def calc_partiality_anisotropy_set(self, my_uc, rotx, roty, miller_indices,
ry, rz, r0, re, nu,
bragg_angle_set, alpha_angle_set, wavelength, crystal_init_orientation,
spot_pred_x_mm_set, spot_pred_y_mm_set, detector_distance_mm,
partiality_model, flag_beam_divergence):
#use III.4 in Winkler et al 1979 (A35; P901) for set of miller indices
O = sqr(my_uc.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
CO = crystal_orientation(O*R, basis_type.direct)
CO_rotate = CO.rotate_thru((1,0,0), rotx
).rotate_thru((0,1,0), roty)
A_star = sqr(CO_rotate.reciprocal_matrix())
S0 = -1*col((0,0,1./wavelength))
#caculate rs
rs_set = r0 + (re * flex.tan(bragg_angle_set))
if flag_beam_divergence:
rs_set += ((ry * flex.cos(alpha_angle_set))**2 + (rz * flex.sin(alpha_angle_set))**2)**(1/2)
#calculate rh
x = A_star.elems * miller_indices.as_vec3_double()
sd_array = x + S0.elems
rh_set = sd_array.norms() - (1/wavelength)
#calculate partiality
if partiality_model == "Lorentzian":
partiality_set = ((rs_set**2)/((2*(rh_set**2))+(rs_set**2)))
elif partiality_model == "Voigt":
partiality_set = self.voigt(rh_set, rs_set, nu)
elif partiality_model == "Lognormal":
partiality_set = self.lognpdf(rh_set, rs_set, nu)
#calculate delta_xy
d_ratio = -detector_distance_mm/sd_array.parts()[2]
calc_xy_array = flex.vec3_double(sd_array.parts()[0]*d_ratio, \
sd_array.parts()[1]*d_ratio, flex.double([0]*len(d_ratio)))
pred_xy_array = flex.vec3_double(spot_pred_x_mm_set, spot_pred_y_mm_set, flex.double([0]*len(d_ratio)))
delta_xy_set = (pred_xy_array - calc_xy_array).norms()
return partiality_set, delta_xy_set, rs_set, rh_set
def calc_partiality_anisotropy_set(self, my_uc, rotx, roty, miller_indices,
ry, rz, r0, re, nu,
bragg_angle_set, alpha_angle_set, wavelength, crystal_init_orientation,
spot_pred_x_mm_set, spot_pred_y_mm_set, detector_distance_mm,
partiality_model, flag_beam_divergence):
#use III.4 in Winkler et al 1979 (A35; P901) for set of miller indices
O = sqr(my_uc.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
CO = crystal_orientation(O*R, basis_type.direct)
CO_rotate = CO.rotate_thru((1,0,0), rotx
).rotate_thru((0,1,0), roty)
A_star = sqr(CO_rotate.reciprocal_matrix())
S0 = -1*col((0,0,1./wavelength))
#caculate rs
rs_set = r0 + (re * flex.tan(bragg_angle_set))
if flag_beam_divergence:
rs_set += ((ry * flex.cos(alpha_angle_set))**2 + (rz * flex.sin(alpha_angle_set))**2)**(1/2)
#calculate rh
x = A_star.elems * miller_indices.as_vec3_double()
sd_array = x + S0.elems
rh_set = sd_array.norms() - (1/wavelength)
#calculate partiality
if partiality_model == "Lorentzian":
partiality_set = ((rs_set**2)/((2*(rh_set**2))+(rs_set**2)))
elif partiality_model == "Voigt":
partiality_set = self.voigt(rh_set, rs_set, nu)
elif partiality_model == "Lognormal":
partiality_set = self.lognpdf(rh_set, rs_set, nu)
#calculate delta_xy
d_ratio = -detector_distance_mm/sd_array.parts()[2]
calc_xy_array = flex.vec3_double(sd_array.parts()[0]*d_ratio, \
sd_array.parts()[1]*d_ratio, flex.double([0]*len(d_ratio)))
pred_xy_array = flex.vec3_double(spot_pred_x_mm_set, spot_pred_y_mm_set, flex.double([0]*len(d_ratio)))
delta_xy_set = (pred_xy_array - calc_xy_array).norms()
return partiality_set, delta_xy_set, rs_set, rh_set
def energies_adp_iso(self,
xray_structure,
parameters,
use_u_local_only,
use_hd,
wilson_b=None,
compute_gradients=False,
tan_b_iso_max=None,
u_iso_refinable_params=None,
gradients=None):
"""
Compute target and gradients for isotropic ADPs/B-factors relative to
restraints.
