def hessian_calculation(self, N, d_coordinates, chains, atoms_hessian, cutoff, d_secondary, matrix_distances, l_coordinates, resrange1 = [], resrange2 = [], l_rem = [], verbose = False):
'''This function calculates the Hessian matrix. At least its supposed to...'''
if verbose == True:
print 'calculating the hessian/second-order derivate Kirchoff/Laplacian matrix'
import math, Numeric
cutoff_sq = cutoff**2
matrix_hessian = Numeric.zeros((3*N,3*N), typecode='d')
for row_sup in range(N):
for col_sup in range(N):
## ## remove local contacts
## if row_sup in resrange1 and col_sup in resrange2:
## continue
if col_sup > row_sup:
xi = l_coordinates[row_sup][0]
xj = l_coordinates[col_sup][0]
yi = l_coordinates[row_sup][1]
yj = l_coordinates[col_sup][1]
zi = l_coordinates[row_sup][2]
zj = l_coordinates[col_sup][2]
x = xj-xi
y = yj-yi
z = zj-zi
dist_sq = x**2+y**2+z**2
## if dist_sq <= cutoff_sq:
sigmoidfactor = goodvibes_core.sigmoid(math.sqrt(dist_sq), cutoff)
## stronger local contacts
if row_sup in resrange1 and col_sup in resrange2:
sigmoidfactor *= 4
vector = [x,y,z]
for row_sub in range(3):
for col_sub in range(3):
if col_sub >= row_sub: #fill supersymmetrical elements when j>=i
value = sigmoidfactor*-vector[row_sub]*vector[col_sub]/dist_sq
matrix_hessian[3*row_sup+row_sub,3*col_sup+col_sub] = value ##upper super off-diagonal; xixj, xiyj, xizj, yiyj, yizj, zizj
matrix_hessian[3*col_sup+col_sub,3*row_sup+row_sub] = value ##lower super off-diagonal; xjxi, yjxi, zjxi, yjyi, zjyi, zjzi
matrix_hessian[3*row_sup+row_sub,3*row_sup+col_sub] -= value ##super diagonal (row); xixi, xiyi, xizi, yiyi, yizi, zizi
matrix_hessian[3*col_sup+col_sub,3*col_sup+row_sub] -= value ##super diagonal (col); xjxj, yjxj, zjxj, yjyj, zjyj, zjzj
if col_sub > row_sub: #fill lower subsymmetrical elements
matrix_hessian[3*row_sup+col_sub,3*col_sup+row_sub] = value #upper super off-diagonal; yixj, zixj, ziyj
matrix_hessian[3*col_sup+row_sub,3*row_sup+col_sub] = value #lower super off-diagonal; xjyi, xjzi, yjzi
matrix_hessian[3*row_sup+col_sub,3*row_sup+row_sub] -= value ##super diagonal; yixi, zixi, ziyi
matrix_hessian[3*col_sup+row_sub,3*col_sup+col_sub] -= value ##super diagonal; yjxj, zjxj, zjyj
return matrix_hessian
def hessian_calculation(self,
N,
matrix_distances,
d_coordinates,
cutoff,
l_coordinates,
verbose=False):
'''This function calculates the Hessian matrix. At least its supposed to...'''
if verbose == True:
print 'calculating the hessian/second-order derivate Kirchoff/Laplacian matrix'
import math, Numeric, time
matrix_hessian = Numeric.zeros(
(3 * len(l_coordinates), 3 * len(l_coordinates)), typecode='d')
##
## write matrix
##
row_sup = 0
col_sup = 0
for row_sup in range(len(l_coordinates)):
for col_sup in range(row_sup + 1, len(l_coordinates)):
xi = l_coordinates[row_sup][0]
xj = l_coordinates[col_sup][0]
yi = l_coordinates[row_sup][1]
yj = l_coordinates[col_sup][1]
zi = l_coordinates[row_sup][2]
zj = l_coordinates[col_sup][2]
x = xj - xi
y = yj - yi
z = zj - zi
vector = [x, y, z]
dist_sq = x**2 + y**2 + z**2
sigmoidfactor = goodvibes_core.sigmoid(math.sqrt(dist_sq),
cutoff)
factor = sigmoidfactor
matrix_hessian = goodvibes_core.fill_matrix_hessian(
matrix_hessian,
factor,
row_sup,
col_sup,
dist_sq,
vector,
)
return matrix_hessian
def hessian_calculation(self, N, d_coordinates, chains, atoms_hessian, cutoff, d_secondary, matrix_distances, l_coordinates, resrange1 = [], resrange2 = [], l_rem = [], verbose = False):
'''This function calculates the Hessian matrix. At least its supposed to...'''
