Eduardo Eloy / modfire

JuntarSubreg.ipynb 357 KB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hoje: 16/02/2023 16:39:12\n",
      "\n",
      "/home/eduardo/reuniao/2Eduardo\n"
     ]
    }
   ],
   "source": [
    "# 01.02.2023\n",
    "import datetime\n",
    "print(\"Hoje: {:%d/%m/%Y %H:%M:%S\\n}\".format(datetime.datetime.now()))\n",
    "import os\n",
    "print(os.getcwd())   # get current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pared Penaf PaivaWest  PaivaEast PaivaIsland\n",
    "#    1    2       3         4          5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time is: 1676565552.9816284\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "TStart = time.time()\n",
    "print('The time is:', time.time())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PP2File(fileName,LIST,modelName):\n",
    "    file = open(fileName, 'w')\n",
    "    file.write(modelName)\n",
    "    file.write(\"\\tWood\\tSoil\\tResist\\tPared\\tPenaf\\tPaivaW\\tPaivaE\\tPaivaI\\n\")\n",
    "    file.write(\"#\\t+\\t-\\t+\\t#\\t#\\t#\\t#\\t#\\n\")\n",
    "    k = 0\n",
    "    for p in LIST:\n",
    "        k += 1    \n",
    "        buf = 'P%06d'%(k)+\"\\t\"+'%.4f'%(p[0])+\"\\t\"+'%.4f'%(p[1])+\"\\t\"+'%.4f'%(p[2])+ \\\n",
    "        \"\\t\"+'%d'%(p[3])+\"\\t\"+'%d'%(p[4])+\"\\t\"+'%d'%(p[5])+\"\\t\"+'%d'%(p[6])+\"\\t\"+'%d'%(p[7])+\"\\n\"    \n",
    "        file.write(buf)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readPPfile(fileName):\n",
    "    file = open(fileName, 'r')\n",
    "    Lines = file.readlines()\n",
    "    file.close()\n",
    "    #------------------\n",
    "    LIST = []\n",
    "    for p in Lines[2:]:\n",
    "        lin = p.split(\"\\t\")        \n",
    "        val = []\n",
    "        for s in lin[1:]:\n",
    "            val.append(float(s))    \n",
    "        LIST.append(val)\n",
    "    return (LIST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pntNumber = 0\n",
    "MasterParPoints = []\n",
    "#-------------------------------------------functions\n",
    "def pnt_dominated(pnt):\n",
    "    # is pnt dominated by MasterParPoints? \n",
    "    #print('pnt=', pnt)        \n",
    "    for k in range(len(MasterParPoints)):        \n",
    "        #print('COMPAR ', MasterParPoints[k])\n",
    "        if MasterParPoints[k][0] >= pnt[0] and MasterParPoints[k][1] <= pnt[1] and MasterParPoints[k][2] >= pnt[2]:\n",
    "            #print('pnt dominated by ',k )\n",
    "            return True\n",
    "    return False\n",
    "    \n",
    "def insert_pnt(npnt):\n",
    "    if pnt_dominated(npnt):\n",
    "        #print ('ignorar')\n",
    "        return 0\n",
    "    # elimination of SumParPoints dominated by pnt\n",
    "    k = 0 \n",
    "    while k < len(MasterParPoints):        \n",
    "        if npnt[0] >= MasterParPoints[k][0] and npnt[1] <= MasterParPoints[k][1] and npnt[2] >= MasterParPoints[k][2]:           \n",
    "            del MasterParPoints[k]\n",
    "        else:\n",
    "            k += 1\n",
    "    MasterParPoints.append(npnt)\n",
    "    if len(MasterParPoints)%500==0:\n",
    "        TTemp = time.time()\n",
    "        print('pnt={:>10d}. ParPnt={:>6d}. Time  : {:>9.2f}'.format(pntNumber,len(MasterParPoints), TTemp - TStart))\n",
    "    return 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter(PPSet, nReg = -1):\n",
    "    # nReg:  1  2  3   4  5\n",
    "    # nReg = 0 : nรฃo alterar os indices dos pontos \n",
    "    global MasterParPoints\n",
    "    global TStart, TFinish\n",
    "    global pntNumber\n",
    "    \n",
    "    MasterParPoints = []\n",
    "    pntNumber = 0\n",
    "    \n",
    "    TStart = time.time()\n",
    "    pntNumber = 0\n",
    "    \n",
    "    for i in range(len(PPSet)):\n",
    "        if nReg < 0:\n",
    "            newp = [0,0,0]\n",
    "        elif nReg == 0:\n",
    "            newp = [0,0,0,  0,0,0,0,0]\n",
    "            for k in range(8):\n",
    "                newp[k] = PPSet[i][k]\n",
    "        else:\n",
    "            newp = [0,0,0,  0,0,0,0,0]\n",
    "            newp[nReg+2] = i+1\n",
    "        for k in range(3):\n",
    "            newp[k] = PPSet[i][k]        \n",
    "        insert_pnt(newp)\n",
    "        pntNumber = pntNumber + 1    \n",
    "    \n",
    "    TFinish = time.time()\n",
    "    print(len(MasterParPoints))\n",
    "    print('Time  : {:>9.2f}'.format(TFinish - TStart))\n",
    "    ResultSet = MasterParPoints.copy()\n",
    "    return ResultSet   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Arred3d(LIST, mult):\n",
    "    for p in LIST:\n",
    "        for k in range(3):\n",
    "            p[k] = int(p[k]*mult[k] + 0.5)/mult[k]        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pntclr(x3, x3_min, x3_max):\n",
    "    clrs = ['red','orange', 'yellow','green', 'lightblue', 'blue', 'magenta']\n",
    "    n = int(((x3 - x3_min)/(x3_max - x3_min))*6+0.5)\n",
    "    if n<0:\n",
    "        n = 0\n",
    "    elif n>6:\n",
    "        n = 6\n",
    "    return clrs[n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "######################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "814"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ParPenPP = readPPfile('ParPenPP.vmt')\n",
    "len(ParPenPP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.8147, 2.4447, 20.944],\n",
       " [2.8145, 2.4448, 20.945],\n",
       " [2.8145, 2.4447, 20.945],\n",
       " [2.8139, 2.4423, 20.948],\n",
       " [2.8147, 2.4449, 20.944]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ParPenPP[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219\n",
      "Time  :      0.04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "219"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ParPenPPF = filter(ParPenPP,1)\n",
    "len(ParPenPPF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.8147, 2.4447, 20.944, 1, 0, 0, 0, 0],\n",
       " [2.8145, 2.4447, 20.945, 3, 0, 0, 0, 0],\n",
       " [2.8139, 2.4423, 20.948, 4, 0, 0, 0, 0],\n",
       " [2.814, 2.4425, 20.948, 7, 0, 0, 0, 0],\n",
       " [2.8093, 2.4391, 20.96, 9, 0, 0, 0, 0]]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ParPenPPF[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Numeraรงรฃo dos pontos da subregiรฃo a partir de 1!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"ParPenPPF.vmt\",ParPenPPF,\"ParPenF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7909 2.815\n",
      "2.4358 2.445\n",
      "20.94 20.988\n"
     ]
    },
    {
     "data": {
