def save_opt_det(phil_params, x, ref_params, SIM):
opt_det = get_optimized_detector(x, ref_params, SIM)
El = ExperimentList()
E = Experiment()
E.detector = opt_det
El.append(E)
El.as_file(phil_params.geometry.optimized_detector_name)
print("Saved detector model to %s" %
phil_params.geometry.optimized_detector_name)
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)
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]
P.simulator.crystal.num_mosaicity_samples = 250 # in practive, the number of mosaic domains we model should be smaller than whats in the crystal .. .
P.simulator.crystal.has_isotropic_mosaicity = False
P.fix.eta_abc = False
E.detector = SIM.detector
E.beam = SIM.D.beam
E.imageset = make_imageset([img], E.beam, E.detector)
#refls = utils.refls_from_sims([img], E.detector, E.beam, thresh=18)
refls = utils.refls_from_sims([spots], E.detector, E.beam, thresh=18)
print("%d REFLS" % len(refls))
utils.refls_to_q(refls, E.detector, E.beam, update_table=True)
utils.refls_to_hkl(refls, E.detector, E.beam, E.crystal, update_table=True)
P.roi.shoebox_size = 20
P.relative_tilt = False
P.roi.fit_tilt = False
P.roi.pad_shoebox_for_background_estimation = 10
P.roi.reject_edge_reflections = False
P.refiner.sigma_r = SIM.D.readout_noise_adu
P.refiner.adu_per_photon = SIM.D.quantum_gain
def update_detector(x, ref_params, SIM, save=None):
"""
Update the internal geometry of the diffBragg instance
:param x: refinement parameters as seen by scipy.optimize (e.g. rescaled floats)
:param ref_params: diffBragg.refiners.Parameters (dict of RangedParameters)
:param SIM: SIM instance (instance of nanoBragg.sim_data.SimData)
:param save: optional name to save the detector
"""
det = SIM.detector
if save is not None:
new_det = Detector()
for pid in range(len(det)):
panel = det[pid]
panel_dict = panel.to_dict()
group_id = SIM.panel_group_from_id[pid]
if group_id not in SIM.panel_groups_refined:
fdet = panel.get_fast_axis()
sdet = panel.get_slow_axis()
origin = panel.get_origin()
else:
Oang_p = ref_params["group%d_RotOrth" % group_id]
Fang_p = ref_params["group%d_RotFast" % group_id]
Sang_p = ref_params["group%d_RotSlow" % group_id]
Xdist_p = ref_params["group%d_ShiftX" % group_id]
Ydist_p = ref_params["group%d_ShiftY" % group_id]
Zdist_p = ref_params["group%d_ShiftZ" % group_id]
Oang = Oang_p.get_val(x[Oang_p.xpos])
Fang = Fang_p.get_val(x[Fang_p.xpos])
Sang = Sang_p.get_val(x[Sang_p.xpos])
Xdist = Xdist_p.get_val(x[Xdist_p.xpos])
Ydist = Ydist_p.get_val(x[Ydist_p.xpos])
Zdist = Zdist_p.get_val(x[Zdist_p.xpos])
origin_of_rotation = SIM.panel_reference_from_id[pid]
SIM.D.reference_origin = origin_of_rotation
SIM.D.update_dxtbx_geoms(det,
SIM.beam.nanoBragg_constructor_beam,
pid,
Oang,
Fang,
Sang,
Xdist,
Ydist,
Zdist,
force=False)
fdet = SIM.D.fdet_vector
sdet = SIM.D.sdet_vector
origin = SIM.D.get_origin()
if save is not None:
panel_dict["fast_axis"] = fdet
panel_dict["slow_axis"] = sdet
panel_dict["origin"] = origin
new_det.add_panel(Panel.from_dict(panel_dict))
if save is not None and COMM.rank == 0:
t = time.time()
El = ExperimentList()
E = Experiment()
E.detector = new_det
El.append(E)
El.as_file(save)
t = time.time() - t
print("Saved detector model to %s (took %.4f sec)" % (save, t),
flush=True)
def convert_crystfel_to_dxtbx(geom_filename,
output_filename,
detdist_override=None):
"""
:param geom_filename: a crystfel geometry file https://www.desy.de/~twhite/crystfel/manual-crystfel_geometry.html
:param output_filename: filename for a dxtbx experiment containing a single detector model (this is a json file)
:param detdist_override: alter the detector distance stored in the crystfel geometry to this value (in millimeters)
"""
geom = load_crystfel_geometry(geom_filename)
dxtbx_det = Detector()
for panel_name in geom['panels'].keys():
P = geom['panels'][panel_name]
FAST = P['fsx'], P['fsy'], P['fsz']
SLOW = P['ssx'], P['ssy'], P['ssz']
# dxtbx uses millimeters
pixsize = 1 / P['res'] # meters
pixsize_mm = pixsize * 1000
detdist = P['coffset'] + P['clen'] # meters
detdist_mm = detdist * 1000
if detdist_override is not None:
detdist_mm = detdist_override
# dxtbx and crystfel both identify the outer corner of the first pixel in memory as the origin of the panel
origin = P['cnx'] * pixsize_mm, P[
'cny'] * pixsize_mm, -detdist_mm # dxtbx assumes crystal as at point 0,0,0
num_fast_pix = P["max_fs"] - P['min_fs'] + 1
num_slow_pix = P["max_ss"] - P['min_ss'] + 1
panel_description = {
'fast_axis': FAST,
'gain': 1.0, # I dont think nanoBragg cares about this parameter
'identifier': '',
'image_size': (num_fast_pix, num_slow_pix),
'mask': [],
'material': 'Si',
'mu': 0, # NOTE for a thick detector set this to appropriate value
'name': panel_name,
'origin': origin,
'pedestal':
0.0, # I dont think nanoBragg cares about this parameter
'pixel_size': (pixsize_mm, pixsize_mm),
'px_mm_strategy': {
'type': 'SimplePxMmStrategy'
},
'raw_image_offset': (0, 0), # not sure what this is
'slow_axis': SLOW,
'thickness':
0, # note for a thick detector set this to appropriate value
'trusted_range': (-1.0, 1e6), # set as you wish
'type': 'SENSOR_PAD'
}
dxtbx_node = Panel.from_dict(panel_description)
dxtbx_det.add_panel(dxtbx_node)
E = Experiment()
E.detector = dxtbx_det
El = ExperimentList()
El.append(E)
El.as_file(output_filename) # this can be loaded into nanoBragg
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)
print("Saved experiment %s" % el_file )
R2.as_file(R_file)
print("Saved refls %s" % R_file)