Case_2_Team2_FB_ElectrostaticForceSystemusingNewtonInterpolationandOutlineImageusingCubicSpline.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**CASE 2 - Fisika Komputasi**"
      ],
      "metadata": {
        "id": "PNRTMlsS96y2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "***Electrostatic Force System using Newton Interpolation and Outline Image using Cubic Spline***\n",
        "---\n",
        "\n",
        "**KELOMPOK 2 - Fisika B**\n",
        "1.   Dhinar Hidayath \t\t       (1306621054)\n",
        "2.   Khansa Farras Callista\tA. (1306621067)\n",
        "3.   Puspa Zuhrotul Kamilah\t   (1306621058)\n",
        "4.   Salma Mardhiyah     \t     (1306621061)\n"
      ],
      "metadata": {
        "id": "4UvvgF_3-DR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Artikel-1 *Interpolation Newton Electrostatic Force System***"
      ],
      "metadata": {
        "id": "kHt-qKe-_uia"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TZElgsQn9vir",
        "outputId": "5379c3c9-0dfe-42bf-97fe-3e4efe6a44fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================ METODE NEWTON INTERPOLATION ================================ \n",
            "\n",
            "Perhitungan-1 Capacitance Gradient Measurement Results\n",
            "---> Perhitungan Kapasitansi pada detik (s) ke-26.55\n",
            "\n",
            "Perhitungan-2 AFM Cantilever Stiffness Measurement\n",
            "---> Perhitungan Tegangan pada detik (s) ke-44\n",
            "\n",
            "\n",
            "======================== Capacitance Gradient Measurement Results =========================== \n",
            "\n",
            "TABEL PERHITUNGAN DENGAN METODE NEWTON INTERPOLATION\n",
            "---------------------------------------------------------------------------------------------\n",
            "|       t        |     f(t)       |        ST-1       |       ST-2       |       ST-3       |\n",
            "|-------------------------------------------------------------------------------------------|\n",
            "|     23.105     |    16.33928    |   -0.0002470741   |  0.0002748344    |  -0.0000982334   |\n",
            "|--------------------------------------------------------------------------------------------\n",
            "|     23.874     |    16.33909    |   0.0001190053    |  0.0000788588    |\n",
