Tema4.Transmisor_Lineal_I_(PAM).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MANUAL DE LABORATORIO DE COMUNICACIONES DIGITALES: PYTHON\n",
    "F. Javier Payán Somet, Juan José Murillo-Fuentes, José Carlos Aradillas Jaramillo \\\n",
    "_Departamento de Teoría de la Señal y Comunicaciones_ \\\n",
    "_Escuela Técnica Superior de Ingeniería_ \\\n",
    "_Universidad de Sevilla_ \n",
    "\n",
    "# Tema 4. Modulador Lineal I (PAM)\n",
    "\n",
    "**Este notebook contiene código del Tema 4**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/gapsc-us/labcomdig/blob/main/Tema4.Transmisor_Lineal_I_(PAM).ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inicialización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Numpy ≥1.16 is required\n",
    "import numpy as np\n",
    "assert np.__version__ >= \"1.16\"\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Ignore useless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\") #Ignorar warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diseño experimental de un transmisor PAM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transmisor PAM discreto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 4.1** Transmisor M-PAM\n",
    "\n",
    "Las funciones que se describen aquí están en el fichero labcomdig.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transmisorpam(Bn,Eb,M,p,L):\n",
    "    '''\n",
    "    [Xn,Bn,An,phi,alfabetopam] = transmisorpam(Bn,Eb,M,p,L) \n",
    "    \n",
    "    Entradas:    \n",
    "     Bn = Secuencia de dígitos binarios\n",
    "     Eb = Energía media por bit transmitida en Julios\n",
    "     M  = Número de síímbolos del código PAM\n",
    "     p  = Pulso paso de baja o paso de banda \n",
    "     L  = Número de puntos a utilizar en la representación de un símbolo\n",
    "    \n",
    "    Devuelve:\n",
    "     Xn = la señal de información (discreta)\n",
    "     Bn = La secuencia de dígitos binarios realmente transmitidos\n",
    "     An = La secuencia de niveles de amplitud transmitidos\n",
    "   \t phi = Pulso básico real normalizado (energía unidad)\n",
    "     alfabetopam = Los niveles de amplitud asociados a cada símbolo\n",
    "    '''    \n",
    "    #Comprobación de parámetros de entrada\n",
    "    if len(Bn)<1 or Eb<=0 or M<2 or p@p==0 or L<1:\n",
    "        raise Exception('Error: revise los parámetros de entrada')\n",
    "    \n",
    "    # Se obtienen en primer lugar los niveles asociado a cada sí­mbolo ¿Cuántos bits hay en cada sí­mbolo?\n",
    "    k = int(np.ceil(np.log2(M)))   \n",
    "    M = 2**(k) # Se Ajusta M a una potencia de dos\n",
    "    \n",
    "    # El alfabeto [Ver la ecuación (4.21)] \n",
    "    alfabetopam = np.sqrt(3*Eb*np.log2(M)/(M**2-1))*(2*(np.arange(M))-M+1)\n",
    "    \n",
    "    # Si Bn no tiene una longitud múltiplo de k, se completa con ceros\n",
    "    Nb = len(Bn)  # Número de bits a transmitir, actualizado\n",
    "    Bn = Bn.squeeze().astype(int) #Comprobación de int y dimensiones   \n",
    "    Bn = np.r_[Bn,np.zeros(int(k*np.ceil(Nb/k)-Nb)).astype(int)] #\n",
    "    Nb = len(Bn)  # Número de bits a transmitir tras la corrección\n",
    "    Ns = Nb//k # Número de símbolos a transmitir\n",
    "    \n",
    "    # La secuencia generada\n",
    "    if M>2:\n",
    "        An = alfabetopam[gray2de(np.reshape(Bn,[Ns,k]))]\n",
    "    else:\n",
    "        An = alfabetopam[Bn]\n",
    "    \n",
    "    # Comprobación de las longitudes y otros datos del pulso suministrado para \n",
    "    # hacer que el número de muestras del mismo sea efectivamente L\n",
    "    Ls = len(p)\n",
    "    if Ls<L:\n",
    "        p = np.r_[p, np.zeros(L-Ls)]\n",
    "    elif Ls>L:\n",
    "        print('La duración del pulso se trunca a {} muestras'.format(str(L)))\n",
    "        p = p[:L] #Debe modificarse si se quiere un pulso de más de L muestras \n",
    "    # Se normaliza la energía del pulso para obtener la base del sistema\n",
    "    phi = p / np.sqrt(p@p) \n",
    "       \n",
