def testGetScannedPairs(self):
"""
The getSequencePairs method must return pairs of
(landmark, trigPoints).
"""
subject = AARead('subject', 'FRRRFRRRFASAASA')
dbParams = DatabaseParameters(landmarks=[AlphaHelix],
trigPoints=[Peaks], distanceBase=1.0)
be = Backend()
be.configure(dbParams)
be.addSubject(subject, '0')
scannedSubject = be.scan(subject)
pairs = list(be.getScannedPairs(scannedSubject))
# First pair.
landmark, trigPoint = pairs[0]
self.assertEqual(Landmark(AlphaHelix.NAME, AlphaHelix.SYMBOL,
0, 9, 2), landmark)
self.assertEqual(TrigPoint(Peaks.NAME, Peaks.SYMBOL, 10), trigPoint)
# Second pair.
landmark, trigPoint = pairs[1]
self.assertEqual(Landmark(AlphaHelix.NAME, AlphaHelix.SYMBOL,
0, 9, 2), landmark)
self.assertEqual(TrigPoint(Peaks.NAME, Peaks.SYMBOL, 13), trigPoint)
self.assertEqual(2, len(pairs))
def __init__(self, sequences, labels, defaultLabel=None, **kwargs):
"""
Base class for using cluster analysis to evaluate how well various
feature finders and database parameter settings can separate a set of
sequences. The clustering is based on feature offset deltas.
@param sequences: Either A C{str} filename of sequences to consider or
a C{light.reads.Reads} instance.
@param labels: A C{dict} with a label for each sequence id in
C{sequences}. These are the known categories of each sequence.
@param defaultLabel: If not C{None}, a label to use for reads whose ids
are not present in C{labels}. If C{None} and a read id has no label
a ValueError is raised.
@param kwargs: See
C{database.DatabaseSpecifier.getDatabaseFromKeywords} for
additional keywords, all of which are optional.
@raises ValueError: If the id of a read is not in labels and no default
label has been set, or if there are no reads in C{sequences}.
"""
if isinstance(sequences, str):
reads = FastaReads(sequences,
readClass=AAReadWithX,
upperCase=True)
else:
reads = sequences
database = DatabaseSpecifier().getDatabaseFromKeywords(**kwargs)
backend = Backend()
backend.configure(database.dbParams)
allOffsetDeltas = []
trueLabels = []
for read in reads:
trueLabel = labels.get(read.id, defaultLabel)
if trueLabel is None:
raise ValueError('Read %r has no corresponding label' %
read.id)
trueLabels.append(trueLabel)
offsetDeltas = Counter()
scannedRead = backend.scan(read)
for landmark, trigPoint in backend.getScannedPairs(scannedRead):
delta = scaleLog(trigPoint.offset - landmark.offset,
database.dbParams.distanceBase)
offsetDeltas[delta] += 1
allOffsetDeltas.append(offsetDeltas)
nReads = len(reads)
if nReads == 0:
raise ValueError('No sequences were found in %r' % sequences)
# Don't check that len(reads) == len(labels). I.e., ignore extra labels
# to make using this class interactively more convenient.
# Create an affinity matrix. Initially set all values to 1.0 so we
# don't need to later initialize the diagonal.
affinity = np.ones((nReads, nReads))
for row, offsetDeltas in enumerate(allOffsetDeltas):
for col in range(row + 1, nReads):
affinity[row,
col] = affinity[col,
row] = (self.affinityFromOffsetDeltas(
allOffsetDeltas[row],
allOffsetDeltas[col]))
self.nReads = nReads
self.affinity = affinity
self.trueLabels = trueLabels