:returns: scitbx.restraints.energies object
"""
result = scitbx.restraints.energies(
compute_gradients=compute_gradients,
gradients=gradients,
gradients_size=xray_structure.scatterers().size(),
gradients_factory=flex.double,
normalization=self.normalization)
if (self.geometry is None):
result.geometry = None
else:
result.geometry = cctbx.adp_restraints.energies_iso(
geometry_restraints_manager=self.geometry,
xray_structure=xray_structure,
parameters=parameters,
wilson_b=wilson_b,
use_hd=use_hd,
use_u_local_only=use_u_local_only,
compute_gradients=compute_gradients,
gradients=result.gradients)
result += result.geometry
if (self.cartesian_ncs_manager is None):
result.cartesian_ncs_manager = None
else:
result.cartesian_ncs_manager = self.cartesian_ncs_manager.energies_adp_iso(
u_isos=xray_structure.extract_u_iso_or_u_equiv(),
average_power=parameters.average_power,
compute_gradients=compute_gradients,
gradients=result.gradients)
result += result.cartesian_ncs_manager
result.finalize_target_and_gradients()
if (compute_gradients):
#XXX highly inefficient code: do something asap by adopting new scatters flags
if (tan_b_iso_max is not None and tan_b_iso_max != 0):
u_iso_max = adptbx.b_as_u(tan_b_iso_max)
if (u_iso_refinable_params is not None):
chain_rule_scale = u_iso_max / math.pi / (
flex.pow2(u_iso_refinable_params) + 1.0)
else:
u_iso_refinable_params = flex.tan(
math.pi *
(xray_structure.scatterers().extract_u_iso() /
u_iso_max - 1. / 2.))
chain_rule_scale = u_iso_max / math.pi / (
flex.pow2(u_iso_refinable_params) + 1.0)
else:
chain_rule_scale = 1.0
result.gradients = result.gradients * chain_rule_scale
return result
def superimpose_powder_arcs(image, params):
#put in some random noise to improve display
image.read()
apply_gaussian_noise(image, params)
pxlsz = image.pixel_size
distance = image.distance
wavelength = image.wavelength
eps = 0.01
beamx = eps + image.beamx / pxlsz
beamy = eps + image.beamy / pxlsz
print(beamx, beamy)
image.read()
data = image.linearintdata
detector_d = flex.double()
detector_radius = flex.double()
for x in range(data.focus()[0]):
dx = x - beamx
dx_sq = dx * dx
for y in range(data.focus()[1]):
dy = y - beamy
radius = math.sqrt(dx_sq + dy * dy) * pxlsz
detector_radius.append(radius)
theta = 0.5 * math.atan(radius / distance)
resol_d = wavelength / (2. * math.sin(theta))
if is_pinwheel_region(dx, dy):
detector_d.append(resol_d)
else:
detector_d.append(0.0)
print(detector_d.size())
d_order_image = flex.sort_permutation(detector_d, reverse=True)
#code = "2oh5"
code = params.viewer.powder_arcs.code
d_min = 2.0
sf = get_mmtbx_icalc(code, d_min)
sf.show_summary()
cell = sf.unit_cell()
millers = sf.indices()
FUDGE_FACTOR = 0.02
intensities = FUDGE_FACTOR * sf.data()
spot_d = cell.d(millers)
spot_two_theta = cell.two_theta(millers, wavelength=wavelength)
spot_radius = distance * flex.tan(spot_two_theta)
d_order_spots = flex.sort_permutation(spot_d, reverse=True)
print(list(d_order_spots))
spot_ptr_min = 0
spot_ptr_max = 1
THREESIGMA = 1.5
SIGMA = 0.20
SCALE = 0.003
for x in range(50000): #len(detector_d)):
if x % 10000 == 0: print(x)
if detector_d[d_order_image[x]] > 2.1:
pxl_radius = detector_radius[d_order_image[x]]
#print "pixel radius",pxl_radius,"resolution",detector_d[d_order_image[x]]
if True: # data[d_order_image[x]]>0:
data[d_order_image[x]] = 0
# increase spot_ptr_max???
while spot_radius[
d_order_spots[spot_ptr_max]] - THREESIGMA < pxl_radius:
spot_ptr_max += 1
print(
x, pxl_radius, "increase spot_ptr_max", spot_ptr_max,
spot_radius[d_order_spots[spot_ptr_max]] - THREESIGMA)
while spot_radius[
d_order_spots[spot_ptr_min]] + THREESIGMA < pxl_radius:
spot_ptr_min += 1
print(x, pxl_radius, "increase spot_ptr_min", spot_ptr_min)
for sidx in range(spot_ptr_min, spot_ptr_max):
spot_rad = spot_radius[d_order_spots[sidx]]
delta_rad = spot_rad - pxl_radius
#gaussian distribution with SIGMA=1 (1 pixel sigma)
#divide through by spot_radius since energy has to be spread around increasing ring
# circumference
#print "changing data ",x,d_order_image[x]
data[d_order_image[x]] += int(
SCALE * (1. /
(math.sqrt(2. * math.pi * SIGMA * SIGMA))) *
math.exp(-0.5 * delta_rad * delta_rad /
(SIGMA * SIGMA)) *
intensities[d_order_spots[sidx]] / spot_rad)
def energies_adp_iso(self,
xray_structure,
parameters,
use_u_local_only,
use_hd,
wilson_b=None,
compute_gradients=False,
tan_b_iso_max=None,
u_iso_refinable_params=None,
gradients=None):
"""
Compute target and gradients for isotropic ADPs/B-factors relative to
restraints.
:returns: scitbx.restraints.energies object
"""
result = scitbx.restraints.energies(
compute_gradients=compute_gradients,
gradients=gradients,
gradients_size=xray_structure.scatterers().size(),
gradients_factory=flex.double,
normalization=self.normalization)
if (self.geometry is None):
result.geometry = None
else:
result.geometry = cctbx.adp_restraints.energies_iso(
geometry_restraints_manager=self.geometry,
xray_structure=xray_structure,
parameters=parameters,
wilson_b=wilson_b,
use_hd = use_hd,
use_u_local_only=use_u_local_only,
compute_gradients=compute_gradients,
gradients=result.gradients)
result += result.geometry
if (self.ncs_groups is not None and \
self.torsion_ncs_groups is not None):
raise Sorry("Cannot have both Cartesian and torsion NCS restraints"+\
" at the same time.")
if (self.ncs_groups is None):
result.ncs_groups = None
else:
result.ncs_groups = self.ncs_groups.energies_adp_iso(
u_isos=xray_structure.extract_u_iso_or_u_equiv(),
average_power=parameters.average_power,
compute_gradients=compute_gradients,
gradients=result.gradients)
result += result.ncs_groups
if (self.torsion_ncs_groups is None):
result.torsion_ncs_groups = None
else:
result.torsion_ncs_groups = self.torsion_ncs_groups.energies_adp_iso(
u_isos=xray_structure.extract_u_iso_or_u_equiv(),
average_power=parameters.average_power,
compute_gradients=compute_gradients,
gradients=result.gradients)
result += result.torsion_ncs_groups
result.finalize_target_and_gradients()
if(compute_gradients):
#XXX highly inefficient code: do something asap by adopting new scatters flags
if(tan_b_iso_max is not None and tan_b_iso_max != 0):
u_iso_max = adptbx.b_as_u(tan_b_iso_max)
if(u_iso_refinable_params is not None):
chain_rule_scale = u_iso_max / math.pi / (flex.pow2(u_iso_refinable_params)+1.0)
else:
u_iso_refinable_params = flex.tan(math.pi*(xray_structure.scatterers().extract_u_iso()/u_iso_max-1./2.))
