Add pyfftw sdp

stumpy/sdp.py

@@ -6,6 +6,13 @@
 
 from . import config
 
+try:  # pragma: no cover
+    import pyfftw
+
+    PYFFTW_IS_AVAILABLE = True
+except ImportError:  # pragma: no cover
+    PYFFTW_IS_AVAILABLE = False
+
 
 @njit(fastmath=config.STUMPY_FASTMATH_TRUE)
 def _njit_sliding_dot_product(Q, T):
@@ -109,6 +116,206 @@ def _pocketfft_sliding_dot_product(Q, T):
     return c2r(False, np.multiply(fft_2d[0], fft_2d[1]), n=next_fast_n)[m - 1 : n]
 
 
+class _PYFFTW_SLIDING_DOT_PRODUCT:
+    """
+    A class to compute the sliding dot product using FFTW via pyfftw.
+
+    This class uses FFTW (via pyfftw) to efficiently compute the sliding dot product
+    between a query sequence, Q, and a time series, T. It preallocates arrays and caches
+    FFTW objects to optimize repeated computations with similar-sized inputs.
+
+    Parameters
+    ----------
+    max_n : int, default=2**20
+        Maximum length to preallocate arrays for. This will be the size of the
+        real-valued array. A complex-valued array of size `1 + (max_n // 2)`
+        will also be preallocated. If inputs exceed this size, arrays will be
+        reallocated to accommodate larger sizes.
+
+    Attributes
+    ----------
+    real_arr : pyfftw.empty_aligned
+        Preallocated real-valued array for FFTW computations.
+
+    complex_arr : pyfftw.empty_aligned
+        Preallocated complex-valued array for FFTW computations.
+
+    rfft_objects : dict
+        Cache of FFTW forward transform objects with
+        (next_fast_n, n_threads, planning_flag) as lookup keys.
+
+    irfft_objects : dict
+        Cache of FFTW inverse transform objects with
+        (next_fast_n, n_threads, planning_flag) as lookup keys.
+
+    Methods
+    -------
+    __call__(Q, T, n_threads=1, planning_flag="FFTW_ESTIMATE")
+        Compute the sliding dot product between `Q` and `T` using FFTW
+        via pyfftw, and cache FFTW objects if not already cached.
+
+    Notes
+    -----
+    The class maintains internal caches of FFTW objects to avoid redundant planning
+    operations when called multiple times with similar-sized inputs and parameters.
+    When `planning_flag=="FFTW_ESTIMATE"`, there will be no planning operation.
+    However, caching FFTW objects is still beneficial as the overhead of creating
+    those objects can be avoided in subsequent calls.
+
+    Examples
+    --------
+    >>> sdp_obj = _PYFFTW_SLIDING_DOT_PRODUCT(max_n=1000)
+    >>> Q = np.array([1, 2, 3])
+    >>> T = np.array([4, 5, 6, 7, 8])
+    >>> result = sdp_obj(Q, T)
+
+    References
+    ----------
+    `FFTW documentation <http://www.fftw.org/>`__
+
+    `pyfftw documentation <https://pyfftw.readthedocs.io/>`__
+    """
+
+    def __init__(self, max_n=2**20):
+        """
+        Initialize the `_PYFFTW_SLIDING_DOT_PRODUCT` object, which can be called
+        to compute the sliding dot product using FFTW via pyfftw.
+
+        Parameters
+        ----------
+        max_n : int, default=2**20
+            Maximum length to preallocate arrays for. This will be the size of the
+            real-valued array. A complex-valued array of size `1 + (max_n // 2)`
+            will also be preallocated.
+
+        Returns
+        -------
+        None
+        """
+        # Preallocate arrays
+        self.real_arr = pyfftw.empty_aligned(max_n, dtype="float64")
+        self.complex_arr = pyfftw.empty_aligned(1 + (max_n // 2), dtype="complex128")
+
+        # Store FFTW objects, keyed by (next_fast_n, n_threads, planning_flag)
+        self.rfft_objects = {}
+        self.irfft_objects = {}
+
+    def __call__(self, Q, T, n_threads=1, planning_flag="FFTW_ESTIMATE"):
+        """
+        Compute the sliding dot product between `Q` and `T` using FFTW via pyfftw,
+        and cache FFTW objects if not already cached.
+
+        Parameters
+        ----------
+        Q : numpy.ndarray
+            Query array or subsequence.
+
+        T : numpy.ndarray
+            Time series or sequence.
+
+        n_threads : int, default=1
+            Number of threads to use for FFTW computations.
+
