def test_finding_closest_match(self):
auto = EPGProfile.from_file(example_file('simple-auto.csv'))
manual = EPGProfile.from_file(example_file('simple-manual.csv'))
auto, manual = _EPGFingerprint(auto), _EPGFingerprint(manual)
finder = auto.find_closest_match_to
self.assertEqual((1941, 3000), finder(manual, 1947, 3000))
self.assertEqual((235, 560), finder(manual, 215, 550))
self.assertEqual((4016, 4638), finder(manual, 4014, 4638))
def test_creating_confusion_matrix(self):
profile = EPGProfile.from_file(example_file('simple-manual.csv'))
other = EPGProfile.from_file(example_file('simple-auto.csv'))
stats = EPGComparator((profile, other)).compare()
self.assertSequenceEqual(
(1953, 2, 0, 6, 1677, 0, 450, 0, 316),
tuple(stats.cfm))
self.assertEqual(2409, sum(stats.cfm.column('c')))
self.assertEqual(1679, sum(stats.cfm.column('np')))
self.assertEqual(316, sum(stats.cfm.column('pd')))
def test_calculate_offset(self):
auto = EPGProfile.from_file(example_file('simple-auto.csv'))
manual = EPGProfile.from_file(example_file('simple-manual.csv'))
auto, manual = _EPGFingerprint(auto), _EPGFingerprint(manual)
expected = dict(
pd=[[20], [10]],
c=[[10, 0], [-446, 2]],
np=[[-6, 2], [0, 0]])
offsets = EPGComparator._calculate_offsets((auto, manual),
fractional=False)
self.assertDictEqual(expected, offsets)
def test_calculate_matches_complex(self):
auto = EPGProfile.from_file(example_file('Ap10-haba-auto.csv'))
manual = EPGProfile.from_file(example_file('Ap10-haba-manual.csv'))
stats = EPGComparator((auto, manual)).compare()
expected = dict(
c=dict(n=71, full_matches=45, loffset=.01, roffset=.02),
pd=dict(n=49, full_matches=37, loffset=0., roffset=.04),
np=dict(n=22, full_matches=20, loffset=.02, roffset=0.))
for waveform in expected:
for name, val in expected[waveform].items():
self.assertAlmostEqual(val, stats.get(waveform, mean)[name],
2, name)
def test_calculate_matches(self):
auto = EPGProfile.from_file(example_file('Ap1-haba-auto.csv'))
manual = EPGProfile.from_file(example_file('Ap1-haba-manual.csv'))
stats = EPGComparator((auto, manual)).compare()
expected = dict(
c=dict(n=33, full_matches=21, loffset=.03, roffset=.06),
pd=dict(n=16, full_matches=8, loffset=0., roffset=0.),
np=dict(n=26, full_matches=20, loffset=.13, roffset=.04))
for waveform in expected:
for name, val in expected[waveform].items():
self.assertAlmostEqual(val, stats.get(waveform, mean)[name],
2, name)
def test_making_fingerprint(self):
profile = EPGProfile.from_file(example_file('simple-manual.csv'))
expected = [None] * 215
expected.extend(['pd'] * 336)
expected.extend(['c'] * 1396)
expected.extend(['np'] * 1054)
expected.extend(['c'] * 1013)
expected.extend(['np'] * 625)
self.assertSequenceEqual(expected, _EPGFingerprint(profile))
def test_finding_closest_match_complex(self):
auto = EPGProfile.from_file(example_file('Ap1-haba-auto.csv'))
manual = EPGProfile.from_file(example_file('Ap1-haba-manual.csv'))
auto, manual = _EPGFingerprint(auto), _EPGFingerprint(manual)
finder = auto.find_closest_match_to
self.assertEqual((0, 19313), finder(manual, 0, 19313))
self.assertEqual(None, finder(manual, 19663, 66200))
self.assertEqual((66201, 98635), finder(manual, 66201, 98635))
self.assertEqual(None, finder(manual, 101423, 101845))
self.assertEqual(None, finder(manual, 203058, 209251))
self.assertEqual(None, finder(manual, 209667, 214063))
self.assertEqual(None, finder(manual, 214507, 234165))
self.assertEqual((249608, 251075), finder(manual, 249608, 251441))
self.assertEqual(None, finder(manual, 251442, 359998))
self.assertEqual(None, finder(manual, 371844, 382845))
self.assertEqual(None, finder(manual, 382985, 389107))
self.assertEqual((389108, 389470), finder(manual, 389108, 389470))
self.assertEqual((424665, 425297), finder(manual, 424316, 425567))
self.assertEqual((425298, 432213), finder(manual, 425568, 432213))