def exercise_restrained_refinement(options):
import random
random.seed(1)
flex.set_random_seed(1)
xs0 = smtbx.development.random_xray_structure(
sgtbx.space_group_info('P1'),
n_scatterers=options.n_scatterers,
elements="random")
for sc in xs0.scatterers():
sc.flags.set_grad_site(True)
sc0 = xs0.scatterers()
uc = xs0.unit_cell()
mi = xs0.build_miller_set(anomalous_flag=False, d_min=options.resolution)
fo_sq = mi.structure_factors_from_scatterers(
xs0, algorithm="direct").f_calc().norm()
fo_sq = fo_sq.customized_copy(sigmas=flex.double(fo_sq.size(), 1))
i, j, k, l = random.sample(xrange(options.n_scatterers), 4)
bond_proxies = geometry_restraints.shared_bond_simple_proxy()
w = 1e9
d_ij = uc.distance(sc0[i].site, sc0[j].site) * 0.8
bond_proxies.append(
geom.bond_simple_proxy(i_seqs=(i, j), distance_ideal=d_ij, weight=w))
d_jk = uc.distance(sc0[j].site, sc0[k].site) * 0.85
bond_proxies.append(
geom.bond_simple_proxy(i_seqs=(j, k), distance_ideal=d_jk, weight=w))
d_ki = min(
uc.distance(sc0[k].site, sc0[i].site) * 0.9, (d_ij + d_jk) * 0.8)
bond_proxies.append(
geom.bond_simple_proxy(i_seqs=(k, i), distance_ideal=d_ki, weight=w))
d_jl = uc.distance(sc0[j].site, sc0[l].site) * 0.9
bond_proxies.append(
geom.bond_simple_proxy(i_seqs=(j, l), distance_ideal=d_jl, weight=w))
d_lk = min(
uc.distance(sc0[l].site, sc0[k].site) * 0.8, 0.75 * (d_jk + d_jl))
bond_proxies.append(
geom.bond_simple_proxy(i_seqs=(l, k), distance_ideal=d_jl, weight=w))
restraints_manager = restraints.manager(bond_proxies=bond_proxies)
xs1 = xs0.deep_copy_scatterers()
xs1.shake_sites_in_place(rms_difference=0.1)
def ls_problem():
xs = xs1.deep_copy_scatterers()
reparametrisation = constraints.reparametrisation(
structure=xs,
constraints=[],
connectivity_table=smtbx.utils.connectivity_table(xs),
temperature=20)
return least_squares.crystallographic_ls(
fo_sq.as_xray_observations(),
reparametrisation=reparametrisation,
restraints_manager=restraints_manager)
gradient_threshold, step_threshold = 1e-6, 1e-6
eps = 5e-3
ls = ls_problem()
t = wall_clock_time()
cycles = normal_eqns_solving.naive_iterations(
ls,
gradient_threshold=gradient_threshold,
step_threshold=step_threshold,
track_all=True)
if options.verbose:
print "%i %s steps in %.6f s" % (cycles.n_iterations, cycles,
t.elapsed())
sc = ls.xray_structure.scatterers()
for p in bond_proxies:
d = uc.distance(*[sc[i_pair].site for i_pair in p.i_seqs])
assert approx_equal(d, p.distance_ideal, eps)
ls = ls_problem()
t = wall_clock_time()
cycles = normal_eqns_solving.levenberg_marquardt_iterations(
ls,
gradient_threshold=gradient_threshold,
step_threshold=step_threshold,
tau=1e-3,
track_all=True)
if options.verbose:
print "%i %s steps in %.6f s" % (cycles.n_iterations, cycles,
t.elapsed())
sc = ls.xray_structure.scatterers()
sc = ls.xray_structure.scatterers()
for p in bond_proxies:
d = uc.distance(*[sc[i].site for i in p.i_seqs])
assert approx_equal(d, p.distance_ideal, eps)
def exercise_restrained_refinement(options):
import random
random.seed(1)
flex.set_random_seed(1)
xs0 = smtbx.development.random_xray_structure(
sgtbx.space_group_info('P1'),
n_scatterers=options.n_scatterers,
elements="random")
for sc in xs0.scatterers():
sc.flags.set_grad_site(True)
sc0 = xs0.scatterers()
uc = xs0.unit_cell()
mi = xs0.build_miller_set(anomalous_flag=False, d_min=options.resolution)
