Butter PB.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5874909",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: PySpice in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (1.5)\n",
      "Requirement already satisfied: cffi>=1.14 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (1.14.6)\n",
      "Requirement already satisfied: requests>=2.23 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (2.26.0)\n",
      "Requirement already satisfied: scipy>=1.4 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (1.7.1)\n",
      "Requirement already satisfied: numpy>=1.18 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (1.20.3)\n",
      "Requirement already satisfied: PyYAML>=5.3 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (6.0)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from requests>=2.23->PySpice) (2021.10.8)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from requests>=2.23->PySpice) (3.2)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from requests>=2.23->PySpice) (1.26.7)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from requests>=2.23->PySpice) (2.0.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install PySpice\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.signal as sps\n",
    "import pandas as pd #Leer archivos de Excel\n",
    "from PySpice.Spice.Netlist import *\n",
    "from PySpice.Unit import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71576080",
   "metadata": {},
   "source": [
    "# Diseño de filtro Butterworth Pasa Bajas\n",
    "\n",
    "## Paola Briseño, UAM - I, 2022.\n",
    "\n",
    "Diseño y simulación (teórico/SPICE) de un filtro pasa-bajas con la siguiente especificación:\n",
    "\n",
    "máxima atenuación en la banda de paso: 0.1 dB\n",
    "mínima atenuación en la banda de rechazo: 60 dB\n",
    "fp=100m1 y fr=1000m2 Hz,con m1=5 y m2=6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b08fbd73",
   "metadata": {},
   "outputs": [],
   "source": [
    "def aproximacion_butterworth(Ap, Ar, fp, fr):\n",
    "# Calcula el orden N y la freuencia de corte fc del filtro pasabajas␣Butterworth, dadas las especificaciones relativas (Ap, fp), (Ar, fr)\n",
    "  N = int(np.ceil((np.log10((10**(Ap/10)-1)/(10**(Ar/10)-1)))/(2*np.log10(fp/fr))))\n",
    "  fc = fp/(10**(Ap/10)-1)**(1/(2*N))\n",
    "  return N, fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c23bb8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def butterworth_pasabajas(N, fc):\n",
    "# Calcula los coeficientes b, a del filtro pasabajas Butterworth de orden N y frecuencia\n",
    "# de corte fc, así como su forma factorizada en la lista s. Cada entrada de s␣ es una sección de primer o segundo orden: s = [[bi, ai], ....]\n",
    "\n",
    "# Calcula los polos\n",
    "  p = np.array([np.exp(1j*(2*k-1)*np.pi/(2*N))*(np.exp(-1j*np.pi/2)) for k in range(1, 2*N+1)])\n",
    "  p = np.array([2*np.pi*fc*pi for pi in p if pi.real <= 0])\n",
    "# Genera los coeficientes de H(s)\n",
    "  a = np.poly(p).real\n",
    "  b = np.array([(2*np.pi*fc)**N])\n",
    "# Ordena los polos para hacer las secciones\n",
    "  inx = np.argsort(p.real)\n",
    "  p = p[inx]\n",
    "# Inicializa la lista de secciones\n",
    "  s = []\n",
    "# Sección de orden uno, si la hay\n",
    "  if N%2 != 0:\n",
    "    bi = np.array([-p[0].real])\n",
    "    ai = np.array([1, -p[0].real])\n",
    "    s.append([bi, ai])\n",
    "    # Quita el polo real de la lista\n",
    "    p = p[1:]\n",
    "# Secciones de orden dos\n",
    "  for i in range(0, p.shape[0], 2):\n",
    "    ai = np.poly(p[i:i+2]).real\n",
    "    bi = np.array([ai[2]])\n",
    "    s.append([bi, ai])\n",
    "  return b, a, s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7ad73a",
   "metadata": {},
   "source": [
    "# Comienza el diseño"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "839f5fa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (Paso 1) Especificación relativa\n",
    "Ap, fp, Ar, fr = 0.1, 100*8, 60, 1000*11\n",
    "# (Paso 2) Parámetros del filtro\n",
    "N, fc = aproximacion_butterworth(Ap, Ar, fp, fr)\n",
    "# (Paso 3) Coeficientes de las secciones\n",
    "b, a, s = butterworth_pasabajas(N, fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9041378",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4182796924477930.5\n",
      "[1.00000000e+00 2.10148858e+04 2.20812713e+08 1.35912820e+12\n",
      " 4.18279692e+15]\n",
      "4\n",
      "1279.93179224373\n"
     ]
    }
   ],
   "source": [
    "#print(b)\n",
    "print(b[0])\n",
    "print(a)\n",
    "print(N)\n",
    "print(fc)\n",
    "#print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e02b0fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pasa-bajas, N=4')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepara gráfica con referencias relevantes (pasa bajas)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.axhline(-Ap, color='gray', linestyle='--')\n",
    "plt.axhline(-Ar, color='gray', linestyle='--')\n",
    "plt.axhline(-3, color='black', linestyle=':')\n",
    "plt.axvline(fp, color='gray', linestyle='--')\n",
    "plt.axvline(fr, color='gray', linestyle='--')\n",
    "plt.axvline(fc, color='black', linestyle=':')\n",
    "# Respuesta en frecuencia total\n",
    "W, H = sps.freqs(b, a)\n",
    "f = W/(2*np.pi)\n",
    "plt.semilogx(f, 20*np.log10(np.abs(H)), linewidth=5, label='total')\n",
    "# Respuesta en frecuencia de cada sección\n",
    "for i, [bi, ai] in enumerate(s):\n",
    "  _, Hi = sps.freqs(bi, ai, W)\n",
    "  plt.semilogx(f, 20*np.log10(np.abs(Hi)), label='sección {}'.format(i))\n",
    "# Documenta la gráfica\n",
    "plt.xlabel('f (Hz)')\n",
    "plt.ylabel('$|H(f)|_{dB}$')\n",
    "plt.title(f'Pasa-bajas, N={N}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8ac40632",
   "metadata": {},
   "outputs": [],
   "source": [
    "def secciones_sallen_key(s, C=1e-6):\n",
    "# Calcula los componentes de cada sección, usando como base C3=1uF.Regresa la lista sk = [[R1, R2, C2], [R1i, R2i, C3i, C4i], ....]\n",
    "# con los␣ componentes de la sección de primer orden (si la hay) y los de las secciones de segundo␣orden\n",
    "\n",
    "# Inicializa lista de componentes de sección\n",
    "  sk = []\n",
    "# Para cada sección\n",
    "  for i, [bi, ai] in enumerate(s):\n",
    "# Sección de primer orden\n",
    "    if ai.shape[0] == 2:\n",
    "      R = 1/(ai[1]*C)\n",
    "      print('sección {}: R1={:6.3f}k, R2={:6.3f}k, C2={:6.3f}u'.format(i, R/1e3, R/1e3, C/1e-6))\n",
    "      sk.append([R/1e3, R/1e3, C/1e-6])\n",
    "      # Sección de segundo orden\n",
    "    else:\n",
    "      m = (ai[1]**2)/(4*ai[2])\n",
    "      R1 = ai[1]/(2*m*ai[2]*C)\n",
    "      R2 = ai[1]/(m*ai[2]*C)-R1\n",
    "      print('sección {}: R1={:6.3f}k, R2={:6.3f}k, C3={:6.3f}u, C4={:6.3f}u'.format(i, R1/1e3, R2/1e3, C/1e-6, m*C/1e-6))\n",
    "      sk.append([R1/1e3, R2/1e3, C/1e-6, m*C/1e-6])\n",
    "  return sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0ac62594",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sallen_key_pasabajas_spice(sk, spiceopus=False):\n",
    "## Genera una lista de red (netlist) para SPICE a partir de los componentes exactos\n",
    "\n",
    "# Cadenas paramétricas de elementos del netlist\n",
    "  MODELOS = '* Modelos\\n.include tl081.mod\\n'\n",
