Part14: Curve_fitting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Part14: Curve_fitting.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOkG8aUgbFEMW7TAECcDBuC"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7nCDhrr1fFh"
      },
      "source": [
        "# Approach - 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4fTBjryz1-J"
      },
      "source": [
        "Help : https://www.youtube.com/watch?v=Dggl0fJJ81k"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BWI-7qEjnXv"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIcM52nIzl6k"
      },
      "source": [
        "x = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]\n",
        "y = [1,2,3,4,5,6,7,8,9,10,9, 8, 7, 6, 5, 4, 3, 2, 1]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHKpsY-iz5VB",
        "outputId": "d9eb500a-2f86-411e-e52a-9344d013bdc8"
      },
      "source": [
        "curve = np.polyfit(x,y,2)\n",
        "curve"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.09951349,  1.99026979, -1.70278638])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_S9-XwNl0MBI",
        "outputId": "8223b661-8a93-4e09-8cb8-4c014cf9e9aa"
      },
      "source": [
        "poly = np.poly1d(curve)\n",
        "poly"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "poly1d([-0.09951349,  1.99026979, -1.70278638])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDExgaGf0WQO",
        "outputId": "885ffd28-2fa2-42e4-d755-531782c91b51"
      },
      "source": [
        "print(poly(19.5))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.7325298540468936\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f5eVIGt3ZOi"
      },
      "source": [
        "# 1*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UM00kUb3b7c",
        "outputId": "193fa984-2ef1-447f-faf0-54da599c6fbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        }
      },
      "source": [
        "import numpy as np\n",
        "x = np.array([0,1,2,3,4,5])\n",
        "y = np.array([0,0.8,0.9,0.1,-0.8,-1])\n",
        "print(x)\n",
        "print(y)\n",
        "\n",
        "p1 = np.polyfit(x,y,1)\n",
        "p2 = np.polyfit(x,y,2)\n",
        "p3 = np.polyfit(x,y,3)\n",
        "print(p1)\n",
        "print(p2)\n",
        "print(p3)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(x,y,'o')\n",
        "xp = np.linspace(-2,6,100)\n",
        "plt.plot(xp,np.polyval(p1,xp),'r-')\n",
        "plt.plot(xp,np.polyval(p2,xp),'b--')\n",
        "plt.plot(xp,np.polyval(p3,xp),'m:')\n",
