# 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. # from bart.common import Utils from bart.common.Analyzer import Analyzer import unittest import pandas as pd import trappy class TestCommonUtils(unittest.TestCase): def __init__(self, *args, **kwargs): super(TestCommonUtils, self).__init__(*args, **kwargs) def test_interval_sum(self): """Test Utils Function: interval_sum""" # A series with a non uniform index # Refer to the example illustrations in the # the interval sum docs-strings which explains # the difference between step-post and ste-pre # calculations values = [0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1] index = [0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12] series = pd.Series(values, index=index) self.assertEqual(Utils.interval_sum(series, 1, step="post"), 8) self.assertEqual(Utils.interval_sum(series, 1, step="pre"), 7) # check left boundary array = [1, 1, 0, 0] series = pd.Series(array) self.assertEqual(Utils.interval_sum(series, 1, step="post"), 2) self.assertEqual(Utils.interval_sum(series, 1, step="pre"), 1) # check right boundary array = [0, 0, 1, 1] series = pd.Series(array) self.assertEqual(Utils.interval_sum(series, 1, step="post"), 1) self.assertEqual(Utils.interval_sum(series, 1, step="pre"), 2) array = [False, False, True, True, True, True, False, False] series = pd.Series(array) self.assertEqual(Utils.interval_sum(series), 4) def test_area_under_curve(self): """Test Utils function: area_under_curve""" array = [0, 0, 2, 2, 2, 1, 1, 1] series = pd.Series(array) # Area under curve post stepping self.assertEqual( Utils.area_under_curve( series, method="rect", step="post"), 8) # Area under curve pre stepping self.assertEqual( Utils.area_under_curve( series, method="rect", step="pre"), 9) array = [1] series = pd.Series(array) # Area under curve post stepping, edge case self.assertEqual( Utils.area_under_curve( series, method="rect", step="post"), 0) # Area under curve pre stepping, edge case self.assertEqual( Utils.area_under_curve( series, method="rect", step="pre"), 0) class TestAnalyzer(unittest.TestCase): def test_assert_statement_bool(self): """Check that asssertStatement() works with a simple boolean case""" rolls_dfr = pd.DataFrame({"results": [1, 3, 2, 6, 2, 4]}) trace = trappy.BareTrace() trace.add_parsed_event("dice_rolls", rolls_dfr) config = {"MAX_DICE_NUMBER": 6} t = Analyzer(trace, config) statement = "numpy.max(dice_rolls:results) <= MAX_DICE_NUMBER" self.assertTrue(t.assertStatement(statement, select=0)) def test_assert_statement_dataframe(self): """assertStatement() works if the generated statement creates a pandas.DataFrame of bools""" rolls_dfr = pd.DataFrame({"results": [1, 3, 2, 6, 2, 4]}) trace = trappy.BareTrace() trace.add_parsed_event("dice_rolls", rolls_dfr) config = {"MIN_DICE_NUMBER": 1, "MAX_DICE_NUMBER": 6} t = Analyzer(trace, config) statement = "(dice_rolls:results <= MAX_DICE_NUMBER) & (dice_rolls:results >= MIN_DICE_NUMBER)" self.assertTrue(t.assertStatement(statement)) statement = "dice_rolls:results == 3" self.assertFalse(t.assertStatement(statement))