def exercise_term_gradients_d_ab(term, x_max=1., n_points=50):
for i in xrange(n_points+1):
x = x_max * i / n_points
grad_finite = term_finite_gradient_d_ab_at_x(term, x)
grad_analytical = term.gradients_d_ab_at_x_sq(x*x)
assert eps_eq(grad_finite.a, grad_analytical.a)
assert eps_eq(grad_finite.b, grad_analytical.b)
def recycle():
for n, first, last in [[(5, 3, 4), (0, 0, 0), (3, 5, 6)],
[(4, 3, 5), (-1, -3, 4), (6, 4, 5)],
[(3, 4, 5), (-2, 3, 0), (-2, 3, 0)],
[(3, 4, 5), (-2, 3, 0), (-2, 3, 3)],
[(3, 4, 5), (-2, 3, 0), (-2, 8, 0)],
[(3, 4, 5), (-2, 3, 0), (-2, 9, 0)],
[(3, 4, 5), (-2, 3, 0), (3, 3, 0)],
[(3, 4, 5), (-2, 3, 0), (4, 3, 0)]]:
gridding = iotbx.xplor.map.gridding(n=n, first=first, last=last)
flex_grid = gridding.as_flex_grid()
data = 20000 * flex.random_double(size=flex_grid.size_1d()) - 10000
data.resize(flex_grid)
stats = maptbx.statistics(data)
iotbx.xplor.map.writer(file_name="tmp.map",
title_lines=["regression test"],
unit_cell=uctbx.unit_cell(
(10, 20, 30, 80, 90, 100)),
gridding=gridding,
data=data,
average=stats.mean(),
standard_deviation=stats.sigma())
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["regression test"]
assert read.gridding.n == n
assert read.gridding.first == first
assert read.gridding.last == last
assert read.unit_cell.is_similar_to(
uctbx.unit_cell((10, 20, 30, 80, 90, 100)))
assert eps_eq(read.average, stats.mean(), eps=1.e-4)
assert eps_eq(read.standard_deviation, stats.sigma(), eps=1.e-4)
assert read.data.origin() == first
assert read.data.last(False) == last
assert read.data.focus() == data.focus()
assert eps_eq(read.data, data, eps=1.e-4)
def exercise_term_gradients_d_ab(term, x_max=1., n_points=50):
for i in range(n_points + 1):
x = x_max * i / n_points
grad_finite = term_finite_gradient_d_ab_at_x(term, x)
grad_analytical = term.gradients_d_ab_at_x_sq(x * x)
assert eps_eq(grad_finite.a, grad_analytical.a)
assert eps_eq(grad_finite.b, grad_analytical.b)
def exercise_integral_dx(gaussian, x_max=1., n_points=1000):
numerical_integral = 0
x_step = x_max / n_points
for i in xrange(n_points+1):
x = x_max * i / n_points
new_value = gaussian.at_x(x)
if (i):
numerical_integral += (prev_value + new_value) * .5
prev_value = new_value
analytical_integral = gaussian.integral_dx_at_x(x, 1.e-3)
assert eps_eq(analytical_integral, gaussian.integral_dx_at_x(x))
assert eps_eq(numerical_integral*x_step, analytical_integral, eps=1.e-5)
def exercise_integral_dx(gaussian, x_max=1., n_points=1000):
numerical_integral = 0
x_step = x_max / n_points
for i in range(n_points + 1):
x = x_max * i / n_points
new_value = gaussian.at_x(x)
if (i):
numerical_integral += (prev_value + new_value) * .5
prev_value = new_value
analytical_integral = gaussian.integral_dx_at_x(x, 1.e-3)
assert eps_eq(analytical_integral, gaussian.integral_dx_at_x(x))
assert eps_eq(numerical_integral * x_step,
analytical_integral,
eps=1.e-5)
def exercise_fft_map_as_xplor_map(space_group_info, n_elements=10, d_min=3):
structure = random_structure.xray_structure(
space_group_info,
elements=["Si"]*n_elements,
volume_per_atom=1000,
min_distance=3.,
general_positions_only=False)
f_calc = structure.structure_factors(
d_min=d_min, anomalous_flag=False).f_calc()
fft_map = f_calc.fft_map()
fft_map.as_xplor_map(
file_name="tmp.map",
gridding_last=[n-1 for n in fft_map.n_real()])
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["cctbx.miller.fft_map"]
assert read.gridding.n == fft_map.n_real()
assert approx_equal(flex.linear_correlation(
read.data.as_1d(),
fft_map.real_map_unpadded(in_place=False).as_1d()).coefficient(), 1)
for first,last in [[(0,0,0),(3,5,6)],
[(-1,-3,4),(6,4,5)],
[(-2,3,0),(-2,3,0)],
[(-2,3,0),(-2,3,3)],
[(-2,3,0),(-2,8,0)],
[(-2,3,0),(-2,9,0)],
[(-2,3,0),(3,3,0)],
[(-2,3,0),(4,3,0)]]:
fft_map.as_xplor_map(
file_name="tmp.map",
gridding_first=first,
gridding_last=last)
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["cctbx.miller.fft_map"]
assert read.gridding.n == fft_map.n_real()
assert read.gridding.first == first
assert read.gridding.last == last
real_map = fft_map.real_map()
first_p1 = [i%n for i,n in zip(first, fft_map.n_real())]
assert eps_eq(read.data[first], real_map[first_p1], eps=1.e-4)
last_p1 = [i%n for i,n in zip(last, fft_map.n_real())]
assert eps_eq(read.data[last], real_map[last_p1], eps=1.e-4)
for x in xrange(1,10):
point = [iround(f+(l-f)*x/10.) for f,l in zip(first,last)]
point_p1 = [i%n for i,n in zip(point, fft_map.n_real())]
assert eps_eq(read.data[point], real_map[point_p1], eps=1.e-4)
def exercise_fft_map_as_xplor_map(space_group_info, n_elements=10, d_min=3):
structure = random_structure.xray_structure(space_group_info,
elements=["Si"] * n_elements,
volume_per_atom=1000,
min_distance=3.,
general_positions_only=False)
f_calc = structure.structure_factors(d_min=d_min,
anomalous_flag=False).f_calc()
fft_map = f_calc.fft_map()
fft_map.as_xplor_map(file_name="tmp.map",
gridding_last=[n - 1 for n in fft_map.n_real()])
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["cctbx.miller.fft_map"]
assert read.gridding.n == fft_map.n_real()
assert approx_equal(
flex.linear_correlation(
read.data.as_1d(),
fft_map.real_map_unpadded(in_place=False).as_1d()).coefficient(),
1)
for first, last in [[(0, 0, 0), (3, 5, 6)], [(-1, -3, 4), (6, 4, 5)],
[(-2, 3, 0), (-2, 3, 0)], [(-2, 3, 0), (-2, 3, 3)],
[(-2, 3, 0), (-2, 8, 0)], [(-2, 3, 0), (-2, 9, 0)],
[(-2, 3, 0), (3, 3, 0)], [(-2, 3, 0), (4, 3, 0)]]:
fft_map.as_xplor_map(file_name="tmp.map",
gridding_first=first,
gridding_last=last)
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["cctbx.miller.fft_map"]
assert read.gridding.n == fft_map.n_real()
assert read.gridding.first == first
assert read.gridding.last == last
real_map = fft_map.real_map()
first_p1 = [i % n for i, n in zip(first, fft_map.n_real())]
assert eps_eq(read.data[first], real_map[first_p1], eps=1.e-4)
last_p1 = [i % n for i, n in zip(last, fft_map.n_real())]
assert eps_eq(read.data[last], real_map[last_p1], eps=1.e-4)
for x in xrange(1, 10):
point = [
iround(f + (l - f) * x / 10.) for f, l in zip(first, last)
]
point_p1 = [i % n for i, n in zip(point, fft_map.n_real())]
assert eps_eq(read.data[point], real_map[point_p1], eps=1.e-4)
def recycle():
for n,first,last in [[(5,3,4),(0,0,0),(3,5,6)],
[(4,3,5),(-1,-3,4),(6,4,5)],
[(3,4,5),(-2,3,0),(-2,3,0)],
[(3,4,5),(-2,3,0),(-2,3,3)],
[(3,4,5),(-2,3,0),(-2,8,0)],
[(3,4,5),(-2,3,0),(-2,9,0)],
[(3,4,5),(-2,3,0),(3,3,0)],
[(3,4,5),(-2,3,0),(4,3,0)]]:
gridding = iotbx.xplor.map.gridding(
n=n, first=first, last=last)
flex_grid = gridding.as_flex_grid()
data = 20000*flex.random_double(size=flex_grid.size_1d())-10000
data.resize(flex_grid)
stats = maptbx.statistics(data)
iotbx.xplor.map.writer(
file_name="tmp.map",
title_lines=["regression test"],
unit_cell=uctbx.unit_cell((10,20,30,80,90,100)),
gridding=gridding,
data=data,
average=stats.mean(),
standard_deviation=stats.sigma())
read = iotbx.xplor.map.reader(file_name="tmp.map")
assert read.title_lines == ["regression test"]
assert read.gridding.n == n
assert read.gridding.first == first
assert read.gridding.last == last
assert read.unit_cell.is_similar_to(
uctbx.unit_cell((10,20,30,80,90,100)))
assert eps_eq(read.average, stats.mean(), eps=1.e-4)
assert eps_eq(read.standard_deviation, stats.sigma(), eps=1.e-4)
assert read.data.origin() == first
assert read.data.last(False) == last
assert read.data.focus() == data.focus()
assert eps_eq(read.data, data, eps=1.e-4)
def exercise_term():
t = gaussian.term(2,3)
assert approx_equal(t.a, 2)
assert approx_equal(t.b, 3)
assert approx_equal(t.at_x_sq(4), 2*math.exp(-3*4))
assert approx_equal(t.at_x(2), 2*math.exp(-3*4))
eps = 1.e-5
for ix in (xrange(10)):
x = ix/10.
assert eps_eq((t.at_x(x+eps)-t.at_x(x-eps))/(2*eps), t.gradient_dx_at_x(x))
for f in [1,-1]:
for t in [gaussian.term(f*2,3),
gaussian.term(f*3,0),
gaussian.term(f*4,1.e-4),
gaussian.term(f*5,-1)]:
exercise_gradient_dx(t)
exercise_integral_dx(t)
exercise_term_gradients_d_ab(t)
def exercise_term():
t = gaussian.term(2, 3)
assert approx_equal(t.a, 2)
assert approx_equal(t.b, 3)
assert approx_equal(t.at_x_sq(4), 2 * math.exp(-3 * 4))
assert approx_equal(t.at_x(2), 2 * math.exp(-3 * 4))
eps = 1.e-5
for ix in (range(10)):
x = ix / 10.