chain_rule_scale = u_iso_max / math.pi / (flex.pow2(u_iso_refinable_params)+1.0)
else:
chain_rule_scale = 1.0
result.gradients = result.gradients * chain_rule_scale
return result
def superimpose_powder_arcs(image,params):
#put in some random noise to improve display
image.read()
apply_gaussian_noise(image,params)
pxlsz = image.pixel_size
distance = image.distance
wavelength = image.wavelength
eps = 0.01
beamx = eps+image.beamx/pxlsz
beamy = eps+image.beamy/pxlsz
print beamx,beamy
image.read()
data = image.linearintdata
detector_d = flex.double()
detector_radius = flex.double()
for x in xrange(data.focus()[0]):
dx = x-beamx
dx_sq = dx*dx;
for y in xrange(data.focus()[1]):
dy = y-beamy
radius = math.sqrt(dx_sq+dy*dy)*pxlsz
detector_radius.append(radius)
theta = 0.5 * math.atan(radius/distance)
resol_d = wavelength/(2.*math.sin(theta))
if is_pinwheel_region(dx,dy):
detector_d.append(resol_d)
else:
detector_d.append(0.0)
print detector_d.size()
d_order_image = flex.sort_permutation(detector_d,reverse=True)
#code = "2oh5"
code = params.viewer.powder_arcs.code
d_min = 2.0
sf = get_mmtbx_icalc(code,d_min)
sf.show_summary()
cell = sf.unit_cell()
millers = sf.indices()
FUDGE_FACTOR = 0.02
intensities = FUDGE_FACTOR*sf.data()
spot_d = cell.d(millers)
spot_two_theta = cell.two_theta(millers,wavelength=wavelength)
spot_radius = distance * flex.tan(spot_two_theta)
d_order_spots = flex.sort_permutation(spot_d,reverse=True)
print list(d_order_spots)
spot_ptr_min = 0
spot_ptr_max = 1
THREESIGMA=1.5
SIGMA = 0.20
SCALE = 0.003
for x in xrange(50000):#len(detector_d)):
if x%10000==0: print x
if detector_d[d_order_image[x]]>2.1:
pxl_radius = detector_radius[d_order_image[x]]
#print "pixel radius",pxl_radius,"resolution",detector_d[d_order_image[x]]
if True:# data[d_order_image[x]]>0:
data[d_order_image[x]]=0
# increase spot_ptr_max???
while spot_radius[d_order_spots[spot_ptr_max]]-THREESIGMA < pxl_radius:
spot_ptr_max += 1
print x, pxl_radius,"increase spot_ptr_max" , spot_ptr_max, spot_radius[d_order_spots[spot_ptr_max]]-THREESIGMA
while spot_radius[d_order_spots[spot_ptr_min]]+THREESIGMA < pxl_radius:
spot_ptr_min += 1
print x, pxl_radius,"increase spot_ptr_min" , spot_ptr_min
for sidx in xrange(spot_ptr_min,spot_ptr_max):
spot_rad = spot_radius[d_order_spots[sidx]]
delta_rad = spot_rad-pxl_radius
#gaussian distribution with SIGMA=1 (1 pixel sigma)
#divide through by spot_radius since energy has to be spread around increasing ring
# circumference
#print "changing data ",x,d_order_image[x]
data[d_order_image[x]] += int(SCALE*(1./(math.sqrt(2.*math.pi*SIGMA*SIGMA)))*math.exp(
-0.5*delta_rad*delta_rad/(SIGMA*SIGMA))*intensities[d_order_spots[sidx]]/spot_rad)
