def main():
with measure("load pm"):
# pm = emzed.io.loadPeakMap("141208_pos001.mzXML")
pm = emzed.io.loadPeakMap("Danu.mzML")
t0 = emzed.utils.toTable("peakmap", (pm, ), type_=object)
path = "pm_only.hdf5"
with atomic_hdf5_writer(path) as add:
add(t0)
peaks = runMetaboFeatureFinder(pm)
peaks = integrate(peaks)
peaks.info()
with atomic_hdf5_writer("peaks.hdf5") as add:
add(peaks)
def test_atomic_hdf5_writer(table, tmpdir, regtest):
path = tmpdir.join("test.hdf5").strpath
with atomic_hdf5_writer(path) as add:
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
t = Hdf5TableProxy(path).toTable()
print(t.int.values, file=regtest)
path = tmpdir.join("test_2.hdf5").strpath
with atomic_hdf5_writer(path, atomic=False) as add:
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
t = Hdf5TableProxy(path).toTable()
print(t.int.values, file=regtest)
def test_atomic_hdf5_writer(table, tmpdir, regtest):
path = tmpdir.join("test.hdf5").strpath
with atomic_hdf5_writer(path) as add:
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
t = Hdf5TableProxy(path).toTable()
print(t.int.values, file=regtest)
path = tmpdir.join("test_2.hdf5").strpath
with atomic_hdf5_writer(path, atomic=False) as add:
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
add(table)
table.replaceColumn("int", table.int + 10, type_=int)
t = Hdf5TableProxy(path).toTable()
print(t.int.values, file=regtest)
def main():
with measure("load pm"):
# pm = emzed.io.loadPeakMap("141208_pos001.mzXML")
pm = emzed.io.loadPeakMap("Danu.mzML")
pm2 = copy.deepcopy(pm)
# create modified copy
pm2.spectra = pm2.spectra[1:]
rtmin, rtmax = pm.rtRange()
pms = [pm, pm2]
n = 10000
integers = list(reversed(range(n)))
for k in range(0, n, 10):
integers[k] = None
flags = [i % 2 == 0 for i in range(n)]
tuples = [tuple(randint(0, 1000, size=10)) for _ in range(100)]
tsub = emzed.utils.toTable("a", (1, 2, 3), type_=int)
with measure("create table"):
t = emzed.utils.toTable("integers", integers, type_=int)
t.addColumn("check", flags, type_=CheckState)
t.addColumn("sub_table", tsub, type_=Table, format_="%r")
t.addColumn("mzmin",
t.apply(lambda: 100 + 900 * np_random() + np_random(), ()),
type_=float)
t.addColumn("mzmax",
t.apply(lambda mzmin: mzmin + 0.1 * np_random(),
(t.mzmin, )),
type_=float)
t.addColumn("rtmin",
t.apply(lambda: rtmin + (rtmax - rtmin) * np_random(), ()),
type_=float)
t.addColumn("rtmax",
t.apply(lambda rtmin: rtmin + 30 + 300 * np_random(),
(t.rtmin, )),
type_=float)
t.addColumn("peakmap",
t.apply(lambda: random.choice(pms), ()),
type_=object)
for i in range(10):
print(i)
t.addColumn("floats_%d" % i, t.integers + 1.1, type_=float)
t.addColumn("strings_%d" % i,
t.integers.apply(str) * (i % 3),
type_=str)
t.addColumn("tuples_%d" % i,
t.apply(lambda: random.choice(tuples), ()),
type_=object)
t.addColumn("peakmaps_%d" % i, pms[i % 2], type_=object)
target_ids = [None] * n
for i in range(0, n, 100):
target_ids[i] = "target_%d" % i
target_ids[i + 1] = "target_%d" % i
target_ids[i + 2] = "target_%d" % i
target_ids[i + 3] = "target_%d" % i
t.addColumn("target_id", target_ids, type_=str)
n, m = t.shape
#for fac in (1, 10):
for fac in (1, ):
n0 = n * fac
with measure("write hdf5 table with %d rows and %d cols" % (n0, m)):
path = "test_%d.hdf5" % (n * fac)
with atomic_hdf5_writer(path) as add:
for i in range(fac):
print(i, "out of", fac)
add(t)
