def run():
mon_lib_srv = monomer_library.server.server()
ener_lib = monomer_library.server.ener_lib()
processed_pdb_file = monomer_library.pdb_interpretation.process(
mon_lib_srv = mon_lib_srv,
ener_lib = ener_lib,
file_name = None,
raw_records = pdb_str,
force_symmetry = True)
pdb_hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
xray_structure = processed_pdb_file.xray_structure()
pdb_hierarchy.write_pdb_file(
file_name = "distorted.pdb",
crystal_symmetry = xray_structure.crystal_symmetry())
geometry_restraints = processed_pdb_file.geometry_restraints_manager(
show_energies = False)
#geometry_restraints.write_geo_file(file_name='start.geo')
states = mmtbx.utils.states(
xray_structure = xray_structure,
pdb_hierarchy = pdb_hierarchy)
states.add(sites_cart = xray_structure.sites_cart())
riding_h_manager = riding.manager(
pdb_hierarchy = pdb_hierarchy,
geometry_restraints = geometry_restraints)
# save shaked model in states
states.add(sites_cart = xray_structure.sites_cart())
#
#xray_structure.scatterers().flags_set_grads(state=False)
xray_structure.scatterers().flags_set_grad_site(
iselection = xray_structure.all_selection().iselection())
#xray_structure.show_scatterer_flags_summary()
use_riding = True
minimized = lbfgs(
xray_structure = xray_structure,
states = states,
geometry_restraints = geometry_restraints,
riding_h_manager = riding_h_manager,
use_riding = use_riding,
verbose = 0)
minimized.states.write(
file_name = "minimized_all_states.pdb",
crystal_symmetry = xray_structure.crystal_symmetry())
pdb_hierarchy.adopt_xray_structure(minimized.xray_structure)
pdb_hierarchy.write_pdb_file(
file_name = "minimized.pdb",
crystal_symmetry = xray_structure.crystal_symmetry())
target_final = geometry_restraints.energies_sites(
sites_cart = xray_structure.sites_cart(),
compute_gradients = True).target
assert (target_final < 1.5), 'Target of final riding model is too large'
def run():
# Read and process PDB file
mon_lib_srv = monomer_library.server.server()
ener_lib = monomer_library.server.ener_lib()
processed_pdb_file = monomer_library.pdb_interpretation.process(
mon_lib_srv=mon_lib_srv,
ener_lib=ener_lib,
file_name=None,
raw_records=pdb_str,
force_symmetry=True)
pdb_hierarchy = processed_pdb_file.all_chain_proxies.pdb_hierarchy
xray_structure = processed_pdb_file.xray_structure()
#
xrs_dc = xray_structure.deep_copy_scatterers()
pdb_hierarchy.write_pdb_file(
file_name="start.pdb",
crystal_symmetry=xray_structure.crystal_symmetry())
# Create grm
geometry_restraints = processed_pdb_file.geometry_restraints_manager(
show_energies=False)
states = mmtbx.utils.states(xray_structure=xray_structure,
pdb_hierarchy=pdb_hierarchy)
states.add(sites_cart=xray_structure.sites_cart())
# shake coordinates
xray_structure.shake_sites_in_place(rms_difference=5.0)
pdb_hierarchy.adopt_xray_structure(xray_structure)
pdb_hierarchy.write_pdb_file(
file_name="distorted.pdb",
crystal_symmetry=xray_structure.crystal_symmetry())
states.add(sites_cart=xray_structure.sites_cart())
#
xray_structure.scatterers().flags_set_grads(state=False)
xray_structure.scatterers().flags_set_grad_site(
iselection=xray_structure.all_selection().iselection())
minimized = lbfgs(xray_structure=xray_structure,
states=states,
geometry_restraints=geometry_restraints,
