Fixed issues with bounds and reshaping in QNAP

proxtoolbox/Algorithms/QNAP.py

@@ -83,7 +83,7 @@ class QNAP(Algorithm):
         ##### PREPROCESSING
 
         iter = self.iter
-        prox1 = self.prox1; 
+        prox1 = self.prox1 
         prox2 = self.prox2
 
         if u.ndim < 3:
@@ -145,9 +145,9 @@ class QNAP(Algorithm):
                         gradfnew_vec = (u[:,0]- tmp_u[:,0])
                 else:
                     if iter>3:
-                        uold_vec= np.concatenate((np.real(u[:,0]), np.imag(u[:,0]))).reshape(self.Ny*self.Nx*2, 1)
+                        uold_vec= np.concatenate((np.real(u[:,0]), np.imag(u[:,0]))).reshape(self.Ny*self.Nx*2)
                         
-                        unew_vec=np.concatenate((np.real(tmp_u[:,0]), np.imag(tmp_u[:,0]))).reshape(self.Ny*self.Nx*2, 1)
+                        unew_vec=np.concatenate((np.real(tmp_u[:,0]), np.imag(tmp_u[:,0]))).reshape(self.Ny*self.Nx*2)
                         
                         gradfold_vec = gradfnew_vec
                         gradfnew_vec = uold_vec-unew_vec
@@ -167,16 +167,16 @@ class QNAP(Algorithm):
                             tmp_u[:,j]=tmp_u_vec
                         gap[iter-1]=gap[iter-1]/(self.Nx*self.Ny)
                     else:
-                        uold_vec= np.array((np.real(u[:,0]), np.imag(u[:,0]))).reshape(self.Ny*self.Nx*2, 1)
-                        unew_vec= np.array((np.real(tmp_u[:,0]), np.imag(tmp_u[:,0]))).reshape(self.Ny*self.Nx*2, 1)
+                        uold_vec= np.array((np.real(u[:,0]), np.imag(u[:,0]))).reshape(self.Ny*self.Nx*2)
+                        unew_vec= np.array((np.real(tmp_u[:,0]), np.imag(tmp_u[:,0]))).reshape(self.Ny*self.Nx*2)
                         gradfnew_vec = uold_vec-unew_vec
 
             elif (p!=1) and (q==1):
                 if (np.all(isreal(u))):
                     tmp2 = u[:,:,0]
-                    uold_vec= tmp2.reshape(self.Nx*self.Ny,1)
+                    uold_vec= tmp2.reshape(self.Nx*self.Ny)
                     tmp2 = tmp_u[:,:,0]
-                    unew_vec = tmp2.reshape(self.Nx*self.Ny,1)
+                    unew_vec = tmp2.reshape(self.Nx*self.Ny)
                     if iter<=3:
                         gradfnew_vec = uold_vec-unew_vec
                     else:
@@ -196,9 +196,9 @@ class QNAP(Algorithm):
                 
                 else:
                     tmp2=np.concatenate((np.real(u[:,:,0]),np.imag(u[:,:,0])), axis=1)
-                    uold_vec=tmp2.reshape(self.Nx*self.Ny*2,1)
+                    uold_vec=tmp2.reshape(self.Nx*self.Ny*2)
                     tmp2= np.concatenate((np.real(tmp_u[:,:,0]), np.imag(tmp_u[:,:,0])), axis=1)
-                    unew_vec = tmp2.reshape(self.Nx*self.Ny*2,1)
+                    unew_vec = tmp2.reshape(self.Nx*self.Ny*2)
                     if iter<=3:
                         gradfnew_vec = uold_vec-unew_vec
                     else:
@@ -212,7 +212,7 @@ class QNAP(Algorithm):
                     # now reshape and replace u 
                         gap[iter-1]=gap[iter-1]/(self.Nx*self.Ny)
                         tmp2=unew_vec.reshape(self.Ny,self.Nx*2)
-                        unew=tmp2[:,1:self.Nx] +1j*tmp2[:,self.Nx+1:2*self.Nx]
+                        unew=tmp2[:,0:self.Nx] +1j*tmp2[:,self.Nx:2*self.Nx]
                         for j in range(self.product_space_dimension):
                             tmp_u[:,:,j]=unew
             else: # product space of 3D arrays
@@ -292,7 +292,7 @@ class QNAP(Algorithm):
             self.u1 = tmp;
             self.u2 = tmp2;
 
-        change=change[0:iter];
+        change=change[1:iter];
         self.iter = iter;
         print(self.iter)
         self.change = change;