From 8ca2b472ce3a63ff186b7a6b8db45f9a387ae442 Mon Sep 17 00:00:00 2001
From: JupyterHub User <1myhisij@jupyter.rwth-aachen.de>
Date: Fri, 15 Sep 2023 08:22:00 +0000
Subject: [PATCH] plots

---
 hp4155/ctlm_part2.ipynb | 742 +++++++++++++++++++++++++++++++++++++++-
 1 file changed, 723 insertions(+), 19 deletions(-)

diff --git a/hp4155/ctlm_part2.ipynb b/hp4155/ctlm_part2.ipynb
index f9d110c..62a3c50 100644
--- a/hp4155/ctlm_part2.ipynb
+++ b/hp4155/ctlm_part2.ipynb
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "ace8d693-f5cb-4aa4-b82d-66c842c3df5a",
    "metadata": {},
    "outputs": [
@@ -161,7 +161,7 @@
      "text": [
       "2.2250738585072014e-308\n",
       "100\n",
-      "[35714.285713825164, 100000.00000378577, 499999.999930111, 14285.714285530066, 19230.769231365877, 22727.272726848576, 55555.555553272694, 49999.999997451996, 45454.54545553223, 49999.999997451996, 71428.57143671338, 83333.33332168518, 35714.285713825164, 38461.53846141788, 16129.032258120591, 15624.999999984373, 499999.999930111, 22727.272727766114, 4.49423283715579e+307, 24999.99999983622, 27777.777778006992, 249999.9999650555, 166666.6666927136, 21739.13043510191, 22727.272726848576, 41666.66666701049, 49999.999997451996, 50000.000001892884, 38461.53846141788, 8.025166923471991, 1.8085668193097093, 1.8650731481688694, 1.868579095084515, 1.905930875699001, 1.9544534175572468, 1.9530410802660825, 1.9884589840565334, 2.0313147481575977, 2.0316696667655436, 2.056462226901814, 2.0915513873260365, 2.0886070661754252, 2.1276595744680864, 2.116464826471049, 2.129344928326249, 2.1620593182594563, 2.147867811623401, 2.1543273974432444, 2.178886588953045, 2.1767901922541095, 2.1599111844520955, 2.180150168743623, 2.1534923184928996, 2.1487631719182447, 2.161741498951554, 2.12762335960239, 2.1195960897691353, 2.104775736145312, 2.1074460283072165, 2.0732005655691155, 2.048407977320025, 2.0528401042842788, 2.005776636713734, 1.979147699834543, 1.972814614610664, 1.9203883793458385, 1.8918612130614105, 1.8545650117764856, 1.836898140324326, 1.7850640123954826, 11.17243536746139, 35714.285713825164, 45454.54545553223, 499999.999930111, 22727.272727766114, 499999.999930111, 45454.54545553223, 17241.379310575107, 50000.000001892884, 50000.000001892884, 24999.99999983622, 4.49423283715579e+307, 71428.57142765033, 55555.55555875528, 38461.538464045625, 24999.99999983622, 38461.53846141788, 249999.9999650555, 499999.999930111, 41666.66666701049, 55555.55555875528, 4.49423283715579e+307, 23809.52380921678, 45454.54545553223, 45454.54545553223, 250000.00007607782, 41666.66666701049, 49999.999997451996, 22727.272727766114, 49999.999997451996]\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"
      ]
     }
    ],
@@ -182,7 +182,7 @@
     "#define the voltage differences\n",
     "derivative=[]\n",
     "for i in range(1,len(voltage)):\n",
-    "    element = (abs(voltage[i]-voltage[i-1])+mini)**(-1)\n",
+    "    element = 0.001*(abs(voltage[i]-voltage[i-1])+mini)**(-1) #0,001 is the delta I\n",
     "    derivative.append(element)\n",
     "\n",
     "print(len(derivative))\n",
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "id": "d7ee8038-f343-4660-99ea-63bd13291c42",
    "metadata": {},
    "outputs": [
@@ -200,14 +200,14 @@
      "output_type": "stream",
      "text": [
       "99\n",