fo_sq = mi.structure_factors_from_scatterers(
xs0, algorithm="direct").f_calc().norm()
fo_sq = fo_sq.customized_copy(sigmas=flex.double(fo_sq.size(), 1))
i, j, k, l = random.sample(xrange(options.n_scatterers), 4)
bond_proxies = geometry_restraints.shared_bond_simple_proxy()
w = 1e9
d_ij = uc.distance(sc0[i].site, sc0[j].site)*0.8
bond_proxies.append(geom.bond_simple_proxy(
i_seqs=(i, j),
distance_ideal=d_ij,
weight=w))
d_jk = uc.distance(sc0[j].site, sc0[k].site)*0.85
bond_proxies.append(geom.bond_simple_proxy(
i_seqs=(j, k),
distance_ideal=d_jk,
weight=w))
d_ki = min(uc.distance(sc0[k].site, sc0[i].site)*0.9, (d_ij + d_jk)*0.8)
bond_proxies.append(geom.bond_simple_proxy(
i_seqs=(k, i),
distance_ideal=d_ki,
weight=w))
d_jl = uc.distance(sc0[j].site, sc0[l].site)*0.9
bond_proxies.append(geom.bond_simple_proxy(
i_seqs=(j, l),
distance_ideal=d_jl,
weight=w))
d_lk = min(uc.distance(sc0[l].site, sc0[k].site)*0.8, 0.75*(d_jk + d_jl))
bond_proxies.append(geom.bond_simple_proxy(
i_seqs=(l, k),
distance_ideal=d_jl,
weight=w))
restraints_manager = restraints.manager(bond_proxies=bond_proxies)
xs1 = xs0.deep_copy_scatterers()
xs1.shake_sites_in_place(rms_difference=0.1)
def ls_problem():
xs = xs1.deep_copy_scatterers()
reparametrisation = constraints.reparametrisation(
structure=xs,
constraints=[],
connectivity_table=smtbx.utils.connectivity_table(xs),
temperature=20)
return least_squares.crystallographic_ls(
fo_sq.as_xray_observations(),
reparametrisation=reparametrisation,
restraints_manager=restraints_manager)
gradient_threshold, step_threshold = 1e-6, 1e-6
eps = 5e-3
ls = ls_problem()
t = wall_clock_time()
cycles = normal_eqns_solving.naive_iterations(
ls,
gradient_threshold=gradient_threshold,
step_threshold=step_threshold,
track_all=True)
if options.verbose:
print "%i %s steps in %.6f s" % (cycles.n_iterations, cycles, t.elapsed())
sc = ls.xray_structure.scatterers()
for p in bond_proxies:
d = uc.distance(*[ sc[i_pair].site for i_pair in p.i_seqs ])
assert approx_equal(d, p.distance_ideal, eps)
ls = ls_problem()
t = wall_clock_time()
cycles = normal_eqns_solving.levenberg_marquardt_iterations(
ls,
gradient_threshold=gradient_threshold,
step_threshold=step_threshold,
tau=1e-3,
track_all=True)
if options.verbose:
print "%i %s steps in %.6f s" % (cycles.n_iterations, cycles, t.elapsed())
sc = ls.xray_structure.scatterers()
sc = ls.xray_structure.scatterers()
for p in bond_proxies:
d = uc.distance(*[ sc[i].site for i in p.i_seqs ])
assert approx_equal(d, p.distance_ideal, eps)
def exercise_direct(space_group_info,
elements,
anomalous_flag=False,
use_random_u_iso=False,
verbose=0):
xs = random_structure.xray_structure(
space_group_info=space_group_info,
elements=elements,
random_u_iso=use_random_u_iso,
volume_per_atom=18.6,
min_distance=1.2)
#show_diagnostics(xs)
reciprocal_volume = xs.unit_cell().reciprocal().volume()
times = []
cuda_platform = direct_summation_cuda_platform()
cuda_platform_float = direct_summation_cuda_platform(float_t="float")
direct_reference = True
for x in xrange(1,7):
print "There are %d scatterers"%len(elements)
number_of_reflections = pow(10.,x)
Volume = number_of_reflections * reciprocal_volume * 2. #2 P1 asymmetric units
Volume *= space_group_info.group().order_z() # take space group into acct.
recip_radius = pow(3.*Volume/(4.*pi),1./3.)
