{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy.sparse as sp\n",
      "import scipy.sparse.linalg as alg\n",
      "import math\n",
      "from time import time\n",
      "\n",
      "import pylab as pl\n",
      "\n",
      "%pylab inline --no-import-all"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# x - bezwymiarowa siatka polozen\n",
      "\n",
      "# Stale 'rho' zadeklerowane dla ponizszych typow potencjalow to bezwymiarowe zmienne\n",
      "# z bezwymiarowego rownania Schrodingera.\n",
      "\n",
      "def rho_Konwent():\n",
      "    k = 1.0\n",
      "    return 0.25 * (2 * k + 1)**2\n",
      "def Konwent(x):\n",
      "    # potencjal Konwenta: v(x) = V_c * (alpha * cosh(C*x) - 1)**2\n",
      "    # bezwymiarowy potencjal Konwenta: V(x) = ( (b / (2*k+1)) * cosh(x) - 1 ) ** 2\n",
      "    k = 1.0\n",
      "    b = 0.03   \n",
      "    return  ( (b / (2.0 * k + 1.0)) * np.cosh(x) - 1) ** 2 \n",
      "\n",
      "\n",
      "def rho_Konwent2(m, V_c, C, h):\n",
      "    # 1/C - stala o wymiarze dlugosci\n",
      "    # m - masa czastki\n",
      "    # V_c - charakterystyczna energia\n",
      "    # h - stala Diraca\n",
      "    return 2.0 * m * V_c / (C * C * h * h)\n",
      "def Konwent2(x):\n",
      "    # potencjal Konwenta: v(x) = V_c * (alpha * cosh(C*x) - 1)**2\n",
      "    # bezwymiarowy potencjal Konwenta: V(x) = (apha*cosh(x) - 1)**2\n",
      "    return  (c * np.cosh(x) - 1) ** 2   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# rozmiar macierzy pieciodiagonalnej:\n",
      "N = 1000\n",
      "    \n",
      "# konce bezwymiarowego przedzialu obustronnie domknietego [x_min,x_max]:\n",
      "x_min = -8.0\n",
      "x_max = 8.0\n",
      "\n",
      "# ilosc wartosci wlasnych i wektorow wlasnych, ktore chcemy wyznaczyc:\n",
      "N_eigvals = 4\n",
      "\n",
      "# wybor potencjalu:\n",
      "potential = Konwent\n",
      "# opcjonalne argumenty wybranego potencjalu:\n",
      "args_potential = ()\n",
      "\n",
      "# wybor parametru rho (odpowiedni dla danego potencjalu):\n",
      "#rho = rho_Konwent\n",
      "rho = rho_Konwent\n",
      "# opcjonalne argumety wejsciowe funkcji zwracajacej wartosc bezwymiarowej zmiennej rho\n",
      "args_rho = ()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential):\n",
      "    \n",
      "    \"\"\"\n",
      "    funkcja 'EigenvalEigenvecProblem5' rozwiazuje algebraiczne zagadnienie wlasne \n",
      "    wykorzystujac pieciopunktowa metode centralnych roznic skonczonych do przyblizenia \n",
      "    drugiej pochodnej.\n",
      "    \"\"\"\n",
      "    \n",
      "    # dyskretna siatka polozen, gdzie N - ilosc punktow siatki:\n",
      "    x = np.linspace(x_min, x_max, N)\n",
      "    \n",
      "    # krok dyskretnej siatki:\n",
      "    dx = (x_max - x_min) / (N - 1)\n",
      "    \n",
      "    # elementy na glownej diagonali macierzy pentadiagonalnej:\n",
      "    diagonal = 30.0 * np.ones(N) + 12.0 * dx * dx * rho(*args_rho) * potential(x, *args_potential)\n",
      "    # elementy na pierwszych diagonalach macierzy pentadiagonalnej:\n",
      "    diag_edge1 = -16.0 * np.ones(N - 1)\n",
      "    # elementy na drugich diagonalach macierzy pentadiagonalnej:\n",
      "    diag_edge2 = 1.0 * np.ones(N - 2)\n",
      "    # tablica niezerowych element\u00f3w macierzy pentadiagonalnej:\n",
      "    pentadiagonal = np.array([diag_edge2, diag_edge1, diagonal, diag_edge1, diag_edge2])\n",
      "    \n",
      "    # numery diagonali, gdzie 0 oznacza diagonale glowna, \n",
      "    # -1 -pierwsza diagonale dolna, 1 -pierwsza diagonale gorna,\n",
      "    # -2 -druga diagonale dolna, 2 - druga diagonale gorna:\n",
      "    offsets = [-2, -1, 0, 1, 2]\n",
      "    # macierz trojdiagonalna (hamiltonian) zapisana w postaci macierzy rzadkiej:\n",
      "    hamilton = sp.diags(pentadiagonal, offsets)\n",
      "        \n",
      "    start = time()\n",
      "    \n",
      "    # wyznaczenie N_eigvals wartosci wlasnych i odpowiadajacych im wektorow wlasnych\n",
      "    eigvals, eigvecs = alg.eigsh(hamilton, k = N_eigvals, which = \"SM\", tol = 1.0E-4)\n",
      "    # argument \"SM\" oznacza, \u017ce wyznaczone zostanie N_eigvals najmniejszych wartosci\n",
      "    # wlasnych i odpowiadajacych im wektorow wlasnych, a argument \"tol\" oznacza wzgledna \n",
      "    # dokladnosc, z jaka wyznaczone zostana wartosci wlasne\n",
      "    \n",
      "    # !UWAGA! Argument 'eigvecs' stanowi macierz o 'N' wierszach i 'k' kolumnach,\n",
      "    # wiec i-ty wektor wlasny ma 'i' kolumn o dlugosci 'N'.\n",
      "    \n",
      "    #print \"'alg.eigsh(...)' time: %.2fs\" % (time() - start)\n",
      "    \n",
      "    eigvals = eigvals / (12.0 * dx * dx * rho(*args_rho))\n",
      "    \n",
      "    return eigvals, eigvecs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# DYSKUSJA BLEDOW\n",
      "\n",
      "eigvals_a, eigvecs_a = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "print eigvals_a\n",
      "\n",
      "N_max = 500\n",
      "N_list = range(100, N_max + 100, 50)\n",
      "measures = 5\n",
      "\n",
      "# blad wzgledny:\n",
      "time_list = []\n",
      "digits_list = []\n",
      "\n",
      "for N in N_list:\n",
      "    eigvals_num, eigvecs_num = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "    \n",
      "    time_start = time()\n",
      "    for m in xrange(measures):\n",
      "        EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "    exec_time = (time() - time_start) / measures\n",
      "    \n",
      "    time_list.append( exec_time )\n",
      "    digits = [-math.log10( abs( (e_num - e_a) / e_a ) )\n",
      "                  for e_num, e_a in zip(eigvals_num, eigvals_a) ]\n",
      "    digits_list.append(digits)\n",
      "    \n",
      "for idx in xrange(N_eigvals):\n",
      "    \n",
      "    digit_data = [digit[idx] for digit in digits_list]\n",
      "    pl.plot(N_list, digit_data, \".-\", label = \"$\\widetilde{E}_{%d}$\" % idx)\n",
      "    \n",
      "pl.xlabel(\"$N_{}^2$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$l_n$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "pl.xlim(None,N_max)\n",
      "pl.show()\n",
      "\n",
      "for idx in xrange(N_eigvals):\n",
      "    \n",
      "    digit_data = [digit[idx] for digit in digits_list]\n",
      "    pl.plot(time_list, digit_data, \".-\")\n",
      "    \n",
      "pl.xlabel(\"$t$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$l_n$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "#pl.xlim(None,1000)\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.55525589  0.55565553  1.00049963  1.11047152  1.32631369  1.60634954]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEUCAYAAAAx56EeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4IiCAAA4oiAAAI7+ACiFk/BpfsPCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbbe1b0c>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAESCAYAAAAWtRmOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSHMQQgjRyf8DGMiEPJU2XK8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc54ea4c>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# WYKRES ZALEZNOSCI ENERGII DLA KOLEJNYCH POZIOMOW ENERGETYCZNYCH:\n",
      "\n",
      "N_eigvals = 6\n",
      "\n",
