def test_big_k(self):
x = np.random.random((300, 3))
reader = DataInMemory(x)
k = 151
c = api.cluster_uniform_time(k=k)
c.estimate(reader)
def test_discretizer(self):
reader_gen = DataInMemory(data=self.generated_data)
# check if exception safe
api.discretizer(reader_gen)._chain[-1].get_output()
api.discretizer(reader_gen, transform=api.tica())._chain[-1].get_output()
api.discretizer(reader_gen, cluster=api.cluster_uniform_time())._chain[-1].get_output()
api.discretizer(reader_gen, transform=api.pca(), cluster=api.cluster_regspace(dmin=10))._chain[-1].get_output()
def test_big_k(self):
x = np.random.random((300, 3))
reader = DataInMemory(x)
k=151
c = api.cluster_uniform_time(k=k)
c.data_producer = reader
c.parametrize()
def test_2d_skip(self):
x = np.random.random((300, 3))
reader = DataInMemory(x)
k = 2
c = api.cluster_uniform_time(k=k, skip=100)
c.estimate(reader)
def test_no_transform(self):
reader_xtc = api.source(self.traj_files, top=self.pdb_file)
api.pipeline([reader_xtc,
api.cluster_kmeans(k=10)])._chain[-1].get_output()
api.pipeline([reader_xtc,
api.cluster_regspace(dmin=10)])._chain[-1].get_output()
api.pipeline([reader_xtc,
api.cluster_uniform_time()])._chain[-1].get_output()
def test_2d_skip(self):
x = np.random.random((300, 3))
reader = DataInMemory(x)
k = 2
c = api.cluster_uniform_time(k=k, skip=100)
c.data_producer = reader
c.parametrize()
def test_1d(self):
x = np.random.random(1000)
reader = DataInMemory(x)
k = 2
c = api.cluster_uniform_time(k=k)
c.data_producer = reader
c.parametrize()
def test_2d(self):
x = np.random.random((300, 3))
reader = DataInMemory(x)
k = 2
c = api.cluster_uniform_time(k=k)
c.data_producer = reader
c.parametrize()
def test_big_k(self):
# TODO: fix this (some error handling should be done in _param_init)
x = np.random.random((300, 3))
reader = DataInMemory(x)
k = 298
c = api.cluster_uniform_time(k=k)
c.data_producer = reader
c.parametrize()
def test_big_k(self):
# TODO: fix this (some error handling should be done in _param_init)
x = np.random.random((300, 3))
reader = DataInMemory(x)
k = 298
c = api.cluster_uniform_time(k=k)
c.data_producer = reader
c.parametrize()
def test_is_parametrized(self):
# construct pipeline with all possible transformers
p = api.pipeline(
[
api.source(self.traj_files, top=self.pdb_file),
api.tica(),
api.pca(),
api.cluster_kmeans(k=50),
api.cluster_regspace(dmin=50),
api.cluster_uniform_time(k=20)
], run=False
)
self.assertFalse(p._is_parametrized(), "If run=false, the pipeline should not be parametrized.")
p.parametrize()
self.assertTrue(p._is_parametrized(), "If parametrized was called, the pipeline should be parametrized.")
def test_no_transform(self):
reader_xtc = api.source(self.traj_files, top=self.pdb_file)
api.pipeline([reader_xtc, api.cluster_kmeans(k=10)])._chain[-1].get_output()
api.pipeline([reader_xtc, api.cluster_regspace(dmin=10)])._chain[-1].get_output()
api.pipeline([reader_xtc, api.cluster_uniform_time()])._chain[-1].get_output()
def test_1d(self):
x = np.random.random(1000)
reader = DataInMemory(x)
k = 2
c = api.cluster_uniform_time(reader, k=k)
