def fit_rotatable(pdb_hierarchy, xray_structure, map_data,
rotatable_h_selection):
unit_cell = xray_structure.unit_cell()
sites_cart = xray_structure.sites_cart()
scatterers = xray_structure.scatterers()
for sel_ in rotatable_h_selection:
ed_val = -1
angle = 0.
angular_step = 1
axis = sel_[0]
points_i_seqs = sel_[1]
sites_frac_best = flex.vec3_double(len(points_i_seqs))
while angle <= 360:
sites_frac_tmp = flex.vec3_double(len(points_i_seqs))
ed_val_ = 0
for i_seq, point_i_seq in enumerate(points_i_seqs):
site_cart_new = rotate_point_around_axis(
axis_point_1=sites_cart[axis[0]],
axis_point_2=sites_cart[axis[1]],
point=sites_cart[point_i_seq],
angle=angle,
deg=True)
site_frac_new = unit_cell.fractionalize(site_cart_new)
ed_val_ += abs(
maptbx.eight_point_interpolation(map_data, site_frac_new))
sites_frac_tmp[i_seq] = site_frac_new
if (ed_val_ > ed_val):
ed_val = ed_val_
sites_frac_best = sites_frac_tmp.deep_copy()
angle += angular_step
for i_seq, point_i_seq in enumerate(points_i_seqs):
scatterers[point_i_seq].site = sites_frac_best[i_seq]
pdb_hierarchy.adopt_xray_structure(xray_structure)
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 fit_rotatable(
pdb_hierarchy,
xray_structure,
map_data,
rotatable_h_selection):
unit_cell = xray_structure.unit_cell()
sites_cart = xray_structure.sites_cart()
scatterers = xray_structure.scatterers()
for sel_ in rotatable_h_selection:
ed_val = -1
angle = 0.
angular_step = 1
axis = sel_[0]
points_i_seqs = sel_[1]
sites_frac_best = flex.vec3_double(len(points_i_seqs))
while angle <= 360:
sites_frac_tmp = flex.vec3_double(len(points_i_seqs))
ed_val_ = 0
for i_seq, point_i_seq in enumerate(points_i_seqs):
site_cart_new = rotate_point_around_axis(
axis_point_1 = sites_cart[axis[0]],
axis_point_2 = sites_cart[axis[1]],
point = sites_cart[point_i_seq],
angle = angle,
deg = True)
site_frac_new = unit_cell.fractionalize(site_cart_new)
ed_val_ += abs(maptbx.eight_point_interpolation(map_data,site_frac_new))
sites_frac_tmp[i_seq] = site_frac_new
if(ed_val_ > ed_val):
ed_val = ed_val_
sites_frac_best = sites_frac_tmp.deep_copy()
angle += angular_step
for i_seq, point_i_seq in enumerate(points_i_seqs):
scatterers[point_i_seq].site = sites_frac_best[i_seq]
pdb_hierarchy.adopt_xray_structure(xray_structure)
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_eight_point_interpolation():
map = flex.double(flex.grid(2,3,5), 10)
for shift in [0,1,-1]:
for index in flex.nested_loop(map.focus()):
x_frac = [float(i)/n+shift for i,n in zip(index, map.focus())]
assert approx_equal(maptbx.eight_point_interpolation(map, x_frac), 10)
assert approx_equal(
maptbx.eight_point_interpolation_with_gradients(map, x_frac,[1,1,1])[0], 10)
assert maptbx.closest_grid_point(map.accessor(), x_frac) == index
for i in xrange(100):
x_frac = [3*random.random()-1 for i in xrange(3)]
assert approx_equal(map.eight_point_interpolation(x_frac), 10)
assert approx_equal(
map.eight_point_interpolation_with_gradients(x_frac,[1,1,1])[0], 10)
map = flex.double(range(30))
map.resize(flex.grid(2,3,5))
for shift in [0,1,-1]:
v = 0
for index in flex.nested_loop(map.focus()):
x_frac = [float(i)/n+shift for i,n in zip(index, map.focus())]
assert approx_equal(map.eight_point_interpolation(x_frac), v)
assert approx_equal(
map[maptbx.closest_grid_point(map.accessor(), x_frac)], v)
assert approx_equal(map.value_at_closest_grid_point(x_frac), v)
v += 1
map = flex.double()
for i in xrange(48): map.append(i%2)
map.resize(flex.grid(2,4,6))
for shift in [0,1,-1]:
for offs in [.0,.5,.25,.75]:
v = offs
for index in flex.nested_loop(map.focus()):
x_frac = [(i+offs)/n+shift for i,n in zip(index, map.focus())]
assert approx_equal(map.eight_point_interpolation(x_frac), v)
if (offs != .5):
assert maptbx.closest_grid_point(map.accessor(), x_frac) == tuple(
[int(i+offs+.5)%n for i,n in zip(index,map.focus())])
v = 1-v
