def __init__(self, fixed, moving):
self.fixed = fixed
self.moving = moving
self.nsde = nsd_engine(self.fixed)
self.d_fixed = math.sqrt(self.nsde.d_fixed)
self.d_moving = math.sqrt(self.nsde.get_mean_distance(self.moving))
self.m_com = self.moving.mean()
self.f_com = self.fixed.mean()
self.n_mov = self.moving - self.m_com
self.d = (self.d_fixed + self.d_moving) / 12
self.n = 6
pi = math.pi
self.domain = [(-pi, pi), (-pi, pi), (-pi, pi), (-self.d, self.d),
(-self.d, self.d), (-self.d, self.d)]
self.x = None
self.optimizer = de.differential_evolution_optimizer(
self,
population_size=12,
f=0.85,
cr=0.95,
n_cross=2,
eps=1e-2,
show_progress=False,
show_progress_nth_cycle=20)
def rosca(m=9, hemisphere=True):
"""
Regular grid on the unit sphere, Rosca (2010).
"""
def truncate(x):
if (abs(x) < 1.e-6): return 0
else: return x
def add(result, new):
foud = False
for s in result:
d = math.sqrt((s[0] - new[0])**2 + (s[1] - new[1])**2 +
(s[2] - new[2])**2)
if (d < 1.e-3): return
result.append(new)
return
d_l = math.sqrt(2) / m
result = flex.vec3_double()
for m_ in xrange(m + 1):
if (m_ == 0):
add(result, [0, 0, 1])
else:
l_m = m_ * d_l
d_phi = math.pi / (4 * m_)
assert l_m >= 0 and l_m <= math.sqrt(2)
for k in xrange(m_ + 1):
arg1 = k * d_phi
arg2 = l_m * math.sqrt(1 - l_m**2 / 4)
x_km = truncate(math.cos(arg1) * arg2)
y_km = truncate(math.sin(arg1) * arg2)
z_m = truncate(1. - l_m**2 / 2)
add(result, [x_km, y_km, z_m])
add(result, [y_km, x_km, z_m])
add(result, [-y_km, x_km, z_m])
add(result, [-x_km, y_km, z_m])
add(result, [x_km, -y_km, z_m])
add(result, [y_km, -x_km, z_m])
add(result, [-x_km, -y_km, z_m])
add(result, [-y_km, -x_km, z_m])
if (not hemisphere):
add(result, [x_km, y_km, -z_m])
add(result, [y_km, x_km, -z_m])
add(result, [-y_km, x_km, -z_m])
add(result, [-x_km, y_km, -z_m])
add(result, [x_km, -y_km, -z_m])
add(result, [y_km, -x_km, -z_m])
add(result, [-x_km, -y_km, -z_m])
add(result, [-y_km, -x_km, -z_m])
for r in result:
assert abs(1. - math.sqrt(r[0]**2 + r[1]**2 + r[2]**2)) < 1.e-6
# XXX for debugging
if (0):
f = "HETATM%5d O HOH A%4d %8.3f%8.3f%8.3f 1.00 23.99 O "
o = open("junk.pdb", "w")
for i, r in enumerate(result):
print >> o, f % (i, i, r[0], r[1], r[2])
o.close()
return result
def tst():
M = 10
xy = []
for ii in range(M):
r = 10.0
phi = ii * (math.pi * 2 / M)
x = r * math.cos(phi)
y = r * math.sin(phi)
xy.append(flex.double([x, y]))
