Updated README, docstrings, API docs

Author: seanlaw

README.rst

@@ -212,12 +212,7 @@ To install stumpy from source, see the instructions in the `documentation <https
 Documentation
 -------------
 
-In order to fully understand and appreciate the underlying algorithms and applications, it is imperative that you read the original publications_. For a more detailed example of how to use STUMPY please consult the latest `documentation <https://stumpy.readthedocs.io/en/latest/>`__ or explore the following tutorials:
-
-1. `The Matrix Profile <https://stumpy.readthedocs.io/en/latest/Tutorial_The_Matrix_Profile.html>`__
-2. `STUMPY Basics <https://stumpy.readthedocs.io/en/latest/Tutorial_STUMPY_Basics.html>`__
-3. `Time Series Chains <https://stumpy.readthedocs.io/en/latest/Tutorial_Time_Series_Chains.html>`__
-4. `Semantic Segmentation <https://stumpy.readthedocs.io/en/latest/Tutorial_Semantic_Segmentation.html>`__
+In order to fully understand and appreciate the underlying algorithms and applications, it is imperative that you read the original publications_. For a more detailed example of how to use STUMPY please consult the latest `documentation <https://stumpy.readthedocs.io/en/latest/>`__ or explore our `hands-on tutorials <https://stumpy.readthedocs.io/en/latest/tutorials.html>`__.
 
 -----------
 Performance

docs/api.rst

@@ -29,6 +29,7 @@ STUMPY API
     stumpy.gpu_mpdist
     stumpy.motifs
     stumpy.match
+    stumpy.mmotifs
     stumpy.snippets
     stumpy.stimp
     stumpy.stimped
@@ -142,6 +143,11 @@ match
 
 .. autofunction:: stumpy.match
 
+mmotifs
+=======
+
+.. autofunction:: stumpy.mmotifs
+
 snippets
 ========
 

stumpy/gpu_aamp.py

@@ -346,7 +346,9 @@ def gpu_aamp(T_A, m, T_B=None, ignore_trivial=True, device_id=0, p=2.0):
 
     This is a convenience wrapper around the Numba `cuda.jit` `_gpu_aamp` function
     which computes the non-normalized matrix profile according to modified version
-    GPU-STOMP.
+    GPU-STOMP. The default number of threads-per-block is set to `512` and may be
+    changed by setting the global parameter `config.STUMPY_THREADS_PER_BLOCK` to an
+    appropriate number based on your GPU hardware.
 
     Parameters
     ----------

stumpy/gpu_ostinato.py

@@ -83,7 +83,7 @@ def gpu_ostinato(Ts, m, device_id=0, normalize=True, p=2.0):
     >>> from numba import cuda
     >>> if __name__ == "__main__":
     ...     all_gpu_devices = [device.id for device in cuda.list_devices()]
-    ...     stumpy.gpu_ostinatoe(
+    ...     stumpy.gpu_ostinato(
     ...         [np.array([584., -11., 23., 79., 1001., 0., 19.]),
     ...          np.array([600., -10., 23., 17.]),
     ...          np.array([  1.,   9.,  6.,  0.])],

stumpy/gpu_stump.py

@@ -380,7 +380,10 @@ def gpu_stump(
     Compute the z-normalized matrix profile with one or more GPU devices
 
     This is a convenience wrapper around the Numba `cuda.jit` `_gpu_stump` function
-    which computes the matrix profile according to GPU-STOMP.
+    which computes the matrix profile according to GPU-STOMP. The default number of
+    threads-per-block is set to `512` and may be changed by setting the global parameter
+    `config.STUMPY_THREADS_PER_BLOCK` to an appropriate number based on your GPU
+    hardware.
 
     Parameters
     ----------

stumpy/motifs.py

@@ -247,6 +247,12 @@ def motifs(
     --------
     stumpy.match : Find all matches of a query `Q` in a time series `T`
     stumpy.mmotifs : Discover the top motifs for the multi-dimensional time series `T`
+    stumpy.stump : Compute the z-normalized matrix profile
+    stumpy.stumped : Compute the z-normalized matrix profile with a distributed dask
+        cluster
+    stumpy.gpu_stump : Compute the z-normalized matrix profile with one or more GPU
+        devices
+    stumpy.scrump : Compute an approximate z-normalized matrix profile
 
     Examples
     --------
@@ -393,7 +399,14 @@ def match(
 
     See Also
     --------
-    stumpy.motifs : Discover the top motifs for time series T
+    stumpy.motifs : Discover the top motifs for time series `T`
+    stumpy.mmotifs : Discover the top motifs for the multi-dimensional time series `T`
+    stumpy.stump : Compute the z-normalized matrix profile
+    stumpy.stumped : Compute the z-normalized matrix profile with a distributed dask
+        cluster
+    stumpy.gpu_stump : Compute the z-normalized matrix profile with one or more GPU
+        devices
+    stumpy.scrump : Compute an approximate z-normalized matrix profile
 
     Examples
     --------