diff --git a/hp4155/.ipynb_checkpoints/measurements-checkpoint.py b/hp4155/.ipynb_checkpoints/measurements-checkpoint.py
index c32828b89e869b04c1c8bb199b4bced1433b6a9c..8d2afca32f1630ef4ae7224196e021e7645c43e3 100644
--- a/hp4155/.ipynb_checkpoints/measurements-checkpoint.py
+++ b/hp4155/.ipynb_checkpoints/measurements-checkpoint.py
@@ -5,6 +5,10 @@ import matplotlib.pyplot as plt
 import pandas as pd
 from datetime import datetime
 import os 
+from sklearn.linear_model import LinearRegression
+import sys
+import numpy as np
+from IPython.display import display, clear_output
 
 
 def I_V_Measurement(start,stop,step):
@@ -61,9 +65,10 @@ def I_V_Measurement(start,stop,step):
     print(df)
     
     #exporting the data frame in an excel file
-    file_name ="\results.csv"
-    path = "C:\Users\user\Desktop"
-    directory = os.path.join(cwd,file_name)
+    file_name ="results.csv"
+    path = r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+    #r"C:\Users\user\Desktop"
+    directory = os.path.join(path,file_name)
     df.to_csv(directory)
     
     del device
@@ -141,7 +146,7 @@ def stress_sampling(V2_stress=10,V3_stress=3,stress_time=30,V2_sampling=10,V3_sa
     
 
 #prepare full measurement
-def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED'):
+def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED',innen=0):
     
     #connect to the device
     device = module.HP4155a('GPIB0::17::INSTR')
@@ -153,6 +158,10 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
     plt.ylabel('Current(A)')
     plt.title("CTLM plot")
     
+    #lists for appending all data values
+    ctlm_voltage = []
+    ctlm_current = []
+    ctlm_resistance = []
     #execute five measurements
     for j in range(len(distances)):
         
@@ -160,7 +169,12 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         device.reset()
         device.inst.write(":PAGE:MEAS")
         device.inst.write(":PAGE:CHAN:MODE SWEEP") #go to sweep page and prepare sweep measurement
-       
+
+        #vsus and vmus are disabled
+        device.disable_vsu(1)
+        device.disable_vsu(2)
+        device.disable_vmu(1)
+        device.disable_vmu(2)
         #smu1 is constant and common
         device.smu_mode_meas(1,'COMM')
         device.smu_function_sweep(1,'CONS')
@@ -168,6 +182,7 @@ 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')
@@ -176,6 +191,7 @@ 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)
@@ -185,7 +201,15 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         device.const_comp(4,10)
     
         # smu1 is common and compliance is automatically set to maximum
-    
+        
+        #define user functions 
+        device.user_function('I','A','I3')
+        print(device.error())
+        device.user_function('V','V','V4-V2')
+        print(device.error())
+        device.user_function('R','OHM','DIFF(V,I)')
+        print(device.error())
+        
         #integration time
         device.integration_time(time)
     
@@ -202,15 +226,22 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
     
         voltage_values = device.return_data('V3')
         current_values = device.return_data('I3')
-    
-        resistance_values=[]
-    
-        for i in range(len(voltage_values)):
-            pass
-            #resistance_values.append(voltage_values[i]/current_values[i])
+        voltage = device.return_data('V') 
+        print(voltage_values)
+        print(current_values)
+        
+        ctlm_voltage.append(voltage_values)
+        ctlm_current.append(current_values)
         
         #plot results of the single measurement
-        plt.plot(voltage_values,current_values,label=f"distance={distances[j]}")
+        #plt.plot(voltage_values,current_values,label=f"distance={distances[j]}")
+        plt.plot(voltage_values,current_values,label='ausgangvoltage')
+        plt.legend()
+        plt.show()
+        plt.figure()
+        plt.plot(voltage,current_values,label='Eingangsvoltage')
+        plt.legend()
+        plt.show()
         
         #save measurement as txt file
         #add title to the results
@@ -220,23 +251,209 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         df = pd.DataFrame(data)
         print(df)
         
-        file_name = field_name+"_CTLM_"+str(j+1)+"_"+date+".txt"
-        cwd = os.getcwd()
-        path = os.path.join(cwd,file_name)
+        file_name = field_name+"_CTLM_"+str(j+1)+".txt"
+        path =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+        directory = os.path.join(path,file_name)
         #export DataFrame to text file (keep header row and index column)
-        #f=open(path, 'a')
-        #f.write('title\n')
-        #df_string = df.to_string()
-        #f.write(df_string)
+        f=open(directory, 'a')
+        f.write('title\n')
+        df_string = df.to_string()
+        f.write(df_string)
+        
+        #plot diagramm
+        #plt.legend()
+        #plt.show()
 
         #wait for confirmation from user after a measurement is done 
         while True:      
             answer = input('please press enter to continue with the next measurement or finish after the last measurement!')
             if answer == "":
                 break
-    
-    #close the connection and plot all the diagramms
-    plt.legend()
-    plt.show()
+
+    #close the connection and plot all the diagramms 
     del device
 
