def propagate_wavefront(cls,
wavefront1,
propagation_distance,
magnification_x=1.0):
wavefront = wavefront1.duplicate()
shape = wavefront.size()
delta = wavefront.delta()
wavenumber = wavefront.get_wavenumber()
wavelength = wavefront.get_wavelength()
fft_scale = numpy.fft.fftfreq(shape) / delta
x = wavefront.get_abscissas()
x_rescaling = wavefront.get_abscissas() * magnification_x
r1sq = x**2 * (1 - magnification_x)
r2sq = x_rescaling**2 * ((magnification_x - 1) / magnification_x)
fsq = (fft_scale**2 / magnification_x)
Q1 = wavenumber / 2 / propagation_distance * r1sq
Q2 = numpy.exp(-1.0j * numpy.pi * wavelength * propagation_distance *
fsq)
Q3 = numpy.exp(1.0j * wavenumber / 2 / propagation_distance * r2sq)
wavefront.add_phase_shift(Q1)
fft = numpy.fft.fft(wavefront.get_complex_amplitude())
ifft = numpy.fft.ifft(fft * Q2) * Q3 / numpy.sqrt(magnification_x)
wf_propagated = GenericWavefront1D.initialize_wavefront_from_arrays(
x_rescaling, ifft, wavelength=wavelength)
return wf_propagated
def propagate_wavefront(cls,
wavefront,
propagation_distance,
shift_half_pixel=False):
shape = wavefront.size()
delta = wavefront.delta()
wavelength = wavefront.get_wavelength()
wavenumber = wavefront.get_wavenumber()
fft_scale = numpy.fft.fftfreq(shape, d=delta)
fft_scale = numpy.fft.fftshift(fft_scale)
x2 = fft_scale * propagation_distance * wavelength
if shift_half_pixel:
x2 = x2 - 0.5 * numpy.abs(x2[1] - x2[0])
p1 = numpy.exp(1.0j * wavenumber * propagation_distance)
p2 = numpy.exp(1.0j * wavenumber / 2 / propagation_distance * x2**2)
p3 = 1.0j * wavelength * propagation_distance
fft = numpy.fft.fft(wavefront.get_complex_amplitude())
fft = fft * p1 * p2 / p3
fft2 = numpy.fft.fftshift(fft)
wavefront_out = GenericWavefront1D.initialize_wavefront_from_arrays(
x2, fft2, wavelength=wavefront.get_wavelength())
# added [email protected] 2018-03-23 to conserve energy - TODO: review method!
wavefront_out.rescale_amplitude(
numpy.sqrt(wavefront.get_intensity().sum() /
wavefront_out.get_intensity().sum()))
return wavefront_out
def get_eigenvector_wavefront(self, mode):
complex_amplitude = self.get_eigenvectors()[mode, :] * np.sqrt(
self.get_eigenvalue(mode))
wf = GenericWavefront1D.initialize_wavefront_from_arrays(
self.abscissas, complex_amplitude)
wf.set_photon_energy(self.photon_energy)
return wf
def _calculate_far_field(self):
#
# undulator emission
#
out = self.calculate_undulator_emission(
electron_energy=self.electron_energy,
electron_current=self.electron_current,
undulator_period=self.undulator_period,
undulator_nperiods=self.undulator_nperiods,
K=self.K,
photon_energy=self.photon_energy,
abscissas_interval_in_far_field=self.
