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mightyscape-1.1-deprecated/extensions/fablabchemnitz/networkx/drawing/tests/test_layout.py
2020-08-30 12:36:33 +02:00

344 lines
13 KiB
Python

"""Unit tests for layout functions."""
import pytest
numpy = pytest.importorskip('numpy')
test_smoke_empty_graphscipy = pytest.importorskip('scipy')
import pytest
import networkx as nx
from networkx.testing import almost_equal
class TestLayout(object):
@classmethod
def setup_class(cls):
cls.Gi = nx.grid_2d_graph(5, 5)
cls.Gs = nx.Graph()
nx.add_path(cls.Gs, 'abcdef')
cls.bigG = nx.grid_2d_graph(25, 25) # > 500 nodes for sparse
@staticmethod
def collect_node_distances(positions):
distances = []
prev_val = None
for k in positions:
if prev_val is not None:
diff = positions[k] - prev_val
distances.append(numpy.dot(diff, diff) ** 0.5)
prev_val = positions[k]
return distances
def test_spring_fixed_without_pos(self):
G = nx.path_graph(4)
pytest.raises(ValueError, nx.spring_layout, G, fixed=[0])
pos = {0: (1, 1), 2: (0, 0)}
pytest.raises(ValueError, nx.spring_layout, G, fixed=[0, 1], pos=pos)
nx.spring_layout(G, fixed=[0, 2], pos=pos) # No ValueError
def test_spring_init_pos(self):
# Tests GH #2448
import math
G = nx.Graph()
G.add_edges_from([(0, 1), (1, 2), (2, 0), (2, 3)])
init_pos = {0: (0.0, 0.0)}
fixed_pos = [0]
pos = nx.fruchterman_reingold_layout(G, pos=init_pos, fixed=fixed_pos)
has_nan = any(math.isnan(c) for coords in pos.values() for c in coords)
assert not has_nan, 'values should not be nan'
def test_smoke_empty_graph(self):
G = []
vpos = nx.random_layout(G)
vpos = nx.circular_layout(G)
vpos = nx.planar_layout(G)
vpos = nx.spring_layout(G)
vpos = nx.fruchterman_reingold_layout(G)
vpos = nx.spectral_layout(G)
vpos = nx.shell_layout(G)
vpos = nx.bipartite_layout(G, G)
vpos = nx.spiral_layout(G)
# FIXME vpos = nx.kamada_kawai_layout(G)
def test_smoke_int(self):
G = self.Gi
vpos = nx.random_layout(G)
vpos = nx.circular_layout(G)
vpos = nx.planar_layout(G)
vpos = nx.spring_layout(G)
vpos = nx.fruchterman_reingold_layout(G)
vpos = nx.fruchterman_reingold_layout(self.bigG)
vpos = nx.spectral_layout(G)
vpos = nx.spectral_layout(G.to_directed())
vpos = nx.spectral_layout(self.bigG)
vpos = nx.spectral_layout(self.bigG.to_directed())
vpos = nx.shell_layout(G)
vpos = nx.spiral_layout(G)
vpos = nx.kamada_kawai_layout(G)
vpos = nx.kamada_kawai_layout(G, dim=1)
def test_smoke_string(self):
G = self.Gs
vpos = nx.random_layout(G)
vpos = nx.circular_layout(G)
vpos = nx.planar_layout(G)
vpos = nx.spring_layout(G)
vpos = nx.fruchterman_reingold_layout(G)
vpos = nx.spectral_layout(G)
vpos = nx.shell_layout(G)
vpos = nx.spiral_layout(G)
vpos = nx.kamada_kawai_layout(G)
vpos = nx.kamada_kawai_layout(G, dim=1)
def check_scale_and_center(self, pos, scale, center):
center = numpy.array(center)
low = center - scale
hi = center + scale
vpos = numpy.array(list(pos.values()))
length = vpos.max(0) - vpos.min(0)
assert (length <= 2 * scale).all()
assert (vpos >= low).all()
assert (vpos <= hi).all()
def test_scale_and_center_arg(self):
sc = self.check_scale_and_center
c = (4, 5)
G = nx.complete_graph(9)
G.add_node(9)
sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5))
# rest can have 2*scale length: [-scale, scale]
sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c)
sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c)
sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c)
sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c)
sc(nx.spiral_layout(G, scale=2, center=c), scale=2, center=c)
sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c)
def test_planar_layout_non_planar_input(self):
