Compensador_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Compensador_v2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mgstingl/c2_fiuba/blob/main/Compensador_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjXWUCTOLjZ4"
      },
      "source": [
        "!pip install control\r\n",
        "\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from math import pi\r\n",
        "from control.matlab import *  # MATLAB-like functions\r\n",
        "import scipy.optimize as op\r\n",
        "from control import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnE9fQjiN0Lw"
      },
      "source": [
        "s = tf(\"s\") # Defino 's' para definir las transferencias"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IGkHdlpT5ntp",
        "outputId": "2d96cd93-f87b-48fb-ea07-d6cdf2f0a61b"
      },
      "source": [
        "vars = {\"fc_\": [10, 8, 6, 5], # Denominador de la frecuencia de crossover\r\n",
        "        \"V_in\": [12, 60],\r\n",
        "        \"V_out\": [1, 50],\r\n",
        "        \"RL\": [27, 50, 200, 500, 1000]}\r\n",
        "\r\n",
        "V_in = vars[\"V_in\"][0]\r\n",
        "\r\n",
        "\r\n",
        "a = 4*2*2*5\r\n",
        "print(a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-zKKnJgQ98Z"
      },
      "source": [
        "#Frecuencia de Swiching\r\n",
        "fs = 200*10**3 # Frecuencia de la triangular del PWM\r\n",
        "Ts =  1/fs\r\n",
        "ton = Ts * 0.5\r\n",
        "Duty = ton/Ts\r\n",
        "\r\n",
        "#Frecuencia de crossover (elegida)\r\n",
        "fc = fs/6; #(1/10;1/5).fs -----------------------------------------------------"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2qqjmkZaeaR",
        "outputId": "4c0a57cc-3e77-42ea-df3d-b23356077186"
      },
      "source": [
        "# Linealización del Delay teórico\r\n",
        "tau = 250 / np.power(10,9)  #Adaptar al calculado en el Clase D\r\n",
        "\r\n",
        "  # Aprox. Padé orden 1\r\n",
        "dnum,dden = pade(tau,1)\r\n",
        "delay = tf(dnum,dden)\r\n",
        "print(\"Transferencia Delay {0}--> con tau = {1}\".format(delay,tau))\r\n",
        "#bode_delay = bode(delay) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Transferencia Delay \n",
            "-s + 8e+06\n",
            "----------\n",
            "s + 8e+06\n",
            "--> con tau = 2.5e-07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYpPiNBWMWR8",
        "outputId": "74148994-d915-4a13-92ce-386231281bae"
      },
      "source": [
        "V_in = 60 # (12;60V)     # Tensión de alimentación -------------------------------\r\n",
        "V_ramp = 6 #Tensión de la triangular de entrada del PWM\r\n",
        "V_ref = V_ramp *0.9 # Tensión pico de la señal in desde la etapa de control (0.1;0.9).V_ramp\r\n",
        "V_out = V_in/2 # (1;50)  -------------------------------------------\r\n",
        "\r\n",
        "R_esr = 0.05 # Chequear si corresponde con el capacitor elegido\r\n",
        "\r\n",
        "RL = 27 # 27: Valor mínimo calculado. -----------------------------------------\r\n",
        "fo = 5000   # Frecuencia de corte del filtro LC\r\n",
        "#Lo = 1.2*10**(-3)\r\n",
        "#Co = 833*10**(-9)\r\n",
        "#wo = 1/np.sqrt(Lo*Co)\r\n",
        "#fo = wo/(2*np.pi)\r\n",
        "#Qo = wo*RL*Co\r\n",
        "\r\n",
        "wo = 2*np.pi*fo\r\n",
        "Qo = 1/np.sqrt(2) #Elijo el valor óptimo o 1.3 \r\n",
        "Lo = RL/(wo*Qo)\r\n",
        "Co = Qo/(RL*wo)\r\n",
        "\r\n",
        "\r\n",
        "f_esr = 1/(2*np.pi*R_esr*Co)\r\n",
        "print(\"fo = {0}, fc = {1},fs = {2} f_esr = {3}\".format(fo,fc,fs,f_esr))\r\n",
        "\r\n",
        "print(\"Lo = {0}mH, Co = {1}nF, RL = {2}, R_esr = {3}\".format(round(Lo*10**3,2),round(Co*10**9,2),round(RL,2),round(R_esr,2)))\r\n",
