def setup_experiment(pars, cif_file, no_voxels, beam_width, theta_min,
theta_max):
'''
beam_width needs to be in microns it is here converted to mm
'''
o11 = pars.parameters['o11']
o12 = pars.parameters['o12']
o21 = pars.parameters['o21']
o22 = pars.parameters['o22']
dety_size = 2048
detz_size = 2048
(dety_center, detz_center) = detector.xy_to_detyz(
[pars.parameters['z_center'], pars.parameters['y_center']], o11, o12,
o21, o22, dety_size, detz_size)
param = {
'no_voxels': no_voxels,
'beam_width': beam_width / 1000.,
'structure_phase_0': cif_file,
'omega_start': 0,
'omega_end': 180,
'omega_step': 1.0,
'omega_sign': pars.parameters['omegasign'],
'wavelength': pars.parameters['wavelength'],
'detz_center': detz_center, #1041.55049636,#detz_center
'dety_center': dety_center, #998.798998,#dety_center
'z_size': pars.parameters['z_size'] / 1000.,
'y_size': pars.parameters['y_size'] / 1000.,
'distance': pars.parameters['distance'] / 1000.,
'o11': pars.parameters['o11'],
'o12': pars.parameters['o12'],
'o21': pars.parameters['o21'],
'o22': pars.parameters['o22'],
'dety_size': dety_size,
'detz_size': detz_size,
'lorentz_apply': 1,
'theta_min': 0,
'theta_max':
12, #i.e only two theta = 2*theta_max or lower is computed for
'tilt_x': pars.parameters['tilt_x'],
'tilt_y': pars.parameters['tilt_y'],
'tilt_z': pars.parameters['tilt_z'],
'wedge': pars.parameters['wedge'],
}
return param
def run(self, start_voxel, end_voxel, log=True, parallel=None):
'''
This is the main algorithmic loop that will calculate the diffraction pattern
based on a per-voxel definition of the sample. The algorithm deals with one voxel at the
time, computing all reflections that would be generated for that voxel in a single go.
Therefore, the run time is linear with voxel number regardless of the number of y-scans.
Input: start_voxel: Used for running in parrallel, equal to 0 for 1 cpu
end_voxel: Used for running in parrallel, equal to number of voxels for 1 cpu
log: used to determine of this process should print to stdout or be quiet.
parallel: Flag to indicate parallel mode. Such a mode requires that we pass the result
of the run to the calling process.
'''
self.param['lorentz_apply']=1
tth_lower_bound = self.param['two_theta_lower_bound']*np.pi/180.
tth_upper_bound = self.param['two_theta_upper_bound']*np.pi/180.
spot_id = 0
# Compute used quanteties once for speed
#--------------------------------------------
# Don't make static dictonary calls in loop, slower..
no_voxels = self.param['no_voxels']
beam_width = self.param['beam_width']
omega_start = self.param['omega_start']*np.pi/180
omega_end = self.param['omega_end']*np.pi/180
wavelength = self.param['wavelength']
scale_Gw = wavelength/(4.*np.pi)
detz_center = self.param['detz_center']
dety_center = self.param['dety_center']
z_size = self.param['z_size']
y_size = self.param['y_size']
distance = self.param['distance']
wavelength = self.param['wavelength']
if log==True:
print('no of voxels ',no_voxels)
# print(start_voxel, end_voxel)
# with open('solution','w') as f:
# for voxel_nbr in range(start_voxel, end_voxel):
# if len(self.param['phase_list']) == 1:
# phase = self.param['phase_list'][0]
# else:
# phase = self.param['phase_voxels_%s' %(self.param['voxel_list'][voxel_nbr])]
# unit_cell = self.param['unit_cell_phase_%i' %phase]
# U = self.param['U_voxels_%s' %(self.param['voxel_list'][voxel_nbr])]
# voxel_eps = np.array(self.param['eps_voxels_%s' %(self.param['voxel_list'][voxel_nbr])])
# B = tools.epsilon_to_b(voxel_eps,unit_cell)
# UB = np.dot(U,B)
# ub = UB.ravel()
# f.write("np.array([")
# for val in ub:
# f.write(str(val)+", ")
# f.write("])\n")
# raise
for voxel_nbr in range(start_voxel, end_voxel):
