{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.float_format = '{:,.5f}'.format\n",
    "import igraph\n",
    "import pandapower as pp\n",
    "import pandapower.plotting\n",
    "from pandapower.test.shortcircuit.test_1ph import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# beauftragt: Y0y0d5, YN0y0d5, Y0yn0d5, YN0yn0d5, Y0y0y0, Y0d5d5, YN0d5d5, Y0d5y0, Y0y0d11 und D0d0d0 \n",
    "# implementiert zusätzlich: YNdy, Ydyn, YNdyn, YNyy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "vg = \"yyy\"\n",
    "net = single_3w_trafo_grid(vg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "hp.pandapower.plotting.simple_plot - WARNING: No or insufficient geodata available --> Creating artificial coordinates. This may take some time\n",
      "hp.pandapower.plotting.collections - WARNING: 1 in collections is of unknown type. Skipping\n"
     ]
    },
    {
     "data": {
      "image/png": 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PKCgoKPH5xo0bM3z4cIYOHUpCQoJNVYqZ/Px8vvzySyZMmMC8efN8Pn/GGWcwcuRILr30Ur9djdZou0j5U4MuIiIi4ueKior44IMP+L//+z9WrVpV4nMOh4O+ffsycuRILrjgAkJDQ22qUv6p1atX88orr/DOO+9w+PDhEp9LSEhg+PDh3H333VSvXt2eAi1otF2k/KlBDxJ7937K1q1jKCw8ZHcp8g+EhcXSqNHj1K59qd2liIiIH3O5XEyfPp3777+f1atXl/hcQkICN9xwAzfffHOlue92sDl06BDvvfceL7/8MmvWrCnxuRo1avDAAw9wyy23UKVKFZsqLKbRdpGKoQY9SCxZ0pKjR3+3uww5AdHRp5GW9pvdZYiIiJ9asGABo0ePZv78+SU+3rVrV2655RYuvfRSoqKibKpOypJn68KECRP47LPPStyrvl69ejz66KNcd911hIWF2VKfRttFKo4a9CDx00/1yM/fCTgABw5HCOHhtYAQn3OLig5RVJRDSEgMLlceLpeT8PBaOBy+v/SdzqMUFu4nJKQKLlcRLlcB4eEJOBwRJufmUliYjcPhfrHgcuUSFhZPSIjviweXK5+CgiwcjnAcjlCczmOEhdUgJCTa5NxCCgr+xOEIweGIxOk8QmhoHKGhsSb/JZwUFPyJy+UkNDSGoqJDhITEEBZW3eRcFwUFWbhcBYSGVv3r3CjCwuJNzoXCwmyczlxCQ2MpKjqMwxFOeHgC7v/mpc898FedsRQVHSlxPfLzMwEnERF1OeOMHabfS0REKq9Vq1bx4IMPMnXq1BIfT0tL45lnnqFXr142VSYVYevWrYwZM4b33nuvxK3aTjvtNJ588kkuuuiiCg/902i7SAVySVBYsKCua84cXAsW1HUdObLOtWBBsmvx4pau3NzMEudt2fK4a84cXFu2PO5yuVyu3NxM1+LFLV0LFiS7jhxZV+LcXbsmuubMcbjWrRvucjqLXAUFOa7ly89wzZsX5zp4cHGJc7Oyprnmzo1wrVo1yFVUlOcqKspzrVo1yDV3boQrK2taiXMPHlzsmjcvzrV8+RmugoIcl9NZ5Fq3brhrzhyHa9euiSXONftZSv8MHmY/S+mfwcPsZyn9M3iY/SylfwYPs5+l9M/gfa1EREQ8tm7d6rr22mtdDofDBRj/pKSkuD7//HOX0+m0u0SpQL/++qurf//+Jf4sAK4uXbq45syZU6F1hIeHuwBXaGioa8mSJRX2vUUqI9/lVQl40dEtaNduDoWFB1m58hzy8nYDsHXrE2zd+jCNGj1Oo0YPARAZmUjbtt8TFlaNFSt6cfToegAyM99i3bphJCffTPPmL+NwhBAWFkubNl8TE9OalSvPIydnCQDZ2dNZvfpi4uP7cvrpHxMSEkFISASnn/4x8fF9Wb36YrKzpwOQk7OElSvPIyamNW3afE1YWCwORwjNm79McvLNrFs3jMzMtwA4enQ9K1b0IiysGm3bfk9kZCIAjRo9RKNGj7N168Ns3foEAHl5u1m58hwKCw/Srt0coqNbAJCUNJSUlDfZtes1Nmy4BZfLSWHhIX79tQ9HjqymbdtZxMWlARAf34/Wrb8gO3sGa9cOxunMx+nMZ+3awWRnz6B16y+Ij+8HQFxcGm3bzuLIkdX8+msfCgsP4XI52bDhFnbteo2UlDdJShpqej2g5L1QRUSkcvvzzz+54447aNGiBe+8846xalq3bl3efPNNVq9ezcUXX6xbpVUyqampTJ06lR9//JEzzzzT+PjixYvp1asXffr04ZdffinXGgoKChgyZIhxt4D77rtP+85FyplG3IOEZ8Tde2zau8GtUaM3O3e+VKI59+bd4CYmXsv27eNKNOfevBvcevXuYPv2Z0o05968G9wGDUazY8f4Es25N+8Gt0GDUeze/Y5Pc+7N84ZD3bq3sn//bJ/m3JvnDYfata8kN3czR46sKdGce/O84VCjxnkA7N8/q0Rz7q34DYdWREU1Ye/eD0o0594816OgYC8uV6FG3EVEKrlDhw7x3HPP8eyzz5ZI8q5Rowb3338/t956q1+Eg4n9XC4X06ZN44EHHvAJC7ziiit4/PHHadq0aZl/X422i1Q8NehBwqxBB3dTuGRJCgB1695K8+YvWj5HXt5uFi5MAqB69XNo23aWT3PuUVh4iPnz4wCIiEima9ctPs25h9OZz6JFjcnP3wVA9+45Ps25h8vlZOXK8zhw4HsAunXLNG3OPTZsuI2dO18CIC1tnWlz7rFjx//YuPF2ADp0WERcXBfLc//88yvWrLkIgFatvqRWrUGW5+bkLObnn7sC0KzZC9Sr92/Lc72vhxp0EZHKa/r06dx0003s2rXL+FiVKlW44447uO+++/zu9lriH4qKinj//fcZM2YM27ZtMz4eHh7Oww8/zOjRowkPDy+T76XUdhF7aMQ9yO3d+4nxeP/+2ca4u5l9+2YYj48e/Y1jxzZannvw4DzjcWHhfg4fXmF57uHDKygs3G/6taUdO7aRo0eLk829ayotL283+/fPNo69f9bSCgsPsXfvx8bx7t2TcbnMR82dznz27HnbON6z522cznzTc10uJ7t3T/aq4ePj3urueDWKiEjw279/P0OGDKF///5Gcx4aGsrw4cPZtGkTTz31lJpzsRQaGsq1117LunXrGD9+PAkJCYB7FH3MmDF07dqVX3/99ZS/j0bbReyjBj2Iee85T0tb57Mn3VvxnvPhdOu2i7Cw6iX2pHvzjIAnJAzizDOzqFq1fYk96d48I+BVq7bnzDOzSEgYVGJPurfikfzqdOu2i+Tk4SX2pHvzHslPS1vnsyfdm/dIfocOi0lJmVhiT7o375H81NRppKZOK7En3VvJPecT6dBhcYk96VbXIzQ0zudzIiIS/KZPn07r1q15++3iN4H79OnDb7/9xiuvvEJSUpKN1UkgiYyM5Pbbb2fTpk2MHj2a0NBQAH7++Wc6derE448/bjTXJ2PcuHHGLdVOP/10HnnkkTKpW0T+nhr0IFU6EM4qOA58A+EiI5NMg+PANxAuPDzeNDgOfAPhwsPjTYPjwCwQLsk0OA7MA+HMguMA00A4s+A4wDQQziw4Dko35+4952bBcWbXw/z2cCIiEqzMVs3j4uJ46623mDFjBs2bN7e5QglUcXFxPP300yxatIhWrVoBJVfTV61adcLPuWrVKsaOHQu4V+wnT56sfeciFUgNetBxmaa1g3m6u1laO5inu5ultQOm6e5mae2Aabq7VVq7Wbq7VVo7+Ka7W6W1g2+6u9OZZ5rWDmbp7nmmae1gnu5udT1ERCT4Wa2ar1mzhuuvv17J7FImOnXqxPLly3nggQdKrKZ37NiRJ5544h+vpmu0XcR+CokLEp6QOI/jNYOehtgT2pacPNw0rR2KV6s9+8ITEgaZprVD8Wp1Ts5PAMTFnWGa1g7Fq9VZWV8BEB3d0jKtvXi1+lXAHUpnldYOxavVAKGhcZZp7eCZHrgBAIcjnNatvzRNawfP9MBFuFzuv7RSUiaaprVD8fRAUVEOUPJ6WAX6iYhI8Ni/fz933nlnicY8Li6O8ePHM2TIEDXmUm6WLVvGkCFDWLNmjfGxDh06MHnyZFJTU4/7tUptF7GfVtCDVL16t1t+Ljq6BYmJ1xrHDRuOsUxrj4xMpH79e4zjxo2fsExrDwuLpXHjsV7njrVMaw8JiaBx4+JR9Pr177FMa3c4QmjYcIxxnJh47XHT2r1/9urVz7ZszgHq1LnaeBwRkUjNmhdanluz5oVERBTX6P21pcXFpVG9+tmmNYmISHDTqrnY6WRX0zXaLuIf1KAHmZCQKEJD4yyDysC9arx9+ziqVz+HiIgkVq7sbZnunp09nfXrRxAX142oqKasXHm+aXAcuFeNV6++mJiYNsTEtGH16otNg+PAvYq/cuX5REU1JS6uG+vXjzANjgPPKn5vIiKSqF79HLZvH2caHAfFq/ihoXHEx6eTnZ1hGhwHxav4DkcECQmXkJe3wzQ4DopX8fPydpCQcAkOR4RpcJzH1q1PkJ2dQXx8+t9eDxERCQ4HDhw47l7zevXq2VyhVBaRkZE8+eSTPnvTH374Ybp27epzL3WNtov4DzXoQSYsLN4yqAxKBsK1bTuLdu3mWqa7e+85b9duLu3bzzcNjoOSgXDt28+nffv5psFxUDIQrn37+bRrN9c0OA5KB8LNpW3bWabBceAbCJeaOsUy3b10IFzr1p+ZBseBbyBc69afmQbHeXjvOU9NnXLc6yEiIsFh3bp1dOnSRavm4lesVtPT0tL45JPiW78qtV3Ef6hBD0JWaeJmgXBW6e5mgXBmwXHgm9YeFhZrGhwHZmntiabBcWCe1m4WHAfmae3gGxwH5mnt4Bsc53I5TdPawSw4zt2kmwXClb4eoNgHEZFgMnPmTNLS0li/3v33olbNxZ+YraYfO3aMwYMH8/DDD7Ny5UqNtov4EYXEBQmz4DHvxrl27cFs3HiHT1q7h3fjXL/+PaxfP8Inrd3Du3Fu0mQcGzbc4pPW7uHdODdv/jKbN4/ySWv38G6cW7R4hT/+eNY0rR1Krmo3azaevXs/Nk1r9/A0zg0bjuHIkV9N09o9PG9kJCXd9Nfx6z5p7R7eb2TExLRh27axlgF9nuvhdObicuUrJE5EJMC5XC7++9//ct999+F5OdWmTRumTJlCo0aN7C1OxERubi4jRoxg8uTJxsfq1KnDnj17ALj//vt56qmnbKpOREANetCwSgbPyVnCzz93AaB27ato2fIdy0C4o0fXs2RJCgBxcd1o126uZSBcXt5uFi5MAiA8vBZdumyyDIQrLDzE4sVNKSj4E4Bu3TItA+GcznxWrDibnJyFAKSlrbMMhHO5nPz227Xs3fs+AB06LD5uINyWLY+wbZv7HeLU1GmWae0AmZkTWbduGMBfzfkNludmZ09n1ar+gDtwr3HjxyzP9b4eatBFRAJXbm4uN910E++++67xsYsvvpi3336bqlWr2liZyPG5XC7Gjx/PPffcg9NZvKWvRYsW/Prrr1o9F7GZRtyD3JEjxSEgubmbKSo6YnnusWMbjMf5+XspKNhneW5e3nbjsdOZS35+puW5+fmZOJ25pl9bWkHBPvLz95rWVFpR0RFyczcbx94/a2lOZz5HjvxqHB869IvluS6Xk0OHlnmdu8w0OM7suY4c+dUyOO7vahQRkcCwa9cuzjrrrBLN+aOPPsqnn36q5lz8nsPh4M4772TGjBlUr17d+Piff/7JTz/9ZF9hIgKoQQ9qxXvOh9OhwyKOHFljGVTmGdVOSBhE586rcTqPmQbHQfGodlzcGaSlrScysp5pcBwUj85HRtYjLW09cXFnmAbHQfHovNN5jM6dV5OQMMg0OA68R+fX0KHDIpKTh5sGx0HJ0fnU1GmWwXFQOhBuIikpE02D4zxKBsJNswyOg+LrERIS4/M5EREJDEuWLKFTp04sWeL+eyw6OprPPvuMRx55hJAQvaySwHHBBRewePFiUlLc05P79+/nvPPOY8KECWjAVsQ++pskSJUOhIuL62KZJl46EC4mppVpcBz4BsJFRzc3DY4D30C46OjmpsFx4BsIFxPTyjQ4DswC4bqYBseBeSCcWXAc+Ka1JyUNNQ2O8ygdCGcVHFf6eoSFVT/1CywiIhXuvffeo2fPnmRmuqfGGjRowE8//cQll1xic2UiJ6dFixYsXryYCy+8EICioiJuueUWRowYQX6+9USgiJQfNehByCytHczT3c3S2gHTdHeztHbANN3dLK0dME13N0trB0zT3a3S2s3S3a3S2sE33d0qrR3M093N0trBPN299PUQEZHAUlRUxKhRo7jmmmvIy8sDoEePHixdupS2bdvaXJ3IqalWrRpTp07l3nvvNT722muvce655/Lnn3/aWJlI5aSQuCDhCYnzSE4ebprWDsWr4EVFOQAkJAwyTWuH4lXw/PxdAMTFnWGa1g7Fq+BHj/4GQHR0S9O0diheBc/Jce91iohINk1rh+JV8KysrwAIDY2zTGsvbrRfBcDhiLBMa4fiVXA3h2VaOxS/8eG5TZpVWjsUTyW4XO53n72vh1Wgn4iI+J+cnByuuOIKZsyYYXzspptu4sUXXyQiwjxIVSRQvffeewwbNsx4I6phw4ZkZGTQpk0bmysTqTy0gh6kmjUbb5nWHheXRr16dxjHKSlvWqa1R0e3oEmTccbxaadNtkxrj4xMpEWL14zjFi1es0xrDwuL5bTTJhvHTZqMs0xrDwmJICXlTeO4Xr07LNPaHY4QmjUbbxzHxw84blp7w4YPGI+johpbNufgXkmPimps+rWlxcf3Iz5+gHF8vOshIiL+ybMn19Och4aG8vLLL/Pqq6+qOZegdPXVVzNv3jySktx36tm2bRtnnXUWixcvtrkykcpDHUPQCcXhCGft2sst08Szs6ezffszREQkExJShVWr0k2D48C92r5hwy2Eh9ciNDSW1asHmgbHgXu1fe3aywkLq0lYWE3Wrr3cNDgO3Kvtq1cPJDQ0lvDwWmzYcItpcBy4V9tXrUonJKQKERHJbN/+jGlwHHhW2y/H4QgnMrI+WVlfmAbHQfFqOziIimpCbu5m0+A4j61bnyA3dzNRUU0Ah2VwHLhX27OyviAysv7fXg8REfE/f/75J+ecc44RBlezZk1mzZrFyJEjcTgcNlcnUn7S0tJYtmwZnTt3BuDAgQOcd955zJ8/3+bKRCoHNehBJiIikdatv7RME/fec9616xbatZtrGhwHJQPhunTZRMeOy0yD46BkIFznzmvo3HmNaXAclAyE69hxGV26bDINjoOSgXDt2s2la9ctpsFxUDoQ7ku6dt1qGhwHvoFwXbtuOm66u/ee865dN1kGx0HJDICuXbce93qIiIj/2b17N7169WLFihUA1KlThx9++IFevXrZW5hIBUlOTub77783/swfOnSICy64gO+//97mykSCnxr0IGSVJm4WCGcWHAe+ae1hYbGmwXHgm9YeGZloGhwHvmnt0dEtTIPjwCytPc00OA7M09rNguPAPK0dfIPjPMwC4azS3c0C+kpfDxER8V87duzgrLPOYs2aNQDUrVuXH374gdatW9tcmUjFqlq1KtOnT+eCCy4A4OjRo/Tr14+vv/7a5spEgptC4oKEWfCYd0Nep851rF072Cet3cO7IW/ceCyrV1/sk9bu4d2Qt2jx2l9j7dVMA+G8G/LTT/+I9etv9klr9/BuyFu3/oItW8b4pLV7eDfkp5/+MXv2vG2a1g6lG/I3OHRomWlau4d3Q+4+9k1r9/BuyGNjO7Fu3Y0+6fkenuvhcITgdOYqJE5ExM9s27aNXr16sWXLFsAdkPX999/TpEkTmysTsU9eXh6XXXYZGRkZAISHh/Ppp58ycOBAmysTCU5q0IOEVTJ4dvZ0Vq3qD0DNmu6VXKtAuJycJfz8cxcAYmLa0L79fMtAuKNH17NkSQoAYWE16dx5jWUgXF7ebpYubUVh4T4A0tLWWQbCFRYe4pdfunPkyK8AdOiw2DIQzunMZ/Xqi9m3z72Knpo6zTIQzuVysn79SDIz3SF2KSkTjxsI553ufry0dvA06TcAkJR0My1aTLAMhPO+HmrQRUT8h2flfPPmzQA0bdqU2bNn07BhQ5srE7FfQUEBV111FZ9++ingbtK/+uor+vbta3NlIsFHI+4iIiJSqe3evZvevXsbzXmLFi344Ycf1JyL/CU8PJwPPviAq666CnA37BdffDHfffedzZWJBB816EHMM1KdkDCIVq2+ZP/+WZZBZZ4R97i4M2jb9jtyc7eaBsdB8Yh7dHRL2rWbR0hIlGlwHBSPuIeERNGu3Tyio1uaBsdB8Yh7bu5W2rb9jri4M0yD46B4xH3//lm0avUlCQmDTIPjoHjEPTPzdVJS3iQ5ebhpcJxHyT3n1sFx4D3iPpyUlDfJzHzdMt3dcz1CQqJMn0tERCpeVlYW5557LuvXu/9eatq0Kd9//z1169a1uTIR/xIWFsbkyZMZPNidp5OXl0d6ejrz5s2zuTKR4KIGPUiVDoSrVWuQaXAc+AbC1ajR2zQ4DnwD4apX72EaHAe+gXDVq/cwDY4D30C4GjV6mwbHgW8gXK1ag0yD48AsEO4G0+A4j9KBcFbBceAbCJeUdINlurv39QgLiz+FKysiImXFc59zTyCcZ8+5mnMRc2FhYbz77rsMGjQIgGPHjtGvXz8WLVpkb2EiQUQNehAyS2sH83R3s7R2wDTd3SytHTBNdzdLawdM093N0toB03R3s7R2wDTd3Sqt3Srd3SytHczT3c3S2sE83b309RAREfvl5OTQp08f41ZqdevWZfbs2TRo0MDewkT8XHh4OB999BEXXnghAIcPH6ZPnz4sX77c5spEgoNC4oKEJyQOQo1be5mltUNxAx8enkBh4X6qVm1vmtYOxavrISGROJ25REbWM01rh+LVdaczF+CvsXbftHYoXl3Py9tBSEgUTmeeaVo7FK+uHz78C2FhNSgoyDJNawfv1fXpREQkkpe3wzKt3buBj4pqTG7u5uMGwnka+KioJuTmbrFMa4fiBj4ysh75+btLXA+rQD8REakYRUVFpKenM2PGDKD4PucpKSk2VyYSOI4dO8aAAQOYPXs2AImJiSxbtkwTKCKnSCvoQacIl6uA00//yDKtPT6+Hw0ajCY/fxdO5zFSUzMs09rj4tJo3vxlCgr+pKjoEK1bT7FMa4+ObsHpp39EYeE+Cgv3cfrpH1mmtUdGJtK69RSKig5RUPAnzZu/bJnWHhYWS2pqBk7nMfLzd9GgwWjLtHb3SvpHuFwF5OX9QULCxZZp7Z6VdHCRm7uZqKgmx01rb9Toob+a882Ay7I5B/dKekLCxeTl/fG310NERCrWgw8+aDTnNWvW5LvvvlNzLnKCqlSpwpQpUzjzzDMBd9jioEGDOHbsmM2ViQQ2NehBauPGO0yDysC9Kr5jx3jjeN26YabBceBeFd+8eZRx/PvvQ0yD48C9Kr5+/c3G8fr1N5sGx4F7Vfz334cYx5s3jzINjgP3qvi6dcOM4x07xpsGx4