def simFunc(x, *args, **kwargs):
# add a new parameter
spacing_1, spacing_2, spacing_3, roughness, layer_occ_1, layer_occ_2, scale, q_offset, wse2_t, mos2_t = x
layer_occ_1 = int(layer_occ_1)
layer_occ_2 = int(layer_occ_2)
layer_occ_2 = layer_occ_2 if layer_occ_1 != 0 else 0
xrr_data, q_z, crystal_params = args[:]
layer_dict = OrderedDict({
"layer_1": {
"wse2": 1
},
"layer_2": {
"vacuum": spacing_2
},
"layer_3": {
"mos2": layer_occ_1
},
"layer_4": {
"vacuum": spacing_3
},
"layer_5": {
"mos2": layer_occ_2
}
})
# build film
#pass new params
film = build_crystal(layer_seq=layer_dict,
substrate=crystal_params[0],
mos2_layer=crystal_params[1],
wse2_layer=crystal_params[2],
vacuum=crystal_params[3],
stack_name="1_1_1",
substrate_spacing=spacing_1,
roughness=roughness,
mos2_thickness=mos2_t,
wse2_thickness=wse2_t)
# simulate
en = 10000
resolution = np.rad2deg(2e-3)
m = xu.simpack.SpecularReflectivityModel(film,
sample_width=10,
beam_width=0.3,
energy=en,
I0=scale)
# resolution_width=resolution, I0=1e4)
q_z_shifted = np.copy(q_z) + q_offset
# print("q_z_shift:", q_z_shifted.shape)
alpha = np.rad2deg(np.arcsin(xu.en2lam(en) * q_z_shifted /
(4 * np.pi))).squeeze()
# print("alpha:", alpha.shape)
xrr_sim = m.simulate(alpha)
del film, layer_dict
if 'fit_return' in list(kwargs.keys()):
return xrr_sim, m.densityprofile(300, plot=False)
return xrr_sim
def test_area_calib_hkl(self):
scannrs = (2, 4, 43, 44, 46, 47)
sang, ang1, ang2 = [
numpy.empty(0),
] * 3
imghkl = numpy.empty((0, 3))
r = self.roi
detdata = numpy.empty((0, r[1] - r[0], r[3] - r[2]))
for scannr, scanhkl, sl in zip(scannrs, self.hkls, self.slices):
(om, tth,
tt), angles = xu.io.gettty08_scan(datfile, scannr, 'om', 'tth',
'tt')
intensity = numpy.zeros(
(len(angles[sl]), r[1] - r[0], r[3] - r[2]))
# read images
for i, nr in enumerate(angles[sl]['Number']):
fname = ccdfile % (scannr, scannr, nr)
intensity[i] = getimage(fname, self.hotpixelmap, roi=self.roi)
if scanhkl == (0, 0, 0):
sang = numpy.concatenate((sang, (0, ) * len(angles[sl])))
else:
sang = numpy.concatenate((sang, om[sl]))
ang1 = numpy.concatenate((ang1, tth[sl]))
ang2 = numpy.concatenate((ang2, tt[sl]))
detdata = numpy.concatenate((detdata, intensity))
imghkl = numpy.concatenate((imghkl, (scanhkl, ) * len(angles[sl])))
# start calibration
GaAs = xu.materials.GaAs
qconv = xu.experiment.QConversion(['z+', 'y-', 'x+', 'z-'],
['z+', 'y-'], [1, 0, 0])
hxrd = xu.HXRD(GaAs.Q(1, 1, 0),
GaAs.Q(0, 0, 1),
en=self.en,
qconv=qconv)
param, eps = xu.analysis.sample_align.area_detector_calib_hkl(
sang,
ang1,
ang2,
detdata,
imghkl,
