def __add_events(self, events):
"""Add events to the class_definitions
If the events are known to trappy just add that class to the
class definitions list. Otherwise, register a class to parse
that event
"""
from trappy.dynamic import DynamicTypeFactory, default_init
from trappy.base import Base
# TODO: scopes should not be hardcoded (nor here nor in the FTrace object)
all_scopes = [
self.thermal_classes, self.sched_classes, self.dynamic_classes
]
known_events = {k: v for sc in all_scopes for k, v in sc.iteritems()}
for event_name in events:
for cls in known_events.itervalues():
if (event_name == cls.unique_word) or \
(event_name + ":" == cls.unique_word):
self.class_definitions[event_name] = cls
break
else:
kwords = {
"__init__": default_init,
"unique_word": event_name + ":",
"name": event_name,
}
trace_class = DynamicTypeFactory(event_name, (Base, ), kwords)
self.class_definitions[event_name] = trace_class
def add_parsed_event(self, name, dfr, pivot=None):
"""Add a dataframe to the events in this trace
This function lets you add other events that have been parsed
by other tools to the collection of events in this instance. For
example, assuming you have some events in a csv, you could add
them to a trace instance like this:
>>> trace = trappy.BareTrace()
>>> counters_dfr = pd.DataFrame.from_csv("counters.csv")
>>> trace.add_parsed_event("pmu_counters", counters_dfr)
Now you can access :code:`trace.pmu_counters` as you would with any
other trace event and other trappy classes can interact with
them.
:param name: The attribute name in this trace instance. As in the example above, if :code:`name` is "pmu_counters", the parsed event will be accessible using :code:`trace.pmu_counters`.
:type name: str
:param dfr: :mod:`pandas.DataFrame` containing the events. Its index should be time in seconds. Its columns are the events.
:type dfr: :mod:`pandas.DataFrame`
:param pivot: The data column about which the data can be grouped
:type pivot: str
"""
from trappy.base import Base
from trappy.dynamic import DynamicTypeFactory, default_init
if hasattr(self, name):
raise ValueError("event {} already present".format(name))
kwords = {
"__init__": default_init,
"unique_word": name + ":",
"name": name,
}
trace_class = DynamicTypeFactory(name, (Base, ), kwords)
self.class_definitions[name] = trace_class
event = trace_class()
self.trace_classes.append(event)
event.data_frame = dfr
if pivot:
event.pivot = pivot
setattr(self, name, event)