def __init__(self,map_data,unit_cell,label,site_cart_1,site_cart_2,step=0.005):
x1,y1,z1 = site_cart_1
x2,y2,z2 = site_cart_2
self.one_dim_point = None
self.peak_value = None
self.peak_site_cart = None
self.status = None
self.bond_length = math.sqrt((x1-x2)**2+(y1-y2)**2+(z1-z2)**2)
alp = 0
self.data = flex.double()
self.dist = flex.double()
self.peak_sites = flex.vec3_double()
i_seq = 0
while alp <= 1.0+1.e-6:
xp = x1+alp*(x2-x1)
yp = y1+alp*(y2-y1)
zp = z1+alp*(z2-z1)
site_frac = unit_cell.fractionalize((xp,yp,zp))
ed_ = maptbx.eight_point_interpolation(map_data, site_frac)
self.dist.append( math.sqrt((x1-xp)**2+(y1-yp)**2+(z1-zp)**2) )
self.data.append(ed_)
self.peak_sites.append(unit_cell.orthogonalize(site_frac))
alp += step
i_seq += 1
i_seq_left, i_seq_right, max_peak_i_seq = self.find_peak()
self.b_estimated, self.q_estimated = None, None
self.a, self.b = None, None
self.peak_data, self.peak_dist = None, None
if([i_seq_left, i_seq_right].count(None) == 0):
self.one_dim_point = self.dist[max_peak_i_seq]
self.peak_value = self.data[max_peak_i_seq]
self.peak_site_cart = self.peak_sites[max_peak_i_seq]
self.peak_data = self.data[i_seq_left:i_seq_right+1]
self.peak_dist = self.dist[i_seq_left:i_seq_right+1]
assert (self.peak_data < 0.0).count(True) == 0
origin = self.dist[max_peak_i_seq]
dist = (self.peak_dist - origin).deep_copy()
sel = self.peak_data > 0.0
data = self.peak_data.select(sel)
dist = dist.select(sel)
if(data.size() > 0):
approx_obj = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data,
distances = dist,
use_weights = False,
optimize_cutoff_radius = False)
a_real = approx_obj.a_real_space()
b_real = approx_obj.b_real_space()
gof = approx_obj.gof()
self.a = ias_scattering_dict[label].array_of_a()[0]
self.b = ias_scattering_dict[label].array_of_b()[0]
self.b_estimated = approx_obj.b_reciprocal_space()-self.b
self.q_estimated = approx_obj.a_reciprocal_space()/self.a
#print "%.2f %.2f"%(self.q_estimated, self.b_estimated)
if(self.b_estimated <= 0.0):
self.b_estimated = self.b
if(self.q_estimated <= 0.0):
self.q_estimated = self.a
self.set_status()
def exercise_1(grid_step,
radius,
shell,
a,
b,
buffer_layer,
site_cart):
xray_structure = xray_structure_of_one_atom(site_cart = site_cart,
buffer_layer = buffer_layer,
a = a,
b = b)
sampled_density = mmtbx.real_space.sampled_model_density(
xray_structure = xray_structure,
grid_step = grid_step)
site_frac = xray_structure.unit_cell().fractionalization_matrix()*site_cart
assert approx_equal(flex.max(sampled_density.data()),
sampled_density.data().value_at_closest_grid_point(site_frac[0]))
around_atom_obj = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = sampled_density.data(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data_exact = around_atom_obj.data()
dist_exact = around_atom_obj.distances()
approx_obj = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data_exact,
distances = dist_exact,
use_weights = False,
optimize_cutoff_radius = True)
assert approx_equal(approx_obj.a_reciprocal_space(), 6.0, 1.e-3)
assert approx_equal(approx_obj.b_reciprocal_space(), 3.0, 1.e-3)
assert approx_obj.gof() < 0.3
assert approx_obj.cutoff_radius() < radius
def exercise_1(grid_step,
radius,
shell,
a,
b,
buffer_layer,
site_cart):
xray_structure = xray_structure_of_one_atom(site_cart = site_cart,
buffer_layer = buffer_layer,
a = a,
b = b)
sampled_density = mmtbx.real_space.sampled_model_density(
xray_structure = xray_structure,
grid_step = grid_step)
site_frac = xray_structure.unit_cell().fractionalization_matrix()*site_cart
assert approx_equal(flex.max(sampled_density.data()),
sampled_density.data().value_at_closest_grid_point(site_frac[0]))
around_atom_obj = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = sampled_density.data(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data_exact = around_atom_obj.data()
