semvec/pypi-packages huggingface dataset:
102217186-abhaijeet-topsis 1.2.0
A Python package for TOPSIS calculation
Requires Python: >=3.6

# **TOPSIS Package**  
_A Python package for decision-making using the TOPSIS method._  

---

## **What is TOPSIS?**  
The **Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)** is a multi-criteria decision analysis method.  
It ranks alternatives based on their closeness to the ideal solution and farthest from the worst solution, making it an excellent tool for decision-making problems.  

---

## **Features**  
- Simple and efficient implementation of the TOPSIS algorithm.  
- Accepts a decision matrix via a CSV file.  
- Supports customizable weights and impacts for criteria.  
- Outputs the TOPSIS scores and ranks into an easy-to-read CSV file.  

---

## **Installation**  
You can install the package from PyPI using:  

```bash
pip install 102217186-abhaijeet-topsis==1.2.0
```

---

## **How to Use**  

### **Run the Package**  

You can run the TOPSIS package directly from the command line using:  

```bash
python -m 102217186_abhaijeet_topsis <input_file> <weights> <impacts> <output_file>
```

### **Parameters**  
- `<input_file>`: Path to the CSV file containing the decision matrix.  
- `<weights>`: Comma-separated weights for each criterion (e.g., `0.2,0.1,0.43,0.3`).  
- `<impacts>`: Comma-separated impacts for each criterion (e.g., `+,+,-,-`, where `+` indicates a benefit criterion and `-` indicates a cost criterion).  
- `<output_file>`: Path to the output CSV file where the results will be saved.  

---

## **Example Usage**  

### Input CSV (`input.csv`)  
![input](https://github.com/user-attachments/assets/bc6d2b80-b7ff-4298-b4f0-c1925d85f166)

### Command  
```bash
python -m 102217186_abhaijeet_topsis input.csv "0.2,0.1,0.4,0.3" "+,+,-,+"
output.csv
```

### Output CSV (`output.csv`)  
![image](https://github.com/user-attachments/assets/f4dcd4f3-15ca-45b3-a228-c76786f9e69d)


---

## **How It Works**  
1. **Normalization**: The decision matrix is normalized to bring all criteria onto a comparable scale.  
2. **Weighting**: Each criterion is multiplied by its corresponding weight.  
3. **Ideal Solutions**: Calculates the ideal best and worst solutions based on the impacts.  
4. **Distance Calculation**: Computes the distance of each alternative from the ideal best and worst solutions.  
5. **Ranking**: Scores and ranks alternatives based on their relative closeness to the ideal solution.  

---

## **License**  
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.  

---

## **Support**  
If you encounter any issues or have questions, feel free to open an issue on [GitHub](https://github.com/yourusername/102217186-abhaijeet-topsis).  

---
ks-digital-twin 0.0.3
The digital_twin project
Requires Python: >=3.11

# Digital Twin

This repository helps you easily create a digital twin of your robot.

## Getting Started

First, you need to authenticate with the kscale api by running
```
pip install kscale
kscale user key
```

To list all the robots you can use as puppets, run
```
kscale robots list
```

## Digital Twin Structure

### Puppets

These are your digital twin robots. They receive actions from the actors and mirror their behavior. Currently, we support the following puppet types:

- [Mujoco](digital_twin/puppet/mujoco.py)
- [PyBullet](digital_twin/puppet/pybullet.py)

### Actors

These are the sources of actions for your puppet robots. Essentially your input sources. Currently, we support the following actor types:

- [Keyboard](digital_twin/actor/keyboard.py)
- [Sinusoid](digital_twin/actor/sinusoid.py)
- [PyKOS](digital_twin/actor/pykos.py)
    - gRPC client for KOS-compatible robots

## Examples

See the [examples](examples) directory for a few examples.