tutorials/14_Export to MOOSE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export geometries to MOOSE\n",
    "\n",
    "content:\n",
    "* [Single patch](#patch)\n",
    "    * [Create geometry](#geometry)\n",
    "    * [Assign Boundary Conditions labels](#bc)\n",
    "    * [Export geometry with Gmsh](#gmsh)\n",
    "    * [Create MOOSE script](#script)\n",
    "* [Multi patch](#patches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='patch'></a>\n",
    "# Single patch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='geometry'></a>\n",
    "## 1) Create Geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp = np.asarray([[[0,0],[-1,0],[-2,0]],\n",
    "                 [[0,1], [-1,2], [-2,2]],\n",
    "                 [[1, 1.5], [1,4],[1,5]],\n",
    "                 [[3, 1.5],[3, 4],[3, 5]]])\n",
    "U = np.asarray([0, 0, 0, 0.5, 1, 1, 1])       \n",
    "V = np.asarray([0, 0, 0, 1, 1, 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygeoiga.nurb.nurb_creation import NURB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrb = NURB(cp, [U,V])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vgx8GoVP3JZ7cN0XrJImIhI7XC506QUoKHHOM6TTBjRnjfM5XkmwbHvj4S15f+n8EkrdBCuBLhT8ehN8ieMkUkSjSdYnWSRIRCY38Bcklk7QzMuDyVwYwNutRSNrrXGA2qxz8/JJO3xeJAipJIuJ+69a5qiDl5MCJ9zzI3DJvQGK2Mxl7bzVnvpHOUhOJGipJIuJ+tWrBXXfBlVdGdUFavdrPuqULyErczNwKvzmn7WxvACNHwIa2puOJSCEqSSLiXl4vJCdD8+bw6KOm0xzQ7AV7OGvwZWyrMJ6xabnzLDYeD59/AzsbGs0mEq0yo6ChREEEEZHDkLdQZMOG8M8/YFmmExUx4e+NXDziQvaWmw4VARu6nHoGfDEa3q1kOp5IVOvSU+skiYiUXP6VtD//POoK0vCf53L9z5eRXWYxlAcCHlhwCXz5uRZ/FHERlSQRcZcovtTIiyN/5ZEZvfCnrYeygD8JZtwMY98gb42jR3kagGd4zFxQERd4dLLWSRIRKZn+/aOqINk23PPhZ7y57HbslO3ONdVy0uD3fvB70XlSnZgAqCSJHEqnFVonSUSkZD75BDZvduYiGWTb0Pvdlxi2rj8kZjgLQO4rDz8OAG8fo9lEJDRUkkQk+nm98PjjMGyYc8FawwWp2+C7+WbdIEjIdn6K7qkOo4fC8vOM5hKR0FJJEpHoln8O0tatTkky5Joh/2P4ilfB4wcPkN4YRoyCza2MZRKR8FFJEpHoVXiStqE9SM99/ymPTrsJO2EfWMCGVvDZt7C7Xom3lU7lkOcTiUXpaaYTqCSJSLSKgrPYRv89gx7fdsGXvNnZc7S9AQz9BXY0PuxtduerkOUTiWXde2idJBGR4NLSnGL06acRL0izl27htPfOYk+puf9ddHbEKFhxTkRziIhZKkkiEl3WroXataFJE5g6NaILRW7a5KfVSxezsewPUBrwJcEvz8Nf94XsNZ7jYQD68XzItikSi56bkLtOUn9zGVSSRCR6eL3QqRPcfjs88UTECtKePdD+iQeZW+ZVKBcA24J/r4XvhoT8tdozNeTbFIlF7ddqnSQREUdeQUpNhZ49I/KSfj9c8+KnfJFxE5Tb50x+WHUqfDIe/FEwa1REjFJJEhHz8hekiRMjMgdpwBczuHdmF+y0zc5PwhBMyhaR2KKSJCJmZWbC+edHrCD9MWMLZw/vSFa5ec4lRLLKwciRWghSRIpQSRIRs9LS4P33oWnTsBakLVv8HPfcxWwq/wOUJyyTsotjLXUi+noibrW2XORO2jgQlSQRMcPrhcWLoXt36NIlbC+TnQ2nP/Ug/yS8ChXCOym7OHrxqZHXFXGbXt08AFxjMINKkohEXt4cpNKloWtX51BbiNk2/N+gT3lv/U2QpEnZIlJyKkkiEll5BSklBX75JSwFacjPM7jh1y4EUqNvUvbr3A3APQwwmkMk2r0+TuskiUg8yV+QwnCpkXmrtnDy2x3ZW2oepBKVk7JbMct0BBFXaLVR6ySJSDz5/vuwFKTsbD8tnr+IJfbYsK2ULSLxRyVJRMLP74eEBOjXD/r2hSpVQrbpqz9+kM9XvgqeAGB2UraIxBaVJBEJL68XrroKRoyAFi1CVpDGL/iL8z8/G3/CXrCAVafAJ79oUraIhIxKkoiET/45SCGaoO3z+Tn2hS4s9v0MHmBnHfh4UlRMyi6OxTQ1HUHEFRZXdtZJ6mAwg0qSiIRHGCZpv/7jd9w79XJIyAbbA+Nehr/vPfKsEdSX90xHEHGFvl2ddZJuNphBJUlEQm/RopAWpG07sznq+VPYnjrD2Xu0qQW8948OrYlIWHlMBxCRGFSvnrOK9hEWJNuGawe8T+XXSrE9bQYEEmHUcBg0x7UFaTA3M9jo78Yi7jB4TIDBYwJGM2hPkoiEzvz5ULs2lC8PQ4ce0ab+9u7k9KFtyCm71JmYvfI0GDIJSAhFUmOasth0BBFXaJpufp0k7UkSkdDweuHMM+H6649oMz4fnPLQ07T7uiI55ZaCLxWGTIAhv+P2giQi7qI9SSJy5PImaaemwosvHvZmfvhjNRd9dzKBUhudB+ZfAiNHhyajiEgJqSSJyJHJX5AmTjysOUjZ2dCy360sLDPIWTE7szx8PBk2Hx/6vCIixaSSJCKHz7adw2tHUJA+Huvl+t/PgLI7wQam9YUf3g191igxi1amI4i4wqwaWidJRNzMspwLyAYCJS5IGRnQ9LHLWFf2a0gBdld3TuvfXS88WaPEPQwwHUHEFe45z5k2fbfBDJq4LSIl5/XC//7nlKNGjUpckF4cMYnSz5ViXbmvAQsmPgmvboz5giQi7qI9SSJSMl4vnHUWpKXBPfdAjRrF/tLt2/00fqYj28v+7vz0SW8Mg2dAdvnw5Y0yw7gGgF58ajiJSHQbNjp3jaT+5jKoJIlI8eUvSBMnlqgg3f3el7yx9moo54NAAox5G/7tG8aw0akOa01HEHGFOrvMr5OkkiQixVO4IBXzENv6TZk0ebktGWXmOgf4158I708FOzm8eUVEjpDmJIlI8WzYABUrlqgg3fDeAGq/U4aMsnPBnwxffA3vzVBBEhFX0J4kETm4XbugXDk47zznsiNJSYf8ktVbttB8wElkJq1yLimy7GwY9hNaMVtE3ER7kkTkwLxeaNIERoxw7hejIPX+5GHqv12dzORVkF0KPvgDho1HBckxlfZMpb3pGCJRb2odi6l1LKMZtCdJRILLm4OUmgqtWx/y6Tsz91D7+ebs9azL/fqr4OvPwhzSffrxvOkIIq7Qr5OzH+dhgxlUkkSkqPwFadKkQ85Benfid9wy6VJI8MO+cvD+X5B+dGSyioiEiUqSiBS0aVOxC5JtQ6tnr8DrG5lv7tH4iEV1o1FcBkB3vjKcRCS6jRrpd25onSQRiRrVq8Mjj8CFFx60IC1es41jBjXHn7IFsGD0R+DtE7GYblWZdNMRRFyhcqbpBCpJIpLH6wW/H044wVlJ+yBuf+9T3l7XG1ICsLcyDFwAmVUjFFREJDJUkkTkvzlItWvDzJngCX7ia0YGNHjkfLaU/8k5vDb/Ehg5OqJRRUQiRSVJJN7ln6T91VcHLEg//bmO88e0gAo7IOCBkSNh4WWRzSoiEkEqSSLxrBhnsdk2XPLsIL7z3QapNuyq6Rxei6OL0obSBDqZjiDiChMaOmskdTCYQSVJJJ69/PJBC9KOHX7qP9mRXRV+dx6YeS18OzSyGWPMMzxmOoKIKzxzhrNX+2mDGVSSROLZ++87p/zXr19k6MtfF9NjwolQcS/4E2H4GFh+noGQIiJm6LIkIvHG64XOnWHbNmcvUqGCZNtwTv8X6TG5GSTthe0N4LldKkghMpbzGcv5pmOIRL2xw/2MHe43mkF7kkTiSf45SDt2QKVKBYa3b/dT56m2ZJT/13ng79vhp7cinzOGpREFi7+IuECaz3QClSSR+FF4knajRgWGh/7kpc8fJ0OFfeBLho9/hXWnmskqIhIFil2SLMtKAKYD62zb7hq+SCIScnPmHPAsNtuG0x5/hCkJzzk/ETY3h0GzwU42FldEJBqUZE/SXcACoFyYsohIuJQvD8ceCx9+WKAgbdqaTf3nW5JVdqHzwOR+MPFZQyFj2wZqUINN++/bOKc3b6Q6NdloKpZI1Nn/vbIq9wHL+V6henXYGNnvlWKVJMuy6gAXAM8C94Y1kYiEzsqVUK+e8zFp0n8/bIB3vv2L26afCeWyIScV3v8bNh9vLGqsy1+QCj/ekOUFHvOTwGrq545vKDKPKYck1lIXgJqsJ5V9BcazSGE9tQGozVqSyS4wvo9UNlALgLqsJpGCkz8yKMUmagBQn5V4CBQY30MZtlANgAaswMIuML6LcqRTBbBpyIoif+adlGcblfHgp/7+/wn/s52K7KAiCfiox+oi4+lUZhflSSKbOqwtMr6VKuymHCnsoxbri4xvphp7KUMqmdRkQ5HxjdQgk1KUYi/Vg7xvG6jJPtIow26qsqXI+Dpqk00K5dgZ9Fp9a6iLjyTKs4NKbCsyvor6BEigItuowI4i4ytpgI2HymylHLuKjK/AOZRehS2UZXeBsQAeVtEAgGpsojR7C4xHw7+9A32vsOkAj4eTbduH/ABGAa1x1nQac6jnt27d2hYRw2bPtu3KlW37gQcKPBwI2HbbJ2+3eQLn4+YTbfDZzoE3fYTroyRPXk2d/XfH0rnI+DyO3n93MqcVGf+LtvvvzqRlkfFfOGv/3aU0KjI+mov3391E1SLjn3DN/rsZpBYZf5tbbLBtD76gf74XeNAG2y7P9qDjj/KUDbZdh9VBx+/idRts+2jmBR2/ng9ssO22/BV0/HJG2GDbZ/FL0PHOjLXBti9mdNDx05hsg21fwydBx1sy0wbbvoW3g443YqkNtv0gLwQdr8omG2z7KR4NOp5Khg22/Tp3FRnz4dl/9wOuLzK+nfL7747g8qj8txfsz7z/I0yA6bZdtM8cck+SZVldgc22bc+wLKvDQZ53M3AzQL169Y64vInIEcg/Sfvmm/c/vH1XJrWeO4Z9qSudB8a/AFP+Zyaj7HctBRfozKDU/tuvch+fc1WB8Z2U33/7WR6hGpsLjG+lyv7bj/E0FdleYHxj7l4igAd4mTLsKTC+JndPAcDtDCyyt2A5/036v5EPSKDgadqLaQqAjVXkzwYwj2MB588ZbHwWrQDYRqWg49NpA8B6agUdn0r7/TmDjf9DWwDmc0zQcS/OHtV/OTHoeN6fbwqnBB1fjfN/4AQ6BR3fnLsX7gcuYAM1i4zvpiwAX3MpS2hSZDyHJACG05N/ObHAWN5hXIAPuYHfODPo1wK8zW2MoeAU42j4t3cJ3xItLKdAHeQJlvU80AvwAak4c5K+tm37mgN9TZs2bezp06eHMqeIFNcBLjUyfMp4rhnXBTw+yCoN7/4L25uazRonPPjxH+R30sKHq0TiWf6iV3QwPN8rlmXNsG27TeHHD7mYpG3bD9u2Xce27QbAlcCvBytIImJQVhZ07VqkIJ39Vh+u+flcsHyw8nR4fqcKUgTdw+umI4jIYdCK2yKxJCUFhg7dX5B8Pj/lnmjAhG1DAQvGvANDJgMJhoPGl3e4lR35DlPkt5HqEU4jEt0O+D1RPfLfKyVaTNK27UnApLAkEZHD5/XCzJnQuzd07AjAkk3raD6wCYHETMgqC28tgD21DQeNHx78PMQLDOR2dlGeirlnKU3MvaZ5R/0oFQkqb0mMifWdw24dVpo7HK0Vt0XcLm8OUqlS0L07lC7Nx5PHc/2EzpAQgC3N4e25aO9R5Hjw8zHXcS3DWE8thnCd6UgichhUkkTcLK8gpaXBr79C6dL0fOdFPtv8EFjA/Mvgy1GmU8aV/AXpUZ4uUpBG0sNQMhF3GXlM7p4kgxlUkkTcKn9BmjgRu/FRNOnXnWXJXznjOr0/4goXpGd5tMhzBnGrgWQi7jPoJGfa9DsGM6gkibjVxIn7C9KOKg2p8UBzssosAtsDw36CFeeYThh3qrGZDkw6YEECSCMDgMx869GISFFpOeaXxjjkOkmHQ+skiYSRzweJub/f7NjB1JU5nDKyMaTshuw0eHOJJmhHmAc/NhY2Hsqzg51UOOBzNXFbpHgiOXH7sNdJEpEo4vXC0UdD7i8hr45byCmjazoFaUd9eG63ClKE5R1ie4dbAfugBUlE3EUlScQtvF7o1An27YMKFej23EDuX9AeLD8sOQ8GrERnsEVW/jlIa6kDB1spWERcR3OSRNwgryClphKYMJGjBj3DirK514Sa3A8mPms2XxwqziRtEXE3lSSRaLd06f6ClPHDRKoMupzM8rMAC774ChZ1M50wLr3L/6kgicQ4lSSRaFevHlx2GYsv70vzz0/GrrANclLg7Xmwo7HpdHHray5lGY15kYdK9HVD6BOeQCIxZkhL8+sk6ew2kWg1bx5UqwZVqzL0Jy99prSGBB/srgEDloM/zXTCuOPBzylM4Q9ONx1FJPY97uzHsfv7wv5SOrtNxE28XujQAfr04fo3P6HPXy3B44OVp8GrG1SQDMibg/QbZ9KCOYe9ncpspTJbQ5hMJDZVzrCpnGF2rSQdbhOJNrmTtO2UFLrWrsTYbb2dx/++HX56y2y2OFV4kvZcjjvsbY2iO6B1kkQOZdSXAefGy+YyqCSJRJN8BenEc6ozq9anzuNfD4M515jNFqd0FptI/FJJEokWtg233IIvMZljuuxjSe1/wZ8Eg6fD5uNNp4tbF/GdCpJInFJJEokWlsWE/71A33GdWFYtBzIqwWurwFfGdLK49g3dOIPf+J0zTEcRkQjTxG0R07xeuP12nh4xkrP/PZNlVXNg/QnwUroKkiEe/LzBnbRkFoAKkkic0p4kEZNy5yBt9WXxbvLbUA6YeR1895HpZHEr/xykVdRnNq1Cuv1B3BLS7YnEqkFtzK+TpJIkYkpuQdqQvZvTe2exvhww5h2Yof9ETSk8Sfs17gv5a4zkipBvUyQWjTzWOdg1wmAGlSQRE7xe7E6dWJe9nQ69/SyrmAAf/AHr2plOFrcidRZbHdYAsJa6Ydm+SKyos9PsGkmgkiRixI71q9lop9O1t82y0uXgxRWQVcl0rLiWiI+qbAn7WWzD6AVonSSRQxn2Te46SQPMZVBJEomk7dtZnLWbZlMvJeFWG//2JvDCAiDBdLK45cFPafaym3JcyPf49WNRRHLp7DaRSPF6yTnqKJ7v2wCsHPzr2sHAxaggmZN3iG0iHUlhnwqSiBSgkiQSCV4vWWeeyUbfNn6vb8PS8+CjqaZTxbX8c5BG040sUk1HEpEoo5IkEm5eL5mnn8HmwA469oZl66+A4T+ZThXXdKkRESkO7VsWCaf0dPacdhrbrd1OQVp2C4x9x3SquPcc/YwVpFfDsKyASCx6tZ2zH6eDwQwqSSJh1HPo15Q+cze/NoRl8x6CX583HUmAt7mNNdTlbW6P+GuP4cKIv6aIG41pZpmOoMNtImHh9XLXTbfy2a6beb81LJv2igqSYR78XMdHWARYQz0jBQmgKYtoyiIjry3iJk232jTdanatJO1JEgk1r5cd7drRt3QmA2+BwJj3YOZNplPFtfxzkDZTjR/oaizLYPoCWidJ5FAG/5C7TtLb5jKoJImEkD3by472bdmbmMVFV0Hgy1Gw8DLTseJa4UnaJguSiLiLSpJIiNizvWxvdxIZSdl06APLvpsAq84yHSuu6Sw2ETkSmpMkEgJ+Pwy95gKnIPW2WPbldBWkKHAs87icL1WQROSwaE+SyBHKybap9L927LtkLTV2e1j7yQLY3tR0rDhnAxZzOJ5jmM9KGpoOJCIupJIkcgSypnmZ1q0dpa/MZA/JrP1wOeypbTpWXPPg50Nu4HdO5yNuiLqC9Iz2aIkUyzOna50kEdfa/edMss5pQ4PEAKUzUmDgesiqZDpWXMs/B2kJTUzHCWoCZ5uOIOIKExqZXydJJUnkMOyYPBNf59bsS7TpcHUZlr+3GfxppmPFtcKTtJ/jEdORgmrJLABm08poDpFo13Kj2TWSQCVJpMTW/TKdlIvaOgXpioose28j2MmmY8U52zVnsQ3gbkDrJIkcyoBxAdMRdHabSEnMWrSN1mNO59+aNh0uq8GyD7aoIEUFiwUcHfUFSUTcRXuSRIrpn+//5NSpHfFVzOG885vD2wtMR4p7Hvw0YjlLacILPGw6jojEGO1JEimGPz/7joZXnsbrv+TA2pNUkKJA3hykaZxEDTaYjiMiMUglSeQQxg/7gqY3XUxWAgyo1R4++Md0pLiXf5L2K9zPRmqajiQiMUiH20QO4vuPP6Td7TeSlQAdTjuPZd/+ZDpS3HP7pUb68ZzpCCKu0O8sZz/OFIMZVJJEChm/bDznDz+f25v24477nnYK0kmXs+zHkaajCXAr77i2IAFM5RTTEURcYWpdrZMkEnWuGHUFftvPGwuf5q9LYeuCG1n26/umY0muwfRlE9X5kh6moxyW9rm/F6ssiRxc+zVaJ0kkqoxfNp46K7dz5WoYdBL8vfF6mKKCZJoHP4/xNAO5nXSquLYgATxHP0DrJIkcynO/ap0kkajS7/WL+HUoPPwHlNsHHDvadKS4lzcH6Un60w29HyISOSpJIrmefaUvP360j32J0LE37EoD0rZD/V9NR4tbhSdpf8BNpiOJSBxRSRIBRn80mL6Pv8e+ROjQB5ZVzjd41cWmYsU1t5/FJiLup5IkcW/UlH/44Zv/IzMpSEGygJQ9UGuaoXTxqxLbaMdfKkgiYowmbktc+2nKbC4f1x5OhBHZ17HnrY8KPqHsOrj+dOh1Hnw4BbY2NxM0jnjwA7CVqrRmBnsoazhRaN3NANMRRFzh7vOc/TizDGbQniSJW9NGjKN5l1actjoA83qwZ9xHRZ+0uzYMnQD+JOjZBUpvjnzQOJJ3iG0ovbEIxFxBAphNK2bTynQMkag3u4bF7Bpm10pSSZK4tPDb32l4XWcSA7Bh+2kwasSBn7yjIXz+PZTZCFddBImZkQsaR/LPQVpIc+wY/fHUiV/oxC+mY4hEvU7LbTotN7tWkg63SdxZPe4vqlx1hjNJ+7xWLBv1+6G/aF1b+Go4XHEZXNoLvhwJdmz+J25CPE3SfpRnAJjA2YaTiES3R3/XOkkiEbX5n4WU6naKU5AuPIplo2YW/4sXdoNxr8IxX8HZ/wtfyDj0NrfFRUESEXfRniSJGzt2+Kk16jReamnzTvNaLBuypOQb+etuqLQMTn3FmcQ984aQ54xHn3MVy2nEyzxoOoqIyH7akyRxYd/0uRz/UF38pdO577RqLBuy+jC3ZMFPA2DZOXDBrVD7n1DGjCse/Pvn5kzmTBUkEYk6KkkS83JmeNl9Ris+/nkDZJaHV9YDCYe/wUAijPocdteCKy6F0ptCljVe5M1B+oVzOJEZpuOIiASlw20S0/wzvew8vTX7kvz0PS8FXtnAERWkPJmVYcRouOEUuLwHfPILBJKOfLtxoPAk7X9pbTpSRPVlsOkIIq7Q9wJnP84igxm0J0liVmCWl+2ntiEzyUfHa5JY9tEm8KeF7gU2toLv3ocGk+HcB0K33RgWT2exHchimrGYZqZjiES9xVUsFlcxu06S9iRJTLJtmHLR2dRPyqFjrwSWDV0B2eVD/0JzekLtadDuDWeZgDlXh/41Ysi5/BzXBQmgK98DMIYLDScRiW5dF5ldIwlUkiRGNXvgBrZfsYVy+yyWD58Pe2qH78V+fhlq/gtd+8K6k2Bbk/C9lsv9xPmczF/8w8mmoxhzH68CKkkih3LfX1onSSS0vF6+bdmClWkfsbW0xfJR02B70/C+ZiDJWWjSnwzdr4SErPC+nst48PM2t9KOqQBxXZBExF1UkiR2eL3saN+eE1fOo/oe4ONfYWOEJgXvqgvffgS1/oWzH47Ma7pA3hykWxnEafxhOo6ISImoJEls8HrZdcqp7E7MoGNvWPvj17C6Q2QzLLoY/r4D2r8OTX6I7GtHocKTtF9Bk9tFxF1UksT9vF72nHoaOxP20LE3LPvtY1jUzUyW8S/BxpZwSR8ou85Mhiigs9hEJBZo4ra43vCfJ3F8qd10uwKW/fMqePuYC+NLhS9HQN/W0O1aGDY+Li+E6yFAGpkqSEH0YpjpCCKu0OsS52fnGoMZVJLEvbZsYcSSZVyz526s/wN78qPw972mU0F6M+fSJRfdBCe9Df/cYTpRxHjwU56dbKcSVzACWzuri1hLXdMRRFxhbXmzaySBDreJW3m9ZDdrxuRnTgFs7Om3waSnTaf6z783wOIucM7/oPJi02kiIu8Q2x+cRin2qiAdQA9G0IMRpmOIRL0e8wL0mGd2GQD9FBP38XrxdezIZt92xh1lg/dqGDvQdKpCLPj+fefw2yW9weMzHSis8s9B+oyryaC06UhR6xYGcQuDTMcQiXq3TLe5ZbrZBSVVksRdvF78Z53FxpxtdOgDy9K7wOjhplMFt7sW/PA21P0LTnnFdJqw0SRtEYlVKkniHjt3Yp99Nhuy052CtOdU+CzKT7WfeyXMuxw6Pg7V5phOExb9eUIFSURikiZui3uUL88Np2QzuR4s8x0LH7thcUILfngH6v8Gl/aC96Y5K3THkIHczlrqMJj/Mx1FRCSktCdJop/XCxMmUL//6Xx8wk6WpVWEQbNNpyq+jCowZjDUmB0zh908+Pk/BpFIDpuooYIkIjFJe5Ikunm90KkTa8lh3S07gUQYsAJIMJ2sZBZeAvMvhQ79YX53V18EN/8cpE1UZzSXmo7kKt0ZZTqCiCt0v9zZj7PVYAbtSZLolVuQdgQCdLhqJ34PMHgGZJc3nezw/PiWc7Zb1/8DzJ6xcbgKT9JWQSq5dKqQThXTMUSiXnopi/RSZtdKUkmS6JRbkDKsBNr03MaySsDoobD5eNPJDt/uWjD+RWj0K7QaYjpNieksttDozRB6M8R0DJGo13tWgN6ztE6SSFFDhpCTlMLxPdKdgjTtNvBeazrVkfv3Jlh1Opx3H5TebDpNiTRhCd0YrYJ0hPowhD4qSSKH1Ge2TZ/ZWidJ5D+28w2R+fQr1L9sL8uq+mD1KVG4WORhsj3w/WBI2gud7zadppic92QRzTmaBSpIIhI3VJIkeni90K4dgZWrqfJYGzZU2QG7q8PHf5pOFlpbj4bf+8Fxn0PjcabTHJQHP0Pow10MAGAddcwGEhGJIJUkiQ5eL5x1Fqxfz7nP3EtGuZngS849ky0G/fEQpDeB8++EhCzTaYLKm4PUm08ozV7TcUREIk4lSczLK0hpadxx1Y1MqPMVYMHbc8CfZjpdePhT4Mc3ocpiaP+66TRFFJ6k/RyPmI4kIhJxWidJzJo3b39BGvrQawzc0sN5fMSXsL2p2WzhtrQzLLgEzngavNfArmg5lGXzEdfrLLYw6cJY0xFEXKHL1c5+nAyDGbQnScyqWRNOPZU5g0fTZ/NVzmO/PwQLLzObK1LGvQ5WAM69z3SSfCz+oS2P8IwKUhhkUopMSpmOIRL1MpMsMpPMrpOkPUlixuLFUK8eVKrE9iFfc/zLlSDFD8vOhl+fN50ucnY0gD8eho5PwIybYUUnY1E8+GnGIhZwDO9wm7Ecse4W3gFgELcaTiIS3W6ZZnaNJNCeJDHB64VTToHbbsPngxpPt4CUXbCjLgwbbzpd5P35IGxrBF3uAE+OkQh5c5D+oS11WGMkQ7zowUh6MNJ0DJGo12O+TY/5WidJ4kneJO3UVHj4Yeo/cDnZ5RZCdhq8sdR0OjN8qfDTAKi6ANpGfj2o/JO0X+Ah1lI34hlERKKRSpJETv6CNGkSZw//ivXlRzkLLL61EOxk0wnNWXwhLOkMZz4FaekRe1ldakRE5MBUkiQy/H7o0WN/QXrizxVMsB9yxob9BLvrmc0XDX5+xTnseOZTEXvJG/hQBUlE5AA0cVsiIyEBvvgCypRhfHoaTy3vDBbwy3Ow4hzT6aLDlmOda7ud9A5MuxXSm4X9JT/iejZTjW+5JOyvJSLiNtqTJOHl9cLLLzu3W7Vic7mGnPt9E+e09wWXwp8Pm80XbSb2B18anPNg2F7Cg59n6Uct1uEnUQUpwjoyiY5MMh1DJOp17J1Ax94JRjOoJEn45M1BeuMN2LYNvx9qv9IYkjIhvSmM/Mp0wuizt7pzXbfm30GDiSHffN4cpH48z0V8F/Lti4jEEpUkCY9Ck7SpVIl6/TrjK70KssrCwPmmE0avv+6GHfXhvHvB8odss4Unab/LLSHbthTffbzCfbxiOoZI1LtvSoD7pphdK+mQJcmyrFTLsv6xLGu2ZVnzLMvqH4lg4mKFC9JRR9H19UdYnzYOAgkwYBlgdhdqVPOlwi/PQ81Z0PKTkGxSZ7FFj66MoStjTMcQiXpdl9h0XRL96yRlAWfZtt0SaAV0tiyrXVhTibvNmwelS+8vSK//OJofdj7njH34B2RWNRrPFeZeCWvbQsfHITHziDdXjl20YpYKkohICRyyJNmOPbl3k3I/zFY7iU779jmfr7oKFiyAo45i1qpl3Pt37nXYfngL1qlfF48Fv7wI5dc6Z7sdJg9+kshmBxVpx18qSCIiJVCsOUmWZSVYljUL2AyMt23777CmEvfxeqFJE/j5Z+d+qVLs3ZfJiR8eC9gw61qYfrvRiK6zsgMsPRdOfx5Sdpb4y/MOsY2kBx78uqiqiEgJFask2bbtt227FVAHaGtZVovCz7Es62bLsqZbljV9y5YtIY4pUc3rhU6dwLahUaP9D1d9rhF2QhZsbAnfDjUY0MUmPAel0uGUV0v0ZfnnIE2nDQHNAYsamaSRSZrpGCJRLzPR+TCpRC9v2/YOy