fixed and refactor code

commit/core.pyx

@@ -91,7 +91,6 @@ cdef class Evaluation :
     cdef public contribution_mask
     cdef public contribution_fibs
     cdef public contribution_voxels
-    cdef public debias
     cdef public verbose
 
     def __init__( self, study_path='.', subject='.' ) :
@@ -118,7 +117,6 @@ cdef class Evaluation :
         self.contribution_mask      = None # set by "fit" method
         self.contribution_voxels    = None # set by "fit" method
         self.contribution_fibs      = None
-        self.debias                 = False
         self.verbose                = 3
 
         # store all the parameters of an evaluation with COMMIT
@@ -763,8 +761,6 @@ cdef class Evaluation :
             logger.error( 'Data not loaded; call "load_data()" first' )
 
         y = self.niiDWI_img[ self.DICTIONARY['MASK_ix'], self.DICTIONARY['MASK_iy'], self.DICTIONARY['MASK_iz'], : ].flatten().astype(np.float64)
-        # y[y < 0] = 0
-        print(f"place of first non-zero voxel in input data: {np.where(y==1.07405806)}")
         return y
 
 
@@ -1223,7 +1219,7 @@ cdef class Evaluation :
         logger.info( f'[ {format_time(time.time() - tr)} ]' )
 
 
-    def fit( self, tol_fun=1e-3, tol_x=1e-6, max_iter=100, x0=None, confidence_map_filename=None, confidence_map_rescale=False, debias=False ) :
+    def fit( self, tol_fun=1e-3, tol_x=1e-6, max_iter=100, x0=None, confidence_map_filename=None, confidence_map_rescale=False, debias=True ) :
         """Fit the model to the data.
 
         Parameters
@@ -1340,19 +1336,15 @@ cdef class Evaluation :
         self.CONFIG['optimization']['max_iter']       = max_iter
         self.CONFIG['optimization']['regularisation'] = self.regularisation_params
 
-        if debias:
-            self.debias = True
-            self.CONFIG['optimization']['x0'] = x0
-            self.confidence_array = confidence_array
-
-
         # run solver
         t = time.time()
         with ProgressBar(disable=self.verbose!=3, hide_on_exit=True) as pb:
             self.x, opt_details = commit.solvers.solve(self.get_y(), self.A, self.A.T, tol_fun=tol_fun, tol_x=tol_x, max_iter=max_iter, verbose=self.verbose, x0=x0, regularisation=self.regularisation_params, confidence_array=confidence_array)
 
-        if self.debias:
-            logger.info( 'Recomputing coefficients' )
+        if (self.regularisation_params['regIC']!=None or self.regularisation_params['regEC']!= None or self.regularisation_params['regISO']!= None) and debias:
+            temp_verb = self.verbose
+            logger.info( 'Running debias' )
+            self.set_verbose(0)
             xic, _, _ = self.get_coeffs() 
             weights_in = pjoin( self.get_config('TRACKING_path'), 'streamline_weights.txt' )
             np.savetxt(weights_in, xic)
@@ -1363,9 +1355,6 @@ cdef class Evaluation :
 
             filter(dictionary_info['filename_tractogram'], tractogram_filtered, minweight=0.000000000000001, weights_in=weights_in, force=True, verbose=0)
 
-            # # RE-RUN COMMIT WITH THE FILTERED TRACTOGRAM
-            # path_COMMIT = os.path.join(local_path, "COMMIT_master_debias")
-
             trk2dictionary.run(
                 filename_tractogram = tractogram_filtered,
                 TCK_ref_image = dictionary_info['TCK_ref_image'],
@@ -1396,9 +1385,11 @@ cdef class Evaluation :
 
             self.A = operator.LinearOperator( self.DICTIONARY, self.KERNELS, self.THREADS, nolut=True if hasattr(self.model, 'nolut') else False )
             self.set_regularisation()
+            self.set_verbose(temp_verb)
 
-            with ProgressBar(disable=self.verbose!=3, hide_on_exit=True) as pb:
-                self.x, opt_details = commit.solvers.solve(self.get_y(), self.A, self.A.T, tol_fun=self.CONFIG['optimization']['tol_fun'], tol_x=self.CONFIG['optimization']['tol_x'], max_iter=self.CONFIG['optimization']['max_iter'], verbose=self.verbose, x0=self.CONFIG['optimization']['x0'], regularisation=self.regularisation_params, confidence_array=self.confidence_array)
+            logger.subinfo('Recomputing coefficients', indent_lvl=1, indent_char='*', with_progress=True)
+            with ProgressBar(disable=self.verbose< 3, hide_on_exit=True, subinfo=True) as pbar:
+                self.x, opt_details = commit.solvers.solve(self.get_y(), self.A, self.A.T, tol_fun=tol_fun, tol_x=tol_x, max_iter=max_iter, verbose=0, x0=x0, regularisation=self.regularisation_params, confidence_array=confidence_array)
 
         self.CONFIG['optimization']['fit_details'] = opt_details
         self.CONFIG['optimization']['fit_time'] = time.time()-t
@@ -1446,7 +1437,7 @@ cdef class Evaluation :
         return xic, xec, xiso
 
 
-    def save_results( self, path_suffix=None, coeffs_format='%.5e', stat_coeffs='sum', save_est_dwi=False, do_reweighting=True, debias=False ) :
+    def save_results( self, path_suffix=None, coeffs_format='%.5e', stat_coeffs='sum', save_est_dwi=False, do_reweighting=True ) :
         """Save the output (coefficients, errors, maps etc).
 
         Parameters