def __init__(self, T, m, excl_zone=None, egress=True):
"""
Initialize the `stumpi` object
Parameters
----------
T : ndarray
The time series or sequence for which the unnormalized matrix profile and
matrix profile indices will be returned
m : int
Window size
excl_zone : int, default None
The half width for the exclusion zone relative to the current
sliding window
egress : bool, default True
If set to `True`, the oldest data point in the time series is removed and
the time series length remains constant rather than forever increasing
"""
self._T = T.copy()
self._T = np.asarray(self._T)
core.check_dtype(self._T)
self._m = m
self._n = self._T.shape[0]
if excl_zone is not None: # pragma: no cover
self._excl_zone = excl_zone
else:
self._excl_zone = int(np.ceil(self._m / 4))
self._egress = egress
mp = stumpy.aamp(self._T, self._m)
self._P = mp[:, 0]
self._I = mp[:, 1]
self._left_I = mp[:, 2]
self._left_P = np.empty(self._P.shape)
self._left_P[:] = np.inf
self._T_isfinite = np.isfinite(self._T)
self._T, self._T_subseq_isfinite = core.preprocess_non_normalized(
self._T, self._m)
self._T_squared = np.sum(core.rolling_window(self._T * self._T,
self._m),
axis=1)
# Retrieve the left matrix profile values
for i, j in enumerate(self._left_I):
if j >= 0:
D = core.mass_absolute(self._T[i:i + self._m],
self._T[j:j + self._m])
self._left_P[i] = D[0]
Q = self._T[-m:]
self._QT = core.sliding_dot_product(Q, self._T)
if self._egress:
self._QT_new = np.empty(self._QT.shape[0])
self._n_appended = 0
def test_check_dtype_float64():
assert core.check_dtype(np.random.rand(10))
def test_check_dtype_float32():
assert core.check_dtype(np.random.rand(10).astype(np.float32))
def test_df_to_array():
a = np.random.rand(10)
assert core.check_dtype(core.df_to_array(a))
df = pd.Series(a)
assert core.check_dtype(core.df_to_array(df))
def test_check_bad_dtype():
for dtype in [np.int32, np.int64, np.float32]:
with pytest.raises(TypeError):
core.check_dtype(np.random.rand(10).astype(dtype))
