def optimize_origin_offset_local_scope(self):
"""Local scope: find the optimal origin-offset closest to the current overall detector position
(local minimum, simple minimization)"""
from libtbx.test_utils import approx_equal
from rstbx.indexing_api import dps_extended
detector = self.imagesets[0].get_detector()
beam = self.imagesets[0].get_beam()
s0 = matrix.col(beam.get_s0())
# construct two vectors that are perpendicular to the beam. Gives a basis for refining beam
axis = matrix.col((1, 0, 0))
beamr0 = s0.cross(axis).normalize()
beamr1 = beamr0.cross(s0).normalize()
beamr2 = beamr1.cross(s0).normalize()
assert approx_equal(s0.dot(beamr1), 0.)
assert approx_equal(s0.dot(beamr2), 0.)
assert approx_equal(beamr2.dot(beamr1), 0.)
# so the orthonormal vectors are self.S0_vector, beamr1 and beamr2
if self.horizon_phil.indexing.mm_search_scope:
scope = self.horizon_phil.indexing.mm_search_scope
plot_px_sz = self.imagesets[0].get_detector()[0].get_pixel_size(
)[0]
plot_px_sz *= self.wide_search_binning
grid = max(1, int(scope / plot_px_sz))
widegrid = 2 * grid + 1
scores = flex.double()
for y in xrange(-grid, grid + 1):
for x in xrange(-grid, grid + 1):
new_origin_offset = x * plot_px_sz * beamr1 + y * plot_px_sz * beamr2
score = 0
for i in range(len(self.imagesets)):
score += self.get_origin_offset_score(
new_origin_offset, self.solution_lists[i],
self.amax_lists[i], self.spot_lists[i],
self.imagesets[i])
scores.append(score)
def igrid(x):
return x - (widegrid // 2)
idxs = [igrid(i) * plot_px_sz for i in xrange(widegrid)]
# if there are several similarly high scores, then choose the closest
# one to the current beam centre
potential_offsets = flex.vec3_double()
sel = scores > (0.9 * flex.max(scores))
for i in sel.iselection():
offset = (idxs[i % widegrid]) * beamr1 + (
idxs[i // widegrid]) * beamr2
potential_offsets.append(offset.elems)
#print offset.length(), scores[i]
wide_search_offset = matrix.col(potential_offsets[flex.min_index(
potential_offsets.norms())])
#plot_max = flex.max(scores)
#idx_max = flex.max_index(scores)
#wide_search_offset = (idxs[idx_max%widegrid])*beamr1 + (idxs[idx_max//widegrid])*beamr2
else:
wide_search_offset = None
# DO A SIMPLEX MINIMIZATION
from scitbx.simplex import simplex_opt
class test_simplex_method(object):
def __init__(selfOO, wide_search_offset=None):
selfOO.starting_simplex = []
selfOO.n = 2
selfOO.wide_search_offset = wide_search_offset
for ii in range(selfOO.n + 1):
selfOO.starting_simplex.append(flex.random_double(
selfOO.n))
selfOO.optimizer = simplex_opt(dimension=selfOO.n,
matrix=selfOO.starting_simplex,
evaluator=selfOO,
tolerance=1e-7)
selfOO.x = selfOO.optimizer.get_solution()
selfOO.offset = selfOO.x[0] * 0.2 * beamr1 + selfOO.x[
1] * 0.2 * beamr2
if selfOO.wide_search_offset is not None:
selfOO.offset += selfOO.wide_search_offset
def target(selfOO, vector):
trial_origin_offset = vector[0] * 0.2 * beamr1 + vector[
1] * 0.2 * beamr2
if selfOO.wide_search_offset is not None:
trial_origin_offset += selfOO.wide_search_offset
target = 0
for i in range(len(self.imagesets)):
target -= self.get_origin_offset_score(
trial_origin_offset, self.solution_lists[i],
self.amax_lists[i], self.spot_lists[i],
self.imagesets[i])
return target
