Fixed #1111 Add module for sliding dot product; Include pyfftw as (soft) dependency

docstring.py

@@ -10,7 +10,7 @@ def get_docstring_args(fd, file_name, func_name, class_name=None):
     Extract docstring parameters from function definition
     """
     docstring = ast.get_docstring(fd)
-    if len(re.findall(r"Parameters", docstring)) != 1:
+    if docstring is None or len(re.findall(r"Parameters", docstring)) != 1:
         msg = "Missing required 'Parameters' section in docstring in \n"
         msg += f"file: {file_name}\n"
         if class_name is not None:
@@ -23,7 +23,7 @@ def get_docstring_args(fd, file_name, func_name, class_name=None):
         msg += f"function/method: {func_name}\n"
         raise RuntimeError(msg)
 
-    if class_name is None:
+    if len(re.findall(r"Returns", docstring)) > 0:
         params_section = re.findall(
             r"(?<=Parameters)(.*)(?=Returns)", docstring, re.DOTALL
         )[0]
@@ -43,7 +43,7 @@ def get_signature_args(fd):
     return set([a.arg for a in fd.args.args if a.arg != "self"])
 
 
-def check_args(doc_args, sig_args, file_name, func_name, class_name=None):
+def check_args(docstring_args, signature_args, file_name, func_name, class_name=None):
     """
     Compare docstring arguments and signature argments
     """

environment.yml

@@ -27,3 +27,5 @@ dependencies:
   - jupyterlab-myst>=2.0.0
   - myst-nb>=1.0.0
   - polars>=1.14.0
+  - fftw>=3.3
+  - pyfftw>=0.15.0

pyproject.toml

@@ -54,6 +54,7 @@ ci = [
     "isort >= 5.11.0",
     'tbb >= 2019.5 ; platform_system == "Linux"',
     "polars >= 1.14.0",
+    "pyfftw >= 0.15.0",
     "lxml"
 ]
 
@@ -86,8 +87,8 @@ isort = ">=5.11.0"
 polars = ">=1.14.0"
 pip = "*"
 lxml = "*"
-fftw = "*"
-pyfftw = "*"
+fftw = ">=3.3"
+pyfftw = ">=0.15.0"
 # readthedocs
 sphinx = ">=3.5.3"
 pydata-sphinx-theme = "*"

stumpy/core.py

@@ -12,10 +12,9 @@
 from numba import cuda, njit, prange
 from scipy import linalg
 from scipy.ndimage import maximum_filter1d, minimum_filter1d
-from scipy.signal import convolve
 from scipy.spatial.distance import cdist
 
-from . import config
+from . import config, sdp
 
 try:
     from numba.cuda.cudadrv.driver import _raise_driver_not_found
@@ -652,7 +651,8 @@ def check_window_size(m, max_size=None, n=None):
 @njit(fastmath=config.STUMPY_FASTMATH_TRUE)
 def _sliding_dot_product(Q, T):
     """
-    A Numba JIT-compiled implementation of the sliding window dot product.
+    A wrapper for numba JIT-compiled implementation of
+    the sliding dot product.
 
     Parameters
     ----------
@@ -667,18 +667,12 @@ def _sliding_dot_product(Q, T):
     out : numpy.ndarray
         Sliding dot product between `Q` and `T`.
     """
-    m = Q.shape[0]
-    l = T.shape[0] - m + 1
-    out = np.empty(l)
-    for i in range(l):
-        out[i] = np.dot(Q, T[i : i + m])
-
-    return out
+    return sdp._njit_sliding_dot_product(Q, T)
 
 
 def sliding_dot_product(Q, T):
     """
-    Use FFT convolution to calculate the sliding window dot product.
+    A wrapper for convolution implementation of the sliding dot product.
 
     Parameters
     ----------
@@ -692,27 +686,8 @@ def sliding_dot_product(Q, T):
     -------
     output : numpy.ndarray
         Sliding dot product between `Q` and `T`.
-
-    Notes
-    -----
-    Calculate the sliding dot product
-
-    `DOI: 10.1109/ICDM.2016.0179 \
-    <https://www.cs.ucr.edu/~eamonn/PID4481997_extend_Matrix%20Profile_I.pdf>`__
-
-    See Table I, Figure 4
-
-    Following the inverse FFT, Fig. 4 states that only cells [m-1:n]
-    contain valid dot products
-
-    Padding is done automatically in fftconvolve step
     """
-    n = T.shape[0]
-    m = Q.shape[0]
-    Qr = np.flipud(Q)  # Reverse/flip Q
-    QT = convolve(Qr, T)
-
-    return QT.real[m - 1 : n]
+    return sdp._convolve_sliding_dot_product(Q, T)
 
 
 @njit(