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')
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 corrVert(f1, f2=None, i_ref=0, j_ref=0, norm=True):
'''
Do the two point vertical correlation from a reference point
pt(i_ref,j_ref). The field has dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Vertical location of the horizontal line. Default=0
*j_ref*: python int.
Vertical location of the horizontal line. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*lags_l*: numpy array.
Array of left/negative lags.
*res_l*: numpy array.
Array of left/negative results.
*lags_r*: numpy array.
Array of right/positive lags.
*res_r*: numpy array.
Array of right/positive results.
'''
f1sum = np.sum(f1, axis=0)
if f2 == None:
f2 = f1
f2sum = f1sum
else:
f2sum = np.sum(f2, axis=0)
res = np.empty(f1[0].shape[0], dtype=float)
res[:] = np.nan
k, dx, dy = f1.shape
for i in range(dx):
if np.isnan(f1sum[i_ref, j_ref]) or np.isnan(f2sum[i_ref, j_ref]):
res[i] = np.nan
else:
res[i] = tt.twoPointCorr(x=f1[:, i_ref, j_ref],
y=f2[:, i, j_ref],
norm=True)
if norm == True:
res = res / res[i_ref]
res_l = res[:i_ref + 1][::-1]
res_r = res[i_ref:]
lags_l = np.arange(len(res_l))
lags_r = np.arange(len(res_r))
return lags_l, res_l, lags_r, res_r
def corrVert(f1,f2=None,i_ref=0,j_ref=0,norm=True):
'''
Do the two point vertical correlation from a reference point
pt(i_ref,j_ref). The field has dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Vertical location of the horizontal line. Default=0
*j_ref*: python int.
Vertical location of the horizontal line. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*lags_l*: numpy array.
Array of left/negative lags.
*res_l*: numpy array.
Array of left/negative results.
*lags_r*: numpy array.
Array of right/positive lags.
*res_r*: numpy array.
Array of right/positive results.
'''
f1sum=np.sum(f1,axis=0)
if f2==None:
f2=f1
f2sum=f1sum
else:
f2sum=np.sum(f2,axis=0)
res=np.empty(f1[0].shape[0], dtype=float)
res[:] = np.nan
k,dx,dy=f1.shape
for i in range(dx):
if np.isnan(f1sum[i_ref,j_ref]) or np.isnan(f2sum[i_ref,j_ref]):
res[i]=np.nan
else:
res[i]=tt.twoPointCorr(x=f1[:,i_ref,j_ref],y=f2[:,i,j_ref],norm=True)
if norm==True:
res=res/res[i_ref]
res_l=res[:i_ref+1][::-1]
res_r=res[i_ref:]
lags_l=np.arange(len(res_l))
lags_r=np.arange(len(res_r))
return lags_l,res_l,lags_r,res_r
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 fitExp(r11,maxlag=None):
if maxlag==None:
maxlag=len(r11)
xdata=np.arange(len(r11[:maxlag]))
ydata=np.array(r11[:maxlag])
try:
popt, pcov = tt.fit_exp_correlation(xdata,ydata)
T=popt
except RuntimeError:
print("Error - curve_fit failed")
T=0
return T
def fitExp(r11, maxlag=None):
if maxlag == None:
maxlag = len(r11)
xdata = np.arange(len(r11[:maxlag]))
ydata = np.array(r11[:maxlag])
try:
popt, pcov = tt.fit_exp_correlation(xdata, ydata)
T = popt
except RuntimeError:
print("Error - curve_fit failed")
T = 0
return T
def generateSpectra(self, doDetrend=True):
'''
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
'''
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()
#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 corrField(f1, f2=None, i_ref=0, j_ref=0, norm=True):
'''
Two point correlation of an entire field with the point pt(i_ref,j_ref) as
reference. The field has the dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Horizontal location of the reference point. Default=0
*j_ref*: python int.
Vertical location of the reference point. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*res*: np.array of shape (N,M).
the two point horizontal correlation.
'''
f1sum = np.sum(f1, axis=0)
if f2 == None:
f2 = f1
f2sum = f1sum
else:
f2sum = np.sum(f2, axis=0)
res = np.empty(f1[0].shape, dtype=float)
res[:] = np.nan
k, dx, dy = f1.shape
if np.isnan(f1sum[i_ref, j_ref]):
res[i_ref, j_ref] = np.nan
else:
for i in range(dx):
for j in range(dy):
if np.isnan(f2sum[i, j]):
res[i, j] = np.nan
else:
res[i, j] = tt.twoPointCorr(x=f1[:, i_ref, j_ref],
y=f2[:, i, j],
norm=True)
if norm == True:
return res / res[i_ref, j_ref]
else:
return res
def corrField(f1,f2=None,i_ref=0,j_ref=0,norm=True):
'''
Two point correlation of an entire field with the point pt(i_ref,j_ref) as
reference. The field has the dimension of NxM with T time realization.
Arguments:
*f1*: np.array of shape (T,N,M).
In general, one of the fields (vx,vy or vz) of a
pyFlowStat.SurfaceTimeSeries object.
*f2*: np.array of shape (T,N,M).
Same as f1. If None, then f1 is used has second field. Default=None
*i_ref*: python int.
Horizontal location of the reference point. Default=0
*j_ref*: python int.
Vertical location of the reference point. Default=0
*Norm*: python bool.
Normalization of the correlation. Default=True.
Returns:
*res*: np.array of shape (N,M).
the two point horizontal correlation.
'''
f1sum=np.sum(f1,axis=0)
if f2==None:
f2=f1
f2sum=f1sum
else:
f2sum=np.sum(f2,axis=0)
res=np.empty(f1[0].shape, dtype=float)
res[:] = np.nan
k,dx,dy=f1.shape
if np.isnan(f1sum[i_ref,j_ref]):
res[i_ref,j_ref] = np.nan
else:
for i in range(dx):
for j in range(dy):
if np.isnan(f2sum[i,j]):
res[i,j]=np.nan
else:
res[i,j]=tt.twoPointCorr(x=f1[:,i_ref,j_ref],y=f2[:,i,j],norm=True)
if norm==True:
return res/res[i_ref,j_ref]
else:
return res
def doAutoCorr(xkey,ykey,Tkey,Lkey=None,threshold=0.1):
xdata=self.data[xkey]
ydata=self.data[ykey]
if checkAutoCorr(xdata,ydata,threshold=threshold):
try:
popt, pcov = tt.fit_exp_correlation(xdata,ydata)
#self.data['Txx']=abs(popt[0])*np.sqrt(np.pi)*0.5*self.data['dt']
self.data[Tkey]=popt[0]*self.data['dt']
if Lkey:
self.data[Lkey]=self.data[Tkey]*self.Umean()
except RuntimeError:
print("Error - curve_fit failed")
self.data[Tkey]=0
if Lkey:
self.data[Lkey]=0
else:
self.data[Tkey]=0
if Lkey:
self.data[Lkey]=0
def generateSpectra(self,doDetrend=True):
'''
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
'''
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();
#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 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'])