dist_exact = around_atom_obj.distances()
approx_obj = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data_exact,
distances = dist_exact,
use_weights = False,
optimize_cutoff_radius = True)
assert approx_equal(approx_obj.a_reciprocal_space(), 6.0, 1.e-3)
assert approx_equal(approx_obj.b_reciprocal_space(), 3.0, 1.e-3)
assert approx_obj.gof() < 0.3
assert approx_obj.cutoff_radius() < radius
def __init__(self,map_data,unit_cell,label,site_cart_1,site_cart_2,step=0.005):
x1,y1,z1 = site_cart_1
x2,y2,z2 = site_cart_2
self.one_dim_point = None
self.peak_value = None
self.peak_site_cart = None
self.status = None
self.bond_length = math.sqrt((x1-x2)**2+(y1-y2)**2+(z1-z2)**2)
alp = 0
self.data = flex.double()
self.dist = flex.double()
self.peak_sites = flex.vec3_double()
i_seq = 0
while alp <= 1.0+1.e-6:
xp = x1+alp*(x2-x1)
yp = y1+alp*(y2-y1)
zp = z1+alp*(z2-z1)
site_frac = unit_cell.fractionalize((xp,yp,zp))
ed_ = maptbx.eight_point_interpolation(map_data, site_frac)
self.dist.append( math.sqrt((x1-xp)**2+(y1-yp)**2+(z1-zp)**2) )
self.data.append(ed_)
self.peak_sites.append(unit_cell.orthogonalize(site_frac))
alp += step
i_seq += 1
i_seq_left, i_seq_right, max_peak_i_seq = self.find_peak()
self.b_estimated, self.q_estimated = None, None
self.a, self.b = None, None
self.peak_data, self.peak_dist = None, None
if([i_seq_left, i_seq_right].count(None) == 0):
self.one_dim_point = self.dist[max_peak_i_seq]
self.peak_value = self.data[max_peak_i_seq]
self.peak_site_cart = self.peak_sites[max_peak_i_seq]
self.peak_data = self.data[i_seq_left:i_seq_right+1]
self.peak_dist = self.dist[i_seq_left:i_seq_right+1]
assert (self.peak_data < 0.0).count(True) == 0
origin = self.dist[max_peak_i_seq]
dist = (self.peak_dist - origin).deep_copy()
sel = self.peak_data > 0.0
data = self.peak_data.select(sel)
dist = dist.select(sel)
if(data.size() > 0):
approx_obj = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data,
distances = dist,
use_weights = False,
optimize_cutoff_radius = False)
a_real = approx_obj.a_real_space()
b_real = approx_obj.b_real_space()
gof = approx_obj.gof()
self.a = ias_scattering_dict[label].array_of_a()[0]
self.b = ias_scattering_dict[label].array_of_b()[0]
self.b_estimated = approx_obj.b_reciprocal_space()-self.b
self.q_estimated = approx_obj.a_reciprocal_space()/self.a
#print "%.2f %.2f"%(self.q_estimated, self.b_estimated)
if(self.b_estimated <= 0.0):
self.b_estimated = self.b
if(self.q_estimated <= 0.0):
self.q_estimated = self.a
self.set_status()
def exercise_4(grid_step,
radius,
shell,
a,
b,
d_min,
volume_per_atom,
use_weights,
optimize_cutoff_radius,
scatterer_chemical_type = "C"):
xray_structure = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P 1"),
elements = ((scatterer_chemical_type)*1),
volume_per_atom = volume_per_atom,
min_distance = 1.5,
general_positions_only = True)
custom_dict = \
{scatterer_chemical_type: eltbx.xray_scattering.gaussian([a],[b])}
xray_structure.scattering_type_registry(custom_dict = custom_dict)
miller_set = miller.build_set(
crystal_symmetry = xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = d_min,
d_max = None)
f_calc = miller_set.structure_factors_from_scatterers(
xray_structure = xray_structure,
algorithm = "direct",
cos_sin_table = False,
exp_table_one_over_step_size = False).f_calc()
fft_map = f_calc.fft_map(grid_step = grid_step, symmetry_flags = None)
fft_map = fft_map.apply_volume_scaling()
site_frac = xray_structure.sites_frac()
m = fft_map.real_map_unpadded()
amm = abs(flex.max(m))
r = abs(amm-abs(m.value_at_closest_grid_point(site_frac[0]))) / amm
assert r < 0.15, r
around_atom_obj_ = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = fft_map.real_map_unpadded(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data = around_atom_obj_.data()
dist = around_atom_obj_.distances()
approx_obj_ = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data,
