diff --git a/hp4155/ctlm.ipynb b/hp4155/ctlm.ipynb
index c656659fee38eab720f30949d394e19b2a24aa7b..2d8f7be1478ea84b53ed0f1f764a88aa678291cb 100644
--- a/hp4155/ctlm.ipynb
+++ b/hp4155/ctlm.ipynb
@@ -450,7 +450,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/hp4155/ctlm_part2.ipynb b/hp4155/ctlm_part2.ipynb
index ad56acdabf562f78508f7416e177bfb6fd2d44c2..efa6d03c82087e054d1e8678e8b35b2505cb0f24 100644
--- a/hp4155/ctlm_part2.ipynb
+++ b/hp4155/ctlm_part2.ipynb
@@ -74,7 +74,8 @@
    ],
    "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))"
+    "print(len(voltage))\n",
+    "\n"
    ]
   },
   {
@@ -103,10 +104,14 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nan\n"
+     "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'"
      ]
     }
    ],
@@ -118,21 +123,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "265c23ad-29f4-4664-94c3-60fa440998f4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "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",
@@ -149,20 +143,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "ace8d693-f5cb-4aa4-b82d-66c842c3df5a",
    "metadata": {},
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "'''lets test the linear regression fitting.\n",
     "for these tasks the are are two methods.\n",
@@ -189,19 +173,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "d7ee8038-f343-4660-99ea-63bd13291c42",
    "metadata": {},
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "second_derivative=[]\n",
     "for i in range(1,len(derivative)):\n",
@@ -214,19 +189,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "580b815f-3b7d-495f-b7b0-adb064811540",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "99\n",
-      "99\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -241,22 +207,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "a15d75eb-a1a7-4d51-a0ac-43151974a038",
    "metadata": {},
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "indexes=[]\n",
     "for i in range(len(second_derivative)):\n",
@@ -276,21 +230,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "60c467d7-aa6a-4703-b81e-ddcb0097b88b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "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",
@@ -309,18 +252,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "188ea879-4cf9-419c-8198-1ed48852804e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "coefficient of determination: 0.9471925862102195\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#now we have to do the linear regression\n",
     "x=np.array(voltage_new).reshape((-1,1)) #column matrix\n",
@@ -334,132 +269,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "8cc96899-4cf2-4de4-85e4-3d1cc4ce8f21",
    "metadata": {},
-   "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: -3.123555169927545e-06\n",
-      "slope: [0.00329592]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# method 2 is multiple linear regression\n",
     "x=np.array(voltage).reshape((-1,1)) #column matrix\n",
@@ -478,63 +291,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "621a7181-9e30-44ed-a658-e07592d134d0",
    "metadata": {},
-   "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 0x231899a2490>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#here is the second linear regression\n",
     "y_pred = model.predict(x)\n",
@@ -547,52 +307,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "02e69af1-785f-43e7-8263-067687ecac42",
    "metadata": {},
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "residuals=(y-y_pred)\n",
     "print(residuals)\n",
@@ -603,25 +321,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "8d8e0281-5b75-45a0-8717-43d204a8c2c3",
    "metadata": {},
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "x_clean = x[~outliers]\n",
     "y_clean = y[~outliers]\n",
@@ -632,18 +335,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "a1e3f89d-8b58-420b-bcd4-ab684883e366",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9995469997854816\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_clean = LinearRegression()\n",
     "model_clean.fit(x_clean, y_clean)\n",
@@ -654,21 +349,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "549ba62d-cb21-405d-9005-96d3300ede9c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#here is the second linear regression\n",
     "plt.figure()\n",
@@ -681,38 +365,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "9dfc34a4-6396-42bd-b67b-b6da1fd58d27",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2318bf83f10>]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "admitance=[]\n",
     "for R in resistance:\n",
@@ -725,31 +381,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "ef8cd500-683e-406b-89bd-14c7823f29ea",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2318bf43ed0>]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure()\n",
     "plt.plot(current,voltage)"
@@ -757,19 +392,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "32e12cf6-a51e-46ef-83c5-7c87505ef6ec",
    "metadata": {},
-   "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#define the voltage differences\n",
     "derivative=[]\n",
@@ -783,19 +409,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "0be31f60-d8fd-4c0f-956b-524cf4a9f078",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "99\n",
