def test_remove(self):
mzs = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]).reshape(-1, 1)
ones = np.ones_like(mzs)
peaks = np.hstack((mzs, ones))
spectra = [
Spectrum(peaks, 0.0, 1, "0"),
Spectrum(peaks, 1.0, 1, "0"),
Spectrum(peaks, 1.5, 2, "0"),
Spectrum(peaks, 2.0, 1, "0")
]
pm = PeakMap(spectra)
pmt = copy.deepcopy(pm)
pmt.remove(0.0, 10.0, 0.0, 2.0)
assert len(pmt) == 1
assert pmt.spectra[0] == Spectrum(peaks, 1.5, 2, "0")
pmt = copy.deepcopy(pm)
pmt.remove(1.0, 3.2, 1.0, 2.0)
assert len(pmt) == 4
assert np.all(pmt.spectra[0].peaks == peaks)
assert np.all(
pmt.spectra[1].peaks.flatten() == [0., 1.0, 4.0, 1.0, 5.0, 1.0])
assert np.all(pmt.spectra[2].peaks == peaks)
assert np.all(
pmt.spectra[3].peaks.flatten() == [0., 1.0, 4.0, 1.0, 5.0, 1.0])
def testFilterIntensity(self, regtest):
data = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]).reshape(-1, 1)
intensities = np.array([10.0, 11.0, 12.0, 13.0, 14.0,
15.0]).reshape(-1, 1)
peaks = np.hstack((data, intensities))
assert peaks.shape == (6, 2)
spec1 = Spectrum(peaks, 0.0, 1, "0")
spec2 = Spectrum(peaks, 0.0, 2, "0")
assert spec1.maxIntensity() == 15.0
pm = PeakMap([spec1, spec2])
pm_x = pm.extract(mslevelmin=1)
assert len(pm_x) == 2
pm_x = pm.extract(mslevelmin=2)
assert len(pm_x) == 1
pm_x = pm.extract(mslevelmin=3)
assert len(pm_x) == 0
pm_x = pm.extract(mslevelmax=1, imin=10.0, imax=15.0)
assert len(pm_x) == 1
assert pm_x[0].peaks.shape == (6, 2)
for spec in pm_x:
print >> regtest, spec.peaks
pm_x = pm.extract(mslevelmax=1, imin=10.0)
assert len(pm_x) == 1
assert pm_x[0].peaks.shape == (6, 2)
for spec in pm_x:
print >> regtest, spec.peaks
pm_x = pm.extract(mslevelmax=1, imax=15.0)
assert len(pm_x) == 1
assert pm_x[0].peaks.shape == (6, 2)
for spec in pm_x:
print >> regtest, spec.peaks
pm_x = pm.extract(mslevelmax=1, imin=10.0)
assert len(pm_x) == 1
assert pm_x[0].peaks.shape == (6, 2)
for spec in pm_x:
print >> regtest, spec.peaks
pm_x = pm.extract(mslevelmax=1, imin=11.0)
assert len(pm_x) == 1
assert pm_x[0].peaks.shape == (5, 2)
for spec in pm_x:
print >> regtest, spec.peaks
pm_x = pm.extract(mslevelmax=1, imin=11.0, imax=13.5)
assert len(pm_x) == 1
assert pm_x[0].peaks.shape == (3, 2)
for spec in pm_x:
print >> regtest, spec.peaks
def _final_spectrum(peaks, spectra):
rt = np.mean([s.rt for s in spectra])
msLevel = 2
polarity = spectra[0].polarity
precursors = [p for spec in spectra for p in spec.precursors]
return Spectrum(np.vstack(peaks), rt, msLevel, polarity, precursors)
def testCompress(self):
t = toTable("a", [])
import numpy
t.compressPeakMaps()
s = Spectrum(
numpy.arange(12, dtype="float64").reshape(-1, 2), 1.0, 1, "+")
pm = PeakMap([s])
s = Spectrum(
numpy.arange(12, dtype="float64").reshape(-1, 2), 1.0, 1, "+")
pm2 = PeakMap([s])
t = toTable("pm", [pm, pm2])
assert len(set(map(id, t.pm.values))) == 2
t.compressPeakMaps()
assert len(set(map(id, t.pm.values))) == 1
def test_proxies(self):
mzs = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]).reshape(-1, 1)
ones = np.ones_like(mzs)
peaks = np.hstack((mzs, ones))
spec = Spectrum(peaks, 0.0, 1, "0")
spec.peaks[:, 0] += 1.0
assert np.linalg.norm(spec.peaks[:, 0] - mzs.flatten(), 1) == 6.0
def check(fun, spec=spec):
before = spec.uniqueId()
exec(fun, dict(spec=spec))
assert "unique_id" not in spec.meta
after = spec.uniqueId()
assert before != after
check("spec.peaks[:, 0] += 1")
check("spec.peaks += 1")
check("spec.peaks -= 1")
check("spec.peaks /= 2.1")
check("spec.peaks //= 2.1")
check("spec.peaks *= 2")
check("spec.peaks %= 1.1")
check("spec.peaks **= 2")
check("spec.rt += 1")
check("spec.msLevel += 1")
