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def test_is_data_diverging():
assert is_data_diverging(diverging_ordinal)
assert is_data_diverging(diverging_continuous)
assert not is_data_diverging(ordinal)
assert not is_data_diverging(continuous)
def test_is_data_diverging():
assert is_data_diverging(diverging_ordinal)
assert is_data_diverging(diverging_continuous)
assert not is_data_diverging(ordinal)
assert not is_data_diverging(continuous)
def test_is_data_diverging():
assert is_data_diverging(diverging_ordinal)
assert is_data_diverging(diverging_continuous)
assert not is_data_diverging(ordinal)
assert not is_data_diverging(continuous)
def test_is_data_diverging():
assert is_data_diverging(diverging_ordinal)
assert is_data_diverging(diverging_continuous)
assert not is_data_diverging(ordinal)
assert not is_data_diverging(continuous)
if self.group_order == "alphabetically":
data_reduced = sorted(list(set(data)))
elif self.group_order == "default":
data_reduced = list(unique_everseen(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["continuous"].mpl_colormap)
elif dtype == "continuous" and is_data_diverging(data):
cmap = get_cmap(cmaps["diverging"].mpl_colormap)
for d in data:
idx = data_reduced.index(d) / n_grps
self.node_colors.append(cmap(idx))
# Add colorbar if required.ListedColormap
logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
logging.debug("dtype: {0}".format(dtype))
if len(data_reduced) > 1 and dtype == "continuous":
self.sm = plt.cm.ScalarMappable(
cmap=cmap,
norm=plt.Normalize(
vmin=min(data_reduced),
def compute_edge_colors(self):
"""Compute the edge colors."""
data = [self.graph.edges[n][self.edge_color] for n in self.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
self.edge_colors.append(cmap(idx))
# Add colorbar if required.
logging.debug("length of data_reduced: {0}".format(len(data_reduced)))
logging.debug("dtype: {0}".format(dtype))
if len(data_reduced) > 1 and dtype == "continuous":
self.sm = plt.cm.ScalarMappable(
cmap=cmap,
norm=plt.Normalize(
vmin=min(data_reduced),
vmax=max(data_reduced), # noqa # noqa
),
)