university/grading-tools/actions/workflows/build.yml/badge.svg)](https://github.com/worldquant-university/grading-tools/actions)
[![codecov](https://codecov.io/gh/worldquant-university/grading-tools/branch/main/graph/badge.svg?token=PV83R6T99N)](https://codecov.io/gh/worldquant-university/grading-tools)

This library allows you to compare student submissions to an answer, and provide
meaningful feedback. It currently accommodates basic Python data structures, `pandas`
Series and DataFrames, `scikit-learn` models, and images.

## Installation

```bash
$ pip install grading-tools
```

## Usage

```python
>>> from grading_tools.graders import PythonGrader
>>> sub = {"snake": "reptile", "frog": "reptile"}
>>> ans = {"snake": "reptile", "frog": "amphibian"}
>>> g = PythonGrader(sub, ans)
>>> g.grade_dict()
>>> g.return_feedback(html=False)
{
    'score': 0,
    'passed': False,
    'comment': "The value for the key `frog` doesn't match the expected result."
}
```

## License

`grading-tools` was created by
[Nicholas Cifuentes-Goodbody](https://github.com/ncgoodbody) at
[WorldQuant University](http://wqu.edu/). It is not currently licensed for reuse of
any kind.

## Contributing

This package uses [Python Semantic Release](https://python-semantic-release.readthedocs.io/en/latest/), so all commit messages must follow the [Angular](https://github.com/angular/angular/blob/main/CONTRIBUTING.md#-commit-message-format) format.
aditya-102103464 1.1.1
Topsis Package

# TOPSIS Python Package

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a method for multicriteria decision analysis. This Python package provides a simple implementation of TOPSIS for ranking alternatives based on multiple criteria.

## Installation

You can install the `topsis` package using pip:

pip install topsis-Aditya-102103464

# Usage

import numpy as np
from topsis-Aditya-102103464 import topsis

# Example decision matrix
import .csv data file
# Criteria weights
weights = [1, 1, 1, 1]

# Impact criteria
impacts = [True, True, True, True]

# Perform TOPSIS analysis
scores = topsis(matrix, weights, is_benefit)
ranking = np.argsort(scores)[::-1]
print("Scores:", scores)
print("Ranking:", ranking+1)

# Output 

A .csv file containing input columns with two additional columns ie. topsis score and ranking will be generated.
topsis-Aditya-102103464 0.1.0
Topsis Package

# TOPSIS Python Package

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a method for multicriteria decision analysis. This Python package provides a simple implementation of TOPSIS for ranking alternatives based on multiple criteria.

## Installation

You can install the `topsis` package using pip:

pip install topsis-Aditya-102103464

# Usage

import numpy as np
from topsis-Aditya-102103464 import topsis

# Example decision matrix
import .csv data file
# Criteria weights
weights = [1, 1, 1, 1]

# Impact criteria
impacts = [True, True, True, True]

# Perform TOPSIS analysis
scores = topsis(matrix, weights, is_benefit)
ranking = np.argsort(scores)[::-1]
print("Scores:", scores)
print("Ranking:", ranking+1)

# Output 

A .csv file containing input columns with two additional columns ie. topsis score and ranking will be generated.
crampon 0.1.0
The purpose of this update is to centralize and streamline shell execution commands in Py65 environments by creating a dedicated utility package. This will enhance maintainability, consistency, and potentially offer performance improvements.
Requires Python: ==3.11.7

# crampon
This repository will host our py65's utilities and other things

# How to use
- poetry init
- brew install pyenv
- pyevn install 3.11.7
- pyenv local 3.11.7
-
Topsis-nikhil-102103482 0.0.2
topsis package for decision making
Keywords: python,video,stream,video stream,camera stream,sockets

# Topsis

TOPSIS is based on the fundamental premise that the best solution has the shortest distance from the positive-ideal solution, and the longest distance from the negative-ideal one. Alternatives are ranked with the use of an overall index calculated based on the distances from the ideal solutions.

It takes 4 arguments :
1.Data.csv file
2.Weights
3.Impacts
4.Result file

Returns file with Topsis Score and Rank

## How to use it?

Open terminal and type pip install Topsis-nikhil-102103482

To get started quickly, just use the following:

from topis_pckg.topsis import topsis
topsis('inputfilename','Weights','Impacts','Outputfilename')
make ensure the weights and impacts should be in ""

eg: "1,1,1,1,1" and "+,-,+,-,+"