def test_reindex_no_resample():
node = Pipeline(steps=dummy_classifier)
node.resample = False
times = pd.date_range(start='2018-01-01', periods=10, freq='1s').values
data = np.arange(50).reshape(10, 5)
df = node._reindex(data, times, None)
assert np.array_equal(df.index.values, times)
def test_reindex_resample_infer_exception():
node = Pipeline(steps=dummy_classifier)
node.resample = True
node.resample_rate = None
times = np.array([
'2018-01-01T00:00:00.000000000',
'2018-01-01T00:00:02.250000000'
], dtype=np.datetime64)
data = np.arange(15).reshape(3, 5)
with pytest.raises(ValueError):
node._reindex(data, times, None)
def test_reindex_upsample_both_infer():
node = Pipeline(steps=dummy_classifier)
node.resample = True
node.resample_direction = 'both'
node.resample_rate = None
times = pd.date_range(start='2018-01-01', periods=3, freq='1s').values
data = np.arange(25).reshape(5, 5)
df = node._reindex(data, times, None)
expected = np.array([
'2017-12-31T23:59:59',
'2018-01-01T00:00:00',
'2018-01-01T00:00:01',
'2018-01-01T00:00:02',
'2018-01-01T00:00:03'
], dtype=np.datetime64)
assert np.array_equal(df.index.values, expected)
def test_reindex_resample_right_given():
node = Pipeline(steps=dummy_classifier)
node.resample = True
node.resample_direction = 'right'
node.resample_rate = 0.5
times = pd.date_range(start='2018-01-01', periods=10, freq='1s').values
data = np.arange(25).reshape(5, 5)
df = node._reindex(data, times, None)
expected = np.array([
'2018-01-01T00:00:00',
'2018-01-01T00:00:02',
'2018-01-01T00:00:04',
'2018-01-01T00:00:06',
'2018-01-01T00:00:08'
], dtype=np.datetime64)
assert np.array_equal(df.index.values, expected)