def main(argv): opts = docopt.docopt(__doc__, argv) with kmers(opts['<ref>'], 'r') as z: K = z.meta['K'] xs = array.array('L', readKmers(z)) Z = len(xs) with kmers(opts['<input>'], 'r') as z0: K0 = z0.meta['K'] if K0 != K: print >> sys.stderr, "mismatched K (%d)" % (K0, ) sys.exit(1) with kmers(opts['<output>'], 'w') as z: z.meta['K'] = K if 'counts' in z0.meta: ys = readKmersAndCounts(z0) writeKmersAndCounts(z, project2(xs, ys)) z.meta['kmers'] = 'kmers' z.meta['counts'] = 'counts' else: ys = readKmers(z0) writeKmers(z, project1(xs, ys)) z.meta['kmers'] = 'kmers' z.meta['hist'] = z0.meta['hist']
def main(argv): opts = docopt.docopt(__doc__, argv) p = 0.01 if opts['-P'] is not None: p = float(opts['-P']) inp = opts['<input>'] out = opts['<output>'] with kmers(out, 'w') as z: h = {} with kmers(inp, 'r') as z0: K = z0.meta['K'] z.meta = z0.meta.copy() del z.meta['kmers'] del z.meta['counts'] xs = readKmersAndCounts(z0) S = 0 if opts['-D'] is None: if opts['-S']: S = long(opts['-S']) random.seed(S) writeKmersAndCounts(z, sampleR(p, xs, h)) else: if opts['-S']: S = long(opts['-S']) writeKmersAndCounts(z, sampleD(p, S, xs, h)) z.meta['K'] = K z.meta['kmers'] = 'kmers' z.meta['counts'] = 'counts' z.meta['hist'] = h
def main(argv): opts = docopt.docopt(__doc__, argv) inp = opts['<input>'] out = opts['<output>'] c = 0 if opts['-c'] is not None: c = int(opts['-c']) C = None if opts['-C'] is not None: C0 = int(opts['-C']) if C0 > 0: C = C0 with kmers(inp, 'r') as z: K = z.meta['K'] h = z.meta['hist'] if c == 0: c = infer(K, h) print >> sys.stderr, 'inferred cutoff:', c xs = readKmersAndCounts(z) with kmers(out, 'w') as w: w.meta = z.meta.copy() del w.meta['kmers'] del w.meta['counts'] writeKmersAndCounts(w, trim(xs, c, C)) w.meta['K'] = K w.meta['kmers'] = 'kmers' w.meta['counts'] = 'counts' w.meta['hist'] = h
def main(argv): opts = docopt.docopt(__doc__, argv) if opts['-X']: K = 27 if opts['-K']: K = int(opts['-K']) buildIndex(K, opts['<input>'], opts['<alleles>']) return idx = index(opts['<alleles>']) for inp in opts['<input>']: with kmers(inp, 'r') as z: K0 = z.meta['K'] if K0 != idx.K: print >> sys.stderr, 'Input "%d" has different k to index' % ( inp, ) sys.exit(1) xs = readKmers(z) cs = [idx.lens[i] for i in xrange(len(idx.lens))] for x in xs: for j in idx[x]: cs[j] -= 1 for j in xrange(len(idx.lens)): assert cs[j] >= 0 if cs[j] == 0: print '%s\t%d\t%s' % (inp, j, idx.names[j])
def main(argv): opts = docopt.docopt(__doc__, argv) for inp in opts['<input>']: with kmers(inp, 'r') as z: itms = z.meta.items() itms.sort() for (k, v) in itms: print k, v
def getK(ins): k = None for fn in ins: with kmers(fn, 'r') as z: k0 = z.meta['K'] if k is None: k = k0 elif k != k0: raise MismatchedK(k, k0) return k
def main(argv): opts = docopt.docopt(__doc__, argv) for inp in opts['<input>']: with kmers(inp, 'r') as z: if 'hist' in z.meta: h = z.meta['hist'].items() h = [(int(f), c) for (f, c) in h] h.sort() for (f,c) in h: print '%s\t%d\t%d' % (inp, f, c)
