def run(pdb_file_name,
n_models,
log,
eps=1.e-7,
output_file_name="ensemble.pdb"):
pdb_inp = iotbx.pdb.input(file_name = pdb_file_name)
pdb_hierarchy = pdb_inp.construct_hierarchy()
xrs = pdb_hierarchy.extract_xray_structure(
crystal_symmetry=pdb_inp.crystal_symmetry_from_cryst1())
tls_extract = mmtbx.tls.tools.tls_from_pdb_inp(
remark_3_records = pdb_inp.extract_remark_iii_records(3),
pdb_hierarchy = pdb_hierarchy)
tlso = tls_extract.tls_params
if(len(tlso)!=1):
raise Sorry("Only one TLS group per PDB is currently supported.")
tlso = tlso[0] # XXX one group only
deg_to_rad_scale = math.pi/180
# Units: T[A], L[deg**2], S[A*deg]
T = matrix.sym(sym_mat3=tlso.t)
L = matrix.sym(sym_mat3=tlso.l)*(deg_to_rad_scale**2)
S = matrix.sqr(tlso.s)*deg_to_rad_scale
# sanity check
if(not adptbx.is_positive_definite(tlso.t, eps)):
raise Sorry("T matrix is not positive definite.")
if(not adptbx.is_positive_definite(tlso.l, eps)):
raise Sorry("L matrix is not positive definite.")
r = analysis.run(T=T, L=L, S=S, log=log)
ensemble_generator(
decompose_tls_object = r,
pdb_hierarchy = pdb_hierarchy,
xray_structure = xrs,
n_models = n_models,
log = log).write_pdb_file(file_name=output_file_name)
def run():
for fn in files:
try:
fn_ = libtbx.env.find_in_repositories(
relative_path="mmtbx/regression/tls/data_tls_analysis/%s" % fn,
test=os.path.isfile)
of = open("phenix_" + fn, "w")
T, L, S = extract(fn_)
# test python implementation
r = analysis.run(T=T, L=L, S=S, log=of, implementation='python')
# test c++ implementation
r2 = analysis.run(T=T, L=L, S=S, log=of, implementation='c++')
# test equivalence
assert approx_equal(r.result.dx, r2.result.dx, 1e-4)
assert approx_equal(r.result.dy, r2.result.dy, 1e-4)
assert approx_equal(r.result.dz, r2.result.dz, 1e-4)
assert approx_equal(r.result.l_x, r2.result.l_x, 1e-4)
assert approx_equal(r.result.l_y, r2.result.l_y, 1e-4)
assert approx_equal(r.result.l_z, r2.result.l_z, 1e-4)
assert approx_equal(r.result.sx, r2.result.sx, 1e-4)
assert approx_equal(r.result.sy, r2.result.sy, 1e-4)
assert approx_equal(r.result.sz, r2.result.sz, 1e-4)
assert approx_equal(r.result.tx, r2.result.tx, 1e-4)
assert approx_equal(r.result.ty, r2.result.ty, 1e-4)
assert approx_equal(r.result.tz, r2.result.tz, 1e-4)
assert approx_equal(r.result.v_x, r2.result.v_x, 1e-4)
assert approx_equal(r.result.v_x_M, r2.result.v_x_M, 1e-4)
assert approx_equal(r.result.v_y, r2.result.v_y, 1e-4)
assert approx_equal(r.result.v_y_M, r2.result.v_y_M, 1e-4)
assert approx_equal(r.result.v_z, r2.result.v_z, 1e-4)
assert approx_equal(r.result.v_z_M, r2.result.v_z_M, 1e-4)
assert approx_equal(r.result.w_L_lx, r2.result.w_L_lx, 1e-4)
assert approx_equal(r.result.w_L_ly, r2.result.w_L_ly, 1e-4)
assert approx_equal(r.result.w_L_lz, r2.result.w_L_lz, 1e-4)
assert approx_equal(r.result.w_M_lx, r2.result.w_M_lx, 1e-4)
assert approx_equal(r.result.w_M_ly, r2.result.w_M_ly, 1e-4)
assert approx_equal(r.result.w_M_lz, r2.result.w_M_lz, 1e-4)
of.close()
print fn, "OK"
except Exception, e:
print fn, str(e)
def exercise_00(pdb_str, formula):
"""
TLS group 6 of 4muy.