_abscissas_interval_in_far_field,
number_of_points=self.number_of_points,
distance_to_screen=self.distance_to_screen,
scan_direction=self.scan_direction,
)
#
#
#
from wofry.propagator.wavefront1D.generic_wavefront import GenericWavefront1D
input_wavefront = GenericWavefront1D.initialize_wavefront_from_arrays(
out["abscissas"], out["electric_field"][:, 0])
input_wavefront.set_photon_energy(photon_energy=self.photon_energy)
self.far_field_wavefront = input_wavefront
def propagator1D_offaxis(input_wavefront, x2_oe, y2_oe, p, q, theta_grazing_in, theta_grazing_out=None,
zoom_factor=1.0, normalize_intensities=False):
from wofry.propagator.wavefront1D.generic_wavefront import GenericWavefront1D
if theta_grazing_out is None:
theta_grazing_out = theta_grazing_in
x1 = input_wavefront.get_abscissas()
field1 = input_wavefront.get_complex_amplitude()
wavelength = input_wavefront.get_wavelength()
x1_oe = -p * numpy.cos(theta_grazing_in) + x1 * numpy.sin(theta_grazing_in)
y1_oe = p * numpy.sin(theta_grazing_in) + x1 * numpy.cos(theta_grazing_in)
# field2 is the electric field in the mirror
field2 = goFromToSequential(field1, x1_oe, y1_oe, x2_oe, y2_oe,
wavelength=wavelength, normalize_intensities=normalize_intensities)
x3 = x1 * zoom_factor
x3_oe = q * numpy.cos(theta_grazing_out) + x3 * numpy.sin(theta_grazing_out)
y3_oe = q * numpy.sin(theta_grazing_out) + x3 * numpy.cos(theta_grazing_out)
# field2 is the electric field in the image plane
field3 = goFromToSequential(field2, x2_oe, y2_oe, x3_oe, y3_oe,
wavelength=wavelength, normalize_intensities=normalize_intensities)
output_wavefront = GenericWavefront1D.initialize_wavefront_from_arrays(x3, field3 / numpy.sqrt(zoom_factor), wavelength=wavelength)
return output_wavefront
def toGenericWavefront(self):
wavelength = self.wise_computation_result.Lambda
position = self.wise_computation_result.S
electric_field = numpy.real(
self.wise_computation_result.Field) + 1j * numpy.imag(
self.wise_computation_result.Field)
return GenericWavefront1D.initialize_wavefront_from_arrays(
x_array=position, y_array=electric_field, wavelength=wavelength)
def test_initializers(self, do_plot=do_plot):
print("# ")
print("# Tests for initializars (1D) ")
print("# ")
x = numpy.linspace(-100, 100, 50)
y = numpy.abs(x)**1.5 + 1j * numpy.abs(x)**1.8
wf0 = GenericWavefront1D.initialize_wavefront_from_steps(
x[0], numpy.abs(x[1] - x[0]), y.size)
wf0.set_complex_amplitude(y)
wf1 = GenericWavefront1D.initialize_wavefront_from_range(
x[0], x[-1], y.size)
wf1.set_complex_amplitude(y)
wf2 = GenericWavefront1D.initialize_wavefront_from_arrays(x, y)
print("wavefront sizes: ", wf1.size(), wf1.size(), wf2.size())
if do_plot:
from srxraylib.plot.gol import plot
plot(wf0.get_abscissas(),
wf0.get_intensity(),
title="initialize_wavefront_from_steps",
show=0)
plot(wf1.get_abscissas(),
wf1.get_intensity(),
title="initialize_wavefront_from_range",
show=0)
plot(wf2.get_abscissas(),
wf2.get_intensity(),
title="initialize_wavefront_from_arrays",
show=1)
numpy.testing.assert_almost_equal(wf0.get_intensity(),
numpy.abs(y)**2, 5)
numpy.testing.assert_almost_equal(wf1.get_intensity(),
numpy.abs(y)**2, 5)
numpy.testing.assert_almost_equal(x, wf1.get_abscissas(), 11)
numpy.testing.assert_almost_equal(x, wf2.get_abscissas(), 11)
def propagate_wavefront(cls,
wavefront1,
propagation_distance1,
magnification_x=1.0,
