def __init__(self,
unit_cell,
map_target,
real_space_gradients_delta,
sites_frac,
map_current = None,
geometry_restraints_manager = None,
real_space_target_weight = None,
restraints_target_weight = None,
sites_cart = None,
target_type = "diffmap"):
adopt_init_args(self, locals())
assert target_type in ["simple", "diffmap"]
if(geometry_restraints_manager is not None):
assert [real_space_target_weight, restraints_target_weight, sites_cart
].count(None)==0
self.geom_tg_obj = geometry_restraints_manager.energies_sites(
sites_cart = sites_cart, compute_gradients = True)
if(target_type == "diffmap"):
assert map_current is not None
self.rsr_tg_obj = maptbx.target_and_gradients_diffmap(
unit_cell = unit_cell,
map_target = map_target,
map_current = map_current,
step = real_space_gradients_delta,
sites_frac = sites_frac)
if(target_type == "simple"):
assert sites_cart is not None
self.rsr_tg_obj = maptbx.target_and_gradients_simple(
unit_cell = unit_cell,
map_target = map_target,
sites_cart = sites_cart,
delta = real_space_gradients_delta,
selection = flex.bool(sites_cart.size(),True))
def data_target_and_grads(self, compute_gradients, x):
"""
Args:
compute_gradients: (bool) when True compute gradients
x: refined parameters
"""
g = None
tg = maptbx.target_and_gradients_simple(
unit_cell=self.unit_cell,
map_target=self.map_data,
sites_cart=self.xray_structure.sites_cart(),
delta=self.real_space_gradients_delta,
selection=self.selection)
t = tg.target() * (-1)
if compute_gradients:
if self.refine_sites:
g = tg.gradients() * (-1)
return t, g
def exercise_03():
"""
Exercise maptbx.target_and_gradients_simple.
"""
def compute_map(xray_structure, d_min=1.5, resolution_factor=1. / 4):
fc = xray_structure.structure_factors(d_min=d_min).f_calc()
fft_map = fc.fft_map(resolution_factor=resolution_factor)
fft_map.apply_sigma_scaling()
result = fft_map.real_map_unpadded()
return result, fc, fft_map
xrs = random_structure.xray_structure(
space_group_info=sgtbx.space_group_info("P212121"),
elements=["N", "C", "O", "S", "P"] * 10,
volume_per_atom=50)
map_target, tmp, tmp = compute_map(xray_structure=xrs)
xrs_sh = xrs.deep_copy_scatterers()
xrs_sh.shake_sites_in_place(mean_distance=0.8)
#
t1 = maptbx.real_space_target_simple(unit_cell=xrs.unit_cell(),
density_map=map_target,
sites_cart=xrs_sh.sites_cart(),
selection=flex.bool(
xrs_sh.scatterers().size(), True))
g1 = maptbx.real_space_gradients_simple(unit_cell=xrs.unit_cell(),
density_map=map_target,
sites_cart=xrs_sh.sites_cart(),
delta=0.25,
selection=flex.bool(
xrs_sh.scatterers().size(),
True))
o = maptbx.target_and_gradients_simple(unit_cell=xrs.unit_cell(),
map_target=map_target,
sites_cart=xrs_sh.sites_cart(),
delta=0.25,
selection=flex.bool(
xrs_sh.scatterers().size(),
True))
assert approx_equal(t1, o.target())
for gi, gj in zip(g1, o.gradients()):
assert approx_equal(gi, gj)
def compute_functional_and_gradients(O):
if (O.number_of_function_evaluations == 0):
O.number_of_function_evaluations += 1
return O.f_start, O.g_start
O.number_of_function_evaluations += 1
#
x_current = O.x
if (O.x_previous is None):
O.x_previous = x_current.deep_copy()
else:
xray.ext.damp_shifts(previous=O.x_previous,
current=x_current,
max_value=10.)
O.x_previous = x_current.deep_copy()
#
O.sites_cart_variable = flex.vec3_double(x_current)
if (O.real_space_target_weight == 0):
rs_f = 0.
rs_g = flex.vec3_double(O.sites_cart_variable.size(), (0, 0, 0))
else:
if (O.local_standard_deviations_radius is None):
if (O.gradients_method == "fd"):
o = maptbx.target_and_gradients_simple(
unit_cell=O.unit_cell,
map_target=O.density_map,
sites_cart=O.sites_cart_variable,
delta=O.real_space_gradients_delta,
selection=O.selection_variable_real_space)
else:
o = maptbx.target_and_gradients_simple(
unit_cell=O.unit_cell,
map_target=O.density_map,
sites_cart=O.sites_cart_variable,
selection=O.selection_variable_real_space,
use_quadratic_interpolation=O.
use_quadratic_interpolation)
rs_f = o.target()
rs_g = o.gradients()
else:
rs_f = local_standard_deviations_target(
unit_cell=O.unit_cell,
density_map=O.density_map,
weight_map=O.weight_map,
weight_map_scale_factor=O.weight_map_scale_factor,
sites_cart=O.sites_cart_variable,
site_radii=O.site_radii)
rs_g = local_standard_deviations_gradients(
unit_cell=O.unit_cell,
density_map=O.density_map,
weight_map=O.weight_map,
weight_map_scale_factor=O.weight_map_scale_factor,
sites_cart=O.sites_cart_variable,
site_radii=O.site_radii,
delta=O.real_space_gradients_delta)
rs_f *= -O.real_space_target_weight
rs_g *= -O.real_space_target_weight
if (O.geometry_restraints_manager is None):
f = rs_f
g = rs_g
else:
if (O.selection_variable is None):
O.sites_cart = O.sites_cart_variable
else:
O.sites_cart.set_selected(O.selection_variable,
O.sites_cart_variable)
if (O.states_collector is not None):
O.states_collector.add(sites_cart=O.sites_cart)
gr_e = O.geometry_restraints_manager.energies_sites(
sites_cart=O.sites_cart, compute_gradients=True)
gr_e_gradients = gr_e.gradients
if (O.selection_variable is not None):
gr_e_gradients = gr_e.gradients.select(O.selection_variable)
f = rs_f + gr_e.target
g = rs_g + gr_e_gradients
return f, g.as_double()