        "yfit = p1[0] * x + p1[1]\n",
        "yresid= y - yfit\n",
        "SSresid = np.sum(yresid**2)\n",
        "SStotal = len(y) * np.var(y)\n",
        "rsq = 1 - SSresid/SStotal\n",
        "print(yfit)\n",
        "print(y)\n",
        "print(rsq)\n",
        "\n",
        "from scipy.stats import linregress\n",
        "slope,intercept,r_value,p_value,std_err = linregress(x,y)\n",
        "print(r_value**2)\n",
        "print(p_value)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5]\n",
            "[ 0.   0.8  0.9  0.1 -0.8 -1. ]\n",
            "[-0.30285714  0.75714286]\n",
            "[-0.16071429  0.50071429  0.22142857]\n",
            "[ 0.08703704 -0.81349206  1.69312169 -0.03968254]\n",
            "[ 0.75714286  0.45428571  0.15142857 -0.15142857 -0.45428571 -0.75714286]\n",
            "[ 0.   0.8  0.9  0.1 -0.8 -1. ]\n",
            "0.5177880184331798\n",
            "0.5177880184331796\n",
            "0.10693129655435198\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lr2jdW3b1lrl"
      },
      "source": [
        "# Approach - 2 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eo5v70cv2Fmx"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from scipy.optimize import curve_fit\n",
        "import numpy as np"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nPk4kf42IC7"
      },
      "source": [
        "#Fitting function\n",
        "def func(x, a, b):\n",
        "    return a*np.exp(b*x)\n",
        "    #return a*x+b   ## you can specify any function as par your problem"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCMveYla2Rco"
      },
      "source": [
        "#Experimental x and y data points    \n",
        "xData = np.array([1, 2, 3, 4, 5])\n",
        "yData = np.array([1, 9, 50, 300, 1500])"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AQyp_Gi2UVy",
        "outputId": "45be1b72-46ec-41e6-af8b-30c8dcf47874",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "#Plot experimental data points\n",
        "plt.plot(xData, yData, 'bo', label='experimental-data')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1d317afa90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD4CAYAAAAAczaOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATjUlEQVR4nO3df4zk9X3f8ecLDuOcnfowtyX07mBRc0pFrKS+rggRlWWZFgOxOKS6Fuk2nF2qVRrSOCWSg40UVEdIiVqFhDZ1tDHUR7vBRsQuVwvHOQGRVakQ72Gbn05YEQ7uBL6NwTjttXEvfveP+ZxZ1nt3OzuzM3v+Ph/SaL7fz+cz83nvB/Y13/vOd3ZSVUiSuuGMcRcgSRodQ1+SOsTQl6QOMfQlqUMMfUnqkE3jLuBktm7dWpOTk+MuQ5JOKwcOHPjLqppYqW9Dh/7k5CTz8/PjLkOSTitJDp6oz9M7ktQhhr4kdYihL0kdYuhLUocY+pLUIYa+JG0gc3MwOQlnnNG7n5sb7vNv6Es2JalL5uZgZgaOHu3tHzzY2weYnh7OHB7pS9IGccstrwf+cUeP9tqHxdCXpA3ihRf6a18LQ1+SNogLLuivfS0MfUnaIG67DTZvfmPb5s299mEx9CVpg5iehtlZuPBCSHr3s7PDexMXvHpHkjaU6enhhvxyHulLUocY+pLUIYa+JHWIoS9JHWLoS1KHnDL0k9yV5EiSJ1fo+5UklWRr20+SO5IsJHk8ya4lY/ckebbd9gz3x5AkrcZqjvQ/BVy5vDHJDuAKYOkHhK8CdrbbDPCJNvbtwK3ATwGXALcmOWeQwiVJ/Ttl6FfVl4BXVui6HfgIUEvadgN3V88jwJYk5wPvBfZX1StV9SqwnxVeSCRJ62tN5/ST7AYOV9XXlnVtA15csn+otZ2ofaXnnkkyn2R+cXFxLeVJkk6g79BPshn4GPBrwy8Hqmq2qqaqampiYmI9ppCkzlrLkf7fBS4CvpbkeWA78FiSHwEOAzuWjN3e2k7ULkkaob5Dv6qeqKq/XVWTVTVJ71TNrqp6GdgHXN+u4rkUeK2qXgK+CFyR5Jz2Bu4VrU2SNEKruWTzHuB/Aj+W5FCSG04y/AHgOWAB+H3gFwCq6hXg14Evt9vHW5skaYRSVaceNSZTU1M1Pz8/7jIk6bSS5EBVTa3U5ydyJalDDH1J6hBDX5I6xNCXpA4x9CWpQwx9SeoQQ1+SOsTQl6QOMfQlqUMMfUnqEENfkjrE0JekDjH0JalDDH1J6hBDX5I6xNCXpA4x9CWpQwx9SeqQ1XxH7l1JjiR5cknbv0vy9SSPJ/lcki1L+j6aZCHJnyV575L2K1vbQpKbh/+jSJJOZTVH+p8CrlzWth94R1X9BPDnwEcBklwMXAf8eHvMf0pyZpIzgd8FrgIuBn62jZUkjdApQ7+qvgS8sqztj6vqWNt9BNjetncDn66qv66qvwAWgEvabaGqnquq7wCfbmMlSSM0jHP6/wL4QtveBry4pO9QaztR+/dJMpNkPsn84uLiEMqTJB03UOgnuQU4BswNpxyoqtmqmqqqqYmJiWE9rSQJ2LTWByb5IPA+4PKqqtZ8GNixZNj21sZJ2iVJI7KmI/0kVwIfAa6pqqNLuvYB1yU5O8lFwE7gT4EvAzuTXJTkTfTe7N03WOmSpH6d8kg/yT3Au4GtSQ4Bt9K7WudsYH8SgEeq6uer6qkk9wJP0zvtc2NV/U17nl8EvgicCdxVVU+tw88jSTqJvH5mZuOZmpqq+fn5cZchSaeVJAeqamqlPj+RK0kdYuhLUocY+pLUIYa+JHWIoS9JHWLoS1KHGPqS1CGGviR1iKEvSR1i6EtShxj6ktQhhr4kdYihL0kdYuhLUocY+pLUIYa+JHWIoS9JHXLK0E9yV5IjSZ5c0vb2JPuTPNvuz2ntSXJHkoUkjyfZteQxe9r4Z5PsWZ8fR5J0Mqs50v8UcOWytpuBB6tqJ/Bg2we4it6Xoe8EZoBPQO9Fgt536/4UcAlw6/EXCknS6Jwy9KvqS8Ary5p3A3vb9l7g2iXtd1fPI8CWJOcD7wX2V9UrVfUqsJ/vfyGRJK2ztZ7TP6+qXmrbLwPnte1twItLxh1qbSdq/z5JZpLMJ5lfXFxcY3mSpJUM/EZuVRVQQ6jl+PPNVtVUVU1NTEwM62klSaw99L/RTtvQ7o+09sPAjiXjtre2E7VLkkZoraG/Dzh+Bc4e4P4l7de3q3guBV5rp4G+CFyR5Jz2Bu4VrU2SNEKbTjUgyT3Au4GtSQ7RuwrnN4B7k9wAHAQ+0IY/AFwNLABHgQ8BVNUrSX4d+HIb9/GqWv7msCRpnaV3Sn5jmpqaqvn5+XGXIUmnlSQHqmpqpT4/kStJHWLoS1KHGPqS1CGGviR1iKEvSR1i6EtShxj6ktQhhr4kdYihL0kdYuhLUocY+pLUIYa+JHWIoS9JHWLoS1KHGPqS1CGGviR1iKEvSR0yUOgn+TdJnkryZJJ7krw5yUVJHk2ykOQzSd7Uxp7d9hda/+QwfgBJ0uqtOfSTbAN+CZiqqncAZwLXAb8J3F5VPwq8CtzQHnID8Gprv72NkySN0KCndzYBP5RkE7AZeAl4D3Bf698LXNu2d7d9Wv/lSTLg/JKkPqw59KvqMPDvgRfohf1rwAHgW1V1rA07BGxr29uAF9tjj7Xx5651fklS/wY5vXMOvaP3i4C/A7wFuHLQgpLMJJlPMr+4uDjo00mSlhjk9M4/Av6iqhar6v8BnwUuA7a00z0A24HDbfswsAOg9b8N+ObyJ62q2aqaqqqpiYmJAcqTJC03SOi/AFyaZHM7N3858DTwMPD+NmYPcH/b3tf2af0PVVUNML8kqU+DnNN/lN4bso8BT7TnmgV+FbgpyQK9c/Z3tofcCZzb2m8Cbh6gbknSGmQjH2xPTU3V