    "  SENAL = '* Fuente de señal\\nVin (vin 0) dc 0 ac 1.0v\\n'\n",
    "  ALIMENTACION = '* Fuentes de alimentación\\nVpp (vp 0) dc 15\\nVnn (0 vn) dc 15\\n'\n",
    "  POrc = '*Seccion {0}\\nR1{0} ({4} vi{0}) {1}k\\nR2{0} (vi{0} {5}) {2}k\\nC2{0} (vi{0} {5}) {3}u\\n'\n",
    "  POopamp = 'X{0} (0 vi{0} vp vn {2}) TL081\\n'\n",
    "  SKrc = '*Seccion {0}\\nR1{0} ({5} n0{0}) {1}k\\nR2{0} (n0{0} vni{0}) {2}k\\nC3{0} (n0{0} {6}) {3}u\\nC4{0} (vni{0} 0) {4}u\\n'\n",
    "  SKopamp = 'X{0} (vni{0} {1} vp vn {1}) TL081\\n'\n",
    "  CTRL = '* Simulacion\\n.control\\ndestroy all\\nac dec 10 0.1Hz 10MegHz\\nplot db(mag(v(vout)/v(vin))) vs frequency\\n+ xlabel \"f (Hz)\"\\n+ ylabel \"|H(f)|dB\"\\n.endc\\n.end'\n",
    "\n",
    "# Inicializa el netlist con el encabezado básico\n",
    "  netlist = MODELOS + SENAL + ALIMENTACION\n",
    "# Agrega cada sección\n",
    "  for i, ski in enumerate(sk):\n",
    "  # Secciones de segundo orden Sallen-Key\n",
    "    if len(ski) == 4:\n",
    "      if i == 0:\n",
    "        netlist = netlist + SKrc.format(i, ski[0], ski[1], ski[2], ski[3], 'vin', 'vout0')\n",
    "        netlist = netlist + SKopamp.format(i, 'vout0')\n",
    "      elif i == len(sk)-1:\n",
    "        netlist = netlist + SKrc.format(i, ski[0], ski[1], ski[2], ski[3], 'vout{}'.format(i-1), 'vout')\n",
    "        netlist = netlist + SKopamp.format(i, 'vout')\n",
    "      else:\n",
    "        netlist = netlist + SKrc.format(i, ski[0], ski[1], ski[2], ski[3], 'vout{}'.format(i-1), 'vout{}'.format(i))\n",
    "        netlist = netlist + SKopamp.format(i, 'vout{}'.format(i))\n",
    "# Sección inversora de primer orden\n",
    "    else:\n",
    "      if i == 0:\n",
    "        netlist = netlist + POrc.format(i, ski[0], ski[1], ski[2], 'vin', 'vout0')\n",
    "        netlist = netlist + POopamp.format(i, 'vin', 'vout0')\n",
    "      elif i == len(sk)-1:\n",
    "        netlist = netlist + POrc.format(i, ski[0], ski[1], ski[2], 'vout{}'.format(i-1), 'vout')\n",
    "        netlist = netlist + POopamp.format(i, 'vout{}'.format(i-1), 'vout')\n",
    "      else:\n",
    "        netlist = netlist + POrc.format(i, ski[0], ski[1], ski[2], 'vout{}'.format(i-1), 'vout{}'.format(i))\n",
    "        netlist = netlist + POopamp.format(i, 'vout{}'.format(i-1), 'vout{}'.format(i))\n",
    "  # Si se prefiere el netlist para SPICEopus\n",
    "  if spiceopus:\n",
    "    netlist = netlist + CTRL\n",
    "  return netlist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a7d247",
   "metadata": {},
   "source": [
    "# Continuación de diseño"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5382f347",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sección 0: R1= 0.135k, R2= 0.135k, C3= 1.000u, C4= 0.854u\n",
      "sección 1: R1= 0.325k, R2= 0.325k, C3= 1.000u, C4= 0.146u\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "cannot load library 'C:\\Users\\ichbi\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\NgSpice\\Spice64_dll\\dll-vs\\ngspice.dll': error 0x7e.  Additionally, ctypes.util.find_library() did not manage to locate a library called 'C:\\\\Users\\\\ichbi\\\\anaconda3\\\\lib\\\\site-packages\\\\PySpice\\\\Spice\\\\NgSpice\\\\Spice64_dll\\\\dll-vs\\\\ngspice.dll'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10584/403240586.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcircuit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCircuit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'PySPICE'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcircuit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw_spice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msallen_key_pasabajas_spice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspiceopus\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcircuit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimulator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mac\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart_frequency\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mu_Hz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstop_frequency\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m@\u001b[0m\u001b[0mu_MHz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumber_of_points\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'dec'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\Netlist.py\u001b[0m in \u001b[0;36msimulator\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1291\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msimulator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1292\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mCircuitSimulator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;32m~\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\Simulation.py\u001b[0m in \u001b[0;36mfactory\u001b[1;34m(cls, circuit, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1166\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msub_cls\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1167\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0msub_cls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcircuit\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   1168\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1169\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown simulator type'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\NgSpice\\Simulation.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, circuit, **kwargs)\u001b[0m\n\u001b[0;32m     97\u001b[0m         \u001b[0mngspice_shared\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ngspice_shared'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mngspice_shared\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ngspice_shared\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNgSpiceShared\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew_instance\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[0m\u001b[0;32m    100\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ngspice_shared\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mngspice_shared\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\NgSpice\\Shared.py\u001b[0m in \u001b[0;36mnew_instance\u001b[1;34m(cls, ngspice_id, send_data, verbose)\u001b[0m\n\u001b[0;32m    447\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    448\u001b[0m             \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"New instance for id {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mngspice_id\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[1;32m--> 449\u001b[1;33m             \u001b[0minstance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mngspice_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mngspice_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msend_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msend_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\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    450\u001b[0m             \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_instances\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mngspice_id\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minstance\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    451\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0minstance\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\NgSpice\\Shared.