def exercise_grad_u_transformations():
uc = uctbx.unit_cell((12.296512479074615, 15.985466222796999, 20.904071214426843, 83.0, 109.0, 129.0))
grad_u_star = (
1681615.347859645,
1740095.5140965185,
2212142.6517873215,
-2802250.0492254314,
-1934508.748421974,
1503834.0901063806,
)
grad_u_cart = adptbx.grad_u_star_as_u_cart(uc, grad_u_star)
assert approx_equal(
grad_u_cart,
(
11121.484290152286,
3713.1538936460488,
4293.4422553333607,
-332.12536958297818,
-340.3709419122527,
406.25522344235526,
),
)
assert approx_equal(grad_u_star, adptbx.grad_u_cart_as_u_star(uc, grad_u_cart))
grad_u_star_array = flex.sym_mat3_double([grad_u_star] * 3)
grad_u_cart_array = adptbx.grad_u_star_as_u_cart(uc, grad_u_star_array)
for g in grad_u_cart_array:
assert approx_equal(grad_u_cart, g)
grad_u_star_array = adptbx.grad_u_cart_as_u_star(uc, grad_u_cart_array)
for g in grad_u_star_array:
assert approx_equal(grad_u_star, g)
def exercise_grad_u_transformations():
uc = uctbx.unit_cell((12.296512479074615, 15.985466222796999,
20.904071214426843, 83.0, 109.0, 129.0))
grad_u_star = (1681615.347859645, 1740095.5140965185, 2212142.6517873215,
-2802250.0492254314, -1934508.748421974, 1503834.0901063806)
grad_u_cart = adptbx.grad_u_star_as_u_cart(uc, grad_u_star)
assert approx_equal(
grad_u_cart,
(11121.484290152286, 3713.1538936460488, 4293.4422553333607,
-332.12536958297818, -340.3709419122527, 406.25522344235526))
assert approx_equal(grad_u_star,
adptbx.grad_u_cart_as_u_star(uc, grad_u_cart))
grad_u_star_array = flex.sym_mat3_double([grad_u_star] * 3)
grad_u_cart_array = adptbx.grad_u_star_as_u_cart(uc, grad_u_star_array)
for g in grad_u_cart_array:
assert approx_equal(grad_u_cart, g)
grad_u_star_array = adptbx.grad_u_cart_as_u_star(uc, grad_u_cart_array)
for g in grad_u_star_array:
assert approx_equal(grad_u_star, g)
def __init__(self,
xray_structure,
restraints_manager,
use_hd,
selection=None):
# Pairwise ADP restraints: 3 mix cases supported:
# o - ()
# o - o
# () - ()
# In SHELX this called SIMU restraints
unit_cell = xray_structure.unit_cell()
n_grad_u_iso = xray_structure.n_grad_u_iso()
u_cart = xray_structure.scatterers().extract_u_cart(unit_cell)
u_iso = xray_structure.scatterers().extract_u_iso()
scatterers = xray_structure.scatterers()
sites_cart = xray_structure.sites_cart()
if (selection is not None):
sites_cart = sites_cart.select(selection)
bond_proxies_simple = restraints_manager.pair_proxies(
sites_cart=sites_cart).bond_proxies.simple
if (selection is None):
selection = flex.bool(scatterers.size(), True)
hd_selection = xray_structure.hd_selection()
result = eval_adp_aniso_restraints(scatterers=scatterers,
u_cart=u_cart,
u_iso=u_iso,
bond_proxies=bond_proxies_simple,
selection=selection,
hd_selection=hd_selection,
n_grad_u_iso=n_grad_u_iso,
use_hd=use_hd)
self.target = result.target
self.number_of_restraints = result.number_of_restraints
if (n_grad_u_iso == 0):
self.gradients_iso = None
else:
self.gradients_iso = result.gradients_iso()
self.gradients_aniso_cart = result.gradients_aniso_cart()
self.gradients_aniso_star = adptbx.grad_u_cart_as_u_star(
unit_cell, self.gradients_aniso_cart)
def __init__(self, xray_structure, restraints_manager, use_hd,
selection = None):
# Pairwise ADP restraints: 3 mix cases supported:
# o - ()
# o - o
# () - ()
# In SHELX this called SIMU restraints
unit_cell = xray_structure.unit_cell()
n_grad_u_iso = xray_structure.n_grad_u_iso()
