Bonairo puzzle .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eba21227",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random \n",
    "import pandas as pd\n",
    "from pyomo.environ import *\n",
    "from shapely.geometry import LineString\n",
    "import itertools\n",
    "import os\n",
    "from pyomo import environ as pe\n",
    "\n",
    "os.environ['NEOS_EMAIL'] = 'XXXXXX@gmail.com' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7f0fc362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(1, 1): -100,\n",
       " (1, 2): -100,\n",
       " (1, 3): -100,\n",
       " (1, 4): -100,\n",
       " (1, 5): 0,\n",
       " (1, 6): -100,\n",
       " (1, 7): 1,\n",
       " (1, 8): -100,\n",
       " (1, 9): 0,\n",
       " (1, 10): -100,\n",
       " (1, 11): -100,\n",
       " (1, 12): -100,\n",
       " (2, 1): -100,\n",
       " (2, 2): -100,\n",
       " (2, 3): 1,\n",
       " (2, 4): -100,\n",
       " (2, 5): -100,\n",
       " (2, 6): -100,\n",
       " (2, 7): 0,\n",
       " (2, 8): 0,\n",
       " (2, 9): -100,\n",
       " (2, 10): -100,\n",
       " (2, 11): 1,\n",
       " (2, 12): 0,\n",
       " (3, 1): -100,\n",
       " (3, 2): -100,\n",
       " (3, 3): 1,\n",
       " (3, 4): -100,\n",
       " (3, 5): -100,\n",
       " (3, 6): -100,\n",
       " (3, 7): -100,\n",
       " (3, 8): -100,\n",
       " (3, 9): -100,\n",
       " (3, 10): -100,\n",
       " (3, 11): -100,\n",
       " (3, 12): -100,\n",
       " (4, 1): -100,\n",
       " (4, 2): -100,\n",
       " (4, 3): -100,\n",
       " (4, 4): -100,\n",
       " (4, 5): -100,\n",
       " (4, 6): 1,\n",
       " (4, 7): -100,\n",
       " (4, 8): 0,\n",
       " (4, 9): 0,\n",
       " (4, 10): -100,\n",
       " (4, 11): 0,\n",
       " (4, 12): -100,\n",
       " (5, 1): -100,\n",
       " (5, 2): -100,\n",
       " (5, 3): -100,\n",
       " (5, 4): -100,\n",
       " (5, 5): -100,\n",
       " (5, 6): -100,\n",
       " (5, 7): -100,\n",
       " (5, 8): -100,\n",
       " (5, 9): -100,\n",
       " (5, 10): -100,\n",
       " (5, 11): -100,\n",
       " (5, 12): 1,\n",
       " (6, 1): -100,\n",
       " (6, 2): -100,\n",
       " (6, 3): -100,\n",
       " (6, 4): -100,\n",
       " (6, 5): -100,\n",
       " (6, 6): -100,\n",
       " (6, 7): 1,\n",
       " (6, 8): -100,\n",
       " (6, 9): 0,\n",
       " (6, 10): -100,\n",
       " (6, 11): 1,\n",
       " (6, 12): 1,\n",
       " (7, 1): 0,\n",
       " (7, 2): 1,\n",
       " (7, 3): 1,\n",
       " (7, 4): -100,\n",
       " (7, 5): -100,\n",
       " (7, 6): -100,\n",
       " (7, 7): -100,\n",
       " (7, 8): -100,\n",
       " (7, 9): -100,\n",
       " (7, 10): -100,\n",
       " (7, 11): -100,\n",
       " (7, 12): -100,\n",
       " (8, 1): -100,\n",
       " (8, 2): -100,\n",
       " (8, 3): -100,\n",
       " (8, 4): -100,\n",
       " (8, 5): -100,\n",
       " (8, 6): -100,\n",
       " (8, 7): -100,\n",
       " (8, 8): 0,\n",
       " (8, 9): -100,\n",
       " (8, 10): 0,\n",
       " (8, 11): 0,\n",
       " (8, 12): -100,\n",
       " (9, 1): -100,\n",
       " (9, 2): -100,\n",
       " (9, 3): -100,\n",
       " (9, 4): 1,\n",
       " (9, 5): 1,\n",
       " (9, 6): -100,\n",
       " (9, 7): -100,\n",
       " (9, 8): 0,\n",
       " (9, 9): -100,\n",
       " (9, 10): -100,\n",
       " (9, 11): -100,\n",
       " (9, 12): -100,\n",
       " (10, 1): -100,\n",
       " (10, 2): -100,\n",
       " (10, 3): -100,\n",
       " (10, 4): -100,\n",
       " (10, 5): -100,\n",
       " (10, 6): -100,\n",
       " (10, 7): -100,\n",
       " (10, 8): -100,\n",
       " (10, 9): -100,\n",
       " (10, 10): -100,\n",
       " (10, 11): 0,\n",
       " (10, 12): 0,\n",
       " (11, 1): 1,\n",
       " (11, 2): -100,\n",
       " (11, 3): -100,\n",
       " (11, 4): 0,\n",
       " (11, 5): -100,\n",
       " (11, 6): 1,\n",
       " (11, 7): -100,\n",
       " (11, 8): -100,\n",
       " (11, 9): 0,\n",
       " (11, 10): -100,\n",
       " (11, 11): -100,\n",
       " (11, 12): -100,\n",
       " (12, 1): -100,\n",
       " (12, 2): -100,\n",
       " (12, 3): -100,\n",
       " (12, 4): -100,\n",
       " (12, 5): 1,\n",
       " (12, 6): 1,\n",
       " (12, 7): -100,\n",
       " (12, 8): 1,\n",
       " (12, 9): -100,\n",
       " (12, 10): -100,\n",
       " (12, 11): -100,\n",
