def extractFeatures(cls, aln):
"""
Extract the data in a cmp.h5 alignment record into a
ConsensusCore-friendly `QvSequenceFeatures` object. Will
extract only the features relevant to this Model, zero-filling
the other features arrays.
"""
if isinstance(aln, CmpH5Alignment):
#
# For cmp.h5 input, we have to use the AlnArray to see where the
# gaps are (see bug 20752), in order to support old files.
#
alnRead = np.fromstring(aln.read(), dtype=np.int8)
gapMask = alnRead == ord("-")
_args = [ alnRead[~gapMask].tostring() ]
for feature in ALL_FEATURES:
if feature in cls.requiredFeatures:
_args.append(asFloatFeature(aln.pulseFeature(feature)[~gapMask]))
else:
_args.append(cc.FloatFeature(int(aln.readLength)))
return cc.QvSequenceFeatures(*_args)
else:
_args = [ aln.read(aligned=False, orientation="native") ]
for feature in ALL_FEATURES:
if feature in cls.requiredFeatures:
_args.append(asFloatFeature(aln.pulseFeature(feature, aligned=False)))
else:
_args.append(cc.FloatFeature(int(aln.readLength)))
return cc.QvSequenceFeatures(*_args)
def extractFeatures(cls, aln):
"""
Extract the data in a cmp.h5 alignment record into a
ConsensusCore-friendly `QvSequenceFeatures` object. Will
extract only the features relevant to this Model, zero-filling
the other features arrays.
"""
if isinstance(aln, CmpH5Alignment):
#
# For cmp.h5 input, we have to use the AlnArray to see where the
# gaps are (see bug 20752), in order to support old files.
#
alnRead = np.fromstring(aln.read(), dtype=np.int8)
gapMask = alnRead == ord("-")
_args = [alnRead[~gapMask].tostring()]
for feature in ALL_FEATURES:
if feature in cls.requiredFeatures:
_args.append(
asFloatFeature(aln.baseFeature(feature)[~gapMask]))
else:
_args.append(cc.FloatFeature(int(aln.readLength)))
return cc.QvSequenceFeatures(*_args)
else:
_args = [aln.read(aligned=False, orientation="native")]
for feature in ALL_FEATURES:
if feature in cls.requiredFeatures:
_args.append(
asFloatFeature(aln.baseFeature(feature,
aligned=False)))
else:
_args.append(cc.FloatFeature(int(aln.readLength)))
return cc.QvSequenceFeatures(*_args)
def extractFeatures(cls, aln):
"""
Extract the data in a cmp.h5 alignment record into a
ConsensusCore-friendly `QvSequenceFeatures` object. Will
extract only the features relevant to this Model, zero-filling
the other features arrays.
Note that we have to use the AlnArray to see where the gaps
are, at least for the moment (see bug 20752).
"""
alnRead = np.fromstring(aln.read(), dtype=np.int8)
gapMask = alnRead == ord("-")
_args = [ alnRead[~gapMask].tostring() ]
for feature in [ "InsertionQV",
"SubstitutionQV",
"DeletionQV",
"DeletionTag",
"MergeQV" ]:
if feature in cls.requiredFeatures:
_args.append(asFloatFeature(aln.pulseFeature(feature)[~gapMask]))
else:
_args.append(cc.FloatFeature(int(aln.readLength)))
return cc.QvSequenceFeatures(*_args)