Metadata-Version: 2.1
Name: GameBenchAPI-PyClient-BigFish
Version: 0.1.19
Summary: A GameBench API Client Library.
Home-page: https://github.com/bigfishgames/GameBenchAPI-PyClient
Author: Big Fish Games, Inc.
Author-email: 
License: UNKNOWN
Description: A Python Client for the GameBench API
        
        [![Build Status](https://travis-ci.com/bigfishgames/GameBenchAPI-PyClient.svg?branch=master)](https://travis-ci.com/bigfishgames/GameBenchAPI-PyClient)
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        Please check out our [ZenHub Board](https://app.zenhub.com/workspaces/gamebenchapi-pyclient-5cabf535a736c27636b0283d/board?repos=180245554) for open issues and feature
        requests.
        
        Repository: [GitHub](https://github.com/bigfishgames/GameBenchAPI-PyClient)
        
        For full documentation, go to the [ReadtheDocs](https://gamebenchapi-pyclient.readthedocs.io/) page.
        
        [PyPi](https://pypi.org/project/GameBenchAPI-PyClient-BigFish/#description)
        
        ## Overview
        To install, run `pip install GameBenchAPI-PyClient-BigFish`
        
        The GameBench API Client library supplies a high-level object-oriented interface to the GameBench API. It is built in
        Python 3.7 and uses the Requests library and Pandas data frames to easily integrate into data analysis software.
        
        The library has two main architectural components; the models and API packages. The API package is responsible for
        URL requests and dealing with the responses. The models are the objects representing the data returned. A mediator
        provides the glue between the api and the models.
        
        As a user of the library, you should only ever need to interact with the models creator class and the model objects
        it can return.
        
        Right now, the models are very thin. They only contain a property that has the data frame assigned. Over time we
        would like to add common functionality, like aggregates, to these classes.
        
        ## The Basics
        To make a request, import the ModelCreator class.
        Instantiating the ModelCreator requires two arguments.  The first is a CamelCase style 'model'
        named after the metric that you are looking for; the model is dynamically imported based on this
        name.  The second argument is a dictionary that must include specific key/value pairs for
        querying the GameBench API.
        
        ```python
        from gamebench_api_client.models.creator.model_creator import ModelCreator
        
        
        
        time_series_request = {
            'session_id': '66d926f47ff5a7a5d853d1058c6305614e1ae6a5'
        }
        
        creator = ModelCreator('Cpu', time_series_request)
        cpu_time_series = creator.get_model()
        
        results = cpu_time_series.data
        
        print(results)
        
        """
              appUsage  daemonUsage    gbUsage  timestamp  totalCpuUsage
        0  1372571.375            0  12.658228       5257      39.688461
        """
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
