def test_matrix_of_pairs():
dic = {(2, 2): 0.22, (3, 2): 0.10, (2, 3): .03}
mat = matrix_of_pairs(dic)
mat_actual = np.array([
[.22, 0.03],
[0.1, 0]
])
assert np.array_equal(mat, mat_actual)
def test_compute_correlations():
n = 1000
nspikes = 3 * n
clusters = np.repeat([0, 1, 2], n)
features = np.zeros((nspikes, 2))
masks = np.ones((nspikes, 2))
# clusters 0 and 1 are close, 2 is far away from 0 and 1
features[:n, :] = np.random.randn(n, 2)
features[n:2*n, :] = np.random.randn(n, 2)
features[2*n:, :] = np.array([[10, 10]]) + np.random.randn(n, 2)
# compute the correlation matrix
correlations = compute_correlations(features, clusters, masks)
matrix = matrix_of_pairs(correlations)
# check that correlation between 0 and 1 is much higher than the
# correlation between 2 and 0/1
assert matrix[0,1] > 100 * matrix[0, 2]
assert matrix[0,1] > 100 * matrix[1, 2]
def test_compute_correlations():
n = 1000
nspikes = 3 * n
clusters = np.repeat([0, 1, 2], n)
features = np.zeros((nspikes, 2))
masks = np.ones((nspikes, 2))
# clusters 0 and 1 are close, 2 is far away from 0 and 1
features[:n, :] = np.random.randn(n, 2)
features[n:2 * n, :] = np.random.randn(n, 2)
features[2 * n:, :] = np.array([[10, 10]]) + np.random.randn(n, 2)
# compute the correlation matrix
correlations = compute_correlations(features, clusters, masks)
matrix = matrix_of_pairs(correlations)
# check that correlation between 0 and 1 is much higher than the
# correlation between 2 and 0/1
assert matrix[0, 1] > 100 * matrix[0, 2]
assert matrix[0, 1] > 100 * matrix[1, 2]
