def exercise_basic(): identity = (1,0,0,0,1,0,0,0,1) I = crystal_orientation(identity,False) #direct space orthorhombic = (1,0,0,0,0.5,0.,0.,0.,0.25) R = crystal_orientation(orthorhombic,True) #reciprocal space assert R.direct_matrix() == (1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 4.0) assert R.reciprocal_matrix() == orthorhombic inverse = (-1,0,0,0,-1,0,0,0,-1) negative = crystal_orientation(inverse,False) assert I == negative.make_positive() assert R.unit_cell().parameters() == (1.0,2.0,4.0,90.,90.,90.) assert approx_equal( R.unit_cell_inverse().parameters(), (1.0,0.5,0.25,90.,90.,90.) )
def get_frames_from_mysql(self,params):
T = Timer("frames")
CART = manager(params)
db = CART.connection()
cursor = db.cursor()
cursor.execute("SELECT * FROM %s_frame;"%params.mysql.runtag)
ALL = cursor.fetchall()
from cctbx.crystal_orientation import crystal_orientation
orientations = [crystal_orientation(
(a[8],a[9],a[10],a[11],a[12],a[13],a[14],a[15],a[16]),False) for a in ALL]
return dict( frame_id = flex.int( [a[0] for a in ALL] ),
wavelength = flex.double( [a[1] for a in ALL] ),
beam_x = flex.double( [a[2] for a in ALL] ),
beam_y = flex.double( [a[3] for a in ALL] ),
distance = flex.double( [a[4] for a in ALL] ),
orientation = orientations,
rotation100_rad = flex.double([a[17] for a in ALL] ),
rotation010_rad = flex.double([a[18] for a in ALL] ),
rotation001_rad = flex.double([a[19] for a in ALL] ),
half_mosaicity_deg = flex.double([a[20] for a in ALL] ),
wave_HE_ang = flex.double([a[21] for a in ALL] ),
wave_LE_ang = flex.double([a[22] for a in ALL] ),
domain_size_ang = flex.double([a[23] for a in ALL] ),
unique_file_name = [a[24] for a in ALL],
)
def __init__(self,detector=None,camera=None,structure=None,simulation=None):
ext.xfel1.__init__(self)
self.detector=detector
self.camera=camera
self.structure=structure
self.sim=simulation
self.uc = self.structure.p1_cell()
self.bmat = matrix.sqr(self.uc.orthogonalization_matrix()).inverse().transpose()
self.Ori = crystal_orientation.crystal_orientation( self.bmat,
crystal_orientation.basis_type.reciprocal )
energy_in_eV = eV_per_inv_meter / (self.camera.lambda0) # lambda in meters
# top hat function for dispersion
self.full_pass_eV = energy_in_eV * matrix.col([1.-(self.sim.bandpass/2.),
1.+(self.sim.bandpass/2.)])
self.full_pass_lambda = eV_per_inv_meter * matrix.col((1./self.full_pass_eV[0],
1./self.full_pass_eV[1]))
intensities = self.structure.p1_intensities()
self.set_indices(intensities.indices())
self.set_intensities(intensities.data())
def read_frames_updated_detail(self):
from cctbx.crystal_orientation import crystal_orientation
from xfel.cxi.util import is_odd_numbered
frames = {'frame_id': flex.int(),
'wavelength': flex.double(),
'cc': flex.double(),
'G': flex.double(),
'BFACTOR': flex.double(),
'RS': flex.double(),
'odd_numbered': flex.bool(),
'thetax': flex.double(),
'thetay': flex.double(),
'orientation': [],
'unit_cell': [],
'unique_file_name': []}
stream = open(self.params.output.prefix + '_frame.db', 'r')
for row in stream:
items = row.split()
CO = crystal_orientation([float(t) for t in items[8:17]], True)
unique_file_name = eval(items[19])
frames['frame_id'].append(int(items[0]))
frames['wavelength'].append(float(items[1]))
frames['cc'].append(float(items[20]))
frames['G'].append(float(items[5]))
frames['BFACTOR'].append(float(items[6]))
frames['RS'].append(float(items[7]))
frames['thetax'].append(float(items[17]))
frames['thetay'].append(float(items[18]))
frames['odd_numbered'].append(is_odd_numbered(unique_file_name))
frames['orientation'].append(CO)
frames['unit_cell'].append(CO.unit_cell())
frames['unique_file_name'].append(unique_file_name)
stream.close()
return frames
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 get_crystal_orientation(ortho_matrix, rot_matrix): #From orthogonalization matrix and rotation matrix, #generate and return crystal orientation O = sqr(ortho_matrix).transpose() R = sqr(rot_matrix).transpose() X = O*R co = crystal_orientation(X, basis_type.direct) return co
def test_reduction(): from libtbx.test_utils import approx_equal uc = unit_cell((10,20,30,90,90,90)) reference = uc.parameters() assert approx_equal (rwgk_niggli(uc).parameters(), reference) CO = crystal_orientation.crystal_orientation(uc.fractionalization_matrix(),True) assert approx_equal ( CO.unit_cell().parameters(), reference) assert approx_equal ( rwgk_niggli(CO).unit_cell().parameters(), reference) return True
def exercise_functions(): orthorhombic = (1,0,0,0,0.5,0.,0.,0.,0.25) R = crystal_orientation(orthorhombic,True) A = R.rotate_thru((1,0,0,),math.pi/2.) assert approx_equal( A.direct_matrix(), (1,0, 0, 0,0,2,0,-4,0) ) B = R.rotate_thru((0,1,0,),math.pi/2.) assert approx_equal( B.direct_matrix(), (0,0,-1, 0,2,0,4, 0,0) ) C = R.rotate_thru((0,0,1,),math.pi/2.) assert approx_equal( C.direct_matrix(), (0,1, 0,-2,0,0,0, 0,4) )
def set_B(self, B):
# also set the unit cell
co = crystal_orientation(B,True)
self._uc = co.unit_cell()
self._B = matrix.sqr(self._uc.fractionalization_matrix()).transpose()
# reset scan-varying data, if the static B has changed
self.reset_scan_points()
def get_labelit_orient(self):
UNI_I = matrix.sqr((0,1,0,1,0,0,0,0,-1)).inverse()
a, b, c = tuple(self.a_axis), tuple(self.b_axis), tuple(self.c_axis)
matXDS = matrix.sqr(a+b+c)
print "matXDS=", matXDS[4]
matRossmann = (UNI_I * matXDS.transpose()).transpose()
orient = crystal_orientation.crystal_orientation(matRossmann, False) # reciprocal flag
rotation_ax = self.get_endstation().rot_axi
orient = orient.rotate_thru(rotation_ax, -self.starting_angle*math.pi/180.)
return orient
def set_orientation(self, orientation, length_unit=1.E-10):
#provide orientation as either an A matrix (Rossmann) or B matrix (Busing & Levy)
# data type can be either scitbx.matrix.sqr or scitbx::mat3
# in either the reciprocal or direct space setting
# or as a cctbx.crystal_orientation.crystal_orientation
# if space group is not triclinic the orientation matrix should be close to
# symmetrized, but exact symmetrization is done by averaging within the constructor.
# length unit defaults to 1.E-10 meters = 1 Angstrom
if "direct_matrix" in dir(orientation):
self.orientation = orientation # data is already a cctbx orientation
else:
from cctbx.crystal_orientation import crystal_orientation
which_setting = [crystal_orientation(orientation,True),
crystal_orientation(orientation,False)]
#kludgy test for space setting: unit cell volume is never < 100 Angstroms^3
conversion_to_A3 = (length_unit*length_unit*length_unit)/1.E-30
select = [a.unit_cell().volume()*conversion_to_A3 > 100.
for a in which_setting]
self.orientation = which_setting[select.index(True)]
def __init__(self,params):
import cPickle as pickle
from dxtbx.model.beam import beam_factory
from dxtbx.model.detector import detector_factory
from dxtbx.model.crystal import crystal_model
from cctbx.crystal_orientation import crystal_orientation,basis_type
from dxtbx.model.experiment.experiment_list import Experiment, ExperimentList
from scitbx import matrix
self.experiments = ExperimentList()
self.unique_file_names = []
self.params = params
data = pickle.load(open(self.params.output.prefix+"_frame.pickle","rb"))
frames_text = data.split("\n")
for item in frames_text:
tokens = item.split(' ')
wavelength = float(tokens[order_dict["wavelength"]])
beam = beam_factory.simple(wavelength = wavelength)
detector = detector_factory.simple(
sensor = detector_factory.sensor("PAD"), # XXX shouldn't hard code for XFEL
distance = float(tokens[order_dict["distance"]]),
beam_centre = [float(tokens[order_dict["beam_x"]]), float(tokens[order_dict["beam_y"]])],
fast_direction = "+x",
slow_direction = "+y",
pixel_size = [self.params.pixel_size,self.params.pixel_size],
image_size = [1795,1795], # XXX obviously need to figure this out
)
reciprocal_matrix = matrix.sqr([float(tokens[order_dict[k]]) for k in [
'res_ori_1','res_ori_2','res_ori_3','res_ori_4','res_ori_5','res_ori_6','res_ori_7','res_ori_8','res_ori_9']])
ORI = crystal_orientation(reciprocal_matrix, basis_type.reciprocal)
direct = matrix.sqr(ORI.direct_matrix())
crystal = crystal_model(
real_space_a = matrix.row(direct[0:3]),
real_space_b = matrix.row(direct[3:6]),
real_space_c = matrix.row(direct[6:9]),
space_group_symbol = "P63", # XXX obviously another gap in the database paradigm
mosaicity = float(tokens[order_dict["half_mosaicity_deg"]]),
)
crystal.domain_size = float(tokens[order_dict["domain_size_ang"]])
#if isoform is not None:
# newB = matrix.sqr(isoform.fractionalization_matrix()).transpose()
# crystal.set_B(newB)
self.experiments.append(Experiment(beam=beam,
detector=None, #dummy for now
crystal=crystal))
self.unique_file_names.append(tokens[order_dict["unique_file_name"]])
self.show_summary()
def crystfel_to_cctbx_coord_system(abasis, bbasis, cbasis):
"""CrystFEL is RHS with z down the beam, and y to the ceiling.
CCTBX.Postrefine is RHS with z to the source, and y to the ceiling.
"""
from scitbx.matrix import sqr
from cctbx import crystal_orientation
a_mat = sqr((abasis[0], bbasis[0], cbasis[0],
abasis[1], bbasis[1], cbasis[1],
abasis[2], bbasis[2], cbasis[2]))
coord_transformation = sqr(( -1, 0, 0,
0, 1, 0,
0, 0, -1))
new_coords = a_mat.__mul__(coord_transformation)
ori = crystal_orientation.crystal_orientation(new_coords, crystal_orientation.basis_type.reciprocal)
logging.debug("\naStar: {}\nbStar: {}\ncStar: {}".format(abasis, bbasis, cbasis))
logging.debug(str(ori))
return ori
def read_frames(self):
from xfel.cxi.util import is_odd_numbered
db = self.connection()
cursor = db.cursor()
cursor.execute("""SELECT
frame_id_1_base,wavelength,c_c,slope,offset,res_ori_1,res_ori_2,res_ori_3,
res_ori_4,res_ori_5,res_ori_6,res_ori_7,res_ori_8,res_ori_9,
unique_file_name
FROM `%s_frame`"""%self.params.mysql.runtag)
ALL = cursor.fetchall()
from cctbx.crystal_orientation import crystal_orientation
orientations = [crystal_orientation(
(a[5],a[6],a[7],a[8],a[9],a[10],a[11],a[12],a[13]),False) for a in ALL]
return dict( frame_id = flex.int( [a[0]-1 for a in ALL] ),
wavelength = flex.double( [a[1] for a in ALL] ),
cc = flex.double( [a[2] for a in ALL] ),
slope = flex.double( [a[3] for a in ALL] ),
offset = flex.double( [a[4] for a in ALL] ),
odd_numbered = flex.bool( [is_odd_numbered(a[14]) for a in ALL] ),
orientation = orientations,
unit_cell = [CO.unit_cell() for CO in orientations],
unique_file_name = [a[14] for a in ALL] )
def read_frames_legacy_detail(self):
from cctbx.crystal_orientation import crystal_orientation
from xfel.cxi.util import is_odd_numbered
# XXX issues with spaces in the file name, and leading and
# trailing single quotes (stripped below).
frames = {'frame_id': flex.int(),
'wavelength': flex.double(),
'cc': flex.double(),
'slope': flex.double(),
'offset': flex.double(),
'odd_numbered': flex.bool(),
'domain_size_ang': flex.double(),
'half_mosaicity_deg': flex.double(),
'orientation': [],
'unit_cell': [],
'unique_file_name': []}
stream = open(self.params.output.prefix + '_frame.db', 'r')
for row in stream:
items = row.split()
CO = crystal_orientation([float(t) for t in items[8:17]], False)
unique_file_name = eval(items[24])
frames['frame_id'].append(int(items[0]))
frames['wavelength'].append(float(items[1]))
frames['cc'].append(float(items[5]))
frames['slope'].append(float(items[6]))
frames['offset'].append(float(items[7]))
frames['domain_size_ang'].append(float(items[23]))
frames['half_mosaicity_deg'].append(float(items[20]))
frames['odd_numbered'].append(is_odd_numbered(unique_file_name))
frames['orientation'].append(CO)
frames['unit_cell'].append(CO.unit_cell())
frames['unique_file_name'].append(unique_file_name)
stream.close()
return frames
def populate_orientation(self):
assert self.xtal.get_A() is not None, "no crystal orientation matrix"
self.frame['current_orientation'] = [
crystal_orientation(self.xtal.get_A(), True)
]
if key == "correction_vectors":
# drop correction_vectors as they aren't as easy to split up
continue
elif key in [
"current_cb_op_to_primitive", "effective_tiling",
"pointgroup", "identified_isoform"
]:
dl[key] = data[key]
dr[key] = data[key]
dm[key] = data[key]
dn[key] = data[key]
continue
elif key == "current_orientation":
from cctbx import crystal_orientation
dl[key] = [
crystal_orientation.crystal_orientation(c)
for c in data[key]
]
dr[key] = [
crystal_orientation.crystal_orientation(c)
for c in data[key]
]
dm[key] = [
crystal_orientation.crystal_orientation(c)
for c in data[key]
]
dn[key] = [
crystal_orientation.crystal_orientation(c)
for c in data[key]
]
continue
def refined_settings_from_refined_triclinic(experiments, reflections, params):
"""Generate a RefinedSettingsList from a triclinic model.
Args:
experiments: The experiments refined with a triclinic model
reflections: A reflection table containing observed centroids
params: The working PHIL parameters.
Returns:
RefinedSettingsList: A list of the refined settings. The highest symmetry
setting will be first item in the list, and the triclinic setting will be last.
"""
if params.nproc is libtbx.Auto:
params.nproc = number_of_processors()
if params.refinement.reflections.outlier.algorithm in ("auto",
libtbx.Auto):
if experiments[0].goniometer is None:
params.refinement.reflections.outlier.algorithm = "sauter_poon"
else:
# different default to dials.refine
# tukey is faster and more appropriate at the indexing step
params.refinement.reflections.outlier.algorithm = "tukey"
assert len(experiments.crystals()) == 1
crystal = experiments.crystals()[0]
used_reflections = copy.deepcopy(reflections)
UC = crystal.get_unit_cell()
refined_settings = RefinedSettingsList()
for item in iotbx_converter(
UC,
params.lepage_max_delta,
best_monoclinic_beta=params.best_monoclinic_beta):
refined_settings.append(BravaisSetting(item))
triclinic = refined_settings.triclinic()
# assert no transformation between indexing and bravais list
assert str(triclinic["cb_op_inp_best"]) == "a,b,c"
Nset = len(refined_settings)
for j in range(Nset):
refined_settings[j].setting_number = Nset - j
for subgroup in refined_settings:
bravais_lattice = str(
bravais_types.bravais_lattice(
group=subgroup["best_subsym"].space_group()))
space_group = lowest_symmetry_space_group_for_bravais_lattice(
bravais_lattice)
orient = crystal_orientation(crystal.get_A(), True).change_basis(
scitbx.matrix.sqr(subgroup["cb_op_inp_best"].c().as_double_array()
[0:9]).transpose())
constrain_orient = orient.constrain(subgroup["system"])
subgroup["bravais"] = bravais_lattice
subgroup.unrefined_crystal = dxtbx_crystal_from_orientation(
constrain_orient, space_group)
with concurrent.futures.ProcessPoolExecutor(
max_workers=params.nproc) as pool:
for i, result in enumerate(
pool.map(
refine_subgroup,
((params, subgroup, used_reflections, experiments)
for subgroup in refined_settings),
)):
refined_settings[i] = result
identify_likely_solutions(refined_settings)
return refined_settings
def refined_settings_factory_from_refined_triclinic(params,
experiments,
reflections,
i_setting=None,
lepage_max_delta=5.0,
nproc=1):
assert len(experiments.crystals()) == 1
crystal = experiments.crystals()[0]
used_reflections = copy.deepcopy(reflections)
UC = crystal.get_unit_cell()
Lfat = RefinedSettingsList()
for item in iotbx_converter(UC, lepage_max_delta):
Lfat.append(BravaisSetting(item))
triclinic = Lfat.triclinic()
# assert no transformation between indexing and bravais list
assert str(triclinic["cb_op_inp_best"]) == "a,b,c"
Nset = len(Lfat)
for j in range(Nset):
Lfat[j].setting_number = Nset - j
for j in range(Nset):
cb_op = Lfat[j]["cb_op_inp_best"].c().as_double_array()[0:9]
orient = crystal_orientation(crystal.get_A(), True)
orient_best = orient.change_basis(scitbx.matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(Lfat[j]["system"])
bravais = Lfat[j]["bravais"]
cb_op_best_ref = Lfat[j][
"best_subsym"].change_of_basis_op_to_reference_setting()
space_group = sgtbx.space_group_info(
number=bravais_lattice_to_lowest_symmetry_spacegroup_number[
bravais]).group()
space_group = space_group.change_basis(cb_op_best_ref.inverse())
bravais = str(bravais_types.bravais_lattice(group=space_group))
Lfat[j]["bravais"] = bravais
Lfat[j].unrefined_crystal = dials_crystal_from_orientation(
constrain_orient, space_group)
args = []
for subgroup in Lfat:
args.append((params, subgroup, used_reflections, experiments))
results = easy_mp.parallel_map(
func=refine_subgroup,
iterable=args,
processes=nproc,
method="multiprocessing",
preserve_order=True,
asynchronous=True,
preserve_exception_message=True,
)
for i, result in enumerate(results):
Lfat[i] = result
identify_likely_solutions(Lfat)
return Lfat
def set_B(self, B):
# also set the unit cell
co = crystal_orientation(B, True)
self._uc = co.unit_cell()
self._B = matrix.sqr(self._uc.fractionalization_matrix()).transpose()
cell = results['cell']
axis = results['axis']
wavelength = results['wavelength']
start = results['starting_frame'] - 1
phi_start = results['phi_start']
phi_width = results['phi_width']
# FIXME really need to rotate the reference frame to put the
# direct beam vector along (0,0,1)
resolution = 1.8
orientation = A + B + C
co = crystal_orientation(orientation, basis_type.direct)
mm = co.unit_cell().max_miller_indices(resolution)
ra = rotation_angles(resolution, co.reciprocal_matrix(), wavelength, axis)
for record in open('SPOT.XDS', 'r').readlines():
lst = record.split()
hkl = tuple(map(int, lst[-3:]))
if hkl == (0, 0, 0):
continue
image = nint(float(lst[2]))
if ra(hkl):
phi1, phi2 = ra.get_intersection_angles()
i = nint(start + (rtod * phi1 - phi_start) / phi_width)
j = nint(start + (rtod * phi2 - phi_start) / phi_width)
if abs(i - image) <= abs(j - image):
def set_B(self, B):
# also set the unit cell
co = crystal_orientation(B,True)
self._uc = co.unit_cell()
self._B = matrix.sqr(self._uc.fractionalization_matrix()).transpose()
def get_predictions_accounting_for_centering(self,experiments,reflections,cb_op_to_primitive,**kwargs):
# interface requires this function to set current_orientation
# in the actual setting used for Miller index calculation
detector = experiments[0].detector
crystal = experiments[0].crystal
if self.horizons_phil.integration.model == "user_supplied":
lower_limit_domain_size = math.pow(
crystal.get_unit_cell().volume(),
1./3.)*self.horizons_phil.integration.mosaic.domain_size_lower_limit # default 10-unit cell block size minimum reasonable domain
actual_used_domain_size = kwargs.get("domain_size_ang",lower_limit_domain_size)
self.block_counter+=1
rot_mat = matrix.sqr(cb_op_to_primitive.c().r().as_double()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
centered_orientation = crystal_orientation(crystal.get_A(),basis_type.reciprocal)
self.current_orientation = centered_orientation
self.current_cb_op_to_primitive = cb_op_to_primitive
primitive_orientation = centered_orientation.change_basis(rot_mat)
self.inputai.setOrientation(primitive_orientation)
from cxi_user import pre_get_predictions
if self.block_counter < 2:
KLUDGE = self.horizons_phil.integration.mosaic.kludge1 # bugfix 1 of 2 for protocol 6, equation 2
self.inputai.setMosaicity(KLUDGE*self.inputai.getMosaicity())
oldbase = self.inputai.getBase()
#print oldbase.xbeam, oldbase.ybeam
newbeam = detector[0].get_beam_centre(experiments[0].beam.get_s0())
newdistance = -detector[0].get_beam_centre_lab(experiments[0].beam.get_s0())[2]
from labelit.dptbx import Parameters
base = Parameters(xbeam = newbeam[0], ybeam = newbeam[1], #oldbase.xbeam, ybeam = oldbase.ybeam,
distance = newdistance, twotheta = 0.0)
self.inputai.setBase(base)
self.inputai.setWavelength(experiments[0].beam.get_wavelength())
self.bp3_wrapper = pre_get_predictions(self.inputai, self.horizons_phil,
raw_image = self.imagefiles.images[self.image_number],
imageindex = self.frame_numbers[self.image_number],
spotfinder = self.spotfinder,
limiting_resolution = self.limiting_resolution,
domain_size_ang = actual_used_domain_size,
)
BPpredicted = self.bp3_wrapper.ucbp3.selected_predictions_labelit_format()
BPhkllist = self.bp3_wrapper.ucbp3.selected_hkls()
self.actual = actual_used_domain_size
primitive_hkllist = BPhkllist
#not sure if matrix needs to be transposed first for outputting HKL's???:
self.hkllist = cb_op_to_primitive.inverse().apply(primitive_hkllist)
if self.horizons_phil.integration.spot_prediction == "dials":
from dials.algorithms.spot_prediction import StillsReflectionPredictor
predictor = StillsReflectionPredictor(experiments[0])
Rcalc = flex.reflection_table.empty_standard(len(self.hkllist))
Rcalc['miller_index'] = self.hkllist
predictor.for_reflection_table(Rcalc, crystal.get_A())
self.predicted = Rcalc['xyzcal.mm']
self.dials_spot_prediction = Rcalc
self.dials_model = experiments
elif self.horizons_phil.integration.spot_prediction == "ucbp3":
self.predicted = BPpredicted
self.inputai.setOrientation(centered_orientation)
if self.inputai.active_areas != None:
self.predicted,self.hkllist = self.inputai.active_areas(
self.predicted,self.hkllist,self.pixel_size)
if self.block_counter < 2:
down = self.inputai.getMosaicity()/KLUDGE
print "Readjusting mosaicity back down to ",down
self.inputai.setMosaicity(down)
return
def organize_input(self,
observations_pickle,
iparams,
avg_mode,
pickle_filename=None):
"""Given the pickle file, extract and prepare observations object and
the alpha angle (meridional to equatorial).
