def test_procrustes2(self): """procrustes disparity should not depend on order of matrices""" m1, m3, disp13 = procrustes(self.data1, self.data3) m3_2, m1_2, disp31 = procrustes(self.data3, self.data1) np.testing.assert_almost_equal(disp13, disp31) # try with 3d, 8 pts per rand1 = np.array([[2.61955202, 0.30522265, 0.55515826], [0.41124708, -0.03966978, -0.31854548], [0.91910318, 1.39451809, -0.15295084], [2.00452023, 0.50150048, 0.29485268], [0.09453595, 0.67528885, 0.03283872], [0.07015232, 2.18892599, -1.67266852], [0.65029688, 1.60551637, 0.80013549], [-0.6607528, 0.53644208, 0.17033891]]) rand3 = np.array([[0.0809969, 0.09731461, -0.173442], [-1.84888465, -0.92589646, -1.29335743], [0.67031855, -1.35957463, 0.41938621], [0.73967209, -0.20230757, 0.52418027], [0.17752796, 0.09065607, 0.29827466], [0.47999368, -0.88455717, -0.57547934], [-0.11486344, -0.12608506, -0.3395779], [-0.86106154, -0.28687488, 0.9644429]]) res1, res3, disp13 = procrustes(rand1, rand3) res3_2, res1_2, disp31 = procrustes(rand3, rand1) np.testing.assert_almost_equal(disp13, disp31)
def test_procrustes(self): """tests procrustes' ability to match two matrices. the second matrix is a rotated, shifted, scaled, and mirrored version of the first, in two dimensions only """ # can shift, mirror, and scale an 'L'? a, b, disparity = procrustes(self.data1, self.data2) np.testing.assert_allclose(b, a) np.testing.assert_almost_equal(disparity, 0.) # if first mtx is standardized, leaves first mtx unchanged? m4, m5, disp45 = procrustes(self.data4, self.data5) np.testing.assert_equal(m4, self.data4) # at worst, data3 is an 'L' with one point off by .5 m1, m3, disp13 = procrustes(self.data1, self.data3) self.assertTrue(disp13 < 0.5 ** 2)
def test_procrustes(self): """tests procrustes' ability to match two matrices. the second matrix is a rotated, shifted, scaled, and mirrored version of the first, in two dimensions only """ # can shift, mirror, and scale an 'L'? a, b, disparity = procrustes(self.data1, self.data2) np.testing.assert_allclose(b, a) np.testing.assert_almost_equal(disparity, 0.) # if first mtx is standardized, leaves first mtx unchanged? m4, m5, disp45 = procrustes(self.data4, self.data5) np.testing.assert_equal(m4, self.data4) # at worst, data3 is an 'L' with one point off by .5 m1, m3, disp13 = procrustes(self.data1, self.data3) self.assertTrue(disp13 < 0.5**2)
def get_procrustes_results(coords_f1, coords_f2, sample_id_map=None, randomize=None, max_dimensions=None, get_eigenvalues=get_mean_eigenvalues, get_percent_variation_explained=get_mean_percent_variation): """ """ # Parse the PCoA files ord_res_1 = OrdinationResults.from_file(coords_f1) ord_res_2 = OrdinationResults.from_file(coords_f2) sample_ids1 = ord_res_1.site_ids coords1 = ord_res_1.site eigvals1 = ord_res_1.eigvals pct_var1 = ord_res_1.proportion_explained sample_ids2 = ord_res_2.site_ids coords2 = ord_res_2.site eigvals2 = ord_res_2.eigvals pct_var2 = ord_res_2.proportion_explained if sample_id_map: sample_ids1 = map_sample_ids(sample_ids1, sample_id_map) sample_ids2 = map_sample_ids(sample_ids2, sample_id_map) # rearrange the order of coords in coords2 to correspond to # the order of coords in coords1 order = list(set(sample_ids1) & set(sample_ids2)) coords1 = reorder_coords(coords1, sample_ids1, order) coords2 = reorder_coords(coords2, sample_ids2, order) if len(order) == 0: raise ValueError('No overlapping samples in the two files') # If this is a random trial, apply the shuffling function passed as # randomize() if randomize: coords2 = randomize(coords2) randomized_coords2 = OrdinationResults(eigvals=eigvals2, proportion_explained=pct_var2, site=coords2, site_ids=order) else: randomized_coords2 = None coords1, coords2 = pad_coords_matrices(coords1, coords2) if max_dimensions: coords1 = filter_coords_matrix(coords1, max_dimensions) coords2 = filter_coords_matrix(coords2, max_dimensions) pct_var1 = pct_var1[:max_dimensions] pct_var2 = pct_var2[:max_dimensions] eigvals1 = eigvals1[:max_dimensions] eigvals2 = eigvals2[:max_dimensions] else: if len(pct_var1) > len(pct_var2): pct_var2 = append(pct_var2, zeros(len(pct_var1) - len(pct_var2))) eigvals2 = append(eigvals2, zeros(len(eigvals1) - len(eigvals2))) elif len(pct_var1) < len(pct_var2): pct_var1 = append(pct_var1, zeros(len(pct_var2) - len(pct_var1))) eigvals1 = append(eigvals1, zeros(len(eigvals2) - len(eigvals1))) # Run the Procrustes analysis transformed_coords_m1, transformed_coords_m2, m_squared =\ procrustes(coords1, coords2) # print coords2 # print transformed_coords_m2 eigvals = get_eigenvalues(eigvals1, eigvals2) pct_var = get_percent_variation_explained(pct_var1, pct_var2) transformed_coords1 = OrdinationResults(eigvals=asarray(eigvals), proportion_explained=asarray(pct_var), site=asarray(transformed_coords_m1), site_ids=order) transformed_coords2 = OrdinationResults(eigvals=asarray(eigvals), proportion_explained=asarray(pct_var), site=asarray(transformed_coords_m2), site_ids=order) # Return the results return (transformed_coords1, transformed_coords2, m_squared, randomized_coords2)