def exercise(flags, space_group_info):
# Prepare a structure compatible with the ShelX model
xs = random_structure.xray_structure(space_group_info,
elements="random",
n_scatterers=10,
use_u_iso=True,
random_u_iso=True,
use_u_aniso=True)
xs.apply_symmetry_sites()
xs.apply_symmetry_u_stars()
for isotropic, sc in itertools.izip(variate(bernoulli_distribution(0.4)),
xs.scatterers()):
sc.flags.set_grad_site(True)
if isotropic:
sc.flags.set_use_u_iso(True)
sc.flags.set_use_u_aniso(False)
sc.flags.set_grad_u_iso(True)
else:
sc.flags.set_use_u_iso(False)
sc.flags.set_use_u_aniso(True)
sc.flags.set_grad_u_aniso(True)
not_origin_centric = (xs.space_group().is_centric()
and not xs.space_group().is_origin_centric())
try:
ins = list(
shelx.writer.generator(xs,
full_matrix_least_squares_cycles=4,
weighting_scheme_params=(0, 0),
sort_scatterers=False))
except AssertionError:
if (not_origin_centric):
print(
"Omitted %s\n because it is centric but not origin centric" %
xs.space_group().type().hall_symbol())
return
raise
else:
if (not_origin_centric):
raise Exception_expected
ins = cStringIO.StringIO("".join(ins))
xs1 = xs.from_shelx(file=ins)
xs.crystal_symmetry().is_similar_symmetry(xs1.crystal_symmetry(),
relative_length_tolerance=1e-3,
absolute_angle_tolerance=1e-3)
uc = xs.unit_cell()
uc1 = xs1.unit_cell()
for sc, sc1 in itertools.izip(xs.scatterers(), xs1.scatterers()):
assert sc.label.upper() == sc1.label.upper()
assert sc.scattering_type == sc1.scattering_type
assert sc.flags.bits == sc1.flags.bits
assert approx_equal(sc.site, sc1.site, eps=1e-6)
if sc.flags.use_u_iso():
assert approx_equal(sc.u_iso, sc1.u_iso, eps=1e-5)
else:
assert approx_equal(adptbx.u_star_as_u_cif(uc, sc.u_star),
adptbx.u_star_as_u_cif(uc1, sc1.u_star),
eps=1e-5)
def exercise_variate_generators():
from scitbx.random \
import variate, normal_distribution, bernoulli_distribution, \
gamma_distribution, poisson_distribution
for i in range(10):
scitbx.random.set_random_seed(0)
g = variate(normal_distribution())
assert approx_equal(g(), -0.917787219374)
assert approx_equal(
g(10),
(1.21838707856, 1.732426915, 0.838038157555, -0.296895169923,
0.246451144946, -0.635474652255, -0.0980626986425, 0.36458295417,
0.534073780268, -0.665073136294))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 0, eps=0.005)
assert approx_equal(stat.biased_variance, 1, eps=0.005)
assert approx_equal(stat.skew, 0, eps=0.005)
assert approx_equal(stat.kurtosis, 3, eps=0.005)
bernoulli_seq = variate(bernoulli_distribution(0.1))
for b in itertools.islice(bernoulli_seq, 10):
assert b in (True, False)
bernoulli_sample = flex.bool(itertools.islice(bernoulli_seq, 10000))
assert approx_equal(bernoulli_sample.count(True) / len(bernoulli_sample),
0.1,
eps=0.01)
# Boost 1.64 changes the exponential distribution to use Ziggurat algorithm
scitbx.random.set_random_seed(0)
g = variate(gamma_distribution())
if (boost_version < 106400):
assert approx_equal(g(), 0.79587450456577546)
assert approx_equal(g(2), (0.89856038848394115, 1.2559307580473893))
else:
assert approx_equal(g(), 0.864758191783)
assert approx_equal(g(2), (1.36660841837, 2.26740986094))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 1, eps=0.005)
assert approx_equal(stat.skew, 2, eps=0.01)