"""
#get general parameters
if iparams.isoform_name is not None:
if "identified_isoform" not in observations_pickle:
return None, "No identified isoform"
if observations_pickle[
"identified_isoform"] != iparams.isoform_name:
return None, "Identified isoform(%s) is not the requested isoform (%s)" % (
observations_pickle["identified_isoform"],
iparams.isoform_name)
if iparams.flag_weak_anomalous:
if avg_mode == 'final':
target_anomalous_flag = iparams.target_anomalous_flag
else:
target_anomalous_flag = False
else:
target_anomalous_flag = iparams.target_anomalous_flag
img_filename_only = ''
if pickle_filename:
img_filename_only = os.path.basename(pickle_filename)
txt_exception = ' {0:40} ==> '.format(img_filename_only)
#for dials integration pickles - also look for experimentxxx.json
if "miller_index" in observations_pickle:
from dxtbx.model.experiment_list import ExperimentListFactory
exp_json_file = os.path.join(
os.path.dirname(pickle_filename),
img_filename_only.split('_')[0] + '_refined_experiments.json')
if os.path.isfile(exp_json_file):
experiments = ExperimentListFactory.from_json_file(
exp_json_file)
dials_crystal = experiments[0].crystal
detector = experiments[0].detector
beam = experiments[0].beam
crystal_symmetry = crystal.symmetry(
unit_cell=dials_crystal.get_unit_cell().parameters(),
space_group_symbol=iparams.target_space_group)
miller_set_all = miller.set(
crystal_symmetry=crystal_symmetry,
indices=observations_pickle['miller_index'],
anomalous_flag=target_anomalous_flag)
observations = miller_set_all.array(
data=observations_pickle['intensity.sum.value'],
sigmas=flex.sqrt(
observations_pickle['intensity.sum.variance'])
).set_observation_type_xray_intensity()
detector_distance_mm = detector[0].get_distance()
alpha_angle_obs = flex.double([0] * len(observations.data()))
wavelength = beam.get_wavelength()
spot_pred_x_mm = observations_pickle['s1'] #a disguise of s1
spot_pred_y_mm = flex.double([0] * len(observations.data()))
#calculate the crystal orientation
O = sqr(dials_crystal.get_unit_cell().orthogonalization_matrix(
)).transpose()
R = sqr(dials_crystal.get_U()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
crystal_init_orientation = crystal_orientation(
O * R, basis_type.direct)
else:
txt_exception += exp_json_file + ' not found'
print txt_exception
return None, txt_exception
else:
#for cctbx.xfel proceed as usual
observations = observations_pickle["observations"][0]
detector_distance_mm = observations_pickle['distance']
mm_predictions = iparams.pixel_size_mm * (
observations_pickle['mapped_predictions'][0])
xbeam = observations_pickle["xbeam"]
ybeam = observations_pickle["ybeam"]
alpha_angle_obs = flex.double([math.atan(abs(pred[0]-xbeam)/abs(pred[1]-ybeam)) \
for pred in mm_predictions])
spot_pred_x_mm = flex.double(
[pred[0] - xbeam for pred in mm_predictions])
spot_pred_y_mm = flex.double(
[pred[1] - ybeam for pred in mm_predictions])
#Polarization correction
wavelength = observations_pickle["wavelength"]
crystal_init_orientation = observations_pickle[
"current_orientation"][0]
#continue reading...
if iparams.flag_LP_correction and "observations" in observations_pickle:
fx = 1 - iparams.polarization_horizontal_fraction
fy = 1 - fx
if fx > 1.0 or fx < 0:
print 'Horizontal polarization fraction is not correct. The value must be >= 0 and <= 1'
print 'No polarization correction. Continue with post-refinement'
else:
phi_angle_obs = flex.double([math.atan2(pred[1]-ybeam, pred[0]-xbeam) \
for pred in mm_predictions])
bragg_angle_obs = observations.two_theta(wavelength).data()
P = ((fx*((flex.sin(phi_angle_obs)**2)+((flex.cos(phi_angle_obs)**2)*flex.cos(bragg_angle_obs)**2)))+\
(fy*((flex.cos(phi_angle_obs)**2)+((flex.sin(phi_angle_obs)**2)*flex.cos(bragg_angle_obs)**2))))
I_prime = observations.data() / P
sigI_prime = observations.sigmas() / P
observations = observations.customized_copy(
data=flex.double(I_prime), sigmas=flex.double(sigI_prime))
#set observations with target space group - !!! required for correct
#merging due to map_to_asu command.
if iparams.target_crystal_system is not None:
target_crystal_system = iparams.target_crystal_system
else:
target_crystal_system = observations.crystal_symmetry(
).space_group().crystal_system()
lph = lbfgs_partiality_handler()
if iparams.flag_override_unit_cell:
uc_constrained_inp = lph.prep_input(
iparams.target_unit_cell.parameters(), target_crystal_system)
else:
uc_constrained_inp = lph.prep_input(
observations.unit_cell().parameters(), target_crystal_system)
uc_constrained = list(
lph.prep_output(uc_constrained_inp, target_crystal_system))
try:
#apply constrain using the crystal system
miller_set = symmetry(unit_cell=uc_constrained,
space_group_symbol=iparams.target_space_group
).build_miller_set(
anomalous_flag=target_anomalous_flag,
d_min=iparams.merge.d_min)
observations = observations.customized_copy(
anomalous_flag=target_anomalous_flag,
crystal_symmetry=miller_set.crystal_symmetry())
except Exception:
a, b, c, alpha, beta, gamma = uc_constrained
txt_exception += 'Mismatch spacegroup (%6.2f,%6.2f,%6.2f,%6.2f,%6.2f,%6.2f)' % (
a, b, c, alpha, beta, gamma)
print txt_exception
return None, txt_exception
#reset systematic absence
sys_absent_negate_flags = flex.bool([
sys_absent_flag[1] == False
for sys_absent_flag in observations.sys_absent_flags()
])
observations = observations.select(sys_absent_negate_flags)
alpha_angle_obs = alpha_angle_obs.select(sys_absent_negate_flags)
spot_pred_x_mm = spot_pred_x_mm.select(sys_absent_negate_flags)
spot_pred_y_mm = spot_pred_y_mm.select(sys_absent_negate_flags)
#remove observations from rejection list
if iparams.rejections:
if pickle_filename in iparams.rejections:
miller_indices_ori_rejected = iparams.rejections[
pickle_filename]
i_sel_flag = flex.bool([True] * len(observations.data()))
cnrej = 0
for miller_index_ori_rejected in miller_indices_ori_rejected:
for i_index_ori, miller_index_ori in enumerate(
observations.indices()):
if miller_index_ori_rejected == miller_index_ori:
i_sel_flag[i_index_ori] = False
cnrej += 1
observations = observations.customized_copy(
indices=observations.indices().select(i_sel_flag),
data=observations.data().select(i_sel_flag),
sigmas=observations.sigmas().select(i_sel_flag))
alpha_angle_obs = alpha_angle_obs.select(i_sel_flag)
spot_pred_x_mm = spot_pred_x_mm.select(i_sel_flag)
spot_pred_y_mm = spot_pred_y_mm.select(i_sel_flag)
#filter resolution
i_sel_res = observations.resolution_filter_selection(
d_max=iparams.merge.d_max, d_min=iparams.merge.d_min)
observations = observations.select(i_sel_res)
alpha_angle_obs = alpha_angle_obs.select(i_sel_res)
spot_pred_x_mm = spot_pred_x_mm.select(i_sel_res)
spot_pred_y_mm = spot_pred_y_mm.select(i_sel_res)
#Filter weak
i_sel = (observations.data() /
observations.sigmas()) > iparams.merge.sigma_min
observations = observations.select(i_sel)
alpha_angle_obs = alpha_angle_obs.select(i_sel)
spot_pred_x_mm = spot_pred_x_mm.select(i_sel)
spot_pred_y_mm = spot_pred_y_mm.select(i_sel)
#filter icering (if on)
if iparams.icering.flag_on:
miller_indices = flex.miller_index()
I_set = flex.double()
sigI_set = flex.double()
alpha_angle_obs_set = flex.double()
spot_pred_x_mm_set = flex.double()
spot_pred_y_mm_set = flex.double()
for miller_index, d, I, sigI, alpha, spot_x, spot_y in zip(
observations.indices(),
observations.d_spacings().data(), observations.data(),
observations.sigmas(), alpha_angle_obs, spot_pred_x_mm,
spot_pred_y_mm):
if d > iparams.icering.d_upper or d < iparams.icering.d_lower:
miller_indices.append(miller_index)
I_set.append(I)
sigI_set.append(sigI)
alpha_angle_obs_set.append(alpha)
spot_pred_x_mm_set.append(spot_x)
spot_pred_y_mm_set.append(spot_y)
observations = observations.customized_copy(indices=miller_indices,
data=I_set,
sigmas=sigI_set)
alpha_angle_obs = alpha_angle_obs_set[:]
spot_pred_x_mm = spot_pred_x_mm_set[:]
spot_pred_y_mm = spot_pred_y_mm_set[:]
#replacing sigI (if set)
if iparams.flag_replace_sigI:
observations = observations.customized_copy(
sigmas=flex.sqrt(observations.data()))
inputs = observations, alpha_angle_obs, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm, wavelength, crystal_init_orientation
return inputs, 'OK'
def get_predictions_accounting_for_centering(self, experiments,
reflections,
cb_op_to_primitive, **kwargs):
# interface requires this function to set current_orientation
# in the actual setting used for Miller index calculation
detector = experiments[0].detector
crystal = experiments[0].crystal
if self.horizons_phil.integration.model == "user_supplied":
lower_limit_domain_size = math.pow(
crystal.get_unit_cell().volume(), 1. / 3.
) * self.horizons_phil.integration.mosaic.domain_size_lower_limit # default 10-unit cell block size minimum reasonable domain
actual_used_domain_size = kwargs.get("domain_size_ang",
lower_limit_domain_size)
self.block_counter += 1
rot_mat = matrix.sqr(
cb_op_to_primitive.c().r().as_double()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
centered_orientation = crystal_orientation(crystal.get_A(),
basis_type.reciprocal)
self.current_orientation = centered_orientation
self.current_cb_op_to_primitive = cb_op_to_primitive
primitive_orientation = centered_orientation.change_basis(rot_mat)
self.inputai.setOrientation(primitive_orientation)
from cxi_user import pre_get_predictions
if self.block_counter < 2:
KLUDGE = self.horizons_phil.integration.mosaic.kludge1 # bugfix 1 of 2 for protocol 6, equation 2
self.inputai.setMosaicity(KLUDGE * self.inputai.getMosaicity())
oldbase = self.inputai.getBase()
#print oldbase.xbeam, oldbase.ybeam
newbeam = detector[0].get_beam_centre(experiments[0].beam.get_s0())
newdistance = -detector[0].get_beam_centre_lab(
experiments[0].beam.get_s0())[2]
from labelit.dptbx import Parameters
base = Parameters(
xbeam=newbeam[0],
ybeam=newbeam[1], #oldbase.xbeam, ybeam = oldbase.ybeam,
distance=newdistance,
twotheta=0.0)
self.inputai.setBase(base)
self.inputai.setWavelength(experiments[0].beam.get_wavelength())
self.bp3_wrapper = pre_get_predictions(
self.inputai,
self.horizons_phil,
raw_image=self.imagefiles.images[self.image_number],
imageindex=self.frame_numbers[self.image_number],
spotfinder=self.spotfinder,
limiting_resolution=self.limiting_resolution,
domain_size_ang=actual_used_domain_size,
)
BPpredicted = self.bp3_wrapper.ucbp3.selected_predictions_labelit_format(
)
BPhkllist = self.bp3_wrapper.ucbp3.selected_hkls()
self.actual = actual_used_domain_size
primitive_hkllist = BPhkllist
#not sure if matrix needs to be transposed first for outputting HKL's???:
self.hkllist = cb_op_to_primitive.inverse().apply(
primitive_hkllist)
if self.horizons_phil.integration.spot_prediction == "dials":
from dials.algorithms.spot_prediction import StillsReflectionPredictor
predictor = StillsReflectionPredictor(experiments[0])
Rcalc = flex.reflection_table.empty_standard(len(self.hkllist))
Rcalc['miller_index'] = self.hkllist
predictor.for_reflection_table(Rcalc, crystal.get_A())
self.predicted = Rcalc['xyzcal.mm']
self.dials_spot_prediction = Rcalc
self.dials_model = experiments
elif self.horizons_phil.integration.spot_prediction == "ucbp3":
self.predicted = BPpredicted
self.inputai.setOrientation(centered_orientation)
if self.inputai.active_areas != None:
self.predicted, self.hkllist = self.inputai.active_areas(
self.predicted, self.hkllist, self.pixel_size)
if self.block_counter < 2:
down = self.inputai.getMosaicity() / KLUDGE
print "Readjusting mosaicity back down to ", down
self.inputai.setMosaicity(down)
return
def postrefine_by_frame(self, frame_no, pickle_filename, iparams,
miller_array_ref, pres_in, avg_mode):
#1. Prepare data
observations_pickle = read_frame(pickle_filename)
pickle_filepaths = pickle_filename.split('/')
img_filename_only = pickle_filepaths[len(pickle_filepaths) - 1]
txt_exception = ' {0:40} ==> '.format(img_filename_only)
if observations_pickle is None:
txt_exception += 'empty or bad input file\n'
return None, txt_exception
inputs, txt_organize_input = self.organize_input(
observations_pickle,
iparams,
avg_mode,
pickle_filename=pickle_filename)
if inputs is not None:
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm, wavelength, crystal_init_orientation = inputs
else:
txt_exception += txt_organize_input + '\n'
return None, txt_exception
#2. Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(),
miller_indices=observations_non_polar.indices())
pair_0 = flex.size_t([pair[0] for pair in matches.pairs()])
pair_1 = flex.size_t([pair[1] for pair in matches.pairs()])
references_sel = miller_array_ref.select(pair_0)
observations_original_sel = observations_original.select(pair_1)
observations_non_polar_sel = observations_non_polar.select(pair_1)
alpha_angle_set = alpha_angle.select(pair_1)
spot_pred_x_mm_set = spot_pred_x_mm.select(pair_1)
spot_pred_y_mm_set = spot_pred_y_mm.select(pair_1)
#4. Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(
references_sel.data(), observations_original_sel, wavelength,
crystal_init_orientation, alpha_angle_set, spot_pred_x_mm_set,
spot_pred_y_mm_set, iparams, pres_in,
observations_non_polar_sel, detector_distance_mm)
except Exception:
txt_exception += 'optimization failed.\n'
return None, txt_exception
#caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, \
a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = refined_params
inputs, txt_organize_input = self.organize_input(
observations_pickle,
iparams,
avg_mode,
pickle_filename=pickle_filename)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm, wavelength, crystal_init_orientation = inputs
observations_non_polar, index_basis_name = self.get_observations_non_polar(
observations_original, pickle_filename, iparams)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell(
(a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
if pres_in is not None:
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(
wavelength=wavelength).data()
ph = partiality_handler()
partiality_fin, dummy, rs_fin, rh_fin = ph.calc_partiality_anisotropy_set(
uc_fin, rotx_fin, roty_fin, observations_original.indices(),
ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, two_theta,
alpha_angle, wavelength, crystal_init_orientation, spot_pred_x_mm,
spot_pred_y_mm, detector_distance_mm, iparams.partiality_model,
iparams.flag_beam_divergence)
#calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O * R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru(
(1, 0, 0), rotx_fin).rotate_thru((0, 1, 0), roty_fin)
#remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.customized_copy(\
indices=observations_non_polar.indices().select(i_sel),
data=observations_non_polar.data().select(i_sel),
sigmas=observations_non_polar.sigmas().select(i_sel))
observations_original_sel = observations_original.customized_copy(\
indices=observations_original.indices().select(i_sel),
data=observations_original.data().select(i_sel),
sigmas=observations_original.sigmas().select(i_sel))
pres = postref_results()
pres.set_params(observations=observations_non_polar_sel,
observations_original=observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_fin_orientation,
detector_distance_mm=detector_distance_mm)
r_change = ((pres.R_final - pres.R_init) / pres.R_init) * 100
r_xy_change = (
(pres.R_xy_final - pres.R_xy_init) / pres.R_xy_init) * 100
cc_change = ((pres.CC_final - pres.CC_init) / pres.CC_init) * 100
txt_postref = '{0:40} => RES:{1:5.2f} NREFL:{2:5d} R:{3:6.1f}% RXY:{4:5.1f}% CC:{5:5.1f}% G:{6:6.4f} B:{7:5.1f} CELL:{8:6.1f}{9:6.1f} {10:6.1f} {11:5.1f} {12:5.1f} {13:5.1f}'.format(
img_filename_only + ' (' + index_basis_name + ')',
observations_original_sel.d_min(),
len(observations_original_sel.data()), r_change, r_xy_change,
cc_change, pres.G, pres.B, a_fin, b_fin, c_fin, alpha_fin,
beta_fin, gamma_fin)
print txt_postref
txt_postref += '\n'
return pres, txt_postref
def exercise_change_basis():
assert approx_equal(O1.unit_cell().parameters(),
(47.659, 47.6861, 49.6444, 62.9615, 73.8222, 73.5269),
1E-3)
reindex = (0.0, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, -1.0
) # swap a & b and take inverse
O2 = O1.change_basis(reindex)
assert approx_equal(O2.unit_cell().parameters(),
(47.6861, 47.659, 49.6444, 73.8222, 62.9615, 73.5269),
1E-3)
rhombohedral_test = crystal_orientation(
(0.002737747939994224, -0.0049133768326561278, 0.0023634556852316566,
0.0062204242383498082, 0.006107332242442573, 0.0047036576949967112,
-0.0057640198753891566, -0.0025891042237382953,
0.0023674924674260264), basis_type.reciprocal)
rhombohedral_reference = crystal_orientation(
(-0.0076361080646872997, 0.0049061665572297979, 0.0023688116121433865,
-0.00011109895272056645, -0.0061110173438898583,
0.0047062769738302939, 0.0031790372319626674, 0.0025876279220667518,
0.0023669727051432361), basis_type.reciprocal)
# Find a similarity transform that maps the two cells onto each other
c_inv_r_best = rhombohedral_test.best_similarity_transformation(
other=rhombohedral_reference,
fractional_length_tolerance=1.00,
unimodular_generator_range=1)
c_inv_r_int = tuple([int(round(ij, 0)) for ij in c_inv_r_best])
assert c_inv_r_int == (-1, 0, 0, 1, -1, 0, 0, 0, 1)
c_inv = sgtbx.rt_mx(sgtbx.rot_mx(c_inv_r_int))
cb_op = sgtbx.change_of_basis_op(c_inv)
rhombohedral_reindex = rhombohedral_test.change_basis(cb_op)
assert rhombohedral_reindex.difference_Z_score(
rhombohedral_reference) < 0.40
assert rhombohedral_reindex.direct_mean_square_difference(
rhombohedral_reference) < 0.1
#an alternative test from ana that should fail (gives high msd~0.22; cell axes don't match):
ana_reference = crystal_orientation(
(0.0023650364919947241, 0.012819317075171401, 0.003042762222847376,
0.0081242553464681254, 0.0050052660206998077, -0.01472465697193685,
-0.01373896574061278, 0.0083781530252581681, -0.0035301340829149005),
basis_type.reciprocal)
ana_current = crystal_orientation(
(-0.014470153848927263, 0.0095185368147633793, 0.00087746490483763798,
-0.0049989006573928716, -0.0079714727432991222, 0.014778692772530192,
0.0051268914129933571, 0.010264066188909109, 0.0044244589492769002),
basis_type.reciprocal)
c_inv_r_best = ana_current.best_similarity_transformation(
other=ana_reference,
fractional_length_tolerance=200.0,
unimodular_generator_range=1)
c_inv_r_int = tuple([int(round(ij, 0)) for ij in c_inv_r_best])
c_inv = sgtbx.rt_mx(sgtbx.rot_mx(c_inv_r_int))
cb_op = sgtbx.change_of_basis_op(c_inv)
ana_reindex = ana_reference.change_basis(cb_op.inverse())
assert 200.0 > ana_reindex.difference_Z_score(ana_current) > 20.
u = uctbx.unit_cell((10., 10., 10., 90., 90., 90.))
