def test_color_different_size_input():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([0, 1, 2])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
t.map(resolution=2, overlap=0.3, clusterer=None)
with pytest.raises(ValueError):
t.color(target, dtype="categorical", ctype="rgb", normalized=False)
def test_color_dtype():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([[0], [0], [0], [1], [1], [1], [2], [2], [2]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
t.map(resolution=2, overlap=0.3, clusterer=None)
with pytest.raises(Exception):
t.color(target, dtype="somecategory", ctype="rgb", normalized=False)
def test_color_numerical_rgb():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([[0], [0], [0], [1], [1.1], [0.9], [2], [2], [2]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
clusterer = DBSCAN(eps=0.2, min_samples=3)
t.map(resolution=2, overlap=0.3, clusterer=clusterer)
t.color(target, dtype="numerical", ctype="rgb", normalized=False)
test_color = ['#0000b2', '#00b200', '#b2b200']
assert t.colorlist == test_color
def test_color_categorical_gray():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([[0], [0], [0], [1], [1], [1], [2], [2], [2]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
clusterer = DBSCAN(eps=0.2, min_samples=3)
t.map(resolution=2, overlap=0.3, clusterer=clusterer)
t.color(target, dtype="categorical", ctype="gray", normalized=False)
test_color = ['#dcdcdc', '#787878', '#464646']
assert t.colorlist == test_color