      "eigvals, eigvecs = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "print eigvals\n",
      "\n",
      "fig, ax1 = pl.subplots(figsize = (8, 5))\n",
      "ax1.set_yticklabels([])\n",
      "ax1.set_ylabel(\"$\\psi_n$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "\n",
      "ax2 = ax1.twinx()\n",
      "\n",
      "num_x = len(eigvecs)\n",
      "dx = (x_max - x_min) / (num_x - 1) \n",
      "x_list = [x_min + i * dx for i in xrange(num_x)]\n",
      "print len(x_list)\n",
      "h_list = [potential(x_i) for x_i in x_list]\n",
      "ax2.plot(x_list, h_list, color = \"black\")\n",
      "\n",
      "#ax2.plot((eigvals), \"o-\")\n",
      "for i in xrange(N_eigvals):\n",
      "    ax2.plot([x_min, x_max], [eigvals[i],eigvals[i]], color = \"orange\")\n",
      "    ax2.plot(x_list, eigvecs[:,i] + eigvals[i], color = \"green\")\n",
      "#plt.savefig(\"num_har_E.png\")\n",
      "\n",
      "ax2.set_xticks([x_min, x_max])\n",
      "ax2.set_xticklabels([\"$x_{min}$\", \"$x_{max}$\"])\n",
      "ax2.set_xlim(x_min, x_max)\n",
      "ax2.set_yticks(eigvals[1:])\n",
      "\n",
      "\n",
      "ax2.set_yticklabels([\"$n = 0, \\ 1$\"] + [\"$n = %d$\" % i for i in range(2,len(eigvals))])\n",
      "ax2.set_ylim(None, eigvals[-1] + 0.1)\n",
      "\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.5552559   0.55565554  1.00049963  1.11047153  1.3263137   1.60634957]\n",
        "550"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAE1CAYAAAC2idmRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4VFX6wPHvTHrvvYcUkpAAgZDQE6SJIIKCWABdRfRn\nWcVd11VYWF1c6ypiRREVXXSlqEhHEmqoCQQSCEkglfRepmXm/v6IRJEiJZkSzud5znMnM5OZ904y\nc977zrnnyCRJkhAEQRAEQbgGckMHIAiCIAiC6REJhCAIgiAI10wkEIIgCIIgXDORQAiCIAiCcM1E\nAiEIgiAIwjUzN3QAvyeTyQwdgiAIgiDonamdFGl0CQSY3ot4JYcOHeKxxx7j8OHDhg5FEAShR5Ak\nCblcjlarRS7vGYV0Uzx47hmvvBGztrZGpVIZOgxBEIQeQ61WY2lp2WOSB1MlXv1uZmVlhVKpNHQY\ngiAIPYZSqcTKysrQYdz0jPIrjJ7EyspKVCAE4SpIkkStopby5nLqFHUo25WotCrade3YWthib2mP\nvaU9vg6+eNh6mGTJV+gaKpUKa2trQ4dx0xMJRDcTX2GATtLRrGqmXlmPRqtBLpNjYWaBu607tha2\nhg5PMIDSplL2Fu8lqyqL7KpsTlSdoKSpBHtLe3zsfXCzdcPa3BorMyvM5Ga0adpoVbfSrG7mXPM5\nWtQt+Dv6E+kWST/vfvTz7keCbwJBzkGG3jVBD0QFwjiIBKKb3WxfYZQ3l5Nems7+0v0crzpOfl0+\nRQ1FWJtb42LjgqWZJTpJh6pdRa2iFrlMTqhLKFHuUUR7RNPfuz/Dg4bjauNq6F0RulCdoo5NeZvY\nlL+JPcV7aNW0MjRgKP28+3F/3P3EeMQQ4hKCtfnVHVW2adoobizmZPVJjlYeZWXWSh7f+Di2Frak\nBKdwS8gtTAifgIuNSzfvmWAIKpVKJBBGQGZsi2nJZLIedRaGUqnE2dm5RycRRyuO8l3Od2w4vYHi\nxmKGBAwhyT+Jvl59CXcLJ8Q5BCvzi9/skiTRom4hvy6fkzUnyanO4dC5Q6SXpBPsHMyokFFM6T2F\nYYHDMJObGWDPhBtR2lTK/7L/x4+5P5JRnkFKSAq3hd9GcnAy4a7hXf4VhCRJ5FTnkFqYytaCraQV\nppHon8jU3lOZFjMNd1v3Ln0+4eo0Khs5WHaQUzWnOF13mpq2GlrULai1auwt7XG0ciTQKZBw13Bi\nPGKI9YrFXH7lY9vjx49zzz33cOLECT3tRfczxb5PJBDdTKfTYWZmhk6n61Hf2da01fBV1ld8fvRz\n6pX13Bt7LxPDJ5Lon/iHb/4/otFqyCjPYEvBFtadWkdZUxl39L6D2X1nMyRgSI96HXuaVnUr606t\n44tjX3Dk3BGmRE1hSu8p3BJyCzYWNnqNpUXdwpb8Law+uZpNeZsYHTqaP/X/E2N7jb3h/1HhyrKr\nsvkm+xs252/mVM0p4n3iifGIIdw1HG97b+wt7bEws6BF3UKjspGixiJO157mWOUxyprKGOQ3iFvD\nbmVy78mEuYZd9PiHDx9m7ty5HDlyxAB71z1Mse8TCYQeWFpa0tzc3CNKbgV1Bfxn/39YdXwVt0Xc\nxoP9HiQ5OBm5rPtO6DlTf4bvsr/js6OfIUPGQ/0f4sH+D4ojSiNysvokSw8uZdWJVQz2H8zsvrO5\nPfJ2vScNl9OgbODbE9+y4ugKSppKeCT+ER5LeAxPO09Dh9ZjtKpbWZm1kg8OfUC9sp67Y+7m9sjb\nSfRLvGQF8nJq22rZU7yHDXkbWH96PS7WLkyPmc7MuJn0cu0FwN69e3nuuefYu3dvd+2O3pli3ycS\nCD1wdHSktLQUR0dHQ4dy3XJrclm0cxHbCrYxd+Bcnhz0JN723nqNQZIk9pXs45OMT/gh9wfujrmb\np5Oeprd7b73GIXTQSTo25W1iyYElZFVmMXfgXOYOmIuvg6+hQ7uiE1UnePfAu3yX8x1To6bydOLT\nxHrFGjosk1WnqOOt9Lf46PBHjAgawRMJT5ASktIlBxU6ScehskP898R/WXV8FZHukcyKm4VnjSdL\nXlvCjh07umAPjIMp9n0igdADDw8PsrOz8fQ0vaOd0qZSXtr5EutOrWNe0jyeGPQEDlYOhg6LypZK\nPjz8IR8e/pAE3wSeH/Y8wwKHGTqsm4JGq+GLY1/w2t7XcLJy4s+Jf2Z6zPRrOso0BtWt1Sw7soz3\nD71Pf5/+LBixgCT/JEOHZTIalA28s/8d3jv4HlOjpvL3YX8nxCWk255PrVWzOX8zXx77ks2nN+NY\n5cinT37aY76SMsW+TyQQehAQEMC+ffsICAgwdChXTaFR8Pre13n34LvMiZ/D34b+zShHtCs0ClZm\nreTVPa8S7BzMwpELGRk80tBh9UhqrZovjn7BK3teIdw1nAUjFjAscJjJj0lRtitZkbmCV/e+SqRb\nJAtGLGB40HBDh2W0JEniy2Nf8rftf+PW8FtZMGIBoS6heo3hi/99wdtb38ZmsA2FDYXcG3svs+Jm\n0de7r17j6Eqm2PeJmSj1wNRO5Vyfu56YD2I4XnWczLmZvDr6VaNMHgBsLGx4ZMAj5D6Ry8y4mTz0\n40Mkf55M6tlUQ4fWY6i1apYdWUbE0ghWn1zN11O/ZuvMrQwPGm7yyQOAtbk1jyU8Rt6TeUyPmc4D\nPzxA8ufJ7C7abejQjM6JqhOM/Hwk7x16jw33bmDF5BV6Tx4ALLWW9G7pTfpD6ex8YCe2FrZM/mYy\nfT/qy+t7X+dUzSmT64y7yzPPPINareaDDz6gsrKyax9cMjJGGNINi46Olo4fP27oMP5QXm2edNvX\nt0mRSyOlrflbDR3OddFoNdLnmZ9LYe+GScmfJ0u7i3YbOiSTpWpXSR8d+kgKfDtQGrdynLSveJ+h\nQ9ILjVYjrchcIQW9HSTd+tWtUsa5DEOHZHDNqmbpL1v/Irm/7i59cPADqV3bbtB4PvvsM2n27NkX\nXKfVaaW0s2nSoz89Kvm95SeFvRsmPbP5GemHUz9ItW21hgn0GnRX3+fq6ir5+vpKy5Yt6/LHFl9h\n6MGAAQNYtmwZAwYMMHQol6RqV/H63tdZcmAJzw19jqeTnsbSzNLQYd2Qdl07K4+t5KVdLxHhFsFL\nyS+R6J9o6LBMgqpdxWeZn/HvPf8mxjOGhSMX3pRjA1TtKj7J+ITFuxczImgEL6e8TIRbhKHD0itJ\nklh7ci3PbHmGlJAUXh/9Ol72XoYOi48++ojMzEw+/vjjS94uSRKZFZlsOL2BXcW7OFB6gCDnIAb4\nDKCPZx9iPWMJdwvHz8HPaMbudFff9/XXX3Pfffd1+eOCEY6BKPtSxs42kMlAxoWN85cvcZvsOm+z\nlIG9HOx+2Z6/3AMqs1dlZxvMrYJIS1jqAYEWho6oa6klWNEE/6qDvpbwkhvEiyn0L0mlg+VN8Go9\n9LGEhW6QKF4rWnXwbgP8pwEm28FCVwjoYe+TSylQw5PVUNQOH3jASBOedb5dgqOqjnZCDcdVUKCB\nc+3gbAb+5uBlBg5ycPxNs5aBuaxjyubfbs0AM9mv/dLvXan7OH/bydMd7by1G7gogdi9ezdr1qxh\n5MiOcV3Z2dnMnz//mvb95ZdfJiEhgezsbJ599tlr+t0/YnRDV/2fhxlLZyBJEhJS5xa46Lob3ULH\nUUarqpUWdUvnXPtanRZve298HHzwsfch2DmY3u69iXKPIsYz5pqnWU5JSWHBggWMGjWqy1+v61XT\nVsNft/2V7ZXbWTp1KXf0vsPQIXULS2AuMLtdybIjy5i451WS/JP4Z/I/xal7v1C2K1mesZxX975K\nnFccqycvZJDfIEOHZTTsgL8DjyrqeWPfG/Q78jGz+s7ihWEv4GHnYejwupyyXdkxgPrAu0ZbkXzt\ntdeora3l9ddfv6r7mwMDf2m/pZN0VLVWUdJYQlVrFc3qZppVzTSpmmhSN1HfrqRd145Wp6Vd135B\n00raSz7XlY7Jz/c7AHiC9bDf/M6GVRfd//wYI39/fxISEvj+++87b8vJyWHbtm2XfJ7Zs2fj7OwM\nwIIFCwAoKChgy5YtjBs37rLxXSujSyAoh1V3XvxC6pNCo6C8pZzy5nLKW8o5U3+G/aX7WXF0BdlV\n2fg4+DDYfzBDA4YyttfYP1zAx9ra2mgGUUq/jKB+bvtz3NPnHnL+L8coTsvsbtbm1jyV+BQPxz/M\nB4c+YPTK0YwKGcWikYuIdI80dHgGoWxX8mnGp7y29zX6evVl7fS1JPglGDoso+Vi48Irt7zCU4lP\nsXj3Ynq/35vHBj7GvMHzeszaLVsLtvL4xseJ9YwlY24GgU6Bhg7pkrpqLQy5TI63vbfe57S5lFVc\n3O8NGzaM1157jYSEBBobGzE3/7XLjo6OJjo6+oqPuWLFCrRaLQ8//DA2NjZkZWX18ATCCNhY2BDq\nEnrJ0cVanZbs6mzSS9LZWbSTF3e8iIedBxPCJ3Bn1J0k+iVeNDLdWJb0zqnO4fGNj9OkamLDvRsY\n6Pv7fLzns7Ww5S9D/sLcAXNZenApw1YMY0L4BP4x4h+ds9z1dG2aNj4+/DFvpr9JvE886+5ed1P+\nL1wvb3tvlt66lGcHP8vi3YuJWBrBk4Oe5Omkp3GydjJ0eNelqKGIZ7c+S0Z5Bu/e+i4TIyYaOqQr\nUiqV2NnZGTqMbqdQKDqXLd+4cSNjxowhPT2dwYMHX7ECMWvWLFxcXHB3d2fgwI73dmFhIcnJyV0a\nn0ggrpGZ3Iw4rzjivOKYO3AuOknH4XOH+en0Tzzw/QOotWpm9JnBjD4ziPOKAwy/pHdNWw2L0hbx\nbfa3LBixgP9L+L8eMfHKjXCwcuCF4S/weMLjvLP/HRI/TWRK1BTmD5/fY5eEblI18cGhD3hn/zsM\nDRzKT/f8RH+f/oYOy2QFOwfzyaRPeH7o8/xr978IWxrG04lP81TiUyZT1VO2K3lj7xu8c+Ad/pz4\nZ1ZOWWk0049fiUqlwtW1Z1R9riQ7O7tz/IODgwMFBQUMHToUuLoKxMSJE1myZAlOTk74+/t3+dfo\nRjeI0pTPwpAkiWOVx1h1YhXfnPgGJysnZvWdxYFPD3DbiNt44IEH9BqPWqvmg0MfsHj3Ymb0mcGi\nkYtws3XTawymok5Rx5v73uTjIx8zo88MXhj2An6OfoYOq0vUttXy3sH3eO/Qe4wJHcOLw18kxjPG\n0GH1OKdrT/PSzpfYWrCVp5Oe5rGBjxnt/Ck6Scd32d/xwo4X6Ofdj7fGvkWwc7Chw7pqjz/+OFFR\nUTzxxBOGDqXLmGLfJxKIbqKTdOwu2s2XWV/y9ZGv6WXdixdue4E7et+BnWX3lt6U7Uo+y/yM1/a+\nRoxHDG+OfZNojytnqkKH6tZqXtv7Gp9lfsbsfrP5y+C/mGwicbL6JEsOLOHb7G+Z0nsKzw97/qY7\nDdEQTlaf5LW9r/Fj7o/M6juLZ5KeMZqqliRJbC3Yyt9//jvmcnNeHf0qo0KMZ3D31Xr44YdJTExk\nzpw5hg6ly5hi3ycSCD147KnHaAtoo8avhn0l+5gcOZlZfWd1+SqW1a3VfH70c9458A7xPvHMHz5f\nzH1wncqby3l176usPLaScWHjeGrQUyT5Jxn9zIs6Sce2gm28c+AdMsszeXTgozw68FGjGCR2sylr\nKmPJgSUsz1zOLSG38MiARxgVMqpbV669HJ2k44dTP/DGvjeoVdSyeNRi7oy60+j/ny9n5syZjB07\nlpkzZxo6lC5jin2fSCD04K9//SseHh4899xzVLRUsOr4KlZmraS6rZr7Yu9jQvgEkvyTrutUqVZ1\nK1sKtvC/7P+xOX8zd/S+g6cSnyLeJ74b9uTm06hsZMXRFSw9uBQXaxfmDpjL9JjpRjdYrqihiM+P\nfs7nxz7H2dqZpwY9xT2x92BtLiZyMLRGZSMrs1byScYntKhbeKj/Q8zuO1svla16RT2rTqxiyYEl\nOFk58dzQ55jSewpmcrNuf+7uNG3aNKZNm8b06dMNHUqXMcW+TyQQerBgwQIsLS07z8c970TVCf57\n/L9sO7ON3JpchgYOZZDfIGI9Y4l0i8Tb3htXG1fM5GboJB1NqiZKGks4U3+Gw+WH2V+6nwOlB0j0\nT2RK7ync0+ceo/3O1dSdX7p6xdEVbD+znfFh45nVdxZjQsdgYWaYWYWqWqv4MfdHvs3+lszyTO6J\nvYcH+z1If+/+Jntk2ZNJksThc4f5JOMTvsv5jhiPGKZGTWVq1NQuHX+g0Cj4+ezPrMxayZb8LYwP\nG89jAx9jRNCIHvN/cfvtt/PQQw8xefJkQ4fSZUyx7xMJhB7861//QqFQsHjx4svep05RR1phGhnl\nGRyvOk5ebR5VrVU0KBvQSTokJBwsHQhwCiDIKYh4n3iS/JMYGjBUJA16Vqeo49sT3/Jl1pfk1uQy\nPmw8t0fezrhe47r1byFJEqdqTrGlYAtrT67lWOUxxvUax13Rd3F75O2i2mBCVO0qdpzdwZqTa/gx\n90fsLe0ZGTySkUEj6e/dnwi3iKs+G6JN00ZGeQYHSg+QVpTGzsKd9PPux72x93J3zN098vNh7Nix\nPPvss106p4GhmWLfJxIIPXjzzTcpLy/nrbfeuubf1eo6ZjuTy+Q95uihJylrKmND3gbWn15PWmEa\noS6hDA8czrDAYcR5xRHuGn7dFYoGZQPHKo5xrPIY+0r2kVaYhpW5FaNDRzOl9xRGh44WSUMPIEkS\nOdU57Craxa7iXWRVZlFQV4C/oz/BzsF42HngauOKhdwCmUyGQqOgXllPVWsV+XX5VLdWE+sVS5J/\nEsMChjG219gemTT81siRI/nnP//Z5fMaGJIp9n0igdCD9957j1OnTvHee+8ZOhShG2m0GjLKM9hd\nvJt9Jfs4UXWCkqYSwlzDCHQKxMfeB18HX5ytnbE0s8TKzAqZTEarumMq9UZVI6VNpRQ3FlPUWES9\nop44rzj6efcjwTeBlJAUkzrVTrh+Gq2G/Lp8SppKqG6tpk5Rh1bSotVpsbWwxcXGBQ9bD3q59iLA\nMcDkxzRcq8TERN59910SE3vOIHFT7Ptu7tmE9MTa2hqFQmHoMIRuZmFmQaJ/4gVnvig0CnJrcylp\nLKG8pZxzzecoaSpBrVWjalchIWFvaY+dhR2uNq7EecUR6BRIgGMAgU6BN13HIHSwMLMgyiOKKI8o\nQ4dilH47Q6NgOCKB0AMbGxuRQNykbCxs6Ofdj37e/QwdiiD0GEqlEhsb458xs6fT/wnJNyFjWkxL\nEATB1IkKhHEQCYQeiAqEIAhC1xEVCOMgEgg9EBUIQRCEriMqEMZBJBB6ICoQgiAIXUdUIIyDSCD0\nQFQgBEEQuoZGowHA3FycA2BoIoHQA1GBEARB6Bqi+mA8RAKhB6ICIQiC0DXE+AfjIRIIPRAVCEEQ\nhK4hKhDGQyQQeiAqEIIgCF1DVCCMh0gg9EBUIARBELqGQqEQFQgjIRIIPbCwsECn09He3m7oUARB\nEEyaUqkUFQgjIRIIPZDJZOJrDEEQhC4gKhDGQyQQemJjYyMSCEEQhBskKhDGQyQQeiKW9BYEQbhx\nogJhPEQCoSeiAiEIgnDjRAXCeIgEQk9EBUIQBOHGiQqE8RAJhJ6ICoQgCMKNExUI4yESCD0RFQhB\nEIQbJyoQxkMkEHoiJpMSBEG4caICYTxEAqEnYh4IQRCEGycqEMZDLKiuJ6ICcWk6SUdpUym5Nbmc\nbThLVWsVVa1V1LTVoGhXoGpXodaqsTCzwMbcBmtza1xsXPCx9+loDj4EOQUR7haOpZmloXdHEABo\n07SRX5dPfl0+FS0VVLVWUd1WTU1bDcp2JRqtBo1Og4XcAlsLW2wtbHGxccHX3hdfB18CnAKIco/C\n084TmUxm6N0xKkqlEhcXF0OHISASCL0RFYgOBXUF7CvZx4GyAxwsO0h2dTbO1s5EuEXQy6UXXvZe\nhLmGkeSfhI25DZZmlliaWaLRaVBoFCjaFdQr6ilvKWdPyR7Km8s523CW4sZigpyCiPaIJs4rjiT/\nJAb5DcLVxtXQuyz0YJIkUdpUyqFzhzhYdpCM8gxO1Zyiuq2aXi69CHMNw8fBBw9bD6Lco3C3dcfa\n3BoLuQUWZha069ppVbfSqmmlXlHPuZZzZFVlUdRQRE51DnKZnBjPGBL9EhkaMJQhAUPwsPMw9G4b\nlKhAGA+RQOjJzVqBUGgUbDuzjc35m9lSsIU2TRvDA4eT6JfIjD4z6OvVFwcrhxt+HlW7iry6PE5W\nnySzIpM39r3BobJD+Dr4MjRwKGNDxzI6dDRutm5dsFfCzayooYjtZ7bz89mfSS1MRSfpSPBNYJDf\nIP6c+GeiPaIJdArETG52Q88jSRKVrZUcrzzO/tL9fHj4Q2Z9P4tg52BuC7+N28JvI8k/6Yafx9SI\nMRDGQyQQenIzVSDUWjWb8zfzv+z/sSFvA/28+zEhbALf3/09fTz7dEtJ1srcij6efejj2YdpMdMA\n0Oq0ZFdns7NwJ18f/5pHfnqESLdIxvYay4TwCST5JyGXiWFAwpWptWp2nN3Bj7k/su3MNhqVjYwO\nHc3o0NEsHrWYYOfgbvmflslkeNt7423vzZheY4CO/+kDZQfYkLeBxzc+TkVLBXf3uZv7Y+9noO/A\nm+LrDlGBMB4igdCTm6ECkV+Xz6cZn/L50c+JcItgRp8ZvDn2TbztvQ0Sj5ncjDivOOK84ngy8UnU\nWjXpJelsKdjC3J/mUttWy5SoKdwZdScjgkZgLhdvB6FDi7qFTXmbWHdqHZvyNxHlHsUdve9gzfQ1\n9PHsY7DE00xuxpCAIQwJGMLiUYvJr8vn66yvuXftvchlcmb3nc3D8Q/jaedpkPj0QVQgjIdMkiTJ\n0EH8lkwmY+HChZ0/Jycnk5ycbLiAushLL72ERqPh5ZdfNnQoXUon6Vifu56lB5eSVZnF7H6zmRM/\nhwi3CEOH9odO155m7cm1rDm5hsKGQqZGTWVm3EyGBgy9KY7khAuptWq25G/hq+NfsTl/M0n+SUzp\nPYXJkZPxcfAxdHhXJEkSB8sO8mnmp6zOWc2kiEk8MegJBvkNMnRoXW78+PH8+c9/5tZbbzV0KF1K\nJpNhZN3xHzLKBMLIQuoSb7zxBpWVlbz55puGDqVLKNuVrDy2krfS38LByoF5SfOYGjUVK3MrQ4d2\nXYoailh1YhUrs1ai0Ci4P+5+ZsbNJNwt3NChCd1IkiTSS9P5Kusrvsv5jki3SO6Pu59p0dNMdrxM\nnaKOzzI/4/1D7+Nt780Lw15gYsTEHpMUJycns3DhQlJSUgwdSpcyxb5PJBB6snTpUnJzc3nvvfcM\nHcoNadO08f7B9/nP/v8Q7xPPX4f8lZFBI3vMh5MkSWRWZLIyayWrjq8i2DmYh/o/xIw+M7pksKdg\nHKpaq/j86Od8mvEpZnIz7o+9n3tj7yXEJcTQoXUZrU7LulPrWLx7MTpJxwvDXuCu6LtMftBlYmIi\nS5YsISkpydChdClT7PvECDI9MfUxEKp2Fe8dfI+wd8M4UHaArfdvZcO9G0gOTu4xyQN0vInjfeJ5\ne9zblM4r5R8j/8Gm/E0EvhPInPVzOFh20OTe5EIHnaRjW8E2pn03jYilEZyqOcUXd3xBzv/l8OKI\nF3tU8gAd4yXuir6LjEcyeGXUK7xz4B2iP4jmy2NfotVpDR3edRNjIK5dfX09f/vb37r8ccWoMT0x\n1bMwNFoNXxz7gpd3vUysZyw/3fsT8T7xhg5LL8zl5kwIn8CE8AmUN5fz+dHPuWfNPdhZ2DEnfg73\nx92Pi42Y0MbYlTeXs+LoCj7N+BQnayceiX+ETyd1XL4ZyGQybou4jQnhE0gtTGVh2kJe3fMqL6e8\nzNSoqSZ3ACDOwrh2//3vf6mqquryxxUVCD0xtQqEVqflq6yviHo/im9OfMM3d35zUyUPv+fj4MPf\nh/+dvCfzeHvc2+wt2UvIkhBmrpvJ7qLdoiphZLQ6LRvzNjLl2ylEfxBNYUMh/5v2PzIeyeCxhMdu\nmuTht2QyGaNCRrHrgV28NfYtFu9eTMInCWzJ32JS/7+iAnFt8vLyCAnpnuqa0Y2BOLFCxqdNIKOj\nwa+XZYBM9ruff3+fS9x+wX0ucbu1DOzlv2kycJCDpxl4m4OFaSXoN0QnwdoW+EcduMjhX26QYmvo\nqIxTjRa+bIJPGjt+nuMEsxzB3bS/YjZpJRr4rAmWN4GXGTziBDMcOt7PwoXOv9cX1IKnOSx2g2E3\n2YG9QgeV2o5Wr4VWCdp00KrruKyWQAtof7fV/XJZdxXPcbkOtjS/o513YDMXJXK7d+9mzZo1jBw5\nEoDs7Gzmz59/Tfu4bt064uPjWbRoEStWrLim3/0jRvcVRuyz8J8N/0H65WWXJAkJ6bLbK94HCd0v\nf5ArPYaqXUWLuuWC1qRqorKhkurWapytnfF18CXcLZxoj2ii3KM6txZmFle1Xzt27ODll18mNTW1\ne164GyRJEhvzNrIgdQEymYy37voX48PGm1x5U5/cgXnAM5LEnuI9fJLxCS/l/siE8AnMiZ/T48aH\nGKt2XTsb8zbyScYn7K3Yy4w+M/ghfg79ffobOjSjJgfuAu7QtfNV1lfcn7aIaOto/jXqX0ZdaXRw\ncKCsrAxHR8erur+yXcmximOcqDrB6brTnK49TV5tHsWNxai0KrzsvPCy98LF2gU7SzvsLOw6t1bm\nVshlcsxkZpjJzLCUd2zN5GbIZXLkMjky/vg9fqnPgeDEC38+sPmZy/6ev78/CQkJfP/995235eTk\nsG3btks+3+zZs3F2dmbfvn0MHTqUtra2P4zxehhdBcLYRqJqdVqq26opayojtzaXkzUnOVl9khNV\nJyhrLmOAzwCGBAwhOTiZEUEjsDa/dGktPT2defPmkZ6eruc9uDJJkthasJV/pP2DVnUrL6e8zB29\n7xAd33WqU9TxVdZXLDuyDLVWzZz4OczuN7tHT+xjKGfrz7I8czkrjq4gyCmIOfFzmB4zHTtLO0OH\nZpJU7So+zfiUxbsXM8hvEIuSF9HPu5+hw7qIubk5CoUCC4tLH7w1qZpIPZvK9rPbSS9JJ6c6h97u\nvYn1iiWWoZH3AAAgAElEQVTSLZIItwgi3CIIdArEycrJaD7rLtf3TZo0ifXr19PY2Mi8efNYvnz5\nVT/mihUrkMvl1NTU8P333/PGG2906dkrIoG4AQ3KBg6UHmBvyV5+PvszxyuPMyJoBOPDxjMpYhJB\nzkGd983MzOTBBx/k6NGjBoz4V5Ik8fPZn1mYtpA6RR2LRi5iWsw0MbVzF5Ekif2l+1mWsYx1J9cx\nttdY5sTP4ZbQW8RrfAPUWjU/5v7IsiPLyCjP4P64+3k4/mH6ePYxdGg9hkKj4OMjH/P63tdJ9E9k\n0chF9PXua+iwAGhvb8fKyor29vYLOv7sqmzWnFzDloItZFVmkeSfxJjQMQwLHEY/737YWhj/97CX\n6vsUCgWzZs3iu+++Y9WqVchkMoKCghg8ePAVKxCzZs26YMXSwsJC/vnPf3b5VxgigehCdYo6tp/Z\nzsa8jWzI20CoSyjTo6dzV/RdKCoU3HHHHZw6dcqgMeokHRtOb+D1fa9T1VrFwpELuTvmbpM/N9yY\nNSgb+O/x/7LsyDKaVE3MiZ/Dg/0fNNgU36boVM0pPsv8jC+OfUGUexRz4udwZ/Sdl634CTeuTdPG\nx4c/5vV9rzMkYAgvDHuBAb4DDBpTc3MzPj4+NDc3c6LqBN/lfMfqnNU0q5u5M+pObgu/jWGBw7Cx\nML3BHJfq+w4fPsz+/ft54okn+OmnnygoKGDq1KkEBARc9eMqlUpefPFFtmzZwgcffMCIESO6LmaR\nQHQPjVZDamEq3+V8x/envsfP1o+ijUVkfp1JsHOw3uNRaBSszFrJf9L/g52lHc8OfpbpMdPF+g96\nJEkSh88dZlnGMlbnrGZIwBDui72PyZGTRdn9Eqpaq/jmxDeszFpJaVMpM+Nm8nD8wyYxTXpP0qZp\n46PDH/HO/ncIdg7m6aSnmRw5We8HHZIksfPUTib8dQIB4wNQtau4K/ou7oq+i0F+g0y+smeKfZ9I\nIPRAo9WwNnMtD7zxAA4DHQh0CuTOqDu5M/rObv8wzKrMYsXRFXyd9TWJ/ok8O/jZHjVzpKlqUbfw\nw6kf+Or4V6SXpHN75O3cH3c/o0JG3dRJnUKj4MfcH1mZtZI9xXuYGDGRmXEzuSX0lpv6dTEGGq2G\ndafWseTAEs41n2PugLncF3sfAU5XfzR8rc7PDHu+0qBSq6jfW8+OpTt63Oqjptj3iQRCTxobGwkI\nCKCuoY5dRbtYc3IN606uw93WnSlRUxgbOpZBfoOu+qyOy5EkiRNVJ/jp9E+sPrma6tZqZvebzQN9\nH6CXa68u2huhK1W2VPJt9rd8lfUVJU0lTIuexpTeUxgeNPym6DRb1C1szt/M2pNr2ZS/iYG+A5kZ\nN5OpUVOxt7Q3dHjCJRwsO8jyzOWszllNX6++zOgzg/Fh4wl0Crzhx25Vt5JWmMaWgi1syNuADBnT\nYqZxV9Rd2DfbM3HiRPLy8rpgL4yLKfZ9IoHQE7VajZ2dHRqNpvM6naQjvSSdH3J/4OezP5Nfl8/Q\ngKEk+CXQ16svfb36EuwcfMVSYYOygfy6fA6fO8yBsgPsOLsDuUzOpIhJTI6cTHJwshjfYEJya3JZ\nnbOadafWUdhQyMSIiUzpPYWxvcaa5Pe6l1OvqOen0z+x9tRafj7zM0n+SUyNmsodve8QY0NMiLJd\nyca8jXyX8x3bz2zH3dad0aGjSfBNoL93fyLcIq64wJ6yXcnZ+rNkVmRy+NxhDp07xNGKowzwGcC4\nXuOYED6BOK+4zkrDsWPHmDlzJllZWfraRb0xxb5PJBB6IkkSZmZmqNVqzM0vfVRZ21bLzqKdZFZk\ncrTiKMcqjlHRUoGnnSeedp5Ym1tjYWaBsl1Jk6qJqtYqVO0qwlzDiPeJZ5DfIIYHDifaI7pHlfZu\nVsWNxfxw6ge+z/2ew+cOMzxwOKNDRzMmdIzJ/Y3bde0cKjvEloItbC3YyvGq44wKGcXU3lOZFDkJ\nVxtXQ4co3CCdpONoxVG2n9lORnkGmRWZnK0/i7