Metadata-Version: 2.1
Name: sas-esppy
Version: 7.1.16
Summary: SAS Event Stream Processing Python Interface
Home-page: https://github.com/sassoftware/python-esppy/
Author: SAS
Author-email: Robert.Levey@sas.com
License: Apache 2.0
Description: # SAS Event Stream Processing Python Interface
        
        The ESPPy package enables you to create
        [SAS Event Stream Processing (ESP)](https://www.sas.com/en_us/software/event-stream-processing.html)
        models programmatically in Python. Using ESPPy, you can connect to 
        an ESP server and interact with projects and their components as 
        Python objects. These objects include projects, continuous queries, 
        windows, events, loggers, SAS Micro Analytic Service modules, 
        routers, and analytical algorithms.
        
        ESPPy has full integration with [Jupyter](https://jupyter.org/) notebooks including visualizing 
        diagrams of your ESP projects, and support for streaming charts and 
        images. This enables you to easily explore and prototype your ESP 
        projects in a familiar notebook interface.
        
        ## Installation
        
        To install ESPPy, use `pip`. This installs
        ESPPy and the Python package dependencies.
        
        ```
        pip install sas-esppy
        ```
        
        ### Additional Requirements
        
        In addition to the Python package dependencies, you also need the 
        `graphviz` command-line tools to fully take advantage of ESPPy. Download them from http://www.graphviz.org/download/.
        
        ### Performance Enhancement
        
        ESPPy uses the `ws4py` websocket Python package. In some cases,
        you can improve performance greatly by installing the `wsaccel` package.
        This may not be available on all platforms though, and is left up to 
        the user to install.
        
        ## The Basics
        
        To import the ESPPy package, use the same method as with any other Python package.
        
        ```
        >>> import esppy
        ```
        
        To connect to an ESP server, use the `ESP` class.  In most cases, the only
        information that is needed is the hostname and port.
        
        ```
        >>> esp = esppy.ESP('http://myesp.com:8777')
        ```
        
        ### Getting Information about the Server
        
        After you have connected to the server, you can get information about the
        server and projects.
        
        ```
        >>> esp.server_info
        {'analytics-license': True,
         'engine': 'esp',
         'http-admin': 8777,
         'pubsub': 8778,
         'version': 'X.X'}
        
        # Currently no projects are loaded
        >>> esp.get_projects()
        {}
        ```
        
        ### Loading a Project
        
        To load a project, use the `load_project` method.
        
        ```
        >>> esp.load_project('project.xml')
        
        >>> esp.get_projects()
        {'project': Project(name='project')}
        ```
        
        To access continous queries and windows within projects, use 
        the `queries` and `windows` attributes of the `Project` and
        `ContinuousQuery` objects, respectively.
        
        ```
        >>> proj = esp.get_project('project')
        >>> proj.queries
        {'contquery': ContinuousQuery(name='contquery', project='project')}
        
        >>> proj.queries['contquery'].windows
        {'w_data': CopyWindow(name='w_data', continuous_query='contquery', project='project'),
         'w_request': SourceWindow(name='w_request', continuous_query='contquery', project='project'),
         'w_calculate': CalculateWindow(name='w_calculate', continuous_query='contquery', project='project')}
        
        >>> dataw = proj.queries['contquery'].windows['w_data']
        ```
        
        As a shortcut, you can drop the `queries` and `windows` attribute name.
        Projects and continuous queries act like dictionaries of those components.
        
        ```
        >>> dataw = proj['contquery']['w_data']
        ```
        
        ### Publishing Event Data
        
        To publish events to a window, use the `publish_events` method.
        It accepts a file name, file-like object, DataFrame, or a string of
        CSV, XML, or JSON data.
        
        ```
        >>> dataw.publish_events('data.csv')
        ```
        
        ### Monitoring Events
        
        You can subscribe to the events of any window in a project. By default,
        all event data are cached in the local window object.
        
        ```
        >>> dataw.subscribe()
        >>> dataw
               time        x        y        z
        id                                    
        6   0.15979 -2.30180  0.23155  10.6510
        7   0.18982 -1.41650  1.18500  11.0730
        8   0.22040 -0.27241  2.22010  11.9860
        9   0.24976 -0.61292  2.22010  11.9860
        10  0.27972  1.33480  4.24950  11.4140
        11  0.31802  3.44590  7.58650  12.5990
        ```
        
        To limit the number of cached events, use the `limit`
        parameter. For example, to only keep the last 20 events, enter 
        the following line:
        
        ```
        >>> dataw.subscribe(limit=20)
        ```
        
        You can also limit the amount of time that events are collected using
        the `horizon` parameter. Use one of the following objects: `datetime`, `date`, `time`,
        or `timedelta`.
        
        ```
        >>> dataw.subscribe(horizon=datetime.timedelta(hours=1))
        ```
        
        You can also perform any DataFrame operation on your ESP windows.
        
        ```
        >>> dataw.info()
        <class 'pandas.core.frame.DataFrame'>
        Int64Index: 2108 entries, 6 to 2113
        Data columns (total 4 columns):
        time    2108 non-null float64
        x       2108 non-null float64
        y       2108 non-null float64
        z       2108 non-null float64
        dtypes: float64(4)
        memory usage: 82.3 KB
        
        >>> dataw.describe()
                    time          x          y          z
        count  20.000000  20.000000  20.000000  20.000000
        mean   69.655050  -4.365320   8.589630  -1.675292
        std     0.177469   1.832482   2.688911   2.108300
        min    69.370000  -7.436700   4.862500  -5.175700
        25%    69.512500  -5.911250   7.007675  -3.061150
        50%    69.655000  -4.099700   7.722700  -1.702500
        75%    69.797500  -2.945400   9.132350  -0.766110
        max    69.940000  -1.566300  14.601000   3.214400
        ```
        
        ### Using ESPPy Visualizations with JupyterLab
        
        NOTE: These instructions assume you have Anaconda installed.
        
        To use jupyterlab visualizations with ESPPy (available in version 6.2 or higher), perform the following steps:
        
        1. Create a new Anaconda environment. For this example, the environment is called esp.
        ```
            $ conda create -n esp python=3.X
        ```
        2. Activate the new environment.
        ```
        $ conda activate esp
        ```
        3. Install the following packages:
        ```
        $ pip install jupyter
        $ pip install jupyterlab
        $ pip install matplotlib
        $ pip install ipympl
        $ pip install pandas
        $ pip install requests
        $ pip install image
        $ pip install ws4py
        $ pip install plotly
        $ pip install ipyleaflet
        $ pip install graphviz
        ```
        4. Install the following Jupyterlab extensions:
        ```
        $ jupyter labextension install @jupyter-widgets/jupyterlab-manager
        $ jupyter labextension install plotlywidget
        $ jupyter labextension install jupyter-leaflet
        ```
        
        5. Install the following packages (WINDOWS ONLY):
        ```
        $ conda install -c conda-forge python-graphviz
        ```
        
        6. Create and change to a working directory.
        ```
        $ cd $HOME
        $ mkdir esppy
        $ cd esppy
        ```
        
        7. Install ESPPy.
        ```
        pip install sas-esppy
        ```
        
        8. Create a notebooks directory to store your notebooks.
        ```
        $ mkdir notebooks
        ```
        
        9. Start the Jupyterlab server. Select an available port. For this example, port 35000 was selected.
        ```
        $ jupyter lab --port 35000
        ```
        
        After you complete these steps, you can use the latest ESP graphics in your Jupyter notebooks.
        
        ### Documentation
        
        To view the full API documentation for ESPPy, see 
        https://sassoftware.github.io/python-esppy/.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
