def exercise_packed(structure_ideal, f_obs,
anomalous_flag,
verbose=0):
sh, ls = shift_all(structure_ideal, f_obs, anomalous_flag)
flag = (random.random() > 0.5)
gradient_flags = randomize_gradient_flags(
xray.structure_factors.gradient_flags(site=flag, u=not flag),
f_obs.anomalous_flag(),
thresholds=(1/2.,0))
u_iso_refinable_params = flex.double()
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)
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)
n_parameters = xray.scatterer_grad_flags_counts(
sh.structure_shifted.scatterers()).n_parameters()
assert n_parameters == sh.structure_shifted.n_parameters()
if (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=n_parameters)
assert sfd.packed().size() == n_parameters
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=n_parameters,
verbose=verbose)
assert map0.packed().size() == n_parameters
correlation = flex.linear_correlation(sfd.packed(), map0.packed())
assert correlation.is_well_defined()
assert correlation.coefficient() > 0.999
def exercise(target_functor,
data_type,
parameter_name,
space_group_info,
anomalous_flag,
cartesian_flag,
n_elements=9,
d_min=2.5,
shake_sigma=0.25,
test_hard=True,
verbose=0):
assert data_type == 'F' or data_type == 'F^2'
if (data_type == 'F^2'
and not target_functor == xray.unified_least_squares_residual):
return
if (parameter_name != "site" and cartesian_flag == True): return
if (parameter_name == "fdp" and not anomalous_flag): return
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(("O", "N", "C") * (n_elements))[:n_elements],
volume_per_atom=100,
random_f_prime_d_min=d_min,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
use_u_iso=False,
random_u_cart_scale=0.3,
random_u_iso=False,
random_occupancy=True)
if (parameter_name in ["u_star", "u_iso"]): shake_sigma = shake_sigma / 2.
random_structure.random_modify_adp_and_adp_flags(
scatterers=structure_ideal.scatterers(),
random_u_iso_scale=0.3,
random_u_iso_min=0.0,
parameter_name=parameter_name)
rnd_f_calc = structure_ideal.structure_factors(
anomalous_flag=anomalous_flag, d_min=d_min,
algorithm="direct").f_calc()
if data_type == 'F':
y_obs = abs(rnd_f_calc)
elif data_type == 'F^2':
y_obs = rnd_f_calc.norm()
y_obs.set_observation_type_xray_intensity()
structure_shake = structure_ideal.random_modify_parameters(
parameter_name, shake_sigma, vary_z_only=False)
assert tuple(structure_ideal.special_position_indices()) \
== tuple(structure_shake.special_position_indices())
target_ftor = target_functor(y_obs)
for structure in (structure_ideal, structure_shake)[:]: #SWITCH
f_calc = y_obs.structure_factors_from_scatterers(
xray_structure=structure, algorithm="direct").f_calc()
target_result = target_ftor(f_calc, compute_derivatives=True)
if (structure == structure_ideal):
assert abs(target_result.target()) < 1.e-5
gradient_flags = xray.structure_factors.gradient_flags(
site=(parameter_name == "site" or random.choice((False, True))),
u_iso=(parameter_name == "u_iso" or random.choice((False, True))),
u_aniso=(parameter_name == "u_star" or random.choice((False, True))),
occupancy=(parameter_name == "occupancy" or random.choice(
(False, True))),
fp=(parameter_name == "fp" or random.choice((False, True))),
fdp=(parameter_name == "fdp" or (anomalous_flag and random.choice(
(False, True)))))
xray.set_scatterer_grad_flags(scatterers=structure.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(
structure.scatterers())
sf = xray.structure_factors.gradients_direct(
xray_structure=structure,
u_iso_refinable_params=None,
miller_set=y_obs,
d_target_d_f_calc=target_result.derivatives(),
n_parameters=0)
if (parameter_name == "site"):
d_analytical = sf.d_target_d_site_frac()
if (cartesian_flag
): # average d_analytical or d_numerical, but not both
structure_ideal.apply_special_position_ops_d_target_d_site(
d_analytical)
if (cartesian_flag):
d_analytical = sf.d_target_d_site_cart()
d_numerical = finite_differences_site(cartesian_flag, target_ftor,
structure)
if (not cartesian_flag
): # aver. d_analytical or d_numerical, but not both
structure_ideal.apply_special_position_ops_d_target_d_site(
d_numerical)
elif (parameter_name == "u_star" and grad_flags_counts.use_u_aniso > 0):
d_analytical = sf.d_target_d_u_star()
d_numerical = finite_differences_u_star(target_ftor, structure)
else:
if (parameter_name == "occupancy"):