O1Mz4OPtiY22BlboVGq6FJ1USdoo46RR0BTgHE\necWR4JtAgm8Cg/wGXXbhugMHDvDkk09y8OBBPe9d9zPFvk8kEHpkZ2dHZWUl9vZXX5bVaDVUtFR0\nJAtaFap2FTYWNjhaOeJm44annadJdSTC9alT1PHzmZ/ZfnY72wq2oWxXMjp0NMMCh5Hkn0SMR4xR\nVZqU7UoyyjPYV7KPvSV72Vm4E39Hf8aFjWNs6FiGBw0XZ1DcBHSSjqrWKsqby1G2K1FpVViaWeJg\n6YCLjQs+9j7X9H+blpbGwoUL2blzZzdGbRim2PeJBEKP3NzcyM3Nxd3d3dChCCauoK6A7We2k16a\nzv7S/ZxrPsdA34Ek+ScR6xlLH88+f1g+7iqt6layq7M5XnmcrKosDpUd4ljlMaLcoxgSMIQhAUMY\nETQCXwffbo9F6Nk2b97M22+/zZYtWwwdSpczxb6v54/QMiKmtqCWYLx6ufail2sv5g6cC3RUKA6W\nHeRA6QHWnlrLS7teorChkGDnYEJdQglyCupozkF42nniauOKq40rLtYu2FjYYCYzu6CSJUkSaq0a\nZbuSemU91a3VVLdVU91aTWlTKWcaznC2/ixn6s9Q1VpFpHskcV5xxHnGMTlyMoP8BokBkEKXEytx\nGheRQOiRqS7pLRg/VxtXxoeNZ3zY+M7rVO0q8uryOFt/lqLGIooai8ioyKC6tbrz++daRS3KdiWS\nJGFhZoGF3AKtpEXVrsJcbo61uTXO1s542HngYeuBh50Hfg5+JPolMiNmBqEuoQQ6Bd7w2UOCcDXE\nSpzGRSQQeiQqEII+WZlb0cezz1VN86zVadHoNGi0Gszl5p2LCAmCMREVCOMiEgg9srW1FQmEYJTM\n5B0rDIqBjYIxUygU2Noa/7oWNwtxiKFHtra2tLa2GjoMQRAEk9Ta2ioSCCMiEgg9srW17bZ12QVB\nEHq6trY2kUAYEZFA6JFIIARBEK6fSCCMi0gg9EgkEIIgCNdPJBDGRSQQeiQSCEEQhOsnEgjjIhII\nPRIJhCAIwvUTCYRxEQmEHtnZ2YkEQhAE4Tq1tbVhZ2dn6DCEX4h5IPTI1taWhoYGQ4chCAIdCz01\nqZqoV9TToGygQdlAs7oZtVaNWqtGo9V0bHUaACzkFp2zdVqaWWJvaY+ztfMFrSctuW6MRAXCuIgE\nQo9sbW05d+6cocMQhB6vXlFPQX0BxY3FlDWVca7lHOeaL2xNqiYcLB0uSAAcrBywNLPsbOeTBuhY\nGff8bJ0anYYWdUtn4tGgbKBeUY+1uTV+jn74O/rj5+BHgFMAEa4R9HbvTaR7pFgf5AaJBMK4iARC\nj8QYCEHoOq3qVk7WnCS7Kpv8+nwK6grIr8unoL4AjVZDL9deBDkF4efoh6+9LynBKfg6+OLr4IuP\nvQ/O1s5dugS6JEnUK+spayqjtKmUsuYyihuLWX96PW/se4PTtadxs3Ujyj2KAb4DGOgzkAS/BAIc\nAy5YyEy4PJFAGBeRQOiRSCAE4dpptBpya3M5UXXiglbWXEakWyTRHtFEuEVwa9ithLmG0cu1Fx62\nHnrvlGUyWecqp7FesRfdrpN0FDcWk12VzeFzh/n82Oc8vvFxJCQG+Q0iJTiFUSGjiPOKE+uQXIZI\nIIyLSCD06EYSCGW7kvLmcipbK1FoFLTr2mnXtWNpZtmxLLONCx62HthZigFGgunSaDXkVOdwpPwI\nh88d5kj5EU5UnSDAMYBYr1j6ePRhZtxMYjxjCHMNw1xuOh9hcpmcYOdggp2DuS3iNqCjalHaVEp6\naTqphal8fORjatpqSA5OZnTIaCZFTsLf0d/AkRsPkUAYF9N59/UAV5NA6CQdRyuOsrtoNyeqT5Bd\nlU1ubS4t6ha87b3xsvPCztIOc7k5ZjIzVFoV9Yp66hR1VLdV42bjRoRbx3euCb4JDA4YTIRbhDii\nEYyOJEkU1Bewr2QfB8oOcOTcEY5XHSfIKYgBvgMY4DOAe2PvpZ93vx47dkAmkxHgFECAUwDTY6YD\nUNZURmphKpvzNzM/dT7BzsFMjpzM7ZG309er7039dYdIIIyLTJIkydBB/JZMJsPIQuoye/bs4fnn\nn2fPnj0XXF/WVMa2M9vYWrCVbWe24W7rzsigkcR5xRHjEUOUR9RVlWR1ko6SxhJO154mpzqHA2UH\nSC9Np0nVREpwChPCJzA+bDy+Dr7duZuCcEkKjYIj5UfYV7Kvs1mbWzMkYAiJfokM9B1IP+9+OFg5\nGDpUo6HRathbspcfc3/kh9wfkCSJe2Lv4d4+9xLjGWPo8PTO2tqahoYGrK173qqxptj3iQRCjzIy\nMnjooYfYe3Avu4p2sbVgK1sLtlLeUs4tIbcwrtc4xvQaQ6BTYJc+77nmc2wr2Mam/E1sLdhKmGsY\nd8fczfSY6QQ4BXTpcwnCeVWtVewq2tWZLByvOk6MRwxDAoYwJGAIg/0Hi/+/ayBJEkcrjvLfE/9l\n1fFVuNm6cU+fe7gv9r6b4nXUarVYWFig1Wp7ZBXGFPs+kUDogU7SkVWZxVfpX/Hh1g+RBciI94ln\nbK+xjO01lgE+A7p0NPiVtOvaSStM45sT37Du1DqiPaK5P/Z+7om9B0crR73EIPRM1a3V7CzaSVph\nGqmFqZQ1lTEscBhDA4YyJGAICX4J2FqI8nNX0Ek69hTv4evjX7M6ZzWJfok8HP8wkyImdZ522tM0\nNzfj4+NDS0uLoUPpFqbY94kEopuUNJaw4+wOtp3ZxrYz23CycmKw52A2vLuBMzvOGEVnrdaq2ZK/\nhS+OfcH2M9uZEjWFOfFzGOw/uEdm+ELXqm2rZVfRLlILU0ktTKW4sZhhgcNICU4hOTiZ/t799ZYY\n38zaNG2syVnDp5mfkluTy6y+s3io/0NEukcaOrQuVVlZSWxsLFVVVYYOpVt0R9+nVqv5+uuvcXR0\nZOPGjSxdurRLx5CIBKIL6CQd+XX5HCo7RFpRGqlnU2lQNpAcnMzYXmMZEzqGEJcQampqiIyMpLa2\n1tAhX6SypZIvj33JJxmfYGFmwZz4OczuOxsXGxdDhyYYiXpFfWfCkFaYxpn6MwwNHEpyUDIpISnE\n+8Sb1FkRPVFuTS6fHf2ML45+QR/PPjye8DiTIif1iL/L2bNnSUlJobCw0NChdIvu6Pv27NnDsmXL\n+PLLL7nrrruYPXs2kyZN6rLHFwnENdBJOkqbSsmvyye/Lp/c2lwyyjPIKM/AxdqFgb4DGRE0gpTg\nFGI8Yy4686GtrQ03NzcUCoWB9uCPSZLErqJdfHzkYzbmbWRK1BQeHfAog/wGiarETaZR2XhBwpBX\nl8dg/8GdFYaBvgN7bLnc1KnaVaw5uYb3Dr5HaVMpjw18jIfjH8bDzsPQoV237Oxspk2bRk5OjqFD\n6Rbd1fc1Njbi5OTE0KFDWbNmDd7e3l322EaXQFR/KSND1XFZukQDkKTLXH++SZe5/pfbLvXYCgla\ndR2t7ZfLzRJUtUOVtqNVasHdDMIsOlq4BfS3gnjrjut7mqp2+LwJPm4CJzk86gT3OoC9OCO0R2rS\nwm4lpLVBqgJOqSHJGpJtIcUGEqzBUuSQJidDCe83wtoWuN0OnnDu+FsKv5IkaP3lc18t/dIA1S+X\nNRLozt+Xi/uRK12+nGO5kJX7688r13NRArF7927WrFnDyJEjgY4kav78+de0b7W1tXzyySd4enry\npz/96Zp+948YXV3L80UY88qYzqNdGTJkMtkFW+Ci6y53/6t9DBtzG+ws7bC1sMXBwg5vCzvsLe3x\ntPPEy96rY2vndcOL5dja2lJdXW0SK8p5As8Bf5F0bD+znQ8Pf8jzhTuZ0WcGjw58lDivOEOHKNyA\nZlUze0v2doxhOJtKTnUOCX4JpESk8J/gZBL9ErEytzJ0mMINigeWA6+31bLi6AruPvQBHjoPHk94\nnOkx07E2N41sYufOnSxYsIBdu3Zd0+8pNApyqnPIr8unuLGY4qZiihqKqGytpF5RT72yYzE1SzNL\n7CzssDSzxMrcqnM9FCszK8zl5shl8ov6lKu9fElxv7Tz1m+/6C7nf9/f35+EhAS+//77zttycnLY\ntm3bJR969uzZODs7A+Dm5sbzzz/PXXfdRWRkJEOHDr2q1+1qGF0Fwpi/wugKHh4e5OTk4OFhmqXE\n0qZSlmcs55OMTwhyDuLRAY8yLWaayXwI3cxa1C3sLd5LWlEaaYVpHK88zgDfASQHJ5MSnEKSf5L4\nO94EtDotm/I38d7B98isyORP/f/EnPg5hLqEGjq0K9q0aRPvvvsumzZtuux9FBoFh84dYk/xHjLK\nMzhedZzixmIi3CIIdw0nyDmIQMdAgpyD8LLzwsXGBRdrF1xsXLA0s9Tj3lzscn3fpEmTWL9+PY2N\njcybN4/ly5df1+PPnz+f+vp63n///RsNtZPRVSB6OlNfD8Pf0Z+FyQt5ccSLbDi9gQ8Pf8i8rfOY\n1XcWcwfMJcItwtAhCr/4fcKQVZnFAJ+OhGHxqMUk+SeJ0ypvQmZyMyZGTGRixERO157m4yMfk/hp\nIvE+8cwdMNdoTwW91CyU5yfa2py/mV1FuzhWeYw+nn0YGjCUO6PuZFHyIiLdIo1yf66GQqHonDRr\n48aNjBkzhvT0dAYPHnzFCsSsWbNwcXHh3//+NyqVikWLFlFRUUFs7MVrtNwIUYHQs6ioKNauXUtU\nVJShQ+kyZ+rPsOzIMlYcXUGsZyyPDnyUyZGTTfZNa6ouVWGI94knOTiZ5OBkkTAIl6VsV7ImZw0f\nH/mY/Lp8Huz/IH/q9yd6ufYydGidVq5cydatW3nlvVfYnL+ZTfmb2HF2B+Fu4YwPG8+o4FEM8htk\nsusBXarvO3z4MPv37+eJJ57gp59+oqCggKlTpxIQcHUTh509e5b09HRUKhUZGRksWbIEubzrBrGJ\nBELPBgwYwLJlyxgwYIChQ+lyqnYVa0+u5aMjH5FXm8dD8Q8xJ35Ol8+sKXSoaathX8k+9pbsZXfR\nbrIqs0TCINywnOoclh1ZxqoTqwhxDuHe2Hu5O+ZuvOy9DBKPWqtmb/Fe/r363xxuPIzcUc64sHGM\n7zWecWHj8LTzNEhcXc0U+z6RQOjZ8OHDeeWVVxg+fLihQ+lWOdU5fHzkY77K+orB/oO5N/ZeJkVM\nEuscXCdJkjhde5q9JXs7WvFeylvKSfRLZGjAUIYFDmNwwGCRMAhdpl3XzvYz2/n6+Nesz11Pf5/+\nTAyfyG0RtxHpFtmtp3WXNJZcUGWIcIvAtdYV5xpnvn796x45QZkp9n0igdCzcePGMW/ePMaNG2fo\nUPSiTdPG6pzV/C/7f+wu3s2okFFMj57OxIiJPTaZkCQJtVZNm6btqpqyXYlaq+5ouo5tq7qVsuYy\nypvLKW8pp7q1GrlMjouNC45Wjthb2mMht0BCQqvTopN0nU0r/e7n393++/vA1Z+9dKWtudwcc7k5\nFnKLjq2ZxUXXXe56CzMLLM0ssTG3wdrc+tetRcf2UtedP3PKwdIBByuHHjFZkrFq07Tx85mf+Snv\nJzac3oCVuRVjQscw2H8wgwMGE+4aft0JhUar6UyOdxfvZk/xHppVzYztNZZbw27trDIsXryYtrY2\nFi9e3MV7ZxxMse8TCYSeTZkyhVmzZjFlyhRDh6J39Yp6fsj9oTOZGBowlAnhE5gQPoEw1zBDh3eB\nNk0b1a3VVLdVU91aTa2ilkZlI42qX5ry8ttWTSvmcnNsLWyvqpnJzGhQNlDdVk1FSwXnms/RoGzA\nz8GPEJcQerv1JsojCh97n85TyyzNLLEws8BMZoZcJsdM3rE9385ff8F1l7jP+Q99SZKQkDrfe+cv\nX8u2Xdfe2TRaza+XdZqLrv/9dRqdBrVWjbJdiaJd0bHVKP7w51ZNK82qZprVzViZWeFg5dCZUDha\nOXZedrB0wMnaCTcbt45me/HW0KPwTYUkSWRVZpFamEp6aTr7S/fTqGwk0j2SSLdIwlzD8LD16DzD\nwVxu3vm3blI1UdFSQXlLOcWNxWRXZ5Nfl0+AYwCDAwYzLGAYwwKH0du990UJyYsvvoitrS0vvvii\ngfa8e5li3ycSCD277777mDBhAvfdd5+hQzGoRmUj289sZ2P+RjbmbcTB0oFxYeMYETiC4UHD8bbv\nutnSzmvXtVPRUkFZUxllzWWd2/KWcqpaq6huraamrYbqtmq0Oi0edh542HrgYeeBq40rztbOOFk5\ndTTrC7fO1s6dl+0s7S57NFzdWs3xquNkVWZxvOo4meWZnKo5RW/33gz0HUiCbwIJfgnEeMSIQajX\nQJIk2jRtNKubaVI1dSYVzapfflY306hspFZR29HaLtzWKeqwNrfGzcYNTztPfBx88HXwxdfet/Oy\nj33H1sPO46JZZm921a3V5NXlcbr2NPl1+dS01VCvrKdOUYdO0mEuN8dMZoa9pT0+Dj742Pvg7+hP\ntEc0vd17X9VXb8888wyBgYE888wzetgj/TPFvk/U/PTM1taW1tZWQ4dhcE7WTtwZfSd3Rt/ZuUzx\ntjPb+DLrSx756RE8bD0YHjScIf5D6O/TnxiPmD+c1Khd105JYwln6s9QUF/AmfoznKk/Q2FDIWXN\nZVS3VuNm64afgx9+jn74Ofjh7+hPpFsknnaeeNh54G7rjoetB/aW9tddkpUkiarWKk7XniavNo/s\n6uzOpEHZriTWM5Y4rzgG+Q5iTvwc+nr1veEJym52MpkMO0s77Cztriv5lCSJJlUTtYpaqlqrKG8u\n51zzOcpbytlXso/yll9+bi6nQdmAj4MPIc4hhLiEdGx/c9nHweemSzA87DoS7SEBQ7rtOVpbW7t0\nISjhxokEQs9MfR6I7iCTyejv05/+Pv15buhz6CQdJ6pOsKtoF2lFabxz4B3y6/IJdw2nv09/QpxD\nsJBboNKqqFPUkV+XT15dHqVNpXjbexPqEtrRnEOZ0nsKQc5B+Dv642Pv02VH9S3qFkoaSyhtKqWk\nqYSixiLyavM6j8LM5eadk9dEe0Tz58Q/E+sZi7+jv1hTxAjJZLKOCpK10x9OqKTWqiltKuVs/VnO\nNnS0TfmbOi7Xn6VB2UCISwhR7lH0du9NlHsUUR4dl+0t7fW0Rz3PpeaBEAxLJBB6JhKIPyaXyYnz\niiPSLZLB/oPJKM/gcPlhDpcdZt3Jdai0Kuws7JCQaFW3Ym9pj7+jP2NCx+Dj4IO3vTdedl44Wztj\nY2FDm6aN8uZymlRNmMvNkSHrHAcAXPS9uqJdQYu65dcy929K3eeaz1HaVIqqXYW/oz8BTgEEOAYQ\n4BTA+LDxPOX2FOGu4bjZuhn4VRS6i6WZZWeSeiltmjby6/I5VXOKk9Un2Zi/kbfS3+J07Wncbd2J\n8oiin3c/+nn1o79Pf8Jdw3vkWQVdTSQQxkckEHomEohLa9O0kVmeSUZ5BpkVHdvTtacJcw0j3iee\n/t79mRY9jWiPaHzsfTqP4nWSjnPN5yhrKqOipYLK1koqWirIqc7p/O67Rd3S2c6fkSAhdWwl6aKR\n/TYWNthZ2OFm64a7rTvhruEk+SXhZuuGt703AY4BuNq4ikqCcEm2FrbEecVdtFaMVqftHDh4tOIo\na06uYX7qfCpbKon1iqWfdz/6e/dnkN8g+nj2EWeV/I5IIIyPUf6HLlq0qPNycnIyycnJBoulq9na\n2lJfX2/oMAxKkiSKGotIL0knvTSdfSX7OFlzkmiPaAb4DCDJP4nHBj5GrFfsH67NIJfJ8Xf0x9/R\nX0/RC8L1MZObdYyTcAlhYsTEzusblY0cqzxGZnkm+0r28fb+tyltKmWg70AG+w8myT+JJP+kHjNh\n0vUSCYTxMfoEoqe5GSsQOknXcdrX2VT2lOwhvSQdCYnB/oMZEjCEu2PuZoDvALGQk3BTcrJ2YkTQ\nCEYEjei8rl5Rz4GyA+wv3c/7h95n1rpZuNm6MTRgaOdMoyHOITdVFUwkENdOo9GwbNkylEolDQ0N\nvPzyy136+EaZQPRkN0MCIUkSp2pOsePsDlILU0krTMPN1o2U4BTujLqTN8e8SbBz8E314ScI18LF\nxoXxYeMZHzYe6EjCT9WcYk/xHraf2c78HfMxl5uTHJzMyKCRJAcnE+oS2qPfUyKBuHarV6/mnnvu\nwdXVlWnTpnHgwAESExO77PFFAqFndnZ2PS6BkCSJM/VnSC1M7UwarMysGBUyijt638GS8Uvwc/Qz\ndJiCYLLkMjnRHtFEe0TzyIBHkCSJvLo8dhbuZEfhDv6R9g/kMnlHdSIomZSQlB5XoWhra8POzjQX\nyjKU3NxcGhoaeOyxxwgNDaW0tLRLEwijm0iq8ksZR1Rw/t9e9tsm+/XyRbddw+2XvO2X280BOznY\nyn7dynvOe7DLlGpghwJSFbCjDTQSjLKFFJuObYiYA0kQ9EaSIF8DaYqOtqMNLGUd78eUX96XgeI9\neRGNBK06aJWgRQdqCbRA+2W2ukv0lpfrQP/o+qxcOH761+v/u56LJpLavXs3a9asYeTIkQBkZ2cz\nf/78q94/pVKJTqfD1taW8ePHs3z5cvz8uu5gzugqEN4vwPjXOsp2V5o6Fy4/3e5lb9P98e+dX8Og\nVdNKm6YNhUaBlbkV9pb2nbPUnW9edl4EOwcT4hJCqEsovg6+fziBTFpaGosWLSItLa2bXsHuUdFS\nQVphGqmFqaSeTaVeWU9ycDKjgkfxt5CUbl9cRxCEy5MB4b+0OXR8dubW5pJ6NpUNhan8pTAVJysn\nUkJSSAnuaD4OPoYN+hrZ29tTUVGBvf2V59LQaDUUNxZzpv4MZxvOUtRY1DnTbFVrFVWtVdQp6mhR\nt6CTdB0TkFl0TEJmZWbVMWum3Kxz9szzP5+fHv5Sn3MyLv3Zd7nPRBky6E9HO2/9psv+vr+/PwkJ\nCXz//fedt+Xk5LBt27ZLPv7s2bNxdnbG2rpjXFlaWhqjRo3q0uQBjLACYWzTeeokHcp2JS3qFmra\nam8xUCgAACAASURBVDr/AatbqylvKaewobBzApk6RR3BzsHEeMYQ6xlLH88+9PHsQ5hrWOcpWQcP\nHuTxxx/n0KFDBt6zK6tpq7kgYShvKWdE0IjOD59Yr9ibbrY9QTBVOklHdlV2x/u5MJWdhTvxsvfq\nfD8nByfjYedh6DAvS5IkzMzM0Gg0mJl1zJmh1WnJr8vnWOUxjlUeI6syixNVJzjXfA4fex9CXUIJ\ncQkhyCkILzuvzgO/81PT21vaY2VmZTQHPpfr+yZNmsT69etpbGxk3rx5LF++/Joet7a2lo8++qhb\n1hARCUQXUmgUFNQXcKLqRGc7XnWcc83n6O3em75effGUPPl2ybcc2XgEd1t3Q4cMdLw5C+oLSC9J\nZ3/ZfvYU76GwoZBhgcM6P2D6efcTk90IQg+h1Wk5VnmM1LMdCcXu4t0EOgV2vt+HBw03ms+n/2/v\nzuOiqvf/gb9mgRl2BtlBQBZFZFNB0EBR3E0tykRTMruWlXUtrX62fe223hYtS1vUvGYpbt28brik\nuYUIioKALIIIyL4M2zDMcn5/oJMmKujMnHPg/Xw8zmNglnPeKMx5zefzOZ8PAFTUV8BzmCdW/LJC\nFxiyqrLgaOGIEOcQhDiFINgpGEGOQfCy9eLlOjKdnfsUCgUSEhKwfft2bNmyBQKBAJ6enhg+fPhd\nWyASEhIgk8nAMAy++eYbvPjii9BqtTh27BhiY2P1VzMFCMNrbm9GVlUWMiozcLLgJBL/SITUUwpL\nU0sEOwUjxOmvP4AB9gMMOoGMltHiSsMVXKy6iAsVF3SXipmbmCPSPVJ3aeVQ16E0kQ3HMQyDuro6\nVFZWorKyElVVVaivr4dcLtdtjY2NkMvlaGpqglKpvG1ra2tDe3s7gI6/vc42U1NTSKVSmJmZwczM\n7JavLSwsIJPJIJPJYGtrq/vazs4OTk5OcHFxgbW1NWc+5ZHOqbVqnCs/pxsEfbr0NOzM7BDuGo4w\n1zCEuYYhxCnE4DOsqrVq5NfmI7Mqs2Or7FhDprK5Eu2l7Xh68tMd75fOHe+X1hJrg9ZjTJ2d+9LS\n0nD69GksWrQIe/bsweXLlxEXF4e+fft2aZ/ffvstli1bBhMTE2i1Whw/fhyDBg3SX80UIIyrtrYW\nfn5+qK2tRbG8GBmVGbhQ8VcTXGljKQY6DMQgh0HwtPWEp03H5m7trlse926LSqk0KsiVctQr6jvW\naGgoxlX5VVyRX0F2dTayq7Mhk8oQ6BiIIKcgRLhFIMItgq6S4KD6+npcuXLllq24uBglJSWoqKhA\ndXU1LCws4OTkBCcnJzg6OsLOzg42Nja3bZaWlpBKpZBIJLdtpqYdy1gzDNPpplKpoFAobtna2tqg\nUCjQ3NyMhoYG1NfX37ZVVlbi2rVr0Gg0cHFxgaurK1xcXODu7g5vb294e3vDx8cHXl5ekEjuvlAa\nMS4to0V+bT7SrqUhrTwNqWWpyKzKhEQk6Vjfw2EgfGQ+cLd21y1K52jhCAtTi7t2bSpUCtS01qBW\nUYuK5opb1hPJr+1YR8bN2g1BjkEIcgrSLTwnqBdg/LjxKCoqMuK/gnHx8dxHAcLIlEolrKysdJ/6\n/u5Ga0V2dTaK5R0n/2J5MUobS9HQ1oB6RT1EQhGsJdYdA3quD95h0LGaoFKthI20Y3npvtZ94WHj\nAU9bT3hYeyDAIQCDHAfBVmprzB+Z3ENlZSUyMzORk5ODnJwcZGdnIycnB62trfD29oaXl9ctm7u7\nO5ydneHo6MiLE29zczPKy8tRXl6Oa9euoaSkBEVFRbh8+TIKCwtx9epVODo6wt/fH4GBgQgKCkJg\nYCACAgLuOWCOGA/DMChvLtet8VHUUITSxlKUNZWhrLEMVS1VUKgVMBObdYwvEEug1qp1m0KlgJbR\nwt7cHvbm9nC0cISXrVfHWAXbfvCx88FA+4GwML39Us2MjAw8+eSTyMzMZOEnNw4+nvsoQBgZwzAw\nNTVFS0uL7pNfd1+vUCvQqGyEltHq7hMIBLCWWMPCxIKaizmstrYWZ86cQVpaGs6ePYu0tDS0trYi\nODgYAwcO1G0BAQFwdXXtFf+XarUaJSUluHTpEi5evIiLFy8iMzMTly5dgouLC8LCwhAREYGIiAgM\nGTIEZma09DlXaRktWlWtaFI2QalRwkRoArFQDLFQDIlYct/vT3/++SeWLFmC5ORkA1TNDXw891GA\nYIFMJsPly5dhZ2fHdinEwCoqKnD8+HEcO3YMx48fx9WrVxEeHo6wsDAMHToUYWFh8PKiWTk7o9Fo\nkJ+fjzNnziAlJQUpKSnIycnBwIEDMXr0aIwZMwbR0dHUStELHDx4EJ999tkdBw32BHw899EoORZY\nWlqiubmZAkQPpFarkZycjL1792LPnj24du0aoqOjMXLkSDz99NMIDQ2FWEx/dl0hEong7+8Pf39/\nJCQkAOiYGCctLQ1HjhzBJ598ghkzZiA0NBRjx47F9OnTERoaSmGsB2pubqagyEH0TsaCGwGC9AwK\nhQJ79+7Fr7/+igMHDsDT0xNTpkzBunXrEB4errtunTw4qVSKqKgoREVF4d1330VraytOnjyJAwcO\n4PHHH4dGo8EjjzyCRx55BFFRURTWeggKENxEMwGxgAIE/6lUKuzfvx8JCQlwdXXFd999h5iYGGRk\nZODcuXN4//33ERkZSeHBwMzNzTF+/Hh88cUXKCgowJ49e2Bvb4+lS5fCzc0Nixcvxrlz53jXNExu\nRQGCmyies4ACBH8VFxdj3bp1WL9+PTw8PDB79mx8+umncHZ2Zru0Xk8gECAwMBCBgYF4++23UVBQ\ngE2bNuGxxx6DhYUF5s2bh/nz51PXIQ9RgOAmaoFgAQUIfmEYBklJSZgyZQqGDBkCuVyOQ4cO4fTp\n03j55ZcpPHCUr68v3nvvPVy+fBnffvstMjIy4OPjgwULFiAjI4Pt8kg3UIDgJgoQLKAAwQ9qtRq/\n/PILQkND8frrr+Pxxx9HSUkJVq1apdfZ3IhhCYVCREdH46effkJubi48PT0xadIkjB49GocPH6bu\nDR6gAMFNFCBYQAGC21QqFb7//nv4+vrihx9+wMcff4wLFy7g6aefhrm5OdvlkQfg6OiIt99+G1eu\nXMH8+fOxaNEiPPTQQ0hKSqIgwWEUILjpgQLExYsXsXfvXn3V0mtQgOAmhmGwfft2DBo0CNu2bcOW\nLVtw7NgxTJ48mS4N7GFMTEwwd+5cZGVl4eWXX8aSJUsQGRmJ48ePs10a6QQFCG56oABx8uRJRERE\n6KuWXoMCBPccP34cERER+OSTT7B69WocPnwYw4cPZ7ssYmAikQjx8fHIzMzEP//5TyQkJCAuLg75\n+flsl0ZuQgGCmx4oQFRVVcHenjtLvvIFBQjuqKiowNy5czFnzhy8+uqrSE1Nxbhx46jFoZcRCoWY\nPXs2Ll26hIiICAwfPhxvvvkmFAoF26URUIDgqgcKEPQme38oQLBPq9VizZo1CAoKgpubG3JychAf\nHw+hkIYF9WZSqRRvvPEGMjMzUVhYiMDAQBw8eJDtsno9ChDcdN/zQFRUVMDV1VWftfQaFCDYdWMA\nnUKhwLFjxxAQEMB2SYRjXFxckJiYiKSkJCxcuBAjR47EqlWrYG1tzXZpvRIFCG7q0set7du346OP\nPsKUKVN0y1CfOnUKUVFRAIAlS5Zg9uzZhquyh6EAwQ6GYXTTS0+cOBEnT56k8EDuauLEicjMzIRU\nKkVISAgNsmQJBQhuumeAKCkpQVlZGV5//XUcPXoUWVlZAIDc3FwMGDAAAODl5YULFy4YttIehAKE\n8TU2NiI+Ph7ffPMNjh49itdff52mmSZdYmFhge+++w6rVq3CzJkz8eabb0KtVrNdVq9CAYKb7hkg\nkpKSMGfOHPz+++8Qi8UYOHAgANxyzfSLL75In+S6wcrKCi0tLWyX0WtkZGQgPDwctra2SE5ORmBg\nINslER6aOnUqLly4gNTUVEyYMAFVVVVsl9RrtLS0wMrKiu0yyN/cM0AsWLAA9vb22LhxI+Li4iCV\nSqFQKGBhYaF7jkajwYgRIwxaaE9CLRDGs3nzZsTGxuKdd97B999/DzMzM7ZLIjzm6OiIpKQkREZG\nIiwsDCkpKWyX1OO1t7eDYRiYmpqyXQr5my6NgdBqtdi7dy+mTZsGAEhLS0N4eLju8d27d+Oxxx4z\nTIU9EAUIw9NqtXj33Xfx1ltv4ciRI5gzZw7bJZEeQiQS4cMPP8Q333yDqVOnYvPmzWyX1KO1tLRQ\n9wVHdSlA1NTUoKmpSddNcfbsWV2AqKurQ1lZGTw8PAxXZQ9DAcKwFAoFZs2apVvwKigoiO2SSA80\nbdo0HDlyBMuWLcNHH31EU2EbCI1/4K4uBQh7e3v4+fkhLy8PANDW1gZTU1OUlpbiq6++wgsvvGDQ\nInsaChCG09DQgHHjxkEkEuHo0aNwcnJiuyTSgwUGBiI5ORk7duzAggULoFKp2C6px6EAwV0Cpoux\nubCwEO+//z7Cw8Nx4MABxMTEwMrKCs8884xuQqm8vDycOHEC6enpGDduHNRqNbZv347ExMSuFyQQ\n9PgkzzAMxGIx2tvb6UoAPaqqqsKECRMwcuRIrFy5kiaFIkbT3NyMGTNmwMzMDImJidRfr0epqal4\n8cUXcebMGbZLMSg+nvu6HCBuyM7ORnZ2Nh5//PHbHtu/fz88PT0xf/58nD59GgAQGhqKU6dO3TLo\n8q4F8fAf8X5YW1ujrKyMRhbrSUlJCcaOHYuZM2fivffeo1lSidEplUrEx8ejvb0dO3fuhFQqZbuk\nHuHo0aN4//33ceTIEbZLMSg+nvu6/REtJSUFw4YN6/SxSZMm4dChQ7oBa6WlpTAzM+tyeOhNqBtD\nf0pKSjBy5Eg8++yz+Ne//kXhgbBCIpFg27ZtsLa2xsMPP0yXausJdWFwV7cDxJUrV+46YPLw4cMY\nP348AGDTpk34xz/+gf/973/3X2EPRQFCPyoqKhAbG4tFixZhyZIlbJdDejkTExP8/PPPcHNzw/Tp\n06FUKtkuifcoQHBXtwOEWHz35TMaGhrQv39/AIC5uTna2tqoKa8TFCAeXE1NDcaOHYs5c+ZQeCCc\nIRKJ8OOPP8LW1hazZs2iWSsfEAUI7ur2GAhD42M/0P0YOXIkPvzwQ0RHR7NdCi81NjZi9OjRGDt2\nLD755BPqtiCco1QqMW3aNLi6umL9+vU0qPc+rVy5EiUlJVixYgXbpRgUH8999BvNEmqBuH9qtRoz\nZ87E0KFDKTwQzpJIJPj111+Rm5uL119/ne1yeItaILiLAgRLKEDcH4ZhsGjRIjAMgzVr1lB4IJxm\nYWGBvXv3Yvfu3fj+++/ZLoeXKEBw190HNBCDoQBxfz7//HMkJyfjxIkT9xyPQwgXyGQy7NmzB1FR\nUfD19UVsbCzbJfFKc3MzPD092S6DdIJaIFhiaWmJpqYmtsvglV27dmHVqlXYu3