/Pz8uMuQpNNKkgNVNbVSn5/IlaQOMfQlqUMMfUnqEENfkjrE0JekDjH0JalDDH1J6hBDX5I6xNCXpA4x9CWpQwx9SeoQQ1+SOsTQl6QOMfQlqUMMfUnqEENfkjrE0JekDjH0JalDBgr9JFuS3Jfk60meSfLTSd6eZH+SZ9v9OW1sktyRZCHJ40l2DedHkCSt1qBH+r8D/FFV/T3gJ4Fn6H3h+YNVtRN4kNe/AP0qYGe7zQCfGHBuSVKf1hz6Sd4GvAu4E6CqvlNV3wJ2A3vbsL3AtW17N3B39TwCbEly/porlyT1bZAj/YuAReA/J/lKkk8meQtwXlW91Ma8DJzXtrcBLy55/KHW9gZJZpLMJ5lfXFwcoDxJ0nKDhP4mYBfwiap6J/C/ef1UDgBVVUD186RVNVtVU1U1NTExMUB5kqTlBgn9Q8Chqnq07d9H70XgG8dP27T7I63/MLBjyeO3tzZJ0oisOfSr6mXgxSQ/1pouB54G9gF7Wtse4P62vQ+4vl3Fcynw2pLTQJKkEdg04OP/NTCX5E3Ac8CH6L2Q3JvkBuAg8IE29gHgamABONrGSpJGaKDQr6qvAlMrdF2+wtgCbhxkPknSYPxEriR1iKEvSR1i6EtShxj6ktQhhr4kdYihL0kdYuhLUocY+pLUIYa+JHWIoS9JHWLoS1KHGPqS1CGGviR1iKEvSR1i6EtShxj6ktQhhr4kdcjAoZ/kzCRfSfL5tn9RkkeTLCT5TPsqRZKc3fYXWv/koHNLkvozjCP9DwPPLNn/TeD2qvpR4FXghtZ+A/Bqa7+9jZMkjdBAoZ9kO/AzwCfbfoD3APe1IXuBa9v27rZP67+8jZckjcigR/q/DXwE+G7bPxf4VlUda/uHgG1texvwIkDrf62Nf4MkM0nmk8wvLi4OWJ4kaak1h36S9wFHqurAEOuhqmaraqqqpiYmJob51JLUeZsGeOxlwDVJrgbeDPwt4HeALUk2taP57cDhNv4wsAM4lGQT8DbgmwPML0nq05qP9Kvqo1W1vaomgeuAh6pqGngYeH8btge4v23va/u0/oeqqtY6vySpf+txnf6vAjclWaB3zv7O1n4ncG5rvwm4eR3mliSdxCCnd76nqv4E+JO2/RxwyQpj/i/wT4cxnyRpbfxEriR1iKEvSR1i6EtShxj6ktQhhr6kdTU3B5OTcMYZvfu5uXFX1G1DuXpHklYyNwczM3D0aG//4MHePsD09Pjq6jKP9CWtm1tueT3wjzt6tNeu8TD0Ja2bF17or13rz9CXtG4uuKC/dq0/Q1/SurntNti8+Y1tmzf32jUehr6kdTM9DbOzcOGFkPTuZ2d9E3ecvHpH0rqanjbkNxKP9CWpQwx9SeoQQ1+SOsTQl6QOMfQlqUMMfUnqkDWHfpIdSR5O8nSSp5J8uLW/Pcn+JM+2+3Nae5LckWQhyeNJdg3rh5Akrc4gR/rHgF+pqouBS4Ebk1xM7wvPH6yqncCDvP4F6FcBO9ttBvjEAHNLktZgzaFfVS9V1WNt+6+AZ4BtwG5gbxu2F7i2be8G7q6eR4AtSc5fc+WSpL4N5Zx+kkngncCjwHlV9VLrehk4r21vA15c8rBDrW35c80kmU8yv7i4OIzyJEnNwKGf5K3AHwK/XFXfXtpXVQVUP89XVbNVNVVVUxMTE4OWJ0laYqDQT3IWvcCfq6rPtuZvHD9t0+6PtPbDwI4lD9/e2iRJIzLI1TsB7gSeqarfWtK1D9jTtvcA9y9pv75dxXMp8NqS00CSpBEY5K9sXgb8HPBEkq+2to8BvwHcm+QG4CDwgdb3AHA1sAAcBT40wNySpDVYc+hX1f8AcoLuy1cYX8CNa51PkjQ4P5ErSR1i6EtShxj6ktQhhr4kdYihL0kdYuhLUocY+pLUIYa+JHWIoS9JHWLoS1KHGPpSn+bmYHISzjijdz83N+6KpNUb5A+uSZ0zNwczM3D0aG//4MHePsD09PjqklbLI32pD7fc8nrgH3f0aK9dOh0Y+lIfXnihv3ZpozH0pT5ccEF/7dJGY+hLfbjtNti8+Y1tmzf32qXTgaEvr0bpw/Q0zM7ChRdC0rufnfVNXJ0+Rh76Sa5M8mdJFpLcvB5zGGKrd/xqlIMHoer1q1FcsxObnobnn4fvfrd3b+DrdDLS0E9yJvC7wFXAxcDPJrl4mHMYYv3xahSpW0Z9pH8JsFBVz1XVd4BPA7uHOYEh1h+vRpG6ZdShvw14ccn+odb2PUlmkswnmV9cXOx7AkOsP16NInXLhnsjt6pmq2qqqqYmJib6frwh1h+vRpG6ZdShfxjYsWR/e2sbGkOsP16NInXLqP/2zpeBnUkuohf21wH/bJgTHA+rW27pndK54IJe4BtiJzY97fpIXTHS0K+qY0l+EfgicCZwV1U9Nex5DDFJWtnI/8pmVT0APDDqeSVJG/CNXEnS+jH0JalDDH1J6hBDX5I6JFU17hpOKMkicHCAp9gK/OWQyhkm6+qPdfXHuvrzg1jXhVW14