radius=1e6):
wavefront = wavefront1.duplicate()
shape = wavefront.size()
delta = wavefront.delta()
wavenumber = wavefront.get_wavenumber()
wavelength = wavefront.get_wavelength()
# radius = -50.0
magnification = (propagation_distance1 + radius) / radius
propagation_distance = propagation_distance1 / magnification
#
# make plane wave by adding a spherical wavefront with radius -R
#
new_phase = 1.0 * wavefront.get_wavenumber() * (
wavefront.get_abscissas()**2) / (-2 * radius)
wavefront.add_phase_shifts(new_phase)
#
# main 2 FFT block
#
fft_scale = numpy.fft.fftfreq(shape) / delta
x = wavefront.get_abscissas()
x_rescaling = wavefront.get_abscissas() * magnification_x
r1sq = x**2 * (1 - magnification_x)
r2sq = x_rescaling**2 * ((magnification_x - 1) / magnification_x)
fsq = (fft_scale**2 / magnification_x)
Q1 = wavenumber / 2 / propagation_distance * r1sq
Q2 = numpy.exp(-1.0j * numpy.pi * wavelength * propagation_distance *
fsq)
Q3 = numpy.exp(1.0j * wavenumber / 2 / propagation_distance * r2sq)
wavefront.add_phase_shift(Q1)
fft = numpy.fft.fft(wavefront.get_complex_amplitude())
ifft = numpy.fft.ifft(fft * Q2) * Q3 / numpy.sqrt(magnification_x)
#
#
#
#
# calculate new phase term and scale coordinates
#
k = wavefront.get_wavenumber()
PHASE1 = numpy.ones(
wavefront.get_abscissas().size) * (k * propagation_distance *
(1 - 1 / magnification))
PHASE2 = k / 2 / propagation_distance * (
magnification - 1) / magnification * (wavefront.get_abscissas() *
magnification)**2
PHASE = PHASE2 + PHASE1
wf_propagated = GenericWavefront1D.initialize_wavefront_from_arrays(
x_rescaling * magnification,
ifft / numpy.sqrt(magnification) * numpy.exp(1j * PHASE),
wavelength=wavelength)
return wf_propagated
def propagate_wavefront_new(
cls,
wavefront,
propagation_distance,
fraunhofer_kernel=True, # set to False for far field propagator
shift_half_pixel=None, # not more used, kept for version compatibility
):
#
# check validity
#
x = wavefront.get_abscissas()
deltax = wavefront.delta()
#
# compute Fourier transform
#
# frequency for axis 1
npixels = wavefront.size()
pixelsize = wavefront.delta()
wavenumber = wavefront.get_wavenumber()
wavelength = wavefront.get_wavelength()
freq_nyquist = 0.5 / pixelsize
if numpy.mod(npixels, 2) == 0:
freq_n = numpy.arange(-npixels // 2, npixels // 2,
1) / (npixels // 2)
else:
freq_n = numpy.arange(-(npixels - 1) // 2,
(npixels + 1) // 2, 1) / ((npixels - 1) // 2)
freq_x = freq_n * freq_nyquist
x2 = freq_x * propagation_distance * wavelength
P1 = numpy.exp(1.0j * wavenumber * propagation_distance)
# fsq = freq_x ** 2
# P2 = numpy.exp(-1.0j * wavenumber / 2 / propagation_distance * fsq)
P2 = numpy.exp(1.0j * wavenumber / 2 / propagation_distance * x**2)
P3 = 1.0j * wavelength * propagation_distance
if fraunhofer_kernel:
exponential = 1.0 + 0j
else:
exponential = numpy.exp(1j * wavenumber / 2 /
propagation_distance * x**2)
F1 = numpy.fft.fft(exponential * wavefront.get_complex_amplitude()
) # Take the fourier transform of the image.
# Now shift the quadrants around so that low spatial frequencies are in
# the center of the 2D fourier transformed image.
F1 *= P1
F1 *= P2
F1 /= numpy.sqrt(P3) # this is 1D -> no sqrt for 2D
F2 = numpy.fft.fftshift(F1)
F2 *= deltax # why??
wavefront_out = GenericWavefront1D.initialize_wavefront_from_arrays(
x_array=x2, y_array=F2, wavelength=wavelength)