hxrd,
GaAs, ['z+', 'y-'],
'x+',
start=(0, 0, 0, 0, 0, 0, xu.en2lam(self.en)),
fix=(False, False, False, False, False, False, False),
debug=False,
plot=False)
self.assertTrue(True)
def test_area_calib_hkl(self):
scannrs = (2, 4, 43, 44, 46, 47)
sang, ang1, ang2 = [numpy.empty(0), ]*3
imghkl = numpy.empty((0, 3))
r = self.roi
detdata = numpy.empty((0, r[1] - r[0], r[3] - r[2]))
for scannr, scanhkl, sl in zip(scannrs, self.hkls, self.slices):
(om, tth, tt), angles = xu.io.gettty08_scan(datfile, scannr,
'om', 'tth', 'tt')
intensity = numpy.zeros((len(angles[sl]),
r[1] - r[0], r[3] - r[2]))
# read images
for i, nr in enumerate(angles[sl]['Number']):
fname = ccdfile % (scannr, scannr, nr)
intensity[i] = getimage(fname, self.hotpixelmap, roi=self.roi)
if scanhkl == (0, 0, 0):
sang = numpy.concatenate((sang, (0,)*len(angles[sl])))
else:
sang = numpy.concatenate((sang, om[sl]))
ang1 = numpy.concatenate((ang1, tth[sl]))
ang2 = numpy.concatenate((ang2, tt[sl]))
detdata = numpy.concatenate((detdata, intensity))
imghkl = numpy.concatenate((imghkl, (scanhkl,)*len(angles[sl])))
# start calibration
GaAs = xu.materials.GaAs
qconv = xu.experiment.QConversion(['z+', 'y-', 'x+', 'z-'],
['z+', 'y-'], [1, 0, 0])
hxrd = xu.HXRD(GaAs.Q(1, 1, 0), GaAs.Q(0, 0, 1),
en=self.en, qconv=qconv)
param, eps = xu.analysis.sample_align.area_detector_calib_hkl(
sang, ang1, ang2, detdata, imghkl, hxrd, GaAs,
['z+', 'y-'], 'x+',
start=(0, 0, 0, 0, 0, 0, xu.en2lam(self.en)),
fix=(False, False, False, False, False, False, False),
debug=False, plot=False)
self.assertTrue(True)
plt.show()
# get the sample to detector distance
# for that determine how much the the com has moved when the detector
# has rotated by 1 degree. We may find this value with delta or nu. Here, we do
# both and calculate the average. The leading coefficient of the function
# x_com = f(delta) gives how much the x_com has moved when delta has changed by
# one degree.
x_com_shift = P.polyfit(delta, x_com, 1)[1]
y_com_shift = P.polyfit(nu, y_com, 1)[1]
pix0_x = P.polyfit(delta, x_com,
1)[0] # pixel 0, reference of the direct beam
pix0_y = P.polyfit(nu, y_com, 1)[0]
sdd = (1 / 2) * (1 / np.tan(np.pi / 180)) * (x_com_shift +
y_com_shift) * 55e-6
param, eps = xu.analysis.sample_align.area_detector_calib(
angle1,
angle2,
ccdimages, ['z-', 'y-'],
'x+',
start=(55e-6, 55e-6, ssd, 45, 0, -0.7, 0),
fix=(True, True, False, False, False, False, True),
wl=xu.en2lam(en))
# qx, qy, qz = hxrd.Ang2Q.area(eta, phi, nu, delta,
# delta=(0, 0, outerangle_offset, 0))
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/>.