    "    # Obtención del tren de pulsos, Xn = np.kron(An,phi) ó\n",
    "    Xn = np.reshape(np.reshape(An,[Ns,1])*phi,[Ns*L,]) #Debe modificarse si se quiere un pulso de más de L muestras\n",
    "    return [Xn,Bn,An,phi,alfabetopam]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 4.2** Asignación Gray a decimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gray2de(b):\n",
    "    '''\n",
    "    Convierte cada columna de la matriz formada por dígitos binarios b en un vector\n",
    "        fila de los valores decimales correspondientes, aplicando codificación de Gray.\n",
    "    '''\n",
    "    if not isinstance(b, (np.ndarray, list, tuple)):  # se comprueba que no sea np.array, lista o tupla\n",
    "       raise Exception('Error: la entrada no es un array, lista o tupla')\n",
    "    b = np.array(b)    # Esto es para que admita también listas y tuplas\n",
    "    c = np.zeros_like(b); c[:,0] = b[:,0]\n",
    "    for i in range(1,np.shape(b)[1]):\n",
    "        c[:,i] = np.logical_xor(c[:,i-1], b[:,i])\n",
    "    c = np.fliplr(c) # Convierte los bits menos significativos en los más significativos\n",
    "    #Comprueba un caso especial.\n",
    "    [n,m] = np.shape(c) \n",
    "    if np.min([m,n]) < 1:\n",
    "        d = []\n",
    "        return\n",
    "    d = c @ 2**np.arange(m)\n",
    "    return d "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 4.3** Ejemplos de uso de Gray2de"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Asignación de Gray de números de 0 a 7 codificados en 3 bits de forma consecutiva:\n",
      " [ 10  34 102]\n",
      "Asignación de Gray de secuencia con números de 0 a 7 ordenados de forma que haya cambios consecutivos de un bit:\n",
      " [10 40 68]\n"
     ]
    }
   ],
   "source": [
    "b =np.array([[0,0,0],[0,0,1],[0,1,0],[0,1,1],[1,0,0],[1,0,1],[1,1,0],[1,1,1]]).T\n",
    "print('Asignación de Gray de números de 0 a 7 codificados en 3 bits de forma consecutiva:\\n',gray2de(b))\n",
    "\n",
    "b =np.array([[0,0,0],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,1,1],[1,0,1],[1,0,0]]).T\n",
    "print('Asignación de Gray de secuencia con números de 0 a 7 ordenados de forma que haya \\\n",
    "cambios consecutivos de un bit:\\n',gray2de(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 4.4** Ejemplos de uso de np.reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sea el vector fila de 12 elementos\n",
      " [[4 1 1 4 2 2]\n",
      " [2 2 4 2 3 2]]\n",
      "Vector a reordenado como Matriz 4x3\n",
      " [[4 1 1]\n",
      " [4 2 2]\n",
      " [2 2 4]\n",
      " [2 3 2]]\n",
      "Vector a reordenado como Matriz 4x3 por columna\n",
      " [[4 1 2]\n",
      " [2 4 3]\n",
      " [1 4 2]\n",
      " [2 2 2]]\n",
      "Vector a como Matriz 3x4\n",
      " [[4 1 1 4]\n",
      " [2 2 2 2]\n",
      " [4 2 3 2]]\n",
      "Vector a reordenado como Matriz 3x4 por columna\n",
      " [[4 2 4 3]\n",
      " [2 1 2 2]\n",
      " [1 4 2 2]]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([[4, 1, 1, 4, 2, 2],[ 2, 2, 4, 2, 3, 2]])\n",
    "print('Sea el vector fila de 12 elementos\\n',a)\n",
    "A=np.reshape(a,newshape=[4,3])\n",
    "print('Vector a reordenado como Matriz 4x3\\n',A)\n",
    "B=np.reshape(a,newshape=[4,3], order='F') #F after Fortran-like\n",
    "print('Vector a reordenado como Matriz 4x3 por columna\\n',B)\n",
    "C=np.reshape(a,newshape=[3,4]) \n",
    "print('Vector a como Matriz 3x4\\n',C)\n",
    "D=np.reshape(a,newshape=[3,4], order='F')\n",
    "print('Vector a reordenado como Matriz 3x4 por columna\\n',D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplos de uso de la función transmisorpam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 4.5** Generación de una señal M-PAM banda base con una señalización Manchester"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generación de una señal M-PAM banda base con una señalización Manchester.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('\\n\\nGeneración de una señal M-PAM banda base con una señalización Manchester.')\n",
