def run_one(space_group_info,
n_elements=9,
volume_per_atom=1000,
d_min=2.0,
anomalous_flag=0,
verbose=0):
if (random.random() < 0.5):
random_f_prime_scale = 0.6
else:
random_f_prime_scale = 0
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(
("O", "N", "C") * (n_elements)
)[:n_elements], #(("O","N","C")*(n_elements/3+1))[:n_elements],
volume_per_atom=volume_per_atom,
min_distance=5,
general_positions_only=True,
random_f_prime_d_min=d_min - 1,
random_f_prime_scale=random_f_prime_scale,
random_f_double_prime=anomalous_flag,
use_u_aniso=True,
use_u_iso=False,
random_u_iso=True,
random_u_cart_scale=.3,
random_occupancy=True)
random_structure.random_modify_adp_and_adp_flags(
scatterers=structure_ideal.scatterers(),
random_u_iso_scale=0.3,
random_u_iso_min=0.0)
if (random.random() < 0.5):
assign_custom_gaussians(structure_ideal,
negative_a=random.random() < 0.5)
if (0 or verbose):
structure_ideal.show_summary().show_scatterers()
f_obs = abs(
structure_ideal.structure_factors(d_min=d_min,
anomalous_flag=anomalous_flag,
algorithm="direct").f_calc())
if (1):
site(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
u_iso(structure_ideal, d_min, f_obs, tan_u_iso=False, verbose=verbose)
u_iso(structure_ideal, d_min, f_obs, tan_u_iso=True, verbose=verbose)
u_star(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
occupancy(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
fp(structure_ideal, d_min, f_obs, verbose=verbose)
if (1 and anomalous_flag):
fdp(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
exercise_gradient_manager(structure_ideal, f_obs, anomalous_flag)
if (1):
exercise_packed(structure_ideal, f_obs, anomalous_flag)
def run_one(space_group_info, n_elements= 9, volume_per_atom=1000, d_min = 2.0,
anomalous_flag=0,
verbose=0):
if (random.random() < 0.5):
random_f_prime_scale=0.6
else:
random_f_prime_scale=0
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(("O","N","C")*(n_elements))[:n_elements],#(("O","N","C")*(n_elements/3+1))[:n_elements],
volume_per_atom=volume_per_atom,
min_distance=5,
general_positions_only=True,
random_f_prime_d_min=d_min-1,
random_f_prime_scale=random_f_prime_scale,
random_f_double_prime=anomalous_flag,
use_u_aniso = True,
use_u_iso = False,
random_u_iso=True,
random_u_cart_scale=.3,
random_occupancy=True)
random_structure.random_modify_adp_and_adp_flags(
scatterers = structure_ideal.scatterers(),
random_u_iso_scale = 0.3,
random_u_iso_min = 0.0)
if (random.random() < 0.5):
assign_custom_gaussians(structure_ideal, negative_a=random.random()<0.5)
if (0 or verbose):
structure_ideal.show_summary().show_scatterers()
f_obs = abs(structure_ideal.structure_factors(
d_min=d_min, anomalous_flag=anomalous_flag, algorithm="direct").f_calc())
if (1):
site(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
u_iso(structure_ideal, d_min, f_obs, tan_u_iso=False, verbose=verbose)
u_iso(structure_ideal, d_min, f_obs, tan_u_iso=True, verbose=verbose)
u_star(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
occupancy(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
fp(structure_ideal, d_min, f_obs, verbose=verbose)
if (1 and anomalous_flag):
fdp(structure_ideal, d_min, f_obs, verbose=verbose)
if (1):
exercise_gradient_manager(structure_ideal, f_obs, anomalous_flag)
if (1):
exercise_packed(structure_ideal, f_obs, anomalous_flag)
def exercise(target_functor, data_type, space_group_info, anomalous_flag,
gradient_flags, occupancy_penalty,
n_elements=9, d_min=None, shake_sigma=0.1,
verbose=0,tan_u_iso=False, param = 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
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(("O","N","C")*(n_elements))[:n_elements],#("Se",)*n_elements,
volume_per_atom=200,
random_u_iso=True,
random_u_cart_scale=.3,
random_occupancy=True,
use_u_aniso = True)
random_structure.random_modify_adp_and_adp_flags(
scatterers = structure_ideal.scatterers(),
random_u_iso_scale = 0.3,
random_u_iso_min = 0.0)
xray.set_scatterer_grad_flags(scatterers = structure_ideal.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,
tan_u_iso = tan_u_iso,
param = param)
if(0):
print
for sc in structure_ideal.scatterers():
print sc.flags.use_u_iso(),sc.flags.grad_u_iso(),sc.flags.use_u_aniso(),\
sc.flags.grad_u_aniso(),sc.u_iso, sc.u_star,sc.flags.tan_u_iso(),\
sc.flags.param, sc.occupancy
rnd_f_calc = structure_ideal.structure_factors(
anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct",
cos_sin_table=True).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()
else:
raise "Error: invalid data type: %s" % data_type
if (0 or verbose):
print "structure_ideal:"
structure_ideal.show_summary().show_scatterers()
print "n_special_positions:", \
structure_ideal.special_position_indices().size()
