DexTAR_VKR_fin_13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from scipy.optimize import fsolve\n",
    "import math\n",
    "import numpy.linalg as LA\n",
    "import sympy as sp\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tol = 1e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y, t1, t2, f1, f2, l, L, d = sp.symbols('x y t1 t2 f1 f2 l L d')\n",
    "la = 13. # l у основания\n",
    "lb = 13. # l у основания\n",
    "lc = 4.25 #L\n",
    "ld = 4.25 #L\n",
    "l = la # l у основания\n",
    "L = lc #L\n",
    "d = 12.\n",
    "work_space = []\n",
    "diff_symb = [x, y, t1, t2, f1, f2]\n",
    "out_and_pass = [x, y, f1, f2]\n",
    "in_and_pass = [t1, t2, f1, f2]\n",
    "func1 = sp.Function('func1')\n",
    "func2 = sp.Function('func2')\n",
    "func3 = sp.Function('func3')\n",
    "func4 = sp.Function('func4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "func1 = x-l*sp.cos(t1)-L*sp.cos(f1)\n",
    "func2 = x-l*sp.cos(t2)-L*sp.cos(f2)-d\n",
    "func3 = y-l*sp.sin(t1)-L*sp.sin(f1)\n",
    "func4 = y-l*sp.sin(t2)-L*sp.sin(f2)\n",
    "F = [func1, func2, func3, func4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 4.25 \\sin{\\left(f_{1} \\right)} & 0\\\\1 & 0 & 0 & 4.25 \\sin{\\left(f_{2} \\right)}\\\\0 & 1 & - 4.25 \\cos{\\left(f_{1} \\right)} & 0\\\\0 & 1 & 0 & - 4.25 \\cos{\\left(f_{2} \\right)}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[1, 0,  4.25*sin(f1),             0],\n",
       "[1, 0,             0,  4.25*sin(f2)],\n",
       "[0, 1, -4.25*cos(f1),             0],\n",
       "[0, 1,             0, -4.25*cos(f2)]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "J_out_and_pass = sp.zeros(len(F),len(out_and_pass))\n",
    "i=0\n",
    "j=0\n",
    "while i < len(F):\n",
    "    j=0\n",
    "    while j < len(out_and_pass):\n",
    "        J_out_and_pass[i,j] = sp.diff(F[i], out_and_pass[j])\n",
    "        j+=1\n",
    "    i+=1\n",
    "J_out_and_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}13.0 \\sin{\\left(t_{1} \\right)} & 0 & 4.25 \\sin{\\left(f_{1} \\right)} & 0\\\\0 & 13.0 \\sin{\\left(t_{2} \\right)} & 0 & 4.25 \\sin{\\left(f_{2} \\right)}\\\\- 13.0 \\cos{\\left(t_{1} \\right)} & 0 & - 4.25 \\cos{\\left(f_{1} \\right)} & 0\\\\0 & - 13.0 \\cos{\\left(t_{2} \\right)} & 0 & - 4.25 \\cos{\\left(f_{2} \\right)}\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "Matrix([\n",
       "[ 13.0*sin(t1),             0,  4.25*sin(f1),             0],\n",
       "[            0,  13.0*sin(t2),             0,  4.25*sin(f2)],\n",
       "[-13.0*cos(t1),             0, -4.25*cos(f1),             0],\n",
       "[            0, -13.0*cos(t2),             0, -4.25*cos(f2)]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "J_in_and_pass = sp.zeros(len(F),len(in_and_pass))\n",
    "i=0\n",
    "j=0\n",
    "while i < len(F):\n",
    "    j=0\n",
