scikit-image rather than imageio

proxtoolbox/Problems/OrbitalTomog/planar_molecule/orbitaltomog_data_processor.py

 import numpy as np
-import numpy.fft as fft
 from scipy.io import loadmat
 from scipy.ndimage import binary_dilation
 import matplotlib.pyplot as plt
-import imageio
+from skimage.io import imread
 from proxtoolbox.Utilities.OrbitalTomog.binning import bin_2d_array
 from proxtoolbox.Utilities.OrbitalTomog.array_tools import shifted_ifft, shifted_fft
 
@@ -12,17 +11,18 @@ def data_processor(config):
     if config['data_filename'][1] == ':':
         path_to_file = config['data_filename']
     else:
-        path_to_file = '../../../InputData/OrbitalTomog/' + config['data_filename']
+        path_to_file = '../../../../InputData/OrbitalTomog/' + config['data_filename']
 
     try:
         if config['data_filename'][-4:] == '.mat':  # for the old  coronene simulations by W. Bennecke
             inp_data = loadmat(path_to_file)
             inp = inp_data["I2"]
         else:
-            inp = imageio.imread(path_to_file)
+            inp = imread(path_to_file)
     except FileNotFoundError:
+        print("Tried path %s, found nothing. "% path_to_file)
         path_to_file = input('Please enter the path to the datafile: ')
-        inp = imageio.imread(path_to_file)
+        inp = imread(path_to_file)
 
     ny, nx = inp.shape
     config['data'] = abs(inp)
@@ -51,7 +51,7 @@ def data_processor(config):
 
     # Object support determination
     try:
-        config['support'] = imageio.imread(config['support_filename'])
+        config['support'] = imread(config['support_filename'])
     except KeyError:  # 'support filename does not exist, so define a support here'
         if 'threshold for support' not in config:
             config['threshold for support'] = 0.1
@@ -71,7 +71,7 @@ def data_processor(config):
 
     # Autocorrelation
     config['threshold for support'] = 0.1
-    autocorrelation = abs(fft.fftshift(fft.ifft2(fft.ifftshift(inp))))
+    autocorrelation = abs(shifted_ifft(inp))
     config['abs_illumination'] = np.ones_like(config['support'])
 
     # Initial guess
@@ -81,8 +81,7 @@ def data_processor(config):
     if ('use_sparsity_with_support' in config
             and config['use_sparsity_with_support']
             and 'sparsity_support' not in config):
-        ac = abs(fft.fftshift(fft.ifft2(fft.ifftshift(inp))))
-        config['sparsity_support'] = binary_dilation(ac > 0.01 * np.amax(ac)).astype(np.uint)
+        config['sparsity_support'] = binary_dilation(autocorrelation > 0.01 * np.amax(autocorrelation)).astype(np.uint)
 
     if config['dataprocessor_plotting']:
         plt.figure(figsize=(15, 4))
@@ -110,55 +109,6 @@ def data_processor(config):
 
     return config
 
-#
-# def bin_2d_array(arr, new_shape):
-#     """"
-#     bins a 2D numpy array
-#      Args:
-#         arr: input array to be binned
-#         new_shape: shape after binning
-#      Returns:
-#          binned np array
-#     """
-#     shape = (new_shape[0], arr.shape[0] // new_shape[0],
-#              new_shape[1], arr.shape[1] // new_shape[1])
-#     if np.any(np.isnan(arr)):
-#         binfactor = 1
-#         for i, s in enumerate(arr.shape):
-#             binfactor *= new_shape[i] / s
-#         return np.nanmean(arr.reshape(shape), axis=(3, 1)) * binfactor
-#     else:
-#         return arr.reshape(shape).sum(-1).sum(1)
-
-
-# def shifted_fft(arr, axes=None):
-#     """
-#     Combined fftshift and fft routine, based on scipy.fftpack
-#
-#     Args:
-#         arr: numpy array
-#         axes: identical to  argument for scipy.fftpack.fft
-#
-#     Returns:
-#         transformed array
-#     """
-#
-#     return fft.ifftshift(fft.fftn(fft.fftshift(arr, axes=axes), axes=axes), axes=axes)
-#
-#
-# def shifted_ifft(arr, axes=None):
-#     """
-#     Combined fftshift and fft routine, based on scipy.fftpack
-#
-#     Args:
-#         arr: numpy array
-#         axes: identical to  argument for scipy.fftpack.fft
-#
-#     Returns:
-#         transformed array
-#     """
-#     return fft.fftshift(fft.ifftn(fft.ifftshift(arr, axes=axes), axes=axes), axes=axes)
-
 
 def support_from_autocorrelation(input_array: np.ndarray,
                                  threshold: float = 0.1,