generate_setup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from numpy import *\n",
    "from numpy.linalg import *\n",
    "from matplotlib.pyplot import *\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def write_directed_vertex_list_to_file(g, filename):\n",
    "    A=array(g.edges())\n",
    "    A=append(A,A[:,[1,0]], axis=0)\n",
    "    A=array(A[A[:,0].argsort()]).astype(int)\n",
    "    savetxt(filename, A, fmt='%i')\n",
    "\n",
    "\n",
    "def unique_rows(a):\n",
    "    a = ascontiguousarray(a)\n",
    "    unique_a = unique(a.view([('', a.dtype)]*a.shape[1]))\n",
    "    return unique_a.view(a.dtype).reshape((unique_a.shape[0], a.shape[1]))\n",
    "\n",
    "\n",
    "def to_undirected_vertex_list(A):\n",
    "    AA=[[min(A[i]), max(A[i])] for i in range(shape(A)[0])]\n",
    "    return unique_rows(AA).astype(int).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFFcXB+DfiIWuVEWxxoYFRZoldo2iWFCjiRURNVhj\nSayJPQpiL0QFW+KnJooaFbFFsUaKCti7YglYkSrCnu+PAYOwC7uwsLvseZ9nHtjZO3furjJn5laB\niMAYY0z7lFJ1ARhjjKkGBwDGGNNSHAAYY0xLcQBgjDEtxQGAMca0FAcAxhjTUhwAGGNMS3EAYIwx\nLcUBgDHGtFRpVRcgL+bm5lSjRg1VF4MxxjRGRETEKyKykCetWgeAGjVqIDw8XNXFYIwxjSEIwmN5\n03IVEGOMaSmlBABBEDYLghAnCMI1Ge8LgiCsFgThniAIUYIgNFPGeRljjBWcsp4AtgLomsf7LgDq\nZG6jAPgp6byMMcYKSCkBgIjOAHiTR5JeALaT6B8AFQRBsFLGuRljjBVMcbUBVAEQk+3108x9jDHG\nVETtGoEFQRglCEK4IAjhL1++VHVxGGOsxCquAPAMQNVsr60z9+VCRBuJyIGIHCws5OrKyhhjrACK\nKwD8BWBoZm+g5gDiiehFMZ2bMcaYFEoZCCYIwk4A7QCYC4LwFMAcAGUAgIh+BRAEoBuAewCSAQxX\nxnkZY/mIiwO2bgWiooD4eKB8ecDWFhg+HOAnbK0nqPOi8A4ODsQjgRkrgLAwYPFi4MgR8XVq6n/v\n6ekBRICLCzBjBuDoqJoysiIhCEIEETnIk1btGoEZY4Xk5we0awfs3y9e+LNf/AEgJUXct3+/mM6P\nh+VoK7WeC4gxpiA/P2DqVCA5Of+0RGK6qVPF115eRVs2pnb4CYCxkiIsTOrF/xHEBjgTAJUAjAOQ\nnj1BVhDg6latwwGAsZJi8WKxeieHMQAsALwAcBVACID1OROlpIjHM63CAYCxkiAuTmzwldKp4yGA\nAQB0IT4BdAVwPWciIiAoCODBl1qFAwBjJcHWrTLf+h7Aboj9r58BOAIZMzcKQp75sJKHAwBjJUFU\nVO7ePpnaALgGwBjiEHwHAL2lJUxJAaKji6qETA1xAGCsJIiPl7pbAvFuvw+AJACvALwFME1WPm/f\nFkHhmLriAMBYSVC+vNTdbwA8gdjzpxwAM4jD8INk5WNiUgSFY+qKAwBjJQDZ2iKjbNlc+80B1ATw\nK8Sun+8AbANgKy0TPT2gceMiLCVTNxwAGNNgcXFx8PX1xZebNiH940epaQIhNvxaAKgNcZKuFdIS\nEgHu7kVUUqaOOAAwpmEyMjJw5MgR9O3bF3Xr1sX169fhvWULyvbqJfbkyaEpgNMQ6/5fAfgDQMWc\niQQB6NaNJ4jTMjwVBGMa4uHDh9i8eTO2bt0KKysreHp6YsuWLTA2NhYTlCsHHDsm3zQQOenpiRPD\nMa3CTwCMqbHU1FTs2rULnTp1gqOjI96/f4/Dhw8jNDQUo0aN+u/iD4izevr6Avr6ip1EX188zkGu\nCSRZCcJPAIypoaioKAQEBGDHjh1o2rQpPD090bt3b+jq6uZ9YNaEblOniv3685ruXRDEO39fX54I\nTkvxEwBjauL9+/fYsGEDnJyc0K1bNxgbGyM0NBQnTpzAN998k//FP4uXFxASAri5Abq6SC2V489c\nTw/Q1RXfDwnhi78W4ycAxlSIiHD+/Hn4+/tj//796NixI+bOnYsuXbpAR0en4Bk7OAB794Li4rC4\nZk1Mc3GB/ocPYj//xo3F3j7c4Kv1OAAwpgKxsbHYvn07AgICAACenp7w9vZGxYq5+ucUSkxqKjYa\nG2Penj1KzZeVDBwAGCsm6enpOHr0KAICAvD333/Dzc0NAQEBaNmyJQQp3TeVISIiAs2aNSuSvJnm\n4wDAWBHL6r65ZcsWVKlSBSNGjMDWrVs/78FTRC5fvgx7e/siPw/TTNwIzFgRSE1Nxc6dOz9130xI\nSMCRI0dw6dKl3N03i1BERAQHACYTPwEwpkSRkZEICAjA//73P9jZ2WHkyJHo1auX/D14lIiIuAqI\n5YkDAGOFFB8fj127dsHf3x///vsvhg8fjrCwMNSsWVOl5Xr+/DmICNbW1iotB1NfHAAYKwAiwrlz\n5xAQEID9+/ejU6dOmD9/Pr766qvCdd9Uoqzqn6JqYGaajwMAYwqIjY3Ftm3bEBAQgFKlSsHT0xM+\nPj6wtLRUddFy4eoflh9uBGYsH+np6Th8+DDc3NxQv3593Lp1C1u2bMGNGzcwZcoUtbz4A9wDiOWP\nnwCYZouLExcyj4oSl0UsXx6wtQWGDy/0SNcHDx586r5ZtWpVjBgxAtu2bSu2HjyFFRERgbVr16q6\nGEyNcQBgmiksDFi8GDhyRHydfUH0wEBgzhzAxUWc4tjRUe5sU1NTERgYiICAAERFRWHw4ME4evQo\nGjVqpOQPULRevHiBDx8+oFq1aqouClNjHACY5vHzy3u2y5QU8ef+/cDRo3LNdhkZGQl/f3/s3LkT\nzZo1w+jRo9GrVy+UK1euCD5A0cuq/uEGYJYXDgBMs2Rd/OVZ9IRITDd1qvg6RxCIj4/Hzp074e/v\nj7i4OAwfPhzh4eGoUaOG8stdzHgAGJMHNwIzzREWJvXib5hj0wEwPnuCrCAQHg4