-      "[2295918367.468986, 39999999994.14683, 242857142788.34436, 70643642.08245775, 67240451.83829193, 746097336.9502603, 308641975.31068957, 227272727.08440652, 206611570.0917475, 1071428571.908469, 850340134.7377707, 3968253966.7669935, 98116169.55561735, 858942546.2779719, 8129552.550612424, 7568359373.900659, 238636363567.8163, inf, inf, 69444444.45381439, 6172839505.246728, 20833333315.17343, 24154589380.04353, 21481354.1687213, 430440771.35928303, 347222222.10459465, 0.22204440027740802, 576923077.0455914, 1478981280.5528402, 49.889253532375676, 0.10219547145567956, 0.0065388474512759675, 0.06979475642041172, 0.09248061069502782, 0.0027603474454596117, 0.06917262107966252, 0.08521692914536874, 0.0007209514027160663, 0.05037029239032119, 0.0721595329861116, 0.006158198987294401, 0.08156534477192649, 0.023818612759653943, 0.02726028253789495, 0.0696602202875625, 0.030682879162623997, 0.013874336458860369, 0.05290853912861865, 0.004567810652435678, 0.0367420586384047, 0.04371440853322024, 0.05811811672245666, 0.010184180821546209, 0.0278873511622859, 0.07375459769809996, 0.017079006811064144, 0.03141316359005189, 0.005620366150795851, 0.07217046443495378, 0.05140000797993658, 0.009078804230072604, 0.0966137736754965, 0.05341169945280647, 0.012534111053696229, 0.10342724311946469, 0.05478323862828188, 0.07055923660548065, 0.03276436166272785, 0.09521401319782576, 16.757058776920367, 398890.72552072123, 347866419.342195, 20661157022.020298, 238636363567.8163, 10847107436.652409, 227272727205.52167, 1282416642.9804366, 564803805.0302639, 0.0, 1250000000.1501553, inf, inf, 1133786847.763597, 949667616.4274548, 517751479.4264198, 336538461.5373367, 8136094673.191322, 62499999982.52775, 229166666599.51776, 578703703.827475, inf, inf, 515357658.23896027, 0.0, 9297520664.772095, 52083333368.11638, 347222222.10459465, 1363636363.414803, 619834710.6881351]\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",
-    "    element=abs(derivative[i]-derivative[i-1])*(abs(voltage[i]-voltage[i-1])+mini)**(-1)\n",
+    "    element= abs(derivative[i]-derivative[i-1])*(abs(voltage[i]-voltage[i-1])+mini)**(-1)\n",
     "    second_derivative.append(element) \n",
     "\n",
     "print(len(second_derivative))\n",
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "580b815f-3b7d-495f-b7b0-adb064811540",
    "metadata": {},
    "outputs": [
@@ -224,21 +224,642 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.2250738585072014e-308\n"
+      "99\n",
+      "99\n"
+     ]
+    }
+   ],
+   "source": [
+    "#we have two less values, we delete also the last 2 voltages \n",
+    "voltage_new = voltage.copy() \n",
+    "current_new = current.copy() \n",
+    "voltage_new.pop()\n",
+    "voltage_new.pop()\n",
+    "current_new.pop()\n",
+    "current_new.pop()\n",
+    "print(len(voltage_new))\n",
+    "print(len(current_new))   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "indexes=[]\n",
+    "for i in range(len(second_derivative)):\n",
+    "    if second_derivative[i]>10:\n",
+    "        indexes.append(i)\n",
+    "print(list(reversed(indexes)))\n",
+    "\n",
+    "for i in list(reversed(indexes)):\n",
+    "    voltage_new.pop(i)\n",
+    "    current_new.pop(i)\n",
+    "\n",
+    "print(voltage_new)\n",
+    "print(current_new)\n",
+    "print(len(voltage_new))\n",
+    "print(len(current_new))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "60c467d7-aa6a-4703-b81e-ddcb0097b88b",
+   "metadata": {},
+   "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",
+    "fig.suptitle('CTLM plot')\n",
+    "ax1.set_title('I(V)')\n",
+    "ax1.set(xlabel='Voltage(V)',ylabel='Current(A)')\n",
+    "ax1.plot(voltage, current,label=\"initial\")\n",
+    "ax1.plot(voltage_new, current_new,label=\"linear\")\n",
+    "ax2.set_title('R(V)')\n",
