perun.processing
================

.. py:module:: perun.processing

.. autoapi-nested-parse::

   Processing Module.



Attributes
----------

.. autoapisummary::

   perun.processing.log


Functions
---------

.. autoapisummary::

   perun.processing.processEnergyData
   perun.processing.processSensorData
   perun.processing.processDataNode
   perun.processing.processRegionsWithSensorData
   perun.processing.addRunAndRuntimeInfoToRegion
   perun.processing.getInterpolatedValues


Module Contents
---------------

.. py:data:: log

.. py:function:: processEnergyData(raw_data: perun.data_model.data.RawData, start: Optional[numpy.number] = None, end: Optional[numpy.number] = None) -> Tuple[Any, Any]

   Calculate total energy and average power from an energy or power time series.

   Using the start and end parameters the results can be limited to certain areas of the application run.

   Parameters
   ----------
   raw_data : RawData
       Raw Data from sensor
   start : Optional[np.number], optional
       Start time of region, by default None
   end : Optional[np.number], optional
       End time of region, by default None

   Returns
   -------
   _type_
      Tuple with total energy in joules and avg power in watts.


.. py:function:: processSensorData(sensorData: perun.data_model.data.DataNode) -> perun.data_model.data.DataNode

   Calculate metrics based on raw values.

   Parameters
   ----------
   sensorData : DataNode
       DataNode with raw sensor data.

   Returns
   -------
   DataNode
       DataNode with computed metrics.


.. py:function:: processDataNode(dataNode: perun.data_model.data.DataNode, perunConfig: configparser.ConfigParser, force_process=False) -> perun.data_model.data.DataNode

   Recursively calculate metrics on the dataNode tree.

   Parameters
   ----------
   dataNode : DataNode
       Root data node tree.
   perunConfig: ConfigParser
       Perun configuration
   force_process : bool, optional
       Force recomputation of child node metrics, by default False

   Returns
   -------
   DataNode
       Data node with computed metrics.


.. py:function:: processRegionsWithSensorData(regions: List[perun.data_model.data.Region], dataNode: perun.data_model.data.DataNode)

   Complete region information using sensor data found on the data node (in place op).

   Parameters
   ----------
   regions : List[Region]
       List of regions that use the same data node.
   dataNode : DataNode
       Data node with sensor data.


.. py:function:: addRunAndRuntimeInfoToRegion(region: perun.data_model.data.Region)

   Process run and runtime stats in region objects (in place operation).

   Parameters
   ----------
   region : Region
       Region object


.. py:function:: getInterpolatedValues(t: numpy.ndarray, x: numpy.ndarray, start: numpy.number, end: numpy.number) -> Tuple[numpy.ndarray, numpy.ndarray]

   Filter timeseries with a start and end limit, and interpolate the values at the edges.

   Parameters
   ----------
   t : np.ndarray
       Original time steps
   x : np.ndarray
       Original values
   start : np.number
       Start of the region of interest
   end : np.number
       End of the roi

   Returns
   -------
   np.ndarray
       ROI values