def propmat(alignment, num_imp, num_changes, transitions, probs):
num_changes = int(num_changes)
orlen = len(alignment)-num_imp
record = 0
newpd = copy(alignment.pd)
newdistarray = copy(alignment.distarray)
origlist = [i for i in alignment[:orlen]]
implist = [i for i in alignment[orlen:]]
# targets = [(random.randint(0,len(implist)-1), random.randint(0,len(alignment[0])-1)) for i in xrange(num_changes)]
targets = [(random.randint(0,len(implist)-1), wl_one(probs)) for i in xrange(num_changes)]
for t in targets:
old = newdistarray[orlen+t[0],t[1]]
new = weightselect(transitions[old])
# new = random.choice(AAS)
newdistarray[orlen+t[0],t[1]] = new
changes = (newdistarray[:,t[1]]==old).astype(int)-(newdistarray[:,t[1]]==new).astype(int)
# pdb.set_trace()
newpd[orlen+t[0]]+=changes
newpd[:,orlen+t[0]]+=changes
record += 1
# newpd = np.tril(newpd,-1)
# newpd += newpd.transpose()
np.fill_diagonal(newpd,0)
inds = Counter([t[0] for t in targets]).keys()
for ind in inds:
seq = implist[ind]
implist[ind] = SeqRecord(Seq(''.join(newdistarray[ind+orlen])), id=seq.id, name=seq.name, description=seq.description, annotations=seq.annotations)
newalign = MultipleSeqAlignment(origlist+implist)
newalign.pd, newalign.distarray = newpd, newdistarray
return record, newalign, targets
