def gradients(self):
return -1. * maptbx.real_space_gradients_simple(
unit_cell=self.unit_cell,
density_map=self.density_map,
sites_cart=self.sites_cart,
delta=self.real_space_gradients_delta,
selection=self.selection)
def _compute_gradients(self):
return -1.*maptbx.real_space_gradients_simple(
unit_cell = self.unit_cell,
density_map = self.target_map,
sites_cart = self.sites_cart,
delta = self.real_space_gradients_delta,
selection = flex.bool(self.sites_cart.size(), True))
def _compute_gradients(self):
return -1. * maptbx.real_space_gradients_simple(
unit_cell=self.unit_cell,
density_map=self.target_map,
sites_cart=self.sites_cart,
delta=self.real_space_gradients_delta,
selection=flex.bool(self.sites_cart.size(), True))
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
O.sites_cart_residue = flex.vec3_double(O.x)
rs_f = maptbx.real_space_target_simple(
unit_cell=O.unit_cell,
density_map=O.density_map,
sites_cart=O.sites_cart_residue,
selection=flex.bool(O.sites_cart_residue.size(),True))
O.real_space_target = rs_f
rs_g = maptbx.real_space_gradients_simple(
unit_cell=O.unit_cell,
density_map=O.density_map,
sites_cart=O.sites_cart_residue,
delta=O.real_space_gradients_delta,
selection=flex.bool(O.sites_cart_residue.size(),True))
O.rs_f = rs_f
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:
O.sites_cart_all.set_selected(O.residue_i_seqs, O.sites_cart_residue)
gr_e = O.geometry_restraints_manager.energies_sites(
sites_cart=O.sites_cart_all, compute_gradients=True)
f = rs_f + gr_e.target
g = rs_g + gr_e.gradients.select(indices=O.residue_i_seqs)
return f, g.as_double()
def gradients(self):
return -1.*maptbx.real_space_gradients_simple(
unit_cell = self.unit_cell,
density_map = self.density_map,
sites_cart = self.sites_cart,
delta = self.real_space_gradients_delta,
selection = self.selection)
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
O.sites_cart_variable = flex.vec3_double(O.x)
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):
rs_f = maptbx.real_space_target_simple(
unit_cell = O.unit_cell,
density_map = O.density_map,
sites_cart = O.sites_cart_variable,
selection = O.selection_variable_real_space)
rs_g = maptbx.real_space_gradients_simple(
unit_cell = O.unit_cell,
density_map = O.density_map,
sites_cart = O.sites_cart_variable,
delta = O.real_space_gradients_delta,
selection = O.selection_variable_real_space)
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()
def e_pot(O, sites_moved):
if ( O.last_sites_moved is None
or O.last_sites_moved.id() is not sites_moved.id()):
O.last_sites_moved = sites_moved
sites_cart = sites_moved
#
if (O.reduced_geo_manager is None):
flags = None
if (O.orca_experiments):
flags = cctbx.geometry_restraints.flags.flags(
nonbonded=False, default=True)
else:
# computing nonbonded interactions only with geo_manager,
# contributions from other restraints below with reduced_geo_manager
flags = cctbx.geometry_restraints.flags.flags(
nonbonded=True, default=False)
geo_energies = O.geo_manager.energies_sites(
sites_cart=sites_cart,
flags=flags,
custom_nonbonded_function=O.custom_nonbonded_function,
compute_gradients=True)
if (0): # XXX orca_experiments
print "geo_energies:"
geo_energies.show()
if (0): # XXX orca_experiments
O.geo_manager.show_sorted(site_labels=O.site_labels)
O.f = geo_energies.target
O.g = geo_energies.gradients
if (O.reduced_geo_manager is not None):
reduced_geo_energies = O.reduced_geo_manager.energies_sites(
sites_cart=sites_cart,
compute_gradients=True)
O.f += reduced_geo_energies.target
O.g += reduced_geo_energies.gradients
O.last_grms = group_args(geo=flex.mean_sq(O.g.as_double())**0.5)
#
if (O.density_map is not None):
rs_f = maptbx.real_space_target_simple(
unit_cell=O.geo_manager.crystal_symmetry.unit_cell(),
density_map=O.density_map,
sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(),True))
rs_g = maptbx.real_space_gradients_simple(
unit_cell=O.geo_manager.crystal_symmetry.unit_cell(),