assert eps_eq((t.at_x(x + eps) - t.at_x(x - eps)) / (2 * eps),
t.gradient_dx_at_x(x))
for f in [1, -1]:
for t in [
gaussian.term(f * 2, 3),
gaussian.term(f * 3, 0),
gaussian.term(f * 4, 1.e-4),
gaussian.term(f * 5, -1)
]:
exercise_gradient_dx(t)
exercise_integral_dx(t)
exercise_term_gradients_d_ab(t)
def driver1(use_fortran_library=False):
n = 25
nbd = flex.int(n)
x = flex.double(n)
l = flex.double(n)
u = flex.double(n)
g = flex.double(n)
if ("--Verbose" in sys.argv[1:]):
iprint = 1000
else:
iprint = -1
for i in range(0, n, 2):
nbd[i] = 2
l[i] = 1.0e0
u[i] = 1.0e2
for i in range(1, n, 2):
nbd[i] = 2
l[i] = -1.0e2
u[i] = 1.0e2
for i in range(n):
x[i] = 3.0e0
minimizer = lbfgsb.minimizer(n=n,
m=5,
l=l,
u=u,
nbd=nbd,
factr=1.0e+7,
pgtol=1.0e-5,
iprint=iprint)
f = 0
while True:
if (minimizer.process(x, f, g, use_fortran_library)):
f = .25e0 * (x[0] - 1.e0)**2
for i in range(1, n):
f = f + (x[i] - x[i - 1]**2)**2
f = 4.e0 * f
t1 = x[1] - x[0]**2
g[0] = 2.e0 * (x[0] - 1.e0) - 1.6e1 * x[0] * t1
for i in range(1, n - 1):
t2 = t1
t1 = x[i + 1] - x[i]**2
g[i] = 8.e0 * t2 - 1.6e1 * x[i] * t1
g[n - 1] = 8.e0 * t1
elif (minimizer.is_terminated()):
break
assert minimizer.task(
) == "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
assert minimizer.f_list().size() == minimizer.n_iteration() + 1
assert minimizer.f_list()[-1] == minimizer.f()
assert minimizer.f_list()[-2] == minimizer.f_previous_iteration()
assert not minimizer.initial_x_replaced_by_projection()
assert minimizer.is_constrained()
assert minimizer.is_fully_constrained()
if ("--soft" not in sys.argv[1:]):
assert minimizer.n_fg_evaluations_total() == 27
assert minimizer.n_fg_evaluations_iter() == 1
assert minimizer.n_intervals_explored_cauchy_search_total() == 48
assert minimizer.n_intervals_explored_cauchy_search_iter() == 1
assert minimizer.n_skipped_bfgs_updates_total() == 0
assert minimizer.n_bfgs_updates_total() == 22
assert minimizer.subspace_argmin_is_within_box() == 1
assert minimizer.n_free_variables() == 25
assert minimizer.n_active_constraints() == 0
assert minimizer.n_variables_leaving_active_set() == 0
assert minimizer.n_variables_entering_active_set() == 0
assert eps_eq(minimizer.theta_bfgs_matrix_current(), 23.2674689856)
assert minimizer.f_previous_iteration() >= 0
assert minimizer.floating_point_epsilon() \
== scitbx.math.floating_point_epsilon_double_get()
assert eps_eq(minimizer.factr_times_floating_point_epsilon(),
minimizer.factr() * minimizer.floating_point_epsilon())
assert eps_eq(minimizer.two_norm_line_search_direction_vector(),
1.56259327735e-05)
assert eps_eq(minimizer.two_norm_line_search_direction_vector_sq(),
minimizer.two_norm_line_search_direction_vector()**2)
assert minimizer.accumulated_time_cauchy_search() > -0.02
assert minimizer.accumulated_time_subspace_minimization() > -0.02
assert minimizer.accumulated_time_line_search() > -0.02
assert eps_eq(minimizer.slope_line_search_function_current(),
9.31496613169e-10)
assert eps_eq(minimizer.slope_line_search_function_start(),
-3.6762390377e-09)
assert eps_eq(minimizer.maximum_relative_step_length(), 1.37484166517)
assert eps_eq(minimizer.relative_step_length_line_search(), 1.0)
assert eps_eq(minimizer.infinity_norm_projected_gradient(),
0.000143369780806)
def exercise_two_models_with_holes(processed_pdb):
selection_strings = ["chain A", "chain B", "chain C", "chain D"]
group = ncs.restraints.group.from_atom_selections(
processed_pdb=processed_pdb,
reference_selection_string=None,
selection_strings=selection_strings,
coordinate_sigma=0.05,
b_factor_weight=0.4321,
special_position_warnings_only=False)
sites_cart = processed_pdb.all_chain_proxies.pdb_atoms.extract_xyz()
ncs_operators = group.operators(sites_cart=sites_cart)
out = StringIO()
ncs_operators.show(sites_cart=sites_cart, out=out, prefix="{*")
assert not show_diff(
out.getvalue(), """\
{*NCS operator 1:
{* Reference selection: "chain A"
{* Other selection: "chain B"
{* Number of atom pairs: 22
{* Rotation={{-0.925533, 0.322815, -0.197938},
{* {0.329616, 0.429511, -0.840758},
{* {-0.186393, -0.843393, -0.503931}}
{* Translation={{163.62}, {-13.0292}, {44.8533}}
{* Histogram of differences:
{* 0.092573 - 0.238983: 1
{* 0.238983 - 0.385393: 2
{* 0.385393 - 0.531803: 7
{* 0.531803 - 0.678214: 3
{* 0.678214 - 0.824624: 4
{* 0.824624 - 0.971034: 5
{* RMS difference with respect to the reference: 0.653687
{*NCS operator 2:
{* Reference selection: "chain A"
{* Other selection: "chain C"
{* Number of atom pairs: 32
{* Rotation={{-0.988874, -0.139883, -0.0506023},
{* {0.0139383, -0.425808, 0.904706},
{* {-0.1481, 0.893935, 0.423021}}
{* Translation={{177.315}, {18.2319}, {1.24026}}
{* Histogram of differences:
{* 0.291817 - 0.418479: 2
{* 0.418479 - 0.545141: 9
{* 0.545141 - 0.671802: 5
{* 0.671802 - 0.798464: 10
{* 0.798464 - 0.925126: 3
{* 0.925126 - 1.051787: 3
{* RMS difference with respect to the reference: 0.677436
{*NCS operator 3:
{* Reference selection: "chain A"
{* Other selection: "chain D"
{* Number of atom pairs: 24
{* Rotation={{0.950594, -0.191857, 0.244055},
{* {-0.192933, -0.981014, -0.0197252},
{* {0.243205, -0.0283355, -0.969561}}
{* Translation={{6.77797}, {56.2476}, {-5.96327}}
{* Histogram of differences:
{* 0.270982 - 0.414517: 3
{* 0.414517 - 0.558053: 3
{* 0.558053 - 0.701588: 7
{* 0.701588 - 0.845124: 6
{* 0.845124 - 0.988659: 2
{* 0.988659 - 1.132195: 3
{* RMS difference with respect to the reference: 0.724248
""")
energies_sites_no_gradients = ncs_operators.energies_sites(
sites_cart=sites_cart, compute_gradients=False)
assert energies_sites_no_gradients.number_of_restraints == 110
assert eps_eq(energies_sites_no_gradients.residual_sum, 7014.03969257)
assert eps_eq(energies_sites_no_gradients.target, 7014.03969257)
assert energies_sites_no_gradients.gradients is None
assert eps_eq(energies_sites_no_gradients.rms_with_respect_to_average, [
0.41226641576521778, 0.38139080907663186, 0.39748408968570492,
0.40001937328488651
])
energies_sites = ncs_operators.energies_sites(sites_cart=sites_cart)
assert energies_sites_no_gradients.number_of_restraints \
== energies_sites.number_of_restraints
assert energies_sites_no_gradients.residual_sum \
== energies_sites.residual_sum
assert energies_sites_no_gradients.target \
== energies_sites.target
assert eps_eq(energies_sites.gradients.norm(), 3349.99455344)
assert eps_eq(energies_sites.rms_with_respect_to_average,
energies_sites_no_gradients.rms_with_respect_to_average)
site_labels = [
'"' + atom.pdb_label_columns() + '"'
for atom in processed_pdb.all_chain_proxies.pdb_atoms
]
out = StringIO()
energies_sites.show_distances_to_average(site_labels=site_labels,
out=out,
prefix="#^")
assert not show_diff(out.getvalue(),
"""\
#^NCS selection: "chain A"
#^ Distance to NCS average
#^ " N GLN A 1 ": 0.4263
#^ " CA GLN A 1 ": 0.2141
#^ " C GLN A 1 ": 0.4052
...
#^ " CA THR B 6 ": 0.4001
#^ " C THR B 6 ": 0.6281
#^NCS selection: "chain C"
#^ Distance to NCS average
#^ " N GLN C 1 ": 0.4135
#^ " CA GLN C 1 ": 0.5070
...
#^ " C SER D 4 ": 0.6943
#^ " CA BSER D 4 ": 0.4444
#^ " N THR D 6 ": 0.3724
#^ " CA THR D 6 ": 0.4129
#^ " C THR D 6 ": 0.4017
""",
selections=[range(5),
range(56, 62),
range(-5, 0)])
for ag, fg in zip(
energies_sites.gradients,
finite_difference_site_gradients(
ncs_operators=ncs_operators,
sites_cart=sites_cart,
sites_average=energies_sites.sites_average)):
assert eps_eq(ag, fg)
#
u_isos = processed_pdb.xray_structure().extract_u_iso_or_u_equiv()
eng = group.energies_adp_iso(u_isos=u_isos,
average_power=1,
compute_gradients=False)
energies_adp_iso_no_gradients = eng
assert eng.number_of_restraints == 110
assert eps_eq(eng.residual_sum, 1.11021057745)
assert eps_eq(eng.target, eng.residual_sum)
assert eng.gradients is None
assert eps_eq(eng.rms_with_respect_to_average, [
3.8233537528289001, 4.4894247897900934, 3.71150443476373,
4.0839849076232442
])
energies_adp_iso = group.energies_adp_iso(u_isos=u_isos, average_power=1)
assert energies_adp_iso.number_of_restraints == eng.number_of_restraints
assert eps_eq(energies_adp_iso.residual_sum, eng.residual_sum)
assert eps_eq(energies_adp_iso.target, eng.target)
assert eps_eq(energies_adp_iso.gradients.norm(), 4.50764745473)
assert eps_eq(energies_adp_iso.rms_with_respect_to_average,
eng.rms_with_respect_to_average)
out = StringIO()
energies_adp_iso.show_differences_to_average(site_labels=site_labels,
out=out,
prefix="Y$")
assert not show_diff(out.getvalue().replace(" 7.66 = ", " 7.65 = "),
"""\
Y$NCS selection: "chain A"
Y$ B-iso NCS ave Difference
Y$ " N GLN A 1 ": 11.77 - 12.08 = -0.3133
Y$ " CA GLN A 1 ": 9.09 - 9.28 = -0.1933
Y$ " C GLN A 1 ": 15.40 - 12.05 = 3.3500
...