#
# Copyright (C) 2016 Dominik Kriegner <*****@*****.**>
from matplotlib.pylab import *
import xrayutilities as xu
import time
mpl.rcParams['font.size'] = 16.0
en = 'CuKa1' # eV
lam = xu.en2lam(en)
resol = 2 * pi / 4998 # resolution in q; to suppress buffer oscillations
h, k, l = (0, 0, 4)
qz = linspace(4.0, 5.0, 3e3)
sub = xu.simpack.Layer(xu.materials.Si, inf)
# xfrom xto nsteps thickness
buf1 = xu.simpack.GradedLayerStack(xu.materials.SiGe,
0.2,
0.8,
100,
10000,
relaxation=1.0)
buf2 = xu.simpack.Layer(xu.materials.SiGe(0.8), 4997.02, relaxation=1.0)
lay1 = xu.simpack.Layer(xu.materials.SiGe(0.6), 49.73, relaxation=0.0)
lay2 = xu.simpack.Layer(xu.materials.SiGe(1.0), 45.57, relaxation=0.0)
InPWZ = xu.materials.InPWZ
# approximate version of other hexagonal polytypes
ainp = InP.a
InP4H = xu.materials.Crystal(
"InP(4H)",
xu.materials.SGLattice(186,
ainp / sqrt(2),
2 * sqrt(4 / 3.) * ainp,
atoms=[In, In, P, P],
pos=[('2a', 0), ('2b', 1 / 4.), ('2a', 3 / 16.),
('2b', 7 / 16.)]))
for energy in [8041]: # eV
lam = xu.en2lam(energy) # e in eV -> lam in angstrom
print(' %d eV = %8.4f A' % (energy, lam))
print('------------------------------------------------------------------'
'-----------------')
print('material | peak | omega | 2theta | phi | '
'tt-om | |F| ')
print('------------------------------------------------------------------'
'-----------------')
exp111 = xu.HXRD(InP.Q(1, 1, -2), InP.Q(1, 1, 1), en=energy)
exphex = xu.HXRD(InPWZ.Q(1, -1, 0), InPWZ.Q(0, 0, 1), en=energy)
# InP ZB reflections
reflections = [[1, 1, 1], [2, 2, 2], [3, 3, 3], [3, 3, 1], [2, 2, 4],
[1, 1, 5]]
mat = InP
imagenrs = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]
images = []
ang1 = []
ang2 = []
# read images and angular positions from the data file
# this might differ for data taken at different beamlines since
# they way how motor positions are stored is not always consistent
for imgnr in imagenrs:
filename = filetmp % imgnr
edf = xu.io.EDFFile(filename)
images.append(edf.data)
ang1.append(float(edf.header['ESRF_ID01_PSIC_NANO_NU']))
ang2.append(float(edf.header['ESRF_ID01_PSIC_NANO_DEL']))
# call the fit for the detector parameters
# detector arm rotations and primary beam direction need to be given.
# in total 9 parameters are fitted, however the severl of them can
# be fixed. These are the detector tilt azimuth, the detector tilt angle, the
# detector rotation around the primary beam and the outer angle offset
# The detector pixel size or the detector distance should be kept unfixed to
# be optimized by the fit.
param, eps = xu.analysis.sample_align.area_detector_calib(
ang1, ang2, images, ['z+', 'y-'], 'x+',
start=(None, None, 1.0, 45, 0, -0.7, 0),
fix=(False, False, True, False, False, False, False),
wl=xu.en2lam(en))
en = 10300.0 # eV
datadir = os.path.join("data", "wire_") # data path for CCD files
# template for the CCD file names
filetmp = os.path.join(datadir, "wire_12_%05d.edf.gz")
# manually selected images
# select images which have the primary beam fully on the CCD
imagenrs = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]
images = []
ang1 = []
ang2 = []
# read images and angular positions from the data file
for imgnr in imagenrs:
filename = filetmp % imgnr
edf = xu.io.EDFFile(filename)
images.append(edf.data)
ang1.append(float(edf.header['ESRF_ID01_PSIC_NANO_NU']))
ang2.append(float(edf.header['ESRF_ID01_PSIC_NANO_DEL']))
# or for newer EDF files (recorded in year >~2013)
# ang1.append(edf.motors['nu'])
# ang2.append(edf.motors['del'])
# call the fit for the detector parameters
# detector arm rotations and primary beam direction need to be given
param, eps = xu.analysis.sample_align.area_detector_calib(
ang1, ang2, images, ['z+', 'y-'], 'x+', start=(45, 0, -0.7, 0),
fix=(False, False, False, False), wl=xu.en2lam(en))
# define materials
InP = xu.materials.InP
InPWZ = xu.materials.InPWZ
# approximate version of other hexagonal polytypes
ainp = InP.a
InP4H = xu.materials.Crystal(
"InP(4H)",
xu.materials.SGLattice(186, ainp / sqrt(2), 2 * sqrt(4/3.) * ainp,
atoms=[In, In, P, P],
pos=[('2a', 0), ('2b', 1/4.),
('2a', 3/16.), ('2b', 7/16.)]))
