def afterScript(complexPlotsIs, complexPlotsEs, complexPlotsPCAs):
if what == 'rays':
return
if not isMono:
return
dump = []
NN = len(complexPlotsEs)
for ic, (complexPlotIs, complexPlotEs, complexPlotPCAs) in enumerate(
zip(complexPlotsIs, complexPlotsEs, complexPlotsPCAs)):
x = complexPlotIs.xaxis.binCenters
y = complexPlotIs.yaxis.binCenters
Ixy = complexPlotIs.total2D
Exy4D = complexPlotEs.total4D
print("solving eigenvalue problem, {0} of {1}...".format(ic+1, NN))
start = time.time()
wN, vN = rco.calc_eigen_modes_4D(Exy4D)
stop = time.time()
print("the eigenvalue problem has taken {0:.4f} s".format(stop-start))
print("solving PCA problem, {0} of {1}...".format(ic+1, NN))
Es = complexPlotPCAs.field3D
start = time.time()
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es)
stop = time.time()
print("the PCA problem has taken {0:.4f} s".format(stop-start))
dump.append([x, y, Ixy, wN, vN, Es, wPCA, vPCA])
pickleName = '{0}-res.pickle'.format(prefix)
with open(pickleName, 'wb') as f:
pickle.dump(dump, f, protocol=2)
print("Done")
def afterScript(complexPlotsIs, complexPlotsEs, complexPlotsPCAs):
if what == 'rays':
return
if not isMono:
return
dump = []
NN = len(complexPlotsEs)
for ic, (complexPlotIs, complexPlotEs, complexPlotPCAs) in enumerate(
zip(complexPlotsIs, complexPlotsEs, complexPlotsPCAs)):
x = complexPlotIs.xaxis.binCenters
y = complexPlotIs.yaxis.binCenters
Ixy = complexPlotIs.total2D
Exy4D = complexPlotEs.total4D
print("solving eigenvalue problem, {0} of {1}...".format(ic + 1, NN))
start = time.time()
wN, vN = rco.calc_eigen_modes_4D(Exy4D)
stop = time.time()
print("the eigenvalue problem has taken {0:.4f} s".format(stop -
start))
print("solving PCA problem, {0} of {1}...".format(ic + 1, NN))
Es = complexPlotPCAs.field3D
start = time.time()
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es)
stop = time.time()
print("the PCA problem has taken {0:.4f} s".format(stop - start))
dump.append([x, y, Ixy, wN, vN, Es, wPCA, vPCA])
pickleName = '{0}-res.pickle'.format(prefix)
with open(pickleName, 'wb') as f:
pickle.dump(dump, f, protocol=2)
print("Done")
def plotFocus():
pickleName = '{0}-res.pickle'.format(prefix)
with open(pickleName, 'rb') as f:
dump = pickle.load(f)
# normalize over all images:
norm = 0.
for ic, d in enumerate(dFocus):
Es = dump[ic][3]
dmax = ((np.abs(Es)**2).sum(axis=0) / Es.shape[0]).max()
if norm < dmax:
norm = dmax
norm = norm**0.5
NN = len(dFocus)
for ic, d in enumerate(dFocus):
x, y, Ixy, Es = dump[ic]
scrStr = 'screen at f+{0:.1f} µm'.format(d*1000)
print("solving PCA problem, {0} of {1}...".format(ic+1, NN))
start = time.time()
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es)
stop = time.time()
print("the PCA problem has taken {0} s".format(stop-start))
print("Top 4 eigen values (PCA) = {0}".format(wPCA))
figP = rco.plot_eigen_modes(x, y, wPCA, vPCA,
xlabel='x (µm)', ylabel='z (µm)')
figP.suptitle('Principal components of one-electron images, ' + scrStr,
fontsize=11)
figP.savefig('PCA-{0}-{1:02d}.png'.format(prefix, ic))
xarr, xnam = (x, 'x') if cut.startswith('hor') else (y, 'z')
xdata = rco.calc_1D_coherent_fraction(Es/norm, xnam, xarr)
fig2D, figXZ = rco.plot_1D_degree_of_coherence(
xdata, xnam, xarr, "nm", isIntensityNormalized=True,
locLegend='lower left')
fig2D.suptitle('Mutual intensity, ' + scrStr, size=11)
figXZ.suptitle('Intensity and Degree of Coherence, ' + scrStr, size=11)
fig2D.savefig('MutualI-{0}-{1:02d}.png'.format(prefix, ic))
figXZ.savefig('IDOC-{0}-{1:02d}.png'.format(prefix, ic))
print("Done")
plt.show()
def afterScript(plots, complexPlotsPCA, beamLine):