F7VXzjxjuM4+zsqabBcR7btj1lPM7N3WIZHAfuVfHc3C2mX1tadvZ0srOnGsfHux4iIlJx3n//fcaNGwdAaGgon376Ka1bt7a5KpHAFBMTw1dffUWjRo0AWLZsGcOGDUMDuiInTw16kImIqEtKykTToDIoGQjXvXsOqanTTIPjoGQgXLdumXTosNg0OA5KBsKlpa0jLW2daXAclAyE69BhMd26ZZoGx0HJQLjU1Gl0755jGhwHpQPhJtKzZ55pcJyH957zs84qsgyOg5J7zs86q+i46e7ee8579sw77vUQEZGKs3TpUm644Qbj+IUXXuCcc86xsSKRwJeQkMCUKVOIiYkB4IMPPuA///mPzVWJBC416EHILKgMfNPaw8JiTYPjwDetPTIy0TQ4DnzT2qOjW5gGx4FvWntcXJppcBz4prXHx/czDY4Ds7T2oabBcR6lA+GsguPAPBDOKt3dLKCv9PUQEZGKl5mZyaBBg8jLywPgpptuYuTIkTZXJRIc2rRpw7vvvmsc33///Uyfbj3BKCLWFBIXJMyCx7wby8TEIaxceb5PWruHd2PZuPETrFx5vk9au4d3o3/aaZNZvXqgT1q7h3ej37r1FH7/fYhPWruHd6Pftu23bNnykE9au0fJRv9bdu+e7JPW7lG60T906BfTtHbwbfQB07R2D+9GPza2vWl6vofneoSERON0HlFInIhIBcnNzeXss89m8eLFAPTo0YPvvvuOiAhlg4iUpbFjx/LII48AEBsby+LFi2nZsqXNVYkEFjXoQcIqGdzdFLrH+eLiutGmzTeWgXDZ2dNZtao/AFFRTWnffr5lIFxOzhJ+/rkLAKGhsXTsuMwyEO7o0fUsX96JoiL3inuHDostA+Hy8nbzyy/dyc3dBEBq6jTLQDh3k34BOTkLAUhJmWgZCOdp0rOyvgI4blp7cZP+KgDJycOPGwjnadIBEhIGWabnQ8nroQZdRKT8uVwurrvuOmN1r0GDBixdupTatWvbXJlI8HE6nQwePJjPPvsMgGbNmrFkyRJq1Khhc2UigUMj7kEuJqY4+CYqqgmhoTGW51ap0tx4HBFRm/DwmpbnRkYW3yc2JCSKiIgky3MjIpIICYky/drSwsNrEhFR/KLJu6bSQkNjiIpqYhx7/6ylhYREEBPTxjiOjW1vea7DEUJsbCevcztZNuelnysmps1x09qPV6OIiJS95557zmjOo6OjmTJlippzkXISEhLC5MmTadu2LQAbN25k8ODBFBYW2lyZSOBQgx7EPKPocXFn0KzZC+zd+4FlUJlnFD06uiUpKRM5dGi5aXAcFI+iR0Qkc9pp7+J05pkGx0HxKLrTmcdpp71LRESyaXAcFK9yHzq0nJSUiURHtzQNjoPiVe69ez+gWbMXiIs7wzQ4zmPr1ifYtm0sDRuOISFhkGVwHHhWuW8kKelmkpJuZt26Gy3T3T1bAxISBtGw4Ri2bRtrGhwHxdfD4dBIpYhIRfj666+57777jON33nmHdu3a2VeQSCUQExPDlClTqFWrFgCzZs3i3nvvtbkqkcChBj1IlQ6Eq1fv36bBceAbCJeUNNQ0OA58A+ESE682DY4D30C4xMSrTYPjwHefeFLSUNPgOPDdJ16v3r9Ng+M8vPeJN278mGVwHJTct9+ixQRatJhgme5eOhCucePHLNPdva9HeHjCSVxRERE5EevWrePyyy/H6XT/fTdmzBguueQSm6sSqRwaNmzI559/Tnh4OADjx49n0qRJNlclEhjUoAchs7R2ME93N0trB0zT3c3S2gHTdHeztHbANN3dLK0dME13N0trByzT3UuntQOW6e5mae1W6e5mae2Aabp76esBjnK9/iIild3BgwdJT0/n4MGDAFx00UVGcJWIVIwePXrw8ssvG8fDhw9n4cKFNlYkEhgUEhckPCFxISFROBwRlmntUNyIVq/ei6NHfyMsrLppWjsUN6KxsR3Jz9+L03nMNK0dihvRqKhGAOTmbjVNa4fiVfuQkCpERNTm0KHlpmnt4L1qf4Do6JYcODDHNK0dSq7aV69+NtnZGZaBcN5vDMTHDyAr6wvLtHbvNwYSEi4mO3uqZVo7FL8xEB+fzoEDc0tcD6tAPxERKRvXX389kydPBiA1NZWffvqJqlWr2luUSCV122238dJLLwHQuHFjfv31V/3/KHIcatCDhKfp8+jePccyrR1g8+b72b79GQC6ddtFZKR1yJt38njnzquJiWllee7+/bNZufJcANq2/Y4aNXpbnnvkyBqWLnWHph0vgR0gLy+ThQuTAWjQYDRNmjxteW5h4SHmz48DID4+ndTUKZbnOp35zJsXCUBkZH26dt1qGQjncjlZtKgReXl/ANCzZ95xA+FWrRpIdnYGUPJ6qEEXESk/06dPp39/9x1J4uLiWLFiBY0bN7a5KpHKq6CggHPOOYf58+cDcMsttxgNu4j40oh7kNqx4wXLzx09up7du98xjrdtG2saHAfu1es//njWON6y5SHT4DhwN8ZbtozxOneMaXAcuBvjLVuKV7X/+ONZ0+A4cDfG27aNNY53737HNDjOw/tnP3BgrmVwHMCePe8Zj/Pzd7Nv30zLc/ftm0l+fnGN3l9bWk7OEg4cmGtak4iIlI8DBw5w0003GcfPPfecmnMRm4WHhzNp0iSqVKkCwMsvv8ycOXNsrkrEf6lBDzIREcmWQWVQMhCuW7dMUlImmgbHQclAuLS0daSmTjMNjoOSo+UdOiymQ4fFpsFxUHK0PDV1Gmlp60yD46B0INxEunXLNA2O8/Dec969e45lcBx47zkfTs+eucTH97NMdy/ec96Pnj1zSU4ebhocByX3nHfvnnPc6yEiImXnzjvvZNeuXQD06dOHoUOtJ7NEpOI0a9aMZ555xjgeOnQohw8ftrEiEf+lBj3oOEyDysA3rT0yMtE0OA5809qjo1uYBseBb1p7XFyaaXAc+Ka1x8f3Mw2OA9+09qSkoabBcR6lA+GsguPANxAuJCTSMt3dNxAu0jQ4DswD+qyuh4iIlJ3p06cb+87j4uJ4/fXXcTgUyiniL2699VZ69uwJwNatWxk9erTNFYn4J+1BDxJm+5q9G9batS8zTWv38G5YGzYcw8qVvX3S2j28G9aUlDdZtSrdJ63dw7thTU3NYN26YT5p7R4l30CYzbZtY33S2j1Kv4Gwd+8nPmntHqXfQDhyZLVPWrtH6TcQANO0dvB9A8HzRoBVQJ/neoSGxlFUlKM96CIiZeTAgQO0atXKWD1/8803ueGGG2yuSkRK27RpE6mpqRw7dgyA77//nl69etlclYh/UYMeJKyCxzxNIUB0dEvLtHYoGQYXEZFEu3ZzTdPawd2kr1rlDuEJCalCu3ZzTdPawd2kr1hxNk6n+5dxauo007R28DTpZ5OfnwkcPzzO06QfPfobgGVaOxQ36Tk5PwGQnDzcNK0dipv0rKyvAEhIGGSZ1l7cpL8KQFzcGZbp+VDyeqhBFxEpG96p7X369GHGjBlaPRfxU//73/+4/fbbAWjUqBGrVq1SqruIF424B7natS8zHteo0duyOQeoWbOv8Tg6uiVVqjSzPLdatZ7G47CwGlSt2s7y3KpV2xEWVsP0a0urUqUZ0dEtTWsqLTIysURKvPfPWlpYWCy1aw82jhMTh1imtYeERFCnznXGcZ0611mmtTscISQmDvGqYfBx0/OPV6OIiJw4jbaLBJbSo+6jRo2yuSIR/6IGPYh5Rsajo1tSt+6t7Nz5kuUeaM9qdEREMg0ajObAgTmmwXFQvBodGhpHw4ZjKCjIMg2Og+LV6IKCLBo2HENoaJxpcBwUr0YfODCHBg1GExGRbBoc57F16xPs3PkSdeveSnR0S8vgOHBPB2zceAe1a19FXFw3Vq483zLdPTt7OmvXDqZmzX7UrNnvr5F33+A48Izwn09cXDdq176KjRvvMA2Og+Lr4XCEmX5eREROjFlqe/369W2sSET+TkhICG+99RbR0dEATJgwQanuIl7UoAep0oFwzZu/aBlUVno/d5MmT5sGx4Hvfu7GjR8zDY4D3/3cjRs/ZhocB777uZs0edo0OM7De3998+YvWgbHQcn99S1bvkObNt9Yprt7769v3fqLv/bK+wbHQelAuG9o2fId0+C40tcjPLzWCVxJERGxotR2kcDUtGlTpbqLWFCDHoTM0toB0zRxs7R2wDTd3SytHTBNdzdLawdM093N0toBy3T30mntgGW6e+m0docjxDLd3TetPYKQkAjTdHeztHaHI8Q03b309dD/diIip06j7SKB7ZZbbtGou4gJhcQFCU9IXGhoLKGhsZZp7VDc4Nateyv798+2TGuH4ga3du0ryc3dzJEja0zT2qG4wa1R4zwA9u+fZZrWDt4Nbiuiopqwd+8HpmntULLBrVGjNzt3vmQZCOf9hkNi4rVs3z7ONK0dSk4D1Kt3B9u3P2Oa1g4lpwEaNBjNjh3jLdPavd9waNBgFLt3v1PielgF+omIyD9z+PBhUlJSlNouEuA2bdpEmzZtOHr0KADz5s2jR48eNlclYi816EHC0/R5dOuWedxAuA0bbmPnzpcASEtbZ5nWDrBjx//YuNGdttmhwyLi4rpYnvvnn1+xZs1FALRq9SW1ag2yPDcnZzE//9wVgGbNXqBevX9bnnv06HqWLEkBoG7dW2ne/EXLc/PydrNwYRIA1aufQ9u2sywD4QoLDzF/fhwAERHJdO26xTIQzunMZ9GixuTnu18Qdu+eYxkI53I5WbnyPA4c+B4oeT3UoIuInJrHH3+cMWPGAHDBBRcwc+ZMrZ6LBKgXX3yRf//b/RqwS5cuLFy4UP8/S6WmWdsgtW/fDMvP5eXtZv/+2cbx3r2fWJ5bWHiIvXs/No53755sGhwH7gZ2z563jeM9e942DY4DdwO7e/dkrxo+Ng2OM6tx//7ZlsFxUPJnP3r0N44d22h57sGD84zHhYX7OXx4heW5hw+voLBwv+nXlnbs2Ebj9m+laxIRkZP3559/8n//938AhIaG8r///U8v5kUC2MiRI2ndujUAixcv5quvvrK3IBGbqUEPMhERSSQnDzcNKoOSI+Bpaessg+Og5Ah4hw6LSUmZaBocByVHwFNTp5GaOs00OA5KB8JNpEOHxabBcR7ee87T0tZZBseB957z4XTrtouwsOqW6e6ekfyEhEGceWYWVau2Nw2Og+KR/KpV23PmmVkkJAwyDY4D75H86nTrtuu410NERE7M008/zaFD7r8rhg0bRosW1hNgIuL/QkNDeeqpp4zjBx98kMLCQhsrErGXGvSgYx5UBuaBcGbBceCb1h4Xl2YaHAe+ae3x8f1Mg+PAN609KWmoaXCcR+lAOKvgOPANhIuMTLJMdy8dCBceHm8aHAe+gXDh4fGmwXFgFtCXZHk9RETkxGzbto2XX34ZgCpVqhhj7iIS2Pr378+ZZ54JwG+//cY777xjc0Ui9lGDHoTM0sSt0trBN93dKq0dfNPdnc4807R2MEt3zzNNawfzdHeztHYwT3c3S2sH83R3s7R2wDTd3SytHTBNd7dKzy99PZzOo+Vw1UVEgt8jjzxCfr77Dd/bb7+d5ORkmysSkbLgcDhK3HbtkUce4dixYzZWJGIfhcQFCbPgseLV6lcBdwiaVVo7FK9WA4SGxlmmtYNntdqdmOtwhNO69Zemae3gWa2+CJerAICUlImmae1QvFpdVJQDYJnWDsWr1Z7QtuTk4aZp7VA8PeDZF56QMMg0rR2Kpwdycn4CIC7uDNO0diieHsjK+gqA6OiWlun5vtdDIXEiIv/U6tWradOmDS6Xixo1arB582aqV69ud1kiUoYGDBjAtGnTAHj22We5++67ba5IpOJpBT2IORwhNGxYPP6XmHjtcdPa69W73XhcvfrZls05QJ06VxuPIyISqVnzQstza9a8kIiI4obV+2tLi4tLo3r1s01rKi06ugWJidcaxw0bjrFMa4+MTKR+/XuM48aNn7BMaw8Li6Vx47Fe5461TGsPCYmgcePirQH1699jmZ5f+nqIiMg/9+CDD+JZU7j//vvVnIsEoaeeesoIfXzqqac4ePCgzRWJVDw16EHMvWrcm4iIJKpXP4ft28dZ7oH2rBqHhsYRH59OdnaGaXAcFK8aOxwRJCRcQl7eDtPgOCheNc7L20FCwiU4HBGmwXEeW7c+QXZ2BvHx6YSGxlkGx4F7FX/79nFUr34OERFJrFzZ2zLdPTt7OuvXjyAurhtRUU1ZufJ80+A4cK/ir159MTExbYiJacPq1RebBseBexV/5crziYpqSlxcN9avH2EaHAfF10P/24mInJgFCxaQkZEBQN26dbn11lttrkhEykNqaipXX+1eyNm3bx//+c9/bK5IpOKpUwhSJfecz6Vt21mWQWWl95ynpk6xTHcvHQjXuvVnpsFx4BsI17r1Z6bBcR7ee85TU6dYBsdByUC4tm1n0a7dXMt0d+895+3azaV9+/mmwXFQMhCuffv5tG8/3zQ4DkoGwrVvP5927eaaBseVvh7h4bX+wRUUEREAl8vF6NGjjeNHH32UKlWq2FiRiJSnxx57jPDwcADGjx9PZmamzRWJVCw16EHILBDOLDgOzNPawTc4DszT2sE3OM7lcpqmtYNZcJy7STcLhLNKdzcLhLNKdzcLhDMLjgPftPawsFjT4DgwS2tPNA2OM7seDkdY+f4BEBEJIjNmzGD+/PkApKSkMGTIEHsLEpFy1bhxY0aMGAHA0aNHefzxx22uSKRiKSQuSHhC4sLCahARkWia1g4lV7WbNRvP3r0fm6a1e3ga54YNx3DkyK+mae0ensY5Kemmv45f90lr9/BunGNi2rBt21jLQDjvxrl27cFs3HiHT1q7h3fjXL/+PaxfP8Inrd3Du3Fu0mQcGzbc4pPW7uH9Rkbz5i+zefMon7R2D+83Mlq0eIU//ni2xPUwC/QTERFfRUVFtG/fnlWrVgHw2Wefcckll9hclYiUt71799K0aVMOHz5MWFgYv/32G82aNbO7LJEKoQY9SHiaPo+0tHWWgXAul5PffruWvXvfB6BDh8XHDYTbsuURtm1zh6alpk6zTGsHyMycyLp1wwD+as5vsDw3O3s6q1b1B9wBb40bP2Z5bk7OEn7+uQsAtWtfRcuW71gGwh09up4lS1IAiIvrRrt2cy0D4fLydrNwYRIA4eG16NJlk2UgXGHhIRYvbkpBwZ8AdOuWaRkI53Tms2LF2eTkLARKXg816CIi/8x7773HNddcA0Dnzp1ZvHixESAlIsHt0Ucf5bHH3K8NL7/8cj788EObKxKpGBpxD1LHjm2w/FxR0RFyczcbx0eOrLY81+nM58iRX43jQ4d+sTzX5XJy6NAyr3OXmQbHmT3XkSO/WgbHla4xN3czRUVHLM/1/tnz8/dSULDP8ty8vO3GY6czl/x8631O+fmZOJ25pl9bWkHBPvLz95rWJCIif8/lcvF///d/xvEzzzyj5lykErn77rtJSEgA4JNPPmHbtm02VyRSMdSgB5nw8DokJAwyDSoD71HtNXTosIjk5OGmwXFQclQ7NXWaZXAclA6Em0hKykTT4DiPkoFw0yyD48B7z/lwOnRYxJEjayzT3T2j8wkJg+jceTVO5zHT4DgoHp2PizuDtLT1REbWMw2Og+LR+cjIeqSlrScu7gzT4DgoHp13Oo/RufPq414PEREx99NPP/Hrr+43iLt06cI555xjc0UiUpFiY2P597//DYDT6eT111+3uSKRiqEGPcg4HGGmQWVgFgjXxTQ4DswD4cyC48A3rT0paahpcJxH6UA4q+A48A2Ei4vrYpnuXjoQLiamlWlwHPgGwkVHNzcNjgPfQLjo6OamwXHgGwgXE9PK8nqIiIi1CRMmGI9vueUWGysREbsMGzaMsDB3uO6bb75JXl6ezRWJlD816EHILE3cKq3dLN3dKq0dfNPdrdLawTzd3SytHczT3c3S2sE83d0srR0wTXc3S2sHTNPdzdLaAdN0d7P0fLPr4T0mLyIivvbu3cunn34KQEJCAv/6179srkhE7JCUlMTFF18MuH8vfPHFFzZXJFL+FBIXJMyCxzyNdlbWVwCEhsZZprUXN9qvAuBwRFimtUPxKribwzKtHYpXwcH9R80qrR2KV8FdLvcqenLycNO0diheBS8qygEgIWGQaVo7FK+C5+fvAiAu7gzTtHYoXgU/evQ3AKKjW5qmtUPxVEJOzk8AREQkm6bng+/1UEiciIi5p59+mgceeACAUaNG8cwzz9hckYjYZd68eZx11lkAdO/enR9//NHmikTKl1bQg1hISAQpKW8ax/Xq3WGZ1u5whNCs2XjjOD5+wHHT2hs2fMB4HBXV2LI5B/dKelRUY9OvLS0+vh/x8QOM42bNxlumtcfFpVGv3h3GcUrKm5Zp7dHRLWjSZJxxfNppky3T2iMjE2nR4jXjuEWL1yzT2sPCYjnttMnGcZMm4yzT80tfDxER8VVUVMSrr3reLHZw880321yRiNipR48etGrVCoD58+cb2RQiwUoNehArLDzEqlXphIRUISIime3bn7HcA+1e3b0chyOcyMj6ZGV9YRocB8Wr7eAgKqoJubmbTYPjPLZufYLc3M1ERTUBHJbBceBebc/K+oLIyPo4HOGsXXu5Zbp7dvZ0tm9/hoiIZEJCqrBqVbppcBy4V9s3bLiF8PBahIbGsnr1QNPgOHCvtq9dezlhYTUJC6vJ2rWXmwbHgXu1ffXqgYSGxhIeXosNG24xDY6D4ushIiLWZsyYwfbt7rtk9O3bl8aNG//NV4hIMHM4HIwcOdI4fuWVV2ysRqT8qUEPUt57ztu1m0vXrlssg8pK7jn/kq5dt5oGx4FvIFzXrpuOm+7uvee8a9dNlsFxUDIQrmvXrbRu/aVlurv3nvOuXbfQrt1c0+A4KBkI16XLJjp2XGYaHAclA+E6d15D585rTIPjoGQgXMeOy+jSZZNpcFzp6xEeXus4V05EpHLzDofzflEuIpXX1VdfTdWqVQF49913OXjwoM0ViZQf7UEPEt570NPSfjMNhDMLf7MKhDMLfzteIJxZ+JtVIJxZ+JtVIJxZ+JtVIJxZ+JtVIJxZ+JtVIJxZ+JtVIJxZGF/pj61efbFPXoCISDByuVzs37+f3bt3k5eXR0FBAYWFhT7/hISEEBYWxp49e7j66qsBSE5OZurUqdSqVYvExETCw8Nt/mlExE4jR440Vs9ffPFFbr31VpsrEikfatCDhKdBDw9PoEqVFj7NuYd3Q3766R+zZ8/bpmntULpJf4NDh5aZNuce3g25+9i3OffwbshjYzuxbt2NPs25h3dDXqfOdaxdO9inOffwbsgbNx7L6tUX+zTnHt4NeYsWr/011l7NNBDOuyE//fSPWL/+Zp/m3MO7IW/d+gu2bBlT4nqYBfqJiAQSl8tFdnY2u3btIjMz0/h36ceZmZlldlukWrVqkZSURFJSEsnJyT6Pk5OTSUxMJDIysky+n4j4l1WrVtGmTRsAWrZsyZo1a3A4HDZXJVL21KAHCU/T59Ghw2LLQDinM5/Vqy9m3z73qHtq6jTLQDiXy8n69SPJzHSHpqWkTDxuIJx3uvvx0trB06TfAEBS0s20aDHBMhAuO3s6q1b1B6BmTfct2awC4XJylvDzz10AiIlpQ/v28y0D4Y4eXc+SJSkAhIXVpHPnNZaBcHl5u1m6tBWFhfsASEtbZxkIV1h4iF9+6c6RI+4gE+/roQZdRAJFYWEhv/32G8uXL2f58uX88ssvbN++nd27d1NQUGB3eaZq1qxJcnIyLVu2pGPHjsY/NWrUsLs0ETlFPXv2NFLc58yZw9lnn21vQSLlIMzuAkRERMR+nmZ82bJlRkO+cuVKjh07dlLPl5CQYKxyJyUlERMTQ1hYmOk/TqeT5cuXM3XqVABOO+00evfuTX5+fomV+szMTPLzzYNDPfbt28e+fftYvXq1cS91gCZNmpRo2NW0iwSekSNHGg36hAkT1KBLUNIKepDQiLubRtxFRP5eYWEha9euNRrxE2nGa9WqVWKs3GzkPDExkYgI8yknK126dGHJEnfA5sKFC+natavPOS6Xi3379pmO1Ht/bOfOnX/byAM0btyYTp06qWkXCRD5+fnUr1+fvXv3EhYWxvbt20lKSrK7LJEypQY9SCgkTiFxIiLHc+DAAb7++msyMjKYMWPGP0pBbtq0aYkGtkOHDlSvXr3Ma9u6datxO7W2bdvyyy+/nNLe0sLCQn7//fcSb0CsWLGCo0eP/u3XdunShfT0dNLT02nVqpX2uIr4mQcffJCnnnoKcK+ijxgxwuaKRMqWGvQgUXpVtnRTWLVqO9NGHHwb95o1L7RsxM2adKtGHHybdKtGHHwb9337Zpo24uDbuB8+vMK0EQffxj0/P9O0EQffxh0wbcTBt3GPiEgyfWMESjbuISGRFBT8qQZdRMrd5s2bmTp1KhkZGcybN4/CwkLLcyuqGTfz4osv8u9//xuAJ598kgceeKDMv8fJNO2NGjUymvWePXsqSV7ED6xYsYL27dsD0KdPH2bOnGlzRSJlSw16kDAbm/Y0hYcP/0JYWA0KCrJMR9nBu0mfTkREInl5OyxH2b2b9KioxuTmbj5uIJynSY+KakJu7hbLUXYobtIjI+uRn7+b+Ph+pqPsUNykh4cnUFi4n6pV25uOskNxkx4SEonTmUtkZD3TUXYobtKdzlwAQkKiTEfZobhJz8vbQUhIFE5nnunWAii+Hjk5PwGoQReRMud0OlmyZInRlK9evdr0vGrVqnHeeeeRlpZW4c24mfPOO4/vvvsOcCc1t27dukK+b+mmfc6cOcf9b3bhhReSnp5Onz59NAovYhOXy0XDhg35448/iIiIICsri9hY8zBgkUCkBj1IWO1rLijIZsGCBAAaNhxD48aPWT6H05nHvHlRACQkXELr1p9ZnutyOfnhh1AAoqKa0LXrpuPWt2hRU3JzNwNw1llFlmntAKtXX0pW1ucA9OyZS0iI9S1ztmx5hG3bxgJw5plZhIfHW567e/d7/P77NQCkpa0nOrq55bkHDvzIihU9AWjXbh7Vq/ewPPfo0Q0sWeJu3k877V0SE6+2PNf7eqhBF5GycPToUb777jsyMjKYNm0ae/bsMT2vcePGDBw4kAEDBtCjRw+/WQ0+ePAgCQkJFBYW0qhRIzZv3mzrWPmWLVuYOnUqU6dOZe7cuaZTB2FhYfTo0cNYXW/SpIkNlYpUXrfeeisvv/wyAJ999hmXXHKJzRWJlB3rLkkCntOZz7p1w4zjHTvGk5OzxPRcl8vJxo13GMfZ2VPJzp5u+dzbtj1lPM7N3UJm5luW52ZmvkVu7hbTry0tO3s62dlTjeONG+/A5XKanpuTs4QdO8Ybx+vWDcPpNA8FOnp0PZs3jzKOf/99CIWFh0zPzcvbzfr1NxvH69ffTF7ebtNzCwsP8fvvQ4zjzZtHcfToetNzS18PEZFTsWLFCm666SZq1arFwIEDmThxYonm3OFw0LVrV5566ilWr17Npk2beP755znnnHP8pjkH+Oabb4wmOD093fY9340bN+bf//43s2bNIisri48++ogrr7yyxIRBYWEhc+bM4c4776Rp06Z07dqVd955h9zcXPsKF6lEBgwYYDz23P1BJFioQQ9S3vvKU1On0b17DjExrVm58jyfJr3kvvKJ9OyZR3x8X1avvti0SffeV37WWUUkJ9/MunXDTJt0733lZ51VRKNGj7N168Ns3fqEz7ne+8p79swjJWUiu3a9xoYNt/g06d77yrt3zyE1dRrZ2TNYu3awT5Puva+8W7dMOnRYzJEjq/n11z4+Tbr3vvK0tHWkpa2jsPDgX2PsJZt0733lHTosplu3TMLCqrFiRS+fJt37eoSFWa/yi4gcT25uLu+99x5nnHEG7du354033iixh7pKlSpGs56ZmcnChQu5//77/TrsLCMjw3icnp5uYyW+qlWrxuDBg3n//ffZu3dviabc2+LFi7nuuuuoV68e9913H5s3b7apYpHK4eyzz6Zq1aoATJs2jaKiIpsrEilDLgkKCxbUdc2Zg2vBgrquoqI816pVg1xz50a4srKmGecUFOS4li8/wzVvXpzr4MHFLpfL5XI6i1zr1g13zZnjcO3aNdE41+o5tmx53DVnDq4tWx43Pmb1HLt2TXTNmeNwrVs33OV0Fh33ObKyprnmzo1wrVo1yFVUlHfc5zh4cLFr3rw41/LlZ7gKCnKO+xxHjqxzLViQ7Fq8uKUrNzfTONfsOXJzM12LF7d0LViQ7DpyZJ1xrtlzmP23tHqO0v8tva+ViMg/sXnzZteoUaNcCQkJLqDEP7Gxsa5hw4a5pk6d6jp69KjdpZ6Q/Px8V/Xq1V2Aq1q1aq78/Hy7S/pHnE6na82aNa5nnnnG1bZtW59r4nA4XBdeeKFr6tSprsLCQrvLFQlKl156qfH/3I8//mh3OSJlRnvQg0TxfdDrUK1aN8t7m5dMd/+W3bsnW97bvHS6+6FDv1imtZdOdwcs09qh5Cp8bGx7y7R2KLkKn5g4hJUrz7e8t7n3Knzjxk+wcuX5lvc2916FP+20yaxePdDy3ubeq/CtW0/h99+HWN5r3nsVvm3bb9my5aES10P3QReRf6KoqIhvvvmGCRMmMGPGDEr/dZ2amsott9zClVdeGbABSXPnzqVXr14ADB48mI8++sjmik6cy+Vi0aJFTJgwgU8++cTn/usNGzZk+PDh3HDDDdSqVcumKkWCz7vvvsu1114LwL333st//vMfmysSKRtq0IOEp+nzSE2dZprWDp4m/QJychYCkJIy0TStHYqb9KysrwCOm9Ze3KS/CkBy8nDLtHYobtIBEhIGWaa1g6dJvwGAuLhutGnzjWlaO7ib9FWr+gMQFdWU9u3nm6a1g7tJ//nnLgCEhsbSseMy07R2cDfpy5d3oqjIPRbfocNi07R2cDfpv/zSndxcd3ie9/VQgy4ix5OVlcVbb73Fq6++ypYtW0p8Ljw8nEsvvZSRI0dy5pln+u3Y+j9111138fzzzwPw/vvvc+WVV9pc0an5888/eeutt3jllVfYtm1bic9FRETwr3/9i5EjR9KtW7eAv3YidsvOzqZ27do4nU5SUlL4/fff7S5JpExoD3qQqlLFOqE8NDSGqKjixNmYGOvb2YSERBAT08Y4jo1tb3muwxFCbGwnr3M7HTet3fu5YmLaWDbnpWuMimpCaGiM5bneP3tERG3Cw2tanhsZ2cB4HBISRUREkuW5ERFJhIREmX5taeHhNYmIqG1ak4iImc2bN3P99ddTr149Ro0aVaI5r1+/Pk8++SR//PEHH3zwAd27dw/4Bs/lchn7z0NDQ7nwwgttrujU1apVi1GjRrFp0yamTZtG3759jeuUn5/P+++/z5lnnkn79u35+OOPcTrNQ1BF5O/Fx8fTvXt3ANatW8f69eYhvSKBRg16kAkLq0F0dEvToDIoXuXeu/cDmjV7gbi4M0yD4zy2bn2CbdvG0rDhGBISBlkGx4FnlftGkpJuJinpZtatu9Ey3d0zip6QMIiGDcewbdtY0+A4KB5Fj4s7g2bNXmDv3g9Mg+OgeBQ9OrolKSkTOXRouWlwHBSPokdEJHPaae/idOaZBsdB8dYApzOP0057l4iIZNPgOCieOjh0aDkpKROPez1ERPbs2cNtt93GaaedxuTJk8nLyzM+d8EFFzBlyhS2bNnCAw88QJ06dWystGz9/vvvbNrknjLq2bNnUN1XPDQ0lH79+jF9+nQ2btzIfffdR3x8cUDoypUrufzyy+ncuTPffvutz/YFEflnvIMlleYuwUINepAJCYmmbdvvTdPES+8Tr1fv37Rp87Vlurv3PvHGjR/j9NM/tkx3994n3qLFBFq0mGCZ7u69T/z00z+mcePHLNPdvfeJt2nzNfXq/ZuUlDdN092994m3bfs9SUlDad36C9N0d+994u3azSEx8Wratp1lmu5ect/+LBITr6Zduzmm6e6l9+0nJQ21vB4iUrnl5OQwZswYmjZtyksvvURBQQEANWrU4O6772bDhg18/fXXpKenExoaanO1Zc87vd37lknBpkmTJowbN44dO3bwzjvv0KVLF+NzP//8MxdccAHnnnsuS5cutbFKkcDk/bvD+3eKSECzL59OylLpZPDSaeJWSesul3kiuVnSustlnu5uldZu9j2t0trNvqdVWrvZ97RKazf7nlZp7Wbf0yqt3eXyTXe3Sr4vfT3mz6+jFHeRSiw3N9f1/PPP+ySyx8TEuB5++GHXwYMH7S6xQpxxxhnGz75x40a7y6kwTqfT9c0337jat2/vk/5+6aWXun7//Xe7SxQJKCkpKS7AFRIS4srKyrK7HJFTppC4IGEWPFa8SnyA6OiWHDgwxzStHUquElevfjbZ2RmWgXDeq8Tx8QPIyvrCMq3de9U+IeFisrOnWqa1Q/GqfXx8OgcOzLVMa4fiVfvq1Xtx9OhvhIVVN01rh+JV+9jYjuTn78XpPGaa1g7Fq/ZRUY0AyM3daprWDsWr9iEhVYiIqM2hQ8tN0/Oh+HocPboOcCokTqSSKSoq4r333mPMmDFs377d+HhYWBg333wzDz/8cFCNsB/P4cOHiYuLw+Vy0bJlS9auXWt3SRXO6XTyySef8NBDDxmj/uAejx86dCiPPPIIdevWtbFCkcBw77338uyzzwLwySef8K9//cvmikROjUbcg1hkZCJt284mPz+TAwe+p0GDUZZp7WFhsbRp8zVFRTlkZ2cQH59umdYeEhLB6ad/jMuVT1bW50RG1rNMa3c4Qmje/GUiI+uRlfU5Llf+cdPaGzV6iPj4dLKzMygqyrFszgGSkobSoMEoDhz4nvz8TNq2nW2Z1h4f348WLV4hJ2chubmbaNv2W8u09ri4NFq3/oIjR37lyJFfad36C8u09ujoFrRt+y25uZvIyVlIixavWKbne64HKBRIpDJx/RWG1rZtW4YMGVKiOb/yyitZt24dL730UqVpzgF++eUXY991jx49bK7GHiEhIVx++eWsXbuWCRMmGNe/qKiIN954g2bNmjFq1Cj2799vc6Ui/s37d8jy5cttrESkbKhBD2Iul5Nt28Yax7t3v3PcPdA7drxgPD5wYK5lcBzAnj3vGY/z83ezb99My3P37ZtJfn7xPm3vry0tJ2cJBw7MNa2ptKNH17N79zvG8bZtY02D48C9ev3HH88ax1u2PGQaHAfuaYItW8Z4nTvGNDgO3NMEW7YUv5Hxxx/PmgbHge/1EJHgN3/+fHr06MHAgQNZs2aN8fELL7yQX375hffff58mTZoc5xmCk/eL6I4dO9pYif0iIiIYMWIEmzZt4oknniAuLg6A3Nxc/vOf/9CkSROeeeYZjh49anOlIv7J+3eIGnQJBmrQg1TJQLiJdOuWedygMu9AuO7dcyyD48A7EG44PXvmEh/fzzLdvTgQrh89e+aSnDzcNDgOSgbCde+eYxkcByUD4bp1yyQlZaJpcByUDIRLS1tHauo00+A4KDnq36HDYjp0WGwaHAclR/1TU6eRlrbONDiu9PUICwuepGIRMbd//36GDBlCjx49WLBggfHxLl26MHfuXGbMmEG7du3sK9Bmy5YtMx536tTpOGdWHjExMTz44INs3ryZu+++m8jISAAOHDjA/fffT6tWrZgzZ47NVYr4n+TkZGMCZfny5borggQ8NehBqHRae1LS0L/Gq83TxL2b80aNHjLG3c2adO+09ubNXyYkJNIy3b10WntISCTNm79smu5eOq09LCyWRo0eMm3SS6e1R0YmkpQ01DTdvXRae3R0C+Lj+5mmu5dOa4+LSyMuLs003b10Wnt8fD+io1uYpruXvh4hIdHlcNVFxF9Mnz6d1q1b8/bbbxsfa9myJV9++SULFy7krLPOsrE6/+BZ5YqIiKB169Y2V+Nf4uPjefbZZ1m/fj3XX389ISHul2pbt27lnHPO4ZZbbuHw4cM2VyniPxwOh/FG3/79+9myZYvNFYmcGjXoQce3Ofcwa9JLN+ceZk166ebcs+fcsyfdu0n3bc7de849e9K9m3Sz5tyjdJNu1px7lG7S8/IyfZpzj9JNekFBtk9z7lG6SS8oyPZpzj18m/RMy+shIsHFs2rev39/du3aBUBcXByvvfYav/76K4MGDcLhcNhcpf0OHTrEunXrAEhNTSUiwjyTpLJr0KABb731FqtWraJnz57GxydMmECbNm20mi7iRWPuEkyU4h4kPCnuHikpEy2bweI08d8ALNPaoXhVOSfnJwCSk4dbBsJ5VpWzsr4CICFhkGUgXPGq8qsAxMWdcdxAOM8bCQDR0S0t09rBs8p/AwAREUm0azfXMhAuO3s6q1b1ByAkpArt2s21DITLyVnCihVn43QeAyA1dZplIJz7jYSzyc/PBEpeD7PEfREJbNOnT+emm24yGnOAPn368Prrr1O/fn0bK/M/P/74o9Fw3nTTTbz22ms2V+T/nE4nL7/8MqNHjy6xF33kyJGMGzeOqlWr2lidiP0yMjIYOHAgAKNGjeKZZ56xuSKRk6cV9CBVs2Zfy89FRiZSo0Zv47h27csszw0Li6V27cHGcWLiENPmHNwr6XXqXGcc16lznWVau8MRQmLiEK8aBls256VrrFGjt2VzDiV/9ujollSp0szy3GrVilclwsJqULVqO8tzq1ZtV2L/uPfXllalSjOio1ua1iQiwcNq1XzixInMmDFDzbkJBcSduJCQEG677TZ+/fVXn9X01NRUraZLpacVdAkmatCDTGhoLBERyaZBZR5btz7Bzp0vUbfurURHt7QMjgP3avTGjXdQu/ZVxMV1Y+XK8y3T3bOzp7N27WBq1uxHzZr9/hoD9w2OA8+e8/OJi+tG7dpXsXHjHabBcVC85zw6uiV1697Kzp0vmQbHQfF0QEREMg0ajObAgTmmwXFQPB0QGhpHw4ZjKCjIMg2Og+LpgIKCLBo2HENoaJxpcBwUTwccODCHBg1G/+31EJHAZLbXvE+fPqxevZqhQ4dqnN2C94tnBcSdmKZNmzJnzhz+97//ER3tzjPR3nQRBcVJcFGDHmRCQ+NMg8o8vPecN2/+omVwHJQMhGvZ8h3atPnGMt3de89569Zf/LU32zc4DkoHwn1Dy5bvmAbHgW8gXPPmL1qmu5cOhGvS5GnT4DjwDYRr3Pgx0+A48A2Ea9z4MdPgOPANhGvS5OnjXg8RCTxaNT81ngR3BcSdHK2mi/hyOBzGKrqC4iTQqUEPQlZp4maBcFbp7maBcFbp7maBcGbBcWCe1m4WHAfmae3gGxwH5mnt4Bsc53I5TdPawTc4zunMN01rB9/guMLCQ6bp+WbXA8zv1S4i/m/mzJlaNT8FCogrO8dbTR85ciRHjhyxuUKRiqUxdwkWCokLEmbBY94Nbo0avdm58yXLQDjvBjcx8Vq2bx/nk9bu4d3g1qt3B9u3P+OT1u7h3eA2aDCaHTvGm6a1Q8nV5wYNRrF79zumae0enjcc6ta9lf37Z5umtXt43nCoXftKcnM3c+TIGp+0dg/PGw41apwHwP79s3zS2j2K33BoRVRUE/bu/cAyrd1zPQoK9uJyFSokTiSAuFwunnjiCcaMGWN8LC4ujueff57rr79ejfk/pIC48rFp0yaGDh3KvHnzjI+1a9eOKVOm0KBBAxsrE6k4U6ZMYdCgQYCC4iSwqUEPElbJ4EePrmfJkhQA6ta9lebNX7R8jry83SxcmARA9ern0LbtLMtAuMLCQ8yfHwdAREQyXbtusQyEczrzWbSoMfn57lHQ7t1zLAPhXC4nK1eex4ED3wPQrVvmcQPhNmy4jZ07XwIgLW2dZVo7wI4d/2PjxtsB6NBhEXFxXSzP/fPPr1iz5iIAWrX6klq1Blmem5OzmJ9/7gpAs2YvUK/evy3P9b4eatBFAsORI0cYMmQIn332mfExJbSfnPHjx3PnnXcC8Nprr3HTTTfZXFHwMEt6r1WrFl988QXdu3e3uTqR8rdz507q1asHwLnnnsusWbNsrkjk5KhBDxLFt1kLISIiyfh4UdEhiopyAHA4wggPr4XVzgan8yiFhfv/OgohPLwWDkeYxbm5FBZmG8fuc80bdJcrn4KCP43jsLB4QkKiLM4t/Otc9xj4jz/G8OGH+Vx1VSy9elUpXQUFBX/ichUC7v33oaFWSfAuCgqycLnce8tDQmIIC6tucS4UFmbjdOb+dW4UYWHxxzn3AE6ne5TQ4YggPDwBMF9N874eatBF/N+2bdsYOHAgK1euBNz7HJ9++mnuu+8+rZqfhGuuuYb33nsPcI+gdujQweaKgs/atWtJT09n06ZNAISHhzNhwgSGDRtmc2Ui5cvlcpGUlMSePXuoUaMG2dnZ+j0tAUkNepBYsqQlR4/+bncZZe6662D7dmjQALy2fAaF6OjTSEv7ze4yRMTCjz/+yCWXXMKff7rfYIyNjeWDDz6gf//+NlcWuLp06cKSJUtwOBwcO3aMyMhIu0sKSvv27WPw4MF89913xsduu+02nnvuOcLCzN94FwkG559/vrFynpWVRXy89QKLiL/Sb+kg4QlNM7vtVyA7diwTcHLsWMnJgEAXFhZLo0aP212GiFh44403uOWWWygoKACgWbNmZGRk0LJlS5srC2yZmZmAe/RazXn5qVmzJjNnzuSee+7hhRdeAODFF19k7dq1fPLJJ9SsWdPmCkXKR926dY3HmZmZatAlIKlBDxK1a19K7dqX2l1GmYuIqAfsJCIiSePgIlLuCgoKuOuuu3jppZeMj5177rl8/PHHampOkdPpNBr0pKTgecPVX4WFhTF+/HjatGnD8OHDKSgoYPbs2aSlpTFlyhRatWpld4kiZc77d8uuXbt0K0cJSLrNmoiICJCdnU2fPn1KNOd33HEHM2fOVHNeBrKzsyksdGeGJCcn21xN5TF06FDmzJlD7dq1AXfie7du3Zg6darNlYmUPe/fLZ43BEUCjRp0ERGp9NasWUNaWhrff+++g0R4eDgTJ07k+eef157dMuL9Ylkr6BXrzDPPZOnSpbRv3x5w349+4MCBPPPMMyiKSIJJ6RV0kUCkBl1ERCq1adOm0bVrVzZv3gxA7dq1mTNnDkOHDrW5suDi/WJZK+gVr0GDBsyfP5/LLrsMcCde33///Vx11VXk5ubaXJ1I2dAKugQDNegiIlJpffjhhwwaNIjDhw8D0KFDB5YtW8aZZ55pc2XBRyvo9ouOjuajjz7iiSeeMD724Ycfkp6ebtw7XSSQaQVdgoEadBERqZQmT57MVVddRVFREQCXXXYZP/74I/Xr17e5suCkFXT/4HA4ePDBB/nqq6+IiYkBYNasWfTt25dDh4LrTjBS+Xg36FpBl0ClBl1ERCqdV199leuvv97Yf3vzzTfz4YcfEh0dbXNlwUsr6P5l4MCBfPvtt8TFxQHwww8/cMEFF3Dw4EGbKxM5eZGRkUaop1bQJVCpQRcRkUpl/PjxjBgxwji+/fbbeeWVVwgJ0V+J5UkNuv8544wzmD17NjVq1ABg4cKF9O7dm3379tlcmcjJ8/x+yczMVAiiBCS9GhERkUpj3Lhx3HnnncbxqFGjeP7553E4HDZWVTl4r2YlJibaWIl469SpE3PmzCEhIQGA5cuX06tXL/7880+bKxM5OZ4tNHl5eRw4cMDeYkROghp0ERGpFP773/8yevRo4/jRRx/l6aefVnNeQTwr6AkJCURERNhcjXhr27YtP/zwg/HGya+//sp5552nlXQJSAqKk0CnBl1ERILeyy+/zD333GMcP/XUUzzyyCNqziuIy+UyGnQFxPmn008/nXnz5lG3bl0AVq5cqT3pEpB0qzUJdGrQRUQkqE2cOJFbb73VOB47diz333+/jRVVPvv27SM/Px/Q/nN/1rx5c77//nvq1KkDwLJly+jbt69xG0KRQKAVdAl0atBFRCRovffee9x4443G8QMPPMBDDz1kY0WVk/cqrCdhWfxTixYtmD17trEn/aeffmLAgAG6T7oEDO/fMZoAkUCkBl1ERILSZ599xnXXXWek+N5111088cQTGmu3QWFhofE4PDzcxkrkn2jVqhWzZs2ievXqAMydO5eLLrqIvLw8ewsT+Qe8f8d4/+4RCRRq0EVEJOgsXryYq6++GqfTCcDIkSN59tln1ZzbxPtFclhYmI2VyD/Vrl07vv32W2JjYwH49ttvGTFihG5bJX7P+3eMGnQJRGrQRUQkqOzcubPEat+QIUN48cUX1ZzbSA16YOrcuTMzZ86kSpUqAEyaNIn//e9/NlclcnxaQZdApwZdRESCxrFjx7jooouM5N6ePXvy2muvERKiv+7spAY9cJ155plMmjTJOL7rrrv49ttvbaxI5Pi0gi6BTq9YREQkKLhcLm688UaWLl0KQMOGDfnss890z20/UFBQYDzWHvTAM3jwYB588EEAnE4ngwcPZsOGDTZXJWLOu0H3/t0jEijUoIuISFB49tlnef/99wGIiYkhIyODWrVq2VyVgFbQg8HYsWMZOHAgAAcOHCA9PV0J2eKXNOIugU4NuoiIBLwZM2YwatQo4/idd96hTZs2NlYk3hQsFvhCQkJ49913adWqFQC///47V155JUVFRTZXJlKSd96IJyhUJJCoQRcRkYD222+/ccUVVxhN4KOPPsrFF19sc1XizXvVXA1d4IqNjSUjI8O4z/SMGTOM0XcRf6EtNRLo1KCLiEjA2r9/P+np6eTk5ABwySWX8PDDD9tclZSm0Kbg0aRJEz799FNCQ0MBGDdunLG1RMQfaEuNBDo16CIiEpAKCwsZPHgwGzduBKBt27a8/fbbSmz3Q96rWAptCnznnHMOL7zwgnF8ww03GOGMInbzbtC1gi6BSK9iREQkIN13333MmjULgFq1ajFlyhRiYmJsrkrMaAU9+IwcOZKbbroJgLy8PAYNGsSuXbtsrkpEK+gS+NSgi4hIwHn77bd5/vnnAfcLsM8//5yGDRvaXJVYUYMefBwOBy+++CI9evQAYNeuXVx00UXk5eXZXJlUdt5TOmrQJRCpQRcRkYCyZcsWbrnlFuP45ZdfNpoE8U9q0INTREQEn332GQ0aNABgyZIljB071uaqpLLTCroEOjXoIiISMJxOJ0OHDuXIkSMADBs2zBizFf8VHR1tPD506JCNlUhZq127Nl999ZWx13fcuHEsW7bM5qqkMvP+HVOlShUbKxE5OWrQRUQkYLz66qvMnTsXgIYNG/Lcc8/ZW5D8I7Vq1TLuTZyZmWlzNVLW2rdvb9w9oaioiCFDhmjUXWzj/TsmOTnZxkpETo4adBERCQhbtmzhvvvuM44nTpxIbGysjRXJPxUWFkadOnUAFCQWpEaPHk379u0BWLNmjUbdxTbev2OSkpJsrETk5KhBFxERv1d6tP3mm2+md+/eNlclJ8LzQnnPnj04nU6bq5GyFh4ezuTJkzXqLrbzXkFXgy6BSA26iIj4vdKj7f/3f/9nb0FywjyjpoWFhWRlZdlcjZSHNm3aaNRdbOdp0ENCQqhdu7bN1YicODXoIiLi1zTaHhy8V7I05h68So+6P/744zZXJJWN5/dLnTp1CA0NtbkakROnBl1ERPyWRtuDh3eDrqC44FV61P2ZZ57RqLtUmKKiIvbs2QMoIE4Clxp0ERHxWxptDx7eL5a1gh7cNOoudtm7d6+RcaH95xKo1KCLiIhf0mh7cNEKeuWiUXexg26xJsFADbqIiPid0qPtw4cP12h7gNMe9MpFo+5iB91iTYKBGnQREfE7EydOLDHa/p///MfeguSUea9maQW9cig96j506FCKiopsrkqCmVbQJRioQRcREb9y5MgRxowZYxxrtD041KlTB4fDAahBr0y8R91XrVrFO++8Y3NFEsx0D3QJBmrQRUTEr4wfP57du3cDcMkll2i0PUiEh4dTq1YtQCPulUl4eDgvvPCCcfzII4+Qm5trY0USzDTiLsEgzO4CREREPLKzs41x9pCQEJ544gmbK5KylJSUxN69e9m9ezdOp5OQEK0TVAY9evSgb9++zJgxgz/++IMJEyZw1113/e3XFRYWsnfvXnbt2kVmZia7du1iz5495ObmUlhYWOKfgoICCgsLCQ0NJSwszPgnPDzceFyzZk2Sk5NJSkoy/h0TE1MB/wWkomjEXYKBGnQREfEbTz/9NDk5OQAMHTqU0047zeaKpCw1atSIlStXUlBQwObNm2nWrJndJUkFefrpp5k5cyYul4snn3yS/v37c/DgwRLNd2ZmZonH3rfMKi9xcXElGnbvx8nJySQnJ9OoUSMj7E7822+//QZAZGQktWvXtrkakZOjBl1ERPzCH3/8wUsvvQRAVFQUjzzyiM0VSVnr0KEDU6ZMAWD58uVq0CsBl8vFzp072bJlC61bt2bVqlXs27ePlJQUu0sDICcnh5ycHNatW2d5TmRkJG3atKFjx4506tSJjh070qpVKzXtfubgwYNs2LABgLZt2xIWpjZHApP+5IqIiF949NFHycvLA+C2226jXr16NlckZa1jx47G4+XLlzN48GAbq5Gy5mnGly9fzrJly1i+fDnLly9n7969J/Q8oaGhJCYm+qxmJyUlkZiYSExMjOkIe2hoKC6Xq8TIu+dxfn4+WVlZJVbovf/tuaWjmby8PJYuXcrSpUuNj3k37Z5/WrdurabdRr/88ovx2Pt3jUigUYMuIiK2W7t2LZMnTwagWrVqjB492t6CpFx4v2jWPbEDX25uLnPnzmXBggUn1Yy3atWKwYMH+zThCQkJhIaGlmPlvg4dOmTavP/xxx+sWLGC9evXlzjfrGmPiIigbdu2dOzYkc6dO3PBBRdQt27dCv05KrPly5cbj9WgSyBTgy4iIrZ76KGHjL2mo0aNombNmjZXJOUhMTGR5ORkdu3axc8//4zL5TJuvSaB4c8//2TGjBlkZGTwzTffHHflGaBmzZolVpkbNmzIOeecw+HDh1m3bh1XXnklTZs2raDqrcXGxhIbG0uLFi1MP3/w4EF++eUX442I5cuX+zTt+fn5RtP+6quvAu5GMT09nfT0dNq2bas/7+VIDboEC4fL5XLZXYSIlXr16rFz507q1q3Ljh077C5HRMrBokWL6NatG+BO+d64cSPR0dE2VyXlJT09nalTpwKwYcMG7UP3cy6Xi3Xr1pGRkcHUqVP56aefLIPbSjfjnTp1omHDhj5N6aOPPspjjz0GwBVXXMEHH3xQ7j9HecjJyTGads9If+mm3Vv9+vVJT09nwIABnH322URGRlZgtcGvRYsWbNiwgcjISA4dOqTtBhKw1KCLX1ODLhLcXC4XvXr14ocffgDg1Vdf5eabb7a5KilPjz32GI8++igAH330kfah+6HCwkJ++uknMjIyyMjIMIK3SqtVqxb9+/enT58+pKWlmTbjZnJycmjatClZWVmAe+9wu3btyvJHsI2naZ87dy4ZGRn8/PPPpudVrVqVPn36kJ6eTt++fYmPj6/gSoPLwYMHqV69OgCdO3dmyZIl9hYkcgrUoItfU4MuEty+/vprLrzwQgCaN2/OmjVrtOoR5KZPn07//v0BuPfee4373ou98vLymDZtGlOmTGH69Ons27fP9LyWLVsaI9tdunQ56b3iL7zwAnfccQcAffr0YebMmSdbul/7448/mDZtGhkZGXz//ffk5+f7nBMSEkL37t1JT0/nsssuo379+jZUGtjmzp1Lr169ABgxYgQTJkywuSKRk6cGXfyaGnSR4OVyuejQoQMrVqwA4OOPP+ayyy6ztygpd7t37yYpKQmAXr168f3339tcUeW2bds2XnvtNd58803+/PNPn8+HhobSo0cPBgwYwIABA2jevHmZfN+8vDxSUlLYtm0bAHPmzOHss88uk+f2V4cOHeLbb79l6tSpTJs2jezsbJ9zQkJCGDBgACNHjuTcc88lJCTEhkoDz3//+1/uueceAN58801uuOEGmysSOXlq0MWvqUEXCV6zZ8/m3HPPBdz3x166dKlejFYSdevWZdeuXVSrVo39+/crOKuCOZ1Ovv32WyZMmMD06dN99pTHxsZy4YUXkp6ezoUXXlhuoY3vvPMO1113HQD9+/c3sgkqg6KiIhYuXGhsIzC7D3uzZs0YMWIEQ4YMUXDm37jyyiv58MMPgeDaMiGVkxp08Wtq0EWC1yWXXMIXX3wBwKeffsqll15qc0VSURQUZ4/s7GwmT57MK6+8wqZNm0p8LiwsjEsuuYTrr7+eXr16ERERUe71FBUV0aRJE7Zv347D4WDz5s00atSo3L+vP1q/fj0ffvghr7/+Ort27SrxuaioKK644gpuueUWpZNbSElJYf369QqIk6CgpQoREalwO3bsYMqUKYA7uX3gwIE2VyQVybvJ8L41kpSPpUuXcv3111OvXj3uueeeEs15vXr1ePzxx/njjz/46KOPuOCCCyqkOQf3+LwnFNLlcvHaa69VyPf1Ry1atOCRRx5h69atfP755/Tu3dv4XG5uLpMmTaJTp0506dKFt99+m2PHjtlYrX/Jyckx0vPbtGmj5lwCnhp0ERGpcG+88QZFRUUA3HTTTXpBVcmoQS9/x44dY9KkSXTu3Jm0tDQmT55Mbm6u8fnzzjuPL7/8ki1btvDQQw+RmJhoS5033HCD8f//m2++SV5eni11+Ivw8HAuvvhivvvuO3777Tduv/12qlWrZnx+yZIlDBkyhHr16nHvvff6TEJURr/88ovxWBMGEgzUoIuISIUqKCjg9ddfB9wraDfeeKPNFUlF834RvWjRIhsrCT65ubk899xz1K9fn6FDh7Js2TLjc9WrV+fOO+9k3bp1fPvttwwaNIiwsDAbq4U6deoY21uysrL47LPPbK3Hn5x22mmMHz+enTt38sYbb5TYV71v3z6effZZmjdvzuWXX87GjRvtK9Rm3r9DOnXqZGMlImVDDbqIiFSoL7/8kt27dwMwaNAg6tata3NFUtGSkpKMfec//fST5S295J8rKipi0qRJtGjRgrvvvrtEQnj79u1588032blzJ8899xwtWrSwsVJfI0aMMB7r9li+YmJiGDZsGD///DMLFy7kmmuuMbYhuFwuPv74Y1q2bMmIESPIzMy0udqKN336dONxjx49bKxEpGyoQRcRkQrl/QJ85MiRNlYidkpPTwfcjWWw3gO7IrhcLr766ivatGnD0KFD+eOPPwBwOBxcfvnlLFq0iOXLl3PDDTcQHR1tc7XmunfvTuvWrQH3GzaeWy9KSQ6Hg65du/LOO++wY8cOnnnmGWrVqgVAYWEhr776Kk2bNuWBBx7gwIED9hZbQbKysliwYAHgnjjwtzefRE6GGnQREakwa9as4YcffgDcqbu9evWyuSKxi6dBByrV7bXK0rx58zjzzDO56KKLWLt2rfHxvn378ssvv/Dhhx/SpUsXv7+NncPhKPFm3SuvvGJjNYGhVq1ajBo1ik2bNvHoo49StWpVwJ098PTTT9OkSROeffbZoA+TmzFjhnGbQO/fKSKBTA26iIhUGO8X3iNHjvT7xkHKz5lnnkmNGjUAmDlzJvn5+TZXFDhWrlxJ3759Oeuss1i4cKHx8W7duvHDDz8wffp02rZta2OFJ+7qq682msz33nuPgwcP2lxRYIiNjeWRRx5h8+bN3H777cbo+/79+7n33ntp0aIFEydOpLCw0OZKy4f3m3tq0CVYqEEXEZEKcejQId555x0AoqOjufbaa22uSOwUFhZG3759AfdtkubNm2dzRf5v8+bNXH311bRv377EtoDTTz+dr776igULFtCzZ08bKzx5sbGxxu+Eo0ePGr8r5J+pVasW48ePZ926dVx77bXGm587duxg2LBhpKam8sUXX+ByuWyutOzk5eXx9ddfA5CQkEDXrl1trkikbKhBFxGRCvH+++9z6NAhAK666iqqV69ub0FiO+8Vr4yMDBsr8W/79+/ntttu47TTTuP99983mqz69eszadIkfv31VwYOHBjwEymlw+KCqZmsKI0aNeLtt99m5cqVDBgwwPj477//ziWXXEK3bt2MPduBbu7cuRw+fBiAfv36ERoaanNFImVDDbqIiJQ7l8ulcDjxccEFFxj3wM7IyFBDZmLq1Km0atWKl156iYKCAgDi4+N57rnnWL9+PUOGDAmaxqR169bGBMDvv//O3Llz7S0ogKWmppKRkcGPP/7ImWeeaXx88eLF9OjRgzvuuIOjR4/aWOGp835TT+PtEkzUoIuISLn76aefWLVqFeDeJ+t9P1+pvKpVq8bZZ58NwLZt21i9erW9BfmR/fv3c+2115Kenm7cOismJoaHH36YTZs2ceeddxIVFWVzlWXP+8073XLt1HXv3p0ff/yRqVOnGkn5LpeLF154gTZt2vDjjz/aXOHJcblcRoMeERHB+eefb3NFImVHDbqIiJS7Tz/91Hg8fPhwGysRf6Mxd1+eVfN3333X+Fjfvn35/fffGTt2LNWqVbOxuvJ10UUXUbt2bQCmTZsW8Ku8/sDhcNC/f39WrFjBf//7X+ONnU2bNnHWWWcF5Gr6ihUr2LFjBwC9e/c2AgZFgoEadBERKVfeKx1hYWEMHDjQ5orEn3jvk63sDfr+/fu57rrrSqyaV6tWjUmTJjFt2jTq1atnc4XlLyIiwvgdkZuby+zZs22uKHiEhoZy1113sWLFCs444wwgcFfTNd4uwUwNuoiIlKu1a9eyZcsWAM4666ygXv2TE9ewYUPatGkDwJIlS4zGtLKZNm0arVq1KpFe3rdvX9asWcOQIUMCPgDuROhNm/KVkpLCvHnzAno13fv2av3797exEpGypwZdRETKlVY65O94/7mYPn26jZVUPM+q+YABA0xXzevWrWtzhRWvd+/eVKlSBXA3Yk6n0+aKgo/3anq3bt2AwFlN37FjB8uXLwegQ4cOlWKyRCoXNegiIlKuvBt075UxEQ/vBv3LL7+0sZKKZbZqfuGFF7J69epKt2ruLTo6mvPOOw+APXv2sHTpUpsrCl4pKSn8+OOPPPvsswGzmj5lyhTjsd70lWCkBl1ERMrN7t27Wbx4MeC+7U/jxo1trkj8UceOHalfvz4AX3/9Ndu3b7e5ovJ17Ngxhg4d6rNq/tZbbzF9+nStCKLwwIoUGhrK3Xffbbqa3rZtW37++WebKyzmcrl4/fXXjeNBgwbZV4xIOVGDLiIi5Wb69OnGva210iFWQkJCuPHGGwFwOp0lXoAHmx07dtCjRw8mTZpkfMyzan799ddX2lXz0vr372/8t1CDXjHMVtM3btxI9+7d+fjjj22uzm3hwoX8+uuvAHTp0oW2bdvaXJFI2VODLiIi5Ub7z+WfGjZsGGFhYQC88cYb5Ofn21xR2Vu4cCGdOnUy9s/GxMRo1dxCnTp16NKlCwCrV682gialfHmvpqelpQHuiY/LL7+cBx980PY8gAkTJhiPR44caWMlIuVHDbqIiJSLY8eOMWvWLAASExPp1KmTzRWJP0tKSuLiiy8GYO/evXzxxRc2V1S2Jk+ezNlnn82ePXsAaNy4MQsXLtSq+XF4Z1Z4p3ZL+fMkvQ8ZMsT42FNPPcVFF11ETk6OLTXt3buXTz/9FICaNWty2WWX2VKHSHlTgy4iIuVi9uzZHDt2DHCPq4aE6K8cOT7vFTHvlbJAVlhYyF133cX1119vTAWcffbZLFmyhNTUVJur82/ah26vyMhI3nrrLZ5//nnj93dGRgZnnHEGmzZtqvB63nrrLeP/oRtuuMEYwxcJNnq1JCIi5ULj7XKievbsyemnnw7Ajz/+yKpVq2yu6NTs37+ffv368fzzzxsfGzlyJN9++y0JCQk2VhYYWrVqZQRL/vDDDxw4cMDegiohh8PBHXfcwcyZM6levToAa9asIS0tje+//77C6igqKuLVV181aho+fHiFfW+RiqYGXUREypzT6TRGUqtUqULv3r1trkgCgcPhCJpV9N9++40uXbrw7bffAhAWFsarr77Kyy+/THh4uM3VBQaHw2G8uVdYWMjXX39tc0WV1/nnn8+SJUs47bTTANi3bx/nn38+L730khEEWp5mzJjBtm3bAHeoYpMmTcr9e4rYRQ26iIiUuWXLlrF7924AzjvvPKKjo22uSALFNddcQ0xMDADvvvuubftdT8WMGTPo2rUrGzZsACAhIYHZs2dz880321xZ4NGYu/9o3rw5ixYtom/fvoB7Vfu2227j5ptvLvdQR4XDSWWiBl1ERMrcd999Zzz2DnoS+TtxcXFcc801ABw5coR3333X5or+OZfLxX/+8x/69+9vvLHQpk0bli5dSs+ePW2uLjD16NGDatWqAe7fKxWxWivWqlWrRkZGBvfdd5/xsTfeeIPevXuzd+/ecvmemzZtMqYnGjVqRJ8+fcrl+4j4CzXoIiJS5pYtW2Y87t69u42VSCAaMWKE8XjChAkB0ZQVFhZy/fXXM2rUKKPeiy++mAULFtCoUSN7iwtg4eHhxu3W/vzzT3bs2GFzRRIaGsq4ceN49913iYyMBGD+/Pl07tyZ33//vcy/n2fvOcDw4cMJDQ0t8+8h4k/UoIuISJnz3Oe5atWqtGjRwuZqJNC0adPGeGNn7dq1zJs3z+aKji8/P5/LL7+ct99+2/jYo48+yqeffkrVqlVtrCw4dOzY0Xjs+d0i9rv66qv58ccfSU5OBmD79u2cddZZZRrueOzYMd566y0AIiIiGDp0aJk9t4i/UoMuIiJlKisri+3btwPQvn173V5NTkqghMXl5uZy6aWX8vnnnwPuJuKTTz7hkUce0Z/9MqIG3X917tyZpUuX0r59e8B9r/Kzzz6bn3/+uUye/5NPPmHfvn0AXHbZZdSqVatMnlfEn+lvDhERKVPeL6C9X1iLnIiLL76Y2rVrA/DFF1+wefNmmyvydfToUQYOHGjcsSAqKoopU6bwr3/9y+bKgosadP+WnJzM7Nmzja0I+/bt45xzzmHRokWn9LxOp9PnFoUilYEadBERKVNq0KUsREZGGnvRCwsLGTNmjM0VlXT48GH69etn3EYtOjqa6dOnK8CqHDRs2JD4+HjA/fslEDIJKpsaNWrw7bffGltTDh48yHnnncePP/540s/50UcfsXLlSgA6depE165dy6RWEX+nBl1ERMqUd0CcGnQ5FXfddZfRmH3wwQfGi3W7HT58mD59+jB37lwAYmNj+fbbbznnnHPsLSxIORwO43fJ3r17FRTnp+Li4vj666+N/w8OHz7MBRdcYPx/ciLy8/N5+OGHjeOnnnoKh8NRVqWK+DU16CIiUqY8K+gxMTEKiJNTEhcXx4MPPgi4b2H2wAMP2FyRe6x9wIABLFiwAIDq1avz3XffceaZZ9pcWXDTmHtgiImJYdq0aVx44YWAO+Stf//+/PTTTyf0PG+88YaxraV3796cd955ZV6riL9Sgy4iImXGOyCuQ4cOuh2OnLIRI0ZQv359AGbMmGFrontubi4XXXSRsSJYvXp1vv/+e9LS0myrqbJQgx44qlSpwpdffkm/fv0AOHLkCBdeeGGJ6arjOXz4MI8//rhx/PTTT5dLnSL+Sg26iIiUGe0/l7IWFRXF2LFjjePRo0fbsgc5Pz+fyy67zNhz7hlr96RXS/lSgx5YIiMj+eyzz4yV75ycHM4///x/tE1l/Pjx7NmzB4BLL72Uzp07l2utIv5GDbqIiJQZNehSHq655hpOP/10ABYuXEhGRkaFfv/CwkKuvPJKI609JiaGmTNnqnGoQAqKCzxRUVF89dVXnHXWWQDs37+fc889l7Vr11p+TVZWFv/5z38ACA0N5YknnqiQWkX8iRp0EREpM2rQpTyEhoby1FNPGccPPPAARUVFFfK9XS4XQ4cONe5zHhUVxdSpU7XnvIKVDorbuXOnzRXJPxEdHc3UqVPp1q0b4G7Ae/fuzZYtW0zPf/rppzl06BAAQ4cOJSUlpcJqFfEXatBFRKTMKCBOykt6ejpnnHEGAGvXruXdd9+tkO87btw443tFRETw5Zdf0qtXrwr53lKSxtwDU2xsLDNnzjSu3+7duxk4cCCHDx8ucd727dt56aWXAPcbYY888kiF1yriD9Sgi4hImcjKymLbtm0AtG/fXgFxUqYcDgfPPPOMcTxmzBhyc3PL9XtOnTq1RHL8Bx98oPuc28i7Qf+ngWPiH6pVq8Y333xjvHG7atUqrr32WpxOp3HOo48+Sn5+PgC33347devWtaVWEbupQRcRkTKxadMm43FqaqqNlUiw6tGjB3379gXgjz/+YMKECeX2vdasWcOVV15p7HUeO3Ysl1xySbl9P/l7bdq0MR5v3LjRxkrkZMTHx5ORkUG1atUA+PLLL3nssccA91TM22+/DbjvjjBq1Cjb6hSxmxp0EREpE7t27TIea+VDysvTTz+Nw+EA4KmnnuLgwYNl/j327dtXYgT3X//6Fw899FCZfx85McnJycbjzMxMGyuRk5WSksKHH35ISIi7BRk7diyffvopDz74oLGaPnr0aGrUqGFnmSK2UoMuIiJlwvsFs/cLaZGy1KZNG6666ioAsrOzefLJJ8v0+QsLC7nsssuMiZB27doxadIk400BsU9MTAxxcXFAyTcEJbBceOGFRlI7wLXXXstXX30FuP/uuO2222yqTMQ/qEEXEZEy4d2gJyUl2ViJBLuxY8cSEREBwH//+18WL15cZs999913M3v2bABq1arFlClTiImJKbPnl1Pj+d2iFfTAdtddd3HttdcClMiSePTRR4mOjrarLBG/oAZdRETKhPeKllbQpTw1btzYSHh2Op1cf/31ZRIY9+abb/K///0PgPDwcL744gsaNGhwys8rZcfzu+Xw4cPG7bgk8DgcDl577TXq1KljfCwuLo5rrrnGxqpE/IMadBERKRNaQZeKdN9999GpUycAfvvtNx599NFTer758+czcuRI43jChAl07979lJ5Typ737xatoge2xYsXs2fPHuM4JyeHf//730Ywo0hlpQZdRETKhGcFPSwsjPj4eJurkWAXFhbGpEmTjFH3//u//zvpUfft27dzySWXUFBQAMBtt93GsGHDyqxWKTtq0IPDkSNHGDp0qHEcFhYGwBtvvFGud2cQCQRq0MXv/PknjBsHF14Ie/e6P3bwIGzebG9dInJ8nhfLSUlJRkKvSHlq3br1KY+6HzlyhIEDB7L3r79wevfuzXPPPVfmtUrZ8N4+o6C4wDV69Gg2//XCrnv37rz11lvG526//Xa+//57u0oTsZ1eQYnfyMmB66+H5GR44AH4+mv4azGDw4ehWTN30751q61lioiJgoICo8HReLtUpFMddR8+fDgrVqwAoGnTpnz88cfGap74H62gB765c+fy0ksvAVClShUmTZrENddcY9z7vKioiH/961/88ccfdpYpYhs16OIXsrOhWzd4910oLIS/boVZgssFs2ZBp06wdm3F1ygi1rz3ESogTirSqYy6f/HFF7z33nsAVK1alSlTpmh7hp/TCnpgKz3a/vTTT9OsWTMAnnzySfr16wfAvn37uOmmm7QfXSolNehiu6IiGDAA1q1zP/67cw8cgPPOg337KqQ8EfkHvF8oawVdKtrJjLpnZWUxYsQI4/iVV16hVatW5VqnnDqtoAe20aNHs2XLFgB69OhR4p7noaGhvPvuu8abMF9//TWTJk2ypU4RO6lBF9t98w0sXPj3zblHURHs3g1vvFG+dYnIP6cEd7HbiY6633bbbca2jPT0dK666qryLlHKgBr0wFV6tP2tt97yySupUaMGr7/+unF85513atRdKh016GK7l16C0NAT+xqn0/11/7SpF5Hy5f1CWSPuYocTGXX/4osv+OijjwB3Q/Dqq6/icDgqrFY5ebGxsVStWhXQiHsgOXz4sOVoe2n9+vXjuuuuA9y3XtOou1Q2atDFVvv2ucPgTqbR3rEDfvqp7GsSkROnEXfxB6VH3YcMGcKxY8dKnFN6tP3FF1/Un9kA47leWkEPHMcbbTfz/PPPa9RdKi2HS29JiY3WroXjb/mrB+wE6gI7fD5bo8YIoqOnlk9xIvKPHTx4kMOHDwPwxBNP8OCDD9pckVRWhYWFdOvWjWXLlgFwzTXX8Pbbbxsr5FdccYWxej5w4EC+/PJLrZ4HmK5duxrTEU6nU9fPz3344YdceeWVgHu0/ddff7VcPfc2ffp0+vfvD0BcXByrV6+mfv365VqriD9Qgy62+vsGvSXwO3Aa8JvJ5wcDn5RDZSJyslq1asXq1avtLkMqsbVr19K5c2eOHj0KuMfd77nnHr744gsuueQSwD3avmbNGq2eB6Du3buzYMECwP2GTOiJ7pOTCrNs2TJ69OhhhDZOmDChxATL3xkyZAhvv/02AH369GHGjBl6Q0aCnm70KbZKTASHw30LNXOPA/8H3Gv62YSEAiIj65ZTdSLyTx04cIAjR44A7hdUInY6/fTTeeedd7j00ksBd4BcvXr1uP32241zNNoeuLzvU68G3X9lZmYyaNAgozm/4YYbGD58+Ak9x/PPP8+sWbPYtWuXMeruvZddJBhpBV1s17cvfPvtie9Dr1cPtm498YA5ESl7w4YNY+LEiQCsXr1at6sSv/Doo4/y2GOPAe6mrrCwENBoe6A799xzmT17NuAOH4uJibG5IiktLy+Ps88+m0WLFgFw5plnMnv2bCIjI0/4uTTqLpWNQuLEdrfeeuLNeUiI++vUnIv4h4KCAuOx9+qWiJ3GjBnDxRdfDGA050ptD3zh4eHGY+/fPeIfXC4Xw4cPN5rz+vXr8/nnn59Ucw5KdZfKRw262O6CC6Bbt3/ebIeGukfjb7yxfOsSkX/O0/yAGnTxHyEhIfz3v/8t8WeyXr16JCQk2FiVnKrSI+7iX8aPH8/kyZMBdyjclClTqFOnzik9p1LdpTJRgy62Cw2FqVMhJeXvm/TQUKhRA2bNgpo1K6Y+Efl73i+SvVe3ROz21FNPlfjzuWrVKkaNGmVjRXKq1KD7r2+++YZ77rnHOJ48eTLt27c/5eetUaMGr7/+unF89913s3///lN+XhF/pAZd/EJ8PCxcCNdcA2Fh7hF2b6Gh7jC5886DpUvh9NPtqVNEzGncUPzR77//bmQjxMTEGGFizz33nJEMLYHHe3uC0+m0sRLxtn79ei6//HLjmjz00ENcdtllZfb8/fr14+qrrwbcwaTjxo0rs+cW8Sdq0MVvxMXBpEmwaxc8/TT06QNpaXDuuXDPPbBxI8ycCY0a2V2piJSmFS3xRw899JDRLNx///28/PLLxuduuukmY4+sBBZN7PifgwcPkp6ezoEDBwB3EKMnoLEsPfnkk8Ze9hdeeIGdO3eW+fcQsZsadPE7tWrBffe5m/HFi93j7M88A02a2F2ZiFjxfpGsBl38wZIlS/j8888BSExM5I477uDmm29m5MiRAOTn53PRRRfpBX4A8g6GU4Nuv6KiIq644grWrVsHQKtWrXj33XcJKT0OWQYaNGjALbfcAkBubm65vAkgYjc16CIicsq0gi7+xOVyMXr0aON4zJgxxq24xo8fz9lnnw3A7t27GThwIIcOHbKjTDlJCqX0Hy6Xi/vuu4+ZM2cCULNmTTIyMoiNjS237/nAAw8QFxcHwFtvvWW8MSASLNSgi4jIKfN+kazbHondZs2axZw5cwBo2rQpw4YNMz4XHh7Op59+SqO/9kstX76cAQMGcPToUTtKlZOgBt1/jB07lueeew6A0NBQPv30U5qU88hjfHw89957L+BevX/ooYfK9fuJVDQ16CIicsq0gi7+wul0llg9f+KJJ3zGoBMSEpg6dSo1atQA4IcffmDQoEHk5uZWaK1yctSg+4dx48bx6KOPGscTJkzgnHPOqZDvfccddxi3bvvss89YunRphXxfkYqgBl1ERE6ZGnTxF59++im//PILAO3bt7dMkW7dujXffvutMSo7a9YsLr30UvLz8yusVjk53r9jQv/u/qxSLl544YUSb4Q9//zz3HTTTRX2/atWrcrDDz9sHN9///0V9r1FypsadBEROWWe/b0AOTk5NlYilVlBQUGJcdenn376uEFVnTp1YubMmcaf3+nTp3PFFVfoTSY/5/kdU6VKlRK3XJOK8dprr3HHHXcYx08//XSJ44py4403GuP0s2fPZtasWRVeg0h5UIMuIiKnLCkpyXicmZlpYyVSmb355pts3LgRgLPPPpvzzz//b7/mjDPOYNq0aURFRQHwxRdfcM0116hJ92Oe3zHJyck2V1L5TJo0ieHDhxvHY8aMKbGSXpEiIiJ4/P/bu/M4G+v//+PPMzPGWLMmuyxJljC2lDUVMiOKyAcTisjHp00SokhalI8MWiTSYvtxUHZSthhFiyWUfd8Hwyzn98d85/qcyVjGnHPe1znncb/durnOdc6Yl2ou53ler+v9fuMN6/GAAQOsbRUBf0ZABwBkmXtAP3jwoMFKEKzOnz+v119/3Xr81ltv3XB3tXHjxpo7d67Cw8MlSV9//bU6duzIuLsNXbx4UadOnZKU/roD75s4caK6detmPe7fv3+6e9BN6NChg6pXry5J2rRpk2bOnGm0HsATCOgAgCxz72TRQYcJY8aM0eHDhyVJbdu2Vd26dTP19Q8++KBmzZplhfSZM2fqscce06VLlzxeK25e2n9jiYDuS2PGjEnXOe/Xr1+mPgTzlpCQEI0cOdJ6/Oqrr7KTCPweAR0AkGV00GHS2bNn9fbbb0tKfcM+fPjwm/p9WrVqpblz51rj7vPmzVPr1q3Zgs1G3K8vjLj7xqhRo9LdY96/f3+9//77xsN5moceekiNGjWSJO3cuVOff/654YqArCGgAwCyjHvQYdLUqVN15swZSVLXrl1VqVKlm/69mjdvrgULFihnzpySpEWLFqlVq1aKj4/3SK3IGvfrCx1073K5XBo2bFi6e8xfe+01W3TO3TkcjnRd9LFjx8rlchmsCMgaAjoAIMsiIiKsPaUJ6PAll8ul2NhY67EnVpNu2rSpFi9erDx58kiSVqxYoQcffFDHjh3L8u+NrCGg+0ZycrJefPHFdPeYjxw5UkOHDrVVOE9zzz33WLe1bNmyRWvXrjVcEXDzCOgAAI9Ie7N88OBBuhfwmVWrVumPP/6QJN13332qVq2aR37fe++9V0uXLlW+fPkkSWvXrlXt2rW1efNmj/z+uDmMuHvfmTNnFBUVpdGjR1vn3n//fWOrtd+o3r17W8fjxo0zWAmQNQR0AIBHpL1ZTkhIsMaNAW9z7567v0H3hDp16mjFihXWh0979uxR/fr1NWvWLI9+H9w4OujetWPHDtWtW1ffffedJCk0NPSKfc/tqn379ipQoIAkacaMGTp69KjhioCbQ0AHAHgEC8XB1w4dOqTZs2dLkm699Va1bdvW49+jevXq2rhxo+rUqSNJunDhgh577DENHTqUPZcNoIPuPYsXL1bdunW1fft2SVKBAgW0ePFiPf3004YruzERERHq3r27JCkxMVGffvqp4YqAm0NABwB4BFutwdc++eQTJSUlSZJ69Oih7Nmze+X7FCtWTN9//706d+5snRs2bJjatWvH4nE+lnZtyZ49u3X7AbLG5XLp/fffV4sWLXT69GlJUpUqVbRhwwY1bdrUbHGZ1LNnT+se+QkTJig5OdlwRUDmEdABAB5BBx2+lJSUpIkTJ0pK3VrN212+iIgIff7553rnnXesADB79mzde++9+vvvv736vfE/adeWokWL2nKxMn9z6dIldevWTc8//7w1EdK6dWutWbNGZcuWNVxd5pUrV07NmzeXJO3du1fffvut4YqAzCOgAwA8gg46fGnevHk6cOCApNT9y0uXLu317+lwOPTiiy9q/vz5yps3r6TUFaNr166tVatWef37B7tLly7p5MmTkhhv94RDhw6pcePGmjx5snVu0KBBmj17trWDgT9yX4vCfY0KwF8Q0AEAHkEHHb7kzcXhrqdly5Zav369KlSoIEk6fvy47r//fn300Uc+rSPYHD582Dpmgbis2bhxo2rXrq1169ZJknLkyKFvvvlGb7zxhkJC/DsetGjRwvrAbuHChdq1a5fhioDM8e+fQACAbZQpU8Y63rp1q7lCEPC2b9+upUuXSkodaX3ggQd8XsOdd96p9evX68EHH5SUOnLfs2dP9enTR4mJiT6vJxikbacnpb/eIHO++uorNWjQwJpAKVmypFavXq327dsbrswzQkND1atXL+vxhAkTDFYDZB4BHQDgEcWLF1fhwoUlSXFxceyFDq8ZP368dfzMM88Y6/jlz59fCxYs0PPPP2+di42NVZMmTbRz504jNQWyuLg46zgyMtJgJf4pPj5ezz77rJ544gklJCRIku69915t2LBBNWrUMFydZ3Xr1k3h4eGSpEmTJunixYuGKwJuHAEdAOARDofDetN84sQJ7dmzx3BFCETnz5+37pmNiIhQTEyM0XrCwsL03nvv6bPPPrMCwerVq1WtWjWNGTOGrdg8aOPGjdYxAT1zVq5cqWrVqmncuHHWue7du2vZsmUqUqSIwcq849Zbb1W7du0kSSdPntT06dMNVwTcOAI6AMBj3N80u3e7AE+ZNWuWzpw5I0nq0KGDChYsaLiiVDExMfr++++tla8vXryo//znP2rUqBHddA9Ju6bkyZNH5cuXN1yNf4iPj1ffvn3VpEkT/fXXX5JS7zcfN26cPv74Y69tTWgH7mtTsD4E/AkBHQDgMbVq1bKOCejwhjlz5ljHPXr0MFdIBurVq6ctW7bo2Weftc79+OOPdNM94OjRo9q/f7+k1A8C/X0hM19I65p/+OGH1rkGDRpoy5Yt6t27d8BvU3fPPfeoUqVKkqS1a9fqyJEjhisCbgxXNwCAx9BBhzclJCRo0aJFklJHWOvVq2e4oivlypVLY8eO1cqVK+mmexD3n9+4q3XNP/jgA61cuTJopg8cDoceeeQRSZLL5dKCBQvMFgTcIAI6AMBjSpQowUJx8Jrly5frwoULklL3Pg8NDTVc0dU1atSIbroHEdBvzLW65v369Qu6yYPo6Gjr2Ol0GqwEuHHB9VMKAPCqfy4Ut3fvXsMVIZC4v8F2f+NtV3TTPYcF4q6NrnnG6tSpo1tvvVWStHjxYlZzh18goAMAPIoxd3iDy+XSvHnzJEnZs2dXs2bNDFd049K66X379rXOpXXTP/jgAyUlJRmszj+wQNzV0TW/upCQELVq1UpS6odjy5YtM1wRcH3B+xMLAPAKAjq8YdOmTTp48KAkqVmzZsqVK5fhijInV65c+u9//3tFN/25555TtWrVNGfOHG4JuQr3BeJq1qwZ1IHT3e+//67WrVvTNb8O92mbtA/5ADvjCgcA8Cj3gO4+lgpkhb+Nt19NRt30rVu3qk2bNqpfv76+//57g9XZE/efp7dnzx7FxMSoatWq6X4u6JpnrFmzZoqIiJCUGtBZ/wF2x08vAMCjSpYsyUJx8Dj3IJI2suqv0rrpP/74o+655x7r/Lp169S4cWO1bNlSv/zyi7kCbcY9oLtv5Rhsjh8/rueff1533HGHPv/8c+vaWqxYMX300Ud0za8iV65c1i0xhw4dYrILtkdABwB4FAvFwdP27t1rBdbatWurWLFiZgvykHvvvVerV6/W3LlzVblyZev8d999pxo1aqhTp07avXu3wQrtIdgXiIuPj9cbb7yhsmXL6v3339fly5clSfny5dOoUaO0c+dOPfXUU3TNr4HV3OFP+EkGAHic+5vodevWGawEgcD9vlF/Hm/PiMPhUHR0tDZv3qzPPvtMJUuWtJ778ssvdeedd6pv3746cuSIwSrNSUlJ0fr16yUF3wJxly9f1ocffqhy5cppyJAhOnfunKTU+8wHDBig3bt3q3///sqRI4fhSu3PfeqGgA67I6ADADyucePG1vH8+fPNFYKAECj3n19LaGioYmJitGPHDo0ePVoFCxaUJCUmJqYLaWfPnjVcqW9t2rRJhw8flpR6j3UwdIlTUlI0bdo068OZo0ePSkr9f+Tpp5/Wzp07NXLkSOXPn99wpf6jaNGiqlOnjiRpy5Yt2rNnj+GKgKsL/KscAMDnGjZsqLx580qSvv32W7aRwk07e/asVqxYIUkqXbq0qlatargi74qIiNBzzz2n3bt3a/DgwdZq9efPn7fGnIcNG2ataB/oAnl64p8uXryoyZMnq0aNGvrXv/5lrcwuSe3atdMff/yhiRMnBswtHr7Gau7wFwR0AIDHhYeHq3nz5pKkkydPas2aNYYrgr9avHixEhMTJUlRUVFyOByGK/KNvHnz6vXXX9euXbv07LPPKlu2bJJS13UYOnSoSpUqpXbt2mnFihUBvRBjIC0OeDU7d+7Uiy++qOLFi+vJJ5/Uli1brOfuv/9+bdiwQdOnT9cdd9xhsEr/FxUVZR0z5g47I6ADALyCRXngCT/++KN13LJlS4OVmFGkSBGNHTtW27Zt0xNPPGGNeCcnJ2vmzJlq2rSpKleurLFjx+rMmTOGq/Us98UBa9WqpeLFi5styIOSk5PldDrVvHlzVahQQe+9955OnTplPV+7dm0tWbJES5cuDeqV6z2patWqKlGihCRp7dq1bLcG2yKgAwC8okWLFgoNDZUkzZ07N6C7fPAe9y2R0u4hDUZly5bVtGnT9Ndff2nQoEEqUqSI9dzWrVv173//W8WLF1fPnj21efNmg5V6TiCOtx85ckRvvvmmypYtq9atW2vRokXWc+Hh4ercubPWrl2r9evXW1uDwTMcDod1DYmPj9eOHTsMVwRkjIAOAPCKAgUKqEGDBpJSRzi3b99uuCL4m+TkZG3atEmSVKZMGWvhtGBWqlQpvfHGG9q7d6++/vprNWzY0Hru/Pnz+uijj1S9enXdd999+vLLL3Xp0iWD1WZNoCwO6HK59OOPP+qJJ55QyZIl9eqrr6bbfrJMmTIaNWqU9u/frylTpqhevXpBcyuHr7nvMOK+fR9gJwR0AIDXMOaOrNi+fbsuXLggKTj3v76W8PBwPf744/r+++/166+/qnfv3sqdO7f1/OrVq9WpUyeVLFlSAwYMUFxcnF9NsbgvDliqVClVq1bNcEWZt2/fPo0dO1Z33323GjRooK+++spaT8HhcKhly5aaP3++du7cqf79+6tw4cKGKw587tcR9+kcwE4I6AAAryGgIyvc30AT0K+uSpUqGjdunA4cOKBx48apcuXK1nPHjh3TqFGjVKtWLZUsWVLPPPOMvvvuOyUkJBis+PoWLVpkhdno6Gi/6Ci7XC5t2rRJQ4cOVc2aNVWqVCn9+9//1q+//mq9pmDBgurfv7927typBQsW6OGHH7ZuBYL3EdDhDxwuf/o4FQDgd+666y5t3bpVDodDR44coUuEG/af//xHY8aMkZQa2B588EHDFfkHl8ulH374QbGxsZo1a1aG2xzmypVLDz30kKKjo9WyZUvb/Vx27txZX3zxhSR7/7dPSEjQihUr5HQ6NW/ePB04cCDD191zzz165pln1K5dO0VERPi4SrgrXbq09u7dq9y5c+vMmTPWwouAXRDQAQBeNWDAAI0aNUqSNHnyZHXt2tVwRfAXDRo0sFZxP378OPeg34QjR45ozpw5cjqdWrZsWYb3pDscDtWvX1/R0dGKjo5WxYoVjXask5KSVKRIEZ08eVJ58uTRsWPHlD17dmP1/NOxY8f07bffyul0atGiRTp//nyGr4uMjFR0dLQeeeQRvxzRD1SPPvqoZs+eLSl1gcU777zTcEVAegR0AIBXrVmzRvfee68kqW3btpo1a5bhiuAPkpOTdcstt+j8+fMqXbq0/v77b9Ml+b34+HgtWbJETqdTCxYs0LFjxzJ8XYUKFRQVFaUGDRooMjJSJUqU8GlgX7VqlRo1aiRJateunaZPn+6z752RM2fOaNOmTVq/fr0WLFigNWvWZLhFV/bs2XX//fcrKipKrVq1srb0gr28+eabevXVVyVJX3zxhTp16mS4IiC9MNMFAAACW926dVW4cGEdO3ZMixYtUkJCAiOeuK4dO3ZYnUnuP/eM3Llzq02bNmrTpo2Sk5O1fv16OZ1OOZ1Obd261Xrdn3/+qdGjR2v06NGSpMKFCysyMlKRkZGqVauW10O7ydXb08L4xo0bFRcXp7i4OO3cufOqry9cuLAefvhhRUdH64EHHki3UB/s6Z/3oRPQYTd00AEAXtetWzd99tlnklLffEdFRRmuCHY3depUdenSRZI0YsQIDRw40HBFgW3nzp2aN2+enE6nfvjhByUnJ1/z9e6hPS24eyK0u1wuVahQQbt27VJoaKiOHj2qAgUKZOn3vJrTp09r06ZNVhC/XhhPU6lSJet2gLp167LIm585fvy4teZCgwYNtGrVKsMVAekR0AEAXjdv3jyrExYVFcWK7rguFogz5+TJk1qxYkW64HrixInrfl3hwoVVrVo1FS9eXEWLFlWxYsXS/Vq0aFHlyJHjmr/HsmXL1KxZM0lS06ZNtWzZspv6MyQlJenIkSM6dOiQDh06pIMHD6b7ddu2bTcUxiMiInT33XdbH0Q0bNhQ5cuXv6maYB8sFAc7I6ADALwuKSlJZcuW1b59++RwOLR7926VKVPGdFmwMRaIsw+Xy6W9e/emG/u+0dD+T/ny5cswvBcoUEDZsmXTBx98oJ9++kmSNHDgQDVt2lQOh0NJSUlKTExUUlJSuuNz585lGMKPHj2a6X3f3cN42ih/pUqVlC1btkz/OWFvLBQHOyOgAwB8YsSIERo0aJCk1JXdR44cabgi2BULxNlfWmiPi4tLF9xvJrSbkBbG04I4YTy4sFAc7IyADgDwicOHD6tUqVJKTExUoUKFtH//flttnQT72LFjhypWrCiJlf/9icvl0unTpzMcKf/nuYsXL3qlhtDQUN12220Zjte7nytcuDD3jgexRYsWqXnz5pKkF154Qe+++67hioD/YRV3AIBP3HbbbXr00Uf19ddf6/jx45o5cyZdC2Ro79691vEdd9xhsBJkhsPhUP78+ZU/f37dddddV32dy+XS2bNn0wX2kydPavDgwTp79qxCQkL0wgsvKGfOnEpKSlJKSoqyZcumsLAwhYWFpTvOkSNHuvBdqFAhgjeuy/264n69AeyAgA4A8JnevXvr66+/liTFxsYS0JGhgwcPWsfFihUzWAm8weFw6JZbbtEtt9yiSpUqSZJmzJihs2fPSpIeeeQRvf322yZLRIArWrSodex+vQHsgCULAQA+c99996lKlSqSpDVr1uiXX34xWxBs6dChQ9ax+xtpBK7Y2FjruHfv3gYrQTCIiIhQ/vz5JaW/3gB2QEAHAPiMw+FI9+Z7/PjxBquBXbl3tAjoge+PP/7QypUrJUkVK1ZU06ZNzRaEoJB2bTl48GCmV/wHvImADgDwqX/961/KnTu3pNTVc8+cOWO4ItiNe0eLEffA5/5B3TPPPCOHw2GwGgSLtGtLQkICfw/BVgjoAACfypMnj7p06SJJunDhgqZMmWK4ItgNHfTgER8fr88//1ySlDNnTnXt2tVwRQgW7tcWxtxhJwR0AIDPPfPMM9ZxbGws44VIJ+3Ncv78+RUREWG4GnjTtGnTdO7cOUlSp06dlC9fPrMFIWi4T+ewUBzshIAOAPC5KlWqqGHDhpKkbdu2WfefAi6Xy3qzTPc8sLlcLhaHgzF00GFXBHQAgBHub8bfffddg5XATs6cOaOEhARJ3H8e6JYuXaotW7ZIku655x5Vr17dbEEIKu7XFwI67ISADgAwok2bNipZsqQk6dtvv9UPP/xguCLYAVusBYeUlBS98sor1uN+/foZrAbBiL3QYVcEdACAEeHh4Ro6dKj1+OWXX+ZedLBAXJCYOXOm4uLiJEnVq1dXu3btDFeEYMOIO+yKgA4AMKZLly6qVKmSJGnt2rWaN2+e4YpgGlusBb7ExEQNGjTIejxy5EiFhPCWFL5FBx12xdUQAGBMWFiY3nzzTevxwIEDlZycbLAimEYHPfBNmjRJf/75pySpcePGeuihhwxXhGCUM2dO3XLLLZLooMNeCOgAAKNat26tevXqSZJ+//13ffHFF4YrgkmHDx+2jgnogefChQsaNmyY9XjkyJFyOBwGK0IwS7vGuF93ANMI6AAAoxwOh9566y3r8ZAhQ6xVvBF83P/b58qVy2Al8Ib//ve/VreyTZs21odzgAlp1xj+zoGdENABAMY1atRILVq0kCTt3btXEyZMMFwRTElKSrKOs2XLZrASeNrJkyetD+NCQkI0YsQIwxUh2KVdY5KSklikFLZBQAcA2IL7vegjRozQ2bNnDVYDUxITE63jsLAwg5XA09566y2dOXNGkhQTE2MtEAmY4n6NYf0T2AUBHQBgC9WrV9cTTzwhSTp+/LjeffddwxXBBPcOOgE9cOzfv19jx46VJGXPnj3dFouAKe7XGPdrD2ASAR0AYBtvvPGG9YZp9OjROnLkiOGK4GuMuAemYcOGWff59u3bVyVLljRcEUBAhz0R0AEAtlG2bFn17NlTknT+/HkNHz7ccEXwNTrogWfbtm2aNGmSJClv3rwaMGCA4YqAVAR02BEBHQBgK4MHD7ZW1p04caL++OMPwxXBl9zfJIeGhhqsBJ7gcrn04osvKiUlRZL08ssvq2DBgoarAlK5B3T39S8AkwjoAABbKVKkiF544QVJqW+YYmJi6GwEEfc9sVlV2f9NnTpVCxYskJS653S/fv0MVwT8T9oHR1LqzgKAHfB/IgDAdgYMGKCKFStKkjZs2KD33nvPcEXwFff7zvlgxr8dPHgwXSAfP348e9vDVljzAnZEQAcA2E6OHDk0efJkq6MxZMgQRt2DBPeEBgaXy6WePXvq9OnTkqROnTqpdevWZosC/oE1L2BHBHQAgC3Vq1fPGnW/fPkyo+5BgntCA8PUqVM1f/58Sam3rYwZM8ZwRcCVCOiwIwI6AMC2hg0blm7Unb3RAx8ddP/3z9H2iRMnsjAcbImADjsioAMAbOufo+6vvfaafv/9d8NVwZu4B92/MdoOf5J2jQkJCWGRONgG/ycCAGztn6PuTz75JMEtgLl3sS5fvmywEtwMRtvhT9Juo2FLR9gJAR0AYHuvv/667rzzTkmMuge6/PnzW8cnTpwwWAkyi9F2+Jvjx49LkgoUKGC4EuB/COgAANuLiIjQZ599xqh7EChatKh1fPDgQYOVIDMYbYe/SUlJ0aFDhySlv+4AphHQAQB+gVXdg0OxYsWs47Q3z7A/Rtvhb06cOGH9HUJAh50Q0AEAfsN91H3jxo16++23DVcET6OD7n8OHDjAaDv8jvv1xf2DQcA0AjoAwG/8c9R9yJAhWrp0qeGq4EnuAZ0Ouv0lJCTo0UcfZbQdfsf9+kIHHXZCQAcA+JV69epp4MCBkqTk5GS1b99eO3fuNFwVPOW2226zjgno9uZyufT0009r/fr1kqRSpUox2g6/QUCHXRHQAQB+Z9iwYWrVqpUk6dSpU4qOjtbZs2cNVwVPCA8PV6FChSQx4m53o0eP1tSpUyVJOXPm1Ny5cxlth99gxB12RUAHAPidkJAQTZs2TZUqVZIkbd26VZ06dVJycrLhyuAJaW+WDx06JJfLZbgaZGThwoXq37+/9Xjy5MmqXr26uYKATKKDDrsioAMA/FLevHnldDqtfbPnz5+vwYMHG64KnpD2ZjkxMZG90G1o+/bt6tChg1JSUiSlrgXRrl07w1UBmUMHHXZFQAcA+K3y5ctr+vTpCg0NlSSNHDlSX331leGqkFVstWZfp0+fVnR0tM6cOSNJatOmjV577TXDVQGZ535tKVKkiMFKgPQI6AAAv9asWTONHj3aetytWzdt3LjRYEXIKlZyt6fk5GR16NBBO3bskCRVrVpVU6ZMsXZVAPxJ2rWlUKFCCg8PN1wN8D9cUQEAfq9v377q3r27pNRtnx555BGCnR9z76CzUJx9vPzyy1q0aJEkqWDBgpo7d65y585tuCog81wul/V3BOPtsBsCOgDA7zkcDo0bN07169eXJB04cEBt27bVpUuXDFeGm0EH3X6mTJmi9957T5IUFhammTNn6vbbbzdcFXBzTp48qcuXL0tigTjYDwEdABAQsmfPrtmzZ6tEiRKSpHXr1qlXr16sAu6H3Dta+/btM1gJpNSfpaeeesp6PHbsWDVu3NhcQUAW7d+/3zomoMNuCOgAgIBRpEgRzZ07Vzly5JCUuvXTO++8Y7gqZNYdd9xhHW/evNlgJfj777/Vpk0bq9vYq1cv9erVy3BVQNb88ssv1nHadp2AXRDQAQABpWbNmvrss8+sxy+//LJiY2MNVoTMKlCggDU+/csvv7C/vSH79+/X/fffr8OHD0uSGjVqpDFjxhiuCsi6uLg46zgyMtJgJcCVCOgAgIDz+OOP6/XXX7ce9+nTR5MmTTJYETIr7U3zhQsXtG3bNsPVBJ/Dhw/r/vvv1+7duyVJFStW1IwZM1jtGgHBPaDXrFnTYCXAlQjoAICANGjQIA0YMMB63KNHD3355ZcGK0JmuHe13N9Mw/uOHz+uZs2aWduplStXTsuWLVPhwoUNVwZkXVJSkn7++WdJUtmyZZU/f37DFQHpEdABAAHJ4XDozTffVL9+/SSlbqvTpUsXzZo1y3BluBEEdDNOnTqlBx54QL///rskqVSpUlq2bJmKFy9uuDLAM7Zt26aLFy9KYrwd9kRABwAELIfDoffff99a1Co5OVkdOnSQ0+k0XBmux33sdOPGjQYrCR6nT59W8+bNrQW0ihUrpuXLl6t06dJmCwM8iPvPYXcEdABAQEvbIz0mJkZS6njjo48+qhkzZpgtDNdUsGBBlSlTRhILxfnCiRMndP/99+unn36SlLojwvLly1WuXDnDlQGeRUCH3RHQAQABLyQkRJ988ok6deokKTWkd+jQQV988YXhynAttWrVksRCcd525MgRNWnSRJs2bZIkFS5cWEuXLlXFihUNVwZ4HgEddkdABwAEhdDQUH3++efq1q2bJCklJUVdunTRp59+argyXA33oXvfgQMH1LhxY/3666+SpKJFi+r7779XlSpVDFcGeB4LxMEfENABAEEjNDRUH3/8sZ555hlJqQvH9ejRQ+PGjTNcGTJCQPeuvXv3qlGjRtZ0QsmSJbVq1SpVqlTJcGWAd7BAHPwBAR0AEFRCQkI0btw4Pffcc9a5Z599VsOHD5fL5TJYGf7JfaE4Arpn/fbbb2rYsKF27dolSbr99tu1atUqlS9f3nBlgPcw3g5/QEAHAAQdh8Oh9957TwMHDrTODR48WB07dtSFCxcMVgZ37gvF/fzzzywU5yFz587VPffcoz179kiS7rjjDq1atcr6dw0EKgI6/AEBHQAQlBwOh0aMGKG33nrLOvfNN9+oQYMG2rdvn8HK4C7tTTQLxWWdy+XSiBEj9Mgjjyg+Pl5S6pTC999/rxIlShiuDvA+94DuPqED2AkBHQAQ1F5++WXNmTNHuXPnliRt2rRJtWvX1po1awxXBkmqXbu2dfz9998brMS/XbhwQR07dtSgQYOscx06dNAPP/yg2267zWBlgG/Ex8dr48aNklIXiCtQoIDhioCMEdABAEGvdevWWrt2rW6//XZJqdtONW7cWJMmTTJcGR566CHreN68eQYr8V/79u3Tfffdp2+++UZS6vTIm2++qS+//FI5c+Y0XB3gG4sXL9bly5clpb+uAHZDQAcAQFKVKlW0YcMGNWnSRJKUmJio7t2767nnnlNSUpLh6oLX3XffrZIlS0qSli9frnPnzhmuyL+sWbNGtWrVsraWyp07t+bMmaNXXnlFDofDcHWA7zidTus4OjraYCXAtRHQAQD4PwULFtSiRYv07LPPWuc++OADtWzZUidPnjRYWfByOBzWm+nLly9r8eLFhivyH5MmTVLjxo119OhRSaljvevWrSOcIOgkJydr/vz5klI/pEr7IBawIwI6AABusmXLprFjx2rixIkKCwuTJC1ZskR169bV1q1bDVcXnNwDpXsXDBlLSkrSf/7zH3Xv3l2JiYmSpKZNm+qnn35S5cqVDVcH+N7atWt14sQJSanj7dmzZzdcEXB1BHQAADLw9NNPa/ny5SpcuLAkaefOnapbt64WLFhguLLg06hRI+XJk0eStGDBArZbu4aTJ0+qRYsWGjNmjHWub9++WrhwoQoWLGiwMsAc9/UrmCCB3RHQAQC4igYNGmjDhg26++67JUnnzp1TVFSUXn75ZSUkJBiuLnhkz57dWtTpxIkTWrt2reGK7GnlypWqVauWli5dKil1GuSjjz7Sf//7X2XLls1wdYA5aZM3ISEhatmypeFqgGsjoAMAcA2lS5fW6tWr9dhjj0lK3Uv67bffVs2aNbV+/XrD1QUPxtyvLj4+Xs8++6yaNGmiv/76S5JUuHBhLV++XE899ZTh6gCzduzYoW3btkmS6tevr0KFChmuCLg2AjoAANeRK1cuTZ8+XaNGjVJ4eLgkaevWrapfvz7ddB9p2bKlQkJS37YQ0P9n5cqVqlatmsaNG2edS5v8uO+++wxWBtgD4+3wNwR0AABugMPhUP/+/RUXF6datWpJklJSUuim+0jBggWtwLl9+3Zt377dcEVmZdQ1z5Ejhz744AOtXLlSpUuXNlwhYA9srwZ/Q0AHACATqlSporVr1+rNN9+km+5j7m+u3btiweZqXfMtW7aoX79+1qQBEOxOnDihH3/8UZJ0xx13qGLFioYrAq6PKzgAAJkUFhamV155hW66jwX7fejX65qXL1/ecIWAvXz77bdKSUmRRPcc/oOADgDATaKb7lsVKlSwOmCrV6+29jUOBnTNgcxzn7SJiooyWAlw47iaAwCQBe7d9MjISEn/66bXqFGDLcE8LK0LlpKSopkzZxquxvvOnj1L1xy4CWfPntW3334rSSpQoIDq169vuCLgxhDQAQDwgCpVqmjdunUaMWKEtef0tm3bVL9+fbVr1y7oFzXzlA4dOljHsbGxcrlcBqvxnkuXLumDDz5QuXLl6JoDN2Hq1Kk6f/68JKldu3YKCwszXBFwYxyuQP2bDQAAQ3777TfFxMQoLi7OOhcaGqpu3brptddeU/HixQ1W5//q1atn3ee/evXqgOqMJScna9q0aRoyZIj27Nljnc+RI4dGjhypvn37EsyB63C5XKpSpYr++OMPSdLmzZtVrVo1w1UBN4YrPAAAHpbWTf/www916623SkoNXh9//LHKly+vAQMG6NSpU4ar9F+9e/e2jmNjYw1W4jkul0vz5s1T9erV1bVr13ThvEOHDvrtt9/omgM3aNWqVVY4v++++wjn8Ct00AEA8KL4+Hi9//77euedd3Tu3DnrfL58+TRgwAD17dtXOXPmNFih/0lISFDx4sV18uRJhYeHa9++fdYHIf7oxx9/1IABA7R69ep05x966CGNHDlSNWrUMFQZ4J8ef/xxTZ8+XZL05ZdfqmPHjoYrAm4cH8MCAOBFuXPn1uDBg7V7924999xz1mrvp0+f1oABA1ShQgV99NFHSkpKMlyp/4iIiFD37t0lSZcvX9akSZMMV3Rzfv31V0VFRalBgwbpwnmdOnW0fPlyLVy4kHAOZNKhQ4c0e/ZsSdKtt96qtm3bGq4IyBwCOgAAPlCoUCGNHj1af/75p2JiYqxR5YMHD6pnz56qXLmyZs6cGbCLnnlaz5495XA4JEkTJkxQcnKy4Ypu3J49e9S1a1fdfffdmj9/vnX+zjvv1OzZs7Vu3To1adLEYIWA//rkk0+sDzx79Oih7NmzG64IyBxG3AEAMOD333/Xq6++qrlz56Y7X7NmTfXr10/t27dXRESEoer8Q8uWLfXdd99JSt3vuFWrVoYrurZffvlFsbGx+vzzz3X58mXrfIkSJTRs2DB16dKFlaaBLEhKSlKZMmV04MABhYSEaPfu3SpdurTpsoBMoYMOAIABlStX1pw5c7RmzRo1bNjQOr9p0yZ17dpVJUqUUP/+/bV7926DVdqbPywWd+nSJU2bNk333nuvatSooY8//tgK5/nz59c777yjHTt2qFu3boRzIIvmzZunAwcOSJJatWpFOIdfooMOAIBhLpdLCxcu1KBBg7Rp06Z0zzkcDjVv3lx9+vRR8+bNFRoaaqhK+0lOTla5cuW0Z88eORwO/fnnnypXrpzpsiRJf//9tyZOnKhPPvlEx48fT/dcnjx51LdvX7300kvKly+fmQKBAPTAAw9o6dKlkqSFCxfqoYceMlwRkHkEdAAAbMLlcmnt2rWKjY3VjBkz0o1BS1KZMmXUq1cvdevWTYULFzZUpb2MGjVKAwYMkCS99NJLevvtt43VkpKSokWLFik2NlYLFiy4Yj2BKlWqqE+fPurUqZPy5MljqEogMG3fvl133nmnJKlcuXLasWMH2xLCLxHQAQCwoaNHj2rSpEmaMGFCuj2xJSk8PFzt27dX7969Va9ePWuxtGB07NgxlShRQpcvX1aBAgW0f/9+5ciRw6c1HD9+XJ999pkmTJhwxS0J2bJl02OPPabevXvr3nvvDer/VoA3Pffcc/rggw8kSe+++65eeOEFswUBN4mADgCAjSUnJ+u7777TuHHjtHDhwiuer169urp166bWrVurVKlSBio0r3Pnzvriiy8kSZMnT1bXrl29/j0TEhK0YsUKff311/rmm2906dKldM+XLFlSPXv2VPfu3XXbbbd5vR4gmJ0/f17FixfXmTNnFBERof3796tgwYKmywJuCgEdAAA/sWvXLk2YMEGTJk3SyZMnr3j+7rvvVnR0tKKjo1WzZs2gGe9cu3at6tevL0mqWLGifvvtN68suHbs2DEtWLBATqdTixcv1vnz5694zYMPPqjevXvr4YcfZtE3wEfcb3Xp2rWrJk+ebLYgIAsI6AAA+JmLFy9q+vTpio2N1U8//ZTha4oVK6aoqChFR0eradOmAb1lm8vlUqNGjfTDDz9Ikj7++GP16NHDI7/v9u3b5XQ65XQ6tWbNmgz3qc+fP7+efPJJ9erVSxUqVMjy9wVw406dOqWyZcvq9OnTCgkJ0ZYtW1S5cmXTZQE3jYAOAIAf+/XXXzV37lw5nU5t2LAhw9fkzJlTDz30kKKiovTwww/r1ltv9XGV3rdmzRrde++9kqTixYvrzz//vKl70ZOSkrR69Wo5nU7NmzdPf/75Z4avK1y4sFq1aqXo6Gg9+OCDypkzZ5bqB3BzBgwYoFGjRkmSYmJi9NlnnxmuCMgaAjoAAAHi4MGD1gj20qVLlZCQcMVrHA6H7rnnHrVo0UJ16tRRZGRkwNyr2bp1azmdTkmpI6/9+/e/7tckJibq999/V1xcnFauXKkFCxbo1KlTGb72rrvusm4hqFOnDlveAYYdOHBA5cuXV0JCgsLDw/Xnn38G7VocCBwEdAAAAtD58+e1dOlSOZ1OzZ8/X0ePHr3qa0uXLq3IyEhFRkaqVq1afhvaf/vtN1WrVk0ul0v58uXT7t27lT9/fut59zC+ceNGxcXFacuWLVcs8JYmNDRUDRo0UHR0tKKiolS+fHlf/VEA3ICePXvqo48+kpS6ivvo0aMNVwRkHQEdAIAAl5KSop9++sm6l/r333+/7te4h/a0fwoVKuSDarMmJiZGn3/+uSTpySefVP369RUXF3fdMJ4mb968atGihaKjo9WiRYt0AR+AfWzfvl2VK1dWcnKy8ubNq127dvnFNQq4HgI6AABBZvfu3VqzZo0VXH/++WfFx8df9+tKlSqlChUqqFixYipatOgVvxYtWtQn92InJyfr2LFjOnTokA4ePJju1127dmnp0qUZLuaWkTvuuMOaHKhVq5bq1aun8PBwL/8JAGRVu3btNHPmTEnSG2+8oUGDBhmuCPAMAjoAAEEuOTlZO3bssAJ7XFycNm3alOE2Ytdzyy23XBHaixYtqly5ciksLMz6J1u2bNZxSkqKkpKSlJiYqKSkJOs4MTFRx48fvyKEHzlyRMnJyZmuzT2MR0ZGqkaNGsqbN2+mfx8AZm3YsEF16tSRJBUpUkQ7d+5U7ty5DVcFeAYBHQAAXCGj0H6jnXbTHA6HpNRt0hwOh6ZMmaLo6GjCOBAgmjVrpmXLlkmSPvzwQ/Xp08dwRYDnENABAMANcblcOnPmTLpudkZj5gcPHtSFCxc8/v1DQkJUpEiRKzr0/xy1L1KkiEaOHKkhQ4ZIktq3b69vvvnG4/UA8L2lS5fqgQcekCSVLVtWW7du5bYUBBQCOgAA8CiXy6Vz585Zgf3w4cO6dOlSuhF291H20NDQq46/FyhQwArft9566w1vbRYfH69y5cpZq9dv3LhRkZGR3vxjA/CylJQU1alTR3FxcZKkadOm6YknnjBcFeBZBHQAABCQPvzwQ/Xt21dS6kjskiVLDFcEICumT5+uxx9/XJJUvXp1xcXFKSQkxHBVgGcR0AEAQEC6fPmy7rzzTv3111+SUt/ct2vXznBVAG7GmTNnVLVqVe3bt0+S9N1336l58+aGqwI8j4+cAABAQAoPD9eoUaOsx71797ZG3gH4l5deeskK5w888IAeeughwxUB3kEHHQAABCyXy6XHHntMs2fPlpS6d/L06dMNVwUgMxYvXmwF8ty5c+u3335T6dKlDVcFeAcBHQAABLQjR46ocuXKOnHihCRG3QF/8s/R9gkTJqhnz56GqwK8hxF3AAAQ0IoUKaJx48ZZjxl1B/yH+2j7/fffr6efftpwRYB30UEHAAAB75+j7o899phmzJhhuCoA18JoO4IRAR0AAAQFRt0B/8FoO4IVI+4AACAoMOoO+A9G2xGs6KADAICgwag7YH//HG3/9ddfVaZMGbNFAT5CQAcAAEGFUXfAvv452j5+/Hj16tXLcFWA7zDiDgAAggqj7oB9/XO0nfvOEWzooAMAgKDzz1H31q1ba/bs2QoJoXcBmLJgwQK1atVKEqPtCF78LQQAAIKOw+FQbGysChYsKEmaO3euhg8fbrgqIHht27ZNTzzxhPX4nXfeIZwjKNFBBwAAQevbb79Vq1atlPZ2aNasWWrbtq3hqoDgcurUKdWtW1d//vmnJKlt27aaOXOmHA6H4coA36ODDgAAglbLli311ltvWY87d+6szZs3G6wICC5JSUl6/PHHrXB+9913a8qUKYRzBC0COgAACGovvfSSOnXqJEm6cOGCWrdurWPHjhmuCggO/fv315IlSyRJhQoV0ty5c5UrVy7DVQHmMOIOAACC3sWLF9WwYUNt3LhRktSwYUMtWbJE4eHhhisDAtfkyZP15JNPSpLCwsK0bNkyNWzY0HBVgFl00AEAQNDLkSOH5syZo9tuu02StGrVKvXr189wVUDgWrt2bbot1MaNG0c4B0RABwAAkCQVL15cc+bMUfbs2SVJEyZM0Pjx4w1XBQSe/fv3q02bNrp8+bIkqU+fPnr66acNVwXYAyPuAAAAbqZMmaKuXbtKSh27XbJkiRo3bmy2KCBAXLx4UQ0aNFBcXJwkqUmTJlq0aJGyZctmuDLAHuigAwAAuOnSpYteeOEFSakrTD/22GP666+/DFcF+D+Xy6Xu3btb4fz222/XjBkzCOeAGwI6AADAP4waNUrNmzeXJJ04cULR0dE6d+6c4aoA/zZq1Ch99dVXkqTcuXPL6XSqYMGChqsC7IURdwAAgAycPn1adevW1Y4dOyRJzZs3T3ePOoAb99VXX6lTp05Kix5z5sxR69atDVcF2A8ddAAAgAzky5dPTqdTt9xyiyRp4cKFevzxx5WYmGi4MsC/zJo1S507d7bC+fDhwwnnwFUQ0AEAAK6iYsWKcjqdypEjhyRp7ty5+te//qWkpCTDlQH+Yf78+erQoYOSk5MlSb169dLAgQMNVwXYFyPuAAAA17F06VK1atVKly5dkiR17txZkydPVkgIvQ7gahYvXqyoqChrO7WYmBh9+umn/NwA18BPBwAAwHU0a9ZMs2fPtlabnjp1qnr16iX6HEDGVq5cqUceecQK5x07dtQnn3xCOAeug58QAACAG9CyZUt98803Cg0NlSR9/PHH6t27t1JSUgxXBtjL999/r1atWunixYuSpLZt22rKlCnWzw6AqyOgAwAA3KA2bdpo2rRpVhdwwoQJ6tGjh3V/LRDsFi9erBYtWuj8+fOSpIcfflhfffWVwsLCDFcG+AcCOgAAQCY8/vjjmjp1qtUN/Oyzz9SlSxcWjkPQmz9/vqKioqzO+cMPP6yZM2cqPDzccGWA/yCgAwAAZNITTzyhr7/+2uoKfvnll+rQoYN1vy0QbGbPnq22bdtaPwNt2rTR7NmzFRERYbgywL8Q0AEAAG7CY489ptmzZ1vdwVmzZunRRx9VQkKC4coA3/rqq6/Uvn17JSYmSpI6dOigb775hs45cBMI6AAAADcpKipKTqfT6hLOnz9fzZo109GjRw1XBnify+XS22+/rU6dOlnrMMTExOiLL76wdjwAkDnsgw4AAJBFK1euVKtWrayFsUqWLCmn06nq1aubLQzwkosXL+qpp57StGnTrHO9evXSuHHj2EoNyAJ+egAAALKocePGWrlypYoXLy5J2rdvn+rXr68ZM2YYrgzwvAMHDqhRo0bpwvnQoUMVGxtLOAeyiA46AACAhxw6dEht2rTR+vXrrXODBw/W0KFDCS4ICOvXr1ebNm106NAhSVKuXLk0ZcoUtW3b1nBlQGAgoAMAAHhQQkKCevbsqSlTpljnHnnkEU2dOlW5c+c2WBmQNVOmTNHTTz+tS5cuSZJKly4tp9OpatWqGa4MCBx8lAsAAOBBERERmjx5st577z2raz5nzhzVr19ff/31l+HqgMxLTk7WSy+9pK5du1rhvGHDhtqwYQPhHPAwOugAAABesnDhQnXo0EFnzpyRJBUsWFAzZ85U48aNzRYG3KDTp0+rY8eOWrhwoXWuV69eGjNmDNuoAV5ABx0AAMBLmjdvrvXr16tixYqSpBMnTuiBBx7Q+PHjDVcGXN/27dtVt25dK5yHhYUpNjZW48ePJ5wDXkIHHQAAwMsy6kL27NlTY8aMUfbs2Q1WBmTsu+++U8eOHdNNf8yYMUNNmjQxXBkQ2OigAwAAeFm+fPk0f/58vfjii9a5iRMnqlatWoqLizNYGZBefHy8+vTpo5YtW1rhvEqVKtqwYQPhHPABAjoAAIAPhIaG6p133tGUKVOsrvlvv/2munXravDgwdbiW4ApK1asUNWqVRUbG2ude+SRR7RmzRrdfvvtBisDggcBHQAAwIc6d+6s9evXq3r16pJSV8gePnw43XQYk9Y1b9q0qf7++29JUs6cOTVmzBjNmjVLefLkMVsgEEQI6AAAAD52991366efftLQoUMVFhYmiW46zJgxY4Zuv/32dF3zhg0basuWLfr3v/9tbRUIwDdYJA4AAMCgzZs3KyYmRr/88ot1rkqVKpo8ebIiIyPNFYaAFh8fr5dffjldMM+RI4dGjRqlPn36EMwBQ/jJAwAAMIhuOnwto3vNJalr167q27cv4RwwiA46AACATdBNhzdl1DWPiIhQYmKikpOTFRISonXr1ql27doGqwSCGx+PAQAA2MS1uukDBgywtr0CMsPlcsnpdF7RNW/YsKF+++03DR06VJKUkpKimJgYJSQkGKoUAB10AAAAG8qom16gQAENHDhQffr0UUREhLni4Dd++OEHDRgwQGvWrLHO5cyZU2+99ZZ1r3liYqLq1aunTZs2SZJeeeUVvfnmm6ZKBoIaAR0AAMCmEhMT9eabb2rEiBFKTEy0zpcsWVLDhg1T586drU474G7Lli0aOHCgFixYkO58o0aN9Omnn6pcuXLpzv/666+KjIxUYmIio+6AQYy4AwAA2FS2bNn02muvafv27ercubMcDockad++ferWrZuqVaumOXPmiH4L0vz111/q3Lmzqlevni6cV6pUSf/v//0/rVix4opwLklVq1bVkCFDJDHqDphEBx0AAMBPXK0rWq9ePb311ltq1KiRocpg2tGjRzV8+HBNmDAhw2mLLl26KDQ09Jq/B6PugHkEdAAAAD+T0X3FktS8eXONHDlS1atXN1MYfO7s2bMaPXq03nvvPcXHx1vnCxQooFdffVW9e/fO1HoFjLoDZjHiDgAA4GcaNGigH3/8UXPnzlXlypWt8wsXLlSNGjX0xBNPaNeuXQYrhLddunRJY8aMUbly5TRs2DArnOfMmVOvvvqqdu/ereeffz7Tiwky6g6YRQcdAADAjyUnJ+uLL77QkCFDtHfvXut8SEiIoqKi1Lt3bzVr1kwhIfRlAsGePXv00Ucf6ZNPPtHRo0et82FhYXr66ac1aNAgFS1aNEvfg1F3wBwCOgAAQABISEjQ+PHjNWLECJ04cSLdc+XLl9czzzyjmJgYFShQwFCFuFkpKSlasmSJYmNjNX/+fKWkpKR7vkOHDnrjjTdUvnx5j31PRt0BMwjoAAAAAeTs2bMaM2aMJkyYoIMHD6Z7LiIiQh07dlSfPn0UGRlpqELcqBMnTmjy5MkaP378FbcshIWFqU2bNhowYIBq1qzple8/fPhwDR48WJJ01113KS4uLtMj8wAyh4AOAAAQgBITE+V0OhUbG6vly5df8XydOnXUu3dvtW/fXjly5DBQIa5mw4YNio2N1ddff33F/d/FixdXz5491aNHjyyPsl8Po+6A7xHQAQAAAtzWrVs1YcIETZ48WWfPnk33XIECBdStWzf16tUrw/2x4RsXLlzQN998o9jYWG3cuPGK55s1a6bevXsrKipKYWFhPquLUXfAtwjoAAAAQeL8+fP68ssvNW7cOG3evPmK5xs3bqzWrVsrOjpaZcuWNVBhcImPj9eSJUvkdDo1d+5cnTp1Kt3zt9xyi5588kn16tVLFStWNFQlo+6ALxHQAQAAgozL5dK6desUGxur6dOn6/Lly1e8pnLlyoqOjlZUVJTq1q3LKvAecuDAAc2fP19Op1PLli3TpUuXrnhNjRo11KdPH3Xo0EG5cuUyUGV6jLoDvkNABwAACGLHjh3TpEmT9PHHH1917/Rbb71VrVq1UnR0tJo1a2aL0OgvXC6XNm/eLKfTKafTqbi4uAxflzNnTj366KPq06eP6tSpI4fD4eNKr41Rd8A3COgAAACQy+XS1q1brSC5bt06ZfQ2MSIiQs2aNVN0dLRatWrl9YXK/NGlS5e0cuVKzZs3T06nU/v27cvwdcWKFVN0dLSio6PVpEkT24+NM+oOeB8BHQAAAFc4cuSIFixYIKfTqSVLlujChQsZvq5GjRqqXbu2IiMjFRkZqapVqyo8PNzH1Zrjcrm0b98+xcXFaePGjYqLi9OaNWt07ty5DF9fo0YN69aBmjVr2q5Tfi2MugPeR0AHAADANV28eFHLly+X0+nUvHnzdOjQoau+Njw8XFWrVrUCeyCF9ozCeFxcnI4fP37Vr8mWLZuaNm1qTRyUKlXKhxV7HqPugHcR0AEAAHDDUlJSFBcXZ4X1LVu2ZDgK7y6j0F6hQgXlyZPHth3kxMRE7d+/X7/88ssNh/E0hQsXVvPmzRUdHa0HH3xQefPm9UHFvsOoO+A9BHQAAADctHPnzumXX36xAmxcXJy2bdt23dAupS6MVqxYMRUtWtT61f047ddbbrnFY0H+0qVLOnz4sA4ePKhDhw5Zv7ofHzx48IaCuCQVKlRIkZGRqlWrlvXhQ8mSJW37wYMnMOoOeA8BHQAAAB6VldCekRw5cljhPWfOnAoLC8vwn5SUFCUlJVn/JCYmWr8eO3ZMhw4d0okTJ276z1W4cOF0UwDBEMavhlF3wDsI6AAAAPC6+Ph4K7T//PPP2rdvn9WxPnPmjOnyLNmyZUvXvb/rrruCPoxfDaPugOcR0AEAAGDUhQsXrDHzf46au587derUTX+P7NmzX3OMPu24QIEChPAbxKg74HkEdAAAAPiFy5cv69KlS+nG2N1H2UNDQ9ONvGfLls06zpEjB8HbCxh1BzyLgA4AAADgpjHqDnhOiOkCAAAAAPivl19+WTVr1pQk/fHHH3r99dcNVwT4LzroAAAAALKEUXfAM+igAwAAAMiSqlWrasiQIZKklJQUxcTEKCEhwXBVgP8hoAMAAADIMkbdgaxjxB0AAACARzDqDmQNHXQAAAAAHsGoO5A1BHQAAAAAHsOoO3DzGHEHAAAA4FGMugM3hw46AAAAAI9i1B24OQR0AAAAAB7HqDuQeYy4AwAAAPAKRt2BzKGDDgAAAMArGHUHMoeADgAAAMBrGHUHbhwj7gAAAAC8ilF34MbQQQcAAADgVYy6AzeGgA4AAADA6xh1B66PEXcAAAAAPsGoO3BtdNABAAAA+ASj7sC1EdABAAAA+Ayj7sDVMeIOAAAAwKcYdQcyRgcdAAAAgE8x6g5kjIAOAAAAwOcYdQeuxIg7AAAAACMYdQfSo4MOAAAAwAhG3YH0COgAAAAAjGHUHfgfRtwBAAAAGMWoO5CKDjoAAAAAoxh1B1IR0AEAAAAYx6g7wIg7AAAAAJtg1B3Bjg46AAAAAFtg1B3BjoAOAAAAwDYYdUcwY8QdAAAAgK0w6o5gRQcdAAAAgK0w6o5gRUAHAAAAYDuMuiMYMeIOAAAAwJYYdUewoYMOAAAAwJYYdUewIaADAAAAsC1G3RFMGHEHAAAAYGuMuiNY0EEHAAAAYGuMuiNYENABAAAA2B6j7ggGjLgDAAAA8AuMuiPQ0UEHAAAA4BcYdUegI6ADAAAA8BuMuiOQMeIOAAAAwK8w6o5ARQcdAAAAgF9h1B2BioAOAAAAwO8w6o5AxIg7AAAAAL/EqDsCDR10AAAAAH6JUXcEGgI6AAAAAL/FqDsCCSPuAAAAAPwao+4IFHTQAQAAAPg1Rt0RKAjoAAAAAPweo+4IBIy4AwAAAAgIjLrD39FBBwAAABAQGHWHvyOgAwAAAAgYjLrDnzHiDgAAACCgMOoOf0UHHQAAAEBAYdQd/oqADgAAACDgMOoOf8SIOwAAAICAxKg7/A0ddAAAAAABiVF3+BsCOgAAAICAxag7/Akj7gAAAAACGqPu8Bd00AEAAAAENEbd4S8I6AAAAAACHqPu8AeMuAMAAAAICoy6w+7ooAMAAAAICoy6w+4I6AAAAACCBqPusDNG3AEAAAAEFUbdYVd00AEAAAAEFUbdYVcEdAAAAABBh1F32BEj7gAAAACCEqPusBs66AAAAACCEqPusBsCOgAAAICgxag77IQRdwAAAABBjVF32AUddAAAAABBjVF32AUBHQAAAEDQY9QddsCIOwAAAACIUXeYRwcdAAAAAMSoO8wjoAMAAADA/2HUHSYx4g4AAAAAbhh1hyl00AEAAADADaPuMIWADgAAAAD/wKg7TGDEHQAAAAAywKg7fI0OOgAAAABkgFF3+BoBHQAAAACuglF3+BIj7gAAAABwDYy6w1fooAMAAADANTDqDl8hoAMAAADAdTDqDl9gxB0AAAAAbgCj7vA2OugAAAAAcAMYdYe3EdABAAAA4AYx6g5vYsQdAAAAADKBUXd4Cx10AAAAAMgERt3hLQR0AAAAAMgkRt3hDYy4AwAAAMBNYNQdnkYHHQAAAABuAqPu8DQCOgAAAADcJEbd4UmMuAMAAABAFjDqDk+hgw4AAAAAWcCoOzyFgA4AAAAAWcSoOzyBEXcAAAAA8ABG3ZFVdNABAAAAwAMYdUdWEdABAAAAwEMYdUdWMOIOAAAAAB7EqDtuFh10AAAAAPAgRt1xswjoAAAAAOBhjLrjZjDiDgAAAABewKg7MosOOgAAAAB4AaPuyCwCOgAAAAB4CaPuyAxG3AEAAADAixh1x42igw4AAAAAXsSoO24UAR0AAAAAvIxRd9wIRtwBAAAAwAcYdcf10EEHAAAAAB9g1B3XQ0AHAAAAAB9h1B3Xwog7AAAAAPgQo+64GjroAAAAAOBDjLrjagjoAAAAAOBjjLojI4y4AwAAAIABjLrjn+igAwAAAIABjLrjnwjoAAAAAGAIo+5wx4g7AAAAABjEqDvSENABAAAAwLDhw4dr8ODBkqS77rpLcXFxOncuQj//LMXHS3nySDVqSIUKGS4UXkVABwAAAADDEhMTVa9ePW3atElSPd1113jt2FFdSUn/e022bNLjj0t9+0p16hgrFV5EQAcAAAAAG9iy5VfVqDFPKSkDJSVKynbFa8LCpKQk6fXXpUGDJIfD52XCi8JMFwAAAAAAkGbOrKqUlKr/9+jKcC7J6qgPGSKFhEivvuqb2uAbdNABAAAAwLCNG6WbWRdu82apWjXP1wMz2GYNAAAAAAwbNy51fD0zwsKk8eO9Uw/MoIMOAAAAAAadOiXddpt0+XLmvzYiQjp6NHWVd/g/OugAAAAAYNDvv99cOJekhARp61bP1gNzCOgAAAAAYFB8vNmvh30Q0AEAAADAoLx5zX497IN70AEAAADAoPh4qUgR6cKFzH9t3rzS4cNSjhyerwu+RwcdAAAAAAzKnVt68snMr+IeGir16EE4DyR00AEAAADAsD/+kKpUkTKTzkJCpG3bpAoVvFcXfIsOOgAAAAAYdtdd0tixmfuaCRMI54GGDjoAAAAA2MT48dKzz0oOh5ScfOXzoaGpv06cKHXv7tva4H0EdAAAAACwkd27UwP4xInSmTP/O58vn9Srl9Szp1SmjKnq4E0EdAAAAACwoYQEadcu6dw5KU8eqXx5KXt201XBmwjoAAAAAADYAIvEAQAAAABgAwR0AAAAAABsgIAOAAAAAIANENABAAAAALABAjoAAAAAADZAQAcAAAAAwAYI6AAAAAAA2AABHQAAAAAAGyCgAwAAAABgAwR0AAAAAABsgIAOAAAAAIANENABAAAAALABAjoAAAAAADZAQAcAAAAAwAYI6AAAAAAA2AABHQAAAAAAGyCgAwAAAABgAwR0AAAAAABsgIAOAAAAAIANENABAAAAALABAjoAAAAAADZAQAcAAAAAwAb+P2pg1/ga98w2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.plotting.simple_plot(net, ext_grid_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Yyy\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.51934</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  0.00000      inf       inf  1.52174  22.24258\n",
       "2  0.00000      inf       inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ydd\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.51934</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  0.00000      inf       inf  1.52174  22.24258\n",
       "2  0.00000      inf       inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ddd\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.51934</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  0.00000      inf       inf  1.52174  22.24258\n",
       "2  0.00000      inf       inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Yynd\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
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       "      <th>0</th>\n",
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       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.33940</td>\n",
       "      <td>0.10727</td>\n",
       "      <td>18.19496</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