    "    while j < len(in_and_pass):\n",
    "        J_in_and_pass[i,j] = sp.diff(F[i], in_and_pass[j])\n",
    "        j+=1\n",
    "    i+=1\n",
    "J_in_and_pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - 18.0625 \\sin{\\left(f_{1} - f_{2} \\right)}$"
      ],
      "text/plain": [
       "-18.0625*sin(f1 - f2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.simplify(J_out_and_pass.det())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 3052.5625 \\sin{\\left(f_{1} - t_{1} \\right)} \\sin{\\left(f_{2} - t_{2} \\right)}$"
      ],
      "text/plain": [
       "3052.5625*sin(f1 - t1)*sin(f2 - t2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.simplify(J_in_and_pass.det())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "symp_sing1 = sp.simplify(J_in_and_pass.det())\n",
    "const1 = symp_sing1.func(*[term for term in symp_sing1.args if not term.free_symbols])\n",
    "symp_sing1 = symp_sing1/const1\n",
    "sing_f_1 = sp.lambdify(diff_symb, symp_sing1, 'numpy')\n",
    "\n",
    "symp_sing2 = sp.simplify(J_out_and_pass.det())\n",
    "const2 = symp_sing2.func(*[term for term in symp_sing2.args if not term.free_symbols])\n",
    "symp_sing2 = symp_sing2/const2\n",
    "sing_f_2 = sp.lambdify(diff_symb, symp_sing2, 'numpy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_val_cartesian, max_val_cartesian = -16, 16\n",
    "min_val_revolute, max_val_revolute = -np.pi, np.pi\n",
    "v1 = np.linspace(min_val_cartesian, max_val_cartesian, int((max_val_cartesian-min_val_cartesian)/2)+1)\n",
    "# v2 = np.linspace(min_val_revolute, max_val_revolute, int((max_val_revolute-min_val_revolute)/2)+1)\n",
    "v2 = np.linspace(min_val_revolute, max_val_revolute, 5)\n",
    "\n",
    "appr = []\n",
    "\n",
    "for i in v1:\n",
    "    for j in v1:\n",
    "        for k in v2:\n",
    "            for o in v2:\n",
    "                for m in v2:\n",
    "                    for p in v2:  \n",
    "                        appr.append([i,j,k,o,m,p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-16., -14., -12., -10.,  -8.,  -6.,  -4.,  -2.,   0.,   2.,   4.,\n",
       "         6.,   8.,  10.,  12.,  14.,  16.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.14159265, -1.57079633,  0.        ,  1.57079633,  3.14159265])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180625"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(appr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_inside(x, y, d):\n",
    "    D1=(2 * (-1*(x)/(y)) * ( (x)**2/(2*y) + y/2 + (la**2-lc**2)/(2*y) ))**2 - 4*(1 + ((x)**2)/(y**2) ) * ( ( (x)**2/(2*y) + y/2 + (la**2-lc**2)/(2*y) )**2 - la**2) \n",
    "    D2=(2 * (-1*(x-d)/(y)) * ( (x-d)**2/(2*y) + y/2 + (la**2-lc**2)/(2*y) ))**2 - 4*(1 + ((x-d)**2)/(y**2) ) * ( ( (x-d)**2/(2*y) + y/2 + (la**2-lc**2)/(2*y) )**2 - la**2)     \n",
    "    if D1 >= 0 and D2 >= 0:\n",