7ImAp2BuYXG3gPeA2jTpo0Ox8WLvIKUkrJ/DhLAia90JTNhI2RUhMEzzGZ0sw2tYW4PaP8a/HObs0TAIWiSdnTrwo+mI4i4Qpeezi93JgtFcc5uq2pZVoXc22nAOcDCMOcSN1i9OmhBemXcZ8zc8wMEPDBgBTqT7QhNfBoS98EZzxbr6W9ypwqSiEgIFGdPUk1gqGVZCTilaqRt2zp/VaBOHbj5Zrjuuv0Fac3WbTwwpZdzyZFRX0B2ebMZY0F6U5h5PbR5F6beCzsaHPTpQ+nNChryKvdHJp+UyKM8DcAzPGY4iUh0e3Sy2TWSACzbDv2OrDZt2tjTp08P+XYlSsyZ45zin2/+EThTkpIfqY4vZTMsuBhGfGMmXywqtxbuaALzL4fRRddO8uDnfH7kB7oaCCclMZEOALo0icghTKxvAdBhZfgPuFmWNcO27TaFH9eK21IyXi907Ai9ejmtKJ+jH73SKUgZlVSQQm1XHfjndjhuOFQpeLQ7bw7SGC6kPVMMBRQRiT0qSVJ8+VfSHjoULGv/0OOfjmFR0giwLRg4z2DIGPbng85q3Gc8vf+hwpO0p3KKwYAiIrFFJUmKJ8ilRvIsXb2Hp5dc4tz59n3IqGEkYszLqJq7N+lzqLJAZ7GJiISZSpIUT79+QQtSIADNBx4DHj+sOAtm3WAuYzyY8gDklIIzn+IMJqsguVA6lUmnsukYIlEvPc35MMnwMk3iGsOHQ3p6kcnaLR6+CX+pNbCvHHwywVC4OJJRBf6+A057kUmTH+OELf8yixNMp5IS6M5XpiOIuEL3Hi5YJ0nimNcLV14JGRlQvnyRgvTiiEksSPvAuTNoVuTzxSEPft79bT1nLU6BM59SQRIRCSOVJAkubw7Sn39CkMvMrNuYzUPzznHu/PQ67GwY4YDxJ28OUl/fJ7T5px0cOxKqapK82zzHwzzHw6ZjiES95yYEeG6C2bWSVJKkqLyClJYGEydC/foFhgMBaPjysZDggzXt4O+7zeSMI4Unab+0fhRkl4YznzIdTUqoPVNpz1TTMUSiXvu1Nu3Xmr3KmUqSFFS4IOWbpJ2nTb/7ySm7FLLT4KM/DISML0HPYsusDH/fCS20N0lEJFxUkqQgj8fZc3SAgvT21/8wMzX3ivSDZ6DrskWGn4SiZ7FNvdfZm3TaC+aCiYjEMJ3dJo4NG6BGDWjRAqZPL7BQZJ7Nm/3cPvMMpxdNfBLSj454zHjiwU9l0tlCNa7nI5wL4uWTWRmm94V2b8Ck/rC9UdDtiIjI4dGeJHEOsR1/PLz8snM/SEGybaj3/AmQmAWbjoPJT0Q4ZHzJO8T2F+0oyy6KFKQ8U++DQAKc8nJE88nhW0sd1lLHdAyRqLe2nMXacgf42Rch2pMU77xe6NTJWSjy0ksP+LTTHulPVvk5kJMC786MYMD4U3gO0m7KHfjJu2vBrOvghI9g8mPOfYlqvfjUdAQRV+jVzdmPc43BDNqTFM/yF6QDzEECGPLDXKYkP+nc+fBPNA8pfA7rUiN/PggeH7R/LfwBRUTiiEpSvNqzB84995AFKT3dz3VT2jp3/rwPNraOYMj48wjPlvxSI9sbwdyroM27kJYe3oByxF7nbl7nbtMxRKLe6+MCvD7O7DpJOtwWr8qUgXfeceYiHaAgATR45jSokAnpR8Evr0QwYHwayO2spxYfcmPJvvD3h+H44XDym84kbolarZhlOoKIK7TaaHaNJNCepPjj9cKYMc7tSy89aEG67JXX2FP+L/AnwkCtxRMuHvzcxQBS2Md2KpW8IAFsORYWXOKUpOTdIc8oIhKPVJLiSd4cpDvvhOzsgz71z3kr+HrPfc6doePBTo5AwPiTNwdpAPdwCd8c2cZ+7wdpO6DN4FBEExGJeypJ8SKvIKWkwM8/Q/KBS08gAKd/1sq5M/0mWN0hIhHjTeFJ2iO48sg2uP4kWNEBTn4DPDkhySgiEs9UkuJB/oI0adJBD7EBNHyyE3byLthZF354LzIZ48xhncVWHFMegPJrocWI0GxPQm4xTVlMU9MxRKLe4soWiytrnSQJt5Eji12Qnvj2Q1Z7fnUWKBw4PzL54lADVtKVMaEtSABLO8PmY5zFJb09OeAilGJMX/SLh0hx9O3q7Me52WAG7UmKZYHcUyeffhr+/feQBWn1to08NfMm587n34CvTHjzxSEL5z1ZTmOOZkFoCxKA7YEp90MNLzT6JbTbFhGJMypJscrrhRNPhCVLnMuMVKt2yC9pMuBYsGyYewUs6RqBkPHFg58h9OFxnFP0N1M9PC8052rYXRNO0ZIN0WgwNzPY6O/GIu4weEyAwWPMrpOkkhSL8uYgbd0a9DpswXR8qyfZCdtgTzX46oswB4w/+ecg+cO9Yrk/Bf6+E476GarPDu9rSYk1zZ2VJCIH1zTdpmm62bWSVJJiTQknaQPMWLmYSemfgW3B23PDnzHOhG2S9sFM7wvZpeGUV8P/WiIiMUolKZYsWFDiggTQ7v32zo0/HoDMquHLF6c+4MbIFiSAfRVhxk3Q4nMotzYyrykiEmNUkmJJnTpOSSpBQTrnlXvxJW+DjCow4cXw5otTk+jAIzwTuYKU56+7nTlmJ78R2dcVEYkRWgIgFixc6BSksmXhi+LPJ5q5eCO/7HndufP+X2EKF588+DkeL7M4gU/obSbEzvowvzuc+AFMehJySpvJIQXMopXpCCKuMKuGM6e2g8EM2pPkdl4vnH463Fyys2VsG9p+cJKzjM7M62BH4/Dki0N5c5Cm0p6GLDcb5q+7nEuVtPzEbA7Z7x4GcA8DTMcQiXr3nOfhnvPM1hSVJDfLm6SdmgpPPVWiL73oxZfwlVoLWWXgu4/CFDD+5J+k/QyPsoJGZgOtbQfrToJ2b4Bl9lRaERG3UUlyq/wFaeLEYs9BAli6eg9jMh927gyZEKaA8cfIWWyHZDl7k6osgsY/mw4jwDCuYRjXmI4hEvWGjQ4wbLTWSZKSCgTgmmsOqyABHPfmSeAJwJIusKFtmELGn2v4NMoKUq75lzuLS2oCd1Sow1rqoDMORQ6lzi6bOrvMrpOkidtu5PE412NLTCxxQbrprU/ZV2Yh5KTAZ9+FKWB8GkYvtlCVH+liOkpB/mSYdguc9ThUWQhbm5tOJCLiCtqT5CZeLzz5pDPrunnzEhekrVv9fLDlOufOF19DuFd+jgMe/LzEAzRgBTae6CtIeWb0BV8ynPym6SQiIq6hkuQWXi+cdRZ8+KFzuZHD0Oi50yHB50zkXRal/5m7SN4cpAd4ha6MMR3n4PZWc67p1nIopG43nUZExBVUktwgryClpTlzkKqWfFXsRz8ex+5yUyGQAB/+EYaQ8aXwJO2B3GE60qH9fRckZ8CJH5pOEtem0p6ptDcdQyTqTa1jMbVO8a4/Gi6akxTtChekEh5iA9i9G55ddrHzbn/zAdjJoc8ZR6LzLLZi2NgKVp4JbQfC1HvA1uFWE/rxvOkIIq7Qr5OzH+dhgxm0JynarVgB5coddkECOOqxiyEpC9KbgrdPaPPFoVJk0IxF7ipIef65HSqsgiY/mk4iIhL1tCcpWu3dC6VLw8UXQ+fOzkVrD8Nbo2awucJ3YFvw7owQh4wvHvwkkcMeynIGk8nm8N4ToxZeDLtqwUlvw+KuptPEpVFcBkB3vjKcRCS6jRrpd270N5dBe5Kikdfr7DX69lvn/mEWpMxMuPPfs5w7vzwLvjIhChh/8g6xfc+FJOBzZ0ECCCTBjJuhyU9QcZnpNHGpMulUJt10DJGoVznT+TBJJSna5M1BSkiAY489ok21ePgmSNkFu2vBnyaP6rpb/jlIv3EmfrfvgP33JvAnQpt3TScREYlqKknRJK8gpabCpEmHPQcJYOT4FSyv8AHYwHv/hCxivHHtJO2D2V0LFnaDEz6CRMO/pomIRDGVpGixfn3ICpLfD1f+0g4s4K87YU/tkMWMN69xb2wVpDzTboVS26DFCNNJRESilsuPG8SQmjXh3nuhR48jKkgAHfo/hp22GTIqwjhdr+tIvM9NrKI+r3Ov6SihtfJM2HwMnPQOzOpjOk1cmUAn0xFEXGFCQ2eNpA4GM1i2HfqLx7Vp08aePn16yLcbk7xeSEqCo48Oyebmr9jGsUOrADYMnA/podluPPHgpxuj+YrLcHbHxaiT3oYLbncOx64/yXQaEZGCHnf249j9fWF/KcuyZti23abw4zrcZpLXC506Qa9ezvXYQuDEd1uDZcO8HipIhyFvDtIoLudMfjMdJ7y8vSCrjLM3SUREilBJMiWvIKWkwBdfgHXkeyxu/XggWWkrITsNRmmuSUkVnqT9m9GdvBGQVc4pSsd9DmnbTKeJG2M5n7GcbzqGSNQbO9zP2OF+oxlUkkzIX5COcJJ2nl17Mxm08i7nzqc/HfH24k1MnsVWHNP7QmIWHD/MdJK4kUYmaeisQpFDSfM5HyapJJnw9NMhLUgAR73QHjwBWNERVp8Rkm3Gk5P5m54Mj6+CBLCpJaw9GVq/h7NehIiI5NHZbSYMGQJbtkCDBiHZ3LuTv2JLwmzwJcEn40OyzXgzlVM4Hi/zObIFPF1pxs1w8Q1QdwqsOdV0GhGRqKE9SZHi9TrXYdu1y7kmW4gKkt/v55YJVzl3Rn0O6MruxeXBz/vcyIV8BxCfBQlg7hWQVTZ3b5KIiORRSYqEvJW0Z8yA9NBes6ndgMvAkwObjoeFl4V027Esbw7SjXzIscwzHcesnNLgvQaOHQmp202niXlj6MoYdHFhkUMZ08RiTBOzy7CoJIVb4UuNNGwYsk2v376N6bu/BduCD/4M2XZjXeFJ2i+g69ox42ZI2gfHf2o6Scx7lft5lftNxxCJeq+e4uHVU8zWFJWkcArhtdiCOe6VDs5ah7N7gq9MSLcdq+L2LLZD2dgK1p2kCdwiIvmoJIVTqVLQpElYCtI3f81iW9Ic52ru3wwJ6bZjWQAPOymvghTMjJuh+lyo85fpJDFtIh2YGOtrcImEwMShfiYO1TpJsWfNGmcF7aOOgilTQl6QALp/09m5MeEZNFn70Dz4qcU6wOJO3lRBCmbulc4K3JrALSICqCSFntcLJ5wATz7p3A/BStqF3f3BCPypm5wzkqb8L+TbjzV5h9j+oS0V2UZMX4/tSGSXgTk9ocUISN1hOo2IiHEqSaGUfw5Sr15heQmfD95YeZ1z58svwvIasST/HKRB3MJ2KpmOFN1m3ARJmXDcZ6aTiIgYp5IUKmGepJ2n47MPO/+J7awLy7qE5TVihSZpH4YNJ8LGltDqY9NJRESM04rboZCZCeefH/aCtG2bnz8CLztHi4b+EpbXiCUP8pIKUolZMPN6OP8uqO511t+SkBpJD9MRRFxh5DHO1IgOBjOoJIVCWhp88IFzJluYChLA8c9dBmX9sKYtbG8atteJFQO5nfXU4hN6m47iLt6ecM4Dzt6kca+bThNzBnGr6QgirjDoJOdg1zsGM+hw25HwemHkSOf2+eeHtSBNn7uNdWVyF44cOiFsr+N2Hvw8wEuUYi97KKuCdDgyK8Oii6HlMEjINp0m5qSRQRoZpmOIRL20HJu0HLPrtqkkHa68OUgPPugcbguzMz7uoIUjDyFvDtJL/I9L+dp0HHebeR2USoem35tOEnPG0oWxaD6hyKGM/SzA2M8CRjOoJB2OvIKUlga//OJ8DqPB384is6wWjjyYwpO0PyU8ZxfGjWXnwq7acMJHppOIiBijklRS+QvSxIlhPcQGEAjArX/mLhz5y7No4ciidBZbGNgJMKs3HPUTlF1nOo2IiBEqSSX1ww8RK0gAN7w+gkCpTZBVDqY+GPbXc6NarOccxqsghdqs68ATcOYmiYjEIZWk4vLnXj/moYdg1qyIFKTMTBiyLW/hyM/D/npuYxEAbNZSlxbMVUEKtW1Hwcozcg+56aK3IhJ/VJKKw+uF446DOXOcy4xUrhyRlz318YchWQtHBuPBzxD68Ar3AzbbiMx7EndmXg+Vl0C9P00niRlD6MMQ+piOIRL1hrS0GNLS7GWkVJIOxeuFTp1g9+6wT9DOb+1aPzNLvez8Aq+FIwvIPwdpOxXRtdjCaH5356K3msAdMkPpw1CVJJFDGtrKw9BWZmuKStLB5BWk1NSIzUHKc+KLl4HHD2u1cGR+mqQdYTmlnaJ0zChI0to+oVCZrVRmq+kYIlGvcoZN5QytkxSdFi82VpCmztrGlspaODKY97hZBSnSZl8LKbuh2bemk8SEUXRnFN1NxxCJeqO+DDDqS62TFJ3q1oULLoh4QQLoNKyDFo48gLF04WGeU0GKpFVnwo560PIT00lERCJK124rbP58qFULKlSAIUMi/vKfTdDCkYV58NOG6fzDyXzNZabjxB/bA95ecNrzUHY97K5lOpGISERoT1J+Xi+ceSZcf72xCL3Ha+HI/PLmIP3JqTRjoek48Wt2L2fNpOM+M51ERCRiVJLy5E3STkmBl14yEuHxESPwpWrhyDz5J2k/yZMsornpSPErvRmsPRlaDkVrJolIvNDhNihYkCZNivgcpDzPzLneeUe0cKTOYotGs6+FC26DGrNhYyvTaVxrELeYjiDiCoPaOMu7dDCYQXuSbBtuuMF4Qerx7iPYSRlaODLX5XypghRt5l4B/iRN4D5CI7mCkVxhOoZI1Bt5rIeRx5qtKdqTZFnw5Zfg8xkrSD6fny83vOic0aaFIwEYwRVsoSq/0sl0FMmTWRkWd4XjhsP4FyGQZDqRK9VhDQBrqWs4iUh0q7PT/KH9+N2T5PXCAw9AIAANGhgrSADtXs9dOHJNu7heONKDn9e4h+YsACwVpGg0+1oosxka/2w6iWsNoxfD6GU6hkjUG/ZNgGHfaJ2kyMubg/TFF7Bpk9Eou/btYcbe3IUjPxlvNItJeXOQ7mEAnfnJdBw5kCVdIKOyDrmJSFyIv5JU+FIjNWsajdP2tUucw2zzL43bhSMLT9IewD2mI8mB+JOduUnNvoOUXabTiIiEVXyVJIPXYgsmIyubRdm/OnuRRsXn+jM6i82F5vSEpH3QfLTpJCIiYRVfJWnjRmcl7SgoSACnvHQtWDas6AB2suk4RqSQRT1WqyC5yZr2sL2BFpYUkZgXH2e37doF5crBuec6lx1JMn9WTlYWzPaNcg61fRF/v5F78JPKPjIozTmMx4f590SKy4I5V8NpL0CZjbCnhulArvIq95mOIOIKr7Zz9uN0MJgh9vckeb3QpAl8nrtAYxQUJICOL93tnNG2sRVklzcdJ6LyDrH9zLkkka2C5EZzejqXKTl2pOkkrjOGCxnDhaZjiES9Mc0sxjSzjGaI7ZKUNwcpORlOOsl0mv18PpiaM8i5usNn35qOE1H55yD9yPnkEJ+HGV1vyzGwoZWzZpKUSFMW0ZRFpmOIRL2mW22abjW7VlLslqQom6Sd3+WvvQoJ2bCtMeyuZzpOxGiSdoyZczXU+QcqLTGdxFUG05fB9DUdQyTqDf4hwOAftE5S6G3eHLUFKRCAb3Y97uxFGjHKdJyIeokHVZBiydyrnDMzj9O1BkUkNsVmSapWDR59NOoKEsBt734GiRmwpzpsbmU6TkQN4hbu5A0VpFixqw6sPDP3kJv5yweIiIRabJUkrxf+/de5fdddUVeQbBsGr7nduTN6qNkwEeLBT08+BWyWcRRvcafpSBJKc3pClcVQ81/TSUREQi52SpLXC2edBX36OMe0otCzn07CTtkO+8rD8vNMxwm7vDlIn9KLc9G1vmLS/MvAlwzHawK3iMSe2FgnKa8gpabC11+DJzq735NzekIp4McBpqOEXeFJ2j8T+6UwLu2r6FzPrcUX8PPLYCeYThT1ntHhZpFieeZ08+skub8k5S9IkyZF3SG2PB9+Nxd/qfWQkwbePqbjhJXOYoszc6+Co7+B+r/Dyg6m00S9CZxtOoKIK0xoZHaNJCjG4TbLsupaljXRsqz5lmXNsyzrrkgEK7ZXX436ggRwy2+XOatr/97PdJSwa8UsruQLFaR4sfgCyC7t7E2SQ2rJLFoyy3QMkajXcqNNy41mTwopzp4kH3Cfbdv/WpZVFphhWdZ427bnhzlbUTVqwKZNRR+vUiWqC9IPk9eRU3Yx+JPg99gqDRuoQQ2KvidbqKKCFC9ySsOiC+Hor2DsWxDQCuoHM4C7AejIJKM5RKLdgHHm5xcfsiTZtr0B2JB7e7dlWQuA2kDkS1KwggSwdStMnercbtzYWQJg926YO7foc5s2hcqVYccOWLCg6PjRRzsXwU1Ph8WLi463aAFlyzprMS1bVnS8ZUsoVQo2bICVKwF45e3raVcWmN+Vf8kimxRqs5a6rCny5dNpg48k6rGKWqwvMv43J2PjoQErqMHGAmM2Fn/TzvlrYClV2VJg3Eci03FWHm/KIiqxrcB4FinM5ETnr4H5lGdngfEMSuGlpfPXwBzKsCdoQQKoytagj0uMmnslHPcFNPwVlmn+mYjECNu2i/0BNABWA+UO9rzWrVvbYeGcRX/wjyFDnOf++Wfw8a++csZ/+in4+LhxzviXXwYfnzLFGf/oo+Djc+Y442+8EXS8HittsO2HeTboeEXSbbDt53go6HgSWTbY9lvcVmRsH8n//TVwbZHxLVT+76+BbkXGl9Pgv78GzikyPpvj/vtroN0h34vivF36iJGPhH02D5Wzufg681mi/GMiZ9oTOdN4Dn3oI9o/JtbHnlif8PSJIvWC6bZdtM9YztihWZZVBvgNeNa27a+DjN8M3AxQr1691qtWrQpNiyv4Igce++kn53OLFlC7NmzfDv/8U/R5rVpB9erO3qcZM4qOt27tHL7buBFmzy46fvLJzp6mdeuC76k65RRnT9OqVbBwIZe904895f6F1afC5MeYzBnsI42GLKcJRS/n8Ctn4SOJJiymISuKjI/nHGw8HM38InuibCzGc67z18CcInuickhiImc5fw3MpBqbC4xnksbvnOH8NTCdyqQXGN9DGaZwqvPXwF+UZyfj6Fz07yCXpQUG48slvaHZd/DKRvCnmE4TtSbmnqujw20iBzexvvN/foeV4f+/xLKsGbZttynyeHFKkmVZScAYYJxt268d6vlt2rSxp0+fflhBDxHkwGPFLHuRtGqVnwYfJwE2PJUFduxdzNXmwO+JSlKcOepHuKYLfPYdLNZV7g9EJUmkeKKhJB1yTpJlWRbwIbCgOAVJ/tPhjT5Q3oaVp8dkQTqUO3hTK2zHk+VnQ0YlaDFCJekg+vGc6QgirtDvLOcE/CkGMxTn7LZTgV7AHMuyZuU+1s+27bFhS3Ug1asHn7xdvXrEoxxKRgasLPs52MDn35iOEzYbqR508vY+UniTuwBUlOJFIAkWXAYtPofETPClmU4UlaZyiukIIq4wta75dZKKc3bbH3CQYyqRtHHjoZ8TJc59+UHw+GHjcZBVyXScsKlJ8PckkRxGcAVvchd+EniH2yKcTIyYewW0fh+ajHUKkxTRPvf3YpUlkYNrv8b8lA33r7gdhQIB+NP3BiQAn39rOo4RPpK4ghF8yA3M4TjTcSRSVnaAPdWdhSVVkoJ6DmdBWc1JEjm45341v05SdF7k7FA6doTevU2nOKAb3x8AidmwowHsbGg6TkRcxig2UY2GLN//mI8kevPJ/jPmWjDHVDyJFDsB5neHpmMgebfpNCIiR8SdJWn7dti589DPM2TImkeduUgjR5iOEjEpZFGNLSTgDzp+PmOZw/HcwZsRTiYRN/cKSNoHTX8wnURE5Ii4syRFsVfGfomduBf2VoMNbU3HiRrjOYev6cab3KWiFOvWnAq7a8Ixo0wnERE5IipJIdZvyi3OjW8/NBskyuTNUVJRigO2BxZc6kzeTtprOo2IyGFTSQqhMd7J5CSmQ1Y5WNLVdJyoU7gotWKm6UgSLvMuh6RMpyhJAXczgLsZYDqGSNS7+zwPd59ntqa48+y2Cy5wLh0SZa4c1dOpnT+/ZDpKxK2gIcO4ht2UPejz8orS+fzILE6IUDqJuNWnOWe5HfslzL/cdJqoMptWpiOIuMLsGuZXH3JnSXr2WdMJiti0axt7PWvBlwL/9jUdJ+Kmckqx133xkcT3XARAO6ZyAjMZxK3hjCeRZic4h9xaDoWkDMgpZTpR1OjELwBM4GzDSUSiW6flWicpZpw98Dpnyc35WhumJPoymD4MJRGfVuaONfO7w0mDnGu6ac2k/R7lGUAlSeRQHv1d6yQdnpNPhh49TKfYz7Zh7r4fndP+v/vAdBwjruALMkijMUtL9HU38b4mc8eqVWfA3io6y01EXMudJSkrC7KzTafY77lR34EnB3bWBX98Xq8qAT9p7MOiZLtHddZbDAskOofcmn3vXMtNRMRl3FmSoszT0+92DrWNf8F0FFfKX5RO4w8oYdGSKDa/OyTvhaPGmU4iIlJimpN0hDZtzSYrbQX4E2He1abjuFZeUXJYpLCPLFKNZpIQWNkBMirDMV/CwktMpxERKRGVpCPU+c3bnAvZrjjLdBTX85EEQGW28htnMpi+msztdoEkpxwdOxIS94FPxbcvg01HEHGFvhc4B7sWGczgzsNtPXpA586mU2DbMCsw3Dk69PVQ03GMWkIT3uEWdlL+iLe1k/IsopnmKMWKeZdDym5o/LPpJFFhMc1YTDPTMUSi3uIqFourmF0ryZ17kvr1M50AgEHf/+NMSM2oDBk1TMcxahptmUZorlWXd+htBFfwJncBaI+Sm604CzIrwNFfw6KLTKcxrivfAzCGCw0nEYluXReZn5/qzpLkz73SfEKC0RgP/HYTlAN+f9hojmhgEcDCJoAHZxb7kSlclHZQgWFce+RBJfICSbD4Qmj6vXMWaCDJdCKj7uNVQCVJ5FDu+0vrJB2e1q3hMrOL06Wn+8koOwcCHvjrPqNZosFVfI6fRI4q4TpJB5NXlF7lXn7m3JBtVwxYcCmU2gb1J5tOIiJSbO4sSVGg2xtPgWXD+hNNR4lpPpK4n1fZRA0SyeF8dMFUV1p2LuSkOYfcRERcQiXpMNg2/J7zpjNh+5uPTceJG7fxNmO5QJO53SinFCw5H5p/A5b5XegiIsWhknQYRv68AlJ2QHYZ2NrCdJy48Ta3aWVuN1twKZRbD7X/MZ1ERKRY3Dlx27C+P/aBisCMG0xHiSs6683lllzgLLp69Newtp3pNMb0YpjpCCKu0OsSZz/OGoMZ3LknqU8f6NbNyEvv3Ak7y//pHGr7+VUjGaLRfI7hRR5kOxXD+jr5L2HyDI9SjU1hfT0JoX0VYEUnaD6aeL70zFrqspa6pmOIRL215S3Wltc6SSV3993GXrrna++Dxw9bm+IstS0AsziBWZwQkdfKK0rNWMRmqkfkNSVEFnSDC/8Pqs2FzceZTmNEj9zL74zkCsNJRKJbj3nm5y+6c0/S7t2wd2/EX9a2YWzGE84vwd8PivjrR7MksinPDjz4I/J6PpKYhzMf7G5e1xwlt1h0MdhWXJ/ldguDuAX9/BA5lFum29wy3exeZ3eWpNNPh549I/6yv/6xE7v0BvClwCpdqy2/y/mSHVSkEcsj+roWAU7jD03mdos9NWD1qXD0aNNJREQOyZ0lyZBeX13vLCatq5lHDRsPV/KFznpzk4XdoMZsqLjMdBIRkYNSSSomnw82lB3jHGr79gPTcSSf/JO53+Qu7uQN05HkYBbknnShvUkiEuVUkorpiU/HQkI27KoNvjKm40gheUXpKy7F59LzEeLGjoawoZWzsKSISBTT/ybF9Or8u6A08MsLpqPIAfhIojujyLvAbg02sJGaZkNJcIsuhjOfgtKbYW8102kiyvk3KiKH0v1yZz/OVoMZ3Lkn6dZb4eqrI/Zy23Zmk1VqqbMQ3pxrIva6buLleB7jKbZRyXASpyAdh5clNNEcpWi18GLn2odNx5hOEnHpVCGdKqZjiES99FIW6aW0TlLJ3XxzRF/uorfvdv7vXXlmRF/XTeZyHHOJnnVvFnA0P3OuVuaOVhtbwY56ziG3mdebThNRvRkCwFD6GM0hEu16zzK/TpI7S9LGjZCYCFUi89vYlIxPnL+p0UMj8npuVIq9VGIbG6mBjyTTcXQJk6hnwaKL4MQPIGkv5JQ2HShi+qgkiRRLn9nmV+Z35+G2zp3hxhsj8lLj587CTtwLmZVgT+2IvKYbXcI3rKEeDVhpOsp+hc9668yPpiNJfosuhqR90Hi86SQiIkG5syRF0DUjc9dG+vN+01HkMOQVpTt4k/GcYzqO5LfyTNhXHpp9azqJiEhQKkmHsNkzy7mMwp8Pm44ih8lHEgO5Az+J1GT9/jkhYlggCZZ0cSZvW5G5nI2ISEmoJB3EXaOed87A2RCZC7dK+D3AywzhOp31Fi0WXgylt0LdKaaTiIgU4c6J2xHy3qy3nL+hbz40HUVC5EFeoj6rNJk7Wiw9H/xJ0PxbWH266TQR0YWxpiOIuEKXq539OBkGM7izJD3wAJQrF9aXCARgX+IG55DA5lZhfa1YMIPW3M3rbI3y9V901luUySoHKzo685J+fpm8da5iWSalTEcQcYXMJPM/D9xZknr2DPtL3Dt8sPPzelP0rP0TzRbRnEU0Nx2jWPIXpRv4kMH0JZsU07Hi18JLoOutUHUBbDnGdJqwu4V3ABjErYaTiES3W6aZXyfJnXOSli2DNWvC+hLvz33NuTGxf1hfJ1aUYydHM59kskxHKZa8otSRiWSTgoX5b8a4tegi53OcnOXWg5H0YKTpGCJRr8d8mx7zza6V5M6SdNllcMcdYdt8IAAZqUsh4IElXcP2OrGkK2OYz7HUY7XpKMXmI4ntVCKJbL7iMk3mNmV3bVjXxpmXJCISRdx5uC3MXv92PFgB2NbYdBSJABsLG0tzlExadBF0fAJKb4K91U2nCYsN1KAGm/bft3PnX22kOjXZaCqWSNTZ/72yKvcBK3duUvXqzhU3Iside5LC7Nmpjzrzkf653XQUiYDCK3Nrj5IBiy90lttoErtnfuUvSMV5XCReHfB7YlPkv1e0J6kQ24btqbPABv4O3yE9iS6Fz3rLIYl3ucV0rPixsSXsrAPNvodZ15lOE3GP059ZtOI7LgbgUZ7GU2ie3N+czDg6k4CPR3i2yDZ+53Qmchal2Mv9vFJkfAKd+JPTqMB27gzyi8CPnM802lKdjfRlcJHxb7gELy2py2qu4+Mi4yPpwUKOpjFL6cnwIuOfcg3LacwxzKM7o4qMf8gNrKMOrZjJRXxXZHwQt7CFapzMX5zHuCLjA7ibXZTndCbTkYlFxl/iQfaRRid+4VT+LDL+DI8SIIHzGctJTCsw5ieBZ3kUgEsYzfF4C4xnUIpXeACAHoygOQsLjO+gwv491dcwjEYsLzC+ieoM5v8AuIEPqM26AuNrqMvHOBeCvoV3qMqWAuPLaMxwrgHgbl6lnLUdErMgIRsScpifUpNR5dtB8h4e3D6SVM9e8PjAkwMJPmaVq8R3NetCYjaPLvsLj+XLHQ8ANn9XKc+4WpVJsP08Mm8ZYDu/1ODMF/q9Rlkm1ipPqRwf989bV3Dcggm1S/NnzdJUyM7hzjlbc1PnPQd+bJDGtOqpVM/Ioe/cnfBPkbfHHNu2Q/7RunVrO6xatrTtiy8Oy6ZHTVxk8wQ291ezncqkj+J8XM2ntg32USw2nuVIPhLJtj+ij92Gf4xnibuPC/7Ppl9pm4R95rOE4eNQT/iQ6/bfzSaxyPgA7rTBtlPIDPr1z9DPBtuuwuag4//jeRtsuyHLgo7fykAbbPt4ZgUd78VQG2z7FP4IOn4JX9tg2+fxY9Dxs/nZBtvuzsig4ycz1Qbbvo4Pg44fw1wbbPtOBgQdr8sqG2y7H88EHa/ANhts+3n+F3Q8kWwbbHsgtxYZyyRl/92h9CoyvtlTwabxDzbHDbO/rnB8kfFlpUvZ9Drb5rpT7Z9rly0yPqtqgs3/Ktg8XMaeWssqMv5HXWyesGyewJ5btWj2Hxvj/L/1BPaqckXHRx6DzZPOx7bUIP/2Wv03nu0pOj7gZGcs5ZHg/3afOd0Zr/JA8PH/dXLGG94ZfPzWLs748f9XnG+k8ACm23bRPmM5Y6HVpk0be/r06SHf7n6jR0OZMnBO6K/FVeeBC1lXZgxMvRPGvRHy7ceqxizlTH5jFN3ZRXnTcULmRGbwL61Nx4gPTX6Anl1h2E+w7DzTaUKmFTN5nKfoxjcHfM5/Z1fmrQtzoJ/L1kHGNB583A9l1kG5NVBhHZRZC2U2QamtkLYNUndCyi5I3gvJmZCQ6eyB8WSDx+98WIHcySl2wc1bQW4f7K0LNm4fYNy2AE/uZwvLSgDbwsp93MLCshOw8GCRgMdKcD7jwUMCHhJJsBJIsBJJ8CSR6Eki0Uom0UogyZNEUkIKyZ4kkhNSSE1MITkphdTEVFI9yaQmpZGWlEJKYjLJCckkehKwPB6SPYl4LA8ey0NSQhIej4dky/mckJhAkpVIQoKHBMvjvEZiAolWIgkJCSQmJJLsSSTRk4DH4yElMYkETwIpiUkkehJITEwkxZNEYmICNSoeZL29MHQWAMuyZti23abw4+483NatW9g2vS55ovOPdELR3dlyYMs4imUcZTpGSF3CaEZzKXfyhiZzR8KKsyAnzbmWWwyUpPzlaDsVDvHswovmHWwRvUMtsBdD4wmZUG0OVFkMFZdB+dW5BWcLpO2A5D2QlAEJWQULTeEmUpI1CQt8qQW2B+xErEACViDJKR8kOR9WMolWCklWCimeNFIT00hLTKNMcinKJJelXEpZyqWUo0JaBSqXqkCVMpWoXqYytSpUpW6l6lQpXZ7ExIQShJNIc2dJmjMHUlKgadOQbnbqzJ2QtBeyyoKvTEi3Hesqs5XGLGM2Lcki1XSckBhD1/2TuUFnvYWdLw2Wn+3MS/rxTdy8+nYzFjKTE9lOBR6nP29yJwtpHnRC6kZi82y+/RL3QK3pzmKhFVZCubw9Oem5e3D2QOK+gkUnr6kc6p9A/kJjewAPBJLw2IlYJJFgJ5NopZJipZGSUJrSiWWpkFKBimkVqVqqCtXLVqNO+Vo0rFyHZtXr07habVKTksPy1yDFt7E01Nh7gMcjnMWdJalXL2jQAL75JqSbvfHz/0FpYNm5Id1uPDiPcQznGpqwmKU0MR0nJHQJEwMWXeiUpGrzYHML02lKpBUzOYlpvM/NLKI5vRnCt1zMzty9SLFzmr/fKTu1/4Yas6HSMii7DspshpSduXt2copfdvYXHWevjbO3JplEO5UUqwylEstSPrkS1UtXp3a5mjSs1IBjazSlbf0WNKxcm4QE7YmJNTX2BD+kFumCBG4tSWGywDPK+Yb96TXTUSRK5C9KA7ibCXRiPseajhW7llzgfG76vWtKUv7DapuoxqdcQyal+ITepqOVgN85rNXwV6iyECqugjLrIW07pOx29vR4fLmHsjhw6dk/nyYBK5BCQiCNZMpRNqESlVKqUqt8DepXqEOTqg1oVacZJzVoQeVSlSLxBxQ5LCpJuZYu9WOnpoMvBXbXMx1Hokj+S5ioIIXZ7lqwvrUzL+mPh02nOajGLOVlHqAb37CD8vsPq0XlBWzLr4DGP0Ptf6DKIii3zpm0nJThlB8IXnwK7OVJxOMvRTKlKZ1QgSpp1alfoR7HVGvKKQ1P4MzGJ1OtbNUI/YFEIkMlKVefD1+CVGDdSaajSBTykcR4nMOwZzGBo1nA22ix0bBYdCF06O9Mzs2Ivv90k8gmh2RsLE7jj/3laOchJ2eHSco2aDwe6k5xDlNWWAVp6c7ZWgk5kLtWTRE2uYe3UkkKlKWMpxo1U+vTpEojTqzTgo7N2tC2wXEkJ2iOjsQvlaRcU3MGQwow/gXTUSTKXcfHXMNwPAQ0RykcFneFjk9Ckx9h9rWm0+yXd1gtER8X8T3LaUwd1pJNSvhfvMw6OHYENJgMVec7E5+T94Lld8YLlyDbedAKJJFol6aMpzK1y9Tj+BotOKvJKVxy/Dk6zCVSDO4sSS+9BKVCt0t7wwYIlFkNdgKsOzVk240nf3AaPRjBRiNT6yLrOj6mFBmazB0uG06EXbWceUlRUJLyzznaQXle414sAth4QluQkndC89Fw1M/O/KBy65z5QB5f8BJkJ5DgK0eqVZGqKbU5pmpzTmvUlstbnctRVRuGLpdIHHNnSTo3tGef3ffhl87y6FtCu6RAPFlNfVZT33SMiNBZb+FmORO4W3zhLOrnN3e45yo+4zN6FphzdMSH1epNhuM+g5ozoMJqSN3h/DmhYBnKPRyWEChNGasaDcs15YwGp3HdyZfRqs7RR5ZBRIrFnSXp778hNRVatgzJ5r5OfxoqAH88FJLtxaPqbORY5jGV9tE5cTXE8helE5iJ8z+ae9f1iTqLu0Lr96HeH84ikxHUklmkkclftOdHzudRnmYgt5e8HFnZcNwXcOyXUGMmlN7ilKHCRQgLTyCNNCpRp3RjTqvfjmvbduPU+ifp9HYRw9xZkvr2Ddk6STk5kFV2gbP0u9f8rn236sSEmFsn6VDyilIAD2BRir1kUNp0rNiwvBP4kp1LlUSoJLVkFk/Qn258wyTOpCOT2EHF/Rc2Pai0LdDmXTjqJ+fssbQdznyhApeasPD4y1DRakCb6qfSs/VFXNXmPK24LBLF3FmSQmjw9zOcY/676piOIi7kIwmAGmzgD07jDe7SobdQyCkNKztAk7Hw86thfanj8NKfJ/ZfPiTvsNoBlV8Bp74K9X9zFlVM3kuBM8hy5wul+CtTv0xzuh7dmQc79KV6ueg7U09EDi7uS9JTUx+AMsC/15uOIi62lSrMpqXmKIXSki5w/t1QcTlsbxSGF3AOkbblHzow6cBzjqrMhdOfh0YTofQmZ0HFfIXICiRTmmocU6kVvVpfzs2nXKnT5kViRNyXpC2pU52flZMfMR1FXEyTucNgyQVOSTrqR5h2W8g2m3e22gQ68Ta3M5TejKL7f+Wo9l9w6otQ708ovZX9e4ly9xCl+WpzYqWOPNDxJi4+4YyQ5RKR6BPXJWnm4o2QsA8yK4Kt3/zkyBQuSulU5jN6mo7lXtuOgvQm0PSHkJSkwqfyT6ATAL5Gv7Kz/evOatRpO5wzXSG3FCVS3qrDeQ3P5+Wuj1CvUu0jziEi7uHOkjRwIKSlHfFmrvviXuc3xIWXHPG24t1EOnI+Y1lPLdNRjMorSv15gh8533Qc91vSBVoPdi6fkXP4Z00+zaM8yrO5p/I/zpun72Jn22ehzB0FD53ZSVSyGnBx80t46cKHtOCiSJxzZ0k67bSQbGZO1lhIAsa9EpLtxbMN1GJDnBekPD6SeITnAEhhH535iW+5xGwot1rSBdq9AQ0mObdLoBUzWUsdtlKViYltyW58Im+et5ydFZ/67/BZIIlKNOWKYy7nxW73UTalTDj+FCLiUu4sSRMnOitun3zyYW9i195MAkk7IScNsvTb4pGqwxpaM4NfOJu96D+aPHfxBi/yEHfyhuYoHY5VZ0B2Kecst2KWpPyH1Z5t3oxHu63j1+Q9/JpbjDx2KseWPpX3ur9Gu4bHhze/iLiaO0vSPfcc8TpJN372hPPb5CpNvAyFM5gcd+skFcdr3MvJ/K3J3IfLlwrLz3bWS+ItDrZgZytm8njSg3TL+YXtqfB4O3jz5EWQDImBMpxa41yGXPU6DSrWi1h8EXE3d5akEPh+1afOn16H2iSMdNZbCCzpAs2/gyoLYesBLsdxwvv023s7HVdl83gHePNkyEyuRJeGl/LxFa9QoVT5iEYWkdgQtyVpX+IGCCTC1hamo0iMy1+UnqA/w+nJNiqbjuUeS3MnwDcZW6AktfJM5fEal9Pvok0srOHj7p2QmVCaTk1uZeN1z5CapDNWReTIxGVJ+nL6r85e+3QdFpLIyCtKDVmhglRSO+vBphZw3DA453+0+uJhHs95g24rdrN9GzTfBltqNuLjmz/m3OY6fC4ioROXJemJH99wbizoZjaIxBUfSSyhKQCP8RQ7qKBDb8W1pAuc8hLDv4arFz/jzDk602LK+Wfz1Z1fUj5Nh9NEJPTcWZI++ghSUw/7y5dkToFkYMp9ocsU58ZzDqczmbXoGniH4sFPK2ZxKaMBzVE6lGa1hrDopAHggcWV4fEOsL5nLz648RPT0UQkxrmzJJ144mF/qW2DL2mbMx9Jp/6HzBaqsYVqpmO4QoAETeYuhlZJk3i8zoV0W7GHTmvh10bQv6MzVjF9DB+YjSciccBjOsBhGTMGJk06rC8dN32xc4HK3TVDmynONWQ51zCMsuwyHcUV8uYofU033uQu7uBN05GiRitm8nWNeszM6UjH9Xt4/EyLGbUocPb/9n3b+XXFr8Yyikh8cGdJevRRGDDgsL70kbEvOj9sl50T0kjxrj1TGca1VGeT6SiukVeUvqQ7OwpfeT5OJR39CT+WPpGO29fweAe48cXLGHh+BXYGuQpR95HdI55PROKLOw+3HYE5Gb9AGvDH/0xHEcFHEj0YSd5ukrqsZg3xtdhhK2Zyc+IA7rhtAjkV1tFtDaTXaMwfD02lWtmqePoH/11ux74dkQ0qInEnrkqSbUNOynqwLdje1HQckVxOQWrDNH7ndB7kpbiYo5T/8iE7EmBQFszzl+b+O77islbn7X9e4ImAwZQiEs/iqiRNn7cTPD7IqGI6ikgRs2jFWLrE/GTuCmznI653ylFiMo+f5qyQfVTly/A/MMp0PBGR/eKqJD3w9UvOL+1r25mOIlJErF/CpBLpbKMyOylPFbbweNvKvNkxnZ2pFq91eJ97OtxgOqKISAHuLEkjRkByyS858Peu0VAWmHJP6DPFuR85n1bMZHWczacJtcJFaS4tmMhZpmMdkZbM4gn6cwaTacgKdlfcyBm3TIfkLBICaSy6bQ5NqzY2HVNEpAh3lqRmzUr8JbYN+9KWgw2scvd/OtFoO5XYjtadCoW8onQdHzOJDqbjHLa8ctSNb9hOBV7nHgItP4ZL7gagVuLRrH5oDgkJCWaDiogcgDuXABgxAn78sURfsmiRHxKyIKtsmELFtyYs5hbeoTw7TEeJCT6SeJ+bsfHQkOVcz4emI5VIUxYxixPowCQepz8NWcHT3eexN7cg9Wh4G+sema+CJCJRzZ0l6fnnYfDgEn3Jw6M+cOYjbTo+PJni3ElM4x1uoypbTEeJOffyGh9yY9QvONmKmdzEewAsphnXMtQpRzzOzu594diRYHsYct73jOg90HBaEZFDc+fhtsPwS/oQqADMvM5wEpGSuYfXqcX6qJ3Mnf9U/k1U41OuIZNSDONa5wmX9HIKEh6m9p5Nu0YtjOYVESkud+5JKiHbhj1pc535SLP6mI4jUiLRegmTRizja7oxkxPpyEQepz/NWEQmpf570oU3QctPAYu/r5+hgiQirhIXJWnFCiB5D/hSAc2BEPfJX5R6MpxEcoxlSSIbABuL0/iDx+lPA1Y6h9XyX16l8x1w4geAxe+9/6JtvVYm4oqIHLa4ONz2zIixznykbUeZjiJy2PKKUhqZ+EjCg59ABEt/3mG1JHK4kDGsoBF1WEs2KUWffPb9cLIz72j81ZM4rWHbiOUUEQkVd+5J+v57GDSo2E//buPbzo35l4YpkHzHRTRmKauobzpKTPORxG7KkUomP9E5IofeWjFz/2G1DkziH9pi4VwqJGhB6vAYnPoqAN/3+Jmzm54R9owiIuHgzj1JdeuW6Onb0v525iP9+UB48gh7KMsetLxCpPhIZDdlwz6Z+0o+53OuZgfleZz+vMmdBQ+pFXbyADjzGQCGX/g9XY85Jyy5REQiwZ17kj7+GL7+ulhPzcgAO3UbBBLBVybMweLXMczjQV6kIttMR4kL4ZzM3YqZtGcK4Kyk/ihPB59zVFjFxdDZWc3+rdNHcXXrriHLJCJigjtL0htvwCefFOupI36dC9iwq054M8W5VsziRR6iMummo8SNwkWpL+8e0fZaMZPRXMJMTuR5HgZgJxV4lkcPXo4A8MMtJwBwaZ3bub3TZUeURUQkGrizJJXAy1NfdiZtL9Vuf4k9eUXpfW7kD047rG0ch3d/OcpbIftivi3ZRm5sB8kZVEk4iq9ufOuwcoiIRBt3zkkqgaW+XyEJ+PN/pqOIhIWPJG7m/dx7Nu2ZylROKcZX2oDFSUzbX44OOecomNOfgdrTsQJJrHt0Xsm+VkQkih1yT5JlWR9ZlrXZsqy5kQgUajmpG8D2wA5dZVxi31V8zhROPegcpbyz1W7HOUX/E64t3pyjYKrMhbMeA2BS78kkJyQfbnQRkahTnMNtQ4DOYc4RFvNWbgHLDxlVTEcRiYgvuXz/HCUbq8hHJqn7V8jO4yOp5OUIAD/0PQmAG465jzMatQvNH0JEJEoc8nCbbduTLctqEIEsxffrr+A5dL976Lvc+Uhr2oc/U5z7issYzzlso5LpKHEtb45SDsH36KSSdfiH1Qr7vxMhaR/VPc35oMcrR7YtEZEo5M45SZWK9x/xb5u+g2Rg6r3hzSNkkcoWUk3HEJyidDBP8/iRv8jpz0B1L5Y/mbWPuPJIvIjIIYXs7DbLsm62LGu6ZVnTt2zZEqrNBvf22zB8+CGftjthhTM3dbVW/A2345nNUzxGJS0BEPusbOj4BADjrphCYqKuhygisSlkJcm27fds225j23abqlWrhmqzwb3/Pnz55UGfsi8nGzzZkFUuvFkEgBbM5TGeoZIWk4x9vc8GT4CmqWdwzrGtTacREQmbmF0n6ZkfBzvzkTa2NB1FJHZUmQv1fwfbw5wHxptOIyISVsVZAuBzYCrQzLKstZZl3RD+WEfuC+8o58acq8wGETFgI9VL9Hix9ekEwF0tH9fp/iIS84pzdpsrW8aajCXOpO3ZvUxHEYm4mmwEYD01+Y6L+D8GH/lGWw+C0ptJtsszoNsTR749EZEoF7OH27IT0wFLF7UVCQk/dLkTgJ96/WA4i4hIZLhzCYC//gLLOuBwIIAzaTsnLXKZ4twIruBrLiWLFNNRJJ9GLMdPCM4+69EdEnzUTTqejkedeuTbExFxAXeWpNSDr8czdd5q58beI5x/IcXmJxG/S/85xbJ9hOAXhTLr4OhvwLaYd9+fR749ERGXcOfhtpdfhg8/PODwG5M+cc5s29wicpniXGum8zp3U4Uwr5ElJdKfx+nBiCPbyHVnggVda/wfZVN0+FpE4oc7S9Lw4fD99wcc/mN97nWplpwXoUDSjEXczRtUYIfpKJLPTbzPWfx6+BsovwIqLQN/Mt//3zuhCyYi4gLuLEmHsCWwyFlpe87VpqOIuNuVl4IFPY+6w3QSEZGIi8mS5EveCliQpYutihy2hEyoMQsCHob2fNF0GhGRiIu5kpSZCSRkgU9nWYkckR6XgwWnVr2QhARdn01E4o87S5LH43wE8dt0ZxE99laLYCCxsfC79J9TLAvgwebAy2UcmB+a/Ag2jL/585DnEhFxA3ees/3vvwccemty3pltx0Quj/A5V/M5mgMWbeqw7vC+sPPd4AnQKLUNaclab0xE4lPM/eo/bdsE58ayc8wGEXGzNu+BDb/1/cZ0EhERY9xZkp58EgYODDq0zbPAObPNq2u2RdLJ/MUH3EA1NpmOIvm8yr304pOSfVGbgZCYTQWrAXUq1A5PMBERF3BnSfrmG/jll6BD/pTcxQwzq0Yuj9CYZdzAR5Rjl+koks9VfM6plHCV7LMfARtGXam5SCIS39xZkg5g0yYgcZ/ObBM5XPV/hZRdJPoq0qlZO9NpRESMiqmSNGFK7l6kDO1FEjksXe4CCx4+6XnTSUREjIupkvTe38OdM9u2NjcdRcSdqs4H2+Kpi/uaTiIiYpw7S1LZslC6dJGH52aMd24s1ZltkZZDEjsoT8Cl/6Ri1S7KkUkxT+E/fgh4AlT1NAtrJhERt3DnOkm//x704R0p83Kv2dYzsnmEL+nBl/QwHUMKac6i4j+5w1Ngw8CLXw5fIBERF4mpX/v9Kbmnn+/RacsiJeOHiivATqBHq66mw4iIRAV3lqQHHoCXC/62m5EBJOqabaacxu+MoAc12GA6iuQzmJu5ifcO/cRTXgELGqedGP5QIiIu4c6SNH48/Flw7ZeZC3YCNmRUMZMpztVjNT34kjLsMR1F8rmQ72nNjEM/sf0bYMMnVwZfpFVEJB65syQF8d6Uz5wz29I16VSkRKxsKLMBy07mlAZtTacREYkaMVOSJq//ybmxoqPZICJuc85DYEGLsqebTiIiElVipiRtsL3OmW2zrjUdRcRdWg0FG77oOdh0EhGRqOLOklS9OlSuXOCh7MTcM9t21zMQSDIoxRrq4HPpqhKxaj212E7FAz8hcQ+kbcPjT+OYmo0jF0xExAXc+T/auHFFHrIT94E/2UAYAfiGbnxDN9MxpJA2h5q03eEp51BbmU6RCSQi4iLu3JNUyKbtewAbMisf8rkiks8xo8CGty590nQSEZGo486SdNtt8NRT++++M3lE7jXbmpjLFOc68itjOZ9arDMdRfIZztXcwZsHfkKF1YDFGU1aRyyTiIhbuPNw259/QoMG++9+v3CMc2NlByNxBGqygfP5iVJkmI4i+XRkIrspG3yw7Gqw/KQFqkc2lIiIS7hzT1Ihy/bMcW7M1pltIsXW8UmwoEPd80wnERGJSjFRkvayxTn9f4fOzhEptqN+ci5oe+mTppOIiESlmChJfisT7Jj4o4hETplNYCfQqHJD00lERKKSO5tFo0ZQp85/9z0+CLhzelWs2EU55nM02WgZhmiyhCZsoGbRgWqzwApQwVM34plERNzCnc3i66/33wwEAGzISTMWR2AMFzKGC03HkELOZHLwgQ79wYKLml4c2UAiIi7izj1J+Sxfm3vV+azyZoOIuEmDyWDDaxc/bjqJiEjUcmdJuu46ePhhAMbO+dNZI2lPNbOZ4tw5/MxkTqcOa0xHkXxGcwn383LRgbRtYCdRuVSlyIcSEXEJdx5umzlz/zpJk5f94zy2s765PEJVtnA6f5DKPtNRJJ+T+ZtNFFoHqdE4sKBSQGeDiogcjDv3JOUzb8s858bWZmaDiLjFyQMBuLhJD8NBRESim+tL0uasVc6N9W3MBhFxixqzwIb+XW8xnUREJKq5viTtYZNzY+WZZoOIuEXpzYBF3Yo1TCcREYlq7pyT1KIF1HTWfslJ2OGstp2lCagmpVOZv2lLFimmo0g+MzmBlTQo+GBCNgmB0kbyiIi4iTtL0qef7r9pJ2bgnN4mJo2jM+PobDqGFHIBYws+UPsvZ9J2Yj0zgUREXMTVh9sCAcCTA4EE01FE3KHVRwCcWvdUw0FERKKfO0vSFVfAPfeweTNgBcCXajpR3DufscykFfVYZTqK5DOes3mUp/97oP4fANx9Rh8zgUREXMSdh9sWLYKsLFau9Dv3s8uazSNUZDutmE0y2aajSD7HMo9l5FsPqfwasOHMxtqTJCJyKO7ck5Rr/JxZznSkjMqmo4i4Q/JeLFsXIRYRKQ5Xl6TJq6Y4N3bVNhtExA1KbQRsSlHVdBIREVdwdUlattvr3EhvYjaIiBu0/gAsaF6+lekkIiKu4M45SW3bQo0abNvpTEJlYyujcQQ2UZ0JnEUmaaajSD5/cBoLae7caTwOgKuP724wkYiIe1i2bYd8o23atLGnT58e8u0WlnLv0WSXXwhvzYf0o8P+eiKu9kBVKLWVfY9mkZKoeUkiInksy5ph23aR65u5+nBbTvI2Z7Xt9Kamo4hEv9QdYHtUkEREismdJalrV/i//8NO3JP7gBaTNO0ivmUpjanPStNRJJ+ptOMZHnHueHx4AlpTTESkuNw5J2ntWkhMhOpZYKsgRYMy7KExy0kix3QUyac+q6jCVkjIBCCVioYTiYi4hzv3JAEBG7D84E8yHUUk+jUaDxZUTdVyGSIixeXakpSTt7Bztq5mLnJIDX8FoEW1Yw0HERFxD9eWpF0Zmc5q2/t0+EDkkGrMAeDsprociYhIcbmzJHXsyL81yzu3d9c0m0UAWEsdvuFi9qI9e9HkJzozm5ZQYSUAV5x4gdlAIiIu4s6J26+/zuuv3Qu7ge0NTacRYDJnMpkzTceQQq7nY+dGqYfBhppla5gNJCLiIu7ckwSs2L3EubFZcyxEDikpA2zXfruLiBjhzp+aHTvS/6s/ndvr2pnNIgBcxig2UY2GLDcdRfKZQwte5n5njSQ7xXQcERFXcWdJ2r6d0lmZzmrba1SSokEKWVRjCwn4TUeRfCqTTll2AJBKBaNZRETcxp0lCQjgc27YusSCyEGVXQ8WVEqqZTqJiIiruLYk2fg1x0KkOMquB6BZJV0EWkSkJNzbMixbJUmkOEqlA3BqvZMNBxERcRd3towLLuC3+qgkRZEVNGQY17CbsqajSD5fcRl/13OWp++hNZJERErEsm075Btt06aNPX369JBvNz/rSQty0uC5jLC+jojr3V8dSm/GfjL03+siIrHAsqwZtm23Kfy4u3fF2AmmE4hEv8R9phOIiLiSK0uSffLJjPgSCKgkRYsr+IIM0mjMUtNRJJ/lNOSt8XtwLnQoIiIl4c6StC+LZD8QcOdVVWJRAn7S2IeFDulEk1T2kWTb2usqInIYXFmSAoG8G0lGc4i4g42lkiQiUmKuLEn+vEWd/SpJIsWRoEuSiIiUmCtLUo4vd7Vtv37wixRHslXKdAQREddxZUlaelonfjoK8KkkRYslNOEdbmEn5U1HkXyGJl3Kbw0gLbGM6SgiIq7jypI0vnM3Bp+E9iRFkWm05TbeYQvVTEeRfB6uexGfHw/lkyuajiIi4jquLEnr0jfjCeAsJilRwSKABz/o7Lao4qkyDysAVdJUXkVESsqVJenup+/jqxFATmnTUSTXVXyOn0SO0jpJUWXtrMcY9APULFvTdBQREddxZUnyB3JPb8vWZFSRg/I462U0qFDfcBAREfdxZ0my80qSJqOKHJTlfK8cU72Z4SAiIu7j8pKkK86LHJTl7Ek6sc7RhoOIiLiPK0tSwM5dcjtLJUnk4GywoVVtlSQRkZJyZUn64ui6jD4a2FfBdBTJNZ9jeJEH2Y5ONY8mb5+YyrijIDFRlyURESkpV14hdtAJNUmv5IXvK5iOIrlmcQKzOMF0DCnk2VNSIC3DdAwREVdy5Z6kFN8uSmUDmZVMR5FcSWRTnh25ayVJtCiX7Sctx3QKERF3cmVJ+vHLWQz/CpWkKHI5X7KDijRiuekoks/C93fz+k+mU4iIuJMrS9L+VZ33VjUbQyTqaQV0EZHD5cqSZJN7dtueGmaDiIiISMxyZUnCyv3tOEN7kkRERCQ83FmS9h9C0GnNIgdlmQ4gIuJe7lwC4MRUtpXZA4tMJ5E8Xo7nMZ5iG5pMH01eau9hcWWbvqaDiIi4kCtL0vsnJBJIBkaZTiJ55nIccznOdAwpZEB7C9fuMBYRMcyVPz2rZWRTWevjRZVS7KUOa0hEi/JEk1q7AlTM1BluIiKHw5Ul6cfPM/ngO9MpJL9L+IY11KMBK01HkXymf2DzwgSVJBGRw+HKkqTJqCIiIhJuxSpJlmV1tixrkWVZSy3LeijcoQ6oRg2wLFptsrlkEdhY2FhsQOslmbKBGthYDOcaAJbQVO9JFMh7X2rugZv/DYBlOR819L6IiBTXIUuSZVkJwNvA+cAxwFWWZR0T7mBBbdoU9OEaBH9cwu9Af/d6T8w64N//Ab6HRESkqOKc3dYWWGrb9nIAy7K+AC4G5oczWEm1vDmB2TUsOi23efT3QJHxvhd4WFzFousim/v+Kjre6xIPa8tb9JgX4JbpRedwdL/cQ3opi96zAvSZXXS8y9UeMpMsbpkWoMf8ouMdeztrOt03JUDXJQXHMxOhS09n/NHJATqtKDiengbdezjjz00I0H5twfG15Sx6dXP67uvjArTaWHB8cWWLvl2d8cFjAjRNLzg+q4bFPec548NGB6izq+D41DoW/To546NG+qmcmW9wVZE/6n4T6zvHRcc0sXj1FOfrJw4tegHckcdYDDrJQ1qOzdjPir43Q1paDG3loXKGzagvi44PamMx8lgPdXbaDPum6Pir7TyMaWbRdKvN4B+Kjj9zuocJjSxabrQZMK7oeL+zPEyta9F+jc1zvxYdv/s8T/T92zvI+yIiIsVTnMNttYE1+e6vzX2sAMuybrYsa7plWdO3bNkSqnwiIiIiRli2ffAzXyzL6g50tm37xtz7vYCTbdu+/UBf06ZNG3v69OkhDZob5oBDs2bqDB4TWp2g9yQaHex94RDf8yIi8cayrBm2bbcp/HhxDretA+rmu18n97Go0qqV6QRSmN4TERFxs+IcbpsGNLEsq6FlWcnAlYCRVYo2li7Z4xIB1auX7HGJCH2viIgcuUPuSbJt22dZ1u3AOJwryn5k2/a8sCcLosae4IcJdFKzQRs3mk4gQeh7RUTkyBXr2m22bY8FxoY5i4iIiEjUcOeK2yIiIiJhppIkIiIiEoRKkoiIiEgQKkkiIiIiQagkiYiIiAShkiQiIiIShEqSiIiISBAqSSIiIiJBqCSJiIiIBKGSJCIiIhKESpKIiIhIECpJIiIiIkGoJImIiIgEoZIkIiIiEoRKkoiIiEgQKkkiIiIiQagkiYiIiAShkiQiIiIShEqSiIiISBAqSSIiIiJBWLZth36jlrUFWBXyDRdUBdga5teQktF7Ep30vkQfvSfRSe9L9InUe1Lftu2qhR8MS0mKBMuyptu23cZ0DvmP3pPopPcl+ug9iU56X6KP6fdEh9tEREREglBJEhEREQnCzSXpPdMBpAi9J9FJ70v00XsSnfS+RB+j74lr5ySJiIiIhJOb9ySJiIiIhI2rS5JlWS9blrXQsiyvZVmjLcuqYDpTvLMs63LLsuZZlhWwLEtniRhkWVZny7IWWZa11LKsh0znEbAs6yPLsjZbljXXdBZxWJZV17KsiZZlzc/92XWX6UwClmWlWpb1j2VZs3Pfl/4mcri6JAHjgRa2bR8PLAYeNpxHYC5wKTDZdJB4ZllWAvA2cD5wDHCVZVnHmE0lwBCgs+kQUoAPuM+27WOAdsBt+l6JClnAWbZttwRaAZ0ty2oX6RCuLkm2bf9s27Yv9+5fQB2TeQRs215g2/Yi0zmEtsBS27aX27adDXwBXGw4U9yzbXsysM10DvmPbdsbbNv+N/f2bmABUNtsKrEde3LvJuV+RHwStatLUiHXAz+aDiESJWoDa/LdX4t+8IsclGVZDYATgL8NRxGcPeKWZc0CNgPjbduO+PuSGOkXLCnLsn4BagQZesS27W9zn/MIzi7T4ZHMFq+K856IiLiJZVllgK+Au23b3mU6j4Bt236gVe5849GWZbWwbTui8/miviTZtn32wcYty+oDdAU62VrPICIO9Z5IVFgH1M13v07uYyJSiGVZSTgFabht21+bziMF2ba9w7KsiTjz+SJaklx9uM2yrM7Ag8BFtm1nmM4jEkWmAU0sy2poWVYycCXwneFMIlHHsiwL+BBYYNv2a6bziMOyrKp5Z6xblpUGnAMsjHQOV5ckYCBQFhhvWdYsy7LeNR0o3lmW1c2yrLVAe+AHy7LGmc4Uj3JPaLgdGIczEXWkbdvzzKYSy7I+B6YCzSzLWmtZ1g2mMwmnAr2As3L/H5llWVYX06GEmsBEy7K8OL/0jbdte0ykQ2jFbREREZEg3L4nSURERCQsVJJEREREglBJEhEREQlCJUlEREQkCJUkERERkSBUkkRERESCUEkSERERCUIlSURERCSI/wdrcrf+YcLNdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrb.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygeoiga.nurb.multipatch_NURBS import Multipatch\n",