check("spec.precursors = [(1, 1, 0)]")
check("spec.polarity = '+'")
pm = PeakMap([spec])
before = pm.uniqueId()
pm.spectra[0].rt += 2
after = spec.uniqueId()
assert before != after
from cPickle import loads, dumps
back = loads(dumps(pm))
back.meta.pop("unique_id", None)
pm.meta.pop("unique_id", None)
assert back.uniqueId() == pm.uniqueId()
# this was broken after pickling, the callback was not pickled and calling
# it whein operatin on peaks rose an excepion
for s in back:
s.peaks += 1
# this was broken because it creates a view which is not continous:
s.peaks = s.peaks[1:-1:2]
assert s.uniqueId()
spec_new = loads(dumps(spec))
assert spec_new.scan_number == spec.scan_number
assert spec_new.rt == spec.rt
assert spec_new.msLevel == spec.msLevel
assert spec_new.precursors == spec.precursors
assert spec_new.polarity == spec.polarity
assert np.linalg.norm(spec_new.peaks - spec.peaks) == 0.0
def _final_spectrum(peaks, spectra):
rt = np.mean([s.rt for s in spectra])
msLevel = 2
polarity = spectra[0].polarity
# precursor_mz = np.mean([mz for s in spectra for (mz, ii) in s.precursors])
# precursor_ii = np.mean([ii for s in spectra for (mz, ii) in s.precursors])
# precursors = [(precursor_mz, precursor_ii)]
precursors = [p for spec in spectra for p in spec.precursors]
return Spectrum(np.vstack(peaks), rt, msLevel, polarity, precursors)
def testTrapezIntegrationSimple():
p0 = np.array((1.0, 1.0, 2.0, 2.0)).reshape(-1,2)
p1 = np.array((2.0, 2.0, 3.0, 3.0)).reshape(-1,2)
p2 = np.array((1.0, 1.0, 2.0, 2.0, 3.0, 3.0)).reshape(-1,2)
p3 = np.array((3.0, 3.0)).reshape(-1,2)
s0 = Spectrum(p0, 0.0, 1, '0')
s1 = Spectrum(p1, 1.0, 1, '0')
s2 = Spectrum(p2, 2.0, 1, '0')
s3 = Spectrum(p3, 3.0, 1, '0')
pm = PeakMap([s0,s1,s2,s3])
integrator = dict(emzed._algorithm_configs.peakIntegrators)["trapez"]
integrator.setPeakMap(pm)
assert integrator.integrate(1.4, 2.5, 0, 3)["area"] == 5.0
assert integrator.integrate(1.4, 2.5, 0, 2)["area"] == 4.0
assert integrator.integrate(0.4, 2.5, 0, 3)["area"] == 6.5
assert integrator.integrate(0.4, 2.5, 0, 2)["area"] == 5.0
assert integrator.integrate(0.4, 3.0, 0, 3)["area"] == 14
# one level 2 spec:
s1 = Spectrum(p1, 1.0, 2, '0')
pm = PeakMap([s0,s1,s2,s3])
integrator.setPeakMap(pm)
assert integrator.integrate(1.4, 2.5, 0, 3, msLevel=1)["area"] == 5.0
assert integrator.integrate(1.4, 2.5, 0, 2, msLevel=1)["area"] == 4.0
assert integrator.integrate(0.4, 2.5, 0, 3, msLevel=1)["area"] == 7.5
assert integrator.integrate(0.4, 2.5, 0, 2, msLevel=1)["area"] == 6.0
assert integrator.integrate(0.4, 3.0, 0, 3, msLevel=1)["area"] == 13.5
# multiple levels shall rise exception:
ex(lambda: integrator.integrate(0.4, 3.0, 0, 3))
def testIntensityInRange(self):
data = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]).reshape(-1, 1)
ones = np.ones_like(data)
peaks = np.hstack((data, ones))
assert peaks.shape == (6, 2)
spec = Spectrum(peaks, 0.0, 1, "0")
assert spec.intensityInRange(0.0, 5.0) == 6.0
assert spec.intensityInRange(0.1, 5.0) == 5.0
assert spec.intensityInRange(0.0, 4.5) == 5.0
assert spec.intensityInRange(0.5, 4.5) == 4.0
assert spec.intensityInRange(2.0, 2.0) == 1.0
assert spec.intensityInRange(2.1, 2.0) == 0.0
assert spec.maxIntensity() == 1.0
def test_proxies(self):
mzs = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0]).reshape(-1, 1)
ones = np.ones_like(mzs)
peaks = np.hstack((mzs, ones))
spec = Spectrum(peaks, 0.0, 1, "0")
spec.peaks[:, 0] += 1.0
assert np.linalg.norm(spec.peaks[:, 0] - mzs.flatten(), 1) == 6.0
def check(fun, spec=spec):
before = spec.uniqueId()
exec(fun, dict(spec=spec))
assert "unique_id" not in spec.meta
after = spec.uniqueId()