Y$ " CA THR A 6 ": 3.98 - 7.65 = -3.6750
Y$ " C THR A 6 ": 14.64 - 10.99 = 3.6475
Y$NCS selection: "chain B"
Y$ B-iso NCS ave Difference
Y$ " N GLU B 2 ": 12.87 - 10.35 = 2.5225
...
Y$ " C SER D 4 ": 7.29 - 10.45 = -3.1575
Y$ " CA BSER D 4 ": 5.23 - 7.00 = -1.7667
Y$ " N THR D 6 ": 4.55 - 8.69 = -4.1425
Y$ " CA THR D 6 ": 8.78 - 7.65 = 1.1250
Y$ " C THR D 6 ": 10.80 - 10.99 = -0.1925
""",
selections=[range(5),
range(32, 37),
range(-5, 0)])
for average_power in [1, 0.69, 0.35]:
finite_difference_gradients = flex.double()
eps = 1.e-6
for i_u_iso in xrange(u_isos.size()):
rs = []
for signed_eps in [eps, -eps]:
u_isos_eps = u_isos.deep_copy()
u_isos_eps[i_u_iso] += signed_eps
energies = group.energies_adp_iso(u_isos=u_isos_eps,
average_power=average_power,
compute_gradients=False)
rs.append(energies.residual_sum)
finite_difference_gradients.append((rs[0] - rs[1]) / (2 * eps))
energies_adp_iso = group.energies_adp_iso(u_isos=u_isos,
average_power=average_power)
assert eps_eq(energies_adp_iso.gradients, finite_difference_gradients)
#
groups = ncs.restraints.groups()
groups.members.append(group)
for coordinate_sigma, b_factor_weight in [(None, 1.234), (0.1, None)]:
groups.members.append(
ncs.restraints.group.from_atom_selections(
processed_pdb=processed_pdb,
reference_selection_string=None,
selection_strings=selection_strings,
coordinate_sigma=coordinate_sigma,
b_factor_weight=b_factor_weight,
special_position_warnings_only=False))
energies_adp_iso = groups.energies_adp_iso(u_isos=u_isos, average_power=1)
assert energies_adp_iso.number_of_restraints == 220
assert eps_eq(energies_adp_iso.residual_sum, 4.2807726061)
assert eps_eq(energies_adp_iso.target, energies_adp_iso.residual_sum)
assert eps_eq(energies_adp_iso.gradients.norm(), 17.3806790658)
energies_adp_iso = groups.energies_adp_iso(u_isos=u_isos,
average_power=1,
normalization=True)
assert energies_adp_iso.number_of_restraints == 220
assert eps_eq(energies_adp_iso.residual_sum, 4.2807726061)
assert eps_eq(energies_adp_iso.target, energies_adp_iso.residual_sum / 220)
assert eps_eq(energies_adp_iso.gradients.norm(), 17.3806790658 / 220)
for rms in energies_adp_iso.rms_with_respect_to_averages:
if (rms is not None):
assert eps_eq(
rms, energies_adp_iso_no_gradients.rms_with_respect_to_average)
assert energies_adp_iso.rms_with_respect_to_averages[2] is None
energies_sites = groups.energies_sites(sites_cart=sites_cart)
assert energies_sites.number_of_restraints == 220
assert eps_eq(energies_sites.residual_sum, 8767.54961571)
assert eps_eq(energies_sites.target, energies_sites.residual_sum)
assert eps_eq(energies_sites.gradients.norm(), 4187.49319181)
energies_sites = groups.energies_sites(sites_cart=sites_cart,
normalization=True)
assert energies_sites.number_of_restraints == 220
assert eps_eq(energies_sites.residual_sum, 8767.54961571)
assert eps_eq(energies_sites.target, energies_sites.residual_sum / 220)
assert eps_eq(energies_sites.gradients.norm(), 4187.49319181 / 220)
for rms in energies_sites.rms_with_respect_to_averages:
if (rms is not None):
assert eps_eq(
rms, energies_sites_no_gradients.rms_with_respect_to_average)
assert energies_sites.rms_with_respect_to_averages[1] is None
out = StringIO()
groups.show_adp_iso_differences_to_average(u_isos=u_isos,
site_labels=site_labels,
out=out,
prefix="W@")
assert not show_diff(out.getvalue(),
"""\
W@NCS restraint group 1:
W@ weight: 0.4321
W@ NCS selection: "chain A"
W@ B-iso NCS ave Difference
W@ " N GLN A 1 ": 11.77 - 12.08 = -0.3133
...
W@ " C THR D 6 ": 10.80 - 10.99 = -0.1925
W@NCS restraint group 3:
W@ b_factor_weight: None => restraints disabled
""",
selections=[range(5), range(-3, 0)])
out = StringIO()
groups.show_operators(sites_cart=sites_cart, out=out, prefix="K&")
assert not show_diff(out.getvalue(),
"""\
K&NCS restraint group 1:
K& NCS operator 1:
K& Reference selection: "chain A"
...
K& 0.824624 - 0.971034: 5
K& RMS difference with respect to the reference: 0.653687
K& NCS operator 2:
K& Reference selection: "chain A"
K& Other selection: "chain C"
K& Number of atom pairs: 32
...
K& 0.845124 - 0.988659: 2
K& 0.988659 - 1.132195: 3
K& RMS difference with respect to the reference: 0.724248
""",
selections=[range(3),
range(15, 21),
range(-3, 0)])
out = StringIO()
groups.show_sites_distances_to_average(sites_cart=sites_cart,
site_labels=site_labels,
out=out,
prefix="[")
assert not show_diff(out.getvalue(),
"""\
[NCS restraint group 1:
[ coordinate_sigma: 0.05
[ weight: 400
[ NCS selection: "chain A"
[ Distance to NCS average
[ " N GLN A 1 ": 0.4263
...
[ " C THR D 6 ": 0.4017
[NCS restraint group 2:
[ coordinate_sigma: None => restraints disabled
[NCS restraint group 3:
[ coordinate_sigma: 0.1
[ weight: 100
[ NCS selection: "chain A"
[ Distance to NCS average
[ " N GLN A 1 ": 0.4263
...
[ " C THR D 6 ": 0.4017
""",
selections=[range(6),
range(120, 129),
range(-1, 0)])
#
selection = groups.selection_restrained()
assert selection.size() == 132
assert selection.count(True) == 110
out = StringIO()
processed_pdb.show_atoms_without_ncs_restraints(
ncs_restraints_groups=groups, out=out, prefix="%&")
assert not show_diff(
out.getvalue(), """\
%&Atoms without NCS restraints:
%&MODEL 1
%&ATOM 20 N ALA B 3 111.517 7.175 -8.669 1.00 10.73 N
%&ATOM 21 CA ALA B 3 112.152 8.103 -8.026 1.00 16.28 C
%&ATOM 22 C ALA B 3 111.702 8.243 -5.903 1.00 9.19 C
%&ATOM 24 CA SER B 4 111.797 9.689 -4.364 1.00 8.91 C
%&TER
%&ATOM 38 N ALA C 3 109.043 27.391 28.663 1.00 15.05 N
%&ATOM 39 CA ALA C 3 109.073 26.531 28.433 1.00 3.14 C
%&ATOM 40 C ALA C 3 108.930 26.867 26.637 1.00 15.22 C
%&TER
%&ATOM 57 N ALA D 3 65.439 9.903 -12.471 1.00 7.59 N
%&ATOM 58 CA ALA D 3 65.019 9.566 -11.201 1.00 15.62 C
%&ATOM 59 C ALA D 3 65.679 11.045 -11.097 1.00 2.65 C
%&ATOM 61 CA CSER D 4 65.657 12.870 -8.333 1.00 5.84 C
%&TER
%&ENDMDL
%&MODEL 2
%&ATOM 86 N ALA B 3 111.984 7.364 -8.288 1.00 3.16 N
%&ATOM 87 CA ALA B 3 112.389 8.456 -7.544 1.00 12.73 C
%&ATOM 88 C ALA B 3 111.615 8.267 -6.238 1.00 7.17 C
%&ATOM 90 CA SER B 4 111.707 9.131 -4.317 1.00 13.83 C
%&TER
%&ATOM 104 N ALA C 3 109.237 27.879 29.334 1.00 6.45 N
%&ATOM 105 CA ALA C 3 109.043 26.399 28.156 1.00 15.21 C
%&ATOM 106 C ALA C 3 108.983 26.760 27.178 1.00 11.73 C
%&TER
%&ATOM 123 N ALA D 3 65.188 10.335 -12.959 1.00 6.94 N
%&ATOM 124 CA ALA D 3 65.543 9.614 -11.242 1.00 5.18 C
%&ATOM 125 C ALA D 3 65.064 11.402 -10.648 1.00 16.01 C
%&ATOM 127 CA CSER D 4 65.127 12.969 -9.218 1.00 9.03 C
%&TER
%&ENDMDL
""")
#
for group in groups.members:
assert group.registry.number_of_additional_isolated_sites == 0
groups.register_additional_isolated_sites(number=10)
for group in groups.members:
assert group.registry.number_of_additional_isolated_sites == 10
groups.register_additional_isolated_sites(number=3)
for group in groups.members:
assert group.registry.number_of_additional_isolated_sites == 13
def driver1(use_fortran_library=False):
n = 25
nbd = flex.int(n)
x = flex.double(n)
l = flex.double(n)
u = flex.double(n)
g = flex.double(n)
if "--Verbose" in sys.argv[1:]:
iprint = 1000
else:
iprint = -1
for i in xrange(0, n, 2):
nbd[i] = 2
l[i] = 1.0e0
u[i] = 1.0e2
for i in xrange(1, n, 2):
nbd[i] = 2
l[i] = -1.0e2
u[i] = 1.0e2
for i in xrange(n):
x[i] = 3.0e0
minimizer = lbfgsb.minimizer(n=n, m=5, l=l, u=u, nbd=nbd, factr=1.0e7, pgtol=1.0e-5, iprint=iprint)
f = 0
while True:
if minimizer.process(x, f, g, use_fortran_library):
f = 0.25e0 * (x[0] - 1.0e0) ** 2
for i in xrange(1, n):
f = f + (x[i] - x[i - 1] ** 2) ** 2
f = 4.0e0 * f
t1 = x[1] - x[0] ** 2
g[0] = 2.0e0 * (x[0] - 1.0e0) - 1.6e1 * x[0] * t1
for i in xrange(1, n - 1):
t2 = t1
t1 = x[i + 1] - x[i] ** 2
g[i] = 8.0e0 * t2 - 1.6e1 * x[i] * t1
g[n - 1] = 8.0e0 * t1
elif minimizer.is_terminated():
break
assert minimizer.task() == "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
assert minimizer.f_list().size() == minimizer.n_iteration() + 1
assert minimizer.f_list()[-1] == minimizer.f()
assert minimizer.f_list()[-2] == minimizer.f_previous_iteration()
assert not minimizer.initial_x_replaced_by_projection()
assert minimizer.is_constrained()
assert minimizer.is_fully_constrained()
if "--soft" not in sys.argv[1:]:
assert minimizer.n_fg_evaluations_total() == 27
assert minimizer.n_fg_evaluations_iter() == 1
assert minimizer.n_intervals_explored_cauchy_search_total() == 48
assert minimizer.n_intervals_explored_cauchy_search_iter() == 1
assert minimizer.n_skipped_bfgs_updates_total() == 0
assert minimizer.n_bfgs_updates_total() == 22
assert minimizer.subspace_argmin_is_within_box() == 1
assert minimizer.n_free_variables() == 25
assert minimizer.n_active_constraints() == 0
assert minimizer.n_variables_leaving_active_set() == 0
assert minimizer.n_variables_entering_active_set() == 0
assert eps_eq(minimizer.theta_bfgs_matrix_current(), 23.2674689856)
