def test_get_undershoot(self):
"""Test get_undershoot"""
A = [0, 0, 0, 1, 1, 1, 1, 1]
B = [2, 2, 2, 2, 2, 2, 2, 2]
trace = trappy.BareTrace()
df = pd.DataFrame({"A": A, "B": B})
trace.add_parsed_event("event", df)
s = SignalCompare(trace, "event:A", "event:B")
expected = (4.0 / 14.0, 1.0)
self.assertEqual(s.get_undershoot(method="rect"), expected)
A = [3, 3, 3, 3, 3, 3, 3, 3]
B = [2, 2, 2, 2, 2, 2, 1, 1]
df = pd.DataFrame({"A": A, "B": B})
trace.event.data_frame = df
s = SignalCompare(trace, "event:A", "event:B")
expected = (float("nan"), 0.0)
result = s.get_undershoot(method="rect")
self.assertTrue(np.isnan(result[0]))
self.assertEqual(result[1], expected[1])
def test_barplot_trace(self):
"""BarPlot plots traces without exploding"""
trace = trappy.BareTrace()
trace.add_parsed_event("event", self.dfr)
trappy.BarPlot(trace, signals=["event:foo",
"event:bar"]).view(test=True)
def setUp(self):
trace = trappy.BareTrace()
data = {
"identifier": [
0,
0,
0,
1,
1,
1,
],
"result": [
"fire",
"blank",
"fire",
"blank",
"fire",
"blank",
],
}
index = pd.Series([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], name="Time")
data_frame = pd.DataFrame(data, index=index)
trace.add_parsed_event("aim_and_fire", data_frame)
self._trace = trace
self.topology = Topology(clusters=[[0], [1]])
def test_exp_ops(self):
"""Test exponentiation: Numeric"""
parser = Parser(trappy.BareTrace())
eqn = "3**3 * 2**4"
self.assertEqual(parser.solve(eqn), 432)
eqn = "3**(4/2)"
self.assertEqual(parser.solve(eqn), 9)
def test_bare_trace_get_duration(self):
"""BareTrace.get_duration() works for a simple case"""
trace = trappy.BareTrace()
trace.add_parsed_event("pmu_counter", self.dfr[0])
trace.add_parsed_event("load_event", self.dfr[1])
self.assertEquals(trace.get_duration(), self.dfr[1].index[-1])
def test_sum_operator(self):
"""Test Addition And Subtraction: Numeric"""
parser = Parser(trappy.BareTrace())
# Simple equation
eqn = "10 + 2 - 3"
self.assertEqual(parser.solve(eqn), 9)
# Equation with bracket and unary ops
eqn = "(10 + 2) - (-3 + 2)"
self.assertEqual(parser.solve(eqn), 13)
def test_bare_trace_can_add_parsed_event(self):
"""The BareTrace() class can add parsed events to its collection of trace events"""
trace = trappy.BareTrace()
trace.add_parsed_event("pmu_counters", self.dfr[0])
self.assertEquals(len(trace.pmu_counters.data_frame), 3)
self.assertEquals(trace.pmu_counters.data_frame["l1_misses"].iloc[0],
24)
trace.add_parsed_event("pivoted_counters", self.dfr[0], pivot="cpu")
self.assertEquals(trace.pivoted_counters.pivot, "cpu")
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_mul_ops(self):
"""Test Mult and Division: Numeric"""
parser = Parser(trappy.BareTrace())
eqn = "(10 * 2 / 10)"
self.assertEqual(parser.solve(eqn), 2)
eqn = "-2 * 2 + 2 * 10 / 10"
self.assertEqual(parser.solve(eqn), -2)
eqn = "3.5 // 2"
self.assertEqual(parser.solve(eqn), 1)
eqn = "5 % 2"
self.assertEqual(parser.solve(eqn), 1)
def test_bare_trace_get_duration_normalized(self):
"""BareTrace.get_duration() works if the trace has been normalized"""
trace = trappy.BareTrace()
trace.add_parsed_event("pmu_counter", self.dfr[0].copy())
trace.add_parsed_event("load_event", self.dfr[1].copy())
basetime = self.dfr[0].index[0]
trace.normalize_time(basetime)
expected_duration = self.dfr[1].index[-1] - basetime
self.assertEquals(trace.get_duration(), expected_duration)
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))
def test_bare_trace_normalize_time_accepts_basetime(self):
"""BareTrace().normalize_time() accepts an arbitrary basetime"""
trace = trappy.BareTrace()
trace.add_parsed_event("pmu_counter", self.dfr[0].copy())
prev_first_time = trace.pmu_counter.data_frame.index[0]
basetime = 3
trace.normalize_time(basetime)
self.assertEquals(trace.basetime, basetime)
exp_first_time = prev_first_time - basetime
self.assertEquals(
round(trace.pmu_counter.data_frame.index[0] - exp_first_time, 7),
0)
def test_conditional_compare(self):
"""Test conditional_compare"""
# Refer to the example in
# bart.common.signal.SignalCompare.conditional_compare
# doc-strings which explains the calculation for the
# data set below
A = [0, 0, 0, 3, 3, 0, 0, 0]
B = [0, 0, 2, 2, 2, 2, 1, 1]
trace = trappy.BareTrace()
df = pd.DataFrame({"A": A, "B": B})
trace.add_parsed_event("event", df)
s = SignalCompare(trace, "event:A", "event:B")
expected = (1.5, 2.0 / 7)
self.assertEqual(
s.conditional_compare("event:A > event:B", method="rect"),
expected)
def test_filter_prev_values(self):
"""Trigger works with a filter that depends on previous values of the same pivot"""
# We generate an example in which we want a trigger whenever the
# identifier is no longer 1 for blank
class my_filter(object):
def __init__(self, val_out):
self.prev_val = 0
self.val_out = val_out
def __call__(self, val):
ret = self.prev_val == self.val_out
self.prev_val = val
return ret
trace = trappy.BareTrace()
data = collections.OrderedDict([
(0.1, ["blank", 1]),
(0.2, ["fire", 1]),
(0.3, ["blank", 0]), # value is no longer 1, trigger
(0.4, ["blank", 1]),
(0.5, ["fire", 0]), # This should NOT trigger
(0.6, ["blank", 0]), # value is no longer 1 for blank, trigger
])
data_frame = pd.DataFrame.from_dict(
data,
orient="index",
)
data_frame.columns = ["result", "identifier"]
trace.add_parsed_event("aim_and_fire", data_frame)
trigger = Trigger(trace,
trace.aim_and_fire,
filters={"identifier": my_filter(1)},
value=-1,
pivot="result")
expected = pd.Series([-1, -1], index=[0.3, 0.6])
assert_series_equal(expected, trigger.generate("blank"))
def test_bare_trace_accepts_name(self):
"""The BareTrace() accepts a name parameter"""
trace = trappy.BareTrace(name="foo")
self.assertEquals(trace.name, "foo")