def main(argv): opts = docopt.docopt(__doc__, argv) L0 = None if opts['-l']: L0 = int(opts['-l']) for inp in opts['<input>']: with kmers(inp, 'r') as z: K = z.meta['K'] L = L0 if L is None: L = 2*K xs = array.array('L', readKmers(z)) S = sparse(2*K, xs) seen = bitvec(S.count()) for i in xrange(S.count()): if seen[i]: continue x = S.select(i) xb = rc(K, x) xp = succ(K, S, xb) if xp == 1: # x isn't the start of a contig continue pth = [x] seen[i] = 1 xn = succ(K, S, x) while len(xn) == 1: if seen[xn[0]] == 1: break x = S.select(xn[0]) pth.append(x) seen[xn[0]] = 1 xb = rc(K, x) j = S.rank(xb) seen[j] = 1 xn = succ(K, S, x) if len(pth)+K-1 < L: continue s = [render(K, pth[0])] for j in xrange(1, len(pth)): s.append("ACGT"[pth[j]&3]) print '>contig_%d\n%s' % (i, ''.join(s))
def main(argv): opts = docopt.docopt(__doc__, argv) with kmers(opts['<ref>'], 'r') as z: K = z.meta['K'] xs = readKmers(z) if opts['-H'] is not None: d = int(opts['-H']) ref = hamming(K, d, xs) elif opts['-L'] is not None: d = int(opts['-L']) ref = levenshtein(K, d, xs) else: ref = ksnp(K, xs) xs = [] for ys in ref: xs += ys xs.sort() with kmers(opts['<output>'], 'w') as z: writeKmers(z, xs) z.meta['kmers'] = 'kmers' z.meta['K'] = K
def main(argv): opts = docopt.docopt(__doc__, argv) nms = [str(i+1) for i in xrange(len(probesMTB))] probes = probesMTB if opts['-p'] is not None: nms = [] probes = [] bad = False with open(opts['-p']) as f: i = 0 ln = 0 for l in f: ln += 1 if l[0] == '#': continue i += 1 t = l.split() if len(t) == 1: nms.append(str(i)) probes.append(probe(t[0])) elif len(t) == 2: nms.append(t[0]) probes.append(probe(t[1])) else: bad = True print >> sys.stderr, '%s line %d, badly formatted.' % (opts['-p'], i) if bad: sys.exit(1) for inp in opts['<input>']: with kmers(inp, 'r') as z: K = z.meta['K'] xs = readKmers(z) xs = sparse(2*K, array.array('L', xs)) res = [] for i in xrange(len(probes)): if findProbe(probes[i], K, xs): res.append('1') else: res.append('0') if opts['-l']: for i in xrange(len(nms)): print '%s\t%s\t%s' % (inp, nms[i], res[i]) else: print inp + '\t' + ''.join(res)
def main(argv): opts = docopt.docopt(__doc__, argv) inp = opts['<input>'] with kmers(inp, 'r') as z: K = z.meta['K'] if 'kmers' not in z.meta: print >> sys.stderr, 'cannot dump "%s" as it contains no k-mers' % (inp,) return if 'counts' in z.meta: xs = readKmersAndCounts(z) for (x, c) in xs: print '%s\t%d' % (render(K, x), c) else: xs = readKmers(z) for x in xs: print render(K, x)
def prep(self, K, fn): with kmers(fn, 'r') as z: fK = z.meta['K'] if fK < K: raise MismatchedK(K, fK) xs = readKmers(z) if self.vec: S = 2 * (fK - K) v = array.array('I', [0 for i in xrange(1 << (2 * K))]) for (x, c) in xs: y = x >> S v[y] += c return v S = 2 * (fK - K) v = array.array('L', []) for x in xs: y = x >> S if len(v) == 0 or v[-1] != y: v.append(y) return v
