def run(space_group_info):
"""
Make sure it work for all space groups and boxes with non-zero origin.
"""
# make up data
xrs = random_structure.xray_structure(
space_group_info = space_group_info,
volume_per_atom = 50,
general_positions_only = False,
u_iso = 0.3,
elements = ('C', 'N', 'O', "S")*10,
min_distance = 1.5)
xrs.scattering_type_registry(table="wk1995")
f_calc = xrs.structure_factors(d_min=2).f_calc()
f_obs = abs(f_calc)
# create fmodel object
fmodel = mmtbx.f_model.manager(
xray_structure = xrs,
f_obs = f_obs)
fmodel.update_all_scales()
mc1 = fmodel.electron_density_map().map_coefficients(
map_type = "2mFo-DFc",
isotropize = False,
exclude_free_r_reflections=False,
fill_missing = False)
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min = fmodel.f_obs().d_min(),
symmetry_flags = maptbx.use_space_group_symmetry,
resolution_factor = 1./3)
# compute OMIT map
r = cfom.run(
crystal_gridding = crystal_gridding,
fmodel = fmodel.deep_copy(),
full_resolution_map = False,
max_boxes = 70,
neutral_volume_box_cushion_width = 0,
box_size_as_fraction=0.3,
log=False)
ccs = get_cc(mc1=mc1, mc2=r.map_coefficients(filter_noise=False), xrs=xrs)
assert flex.mean(ccs) > 0.8
print(" CC(min/max,mean)",ccs.min_max_mean().as_tuple())
def run(space_group_info):
"""
Make sure it work for all space groups and boxes with non-zero origin.
"""
# make up data
xrs = random_structure.xray_structure(
space_group_info = space_group_info,
volume_per_atom = 50,
general_positions_only = False,
u_iso = 0.3,
elements = ('C', 'N', 'O', "S")*10,
min_distance = 1.5)
xrs.scattering_type_registry(table="wk1995")
f_calc = xrs.structure_factors(d_min=2).f_calc()
f_obs = abs(f_calc)
# create fmodel object
fmodel = mmtbx.f_model.manager(
xray_structure = xrs,
f_obs = f_obs)
fmodel.update_all_scales()
mc1 = fmodel.electron_density_map().map_coefficients(
map_type = "2mFo-DFc",
isotropize = False,
exclude_free_r_reflections=False,
fill_missing = False)
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min = fmodel.f_obs().d_min(),
symmetry_flags = maptbx.use_space_group_symmetry,
resolution_factor = 1./3)
# compute OMIT map
r = cfom.run(
crystal_gridding = crystal_gridding,
fmodel = fmodel.deep_copy(),
full_resolution_map = False,
max_boxes = 70,
neutral_volume_box_cushion_width = 0,
box_size_as_fraction=0.3,
log=False)
ccs = get_cc(mc1=mc1, mc2=r.map_coefficients(filter_noise=False), xrs=xrs)
assert flex.mean(ccs) > 0.8
print " CC(min/max,mean)",ccs.min_max_mean().as_tuple()
def run():
"""
This test makes sure that composite full omit maps calculated using Marat's
ASU map code and not using ASU maps exactly match.
"""
# make up data
xrs = random_structure.xray_structure(
space_group_info=space_group_info("P 1"),
volume_per_atom=250,
general_positions_only=False,
elements=('C', 'N', 'O', "S") * 50,
u_iso=0.1,
min_distance=1.0)
xrs.scattering_type_registry(table="wk1995")
f_obs = abs(xrs.structure_factors(d_min=2).f_calc())
# create fmodel object
xrs.shake_sites_in_place(mean_distance=0.3)
sel = xrs.random_remove_sites_selection(fraction=0.1)
xrs = xrs.select(sel)
fmodel = mmtbx.f_model.manager(xray_structure=xrs, f_obs=f_obs)
fmodel.update_all_scales(update_f_part1=False)
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min=fmodel.f_obs().d_min(),
symmetry_flags=maptbx.use_space_group_symmetry,
resolution_factor=0.25)
# compute OMIT maps
r1 = omit_p1_specific(crystal_gridding=crystal_gridding,
fmodel=fmodel.deep_copy(),
max_boxes=70,
map_type="Fo")
r2 = omit_general_obsolete(crystal_gridding=crystal_gridding,
fmodel=fmodel.deep_copy(),
full_resolution_map=False,
map_type="Fo",
n_debias_cycles=1,
neutral_volume_box_cushion_width=0,
box_size_as_fraction=0.3,