# build distance matrix
dmat = []
for ii in range(M):
tmp = []
for jj in range(M):
x1 = xy[ii][0]
x2 = xy[jj][0]
y1 = xy[ii][1]
y2 = xy[jj][1]
dd = math.sqrt((x1 - x2)**2.0 + (y1 - y2)**2.0)
if jj != ii:
tmp.append((jj, dd))
dmat.append(tmp)
spee = classic_spe_engine(dmat, l=1.0, max_cycle=5000)
x, s = spee.embed(2, M)
assert s < 1e-4
print("OK")
def embed(self, n_dimensions, n_points):
x = []
for ii in range(n_points):
x.append(flex.random_double(n_dimensions) * 100)
l = float(self.l)
for mm in range(self.max_cycle):
atom_order = flex.sort_permutation(flex.random_double(len(x)))
strain = 0.0
for ii in atom_order:
n_contacts = len(self.dmat[ii])
jj_index = flex.sort_permutation(
flex.random_double(n_contacts))[0]
jj_info = self.dmat[ii][jj_index]
jj = jj_info[0]
td = jj_info[1]
xi = x[ii]
xj = x[jj]
cd = math.sqrt(flex.sum((xi - xj) * (xi - xj)))
new_xi = xi + l * 0.5 * (td - cd) / (cd + self.eps) * (xi - xj)
new_xj = xj + l * 0.5 * (td - cd) / (cd + self.eps) * (xj - xi)
strain += abs(cd - td)
x[ii] = new_xi
x[jj] = new_xj
l = l - self.dl
return x, strain / len(x)
def tst_dmatrix():
# expected values are the d_jmn at beta=1.0 and j=2, m=-2, -2<=n<=2
expect = [0.593133, 0.64806, 0.433605, 0.193411, 0.0528305]
eps = 1e-4
d2 = dmatrix(2, 1.0) # dmatrix( max_L, beta )
for n in range(-2, 3):
assert abs(expect[n + 2] - d2.djmn(2, -2, n)) < eps
d4 = dmatrix(4, 1.0)
for n in range(-2, 3):
assert abs(expect[n + 2] - d4.djmn(2, -2, n)) < eps
d7 = dmatrix(2, 1.0)
for n in range(-2, 3):
assert abs(expect[n + 2] - d7.djmn(2, -2, n)) < eps
# check agains d(2,2,1) = -(1+cos(beta))*sin(beta)/2.0
for ii in range(10):
expt = -(1.0 + math.cos(ii)) * math.sin(ii) / 2.0
assert abs(dmatrix(2, ii).djmn(2, 2, 1) - expt) < eps
# check beta= multiple of pi/2
assert abs(dmatrix(20, math.pi).djmn(2, 2, 0)) < eps
assert abs(dmatrix(2, math.pi * 2).djmn(2, 2, 0)) < eps
assert abs(
dmatrix(20, math.pi * 0.5).djmn(2, 2, 0) - math.sqrt(6.0) / 4.0) < eps
def kabsch_rotation(reference_sites, other_sites):
"""
Kabsch, W. (1976). Acta Cryst. A32, 922-923.
A solution for the best rotation to relate two sets of vectors
Based on a prototype by Erik McKee and Reetal K. Pai.
This implementation does not handle degenerate situations correctly
(e.g. if all atoms are on a line or plane) and should therefore not
be used in applications. It is retained here for development purposes
only.