+    "ax2.set(xlabel='Voltage(V)',ylabel='Resistance(Ohm)')\n",
+    "ax2.plot(voltage,resistance)\n",
+    "fig.tight_layout() #leave space between plots \n",
+    "ax1.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "188ea879-4cf9-419c-8198-1ed48852804e",
+   "metadata": {},
+   "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",
+    "y = np.array(current_new)\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(x, y)\n",
+    "r_sq = model.score(x, y)\n",
+    "print(f\"coefficient of determination: {r_sq}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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: -3.1235551699275468e-06\n",
+      "slope: [0.00329592]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# method 2 is multiple linear regression\n",
+    "x=np.array(voltage).reshape((-1,1)) #column matrix\n",
+    "y = np.array(current)\n",
+    "\n",
+    "print(x)\n",
+    "print(y)\n",
+    "\n",
+    "model = LinearRegression()\n",
+    "model.fit(x, y)\n",
+    "r_sq = model.score(x, y)\n",
+    "print(f\"coefficient of determination: {r_sq}\")\n",
+    "print(f\"intercept: {model.intercept_}\")\n",
+    "print(f\"slope: {model.coef_}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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"
      ]
     },
     {
-     "ename": "IndexError",
-     "evalue": "pop index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_21670/2406563054.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msecond_derivative\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     11\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msecond_derivative\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmini\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0mvoltage_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\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     13\u001b[0m         \u001b[0mcurrent_new\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: pop index out of range"
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f3d62537340>"
+      ]
+     },
+     "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",
+    "print(y_pred)\n",
+    "plt.figure()\n",
+    "plt.plot(voltage,current,label=\"real curve\")\n",
+    "plt.plot(x,y_pred,label=\"linear\")\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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"
      ]
     }
    ],
+   "source": [
+    "residuals=(y-y_pred)\n",
+    "print(residuals)\n",
+    "threshold = 0.001 #adjust this threshold as needed\n",
+    "outliers = abs(residuals) > threshold\n",
+    "print(outliers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "x_clean = x[~outliers]\n",
+    "y_clean = y[~outliers]\n",
+    "\n",
+    "print(x_clean)\n",
+    "print(y_clean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a1e3f89d-8b58-420b-bcd4-ab684883e366",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9995469997854816\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_clean = LinearRegression()\n",
+    "model_clean.fit(x_clean, y_clean)\n",
+    "y_pred_clean = model_clean.predict(x_clean)\n",
+    "r_sq = model.score(x_clean, y_clean)\n",