"""
pdb_inp = iotbx.pdb.input(source_info=None, lines = pdb_str)
pdb_hierarchy = pdb_inp.construct_hierarchy()
asc = pdb_hierarchy.atom_selection_cache()
cs = pdb_inp.crystal_symmetry_from_cryst1()
tls_extract = mmtbx.tls.tools.tls_from_pdb_inp(
remark_3_records = pdb_inp.extract_remark_iii_records(3),
pdb_hierarchy = pdb_hierarchy)
#
deg_to_rad_scale = math.pi/180
tls_params_one_group = tls_extract.tls_params[0]
T = matrix.sym(sym_mat3=tls_params_one_group.t)
L = matrix.sym(sym_mat3=tls_params_one_group.l)
S = matrix.sqr(tls_params_one_group.s)
origin = tls_params_one_group.origin
tlso = tools.tlso(
t = T.as_sym_mat3(),
l = L.as_sym_mat3(),
s = S,
origin = origin)
log = open("analysis.log","w")
r = analysis.run(T=T, L=L*(deg_to_rad_scale**2), S=S*deg_to_rad_scale,
log=log, find_t_S_using_formula=formula).self_check(show=False)
log.close()
#
rs = flex.double()
for trial in xrange(10):
o = tools.u_tls_vs_u_ens(pdb_str=pdb_str,
dx = r.dx,
dy = r.dy,
dz = r.dz,
sx = r.sx,
sy = r.sy,
sz = r.sz,
lx = r.l_x,
ly = r.l_y,
lz = r.l_z,
tx = r.tx,
ty = r.ty,
tz = r.tz,
vx = r.v_x,
vy = r.v_y,
vz = r.v_z,
w_M_lx = r.w_M_lx,
w_M_ly = r.w_M_ly,
w_M_lz = r.w_M_lz,
origin = origin,
n_models = 10000,
assert_similarity=False)
rs.append(o.r)
return flex.mean(rs)
def run():
for fn in files:
try:
fn_ = libtbx.env.find_in_repositories(
relative_path="mmtbx/regression/tls/data_tls_analysis/%s" % fn,
test=os.path.isfile)
of = open("phenix_" + fn, "w")
T, L, S = extract(fn_)
r = analysis.run(T=T, L=L, S=S, log=of)
of.close()
print fn, "OK"
except Exception, e:
print fn, str(e)
def run():
for fn in files:
try:
fn_ = libtbx.env.find_in_repositories(
relative_path="mmtbx/regression/tls/data_tls_analysis/%s"%fn,
test=os.path.isfile)
of = open("phenix_"+fn, "w")
T, L, S = extract(fn_)
r = analysis.run(T=T, L=L, S=S, log=of)
of.close()
print fn, "OK"
except Exception, e:
print fn, str(e)
def run(pdb_file_name, n_models, log, output_file_name_prefix, eps=1.e-7):
pdb_inp = iotbx.pdb.input(file_name=pdb_file_name)
pdb_hierarchy = pdb_inp.construct_hierarchy()
asc = pdb_hierarchy.atom_selection_cache()
cs = pdb_inp.crystal_symmetry_from_cryst1()
tls_extract = mmtbx.tls.tools.tls_from_pdb_inp(
remark_3_records=pdb_inp.extract_remark_iii_records(3),
pdb_hierarchy=pdb_hierarchy)
for i_group, tls_params_one_group in enumerate(tls_extract.tls_params):
selection = asc.selection(tls_params_one_group.selection_string)
pdb_hierarchy_sel = pdb_hierarchy.select(selection)
xrs = pdb_hierarchy_sel.extract_xray_structure(crystal_symmetry=cs)
deg_to_rad_scale = math.pi / 180
# Units: T[A], L[deg**2], S[A*deg]
T = matrix.sym(sym_mat3=tls_params_one_group.t)
L = matrix.sym(sym_mat3=tls_params_one_group.l)
S = matrix.sqr(tls_params_one_group.s)
origin = tls_params_one_group.origin
tlso = tools.tlso(t=T.as_sym_mat3(),
l=L.as_sym_mat3(),
s=S,
origin=origin)
# sanity check
if (not adptbx.is_positive_definite(tls_params_one_group.t, eps)):
raise Sorry("T matrix is not positive definite.")
