def test_acf_1d(trigonometric, nlags):
_acf = timeseries.acf(trigonometric.sin, nlags=nlags)
# calculate default nlags
if nlags is None:
nlags = int(0.1 * (len(trigonometric.t) / 2 - 1))
assert len(_acf) == nlags
assert_almost_equal(_acf[0], 1)
assert_almost_equal(trigonometric.cos[:nlags], _acf, decimal=1)
def test_acf_col_var(trigonometric):
nlags = 100
sin = np.vstack((trigonometric.sin for _ in range(5)))
_acf = timeseries.acf(sin.T, row_var=False, nlags=nlags)
cos = np.vstack((trigonometric.cos[:nlags] for _ in range(5)))
assert_equal(_acf.shape, (nlags, 5))
assert_almost_equal(_acf[0, :], np.ones(5))
assert_almost_equal(cos, _acf.T, decimal=1)
def test_acf_2d(trigonometric, nlags):
sin = np.vstack((trigonometric.sin for _ in range(5)))
_acf = timeseries.acf(sin, row_var=True, nlags=nlags)
# calculate default nlags
if nlags is None:
nlags = int(0.1 * (len(trigonometric.t) / 2 - 1))
cos = np.vstack((trigonometric.cos[:nlags] for _ in range(5)))
assert_equal(_acf.shape, (5, nlags))
assert_almost_equal(_acf[:, 0], np.ones(5))
assert_almost_equal(cos, _acf, decimal=1)
def test_acf_DataFrame(trigonometric, nlags):
sin = pd.DataFrame(np.vstack((trigonometric.sin for _ in range(5))).T)
_acf = timeseries.acf(sin, nlags=nlags)
# calculate default nlags
if nlags is None:
nlags = int(0.1 * (len(trigonometric.t) / 2 - 1))
cos = np.vstack((trigonometric.cos[:nlags] for _ in range(5))).T
assert_equal(_acf.shape, (nlags, 5))
assert_almost_equal(_acf.iloc[0], np.ones(5))
assert_almost_equal(cos, _acf.values, decimal=1)
def test_nlags(trigonometric):
acf = timeseries.acf(trigonometric.sin, 10)
assert len(acf) == 10
def test_demean(trigonometric):
acf = timeseries.acf(trigonometric.sin + 10, 10, demean=False, norm=False)
acf2 = timeseries.acf(trigonometric.sin, 10, norm=False)
assert_almost_equal(acf - 10**2, acf2)
def test_norm(trigonometric):
acf = timeseries.acf(trigonometric.sin, 10, norm=True)
assert acf[0] == 1.0
acf = timeseries.acf(trigonometric.sin, 10, norm=False)
assert_almost_equal(acf[0], 0.49995)