1 files changed, 0 insertions, 124 deletions
diff --git a/silx/math/fft/npfft.py b/silx/math/fft/npfft.py
deleted file mode 100644
index 20351de..0000000
--- a/silx/math/fft/npfft.py
+++ /dev/null
@@ -1,124 +0,0 @@
-#!/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)
-