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# Copyright 2015-2016 ARM Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Trigger is a representation of the following:
- Event(s) (:mod:`trappy.base.Base`)
- An associated value
- scalar
- vector
- A set of filters
- value based
- function based
"""
import types
from trappy.utils import listify
import pandas as pd
class Trigger(object):
"""Trigger is an event-value relationship which
accepts a trace object to "generate" qualified data
:param trace: A trappy FTrace object
:type trace: :mod:`trappy.trace.FTrace`
:param template: A trappy Event to act as a trigger
:type template: trappy.Base
:param filters: Key value filter pairs
:type filters: dict
The filter can either have a function:
::
def function_based_filter(elem):
if condition:
return True
else:
return False
or a value/list of values
::
f = {}
f["data_column_a"] = function_based_filter
f["data_column_b"] = value
function_based_filter is anything that behaves like a function,
i.e. a callable.
:param value: Value can be a string or a numeric
:type value: str, int, float
:param pivot: This is the column around which the data will be
pivoted
:type pivot: str
"""
def __init__(self, trace, template, filters, value, pivot):
self.template = template
self._filters = filters
self._value = value
self._pivot = pivot
self.trace = trace
def generate(self, pivot_val):
"""Generate the trigger data for a given pivot value
and a trace index
:param pivot_val: The pivot to generate data for
:type pivot_val: hashable
"""
trappy_event = getattr(self.trace, self.template.name)
data_frame = trappy_event.data_frame
data_frame = data_frame[data_frame[self._pivot] == pivot_val]
mask = [True for _ in range(len(data_frame))]
for key, value in self._filters.iteritems():
if hasattr(value, "__call__"):
mask = mask & (data_frame[key].apply(value))
else:
mask = apply_filter_kv(key, value, data_frame, mask)
data_frame = data_frame[mask]
if isinstance(self._value, str):
return data_frame[value]
else:
return pd.Series(self._value, index=data_frame.index)
def apply_filter_kv(key, value, data_frame, mask):
"""Internal function to apply a key value
filter to a data_frame and update the initial
condition provided in mask.
:param value: The value to checked for
:param data_frame: The data to be filtered
:type data_frame: :mod:`pandas.DataFrame`
:param mask: Initial Condition Mask
:type mask: :mod:`pandas.Series`
:return: A **mask** to index the data frame
"""
value = listify(value)
if key not in data_frame.columns:
return mask
else:
for val in value:
mask = mask & (data_frame[key] == val)
return mask
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