        "print(\"wo = {0}, Qo = {1}\".format(round(wo,2),round(Qo,2),round(fo,2)))\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fo = 5033.92809676676, fc = 33333.333333333336,fs = 200000 f_esr = 3821247.1330587114\n",
            "Lo = 1.2mH, Co = 833.0nF, RL = 27, R_esr = 0.05\n",
            "wo = 31629.1, Qo = 0.71\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "seD0rrC1Lqlb",
        "outputId": "bf2cd2c7-e81f-4633-c334-529c178d7df8"
      },
      "source": [
        "# Transferencia Entrada - Salida\r\n",
        "\r\n",
        "G_vd = V_in*(RL*(1+s*R_esr*Co))/(RL+s*(Lo+Co*R_esr*RL)+s**2*Lo*Co*(RL+R_esr))\r\n",
        "G = G_vd/V_ramp\r\n",
        "#bode_G = bode(G)\r\n",
        "bode_G_delay = bode(G*delay)\r\n",
        "print(G*delay)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "        -6.747e-05 s^2 - 1080 s + 1.296e+10\n",
            "---------------------------------------------------\n",
            "1.622e-07 s^3 + 1.305 s^2 + 5.782e+04 s + 1.296e+09\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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F5B1glXP821TVhg81GiUiwohuWYzolsXiTft5ekYBj36+lmdnrueqYdncOLJzpCUacUq1QUNVK/fJmCMiYfsPX1WvDlL3IPBguI5tGLFIbnZznr92MKu3H+SpGet5btYGXpq7kdPauuja74gNUWLUK6HMp5Hpt2Q5neqsu6phRBm92jZh3M8GMO33eVw6oD1fbC33DVHy9lLW7TwUaXlGnBDK66nF+F4VCb7XQoXAjeEUZRhG7emclcZDP+rHkLQ9rCxvw5vzN/Pekm2c07s1vxzVjVM6Nou0RCOGCSVo9FLVYn+DiCSFSY9hGPVEZrKLP+X15rZR3Xh5TiEvz93IlFU7GdGtBSOaezlDFZGqP9c1jECE0rlvbhW2L+tbiGEY4SEzLZHfnXMSc+8+k3t+0JN1O4v418Jifmi9zI1aEGyU2zb4elyniMgAfK+nAJoAllkzjBgjPcnDLad35ZpTc3ho/HTyd/h6mXdrlc4vzuhKMwseRggEez11LnAd0AF4xM9+CLgnjJoMwwgjyQlu8jom8McrTueTFTt4esZ67vjPMjKThdsSC7l8cEfSkkJ5c200RgJeGar6CvCKiPxIVd9tQE2GYTQAHreLi/q348J+bfli7W7+8cFi/jppFeOmr+PaU3PoZiPrGlUQ7PXUVeob5ylHRH5XuV5VH6liM8MwYgwRIe+kVjA0hYzO/Xnmi/U8Nm0diW5YXLySG0+zjoLG9wR7Bk1zftZ+xCvDMGKK3OzmPHfNINbtPMQD78zljfmbeG3eJga3dpHV/YDNKGgEfT31b+fnAw0nxzCMaKB76wxu6pvE/7tmGC/NKeSVORu4YNxsTuuWxbBm9rluYyaUAQtbAjfjG3H2eHtVvSF8sgzDiAbaNE3m7h/0on/CDjYnduLF2YXMLihh0tZZ3DSyi31x1QgJ5ROJD4FZ+KZ9tQECDaMRkpog/OKMrlw/Iod/jZ/O7N1wx3+W0SxJuFkKyC6z4NFYCCVopKrq2LArMQwj6knyuBnZIYE/XjmSWev28K8PF/Pw5G9IdMG8I8u5YURnurS0NGg8E0qP8Eki8oOwKzEMI2YQEU7v0ZI7Bicz+X9OZ2hbD+8s3Mro//2Ca19cQP43u6yneZwSStD4Db7AcVREDorIIRE5GG5hhmHEBie1yeDGvknMuWs0vz2rB6u2H+T6lxZy5iNf8NKcQg4Vl0VaolGPVBs0VDVDVV2qmqKqTZxyk4YQZxhG7NAyI4nfnNWdOWNH89jlp9AsNYEHJq5i2N+n8ecPV7Bmh/2vGQ+E8vXUwCrMB4BNqhrOGfwMw4hBEj0uLj6lPRef0p5lW77jlbkbGb9wC69+uYkBnZpxxZBOXNCvHSmJ7khLNWpBKInwp4CBwHKn3BffvN1NReRWVZ0SLnGGYcQ2/Ts245GfnsKfLujNu19t5c0Fm/nDhK/5y6RVXDqgPT8b2inSEo0aEkrQ+Ba4UVVXAohIb+AvwJ3Ae4AFDcMwgtI8LZGbRnbhxtM6s6BwH28u2MxbC7bwypebyG7i4rqEQi7q344W6TZVT7QTStDocSxgAKjqKhHpqaobrEeoYRg1QUQY2qUFQ7u04L4LS/lgyTZembmGByau4sGPV5N3Uit+NLA9o3u1Isljr6+ikVCCxkoReRoY75R/CqxyZu+zzyIMw6gVmWmJ3HBaZ7qUb6Jtz1ze+2or7y/Zxuerd9I0JYExJ7fh/H5tObVri0hLNfwIJWhcB/wS+B+nPAe4A1/AGBUeWYZhNCZOapPB3T/oxZ1jejK7YA/vf7WVj5dv5+1FW2iemkC/TMXTfg/DumTicYfSU8AIF9UGDVU9Cvyvs1SmqLYHFpEfA/cDvYAhqrrIr+5u4EZ8w5b8WlUnO/YxwGOAG3heVR+q7fENw4g+3C7hjB4tOaNHS4rLvMxcu5uPl29n8vJv+eKF+WSmJXJ2r9ac1bs1p3XLirTcRkkon9x2B/4B9AaSj9lVtUsdj70CuBT4d6Xj9QYuB04G2gGfi0gPp/pJ4GxgK7BQRD5S1VV11GEYRhSSnODmnJPbcM7JbZjS8jsqWvfi4+Xb+cR5AklOcNGrubAzbTOje7amZYYl0RuCUF5PvQTcBzyK73XU9YTWkzwoqroaqGp45YuB8apaAhSKSAEwxKkrUNUNznbjnbYWNAwjzkl0C3l92jCmTxtKyytYULiPqat2MGnJZsa+uxyR5fTr0Iy8Hi3JOOxlZIXidtmHOuFAtJopHUVksarmishyVe3rb6sXASIzgDuOvZ4SkSeAec6sgYjIC8CnTvMxqnqTY78aGKqqt1exz1uAWwBat26dO378+ON1RUVFpKenh7Tub6spoWwbqE1V9sq2YOWq9MeiL/V9Thral2i9vuLJl0OHitivKSzZ5eXr3V42HKhAgbQE6NPCTb+WbjqnlNAuM/7ulXCeE4BRo0YtVtVBJ1SoatAFmIvvyeI94HbgEuCb6rZztv0c32uoysvFfm1mAIP8yk8AV/mVXwAuc5bn/exXA09UpyE3N1f9yc/PD3nd31ZTQtk2UJuq7JVtwcpV6Y9FX+r7nIS6fX35Eq3XV6C6WPSlsn1fUYn+482p+ru3l2ruX6dq9thJmj12kp776Bf6l4krdfrqnfrp1OknbB+L90o4z4mqKrBIq/ibGsrrqd8AqcCvgb8Co4FrQ4lUqnpWKO0qsQ3o6Ffu4NgIYjcMw6B5WiLD2nrIy+tPRYWyavtBXpk8n+0VSbw2bxMvzC7ELZC7/ktO65ZF8kEvw8srIi07pgjl66mFzmoRvnxGuPkIeFNEHsGXCO8OLAAE6C4infEFi8uBKxpAj2EYMYjLJfRp35TzuySSlzeU4jIvizft541pX7Gl1Mujn69FFR79agpdm8Bq1nNq1xZ4bUj3oAQMGiLyUbANVfWiuhxYRC4BxgEtgY9FZKmqnquqK0XkHXwJ7nLgNlX1OtvcDkzG98nti+rXU90wDCMYyQluRnTLomxrInl5p/HdkVJemDiTg8lt+Hz5Zv752RoAUjxw6uaFDOuSybAuLejdton1DfEj2JPGqcAW4C1gPr7/9OsNVX0feD9A3YPAg1XYPwE+qU8dhmE0TpqlJpLb2kNeXh9GNd3DybmnMm/DXt6dtZyNew8zfc0uANKTPAzOac4wZ/iTPu0adxAJFjTa4OsT8TN8r4E+Bt6y/+4Nw4hHWmYkcWH/dmTsX0teXh67DhYzr3Af8zbsZf6GveR/sxuAtEQ3A7OdINI5k34dmpHoaTxBJGDQcF4JfQZ85owz9TNghog8oKpPNJRAwzCMSNCqSTIX9W/HRf3bAbDrUDELCvcxf8M+5hfu5eHJ3wCQnOBiYKfmDOmcSdIBL8PKvCQnxO9gi0ET4U6wOB9fwMgBHifAKyXDMIx4plVGMhf0a8cF/XxBZN/hUl8QKdzLgsJ9PDZtHarwyFeT6d+hGUM6ZzKkcyaDcjJJTwrlQ9XYIFgi/FWgD74cwgOquqLBVBmGYUQ5mWmJjHF6qQMcOFrGSxO/4GhGexYU7uPZmRt4asZ6XAInt2t6PIiUlsb211nBwt9VwGF8/TR+7TfchwCqNk+4YRjGcZqmJHBKKw95eb0AOFxSzpLN37Fg4z4WFO7ldaefCMDjK744HkSGdM6kbdOUSEqvEcFyGo0ns2MYhlHPpCV5OK17Fqd1943GW1LuZfnWA4yftojdksKHS7/ljfmbAeiYmUKn5DJ2pm1mSOcW5LRIrWpcvqggfl60GYZhRDFJHjeDcjIp6ppIXt4Qyr0VrNlxiPmFvieRuWt3Mufd5YDvS64hnTNpXlZG6+0HOal1Bq4oGYDRgoZhGEYE8Lhd9GnflD7tm3LjaZ3Jz8+nQ+9BLNi4j4WF+1hQuI9vD5Ty+upZNEn2MDgnkxYVpTTpsp++7ZtGTnfEjmwYhmEcR0To3jqD7q0zuHJoNgATPp2Oq3UPFhTuY8HGfUzbXcY7a+eSnOCicwYsKVvL0M6ZlHgbLrluQcMwDCNKyUpxkTewA5cO7ADAh5PzSWzXk/mF+8hfsZnHp/s+83UL9PtmDkNyMkkpKmfAkbKwabKgYRiGESM0TRLy+rblvL5tyWuym4HDRrB4034mzFjKzgrhxTmFlHmVx5ZMoUO6i48Gl9I8LbFeNVjQMAzDiFGaJCcw6qRWyPZE8vKGU1zm5eWJMyht2omZX6+nWWpCvR/TgoZhGEackJzgpmemm7y87vRzbwvLZ7vWF8MwDMMIGQsahmEYRsiIbyrY+EVEdgOb/ExNgQMhrmcBe2p5aP/91bRNVfbKtmDlY+v+tlj0pb7PSTCdobSpqS/Ren0FqotFXxrzvRLOcwKQraotT7BWNXF4PC/As6GuE2Bi9Zoep6ZtqrJXtgUr++n3t8WcL/V9Thral2i9vuLJl8Z8r4TznARbGuPrqYk1XK+P49S0TVX2yrZg5YkB2tSWSPlS3+ck1P3Uly/Ren0FqotFXxrzvRLOcxKQuH89VRdEZJGqDoq0jvogXnyJFz/AfIlW4sWXcPnRGJ80asKzkRZQj8SLL/HiB5gv0Uq8+BIWP+xJwzAMwwgZe9IwDMMwQsaChmEYhhEyFjQMwzCMkLGgYRiGYYSMBY0aICK9ROQZEZkgIrdGWk9dEJE0EVkkIhdEWktdEJE8EZnlnJe8SOupCyLiEpEHRWSciFwbaT21RURGOufjeRGZG2k9dUFEOonIByLyoojcFWk9dUFEeovIOyLytIhcVtv9NPqg4VwMu0RkRSX7GBH5RkQKjl0sqrpaVX8B/AQYEQm9gaiJHw5jgXcaVmVo1NAXBYqAZGBrQ2utjhr6cjHQASgjynyp4X0yy7lPJgGvREJvMGp4TvoCE1T1BmBAg4uthhr6ch4wTlVvBa6p9UHD0c08lhbgdGAgsMLP5gbWA12ARGAZ0Nupuwj4FLgi0tpr6wdwNnA5cB1wQaS119EXl1PfGngj0trr6MtdwM+dNhMirb22fvjVvwNkRFp7Hc9JCyAfmA5cH2ntdfSlFfAk8DAwp7bHbPRPGqo6E9hXyTwEKFDVDapaCozH918gqvqRqp4HXNmwSoNTQz/ygGHAFcDNIhJV10FNfFHVCqd+P5DUgDJDoobnZSs+PwC8Daeyemp6n4hIJ+CAqh5qWKXVU0NfrgfuU9XRwPkNq7R6aniv7FLV2/D9c1LrgQxtEqaqaQ9s8StvBYY678wvxffH6ZMI6KopVfqhqrcDiMh1wB6/P7zRTKBzcilwLtAMeCISwmpBlb4AjwHjRGQkMDMSwmpIID8AbgReanBFtSeQL88A94vIFcDGCOiqDYHulRzgHiAN39NGrbCgUQNUdQYwI8Iy6g1VfTnSGuqKqr4HvBdpHfWBqh7B98c25lHV+yKtoT5Q1RVArZPG0YSqbgRuqet+ouq1RBSxDejoV+7g2GKNePEDzJdoJF78APMlZCxoVM1CoLuIdBaRRHxJ448irKk2xIsfYL5EI/HiB5gvoRPp7H+kF+AtYDvff+Z4o2P/AbAW31cI90ZaZ2Pxw3yJziVe/DBf6r7YKLeGYRhGyMRcIlxExuD7ysQNPK+qDwVrn5WVpTk5OcfLhw8fJi0tLaR1f1tNCWXbQG2qsle2BStXpT8Wfanvc9LQvkTr9RVPvjTmeyWc5wRg8eLFezTW5winms5EVS25ubnqT35+fsjr/raaEsq2gdpUZa9sC1auSn8s+lLf5yTU7evLl2i9vgLVxaIvjfleCec5UVUlwBzjsZYID9iZyDAMwwg/sRY0quq00j4cB9p9qISiUqWk3HvsKccwDKPRE1OJcGdkxjGqepNTvhq/Hs5+7W7B6cTSunXr3PHjxx+vKyoqIj09vdr1fyx1sa3I97txCSS5IcktJLkh2SOkeCDFI84CqR4hNUFIT4D0RMFdXkyrpqmkJwipCeASOcEf/2NWZ69sC1Y+th7Iv5oSyrbh8KU6n6Ldl+rWI3VO4smXxnyvhPOcAIwaNWqxqg46oaKqd1bRugCnApP9yncDdwfbprY5jYnLtuk9L03RJ6av0399tlrv/2iFjp2wTH/15ld6w0sL9MdPz9VzH/1Ch/9jmva7f7J2vmuSZo+teuly98c65MGpetG4WXrTKwv13ve/1mn5L1EAACAASURBVHHT1upfXpuicwp269b9R7TcW1GllkC2eH1P61+2nEZgLKcR2N5Y7pVI5TRi7eup451W8PVwvBzfoHv1zgX92pG+by15ed1Caq+qFJWU892RMvYfKWXGl4vo2K0n+w6Xse9wCbsOlrDzUAmb9x5h4cZ9fHekDIAXVswHINHtokNmCp0yU3EfLWFz0kZ6tW1CzzYZZCQnhMNFwzCMGhNTQUNVy0XkdmAyvi+pXlTVlRGWBYCIkJGcQEZyAh0zU9nX0kPegA4B2xeXeflo6he079GPTXuPsGnfYTbvPcKmvUfYsKucaZu/d6tjZgotPaUsKVtLn/ZNGZzTvCFcMgzDOIGYChoAqvoJsTHCbFCSE9y0SnUxolsWIyo9zOTn53PSgGGs3n7QWQ6xeMMOHp++DlUQgQ7pLs48uJJhXTIZ0rlFZJwwDKPREVOJ8JogIhcCF7Zv3/7m119//bg9lpN7CSlpbDxQwZp9XlbtLqXwkFDqDGreLlUZ3DaRIW09NOVIzCb3/MuWCA+PH/HkiyXCLRFe70s8d+4rKfPqoo179Ynp6/Schz7RHCcZP/yvH+ujU7/RNyZOq5GemuoMpY0lwgOvWyI8sJZQ21giPPC6JcKNE0j0uMjNziQ3O5OTZSu9Bw7js5U7eGPmah6b5nuV9cq6mVw1rBOtvfH5RGkYRsNiQSOOaNUkmWtOzaFTyUZ6DRzGuPdnsfyQiz99uJKMRLiFdVx9ajbNUhMjLdUwjBgl1nqEGyHSukkyZ2Un8MFtI3j7lmF0burmf6euZfhD0/nLxFVs++5opCUahhGDWCK8ESX39msqnxaWMX97OQB5HT1c1iORFM+JvdWjwRdLhIfHj3jyxRLhlgj37/39e0CBLKecBxwAljrLn0PZTzwnwgOVq0vubd1/RO99/2vNuWuSDvv75zpt9Y5a6QyljSXCA69bIjywllDbWCI88Hq4EuFR+XpKRDoC5wCbK1XNUtVTnOUvEZAWF7RvlsLfftiXCb8YTnqShxteXsRvxi9hb1FJpKUZhhHlRGXQAB4F7sT3pGGEidzs5kz69Wn85szufLJ8O2c98gXvL9l67EnPMAzjBKIupyEiFwOjVfU3IrIRGKSqe0QkD3gX33Do3wJ3aIAhROpjlNvG9p5226EKXlxRwvoDFeS2dnNL3ySSnFyH5TRq50u0Xl/x5IvlNBpJTgP4HFhRxXIxMB9o6rTbyPc5jSZAurP+A2BdKMeynEboesq9FfrvLwo0565JevETs3XPoeKQt7WcxonlaL2+AtXFoi+W0wi8Hlc5DVU9S1X7VF6ADUBnYJnzlNEB+EpE2qjqQVUtcrb/BEgQkaxI6I9X3C7hltO78sxVuazefpAfPT2XTXsPR1qWYRhRRFTlNFR1uaq2UtUcVc3B9ypqoKruEJE2Ir6ZjERkCD7teyMoN2459+Q2vHnzUL47WsalT81lwwFvpCUZhhElBA0aItJBRO4QkQ9FZKGIzBSRp0TkfBFp6IBzGbBCRJYBjwOXO49QRhjIzc7k3VuHk5rk5qEFxeSv2RVpSYZhRAEBE+Ei8hK++bcnAYuAXUAy0AMYBeQCd6nqzIaRWjOsc1/9JPe+K6ngfxccYdsR4bqTEzm9Q9UTQlki/MRytF5f8eRLNN0rDe1L1CXCgT6B6pz6RKBbsDbRsFgiPHQ9gfhk6nS9+oX5mj12kn6wZGuVbSwRfmI5Wq+vQHWx6Eu03Svxcn2p1iIRrqorgkUhVS1V1YJahzEjZkjxCM9dk8uwLpnc8Z9lzC3YE2lJhmFEiGrzEiKyXES+rrTMEpFHRcSmjGskJHnc/PvqQXTJSufnry1m1bcHIy3JMIwIEEoy+1PgY+BKZ5mIL8exA3g5bMqMqKNpSgIv3zCY9GQP17+8wEbKNYxGSLU9wkXkK1UdWJVNRJarat+wKqwllggPX3Jv66EKHpx/lOZJwj1DU0hPFEuEV1GO1usrnnyJ9nslnL5EXSL82AIsA4b4lQcDy5z1JdVtH+nFEuGh6wlEVdt+uX6Pdr/nE73s6Tl6tLTcEuFVlKP1+gpUF4u+xMK9EmqbaDonqnXrEX4T8IKIFIpIIfACcLOIpAH/qHUYC4CI9BeRL51cykQRaeJXd7eIFIjINyJybn0f2widYV1a8MhP+7Nw435++/ZSKqzLjGE0Cqqd7lVVFwJ9RaSpUz7gV/1OGDQ9j28wwi9E5AbgD8CfRKQ3cDlwMtAO+FxEeqiqdVeOEBf0a8fOgyX8ddIqOJzA6FGRVmQYRrgJ5eup1iLyAjBeVQ+ISG8RuTGMmnoAxzoMTgV+5Kxf7GgoUdVCoAAYEkYdRgjceFpnrh6WzaeFZbyzaEuk5RiGEWZCSYR/CrwE3Kuq/UXEgy+XEZYEuIjMBf6lqh+IyO+AB1Q1Q0SeAOap6utOuxeAT1V1QhX7sKHRGzC5561QHl5wmHUHhD8MTqZnprtefLFEeHj8iCdfYu1eqU9fojkRvlArJb2BpdVtV80+gw2N3hOYAiwG7gP2Ots8AVzlt48XgMuqO5YlwkPXE4hQtp00ZbqO/n/52v+ByVq4uyjotpYIzw9ZSyAsER7YHu33SixcX6p1S4QfdjrxKYCIDMM3V3et0QBDo6vqh6q6RlXPUdVc4C1gvbPZNqCj3246ODYjCkhLEF64djAAN76ykANHyyKsyDCMcBBK0Pgd8BHQVUTmAK8CvwqXIBFp5fx0AX8EnnGqPgIuF5EkEekMdAcWhEuHUXNystJ45qpcNu87wu1vfkW5tyLSkgzDqGeqDRqq+hVwBjAc+Dlwsqp+HUZNPxORtcAafNO6vuToWInva61VwGfAbWpfTkUdw7q04MFL+jJr3R4emLgq0nIMw6hngg2NfmmwDVX1vbAoqiesR3hkk3tvf1PKp4VlXJyjXNLTEuHRdH3Fky/xcK/U1peoS4Tj+w//JXzjTu0H3nWWfcCkQNtF22KJ8ND1BKI2vpR7K/T2N7/S7LGT9LmZ64O2tUR4zbFEeGB7rN0rwezRmAgP2LlPVa8HEJEpQG9V3e6U22IDFRrV4HYJj/6kP9t37ORvH6/G4xKuG9E50rIMw6gjoSTCOx4LGA47gU5h0mPEER63i1/0T+Kc3q25f+IqXpu3KdKSDMOoI9UOIwJME5HJ+D5/Bfgpvn4WhlEtHpfwxBUDufX1xfzpgxV4XELbSIsyDKPWVNsjHEBELgFOd4ozVfX9sKqqBywRHl3JvbIKZdxXJSzf4+WK7srZXS0RbonwuvsSj/dKqL5EYyJcAtXVpE2A7X4MrAQqgEF+9iHAUmdZBlziV7cRWO7UVZmgqWqxRHjoegJRX74cLS3Xq56fpzljJ+kb8zYF3NYS4dVjifDA9ni4VwLZoiERHiynkS8ivxKR/8pfiEiiiIwWkVeAa2sZxFYAl/L9wIT+9kGqegowBvi3M9bVMUap6ilaVfQzop7kBDfPXj2Ik7Pc3PP+cu6csIziMutqYxixRLCcxhjgBuAtpwf2d0AKvuT5FOD/VHVJbQ6qqqsBRKSy/YhfMRln6BIjfkhJdPO73CSWlLVj3PQClm87yHXdrOe4YcQKwT65LQaeAp4SkQQgCziqqt+FU5CIDAVeBLKBq1W1/JgkYIqIKPBvVX02nDqM8OES4ffnnMTATs35n7eXcv+XZTTN3sGYPm0iLc0wjGoIKRFeqx2LfA5U9VfgXlX90GkzA9+ES4uq2L4X8ApwuqoWi0h7Vd3mjE01FfiVqlZ+vXVsWxsaPUaSe3uOVjBu8RE2FQljcjxc1iOR4iOHLREeRj/iyZfGdK8E0h6KT7Wh1kOjh3MBZuCXCK+ifnpV9cD9+IKNJcKrKMdacm/KtOn6pw+Wa/bYSXrhuFn67Hufn9DOEuHVa6lJm3jxpbHdK9GeCG9wRKTzscS3iGTjm1tjo4ikiUiGY08DzsGXNDfigASX8JeL+/DEFQPYebCYB+cX88s3FrPriOU6DCPaCKVz37E/4N1V9XMRSQE8qnqotgd1+n2MA1oCH4vIUlU9FzgNuEtEyvB9jvtLVd0jIl2A953EuQd4U1U/q+3xjejkgn7tGN2zFfe+Op3J3+xmcpmXNd5V/PrMbpGWZhiGQ7VBQ0RuxpcfyAS64pv86BngzNoeVH2dA0/oIKiqrwGvVWHfAPSv7fGM2CE10cPF3RK5+yfD+MOrX/Dy3EImLN7CmGxhwFCb2MkwIk0oc4Qvxdfpbr6qDnBsyzVMc4TXF9YjPDaTe5X179dU3v6mlBV7vCS6ITdLObdLCjlN3dSGeElUWiK8dn6Eoj9WfInaRDi+YAHOHOH4nk6+rm67aFksER66nkBEyhd/+0sffK5jJyzT7vdM0uyxk/SicbP07YWb9UhJebXaQtEZSptoSlRaIjywvbHcK5FKhIeS0/hCRO4BUkTkbOCXwMRahy/DqAU5Td1cl9ePkU32sjs1hzfmb+bOCV/z10mrOLtXa87u3ZrTe7QkLSmkNJ1hGLUklDvsLuBGfOM+/Rz4BHg+nKIMIxBpCcL5Izpz7fAcFhTu451FW5m2ZifvLdlGosfFad2yOLt3a87s1YpWGcmRlmsYcUe1QUNVK4DngOdEJBPo4Dy6GEbEEBGGdmnB0C4tKPdWsHDjfqau2snU1TuYvmYXItCzTROGds5kaOdMBnfOJCs9KdKyDSPmCSURPgO4CF+AWQzsAuaq6m/Drq4OWCI8NpN71emvTouqsrVIWbKrnDX7vBTsr6DU6e7RLk3onFFB75ZJ5DRx0zZdcFUa/6w+fYnW6yuefGnM90o0J8KPJcBvAh5w1uucCCfw8OgJ+IYPWQ6sBu72qxsDfAMUAHeFchxLhIeuJxDRkAiv7TkpKfPqoo379Kn8Ar3uxfna815fIj177CTt+cdP9UdPzdH7PlyhExZt0TXbD2ppuTcmEpWWCA9sbyz3SjQnwj3OvOA/Ae6tbdSqgmPDo/+7kv3HQJKq9hWRVGCViLwFbAGeBM4GtgILReQjVV1Vj5qMOCPR4yI3uzm52c25Na8r0/Pz6dh7EMu3HeDrrQdYse0Aby/cwstzNwKQ4BZap8DA7Us4qU0GPdtkcFKbDNo1TYmsI4YRJYQSNP4CTAZmq+pCp3f2uroeWAMMj45vNNs0ZziRFKAUOIivr0iB+jr6ISLjgYsBCxpGyLhE6N46g+6tM7h0YAcAvBXK+t1FrPr2IN/sPMTclRtZvGk/Hy379vh2qYluWicrH+1aSvdWGXRrlU73VulUWHrPaGSEbZTbkAVUGunWGYb9NXw9zlOB36rqsyJyGTBGVW9y2l0NDFXV26vYp41yG6PvaavTX9f3tDXx5UiZsq2o4viy5UAZO4+62F/y/T2T4FLaprlpny60S3fRwlNK16xUWqYKRw4fjsrrK1Cd5TRi616J5pxGMnAbvrk1Xjy2VLeds+3n+F5DVV4u9mszg//OaYwA3sCX22iFL4fRBbgMeN6v3dXAE9VpsJxG6HoCEcs5jVD0hNrmmP3A0VJdvGmfjl+wSW95+jO9+oX5eurfPz+eKzmWLxn190/0rneX6StzC/Xf732uB46W1psvltMIbG8s90o05zReA9YA5+J7VXUlvgR1tajqWaG0q8QVwGeqWgbsEpE5wCB8OY2Ofu06ANtqsX/DqBNNkhMY2Kk5Azs1p/XhDeTlDQHgUHEZb382k4z23Vmz4xBfrt7MJ8t38NaCLQD8ff4Usluk0jaxhPWeQir2exlW5iU5oXZDohhGJAglaHRT1R+LyMWq+oqIvAnMCqOmzcBo4DVnGPRhwP/hy110d6ae3QZcji/AGEZUkJGcQLdmbvIGdwJgRsZuzjjjDHYcLObtyXNIaJnD8q0HWLB+J/Mm+VJx/1w4mZPaZDCgUzNO7ZLFsC6ZtLD+JEYUE0rQODa06Hci0gfYge+1UZ0IMjz6k8BLIrISEOAlVf3a2eZ2fEl5N75XZCvrqsMwwomI0LZpCqe08pCX5xvifcaMGfQaOIw3Pp2Nt1kHlm05wPtfbeP1eZsB6Nkmg2FdWjC8q6/zYtOUhEi6YBj/RSid+24C3gX6AS8B6cCfVfWZ8MurPda5LzaTe9Xpb8hEeF19qcn1VV6hbDxYwZq9Xlbv87LO6ZToEujR3EVuKw8DWrvJSnHV2Y9w+1JT7F6pnS9RmwiP9cUS4aHrCYQlwgPbw5WoLC4r1/kb9urDn63Rsx+ZcTzBfv7jM/Wxz9fqax9N04qKilr5EajOEuGh6wlErFxfoUBtE+EikgT8CMjB73WWqv6l1iHMMIygJHncDOmcyZDOmdxx7kkU7jnM5JU7mLJyB49MXQvAmxtmc/mQjlzcvz1NU+0VltEwhJLT+BA4gG/cqZLwyjEMoyo6Z6XxizO68oszurLrYDGPvz+LJQfgzx+u5MGPV3Nenzb8dHAnhnXJrKrDrGHUG6EEjQ6qOibsSgzDCIlWTZI5KzuBv+WNPD4MygdLt/HB0m/JaZHKVcOyuWJop0jLNOKUUBLhzwLjVHV5w0iqHywRHpvJver0x2sivK5+lHiVRTvK+WJrOWv3V5CeAGe0Vc7rlkZ6Ymgj+UaLLzVp05jvlahLhOMbZfZrfP0jyvD1zP76mD3QdtG2WCI8dD2BsER4YHs0Jo8XbdynN768QLPHTtLef/pUH/x4le48cLTa7aPRl+raNOZ7JRoT4RfUOkRVg4g8DFyIbzDC9cD1qvqdM53sQ0CiU/cHVZ3ubDMDaAscdXZzjqruCpdGw4hVcrOb8/y1g3lt4nQWHW7O87M28PKcjfx0cEd+c1Z3m4zKqBPBgsZO4BdAN3xPFy+oank9HXcqvnkyykXkn8DdwFhgD3Chqn7rdCScDLT32+5KdQY2NAwjOB0zXFx94QB+f/ZJPDNzPW8t2Mz7S7Zxa15XulXY6LxG7QgWNF7B91pqFnAe0Bv4TX0cVFWn+BXn4RuMEFVd4mdfCaSISJKq2ldbhlFLOrVI5e+X9OWm0zrzz8/W8PDkb2ieJBxqtpVLB7TH5bKvrYzQCZgIF5HlqtrXWfcAC1R1YL0LEJkIvK2qr1eyXwb8Qp1BD53XUy0AL74e6n/TAOJtaPTYTe5Vp98S4XX345t9Xt5cdZRNRUKnDBeX90ykdwt3TPrSmO+VaEyEfxWsXN1CaMOi3wu8jxO8/Own48t1dPWztXd+ZgBTgGtC0WGJ8ND1BMIS4YHtsZg8VlWdNn26frh0mw7/xzTNHjtJf/nGYn3v02kBt49WXxrzvRKNifD+InLQWRd8r4oOOuuqqk2CRSmtZlh0EbkOX7L9TEfgMXsHJ5Bco6rr/fa3zfl5yBlpdwjwarBjGIZRNS4RLurfjnN6t+a5mRt4Ir+AaVrB3rQNXDc8B4/bFWmJRpQS8MpQVbeqNnGWDFX1+K0HDRjVISJjgDuBi1T1iJ+9GfAxcJeqzvGze0Qky1lPwBdsVtRFg2EYkJzg5ldndmfqb8+gR6abv328mgvGzWbRxn2RlmZEKZH6d+IJfK+ZporIUhE5NmLu7fi+1vqzY18qIq2AJGCyiHwNLMU3n8ZzkRBuGPFIpxap/HZgEv++OpeDR8u47JkveWF5CXuL7BsU47+J+Bzh4cJ6hMdmcq86/ZYIr7sf1flSUq58tL6MTzeWkuIRftwjkYHNSmiSEX2+NOZ7JeoS4fGyWCI8dD2BsER4YHusJsJD8eX1idP0J8/M1eyxk3T03z/RFdu+O6FdpH1pzPdKpBLhlu0yDKNK2qe7GH/LMB75SX92Ha3gwnGzeWDiSo6Wx+fbCSM0Qhnl1jCMRoqIcOnADiTtXcfcoixenruR9xOFw823csmA9tXvwIg77EnDMIxqSUsQHrykL+//cgTNk4Q7/rOM8x+fxde7y4/1ozIaCZYIj8JEZWNO7lWn3xLhdfejrr4cPFTE6qJkJqwtZfdRpVemiws7eendJrauL//1WLxXGlUiHHgYWINvqPX3gWaO/Up8n9QeWyqAU5y6XHwDJxYAj1OpF3mgxRLhoesJhCXCA9vjORFenf6SMq/e89IUHfCXKZo9dpL+6s2vdP2uQ9XqqonO6to05nulsSXCpwJ9VLUfsBbfKLeo6huqeoqqngJcDRSq6lJnm6eBm4HuzmKzCRpGBEn0uDg7J4EZf8jjgi4JTFm1g9H/+wVXvzCfqat24rWRdOOSiAQNVZ2i3w+zPg/oUEWznwHjAUSkLdBEVec5EfBV4IcNItYwjKA0SU7gsh6JzLxzFL87uwfrdhZx86uLOP1f+TyZX2AdBOOMaPh66gbg7SrsPwUudtbbA1v96rby3/NsGIYRYVplJPPrM7tza15XPl+1k1e/3MTDk7/hsc/XcVbvVozs3pIRXbPo1CI10lJrRbm3gqNlXt9S6vez1EtxuZfS8gqWfFvOroVbKPFWUFLmpdRbQVm5Uur1UuZVSssr2LSlhE/2LKPcq5RXKOUVFZR7lZ27i3lxwwK8FRV4KxRvhbL/u6M8umI2XlW8FXDo0BGSv/qCClUOHz5C0oJ8KlQ5erSYxC+nUaFKcUkpCbOnUlJSyqLTvCR53PX6ewhbIlxEPgfaVFF1r6p+6LS5FxgEXKp+QkRkKPC8fj80+yDgIf1+mPSRwFhVvSDAsW1o9BhN7lWn3xLhdfejIX3ZVlTB9M1lLN7p5bsS3y3eMkXo1cJN7xZuTmruwlN2hIyM8J6TlNQ0jpTD4TJlz8EjVHiSOVIG+w8X43UlcuBoKWWSwNEy5VBJOWXqptgLxeVKsVcpLoeyiqASg+IWcLvAI+AWxeNy4XY5dgGXgGgFCR43Lj+bVnhJ8HhwOeUKb/nxstdbTmKCB0HwlpeRmJCACMfXy8vLuLZvGp5azpcSKBEeticNreUotw6XA2/5lbfx36+wOji2QMd+FngWYNCgQZqXl3e8bsaMGRwrV7fub6spoWwbqE1V9sq2YOWq9MeiL/V9ToLpDKVNTX2J1usrUF24fLkS3wc363cXMadgL7ML9jBvw15mbvW9tkp0CR1aQPtmKXRonkL7Zim0aZpCcoILj8uFxyVs2L2cgSf1we0SSr0VHC0t50ipl6WbV9OxSSeOlnk5VFzGgaNlrN9STEJaEgeOlnPwaBl7DwnF3iN+igQo8Vsvwy1Cs1QXTVISwHuUNlnNSUvykJ7kdn56SE30kJbkJjnBTUqCm9REN8mJvvUkj4vkBDfLvlrEyBGnkuRxkeRxkeB2keh2/ddEV7FwfQUjIq+n/Ea5PUP9Rrl16lzAT4CRx2yqul1EDorIMGA+cA0wrgElG4ZRB0SEbq0y6NYqg2uH51DurWD5tgMs3fId85avxZWewbbvjrLq24PsPVxa9U4Wz6/avmo1ACkJbpqkePBUVNAuRWnfLJlebTM4tHcnJ3fvTLOUBJqmJrC5YA2nDcmlaUoCK5YsYMzoM/hy9kxGjRoFHPujO6xWfu5Mc9G+WUqtto0VIpXTeALfyLVTRQRgnqr+wqk7HdiiqhsqbfNL4GUgBfjUWQzDiEE8bhcDOjVnQKfmdC7bRF5e7vG6o6Vedh4sptRb4bz3r2DBosX0P2UAZd4KkjwuUhI8pCa6WbpoPmfmjSQlwX18DhDfH/3hx/fnK/f4vnyggNzs5gBsTfI9ITh/h4wQiEjQUNVuQepmACeEeVVdBPQJoyzDMKKAlEQ3OVlp/2XbV+BmcE7mCW03JrvISE5oKGkG1iM8KhOVlgi3RHg4/YgnXxrzvdKoeoQ35GI9wkPXEwjrER7Y3ph7hPuvx+L15b8ei/dKpHqEx+2TxjFEZDewyc/UFDgQ4noWsKeWh/bfX03bVGWvbAtWPrbub4tFX+r7nATTGUqbmvoSrddXoLpY9KUx3yvhPCcA2ara8gRrVZEknhfg2VDXCRBpa3qcmrapyl7ZFqzsp9/fFnO+1Pc5aWhfovX6iidfGvO9Es5zEmxpjEOjT6zhen0cp6ZtqrJXtgUrTwzQprZEypf6Pieh7qe+fInW6ytQXSz60pjvlXCek4DE/eupuiAii7SqRFAMEi++xIsfYL5EK/HiS7j8aIxPGjXh2UgLqEfixZd48QPMl2glXnwJix/2pGEYhmGEjD1pGIZhGCFjQcMwDMMIGQsahmEYRshY0KgBItJLRJ4RkQkicmuk9dQFEUkTkUUiUuWcJLGCiOSJyCznvORFWk9dEBGXiDwoIuNE5NpI66ktIjLSOR/Pi8jcSOupCyLSSUQ+EJEXReSuSOupCyLSW0TeEZGnReSy2u6n0QcN52LYJSIrKtnHiMg3IlJw7GJR1dXqG433J8CISOgNRE38cBgLvNOwKkOjhr4oUAQk89+zO0YFNfTlYnxzxZQRZb7U8D6Z5dwnk4BXIqE3GDU8J32BCap6AzCgwcVWQw19OQ8Yp6q34pteonaEo8dgLC34hmIfCKzws7mB9UAXIBFYBvR26i7CNyz7FZHWXls/gLPxTXR1HXBBpLXX0ReXU98aeCPS2uvoy13Az502EyKtvbZ++NW/A2REWnsdz0kLIB+YDlwfae119KUV8CTwMDCntsds9E8aqjoT2FfJPAQoUNUNqloKjMeZr1xVP1LV8/BNSBY11NCPPHzDz18B3OxMfBU11MQXVT02Ced+fHO0RBU1PC9b8fkB4G04ldVT0/tERDoBB1T1UMMqrZ4a+nI9cJ+qjgbOb1il1VPDe2WXqt6G75+TWo9JFalJmKKd9sAWv/JWYKjzzvxSfH+cPomArppSpR+qejscn3J3j98f3mgm0Dm5FDgXaIZvcq9YoEpfgMeAcSIyEpgZCWE1JJAfADcCLzW4otoTyJdngPtF5ApgYwR01YZA90oOcA+Qhu9po1ZY0KgB6psgakaEZdQbqvpypDXUFVV9D3gv0jrqA/VNfXxjpHXUB6p6X6Q11AequgKoddI4mlDVjcAtdd1PVL2WiCK2AR39yh0cW6wRL36A+RKNkVQUcwAABT5JREFUxIsfYL6EjAWNqlkIdBeRziKSiC9p/FGENdWGePEDzJdoJF78APMldCKd/Y/0ArwFbOf7zxxvdOw/ANbi+wrh3kjrbCx+mC/RucSLH+ZL3RcbsNAwDMMIGXs9ZRiGYYSMBQ3DMAwjZCxoGIZhGCFjQcMwDMMIGQsahmEYRshY0DAMwzBCxoKGEZOIiFdElvotOZHWVF+IyAAReaGO+3jZf84EEblcRO6tuzoQkdtF5Ib62JcRe9jYU0asclRVT6mqQkQEEI2NgRir4h7gb5WNIuJR1fJa7vM84PE6qfqeF4E5zk+jkWFPGkZcICI5zqQzrwIrgI4i8gcRWSgiX4vIA35t7xWRtSIyW0TeEpE7HPsMERnkrGeJyEZn3S0iD/vt6+eOPc/ZZoKIrBGRN5yAhYgMFpG5IrJMRBaISIaIzBSRU/x0zBaR/pX8yAD6qeoyp3y/iLwmInOA1xw/Z4nIV84y3GknIvKE8zv4HN/cCcf2KcApwFcicobf09kS53gE+V1d49iWichrcHxgxY0iMqReTp4RU9iThhGrpIjIUme9EPgt0B249v+3d3YhVlVRHP/9I2ksp4GKYuolqKapoBr8eMiSjJx6CYw+5iUlLIIIYRR6S/AlLJCeoghu0EuESQmClBZCD4M2k1MS9vGWIUapiKbNKI3/HvaSOXO793qpQZg76wcX7tln77XOWdy719lr3buX7f2SBuN4GSBgp6QVwFnKXjz3Uz7/48CBS+h6gVIbYqmkq4ARSXvi3ABwD3CU8vS9XNIosA0Ysj0m6VpgAnifUvhqWFIf0HXROVRYQnF6Ve4GHrQ9IelqYJXtSUl3ULaRWAI8CdwZfW8CfmB6JTAAHLTtcJCv2B6RtAiYbGGrE8BrwAO2j0u6rnJN3wAPAaOXsF3SYaTTSOYqM8JTkdM4bHt/NA3G69s4XkSZGLuBHfG0jKR2NnIbBO6t5Ah6QtZ5YNT2kZD1HXArcAr4zfYYgO3TcX47sEnSq8A64IMGunqBY3VtO21PxPsFwNuxYpkC+qJ9BfCR7SngqKS9lfGPU6pNQnFsb0n6EPjU9pFwGo1sdR+w3fbxuI9qsZ8/gP7G5ko6mXQaSSdxtvJewBbb71U7SBpuMf5vpkO2XXWy1tveXSfrYeBcpWmKFt8p239J+oJSEe5ZYHGDbhN1umHmfW0AfqdM6FcAk830VRgEnopreEPSLsqGdiOSHqO5rda3kNkV15rMMzKnkXQqu4F1EYJB0i2SbqRUxFstaWHE85+ojPmF6