A = []
U = self.param['U_voxels_%s' %(self.param['voxel_list'][voxel_nbr])]
if len(self.param['phase_list']) == 1:
phase = self.param['phase_list'][0]
else:
phase = self.param['phase_voxels_%s' %(self.param['voxel_list'][voxel_nbr])]
unit_cell = self.param['unit_cell_phase_%i' %phase]
self.voxel[voxel_nbr] = variables.voxel_cont(U)
voxel_pos = np.array(self.param['pos_voxels_%s' %(self.param['voxel_list'][voxel_nbr])])
voxel_eps = np.array(self.param['eps_voxels_%s' %(self.param['voxel_list'][voxel_nbr])])
# Calculate the B-matrix based on the strain tensor for each voxel
B = tools.epsilon_to_b(voxel_eps,unit_cell)
# add B matrix to voxel container
self.voxel[voxel_nbr].B = B
V = tools.cell_volume(unit_cell)
voxel_vol = np.pi/6 * self.param['size_voxels_%s' %self.param['voxel_list'][voxel_nbr]]**3
nrefl = 0
for hkl in self.hkl[self.param['phase_list'].index(phase)]:
check_input.interrupt(self.killfile)
Gc = np.dot(B,hkl[0:3])
Gw = np.dot(self.S,np.dot(U,Gc))
# if hkl[0]==0 and hkl[1]==-2 and hkl[2]==0:
# print(np.dot(U,B))
# print(hkl[0:3])
# print(Gw)
# raise
tth = tools.tth2(Gw,wavelength)
# If specified in input file do not compute for two
# thetaoutside range [tth_lower_bound, tth_upper_bound]
# if tth_upper_bound<tth or tth<tth_lower_bound :
# continue
costth = np.cos(tth)
(Omega, Eta, Omega_mat, Gts) = self.find_omega_general(Gw*scale_Gw,scale_Gw,tth)
for omega,eta,Om,Gt in zip(Omega, Eta, Omega_mat, Gts):
if omega_start < omega and omega < omega_end:
#print(np.degrees(omega))
# Calc crystal position at present omega
[tx,ty,tz]= np.dot(Om,voxel_pos)
#The 3 beam y positions that could illuminate the voxel
beam_centre_1 = np.round(ty/beam_width)*beam_width
beam_centre_0 = beam_centre_1+beam_width
beam_centre_2 = beam_centre_1-beam_width
#Compute precentual voxel beam overlap for the three regions
overlaps_info = convexPolygon.compute_voxel_beam_overlap(tx,ty,omega,beam_centre_1, beam_width)
regions = [ beam_centre_0, beam_centre_1,beam_centre_2]
for beam_centre_y,info in zip(regions,overlaps_info):
# perhaps we should have a less restrictive
# comparision here, like: if intensity_modifier<0.0001,
# there is some possibilty to simulate nosie here
intensity_modifier = info[0]
if intensity_modifier==0:
continue
#Lorentz factor
if self.param['lorentz_apply'] == 1:
if eta != 0:
L=1/(np.sin(tth)*abs(np.sin(eta)))
else:
L=np.inf;
else:
L = 1.0
intensity = L*intensity_modifier*hkl[3]
# If no unit cells where activated we continue
dty = beam_centre_y*1000. # beam cetre in microns
cx = info[1]
cy = info[2]
# Compute peak based on cms of illuminated voxel fraction
tx = cx
ty = cy - beam_centre_y # The sample moves not the beam!
# print("tx",tx)
# print("ty",ty)
# print("")
tz = 0
(dety, detz) = self.det_coor(Gt,
costth,
wavelength,
distance,
y_size,
z_size,
dety_center,
detz_center,
self.R,
tx,ty,tz)
#If shoebox extends outside detector exclude it
if (-0.5 > dety) or\
(dety > self.param['dety_size']-0.5) or\
(-0.5 > detz) or\
(detz > self.param['detz_size']-0.5):
continue
if self.param['spatial'] != None :
# To match the coordinate system of the spline file
(x,y) = detector.detyz_to_xy([dety,detz],
self.param['o11'],
self.param['o12'],
self.param['o21'],
self.param['o22'],
self.param['dety_size'],
self.param['detz_size'])
# Do the spatial distortion
(xd,yd) = self.spatial.distort(x,y)
# transform coordinates back to dety,detz
(detyd,detzd) = detector.xy_to_detyz([xd,yd],
self.param['o11'],
self.param['o12'],
self.param['o21'],
self.param['o22'],
self.param['dety_size'],
self.param['detz_size'])
else:
detyd = dety
detzd = detz
if self.param['beampol_apply'] == 1:
#Polarization factor (Kahn, J. Appl. Cryst. (1982) 15, 330-337.)