if (not adptbx.is_positive_definite(tls_params_one_group.l, eps)):
raise Sorry("L matrix is not positive definite.")
r = analysis.run(T=T,
L=L * (deg_to_rad_scale**2),
S=S * deg_to_rad_scale,
log=log).self_check()
ensemble_generator_obj = ensemble_generator(
tls_from_motions_object=r,
pdb_hierarchy=pdb_hierarchy_sel,
xray_structure=xrs,
n_models=n_models,
origin=origin,
log=log)
ensemble_generator_obj.write_pdb_file(
file_name=output_file_name_prefix +
"_ensemble_%s.pdb" % str(i_group))
# get U from TLS
u_from_tls = tools.uaniso_from_tls_one_group(
tlso=tlso, sites_cart=xrs.sites_cart(), zeroize_trace=False)
# get U from ensemble
pdb_hierarchy_from_tls = pdb_hierarchy_sel.deep_copy()
pdb_hierarchy_from_ens = pdb_hierarchy_sel.deep_copy()
u_from_ens = tools.u_cart_from_ensemble(
models=ensemble_generator_obj.states.root.models())
for i in xrange(xrs.sites_cart().size()):
print "atom %d:" % i
print " Ucart(from TLS):", ["%8.5f" % u for u in u_from_tls[i]]
print " Ucart(from ens):", ["%8.5f" % u for u in u_from_ens[i]]
#
u1, u2 = u_from_tls.as_double(), u_from_ens.as_double()
cc = flex.linear_correlation(x=u1, y=u2).coefficient()
r = flex.sum(flex.abs(u1-u2))/\
flex.sum(flex.abs(flex.abs(u1)+flex.abs(u2)))*2
print "%6.4f %6.4f" % (cc, r)
#
pdb_hierarchy_from_tls.atoms().set_uij(u_from_tls)
pdb_hierarchy_from_ens.atoms().set_uij(u_from_ens)
pdb_hierarchy_from_tls.write_pdb_file(
file_name=output_file_name_prefix +
"_u_from_tls_%s.pdb" % str(i_group),
crystal_symmetry=cs)
pdb_hierarchy_from_ens.write_pdb_file(
file_name=output_file_name_prefix +
"_u_from_ensemble_%s.pdb" % str(i_group),
crystal_symmetry=cs)
return ensemble_generator_obj
def run(pdb_file_name,
n_models,
log,
output_file_name_prefix,
eps=1.e-7):
pdb_inp = iotbx.pdb.input(file_name = pdb_file_name)
pdb_hierarchy = pdb_inp.construct_hierarchy()
asc = pdb_hierarchy.atom_selection_cache()
cs = pdb_inp.crystal_symmetry_from_cryst1()
tls_extract = mmtbx.tls.tools.tls_from_pdb_inp(
remark_3_records = pdb_inp.extract_remark_iii_records(3),
pdb_hierarchy = pdb_hierarchy)
for i_group, tls_params_one_group in enumerate(tls_extract.tls_params):
selection = asc.selection(tls_params_one_group.selection_string)
pdb_hierarchy_sel = pdb_hierarchy.select(selection)
xrs = pdb_hierarchy_sel.extract_xray_structure(crystal_symmetry=cs)
deg_to_rad_scale = math.pi/180
# Units: T[A], L[deg**2], S[A*deg]
T = matrix.sym(sym_mat3=tls_params_one_group.t)
L = matrix.sym(sym_mat3=tls_params_one_group.l)
S = matrix.sqr(tls_params_one_group.s)
origin = tls_params_one_group.origin
tlso = tools.tlso(
t = T.as_sym_mat3(),
l = L.as_sym_mat3(),
s = S,
origin = origin)
# sanity check
if(not adptbx.is_positive_definite(tls_params_one_group.t, eps)):
raise Sorry("T matrix is not positive definite.")
