def test_hommola_cospeciation_no_sig():
np.random.seed(1)
hdist = np.array([[0, 3, 8, 8, 9], [3, 0, 7, 7, 8], [
8, 7, 0, 6, 7], [8, 7, 6, 0, 3], [9, 8, 7, 3, 0]])
pdist = np.array([[0, 5, 8, 8, 8], [5, 0, 7, 7, 7], [
8, 7, 0, 4, 4], [8, 7, 4, 0, 2], [8, 7, 4, 2, 0]])
# this matrix was picked because it will generate an r value that's less
# than a standard deviation away from the mean of the normal distribution
# of r vals
randomized_matrix = np.array(
[[0, 0, 0, 1, 0], [0, 0, 0, 0, 1], [1, 0, 0, 0, 0], [1, 0, 0, 0, 0],
[0, 0, 0, 0, 1]])
obs_p, obs_r, obs_perm_stats = hommola_cospeciation(
hdist, pdist, randomized_matrix, 9)
exp_p = .6
exp_r = -0.013679391379114569
exp_perm_stats = np.array([-0.22216543, -0.14836061, -0.04434843,
0.1478281, -0.29105645, 0.56395839, 0.47304992,
0.79125657, 0.06804138])
assert_almost_equal(obs_p, exp_p)
assert_almost_equal(obs_r, exp_r)
assert_allclose(obs_perm_stats, exp_perm_stats)
def test_hommola_cospeciation_sig():
np.random.seed(1)
hdist = np.array([[0, 3, 8, 8, 9], [3, 0, 7, 7, 8], [
8, 7, 0, 6, 7], [8, 7, 6, 0, 3], [9, 8, 7, 3, 0]])
pdist = np.array([[0, 5, 8, 8, 8], [5, 0, 7, 7, 7], [
8, 7, 0, 4, 4], [8, 7, 4, 0, 2], [8, 7, 4, 2, 0]])
matrix = np.array([[1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [
0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 1, 1]])
obs_p, obs_r, obs_perm_stats = hommola_cospeciation(
hdist, pdist, matrix, 9)
exp_p = .1
exp_r = 0.83170965463247915
exp_perm_stats = np.array([-0.14928122, 0.26299538, -0.21125858,
0.24143838, 0.61557855, -0.24258293,
0.09885203, 0.02858, 0.42742399])
assert_almost_equal(obs_p, exp_p)
assert_almost_equal(obs_r, exp_r)
assert_allclose(obs_perm_stats, exp_perm_stats)