if verbose == True:
print 'calculating the hessian/second-order derivate Kirchoff/Laplacian matrix'
import math, Numeric
cutoff_sq = cutoff**2
matrix_hessian = Numeric.zeros((3*N,3*N), typecode='d')
for row_sup in range(N):
for col_sup in range(N):
## remove local contacts
if row_sup in resrange1 and col_sup in resrange2:
continue
if col_sup > row_sup:
xi = l_coordinates[row_sup][0]
xj = l_coordinates[col_sup][0]
yi = l_coordinates[row_sup][1]
yj = l_coordinates[col_sup][1]
zi = l_coordinates[row_sup][2]
zj = l_coordinates[col_sup][2]
x = xj-xi
y = yj-yi
z = zj-zi
dist_sq = x**2+y**2+z**2
## if dist_sq <= cutoff_sq:
sigmoidfactor = goodvibes_core.sigmoid(math.sqrt(dist_sq), cutoff)
vector = [x,y,z]
for row_sub in range(3):
for col_sub in range(3):
if col_sub >= row_sub: #fill supersymmetrical elements when j>=i
value = sigmoidfactor*-vector[row_sub]*vector[col_sub]/dist_sq
matrix_hessian[3*row_sup+row_sub,3*col_sup+col_sub] = value ##upper super off-diagonal; xixj, xiyj, xizj, yiyj, yizj, zizj
matrix_hessian[3*col_sup+col_sub,3*row_sup+row_sub] = value ##lower super off-diagonal; xjxi, yjxi, zjxi, yjyi, zjyi, zjzi
matrix_hessian[3*row_sup+row_sub,3*row_sup+col_sub] -= value ##super diagonal (row); xixi, xiyi, xizi, yiyi, yizi, zizi
matrix_hessian[3*col_sup+col_sub,3*col_sup+row_sub] -= value ##super diagonal (col); xjxj, yjxj, zjxj, yjyj, zjyj, zjzj
if col_sub > row_sub: #fill lower subsymmetrical elements
matrix_hessian[3*row_sup+col_sub,3*col_sup+row_sub] = value #upper super off-diagonal; yixj, zixj, ziyj
matrix_hessian[3*col_sup+row_sub,3*row_sup+col_sub] = value #lower super off-diagonal; xjyi, xjzi, yjzi
matrix_hessian[3*row_sup+col_sub,3*row_sup+row_sub] -= value ##super diagonal; yixi, zixi, ziyi
matrix_hessian[3*col_sup+row_sub,3*col_sup+col_sub] -= value ##super diagonal; yjxj, zjxj, zjyj
return matrix_hessian
def hessian_calculation(self, N, matrix_distances, d_coordinates, cutoff, l_coordinates, verbose=False):
"""This function calculates the Hessian matrix. At least its supposed to..."""
if verbose == True:
print "calculating the hessian/second-order derivate Kirchoff/Laplacian matrix"
import math, Numeric, time
matrix_hessian = Numeric.zeros((3 * len(l_coordinates), 3 * len(l_coordinates)), typecode="d")
##
## write matrix
##
row_sup = 0
col_sup = 0
for row_sup in range(len(l_coordinates)):
for col_sup in range(row_sup + 1, len(l_coordinates)):
xi = l_coordinates[row_sup][0]
xj = l_coordinates[col_sup][0]
yi = l_coordinates[row_sup][1]
yj = l_coordinates[col_sup][1]
zi = l_coordinates[row_sup][2]
zj = l_coordinates[col_sup][2]
x = xj - xi
y = yj - yi
z = zj - zi
vector = [x, y, z]
dist_sq = x ** 2 + y ** 2 + z ** 2
sigmoidfactor = goodvibes_core.sigmoid(math.sqrt(dist_sq), cutoff)
factor = sigmoidfactor
matrix_hessian = goodvibes_core.fill_matrix_hessian(
matrix_hessian, factor, row_sup, col_sup, dist_sq, vector
)
return matrix_hessian