            "|-------------------------------------------------------------------------\n",
            "|     24.437     |   16.339157    |   0.0002156863    |\n",
            "|------------------------------------------------------\n",
            "|      25.1      |    16.3393     | \n",
            "----------------------------------- \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x108 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Kapasitansi pada 26.55 s adalah sekitar 16.339048954860218 pF\n",
            "\n",
            "\n",
            "\n",
            "======================== AFM Cantilever Stiffness Measurement =========================== \n",
            "\n",
            "TABEL PERHITUNGAN DENGAN METODE NEWTON INTERPOLATION\n",
            "---------------------------------------------------------------------------------------------\n",
            "|       t        |     f(t)       |        ST-1       |       ST-2       |       ST-3       |\n",
            "|-------------------------------------------------------------------------------------------|\n",
            "|       39       |    649.6094    |   2.2437250000    |  -0.5319250000   |  -0.1331708333   |\n",
            "|--------------------------------------------------------------------------------------------\n",
            "|       40       |   651.853125   |   1.1798750000    |  -0.9314375000   |\n",
            "|-------------------------------------------------------------------------\n",
            "|       41       |    653.033     |   -0.6830000000   |\n",
            "|------------------------------------------------------\n",
            "|       42       |     652.35     | \n",
            "----------------------------------- \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ],
            "image/png": 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M2ySdCCwDPpqu+7Ck9wJLgasjYlefbf8YeHyApG2w7c3yTkSwq7WTdTtbWb+rjXW7Wlm/K3mfTK3s6dx32LJ6XAkza8uZVVvBSXNqmVlbsbfXZUZN0gtzoHrFhttFJ8/iopNnsWTJEj5y6cLRDmfUlRQXpQl2+YDlenqShD7pXd2TJPGNbWxsSN7/+oXtbGnas0/vXXGRmFlbztxJlcydVMmcSZXMm1TFvMmVzJtcyfjypKfRV/uaFR5FfydmvN6KpXrgEeCMiHhU0k3AbuCbJMOiAXwemBER78/Y7ljgp8A5EfFilnrrBto+o9xVwFUAdXV1py5evPgAH+G+mpubqa6uHtZ92NCNdLt09gTbW4NtbT1sTV+3tQbb2oJtrT3s6XMuf1UpTKkoYkqFkqm8iMkVYnKFmFJRRGVJcmL+wcT/Vg68rp5g555ge1vyndu+z3evh90d+5afUAZ1lUWUF3VTU9rDxHFw/KSgPf1+lhUXsWD6+JE/ENuH/63kp5Fol0WLFi2LiPps64YzcZsOPBIR89P5NwGfjIi3ZZSZD9wbEcel87OBh4DLI+LXOexjn+37U19fH35ywtg0HO3S3N7FKztaWLujlTU7WnllRwuvpK+bdu/b81FeWsScia/2esyeWMGcSZXMmVjJnEkVe3s+xhL/Wxl5ze1drE2/o73f2Ze2t/DYyzv3llk0o5uHNyW9twJe/tLb+qnNRor/reSnEXpyQr+J27ANlUbEZknrJC2IiNXA2cDTkmZExKa02DuBJ9Mga4H/Iknu+k3a+tve7EBq6+hmzY4WXt6eTGu2t6TzrWxv3ncEf0p1GXMnVfKGQyczd1IyFDV3UiVzJ1cytXrcQddjZoWnelwJx8ycwDEzJ+yz/IwvPcSGhjYAjqwJHk7/ss6srRjpEM0sR8N9tvJHgDvTK0pfAi4HviHpJJKhzjXAB9OyHwYOBz4r6bPpsnMiYqukW4BvR8RS4Cv9bG+2Vy4nXHf3BOt3tfLSthZe3Na8N0l7eXsLmxr37FN26vhxHDK5irOOmsq8yVXMn1zF/CmVzJtcRfUonvRv9npce+6Cvee4Vaedv6N1ta+Z5WZYf3EiYgXQt6vvPf2U/QLwhX7WXZnxPuv2Zr2ynXD9ibtX8rs1O6mtLOXFrS28tL2ZNdtb6eh+9WKAmopSDplSxemHTuaQKVXMn1K199XJmR2MDvarfc0ORv41soNC7600nt/SzN/+9Km99wv7l2eSc3bau3u489G1FBeJeZMrOXRKNYsWTOPQqVUcOrWaQ6dUMamqzMOaNub4al+zwuLEzQpKRLCtuZ3ntzTz3JYmnt/azPNbmnhha3PWe5zNrQ6ebng1GXvmc2/1cyfNzKxgOXGzvLWrpYPVW5p4fksTq7c08dzmZp7b2kRDRoJWU1HKEdOqeetxMzhiWjWHT6vm43ev3Pt4pfPm9PB0Q5KozaqtcNJmZmYFzYmbjbo9nd08v6WZZzfvZvXmJElbvbmJrU2vXr05vryEI+vGc95xMziyrpoj68ZzRF111qs28+nxSmZmZgeSEzcbMRHBhoY2nt3UxDObdvPs5iae2bybNdtb6H2iz7iSIo6oq+ZNR0xlwfRqjqgbz1HTxzN9QnnO55/5hGszMztYOXGzYdHbi/b0pkae3ribZzY38eym3eze07W3zJxJFRw9fQIXHD+DBdMncNSM8cyfXEXxAXh4tk+4NjOzg5ETN3vdGlo7eGrjbp7euJunNyWvL2xrpjvtRqssK+ao6eO58MSZHD1jAkfPmMCC6eN9iw0zM7Mh8i+n5Swi2LK7nSc3NPLkxsa9yVrvndcBpk8o59iZE3jLMXXJndpnTGDupEqKDkAvmpmZ2VjnxM2y6j0f7ckNjTy5YTerNjTy1MZGtjcnT6uW4JApVZwybyLvOX0ex6ZJ2uTqcaMcuZmZ2cHLiZsREWxq3MPK9Y2s2tDAqg27eXJDIztbkiStuEgcMa2ahQumcdzMCRw3q4ajZ0ygykOdZmZmI8q/vGPQ9uZ2Vq5v4Il1jaza0MjK9Q17e9KKi8SRdeN589HTOH5Wzd4krby0eJSjNjMzMyduB7nm9i5WrW/kifUNe5O13nPSJPb2pJ0wu4bjnaSZmZnlNSduB5Gu7h5Wb2lixboGVqxt4In1DTy/tZlI75E2d1IlJ8+t5X1vnM8Js5PeNA93mpmZFY5Bf7UlTQPOAGYCbcCTwNKI6Bnm2GwQmxv3sGLdLpavbWD5ugZWrW/c+7SAiZWlnDinlvOPn8FJc2o5YXYtk6rKRjliMzMzez36TdwkLQI+CUwClgNbgXLgIuAwSXcDX4uI3QPUUQvcAhwHBPB+4FzgA8C2tNinI+K+tPyngCuAbuD/RsSDWeo8BFgMTAaWAe+JiI6cj7hAtXd18+SG3Sxfu4vH1ybJ2qbG5HmcZcVFHDNzApecNoeT5tRy0pxa5k6qzPlJA2ZmZlYYBupxOx/4QESs7btCUglwAfAW4IcD1HET8EBEXCypDKgkSdxujIgb+tR5DHAJcCxJ795/SzoyIrr71PnldPvFkr5Nkuj980AHWYi27N7Dsld2seyVJFF7asNuOrqTTs5ZtRWcOm8ip8ydyMlzazlm5gTGlfi8NDMzs4PdQInb1yJic7YVEdEF3DNQxZJqgDOB96XbdAAdA/QCvQNYHBHtwMuSXgBOA36bUaeAs4A/SxfdDlxHgSduveem9SZqy17ZxfpdyQUEZSVFnDCrhvedMZ9T5tZyytyJTJtQPsoRm5mZ2WgYKHFbIelJ4LvADyOiYYh1H0IyHHqbpBNJhjU/mq77sKT3AkuBqyNiFzALeCRj+/XpskyTgYY0ceyvTN5rae9ixboGfrdmZ9Kj9souWjqSjsW6CeOonzeJy884hFPm1nLszBrKSopGOWIzMzPLB4reSw77rpCKgTeTDF+eT5JUfRf4SUS0Zd1o3+3r023OiIhHJd0E7Aa+CWwnOeft88CMiHi/pG8Cj0TEHen2twL3R8TdGXVOScscns7PScscl2X/VwFXAdTV1Z26ePHiXD6PIWto62RL4x4mlvWwq6OIuppyaitK9ynT2B48t6ub53Z18/yuHtY29dATIGD2+CKOmFjEEbXFHDGxiMnl8rlpB1BzczPV1dWjHYZlcJvkJ7dL/nGb5KeRaJdFixYti4j6bOv67XFLzy17EHgwPT/tPJIk7uuSfhERlw6y3/XA+oh4NJ2/G/hkRGzpLSDpX4F709kNwJyM7WenyzLtAGollaS9btnK9MZ/M3AzQH19fSxcuHCQcIfunuUb+NQvVtHWWcTVx/fwtVVFlJd0cc25h1FTUcrv1uzkd2t28fL2VgDKS4s4aU4tF9ZPSs5RmzeRCeWlg+zFXo8lS5YwHG1v+89tkp/cLvnHbZKfRrtdcrqJV0R0SHoaeAY4FTg6h202S1onaUFErAbOBp6WNCMiNqXF3klyexGAnwJ3SfoHkosTjgAe61NnSHoYuJjkytLLgJ/kcgzD4asPrt57+40VO5Jesj1dPXzhv54BoLaylPp5k3j3aXOonz+J4zzsaWZmZq/DgIlbOhR5CfBuoIpkqPTtEfFsjvV/BLgz7bF7Cbgc+Iakk0iGStcAHwSIiKckfR94GugC/rL3ilJJ9wFXRsRG4BPAYklfILlNya05H+0BtrHh1RHjX2zc96rOn33sTA6fWk1RkYc9zczM7MAY6D5uvyE58f/7JLcFWTbUyiNiBdB3jPY9A5S/Hrg+y/LzM96/RHK16aibWVux9/FRVxzZxa3PJR/nrNoKjqwbP5qhmZmZ2UFooHG7TwLzI+La/UnaxoJrz11ARfpcz9pxybKK0mKuPXfBKEZlZmZmB6uBhkrPBFYBu7KtlHQWUBkR92ZbPxZcdHJyJ5KvPrgaaGJWbQXXnrtg73IzMzOzA2mgxG0VcK+kPcDjJPdkKye5aOAk4L+BLw53gPnuopNncdHJs1iyZAkfuXThaIdjZmZmB7GBbgfyE+Anko4gecj8DJL7sN0BXJXLvdzMzMzM7MAZ9HYgEfE88PwIxGJmZmZmA/BNxczMzMwKhBM3MzMzswLhxM3MzMysQAyauEk6UtIvJD2Zzp8g6f8Nf2hmZmZmlimXHrd/BT4FdAJExEqSx2CZmZmZ2QjKJXGrjIjH+izrGo5gzMzMzKx/uSRu2yUdRvJQeCRdDGwa1qjMzMzM7DUGvY8b8JfAzcBRkjYALwN/PqxRmZmZmdlr5HID3peAN0uqAooiomn4wzIzMzOzvgZN3CT9dZ95gEZgWUSsGGTbWuAW4DiSodb3R8Rv03VXAzcAUyNiu6RrgUsz4jo6XbezT53fAf4wjQHgfYPFYWZmZnYwyGWotD6d/jOdvwBYCXxI0g8i4isDbHsT8EBEXCypDKgEkDQHOAdY21swIr4KfDVdfyHwsb5JW4ZrI+LuHGI3MzMzO2jkcnHCbOCUiLg6Iq4GTgWmAWcC7+tvI0k1aZlbASKiIyIa0tU3Ah8nveAhi3cD380hNjMzM7MxI5fEbRrQnjHfCdRFRFuf5X0dAmwDbpO0XNItkqokvQPYEBFPZNtIUiXwVuCHA9R9vaSVkm6UNC6HYzAzMzMreIror9MrLSD9DfBO4CfpoguBnwJfA26OiEv72a4eeAQ4IyIelXQT0EHSC3dORDRKWgPUR8T2jO3eBfx5RFzYT70zgM1AGcnVri9GxOeylLsKuAqgrq7u1MWLFw94nK9Xc3Mz1dXVw7oPGzq3S/5xm+Qnt0v+cZvkp5Fol0WLFi2LiPps6wZN3AAk/R7wxnT21xGxNIdtpgOPRMT8dP5NwHXA8UBrWmw2sBE4LSI2p+V+DPwgIu7KYR8LgWsi4oKBytXX18fSpYOG/LosWbKEhQsXDus+bOjcLvnHbZKf3C75x22Sn0aiXST1m7jlcnECEfE7Sa8A5WmFcyNi7SDbbJa0TtKCiFgNnA08HhFnZwS2howet/S8uD9kgPvESZoREZuUXN56EfBkLsdgZmZmVuhyuR3I20mGRWcCW4G5wLPAsTnU/xHgzvSK0peAywcp/07gZxHR0ieG+4ArI2JjWt9UQMAK4EM5xGFmZmZW8HLpcfs88PvAf0fEyZIWkeOTE9L7q2Xt6kvXz+8z/x3gO1nKnZ/x/qxc9m1mZmZ2sMnlqtLOiNgBFEkqioiHGSAZMzMzM7PhkUuPW4OkauBXJMOUW4GWQbYxMzMzswMslx63d5BcBfox4AHgRZKnJ5iZmZnZCMolcftsRPRERFdE3B4R3wA+MdyBmZmZmdm+cknc3pJl2XkHOhAzMzMzG1i/57hJ+j/AXwCHSlqZsWo88OvhDszMzMzM9jXQxQl3AfcDfw98MmN5U0TsHNaozMzMzOw1BkrcioHdwF/2XSFpkpM3MzMzs5E1UOK2DOh9kKn6rAvg0GGJyMzMzMyy6jdxi4hDRjIQMzMzMxtYTg+ZT59XemY6uyQi7h2+kMzMzMwsm0FvByLpS8BHgafT6aOSvjjcgZmZmZnZvnLpcTsfOCkiegAk3Q4sBz49nIGZmZmZ2b5yuQEvQG3G+5pcK5dUK+luSc9KekbS6RnrrpYUkqak8wslNUpakU6f7afOQyQ9KukFSd+TVJZrPGZmZmaFLJcet78Hlkt6mOTq0jPZ975uA7kJeCAiLk4TrEoASXOAc4C1fcr/T0QM9hzULwM3RsRiSd8GrgD+Ocd4zMzMzApWvz1ukv5J0hkR8V3g94EfAT8ETo+I7w1WsaQakiTvVoCI6IiIhnT1jcDHefV2IzmRJOAs4O500e3ARUOpw8zMzKxQDTRU+hxwg6Q1wMeAdRHx04jYnGPdhwDbgNskLZd0i6QqSe8ANkTEE1m2OV3SE5Lul3RslvWTgYaI6Ern1wOzcozHzMzMrKApYuBOL0nzgEvSqQL4LvDdiHhukO3qgUeAMyLiUUk3AR0kvXDnRERjmhTWR8R2SROAnoholnQ+cFNEHNGnzinAIxFxeDo/B7g/Io7Lsv+rgKsA6urqTl28ePFgn8Xr0tzcTHV19bDuw4bO7ZJ/3Cb5ye2Sf9wm+Wkk2mXRokXLIqI+27pBE7d9CksnA/8GnBARxYOUnU6SZM1P598EXAccD7SmxWYDG4HT+vbkZSZ1GctE0os3PSK60osdrouIcweKpb6+PpYuXZrrYe6XJUuWsHDhwmHdhw2d2yX/uE3yk9sl/7hN8tNItIukfhO3XO7jViLpQkl3kjx0fjXwR4NtlyZi6yQtSBedDTweEdMiYn6a0K0HTomIzZKmp4kZkk5LY9vRp84AHgYuThddBvxksFjMzMzMDgb9XlUq6S3Au0nu4/YYsBi4KiJahlD/R4A70ytKXwIuH6DsxcD/kdQFtAGXpIkaku4DroyIjcAngMWSvkByP7lbhxCPmZmZWcEa6HYgnwLuAq6OiF37U3lErACydvWl6+dnvP8m8M1+yp2f8f4l4LT9icfMzMyskA30kPmzRjIQMzMzMxtYrk9OMDMzM7NR5sTNzMzMrEA4cTMzMzMrEE7czMzMzAqEEzczMzOzAuHEzczMzKxAOHEzMzMzKxBO3MzMzMwKhBM3MzMzswLhxM3MzMysQDhxMzMzMysQTtzMzMzMCoQTNzMzM7MCMayJm6RaSXdLelbSM5JOz1h3taSQNCWdv1TSSkmrJP1G0on91PkdSS9LWpFOJw3nMZiZmZnli5Jhrv8m4IGIuFhSGVAJIGkOcA6wNqPsy8AfRsQuSecBNwNv6KfeayPi7mGM28zMzCzvDFuPm6Qa4EzgVoCI6IiIhnT1jcDHgegtHxG/iYhd6ewjwOzhis3MzMysEA3nUOkhwDbgNknLJd0iqUrSO4ANEfHEANteAdw/wPrr02HVGyWNO5BBm5mZmeUrRcTgpfanYqmepOfsjIh4VNJNQAdJL9w5EdEoaQ1QHxHbM7ZbBHwL+IOI2JGl3hnAZqCMZDj1xYj4XJZyVwFXAdTV1Z26ePHiA32I+2hubqa6unpY92FD53bJP26T/OR2yT9ukzyzcyds2EDztGlUb90Ks2bBpEnDsqtFixYti4j6bOuGM3GbDjwSEfPT+TcB1wHHA61psdnARuC0iNgs6QTgx8B5EfFcDvtYCFwTERcMVK6+vj6WLl26fweSoyVLlrBw4cJh3YcNndsl/7hN8pPbJf+4TfLInXfCVVdBaytLbriBhddcA5WVcPPNcOmlB3x3kvpN3IZtqDQiNgPrJC1IF50NPB4R0yJifprQrQdOSZO2ucCPgPcMlLSlPW5IEnAR8ORwHYOZmZkZn/kMtLbuu6y1NVk+wob7qtKPAHemV5S+BFw+QNnPApOBbyU5GV292aak+4ArI2JjWt9UQMAK4EPDF76ZmZmNeWvXDm35MBrWxC0iVgBZu/rS9fMz3l8JXNlPufMz3p914CI0MzMzG8TcufDKK9mXjzA/OcHMzMxsINdfn5zTlqmyMlk+wpy4mZmZmQ3k0kuTCxHmzUvm580btgsTBjPc57iZmZmZFb5LL02mJUtgzZpRC2PYbgeSTyRtA7IMTh9QU4Dtg5aykeZ2yT9uk/