# added [email protected] 2018-03-23 to conserve energy - TODO: review method!
# wavefront_out.rescale_amplitude(numpy.sqrt(wavefront.get_intensity().sum() /
# wavefront_out.get_intensity().sum()))
return wavefront_out
def calculate_output_wavefront_after_GSM_1D(input_wavefront,
shape=1,
radius=10000.0,
wavy_amplitude=1e-9,
wavy_ripples=1.0,
grazing_angle=1.5e-3,
grazing_angle_out=1.5e-3,
lines_per_meter=None,
error_flag=0,
error_file="",
error_edge_management=0,
write_profile=""):
import numpy
from scipy import interpolate
abscissas = input_wavefront.get_abscissas().copy()
abscissas_on_mirror = abscissas / numpy.sin(grazing_angle)
abscissas_on_output_plane = abscissas_on_mirror * numpy.sin(
grazing_angle_out)
output_wavefront = GenericWavefront1D.initialize_wavefront_from_arrays(
abscissas_on_output_plane,
input_wavefront.get_complex_amplitude(),
wavelength=input_wavefront.get_wavelength(),
)
# )input_wavefront.duplicate()
if shape == 0:
height = numpy.zeros_like(abscissas_on_mirror)
elif shape == 1:
if radius >= 0:
height = radius - numpy.sqrt(radius**2 - abscissas_on_mirror**2)
else:
height = radius + numpy.sqrt(radius**2 - abscissas_on_mirror**2)
elif shape > 1:
if wavy_ripples == 0.0:
height = numpy.zeros_like(abscissas_on_mirror)
else:
period = (abscissas_on_mirror[-1] -
abscissas_on_mirror[0]) / wavy_ripples
if shape == 2:
y = numpy.cos(2 * numpy.pi * abscissas_on_mirror / period)
elif shape == 3:
y = numpy.sin(2 * numpy.pi * abscissas_on_mirror / period)
y -= y.min()
y /= y.max()
y *= wavy_amplitude
height = y
else:
raise Exception("Wrong shape")
if error_flag:
a = numpy.loadtxt(error_file) # extrapolation
if error_edge_management == 0:
finterpolate = interpolate.interp1d(
a[:, 0], a[:, 1], fill_value="extrapolate"
) # fill_value=(0,0),bounds_error=False)
elif error_edge_management == 1:
finterpolate = interpolate.interp1d(a[:, 0],
a[:, 1],
fill_value=(0, 0),
bounds_error=False)
else: # crop
raise Exception("Bad value of error_edge_management")
height_interpolated = finterpolate(abscissas_on_mirror)
height += height_interpolated
#>>>>>>>>>>>>>>>>>>>
# phi = -2 * output_wavefront.get_wavenumber() * height * numpy.sin(grazing_angle)
# Ken's Eq. 7
if lines_per_meter is None:
delta_grating = 0
else:
order = 1
grating_period = 1.0 / lines_per_meter
delta_grating = order * output_wavefront.get_wavelength(
) / grating_period * abscissas_on_mirror
delta_s = abscissas_on_mirror * (numpy.cos(grazing_angle) - numpy.cos(grazing_angle_out)) \
- height * (numpy.sin(grazing_angle) + numpy.sin(grazing_angle_out))
phi = output_wavefront.get_wavenumber() * (delta_s + delta_grating)
#>>>>>>>>>>>>>>>>>>>
output_wavefront.add_phase_shifts(phi)
if error_flag:
profile_limits = a[-1, 0] - a[0, 0]
profile_limits_projected = (a[-1, 0] -
a[0, 0]) * numpy.sin(grazing_angle)
wavefront_dimension = output_wavefront.get_abscissas(
)[-1] - output_wavefront.get_abscissas()[0]
print("profile deformation dimension: %f m" % (profile_limits))
print(
"profile deformation projected perpendicular to optical axis: %f um"
% (1e6 * profile_limits_projected))
print("wavefront window dimension: %f um" %
(1e6 * wavefront_dimension))
if wavefront_dimension <= profile_limits_projected:
print(
"\nWavefront window inside error profile domain: no action needed"
)
else:
if error_edge_management == 0:
print(
"\nProfile deformation extrapolated to fit wavefront dimensions"
)
else:
output_wavefront.clip(a[0, 0] * numpy.sin(grazing_angle),
a[-1, 0] * numpy.sin(grazing_angle))
print(
"\nWavefront clipped to projected limits of profile deformation"
)
# output files
if write_profile != "":
f = open(write_profile, "w")
for i in range(height.size):
f.write("%g %g\n" % (abscissas_on_mirror[i], height[i]))
f.close()
print("File %s written to disk." % write_profile)
return output_wavefront, abscissas_on_mirror, height
