def plotMagPhase(h5fname):
mdata = fdata(h5fname)
lenz2 = (mdata.vars.nz2 - 1) * mdata.vars.dz2
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
saxis = z2axis * mdata.vars.lc
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
sampleFreq = 1.0 / mdata.vars.dz2
lenz2 = (mdata.vars.nz2 - 1) * mdata.vars.dz2
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
xf, yf = readField(h5fname, f1D=1)
intens = np.square(xf) + np.square(yf)
mgx, phx = getMagPhase(xf, mdata.vars.nz2, mdata.vars.rho, lenz2)
mgy, phy = getMagPhase(yf, mdata.vars.nz2, mdata.vars.rho, lenz2)
ax1 = plt.subplot(311)
plt.plot(z2axis, xf, label='x-field')
plt.plot(z2axis, yf, label='y-field')
plt.xlabel('z2')
plt.ylabel('Fields')
plt.legend()
# example for adding subplot
plt.subplot(312, sharex=ax1)
plt.plot(z2axis, mgx, label='x-field')
plt.plot(z2axis, mgy, label='y-field')
plt.xlabel('z2')
plt.ylabel('Mag')
axes = plt.subplot(313, sharex=ax1)
plt.plot(z2axis, phx, label='x-field')
plt.plot(z2axis, phy, label='y-field')
plt.xlabel('z2')
plt.ylabel('Phase')
axes.set_ylim([0, 2. * pi])
nameparts = h5fname.split('_')
basename = nameparts[0]
z = mdata.vars.z
plt.savefig(basename + "_magPhase_z_" + str(z) + ".png")
def getFilteredFields(h5fname, cfr=None, dfr=None, qAv = 0, qScale = None):
mdata = fdata(h5fname)
if (qScale==None):
qScale = mdata.vars.qscale
lenz2 = (mdata.vars.nz2-1) * mdata.vars.dz2
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
xf, yf = readField(h5fname)
if ((cfr != None) and (dfr != None)):
xf = filterField(xf, cfr, dfr, mdata.vars)
yf = filterField(yf, cfr, dfr, mdata.vars)
return xf, yf
def plotSpecPow(h5fname, ftplottype=None):
mdata = fdata(h5fname)
# To select temporal slice of field....
#z2s = 50
#z2e = 80
#z2si = int(np.floor(z2s / dz2))
#z2ei = int(np.floor(z2e / dz2))
#z2axis = (np.arange(z2si,z2ei) - z2si) * dz2
# ...otherwise take full field
lenz2 = (mdata.vars.nz2 - 1) * mdata.vars.dz2
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
xf, yf = readField(h5fname, f1D=1)
#xf = np.concatenate((xf, xf))
#xf = np.concatenate((xf, xf))
#xf = np.concatenate((xf, xf))
#xf = np.concatenate((xf, xf))
#xf[mdata.vars.nz2:] = 0.
#yf = np.concatenate((yf, yf)) # [yf, yf, yf, yf]
#yf = np.concatenate((yf, yf))
#yf = np.concatenate((yf, yf))
#yf = np.concatenate((yf, yf))
#yf[mdata.vars.nz2:] = 0.
intens = np.square(xf) + np.square(yf)
#npads = np.int(np.round(mdata.vars.nz2 / 2))
npads = mdata.vars.nz2
xff = np.fft.fft(xf, n=npads)
yff = np.fft.fft(yf, n=npads)
NumUniquePts = np.int_(np.ceil((npads + 1) / 2))
fs = (mdata.vars.nz2) / lenz2 #sampling frequency
ftfieldtemp = np.zeros(NumUniquePts + 1)
ftfieldtemp = xff[0:NumUniquePts]
ftxpower = np.square(np.absolute(ftfieldtemp))
if np.remainder(npads, 2) == 1:
ftxpower[1:] = ftxpower[1:] * 2
else:
ftxpower[1:-1] = ftxpower[1:-1] * 2
ftfieldtemp = yff[0:NumUniquePts]
ftypower = np.square(np.absolute(ftfieldtemp))
if np.remainder(npads, 2) == 1:
ftypower[1:] = ftypower[1:] * 2
else:
ftypower[1:-1] = ftypower[1:-1] * 2
#print str(mdata.vars.lr)
#ftxaxis = (np.arange(0,NumUniquePts)*(fs/(npads)))*(4.*np.pi*mdata.vars.rho)
#ftxaxis[1:] = 1 / ftxaxis[1:]
#ftxaxis[0] = ftxaxis[-1] + 1
if (ftplottype == 1):
ftxaxis = mdata.vars.lr / ((np.arange(0, NumUniquePts) * (fs /
(npads))) *
