def test_rest(self):
x = Lineage(np.random.random((10, 4)), names=["foo", "bar", "baz", "quux"])
y = x[["foo, bar", Lin.REST]]
expected = np.c_[np.sum(x.X[:, [0, 1]], axis=1), np.sum(x.X[:, [2, 3]], axis=1)]
assert y.shape == (10, 2)
np.testing.assert_array_equal(y.X, expected)
np.testing.assert_array_equal(y.names, ["bar or foo", "rest"])
np.testing.assert_array_equal(
y.colors,
[_compute_mean_color(x.colors[:2]), _compute_mean_color(x.colors[2:])],
)
def update_entries(key):
if key:
res.append(self[rows, key].X.sum(1))
# item = (key, rows) if self._is_transposed else (rows, key)
# res.append(self[item].X.sum(int(not self._is_transposed)))
names.append(" or ".join(self.names[key]))
colors.append(_compute_mean_color(self.colors[key]))
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_return_colors(self):
x = pd.Series(["b", "c", "a", "d", "a"]).astype("category")
expected = pd.Series(
["a or b", "c or d", "a or b", "c or d",
"a or b"]).astype("category")
res, colors = _process_series(x,
keys=["b, a", "d, c"],
colors=["red", "green", "blue", "white"])
assert isinstance(res, pd.Series)
assert is_categorical_dtype(res)
assert isinstance(colors, list)
np.testing.assert_array_equal(res.values, expected.values)
assert set(colors) == {
_compute_mean_color(["red", "green"]),
_compute_mean_color(["blue", "white"]),
}
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:])]
)