rho = np.pi/2.0 + eta + self.param['beampol_direct']*np.pi/180.0
P = 0.5 * (1 + costth*costth -\
self.param['beampol_factor']*np.cos(2*rho)*np.sin(tth)**2)
else:
P = 1.0
overlaps = 0 # set the number overlaps to zero
# if self.param['intensity_const'] != 1:
# intensity = intensity_modifier*int_intensity(hkl[3],
# L,
# P,
# self.param['beamflux'],
# self.param['wavelength'],
# V,
# voxel_vol)
# else:
# intensity = intensity_modifier*hkl[3]
#TODO: try and build up the images as we go
# Mapp reflection to the correct image
self.peak_merger.add_reflection_to_images([self.param['voxel_list'][voxel_nbr],
nrefl,spot_id,
hkl[0],hkl[1],hkl[2],
tth,omega,eta,
dety,detz,
detyd,detzd,
Gw[0],Gw[1],Gw[2],
L,P,hkl[3],intensity,overlaps,dty,1])
A.append([self.param['voxel_list'][voxel_nbr],
nrefl,spot_id,
hkl[0],hkl[1],hkl[2],
tth,omega,eta,
dety,detz,
detyd,detzd,
Gw[0],Gw[1],Gw[2],
L,P,hkl[3],intensity,overlaps,dty,1])
nrefl = nrefl+1
#TODO: When run in parallel the spot id might be duplicated..
#but who cares about spot id anyways huh..
spot_id = spot_id+1
# A = np.array(A)
# if len(A) > 0:
# # sort rows according to omega
# A = A[np.argsort(A,0)[:,A_id['omega']],:]
# # Renumber the reflections
# A[:,A_id['ref_id']] = np.arange(nrefl)
# # Renumber the spot_id
# A[:,A_id['spot_id']] = np.arange(np.min(A[:,A_id['spot_id']]),
# np.max(A[:,A_id['spot_id']])+1)
# else:
# A = np.zeros((0,len(A_id)))
# save reflection info in voxel container, if we are not in parallel mode this
# will be the same object that we ran the run() method upon. Otherwise it is by
# defualt a copy of that object and we will need to pass the result to the calling
# process at the end of the algorithm
self.voxel[voxel_nbr].refs = A
if parallel and log==True:
progress = int(100*(voxel_nbr+1-start_voxel)/float(end_voxel-start_voxel))
print('\rDone approximately %3i percent' %progress, end=' ')
sys.stdout.flush()
elif log==True:
print('\rDone approximately %3i voxel(s) of %3i' %(voxel_nbr+1,self.param['no_voxels']), end=' ')
sys.stdout.flush()
if log==True:
print('\n')
# if we are running in parrallel the memory cannot be shared (easily)
# so wee return our result in order to merge all results
# into a single find_refl.py Object.
if parallel:
result = {"start_voxel": start_voxel,
"end_voxel": end_voxel,
"refl_list": self.voxel}
return result
else:
# if not in parallel we may merge here
# this allows us to include analyse_images_as_clusters()
# easy in profiling
self.peak_merger.analyse_images_as_clusters()
def make_image(self, frame_number=None):
"""
makeimage script produces edf diffraction images using the reflection information
Henning Osholm Sorensen, June 23, 2006.
python translation Jette Oddershede, March 31, 2008
"""
peakshape = self.graindata.param['peakshape']
if peakshape[0] == 0: # spike peak, 2x2 pixels
peak_add = 1
frame_add = 1
peakwsig = 0
elif peakshape[0] == 1: # 3d Gaussian peak
peak_add = max(1, int(round(peakshape[1])))
frame_add = max(1, int(round(peakshape[1])))
peakwsig = peakshape[2]
elif peakshape[0] == 3: # 3d Gaussian peak in 2theta,eta,omega
peak_add = 1
frame_add = 1
cen_tth = int(1.5 * peakshape[1] / Delta_tth)
frame_tth = 2 * cen_tth + 1
fwhm_tth = peakshape[1] / Delta_tth
cen_eta = int(1.5 * peakshape[2] / Delta_eta)
frame_eta = 2 * cen_eta + 1
fwhm_eta = peakshape[2] / Delta_eta
raw_tth_eta = n.zeros((frame_tth, frame_eta))
raw_tth_eta[cen_tth, cen_eta] = 1
filter_tth_eta = ndimage.gaussian_filter(
raw_tth_eta, [0.5 * fwhm_tth, 0.5 * fwhm_eta])
peakwsig = 1.
