def test_plot_connectome_strength_exceptions():
node_coords = np.arange(2 * 3).reshape((2, 3))
# Used to speed-up tests because the glass brain is always plotted
# before any error occurs
kwargs = {'display_mode': 'x'}
# adjacency_matrix is not symmetric
non_symmetric_adjacency_matrix = np.array([[1., 2], [0.4, 1.]])
with pytest.raises(ValueError, match='should be symmetric'):
plot_connectome_strength(non_symmetric_adjacency_matrix, node_coords,
**kwargs)
adjacency_matrix = np.array([[1., 2.], [2., 1.]])
# adjacency_matrix mask is not symmetric
masked_adjacency_matrix = np.ma.masked_array(
adjacency_matrix, [[False, True], [False, False]])
with pytest.raises(ValueError, match='non symmetric mask'):
plot_connectome_strength(masked_adjacency_matrix, node_coords,
**kwargs)
# wrong shapes for node_coords or adjacency_matrix
with pytest.raises(ValueError,
match=r'supposed to have shape \(n, n\).+\(1L?, 2L?\)'):
plot_connectome_strength(adjacency_matrix[:1, :], node_coords,
**kwargs)
with pytest.raises(ValueError, match=r'shape \(2L?, 3L?\).+\(2L?,\)'):
plot_connectome_strength(adjacency_matrix, node_coords[:, 2], **kwargs)
wrong_adjacency_matrix = np.zeros((3, 3))
with pytest.raises(ValueError,
match=r'Shape mismatch.+\(3L?, 3L?\).+\(2L?, 3L?\)'):
plot_connectome_strength(wrong_adjacency_matrix, node_coords, **kwargs)
def test_plot_connectome_strength_deprecation_warning():
with pytest.deprecated_call():
adjacency_matrix = np.array([[1, -2, 0.3, 0.],
[-2, 1, 0, 0],
[0.3, 0, 1, 0],
[0, 0, 0, 1]])
node_coords = np.arange(3 * 4).reshape(4, 3)
plot_connectome_strength(adjacency_matrix, node_coords)
def test_connectome_strength():
# symmetric up to 1e-3 relative tolerance
adjacency_matrix = np.array([[1., -2., 0.3, 0.], [-2.002, 1, 0., 0.],
[0.3, 0., 1., 0.], [0., 0., 0., 1.]])
node_coords = np.arange(3 * 4).reshape(4, 3)
args = adjacency_matrix, node_coords
kwargs = dict()
plot_connectome_strength(*args, **kwargs)
plt.close()
# used to speed-up tests for the net plots
kwargs['display_mode'] = 'x'
# node_coords not an array but a list of tuples
plot_connectome_strength(adjacency_matrix,
[tuple(each) for each in node_coords], **kwargs)
# saving to file
filename = tempfile.mktemp(suffix='.png')
try:
display = plot_connectome_strength(*args,
output_file=filename,
**kwargs)
assert display is None
assert (os.path.isfile(filename) and # noqa: W504
os.path.getsize(filename) > 0)
finally:
os.remove(filename)
plt.close()
# passing node args
plot_connectome_strength(*args, node_size=10, cmap='RdBu')
plt.close()
plot_connectome_strength(*args, node_size=10, cmap=plt.cm.RdBu)
plt.close()
# masked array support
masked_adjacency_matrix = np.ma.masked_array(
adjacency_matrix,
np.abs(adjacency_matrix) < 0.5)
plot_connectome_strength(masked_adjacency_matrix, node_coords, **kwargs)
plt.close()
# sparse matrix support
sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
plot_connectome_strength(sparse_adjacency_matrix, node_coords, **kwargs)
plt.close()
# NaN matrix support
nan_adjacency_matrix = np.array([[1., np.nan, 0.], [np.nan, 1., 2.],
[np.nan, 2., 1.]])
nan_node_coords = np.arange(3 * 3).reshape(3, 3)
plot_connectome_strength(nan_adjacency_matrix, nan_node_coords, **kwargs)
plt.close()
# smoke-test with hemispheric sagital cuts
plot_connectome_strength(*args, display_mode='lzry')
plt.close()