zkdsk/bpP8NBLtMi8ipmZbMSYSt5EgaWl/91yx0eN2yT9uk/zkdsk/bpP8NNrt4nPczMzMzAqEEzczMzOzAuHE7cC5ebQDsKzcLvnHbZKf3C75x22Sn0a1XXyOm5mZmVmBcI+bmZmZWYFw4rYfJJVLekzSE5KekvR36fKzJD0u6UlJt0vyffJGmKRiScsl3ZvOHyLpUUkvSPpe+txcG0FZ2uTDaXuEpCmjHd9YlaVd7pS0Ov379W+SSkc7xrEmS5vcmv7OrJR0t6Tq0Y5xLOrbLhnLvyGpeaTjceK2f9qBsyLiROAk4K2S3gjcDlwSEceR3DfustELccz6KPBMxvyXgRsj4nBgF3DFqEQ1tvVtk18Db2b4761oA+vbLncCRwHHAxX08+xoG1Z92+RjEXFiRJwArAU+PDphjXl92wVJ9cDE0QjGidt+iERvll2aTt1AR0Q8ly7/OfDHoxHfWCVpNvA24JZ0XsBZwN1pkduBi0YluDGqb5sARMTyiFgzakFZf+1yX/q3LYDHgNmjFd9Y1E+b7E7XiSSZ9knpIyxbu0gqBr4KfHw0YnLitp/SrtMVwFaSJO0xoCTNwgEuBuaMUnhj1ddJ/iH1pPOTgYaI6Ern1wOzRiGusezr7Nsmlh++Tj/tkg6Rvgd4YIRjGuu+TpY2kXQbsJmkN/QfRz6sMe/rvLZdPgz8NCI2jUZATtz2U0R0R8RJJP8rPQ04FrgEuFHSY0ATSS+cjQBJFwBbI2LZaMdiCbdJfsqhXb4F/Coi/mcEwxrTBmqTiLgcmEkyVPeukY5tLMvWLpJmAn/CKCbRPnn+dYqIBkkPA2+NiBuANwFIOgc4clSDG1vOAN4u6XygHJgA3ATUSipJe91mAxtGMcax5jVtIumOiPjzUY5rrOu3XST9LTAV+OCoRjj2DPhvJSK6JS0m6fm5bRTjHGuy/a48RXKe+wvJCDaVkl5Iz6MeEb6P236QNBXoTJO2CuBnJCfBPxYRWyWNA+4Dro+Ih0Yz1rFI0kLgmoi4QNIPgB9GxGJJ3wZWRsS3RjXAMSizTTKWrQHqI8IP0R4lff6tXAm8Hzg7ItpGNbAxrLdNgAuBwyLihfQct68CRMQ1oxfd2JXtb1i6vDkiRvRqXw+V7p8ZwMOSVgK/A34eEfcC10p6BlgJ/KeTtrzwCeCvJb1Acs7braMcz5gn6f9KWk/SA7pS0i2DbWMj4ttAHfBbSSskfXa0AxrjBNwuaRWwiuR353OjG5LlA/e4mZmZmRUI97iZmZmZFQgnbmZmZmYFwombmZmZWYFw4mZmZmZWIJy4mZmZmRUIJ25mNmZImpze6mKFpM2SNqTvmyUNy/39JP2VpPcOsP4CSb7Ng5nlxLcDMbMxSdJ1QHP6xJPh2kcJ8DhwSsYzc/uWUVrmjIhoHa5YzOzg4B43MxvzJC2UdG/6/jpJt0v6H0mvSPojSV+RtErSA+lD2JF0qqRfSlom6UFJM7JUfRbweG/Slt58+GlJK9NHGBHJ/56XABdk2d7MbB9O3MzMXuswkqTr7cAdwMMRcTzQBrwtTd7+Ebg4Ik4F/g24Pks9ZwCZDw7/JHByRJwAfChj+VLS5xybmQ3ED5k3M3ut+yOiM33cUDHwQLp8FTAfWAAcB/w8fdB0MbApSz0zgGcy5lcCd0q6B7gnY/lWYOaBC9/MDlZO3MzMXqsdICJ6JHXGqycD95D83RTwVEScPkg9bUB5xvzbgDNJHiD+GUnHp8Oo5WlZM7MBeajUzGzoVgNTJZ0OIKlU0rFZyj0DHJ6WKQLmRMTDwCeAGqA6LXck8OSwR21mBc+Jm5nZEEVEB3Ax8GVJTwArgDdmKXo/SQ8bJMOpd6TDr8uBb0REQ7puEfBfwxmzmR0cfDsQM7NhJOnHwMcj4vl+1tcBd0XE2SMbmZkVIiduZmbDSNICoC4iftXP+t8DOiNixYgGZmYFyYmbmZmZWYHwOW5mZmZmBcKJm5mZmVmBcOJmZmZmViCcuJmZmZkVCCduZmZmZgXCiZuZmZlZgfj/JhXdzaVGkrUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Tegangan pada 44.0 s adalah sekitar 642.1992749999994 V\n"
          ]
        }
      ],
      "source": [
        "# Membuat Grafik dari Data Awal dan Data Akhir\n",
        "def grafik(x, y, detik, fnx, judulx, judul, size):\n",
        "  xnew = np.linspace(x.min(), x.max(), 100) \n",
