Copy over numpy.fft helper functions

Author: ndgrigorian

mkl_fft/interfaces/__init__.py

@@ -25,8 +25,9 @@
 
 from . import numpy_fft
 
+# find scipy, not scipy.fft, to avoid circular dependency
 try:
-    import scipy.fft
+    import scipy
 except ImportError:
     pass
 else:

mkl_fft/interfaces/_numpy_helper.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+# Copyright (c) 2017, Intel Corporation
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#     * Redistributions of source code must retain the above copyright notice,
+#       this list of conditions and the following disclaimer.
+#     * Redistributions in binary form must reproduce the above copyright
+#       notice, this list of conditions and the following disclaimer in the
+#       documentation and/or other materials provided with the distribution.
+#     * Neither the name of Intel Corporation nor the names of its contributors
+#       may be used to endorse or promote products derived from this software
+#       without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+"""
+FFT helper functions copied from `numpy.fft` (with some modification) to
+prevent circular dependencies when patching NumPy.
+"""
+
+import numpy as np
+
+__all__ = ["fftshift", "ifftshift", "fftfreq", "rfftfreq"]
+
+
+def fftshift(x, axes=None):
+    """
+    Shift the zero-frequency component to the center of the spectrum.
+
+    For full documentation refer to `numpy.fft.fftshift`.
+
+    """
+    x = np.asarray(x)
+    if axes is None:
+        axes = tuple(range(x.ndim))
+        shift = [dim // 2 for dim in x.shape]
+    elif isinstance(axes, (int, np.integer)):
+        shift = x.shape[axes] // 2
+    else:
+        shift = [x.shape[ax] // 2 for ax in axes]
+
+    return np.roll(x, shift, axes)
+
+
+def ifftshift(x, axes=None):
+    """
+    The inverse of `fftshift`. Although identical for even-length `x`, the
+    functions differ by one sample for odd-length `x`.
+
+    For full documentation refer to `numpy.fft.ifftshift`.
+
+    """
+    x = np.asarray(x)
+    if axes is None:
+        axes = tuple(range(x.ndim))
+        shift = [-(dim // 2) for dim in x.shape]
+    elif isinstance(axes, (int, np.integer)):
+        shift = -(x.shape[axes] // 2)
+    else:
+        shift = [-(x.shape[ax] // 2) for ax in axes]
+
+    return np.roll(x, shift, axes)
+
+
+def fftfreq(n, d=1.0, device=None):
+    """
+    Return the Discrete Fourier Transform sample frequencies.
+
+    For full documentation refer to `numpy.fft.fftfreq`.
+
+    """
+    if not isinstance(n, (int, np.integer)):
+        raise ValueError("n should be an integer")
+    val = 1.0 / (n * d)
+    results = np.empty(n, int, device=device)
+    N = (n - 1) // 2 + 1
+    p1 = np.arange(0, N, dtype=int, device=device)
+    results[:N] = p1
+    p2 = np.arange(-(n // 2), 0, dtype=int, device=device)
+    results[N:] = p2
+    return results * val
+
+
+def rfftfreq(n, d=1.0, device=None):
+    """
+    Return the Discrete Fourier Transform sample frequencies (for usage with
+    `rfft`, `irfft`).
+
+    For full documentation refer to `numpy.fft.rfftfreq`.
+
+    """
+    if not isinstance(n, (int, np.integer)):
+        raise ValueError("n should be an integer")
+    val = 1.0 / (n * d)
+    N = n // 2 + 1
+    results = np.arange(0, N, dtype=int, device=device)
+    return results * val

mkl_fft/interfaces/numpy_fft.py

@@ -41,6 +41,7 @@
     rfft2,
     rfftn,
 )
+from ._numpy_helper import fftfreq, fftshift, ifftshift, rfftfreq
 
 __all__ = [
     "fft",
@@ -57,11 +58,8 @@
     "irfftn",
     "hfft",
     "ihfft",
+    "fftshift",
+    "fftfreq",
+    "rfftfreq",
+    "ifftshift",
 ]
-
-# It is important to put the following import here to avoid circular imports
-# when patching numpy with mkl_fft
-# Added for completing the namespaces
-from numpy.fft import fftfreq, fftshift, ifftshift, rfftfreq
-
-__all__ += ["fftshift", "ifftshift", "fftfreq", "rfftfreq"]