Yn86TpZL0taELL6JF3TQvfPQK+kpdG/W9JFZ1KjJKTHbJ9sMPZH4PYWsnsoq5gLwBpKPWjivoYi/9ILrAzdPcCVtk/E8W22v7f9JmUL7X6a22ov8Iyk66O9Gp7q499htGQekCuNpCOxvUfSXcC+yE2fAZ6zPS5pG3CQEmIZqwzbCnws6SVgV6W9Rgk7jUdS+RiwuoXu85KGKKVbF1KeyB8Fztg+IOk0TUqh2v5JUo+kbjeur/0O8ImktcDnTK9CdgCPUHIZvwL7on0V8GVl/LCklcAF4BDwme1zTWx1SNLrwFeSpijhq+dDznJgczMbJJ1Lbo2ezGskbaZM5lsvk76bKSWD+5v9JFjSBuBP27VZ0FcDapVcz/9G0gCw0faa2ZKZzB0yPJUkl4lYHXxNKYrT6j8k7zIzV/Kfsf3ibDqM4AZg0yzLTOYIudJIkiRJ2iZXGkmSJEnbpNNIkiRJ2iadRpIkSdI26TSSJEmStkmnkSRJkrRNOo0kSZKkbf4BC2rXFdy8OxIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BbE9X5DSQL3h",
        "outputId": "0d144dc9-d09d-4a9d-c925-659e38a4b1db"
      },
      "source": [
        "#Diseño del compensador\r\n",
        "t = 70*np.pi/90\r\n",
        "\r\n",
        "f_p3 = fs/2\r\n",
        "f_z2 = fo*np.sqrt((1-np.sin(t))/(1+np.sin(t)))\r\n",
        "f_p2 = fo*np.sqrt((1+np.sin(t))/(1-np.sin(t)))\r\n",
        "f_z1 = f_z2/2\r\n",
        "print(\"f_z1 = {0}; f_z2 = {1}; f_p2 = {2}; f_p3 = {3}\\nf_esr = {4} \".format(round(f_z1,2), round(f_z2,2),round(f_p2,2),round(f_p3,2),round(f_esr,2)))\r\n",
        "\r\n",
        "\r\n",
        "#Calculo de los componentes a partir de las frecuencias obtenidas\r\n",
        "C_f3 = 2*10**(-9)\r\n",
        "R_f3 = 1/(2*np.pi*C_f3*f_p2)\r\n",
        "R_f1 = 1/(2*np.pi*C_f3*f_z2) - R_f3\r\n",
        "R_c1 = (2*np.pi*fc*Lo*Co*V_ramp)/(V_in*C_f3)\r\n",
        "R_f2 = R_f1*V_ref/(V_out-V_ref)\r\n",
        "\r\n",
        "C_c1 = 1/(2*np.pi*R_c1*f_z1)\r\n",
        "C_c2 = 1/(2*np.pi*R_c1*f_p3)\r\n",
        "\r\n",
        "\r\n",
        "print(\"C_c1 = {0}nF; C_c2 = {1}pF; C_f3 = {2}nF; \\nR_c1 = {3}; R_f1 = {4}; R_f2 = {5}; R_f3 = {6} \".format(round(C_c1*10**9,2), round(C_c2*10**12,2), round(C_f3*10**9,2),round(R_c1,2),round(R_f1,2), round(R_f2,2)\r\n",
        ",round(R_f3,2)))\r\n",
        "print(\"Se calcularon valores comerciales\")\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "f_z1 = 1173.68; f_z2 = 2347.36; f_p2 = 10795.29; f_p3 = 100000.0\n",
            "f_esr = 3821247.13 \n",
            "C_c1 = 12.95nF; C_c2 = 152.04pF; C_f3 = 2.0nF; \n",
            "R_c1 = 10467.79; R_f1 = 26529.35; R_f2 = 5823.52; R_f3 = 7371.5 \n",
            "Se calcularon valores comerciales\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhOJI6yR4aiG"
      },
      "source": [
        "#Calculo de los componentes a partir de las frecuencias obtenidas\n",
        "\n",
        "#C_c1 = 12.95nF C_c2 = 152.04pF; C_f3 = 2000.0pF; \n",
        "#R_c1 = 10467.79; R_f1 = 26529.35; R_f2 = 5823.52; R_f3 = 7371.5\n",
        "\n",
        "#C_f3 = 2*10**(-9) \n",
        "#R_f3 = 1/(2*np.pi*C_f3*f_p2) # = 7371 ~ 7320\n",
        "#R_f1 = 1/(2*np.pi*C_f3*f_z2) - R_f3 # = 26529 ~ 26580 ~ 26700\n",
        "#R_c1 = (2*np.pi*fc*Lo*Co*V_ramp)/(V_in*C_f3) # = 10467 ~ 10467 ~ 10500\n",
        "#R_f2 = R_f1*V_ref/(V_out-V_ref) # = 5823 ~ 5860 ~ 5900\n",
        "\n",
        "#C_c1 = 1/(2*np.pi*R_c1*f_z1) # = 12.95nF ~ 12.91nF ~ 12nF\n",
        "#C_c2 = 1/(2*np.pi*R_c1*f_p3) # = 152.04pF ~ 151.58pF ~ 12nF\n",
        "\n",
        "#C_f3 = 2*10**(-9) \n",
        "#R_f3 = 7320\n",
        "#R_f1 = 26700\n",
        "#R_c1 = 10500\n",
        "#R_f2 = 5900\n",
        "\n",
        "#C_c1 = 12*10**(-9)\n",
        "#C_c2 = 12*10**(-9)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "XUQzZI0BOKWQ",
        "outputId": "ccc2b992-071b-4d6e-82b3-2d66be47f6f6"
      },
      "source": [
        "#Compensador por adelanto de fase\r\n",
        "G_c = ((1+s*R_c1*C_c1)*(1+s*(R_f1+R_f3)*C_f3))/(s*R_f1*(C_c1+C_c2)*(1+s*R_f3*C_f3)*(1+s*R_c1*(C_c1*C_c2)/(C_c1+C_c2)))\r\n",
        "print (G_c)\r\n",
        "bode_Gc = bode(G_c)\r\n",
        "#bode_Gc_delay = bode(G_c*delay)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  1.205e-16 s^2 + 2.666e-12 s + 1.311e-08\n",