verbose=0)
minimized.states.write(file_name="minimized_all_states.pdb",
crystal_symmetry=xray_structure.crystal_symmetry())
pdb_hierarchy.adopt_xray_structure(minimized.xray_structure)
pdb_hierarchy.write_pdb_file(
file_name="minimized.pdb",
crystal_symmetry=xray_structure.crystal_symmetry())
def exercise_constrained_lbfgs(xray_structure,
constraints_list,
t_celsius,
d_min=0.5,
shake_sites_rmsd=0.5,
shake_u_iso_spread=0,
shake_u_aniso_spread=0,
grad_site=True,
grad_u_iso=True,
grad_u_aniso=False,
grad_occupancy=False,
grad_fp_fdp=False,
lbfgs_m=5,
lbfgs_max_iterations=1000,
verbose=0):
xs = xray_structure
xray.set_scatterer_grad_flags(scatterers=xs.scatterers(),
site=grad_site,
u_iso=grad_u_iso,
u_aniso=grad_u_aniso,
occupancy=grad_occupancy,
fp=grad_fp_fdp,
fdp=grad_fp_fdp,
tan_u_iso=False,
param=0)
xs0 = xs.deep_copy_scatterers()
mi = xs0.build_miller_set(anomalous_flag=False, d_min=d_min)
fo_sq = mi.structure_factors_from_scatterers(
xs0, algorithm="direct").f_calc().norm()
fo_sq = fo_sq.customized_copy(sigmas=flex.double(fo_sq.size(), 1))
fo_sq.set_observation_type_xray_intensity()
y_obs = fo_sq
#y_obs = fo_sq.f_sq_as_f()
if grad_site:
xs.shake_sites_in_place(rms_difference=shake_sites_rmsd)
if not grad_u_aniso: shake_u_aniso_spread = 0
if not grad_u_iso: shake_u_iso_spread = 0
if grad_u_aniso or grad_u_iso:
xs.shake_adp(spread=shake_u_iso_spread,
aniso_spread=shake_u_aniso_spread)
xs1 = xs.deep_copy_scatterers()
core_params = scitbx.lbfgs.core_parameters(m=lbfgs_m,
maxfev=100,
xtol=1e-5)
connectivity_table = smtbx.utils.connectivity_table(xs0)
if constraints_list is None:
from smtbx.development import generate_hydrogen_constraints
constraints_list = generate_hydrogen_constraints(
structure=xs0, connectivity_table=connectivity_table)
reparametrisation = constraints.reparametrisation(xs,
constraints_list,
connectivity_table,
temperature=t_celsius)
lbfgs_termination_params = scitbx.lbfgs.termination_parameters(
traditional_convergence_test=False,
drop_convergence_test_max_drop_eps=1.e-20,
drop_convergence_test_iteration_coefficient=1,
min_iterations=500,
max_iterations=lbfgs_max_iterations)
minimizer = lbfgs(
target_functor=xray.target_functors.unified_least_squares_residual(
y_obs),
xray_structure=xs,
reparametrisation=reparametrisation,
structure_factor_algorithm="direct",
lbfgs_termination_params=lbfgs_termination_params,
lbfgs_core_params=core_params,
reference_structure=xs0,
verbose=verbose)
if verbose > 0:
print "Total parameters: ", xs.n_parameters()
print "Independent parameters: ", reparametrisation.n_independents
print "Reference model: "
xs0.show_angles(distance_cutoff=1.5)
print
print "Starting model: "
xs1.show_angles(distance_cutoff=1.5)
print
print "Refined model: "
xs.show_angles(distance_cutoff=1.5)
print
print "n_iter, n_fun: ", minimizer.minimizer.iter(
), minimizer.minimizer.nfun()
h_selection = xs.element_selection('H')
diff = xray.meaningful_site_cart_differences(
xs0.select(h_selection, negate=True),
xs.select(h_selection, negate=True))
#diff = xray.meaningful_site_cart_differences(xs0, xs)
#assert diff.max_absolute() < 1e-3
if verbose > 0:
diff.show()
print
assert diff.max_absolute() < 2e-2, diff.max_absolute()
diff = xray.meaningful_site_cart_differences(xs0, xs)
if verbose > 0:
diff.show()
