Obstacle Map using Mathplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
    "from math import pi,sin,atan,cos,floor,ceil\n",
    "\n",
    "def obs_map_pt(x,y):\n",
    "    circle = ((np.square(x-225))+ (np.square(y-50)) <=np.square(25))\n",
    "    ellipse = (((np.square(x-150))/np.square(40))+((np.square(y-100))/np.square(20)) -1 <=0)\n",
    "    rhombus = (x*(-3/5)+y-55<0) and (x*(3/5)+y-325<0) and (x*(-3/5)+y-25>0) and (x*(3/5)+y-295 > 0)\n",
    "    rectangle = ((200-y) - (1.73)*x + 135 > 0 and (200-y) + (0.58)*x - 96.35  <= 0 and (200-y) - (1.73)*x - 15.54 <= 0 and (200-y) + (0.58)*x - 84.81 >= 0)\n",
    "    polygon1 = ((y+13*x-340>0) and x+y-100<0 and y+(-7/5)*x+20>0)#triangle1\n",
    "    polygon2 = (y-15>0 and (7/5)*x+y-120<0 and y+(-7/5)*x+20<0)#triangle2\n",
    "    polygon3 = ((7/5)*x+y-120>0 and (-6/5)*x+y+10<0 and (6/5)*x+y-170<0 and (-7/5)*x+y+90>0)#rhombus\n",
    "    \n",
    "    if circle or ellipse or rhombus or rectangle or polygon1 or polygon2 or polygon3 :\n",
    "        obj_val = 0\n",
    "    else:\n",
    "        obj_val = 1\n",
    "    \n",
    "    return obj_val\n",
    "r=1\n",
    "c=1\n",
    "tot=r+c\n",
    "\n",
    "def obs_map(x,y):\n",
    "    circle = ((np.square(x-225))+ (np.square(y-50)) <=np.square(25+tot))\n",
    "    ellipse = (((np.square(x-150))/np.square(40+tot))+((np.square(y-100))/np.square(20+tot)) -1 <=0)\n",
    "    rhombus = (x*(-3/5)+y-55-(tot/sin(pi/2 -atan(-3/5)))<0) and (x*(3/5)+y-325-(tot/sin(pi/2 -atan(3/5)))<0) and (x*(-3/5)+y-25+(tot/sin(pi/2 -atan(-3/5)))>0) and (x*(3/5)+y-295+(tot/sin(pi/2 -atan(3/5))) > 0)\n",
    "    rectangle = ((200-y) - (1.73)*x + 135+(tot/sin(pi/2 -atan(1.73))) > 0 and (200-y) + (0.58)*x - 96.35-(tot/sin(pi/2 -atan(0.58)))  <= 0 and (200-y) - (1.73)*x - 15.54-(tot/sin(pi/2 -atan(1.73)))<= 0 and (200-y) + (0.58)*x - 84.81+(tot/sin(pi/2 -atan(0.58)))>= 0)\n",
    "    polygon1 = ((y+13*x-340+(tot/sin(pi/2 -atan(13)))>0) and x+y-100-(tot/sin(pi/2 -atan(1)))<0 and y+(-7/5)*x+20>0)#triangle\n",
    "    polygon2 = (y-15+tot>0 and (7/5)*x+y-120<0 and y+(-7/5)*x+20<0)#triangle\n",
    "    polygon3 = ((7/5)*x+y-120>0 and (-6/5)*x+y+10-(tot/sin(pi/2 -atan(-6/5)))<0 and (6/5)*x+y-170-(tot/sin(pi/2 -atan(6/5)))<0 and (-7/5)*x+y+90+(tot/sin(pi/2 -atan(-7/5)))>0)#rhombus\n",
    "    if circle or ellipse or rhombus or rectangle or polygon1 or polygon2 or polygon3 :\n",
    "        obj_val = 0\n",
    "    else:\n",
    "        obj_val = 1\n",
    "    \n",
    "    return obj_val\n",
    "\n",
    "# Total hight and width of the entire graph\n",
    "height = 200\n",
    "width = 300\n",
    "plot_x=[]\n",
    "plot_y=[]\n",
    "rigid_x=[]\n",
    "rigid_y=[]\n",
    "\n",
    "for i in range(200):\n",
    "    for j in range(300):\n",
    "           if (obs_map_pt(j,i)==0):\n",
    "            plot_x.append(j)\n",
    "            plot_y.append(200-i)\n",
    "\n",
    "for i in range(200):\n",
    "    for j in range(300):\n",
    "           if (obs_map(j,i)==0):\n",
    "            rigid_x.append(j)\n",
    "            rigid_y.append(200-i)\n",
    "            \n",
    "plt.xlim(0,300)\n",
    "plt.ylim(0,200)\n",
    "#fig, ax = plt.subplots()\n",
    "plt.plot(rigid_x,rigid_y,\".k\")\n",
    "plt.plot(plot_x,plot_y,\".r\")\n",
    "plt.show() \n",
    "plt.pause(5) \n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " ...]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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     "data": {
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