(4. * np.pi * mdata.vars.rho))
sp_x_axis = r'$\lambda (m)$'
else:
ftxaxis = (np.arange(0, NumUniquePts) *
(fs / (npads))) * (4. * np.pi * mdata.vars.rho)
sp_x_axis = r'$\omega / \omega_r$'
sp_title = 'Intensity Spectrum'
# z2axis = [z2axis,z2axis,z2axis,z2axis]
ax1 = plt.subplot(211)
plt.plot(z2axis, xf, label='x field')
plt.plot(z2axis, yf, label='y field')
plt.xlabel(r'$\bar{z}_2$', fontsize=16)
plt.ylabel('Fields', fontsize=16)
plt.legend()
# example for adding subplot
axes = plt.subplot(212)
plt.xlabel(sp_x_axis, fontsize=16)
plt.ylabel('Intensity', fontsize=16)
#print np.len(ftxaxis), np.len(ftxpower)
if ftplottype == 1:
plt.loglog(ftxaxis, ftxpower, label='x intensity')
plt.plot(ftxaxis, ftypower, label='y intensity')
plt.plot(ftxaxis, ftxpower + ftypower, label='combined')
# axes.set_xlim([5.8e-10, 7.2e-10])
else:
plt.semilogy(ftxaxis, ftxpower, label='x intensity')
plt.plot(ftxaxis, ftypower, label='y intensity')
plt.plot(ftxaxis, ftxpower + ftypower, label='combined')
axes.set_xlim([0.8, 1.2])
plt.legend()
nameparts = h5fname.split('_')
basename = nameparts[0]
z = mdata.vars.z
plt.tight_layout()
plt.savefig(basename + "-spec-intensity-z-" + str(z) + ".png")
def spectroT(h5fname, z2s=None, z2e=None, qScale=None):
mdata = fdata(h5fname)
if (qScale == None):
qScale = mdata.vars.qscale
lenz2 = (mdata.vars.nz2 - 1) * mdata.vars.dz2
xf, yf = readField(h5fname, f1D=1)
# dz2 = h5f.root.runInfo._v_attrs.sLengthOfElmZ2
# nz2 = h5f.root.runInfo._v_attrs.nZ2
sampleFreq = 1.0 / mdata.vars.dz2
# z2s = 0.00
# z2e = 0.06
if ((z2s == None) or (z2e == None)):
z2si = 0
z2ei = mdata.vars.nz2
else:
z2si = int(np.floor(z2s / mdata.vars.dz2))
z2ei = int(np.floor(z2e / mdata.vars.dz2))
xfs = xf[z2si:z2ei]
yfs = yf[z2si:z2ei]
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
z2axis = (np.arange(z2si, z2ei) - z2si) * mdata.vars.dz2
if (qScale == 0):
z2axis = z2axis * mdata.vars.lc
freqScale = mdata.vars.lr
xfs = xfs * mdata.vars.fieldScale # * mdata.vars.lc / mdata.vars.c0
sampleFreq = sampleFreq / mdata.vars.lc
xaxisl = r'$ct-z (m)$'
faxisl = r'x-field $(Vm^{-1})$'
saxisl = r'$\omega / \omega_0$'
else:
freqScale = 4. * np.pi * mdata.vars.rho
xaxisl = r'$\bar{z}_2$'
faxisl = 'x-field (Scaled)'
saxisl = r'$\bar{f}$'
ax1 = plt.subplot(211)
plt.plot(z2axis, xfs)
plt.xlabel(xaxisl, fontsize=16)
plt.ylabel(faxisl, fontsize=16)
plt.xlim(200e-6, 210e-6)
plt.subplot(212, sharex=ax1)
# specf = signal.get_window(('tukey',2.0),128)
# freqs, time, Sxx = signal.spectrogram(xfs, sampleFreq, nperseg=128, nfft=2048, \
# window=specf)
Sxx = signal.cwt(xfs, signal.ricker, np.arange(1, 31))
# freqs = freqs * freqScale
# plt.pcolormesh(time, freqs, Sxx)
plt.pcolormesh(z2axis, np.arange(1, 31), Sxx)
plt.xlim(0.0025, 0.0026)
# plt.ylim(0.8, 1.2);
cb = plt.colorbar()
# specP, freqs, time, image = specgram(xfs, \
# NFFT=50, Fs=sampleFreq, noverlap=0)#, cmap=plt.cm.gist_heat)
# freqs = freqs * 3e8
# plt.pcolor(time,freqs,specP)
plt.xlabel(xaxisl, fontsize=16)
plt.ylabel(saxisl, fontsize=16)
plt.tight_layout()
# then either:
#plt.imshow(specP,cmap='PRGn')
# plt.show()
# -or- just
nameparts = h5fname.split('_')
basename = nameparts[0]
z = mdata.vars.z
plt.savefig(basename + "-spectrogram-z-" + str(z) + ".png")
def plotSpecInt(h5fname, ftplottype=None):
mdata = fdata(h5fname)