    "        work_space.append([x,y])\n",
    "        return 1\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kinematic_eq_sing1(z):\n",
    "    x = z[0]\n",
    "    y = z[1]\n",
    "    t1 = z[2]\n",
    "    t2 = z[3]\n",
    "    f1 = z[4]\n",
    "    f2 = z[5]\n",
    "\n",
    "    F = np.zeros(6)\n",
    "    F[0] = (x-l*np.cos(t1)-L*np.cos(f1))\n",
    "    F[1] = (x-l*np.cos(t2)-L*np.cos(f2)-d)\n",
    "    F[2] = (y-l*np.sin(t1)-L*np.sin(f1))\n",
    "    F[3] = (y-l*np.sin(t2)-L*np.sin(f2))\n",
    "    F[4] = sing_f_1(x,y,t1,t2,f1,f2)\n",
    "    return F\n",
    "\n",
    "def kinematic_eq_sing2(z):\n",
    "    x = z[0]\n",
    "    y = z[1]\n",
    "    t1 = z[2]\n",
    "    t2 = z[3]\n",
    "    f1 = z[4]\n",
    "    f2 = z[5]\n",
    "\n",
    "    F = np.zeros(6)\n",
    "    F[0] = (x-l*np.cos(t1)-L*np.cos(f1))\n",
    "    F[1] = (x-l*np.cos(t2)-L*np.cos(f2)-d)\n",
    "    F[2] = (y-l*np.sin(t1)-L*np.sin(f1))\n",
    "    F[3] = (y-l*np.sin(t2)-L*np.sin(f2))\n",
    "    F[4] = sing_f_2(x,y,t1,t2,f1,f2)\n",
    "    \n",
    "    return F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Robot work area:\n"
     ]
    },
    {
     "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": [
    "# окружности в питоне легче всего задавать через сферические координаты\n",
    "# тета меняется от 0 до 2pi\n",
    "theta = np.linspace(0, 2*np.pi, 100)\n",
    "\n",
    "x1 = (la+lc)*np.cos(theta)\n",
    "x2 = (la+lc)*np.sin(theta)\n",
    "x3 = 2*lc*np.cos(theta)\n",
    "x4 = 2*lc*np.sin(theta)\n",
    "x5 = (la+lc)*np.cos(theta)+d\n",
    "x6 = (la+lc)*np.sin(theta)\n",
    "x7 = 2*lc*np.cos(theta)+d\n",
    "x8 = 2*lc*np.sin(theta)\n",
    "\n",
    "#задаю сетку для расчёта рабочей области\n",
    "gr_x = np.linspace(-20, 20, 300)\n",
    "gr_y = np.linspace(-20, 20, 300)\n",
    "# gr_x = np.linspace(-(la+lc), la+lc+d, 250)\n",
    "# gr_y = np.linspace(-(la+lc), la+lc+d, 250)\n",
    "gr_X, gr_Y = np.meshgrid(gr_x,gr_y)\n",
    "    \n",
    "fig, ax = plt.subplots(1)\n",
    "ax.plot(x1, x2)\n",
    "ax.plot(x3, x4)\n",
    "ax.plot(x5, x6)\n",
    "ax.plot(x7, x8)\n",
    "ax.set_aspect(1)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "print('Robot workspace:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#функция подсчёта площади\n",
    "def count_area_new(d_iter):\n",
    "    S_square=40*40\n",
    "    number_of_points = len(gr_x)*len(gr_y) #50 000\n",
    "    number_inside = 0\n",
    "    for i in range(len(gr_x)):\n",
    "        for j in range(len(gr_y)):\n",
    "            number_inside+=is_inside(gr_x[i], gr_y[j], d_iter)\n",
    "#             is_gran(gr_x[i], gr_y[j], d_iter)\n",
    "#     S=(S_square*number_inside/number_of_points) # /2 из-за того, что области на графике две, но в реальности она одна\n",
    "    S = S_square*len(work_space)/number_of_points\n",
