semvec/pypi-packages huggingface dataset:
RackioModbus 0.2
A Rackio extension to enable Modbus TCP Server/Client integration
License: MIT

# rackio-modbus

A Rackio extension to enable Modbus TCP Server/Client integration

## Installation

```
pip install RackioModbus
```

## Usage

```python
from rackio import Rackio, TagEngine
from rackio_modbus import RackioModbus

app = Rackio()

driver = RackioSocket(app, mode="server")
```

## Defining Tags and Modbus bindings

```python
tag_egine = TagEngine()

# Tags definitions

tag_egine.set_tag("T1", "float")
tag_egine.set_tag("T2", "float")
tag_egine.set_tag("T3", "float")

# Modbus mapping

driver.define_mapping("T1", "write", 0, 60) # Tag, mode, lower limit, upper limit
driver.define_mapping("T2", "write", 0, 50)
driver.define_mapping("T3", "read", 0, 110)
```
Topsis-Arth-101803214 1.1.3
Implements Topsis
License: MIT

# TOPSIS

Code by: Arth Duggal 

<hr style = "border:2px solid gray"> </hr>
## What is TOPSIS

TOPSIS is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems. Compensatory methods such as TOPSIS allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion. This provides a more realistic form of modelling than non-compensatory methods, which include or exclude alternative solutions based on hard cut-offs. An example of application on nuclear power plants is provided in.TOPSIS stands for ‘The Technique for Order of Preference by Similarity to the Ideal Solution’ is a multi-criteria decision analysis(MCDA) method.


## How to run

#### Before running, make sure you have pandas installed on your system

Open Terminal and input the following commands


>> pip install Topsis-Arth-101803214



>> python
>>>from topsis.topsis1 import topsis
>>>topsis("input.csv","1,2,1,2","+,+,-,+","output.csv")

## Sample Input

This input was used to test the module

<table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output 


<table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>


## License

© 2020 Arth Duggal

This repository is licensed under the MIT license. See LICENSE for details.