{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Phase Diagram\n",
    "# 2022-01-25\n",
    "\n",
    "import numpy as np\n",
    "from numpy.linalg import pinv, inv,det\n",
    "import matplotlib.pyplot as plt\t\t# For Plotting\n",
    "import os\n",
    "import math\n",
    "import cmath\n",
    "from scipy.signal import find_peaks\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-25 14:43:51\n"
     ]
    }
   ],
   "source": [
    "#print(datetime.now())\n",
    "print(datetime.now().isoformat(' ', 'seconds'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def phasor(x):\n",
    "    print(str(x), \"= \",abs(x), \"<\",cmath.phase(x)*180/(math.pi))\n",
    "\n",
    "def phasor3(x):\n",
    "    print(str(x[0]),\"= \",abs(x[0]), \"<\",cmath.phase(x[0])*180/(math.pi))\n",
    "    print(str(x[1]),\"= \",abs(x[1]), \"<\",cmath.phase(x[1])*180/(math.pi))    \n",
    "    print(str(x[2]),\"= \",abs(x[2]), \"<\",cmath.phase(x[2])*180/(math.pi))  \n",
    "\n",
    "def cart(mag,degree):\n",
    "    rad=degree*(np.pi/180.)\n",
    "    y=complex(mag*(np.cos(rad)),mag*(np.sin(rad)) )\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pD(cVar,title,minXY,maxXY):\n",
    "    titletext=title\n",
    "    x1=np.zeros(2)\n",
    "    y1=np.zeros(2)\n",
    "    x1[1]=cVar[0].real\n",
    "    y1[1]=cVar[0].imag\n",
    "    x2=np.zeros(2)\n",
    "    y2=np.zeros(2)\n",
    "    x2[1]=cVar[1].real\n",
    "    y2[1]=cVar[1].imag\n",
    "    x3=np.zeros(2)\n",
    "    y3=np.zeros(2)\n",
    "    x3[1]=cVar[2].real\n",
    "    y3[1]=cVar[2].imag\n",
    "\n",
    "    xmax=maxXY\n",
    "    ymax=maxXY\n",
    "    xmin=minXY\n",
    "    ymin=minXY\n",
    "    fig=plt.figure(figsize=(6,6))\n",
    "    ax=fig.add_subplot(111)\n",
    "    ax.plot(x1, y1, c='r', label=\"a/0\")\t# default marker is dot(.)\n",
    "    ax.plot(x2, y2, c='b',label=\"b/1\")\t#\n",
    "    ax.plot(x3, y3,c='g', label=\"c/2\")\n",
    "    #ax.scatter(x3, y3, c='r', marker='*',label=label3)\n",
    "    plt.title(titletext)\n",
    "    # plt.legend(loc = 'upper right')\n",
    "    plt.legend(loc = 'lower right')\t\t\n",
    "    plt.grid()                   \n",
    "    plt.xlabel('Real')\n",
    "    plt.ylabel('Imag')\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pD2(cVar1,cVar2,title1,title2,minXY,maxXY):\n",
    "    titletext=title1+ \":solid   \"+title2+ \":dashed\"\n",
    "    x11=np.zeros(2)\n",
    "    y11=np.zeros(2)\n",
    "    x11[1]=cVar1[0].real\n",
    "    y11[1]=cVar1[0].imag\n",
    "    x12=np.zeros(2)\n",
    "    y12=np.zeros(2)\n",
    "    x12[1]=cVar1[1].real\n",
    "    y12[1]=cVar1[1].imag\n",
    "    x13=np.zeros(2)\n",
    "    y13=np.zeros(2)\n",
    "    x13[1]=cVar1[2].real\n",
    "    y13[1]=cVar1[2].imag\n",
    "\n",
    "    x21=np.zeros(2)\n",
    "    y21=np.zeros(2)\n",
    "    x21[1]=cVar2[0].real\n",
    "    y21[1]=cVar2[0].imag\n",
    "    x22=np.zeros(2)\n",
    "    y22=np.zeros(2)\n",
    "    x22[1]=cVar2[1].real\n",
    "    y22[1]=cVar2[1].imag\n",
