def testDetermineAggregationWindow(self):
"""
Verify aggregation window can be determined from the cwtVar distribution
"""
weekPeriod = 604800.0
samplingIntervalInS = numpy.timedelta64(300, 's')
widths = numpy.logspace(0, numpy.log10(40000 / 20), 50)
timeScaleInS = widths * samplingIntervalInS * 4
timeScaleInS = timeScaleInS.astype('timedelta64[s]')
ts = timeScaleInS.astype('float64')
cwtVar = numpy.exp(-(ts - weekPeriod)**2 / (2 * 100000.0**2))
numDataPts = 40000
maxSamplingInterval = (float(numDataPts) / 1000.0 *
samplingIntervalInS)
maxSamplingInterval = maxSamplingInterval.item().total_seconds()
timeScaleInMs = timeScaleInS.astype('timedelta64[ms]')
samplingIntervalInMs = samplingIntervalInS.astype('timedelta64[ms]')
aggregationTimeScaleInMs = param_finder._determineAggregationWindow(
timeScale=timeScaleInMs,
cwtVar=cwtVar,
thresh=0.2,
samplingInterval=samplingIntervalInMs,
numDataPts=40000)
aggregationTimeScaleInS = aggregationTimeScaleInMs.item(
).total_seconds()
self.assertLessEqual(aggregationTimeScaleInS, maxSamplingInterval)
self.assertGreater(aggregationTimeScaleInS,
samplingIntervalInS.astype('float64'))
# if the numDataPts < MIN_ROW_AFTER_AGGREGATION, no aggregation should occur
aggregationTimeScaleInMs = param_finder._determineAggregationWindow(
timeScale=timeScaleInMs,
cwtVar=cwtVar,
thresh=0.2,
samplingInterval=samplingIntervalInMs,
numDataPts=param_finder.MIN_ROW_AFTER_AGGREGATION - 1)
aggregationTimeScaleInS = aggregationTimeScaleInMs.item(
).total_seconds()
self.assertEqual(aggregationTimeScaleInS,
samplingIntervalInS.astype('float64'))
def testDetermineAggregationWindow(self):
"""
Verify aggregation window can be determined from the cwtVar distribution
"""
weekPeriod = 604800.0
samplingIntervalInS = numpy.timedelta64(300, "s")
widths = numpy.logspace(0, numpy.log10(40000 / 20), 50)
timeScaleInS = widths * samplingIntervalInS * 4
timeScaleInS = timeScaleInS.astype("timedelta64[s]")
ts = timeScaleInS.astype("float64")
cwtVar = numpy.exp(-(ts - weekPeriod) ** 2 / (2 * 100000.0 ** 2))
numDataPts = 40000
maxSamplingInterval = float(numDataPts) / 1000.0 * samplingIntervalInS
maxSamplingInterval = maxSamplingInterval.item().total_seconds()
timeScaleInMs = timeScaleInS.astype("timedelta64[ms]")
samplingIntervalInMs = samplingIntervalInS.astype("timedelta64[ms]")
aggregationTimeScaleInMs = param_finder._determineAggregationWindow(
timeScale=timeScaleInMs, cwtVar=cwtVar, thresh=0.2, samplingInterval=samplingIntervalInMs, numDataPts=40000
)
aggregationTimeScaleInS = aggregationTimeScaleInMs.item().total_seconds()
self.assertLessEqual(aggregationTimeScaleInS, maxSamplingInterval)
self.assertGreater(aggregationTimeScaleInS, samplingIntervalInS.astype("float64"))
# if the numDataPts < MIN_ROW_AFTER_AGGREGATION, no aggregation should occur
aggregationTimeScaleInMs = param_finder._determineAggregationWindow(
timeScale=timeScaleInMs,
cwtVar=cwtVar,
thresh=0.2,
samplingInterval=samplingIntervalInMs,
numDataPts=param_finder.MIN_ROW_AFTER_AGGREGATION - 1,
)
aggregationTimeScaleInS = aggregationTimeScaleInMs.item().total_seconds()
self.assertEqual(aggregationTimeScaleInS, samplingIntervalInS.astype("float64"))
def testDetermineAggregationWindow(self):
"""
Verify aggregation window can be determined from the cwtVar distribution
"""
weekPeriod = 604800.0
samplingInterval = numpy.timedelta64(300, 's')
widths = numpy.logspace(0, numpy.log10(40000 / 20), 50)
timeScale = widths * samplingInterval * 4
timeScale = timeScale.astype('float64')
cwtVar = numpy.exp(-(timeScale - weekPeriod) ** 2 / (2 * 100000.0 ** 2))
numDataPts = 40000
maxSamplingInterval = (float(numDataPts) / 1000.0 * samplingInterval)
maxSamplingInterval = maxSamplingInterval.item().total_seconds()
aggregationTimeScale = param_finder._determineAggregationWindow(
timeScale=timeScale,
cwtVar=cwtVar,
thresh=0.2,
samplingInterval=samplingInterval,
numDataPts=40000
)
aggregationTimeScale = aggregationTimeScale.item().total_seconds()
self.assertLessEqual(aggregationTimeScale, maxSamplingInterval)
self.assertGreater(aggregationTimeScale, samplingInterval.astype('float64'))
# if the numDataPts < MIN_ROW_AFTER_AGGREGATION, no aggregation should occur
aggregationTimeScale = param_finder._determineAggregationWindow(
timeScale=timeScale,
cwtVar=cwtVar,
thresh=0.2,
samplingInterval=samplingInterval,
numDataPts=param_finder.MIN_ROW_AFTER_AGGREGATION-1
)
aggregationTimeScale = aggregationTimeScale.item().total_seconds()
self.assertEqual(aggregationTimeScale, samplingInterval.astype('float64'))
def testDetermineAggregationWindow(self):
"""
Verify aggregation window can be determined from the cwtVar distribution
"""
dayPeriod = 86400.0
weekPeriod = 604800.0
samplingInterval = numpy.timedelta64(300, 's')
widths = numpy.logspace(0, numpy.log10(40000 / 20), 50)
timeScale = widths * samplingInterval * 4
timeScale = timeScale.astype('float64')
cwtVar = numpy.exp(-(timeScale - weekPeriod) ** 2 / (2 * 100000.0 ** 2))
numDataPts = 40000
maxSamplingInterval = (float(numDataPts) / 1000.0 * samplingInterval)
aggregationTimeScale = param_finder._determineAggregationWindow(
timeScale=timeScale,
cwtVar=cwtVar,
thresh=0.2,
samplingInterval=samplingInterval,
numDataPts=40000
)
self.assertLessEqual(aggregationTimeScale, maxSamplingInterval)
self.assertGreater(aggregationTimeScale, samplingInterval.astype('float64'))