d_analytical = sf.d_target_d_occupancy()
elif (parameter_name == "u_iso" and grad_flags_counts.use_u_iso > 0):
d_analytical = sf.d_target_d_u_iso()
elif (parameter_name == "fp"):
d_analytical = sf.d_target_d_fp()
elif (parameter_name == "fdp"):
d_analytical = sf.d_target_d_fdp()
else:
raise RuntimeError
d_numerical = finite_differences_scalar(parameter_name, target_ftor,
structure)
linear_regression_test(d_analytical,
d_numerical,
test_hard,
verbose=verbose)
if (parameter_name == "u_iso" and grad_flags_counts.use_u_iso > 0):
u_iso_refinable_params = flex.double()
for scatterer in structure.scatterers():
scatterer.flags.set_tan_u_iso(True)
scatterer.flags.set_grad_u_iso(gradient_flags.u_iso)
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)
sf = xray.structure_factors.gradients_direct(
xray_structure=structure,
u_iso_refinable_params=u_iso_refinable_params,
miller_set=y_obs,
d_target_d_f_calc=target_result.derivatives(),
n_parameters=0)
d_analytical = sf.d_target_d_u_iso()
d_numerical = finite_differences_scalar("tan_u_iso", target_ftor,
structure)
linear_regression_test(d_analytical,
d_numerical,
test_hard,
verbose=verbose)
def exercise_gradient_manager(structure_ideal, f_obs,
anomalous_flag,
verbose=0):
sh, ls = shift_all(structure_ideal, f_obs, anomalous_flag)
grad_manager = xray.structure_factors.gradients(
miller_set=f_obs,
quality_factor=100000,
wing_cutoff=1.e-10)
gradient_flags=xray.structure_factors.gradient_flags(default=True)
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)
if (0):
print "exercise_gradient_manager"
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
if (random.random() > 0.5):
n_parameters = 0
else:
n_parameters = xray.scatterer_grad_flags_counts(
sh.structure_shifted.scatterers()).n_parameters()
assert n_parameters == sh.structure_shifted.n_parameters()
gd = grad_manager(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=None,
miller_set=f_obs,
d_target_d_f_calc=ls.derivatives(),
n_parameters=n_parameters,
algorithm="direct")
gf = grad_manager(
xray_structure=sh.structure_shifted,
u_iso_refinable_params=None,
miller_set=f_obs,
d_target_d_f_calc=ls.derivatives(),
n_parameters=n_parameters,
algorithm="fft")
grad_flags_counts = \
xray.scatterer_grad_flags_counts(sh.structure_shifted.scatterers())
if (n_parameters == 0):
d = gd.d_target_d_site_frac()
f = gf.d_target_d_site_frac()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
d = gd.d_target_d_site_cart()
f = gf.d_target_d_site_cart()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
if(grad_flags_counts.u_iso > 0):
d = gd.d_target_d_u_iso()
f = gf.d_target_d_u_iso()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
if(grad_flags_counts.u_aniso > 0):
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
d = gd.d_target_d_u_cart()
f = gf.d_target_d_u_cart()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
d = gd.d_target_d_occupancy()
f = gf.d_target_d_occupancy()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
d = gd.d_target_d_fp()
f = gf.d_target_d_fp()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
if (anomalous_flag):
d = gd.d_target_d_fdp()
f = gf.d_target_d_fdp()
linear_regression_test(d, f, slope_tolerance=1.e-2, verbose=verbose)
else:
correlation = flex.linear_correlation(gd.packed(), gf.packed())
assert correlation.is_well_defined()
assert correlation.coefficient() > 0.995, correlation.coefficient()
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 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()
def exercise(target_functor, data_type, parameter_name, space_group_info,
anomalous_flag,
cartesian_flag,
n_elements=9,
d_min=2.5,
shake_sigma=0.25,
test_hard=True, verbose=0):
assert data_type == 'F' or data_type == 'F^2'
if (data_type == 'F^2'
and not target_functor == xray.unified_least_squares_residual): return
if (parameter_name != "site" and cartesian_flag == True): return
if (parameter_name == "fdp" and not anomalous_flag): return
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(("O","N","C")*(n_elements))[:n_elements],
volume_per_atom=100,
random_f_prime_d_min=d_min,
random_f_double_prime=anomalous_flag,
use_u_aniso = True,
use_u_iso = False,
random_u_cart_scale = 0.3,
random_u_iso = False,
random_occupancy=True)
if(parameter_name in ["u_star", "u_iso"]): shake_sigma = shake_sigma / 2.
