semvec/pypi-packages huggingface dataset:
py3wifi 0.0.3
Wrapper for 3WiFi Wireless Database
License: MIT
Requires Python: >=3.6
Keywords: wrapper api 3wifi

# Python wrapper for [3WiFi Wireless Database](https://3wifi.stascorp.com/)
[![PyPI pyversions](https://img.shields.io/pypi/pyversions/py3wifi.svg)](https://pypi.python.org/pypi/py3wifi/)
[![PyPI license](https://img.shields.io/pypi/l/py3wifi.svg)](https://pypi.python.org/pypi/py3wifi/)
[![PyPI version shields.io](https://img.shields.io/pypi/v/py3wifi.svg)](https://pypi.python.org/pypi/py3wifi/)
![PyPI - Downloads](https://img.shields.io/pypi/dm/py3wifi)
## Install
```
pip install py3wifi
```
Or you can install from source with:
```
https://github.com/drygdryg/py3wifi.git
cd py3wifi
python setup.py install
```
## API usage
### Authorization
```python
import py3wifi

api_session = py3wifi.Api(login='mylogin', password='mypassword')
api_session.auth()
```
Or with read API key:
```python
api_session = py3wifi.Api(read_key='abcdefghijklmnopqrstuvwxyz123456')
```
### Calling methods
```python
>>> api_session.method('apiquery', params={'bssid': '4C:F2:BF:30:81:A4'})
{'4C:F2:BF:30:81:A4': [{'time': '2017-07-27 10:39:43', 'bssid': '4C:F2:BF:30:81:A4', 'essid': 'IDNet-41', 'sec': 'WPA2', 'key': '87059894216', 'wps': '12345678', 'lat': 54.89953995, 'lon': 69.14550781}]}
```
Or:
```python
>>> api = api_session.get_api()
>>> api.apiquery(bssid='4C:F2:BF:30:81:A4')
{'4C:F2:BF:30:81:A4': [{'time': '2017-07-27 10:39:43', 'bssid': '4C:F2:BF:30:81:A4', 'essid': 'IDNet-41', 'sec': 'WPA2', 'key': '87059894216', 'wps': '12345678', 'lat': 54.89953995, 'lon': 69.14550781}]}
```
See https://3wifi.stascorp.com/apidoc for detailed API guide.
## Usage of 3WiFi AJAX interface
```python
import py3wifi

client = py3wifi.Client(login='mylogin', password='mypassword')
client.auth()
```
```python
>>> client.request('find', {'bssid': '04:95:E6:6C:36:*', 'essid': 'Tenda_◯', 'wps': '□□□□□□□□'})
{'result': True, 'data': [{'id': 93806549, 'time': '2019-12-08 17:30:55', 'comment': 'Router Scan', 'range': '192.168.0.0/16', 'nowifi': False, 'hidden': False, 'bssid': '04:95:E6:6C:36:50', 'essid': 'Tenda_6C3650', 'sec': 'None', 'key': '<empty>', 'wps': '21847080', 'lat': 44.52231216, 'lon': 33.59743881, 'fav': False}], 'found': 1, 'page': {'current': 1, 'count': 1}, 'time': 0.013512849807739}
```
ortografix 0.7.0
Seq2seq model with attention for automatic orthographic simplification
License: MIT
Keywords: seq2seq,ortographic simplification

# ortografix
[![GitHub release][release-image]][release-url]
[![PyPI release][pypi-image]][pypi-url]
[![Build][build-image]][build-url]
[![MIT License][license-image]][license-url]


[release-image]:https://img.shields.io/github/release/akb89/ortografix.svg?style=flat-square
[release-url]:https://github.com/akb89/ortografix/releases/latest
[pypi-image]:https://img.shields.io/pypi/v/ortografix.svg?style=flat-square
[pypi-url]:https://pypi.org/project/ortografix/
[build-image]:https://img.shields.io/github/workflow/status/akb89/ortografix/CI?style=flat-square
[build-url]:https://github.com/akb89/ortografix/actions?query=workflow%3ACI
[license-image]:http://img.shields.io/badge/license-MIT-000000.svg?style=flat-square
[license-url]:LICENSE.txt

Welcome to ortografix, a seq2seq model for automatic ortografic simplification, coded with pytorch 1.4.

## Install
via pip:
```shell
pip3 install ortografix
```
or, after a git clone:
```shell
python3 setup.py install
```

## Train
To train a model, run:
```shell
ortografix train \
--data /abs/path/to/training/data \
--model-type gru \
--shuffle \
--hidden-size 256 \
--num-layers 1 \
--bias \
--dropout 0 \
--learning-rate 0.01 \
--epochs 10 \
--print-every 100 \
--use-teacher-forcing \
--teacher-forcing-ratio 0.5 \
--output-dirpath /abs/path/to/output/directory/whereto/save/model \
--with-attention \
--character-based
```

## Test
### Qualitative evaluation
To qualitatively evaluate the output of the model on a set of 10 randomly selected sentences from a given dev/test set, run:
```shell
ortografix evaluate \
--data /abs/path/to/test/data.txt \
--model /abs/path/to/model/directory/ \
--random 10
```
### Quantitative evaluation
To quantitatively evaluate the output of the model on a given dev/test set, run:
```shell
ortografix evaluate \
--data /abs/path/to/test/data.txt \
--model /abs/path/to/model/directory
```
Quantitative evaluation will return:
1. The sum of all edit (Levenshtein) distance computed across all test pairs
2. The average edit distance computed across all test pairs
3. The average normalized edit distance
4. The average normalized edit similarity

All measure are computed via [textdistance](https://github.com/life4/textdistance).