distances = dist,
use_weights = use_weights,
optimize_cutoff_radius = optimize_cutoff_radius)
assert approx_equal(approx_obj_.a_reciprocal_space(), 6.0, 0.1)
assert approx_equal(approx_obj_.b_reciprocal_space(), 3.0, 0.1)
assert approx_obj_.gof() < 1.0
assert approx_obj_.cutoff_radius() < radius
def exercise_4(grid_step,
radius,
shell,
a,
b,
d_min,
volume_per_atom,
use_weights,
optimize_cutoff_radius,
scatterer_chemical_type = "C"):
xray_structure = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P 1"),
elements = ((scatterer_chemical_type)*1),
volume_per_atom = volume_per_atom,
min_distance = 1.5,
general_positions_only = True)
custom_dict = \
{scatterer_chemical_type: eltbx.xray_scattering.gaussian([a],[b])}
xray_structure.scattering_type_registry(custom_dict = custom_dict)
miller_set = miller.build_set(
crystal_symmetry = xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = d_min,
d_max = None)
f_calc = miller_set.structure_factors_from_scatterers(
xray_structure = xray_structure,
algorithm = "direct",
cos_sin_table = False,
exp_table_one_over_step_size = False).f_calc()
fft_map = f_calc.fft_map(grid_step = grid_step, symmetry_flags = None)
fft_map = fft_map.apply_volume_scaling()
site_frac = xray_structure.sites_frac()
m = fft_map.real_map_unpadded()
amm = abs(flex.max(m))
r = abs(amm-abs(m.value_at_closest_grid_point(site_frac[0]))) / amm
assert r < 0.15, r
around_atom_obj_ = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = fft_map.real_map_unpadded(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data = around_atom_obj_.data()
dist = around_atom_obj_.distances()
approx_obj_ = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data,
distances = dist,
use_weights = use_weights,
optimize_cutoff_radius = optimize_cutoff_radius)
assert approx_equal(approx_obj_.a_reciprocal_space(), 6.0, 0.1)
assert approx_equal(approx_obj_.b_reciprocal_space(), 3.0, 0.1)
assert approx_obj_.gof() < 1.0
assert approx_obj_.cutoff_radius() < radius
def exercise_3(grid_step,
radius,
shell,
a,
b,
d_min,
site_cart,
buffer_layer):
xray_structure = xray_structure_of_one_atom(site_cart = site_cart,
buffer_layer = buffer_layer,
a = a,
b = b)
miller_set = miller.build_set(
crystal_symmetry = xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = d_min,
d_max = None)
f_calc = miller_set.structure_factors_from_scatterers(
xray_structure = xray_structure,
algorithm = "direct",
cos_sin_table = False,
exp_table_one_over_step_size = False).f_calc()
fft_map = f_calc.fft_map(grid_step = grid_step, symmetry_flags = None)
fft_map = fft_map.apply_volume_scaling()
site_frac = xray_structure.sites_frac()
r = abs(abs(flex.max(fft_map.real_map_unpadded()))-\
abs(fft_map.real_map_unpadded().value_at_closest_grid_point(site_frac[0])))/\
abs(flex.max(fft_map.real_map_unpadded())) * 100.0
assert approx_equal(r, 0.0)
around_atom_obj_ = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = fft_map.real_map_unpadded(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data = around_atom_obj_.data()
dist = around_atom_obj_.distances()
approx_obj_ = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data,
distances = dist,
use_weights = False,
optimize_cutoff_radius = True)
assert approx_equal(approx_obj_.a_reciprocal_space(), 6.0, 0.1)
assert approx_equal(approx_obj_.b_reciprocal_space(), 3.0, 0.01)
assert approx_obj_.gof() < 0.6
assert approx_obj_.cutoff_radius() < radius
def exercise_3(grid_step,
radius,
shell,
a,
b,
d_min,
site_cart,
buffer_layer):
xray_structure = xray_structure_of_one_atom(site_cart = site_cart,
buffer_layer = buffer_layer,
a = a,
b = b)
miller_set = miller.build_set(
crystal_symmetry = xray_structure.crystal_symmetry(),
anomalous_flag = False,
d_min = d_min,
d_max = None)
f_calc = miller_set.structure_factors_from_scatterers(
xray_structure = xray_structure,