def main(argv): opts = docopt.docopt(__doc__, argv) if opts['-X']: K = 27 S = [] N = 0 qacgt = [0, 0, 0, 0] for fn in opts['<input>']: with open(fn) as f: for (nm, seq) in readFasta(f): if len(seq) < K: continue for (x, p) in kmersWithPos(K, seq, True): S.append(x) qacgt[x & 3] += 1 N += 1 S.sort() qacgt = [float(c) / float(N) for c in qacgt] S = sparse(2 * K, array.array('L', uniq(S))) lens = [] nms = [] seqs = [] n = 0 tmp = [[] for i in xrange(S.count())] for fn in opts['<input>']: with open(fn) as f: for (nm, seq) in readFasta(f): if len(seq) < K: print >> sys.stderr, "warning: `%s' skipped" % (nm, ) continue nms.append(nm) seqs.append(seq) lens.append(len(seq)) for (x, p) in kmersWithPos(K, seq, True): r = S.rank(x) tmp[r].append((n, p)) n += 1 T = array.array('I', []) U = array.array('I', []) V = array.array('i', []) t = 0 for nps in tmp: T.append(t) t += len(nps) for (n, p) in nps: U.append(n) V.append(p) T.append(t) del tmp gfn = opts['<genes>'] with casket(gfn, 'w') as z: meta = {} meta['K'] = K meta['lens'] = lens meta['qacgt'] = qacgt meta['nms'] = nms meta['seqs'] = seqs z.add_content('__meta__', json.dumps(meta)) write64(z, S.xs, 'S') write32(z, T, 'T') write32(z, U, 'U') write32s(z, V, 'V') return print >> sys.stderr, "loading..." gfn = opts['<genes>'] with casket(gfn, 'r') as z: mf = z.open('__meta__') meta = json.load(mf) K = meta['K'] lens = meta['lens'] qacgt = meta['qacgt'] nms = meta['nms'] seqs = meta['seqs'] S = read64(z, 'S') S = sparse(2 * K, S) T = read32(z, 'T') U = read32(z, 'U') V = read32s(z, 'V') print >> sys.stderr, "done." for fn in opts['<input>']: L = array.array('B', [0 for i in xrange(S.count())]) Y = array.array('L', [0 for i in xrange(S.count())]) with kmers(fn, 'r') as z: sacgt = z.meta['acgt'] xs = readKmers(z) X = array.array('L', xs) M = len(X) resolveAll(K, S, L, Y, X) X = sparse(2 * K, X) g = sum([qp * sp for (qp, sp) in zip(qacgt, sacgt)]) print >> sys.stderr, "g =", g nm = [null(g, M, j) for j in range(0, K + 1)] # counts for computing distribution of prefix lengths cnt = [[0 for j in xrange(K + 1)] for i in xrange(len(nms))] # the k-mers that we pulled by lcp from the sample # for each position of each query. P = [ array.array('L', [0 for j in xrange(lens[i] - K + 1)]) for i in xrange(len(lens)) ] # the length of the lcp for each position of each query. Q = [ array.array('B', [0 for j in xrange(lens[i] - K + 1)]) for i in xrange(len(lens)) ] for i in xrange(S.count()): for j in xrange(T[i], T[i + 1]): n = U[j] p = V[j] y = Y[i] l = L[i] cnt[n][l] += 1 if p > 0: p -= 1 else: p = -(p + 1) y = rc(K, y) if l > Q[n][p]: Q[n][p] = l P[n][p] = y for i in xrange(len(nms)): # iterate over the queries qc = math.log(K * 0.05 / float(lens[i] - K + 1) / 2) # Link up "de Bruijn" sequences m = (1 << (2 * K - 2)) - 1 py = 0 u = unionfind() for j in xrange(lens[i] - K + 1): x = P[i][j] y = x >> 2 if j > 0: d = ham(py, y) if d == 0: u.union(j - 1, j) py = x & m # Gather up the de Bruin fragments udx = {} for j in xrange(lens[i] - K + 1): v = u.find(j) if v not in udx: udx[v] = [] udx[v].append(j) # Index the left hand k-mers idxLhs = {} kx = [] for (jx, js) in udx.iteritems(): q = 0 for j in js: q += math.log1p(-nm[Q[i][j]]) if q > math.log(0.05 / len(js)): continue kx.append((-len(js), jx)) idxLhs[P[i][js[0]]] = jx kx.sort() # Attempt to link up fragments links = {} for (_, jx) in kx: jR = udx[jx][-1] if jR == lens[i] - K + 1: continue x = P[i][jR] xs = [] lnk = None for k in xrange(100): ys = succ(K, X, x) if len(ys) != 1: break x = ys[0] if x