def bogus_kmers(self,count=200):
        """
        m.bogus_kmers(count=200) --  Generate a faked multiple sequence alignment that will reproduce
                                     the probability matrix.
        """

        POW  = math.pow
        #Build p-value inspired matrix
        #Make totals cummulative:
        # A: 0.1 C: 0.4 T:0.2 G:0.3
        #                            ->  A:0.0 C:0.1 T:0.5 G:0.7  0.0
        
        #Take bg into account:
        # We want to pick P' for each letter such that:
        #     P'/0.25  = P/Q
        # so  P'       = 0.25*P/Q
        
        m = []
        for i in range(self.width):
            _col = []
            tot   = 0.0
            for L in ACGT:
                _col.append( tot )
                tot = tot + POW(2,self.logP[i][L]) * 0.25 / self.background[L]
            _col.append(tot)
            #Renormalize
            for idx in range(len(_col)):
                _col[idx] = _col[idx] / _col[-1]
            m.append(_col)

        for p in range(0): #Was 5
            for i in range(len(m)):
                print '%6.4f  '%m[i][p],
            print

        seqs=[]
        for seqnum in range(count+1):
            f = float(seqnum)/(count+1)
            s = []
            for i in range(self.width):
                for j in range(4):
                    if (m[i][j] <= f and f < m[i][j+1]):
                        s.append(ACGT[j])
                        break
            seqs.append(''.join(s))

        del(seqs[0])
        #for i in range(count):
        #    print ">%3d\n%s"%(i,seqs[i])

        return(seqs)
m.bogus_kmers(count=200) --  Generate a faked multiple sequence alignment that will reproduce
                                     the probability matrix.