def test_interpolate_on_linear_data():
linear_data = pd.DataFrame(
[
[0.1],
[0.2],
[0.3],
[0.4],
[0.5],
[0.6],
[0.7],
[0.8],
[0.9],
],
[
pd.Timestamp('2018-01-01 00:00:00.000'),
pd.Timestamp('2018-01-01 00:00:00.100'),
pd.Timestamp('2018-01-01 00:00:00.200'),
pd.Timestamp('2018-01-01 00:00:00.300'),
pd.Timestamp('2018-01-01 00:00:00.400'),
pd.Timestamp('2018-01-01 00:00:00.500'),
pd.Timestamp('2018-01-01 00:00:00.600'),
pd.Timestamp('2018-01-01 00:00:00.700'),
pd.Timestamp('2018-01-01 00:00:00.800'),
],
['A']
)
linear_data_with_nan = linear_data.copy()
linear_data_with_nan.iloc[[2, 3, 5]] = np.NaN
interpolate = Interpolate(rate=10, method='linear')
interpolate.i.data = linear_data_with_nan
interpolate.update()
pd.testing.assert_frame_equal(interpolate.o.data, linear_data.iloc[:-1])
def test_data_not_monotonic(caplog):
data = pd.DataFrame(
[[0, 0], [0, 0]],
[pd.Timestamp('2018-01-01 00:00:00.100'), pd.Timestamp('2018-01-01 00:00:00.000')]
)
node = Interpolate(rate=rate)
node.i.data = data
node.update()
assert caplog.record_tuples[0][2] == 'Data index should be strictly monotonic'
def test_interpolate_on_data_no_jitter():
data = dummy_data_no_jitter
data.reset()
interpolate = Interpolate(rate=rate, method='linear')
looper = Looper(data, interpolate)
dejittered_data, _ = looper.run(chunk_size=8)
pd.testing.assert_frame_equal(dejittered_data, data._data.iloc[:-1])
def test_data_not_monotonic():
data = dummy_data_not_monotonic
node = Interpolate(rate=rate)
looper = Looper(data, node)
with pytest.raises(WorkerInterrupt):
_, _ = looper.run(chunk_size=8)