u_cart = xray_structure.scatterers().extract_u_cart(unit_cell)
u_iso = xray_structure.scatterers().extract_u_iso()
scatterers = xray_structure.scatterers()
sites_cart = xray_structure.sites_cart()
if(selection is not None):
sites_cart = sites_cart.select(selection)
bond_proxies_simple = restraints_manager.pair_proxies(sites_cart =
sites_cart).bond_proxies.simple
if(selection is None):
selection = flex.bool(scatterers.size(), True)
hd_selection = xray_structure.hd_selection()
result = eval_adp_aniso_restraints(
scatterers=scatterers,
u_cart=u_cart,
u_iso=u_iso,
bond_proxies=bond_proxies_simple,
selection=selection,
hd_selection=hd_selection,
n_grad_u_iso=n_grad_u_iso,
use_hd=use_hd)
self.target = result.target
self.number_of_restraints = result.number_of_restraints
if (n_grad_u_iso == 0) :
self.gradients_iso = None
else :
self.gradients_iso = result.gradients_iso()
self.gradients_aniso_cart = result.gradients_aniso_cart()
self.gradients_aniso_star = adptbx.grad_u_cart_as_u_star(unit_cell,
self.gradients_aniso_cart)
def u_star(structure_ideal, d_min, f_obs, verbose=0):
sh = shifted_u_star(f_obs, structure_ideal, 0, 0, 0.0001)
if (0 or verbose):
print "u_star"
sh.structure_shifted.show_summary().show_scatterers()
print
ls = xray.targets_least_squares_residual(
f_obs.data(), sh.f_calc.data(), True, 1)
gradient_flags = randomize_gradient_flags(
xray.structure_factors.gradient_flags(u_aniso=True),
f_obs.anomalous_flag())
xray.set_scatterer_grad_flags(scatterers = sh.structure_shifted.scatterers(),
site = gradient_flags.site,
u_iso = gradient_flags.u_iso,
u_aniso = gradient_flags.u_aniso,
occupancy = gradient_flags.occupancy,
fp = gradient_flags.fp,
fdp = gradient_flags.fdp)
grad_flags_counts = xray.scatterer_grad_flags_counts(sh.structure_shifted.scatterers())
if(grad_flags_counts.n_parameters() > 0):
if (0):
print "u_aniso"
print "gradient_flags.site ", gradient_flags.site
print "gradient_flags.u_iso ", gradient_flags.u_iso
print "gradient_flags.u_aniso ", gradient_flags.u_aniso
print "gradient_flags.occupancy ", gradient_flags.occupancy
print "gradient_flags.fp ", gradient_flags.fp
print "gradient_flags.fdp ", gradient_flags.fdp
cntr_use_u_iso = 0
cntr_use_u_aniso = 0
cntr_grad_u_iso = 0
cntr_grad_u_aniso = 0
for scatterer in sh.structure_shifted.scatterers():
if (scatterer.flags.use_u_iso()): cntr_use_u_iso += 1
if (scatterer.flags.use_u_aniso()): cntr_use_u_aniso += 1
if (scatterer.flags.grad_u_iso()): cntr_grad_u_iso += 1
if (scatterer.flags.grad_u_aniso()): cntr_grad_u_aniso += 1
print "use_u_iso ", cntr_use_u_iso,cntr_grad_u_iso
print "use_u_aniso ", cntr_use_u_aniso,cntr_grad_u_aniso
sfd = xray.structure_factors.gradients_direct(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=None,
miller_set=f_obs,
d_target_d_f_calc=ls.derivatives(),
n_parameters= 0
)
re = resampling(miller_set=f_obs)
map0 = re(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=None,
dp=miller.array(miller_set=f_obs, data=ls.derivatives()),
n_parameters= 0,
verbose=verbose)
grad_flags_counts = xray.scatterer_grad_flags_counts(sh.structure_shifted.scatterers())
if(grad_flags_counts.u_aniso):
sfd_d_target_d_u_cart = sfd.d_target_d_u_cart()
map0_d_target_d_u_cart = map0.d_target_d_u_cart()
top_gradient = None
gradients_1 = []
for i_scatterer,scatterer in enumerate(sh.structure_shifted.scatterers()):
if(scatterer.flags.use_u_iso()): parameter_name = "u_iso"