       " (12, 12): 0}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D =['0   0 1 0 ', \n",
    "'        00',\n",
    "' 00   1   ',\n",
    "'     1  11',\n",
    "'0  1  11 0',\n",
    "'1 01  1   ',\n",
    "' 0  1  0 0',\n",
    "'   0 0 0 1',\n",
    "' 00       ',\n",
    "'  00   0  ',]\n",
    "\n",
    "D = [\n",
    "'    0 1 0   ',\n",
    "'  1   00  10',\n",
    "'  1         ',\n",
    "'     1 00 0 ',\n",
    "'           1',\n",
    "'      1 0 11',\n",
    "'011         ',\n",
    "'       0 00 ',\n",
    "'   11  0    ',\n",
    "'          00',\n",
    "'1  0 1  0   ',\n",
    "'    11 1   0']\n",
    "\n",
    "data = {}\n",
    "counter = 0\n",
    "Nr , Nc= len(D), len(D)\n",
    "for r in range(Nr):\n",
    "    for c in range(Nc):\n",
    "        s= D[r][c]\n",
    "        if s in ['0','1']:\n",
    "            data[r+1,c+1] =  int(s)            \n",
    "        else:\n",
    "            data[r+1,c+1] =  -100            \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "812cd368",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64db34a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(L, LW=1):\n",
    "    if LW > 1:\n",
    "        LW =8\n",
    "        plt.plot([L[0][0],L[1][0]], [L[0][1],L[1][1]], lw=LW)\n",
    "    else:\n",
    "        plt.plot([L[0][0],L[1][0]], [L[0][1],L[1][1]], lw=2, c= 'k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ae24a197",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = 2** (Nc+1)\n",
    "model = AbstractModel()\n",
    "\n",
    "model.r = RangeSet(Nr)\n",
    "model.c = RangeSet(Nc)\n",
    "\n",
    "\n",
    "model.U = Var(model.r,model.c,bounds=(0,1), within=Binary)\n",
    "model.Vr = Var(model.r,bounds=(1,2**Nc),initialize=1, within=Reals)\n",
    "model.Vc = Var(model.c,bounds=(1,2**Nc),initialize=1, within=Reals)\n",
    "\n",
    "model.diff_c = Var(model.c,model.c,bounds=(0,1),initialize=0, within=Binary)\n",
    "model.diff_r = Var(model.r,model.r,bounds=(0,1),initialize=0, within=Binary)\n",
    "\n",
    "\n",
    "def rule_C1(model,c):\n",
    "    return sum( model.U[r,c] for r in model.r) == (Nc/2)\n",
    "model.C1 = Constraint(model.c, rule=rule_C1)\n",
    "\n",
    "def rule_C2(model,r):\n",
    "    return sum( model.U[r,c] for c in model.c) == (Nr/2)\n",
    "model.C2 = Constraint(model.r, rule=rule_C2)\n",
    "\n",
    "\n",
    "def rule_C3A(model,c,r):\n",
    "    if r+2<=Nr:\n",
    "        return model.U[r,c]+ model.U[r+1,c] + model.U[r+2,c] <= 2\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C3A = Constraint(model.c,model.r, rule=rule_C3A)\n",
    "\n",
    "\n",
    "def rule_C3B(model,c,r):\n",
    "    if r+2<=Nr:\n",
    "        return model.U[r,c]+ model.U[r+1,c] + model.U[r+2,c] >= 1\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C3B = Constraint(model.c,model.r, rule=rule_C3B)\n",
    "\n",
    "\n",
    "def rule_C4A(model,c,r):\n",
    "    if c+2<=Nc:\n",
    "        return model.U[r,c]+ model.U[r,c+1] + model.U[r,c+2] <= 2\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C4A = Constraint(model.c,model.r, rule=rule_C4A)\n",
    "\n",
    "\n",
    "def rule_C4B(model,c,r):\n",
    "    if c+2<=Nc:\n",
    "        return model.U[r,c]+ model.U[r,c+1] + model.U[r,c+2] >= 1\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C4B = Constraint(model.c,model.r, rule=rule_C4B)\n",
    "\n",
    "def rule_C5(model,c):\n",
    "    return sum( (2**(r-1))*model.U[r,c] for r in model.r) == model.Vc[c]\n",
    "model.C5 = Constraint(model.c, rule=rule_C5)\n",
    "\n",
    "\n",
    "def rule_C6(model,r):\n",
    "    return sum( (2**(c-1))*model.U[r,c] for c in model.c) == model.Vr[r]\n",
    "model.C6 = Constraint(model.c, rule=rule_C6)\n",
    "\n",
    "\n",
    "def rule_C7A(model,c1,c2):\n",
    "    if c1>c2:\n",
    "        return model.Vc[c1]-model.Vc[c2] >= 1 - M*(1-model.diff_c[c1,c2])\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C7A = Constraint(model.c,model.c, rule=rule_C7A)\n",
    "\n",
    "\n",
    "def rule_C7B(model,c1,c2):\n",
    "    if c1>c2:\n",
    "        return model.Vc[c1]-model.Vc[c2] <= -1 + M*model.diff_c[c1,c2]\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C7B = Constraint(model.c,model.c, rule=rule_C7B)\n",
    "\n",
    "\n",
    "def rule_C8A(model,r1,r2):\n",
    "    if r1>r2:\n",
