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()
exp_eigvals = np.array([
0.73599103, 0.26260032, 0.14926222, 0.06990457, 0.02956972,
0.01931184, 0., 0., 0., 0., 0., 0., 0., 0.
])
exp_site = np.loadtxt(get_data_path('exp_PCoAzeros_site'))
exp_prop_expl = np.array([
0.58105792, 0.20732046, 0.1178411, 0.05518899, 0.02334502,
0.01524651, 0., 0., 0., 0., 0., 0., 0., 0.
])
exp_site_ids = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',
'13'
]
# Note the absolute value because column can have signs swapped
npt.assert_almost_equal(scores.eigvals, exp_eigvals)
npt.assert_almost_equal(np.abs(scores.site), exp_site)
npt.assert_almost_equal(scores.proportion_explained, exp_prop_expl)
npt.assert_equal(scores.site_ids, exp_site_ids)
def pcoa(lines):
"""Run PCoA on the distance matrix present on lines"""
# Parse the distance matrix
dist_mtx = DistanceMatrix.from_file(lines)
# Create the PCoA object
pcoa_obj = PCoA(dist_mtx)
# Get the PCoA results and return them
return pcoa_obj.scores()
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_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 = [str(i) for i in range(matrix.shape[0])]
dist_matrix = DistanceMatrix(matrix, self.ids)
self.ordination = PCoA(dist_matrix)