    "    x23=np.zeros(2)\n",
    "    y23=np.zeros(2)\n",
    "    x23[1]=cVar2[2].real\n",
    "    y23[1]=cVar2[2].imag    \n",
    "    \n",
    "    xmax=maxXY\n",
    "    ymax=maxXY\n",
    "    xmin=minXY\n",
    "    ymin=minXY\n",
    "    fig=plt.figure(figsize=(6,6))\n",
    "    ax=fig.add_subplot(111)\n",
    "    ax.plot(x11, y11, c='r', label=\"a/0\")\t# default marker is dot(.)\n",
    "    ax.plot(x12, y12, c='b',label=\"b/1\")\t#\n",
    "    ax.plot(x13, y13,c='g', label=\"c/2\")\n",
    "    \n",
    "    ax.plot(x21, y21, c='r', linestyle='dashed',label=\"a/0\")\t# default marker is dot(.)\n",
    "    ax.plot(x22, y22, c='b',linestyle='dashed',label=\"b/1\")\t#\n",
    "    ax.plot(x23, y23,c='g', linestyle='dashed',label=\"c/2\")   \n",
    "    \n",
    "    #ax.scatter(x3, y3, c='r', marker='*',label=label3)\n",
    "    plt.title(titletext)\n",
    "    # plt.legend(loc = 'upper right')\n",
    "    plt.legend(loc = 'lower right')\t\t\n",
    "    plt.grid()                   \n",
    "    plt.xlabel('Real')\n",
    "    plt.ylabel('Imag')\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def puConverter(PUold,Sold,Snew,Vold,Vnew):\n",
    "    PUnew=PUold*(Snew/Sold)*(Vold/Vnew)**2\n",
    "    return PUnew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.+0.j] =  [1.] < 0.0\n",
      "[-0.5-0.8660254j] =  [1.] < -119.99999999999999\n",
      "[-0.5+0.8660254j] =  [1.] < 119.99999999999999\n"
     ]
    }
   ],
   "source": [
    "vA=cart(1,0)\n",
    "vB=cart(1,-120)\n",
    "vC=cart(1,120)\n",
    "v3=np.array([[vA],[vB],[vC]])\n",
    "phasor3(v3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAGDCAYAAAABCJbEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYlGRiIhILCoSERGJ5f8AYysGz7WcquIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc224f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pD(v3,\"v3\",-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.6062177826491071+0.3499999999999999j) =  0.7 < 29.999999999999993\n",
      "[0.60621778+0.35j] =  [0.7] < 29.999999999999993\n",
      "[4.2862638e-17-0.7j] =  [0.7] < -90.0\n",
      "[-0.60621778+0.35j] =  [0.7] < 150.00000000000003\n"
     ]
    }
   ],
   "source": [
    "iA=cart(0.7,30)\n",
    "iB=cart(0.7,-90)\n",
    "iC=cart(0.7,150)\n",
    "i3=np.array([[iA],[iB],[iC]])\n",
    "phasor(iA)\n",
    "phasor3(i3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAGDCAYAAAABCJbEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xea1c30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pD(i3,\"i3\",-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAGDCAYAAAABCJbEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8FeXZ//HPRdhFgbDIEhQqIIJlX6VWwIWlVqTFrQVx\nodanImrVuvWppY8oVcuvAq4FC9QFcKlLZVGBSK2IIuKCLKJF2RQMskTBSLh+f8yAISYk5JycOefk\n+369zitzzsyZ+Z4hnCv3zNz3mLsjIiJSVpWiDiAiIqlNhURERGKiQiIiIjFRIRERkZiokIiISExU\nSEREJCYqJCKAmf3RzB4Jp48xs1wzyyhp2TJs52YzmxxL1gLr6mNmG+KwHjezlnHKlG1mI+OxLkkd\nKiSS9MzsTjNbb2Y7zewTM7u5PLfn7p+6ey13zy+Hdd/u7iMBzKy+mf3HzHLMbLuZLTaz3vHepkh5\nUyGRVDAFaOPuRwEnAb80s59FnCkecoFLgAZAXeDPwPNmVjnSVCKHSYVEImdmN5jZk4Veu8fMJgC4\n+2p3/6rA7H1AkYdizKy6mT1S4K/8N83s6HBeEzN7zsy2mdlaM/tVMetoHh7uqRw+b2Fmr5jZLjN7\nCagfw2c9cFjM3feEn20fYEA+QUHJLOa9Ncxsqpl9aWYfAN0Kzb/RzD4Kc35gZkMKzGsZfoYdZvaF\nmc0stPrTzOzDcJ/da2ZW4L2XmNnKcLvzzOzYAvNON7NV4XonhZ9DKhgVEkkGM4BBZnYkQHhu4lzg\nsf0LhF+SucAG4IhC8941s1+ET0cAtYFmQD3gcmB3ge1sAJoAQ4HbzaxfKfI9BrxFUED+L9xG3JjZ\nu8Ae4DlgsrtvCV//kZltL7DorcBx4aN/ETk+Ak4m+PxjgEfMrHE47/+AFwkKVRYwsdB7zyQoTO0J\n9n3/MMNg4GbgZwQtp38Dj4fz6gNPA78n2DcfATo0VwGpkEjk3P0TYBmw/y/ofsDX7v56gWXGAUcC\nnYF/ADsKzGvv7vsLy7cEBaSlu+e7+1vuvtPMmhF8yd0QtgSWA5OBCw+VzcyOIfiC/V93/8bdFwHP\nx/6pv+Pu7YGjgF8ArxZ4/VV3r1Ng0XOBse6+zd3XAxMKrecJd9/k7vvcfSbwIdA9nP0tcCzQJPz8\nr3Kwce6+3d0/BRYCHcPXLwfucPeV7r4XuB3oGLZKBgEr3P1Jd/8W+CvwWaz7Q1KPCokki8eAC8Lp\nX1CgxbGfB94maGGMKWY9/wDmATPMbFN4or4KQStkm7vvKrDsJ0DTEnI1Ab4sdGjtkxI/zWEKv9wf\nB240sw6HyLK+uBxmdqGZLQ8PT20HTuS7w3C/Izjs9IaZrTCzSwqtu2AB+BqoFU4fC9xTYJ3bwvU0\nLZzHgxFgC+aTCkKFRJLFE0AfM8siaJl8r5AUUJng8M73uPu37j7G3dsSnJg/k6DVsQnI3H/4LHQM\nsLGEXJuBumZ2RKH3lZcqwA8OkaVZUTnCFsLfgFFAvbAl8z7hOQt3/8zdf+XuTYBfA/eV8pLf9cCv\n3b1OgUcNd3+tcJ7wvEqz4lYk6UuFRJKCu28FsoG/A/9195UAZlbJzH5tZnUt0B24Aphf1HrMrK+Z\n/TA8z7KT4JDOvvBQ0GvAHeEJ+fbApcAh+4OEh92WAmPMrKqZ/Qj4aTw+s5n1DM+DVA1PpN8AHA0s\nKeYts4Cbwn2RBVxZYN4RgANbw3VfTNAi2b+tc8L3AHwZLruvFDEfCLfZLlxPbTM7J5z3AtDOzH4W\nXpgwGmhUinVKmlEhkWTyGHAa32+NDCE4kbuL4It/IgVOFoeHan4ZPm0EPElQRFYCrxAc7oLg0Flz\ngtbJP4Fb3f3lUuT6BdCD4LDOrcD0w/xcxakG3AvkELSMBgE/cfdNAGZ2cniBwX5jCA5n/ZfgxPn+\nz4W7fwD8BVgMfA78EPhPgfd2A5aE63sOuMrdPy4poLv/k+Cy5BlmtpOglTMwnPcFcA4wLvwMrQpt\nUyoI042tREQkFmqRiIhITCIrJGbWzMwWhh2nVpjZVUUsY2Y2Iew89q6ZdY4iq4iIFC/KoRj2Ate6\n+7LwSpq3zOyl8FjvfgMJjru2IjhGfX/4U0REkkRkLRJ33+zuy8LpXQQnRgtf0z8YmB72H3gdqFOg\np66IiCSBpDhHYmbNgU58/7LHphzcwWkDJXcgExGRBIp8lFEzqwU8BVzt7jtjWM9lwGUA1atX73LM\nMeXZZyx2+/bto1KlpKjjh6Sc8aWc8aWc8bNmzZov3L1Bmd7s7pE9CHrxzgN+W8z8B4ELCjxfDTQu\nab2tW7f2ZLdw4cKoI5SKcsaXcsaXcsYPsNTL+F0e5VVbRnCfiZXuPr6YxZ4DLgyv3uoJ7HD3zQkL\nKSIiJYry0FZvYDjwnpktD1+7mXD8IHd/AJhN0Nt3LcFAchdHkFNERA4hskLiwTDWh7wJTtjcuiIx\niUREpCyS++yPiIgkPRUSERGJiQqJiIjERIVERERiokIiIiIxUSEREZGYqJCIiEhMVEhERCQmKiQi\nIhITFRIREYmJComIiMREhURERGKiQiIiIjFRIRERkZiokIiISExUSEREJCYqJCIiEhMVEhERiYkK\niYiIxESFREREYqJCIiIiMVEhERGRmKiQiIhITFRIREQkJiokIiISExUSERGJiQqJiIjERIVERERi\nokIiIiIxUSEREZGYqJCIiEhMVEhERCQmkRYSM3vYzLaY2fvFzO9jZjvMbHn4+EOiM4qIyKFVjnj7\nU4FJwPRDLPNvdz8zMXFERORwRdoicfdFwLYoM4iISGzM3aMNYNYc+Je7n1jEvD7AU8AGYBNwnbuv\nKGY9lwGXATRo0KDLrFmzyilxfOTm5lKrVq2oY5RIOeNLOeNLOeOnb9++b7l71zK92d0jfQDNgfeL\nmXcUUCucHgR8WJp1tm7d2pPdwoULo45QKsoZX8oZX8oZP8BSL+P3eFJfteXuO909N5yeDVQxs/oR\nxxIRkQKSupCYWSMzs3C6O0HenGhTiYhIQZFetWVmjwN9gPpmtgG4FagC4O4PAEOB/zGzvcBu4Pyw\nCSYiIkki0kLi7heUMH8SweXBIiKSpJL60JaIiCQ/FRIREYmJComIiMREhURERGKiQiIiIjFRIRER\nkZiokIiISExUSEREJCYqJCIiEhMVEhERiYkKiYiIxESFREREYqJCIiIiMVEhERGRmKiQiIhITFRI\nREQkJiokIiISExUSERGJiQqJiIjERIVERERiokIiIiIxUSEREZGYqJCIiEhMVEhERCQmKiQiIhIT\nFRIREYmJComIiMREhURERGKiQiIiIjFRIRERkZiokIiISExUSEREJCaRFhIze9jMtpjZ+8XMNzOb\nYGZrzexdM+uc6IwiInJoUbdIpgIDDjF/INAqfFwG3J+ATCIichgiLSTuvgjYdohFBgPTPfA6UMfM\nGicmnYiIlIa5e7QBzJoD/3L3E4uY9y9gnLu/Gj6fD9zg7kuLWPYyglYLDRo06DJr1qzyjB2z3Nxc\natWqFXWMEilnfClnfCln/PTt2/ctd+9alvdWjneYqLj7Q8BDAMcff7z36dMn2kAlyM7OJtkzgnLG\nm3LGl3Imh6jPkZRkI9CswPOs8DUREUkSyV5IngMuDK/e6gnscPfNUYcSEZHvRHpoy8weB/oA9c1s\nA3ArUAXA3R8AZgODgLXA18DF0SQVEZHiRFpI3P2CEuY7cEWC4oiISBkk+6EtERFJciokIiISExUS\nERGJiQqJiIjERIVEUt7f3/47E9dOjDqGSIWlQiIp7+MvP+bpjU/z6qevRh1FpEJSIZGUd9PJN9Gw\nWkNGzR5F/r78qOOIVDgqJJLyalapyW+O+w3vfP4OD771YNRxRCocFRJJCz+u/2NObXEqv1/we774\n+ouo44hUKCokkhbMjAkDJ7Arbxe3zL8l6jgiFYoKiaSNtg3aMrr7aP627G8s3fS9W9aISDlRIZG0\ncmufW2l4RHDifZ/vizqOSIWgQiJp5ahqR3Hn6XeyZOMSpr8zPeo4IhWCComknWHth3FSs5O44eUb\n2L5ne9RxRNKeComknUpWiUkDJ7H1q638MfuPUccRSXsqJJKWOjXuxOVdL2fSG5N4f8v7UccRSWsq\nJJK2/q/v/1G7em2unHMlwT3SRKQ8qJBI2qpXsx6397ud7HXZzFoxK+o4ImlLhUTS2sjOI+ncuDPX\nvngtuXm5UccRSUsqJJLWMiplMGngJDbu2sjYRWOjjiOSllRIJO31ataLER1G8JfFf2FNzpqo44ik\nHRUSqRDGnTaOGlVqcNXcq3TiXSTOVEikQmhUqxFj+oxh7tq5PL/m+ajjiKQVFRKpMK7odgVtG7Tl\n6rlXs2fvnqjjiKQNFRKpMKpkVGHiwIn8d/t/ues/d0UdRyRtqJBIhdKvRT/ObXcut796O+u2r4s6\njkhaUCGRCufu0++mklXi2hevjTqKSFpQIZEKp1ntZtxy8i08vfJpXvropajjiKQ8FRKpkK7tdS0t\nM1syeu5o8vLzoo4jktJUSKRCqla5GvcMuIdVX6xiwpIJUccRSWkqJFJhDWo1iDNbn8mYV8awadem\nqOOIpCwVEqnQ/tr/r+Tl53HDyzdEHUUkZUVaSMxsgJmtNrO1ZnZjEfMvMrOtZrY8fIyMIqekr+My\nj+N3J/2OR959hH9/8u+o44ikpMgKiZllAPcCA4G2wAVm1raIRWe6e8fwMTmhIaVCuOnkm2h2VDOu\nnHMl+fvyo44jknKibJF0B9a6+8fungfMAAZHmEcqqJpVajK+/3je+fwdHnzrwajjiKQci2okVDMb\nCgxw95Hh8+FAD3cfVWCZi4A7gK3AGuAad19fzPouAy4DaNCgQZdZs5L7jni5ubnUqlUr6hglqig5\n3Z3r3r2ONblr+Ee3f1Cnap04pvtORdmfiaKc8dO3b9+33L1rmd7s7pE8gKHA5ALPhwOTCi1TD6gW\nTv8aWFCadbdu3dqT3cKFC6OOUCoVKeeKLSu88p8q+6+e+1XsgYpRkfZnIihn/ABLvYzf51Ee2toI\nNCvwPCt87QB3z3H3b8Knk4EuCcomFVDbBm0Z3X00k5dNZummpVHHEUkZURaSN4FWZtbCzKoC5wPP\nFVzAzBoXeHoWsDKB+aQCurXPrTQ8oiGjZo9in++LOo5ISoiskLj7XmAUMI+gQMxy9xVm9iczOytc\nbLSZrTCzd4DRwEXRpJWK4qhqR3HX6XexZOMSpi2fFnUckZQQaT8Sd5/t7q3d/Th3Hxu+9gd3fy6c\nvsnd27l7B3fv6+6roswrFcOw9sM4qdlJ3PDyDWzfsz3qOCJJTz3bRQoxMyYNnMQXX3/BH7P/GHUc\nkaSnQiJShE6NO3F518uZ9MYk3vv8vajjiCQ1FRKRYtzW7zbqVK/DlXOu3H85uogUQYVEpBiZNTIZ\n228sr3zyCjNXzIw6jkjSUiEROYSRnUfSuXFnrnvxOnLzcqOOI5KUVEgk6b258U127NkRybYzKmUw\naeAkNu7ayNhFYyPJkHS2bYNly6JOIUlEhUSSWl5+HmfPPJtWE1vxyrpXIsnQq1kvRnQYwV8W/4U1\nOWsiyZA05s6Fli3hvPMgXyMlS0CFRJJa1YyqPHv+s3Rr2o029dsAsPObnQnPMe60cdSoUoOr5l5V\n8U68u8POcJ+fcAL07g1PPw0ZGdHmkqShQiJJr2uTrrzwixc4utbRuDv9H+nPgEcG8P6W9xOWoVGt\nRozpM4a5a+fy/JrnE7bdyC1bBn37wjnnBM+PPRaefx5++MNoc0lSUSGRlLLP93Fu23NZsnEJHR7o\nwOX/upzPcz9PyLav6HYF7Rq04+q5V7P7290J2WZUqm7dChddBF27wooVcPbZQctEpAgqJJJSMipl\ncE2va1h75VpGdRvFlLen0GpiK5ZvX17u266SUYWJAyfy3+3/5a7X7ir37UVm9mx6XHghPP44XH89\nrF0L//M/YBZ1MklSKiSSkurVrMc9A+/h/f95n7PbnE2rWq0A2Lxrc7mew+jboi/ntjuXO169g3Xb\n15XbdhJu3z747LNgunt3tp5yCqxaBX/+M9SuHW02SXoqJJLSjq9/PNOHTOeIykewd99eTp1+Kic9\nfBKL1y8ut23effrdVLJKXPviteW2jYRauDA4hHXWWUFBqV+fVTfeCC1aRJ1MUoQKiaSNSlaJ60+6\nnk+2f8JJD5/E+U+eXy6thma1m3HLybfw9MqnefGjF+O+/oRZsyY499GvH+TkwDXX6PCVlIkKiaSN\nSlaJiztdzJor1/CHH/+B51Y/R5tJbVj0yaK4b+vaXtfSMrMlo+eMJi8/L+7rL3dz5kC7djB/Ptx+\ne3AY64ILVEikTFRIJO3UqlqLMX3HsObKNfym22/o0bQHAGty1rB33964bKNa5WrcM+AeVuesZsKS\nCXFZZ7nLy4OPPgqmTz4ZRo8OTqTfdBPUqBFtNklpKiSStrKOymJ8//FUq1yNPXv3cNr00+jwQAfm\nfDgnLusf1GoQP239U8a8MoZNuzbFZZ3lwh2eeSZogQwaBHv3Qq1a8Je/wNFHR51O0oAKiVQI1TKC\nFsQ3e79h0GOD4tah8f/1/398m/8tv3vpd3FIWQ72dygcMgSqVIG//lU90iXuVEikQjAzhpwwhA+u\n+IDxZ4w/0KHx1U9fjWm9x2Uex/UnXc+j7z3Kvz/5d5zSxsm8ed91KLzvPnj3XRg4UOdBJO5USKRC\nqZpR9UCHxtv63kavrF4ALN20lD1795RpnTedfBPH1D6GUXNGxe0cTJl99RW8/XYw3bcvjB37XYfC\nypWjzSZpS4VEKqR6Netx08k3kVEpg9y8XPo/0p82k9ow4/0Zh92hsWaVmvzljL/w7ufv8uDSB8sp\ncQn27YOpU6FVK/jpT4MT61WrBifS1aFQypkKiVR4tarW4slznqRujbpc8NQFZerQ+PMTfs6pLU7l\n9wt/z9avtpZT0mLs71B48cVwzDEwa1ZQREQSRIVEhGDok6W/WsrDZz18oEPjkg1LSv1+M2PCwAnk\n5uVyy4JbyjFpIQsWfNeh8LHHYPFiOOmkxG1fBBUSkQMyKmUc6ND4t5/+je5NuwPw0kcvleoOjW0b\ntGV099FMXjaZNze+WX5Bt20LWiEAffrAQw+pQ6FESoVEpJBaVWsxsvNIzIwde3YwZOYQWk5syf1v\n3l/iyfRb+9xKwyMaMmrOKPb5vvgGy8sLLt9t2TK4P8ju3VCpEvzqV+pQKJEqVSExs8wiHlXKO5xI\n1GpXr80rF71C2wZt+c3s35TYofGoakdx1+l38cbGN5i6fGp8QhTsUHjNNcH5kIULVTwkaZS2RbIM\n2AqsAT4Mp/9rZsvMrEt5hRNJBl2adCF7RDZPn/v0gQ6Nyz8r/v4nw9oPo3ez3tz48o1s37M99gD/\n+c93HQpnzw76h+gOhZJESltI5gKD3L2+u9cDBgKzgN8A95VXOJFkUbBD4zPnPUPHRh0BmPn+zO/d\nodHMmDhwIl98/QW3Lry1bBvcuDG4Lzp8d490dSiUJFXaQtLV3eftf+LuLwI/dvfXgWrlkkwkCVXN\nqMrgNoMByPk6h4ufvZhWE1txx7/vOOj2u50ad+Lyrpcz6c1JvPv5u6XfwFdfwR//GPQHufRSyM0N\nCseQIepQKEmrtIVkm5ndYGbHho/fAV+aWQYQ5zOKIqmhXs16LL98Of1a9OPmBTfT5t42PP7e4wc6\nNN7W7zbqVq/LlXOuLLmTY8EOhWPGBDeZWrYsGFxRJMmVtpD8AsgCngkfx4SvZQDnlk80keTXul5r\nnjn/GeZfOJ/MGpn88ulfsjpnNQCZNTIZ228siz5ZxMwVMw+9orffDjoUNmsWnBOZMUN3KJSUUapC\n4u5fuPuV7t4pfIxy963unufua8u6cTMbYGarzWytmd1YxPxqZjYznL/EzJqXdVsi5alfi34s/dVS\nFl28iDb12wBw7xv3cmqLU+ncuDPXvngtuXm5B79pzRr429+C6S5dYNEidSiUlFTay38bmNldZjbb\nzBbsf8Sy4fCw2L0EJ+7bAheYWdtCi10KfOnuLYH/B/w5lm2KlKeMShn86JgfAfB57ufc8PINtLu/\nHSfUP4FNuzZx26LbggVzcuCqq4LLeX/3O9gRdnY8+eSgX4hIiintb+2jwCqgBTAGWAfE2nW3O7DW\n3T929zxgBjC40DKDgWnh9JPAqWa6ZEWS39G1jmbVqFVccOIFPPreo1TLqMb4f99F1UceCs6DTJoE\nl1wS9EjXoIqS4kp7GUg9d59iZle5+yvAK2b2SozbbgqsL/B8A9CjuGXcfa+Z7QDqAV/EuG2Rcpd1\nVBZTz57Kld2vZNTsUSzZ8DpHP/U4Th2sUydYvRrOOy/qmEXquH071KkTdYwStaxfPxgmRiJV2kLy\nbfhzs5n9BNhEcPI9aZjZZcBlAA0aNCA7OzvaQCXIzc1N+oygnPHS5+MpvP7oF5z2bR3ePm54cLvb\n7XHorFhO8vPz2Z7E+fbLq107qf/d90v238+YuXuJD+BMoDZwIrAQeAs4qzTvPcQ6ewHzCjy/Cbip\n0DLzgF7hdGWCloiVtO7WrVt7slu4cGHUEUpFOWO3Y4f70Ue7H3mke4cOX0Ydp1SSeX8WpJzxAyz1\nMn6fl6pF4u7/Cid3AH3jUsGCcyytzKwFsBE4n+CS4oKeA0YAi4GhwILwA4ukjD/9CbZsgU6dID8/\n6jQi8VeqQhJ+2V8JNC/4Hnc/q6wb9uCcxyiCVkcG8LC7rzCzPxFUxueAKcA/zGwtsI2g2IikjA8+\ngHvuCTqpf/hhUh/NEimz0p4jeYbgS/154tiT3d1nA7MLvfaHAtN7gHPitT2RRHKH0aODzum33x6M\n/C6SjkpbSPa4+4RyTSKSZp56CubPh4kToUEDOPNM+OijHCD5r4YSORylLST3mNmtwIvAN/tfdPdl\n5ZJKJMV9/TX89rfQvj1cfnnw2nXXQXb2euC4SLOJxFtpC8kPgeFAP747tOXhcxEp5I47YP16ePRR\nDdor6a+0v+JDgB940ANdRA7ho4/gzjvhF78IRj3Zr08f2L69I8uLvyeWSEoq7RAp76ADuyKlcs01\nULUq3HVX1ElEEqO0LZKjgVVm9iYHnyMp8+W/IunohRfg+eeDFkmTJlGnEUmM0haSMt4vVKTi2LMn\nGNT3+OODnyIVRWl7tsc6QKNI2hs/Pjg/Mm9ecGhLpKI4ZCExs10EV2d9bxbg7n5UuaQSSTHr18PY\nscGt1c84o+hl1I9E0tUhC4m7H5moICKp7Lrrgtuujx9/6GXUj0TSkW7HJhKjBQtg1iy48UZo3jzq\nNCKJp65SIjH49ttgPK3mzYO75h6K+pFIulIhEYnBvffCihXwzDNQo0bUaUSioUNbImX02Wdw660w\nYACcpR5VUoGpkIiU0Y03wu7dwf1GzKJOIxIdFRKRMli8GKZNC0b4bd066jQi0dI5EpHDlJ8Po0YF\nQ6D8/velf5/6kUi6UiEROUyTJ8OyZfD448HdD0tL/UgkXenQlshhyMmBm2+GU06B886LOo1IclCL\nROQw/O//wo4dMGHC4Z9gVz8SSVdqkYiU0rJl8MADcMUVwS10RSSgQiJSCu5w5ZVQvz6MGRN1GpHk\nokNbIqXwyCPw2mswZQrU0UVXIgdRi0SkBDt3wvXXQ/fucNFFUacRST5qkYiUYMwY2LIluIVupRj+\n9FI/EklXKiQih/DBB8EVWpdeCt26xbYu9SORdKVDWyLFcA+GiK9VC26/Peo0IslLLRKRYjz1FMyf\nD5MmQYMGsa9P/UgkXalFIlKEr74KBmTs0AF+/euo04gkN7VIRIowbhysXw+PPgqV9b9E5JDUIhEp\nZO1auPNO+OUv4eSTo04jkvxUSEQKueYaqFo1KCYiUrJIGu1mlgnMBJoD64Bz3f3LIpbLB94Ln37q\n7rqhqZSrF16Af/0rKCJNmsR33epHIukqqhbJjcB8d28FzA+fF2W3u3cMHyoiUq727IGrroLjjw9+\nxtt118F5562P/4pFIhbVacTBQJ9wehqQDdwQURYRAMaPh48+gnnzgkNbIlI65u6J36jZdnevE04b\n8OX+54WW2wssB/YC49z9mUOs8zLgMoAGDRp0mTVrVrlkj5fc3FxqHc7t9SJSUXJu2VKNESO6063b\nNv70pxVxTPadq6/uSH5+PhMnvlfywhGrKP/uiZIKOfv27fuWu3ct05vdvVwewMvA+0U8BgPbCy37\nZTHraBr+/AHBuZTjSrPt1q1be7JbuHBh1BFKpaLkPPdc9+rV3f/737jEKdIpp7h36PBl+W0gjirK\nv3uipEJOYKmX8fu+3A5tuftpxc0zs8/NrLG7bzazxsCWYtaxMfz5sZllA52Aj8ojr1RcCxbArFnB\n4IzNm0edRiT1RHWy/TlgRDg9Ani28AJmVtfMqoXT9YHewAcJSygVwrffBjesatEiGCpeRA5fVCfb\nxwGzzOxS4BPgXAAz6wpc7u4jgROAB81sH0HBG+fuKiQSV/feG4zw++yzUKNG1GlEUlMkhcTdc4BT\ni3h9KTAynH4N+GGCo0kF8tlncOutMGAA/PSn5b899SORdKVRhKTCuvFG2L0b7rkHzMp/e7ofiaQr\nDZEiFdLixTBtGlx7LbRuHXUakdSmFolUOPn5MGoUNG0Kt9ySuO3qfiSSrlRIpMKZPBmWLYPHHw/u\nfigisdGhLalQcnLg5pvhlFPgvPOiTiOSHlRIpEL53/+FHTtg4sTEnGAXqQhUSKTCWLYMHngArrgC\nfqgLy0XiRudIpEJwD3qw168fDIUSBfUjkXSlQiIVwiOPwGuvwcMPQ52IvsfVj0TSlQ5tSdrbuTMY\nR6tHDxgxouTlReTwqEUiaW/MGNiyJbiFbqUI/3RSPxJJVyokktY++AAmTICRI6Fr2W7ZEzfZ2ZCd\nvZzvbg4qkh50aEvSljuMHg1HHgm33x51GpH0pRaJpK2nnoL582HSpOBqLREpH2qRSFr66iv47W+h\nQwf49a+jTiOS3tQikbQ0bhysXw+PPQaV9VsuUq7UIpG0s3Yt3HknDBsGP/pR1GlE0p8KiaSda66B\nqlWDYiJA8mMAAAAU+0lEQVQi5U+NfkkrL7wQ9Be56y5o3DjqNCIVgwqJpI09e+Cqq6BNm+CyX5F4\n+fbbb9mwYQN79uwp0/tr167NypUr45yqbKpXr05WVhZVqlSJ2zpVSCRtjB8PH30EL74YHNoSiZcN\nGzZw5JFH0rx5c6wM9x/YtWsXRx55ZDkkOzzuTk5ODhs2bKBFixZxW6/OkUha2LKlGmPHws9+Bqef\nHnUaSTd79uyhXr16ZSoiycTMqFevXplbVsVRIZG0cP/9x7FvX9AqESkPqV5E9iuPz6FCIilvwQLI\nzm7IzTfDscdGnUYk8caNG8ejjz7KN998w3nnnUfLli3p0aMH69atS8j2VUgk5T3wADRuvJvrr486\niUg05s2bxxlnnMGUKVOoW7cua9eu5ZprruGGG25IyPZVSCTlPfYY3HXXO1SvHnUSkfJ19tln06VL\nF9q1a8dDDz0EwM6dO8nLy6NBgwY8++yzjAhvujN06FDmz5+Pu5d7Ll21JSmvcmVo2jS+Jw9FinX1\n1RzuTWVq5OdDRkbxC3TsCH/9a4nrefjhh8nMzGT37t1069aNn//857zyyiuceuqpAGzcuJFmzZoB\nULlyZWrXrk1OTg71y3nUUrVIRERSxIQJE+jQoQM9e/Zk/fr1fPjhh8ydO5eBAwdGmkstEhGRw1GK\nlkNhu+PQjyQ7O5uXX36ZxYsXU7NmTfr06cOePXt44403uP/++wFo2rQp69evJysri71797Jjxw7q\n1asX03ZLQy0SEZEUsGPHDurWrUvNmjVZtWoVr7/+OitWrKBNmzZkhIfNzjrrLKZNmwbAk08+Sb9+\n/RJy2bJaJCIiKWDAgAE88MADtG/fnuOPP56ePXuye/duBgwYcGCZSy+9lOHDh9OyZUsyMzOZMWNG\nQrKpkIiIpIBq1aoxZ86cg147/fTTmT59+oHn1atX54knnkh0tGgObZnZOWa2wsz2mVnXQyw3wMxW\nm9laM7sxkRlFRJLdSy+9ROMkGOY6qnMk7wM/AxYVt4CZZQD3AgOBtsAFZtY2MfFERKS0Ijm05e4r\nocQxX7oDa93943DZGcBg4INyDygiIqWWzOdImgLrCzzfAPQobmEzuwy4DKBBgwZkZ2eXa7hY5ebm\nJn1GUM54U874SlTO2rVrs2vXrjK/Pz8/P6b3x9uePXviut/KrZCY2ctAoyJm3eLuz8Z7e+7+EPAQ\nwPHHH+99+vSJ9ybiKjs7m2TPCMoZb8oZX4nKuXLlypj6gSTL/Uj2q169Op06dYrb+sqtkLj7aTGu\nYiPQrMDzrPA1ERFJIsncIfFNoJWZtTCzqsD5wHMRZxIRicS6des48cQTi5w3Y8YMxo4dy6pVq+jV\nqxfVqlXj7rvvTli2qC7/HWJmG4BewAtmNi98vYmZzQZw973AKGAesBKY5e4rosgrIpLM5syZw4AB\nA8jMzGTChAlcd911Cd1+JIXE3f/p7lnuXs3dj3b3/uHrm9x9UIHlZrt7a3c/zt3HRpFVRCRZ7N27\nlxEjRtC+fXuGDh3K119/jbuzfPlyOnfuTMOGDenWrRtVqlRJaK5kvmpLRCTplGEUefLza8RjFHlW\nr17NlClT6N27N5dccgn33Xcf/fr1o0OHDpHeCliFREQkRTRr1ozevXsDMGzYMCZMmEBeXp6GkRcR\nSSVlGEWeXbt2x+Xy38KtDjPjxRdf5Kmnnop53bFI5qu2RESkgE8//ZTFixcD8Nhjj9GxY0f27t2b\nkHuOHIoKiYhIimjTpg3Tpk2jffv2fPnll7Ro0YLTTvuuy95nn31GVlYW48eP57bbbiMrK4udO3eW\ney4d2hIRSQHNmzdn5cqVB702cuRIRo4ceeB5o0aN2LBhQ6KjqZCIiKSqyZMnRx0B0KEtERGJkQqJ\niIjERIVERERiokIiIiIxUSEREZGYqJCIiKSwzZs3c8YZZ7B8+XJ69epFu3btaN++PTNnzkxYBl3+\nKyKSwubOnUv//v2pWbMm06dPp1WrVmzatIkuXbrQv39/6tSpU+4Z1CIREUkR06dPp3379nTo0IHh\nw4cDQSEZOHAgrVu3plWrVgA0adKEhg0bsnXr1oTkUotEROQwXD33apZ/dnjjyOfn55NxiHHkOzbq\nyF8HHHo0yBUrVnDbbbfx2muvUb9+fbZt20Z+fj6rV6+mbdu2By37xhtvkJeXx3HHHXdYOctKLRIR\nkRSwYMECzjnnHOrXrw9AZmYmS5YsoUePHgctt3nzZoYPH87f//53KlVKzFe8WiQiIoehpJZDUXbt\n2hWXYeQL23+L3f127tzJT37yE8aOHUvPnj3jvr3iqEUiIpIC+vXrxxNPPEFOTg4A27ZtY/78+QdG\n/83Ly2PIkCFceOGFDB06NKHZ1CIREUkB7dq145ZbbuGUU04hIyODrKwsqlevfqClM2vWLBYtWkRO\nTg5Tp04FYOrUqXTs2LHcs6mQiIikiBEjRjBixAgAHnnkkYOGjB82bBjDhg2LJJcKiYhICoqqaBRF\n50hERCQmKiQiIhITFRIREYmJComIiMREhURERGKiQiIikuLGjRvHo48+yjfffMN5551Hy5Yt6dGj\nB+vWrUvI9lVIRERS3Lx58zjjjDOYMmUKdevWZe3atVxzzTXccMMNCdm+ComISIo4++yz6dKlC+3a\nteOhhx4CgvG18vLyaNCgAc8+++yBDotDhw5l/vz5uHu551KHRBGRw9Wnz/dfO/NMuO66IufXyM+H\nwYOLnU92dqk2+/DDD5OZmcnu3bvp1q0bP//5z3nllVc49dRTAdi4cSPNmjUDoHLlytSuXZucnJwD\nIwaXF7VIRERSxIQJE+jQoQM9e/Zk/fr1fPjhhwdubBWlSFokZnYO8EfgBKC7uy8tZrl1wC4gH9jr\n7l0TlVFEpFgltSAKzd9deBj5UrZADl5lNi+//DKLFy+mZs2a9OnThz179vDGG29w//33A9C0aVPW\nr19PVlYWe/fuZceOHdSrV++wt3W4omqRvA/8DFhUimX7untHFRERqch27NhB3bp1qVmzJqtWreL1\n119nxYoVtGnT5sDdF8866yymTZsGwJNPPkm/fv0ws3LPFkmLxN1XAgn5gCIi6WDAgAE88MADtG/f\nnuOPP56ePXuye/fug25sdemllzJ8+HBatmxJZmYmM2bMSEg2S8QZ/WI3bpYNXHeIQ1v/Bb4EHHjQ\n3R86xLouAy4DaNCgQZdZs2bFP3Ac5ebmUqtWrahjlEg540s54ytROWvXrk3Lli3L/P6S7tleVoMH\nD+bBBx+kUaNGh/W+tWvXsmPHjoNe69u371tlPvLj7uXyAF4mOIRV+DG4wDLZQNdDrKNp+LMh8A7w\n49Jsu3Xr1p7sFi5cGHWEUlHO+FLO+EpUzg8++CCm9+/cuTNOSeKjqM8DLPUyft+X26Etdz8tDuvY\nGP7cYmb/BLpTuvMqIiKSIEl7+a+ZHWFmR+6fBs4gaNGIiEgSiaSQmNkQM9sA9AJeMLN54etNzGx2\nuNjRwKtm9g7wBvCCu8+NIq+IiBQvqqu2/gn8s4jXNwGDwumPgQ4JjiYiIocpaQ9tiYhIalAhERFJ\nAevWrePEE08sct6MGTMYO3Ysq1atolevXlSrVo277747YdlUSEREUtycOXMYMGAAmZmZTJgwgev2\nDw6ZICokIiIpYu/evYwYMYL27dszdOhQvv76a9yd5cuX07lzZxo2bEi3bt2oUqVKQnNpGHkRkcN0\nmKPIk59fIx6jyLN69WqmTJlC7969ueSSS7jvvvvo168fHTp0iHTIKRUSEZEU0axZM3r37g3AsGHD\nmDBhAnl5eRVzGHkRkVR2mKPIs2vX7oOGkS/DKPLA9we6NTNefPFFnnrqqbKtME50jkREJEV8+umn\nLF68GIDHHnuMjh07snfv3oTcc+RQVEhERFJEmzZtmDZtGu3bt+fLL7+kRYsWnHbad8MafvbZZ2Rl\nZTF+/Hhuu+02srKy2LlzZ7nn0qEtEZEU0Lx5c1auXHnQayNHjmTkyJEHnjdq1IgNGzYkOpoKiYhI\nqpo8eXLUEQAd2hIRkRipkIiISExUSEREJCYqJCIiEhMVEhERiYkKiYhICtu8eTNnnHEGy5cvp1ev\nXrRr14727dszc+bMhGXQ5b8iIils7ty59O/fn5o1azJ9+nRatWrFpk2b6NKlC/3796dOnTrlnkEt\nEhGRFDF9+nTat29Phw4dGD58OBAUkoEDB9K6dWtatWoFQJMmTWjYsCFbt25NSC61SEREDlOfqX2+\n99qZrc/kupOuK3J+fn4+g08YXOz87IuyS9zmihUruO2223jttdeoX78+27ZtIz8/n9WrV9O2bduD\nln3jjTfIy8vjuOOOK/VnioVaJCIiKWDBggWcc8451K9fH4DMzEyWLFlCjx49Dlpu8+bNDB8+nL//\n/e9UqpSYr3i1SEREDlNJLYjC83ft