def get_live_timeseries():
ts = TimeSeries()
ts.set_live()
ts.dt = 1 / sampling_rate
ts.npts = ts_size
# ts.put('net', 'IU')
ts.put('npts', ts_size)
ts.put('sampling_rate', sampling_rate)
ts.tref = TimeReferenceType.UTC
ts.t0 = datetime.utcnow().timestamp()
ts['delta'] = 0.1
ts['calib'] = 0.1
ts['site_id'] = bson.objectid.ObjectId()
ts['channel_id'] = bson.objectid.ObjectId()
ts['source_id'] = bson.objectid.ObjectId()
ts.set_as_origin('test', '0', '0', AtomicType.TIMESERIES)
ts.data = DoubleVector(np.random.rand(ts_size))
return ts
def get_live_timeseries():
ts = TimeSeries()
ts.set_live()
ts.dt = 1 / sampling_rate
ts.npts = ts_size
# ts.put('net', 'IU')
ts.put("npts", ts_size)
ts.put("sampling_rate", sampling_rate)
ts.tref = TimeReferenceType.UTC
ts.t0 = datetime.utcnow().timestamp()
ts["delta"] = 0.1
ts["calib"] = 0.1
ts["site_id"] = bson.objectid.ObjectId()
ts["channel_id"] = bson.objectid.ObjectId()
ts["source_id"] = bson.objectid.ObjectId()
ts.set_as_origin("test", "0", "0", AtomicType.TIMESERIES)
ts.data = DoubleVector(np.random.rand(ts_size))
return ts
def test_scale():
dts=_CoreTimeSeries(9)
dir=setbasics(dts,9)
d3c=_CoreSeismogram(5)
setbasics(d3c,5)
dts.data[0]=3.0
dts.data[1]=2.0
dts.data[2]=-4.0
dts.data[3]=1.0
dts.data[4]=-100.0
dts.data[5]=-1.0
dts.data[6]=5.0
dts.data[7]=1.0
dts.data[8]=-6.0
# MAD o=f above should be 2
# perf of 0.8 should be 4
# rms should be just over 10=10.010993957
print('Starting tests for time series data of amplitude functions')
ampmad=MADAmplitude(dts)
print('MAD amplitude estimate=',ampmad)
assert(ampmad==3.0)
amprms=RMSAmplitude(dts)
print('RMS amplitude estimate=',amprms)
assert(round(amprms,2)==100.46)
amppeak=PeakAmplitude(dts)
ampperf80=PerfAmplitude(dts,0.8)
print('Peak amplitude=',amppeak)
print('80% clip level amplitude=',ampperf80)
assert(amppeak==100.0)
assert(ampperf80==6.0)
print('Starting comparable tests for 3c data')
d3c.data[0,0]=3.0
d3c.data[0,1]=2.0
d3c.data[1,2]=-4.0
d3c.data[2,3]=1.0
d3c.data[0,4]=np.sqrt(2)*(100.0)
d3c.data[1,4]=-np.sqrt(2)*(100.0)
ampmad=MADAmplitude(d3c)
print('MAD amplitude estimate=',ampmad)
amprms=RMSAmplitude(d3c)
print('RMS amplitude estimate=',amprms)
amppeak=PeakAmplitude(d3c)
ampperf60=PerfAmplitude(d3c,0.6)
print('Peak amplitude=',amppeak)
print('60% clip level amplitude=',ampperf60)
assert(amppeak==200.0)
assert(ampperf60==4.0)
assert(ampmad==3.0)
amptest=round(amprms,2)
assert(amptest==89.48)
print('Trying scaling functions for TimeSeries')
# we need a deep copy here since scaling changes the data
d2=TimeSeries(dts)
amp=_scale(d2,ScalingMethod.Peak,1.0)
print('Computed peak amplitude=',amp)
print(d2.data)
d2=TimeSeries(dts)
amp=_scale(d2,ScalingMethod.Peak,10.0)
print('Computed peak amplitude with peak set to 10=',amp)
print(d2.data)
assert(amp==100.0)
assert(d2.data[4]==-10.0)
print('verifying scale has modified and set calib correctly')
calib=d2.get_double('calib')
assert(calib==10.0)
d2=TimeSeries(dts)
d2.put('calib',6.0)
print('test 2 with MAD metric and initial calib of 6')
amp=_scale(d2,ScalingMethod.MAD,1.0)
calib=d2.get_double('calib')
print('New calib value set=',calib)
assert(calib==18.0)
print('Testing 3C scale functions')
d=Seismogram(d3c)
amp=_scale(d,ScalingMethod.Peak,1.0)
print('Peak amplitude returned by scale funtion=',amp)
calib=d.get_double('calib')
print('Calib value retrieved (assumed inital 1.0)=',calib)
print('Testing python scale function wrapper - first on a TimeSeries with defaults')
d2=TimeSeries(dts)
amp=scale(d2)
print('peak amplitude returned =',amp[0])
assert(amp[0]==100.0)
d=Seismogram(d3c)
amp=scale(d)
print('peak amplitude returned test Seismogram=',amp[0])
assert(amp[0]==200.0)
print('starting tests of scale on ensembles')
print('first test TimeSeriesEnemble with 5 scaled copies of same vector used earlier in this test')
ens=TimeSeriesEnsemble()
scls=[2.0,4.0,1.0,10.0,5.0] # note 4 s the median of this vector
npts=dts.npts
for i in range(5):
d=TimeSeries(dts)
for k in range(npts):
d.data[k]*=scls[i]
d.put('calib',1.0)
ens.member.append(d)
# work on a copy because scaling alters data in place
enscpy=TimeSeriesEnsemble(ens)
amps=scale(enscpy)
print('returned amplitudes for members scaled individually')
for i in range(5):
print(amps[i])
assert(amps[i]==100.0*scls[i])
enscpy=TimeSeriesEnsemble(ens)
amp=scale(enscpy,scale_by_section=True)
print('average amplitude=',amp[0])
#assert(amp[0]==4.0)
avgamp=amp[0]
for i in range(5):
calib=enscpy.member[i].get_double("calib")
print('member number ',i,' calib is ',calib)
assert(round(calib)==400.0)
#print(enscpy.member[i].data)
# similar test for SeismogramEnsemble
npts=d3c.npts
ens=SeismogramEnsemble()
for i in range(5):
d=Seismogram(d3c)
for k in range(3):
for j in range(npts):
d.data[k,j]*=scls[i]
d.put('calib',1.0)
ens.member.append(d)
print('Running comparable tests on SeismogramEnsemble')
enscpy=SeismogramEnsemble(ens)
amps=scale(enscpy)
print('returned amplitudes for members scaled individually')
for i in range(5):
print(amps[i])
assert(round(amps[i])==round(200.0*scls[i]))
print('Trying section scaling of same data')
enscpy=SeismogramEnsemble(ens)
amp=scale(enscpy,scale_by_section=True)
print('average amplitude=',amp[0])
assert(round(amp[0])==800.0)
avgamp=amp[0]
for i in range(5):
calib=enscpy.member[i].get_double("calib")
print('member number ',i,' calib is ',calib)
assert(round(calib)==800.0)