in idxLhs: lnk = idxLhs[x] break xs.append(x) if lnk is not None: links[jx] = xs u.union(jx, lnk) # Gather up the linked fragments vdx = {} for j in [jx for (_, jx) in kx]: v = u.find(j) if v not in vdx: vdx[v] = [] vdx[v].append(j) res = [] for (jxx, jxs) in vdx.iteritems(): # Order the gragments by start position fs = [(udx[jx][0], jx) for jx in jxs] fs.sort() sxs = [] for fj in xrange(len(fs)): (_, jx) = fs[fj] beg = udx[jx][0] end = udx[jx][-1] + 1 if fj == 0: for j in xrange(beg): sxs.append((0, 0)) xs = links.get(jx, None) for j in xrange(beg, end): x = P[i][j] l = Q[i][j] sxs.append((x, l)) if xs: for x in xs: sxs.append((x, 27)) else: if fj < len(fs) - 1: nxt = fs[fj + 1][0] else: nxt = lens[i] - K + 1 for j in xrange(end, nxt): sxs.append((0, 0)) seq = [[0, 0, 0, 0] for j in xrange(len(sxs) + K - 1)] for j in xrange(len(sxs)): (x, l) = sxs[j] p = math.log1p(-nm[l]) for k in xrange(K): seq[j + K - k - 1][x & 3] += p x >>= 2 ax = [] p = None inf = False for j in xrange(len(seq)): b = 0 for k in xrange(4): if seq[j][k] < qc: b |= 1 << k ax.append(fasta(b)) ssj = sum(seq[j]) if p is None: p = ssj else: p = logAdd(p, ssj) if ssj > -1e-300: inf = True dst = counts2cdf(cnt[i]) (_, kd) = ksDistance2(dst, nm) df = math.ceil(len(seq) / float(K)) if inf: q = 1e300 pv = 0.0 else: q = 2 * math.exp(p) pv = chi2(df, q) res.append((pv, q, kd, ''.join(ax))) if len(res) == 0: continue res.sort() if res[0][0] < -2: #ed = lev(seqs[i], res[0][2]) ed = 0 pv = res[0][0] / math.log(10) c2 = res[0][1] kd = res[0][2] a = res[0][3] print '%d\t%d\t%d\t%g\t%g\t%g\t%s\t%s' % ( i, lens[i], len(a), kd, c2, pv, nms[i], a) sys.stdout.flush()
def main(argv): opts = docopt.docopt(__doc__, argv) verbose = opts['-v'] K = int(opts['<k>']) out = opts['<output>'] Z = 1024 * 1024 * 32 if opts['-m'] is not None: Z = 1024 * 1024 * int(opts['-m']) buf = KmerAccumulator2(K) n = 0 tmps = [] acgt = [0, 0, 0, 0] m = 0 d = None if opts['-D'] is not None: d = float(opts['-D']) S = 0 if opts['-S'] is not None: S = int(opts['-S']) cacheYes = set([]) cacheNo = set([]) B = opts['-C'] if B is not None: xs = set([]) for (nm, seq) in readFasta(openFile(B)): xs |= set(kmersList(K, seq, True)) B = xs tmpnm = tmpfile('.pmc') with casket(tmpnm, 'w') as z: nr = 0 for itm in reads(opts['<input>'], K=K, pairs=False, reads=False, kmers=True, both=True, verbose=verbose): xs = itm.kmers[0] for x in xs: acgt[x & 3] += 1 if d is not None: for x in xs: if x in cacheNo: continue if x not in cacheYes: if not sub(S, d, x): cacheNo.add(x) continue cacheYes.add(x) buf.add(x) m += 1 n += 1 if len(cacheYes) > 1000000: cacheYes = set([]) if len(cacheNo) > 1000000: cacheNo = set([]) elif B is not None: found = False for x in xs: if x in B: found = True break if found: buf.addList(xs) for x in xs: m += 1 n += 1 else: buf.addList(xs) for x in xs: m += 1 n += 1 nr += 1 if (nr & 1023) == 0 and buf.mem() >= Z // 2: fn = 'tmps-%d' % (len(tmps), ) tmps.append(fn) writeKmersAndCounts2(z, buf.kmersOnly(), buf.countsOnly(), fn) buf.clear() n = 0 if len(tmps) and len(buf): fn = 'tmps-%d' % (len(tmps), ) tmps.append(fn) writeKmersAndCounts2(z, buf.kmersOnly(), buf.countsOnly(), fn) buf = [] with zotk.kmers(out, 'w') as z: h = {} if len(tmps) == 0: for c in buf.countsOnly(): h[c] = 1 + h.get(c, 0) writeKmersAndCounts2(z, buf.kmersOnly(), buf.countsOnly()) elif len(tmps) == 1: with casket(tmpnm, 'r') as z0: writeKmersAndCounts(z, readKmersAndCounts(z0, tmps[0])) else: with casket(tmpnm, 'r') as z0: xss = [readKmersAndCounts(z0, t) for t in tmps] mergeNinto(K, xss, h, z) n = float(sum(acgt)) acgt = [c / n for c in acgt] z.meta['K'] = K z.meta['kmers'] = 'kmers' z.meta['counts'] = 'counts' z.meta['hist'] = h z.meta['acgt'] = acgt z.meta['reads'] = nr os.remove(tmpnm)
def main(argv): opts = docopt.docopt(__doc__, argv) fns = opts['<input>'] p = None if opts['-p'] is not None: p = float(opts['-p']) if len(fns) == 1 and isFasta(fns[0]): K = 25 seqs = [] with openFile(fns[0]) as f: for (nm, seq) in readFasta(f): xs = set(basics.kmers(K, seq, True)) xs = list(xs) xs.sort() xs = array.array('L', xs) seqs.append((nm.split()[0], xs)) Z = 1 if opts['-a']: Z = len(seqs) print len(seqs) for i in xrange(Z): xnm = seqs[i][0] xs = seqs[i][1] for j in xrange(i + 1, len(seqs)): ynm = seqs[j][0] ys = seqs[j][1] (isec, union, d) = jaccard(xs, ys) if p is None: print '%s\t%s\t%d\t%d\t%d\t%d\t%f' % ( xnm, ynm, len(xs), len(ys), isec, union, d) else: pv = logIx(p, isec + 1, (union - isec) + 1) / math.log(10) q05 = quantBeta(0.05, isec + 1, (union - isec) + 1) q95 = quantBeta(0.95, isec + 1, (union - isec) + 1) print '%s\t%s\t%d\t%d\t%d\t%d\t%f\t-%f\t+%f\t%f' % ( xnm, ynm, len(xs), len(ys), isec, union, d, d - q05, q95 - d, pv) sys.stdout.flush() return Z = 1 if opts['-a']: Z = len(fns) for i in xrange(Z): with kmers(fns[i], 'r') as z0: xK = z0.meta['K'] xs = array.array('L', readKmers(z0)) for j in xrange(i + 1, len(fns)): with kmers(fns[j], 'r') as z1: yK = z1.meta['K'] ys = array.array('L', readKmers(z1)) if xK != yK: print >> sys.stderr, 'mismatched K:', fns[j] sys.exit(1) (isec, union, d) = jaccard(xs, ys) if p is None: print '%s\t%s\t%d\t%d\t%d\t%d\t%f' % ( fns[i], fns[j], len(xs), len(ys), isec, union, d) else: pv = logIx(p, isec + 1, (union - isec) + 1) / math.log(10) q05 = quantBeta(0.05, isec + 1, (union - isec) + 1) q95 = quantBeta(0.95, isec + 1, (union - isec) + 1) print '%s\t%s\t%d\t%d\t%d\t%d\t%f\t-%f\t+%f\t%f' % ( fns[i], fns[j], len(xs), len(ys), isec, union, d, d - q05, q95 - d, pv) sys.stdout.flush()
def main(argv): opts = docopt.docopt(__doc__, argv) K = getK(opts['<input>']) J = K - 1 M = (1 << (2 * (K - J))) - 1 if opts['-r'] is not None: with kmers(opts['-r'], 'r') as z: xs = list(group(K, J, 0, readKmersAndCounts(z))) for fn in opts['<input>']: with kmers(fn, 'r') as z: samXs = readKmersAndCounts(z) i = 0 for (yCtx, _, yGrp) in group(K, J, 0, samXs): while i < len(xs) and xs[i][0] < yCtx: i += 1 assert i < len(xs) assert xs[i][0] == yCtx gt = float(sum([c for (x,c) in xs[i][2]])) gx = [0 for j in xrange(M+1)] for (x,c) in xs[i][2]: gx[x&M] = c st = sum([c for (x,c) in yGrp]) sx = [0 for j in xrange(M+1)] for (x,c) in yGrp: sx[x&M] = c ss = [] b = 0 for j in xrange(M+1): p = float(gx[j])/gt v = 0.0 if 0.0 < p and p < 