if beamLine.source.trajectory is not None:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.gca(projection='3d')
tr = beamLine.source.trajectory
ax.plot(tr[2] / 1e3, tr[0] * 1e3, tr[1] * 1e3)
ax.set_xlabel(r"$z$, m")
ax.set_ylabel(u"$x$, µm")
ax.set_zlabel(u"$y$, µm")
plt.title("trajectory 3D")
plt.savefig(prefix + '0trajectory' + suffix + '.png')
if not complexPlotsPCA:
return
if not ('mono' in prefix):
return
dump = []
start = time.time()
NN = len(complexPlotsPCA)
for ic, complexPlotPCA in enumerate(complexPlotsPCA):
x = complexPlotPCA.xaxis.binCenters
y = complexPlotPCA.yaxis.binCenters
if repeats >= 4:
print("solving PCA problem, {0} of {1}...".format(ic + 1, NN))
Es = complexPlotPCA.field3D
start = time.time()
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es)
stop = time.time()
print("the PCA problem has taken {0:.4f} s".format(stop - start))
dump.append([repeats, x, y, wPCA, vPCA])
pickleName = '{0}-{1}repeats.pickle'.format(prefix, repeats)
with open(pickleName, 'wb') as f:
pickle.dump(dump, f, protocol=2)
print("Done")
if beamLine.source.trajectory is not None:
plt.show()
def afterScript(plots, complexPlotsPCA, beamLine):
if beamLine.source.trajectory is not None:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.gca(projection='3d')
tr = beamLine.source.trajectory
ax.plot(tr[2]/1e3, tr[0]*1e3, tr[1]*1e3)
ax.set_xlabel(r"$z$, m")
ax.set_ylabel(u"$x$, µm")
ax.set_zlabel(u"$y$, µm")
plt.title("trajectory 3D")
plt.savefig(prefix + '0trajectory' + suffix + '.png')
if not complexPlotsPCA:
return
if not ('mono' in prefix):
return
dump = []
start = time.time()
NN = len(complexPlotsPCA)
for ic, complexPlotPCA in enumerate(complexPlotsPCA):
x = complexPlotPCA.xaxis.binCenters
y = complexPlotPCA.yaxis.binCenters
if repeats >= 4:
print("solving PCA problem, {0} of {1}...".format(ic+1, NN))
Es = complexPlotPCA.field3D
start = time.time()
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es)
stop = time.time()
print("the PCA problem has taken {0:.4f} s".format(stop-start))
dump.append([repeats, x, y, wPCA, vPCA])
pickleName = '{0}-{1}repeats.pickle'.format(prefix, repeats)
with open(pickleName, 'wb') as f:
pickle.dump(dump, f, protocol=2)
print("Done")
if beamLine.source.trajectory is not None:
plt.show()
def main():
und = rs.Undulator(**kw)
for harmonic in harmonics:
Eh = harmonic * und.E1
# Eh = Eh * (1 - 1./harmonic/und.Np) # detuned to harmonic's maximum
und.eMin = Eh * (1 - bw/2)
und.eMax = Eh * (1 + bw/2)
und.reset()
energy = np.ones(repeats) * Eh
st = 'st' if harmonic == 1 else 'nd' if harmonic == 2 else 'rd'\
if harmonic == 3 else 'th'
for energySpread in energySpreads:
und.eEspread = energySpread
for emittancex in emittances:
if energySpread > 1e-12:
ses = '{0:.1e}'.format(energySpread)
ses = ses[:-2] + ses[-1] # removes "0" from power
else:
ses = '0'
txt = '{0}{1} harmonic ({2:.0f} eV){5}{3} energy spread{5}' +\
'$\\epsilon_x$ = {4:.0f} pmrad'
cap = txt.format(harmonic, st, Eh, ses, emittancex, ', ')
baseName = 'h{0:02d}-esp{1}-em{2:04.0f}'.format(
harmonic, energySpread, emittancex)
emittancez = 10 if emittancex > 1e-12 else 0
und.dx = np.sqrt(emittancex*betaX) * 1e-3 # in mm
und.dz = np.sqrt(emittancez*betaZ) * 1e-3 # in mm
und.dxprime = \
emittancex*1e-9 / und.dx if und.dx > 0 else 0. # rad
und.dzprime = \
emittancez*1e-9 / und.dz if und.dz > 0 else 0. # rad
Es, Ep = und.multi_electron_stack(energy, theta, psi)
print("Es.shape", Es.shape)
k = binsx * binsz
D = np.array(Es).reshape((repeats, k), order='F').T
J = np.dot(D, D.T.conjugate()) # / repeats
print("solving eigenvalue problem...")
