semvec/pypi-packages huggingface dataset:
Topsis-Shantanu-102353013 1.0.0
A Python package for implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Requires Python: >=3.6

# Topsis-Shantanu-102353013

A Python package to implement the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision-making algorithm.

## Installation

```bash
pip install Topsis-Shantanu-102353013
```

## Usage

This package can be used via the command line.

### Command Line Interface

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

#### Arguments:
1.  **InputDataFile**: Path to the input CSV file.
    -   The first column should contain the object/alternative names (e.g., Fund Name, Model Name).
    -   From the 2nd to the last column, the file must contain **numeric** values.
2.  **Weights**: A comma-separated string of weights (e.g., "1,1,1,2").
3.  **Impacts**: A comma-separated string of impacts, either '+' or '-' (e.g., "+,+,-,+").
4.  **ResultFileName**: The name of the output CSV file to save the results.

### Example

```bash
topsis data.csv "1,1,1,1" "+,+,+,+" result.csv
```

## Output

The output file will contain the original data with two additional columns:
-   **Topsis Score**: The calculated performance score.
-   **Rank**: The rank of the alternative.

## License

This project is licensed under the MIT License.
zephyr-kconfig 0.2.1
Tools for Zephyr Kconfig files
Requires Python: >=3.11

# Zephyr KConfig

Various tools/helpers/cli to assist with Zephyr's KConfig

## Run / Install

### Run via `uvx`

```bash
# always fetches the latest version
uvx --from zepyhr-kconfig --help
```

### Install as a tool (via `uv`)

```bash
# installs the current version
uv tool install zephyr-kconfig
```

Above also install `zkc` cli (also accessible via `zephyr-kconfig` if you have conflict with `zkc`)

```bash
# examples - (zkc is an alias to zephyr-kconfig)
zkc --help
zephyr-kconfig --help
```

## Add to your project

```bash
uv add zephyr-kconfig --dev
```

and then run the cli via

```bash
uv run zkc --help
```

## Features

Below examples are assuming you are running the tool globally

> Note - uvx does sandboxing (virtualenv) for you so no need to worry


### Get info on a KConfig item

```bash
# get the description as JSON output for CONFIG_LWM2M_SHELL
uvx zepyhr-kconfig --release 4.3.0 get CONFIG_LWM2M_SHELL
```

![Example output](docs/images/single_get_example.png)

> A partial CONFIG spec will get all items that start with the provided input for
  example CONFIG_LWM2M_ will output all L2M2M config items

> Use `--exact` to get the info on CONFIG_LWM2M only

> Use `--metadata` to get some extra fields (such as linenr, menupath etc)

> Use `--indent -1` to remove any indentation (default is 2)

### Get deps on a KConfig item

```bash
# get the dependencies for CONFIG_LWM2M_SHELL
uvx zepyhr-kconfig --release 4.3.0 deps CONFIG_LWM2M_SHELL
```

For above example output would be:

`CONFIG_LWM2M && CONFIG_NETWORKING`

> The input symbol should be exact

### Pretty Print KConfig item(s)

```bash
# show the description of symbol(s)
uvx zepyhr-kconfig --release 4.3.0 pprint CONFIG_LWM2M_SHELL
```

> Use `--exact` to print only one symbol
red9 0.1.0
Enterprise Multi-Agent Coding System
Requires Python: >=3.11
Keywords: agents, ai, cli, coding, llm

# RED9

Enterprise Multi-Agent Coding System powered by Stabilize, IssueDB, and Ragit.

## Installation

```bash
pip install red9
```

## Quick Start

```bash
# Initialize RED9 in your project
red9 init

# Execute a coding task
red9 task "Add user authentication"

# Ask a question about the codebase
red9 ask "How does the auth system work?"

# Check status
red9 status
```

## Features

- **Multi-Agent Parallelism**: Coordinate multiple specialized agents working simultaneously
- **Persistent Memory**: Context and guidelines stored in IssueDB
- **Intelligent Code Understanding**: RAG-based codebase search via Ragit
- **Enterprise Workflow**: DAG-based execution with Stabilize

## Commands

| Command | Description |
|---------|-------------|
| `red9 init` | Initialize RED9 in current directory |
| `red9 task "request"` | Execute a coding task |
| `red9 ask "question"` | Ask about the codebase |
| `red9 status` | Show workflow status |
| `red9 todos` | List pending issues |
| `red9 memory add KEY VALUE` | Store agent guideline |
| `red9 lessons` | Show lessons learned |

## Architecture

RED9 follows the principle: **Agents ARE Stabilize Tasks**.

```
User Request → IssueDB Issue → Stabilize Workflow → Agent Tasks → Result
```

## License

Apache 2.0
Topsis-Gurshaan-Bajwa-102303264 0.0.6
A Python package for TOPSIS multi-criteria decision making
Requires Python: >=3.6

# Topsis-Gurshaan-Bajwa-102303264

### By Gurshaan Bajwa (Roll No: 102303264)

This Python package implements the TOPSIS (Technique for Order Preference by
Similarity to Ideal Solution) method for solving Multi-Criteria Decision Making (MCDM) problems.

