finishing up the tools move (previous commit)

proxtoolbox/Problems/OrbitalTomog/orbital_tomog_3d_data_processor.py

@@ -65,6 +65,7 @@ def data_processor(config):
     if config['dataprocessor_plotting']:
         XYZStackViewer(inp, cmap='viridis')
         XYZStackViewer(shifted_fft(inp).real)
+        # TODO: investigate why these viewers crash after a limited time
 
     # Other settings
     config['fresnel_nr'] = 0
@@ -72,62 +73,5 @@ def data_processor(config):
     config['use_farfield_formula'] = True
     config['magn'] = 1
     config['data_sq'] = abs(config['data']) ** 2
-    config['data_zeros'] = 1 # probably here I can put in the mask with NaNs (where we have no sensitivity
 
     return config
-
-
-def bin_array(arr: np.ndarray, new_shape: any, pad_zeros=True) -> np.ndarray:
-    """
-    Reduce the size of an array by binning
-
-    :param arr: original
-    :param new_shape: tuple which must be an integer divisor of the original shape, or integer to bin by that factor
-    :return: new array
-    """
-    # make tuple with new shape
-    if type(new_shape) == int:  # binning factor is given
-        _shape = tuple([i // new_shape for i in arr.shape])
-        binfactor = tuple([new_shape for i in _shape])
-    else:
-        _shape = new_shape
-        binfactor = tuple([s // _shape[i] for i, s in enumerate(arr.shape)])
-    # determine if padding is needed
-    padding = tuple([(0, (binfactor[i] - s % binfactor[i]) % binfactor[i]) for i, s in enumerate(arr.shape)])
-    if pad_zeros and np.any(np.array(padding) != 0):
-        _arr = np.pad(arr, padding, mode='constant', constant_values=0)  # pad array
-        _shape = tuple([s//binfactor[i] for i, s in enumerate(_arr.shape)])  # update binned size due to padding
-    else:
-        _arr = arr  # expected to fail if padding has non-zeros
-    # send to 2d or 3d padding functions
-    try:
-        if len(arr.shape) == 2:
-            out = bin_2d_array(_arr, _shape)
-        elif len(arr.shape) == 3:
-            out = bin_3d_array(_arr, _shape)
-        else:
-            raise NotImplementedError('Cannot only bin 3d or 2d arrays')
-        return out
-    except ValueError:
-        raise ValueError("Cannot bin data with this shape. Try setting pad_zeros=True, or change the binning.")
-
-
-def bin_3d_array(arr: np.ndarray, new_shape: tuple) -> np.ndarray:
-    """"
-    bins a 3D numpy array
-     Args:
-        arr: input array to be binned
-        new_shape: shape after binning, must be an integer divisor of the original shape
-     Returns:
-         binned np array
-    """
-    shape = (new_shape[0], arr.shape[0] // new_shape[0],
-             new_shape[1], arr.shape[1] // new_shape[1],
-             new_shape[2], arr.shape[2] // new_shape[2])
-    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=(5, 3, 1)) * binfactor
-    else:
-        return np.sum(arr.reshape(shape), axis=(5, 3, 1))

proxtoolbox/Problems/OrbitalTomog/phase.py

@@ -5,8 +5,6 @@ from proxtoolbox import Algorithms
 from proxtoolbox import ProxOperators
 from proxtoolbox.ProxOperators.proxoperators import ProxOperator
 from proxtoolbox.Problems.OrbitalTomog import Graphics
-# from .proxoperators import ProxOperator
-#from phase_retrieval.back_end_utilities import norm
 from numpy.linalg import norm
 from numpy import square, sqrt
 
@@ -33,13 +31,6 @@ class Phase(Problem):
             data, data_norm, support,
         """
         self.config = new_config
-        #self.back_end = back_end
-
-        #call data processor, read data
-
-#         module = __import__(self.config['data_filename'])
-#         data_processor = getattr(module, self.config['data_filename'])
-#         data_processor(self.config)
 
         if 'Nz' not in self.config:
             self.config['Nz'] = 1