Updated docstring with "z-normalized"

Author: seanlaw

stumpy/gpu_mpdist.py

@@ -11,9 +11,7 @@
 
 def gpu_mpdist(T_A, T_B, m, percentage=0.05, k=None, device_id=0):
     """
-    Compute the non-normalized (i.e., without z-normalization) matrix profile distance
-    (MPdist) measure between any two time series with one or more GPU devices and
-    `stumpy.gpu_stump`.
+    Compute the z-normalized matrix profile distance (MPdist) measure between any two time series with one or more GPU devices
 
     The MPdist distance measure considers two time series to be similar if they share
     many subsequences, regardless of the order of matching subsequences. MPdist

stumpy/gpu_ostinato.py

@@ -8,7 +8,8 @@
 
 def gpu_ostinato(Ts, m, device_id=0):
     """
-    Find the consensus motif of multiple time series with one or more GPU devices
+    Find the z-normalized consensus motif of multiple time series with one or more GPU
+    devices
 
     This is a wrapper around the vanilla version of the ostinato algorithm
     which finds the best radius and a helper function that finds the most

stumpy/mpdist.py

@@ -288,8 +288,8 @@ def _mpdist_vect(
 
 def mpdist(T_A, T_B, m, percentage=0.05, k=None):
     """
-    Compute the matrix profile distance (MPdist) measure between any two time series
-    with `stumpy.stump`.
+    Compute the  z-normalized matrix profile distance (MPdist) measure between any two
+    time series
 
     The MPdist distance measure considers two time series to be similar if they share
     many subsequences, regardless of the order of matching subsequences. MPdist
@@ -330,8 +330,8 @@ def mpdist(T_A, T_B, m, percentage=0.05, k=None):
 
 def mpdisted(dask_client, T_A, T_B, m, percentage=0.05, k=None):
     """
-    Compute the matrix profile distance (MPdist) measure between any two time series
-    with a distributed dask cluster and `stumpy.stumped`.
+    Compute the z-normalized matrix profile distance (MPdist) measure between any two
+    time series with a distributed dask cluster
 
     The MPdist distance measure considers two time series to be similar if they share
     many subsequences, regardless of the order of matching subsequences. MPdist

stumpy/ostinato.py

@@ -245,7 +245,7 @@ def _ostinato(Ts, m, M_Ts, Σ_Ts, dask_client=None, device_id=None, mp_func=stum
 
 def ostinato(Ts, m):
     """
-    Find the consensus motif of multiple time series
+    Find the z-normalized consensus motif of multiple time series
 
     This is a wrapper around the vanilla version of the ostinato algorithm
     which finds the best radius and a helper function that finds the most
@@ -309,7 +309,8 @@ def ostinato(Ts, m):
 
 def ostinatoed(dask_client, Ts, m):
     """
-    Find the consensus motif of multiple time series with a distributed dask cluster
+    Find the z-normalized consensus motif of multiple time series with a distributed
+    dask cluster
 
     This is a wrapper around the vanilla version of the ostinato algorithm
     which finds the best radius and a helper function that finds the most