      "image/png": 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O6ymsvALMuUI2T5zhh5h14rUcljrnXskoXwzcDJwOXAvc5Zy7zcxiwH8A7wVWASuccxdlXfNQvMCxyjn33wGf2YMf3hctWrR8/fr1E8d2795NS0tLMatYM1R31b3m7AK2MT0dNfoQhx/utTjKXfdde3exbWQby44Iuqu05dEXGfk1HMjYOKpxHsz7g4LvZbZ1P/300zc551ZMO+CcK+kDaAE2AWcFHLsVeL3//HrgbP/5RcDf+s/PB76e9b4G4HvAh/O5h+XLl7tM9913n6tXqnt9qtm63+ScS7jAf9k34VwC5wh5NDU5d9NN5a37TY/f5BL9CcdncFteCvtK6oi+yH/f5Nz6hHNJJh/rE155gWZbd+AhF1CJkmZVmVkjXjdU0jl3e8ApK4D1ZrYVOBtYZ2ZnAn8KXOSXXwa8L2sgfAD4jXPuitLdvYhUXETaVFRGFcD+/bmzqoqtb0Mfowe8u7pkA+yZ1uhI4HWwRQiaAJgarchEvzAlGxw3MwOuAZ52zl0edI5zbknG+dfjdVXdAdyRUX4+XlfVp/zXXwBa8XIMRORgFpEVlc+0t1xZVcU2NDz5gd9+wvt5aRe0t0LMOvCCRo4sqLAJfRWY6BemlC2O04BzgbeY2aP+4wwzu9DMLpzJBc3sGLw/NI4HHvavqQAicrCKyIrKZ9pbPllVxdTeOsMP3JKEW9vgZiMwHRcqMtEvTMlaHM65B/HWrsz3/PNDyq/HG//AOfdMIdcUkRrXz9SM1vwOAdDUVP6sqv6ufnru7GH0wCjnLIVvroa5ExP3QtJxtyThp2uid/mLJyoy0S+MZo6LSPXqxhvR7Jh+6D3+oaB8/IUL4dprobvMc+O6l3UzsHqAjtYOLu3KDBppAem4ubaGTXTAqQMVmegXRmtViUj1SuLtPbFzatHf4KXihtkRtihgGXW0hh3JGqvIHrv4Cd5quDvwZoj3nQFnVk/QALU4RKRaJYELmBY01hAdNAB6e0t2V5GSm5P03NnD/zh2MGykgmmjM5ljF0H7bnzySriiQhUKocAhItWpj2kz+/qAiE6dCQMDJbifPKTTcS/tgljgaKwxLR33pH6wRu952L4bl1aoQiEUOESkOgVkn0atS5UpVYFNw5ObkwwOe3fYHtZN5Rzc0Td1174l3XCcnxwa1sX2YlXsgj5BgUNEqlNWj06S/FMq4/Hc5xRTcnOSNXesmXg9NBxx8uGD3taw6eCxJQn/9a/e87aw95S5QjkocIhIdepnyh4UfYTOcJimp8zrAfZt6OPA+GQn2iUbYDzoZg14LVNngmdmVYXtu3FJ9SxwCMqqEpFqlU4k8rOq8ummise9oLFuXQnvK0DmjPFzlnqzxQNbR1uAx/zno4Nwc4wp4fA0/+eUrKq18OEyVygHBQ4RqV7d3iOZBDvXGyLI1tEBW7eW+8amam9tZ3B4MGDSX4YtwM+Zsp9I6KZN6QCS6IAzqytogLqqRKQG9PUFBw2zSu+54env6qcx1hgy6c/3GFlBIw9VNFs8kwKHiFS9sMUKnSv/7PAg3cu6+evX/XV4NhVEL+Ubpopmi2dS4BCRqrdgQfixZD7beJdYcnOSf334X6OzqQrdnTYRsM5KlVDgEJGaVu49NwLvwc+qCt6Dw3cSEJZVa1nDzVW2qGE2BQ4RqXq7ItYYKfeeG4H34GdVffsJeP+dsPXlgDGZJcCpeC0P5z8w+P218Prr/RaGVeWihtmUVSUiVS2ZhFgsfDZ4uffcCLwHP6sKvODxwCD88iJoyR4oX4KXp5vewm7hAvjaaXBqd1UHimxqcYhI1UomvXkZYUGjWrKqznjNGVNebxuBD90DY+NZJ+4BLsl4vXMnrFlTHQM1BVDgEJGq1dcHoxHZSGbVkVV1z2/umVZ2/WPw0Xv9Fw7YCrwf+HbWiQcOVMdATQEUOESkauUavxjP/ou+zHrv7qXhcw0T3VTZvv5z/4nD657KDhpp1TBQUwAFDhGpWlFpuFD+xQwz9d7dy5UPXUnKhc/qm5jXkSsuVMNATQEUOESkZpV7McNMA5ui98gwoL8Lb+LfJZGnVsdATQGUVSUiVWvnzvBja9fOfjHDoeFRntwxwt6xcZobYpzQNo/21twz9ZKbk7z7hBSXdnmtip2jcEgc5h0yec648xc6vJLwLqq0ahioKYACh4hUpWTSG/wOW9iwGEHjkeeHSfnX3zs2ziPPe1O/o4JHcnOSH/3XmimLGR4+d/p58fTyuB/A2xI2LHh0VO8M8TDqqhKRqlTqhQ2f3DEyETTSUs4rj7yvDX38w8oD4YsZZmsALg05lkjUXDcVKHCISJUq9cKGe6dNsoguTxsaHopezDCbA94ccmxgoOa6qUCBQ0SqVFiiUbF6dpobgr/+wsrT2lvb2R22HlWQFMFZVQsX1mTQAAUOEalS/f1eT06mYvbsnNA2b3Icwhc3rzzyvrr6mduY54c44KoZ3V5VU+AQkarU3e315HR0eOMaHR3F7dlpb01w8qLWiRZGc0OMkxe15syq6l7WTSxwX1gmFy90eC2NbwAfCjk3auXGKqesKhGpWt3dpevNGRoe5bHnhzngD5CnxgNG4gMkNyf5qxOgITN4JIE+vC6p/C5Tc5P+MqnFISJ1Z2h4lE3PTQYNgP3jjoefG2ZoOHxxrOTmJGvuWMNVv8jI+EoCPcAg04OGGTQE/H3e2FiT2VRpChwiUnee3DES2DAYJzodN71h04e+DyPpAfI+greFNYMbb4Trr/cGwtMWLoTrrqvZgXFQV5WI1KGolNuoY+kNmyBjr42odajSwaGGg0QQtThEpO5Epdw2hY58w4Jmb9XFc5ZmFIYNVdTwGEYuChwiUneiUm5d0HT1LJd2MZlZ1Y+3HWymGp0Rni8FDhGpO1EptwcC4kZyc5LmLzSzc+9O//0ZB7uBAcDfMrzoecNVSGMcIlJ3ojKnsruxkpuTvO/f3se4mxz7GBqGzkMzTur2H8Txtvo7uKnFISJ1JypzKrsbq29D35SgAXDJBtgTuOxIBTcIKSMFDhGpO1GZU9ndWJmZVJlGD3hzOSaHRLqAWa71XiMUOESk7kRlVWV3Y7W3Ts2OOmcpfHO1tweHmffwbMSbDXjwU+AQkboTlVWV3Y3V3zU1O+rSLkL24kjhzQY8+ClwiEjdSCahsxM6D0tMdDE9sPV2PvDdUzh7/WI+8N1T+P5vb+U93zG2vmyMO+Ntx13IOUu9lsaWi6Ejci+OqNmABw9lVYlIXUgmoacHRv2eqBefjfHLA7dx1S8+waupvQDsGN3G1b/4KOveMZk1tTCxm2vf6WXaHpLzG/PgnfSXSS0OEakLfX2TQQMgefk8ko99cSJopL2aOsDn7p/63jkN+QSNON5swINfZOAws/lmdlxA+YmluyURkeLL3or2wbsT7Bh9Nvjc4Zl8wg34kzkOeqGBw8zeDfwS+I6ZPWlmp2Qcvr7UNyYiUkzTlo5algQXvC5VQXuKA15roz6CBkS3OC4BljvnXgusAW40s3f5x8JXARMRqUJTtqJdloR3XgCx4PkcZ7xm6ms3njlfI0h9TPxLi+q1izvntgM4535uZqcDd5nZseS/x5WISFVILx3V1weDXX3QEDj1G4B7fjP1tU38vyBrqZeJf2lRLY6RzPENP4isBN4JnFDi+xIRKapkEi6+GAYHgdbotNlpYxyhQcOot6AB0YFjLRAzs+PTBc65EeBtwF+X+sZERIolmYQLLoCdO/2C0QWR5y9ozioI7WOpj/TbbKGBwzn3mHPuN8AtZvZJ8zQDlwO9ZbtDEZFZ6uuD/eE9U9HGgN/i7Ss7RYJ6Sb/Nls88jj8BjgX+E/gF8CxwWilvSkSkmLJTcUnsijx/1168VsYeYBPwTeDDwE6/nA68TTjqJ5MqUz6B4wCwF2gG5gBbnHPhS0uKiFSZaam4w9FdTAuagZuBI4DXA5cB/wIcDVwEbOmnXoMG5Bc4foEXOE4B3gicY2a3lvSuRESKqL8fmjIXJtzQD2ON0W/qA7L3e9oP3AI8Vh+LGYbJJ3D8b+fc3zvnDjjntjvn3gl8t9Q3JiJCEujE+6bqpOBVyx/Z/jIvv3qAn7d9g/lfOAb7TIwFXzyWP3jtfvj360IHvXftJXy9wh3AaH0sZhgmZ+Bwzj0UUHZjaW5HRMSXxJtXN4j3BT/ov84zeDyy/WW2vLKXkVdf5upffIIdo9twOHa9+gxb/+CDXvAI6bJqbyU8YaoNSNRnNlWaFjkUkeoU1FU0St5bXmx9xVu8cNfe7dMWMtyf2suO4/8eNlwKB6bm3iYaob8LL2Fq6maA0AS8GzipPrOp0koWOMzsWDO7z8ye8te6ujji3FPMbMzMzs4qn29mz5jZ1zPKlpvZZjP7rZn9s5lp+RORg1FYb1CevUTpXqix8QOBx196dRts7obvDgDeVL6OVhhYDd3L8Ma+B/ASqAyvpfHXeDmlS+p3YBxK2+IYAz7mnDseLy/hg5mTCdPMLA58CfhBwDU+DzyQVXYl8H7gNf7jbcW8aRGpEmG9QXn2EqXXlmqIBQ+CH3bIYm/Nqj/7FACL53stje5lwBV4GVXvxWvlfAn4Gl7QsHh+N3AQK1ng8AfSH/afjwBPA4sDTv0Q8B3ghcxCM1sOLCIjoJjZUcB859xPnXMO+BZwZkkqICKVFdRVlOecu2QSfnhLM87BguajOCQ+tTuqKT6Htj1dsLoH5m8D4JlXoOdOSH4Z+CTwon/yi8D/AX7ivz6uvhY0DFKWMQ4z6wROBn6WVb4YeBdeKyKzPAZ8Bfh41qUWA89kvH6G4GAkIrUuu6uogDl3fX1w9T8cyveSzbQ0HcoHTvkybYnFGMaC5iPpbD2BX8dugqapgyijB6Dveby020zpNNzfXwun1t/aVNnMRa8VPPsPMGsB7gf6nXO3Zx27FfiKc+6nZnY9cJdz7jYzuwhIOOe+bGbnAyuccxeZ2QrgH51zq/z3vxH4pHPuLwI+twd/reNFixYtX79+/cSx3bt309LSUorqVj3VXXWvKbuAbXhf3E14fyZGLzMFwKZN0NI6zpyFO2nG8bt9v5s41hSHxfO8SX5j4/DSXmid45VP2O9/bvYE8+XLZ1efMpvt7/3000/f5JxbMe2Ac65kD6ARuBf4aMjxLcBW/7Ebr7vqTLyEuyG/fAfwCvCPwFHALzPefw5wda77WL58uct03333uXqlutenmqz7Tc65hJv6rznhl+fwrvP3uN7vft0d8vlmd9nNlzk+w5RHoh930+PeRcfHQ746duPcOTiH/zg8Xpp6ltBsf+/AQy7gf5xSZlUZcA3wtHPu8qBznHNLnHOdzrlO4Dag1zl3h3Ou2znX7pd/HPiWc+5Tzlva/RUze71//fcB/16qOohIBc0iHfc9Hxnhll9O30984jIHoG+D9zw0L3MucKn/vAm4RGMbaTm3X5+F04Bzgc1m9qhfdgl+ToRz7qoZXrcXb+vaZuB7/kNEDjazSMe1pnF2huwnPnGZfPYVbwcOj3tB48Ma20grWeBwzj1IAVvMOufODym/now9zp03k33p7O5ORKpeO95s8aDyCL29cMq50NJ0KCP7Xwq/fD77isc64IWteZxYXzRzXESq0wzTcQcGwLD0OGigidnhELFJU/3ut5GLAoeIVJf0wobn4vVZpL+l4sB55EzHTaWg5VDHP73tXt563PsCzxn4C+heijdH4xt4aTjjeNOWx6He99vIRYFDRKpH9sKGe5jceS8F3EDORQ7ftHqUcTfGL1/8BRu3Tt8BYuEe6D4J79vvCLwpyEvwAlMjfl7uVhQ0wilwiEj1CMqkypRHVlXP343QEG8g+Xh4VlX0BZQ9lYsCh4hUj3wWMMxxTvN8r4kSllW1K3vcJNs6ZU/losAhItUjnwUMQ85JJqGzE17Y5n2tLUwcHfz2fNJwJZICh4hUj6BMqkwhiU7JpNfDNDgIycvnsW9fiu4TPz19ccMx6N8Qcf3jpy3gLQFKOQFQRKQw6fHoPrwuqfS6VLvwWhr9BI5Z9/XBqD828uDdXuTp/tTb+cApjoaXva6r2J7DuPb7L9G9OeLz9+yZbQ3qggKHiFSPXrws2BReKu5u4FX/2O7wtw1mTBQ8hyRfu/ti2u7eCcD9l13G2GeMq/grusmxYMVQfe8lni8FDhGpDr1M3WDBMRk0AHYCF/jPM1odyYz03HNIci1rmMPUXf/iOHpzBQ2A9vreSzxfGuMQkeowkMc5+5mWjtuX8fpS+qYFjbSc6x8lEtCvmeL5UItDRCovidc9lYfxQYibt05Iy6Hj/OXa3fzm8UaOnf9vdKwfjFhCJEJHhxc0ujXpLx9qcYhIZSWBNfmf7o1CGGDsfjnOt/5pHm9Z9SDXXPt+bCY9TfE4bN2qoFEABQ4Rqaw+COldmuZVvL0ZMqXGYlz31eNpnrPXy7pqLPDzNVO8YAocIpK/Xrw1ncx/tDB97aj0IoUx/2fSf7RkvC/96CR46fQAL+I1TL6dWbgsCZ9o43drjsU+C23bIPk1YGHABY4+Gtau9VoY4P1cu1YzxWdAYxwikp/srCfwFiE8z3/ezeQihen1pgbxMqEOEDz2MIgXQKLGJRqB6+CUvqlptyxLwjvXQMNkc2XnXrhgP3AfdC8D6ICNy73NX9MUKGZNLQ4RyU9Y1lOKyUynoEUK9xMdGHINZh/wrtvfDw2NGSd39U0JGhMfl5rcFlb7aZSGAoeI5Ccq62ko62exDXlj1x+89GXmHZqCt/dCa3gf1+S2sBrwLgV1VYlIbjn2wJhYeDBsu9dcFuKNgYS9tx0eGNzBm1Yf4D/mvpPNL/w4+nZawRuMkVJQi0NEcovaAyPOZI9Q0CKFTeQx+85/b1NAeaN3bMc+r1sqV9Boiqe3hVW2VKkocIhIblFdUDcw2SPUjTcW0oEXLDqAa4EbgbkR19jlv/dapmZELQSuI+8ep4XNcO070wPjGgQvFXVViUhuYV1QHUz/Uu8OKEs7D0h5PV8X4y0/BbDQ4GtJfw7eDIcl4gY7/jb9Kp8mjsyUWhwikltQF1TI3hiB0mm6qcmJ4jszDu8chwsumLpgYaZHtr888XzZEW8MPKdneeYrR+6BGZkpBQ4RyS2oC2qA/FsHGWm6YRPF9++fumBhpq2vTO4d/pm3/N8pwSNusHYFrHtH0IdKKairSkTyM4tupMxurqikq7DtMNKzNx7YejvXbPo/fLHrZS5cAfEYWGivlPbWKBUFDhEprd7Ap4GitsN4YOvtfP1nH+GKtx7gg6dGBYyJq+V5g1IodVWJSGkNBD4NFLUdRvLxL5JyB7hwRT5BAzRrvHTU4hCRYEkm9/6O2O87p1Tg0wCOxIrnuP1X/vqH85t5asfdHDbSz5kn/Zp3fTzlrY2V95+7mjVeKgocIjJd0GKF6fl0hXwfZyU2xYDxkFPbjk5NjGU44L+HR2kee4m3n/o0lv6maiHPjZo0a7yU1FUlItMFLVY4SuGJSlnnN4ee6Oj+yMiUEjPDEu+cDBoTB8gjeGjWeCkpcIjIdGHZTYPQ0OCNMTQ0QG8v3oh3A/QaNFjWsYzrJPF6msK8afW+6Z8X1XIY91ZLd9OCyFo0a7y01FUlItOFzBQfBFL+QEUqBVdm7M+RuVXHxLEmWLd/sucrzLxDgzuwLGxUZBT4d7BEB5y5NeLKUgpqcYjIdAEzxfcwfdtW8DKlwrKlBvZ7P4N6vnJzHB2/AzeWVTwGPArEE3CSMqcqQYFDRKYLmCn+frK2bfWlCM+WSpfnmoq3ezjGkvnNpMbHcM6RGh+jc/51/Mnv/w0jz3UyvifmdUvtAfczvH1kTx2AJcqcqgR1VYlIXsKmTsT8Y0HBIz1CMa3na1nS28GvdQiG22nZ9HlOPupcXjdwGOcshUu7vD01du+H+cdsnXzfXOA0CF5dUcpFLQ4RmS49KDGIl8E06DVAzgk4dRxYGXKZni4gkdXztSwJq3vg0EEwB4cOsm/Vhaz61irOWQrfXA2dh0LMoKUJxrKGP7zB8DNmVz+ZFQUOEZkuYFBiLnBpyOm/xctlSrcw4nFYuxbW/QgYgLOOTjGA49i4g65LoGnqxQ+4UTZs2cClXTA3azOnhqxvKW/W+D2F10mKRl1VIjJdyKBE2OpPQwbrxkOSYLvheyteoBm4Ajh7/e9Cp2F4W77O4galLNTiEJFpdi8ILg/7uo5anBCgOaPZsDBxdOh5Q8M5bmxCyA1KWShwiMg0lzB9sl5YOm48Hr04IcAJbfOI+6