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