    "#import matplotlib.pyplot as plt\n",
    "#import numpy as np\n",
    "#from labcomdig import transmisorpam\n",
    "\n",
    "# Datos\n",
    "Bn = np.array([1,0,1,1,1,1,0,1,0,1,0,0,0,1,0]) #Secuencia de bits\n",
    "Eb = 8 #Energía de bit, en Julios\n",
    "L = 8 #Número de muestras por símbolo\n",
    "Rb = 1e6 #Sólo necesario para representar en tiempo\n",
    "M = 8 #Orden de la modulación\n",
    "\n",
    "# Definición del pulso de Manchester\n",
    "p = np.ones(L) # La función evaluada en los puntos elegidos \n",
    "p[L//2:] = -1 # // se usa para división entera (floor)\n",
    "[Xn,Bn,An,phi,alfabeto] = transmisorpam(Bn,Eb,M,p,L)\n",
    "\n",
    "#Actualizamos valores tras las posibles modificaciones\n",
    "L = len(phi) #Número de muestras por símbolo\n",
    "Nb = len(Bn) #Número de bits transmitidos\n",
    "M = len(alfabeto) #Número de símbolos de la constelación\n",
    "k = np.log2(M) # Número de bits por símbolo\n",
    "Ns = Nb/k #Número de símbolos\n",
    "\n",
    "# Representación en tiempo discreto\n",
    "plt.figure(41,figsize=(10, 4))\n",
    "plt.stem(np.arange(Ns*L),Xn, 'b-','bo','b-', use_line_collection = True)\n",
    "plt.grid()\n",
    "plt.xlabel('n'); plt.ylabel('X(n)')\n",
    "plt.title('Código 4.5. Representación en tiempo discreto')\n",
    "plt.show()\n",
    "\n",
    "# Representación en tiempo continuo\n",
    "Tb = 1/Rb #Duración del bit en segundos\n",
    "Ts = Tb*k #Duración tiempo de símbolo\n",
    "Tm = Ts/L #Intervalo entre cada muestra de la señal \n",
    "Td = Tb*Nb #Duración total de la señal\n",
    "t = np.arange(Ns*L)*Tm*1e6 #Otra posibilidad: t = np.arange(0,Td,Tm)*1e6\n",
    "#t es el eje de representación de la señal en tiempo continuo escalado (1e6)\n",
    "Xt = np.sqrt (1/Tm )*Xn\n",
    "plt.figure(42,figsize=(10, 4)), plt.plot(t,Xt), plt.grid()\n",
    "plt.xlabel('t ($\\mu$s)'), plt.ylabel('X(t)')\n",
    "plt.title('Código 4.5. Representación en tiempo continuo')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 4.6** Generación de una señal M-PAM paso de banda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Generación de una señal M-PAM paso de banda con pulso rectangular.\n",
      "Incrementamos de 8 a 32 el número de muestras\n",
      "\n",
      "Definimos el pulso p como el producto de un pulso conformador,\n",
      " rectangular, multiplicado por un coseno de frecuencia angular wc=4pi/T\n"
     ]
    },
    {
     "data": {
      "image/png": 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nrYqISP6Gh+G668JwrZZrKZNWpSwijbRFTkREmirjddgaa96+PXl8GbOUoWbJjrbIiYhIqu3by3cdtqRrx02ZArt3VyNL0WuWbGmLnIiIpNq0qXzXYUu6dtyhQ/DII9XIUvSaJVtqyImISKr9KZdYL/J12NqtrYxZilyzZEsNORERSVU/27NRka/D1m5tZcxS5JolW2rIiYhIqsWLy3cdtqRrx02ZAs9/fjWyFL1myZYaciIikmpgoHzXYUu6dtySJeFWhSxFr1mypbNWRUSkqTJeh62x5qGh5PFlUMaaJTvaIiciIiJSUmrIiYiIiJSUGnIiIiIiJaWGnIiIiEhJFbIhZ2bvNbP1ZrbfzG5qMt85ZnbQzEZjt6HMChURERHJUVHPWn0YuAo4A5g1wbx3ufure1+SiIiISLEUsiHn7rcBmNly4IU5lyMiIiJSSIVsyLXp5Wb2KPAYsAa42t2fbpzJzEaAEYCBgQFqGVyMZ3R0NJPlFFE/ZwflV/5q5N+1axkAtdo9Y4YnUpT8afW3k6Vdvcrey5q7qSjvfV7yyF/2hty3gZOBLcBJwOeBp4GrG2d099XAaoDly5f7UP3qkD1Uq9XIYjlF1M/ZQfmVvxr5jzgi/B0aGhozPJGi5E+rv50s7epV9l7W3E1Fee/zkkf+Qp7s0Cp3f9DdN7n7IXf/IXAl8Pa86xIRERHJQqkbcgkcsLyLEBEREclCIRtyZjbNzGYCU4GpZjbTzMbtBjazM81sIBo+AbgU+HK21YqIiIjko5ANOeASYB9wMfCuaPgSM1sYXStuYTTfacB9ZrYH+BpwG/DBPAqOW7sWFi2C179+BYsWhftlVc8yZQqlzyIiIlI1hWzIufvl7m4Nt8vdfau7z3X3rdF8F7n7gLvPcfdj3f0v3f1AnrWvXQsjI7BlC7gbW7aE+2VsAI3NQqmziEh3FPnHXWNt27e3N3+Zs0j/KvtZq4WzciXs3Tt23N69YfzwcD41dapKWURk8rZvDz/m6t8L9R93kP93Qv2HZ7y2KU02VVQpi/Q3rRpdtnVre+OLrEpZRGTyNm1K/3GXt6QfnocOhZqTVCmL9Dc15Lps4cL2xhdZlbKIyOTt3588vgg/7tJqSKu5Slmkv6kh12WrVsHs2WPHzZ4dxpdNlbKIyOTNmJE8vgg/7tJqSKu5Slmkv6kh12XDw7B6df0D5xx9NMyaBWedVbyDaZPED7BduRLOPvvwl0eZs5ShXpGiW7y4uD/ukn54TpkSak5SpSzS39SQ64HhYTj1VHjRi/aybx/s3FmOsz6TzlL99KfDl8cJJ1D6LEWuV6QMBgbiP1RhcDDcz/vkAGj8ER1qW7Ik1JykSlmkv6kh10Pbts0s7MG0SdLOUt20qdgHBidpdsatiHSu/kN1xQrYvLkYDZ+6xtomavhUKYv0L11+pIcOHEhuJxfhYNoknRxgW7YsRa1XRESkE9oi10PTpx9KHF+Eg2mTNDvAtsgHBifRGbciItIP1JDroec978nCHkybJO0s1cWLi31gcBKdcSsiIv1ADbkeOuqoA4U9mDZJ0gG2q1eHYzOKfGBwkrQsRa1XRESkEzpGrseGh+G668JwrZZrKS1Jqrd+vwpZREREqkRb5ERERERKSg05ERERkZJSQ05ERESkpNSQExERESmpQjbkzOy9ZrbezPab2U0TzHuBmW0zs91mdqOZqVthERER6QuFbMgBDwNXATc2m8nMzgAuBk4DFgHHAlf0ujgRERGRIihkQ87db3P3LwE7J5j1bOAGd9/g7o8DHwDO6XF5IiIiIoVQ9uvInQR8OXb/XmDAzI529zGNQDMbAUYABgYGqPX4wmK7di3j4MGD1Go1du1aBsAllzzM9dcfy44dM1iwYD/nn/8gp5++o6d1tOL22xeMqetZzzrIUUcdoFa7B+CZ+mu1e1rOMjo62vPXOMlEWbKSV/6iUP5q5E/67DcOJ8kzfys1t5OlXd3M3ss6e6Uq636ncsnv7oW9EXav3tRk+gPAG2P3pwMOLGr2vKeccor32ooV7kuXPv7M8AknuM+e7Q6Hb7Nnu998c89Laermm8fXNWVKqLduxYpwqw+3kmXdunXZhYi0kiUreeQvEuVfl3cJXdH42U8aTpJn/lZqbidLu7qZvZd19kpV1v1O9So/sN5T2jSF3LXahlFgXux+ffiJHGppatMm2Lt37Li9e2HlynzqqVu5cnxdhw6FetNUKYuIiEiZlb0htwFYGru/FNjuDbtVi2D//uTxW7dmW0ery0+rt9m0MmYREREps0I25MxsmpnNBKYCU81sppklHc/3GeA8MzvRzI4ELgFuyrDUls1IuSjKwoXZ1tHq8tPqbTatjFlERETKrJANOUKDbB/h0iLvioYvMbOFZjZqZgsB3P0bwIeAdcCW6HZZPiU3t3gxzJ49dtzs2bBqVT711K1aNb6uKVNCvWmqlEVERKTMCtmQc/fL3d0abpe7+1Z3n+vuW2PzXuPuA+4+z93PdfdC7kgbGIDVqw9vHRocDPeHh/Ota3h4fF1LloR601Qpi4iISJmV/fIjpTI8DNddF4aLdHZ2Y11DQ+0/pig6ySIiIlJWhdwiJ9ILa9fCokVhd+uiReF+WVUpi4iIdE5b5KQvrF0LIyOHL0+yZUu4D/nvEm5XlbKIiMjkaIuc9IWka8wV4dp3nahSFhERmRw15KQvpF1jbsuW8uyarO9O3bIleXoZs2jXsIjI5KghJ32h2TXu6rsmi9yYqO9OTWvE1ZUti3s5apZkeTfIu7n8KmWR/qKGnPSFpGvMxRV912TS7tQ0ZcxS9JplvO3b822Qp/0g2L69/eeqUhbpP2rISV9ovMZckry7GGum3drKmKXINct4efe5nPaDoJO+lauURfqPGnLSN4aH4dRTi9vFWDPt1lbGLEWuWcbLu8/lbvatXKUs0n/UkJO+U9QuxppJ637s+c+vRpai1yzj5f2DqJt9K1cpi/QfNeSk7xS1i7Fm0rofW7KkGlmKXrOMl/cPorQfBJ30rVylLNJ/1JCTvlTfzbpiBWzeXI5GRGPN9T5kq5ClDDXLWHn/IEr7QdBJ38pVyiL9Rz07iIhIR/Luczlp+fX73XiuLHUzi/QXbZGTvlfU6zd1UleVsoiIyMS0RU76Wv36UUXrtzStP9WFC9N3t1Qpi4iItEZb5KSv5X39qDSdXFeqSllERKQ1asjlJO9dTY3Ln8wVxMucJe/rR6Xp5LpSVcoiIiKtKWRDzsyOMrMvmtkeM9tiZu9Mme8cMztoZqOx21C21baviN3BbNzY3a5tbr99QfcLTzDZLHlfPypNJ9eVqlIWERFpTSEbcsC1wFPAADAMfMLMTkqZ9y53nxu71bIqslN57wJL2tV16FB3u7a5/vpjOy+wDZPNkvf1o9J0cl2pKmUREZHWFK4hZ2ZzgLcBl7r7qLt/F/gKcFa+lXVP3rvAsujaZseObDa3TDZL3tePStPJdaWqlEVERFpTxLNWlwAH3X1jbNy9wIqU+V9uZo8CjwFrgKvd/enGmcxsBBgBGBgYoNbjCwXt2rWMgwcPUqvV2LVrGQC12j3s2rWM6dOfzYEDU8c9ZsGCJ6nV7u5pXWE5p7J9+8xx46dPP0it9p0x9QLj6o8Pp2WZP38ftdr3e1H+GN3Icswx93DCCWH4ox+9J5o+ubpGR0cnvY4dcwzj6mr2XoTHdD9LJxrzT5Slarrx/hfBROtb43Bdlvnb+b5qZ7hTk8neapYiq8q636k88hexITcX2N0wbjfw7IR5vw2cDGwBTgI+DzwNXN04o7uvBlYDLF++3IeGhrpXcYIjjoBdu3YxNDTEEUeEcfXh444LW5LiuwRnz4YPf3gmva4L4MMfHns5CAgnChx33NRx9dazxOuPD6dlGRnZXLos8fkmq1ardeW5Oqm/21k6kZS/WZaq6db7n7dO17cs80/2M9Ltz85ksreapciqsu53Ko/8hdu1CowC8xrGzQOeaJzR3R90903ufsjdfwhcCbw9gxonJe9dYGn9dnaza5vTT9/RvYKb6GYWERGRsiliQ24jMM3MXhwbtxTY0MJjHbCeVNVlefc1mdZvZzeeq8xZREREyqRwDTl33wPcBlxpZnPM7FXAWwjHv41hZmea2UA0fAJwKfDlLOsVERERyUvhGnKR3wdmATuAzwHvcfcNZrYwulZc/cpUpwH3mdke4GuEBuAHc6lYREREJGNFPNkBd38MeGvC+K2EkyHq9y8CLsquMhEREZHiKOoWOZFclLm7sYmeq8xZREQkWSG3yInkod7dWP1SJvXuxiCbEzjq3Y3Flz+lw59aeWe5/fYFfOQj3ckiIiLp9NUqEumHrtOyynL99cd2LUs35L11spuqlEWKqWrrWNXyNFJDTiTSD12nZZUlrYu2TrJ0qv7lbQZnnRW2CrqHv2edFcaX5Uu9WZZzz4X586v7T0qyUaV1LN5wmz8f3v3ucn/+J6JdqyKRGTOSGxoLF44f1wsLF4YvmUYzOui2Nu8sCxbsT+w6rZMsnWjcTe0+dnr9fta7nDsxUZYDB2DnzjBchjxSPFVaxxqz1OuOK9PnvxXaIicSWbw4dC8WN3s2rFqVzfJXrRq//ClTQl3tyjvL+ec/2LUsnUjaTZ0my13OnWgnCxQ/jxRPldaxKmVplRpyIpF+6Dotqyynn74jl67T6rtUkrZsNlM/GaNIu1o6zQLhMUXKIsVUpXWsSlnapV2rIjHDw3DddWG4Vst/+ZPpe7lKWVrRuEulXfXjZ4qwq2WyWaA4WaSYqrSOVSlLJ9raImdmM8xssZmdaGbP7VVRIiLtanWXik3QG3MRdrW0muXoo+FZz0qfXoQsUkxVWscmyjJ9OkyLNls1+/wXIUsnJmzImdmzzew9ZvZtYDdwP/AjYJuZPWRm15nZr/S6UBGRZpqdkTs4CCecACtWwJo1E590kdXZvZ0sP57l0Ufhxhub58k7ixRTldaxibJ86lPwqle19vnPO0snmjbkzOwCYDPwbuBbhM7rlwFLgFcAlxF2z37LzL5hZi/uZbEiImnSzsidMQM2bz58fN7wMJx6avhSHxxs77my0moWOJwn7Z9T3lmkmKq0jk2UJb6rtOhZOjHRFrlXAivc/Vfc/QPu/k13/6G73+/u33f3G939XGAA+AqwoucVi4jExA9ybtxtMtGZsklnCpvld/DzZLIknamcZxYppiqtY1XKMhlNG3Lu/g53/9FET+Lu+9394+5+ffdKExFprn6Qc/1Mtfj1r1o5U7bxTGGz8deYyuoLfbJZGs9UzjOLFFMW69jtty/oTfEN9Hk5TJcfEZHSSjvIOWn3UJr4rpbGC6Hm3UUblDOLFFMW69j11x/blVonos/LYS1ffsTMZgJ/DJwGLKChEejuL+tuaSIizVWpW7MqZZFiymIdS+uer9v0eTmsnevIfRz4deALwPcAbz67iEhvValbsyplkWLKYh1bsGA/ML57vm7T5+WwdnatvhV4h7uPuPvl7n5F/NbNoszsKDP7opntMbMtZvbOJvNeYGbbzGy3md1oZhn15tg98Q5+e