def spectraAnalysis(fig, v , time, vName, Nbins, mode, normalize=False ):
nterms = np.shape(v) # Number of variables in the file, number of entries in the data.
print(' Number of terms in the {} data series, N = {}'.format(vName, nterms))
# Determine the sampling frequency.
samplingFreq = samplingFrequency( time, None )
print(' sampling frequency = {}'.format(samplingFreq))
deltaT = (time[-1]-time[0])
vw = applyTapering( v , deltaT , samplingFreq )
# Evaluate, Power (P), power spectral energy (E), and power spectral density (S).
P, E, S, freqs = evalSpectra( vw, samplingFreq, normalize )
if(mode == 'S'):
Sbin, fbin = frequencyBins( S , freqs, Nbins )
Vbin = Sbin
if(mode == 'E'):
Ebin, fbin = frequencyBins( E , freqs, Nbins )
Vbin = Ebin
if(mode == 'P'):
Pbin, fbin = frequencyBins( P , freqs, Nbins )
Vbin = Pbin
Refbin = 1.E-1 * fbin[Nbins/2:]**(-5./3.)
if( mode == 'S' ):
if( normalize ):
labelStr = "{}, normalized power spectral density".format(vName)
plotStr = [ "Normalized power spectral density","Frequency [Hz]","f*S/$\sigma^2$ "]
else:
labelStr = "{}, power spectral density: $\Phi(f)$".format(vName)
plotStr = ["Power spectral density" ,"Frequency [Hz]","$\Phi(f)$"]
elif( mode == 'E' ):
labelStr = "{}, energy spectrum: E(f)".format(vName)
plotStr = ["Energy spectrum","Frequency [Hz]","E(f)"]
else:
labelStr = "{}, power spectrum: P(f)".format(vName)
plotStr = ["Power spectrum","Frequency [Hz]","P(f)"]
if( fig is None ):
fig = plt.figure(num=1, figsize=(12.,10.))
fig = addToPlot(fig,fbin[Nbins/2:],np.nanmean(Vbin)*Refbin,"Model -5/3 curve",plotStr,logOn=True)
fig = addToPlot(fig, fbin, Vbin, labelStr, plotStr, logOn=True)
return fig
# =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
# =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
# =*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*
Re_t[i], Re_d[i]))
elif (isinstance(u, np.float)):
print(dStr.format(u, l, nu, t, epsilon, v, eta, Re_t, Re_d))
# The log-law profile for the velocity
z = np.linspace(0., l, int(l / dz))
kappa = 0.41
# Um := mean(U)/u*
Um = us / kappa * np.log(np.maximum((z - d) / z0, 1.))
if (printOn):
ufig = plt.figure(1)
ufig = addToPlot(ufig,
Um,
z,
'<U>', ["Log-law Velocity profile", "<U>", "z"],
logOn=False)
plt.show()
dUm = Um[1:] - Um[:-1]
dz = z[1:] - z[:-1]
zm = z[:-1]
dUmdz = dUm / dz
print('u(z):\n' + ' '.join('{:.2f},'.format(uv) for uv in Um[::c]))
print('z :\n' + ' '.join('{},'.format(zi) for zi in z[::c].astype(int)))
print('v(z):\n' + ' '.join('{},'.format(v) for v in np.zeros(len(z[::c]))))
print('dudz(z):\n' + ' '.join('{:.3f},'.format(s * duz) for duz in dUmdz[::c]))
print('z :\n' + ' '.join('{},'.format(zim) for zim in zm[::c].astype(int)))
# ======= Write 100% F_km ================
f_vtk = vtkWritePointDataStructured2D( f_vtk, F_km, Xt, 'fp_km' )
# ======= Write 00% F_km ================
Ftmp[:,:] = 0.; Ftmp += F_km*id90_km
f_vtk = vtkWritePointDataStructured2D( f_vtk, Ftmp , Xt, 'fp90_km' )
# Close the file at the end.
f_vtk.close(); Ftmp = None
if( printOn ):
CfD = dict()
CfD['title']='F(x,y)'; CfD['label']=fileout; CfD['N']=16
Cfp = addContourf( Xt, Yt, Ft , CfD )
CfD['title']='F_km(x,y), Ana'; CfD['label']=fileout+'_km'
Cfa = addContourf( Xt, Yt, F_km, CfD )
Fym = writeCrossWindSum( Ft, Xt, None, None )
pfig = plt.figure(num=3, figsize=(12.,9.))