Pr3Sd+mkPiU+eQp19fsgH27J/63uwxDqk8BQ4RmSoJH93pLQ/i/McY3tbfQem4N9zgb/ka4cd3Jbjhi604B2/qPIuVne8mZt6ISMzirOz8y4lsquZGGEvBuPOyqrLHODy7ZlFBmS0FDhGZ5GdTdTKZZmt4g6FrmJ5V1dGRO2gkk9DTA/92fYIXn43xwNbb2bj1Fsadl8A77lLct/UWVv3eZDZVQxz2HoBHnwu7qvbaqCQFDhGZFDHFOzurKp8uKvC2gx31r5m8fB7Jxy7l1dTeKefsT+3jc/dnfV6TF0iCacZ4JSmrSkQm5UhWSv+dP3cuXH117tYGeNvBvuEdo8RW9/Lg8zcxvjd4nnnQwPjieWFX1eS/SlLgEJFJIYsbpsU6wG0t7JJnnjfKyJt7+dHgDdEfHZCKG7zTX0dhNyBFp64qEZkUsLjhhAQz6iF6z0dG+I+hmyLPSTRCf1c+V7OZ3YQUlQKHiHjSW8UGjXHEgPOYUQ+RNY1PDIQHiRmcdxJ0L8t1pQbgxpndhBSVAoeITF2bKsg4cA3TtoLNpbcXXnmJidTbwEs7uOYRSG7OdbUDKGhUBwUOEclvw4z9FLx17MAAGMaf/d57oy+dgr4NUWfk1Y8lZaLAISL5L/1U4BJRqRS0HOroOeWLvPW486IvPQzeUolZqxzSBfyosA+WklLgEJH8l34qcN5dPA47tnuT/n70XzdHX7q1A2+ZxFeZnLPuUNCoPkrHFal3SeCVPM5rouCEpp4e+Or19/Dr4y6KvnS8if4uZUvVCrU4ROpdH964c5SFwLUUPDa9bh082/nZ6Es3L+Tad15L9zINfNcKtThE6l2ucYuFwNeYUULTZ68Y5Z/OvJW2xNHsGN1G8rEv8uDQHVPO2fG3Owq/sFSUAodIvVsA7Iw4vhO4wH9eQPD47BWjvGblMHOajwHgiLnHsvbUywCmBQ+pLeqqEpHcZpCKe+SJI8yZuu0GcxoSdJ/06YnXXUuUZluLShY4zOxYM7vPzJ4ysyfN7OKIc08xszEzO9t/3WFmD5vZo/57L8w49xwz22xmj5vZ982srVR1EKkL+W5tUUAq7qpVsPCo4B2Y2hKLwXlB40fvU8ZULSpli2MM+Jhz7njg9cAHzez47JPMLA58CfhBRvF24E+dc68F/gT4lJkdbWYNeL2tpzvnTgQeB6LTNUQkWr4ptnmet2oVbNjgpeEG2fFsA3zWKWjUsJIFDufcdufcw/7zEeBpYHHAqR8CvgO8kPHe/c65V/2Xh2TcZ3pfmblmZsB84NnS1ECkTkQtbJhWQCruBn8GePLyeeybuu0G+/Z65V3qoapp5pwr/YeYdQIPAEudc69klC8GbgZOx0v2u8s5d5t/7FjgbuD3gU84577hl5/tn7sH+A1e62PaCmpm1oO3+g6LFi1avn79+olju3fvpqWlpfgVrQGqu+oeaBewDW8sIwb7W8fZuyiF85eYcg72jcQ56ojJvzV37YJdw+O0to3T0OhIjRmv7HuJOYc0kmicO+0jDhwwdj0fY/dwjOXLi1q9SPq9z7zup59++ibn3Irs8pJnVZlZC16L4sOZQcN3BfBJ59y4ZS2875z7HXCimR0N3GFmt+H9570WOBn4b+BfgE8DX8j+XOfcADAAsGLFCrdy5cqJYxs3biTzdT1R3VdW+jYqIu+6J2HrbXt45HOv4LJW/mhIwf0/aOUfPpwgmYR/vXWU939mmHizN7/7wa2309rSxolHnsp41r/nVAr++W9befDuBB0dsHVrsWqWm37vK4t+3ZJmVZlZI17QSDrnbg84ZQWw3sy2AmcD68zszMwTnHPPAk8AbwRe65f9l/OaSrcA/6NU9y9Sd/rg6Q/tnhY0wFs+5MgTR7zT+uCv/mZq1lTy8S9y4pFvJPuPwPR7uz86QiKR33azUt1KmVVleAsxP+2cuzzoHOfcEudcp3OuE7gN6HXO3WFmx5hZs3+dw4A3AL/Ca0wfb2aH+5f4M7yxExEphiHYG5INBZOZUkND0JZ13s7R6OHGw48aZ2Agv+1mpbqVsqvqNOBcYLOZPeqXXYKfm+GcuyrivX8MfMXMHN5g+GXOuc0AZvZZ4AEzO4C3e8D5Jbl7kXrUDs3bY+xdHBw8dm6PwR9De7uXNXVExnlRe24AJJpi/E8FjYNCyQKHc+5BvC/9fM8/P+P5D4ETQ867CogKOiIyU/3wx//SEjjGkUrBc4/Pg7d43U0DX5vLBz7rdVddfPfppNwYjz53P6898s2B3VUntM0rUyWk1DRzXEQmdUPn2XNZ+o/zaNzFxMrm+/fBU/7AOHjdTQ8nPsWVnzNe2BbjmZFfAfCF+9/Do8/dj3Nu4rF/bB8rjmylvTVXzq/UCq1VJSJTdcNrult4DVlpnCdNfbnnj9bxIN/gwR9PLf/C/e+Zdkn3D6VP+5fyUYtDRGZkYeLovM6L5xj7kNqjwCEiM9J94qdznwT0LO8p8Z1IuamrSqTeJfFWvh3Cy3nsJ3L59AcGd7Bj3wHe1HkWf3T4KbQlFofutQGw7h3rSnLbUjkKHCL1LIm3MM+o/3rQfw2BwSMdNNKOmHvsxM+gvTbUTXVwUleVSD3rYzJopI0SuvdGZtDIlr3XBqib6mClwCFSz8L22BjEm4XVidcqyVNbwlsAO25x1q5Yq26qg5S6qkTqWTtekAiTo+sqW8xi3PPe53j7cYtmf29StdTiEKln+ezFEdF1FUQzxA9+anGI1Ls8FgZyQ44fD+7MeZ5miNcHBQ6RepUELsDbvCmH0aNSkQPjAGf94VFFuS2pfuqqEqlXfeQVNMbmjPPkR0Yiz2lp0FdJPVGLQ6RehWVUAQ5vbanRo1M8+ZERnlm9L/JSf67B8LqiPxNE6lV7pW9AapUCh0i96gcCtogFMP//5j7bwOv+rpVj7pwTehl1U9Uf/cZF6lU3cC2wMPq0hn0xln51Hm1zGqcda2mIqZuqDmmMQ+Rg1gtcCVwGnI6Xensh3sbOfURP/suQ2N7AmzraSnOPUnMUOEQOVumgkcn5ZVcDwduKB9N4iGRQV5XIwWog4lghQSOBNx4i4lPgECm3JN7igTH/Z6//0/D6ADIXF+z1X2c/erOu2Zvx3gb/daoI99qBF4DyWKdK6oe6qkTKKWj/i8zupFRG+XmEf/mn37OO6V1SKaZ3Uc1EHNhahOvIQUctDpFyCtr/IkyuFsNA1s9C5PMvX1tpSAgFDpEovXj/StJdRPMI3p8iu6toFXAIU7uX5pB3FlNeUlk/8zQOdB4GybA0XAPW4rVmRAKoq0okTFBW0m7gfP95d8h5KWBDwPVeLebN4XUlpX8WEDyGgMGd0JMAboJujV9IgdTiEAkT1gU0xtT9KQrtKspjGXNgMjCE6cn6mYdXgUv856Oj0FfAPhsiaQocImGi/orPXCCw0Owlh5etlCuA3IDXZRQksytpnf86swUS8L4XgTXAtzPKhiIWOhQJo8Ah9SEJbGYyBTaffbSj/nVkTojL1TLI1oGXrTQe8d44XlfYOrxAk/3IHn9Yh9cScv7PdUxZSmQzcARTgwZAuyb2yQwocMjBL50Cux/vizW9j3ZU8Ej65wYxpk6IKyT7KN/3zjajKQlkbNjXGnLaGWfM8nOkLilwyMEvKAU21z7afYQHjgVMnRAX1FXUxfSVZw8BbszjvcXIaMqq23DIaffcM8vPkbqkrCo5+IX140f170cd2xVQto6Zf9nP5r1hMu6/Fzgu7DSNccgMqMUhB7+wfvyo/v2ZHqsWc70fQRnFmRYsKMfNyMFGgUMOfv14C/VlyrVwXz8wffsJr/upFhb82+P9mMmkcpFcFDjk4NeN9w3ahDc4nc/Cfd3AdUzd5Ggh3sZHVTJhrrcXGhrAzPvZ24s3KN7GxPhMrkzhXUHdbiI5aIxD6kM3sJHClhPvpmqCRLbeXrgyow8qlfJfXwXr/KCxKo/rKB1XZkItDpEaNBDSBzWQkQkWtOpJpkQC+muh202qjgKHSLn5+3H0mtfNlPlYldVMWLVq+jkx81oYQQqZxD4woHWqZGYUOETKyZ+M2DsYnO20YcNk8Fi1ynudLWx6CeS/DBYoaMjMKXCIlJM/GTEq2ykdLIKCRi5zM553RZzXFXVQJAcFDqlbf78KjvG7fo4x73W2oMylZC+0ZHQdxQ16M98b0hUVi0HS348jV5eSFdJ0yLAHJjLBfgR0BayF1dUFP/rRzK4vAsqqkjr196vgsg2w13+9De81q+Bz/pdqWOZSdhfTOHCl/951a/C6okann+ectzotFLyFRt7a0wso+tLxYeNG7/NFikEtDqlL12YEjbS9fnlaWOZSmIEN5OyKOgB8mtLsyhozZUlJeShwSF16No/ysMylMCmYWCMq6q2/A9Z1hG+1MRNzGuBbN2rAW8pDgUNqWjIJnZ3e+EFnp/c6H4eFlB+d8Tw2g38dvQlvfahckv3eRD2X9ShkbCPzfXsPKGhI+WiMQ2pWMgk9Pd4WqACDg95riP4STfbCSEB5E3BB1+S1Z+LKPfmdF3Sfvb35j0MoK0oqSS0OqVl9fZNBIy2ffbT7BryxhmwtTA6M9/XBeMDyJHPnwsK508sLFXSf+Y6pKCtKKk2BQ2pW2F4SQ0NT02jjcWhpgU2bvO6swZABiJfw3heLea2XbH/w2le55v9t5+qHtnPb095j/ePbecM7sneJmtn95xpTSXdLKWhIpSlwSM0KW6AvkfDSZtNfxOPjsMfvQhocDJ9dvcC894V1F239ZSP/7945U+ZmNDbBxf80PKPgkX3/8Yi9y6OOiZSbAofUrP5+L0hkSiRgb3aebRbH9OCRAHbnGF/Yvy9G8qvzppXHYtD90aBRk3BBCwz2ROToRh0TKTcNjkvJDA2P8vgLr7B/3PtGbjQ4aVEr7a3ZuyoFv/fJHSPsHRunuSHGCW3eF/aTO0b4/u1N3PSV+ex6PkZmCJg7F973vqmT9sI4oCMOQyloj0N/D7w3j/ft3B78p3/bUd6AyFo/xzbqHjo6vKCRPYC/zt8+9qqrprZ61q6dPCZSDRQ4pCSGhkd5+LnhKdtfHHCw6blhgMjgMTQ8yiPPD5Pyvzz3jo2z6blhDNh45xyu/vtWXt03vbG8Z09+QQO8L++tW6eWvf9b4+zdE90IX3hU8EDEju0xOjomv+Bn+kW/bp2ChFQ/dVVJSTy5YyRwzyTnH8v13lRWt5HDW9oj+dV5gUGjEBYyw/r9fSM0NIb3Vx0yZ5zuj0y/9/FxuO0b8zRrW+qGWhxSEnvHwrfaizqW63hYV1EhnAue5/Hms0axxnGu7Z/PyMt+cDLAQdvRKbo/MsKbVu+bdq2bv9zKmrMSmoAndUOBQ0qiuSEWGgCaG6JbDFHvXXhUih3Pzu4/246O8M990+p904JDlERjjNuvyz1mI3IwUVeVlMQJbfMC/+My/1iu94bp/sgIh8yZGlSaDsl/I/Go7VJz3Ve2uBX+HpGDQckCh5kda2b3mdlTZvakmV0cce4pZjZmZmf7rzvM7GEze9R/74UZ5zaZ2YCZ/drMfmlm/7NUdZCZa29N8LojW2mKTWY9NRosPzJ3VlXU8Tet3seFnx+m7egxzBxtR49x4eeHice97KObboKFCyfPb2nxXptBU1P0dqntrQlWHNma1z+K5oYYJ+eZISZysCllV9UY8DHn3MNmNg/YZGY/dM49lXmSmcWBLwE/yCjeDvypc+5VM2sBnjCz7zrnnsVbuPoF59wfmFkMWFDCOtS9oOyoGPC6PAPATL5YH9n+cuTx7O4kA772icnjYYFh40ZYuTL6s2d6zyL1pGSBwzm3HS8A4JwbMbOngcXAU1mnfgj4DnBKxnv3Zxw/hKktowuAP/LPGwd2FP3mBfCCxkN++mymcZgoL/aX7CPbX2bLKzlm8GXpnN9c1HsQkWhlGeMws07gZOBnWeWLgXcxfbO0dFfX43jbF3zJOfesmR3qH/6835V1q5ktKunN17F80maLbWuBQWPJ/GZOPurQot+HiIQzV+L9JP2upvuBfufc7VnHbgW+4pz7qZldD9zlnLst65yjgTuA1Xj747wI/KVz7jYz+yhwsnPu3IDP7cHfaG3RokXL169fP3Fs9+7dtLS05HX/u/buYtvINvan9k8pj1mMjtYOFjQXt6ds74EUr2Yty5poiNMUL06Mz7fu+1PjjI7l3sno0EMai3FbE15+NWjd2uJ8XiG/94ON6q66z8Tpp5++yTm3Iru8pIHDzBqBu4B7nXOXBxzfwuSaEW3AKNDjnLsj67xrgXvwurR2A/Occ+NmdizwfefcCVH3sWLFCvfQQw9NvN64cSMrc3V2A8nNSXru7GH0QPACdjFifOusb9G9rDgJ/FHdNCvyGFPIRz51z565Haa5Icbbjytug+/ffrWdsI+d7efl+3s/GKnuKyt9GxUx27qbWWDgKGVWlQHXAE8HBQ0A59wS51ync64TuA3odc7dYWbHmFmzf53DgDcAv3JelLsTWOlfoovpYyZF07ehLzRoAIwzTt+GHJs/FCCqm6YU3UJRn5UraEBpUlGjxiuU+ipSHUqZVXUacC6w2cwe9csuAdoBnHNXRbz3j4GvmFl6IdPLnHOb/WOfBG40syvwuq3WFP/WvdbG4HDApgxZhoZDNoWYgajv6lyzrYsp12flm1U1E+nxisyWVyk/T0QKV8qsqgcJ3/og6PzzM57/EDgx5LxB4E2zvb8o6S6qfLS3hmwKMQP+6haBcs22Lqawmdul6JoKcvJRh2rAW6SKaeZ4gFxdVGkxYvR3FW9lu2rppjmhbR7xrJCvWdIikqa1qgLk0/00t3EuV6++umgD41A93TTpz8reD0NdRSICChyB2lvbI8c3Olo72PrhrSX57GrpptEMahEJo66qAP1d/SQag780E42JonZPiYjUGgWOAN3LuhlYPUBHq7f+dty8PSA6WjsYWD1Q1O4pEZFao66qEN3LuhUgREQCqMUhIiIFUeAQEZGCKHCIiEhBFDhERKQgChwiIlIQBQ4RESmIAoeIiBREgUNERAqiwCEiIgVR4BARkYKUdM/xamFmLwKZy922ATsqdDuVprrXJ9W9Ps2m7jsAnHNvyz5QF4Ejm5k9FLQBez1Q3VX3eqO6F7/u6qoSEZGCKHCIiEhB6jVwDFT6BipIda9Pqnt9Kknd63KMQ0REZq5eWxwiIjJDNR04zOxYM7vPzJ4ysyfN7OKAcz5hZo/6jyfMLGVmC/xjF/tlT5rZhzPes8DMfmhmv/F/HlbGauWlhHX/jJlty3jfGWWsVl7yrHurmd1pZo/556zJOHae/7v9jZmdl1G+3Mw2m9lvzeyfzczKVad8lbDuG83sVxm/9yPKVadCFKH+3zezl83srqz3LDGzn/m/+/9rZk3lqE8hSlj3681sS8bv/rU5b8Y5V7MP4Cjgdf7zecCvgeMjzl8N/If/fCnwBJDA20L3R8Dv+8e+DHzKf