3mQZeNytm/v3TJe//oVlclSpfclqyz1978XWeLL6eQg5zR5HfxcxSy9/Pxn8XlJWk4VsmSxjm3fPkOfl4y1s0VuL/BQrwppcC3wFOFs2GXAP5nZve6+IT6TmZ0BXAy8HngY+CJwRTSuFLZvH3tF6l5dXbrxytf1Lom6aWwWq1CWKr0vWWWxnmQZv5zxBznPmtV5t2aXXALnnRd+nScd/Ay9ff+rk6U3n/8sPi9py6lKlt6vY9l995f589JN5o1H+KXNaPZHwEnAe9z9UM8KMpsDPA6c7O4bo3FrgJ+5+8UN834W2OzufxHdPw1Y6+7Pa7aM5cuX+/r163tSf90LXwi7dj3N8uXTuOeeMG7ZMsYN/+IX4w+yhLCp99RTu1fP3Xcnbzo2g9e+dnxd9WFIn6Ys6Xbt2sURRxwx4XztZGmn/vhwEd8XGH+/F8tpZbixlqK+/52u15PJ0s5r+eSTyVm6uZ5N9Hq1W3MvsnTrvYfm73mrir6OtaOo3/3LlsFHPxqGa7UaQz3oj9DMfuDuyxOntdGQ+yrwGkLvDj8GDsSnu/t/n2Sd9eW8HPieu8+KjbuIcD27NzfMey/wQXf/fHR/PvBzYL6772yYdwQYARgYGDjllltu6Ua5qT72seP56U9nMXXq1Kbz3Xvvc4CkPkOcOXMOcvzxo9x//1yAjoYB7r9/Lnv2TE1dztKlu9vOl1WWev3xaWXI4u68+MV7KpElabjbWdpZft6v2dKluyd8LZLe/zJm6dVy2lmv4sNZvF6TzVJ/73uZpUzf/fVlVOH/WFqWWbMO8oUv3AXA6Ogoc+fO7Uotca973etSG3K4e0s34FPNbq0+TwvLeQ2wrWHc7wC1hHkfAN4Yuz+dcBLGombLOOWUUzwL69atm3CewUH30P4fe5sxw33FijDPihWdD9fvz5iRvJzBwU7TZZMlaVoZsixd+nhlsmTxvrSz/LQ8Wb1mjXUl1Zn0/pcxS6+W0856lfXnZbJZ6u99L7OU6bu/nZrLnsW9tf/7nQDWe0qbpuU98u5+brNb++3LVKPAvIZx84AnWpi3Ppw0byElXVl+9uzODthsJulgztmzw/K7RVnapyytWbs27Fa5804YHR3fiXcWrxmE3TqTPSi9SlkmWk6nB5+nyeLzAtXIUl/P6sd89Wo5MPE6NtmTBaqUpReKeEHgjcC0hn5blwIbEubdEE2Lz7fdG3arFln9yvKDg2EFHRwM9zs5YLOZ+lWsG5fTzYM3x2bxCmWp0vuSVRbvWpb6yRr1Y2N27gy/kY8+OpvXDMb+89iyBTZu7KwBVKUszZZTf/8nurp+u7L4vEDyZ6ZMWeonBdTXM/fD73vv1zEft46NjHS+juWbZfznZTJZeqVpQ87MbjezV0/0JGZ2hJmtNLM/nGxB7r4HuA240szmmNmrgLcAaxJm/wxwnpmdaGZHApcAN022hqwND4crUR86NL6D37Itp76MO+64szJZqvS+ZJWlm+//pk3jr+B+4ADMnZvNazY4GP55xB06FOpqV5WyNFtO/f3v9o+F+DKy/lyWKUtSrwfu4f3v9To2MLB/3Dq2d2/n61ieWZI+L5PJ0isTXX7kZuBzZrYP+AqwHngEeBI4EjgReDXwRuBLwPu6VNfvAzcCO4CdhDNlN5jZQsKJFie6+1Z3/4aZfQhYB8wC/gG4rEs1iEgB5H3V9SpdQb6bWaS40t7nLNaztJ4dOl3H8sxSls9L04acu99kZmuBdwC/DZwHPKc+mdCo+ibwcnf/abeKcvfHCBcgbhy/FZjbMO4a4JpuLVtEiiXvq65X6Qry3cwixZX2Pmexni1YsJ/t28f37NDpOpZnlrJ8XiY8Rs7dD7j7Z939ze5+JGFL3AuAme7+S+5+EfAfvS5URPpLlgc4N9ONg5+rlEWKKeuTaNKcf/6D49ax6dPh4MFQWysn2BQlS9Lnpd0sWZiwIWdm15rZM81rd9/t7tvc/UA0/UTg7h7WKCJ9pvGkgF4f4NzMZA9+rlIWKaY8TqJJc/rpO8acxFGv4emnw/SJTrApUpbGk17azZKVVs5aPR34oZm9Ij7SgvcD/wo82IviRKQ/JZ0U0OsDnJuZzMHPVcoixZTXSTRp4idxzJ0LTz01dnqzE2yqlCUrrTTklgHfAO40s6vNbLqZvQS4C7gIONvdf6uHNYpIn4jvgkyS1UkBaZod/Ny4q6UKWbSrtdiKvo41q6HxOoZVyJLX52Wis1Zx933AH5rZlwhnkv4G8ELgW8Bb3F0b4kWkY/Ev8G9/e/xWorisTgpotvykg5/rDk97DmedVf4sRe8svB+V6fNSryFtPauP/8lPqpFlZAQuuGABPehqtal2Lgi8EdgMHA8cAj6hRpyITEbSxT7TZHlSQJq0kwXGs0pk2bs3XMdLiqFsnxdo/TNThSx798L11x+bXUGRlhpyZnYO8CNgL3Ac8FHgq2b2STOb07PqRAokfiZVN7tpymOTfFGyJF3sM0nWJwWkaTz4uRNly1KEXVoSlO3zAuNPsGlX2bKkXUevl1o5a/XLwN8B73P3M919s7tfCrwKeC1wn5m9psd1iuSq8ZdwN7tpqm+Sz6oxl0WW229f0NLjW2kk5HVSQJr4wc/t/nMqY5Yi7NKSoIyfFxh7gk07yphlwYLsrxbcyha5I4Cl7v738ZHu/i/Aywk9Pvxz90sTKY6kX8Ld7KYpy11YWWRpdffCRI2EouxSSdP6rtZyZil6zf1Gn5fiSPu8nH9+9hfxaKUhN+TuiV/x7r7f3S8ATutuWSLFom6a2ntMq7sXkr4M87rGWieaXZctfr9sWbK+Xpe0Rp+X4kj7vJx++o7Ma2mlZ4cmhyA+M893ulOOSDGl/RLutJumdpbRbVlkaXX3QtKX4Zo14cDnou1SSVPf1eIeag9ZvNRZ8rhel0ysup+XcmfJ+/PSzlmrIn0r6ZfwlCmweHH7z7V4cb67sLLI0s7uhaJ8GXZDPcsdd9xZ+ixSTFX8vFQhS57UkBNpQdIv4SVLYGCg/ecaGMh3F1YWWfLYvSAi0o/UkBNpUeOvx04aPmnPlVc3TVXIIiLSz9SQExERESkpNeRERERESqpQDTkzO8rMvmhme8xsi5m9s8m855jZQTMbjd2GsqtWREREJF+FasgB1wJPAQPAMPAJMzupyfx3ufvc2K2WRZG9VpTuk7qhSllERESKZlreBdRFfba+DTjZ3UeB75rZV4CzgItzLS5DSd0nTemwuZ30XCMjYTiLA9K7mSWtKyjQwfUiItK/CtOQA5YAB919Y2zcvcCKJo95uZk9CjwGrAGudvenk2Y0sxFgBGBgYIBardaVopsZHR1tezkXXngqe/fOHDPu0CF44IGD1GrfYdeuZQDUavdMOHzhhTPHPdfevXDhhU9yzDF3txunLaOjo1x44ZOTzgKwa9cyHnjg2Rw4MHXMc2WVJU2z+g8ePEitVkvMEr9fFO2sV61k6WTdrxLlHx23/neyXjWbL2utZql/9lt9TNVo3c8hv7sX4ga8BtjWMO53gFrK/McCiwm7h38J+DHw560s65RTTvEsrFu3ru3HmLmHa1uPv7m7r1gRbq0Mpz2XWeeZWrVu3bquZKnfT3ueLLIkuflm9xkzQg2Dg+4nnDC2/qVLH39muD6+8TE335x11ckmypI0PFGWTtb9KlH+de7e3vdVXavzZa3VLPXPfquPqRqt++t68rzAek9p02R2jJyZ1czMU27fBUaBeQ0Pmwc8kfR87v6gu29y90Pu/kPgSuDtvU3Re93sPintuarUFVRWWeKSdhlv3Nj8+L+03dx5H+dXpSwiIv0os4acuw+5u6XcXg1sBKaZ2YtjD1sKbGh1EYBNOFfBdbP7pKTnqlpXUFlliVu5MuzWjTt0CDZtau8xe/eG8XmqUhYRkX5UmLNW3X0PcBtwpZnNMbNXAW8hHPs2jpmdaWYD0fAJwKXAl7Oqt1e62X1S0nNVrSuoPE502Lo1efz+Jv3Epz0mbXxWqpRFRKQfFaYhF/l9YBawA/gc8B533wBgZguja8XVd6adBtxnZnuArxEagR/Moeauq1L