assert minimizer.f_previous_iteration() >= 0
assert minimizer.floating_point_epsilon() == scitbx.math.floating_point_epsilon_double_get()
assert eps_eq(
minimizer.factr_times_floating_point_epsilon(), minimizer.factr() * minimizer.floating_point_epsilon()
)
assert eps_eq(minimizer.two_norm_line_search_direction_vector(), 1.56259327735e-05)
assert eps_eq(
minimizer.two_norm_line_search_direction_vector_sq(), minimizer.two_norm_line_search_direction_vector() ** 2
)
assert minimizer.accumulated_time_cauchy_search() > -0.02
assert minimizer.accumulated_time_subspace_minimization() > -0.02
assert minimizer.accumulated_time_line_search() > -0.02
assert eps_eq(minimizer.slope_line_search_function_current(), 9.31496613169e-10)
assert eps_eq(minimizer.slope_line_search_function_start(), -3.6762390377e-09)
assert eps_eq(minimizer.maximum_relative_step_length(), 1.37484166517)
assert eps_eq(minimizer.relative_step_length_line_search(), 1.0)
assert eps_eq(minimizer.infinity_norm_projected_gradient(), 0.000143369780806)
def exercise_fit():
x = flex.double((0.1, 0.2, 0.5))
y = flex.double((3, 2, 1))
sigmas = flex.double((0.04, 0.02, 0.01))
gf = gaussian.fit(x, y, sigmas, gaussian.sum((1, 2), (4, 5)))
assert approx_equal(gf.array_of_a(), (1, 2))
assert approx_equal(gf.array_of_b(), (4, 5))
assert approx_equal(gf.c(), 0)
assert not gf.use_c()
assert approx_equal(gf.table_x(), x)
assert approx_equal(gf.table_y(), y)
assert approx_equal(gf.table_sigmas(), sigmas)
assert approx_equal(
gf.fitted_values(),
[2.8632482881537511, 2.4896052951221748, 0.94088903489182252])
reference_gaussian = gaussian.sum((1, 2, 3), (4, 5, 6))
gf = gaussian.fit(x, reference_gaussian, sigmas,
gaussian.sum((1, 2), (4, 5)))
assert approx_equal(gf.array_of_a(), (1, 2))
assert approx_equal(gf.array_of_b(), (4, 5))
assert approx_equal(gf.c(), 0)
assert approx_equal(gf.table_x(), x)
assert approx_equal(gf.table_y(), reference_gaussian.at_x(x))
assert approx_equal(gf.table_sigmas(), sigmas)
assert isinstance(gf.sort(), gaussian.fit)
assert gf.sort().table_x() == gf.table_x()
assert gf.sort().table_y() == gf.table_y()
assert gf.sort().table_sigmas() == gf.table_sigmas()
assert approx_equal(gf.differences(),
gf.at_x(x) - reference_gaussian.at_x(x))
c_fit = gaussian.fit(
flex.double([
0.0, 0.066666666666666666, 0.13333333333333333, 0.2,
0.26666666666666666
]),
gaussian.sum((2.657506, 1.078079, 1.490909, -4.2410698, 0.71379101),
(14.780758, 0.776775, 42.086842, -0.000294, 0.239535),
4.2979832), flex.double(5, 0.0005),
gaussian.sum((1.1423916, 4.1728425, 0.61716694),
(0.50733125, 14.002512, 41.978928)))
differences = flex.double([
-0.064797341823577881, 0.003608505180995536, 0.098159179757290715,
0.060724224581695019, -0.10766283796372011
])
assert approx_equal(c_fit.differences(), differences)
assert approx_equal(
c_fit.significant_relative_errors(),
[0.0107212, 0.0005581, 0.0213236, 0.0169304, 0.0385142])
gf = gaussian.fit(x, reference_gaussian, flex.double(x.size(), 1),
gaussian.sum((1, 2), (4, 5)))
assert list(gf.bound_flags(False, False)) == [False, False, False, False]
assert list(gf.bound_flags(True, False)) == [True, False, True, False]
assert list(gf.bound_flags(False, True)) == [False, True, False, True]
sgf = gf.apply_shifts(flex.double((3, -3, 4, 6)), True)
assert approx_equal(sgf.array_of_a(), (1 + 3, 2 + 4))
assert approx_equal(sgf.array_of_b(),
((math.sqrt(4) - 3)**2, (math.sqrt(5) + 6)**2))
assert approx_equal(sgf.c(), 0)
assert not sgf.use_c()
sgf = gf.apply_shifts(flex.double((3, -3, 4, 6)), False)
assert approx_equal(sgf.array_of_a(), (1 + 3, 2 + 4))
assert approx_equal(sgf.array_of_b(), (4 - 3, 5 + 6))
assert approx_equal(sgf.c(), 0)
assert not sgf.use_c()
differences = sgf.differences()
for use_sigmas in [False, True]:
assert approx_equal(sgf.target_function(2, use_sigmas, differences),
25.0320634)
assert approx_equal(sgf.target_function(4, use_sigmas, differences),
256.2682575)
assert approx_equal(sgf.gradients_d_abc(2, use_sigmas, differences),
[15.6539271, -4.1090114, 10.4562306, -1.6376781])
gfc = gaussian.fit(x, reference_gaussian, flex.double(x.size(), 1),
gaussian.sum((1, 2), (4, 5), 6))
assert list(gfc.bound_flags(False,
False)) == [False, False, False, False, False]
assert list(gfc.bound_flags(True,
False)) == [True, False, True, False, True]
assert list(gfc.bound_flags(False,
True)) == [False, True, False, True, False]
sgfc = gfc.apply_shifts(flex.double((3, -3, 4, 6, -5)), True)
assert approx_equal(sgfc.array_of_a(), (1 + 3, 2 + 4))
assert approx_equal(sgfc.array_of_b(),
((math.sqrt(4) - 3)**2, (math.sqrt(5) + 6)**2))
assert approx_equal(sgfc.c(), 6 - 5)
assert sgfc.use_c()
sgfc = gfc.apply_shifts(flex.double((3, -3, 4, 6, -5)), False)
assert approx_equal(sgfc.array_of_a(), (1 + 3, 2 + 4))
assert approx_equal(sgfc.array_of_b(), (4 - 3, 5 + 6))
assert approx_equal(sgfc.c(), 6 - 5)
assert sgfc.use_c()
differences = sgfc.differences()
for use_sigmas in [False, True]:
assert approx_equal(sgfc.target_function(2, use_sigmas, differences),
44.8181444)
assert approx_equal(sgfc.target_function(4, use_sigmas, differences),
757.3160329)
assert approx_equal(
sgfc.gradients_d_abc(2, use_sigmas, differences),
[21.1132071, -6.0532695, 13.6638274, -2.2460994, 22.7860809])
differences = c_fit.differences()
gabc = c_fit.gradients_d_abc(2, False, differences)
assert approx_equal(gabc, [
-0.016525391425206391, 0.0074465239375589107, 0.020055876723667564,
0.00054794635257838251, -0.018754011379726425, -0.0011194004809549143
])
assert approx_equal(
c_fit.gradients_d_shifts(flex.double((0.1, 0.4, 0.2, 0.5, 0.3, 0.6)),
gabc),
[-0.0165254, 0.01656512, 0.0200559, 0.0046488, -0.0187540, -0.0158487])
g5c = gaussian.sum(
(2.657505989074707, 1.0780789852142334, 1.4909089803695679,
-4.2410697937011719, 0.71379101276397705),
(14.780757904052734, 0.77677500247955322, 42.086841583251953,
-0.00029399999766610563, 0.23953500390052795), 4.2979831695556641)
for include_constant_term in (False, True):
a = flex.double(g5c.array_of_a())
b = flex.double(g5c.array_of_b())
permutation = flex.sort_permutation(data=flex.abs(a), reverse=True)[:4]
gf = gaussian.fit(
flex.double([0]), g5c, flex.double(1, 1),
gaussian.sum(iter(a.select(permutation)),
iter(b.select(permutation)), 0,
include_constant_term))
assert approx_equal(gf.differences(), [-5.01177418232])
shifts = flex.double(8, -1)
if (include_constant_term): shifts.append(-.2)
sgf = gf.apply_shifts(shifts, False)
assert approx_equal(sgf.array_of_a(),
[-5.2410698, 1.657506, 0.49090898, 0.078078985])
assert approx_equal(sgf.array_of_b(),
[-1.0002940, 13.780758, 41.086842, -0.223225])
if (include_constant_term):
assert approx_equal(sgf.c(), -.2)
expected_gradients = [1, 0, 1, 0, 1, 0, 1, 0]
if (include_constant_term): expected_gradients.append(1)
assert approx_equal(fit_finite_diff_gradients(sgf, 0),
expected_gradients,
eps=1.e-4)
for i in range(10):
gf = gaussian.fit(flex.double([i / 10.]), g5c, flex.double(1, 1),
sgf)
differences = flex.double([0.5])
assert approx_equal(gf.gradients_d_abc(2, False, differences),
fit_finite_diff_gradients(gf,
gf.table_x()[0]),
eps=1.e-3)
for sigma in [0.04, 0.02, 0.01]:
gf = gaussian.fit(flex.double([i / 20.]), g5c,
flex.double([sigma]), sgf)
for power in [2, 4]:
for use_sigmas in [False, True]:
differences = gf.differences()
an = gf.gradients_d_abc(power, use_sigmas, differences)
fi = fit_finite_diff_target_gradients(
gf, power, use_sigmas)
assert eps_eq(an, fi, eps=1.e-3)
def exercise_gradient_dx(gaussian, x_max=1., n_points=50):
for i in range(n_points + 1):
x = x_max * i / n_points
grad_finite = finite_gradient_dx_at_x(gaussian, x)
grad_analytical = gaussian.gradient_dx_at_x(x)
assert eps_eq(grad_finite, grad_analytical)
def exercise_minimizer_interface():
n = 25
m = 5
l = flex.double(n, -1)
u = flex.double(n, 1)
nbd = flex.int(n)
factr = 1.0e+7
pgtol = 1.0e-5
iprint = -1
for enable_stp_init in [False, True]:
minimizer = lbfgsb.ext.minimizer(n, m, l, u, nbd, enable_stp_init,
factr, pgtol, iprint)
assert minimizer.n() == n
assert minimizer.m() == m
assert minimizer.l().id() == l.id()
assert minimizer.u().id() == u.id()
assert minimizer.nbd().id() == nbd.id()
assert minimizer.enable_stp_init() == enable_stp_init
assert eps_eq(minimizer.factr(), factr)
assert eps_eq(minimizer.pgtol(), pgtol)
assert eps_eq(minimizer.iprint(), iprint)
assert not minimizer.requests_f_and_g()
assert not minimizer.is_terminated()
assert minimizer.task() == "START"
x = flex.double(n, 0)
f = 1
g = flex.double(n, -1)
assert minimizer.process(x, f, g, False)
assert minimizer.task() == "FG_START"
assert minimizer.f() == 0
if (not enable_stp_init):
try:
minimizer.requests_stp_init()
except RuntimeError as e:
assert str(e).endswith(
": SCITBX_ASSERT(enable_stp_init()) failure.")