for energy in [8041]: # eV
lam = xu.en2lam(energy) # e in eV -> lam in Angstroem
print(' %d eV = %8.4f A' % (energy, lam))
print('------------------------------------------------------------------'
'-----------------')
print('material | peak | omega | 2theta | phi | '
'tt-om | |F| ')
print('------------------------------------------------------------------'
'-----------------')
exp111 = xu.HXRD(InP.Q(1, 1, -2), InP.Q(1, 1, 1), en=energy)
exphex = xu.HXRD(InPWZ.Q(1, -1, 0), InPWZ.Q(0, 0, 1), en=energy)
# InP ZB reflections
reflections = [[1, 1, 1], [2, 2, 2], [3, 3, 3],
[3, 3, 1], [2, 2, 4], [1, 1, 5]]
mat = InP
InPWZ = xu.materials.InPWZ
# approximate version of other hexagonal polytypes
ainp = InP.a
InP4H = xu.materials.Crystal(
"InP(4H)",
xu.materials.SGLattice(186,
ainp / sqrt(2),
2 * sqrt(4 / 3.) * ainp,
atoms=[In, In, P, P],
pos=[('2a', 0), ('2b', 1 / 4.), ('2a', 3 / 16.),
('2b', 7 / 16.)]))
for energy in [8041]: # eV
lam = xu.en2lam(energy) # e in eV -> lam in Angstroem
print(' %d eV = %8.4f A' % (energy, lam))
print('------------------------------------------------------------------'
'-----------------')
print('material | peak | omega | 2theta | phi | '
'tt-om | |F| ')
print('------------------------------------------------------------------'
'-----------------')
exp111 = xu.HXRD(InP.Q(1, 1, -2), InP.Q(1, 1, 1), en=energy)
exphex = xu.HXRD(InPWZ.Q(1, -1, 0), InPWZ.Q(0, 0, 1), en=energy)
# InP ZB reflections
reflections = [[1, 1, 1], [2, 2, 2], [3, 3, 3], [3, 3, 1], [2, 2, 4],
[1, 1, 5]]
mat = InP
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/>.
#
# Copyright (C) 2016 Dominik Kriegner <*****@*****.**>
import time
import xrayutilities as xu
from matplotlib.pylab import *
mpl.rcParams['font.size'] = 16.0
en = 'CuKa1' # eV
lam = xu.en2lam(en)
resol = 2*pi/4998 # resolution in q; to suppress buffer oscillations
H, K, L = (0, 0, 4)
qz = linspace(4.0, 5.0, 3000)
sub = xu.simpack.Layer(xu.materials.Si, inf)
# xfrom xto nsteps thickness
buf1 = xu.simpack.GradedLayerStack(xu.materials.SiGe, 0.2, 0.8, 100, 10000,
relaxation=1.0)
buf2 = xu.simpack.Layer(xu.materials.SiGe(0.8), 4997.02, relaxation=1.0)
lay1 = xu.simpack.Layer(xu.materials.SiGe(0.6), 49.73, relaxation=0.0)
lay2 = xu.simpack.Layer(xu.materials.SiGe(1.0), 45.57, relaxation=0.0)
# create superlattice stack
# lattice param. are adjusted to the relaxation parameter of the layers
# Note that to create a superlattice you can use summation and multiplication
pls = xu.simpack.PseudomorphicStack001('SL 5/5', sub+buf1+buf2+5*(lay1+lay2))
