def getIntData(h5fname, dataName, irtype=0):
mdata = fdata(h5fname)
h5in = tables.open_file(h5fname, 'r')
npts = h5in.root._f_get_child(dataName).shape[0]
meshName = h5in.root._f_get_child(dataName)._v_attrs.vsMesh
mmin = h5in.root._f_get_child(meshName)._v_attrs.vsLowerBounds
mmax = h5in.root._f_get_child(meshName)._v_attrs.vsUpperBounds
lenz2 = mmax - mmin
dz2 = lenz2 / (npts - 1)
z2axis = (np.arange(0, npts)) * dz2
dataT = h5in.root._f_get_child(dataName).read()
if (irtype == iav):
if (npts > 1):
dataR = np.trapz(dataT, x=z2axis) / lenz2 # average
else:
dataR = dataT
elif (irtype == ipeak):
dataR = np.max(dataT) # peak
else:
dataR = dataT # temporal
h5in.close()
return dataR
def getPow(h5fname, cfr=None, dfr=None, irtype = 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)
if (((irtype == ipeak) or (irtype == icycav)) and (mdata.vars.q1d==1)):
xf, phx = getMagPhase.getMagPhase(xf, mdata.vars.nz2, mdata.vars.rho, lenz2)
yf, phy = getMagPhase.getMagPhase(yf, mdata.vars.nz2, mdata.vars.rho, lenz2)
intens = np.square(xf) + np.square(yf)
# intens = np.square(yf)
#tintens = getFiltPow(ij, dfr, cfr)
#ens[fcount] = np.trapz(tintens, x=z2axis)
# trar = 2. * np.pi * np.square(35.e-6) / mdata.vars.lg / mdata.vars.lc
transArea = mdata.vars.transArea
if (mdata.vars.q1d==1):
if (irtype == iav):
power = np.trapz(intens, x=z2axis) / lenz2 * transArea # average
elif (irtype == ipeak):
power = np.max(intens * transArea) # peak
else:
power = intens * transArea # temporal
else:
tintensx = np.trapz(intens, x=xaxis, axis=0)
tintensxy = np.trapz(tintensx, x=yaxis, axis=0)
if (irtype == iav):
power = np.trapz(tintensxy, x=z2axis) / lenz2
elif (irtype == ipeak):
power = np.max(intens * transArea) # * transverse area???
else:
power = tintensxy
if (qScale == 0):
power = power * mdata.vars.powScale # * mdata.vars.lc / mdata.vars.c0
return power
def plotPowVsZ(basename, cfr=None, dfr=None):
filelist = getFileSlices(basename)
print filelist
mdata = fdata(filelist[0])
sampleFreq = 1.0 / mdata.vars.dz2
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
fcount = 0
pows = np.zeros(len(filelist))
zData = np.zeros(len(filelist))
if (mdata.vars.iMesh == iPeriodic):
gAv = 1 # for average...
else:
gAv = 2 # for peak...
for ij in filelist:
pows[fcount] = getPow(ij, cfr, dfr, irtype=gAv, qScale=0)
zData[fcount] = getZData(ij)
fcount += 1
# plotLab = 'SI Power'
# axLab = 'Power (W)'
if (mdata.vars.iMesh == iPeriodic):
plotLab = 'Power'
axLab = 'Power (W)'
else:
plotLab = 'Peak Power'
axLab = 'Power (W)'
ax1 = plt.subplot(111)
plt.semilogy(zData, pows, label=plotLab)
#ax1.set_title(axLab)
plt.xlabel('z (m)')
plt.ylabel(axLab)
#plt.legend()
if ((cfr == None) or (dfr == None)):
opname = basename + "-unfiltered-power.png"
else:
opname = basename + "-filt-" + str(cfr) + "-" + str(dfr) + "-power.png"
plt.savefig(opname)
def plotPowVsZ(basename):
filelist = getIntFileSlices(basename)
print filelist
mdata = fdata(filelist[0])
sampleFreq = 1.0 / mdata.vars.dz2
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
fcount = 0
pows = np.zeros(len(filelist))
zData = np.zeros(len(filelist))
if (mdata.vars.iMesh == iPeriodic):
gAv = iav # for average...
plotLab = 'Power'
axLab = 'Power (W)'
else:
gAv = ipeak # for peak...
