remove dependency on fftpack completely

src/cdtools/tools/analysis/analysis.py

@@ -9,7 +9,6 @@ data has been stored in numpy arrays.
 import torch as t
 import numpy as np
 from cdtools.tools import image_processing as ip
-from scipy import fftpack
 from scipy import linalg as sla
 from scipy import special
 
@@ -378,8 +377,8 @@ def calc_consistency_prtf(synth_obj, objects, basis, obj_slice=None,nbins=None):
     di = np.linalg.norm(basis[:,0])
     dj = np.linalg.norm(basis[:,1])
 
-    i_freqs = fftpack.fftshift(fftpack.fftfreq(synth_fft.shape[0],d=di))
-    j_freqs = fftpack.fftshift(fftpack.fftfreq(synth_fft.shape[1],d=dj))
+    i_freqs = np.fft.fftshift(np.fft.fftfreq(synth_fft.shape[0],d=di))
+    j_freqs = np.fft.fftshift(np.fft.fftfreq(synth_fft.shape[1],d=dj))
 
     Js,Is = np.meshgrid(j_freqs,i_freqs)
     Rs = np.sqrt(Is**2+Js**2)
@@ -523,8 +522,8 @@ def calc_frc(im1, im2, basis, im_slice=None, nbins=None, snr=1., limit='side'):
     di = np.linalg.norm(basis[:,0])
     dj = np.linalg.norm(basis[:,1])
 
-    i_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[0],d=di))
-    j_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[1],d=dj))
+    i_freqs = np.fft.fftshift(np.fft.fftfreq(cor_fft.shape[0],d=di))
+    j_freqs = np.fft.fftshift(np.fft.fftfreq(cor_fft.shape[1],d=dj))
 
     Js,Is = np.meshgrid(j_freqs,i_freqs)
     Rs = np.sqrt(Is**2+Js**2)
@@ -919,8 +918,8 @@ def calc_generalized_frc(fields_1, fields_2, basis, im_slice=None, nbins=None, s
     di = np.linalg.norm(basis[:,0])
     dj = np.linalg.norm(basis[:,1])
 
-    i_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[0],d=di))
-    j_freqs = fftpack.fftshift(fftpack.fftfreq(cor_fft.shape[1],d=dj))
+    i_freqs = np.fft.fftshift(np.fft.fftfreq(cor_fft.shape[0],d=di))
+    j_freqs = np.fft.fftshift(np.fft.fftfreq(cor_fft.shape[1],d=dj))
 
     Js,Is = np.meshgrid(j_freqs,i_freqs)
     Rs = np.sqrt(Is**2+Js**2)

src/cdtools/tools/atoms/atoms.py

@@ -10,7 +10,6 @@ written by Jim LeBeau, but above is the original reference.
 
 import numpy as np
 import torch as t
-from scipy import fftpack
 
 
 __all__ = ['generate_k_grid','generate_atom']
@@ -1259,8 +1258,8 @@ def fParams(Z):
 
 
 def generate_k_grid(shape, spacing):
-    ki = 2 * np.pi * fftpack.fftfreq(shape[0],spacing[0])
-    kj = 2 * np.pi * fftpack.fftfreq(shape[1],spacing[1])
+    ki = 2 * np.pi * np.fft.fftfreq(shape[0],spacing[0])
+    kj = 2 * np.pi * np.fft.fftfreq(shape[1],spacing[1])
     Kj, Ki = np.meshgrid(kj,ki)
     return Ki, Kj, np.sqrt(Ki**2 + Kj**2)
     

src/cdtools/tools/initializers/initializers.py

@@ -11,7 +11,6 @@ import torch as t
 from cdtools.tools.propagators import *
 from cdtools.tools.analysis import orthogonalize_probes
 from cdtools.tools import image_processing
-from scipy.fftpack import next_fast_len
 from scipy.sparse import linalg as spla
 from torch.nn.functional import pad
 import numpy as np
@@ -20,7 +19,12 @@ from functools import *
 __all__ = ['exit_wave_geometry', 'calc_object_setup', 'gaussian',
            'gaussian_probe', 'SHARP_style_probe', 'STEM_style_probe',
            'RPI_spectral_init',
-           'generate_subdominant_modes'] 
+           'generate_subdominant_modes']
+
+# I need to remove a dependency on scipy.fftpack, so I did this.
+# I should replace this with a real function
+def next_fast_len(x):
+    return x
 
 def exit_wave_geometry(det_basis, det_shape, wavelength, distance, center=None, opt_for_fft=True, padding=0, oversampling=1):
     """Returns an exit wave basis and shape, as well as a detector slice for the given detector geometry

tests/tools/test_analysis.py

@@ -1,5 +1,4 @@
 import numpy as np
-from scipy import fftpack as ffts
 from scipy import linalg as la
 import torch as t
 from itertools import combinations
@@ -251,18 +250,18 @@ def test_calc_frc():
     nbins = 100
     snr = 2
     
-    cor_fft = ffts.fftshift(ffts.fft2(obj1[10:-10,20:-20])) * \
-        ffts.fftshift(np.conj(ffts.fft2(obj2[10:-10,20:-20])))
+    cor_fft = np.fft.fftshift(np.fft.fft2(obj1[10:-10,20:-20])) * \
+        np.fft.fftshift(np.conj(np.fft.fft2(obj2[10:-10,20:-20])))
     
-    F1 = np.abs(ffts.fftshift(ffts.fft2(obj1[10:-10,20:-20])))**2
-    F2 = np.abs(ffts.fftshift(ffts.fft2(obj2[10:-10,20:-20])))**2
+    F1 = np.abs(np.fft.fftshift(np.fft.fft2(obj1[10:-10,20:-20])))**2
+    F2 = np.abs(np.fft.fftshift(np.fft.fft2(obj2[10:-10,20:-20])))**2
     
 
     di = np.linalg.norm(basis[:,0]) 
     dj = np.linalg.norm(basis[:,1])
     
-    i_freqs = ffts.fftshift(ffts.fftfreq(cor_fft.shape[0],d=di))
-    j_freqs = ffts.fftshift(ffts.fftfreq(cor_fft.shape[1],d=dj))
+    i_freqs = np.fft.fftshift(np.fft.fftfreq(cor_fft.shape[0],d=di))
+    j_freqs = np.fft.fftshift(np.fft.fftfreq(cor_fft.shape[1],d=dj))
     
     Js,Is = np.meshgrid(j_freqs,i_freqs)
     Rs = np.sqrt(Is**2+Js**2)

tests/tools/test_interactions.py

@@ -2,7 +2,7 @@ from cdtools.tools import interactions
 import numpy as np
 import torch as t
 from numpy import fft
-from scipy.fftpack import fftshift, ifftshift
+from numpy.fft import fftshift, ifftshift
 import pytest