semvec/pypi-packages huggingface dataset:
Topsis-Sameer-102303773 1.0.2
A Python package implementing the TOPSIS method

# 🚀 TOPSIS – Python Package

## Project Description

This package implements **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** using **Python 🐍**.

TOPSIS is a multi-criteria decision-making (MCDM) technique used to rank alternatives based on their distance from an ideal best solution and an ideal worst solution. The alternative closest to the ideal best and farthest from the ideal worst is considered the best option.

This package allows users to perform TOPSIS analysis easily and can be executed directly from the command line after installation.

---

## Package Name

Topsis-Sameer-102303773

---

## Installation

Install the package from PyPI using pip:

pip install Topsis-Sameer-102303773

---

## Usage (Command Line)

Run the TOPSIS method using the following command:

topsis input.csv "1,1,1,2" "+,+,-,+" output.csv

---

## Input File Format

The input file must be in CSV format.

The first column should contain the names of the alternatives.

The remaining columns should contain numeric values only.

A minimum of three columns is required.

Example input file:

Fund Name,P1,P2,P3,P4  
M1,0.67,0.45,6.5,42.6  
M2,0.60,0.36,3.6,53.3  
M3,0.82,0.67,3.8,63.1

---

## Weights and Impacts

Weights represent the importance of each criterion.

Impacts specify whether a criterion is beneficial or non-beneficial.

Use `+` if a higher value is better.

Use `-` if a lower value is better.

The number of weights and impacts must match the number of criteria columns.

---

## Output File Format

The output is a CSV file containing all original columns along with:

Topsis Score – Relative closeness of each alternative to the ideal solution.

Rank – Ranking of alternatives based on the TOPSIS score.

A higher TOPSIS score indicates a better alternative.

---

## Methodology

The TOPSIS algorithm follows these steps:

1. Read the input data from the CSV file
2. Normalize the decision matrix
3. Apply weights to the normalized matrix
4. Determine the ideal best and ideal worst values
5. Calculate the distance from ideal solutions
6. Compute the TOPSIS score
7. Rank the alternatives based on the score

---

## Error Handling

The package handles the following errors:

Incorrect number of command-line arguments  
Input file not found  
Non-numeric values in criteria columns  
Mismatch between number of weights, impacts, and criteria  
Invalid impact values (only + or - allowed)

---

## Dependencies

Python 3.x  
Pandas  
NumPy

---

## Conclusion

This package provides a simple and effective way to perform TOPSIS analysis using Python. It is suitable for academic assignments, projects, and real-world decision-making problems involving multiple criteria.

Happy Decision Making ✨
jolly-roger 0.8.0
The pirate flagger
Requires Python: >=3.11

# jolly-roger

[![Actions Status][actions-badge]][actions-link]
[![Documentation Status][rtd-badge]][rtd-link]

[![PyPI version][pypi-version]][pypi-link]
<!-- [![Conda-Forge][conda-badge]][conda-link] -->
[![PyPI platforms][pypi-platforms]][pypi-link]

<!-- [![GitHub Discussion][github-discussions-badge]][github-discussions-link] -->

<!-- SPHINX-START -->

<!-- prettier-ignore-start -->
[actions-badge]:            https://github.com/flint-crew/jolly-roger/workflows/CI/badge.svg
[actions-link]:             https://github.com/flint-crew/jolly-roger/actions
[conda-badge]:              https://img.shields.io/conda/vn/conda-forge/jolly-roger
[conda-link]:               https://github.com/conda-forge/jolly-roger-feedstock
[github-discussions-badge]: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github
[github-discussions-link]:  https://github.com/flint-crew/jolly-roger/discussions
[pypi-link]:                https://pypi.org/project/jolly-roger/
[pypi-platforms]:           https://img.shields.io/pypi/pyversions/jolly-roger
[pypi-version]:             https://img.shields.io/pypi/v/jolly-roger
[rtd-badge]:                https://readthedocs.org/projects/jolly-roger/badge/?version=latest
[rtd-link]:                 https://jolly-roger.readthedocs.io/en/latest/?badge=latest

<!-- prettier-ignore-end -->

The pirate flagger!

<img src="logo.png" alt="The Jolly Roger Flag" style="width:400px;"/>


## Installation

`pip install jolly-roger`

## About

This package attempts to flag or modify visibilities that are contaminated by the Sun (or potentially some other bright source). There are two main modes that are currently supported in `jolly-roger`.