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, ngspice_id, send_data, verbose)\u001b[0m\n\u001b[0;32m    477\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    478\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_library_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 479\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_library\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mverbose\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    480\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_ngspice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msend_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    481\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\NgSpice\\Shared.py\u001b[0m in \u001b[0;36m_load_library\u001b[1;34m(self, verbose)\u001b[0m\n\u001b[0;32m    537\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    538\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 539\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_ngspice_shared\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mffi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlibrary_path\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    540\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m         \u001b[1;31m# Note: cannot yet execute command\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\cffi\\api.py\u001b[0m in \u001b[0;36mdlopen\u001b[1;34m(self, name, flags)\u001b[0m\n\u001b[0;32m    148\u001b[0m                             \"or an already-opened 'void *' handle\")\n\u001b[0;32m    149\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m             \u001b[0mlib\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunction_cache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_make_ffi_library\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\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    151\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_function_caches\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunction_cache\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_libraries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlib\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\cffi\\api.py\u001b[0m in \u001b[0;36m_make_ffi_library\u001b[1;34m(ffi, libname, flags)\u001b[0m\n\u001b[0;32m    830\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_make_ffi_library\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mffi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlibname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\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    831\u001b[0m     \u001b[0mbackend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mffi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 832\u001b[1;33m     \u001b[0mbackendlib\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_load_backend_lib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlibname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\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    833\u001b[0m     \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    834\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0maccessor_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\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[1;32m~\\anaconda3\\lib\\site-packages\\cffi\\api.py\u001b[0m in \u001b[0;36m_load_backend_lib\u001b[1;34m(backend, name, flags)\u001b[0m\n\u001b[0;32m    825\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfirst_error\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    826\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"%s.  Additionally, %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mfirst_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 827\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\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    828\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_library\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    829\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: cannot load library 'C:\\Users\\ichbi\\anaconda3\\lib\\site-packages\\PySpice\\Spice\\NgSpice\\Spice64_dll\\dll-vs\\ngspice.dll': error 0x7e.  Additionally, ctypes.util.find_library() did not manage to locate a library called 'C:\\\\Users\\\\ichbi\\\\anaconda3\\\\lib\\\\site-packages\\\\PySpice\\\\Spice\\\\NgSpice\\\\Spice64_dll\\\\dll-vs\\\\ngspice.dll'"
     ]
    }
   ],
   "source": [
    "# (Paso 4) Calcula componentes de las secciones\n",
    "sk = secciones_sallen_key(s)\n",
    "# (Paso 5) Simulación realista (SPICE) vs modelo (Python/Octave)\n",
    "circuit = Circuit('PySPICE')\n",
    "circuit.raw_spice = sallen_key_pasabajas_spice(sk, spiceopus=True)\n",
    "sim = circuit.simulator().ac(start_frequency=10@u_Hz, stop_frequency=0.1@u_MHz, number_of_points=10, variation='dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "133c993b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.13459160096231393, 0.13459160096231393, 1.0, 0.8535533905932735],\n",
       " [0.32493286842472563, 0.32493286842472563, 1.0, 0.14644660940672646]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d8b4212",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.5'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PySpice\n",
    "PySpice.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "124a4462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".title PySPICE\r\n",
      "* Modelos\n",
      ".include tl081.mod\n",
      "* Fuente de señal\n",
      "Vin (vin 0) dc 0 ac 1.0v\n",
      "* Fuentes de alimentación\n",
      "Vpp (vp 0) dc 15\n",
      "Vnn (0 vn) dc 15\n",
      "*Seccion 0\n",
      "R10 (vin n00) 0.13459160096231393k\n",
      "R20 (n00 vni0) 0.13459160096231393k\n",
      "C30 (n00 vout0) 1.0u\n",
      "C40 (vni0 0) 0.8535533905932735u\n",
      "X0 (vni0 vout0 vp vn vout0) TL081\n",
      "*Seccion 1\n",
      "R11 (vout0 n01) 0.32493286842472563k\n",
      "R21 (n01 vni1) 0.32493286842472563k\n",
      "C31 (n01 vout) 1.0u\n",
      "C41 (vni1 0) 0.14644660940672646u\n",
      "X1 (vni1 vout vp vn vout) TL081\n",
      "* Simulacion\n",
      ".control\n",
      "destroy all\n",
      "ac dec 10 0.1Hz 10MegHz\n",
      "plot db(mag(v(vout)/v(vin))) vs frequency\n",
      "+ xlabel \"f (Hz)\"\n",
      "+ ylabel \"|H(f)|dB\"\n",
      ".endc\n",
      ".end\r\n",
      "\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Despliega el netlist SPICE\n",
    "print(circuit)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b1b05d6",
   "metadata": {},
   "source": [
    "# Mediciones de la implementación:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78eb9a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frec (Hz)</th>\n",
       "      <th>Vin (V)</th>\n",
       "      <th>Vout (V)</th>\n",
       "      <th>Vout (V)dB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>200.1</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.537443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>250.8</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-0.537443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>301.1</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.92</td>\n",
       "      <td>-0.724243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>350.9</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-0.915150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>401.1</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.88</td>\n",
