def test_1d_array(self):
# input arrays should be flat
bad_input = np.array([3, 2, 1, 3, 2]).reshape(5, 1)
good_input = np.array([32, 20, 10, 3, 2])
with pytest.raises(ValueError):
_align_to(bad_input, good_input)
_align_to(good_input, bad_input)
def test_events_before_first_aligning_event(self):
a = np.arange(0, 10, 1)
b = np.arange(3.5, 8.5, 1)
good_output = np.array(
[np.nan, np.nan, np.nan, np.nan, 0.5, 0.5, 0.5, 0.5, 0.5, 1.5])
actual_output = _align_to(a, b)
np.testing.assert_allclose(actual_output, good_output)
def test_list_input(self):
bad_input_1 = [3, 2, 1, 3, 2]
bad_input_2 = [2, 3, 10, 2]
good_input = np.array(bad_input_1)
# one bad input
with pytest.raises(TypeError):
_align_to(bad_input_1, good_input)
_align_to(good_input, bad_input_2)
# one two bad inputs
with pytest.raises(TypeError):
_align_to(bad_input_1, bad_input_2)
def test_no_beyond(self):
a = np.arange(0, 10, 2)
b = np.arange(1.1, 3, 0.5)
expected = np.array([np.nan, 0.4, np.nan, np.nan, np.nan])
actual = _align_to(a, b, no_beyond=True)
np.testing.assert_allclose(actual, expected)
def test_normal_case(self):
a = np.arange(0, 10, 2)
b = np.arange(0.6, 8, 2)
expected = np.array([np.nan, 1.4, 1.4, 1.4, 1.4])
actual = _align_to(a, b)
np.testing.assert_allclose(actual, expected)
def test_two_scaler_input(self):
bad_input_1 = 1
bad_input_2 = 2
with pytest.raises(TypeError):
_align_to(to_be_aligned=bad_input_1, to_align_to=bad_input_2)