    "mult = Multipatch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult.add_patch(nrb, \"biquadratic\", kappa=10)\n",
    "mult.define_topology()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Boundary conditions labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult.assign_boundary_condition(name_patch = \"biquadratic\", id_boundary=\"bot_BC\", face=0)\n",
    "mult.assign_boundary_condition(name_patch = \"biquadratic\", id_boundary=\"top_BC\", face=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "biquadratic {0: 'bot_BC', 1: 'top_BC'}\n"
     ]
    }
   ],
   "source": [
    "for name in mult.geometry.keys():\n",
    "    print(name, mult.geometry[name].get(\"BC\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='gmsh'></a>\n",
    "## 2) Export geometry with Gmsh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygeoiga.nurb.nrb_to_gmsh import convert_geometry_mp_to_gmsh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info    : Running '/home/danielsk78/anaconda3/envs/IGA/lib/python3.7/site-packages/gmsh-4.7.1-Linux64-sdk/lib/../bin/gmsh -2 /tmp/tmpnc_8hpda.geo -format msh2 -bin -o /tmp/tmp35f4x07l.msh' [Gmsh 4.7.1, 1 node, max. 1 thread]\n",
      "Info    : Started on Mon Mar  1 03:41:49 2021\n",
      "Info    : Reading '/tmp/tmpnc_8hpda.geo'...\n",
      "Info    : Done reading '/tmp/tmpnc_8hpda.geo'\n",
      "Info    : Meshing 1D...\n",
      "Info    : [  0%] Meshing curve 1 (Nurb)\n",
      "Info    : [ 30%] Meshing curve 2 (Nurb)\n",
      "Info    : [ 50%] Meshing curve 3 (Nurb)\n",
      "Info    : [ 80%] Meshing curve 4 (Nurb)\n",
      "Info    : Done meshing 1D (Wall 0.0182228s, CPU 0.034719s)\n",
      "Info    : Meshing 2D...\n",
      "Info    : Meshing surface 6 (Plane, Frontal-Delaunay)\n",
      "Info    : Done meshing 2D (Wall 0.00365863s, CPU 0.006848s)\n",
      "Info    : 38 nodes 72 elements\n",
      "Info    : Writing '/tmp/tmp35f4x07l.msh'...\n",
      "Info    : Done writing '/tmp/tmp35f4x07l.msh'\n",
      "Info    : Stopped on Mon Mar  1 03:41:49 2021 (From start: Wall 0.0337383s, CPU 0.090717s)\n",
      "2.2 8 False\n"
     ]
    }
   ],
   "source": [
    "mesh, script, physical_tag_id = convert_geometry_mp_to_gmsh(mult.geometry, \n",
    "                                                            size=1, # Size of the mesh \n",
    "                                                            save_geo=  \"data/biquadratic.geo\",\n",
    "                                                            save_msh= \"data/biquadratic.msh\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='script'></a>\n",
    "## 3) Create script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygeoiga.FE_solvers.run_moose import create_script, convert_msh_to_exodus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/danielsk78/GitProjects/IGA_master_thesis/pygeoiga/tutorials/data/biquadratic.i'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = create_script(\"data/biquadratic.msh\", \n",
    "                          topology_info=physical_tag_id,\n",
    "                          bot_bc=40, \n",
    "                          top_bc=10, \n",
    "                          geometry=mult.geometry)\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert_msh_to_exodus(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='patches'></a>\n",
    "# Multi patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create multipatch structure\n",
    "c_d1 = np.array([[[0, 0], [0, 100], [0, 200]],\n",
    "                          [[50, 0], [85.5, 100], [121, 200]],\n",
    "                          [[100, 0], [171, 100], [242, 200]]])\n",
    "k_d1 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d1 = NURB(c_d1, k_d1)\n",
    "\n",
    "c_d2 = np.array([[[100, 0], [171, 100], [242, 200]],\n",
    "                 [[550, 0], [585.5, 100], [621, 200]],\n",
    "                 [[1000, 0], [1000,  100], [1000,  200]]])\n",
    "k_d2 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d2 = NURB(c_d2, k_d2)\n",
    "\n",
    "c_d3 = np.array([[[0, 200], [0, 250], [0, 300]],\n",
    "                 [[121, 200], [138, 250], [155, 300]],\n",
    "                 [[242, 200],  [276, 250], [310, 300]]])\n",
    "k_d3 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d3 = NURB(c_d3, k_d3)\n",
    "\n",
    "c_d4 = np.array([[[ 242,  200], [ 276.,  250.], [ 310.,  300]],\n",
    "                 [[ 621.,  200.], [ 638.,  250], [ 655.,  300.]],\n",
    "                 [[1000.,  200.], [1000.,  250.], [1000.,  300.]]])\n",
    "k_d4 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d4 = NURB(c_d4, k_d4)\n",
    "\n",
    "c_d5 = np.array([[[  0., 300], [  0., 500], [  0., 700.]],\n",
    "                 [[155., 300.], [225., 500.], [295., 700.]],\n",
    "                 [[310., 300.], [450., 500], [590., 700]]])\n",
    "k_d5 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d5 = NURB(c_d5, k_d5)\n",
    "\n",
    "c_d6 = np.array([[[ 310.,  300], [ 450.,  500], [ 590.,  700]],\n",
    "                 [[ 655.,  300], [ 725.,  500], [ 795.,  700]],\n",
    "                 [[1000.,  300], [1000.,  500], [1000.,  700]]])\n",
    "k_d6 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d6 = NURB(c_d6, k_d6)\n",
    "\n",
    "c_d7 = np.array([[[  0. , 700], [  0. , 750], [  0. , 800.]],\n",
    "                 [[295. , 700], [312.5, 750], [330. , 800]],\n",
    "                 [[590. , 700], [625. , 750], [660. , 800]]])\n",
    "k_d7 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d7 = NURB(c_d7, k_d7)\n",
    "\n",
    "c_d8 = np.array([[[ 590. ,  700], [ 625. ,  750], [ 660. ,  800]],\n",
    "              [[ 795. ,  700], [ 812.5,  750], [ 830. ,  800]],\n",