algorithm = "direct",
cos_sin_table = False,
exp_table_one_over_step_size = False).f_calc()
fft_map = f_calc.fft_map(grid_step = grid_step, symmetry_flags = None)
fft_map = fft_map.apply_volume_scaling()
site_frac = xray_structure.sites_frac()
r = abs(abs(flex.max(fft_map.real_map_unpadded()))-\
abs(fft_map.real_map_unpadded().value_at_closest_grid_point(site_frac[0])))/\
abs(flex.max(fft_map.real_map_unpadded())) * 100.0
assert approx_equal(r, 0.0)
around_atom_obj_ = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = fft_map.real_map_unpadded(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data = around_atom_obj_.data()
dist = around_atom_obj_.distances()
approx_obj_ = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data,
distances = dist,
use_weights = False,
optimize_cutoff_radius = True)
assert approx_equal(approx_obj_.a_reciprocal_space(), 6.0, 0.1)
assert approx_equal(approx_obj_.b_reciprocal_space(), 3.0, 0.01)
assert approx_obj_.gof() < 0.6
assert approx_obj_.cutoff_radius() < radius
def exercise_2(grid_step,
radius,
shell,
a,
b,
volume_per_atom,
scatterer_chemical_type = "C"):
xray_structure = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P 1"),
elements = ((scatterer_chemical_type)*1),
volume_per_atom = volume_per_atom,
min_distance = 1.5,
general_positions_only = True)
custom_dict = \
{scatterer_chemical_type: eltbx.xray_scattering.gaussian([a],[b])}
xray_structure.scattering_type_registry(custom_dict = custom_dict)
sampled_density = mmtbx.real_space.sampled_model_density(
xray_structure = xray_structure,
grid_step = grid_step)
site_frac = xray_structure.sites_frac()
r = abs(abs(flex.max(sampled_density.data()))-\
abs(sampled_density.data().value_at_closest_grid_point(site_frac[0])))/\
abs(flex.max(sampled_density.data())) * 100.0
assert r < 10.
around_atom_obj = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = sampled_density.data(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data_exact = around_atom_obj.data()
dist_exact = around_atom_obj.distances()
approx_obj = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data_exact,
distances = dist_exact,
use_weights = False,
optimize_cutoff_radius = True)
assert approx_equal(approx_obj.a_reciprocal_space(), 6.0, 1.e-2)
assert approx_equal(approx_obj.b_reciprocal_space(), 3.0, 1.e-2)
assert approx_obj.gof() < 0.3
assert approx_obj.cutoff_radius() < radius
def exercise_2(grid_step,
radius,
shell,
a,
b,
volume_per_atom,
scatterer_chemical_type = "C"):
xray_structure = random_structure.xray_structure(
space_group_info = sgtbx.space_group_info("P 1"),
elements = ((scatterer_chemical_type)*1),
volume_per_atom = volume_per_atom,
min_distance = 1.5,
general_positions_only = True)
custom_dict = \
{scatterer_chemical_type: eltbx.xray_scattering.gaussian([a],[b])}
xray_structure.scattering_type_registry(custom_dict = custom_dict)
sampled_density = mmtbx.real_space.sampled_model_density(
xray_structure = xray_structure,
grid_step = grid_step)
site_frac = xray_structure.sites_frac()
r = abs(abs(flex.max(sampled_density.data()))-\
abs(sampled_density.data().value_at_closest_grid_point(site_frac[0])))/\
abs(flex.max(sampled_density.data())) * 100.0
assert r < 10.
around_atom_obj = mmtbx.real_space.around_atom(
unit_cell = xray_structure.unit_cell(),
map_data = sampled_density.data(),
radius = radius,
shell = shell,
site_frac = list(xray_structure.sites_frac())[0])
data_exact = around_atom_obj.data()
dist_exact = around_atom_obj.distances()
approx_obj = maptbx.one_gaussian_peak_approximation(
data_at_grid_points = data_exact,
distances = dist_exact,
use_weights = False,
optimize_cutoff_radius = True)
assert approx_equal(approx_obj.a_reciprocal_space(), 6.0, 1.e-2)
assert approx_equal(approx_obj.b_reciprocal_space(), 3.0, 1.e-2)
assert approx_obj.gof() < 0.3
assert approx_obj.cutoff_radius() < radius