iwpkzZzBs2DBU\nr14dJ0+exMKFC/Hw4UPMmzevRFz8Ae4BxOTDTwBMcyxe/F/1TjaJOX6vBODrHGkoJQV33N3R8+NH\n6OjowNPTE76+vrAooVMiX758GStWSF36nbFPOAAwzRAXJzb45rXCFYC9ACwBtM6xXyBCzVu38Ptf\nf8HBxaVE143HxcUhMTFR5SORmfrjAKCpirD7o1raulWuZNsADAUg7fJetmxZOF6/DnTrpsSCqZ+s\n6p+SHOSYcnAA0DRF1P1R7UVFff5ZpXgMIARAgKwEKSlAdLSSC6Z+eAAYkxc3AmsSPz+gXTuxe2Nq\nau4LYkqKuG//fjGdn58qSlk04uPzTfIbgC8B5Fnx8fatkgqkvrgHEJMXBwBNkb37Yz714J91fywp\nQaB8+XyTbAcwLL9EJibKKI1a4x5ATF4cADRBZvfHtcnJcABQDoB7jiQnAdQHoA+gPcTqkOzdHzVR\nYmIiDh06hAkTJmDp0aPI3f/nPxcAPEPu3j/Zka6uuCB6Cfbq1SvEx8fjiy++UHVRmAbgAKAJMrs/\nVgYwG4BHjrdfAegDYAGANwAcAAzIejMlRTxeA0gkEkRERGDx4sVo3749rKyssHz5clSuXBlddu7M\nc1GVbRC/A6M88v+QmgqvS5cQEhICyu8pSkNdvnwZdnZ2KFWK/7RZ/pTSCCwIQlcAqyCOwfEnoiU5\n3m8H4ACAh5m7AolovjLOXeJl6/7YJ3NXOICn2ZIEAmiI/+5+5wIwB3ALQH0iICgIePlSLXsHPXv2\nDMePH8exY8dw/PhxmJub46uvvsLUqVPRtm1bGBoa/pfYxUVs35By8d6Q34kEAUL37qjfujXGjBmD\n9PR0jBw5EsOGDStRYwG4+ocpotC3CYIg6ABYB8AFQAMA3wqC0EBK0rNE1DRz44u/vOTo/ngdQJNs\nrw0A1M7cDwAQBLm7URa15ORkHD16FFOmTEGjRo1ga2uLoKAgdOzYEREREbh58yZWrVqF7t27f37x\nB8SeTXp6BTuxnh7KzZmDiRMn4tq1a9iyZQuuXbuGOnXqYMCAATh58iQkEknhP6CKcQ8gpghlPAE4\nAbhHRA8AQBCEXQB6AbihhLyZHN0fEwHkvIc1BpCQ9UKF3R+JCNHR0Th69CiOHTuGf/75B02bNkWX\nLl2wefNm2Nvby7+ClqOjOLGbvHMBZUorUwZlfX0BBwcAgCAIaNmyJVq2bIl3795hx44dmDx5MhIT\nEzFy5Ei4u7ujUqVKBfm4KhcREYGFCxequhhMQyijorAKgJhsr59m7suppSAIUYIgHBEEoaESzqsd\n5Oj+aAjgfc7DkKM+vBi7P8bGxuL333/H0KFDUblyZfTp0wePHj3CuHHj8OzZM5w9exazZ8+Gk5OT\n4ssnenmJQUBfX3yyyYsggPT0sKB8efiXKSM1SYUKFTB27FhcvXoVO3fuxL1792BjY4O+ffvi6NGj\nGvVU8ObNG7x69Qp16tRRdVGYhiiulqLLAKoRkS2ANQD2y0ooCMIoQRDCBUEIf/nyZTEVT43J0f2x\nIYDIbK+TANzP3P9JEXZ/TE1NxcmTJzFt2jTY2dmhXr16CAwMRMuWLXHu3Dncu3cP69atQ69evZSz\nmIqXFxASAri5Abq6uauF9PTE/W5uEM6cwdALF/DTTz8hKChIZpaCIMDJyQn+/v54/PgxvvrqK8ya\nNQu1atXCwoUL8ezZs8KXu4hduXIFTZs25QZgJjdlVAE9A1A122vrzH2fENH7bL8HCYKwXhAEcyJ6\nlTMzItoIYCMAODg4lMyuGoqwtQX27gVSU5EOIB1ARuaWCvEf0A3ADxDnwekOYB7ENoH6WXno6Sm1\n+yMR4ebNmzh27BiOHTuGc+fOoWHDhvjqq6+wdu1aODk5oYyMO26lcXAQv5eXL8X2jeho8SnHxET8\nrO7unxq96wDYt28fevTogSNHjsAhsypIFmNjY4wePRqjR49GREQENm3ahMaNG6N169YYOXIkXFxc\n1Gbh9+x4ABhTGBEVaoN4DXoAcQBmWYg3ow1zpKkEQMj83QnAk6zXeW329vak9WJjKaNsWSKA5gCE\nHNscsU8MHQeoHkC6ALUF6GHmfgIovUwZksTGFqoYr169ol27dpGHhwdZW1tTtWrVaOTIkfTnn3/S\n69evlfRhi9b+/fvJysqK7t+/r/CxCQkJ5O/vT87OzmRtbU0///wzPX78uAhKWXD9+/en7du3q7oY\nTMUAhJO81295E+aZCdANwB2INQ+zMvd9B+C7zN/HQeyUEgngHwAt5clX2wNAaGgodenShYL19SlD\nED5d0BXZMgSBjhsbU6NGjWjz5s2Umpoq17k/fPhAISEhNGvWLHJwcCAjIyNydXWl1atX0+3bt0ki\nkRTxpy8a69ato7p169LLly8LnEdkZCSNGzeOTE1NycXFhfbt20dpaWlKLGXB1K5dm65fv67qYjAV\nK/YAUFSbtgaAqKgo6tWrF1WpUoX8/Pwo7fx5In39AgUA0tcnSWgoHTt2jLp06UJWVla0aNGiXHft\nEomE7ty5Q2vXrqUePXqQsbEx2dvb04wZM+j06dP04cMHFX0byjd9+nRq0aIFJScnFyqfpKQk2rZt\nG7Vq1YqsrKxo5syZ9ODBAyWVUjHv3r0jQ0NDSk9PV8n5mfrgAKChbt++Td988w1VrFiRVqxY8fkF\nav16xYOAvr54XDZRUVHk7u5OJiYm5OnpSevWraNRo0ZRjRo1qHLlyjR8+HDauXMnxcXFFfOnLz4Z\nGRk0aNAgcnNzU9oF8/r16/T999+TmZkZde7cmf78889iDZp///03tWzZstjOx9QXBwAN8/DhQxo+\nfDiZm5vTL7/8QgkJCdITZgWB/KqDBEHqxf/jx4904cIFmjNnDtnb21PZsmWpTJky1LhxY/rtt980\ntlqnID58+EAdOnSgcePGKfVzp6Sk0I4dO6ht27ZkaWlJP/74I925c0dp+cvi6+tL48ePL/LzMPWn\nSADg/mIq9Pz5c4wZMwb29vawtrbG3bt3MWPGjNwjYLMo0P0RISGAlxcePXqEDRs2oG/fvrCwsICX\nlxeSk5OxZMkSxMfH4+3btxg1ahTmzJmDVq1aYe/evcjIyCj6D69iZcuWRWBgIEJCQrBs2TKl5aur\nq4uBAwfi9OnTOHPmDCQSCb788kt06NABO3fuxIcPH5R2ruy4BxArEHkjhSq2kvoEEBsbS5MnTyZT\nU1P64YcfCtYgGRdH5ONDNGQIkaur+NPHhxIePKADBw7Q2LFjqU6dOmRpaUmDBw+m7du30/Pnz2Vm\nl56eTnv27KEWLVpQrVq1aM2aNZSYmFiIT6kZYmJiqGrVqrRz584iO0dqairt3r2bOnXqRObm5jRp\n0iS6ceOGUs9Rt25dioqKUmqeTDOBq4DU05s3b2jmzJlkampK48aNy/OCLK/09HQKDQ2lhQsXUps2\nbcjQ0JA6duxI3t7edOXKFcrIyFA4z/Pnz1OfPn3I3NycZsyYoZRyqrOoqCiysLCgU6dOFfm57t27\nR9OnT6dKlSpR69atafv27YVujI6PjycDAwP6+PGjkkrJNBkHADXz/v17WrBgAZmbm5Onpyc9evSo\nUPk9efKEAgICaMCAAWRmZkYNGjSg77//noKCgigpKUlJpRYvVuPGjSMTExNyd3en6OhopeWtbk6e\nPEkWFhbF9hnT0tIoMDCQunbtSmZmZjR+/PgC38GHhIRQ8+bNlVxCpqk4AKiJ5ORkWrp0KVlaWtKg\nQYMK3BiYmJhIQUFBNHHiRLKxsSEzMzMaMGAABQQEUExMjJJLndvr169p0aJFZGVlRV26dKFjx46V\nyAbjHTt2ULVq1ejp06fFet6HDx/STz/9RJUrV6bmzZvT5s2bFap+W758OY0ZM6YIS8g0CQcAFUtN\nTaW1a9dS5cqVqW/fvnTt2jWFjs/IyKArV67QkiVLqEOHDmRgYEBt2rShhQsXUlhYmMr6eqemptLm\nzZupYcOGZGtrS9u2bStR4wOIiLy9vcnW1pbevXtX7Of++PEj/fXXX+Tq6komJibk5eVFly9fzve4\nwYMHU0BAgPQ3Y2OJvL2JBg0S24oGDRJfl+BuvtqOA4CKfPz4kQICAqh69erUrVs3Cg8Pl/vYFy9e\n0LZt22jQoEFkaWlJdevWpXHjxtFff/1F79+/L8JSK04ikVBwcDB16tSJKleuTIsXL6Y3b96oulhK\nIZFIaOzYsdSxY0eVBreYmBiaN28eVa1alRwcHGjDhg0y/x/Y2NjQ1atXP98ZGkrk5kakqytu2bsJ\n6+mJ+9zcxHSsROEAoKhC3iVlZGTQjh07qE6dOtSuXTs6d+5cvsekpKTQsWPHaOrUqWRra0sVKlSg\nvn370oYNG1Q2mrQgrl69SkOHDiUTExOaMGGCRpVdlvT0dOrVqxcNHjxY5VVd6enpFBQURL1796YK\nFSqQp6cnhYaGfipXQkIC6evrfz4VRSHHizDNxgFAXoW8S5JIJBQYGEiNGjWi5s2b08mTJ2WeSiKR\nUHR0NC1btoy6dOlChoaG1LJlS5o7dy5duHBB43twPH36lKZNm0ZmZmb09ddf0z///KPqIhVKUlIS\nNW/enGbOnKnqonzy/PlzWrRoEdWsWZOaNm1K69ato+DgYHJ0dPwvUY4R42sAsgeoLEDDFBgxzjQX\nBwB5FOIuSSKRUFBQENnb21PTpk3p0KFDUu8U4+Li6H//+x+5u7tT5cqVqUaNGjR69Gjau3cvvX37\ntug+mwq9f/+eVq5cSTVq1KBWrVpRYGCgxs5P8/LlS6pTpw75+fmpuiifycjIoGPHjtHXX39Nurq6\nVK9ePbpw4QJJLl3KNV3IXoD2AfRdXgEgKwiEhan6ozEl4ACQn0LMq3Pq1Clq1aoV2djY0J49ez7r\nZ//hwwc6deoUzZgxg+zt7cnY2Jh69uxJa9eupbt376q8OqE4ffz4kf744w9ycnKi2rVr07p165Ta\nRbW43Lt3j6ysrOjAgQOqLopU/fv3p759+1Lt2rXpuJGRzFljZ+UXAASBqE8fVX+ckkHFDe8cAPIS\nGir14n8DoPYAGQP0BUCBUv5IUkqVol5VqtBvv/1G6enpJJFI6NatW7R69Wrq3r07GRkZkaOjI82a\nNYvOnDmjFlMEq5pEIqGzZ89S7969ycLCgmbPnk3//vuvqoulkNDQUDI3N1fLaq1GjRpRREQESf79\nl9Iz140oUAAAxCpP7h1UcGrS8M4BIC9ubrmqfT4CVAegZQClA3QSIH2Abuf4A8kQBErp3p3+/PNP\n8vT0pGrVqpG1tTV5eHjQ7t276dWrV8ovbwly584d8vLyIhMTE/Lw8FC4e6wqHTx4kCpVqkR3795V\ndVE+SUpKIj09PbG3krd37ouOogFAT0+cXoQpTo0a3jkAyBIbK/WPJBogA4Ak2fZ1Bmi2tKcAgAZ0\n6EArV66kGzduaFW1jrK8fPmS5s+fTxUrViQXFxc6ceKERnyPGzZsoNq1a6vNVNkXLlygZs2aiS8G\nDcrzwiNXAADofqtWdODAAfr7778pLCyMbt++Tc+fP6fExESN+DdSCSlVym0BKpd5XTEAqK6MKuWi\noEgAUMaawJpj61a5kxKAa1L2l9PTw66uXYGJE5VVKq1jbm6On376CT/88AN27NiB8ePHo1y5cpgy\nZQoGDBhQ9OsJF9CoUaMQExMDV1dXnDp1Cvr6+iotz+XLl/+bATQ+Xil5vnv0CP7+/khISMD79+8/\n+5mamgpDQ0MYGxvDyMgo10959xkbG8PAwACCICilzCoVFgZMnQokJ+d6ay0AT1nHJSeLxzk6iutb\nq4h2BYCoKCA1NdfuegAsASwFMAnAKQAhANpLyUJISREXIGeFpqurixEjRmD48OEIDg7GsmXLMGPG\nDEyYMAGjRo1C+fLlVV3EXObPn48nT57gm2++QWBgIEqXVt2fUEREBJydncUXMr6r9MwtI3NLhfhH\nL6vUzTp0wF/bt0vPKz0diYmJUoND1s+EhAS8ffsWT548+WxfznQpKSkwMDBQWjApVUpFM9svXgyk\npBTs2JQU8fi9e5VbJgVoVwCQcZdUBsB+AOMBeANwANAfQDlZ+bx9WwSF016lSpVCt27d0K1bN1y5\ncgXLli1DzZo1MWzYMEycOBE1atRQdRE/EQQBmzZtgqurKyZMmIB169ap7E42IiICXl5eAIDUevWg\nU7o0yqSnf5ZmIYB52V7/DmAOgLnSMtTTAxo3lnm+0qVLo0KFCqhQoULhCg4gIyNDrmASHx+Pp0+f\nSn0v6/fk5GTo6+vLHTDyCjCGhobQ0dGR70PExQFHjoiVOlLMADAd4g3mIgDtciYgAoKCgJcvAQuL\ngn6VhaJdASCPO0pbiHf9WVoCGCYrsYmJ8srEPmNnZ4fff/8dMTExWLNmDezt7dG5c2dMmTIFjo6O\nqi4eAHExmT179qBNmzbw9vbG9OnTi70MqampuHv3LipVqoTZs2djz/r1iJJIcqWbCxkXe2mIAHd3\npZUxLzo6OihfvrxSnvIyMjKQlJSUZ5DI+vns2TOZ771//x7JycnQ09OTK2C0DQ1F84wMqRdRbwAN\nAJQFsAtADwBXAXyRM6EgiFXTP/xQ6O+hILQrANjaio9bUqqBogDUBSABsB7ACwDu0vLI5y6JKUfV\nqlXh4+OD2bNnIyAgAP369UONGjUwZcoUuLq6qu6RP5OxsTGCgoLQsmVLVKlSBUOGDCnW8wcHB8PA\nwABNmjTBgAEDcDgsDGV/+AHYv1/mHWmeBAHo1k1ld6KFoaOjA2NjYxgbGxc6L4lEIjWYSAsqZW/d\nQumPH6Xm45zt92EAdgIIgljL8BlVVynL21qsiq24egERQFMBqpDZYt8VoLvcV1qtfPz4kXbu3EkO\nDg5Ut25d+vXXXwu9kIoyXLt2jSwtLen48ePFcr7o6GgaPHgw6evrk62tLb148eK/N2WMcZFr45HA\ninN1lfv77QrQKlnvu7oqtVjgNYFlsLQEXFzEu50clgJ4CyARwBEAtaUcngEgytoa8WXLFmkxWW6l\nS5fGN998g9DQUGzatAmHDx9GjRo1MGfOHMTFxamsXA0bNsQff/yBgQMHIjIyssjOc/HiRfTs2ROd\nO3dGw4YN0a9fP4wePRqVKlX6L5GjI+DrCyjaO0lfXzxOhb1RNEV6ejouXbqEJUuW4ER4uNQ07wAc\nhdjgng5gB4AzALrKylSVVcryRgpVbMU5ElieTaKnRwt69aJKlSrRpk2bNHaOm5Li1q1bNHr0aKpQ\noQKNHDmSbt68qbKy7N69m6ytrenJkydKyzNrzqk2bdpQzZo1yc/P79NTj52dncyRyZL16ymlVCmZ\n00IU56AkTZeRkUGXL1+mZcuWUffu3al8+fLUuHFjmjBhAkUPHUoSKTUKcQA5AGQIUHmAnAE6Juvf\noAgG34EHguWjEHMBERFFRETQl19+SXZ2dnTmzJmiKSOTW1xcHM2dO5csLS3J1dWVTp06pZJBS8uW\nLaOGDRsWeqK/rOquJk2akK2tLf3vf//7bLbY1NRU0tPTk1kFduTIEepXowZlZE1LoKeX+6KjqyvO\n/cPVPp+RSCR07do1Wr16Nbm5uZGpqSnVq1ePvLy86I8//vh8EGAeVcpyb0VQpcwBQB6FHLotkUho\n165dVK1aNRowYAA9fvy46MrK5JKcnEwbNmygevXqUbNmzeh///tfsc7HJJFIaOLEidSuXTtKTU1V\n+PiUlBTy8/OjWrVqUevWrenw4cNSA1l4eDg1btxYZhns7e3pzz//FHfExYl3mEOGiHXNQ4aIr7kd\ni4jE7+v27dv066+/0oABA8jS0pJq1apFI0aMoN9//52ePXuWdwZSppaReyuiCfg4AMgrLEz8ByjE\nXVJSUhLNmTOHzMzMaO7cuRo542VJk5GRQQcPHqS2bdtStWrVyNfXl+Lj44vl3Onp6dS3b1/65ptv\nPpspNi/v3r2jJUuWUKVKlcjV1TXfBYU2bNhA7u7uUt8LDAwkOzs7uc+tjR4+fEibN2+mwYMHU5Uq\nVcja2pqGDBlCW7ZsoUePHimWmRo2vHMAUJQS7pIeP35MAwYMoGrVqtGuXbt43hQ1ERYWRt9++y2Z\nmprSlClTlFpHL0tycjK1atWKfvzxxzzTvXjxgqZPn05mZmY0ePBgioqKkiv/UaNG0erVq3PtT09P\np4YNG9KhQ4cKVO6S6tmzZ/T777+Th4cH1axZkywtLWnAgAG0YcMGunPnTuH/VgtZpaxsHABU6MyZ\nM2RnZ0dffvklRUREqLo4LNPjx49p8uTJZGpqSgMHDizyf5tXr15RvXr1aM2aNbneu3///qdZUceO\nHUsPHz5UKG8HBwc6f/58rv07duygFi1aaP3NR2xsLO3evZu+++47qlu3LpmamlKfPn1ozZo1dP36\n9aL5fngPZipDAAAgAElEQVQ2UA4AWdLT02nTpk1UqVIl8vT0pNjYWFUXiWV69+4dLV26lKytrald\nu3Z06NChIqsuefDgAVWuXJkCAwOJiCgyMpK+/fZbMjMzo1mzZhXo/8WHDx9IT0+PEhMTP9uflpZG\ntWvXznNZ0pLqzZs3tG/fPpowYQI1atSIypcvT66urrRs2TK6cuVK8VWHKaFKWRk4AKiJd+/e0ZQp\nU8jc3Jx8fX3FeduZWkhLS6MdO3aQnZ0d1a9fnzZu3EgpKSlKP094eDhVqFCBWrRoQVZWVuTj41Oo\n9ojLly9TgwYNcu339/enDh06FKaoGuP9+/d0+PBhmjJlCjVr1oyMjIzoq6++oiVLltClS5dUv762\nihveOQComVu3blH37t2pTp06MtcPZqohkUjo77//pu7du1PFihVp3rx59PLly0Lnm9UQ3apVK7Ky\nsiJjY2OKjIwsdL7+/v40ZMiQz/alpqZStWrV6MKFC4XOXx0lJSXRsWPHaMaMGdS8eXMyMDCgdu3a\n0fz58+ns2bN8Y5UDBwA1FRQURPXq1aOuXbuqdNASk+7GjRvk6elJFSpUoO+++45u376tcB5paWn0\n22+/UaNGjcjOzo52795N6enpFBAQQLVq1Sr0cpheXl60cuXKz/atWbOGunfvXqh81UlqaiqdPn2a\n5syZQ61btyYDAwNq1aoVzZ49m/7++2+1mAJEnXEAUGNpaWm0YsUKMjc3p++//77Qg4aY8v3777/0\n008/kYWFBfXs2ZNCQkLyfWpLSkqitWvXUo0aNahdu3YUHByc65i5c+eSvb09JSQkFLhsTk5Onw0+\nTEpKIisrK7p8+XKB81S1tLQ0unDhAi1atIg6duxIhoaG5OjoSD/++CMFBwcX6vvSRhwANEBcXByN\nGjWKKlasSL/++itPK6GGkpKSyM/Pj+rUqUOOjo60a9euXPXLb968oYULF1LFihWpd+/edPHiRZn5\nSSQS8vDwoG7duhWonjotLY309fU/uyD6+PhQv379FM5LldLT0yk8PJx8fHzIxcWFjI2NqUmTJjRp\n0iT666+/6N27d6ouokbjAKBBrly5Qm3atKEmTZrQ6dOnVV0cJkVGRgbt37+fWrduTdWrV6cVK1bQ\n7du36YcffiBTU1MaNmwYXb9+Xa680tLSqGvXrjRy5EiF24IiIyOpfv36n17Hx8eThYWF3OdWlYyM\nDIqMjKSVK1dSr169qEKFCmRjY0Njx46lPXv2KKXNhf2HA4CGkUgk9Oeff1L16tXp66+/VrhfOCs+\nf/75J9WqVYsEQSB7e3uZE7LlJSEhgZo1a0bz589X6LjNmzfTwIEDP72eN29ergZhdSCRSOjmzZu0\nfv166tevH5mbm1Pt2rVp5MiRtHPnzs+nsGZKxwFAQyUnJ9P8+fPJzMyMZs+enauvN1Ody5cvU//+\n/cnc3Jx+/vlnCg8Pp4kTJ5KJiQkNHjyYrly5olB+L168oBo1atCWLVvkPmbcuHG0bNkyIiJ6/fo1\nmZmZ0b179xQ6b1GQSCR0//598vf3p4EDB5KVlRVVq1aNhg0bRtu2bSuW0dfsPxwANNyTJ09o4MCB\nZG1tTTt27OBuoyoikUjo1KlT1KVLF6pSpQotX748V4Pk27dvydvbm6pUqUIdO3akoKAguf+9bt68\nSRUrVqTg4GC50rdo0eJTNeH06dNp1KhRin0gJYqJiaFt27aRu7s7VatWjSpVqkQDBw6kTZs20b17\n9/j/rAoVewCAuNbBbQD3AEyX8r4AYHXm+1EAmsmTr7YGgCznzp0je3t7atmyJYXxtL3FJiMjg/bt\n20fOzs5Ut25dCggIyHd2zw8fPtD27dupSZMm1KBBA7mOIRL/jS0sLPLtxfPx40cyMDCgd+/e0YsX\nL8jU1LRY76z//fdf2rlzJ40aNYpq165NZmZm1K9fP1q3bh3dvHmTL/hqpFgDAAAdAPcB1IK4BnIk\ngAY50nSDuNCWAKA5gEvy5K3tAYBIvBht3ryZrKysaPjw4Vx/WoTS0tJo69atZGNjQ/b29rRnzx6F\ne2dJJBI6ceIEde3alSpVqkQLFy6kV69e5XnMnj17qEqVKrlnooyNJfL2Jho0iN63bUv7jYyIvL1p\n5siRNHHiREU/nkJevXpFe/fupbFjx1KDBg2oQoUK1LNnT1qxYgVFRkbybKNqrLgDQAsAR7O9ngFg\nRo40GwB8m+31bQBW+eXNAeA/8fHx9OOPP5KZmRl5e3sXaL55Jl1iYiKtXLmSqlatSh07dqQTJ04o\n5Y42Ojqahg8fTiYmJjRmzBi6e/euzLSrVq0iGxsbev36tTjFcNZiLjkWHMnQ1aUUgFJcXMR0SvLu\n3Ts6ePAgTZo0iZo2bUpGRkbUtWtX8vHxobCwMO6mrEGKOwD0A+Cf7fUQAGtzpDkE4Mtsr08CcMgv\nbw4Aud25c4d69uxJX3zxBR04cIAfvQvh9evXNG/ePLKwsKC+fftSqBIvqNm9ePGCZs2aRebm5uTm\n5kbnzp2T+u82ZcoU8q1dmyTFMKtkYmIiBQcH07Rp08jR0ZEMDQ2pQ4cOtHDhQjp//nyxLqTDlEuj\nAwCAUQDCAYRXq1atKL8njXb06FGysbGhzp07q30/cHUTExNDkyZNIhMTE/Lw8KBbt24Vy3kTExNp\n7dq19MUXX5CzszP9+eefn91ZZ6xbR6k6Onlf+As4r3xKSgr9/fff9NNPP1GrVq3IwMCAWrduTT//\n/DOdPn26SCbCY6rBVUBaIi0tjVavXk0WFhY0fvx4sfqAyXTr1i3y8PAgExMTmjx5MsXExKikHOnp\n6RQYGEgtW7akmjVr0urVqynp9Olci4qkAuQBUDWIC4w3AShIzpWlPnz4QOfOnaP58+dT+/btydDQ\nkJydnWn69Ol07Ngx7mJcghV3ACgN4AGAmtkagRvmSNM9RyNwqDx5cwCQz8uXL2nMmDFkaWlJ69at\nU/10uGomLCyM+vbtSxYWFjRv3jy1CpQXL16kfv360aGyZSkjx4U9EaA5AD0EKAOgg5mB4KGU6qCM\n3r0pNDSUlixZQl26dCEjIyOys7OjKVOm0KFDh4ptSUymeqroBtoNwJ3M3kCzMvd9B+C7zN8FAOsy\n34+Wp/6fOAAoLCoqitq3b0+NGjXSyoVBssvqjdOxY0eqWrUqrVq1Sn3vemNjKaNcudx39lK2xgDt\nkbI/BaAv69Wj8ePHU2BgoFoFOVa8eCCYFpNIJLR3716qWbMm9enTh+7fv6/qIhWr9PR02rNnDzk4\nOJCNjQ1t3bpV/Rs0vb1z9faRtv0LUDmAbkp5L0NXV1x0hGk9RQJAKbASRRAE9OnTBzdu3IC9vT2c\nnJwwa9YsJCYmqrpoRSotLQ2bN29Gw4YNsXTpUsyePRvXrl3DsGHDUKZMGVUXL29RUUBqap5JPgIY\nBGAYgPpS3i+VmgpERxdB4VhJxgGghNLV1cXMmTMRGRmJmJgY1K9fH7/99hskEomqi6ZUiYmJWL58\nOWrVqoU//vgDv/76Ky5evIhevXqhVCkN+e8dH5/n2xKIXevKAlibV8K3b5VXJqYVNOQvhBVUlSpV\nsH37duzZswdr165Fy5YtcenSJVUXq9BevXqFOXPmoGbNmrh06RIOHjyI4OBgtGvXDoIgqLp4iilf\nXuZbBGAEgFgAewHk+SxjYqLUYrGSjwOAlmjevDkuXryIMWPGoE+fPhg2bBieP3+u6mIp7MmTJ5g4\ncSLq1q2Lf//9FxcuXMDu3bthZ2en6qIVnK0toKsr9S0vADcBHASgl1ceenpA48bKLxsr0TgAaJFS\npUph6NChuHXrFqpUqQJbW1ssXrwYqfnUP6uDGzduwN3dHXZ2dihXrhyuX7+ODRs2oE6dOqouWuG5\nu0vd/RjiAJqrACoBMMzcdkhLTCQzH8Zk4QCghYyMjPDLL78gNDQUYWFhaNCgAfbt25fVpVetXLp0\nCb1790aHDh1Qt25d3Lt3Dz4+PrCyslJ10ZTH0hJwcQFyVF1Vh1gFlAogMds2KOfxggB06wZYWBRD\nYVlJwgFAi9WqVQuBgYHYtGkTfv75Z3Tq1AnRatCThIhw9OhRtG/fHgMGDEDnzp3x8OFDzJw5EyYl\ntZ57xgyxGqcAPpYuDZo+XckFYtqAAwBDx44dceXKFfTt2xedOnXCuHHj8Pr1a/kziIsDfHyAwYOB\nHj3Enz4+wMuXCpUjIyMDf/zxB+zt7TF16lR4enri7t27GDt2LPQKeHHUGI6OgK8voK+v0GESPT0s\nrVgRQ1evRkpKShEVjpVY8g4YUMXGA8GK3+vXr2n8+PFkYWFBq1evznsQVR7TFpOenrjPzS3faYtT\nU1Npw4YNVLt2bWrZsiUdPHhQe+ebX7+eUnR0KEOB2UCTkpKof//+5OjoSE+fPlX1J2AqBh4JzArr\n2rVr1KlTJ2rQoAEdP348d4L168ULUCGmLY6PjycfHx+ysrKibt260ZkzZ4rhk6m3yMhI6mJmRh97\n9iTS1aX0nFNEZAXWPn0+mwBOIpHQokWLqHLlygVaqJ6VHBwAmFJIJBLav38/1apVi3r16vXfgiZZ\nF/8CTlscGxtLM2fOJDMzMxo4cCBdvXpVhZ9SvfTt25d8fX3FF3FxdNvTk45WqkTk6ko0ZIg43UNc\nnMzjDxw4QObm5rRt27ZiKjFTNxwAmFKlpqbSkiVLyMzMjNYMHUoSPT2pF/mdANUHSB+gWgCdyTlf\njZ4eeX/9NZmYmJCXl5fWzVOUn6ioKKpYseJnk9YFBgZSr169FMrn2rVr9MUXX9DkyZN5ZlgtpEgA\n4EZglq9y5cph2rRpiI6ORovTpyGR0th4HMA0AFsAJAA4A3GR6OwoJQXdIiNx8+ZNrF+/HrVq5Uyh\n3RYsWICpU6fCwMDg076EhAQYGRkplE/Dhg0RGhqKyMhIuLq64i1PEcFk4ADA5GalowP7uDjoSHlv\nDoCfIS72UApAlcwtOx0AjZ48QUVNmaOnGF27dg1nzpyBl5fXZ/sTExNhaGiocH6mpqYIDg5GvXr1\n4OzsjFu3bimrqKwE4b9EJr+tW6XuzoC4hudLALUBWAMYB0Bqp0RBkJmPNluwYAEmT5782d0/UPAA\nAAClS5fGqlWrMH36dLRp0wZBQUHKKCorQTgAMPnJmLY4FuJ0xXsAnIU4dcEVAAul5ZGSwtMW53Dj\nxg2cPn0aY8aMyfVeYmKiwlVAOXl4eGDfvn3w9PSEj4+P2PjHGDgAMEXImLY4a4jWeABWAMwBTAYg\n836T66Q/s2DBAkyaNEnqnX5CQkKBnwCya9WqFS5duoTdu3djyJAhPGiMAeAAwBQhY9piE4jVPtln\nsslzQuaSOp1DAdy8eRMnT57E2LFjpb5fmCqgnKpWrYqzZ88iIyMDbdq0wbNnz5SSL9NcHACY/PKY\ntng4gDUA4gC8BbACgKu0hDxt8WcWLlyI77//XmY1jzIDAADo6+vjf//7H/r06QMnJyf8888/Ssub\naR4OAEx+eUw3/BMARwB1AdgAsAMwS1pC4mmLs9y+fRvHjh3DuHHjZKYpSDfQ/AiCgBkzZuDXX39F\njx49sG3bNqXmzzQHBwAmPxnTFgPiSlXrAbwD8C+A1QByPitkALhfvz7SuQoIgHj3P3HiRBgbG8tM\no+wngOx69OiBkJAQLFiwAFOmTEF6enqRnIepLw4ATDGFmLYYurpYqqODJk2aIDg4WLnl0jB37txB\ncHAwxo8fn2e6ogwAANCgQQOEhoYiKioK3bt350FjWoYDAFNMAacthr4+dJYvh19YGBYvXowJEyag\na9euuHbtWtGUU80tXLgQEyZMQPk81gMGlNMNND+mpqY4cuQIbGxs4OzsjJs3bxbp+ZgakXfOCFVs\nPBeQGivkbKAfPnygVatWkYWFBX333XcUGxurog9S/O7cuUPm5ub07t27fNNWrlyZYmJiiqFUooCA\nALKwsKDDhw8X2zmZcoHnAmJFzssLCAkB3NzEnkE5q4X09MT9bm5iuhxTHJQtWxYTJkzArVu3oKur\niwYNGsDb21sj1icurEWLFmHcuHH53v0DRV8FlJOHhwf2798PT09PeHt786CxEk5Q539gBwcHCg8P\nV3UxWH5evhSnd4iOFgd5mZiIXT3d3eVep/bOnTuYNm0arl69Cm9vb3z99dcQpDQ2a7r79+/D2dkZ\n9+7dQ4UKFfJMS0QoXbo0Pnz4gNKlSxdTCUUxMTFwc3NDvXr14O/vX/JXZCtBBEGIICIHudJyAGDq\n5PTp05g8eTJ0dXWxYsUKODs7q7pISuXh4YGqVati3rx5+aZNTk6GmZmZykbtJicnY8SIEbh37x72\n7dsHa2trlZSDKUaRAMBVQEyttGvXDmFhYRg1ahT69OmDgQMH4vHjx6oullI8ePAABw4cwPfffy9X\n+uKu/skpa9BY37594ezszIPGSiAOAEzt6OjowN3dHbdv30adOnXQrFkzzJo1CwkJCaouWqH88ssv\nGDNmDEzkHAeh6gAAiIPGpk+fjg0bNqBnz57YyjO5ligcAJjaMjQ0xLx58xAZGYmnT5+ibt262LRp\nEzIyMlRdNIU9fPgQ+/btw6RJk+Q+pji6gMrL1dUVp0+fxqJFizB58mQeNFZCcABgas/a2hrbtm3D\nwYMH8dtvv8HOzg4nTpxQdbEU8ssvv8DLywumpqZyH6OsmUCVJWvQ2LVr19CtWzceNFYCcABgGsPB\nwQEhISGYO3cuRo8eDVdXV40YtPTo0SMEBgYqdPcPqEcVUE4mJiYICgpCo0aN4OTkpBHfP5ONAwDT\nKIIgoE+fPrhx4wY6dOiANm3aYPz48Xj16pWqiybT4sWLMXr0aJiZmSl0nDoGAEBcaWz58uWYNWsW\n2rZti8OHD6u6SKyAOAAwjVSuXDlMnjz50x2ojY0Nli1bhg8fPqi4ZJ978uQJ9uzZg8mTJyt8bFHM\nBKpM7u7uOHDgAEaNGoUlS5bwoDENxAGAaTRzc3OsWbMGZ8+exalTp9CwYUMEBgaqzcVo8eLFGDly\nJMzNzRU+Vl2fALJr0aIFLl26hD179mDQoEFITk5WdZGYAjgAsBKhfv36OHToEPz8/DB37ly0bdsW\nqh5EGBMTg927d2PKlCkFOl4TAgAgNtKfPXsWgiCgTZs2ePr0qaqLxOTEAYCVKJ07d8aVK1cwdOhQ\n9OzZE8OGDVPZBWnJkiXw9PSEhZzTYeSkTt1A86Onp4fff/8d/fv3h7OzMy5cuKDqIjE5cABgJY6O\njg48PT1x+/ZtWFtbo0mTJpgzZw4SExOLrQxPnz7Fzp07MXXq1ALnoW7dQPMjCAJ+/PFHbNy4Eb17\n98bmzZtVXSSWDw4ArMQyMjLCokWLcPnyZdy7dw/16tXDli1bIJFIivzc3t7eGDFiBCwtLQuch6ZU\nAeXUvXt3hISEYMmSJfj+++950JgaK1QAEATBVBCE44Ig3M38KXWMuyAIjwRBiBYE4aogCDy7GytW\n1atXx44dOxAYGAh/f384ODjg1KlTRXa+Z8+eYceOHYW6+wc0NwAAYq+sS5cu4ebNm3BxccGbN29U\nXSQmRWGfAKYDOElEdQCczHwtS3siairvLHWMKZuzszPOnTuH6dOnw8PDA71798adO3eUfh5vb28M\nHz4cFStWLFQ+6t4NND8mJiY4fPgwbG1t4ezsjBs3bqi6SCyHwgaAXgC2Zf6+DUDvQubHWJESBAH9\n+/fHzZs30aJFC7Rs2RKTJk1S2h3q8+fP8fvvv+OHH34odF6a/ASQpXTp0li2bBlmz56Ntm3b4uDB\ng6ouEsumsAGgIhG9yPz9XwCybnkIwAlBECIEQRhVyHMyVmi6urqYNm0abty4gdTUVNSvXx+rVq3C\nx48fC5Wvj48Phg0bhkqVKhW6jCUhAGQZNmwYDh48iO+++w6LFy9Wm3Ea2i7fACAIwglBEK5J2Xpl\nT5e5FqWsf9UviagpABcAYwVBaJPH+UYJghAuCEL4y5cvFfksjCnM0tISfn5+OHXqFI4cOYJGjRrh\nr7/+KtAF6sWLF9i+fTt+/PFHpZRNk7qByqN58+YIDQ1FYGAgBg4cyIPG1EC+AYCIOhFRIynbAQCx\ngiBYAUDmzzgZeTzL/BkHYB8ApzzOt5GIHIjIoaD9pxlTVMOGDREcHIxVq1ZhxowZ6NixI65evapQ\nHkuXLsWQIUNgZWWllDJpWjdQeVSpUgVnzpyBjo4OWrdujZiYGFUXSasVtgroLwDDMn8fBuBAzgSC\nIBgIgmCU9TuArwBcK+R5GSsSXbt2RWRkJPr374+uXbtixIgRePHiRb7HxcbGYuvWrZg2bZrSylKS\nqoCy09PTw2+//YYBAwbwoDEVK2wAWAKgsyAIdwF0ynwNQRAqC4IQlJmmIoBzgiBEAggFcJiIggt5\nXsaKTOnSpfHdd9/h9u3bMDc3R6NGjbBgwYI8qyyWLl2KwYMHo3Llykopg0QiQXJyMgwMDJSSn7rJ\nGjTm7+/Pg8ZUiBeFZywfDx8+xPTp03HhwgX88ssvGDRoEEqV+u/eKTY2FjY2NoiOjkaVKlWUcs7E\nxERUrFgRSUlJSslPnd26dQu9evVC165dsWzZMpQuXVrVRdJovCg8Y0pUs2ZN7N69G7t27cLatWvh\n7OyMs2fPfnrf19cXAwcOVNrFHyi51T/S1K9fH5cuXcLt27d50Fgx4wDAmJxatWqFixcvYtKkSRg8\neDD69euH0NBQBAQEYPr0vMZAKk6bAgAAVKhQAYcPH0aTJk3g5OSE69evq7pIWoEDAGMKKFWqFAYO\nHIhbt27Bzs4Obdu2RbVq1ZR+sdb0UcAFoaOjA19fX/z8889o3749DxorBhwAGCsAPT09jB49Grq6\nurCxsUG9evWwbt06pU18pm1PANkNHToUBw8ehJeXF3755RceNFaEOAAwVkDLli3DgAEDsHPnThw7\ndgz79u2Dra0tgoKCCn3R0uYAAIjzNoWGhmL//v349ttvedBYEeEAwFgBvH79Ghs3bsSMGTMAAE2a\nNMHx48fh4+ODyZMno0uXLoiOji5w/toeAACgcuXKCAkJQZkyZXjQWBHhAMBYASxfvhz9+vVD9erV\nP+0TBAGurq6Ijo5Gjx490LFjR4wePRqxsbHyZxwXB/j4wG75csy+dAkYPBjw8QG0dFoUPT09bN++\nHd9++y2cnZ1x/vx5VRepZCEitd3s7e2JMXXz6tUrMjU1pYcPH+aZ7s2bNzR58mQyMzOjX375hVJS\nUmQnDg0lcnMj0tUVN+C/TU9P3OfmJqbTUkFBQWRhYUH+/v6qLopaAxBOcl5j+QmAMQWtWLECffr0\nQY0aNfJMZ2JigmXLluGff/5BeHg46tevj127duVuH/DzA9q1A/bvB1JTxS27lBRx3/79Yjo/P2V+\nHI3h4uKCs2fPwsfHBxMmTOCVxpSARwIzpoA3b96gTp06CA8PR82aNRU6NiQkBJMnT0bZsmWxfPly\ntGjRQryYT50KKNLIqa8P+PoCXl4Klr5kePfuHb799lt8/PgRu3fvhpmZmaqLpFZ4JDBjRWTlypXo\n3bu3whd/AGjbti3CwsLg5eWFr7/+GjM7d4Zk8uRcF/83ANwAGACoDuB/OTNKThaDhpbeHFWoUAGH\nDh2CnZ0dnJ2d8x40ltmmgsGDgR49tL5NJRd564pUsXEbAFMnb968ITMzM7p//36h80pMTKQb9etT\neva6/sztG4D6A5QA0FmAjAG6ljOdIBD16aOET6XZtm/fTubm5rR///7P39DiNhVwGwBjyrdq1Sr0\n6NEDtWrVKnReBklJsHn0CDo59icB2AtgAQBDAF9CXHf1t5wZEAFBQVp/JztkyBAcPnwYY8eOxaJF\ni8T2FW5TkRtPu8eYHN69e4e1a9fin3/+UU6GW7dK3X0H4h9l3Wz7mgA4LS2xIIj5KGH9YU3m5OSE\n0NBQuLm5wTIwEJ43b0JIScn/QKL/qtMArWxT4ScAxuSwevVquLq6onbt2srJMCoq950pgEQAxjn2\nGQNIkJZHSgo+XrnCUyVAHDR2ZtkyDI2MlHrxvwtAF8BgaQdrcZsKPwEwlo/4+HisWbNGuStXxcdL\n3W0I4H3OpABkTQt3bPdu9Ny9GwYGBjAyMoKhoWGun9L25fdemTJllPdZi0m55ctBEonU98YCcMzr\n4JQUYPFiYO/eoiia2uIAwFg+Vq9eDRcXF9SpU0d5mZYvL3V3XQDpEO9Ys84WCaChjGy6DxqEtC1b\nkJiYiMTERCQkJOT6PfvPuLg4PHjwQOp7WT8TEhKgo6NToMAh6z0DA4PPFtFRurg44MgRCFKehnYB\nqACgJYB7so7P3qaiRWuRcwBgLA/v37/H6tWrlT8Fga2teLeZoxrIAEAfAD8D8AdwBeLC21KfPfT0\ngMaNoaOjg/Lly6O8jKCiKCJCWlpankEie5B5/fq1zPeyfiYnJ0NfX1+pQaVcuXIQBEEstIw2lfeZ\n3+Xfmd9nnrSwTYUDAGN5WLNmDbp06YK6devmn1gR7u6gOXMgSHlrPQAPAJYAzAD4QcYTABHg7q7c\nckGc06hcuXIoV64czM3NlZKnRCJBUlKSXE8pb9++xZMnT/JMk5iYCIlE8ik4rHv/Hj2ltKn8BGAE\nAGt5CpmSAhRiAj9NxAGAMRkSEhKwatUqnDlzRul533rzBv/q6aFNamqunhimAPbnl4EgAN26aUx1\nRalSpWBkZAQjIyNYWVkpJc+0tLRPQcJk6FAgJOSz968COAHxKUpub98qpWyagnsBMSbD2rVr0alT\nJ9SvX19peWZkZMDX1xdffvklXo0cCUFfv2AZ6ekBmVNRa6uyZcvC1NQU1apVg5F17nv80wAeAagG\noBIAX4hjLJrllamJidLLqc74CYAxKRITE7Fy5UqcPn1aaXnevn0bw4cPR7ly5RAaGioOKKtRo+Bz\nATnINd2LdpDSpjIKwDfZkvhCDAgyh31ltqloE34CYEyKdevWoUOHDrCxsSl0XhkZGVi+fDlatWqF\ngafQn5cAAAmFSURBVAMH4uTJk/+NJvbyEi/m+vpitU5eBEHrJ4KTSUpbiD7EO/+szRDiWACZlWZF\n1KaizvgJgLEcEhMTsXz5cpw6darQed25cwceHh7Q0dHBpUuX8MUXX+RO5OUFODqK/dCDgsQLffbB\nTHp64sWpWzex2ofv/HOztARcXMTpHWQMjJub1/Ea1qaiLBwAGMth/fr1aNeuHRo0aFDgPDIyMrB6\n9WosWrQIP//8M8aNG5d3P3gHB7EK4+VLsStidLTYIGliIlZLuLtr3cVJYTNmAEePKladlkVL21Q4\nADCWTVJSEpYvX44TJ04UOI979+5h+PDhAIB//vlHsekjLCy0qh+6Ujk6itVj3KYiN24DYCwbPz8/\ntG7dGo0aNVL4WIlEglWrVqF58+bo27cvQkJClDd3EJMPt6kohJ8AGMuUnJwMX19fHD9+XOFj79+/\nDw8PD2RkZODixYvKnTaCKYbbVOTGAYCxTL/++itatWqFxgp0BZRIJFi3bh3mzZuHmTNnYuLEidDR\nyTnLPyt23KYiFw4AjEG8+1+6dCmCg4PlPubBgwfw8PBAWloazp8/j3r16hVhCVmBcJtKnrgNgDEA\nGzduRIsWLdCkSZN802bd9Ts5OcHV1RVnz57liz/TSPwEwLReSkoKfHx8EBQUlG/ahw8fYsSIEUhO\nTsa5c+eUOk0EY8WNnwCY1tu0aROcnJzQtGlTmWkkEgn8/Pzg6OiIrl274vz583zxZxqPnwCYVktN\nTYW3tzcOHjwoM83jx48xYsQIJCQk4OzZs0qZHoIxdcABgGmHuDixN0hUlLgcY/nygK0tfktPh729\nPZo1yz1HJBFh48aNmD17NqZMmYKpU6eidGn+k2ElB/9vZiVbWJjYH/zIEfF1ttkiKTAQQ1JS0K9d\nOzGd43+rxj558gSenp54+/YtTp8+jYYNZS3KyJjm4jYAVnL5+QHt2okThKWm5lp+UUhJgS4Ak5AQ\nMZ2fH4gImzZtgr29Pdq3b4+LFy/yxZ+VWPwEwEomPz/554QhApKTIZkyBWvXrMF2fX2cOnWqQNNB\nMKZJ+AmAlTxhYVIv/oMhzgtvDKAuci8SXiolBd/du4d/1q7liz/TCoUKAIIgfC0IwnVBECSCIMic\nUEMQhK6CINwWBOGeIAjTC3NOxvK1ePHnc79kmg7gAYD3AP4CMBtARI40ZdPTUXrp0iIvImPqoLBP\nANcA9AEgc9VsQRB0AKwD