cvrK2t2S6HkC7z\n8/PD1q1bMXv2bOTm5rJdDq80NTXRVAAcRQGCJdbW1mhsbGS7DN7Iy8vDggULsHPnTri7u7NdDiHd\nFhMTg48++ghTp06FXC5nuxzeaGxshI2NDdtlkE5QgGAJBYiua2lpQVxcHD744IM7TmJGCB8888wz\nGDt2LObPn8+7Efdskcvl1OLIURQgWGJjY0OfQrqAYRgsWLAAw4YNw4IFC9guh5AHtnLlShQXF2PV\nqlVsl8IL1ALBXTQKjSXUAtE1q1evxqVLl3Dq1Cm64oL0CBKJBNu3b0dkZCQiIiIQGRnJdkmc1tjY\nSC0QHEUtECyhFoh7y8zMxHvvvYft27fDzMyM7XII0Zt+/fph7dq1mDlzJmpra9kuh9OoC4O7KECw\nhFog7q6trQ1PPvkkPv30U/j4+LBdDiF6N23aNMTFxeGFF16g8RB3QV0Y3EUBgiUUIO7uzTffRP/+\n/TFv3jy2SyHEYD766CNkZmYiMTGR7VI4SalUQqvVQiKRsF0K6QSNgWAJdWHc2aFDh7B9+3acP3+e\nxj2QHs3MzAybNm3CpEmTEB0dTZco/82N1gd6H+AmaoFgCbVAdE4ul2P+/PnYsGED+vTpw3Y5hBjc\n0KFD8dJLL9GlnZ2g8Q/cRgGCJVZWVmhqaqI3jL9ZtmwZJk2ahLFjx7JdCiFGs2zZMsjlclov42/o\nCgxuoy4MlojFYkilUrS0tNBCMdedOHECu3btQlZWFtulEGJUYrEYP/74I2JiYnRLgBMaQMl11ALB\nIurG+EtbWxsWLFiAr7/+Gra2tmyXQ4jRDRo0CM899xwWL17MdimcQV0Y3EYBgkU0kPIvH3zwAQYN\nGoS4uDi2SyGENW+99RbS09Oxd+9etkvhBGqB4DbqwmARtUB0yM7Oxvfff48LFy6wXQohrDIzM8O3\n336Lf/zjH4iJien1q1BSCwS3cTJALF++XPd1TEwMYmJiWKvFkKytrXt9CwTDMPjnP/+Jt99+m/p9\nCQEwduxYREdHY/ny5fjss8/YLodVNIiS2zgfIHoyGxubXt8CsWvXLly7dg0vvPAC26UQwhmff/45\nBg0ahAULFqB///5sl8MauVwOmUzGdhnkDmgMBIt6exdGW1sbXn31VXz55ZcwMTFhuxxCOMPJyQlv\nvPEGli5dynYprKIWCG6jAMGi3j6I8osvvkBISAjGjRvHdimEcM7LL7+M7OxsHDp0iO1SWCOXy2kQ\nJYdxsgujt+jNLRClpaVYsWIFUlNT2S6FEE6SSCT4/PPP8eqrryI9PR1ice97u6YWCG6jFggW9eZB\nlO+++y6effZZeHt7s10KIZw1ffp0ODg4YN26dWyXwgoKENzW+yIth9jY2CAnJ4ftMowuOzsbe/bs\nQV5eHtulEMJpAoEAK1euxIQJEzB79mzWTqZKtRIKtQIMw4ABA7FQDEtTSwgFhv0MSl0Y3EYBgkX6\n7MKoaK5Aenk6CuoKkF+Xj8L6QlS3VqO2tRZ1ijq0qdugZbTQMlqYiExgLbHWbX3M+qCvTV/0te7Y\nvGXeCHAIgIOFg15q+7s333wTr7/+Os04SUgXhISEYPz48fjyyy/x7rvv6n3/8jY5jl05hjPXziC7\nOhuF9YWobKlEc3szlGol1Fo1GHS+Zo8AApiKTGElsYKDuQO8bL0wvO9wTOs/DcFOwQ+8iia1QHCb\ngOHYak4CgaDXLDB14MABrFixAgcOHOj2a/Nr85FUkITjV4/jTNkZNCmbMMRlCPr36Q8/Oz/42PnA\n0cIRfcz6wM7MDlKxFCKhCEKBEO2adjQpm9CobESjshFVLVUoaSzp2OQluFx/GdnV2RALxQhwCECA\nQwCCHYMR5hqGYKdgSMSS+/6Z//zzT8THxyMvLw9SqfS+90NIb1JYWIhhw4YhNzf3vlepZRgGebV5\n2Je/D0evHEVmVSbKm8qh1CghFophK7WFi6UL+tn2g18fP/Sz7QdPG0942XrBxcoFErEEJkITCAVC\nKNQKNLZ1vHfk1+UjqyoLubW5yKvLw5WGK5C3ySEQCOBq6Ypoz2g8Hfo0Yr1ju91iYWdnh/z8/F6x\nMi8fz30UIFiUnJyMV199FcnJyfd8LsMwOF16GolZidibtxetqlZM9J2I0V6jEekeCV873wdO+38/\nXmVLJbKrs5FdnY0LlReQWpaKvNo8DHQYiDDXMIS5hCHMNQxBTkEQC+/dmMUwDEaNGoV58+Zh/vz5\nequVkN7g+eefh5WVFT799NMuPV/eJsfBywexK3cXUspSUNxQDI1WA6lYCi+ZF8JcwjDRdyIm+02G\njVS/3QQarQYnr57EpoxNOFp0FFfkVyAUCDHEeQieC3sOCcEJEIvu/p7BMAxMTEzQ2toKU1NTvdbH\nRXw891GAYFFWVhZmzJiB7OzsOz+nKgs/Z/6MxIuJkIqlmB04G9P9pyPIMUivgaGrFCqFLkyklach\ntSwVV+VXMdhlMCLcIhDhFoFI90i4W7vfVt++ffuwdOlSZGRk9MoR5YQ8iLKyMgQHByMzM/O2WVs1\nWg3OlZ/Djuwd+KP4D1yquYQmZROEAiHcrd0R7hqOKf2nYKLvRDhbOhu9drVWja0Xt2JN2hqklqWC\nAYOH+j6E90e/j2jP6E5f09LSAnt7eygUCiNXyw4+nvsoQLCotLQUkZGRKC0tveV+lUaF3y79hm9S\nv0FBXQHmBM/BrMBZCHEKYSU03Iu8TY7Ua6lIKU3B6bLTSClNgVgoRoR7hC5UDHUZitEPjcZbb71F\nC2YRcp9ee+01tLS0YPlny7EjeweSCpJwrvwcypvLwTAMZGYyBDoGItYrFo8OfBSDHAcZfKBjd2kZ\nLX7J+AWf/vkpsqqyYCO1wdzgufhg9Aewlv413qG8vByDBw9GRUUFi9UaDx/PfRQgWNTY2Ah3d3fd\nQMqK5gqsPbsW35/9Hj52PlgUvgiP+D8CExG/ZmlkGAZXGq4gpSylYytNwblr54B6YE7MHES6RyLC\nLQIBDgEQCUVsl0sIp6m1ahwtOopfc37FsaJjyKnMAUwAM7EZvGXeGNF3BOL84xDTLwZSMb/GFTW0\nNWD50eXYmLERjcpGjPQYia8mfoVg52Dk5uZi6tSpveZqLT6e+yhAsEir1UJsIsbxwuP49uy32Je/\nD08MegIvhr+IYKdgtsvTG4ZhEDEiAo8+/yis/K10oaKiuQJDXYfqWiki3CPgakULapHei2EYZFRm\n4L+X/os/rvyBrOos1LbWQiAQwMXSBaHOoVBlq+BY64hN325iu1y9YRgGv+b8irePvo3cmlx42nri\nSfcnkfRJEtJS09guzyj4eO6jAMEShUqBxIuJ+Mfaf8CzvydeingJ80LnQWbW8xaOOXjwIF555RVk\nZmZCKPyrObW2tRZnys7oWirOlJ2BhYnFLV0fQ1yGwMK0dy9pTHomhmFwtvwsdufuxomrJ5BdnY3q\n1mpdV0T/Pv0xynMUHg94HENdhuq6L6urqzFgwABkZmbCzc2N5Z9C/y7XXcbipMXYn78fjIbBs8Oe\nxb/H/vuW7o2eiI/nPgoQRlZQV4Afzv6ADec3YJjbMKStScOfP/0JH28ftkszCIZhEB0djRdeeAGz\nZ8++53ML6gp0geJ06WlcrLoIVytXBDoGdmwOHbcD7AfAVNTzR2aTnuFKwxXsz9+PUyWnkFmZiauN\nVyFv65iF9kZYeMj9IUz3n46HPB6657iFV155BQKBACtWrDBG+az4OfFnfHjiQ1R5VqGhrQERbhH4\nbNxneMjjIbZLMwg+nvsoQBiBSqPC7rzd+C7tO5yvOI95ofPw7NBn4WvniyFDhmDt2rUYOnQo22Ua\nxNGjR7Fw4UJkZ2dDJOr+eAe1Vo2CugJcrLp4y1YsL9ZNeOVn5wdfO1/drbOlMycHm5KerbW9FafL\nTiO5JBkXKi8gtzYXpY2laGhrgJbRwsLEAq5WrhhoPxCR7pGY6DsRoc6h9/W7WlZWhqCgIOTm5sLB\nwTATvrHtu+++w7lz5/DDDz9gf/5+vPn7m7hQeQGOFo54adhLeG3EazAV95wPEXw891GAMBCGYXCu\n/BwSsxKxOXMzfO18sXDoQsQNjLtlIqbY2FgsW7YMY8eOZbFawxkzZgzmzZuHhIQEve63Td2G3Jpc\nZFdno6CuAAX1BR2zcNbmo1XVCh87H/ja+cLXzlc3w+aN2Tbtze0pYJBuU6qVyKjMQHpFOnKqc5BX\nm4dieTEqWyrR0NYAtVYNkUAEG4kNnK2c4Wvni6HOQxHjFYNI90i9n+yee+452Nvb48MPP9Trfrni\n448/hlwuxyeffKK7r7K5EksPLcWO7B1QaVSIcI/A6yNex7QB03j/N22oc9/777+PkJAQXLx4EW++\n+eZdn6vVarF06dIut2zRxfh6pGW0SC9Px67cXUi8mAgto0V8YDwOzT2EAIeATl9jZ2eHuro6I1dq\nHGlpabh8+TJmzZql931LxVKEOIcgxDnktsfkbXJcrr+MgroCXK67jKzqLCQVJOlm2lSoFXC3dteF\nCmdLZziaO8LJ0gmOFo5wsui4dbBw6NIEWYTfGhQNuFRzCfl1+SiqL8LVxqsobSxFRXMFalprIFfK\noVApoGE0EAqEMBObwUZqAxdLF/jb+yNuYBwi3SMR5REFa4nx+unfeOMNhIeH47XXXuuR08LX19ff\nNgOlk6UTNj26CT898hO2ZG7BytMrEbctDqYiU0T1jcKzQ59F3MA4urrrusOHD4NhGEybNg3p6ek4\nceIEoqM7n3ejvr4eGzZswLFjx7q8f3p3fABaRouc6hyklKXgSNERHLx8EHZmdpjSfwp+ifsFYa5h\n90zFMpkM9fX1RqrYuL744gssXrwYJibGvQzVRmqDIS5DMMRlSKePt7S36MJESWMJqlqqUNpUinMV\n51DZXImqlipUtVShVlELG4kNHC0cITOTQSaVwVZqC5nZ9VvprffZSGxgaWoJC1OLjlsTC3ojMzCG\nYdCuaUejshEVLRWoaKrQ/f9VtlSiuqUatYqO9WBuTN3e3N6MVlUrlJqOdR4AQCgQQiKSwNzEHNYS\na9ib28PL1gujPEehf5/+CHEOQahzKCxNLVn+if/i7e2NyZMn49tvv8WyZcvYLkfv6urq4Ofn1+lj\nAoEAs4NnY3bwbLSr27EmdQ02XNiA2b/OhpbRwlvmjZEeI/FYwGMY7zO+134Q+PPPPzFkSMf74ODB\ng3HkyJE7BgiZTIZXX30Vu3fv7vL+e+e/ajcwDIOGtgaUN5ejRF6CvNo8XKq9hOzqbJwrPwcHcwdE\nuEdgpMdIfDjmQ3jaenZr/z21BeLKlSs4dOgQfvjhB7ZLuY2FqQX87f3hb+9/1+dptBrUKepQ1VKF\n+rZ6NLQ1oF5Rr/u6pLEEmVWZuvvkbXK0qFrQ0t6C5vZmtKhaYCI0uS1U3PheIpJAKpZCIpZAIpLc\ncisVdX6/WCiGWCiGSCDquBWKbvn6bo8JBUIIcHugvVPI7ey5DBhotBqotWpoGA00Wo3u9ub7nXnJ\n7wAAG8hJREFU1Fq1bgVHpUaJNlUblNqO21ZVq25rU7dBoVJAoVagRdUChVoBhUqBNnUblBollGol\nlBol2jXtUKo7btVate5YWkZ7S70ioQgmQhOYikxhZmKmCwQ2Ehv0s+0HB3MHuFi5oK91X3jaesLX\nzhceNh68DXpLly7F5MmTsWTJkh433XN9fT1ksntflWYqNsXi4YuxePhiMAyDAwUHsOH8Bvxe9Ds2\nXtgILaOFtcQa7tbuGGg/EIGOgR3r+zgFw1vmzel5dk6cOIGdO3di1KhRADpmL3777be7/PqqqiqY\nm5sDACwsLPQ+KRfnAsTFH4FXv+p467rx9iUAIBBAd99tm+Cm593h8dv2d9MmFABmAsBS2LFZXP/a\nWgi4iIA+IkAmAAIATLhRqAkADwBoBNouA3mbgfuY7+STG9M9bL573xTfeAGoWQVgN38vvRIBcLi+\n3ZPp9e02yutb7a13MwDU17duUjNA2/Wt/W+bigHaAbTe/P3Nj6Pz57d39nzc+r2yk9tb7sOt94kB\nSASAqaDj9uav/36fVABYCAH767fmN9+a3vq9+fW/0Ru31kLASgiIBDf/oyoAyG/9h9MCaL6+lXf/\n352LQgCUfQZgx/0vcMdVOx8D0P4rsLnrrxEAmHh9g2PHJtcAF9rlyG2X41J5Fs5e3YHdaqBcDVRr\nOn5/bvwOWV6/NRcAJgJAJOj4PRYLOt4PxNe/7yxy36mxudPnAigt6Nju+vNc36m7uzvCw8Px22+/\n6R7Lzs7GoUOHOn3dU089BVtbW2i1Wt3gdY1Gc18D2e+GcwEi8H1g7HsdAwoFEIABAwEj6Px/4Q4Y\nMB1v0ALccisQCP567G/PZxgGGqbjk5NKo4JSpUSrqhW1ilo0KZvgZOkEd2t39O/TH/59Oj69DnUd\nir7WfR9o8M7atWuRkpKCdevW3fc+uKa+vh4+Pj7IyMiAu7s72+UYVbumHc3tzbqtSdl0y/fN7c1o\nav/rvhZVi+4Td5u6DQr1TV/f4X4to4WZ2EzXMmEqMoWpyBQmIhPd16YiU90n8Xs9duN7m5u/7+T5\nEnHHsW60htz42lRkekstNx7j2hTKPdXevXvxzjvv4OzZs7wfSHiz0NBQbNiwAYMHD36g/dgAGHl9\nuxnDMKhprUFRQxEqmju6vmpaa1DbWotWVStUWhVUmo6tRavSfa9hNH/bEW5Z7ryzpc//fh/DMMAw\nwGrYTY8nHb7tdVFRUfj3v/+N8PBwyOXyW9YQCggIQEBA52PrbnByckJLSwuAjpmP9X3FDucCBIqA\nQ3M7T1VsUaqVqGiuQEnj9S6MmktYn74eC/cuhFAgRIRbBEZ5jsJE34nwt/fv1h9xTxwD8cMPP+Dh\nhx/mdXhQqpWobKnUjYmoU9Shvq1e111R31bfcd+N76/fqjQqWEmsYGVqBUtTS91mJfnr+xuPOVs6\nw8LEAmYmZpCKpZCKpTAT3/T1Tfff/JhYKO5RJwryYCZNmoQlS5bg2LFjiImJYbscvamrq+tSF8b9\nEggEcLBwgIMFNy6DFSTc/jetUCgglXZMT75v3z6MGzcOycnJGD58+F1bIBISEiCTyRAVFYXU1FRM\nnjwZqampiI2NBQAUFRWhX79+D1wz9wIEB0nEEnjaesLT1hNRHlG6+xmGQbG8GKdLT+NI0RGsPL0S\nAPBw/4cRHxiPKI+oe34Kk8lkPWoMRHt7O1