def test_inputs_from_dict2D(self):
"""inputs_from_dict2D makes an array object and PhyloNode list"""
matrix = [('1', '2', 0.86), ('2', '1', 0.86), \
('1', '3', 0.92), ('3', '1', 0.92), ('2', '3', 0.67), \
('3', '2', 0.67)]
row_order = ['3', '2', '1']
matrix_d2d = Dict2D(matrix, RowOrder=row_order, \
ColOrder=row_order, Pad=True, Default = 999999999999999)
matrix_array, PhyloNode_order = inputs_from_dict2D(matrix_d2d)
self.assertFloatEqual(matrix_array[0][2], 0.92)
self.assertFloatEqual(matrix_array[1][0], 0.67)
self.assertEqual(PhyloNode_order[0].Name, '3')
self.assertEqual(PhyloNode_order[2].Name, '1')
def test_inputs_from_dict2D(self):
"""inputs_from_dict2D makes an array object and PhyloNode list"""
matrix = [('1', '2', 0.86), ('2', '1', 0.86), \
('1', '3', 0.92), ('3', '1', 0.92), ('2', '3', 0.67), \
('3', '2', 0.67)]
row_order = ['3', '2', '1']
matrix_d2d = Dict2D(matrix, RowOrder=row_order, \
ColOrder=row_order, Pad=True, Default = 999999999999999)
matrix_array, PhyloNode_order = inputs_from_dict2D(matrix_d2d)
self.assertFloatEqual(matrix_array[0][2], 0.92)
self.assertFloatEqual(matrix_array[1][0], 0.67)
self.assertEqual(PhyloNode_order[0].Name, '3')
self.assertEqual(PhyloNode_order[2].Name, '1')
def PCoA(pairwise_distances):
"""runs principle coordinates analysis on a distance matrix
Takes a dictionary with tuple pairs mapped to distances as input.
Returns a cogent Table object.
"""
items_in_matrix = []
for i in pairwise_distances:
if i[0] not in items_in_matrix:
items_in_matrix.append(i[0])
if i[1] not in items_in_matrix:
items_in_matrix.append(i[1])
dict2d_input = [(i[0], i[1], pairwise_distances[i]) for i in pairwise_distances]
dict2d_input.extend([(i[1], i[0], pairwise_distances[i]) for i in pairwise_distances])
dict2d_input = Dict2D(dict2d_input, RowOrder=items_in_matrix, ColOrder=items_in_matrix, Pad=True, Default=0.0)
matrix_a, node_order = inputs_from_dict2D(dict2d_input)
point_matrix, eigvals = principal_coordinates_analysis(matrix_a)
return output_pca(point_matrix, eigvals, items_in_matrix)
def PCoA(pairwise_distances):
"""runs principle coordinates analysis on a distance matrix
Takes a dictionary with tuple pairs mapped to distances as input.
Returns a cogent Table object.
"""
items_in_matrix = []
for i in pairwise_distances:
if i[0] not in items_in_matrix:
items_in_matrix.append(i[0])
if i[1] not in items_in_matrix:
items_in_matrix.append(i[1])
dict2d_input = [(i[0], i[1], pairwise_distances[i]) for i in \
pairwise_distances]
dict2d_input.extend([(i[1], i[0], pairwise_distances[i]) for i in \
pairwise_distances])
dict2d_input = Dict2D(dict2d_input, RowOrder=items_in_matrix, \
ColOrder=items_in_matrix, Pad=True, Default=0.0)
matrix_a, node_order = inputs_from_dict2D(dict2d_input)
point_matrix, eigvals = principal_coordinates_analysis(matrix_a)
return output_pca(point_matrix, eigvals, items_in_matrix)