assert approx_equal(stat.biased_variance, 1, eps=0.005)
scitbx.random.set_random_seed(0)
g = variate(gamma_distribution(alpha=2, beta=3))
assert approx_equal(g(), 16.670850592722729)
assert approx_equal(g(2), (10.03662877519449, 3.9357158398972873))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 6, eps=0.005)
assert approx_equal(stat.skew, 2 / math.sqrt(2), eps=0.05)
assert approx_equal(stat.biased_variance, 18, eps=0.05)
mean = 10.0
pv = variate(poisson_distribution(mean))
draws = pv(1000000).as_double()
m = flex.mean(draws)
v = flex.mean(draws * draws) - m * m
assert approx_equal(m, mean, eps=0.05)
assert approx_equal(v, mean, eps=0.05)
def exercise_variate_generators():
from scitbx.random \
import variate, normal_distribution, bernoulli_distribution, \
gamma_distribution, poisson_distribution
for i in xrange(10):
scitbx.random.set_random_seed(0)
g = variate(normal_distribution())
assert approx_equal(g(), -1.2780081289048213)
assert approx_equal(g(10),
(-0.40474189234755492, -0.41845505596083288,
-1.8825790263067721, -1.5779112018107659,
-1.1888174422378859, -1.8619619179878537,
-0.53946818661388318, -1.2400941724410812,
0.64511959841907285, -0.59934120033270688))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 0, eps=0.005)
assert approx_equal(stat.biased_variance, 1, eps=0.005)
assert approx_equal(stat.skew, 0, eps=0.005)
assert approx_equal(stat.kurtosis, 3, eps=0.005)
bernoulli_seq = variate(bernoulli_distribution(0.1))
for b in itertools.islice(bernoulli_seq, 10):
assert b in (True, False)
bernoulli_sample = flex.bool(itertools.islice(bernoulli_seq, 10000))
assert approx_equal(
bernoulli_sample.count(True)/len(bernoulli_sample),
0.1,
eps = 0.01)
scitbx.random.set_random_seed(0)
g = variate(gamma_distribution())
assert approx_equal(g(), 0.79587450456577546)
assert approx_equal(g(2), (0.89856038848394115, 1.2559307580473893))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 1, eps=0.005)
assert approx_equal(stat.skew, 2, eps=0.005)
assert approx_equal(stat.biased_variance, 1, eps=0.005)
scitbx.random.set_random_seed(0)
g = variate(gamma_distribution(alpha=2, beta=3))
assert approx_equal(g(), 16.670850592722729)
assert approx_equal(g(2), (10.03662877519449, 3.9357158398972873))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 6, eps=0.005)
assert approx_equal(stat.skew, 2/math.sqrt(2), eps=0.05)
assert approx_equal(stat.biased_variance, 18, eps=0.05)
mean = 10.0
pv = variate(poisson_distribution(mean))
draws = pv(1000000).as_double()
m = flex.mean(draws)
v = flex.mean(draws*draws) - m*m
assert approx_equal(m,mean,eps=0.05)
assert approx_equal(v,mean,eps=0.05)
def exercise(flags, space_group_info):
# Prepare a structure compatible with the ShelX model
xs = random_structure.xray_structure(
space_group_info, elements="random", n_scatterers=10, use_u_iso=True, random_u_iso=True, use_u_aniso=True
)
xs.apply_symmetry_sites()
xs.apply_symmetry_u_stars()
for isotropic, sc in itertools.izip(variate(bernoulli_distribution(0.4)), xs.scatterers()):
sc.flags.set_grad_site(True)
if isotropic:
sc.flags.set_use_u_iso(True)
sc.flags.set_use_u_aniso(False)
sc.flags.set_grad_u_iso(True)
else:
sc.flags.set_use_u_iso(False)