MIN = test_simplex_method(wide_search_offset=wide_search_offset)
new_offset = MIN.offset
if self.horizon_phil.indexing.plot_search_scope:
scope = self.horizon_phil.indexing.mm_search_scope
plot_px_sz = self.imagesets[0].get_detector()[0].get_pixel_size(
)[0]
grid = max(1, int(scope / plot_px_sz))
scores = flex.double()
for y in xrange(-grid, grid + 1):
for x in xrange(-grid, grid + 1):
new_origin_offset = x * plot_px_sz * beamr1 + y * plot_px_sz * beamr2
score = 0
for i in range(len(self.imagesets)):
score += self.get_origin_offset_score(
new_origin_offset, self.solution_lists[i],
self.amax_lists[i], self.spot_lists[i],
self.imagesets[i])
scores.append(score)
def show_plot(widegrid, excursi):
excursi.reshape(flex.grid(widegrid, widegrid))
plot_max = flex.max(excursi)
idx_max = flex.max_index(excursi)
def igrid(x):
return x - (widegrid // 2)
idxs = [igrid(i) * plot_px_sz for i in xrange(widegrid)]
from matplotlib import pyplot as plt
plt.figure()
CS = plt.contour(
[igrid(i) * plot_px_sz for i in xrange(widegrid)],
[igrid(i) * plot_px_sz for i in xrange(widegrid)],
excursi.as_numpy_array())
plt.clabel(CS, inline=1, fontsize=10, fmt="%6.3f")
plt.title("Wide scope search for detector origin offset")
plt.scatter([0.0], [0.0], color='g', marker='o')
plt.scatter([new_offset[0]], [new_offset[1]],
color='r',
marker='*')
plt.scatter([idxs[idx_max % widegrid]],
[idxs[idx_max // widegrid]],
color='k',
marker='s')
plt.axes().set_aspect("equal")
plt.xlabel("offset (mm) along beamr1 vector")
plt.ylabel("offset (mm) along beamr2 vector")
plt.savefig("search_scope.png")
#changing value
trial_origin_offset = (idxs[idx_max % widegrid]) * beamr1 + (
idxs[idx_max // widegrid]) * beamr2
return trial_origin_offset
show_plot(widegrid=2 * grid + 1, excursi=scores)
return dps_extended.get_new_detector(self.imagesets[0].get_detector(),
new_offset)
def run(args):
assert args in [[], ["--verbose"]]
if (len(args) != 0):
cout = sys.stdout
else:
cout = null_out()
edge_list_bonds = [(0,1),(0,4),(1,2),(2,3),(3,4)]
bond_list = [
(("C1*", "C2*"), 1.529),
(("C1*", "O4*"), 1.412),
(("C2*", "C3*"), 1.526),
(("C3*", "C4*"), 1.520),
(("C4*", "O4*"), 1.449)]
angle_list = [
(("C1*", "C2*", "C3*"), 101.3),
(("C2*", "C3*", "C4*"), 102.3),
(("C3*", "C4*", "O4*"), 104.2),
(("C4*", "O4*", "C1*"), 110.0)]
sites_cart, geo_manager = cctbx.geometry_restraints.manager \
.construct_non_crystallographic_conserving_bonds_and_angles(
sites_cart=sites_cart_3p,
edge_list_bonds=edge_list_bonds,
edge_list_angles=[])
for bond_atom_names,distance_ideal in bond_list:
i,j = [atom_names.index(atom_name) for atom_name in bond_atom_names]
bond_params = geo_manager.bond_params_table[i][j]
assert approx_equal(bond_params.distance_ideal, distance_ideal, eps=1.e-2)
bond_params.distance_ideal = distance_ideal
bond_params.weight = 1/0.02**2
assert geo_manager.angle_proxies is None
geo_manager.angle_proxies = cctbx.geometry_restraints.shared_angle_proxy()
for angle_atom_names,angle_ideal in angle_list:
i_seqs = [atom_names.index(atom_name) for atom_name in angle_atom_names]
geo_manager.angle_proxies.append(cctbx.geometry_restraints.angle_proxy(
i_seqs=i_seqs,
angle_ideal=angle_ideal,
weight=1/3**2))
geo_manager.show_sorted(
site_labels=atom_names, sites_cart=sites_cart, f=cout)