start = time.time()
dotc4 = rco.calc_degree_of_transverse_coherence_4D(J)
# print('dotc4', dotc4)
wN, vN = rco.calc_eigen_modes_4D(J, eigenN=4)
stop = time.time()
print("the eigenvalue problem has taken {0:.4} s".format(
stop-start))
print("vN.shape", vN.shape)
print("Top 4 eigen values (4D) = {0}".format(wN))
figE4 = rco.plot_eigen_modes(theta*p, psi*p, wN, vN,
xlabel='x (mm)', ylabel='z (mm)')
figE4.axes[0].xaxis.set_ticklabels([])
figE4.axes[1].xaxis.set_ticklabels([])
figE4.suptitle('Eigen modes of mutual intensity,\n'
+ cap, fontsize=11)
plt.text(0.05, 0.05, 'DoTC={0:.3f}'.format(dotc4),
transform=figE4.axes[0].transAxes,
ha='left', va='bottom', color='w', size=10)
figE4.savefig('Modes-{0}-{1}.png'.format('s', baseName))
print("solving PCA problem...")
start = time.time()
dotcP = rco.calc_degree_of_transverse_coherence_PCA(Es)
# print('dotcP', dotcP)
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es, eigenN=4)
stop = time.time()
print("the PCA problem has taken {0:.4} s".format(stop-start))
print("vPCA.shape", vPCA.shape)
print("Top 4 eigen values (PCA) = {0}".format(wPCA))
figEP = rco.plot_eigen_modes(theta*p, psi*p, wPCA, vPCA,
xlabel='x (mm)', ylabel='z (mm)')
figEP.axes[0].xaxis.set_ticklabels([])
figEP.axes[1].xaxis.set_ticklabels([])
figEP.suptitle('Principal components of one-electron images,\n'
+ cap, fontsize=11)
plt.text(0.05, 0.05, 'DoTC={0:.3f}'.format(dotcP),
transform=figEP.axes[0].transAxes,
ha='left', va='bottom', color='w', size=10)
figEP.savefig('Components-{0}-{1}.png'.format('s', baseName))
xdata = rco.calc_1D_coherent_fraction(Es, 'x', theta*p, p)
ydata = rco.calc_1D_coherent_fraction(Es, 'z', psi*p, p)
fig2D1, figXZ = rco.plot_1D_degree_of_coherence(
xdata, 'x', theta*p)
fig2D2, figXZ = rco.plot_1D_degree_of_coherence(
ydata, 'z', psi*p, fig2=figXZ)
fig2D1.suptitle('Mutual intensity for horizontal cut,\n '
+ cap, size=11)
fig2D2.suptitle('Mutual intensity for vertical cut,\n '
+ cap, size=11)
figXZ.suptitle('Intensity and Degree of Coherence,\n '
+ cap, size=11)
fig2D1.savefig('MutualI-x-{0}-{1}.png'.format('s', baseName))
fig2D2.savefig('MutualI-z-{0}-{1}.png'.format('s', baseName))
figXZ.savefig('DOC-{0}-{1}.png'.format('s', baseName))
plt.show()
def main():
und = rs.Undulator(**kw)
for harmonic in harmonics:
Eh = harmonic * und.E1
# Eh = Eh * (1 - 1./harmonic/und.Np) # detuned to harmonic's maximum
und.eMin = Eh * (1 - bw / 2)
und.eMax = Eh * (1 + bw / 2)
und.reset()
energy = np.ones(repeats) * Eh
st = 'st' if harmonic == 1 else 'nd' if harmonic == 2 else 'rd'\
if harmonic == 3 else 'th'
for energySpread in energySpreads:
und.eEspread = energySpread
for emittancex in emittances:
if energySpread > 1e-12:
ses = '{0:.1e}'.format(energySpread)
ses = ses[:-2] + ses[-1] # removes "0" from power
else:
ses = '0'
txt = '{0}{1} harmonic ({2:.0f} eV){5}{3} energy spread{5}' +\
'$\\epsilon_x$ = {4:.0f} pmrad'
cap = txt.format(harmonic, st, Eh, ses, emittancex, ', ')
baseName = 'h{0:02d}-esp{1}-em{2:04.0f}'.format(
harmonic, energySpread, emittancex)
emittancez = 10 if emittancex > 1e-12 else 0
und.dx = np.sqrt(emittancex * betaX) * 1e-3 # in mm
und.dz = np.sqrt(emittancez * betaZ) * 1e-3 # in mm
und.dxprime = \
emittancex*1e-9 / und.dx if und.dx > 0 else 0. # rad
und.dzprime = \
emittancez*1e-9 / und.dz if und.dz > 0 else 0. # rad
Es, Ep = und.multi_electron_stack(energy, theta, psi)
print("Es.shape", Es.shape)
k = binsx * binsz
D = np.array(Es).reshape((repeats, k), order='F').T
J = np.dot(D, D.T.conjugate()) # / repeats
print("solving eigenvalue problem...")
