minor documentation changes

proxtoolbox/Problems/OrbitalTomog/coronene_config.py

@@ -24,9 +24,8 @@ new_config = {
     # What type of measurements are we working with?
     # Options are: 'single diffraction', 'diversity diffraction',
     #              'ptychography', and 'complex'
-    # Options are: '2D_ARPES', '3D_ARPES',
-    #              '2D_time', and '3D_time'
-    'experiment': '2D_ARPES',
+    # Orbital imaging options are: '2D ARPES', '3D ARPES', '2D time', and '3D time''
+    'experiment': '2D ARPES',
 
     # Next we move to things that most of our users will know
     # better than we will.  Some of these may be overwritten in the

proxtoolbox/ProxOperators/P_binary.py

 import numpy as np
 from numpy import shape, real, zeros
 from .proxoperators import ProxOperator
-          
+
+
 class P_binary(ProxOperator):
     """
     Projection subroutine for projecting onto nonnegativity and support constraints
     """
+
     def __init__(self, config):
         """
         Initialization
@@ -13,11 +15,11 @@ class P_binary(ProxOperator):
         Parameters
         ----------
         config : dict - Dictionary containing the problem configuration. It must contain the following mapping:
-        support_idx : array_like - vector of indeces of the nonzero elements of the array
-        """  
-        self.support_idx = config['support_idx'] 
+        support_idx : array_like - vector of indices of the nonzero elements of the array
+        """
+        self.support_idx = config['support_idx']
 
-    def work(self,u):
+    def work(self, u):
         """
         Parameters
         ----------
@@ -26,18 +28,10 @@ class P_binary(ProxOperator):
         Returns
         -------
         p_binary : array_like - the projection IN THE PHYSICAL (time) DOMAIN onto 0 or 1.
-        """ 
+        """
 
         support_idx = self.support_idx
-        p_binary=zeros(shape(u), np.float64)
-        p_binary[support_idx-1] = np.sign(real(u[support_idx-1])-0.5)
-        p_binary[support_idx-1] = np.maximum(p_binary[support_idx-1],1)
+        p_binary = zeros(shape(u), np.float64)
+        p_binary[support_idx - 1] = np.sign(real(u[support_idx - 1]) - 0.5)
+        p_binary[support_idx - 1] = np.maximum(p_binary[support_idx - 1], 1)
         return p_binary
-
-
-
-
-
-
-
-

proxtoolbox/ProxOperators/P_nonneg_sparsity.py

@@ -4,8 +4,9 @@ import numpy as np
 
 class P_nonneg_sparsity(ProxOperator):
     """
-    Projection subroutine for projecting onto support constraints
+    Projection subroutine for projecting onto sparsity constraints
     """
+
     def __init__(self, config):
         """
         Initialization 
@@ -33,9 +34,9 @@ class P_nonneg_sparsity(ProxOperator):
         a = u.shape
         b = 1
         for i in range(len(a)):
-            b=a[i]*b
+            b = a[i] * b
         tmp = np.real(u)
-        tmp[tmp<0] = 0
+        tmp[tmp < 0] = 0
         tmp = np.reshape(tmp, b)
         I = np.argsort(tmp)  # gives indices of sorted array in ascending order
         I = np.flip(I, axis=0)  # reverses the array so that I gives the indices of sorted array in descending order