def exercise_core_LS(target_class, verbose):
n_refl = 10
f_calc = flex.polar(
flex.random_double(n_refl) * 10 - 5,
flex.random_double(n_refl) * 10 - 5)
f_obs = flex.abs(f_calc) + (flex.random_double(n_refl) * 2 - 1)
weights = flex.random_double(n_refl)
r = xray.targets_least_squares_residual(f_obs, weights, f_calc, True, 0)
scale_factor = r.scale_factor()
gr_ana = r.derivatives()
gr_fin = flex.complex_double()
eps = 1.e-6
for i_refl in xrange(n_refl):
gc = []
for i_part in [0, 1]:
fc0 = f_calc[i_refl]
ts = []
for signed_eps in [eps, -eps]:
if (i_part == 0):
f_calc[i_refl] = complex(fc0.real + signed_eps, fc0.imag)
else:
f_calc[i_refl] = complex(fc0.real, fc0.imag + signed_eps)
r = xray.targets_least_squares_residual(
f_obs, weights, f_calc, False, scale_factor)
ts.append(r.target())
f_calc[i_refl] = fc0
gc.append((ts[0] - ts[1]) / (2 * eps))
gr_fin.append(complex(*gc))
if (verbose):
print "ana:", list(gr_ana)
print "fin:", list(gr_fin)
assert approx_equal(gr_fin, gr_ana)
def exercise_core_LS(target_class, verbose):
n_refl = 10
f_calc = flex.polar(
flex.random_double(n_refl)*10-5,
flex.random_double(n_refl)*10-5)
f_obs = flex.abs(f_calc) + (flex.random_double(n_refl)*2-1)
weights = flex.random_double(n_refl)
r = xray.targets_least_squares_residual(
f_obs, weights, f_calc, True, 0)
scale_factor = r.scale_factor()
gr_ana = r.derivatives()
gr_fin = flex.complex_double()
eps = 1.e-6
for i_refl in xrange(n_refl):
gc = []
for i_part in [0,1]:
fc0 = f_calc[i_refl]
ts = []
for signed_eps in [eps,-eps]:
if (i_part == 0):
f_calc[i_refl] = complex(fc0.real + signed_eps, fc0.imag)
else:
f_calc[i_refl] = complex(fc0.real, fc0.imag + signed_eps)
r = xray.targets_least_squares_residual(
f_obs, weights, f_calc, False, scale_factor)
ts.append(r.target())
f_calc[i_refl] = fc0
gc.append((ts[0]-ts[1])/(2*eps))
gr_fin.append(complex(*gc))
if (verbose):
print "ana:", list(gr_ana)
print "fin:", list(gr_fin)
assert approx_equal(gr_fin, gr_ana)
def shift_all(structure_ideal, f_obs, anomalous_flag):
sh = shifted_site(None, structure_ideal, 0, 0, 0.01)
sh = shifted_u_iso(None, sh.structure_shifted, 0, 0.05)
sh = shifted_u_star(None, sh.structure_shifted, 0, 0, 0.0001)
sh = shifted_occupancy(None, sh.structure_shifted, 0, 0.2)
if (anomalous_flag):
sh = shifted_fdp(None, sh.structure_shifted, 0, 2)
sh = shifted_fp(f_obs, sh.structure_shifted, 0, -0.2)
ls = xray.targets_least_squares_residual(
f_obs.data(), sh.f_calc.data(), True, 1)
return sh, ls
def site(structure_ideal, d_min, f_obs, verbose=0):
sh = shifted_site(f_obs, structure_ideal, 0, 0, 0.01)
if (0 or verbose):
print("site")
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(site=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)
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)
sfd_d_target_d_site_cart = sfd.d_target_d_site_cart()
top_gradient = None
for i_scatterer, scatterer in enumerate(sh.structure_shifted.scatterers()):
for i_xyz in (0, 1, 2):
direct_summ = sfd_d_target_d_site_cart[i_scatterer][i_xyz]
if (top_gradient is None): top_gradient = direct_summ
fast_gradie = map0.d_target_d_site_cart()[i_scatterer][i_xyz]
match = judge(scatterer, "site", direct_summ, fast_gradie,
top_gradient)
if (0 or verbose):
print("direct summ[%d][%d]: " % (i_scatterer, i_xyz),
direct_summ)
print("fast gradie[%d][%d]: " % (i_scatterer, i_xyz),
fast_gradie, match)
print()
assert not match.is_bad
sys.stdout.flush()
def site(structure_ideal, d_min, f_obs, verbose=0):