    "    return S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181.79555555555555"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_area_new(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Robot work area:\n"
     ]
    },
    {
     "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": [
    "# отрисовка рабочей области\n",
    "print('Robot work area:')\n",
    "fig, ax = plt.subplots(1)\n",
    "ax.plot(x1, x2)\n",
    "ax.plot(x3, x4)\n",
    "ax.plot(x5, x6)\n",
    "ax.plot(x7, x8)\n",
    "plt.scatter(*zip(*work_space), color = 'orange', s=0.2)\n",
    "# plt.scatter(*zip(*gran), color = 'black')\n",
    "plt.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
      "  improvement from the last ten iterations.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
      "  improvement from the last five Jacobian evaluations.\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "z1 = []\n",
    "i=0\n",
    "while i < len(appr):\n",
    "    zGuess = appr[i]\n",
    "    x_tmp1 = fsolve(kinematic_eq_sing1, zGuess, xtol=tol)\n",
    "    f_tmp1 = kinematic_eq_sing1(x_tmp1)\n",
    "    if np.linalg.norm(np.array(f_tmp1)) < tol:\n",
    "        z1.append(x_tmp1)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": [
    "sing_xy1 = []\n",
    "for i in z1:\n",
    "    sing_xy1.append([i[0],i[1]])\n",
    "    \n",
    "plt.scatter(*zip(*sing_xy1), color = 'orange', s=0.3)\n",
    "plt.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "z2 = []\n",
    "i=0\n",
    "while i < len(appr):\n",
    "    zGuess = appr[i]\n",
    "    x_tmp2 = fsolve(kinematic_eq_sing2, zGuess, xtol=tol)\n",
    "    f_tmp2 = kinematic_eq_sing2(x_tmp2)\n",
    "    if np.linalg.norm(np.array(f_tmp2)) < tol:\n",
    "        z2.append(x_tmp2)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "sing_xy2 = []\n",
    "for i in z2:\n",
    "    sing_xy2.append([i[0],i[1]])\n",
    "    \n",
    "plt.scatter(*zip(*sing_xy2), color = 'orange', s=0.01)\n",
    "plt.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xMin = -20\n",
    "xMax = 20\n",
    "yMin = -20\n",
    "yMax = 20\n",
    "num_squares = 2**13\n",
    "square_size = abs(xMax-xMin)/np.sqrt(num_squares)\n",
    "ox = np.linspace(xMin, xMax, int(np.sqrt(num_squares)+1))\n",
    "oy = np.linspace(yMin, yMax, int(np.sqrt(num_squares)+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sing_squares_sing1 = []\n",
    "sing_squares_sing2 = []\n",
    "ws_new_2 = []\n",
    "\n",
    "i = 0\n",
    "while i < len(ox)-1:\n",
    "    j = 0\n",
    "    while j < len(oy)-1:\n",
    "        found1 = False\n",
    "        found2 = False\n",
    "        for s1 in sing_xy1:\n",
    "            if found1 == True:\n",
    "                break\n",
    "            if s1[0] >= ox[i]-tol:\n",
    "                if s1[0] <= ox[i+1] + tol:\n",