framedimy = int(self.graindata.param['dety_size'] + 2 * frame_add)
framedimz = int(self.graindata.param['detz_size'] + 2 * frame_add)
totalrefl = 0
if frame_number == None:
no_frames = list(range(len(self.graindata.frameinfo)))
print('Generating diffraction images')
else:
no_frames = [frame_number]
for i in no_frames:
check_input.interrupt(self.killfile)
t1 = time.clock()
nrefl = 0
frame = n.zeros((framedimy, framedimz))
omega = self.graindata.frameinfo[i].omega
omega_step = self.graindata.param['omega_step']
# Jettes hack to add relative movement of sample and detector, modelled to be Gaussian in y and z direction with a spread of 1 micron
# movement of 1 micron along x judged to be irrelevant, at least for farfield data
y_move = n.random.normal(0, 1. / self.graindata.param['dety_size'])
z_move = n.random.normal(0, 1. / self.graindata.param['detz_size'])
# loop over grains
for j in range(self.graindata.param['no_grains']):
# loop over reflections for each grain
gr_pos = n.array(self.graindata.param['pos_grains_%s' % j])
for k in range(len(self.graindata.grain[j].refs)):
# exploit that the reflection list is sorted according to omega
if self.graindata.grain[j].refs[k,A_id['omega']]*180/n.pi > \
omega+omega_step+2*peakwsig:
break
elif self.graindata.grain[j].refs[k,A_id['omega']]*180/n.pi < \
omega-2*peakwsig:
continue
dety = self.graindata.grain[j].refs[
k, A_id['detyd']] # must be spot position after
detz = self.graindata.grain[j].refs[
k, A_id['detzd']] # applying spatial distortion
#apply hack
# dety = self.graindata.grain[j].refs[k,A_id['dety']] + y_move
# detz = self.graindata.grain[j].refs[k,A_id['detz']] + z_move
ndety = int(round(dety))
ndetz = int(round(detz))
yrange = list(
range(ndety + frame_add - peak_add,
ndety + frame_add + peak_add + 1))
zrange = list(
range(ndetz + frame_add - peak_add,
ndetz + frame_add + peak_add + 1))
intensity = int(
round(self.graindata.grain[j].refs[k, A_id['Int']]))
nrefl = nrefl + 1
totalrefl = totalrefl + 1
# Gaussian along omega
if peakshape[0] == 1 or peakshape[0] == 3:
fraction = norm.cdf((omega-self.graindata.grain[j].refs[k,A_id['omega']]*180/n.pi+omega_step)/(0.5*peakwsig))\
-norm.cdf((omega-self.graindata.grain[j].refs[k,A_id['omega']]*180/n.pi)/(0.5*peakwsig))
else:
fraction = 1.
if peakshape[0] == 3:
# Gaussian peaks along 2theta,eta
tth = self.graindata.grain[j].refs[k, A_id['tth']]
eta = self.graindata.grain[j].refs[k, A_id['eta']]
Om = tools.form_omega_mat_general(
self.graindata.grain[j].refs[k, A_id['omega']], 0,
-1. * self.graindata.param['wedge'] * n.pi / 180.)
[tx, ty, tz] = n.dot(Om, gr_pos)
for t in range(frame_tth):
tth_present = tth + (
t - cen_tth) * Delta_tth * n.pi / 180.
for e in range(frame_eta):
eta_present = eta + (
e - cen_eta) * Delta_eta * n.pi / 180.
[dety_present,
detz_present] = detector.det_coor2(
tth_present,
eta_present,
self.graindata.param['distance'],
self.graindata.param['y_size'],
self.graindata.param['z_size'],
self.graindata.param['dety_center'],
self.graindata.param['detz_center'],
self.graindata.R,
tx,
ty,
tz,
)
if self.graindata.param['spatial'] != None:
from ImageD11 import blobcorrector
self.spatial = blobcorrector.correctorclass(
self.graindata.param['spatial'])
# To match the coordinate system of the spline file
# SPLINE(i,j): i = detz; j = (dety_size-1)-dety
# Well at least if the spline file is for frelon2k
(x, y) = detector.detyz_to_xy(
[dety_present, detz_present],
self.graindata.param['o11'],
self.graindata.param['o12'],
self.graindata.param['o21'],
self.graindata.param['o22'],
self.graindata.param['dety_size'],
self.graindata.param['detz_size'])
# Do the spatial distortion
(xd, yd) = self.spatial.distort(x, y)
# transform coordinates back to dety,detz
(dety_present,
detz_present) = detector.xy_to_detyz(
[xd, yd], self.graindata.param['o11'],
self.graindata.param['o12'],
self.graindata.param['o21'],
self.graindata.param['o22'],
self.graindata.param['dety_size'],
self.graindata.param['detz_size'])
y = int(round(dety_present))
z = int(round(detz_present))
try:
frame[y + frame_add, z + frame_add] = frame[
y + frame_add, z +
frame_add] + fraction * intensity * filter_tth_eta[
t, e]
except:
# FIXME print("Unhandled exception in make_image.py")
pass
else:
# Generate spikes, 2x2 pixels
for y in yrange:
for z in zrange:
if y > 0 and y < framedimy and z > 0 and z < framedimz and abs(
dety + frame_add - y) < 1 and abs(
detz + frame_add - z) < 1:
# frame[y-1,z] = frame[y-1,z] + fraction*intensity*(1-abs(dety+frame_add-y))*(1-abs(detz+frame_add-z))
y = int(round(y))
z = int(round(z))
frame[y, z] = frame[
y, z] + fraction * intensity * (
1 - abs(dety + frame_add - y)) * (
1 - abs(detz + frame_add - z))
# 2D Gaussian on detector
if peakshape[0] == 1:
frame = ndimage.gaussian_filter(frame, peakshape[1] * 0.5)
# add background
if self.graindata.param['bg'] > 0:
frame = frame + self.graindata.param['bg'] * n.ones(
(framedimy, framedimz))
# add noise
if self.graindata.param['noise'] != 0:
frame = n.random.poisson(frame)
# apply psf
if self.graindata.param['psf'] != 0:
frame = ndimage.gaussian_filter(
frame, self.graindata.param['psf'] * 0.5)
# resize, convert to integers and flip to same orientation as experimental frames
frame = frame[frame_add:framedimy - frame_add,
frame_add:framedimz - frame_add]
# limit values above 16 bit to be 16bit
frame = n.clip(frame, 0, 2**16 - 1)
# convert to integers
frame = n.uint16(frame)
#flip detector orientation according to input: o11, o12, o21, o22
frame = detector.trans_orientation(frame,
self.graindata.param['o11'],
self.graindata.param['o12'],
self.graindata.param['o21'],
self.graindata.param['o22'],
'inverse')
# Output frames
if '.edf' in self.graindata.param['output']:
self.write_edf(i, frame)
if '.edf.gz' in self.graindata.param['output']:
self.write_edf(i, frame, usegzip=True)
if '.tif' in self.graindata.param['output']:
self.write_tif(i, frame)
if '.tif16bit' in self.graindata.param['output']:
self.write_tif16bit(i, frame)
print('\rDone frame %i took %8f s' % (i + 1, time.clock() - t1),
end=' ')
sys.stdout.flush()
def test3(self): # o11, o12, o21, o22 = -1, 0, 0, -1
detyz = n.array([841.38745747, 62.2754412563])
xy = detector.xy_to_detyz(detyz, -1, 0, 0, -1, 1024, 1024)
detyz2 = detector.detyz_to_xy(xy, -1, 0, 0, -1, 1024, 1024)
self.assertEqual(detyz.all(), detyz2.all())
def test2(self): # o11, o12, o21, o22 = 1, 0, 0, 1
