The ESPPy package enables you to create SAS Event Stream Processing (ESP) 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 notebooks including visualizing diagrams of your ESP projects, and support for streaming charts and images. This allows you to easily explore and prototype your ESP projects in a familiar notebook interface.
To install ESPPy, you can use either pip or conda. This will install
ESPPy as well as the Python package dependencies.
pip install sas-esppy
or, if you are using an Anaconda distribution:
conda install -c sas-institute sas-esppy
In addition to the Python package dependencies, you will also need the
graphviz command-line tools to fully take advantage of ESPPy. These can
be downloaded from http://www.graphviz.org/download/.
Also, 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.
Importing the package is done just as it is with any other Python package.
>>> import esppy
To connect to an ESP server, you 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')
Now that we have a connection to the server, we can get information about the server and projects.
>>> esp.server_info
{'analytics-license': True,
'engine': 'esp',
'http-admin': 8777,
'pubsub': 8778,
'version': '5.2'}
# Currently no projects are loaded
>>> esp.get_projects()
{}
Loading a project is done with the load_project method.
>>> esp.load_project('project.xml')
>>> esp.get_projects()
{'project': Project(name='project')}
Continous queries and windows within projects can be accessed using
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']
You can even drop the queries and windows attribute name as a shortcut.
projects and continuous queries act like dictionaries of those components.
>>> dataw = proj['contquery']['w_data']
To publish events to a window, you simply use the publish_events method.
It will accept a file name, file-like object, DataFrame, or a string of
CSV, XML, or JSON data.
>>> dataw.publish_events('data.csv')
You can subscribe to the events of any window in a project. By default, all event data will be 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
You can limit the number of cached events using the limit
parameter. For example, to only keep the last 20 events, you would do
the following.
>>> dataw.subscribe(limit=20)
You can also limit the amount of time that events are collected using
the horizon parameter. It will take a datetime, date, time,
or timedelta object.
>>> 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
The full API documentation of ESPPy is available at https://sassoftware.github.io/python-esppy/.