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       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  3.33940  0.10727  18.19496  1.52174  22.24258\n",
       "2  0.00000      inf       inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ydyn\")  # Extra vector group\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
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       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
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       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.83645</td>\n",
       "      <td>0.00670</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  0.00000      inf       inf  1.52174  22.24258\n",
       "2  8.83645  0.00670   1.13718  0.11730   2.66335"
      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNynd\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
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       "      <td>88.22838</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.49934</td>\n",
       "      <td>0.21021</td>\n",
       "      <td>15.31723</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171\n",
       "1  3.49934  0.21021 15.31723  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
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     "metadata": {},
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   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdyn\")  # Extra vector group\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
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       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
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       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.04239</td>\n",
       "      <td>0.01219</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.79376  4.86639 85.86380 15.80517 158.05171\n",
       "1  0.00000      inf      inf  1.52174  22.24258\n",
       "2  9.04239  0.01219  0.98955  0.11730   2.66335"
      ]
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     "metadata": {},
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   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNyd\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ikss_ka</th>\n",
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       "      <th>rk_ohm</th>\n",
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       "      <td>15.80517</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
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       "      <td>22.24258</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171\n",
       "1  0.00000      inf      inf  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdy\")  # Extra vector group\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
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       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.79376  4.86639 85.86380 15.80517 158.05171\n",
       "1  0.00000      inf      inf  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdd\")\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.84354  3.76841 75.01993 15.80517 158.05171\n",
       "1  0.00000      inf      inf  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNyy\")  # Extra vector group\n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.51934</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  0.00000      inf       inf  1.52174  22.24258\n",
       "2  0.00000      inf       inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)  # warum dasselbe Ergebnis wie Yyy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single-bus fault"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "all-bus fault vs single-bus fault vs LU factorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ynynd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ynynd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.78326</td>\n",
       "      <td>5.12452</td>\n",
       "      <td>88.22838</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.49934</td>\n",
       "      <td>0.21021</td>\n",
       "      <td>15.31723</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171\n",
       "1  3.49934  0.21021 15.31723  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ynynd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1.78326</td>\n",
       "      <td>5.12452</td>\n",
       "      <td>88.22838</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# now with LU factorization instead of Y inversion\n",
    "net = single_3w_trafo_grid(\"Ynynd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=0, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.78326</td>\n",
       "      <td>5.12452</td>\n",
       "      <td>88.22838</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# now with LU factorization instead of Y inversion\n",
    "net = single_3w_trafo_grid(\"Ynynd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=1, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.49934</td>\n",
       "      <td>0.21021</td>\n",
       "      <td>15.31723</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm   xk_ohm\n",
       "1  3.49934  0.21021 15.31723 1.52174 22.24258"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# now with LU factorization instead of Y inversion\n",
    "net = single_3w_trafo_grid(\"Ynynd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=2, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm  xk_ohm\n",
       "2  0.00000      inf      inf 0.11730 2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ydd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ydd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.51934</td>\n",
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       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171\n",
       "1  0.00000      inf       inf  1.52174  22.24258\n",
       "2  0.00000      inf       inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ydd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=0, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.51934</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm   xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.51934 15.80517 158.05171 15.80517 158.05171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ydd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=1, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
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       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm   xk_ohm\n",
       "1  0.00000      inf      inf 1.52174 22.24258"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"Ydd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=2, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm  xk_ohm\n",
       "2  0.00000      inf      inf 0.11730 2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## YNyd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNyd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.78326</td>\n",
       "      <td>5.12452</td>\n",
       "      <td>88.22838</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171\n",
       "1  0.00000      inf      inf  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNyd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=0, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.78326</td>\n",
       "      <td>5.12452</td>\n",
       "      <td>88.22838</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.78326  5.12452 88.22838 15.80517 158.05171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNyd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=1, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm   xk_ohm\n",
       "1  0.00000      inf      inf 1.52174 22.24258"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNyd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=2, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm  xk_ohm\n",
       "2  0.00000      inf      inf 0.11730 2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## YNdd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.84354</td>\n",
       "      <td>3.76841</td>\n",
       "      <td>75.01993</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.84354  3.76841 75.01993 15.80517 158.05171\n",
       "1  0.00000      inf      inf  1.52174  22.24258\n",
       "2  0.00000      inf      inf  0.11730   2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=0, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.84354</td>\n",
       "      <td>3.76841</td>\n",
       "      <td>75.01993</td>\n",
       "      <td>15.80517</td>\n",
       "      <td>158.05171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm   rk_ohm    xk_ohm\n",
       "0  1.84354  3.76841 75.01993 15.80517 158.05171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=1, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>1.52174</td>\n",
       "      <td>22.24258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm   xk_ohm\n",
       "1  0.00000      inf      inf 1.52174 22.24258"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "net = single_3w_trafo_grid(\"YNdd\")  \n",
    "sc.calc_sc(net, fault=\"1ph\", case=\"max\", bus=2, inverse_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th></th>\n",
       "      <th>ikss_ka</th>\n",
       "      <th>rk0_ohm</th>\n",
       "      <th>xk0_ohm</th>\n",
       "      <th>rk_ohm</th>\n",
       "      <th>xk_ohm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>inf</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.11730</td>\n",
       "      <td>2.66335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ikss_ka  rk0_ohm  xk0_ohm  rk_ohm  xk_ohm\n",
       "2  0.00000      inf      inf 0.11730 2.66335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(net.res_bus_sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further vector groups, we included the check in the test, all the vector groups passed."
   ]
  }
 ],
 "metadata": {
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