+#tlm/ctlm final part
+def tlm_final(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),comp=10,distances=(5,10,15,25,45),time='MED',innen=0):
+    
+    #connect to the device
+    device = module.HP4155a('GPIB0::17::INSTR')
+    date = str(datetime.today().replace(microsecond=0))
+    
+    #initialize figure
+    fig, (ax1, ax2) = plt.subplots(2,sharex=True,figsize=(8,6)) #the plots share the same x axis 
+    fig.suptitle('CTLM plot')
+    ax1.set_title('I(V)')
+    ax1.set(xlabel='Voltage(V)',ylabel='Current(A)')
+    ax2.set_title('R(V)')
+    ax2.set(xlabel='Voltage(V)',ylabel='Resistance(Ohm)')          
+            
+    #repeat five times
+    for j in range(len(distances)):
+        #setup
+        device.reset()
+        device.inst.write(":PAGE:MEAS")
+        device.inst.write(":PAGE:CHAN:MODE SWEEP") #go to sweep page and prepare sweep measurement
+
+        #disable vmus and vsus
+        device.disable_vsu(1)
+        device.disable_vsu(2)
+        device.disable_vmu(1)
+        device.disable_vmu(2)
+
+        #smu1 is constant and common
+        device.smu_mode_meas(1,'COMM')
+        device.smu_function_sweep(1,'CONS')
+    
+        #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')
+        device.smu_function_sweep(3,'VAR1')
+    
+        #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)
+    
+        #compliance of smu2 and smu4 is 10V
+        device.const_comp(2,10)
+        device.const_comp(4,10)
+
+        #define user functions
+        device.user_function('I','A','I3')
+        device.user_function('V','V','V4-V2')
+        device.user_function('R','OHM','DIFF(V,I)')
+        device.user_function('VS','V','V3')
+        
+
+        #integration time
+        device.integration_time(time)
+        
+        #define start-step-stop
+        device.start_value_sweep(start)
+        device.step_sweep(step)
+        device.stop_value_sweep(stop)
+
+        #display variables
+        device.display_variable('X','V')
+        device.display_variable('Y1','I')
+        device.display_variable('Y2','R')
+
+        device.display_variable_min_max('X','MIN',-10)
+        device.display_variable_min_max('X','MAX',10)
+        device.display_variable_min_max('Y1','MIN',start)
+        device.display_variable_min_max('Y1','MAX',stop)
+        device.display_variable_min_max('Y2','MIN',0)
+        device.display_variable_min_max('Y2','MAX',200)
+
+        #start measurement
+        device.single_measurement()
+        while device.operation_completed() == False:
+                pass
+            
+        #return data from the device
+        
+        V=device.return_data('V')
+        I=device.return_data('I')
+        R=device.return_data('R')
+        
+        # now we have to remove resistance values that R=inf(nan) that means that the current is zero
+        for i in range(len(R)):
+            if R[i]>10**6:
+                R[i]=float('NAN')
+                
+        # plot the results
+        ax1.plot(V,I,label=f"distance={distances[j]}")
+        ax2.plot(V,R,label=f"distance={distances[j]}")
+        ax1.legend(loc='best')
+        ax2.legend(loc="best")
+        clear_output(wait=True)
+        fig.tight_layout()
+        display(fig)
+        
+        #export data frame to csv(for evaluation) and txt 
+        header = ['Voltage(V)', 'Current(A)','Resistance(Ohm)']
+
+        data = {header[0]:V,header[1]:I,header[2]:R}
+        df = pd.DataFrame(data)
+        print(df)
+        
+        #export to txt 
+        #check tlm or ctlm
+        if(innen==0):
+            #specify path and file_name
+            file_name = field_name+"_TLM_"+str(j+1)+".txt"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_TLM_"+str(j+1)+"_"str(i)+".txt"
+                path= os.path.join(location,file_name)
+                i=i+1
+        else:
+            #specify path and file_name
+            file_name = field_name+"_CTLM_"+str(j+1)+".txt"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_CTLM_"+str(j+1)+"_"str(i)+".txt"
+                path= os.path.join(location,file_name)
+                i=i+1
+            
+        title = "measured field:"+field_name+"\ndistance:"+str(distances[j])+"\nI:"+str(start)+"A to "+str(stop)+"A with step:"+str(step)+"\n"
+        
+        f=open(path, 'a')
+        f.write(title)
+        df_string = df.to_string()
+        f.write(df_string)
+        f.close()
+        
+        #export to csv for evaluataion
+        
+         if(innen==0):
+            #specify path and file_name
+            file_name = field_name+"_TLM_"+str(j+1)+".csv"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_TLM_"+str(j+1)+"_"str(i)+".csv"
+                path= os.path.join(location,file_name)
+                i=i+1
+        else:
+            #specify path and file_name
+            file_name = field_name+"_CTLM_"+str(j+1)+".csv"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_CTLM_"+str(j+1)+"_"str(i)+".csv"
+                path= os.path.join(location,file_name)
+                i=i+1
+        
+        df.to_csv(path)
+        
+        # give user confirmation to do the next measurement
+        
+        while True:
+            answer=input("Press enter to continue or anything else to stop the programm:")
+            if answer=="":
+                break
+            else:
+                sys.exit()
diff --git a/hp4155/.ipynb_checkpoints/I-V measurement-checkpoint.ipynb b/hp4155/.ipynb_checkpoints/tlm_eval-checkpoint.ipynb
similarity index 100%
rename from hp4155/.ipynb_checkpoints/I-V measurement-checkpoint.ipynb
rename to hp4155/.ipynb_checkpoints/tlm_eval-checkpoint.ipynb
diff --git a/hp4155/ctlm_part2.ipynb b/hp4155/ctlm_part2.ipynb
index efa6d03c82087e054d1e8678e8b35b2505cb0f24..b22d0f45621cb38123b853b4e886cc48013a62ef 100644
--- a/hp4155/ctlm_part2.ipynb
+++ b/hp4155/ctlm_part2.ipynb
@@ -7,20 +7,30 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "' this is the part 2 of the single step of ctlm measurement. \\nHere we save the results in a txt file and we do the linear regression\\n'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pandas in /opt/conda/lib/python3.9/site-packages (2.1.0)\n",
+      "Requirement already satisfied: numpy>=1.22.4 in /opt/conda/lib/python3.9/site-packages (from pandas) (1.25.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.9/site-packages (from pandas) (2021.3)\n",
+      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.9/site-packages (from pandas) (2023.3)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
+      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.9/site-packages (1.3.0)\n",
+      "Requirement already satisfied: scipy>=1.5.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn) (1.11.2)\n",
+      "Requirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.9/site-packages (from scikit-learn) (1.3.2)\n",
+      "Requirement already satisfied: numpy>=1.17.3 in /opt/conda/lib/python3.9/site-packages (from scikit-learn) (1.25.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn) (3.2.0)\n"
+     ]
     }
    ],
    "source": [
     "''' this is the part 2 of the single step of ctlm measurement. \n",
     "Here we save the results in a txt file and we do the linear regression\n",
-    "'''"
+    "'''\n",
+    "\n",
+    "!pip install pandas\n",
+    "!pip install -U scikit-learn"
    ]
   },
   {
@@ -74,8 +84,7 @@
    ],
    "source": [
     "voltage = [-9.953434, -9.953406, -9.953396, -9.953398, -9.953468, -9.95352, -9.953564, -9.953582, -9.953562, -9.95354, -9.95352, -9.953506, -9.953494, -9.953522, -9.953548, -9.95361, -9.953674, -9.953672, -9.953628, -9.953628, -9.953588, -9.953552, -9.953556, -9.953562, -9.953608, -9.953652, -9.953676, -9.953696, -9.953716, -9.95369, -9.829082, -9.276158, -8.739986, -8.20482, -7.680142, -7.16849, -6.656468, -6.153566, -5.661274, -5.169068, -4.682796, -4.204682, -3.725894, -3.255894, -2.783408, -2.31378, -1.851258, -1.38568, -0.921498, -0.462548, -0.003156, 0.459826, 0.91851, 1.382872, 1.848256, 2.310846, 2.780854, 3.252642, 3.727752, 4.20226, 4.684606, 5.17279, 5.65992, 6.15848, 6.663748, 7.170638, 7.691366, 8.219946, 8.759156, 9.303552, 9.863756, 9.953262, 9.95329, 9.953312, 9.953314, 9.953358, 9.95336, 9.953382, 9.953324, 9.953344, 9.953324, 9.953284, 9.953284, 9.95327, 9.953252, 9.953278, 9.953318, 9.953344, 9.953348, 9.95335, 9.953374, 9.953392, 9.953392, 9.953434, 9.953456, 9.953434, 9.95343, 9.953454, 9.953434, 9.95339, 9.95337]\n",
-    "print(len(voltage))\n",
-    "\n"
+    "print(len(voltage))"
    ]
   },
   {
@@ -104,14 +113,10 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'numpy.ndarray' object has no attribute 'index'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_9327/792668923.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#we need to find element which has current value 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresistance\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'NAN'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresistance\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mresistance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresistance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'index'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nan\n"
      ]
     }
    ],
@@ -123,10 +128,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "265c23ad-29f4-4664-94c3-60fa440998f4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Here we plot the current and the resistance\n",
     "fig, (ax1, ax2) = plt.subplots(2,sharex=True) #the plots share the same x axis \n",
@@ -143,10 +161,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "ace8d693-f5cb-4aa4-b82d-66c842c3df5a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.2250738585072014e-308\n",
+      "100\n",