if(scatterer.flags.use_u_aniso()): parameter_name = "u_star"
if(parameter_name == "u_iso" and scatterer.flags.grad_u_iso()):
direct_summ = sfd.d_target_d_u_iso()[i_scatterer]
if (top_gradient is None): top_gradient = direct_summ
fast_gradie = map0.d_target_d_u_iso()[i_scatterer]
sys.stdout.flush()
gradients_1.append([direct_summ, fast_gradie])
match = judge(scatterer, parameter_name, direct_summ, fast_gradie,
top_gradient)
if (0 or verbose):
print "direct summ[%d]: " % i_scatterer, direct_summ
print "fast gradie[%d]: " % i_scatterer, fast_gradie, match
print
assert not match.is_bad
if parameter_name == "u_star" and scatterer.flags.grad_u_aniso():
sfd_star = sfd.d_target_d_u_star()[i_scatterer]
sfd_cart = adptbx.grad_u_star_as_u_cart(
structure_ideal.unit_cell(), sfd_star)
assert approx_equal(
sfd_star,
adptbx.grad_u_cart_as_u_star(structure_ideal.unit_cell(), sfd_cart))
for ij in xrange(6):
direct_summ = sfd_d_target_d_u_cart[i_scatterer][ij]
if (top_gradient is None): top_gradient = direct_summ
fast_gradie = map0_d_target_d_u_cart[i_scatterer][ij]
gradients_1.append([direct_summ, fast_gradie])
match =judge(scatterer,"u_star",direct_summ,fast_gradie,top_gradient)
if (0 or verbose or match.is_bad):
print "direct summ[%d][%d]: " % (i_scatterer, ij), direct_summ
print "fast gradie[%d][%d]: " % (i_scatterer, ij),fast_gradie,match
print
assert not match.is_bad
# Making sure that gradients_1 = gradients_2
for i_scatterer,scatterer in enumerate(sh.structure_shifted.scatterers()):
if(not scatterer.flags.use_u_iso()):
scatterer.u_iso = -12345.0
if(not scatterer.flags.use_u_aniso()):
scatterer.u_star =(-999.,-999.,-999.,-999.,-999.,-999.)
sfd = xray.structure_factors.gradients_direct(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=None,
miller_set=f_obs,
d_target_d_f_calc=ls.derivatives(),
n_parameters= 0
)
re = resampling(miller_set=f_obs)
map0 = re(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=None,
dp=miller.array(miller_set=f_obs, data=ls.derivatives()),
n_parameters= 0,
verbose=verbose)
grad_flags_counts = \
xray.scatterer_grad_flags_counts(sh.structure_shifted.scatterers())
if(grad_flags_counts.u_aniso):
sfd_d_target_d_u_cart = sfd.d_target_d_u_cart()
map0_d_target_d_u_cart = map0.d_target_d_u_cart()
gradients_2 = []
for i_scatterer,scatterer in enumerate(sh.structure_shifted.scatterers()):
if(scatterer.flags.use_u_iso()): parameter_name = "u_iso"
if(scatterer.flags.use_u_aniso()): parameter_name = "u_star"
if(parameter_name == "u_iso" and scatterer.flags.grad_u_iso()):
direct_summ = sfd.d_target_d_u_iso()[i_scatterer]
fast_gradie = map0.d_target_d_u_iso()[i_scatterer]
gradients_2.append([direct_summ, fast_gradie])
if parameter_name == "u_star" and scatterer.flags.grad_u_aniso():
sfd_star = sfd.d_target_d_u_star()[i_scatterer]
sfd_cart = adptbx.grad_u_star_as_u_cart(
structure_ideal.unit_cell(), sfd_star)
assert approx_equal(
sfd_star,
adptbx.grad_u_cart_as_u_star(structure_ideal.unit_cell(), sfd_cart))
for ij in xrange(6):
direct_summ = sfd_d_target_d_u_cart[i_scatterer][ij]
fast_gradie = map0_d_target_d_u_cart[i_scatterer][ij]
gradients_2.append([direct_summ, fast_gradie])
for g1,g2 in zip(gradients_1, gradients_2):
assert approx_equal(g1, g2)
sys.stdout.flush()
def d_target_d_u_star(self):
if (self.d_target_d_u_star_was_used):
raise RuntimeError(expensive_function_call_message)
self.d_target_d_u_star_was_used = True
