semvec/pypi-packages huggingface dataset:
appengine-sdk 1.9.88
Un-official `pip install`-able AppEngine SDK.
License: MIT
Keywords: google appengine gae sdk

===================================================
Un-official `pip install`-able AppEngine SDK
===================================================

| |version|

.. |version| image:: http://img.shields.io/pypi/v/appengine-sdk.svg?style=flat
    :alt: PyPI Package latest release
    :target: https://pypi.python.org/pypi/appengine-sdk

It provides working bins for ``appcfg``, ``dev_appserver`` and ``remote_api_shell``. And they work on Windows!

Usage::

    pip install appengine-sdk

You can always `waste your time manually copying files <https://cloud.google.com/appengine/downloads>`_
if you don't like this un-official package.
homeassistant-magiwand 0.1.1
Allow MagiQuest Wands to call into HomeAssistant Webhooks
License: MIT
Requires Python: >=3.5
Keywords: wand,magiquest,IR,HomeAssistant

# homeassistant-magiwand

[![PyPI version](https://badge.fury.io/py/homeassistant-magiwand.svg)](https://badge.fury.io/py/homeassistant-magiwand)

This code can be used to connect MagiQuest IR wands to an instance of HomeAssistant.  This extends a related project of mine, pywand [[source](https://gitlab.com/rveach/wand)] [[PyPI](https://pypi.org/project/pywand/)]

This project ships with a script that will monitor a lirc device, and trigger HomeAssistant Automations via web api call. Currently, the scope is pretty narrow, but I hope to expand it over time to meet my needs.

## Installation, Setup, and Config

#### tldr:

- Enable your infrared device
- Install python3
- `pip install homeassistant-magiwand`
- Enable a Ling-Lived Access Token in HomeAssistant
- Enable api and add an Automation to run in HomeAssistant
- Edit `/etc/wand_config.ini`
- Run `ha_wand_service.py`

#### More Details

- [pywand: Installation and Pre-Reqs](https://gitlab.com/rveach/wand/-/wikis/Installation-and-Pre-Reqs)
- [pywand: Configuration](https://gitlab.com/rveach/wand/-/wikis/pywand-Configuration)
- Enable the REST API for HomeAssistant: [HomeAssistant: REST API](https://developers.home-assistant.io/docs/en/external_api_rest.html)
- Create an Automation for our sensor. This is what will be triggered when the wand is waved. [HomeAssistant: Automation](https://www.home-assistant.io/integrations/automation/)
- Create a Long-Lived Access Token within your HomeAssistant Instance.
- Continue configuring the wand configuration file with the HOMEASSISTANT section. See [example_wand_config.ini](https://gitlab.com/rveach/homeassistant-magiwand/blob/master/example_wand_config.ini) for more information.
- Execute the service by running `ha_wand_service.py`

## Running the service

Just make sure the config file is right, see previous sections

#### Usage

```
ha_wand_service.py -h 
usage: ha_wand_service.py [-h] [--loglevel LOGLEVEL]

optional arguments:
  -h, --help           show this help message and exit
  --loglevel LOGLEVEL  Loglevel: CRITICAL, ERROR, WARNING (default), INFO,
                       DEBUG
```
pytorch-pipeline 0.0.1
Simple ETL Pipeline for PyTorch
License: MIT

# PyTorch Pipeline: Simple ETL Pipeline for PyTorch

PyTorch Pipeline is a simple ETL framework for PyTorch.
It is an alternative to [tf.data](https://www.tensorflow.org/api_docs/python/tf/data) in TensorFlow


# Requirements

- Python 3.6+
- PyTorch 1.2+


# Installation

To install PyTorch Pipeline:

```bash
pip install pytorch_pipeilne
```


# Basic Usage

```py
import pytorch_pipeilne as pp

d = pp.TextDataset('/path/to/your/text')
d.shuffle(buffer_size=100).batch(batch_size=10).first()
```

# Usage with PyTorch

```py
from torch.utils.data import DataLoader
import pytorch_pipeilne as pp


d = pp.Dataset(range(1_000)).parallel().shuffle(100).batch(10)
loader = DataLoader(d, num_workers=4, collate_fn=lambda x: x)
for x in loader:
    ...
```

# Usage with LineFlow

You can use PyTorch Pipeline with pre-defined datasets in [LineFlow](https://github.com/tofunlp/lineflow):

```py
from torch.utils.data import DataLoader
from lineflow.datasets.wikitext import cached_get_wikitext
import pytorch_pipeilne as pp

dataset = cached_get_wikitext('wikitext-2')
# Preprocessing dataset
train_data = pp.Dataset(dataset['train']) \
    .flat_map(lambda x: x.split() + ['<eos>']) \
    .window(35) \
    .parallel() \
    .shuffle(64 * 100) \
    .batch(64)

# Iterating dataset
loader = DataLoader(train_data, num_workers=4, collate_fn=lambda x: x)
for x in loader:
    ...
```
arrayfiles 0.0.1
Array-like File Access in Python
License: MIT