/4p4EuVrmsZ6/4Z4OOVrt9s6w5ckv69AYcDu4AmYAHw3/7Pw/znh/nn/Rx4PWDA94C3V7quZaz7RmBFpetXyvr7r7v8fwt3Zb3nFuB/+c+vAtZWuq5lrPv1wNmF3EtNtzicc9udcw/7z0eAp4HFEW85B/i2//yPgZ8550adc2PA/cBZ/rF3Ajf4z28Azizyrc9aCete9fKsuwPm+a2GFrx/QGPAW4EfOud2OedeAn4IvM3MjgLmO+d+6rx/Td+idn/vBdW9bDdfBLOsP865DcBI5sn+eW8BbvOLavnffEF1n6maDhyZzKwTOBn4WcjxBN4/ku/4RU8AbzSzhf6xM4Bj/WOLnHPb/efPAYtKdd/FUOS6A1xkZo+b2bXV2E2XKaLuX8cLkM8Cm4GLnXPjeP/Qfpdx3jN+2WL/eXZ51Spi3dOu87sq/q4au+myzaD+YRYCL/t/RMHB+bvPpd//N/9VMzsk18kHReAwsxa8L8UPO+deCTltNfAT59wuAOfc08CXgB8A3wceBVLZb/L/+qza1LMS1P1K4DjgtcB24CuluvfZylH3t+LV62i8unzdzOaX9QZLqAR173bOLQPe6D/OLeoNF5l+90Wt+6eBPwJOwevG/GSue6j5wGFmjXj/Iyadc7dHnPq/mOyqAcA5d41zbrlz7k3AS3h9hgDP+10X+D9fKP6dz14p6u6ce945l/L/SvkmcGpp7n528qj7GuB25/ktsAXvH8c2praujvHLtvnPs8urTgnqjnMu/XMEuJkq/b3DrOofZidwqJk1+K8Pxt99KL8LzDnnXgWuI4/ffU0HDr85fQ3wtHPu8ojzWoE3A/+eVX6E/7Mdr4//Zv/Qd4F0xsl52e+rBqWqezpg+t6F161VVfKs+xDeYCBmtgj4Q7zB4HuBPzezw/xuuD8H7vW7Jl8xs9f7138ftft7L6juZtZgZm3++Y3AX1CFv3eYdf0D+b0K9wFn+0W1/G++oLr756X/SDa8sZ3cv/tCRtKr7QG8Aa8b6XG85tmjeP31FwIXZpx3PrA+4P0/Bp4CHgO6MsoXAhuA3+BlHC2odF3LWPcb8fpGH8cLoEdVuq4zqTteU/0Hfl2eAN6b8f4LgN/6jzUZ5Sv8c/8Lr6/YKl3XctQdmAts8q/5JPA1IF7pupao/j8GXgT24o1lvNUv/z28rLrfArcCh1S6rmWs+39knH8T0JLrXjRzXEREClLTXVUiIlJ+ChwiIlIQBQ4RESmIAoeIiBREgUNERAqiwCFSQWErlopUMwUOkcr6J6p8eQ+RbAocImVgZqf4i8jNMbO5/l4JS10RVywVKZeG3KeIyGw5535hZt8FvgA0Azc556pyWQ+RXBQ4RMrnc8AvgH3A31T4XkRmTF1VIuWzEG9znXnAnArfi8iMKXCIlM/VwN8BSbz9UERqkrqqRMrAzN4HHHDO3WxmceA/zewtwGfx9ktoMbNngP/tnLu3kvcqkotWxxURkYKoq0pERAqiwCEiIgVR4BARkYIocIiISEEUOEREpCAKHCIiUhAFDhERKYgCh4iIFOT/B6O0i/0Jwj/0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :     15.47\n"
     ]
    }
   ],
   "source": [
    "PPSet = ParPenPP\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min,x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show()  \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7911 2.815\n",
      "2.4358 2.4447\n",
      "20.941 20.988\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :      3.93\n"
     ]
    }
   ],
   "source": [
    "PPSet = ParPenPPF\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min,x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show()    \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "742"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaWestPP = readPPfile('PaivaWestPP.vmt')\n",
    "len(PaivaWestPP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.2948, 2.6232, 17.355],\n",
       " [2.2948, 2.6232, 17.355],\n",
       " [2.2948, 2.6231, 17.355],\n",
       " [2.2944, 2.6224, 17.352],\n",
       " [2.2944, 2.6224, 17.352]]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaWestPP[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "251\n",
      "Time  :      0.04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "251"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaWestPPF = filter(PaivaWestPP, 3)\n",
    "len(PaivaWestPPF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.2948, 2.6231, 17.355, 0, 0, 3, 0, 0],\n",
       " [2.2944, 2.6223, 17.352, 0, 0, 6, 0, 0],\n",
       " [2.2945, 2.6227, 17.353, 0, 0, 7, 0, 0],\n",
       " [2.2946, 2.6233, 17.356, 0, 0, 8, 0, 0],\n",
       " [2.2958, 2.625, 17.361, 0, 0, 21, 0, 0]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaWestPPF[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"PaivaWestPPF.vmt\",PaivaWestPPF,\"PWestF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "681"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaEastPP = readPPfile('PaivaEastPP.vmt')\n",
    "len(PaivaEastPP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.3712, 0.5059, 2.981],\n",
       " [0.3716, 0.5083, 2.986],\n",
       " [0.3724, 0.5098, 2.983],\n",
       " [0.3719, 0.506, 2.98],\n",
       " [0.3717, 0.5083, 2.986]]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaEastPP[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "362\n",
      "Time  :      0.06\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "362"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaEastPPF = filter(PaivaEastPP,4)\n",
    "len(PaivaEastPPF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.3724, 0.5098, 2.983, 0, 0, 0, 3, 0],\n",
       " [0.3719, 0.506, 2.98, 0, 0, 0, 4, 0],\n",
       " [0.3717, 0.5083, 2.986, 0, 0, 0, 5, 0],\n",
       " [0.3723, 0.5083, 2.985, 0, 0, 0, 6, 0],\n",
       " [0.3724, 0.5086, 2.982, 0, 0, 0, 7, 0]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaEastPPF[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"PaivaEastPPF.vmt\",PaivaEastPPF,\"PEastF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "579"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaIslandsPP = readPPfile('PaivaIslandsPP.vmt')\n",
    "len(PaivaIslandsPP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.3446, 0.3875, 2.347],\n",
       " [0.3289, 0.3781, 2.371],\n",
       " [0.329, 0.3781, 2.371],\n",
       " [0.3291, 0.3782, 2.371],\n",
       " [0.3291, 0.3782, 2.371]]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaIslandsPP[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "285\n",
      "Time  :      0.04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "285"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaIslandsPPF = filter(PaivaIslandsPP,5)\n",
    "len(PaivaIslandsPPF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.329, 0.3781, 2.371, 0, 0, 0, 0, 3],\n",
       " [0.3306, 0.3786, 2.386, 0, 0, 0, 0, 6],\n",
       " [0.3299, 0.3782, 2.376, 0, 0, 0, 0, 7],\n",
       " [0.3316, 0.3787, 2.385, 0, 0, 0, 0, 8],\n",
       " [0.3309, 0.3783, 2.375, 0, 0, 0, 0, 10]]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PaivaIslandsPPF[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"PaivaIslandsPPF.vmt\",PaivaIslandsPPF,\"PIslanF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2881 2.2991\n",
      "2.6152 2.6303\n",
      "17.343 17.383\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :     13.03\n"
     ]
    }
   ],
   "source": [
    "PPSet = PaivaWestPP\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min,x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show()\n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2881 2.2991\n",
      "2.6152 2.6303\n",
      "17.343 17.383\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :      4.32\n"
     ]
    }
   ],
   "source": [
    "PPSet = PaivaWestPPF\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min,x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show() \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(814, 742, 681, 579)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ParPenPP),len(PaivaWestPP),len(PaivaEastPP),len(PaivaIslandsPP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219, 251, 362, 285)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ParPenPPF),len(PaivaWestPPF),len(PaivaEastPPF),len(PaivaIslandsPPF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "##################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joining Paiva West + East\n",
      "pnt=       987. ParPnt=   500. Time  :      0.09\n",
      "pnt=      1554. ParPnt=   500. Time  :      0.19\n",
      "pnt=      1576. ParPnt=   500. Time  :      0.20\n",
      "pnt=      1581. ParPnt=   500. Time  :      0.20\n",
      "pnt=      1642. ParPnt=   500. Time  :      0.22\n",
      "pnt=      1701. ParPnt=   500. Time  :      0.24\n",
      "pnt=      1715. ParPnt=   500. Time  :      0.24\n",
      "pnt=      1730. ParPnt=   500. Time  :      0.24\n",
