def testConvexHull2D():
h = pyopenms.ConvexHull2D()
points = numpy.arange(10.0, dtype=numpy.float32).reshape(-1, 2)
h.setHullPoints(points)
p = h.getHullPoints()
assert numpy.linalg.norm(p - points) < 1e-6
h.expandToBoundingBox()
hullp = h.getHullPoints()
assert set(hullp[:,0]) == set((0.0, 8.0))
assert set(hullp[:,1]) == set((1.0, 9.0))
box = h.getBoundingBox()
assert box.minPosition() == [ 0.0, 1.0]
assert box.maxPosition() == [ 8.0, 9.0]
h.addPoint([-10.0, -10.0])
box = h.getBoundingBox()
assert box.minPosition() == [ -10.0, -10.0]
assert box.maxPosition() == [ 8.0, 9.0]
h.expandToBoundingBox()
hullp = h.getHullPoints()
assert set(hullp[:,0]) == set((-10.0, 8.0))
assert set(hullp[:,1]) == set((-10.0, 9.0))
box = h.getBoundingBox()
assert box.minPosition() == [ -10.0, -10.0]
assert box.maxPosition() == [ 8.0, 9.0]
def test_convex_hull_1():
ch = pyopenms.ConvexHull2D()
points = [[1.0, 1.0], [2.0, 1.0], [1.0, 2.0], [2.0, 2.0]]
for p in points:
ch.addPoint(p)
assert ch.encloses([1.5, 1.5])
assert not ch.encloses([.5, 1.5])
hp = ch.getHullPoints()
# order may change, so we use sort here:
assert sorted(hp.tolist()) == sorted(points)
ch.setHullPoints(hp)
hp = ch.getHullPoints()
# order may change, so we use sorted here:
assert sorted(hp.tolist()) == sorted(points)
ch.addPoints(hp + 1.0)
ch.addPoints(hp)
hp = ch.getHullPoints()
assert hp.shape == (6, 2)
bb = ch.getBoundingBox()
assert bb.minPosition() == [1.0, 1.0]
assert bb.maxPosition() == [3.0, 3.0]
def test_convex_hull_0():
ch = pyopenms.ConvexHull2D()
def main(options):
starttime = time.time()
tolerance = float(options.tolerance)
precursorshift = float(options.precursorshift)
mswindow = float(options.mswindow)
rtwindow = float(options.rtwindow)
# load file
timed("loading experiment", starttime)
exp = glyxtoolms.lib.openOpenMSExperiment(options.infile)
# assure sorted spectra
exp.sortSpectra()
timed("sort spectra", starttime)
ms1, ms2 = sortSpectra(exp)
links = []
noresult = 0
timed("finding precursors " + str(len(ms2)), starttime)
for mz, spec2, spec1 in ms2:
charge = spec2.getPrecursors()[0].getCharge()
if charge == 0:
charge = 1
peaks, results = findMonoIsotopicPeak(mz, charge, spec1, tolerance,
precursorshift, mswindow)
if len(results) == 0:
noresult += 1
continue
best = min(results, key=lambda r: r["mass"] * r["error"] / r["sum"]**2)
rt = spec2.getRT()
link = Link(rt, best)
link.rt1 = spec1.getRT()
link.nativeId = spec1.getNativeID()
link.peaks = peaks
links.append(link)
print "could not find suitable starting pattern for ", noresult, "spectra from ", len(
ms2)
timed("group precursors", starttime)
# group precursors
for l1 in links:
for l2 in links:
if l1.charge != l2.charge:
continue
if abs(l1.mz - l2.mz) > tolerance:
continue
if abs(l1.rt - l2.rt) > rtwindow:
continue
l1.near.add(l2)
timed("group into features", starttime)
# group into features
features = []
while True:
# find link without feature
working = set()
for link in links:
if link.feature == None:
working.add(link)
break
if len(working) == 0:
break
feature = Feature()
features.append(feature)
while len(working) > 0:
current = working.pop()
current.feature = feature
feature.ms2.append(current)