else:
raise Exception_expected
else:
assert not minimizer.process(x, f, g)
assert minimizer.requests_stp_init()
assert approx_equal(minimizer.relative_step_length_line_search(),
0.2)
assert approx_equal(minimizer.current_search_direction(), [1] * n)
minimizer.set_relative_step_length_line_search(value=0.3)
assert approx_equal(minimizer.relative_step_length_line_search(),
0.3)
assert minimizer.process(x, f, g)
assert minimizer.f() == 1
assert minimizer.task() == "FG_LNSRCH"
assert not minimizer.is_terminated()
if (not enable_stp_init):
assert approx_equal(x, [0.2] * n)
else:
assert approx_equal(x, [0.3] * n)
minimizer.request_stop()
assert not minimizer.process(x, f, g)
assert minimizer.task() == "STOP: NO RESTORE"
minimizer.request_stop_with_restore()
assert minimizer.task() == "STOP: CPU"
minimizer.request_restart()
assert not minimizer.requests_f_and_g()
assert not minimizer.is_terminated()
assert minimizer.task() == "START"
minimizer = lbfgsb.minimizer(n=n)
assert minimizer.l().size() == n
assert minimizer.u().size() == n
assert minimizer.nbd().size() == n
assert minimizer.nbd().all_eq(0)
def recycle(miller_array, column_root_label, column_types=None, verbose=0):
original_dataset = to_mtz(miller_array, column_root_label, column_types)
label_decorator = mtz.label_decorator()
written = original_dataset.mtz_object()
if 0 or verbose:
written.show_summary()
original_dataset.mtz_object().write(file_name="tmp_iotbx_mtz.mtz")
restored = mtz.object(file_name="tmp_iotbx_mtz.mtz")
if 0 or verbose:
restored.show_summary()
assert restored.title() == written.title()
assert [line.rstrip() for line in restored.history()] == list(written.history())
assert restored.space_group_name() == written.space_group_name()
assert restored.space_group_number() == written.space_group_number()
assert restored.space_group() == written.space_group()
assert restored.point_group_name() == written.point_group_name()
assert restored.lattice_centring_type() == written.lattice_centring_type()
assert restored.n_batches() == written.n_batches()
assert restored.n_reflections() == written.n_reflections()
assert eps_eq(restored.max_min_resolution(), written.max_min_resolution(), eps=1.0e-5)
assert restored.n_crystals() == written.n_crystals()
assert restored.n_active_crystals() == written.n_active_crystals()
assert restored.n_crystals() == 2
for rx, wx in zip(restored.crystals(), written.crystals()):
assert rx.name() == wx.name()
assert rx.project_name() == wx.project_name()
assert rx.unit_cell().is_similar_to(wx.unit_cell())
assert rx.n_datasets() == wx.n_datasets()
for rd, wd in zip(rx.datasets(), wx.datasets()):
assert rd.name() == wd.name()
assert rd.wavelength() == wd.wavelength()
assert rd.n_columns() == wd.n_columns()
miller_set = restored.crystals()[1].miller_set()
assert miller_set.indices().size() == restored.n_reflections()
crystal_symmetry = restored.crystals()[1].crystal_symmetry()
restored_dataset = restored.crystals()[1].datasets()[0]
if not miller_array.anomalous_flag():
if miller_array.sigmas() is None:
if miller_array.is_complex_array():
assert restored_dataset.n_columns() == 3 + 2
group = restored.extract_complex(
column_label_ampl=column_root_label, column_label_phi=label_decorator.phases(column_root_label)
)
elif miller_array.is_hendrickson_lattman_array():
assert restored_dataset.n_columns() == 3 + 4
deco = label_decorator.hendrickson_lattman
group = restored.extract_hendrickson_lattman(
column_label_a=deco(column_root_label, 0),
column_label_b=deco(column_root_label, 1),
column_label_c=deco(column_root_label, 2),
column_label_d=deco(column_root_label, 3),
)
else:
assert restored_dataset.n_columns() == 3 + 1
group = restored.extract_reals(column_label=column_root_label)
r = miller.array(
miller_set=miller.set(crystal_symmetry=crystal_symmetry, indices=group.indices, anomalous_flag=False),
data=group.data,
)
else:
assert restored_dataset.n_columns() == 3 + 2
group = restored.extract_observations(
column_label_data=column_root_label, column_label_sigmas=label_decorator.sigmas(column_root_label)
)
r = miller.array(
miller_set=miller.set(crystal_symmetry=crystal_symmetry, indices=group.indices, anomalous_flag=False),
data=group.data,
sigmas=group.sigmas,
)
else:
if miller_array.sigmas() is None:
if miller_array.is_complex_array():
assert restored_dataset.n_columns() == 3 + 4
group = restored.extract_complex_anomalous(
column_label_ampl_plus=label_decorator.anomalous(column_root_label, "+"),
column_label_phi_plus=label_decorator.phases(column_root_label, "+"),
column_label_ampl_minus=label_decorator.anomalous(column_root_label, "-"),
column_label_phi_minus=label_decorator.phases(column_root_label, "-"),
)
elif miller_array.is_hendrickson_lattman_array():
assert restored_dataset.n_columns() == 3 + 8
deco = label_decorator.hendrickson_lattman
group = restored.extract_hendrickson_lattman_anomalous(
column_label_a_plus=deco(column_root_label, 0, "+"),
column_label_b_plus=deco(column_root_label, 1, "+"),
column_label_c_plus=deco(column_root_label, 2, "+"),
column_label_d_plus=deco(column_root_label, 3, "+"),
column_label_a_minus=deco(column_root_label, 0, "-"),
column_label_b_minus=deco(column_root_label, 1, "-"),
column_label_c_minus=deco(column_root_label, 2, "-"),
column_label_d_minus=deco(column_root_label, 3, "-"),
)
else:
assert restored_dataset.n_columns() == 3 + 2
group = restored.extract_reals_anomalous(
column_label_plus=label_decorator.anomalous(column_root_label, "+"),
column_label_minus=label_decorator.anomalous(column_root_label, "-"),
)
r = miller.array(
miller_set=miller.set(crystal_symmetry=crystal_symmetry, indices=group.indices, anomalous_flag=True),
data=group.data,
)
else:
assert restored_dataset.n_columns() == 3 + 4
group = restored.extract_observations_anomalous(
column_label_data_plus=label_decorator.anomalous(column_root_label, "+"),
column_label_sigmas_plus=label_decorator.sigmas(column_root_label, "+"),
column_label_data_minus=label_decorator.anomalous(column_root_label, "-"),
column_label_sigmas_minus=label_decorator.sigmas(column_root_label, "-"),
)
r = miller.array(
miller_set=miller.set(crystal_symmetry=crystal_symmetry, indices=group.indices, anomalous_flag=True),
data=group.data,
sigmas=group.sigmas,
)
verify_miller_arrays(miller_array, r)
restored_miller_arrays = restored.as_miller_arrays()
assert len(restored_miller_arrays) == 1
thff = restored_miller_arrays[0].info().type_hints_from_file
assert thff is not None
assert miller_array.is_hendrickson_lattman_array() == (thff == "hendrickson_lattman")
verify_miller_arrays(miller_array, restored_miller_arrays[0])
mtz_object = miller_array.as_mtz_dataset(column_root_label=column_root_label).mtz_object()
restored_miller_arrays = mtz_object.as_miller_arrays()
assert len(restored_miller_arrays) == 1
verify_miller_arrays(miller_array, restored_miller_arrays[0])
if miller_array.is_bool_array() or miller_array.is_integer_array() or miller_array.is_real_array():
cb_op = miller_array.change_of_basis_op_to_niggli_cell()
mtz_object.change_basis_in_place(cb_op=cb_op)
cb_array = miller_array.change_basis(cb_op=cb_op)
assert mtz_object.space_group() == cb_array.space_group()
for mtz_crystal in mtz_object.crystals():
assert mtz_crystal.unit_cell().is_similar_to(cb_array.unit_cell())
restored_miller_arrays = mtz_object.as_miller_arrays()
assert len(restored_miller_arrays) == 1
verify_miller_arrays(cb_array, restored_miller_arrays[0])
mtz_object.change_basis_in_place(cb_op=cb_op.inverse())
assert mtz_object.space_group() == miller_array.space_group()
for mtz_crystal in mtz_object.crystals():
assert mtz_crystal.unit_cell().is_similar_to(miller_array.unit_cell())
restored_miller_arrays = mtz_object.as_miller_arrays()
assert len(restored_miller_arrays) == 1
verify_miller_arrays(miller_array, restored_miller_arrays[0])
def exercise_two_models_with_holes(processed_pdb):
selection_strings=["chain A", "chain B", "chain C", "chain D"]
group = ncs.restraints.group.from_atom_selections(
processed_pdb=processed_pdb,
reference_selection_string=None,
selection_strings=selection_strings,
coordinate_sigma=0.05,
b_factor_weight=0.4321,
special_position_warnings_only=False)
sites_cart = processed_pdb.all_chain_proxies.pdb_atoms.extract_xyz()
ncs_operators = group.operators(sites_cart=sites_cart)
out = StringIO()
ncs_operators.show(sites_cart=sites_cart, out=out, prefix="{*")
assert not show_diff(out.getvalue(), """\
{*NCS operator 1:
{* Reference selection: "chain A"
{* Other selection: "chain B"
{* Number of atom pairs: 22
{* Rotation={{-0.925533, 0.322815, -0.197938},
{* {0.329616, 0.429511, -0.840758},
{* {-0.186393, -0.843393, -0.503931}}
{* Translation={{163.62}, {-13.0292}, {44.8533}}
{* Histogram of differences:
{* 0.092573 - 0.238983: 1
{* 0.238983 - 0.385393: 2
{* 0.385393 - 0.531803: 7
{* 0.531803 - 0.678214: 3
{* 0.678214 - 0.824624: 4
{* 0.824624 - 0.971034: 5
{* RMS difference with respect to the reference: 0.653687
{*NCS operator 2:
{* Reference selection: "chain A"
{* Other selection: "chain C"
{* Number of atom pairs: 32
{* Rotation={{-0.988874, -0.139883, -0.0506023},
{* {0.0139383, -0.425808, 0.904706},
{* {-0.1481, 0.893935, 0.423021}}
{* Translation={{177.315}, {18.2319}, {1.24026}}
{* Histogram of differences:
{* 0.291817 - 0.418479: 2
{* 0.418479 - 0.545141: 9
{* 0.545141 - 0.671802: 5
{* 0.671802 - 0.798464: 10
{* 0.798464 - 0.925126: 3
{* 0.925126 - 1.051787: 3
{* RMS difference with respect to the reference: 0.677436
{*NCS operator 3:
{* Reference selection: "chain A"
{* Other selection: "chain D"
{* Number of atom pairs: 24
{* Rotation={{0.950594, -0.191857, 0.244055},
{* {-0.192933, -0.981014, -0.0197252},
{* {0.243205, -0.0283355, -0.969561}}
{* Translation={{6.77797}, {56.2476}, {-5.96327}}
{* Histogram of differences:
{* 0.270982 - 0.414517: 3
{* 0.414517 - 0.558053: 3
{* 0.558053 - 0.701588: 7
{* 0.701588 - 0.845124: 6
{* 0.845124 - 0.988659: 2
{* 0.988659 - 1.132195: 3
{* RMS difference with respect to the reference: 0.724248
""")
energies_sites_no_gradients = ncs_operators.energies_sites(
sites_cart=sites_cart, compute_gradients=False)
assert energies_sites_no_gradients.number_of_restraints == 110
assert eps_eq(energies_sites_no_gradients.residual_sum, 7014.03969257)
assert eps_eq(energies_sites_no_gradients.target, 7014.03969257)
assert energies_sites_no_gradients.gradients is None
assert eps_eq(energies_sites_no_gradients.rms_with_respect_to_average,
[0.41226641576521778, 0.38139080907663186,
0.39748408968570492, 0.40001937328488651])
energies_sites = ncs_operators.energies_sites(sites_cart=sites_cart)
assert energies_sites_no_gradients.number_of_restraints \
== energies_sites.number_of_restraints
assert energies_sites_no_gradients.residual_sum \
== energies_sites.residual_sum
assert energies_sites_no_gradients.target \
== energies_sites.target
assert eps_eq(energies_sites.gradients.norm(), 3349.99455344)
assert eps_eq(energies_sites.rms_with_respect_to_average,
energies_sites_no_gradients.rms_with_respect_to_average)
site_labels = [
'"'+atom.pdb_label_columns()+'"' for atom in
processed_pdb.all_chain_proxies.pdb_atoms]
out = StringIO()
energies_sites.show_distances_to_average(
site_labels=site_labels, out=out, prefix="#^")
assert not show_diff(out.getvalue(), """\
#^NCS selection: "chain A"
#^ Distance to NCS average
#^ " N GLN A 1 ": 0.4263
#^ " CA GLN A 1 ": 0.2141
#^ " C GLN A 1 ": 0.4052
...
