Taller2_Coriolis_3b.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#*********************************\n",
    "# simulation parameters:\n",
    "#*********************************\n",
    "# initial latitude (degrees):\n",
    "lat0 = 40 \n",
    "# initial velocity in x (m/s):\n",
    "Vx0  = 0\n",
    "# initial velocity in y (m/s):\n",
    "Vy0  = -93.5\n",
    "# simulation time (hours):\n",
    "Time_period = 12/np.sin(lat0*np.pi/180)\n",
    "# time step (s):\n",
    "dt = 10\n",
    "# consider f constant?\n",
    "f_constant = True\n",
    "# Earth's angular speed of rotation:\n",
    "omega = 2*2.*np.pi/(24.*60.*60.) \n",
    "\n",
    "#*********************************\n",
    "# definition of functions:\n",
    "#*********************************\n",
    "\n",
    "def calc_acc(vx, vy, lat, w=omega):\n",
    "    f=calc_f(lat, w)\n",
    "    ax = f*vy\n",
    "    ay = -f*vx\n",
    "    return ax, ay\n",
    "\n",
    "def calc_next_vel(vx, vy, ax, ay, delta_t=dt):\n",
    "    vx_new = vx+ax*delta_t\n",
    "    vy_new = vy+ay*delta_t\n",
    "    return vx_new, vy_new\n",
    "\n",
    "def calc_next_pos(x, y, vx, vy, delta_t=dt):\n",
    "    x_new = x+vx*delta_t\n",
    "    y_new = y+vy*delta_t\n",
    "    return x_new, y_new\n",
    "\n",
    "def calc_f(lat, w):\n",
    "    return 2*w*np.sin(lat)\n",
    "\n",
    "def calc_lat(lat0, y, y0=0, r=6.37e6):\n",
    "    #lat0 must be in radians\n",
    "    dlat = (y-y0)/r\n",
    "    return lat0+dlat\n",
    "\n",
    "\n",
    "#********************************************************************\n",
    "# main code starts here:\n",
    "#********************************************************************\n",
    "\n",
    "# convert initial latitude to radians\n",
    "lat0 = lat0*np.pi/180.\n",
    "\n",
    "# define arrays and set initial values:\n",
    "x   = np.array([0.])\n",
    "y   = np.array([0.])\n",
    "vx  = np.array([Vx0])\n",
    "vy  = np.array([Vy0])\n",
    "lat = np.array([lat0])\n",
    "\n",
    "# compute initial acceleration:\n",
    "ax0, ay0 = calc_acc(vx[-1], vy[-1], lat0) \n",
    "ax = np.array([ax0])\n",
    "ay = np.array([ay0])\n",
    "\n",
    "# compute new positions, velocities and accelerations throughout time:\n",
    "t = 0\n",
    "while t < Time_period*60*60.:\n",
    "    vx_new, vy_new = calc_next_vel(vx[-1], vy[-1], ax[-1], ay[-1])\n",
    "    x_new, y_new = calc_next_pos(x[-1], y[-1], vx[-1], vy[-1])\n",
    "    x = np.append(x, x_new)\n",
    "    y = np.append(y, y_new)\n",
    "    vx = np.append(vx, vx_new)\n",
    "    vy = np.append(vy, vy_new)\n",
    "    lat = np.append(lat, calc_lat(lat0, y[-1], y[0]))\n",
    "    if f_constant:\n",
    "        ax_new, ay_new = calc_acc(vx[-1], vy[-1], lat0)\n",
    "    else:\n",
    "        ax_new, ay_new = calc_acc(vx[-1], vy[-1], lat[-1])\n",
    "    ax = np.append(ax, ax_new)\n",
    "    ay = np.append(ay, ay_new)\n",
    "    t+= dt\n",
    "\n",
    "#*********************************\n",
    "# create plot:\n",
    "#*********************************\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(x*1e-3, y*1e-3)\n",
    "plt.xlabel('x (km)', fontsize=16)\n",
    "plt.ylabel('y (km)', fontsize=16)\n",
    "lims=plt.axis('equal')\n",
    "plt.savefig('plot3b.png')\n",
    "plt.show()"
   ]
  },
  {
   "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
}