varLabel = '$fp_y(x) = \sum_y fp(x,y)$'
axLabels = ['Cross Wind Integrated Footprint', 'x', 'sum_y fp(x,y) ']
pfig = addToPlot(pfig, Xt[0,:], Fym, varLabel, axLabels, False )
Ft = Zt = Xt = Yt = None
F_km = None
plt.show()
# Process data vr --> vp
if( mode == 'mean'):
vp = np.mean( vr, axis=axs ); zp = z
if( vr2 is not None ): vp2 = np.mean( vr2, axis=axs )
plotStr = ["mean({}) vs z ".format(varname), varname ,"z"]
elif( mode == 'std'):
vp = np.std( vr, axis=axs ); zp = z
if( vr2 is not None ): vp2 = np.std( vr2, axis=axs )
if(meanErrorOn):
N = len( vr[:,0,0,0] )
vmerr = vp/np.sqrt(N)
if( len(vmerr.shape) == 3 ): vmerr = vmerr[:,0,0]
plotStr = ["std. error of mean({}) vs z ".format(varname), varname ,"z"]
fig = addToPlot(fig, vmerr, zp,'{}({}), {}'\
.format('std error of mean',varname,fileList[fn].split('_')[-1]), plotStr, False )
'''
N2 = len( vr2[:,0,0,0] )
vmerr2 = vp2/np.sqrt(N2)
if( len(vmerr2.shape) == 3 ): vmerr2 = vmerr2[:,0,0]
fig = addToPlot(fig, vmerr2, zp,'{}({}), {}'\
.format('std error of mean',varname,fileList[fn]), plotStr, False )
'''
plotStr = ["std({}) vs z ".format(varname), varname ,"z"]
elif( mode == 'var' ):
vp = np.var( vr, axis=axs ); zp = z
if( vr2 is not None ): vp2 = np.var( vr2, axis=axs )
plotStr = ["var({}) vs z ".format(varname), varname ,"z"]
for iy in iyList:
# Read data
y, xdict = readVariableFromDataset(varList[iy], ds, 1)
if ('time' not in list(xdict.keys())):
print(' time not in dict = {} '.format(xdict.keys()))
print(' adding time=[None] ...')
xdict['time'] = np.array([None])
y = y.reshape((1, len(y))) # to ensure len(np.shape(y)) == 2
# If time is the only parameter, use it on x-axis
if (len(np.shape(y)) == 1):
xStr = 'time'
labelStr = ' {}({}) '.format(varList[iy], 'time')
plotTxt = [labelStr, xStr, varList[iy]]
fig = addToPlot(fig, xdict[xStr], y, labelStr, plotTxt, logOn)
if (writeAscii):
print(' (1) Writing data to ascii file: {}.dat'.format(
varList[iy]))
np.savetxt(varList[iy] + '_ts.dat',
np.c_[xdict[xStr],
y]) # x,y,z equal sized 1D arrays
elif (len(np.shape(y)) == 2):
time = xdict['time']
xList = list(xdict.keys())
xList.remove('time') # remove time ...
xStr = xList[0] # and take what is left ... z-something, typically
tskip = min(tskip, len(time) - 1)
# Plot profiles for chosen time instances
fig.append(pl.figure(num=j, figsize=(10., 8.)))
for fn in fileNos:
lblList.append(fileList[fn])
for j in xrange(len(pId)):
d = pl.loadtxt(fileList[fn])
"""
Cols: 0 1 2 3 4 5 6
Vars: Center Umean Umax Umin Width Id time
"""
if (pId[j] == 0):
x = d[:, 6]
y = d[:, 0]
if (pId[j] == 1):
x = d[:, 6]
y = d[:, 1]
if (pId[j] == 2):
x = d[:, 6]
y = d[:, 4]
fig[j] = addToPlot(fig[j], x, y, fileList[fn],
[selections[j], xlb[j], ylb[j]], 0)
for j in xrange(len(pId)):
ax, = fig[j].get_axes()
lns = ax.get_lines()
ax.legend(lns, lblList, loc=1)
pl.show()
if (len(veldict.keys()) == 2):
umag = np.mean(umag, axis=(0))
pt = np.mean(pt, axis=(0))
for dstr in veldict.keys():
if ('z' in dstr): z = veldict[dstr]
veldict = None
dudz = (umag[1:] - umag[:-1]) / (z[1:] - z[:-1])
dptdz = (pt[1:] - pt[:-1]) / (z[1:] - z[:-1])
zm = 0.5 * (z[1:] + z[:-1])
Ri = (9.81 / np.mean(pt)) * (dptdz) / (dudz**2 +
1e-9) * (dudz > 1e-3).astype(float)
plotStr = ["Local Ri vs z ", "Ri", "z"]
fig = addToPlot(fig, Ri[2:-4], zm[2:-4], '{}'.format(fileList[fn]),
plotStr, False)
plotStr = ["dpt/dz vs z ", "dpt/dz", "z"]
fig2 = addToPlot(fig2, dptdz[2:-4], zm[2:-4], '{}'.format(fileList[fn]),
plotStr, False)
plotStr = ["du/dz vs z ", "du/dz", "z"]
fig3 = addToPlot(fig3, dudz[2:-4], zm[2:-4], '{}'.format(fileList[fn]),
plotStr, False)
plt.legend(loc=0)
plt.show()
avar = args.appendv
af = args.appendf
nY = len(args.yvars)