4AKgAYBvBUEo+ETrjOUlLk5s8JWyKEgjiKtEAYCQud3PmYhInEDs5csi\nLSZj6qBQAYCIbhLR7XySOQG4R0QPiCgNwC4AvQpzXsZk2ro1z7fHQAwC9QFYAegmLZEg5JsPYyVB\ncbQBVAEQk+3108x9jClfVFSu3j7ZrQeQAOAsxEfXctISpaSIs0cyVsLlGwAEQTghCMI1KVuR3MUL\ngjBKEIRwQRDCX/JjOFNUfHy+SXQAfAnxTsRPVqK3b5VXJsbUVL7dQImoUyHP8QxA1WyvrTP3yTrf\nRgAbAcDBwUH66s6MyVK+vNxJ0yGlDSCLiYkySsOYWiuOKqAwAHUEQagpCEJZAN9A7ITBmPLZ2gK6\nurl2x0FsfEoEkAHgKICdADpKy0NPT1w0hLESrrDdQN0EQXgKoAWAw4IgHM3cX1kQhCAAIKJ0AOMg\n/s3dBPAHEV0vXLEZk8HdXepuAWJ1jzUAEwBTAawE0FNaYiKZ+TBWkhRqJDAR7QOwT8r+58jWwYKI\nggDkP9k6Y4VlaQm4uIjTP2TrCmoBIESe4wVBXCuWlwtkWoBHArOSZ8YMsRqnIPT0xOMZ0wIcAFjJ\n4+gI+PoC+vr5p81OX188zkHmoHbGShSeDI6VTF5e4s+pU8V+/VJGBn8iCOKdv6/vf8cxpgX4CYCV\nXF5eQEgI4OYm9gzKWS2kpyfud3MT0/HFn2kZfgJgJZuDA7B3rzi3z9at4gjft2/Ffv6NG4u9fbjB\nl2kpDgBMO1hYAD/8oOpSMKZWuAqIMca0FAcAxhjTUhwAGGNMS3EAYIwxLcUBgDHGtBQHAMYY01Ic\nABhjTEtxAGCMMS0lUF5zpKiYIAgvATwu4tOYA3hVxOfQdPwdyYe/J/nw9ySfgn5P1YlIruHtah0A\nioMgCOFE/2/vfl6sKsMAjn+/WG0isNDUtKDFbGwjLSSihUGUzmYqMmyTRCAG/QFCUNCqtdEPIiRb\nVLQxB1JD3dhG6AdDKSUNEeRgDRQYURDS0+K+0QU7956Ie871vs8Hhvuee14uDw/PmWfOe849E/n4\nxxEyR+1kntrJPLXTRZ5yCSillCqVDSCllCqVDQDe6DuAa0DmqJ3MUzuZp3YmnqfqrwGklFKt8gwg\npZQqVVUDUHer59U/1car6+pO9YK6rB7oMsZpoN6inlS/Ka83N8z7Tv1SXVI/7TrOvoyrDwcOlv1f\nqHf3EWffWuRph3q51M+S+nwfcfZJPaSuquca9k+0lqpqAMA54FHgTNMEdQ3wCrAL2Ao8oW7tJryp\ncQA4HRFzwOmy3eT+iNhWy219LetjFzBXfvYBr3Ua5BT4D8fRx6V+tkXEi50GOR3eAnaO2D/RWqqq\nAUTEVxFxYcy07cByRHwbEX8A7wELk49uqiwAh8v4MPBwj7FMmzb1sQC8HQNngbXqpq4D7VkeRy1E\nxBng5xFTJlpLVTWAljYD3w9tXyzv1WRDRFwq4x+ADQ3zAjilfqbu6ya03rWpj6yh9jm4tyxtHFfv\n6ia0a8pEa2nm/iewegrY+C+7nouIo13HM61G5Wl4IyJCbbpV7L6IWFFvBU6qX5e/aFJq43Pgjoj4\nVZ0HPmCw1JE6MnMNICIe+J8fsQLcPrS9pbw3U0blSf1R3RQRl8rp5mrDZ6yU11X1CIPT/llvAG3q\no4oaGmNsDiLil6HxMfVVdV1E5HOC/jHRWsoloKt9Asypd6o3AHuAxZ5j6toisLeM9wJXnTmpN6o3\n/T0GHmRwkX3WtamPReDJcgfHPcDloSW1WozNk7pRtYy3M/h99FPnkU63idbSzJ0BjKI+ArwMrAc+\nVJci4iH1NuDNiJiPiCvqs8BHwBrgUESc7zHsPrwEvK8+zeBprI8DDOeJwXWBI+X4vQ54JyJO9BRv\nZ5rqQ91f9r8OHAPmgWXgN+CpvuLtS8s8PQY8o14Bfgf2RGXfTFXfBXYA69SLwAvA9dBNLeU3gVNK\nqVK5BJRSSpXKBpBSSpXKBpBSSpXKBpBSSpXKBpBSSpXKBpBSSpXKBpBSSpXKBpBSSpX6Cy0LWrb5\nffdOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x88f90b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g=nx.barabasi_albert_graph(11, 2)\n",
    "nx.draw_networkx(g)\n",
    "write_directed_vertex_list_to_file(g,'parameters\\\\N0011_BA002_001.net')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#A=loadtxt('N0010_BA002_001.net')\n",
    "#g=nx.Graph(to_undirected_vertex_list(A))\n",
    "#nx.draw_networkx(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#w=random.rand(10)\n",
    "#savetxt('N0010_U001.Freq', w, fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#theta0=random.uniform(-pi,pi,10)\n",
    "#savetxt('N0010_R001.Ic', theta, fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#theta1=random.uniform(-pi,pi,10)\n",
    "#savetxt('N0010_R002.Ic', theta1, fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generate_mean_zero_frequencies(n, a = pi, tol = 0.01):\n",
    "\n",
    "    w = zeros(n)\n",
    "    w[n-2] = -a\n",
    "    w[n-1] = a\n",
    "\n",
    "    for i in range(1000):\n",
    "        w[range(n-2)] = random.uniform(-a,a,n-2)\n",
    "        if abs(mean(w)) < 0.01:\n",
    "            return(sort(w))\n",
    "            break\n",
    "\n",
    "    return nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w = generate_mean_zero_frequencies(10)\n",
    "savetxt('parameters/N0010_U001.Freq', w, fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}