def _calc_g_zs_uniform(self, g_z, g_zs):
if self.cex:
self.cex._calc_g_zs_uniform(g_z, g_zs, LAMBDA_0, LAMBDA_1, *self.shape)
else:
segments = g_zs.shape[1]
pg_zs = g_zs[:, 0]
for r in range(1, segments):
g_zs[:, r] = pg_zs = correlate(pg_zs, LAMBDA_ARRAY, 1) * g_z
def _calc_g_zs_uniform(self, g_z, g_zs):
if self.cex:
self.cex._calc_g_zs_uniform(g_z, g_zs, LAMBDA_0, LAMBDA_1, *self.shape)
else:
segments = g_zs.shape[1]
pg_zs = g_zs[:, 0]
for r in range(1, segments):
g_zs[:, r] = pg_zs = correlate(pg_zs, LAMBDA_ARRAY, 1) * g_z
def _calc_g_zs(self, g_z, c_i, g_zs):
if self.cex:
self.cex._calc_g_zs(g_z, c_i, g_zs, LAMBDA_0, LAMBDA_1, *self.shape)
else:
pg_zs = g_zs[:, 0]
segment_iterator = enumerate(c_i[::-1])
next(segment_iterator)
for r, c in segment_iterator:
g_zs[:, r] = pg_zs = (correlate(pg_zs, LAMBDA_ARRAY, 1) + c) * g_z
def _calc_g_zs(self, g_z, c_i, g_zs):
if self.cex:
self.cex._calc_g_zs(g_z, c_i, g_zs, LAMBDA_0, LAMBDA_1, *self.shape)
else:
pg_zs = g_zs[:, 0]
segment_iterator = enumerate(c_i[::-1])
next(segment_iterator)
for r, c in segment_iterator:
g_zs[:, r] = pg_zs = (correlate(pg_zs, LAMBDA_ARRAY, 1) + c) * g_z
def SCFeqns(phi_z, chi, chi_s, sigma, n_avg, p_i, phi_b=0):
""" System of SCF equation for terminally attached polymers.
Formatted for input to a nonlinear minimizer or solver.
The sign convention here on u is "backwards" and always has been.
It saves a few sign flips, and looks more like Cosgrove's.
"""
# let the solver go negative if it wants
phi_z = abs(phi_z)
# penalize attempts that overfill the lattice
toomuch = phi_z > .99999
penalty_flag = toomuch.any()
if penalty_flag:
penalty = np.where(toomuch, phi_z - .99999, 0)
phi_z[toomuch] = .99999
# calculate new g_z (Boltzmann weighting factors)
u_prime = log((1.0 - phi_z) / (1.0 - phi_b))
u_int = 2 * chi * (correlate(phi_z, LAMBDA_ARRAY, 1) - phi_b)
u_int[0] += chi_s
u_z = u_prime + u_int
g_z = exp(u_z)
# normalize g_z for numerical stability
u_z_avg = np.mean(u_z)
g_z_norm = g_z / exp(u_z_avg)
phi_z_new = calc_phi_z(g_z_norm, n_avg, sigma, phi_b, u_z_avg, p_i)
eps_z = phi_z - phi_z_new
if penalty_flag:
np.copysign(penalty, eps_z, penalty)
eps_z += penalty
return eps_z
def SCFeqns(phi_z, chi, chi_s, sigma, n_avg, p_i, phi_b=0):
""" System of SCF equation for terminally attached polymers.
Formatted for input to a nonlinear minimizer or solver.
The sign convention here on u is "backwards" and always has been.
It saves a few sign flips, and looks more like Cosgrove's.
"""
# let the solver go negative if it wants
phi_z = abs(phi_z)
# penalize attempts that overfill the lattice
toomuch = phi_z > .99999
penalty_flag = toomuch.any()
if penalty_flag:
penalty = np.where(toomuch, phi_z - .99999, 0)
phi_z[toomuch] = .99999
# calculate new g_z (Boltzmann weighting factors)
u_prime = log((1.0 - phi_z) / (1.0 - phi_b))
u_int = 2 * chi * (correlate(phi_z, LAMBDA_ARRAY, 1) - phi_b)
u_int[0] += chi_s
u_z = u_prime + u_int
g_z = exp(u_z)
# normalize g_z for numerical stability
u_z_avg = np.mean(u_z)
g_z_norm = g_z / exp(u_z_avg)
phi_z_new = calc_phi_z(g_z_norm, n_avg, sigma, phi_b, u_z_avg, p_i)
eps_z = phi_z - phi_z_new
if penalty_flag:
np.copysign(penalty, eps_z, penalty)
eps_z += penalty
return eps_z
def _autocorrelation(means):
from numpy.core import multiarray
result = multiarray.correlate(means, means, mode=2)
return result[round(result.size / 2):]
def _calc_g_zs_uniform(g_z, g_zs):
segments = g_zs.shape[1]
pg_zs = g_zs[:, 0]
for r in range(1, segments):
g_zs[:, r] = pg_zs = correlate(pg_zs, LAMBDA_ARRAY, 1) * g_z
def _calc_g_zs(g_z, c_i, g_zs):
pg_zs = g_zs[:, 0]
segment_iterator = enumerate(c_i[::-1])
next(segment_iterator)
for r, c in segment_iterator:
g_zs[:, r] = pg_zs = (correlate(pg_zs, LAMBDA_ARRAY, 1) + c) * g_z
