def test_Y_values(self):
X, Y = self.X, self.Y
Y[0, 0] = -1
with npt.assert_raises(ValueError):
cca(Y, X)
Y[0] = 0
with npt.assert_raises(ValueError):
cca(Y, X)
def test_scaling2(self):
scores = cca(self.Y, self.X, scaling=2)
# Load data as computed with vegan 2.0-8
vegan_features = pd.DataFrame(
np.loadtxt(get_data_path(
'example3_species_scaling2_from_vegan')),
index=self.feature_ids,
columns=self.pc_ids)
vegan_samples = pd.DataFrame(
np.loadtxt(get_data_path(
'example3_site_scaling2_from_vegan')),
index=self.sample_ids,
columns=self.pc_ids)
sample_constraints = pd.DataFrame(
np.loadtxt(get_data_path(
'example3_sample_constraints_scaling2')),
index=self.sample_ids,
columns=self.pc_ids)
mat = np.loadtxt(get_data_path(
'example3_biplot_scaling2'))
cropped_pc_ids = self.pc_ids[:mat.shape[1]]
biplot_scores = pd.DataFrame(mat,
index=self.env_ids,
columns=cropped_pc_ids)
proportion_explained = pd.Series([0.466911, 0.238327, 0.100548,
0.104937, 0.044805, 0.029747,
0.012631, 0.001562, 0.000532],
index=self.pc_ids)
eigvals = pd.Series([0.366136, 0.186888, 0.078847, 0.082288,
0.035135, 0.023327, 0.009905, 0.001225,
0.000417], index=self.pc_ids)
exp = OrdinationResults(
'CCA', 'Canonical Correspondence Analysis',
samples=vegan_samples,
features=vegan_features,
sample_constraints=sample_constraints,
biplot_scores=biplot_scores,
proportion_explained=proportion_explained,
eigvals=eigvals)
assert_ordination_results_equal(scores, exp,
decimal=6)
def test_all_zero_row(self):
X, Y = pd.DataFrame(np.zeros((3, 3))), pd.DataFrame(np.zeros((3, 3)))
with npt.assert_raises(ValueError):
cca(X, Y)
def test_scaling(self):
X, Y = self.X, self.Y
with npt.assert_raises(NotImplementedError):
cca(Y, X, 3)
def test_shape(self):
X, Y = self.X, self.Y
with npt.assert_raises(ValueError):
cca(Y, X[:-1])
