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()
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)