def simulate(energy_matrix,genome,steps):
"""Return genomic position of TF with given energy matrix after given
number of steps."""
w = len(energy_matrix) # width of binding sites, in bases
L = len(genome)
binding_landscape = score_genome(energy_matrix,genome)
def binding_probability(i):
"""Return probability, up to a constant, of occupying ith position in
genome"""
delta_G = binding_landscape[i]
return exp(-delta_G)
def diffusion3d(i):
"""Choose a position at random"""
return random.randrange(L-w+1)
return mh(p=binding_probability,proposal=diffusion3d,x0=0,iterations=steps)
def theoretical_probabilities(energy_matrix,genome):
binding_landscape = score_genome(energy_matrix,genome)
Z = float(sum([exp(-score) for score in binding_landscape]))
return [exp(-score)/Z for score in binding_landscape]