CO = crystal_orientation(u.fractionalization_matrix(), True)
assert approx_equal(
CO.unit_cell().parameters(),
CO.change_basis((1, 0, 0, 0, 1, 0, 0, 0, 1)).unit_cell().parameters())
u = uctbx.unit_cell((2, 3, 5))
CO = crystal_orientation(u.fractionalization_matrix(), True)
assert approx_equal(
CO.change_basis((0, 1, 0, 0, 0, 1, 1, 0, 0)).unit_cell().parameters(),
(5, 2, 3, 90, 90, 90))
cb_op = sgtbx.change_of_basis_op("y,z,x")
assert approx_equal(
CO.change_basis(cb_op).unit_cell().parameters(), (5, 2, 3, 90, 90, 90))
import scitbx.math
from scitbx import matrix
fmx = matrix.sqr(
uctbx.unit_cell((10, 13, 17, 85, 95, 105)).fractionalization_matrix())
crm = matrix.sqr(
scitbx.math.r3_rotation_axis_and_angle_as_matrix(axis=(-3, 5, -7),
angle=37,
deg=True))
co = crystal_orientation(crm * fmx.transpose(), True)
assert approx_equal(co.crystal_rotation_matrix(), crm)
identity = (1, 0, 0, 0, 1, 0, 0, 0, 1)
I = crystal_orientation(identity, False) #direct space
orthorhombic = (1, 0, 0, 0, 0.5, 0., 0., 0., 0.25)
R = crystal_orientation(orthorhombic, True) #reciprocal space
assert R.direct_matrix() == (1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 4.0)
assert R.reciprocal_matrix() == orthorhombic
inverse = (-1, 0, 0, 0, -1, 0, 0, 0, -1)
negative = crystal_orientation(inverse, False)
assert I == negative.make_positive()
assert R.unit_cell().parameters() == (1.0, 2.0, 4.0, 90., 90., 90.)
assert approx_equal(R.unit_cell_inverse().parameters(),
(1.0, 0.5, 0.25, 90., 90., 90.))
O1 = crystal_orientation(
(-0.015553395334476732, -0.0028287158782335244, 0.018868416534039902,
-0.0016512962184570643, -0.020998220575299865, 0.0012056332661160732,
0.015789188025134133, -0.011166135863736777, 0.013045365404272641), True)
def exercise_change_basis():
assert approx_equal(O1.unit_cell().parameters(),
(47.659, 47.6861, 49.6444, 62.9615, 73.8222, 73.5269),
1E-3)
reindex = (0.0, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, -1.0
) # swap a & b and take inverse
O2 = O1.change_basis(reindex)
assert approx_equal(O2.unit_cell().parameters(),
(47.6861, 47.659, 49.6444, 73.8222, 62.9615, 73.5269),
1E-3)
rhombohedral_test = crystal_orientation(
def is_similar_to(self, other, angle_tolerance=0.01,
mosaicity_tolerance=0.8, uc_rel_length_tolerance=0.01,
uc_abs_angle_tolerance=1.):
'''
Test similarity of this to another crystal model
:param other: the crystal model to test against
:type other: crystal_model
:param angle_tolerance: maximum tolerated orientation difference in degrees
:type angle_tolerance: float
:param mosaicity_tolerance: minimum tolerated fraction of the larger
mosaicity for the smaller mosaicity to retain similarity
:type mosaicity_tolerance:float
:param uc_rel_length_tolerance: relative length tolerance to pass to
unit_cell.is_similar_to
:type uc_rel_length_tolerance: float
:param uc_abs_angle_tolerance: absolute angle tolerance to pass to
unit_cell.is_similar_to
:type uc_abs_angle_tolerance: float
:returns: Whether the other crystal model is similar to this
:rtype: bool
'''
# space group test
if self.get_space_group() != other.get_space_group(): return False
# mosaicity test
m_a, m_b = self.get_mosaicity(deg=True), other.get_mosaicity(deg=True)
min_m, max_m = min(m_a, m_b), max(m_a, m_b)
if min_m <= 0.0:
if max_m > 0.0: return False
elif min_m / max_m < mosaicity_tolerance: return False
# static orientation test
U_a, U_b = self.get_U(), other.get_U()
assert U_a.is_r3_rotation_matrix()
assert U_b.is_r3_rotation_matrix()
R_ab = U_b * U_a.transpose()
uq = R_ab.r3_rotation_matrix_as_unit_quaternion()
angle = uq.unit_quaternion_as_axis_and_angle(deg=True)[0]
if abs(angle) > angle_tolerance: return False
# static unit cell test
uc_a, uc_b = self.get_unit_cell(), other.get_unit_cell()
if not uc_a.is_similar_to(uc_b,
relative_length_tolerance=uc_rel_length_tolerance,
absolute_angle_tolerance=uc_abs_angle_tolerance): return False
# scan varying tests
if self.num_scan_points > 0:
if other.num_scan_points != self.num_scan_points: return False
for i in range(self.num_scan_points):
U_a, U_b = self.get_U_at_scan_point(i), other.get_U_at_scan_point(i)
assert U_a.is_r3_rotation_matrix()
assert U_b.is_r3_rotation_matrix()
R_ab = U_b * U_a.transpose()
uq = R_ab.r3_rotation_matrix_as_unit_quaternion()
angle = uq.unit_quaternion_as_axis_and_angle(deg=True)[0]
if abs(angle) > angle_tolerance: return False
B_a, B_b = self.get_B_at_scan_point(i), other.get_B_at_scan_point(i)
uc_a = crystal_orientation(B_a,True).unit_cell()
uc_b = crystal_orientation(B_b,True).unit_cell()
if not uc_a.is_similar_to(uc_b,
relative_length_tolerance=uc_rel_length_tolerance,
absolute_angle_tolerance=uc_abs_angle_tolerance): return False
return True
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, 1.0 / 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
if sum(spot_pred_y_mm_set) == 0:
# hack for dials integration - spot_pred_x_mm_set is s1 * to be fixed *
delta_xy_set = (spot_pred_x_mm_set - sd_array).norms()
else:
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 test_compare_example():
experiments = ExperimentListFactory.from_json(json_is, check_format=False)
for experiment in experiments:
header = {
'DIM': '2',
'DENZO_X_BEAM': '97.185',
'RANK': '0',
'PREFIX': 'step5_000009',
'BEAM_CENTER_Y': '97.13',
'BEAM_CENTER_X': '97.13',
'WAVELENGTH': '1.30432',
'OSC_START': '0',
'ADC_OFFSET': '10',
'BYTE_ORDER': 'little_endian',
'DIRECT_SPACE_ABC':
'2.7790989649304656,3.721227037283121,0.616256870237976,-4.231398741367156,1.730877297917864,1.0247061633019547,2.9848502901631253,-4.645083818143041,28.595825588147285',
'OSC_RANGE': '0',
'DIALS_ORIGIN': '-97.185,97.185,-50',
'MOSFLM_CENTER_X': '97.13',
'MOSFLM_CENTER_Y': '97.13',
'CLOSE_DISTANCE': '50',
'BEAMLINE': 'fake',
'TWOTHETA': '0',
'ADXV_CENTER_Y': '96.965',
'ADXV_CENTER_X': '97.185',
'HEADER_BYTES': '1024',
'DETECTOR_SN': '000',
'DISTANCE': '50',
'PHI': '0',
'SIZE1': '1765',
'SIZE2': '1765',
'XDS_ORGX': '884',
'XDS_ORGY': '884',
'DENZO_Y_BEAM': '97.185',
'TIME': '1',
'TYPE': 'unsigned_short',
'PIXEL_SIZE': '0.11'
}
# the header of the simulated image, containing ground truth orientation
rsabc = sqr([float(v) for v in header['DIRECT_SPACE_ABC'].split(',')
]) * permute.inverse()
rsa = rsabc[0:3]
rsb = rsabc[3:6]
rsc = rsabc[6:9]
header_cryst = Crystal(rsa, rsb, rsc,
'P1').change_basis(CB_OP_C_P.inverse())
header_cryst.set_space_group(experiment.crystal.get_space_group())
print('Header crystal')
print(header_cryst)
expt_crystal = experiment.crystal
print('Integrated crystal')
print(expt_crystal)
header_ori = crystal_orientation.crystal_orientation(
header_cryst.get_A(), crystal_orientation.basis_type.reciprocal)
expt_ori = crystal_orientation.crystal_orientation(
expt_crystal.get_A(), crystal_orientation.basis_type.reciprocal)
print('Converted to cctbx')
header_ori.show()
expt_ori.show()
# assert the equivalence between dxtbx crystal object and the cctbx crystal_orientation object
assert approx_equal(header_cryst.get_U(), header_ori.get_U_as_sqr())
assert approx_equal(header_cryst.get_A(),
header_ori.reciprocal_matrix())
assert approx_equal(
header_cryst.get_B(),
sqr(header_ori.unit_cell().fractionalization_matrix()).transpose())
assert approx_equal(expt_crystal.get_U(), expt_ori.get_U_as_sqr())
assert approx_equal(expt_crystal.get_A(), expt_ori.reciprocal_matrix())
assert approx_equal(
expt_crystal.get_B(),
sqr(expt_ori.unit_cell().fractionalization_matrix()).transpose())
cb_op_align = sqr(
expt_ori.best_similarity_transformation(header_ori, 50, 1))
print('XYZ angles',
cb_op_align.r3_rotation_matrix_as_x_y_z_angles(deg=True))
aligned_ori = expt_ori.change_basis(cb_op_align)
U_integrated = aligned_ori.get_U_as_sqr()
U_ground_truth = header_ori.get_U_as_sqr()
missetting_rot = U_integrated * U_ground_truth.inverse()
print("determinant", missetting_rot.determinant())
assert approx_equal(missetting_rot.determinant(), 1.0)
assert missetting_rot.is_r3_rotation_matrix()
angle, axis = missetting_rot.r3_rotation_matrix_as_unit_quaternion(
).unit_quaternion_as_axis_and_angle(deg=True)
print("Angular offset is %13.10f deg." % (angle))
assert approx_equal(angle, 0.2609472065)
def postrefine_by_frame(self, frame_no, pres_in, iparams, miller_array_ref, avg_mode):
#Prepare data
if pres_in is None:
return None, 'Found empty pickle file'
observations_pickle = pickle.load(open(pres_in.pickle_filename,"rb"))
wavelength = observations_pickle["wavelength"]
crystal_init_orientation = observations_pickle["current_orientation"][0]
pickle_filename = pres_in.pickle_filename
pickle_filepaths = pickle_filename.split('/')
img_filename_only = pickle_filepaths[len(pickle_filepaths)-1]
txt_exception = ' {0:40} ==> '.format(img_filename_only)
inputs, txt_organize_input = self.organize_input(observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename)
if inputs is not None:
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, \
detector_distance_mm, identified_isoform, mapped_predictions, xbeam, ybeam = inputs
else:
txt_exception += txt_organize_input + '\n'
return None, txt_exception
#Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar, index_basis_name = self.get_observations_non_polar(observations_original, pickle_filename, iparams)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(),
miller_indices=observations_non_polar.indices())
I_ref_match = flex.double([miller_array_ref.data()[pair[0]] for pair in matches.pairs()])
miller_indices_ref_match = flex.miller_index((miller_array_ref.indices()[pair[0]] for pair in matches.pairs()))
I_obs_match = flex.double([observations_non_polar.data()[pair[1]] for pair in matches.pairs()])
sigI_obs_match = flex.double([observations_non_polar.sigmas()[pair[1]] for pair in matches.pairs()])
miller_indices_original_obs_match = flex.miller_index((observations_original.indices()[pair[1]] \
for pair in matches.pairs()))
miller_indices_non_polar_obs_match = flex.miller_index((observations_non_polar.indices()[pair[1]] \
for pair in matches.pairs()))
alpha_angle_set = flex.double([alpha_angle[pair[1]] for pair in matches.pairs()])
spot_pred_x_mm_set = flex.double([spot_pred_x_mm[pair[1]] for pair in matches.pairs()])
spot_pred_y_mm_set = flex.double([spot_pred_y_mm[pair[1]] for pair in matches.pairs()])
references_sel = miller_array_ref.customized_copy(data=I_ref_match, indices=miller_indices_ref_match)
observations_original_sel = observations_original.customized_copy(data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_original_obs_match)
observations_non_polar_sel = observations_non_polar.customized_copy(data=I_obs_match,
sigmas=sigI_obs_match,
indices=miller_indices_non_polar_obs_match)
#Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(I_ref_match,
observations_original_sel, wavelength,
crystal_init_orientation, alpha_angle_set,
spot_pred_x_mm_set, spot_pred_y_mm_set,
iparams,
pres_in,
observations_non_polar_sel,
detector_distance_mm)
except Exception:
txt_exception += 'optimization failed.\n'
return None, txt_exception
#caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin, \
a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = refined_params
inputs, txt_organize_input = self.organize_input(observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, \
detector_distance_mm, identified_isoform, mapped_predictions, xbeam, ybeam = inputs
observations_non_polar, index_basis_name = self.get_observations_non_polar(observations_original, pickle_filename, iparams)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell((a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(wavelength=wavelength).data()
ph = partiality_handler()
partiality_fin, dummy, rs_fin, rh_fin = ph.calc_partiality_anisotropy_set(uc_fin, rotx_fin, roty_fin,
observations_original.indices(),
ry_fin, rz_fin, r0_fin, re_fin, voigt_nu_fin,
two_theta, alpha_angle, wavelength,
crystal_init_orientation,
spot_pred_x_mm, spot_pred_y_mm,
detector_distance_mm,
iparams.partiality_model,
iparams.flag_beam_divergence)
#calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O*R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru((1,0,0), rotx_fin
).rotate_thru((0,1,0), roty_fin)
#remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.select(i_sel)
observations_original_sel = observations_original.select(i_sel)
mapped_predictions = mapped_predictions.select(i_sel)
pres = postref_results()
pres.set_params(observations = observations_non_polar_sel,
observations_original = observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_init_orientation,
detector_distance_mm=detector_distance_mm,
identified_isoform=identified_isoform,
mapped_predictions=mapped_predictions,
xbeam=xbeam,
ybeam=ybeam)
r_change, r_xy_change, cc_change, cc_iso_change = (0,0,0,0)
try:
r_change = ((pres.R_final - pres.R_init)/pres.R_init)*100
r_xy_change = ((pres.R_xy_final - pres.R_xy_init)/pres.R_xy_init)*100
cc_change = ((pres.CC_final - pres.CC_init)/pres.CC_init)*100
cc_iso_change = ((pres.CC_iso_final - pres.CC_iso_init)/pres.CC_iso_init)*100
except Exception:
pass
txt_postref= ' {0:40} ==> RES:{1:5.2f} NREFL:{2:5d} R:{3:8.2f}% RXY:{4:8.2f}% CC:{5:6.2f}% CCISO:{6:6.2f}% G:{7:10.3e} B:{8:7.1f} CELL:{9:6.2f} {10:6.2f} {11:6.2f} {12:6.2f} {13:6.2f} {14:6.2f}'.format(img_filename_only+' ('+index_basis_name+')', observations_original_sel.d_min(), len(observations_original_sel.data()), r_change, r_xy_change, cc_change, cc_iso_change, pres.G, pres.B, a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin)
print txt_postref
txt_postref += '\n'
return pres, txt_postref
dm = {}
dn = {}
for key in data:
if key == "correction_vectors":
# drop correction_vectors as they aren't as easy to split up
continue
elif key in ["current_cb_op_to_primitive", "effective_tiling", "pointgroup", "identified_isoform"]:
dl[key] = data[key]
dr[key] = data[key]
dm[key] = data[key]
dn[key] = data[key]
continue
elif key == "current_orientation":
from cctbx import crystal_orientation
dl[key] = [crystal_orientation.crystal_orientation(c) for c in data[key]]
dr[key] = [crystal_orientation.crystal_orientation(c) for c in data[key]]
dm[key] = [crystal_orientation.crystal_orientation(c) for c in data[key]]
dn[key] = [crystal_orientation.crystal_orientation(c) for c in data[key]]
continue
try:
assert len(data[key]) == len(sel_l)
islist = True
except TypeError, e:
islist = False
if islist:
val_l = []
val_r = []
val_m = []
val_n = []
rot_mat = rotation_vector.axis_and_angle_as_r3_rotation_matrix(
omegas[omegaidx] + dangles[omegaidx][n] * 0.1)
HP = (rot_mat * matrix.sqr(Amat)
) * matrix.col(hkl + difference) + beam_vector
#print "%10.7f %10.7f %10.7f"%HP.elems
assert approx_equal(H1, HP, eps=1E-5)
#print
if __name__ == '__main__':
wavelength = 1.2
resolution = 3.0
Amat = (0.0038039968697463817, 0.004498689311366309, 0.0044043429203887785,
-0.00477859183801569, 0.006594300357213904, -0.002402759536958918,
-0.01012056453488894, -0.0014226943325514182, 0.002789954423701981)
orient = crystal_orientation(Amat, True)
calc = partial_spot_position_partial_H(
limiting_resolution=resolution,
orientation=orient.reciprocal_matrix(),
wavelength=wavelength,
axial_direction=(0., 1., 0.))