detyz = [10, 20]
(x, y) = detector.xy_to_detyz(detyz, 1, 0, 0, 1, 1024, 1024)
self.assertEqual(detyz, [y, x])
def make_image(self, grainno=None, refl=None):
from scipy import ndimage
if grainno == None:
do_grains = list(range(self.graindata.param['no_grains']))
else:
do_grains = [grainno]
# loop over grains
for grainno in do_grains:
gr_pos = n.array(self.graindata.param['pos_grains_%s' \
%(self.graindata.param['grain_list'][grainno])])
B = self.graindata.grain[grainno].B
SU = n.dot(self.graindata.S, self.graindata.grain[grainno].U)
if refl == None:
do_refs = list(range(len(self.graindata.grain[grainno].refs)))
else:
do_refs = [refl]
# loop over reflections for each grain
for nref in do_refs:
# exploit that the reflection list is sorted according to omega
print('\rDoing reflection %i of %i for grain %i of %i' %
(nref + 1, len(self.graindata.grain[grainno].refs),
grainno + 1, self.graindata.param['no_grains']),
end=' ')
sys.stdout.flush()
#print 'Doing reflection: %i' %nref
if self.graindata.param['odf_type'] == 3:
intensity = 1
else:
intensity = self.graindata.grain[grainno].refs[nref,
A_id['Int']]
hkl = n.array([
self.graindata.grain[grainno].refs[nref, A_id['h']],
self.graindata.grain[grainno].refs[nref, A_id['k']],
self.graindata.grain[grainno].refs[nref, A_id['l']]
])
Gc = n.dot(B, hkl)
for i in range(self.odf.shape[0]):
for j in range(self.odf.shape[1]):
for k in range(self.odf.shape[2]):
check_input.interrupt(self.killfile)
if self.odf[i, j, k] > self.odf_cut:
Gtmp = n.dot(self.Uodf[i, j, k], Gc)
Gw = n.dot(SU, Gtmp)
Glen = n.sqrt(n.dot(Gw, Gw))
tth = 2 * n.arcsin(Glen /
(2 * abs(self.graindata.K)))
costth = n.cos(tth)
Qw = Gw * self.graindata.param[
'wavelength'] / (4. * n.pi)
(Omega, eta) = tools.find_omega_general(
Qw, tth, self.wx, self.wy)
try:
minpos = n.argmin(
n.abs(Omega -
self.graindata.grain[grainno].
refs[nref, A_id['omega']]))
except:
print(Omega)
if len(Omega) == 0:
continue
omega = Omega[minpos]
# if omega not in rotation range continue to next step
if (self.graindata.param['omega_start']*n.pi/180) > omega or\
omega > (self.graindata.param['omega_end']*n.pi/180):
continue
Om = tools.form_omega_mat_general(
omega, self.wx, self.wy)
Gt = n.dot(Om, Gw)
# Calc crystal position at present omega
[tx, ty, tz] = n.dot(Om, gr_pos)
(dety, detz) = detector.det_coor(
Gt, costth,
self.graindata.param['wavelength'],
self.graindata.param['distance'],
self.graindata.param['y_size'],
self.graindata.param['z_size'],
self.graindata.param['dety_center'],
self.graindata.param['detz_center'],
self.graindata.R, tx, ty, tz)
if self.graindata.param['spatial'] != None:
# To match the coordinate system of the spline file
# SPLINE(i,j): i = detz; j = (dety_size-1)-dety
# Well at least if the spline file is for frelon2k
(x, y) = detector.detyz_to_xy(
[dety, detz],
self.graindata.param['o11'],
self.graindata.param['o12'],
self.graindata.param['o21'],
self.graindata.param['o22'],
self.graindata.param['dety_size'],
self.graindata.param['detz_size'])
# Do the spatial distortion
(xd, yd) = self.spatial.distort(x, y)
# transform coordinates back to dety,detz
(dety, detz) = detector.xy_to_detyz(
[xd, yd], self.graindata.param['o11'],
self.graindata.param['o12'],
self.graindata.param['o21'],
self.graindata.param['o22'],
self.graindata.param['dety_size'],
self.graindata.param['detz_size'])
if dety > -0.5 and dety <= self.graindata.param['dety_size']-0.5 and\
detz > -0.5 and detz <= self.graindata.param['detz_size']-0.5:
dety = int(round(dety))
detz = int(round(detz))
frame_no = int(n.floor((omega*180/n.pi-self.graindata.param['omega_start'])/\
