Exponential Moving Average

The flir-filter-ema plugin implements an exponential moving average across a set of data. It also forms an example of how to write a filter plugin purely in Python. Filters written in Python have the same functionality and set of entry points as any other filter.

The plugin_info entry point that returns details of the plugin and the default configuration

def plugin_info():
    """ Returns information about the plugin
    Args:
    Returns:
        dict: plugin information
    Raises:
    """
    return {
        'name': 'ema',
        'version': '2.1.0',
        'mode': "none",
        'type': 'filter',
        'interface': '1.0',
        'config': _DEFAULT_CONFIG
    }

The plugin_init entry point that initialises the plugin

def plugin_init(config, ingest_ref, callback):
    """ Initialise the plugin
    Args:
        config: JSON configuration document for the Filter plugin configuration category
        ingest_ref:
        callback:
    Returns:
        data: JSON object to be used in future calls to the plugin

    ...
    return data

The plugin_reconfigure entry point that us called whenever the configuration is changed

def plugin_reconfigure(handle, new_config):
   """ Reconfigures the plugin
   Args:
       handle: handle returned by the plugin initialisation call
       new_config: JSON object representing the new configuration category for the category
   Returns:
       new_handle: new handle to be used in the future calls
   """
   global rate, datapoint
   ...
   return new_handle

The plugin_shutdown entry point called to terminate the plugin

def plugin_shutdown(handle):
    """ Shutdowns the plugin doing required cleanup.
    Args:
        handle: handle returned by the plugin initialisation call
    Returns:
        plugin shutdown
    """

And the plugin_ingest call that is called to do the actual data processing

def plugin_ingest(handle, data):
""" Modify readings data and pass it onward
Args:
    handle: handle returned by the plugin initialisation call
    data: readings data
"""

Python filters are added in the same way as any other filters.

• Click on the Applications add icon for your service or task.

• Select the ema plugin from the list of available plugins.

• Name your ema filter.

• Click Next and you will be presented with the following configuration page

• Configure your ema filter

  • EMA datapoint: The name of the data point to create within the asset

  • Rate: The rate controls the rate of the average generated, in this case it is the percentage the current value contribute to the average value.

• Enable your plugin and click Done