atWoV9+/axXUqnlGolShtLcVV+FVfl10fbt1SgsrlS\nFxgqmivQqmqFg4WD7oqMPuZ9OgZNmsngYeOBUOdQ3fc3bu3M7GAmNqOTOzEqoVCIV155BStWrOhR\nAaK+vh52dnZsl8GqrKws3fgHKysrXL58GQ891DGRVldaIMaMGYN9+/Zhx44dEAgEGD9+PMrKyjB2\n7Fhcvnz5lue2tLTghx9+QE5ODr788kssWLAAFhZ3nwWY5oHQI4ZhcKnmEn679BsSsxJR21qLmYEz\nMSdoDga7dN4Ml56ejqeffhrnz583crWGsWXLFqxbtw6///47K8dXa9UobihGfl1+x2Wc9Zd1YaFE\nXoI6RR3crN3gYePx12WcFs5wsnSCk4WT7lZmJqMmeMIbra2t8PLywsmTJ9G/f3+2y3lgKpUKZmZm\nUKlUvSaQG/Pc98cff+glbFILhB4JBAIMdBiIgQ4DsSx6GbKrs5F4MRGPbn0UjhaOWBi2EPGB8TA3\nMde9pqe1QKxevRpLliwx+HGqWqpwseoisquzdWEhvzYfV+VX4WzZMYmPXx8/+Mh8MMJ9BPra9IWH\njQecLJx4O+KekDsxNzfHc889h6+++gqrV69mu5wHduMKjN4SHoxNqVTqZT/UAmEEGq0GBy4fwHdp\n3+FUySk8GfQkFoYtRIBDABobG+Hm5oampia2y3xg6enpmD59OgoLC28Z7PMgmpRNyKrOwsWqi8is\nytRNZd2uaUeQY5BuKmu/Pn7ws/NDP1k/3i1pTIg+XLt2DYGBgbhy5Qqsrfl79ROAXreUN8DPcx+1\nQBiBSCjCZL/JmOw3GVflV7H23FrE/hSLAIcALApfhNa2VqhUKqNPuKRvq1evxsKFC+87PKg0KmRU\nZugW0kopS0FpY6nu2u0gxyBM9p2MQMdAuFq50qcTQm7i6uqK2NhY/Pzzz3jhhRfYLueB1NXV9frx\nD3xALRAsade0Y2f2Tnx95mukZKdg2bhlWDxyMezN7dku7b7U1dXBx8cHubm5cHR0vOfzGYZBSWMJ\nUkr/WnnzfMV59JP1Q4RbBCLdIxHhFoEAhwDqciCki44ePYqXXnoJmZmZvA7Y+/btw9dff439+/ez\nXYrR8PHcRy0QLDEVmWJW0CzMCpoFj0gP5ITkwO9rPzzi/wgWhS/CUFd+rc65YcMGTJ069Y7hobm9\nGWnX0pBSmoLTZaeRUpoCDaPRBYV/jf4XwlzDjLqWACE9TUxMDDQaDU6cOIGRI/8+8wF/UAsEP1CA\n4ABXuGKp31J8//j3WH9uPeK2xcHVyhUvDXsJjwc8DlMRt6eo1Wq1WLNmDTZv7pgy7uY1Qm60LhTU\nFSDYKRiR7pGYOWgmVk5YCU8bT15/SiKEawQCAZ5//nmsWbOG1wGiq9NYE3ZRgOCAG5NJ2Zvb442o\nN7B0xFLsyduDr898jVcPvIrZQbMxK3BWlxbnYkPi7kQI/AXY3bIbb/30FlKvperWCIl0i8Qzg59B\niFPILcuYE0IMIyEhAf/3f/+HiooKODs7s13OfaEWCH6gAMEBf7+UUyQUYbr/dEz3n47cmlxsvrgZ\ns3+dDQCYFTgL0wdMx2CXwazMU9CobMS58nNILUtFWnkaUstSUVJTgv7R/SEQCPBK5CsY5jaMM7O7\nEdLb2NraYsaMGVi3bl23Fl7ikvr6enh6dm9hQmJ8NIiSAxYtWoQBAwbgpZdeuuNzGIbB2fKzSLyY\niL35e1GnqMMEnwkY7TUaEe4R8Lf312uguDHIMbs6G9nV2UivSEfatTRclV9FiFMIwlzDEO4aDk8T\nT0x7aBpKS0phacmdpY4J6c3Onz+PadOmoaioSO8LKBlDQkICYmNj8dRTT7FditHw8dxHLRAc0JXJ\npAQCAcJcwxDmGobPx3+OKw1XkFSQhEOFh/DBiQ9Q21qLUOdQ9O/TH352fvCx8+mYgtmsD/qY94FE\nJNEt6dyuaUejslG3VbdUd8zU2FiCq/KrKKwvRE5NDqwl1ghwCECAQwBGeY7C0uFLEeAQcMvytx9/\n/DGemPEEhQdCOCQ0NBTOzs44ePAgJk2axHY53UZjIPiBAgQH2NnZobi4uFuv8bL1wsKwhVgYthAA\nUN1SjfMV5ztmZKzLx6mSU6hurUZtay1qFbVQqpXQMlpoGS3EQjFspDawlljDRmKDPuZ9dFM7hziF\noJ+sHwIcAmArtb1rDVqtFuvWrUNiYuJ9/+yEEMN45plnsH79el4GCBoDwQ8UIDhAJpMhPT39gfbh\nYOGAcT7jMM5nnJ6qurejR4/C0tISYWFhRjsmIaRr4uPj8cYbb6CqqqpLc7NwCbVA8AOtFsQBdnZ2\nqK2tZbuMblu7di0WLFjAyStDCOntbGxsMH36dGzatIntUrqttraWWiB4gAIEBzg6OqK6uprtMrql\npqYGSUlJePLJJ9kuhRByBze6Mfg0OE+r1aKurg729vyclbc3oQDBAQ4ODrwLEJs2bcK0adOomZEQ\nDouOjoZarcbp06fZLqXL6uvrYWVlxfu1gXoDChAc4OjoiKqqKrbL6DKGYbBhwwbMnz+f7VIIIXch\nEAgwf/58rF+/nu1SuqyqqgoODjSPDB9QgOAAS0tLaDQatLa2sl1Kl1y4cAGNjY28niqXkN7iqaee\nws6dO9HU1MR2KV1SXV3Nu0GfvRUFCA4QCAS86sbYuHEjEhISIBTSrw8hXOfi4oLo6Gj8+uuvbJfS\nJdXV1dQCwRN0BuAIvnRjqFQqbN68GXPnzmW7FEJIF82dOxc//fQT22V0CXVh8AcFCI7gSwvEgQMH\n4OvrCz8/P7ZLIYR00dSpU3H+/HmUlJSwXco9URcGf1CA4Ai+XMq5cePGXjU/PSE9gVQqxWOPPYZf\nfvmF7VLuibow+IMCBEc4ODhwvgujvr4ehw4dwhNPPMF2KYSQbkpISMCmTZs4PycEdWHwBwUIjuBD\nF8bWrVsxYcIE2NrefY0MQgj3PPTQQ1AoFDh37hzbpdwVdWHwBwUIjuDDIMobV18QQvhHIBBgzpw5\nnJ/amlog+IMCBEdwvQUiLy8PRUVFmDBhAtulEELu09y5c7FlyxaoVCq2S7kjaoHgDwoQHMH1QZQ/\n/fQTZs+eDbGYFnAlhK/8/Pzg7e2NgwcPsl1Kp26sg9GnTx+2SyFdQAGCI7g8iJJhGJr7gZAeIiEh\ngbNzQtTV1dE6GDxCAYIjuNyFcebMGZiamiI0NJTtUgghD+iJJ55AUlISGhoa2C7lNtR9wS8UIDjC\n0tISWq0WLS0tbJdymy1btiA+Ph4CgYDtUgghD6hPnz4YM2YMJ6e2pgGU/EIBgiO4uh6GRqPBtm3b\nEB8fz3YphBA9mTVrFhITE9ku4zY0iRS/cHJE3PLly3Vfx8TEICYmhrVajOnGQEovLy+2S9E5fvw4\nnJyc4O/vz3YphBA9efjhh/Hss8+isrISTk5ObJejQ10Y/ML5ANGbODg4oLKyku0ybpGYmIhZs2ax\nXQYhRI/Mzc0xZcoUbN++HYsWLWK7HB3qwuAX6sLgEBcXF1RUVLBdhk57ezt27tyJmTNnsl0KIUTP\nuNiNUV5eDhcXF7bLIF1EAYJD3NzcUFZWxnYZOocPH8aAAQPg6enJdimEED0bP348Ll26hKtXr7Jd\nik5ZWRnc3NzYLoN0EQUIDnF1dcW1a9fYLkNny5Yt1H1BSA9lamqKuLg4TrVCXLt2Da6urmyXQbqI\nAgSHcKkForW1Fbt378aMGTPYLoUQYiDx8fGcChDUAsEvFCA4hEstEPv27UN4eDinRmgTQvRr1KhR\nqKioQG5uLtulQKVSoa6ujt5zeIQCBIdwqQXixuRRhJCeSyQS4YknnsCWLVvYLgUVFRVwcHCASCRi\nuxTSRRQgOMTR0RH19fVob29ntY7GxkYcPnwYcXFxrNZBCDG8G90YDMOwWgd1X/APBQgOEYlEcHR0\nZP1Szt9++w0xMTGQyWSs1kEIMbyIiAi0t7fj/PnzrNZBAyj5hwIEx7i6urLejUHdF4T0HgKBgBOD\nKakFgn8oQHCMm5sbqwMpa2pq8Oeff2LatGms1UAIMa4bAUKr1bJWw7Vr1yhA8AwFCI5huwVix44d\nmDRpEiwsLFirgRBiXEFBQbC0tERycjJrNZSVlVEXBs9QgOAYtlsgaO0LQnofgUDA+tTW1ALBPxQg\nOIbNFoiysjJkZGRg4sSJrByfEMKe+Ph4bNu2DWq1mpXjUwsE/1CA4Bg254LYunUrHnnkEUgkElaO\nTwhhj6+vLzw8PHD06FFWjk+DKPmHAgTHsNmFQd0XhPRus2bNYmVSqaamJqjVatjY2Bj92OT+UYDg\nGDc3N5SWlhp9UpeCggIUFxdj9OjRRj0uIYQ7Zs6cid9++w1KpdKox73R+iAQCIx6XPJgKEBwjI2N\nDUxNTVFTU2PU427duhUzZsyAWCw26nEJIdzh5uaG4OBg7N+/36jHLSoqgre3t1GPSR4cBQgO8vb2\nRmFhoVGPSZNHEUIAdroxCgsL0a9fP6Mekzw4ChAc1K9fP6MGiMzMTDQ2NmLEiBFGOyYhhJsef/xx\nJCUlobm52WjHLCwspBYIHqIAwUHe3t4oKioy2vESExMRHx8PoZB+HQjp7fr06YOoqCjs2rXLaMek\nLgx+ojMGBxmzC4NhGOq+IITcwtjdGNSFwU8UIDioX79+RmuBSElJgUQiweDBg41yPEII902fPh0n\nTpxAbW2twY/FMAy1QPAUBQgOMmYLxObNmzF79my6fIoQomNlZYUJEyZg586dBj9WXV0dBAIBZDKZ\nwY9F9IsCBAd5eHigrKwMKpXKoMdRq9XYunUrTR5FCLmNsboxaAAlf1GA4CBTU1O4uLigpKTEoMc5\ncuQIPD094evra9DjEEL4Z9KkSbhw4YLBp9YvKiqi8Q88RQGCo4zRjXGj+4IQQv5OKpVi+vTp2LZt\nm0GPQy0Q/EUBgqMMPReEQqHArl27MHPmTIMdgxDCb8boxqArMPiLAgRHGboFYt++fRgyZAhcXFwM\ndgxCCL+NGTMGxcXFKCgoMNgxqAuDvyhAcJS/vz9ycnIMtn/qviCE3ItYLMaMGTOQmJhosGPk5uZi\nwIABBts/MRwKEBwVGBiIrKwsg+y7oaEBhw8fRlxcnEH2TwjpOeLj47FlyxaDrBDc3NyMmpoaeHp6\n6n3fxPAoQHCUr68vysrK0Nraqvd9//e//8WYMWPoumtCyD2NGDECTU1NyMzM1Pu+L126hP79+0Mk\nEul938TwKEBwlFgsRv/+/Q3SjfHLL79Q9wUhpEuEQiFmzZqFX375Re/7vnTpEgYOHKj3/RLjoADB\nYYGBgbh48aJe91lcXIz09HRMnTpVr/slhPRcCQkJ+Pnnn6HRaPS635ycHPj7++t1n8R4KEBw2KBB\ng/QeIDZt2oSZM2dCKpXqdb+EkJ5r0KBBcHNzw6FDh/S638zMTAQGBup1n8R4KEBwmL4HUjIMg//8\n5z+YN2+e3vZJCOkd5s2bh//85z963eeFCxcQEhKi130S46EAwWH67sI4deoUJBIJwsPD9bZPQkjv\nMGvWLCQlJaG+vl4v+6uvr0ddXR3NQsljFCA4zMvLC3V1dWhsbNTL/m60PtDKm4SQ7pLJZJgwYQK2\nbt2ql/1lZGQgKCgIQiGdhviK/uc4TCgUYtCgQcjIyHjgfbW0tGDnzp2YM2eOHiojhPRG8+bNw4YN\nG/Syr/T0dISGhuplX4QdFCA4Ljw8HGfOnHng/WzZsgXR0dE0dTUh5L6NGzcO5eXlSE9Pf+B9nTlz\nBsOGDdNDVYQtFCA4btiwYQ8cIBiGwerVq/Hiiy/qqSpCSG8kFovx3HPP4dtvv33gfVGA4D8KEBwX\nERHxwAEiJSUFzc3NGDdunJ6qIoT0Vs888wy2b9+OhoaG+95HbW0tqquraQ4InqMAwXF+fn6Qy+Uo\nLy+/732sXr0azz//PA1WIoQ8MGdnZ0ycOBE//fTTfe/jzz//xLBhw+g9iefof4/jhEIhoqOjcezY\nsft6fXV1Nfbs2UNzPxBC9OaFF17AmjVr7nuBrWPHjmHUqFF6rooYGwUIHoiJicEff/xxX69dv349\nHn30UdjZ2em3KEJIrxUVFQUTExP8/vvv9/X6P/74AzExMfotihidgDHEGq0PQCAQGGTZWD47f/48\nZsyYgfz8/G69rq2tDd7e3khKSkJwcLCBqiOE9EY//vgjEhMTcfDgwW69rq6uDp6enqipqYFEIjFQ\ndfzDx3MftUDwQEhICFpbW5GXl9et123cuBFDhgyh8EAI0bs5c+YgJycHZ8+e7dbrkpKSMHr0aAoP\nPQAFCB4QCASYMmUK9u7d2+XXqNVqfPrpp/h//+//GbAyQkhvZWpqiiVLluDjjz/u1uv27NmDhx9+\n2EBVEWOiAMET06dPx44dO7r8/I0bN8Ld3R1RUVEGrIoQ0pstWLAAJ0+e7PLEUgqFAvv376cA0UNQ\ngOCJ8ePHIy8vD4WFhfd8bktLC9599118+umnRqiMENJbWVhY4N1338Vrr73Wpf77PXv2YOjQoXB1\ndTVCdcTQKEDwhImJCeLj47s0D/2KFSsQHR2NiIgII1RGCOnNFixYgNLSUiQlJd3zuT/++CPmzp1r\nhKqIMdBVGDySm5uL6OhoFBcXw8zMrNPn5OTkIDo6GqmpqejXr5+RKySE9Eb79u3DokWLcP78eVhb\nW3f6nOzsbIwZMwZXrlyBVCo1coXcx8dzH7VA8MiAAQMQFRWFVatWdfp4e3s75syZgw8//JDCAyHE\naCZPnozY2FgsXrz4js9Zvnw5Xn75ZQoPPQi1QPBMfn4+hg8fjpSUFPj4+OjuZxgGixYtwtWrV/G/\n//0PAoGAxSoJIb1NU1MTBg8ejLfeegtPP/30LY/t378fCxcuRE5ODszNzVmqkNv4eO4Ts10A6R4/\nPz8sX74c06ZNw/79++Hh4YHW1lYsWbIEp0+fxh9//EHhgRBidFZWVti7dy9Gjx4NuVyOl156CSKR\nCKdPn8a8efOwY8cOCg89DCdbIAghhJDehmOn43viXAsE3/4BCSGEkN6IBlESQgghpNsoQBBCCCGk\n2yhAEEIIIaTbKEAQQgghpNsoQBBCCCGk24x6FYZGo8HWrVtRWFiIvn374syZM1iyZAm8vb2NWQYh\nhBBiFD35vGfUFogLFy7gscceg7e3N7RaLWbMmAEXFxdjlkAIIYQYTU8+7xk1QAwZMgQSiQTJycmI\niYlBTEzMHReFAoADBw4YsTpCCCFEv7p73uMTowaI1NRU1NTU4OLFi+jXrx9OnDhx1+dPmDDBSJUR\nQggh+tfd8x6fiJYvX77cWAf78ccfUVJSAhsbG8jlcpibm6OiogLr1q1Dnz59sHr1apiZmeHgwYPQ\naDTYtm0bIiMjkZycjLVr18LW1hZHjhxBcHCwsUomhBBC7lt3znsymQxJSUn4+eefMWLECOTn5yMt\nLQ1HjhxBZmYmBg8ezPaPcwujDqJ85513bruvqqoKTk5OsLKygq+vL4RCIaytrWFrawtbW1sAgKur\nKzw9PWFiYkKLsRBCCOGN7pz3vvzyS6xYsQJFRUWQSqXw9/dHYWEhwsPDERISwkL1d8f6ZZzJycmI\njY3FqVOnEBsbi+zsbFhZWeHcuXOQyWQAgJSUFIwdOxZnz57tMX1HhBBCeqc7nff8/PyQlpYGpVKJ\nnJwcfPfdd8jMzERraytKSkrYLvs2nFuNkxBCCCHcx3oLBCGEEEL4hwIEIYQQQrqNAgQhhBBCuo0C\nBCGEEEK6jQIEIYQQQrqNAgQhhBBCuo0CBCGEEEK6jQIEIYQQQrqNAgQhhBBCuu3/A+CkcdH/Ba1e\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbc7734c>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(x_min, x_max, N)\n",