random_structure.random_modify_adp_and_adp_flags(
scatterers = structure_ideal.scatterers(),
random_u_iso_scale = 0.3,
random_u_iso_min = 0.0,
parameter_name = parameter_name)
rnd_f_calc = structure_ideal.structure_factors(
anomalous_flag=anomalous_flag, d_min=d_min, algorithm="direct").f_calc()
if data_type == 'F':
y_obs = abs(rnd_f_calc)
elif data_type == 'F^2':
y_obs = rnd_f_calc.norm()
y_obs.set_observation_type_xray_intensity()
structure_shake = structure_ideal.random_modify_parameters(
parameter_name, shake_sigma, vary_z_only=False)
assert tuple(structure_ideal.special_position_indices()) \
== tuple(structure_shake.special_position_indices())
target_ftor = target_functor(y_obs)
for structure in (structure_ideal, structure_shake)[:]: #SWITCH
f_calc = y_obs.structure_factors_from_scatterers(
xray_structure=structure,
algorithm="direct").f_calc()
target_result = target_ftor(f_calc, compute_derivatives=True)
if (structure == structure_ideal):
assert abs(target_result.target()) < 1.e-5
gradient_flags=xray.structure_factors.gradient_flags(
site=(parameter_name=="site" or random.choice((False,True))),
u_iso=(parameter_name=="u_iso" or random.choice((False,True))),
u_aniso=(parameter_name=="u_star" or random.choice((False,True))),
occupancy=(parameter_name=="occupancy" or random.choice((False,True))),
fp=(parameter_name=="fp" or random.choice((False,True))),
fdp=(parameter_name=="fdp" or (anomalous_flag
and random.choice((False,True)))))
xray.set_scatterer_grad_flags(scatterers = structure.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(structure.scatterers())
sf = xray.structure_factors.gradients_direct(
xray_structure=structure,
u_iso_refinable_params=None,
miller_set=y_obs,
d_target_d_f_calc=target_result.derivatives(),
n_parameters=0)
if (parameter_name == "site"):
d_analytical = sf.d_target_d_site_frac()
if (cartesian_flag): # average d_analytical or d_numerical, but not both
structure_ideal.apply_special_position_ops_d_target_d_site(d_analytical)
if (cartesian_flag):
d_analytical = sf.d_target_d_site_cart()
d_numerical = finite_differences_site(
cartesian_flag, target_ftor, structure)
if (not cartesian_flag): # aver. d_analytical or d_numerical, but not both
structure_ideal.apply_special_position_ops_d_target_d_site(d_numerical)
elif (parameter_name == "u_star" and grad_flags_counts.use_u_aniso > 0):
d_analytical = sf.d_target_d_u_star()
d_numerical = finite_differences_u_star(target_ftor, structure)
else:
if (parameter_name == "occupancy"):
d_analytical = sf.d_target_d_occupancy()
elif (parameter_name == "u_iso" and grad_flags_counts.use_u_iso > 0):
d_analytical = sf.d_target_d_u_iso()
elif (parameter_name == "fp"):
d_analytical = sf.d_target_d_fp()
elif (parameter_name == "fdp"):
d_analytical = sf.d_target_d_fdp()
else:
raise RuntimeError
d_numerical = finite_differences_scalar(
parameter_name, target_ftor, structure)
linear_regression_test(d_analytical, d_numerical, test_hard,
verbose=verbose)
if (parameter_name == "u_iso" and grad_flags_counts.use_u_iso > 0):
u_iso_refinable_params = flex.double()
for scatterer in structure.scatterers():
scatterer.flags.set_tan_u_iso(True)
scatterer.flags.set_grad_u_iso(gradient_flags.u_iso)
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)
sf = xray.structure_factors.gradients_direct(
xray_structure=structure,
u_iso_refinable_params = u_iso_refinable_params,
miller_set=y_obs,
d_target_d_f_calc=target_result.derivatives(),
n_parameters=0)
d_analytical = sf.d_target_d_u_iso()
d_numerical = finite_differences_scalar("tan_u_iso",target_ftor, structure)
linear_regression_test(d_analytical, d_numerical, test_hard,
verbose=verbose)
