def clusterize_by_rt(self, error_rt):
"""
PUBLIC function
Provide a basic clustering home made
:param error_rt:
return: list of clusters(as set)
"""
if self.rt_method == 1:
logging.info("Basic clustering with rt_error:{0}".format(error_rt))
if not isinstance(error_rt, float):
raise TypeError("[clusterize]: args[0] is not a float")
return clusterize_basic(self.peakels, self.BASIC_RT_CALLABLE, error_rt)
elif self.rt_method == 2:
rts = [[x.rt] for x in self.peakels]
matrix_dist = sp.spatial.distance.pdist(np.array(rts)) # metric = eclidean by default
return list(clusterize_hierarchical(self.peakels, matrix_dist, "", error_rt).values())
elif self.rt_method == 3:
logging.info("DB SCAN clustering with error_rt:{0}".format(error_rt))
rts = [[x.rt] for x in self.peakels]
clusters = clusterize_dbscan(rts, self.peakels, eps=0.35)
# with open('clusters.txt', 'w') as f:
# for c in clusters:
# for fe in c:
# f.write(str(fe.rt) + '\n')
# f.write('\n')
return clusters # eps=error_rt / 2.0, min_samples=1)
else:
raise ValueError("wrong clustering technique !")
def clusterize_by_rt(self, error_rt):
"""
PUBLIC function
Provide a basic clustering home made
:param error_rt:
return: list of clusters(as set)
"""
if self.rt_method == 1:
logging.info("Basic clustering with rt_error:{0}".format(error_rt))
if not isinstance(error_rt, float):
raise TypeError("[clusterize]: args[0] is not a float")
return clusterize_basic(self.peakels, self.BASIC_RT_CALLABLE, error_rt)
elif self.rt_method == 2:
rts = [[x.rt] for x in self.peakels]
matrix_dist = sp.spatial.distance.pdist(np.array(rts)) # metric = eclidean by default
return list(clusterize_hierarchical(self.peakels, matrix_dist, "", error_rt).values())
elif self.rt_method == 3:
logging.info('DB SCAN clustering with error_rt:{0}'.format(error_rt))
rts = [[x.rt] for x in self.peakels]
clusters = clusterize_dbscan(rts, self.peakels, eps=0.35)
# with open('clusters.txt', 'w') as f:
# for c in clusters:
# for fe in c:
# f.write(str(fe.rt) + '\n')
# f.write('\n')
return clusters # eps=error_rt / 2.0, min_samples=1)
else:
raise ValueError("wrong clustering technique !")
def test_clusterize_dbscan_rt(self):
clusters = clusterize_dbscan([[x.rt] for x in self.features], self.features, eps=3.0, min_samples=1)
for c in clusters:
for f in c:
print(f.rt)
print('\n')
print(("len clusters dbscan: {0}".format(len(clusters))))
self.assertGreaterEqual(4, len(clusters))
def test_dbscan_clustering_for_alignment(self):
f1 = Peakel(1256.52, 0.0, 0.0, 100.0)
f2 = Peakel(1258.52, 0.0, 0.0, 500.52)
f3 = Peakel(1257.52, 0.0, 0.0, 101.52)
f4 = Peakel(1600.52, 0.0, 0.0, 99.52)
f7 = Peakel(1600.86, 0.0, 0.0, 107.12)
f5 = Peakel(1600.52, 0.0, 0.0, 3205.52)
f6 = Peakel(1456.52, 0.0, 0.0, 600.52)
peakels = [f1, f2, f3, f4, f5, f6, f7]
peakels_by_sample = {'a': {f1, f2, f4}, 'b': {f3, f5, f6}}
sample_by_peakel = {f1: 'a', f2: 'a', f4: 'a', f3: 'b', f5: 'b', f6: 'b', f7: 'b'}
values = [[x.moz, x.rt] for x in peakels]
clusters = clusterize_dbscan(values, peakels, eps=5, min_samples=1)
for c in clusters:
for p in c:
print(p.moz, p.rt, sample_by_peakel[p])
print('\n')