if(not adptbx.is_positive_definite(tls_params_one_group.l, eps)):
raise Sorry("L matrix is not positive definite.")
r = analysis.run(T=T, L=L*(deg_to_rad_scale**2), S=S*deg_to_rad_scale,
log=log).self_check()
ensemble_generator_obj = ensemble_generator(
tls_from_motions_object = r,
pdb_hierarchy = pdb_hierarchy_sel,
xray_structure = xrs,
n_models = n_models,
origin = origin,
log = log)
ensemble_generator_obj.write_pdb_file(
file_name=output_file_name_prefix+"_ensemble_%s.pdb"%str(i_group))
# get U from TLS
u_from_tls = tools.uaniso_from_tls_one_group(
tlso = tlso,
sites_cart = xrs.sites_cart(),
zeroize_trace = False)
# get U from ensemble
pdb_hierarchy_from_tls = pdb_hierarchy_sel.deep_copy()
pdb_hierarchy_from_ens = pdb_hierarchy_sel.deep_copy()
u_from_ens = tools.u_cart_from_ensemble(
models = ensemble_generator_obj.states.root.models())
for i in xrange(xrs.sites_cart().size()):
print "atom %d:"%i
print " Ucart(from TLS):", ["%8.5f"%u for u in u_from_tls[i]]
print " Ucart(from ens):", ["%8.5f"%u for u in u_from_ens[i]]
#
u1, u2 = u_from_tls.as_double(), u_from_ens.as_double()
cc = flex.linear_correlation(x=u1, y=u2).coefficient()
r = flex.sum(flex.abs(u1-u2))/\
flex.sum(flex.abs(flex.abs(u1)+flex.abs(u2)))*2
print "%6.4f %6.4f"%(cc, r)
#
pdb_hierarchy_from_tls.atoms().set_uij(u_from_tls)
pdb_hierarchy_from_ens.atoms().set_uij(u_from_ens)
pdb_hierarchy_from_tls.write_pdb_file(
file_name = output_file_name_prefix+"_u_from_tls_%s.pdb"%str(i_group),
crystal_symmetry = cs)
pdb_hierarchy_from_ens.write_pdb_file(
file_name = output_file_name_prefix+"_u_from_ensemble_%s.pdb"%str(i_group),
crystal_symmetry = cs)
return ensemble_generator_obj
def u_tls_vs_u_ens(
pdb_str,
dx=0,dy=0,dz=0,
sx=0,sy=0,sz=0,
lx=[1,0,0],ly=[0,1,0],lz=[0,0,1],
tx=0,ty=0,tz=0,
vx=[1,0,0],vy=[0,1,0],vz=[0,0,1],
w_M_lx=[0,0,0], w_M_ly=[0,0,0], w_M_lz=[0,0,0],
origin=None,
n_models=10000,
assert_similarity=True):
from mmtbx.tls import analysis, tls_as_xyz
from scitbx import matrix
from libtbx.utils import null_out
#
print "INPUTS:","-"*73
print "dx :", dx
print "dy :", dy
print "dz :", dz
print "sx :", sx
print "sy :", sy
print "sz :", sz
print "lx :", [i for i in lx]
print "ly :", [i for i in ly]
print "lz :", [i for i in lz]
print "tx :", tx
print "ty :", ty
print "tz :", tz
print "vx :", [i for i in vx]
print "vy :", [i for i in vy]
print "vz :", [i for i in vz]
print "w_M_lx:", [i for i in w_M_lx]
print "w_M_ly:", [i for i in w_M_ly]
print "w_M_lz:", [i for i in w_M_lz]
print "origin:", origin
print "-"*79
