def computeRate(selector):
myMultistrand = MergeSim()
myMultistrand.setOptionsFactory2(genOptions, NUMOFPATHS, selector)
myMultistrand.setNumOfThreads(NUMOFTHREADS)
myMultistrand.setTerminationCriteria(TERMINATIONCRIT)
myMultistrand.setLeakMode() # the new leak object -- faster bootstrapping.
myMultistrand.printTrajectory()
return 0.0, 0.0, 0.0
myMultistrand.run()
myFSR = myMultistrand.results
low, high = myFSR.doBootstrap()
return myFSR.k1(), low, high
# FD: This script is now set to use 4 threads and just 50,000 trajectories.
# FD: This is different from the results in case1.pdf
# FD: The results of this study heavily depend on the parameterization of the Metropolis model: JS or DNA23 (see below).
from multistrand.objects import StopCondition, Domain, Complex, Strand
from multistrand.options import Options, Literals
from multistrand.concurrent import MergeSim
from multistrand._options.interface import FirstStepResult
import numpy as np
ATIME_OUT = 10.0
myMultistrand = MergeSim()
myMultistrand.setNumOfThreads(8)
myMultistrand.setLeakMode()
def first_step_simulation(strand_seq, trials, T=25, material="DNA"):
print(
"Running first step mode simulations for %s (with Boltzmann sampling)..."
% (strand_seq))
# Using domain representation makes it easier to write secondary structures.
onedomain = Domain(name="onedomain", sequence=strand_seq)
gdomain = Domain(name="gdomain", sequence="TTTT")
top = Strand(name="top", domains=[onedomain])
bot = top.C
dangle = Strand(name="Dangle", domains=[onedomain, gdomain])