    "                    if s1[1] >= oy[j] - tol:\n",
    "                        if s1[1] <= oy[j+1] + tol:\n",
    "                            sing_squares_sing1.append([[ox[i], ox[i+1]],  [oy[j], oy[j+1]]])            \n",
    "                            found1 = True\n",
    "        for s2 in sing_xy2:\n",
    "            if found2 == True:\n",
    "                break\n",
    "            if s2[0] >= ox[i]-tol:\n",
    "                if s2[0] <= ox[i+1] + tol:\n",
    "                    if s2[1] >= oy[j] - tol:\n",
    "                        if s2[1] <= oy[j+1] + tol:\n",
    "                            sing_squares_sing2.append([[ox[i], ox[i+1]],  [oy[j], oy[j+1]]])            \n",
    "                            found2 = True\n",
    "        if (is_inside(ox[i]+square_size/2, oy[j]+square_size/2, d) == 1) and (found1 == False) and (found2 == False):\n",
    "            ws_new_2.append([[ox[i], ox[i+1]],  [oy[j], oy[j+1]]])  \n",
    "        j+=1\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "258\n",
      "329\n",
      "605\n"
     ]
    }
   ],
   "source": [
    "print(len(sing_squares_sing1))\n",
    "print(len(sing_squares_sing2))\n",
    "print(len(ws_new_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "plt.figure()\n",
    "currentAxis = plt.gca()\n",
    "for i in ws_new_2:\n",
    "    someX, someY = i[0][0], i[1][0]\n",
    "    currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color='Orange'))\n",
    "for i in sing_squares_sing1:\n",
    "    someX, someY = i[0][0], i[1][0]\n",
    "    currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color='Blue'))\n",
    "for i in sing_squares_sing2:\n",
    "    someX, someY = i[0][0], i[1][0]\n",
    "    currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color='Red'))\n",
    "\n",
    "currentAxis.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "fig.set_size_inches(100, 100)\n",
    "plt.savefig('sing_ws_sq_13.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEGCAYAAACO8lkDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAThElEQVR4nO3df6wlZX3H8c9nF3GNmgjhurvlRxfN2gBWt/V0o/1hKGJdiCllWxIwbWkkWW0gtkn/EEpT6Q/6g7bSpLGta9jAHwISCrJRFIFqaVoR7lakS7eUBaGuLMvFlmjTgln22z/O3Ovc88zce+6955xnzsz7ldzcOTNz5n6fYTnf83yfZ2YcEQIAoGxd7gAAAM1DcgAAJEgOAIAEyQEAkCA5AAASx+UOYBROOumk2LJlS+4wAGCq7Nu374WImKna1orksGXLFs3OzuYOAwCmiu1n6rZRVgIAJEgOAIAEyQEAkCA5AAASJAcAQILkAABIkBwAAAmSAwAg0YqL4IDs7tgkvXRE/e9bx4qVQyxv2CjtfG5ycQJDytpzsL3H9vO295fWXWP727YfKX7OzxkjMJSXjhQLx0orh1heeB/QLLnLSjdK2lGx/vqI2Fb83D3hmACg87KWlSLiAdtbcsYArMhC+UhaXCpag5u9+HiUmtAAuXsOda6w/WhRdjqhagfbu2zP2p6dm5ubdHzoqkVloBEkhkWOVfwNII8mJoe/kfRmSdskHZb0F1U7RcTuiOhFRG9mpvKOswCAVWrcbKWIWPjaZPtTkj6XMRxgoJQ0IfOlJkpMyKRxPQfbm0svL5S0v25fYCJylnkoMSGTrD0H27dIOlvSSbYPSfqYpLNtb5MUkp6W9KFsAQJAR+WerXRJxeobJh4IMChHKakOJSZk0LiyEtAITUkMZU2MCa1FcgAAJBo3WwnIqknlpCqUmDAh9ByAsiYnhrJpiRNTi54DuqPcKyh/8256b6FOVS9ivi30LLBG9BzQHeUEULc8jaraMu1tQnYkBwBAgrIS2q22ZLRuYHnwJnorfGjPwvJq37fSYwzRFgavsQYkB7RbbXml7kE8kj4Q44pmdBZu8y0t2RaJEhNWhbISACBBzwHtM8zsow0bFy+XZzElx2hIWalcHqqKeXB9GSUmrBDJAe2zVGKoKhlVfVjWPtRnueUVPkN6Jccox1T3AT+/flHZqYQSE4ZEWQkAkKDngPZZVFoZKMvMG6ps1ECDz5seXJ4vGw1zDoAlkBzQPsPU1IcqGzXZMqUnxhWwRpSVAAAJeg5on7qS0aKZOg0vH61a8X1vqHMA1KPngPapKxnVlpLapGjXUOcAqEfPAe2x7PUNpe9CddcDTLuFAeclekZc84AhkBzQHst+2Jc+LMsfinXXBEyT5PqNIXpGbUyOGBnKSgCABD0HTJ/aaxSGUFlSmfbB6dJ3vJU+uGjwuglKTShk7TnY3mP7edv7S+tOtH2v7SeK3yfkjBENNIqB5VYNTo9iwLlqIBtdlrusdKOkHQPrrpR0f0RslXR/8RoAMEFZy0oR8YDtLQOrL5B0drF8k6SvSProxIJCM438Oc/LPexnmoyyLet4DjUk5e85VNkYEYclqfj9xszxoAlGXu6Yxltm1BllW47xHGpIamZyGIrtXbZnbc/Ozc3lDgcAWqWJs5WO2N4cEYdtb5b0fNVOEbFb0m5J6vV6U/BcR6zYyEtJZS26IG7RnVZHUFaSfnAMLpjrrCYmh72SLpX0J8Xvu/KGg2zG+oHdggviKp91PYKyUpVpTp5YldxTWW+R9FVJP2L7kO3L1E8K77X9hKT3Fq8BABOUe7bSJTWb3jPRQNAcYy0lDagqmUxNiWkNF74NddyKHgQlpk5pYlkJXZbjg7nq2cyNLy+N606r3JMJfVM7WwkAMD4kBzRLMvNmNcsrfF/lc5Ub/r9GOebKc7bKc7dh48Btvyv25TnUnUBZCc3SmFp2Qy+Mq5qh1JhzhjZp+NcjIJNV92BW29sZ4hh8Y8cE0XMAqvBtHB1HzwEAkKDnAIzCwrUGpdtXlK8HqHtAEdcMoKHoOQCjsPDBX3P9Qd3DhbhmAA1FcgAAJEgOwChUXRuw7LUIg+uB5mDMARiF5cYNGFfAlKHnAABI0HMAxqU8Q4lZSZgy9ByAcamdrQQ0H8kBAJAgOQDjUjtbCWg+xhyAcWGMAVOMngMAIEFyAAAkSA4AgATJAQCQIDkAABKNna1k+2lJ35P0iqSjEdHLGxEAdEdjk0PhZyPihdxBAEDXUFYCACSanBxC0pds77O9a3Cj7V22Z23Pzs3NZQgPANqrycnhpyLixyWdJ+ly2+8ub4yI3RHRi4jezMxMnggBoKUamxwi4tni9/OS7pS0PW9EANAdjUwOtl9r+/Xzy5J+TtL+vFEBQHc0dbbSRkl32pb6Md4cEV/MGxIAdEcjk0NEPCXp7bnjAICuamRZCQCQF8kBAJAgOQAAEiQHAECC5AAASJAcAAAJkgMAIEFyAAAkSA4AgATJAQCQIDkAABIkBwBAguQAAEiQHAAACZIDACBBcgAAJEgOAIAEyQEAkCA5AAASJAcAQILkAABIkBwAAAmSAwAg0djkYHuH7cdtH7R9Ze54AKBLGpkcbK+X9AlJ50k6U9Ilts/MGxUAdMeyycH2FbZPmEQwJdslHYyIpyLi+5JulXTBhGMAgM4apuewSdLDtm8rSj0ed1CSTpb0rdLrQ8W6BbZ32Z61PTs3NzeBkACgO5ZNDhHxO5K2SrpB0q9JesL2H9l+8xjjqkpAMRDX7ojoRURvZmZmjKEAQPcMNeYQESHpueLnqKQTJN1u+7oxxXVI0qml16dIenZMfwsAMGCYMYeP2N4n6TpJ/yTpRyPi1yW9Q9IvjimuhyVttX267eMlXSxp75j+FgBgwHFD7HOSpJ0R8Ux5ZUQcs/3+cQQVEUdtXyHpHknrJe2JiMfG8bcAAKllk0NE/O4S2w6MNpxFx75b0t3jOj4AoF4jr3MAAORFcgAAJEgOAIAEyQEAkCA5AAASJAcAQILkAABIkBwAAAmSAwAgQXIAACRIDgCABMkBAJAgOQAAEiQHAECC5AAASJAcAAAJkgMAIEFyAAAkSA4AgATJAQCQIDkAABIkBwBAguQAAEg0LjnYvsb2t20/UvycnzsmAOia43IHUOP6iPjz3EEAQFc1rucAAMivqcnhCtuP2t5j+4SqHWzvsj1re3Zubm7S8QFAqzkiJv9H7fskbarYdLWkByW9ICkk/YGkzRHxwaWO1+v1YnZ2duRxAkCb2d4XEb2qbVnGHCLi3GH2s/0pSZ8bczgAgAGNKyvZ3lx6eaGk/bliAYCuauJspetsb1O/rPS0pA/lDQcAuqdxySEifiV3DADQdY0rKwEA8iM5AAASJAcAQILkAABING5AGmiNOzZJLx3pL2/YKO18Lm88wArQcwDGZT4xDC4DU4DkAABIkByAcdmwsXoZmAKMOQDjwhgDphg9BwBAgp4DMAoLM5PWSTrWX1eeoVSeuVS3D9Ag9ByAUVj44D9WsW5guW4foEFIDgCABMkBGIWF2UjrKtYNLNftAzQIYw7AKCw3bsC4AqYMPQcAQIKeA1ClbnbRsssr2XeFx2BmEyaIngNQpW520bLLK9l3hcdgZhMmiOQAAEhQVkKzrLqcU15e4fsqyzXlfZui5rtc5Tlb5bmbnz01eEEf5a3OITmgWVZdzikvr/B9leWapiUGqTamynO2ynM3zPmnvNUJJAc0y4aNk//wKV9rsOhbeAPd7P7v8rf3kZ6z+d7JEsmRazM6IUtysH2RpGsknSFpe0TMlrZdJekySa9I+khE3JMjRmRSLlfMfxCOywciXdfkxFBWjnOk52yJpFB1vtBauXoO+yXtlPTJ8krbZ0q6WNJZkn5I0n223xIRr0w+RADorizJISIOSJKdfMu5QNKtEfGypG/aPihpu6SvTjZCNMJYS0ylwd2ml5LqVJWY1jyQXlNWopTUOU0bczhZ0oOl14eKdeiisZaYWjTAWjuIvBoD76eU1FljSw6275O0qWLT1RFxV93bKtZV/uu0vUvSLkk67bTTVhUjAKDa2JJDRJy7ircdknRq6fUpkp6tOf5uSbslqdfr8fWm7UZeYlo3sNzEqavDGmVb1kkbZvrnmlJSpzWtrLRX0s22P67+gPRWSQ/lDQmNMPISU918/2k0yrYc4wI3SMp0+wzbF9o+JOldkj5v+x5JiojHJN0m6d8kfVHS5cxUAoDJyzVb6U5Jd9Zsu1bStZONCFNlUYlplWWU5OE709x7GHh40KrKbwO3z0DnNa2sBCyvruwxTLmpcvbNNCcGaVH88+dm2NIbs5FQg7uyAgAS9BzQHsuWVFpw4Vud5IK4IUpllJCwBJID2mPZkkqLLnyrs9Au7pGEtaGshPZJBpsr1tfts7BctW6Y5dW+b0THmG/XMOcAWAI9B7QP8/Q5B1gzeg7opmUfajOhBw2N+hhtLZdh4ug5oH3qHjVa+4CcukdnNtkSj/CUhjsHwBJIDmiful5B3QNyqoz7QUOrNexg8jDnAFgCZSUAQIKeA9pnqesdqh6QUy7BzK8fquxUtbySfVd4jLpnXde1pQqzlTAkkgPaZ5g7uJY/QKuWm16XXy7+QVzbgBWirAQASNBzQLvVlpiWeEDOzdbIS0IjP8aQbZEoJWFVSA5ot9oS03IPyGn6dQ5afh9KSVgDykoAgATJAd1Rd2+laS+7VLVl2tuE7CgroTvqZiCN/PnUE1JVNmr6LCtMDXoOAIAEyQEom5ZyzLTEialFWQkoW+kzmCeNGUiYEHoOQJUmfjNvYkxoLXoOQJUmDVLTW0AGWXoOti+y/ZjtY7Z7pfVbbP+f7UeKn7/NER8AdF2unsN+STslfbJi25MRsW3C8QD1lrrL6yT+NpBBluQQEQckyW7ooB9QlqPERCkJmTVxQPp021+3/Q+2f6ZuJ9u7bM/anp2bm5tkfADQemPrOdi+T9Kmik1XR8RdNW87LOm0iPiO7XdI+qztsyLiu4M7RsRuSbslqdfr8TULk1H7EKBRKI5HKQkNMLbkEBHnruI9L0t6uVjeZ/tJSW+RNDvi8IDVqbs9xVrKTZSQ0ECNKivZnrG9vlh+k6Stkp7KGxUAdE+uqawX2j4k6V2SPm/7nmLTuyU9avsbkm6X9OGI+K8cMQIrslAKGnzwzjLLlJDQUI6Y/i5tr9eL2VkqTwCwErb3RUSvalujykoAgGYgOQAAEiQHAECC5AAASJAcAAAJkgMAIEFyAAAkSA4AgEQrLoKzPSfpmeLlSZJeyBhOTl1uu9Tt9ne57VK327+Wtv9wRMxUbWhFciizPVt3xV/bdbntUrfb3+W2S91u/7jaTlkJAJAgOQAAEm1MDrtzB5BRl9sudbv9XW671O32j6XtrRtzAACsXRt7DgCANSI5AAASrUgOtv/M9r/bftT2nbbfUNp2le2Dth+3/b6ccY6L7YtsP2b7mO3ewLYutH9H0b6Dtq/MHc+42d5j+3nb+0vrTrR9r+0nit8n5IxxXGyfavvLtg8U/+Z/o1jflfZvsP2Q7W8U7f+9Yv3ptr9WtP8zto9f699qRXKQdK+kt0bE2yT9h6SrJMn2mZIulnSWpB2S/nr+GdUts1/STkkPlFd2of1Fez4h6TxJZ0q6pGh3m92o/n/Psisl3R8RWyXdX7xuo6OSfisizpD0TkmXF/+9u9L+lyWdExFvl7RN0g7b75T0p5KuL9r/35IuW+sfakVyiIgvRcTR4uWDkk4pli+QdGtEvBwR35R0UNL2HDGOU0QciIjHKzZ1of3bJR2MiKci4vuSblW/3a0VEQ9IGny2+gWSbiqWb5L0CxMNakIi4nBE/Eux/D1JBySdrO60PyLif4qXryp+QtI5km4v1o+k/a1IDgM+KOkLxfLJkr5V2naoWNcVXWh/F9o4jI0RcVjqf4BKemPmeMbO9hZJPybpa+pQ+22vt/2IpOfVr5o8KenF0hfkkfw/cNxaDzAptu+TtKli09URcVexz9Xqdzs/Pf+2iv2ncu7uMO2velvFuqls/xK60EYMsP06SX8n6Tcj4rt21T+DdoqIVyRtK8ZW75R0RtVua/07U5McIuLcpbbbvlTS+yW9J35w8cYhSaeWdjtF0rPjiXC8lmt/jda0fwldaOMwjtjeHBGHbW9W/1tlK9l+lfqJ4dMRcUexujPtnxcRL9r+ivpjL2+wfVzRexjJ/wOtKCvZ3iHpo5J+PiL+t7Rpr6SLbb/a9umStkp6KEeMmXSh/Q9L2lrM1jhe/QH4vZljymGvpEuL5Usl1fUmp5r7XYQbJB2IiI+XNnWl/TPzszFtv0bSueqPu3xZ0i8Vu42k/a24Qtr2QUmvlvSdYtWDEfHhYtvV6o9DHFW/C/qF6qNML9sXSvorSTOSXpT0SES8r9jWhfafL+kvJa2XtCcirs0c0ljZvkXS2erfqvmIpI9J+qyk2ySdJuk/JV0UEYOD1lPP9k9L+kdJ/yrpWLH6t9Ufd+hC+9+m/oDzevW/3N8WEb9v+03qT8Y4UdLXJf1yRLy8pr/VhuQAABitVpSVAACjRXIAACRIDgCABMkBAJAgOQAAEiQHAECC5AAASJAcgDGw/RPF80U22H5tce/9t+aOCxgWF8EBY2L7DyVtkPQaSYci4o8zhwQMjeQAjElxr6eHJb0k6SeLu2kCU4GyEjA+J0p6naTXq9+DAKYGPQdgTGzvVf9maKdL2hwRV2QOCRja1DzPAZgmtn9V0tGIuLl4zvU/2z4nIv4+d2zAMOg5AAASjDkAABIkBwBAguQAAEiQHAAACZIDACBBcgAAJEgOAIDE/wOYMk7I4r3c2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "currentAxis = plt.gca()\n",
