Propagation Module¶
Event model propagation algorithms.
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Description¶
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class
pycpa.propagation.BusyWindowPropagationEventModel(task, task_results, nonrecursive=True)[source]¶ Derive an output event model from busy window and in_event_model (used as reference). Typically provides better results than JitterPropagationEventModel.
This results from Theorems 1, 2 and 3 from [Schliecker2008].
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class
pycpa.propagation.JitterBminPropagationEventModel(task, task_results, nonrecursive=True)[source]¶ Derive an output event model from response time jitter, the b_min as well as the in_event_model (used as reference).
Uses a reference to task.deltamin_func
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class
pycpa.propagation.JitterOffsetPropagationEventModel(task, task_results, nonrecursive=True)[source]¶ - Derive an output event model from response time jitter
- and in_event_model (used as reference). Also calculates the offset attribute.
This corresponds to Equations 1 (non-recursive) and 2 (recursive from [Schliecker2009] This is equivalent to Equation 5 in [Henia2005] or Equation 4.6 in [Richter2005].
Uses a reference to task.deltamin_func
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class
pycpa.propagation.JitterPropagationEventModel(task, task_results, nonrecursive=True)[source]¶ - Derive an output event model from response time jitter
- and in_event_model (used as reference).
This corresponds to Equations 1 (non-recursive) and 2 (recursive from [Schliecker2009] This is equivalent to Equation 5 in [Henia2005] or Equation 4.6 in [Richter2005].
Uses a reference to task.deltamin_func
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class
pycpa.propagation.OptimalPropagationEventModel(task, task_results, nonrecursive=True)[source]¶ Optimal event model based on jitter and busy_window propagation. For some schedulers, such as FIFO and EDF neither busy_window nor jitter propagation is optimal. This will try both and chooses the best result.