+      "[35.71428571382516, 100.00000000378577, 499.999999930111, 14.285714285530066, 19.230769231365876, 22.727272726848575, 55.5555555532727, 49.999999997451994, 45.45454545553223, 49.999999997451994, 71.42857143671338, 83.33333332168517, 35.71428571382516, 38.46153846141788, 16.129032258120592, 15.624999999984373, 499.999999930111, 22.727272727766113, 4.49423283715579e+304, 24.99999999983622, 27.777777778006993, 249.9999999650555, 166.6666666927136, 21.73913043510191, 22.727272726848575, 41.66666666701049, 49.999999997451994, 50.000000001892886, 38.46153846141788, 0.008025166923471992, 0.0018085668193097094, 0.0018650731481688694, 0.001868579095084515, 0.001905930875699001, 0.0019544534175572467, 0.0019530410802660826, 0.0019884589840565335, 0.0020313147481575977, 0.0020316696667655437, 0.002056462226901814, 0.0020915513873260363, 0.002088607066175425, 0.0021276595744680864, 0.002116464826471049, 0.002129344928326249, 0.002162059318259456, 0.0021478678116234012, 0.0021543273974432443, 0.002178886588953045, 0.0021767901922541094, 0.0021599111844520956, 0.002180150168743623, 0.0021534923184928995, 0.002148763171918245, 0.002161741498951554, 0.00212762335960239, 0.0021195960897691353, 0.002104775736145312, 0.0021074460283072166, 0.0020732005655691157, 0.002048407977320025, 0.002052840104284279, 0.002005776636713734, 0.001979147699834543, 0.001972814614610664, 0.0019203883793458385, 0.0018918612130614106, 0.0018545650117764857, 0.0018368981403243262, 0.0017850640123954827, 0.011172435367461389, 35.71428571382516, 45.45454545553223, 499.999999930111, 22.727272727766113, 499.999999930111, 45.45454545553223, 17.241379310575105, 50.000000001892886, 50.000000001892886, 24.99999999983622, 4.49423283715579e+304, 71.42857142765033, 55.55555555875528, 38.461538464045624, 24.99999999983622, 38.46153846141788, 249.9999999650555, 499.999999930111, 41.66666666701049, 55.55555555875528, 4.49423283715579e+304, 23.809523809216778, 45.45454545553223, 45.45454545553223, 250.00000007607784, 41.66666666701049, 49.999999997451994, 22.727272727766113, 49.999999997451994]\n"
+     ]
+    }
+   ],
    "source": [
     "'''lets test the linear regression fitting.\n",
     "for these tasks the are are two methods.\n",
@@ -173,10 +201,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "d7ee8038-f343-4660-99ea-63bd13291c42",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99\n",
+      "[2295918.367468986, 39999999.99414683, 242857142.78834438, 70643.64208245775, 67240.45183829192, 746097.3369502604, 308641.9753106898, 227272.72708440654, 206611.57009174748, 1071428.5719084693, 850340.1347377702, 3968253.9667669935, 98116.16955561754, 858942.5462779719, 8129.552550612436, 7568359.373900659, 238636363.56781635, inf, inf, 69444.44445381437, 6172839.505246729, 20833333.315173436, 24154589.38004353, 21481.354168721293, 430440.77135928307, 347222.2221045946, 0.0002220446049137159, 576923.0770455912, 1478981.2805528403, 0.04988925353237568, 0.00010219547145567937, 6.538847451275899e-06, 6.979475642041189e-05, 9.24806106950275e-05, 2.760347445459181e-06, 6.917262107966249e-05, 8.521692914536852e-05, 7.209514027161509e-07, 5.0370292390321e-05, 7.215953298611137e-05, 6.158198987294379e-06, 8.15653447719269e-05, 2.3818612759654342e-05, 2.7260282537895074e-05, 6.966022028756236e-05, 3.068287916262347e-05, 1.387433645885984e-05, 5.2908539128618934e-05, 4.567810652435912e-06, 3.674205863840431e-05, 4.371440853322009e-05, 5.811811672245678e-05, 1.0184180821545506e-05, 2.7887351162285667e-05, 7.37545976981003e-05, 1.707900681106398e-05, 3.141316359005189e-05, 5.620366150795954e-06, 7.217046443495352e-05, 5.1400007979936715e-05, 9.078804230072549e-06, 9.661377367549686e-05, 5.341169945280666e-05, 1.2534111053696311e-05, 0.00010342724311946446, 5.4783238628281605e-05, 7.055923660548066e-05, 3.276436166272787e-05, 9.521401319782578e-05, 0.016757058776920363, 398.89072552072116, 347866.419342195, 20661157.022020295, 238636363.56781635, 10847107.43665241, 227272727.20552164, 1282416.6429804363, 564803.805030264, 0.0, 1250000.0001501555, inf, inf, 1133786.8477635968, 949667.6164274549, 517751.47942641965, 336538.46153733676, 8136094.673191323, 62499999.98252775, 229166666.59951782, 578703.703827475, inf, inf, 515357.65823896026, 0.0, 9297520.664772095, 52083333.36811638, 347222.2221045946, 1363636.363414803, 619834.7106881351]\n"
+     ]
+    }
+   ],
    "source": [
     "second_derivative=[]\n",
     "for i in range(1,len(derivative)):\n",
@@ -189,10 +226,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "580b815f-3b7d-495f-b7b0-adb064811540",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99\n",
+      "99\n"
+     ]
+    }
+   ],
    "source": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -207,10 +253,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "a15d75eb-a1a7-4d51-a0ac-43151974a038",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[98, 97, 96, 95, 94, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 77, 76, 75, 74, 73, 72, 71, 70, 28, 27, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n",
+      "[-9.953676, -9.95369, -9.829082, -9.276158, -8.739986, -8.20482, -7.680142, -7.16849, -6.656468, -6.153566, -5.661274, -5.169068, -4.682796, -4.204682, -3.725894, -3.255894, -2.783408, -2.31378, -1.851258, -1.38568, -0.921498, -0.462548, -0.003156, 0.459826, 0.91851, 1.382872, 1.848256, 2.310846, 2.780854, 3.252642, 3.727752, 4.20226, 4.684606, 5.17279, 5.65992, 6.15848, 6.663748, 7.170638, 7.691366, 8.219946, 8.759156, 9.303552, 9.953324, 9.953434]\n",
+      "[-0.024, -0.021, -0.02, -0.019, -0.018, -0.017, -0.016, -0.015, -0.014, -0.013, -0.012, -0.011, -0.01, -0.009, -0.008, -0.007, -0.006, -0.005, -0.004, -0.003, -0.002, -0.001, 0.0, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.028, 0.043]\n",
+      "44\n",
+      "44\n"
+     ]
+    }
+   ],
    "source": [
     "indexes=[]\n",
     "for i in range(len(second_derivative)):\n",
@@ -230,10 +288,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "60c467d7-aa6a-4703-b81e-ddcb0097b88b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Here we plot the current and the resistance\n",
     "fig, (ax1, ax2) = plt.subplots(2,sharex=True) #the plots share the same x axis \n",
@@ -252,10 +323,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "188ea879-4cf9-419c-8198-1ed48852804e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coefficient of determination: 0.9471925862102195\n"
+     ]
+    }
+   ],
    "source": [
     "#now we have to do the linear regression\n",
     "x=np.array(voltage_new).reshape((-1,1)) #column matrix\n",
@@ -269,10 +348,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "8cc96899-4cf2-4de4-85e4-3d1cc4ce8f21",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.953434e+00]\n",
+      " [-9.953406e+00]\n",
+      " [-9.953396e+00]\n",
+      " [-9.953398e+00]\n",
+      " [-9.953468e+00]\n",
+      " [-9.953520e+00]\n",
+      " [-9.953564e+00]\n",
+      " [-9.953582e+00]\n",
+      " [-9.953562e+00]\n",
+      " [-9.953540e+00]\n",
+      " [-9.953520e+00]\n",
+      " [-9.953506e+00]\n",
+      " [-9.953494e+00]\n",
+      " [-9.953522e+00]\n",
+      " [-9.953548e+00]\n",
+      " [-9.953610e+00]\n",
+      " [-9.953674e+00]\n",
+      " [-9.953672e+00]\n",
+      " [-9.953628e+00]\n",
+      " [-9.953628e+00]\n",
+      " [-9.953588e+00]\n",
+      " [-9.953552e+00]\n",
+      " [-9.953556e+00]\n",
+      " [-9.953562e+00]\n",
+      " [-9.953608e+00]\n",
+      " [-9.953652e+00]\n",
+      " [-9.953676e+00]\n",
+      " [-9.953696e+00]\n",
+      " [-9.953716e+00]\n",
+      " [-9.953690e+00]\n",
+      " [-9.829082e+00]\n",
+      " [-9.276158e+00]\n",
+      " [-8.739986e+00]\n",
+      " [-8.204820e+00]\n",
+      " [-7.680142e+00]\n",
+      " [-7.168490e+00]\n",
+      " [-6.656468e+00]\n",
+      " [-6.153566e+00]\n",
+      " [-5.661274e+00]\n",
+      " [-5.169068e+00]\n",
+      " [-4.682796e+00]\n",
+      " [-4.204682e+00]\n",
+      " [-3.725894e+00]\n",
+      " [-3.255894e+00]\n",
+      " [-2.783408e+00]\n",
+      " [-2.313780e+00]\n",
+      " [-1.851258e+00]\n",
+      " [-1.385680e+00]\n",
+      " [-9.214980e-01]\n",
+      " [-4.625480e-01]\n",
+      " [-3.156000e-03]\n",
+      " [ 4.598260e-01]\n",
+      " [ 9.185100e-01]\n",
+      " [ 1.382872e+00]\n",
+      " [ 1.848256e+00]\n",
+      " [ 2.310846e+00]\n",
+      " [ 2.780854e+00]\n",
+      " [ 3.252642e+00]\n",
+      " [ 3.727752e+00]\n",
+      " [ 4.202260e+00]\n",
+      " [ 4.684606e+00]\n",
+      " [ 5.172790e+00]\n",
+      " [ 5.659920e+00]\n",
+      " [ 6.158480e+00]\n",
+      " [ 6.663748e+00]\n",
+      " [ 7.170638e+00]\n",
+      " [ 7.691366e+00]\n",
+      " [ 8.219946e+00]\n",
+      " [ 8.759156e+00]\n",
+      " [ 9.303552e+00]\n",
+      " [ 9.863756e+00]\n",
+      " [ 9.953262e+00]\n",
+      " [ 9.953290e+00]\n",
+      " [ 9.953312e+00]\n",
+      " [ 9.953314e+00]\n",
+      " [ 9.953358e+00]\n",
+      " [ 9.953360e+00]\n",
+      " [ 9.953382e+00]\n",
+      " [ 9.953324e+00]\n",
+      " [ 9.953344e+00]\n",
+      " [ 9.953324e+00]\n",
+      " [ 9.953284e+00]\n",
+      " [ 9.953284e+00]\n",
+      " [ 9.953270e+00]\n",
+      " [ 9.953252e+00]\n",
+      " [ 9.953278e+00]\n",
+      " [ 9.953318e+00]\n",
+      " [ 9.953344e+00]\n",
+      " [ 9.953348e+00]\n",
+      " [ 9.953350e+00]\n",
+      " [ 9.953374e+00]\n",
+      " [ 9.953392e+00]\n",
+      " [ 9.953392e+00]\n",
+      " [ 9.953434e+00]\n",
+      " [ 9.953456e+00]\n",
+      " [ 9.953434e+00]\n",
+      " [ 9.953430e+00]\n",
+      " [ 9.953454e+00]\n",
+      " [ 9.953434e+00]\n",
+      " [ 9.953390e+00]\n",
+      " [ 9.953370e+00]]\n",
+      "[-0.05  -0.049 -0.048 -0.047 -0.046 -0.045 -0.044 -0.043 -0.042 -0.041\n",
+      " -0.04  -0.039 -0.038 -0.037 -0.036 -0.035 -0.034 -0.033 -0.032 -0.031\n",
+      " -0.03  -0.029 -0.028 -0.027 -0.026 -0.025 -0.024 -0.023 -0.022 -0.021\n",
+      " -0.02  -0.019 -0.018 -0.017 -0.016 -0.015 -0.014 -0.013 -0.012 -0.011\n",
+      " -0.01  -0.009 -0.008 -0.007 -0.006 -0.005 -0.004 -0.003 -0.002 -0.001\n",
+      "  0.     0.001  0.002  0.003  0.004  0.005  0.006  0.007  0.008  0.009\n",
+      "  0.01   0.011  0.012  0.013  0.014  0.015  0.016  0.017  0.018  0.019\n",
+      "  0.02   0.021  0.022  0.023  0.024  0.025  0.026  0.027  0.028  0.029\n",
