def tls_refinery(sites_cart,
selection,
u_cart=None,
u_iso=None,
use_minimizer=False,
max_iterations=100):
sites_cart_ = sites_cart.select(selection)
cm = sites_cart_.mean_weighted(weights=flex.double(sites_cart_.size(), 1))
assert [u_cart, u_iso].count(None) == 1
if (not use_minimizer):
obj = tools.tls_ls_derivative_coefficients(
origin=cm, sites_cart=sites_cart_, u_iso=u_iso.select(selection))
def s1(use_generalized_inverse=True):
if (not use_generalized_inverse):
obj.a.matrix_inversion_in_place()
res = obj.a.matrix_multiply(obj.b)
else:
es = scitbx.linalg.eigensystem.real_symmetric(
m=obj.a, relative_epsilon=1.e-12, absolute_epsilon=0)
a = es.generalized_inverse_as_packed_u(
).matrix_packed_u_as_symmetric()
res = a.matrix_multiply(obj.b)
return res
result = s1()
target = tools.ls_target_from_iso_tls(t=result[0],
l=tuple(result[1:7]),
s=tuple(result[7:]),
origin=cm,
sites_cart=sites_cart_,
u_isos=u_iso.select(selection))
#print "target:",target
class foo:
pass
foo.f = target
return foo
else:
if (u_cart is not None):
return tools.tls_from_uaniso_minimizer(
uaniso=u_cart.select(selection),
T_initial=[0, 0, 0, 0, 0, 0],
L_initial=[0, 0, 0, 0, 0, 0],
S_initial=[0, 0, 0, 0, 0, 0, 0, 0, 0],
refine_T=True,
refine_L=True,
refine_S=True,
origin=cm,
sites=sites_cart_,
max_iterations=max_iterations)
else:
minimized = tools.tls_from_uiso_minimizer(
uiso=u_iso.select(selection),
T_initial=[0],
L_initial=[0, 0, 0, 0, 0, 0],
S_initial=[0, 0, 0],
refine_T=True,
refine_L=True,
refine_S=True,
origin=cm,
sites=sites_cart_,
max_iterations=max_iterations)
if (0): # DEBUG
print("Minimization:", xxx.f)
print("T_min:", minimized.T_min)
print("L_min:", minimized.L_min)
print("S_min:", minimized.S_min)
print()
return minimized
def tls_refinery(sites_cart, selection, u_cart=None, u_iso=None,
use_minimizer=False, max_iterations=100):
sites_cart_ = sites_cart.select(selection)
cm = sites_cart_.mean_weighted(weights=flex.double(sites_cart_.size(),1))
assert [u_cart, u_iso].count(None)==1
if(not use_minimizer):
obj = tools.tls_ls_derivative_coefficients(
origin = cm,
sites_cart = sites_cart_,
u_iso = u_iso.select(selection))
def s1(use_generalized_inverse=True):
if(not use_generalized_inverse):
obj.a.matrix_inversion_in_place()
res = obj.a.matrix_multiply(obj.b)
else:
es = scitbx.linalg.eigensystem.real_symmetric(
m=obj.a,
relative_epsilon=1.e-12,
absolute_epsilon=0)
a = es.generalized_inverse_as_packed_u().matrix_packed_u_as_symmetric()
res = a.matrix_multiply(obj.b)
return res
result = s1()
target = tools.ls_target_from_iso_tls(
t = result[0],
l = tuple(result[1:7]),
s = tuple(result[7:]),
origin = cm,
sites_cart = sites_cart_,
u_isos = u_iso.select(selection))
#print "target:",target
class foo: pass
foo.f = target
return foo
else:
if(u_cart is not None):
return tools.tls_from_uaniso_minimizer(
uaniso = u_cart.select(selection),
T_initial = [0,0,0,0,0,0],
L_initial = [0,0,0,0,0,0],
S_initial = [0,0,0,0,0,0,0,0,0],
refine_T = True,
refine_L = True,
refine_S = True,
origin = cm,
sites = sites_cart_,
max_iterations = max_iterations)
else:
minimized = tools.tls_from_uiso_minimizer(
uiso = u_iso.select(selection),
T_initial = [0],
L_initial = [0,0,0,0,0,0],
S_initial = [0,0,0],
refine_T = True,
refine_L = True,
refine_S = True,
origin = cm,
sites = sites_cart_,
max_iterations = max_iterations)
if(0): # DEBUG
print "Minimization:", xxx.f
print "T_min:", minimized.T_min
print "L_min:", minimized.L_min
print "S_min:", minimized.S_min
print
return minimized