def test_plot_connectome():
node_color = ['green', 'blue', 'k', 'cyan']
# 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(edge_threshold=0.38,
title='threshold=0.38',
node_size=10, node_color=node_color)
plot_connectome(*args, **kwargs)
plt.close()
# used to speed-up tests for the next plots
kwargs['display_mode'] = 'x'
# node_coords not an array but a list of tuples
plot_connectome(adjacency_matrix,
[tuple(each) for each in node_coords],
**kwargs)
# saving to file
filename = tempfile.mktemp(suffix='.png')
try:
display = plot_connectome(*args, output_file=filename, **kwargs)
assert_true(display is None)
assert_true(os.path.isfile(filename) and
os.path.getsize(filename) > 0)
finally:
os.remove(filename)
plt.close()
# with node_kwargs, edge_kwargs and edge_cmap arguments
plot_connectome(*args,
edge_threshold='70%',
node_size=[10, 20, 30, 40],
node_color=np.zeros((4, 3)),
edge_cmap='RdBu',
colorbar=True,
node_kwargs={
'marker': 'v'},
edge_kwargs={
'linewidth': 4})
plt.close()
# masked array support
masked_adjacency_matrix = np.ma.masked_array(
adjacency_matrix, np.abs(adjacency_matrix) < 0.5)
plot_connectome(masked_adjacency_matrix, node_coords,
**kwargs)
plt.close()
# sparse matrix support
sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
plot_connectome(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(nan_adjacency_matrix, nan_node_coords, **kwargs)
plt.close()
# smoke-test where there is no edge to draw, e.g. when
# edge_threshold is too high
plot_connectome(*args, edge_threshold=1e12)
plt.close()
# with colorbar=True
plot_connectome(*args, colorbar=True)
plt.close()
# smoke-test with hemispheric saggital cuts
plot_connectome(*args, display_mode='lzry')
plt.close()
def test_plot_connectome():
import matplotlib.pyplot as plt
plt.switch_backend('template')
node_color = ['green', 'blue', 'k', 'cyan']
# 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(edge_threshold=0.38,
title='threshold=0.38',
node_size=10,
node_color=node_color)
plot_connectome(*args, **kwargs)
# used to speed-up tests for the next plots
kwargs['display_mode'] = 'x'
# node_coords not an array but a list of tuples
plot_connectome(adjacency_matrix, [tuple(each) for each in node_coords],
**kwargs)
# saving to file
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
display = plot_connectome(*args, output_file=fp.name, **kwargs)
assert_true(display is None)
assert_true(os.path.isfile(fp.name) and os.path.getsize(fp.name) > 0)
# with node_kwargs, edge_kwargs and edge_cmap arguments
plot_connectome(*args,
edge_threshold='70%',
node_size=[10, 20, 30, 40],
node_color=np.zeros((4, 3)),
edge_cmap='RdBu',
node_kwargs={'marker': 'v'},
edge_kwargs={'linewidth': 4})
# masked array support
masked_adjacency_matrix = np.ma.masked_array(
adjacency_matrix,
np.abs(adjacency_matrix) < 0.5)
plot_connectome(masked_adjacency_matrix, node_coords, **kwargs)
# sparse matrix support
sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
plot_connectome(sparse_adjacency_matrix, node_coords, **kwargs)
# 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(nan_adjacency_matrix, nan_node_coords, **kwargs)
def test_plot_connectome():
node_color = ['green', 'blue', 'k', 'cyan']
# 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(edge_threshold=0.38,
title='threshold=0.38',
node_size=10, node_color=node_color)
plot_connectome(*args, **kwargs)
plt.close()
# used to speed-up tests for the next plots
kwargs['display_mode'] = 'x'
# node_coords not an array but a list of tuples
plot_connectome(adjacency_matrix,
[tuple(each) for each in node_coords],
**kwargs)
# saving to file
filename = tempfile.mktemp(suffix='.png')
try:
display = plot_connectome(*args, output_file=filename, **kwargs)
assert_true(display is None)
assert_true(os.path.isfile(filename) and
os.path.getsize(filename) > 0)
finally:
os.remove(filename)
plt.close()
# with node_kwargs, edge_kwargs and edge_cmap arguments
plot_connectome(*args,
edge_threshold='70%',
node_size=[10, 20, 30, 40],
node_color=np.zeros((4, 3)),
edge_cmap='RdBu',
colorbar=True,
node_kwargs={
'marker': 'v'},
edge_kwargs={
'linewidth': 4})
plt.close()
# masked array support
masked_adjacency_matrix = np.ma.masked_array(
adjacency_matrix, np.abs(adjacency_matrix) < 0.5)
plot_connectome(masked_adjacency_matrix, node_coords,
**kwargs)
plt.close()
# sparse matrix support
sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
plot_connectome(sparse_adjacency_matrix, node_coords,
**kwargs)
plt.close()
# NaN matrix support
node_color = ['green', 'blue', 'k']