+      "  0.03   0.031  0.032  0.033  0.034  0.035  0.036  0.037  0.038  0.039\n",
+      "  0.04   0.041  0.042  0.043  0.044  0.045  0.046  0.047  0.048  0.049\n",
+      "  0.05 ]\n",
+      "coefficient of determination: 0.9197815254258749\n",
+      "intercept: -3.1235551699275468e-06\n",
+      "slope: [0.00329592]\n"
+     ]
+    }
+   ],
    "source": [
     "# method 2 is multiple linear regression\n",
     "x=np.array(voltage).reshape((-1,1)) #column matrix\n",
@@ -291,10 +492,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "621a7181-9e30-44ed-a658-e07592d134d0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-3.28088667e-02 -3.28087744e-02 -3.28087415e-02 -3.28087481e-02\n",
+      " -3.28089788e-02 -3.28091502e-02 -3.28092952e-02 -3.28093545e-02\n",
+      " -3.28092886e-02 -3.28092161e-02 -3.28091502e-02 -3.28091040e-02\n",
+      " -3.28090645e-02 -3.28091567e-02 -3.28092424e-02 -3.28094468e-02\n",
+      " -3.28096577e-02 -3.28096511e-02 -3.28095061e-02 -3.28095061e-02\n",
+      " -3.28093743e-02 -3.28092556e-02 -3.28092688e-02 -3.28092886e-02\n",
+      " -3.28094402e-02 -3.28095852e-02 -3.28096643e-02 -3.28097302e-02\n",
+      " -3.28097962e-02 -3.28097105e-02 -3.23990122e-02 -3.05766178e-02\n",
+      " -2.88094366e-02 -2.70455712e-02 -2.53162734e-02 -2.36299082e-02\n",
+      " -2.19423236e-02 -2.02847978e-02 -1.86622417e-02 -1.70399690e-02\n",
+      " -1.54372544e-02 -1.38614279e-02 -1.22833800e-02 -1.07342966e-02\n",
+      " -9.17701951e-03 -7.62916221e-03 -6.10472572e-03 -4.57021690e-03\n",
+      " -3.04030918e-03 -1.52764573e-03 -1.35254853e-05  1.51242712e-03\n",
+      "  3.02421386e-03  4.55471484e-03  6.08858425e-03  7.61324486e-03\n",
+      "  9.16235462e-03  1.07173311e-02  1.22832567e-02  1.38471981e-02\n",
+      "  1.54369729e-02  1.70459894e-02  1.86515319e-02  2.02947468e-02\n",
+      "  2.19600708e-02  2.36307407e-02  2.53470197e-02  2.70891782e-02\n",
+      "  2.88663723e-02  3.06606591e-02  3.25070479e-02  3.28020527e-02\n",
+      "  3.28021450e-02  3.28022175e-02  3.28022241e-02  3.28023691e-02\n",
+      "  3.28023757e-02  3.28024482e-02  3.28022570e-02  3.28023230e-02\n",
+      "  3.28022570e-02  3.28021252e-02  3.28021252e-02  3.28020791e-02\n",
+      "  3.28020197e-02  3.28021054e-02  3.28022373e-02  3.28023230e-02\n",
+      "  3.28023361e-02  3.28023427e-02  3.28024218e-02  3.28024812e-02\n",
+      "  3.28024812e-02  3.28026196e-02  3.28026921e-02  3.28026196e-02\n",
+      "  3.28026064e-02  3.28026855e-02  3.28026196e-02  3.28024746e-02\n",
+      "  3.28024087e-02]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f8dbad2f220>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#here is the second linear regression\n",
     "y_pred = model.predict(x)\n",
@@ -307,10 +563,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "02e69af1-785f-43e7-8263-067687ecac42",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-1.71911333e-02 -1.61912256e-02 -1.51912585e-02 -1.41912519e-02\n",
+      " -1.31910212e-02 -1.21908498e-02 -1.11907048e-02 -1.01906455e-02\n",
+      " -9.19071141e-03 -8.19078392e-03 -7.19084984e-03 -6.19089599e-03\n",
+      " -5.19093554e-03 -4.19084325e-03 -3.19075756e-03 -2.19055321e-03\n",
+      " -1.19034227e-03 -1.90348862e-04  8.09506117e-04  1.80950612e-03\n",
+      "  2.80937428e-03  3.80925563e-03  4.80926881e-03  5.80928859e-03\n",
+      "  6.80944020e-03  7.80958522e-03  8.80966432e-03  9.80973024e-03\n",
+      "  1.08097962e-02  1.18097105e-02  1.23990122e-02  1.15766178e-02\n",
+      "  1.08094366e-02  1.00455712e-02  9.31627335e-03  8.62990821e-03\n",
+      "  7.94232359e-03  7.28479777e-03  6.66224168e-03  6.03996904e-03\n",
+      "  5.43725441e-03  4.86142791e-03  4.28337996e-03  3.73429657e-03\n",
+      "  3.17701951e-03  2.62916221e-03  2.10472572e-03  1.57021690e-03\n",
+      "  1.04030918e-03  5.27645734e-04  1.35254853e-05 -5.12427123e-04\n",
+      " -1.02421386e-03 -1.55471484e-03 -2.08858425e-03 -2.61324486e-03\n",
+      " -3.16235462e-03 -3.71733112e-03 -4.28325667e-03 -4.84719807e-03\n",
+      " -5.43697292e-03 -6.04598936e-03 -6.65153189e-03 -7.29474682e-03\n",
+      " -7.96007079e-03 -8.63074074e-03 -9.34701967e-03 -1.00891782e-02\n",
+      " -1.08663723e-02 -1.16606591e-02 -1.25070479e-02 -1.18020527e-02\n",
+      " -1.08021450e-02 -9.80221750e-03 -8.80222409e-03 -7.80236911e-03\n",
+      " -6.80237570e-03 -5.80244821e-03 -4.80225705e-03 -3.80232297e-03\n",
+      " -2.80225705e-03 -1.80212521e-03 -8.02125210e-04  1.97920933e-04\n",
+      "  1.19798026e-03  2.19789457e-03  3.19776273e-03  4.19767703e-03\n",
+      "  5.19766385e-03  6.19765726e-03  7.19757816e-03  8.19751883e-03\n",
+      "  9.19751883e-03  1.01973804e-02  1.11973079e-02  1.21973804e-02\n",
+      "  1.31973936e-02  1.41973145e-02  1.51973804e-02  1.61975254e-02\n",
+      "  1.71975913e-02]\n",
+      "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
+      "  True  True  True  True  True False False  True  True  True  True  True\n",
+      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
+      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
+      "  True False False False  True  True  True  True  True  True  True  True\n",
+      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
+      "  True  True  True  True  True  True  True  True  True  True False False\n",
+      "  True  True  True  True  True  True  True  True  True  True  True  True\n",
+      "  True  True  True  True  True]\n"
+     ]
+    }
+   ],
    "source": [
     "residuals=(y-y_pred)\n",
     "print(residuals)\n",
@@ -321,10 +619,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "8d8e0281-5b75-45a0-8717-43d204a8c2c3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-9.953672e+00]\n",
+      " [-9.953628e+00]\n",
+      " [-4.625480e-01]\n",
+      " [-3.156000e-03]\n",
+      " [ 4.598260e-01]\n",
+      " [ 9.953284e+00]\n",
+      " [ 9.953270e+00]]\n",
+      "[-0.033 -0.032 -0.001  0.     0.001  0.032  0.033]\n"
+     ]
+    }
+   ],
    "source": [
     "x_clean = x[~outliers]\n",
     "y_clean = y[~outliers]\n",
@@ -335,10 +648,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "a1e3f89d-8b58-420b-bcd4-ab684883e366",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9995469997854816\n"
+     ]
+    }
+   ],
    "source": [
     "model_clean = LinearRegression()\n",
     "model_clean.fit(x_clean, y_clean)\n",
@@ -349,10 +670,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "549ba62d-cb21-405d-9005-96d3300ede9c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#here is the second linear regression\n",
     "plt.figure()\n",
@@ -365,10 +699,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "9dfc34a4-6396-42bd-b67b-b6da1fd58d27",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8dbac382e0>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "admitance=[]\n",
     "for R in resistance:\n",
@@ -381,10 +745,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "ef8cd500-683e-406b-89bd-14c7823f29ea",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8dbab9ed30>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure()\n",
     "plt.plot(current,voltage)"
@@ -392,10 +779,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "32e12cf6-a51e-46ef-83c5-7c87505ef6ec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100\n",
+      "[0.02800000000036107, 0.009999999999621423, -0.002000000000279556, -0.07000000000090267, -0.05199999999838667, -0.04400000000082116, -0.018000000000739647, 0.020000000001019203, 0.0219999999995224, 0.020000000001019203, 0.013999999998404178, 0.012000000001677336, -0.02800000000036107, -0.026000000000081513, -0.06199999999978445, -0.064000000000064, 0.002000000000279556, 0.0439999999990448, 0.0, 0.04000000000026205, 0.035999999999702936, -0.004000000000559112, -0.005999999999062311, -0.04599999999932436, -0.04400000000082116, -0.023999999999801958, -0.020000000001019203, -0.019999999999242846, 0.026000000000081513, 124.60800000000027, 552.9239999999991, 536.1720000000005, 535.1660000000003, 524.6779999999998, 511.65199999999976, 512.0219999999999, 502.9020000000006, 492.2919999999999, 492.20599999999945, 486.27200000000045, 478.1139999999997, 478.7880000000002, 469.9999999999997, 472.48599999999993, 469.62800000000016, 462.5219999999999, 465.57800000000003, 464.18199999999996, 458.95, 459.392, 462.98199999999997, 458.684, 464.362, 465.3839999999998, 462.59000000000026, 470.008, 471.78799999999967, 475.11000000000035, 474.5079999999997, 482.3459999999997, 488.18400000000037, 487.1299999999996, 498.56000000000034, 505.26800000000003, 506.89000000000027, 520.7280000000001, 528.5799999999998, 539.2100000000006, 544.3959999999989, 560.2040000000006, 89.50600000000009, 0.02800000000036107, 0.0219999999995224, 0.002000000000279556, 0.0439999999990448, 0.002000000000279556, 0.0219999999995224, -0.05799999999922534, 0.019999999999242846, -0.019999999999242846, -0.04000000000026205, 0.0, -0.014000000000180535, -0.01799999999896329, 0.025999999998305157, 0.04000000000026205, 0.026000000000081513, 0.004000000000559112, 0.002000000000279556, 0.023999999999801958, 0.01799999999896329, 0.0, 0.042000000000541604, 0.0219999999995224, -0.0219999999995224, -0.003999999998782755, 0.023999999999801958, -0.020000000001019203, -0.0439999999990448, -0.020000000001019203]\n"
+     ]
+    }
+   ],
    "source": [
     "#define the voltage differences\n",
     "derivative=[]\n",
@@ -404,19 +800,28 @@
     "    derivative.append(element)\n",
     "\n",
     "print(len(derivative))\n",
-    "print(derivative)"
+    "print(derivative)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "0be31f60-d8fd-4c0f-956b-524cf4a9f078",
+   "execution_count": 23,
+   "id": "da6324fd-ec15-48d3-91a7-98bd6bc5486c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99\n",
