diff --git a/Code/S_get_symbolic_expr_error.py b/Code/S_get_symbolic_expr_error.py
index 0e7bad6..fede410 100644
--- a/Code/S_get_symbolic_expr_error.py
+++ b/Code/S_get_symbolic_expr_error.py
@@ -9,32 +9,34 @@ from os import path
 from sympy import Symbol, lambdify, N
 
 def get_symbolic_expr_error(data,expr):
-    N_vars = len(data[0])-1
-    possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
-    variables = []
-    for i in range(N_vars):
-        variables = variables + [possible_vars[i]]
-    eq = parse_expr(expr)
-    f = lambdify(variables, N(eq))
-    real_variables = []
+    try:
+        N_vars = len(data[0])-1
+        possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
+        variables = []
+        for i in range(N_vars):
+            variables = variables + [possible_vars[i]]
+        eq = parse_expr(expr)
+        f = lambdify(variables, N(eq))
+        real_variables = []
 
-    for i in range(len(data[0])-1):
-        check_var = "x"+str(i)
-        if check_var in np.array(variables).astype('str'):
-            real_variables = real_variables + [data[:,i]]
-    
-    # Remove accidental nan's
-    good_idx = np.where(np.isnan(f(*real_variables))==False)
-   
-    # use this to get rid of cases where the loss gets complex because of transformations of the output variable
-    if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
+        for i in range(len(data[0])-1):
+            check_var = "x"+str(i)
+            if check_var in np.array(variables).astype('str'):
+                real_variables = real_variables + [data[:,i]]
+        
+        # Remove accidental nan's
+        good_idx = np.where(np.isnan(f(*real_variables))==False)
+       
+        # use this to get rid of cases where the loss gets complex because of transformations of the output variable
+        if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
+            return 1000000
+        else:
+            try:
+                #return np.sqrt(np.mean((f(*real_variables)[good_idx]-data[good_idx][:,-1])**2))/np.sqrt(np.mean(data[good_idx][:,-1]**2))
+                return np.mean(np.log2(1+abs(f(*real_variables)[good_idx]-data[good_idx][:,-1])*2**30))
+            except:
+                # use this for the case in which the expression is just one number (i.e. not array)
+                #return np.sqrt(np.mean((f(*real_variables)-data[:,-1])**2))/np.sqrt(np.mean(data[:,-1]**2))
+                return np.mean(np.log2(1+abs(f(*real_variables)-data[:,-1])*2**30))
+    except:
         return 1000000
-    else:
-        try:
-            #return np.sqrt(np.mean((f(*real_variables)[good_idx]-data[good_idx][:,-1])**2))/np.sqrt(np.mean(data[good_idx][:,-1]**2))
-            return np.mean(np.log2(1+abs(f(*real_variables)[good_idx]-data[good_idx][:,-1])*2**30))
-        except:
-            # use this for the case in which the expression is just one number (i.e. not array)
-            #return np.sqrt(np.mean((f(*real_variables)-data[:,-1])**2))/np.sqrt(np.mean(data[:,-1]**2))
-            return np.mean(np.log2(1+abs(f(*real_variables)-data[:,-1])*2**30))
-