# ArrayFiles: Array-like File Access in Python
[![Build Status](https://travis-ci.com/yasufumy/arrayfiles.svg?branch=master)](https://travis-ci.com/yasufumy/arrayfiles)
[![Build Status](https://github.com/yasufumy/arrayfiles/workflows/Run%20CI%20build/badge.svg)](https://github.com/yasufumy/arrayfiles/actions?query=workflow%3A%22Run+CI+build%22)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/yasufumy/arrayfiles.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/yasufumy/arrayfiles/context:python)
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/2de6c7b86da743a7ba1a567d29213dca)](https://www.codacy.com/manual/yasufumy/arrayfiles?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=yasufumy/arrayfiles&amp;utm_campaign=Badge_Grade)
 [![Codacy Badge](https://api.codacy.com/project/badge/Coverage/b2c2289a10fd4f2284f436c961e81258)](https://www.codacy.com/manual/yasufumy/arrayfiles?utm_source=github.com&utm_medium=referral&utm_content=yasufumy/arrayfiles&utm_campaign=Badge_Coverage)
[![codecov](https://codecov.io/gh/yasufumy/arrayfiles/branch/master/graph/badge.svg)](https://codecov.io/gh/yasufumy/arrayfiles)

ArrayFiles allows you to access an arbitrary line of a text file.

If your interest is in using arrayfiles for Deep Learning, please check [LineFlow](https://github.com/yasufumy/lineflow).

## Installation

To install arrayfiles:

```bash
pip install arrayfiles
```

## Usage

```py
import arrayfiles

data = arrayfiles.read_text('/path/to/text')

data[0] # Access the first line of your text
data[-1] # Access the last line of your text
data[10:100] # Access the 10th line to the 100 the line of your text
```
url-to-desktop 1.0.0
Converts .url files to .desktop files
License: MIT
Requires Python: >=3.6
Keywords: url,link,convert,url convert,desktop conversor,url linux

# url-to-desktop
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/a85c79e5dac14a51883f8de2aa7cd6d9)](https://app.codacy.com/manual/xandao6/url-to-dektop?utm_source=github.com&utm_medium=referral&utm_content=xandao6/url-to-dektop&utm_campaign=Badge_Grade_Dashboard)
[![HitCount](http://hits.dwyl.io/xandao6/url-to-desktop.svg)](http://hits.dwyl.io/xandao6/url-to-desktop)

It is a command line software through which you can convert .url files, used in Windows, into .desktop files used in linux.

---
## Installation
url-to-desktop is available on PyPI. You can install url-to-desktop by pip command: 

```bash
pip3 install url-to-desktop
```

## Usage
<img src="/src/images/before conversion.png"><br>
<img src="/src/images/converting.png"><br>
<img src="/src/images/after conversion.png"><br>

Example -  
To convert in the current directory 
```bash
url-to-desktop
```
To convert in a specific directory
```bash 
url-to-desktop /path/
````

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[![GitHub](https://img.shields.io/github/license/xandao6/url-to-desktop?style=for-the-badge)](https://github.com/xandao6/url-to-dektop/blob/master/LICENSE)

***If you like the project, support us by star***
topsis-hm 1.0.1
A Python package to get toposis rankings for any table.
License: MIT

### UCS633 Project Submission
* **Name** - *Haymant Mangla*
* **Roll no.** - *101703228*

# topsis-hm

topsis-hm is a Python package for displaying ranking of all criteria using Topsis technique to get good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form.

## Topsis Description

Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is one of the multi-criteria models in making decision which is known for its  simplicity, rationality, comprehensibility and good computational efficiency. Multi-criteria decision making (MCDM) refers to making choice of the best alternative from among a finite set of decision alternatives in terms of multiple, usually conflicting criteria.

## Getting Started

These instructions will help you to install and use this package for general use.

## Prerequisites

Your csv file should not have categorical data


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install topsis-hm
```

## Usage
You can import it either in Python IDLE or run directly through command prompt

### For Command Prompt

If you want to use this package on "data.csv" file with 4 columns. You need to change the directory where "data.csv" is stored then. Here -w represents weights which signifies weight of each feature or column in our dataset and -i represents impacts which signifies impact of each column or feature in our data. If a feature is good we will use + to denote else we will use -

```bash
toposis-hm data.csv -w 1 1 1 1 -i + + - +
```
You can use the following command for help

```bash
topsis-hm -h
```

### For Python IDLE

```python
from topsis-hm.topsis import top
top(X,weights,impacts)

#X should be a matrix
#impacts should be a list of string + for positive impact - for negative impact
#weights should be a list of int or float
```


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)