1.0: v = logBinGe(p, st, sx[j]) if v < -10: b |= 1 << j ss.append('%3.2g' % (v,)) if b > 0: print '%s\t%s\t%s' % (render(J, yCtx), fasta(b), '\t'.join(ss)) i += 1 return # Parse files in parallel to get global distribution N = len(opts['<input>']) h = heap.heap() i = 0 for fn in opts['<input>']: (_, xs) = kfset.read(fn) i += 1 h.push(Group(K, J, i, xs)) while len(h) > 0: xfs = [] g = h.pop() gy = g.this()[0] xfs.append(g.this()) g.next() if g.valid(): h.push(g) for x in h.xs: assert x.valid() while len(h) > 0 and h.front().this()[0] == gy: g = h.pop() xfs.append(g.this()) g.next() if g.valid(): h.push(g) for i in xrange(len(h.xs)): assert h.xs[i].valid() ds = [] gc = [0 for i in xrange(M+1)] for (_, n, xc) in xfs: t = sum([c for (x,c) in xc]) d = [0 for i in xrange(M+1)] for (x,c) in xc: j = x & M gc[j] += c d[j] = c ds.append((n, d)) res = ['*' for i in xrange(N)] seen = set([]) gt = float(sum(gc)) for (n, d) in ds: t = sum(d) b = [0 for i in xrange((M+1)/4)] for i in xrange(M+1): p = float(gc[i])/gt if 0.0 < p and p < 1.0: #vL = logBinLe(p, t, d[i]) #vG = logBinGe(p, t, d[i]) #v = min(vL, vG) v = logBinGe(p, t, d[i]) if v > -10: w = i >> 2 j = i & 3 b[w] |= 1 << j res[n-1] = ''.join([fasta(b0) for b0 in b]) seen.add(res[n-1]) if len(seen) > 1: print '%s\t%s' % (render(J, gy), '\t'.join(res))
def main(argv): opts = docopt.docopt(__doc__, argv) K = None out = opts['<output>'] px = list(pairs(opts['<input>'])) if len(px) == 1: with kmers(out, 'w') as z: h = {} acgt = [0, 0, 0, 0] ix = px[0] if len(ix) == 1: with kmers(ix[0], 'r') as z0: K = z0.meta['K'] xs = readKmersAndCounts(z0) zs = hist(xs, h, acgt) writeKmersAndCounts(z, xs) else: with kmers(ix[0], 'r') as z0, kmers(ix[1], 'r') as z1: K = z0.meta['K'] K1 = z1.meta['K'] if K1 != K: print >> sys.stderr, "mismatched K" sys.exit(1) xs = readKmersAndCounts(z0) ys = readKmersAndCounts(z1) zs = hist(merge(xs, ys), h, acgt) writeKmersAndCounts(z, zs) n = float(sum(acgt)) acgt = [c/n for c in acgt] z.meta['hist'] = h z.meta['acgt'] = acgt return tmps = [] tmpnm = tmpfile('.pmc') with casket(tmpnm, 'w') as z: for ix in px: if len(ix) == 1: nm = 'tmp-' + str(len(tmps)) tmps.append(nm) with kmers(ix[0], 'r') as z0: if K is None: K = z0.meta['K'] else: K0 = z0.meta['K'] if K0 != K: print >> sys.stderr, "mismatched K" sys.exit(1) xs = readKmersAndCounts(z0) writeKmersAndCounts(z, xs, nm) else: nm = 'tmp-' + str(len(tmps)) tmps.append(nm) with kmers(ix[0], 'r') as z0, kmers(ix[1], 'r') as z1: if K is None: K = z0.meta['K'] else: K0 = z0.meta['K'] if K0 != K: print >> sys.stderr, "mismatched K" sys.exit(1) K1 = z1.meta['K'] if K1 != K: print >> sys.stderr, "mismatched K" sys.exit(1) xs = readKmersAndCounts(z0) ys = readKmersAndCounts(z1) writeKmersAndCounts(z, merge(xs, ys), nm) assert K is not None with kmers(out, 'w') as z: h = {} acgt = [0, 0, 0, 0] with casket(tmpnm, 'r') as z0: xss = [readKmersAndCounts(z0, t) for t in tmps] mergeNinto(K, xss, h, acgt, z) n = float(sum(acgt)) acgt = [c/n for c in acgt] z.meta['K'] = K z.meta['kmers'] = 'kmers' z.meta['counts'] = 'counts' z.meta['hist'] = h z.meta['acgt'] = acgt os.remove(tmpnm)