Butter PA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b3ab244",
   "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: scipy>=1.4 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (1.7.1)\n",
      "Requirement already satisfied: ply>=3.11 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (3.11)\n",
      "Requirement already satisfied: numpy>=1.18 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (1.20.3)\n",
      "Requirement already satisfied: matplotlib>=3.2 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (3.4.3)\n",
      "Requirement already satisfied: cffi>=1.14 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (1.14.6)\n",
      "Requirement already satisfied: PyYAML>=5.3 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (6.0)\n",
      "Requirement already satisfied: requests>=2.23 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from PySpice) (2.26.0)\n",
      "Requirement already satisfied: pycparser in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from cffi>=1.14->PySpice) (2.20)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from matplotlib>=3.2->PySpice) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from matplotlib>=3.2->PySpice) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from matplotlib>=3.2->PySpice) (8.4.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from matplotlib>=3.2->PySpice) (0.10.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from matplotlib>=3.2->PySpice) (3.0.4)\n",
      "Requirement already satisfied: six in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib>=3.2->PySpice) (1.16.0)\n",
      "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: charset-normalizer~=2.0.0 in c:\\users\\ichbi\\anaconda3\\lib\\site-packages (from requests>=2.23->PySpice) (2.0.4)\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"
     ]
    }
   ],
   "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": "412244a2",
   "metadata": {},
   "source": [
    "# Diseño de filtro Butterworth Pasa Altas\n",
    "\n",
    "## Paola Briseño, UAM - I, 2022.\n",
    "\n",
    "Diseño y simulación (teórico/SPICE) de un filtro pasa-altas 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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acf8c4af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def aproximacion_butterworth(Ap, Ar, fp, 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": "442e1b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def butterworth_pasaaltas(N, fc):\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([[1]+[0]*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([1,0])\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([1,0,0])\n",
    "        s.append([bi, ai])\n",
    "    return b, a, s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5af289c3",
   "metadata": {},
   "source": [
    "#  Comienza el diseño"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "771b1fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (Paso 1) Especificación relativa\n",
    "Ap, fp, Ar, fr = 0.1, 500, 60, 6000\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_pasaaltas(N, fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6169ec8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 0]\n",
      "[1.00000000e+00 1.31343036e+04 8.62549659e+07 3.31818409e+11\n",
      " 6.38244160e+14]\n",
      "4\n",
      "799.9573701523312\n"
     ]
    }
   ],
   "source": [
    "print(b[0])\n",
    "print(a)\n",
    "print(N)\n",
    "print(fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "756924c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 0, 0]), array([1.00000000e+00, 3.84694847e+03, 2.52634946e+07])]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8ea6a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pasa-altas, N=4')"
      ]
     },
     "execution_count": 7,
     "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 altas)\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",
    "# Respuesta en frecuencia total\n",
    "W, H = sps.freqs(b[0],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-altas, N={N}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "721e482d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def secciones_sallen_key(s, C=1e-6):\n",
    "# Calcula los componentes de cada sección, usando como base C1=1uF.Regresa la lista sk = [[R1, R2, C2], [R1i, R2i, C1i, C2i], ....]\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 {}: R3={:6.3f}k, R4={: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",
    "      R3 = ai[1]/(2*m*ai[2]*C)\n",
    "      R4 = ai[1]/(m*ai[2]*C)-R3\n",
    "      print('sección {}: R3={:6.3f}k, R4={:6.3f}k, C1={:6.3f}u, C2={:6.3f}u'.format(i, R3/1e3, R4/1e3, C/1e-6, m*C/1e-6))\n",
    "      sk.append([R3/1e3, R4/1e3, C/1e-6, m*C/1e-6])\n",
    "  return sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "62650b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sallen_key_pasaaltas_spice(sk, spiceopus=False):\n",
    "    \"\"\"Genera una lista de red (netlist) para SPICE a partir de los componentes␣exactos\"\"\"\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}\\nR3{0} ({4} vi{0}) {1}k\\nR4{0} (vi{0} {5}) {2}k\\nC2{0}␣(vi{0} {5}) {3}u\\n'\n",
    "    POrc = '*Seccion {0}\\nC1{0} ({4} vi{0}) {1}u\\nC2{0} (vi{0} {5}) {2}u\\nR2{0} (vi{0} {5}) {3}u\\n'\n",
    "    POopamp = 'X{0} (0 vi{0} vp vn {2}) TL081\\n'\n",
    "    #SKrc = '*Seccion {0}\\nR3{0} ({5} n0{0}) {1}k\\nR4{0} (n0{0} vni{0}) {2}k\\nC1{0} (n0{0} {6}) {3}u\\nC2{0} (vni{0} 0) {4}u\\n'\n",
    "    SKrc = '*Seccion {0}\\nC1{0} ({5} n0{0}) {1}u\\nC2{0} (n0{0} vni{0}) {2}u\\nR3{0} (n0{0} {6}) {3}k\\nR4{0} (vni{0} 0) {4}k\\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",
    "    # 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 + SKrc.format(i, ski[2], ski[3], ski[1], ski[0], 'vin', 'vout0')\n",
    "                netlist = netlist + SKopamp.format(i, 'vout0')\n",
    "            elif i == len(sk)-1:\n",
    "                netlist = netlist + SKrc.format(i, ski[2], ski[3], ski[1], ski[0], 'vout{}'.format(i-1), 'vout')\n",
    "                netlist = netlist + SKopamp.format(i, 'vout')\n",
    "            else:\n",
    "                netlist = netlist + SKrc.format(i, ski[2], ski[3], 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[2], ski[1], ski[0],'vin', 'vout0')\n",
    "                netlist = netlist + POopamp.format(i, 'vin', 'vout0')\n",
    "            elif i == len(sk)-1:\n",
    "                netlist = netlist + POrc.format(i, ski[2], ski[1], ski[0],'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[2], ski[1], ski[0],'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",
    "\n",
    "    return netlist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eabd34e7",
   "metadata": {},
   "source": [
    "# Continuación del diseño"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "899415ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sección 0: R3= 0.215k, R4= 0.215k, C1= 1.000u, C2= 0.854u\n",
      "sección 1: R3= 0.520k, R4= 0.520k, C1= 1.000u, C2= 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_12844/3223382808.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_pasaaltas_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_pasaaltas_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": 11,
   "id": "781370c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.21534656153970227, 0.21534656153970227, 1.0, 0.8535533905932734],\n",
       " [0.5198925894795611, 0.5198925894795611, 1.0, 0.14644660940672644]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a731dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.5'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import PySpice\n",