rotation_scattering(calc, matrix.col((0., 1., 0.)), Amat, wavelength)
test_finite_differences(calc, matrix.col((0., 1., 0.)), Amat, wavelength)
print "OK"
) # from C to P
print(str(CB_OP_C_P))
icount = 0
from scitbx.array_family import flex
angles = flex.double()
for stuff in get_items():
#print stuff
icount += 1
print("Iteration", icount)
# work up the crystal model from integration
direct_A = stuff["integrated_crystal_model"].get_A_inverse_as_sqr()
permute = sqr((0, 0, 1, 0, 1, 0, -1, 0, 0))
sim_compatible = direct_A * permute # permute columns when post multiplying
from cctbx import crystal_orientation
integrated_Ori = crystal_orientation.crystal_orientation(
sim_compatible, crystal_orientation.basis_type.direct)
#integrated_Ori.show(legend="integrated")
# work up the crystal model from postrefinement
direct_A = stuff["postref"].inverse()
permute = sqr((0, 0, 1, 0, 1, 0, -1, 0, 0))
sim_compatible = direct_A * permute # permute columns when post multiplying
from cctbx import crystal_orientation
postref_Ori = crystal_orientation.crystal_orientation(
sim_compatible, crystal_orientation.basis_type.direct)
# work up the ground truth from header
header_Ori = crystal_orientation.crystal_orientation(
stuff["ABC"], crystal_orientation.basis_type.direct)
#header_Ori.show(legend="header_Ori")
def run(args):
distance = 125
centre = (97.075, 97.075)
pix_size = (0.11, 0.11)
image_size = (1765, 1765)
wavelength = args.w or 1.0
# 1. Make a dummy detector
detector = detector_factory.simple('SENSOR_UNKNOWN', # Sensor
distance,
centre,
'+x','-y', # fast/slow direction
pix_size,
image_size)
# 2. Get the miller array!
mill_array = process_mtz(args.mtzfile[0]).as_intensity_array()
ortho = sqr(mill_array.crystal_symmetry().unit_cell().reciprocal() \
.orthogonalization_matrix())
# 3.Create some image_pickle dictionairies that contain 'full' intensities,
# but are otherwise complete.
im = 0
while im < args.n:
im += 1
A = sqr(flex.random_double_r3_rotation_matrix()) * ortho
orientation = crystal_orientation(A, basis_type.reciprocal)
pix_coords, miller_set = get_pix_coords(wavelength, A, mill_array, detector)
if len(miller_set) > 10: # at least 10 reflections
miller_set = cctbx.miller.set(mill_array.crystal_symmetry(), miller_set,
anomalous_flag=False)
obs = mill_array.common_set(miller_set)
temp_dict = {'observations': [obs],
'mapped_predictions': [pix_coords],
'pointgroup': None,
'current_orientation': [orientation],
'xbeam': centre[0],
'ybeam': centre[1],
'wavelength': wavelength}
old_node = ImageNode(dicti=temp_dict, scale=False)
# Remove all reflection that are not at least p partial
partial_sel = (old_node.partialities > p_threshold)
temp_dict['full_observations'] = [obs.select(partial_sel)]
temp_dict['observations'] = [obs.select(partial_sel)
* old_node.partialities.select(partial_sel)]
temp_dict['mapped_predictions'] = \
[temp_dict['mapped_predictions'][0].select(partial_sel)]
if logging.Logger.root.level <= logging.DEBUG: # debug!
before = temp_dict['full_observations'][0]
after = temp_dict['observations'][0] / old_node.partialities
assert sum(abs(before.data() - after.data())) < eps
if args.r:
partials = list(temp_dict['observations'][0].data())
jiggled_partials = flex.double([random.gauss(obs, args.r * obs)
for obs in partials])
temp_dict['observations'][0] = temp_dict['observations'][0] \
.customized_copy(data=jiggled_partials)
pkl_name = "simulated_data_{0:04d}.pickle".format(im)
with(open(pkl_name, 'wb')) as pkl:
cPickle.dump(temp_dict, pkl)
''' Only works with no noise:
def run_sim2smv(img_prefix=None, simparams=None,pdb_lines=None,crystal=None,spectra=None,rotation=None,rank=None,fsave=None,sfall_cluster=None,quick=False):
smv_fileout = fsave
direct_algo_res_limit = simparams.direct_algo_res_limit
wavlen, flux, real_wavelength_A = next(spectra) # list of lambdas, list of fluxes, average wavelength
real_flux = flex.sum(flux)
assert real_wavelength_A > 0
# print(rank, " ## real_wavelength_A/real_flux = ", real_wavelength_A, real_flux*1.0/simparams.flux)
if quick:
wavlen = flex.double([real_wavelength_A])
flux = flex.double([real_flux])
# GF = gen_fmodel(resolution=simparams.direct_algo_res_limit,pdb_text=pdb_lines,algorithm=simparams.fmodel_algorithm,wavelength=real_wavelength_A)
# GF.set_k_sol(simparams.k_sol)
# GF.make_P1_primitive()
sfall_main = sfall_cluster["main"] #GF.get_amplitudes()
# use crystal structure to initialize Fhkl array
# sfall_main.show_summary(prefix = "Amplitudes used ")
N = crystal.number_of_cells(sfall_main.unit_cell())
#print("## number of N = ", N)
SIM = nanoBragg(detpixels_slowfast=(simparams.detector_size_ny,simparams.detector_size_nx),pixel_size_mm=simparams.pixel_size_mm,\
Ncells_abc=(N,N,N),wavelength_A=real_wavelength_A,verbose=0)
# workaround for problem with wavelength array, specify it separately in constructor.
# SIM.adc_offset_adu = 0 # Do not offset by 40
SIM.adc_offset_adu = 10 # Do not offset by 40
SIM.seed = 0
# SIM.randomize_orientation()
SIM.mosaic_spread_deg = simparams.mosaic_spread_deg # interpreted by UMAT_nm as a half-width stddev
SIM.mosaic_domains = simparams.mosaic_domains # 77 seconds.
SIM.distance_mm = simparams.distance_mm
## setup the mosaicity
UMAT_nm = flex.mat3_double()
mersenne_twister = flex.mersenne_twister(seed=0)
scitbx.random.set_random_seed(1234)
rand_norm = scitbx.random.normal_distribution(mean=0, sigma=SIM.mosaic_spread_deg * math.pi/180.)
g = scitbx.random.variate(rand_norm)
mosaic_rotation = g(SIM.mosaic_domains)
for m in mosaic_rotation:
site = col(mersenne_twister.random_double_point_on_sphere())
UMAT_nm.append( site.axis_and_angle_as_r3_rotation_matrix(m,deg=False) )
SIM.set_mosaic_blocks(UMAT_nm)
######################
SIM.beamcenter_convention=convention.ADXV
SIM.beam_center_mm=(simparams.beam_center_x_mm, simparams.beam_center_y_mm) # 95.975 96.855
######################
# get same noise each time this test is run
SIM.seed = 0
SIM.oversample=simparams.oversample
SIM.wavelength_A = real_wavelength_A
SIM.polarization=simparams.polarization
# this will become F000, marking the beam center
SIM.default_F=simparams.default_F
#SIM.missets_deg= (10,20,30)
SIM.Fhkl=sfall_main
Amatrix_rot = (rotation * sqr(sfall_main.unit_cell().orthogonalization_matrix())).transpose()
SIM.Amatrix_RUB = Amatrix_rot
#workaround for failing init_cell, use custom written Amatrix setter
# print("## inside run_sim2smv, Amat_rot = ", Amatrix_rot)
Amat = sqr(SIM.Amatrix).transpose() # recovered Amatrix from SIM
Ori = crystal_orientation.crystal_orientation(Amat, crystal_orientation.basis_type.reciprocal)
SIM.xtal_shape=shapetype.Gauss # both crystal & RLP are Gaussian
SIM.progress_meter=False
# SIM.show_params()
# flux is always in photons/s
SIM.flux=real_flux
SIM.exposure_s=simparams.exposure_s
# assumes round beam
SIM.beamsize_mm=simparams.beamsize_mm #cannot make this 3 microns; spots are too intense
temp=SIM.Ncells_abc
SIM.Ncells_abc=temp
# print("## domains_per_crystal = ", crystal.domains_per_crystal)
SIM.raw_pixels *= crystal.domains_per_crystal # must calculate the correct scale!
# print("## Initial raw_pixels = ", flex.sum(SIM.raw_pixels))
for x in range(len(flux)):
# CH = channel_pixels(wavlen[x],flux[x],N,UMAT_nm,Amatrix_rot,sfall_cluster[x],rank)
# print("## in loop wavlen/flux/real_wavelength_A = ", wavlen[x], flux[x]/real_flux, real_wavelength_A)
CH = channel_pixels(simparams=simparams,single_wavelength_A=wavlen[x],single_flux=flux[x],N=N,UMAT_nm=UMAT_nm, \
Amatrix_rot=Amatrix_rot,sfall_channel=sfall_cluster[x],rank=rank)
SIM.raw_pixels += CH.raw_pixels * crystal.domains_per_crystal
CHDBG_singleton.extract(channel_no=x, data=CH.raw_pixels)
CH.free_all()
# print("## sum raw_pixels after ", x, "is", flex.sum(SIM.raw_pixels))
# rough approximation to water: interpolation points for sin(theta/lambda) vs structure factor
bg = flex.vec2_double([(0,2.57),(0.0365,2.58),(0.07,2.8),(0.12,5),(0.162,8),(0.2,6.75),(0.18,7.32),(0.216,6.75),(0.236,6.5),(0.28,4.5),(0.3,4.3),(0.345,4.36),(0.436,3.77),(0.5,3.17)])
SIM.Fbg_vs_stol = bg
SIM.amorphous_sample_thick_mm = simparams.water_sample_thick_mm
SIM.amorphous_density_gcm3 = simparams.water_density_gcm3
SIM.amorphous_molecular_weight_Da = simparams.water_molecular_weight_Da
SIM.flux=real_flux
SIM.beamsize_mm=simparams.beamsize_mm # square (not user specified)
SIM.exposure_s=simparams.exposure_s # multiplies flux x exposure
SIM.add_background()
# rough approximation to air
bg = flex.vec2_double([(0,14.1),(0.045,13.5),(0.174,8.35),(0.35,4.78),(0.5,4.22)])
SIM.Fbg_vs_stol = bg
SIM.amorphous_sample_thick_mm = simparams.air_sample_thick_mm # between beamstop and collimator
SIM.amorphous_density_gcm3 = simparams.air_density_gcm3
SIM.amorphous_sample_molecular_weight_Da = simparams.air_molecular_weight_Da # nitrogen = N2
SIM.add_background()
#apply beamstop mask here
# settings for CCD
SIM.detector_psf_kernel_radius_pixels=simparams.detector_psf_kernel_radius_pixels
SIM.detector_psf_type=shapetype.Unknown # for CSPAD
SIM.detector_psf_fwhm_mm=simparams.detector_psf_fwhm_mm
SIM.apply_psf()
SIM.add_noise()
extra = "PREFIX=%s;\nRANK=%d;\n"%(img_prefix,rank)
SIM.to_smv_format_py(fileout=smv_fileout,intfile_scale=1,rotmat=True,extra=extra,gz=True)
SIM.free_all()
def test_compare_example():
experiments = ExperimentListFactory.from_json(json_is, check_format=False)
for experiment in experiments:
header = {
"DIM": "2",
"DENZO_X_BEAM": "97.185",
"RANK": "0",
"PREFIX": "step5_000009",
"BEAM_CENTER_Y": "97.13",
"BEAM_CENTER_X": "97.13",
"WAVELENGTH": "1.30432",
"OSC_START": "0",
"ADC_OFFSET": "10",
"BYTE_ORDER": "little_endian",
"DIRECT_SPACE_ABC":
"2.7790989649304656,3.721227037283121,0.616256870237976,-4.231398741367156,1.730877297917864,1.0247061633019547,2.9848502901631253,-4.645083818143041,28.595825588147285",
"OSC_RANGE": "0",
"DIALS_ORIGIN": "-97.185,97.185,-50",
"MOSFLM_CENTER_X": "97.13",
"MOSFLM_CENTER_Y": "97.13",
"CLOSE_DISTANCE": "50",
"BEAMLINE": "fake",
"TWOTHETA": "0",
"ADXV_CENTER_Y": "96.965",
"ADXV_CENTER_X": "97.185",
"HEADER_BYTES": "1024",
"DETECTOR_SN": "000",
"DISTANCE": "50",
"PHI": "0",
"SIZE1": "1765",
"SIZE2": "1765",
"XDS_ORGX": "884",
"XDS_ORGY": "884",
"DENZO_Y_BEAM": "97.185",
"TIME": "1",
"TYPE": "unsigned_short",
"PIXEL_SIZE": "0.11",
}
# the header of the simulated image, containing ground truth orientation
rsabc = (sqr([float(v)
for v in header["DIRECT_SPACE_ABC"].split(",")]) *
permute.inverse())
rsa = rsabc[0:3]
rsb = rsabc[3:6]
rsc = rsabc[6:9]
header_cryst = Crystal(rsa, rsb, rsc,
"P1").change_basis(CB_OP_C_P.inverse())
header_cryst.set_space_group(experiment.crystal.get_space_group())
print("Header crystal")
print(header_cryst)
expt_crystal = experiment.crystal
print("Integrated crystal")
print(expt_crystal)
header_ori = crystal_orientation.crystal_orientation(
header_cryst.get_A(), crystal_orientation.basis_type.reciprocal)
expt_ori = crystal_orientation.crystal_orientation(
expt_crystal.get_A(), crystal_orientation.basis_type.reciprocal)
print("Converted to cctbx")
header_ori.show()
expt_ori.show()
# assert the equivalence between dxtbx crystal object and the cctbx crystal_orientation object
assert approx_equal(header_cryst.get_U(), header_ori.get_U_as_sqr())
assert approx_equal(header_cryst.get_A(),
header_ori.reciprocal_matrix())
assert approx_equal(
header_cryst.get_B(),
sqr(header_ori.unit_cell().fractionalization_matrix()).transpose(),
)
assert approx_equal(expt_crystal.get_U(), expt_ori.get_U_as_sqr())
assert approx_equal(expt_crystal.get_A(), expt_ori.reciprocal_matrix())
assert approx_equal(
expt_crystal.get_B(),
sqr(expt_ori.unit_cell().fractionalization_matrix()).transpose(),
)
cb_op_align = sqr(
expt_ori.best_similarity_transformation(header_ori, 50, 1))
print("XYZ angles",
cb_op_align.r3_rotation_matrix_as_x_y_z_angles(deg=True))
aligned_ori = expt_ori.change_basis(cb_op_align)
U_integrated = aligned_ori.get_U_as_sqr()
U_ground_truth = header_ori.get_U_as_sqr()
missetting_rot = U_integrated * U_ground_truth.inverse()
print("determinant", missetting_rot.determinant())
assert approx_equal(missetting_rot.determinant(), 1.0)
assert missetting_rot.is_r3_rotation_matrix()
angle, axis = missetting_rot.r3_rotation_matrix_as_unit_quaternion(
).unit_quaternion_as_axis_and_angle(deg=True)
print("Angular offset is %13.10f deg." % (angle))
assert approx_equal(angle, 0.2609472065)
def run_sim2smv(prefix, crystal, spectra, rotation, quick=False):
direct_algo_res_limit = 2.0
wavlen, flux, wavelength_A = next(
spectra) # list of lambdas, list of fluxes, average wavelength
if quick:
wavlen = flex.double([wavelength_A])
flux = flex.double([flex.sum(flux)])
print("Quick sim, lambda=%f, flux=%f" % (wavelength_A, flux[0]))
#sfall = fcalc_from_pdb(resolution=direct_algo_res_limit,pdb_text=pdb_lines,algorithm="direct",wavelength=SIM.wavelength_A)
sfall = fmodel_from_pdb(resolution=direct_algo_res_limit,
pdb_text=pdb_lines,
algorithm="fft",
wavelength=wavelength_A)
# use crystal structure to initialize Fhkl array
sfall.show_summary(prefix="Amplitudes used ")
N = crystal.number_of_cells(sfall.unit_cell())
#SIM = nanoBragg(detpixels_slowfast=(2000,2000),pixel_size_mm=0.11,Ncells_abc=(5,5,5),verbose=0)
SIM = nanoBragg(
detpixels_slowfast=(2000, 2000),
pixel_size_mm=0.11,
Ncells_abc=(N, N, N),
# workaround for problem with wavelength array, specify it separately in constructor.
wavelength_A=wavelength_A,
verbose=0)
SIM.adc_offset_adu = 0 # Do not offset by 40
SIM.adc_offset_adu = 10 # Do not offset by 40
import sys
if len(sys.argv) > 2:
SIM.seed = -int(sys.argv[2])
print("GOTHERE seed=", SIM.seed)
if len(sys.argv) > 1:
if sys.argv[1] == "random": SIM.randomize_orientation()
SIM.mosaic_domains = 25 # 77 seconds. With 100 energy points, 7700 seconds (2 hours) per image
# 3000000 images would be 100000 hours on a 60-core machine (dials), or 11.4 years
# using 2 nodes, 5.7 years. Do this at SLAC? NERSC? combination of all?
# SLAC downtimes: Tues Dec 5 (24 hrs), Mon Dec 11 (72 hrs), Mon Dec 18 light use, 24 days
SIM.mosaic_spread_deg = 0.05 # interpreted by UMAT_nm as a half-width stddev
SIM.distance_mm = 141.7
UMAT_nm = flex.mat3_double()
mersenne_twister = flex.mersenne_twister(seed=0)
scitbx.random.set_random_seed(1234)
rand_norm = scitbx.random.normal_distribution(mean=0,
sigma=SIM.mosaic_spread_deg *
math.pi / 180.)
g = scitbx.random.variate(rand_norm)
mosaic_rotation = g(SIM.mosaic_domains)
for m in mosaic_rotation:
site = col(mersenne_twister.random_double_point_on_sphere())
UMAT_nm.append(site.axis_and_angle_as_r3_rotation_matrix(m, deg=False))
SIM.set_mosaic_blocks(UMAT_nm)
#SIM.detector_thick_mm = 0.5 # = 0 for Rayonix
#SIM.detector_thicksteps = 1 # should default to 1 for Rayonix, but set to 5 for CSPAD
#SIM.detector_attenuation_length_mm = default is silicon
# get same noise each time this test is run
SIM.seed = 1
SIM.oversample = 1
SIM.wavelength_A = wavelength_A
SIM.polarization = 1
# this will become F000, marking the beam center
SIM.default_F = 0
#SIM.missets_deg= (10,20,30)
print("mosaic_seed=", SIM.mosaic_seed)
print("seed=", SIM.seed)
print("calib_seed=", SIM.calib_seed)
print("missets_deg =", SIM.missets_deg)
SIM.Fhkl = sfall
print("Determinant", rotation.determinant())
Amatrix_rot = (
rotation *
sqr(sfall.unit_cell().orthogonalization_matrix())).transpose()
print("RAND_ORI", prefix, end=' ')
print(" ".join([i for i in Amatrix_rot]))
SIM.Amatrix_RUB = Amatrix_rot
#workaround for failing init_cell, use custom written Amatrix setter
print("unit_cell_Adeg=", SIM.unit_cell_Adeg)
print("unit_cell_tuple=", SIM.unit_cell_tuple)
Amat = sqr(SIM.Amatrix).transpose() # recovered Amatrix from SIM
from cctbx import crystal_orientation
Ori = crystal_orientation.crystal_orientation(
Amat, crystal_orientation.basis_type.reciprocal)
print("Python unit cell from SIM state", Ori.unit_cell())
