def compute_edge_colors(G):
"""
Compute the edge colors.
NOTE: This function is copied from the nxviz package!
"""
data = [G.graph.edges[n][G.edge_color] for n in G.edges]
data_reduced = sorted(list(set(data)))
dtype = infer_data_type(data)
n_grps = num_discrete_groups(data)
if dtype == "categorical" or dtype == "ordinal":
if n_grps <= 8:
cmap = get_cmap(cmaps["Accent_{0}".format(n_grps)].mpl_colormap)
else:
cmap = n_group_colorpallet(n_grps)
elif dtype == "continuous" and not is_data_diverging(data):
cmap = get_cmap(cmaps["weights"])
for d in data:
idx = data_reduced.index(d) / n_grps
G.edge_colors.append(cmap(idx))
# Add colorbar if required.
if len(data_reduced) > 1 and dtype == "continuous":
G.sm = plt.cm.ScalarMappable(
cmap=cmap,
norm=plt.Normalize(
vmin=min(data_reduced),
vmax=max(data_reduced),
),
)
G.sm._A = []
return cmap
def test_binomial():
assert infer_data_type(binomial) == "categorical"
assert infer_data_type(binomial_float) == "categorical"
assert infer_data_type(binomial_integer) == "categorical"
def test_infer_data_type():
assert infer_data_type(categorical) == "categorical"
assert infer_data_type(ordinal) == "ordinal"
assert infer_data_type(continuous) == "continuous"
def test_unknown_data_type():
with pytest.raises(ValueError):
infer_data_type(unknown_type)
def test_infer_data_type():
assert infer_data_type(categorical) == 'categorical'
assert infer_data_type(ordinal) == 'ordinal'
assert infer_data_type(continuous) == 'continuous'
