semvec/pypi-packages huggingface dataset:
aiorobot 0.3.0
asyncio interface for iRobot Root
License: MIT

# Root robot

Python async API for iRobot Root (coding robot) over bluetooth-low-energy protocol.

Protocol specifications from <https://github.com/RootRobotics/root-robot-ble-protocol>.

## Installation

Install the `aiorobot` package from PyPI with `pip`.

```sh
pip install aiorobot
```

## Quickstart

To simply run the robot, use the `run` function of `aiorobot` module.
It takes coroutine callbacks for different root robot events.

```python
from aiorobot import run

async def main(robot):
    for i in range(4):
        await robot.led.on((0, i * 80, 100))
        await robot.motor.drive(150)
        await robot.motor.rotate(900)
    await robot.disconnect()

run(started=main)
```

This will search for a root robot in bluetooth devices, connect to it and call the `main` coroutine when the root is ready.
So make sure you have bluetooth enabled and working on your computer.

Accepted keyword-arguments of `run` function are event names listed in [aiorobot/events.py](https://github.com/entwanne/aiorobot/blob/master/aiorobot/events.py).

You can also directly get a robot and interact with it with `get_robot` function that you can use as an async context-manager to start the connection.

```python
import asyncio
from aiorobot import get_robot

async def main():
    async with get_robot() as robot:
        await robot.motor.drive(150)

asyncio.run(main())
```

Then you will need to handle events yourself (iterate over `robot.events` or call `robots.events.process()`) to get updates from the robot.

See more code examples in [aiorobot/examples](https://github.com/entwanne/aiorobot/tree/master/aiorobot/examples) directory.
TOPSIS-Harsh-101803327 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Harsh 101803327**

***

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution 
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, 
and greatest distance from the negative-ideal solution. 

<br>

## How to install this package:
```
>> pip install TOPSIS-Harsh-101803327
```


### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)


Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------- | ------------- 
M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.7722 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.2255 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.4388 | 4
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.5238 | 3
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.8113 | 1


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
ml-accuracy-calc 0.1
This is a package that houses the  functions which can produce accuracy results for each algorithm in the categories of  Clustering,Regression & Classification based on passing the arguments - independent and dependent variables/features
License: MIT
Keywords: Machiene,Machiene Learning,Learning,accuracy,class and object

# ml-accuracy-calc
  This package consists of various algorithms  which can produce accuracy results for each algorithm in the categories of  Clustering,Regression & Classification based on given the arguments - independent and dependent variables/features
## Development Status:
Under Development (Early release)

### Developed by:

 [Vishal Balaji Sivaraman](https://github.com/TheSocialLion)

### Installation:

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install ML-Accuracy-Calc

```bash
pip install ml-accuracy-calc
```

### To import physicscalc:

```python
import ml-accuracy-calc
```


## License:
[MIT](https://choosealicense.com/licenses/mit/)
pandoc-import-code 1.0.0
Yet another pandoc filter to include external code files.
License: MIT

<!-- spellcheck-language "en_GB" -->
<!-- markdownlint-disable commands-show-output -->

# Pandoc-import-code

![PyPI](https://img.shields.io/pypi/v/pandoc-import-code)
![GitHub release (latest SemVer including pre-releases)](https://img.shields.io/github/v/release/noelmace/pandoc-import-code?include_prereleases)

Pandoc filter to include external code files as fenced code blocks using the
[Vuepress syntax](https://vuepress.vuejs.org/guide/markdown.html#import-code-snippets).

## Install

To install pandoc-import-code, open the command line and type:

```bash
pip install pandoc-import-code
```

Python 3.6+ and PyPy3 are supported.

## Usage

**:warning: pandoc-import-code requires Pandoc 2.11+, as it relies on [Panflute 2](https://github.com/sergiocorreia/panflute/releases/tag/2.0.3)**

### Command

```shell-session
$ pandoc source.md --filter pandoc-import-code -o output.md
```

### Syntax

```raw
<<< @/<path>#[region]
```

- **path** : path to a code file to import
  - relative to the pandoc command working directory
- **region** : custom region name for partial import
  - syntax inspired by
    [VS Code](https://code.visualstudio.com/docs/editor/codebasics#_folding)

### Example

```shell-session
$ pandoc docs/index.md --filter pandoc-import-code -o out.md
```

_Source (`./docs/index.md`)_

<!-- prettier-ignore -->
```md
# Code Sample

<<< @/samples/hello-world.html#title

```

_Code sample (`./samples/hello-world.html`)_

<!-- prettier-ignore -->
```html
<!DOCTYPE html>
<html lang="en">
  <head>
    <title>Document</title>
  </head>
  <body>
    <!-- #region title -->
    <h1>
      Hello World!
    </h1>
    <!-- #endregion title -->
  </body>
</html>
```

_Output (`./out.md`)_

<!-- prettier-ignore -->
````md
# Code Sample

``` {.html}
<h1>
  Hello World
</h1>
```

````

#### Limitations

- as line-highlighting isn't supported by pandoc, any `{1-2}` parameter will be
  ignored

## Dev Install

After cloning the repository and opening the pandoc-import-code folder:

`python setup.py install`: installs the package locally

`python setup.py develop`: installs locally with a symlink so changes are
automatically updated