#
#p1 = "dx"+str(dx)+"_"+"dy"+str(dy)+"_"+"dz"+str(dz)
#p2 = "sx"+str(sx)+"_"+"sy"+str(sy)+"_"+"sz"+str(sz)
#p3 = "lx"+"".join([str(i) for i in lx])+"_"+\
# "ly"+"".join([str(i) for i in ly])+"_"+\
# "lz"+"".join([str(i) for i in lz])
#prefix = "_".join([p1,p2,p3])
prefix="u_tls_vs_u_ens"
#
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
xrs = pdb_inp.xray_structure_simple()
sites_cart = xrs.sites_cart()
xrs.set_sites_cart(sites_cart)
ph = pdb_inp.construct_hierarchy()
ph.atoms().set_xyz(sites_cart)
if(origin is None):
origin = sites_cart.mean()
#
o_tfm = analysis.tls_from_motions(
dx=dx,dy=dy,dz=dz,
l_x=matrix.col(lx),l_y=matrix.col(ly),l_z=matrix.col(lz),
sx=sx,sy=sy,sz=sz,
tx=tx,ty=ty,tz=tz,
v_x=matrix.col(vx),v_y=matrix.col(vy),v_z=matrix.col(vz),
w_M_lx=matrix.col(w_M_lx),
w_M_ly=matrix.col(w_M_ly),
w_M_lz=matrix.col(w_M_lz))
#
u_cart_from_tls = get_u_cart(o_tfm=o_tfm, origin=origin, sites_cart=sites_cart)
tlso_ = tlso(
t = o_tfm.T_M.as_sym_mat3(),
l = o_tfm.L_M.as_sym_mat3(),
s = o_tfm.S_M.as_mat3(),
origin = origin)
if(assert_similarity):
T = matrix.sym(sym_mat3=tlso_.t)
L = matrix.sym(sym_mat3=tlso_.l)
S = matrix.sqr(tlso_.s)
o_tfm = analysis.run(T=T, L=L, S=S, log=null_out()).self_check()
#
r = tls_as_xyz.ensemble_generator(
tls_from_motions_object = o_tfm,
pdb_hierarchy = ph,
xray_structure = xrs,
n_models = n_models,
origin = origin,
log = null_out())
r.write_pdb_file(file_name="%s_ensemble.pdb"%prefix)
#
xyz_all = []
for m in r.states.root.models():
xyz_all.append(m.atoms().extract_xyz())
#
n_atoms = xyz_all[0].size()
xyz_atoms_all = []
for i in xrange(n_atoms):
xyz_atoms = flex.vec3_double()
for xyzs in xyz_all:
xyz_atoms.append(xyzs[i])
xyz_atoms_all.append(xyz_atoms)
###
u1 = u_cart_from_tls.as_double()
u2 = flex.double()
for i in xrange(n_atoms):
ui=flex.double(u_cart_from_xyz(sites_cart=xyz_atoms_all[i]))
u2.extend(ui)
r = flex.sum(flex.abs(u1-u2))/\
flex.sum(flex.abs(flex.abs(u1)+flex.abs(u2)))*2
print "R(U_tls,U_ens)=%6.4f"%(r)
print "-"*79
###
for i in xrange(n_atoms):
print "atom %d:"%i
ut=["%8.5f"%u for u in u_cart_from_tls[i]]
ue=["%8.5f"%u for u in u_cart_from_xyz(sites_cart=xyz_atoms_all[i])]
print " Ucart(from TLS):", ut
print " Ucart(from ens):", ue
if(assert_similarity):
for j in xrange(6):
assert approx_equal(abs(float(ut[j])), abs(float(ue[j])), 1.e-3)
def __init__(self,
pdb_str,
dx=0,
dy=0,
dz=0,
sx=0,
sy=0,
sz=0,
lx=[1, 0, 0],
ly=[0, 1, 0],
lz=[0, 0, 1],
tx=0,
ty=0,
tz=0,
vx=[1, 0, 0],
vy=[0, 1, 0],
vz=[0, 0, 1],
w_M_lx=[0, 0, 0],
w_M_ly=[0, 0, 0],
w_M_lz=[0, 0, 0],
origin=None,
n_models=10000,