def lbfgs(sites_cart):
for i_lbfgs_restart in xrange(3):
minimized = cctbx.geometry_restraints.lbfgs.lbfgs(
sites_cart=sites_cart,
geometry_restraints_manager=geo_manager)
assert is_below_limit(value=minimized.final_target_value, limit=1e-10)
return minimized
lbfgs(sites_cart=sites_cart_3p)
lbfgs(sites_cart=sites_cart_2p)
conformer_counts = [0] * 4
sites_cart = sites_cart.deep_copy()
mt = flex.mersenne_twister(seed=0)
for i_trial in xrange(20):
while True:
for i in xrange(sites_cart.size()):
sites_cart[i] = mt.random_double_point_on_sphere()
try:
lbfgs(sites_cart=sites_cart)
except RuntimeError, e:
if (not str(e).startswith(
"Bond distance > max_reasonable_bond_distance: ")):
raise
else:
break
rmsd_list = flex.double()
for reference_sites in [
sites_cart_3p,
sites_cart_2p,
sites_cart_a,
sites_cart_b]:
sup = scitbx.math.superpose.least_squares_fit(
reference_sites=reference_sites,
other_sites=sites_cart)
rmsd = reference_sites.rms_difference(sup.other_sites_best_fit())
rmsd_list.append(rmsd)
oline = " ".join(["%.3f" % rmsd for rmsd in rmsd_list])
print >> cout, oline
assert is_below_limit(min(rmsd_list), 1e-3)
conformer_counts[flex.min_index(rmsd_list)] += 1
def run(args):
assert args in [[], ["--verbose"]]
if (len(args) != 0):
cout = sys.stdout
else:
cout = null_out()
edge_list_bonds = [(0, 1), (0, 4), (1, 2), (2, 3), (3, 4)]
bond_list = [(("C1*", "C2*"), 1.529), (("C1*", "O4*"), 1.412),
(("C2*", "C3*"), 1.526), (("C3*", "C4*"), 1.520),
(("C4*", "O4*"), 1.449)]
angle_list = [
(("C1*", "C2*", "C3*"), 101.3), (("C2*", "C3*", "C4*"), 102.3),
(("C3*", "C4*", "O4*"), 104.2), (("C4*", "O4*", "C1*"), 110.0)
]
sites_cart, geo_manager = cctbx.geometry_restraints.manager \
.construct_non_crystallographic_conserving_bonds_and_angles(
sites_cart=sites_cart_3p,
edge_list_bonds=edge_list_bonds,
edge_list_angles=[])
for bond_atom_names, distance_ideal in bond_list:
i, j = [atom_names.index(atom_name) for atom_name in bond_atom_names]
bond_params = geo_manager.bond_params_table[i][j]
assert approx_equal(bond_params.distance_ideal,
distance_ideal,
eps=1.e-2)
bond_params.distance_ideal = distance_ideal
bond_params.weight = 1 / 0.02**2
assert geo_manager.angle_proxies is None
geo_manager.angle_proxies = cctbx.geometry_restraints.shared_angle_proxy()
for angle_atom_names, angle_ideal in angle_list:
i_seqs = [
atom_names.index(atom_name) for atom_name in angle_atom_names
]
geo_manager.angle_proxies.append(
cctbx.geometry_restraints.angle_proxy(i_seqs=i_seqs,
angle_ideal=angle_ideal,
weight=1 / 3**2))
geo_manager.show_sorted(site_labels=atom_names,
sites_cart=sites_cart,
f=cout)
def lbfgs(sites_cart):
for i_lbfgs_restart in range(3):
minimized = cctbx.geometry_restraints.lbfgs.lbfgs(
sites_cart=sites_cart, geometry_restraints_manager=geo_manager)
assert is_below_limit(value=minimized.final_target_value,
limit=1e-10)
return minimized
lbfgs(sites_cart=sites_cart_3p)
lbfgs(sites_cart=sites_cart_2p)
conformer_counts = [0] * 4
sites_cart = sites_cart.deep_copy()
mt = flex.mersenne_twister(seed=0)
for i_trial in range(20):
while True:
for i in range(sites_cart.size()):
sites_cart[i] = mt.random_double_point_on_sphere()
try:
lbfgs(sites_cart=sites_cart)
except RuntimeError as e:
if (not str(e).startswith(
"Bond distance > max_reasonable_bond_distance: ")):
raise
else:
break
rmsd_list = flex.double()
for reference_sites in [
sites_cart_3p, sites_cart_2p, sites_cart_a, sites_cart_b
]:
sup = scitbx.math.superpose.least_squares_fit(
reference_sites=reference_sites, other_sites=sites_cart)
rmsd = reference_sites.rms_difference(sup.other_sites_best_fit())
rmsd_list.append(rmsd)
oline = " ".join(["%.3f" % rmsd for rmsd in rmsd_list])
print(oline, file=cout)
assert is_below_limit(min(rmsd_list), 1e-3)
conformer_counts[flex.min_index(rmsd_list)] += 1
print("conformer_counts:", conformer_counts)
#
if (libtbx.env.has_module("iotbx")):
import iotbx.pdb.hierarchy
hierarchy = iotbx.pdb.hierarchy.root()
model = iotbx.pdb.hierarchy.model(id="")
chain = iotbx.pdb.hierarchy.chain(id="A")
model.append_chain(chain)
hierarchy.append_model(model)
#
sites_cart_pentagon = pentagon_sites_cart()
for i_stack, sites_cart in enumerate(
[sites_cart_3p, sites_cart_2p, sites_cart_a, sites_cart_b]):
atom_group = iotbx.pdb.hierarchy.atom_group(resname=" U",
altloc="")
sup = scitbx.math.superpose.least_squares_fit(
reference_sites=sites_cart_pentagon, other_sites=sites_cart)
sites_cart_out = sup.other_sites_best_fit()
for site_label, site_cart in zip(atom_names, sites_cart_out):
atom = iotbx.pdb.hierarchy.atom()
atom.name = " %-3s" % site_label
atom.xyz = matrix.col(site_cart) + matrix.col(
(0, 0, i_stack * 1.5))
atom.occ = 1
atom.b = 20
atom.element = " " + site_label[0]
atom_group.append_atom(atom)
residue_group = iotbx.pdb.hierarchy.residue_group(resseq="%4d" %
(i_stack + 1),
icode=" ")
residue_group.append_atom_group(atom_group)
chain.append_residue_group(residue_group)
hierarchy.atoms().reset_serial()
pdb_str = hierarchy.as_pdb_string(append_end=True)
file_name = "puckers.pdb"
print("Writing file:", file_name)
open(file_name, "w").write("""\
REMARK random_puckers.py
REMARK 1 = 3'
REMARK 2 = 2'
REMARK 3 = A
REMARK 4 = B
""" + pdb_str)
#
print("OK")
def __init__(self, hex_cell, vertices_cart):
assert len(vertices_cart) > 0
vertices_hex = hex_cell.fractionalize(sites_cart=vertices_cart)
self.min = vertices_hex.min()
self.max = vertices_hex.max()
self.pivot = vertices_hex[flex.min_index(vertices_hex.dot())]
def __init__(self, hex_cell, vertices_cart): assert len(vertices_cart) > 0 vertices_hex = hex_cell.fractionalize(sites_cart=vertices_cart) self.min = vertices_hex.min() self.max = vertices_hex.max() self.pivot = vertices_hex[flex.min_index(vertices_hex.dot())]
def optimize_origin_offset_local_scope(self):
"""Local scope: find the optimal origin-offset closest to the current overall detector position
(local minimum, simple minimization)"""
from libtbx.test_utils import approx_equal
from rstbx.indexing_api import dps_extended
detector = self.imagesets[0].get_detector()
beam = self.imagesets[0].get_beam()
s0 = matrix.col(beam.get_s0())
# construct two vectors that are perpendicular to the beam. Gives a basis for refining beam
axis = matrix.col((1,0,0))
beamr0 = s0.cross(axis).normalize()
beamr1 = beamr0.cross(s0).normalize()
beamr2 = beamr1.cross(s0).normalize()
assert approx_equal(s0.dot(beamr1), 0.)
assert approx_equal(s0.dot(beamr2), 0.)
assert approx_equal(beamr2.dot(beamr1), 0.)