The flags based on the projected baseline length should the array be tracking the Sun. The projected baseline length between some phased direction being tracked and the Sun can be significantly different. `jolly-roger` attempts to leverage this by only flagging data where the projected baseline length is between some nominal range that corresponds to angular scales associated with the Sun.

The second mode is the application of a notch filter in delay space, where the nulling is applied to the expected delay of the Sun for each timestep and baseline. This mode reads the data row-wise as it appears in the measurement set, so can be fast enough to apply to long-tracks ASKAP observations while also using a limited set of computing resources. A ten hour ASKAP continuum observation can be processed in ~2 minutes, with the process being mostly I/O bound.

`jolly-roger` makes no guarentess about removing all contaminated visibilities, nor does it attempt to peel/subtract the Sun from the visibility data.

## Usage

For the flagger run:

```
jolly_flagger --help
```

For the nulling filter run:

```
jolly_tractor --help
```

Details are provide in the [docs][rtd-link].
iflow-mcp_jin38324_oci-documentation-mcp-server 0.0.1
An  Model Context Protocol (MCP) server for OCI Documentation
Requires Python: >=3.10

*Inspired by: https://github.com/awslabs/mcp/tree/main/src/aws-documentation-mcp-server*

# OCI Documentation MCP Server

Model Context Protocol (MCP) server for OCI Documentation

This MCP server provides tools to search for content, and access OCI documentation.

## Features

- **Read Documentation**: Fetch and convert OCI documentation pages to markdown format
- **Search Documentation**: Search OCI documentation using search engine

## Prerequisites

### Installation Requirements

1. Install `uv` from [Astral](https://docs.astral.sh/uv/getting-started/installation/) or the [GitHub README](https://github.com/astral-sh/uv#installation)
2. Install Python 3.10 or newer using `uv python install 3.10` (or a more recent version)

## Installation

MCP config:

```json
{
  "mcpServers": {
      "oci-documentation-mcp-server": {
        "command": "uvx",
        "args": [
          "--from",
          "oci-documentation-mcp-server@latest",
          "python",
          "-m",
          "oci_documentation_mcp_server.server"
        ],
        "env": {
          "FASTMCP_LOG_LEVEL": "ERROR"
        },
        "disabled": false,
        "autoApprove": []
    },
  }
}

```


If above doesn't work, try below one:

```json
{
  "mcpServers": {
    "oci-documentation-mcp-server": {
        "command": "uvx",
        "args": ["oci-documentation-mcp-server@latest"],
        "env": {
          "FASTMCP_LOG_LEVEL": "ERROR"
        },
        "disabled": false,
        "autoApprove": []
    }
  }
}
```

## Basic Usage
Example:
 - In Cursor ask: `Write a function to download files for OCI Object Storage.`

 ![Cursor_MCP](./image/Cursor_MCP.gif)
 




## Tools

### read_documentation

Fetches an OCI documentation page and converts it to markdown format.

```python
read_documentation(url: str) -> str
```

### search_documentation

Searches OCI documentation using the search engine.

```python
search_documentation(search_phrase: str, limit: int) -> list[dict]
```
anor 0.1.0
Anor Cloud GPU CLI - Managed GPU compute made simple
Requires Python: >=3.8
Keywords: ai, cli, cloud, compute, gpu, machine-learning

# Anor CLI

Managed GPU compute made simple.

## Installation

```bash
pip install anor
```

## Quick Start

```bash
# Login to your account
anor login

# Run a training job
anor run --grade standard --gpus 8 --max-rate 2.20 "python train.py"

# List your jobs
anor jobs list

# Get job logs
anor jobs logs <job-id>

# View pricing
anor pricing
```

## Commands

| Command | Description |
|---------|-------------|
| `anor login` | Login and store credentials |
| `anor logout` | Clear stored credentials |
| `anor whoami` | Show current user |
| `anor run` | Submit a compute job |
| `anor jobs list` | List your jobs |
| `anor jobs get <id>` | Get job details |
| `anor jobs logs <id>` | Get job logs |
| `anor jobs cancel <id>` | Cancel a job |
| `anor pricing` | Show current pricing |

## Compute Grades

| Grade | Price | GPU Types |
|-------|-------|-----------|
| Standard | $1.80/GPU/hr | T4, V100, L4 |
| Performance | $2.50/GPU/hr | A100, A100-80GB |
| Premium | $4.00/GPU/hr | H100, H200, B200 |

## Documentation

https://docs.anor.cloud