Calibration Factor with Polynomial Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Cell Calibration Factor using Polinomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepared Scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this project, you should prepare an object with certain weight as referrence. This object must have known weight with real scale. Choose the number you want to scale. Then pick the range number of calibration factor. Make sure the value is in the range of measured scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, I use a 1 kg of rice which I have measured before with real scale. I choose the 1000 and the range of calibration factor is 100-1000. Then, proceed with the arduino code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the Measured Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "calFactor_Y = ([100,200,300,400,500,600,700,800,900,1000]) #Range of the calibration factor\n",
    "measuredValue_X = ([4336,2168,1445,1084,867,722,619,542,482,434]) #Measured Value (1000 is in the range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(measuredValue_X,calFactor_Y)\n",
    "plt.xlabel('Measured Value (gr)')\n",
    "plt.ylabel('Calibration Factor')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Polynomial Eq using Numpy Polyfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = np.polyfit(measuredValue_X,calFactor_Y,4) #Get the eq by polynomial regression 4 order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(300,4500,1000)\n",
    "plt.scatter(measuredValue_X,calFactor_Y)\n",
    "plt.plot(x,np.polyval(poly,x),'y')\n",
    "plt.xlabel('Measured Value (gr)')\n",
    "plt.ylabel('Calibration Factor')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Polynomial Equation : y =  9.897259959616193e-11 x^4 + -8.944295015743029e-07 x^3 + 0.0027555337492449444 x^2 + -3.6672135448936887 x^1 + 2124.8597232125812\n"
     ]
    }
   ],
   "source": [
    "print('The Polynomial Equation : y = ',poly[0],'x^4 +',poly[1],'x^3 +',poly[2],'x^2 +',poly[3],'x^1 +',poly[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Calibration Factor of Real Value with Poly Eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Calibration Factor is :  417.7230255856957\n"
     ]
    }
   ],
   "source": [
    "rightCalFactor = 0\n",
    "myRealValue = 1000\n",
    "for i in range(len(poly)):\n",
    "    rightCalFactor += poly[i]*(myRealValue**(4-i))\n",
    "print('The Calibration Factor for 1000 gram is : ', rightCalFactor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Calibration Factor 1 kg is :  417723.02558569575\n"
     ]
    }
   ],
   "source": [
    "print('The Calibration Factor 1 kg is : ', rightCalFactor*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}