def do_index(reciprocal_space_vectors, verbose=True):
D = dps_core()
D.setMaxcell(algorithm_parameters.max_cell_edge_for_dps_fft)
D.setXyzData(reciprocal_space_vectors)
hemisphere_shortcut(ai = D,
characteristic_sampling = algorithm_parameters.directional_sampling_granularity,
max_cell = algorithm_parameters.max_cell_edge_basis_choice)
M = SelectBasisMetaprocedure(D)
from rstbx.dps_core.lepage import iotbx_converter
L = iotbx_converter(D.getOrientation().unit_cell().minimum_cell(),5.0)
supergroup = L[0]
triclinic = D.getOrientation().unit_cell()
cb_op = supergroup['cb_op_inp_best'].c().as_double_array()[0:9]
orient = D.getOrientation()
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(supergroup['system'])
D.setOrientation(constrain_orient)
if verbose:
for subgroup in L:
print subgroup.short_digest()
print "\ntriclinic cell=%s volume(A^3)=%.3f"%(triclinic,triclinic.volume())
print "\nafter symmetrizing to %s:"%supergroup.reference_lookup_symbol()
M.show_rms()
return D,L
def do_index(reciprocal_space_vectors, verbose=True):
D = dps_core()
D.setMaxcell(algorithm_parameters.max_cell_edge_for_dps_fft)
D.setXyzData(reciprocal_space_vectors)
hemisphere_shortcut(
ai=D,
characteristic_sampling=algorithm_parameters.
directional_sampling_granularity,
max_cell=algorithm_parameters.max_cell_edge_basis_choice)
M = SelectBasisMetaprocedure(D)
from rstbx.dps_core.lepage import iotbx_converter
L = iotbx_converter(D.getOrientation().unit_cell().minimum_cell(), 5.0)
supergroup = L[0]
triclinic = D.getOrientation().unit_cell()
cb_op = supergroup['cb_op_inp_best'].c().as_double_array()[0:9]
orient = D.getOrientation()
orient_best = orient.change_basis(matrix.sqr(cb_op).transpose())
constrain_orient = orient_best.constrain(supergroup['system'])
D.setOrientation(constrain_orient)
if verbose:
for subgroup in L:
print subgroup.short_digest()
print "\ntriclinic cell=%s volume(A^3)=%.3f" % (triclinic,
triclinic.volume())
print "\nafter symmetrizing to %s:" % supergroup.reference_lookup_symbol(
)
M.show_rms()
return D, L
def index_wrapper(positions, info, pdb_object):
from rstbx.dps_core import dps_core
from rstbx.dps_core.sampling import HemisphereSamplerBase as HemisphereSampler
from cctbx.uctbx import unit_cell
uc = pdb_object.unit_cell()
sampling = get_recommended_sampling(pdb_object, info, positions)
raw_spot_input = flex.vec3_double()
pixel_sz = info.D.pixel_sz
for pos in positions:
raw_spot_input.append((pos[0] * pixel_sz, pos[1] * pixel_sz, 0.0))
#convert raw film to camera, using labelit coordinate convention
camdata = flex.vec3_double()
auxbeam = col((info.C.Ybeam, info.C.Zbeam, 0.0))
film_2_camera = sqr((-1, 0, 0, 0, -1, 0, 0, 0, 1)).inverse()
for x in range(len(raw_spot_input)):
camdata.append(auxbeam + film_2_camera * col(raw_spot_input[x]))
#convert camera to reciprocal space xyz coordinates
xyzdata = flex.vec3_double()
for x in range(len(camdata)):
cam = col(camdata[x])
auxpoint = col((cam[0], cam[1], info.C.distance))
xyz = (auxpoint / (info.C.lambda0 * 1E10 * auxpoint.length()))
xyz = xyz - col((0.0, 0.0, 1.0 / (info.C.lambda0 * 1E10)))
#translate recip. origin
xyzdata.append(xyz)
core_ai = dps_core()
core_ai.setXyzData(xyzdata)
core_ai.setMaxcell(1.25 * max(uc.parameters()[0:3]))
H = HemisphereSampler(characteristic_grid=sampling,
max_cell=1.25 * max(uc.parameters()[0:3]))
H.hemisphere(core_ai, size=30,
cutoff_divisor=4.) # never change these parameters
from rstbx.dps_core.basis_choice import SelectBasisMetaprocedure as SBM
M = SBM(core_ai)
return core_ai, uc
def index_wrapper(positions,info,pdb_object):
from rstbx.dps_core import dps_core
from rstbx.dps_core.sampling import HemisphereSamplerBase as HemisphereSampler
from cctbx.uctbx import unit_cell
uc = pdb_object.unit_cell()
sampling = get_recommended_sampling(pdb_object, info, positions)
raw_spot_input = flex.vec3_double()
pixel_sz = info.D.pixel_sz
for pos in positions:
raw_spot_input.append((pos[0]*pixel_sz, pos[1]*pixel_sz, 0.0))
#convert raw film to camera, using labelit coordinate convention
camdata = flex.vec3_double()
auxbeam = col((info.C.Ybeam,info.C.Zbeam,0.0));
film_2_camera = sqr((-1,0,0,0,-1,0,0,0,1)).inverse();
for x in xrange(len(raw_spot_input)):
camdata.append( auxbeam + film_2_camera * col(raw_spot_input[x]) )
#convert camera to reciprocal space xyz coordinates
xyzdata = flex.vec3_double()
for x in xrange(len(camdata)):
cam = col(camdata[x])
auxpoint = col((cam[0],cam[1],info.C.distance));
xyz = ( auxpoint / (info.C.lambda0*1E10 * auxpoint.length()) );
xyz = xyz - col((0.0, 0.0, 1.0/(info.C.lambda0*1E10))); #translate recip. origin
xyzdata.append( xyz );
core_ai = dps_core()
core_ai.setXyzData(xyzdata)
core_ai.setMaxcell(1.25*max(uc.parameters()[0:3]))
H = HemisphereSampler(
characteristic_grid = sampling,
max_cell=1.25*max(uc.parameters()[0:3]))
H.hemisphere(core_ai,size=30,cutoff_divisor=4.) # never change these parameters
from rstbx.dps_core.basis_choice import SelectBasisMetaprocedure as SBM
M = SBM(core_ai)
return core_ai,uc