+      "[-18.000000000739647, -11.999999999900979, -68.00000000062312, 18.000000002516003, 7.99999999756551, 26.000000000081513, 38.00000000175885, 1.999999998503199, -1.999999998503199, -6.000000002615025, -1.9999999967268423, -40.000000002038405, 2.000000000279556, -35.999999999702936, -2.000000000279556, 66.00000000034356, 41.99999999876525, -43.9999999990448, 40.00000000026205, -4.000000000559112, -40.00000000026205, -1.999999998503199, -40.00000000026205, 1.999999998503199, 20.000000001019203, 3.999999998782755, 1.7763568394002505e-09, 45.99999999932436, 124582.00000000019, 428315.9999999988, -16751.99999999859, -1006.0000000001992, -10488.000000000511, -13026.000000000011, 370.0000000001751, -9119.999999999322, -10610.000000000695, -86.00000000046748, -5933.999999999002, -8158.000000000754, 674.0000000004898, -8788.000000000466, 2486.0000000002174, -2857.9999999997767, -7106.000000000278, 3056.0000000001537, -1396.0000000000719, -5231.999999999971, 442.0000000000073, 3589.999999999975, -4297.9999999999445, 5677.999999999997, 1021.999999999764, -2793.999999999528, 7417.999999999722, 1779.9999999996885, 3322.000000000685, -602.0000000006576, 7838.000000000022, 5838.000000000648, -1054.0000000007694, 11430.000000000746, 6707.999999999685, 1622.0000000002415, 13837.999999999794, 7851.999999999748, 10630.000000000791, 5185.99999999833, 15808.000000001697, -470698.0000000005, -89477.99999999972, -6.000000000838668, -19.999999999242846, 41.99999999876525, -41.99999999876525, 19.999999999242846, -79.99999999874774, 77.99999999846818, -39.99999999848569, -20.000000001019203, 40.00000000026205, -14.000000000180535, -3.999999998782755, 43.999999997268446, 14.000000001956892, -14.000000000180535, -21.9999999995224, -2.000000000279556, 21.9999999995224, -6.000000000838668, -17.99999999896329, 42.000000000541604, -20.000000001019203, -43.9999999990448, 18.000000000739647, 27.999999998584713, -44.00000000082116, -23.9999999980256, 23.9999999980256]\n"
+     ]
+    }
+   ],
    "source": [
     "second_derivative=[]\n",
     "for i in range(1,len(derivative)):\n",
-    "    element = (derivative[i]-derivative[i-1]) #0,001 is the delta I\n",
+    "    element = (derivative[i]-derivative[i-1])/0.001 #0,001 is the delta I\n",
     "    second_derivative.append(element)\n",
     "\n",
     "print(len(second_derivative))\n",
@@ -426,9 +831,108 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40915d35-183c-4d2b-8a67-d62ecebafba6",
+   "id": "2957e4d3-d416-4794-84b7-d3d943703790",
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "40915d35-183c-4d2b-8a67-d62ecebafba6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99\n",
+      "99\n",
+      "[70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 36, 35, 34, 33, 32, 31, 30, 29, 28, 26]\n",
+      "[-9.953434, -9.953406, -9.953396, -9.953398, -9.953468, -9.95352, -9.953564, -9.953582, -9.953562, -9.95354, -9.95352, -9.953506, -9.953494, -9.953522, -9.953548, -9.95361, -9.953674, -9.953672, -9.953628, -9.953628, -9.953588, -9.953552, -9.953556, -9.953562, -9.953608, -9.953652, -9.953696, -6.153566, 9.953262, 9.95329, 9.953312, 9.953314, 9.953358, 9.95336, 9.953382, 9.953324, 9.953344, 9.953324, 9.953284, 9.953284, 9.95327, 9.953252, 9.953278, 9.953318, 9.953344, 9.953348, 9.95335, 9.953374, 9.953392, 9.953392, 9.953434, 9.953456, 9.953434, 9.95343, 9.953454, 9.953434]\n",
+      "[-0.05, -0.049, -0.048, -0.047, -0.046, -0.045, -0.044, -0.043, -0.042, -0.041, -0.04, -0.039, -0.038, -0.037, -0.036, -0.035, -0.034, -0.033, -0.032, -0.031, -0.03, -0.029, -0.028, -0.027, -0.026, -0.025, -0.023, -0.013, 0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027, 0.028, 0.029, 0.03, 0.031, 0.032, 0.033, 0.034, 0.035, 0.036, 0.037, 0.038, 0.039, 0.04, 0.041, 0.042, 0.043, 0.044, 0.045, 0.046, 0.047, 0.048]\n",
+      "56\n",
+      "56\n",
+      "[[-9.953434]\n",
+      " [-9.953406]\n",
+      " [-9.953396]\n",
+      " [-9.953398]\n",
+      " [-9.953468]\n",
+      " [-9.95352 ]\n",
+      " [-9.953564]\n",
+      " [-9.953582]\n",
+      " [-9.953562]\n",
+      " [-9.95354 ]\n",
+      " [-9.95352 ]\n",
+      " [-9.953506]\n",
+      " [-9.953494]\n",
+      " [-9.953522]\n",
+      " [-9.953548]\n",
+      " [-9.95361 ]\n",
+      " [-9.953674]\n",
+      " [-9.953672]\n",
+      " [-9.953628]\n",
+      " [-9.953628]\n",
+      " [-9.953588]\n",
+      " [-9.953552]\n",
+      " [-9.953556]\n",
+      " [-9.953562]\n",
+      " [-9.953608]\n",
+      " [-9.953652]\n",
+      " [-9.953696]\n",
+      " [-6.153566]\n",
+      " [ 9.953262]\n",
+      " [ 9.95329 ]\n",
+      " [ 9.953312]\n",
+      " [ 9.953314]\n",
+      " [ 9.953358]\n",
+      " [ 9.95336 ]\n",
+      " [ 9.953382]\n",
+      " [ 9.953324]\n",
+      " [ 9.953344]\n",
+      " [ 9.953324]\n",
+      " [ 9.953284]\n",
+      " [ 9.953284]\n",
+      " [ 9.95327 ]\n",
+      " [ 9.953252]\n",
+      " [ 9.953278]\n",
+      " [ 9.953318]\n",
+      " [ 9.953344]\n",
+      " [ 9.953348]\n",
+      " [ 9.95335 ]\n",
+      " [ 9.953374]\n",
+      " [ 9.953392]\n",
+      " [ 9.953392]\n",
+      " [ 9.953434]\n",
+      " [ 9.953456]\n",
+      " [ 9.953434]\n",
+      " [ 9.95343 ]\n",
+      " [ 9.953454]\n",
+      " [ 9.953434]]\n",
+      "[-0.05  -0.049 -0.048 -0.047 -0.046 -0.045 -0.044 -0.043 -0.042 -0.041\n",
+      " -0.04  -0.039 -0.038 -0.037 -0.036 -0.035 -0.034 -0.033 -0.032 -0.031\n",
+      " -0.03  -0.029 -0.028 -0.027 -0.026 -0.025 -0.023 -0.013  0.021  0.022\n",
+      "  0.023  0.024  0.025  0.026  0.027  0.028  0.029  0.03   0.031  0.032\n",
+      "  0.033  0.034  0.035  0.036  0.037  0.038  0.039  0.04   0.041  0.042\n",
+      "  0.043  0.044  0.045  0.046  0.047  0.048]\n",
+      "coefficient of determination: 0.9513024191985002\n",
+      "intercept: -0.0010460134789201804\n",
+      "slope: [0.00357815]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -478,10 +982,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "84abbb6d-5542-45d1-9648-a90869445ee6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99\n",
+      "99\n",
+      "[98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n",
+      "[-9.95369, -9.829082, -9.276158, -8.739986, -8.20482, -7.680142, -7.16849, -6.656468, -6.153566, -5.661274, -5.169068, -4.682796, -4.204682, -3.725894, -3.255894, -2.783408, -2.31378, -1.851258, -1.38568, -0.921498, -0.462548, -0.003156, 0.459826, 0.91851, 1.382872, 1.848256, 2.310846, 2.780854, 3.252642, 3.727752, 4.20226, 4.684606, 5.17279, 5.65992, 6.15848, 6.663748, 7.170638, 7.691366, 8.219946, 8.759156, 9.303552, 9.863756]\n",
+      "[-0.021, -0.02, -0.019, -0.018, -0.017, -0.016, -0.015, -0.014, -0.013, -0.012, -0.011, -0.01, -0.009, -0.008, -0.007, -0.006, -0.005, -0.004, -0.003, -0.002, -0.001, 0.0, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.02]\n",
+      "42\n",
+      "42\n",
+      "[[-9.953690e+00]\n",
+      " [-9.829082e+00]\n",
+      " [-9.276158e+00]\n",
+      " [-8.739986e+00]\n",
+      " [-8.204820e+00]\n",
+      " [-7.680142e+00]\n",
+      " [-7.168490e+00]\n",
+      " [-6.656468e+00]\n",
+      " [-6.153566e+00]\n",
+      " [-5.661274e+00]\n",
+      " [-5.169068e+00]\n",
+      " [-4.682796e+00]\n",
+      " [-4.204682e+00]\n",
+      " [-3.725894e+00]\n",
+      " [-3.255894e+00]\n",
+      " [-2.783408e+00]\n",
+      " [-2.313780e+00]\n",
+      " [-1.851258e+00]\n",
+      " [-1.385680e+00]\n",
+      " [-9.214980e-01]\n",
+      " [-4.625480e-01]\n",
+      " [-3.156000e-03]\n",
+      " [ 4.598260e-01]\n",
+      " [ 9.185100e-01]\n",
+      " [ 1.382872e+00]\n",
+      " [ 1.848256e+00]\n",
+      " [ 2.310846e+00]\n",
+      " [ 2.780854e+00]\n",
+      " [ 3.252642e+00]\n",
+      " [ 3.727752e+00]\n",
+      " [ 4.202260e+00]\n",
+      " [ 4.684606e+00]\n",
+      " [ 5.172790e+00]\n",
+      " [ 5.659920e+00]\n",
+      " [ 6.158480e+00]\n",
+      " [ 6.663748e+00]\n",
+      " [ 7.170638e+00]\n",
+      " [ 7.691366e+00]\n",
+      " [ 8.219946e+00]\n",
+      " [ 8.759156e+00]\n",
+      " [ 9.303552e+00]\n",
+      " [ 9.863756e+00]]\n",
+      "[-0.021 -0.02  -0.019 -0.018 -0.017 -0.016 -0.015 -0.014 -0.013 -0.012\n",
+      " -0.011 -0.01  -0.009 -0.008 -0.007 -0.006 -0.005 -0.004 -0.003 -0.002\n",
+      " -0.001  0.     0.001  0.002  0.003  0.004  0.005  0.006  0.007  0.008\n",
+      "  0.009  0.01   0.011  0.012  0.013  0.014  0.015  0.016  0.017  0.018\n",
+      "  0.019  0.02 ]\n",
+      "coefficient of determination: 0.9996847110042263\n",
+      "intercept: -1.1363930831026318e-05\n",
+      "slope: [0.00208319]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -494,7 +1074,7 @@
     "print(len(current_new))   \n",
     "indexes=[]\n",
     "for i in range(len(second_derivative)):\n",
-    "    if abs(derivative[i])>100:\n",
+    "    if abs(derivative[i])<1:\n",
     "        indexes.append(i)\n",
     "print(list(reversed(indexes)))\n",
     "\n",
@@ -505,7 +1085,27 @@
     "print(voltage_new)\n",
     "print(current_new)\n",
     "print(len(voltage_new))\n",
-    "print(len(current_new))"
+    "print(len(current_new))\n",
+    "\n",
+    "x=np.array(voltage_new).reshape((-1,1)) #column matrix\n",
+    "y = np.array(current_new)\n",
+    "\n",
+    "print(x)\n",
+    "print(y)\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(x, y)\n",
+    "r_sq = model.score(x, y)\n",
+    "current_new_pred=model.predict(x)\n",
+    "print(f\"coefficient of determination: {r_sq}\")\n",
+    "print(f\"intercept: {model.intercept_}\")\n",
+    "print(f\"slope: {model.coef_}\")\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(voltage_new,current_new,label=\"linear part\")\n",
+    "plt.plot(voltage_new,current_new_pred,label='after regression')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -515,6 +1115,14 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "478cc779-ccb0-4f26-a28f-8b295d0e213d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/hp4155/measurements.py b/hp4155/measurements.py
index 85938847c9f96a10ddc0c77f93bdce713b8e3d2b..85e4cdf6553c2f030bd344f60ac79b5ee6e6a96a 100644
--- a/hp4155/measurements.py
+++ b/hp4155/measurements.py
@@ -8,6 +8,7 @@ import os
 from sklearn.linear_model import LinearRegression
 import sys
 import numpy as np
+from IPython.display import display, clear_output
 