    "PySpice.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "75e0ccfc",
   "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",
      "C10 (vin n00) 1.0u\n",
      "C20 (n00 vni0) 0.8535533905932734u\n",
      "R30 (n00 vout0) 0.21534656153970227k\n",
      "R40 (vni0 0) 0.21534656153970227k\n",
      "X0 (vni0 vout0 vp vn vout0) TL081\n",
      "*Seccion 1\n",
      "C11 (vout0 n01) 1.0u\n",
      "C21 (n01 vni1) 0.14644660940672644u\n",
      "R31 (n01 vout) 0.5198925894795611k\n",
      "R41 (vni1 0) 0.5198925894795611k\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": "02ba4dc4",
   "metadata": {},
   "source": [
    "# Mediciones d ela implementación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dc72ca8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>51.54</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.38</td>\n",
       "      <td>-8.404328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101.60</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-9.370422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>149.00</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-7.958800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201.60</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-7.958800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>248.70</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-7.958800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>309.80</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>-11.372725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>409.80</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-10.752040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>508.10</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-9.629721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>607.60</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.37</td>\n",
       "      <td>-8.635966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>798.70</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-7.958800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1004.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.44</td>\n",
       "      <td>-7.130946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1218.00</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-6.196078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1506.00</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>1818.00</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.60</td>\n",
       "      <td>-4.436975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.65</td>\n",
       "      <td>-3.741733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2513.00</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.73</td>\n",
       "      <td>-2.733543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3036.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.78</td>\n",
       "      <td>-2.158108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3313.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.81</td>\n",
       "      <td>-1.830300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3521.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-1.310031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3632.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.87</td>\n",
       "      <td>-1.209615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3823.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.86</td>\n",
       "      <td>-1.310031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4000.00</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.89</td>\n",
       "      <td>-1.012200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4644.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-0.264565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4958.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.340667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6041.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.506117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7109.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.748530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10150.00</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.213957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>12000.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.437640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14280.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.437640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>16120.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.655707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>17930.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.583625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>20100.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.437640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>25100.00</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.23</td>\n",
       "      <td>1.798102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>30300.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.583625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34096.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.437640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>45870.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.437640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>61220.00</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.213957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>82640.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.138097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>99730.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.363717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>149000.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.906460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>199000.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.587676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frec (Hz)  Vin (V)  Vout(V)  Vout (V)dB\n",
       "0       51.54     1.16     0.38   -8.404328\n",
       "1      101.60     1.16     0.34   -9.370422\n",
       "2      149.00     1.08     0.40   -7.958800\n",
       "3      201.60     1.16     0.40   -7.958800\n",
       "4      248.70     1.12     0.40   -7.958800\n",
       "5      309.80     0.96     0.27  -11.372725\n",
       "6      409.80     1.04     0.29  -10.752040\n",
       "7      508.10     1.02     0.33   -9.629721\n",
       "8      607.60     1.02     0.37   -8.635966\n",
       "9      798.70     1.00     0.40   -7.958800\n",
       "10    1004.00     1.10     0.44   -7.130946\n",
       "11    1218.00     1.08     0.49   -6.196078\n",
       "12    1506.00     1.04     0.54   -5.352125\n",
       "13    1818.00     1.06     0.60   -4.436975\n",
       "14    2016.00     1.04     0.65   -3.741733\n",
       "15    2513.00     1.04     0.73   -2.733543\n",
       "16    3036.00     1.02     0.78   -2.158108\n",
       "17    3313.00     1.02     0.81   -1.830300\n",
       "18    3521.00     1.02     0.86   -1.310031\n",
       "19    3632.00     1.02     0.87   -1.209615\n",
       "20    3823.00     1.02     0.86   -1.310031\n",
       "21    4000.00     1.02     0.89   -1.012200\n",
       "22    4644.00     1.12     0.97   -0.264565\n",
       "23    4958.00     1.10     1.04    0.340667\n",
       "24    6041.00     1.12     1.06    0.506117\n",
       "25    7109.00     1.12     1.09    0.748530\n",