G = nx.complete_graph(9)
pytest.raises(nx.NetworkXException, nx.planar_layout, G)
def test_smoke_planar_layout_embedding_input(self):
embedding = nx.PlanarEmbedding()
embedding.set_data({0: [1, 2], 1: [0, 2], 2: [0, 1]})
nx.planar_layout(embedding)
def test_default_scale_and_center(self):
sc = self.check_scale_and_center
c = (0, 0)
G = nx.complete_graph(9)
G.add_node(9)
sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5))
sc(nx.spring_layout(G), scale=1, center=c)
sc(nx.spectral_layout(G), scale=1, center=c)
sc(nx.circular_layout(G), scale=1, center=c)
sc(nx.shell_layout(G), scale=1, center=c)
sc(nx.spiral_layout(G), scale=1, center=c)
sc(nx.kamada_kawai_layout(G), scale=1, center=c)
def test_circular_planar_and_shell_dim_error(self):
G = nx.path_graph(4)
pytest.raises(ValueError, nx.circular_layout, G, dim=1)
pytest.raises(ValueError, nx.shell_layout, G, dim=1)
pytest.raises(ValueError, nx.shell_layout, G, dim=3)
pytest.raises(ValueError, nx.planar_layout, G, dim=1)
pytest.raises(ValueError, nx.planar_layout, G, dim=3)
def test_adjacency_interface_numpy(self):
A = nx.to_numpy_array(self.Gs)
pos = nx.drawing.layout._fruchterman_reingold(A)
assert pos.shape == (6, 2)
pos = nx.drawing.layout._fruchterman_reingold(A, dim=3)
assert pos.shape == (6, 3)
def test_adjacency_interface_scipy(self):
A = nx.to_scipy_sparse_matrix(self.Gs, dtype='d')
pos = nx.drawing.layout._sparse_fruchterman_reingold(A)
assert pos.shape == (6, 2)
pos = nx.drawing.layout._sparse_spectral(A)
assert pos.shape == (6, 2)
pos = nx.drawing.layout._sparse_fruchterman_reingold(A, dim=3)
assert pos.shape == (6, 3)
def test_single_nodes(self):
G = nx.path_graph(1)
vpos = nx.shell_layout(G)
assert not vpos[0].any()
G = nx.path_graph(4)
vpos = nx.shell_layout(G, [[0], [1, 2], [3]])
assert not vpos[0].any()
assert vpos[3].any() # ensure node 3 not at origin (#3188)
def test_smoke_initial_pos_fruchterman_reingold(self):
pos = nx.circular_layout(self.Gi)
npos = nx.fruchterman_reingold_layout(self.Gi, pos=pos)
def test_fixed_node_fruchterman_reingold(self):
# Dense version (numpy based)
pos = nx.circular_layout(self.Gi)
npos = nx.spring_layout(self.Gi, pos=pos, fixed=[(0, 0)])
assert tuple(pos[(0, 0)]) == tuple(npos[(0, 0)])
# Sparse version (scipy based)
pos = nx.circular_layout(self.bigG)
npos = nx.spring_layout(self.bigG, pos=pos, fixed=[(0, 0)])
for axis in range(2):
assert almost_equal(pos[(0, 0)][axis], npos[(0, 0)][axis])
def test_center_parameter(self):
G = nx.path_graph(1)
vpos = nx.random_layout(G, center=(1, 1))
vpos = nx.circular_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
vpos = nx.planar_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
vpos = nx.spring_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
vpos = nx.spectral_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
vpos = nx.shell_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
vpos = nx.spiral_layout(G, center=(1, 1))
assert tuple(vpos[0]) == (1, 1)
def test_center_wrong_dimensions(self):
G = nx.path_graph(1)
assert id(nx.spring_layout) == id(nx.fruchterman_reingold_layout)
pytest.raises(ValueError, nx.random_layout, G, center=(1, 1, 1))
pytest.raises(ValueError, nx.circular_layout, G, center=(1, 1, 1))
pytest.raises(ValueError, nx.planar_layout, G, center=(1, 1, 1))
pytest.raises(ValueError, nx.spring_layout, G, center=(1, 1, 1))
pytest.raises(ValueError, nx.spring_layout, G, dim=3, center=(1, 1))
pytest.raises(ValueError, nx.spectral_layout, G, center=(1, 1, 1))
pytest.raises(ValueError, nx.spectral_layout, G, dim=3, center=(1, 1))
pytest.raises(ValueError, nx.shell_layout, G, center=(1, 1, 1))