return adptbx.grad_u_cart_as_u_star(self.xray_structure().unit_cell(),
self.d_target_d_u_cart())
def d_target_d_u_star(self):
if (self.d_target_d_u_star_was_used):
raise RuntimeError(expensive_function_call_message)
self.d_target_d_u_star_was_used = True
return adptbx.grad_u_cart_as_u_star(
self.xray_structure().unit_cell(), self.d_target_d_u_cart())
def u_iso(structure_ideal, d_min, f_obs, tan_u_iso, verbose=0):
sh = shifted_u_iso(f_obs, structure_ideal, 0, 0.05)
if (0 or verbose):
print("u_iso")
sh.structure_shifted.show_summary().show_scatterers()
print()
ls = xray.targets_least_squares_residual(f_obs.data(), sh.f_calc.data(),
True, 1)
gradient_flags = randomize_gradient_flags(
xray.structure_factors.gradient_flags(u_iso=True),
f_obs.anomalous_flag())
if (tan_u_iso):
u_iso_refinable_params = flex.double()
else:
u_iso_refinable_params = None
for scatterer in sh.structure_shifted.scatterers():
scatterer.flags.set_grad_site(gradient_flags.site)
scatterer.flags.set_grad_u_iso(gradient_flags.u_iso)
scatterer.flags.set_grad_u_aniso(gradient_flags.u_aniso)
scatterer.flags.set_grad_occupancy(gradient_flags.occupancy)
scatterer.flags.set_grad_fp(gradient_flags.fp)
scatterer.flags.set_grad_fdp(gradient_flags.fdp)
if (tan_u_iso):
scatterer.flags.set_tan_u_iso(True)
param = random.randint(90, 120)
scatterer.flags.param = param
value = math.tan(scatterer.u_iso * math.pi / adptbx.b_as_u(param) -
math.pi / 2)
u_iso_refinable_params.append(value)
if (0):
print("u_iso")
print("gradient_flags.site ", gradient_flags.site)
print("gradient_flags.u_iso ", gradient_flags.u_iso)
print("gradient_flags.u_aniso ", gradient_flags.u_aniso)
print("gradient_flags.occupancy ", gradient_flags.occupancy)
print("gradient_flags.fp ", gradient_flags.fp)
print("gradient_flags.fdp ", gradient_flags.fdp)
cntr_use_u_iso = 0
cntr_use_u_aniso = 0
cntr_grad_u_iso = 0
cntr_grad_u_aniso = 0
for scatterer in sh.structure_shifted.scatterers():
if (scatterer.flags.use_u_iso()): cntr_use_u_iso += 1
if (scatterer.flags.use_u_aniso()): cntr_use_u_aniso += 1
if (scatterer.flags.grad_u_iso()): cntr_grad_u_iso += 1
if (scatterer.flags.grad_u_aniso()): cntr_grad_u_aniso += 1
print("use_u_iso ", cntr_use_u_iso, cntr_grad_u_iso)
print("use_u_aniso ", cntr_use_u_aniso, cntr_grad_u_aniso)
grad_flags_counts = \
xray.scatterer_grad_flags_counts(sh.structure_shifted.scatterers())
if (grad_flags_counts.n_parameters() > 0):
sfd = xray.structure_factors.gradients_direct(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=u_iso_refinable_params,
miller_set=f_obs,
d_target_d_f_calc=ls.derivatives(),
n_parameters=0)
re = resampling(miller_set=f_obs)
map0 = re(xray_structure=sh.structure_shifted,
u_iso_refinable_params=u_iso_refinable_params,
dp=miller.array(miller_set=f_obs, data=ls.derivatives()),
n_parameters=0,
verbose=verbose)
if (grad_flags_counts.u_aniso > 0):
sfd_d_target_d_u_cart = sfd.d_target_d_u_cart()
map0_d_target_d_u_cart = map0.d_target_d_u_cart()
top_gradient = None
gradients_1 = []
for i_scatterer, scatterer in enumerate(
sh.structure_shifted.scatterers()):
if (0):
print("i_scatterer= ", i_scatterer,scatterer.flags.use_u_iso(),\
scatterer.flags.grad_u_iso(), scatterer.flags.use_u_aniso(),\
scatterer.flags.grad_u_aniso(), scatterer.u_iso, scatterer.u_star)
if (scatterer.flags.use_u_iso()): parameter_name = "u_iso"
if (scatterer.flags.use_u_aniso()): parameter_name = "u_star"