       "      <td>-1.110347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>499.0</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-1.310031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>607.0</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.78</td>\n",
       "      <td>-2.158108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>710.7</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.74</td>\n",
       "      <td>-2.615366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>857.6</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.70</td>\n",
       "      <td>-3.098039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>952.9</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.66</td>\n",
       "      <td>-3.609121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1101.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-4.152166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-4.436975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1311.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.54</td>\n",
       "      <td>-5.352125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1520.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.54</td>\n",
       "      <td>-5.352125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-6.744843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2520.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.42</td>\n",
       "      <td>-7.535014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3006.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-7.958800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4065.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.36</td>\n",
       "      <td>-8.873950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5025.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-9.897000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6160.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.32</td>\n",
       "      <td>-9.897000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7576.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-10.457575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>9554.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-10.457575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1073.0</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.28</td>\n",
       "      <td>-11.056839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2078.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.26</td>\n",
       "      <td>-11.700533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frec (Hz)  Vin (V)  Vout (V)  Vout (V)dB\n",
       "0       200.1     1.14      0.94   -0.537443\n",
       "1       250.8     1.12      0.94   -0.537443\n",
       "2       301.1     1.16      0.92   -0.724243\n",
       "3       350.9     1.14      0.90   -0.915150\n",
       "4       401.1     1.14      0.88   -1.110347\n",
       "5       499.0     1.12      0.86   -1.310031\n",
       "6       607.0     1.14      0.78   -2.158108\n",
       "7       710.7     1.12      0.74   -2.615366\n",
       "8       857.6     1.08      0.70   -3.098039\n",
       "9       952.9     1.06      0.66   -3.609121\n",
       "10     1101.0     1.02      0.62   -4.152166\n",
       "11     1200.0     1.08      0.60   -4.436975\n",
       "12     1311.0     1.04      0.54   -5.352125\n",
       "13     1520.0     1.04      0.54   -5.352125\n",
       "14     2022.0     1.06      0.46   -6.744843\n",
       "15     2520.0     1.04      0.42   -7.535014\n",
       "16     3006.0     1.00      0.40   -7.958800\n",
       "17     4065.0     1.02      0.36   -8.873950\n",
       "18     5025.0     0.98      0.32   -9.897000\n",
       "19     6160.0     1.02      0.32   -9.897000\n",
       "20     7576.0     1.02      0.30  -10.457575\n",
       "21     9554.0     1.00      0.30  -10.457575\n",
       "22     1073.0     1.02      0.28  -11.056839\n",
       "23     2078.0     0.98      0.26  -11.700533"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraer ruta del archivo de mediciones\n",
    "path = 'med_PB.xlsx'\n",
    "#Datos completos de la tabla:\n",
    "datos = pd.read_excel(path)\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9db7b6c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frec (Hz)</th>\n",
       "      <th>Vin (V)</th>\n",
       "      <th>Vout(V)</th>\n",
       "      <th>Vout (V)dB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.440</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.960</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>155.340</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>204.230</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252.690</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>303.220</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>404.760</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>508.420</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>600.900</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>802.180</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1007.720</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.8552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1209.260</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1518.950</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7184.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1822.900</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.8500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2027.570</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-16.0110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2508.400</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.5050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3010.360</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3297.830</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-33.1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3558.260</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-36.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3839.240</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-39.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4018.380</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-40.3300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5124.800</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-48.4800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6057.500</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-54.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7051.900</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-59.6500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10156.670</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-72.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12005.210</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-77.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14190.183</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-82.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16025.110</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-86.1300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frec (Hz)  Vin (V)  Vout(V)  Vout (V)dB\n",
       "0      50.440        1      NaN      0.0225\n",
       "1      99.960        1      NaN      0.0225\n",