plotLab = 'Peak Power'
axLab = 'Power (W)'
for ij in filelist:
pows[fcount] = getPowFromInt(ij, irtype=gAv, qScale=0)
zData[fcount] = getZData(ij)
fcount += 1
ax1 = plt.subplot(111)
plt.semilogy(zData, pows, label=plotLab)
#ax1.set_title(axLab)
plt.xlabel('z (m)')
plt.ylabel(axLab)
#plt.legend()
opname = basename + "-unfiltered-power.png"
plt.savefig(opname)
# plt.show()
outfilename = 'powers.h5'
h5o = tables.open_file(outfilename, 'w')
h5o.create_array('/', 'power_SI', pows)
h5o.create_array('/', 'z_SI', zData)
h5o.close()
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 plotBeamRVsZ(basename):
filelist = getFileSlices(basename)
#print filelist
mdata = fdata(filelist[0])
sampleFreq = 1.0 / mdata.vars.dz2
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
fcount = 0
radx = np.zeros(len(filelist))
rady = np.zeros(len(filelist))
zData = np.zeros(len(filelist))
gAv = 1
for ij in filelist:
radx[fcount] = getIntData(ij, 'sigmaXSI', irtype=gAv)
rady[fcount] = getIntData(ij, 'sigmaYSI', irtype=gAv)
zData[fcount] = getZData(ij)
fcount += 1
# plotLab = 'SI Power'
# axLab = 'Power (W)'
plotLab = r'$\sigma_x$'
axLab = r'$\sigma_x, \sigma_y (m)$'
ax1 = plt.subplot(111)
plt.plot(zData, radx, label=r'$\sigma_x$')
plt.plot(zData, rady, label=r'$\sigma_y$')
#ax1.set_title(axLab)
plt.xlabel('z (m)')
plt.ylabel(axLab)
plt.legend()
plt.tight_layout()
opname = basename + "-beamRadiusVsZ.png"
plt.savefig(opname)
def plotPowVsZ2(fname):
mdata = fdata(fname)
sampleFreq = 1.0 / mdata.vars.dz2
lenz2 = (mdata.vars.nz2-1) * mdata.vars.dz2
z2axis = (np.arange(0,mdata.vars.nz2)) * mdata.vars.dz2
saxis = z2axis * mdata.vars.lc * 1e6
xaxis = (np.arange(0,mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0,mdata.vars.ny)) * mdata.vars.dybar
z = mdata.vars.z
gAv = iav # for average...
plotLab = 'Power'
axLab = 'Power (W)'
pows = getPowFromInt(fname, irtype = itemp, qScale = 0)
ax1 = plt.subplot(111)
plt.plot(saxis, pows)
plt.xlabel(r'$ct-z (\mu m)$')
plt.ylabel(axLab)
ax1.set_title('z = ' + str(z) + 'm')
#plt.legend()
nameparts = fname.split('_')
basename = nameparts[0]
#plt.show()
plt.savefig(basename + "-powvsz2-step-" + str(mdata.vars.step) + "-z-" + \
str(z) + "m" + ".png")
def getPowFromInt(h5fname, cfr=None, dfr=None, irtype=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
power = getIntData(h5fname, 'powerSI', irtype=irtype)
return power
def readField(h5fname, f1D=0, xn=None, yn=None):
mdata = fdata(h5fname)
h5f = tables.open_file(h5fname, mode='r')
# 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
if (xn == None):
xn = int(np.rint(mdata.vars.nx / 2))
if (yn == None):
yn = int(np.rint(mdata.vars.ny / 2))
if (mdata.vars.q1d == 1):
xf = h5f.root.aperp[:, 0]
else:
if (f1D == 0):
xf = h5f.root.aperp[:, :, :, 0]
else:
print(xn, yn)
print(shape(h5f.root.aperp))
xf = h5f.root.aperp[xn, yn, :, 0]
# xfs = xf[z2si:z2ei] # for selecting slice...
if (mdata.vars.q1d == 1):
yf = h5f.root.aperp[:, 1]
else:
if (f1D == 0):
yf = h5f.root.aperp[:, :, :, 1]
else:
yf = h5f.root.aperp[xn, yn, :, 1]
h5f.close()
return xf, yf
def plotEn(basename):
filelist = getIntFileSlices(basename)
print filelist
mdata = fdata(filelist[0])
sampleFreq = 1.0 / mdata.vars.dz2
lenz2 = (mdata.vars.nz2 - 1) * mdata.vars.dz2
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
saxis = z2axis * mdata.vars.lc * 1e6
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
z = mdata.vars.z
gAv = iav # for average...
