semvec/pypi-packages huggingface dataset:
data-genie-mkeshav 0.4.8
A small genie to generate test data

[![CircleCI branch](https://img.shields.io/circleci/project/github/mkeshav/data-genie/master.svg)](https://circleci.com/gh/mkeshav/data-genie/tree/master)
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[![Documentation Status](https://readthedocs.org/projects/data-genie/badge/?version=latest)](https://data-genie.readthedocs.io/en/latest/?badge=latest)
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=mkeshav_data-genie&metric=alert_status)](https://sonarcloud.io/dashboard?id=mkeshav_data-genie)

# Data Genie

Genie that can satisfy your data wish.
Supports generation of fixedwidth, csv and json content

# Install
python3 -m pip install data-genie-mkeshav

# Run tests
- All tests (`docker-compose run --rm test`)
- Single test in a file(`docker-compose run --rm test bash -c "python setup.py develop &&  pytest tests/test_fw.py -k 'test_float'"`)
- `docker inspect --format='{{.Id}} {{.Parent}}'     $(docker images --filter since=<image_id> --quiet)` to check dependent child images

- Detailed [Documentation](https://data-genie.readthedocs.io)

# Sonar Scan
Gitignored .env file contains the SONAR_CLOUD_TOKEN variable (locally)

- `docker-compose run --rm test ./scan`

# Release
increment version in init.py
`./tag-master`
`git push origin master`
decorpy 0.3
A package exposing a collection of ready-to-use python decorators.
Keywords: decorator,timer,debug,typing

# Decorpy

![Travis](https://travis-ci.org/alexZajac/decorpy.svg?branch=master)
[![Coverage Status](https://coveralls.io/repos/github/alexZajac/decorpy/badge.svg)](https://coveralls.io/github/alexZajac/decorpy)
[![Updates](https://pyup.io/repos/github/alexZajac/decorpy/shield.svg)](https://pyup.io/repos/github/alexZajac/decorpy/)
[![Python 3](https://pyup.io/repos/github/alexZajac/decorpy/python-3-shield.svg)](https://pyup.io/repos/github/alexZajac/decorpy/)

A collection of python decorators including time measurement, type checking and debugging.

## Installation

Install the package directly using `Pip` with [Pypi](https://pypi.org/project/decorpy/) 

`pip install decorpy`

Or using `git`

`git clone https://github.com/alexZajac/decorpy.git`

`cd decorpy`

`python setup.py install`

## Examples

### Measure execution time of a function
[Link to gist](https://gist.github.com/alexZajac/f5b594307848f0b23b9ca4fea202633d)

### Debugging a function with its signature and return value

[Link to gist](https://gist.github.com/alexZajac/e4d222757a1095abf6f8cbdcef1e1870)

### Runtime type-checking on function parameters and return

[Link to gist](https://gist.github.com/alexZajac/fe96e566e4f4aab4f80312abb49d25e8)
leanbase 1.6.0
A client for the leanbase API.

# leanbase-python

A client for the [Leanbase](https://leanbase.io/) API in python.

## Usage

```python
import leanbase as lb

lb.configure(api_key='api_key')

lb.await_initialisation(timeout=0.25) # optional

if lb.user(user).can_access('quarter_pounds'):
  # Do this
else:
  # Do that.

lb.user(user).completed('arduous_goal') # optional, recommended.
```

## Architecture

The code and the runtime is divided into the following modules. Storage, Client, Models and API.

Using the client is effectively using the API provided with it. At configuration, 
1. a client and stores are created
1. stores are connected to the client (for auto-updates)
1. the client requests for the first payload and updates the store accordingly.

During usage, 
1. the stores are queried for the feature along with its segments definitions
1. the evaluation algorithm is invoked.
1. the client and stores ensure that the feature definition is always up-to-date