# fastest option, least realistic
#SIM.xtal_shape=shapetype.Tophat # RLP = hard sphere
#SIM.xtal_shape=shapetype.Square # gives fringes
SIM.xtal_shape = shapetype.Gauss # both crystal & RLP are Gaussian
#SIM.xtal_shape=shapetype.Round # Crystal is a hard sphere
# only really useful for long runs
SIM.progress_meter = False
# prints out value of one pixel only. will not render full image!
#SIM.printout_pixel_fastslow=(500,500)
#SIM.printout=True
SIM.show_params()
# flux is always in photons/s
SIM.flux = 1e12
SIM.exposure_s = 1.0 # so total fluence is e12
# assumes round beam
SIM.beamsize_mm = 0.003 #cannot make this 3 microns; spots are too intense
temp = SIM.Ncells_abc
print("Ncells_abc=", SIM.Ncells_abc)
SIM.Ncells_abc = temp
print("Ncells_abc=", SIM.Ncells_abc)
print("xtal_size_mm=", SIM.xtal_size_mm)
print("unit_cell_Adeg=", SIM.unit_cell_Adeg)
print("unit_cell_tuple=", SIM.unit_cell_tuple)
print("missets_deg=", SIM.missets_deg)
print("Amatrix=", SIM.Amatrix)
print("beam_center_mm=", SIM.beam_center_mm)
print("XDS_ORGXY=", SIM.XDS_ORGXY)
print("detector_pivot=", SIM.detector_pivot)
print("xtal_shape=", SIM.xtal_shape)
print("beamcenter_convention=", SIM.beamcenter_convention)
print("fdet_vector=", SIM.fdet_vector)
print("sdet_vector=", SIM.sdet_vector)
print("odet_vector=", SIM.odet_vector)
print("beam_vector=", SIM.beam_vector)
print("polar_vector=", SIM.polar_vector)
print("spindle_axis=", SIM.spindle_axis)
print("twotheta_axis=", SIM.twotheta_axis)
print("distance_meters=", SIM.distance_meters)
print("distance_mm=", SIM.distance_mm)
print("close_distance_mm=", SIM.close_distance_mm)
print("detector_twotheta_deg=", SIM.detector_twotheta_deg)
print("detsize_fastslow_mm=", SIM.detsize_fastslow_mm)
print("detpixels_fastslow=", SIM.detpixels_fastslow)
print("detector_rot_deg=", SIM.detector_rot_deg)
print("curved_detector=", SIM.curved_detector)
print("pixel_size_mm=", SIM.pixel_size_mm)
print("point_pixel=", SIM.point_pixel)
print("polarization=", SIM.polarization)
print("nopolar=", SIM.nopolar)
print("oversample=", SIM.oversample)
print("region_of_interest=", SIM.region_of_interest)
print("wavelength_A=", SIM.wavelength_A)
print("energy_eV=", SIM.energy_eV)
print("fluence=", SIM.fluence)
print("flux=", SIM.flux)
print("exposure_s=", SIM.exposure_s)
print("beamsize_mm=", SIM.beamsize_mm)
print("dispersion_pct=", SIM.dispersion_pct)
print("dispsteps=", SIM.dispsteps)
print("divergence_hv_mrad=", SIM.divergence_hv_mrad)
print("divsteps_hv=", SIM.divsteps_hv)
print("divstep_hv_mrad=", SIM.divstep_hv_mrad)
print("round_div=", SIM.round_div)
print("phi_deg=", SIM.phi_deg)
print("osc_deg=", SIM.osc_deg)
print("phisteps=", SIM.phisteps)
print("phistep_deg=", SIM.phistep_deg)
print("detector_thick_mm=", SIM.detector_thick_mm)
print("detector_thicksteps=", SIM.detector_thicksteps)
print("detector_thickstep_mm=", SIM.detector_thickstep_mm)
print("***mosaic_spread_deg=", SIM.mosaic_spread_deg)
print("***mosaic_domains=", SIM.mosaic_domains)
print("indices=", SIM.indices)
print("amplitudes=", SIM.amplitudes)
print("Fhkl_tuple=", SIM.Fhkl_tuple)
print("default_F=", SIM.default_F)
print("interpolate=", SIM.interpolate)
print("integral_form=", SIM.integral_form)
from libtbx.development.timers import Profiler
P = Profiler("nanoBragg")
# now actually burn up some CPU
#SIM.add_nanoBragg_spots()
del P
# simulated crystal is only 125 unit cells (25 nm wide)
# amplify spot signal to simulate physical crystal of 4000x larger: 100 um (64e9 x the volume)
print(crystal.domains_per_crystal)
SIM.raw_pixels *= crystal.domains_per_crystal
# must calculate the correct scale!
for x in range(len(flux)):
print("+++++++++++++++++++++++++++++++++++++++ Wavelength", x)
CH = channel_pixels(wavlen[x], flux[x], N, UMAT_nm, Amatrix_rot, sfall)
SIM.raw_pixels += CH.raw_pixels * crystal.domains_per_crystal
CH.free_all()
if quick: SIM.to_smv_format(fileout=prefix + "_intimage_001.img")
# rough approximation to water: interpolation points for sin(theta/lambda) vs structure factor
bg = flex.vec2_double([(0, 2.57), (0.0365, 2.58), (0.07, 2.8), (0.12, 5),
(0.162, 8), (0.2, 6.75),
(0.18, 7.32), (0.216, 6.75), (0.236, 6.5),
(0.28, 4.5), (0.3, 4.3), (0.345, 4.36),
(0.436, 3.77), (0.5, 3.17)])
SIM.Fbg_vs_stol = bg
SIM.amorphous_sample_thick_mm = 0.1
SIM.amorphous_density_gcm3 = 1
SIM.amorphous_molecular_weight_Da = 18
SIM.flux = 1e12
SIM.beamsize_mm = 0.003 # square (not user specified)
SIM.exposure_s = 1.0 # multiplies flux x exposure
SIM.add_background()
if quick: SIM.to_smv_format(fileout=prefix + "_intimage_002.img")
# rough approximation to air
bg = flex.vec2_double([(0, 14.1), (0.045, 13.5), (0.174, 8.35),
(0.35, 4.78), (0.5, 4.22)])
SIM.Fbg_vs_stol = bg
#SIM.amorphous_sample_thick_mm = 35 # between beamstop and collimator
SIM.amorphous_sample_thick_mm = 10 # between beamstop and collimator
SIM.amorphous_density_gcm3 = 1.2e-3
SIM.amorphous_sample_molecular_weight_Da = 28 # nitrogen = N2
print("amorphous_sample_size_mm=", SIM.amorphous_sample_size_mm)
print("amorphous_sample_thick_mm=", SIM.amorphous_sample_thick_mm)
print("amorphous_density_gcm3=", SIM.amorphous_density_gcm3)
print("amorphous_molecular_weight_Da=", SIM.amorphous_molecular_weight_Da)
SIM.add_background()
#apply beamstop mask here
# set this to 0 or -1 to trigger automatic radius. could be very slow with bright images
# settings for CCD
SIM.detector_psf_kernel_radius_pixels = 5
#SIM.detector_psf_fwhm_mm=0.08;
#SIM.detector_psf_type=shapetype.Fiber # rayonix=Fiber, CSPAD=None (or small Gaussian)
SIM.detector_psf_type = shapetype.Unknown # for CSPAD
SIM.detector_psf_fwhm_mm = 0
#SIM.apply_psf()
print("One pixel-->", SIM.raw_pixels[500000])
# at this point we scale the raw pixels so that the output array is on an scale from 0 to 50000.
# that is the default behavior (intfile_scale<=0), otherwise it applies intfile_scale as a multiplier on an abs scale.
if quick: SIM.to_smv_format(fileout=prefix + "_intimage_003.img")
print("quantum_gain=",
SIM.quantum_gain) #defaults to 1. converts photons to ADU
print("adc_offset_adu=", SIM.adc_offset_adu)
print("detector_calibration_noise_pct=",
SIM.detector_calibration_noise_pct)
print("flicker_noise_pct=", SIM.flicker_noise_pct)
print("readout_noise_adu=", SIM.readout_noise_adu
) # gaussian random number to add to every pixel (0 for PAD)
# apply Poissonion correction, then scale to ADU, then adc_offset.
# should be 10 for most Rayonix, Pilatus should be 0, CSPAD should be 0.
print("detector_psf_type=", SIM.detector_psf_type)
print("detector_psf_fwhm_mm=", SIM.detector_psf_fwhm_mm)
print("detector_psf_kernel_radius_pixels=",
SIM.detector_psf_kernel_radius_pixels)
SIM.add_noise() #converts phtons to ADU.
print("raw_pixels=", SIM.raw_pixels)
SIM.to_smv_format(fileout=prefix + ".img", intfile_scale=1)
# try to write as CBF
if False:
import dxtbx
from dxtbx.format.FormatCBFMiniPilatus import FormatCBFMiniPilatus
img = dxtbx.load(prefix + ".img")
print(img)
FormatCBFMiniPilatus.as_file(detector=img.get_detector(),
beam=img.get_beam(),
gonio=img.get_goniometer(),
scan=img.get_scan(),
data=img.get_raw_data(),
path=prefix + ".cbf")
SIM.free_all()
def tst_all():
F = foo()
assert F == (1, 2, 3, 4)
size = 1516
D = detector(slowpixels=1516, fastpixels=1516, pixel_size=0.00011)
D.set_region_of_interest(0, int(0.6 * size), int(0.4 * size),
int(0.6 * size))
D.set_oversampling(1)
C = camera()
C.distance = 0.18166
C.Ybeam = 0.08338
C.Zbeam = 0.08338
C.lambda0 = 6.2E-10
C.dispersion = 0.002
C.dispsteps = 4
C.hdivrange = 0
C.vdivrange = 0
C.hdivstep = 1
C.vdivstep = 1
C.source_distance = 10.
C.fluence = 1.E24
Amat = sqr((127.6895065259495, 0.6512077339887, -0.4403031342553,
-1.1449112128916, 225.3922539826207, 1.8393136632579,
1.0680694468752, -2.4923062985132, 306.0953037195841))
PSII = amplitudes_from_pdb(8., "fft", True)
from cctbx import crystal_orientation
X = crystal()
X.orientation = crystal_orientation.crystal_orientation(
Amat, crystal_orientation.basis_type.direct)
X.miller = PSII.indices()
X.amplitudes = PSII.data()
X.Na = 6
X.Nb = 6
X.Nc = 6
SIM = fast_bragg_simulation()
SIM.set_detector(D)
SIM.set_camera(C)
SIM.set_crystal(X)
SIM.sweep_over_detector()
data = D.raw
scale_factor = 55000. / flex.max(data)
#print "scale_factor",scale_factor
fileout = "intimage_001.img"
SIM.to_smv_format(fileout=fileout,
intfile_scale=scale_factor,
saturation=40000)
import os
assert os.path.isfile(fileout)
os.remove(fileout)
#simulation is complete, now we'll autoindex the image fragment and verify
# that the indexed cell is similar to the input cell.
if (not libtbx.env.has_module("annlib")):
print "Skipping some tests: annlib not available."
return
# 1. Analysis of the image to identify the Bragg peak centers.
# This step uses an inefficient algorithm and implementation and
# is most time consuming; but the code is only for testing, not production
from rstbx.diffraction.fastbragg.tst_utils_clustering import specific_libann_cluster
M = specific_libann_cluster(scale_factor * data,
intensity_cutoff=25,
distance_cutoff=17)
# M is a dictionary of peak intensities indexed by pixel coordinates
# 2. Now autoindex the pattern
from rstbx.diffraction.fastbragg.tst_utils_clustering import index_wrapper
SIM.C = C
SIM.D = D
ai, ref_uc = index_wrapper(M.keys(), SIM, PSII)
tst_uc = ai.getOrientation().unit_cell()
#print ref_uc # (127.692, 225.403, 306.106, 90, 90, 90)
#print tst_uc # (106.432, 223.983, 303.102, 90.3185, 91.5998, 90.5231)
# 3. Final assertion. In the given orientation,
# the unit cell A vector is into the beam and is not well sampled,
# so tolerances have to be fairly relaxed, 5%.
# Labelit does better with the target_cell restraint, but this improved
# algorithm is not used here for the test
assert ref_uc.is_similar_to(tst_uc,
relative_length_tolerance=0.20,
absolute_angle_tolerance=2.0)
def run(args):
distance = 125
centre = (97.075, 97.075)
pix_size = (0.11, 0.11)
image_size = (1765, 1765)
wavelength = args.w or 1.0
# 1. Make a dummy detector
detector = detector_factory.simple(
'SENSOR_UNKNOWN', # Sensor
distance,
centre,
'+x',
'-y', # fast/slow direction
pix_size,
image_size)
# 2. Get the miller array!
mill_array = process_mtz(args.mtzfile[0]).as_intensity_array()
ortho = sqr(mill_array.crystal_symmetry().unit_cell().reciprocal() \
.orthogonalization_matrix())
# 3.Create some image_pickle dictionairies that contain 'full' intensities,
# but are otherwise complete.
im = 0
while im < args.n:
im += 1
A = sqr(flex.random_double_r3_rotation_matrix()) * ortho
orientation = crystal_orientation(A, basis_type.reciprocal)
pix_coords, miller_set = get_pix_coords(wavelength, A, mill_array,
detector)
if len(miller_set) > 10: # at least 10 reflections
miller_set = cctbx.miller.set(mill_array.crystal_symmetry(),
miller_set,
anomalous_flag=False)
obs = mill_array.common_set(miller_set)
temp_dict = {
'observations': [obs],
'mapped_predictions': [pix_coords],
'pointgroup': None,
'current_orientation': [orientation],
'xbeam': centre[0],
'ybeam': centre[1],
'wavelength': wavelength
}
old_node = ImageNode(dicti=temp_dict, scale=False)
# Remove all reflection that are not at least p partial
partial_sel = (old_node.partialities > p_threshold)
temp_dict['full_observations'] = [obs.select(partial_sel)]
temp_dict['observations'] = [
obs.select(partial_sel) *
old_node.partialities.select(partial_sel)
]
temp_dict['mapped_predictions'] = \
[temp_dict['mapped_predictions'][0].select(partial_sel)]
if logging.Logger.root.level <= logging.DEBUG: # debug!
before = temp_dict['full_observations'][0]
after = temp_dict['observations'][0] / old_node.partialities
assert sum(abs(before.data() - after.data())) < eps
if args.r:
partials = list(temp_dict['observations'][0].data())
jiggled_partials = flex.double(
[random.gauss(obs, args.r * obs) for obs in partials])
temp_dict['observations'][0] = temp_dict['observations'][0] \
.customized_copy(data=jiggled_partials)
pkl_name = "simulated_data_{0:04d}.pickle".format(im)
with (open(pkl_name, 'wb')) as pkl:
cPickle.dump(temp_dict, pkl)
''' Only works with no noise:
def image_case_factory(item_no):
mosflm = sqr(
(1, 0, 0, 0, 1, 0, 0, 0, 1)) # mosflm lab vectors in their own frame
labelit = sqr(
(0, 0, -1, -1, 0, 0, 0, 1, 0)) # mosflm basis vectors in labelit frame
MLx = (0, 0, -1)
MLy = (-1, 0, 0)
MLz = (0, 1, 0) # "virtual" rotation axis being used by cctbx.xfel for CXI
LM = mosflm * labelit.inverse(
) # converts labelit frame coords to mosflm frame
SWAPXY = sqr((0, 1, 0, 1, 0, 0, 0, 0, 1)) # in labelit frame
SWAPZ = sqr((1, 0, 0, 0, 1, 0, 0, 0, -1)) # in labelit frame
R90 = sqr((0, -1, 0, 1, 0, 0, 0, 0,
1)) # in labelit frame, rotation 90 on beam axis
CONTAINER_SZ = 1000
WAVELENGTH = 1.32 # given by James Holton
container_no = item // CONTAINER_SZ
filename = "/net/viper/raid1/sauter/fake/result/basic00/"
filename = os.path.join(filename, "r%02d" % container_no, TRIAL,
"integration", "int-data_%05d.pickle" % item_no)
trial_results = pickle.load(open(filename, "rb"))
current_hexagonal_ori = trial_results["current_orientation"][0]
current_cb_op_to_primitive = trial_results["current_cb_op_to_primitive"][0]
current_triclinic_ori = current_hexagonal_ori.change_basis(
current_cb_op_to_primitive)
filename = "/net/viper/raid1/sauter/fake/holton/mosflm_matrix"
filename = os.path.join(filename, "%02d" % container_no,
"%05d.mat" % item_no)
lines = open(filename).readlines()
A0 = lines[0].strip().split()
A1 = lines[1].strip().split()
A2 = lines[2].strip().split()
A = sqr(
(float(A0[0]), float(A0[1]), float(A0[2]), float(A1[0]), float(A1[1]),
float(A1[2]), float(A2[0]), float(A2[1]), float(A2[2])))
A = A / WAVELENGTH
Holton_hexagonal_ori = crystal_orientation(SWAPZ * SWAPXY * R90 * LM * A,
True)
Holton_triclinic_ori = Holton_hexagonal_ori.change_basis(
current_cb_op_to_primitive)
c_inv_r_best = Holton_triclinic_ori.best_similarity_transformation(
other=current_triclinic_ori,
fractional_length_tolerance=50.,
unimodular_generator_range=1)
c_inv_r_int = tuple([int(round(ij, 0)) for ij in c_inv_r_best])
from cctbx import sgtbx
c_inv = sgtbx.rt_mx(sgtbx.rot_mx(c_inv_r_int))
cb_op = sgtbx.change_of_basis_op(c_inv)
comparison_triclinic = Holton_triclinic_ori.change_basis(cb_op)
comparison_hexagonal = comparison_triclinic.change_basis(
current_cb_op_to_primitive.inverse())
print(
sqr(current_hexagonal_ori.direct_matrix()) -
sqr(comparison_hexagonal.direct_matrix()))
print("item %d" % item_no)
SC = ScoringContainer()
SC.model = current_hexagonal_ori
SC.reference = comparison_hexagonal
return SC
def postrefine_by_frame(self, frame_no, pickle_filename, iparams, miller_array_ref, pres_in, avg_mode):
# 1. Prepare data
observations_pickle = pickle.load(open(pickle_filename, "rb"))
crystal_init_orientation = observations_pickle["current_orientation"][0]
wavelength = observations_pickle["wavelength"]
pickle_filepaths = pickle_filename.split("/")
img_filename_only = pickle_filepaths[len(pickle_filepaths) - 1]
txt_exception = " {0:40} ==> ".format(img_filename_only)
inputs, txt_organize_input = self.organize_input(
observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename
)
if inputs is not None:
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm = inputs
else:
txt_exception += txt_organize_input + "\n"