      "pnt=      1733. ParPnt=   500. Time  :      0.25\n",
      "pnt=      1743. ParPnt=   500. Time  :      0.25\n",
      "pnt=      1828. ParPnt=   500. Time  :      0.27\n",
      "pnt=      7562. ParPnt=  1000. Time  :      0.92\n",
      "pnt=      7567. ParPnt=  1000. Time  :      0.92\n",
      "pnt=     15419. ParPnt=  1500. Time  :      2.29\n",
      "pnt=     33773. ParPnt=  2000. Time  :      6.68\n",
      "pnt=     33837. ParPnt=  2000. Time  :      6.69\n",
      "pnt=     59467. ParPnt=  2500. Time  :     14.26\n",
      "pnt=     60431. ParPnt=  2500. Time  :     14.45\n",
      "pnt=     60553. ParPnt=  2500. Time  :     14.48\n",
      "pnt=     60641. ParPnt=  2500. Time  :     14.50\n",
      "pnt=     60701. ParPnt=  2500. Time  :     14.51\n",
      "pnt=     60725. ParPnt=  2500. Time  :     14.51\n",
      "pnt=     67346. ParPnt=  2500. Time  :     16.14\n",
      "Joined West+East\n",
      "2779\n",
      "Time  :     21.26\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2779"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MasterParPoints = []   \n",
    "\n",
    "TStart = time.time()\n",
    "pntNumber = 0\n",
    "#---------------------------------Joining Paivas\n",
    "print(\"Joining Paiva West + East\")\n",
    "\n",
    "for i in range(len(PaivaWestPPF)):\n",
    "    for j in range(len(PaivaEastPPF)):\n",
    "        newp = [0,0,0,  0,0,0,0,0]\n",
    "        for k in range(3):\n",
    "            newp[k] = PaivaWestPPF[i][k] + PaivaEastPPF[j][k]\n",
    "        newp[5] = PaivaWestPPF[i][5]\n",
    "        newp[6] = PaivaEastPPF[j][6]\n",
    "        insert_pnt(newp)\n",
    "        pntNumber = pntNumber + 1\n",
    "\n",
    "TFinish = time.time()\n",
    "print(\"Joined West+East\")\n",
    "print(len(MasterParPoints))\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))\n",
    "PaivaPP_WE = MasterParPoints.copy()\n",
    "len(PaivaPP_WE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6571 2.6791\n",
      "3.1139 3.1526\n",
      "20.275 20.369\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :     65.72\n"
     ]
    }
   ],
   "source": [
    "PPSet = PaivaPP_WE\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min, x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show() \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6571 2.6791\n",
      "3.1139 3.1526\n",
      "20.275 20.369\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :     65.40\n"
     ]
    }
   ],
   "source": [
    "PPSet = PaivaPP_WE\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X3[i],X2[i], color=pntclr(X1[i],x1_min, x1_max))\n",
    "plt.xlabel('x3')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show() \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"PaivaPP_WE.vmt\", PaivaPP_WE, \"Paiva_WE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joining Paiva WestEast + Islands\n",
      "pnt=       976. ParPnt=   500. Time  :      0.40\n",
      "pnt=      2691. ParPnt=  1000. Time  :      0.74\n",
      "pnt=      3893. ParPnt=  1500. Time  :      1.27\n",
      "pnt=      3895. ParPnt=  1500. Time  :      1.27\n",
      "pnt=      3920. ParPnt=  1500. Time  :      1.28\n",
      "pnt=     14673. ParPnt=  2000. Time  :      5.15\n",
      "pnt=     14854. ParPnt=  2000. Time  :      5.27\n",
      "pnt=     15566. ParPnt=  2000. Time  :      5.76\n",
      "pnt=     15832. ParPnt=  2000. Time  :      5.87\n",
      "pnt=     15858. ParPnt=  2000. Time  :      5.90\n",
      "pnt=     15859. ParPnt=  2000. Time  :      5.90\n",
      "pnt=     15860. ParPnt=  2000. Time  :      5.90\n",
      "pnt=     15912. ParPnt=  2000. Time  :      5.92\n",
      "pnt=     15913. ParPnt=  2000. Time  :      5.93\n",
      "pnt=     15914. ParPnt=  2000. Time  :      5.93\n",
      "pnt=     15918. ParPnt=  2000. Time  :      5.93\n",
      "pnt=     15919. ParPnt=  2000. Time  :      5.93\n",
      "pnt=     22255. ParPnt=  2500. Time  :      8.46\n",
      "pnt=     26704. ParPnt=  2500. Time  :     10.00\n",
      "pnt=     45054. ParPnt=  3000. Time  :     13.45\n",
      "pnt=     45055. ParPnt=  3000. Time  :     13.45\n",
      "pnt=     45072. ParPnt=  3000. Time  :     13.46\n",
      "pnt=     46773. ParPnt=  3000. Time  :     13.58\n",
      "pnt=     88603. ParPnt=  3500. Time  :     18.49\n",
      "pnt=    144235. ParPnt=  4000. Time  :     25.15\n",
      "pnt=    144815. ParPnt=  4000. Time  :     25.20\n",
      "pnt=    144822. ParPnt=  4000. Time  :     25.20\n",
      "pnt=    284048. ParPnt=  4000. Time  :     42.69\n",
      "pnt=    284057. ParPnt=  4000. Time  :     42.71\n",
      "pnt=    284062. ParPnt=  4000. Time  :     42.72\n",
      "pnt=    284072. ParPnt=  4000. Time  :     42.74\n",
      "pnt=    285225. ParPnt=  4000. Time  :     44.41\n",
      "pnt=    285501. ParPnt=  4000. Time  :     44.97\n",
      "pnt=    290174. ParPnt=  4000. Time  :     50.81\n",
      "pnt=    292063. ParPnt=  4000. Time  :     53.27\n",
      "pnt=    292064. ParPnt=  4000. Time  :     53.27\n",
      "pnt=    303271. ParPnt=  4500. Time  :     64.39\n",
      "pnt=    303273. ParPnt=  4500. Time  :     64.39\n",
      "pnt=    356676. ParPnt=  5000. Time  :    103.64\n",
      "pnt=    377625. ParPnt=  5500. Time  :    118.21\n",
      "pnt=    377708. ParPnt=  5500. Time  :    118.36\n",
      "pnt=    377730. ParPnt=  5500. Time  :    118.42\n",
      "pnt=    378146. ParPnt=  5500. Time  :    119.33\n",
      "pnt=    396216. ParPnt=  6000. Time  :    140.91\n",
      "pnt=    444020. ParPnt=  6500. Time  :    176.85\n",
      "pnt=    444215. ParPnt=  6500. Time  :    176.96\n",
      "pnt=    444225. ParPnt=  6500. Time  :    176.98\n",
      "pnt=    559063. ParPnt=  7000. Time  :    238.23\n",
      "pnt=    559072. ParPnt=  7000. Time  :    238.24\n",
      "pnt=    675561. ParPnt=  7500. Time  :    301.85\n",
      "pnt=    732035. ParPnt=  8000. Time  :    340.87\n",
      "pnt=    766561. ParPnt=  8500. Time  :    372.61\n",
      "Joined Paiva WestEast+Islands\n",
      "8699\n",
      "Time  :    389.66\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8699"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MasterParPoints = []   \n",
    "\n",
    "TStart = time.time()\n",
    "pntNumber = 0\n",
    "#---------------------------------Joining Paivas\n",
    "print(\"Joining Paiva WestEast + Islands\")\n",
    "\n",
    "for i in range(len(PaivaPP_WE)):\n",
    "    for j in range(len(PaivaIslandsPPF)):\n",
    "        newp = [0,0,0,  0,0,0,0,0]\n",
    "        for k in range(3):\n",
    "            newp[k] = PaivaPP_WE[i][k] + PaivaIslandsPPF[j][k]\n",
    "        newp[5] = PaivaPP_WE[i][5]\n",
    "        newp[6] = PaivaPP_WE[i][6]\n",
    "        newp[7] = PaivaIslandsPPF[j][7]\n",
    "        insert_pnt(newp)\n",
    "        pntNumber = pntNumber + 1 \n",
    "\n",
    "TFinish = time.time()\n",
    "print(\"Joined Paiva WestEast+Islands\")\n",
    "print(len(MasterParPoints))\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))\n",
    "Paiva_WEI = MasterParPoints.copy()\n",
    "len(Paiva_WEI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9861 3.0248\n",
      "3.492 3.5402\n",
      "22.642 22.78\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :    409.43\n"
     ]
    }
   ],
   "source": [
