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_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)