       "2     155.340        1      NaN      0.0225\n",
       "3     204.230        1      NaN      0.0225\n",
       "4     252.690        1      NaN      0.0225\n",
       "5     303.220        1      NaN      0.1440\n",
       "6     404.760        1      NaN      0.0225\n",
       "7     508.420        1      NaN      0.0225\n",
       "8     600.900        1      NaN      0.0225\n",
       "9     802.180        1      NaN      0.0225\n",
       "10   1007.720        1      NaN     -0.8552\n",
       "11   1209.260        1      NaN     -2.0210\n",
       "12   1518.950        1      NaN  -7184.0000\n",
       "13   1822.900        1      NaN    -12.8500\n",
       "14   2027.570        1      NaN    -16.0110\n",
       "15   2508.400        1      NaN    -23.5050\n",
       "16   3010.360        1      NaN    -30.0000\n",
       "17   3297.830        1      NaN    -33.1700\n",
       "18   3558.260        1      NaN    -36.0000\n",
       "19   3839.240        1      NaN    -39.1600\n",
       "20   4018.380        1      NaN    -40.3300\n",
       "21   5124.800        1      NaN    -48.4800\n",
       "22   6057.500        1      NaN    -54.3200\n",
       "23   7051.900        1      NaN    -59.6500\n",
       "24  10156.670        1      NaN    -72.1400\n",
       "25  12005.210        1      NaN    -77.9700\n",
       "26  14190.183        1      NaN    -82.8000\n",
       "27  16025.110        1      NaN    -86.1300"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraer ruta del archivo de mediciones Spice\n",
    "path = 'PB_spice.xlsx'\n",
    "#Datos completos de la tabla:\n",
    "spice = pd.read_excel(path)\n",
    "#spice\n",
    "spice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "35a8741a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pasa-Bajas, N=4')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiIifqdAFH3OuQ3AFVmsfzPd9w54KD9ziYj4i+joaAAWLFjgaQ4R8V8FougTEcmN8PBwryP4XPfu3b2OICJ+zlIukBVOERERbsmSTA/3EBEREfE7ZrbUOReR0+ML0pQtIiI5cvjwYQ4fPux1DJ/SXIQicjoq+kQk4E2fPp3p06d7HcOnmjdvTvPmzb2OISJ+TGP6REQCQI8ePbyOICJ+TkWfiEgA6Nq1q9cRRMTPqXtXRCQAFIZxiyKSO7rSJyISAG666SZA8/SJSPZU9IlIwIuIyPEMBwXGAw884HUEEfFzKvpEJODVr1/f6wg+16lTJ68jiIif05g+EQl4+/btY9++fV7H8KnC8B5FJHdU9IlIwJs5cyYzZ870OoZPtWvXjnbt2nkdQ0T8mLp3RUQCQO/evb2OICJ+TkWfiEgAaN++vdcRRMTPqXtXRCQA7Nq1i127dnkdQ0T8mK70iYgEgNtvvx3QPH0ikj0VfSIS8CIjI72O4HOPP/641xFExM+p6BORgFenTh2vI/hcmzZtvI4gIn5OY/pEJOAVhvFuO3fuZOfOnV7HEBE/pit9IhLwPv/8cwC6d+/ubRAf6ty5M6AxfSKSPRV9IiIBoF+/fl5HEBE/p6JPRCQAtGrVyusIIuLnNKZPRCQAbNmyhS1btngdQ0T8mK70iYgEgG7dugEa0yci2VPRJyIBr0mTJl5H8LlnnnnG6wgi4udU9IlIwKtZs6bXEXyuWbNmXkcQET+nMX0iEvAKwxx2GzZsYMOGDV7HEBE/pit9IhLwZs+eDQT2PH333nsvoDF9IpI9FX0iIgHghRde8DqCiPg5FX0iIgEgKirK6wgi4uc0pk9EJADExcURFxfndQwR8WO60iciEgDuu+8+QGP6RCR7BaLoM7OqwHtARSAZGOecG3nSPtHALGBj6qoZzrlB+RhTRPxU06ZNvY7gcy+99JLXEUTEzxWIog84DjzunFtmZqWApWY2xzm3+qT9vnPO3eJBPhHxY1WrVvU6gs81btzY6wgi4ucKxJg+59wO59yy1O8PAL8Blb1NJSIFRWF4Lu3KlStZuXKl1zFExI8ViKIvPTO7ELgS+CmLzZFmttzMvjSzetkc38vMlpjZkvj4eF9GFRE/MW/ePObNm+d1DJ96+OGHefjhh72OISJ+rKB07wJgZiWBj4E+zrn9J21eBlR3zh00s5uAT4CLT27DOTcOGAcQERHhfJtYRCR/DB8+3OsIIuLnCkzRZ2ZFSCn4pjjnZpy8PX0R6Jz7wszGmFl559yu/MwpIuKFhg0beh1BRPxcgejeNTMD3gZ+c879O5t9Kqbuh5ldTcp7251/KUVEvBMbG0tsbKzXMUTEjxWUK33XAt2AFWYWm7puAFANwDn3JnA78ICZHQeOAJ2dc+q+FZFCoU+fPoDm6ROR7BWIos859z1gp9nnDeCN/EkkIgVJq1atvI7gc6+99prXEUTEzxWIok9EJDcqVqzodQSfCw8P9zqCiPi5AjGmT0QkNzZs2MCGDRu8juFTixcvZvHixV7HEBE/pit9IhLwvv32WwBq1qzpcRLfeeKJJwCN6ROR7KnoExEJAG+8oSHNInJqKvpERAJA/fr1vY4gIn5OY/pERALAokWLWLRokdcxRMSP6UqfiEgAGDBgAKAxfSKSPRV9IhLwbrnlFq8j+Nxbb73ldQQR8XMq+kQk4JUvX97rCD5Xp04dryOIiJ/TmD4RCXhxcXHExcV5HcOnFi5cyMKFC72OISJ+TFf6RCTgxcTEAIF9Ney5554DNKZPRLKnok9EJABMnDjR6wgi4udU9ImIBIBAftqIiOQNjekTEQkAc+fOZe7cuV7HEBE/pit9IiIB4MUXXwSgWbNmHicREX+lok9EAt5tt93mdQSfe//9972OICJ+TkWfiAS8MmXKeB3B56pWrep1BBHxcxrTJyIBb+XKlaxcudLrGD41e/ZsZs+e7XUMEfFjutInIgFvyZIlANSvX9/jJL4zbNgwAFq1auVxEhHxV2dd9JnZpUAV59wcMyvmnDvig1wiInIWpk6d6nUEEfFzObnSNxaYZWaPAMfM7Dfn3MA8ziUiImehYsWKXkcQET+XkzF9K5xzrwJ/OefuAM7N40wiInKWPvvsMz777DOvY4iIH8vJlb5rzOwNoJqZXQZYHmcSEZGz9MorrwDQpk0bj5OIiL8666LPOdfQzKoAVwF3ABfmdSgRkbzUsWNHryP43EcffeR1BBHxczm5kaMLsNg5NwuYlfeRRETyVvHixb2O4HPly5f3OoKI+LmcdO/GA2PMLBTYBax1zvXL21giInknNjYWgPDwcE9z+NKMGTMAaN++vcdJRMRfnfWNHM65r4GfnHNNgLuBknmeSkQkD8XGxqYVfoFq1KhRjBo1yusYIuLHcjo5c2kzuwpYAZTIwzwiIpIDs2ZptI2InFpOH8P2GHAt8Cag5/6IiHisTJkyheIZwyKSc6e90mdm1bLZ9Enq68Q+e51z+/MsmYiInLFp06YB0KlTJ4+TiIi/OpPu3XcBR+b5+FzqV0v9fhLwXp4lExGRMzZ27FhARZ+IZM+cc6ffyw+YWStgJBAMTHDODTtpu6Vuvwk4DHR3zi07VZsRERHuxIPYRUhIgC+/hAULoEkTuPFGKFkSgoO9Tia5lJiYCECRIkU8TuI7hw8fBgrH9DQihZWZLXXOReT0+JzM01cCOOqcS8rpSXNwzmBgNNAc2AosNrNPnXOr0+3WGrg49dWIlGcEN8qvjP5i465DTPx+Y5bbLItnp2T3OBXLauds981mfRatZ7/vmbedXbas1gYnHqPEnl2U2Ls75bVnNyX27qLE3r/T1hdPXV/s4L7/Hfjaa2nfHitWgoTiJTlWoiTHipfkWIlSacsZv6auL16KhNR9T6xPKlo0yzeT5Ts5i/d3Nv/22e2fF//2QWaEBBlBQUawQXBwEMFmBAdBcFAQwUEn9kn3fbARZEZwUMr6sCJBhIUEERYSnPK1SLrvQ4LO6mfyZIFc7J2gYk9ETudMxvQFAZ2BLkBDIAEIM7O/gC+Acc65dT5NCVcD651zG1IzTQXaAemLvnbAey7l0uWPZlbWzC5wzu3IttU//oBJk+Dyy32XPD/9+ivF5n/P4V0lWXNeDU8iXPLXRi77cz0rzq/l0wyX/LWRK3esZWvp89hfrCTlD+1Nfe2h/OG9aculEw5nefz+0OLsKlGWnSXKsqt4RXaVv5Qaf2+l8eZfCcaRhLGw5lX8esHFlDp2mJLHDlPq2CFKHj1C6X1/UfLYJs5JSFlfIvHoafMmBgVzMLQ4B8OKcyCsOAdCU76mLJfgYOrygXTLFQ/Ec/GuLfxa0bf/lifk9X+7vG6vSHAQoSFGaEgwxYsEUzwsmJKhIRQPC6FEaDDFQ0PS1pUqGkLZEqGcU7wIZYuH8vfXX1B882YuuP9+iIzMdRZ/NHnyZAC6du3qcRIR8VvOuVO+gIXAs8DlQFC69ecCHYCPga6nayc3L+B2Urp0Tyx3A944aZ/PgevSLc8DIk7V7lXgnF4B8/q7aCm3tlxVt6jaZe7TS653E69q416+vpt7stUj7t4Oz7q23V5xje+f6Go/PsNVf+rzTK/bug53h0NCXaIFucMhoe62rsOz3O/kV80nZrnLHp3qrr3/bdfyntfd7XcNc91vf8490qavG9DiQTc0qrt7PbKjm9TgZvdRvRvc17UauUXVLnMrzr/IbSx7gdtVrLQ7FhTi+b9foL+SwR23IDeowyOuUY8X3bTFf7gff9/lnh4y3HXs1MmdMGjQINelS5e05WeffdZ17949bblfv36uZ8+eacuPP/64e/DBB9OWH330Uffoo4+mLT/44IPu8ccfT1vu2bOn69evX9py9+7d3bPPPpu23KVLFzdo0KC05U6dOrmhQ4emLbdv394NHz48bblNmzbutddec1FRUS4qKsq1atXKjR49Om1706ZN3bhx49KWo6Ki3DvvvOOccy4hIcFFRUW5999/3znn3KFDh1xUVJSbOnWqc865vXv3uqioKPfxxx8755yLj493UVFR7tNPP3XOObdjxw4XFRXlvvzyS+ecc3/88YeLiopyc+bMcc459/vvv7uoqCi3YMEC55xza9ascVFRUe6HH35wzjm3YsUKFxUV5X7++WfnnHO//PKLi4qKcr/88otzzrmff/7ZRUVFuRUrVjjnnPvhhx9cVFSUW7NmjXPOuQULFrioqCj3+++/O+ecmzNnjouKinJ//PGHc865L7/80kVFRbkdO3Y455z79NNPXVRUlIuPj3fOOffxxx+7qKgot3fvXuecc1OnTnVRUVHu0KFDzjnn3n//fRcVFeUSEhKcc8698847LioqKu3fcty4ca5p06Zpy6NHj3atWrVKW37ttddcmzZt0paHDx/u2rdvn7Y8dOhQ1ykAfvZO0M+e73/2gCXO5byeOpPu3VXOucFmVsw5l5yuWPw7teD72Mx83XeSVb+Oy8E+mFkvoBekPDw42Yygrl05fuutDBw4kBYtWhAdHc2xY8d44YUXaN26Nddffz2HDh1iyJAhtGnThsjISPbv38+wYcO49dZbufrqq9mzZw/Dhw+nw+23c1WDBsTHx/Pqq6/SsWNHwsPD2blzJ6NGjeKuu+6ifv36bN22jTGjR9OtWzcuvfRSNm/ezFtvvcU999zDxRdfzIYNG5gwYQI9evSgZs2arFu3jnfeeYf77ruP6tWr89tvv/H+++/z4EMPUaVyZXaOHk2Fr78mGEjC+KReNF/WuS63/+5npXXc99y6akHalTJfZUh/nuMWxLir2/Pq9V1IDM7dj+GyypfSpfMQrvljBT9Wu4xllS89o+OSgoLZX7Qk+4vmbp7ysOMJaVcU71n6KV2XfeHzf8sT8vq/XX79LJxtliCXzICP36DW5c15rUgltpc+D7iUkKo16DLhR+pVKsPvx8/hcEhpkpIdwUE571LOb3PmzAGgbdu2HicREX912hs5zGylc65+6uDBq/Ip18kZIoHnnXMtU5f7Azjnhqbb5y1ggXPug9TlOCDanaJ7N8LM/RgayldPPEF8rVoA1KtXj4YNG5KYmMiUKVMyHRMeHk54eDiHDx9m+vTpmduMiKB+/frs27ePmTNnZtoeGRlJnTp12LVrF59//nmm7U2aNKFmzZrs3LmT2bMzT4HYtGlTqlatypYtW5g3b17a+grr19Ny+HCCkpI4ZsF06TzkjIuWvNJg229Mmfo0RZKOkxgc4rMM+XUeL+X3e8zr8/nTf6P0WY4HhzD3ooY0X/8TAP8Jb82YazoSX/KcTMeFBiVTrVgiFxZPpG65YPr8owNBQcbcuXPZunVrhn1Lly6d9viz2bNns3Pnzgzby5UrR5s2bQD47LPP2L17d4btFStWpFWrVkDK49T27884+1WVKlVo1qwZANOnT0+7aeOEGjVqEBUVBcCUKVPSblw5oXbt2jRu3BiASZMmZXqvBfVz74RWrVpRsWJFNmzYwLfffptp+y233EL58uWJi4sjJiYm0/bbbruNMmXKsHLlSrK6ua9jx44UL1482