def test_cosine_score_swapped(random_spectrum, another_random_spectrum,
mz_tolerance, min_matched_peaks):
"""Cosine scores should not change if comparing spectrum 1 with spectrum 2
or spectrum 2 with spectrum 1."""
mz1, data1 = random_spectrum
mz2, data2 = another_random_spectrum
cosine = cosine_score(mz1, data1, mz2, data2, mz_tolerance,
min_matched_peaks)
cosine_swapped = cosine_score(mz2, data2, mz1, data1, mz_tolerance,
min_matched_peaks)
assert pytest.approx(cosine) == cosine_swapped
def test_query_analog(db):
"""Build an analog and try to find the original spectrum in the database.
"""
p, (mzs, spectra) = db
for i, (mz, data) in enumerate(zip(mzs, spectra)):
mz = mzs[i] - 50
data = spectra[i].copy()
data[:, MZ] = data[:, MZ] - 50
filtered_analog = filter_data(mz, data, 0, 17, 50, 6)
filtered_orig = filter_data(mzs[i], spectra[i], 0, 17, 50, 6)
score = cosine_score(mzs[i], filtered_orig,
mz, filtered_analog,
0.02, 0)
results = query(str(p), [i], [mz], [filtered_analog], [],
0.02, 0, 0, 17, 50, 6, 0., 100.)
assert i in results
seen_i = False
for r in results[i]:
assert 'id' in r
assert 'bank_id' in r
assert 'name' in r
assert 'score' in r
if r['id'] == i:
seen_i = True
assert r['score'] == pytest.approx(score)
assert seen_i
def get_cosine(self, params):
parent_mass = [p[0].parent_mass for p in params]
spectrum = [
p[0].fragmolspectrum_set.get(energy=p[1]).spectrum for p in params
]
# filter_data(data, mz_parent, min_intensity, parent_filter_tolerance, matched_peaks_window,min_matched_peaks_search)
spectrum = [
filter_data(
np.array(spectrum[i]),
parent_mass[i],
self.frag_compare_conf.filter_min_intensity,
self.frag_compare_conf.filter_parent_filter_tolerance,
self.frag_compare_conf.filter_matched_peaks_window,
self.frag_compare_conf.filter_min_matched_peaks_search,
) for i in range(2)
]
# cosine_score(spectrum1_mz, spectrum1_data, spectrum2_mz, spectrum2_data, mz_tolerance, min_matched_peaks)
return cosine_score(
parent_mass[0],
spectrum[0],
parent_mass[1],
spectrum[1],
self.frag_compare_conf.cosine_mz_tolerance,
self.frag_compare_conf.cosine_min_matched_peaks,
)
def test_cosine_score_identity(random_spectrum, mz_tolerance,
min_matched_peaks):
"""Cosine scores between one spectra and itself should always be 1."""
mz, data = random_spectrum
cosine = cosine_score(mz, data, mz, data, mz_tolerance, min_matched_peaks)
assert pytest.approx(cosine) == 1.0
def test_cosine_score_empty(random_spectrum, mz_tolerance, min_matched_peaks):
"""Empty spectra should result in a null cosine score"""
mz, _ = random_spectrum
data = np.empty((0, 2), dtype=np.float32)
cosine = cosine_score(mz, data, mz, data, mz_tolerance, min_matched_peaks)
assert pytest.approx(cosine) == 0.0
def test_cosine_score_known(known_cosines):
"""`cosine_score` should return `expected` values"""
mz1, mz2, data1, data2, mz_tolerance, \
min_matched_peaks, expected = known_cosines
cosine = cosine_score(mz1, data1, mz2, data2, mz_tolerance,
min_matched_peaks)
assert pytest.approx(cosine, rel=1e-4) == expected
def test_cosine_score_python_cython(random_spectrum, another_random_spectrum,
mz_tolerance, min_matched_peaks):
"""Cythonized `cosine_score` and it's fallback Python version should give
the same results.
"""
args = (*random_spectrum, *another_random_spectrum, mz_tolerance,
min_matched_peaks)
cosine_p = cosine_score.__wrapped__(*args)
cosine_c = cosine_score(*args)
assert pytest.approx(cosine_p) == cosine_c