def setup(self):
with open(get_data_path('PCoA_sample_data_3'), 'U') as lines:
dist_matrix = DistanceMatrix.from_file(lines)
self.ordination = PCoA(dist_matrix)
self.ids = [
'PC.636', 'PC.635', 'PC.356', 'PC.481', 'PC.354', 'PC.593',
'PC.355', 'PC.607', 'PC.634'
]
def test_values(self):
"""Adapted from cogent's `test_principal_coordinate_analysis`:
"I took the example in the book (see intro info), and did the
principal coordinates analysis, plotted the data and it looked
right"."""
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
ordination = PCoA(self.dist_matrix)
scores = ordination.scores()
# Note the absolute value because column can have signs swapped
npt.assert_almost_equal(np.abs(scores.species[0, 0]),
0.24078813304509292)
# cogent returned the scores transposed
npt.assert_almost_equal(np.abs(scores.species[0, 1]),
0.23367716219400031)
def test_input(self):
with npt.assert_raises(TypeError):
PCoA([[1, 2], [3, 4]])
def setup(self):
matrix = np.loadtxt(get_data_path('PCoA_sample_data_2'))
self.ids = map(str, range(matrix.shape[0]))
dist_matrix = DistanceMatrix(matrix, self.ids)
self.ordination = PCoA(dist_matrix)