if (parameter_name == "u_iso" and scatterer.flags.grad_u_iso()
and scatterer.flags.use_u_iso()):
direct_summ = sfd.d_target_d_u_iso()[i_scatterer]
if (top_gradient is None): top_gradient = direct_summ
fast_gradie = map0.d_target_d_u_iso()[i_scatterer]
sys.stdout.flush()
gradients_1.append([direct_summ, fast_gradie])
match = judge(scatterer, parameter_name, direct_summ,
fast_gradie, top_gradient)
if (0 or verbose):
print("direct summ[%d]: " % i_scatterer, direct_summ)
print("fast gradie[%d]: " % i_scatterer, fast_gradie,
match)
print()
assert not match.is_bad
if (parameter_name == "u_star" and scatterer.flags.grad_u_aniso()
and scatterer.flags.use_u_aniso()):
sfd_star = sfd.d_target_d_u_star()[i_scatterer]
sfd_cart = adptbx.grad_u_star_as_u_cart(
structure_ideal.unit_cell(), sfd_star)
assert approx_equal(
sfd_star,
adptbx.grad_u_cart_as_u_star(structure_ideal.unit_cell(),
sfd_cart))
for ij in range(6):
direct_summ = sfd_d_target_d_u_cart[i_scatterer][ij]
if (top_gradient is None): top_gradient = direct_summ
fast_gradie = map0_d_target_d_u_cart[i_scatterer][ij]
gradients_1.append([direct_summ, fast_gradie])
match = judge(scatterer, "u_star", direct_summ,
fast_gradie, top_gradient)
if (0 or verbose or match.is_bad):
print("direct summ[%d][%d]: " % (i_scatterer, ij),
direct_summ)
print("fast gradie[%d][%d]: " % (i_scatterer, ij),
fast_gradie, match)
print()
assert not match.is_bad
# Making sure that gradients_1 = gradients_2
for i_scatterer, scatterer in enumerate(
sh.structure_shifted.scatterers()):
if (not scatterer.flags.use_u_iso()):
scatterer.u_iso = -12345.0
if (not scatterer.flags.use_u_aniso()):
scatterer.u_star = (-999., -999., -999., -999., -999., -999.)
sfd = xray.structure_factors.gradients_direct(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=u_iso_refinable_params,
miller_set=f_obs,
d_target_d_f_calc=ls.derivatives(),
n_parameters=0)
re = resampling(miller_set=f_obs)
map0 = re(xray_structure=sh.structure_shifted,
u_iso_refinable_params=u_iso_refinable_params,
dp=miller.array(miller_set=f_obs, data=ls.derivatives()),
n_parameters=0,
verbose=verbose)
grad_flags_counts = \
xray.scatterer_grad_flags_counts(sh.structure_shifted.scatterers())
if (grad_flags_counts.u_aniso):
sfd_d_target_d_u_cart = sfd.d_target_d_u_cart()
map0_d_target_d_u_cart = map0.d_target_d_u_cart()
gradients_2 = []
for i_scatterer, scatterer in enumerate(
sh.structure_shifted.scatterers()):
if (scatterer.flags.use_u_iso()): parameter_name = "u_iso"
if (scatterer.flags.use_u_aniso()): parameter_name = "u_star"
if (parameter_name == "u_iso" and scatterer.flags.grad_u_iso()
and scatterer.flags.use_u_iso()):
direct_summ = sfd.d_target_d_u_iso()[i_scatterer]
fast_gradie = map0.d_target_d_u_iso()[i_scatterer]
gradients_2.append([direct_summ, fast_gradie])
if (parameter_name == "u_star" and scatterer.flags.grad_u_aniso()
and scatterer.flags.use_u_aniso()):
sfd_star = sfd.d_target_d_u_star()[i_scatterer]
sfd_cart = adptbx.grad_u_star_as_u_cart(
structure_ideal.unit_cell(), sfd_star)
assert approx_equal(
sfd_star,
adptbx.grad_u_cart_as_u_star(structure_ideal.unit_cell(),
sfd_cart))
for ij in range(6):
direct_summ = sfd_d_target_d_u_cart[i_scatterer][ij]
fast_gradie = map0_d_target_d_u_cart[i_scatterer][ij]
gradients_2.append([direct_summ, fast_gradie])
for g1, g2 in zip(gradients_1, gradients_2):
assert approx_equal(g1, g2)
sys.stdout.flush()