ye7dOnShSJFirB48WJWrVqVaXv37t0BWLRoEWvXrs2wrUiRInTp0gWAhQsXsnHjxgzbixcvTseOHQH0s6efPe655x6f38jxlZnFABXN7F5gOSlX/04/kCnvLAYuNrMawDZSxhjeddI+nwIPp473awTsO1XBB3C4bFm+euihtIKvIIuvVYuvnniCq44msqx2I667oBoV169P236itK9frz4lS5bkzz//zPThAnD5FVdQtGhRduzYwR9/bMm0/corwylSJJRt27axbdu2jJdSz6vKV5GTqbz8Z2Ir1aBCqXK05GCGDFc3bAjAxk2biI+PzxAuODiYBg0aAPD7ht/ZvfvvDOcODS3C5ZdfAVzChCrBVF7+M3EX1aXchVVpxkGKFi1K/fr1AYiLi+PAgQMZji9evDiXXloXh+O3337L9OFVqmQpLq5dG4BVq1Zx7NhRnPvflZ4yZUpTK/Vn5ddfV2T6cCt7zjnUuPBCAJb/upykpOQM28uVK0e1ainTXv7yyy+ZLkOfd955VKpUieSkZH5dcYgRZZ7lkg2rWFOzHqWqV6NTxVJUrFiRxMTELH+xVKpUiQrnncfRo0dZs2ZNpu1VqlShXLlyHD58mHXrUobhpmUoV413a79B3Q2/sbb2ZZQIKcH1HMpwfPXqF1K6dGn279/Pps2b09Y7l9JOteoXUrRYcfbs3ce2shfyQo+B1NuwmhU16pFYrQbNLziXIqGh7Nt3gPi//8Y5SMZwDpIclD6nHMedceDQYQ4cPsZxZxx3cNwZSS7nV9yyuoJbaf9fPPLDVLot+y+dl3/Nu1fdwpuNOrC3WOm04xKSg1h/KIz1h8KYGw8fDJ3HjXXO4/zjSZRxEOxnFwFP/EItDDetiEjOnNGULWZWE1hAypx9lwH1SLmhY6VzLl8mhTKzm4DXSJmyZaJzboiZ3Q/gnHszdcqWN4BWpEzZco9z7pTzsWjKFpGCITnZkZCUzLHEZI4kJnHwWCL7jhznwNFEDhw9nvpK+X7/0UR2H0og/sAxdh04RvzBYxw4ejzLdqvv2U6f7/9Du9ULORRalLcb3sqEhrdxMOzUd8KWLV6Emy67gDuuqkJ41bK5urM4r0RHRwOwYMECT3OIiO/kdsqWM56nz8xqO+fWplsuCdR3zv2Y05N7TUWfSOFwNDGJnfuOsmXPYf74O+W15e/DbN59mPV/HaTajo089v0UWq9dxJ6ipXirUQfebXALR0KLnrbt+pVL0+2a6rQLr0zRIprTUUR8x+dFn5mZO81OZ7KPP1LRJyLHk5L5Pf4Qq3fsY/fCGK56+1WuXBlDfImyjL6mI/8Jb01CyOm7TCuUCuO+JjXp0qg6xUJV/IlI3suPom8BKXfpznLO/ZFufShwHXA38I1zblJOQ3hFRZ9I4bBo0SKAtAHlp5P03fcceao/JWO+Z/e55/Nqo05MrXcjx4NPPwy6XIlQ+jS7mDuvrkZIcFCucp+N8ePHA9CzZ898O6eI5K/cFn1n8onUCkgCPjCzHWa22sw2AuuAO4FXC2LBJwXX/Pnzee6557K8E0okK2vXrs101+SpBF9/HSV/+BbmzKFcreq8+OUolk/rwwv7f6F48Kn/UN59KIFnZ63ilte/Z9H6XbmNfsamTZvGtGnT8u18IlLwnMmVvtrAOuecS52PrzxwxDm3Nx/y+ZSu9BU8MTExREdHk5CQAEDdunUJDw/noosu4qKLLqJWrVpcdNFFnH/++X4xuF78w4k7W09MnXFWnIPPP4dnn4Xly0muW48f7+7Nv4rXZfnWfac9/I6rqvBsm7qULqq7akUkd/Lj2bszgKpmthZYAfwKrDCzX51z8Tk9sUhOLFiwgOPHU+7ENDOOHTvGokWLmDp1KsnJ/5sipUSJEtSsWTNDIXjiVa1aNUJCzvqx01JYmUGbNnDzzfDRRwQNHEjjp+5jVoMGbOzTj1eCavLflTvJ7u/nD5du5Yf1u3j59iu47uLy+ZtdRCSdM52yJYyUx7B9AUwF6pIybQvOuYq+DOhLutJX8MTExNC0aVMSEhIIDQ1l3rx5REZGkpCQwKZNm/j999/TXuvXr+f3339nw4YNHDt2LK2NkJAQLrzwwgyF4IniMD4+Pu1qYmSAPqO1MMrVlb6THT8OU6bA88/Dpk1w7bVse3wA/zp6AZ8u337KQ3vfWIs+zWoT5IMnfYwZMwaABx98MM/bFhH/kG9TtqSebKNzrka65XOcc3tyenKvqegrmGJiYliwYMEZF2bJycls3749rQg8uTDcty9zF11QUBB33XUXLVq0oF69elxyySUUL37qudvEf52Y6f/Ekw/yREICTJwIgwfD9u3QrBlrHnqCp7aVYPmWvdkedkOdCrzW6UrKFM/b7t7WrVsD8OWXX+ZpuyLiP/K76NvgnKuZ05P5GxV94pzj77//5vfff+ff//4306dP58T/E0FBQWldxmZGzZo1qVevXoZXnTp1KFasmJdvQbx25Ai8+SYMHQrx8bibbyGmwQ389P0KvqtcL8vHz11Yrjjv3ns11cuV8CCwiBRU+TFlyxvAL8AyYEb6K30FnYo+Se/kruOvvvqK8uXLs2rVqgyvtWvXpo0rDAoKyrIY3LNnj7qJC5uDB2HUqJTi7+BBnBkJIaHc2enFLAu/8iXDeO/eq6lbqXQWjYmIZJYfRV8vUsbznXj82l/AKmA1sNo5V2DnCFDRJyc7k67jhIQE1q1bl6kYXLduHUlJSRn2DQ4O5qGHHqJ9+/aEh4dTpkyZ/HgbcpKFCxcCpD0Y3qcGDoQXXwTncMBPXR+mc+VWWe5aqmgIE7s3pOGF5+b6tCNHjgTg0UcfzXVbIuKf8rV7N/WEVfhfEVjfOdctpyf3moo+yUvHjh1j7dq1DBkyJEM3cXq1atXiqquuokGDBmmvc8/N/S98ObU8vZHjdGJioGlTOHYMkpOhZk2+H/8hD3wbn+UzgIsWCWLyPxsRkcvCr23btgB8+umnuWpHRPxXflzpq3aGbe11zu3PaRAvqOgTXzi5m/jDDz8kKCiIZcuWsWzZMpYuXcrmzZvT9r/wwgtp0KBBhmLw999/P6ubVeTU8rXog5TCb8ECKF485cpfiRJseXcanZYksH3f0Uy7lyoawtRe11Cvkq4Ei0j28qPo+yabTScOtNTvJznn3stpEC+o6BNfOV038e7du/nll19YunRpWjG4fv36tO1mhnOOkJAQ7rvvPqKioqhevToXXnghFSpU0MTTZynfi770Vq5MmeNv1y52T3iXjtvL83v8oUy7lS8ZyvT7IqlZoWT+ZxSRAiHfu3cDiYo+8Sd79+4lNjaW4cOH88UXX2S7X7FixahevXpaEXjy14oVKxIUlH/PfC0IPC36AHbuTJngedkyDg0bTsewhqzanrljpHLZYnz68LWUKxl21qcYMWIEAH379s11XBHxT/nxRA4RyQdly5YlOjqasLAwvvnmm7Tu4U8++YTzzz+fzZs3s2nTpgxfly5dyq5dGZ/vGhoaSrVq1bItCitVqpThiSRnO+9hQeT5HIsVK6Z093btSoknH+ej+x+kbc32rNudsat3294jPDhlGZN7NKJI8NkV7noWtYicjq706Uqf+KGzKcQOHjzIH3/8kakgPPF1586dGfYPDg6matWqVK9eneLFizNnzhySk5MJCwtLe8KJ+EhSEjz5JPz73xxtdRO3XPMg649k3q3bNdUZfGv9/M8nIn5N3bu5oKJPCoOjR49mWxSuWLGC/ftTuhnNjGeffZYXXnjB48SFwJgx8MgjHKt3Gbe0eIp1IZnn6nvptsu4q9GZ3kcnIoWBir5cUNEnhV1MTAw33ngjx44dwznHOeecw4gRI+jevXtAjQucO3cuAM2aNfM4STpffAGdOnGsZGluu3kAq8tfmGFzaEgQnz9yHbXPL3VGzQ0bNgyAfv365XVSEfETuS36AudTXUTOWmRkJPPnz2fIkCFMnDiRSy+9lH/+8580atQooMaIbd26la1bt3odI6ObboLvviMs2Jj1n6eI2rA0w+aE48n0/uAXjiYmZdNARrGxscTGxvogqIgEChV9IoVcZGQk/fv355577uH7779nypQpbN++ncaNG9OtWze2b9/udcTAFR4OP/5ISJ2LmfjxILr8kvGu7TU7D/Dy7Lgzamrq1KlMnTrVByFFJFCo6BORNGbGXXfdRVxcHAMGDODDDz+kdu3aDBs2jGPHjnkdLzBVqYJ9+y20aMGQr8cwYP7bmEtO2zzxh40sXBvvYUARCRQq+kQkk5IlSzJkyBBWr15N8+bN6d+/P/Xq1ePTTz/N8vFykkulShH82afs7t6TXotnMuaTYRRN/N90Lv0//pVDxzI/wi29wYMHM3jwYF8nFZECTEWfiGSrZs2azJw5kzlz5hAWFka7du1o1aoVv/32m9fRzkrp0qUpXTrzHbJ+JSSEchPfIubhZ2i5NoapHwyg/KE9AGzfd5RXvl57ysPj4uKIizuzrmARKZx0967u3hU5I4mJiYwZM4bnnnuOQ4cO8fDDD/Pcc89RtmxZr6MFlORkx2u9h3P/uIH8XawM3e94nvXlqxFkMOuh67isip7PK1JY6e5dEckXRYoU4dFHH2XdunXce++9jBw5ktq1azNhwgSSks7sDlM5vaAgo8MLD9Gt678ITUpkxuQnaLwplmQH/Wb8yvGk5NM3IiKSBRV9InJWKlSowFtvvcXSpUupU6cOPXv25Oqrr+aHH37wOlq2Zs+ezezZs72OccaqlytB03/czG3dXmFHqXK8++Fz3PHr16zavp/3YjZneczAgQMZOHBgPicVkYJERZ+I5MiVV17Jt99+ywcffMBff/3FddddR5cuXdi2bZvX0TLZuXNnpsfR+bue19ekVJ2LuL3rcGKqXc7wL0cx4r//5uBzgzjwzbeZ9t+yZQtbtmzxIKmIFBQa06cxfSK5dujQIf71r3/x8ssvExwczIABA3j88ccpWrSo19EAmDRpEgDdu3f3NMfZ+uWPPbQfu4jg48cZ88lQWqz/iWQgKbQoRRbMBz0nWaRQ0Zg+EfFciRIlGDRoEL/99hutWrXimWeeoW7dugwbNoyXXnopoJ7ukZ+urHYOHRpU4XhwCLGV6uBI+dC2xAT2/Pdrr+OJSAGjok9E8kyNGjX4+OOP0551279/f55++mmio6P5/vvvPU5XMPVtUYdiRYL5sdplOIxkIDE4hPHB1TLs179/f/r37+9NSBEpEFT0iUiea9q0Kf/85z8xMwASEhK4+eabee6559i0aVO+5ylXrhzlypXL9/PmhYplitKzSU2WVb6UdeWrsrnsBXTpPIQxx85j6ea/0/bbvXs3u3fv9jCpiPg7FX0i4hM33ngjRYsWJTg4mNDQUC699FIGDx5MzZo1adGiBdOnT8+3R7u1adOGNm3a5Mu5fOG+JjWpUCqMQ6HF2FK2IssqXwrAa3PXpe0zbtw4xo0b51VEESkA/L7oM7PhZrbGzH41s5lmVjab/TaZ2QozizUz3Z0h4rHIyEjmzZvH4MGDWbBgAT/++CObNm3iueeeIy4ujk6dOlG5cmUee+wxVq1a5XVcv1YiLIS+LWpnWv/dul0s2fR3FkeIiGTm93fvmlkLYL5z7riZ/QvAOfdUFvttAiKcc7vOtG3dvSvijaSkJObOncvbb7/NJ598QmJiIpGRkfTo0YOOHTtSsmTJPD3fZ599BlCgr/YdT0omruZl7A4K4x+d/veM3esvLs/7/2xE3759ARgxYoRXEUXExwL+7l3n3NfOuRNPGv8RqOJlHhHJveDgYFq2bMn06dPZtm0br7zyCnv37uWf//wnF1xwAb169eLnn38mr/4oDYTxbiHBQVQsk3kKnBNX+44cOcKRI0c8SCYiBYXfF30nuRf4MpttDvjazJaaWa98zCQiuVChQoW0Lt4ffviBO+64gylTptCoUSOuuOIKRo4cWeALtrxybvFQioUGZ1o/ct46Ro8ezejRoz1IJSIFhV8UfWY218xWZvFql26fp4HjwJRsmrnWOdcAaA08ZGZNsjlXLzNbYmZL4uPj8/y9iEjOmBmNGzdm4sSJ7Nixg7feeouiRYvSp08fKlWqxJ133smoUaMYMmRIoZ33zwyqnVs80/rv1u3ilz/2eJBIRAoSvx/TB2BmdwP3A02dc4fPYP/ngYPOuVMObtGYPhH/t3z5ct5++20mTZrEgQMHAAgJCeHNN9/k3nvvTZsW5lQK6hM5MomMJLlUKZq2GMDGXYcybKqUtJOrE1fw2muveZNNRHwu4Mf0mVkr4CmgbXYFn5mVMLNSJ74HWgAr8y+liPjKFVdcwahRo3jiiScICkr5yDp+/Dg9evSgVq1aPP3006xYseKUbVSsWJGKFSvmR1yfCzLjweiLMq3fHnQ+h6yYB4lEpKDw+6IPeAMoBcxJnY7lTQAzq2RmX6Tucz7wvZktB34G/uucm+1NXBHxhWbNmhEWFkZwcDDFihVjwIABXHTRRQwbNozLL7+c+vXr8+KLL7J+/fpMx7Zq1YpWrVp5kNo32oVX5vzSYRlXmlH5hq7eBBKRAqFAdO/6irp3RQqWmJgYFixYQHR0NJGRkQD89ddffPTRR3zwwQdpj3pr2LAhnTt3pmPHjlSpEkA3/EdGQunS8NVXjF3wO/+avSbD5uKhwSzqdyNli4d6FFBEfCm33bsq+lT0iQSMLVu2MG3aND744AOWLVuGmXH99ddTp04dIiMjueeee7yOmDvpir59RxJpPHQehxKSMuzyRMs6PHRDLY8CiogvBfyYPhGRM1W1alX69u3L0qVLiYuL44UXXiA+Pp7x48fTo0cPWrduzbvvvsu+ffu8jpprZYoVoWPDqpnWT1q0iYTjyR4kEhF/V6iLvri4uLS7+hITE4mOjmby5MkAHD58mOjoaKZNmwbAvn37iI6OZsaMGQDs2rWL6OjotJn+d+7cSXR0NLNnpwwl3LJlC9HR0cydOxeADRs2EB0dzcKFC9POHR0dzaJFiwBYuXIl0dHRLF68GIDY2Fiio6OJjY0FYPHixURHR7NyZcr9KYsWLSI6Opq4uDgAFi5cSHR0NBs2bABg7ty5REdHs2XLFgBmz55NdHQ0O3fuBFKeUBAdHc2uXSkPMJkxYwbR0dFpvwynTZtGdHQ0hw+n3DszefJkoqOjSUxMBFLuhoyOjk77txw/fjzNmjVLWx4zZgytW7dOWx45ciRt27ZNWx4xYgQdOnRIWx42bBidO3dOWx48eDBdu/5vfNLAgQMzXKXp378/vXr9bzrGvn378tBDD6Ut9+nThz59+qQtP/TQQ2lPLADo1asX/fv3T1u+5557GDhwYNpy165dGTz4f0896Ny5M8OGDUtb7tChQ4YnH7Rt25aRI0emLbdu3ZoxY8akLTdr1ozx48enLUdHR+tnz8c/e7Vr1+bZZ5+lV69