{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "    This example shows how to use the Mesh class in order to generate a \n",
    "    geometry similar to those that may define a lifting surface planform.\n",
    "\n",
    "   Y ^   B              This geometry is positioned in space with two chords\n",
    "     |   +---+          simulating the root and tip chords, using points\n",
    "     |  /    |          A and B to store their respective leading edge\n",
    "     | /     | b        coordinates.\n",
    "     |/      |          \n",
    "     +-------+---->     With the parameters n and m, the grid density in the \n",
    "     A   c         X    chordwise and spanwise directions will be defined.\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from vlm import Mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geometry definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "c = 1  # root chord length\n",
    "b = 10  # panel span length\n",
    "n = 2  # number of panels chordwise\n",
    "m = 3  # number of panels spanwise\n",
    "\n",
    "# Wing\n",
    "A = np.array([0, 0])  # root chord leading edge coordinates\n",
    "B = np.array([c/2, b])  # tip chord leading edge coordinates\n",
    "\n",
    "leading_edges_coord = [A, B]\n",
    "chord_lengths = [c, c/2]\n",
    "\n",
    "mesh = Mesh(leading_edges_coord, chord_lengths, n, m)\n",
    "\n",
    "Points = mesh.points()\n",
    "Panels = mesh.panels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Point |    Coordinates \n",
      "------------------------\n",
      "   0   | [0 0]\n",
      "   1   | [0.17 3.33]\n",
      "   2   | [0.33 6.67]\n",
      "   3   | [ 0.5 10. ]\n",
      "   4   | [0.5 0. ]\n",
      "   5   | [0.58 3.33]\n",
      "   6   | [0.67 6.67]\n",
      "   7   | [ 0.75 10.  ]\n",
      "   8   | [1. 0.]\n",
      "   9   | [1.   3.33]\n",
      "  10   | [1.   6.67]\n",
      "  11   | [ 1. 10.]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Panel | Chrd % |  Span  |   Points coordinates\n",
      "------------------------------------------------\n",
      "    0  |  25.0  |   3.33 |  [0.5 0. ] [0 0] [0.17 3.33] [0.58 3.33]\n",
      "    1  |  25.0  |   3.33 |  [0.58 3.33] [0.17 3.33] [0.33 6.67] [0.67 6.67]\n",
      "    2  |  25.0  |   3.33 |  [0.67 6.67] [0.33 6.67] [ 0.5 10. ] [ 0.75 10.  ]\n",
      "    3  |  75.0  |   3.33 |  [1. 0.] [0.5 0. ] [0.58 3.33] [1.   3.33]\n",
      "    4  |  75.0  |   3.33 |  [1.   3.33] [0.58 3.33] [0.67 6.67] [1.   6.67]\n",
      "    5  |  75.0  |   3.33 |  [1.   6.67] [0.67 6.67] [ 0.75 10.  ] [ 1. 10.]\n"
     ]
    }
   ],
   "source": [
    "# Points of the mesh\n",
    "print('\\n Point |    Coordinates ')\n",
    "print('------------------------')\n",
    "for p, i in zip(Points, range(len(Points))):\n",
    "    print('  %2s   |' % i, np.round(p, 2))\n",
    "\n",
    "# Mesh\n",
    "plt.style.use('ggplot')\n",
    "colors = plt.cm.Blues(np.linspace(0, 1, len(Panels) + 5))\n",
    "\n",
    "for p, i in zip(Panels, range(len(Panels))):\n",
    "    col = colors[i + 4]\n",
    "    \n",
    "    x = [p.P1[0], p.P2[0], p.P3[0], p.P4[0]]\n",
    "    y = [p.P1[1], p.P2[1], p.P3[1], p.P4[1]]\n",
    "    \n",
    "    plt.fill(x, y, c=col, alpha=0.65)\n",
    "    plt.plot(x, y, c='w', lw=2)\n",
    "    centre = (p.P1 + p.P3) / 2\n",
    "    plt.text(centre[0], centre[1], i, color='k', fontsize=20)\n",
    "\n",
    "for p in Points:\n",
    "    plt.plot(p[0], p[1], 'bo')\n",
    "plt.show()\n",
    "\n",
    "# Panels distribution\n",
    "print('\\n Panel | Chrd % |  Span  |   Points coordinates')\n",
    "print('------------------------------------------------')\n",
    "for p, i in zip(Panels, range(len(Panels))):\n",
    "    print('  %3s  |  %4.1f  | %6.2f | '\n",
    "         % (i, 100*p.chordwise_position,  p.span),\n",
    "            np.round(p.P1, 2), np.round(p.P2, 2),\n",
    "            np.round(p.P3, 2), np.round(p.P4, 2))"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}