-      "[-0.018000000000739647, -0.011999999999900979, -0.06800000000062312, 0.018000000002516003, 0.00799999999756551, 0.026000000000081513, 0.03800000000175885, 0.001999999998503199, -0.001999999998503199, -0.006000000002615025, -0.0019999999967268423, -0.040000000002038405, 0.002000000000279556, -0.035999999999702936, -0.002000000000279556, 0.06600000000034356, 0.04199999999876525, -0.0439999999990448, 0.04000000000026205, -0.004000000000559112, -0.04000000000026205, -0.001999999998503199, -0.04000000000026205, 0.001999999998503199, 0.020000000001019203, 0.003999999998782755, 1.7763568394002505e-12, 0.04599999999932436, 124.58200000000019, 428.3159999999988, -16.75199999999859, -1.0060000000001992, -10.488000000000511, -13.02600000000001, 0.3700000000001751, -9.119999999999322, -10.610000000000696, -0.08600000000046748, -5.933999999999003, -8.158000000000754, 0.6740000000004898, -8.788000000000466, 2.4860000000002174, -2.8579999999997767, -7.106000000000279, 3.0560000000001537, -1.3960000000000719, -5.231999999999971, 0.4420000000000073, 3.589999999999975, -4.297999999999945, 5.677999999999997, 1.021999999999764, -2.793999999999528, 7.417999999999722, 1.7799999999996885, 3.322000000000685, -0.6020000000006576, 7.838000000000022, 5.838000000000648, -1.0540000000007694, 11.430000000000746, 6.707999999999686, 1.6220000000002415, 13.837999999999795, 7.851999999999748, 10.630000000000791, 5.18599999999833, 15.808000000001698, -470.69800000000055, -89.47799999999972, -0.006000000000838668, -0.019999999999242846, 0.04199999999876525, -0.04199999999876525, 0.019999999999242846, -0.07999999999874774, 0.07799999999846818, -0.03999999999848569, -0.020000000001019203, 0.04000000000026205, -0.014000000000180535, -0.003999999998782755, 0.043999999997268446, 0.014000000001956892, -0.014000000000180535, -0.0219999999995224, -0.002000000000279556, 0.0219999999995224, -0.006000000000838668, -0.01799999999896329, 0.042000000000541604, -0.020000000001019203, -0.0439999999990448, 0.018000000000739647, 0.027999999998584713, -0.04400000000082116, -0.0239999999980256, 0.0239999999980256]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "second_derivative=[]\n",
     "for i in range(1,len(derivative)):\n",
@@ -808,76 +425,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "40915d35-183c-4d2b-8a67-d62ecebafba6",
    "metadata": {},
-   "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, 69, 57, 48, 40, 37, 34, 29, 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",
-      "[-9.829082, -9.276158, -8.739986, -8.20482, -7.16849, -6.656468, -5.661274, -5.169068, -4.204682, -3.725894, -3.255894, -2.783408, -2.31378, -1.851258, -1.38568, -0.462548, -0.003156, 0.459826, 0.91851, 1.382872, 1.848256, 2.310846, 2.780854, 3.727752, 4.20226, 4.684606, 5.17279, 5.65992, 6.15848, 6.663748, 7.170638, 7.691366, 8.219946, 8.759156, 9.863756]\n",
-      "[-0.02, -0.019, -0.018, -0.017, -0.015, -0.014, -0.012, -0.011, -0.009, -0.008, -0.007, -0.006, -0.005, -0.004, -0.003, -0.001, 0.0, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014, 0.015, 0.016, 0.017, 0.018, 0.02]\n",
-      "35\n",
-      "35\n",
-      "[[-9.829082e+00]\n",
-      " [-9.276158e+00]\n",
-      " [-8.739986e+00]\n",
-      " [-8.204820e+00]\n",
-      " [-7.168490e+00]\n",
-      " [-6.656468e+00]\n",
-      " [-5.661274e+00]\n",
-      " [-5.169068e+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",
-      " [-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",
-      " [ 3.727752e+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",
-      " [ 9.863756e+00]]\n",
-      "[-0.02  -0.019 -0.018 -0.017 -0.015 -0.014 -0.012 -0.011 -0.009 -0.008\n",
-      " -0.007 -0.006 -0.005 -0.004 -0.003 -0.001  0.     0.001  0.002  0.003\n",
-      "  0.004  0.005  0.006  0.008  0.009  0.01   0.011  0.012  0.013  0.014\n",
-      "  0.015  0.016  0.017  0.018  0.02 ]\n",
-      "coefficient of determination: 0.9996712099428398\n",
-      "intercept: 1.3273642604055857e-05\n",
-      "slope: [0.00208127]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -927,24 +478,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "84abbb6d-5542-45d1-9648-a90869445ee6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "99\n",
-      "99\n",
-      "[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, 37, 36, 35, 34, 33, 32, 31, 30, 29]\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, -9.953676, -9.953696, -9.953716, 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]\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, -0.024, -0.023, -0.022, 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]\n",
-      "58\n",
-      "58\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -996,7 +533,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/hp4155/measurements.py b/hp4155/measurements.py
index 08752f29e6126aca1aa54ac83203129c67daeee7..56e21f466cf289a95691752cee651de72d1b32e4 100644
--- a/hp4155/measurements.py
+++ b/hp4155/measurements.py
@@ -7,6 +7,7 @@ from datetime import datetime
 import os 
 from sklearn.linear_model import LinearRegression
 import sys
+import numpy as np
 
 
 def I_V_Measurement(start,stop,step):
@@ -277,9 +278,20 @@ 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.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 i in range(len(distances)):
+    for j in range(len(distances)):
         #setup
         device.reset()
         device.inst.write(":PAGE:MEAS")
@@ -348,6 +360,20 @@ def tlm_final(field_name ='M00',start=-50*10**(-3),stop=50*10**(-3),step=10**(-3
         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')
+        
+        
+                
+