       "26   10150.00     1.12     1.15    1.213957\n",
       "27   12000.00     1.10     1.18    1.437640\n",
       "28   14280.00     1.10     1.18    1.437640\n",
       "29   16120.00     1.10     1.21    1.655707\n",
       "30   17930.00     1.10     1.20    1.583625\n",
       "31   20100.00     1.10     1.18    1.437640\n",
       "32   25100.00     1.14     1.23    1.798102\n",
       "33   30300.00     1.10     1.20    1.583625\n",
       "34   34096.00     1.10     1.18    1.437640\n",
       "35   45870.00     1.10     1.18    1.437640\n",
       "36   61220.00     1.08     1.15    1.213957\n",
       "37   82640.00     1.10     1.14    1.138097\n",
       "38   99730.00     1.10     1.17    1.363717\n",
       "39  149000.00     1.10     1.11    0.906460\n",
       "40  199000.00     1.10     1.07    0.587676"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraer ruta del archivo de mediciones\n",
    "path = 'med_PA.xlsx'\n",
    "#Datos completos de la tabla:\n",
    "datos = pd.read_excel(path)\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f2ba412d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</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>50.100</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-96.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>99.960</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-71.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>155.340</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-57.036000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>204.230</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-48.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>252.690</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-42.509000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>303.220</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-37.270000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>404.760</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-29.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>508.420</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-23.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>600.900</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>802.180</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-14.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1007.720</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1209.260</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.804000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1518.950</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1822.900</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.737000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2027.570</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.156000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2508.400</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.570000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3010.360</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3297.830</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3558.260</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3839.240</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4018.380</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5124.800</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6065.300</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7051.900</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10156.670</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12005.210</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.088300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14190.183</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16025.110</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.074000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>18097.300</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20437.500</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25284.280</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>30343.800</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>35325.400</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>45050.200</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>61058.540</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.492800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>82752.180</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>99311.430</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>149707.580</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>202897.500</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Frec (Hz)  Vin (V)  Vout(V)  Vout (V)dB\n",
       "0       50.100        1      NaN  -96.550000\n",
       "1       99.960        1      NaN  -71.560000\n",
       "2      155.340        1      NaN  -57.036000\n",
       "3      204.230        1      NaN  -48.320000\n",
       "4      252.690        1      NaN  -42.509000\n",
       "5      303.220        1      NaN  -37.270000\n",
       "6      404.760        1      NaN  -29.720000\n",
       "7      508.420        1      NaN  -23.910000\n",
       "8      600.900        1      NaN  -19.840000\n",
       "9      802.180        1      NaN  -14.610000\n",
       "10    1007.720        1      NaN  -10.540000\n",
       "11    1209.260        1      NaN   -8.804000\n",
       "12    1518.950        1      NaN   -5.890000\n",
       "13    1822.900        1      NaN   -4.737000\n",
       "14    2027.570        1      NaN   -4.156000\n",
       "15    2508.400        1      NaN   -3.570000\n",
       "16    3010.360        1      NaN   -2.410000\n",
       "17    3297.830        1      NaN   -1.250000\n",
       "18    3558.260        1      NaN   -1.250000\n",
       "19    3839.240        1      NaN    0.088000\n",
       "20    4018.380        1      NaN    0.088302\n",
       "21    5124.800        1      NaN    0.083302\n",
       "22    6065.300        1      NaN    0.088302\n",
       "23    7051.900        1      NaN    0.492790\n",
       "24   10156.670        1      NaN    0.492800\n",
       "25   12005.210        1      NaN   -0.088300\n",
       "26   14190.183        1      NaN    0.492700\n",
       "27   16025.110        1      NaN    1.074000\n",
       "28   18097.300        1      NaN    0.492800\n",
       "29   20437.500        1      NaN    0.492800\n",
       "30   25284.280        1      NaN    0.492800\n",
       "31   30343.800        1      NaN    0.492800\n",
       "32   35325.400        1      NaN    0.492800\n",
       "33   45050.200        1      NaN    0.492800\n",
       "34   61058.540        1      NaN    0.492800\n",
       "35   82752.180        1      NaN    0.088300\n",
       "36   99311.430        1      NaN    0.088300\n",
       "37  149707.580        1      NaN    0.088300\n",
       "38  202897.500        1      NaN    0.088300"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extraer ruta del archivo de mediciones Spice\n",
    "path = 'PA_spice.xlsx'\n",
    "#Datos completos de la tabla:\n",
    "spice = pd.read_excel(path)\n",
    "#spice\n",
    "spice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d8afcd09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pasa-altas, N=4')"
      ]
     },
     "execution_count": 32,
     "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 altas)\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[0],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='blue', 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-altas, N={N}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9863d681",
   "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
}