2nXwMPKlaIGUxv5b7O63c+dOfvKTnzB27Fh69uwZl22Uhlok\nIiIpoF+/fjzxxBPk5OQAsG3bNubPn39g9N+8vDyGDBnChRdeyNChQxOaTS0SEZEU0K5dO2655RZO\nOeUUMjIyyMrKonr16gdaOrNmzWLRokXk5OQwdepUAKZOnUrHjh3LPZsKiYhIihgxYgQjRowA4JFH\nHjloyPhhw4YxbNiwSHKpkIiIpKCoikZRdI5ERERiokIiIiIxUSERESkFd486QlyUx+dQIRERKUH1\n6tXJyclJ+WLi7uTk5FC9evW4rlcn20VESpCVlcWGDRvKPHbVnj174v7lXVbVq1cnKysrruuMpJCY\n2V3AT4E84CPgYnffXsRyA4B7gAxgsruPS2hQERGgSpUqtGjRoszvz87OplOnTnFMlFyiOrT1EnCi\nu7cH1gA3FV7AzDKAe4GBQFvgAjNrW3g5ERGJViSFxN1fdPe94dPXgaLaWd2Bte7+sbvnATOAwYnK\nKCIipZMMJ9svAeYU8XpTYH2B5xvC10REJImU2zkSM3sZaFTErFvc/dlwmVuAvcCjcdjeZcBl4dNv\nzOz9WNdZzuoDX0QdohSUM76UM76UM36OL+sby62QuPtph5pvZhcBZwKnetHX1G0EmhV4nhW+Vtz2\nHgIeCte91N27Hm7mREqFjKCc8aac8aWc8WNmS8v63kgObYVXY/0OOMvdvy5msTeBVmbWwsyqAucD\nzyUqo4iIlE5U50gmAUcCL5nZcjN7AMDMmpjZbIDwZPwoYB6wEpjl7isiyisiIsWIpB+Ju7cs5vVN\nwKACz2cDs8uwiYfKGC2RUiEjKGe8KWd8KWf8lDmjpXqXfxERiVYyXP4rIiIpLC0KiZndZWarzOxd\nM/unmdUpZrkBZrbazNaa2Y0JzniOma0ws31mVuzVG2a2zszeC88dlfkqirI6jJyR7ctw+5lm9pKZ\nfRj+rFvMcvnhvlxuZgm7WKOk/WNm1cxsZjh/iZk1T1S2QjlKynmRmW0tsA9HRpDxYTPbUtwl/RaY\nEH6Gd82sc6IzhjlKytnHzHYU2Jd/iCBjMzNbaGYfhP/PrypimcPfn+6e8g/gDKByOP1n4M9FLJNB\nMK7XD4CqwDtA2wRmPIHgOu1soOshllsH1I9wX5aYM+p9GWa4E7gxnL6xqH/zcF5uBPuwxP0D/AZ4\nIJw+H5iZpDkvAiYlOluhDD8GOgPvFzN/EEGnZgN6AkuSNGcf4F8R78vGQOdw+kiCIaoK/5sf9v5M\nixaJp8CQK+6+0t1XJ2p7ZVXKnMkwfM1gYFo4PQ04O8HbP5TS7J+C+Z8ETjUzS2BGSI5/xxK5+yJg\n2yEWGQxM98DrQB0za5yYdN8pRc7Iuftmd18WTu8iuCK28Ighh70/06KQFJLqQ6448KKZvRX21k9G\nybAvj3b3zeH0Z8DRxSxX3cyWmtnrZpaoYlOa/XNgmfCPoB1AvYSkKyJDqLh/x5+HhzieNLNmRcyP\nWjL8PpZWLzN7x8zmmFm7KIOEh1M7AUsKzTrs/Zky9yNJ9JArZVGajKXwI3ffaGYNCfrZrAr/0omb\nOOUsd4fKWfCJu7uZFXf54bHh/vwBsMDM3nP3j+KdNY09Dzzu7t+Y2a8JWlH9Is6UqpYR/D7mmtkg\n4BmgVRRBzKwW8BRwtbvvjHV9KVNIPMFDrpRFSRlLuY6N4c8tZvZPgsMPcS0kcchZ7vsSDp3TzD43\ns8buvjlsdm8pZh379+fHZpZN8BdYeReS0uyf/ctsMLPKQG0gp5xzFVZiTncvmGkywbmpZJOQ38dY\nFfzCdvfZZnafmdV394SOwWVmVQiKyKPu/nQRixz2/kyLQ1uWJkOumNkRZnbk/mmCiwiScfDJZNiX\nzwEjwukRwPdaUmZW18yqhdP1gd7ABwnIVpr9UzD/UGBBMX8AlacScxY6Nn4WwTH1ZPMccGF4tVFP\nYEeBw55Jw8wa7T8PZmbdCb5/E/rHQ7j9KcBKdx9fzGKHvz+jvIIgjlcirCU4prc8fOy/GqYJMLvQ\n1QhrCP4ivSXBGYcQHGv8BvgcmFc4I8HVM++EjxWJzljanFHvy3D79YD5wIfAy0Bm+HpXgrtpApwE\nvBfuz/eASxOY73v7B/gTwR87ANWBJ8Lf3TeAHyR6H5Yy5x3h7+I7wEKgTQQZHwc2A9+Gv5uXApcD\nl4fzjeAmeB+F/87FXhUZcc5RBfbl68BJEWT8EcF52HcLfF8OinV/qme7iIjEJC0ObYmISHRUSERE\nJCYqJCIiEhMVEhERiYkKiYiIxESFRCQO7LtRht83s+etmBGoS7mudWG/F5GUoEIiEh+73b2ju59I\nMHDfFVEHEkkUFRKR+FtMgUHuzOx6M3szHPhwTIHXnwkH51yRxAN0ipRIhUQkjswsAziVcKgRMzuD\nYGC+7kBHoIuZ/Thc/BJ370LQG3+0mSV69F+RuFAhEYmPGma2nGDspEzgpfD1M8LH2wSjv7bhuxFf\nR5vZ/uEymhHRSLAisVIhEYmP3e7eETiW4G6D+8+RGHBHeP6ko7u3dPcpZtYHOA3o5e4dCApN9SiC\ni8RKhUQkjtx9BzAauDYcHn4ecEl4/wfMrGl4r5nawJfu/rWZtSG4palISkqZ+5GIpAp3f9vM3gUu\ncPd/mNkJwOJwBPFcYBgwF7g8XG41weEtkZSk0X9FRCQmOrQlIiIxUSEREZGYqJCIiEhMVEhERCQm\nKiQiIhITFRIREYmJComIiMREhURERGLy/wGciEwY9gutQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0b6f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pD2(v3,i3,\"v3\",\"i3\",-2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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
}