def testSimilarityMap(self):
# Morgan2 BV
refWeights = [0.5, 0.5, 0.5, -0.5, 0.5, 0.5]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'))
for w,r in zip(weights, refWeights): self.assertEqual(w, r)
fig, maxWeight = sm.GetSimilarityMapForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, radius=2, fpType='bv'))
self.assertEqual(maxWeight, 0.5)
weights, maxWeight = sm.GetStandardizedWeights(weights)
self.assertEqual(maxWeight, 0.5)
refWeights = [1.0, 1.0, 1.0, -1.0, 1.0, 1.0]
for w,r in zip(weights, refWeights): self.assertEqual(w, r)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, fpType='count'))
self.assertTrue(weights[3] < 0)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetMorganFingerprint(m, i, fpType='bv', useFeatures=True))
self.assertTrue(weights[3] < 0)
# hashed AP BV
refWeights = [0.09523, 0.17366, 0.17366, -0.23809, 0.17366, 0.17366]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetAPFingerprint(m, i, fpType='bv', nBits=1024))
for w,r in zip(weights, refWeights): self.assertAlmostEqual(w, r, 4)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetAPFingerprint(m, i, fpType='normal'))
self.assertTrue(weights[3] < 0)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetAPFingerprint(m, i, fpType='hashed'))
self.assertTrue(weights[3] < 0)
# hashed TT BV
refWeights = [0.5, 0.5, -0.16666, -0.5, -0.16666, 0.5]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetTTFingerprint(m, i, fpType='bv', nBits=1024, nBitsPerEntry=1))
for w,r in zip(weights, refWeights): self.assertAlmostEqual(w, r, 4)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetTTFingerprint(m, i, fpType='normal'))
self.assertTrue(weights[3] < 0)
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetTTFingerprint(m, i, fpType='hashed'))
self.assertTrue(weights[3] < 0)
# RDK fingerprint BV
refWeights = [0.42105, 0.42105, 0.42105, -0.32895, 0.42105, 0.42105]
weights = sm.GetAtomicWeightsForFingerprint(self.mol1, self.mol2, lambda m, i: sm.GetRDKFingerprint(m, i, nBits=1024, nBitsPerHash=1))
for w,r in zip(weights, refWeights): self.assertAlmostEqual(w, r, 4)
def fp_maps(results, query, fingerprint, fpradius, nbits, features, metric,
out_file, multfile):
if fingerprint == "ecfp" or fingerprint == "fcfp":
fp_func = lambda m, idx: SimilarityMaps.GetMorganFingerprint(
m,
atomId=idx,
radius=fpradius,
fpType='bv',
nBits=nbits,
useFeatures=features)
elif fingerprint == "rdkit":
fp_func = lambda m, idx: SimilarityMaps.GetRDKFingerprint(
m, atomId=idx, fpType="bv", nBits=nbits)
elif fingerprint == "ap":
fp_func = lambda m, idx: SimilarityMaps.GetAPFingerprint(
m, atomId=idx, fpType="bv", nBits=nbits)
else:
fp_func = lambda m, idx: SimilarityMaps.GetTTFingerprint(
m, atomId=idx, fpType="bv", nBits=nbits)
if multfile:
sim_map_mf(results, query, fp_func, metric, out_file)
else:
sim_map(results, query, fp_func, metric, out_file)