def _createPropertyFilters(self) -> None:
self.propertyFilters = list()
if self.subruns.size == 1:
self.propertyFilters.append(None)
else:
for subrun_index in range(self.subruns.size):
subrun_start_time = self.times[2 * subrun_index]
subrun_stop_time = self.times[2 * subrun_index + 1]
# create a Boolean time series property as the filter
time_filter = BoolTimeSeriesProperty('filter')
time_filter.addValue(subrun_start_time, True)
time_filter.addValue(subrun_stop_time, False)
self.propertyFilters.append(time_filter)
def setUp(self):
if self.__class__._source is not None:
return
height = FloatTimeSeriesProperty("height")
height.addValue("2007-11-30T16:17:00", 1)
height.addValue("2007-11-30T16:17:10", 2)
height.addValue("2007-11-30T16:17:20", 3)
height.addValue("2007-11-30T16:17:30", 4)
height.addValue("2007-11-30T16:17:40", 5)
filter = BoolTimeSeriesProperty("filter")
filter.addValue("2007-11-30T16:16:50", False)
filter.addValue("2007-11-30T16:17:25", True)
filter.addValue("2007-11-30T16:17:39", False)
self.__class__._source = height
self.__class__._filter = filter
def setUp(self):
if self._test_ws is not None:
return
alg = run_algorithm('CreateWorkspace',
DataX=[1, 2, 3, 4, 5],
DataY=[1, 2, 3, 4, 5],
NSpec=1,
child=True)
ws = alg.getProperty("OutputWorkspace").value
run = ws.run()
start_time = DateAndTime("2008-12-18T17:58:38")
nanosec = 1000000000
# === Float type ===
temp1 = FloatTimeSeriesProperty("TEMP1")
tempvalue = -0.00161
for i in range(self._ntemp):
temp1.addValue(start_time + i * nanosec, tempvalue)
run.addProperty(temp1.name, temp1, True)
# === Int type ===
raw_frames = Int64TimeSeriesProperty("raw_frames")
values = [17, 1436, 2942, 4448, 5955, 7461]
for value in values:
raw_frames.addValue(start_time + i * nanosec, value)
run.addProperty(raw_frames.name, raw_frames, True)
# === String type ===
icp_event = temp1 = StringTimeSeriesProperty("icp_event")
values = [
'CHANGE_PERIOD 1',
'START_COLLECTION PERIOD 1 GF 0 RF 0 GUAH 0.000000', 'BEGIN',
'STOP_COLLECTION PERIOD 1 GF 1053 RF 1053 GUAH 0.000000 DUR 22'
]
for value in values:
icp_event.addValue(start_time + i * nanosec, value)
run.addProperty(icp_event.name, icp_event, True)
# === Boolean type ===
period_1 = temp1 = BoolTimeSeriesProperty("period 1")
values = [True]
for value in values:
period_1.addValue(start_time + i * nanosec, value)
run.addProperty(period_1.name, period_1, True)
self.__class__._test_ws = ws
def setUp(self):
if self.__class__._source is not None:
return
height = FloatTimeSeriesProperty("height")
height.addValue("2007-11-30T16:17:00",1)
height.addValue("2007-11-30T16:17:10",2)
height.addValue("2007-11-30T16:17:20",3)
height.addValue("2007-11-30T16:17:30",4)
height.addValue("2007-11-30T16:17:40",5)
filter = BoolTimeSeriesProperty("filter")
filter.addValue("2007-11-30T16:16:50",False)
filter.addValue("2007-11-30T16:17:25",True)
filter.addValue("2007-11-30T16:17:39",False)
self.__class__._source = height
self.__class__._filter = filter
def test_addFilter_filters_log(self):
height_log = FloatTimeSeriesProperty("height_log")
height_log.addValue("2008-Jun-17 11:10:44", -0.86526)
height_log.addValue("2008-Jun-17 11:10:45", -1.17843)
height_log.addValue("2008-Jun-17 11:10:47", -1.27995)
height_log.addValue("2008-Jun-17 11:20:15", -1.38216)
height_log.addValue("2008-Jun-17 11:20:16", -1.87435)
height_log.addValue("2008-Jun-17 11:20:17", -2.70547)
height_log.addValue("2008-Jun-17 11:20:19", -2.99125)
height_log.addValue("2008-Jun-17 11:20:20", -3)
height_log.addValue("2008-Jun-17 11:20:27", -2.98519)
height_log.addValue("2008-Jun-17 11:20:29", -2.68904)
period_log = BoolTimeSeriesProperty("period 7")
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:18", False)
period_log.addValue("2008-Jun-17 11:11:30", False)
period_log.addValue("2008-Jun-17 11:11:42", False)
period_log.addValue("2008-Jun-17 11:11:52", False)
period_log.addValue("2008-Jun-17 11:12:01", False)
period_log.addValue("2008-Jun-17 11:12:11", False)
period_log.addValue("2008-Jun-17 11:12:21", True)
period_log.addValue("2008-Jun-17 11:12:32", False)
self.assertEquals(height_log.size(), 10)
filter = LogFilter(height_log)
filter.addFilter(period_log)
filtered = filter.data()
self.assertEquals(filtered.size(), 1)
def test_addFilter_filters_log(self):
height_log = FloatTimeSeriesProperty("height_log");
height_log.addValue("2008-Jun-17 11:10:44", -0.86526)
height_log.addValue("2008-Jun-17 11:10:45", -1.17843)
height_log.addValue("2008-Jun-17 11:10:47", -1.27995)
height_log.addValue("2008-Jun-17 11:20:15", -1.38216)
height_log.addValue("2008-Jun-17 11:20:16", -1.87435)
height_log.addValue("2008-Jun-17 11:20:17", -2.70547)
height_log.addValue("2008-Jun-17 11:20:19", -2.99125)
height_log.addValue("2008-Jun-17 11:20:20", -3);
height_log.addValue("2008-Jun-17 11:20:27", -2.98519)
height_log.addValue("2008-Jun-17 11:20:29", -2.68904)
period_log = BoolTimeSeriesProperty("period 7")
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:13", False)
period_log.addValue("2008-Jun-17 11:11:18", False)
period_log.addValue("2008-Jun-17 11:11:30", False)
period_log.addValue("2008-Jun-17 11:11:42", False)
period_log.addValue("2008-Jun-17 11:11:52", False)
period_log.addValue("2008-Jun-17 11:12:01", False)
period_log.addValue("2008-Jun-17 11:12:11", False)
period_log.addValue("2008-Jun-17 11:12:21", True)
period_log.addValue("2008-Jun-17 11:12:32", False)
self.assertEquals(height_log.size(), 10);
filter = LogFilter(height_log)
filter.addFilter(period_log)
filtered = filter.data()
self.assertEquals(filtered.size(), 1)
