{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example - 22 - PV Small North - Venus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from AMAT.planet import Planet\n",
    "from AMAT.vehicle import Vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook simulates the atmospheric entry of the Pioneer Venus Small North probe.\n",
    "https://en.wikipedia.org/wiki/Pioneer_Venus_Multiprobe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the planet and atmosphere model.\n",
    "planet=Planet(\"VENUS\")    \n",
    "planet.loadAtmosphereModel('../atmdata/Venus/venus-gram-avg.dat', 0 , 1 , 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the vehicle\n",
    "vehicle=Vehicle('PV-Small-North', 91, 190, 0.00, 0.46, 0.0, 0.19, planet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up entry parameters\n",
    "vehicle.setInitialState(180.0,0.0,0.0,11.54,0.0,-68.7,0.0,0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up solver\n",
    "vehicle.setSolverParams(1E-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Propogate vehicle entry trajectory\n",
    "vehicle.propogateEntry (2400.0,0.1,0.0)\n",
    "# bank angle = 20 deg arbitrary, actual profile will give more realistic results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import rcParams to set figure font type\n",
    "from matplotlib import rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "plt.rc('font',family='Times New Roman')\n",
    "params = {'mathtext.default': 'regular' }          \n",
    "plt.rcParams.update(params)\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(vehicle.v_kmsc, vehicle.h_kmc, 'k-', linewidth=2.0)\n",
    "plt.xlabel('Speed, km/s',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\n",
    "ax=plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(vehicle.acc_net_g, vehicle.h_kmc, 'b-', linewidth=2.0)\n",
    "plt.xlabel('Deceleration, Earth g',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\n",
    "ax=plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(vehicle.q_stag_total, vehicle.h_kmc,'r-', linewidth=2.0)\n",
    "plt.xlabel('Stagnation point heat-rate, '+r'$W/cm^2$',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\n",
    "ax=plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.plot(vehicle.heatload/1.0E3, vehicle.h_kmc, 'm-', linewidth=2.0)\n",
    "plt.xlabel('Stagnation point heat-load, '+r'$kJ/cm^2$',fontsize=14)\n",
    "plt.ylabel('Altitude, km', fontsize=14)\n",
    "ax=plt.gca()\n",
    "ax.tick_params(direction='in')\n",
    "ax.yaxis.set_ticks_position('both')\n",
    "ax.xaxis.set_ticks_position('both')\n",
    "ax.tick_params(axis='x',labelsize=14)\n",
    "ax.tick_params(axis='y',labelsize=14)\n",
    "\n",
    "plt.savefig('../plots/pv-small-north-venus.png',bbox_inches='tight')\n",
    "plt.savefig('../plots/pv-small-north-venus.pdf', dpi=300,bbox_inches='tight')\n",
    "plt.savefig('../plots/pv-small-north-venus.eps', dpi=300,bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  }
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