#^ " CA THR B 6 ": 0.4001
#^ " C THR B 6 ": 0.6281
#^NCS selection: "chain C"
#^ Distance to NCS average
#^ " N GLN C 1 ": 0.4135
#^ " CA GLN C 1 ": 0.5070
...
#^ " C SER D 4 ": 0.6943
#^ " CA BSER D 4 ": 0.4444
#^ " N THR D 6 ": 0.3724
#^ " CA THR D 6 ": 0.4129
#^ " C THR D 6 ": 0.4017
""", selections=[range(5),range(56,62),range(-5,0)])
for ag,fg in zip(energies_sites.gradients,
finite_difference_site_gradients(
ncs_operators=ncs_operators,
sites_cart=sites_cart,
sites_average=energies_sites.sites_average)):
assert eps_eq(ag, fg)
#
u_isos = processed_pdb.xray_structure().extract_u_iso_or_u_equiv()
eng = group.energies_adp_iso(
u_isos=u_isos, average_power=1, compute_gradients=False)
energies_adp_iso_no_gradients = eng
assert eng.number_of_restraints == 110
assert eps_eq(eng.residual_sum, 1.11021057745)
assert eps_eq(eng.target, eng.residual_sum)
assert eng.gradients is None
assert eps_eq(eng.rms_with_respect_to_average,
[3.8233537528289001, 4.4894247897900934,
3.71150443476373, 4.0839849076232442])
energies_adp_iso = group.energies_adp_iso(u_isos=u_isos, average_power=1)
assert energies_adp_iso.number_of_restraints == eng.number_of_restraints
assert eps_eq(energies_adp_iso.residual_sum, eng.residual_sum)
assert eps_eq(energies_adp_iso.target, eng.target)
assert eps_eq(energies_adp_iso.gradients.norm(), 4.50764745473)
assert eps_eq(energies_adp_iso.rms_with_respect_to_average,
eng.rms_with_respect_to_average)
out = StringIO()
energies_adp_iso.show_differences_to_average(
site_labels=site_labels, out=out, prefix="Y$")
assert not show_diff(out.getvalue().replace(" 7.66 = ", " 7.65 = "), """\
Y$NCS selection: "chain A"
Y$ B-iso NCS ave Difference
Y$ " N GLN A 1 ": 11.77 - 12.08 = -0.3133
Y$ " CA GLN A 1 ": 9.09 - 9.28 = -0.1933
Y$ " C GLN A 1 ": 15.40 - 12.05 = 3.3500
...
Y$ " CA THR A 6 ": 3.98 - 7.65 = -3.6750
Y$ " C THR A 6 ": 14.64 - 10.99 = 3.6475
Y$NCS selection: "chain B"
Y$ B-iso NCS ave Difference
Y$ " N GLU B 2 ": 12.87 - 10.35 = 2.5225
...
Y$ " C SER D 4 ": 7.29 - 10.45 = -3.1575
Y$ " CA BSER D 4 ": 5.23 - 7.00 = -1.7667
Y$ " N THR D 6 ": 4.55 - 8.69 = -4.1425
Y$ " CA THR D 6 ": 8.78 - 7.65 = 1.1250
Y$ " C THR D 6 ": 10.80 - 10.99 = -0.1925
""", selections=[range(5),range(32,37),range(-5,0)])
for average_power in [1, 0.69, 0.35]:
finite_difference_gradients = flex.double()
eps = 1.e-6
for i_u_iso in xrange(u_isos.size()):
rs = []
for signed_eps in [eps, -eps]:
u_isos_eps = u_isos.deep_copy()
u_isos_eps[i_u_iso] += signed_eps
energies = group.energies_adp_iso(
u_isos=u_isos_eps,
average_power=average_power,
compute_gradients=False)
rs.append(energies.residual_sum)
finite_difference_gradients.append((rs[0]-rs[1])/(2*eps))
energies_adp_iso = group.energies_adp_iso(
u_isos=u_isos, average_power=average_power)
assert eps_eq(energies_adp_iso.gradients, finite_difference_gradients)
#
groups = ncs.restraints.groups()
groups.members.append(group)
for coordinate_sigma,b_factor_weight in [
(None,1.234),
(0.1,None)]:
groups.members.append(
ncs.restraints.group.from_atom_selections(
processed_pdb=processed_pdb,
reference_selection_string=None,
selection_strings=selection_strings,
coordinate_sigma=coordinate_sigma,
b_factor_weight=b_factor_weight,
special_position_warnings_only=False))
energies_adp_iso = groups.energies_adp_iso(u_isos=u_isos, average_power=1)
assert energies_adp_iso.number_of_restraints == 220
assert eps_eq(energies_adp_iso.residual_sum, 4.2807726061)
assert eps_eq(energies_adp_iso.target, energies_adp_iso.residual_sum)
assert eps_eq(energies_adp_iso.gradients.norm(), 17.3806790658)
energies_adp_iso = groups.energies_adp_iso(
u_isos=u_isos, average_power=1, normalization=True)
assert energies_adp_iso.number_of_restraints == 220
assert eps_eq(energies_adp_iso.residual_sum, 4.2807726061)
assert eps_eq(energies_adp_iso.target, energies_adp_iso.residual_sum/220)
assert eps_eq(energies_adp_iso.gradients.norm(), 17.3806790658/220)
for rms in energies_adp_iso.rms_with_respect_to_averages:
if (rms is not None):
assert eps_eq(
rms, energies_adp_iso_no_gradients.rms_with_respect_to_average)
assert energies_adp_iso.rms_with_respect_to_averages[2] is None
energies_sites = groups.energies_sites(sites_cart=sites_cart)
assert energies_sites.number_of_restraints == 220
assert eps_eq(energies_sites.residual_sum, 8767.54961571)
assert eps_eq(energies_sites.target, energies_sites.residual_sum)
assert eps_eq(energies_sites.gradients.norm(), 4187.49319181)
energies_sites = groups.energies_sites(
sites_cart=sites_cart, normalization=True)
assert energies_sites.number_of_restraints == 220
assert eps_eq(energies_sites.residual_sum, 8767.54961571)
assert eps_eq(energies_sites.target, energies_sites.residual_sum/220)
assert eps_eq(energies_sites.gradients.norm(), 4187.49319181/220)
for rms in energies_sites.rms_with_respect_to_averages:
if (rms is not None):
assert eps_eq(
rms, energies_sites_no_gradients.rms_with_respect_to_average)
assert energies_sites.rms_with_respect_to_averages[1] is None
out = StringIO()
groups.show_adp_iso_differences_to_average(
u_isos=u_isos, site_labels=site_labels, out=out, prefix="W@")
assert not show_diff(out.getvalue(), """\
W@NCS restraint group 1:
W@ weight: 0.4321
W@ NCS selection: "chain A"
W@ B-iso NCS ave Difference
W@ " N GLN A 1 ": 11.77 - 12.08 = -0.3133
...
W@ " C THR D 6 ": 10.80 - 10.99 = -0.1925
W@NCS restraint group 3:
W@ b_factor_weight: None => restraints disabled
""", selections=[range(5),range(-3,0)])
out = StringIO()
groups.show_operators(sites_cart=sites_cart, out=out, prefix="K&")
assert not show_diff(out.getvalue(), """\
K&NCS restraint group 1:
K& NCS operator 1:
K& Reference selection: "chain A"
...
K& 0.824624 - 0.971034: 5
K& RMS difference with respect to the reference: 0.653687
K& NCS operator 2:
K& Reference selection: "chain A"
K& Other selection: "chain C"
K& Number of atom pairs: 32
...
K& 0.845124 - 0.988659: 2
K& 0.988659 - 1.132195: 3
K& RMS difference with respect to the reference: 0.724248
""", selections=[range(3),range(15,21),range(-3,0)])
out = StringIO()
groups.show_sites_distances_to_average(
sites_cart=sites_cart, site_labels=site_labels, out=out, prefix="[")
assert not show_diff(out.getvalue(), """\
[NCS restraint group 1:
[ coordinate_sigma: 0.05
[ weight: 400
[ NCS selection: "chain A"
[ Distance to NCS average
[ " N GLN A 1 ": 0.4263
...
