imgname=filelist[i]
img = dxtbx.load(imgname)
detector = img.get_detector()
beam = img.get_beam()
scan = img.get_scan()
gonio = img.get_goniometer()
print "s0 from ",imgname,": ",beam.get_s0()
print "wavelength from ",imgname,": ",beam.get_wavelength()
crystal = copy.deepcopy(experiments.crystals()[0])
axis = gonio.get_rotation_axis()
start_angle, delta_angle = scan.get_oscillation()
crystal.rotate_around_origin(axis, start_angle + (delta_angle/2), deg=True)
if (output_format == "json"):
exp_list = ExperimentList()
exp_list.append(Experiment(imageset=ImageSetFactory.make_imageset(list([imgname])),
beam=beam,
detector=detector,
goniometer=gonio,
scan=scan,
crystal=crystal))
if (add_background_images==True):
if (len(bkglist) != 1):
bkgname=bkglist[i]
exp_list[0].imageset.external_lookup.pedestal.filename=os.path.abspath(bkgname)
dump.experiment_list(exp_list,json_dir+"/experiments_for_lunus_{0:05d}.json".format(imnum))
else:
from scitbx import matrix
A_matrix = matrix.sqr(crystal.get_A()).inverse()
At = np.asarray(A_matrix.transpose()).reshape((3,3))
def build_statistics_rejecting_outliers(self,refl_gen):
# XXX duplicates behavior of above function; should refactor to cover both cases with same code.
n_total = 0
n_file = 0
sum_delta_x = 0
sum_delta_y = 0
deltax = {}
deltay = {}
delRoverR = {}
panel_deltax = {}
panel_deltay = {}
panel_deltapsi = {}
cumPANNO = {}
cumOBS = {}
cumCALC = {}
run_nos = {}
print ("Reading %d refl files, WITH REJECTION printing the first 10"%(len(refl_gen)))
for item in refl_gen:
strong_refls = item["strong_refls"]
nrefls = len(strong_refls)
keep_refls = flex.bool(nrefls, True)
if self.params.dispatch.by_run==True:
run_match = RUN.match(item["strfile"])
run_token = int(run_match.group(1)); run_nos[run_token]=run_nos.get(run_token,0); run_nos[run_token]+=1
deltax[run_token] = deltax.get(run_token, flex.double())
deltay[run_token] = deltay.get(run_token, flex.double())
delRoverR[run_token] = delRoverR.get(run_token, flex.double())
OBS = flex.vec2_double()
CALC = flex.vec2_double()
if n_file < 1000: print (item["strfile"], nrefls)
n_total += nrefls
n_file += 1
fcalc = strong_refls["xyzcal.mm"]
fobs = strong_refls["xyzobs.mm.value"]
delpsi = strong_refls["delpsical.rad"]
panno = strong_refls["panel"]
if n_file==1:
for ipanel in range(256):
panel_deltax[ipanel] = panel_deltax.get(ipanel, flex.double())
panel_deltay[ipanel] = panel_deltay.get(ipanel, flex.double())
panel_deltapsi[ipanel] = panel_deltapsi.get(ipanel, flex.double())
for isensor in range(32):
cumOBS[isensor] = cumOBS.get(isensor, flex.vec3_double())
cumCALC[isensor] = cumCALC.get(isensor, flex.vec3_double())
cumPANNO[isensor] = cumPANNO.get(isensor, flex.int())
expt_files = glob.glob(self.params.input.expt_glob)
expt_file = expt_files[0]
from dxtbx.model.experiment_list import ExperimentList
int_expt = ExperimentList.from_file(expt_file, check_format=False)[0]
D = int_expt.detector
B = int_expt.beam
Beam = D.hierarchy().get_beam_centre_lab((0,0,-1)) # hard code the sample to source vector for now
distance = D.hierarchy().get_distance()
for irefl in range(len(strong_refls)):
trial_delta_x = (fcalc[irefl][0] - fobs[irefl][0])
trial_delta_y = (fcalc[irefl][1] - fobs[irefl][1])
#if irefl==26 and nrefls==400: import ipdb;ipdb.set_trace()
try:
maha_distance = self.record_MCD_models[panno[irefl]].rob_cov.mahalanobis(X=[ (trial_delta_x,trial_delta_y,delpsi[irefl]) ])[0]
except KeyError:
maha_distance = None
if (
maha_distance is None or
maha_distance >= (self.params.residuals.mcd_filter.mahalanobis_distance)**2
):
keep_refls[irefl]=False
continue
sum_delta_x += trial_delta_x
sum_delta_y += trial_delta_y
panel = D[panno[irefl]]
panel_deltax[panno[irefl]].append( trial_delta_x )
panel_deltay[panno[irefl]].append( trial_delta_y )
panel_deltapsi[panno[irefl]].append( delpsi[irefl] )
CALC.append(panel.get_lab_coord(fcalc[irefl][0:2])[0:2]) # take only the xy coords; assume z along beam
OBS.append(panel.get_lab_coord(fobs[irefl][0:2])[0:2])
cumCALC[panno[irefl]//8].append(panel.get_lab_coord(fcalc[irefl][0:2]))
cumOBS[panno[irefl]//8].append(panel.get_lab_coord(fobs[irefl][0:2]))
cumPANNO[panno[irefl]//8].append(panno[irefl])
if self.params.dispatch.by_run==True:
for irefl in range(len(strong_refls)):
deltax[run_token].append( (fcalc[irefl][0] - fobs[irefl][0]) )
deltay[run_token].append( (fcalc[irefl][1] - fobs[irefl][1]) )
R_sq = CALC.dot(CALC)
DIFF = CALC-OBS
Dr_over_r = (DIFF.dot(CALC))/R_sq
if self.params.dispatch.by_run==True:
for irefl in range(len(Dr_over_r)):
delRoverR[run_token].append( Dr_over_r[irefl] )
if self.params.output.observations_filtered_replaces is not None:
filtered_file = item["obsfile"].replace(self.params.output.observations_filtered_replaces,"filtered")
item["strong_refls"].select(keep_refls).as_file(filtered_file)
from libtbx import adopt_init_args
obj = dict(n_total=n_total, n_file=n_file, sum_delta_x=sum_delta_x,
sum_delta_y=sum_delta_y, deltax=deltax, deltay=deltay, distance=distance,
delRoverR=delRoverR, panel_deltax=panel_deltax, panel_deltay=panel_deltay, panel_deltapsi=panel_deltapsi,
cumOBS=cumOBS, cumCALC=cumCALC, cumPANNO=cumPANNO, O="ok")
adopt_init_args(self, obj)
self.ordered_run_nos = sorted(list(run_nos.keys()))
self.run_nos = run_nos
