diff --git a/hp4155/I-V_new.ipynb b/hp4155/I-V_new.ipynb
index ef2bbf5ca4fa5fd75e9c4523e6bbc2c61ba3143b..45a841d30a954312290f9bd991a8fa2ead71dc12 100644
--- a/hp4155/I-V_new.ipynb
+++ b/hp4155/I-V_new.ipynb
@@ -58,30 +58,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "2bcfdbc2-8b0a-4d41-b8bf-fca0da5b1e08",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Voltage(V)  Current(A)\n",
-      "0     -10.00020      -0.050\n",
-      "1     -10.00016      -0.049\n",
-      "2     -10.00018      -0.048\n",
-      "3     -10.00012      -0.047\n",
-      "4     -10.00020      -0.046\n",
-      "..          ...         ...\n",
-      "96      9.99958       0.046\n",
-      "97      9.99968       0.047\n",
-      "98      9.99960       0.048\n",
-      "99      9.99964       0.049\n",
-      "100     9.99960       0.050\n",
-      "\n",
-      "[101 rows x 2 columns]\n"
-     ]
-    },
     {
      "name": "stdin",
      "output_type": "stream",
@@ -94,112 +74,24 @@
      "output_type": "stream",
      "text": [
       "     Voltage(V)  Current(A)\n",
-      "0     -10.00016      -0.050\n",
-      "1     -10.00018      -0.049\n",
-      "2     -10.00012      -0.048\n",
+      "0     -10.00026      -0.050\n",
+      "1     -10.00020      -0.049\n",
+      "2     -10.00028      -0.048\n",
       "3     -10.00022      -0.047\n",
-      "4     -10.00014      -0.046\n",
+      "4     -10.00024      -0.046\n",
       "..          ...         ...\n",
-      "96      9.99966       0.046\n",
+      "96      9.99952       0.046\n",
       "97      9.99960       0.047\n",
-      "98      9.99970       0.048\n",
-      "99      9.99958       0.049\n",
+      "98      9.99960       0.048\n",
+      "99      9.99952       0.049\n",
       "100     9.99964       0.050\n",
       "\n",
       "[101 rows x 2 columns]\n"
      ]
     },
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "please press enter to continue with the next measurement or finish after the last measurement! \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Voltage(V)  Current(A)\n",
-      "0     -10.00010      -0.050\n",
-      "1     -10.00022      -0.049\n",
-      "2     -10.00012      -0.048\n",
-      "3     -10.00022      -0.047\n",
-      "4     -10.00018      -0.046\n",
-      "..          ...         ...\n",
-      "96      9.99964       0.046\n",
-      "97      9.99956       0.047\n",
-      "98      9.99962       0.048\n",
-      "99      9.99960       0.049\n",
-      "100     9.99962       0.050\n",
-      "\n",
-      "[101 rows x 2 columns]\n"
-     ]
-    },
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "please press enter to continue with the next measurement or finish after the last measurement! \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Voltage(V)  Current(A)\n",
-      "0     -10.00022      -0.050\n",
-      "1     -10.00018      -0.049\n",
-      "2     -10.00022      -0.048\n",
-      "3     -10.00020      -0.047\n",
-      "4     -10.00016      -0.046\n",
-      "..          ...         ...\n",
-      "96      9.99960       0.046\n",
-      "97      9.99964       0.047\n",
-      "98      9.99964       0.048\n",
-      "99      9.99960       0.049\n",
-      "100     9.99968       0.050\n",
-      "\n",
-      "[101 rows x 2 columns]\n"
-     ]
-    },
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "please press enter to continue with the next measurement or finish after the last measurement! \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "     Voltage(V)  Current(A)\n",
-      "0     -10.00022      -0.050\n",
-      "1     -10.00016      -0.049\n",
-      "2     -10.00020      -0.048\n",
-      "3     -10.00022      -0.047\n",
-      "4     -10.00016      -0.046\n",
-      "..          ...         ...\n",
-      "96      9.99966       0.046\n",
-      "97      9.99956       0.047\n",
-      "98      9.99964       0.048\n",
-      "99      9.99960       0.049\n",
-      "100     9.99962       0.050\n",
-      "\n",
-      "[101 rows x 2 columns]\n"
-     ]
-    },
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "please press enter to continue with the next measurement or finish after the last measurement! \n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -237,7 +129,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,
diff --git a/hp4155/__pycache__/measurements.cpython-311.pyc b/hp4155/__pycache__/measurements.cpython-311.pyc
index 135684d838e3c66a96f49d100f3aff051ffa51da..c63678c562a8e2fc94f825304eb249c92335abac 100644
Binary files a/hp4155/__pycache__/measurements.cpython-311.pyc and b/hp4155/__pycache__/measurements.cpython-311.pyc differ
diff --git a/hp4155/__pycache__/module.cpython-311.pyc b/hp4155/__pycache__/module.cpython-311.pyc
index f4bbb5db365a55ac6b34899f2c06ab339754ead9..d315b5ac9dbebade13b73206fd6fbb9b57be82a6 100644
Binary files a/hp4155/__pycache__/module.cpython-311.pyc and b/hp4155/__pycache__/module.cpython-311.pyc differ
diff --git a/hp4155/measurements.py b/hp4155/measurements.py
index 6a840af0d761ebe354d5ed0622f106900b07cca7..1498546706403441bc1928f19343f75a1d3972e4 100644
--- a/hp4155/measurements.py
+++ b/hp4155/measurements.py
@@ -173,6 +173,7 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         #smu2 is constant and I
         device.smu_mode_meas(2,'I')
         device.smu_function_sweep(2,'CONS')
+        device.cons_smu_value(2,0)
     