[ " C THR D 6 ": 0.4017
[NCS restraint group 2:
[ coordinate_sigma: None => restraints disabled
[NCS restraint group 3:
[ coordinate_sigma: 0.1
[ weight: 100
[ NCS selection: "chain A"
[ Distance to NCS average
[ " N GLN A 1 ": 0.4263
...
[ " C THR D 6 ": 0.4017
""", selections=[range(6),range(120,129),range(-1,0)])
#
selection = groups.selection_restrained()
assert selection.size() == 132
assert selection.count(True) == 110
out = StringIO()
processed_pdb.show_atoms_without_ncs_restraints(
ncs_restraints_groups=groups, out=out, prefix="%&")
assert not show_diff(out.getvalue(), """\
%&Atoms without NCS restraints:
%&MODEL 1
%&ATOM 20 N ALA B 3 111.517 7.175 -8.669 1.00 10.73 N
%&ATOM 21 CA ALA B 3 112.152 8.103 -8.026 1.00 16.28 C
%&ATOM 22 C ALA B 3 111.702 8.243 -5.903 1.00 9.19 C
%&ATOM 24 CA SER B 4 111.797 9.689 -4.364 1.00 8.91 C
%&TER
%&ATOM 38 N ALA C 3 109.043 27.391 28.663 1.00 15.05 N
%&ATOM 39 CA ALA C 3 109.073 26.531 28.433 1.00 3.14 C
%&ATOM 40 C ALA C 3 108.930 26.867 26.637 1.00 15.22 C
%&TER
%&ATOM 57 N ALA D 3 65.439 9.903 -12.471 1.00 7.59 N
%&ATOM 58 CA ALA D 3 65.019 9.566 -11.201 1.00 15.62 C
%&ATOM 59 C ALA D 3 65.679 11.045 -11.097 1.00 2.65 C
%&ATOM 61 CA CSER D 4 65.657 12.870 -8.333 1.00 5.84 C
%&TER
%&ENDMDL
%&MODEL 2
%&ATOM 86 N ALA B 3 111.984 7.364 -8.288 1.00 3.16 N
%&ATOM 87 CA ALA B 3 112.389 8.456 -7.544 1.00 12.73 C
%&ATOM 88 C ALA B 3 111.615 8.267 -6.238 1.00 7.17 C
%&ATOM 90 CA SER B 4 111.707 9.131 -4.317 1.00 13.83 C
%&TER
%&ATOM 104 N ALA C 3 109.237 27.879 29.334 1.00 6.45 N
%&ATOM 105 CA ALA C 3 109.043 26.399 28.156 1.00 15.21 C
%&ATOM 106 C ALA C 3 108.983 26.760 27.178 1.00 11.73 C
%&TER
%&ATOM 123 N ALA D 3 65.188 10.335 -12.959 1.00 6.94 N
%&ATOM 124 CA ALA D 3 65.543 9.614 -11.242 1.00 5.18 C
%&ATOM 125 C ALA D 3 65.064 11.402 -10.648 1.00 16.01 C
%&ATOM 127 CA CSER D 4 65.127 12.969 -9.218 1.00 9.03 C
%&TER
%&ENDMDL
""")
#
for group in groups.members:
assert group.registry.number_of_additional_isolated_sites == 0
groups.register_additional_isolated_sites(number=10)
for group in groups.members:
assert group.registry.number_of_additional_isolated_sites == 10
groups.register_additional_isolated_sites(number=3)
for group in groups.members:
assert group.registry.number_of_additional_isolated_sites == 13
def exercise_minimizer_interface():
n = 25
m = 5
l = flex.double(n, -1)
u = flex.double(n, 1)
nbd = flex.int(n)
factr = 1.0e7
pgtol = 1.0e-5
iprint = -1
for enable_stp_init in [False, True]:
minimizer = lbfgsb.ext.minimizer(n, m, l, u, nbd, enable_stp_init, factr, pgtol, iprint)
assert minimizer.n() == n
assert minimizer.m() == m
assert minimizer.l().id() == l.id()
assert minimizer.u().id() == u.id()
assert minimizer.nbd().id() == nbd.id()
assert minimizer.enable_stp_init() == enable_stp_init
assert eps_eq(minimizer.factr(), factr)
assert eps_eq(minimizer.pgtol(), pgtol)
assert eps_eq(minimizer.iprint(), iprint)
assert not minimizer.requests_f_and_g()
assert not minimizer.is_terminated()
assert minimizer.task() == "START"
x = flex.double(n, 0)
f = 1
g = flex.double(n, -1)
assert minimizer.process(x, f, g, False)
assert minimizer.task() == "FG_START"
assert minimizer.f() == 0
if not enable_stp_init:
try:
minimizer.requests_stp_init()
except RuntimeError, e:
assert str(e).endswith(": SCITBX_ASSERT(enable_stp_init()) failure.")
else:
raise Exception_expected
else:
assert not minimizer.process(x, f, g)
assert minimizer.requests_stp_init()
assert approx_equal(minimizer.relative_step_length_line_search(), 0.2)
assert approx_equal(minimizer.current_search_direction(), [1] * n)
minimizer.set_relative_step_length_line_search(value=0.3)
assert approx_equal(minimizer.relative_step_length_line_search(), 0.3)
assert minimizer.process(x, f, g)
assert minimizer.f() == 1
assert minimizer.task() == "FG_LNSRCH"
assert not minimizer.is_terminated()
if not enable_stp_init:
assert approx_equal(x, [0.2] * n)
else:
assert approx_equal(x, [0.3] * n)
minimizer.request_stop()
assert not minimizer.process(x, f, g)
assert minimizer.task() == "STOP: NO RESTORE"
minimizer.request_stop_with_restore()
assert minimizer.task() == "STOP: CPU"
minimizer.request_restart()
assert not minimizer.requests_f_and_g()
assert not minimizer.is_terminated()
assert minimizer.task() == "START"
minimizer = lbfgsb.minimizer(n=n)
assert minimizer.l().size() == n
assert minimizer.u().size() == n
assert minimizer.nbd().size() == n
assert minimizer.nbd().all_eq(0)
def exercise_gradient_dx(gaussian, x_max=1., n_points=50):
for i in xrange(n_points+1):
x = x_max * i / n_points
grad_finite = finite_gradient_dx_at_x(gaussian, x)
grad_analytical = gaussian.gradient_dx_at_x(x)
assert eps_eq(grad_finite, grad_analytical)
def recycle(miller_array, column_root_label, column_types=None, verbose=0):
original_dataset = to_mtz(miller_array, column_root_label, column_types)
label_decorator = mtz.label_decorator()
written = original_dataset.mtz_object()
if (0 or verbose):
written.show_summary()
original_dataset.mtz_object().write(file_name="tmp_iotbx_mtz.mtz")
restored = mtz.object(file_name="tmp_iotbx_mtz.mtz")
if (0 or verbose):
restored.show_summary()
assert restored.title() == written.title()
assert [line.rstrip() for line in restored.history()] \
== list(written.history())
assert restored.space_group_name() == written.space_group_name()
assert restored.space_group_number() == written.space_group_number()
assert restored.space_group() == written.space_group()
assert restored.point_group_name() == written.point_group_name()
assert restored.lattice_centring_type() == written.lattice_centring_type()
assert restored.n_batches() == written.n_batches()
assert restored.n_reflections() == written.n_reflections()
assert eps_eq(restored.max_min_resolution(),
written.max_min_resolution(),
eps=1.e-5)
assert restored.n_crystals() == written.n_crystals()
assert restored.n_active_crystals() == written.n_active_crystals()
assert restored.n_crystals() == 2
for rx, wx in zip(restored.crystals(), written.crystals()):
assert rx.name() == wx.name()
assert rx.project_name() == wx.project_name()
assert rx.unit_cell().is_similar_to(wx.unit_cell())
assert rx.n_datasets() == wx.n_datasets()
for rd, wd in zip(rx.datasets(), wx.datasets()):
assert rd.name() == wd.name()
assert rd.wavelength() == wd.wavelength()
assert rd.n_columns() == wd.n_columns()
miller_set = restored.crystals()[1].miller_set()
assert miller_set.indices().size() == restored.n_reflections()
crystal_symmetry = restored.crystals()[1].crystal_symmetry()
restored_dataset = restored.crystals()[1].datasets()[0]
if (not miller_array.anomalous_flag()):
if (miller_array.sigmas() is None):
if (miller_array.is_complex_array()):
assert restored_dataset.n_columns() == 3 + 2
group = restored.extract_complex(
column_label_ampl=column_root_label,
column_label_phi=label_decorator.phases(column_root_label))
elif (miller_array.is_hendrickson_lattman_array()):
assert restored_dataset.n_columns() == 3 + 4
deco = label_decorator.hendrickson_lattman
group = restored.extract_hendrickson_lattman(
column_label_a=deco(column_root_label, 0),
column_label_b=deco(column_root_label, 1),
column_label_c=deco(column_root_label, 2),
column_label_d=deco(column_root_label, 3))
else:
assert restored_dataset.n_columns() == 3 + 1
group = restored.extract_reals(column_label=column_root_label)
r = miller.array(miller_set=miller.set(
crystal_symmetry=crystal_symmetry,
indices=group.indices,
anomalous_flag=False),
data=group.data)
else:
assert restored_dataset.n_columns() == 3 + 2
group = restored.extract_observations(
column_label_data=column_root_label,
column_label_sigmas=label_decorator.sigmas(column_root_label))
r = miller.array(miller_set=miller.set(
crystal_symmetry=crystal_symmetry,
indices=group.indices,
anomalous_flag=False),
data=group.data,
sigmas=group.sigmas)
else:
if (miller_array.sigmas() is None):
if (miller_array.is_complex_array()):
assert restored_dataset.n_columns() == 3 + 4
group = restored.extract_complex_anomalous(
column_label_ampl_plus=label_decorator.anomalous(
column_root_label, "+"),
column_label_phi_plus=label_decorator.phases(
column_root_label, "+"),
column_label_ampl_minus=label_decorator.anomalous(
column_root_label, "-"),
column_label_phi_minus=label_decorator.phases(
column_root_label, "-"))
elif (miller_array.is_hendrickson_lattman_array()):
assert restored_dataset.n_columns() == 3 + 8
deco = label_decorator.hendrickson_lattman
group = restored.extract_hendrickson_lattman_anomalous(
column_label_a_plus=deco(column_root_label, 0, "+"),
column_label_b_plus=deco(column_root_label, 1, "+"),
column_label_c_plus=deco(column_root_label, 2, "+"),
column_label_d_plus=deco(column_root_label, 3, "+"),
column_label_a_minus=deco(column_root_label, 0, "-"),
column_label_b_minus=deco(column_root_label, 1, "-"),
column_label_c_minus=deco(column_root_label, 2, "-"),
