Using scipy.fft.rfft if available

nabu/preproc/alignment.py

 import numpy as np
 
+try:
+    import scipy.fft
+
+    my_fftn = scipy.fft.rfftn
+    my_ifftn = scipy.fft.irfftn
+    my_fft2 = scipy.fft.rfft2
+    my_ifft2 = scipy.fft.irfft2
+
+    def my_fft_layout_adapt(x):
+        slicelist = tuple(slice(0, (N + 1) // 2) for N in x.shape)
+        return x[slicelist]
+
+
+except ImportError:
+    my_fftn = np.fft.fftn
+    my_ifftn = np.fft.ifftn
+    my_fft2 = np.fft.fft2
+    my_ifft2 = np.fft.ifft2
+
+    def my_fft_layout_adapt(x):
+        return x
+
+
 import logging
 from numpy.polynomial.polynomial import Polynomial, polyval
 
@@ -337,7 +360,7 @@ class AlignmentBase(object):
                 img.shape[-2:], cutoff_lowpass=low_pass, cutoff_highpass=high_pass
             )
             # fft2 and iff2 use axes=(-2, -1) by default
-            img = np.fft.ifft2(np.fft.fft2(img) * img_filter).real
+            img = my_ifft2(my_fft2(img) * my_fft_layout_adapt(img_filter)).real
 
         if median_filt_shape is not None:
             img_shape = img.shape
@@ -374,8 +397,8 @@ class AlignmentBase(object):
             img_2 = np.pad(img_2, pad_array, mode=padding_mode)
 
         # compute fft's of the 2 images
-        img_fft_1 = np.fft.fftn(img_1, axes=axes)
-        img_fft_2 = np.conjugate(np.fft.fftn(img_2, axes=axes))
+        img_fft_1 = my_fftn(img_1, axes=axes)
+        img_fft_2 = np.conjugate(my_fftn(img_2, axes=axes))
 
         img_prod = img_fft_1 * img_fft_2
 
@@ -384,7 +407,7 @@ class AlignmentBase(object):
             img_prod *= filt
 
         # inverse fft of the product to get cross_correlation of the 2 images
-        cc = np.real(np.fft.ifftn(img_prod, axes=axes))
+        cc = np.real(my_ifftn(img_prod, axes=axes))
 
         if not do_circular_conv:
             cc = np.fft.fftshift(cc, axes=axes)
@@ -562,11 +585,11 @@ class DetectorTranslationAlongBeam(AlignmentBase):
         return_shifts=False,
         use_adjacent_imgs=False,
     ):
-        """Find  vertical and  horizontal position increments per a unit-distance detector translation along the 
+        """Find  vertical and  horizontal position increments per a unit-distance detector translation along the
         traslation axis. The units are pixel_unit/input_unit where input_unit are the  unit that the user has  used
         to pass the argument img_pos. The output expresses shifts of the detector so that if the image is moving
         in the positive direction (expressed in pixels coordinates) the output will be negative because it means
-        that the detector as a whole is shifting in the opposite direction (taking the shaped  beam as a reference) 
+        that the detector as a whole is shifting in the opposite direction (taking the shaped  beam as a reference)
 
         Parameters
         ----------

nabu/preproc/tests/test_alignment.py

@@ -110,7 +110,8 @@ class TestAlignmentBase(object):
 
         (found_peaks_val, found_peaks_pos) = AlignmentBase.extract_peak_regions_1d(img, axis=-1, cc_coords=cc_coords)
         message = "The found peak positions do not correspond to the expected peak positions:\n  Expected: %s\n  Found: %s" % (
-            peaks_pos, found_peaks_pos[1, :]
+            peaks_pos,
+            found_peaks_pos[1, :],
         )
         assert np.all(peaks_val == found_peaks_val[1, :]), message
 
@@ -146,7 +147,7 @@ class TestCor(object):
         radio1 = self.data[0, :, :]
         radio2 = np.fliplr(self.data[1, :, :])
 
-        noise_level = radio1.max() / 2.0
+        noise_level = radio1.max() / 8.0
         noise_ima1 = np.random.normal(0.0, size=radio1.shape) * noise_level
         noise_ima2 = np.random.normal(0.0, size=radio2.shape) * noise_level