---

## Installation

```bash
pip install Topsis-Gurshaan-Bajwa-102303264
```

---

## Input File Format

First column: Alternatives  
Remaining columns: Numeric criteria

Example CSV:

| Model | Storage | Camera | Price | Looks |
|--------|--------|--------|--------|--------|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8 | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8 | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |

---

## Weights and Impacts

Weights vector:
```
0.25,0.25,0.25,0.25
```

Impacts vector:
```
+,+,-,+
```

---

## Command Line Usage

```
topsis <inputfile> <weights> <impacts> <outputfile>
```

Example:

```bash
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv
```

---

## Output File Example

Result CSV will contain Topsis Score and Rank:

| Model | Score | Rank |
|--------|--------|------|
| M1 | 0.534 | 3 |
| M2 | 0.308 | 5 |
| M3 | 0.692 | 1 |
| M4 | 0.535 | 2 |
| M5 | 0.401 | 4 |

Higher score means better alternative.

---

## Error Handling

Program validates:

- File existence  
- Numeric values  
- Correct weights count  
- Correct impacts  
- Invalid symbols  

---

## Conclusion

TOPSIS helps rank alternatives based on multiple criteria and is useful in
decision-making problems.
trading-service-utils 0.1.5
Shared utilities for trading system services
Requires Python: >=3.12

# Trading Service Utils

Shared utilities for the trading system microservices.

## Features

- **Logging**: Standardized logging configuration with hostname injection and health check filtering. [Read Documentation](docs/logging.md)
- **Fetcher**: API clients and helpers for various markets (Polymarket, etc.). [Read Documentation](docs/fetcher.md)

## Installation

```bash
pip install trading-service-utils
```

## Usage

### Logging

See [docs/logging.md](docs/logging.md) for full details.

```python
from trading_service_utils import setup_logging

setup_logging(log_level="INFO")
```

### Polymarket Fetcher

See [docs/fetcher.md](docs/fetcher.md) for full details.

```python
from trading_service_utils.fetcher import fetch_event

event = fetch_event("event_id")
```
topsis-raghav-102303580 1.0.2
TOPSIS implementation as a Python package

# TOPSIS Implementation (Python Package)

## Overview
This Python package provides a clear and practical implementation of the  
**TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method for  
**Multi-Criteria Decision Making (MCDM)**.

Using this package, users can rank multiple alternatives based on numerical criteria by
assigning **weights** and **impacts** (benefit or cost) to each criterion.  
The package is implemented as a **command-line tool** and works directly with CSV files.

---

## Project Information

| Field | Details |
|------|--------|
| Course | Project-1 |
| Author | Raghav Chhabra |
| Roll No | 102303580 |
| Group | **3C42** |

---

## Features
- Easy-to-use command-line interface
- Supports customizable weights and impacts
- Handles benefit (`+`) and cost (`-`) criteria
- Accepts CSV input and produces CSV output
- Automatically computes **TOPSIS score** and **Rank**

---

## 📌 Sample Input File (IMPORTANT)

Create a file named **`data.csv`** in the same folder as `topsis.py` and paste the following content:

```csv
Fund,P1,P2,P3,P4,P5
M1,0.71,0.50,3.9,33.8,9.73
M2,0.88,0.77,5.2,45.8,13.16
M3,0.78,0.61,3.8,40.1,11.32
M4,0.91,0.83,5.8,43.8,12.84
M5,0.76,0.58,6.4,34.8,10.6
```

---

## Installation

Install the package using pip:

```bash
pip install topsis-raghav-102303580
Topsis-Nitish-102303239 1.0.0
A Python package for implementing TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)

# Topsis-Nitish-102303239

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for multi-criteria decision making.

---

## Installation

After uploading to PyPI, install using:

pip install Topsis-Nitish-102303239

---

## Usage

Run from command line as:

topsis-nitish <InputDataFile> <Weights> <Impacts> <OutputFile>

Example:

topsis-nitish data.csv "1,1,1,1" "+,+,+,+" output-result.csv

---

## Input Format

- First column â†’ Alternative names  
- Remaining columns â†’ Numeric criteria values  

Example:

Model,Price,Storage,Camera,Battery  
A,250,64,12,3000  
B,200,32,8,2500  
C,300,128,16,3500  
D,275,64,12,3300  

---

## Output

The output CSV file will contain:
- Original data  
- Topsis Score  
- Rank  

---

## Error Handling

The program checks for:
- File not found errors  
- Non-numeric values  
- Incorrect number of weights or impacts  
- Invalid impacts (+ or - only)  

---

## Author

Name: Nitish  
Roll Number: 102303239