aggiunto i fattori cdim per aggiustare separatamente la regolarizzazione sulle diverse direzioni

XRStools/XRS_swissknife.py

@@ -2601,9 +2601,7 @@ def superR_getVolume_Esynt(mydata):
 
     print(" GROUPNAME ", scalprods_groupname)
     
-    target_address = mydata["target_address"]
     output_prefix = mydata["output_prefix"]
-    target_filename, target_groupname = split_hdf5_address(target_address)
 
     interpolation_dict =   json.load( open(mydata["dict_interp"],"r") )
         

XRStools/superr.py

@@ -57,7 +57,7 @@ def my_denoise_tv_chambolle_positive(image, weight=0.1,  n_iter_max=200):
         
 
 def _denoise_tv_chambolle_nd(image, weight=0.1, eps=2.e-4, n_iter_max=200,
-                             positivity=False):
+                             positivity=False, cdim = None):
     """Perform total-variation denoising on n-dimensional images.
 
     Parameters
@@ -139,7 +139,10 @@ def _denoise_tv_chambolle_nd(image, weight=0.1, eps=2.e-4, n_iter_max=200,
                 slices_d[ax] = slice(1, None)
                 slices_p[ax+1] = slice(0, -1)
                 slices_p[0] = ax
-                d[tuple(slices_d)] += p[tuple(slices_p)]
+                if cdim is None:
+                    d[tuple(slices_d)] += p[tuple(slices_p)]
+                else:
+                    d[tuple(slices_d)] += p[tuple(slices_p)]  *cdim[ax]                    
                 slices_d[ax] = slice(None)
                 slices_p[ax+1] = slice(None)
             out_nopos = image + d
@@ -161,7 +164,11 @@ def _denoise_tv_chambolle_nd(image, weight=0.1, eps=2.e-4, n_iter_max=200,
         for ax in range(ndim):
             slices_g[ax+1] = slice(0, -1)
             slices_g[0] = ax
-            g[tuple(slices_g)] = np.diff(out, axis=ax)
+            if cdim is None:
+                g[tuple(slices_g)] = np.diff(out, axis=ax)
+            else:
+                g[tuple(slices_g)] = np.diff(out, axis=ax)*cdim[ax]
+                
             slices_g[ax+1] = slice(None)
 
         norm = np.sqrt((g ** 2).sum(axis=0))[np.newaxis, ...]
@@ -242,7 +249,7 @@ def    Fista( scalDD, scalDS , scalSS,  solution      , niter=500, beta=0.1 ):
         solution[:] =  y - grad/Lip
         
         # solution[:]=skimage.restoration.denoise_tv_chambolle(solution, weight=beta, eps=0.000002) 
-        solution[:]=_denoise_tv_chambolle_nd(solution, weight=beta, eps=0.000002, positivity=True)
+        solution[:]=_denoise_tv_chambolle_nd(solution, weight=beta, eps=0.000002, positivity=True, cdim = [0,0,1])
 
 
         ## solution[:] = np.maximum(solution, 0)