def exercise_01(d_min=1.0):
"""
Exercise maptbx.target_and_gradients_diffmap in action: minimization.
"""
xrs = get_xrs()
map_target, f_calc = get_map(xrs=xrs)
assert approx_equal(xrs.sites_cart(), [[0,0,0]])
for sx in [-1,0,1]:
for sy in [-1,0,1]:
for sz in [-1,0,1]:
xrs_cp = xrs.deep_copy_scatterers()
xrs_cp = xrs_cp.translate(x=0.3*sx, y=0.5*sy, z=0.7*sz)
assert approx_equal(xrs_cp.sites_cart(), [[0.3*sx,0.5*sy,0.7*sz]],1.e-6)
crystal_gridding = maptbx.crystal_gridding(
unit_cell = xrs_cp.unit_cell(),
space_group_info = xrs_cp.space_group_info(),
pre_determined_n_real = map_target.accessor().all())
o = minimization.run(
xray_structure = xrs_cp,
miller_array = f_calc,
crystal_gridding = crystal_gridding,
map_target = map_target,
step = d_min/4,
target_type = "diffmap")
assert approx_equal(xrs.sites_cart(), [[0,0,0]])
def refine(self, weight, sites_cart=None, xray_structure=None):
assert xray_structure is not None and [sites_cart,xray_structure].count(None)==1
self.refined = minimization.run(
xray_structure = xray_structure,
miller_array = self.miller_array,
crystal_gridding = self.crystal_gridding,
map_target = self.map_target,
max_iterations = self.max_iterations,
min_iterations = self.min_iterations,
step = self.step,
real_space_target_weight = weight,
restraints_target_weight = self.restraints_target_weight,
geometry_restraints_manager = self.geometry_restraints_manager,
target_type = "diffmap")
def exercise_02():
"""
Exercise maptbx.target_and_gradients_diffmap in action: minimization
(bigger model).
"""
def compute_map(xray_structure, d_min=1.5, resolution_factor=1./4):
fc = xray_structure.structure_factors(d_min = d_min).f_calc()
fft_map = fc.fft_map(resolution_factor=resolution_factor)
fft_map.apply_sigma_scaling()
result = fft_map.real_map_unpadded()
return result, fc, fft_map
xrs = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P212121"),
elements = ["N","C","O","S","P"]*10,
volume_per_atom = 50)
map_target,tmp,tmp = compute_map(xray_structure = xrs)
xrs_sh = xrs.deep_copy_scatterers()
xrs_sh.shake_sites_in_place(mean_distance=0.8)
start_error = flex.mean(xrs.distances(other = xrs_sh))
assert start_error>0.7
map_current, miller_array, crystal_gridding = compute_map(
xray_structure = xrs_sh)
for step in [miller_array.d_min()/4]*5:
minimized = minimization.run(
xray_structure = xrs_sh,
miller_array = miller_array,
crystal_gridding = crystal_gridding,
map_target = map_target,
max_iterations = 500,
min_iterations = 25,
step = step,
geometry_restraints_manager = None,
target_type = "diffmap")
xrs_sh = minimized.xray_structure
map_current = minimized.map_current
final_error = flex.mean(xrs.distances(other = minimized.xray_structure))
assert approx_equal(start_error, 0.8, 1.e-3)
assert final_error < 1.e-4
def exercise_04():
"""
Exercise maptbx.target_and_gradients_simple in action: minimization
(bigger model).
"""
def compute_map(xray_structure, d_min=1., resolution_factor=1./4):
fc = xray_structure.structure_factors(d_min = d_min).f_calc()
fft_map = fc.fft_map(resolution_factor=resolution_factor)
fft_map.apply_sigma_scaling()
result = fft_map.real_map_unpadded()
return result, fc, fft_map
xrs = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P212121"),
elements = ["N","C","O","S","P"]*10,
volume_per_atom = 150)
map_target,tmp,tmp = compute_map(xray_structure = xrs)
xrs_sh = xrs.deep_copy_scatterers()
xrs_sh.shake_sites_in_place(mean_distance=0.3)
start_error = flex.mean(xrs.distances(other = xrs_sh))
assert start_error > 0.29
map_current, miller_array, crystal_gridding = compute_map(
xray_structure = xrs_sh)
xrs_sh_ = xrs_sh.deep_copy_scatterers()
minimized = minimization.run(
xray_structure = xrs_sh_,
miller_array = miller_array,
crystal_gridding = crystal_gridding,
map_target = map_target,
max_iterations = 500,
min_iterations = 25,
step = 0.5,
geometry_restraints_manager = None,
target_type = "simple")
xrs_sh_ = xrs_sh_.replace_sites_cart(minimized.sites_cart)
final_error = flex.mean(xrs.distances(other = xrs_sh_))
assert final_error < 0.015