1 files changed, 124 insertions, 0 deletions
diff --git a/silx/math/fft/npfft.py b/silx/math/fft/npfft.py
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+++ b/silx/math/fft/npfft.py
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+#!/usr/bin/env python
+# coding: utf-8
+# /*##########################################################################
+#
+# Copyright (c) 2018-2019 European Synchrotron Radiation Facility
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+# THE SOFTWARE.
+#
+# ###########################################################################*/
+import numpy as np
+
+from .basefft import BaseFFT
+
+
+class NPFFT(BaseFFT):
+    """Initialize a numpy plan.
+
+    Please see FFT class for parameters help.
+    """
+    def __init__(
+        self,
+        shape=None,
+        dtype=None,
+        template=None,
+        shape_out=None,
+        axes=None,
+        normalize="rescale",
+    ):
+        super(NPFFT, self).__init__(
+            shape=shape,
+            dtype=dtype,
+            template=template,
+            shape_out=shape_out,
+            axes=axes,
+            normalize=normalize,
+        )
+        self.backend = "numpy"
+        self.real_transform = False
+        if template is not None and np.isrealobj(template):
+            self.real_transform = True
+        # For numpy functions.
+        # TODO Issue warning if user wants ifft(fft(data)) = N*data ?
+        if normalize != "ortho":
+            self.normalize = None
+        self.set_fft_functions()
+        #~ self.allocate_arrays() # not needed for this backend
+        self.compute_plans()
+
+
+    def set_fft_functions(self):
+        # (fwd, inv) = _fft_functions[is_real][ndim]
+        self._fft_functions = {
+            True: {
+                1: (np.fft.rfft, np.fft.irfft),
+                2: (np.fft.rfft2, np.fft.irfft2),
+                3: (np.fft.rfftn, np.fft.irfftn),
+            },
+            False: {
+                1: (np.fft.fft, np.fft.ifft),
+                2: (np.fft.fft2, np.fft.ifft2),
+                3: (np.fft.fftn, np.fft.ifftn),
+            }
+        }
+
+
+    def _allocate(self, shape, dtype):
+        return np.zeros(self.queue, shape, dtype=dtype)
+
+
+    def compute_plans(self):
+        ndim = len(self.shape)
+        funcs = self._fft_functions[self.real_transform][np.minimum(ndim, 3)]
+        if np.version.version[:4] in ["1.8.", "1.9."]:
+            # norm keyword was introduced in 1.10 and we support numpy >= 1.8
+            self.numpy_args = {}
+        else:
+            self.numpy_args = {"norm": self.normalize}
+        # Batched transform
+        if (self.user_axes is not None) and len(self.user_axes) < ndim:
+            funcs = self._fft_functions[self.real_transform][np.minimum(ndim-1, 3)]
+            self.numpy_args["axes"] = self.user_axes
+            # Special case of batched 1D transform on 2D data
+            if ndim == 2:
+                assert len(self.user_axes) == 1
+                self.numpy_args["axis"] = self.user_axes[0]
+                self.numpy_args.pop("axes")
+        self.numpy_funcs = funcs
+
+
+    def fft(self, array):
+        """
+        Perform a (forward) Fast Fourier Transform.
+
+        :param numpy.ndarray array:
+            Input data. Must be consistent with the current context.
+        """
+        return self.numpy_funcs[0](array, **self.numpy_args)
+
+
+    def ifft(self, array):
+        """
+        Perform a (inverse) Fast Fourier Transform.
+
+        :param numpy.ndarray array:
+            Input data. Must be consistent with the current context.
+        """
+        return self.numpy_funcs[1](array, **self.numpy_args)
+