return None, txt_exception
# 2. Determine polarity - always do this even if flag_polar = False
# the function will take care of it.
polar_hkl, cc_iso_raw_asu, cc_iso_raw_rev = self.determine_polar(
observations_original, iparams, pickle_filename, pres=pres_in
)
# 3. Select data for post-refinement (only select indices that are common with the reference set
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
matches = miller.match_multi_indices(
miller_indices_unique=miller_array_ref.indices(), miller_indices=observations_non_polar.indices()
)
I_ref_match = flex.double([miller_array_ref.data()[pair[0]] for pair in matches.pairs()])
miller_indices_ref_match = flex.miller_index((miller_array_ref.indices()[pair[0]] for pair in matches.pairs()))
I_obs_match = flex.double([observations_non_polar.data()[pair[1]] for pair in matches.pairs()])
sigI_obs_match = flex.double([observations_non_polar.sigmas()[pair[1]] for pair in matches.pairs()])
miller_indices_original_obs_match = flex.miller_index(
(observations_original.indices()[pair[1]] for pair in matches.pairs())
)
miller_indices_non_polar_obs_match = flex.miller_index(
(observations_non_polar.indices()[pair[1]] for pair in matches.pairs())
)
alpha_angle_set = flex.double([alpha_angle[pair[1]] for pair in matches.pairs()])
spot_pred_x_mm_set = flex.double([spot_pred_x_mm[pair[1]] for pair in matches.pairs()])
spot_pred_y_mm_set = flex.double([spot_pred_y_mm[pair[1]] for pair in matches.pairs()])
references_sel = miller_array_ref.customized_copy(data=I_ref_match, indices=miller_indices_ref_match)
observations_original_sel = observations_original.customized_copy(
data=I_obs_match, sigmas=sigI_obs_match, indices=miller_indices_original_obs_match
)
observations_non_polar_sel = observations_non_polar.customized_copy(
data=I_obs_match, sigmas=sigI_obs_match, indices=miller_indices_non_polar_obs_match
)
# 4. Do least-squares refinement
lsqrh = leastsqr_handler()
try:
refined_params, stats, n_refl_postrefined = lsqrh.optimize(
I_ref_match,
observations_original_sel,
wavelength,
crystal_init_orientation,
alpha_angle_set,
spot_pred_x_mm_set,
spot_pred_y_mm_set,
iparams,
pres_in,
observations_non_polar_sel,
detector_distance_mm,
)
except Exception:
txt_exception += "optimization failed.\n"
return None, txt_exception
# caculate partiality for output (with target_anomalous check)
G_fin, B_fin, rotx_fin, roty_fin, ry_fin, rz_fin, r0_fin, re_fin, a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin = (
refined_params
)
inputs, txt_organize_input = self.organize_input(
observations_pickle, iparams, avg_mode, pickle_filename=pickle_filename
)
observations_original, alpha_angle, spot_pred_x_mm, spot_pred_y_mm, detector_distance_mm = inputs
observations_non_polar = self.get_observations_non_polar(observations_original, polar_hkl)
from cctbx.uctbx import unit_cell
uc_fin = unit_cell((a_fin, b_fin, c_fin, alpha_fin, beta_fin, gamma_fin))
if pres_in is not None:
crystal_init_orientation = pres_in.crystal_orientation
two_theta = observations_original.two_theta(wavelength=wavelength).data()
from mod_leastsqr import calc_partiality_anisotropy_set
partiality_fin, dummy, rs_fin, rh_fin = calc_partiality_anisotropy_set(
uc_fin,
rotx_fin,
roty_fin,
observations_original.indices(),
ry_fin,
rz_fin,
r0_fin,
re_fin,
two_theta,
alpha_angle,
wavelength,
crystal_init_orientation,
spot_pred_x_mm,
spot_pred_y_mm,
detector_distance_mm,
iparams.partiality_model,
iparams.flag_beam_divergence,
)
# calculate the new crystal orientation
O = sqr(uc_fin.orthogonalization_matrix()).transpose()
R = sqr(crystal_init_orientation.crystal_rotation_matrix()).transpose()
from cctbx.crystal_orientation import crystal_orientation, basis_type
CO = crystal_orientation(O * R, basis_type.direct)
crystal_fin_orientation = CO.rotate_thru((1, 0, 0), rotx_fin).rotate_thru((0, 1, 0), roty_fin)
# remove reflections with partiality below threshold
i_sel = partiality_fin > iparams.merge.partiality_min
partiality_fin_sel = partiality_fin.select(i_sel)
rs_fin_sel = rs_fin.select(i_sel)
rh_fin_sel = rh_fin.select(i_sel)
observations_non_polar_sel = observations_non_polar.customized_copy(
indices=observations_non_polar.indices().select(i_sel),
data=observations_non_polar.data().select(i_sel),
sigmas=observations_non_polar.sigmas().select(i_sel),
)
observations_original_sel = observations_original.customized_copy(
indices=observations_original.indices().select(i_sel),
data=observations_original.data().select(i_sel),
sigmas=observations_original.sigmas().select(i_sel),
)
pres = postref_results()
pres.set_params(
observations=observations_non_polar_sel,
observations_original=observations_original_sel,
refined_params=refined_params,
stats=stats,
partiality=partiality_fin_sel,
rs_set=rs_fin_sel,
rh_set=rh_fin_sel,
frame_no=frame_no,
pickle_filename=pickle_filename,
wavelength=wavelength,
crystal_orientation=crystal_fin_orientation,
detector_distance_mm=detector_distance_mm,
)
r_change, r_xy_change, cc_change, cc_iso_change = (0, 0, 0, 0)
try:
r_change = ((pres.R_final - pres.R_init) / pres.R_init) * 100
r_xy_change = ((pres.R_xy_final - pres.R_xy_init) / pres.R_xy_init) * 100
cc_change = ((pres.CC_final - pres.CC_init) / pres.CC_init) * 100
cc_iso_change = ((pres.CC_iso_final - pres.CC_iso_init) / pres.CC_iso_init) * 100
except Exception:
pass
txt_postref = " {0:40} ==> RES:{1:5.2f} NREFL:{2:5d} R:{3:8.2f}% RXY:{4:8.2f}% CC:{5:6.2f}% CCISO:{6:6.2f}% G:{7:10.3e} B:{8:7.1f} CELL:{9:6.2f} {10:6.2f} {11:6.2f} {12:6.2f} {13:6.2f} {14:6.2f}".format(
img_filename_only + " (" + polar_hkl + ")",
observations_original_sel.d_min(),
len(observations_original_sel.data()),
r_change,
r_xy_change,
cc_change,
cc_iso_change,
pres.G,
pres.B,
a_fin,
b_fin,
c_fin,
alpha_fin,
beta_fin,
gamma_fin,
)
print txt_postref
txt_postref += "\n"
return pres, txt_postref
def populate_orientation(self): assert self.xtal.get_A_at_scan_point(self.scan_no) is not None, "no crystal orientation matrix" self.frame['current_orientation'] = [crystal_orientation(self.xtal.get_A_at_scan_point(self.scan_no).elems, True)]
def get_unit_cell_at_scan_point(self, t): B = self.get_B_at_scan_point(t) co = crystal_orientation(B) return co.unit_cell()
GF = gen_fmodel(resolution=3.0,pdb_text=pdb_lines,algorithm="fft",wavelength=1.7)
CB_OP_C_P = GF.xray_structure.change_of_basis_op_to_primitive_setting() # from C to P
print(str(CB_OP_C_P))
icount=0
from scitbx.array_family import flex
angles=flex.double()
for stuff in get_items():
#print stuff
icount+=1
print("Iteration",icount)
from cctbx import crystal_orientation
# work up the ground truth from header
header_Ori = crystal_orientation.crystal_orientation(stuff["coarse"], crystal_orientation.basis_type.direct)
header_Ori.show(legend="header_Ori")
C2_ground_truth = header_Ori.change_basis(CB_OP_C_P.inverse())
C2_ground_truth.show(legend="coarse_ground_truth")
# work up the ground truth from mosaic ensemble
mosaic_Ori = crystal_orientation.crystal_orientation(stuff["fine"], crystal_orientation.basis_type.direct)
mosaic_Ori.show(legend="mosaic_Ori")
fine_ground_truth = mosaic_Ori.change_basis(CB_OP_C_P.inverse())
fine_ground_truth.show(legend="fine_ground_truth")
# now calculate the angle as mean a_to_a,b_to_b,c_to_c
aoff = C2_ground_truth.a.angle(fine_ground_truth.a,deg=True)
boff = C2_ground_truth.b.angle(fine_ground_truth.b,deg=True)
def populate_orientation(self): assert self.xtal.get_A() is not None, "no crystal orientation matrix" self.frame['current_orientation'] = [crystal_orientation(self.xtal.get_A().elems, True)]
def get_unit_cell_at_scan_point(self, t):
B = self.get_B_at_scan_point(t)
co = crystal_orientation(B)
return co.unit_cell()
def exercise_basic():
identity = (1,0,0,0,1,0,0,0,1)
I = crystal_orientation(identity,False) #direct space
orthorhombic = (1,0,0,0,0.5,0.,0.,0.,0.25)
R = crystal_orientation(orthorhombic,True) #reciprocal space
assert R.direct_matrix() == (1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 4.0)
assert R.reciprocal_matrix() == orthorhombic
inverse = (-1,0,0,0,-1,0,0,0,-1)
negative = crystal_orientation(inverse,False)
assert I == negative.make_positive()
assert R.unit_cell().parameters() == (1.0,2.0,4.0,90.,90.,90.)
assert approx_equal( R.unit_cell_inverse().parameters(), (1.0,0.5,0.25,90.,90.,90.) )
O1 = crystal_orientation((-0.015553395334476732, -0.0028287158782335244, 0.018868416534039902,
-0.0016512962184570643, -0.020998220575299865, 0.0012056332661160732,
0.015789188025134133, -0.011166135863736777, 0.013045365404272641),True)
def exercise_change_basis():
assert approx_equal(O1.unit_cell().parameters(),
(47.659, 47.6861, 49.6444, 62.9615, 73.8222, 73.5269),1E-3)
reindex = (0.0, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, -1.0) # swap a & b and take inverse
O2 = O1.change_basis(reindex)
assert approx_equal(O2.unit_cell().parameters(),
(47.6861, 47.659, 49.6444, 73.8222, 62.9615, 73.5269),1E-3)
rhombohedral_test = crystal_orientation((
0.002737747939994224, -0.0049133768326561278, 0.0023634556852316566,
0.0062204242383498082, 0.006107332242442573, 0.0047036576949967112,
-0.0057640198753891566, -0.0025891042237382953, 0.0023674924674260264),basis_type.reciprocal)
rhombohedral_reference = crystal_orientation((
-0.0076361080646872997, 0.0049061665572297979, 0.0023688116121433865,
def tst_all():
F = foo()
assert F == (1,2,3,4)
size=1516
D = detector(slowpixels=1516,fastpixels=1516,pixel_size=0.00011)
D.set_region_of_interest(0,int(0.6*size),int(0.4*size),int(0.6*size))
D.set_oversampling(1)
C = camera()
C.distance = 0.18166
C.Ybeam = 0.08338
C.Zbeam = 0.08338
C.lambda0 = 6.2E-10
C.dispersion = 0.002
C.dispsteps = 4
C.hdivrange = 0
C.vdivrange = 0
C.hdivstep = 1
C.vdivstep = 1
C.source_distance = 10.
C.fluence = 1.E24
Amat = sqr((127.6895065259495, 0.6512077339887,-0.4403031342553,
-1.1449112128916,225.3922539826207,1.8393136632579,
1.0680694468752,-2.4923062985132,306.0953037195841))
PSII = amplitudes_from_pdb(8.,"fft",True)
from cctbx import crystal_orientation
X = crystal()
X.orientation = crystal_orientation.crystal_orientation(
Amat,crystal_orientation.basis_type.direct)
X.miller = PSII.indices()
X.amplitudes = PSII.data()
X.Na = 6; X.Nb = 6; X.Nc = 6
SIM = fast_bragg_simulation()
SIM.set_detector(D)
SIM.set_camera(C)
SIM.set_crystal(X)
SIM.sweep_over_detector()
data = D.raw
scale_factor = 55000./flex.max(data)
#print "scale_factor",scale_factor
fileout="intimage_001.img"
SIM.to_smv_format(fileout=fileout,
intfile_scale = scale_factor, saturation=40000)
import os
assert os.path.isfile(fileout)
os.remove(fileout)
#simulation is complete, now we'll autoindex the image fragment and verify
# that the indexed cell is similar to the input cell.
if (not libtbx.env.has_module("annlib")):
print "Skipping some tests: annlib not available."
return
# 1. Analysis of the image to identify the Bragg peak centers.
# This step uses an inefficient algorithm and implementation and
# is most time consuming; but the code is only for testing, not production
from rstbx.diffraction.fastbragg.tst_utils_clustering import specific_libann_cluster
M=specific_libann_cluster(scale_factor*data,intensity_cutoff = 25,distance_cutoff=17)
# M is a dictionary of peak intensities indexed by pixel coordinates
# 2. Now autoindex the pattern
from rstbx.diffraction.fastbragg.tst_utils_clustering import index_wrapper
SIM.C = C
SIM.D = D
ai,ref_uc = index_wrapper(M.keys(), SIM, PSII)
tst_uc = ai.getOrientation().unit_cell()
#print ref_uc # (127.692, 225.403, 306.106, 90, 90, 90)
#print tst_uc # (106.432, 223.983, 303.102, 90.3185, 91.5998, 90.5231)
# 3. Final assertion. In the given orientation,
# the unit cell A vector is into the beam and is not well sampled,
# so tolerances have to be fairly relaxed, 5%.
# Labelit does better with the target_cell restraint, but this improved
# algorithm is not used here for the test
assert ref_uc.is_similar_to(tst_uc, relative_length_tolerance=0.20,
absolute_angle_tolerance= 2.0)
def exercise_change_basis():
assert approx_equal(O1.unit_cell().parameters(),
(47.659, 47.6861, 49.6444, 62.9615, 73.8222, 73.5269),1E-3)
reindex = (0.0, -1.0, 0.0, -1.0, 0.0, 0.0, 0.0, 0.0, -1.0) # swap a & b and take inverse
O2 = O1.change_basis(reindex)
assert approx_equal(O2.unit_cell().parameters(),
(47.6861, 47.659, 49.6444, 73.8222, 62.9615, 73.5269),1E-3)
rhombohedral_test = crystal_orientation((
0.002737747939994224, -0.0049133768326561278, 0.0023634556852316566,
0.0062204242383498082, 0.006107332242442573, 0.0047036576949967112,
-0.0057640198753891566, -0.0025891042237382953, 0.0023674924674260264),basis_type.reciprocal)
rhombohedral_reference = crystal_orientation((
-0.0076361080646872997, 0.0049061665572297979, 0.0023688116121433865,
-0.00011109895272056645, -0.0061110173438898583, 0.0047062769738302939,
0.0031790372319626674, 0.0025876279220667518, 0.0023669727051432361),basis_type.reciprocal)
# Find a similarity transform that maps the two cells onto each other
c_inv_r_best = rhombohedral_test.best_similarity_transformation(
other = rhombohedral_reference, fractional_length_tolerance = 1.00,
unimodular_generator_range=1)
c_inv_r_int = tuple([int(round(ij,0)) for ij in c_inv_r_best])
assert c_inv_r_int == (-1, 0, 0, 1, -1, 0, 0, 0, 1)
c_inv = sgtbx.rt_mx(sgtbx.rot_mx(c_inv_r_int))
cb_op = sgtbx.change_of_basis_op(c_inv)
rhombohedral_reindex = rhombohedral_test.change_basis(cb_op)
assert rhombohedral_reindex.difference_Z_score(rhombohedral_reference) < 0.40
assert rhombohedral_reindex.direct_mean_square_difference(rhombohedral_reference) < 0.1
#an alternative test from ana that should fail (gives high msd~0.22; cell axes don't match):
ana_reference = crystal_orientation((
0.0023650364919947241, 0.012819317075171401, 0.003042762222847376,
0.0081242553464681254, 0.0050052660206998077, -0.01472465697193685,
-0.01373896574061278, 0.0083781530252581681, -0.0035301340829149005),basis_type.reciprocal)
ana_current = crystal_orientation((
-0.014470153848927263, 0.0095185368147633793, 0.00087746490483763798,
-0.0049989006573928716, -0.0079714727432991222, 0.014778692772530192,
0.0051268914129933571, 0.010264066188909109, 0.0044244589492769002),basis_type.reciprocal)
c_inv_r_best = ana_current.best_similarity_transformation(
other = ana_reference,
fractional_length_tolerance = 200.0,
unimodular_generator_range=1)
c_inv_r_int = tuple([int(round(ij,0)) for ij in c_inv_r_best])
c_inv = sgtbx.rt_mx(sgtbx.rot_mx(c_inv_r_int))
cb_op = sgtbx.change_of_basis_op(c_inv)
ana_reindex = ana_reference.change_basis(cb_op.inverse())
assert 200.0 > ana_reindex.difference_Z_score(ana_current) > 20.
u = uctbx.unit_cell((10., 10., 10., 90., 90., 90.))
CO = crystal_orientation(u.fractionalization_matrix(),True)
assert approx_equal(
CO.unit_cell().parameters(),
CO.change_basis((1,0,0, 0,1,0, 0,0,1)).unit_cell().parameters())
u = uctbx.unit_cell((2,3,5))
CO = crystal_orientation(u.fractionalization_matrix(),True)
assert approx_equal(
CO.change_basis((0,1,0, 0,0,1, 1,0,0)).unit_cell().parameters(),
(5,2,3,90,90,90))
cb_op = sgtbx.change_of_basis_op("y,z,x")
assert approx_equal(
CO.change_basis(cb_op).unit_cell().parameters(),
(5,2,3,90,90,90))
import scitbx.math
from scitbx import matrix
fmx = matrix.sqr(
uctbx.unit_cell((10, 13, 17, 85, 95, 105)).fractionalization_matrix())
crm = matrix.sqr(scitbx.math.r3_rotation_axis_and_angle_as_matrix(
axis=(-3,5,-7), angle=37, deg=True))
co = crystal_orientation(crm * fmx.transpose(), True)
assert approx_equal(co.crystal_rotation_matrix(), crm)
def run_sim2smv(img_prefix=None,
simparams=None,
pdb_lines=None,
crystal=None,
spectra=None,
rotation=None,
rank=None,
fsave=None,
sfall_cluster=None,
quick=False):
smv_fileout = fsave
direct_algo_res_limit = simparams.direct_algo_res_limit
wavlen, flux, real_wavelength_A = next(
spectra) # list of lambdas, list of fluxes, average wavelength
real_flux = flex.sum(flux)
assert real_wavelength_A > 0
# print(rank, " ## real_wavelength_A/real_flux = ", real_wavelength_A, real_flux*1.0/simparams.flux)
if quick:
wavlen = flex.double([real_wavelength_A])
flux = flex.double([real_flux])
# GF = gen_fmodel(resolution=simparams.direct_algo_res_limit,pdb_text=pdb_lines,algorithm=simparams.fmodel_algorithm,wavelength=real_wavelength_A)
# GF.set_k_sol(simparams.k_sol)
# GF.make_P1_primitive()
sfall_main = sfall_cluster["main"] #GF.get_amplitudes()
# use crystal structure to initialize Fhkl array
# sfall_main.show_summary(prefix = "Amplitudes used ")
N = crystal.number_of_cells(sfall_main.unit_cell())
#print("## number of N = ", N)
SIM = nanoBragg(detpixels_slowfast=(simparams.detector_size_ny,simparams.detector_size_nx),pixel_size_mm=simparams.pixel_size_mm,\
Ncells_abc=(N,N,N),wavelength_A=real_wavelength_A,verbose=0)
# workaround for problem with wavelength array, specify it separately in constructor.
# SIM.adc_offset_adu = 0 # Do not offset by 40
SIM.adc_offset_adu = 10 # Do not offset by 40
SIM.seed = 0
# SIM.randomize_orientation()
SIM.mosaic_spread_deg = simparams.mosaic_spread_deg # interpreted by UMAT_nm as a half-width stddev
SIM.mosaic_domains = simparams.mosaic_domains # 77 seconds.
SIM.distance_mm = simparams.distance_mm
## setup the mosaicity
UMAT_nm = flex.mat3_double()
mersenne_twister = flex.mersenne_twister(seed=0)
scitbx.random.set_random_seed(1234)
rand_norm = scitbx.random.normal_distribution(mean=0,
sigma=SIM.mosaic_spread_deg *
math.pi / 180.)
g = scitbx.random.variate(rand_norm)
mosaic_rotation = g(SIM.mosaic_domains)
for m in mosaic_rotation:
site = col(mersenne_twister.random_double_point_on_sphere())
UMAT_nm.append(site.axis_and_angle_as_r3_rotation_matrix(m, deg=False))
SIM.set_mosaic_blocks(UMAT_nm)
######################
SIM.beamcenter_convention = convention.ADXV
SIM.beam_center_mm = (simparams.beam_center_x_mm,
simparams.beam_center_y_mm) # 95.975 96.855
######################
# get same noise each time this test is run
SIM.seed = 0
SIM.oversample = simparams.oversample
SIM.wavelength_A = real_wavelength_A
SIM.polarization = simparams.polarization
# this will become F000, marking the beam center
SIM.default_F = simparams.default_F
#SIM.missets_deg= (10,20,30)
SIM.Fhkl = sfall_main
Amatrix_rot = (
rotation *
sqr(sfall_main.unit_cell().orthogonalization_matrix())).transpose()
SIM.Amatrix_RUB = Amatrix_rot
#workaround for failing init_cell, use custom written Amatrix setter
# print("## inside run_sim2smv, Amat_rot = ", Amatrix_rot)
Amat = sqr(SIM.Amatrix).transpose() # recovered Amatrix from SIM
Ori = crystal_orientation.crystal_orientation(
Amat, crystal_orientation.basis_type.reciprocal)
print(fsave, "Amatrix_rot", Amatrix_rot)
print(fsave, "Amat", Amat)
print(fsave, "Ori", Ori)
print(fsave, "rotation", rotation)
print(fsave, "sqr(sfall_main.unit_cell().orthogonalization_matrix())",
sqr(sfall_main.unit_cell().orthogonalization_matrix()))
SIM.free_all()
def run_sim2smv(ROI,
prefix,
crystal,
spectra,
rotation,
rank,
tophat_spectrum=True,
quick=False):
smv_fileout = prefix + ".img"
direct_algo_res_limit = 1.7
wavlen, flux, wavelength_A = next(
spectra) # list of lambdas, list of fluxes, average wavelength
if tophat_spectrum:
sum_flux = flex.sum(flux)
#from IPython import embed; embed()
ave_flux = sum_flux / 60. # 60 energy channels
for ix in range(len(wavlen)):
energy = 12398.425 / wavlen[ix]
if energy >= 7090 and energy <= 7150:
flux[ix] = ave_flux
else:
flux[ix] = 0.
if quick:
wavlen = flex.double([wavelength_A])
flux = flex.double([flex.sum(flux)])
print("Quick sim, lambda=%f, flux=%f" % (wavelength_A, flux[0]))
#from matplotlib import pyplot as plt
#plt.plot(flux,"r-")
#plt.title(smv_fileout)
#plt.show()
GF = gen_fmodel(resolution=direct_algo_res_limit,
pdb_text=pdb_lines,
algorithm="fft",
wavelength=wavelength_A)
GF.set_k_sol(0.435)
GF.make_P1_primitive()
sfall_main = GF.get_amplitudes()
# use crystal structure to initialize Fhkl array
sfall_main.show_summary(prefix="Amplitudes used ")
N = crystal.number_of_cells(sfall_main.unit_cell())
#SIM = nanoBragg(detpixels_slowfast=(2000,2000),pixel_size_mm=0.11,Ncells_abc=(5,5,5),verbose=0)
SIM = nanoBragg(
detpixels_slowfast=(3000, 3000),
pixel_size_mm=0.11,
Ncells_abc=(N, N, N),
# workaround for problem with wavelength array, specify it separately in constructor.
wavelength_A=wavelength_A,
verbose=0)
SIM.adc_offset_adu = 0 # Do not offset by 40
SIM.adc_offset_adu = 10 # Do not offset by 40
import sys
if len(sys.argv) > 2:
SIM.seed = -int(sys.argv[2])
print("GOTHERE seed=", SIM.seed)
if len(sys.argv) > 1:
if sys.argv[1] == "random": SIM.randomize_orientation()
SIM.mosaic_domains = 50 # 77 seconds. With 100 energy points, 7700 seconds (2 hours) per image
# 3000000 images would be 100000 hours on a 60-core machine (dials), or 11.4 years
# using 2 nodes, 5.7 years. Do this at SLAC? NERSC? combination of all?
# SLAC downtimes: Tues Dec 5 (24 hrs), Mon Dec 11 (72 hrs), Mon Dec 18 light use, 24 days
SIM.mosaic_spread_deg = 0.05 # interpreted by UMAT_nm as a half-width stddev
SIM.distance_mm = 141.7
UMAT_nm = flex.mat3_double()
mersenne_twister = flex.mersenne_twister(seed=0)
scitbx.random.set_random_seed(1234)
rand_norm = scitbx.random.normal_distribution(mean=0,
sigma=SIM.mosaic_spread_deg *
math.pi / 180.)