    "        return model.Vr[r1]-model.Vr[r2] >= 1 - M*(1-model.diff_r[r1,r2])\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C8A = Constraint(model.r,model.r, rule=rule_C8A)\n",
    "\n",
    "\n",
    "def rule_C8B(model,r1,r2):\n",
    "    if r1>r2:\n",
    "        return model.Vr[r1]-model.Vr[r2] <= -1 + M*model.diff_r[r1,r2]\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C8B = Constraint(model.r,model.r, rule=rule_C8B)\n",
    "\n",
    "\n",
    "def rule_OF(model):\n",
    "    return  sum(model.U[r,c] for r in model.r for c in model.c) \n",
    "model.obj1 = Objective(rule=rule_OF, sense=minimize)\n",
    "instance = model.create_instance()  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "54d10feb",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SolverFactory('cbc')\n",
    "opt.options = {'sec': 1600, 'threads': 4, 'ratio': 0.4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ea6e8d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for r in instance.r:\n",
    "    for c in instance.c:\n",
    "        if data[r,c]>=0:\n",
    "            instance.U[r,c].fix(data[r,c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "15ebe5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is feasible and optimal and nothing more\n",
      "OF=  72.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "results = opt.solve(instance)\n",
    "\n",
    "from pyomo.opt import SolverStatus, TerminationCondition\n",
    "if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n",
    "     print (\"this is feasible and optimal and nothing more\")\n",
    "elif results.solver.termination_condition == TerminationCondition.infeasible:\n",
    "     print (\"do something about it? or exit?\")\n",
    "else:\n",
    "     print ('ERROR ' + str(results.solver))\n",
    "print('OF= ', value(instance.obj1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8c04ae2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9EamLnQkXyfgZcvVkv7xSiv473he5W09mNEYTZjTW7X7nxSYf/aelIxL8lwIeupQCmrkmfMwVo+k/rRwkXsaE6PnP11wx59h/D1fHfhIU0wJidMsaHGpaJ536PK7N2Llr+u+5YJ8W6CNbloPLy5gQPf9p6YgE/82hM61j9ytXjGY+Zx/zsWv6TyvPt5cxgZ7/fM3H7oH/cudjN8a83hjzPmPMsTHmJPXk6e8vGGO+zxjzIWPMJ5PX7zXGfPrcxuRhZweGw2xlh0Nbfum0doCMOgyT8mmlFP339A5IRi0Z2vJp8DIm0POflg4E/+XRGSHtmqc/DLwGuAe8EkBEzKnvfwT4DuAvgf8Xu+7U3wD+VxH5tml1Oztj11wz0cc1TzX9p7WWppcxgZ7/fF3z1BP/5T5jF5G3JhX8xJjKHwG+NXm7KSJb2LVOAb7ZGOM6t+Z4Wq38dRiTrh41LQc6mHT1aPrvGQdamNn1eBkT6PlPSweC//LojMHFBKUV7GkhwG8mr+9LXi9g16BaPJ1Ovv/2YC/nut0l0uqQ72wd7O2YZbIJuNfJdxYD9jL13gwtL2MCPf9p6UDwXx6dMbiYoPQ5p/7+q+T1Y6c++6zRAsaY68B1gKtXr3Lr1q3cP2Kl36eYuxY46vU4mPF7tLRWVvoUHQgdHfU4OJisA8r+u9KnGE3dJZ3WoMfBk5O1fIwJUPSfkg4E/+XRGYeLjv3PT/39EuCjwItPffaR0QIichO4CfYee61Wy/8rymW4fTt3NcVKhZm/R02rDDjQKS6TTdiZds860CpVqF2bouVlTKDnPy0dCP7LozMGF7diPoh9qArwpcnrlyev94HfcqAxm2rVPjzJQxzbZbeWRqvKg7tcmYWwy3vNklL038WqfSiUBxPb5dim4WVMoOc/LR0I/sujM65YylExrwNehz2FfFXy8buT17cAbwXezPNHxdwQkf95Wt1hVEwYFZOJMCrm3I/qOEPw39w6uUfFANewI11edeqzNyTbi7Ed+w9gz9y/KXn9l8CbUtafn1IJ1tftk/EsGAMbG+kSC6lplbD/IzPqYIANUiUG0/TfhRJcymnXpY3ZnYWXMYGe/7R0IPgvj864kmHmacLSzjIMM0+fR5h56sXMyecR/DeXjosz9vPB2ho0m7aR56FQsOXSBoWq1hrQxHbScwkl5ZbRJiBeg1LTBu88mIItl7az8DIm0POflg4E/+XRGWVSEhmtLWR3TC0kIbtjyO74PM5xdsKJBP+F7I4TabdFGg2btzmOzwbEST7nRsPud2602iLSEJt3PZazrjzJx95I9ssrpei/47bI3UaSpzoeyXJ3kqe6Yfc7Lzb56D8tHZHgvxRM69j9usc+jsNDO8242+Wo16NYqdghUZub7ldgUdM6xKYJ6HJ01KNYrGCHNG7ifAUlTf/dP0xWlulyNOhRLFXsUK/Lm25XsPEyJtDzn5YOBP9NYdo9dn/P2Mewu7vrnZaPNmlq+WiTppaPNmlq5dFhyhm7Xw9PA4FAIBA69kAgEPCN0LEHAoGAZ4SOPRAIBDwjdOyBQCDgGaFjDwQCAc9wkY99ubk/SMaMdli50rc5ki9W4fKW+zG3WlqDgR3b2+mw0u/bXNbVKmxtLWBsr6L/1OwaYOcBdFhZ6WOTllaBLZzPA1D032AwoNVq0el06Pf7lMtlqtUqW1tbXHXoPy0dwEv/qcT5pHGQWtvCxrEf74vcrSezvKIJs7zqdr/zorW/L1Kv21l3UTR+Nl69bvc7Lzap2rUvInWxM3QjGT9zt57slxNF/+3v70u9XpcoiiSKIgGe2+I4liiKpF6vy35O/2npiIiX/nMd5zx0KQU081r4mOvEy7wgirl2FP1348YNKRQKYow50yGNbsYYKRQKciOj/7R0RMRL/y0izh+ujv25oJgWECNb1uDQ0joJimkBMbpl7QQ1/adm10mnPk9oZuzcFf130ilN65BGtyydk5aOiHjpv0XF+bSO3a9cMZq5o33MJ+5l7m3FfPaK/mu329RqNY4z+K9QKLC3t8fLU/hPSwfw0n+LjHOVfOzGmLIx5meNMX9mjPmEMeYDxphvcFV/Kp7eARlmKytDW37ZtHZ2YJhRZzi05dOi6T81u3aAjDoMk/IpUfTfzs4Ow4z+Gw6H7KT0n5YO4KX/VI/fUzg5YzfGfCbQwS7QuY9d4Pr12JWUv0FE/t2kss7O2DXXZ/RxzU4v17dUXDNW0X+DwYDHHnuMT+TwXxRF3LlzZ+poDy0dwEv/LTrONc7Yvxx7BAF8tYi8EbiZvP9eRxrTeabloBKTrh4trZYDHWPS1aPpPzW7HOhg0tWj6L+WA/8ZY2bWo6UDeOk/1eN3BFcd++l/SV9ijHkJ8EXJ+7+VvF8s9zr5/tuDvZy7110