semvec/pypi-packages huggingface dataset:
test2333 3.0.0a7
BGmi is a cli tool for subscribed bangumi.
License: MIT
Requires Python: >=3.6,<4.0
Keywords: bangumi,bgmi,feed

# BGmi-ng

![Azure DevOps tests](https://img.shields.io/azure-devops/tests/BGmi/BGmi-NG/2/master?logo=azure-pipelines)
![Azure DevOps coverage](https://img.shields.io/azure-devops/coverage/BGmi/BGmi-NG/2/master)

repo for `BGmi` 3.0
searchconsole 0.0.4
A wrapper for the Google Search Console API.
License: MIT
Keywords: data analysis search console google api seo

# Google Search Console for Python

[![Build Status](https://travis-ci.org/joshcarty/google-searchconsole.svg?branch=master)](https://travis-ci.org/joshcarty/google-searchconsole)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

`google-searchconsole` takes the pain out of working with the [Google Search
Console](https://support.google.com/webmasters/answer/4559176?hl=en) Search Analytics Query API. It is written in Python and provides
convenient features to make querying a site's search analytics data easier.

* **Authentication.** We provide a few different ways to make generating
credentials and authenticating with your account easier. You can use stored
fies as well as a way to do the OAuth2 flow interactively.
* **Querying.** Easier to query by date ranges and filter by various
dimensions. No longer posting large nested JSON, the query object lets you make
complex queries with ease.
* **Exploration.** You can traverse your account hierarchy, with an account
containing webproperties with clear permission levels.
* **Exports.** Clean JSON and pandas.DataFrame outputs so you can easily
analyse your data in Python or Excel.

This package is built on top of
[Google's own API Client](https://developers.google.com/webmaster-tools/search-console-api-original/v3/prereqs)
and is heavily inspired, from design to implementation, by [@debrouwere](https://github.com/debrouwere)'s
fantastic [`google-analytics`](https://github.com/debrouwere/google-analytics) package.

## Quickstart

First, install the package using:

`pip3 install git+https://github.com/joshcarty/google-searchconsole`

Then, create a new project in the [Google Developers Console](https://console.developers.google.com),
enable the  Google Search Console API under "APIs & Services". Next, create credentials
for an OAuth client ID, choosing the Other Application type. Download a JSON copy of
your client secrets.

After that, executing your first query is as easy as

```python
import searchconsole
account = searchconsole.authenticate(client_config='client_secrets.json')
webproperty = account['https://www.example.com/']
report = webproperty.query.range('today', days=-7).dimension('query').get()
print(report.rows)
```

The above example will use your client configuration file to interactively
generate your credentials.


### Saving Credentials
If you wish to save your credentials, to avoid going
through the OAuth consent screen in the future, you can specify a path to save
them by specifying `serialize='path/to/credentials.json`.

When you want to authenticate a new account you run:

```python
account = searchconsole.authenticate(client_config='client_secrets.json',
                                     serialize='credentials.json')
```
Which will save your credentials to a file called `credentials.json`.

From then on, you can authenticate with:

```python
account = searchconsole.authenticate(client_config='client_secrets.json',
                                     credentials='credentials.json')
```
pywisc 0.75
Wisc calculations
License: MIT
Requires Python: >=3.6

[![Build Status](https://travis-ci.org/cluster311/pywisc.svg?branch=master)](https://travis-ci.org/cluster311/pywisc)

# Py Wisc

Calculos para WISC en diferentes versiones y diferentes paises.  
**Status**: En desarrollo. No funciona todavía.

## Install

Publicado en [Pypi](https://pypi.org/project/pywisc/)

```
pip install pywisc
```

## Comandos

Esta aplicación se puede usar desde la línea de comandos

```
$ wisc
##########################
Calculador WISC 4. Versión español, Argentina
##########################
Fecha de nacimiento 
	Formato: Formatos permitidos: AAAA-MM-DD o DD/MM/AAAA. Por ejemplo 2020-12-01 o 01/12/2020
2014-03-12
Fecha de evaluación 
	Formato: Formatos permitidos: AAAA-MM-DD o DD/MM/AAAA. Por ejemplo 2020-12-01 o 01/12/2020
	Predeterminado: 2020-05-01

Comprension verbal-Semejanzas (CV-S):10
Comprension verbal-Vocabulario (CV-V):10
Comprension verbal-Comprensión (CV-C):10
Razonamiento Perceptivo-Construcción de cubos (RP-CC):10
Razonamiento Perceptivo-Conceptos (RP-Co):10
Razonamiento Perceptivo-Matrices (RP-M):10
Memoria Operativa-Retención de dígitos (MO-RD):10
Memoria Operativa-Letras y números (MO-LN):10
Velocidad de procesamiento-Claves (VP-Cl):10
Velocidad de procesamiento-Búsqueda de símbolos (VP-BS):10
Calc over {'S': 10, 'V': 10, 'C': 10, 'CC': 10, 'Co': 10, 'M': 10, 'RD': 10, 'LN': 10, 'Cl': 10, 'BS': 10}
CI calculado: 89
```

## En Python

```python
from pywisc.wisc import Wisc
from pywisc.evaluacion import Evaluacion


directas_por_subtest =  {'S': 10, 'V': 10, 'C': 10, 'CC': 10,
                         'Co': 10, 'M': 10, 'RD': 10, 'LN': 10,
                         'Cl': 10, 'BS': 10}

w = Wisc(wisc_version=4, language='es', country='ar')
e = Evaluacion(wisc=w)
reqs = {'born_date': '2014-03-01', 'test_date': '2020-04-29'}
e.validate_reqs(reqs=reqs)
e.calculate_age()
ci = e.calculate_ci(directas=directas_por_subtest)
assert ci == 89
```

## Tests

```
python -m pytest -s
```