d_min = 1./recip_radius
if 0:
cos_sin_table = math_module.cos_sin_table(2**10)
if 1:
cos_sin_table = False
if direct_reference:
timer = wall_clock_time()
cpu_direct = xs.structure_factors(d_min=d_min,algorithm="direct",
cos_sin_table=cos_sin_table)
cpu_time = timer.elapsed()
cpu_direct_f = cpu_direct.f_calc()
timer = wall_clock_time()
simple_direct = xs.structure_factors(d_min=d_min,algorithm=direct_summation_simple())
simple_time = timer.elapsed()
simple_direct_f = simple_direct.f_calc()
else:
cpu_time=0
simple_time=0
timer = wall_clock_time()
gpu_direct = xs.structure_factors(d_min=d_min,algorithm=cuda_platform)
gpu_time = timer.elapsed()
gpu_direct_f = gpu_direct.f_calc()
gpu_amplitude = gpu_direct_f.as_amplitude_array()
sum_amp = flex.sum(gpu_amplitude.data())
timer = wall_clock_time()
gpuf_direct = xs.structure_factors(d_min=d_min,algorithm=cuda_platform_float)
gpuf_time = timer.elapsed()
gpuf_direct_f = gpuf_direct.f_calc()
gpuf_amplitude = gpuf_direct_f.as_amplitude_array()
abs_diff = flex.abs(gpuf_amplitude.data() - gpu_amplitude.data())
sum_diff = flex.sum(abs_diff)
gpuf_Rfactor = 100.*sum_diff/sum_amp
timer = wall_clock_time()
fft_algorithm = xs.structure_factors(d_min=d_min,algorithm="fft")
fft_time = timer.elapsed()
fft_f = fft_algorithm.f_calc()
fft_amplitude = fft_f.as_amplitude_array()
abs_diff = flex.abs(fft_amplitude.data() - gpu_amplitude.data())
sum_diff = flex.sum(abs_diff)
fft_Rfactor = 100.*sum_diff/sum_amp
times.append((number_of_reflections,cpu_time,d_min,
gpu_time,
gpuf_time,gpuf_Rfactor,
simple_time,
fft_time,fft_Rfactor))
# doesn't assert correctly: assert approx_equal(cpu_direct_f.data(), fft_f.data())
if direct_reference:
assert approx_equal(cpu_direct_f.data(), gpu_direct_f.data(), eps=1e-6)
assert approx_equal(cpu_direct_f.data(), simple_direct_f.data(), eps=1e-6)
show_times_vs_complexity(times, header="run time vs. # reflections")
def exercise_direct(space_group_info,
elements,
anomalous_flag=False,
use_random_u_iso=False,
verbose=0):
xs = random_structure.xray_structure(space_group_info=space_group_info,
elements=elements,
random_u_iso=use_random_u_iso,
volume_per_atom=18.6,
min_distance=1.2)
#show_diagnostics(xs)
reciprocal_volume = xs.unit_cell().reciprocal().volume()
times = []
cuda_platform = direct_summation_cuda_platform()
cuda_platform_float = direct_summation_cuda_platform(float_t="float")
direct_reference = True
for x in range(1, 7):
print("There are %d scatterers" % len(elements))
number_of_reflections = pow(10., x)
Volume = number_of_reflections * reciprocal_volume * 2. #2 P1 asymmetric units
Volume *= space_group_info.group().order_z(
) # take space group into acct.
recip_radius = pow(3. * Volume / (4. * pi), 1. / 3.)
d_min = 1. / recip_radius
if 0:
cos_sin_table = math_module.cos_sin_table(2**10)
if 1:
cos_sin_table = False
if direct_reference:
timer = wall_clock_time()
cpu_direct = xs.structure_factors(d_min=d_min,
algorithm="direct",
cos_sin_table=cos_sin_table)
cpu_time = timer.elapsed()
cpu_direct_f = cpu_direct.f_calc()
timer = wall_clock_time()
simple_direct = xs.structure_factors(
d_min=d_min, algorithm=direct_summation_simple())
simple_time = timer.elapsed()
simple_direct_f = simple_direct.f_calc()
else:
cpu_time = 0
simple_time = 0
timer = wall_clock_time()
gpu_direct = xs.structure_factors(d_min=d_min, algorithm=cuda_platform)
gpu_time = timer.elapsed()
gpu_direct_f = gpu_direct.f_calc()
gpu_amplitude = gpu_direct_f.as_amplitude_array()
sum_amp = flex.sum(gpu_amplitude.data())
timer = wall_clock_time()
gpuf_direct = xs.structure_factors(d_min=d_min,
algorithm=cuda_platform_float)
gpuf_time = timer.elapsed()
gpuf_direct_f = gpuf_direct.f_calc()
gpuf_amplitude = gpuf_direct_f.as_amplitude_array()
abs_diff = flex.abs(gpuf_amplitude.data() - gpu_amplitude.data())
sum_diff = flex.sum(abs_diff)
gpuf_Rfactor = 100. * sum_diff / sum_amp
timer = wall_clock_time()
fft_algorithm = xs.structure_factors(d_min=d_min, algorithm="fft")
fft_time = timer.elapsed()
fft_f = fft_algorithm.f_calc()
fft_amplitude = fft_f.as_amplitude_array()
abs_diff = flex.abs(fft_amplitude.data() - gpu_amplitude.data())
sum_diff = flex.sum(abs_diff)
fft_Rfactor = 100. * sum_diff / sum_amp
times.append(
(number_of_reflections, cpu_time, d_min, gpu_time, gpuf_time,
gpuf_Rfactor, simple_time, fft_time, fft_Rfactor))
# doesn't assert correctly: assert approx_equal(cpu_direct_f.data(), fft_f.data())
if direct_reference:
assert approx_equal(cpu_direct_f.data(),
gpu_direct_f.data(),
eps=1e-6)
assert approx_equal(cpu_direct_f.data(),
simple_direct_f.data(),
eps=1e-6)
show_times_vs_complexity(times, header="run time vs. # reflections")