e/OMf/6BVq1asWbOG7t27c+6559K+fXs+/PBDBg0aRLNmzRg6dCgxMTF+/7MXGxub9rN377U1wGUs8OIPHKPvq++lLetnT597J+hzL3B+9nIqJNctiIj4MTOjQoUK3HDDDdx999307t2bL7/8kpiYGGbOnElISAhJSUl88803hIWF0aVLF68jn7Gq5xanzP6N7CuT8W7eXw6W8iiRiPgzjenTmD6RgJfVPH1JSUl8++23PPXUU2l/7QP069ePoUOH5nPCM9S4MZQsCV9/nbYq5vfd3Dn+x0y7zvm/Jlx8voo/kUCiMX0iIqdRpUqVTHfxBgcHc8MNNzBy5EiKFSuWNsnz66+/zoQJE/Lsub++dk3Nc6l1XslM6yf/uNmDNCLiz1T0iUjAa9asWYaxV+lFRkYyb948hgwZwvTp04mIiKBnz560aNGCjRs35nPSs2dmdG1ULdP6Gcu2cejYcQ8SiYi/UtEnIoVeZGQk/fv354477mD+/PmMHTuWn376ifr16zNq1CiSk/37btj2V1WhWJHgDOsOHDvOrNjtHiUSEX+kok9EAt706dOZPn36Ge0bFBTE/fffz6pVq4iKiuLRRx/l+uuvZ82aNac/2COlixah7L51mdZP+UldvCLyPyr6RCTgHT58OG0qhDNVtWpV/vvf//Lee++xZs0awsPDGTp0aNoUCv6mQelDmdat2r6f1dv3e5BGRPyRij4RkWyYGd26dWP16tW0adOGAQMG0KhRo7S5vPzJ6Bf7cUWVMpnWf7h0iwdpRMQfqegTETmN888/nw8//JCPP/6Y7du307BhQ7p3787gwYOJiYnxOl6a2yMyP6FjVux2PaFDRAAVfSIiZ6x9+/asXr2a5s2b8+677zJw4ECio6P5/vvvvY5G165d+e/o5wkNyfix/vehBOav+dOjVCLiT1T0iUjAq1GjBjVq1MiTts4991yuv/56goJSPj4TEhJo3749U6dOJSkpKU/OkRN16tShXu2atKxXMdO2D5ds9SCRiPgbFX0iEvCioqKIiorKs/aio6MJCwsjODiY0NBQSpUqxZ133snll1/Ohx9+6MkUL88++yzPPvssd1xVJdO2BWvj+evA0XzPJCL+RUWfiMhZOjGh8+DBg1mwYAHr1q1Le1B8x44dCQ8PZ8aMGZ4Uf9fWKs8FZYpmWJeU7Ji5bFu+ZxER/6KiT0QC3pQpU5gyZUqetnliQufIyEiCgoLo2LEjv/76K//5z39ISEigQ4cONGjQgFmzZuXLI906d+5M586dCQ4y2jeonGn7J5qoWaTQU9EnIgEvMTExX+bXCw4O5s4772TVqlW8//77HDp0iFtvvZWIiAg+//xznxZ/4eHhhIeHA3D7VZnv4v1tx37W/nnAZ+cXEf+nok9EJI8FBwfTtWtXfvvtNyZNmsTevXtp06YNjRo14ssvv/RJ8devXz/69esHQI3yJbiiatlM+3yqq30ihZqKPhERHwkJCeHuu+9mzZo1vP3228THx3PTTTcRGRnJ119/nbPi7wyPaXtFpUzrPl2+PV+6mkXEP6noExHxsSJFinDvvfcSFxfHuHHj2LFjBy1btuS6667j9ddf56WXXjqzSZ7Nst3UoUMHOnTokLbc5vILMu3+x9+Hid2yN4fvQkQKuhCvA4iI+Frt2rW9jgBAaGgoPXv25B//+AcTJ05k4MCB9O7dO23b3Llzuf7663PUdmRkZIbl80oXpfFF5fhh/e4M62fFbufKaufk7A2ISIGmK30iEvAaN25M48aNvY6RJiwsjAceeIDevXtjqZfjEhISaNOmDcOHD2fv3r1n3Wbfvn3p27dvhnXtrsh8F+/nv+4gKVldvCKFkYo+ERGPNGvWjKJFi6ZN8nzRRRfx5JNPUqVKFR599FE2bNiQq/Zb1q9IaHDGj/ldB4+x6PdduWpXRAomFX0iEvAmTZrEpEmTvI6RycmTPC9dupRly5bRvn17xowZw8UXX0yHDh344YcfWLRoEUO3biUmm6uAbdu2pW3bthnWlSlWhOg6FTLt+8WKHb54OyLi5zSmT0TEQ5GRkRnG41155ZW89957DBs2jNGjRzN27FhmzJiR8qzf5GTCtm5lXkxMpjF8TZs2zbL9tuGV+Hr1nxnWfbXqTwa3SyYkWH/3ixQm+j9eRMQPVapUiSFDhrBlyxbatm1LcnIyyUBCcjILFizItP+jjz7Ko48+mmn9DXXOo2iRjB/1fx9K4OeNf/souYj4KxV9IiJ+rESJEvTr14+iRVOep5tM5jt1T3l8WAjRtc/LtP6LleriFSlsVPSJiPi5yMhI5s+fzz8qVMABn3zySaZ9WrduTevWrbM8vvVlFTOt+2rVn7qLV6SQ0Zg+EQl49erV8zpCrkVGRhJ58cWUKVqUkSNH0rJlywxFXps2bbI99sZLziM0OIiEpOS0dfEHjrF08x6urnGuT3OLiP+wwvxInoiICLdkyRKvY4iInJlrr+VoWBgN4+P566+/+PXXXzn//PPP6NAe7y5m7m9/ZVjXvfGFPN+24BfEIoWFmS11zkXk9Hh174pIwEtMTCQxMdHrGHmiaHAwH3zwAfv376d79+4kJyef/iCgdf0LMq2bvXInyeriFSk0/L7oM7PnzWybmcWmvm7KZr9WZhZnZuvNrF9+5xQR/zVlyhSmTJnidYw8U79+fV555RVmz57N66+/DqRM9NysWbNsj2l26fkUCc74MN6d+4/yi57FK1JoFJQxfa8650Zkt9HMgoHRQHNgK7DYzD51zq3Or4AiIvnpgQceYPbs2Tz55JNER0fTqVOnU+5fpngRrq1VngVx8RnWz1n9J1dV17N4RQoDv7/Sd4auBtY75zY45xKAqUA7jzOJiPiMmTFx4kTKlSvHXXfdRZcuXejZs+cpj2lZL/NdvHNW7/RVRBHxMwWl6HvYzH41s4lmltWfpJWBLemWt6auy8TMepnZEjNbEh8fn9UuIiL+K93Nd+XLl+fdd99l9erV9O3b97SHNr30PCxjDy+/xx9iQ/zBvE4pIn7IL4o+M5trZiuzeLUDxgIXAeHADuCVrJrIYl2Wo5Odc+OccxHOuYgKFTI/k1JExG+dXLEBzZs3p2/fvowdO5b69euf8vDzShUlvGrZTOvnnPSYNhEJTH5R9Dnnmjnn6mfxmuWc+9M5l+ScSwbGk9KVe7KtQNV0y1WA7fmRXUT8X3h4OOHh4V7H8JkhQ4ZQvXp1Nm3axLZt2065b/O6mad4UdEnUjj4RdF3KmaWfp6B24CVWey2GLjYzGqYWSjQGfg0P/KJiP8L9KIvNDSUr776Cuccd9999ymncWmRRdG39I897Dp4zJcRRcQP+H3RB7xsZivM7FfgBuD/AMyskpl9AeCcOw48DHwF/AZMd86t8iqwiPiXw4cPc/jwYa9j+FTNmjV59dVXmTdvHr1792bo0KHExMRk2u+iCiW5sFzxDOucg/knTdwMEBMTk207IlLw+P2ULc65btms3w7clG75C+CL/MolIgXH9OnTAejevbu3QXyoefPmOOe44YYbGD16NGZGcHAwd9xxB5UqVcqw7/ENu9mzdV+GdYOXF+fndHf3bt++nQ8//JDk5GTCwsKYN28ekZGR+fJeRMQ3/L7oExGR0+vRowcAa9eu5ZtvvsE5x/Hjx/noo48IDQ3NsG+yg6OJSRnWrQI2fhectpyQkMDx48cBOHr0qIo+kQCgok9EJAB07doVSOmSHTFiBAkJCYSGhmZZrCUlOxoOmcvfhxIyrJ/YPYIbLzk/rZ0bb7yRY8eO4ZzjnXfeoWXLljRs2DB/3pCI5LmCMKZPRERO48S4xcjISObNm8fgwYOzvToXHGRE18k8ZdX8Nf8b1xcZGcn8+fMZMmQIw4cP5+jRo1xzzTU89dRTHDlyxKfvRUR8Q1f6REQCwE03pQxxXrBgAZGRkaftir3xkvOYsSzj9C7frInHOYelzgeYvp0ePXrwxBNP8PLLLzNr1izefvttrr32Wh+8ExHxFV3pE5GAFxERQUREhNcxfOqBBx7ggQceOOP9r7+4AsFBGSd73rb3COv+yvrpHGXLlmX8+PF8/fXXHD16lOuvv54+ffpw6NChXOUWkfyjok9EAl79+vVP+7SKgq5Tp0506tTpjPcvU6wIEdUzP9UyfRdvVpo3b87KlSt58MEHGTlyJJdddhnz588/67wikv9U9IlIwNu3bx/79u07/Y4FWE7e442XnJdp3emKPoCSJUvyxhtvsHDhQoKDg2natCn3338/+/fvP6vzi0j+UtEnIgFv5syZzJw50+sYPtWuXTvatWt3VsdkVfQt3byHfYcTz+j4Jk2asHz5ch5//HHGjx9P/fr1mT179lllEJH8o6JPRCQA9O7dm969e5/VMbXOK0mVc4plWJeU7PhuffwZt1G8eHFGjBjBDz/8QMmSJWndujX33HMPe/bsOassIuJ7KvpERAJA+/btad++/VkdY2bcUCdnXbwnu+aaa/jll18YMGAA77//PnXr1mXWrFln3Y6I+I6KPhGRgsS5LFfv2rWLXbt2nXVzWXXxLoyLJzk56/OcSlhYGEOGDOHnn3/mvPPO49Zbb+XOO+8kPv7MrxyKiO+o6BMRCQC33347t99++1kfF3lROcJCMv4q2H0ogd925vymjAYNGrB48WIGDRrExx9/TN26dZk2bRoum4JVRPKHij4RCXhnMllxgWCW7abHH3+cxx9//KybLFokmEY1y2Va/926s79qmF5oaCjPPvssy5Yto0aNGnTu3JkOHTqwc+fOXLUrIjmnok9EAl6dOnWoU6eO1zF8qk2bNrRp0yZHxza5uHymdd+ty5su2fr167No0SL+9a9/8cUXX1C3bl3ee+89XfUT8YCKPhEJeDkd71aQ7Ny5M8dX0a6/OPNzeBdv3MORhKTcxgIgJCSEJ598kuXLl3PppZdy9913c/PNN7Nly5Y8aV9EzoyKPhEJeJ9//jmff/651zF8qnPnznTu3DlHx9Y+vyTnlQrLsC4hKZmfNu7Oi2hp6tSpw7fffsvIkSNZuHAh9erVY/z48brqJ5JPVPSJiASAfv360a9fvxwda2ZZXu37dm3eXx0NDg6md+/erFixgoiICHr16kXz5s3ZuHFjnp9LRDJS0SciEgBatWpFq1atcnx8k9q+G9eXlZo1azJ37lzefPNNfv75Z+rXr8/rr79OcnKyz84pUtip6BMRCQBbtmzJ1Ri5a2tlLvrW/XWQHfuO5CbWKQUFBXHfffexcuVKmjRpQu/evYmKimLt2rU+O6dIYaaiT0QkAHTr1o1u3brl+PjyJcOoV6l0pvW5nbrlTFSrVo0vvviCSZMmsXLlSq644gpGjBhBUlLe3EgiIilU9IlIwGvSpAlNmjTxOoZPPfPMMzzzzDO5aiOrcX35UfRByrjCu+++m9WrV9OyZUueeOIJGjduzKpVq/Ll/CKFgYo+EQl4NWvWpGbNml7H8KlmzZrRrFmzXLWR1Xx936/L2SPZcuqCCy5g5syZTJ06lQ0bNnDllVfSq1cvXnzxRWJiYvIth0ggUtEnIgEvN3PYFRQbNmxgw4YNuWrjqgvPoWiRjL8W9hxOZNX2nD+SLSfMjE6dOrF69WqaNGnC+PHjefbZZ4mOjuaHH37I1ywigURFn4gEvNmzZzN79myvY/jUvffey7333purNsJCgrkmi0eyfevDu3hPpUKFCjRt2pSgoJRfVQkJCbRv354pU6Zw/PhxTzKJFGQq+kREAsALL7zACy+8kOt2sh7X503RBxAdHU1YWBjBwcGEhoZSsmRJunbtyiWXXMKECRNISEjwLJtIQaOiT0QkAERFRREVFZXrdrIa17d08x4OHfPmylpkZCTz5s1j8ODBLFiwgHXr1vHJJ59wzjnn0LNnT2rVqsUbb7zBkSO+m1pGJFCo6BMRCQBxcXHExcXlup1a55WkYumiGdYlJrk8fyTb2YiMjKR///5ERkYSFBREu3bt+Pnnn5k9ezbVq1fnkUceoUaNGgwfPpwDBw54llPE36noExEpSLJ5Tu19993Hfffdl+vmUx7Jlvlqny8eyZYbZkbLli357rvvWLhwIZdffjlPPvkkF154IYMGDWLPnj1eRxTxOyr6RCTgNW3alKZNm3odI/fMst300ksv8dJLL+XJaa6v7V/j+k6nSZMmfP311/z0009cd911PPfcc1SvXp0BAwYQH++/uUXym4o+EQl4VatWpWrVql7H8KnGjRvTuHHjPGnrulrlM9WXv8cfYtte/x43d/XVVzNr1ixiY2Np3bo1w4YNo3r16vzf//0f27Zt8zqeiOdU9IlIwMvtc2kLgpUrV7Jy5co8aevcEqHUr1Qm0/rv1haMq2ZXXHEF06ZNY/Xq1XTs2JHXX3+dmjVrcv/997Nx40av44l4RkWfiAS8efPmMW/ePK9j+NTDDz/Mww8/nGftZTWub9Hv3t3MkROXXHIJkyZNYt26ddx777288847XHzxxdx9992sWbPG63gi+c7viz4zm2ZmsamvTWYWm81+m8xsRep+S/I5poiIp4YPH87w4cPzrL1ra2Uu+mI27MZlcyOJP6tRowZjx45lw4YNPPLII3z44YfUrVuXjh07snz5cq/jieQbvy/6nHOdnHPhzrlw4GNgxil2vyF134j8SSci4h8aNmxIw4YN86y9q6qfQ2hwxl8R8QeO8Xv8wTw7R36rXLkyr776Kps2baJfv37Mnj2b8PBw2rZty08//eR1PBGf8/ui7wQzM6Aj8IHXWURE/E1sbCyxsbF51l7RIsFcWa1spvUxBayLNyvnnXceL730Eps3b2bQoEH88MMPXHPNNTRv3pwxY8bw0ksvERMT43VMkTxXYIo+4HrgT+fcumy2O+BrM1tqZr2ya8TMepnZEjNbolv5RSRQ9OnThz59+uRpm5EXZX4Ob8yGgl/0nXDOOefw7LPPsmnTJl5++WWWLl3KQw89xNNPP02TJk34z3/+43VEkTxl/jA+w8zmAhWz2PS0c25W6j5jgfXOuVeyaaOSc267mZ0HzAEecc59e6rzRkREuCVLNPxPJNDt3LkTgIoVs/qYKUCaNIGQEJg/P9OmE1f5wsPD8+x0P2/8m45vZbzidW6JUJY83YygoOznDCyoBg0axPPPP59h3OIll1xChw4d6NChA+Hh4dgp5koU8TUzW5qbIWx+caXPOdfMOVc/i9eJgi8EaA9MO0Ub21O//gXMBK7Oj+wi4v8qVqxY8Au+0wgPD8/Tgg/giqplKFok46+Jvw8lEPdnYD7qrHnz5hQtWpTg4GCKFi3KY489xgUXXMDQoUNp0KABF110EX379iUmJobk5GSv44qcNb8o+s5AM2CNc25rVhvNrISZlTrxPdACyJsJq0SkwNuwYQMbNmzwOoZPLV68mMWLF+dpm2EhwURUPzfT+