    "for i in ws_new_2:\n",
    "    someX, someY = i[0][0], i[1][0]\n",
    "    currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color='Orange'))\n",
    "\n",
    "currentAxis.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "fig.set_size_inches(100, 100)\n",
    "plt.savefig('ws_sq_13.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws_components = []\n",
    "ws_to_check = ws_new_2.copy()\n",
    "\n",
    "while len(ws_to_check) > 0:\n",
    "    curr_component = []\n",
    "    init_box = ws_to_check.pop(0)\n",
    "    curr_component.append(init_box)\n",
    "    for i in curr_component:\n",
    "        for j in ws_to_check:\n",
    "            if np.sqrt( (i[0][0]+square_size/2-(j[0][0]+square_size/2))**2 + (i[1][0]+square_size/2-(j[1][0]+square_size/2))**2 ) < (square_size+square_size*tol):\n",
    "                curr_component.append(j)\n",
    "                ws_to_check.remove(j)\n",
    "    ws_components.append(curr_component)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ws_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "812.3523197174072\n"
     ]
    }
   ],
   "source": [
    "end = time.time()\n",
    "print(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "plt.figure()\n",
    "currentAxis = plt.gca()\n",
    "for i in ws_components:\n",
    "    rgb = (random.random(), random.random(), random.random())\n",
    "    for j in i:\n",
    "        someX, someY = j[0][0], j[1][0]\n",
    "        currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color=rgb))\n",
    "currentAxis.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "fig.set_size_inches(100, 100)\n",
    "plt.savefig('ws_components_sq_13.png', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": [
    "plt.figure()\n",
    "currentAxis = plt.gca()\n",
    "for i in ws_components:\n",
    "    rgb = (random.random(), random.random(), random.random())\n",
    "    for j in i:\n",
    "        someX, someY = j[0][0], j[1][0]\n",
    "        currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color=rgb))\n",
    "for i in sing_squares_sing1:\n",
    "    someX, someY = i[0][0], i[1][0]\n",
    "    currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color='Blue'))\n",
    "for i in sing_squares_sing2:\n",
    "    someX, someY = i[0][0], i[1][0]\n",
    "    currentAxis.add_patch(Rectangle((someX, someY), square_size, square_size, alpha=1,color='Red'))\n",
    "\n",
    "currentAxis.axis('equal')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "fig.set_size_inches(100, 100)\n",
    "plt.savefig('ws_component_sing_sq_13.png', dpi=500)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}