# so the orthonormal vectors are self.S0_vector, beamr1 and beamr2
if self.horizon_phil.indexing.mm_search_scope:
scope = self.horizon_phil.indexing.mm_search_scope
plot_px_sz = self.imagesets[0].get_detector()[0].get_pixel_size()[0]
plot_px_sz *= self.wide_search_binning
grid = max(1,int(scope/plot_px_sz))
widegrid = 2 * grid + 1
scores = flex.double()
for y in xrange(-grid,grid+1):
for x in xrange(-grid,grid+1):
new_origin_offset = x*plot_px_sz*beamr1 + y*plot_px_sz*beamr2
score = 0
for i in range(len(self.imagesets)):
score += self.get_origin_offset_score(new_origin_offset,
self.solution_lists[i],
self.amax_lists[i],
self.spot_lists[i],
self.imagesets[i])
scores.append(score)
def igrid(x): return x - (widegrid//2)
idxs = [igrid(i)*plot_px_sz for i in xrange(widegrid)]
# if there are several similarly high scores, then choose the closest
# one to the current beam centre
potential_offsets = flex.vec3_double()
sel = scores > (0.9*flex.max(scores))
for i in sel.iselection():
offset = (idxs[i%widegrid])*beamr1 + (idxs[i//widegrid])*beamr2
potential_offsets.append(offset.elems)
#print offset.length(), scores[i]
wide_search_offset = matrix.col(
potential_offsets[flex.min_index(potential_offsets.norms())])
#plot_max = flex.max(scores)
#idx_max = flex.max_index(scores)
#wide_search_offset = (idxs[idx_max%widegrid])*beamr1 + (idxs[idx_max//widegrid])*beamr2
else:
wide_search_offset = None
# DO A SIMPLEX MINIMIZATION
from scitbx.simplex import simplex_opt
class test_simplex_method(object):
def __init__(selfOO, wide_search_offset=None):
selfOO.starting_simplex=[]
selfOO.n = 2
selfOO.wide_search_offset = wide_search_offset
for ii in range(selfOO.n+1):
selfOO.starting_simplex.append(flex.random_double(selfOO.n))
selfOO.optimizer = simplex_opt(dimension=selfOO.n,
matrix=selfOO.starting_simplex,
evaluator=selfOO,
tolerance=1e-7)
selfOO.x = selfOO.optimizer.get_solution()
selfOO.offset = selfOO.x[0]*0.2*beamr1 + selfOO.x[1]*0.2*beamr2
if selfOO.wide_search_offset is not None:
selfOO.offset += selfOO.wide_search_offset
def target(selfOO, vector):
trial_origin_offset = vector[0]*0.2*beamr1 + vector[1]*0.2*beamr2
if selfOO.wide_search_offset is not None:
trial_origin_offset += selfOO.wide_search_offset
target = 0
for i in range(len(self.imagesets)):
target -= self.get_origin_offset_score(trial_origin_offset,
self.solution_lists[i],
self.amax_lists[i],
self.spot_lists[i],
self.imagesets[i])
return target
MIN = test_simplex_method(wide_search_offset=wide_search_offset)
new_offset = MIN.offset
if self.horizon_phil.indexing.plot_search_scope:
scope = self.horizon_phil.indexing.mm_search_scope
plot_px_sz = self.imagesets[0].get_detector()[0].get_pixel_size()[0]
grid = max(1,int(scope/plot_px_sz))
scores = flex.double()
for y in xrange(-grid,grid+1):
for x in xrange(-grid,grid+1):
new_origin_offset = x*plot_px_sz*beamr1 + y*plot_px_sz*beamr2
score = 0
for i in range(len(self.imagesets)):
score += self.get_origin_offset_score(new_origin_offset,
self.solution_lists[i],
self.amax_lists[i],
self.spot_lists[i],
self.imagesets[i])
scores.append(score)
def show_plot(widegrid,excursi):
excursi.reshape(flex.grid(widegrid, widegrid))
plot_max = flex.max(excursi)
idx_max = flex.max_index(excursi)
def igrid(x): return x - (widegrid//2)
idxs = [igrid(i)*plot_px_sz for i in xrange(widegrid)]
from matplotlib import pyplot as plt
plt.figure()
CS = plt.contour([igrid(i)*plot_px_sz for i in xrange(widegrid)],
[igrid(i)*plot_px_sz for i in xrange(widegrid)], excursi.as_numpy_array())
plt.clabel(CS, inline=1, fontsize=10, fmt="%6.3f")
plt.title("Wide scope search for detector origin offset")
plt.scatter([0.0],[0.0],color='g',marker='o')
plt.scatter([new_offset[0]] , [new_offset[1]],color='r',marker='*')
plt.scatter([idxs[idx_max%widegrid]] , [idxs[idx_max//widegrid]],color='k',marker='s')
plt.axes().set_aspect("equal")
plt.xlabel("offset (mm) along beamr1 vector")
plt.ylabel("offset (mm) along beamr2 vector")
plt.show()
#changing value
trial_origin_offset = (idxs[idx_max%widegrid])*beamr1 + (idxs[idx_max//widegrid])*beamr2
return trial_origin_offset
show_plot(widegrid = 2 * grid + 1, excursi = scores)
return dps_extended.get_new_detector(self.imagesets[0].get_detector(), new_offset)