for link in current.near:
if link.feature == None:
working.add(link)
timed("calculate precursor positions", starttime)
# calculate mz and charge of feature
for feature in features:
feature.mz = sum([l.mz for l in feature.ms2]) / len(feature.ms2)
feature.charge = feature.ms2[0].charge
# get lowest error
feature.error = min([l.result["error"] for l in feature.ms2])
feature.extendRTDomain(ms1, tolerance)
# calculate dimensions
masses = [l.result["x"][-1] for l in feature.ms2]
feature.mzLow = feature.mz
feature.mzHigh = max(masses)
timed("merge features", starttime)
# check features against each other
# remove feature if mzLow is within other feature and rtLow and rtHigh within too
todelete = set()
for f1 in features:
for f2 in features:
if f1 == f2:
continue
if abs(f1.mz - f2.mz) > tolerance:
continue
if not (f1.mzLow <= f2.mzLow <= f1.mzHigh):
continue
if not (f1.rtLow <= f2.rtLow <= f1.rtHigh):
continue
if not (f1.rtLow <= f2.rtHigh <= f1.rtHigh):
continue
todelete.add(f2)
for f in todelete:
features.remove(f)
newfeatures = set()
# merge features
for f1 in features:
if f1.rtHigh == 0.0:
print "zero", "f1", [ms.rt for ms in f1.ms2]
for f2 in features:
if f1 == f2:
continue
if abs(f1.mzLow - f2.mzLow) > tolerance:
continue
if f1.charge != f2.charge:
continue
if not ((f1.rtLow <= f2.rtLow <= f1.rtHigh)
or abs(f2.rtLow - f1.rtHigh) < 30):
continue
todelete.add(f1)
todelete.add(f2)
# create new feature
newf = Feature()
newf.error = min((f1.error, f2.error))
newf.mz = min((f1.mz, f2.mz))
newf.mzLow = min((f1.mzLow, f2.mzLow))
newf.mzHigh = max((f1.mzHigh, f2.mzHigh))
newf.rtLow = min((f1.rtLow, f2.rtLow))
newf.rtHigh = max((f1.rtHigh, f2.rtHigh))
if newf.rtHigh == 0.0:
print "zero", "newf"
newf.ms2 = sorted(set(f1.ms2).union(set(f2.ms2)),
key=lambda ms: ms.rt)
newf.rt = (f1.rt + f2.rt) / 2.0
newfeatures.add(newf)
features += list(newfeatures)
timed("write featuremap", starttime)
fm = pyopenms.FeatureMap()
N = 0
for feature in features:
# calc bounding boxes from pattern
f = pyopenms.Feature()
f.ensureUniqueId()
f.setRT(feature.rt)
f.setMZ(feature.mz)
f.setCharge(feature.charge)
hulls = []
sumIntensity = 0
for i in feature.pattern:
pattern = feature.pattern[i]
minRT = feature.rtLow - 1 # eliminate rounding error by adding a rt tolerance
maxRT = feature.rtHigh + 1
#minRT = min([p[0] for p in pattern])
#maxRT = max([p[0] for p in pattern])
minMZ = min([p[1] for p in pattern]) - tolerance
maxMZ = max([p[1] for p in pattern]) + tolerance
sumIntensity += sum([p[2] for p in pattern])
h = pyopenms.ConvexHull2D()
h.addPoint([minRT, minMZ])
h.addPoint([maxRT, minMZ])
h.addPoint([maxRT, maxMZ])
h.addPoint([minRT, maxMZ])
hulls.append(h)
f.setConvexHulls(hulls)
f.getConvexHull().expandToBoundingBox()
f.setIntensity(sumIntensity)
f.setOverallQuality(feature.error)
# check if boundingbox is fullfilles
h = f.getConvexHull()
b = h.getBoundingBox()
minRT, minMZ = b.minPosition()
maxRT, maxMZ = b.maxPosition()
if sumIntensity == 0:
N += 1
continue
if charge == 0:
N += 1
continue
fm.push_back(f)
print "ignoring ", N, " features that have no peaks or a charge of 0"
fxml = pyopenms.FeatureXMLFile()
fxml.store(options.outfile, fm)