    "PPSet = Paiva_WEI\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min, x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show()  \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9861 3.0248\n",
      "3.492 3.5402\n",
      "22.642 22.78\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :    409.15\n"
     ]
    }
   ],
   "source": [
    "PPSet = Paiva_WEI\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X3[i], color=pntclr(X2[i],x2_min, x2_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x3')    \n",
    "plt.grid()    \n",
    "plt.show()    \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9861 3.0248\n",
      "3.492 3.5402\n",
      "22.642 22.78\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :    416.40\n"
     ]
    }
   ],
   "source": [
    "PPSet = Paiva_WEI\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X2[i],X3[i], color=pntclr(X1[i],x1_min, x1_max))\n",
    "plt.xlabel('x2')    \n",
    "plt.ylabel('x3')    \n",
    "plt.grid()    \n",
    "plt.show()   \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nMasterParPoints = []\\npntNumber = 0\\n\\nTStart = time.time()\\n#--------------------------------Direct joining Paivas\\nprint(\"Joining Paiva West + East + Islands directly\")\\n\\nfor i in range(len(PaivaWestPPF)):\\n    for j in range(len(PaivaEastPPF)):\\n        for r in range(len(PaivaIslandsPPF)):            \\n            newp = [0,0,0,  0,0,0,0,0]\\n            for k in range(3):                \\n                newp[k] = PaivaWestPPF[i][k] + PaivaEastPPF[j][k] + PaivaIslandsPPF[r][k]\\n            newp[5] = PaivaWestPPF[i][5]\\n            newp[6] = PaivaEastPPF[j][6]\\n            newp[7] = PaivaIslandsPPF[r][7]\\n            pntNumber = pntNumber + 1\\n            insert_pnt(newp)\\n\\nTFinish = time.time()\\nprint(\"Joined West+East+Islands\")\\nprint(len(MasterParPoints))\\nprint(\\'Time  : {:>9.2f}\\'.format(TFinish - TStart))\\nPaiva_d = MasterParPoints.copy()\\nlen(Paiva_d)\\n'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# West + East + Islands  DIRECTLY\n",
    "\"\"\"\n",
    "MasterParPoints = []\n",
    "pntNumber = 0\n",
    "\n",
    "TStart = time.time()\n",
    "#--------------------------------Direct joining Paivas\n",
    "print(\"Joining Paiva West + East + Islands directly\")\n",
    "\n",
    "for i in range(len(PaivaWestPPF)):\n",
    "    for j in range(len(PaivaEastPPF)):\n",
    "        for r in range(len(PaivaIslandsPPF)):            \n",
    "            newp = [0,0,0,  0,0,0,0,0]\n",
    "            for k in range(3):                \n",
    "                newp[k] = PaivaWestPPF[i][k] + PaivaEastPPF[j][k] + PaivaIslandsPPF[r][k]\n",
    "            newp[5] = PaivaWestPPF[i][5]\n",
    "            newp[6] = PaivaEastPPF[j][6]\n",
    "            newp[7] = PaivaIslandsPPF[r][7]\n",
    "            pntNumber = pntNumber + 1\n",
    "            insert_pnt(newp)\n",
    "\n",
    "TFinish = time.time()\n",
    "print(\"Joined West+East+Islands\")\n",
    "print(len(MasterParPoints))\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))\n",
    "Paiva_d = MasterParPoints.copy()\n",
    "len(Paiva_d)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"PaivaPP_WEI.vmt\", Paiva_WEI, \"Paiva\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3.0136999999999996, 3.5103999999999997, 22.678, 0, 0, 3, 450, 103],\n",
       " [3.0134999999999996, 3.5099, 22.676, 0, 0, 3, 452, 103],\n",
       " [3.0132999999999996, 3.5096, 22.675, 0, 0, 6, 450, 103],\n",
       " [3.0134, 3.51, 22.676000000000002, 0, 0, 7, 450, 103],\n",
       " [3.0149, 3.5128000000000004, 22.684, 0, 0, 21, 404, 103]]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Paiva_WEI[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Paiva_WEI2 = readPPfile(\"PaivaPP_WEI.vmt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3.0137, 3.5104, 22.678, 0.0, 0.0, 3.0, 450.0, 103.0],\n",
       " [3.0135, 3.5099, 22.676, 0.0, 0.0, 3.0, 452.0, 103.0],\n",
       " [3.0133, 3.5096, 22.675, 0.0, 0.0, 6.0, 450.0, 103.0],\n",
       " [3.0134, 3.51, 22.676, 0.0, 0.0, 7.0, 450.0, 103.0],\n",
       " [3.0149, 3.5128, 22.684, 0.0, 0.0, 21.0, 404.0, 103.0]]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Paiva_WEI2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pnt=       573. ParPnt=   500. Time  :      0.08\n",
      "pnt=      1148. ParPnt=  1000. Time  :      0.29\n",
      "pnt=      1719. ParPnt=  1500. Time  :      0.65\n",
      "pnt=      2284. ParPnt=  2000. Time  :      1.14\n",
      "pnt=      2907. ParPnt=  2500. Time  :      1.83\n",
      "pnt=      3538. ParPnt=  3000. Time  :      2.67\n",
      "pnt=      4124. ParPnt=  3500. Time  :      3.65\n",
      "pnt=      4811. ParPnt=  4000. Time  :      4.89\n",
      "pnt=      4812. ParPnt=  4000. Time  :      4.90\n",
      "pnt=      5606. ParPnt=  4500. Time  :      6.34\n",
      "pnt=      5607. ParPnt=  4500. Time  :      6.34\n",
      "pnt=      6375. ParPnt=  5000. Time  :      8.07\n",
      "pnt=      6376. ParPnt=  5000. Time  :      8.08\n",
      "pnt=      6377. ParPnt=  5000. Time  :      8.08\n",
      "pnt=      7110. ParPnt=  5500. Time  :      9.94\n",
      "pnt=      7745. ParPnt=  6000. Time  :     11.81\n",
      "pnt=      8418. ParPnt=  6500. Time  :     13.91\n",
      "6696\n",
      "Time  :     14.76\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6696"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Paiva_WEIF = filter(Paiva_WEI2, 0)\n",
    "len(Paiva_WEIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3.0135, 3.5099, 22.676, 0.0, 0.0, 3.0, 452.0, 103.0],\n",
       " [3.0133, 3.5096, 22.675, 0.0, 0.0, 6.0, 450.0, 103.0],\n",
       " [3.0149, 3.5128, 22.684, 0.0, 0.0, 21.0, 404.0, 103.0],\n",
       " [3.0147, 3.5123, 22.684, 0.0, 0.0, 21.0, 450.0, 103.0],\n",
       " [3.0145, 3.5118, 22.682, 0.0, 0.0, 21.0, 452.0, 103.0]]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Paiva_WEIF[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9861 3.0248\n",
      "3.492 3.5402\n",
      "22.642 22.78\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time  :    261.94\n"
     ]
    }
   ],
   "source": [
    "PPSet = Paiva_WEIF\n",
    "TStart = time.time()\n",
    "X1 = [PPSet[i][0] for i in range(len(PPSet))]\n",
    "X2 = [PPSet[i][1] for i in range(len(PPSet))]\n",
    "X3 = [PPSet[i][2] for i in range(len(PPSet))]\n",
    "x1_min = min(X1); x1_max = max(X1)\n",
    "print(x1_min, x1_max)\n",
    "x2_min = min(X2); x2_max = max(X2)\n",
    "print(x2_min, x2_max)\n",
    "x3_min = min(X3); x3_max = max(X3)\n",
    "print(x3_min, x3_max)\n",
    "#################################\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize =(6,6))\n",
    "for i in range(len(X1)):\n",
    "    plt.scatter(X1[i],X2[i], color=pntclr(X3[i],x3_min, x3_max))\n",
    "plt.xlabel('x1')    \n",
    "plt.ylabel('x2')    \n",
    "plt.grid()    \n",
    "plt.show()  \n",
    "TFinish = time.time()\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ===================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining ParPen + Paiva "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(219, 6696, 1466424)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ParPenPPF), len(Paiva_WEIF), len(ParPenPPF)*len(Paiva_WEIF) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Joining ParPen + Paiva\n",
      "pnt=       499. ParPnt=   500. Time  :      0.07\n",
      "pnt=       999. ParPnt=  1000. Time  :      0.27\n",
