def test_exp_P1():
"""Create a mock experiments object."""
exp = Experiment()
exp_dict = {
"__id__": "crystal",
"real_space_a": [1.0, 0.0, 0.0],
"real_space_b": [0.0, 1.0, 0.0],
"real_space_c": [0.0, 0.0, 1.0],
"space_group_hall_symbol": " P 1",
}
crystal = Crystal.from_dict(exp_dict)
exp.crystal = crystal
return exp
def create_datastructures_for_target_mtz(experiments, mtz_file):
"""Read a merged mtz file and extract miller indices, intensities and
variances."""
m = mtz.object(mtz_file)
ind = m.extract_miller_indices()
cols = m.columns()
col_dict = {c.label(): c for c in cols}
r_t = flex.reflection_table()
if "I" in col_dict: # nice and simple
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["I"].extract_values().as_double()
r_t["variance"] = flex.pow2(col_dict["SIGI"].extract_values().as_double())
elif "IMEAN" in col_dict: # nice and simple
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["IMEAN"].extract_values().as_double()
r_t["variance"] = flex.pow2(col_dict["SIGIMEAN"].extract_values().as_double())
elif "I(+)" in col_dict: # need to combine I+ and I- together into target Ih
if col_dict["I(+)"].n_valid_values() == 0: # use I(-)
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["I(-)"].extract_values().as_double()
r_t["variance"] = flex.pow2(
col_dict["SIGI(-)"].extract_values().as_double()
)
elif col_dict["I(-)"].n_valid_values() == 0: # use I(+)
r_t["miller_index"] = ind
r_t["intensity"] = col_dict["I(+)"].extract_values().as_double()
r_t["variance"] = flex.pow2(
col_dict["SIGI(+)"].extract_values().as_double()
)
else: # Combine both - add together then use Ih table to calculate I and sigma
r_tplus = flex.reflection_table()
r_tminus = flex.reflection_table()
r_tplus["miller_index"] = ind
r_tplus["intensity"] = col_dict["I(+)"].extract_values().as_double()
r_tplus["variance"] = flex.pow2(
col_dict["SIGI(+)"].extract_values().as_double()
)
r_tminus["miller_index"] = ind
r_tminus["intensity"] = col_dict["I(-)"].extract_values().as_double()
r_tminus["variance"] = flex.pow2(
col_dict["SIGI(-)"].extract_values().as_double()
)
r_tplus.extend(r_tminus)
r_tplus.set_flags(
flex.bool(r_tplus.size(), False), r_tplus.flags.bad_for_scaling
)
r_tplus = r_tplus.select(r_tplus["variance"] != 0.0)
Ih_table = create_Ih_table(
[experiments[0]], [r_tplus], anomalous=True
).blocked_data_list[0]
r_t["intensity"] = Ih_table.Ih_values
inv_var = (
Ih_table.weights * Ih_table.h_index_matrix
) * Ih_table.h_expand_matrix
r_t["variance"] = 1.0 / inv_var
r_t["miller_index"] = Ih_table.miller_index
else:
assert 0, """Unrecognised intensities in mtz file."""
r_t = r_t.select(r_t["variance"] > 0.0)
r_t["d"] = (
miller.set(
crystal_symmetry=crystal.symmetry(
space_group=m.space_group(), unit_cell=m.crystals()[0].unit_cell()
),
indices=r_t["miller_index"],
)
.d_spacings()
.data()
)
r_t.set_flags(flex.bool(r_t.size(), True), r_t.flags.integrated)
exp = Experiment()
exp.crystal = deepcopy(experiments[0].crystal)
exp.identifier = str(uuid.uuid4())
r_t.experiment_identifiers()[len(experiments)] = exp.identifier
r_t["id"] = flex.int(r_t.size(), len(experiments))
# create a new KB scaling model for the target and set as scaled to fix scale
# for targeted scaling.
params = Mock()
params.KB.decay_correction.return_value = False
exp.scaling_model = KBScalingModel.from_data(params, [], [])
exp.scaling_model.set_scaling_model_as_scaled() # Set as scaled to fix scale.
return exp, r_t
import pylab as plt
plt.imshow(img, vmax=100)
plt.title("Ground truth image")
plt.show()
SIM.D.raw_pixels *= 0
P = phil_scope.extract()
E = Experiment()
if "G" in args.perturb:
P.init.G = SIM.D.spot_scale * 10
else:
P.init.G = SIM.D.spot_scale
if "crystal" in args.perturb:
E.crystal = C2
else:
E.crystal = C
if "Nabc" in args.perturb:
P.init.Nabc = 20, 20, 20
else:
P.init.Nabc = SIM.crystal.Ncells_abc
if "detz_shift" in args.perturb:
P.init.detz_shift = 1
else:
P.init.detz_shift = 0
if "eta" in args.perturb:
P.init.eta_abc = [0.12, 0.13, 0.14]
from __future__ import division
from simtbx.diffBragg import utils
from simtbx.nanoBragg.tst_nanoBragg_basic import pdb_lines
from simtbx.diffBragg.phil import hopper_phil, philz
from libtbx.phil import parse
from simtbx.nanoBragg import tst_nanoBragg_multipanel
from dxtbx.model import Experiment
import numpy as np
# make a dummie experiment
expt = Experiment()
expt.detector = tst_nanoBragg_multipanel.whole_det
expt.beam = tst_nanoBragg_multipanel.beam
expt.crystal = tst_nanoBragg_multipanel.cryst
# write a dummie PDB file
PDB = "1234.pdb"
o = open(PDB, "w")
o.write(pdb_lines)
o.close()
# Create a miller array from on-disk PDB file
F = utils.get_complex_fcalc_from_pdb(PDB,
wavelength=expt.beam.get_wavelength(),
dmin=2, dmax=20, k_sol=0.2, b_sol=20)
F = F.as_amplitude_array()
Fmap = {h: amp for h,amp in zip(F.indices(), F.data())}
# Create a sim_data class instance as would be done for hopper_utils.refine for example
phil_scope = parse(hopper_phil+philz)
for i in range(N):
iset = isets[i]
path = iset.get_path(0)
d = df2_filt0.query("imgpaths=='%s'" % path)
if has_master:
master_index = iset.indices()[0]
d = d.query("master_indices==%d" % master_index)
if len(d) != 1:
continue
A = d.Amats.values[0]
#break
C = deepcopy(crystals[i])
#C.set_A(A)
Ex = Experiment()
Ex.crystal = C
Ex.imageset = iset
Ex.beam = beams[i]
Ex.detector = D
El2.append(Ex)
Rsel = R.select(R['id']==i)
nref = len(Rsel)
Rsel['id'] = flex.int(nref, new_id)
R2.extend(Rsel)
new_id += 1
print (new_id)
el_file = "%s.expt" % args.tag
R_file = "%s.refl" % args.tag
El2.as_file(el_file)