+    "print(r_sq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "549ba62d-cb21-405d-9005-96d3300ede9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hvPvuu5f/BQjRWVnMcGQN+dv+zNLKQ6zy70ZNkB9DAqL5Q/zPuD7qerwMXmQVVQB5Dgmhzd1QO4N0Q+0YZrMZo9GIt7c3eXl5TJw4kSNHjuDp6ens0C5KvnvRKdWWY85+n/W7l5BuqGOrjw8euDElciKzh97HsLBhPyieVVTB7W9v4f37Uxgfd2U3ix3WDbXoOmpra7nmmmswGo1orZuuaoQQdnRqHxVb/8zKorUs7+bNCT93enr05JEhP2F63B2E+IRcdHZHnLxLIhBN/P39Of9KSwhhB2YTHP6SA9v+wj9rcvm3XzcaA/1ICR7ME/EPMqHvBNzdLn44dkAXQ026VCLQWjukQybRcXXGqk3hQmrKMGb9nbV73ifdw8huby98AgKZFnMTs4bcy4CgAc6OEOhCicDb25uysjJCQkIkGbgIrTVlZWV4e9u/FYUQbXJiF6e3/pmPi//Dim4+lPkb6OcVzlPDHmBq7G34e/o7O8If6DKJICIiguLiYkpKSpwdimhH3t7eREREODsMIcBsRB/4nKztfyG9/ijfdvPF0t2P8T1GMjt+LqPCR+GmOmaL/S6TCDw8PIiOjnZ2GEIIV1NdQu2OxXy5/0PSvSzkeHoSEBDMT2KnMXPIHCL8O/6JSpdJBEII0a6Kszi69X9YenIzn3Xz4Zy/O4N8w/nv+Ae5of9N+Lj7ODvCVpNEIIQQrWVqwLLvUzZl/pl042k2+frgHuDHpPCxzB4+l4SwhE55j1ISgRBCXErVSSq3v8Nnh5axzBuOeXgQ6h3C/EGzmTH4bsJ87dsbaHuTRCCEEC3RGo5t5/DW10k/s50vu/lQ7+9BYkAMjwyfx8R+k/AweDg7SruQRCCEEM0Z6zHuXc63WW+Rbikj29sbb39/boqcyKzhcxkUPMjZEdqdJAIhhACoLKZ06yI+zl3JCm83zni608ejB48PmcNtg2bS3evHHTB2FZIIhBCuS2t04WZ2b32Nf5bv5utuPpj8PBkbNJjfjZjP2D5pGNzaNj53ZyCJQAjhehprqd/9T/6986+kU8VBL0/8/AOYFX0Ts4Y/SL+Afs6OsF1JIhBCuI6KIo5nvMGygi9Y6eNOpZeBAV7h/Db+AW6OnY6vh6+zI7wgR3arJYlACNG1aY0l/zu2bn2d9KqDrPfxxs3Pi2tDE5mduICkXkmdqu2/I2KVRCCE6Joaqqne+T6f7/k7S1UthZ4eBPsF8uCA6dwRfz+9uvVydoQdhl0SgVJqCvAGYAD+prVeeN50ZZt+I1AL3Ku1zlZK9QU+AHoBFmCx1voNe8QkhHBRZXnkZbxO+tG1/MvHg1ovN+J9+/HS8IeY3P8mPA0y2NL52pwIlFIGYBEwCSgGdiilVmmtDzQrdgMQa/tJBd62/TYBv7YlBX8gSyn19XnzCiHExVksmPK+Zv3W10mvyWObjzee3by5odcYZicuYEjYUGdH2GZ5JdUAeBrs34OpPa4IUoBcrXU+gFJqKXAr0PxgfivwgbaOIrJVKRWolOqttT4JnATQWp9TSh0E+pw3rxBCtKy+ivKsJazc9x7L3Bs45e5Ob79gHh04i9uH3UuQd5CzI7SLo2W1/OHLgyT0DSQ5yv7bZI9E0Ac41ux9Mdaz/UuV6YMtCQAopaKAEcA2O8QkhOjKSnPYt/lV0k+s498+nhi9Fal+sTyV+AvG95t4yWEfOzKtNfmlNWQVVpBZVE5mYQX5pTV4e7jx2p3Dce+gVwQt3cI+v6HTRcsopfyAT4DHtNZVLa5EqbnAXIDIyMgri1QI0XlZLDQeXs1XO14nve4Ye7298PX14faIa5g98hFiAmOcHeEVaTCZ2Xe8kszCCnYUVpB9tILymkYAAn09GBkZxIykCCYP7klMmJ9DYrBHIigG+jZ7HwGcaG0ZpZQH1iTwkdZ65YVWorVeDCwGSEpKkoFqhXAVdWc5tf0dlh/6iE88zJQbDET5hfH0kDlMHXwXfp6OOTg6yrl6I9lHz7KjoJztheXsPnaWBpMFgOjQblw7qAdJ/YJIigoiJtQPNzfHN221RyLYAcQqpaKB48As4K7zyqwCFtjuH6QClVrrk7bWRH8HDmqtX7NDLEKILkKfPsCOLS+TfiqD73w80V6K8YGDmZ30GKP6jO00bf/PnKsns7CC7QXl7Cgs5+DJKiwaDG6KoeEB3DOqH8lRwYzsF0SYv5dTYmxzItBam5RSC4CvsDYfXaK13q+Uesg2/R1gNdamo7lYm4/eZ5t9LPATYK9Sapfts2e01qvbGpcQohOymKk98Bn/ynqT9MZT5Hl60r1bN+b0m8KdIxfQx6+PsyO8KK01x8rr2F5YzvaCMnYUVlBQWgOAj4eBEZGBPHxtLMlRwYyIDKSbV8e4l6G0I59bdpCkpCSdmZnp7DCEEPZSW07h1j+zNGcFn3taqHZz4yrPEO6K/xlTBs7A293b2RG2yGLR5JypZntBGdsLK9hRUM6pqnoAuvt4kBwVTEp0EMlRwQzt0x0PB9zovRxKqSytddL5n3eMdCSEcEnmk7vZtHkh6aVZbPbxwt0Lrg8ZwezkXxHfc0SHq/4xWzQHT1axraCcbfll7Cgsp6LWCECvAG+So4NJiQ4mJSqY2B7tU79vD5IIhBDty2ykct9yPs1+m6XmMo57uNPD158FA27j9hE/J9Qn1NkRNjGaLew7Xsm2gvKmOv5z9SYAIoN9ue6qnqREB5MaHULfYJ8Ol7haSxKBEKJ91JRycMufWJq/ii89ocHNjaRufflVws+5ZsDNeLg5f9hHo9nC3uOVbM0vY2t+OVmF5dQ0mgGICevGzfHhjIqxnvX37u7j5GjtRxKBEMKhjMU7+GbLy6RX7GWntyc+3gam9khhVsrjxIU4d9hHo9nCnuLvD/xlZBVVUGs78Mf28GN6YgSjYkJIjg6ih3/HvE9hD5IIhBD2Z2rkzO6PWLH7r3ysqyh1NxDpG8hvBt7JrQkPEuAZ4JSwzBbN/hOVZOSVsSWvjMxmZ/wDe/pzx8gIUmNCSIkOJtTPOU05nUESgRDCbnTVKXZueZX0on/zjZcbZjfF1X4xzB75MGOiJuGm2rfVjMWiOXTqHBn5ZWTklbGtoKypjn+A7Yx/TH/rgT/EhQ7855NEIIRos7qizazOeIX0c4c57OmBv7cHd4ePY2bK4/Tt3n7DPmqtySupbjrj35pf1tSqJyrEl5vjezMqJoTRMSH0COi6VT2XSxKBEOLKmBo4lr2EZXvfZaWq4ZzBjTjfYH531T3cFH8fPu6Ov5n6/QNcW/JK2ZJXRkZ+GSXnGgDoE+jDtYN6MqZ/CKP7hxAe2HVu7tqbJAIhxGWxVBazZdNLpB//jo2ebhgMiokBA5md/EsSI9Ic3oTydFW97Yy/lM25ZRw/WwdAmL+X9aAfYz3wRwb7dtrmnO1NEoEQ4tK0pirvP3y+/Y8srS3kqIc7IV5ezOs7kRmpj9PTgcM+VtYaycgvazrrzz1jHaClu48Ho2NCmDc+hjH9Q+gf5icH/iskiUAIcWHGOo7seJulBz/iC7d66tzcSPAN4xfD7mfS4LvxMNi/7X+90Ux2UQWbckvZnFvK3uOVWLS1r56U6GDuGBnB2AGhXNU7AEMneXK3o5NEIIT4EWN5Ad9t+gPpp7eQ6WnAywA3Bg1jVuoTDO410q7rslg0B05WNR34txeU02CyYHBTJPQNZMG1saQNCCWhbyCe7s7tq6erkkQghLDSmrIj/2ZF5ussry/mjLs7fTy9+VX0TUxL/hWBPvYbIvFoWW3TgX9LXmlTy564nn7MTokkbUAoqTHB+Hs7/2ljVyCJQAgXpxuq2bvtTdJzPuYrgxGjUozpFs5vEx7i6rjpGNwMbV5HeU2j7eZuKZtySzlWbr3B2yvAm2sH9SQtNoQx/UPpKU06nUISgRAuqqHkMGs2v0h6SSb7Pd3pZoA7Q5OYOfopotvY9UO90UxmYQUbc0vYnFvK/hNVaA3+3u6MjgnhwatjGNM/lP5h3eQGbwcgiUAIV6I1Jw58wvLsRXxiPM1Zg4H+Xn7814Dp3Jz0MN2ucNjH5vX8m3JK2VForef3MCgSI4P41XVxpMWGMqxPd4cMvi7aRhKBEC5A11exLeOPpOd9zjp3a9861/pFMjvxEZL733BFZ+Wnq+rZmFPKxpwSNuWUUmYbcH1gT3/uGdWPtNhQUqKCO8woXOLC5BsSogurOb2PVZteJL1iDwUeBoLcFQ/0HMsdo5+md2DUZS2rttHEtoJyNtkO/kdOW9vzh/p5MS4ujKtjQ0kbECpdN3RCkgiE6GosFvL3/pOlu//KKnM5NW5uDPUK4P8NnM3kxHl4tXLYx++rezbYzvgzCytoNFvwdHcjNTqYGSMjuDo2jEG9/KWev5OTRCBEF2GuLWf95oWkF61hq4fGQ2tu8O/PrORfM6zf+FYt42Rlna26x9rCp9xW3TOolz/3jo3i6thQkqOC8fZoe0si0XFIIhCik6s4voOVW15iedUhTrgb6OXuxqO9r2HamKcJ8bt41w91jWa2FZQ11fV/X90T5u/FhLgwro4LZeyA0C49KIuQRCBE52Qxs3/nEtL3vcu/LVU0uilSvIJ5YshPmTD8ftzdWv7X1lpz8OQ5NuSUsDGnhB0F1uoeL3c3W/cNfbk6LpSBPaW6x5VIIhCiE2msPs3aTX8g/fh37HEHH62Z1n0gs0Y9yYDwlBbnKTnXwKbcEjYcsVb5lFZbu2ke2NOfOWP6cXVsGCnRUt3jyiQRCNEJnCrayMdbX2FFTT7lBjeiDO481XcyU0c/ib9P8A/KNpjMZBVWsD6nhI1HSjlwsgqA4G6eXB0bytWx1hY+8hSv+J4kAiE6KG0ykpn1FksP/pNvqcECjPcKY3b8zxg19K4fDPtYVFbD+iMlbDhSwpa8MmobzXgYFCP7BfHE9QMZHxfG4N4BuElvnaIFkgiE6GBqq47z5abfk35yMznuigCt+WlwPHeOeZaIsCEA1DSYyMgrYUNOCeuPlFBUVgtAZLAvtydGMD4ujFH9Q/CTh7lEK8hfiRAdxNG8tSzd8Rqf1R3jnJsbgwye/D76FqakPo6Xhx8HTlaxal0uG46UkFVUgdGs8fEwMKZ/CPePjWZ8XBhRod2cvRmiE5JEIIQTWUwNbNr2Ouk5K9ikGnDXmknevblrxC/o0+cGNueW8cxneWzMKaG02tqm/6reAdyfFs342DBGRgXh5S43eUXbSCIQwgkqy/P5bNMLLC3dQbFBEabh5yEjiYtcwM5TfvzX1yXsO/4tYL3JmzYglPG2bhykCwdhb5IIhGhHhw99Snr2Ir5sOEW9m2KEmzeTuk3mYM1NvJVZTfXm0xjczpAYGcjjk+MYFxfG0PDucpNXOJQkAiEczNhYw7cZr5Ce/y+y3Yx4WzQp5h6UVNzMhjMD2QD0CWxgakI442LDGDMghAAZmUu0I7skAqXUFOANwAD8TWu98Lzpyjb9RqAWuFdrnd2aeYXorEpLDvDx5hdZXr6HUoOit0WTUD6AnWXT+dotjFExIfw2JYzxcWEyQItwqjYnAqWUAVgETAKKgR1KqVVa6wPNit0AxNp+UoG3gdRWzitEp6EtFrbu/oj0XYvZqCowKcVV9Z50rxhDledt9B/YmwduDiM1OgQfT7nJKzoGe1wRpAC5Wut8AKXUUuBWoPnB/FbgA621BrYqpQKVUr2BqFbMK0SHZrFodhceY/WWl9het5l8T42ftjC8Kozufj8hOfE6xseF0TfY19mhCtEieySCPsCxZu+LsZ71X6pMn1bOC4BSai4wFyAyMrJtEQvRRuU1jWzMKWHzvg2cKf87OX4nqTK40Q/NDJ3MxFFPkzpwAB4yLKPoBOyRCFqq2NStLNOaea0far0YWAyQlJTUYhkhHMVs0ew6dpb1R0pYd/gUprOf4Re8gUO+9bgFwBiLP3fEzWFC6jyUmxz8Redij0RQDPRt9j4CONHKMp6tmFcIpzhTVc+6I9YuHDbllFLfUEZC0Eoagw5xvC8Emy38zG8Qd455ml7hSc4OV4grZo9EsAOIVUpFA8eBWcBd55VZBSyw3QNIBSq11ieVUiWtmFeIdtFospBZVG7rvK2Ug7ZeOwcF5jIm/Auy3E9ywE0Rb3ZjQcQUJo95Gk+fQOcGLYQdtDkRaK1NSqkFwFdYm4Au0VrvV0o9ZJv+DrAaa9PRXKzNR++72LxtjUmI1ios/b9eOzPy/6/XzsRIf+aN3M6hmjVkq1pKtOYG9xBmD5/LkKF3gTT1FF2Isjbk6VySkpJ0Zmams8MQnVB1g4ktuaVsyLGe9R8tt/ba2S/El/FxYYyMMHLi5Nt8UrKV026a3mYLM4PimT7mWYJ6DnVy9EK0jVIqS2v9o3pMebJYdGkWi2b/iaqm7pqziyowWTS+ngZGx4Tws6ujGRcbxrnKjaRnPsF/7z2GUSlGaXee6TeV8aOfwODl5+zNEMKhJBGILqe0uoGNOSWsP1zCxpxSymqsvXYO7h3Az66OYVxcKEn9gtE08tX213l69Ur26Xp8LRZmePZiVuICYgbdJtU/wmVIIhCdntFsIbuowlrXn1PCvuPWm7whtqEZx8WFkRYbSg9/a6+dp8pzeXvN43xSkkWFG0SbLDwTmswtac/iFxLrzE0RwikkEYhO6WhZrW1MXuvQjNUNJgxuipGR1qEZx8WGMST8/4Zm1Fqz/dCnpO9cxH8aTgEwQXsxu/8dpKY+hvKQrp2F65JEIDqFc/VGMvLK2JhjvdH7/dCMfQJ9uGV4OOPjWu61s7ahmn9te5X0gi/Io5FAs4X7vCO4M/mXhMdOccamCNHhSCIQHZLZotl7vJKNR6z1/NlHf3iT9/6x0VwdG0p0aMu9dhac2cuyjJf4vGIv1QoGGy282GssU9J+i1dg3xbWKITrkkQgOozjZ+uaDvybckuprDMCMLRPAHPHxXB1bBgj+wXh6d5yFw5mi5mN+9NJ3/s3thjLcNea67UPswfdTfzIn6M8vNpzc4ToNCQRCKepaTCxraCMDUdK2ZhTQl5JDQC9AryZPLgnV8eFMbZ/CCF+Fz+An60r49OMl1l27GuOY6KHycyCbjHcnvoEodHj22NThOjUJBGIdmOxaA6ctLbp33iklMyicoxmjbeHG6nRIdyV2o9xsaEM6OHXqkFaDp7YRvrWV1hddYQGBUlGza/6XMM1ac/i4d+rHbZIiK5BEoFwqFOV9WzMKWFTbimbc0sprba26b+qdwD3j41mXJy1usfbo3WDtBjNRr7e8y7p+99nl7kKH4uFqcqfWYPvJS7xZ2CQP2khLpf81wi7qmkwsTW/rKmeP/dMNQChfl6kDfhxm/7WOnPuBB9vXcjHx9dTpixEGk38JmAgt45+moC+LQ5hIYRoJUkEok3MFs2e4rNsyillY24pO49WNFX3pESHMDOpL2mxoQzq5X/ZY/JqrckuWkd65mt8W12AGbjaqJjd73rGjH0Kt26hjtkoIVyMJAJx2YrKaqxn/DmlbMkrparehFIwJDyAB9JiGBcbSuJlVPecr9ZYy+qdfyX98DKOWGrwN1u42xDIzOHz6Bt/N7jJWL9C2JMkAnFJ5TWNZOSVsSm3lE25JRwrrwOsD3PdMLQ3abGhjB0QSnA3zzat59jZfJZmvMSnp7dxTmniGk08HzSMG8c+g0/vBDtsiRCiJZIIxI/UG83sKCxvusG7/0QVWoO/lzupMSH8LC2GtNhQYi7wMNflsGgLm/O+JD37L2yqPY4BuM7oxuz+tzJi1K9RvkH22SghxAVJIhCYzBb2Hq9kS14Zm3JKyTpaQaPJgodBMSIyiF9eF0dabCjxfbrjbqfB2Ksaq/gs8y8sy/2Uo7qeUJOZh9zDmDHyF/QYPANk3F8h2o0kAhektSb3TDWbc0vZlFvGtvwyzjWYABjUy5+fjOpHWmwoKVHBdPOy75/IkZL9LN32Cl+UZlOnYESDiQWhI7ku7Rk8egy267qEEK0jicBFnDhbx2ZbVc+WvDLOnGsAoG+wDzfF92bsgFBG9w8h9BJP8V4Jo8XId4dXkr57MZkNZ/CyWLjJ5M6suDu5KvVh8A6w+zqFEK0niaCLOlv7fzd4t+SVUVBq7b4hpJsno/uHkDbAeoO3b7Cvw2IorS3lk8w3WF74JWe0kT5GE7/2Cue2lEcJHHizVP8I0UFIIugiqhtM7CgoZ0teKRn5ZU03eLt5GkiNCeHu1EjGDghlYE//pj76HUFrzZ5TmaRv/xNfVezHpGBMvZHneo0lLe0pDDLwixAdjiSCTqreaCa7qIIteWVsyStld3ElZovG0+DGiMhAHpsYx9gBIQzvG4iHnW7wXjQeUz1rDqSTvu9dDhgr8LNYmGnyZOZVdxGd/HOQcX+F6LAkEXQSDSYzO4+eZWt+GRl5Zew8dpZGkwWDmyI+ojsPjY9hTP/Qy+q3xx5OnDvOsh2vsfLYt5zFTP9GI//lG8XNyb+kW+xkGfdXiE5AEkEH1WiysLv4LBl51gN/9tEKGkyWpid454zux5j+oSRHB+Nn55Y9l6K1Zuux9aRn/g/rq/IAzbX1Jmb3mUDy2KdQwVHtGo8Qom0kEXQQRrOFPcWVTWf8mUXl1ButB/6regVwz6h+jIoJISU6mO4+HpdeoANUN1azat97LD3wEQXmaoLMZh6w+HDHkHvpPfIB8HTcjWchhONIInCS7x/i2ppfTkZ+GZmF5dQ2mgFrW/5ZyZGM7h9CanQwgb5t67qhrfIr8kjf8SdWndxMLRaGNTTy//ximXz143hFj5fqHyE6OUkE7eT7M/5tBWVsyy8ns7CcGtuBP66nHzNGRjA6JoTUmJA299ljDyaLifUFX5G+cxHbao7hoTU31JuY1W8Kw8Y8Dt0jnB2iEMJOJBE4SIPJzO5jlWzLL2NbQTlZRRXUGa0H/tgefkxPjCA1JphRMY55iOtKVdRX8Mnu/2X5kRWctNTRy2TiUe3P9IS5BA+/BzwubxwBIUTHJ4nATmobTWQXnWV7gfXAv+vYWRpMFsBa1TMzuS+p0cGkRAdfcgxeZ9hfspd/7nidNSWZNKJJrWvgycAhjJ/wG9wjR0n1jxBdmCSCK1RZaySzqJztBeVsKyhn3/FKTBaNm4Ih4d25Z1Q/UqKDSYkKJqgDVPW0pNHcyFc5n7N091/ZU38aH4uFafUWZsVMZcDoxyCgt7NDFEK0A0kErXSmqp7theXssB34D58+h9bgaXBjeN/uzBsfQ0p0CImRgfh7O6dVT2udqjnF8p3v8En+vyjXjUQ1GnnKLYipiT/Hf9gscO+YiUsI4RhtSgRKqWBgGRAFFAJ3aq0rWig3BXgDMAB/01ovtH3+KnAL0AjkAfdprc+2JSZ70FpzrLyO7YXlbC8oY3tBOYVltQD4ehoY2S+IG4f1JiU6mIS+ge36ANeV0lqTeWoH6Zlv8J/yPVi0ZnxdA7NDEhk18XHcIpKdHaIQwkmU1vrKZ1bqFaBca71QKfUUEKS1fvK8MgbgCDAJKAZ2ALO11geUUpOB/2itTUqplwHOn78lSUlJOjMz84rjPp/JbOHQqXPsKCwns7CCzKJyTldZe+fs7uNBclRwU/3+4PCAdumywV5qjbV8cfhj0vcuIbexnO5mM9Mb4M7YGUSMehj8wpwdohCinSilsrTWSed/3taqoVuBCbbX7wPrgPMP5ClArtY63xbIUtt8B7TWa5uV2wrMaGM8rdJgMpNVWMEO20E/u6iiqSlnn0AfRsWEkNQviJToEGJ7+Dm0kzZHKaoqYunOt/i8aC3ntIlBDY383qMHNyQtwHvIdDB07OorIUT7aWsi6Km1PgmgtT6plOrRQpk+wLFm74uB1BbK3Y+1msmhKuuM3PO3bew9XolSMKhXALePjGBkvyCSooLpE+jj6BAcxmwxs7l4A//M/gubK4/grjWTauu5q+cohk96HBWe4OwQhRAd0CUTgVLqG6BXC5OebeU6Wjqd/kF9lFLqWcAEfHSROOYCcwEiIyNbueofqm4wce+72zl0qoo/3jGcyUN6EtDBb+y2RmVDJZ8d+CdLD/yDYtM5wkwm5je6MWPQbMJSfg7dQpwdohCiA7tkItBaX3ehaUqp00qp3rargd7AmRaKFQN9m72PAE40W8Yc4GZgor7IDQut9WJgMVjvEVwq7pb8dX0eu4+d5a27RzJlaEu5rXM5XH6Y9OxFfHl8PfVYSKyv51GvCCamPoLHVbeAQRqFCSEura1HilXAHGCh7ffnLZTZAcQqpaKB48As4C5oak30JDBea13bxlgu6WytkUBfz06dBIwWI98WrCV919tkVxfhbbFwU20Ds8PHMXDKr6HnEGeHKIToZNqaCBYCy5VSDwBHgTsAlFLhWJuJ3mhrEbQA+Apr89ElWuv9tvn/AngBXyvrk6tbtdYPtTGmLqmktoQV+z/g48PLKDHXEWE08rjRg9uG3kf3pAfAJ8jZIQohOqk2JQKtdRkwsYXPTwA3Nnu/GljdQrkBbVl/V6e1ZlfJLtKz3+Lr09swoUmrreN532jSxj6K28AbwK3jP8MghOjYpBK5A6o31bM6dxXpe/6XQ3Wn8DdbmF3bwMzI6+h34y8hbKCzQxRCdCGSCDqQ4nPFLN/7LivzPqPS0khsYyPPmby5afiD+CbOAe/uzg5RCNEFSSJwMou2kHEig6W73mF96S7ctGZiTS2zAwYxctyjqNhJ4NZ5nmQWQnQ+kgic5FzjOVYdWcnSfUsobCgn2Gxmbq2RGVE30uuWRyCkv7NDFEK4CEkE7Sy3Ipele//OqsI11GkT8fUNLNR+TEqYj+eIe8DL39khCiFcjCSCdmCymFh39DvSd/+V7WcP46k1N1TXMjtwGEOuexRirpGBX4QQTiOJwIHK6spYeXgZyw58yGnjOcKNJn5ZZ2TagNsImvoLCI52dohCCCGJwBH2luwlfc/fWFO8DiMWRtXV8YwKYnziQxiGzwLPbs4OUQghmrhcImjL+AsX02Bu4KuCNaTv+V/2nSvC12JhRnUts0JHEjPuUYhKk+ofIUSH5FKJwBHH4ZPVJ1l+8CM+ObycCnMd0Y1Gnqk3c0vcHfilPgSBV9ZTqhBCtBeXSgT2orVm+6ntpO/5O9+dygCtmVBbx2z3MFJHzkfF3wEenXdcAyGEa5FEcBlqjDX8K3cV6Xv/Tn7daQLNZu47V8udvUYTfs0jEDlKqn+EEJ2OJIJWKKgsYOn+D/g873NqLEYGNzTwYj1MGXwXXilzISDc2SEKIcQVk0RwAWaLmQ3FG0jf+3cySnfjrjVTamqZ7RnOsJRfoIZOB3cvZ4cphBBt5lKJoDUNhs7Wn2Vlzics2/8+Jxoq6GEy8fC5Oqb3GU/odQ9DRJLjAxVCiHbkUokAQF2gDv9A2QGW7v+A1YVraNBmkuvqebzRwDXD5uCedD/492znSIUQon24XCJozmg2srZoLUv3vcuuisP4WDS3VlczyyeK2LEL4Kqp4O7p7DCFEMKhXDIRnKk9w8eHl/HxwX9SZqymn9HIk9V1TI2cTMD18yF8hLNDFEKIduNSiaDcdAQV8jHXf7wfs9ZcXVfHXSZPRsc/gFvSfdAt1NkhCiFEu3OpRBB54pfs9PNjemU1M/3i6DvuFzDoZjC41G4QQogfcKkj4ENnq3ikohKfeRuh1zBnhyOEEB2CS42B+Mf4jYx1+1iSgBBCNONSiUAIIcSPSSIQQggXJ4lACCFcnCQCIYRwcS6XCBw1QpkQQnRWLpUIZKgAIYT4MZdKBEIIIX5MEoEQQrg4SQRCCOHi2pQIlFLBSqmvlVI5tt9BFyg3RSl1WCmVq5R6qoXpjyultFJKen0TQoh21tYrgqeAb7XWscC3tvc/oJQyAIuAG4DBwGyl1OBm0/sCk4CjbYxFCCHEFWhrIrgVeN/2+n3gthbKpAC5Wut8rXUjsNQ23/deB34DSLtOIYRwgrYmgp5a65MAtt89WijTBzjW7H2x7TOUUlOB41rr3ZdakVJqrlIqUymVWVJS0sawhRBCfO+S3VArpb4BerUw6dlWrqOl1vtaKeVrW8bk1ixEa70YWAyQlJQkVw9CCGEnl0wEWuvrLjRNKXVaKdVba31SKdUbONNCsWKgb7P3EcAJoD8QDey2DSgfAWQrpVK01qcuYxuEEEK0QVurhlYBc2yv5wCft1BmBxCrlIpWSnkCs4BVWuu9WuseWusorXUU1oSRKElACCHaV1sTwUJgklIqB2vLn4UASqlwpdRqAK21CVgAfAUcBJZrrfe3cb1CCCHspE1DVWqty4CJLXx+Arix2fvVwOpLLCuqLbEIIYS4MvJksRBCuDhJBEII4eIkEQghhIuTRCCEEC5OEoEQQrg4SQRCCOHiJBEIIYSLk0QghBAuThKBEEK4OEkEQgjh4iQRCCGEi5NEIIQQLk4SgRBCuDhJBEII4eLa1A11ZzMkPIC6RrOzwxBCiA7FpRLBzORIZiZHOjsMIYToUKRqSAghXJwkAiGEcHGSCIQQwsVJIhBCCBcniUAIIVycJAIhhHBxkgiEEMLFSSIQQggXp7TWzo7hsimlSoCiK5g1FCi1czj20lFjk7guT0eNCzpubBLX5WlLXP201mHnf9gpE8GVUkplaq2TnB1HSzpqbBLX5emocUHHjU3iujyOiEuqhoQQwsVJIhBCCBfnaolgsbMDuIiOGpvEdXk6alzQcWOTuC6P3eNyqXsEQgghfszVrgiEEEKcRxKBEEK4uC6XCJRSdyil9iulLEqppPOmPa2UylVKHVZKXX+B+YOVUl8rpXJsv4McFOcypdQu20+hUmrXBcoVKqX22splOiKW89b3vFLqeLPYbrxAuSm2/ZirlHqqHeJ6VSl1SCm1Ryn1qVIq8ALl2mV/XWr7ldWbtul7lFKJjoql2Tr7KqW+U0odtP0PPNpCmQlKqcpm3+9zjo6r2bov+t04aZ8NbLYvdimlqpRSj51Xpl32mVJqiVLqjFJqX7PPWnU8avP/o9a6S/0AVwEDgXVAUrPPBwO7AS8gGsgDDC3M/wrwlO31U8DL7RDzn4DnLjCtEAhtx/33PPD4JcoYbPsvBvC07dfBDo5rMuBue/3yhb6X9thfrdl+4Ebg34ACRgHb2uG76w0k2l77A0daiGsC8EV7/T1dznfjjH3Wwvd6CutDV+2+z4BxQCKwr9lnlzwe2eP/sctdEWitD2qtD7cw6VZgqda6QWtdAOQCKRco977t9fvAbQ4J1EYppYA7gXRHrsfOUoBcrXW+1roRWIp1vzmM1nqt1tpke7sViHDk+i6hNdt/K/CBttoKBCqlejsyKK31Sa11tu31OeAg0MeR67Szdt9n55kI5Gmtr6TXgjbTWm8Ays/7uDXHozb/P3a5RHARfYBjzd4X0/I/SU+t9Umw/mMBPRwc19XAaa11zgWma2CtUipLKTXXwbF8b4Ht0nzJBS5FW7svHeV+rGeOLWmP/dWa7XfqPlJKRQEjgG0tTB6tlNqtlPq3UmpIe8XEpb8bZ/9dzeLCJ2TO2metOR61eb91ysHrlVLfAL1amPSs1vrzC83WwmcObTvbyjhnc/GrgbFa6xNKqR7A10qpQ7YzB4fEBbwNvIB137yAtdrq/vMX0cK8bd6XrdlfSqlnARPw0QUWY/f91VKoLXx2/va3+99b04qV8gM+AR7TWledNzkba9VHte3+z2dAbHvExaW/G2fuM09gKvB0C5Oduc9ao837rVMmAq31dVcwWzHQt9n7COBEC+VOK6V6a61P2i5Lz1xJjHDpOJVS7sB0YORFlnHC9vuMUupTrJeBbTqwtXb/KaX+F/iihUmt3Zd2jUspNQe4GZiobZWjLSzD7vurBa3Zfofso0tRSnlgTQIfaa1Xnj+9eWLQWq9WSr2llArVWju8c7VWfDdO2Wc2NwDZWuvT509w5j6jdcejNu83V6oaWgXMUkp5KaWisWb07RcoN8f2eg5woSsMe7gOOKS1Lm5polKqm1LK//vXWG+Y7muprL2cVyc77QLr2wHEKqWibWdSs7DuN0fGNQV4Epiqta69QJn22l+t2f5VwE9tLWFGAZXfX+I7iu1+09+Bg1rr1y5QppetHEqpFKzHgDJHxmVbV2u+m3bfZ81c8MrcWfvMpjXHo7b/Pzr6Tnh7/2A9eBUDDcBp4Ktm057Fenf9MHBDs8//hq2FERACfAvk2H4HOzDW94CHzvssHFhtex2DtQXAbmA/1ioSR++/fwB7gT22P6be58dle38j1lYpee0UVy7WetBdtp93nLm/Wtp+4KHvv0+sl+uLbNP30qwFmwNjSsNaJbCn2X668by4Ftj2zW6sN93HODqui303zt5ntvX6Yj2wd2/2WbvvM6yJ6CRgtB3DHrjQ8cje/4/SxYQQQrg4V6oaEkII0QJJBEII4eIkEQghhIuTRCCEEC5OEoEQQrg4SQRCCOHiJBEIIYSL+//8s/mGvo4XkwAAAABJRU5ErkJggg==\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",
+    "plt.plot(voltage,current,label=\"real curve\")\n",