# Append the x- and a-variables into the variable list.
varList.append(xvar)
varList.append(avar)
fileNos, fileList = filesFromList( "*"+strKey+"*" )
pfig = plt.figure(num=1, figsize=(12,10))
for fn in fileNos:
dat = extractFromCSV( fileList[fn] , varList )
a = dat[-1]; x = dat[-2]; y = dat[:-2]
dat = None
for i in xrange(nY):
astr ='_'+str(int(a[0]/100.))
y[i]+=(a*af)
axLabels = ["{}(z)".format(varList[i]),"{} +x/100 [m/s]".format(varList[i]),"z [m]"]
varLabel = varList[i]+astr
pfig = addToPlot(pfig,y[i], x, varLabel, axLabels, False )
if(args.labels): pfig = userLabels( pfig )
plt.legend(loc=0)
plt.grid(True)
plt.show()
Fo = np.array( extractColumns( raw , [7,8,9] ) )
Tp = np.array( extractColumns( raw , [10,11,12] ) )
Tv = np.array( extractColumns( raw , [13,14,15] ) )
To = np.array( extractColumns( raw , [16,17,18] ) )
time.extend(t)
for n in xrange(3):
Ftot[n].extend( Fp[n]+Fv[n]+Fo[n] )
Ttot[n].extend( Tp[n]+Tv[n]+To[n] )
#print np.shape(t), np.shape(time), np.shape(Fp[0]),np.shape(Ftot[0])
fig = pl.figure(1, figsize=(8.5,8.5))
if( not args.torgue ):
plotTxt = ["Forces", "Iter/Time (s)", "F (N)"]
fig = addToPlot(fig, time, Ftot[0],'Fx', plotTxt, False)
fig = addToPlot(fig, time, Ftot[1],'Fy', plotTxt, False)
fig = addToPlot(fig, time, Ftot[2],'Fz', plotTxt, False)
else:
plotTxt = ["Torques", "Iter/Time (s)", "T (Nm)"]
fig = addToPlot(fig, time, Ttot[0],'Tx', plotTxt, False)
fig = addToPlot(fig, time, Ttot[1],'Ty', plotTxt, False)
fig = addToPlot(fig, time, Ttot[2],'Tz', plotTxt, False)
if(args.labels):
fig = userLabels( fig )
pl.legend(loc=0)
pl.grid(True)
pl.show()
dtime = np.mean( time[1:] - time[:-1] )
ltime = (time[-1] - time[0])/Nt
f1 = 1./ltime
f2 = 1./(2.*dtime)
nsteps= int( (f2-f1)/f1 )
freq = np.linspace(f1,f2,nsteps,endpoint=False)
print(' f1 = {}, f2 = {}, nsteps = {} '.format(f1,f2,nsteps))
print(' freq = {} '.format( freq ))
sig = v[:,kIds[0], ijk1[1], ijk1[0]] - np.mean( v[:,kIds[0], ijk1[1], ijk1[0]] )
fig1 = plt.figure()
plotStr = [" Signal "," time "," v(time) "]
fig1 = addToPlot(fig1, time, \
ndimage.gaussian_filter(sig, sigma=60.),'Filtered', plotStr )
fig1 = addToPlot(fig1, time,sig,'Signal', plotStr )
WD1 = wtDataset(sig, time, f=freq )
if( complexOn ): tt = "complex"
else: tt = "real"
if('hist' in mode):
Qstr = ''' Enter the index of desired {} values
given the range values {}...{} with indecies {}...{}.'''
if( 'spectro' in mode ):
histmode = 'frequency'