3SVXKUtfJLuO8d3OnqVIWEZF+VKiGnLs/5u5vdfc57r7Q3T8bm7bVw7Xitkb3L3L3gWjeY939L939QH7VS7/oZJdx3ru501Qpi4hIPypUQ06kDDrZZZz3bu526iprFhGRfqSGnEgHOtllXJRdw42qlEVEpN+oISciIl1Xpe75qpRFqqdIPTuIlFL9i3n//vDFPGtWOFmgcfyqVcXfcpWUZWCgnFkkP7ffvoCPfKQa3fOpq0EpOm2RE5mEtAvqPvTQzNJdNDcty8aNugCwtOf6649t+/qEaTZtyve6hZ1cazFN3lmkmtSQE5mEtC/5xx6bUbov7LQsjzyifz7Snh07kq9f0+z6hGnSHpPVdQs7udZimryzSDWpIScyCe1+ARf5C7tKWSRfCxYkt1jK2D1fVbsalOpQQ05kEtK/gJN7iyvyF3a7tRU5i+Tr/PMfrEz3fFXtalCqQw05kUlI+pJPU/Qv7CplkXydfvqOynTPV9WuBqU61JATmYT4l3wzZfjCrlIWyV+VuuerUhapHjXkRCap/sWc1gAaHCzPF3aVsoiI9AM15ES6pEp9kFYpi4hIlakhJ9IlY4+l8VLvglR/qiIi5aCeHUS6aHg43Gq1OxkaGsq7nEmpZxERkeLSFrmCa7ePvyL349dJf4VFziMiIpI3bZErsHb7+CtyP36d9FeY9Jii5BERESkCbZErsHb7+CtyP36d9FeY9Jii5BERESmCQjXkzOy9ZrbezPab2U0tzH+BmW0zs91mdqOZddBpSnG128dfkfvx66S/wrTHFCGPiIhIERSqIQc8DFwF3DjRjGZ2BnAxcBqwCDgWuKKXxWWt3T7+ityPXyf9FaY9pgh5REREiqBQDTl3v83dvwTsbGH2s4Eb3H2Duz8OfAA4p4flZa7dPv6K3I9fJ/0V6lpmIiIizZX5ZIeTgC/H7t8LDJjZ0e4+riFoZiPACMDAwAC1Wq3nBY6Ojk5qOcccAxdcsIDrrz+WHTtmsGDBfp71rIPMmHGAWu0edu1aBvDM8IwZcMEFD4+Z//zzH+SYY3aQQdwxGrO3myU85p5xj8krT7sm+96XnfIrf2P+pM9443Cz+YqkWZaDBw8+k70MWbpN6372+c3dM11gK8zsKuCF7n5Ok3keAP7A3b8R3Z8OPAUsdvfNzZ5/+fLlvn79+u4VnKJWq3X9WmL1p6vV0oeLoJXsZcnSiV6892Wi/MrfmL/Vz3sZvguaZdm1axf33HNE0/mqTOt+b/Kb2Q/cfXnStMx2rZpZzcw85fbdDp5yFJgXu18ffmLy1YqIiIgUX2a7Vt19qMtPuQFYCtwa3V8KbE/arSoiIiJSRYU62cHMppnZTGAqMNXMZppZWmPzM8B5ZnaimR0JXALclFGpIiIiIrkrVEOO0BjbR7isyLui4UsAzGyhmY2a2UKA6Ni4DwHrgC3R7bI8ihYRERHJQ6HOWnX3y4HLU6ZtBeY2jLsGuKbnhYmIiIgUUNG2yImIiIhIi9SQExERESkpNeRKZO1auPtuuPNOWLQItm9PHr92bZ5VtqZKWURERPJSqGPkJN3atTAycriT+S1bQhdXu3ePHz8yEoaHh/OpdSJVyiIiIpInbZEriZUrYe/eseMOHYJHHhk/fu/eMH9RVSmLiIhIntSQK4mtW3s7f5aqlEVERCRPasiVxMKFvZ0/S1XKIiIikic15Epi1SqYPXvsuClT4PnPHz9+9uwwf1FVKYuITCzt5KZ2HlOUE5+qlEWqQQ25khgehtWrYXAQzODoo0Pj55FHYNascN8sTF+9utgnB1Qpi4g0l3Ry08aNzRtA27cnn/iUdwOoSlmkOtSQK5HhYdi8GdasgX374Omnw/idO8P9NWvC9DI0fKqURUTSpZ3ctGlT+mM2bSrmiU9VyiLVoYZcCSV9mZT1i6FKWURkvLSTlepbqNqZlveJT1XKItWhhlwJpX0BlPGLoUpZRGS8tJOVZsxIf0zatLxPfKpSFqkONeRKKO0LoIxfDFXKIiLjpZ3ctHhx+mMWLy7miU9VyiLVoYZcCSV9mZT1i6FKWURkvMaTmwYHYckSGBhIf8zAwPjHFOHEpyplkepQQ66Ekr5MyvrFUKUsIpKsfnLToUPhb7OGT9pjivKdUKUsUg3qa7Wkhoer82VQpSwiIiJZKtQWOTN7r5mtN7P9ZnbTBPOeY2YHzWw0dhvKpFARERGRAijaFrmHgauAM4BZLcx/l7u/urcliYiIiBRToRpy7n4bgJktB16YczkiIiIihVaohlwHXm5mjwKPAWuAq9396aQZzWwEGAEYGBigVqv1vLjR0dFMllNE/ZwdlF/5lb9Z/l27lgFQq90zZrhxWhk0Zjl48OAz2cuWpRu07mefv8wNuW8DJwNbgJOAzwNPA1cnzezuq4HVAMuXL/ehoaGeF1ir1chiOUXUz9lB+ZVf+ZvlP+KI8HdoaGjMcOO0MmjMsmvXrtJm6Qat+9nnz+xkBzOrmZmn3L7b7vO5+4PuvsndD7n7D4Ergbd3v3IRERGRYspsi5y7D/V6EYD1eBkiIiIihVG0y49MM7OZwFRgqpnNNLPExqaZnWlmA9HwCcClwJezq1ZEREQkX4VqyAGXAPuAi4F3RcOXAJjZwuhacfVeOE8D7jOzPcDXgNuAD2ZfsoiIiEg+CnWyg7tfDlyeMm0rMDd2/yLgokwKExERESmgom2RExEREZEWqSEnIiIiUlJqyImISGbWroW774Y774RFi2D79vRpa9fmVWVrqpRFyqtQx8iJiEh1rV0LIyOwf3+4v2ULTJmSPm1kJAwPD2df60TSshxzzPTSZZFy0xY5ERHJxMqVsHfv2HGHDsGmTcnT9u4N44soLcu2bTNLl0XKTQ05ERHJxNatyeP370+fljY+b2l1HTgwpXRZpNzUkBMRkUwsXJg8fsaM9Glp4/OWVtf06YdKl0XKTQ05ERHJxKpVMHv22HFTpsDixcnTZs8O44soLcvznvdk6bJIuakhJyIimRgehtWrYXAQzMLfJUtgYCB52urVxT05IC3LUUcdKF0WKTc15EREJDPDw7B5czgxYPPm0IhLm1b0hk+Vskh5qSEnIiK5qNK11upZ7r33OaXPIuWihpyIiGQu7VprZWwAjc1ipc4i5aOGnIiIZK5K11qrUhYpHzXkREQkc1W61lqVskj5qCEnIiKZq9K11qqURcpHDTkREclcla61VqUsUj5qyImISOaqdK21sVm81FmkfArTkDOzGWZ2g5ltMbMnzOzfzOzMCR5zgZltM7PdZnajmc3Iql4REZmcKl1rrZ7ljjvuLH0WKZfCNOSAacBDwArgOcClwK1mtihpZjM7A7gYOA1YBBwLXJFFoSIiIiJFUJiGnLvvcffL3X2zux9y938ENgGnpDzkbOAGd9/g7o8DHwDOyahcERERkdyZu+ddQyIzGwC2AMvc/ScJ0+8FPujun4/uzwd+Dsx3950J848AIwADAwOn3HLLLb0sH4DR0VHmzp3b8+UUUT9nB+VXfuXv1/z9nB2Uv1f5X/e61/3A3ZcnTZvW9aV1gZlNB9YCn05qxEXmArtj9+vDzwbGNeTcfTWwGmD58uU+NDTUtXrT1Go1slhOEfVzdlB+5Vf+fs3fz9lB+fPIn9muVTOrmZmn3L4bm28KsAZ4Cnhvk6ccBebF7teHn+h68SIiIiIFVKhdq2ZmwI2Ekxfe5O77msz7WWCTu6+M7r8e+Ky7P6+F5fycsNu21+YDj2awnCLq5+yg/Mqv/P2av5+zg/L3Kv+guz83aULRGnKfBJYBp7v76ATzvhG4CXg98AjwD8D33f3iHpfZMjNbn7ZPu+r6OTsov/Irf7/m7+fsoPx55C/MWatmNgj8LqEht83MRqPbcDR9YXR/IYC7fwP4ELCOsHVtC3BZLsWLiIiI5KAwJzu4+xbAmkzfSjjBIT7uGuCaHpcmIiIiUkiF2SJXUavzLiBH/ZwdlF/5+1s/5+/n7KD8mecv1DFyIiIiItI6bZETERERKSk15ERERERKSg05ERERkZJSQ64HzOwoM/uime0xsy1m9s68a+oVM