      "eigvals, eigvecs = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "# WYKRES FUNKCJI FALOWEJ:\n",
      "\n",
      "fig, ax1 = pl.subplots()\n",
      "ax2 = ax1.twinx()\n",
      "\n",
      "#ax1.set_figure(figsize = (10, 6))\n",
      "\n",
      "\n",
      "for idx in range(N_eigvals)[0:4]:\n",
      "    \n",
      "    psi = eigvecs[:,idx]\n",
      "    ax2.plot(x, psi + eigvals[idx], label = \"$\\psi_{%d}(x)$\" % idx)\n",
      "    \n",
      "#pl.legend(bbox_to_anchor=(1.16, 0.7))\n",
      "ax1.set_ylabel(\"$\\psi_n(x)$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "ax1.set_yticks([])\n",
      "ax2.set_xlabel(\"$x$\", {\"fontsize\": \"xx-large\"})\n",
      "ax2.set_xlim(x_min, x_max)\n",
      "#pl.ylim(-shift, shift * N_eigvals)\n",
      "ax2.set_xticklabels([x_min, x_max], [\"$x_{min}$\", \"$x_{max}$\"], size = 15)\n",
      "\n",
      "ax2.set_yticks(eigvals)\n",
      "ax2.set_yticklabels([\"$n = %d$\" % i for i in range(0,len(eigvals))])\n",
      "\n",
      "#plt.savefig(\"num_harr_psi3.png\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAasAAAD5CAYAAACZIBolAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMayIiEh4/weyQHdhaun66gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcdd396c>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(x_min, x_max, N)\n",
      "eigvals, eigvecs = EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)\n",
      "\n",
      "\n",
      "# WYKRES FUNKCJI FALOWEJ:\n",
      "\n",
      "pl.figure(figsize = (10, 6))\n",
      "\n",
      "shift = 0.3\n",
      "\n",
      "for idx in range(N_eigvals)[0:3]:\n",
      "    \n",
      "    psi = eigvecs[:,idx]\n",
      "    pl.plot(x, psi + idx * shift, label = \"$\\psi_{%d}(x)$\" % idx)\n",
      "    \n",
      "pl.legend(bbox_to_anchor=(1.16, 0.7))\n",
      "pl.xlabel(\"$x$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$\\psi_n(x)$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "pl.xlim(x_min, x_max)\n",
      "#pl.ylim(-shift, shift * N_eigvals)\n",
      "pl.xticks([x_min, 0.0, x_max], [\"$x_{min}$\", \"$0.0$\", \"$x_{max}$\"], size = 15)\n",
      "pl.yticks([])\n",
      "#plt.savefig(\"num_harr_psi3.png\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAsIAAAGBCAYAAABsNteGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwBQSUIYAIBKEsIAAFTSb14Romzj+akWAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcd808ec>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.figure(figsize = (10, 6))\n",
      "x = np.linspace(-3.0, 9.0, 300)\n",
      "pl.ylim(0.0,2.0)\n",
      "#pl.xlim(0.0, 200.0)\n",
      "pl.plot(x, (1.0 - np.exp(-x)) ** 2)\n",
      "#plt.plot(np.exp(-2.0 * x) - 2.0 * np.exp(-x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[<matplotlib.lines.Line2D at 0xbcd582c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAAFtCAYAAADBBfTRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTUSgAgAAaCICFQAAQBMRqAAAAJro/wERsQL8ycUTjwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbcd506c>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# WYKRES GESTOSCI PRAWDOPODOBIENSTWA:\n",
      "\n",
      "pl.figure(figsize = (10, 6))\n",
      "\n",
      "#shift2 = shift**2\n",
      "shift2 = 0.012\n",
      "\n",
      "for idx in range(N_eigvals)[0:4]:\n",
      "    \n",
      "    psi = eigvecs[:,idx]\n",
      "    pl.plot(x, psi**2 + idx * shift2, label = \"$P_{%d}(x)$\" % idx)\n",
      "    \n",
      "pl.legend(bbox_to_anchor=(1.16, 0.7))\n",
      "pl.xlabel(\"$x$\", {\"fontsize\": \"xx-large\"})\n",
      "pl.ylabel(\"$P_i(x)$\", {\"fontsize\": \"xx-large\", \"rotation\": \"horizontal\"})\n",
      "#pl.xlim(x_min, x_max)\n",
      "#pl.ylim(-shift2, shift2 * N_eigvals)\n",
      "pl.xticks([x_min, 0.0, x_max], [\"$x_{min}$\", \"$0.0$\", \"$x_{max}$\"], size = 15)\n",
      "pl.yticks([])\n",
      "#pl.savefig(\"num_harr_P3.png\")\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "x and y must have same first dimension",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-11-73ec2a18dac3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mpsi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meigvecs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpsi\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0midx\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mshift2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"$P_{%d}(x)$\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbbox_to_anchor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   2465\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2466\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2467\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2468\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2469\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3891\u001b[0m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3893\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3894\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3895\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    320\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    321\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m                 \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    323\u001b[0m                     \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    324\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    298\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    301\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m    238\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 240\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y must have same first dimension\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    241\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y can be no greater than 2-D\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAFtCAYAAADF8CTsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKwCAJMIKACDJ/wCmruOzjR1NoAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbcd5c2c>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = lambda x: x**2\n",
      "x = 1\n",
      "result = %timeit -o g(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aaa = 1\n",
      "b = print aaa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit EigenvalEigenvecProblem5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit EigenvalEigenvecProblem5(N, x_min, x_max, N_eigvals, potential, *args_potential)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print eigvals\n",
      "print [\"n = %d\" % i for i in range(0,eigvals)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}