sh = shifted_site(f_obs, structure_ideal, 0, 0, 0.01)
if (0 or verbose):
print "site"
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(site=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)
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)
sfd_d_target_d_site_cart = sfd.d_target_d_site_cart()
top_gradient = None
for i_scatterer,scatterer in enumerate(sh.structure_shifted.scatterers()):
for i_xyz in (0,1,2):
direct_summ = sfd_d_target_d_site_cart[i_scatterer][i_xyz]
if (top_gradient is None): top_gradient = direct_summ
fast_gradie = map0.d_target_d_site_cart()[i_scatterer][i_xyz]
match = judge(scatterer, "site", direct_summ, fast_gradie, top_gradient)
if (0 or verbose):
print "direct summ[%d][%d]: " % (i_scatterer,i_xyz), direct_summ
print "fast gradie[%d][%d]: " % (i_scatterer,i_xyz), fast_gradie, match
print
assert not match.is_bad
sys.stdout.flush()
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 timings(structure, d_min, fft_only=False,
wing_cutoff_reference=1.e-6,
wing_cutoff_others=1.e-3):
structure_ng = structure.deep_copy_scatterers()
structure_5g = structure.deep_copy_scatterers()
structure_4g = structure.deep_copy_scatterers()
structure_2g = structure.deep_copy_scatterers()
structure_1g = structure.deep_copy_scatterers()
structure_ng.scattering_type_registry(d_min=d_min, table="n_gaussian")
structure_5g.scattering_type_registry(table="wk1995")
structure_4g.scattering_type_registry(table="it1992")
custom_dict = {}
custom_dict.update(eltbx.xray_scattering.two_gaussian_agarwal_isaacs.table)
structure_2g.scattering_type_registry(custom_dict=custom_dict)
custom_dict.update(eltbx.xray_scattering.one_gaussian_agarwal_1978.table)
structure_1g.scattering_type_registry(custom_dict=custom_dict)
miller_set = miller.build_set(
crystal_symmetry=structure,
d_min=d_min,
anomalous_flag=False)
miller_set.show_summary()
print "d_min:", d_min
if (fft_only):
timer = user_plus_sys_time()
f_calc_reference = xray.structure_factors.from_scatterers(
miller_set=miller_set,
wing_cutoff=wing_cutoff_reference,
exp_table_one_over_step_size=0)(
xray_structure=structure,
miller_set=miller_set,
algorithm="fft").f_calc().data()
print "fft exp function wing_cutoff=%3.1e: %.2f seconds" % (
wing_cutoff_reference, timer.elapsed())
else:
timer = user_plus_sys_time()
f_calc_reference = xray.structure_factors_simple(
structure_5g.unit_cell(),
structure_5g.space_group(),
miller_set.indices(),
structure_5g.scatterers(),
structure_5g.scattering_type_registry()).f_calc()
print "direct simple: %.2f seconds" % timer.elapsed()
f_calc_reference = flex.abs(f_calc_reference)
print "wing_cutoff for following fft calculations: %3.1e"%wing_cutoff_others
for structure in (structure_ng, structure_5g, structure_4g,
structure_2g, structure_1g):
structure.scattering_type_registry().show_summary()
if (not fft_only):
for calc_type,cos_sin_flag in (("direct cos+sin function:",False),
("direct cos+sin table:",True)):
timer = user_plus_sys_time()
f_calc = miller_set.structure_factors_from_scatterers(
xray_structure=structure,
algorithm="direct",
cos_sin_table=cos_sin_flag).f_calc()
print " %-24s %.2f seconds," % (calc_type, timer.elapsed()),
ls = xray.targets_least_squares_residual(
f_calc_reference, f_calc.data(), False, 1)
print "r-factor: %.6f" % ls.target()
for calc_type,exp_table_one_over_step_size in (("fft exp function:",0),
("fft exp table:",-100)):
timer = user_plus_sys_time()
