semvec/pypi-packages huggingface dataset:
topsis-adityapandey-102217092 0.5
A Python package for TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Requires Python: >=3.6
Keywords: topsis decision-making MCDM ranking

# Topsis Python Package

**Developed by:** Aditya Pandey - 102217092

## Overview

The Topsis (Technique for Order of Preference by Similarity to Ideal Solution) method is a popular multi-criteria decision-making technique. It helps to evaluate and select the best alternative from a set of options based on their proximity to the ideal solution. This Python package implements the Topsis method, making it easy to apply the technique on datasets with multiple criteria.

## Features
- Computes Topsis scores based on input data, weights, and impacts.
- Ranks alternatives according to the Topsis methodology.
- Supports data input through Excel files.
- Provides an easy-to-use command-line interface for seamless integration.

## Installation

To install the Topsis Python package, use `pip` with the following command:

```bash
pip install 102217092-Aditya-topsis
```

## Usage

### Command-Line Input

To use the Topsis package from the command line, run the following command:

```sh
python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

- **Input File Type**: The input file must be an Excel file.
- **Data Format**: The second to last columns of the data file MUST contain numeric values.
- **Impacts**: Impacts should be either '+' (positive) or '-' (negative).
- **Weights and Impacts**: Weights and impacts should be enclosed in double quotes and separated by commas.
- **Output**: The output will include a 'Topsis Score' column and a 'Rank' column added to the data. The results will be saved to a CSV file specified in the command-line arguments.

## Example

### Command-Line Input Example

```sh
python 102217092.py data.csv "1,1,1,1,1" "+,+,-,+,-" 102217092-result.csv
```
Topsis-aditya-102217092 0.5
A Python package for TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Requires Python: >=3.6
Keywords: topsis decision-making MCDM ranking

# Topsis Python Package

**Developed by:** Aditya Pandey - 102217092

## Overview

The Topsis (Technique for Order of Preference by Similarity to Ideal Solution) method is a popular multi-criteria decision-making technique. It helps to evaluate and select the best alternative from a set of options based on their proximity to the ideal solution. This Python package implements the Topsis method, making it easy to apply the technique on datasets with multiple criteria.

## Features
- Computes Topsis scores based on input data, weights, and impacts.
- Ranks alternatives according to the Topsis methodology.
- Supports data input through Excel files.
- Provides an easy-to-use command-line interface for seamless integration.

## Installation

To install the Topsis Python package, use `pip` with the following command:

```bash
pip install 102217092-Aditya-topsis
```

## Usage

### Command-Line Input

To use the Topsis package from the command line, run the following command:

```sh
python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

- **Input File Type**: The input file must be an Excel file.
- **Data Format**: The second to last columns of the data file MUST contain numeric values.
- **Impacts**: Impacts should be either '+' (positive) or '-' (negative).
- **Weights and Impacts**: Weights and impacts should be enclosed in double quotes and separated by commas.
- **Output**: The output will include a 'Topsis Score' column and a 'Rank' column added to the data. The results will be saved to a CSV file specified in the command-line arguments.

## Example

### Command-Line Input Example

```sh
python 102217092.py data.csv "1,1,1,1,1" "+,+,-,+,-" 102217092-result.csv
```
beangrow 1.0.1
Returns calculations on portfolios in Beancount Resources
Requires Python: >=3.10
Keywords: accounting, ledger, beancount, returns, investments

# Compute Returns

This directory contains code which computes investment returns on a variety of
assets, as recorded by Beancount, fed directly from a Beancount file.

See this document for details:
https://docs.google.com/document/d/1nPsMIunLnDvdsg6TSsd0PZb7jngojNpFlqnaX36WRp8/

## Scripts

There are three related scripts:

- configure.py: This attempts to automatically infer and generate configuration
  to compute returns from an existing Beancount ledger.

- compute_returns.py: This extracts data for each of the investments defined in
  the configuration and computes the returns and generates output for each
  requested returns report.

- download_prices.py: The compute_returns.py script outputs a list of missing
  (or inadequately dated) price directives to properly do its job as a
  side-product. This script can read that file and fetch those missing dates,
  which you can insert in your ledger and then rerun compute_returns.py for a
  more precise calculation.

## Example

To run the example:
```
cd example
uv run beangrow-returns ledger.beancount config.pbtxt ./reports
```

The reports are available at `example/reports/groups`.
savetxt 0.0.5
CLI program to save and view text with corresponding tags.
Requires Python: >=3.8

save some text (usually URLs) with associated tags

the most common way i use it (and the intended purpose for myself)

```bash
open $(savetxt cat links | fzf | awk '{print $NF}')
```
currently by default the file is stored in your home directory.

functionality:
- put 
```bash
savetxt put <filename> <tags> <link>
```
eg:
```commandline
❯ savetxt put linksfile google,youtube https://www.youtube.com/watch\?v\=xvFZjo5PgG0
❯ savetxt put linksfile hackernews,front https://news.ycombinator.com/
# file is created in $HOME/savetxt/ 
```

- cat
```bash
savetxt cat <filename>
```
eg:
```commandline
❯ savetxt cat linksfile
                      tags                                        value
0    ['google', 'youtube']  https://www.youtube.com/watch?v=xvFZjo5PgG0
1  ['hackernews', 'front']                https://news.ycombinator.com/
```