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)