def testTwoNonOverlappingFeatureUniqueOffsets(self):
"""
If the sequence contains two non-overlapping features, the unique
offsets method must return the correct value.
"""
read = AARead('id', 'FRRRFRRRFW')
s = SequenceFeatureAnalysis(read, landmarks=['AlphaHelix'],
trigPoints=['AminoAcids'])
self.assertEqual(
{
'AlphaHelix': {0, 1, 2, 3, 4, 5, 6, 7, 8},
'AminoAcids': {9},
},
s.uniqueOffsets())
def testOneFeatureUniqueOffsets(self):
"""
If the sequence contains a single feature, the unique offsets method
must return the correct value.
"""
read = AARead('id', 'FRRRFRRRF')
s = SequenceFeatureAnalysis(read, landmarks=['AlphaHelix'],
trigPoints=['Peaks'])
self.assertEqual(
{
'AlphaHelix': {0, 1, 2, 3, 4, 5, 6, 7, 8},
'Peaks': set(),
},
s.uniqueOffsets())
def testTwoSpreadOutNonOverlappingFeatureUniqueOffsets(self):
"""
If the sequence contains two non-overlapping features with other AAs
before and after them, the unique offsets method must return the
correct value.
"""
read = AARead('id', 'XXFRRRFRRRFXXWX')
s = SequenceFeatureAnalysis(read, landmarks=['AlphaHelix'],
trigPoints=['AminoAcids'])
self.assertEqual(
{
'AlphaHelix': {2, 3, 4, 5, 6, 7, 8, 9, 10},
'AminoAcids': {13},
},
s.uniqueOffsets())
def testPairwiseOffsetOverlaps(self):
"""
The pairwiseOffsetOverlaps method must return the correct overlap
matrix.
"""
read = AARead('id', 'FRRRFRRRFWNPNW')
s = SequenceFeatureAnalysis(read,
landmarks=['AlphaHelix', 'BetaTurn'],
trigPoints=['AminoAcids'])
# The order in the overlaps array is AlphaHelix, BetaTurn,
# AminoAcids, so the 0.2 value corresponds to the
self.assertTrue(np.array_equal(
np.array([
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.2],
[0.0, 0.2, 1.0],
]),
s.pairwiseOffsetOverlaps()))
def testTwoOverlappingFeatureUniqueOffsets(self):
"""
If the sequence contains two features that overlap, the unique offsets
method must return the correct value, which will exclude the offset(s)
in common.
"""
read = AARead('id', 'AGYGSTWT')
s = SequenceFeatureAnalysis(read,
landmarks=['AlphaHelix', 'Prosite'],
trigPoints=['AminoAcids'])
# There is an AminoAcid trig point at offset 6, but that's also in
# the Prosite feature, so offset 6 is not unique to either finder.
self.assertEqual(
{
'AlphaHelix': set(),
'AminoAcids': set(),
'Prosite': {0, 1, 2, 3, 4, 5, 7},
},
s.uniqueOffsets())
def testMissingNames(self):
"""
The __init__ method of SequenceFeatureAnalysis must set the correct
missing landmark and trig point names based on what's in the sequence
and what we were looking for.
"""
read = AARead('id', 'FRRRFRRRF')
s = SequenceFeatureAnalysis(read,
landmarks=['AlphaHelix', 'BetaTurn'],
trigPoints=['AminoAcids'])
self.assertEqual(set(['BetaTurn']), s.landmarksNotFound)
self.assertEqual(set(['AminoAcids']), s.trigPointsNotFound)
def testPrintDensities(self):
"""
The printDensities method must return the correct string.
"""
read = AARead('id', 'AAFRRRFRRRFWNPNWXX')
s = SequenceFeatureAnalysis(
read,
landmarks=['AlphaHelix', 'AlphaHelix_pi', 'BetaTurn'],
trigPoints=['AminoAcids'])
# Note that AlphaHelix_pi does not appear in the output as it finds
# no features (it will be present in s.landmarksNotFound). Arguably
# it should also be in the output.
self.assertEqual(
(
'Feature densities:\n'
' OVERALL UNIQUE\n'
' AlphaHelix: 50.00% (9/18) 50.00% (9/18)\n'
' BetaTurn: 22.22% (4/18) 16.67% (3/18)\n'
' AminoAcids: 11.11% (2/18) 5.56% (1/18)\n'
'\n'
'22.22% (4/18) of sequence offsets were not covered by any '
'feature.'
),
s.printDensities())
def testOffsets(self):
"""
The __init__ method of SequenceFeatureAnalysis must set the correct
landmark and trig point offsets.
"""
read = AARead('id', 'FRRRFRRRFWNPNW')
s = SequenceFeatureAnalysis(read,
landmarks=['AlphaHelix', 'BetaTurn'],
trigPoints=['AminoAcids'])
self.assertEqual(
{
'AlphaHelix': {0, 1, 2, 3, 4, 5, 6, 7, 8},
'AminoAcids': {9, 13},
'BetaTurn': {10, 11, 12, 13},
},
s.offsets)