class TestPCoAResultsExtensive(object):
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)
def test_values(self):
results = self.ordination.scores()
npt.assert_equal(len(results.eigvals), len(results.site[0]))
expected = np.array([[-0.028597, 0.22903853, 0.07055272,
0.26163576, 0.28398669, 0.0],
[0.37494056, 0.22334055, -0.20892914,
0.05057395, -0.18710366, 0.0],
[-0.33517593, -0.23855979, -0.3099887,
0.11521787, -0.05021553, 0.0],
[0.25412394, -0.4123464, 0.23343642,
0.06403168, -0.00482608, 0.0],
[-0.28256844, 0.18606911, 0.28875631,
-0.06455635, -0.21141632, 0.0],
[0.01727687, 0.012458, -0.07382761,
-0.42690292, 0.1695749, 0.0]])
npt.assert_almost_equal(*normalize_signs(expected, results.site))
expected = np.array([0.3984635, 0.36405689, 0.28804535, 0.27479983,
0.19165361, 0.0])
npt.assert_almost_equal(results.eigvals, expected)
expected = np.array([0.2626621381, 0.2399817314, 0.1898758748,
0.1811445992, 0.1263356565, 0.0])
npt.assert_almost_equal(results.proportion_explained, expected)
npt.assert_equal(results.site_ids, self.ids)
class TestPCoAEigenResults(object):
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):
results = self.ordination.scores()
npt.assert_almost_equal(len(results.eigvals), len(results.site[0]))
expected = np.loadtxt(get_data_path('exp_PCoAEigenResults_site'))
npt.assert_almost_equal(*normalize_signs(expected, results.site))
expected = np.array([0.51236726, 0.30071909, 0.26791207, 0.20898868,
0.19169895, 0.16054235, 0.15017696, 0.12245775,
0.0])
npt.assert_almost_equal(results.eigvals, expected)
expected = np.array([0.2675738328, 0.157044696, 0.1399118638,
0.1091402725, 0.1001110485, 0.0838401162,
0.0784269939, 0.0639511764, 0.0])
npt.assert_almost_equal(results.proportion_explained, expected)
npt.assert_equal(results.site_ids, self.ids)
class TestPCoAEigenResults(object):
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):
results = self.ordination.scores()
npt.assert_almost_equal(len(results.eigvals), len(results.site[0]))
expected = np.loadtxt(get_data_path('exp_PCoAEigenResults_site'))
npt.assert_almost_equal(*normalize_signs(expected, results.site))
expected = np.array([
0.51236726, 0.30071909, 0.26791207, 0.20898868, 0.19169895,
0.16054235, 0.15017696, 0.12245775, 0.0
])
npt.assert_almost_equal(results.eigvals, expected)
expected = np.array([
0.2675738328, 0.157044696, 0.1399118638, 0.1091402725,
0.1001110485, 0.0838401162, 0.0784269939, 0.0639511764, 0.0
])
npt.assert_almost_equal(results.proportion_explained, expected)
npt.assert_equal(results.site_ids, self.ids)
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 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()
class TestPCoAResultsExtensive(object):
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)
def test_values(self):
results = self.ordination.scores()
npt.assert_equal(len(results.eigvals), len(results.site[0]))
expected = np.array([
[-0.028597, 0.22903853, 0.07055272, 0.26163576, 0.28398669, 0.0],
[
0.37494056, 0.22334055, -0.20892914, 0.05057395, -0.18710366,
0.0
],
[
-0.33517593, -0.23855979, -0.3099887, 0.11521787, -0.05021553,
0.0
],
[0.25412394, -0.4123464, 0.23343642, 0.06403168, -0.00482608, 0.0],
[
-0.28256844, 0.18606911, 0.28875631, -0.06455635, -0.21141632,
0.0
],
[0.01727687, 0.012458, -0.07382761, -0.42690292, 0.1695749, 0.0]
])
npt.assert_almost_equal(*normalize_signs(expected, results.site))
expected = np.array(
[0.3984635, 0.36405689, 0.28804535, 0.27479983, 0.19165361, 0.0])
npt.assert_almost_equal(results.eigvals, expected)
expected = np.array([
0.2626621381, 0.2399817314, 0.1898758748, 0.1811445992,
0.1263356565, 0.0
])
npt.assert_almost_equal(results.proportion_explained, expected)
npt.assert_equal(results.site_ids, self.ids)
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)