+        planning_flag : str, default="FFTW_ESTIMATE"
+            The planning flag that will be used in FFTW for planning.
+            See pyfftw documentation for details. Current options, ordered
+            ascendingly by the level of aggressiveness in planning, are:
+            "FFTW_ESTIMATE", "FFTW_MEASURE", "FFTW_PATIENT", and "FFTW_EXHAUSTIVE".
+            The more aggressive the planning, the longer the planning time, but
+            the faster the execution time.
+
+        Returns
+        -------
+        out : numpy.ndarray
+            Sliding dot product between `Q` and `T`.
+
+        Notes
+        -----
+        The planning_flag is defaulted to "FFTW_ESTIMATE" to be aligned with
+        MATLAB's FFTW usage (as of version R2025b)
+        See: https://www.mathworks.com/help/matlab/ref/fftw.html
+
+        This implementation is inspired by the answer on StackOverflow:
+        https://stackoverflow.com/a/30615425/2955541
+        """
+        m = Q.shape[0]
+        n = T.shape[0]
+        next_fast_n = pyfftw.next_fast_len(n)
+
+        # Update preallocated arrays if needed
+        if next_fast_n > len(self.real_arr):
+            self.real_arr = pyfftw.empty_aligned(next_fast_n, dtype="float64")
+            self.complex_arr = pyfftw.empty_aligned(
+                1 + (next_fast_n // 2), dtype="complex128"
+            )
+
+        real_arr = self.real_arr[:next_fast_n]
+        complex_arr = self.complex_arr[: 1 + (next_fast_n // 2)]
+
+        # Get or create FFTW objects
+        key = (next_fast_n, n_threads, planning_flag)
+
+        rfft_obj = self.rfft_objects.get(key, None)
+        irfft_obj = self.irfft_objects.get(key, None)
+
+        if rfft_obj is None or irfft_obj is None:
+            rfft_obj = pyfftw.FFTW(
+                input_array=real_arr,
+                output_array=complex_arr,
+                direction="FFTW_FORWARD",
+                flags=(planning_flag,),
+                threads=n_threads,
+            )
+            irfft_obj = pyfftw.FFTW(
+                input_array=complex_arr,
+                output_array=real_arr,
+                direction="FFTW_BACKWARD",
+                flags=(planning_flag, "FFTW_DESTROY_INPUT"),
+                threads=n_threads,
+            )
+            self.rfft_objects[key] = rfft_obj
+            self.irfft_objects[key] = irfft_obj
+        else:
+            # Update the input and output arrays of the cached FFTW objects
+            # in case their original input and output arrays were reallocated
+            # in a previous call
+            rfft_obj.update_arrays(real_arr, complex_arr)
+            irfft_obj.update_arrays(complex_arr, real_arr)
+
+        # Compute the (circular) convolution between T and Q[::-1],
+        # each zero-padded to length `next_fast_n` by performing
+        # the following three steps:
+
+        # Step 1
+        # Compute RFFT of T (zero-padded)
+        # Must make a copy of output to avoid losing it when the array is
+        # overwritten when computing the RFFT of Q in the next step
+        rfft_obj.input_array[:n] = T
+        rfft_obj.input_array[n:] = 0.0
+        rfft_obj.execute()
+        rfft_T = rfft_obj.output_array.copy()
+
+        # Step 2
+        # Compute RFFT of Q (reversed, scaled, and zero-padded)
+        # Scaling is required because the thin wrapper `execute`
+        # that will be called below does not perform normalization
+        np.multiply(Q[::-1], 1.0 / next_fast_n, out=rfft_obj.input_array[:m])
+        rfft_obj.input_array[m:] = 0.0
+        rfft_obj.execute()
+        rfft_Q = rfft_obj.output_array
+
+        # Step 3
+        # Convert back to time domain by taking the inverse RFFT
+        np.multiply(rfft_T, rfft_Q, out=irfft_obj.input_array)
+        irfft_obj.execute()
+
+        return irfft_obj.output_array[m - 1 : n]  # valid portion
+
+
+if PYFFTW_IS_AVAILABLE:  # pragma: no cover
+    _pyfftw_sliding_dot_product = _PYFFTW_SLIDING_DOT_PRODUCT(max_n=2**20)
+
+
 def _sliding_dot_product(Q, T):
     """
     Compute the sliding dot product between `Q` and `T`