 
 def I_V_Measurement(start,stop,step):
@@ -206,7 +207,7 @@ def ctlm(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3),com
         print(device.error())
         device.user_function('V','V','V4-V2')
         print(device.error())
-        device.user_function('R','OHM','V/I')
+        device.user_function('R','OHM','DIFF(V,I)')
         print(device.error())
         
         #integration time
@@ -278,18 +279,15 @@ def tlm_final(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3
     #connect to the device
     device = module.HP4155a('GPIB0::17::INSTR')
     date = str(datetime.today().replace(microsecond=0))
-    voltage=[]
-    current=[]
-    restistance=[]
     
     #initialize figure
-    fig, (ax1, ax2) = plt.subplots(2,sharex=True) #the plots share the same x axis 
+    fig, (ax1, ax2) = plt.subplots(2,sharex=True,figsize=(8,6)) #the plots share the same x axis 
     fig.suptitle('CTLM plot')
     ax1.set_title('I(V)')
     ax1.set(xlabel='Voltage(V)',ylabel='Current(A)')
     ax2.set_title('R(V)')
-    ax2.set(xlabel='Voltage(V)',ylabel='Resistance(Ohm)')
-    
+    ax2.set(xlabel='Voltage(V)',ylabel='Resistance(Ohm)')          
+            
     #repeat five times
     for j in range(len(distances)):
         #setup
@@ -331,7 +329,7 @@ def tlm_final(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3
         #define user functions
         device.user_function('I','A','I3')
         device.user_function('V','V','V4-V2')
-        device.user_function('R','OHM','V/I')
+        device.user_function('R','OHM','DIFF(V,I)')
         device.user_function('VS','V','V3')
         