plotLab = 'Energy'
axLab = 'Energy (J)'
ens = np.zeros(len(filelist))
zData = np.zeros(len(filelist))
fcount = 0
for ij in filelist:
ens[fcount] = getEnFromInt(ij, qScale=0)
zData[fcount] = getZData(ij)
fcount += 1
ax1 = plt.subplot(111)
plt.semilogy(zData, ens)
plt.xlabel(r'$z (m)$')
plt.ylabel(axLab)
ax1.set_title('Energy')
#plt.legend()
opname = basename + "-Energy.png"
plt.savefig(opname)
def getEnFromInt(h5fname, 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
saxis = z2axis * mdata.vars.lc
taxis = saxis / mdata.vars.c0
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
power = getIntData(h5fname, 'powerSI', irtype=itemp)
energy = np.trapz(power, x=taxis)
return energy
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 plotPowVsZ(basename):
filelist = getIntFileSlices(basename)
print(filelist)
mdata = fdata(filelist[0])
fcount = 0
pows = np.zeros(len(filelist))
zData = np.zeros(len(filelist))
if (mdata.vars.iMesh == iPeriodic):
gAv = iav # for average...
plotLab = 'Power'
axLab = 'Power (W)'
else:
gAv = ipeak # for peak...
plotLab = 'Peak Power'
axLab = 'Power (W)'
for ij in filelist:
pows[fcount] = getPowFromInt(ij, irtype = gAv, qScale = 0)
zData[fcount] = getZData(ij)
fcount += 1
plt.semilogy(zData, pows, label=plotLab)
plt.xlabel('z (m)')
plt.ylabel(axLab)
opname = basename + "-unfiltered-power.png"
plt.savefig(opname)
outfilename = 'powers.h5'
h5o = tables.open_file(outfilename,'w')
h5o.create_array('/','power_SI',pows)
h5o.create_array('/','z_SI',zData)
h5o.close()
def plot_intensity(file_name_in):
# iTemporal = 0
# iPeriodic = 1
##################################################################
#
##
filename = file_name_in
filename_base = (file_name_in.split('.')[0]).strip()
mdata = fdata(filename)
# H5PY option below
#hf=h5py.File(filename, 'r')
#n1=hf.get('aperp')
#n1=np.array(n1)
hf = tables.open_file(file_name_in, 'r')
n1 = hf.root.aperp.read()
xpol = n1[0, :, :, :]
ypol = n1[1, :, :, :]
z2axis = np.multiply((np.arange(0, mdata.vars.nz2)), mdata.vars.dz2)
intensity2 = np.divide(
np.trapz(np.add(np.square(xpol), np.square(ypol)), x=z2axis, axis=0),
(np.multiply(mdata.vars.nz2, mdata.vars.dz2)))
intensity = np.transpose(intensity2)
dx = mdata.vars.dx
dy = mdata.vars.dy
nx = mdata.vars.nx
ny = mdata.vars.ny
#x=(np.arange(-nx/2,nx/2)*dx*1e3)
#y=(np.arange(-ny/2,ny/2)*dy *1e3)
#X,Y=np.meshgrid(x,y)
# font = matplotlib.font_manager.FontProperties(family='serif')
plt.clf()
plt.figure(1)
ax2 = plt.subplot(111)
ax2.set_ylabel(r'y [$mm$]', fontsize=12)
ax2.yaxis.set_major_formatter(mtick.ScalarFormatter(useMathText=True))
ax2.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax2.set_xlabel(r'x [$mm$]', fontsize=12)
ax2.xaxis.set_major_formatter(mtick.ScalarFormatter(useMathText=True))
ax2.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
plt.imshow(np.multiply(intensity, mdata.vars.intensScale),
cmap='jet',
interpolation='bilinear',
extent=[
-nx * dx * 1e3 / 2., nx * dx * 1e3 / 2.,
-ny * dy * 1e3 / 2., ny * dy * 1e3 / 2.