max_boxes=70,
log=sys.stdout)
r3 = cfom.run(crystal_gridding=crystal_gridding,
fmodel=fmodel.deep_copy(),
full_resolution_map=False,
neutral_volume_box_cushion_width=0,
box_size_as_fraction=0.3,
max_boxes=70,
log=sys.stdout)
assert approx_equal(r1.r, r2.r)
def r_factor(x, y):
x = flex.abs(abs(x).data())
y = flex.abs(abs(y).data())
sc = flex.sum(x * y) / flex.sum(y * y)
return flex.sum(flex.abs(x - sc * y)) / flex.sum(x + sc * y) * 2
print(abs(r1.map_coefficients).data().min_max_mean().as_tuple())
print(abs(r2.map_coefficients).data().min_max_mean().as_tuple())
cc1 = flex.linear_correlation(
x=abs(r1.map_coefficients).data(),
y=abs(r2.map_coefficients).data()).coefficient()
assert approx_equal(cc1, 1.0)
cc2 = flex.linear_correlation(
x=abs(r1.map_coefficients).data(),
y=abs(r3.map_coefficients(filter_noise=False)).data()).coefficient()
assert cc2 > 0.8, cc2
assert approx_equal(r_factor(x=r1.map_coefficients, y=r2.map_coefficients),
0.0)
cc3 = flex.linear_correlation(x=r1.r, y=r3.r).coefficient()
assert cc3 > 0.95
for cutoff in [0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99]:
print(
maptbx.cc_peak(
cutoff=cutoff,
map_coeffs_1=r1.map_coefficients,
map_coeffs_2=r3.map_coefficients(filter_noise=False)),
"CCpeak", cutoff)
def run():
"""
This test makes sure that composite full omit maps calculated using Marat's
ASU map code and not using ASU maps exactly match.
"""
# make up data
xrs = random_structure.xray_structure(
space_group_info = space_group_info("P 1"),
volume_per_atom = 250,
general_positions_only = False,
elements = ('C', 'N', 'O', "S")*50,
u_iso = 0.1,
min_distance = 1.0)
xrs.scattering_type_registry(table="wk1995")
f_obs = abs(xrs.structure_factors(d_min=2).f_calc())
# create fmodel object
xrs.shake_sites_in_place(mean_distance=0.3)
sel = xrs.random_remove_sites_selection(fraction=0.1)
xrs = xrs.select(sel)
fmodel = mmtbx.f_model.manager(
xray_structure = xrs,
f_obs = f_obs)
fmodel.update_all_scales(update_f_part1=False)
crystal_gridding = fmodel.f_obs().crystal_gridding(
d_min = fmodel.f_obs().d_min(),
symmetry_flags = maptbx.use_space_group_symmetry,
resolution_factor = 0.25)
# compute OMIT maps
r1 = omit_p1_specific(
crystal_gridding = crystal_gridding,
fmodel = fmodel.deep_copy(),
max_boxes=70,
map_type = "Fo")
r2 = omit_general_obsolete(
crystal_gridding = crystal_gridding,
fmodel = fmodel.deep_copy(),
full_resolution_map = False,
map_type = "Fo",
n_debias_cycles = 1,
neutral_volume_box_cushion_width = 0,
box_size_as_fraction=0.3,
max_boxes=70,
log=sys.stdout)
r3 = cfom.run(
crystal_gridding = crystal_gridding,
fmodel = fmodel.deep_copy(),
full_resolution_map = False,
neutral_volume_box_cushion_width = 0,
box_size_as_fraction=0.3,
max_boxes=70,
log=sys.stdout)
assert approx_equal(r1.r, r2.r)
def r_factor(x,y):
x = flex.abs(abs(x).data())
y = flex.abs(abs(y).data())
sc = flex.sum(x*y)/flex.sum(y*y)
return flex.sum(flex.abs(x-sc*y))/flex.sum(x+sc*y)*2
print abs(r1.map_coefficients).data().min_max_mean().as_tuple()
print abs(r2.map_coefficients).data().min_max_mean().as_tuple()
cc1=flex.linear_correlation(
x=abs(r1.map_coefficients).data(),
y=abs(r2.map_coefficients).data()).coefficient()
assert approx_equal(cc1, 1.0)
cc2=flex.linear_correlation(
x=abs(r1.map_coefficients).data(),
y=abs(r3.map_coefficients(filter_noise=False)).data()).coefficient()
assert cc2 > 0.8, cc2
assert approx_equal(r_factor(
x=r1.map_coefficients, y=r2.map_coefficients), 0.0)
cc3=flex.linear_correlation(x=r1.r, y=r3.r).coefficient()
assert cc3>0.95
for cutoff in [0.5,0.6,0.7,0.8,0.9,0.95,0.99]:
print maptbx.cc_peak(
cutoff = cutoff,
map_coeffs_1 = r1.map_coefficients,
map_coeffs_2 = r3.map_coefficients(filter_noise=False)), "CCpeak", cutoff