 
@@ -370,43 +368,92 @@ def tlm_final(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3
         for i in range(len(R)):
             if R[i]>10**6:
                 R[i]=float('NAN')
-        
-        #plot data
-        ax1.plot(V)
                 
-            
-
+        # plot the results
+        ax1.plot(V,I,label=f"distance={distances[j]}")
+        ax2.plot(V,R,label=f"distance={distances[j]}")
+        ax1.legend(loc='best')
+        ax2.legend(loc="best")
+        clear_output(wait=True)
+        fig.tight_layout()
+        display(fig)
         
+        #export data frame to csv(for evaluation) and txt 
+        header = ['Voltage(V)', 'Current(A)','Resistance(Ohm)']
 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+        data = {header[0]:V,header[1]:I,header[2]:R}
+        df = pd.DataFrame(data)
+        print(df)
+        
+        #export to txt 
+        #check tlm or ctlm
+        if(innen==0):
+            #specify path and file_name
+            file_name = field_name+"_TLM_"+str(j+1)+".txt"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_TLM_"+str(j+1)+"_"str(i)+".txt"
+                path= os.path.join(location,file_name)
+                i=i+1
+        else:
+            #specify path and file_name
+            file_name = field_name+"_CTLM_"+str(j+1)+".txt"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_CTLM_"+str(j+1)+"_"str(i)+".txt"
+                path= os.path.join(location,file_name)
+                i=i+1
+            
+        title = "measured field:"+field_name+"\ndistance:"+str(distances[j])+"\nI:"+str(start)+"A to "+str(stop)+"A with step:"+str(step)+"\nDate:"+date+"\n"
+        
+        f=open(path, 'a')
+        f.write(title)
+        df_string = df.to_string()
+        f.write(df_string)
+        f.close()
+        
+        #export to csv for evaluataion
+        
+         if(innen==0):
+            #specify path and file_name
+            file_name = field_name+"_TLM_"+str(j+1)+".csv"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_TLM_"+str(j+1)+"_"str(i)+".csv"
+                path= os.path.join(location,file_name)
+                i=i+1
+        else:
+            #specify path and file_name
+            file_name = field_name+"_CTLM_"+str(j+1)+".csv"
+            location =r"\\fileserver.cst.rwth-aachen.de\public\Datentransfer\Asonitis, Alexandros"
+            path= os.path.join(location,file_name)
+        
+            #check if file name exists
+            i=1
+            while os.path.exists(path):
+                file_name = field_name+"_CTLM_"+str(j+1)+"_"str(i)+".csv"
+                path= os.path.join(location,file_name)
+                i=i+1
+        
+        df.to_csv(path)
+        
+        # give user confirmation to do the next measurement
+        
+        while True:
+            answer=input("Press enter to continue or anything else to stop the programm:")
+            if answer=="":
+                break
+            else:
+                sys.exit()
diff --git a/hp4155/module.py b/hp4155/module.py
index 6dbbfb315272e4949e47a680f1857446cc437ae5..3f0364606a70e2fd837b56cc8c991e800e676d09 100644
--- a/hp4155/module.py
+++ b/hp4155/module.py
@@ -9,8 +9,8 @@ class HP4155a(object):
         self.inst.timeout = None
     
     def idn(self):
-       return self.inst.query("*IDN?")
-    
+        return self.inst.query("*IDN?")
+        
     def __del__(self):
         self.rm.close()
     
