def ic_at(motif, other, offset):
'''
Caculates the information content, ic, for a specific alignment. The approach makes a temporary motif object containing the overlapping sequences in the alignemnt and taking the average of the pssm.
Parameters
----------
motif, other: Motif objects
The motifs of interest
offset: int
The offset value that results in the alignment of interest.
'''
#Pull the sequences containined in the aligned region of the motifs from each of the motif instances.
alignment_len = min(len(motif) - offset, len(other))
motif_seqs = [
site[offset:alignment_len + offset] for site in motif.instances
]
other_seqs = [site[:alignment_len] for site in other.instances]
# Create the motif and compute the IC
amotif = Motif(instances=Instances(motif_seqs + other_seqs))
amotif.pseudocounts = dict(A=0.25, C=0.25, G=0.25, T=0.25)
#print('Motif Seqs: ' , motif_seqs)
#print('Other Seqs: ' , other_seqs)
#print('Offset ', offset)
#print('IC: ' , amotif.pssm.mean(), '\n\n')
return amotif.pssm.mean()