+    "plt.plot(x,y_pred,label=\"linear\")\n",
+    "plt.plot(x_clean,y_pred_clean,label=\"second regression\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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 0x7f3d623c40a0>]"
+      ]
+     },
+     "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",
+    "    admitance.append(1/R)\n",
+    "print(admitance)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(voltage,admitance)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ef8cd500-683e-406b-89bd-14c7823f29ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f3d6239ba60>]"
+      ]
+     },
+     "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "#define the voltage differences\n",
+    "derivative=[]\n",
+    "for i in range(1,len(voltage)):\n",
+    "    element = (voltage[i]-voltage[i-1])/0.001 #0,001 is the delta I\n",
+    "    derivative.append(element)\n",
+    "\n",
+    "print(len(derivative))\n",
+    "print(derivative)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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"
+     ]
+    }
+   ],
+   "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",
+    "    second_derivative.append(element)\n",
+    "\n",
+    "print(len(second_derivative))\n",
+    "print(second_derivative)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f3d621d7790>]"
+      ]
+     },
+     "execution_count": 29,
+     "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": [
     "#we have two less values, we delete also the last 2 voltages \n",
     "voltage_new = voltage.copy() \n",
@@ -247,15 +868,98 @@
     "voltage_new.pop()\n",
     "current_new.pop()\n",
     "current_new.pop()\n",
+    "print(len(voltage_new))\n",
+    "print(len(current_new))   \n",
+    "indexes=[]\n",
     "for i in range(len(second_derivative)):\n",
-    "    if second_derivative>100\n",
-    "    \n"
+    "    if abs(second_derivative[i])<1 or abs(second_derivative[i])>100 :\n",
+    "        indexes.append(i)\n",
+    "print(list(reversed(indexes)))\n",
+    "\n",
+    "for i in list(reversed(indexes)):\n",
+    "    voltage_new.pop(i)\n",
+    "    current_new.pop(i)\n",
+    "\n",
+    "print(voltage_new)\n",
+    "print(current_new)\n",
+    "print(len(voltage_new))\n",
+    "print(len(current_new))\n",
+    "plt.plot(current_new,voltage_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f3d6226c700>]"
+      ]
+     },
+     "execution_count": 28,
+     "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": [
+    "#we have two less values, we delete also the last 2 voltages \n",
+    "voltage_new = voltage.copy() \n",
+    "current_new = current.copy() \n",
+    "voltage_new.pop()\n",
+    "voltage_new.pop()\n",
+    "current_new.pop()\n",
+    "current_new.pop()\n",
+    "print(len(voltage_new))\n",
+    "print(len(current_new))   \n",
+    "indexes=[]\n",
+    "for i in range(len(second_derivative)):\n",
+    "    if abs(derivative[i])>100:\n",
+    "        indexes.append(i)\n",
+    "print(list(reversed(indexes)))\n",
+    "\n",
+    "for i in list(reversed(indexes)):\n",
+    "    voltage_new.pop(i)\n",
+    "    current_new.pop(i)\n",
+    "\n",
+    "print(voltage_new)\n",
+    "print(current_new)\n",
+    "print(len(voltage_new))\n",
+    "print(len(current_new))\n",
+    "plt.plot(current_new,voltage_new)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a15d75eb-a1a7-4d51-a0ac-43151974a038",
+   "id": "92f48ccd-4c58-431f-bb6a-0769f10223da",
    "metadata": {},
    "outputs": [],
    "source": []
-- 
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