{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "  m=1 # [kg] mass of the object\n",
    "  h=5;xNum=h # initial position\n",
    "  v=0 # initial velocity\n",
    "  g=9.776 #[m/s^2] gravitational constant Bogota\n",
    "  F=-m*g\n",
    "  dt=0.03 # [s] time advance\n",
    "  t=0 # [s] initial time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Time = []\n",
    "PositionNum=[]\n",
    "PositionAnal=[]\n",
    "while xNum>0:  \n",
    "  t=t+dt # time evolution\n",
    "  Time.append(t)\n",
    "  a=F/m # acceleration \"evolution\"\n",
    "  v=v+a*dt # velocity evolution\n",
    "  xNum=xNum+v*dt # position evolution\n",
    "  xAnal=h-g*t**2/2 # position evolution\n",
    "  PositionNum.append(xNum)\n",
    "  PositionAnal.append(xAnal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "from matplotlib import pyplot\n",
    "pyplot.plot(Time, PositionNum)\n",
    "pyplot.plot(Time, PositionAnal)\n",
    "pyplot.xlabel('t [s]');\n",
    "pyplot.ylabel('x [m]');\n",
    " "
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
