def generateCorrelations(self,doDetrend=True):
# auto correlation corefficient of u
if doDetrend:
ux=signal.detrend(self.ux());
uy=signal.detrend(self.uy());
uz=signal.detrend(self.uz());
umag=signal.detrend(self.Umag());
else:
ux=self.ux();
uy=self.uy();
uz=self.uz();
umag=self.Umag();
#ux=ux[-samples:-1]
#uy=uy[-samples:-1]
#uz=uz[-samples:-1]
self.data['r11'],self.data['taur11'] = tt.xcorr_fft(ux, maxlags=None, norm='coeff')
self.data['r22'],self.data['taur22'] = tt.xcorr_fft(uy, maxlags=None, norm='coeff')
self.data['r33'],self.data['taur33'] = tt.xcorr_fft(uz, maxlags=None, norm='coeff')
self.data['r12'],self.data['taur12'] = tt.xcorr_fft(ux,y=uy, maxlags=None, norm='coeff')
self.data['r13'],self.data['taur13'] = tt.xcorr_fft(ux,y=uz, maxlags=None, norm='coeff')
self.data['r23'],self.data['taur23'] = tt.xcorr_fft(uy,y=uz, maxlags=None, norm='coeff')
self.data['rmag'],self.data['taurmag'] = tt.xcorr_fft(umag, maxlags=None, norm='coeff')
# auto correlation of u
self.data['R11'],self.data['tauR11'] = tt.xcorr_fft(ux, maxlags=None, norm='biased')
self.data['R22'],self.data['tauR22'] = tt.xcorr_fft(uy, maxlags=None, norm='biased')
self.data['R33'],self.data['tauR33'] = tt.xcorr_fft(uz, maxlags=None, norm='biased')
def generateStatistics(self,doDetrend=True):
'''
Generates statistics and populates member variable data.
Arguments:
doDetrend: detrend data bevor sigbal processing
Populates the "data" python dict with with the following keys:
rii: [numpy.array of shape=(?)] Auto-correlation coefficent rii. For i=1,2,3
taurii: [numpy.array of shape=(?)] Time lags for rii. For i=1,2,3
Rii: [numpy.array of shape=(?)] Auto-correlation Rii. For i=1,2,3
tauRii: [numpy.array of shape=(?)] Time lags for Rii. For i=1,2,3
uifrq: [numpy.array of shape=(?)] u1 in frequency domain. For i=1,2,3
uiamp: [numpy.array of shape=(?)] amplitude of u1 in frequency domain. For i=1,2,3
Seiifrq:[numpy.array of shape=(?)] Frequencies for energy spectrum Seii. For i=1,2,3
Seii: [numpy.array of shape=(?)] Energy spectrum Seii derived from Rii. For i=1,2,3
'''
# auto correlation corefficient of u
if doDetrend:
ux=signal.detrend(self.ux());
uy=signal.detrend(self.uy());
uz=signal.detrend(self.uz());
umag=signal.detrend(self.Umag());
else:
ux=self.ux();
uy=self.uy();
uz=self.uz();
umag=self.Umag();
#ux=ux[-samples:-1]
#uy=uy[-samples:-1]
#uz=uz[-samples:-1]
self.data['r11'],self.data['taur11'] = tt.xcorr_fft(ux, maxlags=None, norm='coeff')
self.data['r22'],self.data['taur22'] = tt.xcorr_fft(uy, maxlags=None, norm='coeff')
self.data['r33'],self.data['taur33'] = tt.xcorr_fft(uz, maxlags=None, norm='coeff')
self.data['r12'],self.data['taur12'] = tt.xcorr_fft(ux,y=uy, maxlags=None, norm='coeff')
self.data['r13'],self.data['taur13'] = tt.xcorr_fft(ux,y=uz, maxlags=None, norm='coeff')
self.data['r23'],self.data['taur23'] = tt.xcorr_fft(uy,y=uz, maxlags=None, norm='coeff')
self.data['rmag'],self.data['taurmag'] = tt.xcorr_fft(umag, maxlags=None, norm='coeff')
# auto correlation of u
self.data['R11'],self.data['tauR11'] = tt.xcorr_fft(ux, maxlags=None, norm='none')
self.data['R22'],self.data['tauR22'] = tt.xcorr_fft(uy, maxlags=None, norm='none')
self.data['R33'],self.data['tauR33'] = tt.xcorr_fft(uz, maxlags=None, norm='none')
#u in frequency domain
self.data['u1frq'],self.data['u1amp'] = tt.dofft(sig=ux,samplefrq=self.data['frq'])
self.data['u2frq'],self.data['u2amp'] = tt.dofft(sig=uy,samplefrq=self.data['frq'])
self.data['u3frq'],self.data['u3amp'] = tt.dofft(sig=uz,samplefrq=self.data['frq'])
#Time energy sectrum Se11 (mean: Rii in frequency domain...)