start = time.time()
dotc4 = rco.calc_degree_of_transverse_coherence_4D(J)
# print('dotc4', dotc4)
wN, vN = rco.calc_eigen_modes_4D(J, eigenN=4)
stop = time.time()
print(
"the eigenvalue problem has taken {0:.4} s".format(stop -
start))
print("vN.shape", vN.shape)
print("Top 4 eigen values (4D) = {0}".format(wN))
figE4 = rco.plot_eigen_modes(theta * p,
psi * p,
wN,
vN,
xlabel='x (mm)',
ylabel='z (mm)')
figE4.axes[0].xaxis.set_ticklabels([])
figE4.axes[1].xaxis.set_ticklabels([])
figE4.suptitle('Eigen modes of mutual intensity,\n' + cap,
fontsize=11)
plt.text(0.05,
0.05,
'DoTC={0:.3f}'.format(dotc4),
transform=figE4.axes[0].transAxes,
ha='left',
va='bottom',
color='w',
size=10)
figE4.savefig('Modes-{0}-{1}.png'.format('s', baseName))
print("solving PCA problem...")
start = time.time()
dotcP = rco.calc_degree_of_transverse_coherence_PCA(Es)
# print('dotcP', dotcP)
wPCA, vPCA = rco.calc_eigen_modes_PCA(Es, eigenN=4)
stop = time.time()
print("the PCA problem has taken {0:.4} s".format(stop -
start))
print("vPCA.shape", vPCA.shape)
print("Top 4 eigen values (PCA) = {0}".format(wPCA))
figEP = rco.plot_eigen_modes(theta * p,
psi * p,
wPCA,
vPCA,
xlabel='x (mm)',
ylabel='z (mm)')
figEP.axes[0].xaxis.set_ticklabels([])
figEP.axes[1].xaxis.set_ticklabels([])
figEP.suptitle(
'Principal components of one-electron images,\n' + cap,
fontsize=11)
plt.text(0.05,
0.05,
'DoTC={0:.3f}'.format(dotcP),
transform=figEP.axes[0].transAxes,
ha='left',
va='bottom',
color='w',
size=10)
figEP.savefig('Components-{0}-{1}.png'.format('s', baseName))
xdata = rco.calc_1D_coherent_fraction(Es, 'x', theta * p, p)
ydata = rco.calc_1D_coherent_fraction(Es, 'z', psi * p, p)
fig2D1, figXZ = rco.plot_1D_degree_of_coherence(
xdata, 'x', theta * p)
fig2D2, figXZ = rco.plot_1D_degree_of_coherence(ydata,
'z',
psi * p,
fig2=figXZ)
fig2D1.suptitle('Mutual intensity for horizontal cut,\n ' +
cap,
size=11)
fig2D2.suptitle('Mutual intensity for vertical cut,\n ' + cap,
size=11)
figXZ.suptitle('Intensity and Degree of Coherence,\n ' + cap,
size=11)
fig2D1.savefig('MutualI-x-{0}-{1}.png'.format('s', baseName))
fig2D2.savefig('MutualI-z-{0}-{1}.png'.format('s', baseName))
figXZ.savefig('DOC-{0}-{1}.png'.format('s', baseName))
plt.show()