qdbN3ToDyrJfFVNjbuO5ayrP9bVH+vqT9fq8vSOJHWIoS9JHfKDHvqz4y7gBKyrP9bVH+vqT6fq+oE+py9JeqMf9CN9SdIShr4kdchpH/pJ7kpyJMmTJ+hPkjvaF7E/nmTXBqnr3UleS/LVdvu1EdW1I8nDSZ5O8lSSD68wZuRrtsq6Rr5mSd6c5E+TfK3V9W9XGHN2ks+09Xo0yeQGqeuDSRaXrNe/XO+6lsx9ZpKvJPn8Cn0jX69V1DTOtXo+yRNt3vkV+of7+1hVp/UNeBewC3jyBP1XA18AAlwKPLpB6no38PkxrNf5wK62/cPAnwMXj3vNVlnXyNesrcFb2/ZZwKPApcvG/ALwe237OuAzG6SuDwL/cdT/j7W5bwL+YKX/XuNYr1XUNM61eh7YepL+of4+nvZH+lX1JeCVkwzZDdxdPY8AW5KcvwHqGouqeqmqHmvbfwU8w7LvKWYMa7bKukaurcH/artntdvyqx92A3vb9n3A5UmyAeoaiyTbgZ8BPnmCISNfr1XUtJEN9ffxtA/9VTjll7GP0U+3f55/IcmPj3ry9s/qd9I7SlxqrGt2krpgDGvWTgt8FTgC7K+qE65XVR0DXgPO3QB1AfyTdkrgviQ7VuhfD78NfAT47gn6x7Fep6oJxrNW0Hux/uMkB5LMrNA/1N/HLoT+RvUYvb+P8ZPAfwD+2ygnT/JW4A+BX66qb49y7pM5RV1jWbOq+puq+vv0vtP5kiTvGMW8p7KKuv47MFlVPwHs5/Wj63WT5H3Akao6sN5zrdYqaxr5Wi3xD6tqF3AVcGOSd63nZF0I/XX/Mva1qKpvH//nefW+TeysJFtHMXeSs+gF61xVfXaFIWNZs1PVNc41a3N+C3gYuHJZ1/fWK8km4G3AN8ddV1V9s6r+uu1+EvgHIyjnMuCaJM8Dnwbek+S/Lhsz6vU6ZU1jWqvjcx9u90eAzwGXLBsy1N/HLoT+PuD69g74pcBrVfXSuItK8iPHz2MmuYTef4t1D4o2553AM1X1WycYNvI1W01d41izJBNJtrTtHwL+MfD1ZcP2AXva9vuBh6q9AzfOupad972G3vsk66qqPlpV26tqkt6btA9V1T9fNmyk67WamsaxVm3etyT54ePbwBXA8iv+hvr7OPLvyB22JPfQu6pja5JDwK303tSiqn6P3vfxXg0sAEeBD22Qut4P/Kskx4D/A1y33kHRXAb8HPBEOx8M8DHggiW1jWPNVlPXONbsfGBvkjPpvcjcW1WfT/JxYL6q9tF7sfovSRbovXl/3TrXtNq6finJNcCxVtcHR1DXijbAep2qpnGt1XnA59qxzCbgD6rqj5L8PKzP76N/hkGSOqQLp3ckSY2hL0kdYuhLUocY+pLUIYa+JHWIoS9JHWLoS1KH/H82QRqJcLH5pQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ozVO0BZ2X8w",
        "outputId": "e50358b2-ccf1-411c-ce28-5c56b48b0a48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Initial guess for the parameters\n",
        "initialGuess = [1.0,1.0]    \n",
        " \n",
        "#Perform the curve-fit\n",
        "popt, pcov = curve_fit(func, xData, yData, initialGuess)\n",
        "print(popt)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.45065853 1.62210344]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUI5HeFB1GbA",
        "outputId": "dacf528f-e321-4a3d-cb7e-8668a96c68e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "#x values for the fitted function\n",
        "xFit = np.arange(0.0, 5.0, 0.01)\n",
        " \n",
        "#Plot the fitted function\n",
        "plt.plot(xData, yData, 'bo', label='experimental-data')\n",
        "plt.plot(xFit, func(xFit, *popt), 'r', label='fit params: a=%5.3f, b=%5.3f' % tuple(popt))\n",
        " \n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAAw-83l5EGO"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}