diff --git a/src/toolbox_scs/routines/boz.py b/src/toolbox_scs/routines/boz.py
index 1f231334d476cb429bea8cd7bd4d7b3e37d6bd1d..f118fb80e1b5cd49545d3f4b8429becc6aceea4f 100644
--- a/src/toolbox_scs/routines/boz.py
+++ b/src/toolbox_scs/routines/boz.py
@@ -1071,16 +1071,17 @@ def nl_crit(p, domain, alpha, arr_dark, arr, tid, rois, mask, flat_field,
     # drop saturated shots
     d = data.where(data['sat_sat'] == False, drop=True)
 
-    # calculated error from transmission of 1.0
-    v = d['n'].values.flatten()/d['0'].values.flatten()
-    err1 = 1e8*np.nanmean((v - 1.0)**2)
+    v_1 = snr(d['n'].values.flatten(), d['0'].values.flatten(),
+            methods=['weighted'])
+    err_1 = 1e8*v_1['weighted']['s']**2
 
-    err2 = np.sum((Fmodel-np.arange(2**9))**2)
+    v_2 = snr(d['p'].values.flatten(), d['0'].values.flatten(),
+            methods=['weighted'])
+    err_2 = 1e8*v_2['weighted']['s']**2
 
-    # print(f'{err}: {p}')
-    # logging.info(f'{err}: {p}')
+    err_a = np.sum((Fmodel-np.arange(2**9))**2)
 
-    return (1.0 - alpha)*err1 + alpha*err2
+    return (1.0 - alpha)*0.5*(err_1 + err_2) + alpha*err_a
 
 
 def nl_fit(params, domain):
@@ -1173,10 +1174,27 @@ def inspect_nl_fit(res_fit):
     return f
 
 
-def snr(sig, ref, verbose=False):
-    """ Compute mean, std and SNR with and without weight from transmitted signal sig
-        and I0 signal ref
+def snr(sig, ref, methods=None, verbose=False):
+    """ Compute mean, std and SNR from transmitted signal sig and I0 signal ref.
+
+    Inputs
+    ------
+    sig: 1D signal samples
+    ref: 1D reference samples
+    methods: None by default or list of strings to select which methods to use.
+        Possible values are 'direct', 'weighted', 'diff'. In case of None, all
+        methods will be calculated.
+    verbose: booleand, if True prints calculated values
+
+    Returns
+    -------
+    dictionnary of [methods][value] where value is 'mu' for mean and 's' for
+    standard deviation.
+
     """
+    if methods is None:
+        methods = ['direct', 'weighted', 'diff']
+
     w = ref
     x = sig/ref
 
@@ -1187,30 +1205,37 @@ def snr(sig, ref, verbose=False):
     ref = ref[mask]
     x = x[mask]
 
-    # direct mean and std
-    mu = np.mean(x)
-    s = np.std(x)
-    if verbose:
-        print(f'mu: {mu}, s: {s}, snr: {mu/s}')
     res = {}
-    res['direct'] = {'mu': mu, 's':s}
+
+    # direct mean and std
+    if 'direct' in methods:
+        mu = np.mean(x)
+        s = np.std(x)
+        if verbose:
+            print(f'mu: {mu}, s: {s}, snr: {mu/s}')
+
+        res['direct'] = {'mu': mu, 's':s}
 
     # weighted mean and std
-    wmu = np.sum(sig)/np.sum(ref)
-    v1 = np.sum(w)
-    v2 = np.sum(w**2)
-    ws = np.sqrt(np.sum(w*(x - wmu)**2)/(v1 - v2/v1))
+    if 'weighted' in methods:
+        wmu = np.sum(sig)/np.sum(ref)
+        v1 = np.sum(w)
+        v2 = np.sum(w**2)
+        ws = np.sqrt(np.sum(w*(x - wmu)**2)/(v1 - v2/v1))
+
+        if verbose:
+            print(f'weighted mu: {wmu}, s: {ws}, snr: {wmu/ws}')
 
-    if verbose:
-        print(f'weighted mu: {wmu}, s: {ws}, snr: {wmu/ws}')
-    res['weighted'] = {'mu': wmu, 's':ws}
+        res['weighted'] = {'mu': wmu, 's':ws}
 
     # noise from diff
-    dmu = np.mean(x)
-    ds = np.std(np.diff(x))/np.sqrt(2)
-    if verbose:
-        print(f'diff mu: {dmu}, s: {ds}, snr: {dmu/ds}')
-    res['diff'] = {'mu': dmu, 's':ds}
+    if 'diff' in methods:
+        dmu = np.mean(x)
+        ds = np.std(np.diff(x))/np.sqrt(2)
+        if verbose:
+            print(f'diff mu: {dmu}, s: {ds}, snr: {dmu/ds}')
+
+        res['diff'] = {'mu': dmu, 's':ds}
 
     return res