test.sh

@@ -154,27 +154,58 @@ gen_ray_coveragerc()
     # Generate a .coveragerc_override file that excludes Ray functions and tests
     gen_coveragerc_boilerplate
     echo "    def .*_ray_*" >> .coveragerc_override
-    echo "    def ,*_ray\(*" >> .coveragerc_override
+    echo "    def .*_ray\(*" >> .coveragerc_override
     echo "    def ray_.*" >> .coveragerc_override
     echo "    def test_.*_ray*" >> .coveragerc_override
 }
 
-set_ray_coveragerc()
+check_fftw_pyfftw()
 {
-    # If `ray` command is not found then generate a .coveragerc_override file
-    if ! command -v ray &> /dev/null
+    if ! command -v fftw-wisdom &> /dev/null \
+    || ! python -c "import pyfftw" &> /dev/null;
     then
-        echo "Ray Not Installed"
+        echo "FFTW and/or pyFFTW Not Installed"
+    else
+        echo "FFTW and pyFFTW Installed"
+    fi
+}
+
+gen_pyfftw_coveragerc()
+{
+    gen_coveragerc_boilerplate
+    echo "    class .*PYFFTW*" >> .coveragerc_override
+    echo "    def test_.*pyfftw*" >> .coveragerc_override
+}
+
+set_coveragerc()
+{
+    fcoveragerc=""
+
+    if ! command -v ray &> /dev/null; 
+    then
+        echo "Ray not installed"
         gen_ray_coveragerc
-        fcoveragerc="--rcfile=.coveragerc_override"
     else
-        echo "Ray Installed"
+        echo "Ray installed"
+    fi
+
+    if ! command -v fftw-wisdom &> /dev/null \
+    || ! python -c "import pyfftw" &> /dev/null;
+    then
+        echo "FFTW and/or pyFFTW not Installed"
+        gen_pyfftw_coveragerc
+    else
+        echo "FFTW and pyFFTW Installed"
+    fi
+
+    if [ -f .coveragerc_override ]; then
+        fcoveragerc="--rcfile=.coveragerc_override"
     fi
 }
 
 show_coverage_report()
 {
-    set_ray_coveragerc
+    set_coveragerc
     coverage report --show-missing --fail-under=100 --skip-covered --omit=fastmath.py,docstring.py,versions.py $fcoveragerc
     check_errs $?
 }
@@ -361,6 +392,7 @@ check_print
 check_pkg_imports
 check_naive
 check_ray
+check_fftw_pyfftw
 
 
 if [[ -z $NUMBA_DISABLE_JIT || $NUMBA_DISABLE_JIT -eq 0 ]]; then
@@ -405,4 +437,4 @@ else
     test_coverage
 fi
 
-clean_up
+clean_up

tests/test_sdp.py

@@ -74,6 +74,9 @@ def get_sdp_function_names():
         if func_name.endswith("sliding_dot_product"):
             out.append(func_name)
 
+    if sdp.PYFFTW_IS_AVAILABLE:  # pragma: no cover
+        out.append("_pyfftw_sliding_dot_product")
+
     return out
 
 
@@ -152,3 +155,25 @@ def test_sdp_power2():
             raise e
 
     return
+
+
+def test_pyfftw_sdp_max_n():
+    if not sdp.PYFFTW_IS_AVAILABLE:  # pragma: no cover
+        pytest.skip("Skipping Test pyFFTW Not Installed")
+
+    # When `len(T)` larger than `max_n` in pyfftw_sdp,
+    # the internal preallocated arrays should be resized.
+    # This test checks that functionality.
+    max_n = 2**10
+    sdp_func = sdp._PYFFTW_SLIDING_DOT_PRODUCT(max_n)
+
+    # len(T) > max_n to trigger array resizing
+    T = np.random.rand(max_n + 1)
+    Q = np.random.rand(2**8)
+
+    comp = sdp_func(Q, T)
+    ref = naive.rolling_window_dot_product(Q, T)
+
+    np.testing.assert_allclose(comp, ref)
+
+    return