f_calc = xray.structure_factors.from_scatterers(
miller_set=miller_set,
wing_cutoff=wing_cutoff_others,
exp_table_one_over_step_size=exp_table_one_over_step_size)(
xray_structure=structure,
miller_set=miller_set,
algorithm="fft").f_calc()
print " %-24s %.2f seconds," % (calc_type, timer.elapsed()),
ls = xray.targets_least_squares_residual(
f_calc_reference, f_calc.data(), False, 1)
print "r-factor: %.6f" % ls.target()
print
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 timings(structure,
d_min,
fft_only=False,
wing_cutoff_reference=1.e-6,
wing_cutoff_others=1.e-3):
structure_ng = structure.deep_copy_scatterers()
structure_5g = structure.deep_copy_scatterers()
structure_4g = structure.deep_copy_scatterers()
structure_2g = structure.deep_copy_scatterers()
structure_1g = structure.deep_copy_scatterers()
structure_ng.scattering_type_registry(d_min=d_min, table="n_gaussian")
structure_5g.scattering_type_registry(table="wk1995")
structure_4g.scattering_type_registry(table="it1992")
custom_dict = {}
custom_dict.update(eltbx.xray_scattering.two_gaussian_agarwal_isaacs.table)
structure_2g.scattering_type_registry(custom_dict=custom_dict)
custom_dict.update(eltbx.xray_scattering.one_gaussian_agarwal_1978.table)
structure_1g.scattering_type_registry(custom_dict=custom_dict)
miller_set = miller.build_set(crystal_symmetry=structure,
d_min=d_min,
anomalous_flag=False)
miller_set.show_summary()
print("d_min:", d_min)
if (fft_only):
timer = user_plus_sys_time()
f_calc_reference = xray.structure_factors.from_scatterers(
miller_set=miller_set,
wing_cutoff=wing_cutoff_reference,
exp_table_one_over_step_size=0)(xray_structure=structure,
miller_set=miller_set,
algorithm="fft").f_calc().data()
print("fft exp function wing_cutoff=%3.1e: %.2f seconds" %
(wing_cutoff_reference, timer.elapsed()))
else:
timer = user_plus_sys_time()
f_calc_reference = xray.structure_factors_simple(
structure_5g.unit_cell(), structure_5g.space_group(),
miller_set.indices(), structure_5g.scatterers(),
structure_5g.scattering_type_registry()).f_calc()
print("direct simple: %.2f seconds" % timer.elapsed())
f_calc_reference = flex.abs(f_calc_reference)
print("wing_cutoff for following fft calculations: %3.1e" %
wing_cutoff_others)
for structure in (structure_ng, structure_5g, structure_4g, structure_2g,
structure_1g):
structure.scattering_type_registry().show_summary()
if (not fft_only):
for calc_type, cos_sin_flag in (("direct cos+sin function:",
False), ("direct cos+sin table:",
True)):
timer = user_plus_sys_time()
f_calc = miller_set.structure_factors_from_scatterers(
xray_structure=structure,
algorithm="direct",
cos_sin_table=cos_sin_flag).f_calc()
print(" %-24s %.2f seconds," % (calc_type, timer.elapsed()),
end=' ')
ls = xray.targets_least_squares_residual(
f_calc_reference, f_calc.data(), False, 1)
print("r-factor: %.6f" % ls.target())
for calc_type, exp_table_one_over_step_size in (("fft exp function:",
0), ("fft exp table:",
-100)):
timer = user_plus_sys_time()
f_calc = xray.structure_factors.from_scatterers(
miller_set=miller_set,
wing_cutoff=wing_cutoff_others,
exp_table_one_over_step_size=exp_table_one_over_step_size)(
xray_structure=structure,
miller_set=miller_set,
algorithm="fft").f_calc()
print(" %-24s %.2f seconds," % (calc_type, timer.elapsed()),
end=' ')
ls = xray.targets_least_squares_residual(f_calc_reference,
f_calc.data(), False, 1)
print("r-factor: %.6f" % ls.target())
print()