erU4n3397sJdz3SWyCRTt6pDvbB3s7Zjl8l+n08l1tgn2dkJ3hv+0dAAv/ad6/I7gaoLSe4FfA14NvGfM9y8B/urkjTHmOnAd4OrVq9y6dSv3D1i50qcY5a6Go0GPgyen/x4trZV+n2J+GY56PQ5m+FjVf0p2raz0KToQOjrqcXAwWQd0/dfv9/MLAb1eb+qxp6UDfvpP8/gdxUnHLiL3jTFfBdSBa8CzwKewqynfBz48sv9Nkls1TzzxhNRqtfw/4qkyPHs7dzXFUoXatRm/R0urXIbbDnQqFWb6WNN/anaVAQc6xeXyX7lc5rYD/1Vm+E9LB/DSf6rH7wguc8VcEJGfF5F/Abwd+Jrk812RvNdYKbhYtQ9P8mBiu0TVsmhVq/bhSR7i2C4lNgtN/6nZVcU+/MwlhF12cAaK/qtWq0Q5/RfHMasz/KelA3jpP9XjdwRn49iNMb8C/DXwFPBKYAX4OPAVIvK7k8qFUTFhVEwmwqiYMCpmlCXznw+jYgB+B/jvsJmUPhf4JeDLpnXqTrlQgkvr2FEMWTBwaSNdYiEtrVIJ1tftk/FMMgY2NtIlFtL0n5pdJSCnTWyQKjGYov9KpRLr6+uYjP4zxrCxsTGzs9XSAbz0n+rxO4Kzjl1EtkXk80TkRSLy2WKzKc3/ODcPj2zby7EsmNiWXzat7W17OZaFOLbl06LpPzW7trG3UzIJJeVToui/7e1t4oz+i+OY7ZT+09IBvPSf6vF7Cr/yscdrUGra6cXzYAq2XNrp8Jpaa2vQbNrpxfNQKNhyaadzg67/1Oxawz7Dn1OHQlJuOf23trZGs9mkMKf/CoUCzWYz9TR/LR3AS/+pHr+nmZRERmsL2R1TErI7huyO46wK2R3Phf9CdkdXHLdF7jaSfM7xSDa4k3zODbvfedFqt0UaDZu3OY7PBsRJPudGw+53XmxStastIg2xeddjORuKJ/nYG8l+OVH0X7vdlkajIVEUSRzHZzqkk3zijUZD2jn9p6UjIl76z3WcT+vY/cruOI77h8kKLF2OBj2KpYodEnV5cwErKClpHR7aacbdLke9HsVKxQ6J2txcwApKiv5Ts+sQmyagy9FRj2Kxgh3SuIn7FZT0/Hd4eEir1aLb7dLr9ahUKqyurrK5uel0BSAtHcBL/7mK82mjYvw9Yx/D7u6ud1o+2qSp5aNNmlo+2qSplUeHKWfsfj08DQQCgUDo2AOBQMA3QsceCAQCnhE69kAgEPCM0LEHAoGAZ4SOPRAIBDzD1UIby8tgYMeMdjqs9Ps2R3K1Cltb7sd8a2ndHyRjezusXOnbXNYXq3B5y/3Yck3/adnlq/98jD8v22qAnUfRYWWlj103oIrNn+hIZ9I4SK1tYePY9/dF6nU7myuKxs/yqtftfudF63hf5G49mY0XTZiNV7f7nRebNO3y1X8+xp+XbbUvNjdilGzjZj7Xk/1mw0OXUkAzr4qWlpf5WxTt8tV/Psafl23lPlfRw9WxnzTUtEYa3bI2mJbWc4E+LchHtqwBr+k/Lbt89Z+P8edlW5106lO7wpFtduc+rWP3K1dMuw21Ghwfz1+2UIC9vfRpMrW0hm24UwPJoGMK8Ohe+nSmmv7TsstX//kYf162VRuoARl0KAB7TEodrbKCkjHmbxtjftkY85fGmHvGmD83xvxbY0zZlcZMdnZgOMxWdji05ZdN6+kdkIw6MrTl06LpPy27fPWfj/HnZVvtABl1GCbl58fJGbuxa0w9BbwU6AH/Cfg64L8BfltEvnhSWWdn7Jrrg2ppebkOKXp2+eo/H+PPy7Za7Jq7GmfsL8F26gBvFpF/DPxg8v4LHWlMp9XKX4cx6erR0nrGgQ4mXT2a/tOyy1f/+Rh/XraVAx1MpnqcdOwi8izwzuTtjxpjfhL458BfA9/lQmMmnU6+/8BgL7G6KZZp1dK618l3BgP2EvXeEtkEenb56j8f48/LtuqQ72wd7O2Y+ZeOdjlB6eeArwaeACrJZ+8Hfnd0R2PMdeA6wNWrV7l161Zu8ZV+n2LuWuCo1+Ngxu/R0lq50qcYOdAZ9Dh4crIOKPtPyS5v/edj/HnYVisrfYoOhI6OehwcTNYZh5OO3RjzCPAe7GPcNwE3sR33O4D3GGMeE5EPn+wvIjeTfXjiiSekVqvl/xHlMty+nbuaYqXCzN+jpfVUGZ51oFOqULs2RQd0/adll6/+8zH+vGyrMuBAp5giJkZwdY/9cR4sA/8bIvIJ4D8n71+cfL9YqlX7QCMPcWyXqFoWrYtV+0AoDya2S4nNQtN/Wnb56j8f48/LtqpiH37mEsIu2zgfrkbFxMAfA38D+C/A/wf8Pewtmb8AviDp7J9HGBWzJKMSwqiY7DoQ4m8SYVQM53ZUjIgMgdcAPw9cAt6IPVP/BeA1kzp1p5RKsL5un1ZnwRjY2EiX7EdL60IJLq1jn4xnEoJLG+mSJWn6T8suX/3nY/x52VYlIKdNbJAlMZizCUoiciAi/72IfI6IvCh5/Qci8kFXGjPZ3raXSFmIY1t+2bQe2baXmFkwsS2fFk3/adnlq/98jD8v22obezslk1BSfn78yse+tgbNpp3yOw+Fgi2Xdjq3pla8BqWmnTI9D6Zgy6WdYg26/tOyy1f/+Rh/XrbVGtDkwSPI1EJJuTlsOs2cmWmcbyG7Y0q8zHinaJev/vMx/rxsq5Dd0Q3ttkijYXMpx/HZRjrJsdxo2P3Oi9ZxW+RuI8lRHY9kuDvJUd2w+50XmzTt8tV/Psafl23VFpGG2LzrsZztCk/ysTeS/WYzrWP3K7vjOA4P7dTfbpejXo9ipWKHKW1uul/BRkvr/mGyqkyXo0GPYqlih3ld3nS/qoym/7Ts8tV/Psafl211iE0T0OXoqEexWMEOadxkngel00bF+HvGPobd3V3vtHy0SVPLR5s0tXy0SVMrjw5Tztj9engaCAQCgdCxBwKBgG+Ejj0QCAQ8I3TsgUAg4BmhYw8EAgHPCB17IBAIeIbLhTaWk/uDZBxsh5UrfZv3+WIVLm+5Hwc7GNhxsJ0OK/2+zftcrcLWlttxsFo6EPyXXww7ZrnDykofm6O7CmyRJbnTVJTaajAY0Gq16HQ69Pt9yuUy1WqVra0trrr2n5dtpaAzaRyk1rawcezH+yJ368nMtWjCzLW63S8v+/si9bqdoRZF42eu1et2v/OgIxL8l98oEamLnU0YyfhZhvVkv5wotdX+/r7U63WJokiiKBLguS2OY4miSOr1uuy78J+XbeVWh4cupYCPuSZ8zN+iaZem/xaQF2QiSm1148YNKRQKYow506GPbsYYKRQKcuM85L+xYqLTViFXTD6eC/RpQT6yZQ34kwCcFnyjW5ZA1NIRCf7L67/nDuB5DoOMHYZSW5106tM69NEtc+fuZVstRmdax+5XrphhG+7UQI7nL2sK8Ohe+tSf7TbUanCcQatQgL29dKk/tXQg+C+PjhUDakAGLQrAHqnTtCq1VbvdplarcZzBf4VCgb29PV6e1n9ettXidBa+gpIx5nFjjEzaXGik4ukdkGG2sjK05dOyswPDjFrDoS2/TDoQ/JdHx4oBGbUYJuVTotRWOzs7DDP6bzgcsjOP/7xsK8WYOIWrNU8/G/iekY9fC3wh8KSIPDGprLMzdh/XTPRxHVLw038LXt/yDEptNRgMeOyxx/hEDv9FUcSdO3dmj5bxsq3O/5qnHxaRbz/ZgO/DLmwN8K9caMzkmZaDSky6eloOtIyZXY+WDgT/5dGxYvm1MOnqUWqrlgP/GWPS1eNlW2npPJ9FTVD6Fuxi1n8K/MyCNM5yr5PvDAbsJeq97uz9Op18ZxZgLx27M7S0dCD4L4+OFSPfmRnYS+/laatOp5PrbB3s7ZhuGv952VaKMTGC8wlKxpgY+Nbk7Y+KyCfH7HMduA5w9epVbt26lVt35UqfYpS7Go4GPQ6enP57Vvp9ivmlOOr1OJhiu5YOBP/l0QFYWelTdCB2dNTj4GCGllJb9fv9/CJAr9ebeYz72FaaMTHKImae/k/YG0JPA/963A4ichO4CfYee61Wy6/6VBmevZ27mmKpQu3ajN9TLsNtB1qVClNt19KB4L88OlYMcKBVXJ62KpfL3Hbgv0oa/3nZVooxMYLTWzHGmAvAm5O37xSRj7usfyoXq/aBUB5MbJfdmkW1ah/U5CGO7bJby6ADwX95dKwY9kFXLjHsEmkzUGqrarVKlNN/cRyzmsZ/XraVYkyM4HQcuzHmHwH/B/Ax4DER+fCsMmFUzJKM6gj+y65jxQijYp5PGBVzjkfFJCIG+M7k7bvSdOpOuVCCS+vYp8hZMHBpI12ypFIJ1tftU/hMUgY2NmYHoJYOBP/l0bFiQE7/sUGqJFBKbVUqlVhfX8dk9J8xho2NjXSJwbxsK8WYGMFZx57Mcl0Vm+zgLa7qnYtHtu0lZhZMbMunZXvbXvplIY5t+WXSgeC/PDpWDHvpnEksKZ8Spbba3t4mzui/OI7Znsd/XraVYkycwq987PEalJp2yvQ8mIItl3Y6PMDaGjSbdirzPBQKtlzaqc9aOhD8l0fHigFN7FTwucSScsvXVmtrazSbTQpz+q9QKNBsNtOnE7BiHraVYkycZs7MNM63kN1xyXREgv/OTcZACdkdz01bheyObjhui9xtJDmq4wk5qht2v7y02yKNhs0RHcdng+8kd3SjYfc7DzoiwX/5jRKRhtgc27GcDfuT3NuNZL+cKLVVu92WRqMhURRJHMdnOvSTfOyNRkPaLvznZVu51ZnWsfuV3XEc9w+TVWW6HA16FEsVO8zr8qb7FYAOD+2U5m6Xo16PYqVih19tbrpd7UVLB4L/8othp4R3OTrqUSxWsMPXNnG/gpJOWx0eHtJqteh2u/R6PSqVCqurq2xubrpfQcnLtnKjM21UjL9n7GPY3d31TstHmzS1fLRJU8tHmzS18ugw5Yzdr4engUAgEAgdeyAQCPhG6NgDgUDAM0LHHggEAp4ROvZAIBDwjNCxBwKBgGcsIh/7UjEYDGi1WnQ6Hfr9PuVymWq1ytbWlvsxtwyw41M7rKz0sfmYq8AWbsfBaulgs+61WtDpsNLv27zZ1SpsbS1gzPwgGYfdYeVK3+Ydv1iFy1tux8xr2hT8lw8tmzS1NPw3aRyk1raocez7+/tSr9cliiKJomjsLLl6vS77+/su1ESkLnbmWCTjZ5TVk/3Og46I7O+L1Ot2hl8UjZ/5V6/b/fJyvC9yt57MnIwmzJys2/3Oi03Bf+fDJk0tx/7jYUspoJrX4hznmpgs5WFeGk2bgv/Oh02aWgvw30PVsZ906tM69NEte+d+0tnOY3KWTldLRx4E4LTgG92yHsjPHVTTDqiRLcvBpWlT8N/5sElTa0H+m9axe5Urpt1uU6vVOD4+nrtsoVBgb29vjjSjbaAGzK9lU3LukS4lp5YO0G5DrQYZ/EehAHt76VOnDttwpwaSQcsU4NG9dKlnNW0K/jvLstqkqbVA/6msoJQIfYUx5j3GmGeMMUNjzB8ZY77FpcY0dnZ2GA6HmcoOh0N2dnbmUQOyadlyabW0dICdHcjoP4ZDWz4tT++AZNSSoS2fBk2bgv/Osqw2aWpp+u8Uzs7YjTHrwC9jR9rsAn8IfC7wJyLyrZPKuTpjV12f0ZM1E89Kebi+qqZNwX/jWTabNLUW7D+tM/Z3Yjv17xWRV4vIN4vI10/r1F3SarVy12GMSVlPfi27nuGserR0sMOvckuZdPU840ALM7seTZuC/ybILJlNmlqa/hvBScdujKkAleTty40xh8aYp40xv2CMedyFxiw6nU6us3Wwt2O63W4aNfKdRYO9TTJLS0sH6HTynVmAvXRM4797nXxnS2Avh+/N0NK0KfhvPMtmk6aWpv9GcDVBqXTq778L/FzyWgdeZoy5JiL3T3YwxlwHrgNcvXqVW7du5f4B/X4/dx0AvV5v5u9ZWelTLObXOjrqcXAwWUtLB2Cl38eBFEe9Hgez/HelTzFyoDXocfDkZC1Vm4L/JusskU2aWpr+G8VVx/4Xp/7+IRH5EWPM3wHeD3wR8N8CByc7iMhN4CbYe+y1Wi33DyiXy9y+fTt3PZVKhdm/pwzk1yoWZ2lp6WBnvznwXzGN/54qw7MOtEoVatemaGnaFPw3WWeZbNLU0vTfCK7usd8BPjzl+4850plItVolivL9G47jmNXV1TRq2IeSudSwy2Etgw52SnNO/xHHdtmyWVys2odPeTCxXfZtGpo2Bf+NZ9ls0tTS9N8ILkfFfDvwY8AzPLgV8zLgV0XkqyeVC6NiwqiYTCzBqIQXTMtH/4VRMeNZglEx7wDejF2p9X8AXoTt6P+hQ42JlEol1tfXMcZkKm+MYWNjI2VisBKwjh1xkkkN2GB2wi4tHaBUgvV1+xQ+k5SBjY10SYwulOBSTrsubcw+gDVtCv4bI7OENmlqafpvBGcdezLL9e0i8jdF5KKIfIGI/DMRecaVxiy2t7eJ4zhT2TiO2d7enkcNe5sjk1pSfpl0gO1te+mXSSq25dPyyLa9nM2CiW35NGjaFPx3lmW1SVNL03+n8Cof+9raGs1mk0KhMFe5QqFAs9mcI50AwBrQxE7bn0stKZdWS0sHWFuDZtNOZZ5LqmDLzeO/eA1KTTs9ex5MwZZLO3Vc06bgvwcss02aWpr+O82kJDJaW8juuGw64mcmv5Dd8fz4L2R3DNkdJ9Fut6XRaEgURRLH8ZkO/SQfe6PRkHa77UJNRBpi86HHctbEkzzpjWS/86AjIu22SKNhc0TH8dngO8kd3WjY/fJy3Ba520jyYccT8mE37H7nxabgv/Nhk6aWY/9N69i9yu44jsPDQ1qtFt1ul16vR6VSYXV1lc3NzQWsoHSInb7f5eioR7FYwQ413MTtykZaOsDhoZ3S3O1y1OtRrFTs8KvNzQWslnOYrGDT5WjQo1iq2CFllzfdrmCjaVPwXz60bNLUcuS/aaNivD1jH8fu7q53Wj7apKnlo02aWj7apKmVR4cpZ+xePTwNBAKBgGejYgKBQCAQOvZAIBDwjtCxBwKBgGeEjj0QCAQ8I3TsgUAg4Bmu8rEvL/cHydjUDitX+jYX88UqXN5yPw52MLDjUzsdVvp9m4+5WoWtLbfje320SVNL03+KWoPBgFarRafTod/vUy6XqVarbG1tuZ2z4aNNmloa/ps0DlJrW9g49uN9kbv1ZDZZNGE2Wd3ul5f9fZF63c4ci6LxM8rqdbtfsOmF09L0n6LW/v6+1Ot1iaJIoigaO8u6Xq/L/jnyn5pNmlqO/cdDl1JAM9eEVg4NH23S1PIx/4go5kXy0SZNrQX47+Hq2J9z4DTnjWxZA/GkU5rWIY1uWTonH23S1NL0n6LWSac0rUMa3TJ1Tj7apKm1IP9N69j9yhUzbMOdGsjx/GVNAR7dS5+Os92GWg2OM2gVCrC3ly4lp482aWpp+k9Rq91uU6vVOM7gv0KhwN7eXro01T7apKm1QP8tfAUlY0zLGCNjtm9xUX9qnt4BGWYrK0NbPi07OzDMqDUc2vJp8NEmTS1N/ylq7ezsMMzov+FwyM4S+k/NJk0tzfg7hZMzdmNMC3gD8B+B3zv11b8Xkd+YVtbZGbvmmolaa0H6aJOmlo/raKK4vq+PNmlqLdh/WmueAvysiHz7qW1qp+6UZ1oOKjHp6mk50DJmdj0+2qSppek/Ra2WA/8ZY2bX46NNmlqa8TeC6479x40x94wxfWPM240xn+m4/snc6+T7zwj20uded/Z+nU6+s02wtxO6M7R8tElTS9N/ilqdTifX2SbY2wndJfKfmk2aWprxN4KrCUr3gF8DesDnAF8L/FPgC4D66M7GmOvAdYCrV69y69at3D9g5UqfYpS7Go4GPQ6enP57Vvp9ivmlOOr1OJhiu482aWqp+k9Rq9/v5xcCer3e1GPPR5s0tTT9N4qrjv0fy6mb9caYHeCtwNcbYwoiZx8Ji8hN4CbYe+y1Wi3/L3iqDM/ezl1NsVShdm3G7ymX4bYDrUqFqbb7aJOmlqb/FLXK5TK3HfivskT+U7NJU0sz/kZwdSvmZSPvzanXFznSmM7Fqn3QkAcT26WwZlGt2od3eYhjuxzWNHy0SVNL03+KWtVqlSin/+I4ZnWJ/Kdmk6aWZvyNFnM0KuZTwG3gg8BLsbdiPg34JRF53q2Y04RRMcsxKiGMiplAGBXjl02aWh6Mivkx4CXANwB/D/gD4LuBb3RU/2wulODSOg8uFubFwKWNdEl4SiVYX7cjMzJJGdjYmN0B+miTppam/xS1SqUS6+vrmIz+M8awsbExO7GVjzZpamnG3whOOnYRebOIVEXksohcEpEvEpEfFMk6Mj8jj2zbS5csmNiWT8v2tr0dkIU4tuXT4KNNmlqa/lPU2t7eJs7ovziO2V5C/6nZpKmlGX+n8Csfe7wGpaadijsPpmDLpZ06DrC2Bs2mnd4+D4WCLZd26r2PNmlqafpPUWttbY1ms0lhTv8VCgWazWbqqfde2qSppRl/p5mUREZrC9kdH2KbNLVCdsely074gtukqRWyOzriuC1yt5HkPo5HMqed5D5u2P3y0m6LNBo2b3gcn+2QTvKJNxp2v2DTC6el6T9FrXa7LY1GQ6IokjiOz3RIJ/nEG42GtM+R/9Rs0tRy7L9pHbtf2R3Hcf8wWa2ky9GgR7FUscOHLm+6Xy3