column_label_d_minus=deco(column_root_label, 3, "-"))
else:
assert restored_dataset.n_columns() == 3 + 2
group = restored.extract_reals_anomalous(
column_label_plus=label_decorator.anomalous(
column_root_label, "+"),
column_label_minus=label_decorator.anomalous(
column_root_label, "-"))
r = miller.array(miller_set=miller.set(
crystal_symmetry=crystal_symmetry,
indices=group.indices,
anomalous_flag=True),
data=group.data)
else:
assert restored_dataset.n_columns() == 3 + 4
group = restored.extract_observations_anomalous(
column_label_data_plus=label_decorator.anomalous(
column_root_label, "+"),
column_label_sigmas_plus=label_decorator.sigmas(
column_root_label, "+"),
column_label_data_minus=label_decorator.anomalous(
column_root_label, "-"),
column_label_sigmas_minus=label_decorator.sigmas(
column_root_label, "-"))
r = miller.array(miller_set=miller.set(
crystal_symmetry=crystal_symmetry,
indices=group.indices,
anomalous_flag=True),
data=group.data,
sigmas=group.sigmas)
verify_miller_arrays(miller_array, r)
restored_miller_arrays = restored.as_miller_arrays()
assert len(restored_miller_arrays) == 1
thff = restored_miller_arrays[0].info().type_hints_from_file
assert thff is not None
assert miller_array.is_hendrickson_lattman_array() \
== (thff == "hendrickson_lattman")
verify_miller_arrays(miller_array, restored_miller_arrays[0])
mtz_object = miller_array.as_mtz_dataset(
column_root_label=column_root_label).mtz_object()
restored_miller_arrays = mtz_object.as_miller_arrays()
assert len(restored_miller_arrays) == 1
verify_miller_arrays(miller_array, restored_miller_arrays[0])
if (miller_array.is_bool_array() or miller_array.is_integer_array()
or miller_array.is_real_array()):
cb_op = miller_array.change_of_basis_op_to_niggli_cell()
mtz_object.change_basis_in_place(cb_op=cb_op)
cb_array = miller_array.change_basis(cb_op=cb_op)
assert mtz_object.space_group() == cb_array.space_group()
for mtz_crystal in mtz_object.crystals():
assert mtz_crystal.unit_cell().is_similar_to(cb_array.unit_cell())
restored_miller_arrays = mtz_object.as_miller_arrays()
assert len(restored_miller_arrays) == 1
verify_miller_arrays(cb_array, restored_miller_arrays[0])
mtz_object.change_basis_in_place(cb_op=cb_op.inverse())
assert mtz_object.space_group() == miller_array.space_group()
for mtz_crystal in mtz_object.crystals():
assert mtz_crystal.unit_cell().is_similar_to(
miller_array.unit_cell())
restored_miller_arrays = mtz_object.as_miller_arrays()
assert len(restored_miller_arrays) == 1
verify_miller_arrays(miller_array, restored_miller_arrays[0])
def exercise_fit():
x = flex.double((0.1, 0.2, 0.5))
y = flex.double((3,2,1))
sigmas = flex.double((0.04,0.02,0.01))
gf = gaussian.fit(
x, y, sigmas,
gaussian.sum((1,2), (4,5)))
assert approx_equal(gf.array_of_a(), (1,2))
assert approx_equal(gf.array_of_b(), (4,5))
assert approx_equal(gf.c(), 0)
assert not gf.use_c()
assert approx_equal(gf.table_x(), x)
assert approx_equal(gf.table_y(), y)
assert approx_equal(gf.table_sigmas(), sigmas)
assert approx_equal(gf.fitted_values(),
[2.8632482881537511, 2.4896052951221748, 0.94088903489182252])
reference_gaussian = gaussian.sum((1,2,3), (4,5,6))
gf = gaussian.fit(
x, reference_gaussian, sigmas,
gaussian.sum((1,2), (4,5)))
assert approx_equal(gf.array_of_a(), (1,2))
assert approx_equal(gf.array_of_b(), (4,5))
assert approx_equal(gf.c(), 0)
assert approx_equal(gf.table_x(), x)
assert approx_equal(gf.table_y(), reference_gaussian.at_x(x))
assert approx_equal(gf.table_sigmas(), sigmas)
assert isinstance(gf.sort(), gaussian.fit)
assert gf.sort().table_x() == gf.table_x()
assert gf.sort().table_y() == gf.table_y()
assert gf.sort().table_sigmas() == gf.table_sigmas()
assert approx_equal(gf.differences(), gf.at_x(x)-reference_gaussian.at_x(x))
c_fit = gaussian.fit(
flex.double([0.0, 0.066666666666666666, 0.13333333333333333,
0.2, 0.26666666666666666]),
gaussian.sum(
(2.657506, 1.078079, 1.490909, -4.2410698, 0.71379101),
(14.780758, 0.776775, 42.086842, -0.000294, 0.239535),
4.2979832),
flex.double(5, 0.0005),
gaussian.sum(
(1.1423916, 4.1728425, 0.61716694),
(0.50733125, 14.002512, 41.978928)))
differences = flex.double([-0.064797341823577881, 0.003608505180995536,
0.098159179757290715, 0.060724224581695019, -0.10766283796372011])
assert approx_equal(c_fit.differences(), differences)
assert approx_equal(c_fit.significant_relative_errors(),
[0.0107212, 0.0005581, 0.0213236, 0.0169304, 0.0385142])
gf = gaussian.fit(
x, reference_gaussian, flex.double(x.size(), 1),
gaussian.sum((1,2), (4,5)))
assert list(gf.bound_flags(False, False)) == [False,False,False,False]
assert list(gf.bound_flags(True, False)) == [True,False,True,False]
assert list(gf.bound_flags(False, True)) == [False,True,False,True]
sgf = gf.apply_shifts(flex.double((3,-3,4,6)), True)
assert approx_equal(sgf.array_of_a(), (1+3,2+4))
assert approx_equal(sgf.array_of_b(),
((math.sqrt(4)-3)**2,(math.sqrt(5)+6)**2))
assert approx_equal(sgf.c(), 0)
assert not sgf.use_c()
sgf = gf.apply_shifts(flex.double((3,-3,4,6)), False)
assert approx_equal(sgf.array_of_a(), (1+3,2+4))
assert approx_equal(sgf.array_of_b(), (4-3,5+6))
assert approx_equal(sgf.c(), 0)
assert not sgf.use_c()
differences = sgf.differences()
for use_sigmas in [False, True]:
assert approx_equal(sgf.target_function(2, use_sigmas, differences),
25.0320634)
assert approx_equal(sgf.target_function(4, use_sigmas, differences),
256.2682575)
assert approx_equal(
sgf.gradients_d_abc(2, use_sigmas, differences),
[15.6539271, -4.1090114, 10.4562306, -1.6376781])
gfc = gaussian.fit(
x, reference_gaussian, flex.double(x.size(), 1),
gaussian.sum((1,2), (4,5), 6))
assert list(gfc.bound_flags(False, False)) == [False,False,False,False,False]
assert list(gfc.bound_flags(True, False)) == [True,False,True,False,True]
assert list(gfc.bound_flags(False, True)) == [False,True,False,True,False]
sgfc = gfc.apply_shifts(flex.double((3,-3,4,6,-5)), True)
assert approx_equal(sgfc.array_of_a(), (1+3,2+4))
assert approx_equal(sgfc.array_of_b(),
((math.sqrt(4)-3)**2,(math.sqrt(5)+6)**2))
assert approx_equal(sgfc.c(), 6-5)
assert sgfc.use_c()
sgfc = gfc.apply_shifts(flex.double((3,-3,4,6,-5)), False)
assert approx_equal(sgfc.array_of_a(), (1+3,2+4))
assert approx_equal(sgfc.array_of_b(), (4-3,5+6))
assert approx_equal(sgfc.c(), 6-5)
assert sgfc.use_c()
differences = sgfc.differences()
for use_sigmas in [False, True]:
assert approx_equal(sgfc.target_function(2, use_sigmas, differences),
44.8181444)
assert approx_equal(sgfc.target_function(4, use_sigmas, differences),
757.3160329)
assert approx_equal(
sgfc.gradients_d_abc(2, use_sigmas, differences),
[21.1132071, -6.0532695, 13.6638274, -2.2460994, 22.7860809])
differences = c_fit.differences()
gabc = c_fit.gradients_d_abc(2, False, differences)
assert approx_equal(
gabc,
[-0.016525391425206391, 0.0074465239375589107, 0.020055876723667564,
0.00054794635257838251, -0.018754011379726425, -0.0011194004809549143])
assert approx_equal(
c_fit.gradients_d_shifts(flex.double((0.1,0.4,0.2,0.5,0.3,0.6)), gabc),
[-0.0165254, 0.01656512, 0.0200559, 0.0046488, -0.0187540, -0.0158487])
g5c = gaussian.sum(
(2.657505989074707, 1.0780789852142334, 1.4909089803695679,
-4.2410697937011719, 0.71379101276397705),
(14.780757904052734, 0.77677500247955322, 42.086841583251953,
-0.00029399999766610563, 0.23953500390052795),
4.2979831695556641)
for include_constant_term in (False, True):
a = flex.double(g5c.array_of_a())
b = flex.double(g5c.array_of_b())
permutation = flex.sort_permutation(data=flex.abs(a), reverse=True)[:4]
gf = gaussian.fit(
flex.double([0]),
g5c,
flex.double(1, 1),
gaussian.sum(
iter(a.select(permutation)),
iter(b.select(permutation)), 0, include_constant_term))
assert approx_equal(gf.differences(), [-5.01177418232])
shifts = flex.double(8,-1)
if (include_constant_term): shifts.append(-.2)
sgf = gf.apply_shifts(shifts, False)
assert approx_equal(sgf.array_of_a(),
[-5.2410698, 1.657506, 0.49090898, 0.078078985])
assert approx_equal(sgf.array_of_b(),
[-1.0002940, 13.780758, 41.086842, -0.223225])
if (include_constant_term):
assert approx_equal(sgf.c(), -.2)
expected_gradients = [1,0,1,0,1,0,1,0]
if (include_constant_term): expected_gradients.append(1)
assert approx_equal(
fit_finite_diff_gradients(sgf, 0),
expected_gradients,
eps=1.e-4)
for i in xrange(10):
gf = gaussian.fit(
flex.double([i / 10.]),
g5c,
flex.double(1, 1),
sgf)
differences = flex.double([0.5])
assert approx_equal(
gf.gradients_d_abc(2, False, differences),
fit_finite_diff_gradients(gf, gf.table_x()[0]),
eps=1.e-3)
for sigma in [0.04,0.02,0.01]:
gf = gaussian.fit(
flex.double([i / 20.]),
g5c,
flex.double([sigma]),
sgf)
for power in [2,4]:
for use_sigmas in [False, True]:
differences = gf.differences()
an=gf.gradients_d_abc(power, use_sigmas, differences)
fi=fit_finite_diff_target_gradients(gf, power, use_sigmas)
assert eps_eq(an, fi, eps=1.e-3)