self.graindata.param['omega_step']))
self.frames[frame_no][
dety, detz] = self.frames[frame_no][
dety,
detz] + intensity * self.odf[i, j,
k]
def run(self):
spot_id = 0
# Generate orientations of the grains and loop over all grains
print('no of grains ', self.param['no_grains'])
for grainno in range(self.param['no_grains']):
A = []
U = self.param['U_grains_%s' % (self.param['grain_list'][grainno])]
if len(self.param['phase_list']) == 1:
phase = self.param['phase_list'][0]
else:
phase = self.param['phase_grains_%s' %
(self.param['grain_list'][grainno])]
unit_cell = self.param['unit_cell_phase_%i' % phase]
self.grain.append(variables.grain_cont(U))
gr_pos = n.array(self.param['pos_grains_%s' %
(self.param['grain_list'][grainno])])
gr_eps = n.array(self.param['eps_grains_%s' %
(self.param['grain_list'][grainno])])
# Calculate the B-matrix based on the strain tensor for each grain
B = tools.epsilon_to_b(gr_eps, unit_cell)
# add B matrix to grain container
self.grain[grainno].B = B
V = tools.cell_volume(unit_cell)
grain_vol = n.pi / 6 * self.param[
'size_grains_%s' % self.param['grain_list'][grainno]]**3
# print 'GRAIN NO: ',self.param['grain_list'][grainno]
# print 'GRAIN POSITION of grain ',self.param['grain_list'][grainno],': ',gr_pos
# print 'STRAIN TENSOR COMPONENTS (e11 e12 e13 e22 e23 e33) of grain ',self.param['grain_list'][grainno],':\n',gr_eps
# print 'U of grain ',self.param['grain_list'][grainno],':\n',U
nrefl = 0
# Calculate these values:
# totalnr, grainno, refno, hkl, omega, 2theta, eta, dety, detz
# For all reflections in Ahkl that fulfill omega_start < omega < omega_end.
# All angles in Grain are in degrees
for hkl in self.hkl[self.param['phase_list'].index(phase)]:
check_input.interrupt(self.killfile)
Gc = n.dot(B, hkl[0:3])
Gw = n.dot(self.S, n.dot(U, Gc))
tth = tools.tth2(Gw, self.param['wavelength'])
costth = n.cos(tth)
(Omega, Eta) = tools.find_omega_general(
Gw * self.param['wavelength'] / (4. * n.pi), tth, self.wx,
self.wy)
if len(Omega) > 0:
for solution in range(len(Omega)):
omega = Omega[solution]
eta = Eta[solution]
if (self.param['omega_start']*n.pi/180) < omega and\
omega < (self.param['omega_end']*n.pi/180):
# form Omega rotation matrix
Om = tools.form_omega_mat_general(
omega, self.wx, self.wy)
Gt = n.dot(Om, Gw)
# Calc crystal position at present omega and continue if this is outside the illuminated volume
[tx, ty, tz] = n.dot(Om, gr_pos)
if self.param['beam_width'] != None:
if n.abs(ty) > self.param['beam_width'] / 2.:
continue
# Calc detector coordinate for peak
(dety, detz) = detector.det_coor(
Gt, costth, self.param['wavelength'],
self.param['distance'], self.param['y_size'],
self.param['z_size'],
self.param['dety_center'],
self.param['detz_center'], self.R, tx, ty, tz)
#If shoebox extends outside detector exclude it
if (-0.5 > dety) or\
(dety > self.param['dety_size']-0.5) or\
(-0.5 > detz) or\
(detz > self.param['detz_size']-0.5):
continue
if self.param['spatial'] != None:
# To match the coordinate system of the spline file
(x, y) = detector.detyz_to_xy(
[dety, detz], self.param['o11'],
self.param['o12'], self.param['o21'],
self.param['o22'], self.param['dety_size'],
self.param['detz_size'])
# Do the spatial distortion
(xd, yd) = self.spatial.distort(x, y)
# transform coordinates back to dety,detz
(detyd, detzd) = detector.xy_to_detyz(
[xd, yd], self.param['o11'],
self.param['o12'], self.param['o21'],
self.param['o22'], self.param['dety_size'],
self.param['detz_size'])
else:
detyd = dety
detzd = detz
if self.param['beampol_apply'] == 1:
#Polarization factor (Kahn, J. Appl. Cryst. (1982) 15, 330-337.)