n8NBOc+92Oer1KFYqdkje5qbjFYA8tElTS9N/ilqHh4e0Wi263S69Xo9KpcLq6iqbm5uOVwDy0CZNLUf+mzYqxt8z9jHs7u56p+WjTZpaPtqkqeWjTZpaeXSYcsbu18PTQCAQCISOPRAIBHwjdOyBQCDgGaFjDwQCAc8IHXsgEAh4RujYA4FAwDNc5WNfXgYDOza602Gl37e5wKtV2NpyPw77/iAZn9ph5Urf5mO+WIXLW27H92raxABoAR1WVvpAGagCW8A51fIxJrBZC1utFp1Oh36/T7lcplqtsrW15Xgctp5NXraVhk2TxkFqbQsbx76/L1Kv21mLUTR+NmO9bvfLy/G+yN16MqMsmjCjrG73Oy82yb6I1EUkSrbTzRYnn9WT/c6Jlo8xISL7+/tSr9cliiKJomjszMl6vS77ee1StMnLtnJsEw9dSgHNXCdaOTQ0bZIbIlIQESPTm88k+50DLR9jQs53rpMpRvnXVguw6eHq2E8cOM15o1vW4HguKKYFxMiWJTg0bXquo52nGbN2uEpaPsaEPOjUp3Xoo1umzl3RJi/bakE2TevY/coV025DrQbHxzN3fR6FAuztpU8HO2zDnRpIBi1TgEf30qXk1LSJNlADMmhRAPaAJdPyMSaAdrtNrVbjOINdhUKBvb29dKlnFW3ysq0WaJPGCkqnxT7PGHNojJFke9y1xkR2dmCYcW2P4dCWT8vTO5B1HREZ2vJp0LSJHSDr2ijDpPySafkYE8DOzg7DjHYNh0N20tqlaJOXbaV6/D7A6Rm7MeYCcAt4BQ9G3HyBiHxoUhlnZ+yaa3ZqrQWpaRMD4DEghxYRcIfZI1iUtHyMCfxZs/MMPrbVgm3SPGP/IeBLgO9xXO9sWq38dRiTrp5nHGhhZtejaRMOtDAp61HS8jEmgJYDu4wxs+tRtMnLtlI9fs/ibBy7Mea1wFuANwO/46re1HQ6+f4zgr306XZn73evk++/PdjLuXsztDRtokO+M2iwt0iWSMvHmAA6nU6us3Wwt2O6s+xStMnLtlI9fs/ipGM3xjwKvBv4JRH5MWNMbcb+14HrAFevXuXWrVu5f8NKv08xdy1w1OtxMOP3rFzpU4wcaA16HDw5WUvVppU+RQdiR0c9Dg6WQ8vHmADo9/v5hYBerzf12NO0yce20rRpFFdn7HXgs4GiMeY/AI+c+u6njDHvEJFfPvlARG4CN8HeY6/Vavl/QbkMt2/nrqZYqTDz9zxVhmcdaJUq1K5N0dK0iTLgQKu4RFo+xgRQLpe57cCuyiy7FG3ysq1Uj9+zuLrHbpLXVwFfC3zpqe9ejT2SF0u1ah805CGO7VJss7hYtQ9P8mBiuxzWNDRtoop9IJlLDFgiLR9jAqhWq0Q57YrjmNVZdina5GVbqR6/Z1nIOPbkVsxu8jaMihnHEjxVHxEjjIoZJ7NkMUEYFTORZWsrj0bFvHCUSrC+bp8iZ8EY2NhIl4TnQgkurfPgQmVuMbi0MTvYNW2iBOS0iQ3SJetS0vIxJoBSqcT6+jomo13GGDY2NmYnBlO0ycu2Uj1+z7KQjl1EbolN7mGmna07Z3vbXrpkIY5t+bQ8sm0vx7JgYls+DZo2sY29xZFJLCm/ZFo+xgSwvb1NnNGuOI7ZTmuXok1etpXq8fsAf87YAdbWoNm0U3HnoVCw5VJPvQfiNSg17fTieTAFWy7tNGtNm1gDmtgp+3OJJeWWUMvHmADW1tZoNpsU5rSrUCjQbDbTpRMAVZu8bCvV4/cUk5LIaG0hu+MS2hSyO56PmJCQ3fHctFXI7uiIdluk0bA5juP4rPNOch83Gna/vBy3Re42knzO8YR8zg2733mxSdoi0hCbCz2Ws812kiO9kex3TrR8jAkRabfb0mg0JIoiieP4TId+ko+90WhIO69dijZ52VaObZrWsfuV3XEch4d2Sm63y1GvR7FSscOHNjcXsALLYbICS5ejQY9iqWKHRF3edLsCi6ZNHGKn7nc5OupRLFawwww3cb+CkpKWjzEBHB4e0mq16Ha79Ho9KpUKq6urbG5uOl5BSc8mL9vKkU3TRsX4e8Y+ht3dXe+0fLRJU8tHmzS1fLRJUyuPDlPO2P16eBoIBAKB0LEHAoGAb4SOPRAIBDwjdOyBQCDgGaFjDwQCAc8IHXsgEAh4hrMVlJaW+4NkbGqHlSt9m4v5YhUub7kfc6ulNRjYcbCdDiv9vs37XK3C1tYCxvYG/50bLR/9pxh/g8GAVqtFp9Oh3+9TLpepVqtsbW05nQegojNpHKTWtrBx7Mf7InfryWyyaMJssrrd77xo7e+L1Ot2hloUjZ+5Vq/b/c6LTZpamv7zsa18tElE9vf3pV6vSxRFEkXR2Jm79Xpd9nPa5VqHhy6lgGZei3Oca+IFt0lTS9N/PraVjzaJXq6dReg8XB37c0ExLSBGtqzBoaV1clBNO6BGt6wHV/BfPv/52FY+2iQPOttpHe3olqVzX5TOtI7dr1wxwzbcqYEcz1/WFODRvfTpOLW02m2o1eA4g06hAHt76VN/Bv+dZV7/+dhWPtoEtNttarUaxxnsKhQK7O3tpUp9vEgdlRWUjDHfa4zpGWM+bow5Tv5uGpM1m30Gnt4BGWYrK0Nbftm0dnZgmFFnOLTl0xL8d5Z5/edjW/loE7Czs8Mwo13D4ZCdlHZp6Yzi7IzdGPMzwIuBp4CXAn8f+4/j7SLy5knlnJ2xa67P6MmaiWcI/htPWv/52FY+2oTemrGL1lE5YxeRbxKR14nIPxGRBvD/JF9VXGlM5ZmWg0pMunq0tFoOdIxJV0/w3wSZlP7zsa18tAloObDLGDOzHi2dcTgdx26M+SrgtcDnY1cb/gh2HbPFc6+T77892Mu5e93l0ep08p0tgb0c7i6RTZpamv7zsa18tAnodDq5zqLB3ibpzrBLS2ccricovQL4tlPv3wv86ehOxpjrwHWAq1evcuvWrdzCK1f6FKPc1XA06HHw5PTfo6W10u9TzC/DUa/HwQwfB/9N0UnjPw/bykebAPr9fn4hoNfrTe27tHTG4bRjF5EfMMb8EPAY8C7g64GqMeYLReRTp/a7CdwEe4+9VqvlF3+qDM/ezl1NsVShdm3G79HSKpfhtgOdSoWZPg7+m6yTxn8+tpWPNgHlcpnbDuyqzLBLS2ccTu6xG2M+wxgTAYjIp0Tkj4FfT75+HJz845/Oxap9eJIHE9ulsJZFq1q1D5/yEMd22a1ZBP+NJ63/fGwrH20CqtUqUU674jhmdYZdWjrjcDIqxhjzONABbmFvvXwO8LXAZwC/LSJfPKlsGBXz8I1K8NJ/PraVjzYRRsXMw7PYM/QvBv5H7IPTDwHvBL7akcZ0LpTg0jpgMlZg4NJGusRCWlqlEqyv25EFmWQMbGykS8wU/DdGZg7/+dhWPtoElEol1tfXMRntMsawsbExM2GXls44nHTsIvJhEXmtiHyuiFxMtpeJyJtE5MiFRioe2baXY1kwsS2/bFrb2/ZyNgtxbMunJfjvLPP6z8e28tEmYHt7mzijXXEcs53SLi2dUfzKxx6vQalppxfPgynYcmmnw2tqra1Bs2mnZ89DoWDLpZ3ODcF/p8niPx/bykebgLW1NZrNJoU57SoUCjSbzVTpBDR1nsekJDJaW8jumBJPs+t56T8f28pHmyRkdzxfHbuIyHFb5G4jyeccj2SDO8nn3LD7nRetdluk0bB5r+P47AF1kg+70bD7nRebNLU0/edjW/lok4i0221pNBoSRZHEcXymoz3Jk95oNKSd0y7XOtM6dr+yO47j/mGyAkuXo0GPYqlih0Rd3lzACkBKWoeHdpp2t8tRr0exUrFDyjY3F7CCTfDfudHy0X+K8Xd4eEir1aLb7dLr9ahUKqyurrK5uel0BSVXOtNGxfh7xj6G3d1d77R8tElTy0ebNLV8tElTK48OU87Y/Xp4GggEAoHQsQcCgYBvhI49EAgEPCN07IFAIOAZoWMPBAIBzwgdeyAQCHiG64U2lpAB0AI6rKz0gTJQBbYAx2NuBwM7vrfTYaXft/msq1XY2nI7vvf+IBnb22HlSt/msr5Yhctbzsf2DgYDWq0WnU6Hfr9PuVymWq2ytbXldGxvIqbjP82YUGwrLf+pxoSH/lOxadI4SK1tcePY90WkLiJRsp2WjZPP6sl+eaX2Rep1O/MuisbPyKvX7X55ON4XuVtPZuNFE2bj1e1+uU3al3q9LlEUSRRFY2fJ1et12c9rkxXT8Z9mTCi2lZb/VGPCQ/+5tomHL6XADREpiIiZIW+S/c5BXhAP82ckYko5SBRjQjPXjpL/VGPCQ/8twqaHrGM/OYDn+RkZD+SToJgWEKNbluB4LiimBcTIljHgTw7gaQfv6Jb5QNbyn2ZMKLaVlv9UY8JD/y3Kpmkdu2e5YtpADTjOULYA7AEp02S221CrwXEGrUIB9vbSpTQdtuFODSSDjinAo3up05m2221qtRrHGWwqFArs7e2lTzOq5T/NmFBsKy3/qcaEh/5bpE0LX0HJGPMuY8wHjDEfMcYcG2N+3xjzXcaYT3dRf3p2gGHGssOkfFqpHRhm1BoObfk0PL0DklFHhrZ8SnZ2dhhmtGk4HLKT1iYrpuM/zZhQbCst/6nGhIf+U7XpFK7WPBXgA8BvY4cYfGXy1Q+KyHdPK+vujH0APAbkWDORCLjDzJERWmtBergOZCKmtJamYkxorhmr5D/VmPDQf4u2SWPN0y8TkWsi8kYRqQHvTT7/Okf1p6DloA6Trp6WAy1jZtfzjAMdTKp6Wg5sMsakq0fLf5oxodhWWv5TjQkP/adq0wiu1jz9zZGPXpS83nVRfzo65DszA3vp3U0h1cn33x7s5Vx3hta9Tr7/9mAv5+7NtqnT6eQ6MwN76d2dZZMV0/GfZkwotpWW/1RjwkP/qdo0gvMJSsaYtwGvAD4KfOeEfa4D1wGuXr3KrVu3cuuurPQpFnNXw9FRj4OD6b9npd/HgRRHvR4HU2xfudKnGDnQGfQ4eHKyDkC/388vBPR6vZntqeY/zZhQbCst/6nGhIf+07RpFGcduzEmAn4K+EbgT4F1Efm9cfuKyE3gJth77LVazcEvKAO3c9dSLFaY+XvKZbjtQKsyQ+upMjzrQKdUoXZtig5QLpe57cCmyiybrJiO/zRjQrGttPynGhMe+k/VphFcjYp5KbCL7dTfB7xcRA5c1J2eKvZBVx5iYDWFVNU+PMklFdvlxKZxsWofnuTBxHYpsRlUq1WinDbFcczqLJusmI7/NGNCsa20/KcaEx76T9Wm0WKORsXcAT4fe0Pzp4FPJl99WES+f1rZMComjIrJKBZGxYRRMWdZMv/5MCrm85PXCPhm4E3J9kZH9aegBKxjRzFkwQAbpEoCVSrB+rp9Mp5JysDGxuzEQhdKcCmnTZc2UiUWKpVKrK+vYzLaZIxhY2MjXRIoLf9pxoRiW2n5TzUmPPSfqk0juBoVYyZsj7uoPz3b2EvnLMRJ+bRS2/ZyLJNUbMun4ZFtezmWBRPb8inZ3t4mzmhTHMdsp7XJiun4TzMmFNtKy3+qMeGh/1RtOoVn+djXgCZ2Kvg8FJJyKacjA6ytQbNppxfPJVWw5dJOs47XoNS004vnwRRsubRTrIG1tTWazSaFOW0qFAo0m830U8etmI7/NGNCsa20/KcaEx76T9Wm00xKIqO1heyOKQnZHUN2x7FmheyO58F/IbujM9oi0hCbYzsekT3Jvd1I9ssr1RZpNGze5jg+GxAn+ZwbDbtfHo7bIncbST7neEI+54bdL7dJbWk0GhJFkcRxfObgPcm93Wg0pJ3XJium4z/NmFBsKy3/qcaEh/5zbdO0jt2z7I7jOMROCe9ydNSjWKxgh69t4ny1nMNDO8242+Wo16NYqdghUZubjldgOUxWYOlyNOhRLFXskKjLm85XlTk8PKTVatHtdun1elQqFVZXV9nc3HS/Wo6W/zRjQrGttPynGhMe+s+VTdNGxXh8xv58dnd3vdPy0SZNLR9t0tTy0SZNrTw6TDlj9+zhaSAQCARCxx4IBAKeETr2QCAQ8IzQsQcCgYBnhI49EAgEPCN07IFAIOAZzhfaWDoGAzs2tdNhpd+3uZirVdjacjw2GpvN7ZkW3OuwcqVv8zFfrMLlLbdjbjVt0tRigB1f3mFlpY/Np14FtnA7vlxLBz/9pxXn4OXxOxgMaLVadDod+v0+5XKZarXK1taWu3kAk8ZBam0LG8e+vy9Sr9tZY1E0fjZZvW73y8vxvsjdejKjLJowo6xu9zsvNmlqyb6I1MXO/Ixk/IzQerLfedARP/2nFeciXh6/+/v7Uq/XJYoiiaJo7Mzder0u+ylt4qFLKaCWf0T08lpo2qSppZbDxcP8QZp2+Zi/RdGuReTaebg69pOgmBYQo1vW4HguKKYFxMiWJTg0bdLUeq5TmidksnROWjrip/+04lzEy+P3pFOf1qGPbmk692kdu1+5YtptqNXg+Hj+soUC7O2lT8c5bMOdGkgGLVOAR/fSpeTUtElTizZQAzJoUQD2SJdSV0sHP/2nFefg5fHbbrep1WocZ7CpUCiwt7c3MfWxxgpKGGNeb4x5nzHm2Bhzcsmhy84ODIfZyg6Htnxant4ByaglQ1s+DZo2aWqxA2TUYpiUXyYd/PSfVpyDl8fvzs4Ow4w2DYdDduaKiQc4O2M3xvww8BrgHvBKABGZuSaUszN2tXU00VufUdMmTS21tUg9XAfXiqFil4/rkIKaXYteM1bljF1E3pqI/ISrOuei1cpfhzHp6nnGgRZmdj2aNmlq4UALk6IeLR389J9WnIOXx2/LgU3GmEz1+DNBqdPJ998e7OVctzt7v3udfP/twV7O3ZuhpWmTphYd8p1tgr2dMEtLSwc//acV5+Dl8dvpdHKdrYO9HdNNFRNneUEmKBljrgPXAa5evcqtW7dy17nS71PMXQsc9XoczPg9K1f6FCMHWoMeB09O1lK1SVNrpU/RgdjRUY+Dg8laWjrgqf+U4hz8PH77/X5+EaDX683dR74gHbuI3ARugr3HXqvV8ldaLsPt27mrKVYqzPw9T5XhWQdapQq1a1O0NG3S1KIMONAqztLS0sFP/2nFOXh5/JbLZW47sKmSKibO4s+tmGrVPjzJQxzbpbBmcbFqH57kwcR2OaxpaNqkqUUV+/Aulxh2Obtl0MFP/2nFOXh5/FarVaKcNsVxzGqqmDiLy1ExrwNehz2deFXy8buT17eIyNG4cmFUTBgVk1GMMComjIo5w5Idv16MigGuAW/gQadO8v4NwIsd6oynVIL1dftkPAvGwMZGusRCF0pwaR07siCTGFzamB3smjZpalECcvqPDWYnttLSwU//acU5eHn8lkol1tfXMRltMsawsbGRKTGYy+GObxObkGLc9iFXOlPZ3raXY1mIY1s+LY9s28uxLJjYlk+Dpk2aWmxjbwdkEkvKL5MOfvpPK87By+N3e3ubOKNNcRyzPVdMPMCfe+wAa2vQbNrpxfNQKNhyqadzA/EalJp2evE8mIItl3aataZNmlqsAU3s9Pa5xJJyabW0dPDTf1pxDl4ev2trazSbTQpz2lQoFGg2mxPTCcxkUhIZrS1kd1xCm3zMThiyO+azK2R3DNkd59kWlo+93RZpNGze5jg+GxAn+ZwbDbtfXo7bIncbST7neEI+54bd77zYpKklbRFpiM0bHsvZEDnJJ95I9jsPOuKn/7TiXMTL47fdbkuj0ZAoiiSO4zMd+kk+9kajIe2UNk3r2P3K7jiOw0M7zbjb5ajXo1ip2CFRm5sLWIHlMFmBpcvRoEexVLFDoi5vul1ZRtMmTS0OsdPcuxwd9SgWK9gheZu4XdlISwc//acV5+Dl8Xt4eEir1aLb7dLr9ahUKqyurrK5uTnXg9Jpo2L8PWMfw+7urndaPtqkqeWjTZpaPtqkqZVHhyln7H49PA0EAoFA6NgDgUDAN0LHHggEAp4ROvZAIBDwjNCxBwKBgGeEjj0QCAQ8I3TsgUAg4BmhYw8EAgHPCB17IBAIeEbo2AOBQMAzQsceCAQCnuGsYzfGXDDGfJ8x5kPGmE8mr99rjPl0VxqBQCAQmI3LM/YfAr4Hu5ji/5m8vg34MYcagUAgEJiBk47dGPMI8K3J200R2cKudQrwzcYY1/lJA4FAIDABV2fsK9gzdIDfTF7fl7xewK7jFQgEAgEFLjiq53NO/f1XyevHTn32Wad3NsZcB64nb+8ZYw4c/Y5ZFIEjz7R8tElTy0ebNLV8tElTK4/OY5O+cNWx//mpv18CfBR48anPPnJ6ZxG5CdwEMMb8lkxaBcQxPmr5aJOmlo82aWr5aJOm1qJ0XN2K+SBwL/n7S5PXL09e7wO/5UgnEAgEAjNw0rGLyBHwvyVvW8aYn8YuvghwU0QGLnQCgUAgMBuXwx3fCvwA9sz9m5LXfwm8aUa5mw5/wyx81PLRJk0tH23S1PLRJk2thegYuyZqIBAIBHwhpBQIBAIBzwgdeyAQCHjGC9Kxa+aVMca83hjzPmPMsTFGjDELufdkjHmXMeYDxpiPJFq/b4z5rkXlykn81TPGfDzR6xljmsaYeEF6n2eMOTzxoTHmccf1t07VfXr7Fpc6I5pfYYx5jzHmGWPM0BjzRy71jDGPT7BpIXFojPnbxphfNsb8pTHmnjHmz40x/9YYU3asUzbG/Kwx5s+MMZ9I4v4bHNQ79Vh12W+k0HLWb6TQct53uBrHPi8/BHwH8JfYvDLr2LwyjwDf5ljrGvAi4HeAVzqu+zTXgQ8AvwiUga8EfhAoAN+9AL2/CRwAvwq8FPj7wJsBk7w6wxhzAfg3wBWX9U7gPwK/d+r97y5CxBizDvwy9hjYBf4Q+FzgCYcyzwLvGPnstcAXAk861MEYY4BfwcZCDxuHXwf8Q6ACfLEjnc8Efg07OWYfeA/weuDfGGNERP5djuqvMf1YddlvzNKa9b1LLfd9h4iobthGGAICfE3y2Vcn7/8auLog3W9KNGRB9X/pyPtfT/Q+oOTX/zvR+78WUPePAJ8Etk98CDzuWKOV1Lup5K8/SvS+R0Mv0fws7MxsAd7ouO7PPNU2X5989k+S9x91qLN+SudK8tmPJ+8/6EjjecfqovqNWf2Cy35jUl2L6DteiFsxXuaVEZHfHPnoRcnr3UVpGmO+yhjzDmPMLwAb2Bm+TccarwXeAvwvwG2XdU/gx5PbCH1jzNuTM0SnGGMq2LNYgJcnt5ieNsb8gutbTCN8C3ZG9p8CP+OyYhF5Fnhn8vZHjTE/CfxzbKf3XQ6lPnHq7y8xxrwE+KLk/d9K3i8CL/sNWEzf8ULcipkrr8x5xBjzNuAV2NQK37lAqVdw9hL0vdhOwwnGmEeBdwO/JCI/Zoypuap7DPewl/g9bIx8LfBPgS8A6o61Sqf+/rvAzyWvdeBlxphrInLfpWDy7OMkA+qPisgnXdaf8HPYs9gnePCP6/24vZ31Xmw7vRp7G2aUl/DguHaJ9/0GOOw7XF4Oprwc+Uqefyn3klOfbSxId6G3YhKNk1z0gv1vu6Lgz0/Ddn6/muj+MfBpjup+U1LnrwP/AXvGftJO/4nkkt+Rlhl5v5Po/Feg4Nhn5VN2fGfy2d859ZnzdsN26oJN+HRpAfU/Anw80fi2JBa/LXn/V8BnO465f4CdgPgd2Gc6J7dEIgf1j7sVs5B+Y1a/4LLfmFaX677jhbgV42VeGWPMS7EP4b4Re4n4chFZSNZKY8xnGGMiABH5lIj8MbbzBXgcmzHOiVTy+irsGfSXnvru1dgO0hUvm6BteHBp6oo7wIenfP+xKd/NTfLw+eSB9jtF5OMu6094HPuwDeA3ROQTwH9O3r84+d4VF0Tk50XkXwBvB74m+Xw30V0EXvYbsKC+w/WZQ8r/XE3sf6a/AH46eRXgf1+A1uuwD+ZOHkhI8r4FFB3q3EnqHgI3sA+UfpwFPJzDHqTPYkd13MA+Tf9kov/+BbZb7ZQPH3dc96ewHdHNxK7/muj84oJs+fak/o8C/xo7KkaA9yxA6x+xgDPnEY341HHUA36SBw+I/xwHZ9KntH4F+7D+XUA30fgYcC1nvVOPVZf9RgotZ/1GCi3nfYfzAEtp6AXsZdyfYC/f/gT4fuyZgGutt51y5uj2uEOdSRofWoBNn429NfJn2LOYe9jhc++YN+jm1K0twndJ3T8KdIBnsLcUPoh9+BcvyBYD/DNs53cPewvr7cDlBeicdH7NRbVNorUC/HtsR/7J5PXngS9yrLODfZbzSeyVzy8Cqw7qnXqsuuw3Umg56zdSaDnvO0KumEAgEPCMkFIgEAgEPCN07IFAIOAZoWMPBAIBzwgdeyAQCHhG6NgDgUDAM0LHHggEAp4ROvZAIBDwjNCxBwKBgGeEjj0QCAQ84/8HHUar/ZfKRtsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "KOLOR = ['grey','k','grey','y']\n",
    "\n",
    "for (r,c) in data:\n",
    "    vv = value(instance.U[r,c])\n",
    "    if vv==0 and data[r,c] == 0 :\n",
    "        ss= 300\n",
    "        K = 'k'\n",
    "    elif vv==0 and data[r,c] == -100 :\n",
    "        ss= 300\n",
    "        K = 'r'  \n",
    "    elif vv==1 and data[r,c] == 1 :\n",
    "        ss= 300\n",
    "        K = 'yellow'  \n",
    "    if vv==1 and data[r,c] == -100 :\n",
    "        ss= 300\n",
    "        K = 'gold'  \n",
    "    plt.scatter(c,1+Nr-r,s=ss, c=K, zorder=3)\n",
    "plt.xticks(range(Nr+1), fontweight='bold', fontsize=13)\n",
    "plt.yticks(range(Nr+1), fontweight='bold', fontsize=13)\n",
    "\n",
    "plt.savefig(f'Bonairo puzzle   .png', format='png', dpi=400)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1de93e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "D =[\n",
    "'0   0 1 0 ', \n",
    "'        00',\n",
    "' 00   1   ',\n",
    "'     1  11',\n",
    "'0  1  11 0',\n",
    "'1 01  1   ',\n",
    "' 0  1  0 0',\n",
    "'   0 0 0 1',\n",
    "' 00       ',\n",
    "'  00   0  ',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6b763c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "KOLOR = ['grey','k','grey','y']\n",
    "\n",
    "for (r,c) in data:\n",
    "    vv = value(instance.U[r,c])\n",
    "    if vv==0 and data[r,c] == 0 :\n",
    "        ss= 300\n",
    "        K = 'k'\n",
    "    elif vv==0 and data[r,c] == -100 :\n",
    "        ss= 300\n",
    "        K = 'w'  \n",
    "    elif vv==1 and data[r,c] == 1 :\n",
    "        ss= 300\n",
    "        K = 'yellow'  \n",
    "    if vv==1 and data[r,c] == -100 :\n",
    "        ss= 300\n",
    "        K = 'w'  \n",
    "    plt.scatter(c,1+Nr-r,s=ss, c=K, zorder=3)\n",
    "plt.xticks(range(Nr+1), fontweight='bold', fontsize=13)\n",
    "plt.yticks(range(Nr+1), fontweight='bold', fontsize=13)\n",
    "\n",
    "plt.savefig(f'Base Bonairo puzzle   .png', format='png', dpi=400)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "42397cbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0\n",
      "2 0\n",
      "3 0\n",
      "4 0\n",
      "5 0\n",
      "6 0\n",
      "7 0\n",
      "8 0\n",
      "9 0\n",
      "10 0\n",
      "11 0\n",
      "12 0\n"
     ]
    }
   ],
   "source": [
    "for c in instance.c:\n",
    "    print(c, value(instance.Vc[c]))\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9691cd39",
   "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": "3.6.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}