      "pnt=      1499. ParPnt=  1500. Time  :      0.58\n",
      "pnt=      1999. ParPnt=  2000. Time  :      1.01\n",
      "pnt=      2499. ParPnt=  2500. Time  :      1.58\n",
      "pnt=      2999. ParPnt=  3000. Time  :      2.27\n",
      "pnt=      3499. ParPnt=  3500. Time  :      3.08\n",
      "pnt=      3999. ParPnt=  4000. Time  :      4.03\n",
      "pnt=      4499. ParPnt=  4500. Time  :      5.09\n",
      "pnt=      4999. ParPnt=  5000. Time  :      6.27\n",
      "pnt=      5499. ParPnt=  5500. Time  :      7.59\n",
      "pnt=      5999. ParPnt=  6000. Time  :      9.08\n",
      "pnt=      6499. ParPnt=  6500. Time  :     10.74\n",
      "pnt=      8375. ParPnt=  7000. Time  :     14.48\n",
      "pnt=      8376. ParPnt=  7000. Time  :     14.48\n",
      "pnt=     11188. ParPnt=  7500. Time  :     20.91\n",
      "pnt=     13827. ParPnt=  7500. Time  :     26.33\n",
      "pnt=     13828. ParPnt=  7500. Time  :     26.33\n",
      "pnt=     16396. ParPnt=  4000. Time  :     33.45\n",
      "pnt=     17003. ParPnt=  4500. Time  :     34.89\n",
      "pnt=     17845. ParPnt=  5000. Time  :     37.22\n",
      "pnt=     18632. ParPnt=  5500. Time  :     39.54\n",
      "pnt=     19138. ParPnt=  6000. Time  :     41.24\n",
      "pnt=     19638. ParPnt=  6500. Time  :     43.10\n",
      "pnt=     20294. ParPnt=  7000. Time  :     45.44\n",
      "pnt=     20295. ParPnt=  7000. Time  :     45.44\n",
      "pnt=     20296. ParPnt=  7000. Time  :     45.45\n",
      "pnt=     20297. ParPnt=  7000. Time  :     45.45\n",
      "pnt=     20303. ParPnt=  7000. Time  :     45.47\n",
      "pnt=     20304. ParPnt=  7000. Time  :     45.47\n",
      "pnt=     20312. ParPnt=  7000. Time  :     45.49\n",
      "pnt=     20317. ParPnt=  7000. Time  :     45.51\n",
      "pnt=     22270. ParPnt=  7500. Time  :     49.79\n",
      "pnt=     22281. ParPnt=  7500. Time  :     49.80\n",
      "pnt=     24578. ParPnt=  8000. Time  :     54.75\n",
      "pnt=     28209. ParPnt=  8000. Time  :     60.57\n",
      "pnt=     28210. ParPnt=  8000. Time  :     60.58\n",
      "pnt=     28216. ParPnt=  8000. Time  :     60.61\n",
      "pnt=     28469. ParPnt=  8000. Time  :     61.70\n",
      "pnt=     30578. ParPnt=  8500. Time  :     68.87\n",
      "pnt=     32165. ParPnt=  9000. Time  :     71.89\n",
      "pnt=     32759. ParPnt=  9500. Time  :     74.65\n",
      "pnt=     39357. ParPnt= 10000. Time  :     82.26\n",
      "pnt=     45615. ParPnt= 10000. Time  :     89.59\n",
      "pnt=     45616. ParPnt= 10000. Time  :     89.59\n",
      "pnt=     45617. ParPnt= 10000. Time  :     89.60\n",
      "pnt=     45618. ParPnt= 10000. Time  :     89.61\n",
      "pnt=     51554. ParPnt= 10500. Time  :     98.84\n",
      "pnt=     59290. ParPnt= 10500. Time  :    105.48\n",
      "pnt=     69911. ParPnt= 10500. Time  :    119.32\n",
      "pnt=     69912. ParPnt= 10500. Time  :    119.33\n",
      "pnt=     73104. ParPnt= 10500. Time  :    126.66\n",
      "pnt=     90171. ParPnt= 10500. Time  :    142.49\n",
      "pnt=     90439. ParPnt= 10500. Time  :    143.07\n",
      "pnt=     90448. ParPnt= 10500. Time  :    143.09\n",
      "pnt=     90466. ParPnt= 10500. Time  :    143.12\n",
      "pnt=     90553. ParPnt= 10500. Time  :    143.35\n",
      "pnt=     91475. ParPnt= 10500. Time  :    145.44\n",
      "pnt=     91515. ParPnt= 10500. Time  :    145.46\n",
      "pnt=     94979. ParPnt= 10500. Time  :    150.18\n",
      "pnt=     97739. ParPnt=  9000. Time  :    163.22\n",
      "pnt=     97746. ParPnt=  9000. Time  :    163.25\n",
      "pnt=     97748. ParPnt=  9000. Time  :    163.26\n",
      "pnt=     97822. ParPnt=  9000. Time  :    163.56\n",
      "pnt=    100004. ParPnt=  9500. Time  :    168.96\n",
      "pnt=    110990. ParPnt= 10000. Time  :    186.32\n",
      "pnt=    116506. ParPnt= 10500. Time  :    195.46\n",
      "pnt=    132002. ParPnt= 11000. Time  :    216.31\n",
      "pnt=    132042. ParPnt= 11000. Time  :    216.36\n",
      "pnt=    134172. ParPnt= 11000. Time  :    219.47\n",
      "pnt=    142116. ParPnt=  9500. Time  :    240.43\n",
      "pnt=    145103. ParPnt=  9000. Time  :    255.57\n",
      "pnt=    146790. ParPnt=  9500. Time  :    262.22\n",
      "pnt=    148168. ParPnt= 10000. Time  :    266.07\n",
      "pnt=    152163. ParPnt= 10500. Time  :    278.96\n",
      "pnt=    152172. ParPnt= 10500. Time  :    278.98\n",
      "pnt=    158395. ParPnt= 11000. Time  :    294.90\n",
      "pnt=    165286. ParPnt= 11500. Time  :    309.21\n",
      "pnt=    165295. ParPnt= 11500. Time  :    309.23\n",
      "pnt=    165296. ParPnt= 11500. Time  :    309.24\n",
      "pnt=    165317. ParPnt= 11500. Time  :    309.27\n",
      "pnt=    165318. ParPnt= 11500. Time  :    309.28\n",
      "pnt=    197281. ParPnt=  9000. Time  :    365.09\n",
      "pnt=    198488. ParPnt=  9500. Time  :    371.17\n",
      "pnt=    199751. ParPnt= 10000. Time  :    376.14\n",
      "pnt=    200606. ParPnt= 10500. Time  :    380.60\n",
      "pnt=    203375. ParPnt= 11000. Time  :    388.51\n",
      "pnt=    203377. ParPnt= 11000. Time  :    388.52\n",
      "pnt=    207188. ParPnt= 11500. Time  :    400.26\n",
      "pnt=    285036. ParPnt= 12000. Time  :    467.68\n",
      "pnt=    380817. ParPnt= 12500. Time  :    568.79\n",
      "pnt=    565292. ParPnt= 13000. Time  :    706.43\n",
      "pnt=    584335. ParPnt= 13000. Time  :    721.14\n",
      "pnt=    587652. ParPnt= 13000. Time  :    729.57\n",
      "pnt=    587958. ParPnt= 13000. Time  :    731.59\n",
      "pnt=    588010. ParPnt= 13000. Time  :    732.06\n",
      "pnt=    590708. ParPnt= 13500. Time  :    738.60\n",
      "pnt=    635476. ParPnt= 14000. Time  :    813.35\n",
      "pnt=    648619. ParPnt= 14000. Time  :    830.26\n",
      "pnt=    648638. ParPnt= 14000. Time  :    830.33\n",
      "pnt=    650729. ParPnt= 14000. Time  :    832.55\n",
      "pnt=    650730. ParPnt= 14000. Time  :    832.56\n",
      "pnt=    651663. ParPnt= 14000. Time  :    837.93\n",
      "pnt=    651664. ParPnt= 14000. Time  :    837.94\n",
      "pnt=    652031. ParPnt= 14000. Time  :    838.50\n",
      "pnt=    652037. ParPnt= 14000. Time  :    838.51\n",
      "pnt=    652043. ParPnt= 14000. Time  :    838.53\n",
      "pnt=    652044. ParPnt= 14000. Time  :    838.54\n",
      "pnt=    652062. ParPnt= 14000. Time  :    838.56\n",
      "pnt=    652063. ParPnt= 14000. Time  :    838.57\n",
      "pnt=    652077. ParPnt= 14000. Time  :    838.60\n",
      "pnt=    652098. ParPnt= 14000. Time  :    838.69\n",
      "pnt=    652106. ParPnt= 14000. Time  :    838.72\n",
      "pnt=    652128. ParPnt= 14000. Time  :    838.82\n",
      "pnt=    652130. ParPnt= 14000. Time  :    838.84\n",
      "pnt=    652135. ParPnt= 14000. Time  :    838.86\n",
      "pnt=    652147. ParPnt= 14000. Time  :    838.90\n",
      "pnt=    652170. ParPnt= 14000. Time  :    839.02\n",
      "pnt=    652181. ParPnt= 14000. Time  :    839.04\n",
      "pnt=    652192. ParPnt= 14000. Time  :    839.08\n",
      "pnt=    652202. ParPnt= 14000. Time  :    839.15\n",
      "pnt=    652209. ParPnt= 14000. Time  :    839.17\n",
      "pnt=    652210. ParPnt= 14000. Time  :    839.18\n",
      "pnt=    664291. ParPnt= 14000. Time  :    858.89\n",
      "pnt=    692135. ParPnt= 14500. Time  :    904.90\n",
      "pnt=    692386. ParPnt= 14500. Time  :    906.31\n",