assert before != after
check("spec.peaks[:, 0] += 1")
check("spec.peaks += 1")
check("spec.peaks -= 1")
check("spec.peaks /= 2.1")
check("spec.peaks //= 2.1")
check("spec.peaks *= 2")
check("spec.peaks %= 1.1")
check("spec.peaks **= 2")
check("spec.rt += 1")
check("spec.msLevel += 1")
check("spec.precursors = [(1, 1)]")
check("spec.polarity = '+'")
pm = PeakMap([spec])
before = pm.uniqueId()
pm.spectra[0].rt += 2
after = spec.uniqueId()
assert before != after
from cPickle import loads, dumps
back = loads(dumps(pm))
back.meta.pop("unique_id", None)
pm.meta.pop("unique_id", None)
assert back.uniqueId() == pm.uniqueId()
def test_0():
ii = np.linspace(1000.0, 2000.0, 21)
mzs = np.linspace(100.0, 1100.0, 21)
peaks = np.vstack((mzs, ii)).T
spec = Spectrum(peaks, rt=100.0, msLevel=1, polarity="+")
assert (1.0 - 1e-3) <= spec.cosine_distance(spec, 0.001) <= 1.0 + 1e-3
# enforce only 10 matches
mzs[10:] = 0.0
peaks = np.vstack((mzs, ii)).T
other = Spectrum(peaks, rt=100.0, msLevel=1, polarity="+")
assert (1.0 - 1e-3) <= spec.cosine_distance(other, 0.001) <= 1.0 + 1e-3
# enforce only 5 matches
mzs[5:] = 0.0
peaks = np.vstack((mzs, ii)).T
other = Spectrum(peaks, rt=100.0, msLevel=1, polarity="+")
assert (0.0 - 1e-3) <= spec.cosine_distance(other, 0.001,
min_matches=10) <= 0.0 + 1e-3
ii = np.linspace(1000.0, 2000.0, 21)
mzs = np.linspace(100.0, 1100.0, 21) + 800.0
peaks = np.vstack((mzs, ii)).T
other = Spectrum(peaks, rt=100.0, msLevel=1, polarity="+")
assert (0.99963 - 1e-3) <= spec.cosine_distance(
other, 0.001, min_matches=5) <= 0.99963 + 1e-3
ii = np.linspace(1000.0, 2000.0, 21)
mzs = np.linspace(100.0, 1100.0, 21) + 200
peaks = np.vstack((mzs, ii)).T
s1 = Spectrum(peaks,
rt=1.0,
msLevel=2,
polarity="+",
precursors=[(10.0, 1000)])
s2 = Spectrum(peaks,
rt=1.0,
msLevel=2,
polarity="+",
precursors=[(210.0, 1000)])
assert abs(
s1.cosine_distance(s2, 0.001, consider_precursor_shift=True) -
0.99986) < 1e-3
def testActions():
t = buildTable()
n = len(t)
recorder = RecordingObject()
model = TableModel(t, recorder)
t_orig = t.copy()
action = DeleteRowsAction(model, [0], [0])
action.do()
assert len(model.table) == len(t_orig) - 1
assert model.table.rows[0] == t_orig.rows[1]
action.undo()
assert len(model.table) == len(t_orig)
assert model.table.rows[0] == t_orig.rows[0]
action = CloneRowAction(model, 0, 0)
action.do()
assert model.table.rows[0] == t_orig.rows[0]
assert model.table.rows[1] == t_orig.rows[0]
assert model.table.rows[2] == t_orig.rows[1]
assert len(model.table) == n + 1
action.undo()
assert len(model.table) == n
assert model.table.rows[0] == t_orig.rows[0]
assert model.table.rows[1] == t_orig.rows[1]
action = SortTableAction(model, [("mz", True)])
action.do()
assert model.table.mz.values == (None, 1.0, 2.0)
action.undo()
assert model.table.mz.values == t_orig.mz.values
action = SortTableAction(model, [("mz", False)])
action.do()
assert model.table.mz.values == (2.0, 1.0, None)
action.undo()
assert model.table.mz.values == t_orig.mz.values
class Index(object):
def row(self):
return 0
def column(self):
return 0
action = ChangeValueAction(model, Index(), 0, 0, 3.0)
action.do()
assert model.table.rows[0][0] == 3.0
action.undo()
assert model.table.rows[0][0] == 1.0
t = buildTable()
import numpy
peak = numpy.array(((1.0, 100.0), ))
specs = [Spectrum(peak, rt, 1, "+") for rt in range(9, 15)]
pm = PeakMap(specs)
t.replaceColumn("peakmap", pm)
model.table = emzed.utils.integrate(t, "no_integration")
action = IntegrateAction(model, 0, "", "trapez", 0, 100, {0: 0})
action.do()
assert model.table.area.values[0] == 500.0
action.undo()
assert model.table.area.values[0] == None