5thZjdEOZ8ws38zszNj008zs5+Y2V4zWxddZqZyzOzFZvakmd0cG9cv2X/LzP49Wt8fMLPXROMrn9/MFpnZ18zscTPbZmYfM7Np0bRK5Tez95rZejPbb2Y3NUxLzWrBX5vZzuj2oeji76WSlt/MTjWzb5nZY2b2czP7gpk9Pza90vkb5rks6q3p9Ni4yuc3s9lm9nEze9TMdpvZt2PTep5fDbneuJbQxdgAMAx8wsxOyreknpkGPASsAJ4DXArcGv2Dmw/cFo07ClgPfD6vQnvsWuBf6nf6JbuZvQH4a+BcQj/HrwUe7Jf8wMeBHcDzCdfAXAH8fkXzPwxcReh95xktZB0B3gosBV4G/BrhmqFlk5gfOJJwpuIiYJDQTeSnYtOrnh8AMzsOeDvhAv1x/ZB/NWHdf2n094LYtN7nd3fdungD5hAacUti49YAf5V3bRm+BvcBb4tW4O81vDb7gBPyrrHLeX8LuBW4HLg5Gtcv2b8HnJcwvl/y/zuhO8H6/f8J/H2V8xP+md3U6nsdrSMjsennAXfnnaNb+ROm/zLwROx+X+QHvg68CdhM6J2pL/IDLwF+AcxLmb/n+bVFrvuWAAfdfWNs3L1AVbfIjWFmA4TXYAMh8731ae6+B3iACr0WZjYPuBK4sGFSP2SfCiwHnmtm95vZf0a7FmfRB/kjfwv8VrRr5RjgTOAb9E9+mDjrmOlU//vwtYTvv7rK5zezdwBPufvXEiZXPf+vEnqWuiLatfpDM3tbbHrP86sh131zgd0N43YTdjtVmplNB9YCn3b3n9Afr8UHgBvc/aGG8f2QfQCYTtid8hrCrsWXA5fQH/kB7iR8Kf8C+E/CbsUv0T/5YeKsjdN3A3PLeJzURMzsZcBfAn8WG13p/GY2F/gg8Ccps1Q6P/BC4GRCrhcA7wU+bWYvjab3PL8act03CsxrGDePcNxEZZnZFMIu5KcIKzJU/LUws2XA6cBHEiZXOntkX/T3f7n7I+7+KKHLvDfRB/mjdf6bhOPD5gDzCcdL/TV9kD9moqyN0+cBox7tZ6oKMzuesHvxj939O7FJVc9/BbDG3TelTK96/n3AAeAqd3/K3e8k9AH/X6PpPc+vhlz3bQSmmdmLY+OWMnZTe6VEvyxuIGyheZu7H4gmbSBkr883BziO6rwWQ4QDnLea2TbgIuBtZvavVD877v44YStU0hdS5fMTDmp+EfAxd9/v7jsJB7m/if7IXzdR1jHTqeD3YXSW7u3AB9x9TcPkquc/DfgjC2dtbyN8Jm41s/dH06ue/74Jpvc8vxpyXRYdH3IbcKWZzTGzVwFvIWytqqpPEM7WebO774uN/yJwspm9zcxmEnY53Bftdq2C1YR/WMui2yeBfwLOoPrZ6z4F/KGZLTCzIwm7V/6RPsgfbYHcBLzHzKaZ2RHA2YRjYCqXP8o4E5gKTDWzmRYutTJR1s8Af2pmx5jZCwjHk96UQ4RJScsfHRt5B3Ctu38y4aGVzk9oyJ3M4e/BhwlnZV4bPbTq+b8NbAX+PJrnVYQf+d+MHtr7/HmfAVLFG+GX+peAPdEb/M68a+ph1kHCFpknCZuQ67fhaPrpwE8Im59rwKK8a+7ha3E50Vmr/ZKdcIzcx4FdwDbg74CZfZR/WZTtceBR4AvAgirmj9Zvb7hdPlFWwIAPAY9Ftw8R9fNdpltafuCyaDj+/TfaL/kT5tvM2LNWK5+fcJzsXYT/+T8Gfj3L/BYtSERERERKRrtWRUREREpKDTkRERGRklJDTkRERKSk1JATERERKSk15ERERERKSg05ERERkZJSQ05ERESkpNSQExERESkpNeRERERESmpa3gWIiJSZmdUI3fLsAkaAQ4T+Fd/n7ofyq0xE+oG2yImITN4w8DTwSuC9wJ8Av5lnQSLSH9TXqojIJERb5Ga4+yti474FbHH383MrTET6grbIiYhM3n0N9x8GFuRRiIj0FzXkREQm70DDfUffryKSAX3RiIiIiJSUGnIiIiIiJaWGnIiIiEhJ6axVERERkZLSFjkRERGRklJDTkRERKSk1JATERERKSk15ERERERKSg05ERERkZJSQ05ERESkpNSQExERESkpNeRERERESur/A9W63UaBh1ofAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('\\n\\nGeneración de una señal M-PAM paso de banda con pulso rectangular.')\n",
    "#import numpy as np\n",
    "#import matplotlib.pyplot as plt\n",
    "#from labcomdig import transmisorpam\n",
    "\n",
    "# Datos\n",
    "Bn = np.array([1,0,1,1,1,1,0,1,0,1,0,0,0,1,0]) #Secuencia de bits\n",
    "Eb = 8 #Energía de bit, en Julios\n",
    "print('Incrementamos de 8 a 32 el número de muestras\\n')\n",
    "L = 32 #Número de muestras por símbolo, se incrementa de 8 a 32\n",
    "Rb = 1e6 #Sólo necesario para representar en tiempo\n",
    "M = 8 #Orden de la modulación\n",
    "\n",
    "print('Definimos el pulso p como el producto de un pulso conformador,\\n \\\n",
    "rectangular, multiplicado por un coseno de frecuencia angular wc=4pi/T')\n",
    "\n",
    "# Definición del pulso conformador g: rectangular\n",
    "g = np.ones(L) # \n",
    "\n",
    "#Definición de pulso p\n",
    "wc = 4*np.pi/L #frecuencia angular en discreto\n",
    "p = g*np.cos(wc*np.arange(L)) \n",
    "\n",
    "# Transmision\n",
    "[Xn,Bn,An,phi,alfabeto] = transmisorpam(Bn,Eb,M,p,L)\n",
    "\n",
    "#Actualizamos valores tras las posibles modificaciones\n",
    "L = len(phi) #Número de muestras por símbolo\n",
    "Nb = len(Bn) #Número de bits transmitidos\n",
    "M = len(alfabeto) #Número de símbolos de la constelación\n",
    "k = np.log2(M) # Número de bits por símbolo\n",
    "Ns = Nb/k #Número de símbolos\n",
    "\n",
    "# Representación en tiempo discreto\n",
    "plt.figure(43,figsize=(10, 4))\n",
    "plt.stem(np.arange(Ns*L),Xn,'b-','bo','b-', use_line_collection = True)\n",
    "plt.grid()\n",
    "plt.xlabel('n'), plt.ylabel('X(n)')\n",
    "plt.title('Código 4.6. Representación en tiempo discreto')\n",
    "plt.show()\n",
    "\n",
    "# Se definen los parámetros en tiempo continuo\n",
    "Tb = 1/Rb # Duración del bit en segundos\n",