diff --git a/hp4155/old/.ipynb_checkpoints/I-V measurement-checkpoint.ipynb b/hp4155/old/.ipynb_checkpoints/I-V measurement-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..363fcab7ed6e9634e198cf5555ceb88932c9a245
--- /dev/null
+++ b/hp4155/old/.ipynb_checkpoints/I-V measurement-checkpoint.ipynb	
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hp4155/.ipynb_checkpoints/first_goal-checkpoint.ipynb b/hp4155/old/.ipynb_checkpoints/first_goal-checkpoint.ipynb
similarity index 100%
rename from hp4155/.ipynb_checkpoints/first_goal-checkpoint.ipynb
rename to hp4155/old/.ipynb_checkpoints/first_goal-checkpoint.ipynb
diff --git a/hp4155/I-V measurement.ipynb b/hp4155/old/I-V measurement.ipynb
similarity index 100%
rename from hp4155/I-V measurement.ipynb
rename to hp4155/old/I-V measurement.ipynb
diff --git a/hp4155/first_goal.ipynb b/hp4155/old/first_goal.ipynb
similarity index 100%
rename from hp4155/first_goal.ipynb
rename to hp4155/old/first_goal.ipynb
diff --git a/hp4155/tlm_eval.ipynb b/hp4155/tlm_eval.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..08e2b268e054bb2bb961fd395117c696e5c32764
--- /dev/null
+++ b/hp4155/tlm_eval.ipynb
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "61ed7a95-1234-470b-918a-adfcbf186de9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install pandas\n",
+    "#!pip install -U scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e80894aa-21c6-4864-99c9-fc1e644aea96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "import os \n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import sys\n",
+    "from IPython.display import display, clear_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "442dbcdb-262f-4e2c-9165-c74eeb5a6707",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "101\n"
+     ]
+    }
+   ],
+   "source": [
+    "resistance = [199.0687, 203.1307, 207.3624, 211.7744, 216.3797, 221.1893, 226.2174, 231.4787, 236.9896, 242.7693, 248.838, 255.2181, 261.9341, 269.0141, 276.4874, 284.3889, 292.7551, 301.6264, 311.0509, 321.0848, 331.7863, 343.2259, 355.4841, 368.6504, 382.8311, 398.1461, 414.7365, 432.7694, 452.4416, 473.9852, 491.4541, 488.2188, 485.5548, 482.6365, 480.0089, 477.8993, 475.462, 473.3512, 471.7728, 469.9153, 468.2796, 467.1869, 465.7367, 465.1277, 463.9013, 462.756, 462.8145, 461.8933, 460.749, 462.548, -9.9e+307, 459.826, 459.255, 460.9573, 462.064, 462.1692, 463.4757, 464.6631, 465.969, 466.9178, 468.4606, 470.2536, 471.66, 473.7292, 475.982, 478.0425, 480.7104, 483.5262, 486.6198, 489.6606, 493.1878, 473.9649, 452.4223, 432.7527, 414.7214, 398.1343, 382.8215, 368.6438, 355.4759, 343.2188, 331.7775, 321.0737, 311.0401, 301.6142, 292.7427, 284.3794, 276.4811, 269.0093, 261.9302, 255.2141, 248.8343, 242.7657, 236.9855, 231.4752, 226.2149, 221.1874, 216.3789, 211.7756, 207.3632, 203.1304, 199.0674]\n",
+    "print(len(resistance))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e2cc3ac6-0f01-4012-b62a-820286b264a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "101\n"
+     ]
+    }
+   ],
+   "source": [
+    "voltage = [-9.953434, -9.953406, -9.953396, -9.953398, -9.953468, -9.95352, -9.953564, -9.953582, -9.953562, -9.95354, -9.95352, -9.953506, -9.953494, -9.953522, -9.953548, -9.95361, -9.953674, -9.953672, -9.953628, -9.953628, -9.953588, -9.953552, -9.953556, -9.953562, -9.953608, -9.953652, -9.953676, -9.953696, -9.953716, -9.95369, -9.829082, -9.276158, -8.739986, -8.20482, -7.680142, -7.16849, -6.656468, -6.153566, -5.661274, -5.169068, -4.682796, -4.204682, -3.725894, -3.255894, -2.783408, -2.31378, -1.851258, -1.38568, -0.921498, -0.462548, -0.003156, 0.459826, 0.91851, 1.382872, 1.848256, 2.310846, 2.780854, 3.252642, 3.727752, 4.20226, 4.684606, 5.17279, 5.65992, 6.15848, 6.663748, 7.170638, 7.691366, 8.219946, 8.759156, 9.303552, 9.863756, 9.953262, 9.95329, 9.953312, 9.953314, 9.953358, 9.95336, 9.953382, 9.953324, 9.953344, 9.953324, 9.953284, 9.953284, 9.95327, 9.953252, 9.953278, 9.953318, 9.953344, 9.953348, 9.95335, 9.953374, 9.953392, 9.953392, 9.953434, 9.953456, 9.953434, 9.95343, 9.953454, 9.953434, 9.95339, 9.95337]\n",
+    "print(len(voltage))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e4bda2ac-3256-40b3-8893-ed13c08d487a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "101\n",
+      "nan\n"
+     ]
+    }
+   ],
+   "source": [
+    "current=[-0.05, -0.049, -0.048, -0.047, -0.046, -0.045, -0.044, -0.043, -0.042, -0.041, -0.04, -0.039, -0.038, -0.037, -0.036, -0.035, -0.034, -0.033, -0.032, -0.031, -0.03, -0.029, -0.028, -0.027, -0.026, -0.025, -0.024, -0.023, -0.022, -0.021, -0.02, -0.019, -0.018, -0.017, -0.016, -0.015, -0.014, -0.013, -0.012, -0.011, -0.01, -0.009, -0.008, -0.007, -0.006, -0.005, -0.004, -0.003, -0.002, -0.001, 0.0, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.02, 0.021, 0.022, 0.023, 0.024, 0.025, 0.026, 0.027, 0.028, 0.029, 0.03, 0.031, 0.032, 0.033, 0.034, 0.035, 0.036, 0.037, 0.038, 0.039, 0.04, 0.041, 0.042, 0.043, 0.044, 0.045, 0.046, 0.047, 0.048, 0.049, 0.05]\n",
+    "print(len(current))\n",
+    "\n",
+    "resistance[current.index(0)]=float('NAN')\n",
+    "print(resistance[current.index(0)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a5746d39-afb4-493f-a56c-20a34a775052",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+      "   0. 125. 553. 536. 535. 525. 512. 512. 503. 492. 492. 486. 478. 479.\n",
+      " 470. 472. 470. 463. 466. 464. 459. 459. 463. 459. 464. 465. 463. 470.\n",
+      " 472. 475. 475. 482. 488. 487. 499. 505. 507. 521. 529. 539. 544. 560.\n",
+      "  90.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+      "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+      "   0.   0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "derivative=np.abs(np.diff(voltage)/np.diff(current))\n",
+    "derivative=np.around(derivative)\n",
+    "print(derivative)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "47dbf7e2-bfde-46c3-a949-f44c27ba7c5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,\n",
+      "       46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62,\n",
+      "       63, 64, 65, 66, 67, 68, 69, 70]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "indexes=np.where(derivative>1)\n",
+    "print(indexes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f8cfc3a7-fb93-47be-a033-7aac03b1b1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.02"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "voltage_new=[voltage[index] for index in indexes[0]]\n",
+    "current_new=[current[index] for index in indexes[0]]\n",
+    "\n",
+    "voltage_new.pop()\n",
+    "voltage_new.pop(0)\n",
+    "current_new.pop(0)\n",
+    "current_new.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "17c70e37-532e-4c2b-a6a0-1d748b034358",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fafcbeda310>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(voltage_new,current_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "23a0ebf1-c8d9-47fc-b81e-df7bd2fe4571",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "coefficient of determination: 0.9997053460919436\n",
+      "intercept: 8.975250494780559e-06\n",
+      "slope: [0.00208562]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x=np.array(voltage_new).reshape((-1,1)) #column matrix\n",
+    "y = np.array(current_new)\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(x, y)\n",
+    "r_sq = model.score(x, y)\n",
+    "current_new_pred=model.predict(x)\n",
+    "print(f\"coefficient of determination: {r_sq}\")\n",
+    "print(f\"intercept: {model.intercept_}\")\n",
+    "print(f\"slope: {model.coef_}\")\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(voltage_new,current_new,label=\"linear part\")\n",
+    "plt.plot(voltage_new,current_new_pred,label='after regression')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "24f0de1e-9677-4855-bbec-ad6be5608771",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "press enter to continue: \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(2,sharex=True,figsize=(10,10)) #the plots share the same x axis \n",
+    "fig.suptitle('CTLM plot')\n",
+    "ax1.set_title('I(V)')\n",
+    "ax1.set(xlabel='Voltage(V)',ylabel='Current(A)')\n",
+    "ax2.set_title('R(V)')\n",
+    "ax2.set(xlabel='Voltage(V)',ylabel='Resistance(Ohm)')\n",
+    "for i in range(5):\n",
+    "    ax1.plot(voltage, current,label=f\"{i}\")\n",
+    "    ax2.plot(voltage,resistance,label=f\"{i}\")\n",
+    "    ax1.legend(loc='best')\n",
+    "    ax2.legend(loc=\"best\")\n",
+    "    clear_output(wait=True)\n",
+    "    fig.tight_layout()\n",
+    "    display(fig)\n",
+    "    while True:\n",
+    "        answer=input(\"press enter to continue:\")\n",
+    "        if answer==\"\":\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4121a876-63d5-46db-9c59-634c46587233",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hp4155/tlm_final.ipynb b/hp4155/tlm_final.ipynb
index 337921a303dcd8bd03e2b40e7ec626707bd92f02..2fc1016e060c7bef25026d79022af43121737e17 100644
--- a/hp4155/tlm_final.ipynb
+++ b/hp4155/tlm_final.ipynb
@@ -38,7 +38,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,