gusts.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcApparent(wind, boat):\n",
    "    return np.linalg.norm(wind+boat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcSpeed(vector):\n",
    "    return int(np.linalg.norm(vector))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dotproduct(v1, v2):\n",
    "  return sum((a*b) for a, b in zip(v1, v2))\n",
    "def length(v):\n",
    "  return math.sqrt(dotproduct(v, v))\n",
    "def angle(v1, v2):\n",
    "  return math.acos(dotproduct(v1, v2) / (length(v1) * length(v2)))\n",
    "def angleDegrees(v1, v2):\n",
    "    return int(math.degrees(angle(v1,v2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wind: 15 kn\n",
      "gust: 10 kn\n",
      "upwind speed: 11 kn\n",
      "downwind speed: 21 kn\n"
     ]
    }
   ],
   "source": [
    "wind = np.array([0,0,0,15]); print('wind:', calcSpeed(wind),'kn')\n",
    "gust = np.array([0,0,0,10]); print('gust:', calcSpeed(gust),'kn')\n",
    "upwind = np.array([0,0,8,8]); print('upwind speed:', calcSpeed(upwind),'kn')\n",
    "downwind = np.array([0,0,15,-15]); print('downwind speed:', calcSpeed(downwind),'kn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apparent up:   19 ° with gust: 13 °\n",
      "apparent down: 90 ° with gust: 56 °\n"
     ]
    }
   ],
   "source": [
    "print('apparent up:  ', int(angleDegrees(wind, upwind+wind)),'°', \\\n",
    "      'with gust:', int(angleDegrees(wind, upwind+wind+gust)),'°')\n",
    "print('apparent down:', int(angleDegrees(wind, downwind+wind)),'°', \\\n",
    "      'with gust:', int(angleDegrees(wind, downwind+wind+gust)),'°')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apparent up:   24 kn with gust: 33 kn\n",
      "apparent down: 15 kn with gust: 18 kn\n"
     ]
    }
   ],
   "source": [
    "print('apparent up:  ', int(calcApparent(wind, upwind)),'kn', \\\n",
    "      'with gust:', int(calcApparent(wind+gust, upwind)),'kn')\n",
    "print('apparent down:', int(calcApparent(wind, downwind)),'kn', \\\n",
    "      'with gust:', int(calcApparent(wind+gust, downwind)),'kn')"
   ]
  },
  {
   "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": [
    "soa = np.array([wind, gust+[0,15,0,0], upwind, upwind+wind, upwind+wind+gust])\n",
    "X, Y, U, V = zip(*soa)\n",
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "ax.quiver(X, Y, U, V, angles='xy',\\\n",
    "          color=['green','blue','grey','green','blue'], \\\n",
    "          scale_units='xy', scale=1)\n",
    "ax.set_xlim([-1, 40])\n",
    "ax.set_ylim([-1, 40])\n",
    "plt.draw()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0, 0, 0, 0) (0, 15, 0, 0, 0)\n",
      "(0, 0, 8, 8, 8) (15, 10, 8, 23, 33)\n"
     ]
    }
   ],
   "source": [
    "soa = np.array([wind, gust+[0,15,0,0], upwind, upwind+wind, upwind+wind+gust])\n",
    "X, Y, U, V = zip(*soa)\n",
    "ax = plt.gca()\n",
    "qv = plt.quiver(X, Y, U, V, angles='xy',\\\n",
    "          color=['green','blue','grey','green','blue'], \\\n",
    "          scale_units='xy', scale=1, \\\n",
    "          label=['green','blue','grey','green','blue'])\n",
    "plt.quiverkey(qv, 0.5, 0.5, 0, 'gust', coordinates='axes',\\\n",
    "              color='r', labelcolor='blue')\n",
    "\n",
    "ax.set_xlim([-1, 40])\n",
    "ax.set_ylim([-1, 40])\n",
    "\n",
    "\n",
    "plt.draw()\n",
    "plt.show()\n",
    "\n",
    "print(X, Y)\n",
    "print(U, V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "soa = np.array([wind, gust+[0,15,0,0], downwind, downwind+wind, downwind+wind+gust])\n",
    "X, Y, U, V = zip(*soa)\n",
    "plt.figure()\n",
    "ax = plt.gca()\n",
    "ax.quiver(X, Y, U, V, angles='xy',\\\n",
    "          color=['green','blue','grey','green','blue'], \\\n",
    "          scale_units='xy', scale=1)\n",
    "ax.set_xlim([-1, 60])\n",
    "ax.set_ylim([-30, 30])\n",
    "plt.draw()\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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}