    "Ts = Tb*k #Duración tiempo de símbolo\n",
    "Tm = Ts/L #Intervalo entre cada muestra de la señal \n",
    "Td = Tb*Nb # Duración de la señal\n",
    "t = np.arange(0,Td,Tm)*1e6 # Eje de representación de la señal en tiempo continuo escalado\n",
    "Xt = np.sqrt(1/Tm)*Xn\n",
    "plt.figure(44,figsize=(10, 4)), plt.plot(t,Xt), plt.grid()\n",
    "plt.xlabel('t ($\\mu$s)'), plt.ylabel('X(t)')\n",
    "plt.title('Código 4.6. Representación en tiempo continuo')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio propuesto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de entrar en objeto de este ejercicio, simular una transmisión PAM, se pide la realización de los siguientes apartados, para afianzar conceptos teóricos:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.** Se desea transmitir la secuencia de dígitos binarios {1,0,0,1,1,1,0,0,1,0,0} mediante una señal PAM paso de baja, con $M=4$, $L=4$ y una energía media por símbolo $E_{av}=5$ J. El pulso conformador paso de baja es rectangular. Sin utilizar transmisorpam en los apartados 1.a) a 1.d), esto es, realizando los cálculos y representaciones a mano, se pide:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   a) Determinar la secuencia de símbolos a transmitir.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   b) Determinar la base del sistema.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "markdown",
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   "source": [
    "   c) Representar gráficamente la señal discreta a la salida del transmisor utilizando las funciones descritas en este capítulo.\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   d) Calcular la energía promedio de la salida completa y de la salida correspondiente a los 8 primeros bits. Repita el resultado cambiando el último bit, el undécimo, a 1. Compare los resultados con $E_{av}$ , la energía promedio de bit deseada y explique las diferencias, si las hay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " e) Comprobar los apartados anteriores con Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "markdown",
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   "source": [
    "**2.** Suponer que en el apartado anterior el régimen binario de la fuente es $R_b = 1$ kbps.\n",
    "\n",
    "Determinar:\n",
    "\n",
    "a) La duración del pulso básico en segundos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) El tiempo de muestreo en segundos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) La tasa de transmisión en baudios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tras estas preguntas sobre aspectos más teóricos, se pasa a diseñar un transmisor PAM paso de banda, con $M=8$, que utiliza como pulso conformador paso de baja el mostrado en la Figura 4.13. Se desea transmitir $N_b = 10^4$ bits, con una tasa $R_b=10^4$ bps y una energía media por bit $E_{bav} = 8$ J. La frecuencia portadora viene dada por $\\omega_c =8\\pi/T$. Se utilizarán $L = 64$ muestras para transmitir cada símbolo M-ario.\n",
    "\n",
    " <img src=https://raw.githubusercontent.com/gapsc-us/labcomdig/main/figures/pulsoConformador.png style=\"max-width:30%;width:auto;height:auto;\" align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** Determinar la definición del pulso conformador paso de banda utilizado mediante un código en Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.** Representar la función correspondiente a la base del sistema, tanto en tiempo discreto como continuo. Comprobar que la energía de la misma es 1 J."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.** Calcular el tiempo de separación entre las muestras de la señal discreta generada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.** Determinar la duración de la señal transmitida."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.** Obtener una representación de la señal transmitida correspondiente a los primeros 4 símbolos generados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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