assert_similarity=True,
show=False,
log=sys.stdout,
write_pdb_files=False,
smear_eps=0):
from mmtbx.tls import analysis, tls_as_xyz
from scitbx import matrix
from libtbx.utils import null_out
if (show):
print >> log, "INPUTS:", "-" * 73
print >> log, "dx :", dx
print >> log, "dy :", dy
print >> log, "dz :", dz
print >> log, "sx :", sx
print >> log, "sy :", sy
print >> log, "sz :", sz
print >> log, "lx :", [i for i in lx]
print >> log, "ly :", [i for i in ly]
print >> log, "lz :", [i for i in lz]
print >> log, "tx :", tx
print >> log, "ty :", ty
print >> log, "tz :", tz
print >> log, "vx :", [i for i in vx]
print >> log, "vy :", [i for i in vy]
print >> log, "vz :", [i for i in vz]
print >> log, "w_M_lx:", [i for i in w_M_lx]
print >> log, "w_M_ly:", [i for i in w_M_ly]
print >> log, "w_M_lz:", [i for i in w_M_lz]
print >> log, "origin:", origin
print >> log, "-" * 79
#
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
ph = pdb_inp.construct_hierarchy()
xrs = ph.extract_xray_structure(
crystal_symmetry=pdb_inp.crystal_symmetry())
sites_cart = xrs.sites_cart()
ph.atoms().set_xyz(sites_cart)
if (origin is None):
origin = sites_cart.mean()
#
o_tfm = analysis.tls_from_motions(dx=dx,
dy=dy,
dz=dz,
l_x=matrix.col(lx),
l_y=matrix.col(ly),
l_z=matrix.col(lz),
sx=sx,
sy=sy,
sz=sz,
tx=tx,
ty=ty,
tz=tz,
v_x=matrix.col(vx),
v_y=matrix.col(vy),
v_z=matrix.col(vz),
w_M_lx=matrix.col(w_M_lx),
w_M_ly=matrix.col(w_M_ly),
w_M_lz=matrix.col(w_M_lz))
#
self.u_cart_tls = get_u_cart(o_tfm=o_tfm,
origin=origin,
sites_cart=sites_cart)
tlso_ = tlso(t=o_tfm.T_M.as_sym_mat3(),
l=o_tfm.L_M.as_sym_mat3(),
s=o_tfm.S_M.as_mat3(),
origin=origin)
if (assert_similarity):
T = matrix.sym(sym_mat3=tlso_.t)
L = matrix.sym(sym_mat3=tlso_.l)
S = matrix.sqr(tlso_.s)
o_tfm = analysis.run(T=T, L=L, S=S, log=null_out()).self_check()
#
r = tls_as_xyz.ensemble_generator(tls_from_motions_object=o_tfm,
pdb_hierarchy=ph,
xray_structure=xrs,
n_models=n_models,
origin=origin,
use_states=write_pdb_files,
log=null_out())
if (write_pdb_files):
r.write_pdb_file(file_name="ensemble_%s.pdb" % str(n_models))
#
xyz_all = r.sites_cart_ens
n_atoms = xyz_all[0].size()
###
xyz_atoms_all = all_vs_all(xyz_all=xyz_all)
###
self.u_cart_ens = flex.sym_mat3_double()
for i in xrange(n_atoms):
self.u_cart_ens.append(
u_cart_from_xyz(sites_cart=xyz_atoms_all[i]))
u1 = self.u_cart_tls.as_double()
u2 = self.u_cart_ens.as_double()
#self.r = flex.sum(flex.abs(u1-u2))/\
# flex.sum(flex.abs(flex.abs(u1)+flex.abs(u2)))*2
# LS
#diff = u1-u2
#self.rLS = math.sqrt(flex.sum(diff*diff)/(9.*diff.size()))