self.data['Se11frq'],self.data['Se11'] = tt.dofft(sig=self.data['R11'],samplefrq=self.data['frq'])
self.data['Se22frq'],self.data['Se22'] = tt.dofft(sig=self.data['R22'],samplefrq=self.data['frq'])
self.data['Se33frq'],self.data['Se33'] = tt.dofft(sig=self.data['R33'],samplefrq=self.data['frq'])
def generateAutoCorrelations(self,doDetrend=True):
# auto correlation corefficient of u
if doDetrend:
ux=signal.detrend(self.ux());
uy=signal.detrend(self.uy());
uz=signal.detrend(self.uz());
else:
ux=self.ux();
uy=self.uy();
uz=self.uz();
self.data['r11'],self.data['taur11'] = tt.xcorr_fft(ux, maxlags=None, norm='coeff')
self.data['r22'],self.data['taur22'] = tt.xcorr_fft(uy, maxlags=None, norm='coeff')
self.data['r33'],self.data['taur33'] = tt.xcorr_fft(uz, maxlags=None, norm='coeff')
def generateCorrelations(self, doDetrend=True):
# auto correlation corefficient of u
if doDetrend:
ux = signal.detrend(self.ux())
uy = signal.detrend(self.uy())
uz = signal.detrend(self.uz())
umag = signal.detrend(self.Umag())
else:
ux = self.ux()
uy = self.uy()
uz = self.uz()
umag = self.Umag()
#ux=ux[-samples:-1]
#uy=uy[-samples:-1]
#uz=uz[-samples:-1]
self.data['r11'], self.data['taur11'] = tt.xcorr_fft(ux,
maxlags=None,
norm='coeff')
self.data['r22'], self.data['taur22'] = tt.xcorr_fft(uy,
maxlags=None,
norm='coeff')
self.data['r33'], self.data['taur33'] = tt.xcorr_fft(uz,
maxlags=None,
norm='coeff')
self.data['r12'], self.data['taur12'] = tt.xcorr_fft(ux,
y=uy,
maxlags=None,
norm='coeff')
self.data['r13'], self.data['taur13'] = tt.xcorr_fft(ux,
y=uz,
maxlags=None,
norm='coeff')
self.data['r23'], self.data['taur23'] = tt.xcorr_fft(uy,
y=uz,
maxlags=None,
norm='coeff')
self.data['rmag'], self.data['taurmag'] = tt.xcorr_fft(umag,
maxlags=None,
norm='coeff')
# auto correlation of u
self.data['R11'], self.data['tauR11'] = tt.xcorr_fft(ux,
maxlags=None,
norm='biased')
self.data['R22'], self.data['tauR22'] = tt.xcorr_fft(uy,
maxlags=None,
norm='biased')
self.data['R33'], self.data['tauR33'] = tt.xcorr_fft(uz,
maxlags=None,
norm='biased')
def generateAutoCorrelations(self, doDetrend=True):
# auto correlation corefficient of u
if doDetrend:
ux = signal.detrend(self.ux())
uy = signal.detrend(self.uy())
uz = signal.detrend(self.uz())
else:
ux = self.ux()
uy = self.uy()
uz = self.uz()
self.data['r11'], self.data['taur11'] = tt.xcorr_fft(ux,
maxlags=None,
norm='coeff')
self.data['r22'], self.data['taur22'] = tt.xcorr_fft(uy,
maxlags=None,
norm='coeff')
self.data['r33'], self.data['taur33'] = tt.xcorr_fft(uz,
maxlags=None,
norm='coeff')