kAY15eVyMhI5s2bx+DBg5k/fz6vvPIK8+fPZ+fOnUyYMIFLLrmEUaNG0bhxY6pVq8YjjzzCggULOH78uNfRRc6IX3Tvno6ZTQJ+dM69mW5dJWCCc+4mM6tJytU9gBDgP865IadrV927IoXDpEmTAOjevbunOXLtFN270dHRACxYsCBPTzn6m/UM/youw7rmdc9n/D8K5yQJe/fu5fPPP+fjjz9m9uzZHD16lAoVKtCuXTs6dOjAjTfeSGhoqNcxJUDltns3JC/D+IpzrnsW67YDN6V+vwG4Ip9jiYj4jTfeeMMn7WZ1M8ePG3aTlOwIDsBxfadTtmxZunbtSteuXTl06BBffvklH3/8MVOnTmXChAmUKVOGtm3b0r59e1q2bEmxYsW8jiySpkAUfSIicmr169f3SbuXVS5DidBgDiUkpa07cPQ4q7fv57IqmR/VVpiUKFGC22+/ndtvv52jR48yZ84cZsyYwaxZs3j//fcpUaIEN910E5dddhlJSUm0bNmSyMhIr2NLIVZQxvSJiMgpLFq0iEWLFuV5u0WCg2hYI4txfRt25fm5CrKiRYvSpk0b3nnnHf7880++/vprunbtyty5cxk4cCAvvPAC119/PW+//bbXUaUQU9EnIlKQZDMOe8CAAQwYMMAnp2ycRRdvQXsOb34qUqQIzZs358033+Txxx8nKCjlV21SUhI9evQgIiKCCRMmcOjQIY+TSmGjok9EAt4tt9zCLbfc4nWM3DvFHHFvvfUWb731lk9OG1kz83N4F2/8m8QkTVtyOjfeeCNhYWEEBwdTrFgxHnvsMY4ePUrPnj2pVKkSjzzyCKtWrfI6phQSKvpEJOCVL1+e8uUzFy6BpE6dOtSpU8cnbdetVJrSRTMOAT+UkMSKbft8cr5Akn4amHnz5vHKK6+wYsUKvvvuO9q0acO4ceOoX78+TZo04YMPPuDYsWNeR5YApqJPRAJeXFwccXFxp9+xAFu4cCELFy70SdvBQUajmlk8kk1dvGckMjKS/v37p93EYWZcd911TJ48ma1bt/Lyyy+zbds27rrrLqpWrUq/fv0Cfl5J8YaKPhEJeDExMcTExJx+xwLsueee47nnnvNZ+5Eq+nyiQoUKPPHEE6xbt46vvvqKa6+9lhEjRlCrVi1at27NrFmzNPmz5BlN2SIiEgAmTpzo0/azmq9vyea/OXY8ibCQYJ+euzAICgqiRYsWtGjRgq1btzJhwgTGjx/PrbfeSpUqVejZsyc9evSgUqVKXkeVAkxX+kREAkDNmjWpWbOmz9qvc34pzi2R8UkTRxOTWb5F4/ryWpUqVXj++efZvHkzM2bMoG7dujz33HNUq1aNDh06MGfOHD37V3JERZ+ISACYO3cuc+fO9Vn7QUHGNTUzz9e36HfN1+crISEh3HbbbXz11VesW7eOxx57jIULF9KiRQvq1KnDK6+8wu7d6mKXM6eiT0QkALz44ou8+OKLPj2HxvV5p1atWrz88sts3bqVyZMnU7FiRfr27UvlypXp1q0bixYtwmUzh6PICVaYf0giIiLckiVLvI4hIj62b19KF2SZMgX8sWFRURAUBN98k2nTli1bAKhatarPTr/+r4M0+3fGO4RDg4P49fkWFC2icX35bcWKFbz11lu89957HDhwgMsuu4wHHniALl26ULp0aa/jiQ+Y2VLnXEROj9eVPhEJeGXKlCn4Bd9pVK1a1acFH8BFFUpQoVRYhnUJScn88sden55XsnbZZZfxxhtvsH37dsaNG0dISAgPPvgglStX5v777yc2NtbriOJnVPSJSMBbuXIlK1eu9DqGT82ePZvZs2f79BxmxjVZdPH+vPFvn55XTq1kyZL07NmTpUuX8tNPP3H77bfz7rvvcuWVVxIZGcm7777LkSNHvI4pfkBFn4gEvCVLlhDoQzmGDRvGsGHDfH6eq2tkvpnj500a1+cPzIyrr76ad955h+3bt/Pqq6+yZ88eunfvTuXKlXnssccCfpJyOTXN0yciEgCmTp2aL+dplEXRt3TzHhKOJxMaousI/uKcc86hT58+PProoyxYsIA333yT119/nVdffZUbb7yRG2+8EeccTZs2TXtSiAQ+FX0iIgGgYsWK+XKeWhVKck7xIuw5nJi27mhiMiu376NBtXPyJYOcOTPjhhtu4IYbbmDnzp1MnDiRUaNGMX/+fCDlru9vvvlGhV8hoT/LREQCwGeffcZnn33m8/MEBRkNL8yii1fj+vxexYoVGTBgAI888ghBQSm//o8dO8YTTzzBoUOHPE4n+UFFn4hIQZLNNFuvvPIKr7zySr5EyHJcn4q+AuPGG28kLCyM4OBgQkJC+OGHH7jsssuYN2+e19HEx9S9KyIBr2PHjl5HyBtm2W766KOP8i1GoxqZ7+BdvOlvkpIdwUHZZxT/EBkZybx581iwYAHR0dEkJibSo0cPmjVrRo8ePRg+fDhly5b1Oqb4gIo+EQl4xYsX9zqCz5UvXz7fznXpBaUoGRbCwWPH09YdOHqcNTv3U69SYM+HGCgiIyMzjONbvnw5zz//PCNGjOC///0vY8eOpV27dh4mFF9Q966IBLzY2NiAn6h2xowZzJgxI1/OFRIcxFXVM9+0oS7egqtYsWL861//4qeffqJChQrceuutdO7cmb/++svraJKHVPSJSMArDEXfqFGjGDVqVL6dT+P6AlNERARLlizhxRdfZObMmVx66aVMnjxZz/UNECr6REQCwKxZs5g1a1a+nS+r+fp+3vi3ioMAUKRIEZ5++mliY2OpU6cO3bp14+abb+aPP/7wOprkkoo+EZEAkN/PF76sShnCTpqMefehBH6P19QfgeLSSy/lu+++Y+TIkSxcuJB69eoxduxYkpOTvY4mOaSiT0QkAEybNo1p06bl2/nCQoK5slrZTOvVxRtYgoOD6d27NytXriQyMpIHH3yQ6Oho1q5d63U0yQEVfSIiAWDs2LGMHTs2X895dRZTt/y8Uc/hDUQ1atTgq6++4p133mHFihVcccUVvPzyyxw/fvz0B4vfUNEnIgGvS5cudOnSxesYPvXFF1/wxRdf5Os5sxrX95PG9QUsM6N79+6sXr2a1q1b89RTT9GoUSOWL1/udTQ5Qyr6RCTgFSlShCJFingdw6eKFy+e7/MRXlmtLCEnTca8Y99Rtu45kq85JH9dcMEFzJgxg48++oht27YRERHBM888w9GjR72OJqehok9EAt7ixYtZvHix1zF8avLkyUyePDlfz1k8NITLqmS+eUTj+gqHDh06sHr1arp06cKQIUO48sorWbRokdex5BRU9IlIwFu1ahWrVq3yOoZPTZgwgQkTJuT7eTVfX+F27rnnMmnSJGbPns3hw4e57rrrePTRRzl48KDX0SQLflH0mdkdZrbKzJLNLOKkbf3NbL2ZxZlZy2yOP9fM5pjZutSvmaeKFxEJYHPmzGHOnDn5ft4s5+vbpKKvsGnZsiUrV67koYce4vXXX6d+/fp8/fXXxMTEMHToUGJiYnxyXl+3H2j85dm7K4H2wFvpV5pZXaAzUA+oBMw1s9rOuaSTju8HzHPODTOzfqnLT/k+toiIf/BqzOJV1c/FDNLfu7Fx1yH+2n+U80oX9SSTeKNUqVK8/vrrdOrUiR49etCyZUuCg4NJTk4mJCSEhx56iBo1auTZ+TZu3Mjo0aNJSkoiLCyMefPmZXiesGTmF0Wfc+43SLkz6CTtgKnOuWPARjNbD1wNnFzStwOiU79/F1iAij4RKUQmTZoEQPfu3fP1vGWKFeHSiqVZvWN/hvU/b/qbWy6vlK9ZxD9cd911xMbG0rp1axYsWABAYmIir732ms/OeeTIEe677z6GDh1KixYtAv7GrZzyi6LvFCoDP6Zb3pq67mTnO+d2ADjndpjZefkRTkQk32UzHYpXRR+kjOs7uej7aYOKvsKsaNGivPTSSzRt2pSEhARCQ0OZOXMmDRs2zLNzLF68mNtuu42EhATMjM2bN3PLLbdQoUIF7rrrLrp160aDBg2yuqBUaFl+zadkZnOBillseto5Nyt1nwVAX+fcktTl0UCMc25y6vLbwBfOuY9Panuvc65suuU9zrksx/WZWS+gV+pifVK6liVvlAH2eR0il/zpPeRnFl+dKy/bzW1bOT0+J8eVB3bl4FySNX/6/zKn/Ok95GeW8wEDDgB5+Yy+E++hBFAql+0XpM+WOs65Ujk4F5CPV/qcc81ycNhWoGq65SrA9iz2+9PMLki9yncB8NcpcowDxgGY2RLnXER2+8rZMbNxzrlep9/Tf/nTe8jPLL46V162m9u2cnp8To7TZ0ve8qf/L3PKn96DPlvytq38/mw52/Ok5xd3757Cp0BnMwszsxrAxcDP2ex3d+r3dwOz8imfZPSZ1wHygD+9h/zM4qtz5WW7uW0rp8f7089EYRUI/w386T3osyVv2yowny351r17yhBmtwGvAxWAvUCsc65l6rangXuB40Af59yXqesnAG8655aYWTlgOlAN+AO4wzl32jkD9Ne4iPiCPltExBdy+9niF0WfV8ysV2p3r4hIntFni4j4Qm4/Wwp10SciIiJSWPj7mD4RERERyQMq+kREREQKARV9IiIiIoWAir50zOxWMxtvZrPMrIXXeUQkMJjZpWb2ppl9ZGYPeJ1HRAKHmZUws6Vmdsvp9g34os/MJprZX2a28qT1rcwszszWm1k/AOfcJ865nkB3oJMHcUWkgDjLz5bfnHP3Ax0BTeUiItk6m8+WVE+RMm3daQV80QdMAlqlX2FmwcBooDVQF7jTzOqm2+WZ1O0iItmZxFl8tphZW+B7YF7+xhSRAmYSZ/jZYmbNgNXAn2fScMAXfc65b4GTJ2q+GljvnNvgnEsApgLtLMW/gC+dc8vyO6uIFBxn89mSuv+nzrnGQJf8TSoiBclZfrbcAFwD3AX0NLNT1nX59uxdP1MZ2JJueSvQCHgEaAaUMbNazrk3vQgnIgVWlp8tZhYNtAfCgC/yP5aIFHBZfrY45x4GMLPuwC7nXPKpGimsRZ9lsc4550YBo/I7jIgEjOw+WxYAC/I3iogEkCw/W9K+cW7SmTQS8N272dgKVE23XAXY7lEWEQkc+mwREV/Ik8+Wwlr0LQYuNrMaZhYKdAY+9TiTiBR8+mwREV/Ik8+WgC/6zOwDIAaoY2ZbzeyfzrnjwMPAV8BvwHTn3Covc4pIwaLPFhHxBV9+tphz7vR7iYiIiEiBFvBX+kRERERERZ+IiIhIoaCiT0RERKQQUNEnIiIiUgio6BMREREpBFT0iYiIiBQCKvpERERECgEVfSIip2Bmvc3sNzObksW2K81sQur33c3sjZO2LzCziFO0PdXMLs771CIimanoExE5tQeBm5xzXbLYNgB4PRdtjwWezMXxIiJnTEWfiEg2zOxNoCbwqZn930nbSgGXO+eWn0E7bc0sNvUVZ2YbUzd9BzQzs5A8Dy8ichJ90IiIZMM5d7+ZtQJucM7tOmlzBLDypHWdzOy6dMu1Utv5lNSHo5vZdGBh6vpkM1sPXAEs9cFbEBFJoyt9IiI5cwEQf9K6ac658BMvYEn6jWb2JHDEOTc63eq/gEo+TSoigq70iYjk1BGg6JnubGZNgTuAJidtKpraloiIT+lKn4hIzvxGavft6ZhZdWAM0NE5d3KBVxtYlcfZREQy0ZU+EZEccM6tMbMyZlbKOXfgNLt3B8oBM80MYLtz7iYzO5+U7t4dPo4rIoI557zOICJSIKXe0XvAOTchF8fvd869nbfJREQyU/euiEjOjQWO5eL4vcC7eRNFROTUdKVPREREpBDQlT4RERGRQkBFn4iIiEghoKJPREREpBBQ0SciIiJSCKjoExERESkE/h/duKXiHCNHAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Prepara gráfica con referencias relevantes (pasa bajas)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.axhline(-Ap, color='gray', linestyle='--')\n",
    "plt.axhline(-Ar, color='gray', linestyle='--')\n",
    "plt.axhline(-3, color='black', linestyle=':')\n",
    "plt.axhline(0, color='black', linestyle=':')\n",
    "plt.axvline(fp, color='gray', linestyle='--')\n",
    "plt.axvline(fr, color='gray', linestyle='--')\n",
    "plt.axvline(fc, color='black', linestyle=':')\n",
    "\n",
    "# Respuesta en frecuencia total\n",
    "W, H = sps.freqs(b, a)\n",
    "f = W/(2*np.pi)\n",
    "plt.semilogx(f, 20*np.log10(np.abs(H)), linewidth=5, label='total')\n",
    "\n",
    "#Gráfica de las mediciones en la simulación spice\n",
    "f_spice = spice.iloc[1:, 0]\n",
    "H_spice = spice.iloc[1:, 3]\n",
    "plt.semilogx(f_spice, H_spice, color='red', linestyle='-', marker='.', label='SPICE')\n",
    "plt.axis([100, 10000, -10, 10])\n",
    "\n",
    "#Gráfica de las mediciones en la implementación\n",
    "f_med = datos.iloc[1:, 0]\n",
    "H_med = datos.iloc[1:, 3]\n",
    "plt.semilogx(f_med, H_med, color='black', linestyle='-', marker='.', label='MEDICIONES')\n",
    "plt.axis([100, 10000, -10, 10])\n",
    "\n",
    "# Documenta la gráfica\n",
    "plt.xlabel('f (Hz)')\n",
    "plt.ylabel('$|H(f)|_{dB}$')\n",
    "plt.title(f'Pasa-Bajas, N={N}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b86f427",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}