sc.flags.set_use_u_aniso(True)
sc.flags.set_grad_u_aniso(True)
not_origin_centric = xs.space_group().is_centric() and not xs.space_group().is_origin_centric()
try:
ins = list(
shelx.writer.generator(
xs, full_matrix_least_squares_cycles=4, weighting_scheme_params=(0, 0), sort_scatterers=False
)
)
except AssertionError:
if not_origin_centric:
print("Omitted %s\n because it is centric but not origin centric" % xs.space_group().type().hall_symbol())
return
raise
else:
if not_origin_centric:
raise Exception_expected
ins = cStringIO.StringIO("".join(ins))
xs1 = xs.from_shelx(file=ins)
xs.crystal_symmetry().is_similar_symmetry(
xs1.crystal_symmetry(), relative_length_tolerance=1e-3, absolute_angle_tolerance=1e-3
)
uc = xs.unit_cell()
uc1 = xs1.unit_cell()
for sc, sc1 in itertools.izip(xs.scatterers(), xs1.scatterers()):
assert sc.label.upper() == sc1.label.upper()
assert sc.scattering_type == sc1.scattering_type
assert sc.flags.bits == sc1.flags.bits
assert approx_equal(sc.site, sc1.site, eps=1e-6)
if sc.flags.use_u_iso():
assert approx_equal(sc.u_iso, sc1.u_iso, eps=1e-5)
else:
assert approx_equal(
adptbx.u_star_as_u_cif(uc, sc.u_star), adptbx.u_star_as_u_cif(uc1, sc1.u_star), eps=1e-5
)
def exercise_variate_generators():
from scitbx.random \
import variate, normal_distribution, bernoulli_distribution, \
gamma_distribution, poisson_distribution
for i in xrange(10):
scitbx.random.set_random_seed(0)
g = variate(normal_distribution())
assert approx_equal(g(), -1.2780081289048213)
assert approx_equal(
g(10),
(-0.40474189234755492, -0.41845505596083288, -1.8825790263067721,
-1.5779112018107659, -1.1888174422378859, -1.8619619179878537,
-0.53946818661388318, -1.2400941724410812, 0.64511959841907285,
-0.59934120033270688))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 0, eps=0.005)
assert approx_equal(stat.biased_variance, 1, eps=0.005)
assert approx_equal(stat.skew, 0, eps=0.005)
assert approx_equal(stat.kurtosis, 3, eps=0.005)
bernoulli_seq = variate(bernoulli_distribution(0.1))
for b in itertools.islice(bernoulli_seq, 10):
assert b in (True, False)
bernoulli_sample = flex.bool(itertools.islice(bernoulli_seq, 10000))
assert approx_equal(bernoulli_sample.count(True) / len(bernoulli_sample),
0.1,
eps=0.01)
scitbx.random.set_random_seed(0)
g = variate(gamma_distribution())
assert approx_equal(g(), 0.79587450456577546)
assert approx_equal(g(2), (0.89856038848394115, 1.2559307580473893))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 1, eps=0.005)
assert approx_equal(stat.skew, 2, eps=0.005)
assert approx_equal(stat.biased_variance, 1, eps=0.005)
scitbx.random.set_random_seed(0)
g = variate(gamma_distribution(alpha=2, beta=3))
assert approx_equal(g(), 16.670850592722729)
assert approx_equal(g(2), (10.03662877519449, 3.9357158398972873))
stat = basic_statistics(flex.double(itertools.islice(g, 1000000)))
assert approx_equal(stat.mean, 6, eps=0.005)
assert approx_equal(stat.skew, 2 / math.sqrt(2), eps=0.05)
assert approx_equal(stat.biased_variance, 18, eps=0.05)
mean = 10.0
pv = variate(poisson_distribution(mean))
draws = pv(1000000).as_double()
m = flex.mean(draws)
v = flex.mean(draws * draws) - m * m
assert approx_equal(m, mean, eps=0.05)
assert approx_equal(v, mean, eps=0.05)