"""
assert reference_sites.size() == other_sites.size()
sts = matrix.sqr(other_sites.transpose_multiply(reference_sites))
eigs = eigensystem.real_symmetric((sts * sts.transpose()).as_sym_mat3())
vals = list(eigs.values())
vecs = list(eigs.vectors())
a3 = list(matrix.col(vecs[:3]).cross(matrix.col(vecs[3:6])))
a = matrix.sqr(list(vecs[:6]) + a3)
b = list(a * sts)
for i in xrange(3):
d = math.sqrt(math.fabs(vals[i]))
if (d > 0):
for j in xrange(3):
b[i * 3 + j] /= d
b3 = list(matrix.col(b[:3]).cross(matrix.col(b[3:6])))
b = matrix.sqr(b[:6] + b3)
return b.transpose() * a
def add(result, new):
foud = False
for s in result:
d = math.sqrt((s[0] - new[0])**2 + (s[1] - new[1])**2 +
(s[2] - new[2])**2)
if (d < 1.e-3): return
result.append(new)
return
def nsd(self, moving, d_moving=None):
if self.d_moving is None:
self.d_moving = self.get_mean_distance(moving)
if d_moving is not None:
self.d_moving = d_moving
# loop over all sites in fixed, find the minimum for each site
tot_rho_mf = 0
tot_rho_fm = 0
for site in moving:
dd = self.fixed - site
dd = flex.min(dd.norms())
tot_rho_mf += dd * dd
for site in self.fixed:
dd = moving - site
dd = flex.min(dd.norms())
tot_rho_fm += dd
tot_rho_fm = tot_rho_fm / (self.fixed.size() * self.d_fixed)
tot_rho_mf = tot_rho_mf / (moving.size() * self.d_moving)
result = math.sqrt((tot_rho_fm + tot_rho_mf) / 2.0)
return result
def get_mean_sigma( nlm_array ): coef = nlm_array.coefs() mean = abs( coef[0] ) var = flex.sum( flex.norm(coef) ) sigma = math.sqrt( var-mean*mean ) return mean, sigma
def scan( self ):
fft = fftpack.complex_to_complex_2d( self.ngrid, self.ngrid )
inversion = False
for beta in self.beta:
self.cc_obj.set_beta( beta )
mm = self.cc_obj.mm_coef(0,inversion)
if( self.pad > 0):
mm = self.cc_obj.mm_coef(self.pad, inversion)
fft_input= mm
scores = fft.backward( fft_input ).as_1d()
self.scores = self.scores.concatenate( -flex.norm( scores ) )
self.best_indx = flex.min_index( self.scores )
self.best_score = math.sqrt( -self.scores[ self.best_indx ])
if self.check_inversion:
### Inversion of the Spherical Harmonics ###
inversion = True
inversion_scores = flex.double()
for beta in self.beta:
self.cc_obj.set_beta( beta )
mm = self.cc_obj.mm_coef(0,inversion)
if( self.pad > 0):
mm = self.cc_obj.mm_coef(self.pad, inversion)
fft_input= mm.deep_copy()
scores = fft.backward( fft_input ).as_1d()
inversion_scores = inversion_scores.concatenate( -flex.norm( scores ) )
inv_best_indx = flex.min_index( inversion_scores )
inv_best_score = math.sqrt(-inversion_scores[ inv_best_indx ] )
if( inv_best_score < self.best_score ):
self.score = inversion_scores
self.best_indx = inv_best_indx
self.best_score = inv_best_score
self.inversion = True
else:
self.inversion = False
b=self.best_indx//(self.ngrid*self.ngrid)
a=(self.best_indx - self.ngrid*self.ngrid*b ) // self.ngrid
g=self.best_indx - self.ngrid*self.ngrid*b - self.ngrid*a
b = self.beta[b]
g = math.pi*2.0 *( float(g)/(self.ngrid-1) )
a = math.pi*2.0 *( float(a)/(self.ngrid-1) )
self.best_ea = (a, b, g )
self.find_top( self.topn )
if( self.refine ):
self.refined = []
self.refined_moving_nlm = []
self.refined_score = flex.double()
for t in self.top_align:
r = self.run_simplex( t )
self.refined.append ( r )
self.refined_score.append( self.get_cc( self.target( r ) ) )
self.refined_moving_nlm.append( self.cc_obj.rotate_moving_obj( r[0],r[1], r[2], self.inversion ) )
orders=flex.sort_permutation( self.refined_score, True )
self.best_score = -self.refined_score[orders[0]]
# show the refined results
if( self.show_result ):
print("refined results:")
for ii in range( self.topn ):
o = orders[ii]
o = ii
print(ii, ":", list( self.refined[o] ), ":", self.refined_score[o])
ea = self.refined[ orders[0] ]
self.best_ea = (ea[0], ea[1], ea[2] )
self.moving_nlm = self.cc_obj.rotate_moving_obj( ea[0],ea[1], ea[2], self.inversion )
def rms(flex_double):
return math.sqrt(flex.mean(flex.pow2(flex_double)))