])
#plt.xlabel('x ($mm$)', fontproperties=font)
#plt.ylabel('y ($mm$)', fontproperties=font)
#==============================================================================
# ax= plt.axes()
# for label in ax.get_xticklabels():
# label.set_fontproperties(font)
# for label in ax.get_yticklabels():
# label.set_fontproperties(font)
#==============================================================================
#cbar=plt.colorbar()
def fmt(x, pos):
a, b = '{:.1e}'.format(x).split('e')
b = int(b)
#return r'${} {}$'.format(a, b)
return r'${} \times 10^{{{}}}$'.format(a, b)
cbar = plt.colorbar(format=ticker.FuncFormatter(fmt),
label=r'Intensity [$W/m^2$]')
#cbar=plt.colorbar(format=ticker.ScalarFormatter(useMathText=True),label='Intensity $W/m^2$')
#cbar.set_label(family=font)
for label in cbar.ax.yaxis.get_ticklabels():
label.set_family('serif')
plt.savefig("Intensity" + filename_base + ".png")
plt.show()
def plotPowZZ2(basename, cfr=None, dfr=None):
filelist = getFileSlices(basename)
print(filelist)
mdata = fdata(filelist[0])
sampleFreq = 1.0 / mdata.vars.dz2
lenz2 = (mdata.vars.nz2 - 1) * mdata.vars.dz2
z2axis = (np.arange(
0, mdata.vars.nz2)) * mdata.vars.dz2 * mdata.vars.lc * 1.e6
xaxis = (np.arange(0, mdata.vars.nx)) * mdata.vars.dxbar
yaxis = (np.arange(0, mdata.vars.ny)) * mdata.vars.dybar
fcount = 0
pows = np.zeros(len(filelist))
zData = np.zeros(len(filelist))
# if (mdata.vars.iMesh == iPeriodic):
# gAv = 1 # for average...
# else:
# gAv = 2 # for peak...
gAv = 3 # for cycle averaged
pows = np.ones([len(filelist), mdata.vars.nz2])
powsN = np.ones([len(filelist), mdata.vars.nz2])
for ij in filelist:
pows[-1 - fcount, :] = getPow(ij, cfr, dfr, irtype=gAv, qScale=0)
mv = np.max(pows[-1 - fcount, :])
if (mv != 0.):
powsN[-1 - fcount, :] = pows[-1 - fcount, :] / np.max(
pows[-1 - fcount, :])
else:
powsN[-1 - fcount, :] = 0.
zData[fcount] = getZData(ij)
fcount += 1
# print fcount
# plotLab = 'SI Power'
# axLab = 'Power (W)'
# if (mdata.vars.iMesh == iPeriodic):
# plotLab = 'SI Power'
# axLab = 'Power (W)'
# else:
# plotLab = 'SI Peak Power'
axLab = 'Power (W)'
ax1 = plt.subplot(111)
im = plt.imshow(powsN, aspect='auto', interpolation='bilinear', \
extent=[z2axis[0], z2axis[-1], zData[0], zData[-1]])
ax1.set_title('Power')
plt.xlabel(r'$ct-z (\mu m)$')
plt.ylabel('z (m)')
cb = plt.colorbar(im)
plt.tight_layout()
#plt.legend()
if ((cfr == None) or (dfr == None)):
opname = basename + "-powerALL.png"
else:
opname = basename + "-filt-" + str(cfr) + "-" + str(
dfr) + "-powerALL.png"
plt.savefig(opname)
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 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")
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")
from numpy.lib.scimath import *
from cmath import sin, cos
from scipy import *
from puffdata import fdata
import h5py
iTemporal = 0
iPeriodic = 1
##################################################################
#
##
filename = sys.argv[1]
mdata = fdata(filename)
hf = h5py.File(filename, 'r')
n1 = hf.get('aperp')
n1 = np.array(n1)
n2 = np.zeros((n1.shape[3], n1.shape[2], n1.shape[1], n1.shape[0]))
for i in range(n1.shape[0]):
for j in range(n1.shape[2]):
n2[:, j, :, i] = n1[i, :, j, :].T
xpol = n1[0, :, :, :]
ypol = n1[1, :, :, :]
#xpol=n2[:,:,:,0]
#ypol=n2[:,:,:,1]
z2axis = (np.arange(0, mdata.vars.nz2)) * mdata.vars.dz2