pytest.raises(ValueError, nx.spiral_layout, G, center=(1, 1, 1))
def test_empty_graph(self):
G = nx.empty_graph()
vpos = nx.random_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.circular_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.planar_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.bipartite_layout(G, G)
assert vpos == {}
vpos = nx.spring_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.fruchterman_reingold_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.spectral_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.shell_layout(G, center=(1, 1))
assert vpos == {}
vpos = nx.spiral_layout(G, center=(1, 1))
assert vpos == {}
def test_bipartite_layout(self):
G = nx.complete_bipartite_graph(3, 5)
top, bottom = nx.bipartite.sets(G)
vpos = nx.bipartite_layout(G, top)
assert len(vpos) == len(G)
top_x = vpos[list(top)[0]][0]
bottom_x = vpos[list(bottom)[0]][0]
for node in top:
assert vpos[node][0] == top_x
for node in bottom:
assert vpos[node][0] == bottom_x
vpos = nx.bipartite_layout(G, top,
align='horizontal',
center=(2, 2),
scale=2,
aspect_ratio=1)
assert len(vpos) == len(G)
top_y = vpos[list(top)[0]][1]
bottom_y = vpos[list(bottom)[0]][1]
for node in top:
assert vpos[node][1] == top_y
for node in bottom:
assert vpos[node][1] == bottom_y
pytest.raises(ValueError, nx.bipartite_layout, G, top, align='foo')
def test_kamada_kawai_costfn_1d(self):
costfn = nx.drawing.layout._kamada_kawai_costfn
pos = numpy.array([4.0, 7.0])
invdist = 1 / numpy.array([[0.1, 2.0], [2.0, 0.3]])
cost, grad = costfn(pos, numpy, invdist, meanweight=0, dim=1)
assert almost_equal(cost, ((3 / 2.0 - 1) ** 2))
assert almost_equal(grad[0], -0.5)
assert almost_equal(grad[1], 0.5)
def test_kamada_kawai_costfn_2d(self):
costfn = nx.drawing.layout._kamada_kawai_costfn
pos = numpy.array([[1.3, -3.2],
[2.7, -0.3],
[5.1, 2.5]])
invdist = 1 / numpy.array([[0.1, 2.1, 1.7],
[2.1, 0.2, 0.6],
[1.7, 0.6, 0.3]])
meanwt = 0.3
cost, grad = costfn(pos.ravel(), numpy, invdist,
meanweight=meanwt, dim=2)
expected_cost = 0.5 * meanwt * numpy.sum(numpy.sum(pos, axis=0) ** 2)
for i in range(pos.shape[0]):
for j in range(i + 1, pos.shape[0]):
diff = numpy.linalg.norm(pos[i] - pos[j])
expected_cost += (diff * invdist[i][j] - 1.0) ** 2
assert almost_equal(cost, expected_cost)
dx = 1e-4
for nd in range(pos.shape[0]):
for dm in range(pos.shape[1]):
idx = nd * pos.shape[1] + dm
pos0 = pos.flatten()
pos0[idx] += dx
cplus = costfn(pos0, numpy, invdist,
meanweight=meanwt, dim=pos.shape[1])[0]
pos0[idx] -= 2 * dx
cminus = costfn(pos0, numpy, invdist,
meanweight=meanwt, dim=pos.shape[1])[0]
assert almost_equal(grad[idx], (cplus - cminus) / (2 * dx),
places=5)
def test_spiral_layout(self):
G = self.Gs
# a lower value of resolution should result in a more compact layout
# intuitively, the total distance from the start and end nodes
# via each node in between (transiting through each) will be less,
# assuming rescaling does not occur on the computed node positions
pos_standard = nx.spiral_layout(G, resolution=0.35)
pos_tighter = nx.spiral_layout(G, resolution=0.34)
distances = self.collect_node_distances(pos_standard)
distances_tighter = self.collect_node_distances(pos_tighter)
assert sum(distances) > sum(distances_tighter)
# return near-equidistant points after the first value if set to true
pos_equidistant = nx.spiral_layout(G, equidistant=True)
distances_equidistant = self.collect_node_distances(pos_equidistant)
for d in range(1, len(distances_equidistant) - 1):
# test similarity to two decimal places
assert almost_equal(
distances_equidistant[d],
distances_equidistant[d+1],
2
)