# theta = np.arccos( GetDot(dna, start, end) )
# print theta*se/(2*np.sin(.5*theta))
#
Ecurr = FMC.Boltzmann_Energy(dna.params[start + unwrap])
print dna.params[start + unwrap]
print Ecurr
minimum = False
# optimize the Bert/John parameters
while minimum == False:
a, Ecurr, minimum = FindMinimum(dna, nucl, a, dyads, start=start, end=end, unwrap=unwrap)
Etemp = FMC.Boltzmann_Energy(dna.params[start + unwrap])
print Etemp
dna.write2disk(str(file_name) + '_' + str(unwrap) + '_unwrap_optimized')
np.savetxt(str(file_name) + '_' + str(unwrap) + '_unwrap_input_params_optimized.dat', a)
E_res = np.array([])
for unwrap in range(min_unwrap,max_unwrap+1):
unwrap = -unwrap
E = []
j = 0
print start
print end
number_of_iterations = 1000
minimum = False
# optimize the Bert/John parameters
while minimum == False:
a, Ecurr, minimum = FindMinimum(dna,
nucl,
a,
dyads,
start=start,
end=end,
unwrap=unwrap)
Etemp = FMC.Boltzmann_Energy(dna.params[start + unwrap])
print Etemp
dna.write2disk(str(file_name) + '_' + str(nld) + '_nld_optimized')
np.savetxt(
str(file_name) + '_' + str(nld) + '_nld_input_params_optimized.dat', a)
##############Optimize linker DNA using Metropolis-Hastings Monte Carlo##############
nld_start = 100
nld_end = 17
n = 0
E_res = np.array([])
E_link_series = np.array([])
# for unwrap in range(min_unwrap,max_unwrap+1):
for nld in range(int(nld_start), int(nld_end - 1), -1):
n += 1