        "  spl = make_interp_spline(x, y, k=2) # type: BSpline\n",
        "  ynew = spl(xnew)\n",
        "\n",
        "  plt.figure(figsize=(size))\n",
        "  plt.scatter(x, y)\n",
        "  plt.scatter(detik, fnx, color='red')\n",
        "  plt.plot(xnew, ynew)\n",
        "  plt.xlabel('Time (s)')\n",
        "  plt.ylabel(judulx)\n",
        "  plt.title(judul)\n",
        "  plt.grid()\n",
        "  plt.show()\n",
        "  return\n",
        "\n",
        "# Membuat Fungsi Perhitungan Dengan Metode Newton Interpolation\n",
        "def newton_interpolation(x, fy, t):\n",
        "    for i in range(len(x)-1):\n",
        "        list_fz = []\n",
        "        n = i\n",
        "        for j in range(len(fy[i])-1):\n",
        "            fz = (fy[i][j+1]-fy[i][j])/ (x[n+1]-x[j])\n",
        "            list_fz.append(fz)\n",
        "            n += 1\n",
        "        fy.append(list_fz)\n",
        "\n",
        "    fnx = fy[len(fy)-1][0]\n",
        "    for i in range(1, len(fy)):\n",
        "        fnx = fy[len(fy)-1-i][0]+(t-x[len(fy)-1-i])*fnx\n",
        "    return fnx\n",
        "\n",
        "# Membuat Fungsi Mencetak Tabel Perhitungan\n",
        "def tabel(x, y, fy, judul1):\n",
        "    a, b = 0, 0\n",
        "    print(\"\\n\\n\"+\"=\"*24, judul1,\"=\"*27, \"\\n\")\n",
        "    print(\"TABEL PERHITUNGAN DENGAN METODE NEWTON INTERPOLATION\")\n",
        "    print(\"-\"*93)\n",
        "    print(\"{}{:^16}{}{:^15}\".format(\"|\",\"t\",\"|\",\"f(t)\"),\"|\", end=\" \")\n",
        "    for i in range(1, len(y)):\n",
        "        print(\"{:>10}{}{:^6}\".format(\"ST-\",i,\"\"), \"|\", end=\"\")\n",
        "        if i == len(y)-1:\n",
        "          print(\"\")\n",
        "          \n",
        "    n = len(fy)\n",
        "    print(\"|\"+\"-\"*91+\"|\")\n",
        "    for i in range(len(fy)):\n",
        "        print(\"{}{:^16}{}{:^16}{}\".format(\"|\",x[i],\"|\",y[i],\"|\"),end=\" \")\n",
        "        for j in range(n-1):\n",
        "            print(\"{:^17.10f}\".format(fy[j+1][i]),\"|\", end=\"\")\n",
        "            if j == (n-2):\n",
        "              print(\"\")\n",
        "              print(\"|\"+\"-\"*(92+a-b))\n",
        "              a += 1\n",
        "              b += 20\n",
        "        n -= 1\n",