density_map=O.density_map,
sites_cart=sites_cart,
delta=O.real_space_gradients_delta,
selection=flex.bool(sites_cart.size(),True))
rs_f *= -O.real_space_target_weight
rs_g *= -O.real_space_target_weight
O.f += rs_f
O.g += rs_g
O.last_grms.real = flex.mean_sq(rs_g.as_double())**0.5
O.last_grms.real_or_xray = "real"
#
O.last_grms.total = flex.mean_sq(O.g.as_double())**0.5
return O.f
def check():
map = flex.double(flex.grid(22,26,36).set_focus(22,26,34))
site_frac = [i/n for i,n in zip(grid_point, map.focus())]
sites_cart = flex.vec3_double([uc.orthogonalize(site_frac)])
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, 0)
terms = maptbx.real_space_target_simple_per_site(
unit_cell=uc, density_map=map, sites_cart=sites_cart)
assert approx_equal(terms, [0])
grads = maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=0.1,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(grads, [(0,0,0)])
grid_point_mod = [i%n for i,n in zip(grid_point, map.focus())]
map[grid_point_mod] = 1
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, 1)
terms = maptbx.real_space_target_simple_per_site(
unit_cell=uc, density_map=map, sites_cart=sites_cart)
assert approx_equal(terms, [1])
grads = maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=0.1,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(grads, [(0,0,0)])
i,j,k = grid_point_mod
u,v,w = map.focus()
map[((i+1)%u,j,k)] = 0.3
map[(i,(j+1)%v,k)] = 0.5
map[(i,j,(k+1)%w)] = 0.7
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, 1)
for delta in [0.1, 0.2]:
grads = maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=delta,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(grads, [(0.3,0.5,0.7)])
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 exercise_real_space_gradients_simple(timing):
uc = uctbx.unit_cell((11,13,17))
def check():
map = flex.double(flex.grid(22,26,36).set_focus(22,26,34))
site_frac = [i/n for i,n in zip(grid_point, map.focus())]
sites_cart = flex.vec3_double([uc.orthogonalize(site_frac)])
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, 0)
terms = maptbx.real_space_target_simple_per_site(
unit_cell=uc, density_map=map, sites_cart=sites_cart)
assert approx_equal(terms, [0])
grads = maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=0.1,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(grads, [(0,0,0)])
grid_point_mod = [i%n for i,n in zip(grid_point, map.focus())]
map[grid_point_mod] = 1
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, 1)
terms = maptbx.real_space_target_simple_per_site(
unit_cell=uc, density_map=map, sites_cart=sites_cart)
assert approx_equal(terms, [1])
grads = maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=0.1,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(grads, [(0,0,0)])
i,j,k = grid_point_mod
u,v,w = map.focus()
map[((i+1)%u,j,k)] = 0.3
map[(i,(j+1)%v,k)] = 0.5
map[(i,j,(k+1)%w)] = 0.7
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, 1)
for delta in [0.1, 0.2]:
grads = maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=delta,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(grads, [(0.3,0.5,0.7)])
for grid_point in [(0,0,0), (3,4,5), (-3,15,20)]:
check()
for i_trial in xrange(10):
grid_point = [random.randrange(-100,100) for i in [0,1,2]]
check()
if (timing): n = 1000000
else: n = 10
sites_cart = flex.vec3_double(flex.random_double(size=n*3)*40-20)
map = flex.double(flex.grid(22,26,36).set_focus(22,26,34), 1)
target = maptbx.real_space_target_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart,
selection=flex.bool(sites_cart.size(), True))
assert approx_equal(target, n)
t0 = time.time()
maptbx.real_space_gradients_simple(
unit_cell=uc, density_map=map, sites_cart=sites_cart, delta=0.1,
selection=flex.bool(sites_cart.size(), True))
tm = time.time() - t0
msg = "real_space_gradients_simple: %.2f s / %d sites" % (tm, n)
if (tm >= 0.01): msg += ", %.0f sites / s" % (n / tm)
if (timing): print msg