         #smu3 is var1 and I
         device.smu_mode_meas(3,'I')
@@ -181,6 +182,7 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         #smu4 is constant and I
         device.smu_mode_meas(4,'I')
         device.smu_function_sweep(4,'CONS')
+        device.cons_smu_value(4,0)
     
         #select compliance of smu3
         device.comp('VAR1',comp)
@@ -198,6 +200,11 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         device.start_value_sweep(start)
         device.step_sweep(step)
         device.stop_value_sweep(stop)
+
+        while True:      
+            answer = input('please press enter to continue with the next measurement or finish after the last measurement!')
+            if answer == "":
+                break
     
         #start measurement
         device.single_measurement()
@@ -206,10 +213,16 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
     
     
         voltage_values = device.return_data('V3')
+        v2 = device.return_data('V2')
+        v4 = device.return_data('V4')
         current_values = device.return_data('I3')
         
         ctlm_voltage.append(voltage_values)
         ctlm_current.append(current_values)
+
+        voltage=[]
+        for i in range(len(v2)):
+            voltage.append(v4[i]-v2[i])
     
         resistance_values=[]
     
@@ -219,6 +232,7 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         
         #plot results of the single measurement
         plt.plot(voltage_values,current_values,label=f"distance={distances[j]}")
+        plt.plot(voltage,current_values,label="v4-v2")
         
         #save measurement as txt file
         #add title to the results
diff --git a/hp4155/module.py b/hp4155/module.py
index 70b4cca0ea46a8461deef64822f70138db92ddd9..73132cc0be550f39d0085c999dad135bc417744f 100644
--- a/hp4155/module.py
+++ b/hp4155/module.py
@@ -103,6 +103,10 @@ class HP4155a(object):
     def stop_value_sweep(self,value):
         command=f":PAGE:MEAS:VAR1:STOP {value}"
         self.inst.write(command)
+
+    def cons_smu_value(self,smu_number,value):
+        command =f"PAGE:MEAS:CONS:SMU{smu_number} {value}"
+        self.inst.write(command)
         
         ''' smu1 is constant and common, SMU3 is controlled through the Var1(v) which has a start value, a stop value, and a step
         We don't need SMU2 and SMU4