{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "  m=1 # [kg] mass of the object\n",
    "  x=5;# initial position\n",
    "  v=0 # initial velocity\n",
    "  g=9.776 #[m/s^2] gravitational constant Bogota\n",
    "  F=-m*g\n",
    "  dt=0.003 # [s] time advance\n",
    "  t=0 # [s] initial time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Time = []\n",
    "Position=[]\n",
    "while x>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",
    "  x=x+v*dt # position evolution\n",
    "  Position.append(x)"
   ]
  },
  {
   "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, Position)\n",
    "pyplot.xlabel('t [s]');pyplot.ylabel('x [m]');\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"model.ipynb\">download model.ipynb</a>"
   ]
  }
 ],
 "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
}