      "pnt=    692623. ParPnt= 14500. Time  :    907.40\n",
      "pnt=    692624. ParPnt= 14500. Time  :    907.42\n",
      "pnt=    692625. ParPnt= 14500. Time  :    907.43\n",
      "pnt=    692665. ParPnt= 14500. Time  :    907.79\n",
      "pnt=    692692. ParPnt= 14500. Time  :    908.03\n",
      "pnt=    692760. ParPnt= 14500. Time  :    908.38\n",
      "pnt=    692764. ParPnt= 14500. Time  :    908.40\n",
      "pnt=    692765. ParPnt= 14500. Time  :    908.42\n",
      "pnt=    692766. ParPnt= 14500. Time  :    908.43\n",
      "pnt=    723118. ParPnt= 15000. Time  :    959.30\n",
      "pnt=    723129. ParPnt= 15000. Time  :    959.33\n",
      "pnt=    934702. ParPnt= 15500. Time  :   1125.30\n",
      "pnt=    949258. ParPnt= 14000. Time  :   1166.76\n",
      "pnt=    949264. ParPnt= 14000. Time  :   1166.81\n",
      "pnt=    952064. ParPnt= 14500. Time  :   1174.86\n",
      "pnt=    952067. ParPnt= 14500. Time  :   1174.89\n",
      "pnt=    954845. ParPnt= 15000. Time  :   1194.54\n",
      "pnt=    954858. ParPnt= 15000. Time  :   1194.57\n",
      "pnt=    954944. ParPnt= 15000. Time  :   1194.97\n",
      "pnt=    954961. ParPnt= 15000. Time  :   1195.01\n",
      "pnt=    954970. ParPnt= 15000. Time  :   1195.04\n",
      "pnt=    955068. ParPnt= 15000. Time  :   1195.45\n",
      "pnt=    955069. ParPnt= 15000. Time  :   1195.47\n",
      "pnt=    955070. ParPnt= 15000. Time  :   1195.48\n",
      "pnt=    955071. ParPnt= 15000. Time  :   1195.49\n",
      "pnt=    955073. ParPnt= 15000. Time  :   1195.51\n",
      "pnt=    955075. ParPnt= 15000. Time  :   1195.52\n",
      "pnt=    961710. ParPnt= 15500. Time  :   1221.50\n",
      "pnt=   1170178. ParPnt= 16000. Time  :   1584.82\n",
      "pnt=   1237917. ParPnt= 16000. Time  :   1727.61\n",
      "pnt=   1237919. ParPnt= 16000. Time  :   1727.64\n",
      "pnt=   1264072. ParPnt= 16500. Time  :   1799.28\n",
      "pnt=   1422542. ParPnt= 17000. Time  :   2176.65\n",
      "pnt=   1422546. ParPnt= 17000. Time  :   2176.67\n",
      "pnt=   1422930. ParPnt= 17000. Time  :   2178.60\n",
      "pnt=   1424514. ParPnt= 17000. Time  :   2181.75\n",
      "Joined ParPen + Paiva\n",
      "17141\n",
      "Time  :   2280.79\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "17141"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MasterParPoints = []   \n",
    "\n",
    "TStart = time.time()\n",
    "pntNumber = 0\n",
    "#---------------------------------Joining Paivas\n",
    "print(\"Joining ParPen + Paiva\")\n",
    "\n",
    "for i in range(len(ParPenPPF)):\n",
    "    for j in range(len(Paiva_WEIF)):\n",
    "        newp = [0,0,0,  0,0,0,0,0]\n",
    "        for k in range(3):\n",
    "            newp[k] = ParPenPPF[i][k] + Paiva_WEIF[j][k]\n",
    "        newp[3] = ParPenPPF[i][3]\n",
    "        newp[4] = ParPenPPF[i][4]\n",
    "        newp[5] = Paiva_WEIF[j][5]\n",
    "        newp[6] = Paiva_WEIF[j][6]\n",
    "        newp[7] = Paiva_WEIF[j][7]\n",
    "        insert_pnt(newp)\n",
    "        pntNumber = pntNumber + 1 \n",
    "\n",
    "TFinish = time.time()\n",
    "print(\"Joined ParPen + Paiva\")\n",
    "print(len(MasterParPoints))\n",
    "print('Time  : {:>9.2f}'.format(TFinish - TStart))\n",
    "ValeSousaPP = MasterParPoints.copy()\n",
    "len(ValeSousaPP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"1mWithLock\"+str(len(ValeSousaPP))+\".vmt\", ValeSousaPP, \"VSousa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5.8359000000000005, 5.976, 43.632, 1, 0, 212.0, 93.0, 103.0],\n",
       " [5.8387, 5.984500000000001, 43.607, 1, 0, 223.0, 91.0, 65.0],\n",
       " [5.8372, 5.9821, 43.623, 1, 0, 223.0, 91.0, 103.0],\n",
       " [5.838, 5.978, 43.611999999999995, 1, 0, 223.0, 93.0, 65.0],\n",
       " [5.8359000000000005, 5.9757, 43.631, 1, 0, 223.0, 93.0, 86.0]]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ValeSousaPP[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5.8359, 5.976, 43.632, 1.0, 0.0, 212.0, 93.0, 103.0],\n",
       " [5.8387, 5.9845, 43.607, 1.0, 0.0, 223.0, 91.0, 65.0],\n",
       " [5.8372, 5.9821, 43.623, 1.0, 0.0, 223.0, 91.0, 103.0],\n",
       " [5.838, 5.978, 43.612, 1.0, 0.0, 223.0, 93.0, 65.0],\n",
       " [5.8359, 5.9757, 43.631, 1.0, 0.0, 223.0, 93.0, 86.0]]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ValeSousaPP2 = readPPfile(\"1mWithLock\"+str(len(ValeSousaPP))+\".vmt\")\n",
    "ValeSousaPP2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pnt=       507. ParPnt=   500. Time  :      0.07\n",
      "pnt=      1066. ParPnt=  1000. Time  :      0.28\n",
      "pnt=      1611. ParPnt=  1500. Time  :      0.63\n",
      "pnt=      2112. ParPnt=  2000. Time  :      1.09\n",
      "pnt=      2619. ParPnt=  2500. Time  :      1.67\n",
      "pnt=      3134. ParPnt=  3000. Time  :      2.39\n",
      "pnt=      3690. ParPnt=  3500. Time  :      3.29\n",
      "pnt=      4298. ParPnt=  4000. Time  :      4.32\n",
      "pnt=      4915. ParPnt=  4500. Time  :      5.52\n",
      "pnt=      5455. ParPnt=  5000. Time  :      6.91\n",
      "pnt=      6023. ParPnt=  5500. Time  :      8.57\n",
      "pnt=      6636. ParPnt=  6000. Time  :     10.34\n",
      "pnt=      7222. ParPnt=  6500. Time  :     12.20\n",
      "pnt=      7753. ParPnt=  7000. Time  :     14.08\n",
      "pnt=      8326. ParPnt=  7500. Time  :     16.29\n",
      "pnt=      8903. ParPnt=  8000. Time  :     18.57\n",
      "pnt=      9497. ParPnt=  8500. Time  :     21.01\n",
      "pnt=     10007. ParPnt=  9000. Time  :     23.48\n",
      "pnt=     10530. ParPnt=  9500. Time  :     26.19\n",
      "pnt=     11081. ParPnt= 10000. Time  :     29.15\n",
      "pnt=     11711. ParPnt= 10500. Time  :     32.76\n",
      "pnt=     12360. ParPnt= 11000. Time  :     36.61\n",
      "pnt=     12967. ParPnt= 11500. Time  :     40.52\n",
      "pnt=     13599. ParPnt= 12000. Time  :     44.75\n",
      "pnt=     14192. ParPnt= 12500. Time  :     48.83\n",
      "pnt=     14696. ParPnt= 13000. Time  :     52.60\n",
      "pnt=     15196. ParPnt= 13500. Time  :     56.55\n",
      "pnt=     15724. ParPnt= 14000. Time  :     60.81\n",
      "pnt=     16309. ParPnt= 14500. Time  :     65.70\n",
      "pnt=     16310. ParPnt= 14500. Time  :     65.71\n",
      "pnt=     16902. ParPnt= 15000. Time  :     70.85\n",
      "15143\n",
      "Time  :     72.61\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15143"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ValeSousaPP2F = filter(ValeSousaPP2, 0)\n",
    "len(ValeSousaPP2F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[5.8359, 5.976, 43.632, 1.0, 0.0, 212.0, 93.0, 103.0],\n",
       " [5.8387, 5.9845, 43.607, 1.0, 0.0, 223.0, 91.0, 65.0],\n",
       " [5.8372, 5.9821, 43.623, 1.0, 0.0, 223.0, 91.0, 103.0],\n",
       " [5.838, 5.978, 43.612, 1.0, 0.0, 223.0, 93.0, 65.0],\n",
       " [5.8365, 5.9756, 43.628, 1.0, 0.0, 223.0, 93.0, 103.0]]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ValeSousaPP2F[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "PP2File(\"1mWithLock\"+str(len(ValeSousaPP2F))+\".vmt\", ValeSousaPP2F, \"VSousa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here\n"
     ]
    }
   ],
   "source": [
    "print(\"Here\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ---------------------------------------------"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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