print
structure_ideal_cp = structure_ideal.deep_copy_scatterers()
structure_shake = structure_ideal
if (gradient_flags.site):
structure_shake = structure_shake.random_modify_parameters(
"site", shake_sigma)
if (gradient_flags.occupancy):
structure_shake = structure_shake.random_modify_parameters(
"occupancy", shake_sigma)
if (occupancy_penalty is not None):
structure_shake.scatterers()[-1].occupancy = 0
if (gradient_flags.u_aniso):
shift_u_aniso(structure_shake, 0.001)
if (gradient_flags.u_iso):
shift_u_iso(structure_shake, 0.1)
assert tuple(structure_ideal.special_position_indices()) \
== tuple(structure_shake.special_position_indices())
if (0 or verbose):
print "structure_shake:"
structure_shake.show_summary().show_scatterers()
print
for i_trial in xrange(10):
try:
minimizer = xray.minimization.lbfgs(
target_functor=target_functor(y_obs),
xray_structure=structure_shake,
occupancy_penalty=occupancy_penalty,
structure_factor_algorithm="direct")
except RuntimeError, e:
if (str(e).find("debye_waller_factor_exp: arg_limit exceeded") < 0):
raise
else:
break
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(target_functor, data_type, space_group_info, anomalous_flag,
gradient_flags, occupancy_penalty,
n_elements=9, d_min=None, shake_sigma=0.1,
verbose=0,tan_u_iso=False, param = 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
structure_ideal = random_structure.xray_structure(
space_group_info,
elements=(("O","N","C")*(n_elements))[:n_elements],#("Se",)*n_elements,
volume_per_atom=200,
random_u_iso=True,
random_u_cart_scale=.3,
random_occupancy=True,
use_u_aniso = True)
random_structure.random_modify_adp_and_adp_flags(
scatterers = structure_ideal.scatterers(),
random_u_iso_scale = 0.3,
random_u_iso_min = 0.0)
xray.set_scatterer_grad_flags(scatterers = structure_ideal.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,
tan_u_iso = tan_u_iso,
param = param)
if(0):
print()
for sc in structure_ideal.scatterers():
print(sc.flags.use_u_iso(),sc.flags.grad_u_iso(),sc.flags.use_u_aniso(),\
sc.flags.grad_u_aniso(),sc.u_iso, sc.u_star,sc.flags.tan_u_iso(),\
sc.flags.param, sc.occupancy)
rnd_f_calc = structure_ideal.structure_factors(
anomalous_flag=anomalous_flag,
d_min=d_min,
algorithm="direct",
cos_sin_table=True).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()
else:
raise "Error: invalid data type: %s" % data_type
if (0 or verbose):
print("structure_ideal:")
structure_ideal.show_summary().show_scatterers()
print("n_special_positions:", \
structure_ideal.special_position_indices().size())
print()
structure_ideal_cp = structure_ideal.deep_copy_scatterers()
structure_shake = structure_ideal
if (gradient_flags.site):
structure_shake = structure_shake.random_modify_parameters(
"site", shake_sigma)
if (gradient_flags.occupancy):
structure_shake = structure_shake.random_modify_parameters(
"occupancy", shake_sigma)
if (occupancy_penalty is not None):
structure_shake.scatterers()[-1].occupancy = 0
if (gradient_flags.u_aniso):
shift_u_aniso(structure_shake, 0.001)
if (gradient_flags.u_iso):
shift_u_iso(structure_shake, 0.1)
assert tuple(structure_ideal.special_position_indices()) \
== tuple(structure_shake.special_position_indices())
if (0 or verbose):
print("structure_shake:")
structure_shake.show_summary().show_scatterers()
print()
for i_trial in range(10):
try:
minimizer = xray.minimization.lbfgs(
target_functor=target_functor(y_obs),
xray_structure=structure_shake,
occupancy_penalty=occupancy_penalty,
structure_factor_algorithm="direct")
except RuntimeError as e:
if (str(e).find("debye_waller_factor_exp: arg_limit exceeded") < 0):
raise
else:
break
else:
raise RuntimeError("Too many xray.minimization.lbfgs failures.")
if (0 or verbose):
print("first:", minimizer.first_target_value)
print("final:", minimizer.final_target_value)
print()
assert minimizer.final_target_value < minimizer.first_target_value
if (0 or verbose):
print("minimized structure_shake:")
structure_shake.show_summary().show_scatterers()
print()
f_final = y_obs.structure_factors_from_scatterers(
xray_structure=structure_shake,
algorithm="direct",
cos_sin_table=True).f_calc()
if data_type == 'F':
f_final = abs(f_final)
elif data_type == 'F^2':
f_final = f_final.norm()
c = flex.linear_correlation(y_obs.data(), f_final.data())
assert c.is_well_defined()
if (0 or verbose):
label = gradient_flags.string_of_true()
if (anomalous_flag):
label += ",anomalous"
print("correlation: %10.8f" % c.coefficient(), label)
print()
c_coefficient = c.coefficient()
if(c_coefficient <= 0.999):
print(c_coefficient)
if data_type == 'F':
assert c_coefficient > 0.999
elif data_type == 'F^2':
assert c_coefficient > 0.9
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)
