def test_non_unique_names(self):
with pytest.raises(ValueError):
_ = Lineage(
np.random.random((10, 3, 1)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), "#ffffff", "#ff00FF"],
)
def test_correct_names_to_ixs(self):
x = np.random.random((10, 3))
l = Lineage(x, names=["foo", "bar", "baz"])
y = l[["baz", "bar"]]
assert y._names_to_ixs == {"baz": 0, "bar": 1}
def test_automatic_color_assignment(self):
x = np.random.random((10, 3))
l = Lineage(x, names=["foo", "bar", "baz"])
gt_colors = [colors.to_hex(c) for c in _create_categorical_colors(3)]
np.testing.assert_array_equal(l.colors, gt_colors)
def test_wrong_colors(self):
with pytest.raises(ValueError):
_ = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), "foobar"],
)
def test_rest_all(self):
x = Lineage(np.random.random((10, 4)), names=["foo", "bar", "baz", "quux"])
y = x[[Lin.REST]]
assert y.shape == (10, 1)
np.testing.assert_array_equal(y.X[:, 0], np.sum(x.X, axis=1))
np.testing.assert_array_equal(y.names, ["rest"])
def test_wrong_number_of_dimensions(self):
with pytest.raises(ValueError):
_ = Lineage(
np.random.random((10, 3, 1)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), "#ffffff", "#ff00FF"],
)
def test_colors_length_mismatch(self):
with pytest.raises(ValueError):
_ = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5)],
)
def test_creation(self):
x = np.random.random((10, 3))
names = ["foo", "bar", "baz"]
colors = ["#000000", "#ababab", "#ffffff"]
l = Lineage(x, names=names, colors=colors)
np.testing.assert_array_equal(l, x)
np.testing.assert_array_equal(l.names, np.array(names))
np.testing.assert_array_equal(l.colors, np.array(colors))
def test_names_setter_wrong_type(self):
l = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
with pytest.raises(TypeError):
l.names = ["foo1", "bar1", 3]
def test_color_setter_wrong_colors(self):
l = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
with pytest.raises(ValueError):
l.colors = ["#ffffff", "#ffffff", "foo"]
def test_others_no_effect(self):
names = ["foo", "bar", "baz", "quux"]
x = Lineage(np.random.random((10, 4)), names=names)
y = x[names + [Lin.OTHERS]]
np.testing.assert_array_equal(x.X, y.X)
np.testing.assert_array_equal(x.names, y.names)
np.testing.assert_array_equal(x.colors, y.colors)
def test_names_setter_non_unique(self):
l = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
with pytest.raises(ValueError):
l.names = ["foo1", "bar1", "bar1"]
def test_others(self):
names = ["foo", "bar", "baz", "quux"]
x = Lineage(np.random.random((10, 4)), names=names)
y = x[["foo, baz"] + [Lin.OTHERS]]
expected = x[["foo, baz", "bar", "quux"]]
np.testing.assert_array_equal(y.X, expected.X)
np.testing.assert_array_equal(y.names, expected.names)
np.testing.assert_array_equal(y.colors, expected.colors)
def test_row_subset(self):
x = Lineage(np.random.random((10, 4)), names=["foo", "bar", "baz", "quux"])
y = x[:5, ["foo, bar"]]
expected = np.sum(x.X[:5, [0, 1]], axis=1)[..., np.newaxis]
assert y.shape == (5, 1)
np.testing.assert_array_equal(y.X, expected)
np.testing.assert_array_equal(y.names, ["bar or foo"])
np.testing.assert_array_equal(y.colors, [_compute_mean_color(x.colors[:2])])
def test_column_subset_boolean_invalid_dim(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
with pytest.raises(IndexError):
y = l[:, [True]]
def test_same_names(self):
x = Lineage(np.random.random((10, 4)), names=["foo", "bar", "baz", "quux"])
y = x[np.arange(len(x)), ["foo"] * len(x)]
expected = x["foo"]
assert y.shape == (10, 1)
np.testing.assert_array_equal(y.X, expected.X)
np.testing.assert_array_equal(y.names, ["mixture"])
np.testing.assert_array_equal(y.colors, ["#000000"])
def test_column_invalid_name(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
with pytest.raises(KeyError):
y = l["quux"]
def test_reordering(self):
x = np.random.random((10, 3))
l = Lineage(
x, names=["foo", "bar", "baz"], colors=["#ff0000", "#00ff00", "#0000ff"]
)
y = l[["baz", "bar", "foo"]]
np.testing.assert_array_equal(y.names, ["baz", "bar", "foo"])
np.testing.assert_array_equal(y.colors, ["#0000ff", "#00ff00", "#ff0000"])
def test_colors_setter(self):
l = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
colors = ["#ffffff", "#ffffff", "#ffffff"]
l.colors = colors
np.testing.assert_array_equal(l.colors, np.array(colors))
def test_subset_same_instance(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[0, 0]
assert isinstance(y, Lineage)
def test_singleton_column_name(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[:, "foo"]
np.testing.assert_array_equal(x[:, 0], np.array(y)[:, 0])
def test_remove_duplicates(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[0, ["foo", 2, "bar", 0, 0, "foo"]]
np.testing.assert_array_equal(x[[[0]], [0, 2, 1]], np.array(y))
def test_comb_row_int_col_names(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[[0, 1], "baz"]
np.testing.assert_array_equal(x[[0, 1], :][:, [2]], np.array(y))
def test_column_subset_with_names(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[:, ["foo", "bar"]]
np.testing.assert_array_equal(x[:, [0, 1]], np.array(y))
def test_row_subset_with_ints(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[[1, 2, 3], :]
np.testing.assert_array_equal(x[[1, 2, 3], :], np.array(y))
def test_column_subset_boolean(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l[:, [False, False, True]]
np.testing.assert_array_equal(x[:, -1], y.X.squeeze())
def test_same_indices(self):
x = Lineage(np.random.random((10, 4)), names=["foo", "bar", "baz", "quux"])
half = len(x) // 2
y = x[[0] * len(x), ["foo"] * half + ["bar"] * half]
expected = np.array([x[0, "foo"].X[0, 0]] * half + [x[0, "bar"].X[0, 0]] * half)
assert y.shape == (10, 1)
np.testing.assert_array_equal(y.X.squeeze(), expected)
np.testing.assert_array_equal(y.names, ["mixture"])
np.testing.assert_array_equal(y.colors, ["#000000"])
def test_names_setter(self):
l = Lineage(
np.random.random((10, 3)),
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
names = ["foo1", "bar1", "baz1"]
l.names = names
np.testing.assert_array_equal(l.names, np.array(names))
def test_no_mixing(self):
x = Lineage(np.random.random((10, 4)), names=["foo", "bar", "baz", "quux"])
y = x[["foo", Lin.REST]]
expected = np.c_[np.sum(x.X[:, [0]], axis=1), np.sum(x.X[:, [1, 2, 3]], axis=1)]
assert y.shape == (10, 2)
np.testing.assert_array_equal(y.X, expected)
np.testing.assert_array_equal(y.names, ["foo", "rest"])
np.testing.assert_array_equal(
y.colors, [x.colors[0], _compute_mean_color(x.colors[1:])]
)
def test_singleton_column_first_index_assignment(self):
x = np.random.random((10, 3))
l = Lineage(
x,
names=["foo", "bar", "baz"],
colors=[(0, 0, 0), (0.5, 0.5, 0.5), (1, 1, 1)],
)
y = l["baz"]
np.testing.assert_array_equal(x[:, 2], np.array(y)[:, 0])
np.testing.assert_array_equal(y.names, ["baz"])