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)