def test_color_values_size(self):
nodes = {"a": [1, 2, 3], "b": [4, 5, 6, 7, 8, 9]}
graph = {"nodes": nodes}
n_samples = np.max([i for s in graph["nodes"].values() for i in s]) + 1
color_values = np.arange(n_samples)
color_values = _scale_color_values(color_values)
assert len(color_values) == len(nodes["a"]) + len(nodes["b"]) + 1
def test_color_values_type(self):
nodes = {"a": [1, 2, 3], "b": [4, 5, 6]}
graph = {"nodes": nodes}
n_samples = np.max([i for s in graph["nodes"].values() for i in s]) + 1
color_values = np.arange(n_samples)
color_values = _scale_color_values(color_values)
assert type(color_values) == np.ndarray
assert min(color_values) == 0
assert max(color_values) == 1
def test_color_values_scaled(self):
nodes = {"a": [1, 2, 3], "b": [4, 5, 6]}
graph = {"nodes": nodes}
cv = np.array([6, 5, 4, 3, 2, 1])
color_values = _scale_color_values(cv)
# np.testing.assert_almost_equal(min(color_values), 0)
# np.testing.assert_almost_equal(
# max(color_values), 1
# ), "Scaler might have floating point issues, 1.0000...0002"
# _build_histogram in visuals.py assumes/needs this
assert min(color_values) == 0
assert max(color_values) == 1
def test_node_averages_multiple_color_value_vectors(self):
nodes = {"a": [0, 1, 2], "b": [3, 4, 5]}
graph = {"nodes": nodes, "links": {}}
n_samples = np.max([i for s in graph["nodes"].values() for i in s]) + 1
color_values_1 = np.arange(n_samples)
color_values_2 = np.flip(color_values_1)
color_values = np.column_stack((color_values_1, color_values_2))
color_values = _scale_color_values(color_values)
# (Pdb) color_values
# array([[0. , 1. ],
# [0.2, 0.8],
# [0.4, 0.6],
# [0.6, 0.4],
# [0.8, 0.2],
# [1. , 0. ]])
X = np.arange(n_samples).reshape(-1, 1)
lens = np.copy(X)
graph_data = _format_mapper_data(
graph=graph,
color_values=color_values,
node_color_function="mean",
X=None,
X_names=[],
lens=lens,
lens_names=[],
custom_tooltips=None,
)
nodes = {node["name"]: node for node in graph_data["nodes"]}
assert len(nodes["a"]["color"][0]) == 2
assert np.array(nodes["a"]["tooltip"]["histogram"]).shape[0] == 2
np.testing.assert_almost_equal(np.array([0.2, 0.8]),
nodes["a"]["color"][0])
np.testing.assert_almost_equal(np.array([0.8, 0.2]),
nodes["b"]["color"][0])
def test__scale_color_values_many_columns(self):
cv1 = np.array([6, 5, 4, 3, 2, 1])
cv2 = np.array([1, 2, 3, 4, 5, 6])
cv = np.column_stack([cv1, cv2])
color_values = _scale_color_values(cv)
assert color_values.shape[1] == 2