    "              [[1000. ,  700], [1000. ,  750], [1000. ,  800]]])\n",
    "k_d8 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d8 = NURB(c_d8, k_d8)\n",
    "\n",
    "c_d9 = np.array([[[   0.,  800], [   0.,  900], [ 0., 1000]],\n",
    "                 [[ 330.,  800], [ 365.,  900], [ 400., 1000]],\n",
    "                  [[660.,  800], [ 730.,  900], [ 800., 1000]]])\n",
    "k_d9 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d9 = NURB(c_d9, k_d9)\n",
    "\n",
    "c_d10 = np.array([[[ 660.,  800.], [ 730.,  900], [ 800., 1000]],\n",
    "                  [[ 830.,  800], [ 865.,  900], [ 900., 1000]],\n",
    "                  [[1000.,  800], [1000.,  900], [1000., 1000]]])\n",
    "k_d10 = [[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1]]\n",
    "nrb_d10 = NURB(c_d10, k_d10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygeoiga.nurb.multipatch_NURBS import Multipatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = 3.1 #Thermal conductivity\n",
    "b = 0.9 #Thermal conductivity\n",
    "y = 3 #Thermal conductivity\n",
    "\n",
    "mult2 = Multipatch()\n",
    "mult2.add_patch(nrb_d1, \"d1\", position=(1,1), kappa = r)\n",
    "mult2.add_patch(nrb_d2, \"d2\", position=(1,2), kappa = r)\n",
    "mult2.add_patch(nrb_d3, \"d3\", position=(2,1), kappa = r)\n",
    "mult2.add_patch(nrb_d4, \"d4\", position=(2,2), kappa = b)\n",
    "mult2.add_patch(nrb_d5, \"d5\", position=(3,1), kappa = r)\n",
    "mult2.add_patch(nrb_d6, \"d6\", position=(3,2), kappa = y)\n",
    "mult2.add_patch(nrb_d7, \"d7\", position=(4,1), kappa = b)\n",
    "mult2.add_patch(nrb_d8, \"d8\", position=(4,2), kappa = y)\n",
    "mult2.add_patch(nrb_d9, \"d9\", position=(5,1), kappa = y)\n",
    "mult2.add_patch(nrb_d10, \"d10\", position=(5,2), kappa = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult2.define_topology()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult2.assign_boundary_condition(name_patch = \"d1\", id_boundary=\"bot_BC\", face=0)\n",
    "mult2.assign_boundary_condition(name_patch = \"d2\", id_boundary=\"bot_BC\", face=0)\n",
    "mult2.assign_boundary_condition(name_patch = \"d9\", id_boundary=\"top_BC\", face=2)\n",
    "mult2.assign_boundary_condition(name_patch = \"d10\", id_boundary=\"top_BC\", face=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d1 {0: 'bot_BC'}\n",
      "d2 {0: 'bot_BC'}\n",
      "d3 None\n",
      "d4 None\n",
      "d5 None\n",
      "d6 None\n",
      "d7 None\n",
      "d8 None\n",
      "d9 {2: 'top_BC'}\n",
      "d10 {2: 'top_BC'}\n"
     ]
    }
   ],
   "source": [
    "for name in mult2.geometry.keys():\n",
    "    print(name, mult2.geometry[name].get(\"BC\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info    : Running '/home/danielsk78/anaconda3/envs/IGA/lib/python3.7/site-packages/gmsh-4.7.1-Linux64-sdk/lib/../bin/gmsh -2 /tmp/tmp94lvtkmm.geo -format msh2 -bin -o /tmp/tmpi4h1al67.msh' [Gmsh 4.7.1, 1 node, max. 1 thread]\n",
      "Info    : Started on Mon Mar  1 03:42:11 2021\n",
      "Info    : Reading '/tmp/tmp94lvtkmm.geo'...\n",
      "Info    : Done reading '/tmp/tmp94lvtkmm.geo'\n",
      "Info    : Meshing 1D...\n",
      "Info    : [  0%] Meshing curve 1 (Nurb)\n",
      "Info    : [ 10%] Meshing curve 2 (Nurb)\n",
      "Info    : [ 10%] Meshing curve 3 (Nurb)\n",
      "Info    : [ 20%] Meshing curve 4 (Nurb)\n",
      "Info    : [ 20%] Meshing curve 7 (Nurb)\n",
      "Info    : [ 20%] Meshing curve 8 (Nurb)\n",
      "Info    : [ 30%] Meshing curve 9 (Nurb)\n",
      "Info    : [ 30%] Meshing curve 12 (Nurb)\n",
      "Info    : [ 30%] Meshing curve 13 (Nurb)\n",
      "Info    : [ 40%] Meshing curve 14 (Nurb)\n",
      "Info    : [ 40%] Meshing curve 17 (Nurb)\n",
      "Info    : [ 50%] Meshing curve 18 (Nurb)\n",
      "Info    : [ 50%] Meshing curve 21 (Nurb)\n",
      "Info    : [ 50%] Meshing curve 22 (Nurb)\n",
      "Info    : [ 60%] Meshing curve 23 (Nurb)\n",
      "Info    : [ 60%] Meshing curve 26 (Nurb)\n",
      "Info    : [ 60%] Meshing curve 27 (Nurb)\n",
      "Info    : [ 70%] Meshing curve 30 (Nurb)\n",
      "Info    : [ 70%] Meshing curve 31 (Nurb)\n",
      "Info    : [ 80%] Meshing curve 32 (Nurb)\n",
      "Info    : [ 80%] Meshing curve 35 (Nurb)\n",
      "Info    : [ 80%] Meshing curve 36 (Nurb)\n",
      "Info    : [ 90%] Meshing curve 39 (Nurb)\n",
      "Info    : [ 90%] Meshing curve 40 (Nurb)\n",
      "Info    : [ 90%] Meshing curve 41 (Nurb)\n",
      "Info    : [100%] Meshing curve 44 (Nurb)\n",
      "Info    : [100%] Meshing curve 45 (Nurb)\n",
      "Info    : Done meshing 1D (Wall 0.00842198s, CPU 0.01548s)\n",
      "Info    : Meshing 2D...\n",
      "Info    : [  0%] Meshing surface 6 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 10%] Meshing surface 11 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 20%] Meshing surface 16 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 30%] Meshing surface 20 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 40%] Meshing surface 25 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 50%] Meshing surface 29 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 60%] Meshing surface 34 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 70%] Meshing surface 38 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 80%] Meshing surface 43 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 90%] Meshing surface 47 (Plane, Frontal-Delaunay)\n",
      "Info    : Done meshing 2D (Wall 0.0218121s, CPU 0.043489s)\n",
      "Info    : 260 nodes 573 elements\n",
      "Info    : Writing '/tmp/tmpi4h1al67.msh'...\n",
      "Info    : Done writing '/tmp/tmpi4h1al67.msh'\n",
      "Info    : Stopped on Mon Mar  1 03:42:11 2021 (From start: Wall 0.0364031s, CPU 0.099264s)\n",
      "2.2 8 False\n"
     ]
    }
   ],
   "source": [
    "mesh, script, physical_tag_id = convert_geometry_mp_to_gmsh(mult2.geometry, \n",
    "                                                            size=90, # Size of the mesh \n",
    "                                                            save_geo=  \"data/fault.geo\",\n",
    "                                                            save_msh= \"data/fault.msh\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/danielsk78/GitProjects/IGA_master_thesis/pygeoiga/tutorials/data/fault.i'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = create_script(\"data/fault.msh\", \n",
    "                          topology_info=physical_tag_id,\n",
    "                          bot_bc=40, \n",
    "                          top_bc=10, \n",
    "                          geometry=mult2.geometry)\n",
    "file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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