#
# Merritt / Murshudov
e = smear_eps
eps = matrix.sqr([e, 0, 0, 0, e, 0, 0, 0, e])
I = matrix.sqr([2, 0, 0, 0, 2, 0, 0, 0, 2])
def add_const(u):
es = eigensystem.real_symmetric(u)
vecs = es.vectors()
l_z = matrix.col((vecs[0], vecs[1], vecs[2]))
l_y = matrix.col((vecs[3], vecs[4], vecs[5]))
l_x = matrix.col((vecs[6], vecs[7], vecs[8]))
#l_x = l_y.cross(l_z)
u = matrix.sym(sym_mat3=u)
R = matrix.sqr([
l_x[0], l_y[0], l_z[0], l_x[1], l_y[1], l_z[1], l_x[2], l_y[2],
l_z[2]
])
uD = R.transpose() * u * R
result = R * (uD + eps) * R.transpose()
tmp = R * uD * R.transpose()
for i in xrange(6):
assert approx_equal(tmp[i], u[i])
return R * (uD + eps) * R.transpose()
self.KL = 0
self.CC = 0
n1, n2, d1, d2 = 0, 0, 0, 0
for u1, u2 in zip(self.u_cart_tls, self.u_cart_ens):
for i in xrange(6):
n1 += abs(u1[i] - u2[i])
d1 += (abs(u1[i]) + abs(u2[i]))
u1 = add_const(u=u1)
u2 = add_const(u=u2)
for i in xrange(6):
n2 += abs(u1[i] - u2[i])
d2 += (abs(u1[i]) + abs(u2[i]))
iu1 = u1.inverse()
iu2 = u2.inverse()
self.KL += (u1 * iu2 + u2 * iu1 - I).trace()
diu1 = iu1.determinant()
diu2 = iu2.determinant()
den = (iu1 + iu2).determinant()
self.CC += (diu1 * diu2)**0.25 / (den / 8)**0.5
self.KL = self.KL / self.u_cart_ens.size()
self.CC = self.CC / self.u_cart_ens.size()
self.R1 = n1 / d1 * 2
self.R2 = n2 / d2 * 2
self.r = self.R1
#
###
for i in xrange(n_atoms):
ut = ["%8.5f" % u for u in self.u_cart_tls[i]]
ue = ["%8.5f" % u for u in self.u_cart_ens[i]]
if (assert_similarity):
for j in xrange(6):
assert approx_equal(abs(float(ut[j])), abs(float(ue[j])),
1.e-3)
#
if (write_pdb_files):
ph.atoms().set_uij(self.u_cart_tls)
ph.write_pdb_file(file_name="u_from_tls.pdb",
crystal_symmetry=xrs.crystal_symmetry())
ph.atoms().set_uij(self.u_cart_ens)
ph.write_pdb_file(file_name="u_from_ens.pdb",
crystal_symmetry=xrs.crystal_symmetry())
def __init__(self,
pdb_str,
dx=0,
dy=0,
dz=0,
sx=0,
sy=0,
sz=0,
lx=[1, 0, 0],
ly=[0, 1, 0],
lz=[0, 0, 1],
tx=0,
ty=0,
tz=0,
vx=[1, 0, 0],
vy=[0, 1, 0],
vz=[0, 0, 1],
w_M_lx=[0, 0, 0],
w_M_ly=[0, 0, 0],
w_M_lz=[0, 0, 0],
origin=None,
n_models=10000,
assert_similarity=True,
show=False,
log=sys.stdout,
write_pdb_files=False):
from mmtbx.tls import analysis, tls_as_xyz
from scitbx import matrix
from libtbx.utils import null_out
if (show):
print >> log, "INPUTS:", "-" * 73
print >> log, "dx :", dx
print >> log, "dy :", dy
print >> log, "dz :", dz
print >> log, "sx :", sx
print >> log, "sy :", sy
print >> log, "sz :", sz
print >> log, "lx :", [i for i in lx]
print >> log, "ly :", [i for i in ly]