# To select temporal slice of field....
#z2s = 50 # start in units of z2
#z2e = 80 # End in units of z2
# (everything outside this domain will be set to zero)
#z2si = int(np.floor(z2s / dz2))
#z2ei = int(np.floor(z2e / dz2))
#z2axis = (np.arange(z2si,z2ei) - z2si) * dz2
# ...otherwise take full field
lenz2 = (mdata.vars.nz2-1) * mdata.vars.dz2
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
npads = mdata.vars.nz2
NumUniquePts = np.int_(np.ceil((npads+1)/2))
fs = (mdata.vars.nz2)/lenz2 #sampling frequency
# xpows = np.zeros(NumUniquePts)
# ypows = np.zeros(NumUniquePts)
ftfieldtemp = np.zeros(NumUniquePts)
# for ix in np.arange(0, mdata.vars.nx):
# for iy in np.arange(0, mdata.vars.ny):
# print ix, iy
xf, yf = readField(h5fname, f1D=1)
xff = np.fft.fft(xf)
yff = np.fft.fft(yf)
ftfieldtemp = xff[0:NumUniquePts]
ftxpower = np.square(np.absolute(ftfieldtemp))
if np.remainder(npads,2) == 1:
ftxpower[1:] = ftxpower[1:]*2
else:
ftxpower[1:-1] = ftxpower[1:-1]*2
# xpows = xpows + ftxpower
ftfieldtemp = yff[0:NumUniquePts]
ftypower = np.square(np.absolute(ftfieldtemp))
if np.remainder(npads, 2) == 1:
ftypower[1:] = ftypower[1:]*2
else:
ftypower[1:-1] = ftypower[1:-1]*2
# ypows = ypows + ftypower
# For padding with zeros!!!
#xf = np.concatenate((xf, xf))
#xf = np.concatenate((xf, xf))
#xf = np.concatenate((xf, xf))
#xf = np.concatenate((xf, xf))
#xf[mdata.vars.nz2:] = 0.
#yf = np.concatenate((yf, yf)) # [yf, yf, yf, yf]
#yf = np.concatenate((yf, yf))
#yf = np.concatenate((yf, yf))
#yf = np.concatenate((yf, yf))
#yf[mdata.vars.nz2:] = 0.
#intens = np.square(xf) + np.square(yf)
#npads = np.int(np.round(mdata.vars.nz2 / 2))
#print str(mdata.vars.lr)
#ftxaxis = (np.arange(0,NumUniquePts)*(fs/(npads)))*(4.*np.pi*mdata.vars.rho)
#ftxaxis[1:] = 1 / ftxaxis[1:]
#ftxaxis[0] = ftxaxis[-1] + 1
if (ftplottype == 1):
ftxaxis = mdata.vars.lr / ((np.arange(0,NumUniquePts)*(fs/(npads)))*(4.*np.pi*mdata.vars.rho))
sp_x_axis=r'$\lambda (nm)$'
else:
ftxaxis = (np.arange(0,NumUniquePts)*(fs/(npads)))*(4.*np.pi*mdata.vars.rho)
sp_x_axis=r'$\omega / \omega_r$'
sp_title='Intensity Spectrum'
# z2axis = [z2axis,z2axis,z2axis,z2axis]
# ax1 = plt.subplot(211)
# plt.plot(z2axis, xf, label='x field')
# plt.plot(z2axis, yf, label='y field')
# plt.xlabel(r'$\bar{z}_2$', fontsize=16)
# plt.ylabel('Fields', fontsize=16)
# plt.legend()
# Adding subplot
axes = plt.subplot(111)
plt.xlabel(sp_x_axis, fontsize=16)
plt.ylabel('Intensity (a.u.)', fontsize=16)
##### SAVE DATA
# outfilename = 'specpow.h5'
#
# h5=tables.open_file(outfilename,'w')
#
# h5.create_array('/','wavelens', ftxaxis)
# h5.root.wavelens._v_attrs.vsKind='structured'
# h5.root.wavelens._v_attrs.vsType='mesh'
# h5.root.wavelens._v_attrs.vsStartCell=0
# #h5.root.zSeries._v_attrs.vsNumCells=numTimes-1 # -1 as zonal
# h5.root.wavelens._v_attrs.vsLowerBounds=ftxaxis[0]
# h5.root.wavelens._v_attrs.vsUpperBounds=ftxaxis[-1]
# h5.root.wavelens._v_attrs.vsAxisLabels="lambda (m)"
#
fttpower = ftxpower + ftypower
#
#
#
# h5.create_array('/','specpow',fttpower)
# h5.root.specpow._v_attrs.vsMesh='wavelens'
# h5.root.specpow._v_attrs.vsType='variable'
# h5.root.specpow._v_attrs.vsAxisLabels='Power (a.u.)'
#
#
# h5.close()
if ftplottype==1:
#plt.loglog(ftxaxis, ftxpower, label='x intensity')
#plt.plot(ftxaxis, ftxpower, label='x power')
#plt.plot(ftxaxis, ftypower, label='y power')
plt.plot(ftxaxis*1e9, fttpower / np.max(fttpower), label='Intensity')
axes.set_xlim([90, 110])
else:
# plt.semilogy(ftxaxis, ftxpower, label='x intensity')
# plt.plot(ftxaxis, ftypower, label='y intensity')
plt.plot(ftxaxis, fttpower / np.max(fttpower), label='Intensity')
axes.set_xlim([0.8, 1.2])
# plt.legend()
nameparts = h5fname.split('_')
basename = nameparts[0]
z = mdata.vars.z
plt.tight_layout()
plt.savefig(basename + "-spec-intensity-z-" + str(z) + ".png")