Util Module¶
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Description¶
Various utility functions
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pycpa.util.additive_extension(additive_func, q, q_max, cache=None, cache_offset=1)[source]¶ Additive extension for event models. Any sub- or super- additive function additive_func valid in the domain q in [0, q_max] is extended and the approximited value f(q) is returned. NOTE: this cannot be directly used with delta curves, since they are “1-off”, thus if you supply a delta function to additive_func, note to add 1 and supply q-1. e.g. util.additive_extension(lambda x: self.delta_min(x + 1), n - 1, q_max)
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pycpa.util.bitrate_str_to_bits_per_second(bitrate_str)[source]¶ Convert bitrate strings like “100MBit/s” or “1 Gbit/s” to an integer representation in Bit/s.
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pycpa.util.breadth_first_search(task, func=None, get_reachable_tasks=<function get_next_tasks>)[source]¶ returns a set of nodes (tasks) which is reachable starting from the starting task. calls func on the first discover of a task.
get_reachable_tasks(task) specifies a function which returns all tasks considered immediately reachable for a given task.
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pycpa.util.combinations_with_replacement(iterable, r)[source]¶ combinations_with_replacement(‘ABC’, 2) –> AA AB AC BB BC CC
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pycpa.util.cycles_to_time(value, freq, base_time, rounding='ceil')[source]¶ Converts the cycle/bittimes to an absolute time in base_time
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pycpa.util.dijkstra(source)[source]¶ Calculates a distance-map from the source node based on the dijkstra algorithm The edge weight is 1 for all linked tasks
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pycpa.util.generate_distance_map(system)[source]¶ Precomputes a distance-map for all tasks in the system.
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pycpa.util.get_next_tasks(task)[source]¶ return the list of next tasks for task object. required for _breadth_first_search
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pycpa.util.get_path(t_src, t_dst)[source]¶ Find path between tasks t_src and t_dst. Returns a path as list() or None if no path was found. NOTE: There is no protection against cycles!
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pycpa.util.recursive_max_additive(additive_func, q, q_max, cache=None, cache_offset=1)[source]¶ Sub-additive extension for event models. Any sub-additive function additive_func valid in the domain q in [0, q_max] is extended and the value f(q) is returned. It is optional to supply a cache dictionary for speedup.
NOTE: this cannot be directly used with delta curves, since they are “1-off”, thus if you supply a delta function to additive_func, note to add 1 and supply q-1. e.g. ret = util.recursive_max_additive(lambda x: self.delta_min(x + 1), n - 1, q_max, self.delta_min_cache)
By default, the cache is filled according to the delta domain notion, so it can be used with delta-based event models. To override this behavior, change the cache_offset parameter to zero
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pycpa.util.recursive_min_additive(additive_func, q, q_max, cache=None, cache_offset=1)[source]¶ Super-additive extension for event models. Any additive function additive_func valid in the domain q in [0, q_max] is extended and the value f(q) is returned. It is optional to supply a cache dictionary for speedup.
NOTE: this cannot be directly used with delta curves, since they are “1-off”, thus if you supply a delta function to additive_func, note to add 1 and supply q-1. e.g. ret = util.recursive_min_additive(lambda x: self.delta_plus(x + 1), n - 1, q_max, self.delta_plus_cache)
By default, the cache is filled according to the delta domain notion, so it can be used with delta-based event models. To override this behavior, change the cache_offset parameter to zero
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pycpa.util.time_str_to_time(time_str, base_out, rounding='ceil')[source]¶ Convert strings like “100us” or “10 ns” to an integer representation in base_out.
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pycpa.util.time_to_cycles(value, freq, base_time, rounding='ceil')[source]¶ Converts an absolute time given in the base_time domain into cycles
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pycpa.util.time_to_time(value, base_in, base_out, rounding='ceil')[source]¶ Convert an absolute time given in base_in to another absolute time given in base_out