print >> log, "lz :", [i for i in lz]
print >> log, "tx :", tx
print >> log, "ty :", ty
print >> log, "tz :", tz
print >> log, "vx :", [i for i in vx]
print >> log, "vy :", [i for i in vy]
print >> log, "vz :", [i for i in vz]
print >> log, "w_M_lx:", [i for i in w_M_lx]
print >> log, "w_M_ly:", [i for i in w_M_ly]
print >> log, "w_M_lz:", [i for i in w_M_lz]
print >> log, "origin:", origin
print >> log, "-" * 79
#
pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)
ph = pdb_inp.construct_hierarchy()
xrs = ph.extract_xray_structure(
crystal_symmetry=pdb_inp.crystal_symmetry())
sites_cart = xrs.sites_cart()
ph.atoms().set_xyz(sites_cart)
if (origin is None):
origin = sites_cart.mean()
#
o_tfm = analysis.tls_from_motions(dx=dx,
dy=dy,
dz=dz,
l_x=matrix.col(lx),
l_y=matrix.col(ly),
l_z=matrix.col(lz),
sx=sx,
sy=sy,
sz=sz,
tx=tx,
ty=ty,
tz=tz,
v_x=matrix.col(vx),
v_y=matrix.col(vy),
v_z=matrix.col(vz),
w_M_lx=matrix.col(w_M_lx),
w_M_ly=matrix.col(w_M_ly),
w_M_lz=matrix.col(w_M_lz))
#
self.u_cart_tls = get_u_cart(o_tfm=o_tfm,
origin=origin,
sites_cart=sites_cart)
tlso_ = tlso(t=o_tfm.T_M.as_sym_mat3(),
l=o_tfm.L_M.as_sym_mat3(),
s=o_tfm.S_M.as_mat3(),
origin=origin)
if (assert_similarity):
T = matrix.sym(sym_mat3=tlso_.t)
L = matrix.sym(sym_mat3=tlso_.l)
S = matrix.sqr(tlso_.s)
o_tfm = analysis.run(T=T, L=L, S=S, log=null_out()).self_check()
#
r = tls_as_xyz.ensemble_generator(tls_from_motions_object=o_tfm,
pdb_hierarchy=ph,
xray_structure=xrs,
n_models=n_models,
origin=origin,
use_states=write_pdb_files,
log=null_out())
if (write_pdb_files):
r.write_pdb_file(file_name="ensemble_%s.pdb" % str(n_models))
#
xyz_all = r.sites_cart_ens
n_atoms = xyz_all[0].size()
###
xyz_atoms_all = all_vs_all(xyz_all=xyz_all)
###
self.u_cart_ens = flex.sym_mat3_double()
for i in xrange(n_atoms):
self.u_cart_ens.append(
u_cart_from_xyz(sites_cart=xyz_atoms_all[i]))
u1 = self.u_cart_tls.as_double()
u2 = self.u_cart_ens.as_double()
self.r = flex.sum(flex.abs(u1-u2))/\
flex.sum(flex.abs(flex.abs(u1)+flex.abs(u2)))*2
###
for i in xrange(n_atoms):
ut = ["%8.5f" % u for u in self.u_cart_tls[i]]
ue = ["%8.5f" % u for u in self.u_cart_ens[i]]
if (assert_similarity):
for j in xrange(6):
assert approx_equal(abs(float(ut[j])), abs(float(ue[j])),
1.e-3)
#
if (write_pdb_files):
ph.atoms().set_uij(self.u_cart_tls)
ph.write_pdb_file(file_name="u_from_tls.pdb",
crystal_symmetry=xrs.crystal_symmetry())
ph.atoms().set_uij(self.u_cart_ens)
ph.write_pdb_file(file_name="u_from_ens.pdb",
crystal_symmetry=xrs.crystal_symmetry())