g = scitbx.random.variate(rand_norm)
mosaic_rotation = g(SIM.mosaic_domains)
for m in mosaic_rotation:
site = col(mersenne_twister.random_double_point_on_sphere())
UMAT_nm.append(site.axis_and_angle_as_r3_rotation_matrix(m, deg=False))
SIM.set_mosaic_blocks(UMAT_nm)
#SIM.detector_thick_mm = 0.5 # = 0 for Rayonix
#SIM.detector_thicksteps = 1 # should default to 1 for Rayonix, but set to 5 for CSPAD
#SIM.detector_attenuation_length_mm = default is silicon
# get same noise each time this test is run
SIM.seed = 1
SIM.oversample = 1
SIM.wavelength_A = wavelength_A
SIM.polarization = 1
# this will become F000, marking the beam center
SIM.default_F = 0
#SIM.missets_deg= (10,20,30)
print("mosaic_seed=", SIM.mosaic_seed)
print("seed=", SIM.seed)
print("calib_seed=", SIM.calib_seed)
print("missets_deg =", SIM.missets_deg)
SIM.Fhkl = sfall_main
print("Determinant", rotation.determinant())
Amatrix_rot = (
rotation *
sqr(sfall_main.unit_cell().orthogonalization_matrix())).transpose()
print("RAND_ORI", prefix, end=' ')
for i in Amatrix_rot:
print(i, end=' ')
print()
SIM.Amatrix_RUB = Amatrix_rot
#workaround for failing init_cell, use custom written Amatrix setter
print("unit_cell_Adeg=", SIM.unit_cell_Adeg)
print("unit_cell_tuple=", SIM.unit_cell_tuple)
Amat = sqr(SIM.Amatrix).transpose() # recovered Amatrix from SIM
from cctbx import crystal_orientation
Ori = crystal_orientation.crystal_orientation(
Amat, crystal_orientation.basis_type.reciprocal)
print("Python unit cell from SIM state", Ori.unit_cell())
# fastest option, least realistic
#SIM.xtal_shape=shapetype.Tophat # RLP = hard sphere
#SIM.xtal_shape=shapetype.Square # gives fringes
SIM.xtal_shape = shapetype.Gauss # both crystal & RLP are Gaussian
#SIM.xtal_shape=shapetype.Round # Crystal is a hard sphere
# only really useful for long runs
SIM.progress_meter = False
# prints out value of one pixel only. will not render full image!
#SIM.printout_pixel_fastslow=(500,500)
#SIM.printout=True
SIM.show_params()
# flux is always in photons/s
SIM.flux = 1e12
SIM.exposure_s = 1.0 # so total fluence is e12
# assumes round beam
SIM.beamsize_mm = 0.003 #cannot make this 3 microns; spots are too intense
temp = SIM.Ncells_abc
print("Ncells_abc=", SIM.Ncells_abc)
SIM.Ncells_abc = temp
print("Ncells_abc=", SIM.Ncells_abc)
print("xtal_size_mm=", SIM.xtal_size_mm)
print("unit_cell_Adeg=", SIM.unit_cell_Adeg)
print("unit_cell_tuple=", SIM.unit_cell_tuple)
print("missets_deg=", SIM.missets_deg)
print("Amatrix=", SIM.Amatrix)
print("beam_center_mm=", SIM.beam_center_mm)
print("XDS_ORGXY=", SIM.XDS_ORGXY)
print("detector_pivot=", SIM.detector_pivot)
print("xtal_shape=", SIM.xtal_shape)
print("beamcenter_convention=", SIM.beamcenter_convention)
print("fdet_vector=", SIM.fdet_vector)
print("sdet_vector=", SIM.sdet_vector)
print("odet_vector=", SIM.odet_vector)
print("beam_vector=", SIM.beam_vector)
print("polar_vector=", SIM.polar_vector)
print("spindle_axis=", SIM.spindle_axis)
print("twotheta_axis=", SIM.twotheta_axis)
print("distance_meters=", SIM.distance_meters)
print("distance_mm=", SIM.distance_mm)
print("close_distance_mm=", SIM.close_distance_mm)
print("detector_twotheta_deg=", SIM.detector_twotheta_deg)
print("detsize_fastslow_mm=", SIM.detsize_fastslow_mm)
print("detpixels_fastslow=", SIM.detpixels_fastslow)
print("detector_rot_deg=", SIM.detector_rot_deg)
print("curved_detector=", SIM.curved_detector)
print("pixel_size_mm=", SIM.pixel_size_mm)
print("point_pixel=", SIM.point_pixel)
print("polarization=", SIM.polarization)
print("nopolar=", SIM.nopolar)
print("oversample=", SIM.oversample)
print("region_of_interest=", SIM.region_of_interest)
print("wavelength_A=", SIM.wavelength_A)
print("energy_eV=", SIM.energy_eV)
print("fluence=", SIM.fluence)
print("flux=", SIM.flux)
print("exposure_s=", SIM.exposure_s)
print("beamsize_mm=", SIM.beamsize_mm)
print("dispersion_pct=", SIM.dispersion_pct)
print("dispsteps=", SIM.dispsteps)
print("divergence_hv_mrad=", SIM.divergence_hv_mrad)
print("divsteps_hv=", SIM.divsteps_hv)
print("divstep_hv_mrad=", SIM.divstep_hv_mrad)
print("round_div=", SIM.round_div)
print("phi_deg=", SIM.phi_deg)
print("osc_deg=", SIM.osc_deg)
print("phisteps=", SIM.phisteps)
print("phistep_deg=", SIM.phistep_deg)
print("detector_thick_mm=", SIM.detector_thick_mm)
print("detector_thicksteps=", SIM.detector_thicksteps)
print("detector_thickstep_mm=", SIM.detector_thickstep_mm)
print("***mosaic_spread_deg=", SIM.mosaic_spread_deg)
print("***mosaic_domains=", SIM.mosaic_domains)
print("indices=", SIM.indices)
print("amplitudes=", SIM.amplitudes)
print("Fhkl_tuple=", SIM.Fhkl_tuple)
print("default_F=", SIM.default_F)
print("interpolate=", SIM.interpolate)
print("integral_form=", SIM.integral_form)
from libtbx.development.timers import Profiler
P = Profiler("nanoBragg")
# now actually burn up some CPU
#SIM.add_nanoBragg_spots()
del P
# simulated crystal is only 125 unit cells (25 nm wide)
# amplify spot signal to simulate physical crystal of 4000x larger: 100 um (64e9 x the volume)
print(crystal.domains_per_crystal)
SIM.raw_pixels *= crystal.domains_per_crystal
# must calculate the correct scale!
output = StringIO() # open("myfile","w")
make_response_plot = response_plot(False, title=prefix)
for x in range(0, 100, 2): #len(flux)):
if flux[x] == 0.0: continue
print("+++++++++++++++++++++++++++++++++++++++ Wavelength", x)
CH = channel_pixels(ROI, wavlen[x], flux[x], N, UMAT_nm, Amatrix_rot,
GF, output)
incremental_signal = CH.raw_pixels * crystal.domains_per_crystal
if x in [26, 40, 54, 68]: # subsample 7096, 7110, 7124, 7138 eV
print("----------------------> subsample", x)
make_response_plot.incr_subsample(x, ROI, incremental_signal)
make_response_plot.increment(x, ROI, incremental_signal)
SIM.raw_pixels += incremental_signal
CH.free_all()
message = output.getvalue().split()
miller = (int(message[4]), int(message[5]), int(message[6]))
intensity = float(message[9])
#SIM.to_smv_format(fileout=prefix + "_intimage_001.img")
pixels = SIM.raw_pixels
roi_pixels = pixels[ROI[1][0]:ROI[1][1], ROI[0][0]:ROI[0][1]]
print("Reducing full shape of", pixels.focus(), "to ROI of",
roi_pixels.focus())
SIM.free_all()
make_response_plot.plot(roi_pixels, miller)
return dict(roi_pixels=roi_pixels, miller=miller, intensity=intensity)
def __init__(self, params):
import cPickle as pickle
from dxtbx.model import BeamFactory
from dxtbx.model import DetectorFactory
from dxtbx.model.crystal import CrystalFactory
from cctbx.crystal_orientation import crystal_orientation, basis_type
from dxtbx.model import Experiment, ExperimentList
from scitbx import matrix
self.experiments = ExperimentList()
self.unique_file_names = []
self.params = params
data = pickle.load(
open(self.params.output.prefix + "_frame.pickle", "rb"))
frames_text = data.split("\n")
for item in frames_text:
tokens = item.split(' ')
wavelength = float(tokens[order_dict["wavelength"]])
beam = BeamFactory.simple(wavelength=wavelength)
detector = DetectorFactory.simple(
sensor=DetectorFactory.sensor(
"PAD"), # XXX shouldn't hard code for XFEL
distance=float(tokens[order_dict["distance"]]),
beam_centre=[
float(tokens[order_dict["beam_x"]]),
float(tokens[order_dict["beam_y"]])
],
fast_direction="+x",
slow_direction="+y",
pixel_size=[self.params.pixel_size, self.params.pixel_size],
image_size=[1795,
1795], # XXX obviously need to figure this out
)
reciprocal_matrix = matrix.sqr([
float(tokens[order_dict[k]]) for k in [
'res_ori_1', 'res_ori_2', 'res_ori_3', 'res_ori_4',
'res_ori_5', 'res_ori_6', 'res_ori_7', 'res_ori_8',
'res_ori_9'
]
])
ORI = crystal_orientation(reciprocal_matrix, basis_type.reciprocal)
direct = matrix.sqr(ORI.direct_matrix())
transfer_dict = dict(
__id__="crystal",
ML_half_mosaicity_deg=float(
tokens[order_dict["half_mosaicity_deg"]]),
ML_domain_size_ang=float(
tokens[order_dict["domain_size_ang"]]),
real_space_a=matrix.row(direct[0:3]),
real_space_b=matrix.row(direct[3:6]),
real_space_c=matrix.row(direct[6:9]),
space_group_hall_symbol=self.params.target_space_group.type().
hall_symbol(),
)
crystal = CrystalFactory.from_dict(transfer_dict)
""" old code reflects python-based crystal model
crystal = Crystal(
real_space_a = matrix.row(direct[0:3]),
real_space_b = matrix.row(direct[3:6]),
real_space_c = matrix.row(direct[6:9]),
space_group_symbol = self.params.target_space_group.type().lookup_symbol(),
mosaicity = float(tokens[order_dict["half_mosaicity_deg"]]),
)
crystal.domain_size = float(tokens[order_dict["domain_size_ang"]])
"""
#if isoform is not None:
# newB = matrix.sqr(isoform.fractionalization_matrix()).transpose()
# crystal.set_B(newB)
self.experiments.append(
Experiment(
beam=beam,
detector=None, #dummy for now
crystal=crystal))
self.unique_file_names.append(
tokens[order_dict["unique_file_name"]])
self.show_summary()
rot_mat = rotation_vector.axis_and_angle_as_r3_rotation_matrix(
omegas[omegaidx] + dangles[omegaidx][n]*0.1)
HP = (rot_mat * matrix.sqr(Amat))*matrix.col(hkl+difference) + beam_vector
#print "%10.7f %10.7f %10.7f"%HP.elems
assert approx_equal(H1,HP,eps=1E-5)
#print
if __name__ == '__main__':
wavelength = 1.2
resolution = 3.0
Amat = (0.0038039968697463817, 0.004498689311366309, 0.0044043429203887785,
-0.00477859183801569, 0.006594300357213904, -0.002402759536958918,
-0.01012056453488894, -0.0014226943325514182, 0.002789954423701981)
orient = crystal_orientation(Amat,True)
calc = partial_spot_position_partial_H(
limiting_resolution = resolution,
orientation = orient.reciprocal_matrix(),
wavelength = wavelength,
axial_direction = (0.,1.,0.)
)
rotation_scattering(calc,matrix.col((0.,1.,0.)),Amat,wavelength)
test_finite_differences(calc,matrix.col((0.,1.,0.)),Amat,wavelength)
print "OK"
def refined_settings_factory_from_refined_triclinic(
params, experiments, reflections, i_setting=None,
lepage_max_delta=5.0, nproc=1, refiner_verbosity=0):
assert len(experiments.crystals()) == 1
crystal = experiments.crystals()[0]
used_reflections = copy.deepcopy(reflections)
UC = crystal.get_unit_cell()
from rstbx.dps_core.lepage import iotbx_converter
Lfat = refined_settings_list()
for item in iotbx_converter(UC, lepage_max_delta):
Lfat.append(bravais_setting(item))
supergroup = Lfat.supergroup()
triclinic = Lfat.triclinic()
triclinic_miller = used_reflections['miller_index']
# assert no transformation between indexing and bravais list
assert str(triclinic['cb_op_inp_best'])=="a,b,c"
Nset = len(Lfat)
for j in xrange(Nset): Lfat[j].setting_number = Nset-j
from cctbx.crystal_orientation import crystal_orientation
from cctbx import sgtbx
from scitbx import matrix
for j in xrange(Nset):
cb_op = Lfat[j]['cb_op_inp_best'].c().as_double_array()[0:9]
orient = crystal_orientation(crystal.get_A(),True)
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(Lfat[j]['system'])
bravais = Lfat[j]["bravais"]
cb_op_best_ref = Lfat[j]['best_subsym'].change_of_basis_op_to_reference_setting()
space_group = sgtbx.space_group_info(
number=bravais_lattice_to_lowest_symmetry_spacegroup_number[bravais]).group()
space_group = space_group.change_basis(cb_op_best_ref.inverse())
bravais = str(bravais_types.bravais_lattice(group=space_group))
Lfat[j]["bravais"] = bravais
Lfat[j].unrefined_crystal = dials_crystal_from_orientation(
constrain_orient, space_group)
args = []
for subgroup in Lfat:
args.append((
params, subgroup, used_reflections, experiments, refiner_verbosity))
results = easy_mp.parallel_map(
func=refine_subgroup,
iterable=args,
processes=nproc,
method="multiprocessing",
preserve_order=True,
asynchronous=True,
preserve_exception_message=True)
for i, result in enumerate(results):
Lfat[i] = result
return Lfat
def run(self, experiments, reflections):
self.logger.log_step_time("POSTREFINEMENT")
if (not self.params.postrefinement.enable) or (self.params.scaling.algorithm != "mark0"): # mark1 implies no scaling/post-refinement
self.logger.log("No post-refinement was done")
if self.mpi_helper.rank == 0:
self.logger.main_log("No post-refinement was done")
return experiments, reflections
target_symm = symmetry(unit_cell = self.params.scaling.unit_cell, space_group_info = self.params.scaling.space_group)
i_model = self.params.scaling.i_model
miller_set = self.params.scaling.miller_set
# Ensure that match_multi_indices() will return identical results
# when a frame's observations are matched against the
# pre-generated Miller set, self.miller_set, and the reference
# data set, self.i_model. The implication is that the same match
# can be used to map Miller indices to array indices for intensity
# accumulation, and for determination of the correlation
# coefficient in the presence of a scaling reference.
assert len(i_model.indices()) == len(miller_set.indices())
assert (i_model.indices() == miller_set.indices()).count(False) == 0
new_experiments = ExperimentList()
new_reflections = flex.reflection_table()
experiments_rejected_by_reason = {} # reason:how_many_rejected
for experiment in experiments:
exp_reflections = reflections.select(reflections['exp_id'] == experiment.identifier)
# Build a miller array for the experiment reflections with original miller indexes
exp_miller_indices_original = miller.set(target_symm, exp_reflections['miller_index'], not self.params.merging.merge_anomalous)
observations_original_index = miller.array(exp_miller_indices_original,
exp_reflections['intensity.sum.value'],
flex.double(flex.sqrt(exp_reflections['intensity.sum.variance'])))
assert exp_reflections.size() == exp_miller_indices_original.size()
assert observations_original_index.size() == exp_miller_indices_original.size()
# Build a miller array for the experiment reflections with asu miller indexes
exp_miller_indices_asu = miller.set(target_symm, exp_reflections['miller_index_asymmetric'], True)
observations = miller.array(exp_miller_indices_asu, exp_reflections['intensity.sum.value'], flex.double(flex.sqrt(exp_reflections['intensity.sum.variance'])))
matches = miller.match_multi_indices(miller_indices_unique = miller_set.indices(), miller_indices = observations.indices())
pair1 = flex.int([pair[1] for pair in matches.pairs()]) # refers to the observations
pair0 = flex.int([pair[0] for pair in matches.pairs()]) # refers to the model
assert exp_reflections.size() == exp_miller_indices_original.size()
assert observations_original_index.size() == exp_miller_indices_original.size()
# narrow things down to the set that matches, only
observations_pair1_selected = observations.customized_copy(indices = flex.miller_index([observations.indices()[p] for p in pair1]),
data = flex.double([observations.data()[p] for p in pair1]),
sigmas = flex.double([observations.sigmas()[p] for p in pair1]))
observations_original_index_pair1_selected = observations_original_index.customized_copy(indices = flex.miller_index([observations_original_index.indices()[p] for p in pair1]),
data = flex.double([observations_original_index.data()[p] for p in pair1]),
sigmas = flex.double([observations_original_index.sigmas()[p] for p in pair1]))
I_observed = observations_pair1_selected.data()
MILLER = observations_original_index_pair1_selected.indices()
ORI = crystal_orientation(experiment.crystal.get_A(), basis_type.reciprocal)
Astar = matrix.sqr(ORI.reciprocal_matrix())
Astar_from_experiment = matrix.sqr(experiment.crystal.get_A())
assert Astar == Astar_from_experiment
WAVE = experiment.beam.get_wavelength()
BEAM = matrix.col((0.0,0.0,-1./WAVE))
BFACTOR = 0.
MOSAICITY_DEG = experiment.crystal.get_half_mosaicity_deg()
DOMAIN_SIZE_A = experiment.crystal.get_domain_size_ang()
# calculation of correlation here
I_reference = flex.double([i_model.data()[pair[0]] for pair in matches.pairs()])
I_invalid = flex.bool([i_model.sigmas()[pair[0]] < 0. for pair in matches.pairs()])
use_weights = False # New facility for getting variance-weighted correlation
if use_weights:
# variance weighting
I_weight = flex.double([1./(observations_pair1_selected.sigmas()[pair[1]])**2 for pair in matches.pairs()])
else:
I_weight = flex.double(len(observations_pair1_selected.sigmas()), 1.)
I_weight.set_selected(I_invalid, 0.)
"""Explanation of 'include_negatives' semantics as originally implemented in cxi.merge postrefinement:
include_negatives = True
+ and - reflections both used for Rh distribution for initial estimate of RS parameter
+ and - reflections both used for calc/obs correlation slope for initial estimate of G parameter
+ and - reflections both passed to the refinery and used in the target function (makes sense if
you look at it from a certain point of view)
include_negatives = False
+ and - reflections both used for Rh distribution for initial estimate of RS parameter
+ reflections only used for calc/obs correlation slope for initial estimate of G parameter
+ and - reflections both passed to the refinery and used in the target function (makes sense if
you look at it from a certain point of view)
"""
# RB: By design, for MPI-Merge "include negatives" is implicitly True
SWC = simple_weighted_correlation(I_weight, I_reference, I_observed)
if self.params.output.log_level == 0:
self.logger.log("Old correlation is: %f"%SWC.corr)
if self.params.postrefinement.algorithm == "rs":
Rhall = flex.double()
for mill in MILLER:
H = matrix.col(mill)
Xhkl = Astar*H
Rh = ( Xhkl + BEAM ).length() - (1./WAVE)
Rhall.append(Rh)
Rs = math.sqrt(flex.mean(Rhall*Rhall))
RS = 1./10000. # reciprocal effective domain size of 1 micron
RS = Rs # try this empirically determined approximate, monochrome, a-mosaic value
current = flex.double([SWC.slope, BFACTOR, RS, 0., 0.])
parameterization_class = rs_parameterization
refinery = rs_refinery(ORI=ORI, MILLER=MILLER, BEAM=BEAM, WAVE=WAVE, ICALCVEC = I_reference, IOBSVEC = I_observed)
elif self.params.postrefinement.algorithm == "eta_deff":
eta_init = 2. * MOSAICITY_DEG * math.pi/180.
