Add param "isconstant" to stumpy/mmotifs.py (#877)

* pass the optional argument to ensure correct output when isconstant is provided by user

* add param isconstant to mmotifs

* add naive test function

* Update test_mmotifs.py

Author: NimaSarajpoor

stumpy/mmotifs.py

@@ -12,7 +12,13 @@
 from .mstump import mdl
 
 
-@core.non_normalized(aamp_mmotifs)
+@core.non_normalized(
+    aamp_mmotifs,
+    exclude=[
+        "normalize",
+        "T_subseq_isconstant",
+    ],
+)
 def mmotifs(
     T,
     P,
@@ -27,6 +33,7 @@ def mmotifs(
     include=None,
     normalize=True,
     p=2.0,
+    T_subseq_isconstant=None,
 ):
     """
     Discover the top motifs for the multi-dimensional time series `T`
@@ -92,6 +99,15 @@ def mmotifs(
         and the Euclidean distance, respectively. This parameter is ignored when
         `normalize == True`.
 
+    T_subseq_isconstant : numpy.ndarray, function, or list, default None
+        A parameter that is used to show whether a subsequence of a time series in `T`
+        is constant (True) or not. T_subseq_isconstant can be a 2D boolean numpy.ndarry
+        or a function that can be applied to each time series in `T`. Alternatively, for
+        maximum flexibility, a list (with length equal to the total number of time
+        series) may also be used. In this case, T_subseq_isconstant[i] corresponds to
+        the i-th time series T[i] and each element in the list can either be a 1D
+        boolean np.ndarray, a function, or None.
+
     Returns
     -------
     motif_distances: numpy.ndarray
@@ -154,7 +170,9 @@ def mmotifs(
         warnings.warn(msg)
         max_motifs = 1
 
-    T, M_T, Σ_T, T_subseq_isconstant = core.preprocess(T, m)
+    T, M_T, Σ_T, T_subseq_isconstant = core.preprocess(
+        T, m, T_subseq_isconstant=T_subseq_isconstant
+    )
     P = P.copy()
 
     excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))
@@ -214,6 +232,9 @@ def mmotifs(
             normalize=normalize,
             p=p,
             T_subseq_isconstant=T_subseq_isconstant[subspace_k],
+            Q_subseq_isconstant=np.expand_dims(
+                T_subseq_isconstant[subspace_k, motif_idx], axis=1
+            ),
         )
 
         if len(query_matches) > min_neighbors:

tests/test_mmotifs.py

@@ -193,3 +193,37 @@ def test_mmotifs_two_motif_pairs_max_motifs_2(T):
     npt.assert_array_almost_equal(motif_indices_ref, motif_indices_cmp)
     npt.assert_array_almost_equal(motif_subspaces_ref, motif_subspaces_cmp)
     npt.assert_array_almost_equal(motif_mdls_ref, motif_mdls_cmp)
+
+
+@pytest.mark.parametrize("T", test_data)
+def test_mmotifs_with_default_parameters_with_isconstant(T):
+    motif_distances_ref = np.array([[0.0000000e00, 1.1151008e-07]])
+    motif_indices_ref = np.array([[2, 9]])
+    motif_subspaces_ref = [np.array([1])]
+    motif_mdls_ref = [np.array([232.0, 250.57542476, 260.0, 271.3509059])]
+
+    m = 4
+    excl_zone = int(np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM))
+
+    # The following `T_subseq_isconstant` is basically equivalent to
+    # `T_subseq_isconstant=None` (default). The goal is to test its
+    # functionality.
+    T_subseq_isconstant = [
+        None,
+        naive.rolling_isconstant(T[1], m),
+        None,
+        naive.is_ptp_zero_1d,
+    ]
+
+    P, I = naive.mstump(T, m, excl_zone, T_subseq_isconstant=T_subseq_isconstant)
+    (
+        motif_distances_cmp,
+        motif_indices_cmp,
+        motif_subspaces_cmp,
+        motif_mdls_cmp,
+    ) = mmotifs(T, P, I, T_subseq_isconstant=T_subseq_isconstant)
+
+    npt.assert_array_almost_equal(motif_distances_ref, motif_distances_cmp)
+    npt.assert_array_almost_equal(motif_indices_ref, motif_indices_cmp)
+    npt.assert_array_almost_equal(motif_subspaces_ref, motif_subspaces_cmp)
+    npt.assert_array_almost_equal(motif_mdls_ref, motif_mdls_cmp)