def exercise_value(f_c_in_p1, f_o, flags, n_sampled):
""" where we check against the trusted correlation map implemented
in cctbx.translation_search """
crystal_gridding = f_o.crystal_gridding(
symmetry_flags=translation_search.symmetry_flags(
is_isotropic_search_model=False, have_f_part=False),
resolution_factor=1 / 3)
correlation_map = translation_search.fast_nv1995(
gridding=crystal_gridding.n_real(),
space_group=f_o.space_group(),
anomalous_flag=f_o.anomalous_flag(),
miller_indices_f_obs=f_o.indices(),
f_obs=f_o.data(),
f_part=flex.complex_double(), ## no sub-structure is already fixed
miller_indices_p1_f_calc=f_c_in_p1.indices(),
p1_f_calc=f_c_in_p1.data()).target_map()
nx, ny, nz = n = crystal_gridding.n_real()
def sampled_indices():
from random import randrange
yield (0, 0, 0)
for i in xrange(n_sampled - 1):
yield randrange(0, n[0]), randrange(0, n[1]), randrange(0, n[2])
f_o_sq = f_o.as_intensity_array()
for i, j, k in sampled_indices():
x = (i / nx, j / ny, k / nz)
if random.random() > 0.5: f_o_or_f_o_sq = f_o
else: f_o_or_f_o_sq = f_o_sq
gos = symmetrised_shifted_structure_factors(f_o_or_f_o_sq, f_c_in_p1,
x).misfit(f_o_or_f_o_sq)
assert approx_equal(gos.correlation, correlation_map[i, j,
k]), (i, j, k)
def exercise_value(f_c_in_p1, f_o, flags, n_sampled):
""" where we check against the trusted correlation map implemented
in cctbx.translation_search """
crystal_gridding = f_o.crystal_gridding(
symmetry_flags=translation_search.symmetry_flags(
is_isotropic_search_model=False,
have_f_part=False),
resolution_factor=1/3)
correlation_map = translation_search.fast_nv1995(
gridding=crystal_gridding.n_real(),
space_group=f_o.space_group(),
anomalous_flag=f_o.anomalous_flag(),
miller_indices_f_obs=f_o.indices(),
f_obs=f_o.data(),
f_part=flex.complex_double(), ## no sub-structure is already fixed
miller_indices_p1_f_calc=f_c_in_p1.indices(),
p1_f_calc=f_c_in_p1.data()).target_map()
nx, ny, nz = n = crystal_gridding.n_real()
def sampled_indices():
from random import randrange
yield (0,0,0)
for i in xrange(n_sampled - 1):
yield randrange(0, n[0]), randrange(0, n[1]), randrange(0, n[2])
f_o_sq = f_o.as_intensity_array()
for i,j,k in sampled_indices():
x = (i/nx, j/ny, k/nz)
if random.random() > 0.5: f_o_or_f_o_sq = f_o
else: f_o_or_f_o_sq = f_o_sq
gos = symmetrised_shifted_structure_factors(
f_o_or_f_o_sq, f_c_in_p1, x).misfit(f_o_or_f_o_sq)
assert approx_equal(gos.correlation, correlation_map[i,j,k]), (i,j,k)
def exercise_gradient(f_c_in_p1, f_o, flags, n_sampled):
""" where we use finite differences to check our derivatives """
from random import random
from scitbx.math import approx_equal_relatively
f_o_sq = f_o.as_intensity_array()
for i in xrange(n_sampled):
x = mat.col((random(), random(), random()))
gos = symmetrised_shifted_structure_factors(
f_o_sq, f_c_in_p1, x, compute_gradient=True).misfit(f_o_sq)
for j,e in enumerate([ (1,0,0), (0,1,0), (0,0,1) ]):
h = mat.col(e)*1e-3
fm3, fm2, fm1, f1, f2, f3 = [
symmetrised_shifted_structure_factors(
f_o_sq, f_c_in_p1, y).misfit(f_o_sq).value
for y in (x-3*h, x-2*h, x-h, x+h, x+2*h, x+3*h) ]
finite_diff = ((f3 - fm3)/60 - 3/20*(f2 - fm2) + 3/4*(f1 - fm1))/abs(h)
assert approx_equal_relatively(gos.gradient[j], finite_diff,
relative_error=1e-2,
near_zero_threshold=1e-6), \
(j, i, tuple(x), gos.gradient[j], finite_diff)
def exercise_gradient(f_c_in_p1, f_o, flags, n_sampled):
""" where we use finite differences to check our derivatives """
from random import random
from scitbx.math import approx_equal_relatively
f_o_sq = f_o.as_intensity_array()
for i in xrange(n_sampled):
x = mat.col((random(), random(), random()))
gos = symmetrised_shifted_structure_factors(
f_o_sq, f_c_in_p1, x, compute_gradient=True).misfit(f_o_sq)
for j, e in enumerate([(1, 0, 0), (0, 1, 0), (0, 0, 1)]):
h = mat.col(e) * 1e-3
fm3, fm2, fm1, f1, f2, f3 = [
symmetrised_shifted_structure_factors(f_o_sq, f_c_in_p1,
y).misfit(f_o_sq).value
for y in (x - 3 * h, x - 2 * h, x - h, x + h, x + 2 * h,
x + 3 * h)
]
finite_diff = ((f3 - fm3) / 60 - 3 / 20 * (f2 - fm2) + 3 / 4 *
(f1 - fm1)) / abs(h)
assert approx_equal_relatively(gos.gradient[j], finite_diff,
relative_error=1e-2,
near_zero_threshold=1e-6), \
(j, i, tuple(x), gos.gradient[j], finite_diff)