D_eff_init = 2. * DOMAIN_SIZE_A
current = flex.double([SWC.slope, BFACTOR, eta_init, 0., 0., D_eff_init])
parameterization_class = eta_deff_parameterization
refinery = eta_deff_refinery(ORI=ORI, MILLER=MILLER, BEAM=BEAM, WAVE=WAVE, ICALCVEC = I_reference, IOBSVEC = I_observed)
func = refinery.fvec_callable(parameterization_class(current))
functional = flex.sum(func * func)
if self.params.output.log_level == 0:
self.logger.log("functional: %f"%functional)
self.current = current;
self.parameterization_class = parameterization_class
self.refinery = refinery;
self.observations_pair1_selected = observations_pair1_selected;
self.observations_original_index_pair1_selected = observations_original_index_pair1_selected
error_detected = False
try:
self.run_plain()
result_observations_original_index, result_observations, result_matches = self.result_for_cxi_merge()
assert result_observations_original_index.size() == result_observations.size()
assert result_matches.pairs().size() == result_observations_original_index.size()
except (AssertionError, ValueError, RuntimeError) as e:
error_detected = True
reason = repr(e)
if not reason:
reason = "Unknown error"
if not reason in experiments_rejected_by_reason:
experiments_rejected_by_reason[reason] = 1
else:
experiments_rejected_by_reason[reason] += 1
if not error_detected:
new_experiments.append(experiment)
new_exp_reflections = flex.reflection_table()
new_exp_reflections['miller_index_asymmetric'] = flex.miller_index(result_observations.indices())
new_exp_reflections['intensity.sum.value'] = flex.double(result_observations.data())
new_exp_reflections['intensity.sum.variance'] = flex.double(flex.pow(result_observations.sigmas(),2))
new_exp_reflections['exp_id'] = flex.std_string(len(new_exp_reflections), experiment.identifier)
new_reflections.extend(new_exp_reflections)
'''
# debugging
elif reason.startswith("ValueError"):
self.logger.log("Rejected b/c of value error exp id: %s; unit cell: %s"%(exp_id, str(experiment.crystal.get_unit_cell())) )
'''
# report rejected experiments, reflections
experiments_rejected_by_postrefinement = len(experiments) - len(new_experiments)
reflections_rejected_by_postrefinement = reflections.size() - new_reflections.size()
self.logger.log("Experiments rejected by post-refinement: %d"%experiments_rejected_by_postrefinement)
self.logger.log("Reflections rejected by post-refinement: %d"%reflections_rejected_by_postrefinement)
all_reasons = []
for reason, count in six.iteritems(experiments_rejected_by_reason):
self.logger.log("Experiments rejected due to %s: %d"%(reason,count))
all_reasons.append(reason)
comm = self.mpi_helper.comm
MPI = self.mpi_helper.MPI
# Collect all rejection reasons from all ranks. Use allreduce to let each rank have all reasons.
all_reasons = comm.allreduce(all_reasons, MPI.SUM)
all_reasons = set(all_reasons)
# Now that each rank has all reasons from all ranks, we can treat the reasons in a uniform way.
total_experiments_rejected_by_reason = {}
for reason in all_reasons:
rejected_experiment_count = 0
if reason in experiments_rejected_by_reason:
rejected_experiment_count = experiments_rejected_by_reason[reason]
total_experiments_rejected_by_reason[reason] = comm.reduce(rejected_experiment_count, MPI.SUM, 0)
total_accepted_experiment_count = comm.reduce(len(new_experiments), MPI.SUM, 0)
# how many reflections have we rejected due to post-refinement?
rejected_reflections = len(reflections) - len(new_reflections);
total_rejected_reflections = self.mpi_helper.sum(rejected_reflections)
if self.mpi_helper.rank == 0:
for reason, count in six.iteritems(total_experiments_rejected_by_reason):
self.logger.main_log("Total experiments rejected due to %s: %d"%(reason,count))
self.logger.main_log("Total experiments accepted: %d"%total_accepted_experiment_count)
self.logger.main_log("Total reflections rejected due to post-refinement: %d"%total_rejected_reflections)
self.logger.log_step_time("POSTREFINEMENT", True)
return new_experiments, new_reflections
def is_similar_to(
self,
other,
angle_tolerance=0.01,
mosaicity_tolerance=0.8,
uc_rel_length_tolerance=0.01,
uc_abs_angle_tolerance=1.0,
):
"""
Test similarity of this to another crystal model
:param other: the crystal model to test against
:type other: crystal_model
:param angle_tolerance: maximum tolerated orientation difference in degrees
:type angle_tolerance: float
:param mosaicity_tolerance: minimum tolerated fraction of the larger
mosaicity for the smaller mosaicity to retain similarity
:type mosaicity_tolerance:float
:param uc_rel_length_tolerance: relative length tolerance to pass to
unit_cell.is_similar_to
:type uc_rel_length_tolerance: float
:param uc_abs_angle_tolerance: absolute angle tolerance to pass to
unit_cell.is_similar_to
:type uc_abs_angle_tolerance: float
:returns: Whether the other crystal model is similar to this
:rtype: bool
"""
# space group test
if self.get_space_group() != other.get_space_group():
return False
# mosaicity test
m_a, m_b = self.get_mosaicity(deg=True), other.get_mosaicity(deg=True)
min_m, max_m = min(m_a, m_b), max(m_a, m_b)
if min_m <= 0.0:
if max_m > 0.0:
return False
elif min_m / max_m < mosaicity_tolerance:
return False
# static orientation test
U_a, U_b = self.get_U(), other.get_U()
assert U_a.is_r3_rotation_matrix()
assert U_b.is_r3_rotation_matrix()
R_ab = U_b * U_a.transpose()
uq = R_ab.r3_rotation_matrix_as_unit_quaternion()
angle = uq.unit_quaternion_as_axis_and_angle(deg=True)[0]
if abs(angle) > angle_tolerance:
return False
# static unit cell test
uc_a, uc_b = self.get_unit_cell(), other.get_unit_cell()
if not uc_a.is_similar_to(
uc_b,
relative_length_tolerance=uc_rel_length_tolerance,
absolute_angle_tolerance=uc_abs_angle_tolerance,
):
return False
# scan varying tests
if self.num_scan_points > 0:
if other.num_scan_points != self.num_scan_points:
return False
for i in range(self.num_scan_points):
U_a, U_b = self.get_U_at_scan_point(
i), other.get_U_at_scan_point(i)
assert U_a.is_r3_rotation_matrix()
assert U_b.is_r3_rotation_matrix()
R_ab = U_b * U_a.transpose()
uq = R_ab.r3_rotation_matrix_as_unit_quaternion()
angle = uq.unit_quaternion_as_axis_and_angle(deg=True)[0]
if abs(angle) > angle_tolerance:
return False
B_a, B_b = self.get_B_at_scan_point(
i), other.get_B_at_scan_point(i)
uc_a = crystal_orientation(B_a, True).unit_cell()
uc_b = crystal_orientation(B_b, True).unit_cell()
if not uc_a.is_similar_to(
uc_b,
relative_length_tolerance=uc_rel_length_tolerance,
absolute_angle_tolerance=uc_abs_angle_tolerance,
):
return False
return True
def apply_symmetry(self, crystal_model):
"""Apply symmetry constraints to a crystal model.
Returns the crystal model (with symmetry constraints applied) in the same
setting as provided as input. The cb_op returned by the method is
that necessary to transform that model to the user-provided
target symmetry.
Args:
crystal_model (dxtbx.model.Crystal): The input crystal model to which to
apply symmetry constraints.
Returns: (dxtbx.model.Crystal, cctbx.sgtbx.change_of_basis_op):
The crystal model with symmetry constraints applied, and the change_of_basis_op
that transforms the returned model to the user-specified target symmetry.
"""
if not (self.target_symmetry_primitive
and self.target_symmetry_primitive.space_group()):
return crystal, sgtbx.change_of_basis_op()
target_space_group = self.target_symmetry_primitive.space_group()
A = crystal_model.get_A()
max_delta = self._max_delta
items = iotbx_converter(crystal_model.get_unit_cell(),
max_delta=max_delta)
target_sg_ref = target_space_group.info().reference_setting().group()
best_angular_difference = 1e8
best_subgroup = None
for item in items:
if bravais_lattice(group=target_sg_ref) != item["bravais"]:
continue
if item["max_angular_difference"] < best_angular_difference:
best_angular_difference = item["max_angular_difference"]
best_subgroup = item
if best_subgroup is None:
return None, None
cb_op_inp_best = best_subgroup["cb_op_inp_best"]
best_subsym = best_subgroup["best_subsym"]
ref_subsym = best_subgroup["ref_subsym"]
cb_op_ref_best = ref_subsym.change_of_basis_op_to_best_cell()
cb_op_best_ref = cb_op_ref_best.inverse()
cb_op_inp_ref = cb_op_best_ref * cb_op_inp_best
cb_op_ref_inp = cb_op_inp_ref.inverse()
orient = crystal_orientation(A, True)
orient_ref = orient.change_basis(
scitbx.matrix.sqr(
(cb_op_inp_ref).c().as_double_array()[0:9]).transpose())
constrain_orient = orient_ref.constrain(best_subgroup["system"])
direct_matrix = constrain_orient.direct_matrix()
a = scitbx.matrix.col(direct_matrix[:3])
b = scitbx.matrix.col(direct_matrix[3:6])
c = scitbx.matrix.col(direct_matrix[6:9])
model = Crystal(a, b, c, space_group=target_sg_ref)
assert target_sg_ref.is_compatible_unit_cell(model.get_unit_cell())
model = model.change_basis(cb_op_ref_inp)
if self.cb_op_inp_best is not None:
# Then the unit cell has been provided: this is the cb_op to map to the
# user-provided input unit cell
return model, self.cb_op_inp_best.inverse() * cb_op_inp_best
if not self.cb_op_ref_inp.is_identity_op():
if self.target_symmetry_inp.space_group(
) == best_subsym.space_group():
# Handle where e.g. the user has requested I2 instead of the reference C2
return model, cb_op_inp_best
# The user has specified a setting that is not the reference setting
return model, self.cb_op_ref_inp * cb_op_inp_ref
# Default to reference setting
# This change of basis op will ensure that we get the best beta angle without
# changing the centring (e.g. from C2 to I2)
cb_op_ref_best = ref_subsym.change_of_basis_op_to_best_cell(
best_monoclinic_beta=False)
return model, cb_op_ref_best * cb_op_inp_ref
def run_sim2smv(prefix,
crystal,
spectra,
rotation,
rank,
gpu_channels_singleton,
params,
quick=False,
save_bragg=False,
sfall_channels=None):
smv_fileout = prefix + ".img"
burst_buffer_expand_dir = os.path.expandvars(params.logger.outdir)
burst_buffer_fileout = os.path.join(burst_buffer_expand_dir, smv_fileout)
reference_fileout = os.path.join(".", smv_fileout)
if not quick:
if not write_safe(reference_fileout):
print("File %s already exists, skipping in rank %d" %
(reference_fileout, rank))
return
direct_algo_res_limit = 1.7
wavlen, flux, wavelength_A = next(
spectra) # list of lambdas, list of fluxes, average wavelength
assert wavelength_A > 0
# use crystal structure to initialize Fhkl array
N = crystal.number_of_cells(sfall_channels[0].unit_cell())
#SIM = nanoBragg(detpixels_slowfast=(2000,2000),pixel_size_mm=0.11,Ncells_abc=(5,5,5),verbose=0)
SIM = nanoBragg(
detpixels_slowfast=(3000, 3000),
pixel_size_mm=0.11,
Ncells_abc=(N, N, N),
# workaround for problem with wavelength array, specify it separately in constructor.
wavelength_A=wavelength_A,
verbose=0)
SIM.adc_offset_adu = 0 # Do not offset by 40
SIM.adc_offset_adu = 10 # Do not offset by 40
import sys
if len(sys.argv) > 2:
SIM.seed = -int(sys.argv[2])
if len(sys.argv) > 1:
if sys.argv[1] == "random": SIM.randomize_orientation()
SIM.mosaic_spread_deg = 0.05 # interpreted by UMAT_nm as a half-width stddev
SIM.mosaic_domains = 25 # 77 seconds. With 100 energy points, 7700 seconds (2 hours) per image
# 3000000 images would be 100000 hours on a 60-core machine (dials), or 11.4 years
# using 2 nodes, 5.7 years. Do this at SLAC? NERSC? combination of all?
# SLAC downtimes: Tues Dec 5 (24 hrs), Mon Dec 11 (72 hrs), Mon Dec 18 light use, 24 days
# mosaic_domains setter must come after mosaic_spread_deg setter
SIM.distance_mm = 141.7
UMAT_nm = flex.mat3_double()
mersenne_twister = flex.mersenne_twister(seed=0)
scitbx.random.set_random_seed(1234)
rand_norm = scitbx.random.normal_distribution(mean=0,
sigma=SIM.mosaic_spread_deg *
math.pi / 180.)
g = scitbx.random.variate(rand_norm)
mosaic_rotation = g(SIM.mosaic_domains)
for m in mosaic_rotation:
site = col(mersenne_twister.random_double_point_on_sphere())
UMAT_nm.append(site.axis_and_angle_as_r3_rotation_matrix(m, deg=False))
SIM.set_mosaic_blocks(UMAT_nm)
#SIM.detector_thick_mm = 0.5 # = 0 for Rayonix
#SIM.detector_thicksteps = 1 # should default to 1 for Rayonix, but set to 5 for CSPAD
#SIM.detector_attenuation_length_mm = default is silicon
# get same noise each time this test is run
SIM.seed = 1
SIM.oversample = 1
SIM.wavelength_A = wavelength_A
SIM.polarization = 1
# this will become F000, marking the beam center
SIM.default_F = 0
SIM.Fhkl = sfall_channels[0] # instead of sfall_main
Amatrix_rot = (rotation * sqr(
sfall_channels[0].unit_cell().orthogonalization_matrix())).transpose()
SIM.Amatrix_RUB = Amatrix_rot
#workaround for failing init_cell, use custom written Amatrix setter
print("unit_cell_Adeg=", SIM.unit_cell_Adeg)
print("unit_cell_tuple=", SIM.unit_cell_tuple)
Amat = sqr(SIM.Amatrix).transpose() # recovered Amatrix from SIM
from cctbx import crystal_orientation
Ori = crystal_orientation.crystal_orientation(
Amat, crystal_orientation.basis_type.reciprocal)
# fastest option, least realistic
SIM.xtal_shape = shapetype.Gauss_argchk # both crystal & RLP are Gaussian
# only really useful for long runs
SIM.progress_meter = False
# prints out value of one pixel only. will not render full image!
# flux is always in photons/s
SIM.flux = 1e12
SIM.exposure_s = 1.0 # so total fluence is e12
# assumes round beam
SIM.beamsize_mm = 0.003 #cannot make this 3 microns; spots are too intense
temp = SIM.Ncells_abc
SIM.Ncells_abc = temp
# rough approximation to water: interpolation points for sin(theta/lambda) vs structure factor
water_bg = flex.vec2_double([(0, 2.57), (0.0365, 2.58), (0.07, 2.8),
(0.12, 5), (0.162, 8), (0.18, 7.32),
(0.2, 6.75), (0.216, 6.75), (0.236, 6.5),
(0.28, 4.5), (0.3, 4.3), (0.345, 4.36),
(0.436, 3.77), (0.5, 3.17)])
assert [a[0] for a in water_bg] == sorted([a[0] for a in water_bg])
# rough approximation to air
air_bg = flex.vec2_double([(0, 14.1), (0.045, 13.5), (0.174, 8.35),
(0.35, 4.78), (0.5, 4.22)])
assert [a[0] for a in air_bg] == sorted([a[0] for a in air_bg])
# simulated crystal is only 125 unit cells (25 nm wide)
# amplify spot signal to simulate physical crystal of 4000x larger: 100 um (64e9 x the volume)
SIM.raw_pixels *= crystal.domains_per_crystal
# must calculate the correct scale!
use_exascale_api = os.environ.get("USE_EXASCALE_API", "True")
cache_fhkl_on_gpu = os.environ.get("CACHE_FHKL_ON_GPU", "True")
assert use_exascale_api in ["True", "False"
] and cache_fhkl_on_gpu in ["True", "False"]
use_exascale_api = (use_exascale_api == "True")
cache_fhkl_on_gpu = (cache_fhkl_on_gpu == "True")
QQ = Profiler("nanoBragg Bragg spots rank %d" % (rank))
if use_exascale_api:
#something new
devices_per_node = int(os.environ["DEVICES_PER_NODE"])
SIM.device_Id = rank % devices_per_node
assert gpu_channels_singleton.get_deviceID() == SIM.device_Id
if cache_fhkl_on_gpu: #flag to switch on GPU energy channels
if gpu_channels_singleton.get_nchannels() == 0: # if uninitialized
P = Profiler("Initialize the channels singleton rank %d" %
(rank))
for x in range(len(flux)):
gpu_channels_singleton.structure_factors_to_GPU_direct(
x, sfall_channels[x].indices(),
sfall_channels[x].data())
del P
import time
print("datetime for channels singleton rank %d" % (rank),
time.time())
# allocate GPU arrays
SIM.allocate()
# loop over energies
for x in range(len(flux)):
P = Profiler("USE_EXASCALE_API nanoBragg Python and C++ rank %d" %
(rank))
print("USE_EXASCALE_API+++++++++++++++++++++++ Wavelength", x)
# from channel_pixels function
SIM.wavelength_A = wavlen[x]
SIM.flux = flux[x]
if cache_fhkl_on_gpu: # new interface, use singleton to store all-image energy channels
SIM.add_energy_channel_from_gpu_amplitudes(
x, gpu_channels_singleton)
else: # old interface, host-to-device each energy channel, each image
SIM.Fhkl = sfall_channels[x]
SIM.add_energy_channel_cuda()
del P
SIM.scale_in_place(
crystal.domains_per_crystal) # apply scale directly on GPU
SIM.wavelength_A = wavelength_A # return to canonical energy for subsequent background
if add_background_algorithm == "cuda":
QQ = Profiler("nanoBragg background rank %d" % (rank))
SIM.Fbg_vs_stol = water_bg
SIM.amorphous_sample_thick_mm = 0.1
SIM.amorphous_density_gcm3 = 1
SIM.amorphous_molecular_weight_Da = 18
SIM.flux = 1e12
SIM.beamsize_mm = 0.003 # square (not user specified)
SIM.exposure_s = 1.0 # multiplies flux x exposure
SIM.add_background()
SIM.Fbg_vs_stol = air_bg
SIM.amorphous_sample_thick_mm = 10 # between beamstop and collimator
SIM.amorphous_density_gcm3 = 1.2e-3
SIM.amorphous_sample_molecular_weight_Da = 28 # nitrogen = N2
SIM.add_background()
# deallocate GPU arrays
SIM.get_raw_pixels() # updates SIM.raw_pixels from GPU
SIM.deallocate()
SIM.Amatrix_RUB = Amatrix_rot # return to canonical orientation
del QQ
else:
for x in range(len(flux)):
if rank in [0, 7]:
P = Profiler("nanoBragg Python and C++ rank %d" % (rank))
if rank in [0, 7]:
print("+++++++++++++++++++++++++++++++++++++++ Wavelength", x)
CH = channel_pixels(wavlen[x], flux[x], N, UMAT_nm, Amatrix_rot,
rank, sfall_channels[x])
SIM.raw_pixels += CH.raw_pixels * crystal.domains_per_crystal
CHDBG_singleton.extract(channel_no=x, data=CH.raw_pixels)
CH.free_all()
if rank in [0, 7]: del P
if add_background_algorithm in ["jh", "sort_stable"]:
QQ = Profiler("nanoBragg background rank %d" % (rank))
SIM.Fbg_vs_stol = water_bg
SIM.amorphous_sample_thick_mm = 0.1
SIM.amorphous_density_gcm3 = 1
SIM.amorphous_molecular_weight_Da = 18
SIM.flux = 1e12
SIM.beamsize_mm = 0.003 # square (not user specified)
SIM.exposure_s = 1.0 # multiplies flux x exposure
SIM.add_background(
sort_stable=(add_background_algorithm == "sort_stable"))
SIM.Fbg_vs_stol = air_bg
SIM.amorphous_sample_thick_mm = 10 # between beamstop and collimator
SIM.amorphous_density_gcm3 = 1.2e-3
SIM.amorphous_sample_molecular_weight_Da = 28 # nitrogen = N2
SIM.add_background(
sort_stable=(add_background_algorithm == "sort_stable"))
del QQ
SIM.detector_psf_kernel_radius_pixels = 5
SIM.detector_psf_type = shapetype.Unknown # for CSPAD
SIM.detector_psf_fwhm_mm = 0
#SIM.apply_psf()
QQ = Profiler("nanoBragg noise rank %d" % (rank))
#SIM.add_noise() #converts phtons to ADU.
del QQ
extra = "PREFIX=%s;\nRANK=%d;\n" % (prefix, rank)
SIM.to_smv_format_py(fileout=burst_buffer_fileout,
intfile_scale=1,
rotmat=True,
extra=extra,
gz=True)
SIM.free_all()