        "    print(\"\")\n",
        "    print(\"-\"*35, \"\\n\")\n",
        "    return\n",
        "\n",
        "# Meng-import modul yang dibutuhkan\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.interpolate import make_interp_spline, BSpline\n",
        "\n",
        "# Judul dan Identitas Mahasiswa\n",
        "print(\"=\"*32,\"METODE NEWTON INTERPOLATION\",\"=\"*32, \"\\n\")\n",
        "\n",
        "# Menginisiasikan Data Awal - Capacitance Gradient Measurement Results\n",
        "C        = np.array([23.105, 23.874, 24.437, 25.1])\n",
        "tc       = np.array([16.33928, 16.33909, 16.339157, 16.3393])\n",
        "fxc      = []        \n",
        "fxc.append(tc)\n",
        "judulx_c = 'Capacitance (pF)'\n",
        "judul_c  = 'Capacitance Gradient Measurement Results'\n",
        "size_c   = 13, 1.5\n",
        "\n",
        "# Menginisiasikan Data Awal - AFM Cantilever Stiffness Measurement\n",
        "V        = np.array([39, 40, 41, 42])\n",
        "tv       = np.array([649.6094, 651.853125, 653.033, 652.35])\n",
        "fxv      = []        \n",
        "fxv.append(tv)\n",
        "judulx_v = 'Voltage (V)'\n",
        "judul_v  = 'AFM Cantilever Stiffness Measurement'\n",
        "size_v   = 10, 2\n",
        "\n",
        "# Meminta pengguna untuk memasukkan waktu yang diinginkan\n",
        "detik_c  = float(input(\"Perhitungan-1 \"+judul_c+\"\\n---> Perhitungan Kapasitansi pada detik (s) ke-\"))\n",
        "detik_v  = float(input(\"\\n\"+\"Perhitungan-2 \"+judul_v+\"\\n---> Perhitungan Tegangan pada detik (s) ke-\"))\n",
        "\n",
        "# Memanggil Fungsi Perhitungan dan Fungsi Mencetak Tabel Perhitungan\n",
        "fnx_c = newton_interpolation(C, fxc, detik_c)\n",
        "tabel(C, tc, fxc, judul_c)\n",
        "graph_c  = grafik(C, tc, detik_c, fnx_c, judulx_c, judul_c, size_c)\n",
        "print(f\"\\nKapasitansi pada {detik_c} s adalah sekitar {fnx_c} pF\\n\")\n",
        "\n",
        "fnx_v = newton_interpolation(V, fxv, detik_v)\n",
        "tabel(V, tv, fxv, judul_v)\n",
        "graph_v  = grafik(V, tv, detik_v, fnx_v, judulx_v, judul_v, size_v)\n",
        "print(f\"\\nTegangan pada {detik_v} s adalah sekitar {fnx_v} V\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Artikel-2 *Cubic Spline***"
      ],
      "metadata": {
        "id": "dHJaolugAJkS"
      }
    }
  ]
}