def test_more_map_operations(self):
transformed = TubSequence.build_pipeline(
self.sequence,
x_transform=lambda record: record.underlying['user/angle'],
y_transform=lambda record: record.underlying['user/throttle'])
transformed_2 = TubSequence.build_pipeline(
self.sequence,
x_transform=lambda record: record.underlying['user/angle'] * 2,
y_transform=lambda record: record.underlying['user/throttle'] * 2)
transformed_3 = TubSequence.map_pipeline(
x_transform=lambda x: x,
y_transform=lambda y: y,
pipeline=transformed_2
)
self.assertEqual(len(transformed), size)
self.assertEqual(len(transformed_2), size)
self.assertEqual(len(transformed_3), size)
transformed_list = list(transformed)
transformed_list_2 = list(transformed_3)
index = np.random.randint(0, 9)
x1, y1 = transformed_list[index]
x2, y2 = transformed_list_2[index]
self.assertAlmostEqual(x1 * 2, x2)
self.assertAlmostEqual(y1 * 2, y2)
def test_iterator_consistency(self):
extract = TubSequence.build_pipeline(
self.sequence,
x_transform=lambda record: record.underlying['user/angle'],
y_transform=lambda record: record.underlying['user/throttle'])
# iterate twice through half the data
r1 = list()
r2 = list()
for r in r1, r2:
iterator = iter(extract)
for i in range(size // 2):
r.append(next(iterator))
self.assertEqual(r1, r2)
# now transform and iterate through pipeline twice to see iterator
# doesn't exhaust
transformed = TubSequence.map_pipeline(
x_transform=lambda x: 2 * x,
y_transform=lambda y: 3 * y,
pipeline=extract)
l1 = list(transformed)
l2 = list(transformed)
self.assertEqual(l1, l2)
for e, t in zip(extract, transformed):
ex, ey = e
tx, ty = t
self.assertAlmostEqual(2 * ex, tx)
self.assertAlmostEqual(3 * ey, ty)