diff --git a/commit/solvers.py b/commit/solvers.py
index 264afbe1..9cf4c6d6 100755
--- a/commit/solvers.py
+++ b/commit/solvers.py
@@ -11,9 +11,10 @@
 import warnings
 eps = np.finfo(float).eps
 
- # removed, for now, projection_onto_l2_ball
-from commit.proximals import non_negativity, omega_group_sparsity, prox_group_sparsity, soft_thresholding
 list_regularizers = [None, 'sparsity', 'group_sparsity']
+from commit.proximals import non_negativity, omega_group_sparsity, prox_group_sparsity, soft_thresholding
+# removed, for now, projection_onto_l2_ball
+
 
 def init_regularisation(
     commit_evaluation,
@@ -129,41 +130,38 @@ def init_regularisation(
 
 
 def regularisation2omegaprox(regularisation):
-    lambdaIC  = float(regularisation.get('lambdaIC'))
-    lambdaEC  = float(regularisation.get('lambdaEC'))
-    lambdaISO = float(regularisation.get('lambdaISO'))
+    lambdaIC  = regularisation.get('lambdaIC')
+    lambdaEC  = regularisation.get('lambdaEC')
+    lambdaISO = regularisation.get('lambdaISO')
     if lambdaIC<0.0 or lambdaEC<0.0 or lambdaISO<0.0:
         raise ValueError('Negative regularisation strengths are not allowed')
 
-    regIC  = regularisation.get('regIC')
-    regEC  = regularisation.get('regEC')
-    regISO = regularisation.get('regISO')
-    if not regIC in list_regularizers:
+    if not regularisation['regIC'] in list_regularizers:
         raise ValueError('Regularizer for the IC compartment not implemented')
-    if not regEC in list_regularizers:
+    if not regularisation['regEC'] in list_regularizers:
         raise ValueError('Regularizer for the EC compartment not implemented')
-    if not regISO in list_regularizers:
+    if not regularisation['regISO'] in list_regularizers:
         raise ValueError('Regularizer for the ISO compartment not implemented')
 
     # Intracellular Compartment
     startIC = regularisation.get('startIC')
     sizeIC  = regularisation.get('sizeIC')
-    if regIC is None:
+    if regularisation['regIC'] is None:
         omegaIC = lambda x: 0.0
         if regularisation.get('nnIC')==True:
             proxIC = lambda x, _: non_negativity(x,startIC,sizeIC)
         else:
             proxIC = lambda x, _: x
-    elif regIC == 'sparsity':
+    elif regularisation['regIC'] == 'sparsity':
         omegaIC = lambda x: lambdaIC * np.linalg.norm(x[startIC:sizeIC],1)
         if regularisation.get('nnIC'):
             proxIC = lambda x, scaling: non_negativity(soft_thresholding(x,scaling*lambdaIC,startIC,sizeIC),startIC,sizeIC)
         else:
             proxIC = lambda x, _: non_negativity(x,startIC,sizeIC)
-    # elif regIC == 'l2':
+    # elif regularisation['regIC'] == 'smoothness':
     #     omegaIC = lambda x: lambdaIC * np.linalg.norm(x[startIC:sizeIC])
     #     proxIC  = lambda x: projection_onto_l2_ball(x, lambdaIC, startIC, sizeIC)
-    elif regIC == 'group_sparsity':
+    elif regularisation['regIC'] == 'group_sparsity':
         structureIC = regularisation.get('structureIC')
         groupWeightIC = regularisation.get('weightsIC')
         if not len(structureIC) == len(groupWeightIC):
@@ -189,38 +187,38 @@ def regularisation2omegaprox(regularisation):
     # Extracellular Compartment
     startEC = regularisation.get('startEC')
     sizeEC  = regularisation.get('sizeEC')
-    if regEC is None:
+    if regularisation['regIC'] is None:
         omegaEC = lambda x: 0.0
         if regularisation.get('nnEC')==True:
             proxEC = lambda x, _: non_negativity(x,startEC,sizeEC)
         else:
             proxEC = lambda x, _: x
-    elif regEC == 'sparsity':
+    elif regularisation['regIC'] == 'sparsity':
         omegaEC = lambda x: lambdaEC * np.linalg.norm(x[startEC:(startEC+sizeEC)],1)
         if regularisation.get('nnEC'):
             proxEC = lambda x, scaling: non_negativity(soft_thresholding(x,scaling*lambdaEC,startEC,sizeEC),startEC,sizeEC)
         else:
             proxEC = lambda x, scaling: soft_thresholding(x,scaling*lambdaEC,startEC,sizeEC)
-    # elif regEC == 'l2':
+    # elif regularisation['regIC'] == 'smoothness':
     #     omegaEC = lambda x: lambdaEC * np.linalg.norm(x[startEC:(startEC+sizeEC)])
     #     proxEC  = lambda x: projection_onto_l2_ball(x, lambdaEC, startEC, sizeEC)
 
     # Isotropic Compartment
     startISO = regularisation.get('startISO')
     sizeISO  = regularisation.get('sizeISO')
-    if regISO is None:
+    if regularisation['regISO'] is None:
         omegaISO = lambda x: 0.0
         if regularisation.get('nnISO')==True:
             proxISO = lambda x, _: non_negativity(x,startISO,sizeISO)
         else:
             proxISO  = lambda x, _: x
-    elif regISO == 'sparsity':
+    elif regularisation['regISO'] == 'sparsity':
         omegaISO = lambda x: lambdaISO * np.linalg.norm(x[startISO:(startISO+sizeISO)],1)
         if regularisation.get('nnISO'):
             proxISO  = lambda x, scaling: non_negativity(soft_thresholding(x,scaling*lambdaISO,startISO,sizeISO),startISO,sizeISO)
         else:
             proxISO  = lambda x, scaling: soft_thresholding(x,scaling*lambdaISO,startISO,sizeISO)
-    # elif regISO == 'l2':
+    # elif regularisation['regISO'] == 'l2':
     #     omegaISO = lambda x: lambdaISO * np.linalg.norm(x[startISO:(startISO+sizeISO)])
     #     proxISO  = lambda x: projection_onto_l2_ball(x, lambdaISO, startISO, sizeISO)