rho = n.pi / 2.0 + eta + self.param[
'beampol_direct'] * n.pi / 180.0
P = 0.5 * (1 + costth*costth -\
self.param['beampol_factor']*n.cos(2*rho)*n.sin(tth)**2)
else:
P = 1.0
#Lorentz factor
if self.param['lorentz_apply'] == 1:
if eta != 0:
L = 1 / (n.sin(tth) * abs(n.sin(eta)))
else:
L = n.inf
else:
L = 1.0
overlaps = 0 # set the number overlaps to zero
if self.param['intensity_const'] != 1:
intensity = int_intensity(
hkl[3], L, P, self.param['beamflux'],
self.param['wavelength'], V, grain_vol)
else:
intensity = hkl[3]
A.append([
self.param['grain_list'][grainno], nrefl,
spot_id, hkl[0], hkl[1], hkl[2], tth, omega,
eta, dety, detz, detyd, detzd, Gw[0], Gw[1],
Gw[2], L, P, hkl[3], intensity, overlaps
])
nrefl = nrefl + 1
spot_id = spot_id + 1
# print 'Length of Grain', len(self.grain[0].refl)
A = n.array(A)
if len(A) > 0:
# sort rows according to omega
A = A[n.argsort(A, 0)[:, A_id['omega']], :]
# Renumber the reflections
A[:, A_id['ref_id']] = n.arange(nrefl)
# Renumber the spot_id
A[:,
A_id['spot_id']] = n.arange(n.min(A[:, A_id['spot_id']]),
n.max(A[:, A_id['spot_id']]) + 1)
else:
A = n.zeros((0, len(A_id)))
# save reflection info in grain container
self.grain[grainno].refs = A
print('\rDone %3i grain(s) of %3i' %
(grainno + 1, self.param['no_grains']),
end=' ')
sys.stdout.flush()
print('\n')
def get_measured_data(grain, flt, number_y_scans, ymin, ystep, param):
'''
np array size (no_dtys,no_refl,3)
The data is sorted in falling priority:
1. sorted by dty low to high
2. sorted by h low to high
3. sorted by k low to high
4. sorted by l low to high
'''
o11 = param['o11']
o12 = param['o12']
o21 = param['o21']
o22 = param['o22']
dety_size = param['dety_size']
detz_size = param['detz_size']
n = len(flt.sc[grain.mask])
sc = flt.sc[grain.mask]
fc = flt.fc[grain.mask]
omega = flt.omega[grain.mask]
dty = flt.dty[grain.mask]
h = flt.h[grain.mask]
k = flt.k[grain.mask]
l = flt.l[grain.mask]
all_measured_data = []
for i in range(n):
(dety, detz) = detector.xy_to_detyz([sc[i], fc[i]], o11, o12, o21, o22,
dety_size, detz_size)
all_measured_data.append(
[dety, detz, omega[i], dty[i], h[i], k[i], l[i]])
all_measured_data.sort(key=lambda x: x[3])
data_by_dty = []
k = len(all_measured_data)
print("before binning in dty", k)
for i in range(number_y_scans):
data_by_dty.append([])
print("number_y_scans", number_y_scans)
# currdata=[]
# currdata.append(all_measured_data[0])
# dty_curr = all_measured_data[0][3]
# for i in range(1,len(all_measured_data)):
# if dty_curr!=all_measured_data[i][3]:
# currdata_sorted = sorted(currdata, key=lambda x: (x[4], x[5], x[6]))
# iy = np.round( (dty_curr - ymin) / ystep ).astype(int)
# data_by_dty[iy] = np.asarray(currdata_sorted)
# currdata = []
# dty_curr = all_measured_data[i][3]
# currdata.append(all_measured_data[i])
p = 0
for peak in all_measured_data:
iy = np.round((peak[3] - ymin) / ystep).astype(int)
# print(peak[3],"=>",iy)
data_by_dty[iy].append(peak)
p += 1
print("appended", p)
for i, dty in enumerate(data_by_dty):
data_by_dty[i] = sorted(dty, key=lambda x: (x[4], x[5], x[6]))
k = 0
for dty in data_by_dty:
k += len(dty)
print("after sorting and binning in dty", k)
#raise
return data_by_dty