# Overriding 'node_color' for 3 elements of size 3.
kwargs['node_color'] = node_color
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(nan_adjacency_matrix, nan_node_coords, **kwargs)
plt.close()
# smoke-test where there is no edge to draw, e.g. when
# edge_threshold is too high
plot_connectome(*args, edge_threshold=1e12)
plt.close()
# with colorbar=True
plot_connectome(*args, colorbar=True)
plt.close()
# smoke-test with hemispheric saggital cuts
plot_connectome(*args, display_mode='lzry')
plt.close()
# test node_color as a string with display_mode='lzry'
plot_connectome(*args, node_color='red', display_mode='lzry')
plt.close()
plot_connectome(*args, node_color=['red'], display_mode='lzry')
plt.close()
def test_plot_connectome_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(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(masked_adjacency_matrix, node_coords, **kwargs)
# edges threshold is neither a number nor a string
with pytest.raises(TypeError,
match='should be either a number or a string'):
plot_connectome(adjacency_matrix,
node_coords,
edge_threshold=object(),
**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(adjacency_matrix[:1, :], node_coords, **kwargs)
with pytest.raises(ValueError, match=r'shape \(2L?, 3L?\).+\(2L?,\)'):
plot_connectome(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(wrong_adjacency_matrix, node_coords, **kwargs)
# a few not correctly formatted strings for 'edge_threshold'
wrong_edge_thresholds = ['0.1', '10', '10.2.3%', 'asdf%']
for wrong_edge_threshold in wrong_edge_thresholds:
with pytest.raises(
ValueError,
match='should be a number followed by the percent sign'):
plot_connectome(adjacency_matrix,
node_coords,
edge_threshold=wrong_edge_threshold,
**kwargs)
# specifying node sizes via node_kwargs
with pytest.raises(ValueError,
match="Please use 'node_size' and not 'node_kwargs'"):
plot_connectome(adjacency_matrix,
node_coords,
node_kwargs={'s': 50},
**kwargs)
# specifying node colors via node_kwargs
with pytest.raises(ValueError,
match="Please use 'node_color' and not 'node_kwargs'"):
plot_connectome(adjacency_matrix,
node_coords,
node_kwargs={'c': 'blue'},
**kwargs)
def test_plot_connectome():
node_color = ["green", "blue", "k", "cyan"]
# symmetric up to 1e-3 relative tolerance
adjacency_matrix = np.array(
[[1.0, -2.0, 0.3, 0.0], [-2.002, 1, 0.0, 0.0], [0.3, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]
)
node_coords = np.arange(3 * 4).reshape(4, 3)
args = adjacency_matrix, node_coords
kwargs = dict(edge_threshold=0.38, title="threshold=0.38", node_size=10, node_color=node_color)
plot_connectome(*args, **kwargs)
# used to speed-up tests for the next plots
kwargs["display_mode"] = "x"
# node_coords not an array but a list of tuples
plot_connectome(adjacency_matrix, [tuple(each) for each in node_coords], **kwargs)
# saving to file
with tempfile.NamedTemporaryFile(suffix=".png") as fp:
display = plot_connectome(*args, output_file=fp.name, **kwargs)
assert_true(display is None)
assert_true(os.path.isfile(fp.name) and os.path.getsize(fp.name) > 0)
# with node_kwargs, edge_kwargs and edge_cmap arguments
plot_connectome(
*args,
edge_threshold="70%",
node_size=[10, 20, 30, 40],
node_color=np.zeros((4, 3)),
edge_cmap="RdBu",
node_kwargs={"marker": "v"},
edge_kwargs={"linewidth": 4}
)
# masked array support
masked_adjacency_matrix = np.ma.masked_array(adjacency_matrix, np.abs(adjacency_matrix) < 0.5)
plot_connectome(masked_adjacency_matrix, node_coords, **kwargs)
# sparse matrix support
sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
plot_connectome(sparse_adjacency_matrix, node_coords, **kwargs)
# NaN matrix support
nan_adjacency_matrix = np.array([[1.0, np.nan, 0.0], [np.nan, 1.0, 2.0], [np.nan, 2.0, 1.0]])
nan_node_coords = np.arange(3 * 3).reshape(3, 3)
plot_connectome(nan_adjacency_matrix, nan_node_coords, **kwargs)
# smoke-test where there is no edge to draw, e.g. when
# edge_threshold is too high
plot_connectome(*args, edge_threshold=1e12)
def test_plot_connectome():
import matplotlib.pyplot as plt
plt.switch_backend('template')
node_color = ['green', 'blue', 'k', 'cyan']
# 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(edge_threshold=0.38,
title='threshold=0.38',
node_size=10, node_color=node_color)
plot_connectome(*args, **kwargs)
# used to speed-up tests for the next plots
kwargs['display_mode'] = 'x'
# node_coords not an array but a list of tuples
plot_connectome(adjacency_matrix,
[tuple(each) for each in node_coords],
**kwargs)
# saving to file
with tempfile.NamedTemporaryFile(suffix='.png') as fp:
display = plot_connectome(*args, output_file=fp.name,
**kwargs)
assert_true(display is None)
assert_true(os.path.isfile(fp.name) and
os.path.getsize(fp.name) > 0)
# with node_kwargs, edge_kwargs and edge_cmap arguments
plot_connectome(*args,
edge_threshold='70%',
node_size=[10, 20, 30, 40],
node_color=np.zeros((4, 3)),
edge_cmap='RdBu',
node_kwargs={
'marker': 'v'},
edge_kwargs={
'linewidth': 4})
# masked array support
masked_adjacency_matrix = np.ma.masked_array(
adjacency_matrix, np.abs(adjacency_matrix) < 0.5)
plot_connectome(masked_adjacency_matrix, node_coords,
**kwargs)
# sparse matrix support
sparse_adjacency_matrix = sparse.coo_matrix(adjacency_matrix)
plot_connectome(sparse_adjacency_matrix, node_coords,
**kwargs)
# 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(nan_adjacency_matrix, nan_node_coords, **kwargs)