print
# XXX why does this tolerance have to be so high?
assert diff.max_absolute() < 0.5, diff.max_absolute()
def __init__(self, gradient_only):
print "scitbx.lbfgs: use gradient_only line search: ", gradient_only
adopt_init_args(self, locals())
self.lbfgs_core_params = scitbx.lbfgs.core_parameters(
stpmin=1.e-10,
stpmax=500)
self.lbfgs_termination_params = scitbx.lbfgs.termination_parameters(
max_iterations = 6000)
self.f=None
self.g=None
self.x = flex.double(x0)
self.minimizer = scitbx.lbfgs.run(
gradient_only = gradient_only,
target_evaluator = self,
core_params = self.lbfgs_core_params,
termination_params = self.lbfgs_termination_params,
exception_handling_params = scitbx.lbfgs.exception_handling_parameters(
ignore_line_search_failed_step_at_lower_bound = True))
def compute_functional_and_gradients(self):
self.f = func(x1=self.x[0], x2 = self.x[1], x3 = self.x[2])
self.g = grad(x1=self.x[0], x2 = self.x[1], x3 = self.x[2])
return self.f, self.g
if(__name__ == "__main__"):
t=lbfgs(True)
assert approx_equal(t.x, [2, -5, 500])
t=lbfgs(False)
assert approx_equal(t.x, [2, -5, 500])
def exercise_constrained_lbfgs(xray_structure,
constraints_list,
t_celsius,
d_min=0.5,
shake_sites_rmsd=0.5,
shake_u_iso_spread=0,
shake_u_aniso_spread=0,
grad_site=True,
grad_u_iso=True,
grad_u_aniso=False,
grad_occupancy=False,
grad_fp_fdp=False,
lbfgs_m=5,
lbfgs_max_iterations=1000,
verbose=0):
xs = xray_structure
xray.set_scatterer_grad_flags(scatterers=xs.scatterers(),
site=grad_site,
u_iso=grad_u_iso,
u_aniso=grad_u_aniso,
occupancy=grad_occupancy,
fp=grad_fp_fdp,
fdp=grad_fp_fdp,
tan_u_iso=False,
param=0)
xs0 = xs.deep_copy_scatterers()
mi = xs0.build_miller_set(anomalous_flag=False, d_min=d_min)
fo_sq = mi.structure_factors_from_scatterers(
xs0, algorithm="direct").f_calc().norm()
fo_sq = fo_sq.customized_copy(sigmas=flex.double(fo_sq.size(), 1))
fo_sq.set_observation_type_xray_intensity()
y_obs = fo_sq
#y_obs = fo_sq.f_sq_as_f()
if grad_site:
xs.shake_sites_in_place(rms_difference=shake_sites_rmsd)
if not grad_u_aniso: shake_u_aniso_spread = 0
if not grad_u_iso: shake_u_iso_spread = 0
if grad_u_aniso or grad_u_iso:
xs.shake_adp(spread=shake_u_iso_spread, aniso_spread=shake_u_aniso_spread)
xs1 = xs.deep_copy_scatterers()
core_params = scitbx.lbfgs.core_parameters(m=lbfgs_m, maxfev=100, xtol=1e-5)
connectivity_table = smtbx.utils.connectivity_table(xs0)
if constraints_list is None:
from smtbx.development import generate_hydrogen_constraints
constraints_list = generate_hydrogen_constraints(
structure=xs0, connectivity_table=connectivity_table)
reparametrisation = constraints.reparametrisation(
xs,
constraints_list,
connectivity_table,
temperature=t_celsius)
lbfgs_termination_params=scitbx.lbfgs.termination_parameters(
traditional_convergence_test=False,
drop_convergence_test_max_drop_eps=1.e-20,
drop_convergence_test_iteration_coefficient=1,
min_iterations=500,
max_iterations=lbfgs_max_iterations)
minimizer = lbfgs(
target_functor=xray.target_functors.unified_least_squares_residual(y_obs),
xray_structure=xs,
reparametrisation=reparametrisation,
structure_factor_algorithm="direct",
lbfgs_termination_params=lbfgs_termination_params,
lbfgs_core_params=core_params,
reference_structure=xs0,
verbose=verbose)
if verbose > 0:
print "Total parameters: ", xs.n_parameters()
print "Independent parameters: ", reparametrisation.n_independents
print "Reference model: "
xs0.show_angles(distance_cutoff=1.5)
print
print "Starting model: "
xs1.show_angles(distance_cutoff=1.5)
print
print "Refined model: "
xs.show_angles(distance_cutoff=1.5)
print
print "n_iter, n_fun: ", minimizer.minimizer.iter(), minimizer.minimizer.nfun()
h_selection = xs.element_selection('H')
diff = xray.meaningful_site_cart_differences(
xs0.select(h_selection, negate=True),
xs.select(h_selection, negate=True))
#diff = xray.meaningful_site_cart_differences(xs0, xs)
#assert diff.max_absolute() < 1e-3
if verbose > 0:
diff.show()
print
assert diff.max_absolute() < 2e-2, diff.max_absolute()
diff = xray.meaningful_site_cart_differences(xs0, xs)
if verbose > 0:
diff.show()
print
# XXX why does this tolerance have to be so high?
assert diff.max_absolute() < 0.5, diff.max_absolute()