            "-------------------------------------------\n",
            "1.057e-22 s^3 + 7.435e-17 s^2 + 4.557e-12 s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lxHJpl4YA5BcU8vG4GRTUac7CbUeZuGY/x0/lMWr1FPq2qMdFnRoQk22jBPiaJ53DPKBHsWXzS1hWpURkKDACcALvqeq/vHk8U3FhIQ5uP7c516Y0ZuSsrbw3exsTV+/n5j5NeXBwKztVUA6FhcqmYwXM+nEtE1fvY+9x17/LmsQ6uKN/cwa1qU9KszrMnzOb1FTXDQFpuwP7VF6I00GLOCepA1tw98AWFBYqH46dzqGIJCat3s+z368G4OOtcxnasQFXdk/yc8TBodTOQUQaAElApIh0x/XrFaAW4NX7GN2DC70BXADsBhaJyFhVXevN45rKiY0I5fEL23Jr32aMmLaJ0Qt3MmbRLm7q05SuofYvv9IUFCqLtx9lwur9TFi9jwMncghz7mBgm3geu6ANg9rUZ93SBaSmtvd3qD7hcAgtaju5I7Udf7ioLZsPZvDWuPlszCrkuQnr+c+kDXSu5yAv4QCD29b3d7g1Vlm/HC4ChgONgZeKLD8JPOXFmAB6A5tVdSuAiIwBhgHWOQSAhFoR/OOqztw7sCWvTd/Ex/N34EBZmbeW+1JbkBBrvyRUlR0nCvjHT2sZu2IvB07kEB7iILVtfZKd6Tx09SBiI/57vj1YB1AREVonxnJFyzBSUwew7XAmXy7exWfzt3L3x4tJiA2nV3whyZ0ySY6305hVqdTOQVU/Aj4SkWtU9RsfxgSuXyy7irR3A318HIOppKb1onj+uq48dF4rnv5sDh/N386nC3dwS99m3D2gBQ3igq+T2H0six+W7+X7ZXvYdDCbUOd2UtsmcHnXRpzXLoGY8BDS0tL+p2Mw/9U8PponhrYjJWwf2qADXyzayYT1B/nphTQGtalPn7gCBqnaXXNVQEp7+lFEblHV0SLyOCU/If1SCS+rmqBErgWGqupd7vatQB9VfajINvcA9wAkJiamjBkzhoyMDGJiYn7ZT1nt0/PFt6kMT/d1tu1KW1/Sck9yLL7OXzlnShRjt+Qxb28+DoHeDZ0MTQ6lWS2nV3Iu629RUeXZz+ltM/OURfvzmb83nw3HXKfX2tRx0L1uAQOaRRMTJiW+rqxl1T3n8q4vz2e3eHv3kQyWHAtj2s58TuQqTWMdDG0eSu8GTkIq+PyNt3Mu73tcvF1V7/PgwYOXqGrPEleqaokTcK/7v38qaSrtdVUxAecAk4q0/wj8sbTtU1JSVFV1xowZWlRZ7dPzxbepDE/3dbbtSltf0nJPcize9nfOO49k6p/HrtYOz07QZk+M0xvema8vfzFFCwoKPdq/pzmX9beoKE/3k52Xry+MmaL3fbJYWz81Xps9MU4HvzBDX5u2UXceySxzX4GasyfbeppzRT7Lp3Lz9c+fTNbzX0zTZk+M077/nKpvp23W46dyzx64h3FWZFtP3s+SllXkPS8vYLGW8r1a1mmld9z//UuFuqTKWQS0FpHmwB7gBuAmP8RhvKBJ3Sj+dHlHHhvShi8W7eSDuduZvzWHH3fN5Ja+zRjWLYm60WH+DrPc8gsK+Xn7Ucat3MdPK/dx/FQe8TFHublvU67u3phOSbXsdIcXRYQ6GdQ4lGdvGsjMjYcYOWsrz01wPc1/V//mDD832U7XlYMnT0jXB+4Gkotur6p3eCsoVc0XkYeASbhuZR2lqmu8dTzjH3GRodwzsCW3n9ucF7+YzvyjIfzlx7X846d1nNcugWtSGuOs5qXC8wsKWbjtKD+t2sek1fs5kplLZKiTizom0sJ5hAeuPo8QpyeV8U1VcTiEwe0SGNwugZW703l12mZenLKR9+Zss06iHDx5zuEHYDau4UJ9NsiwukafG3/WDU3AC3U66NsohCdv6s+G/Sf5Zuluvlu2h8lrDxAbCtdkrOGKbo3o2rh2tajhdDI7jwVbjzJ9/QEmrTnA0cxcosKcnNcugUs6NyS1bX2iwlwXlq1j8K8ujWvz3m09WbX7OCOmbfylk7h7QHNu62edRFk86RyiVPUJr0diDNC2QSxPXdKeP1zUltmbDvPmxKV8tnAnH87bTlxkKP1bx5NYmEe749k+u9spv6CQFbvTmb3pMD8tPsXWyVMoKFSiw5yc3z6RSzo3YFCbBCLDrIhcddW5cRzv3daLlbvTGTF1Ey9M3sj7c7bx0HmtuaVvU4/rTAUTTzqHcSJyiRYZR9oYbwtxOhjcLgHZH0H33ucya9MhZm48xKyNhzh4MpdRq6fRNjGWc1rWQ47nEbvjGG0SK39nTm5+IVsOZbB+/wnW7TvJun0nWL4znZM5+YhAci0H9w1qQf9W9enRrLZ9qQSYLo1r8/7wXqzYlc5/Jq3nb+PW8sHcbTx+YRuGdU3CUQ1+mVYXnnQOjwJPiUgOkIfrSWlV1VpejcwYt7ioUC7v2ojLuzZCVRk9bgZZtZoxa9MhxizaSXZeIR+smQdA3Qih89afaVk/hqMHctns3EpUWAg79uaTt/YAUWFOMnPySc/KI/1Urvu/eaRn5bL1UCabD2b8Mo5BmNNB68QYLuvaiAGt4+nXsh7Lf55Hamo7f/45TBXo2qQ2n97Vl9mbDvGvCev5zRcrGDlrG08MbcugNvbUNXg2nsOZpRWN8RMRoUmsg9RBLbl3UEsKC5WvJ86gbnJHNhw4yZyVmzl0ModF24+SlVvA95v/+2zxOysXn7G/EIdQOyqUuMhQmtaNYnC7BNo3rEX7BrE0j4+2awY13IDW9Tm3ZTw/rtzLC5M3MPyDRZzToh5DG/js8mq15cndSiUV2DsO7FBVGxHO+JXDISREOUjtkMiQDol0lN2kpg4AYPqMGfQ6pz+ZOQXMmD2PTt1SyMrNJzo8hLjIUOpEhxEd5rTbS4Ocw+Ea7fDiTg35bOEOXp2+mQVbc1mds4LfX9Q2aAtHenJa6U1cFVhXududgdVAnIjcr6qTvRWcMZXhEPllbIGGMQ46N7YxqkzpwkIcDD+3OVf1aMwTH83g++V7+GnVPu4b1JK7B7QIuhsOPPnNvBforqopqpoCdAO24qqY+h9vBmeMMb4WFxnKDe3CmPrbQQxqU5+XpmzkvBfT+H7ZHgqr+XM3VcmTzqFN0QfQ1FU2u526K6YaY0xN1KxeNG/dksIX9/QlPiacx75YzjVvz2PFrnR/h+YTnnQOa0TkLREZ5J7eBNa6R4M7c9RyY4ypQfq0qMcPD57L89d2YdfRUwx7Yy6//2oFB09mn/3FAcyTaw7DgQeAx9ztucDvcHUMg70TljHGVB8Oh3BdzyYM7dSA16dvZtTcbUxYvZ9Hzm9Fixp6qsmTW1lPAS+6p+IyqjwiY4yppmIjQvnjJe25vlcT/v7TOv45fj0NooSQpIOktk3wd3hV6qynlUSktYh8LSJrRWTr6ckXwRljTHXUon4Mo4b34oPbewEw/INF3P3xYnYdzfJzZFXHk2sOHwBvAfm4TiN9DIz2ZlDGGBMIBrdN4G/9I3liaDvmbj7MkJdm8vKUjWTnBf5DdJ50DpGqOg3XqHE7VPXPwKXeDcsYYwJDqEO4P7Ul0x4fxIUdGzBi2iaGvDSTyWv2nx6sLCB50jnkiIgD2CQiD4nIVUDVjDFpjDE1RMO4SF67sTuf392XqDAn93yyhOEfLGJ/ZqG/Q6sQTzqHR4Eo4BEgBbgVuM2bQRljTKA6p2U9fnpkAM9e1oElO47xzJxTvDh5A6dyA+tU01k7B1VdpKoZqrpbVW9X1atVdYEvgjPGmEAU6nRwZ//mTH98EL0aOnlt+uaAO9VU6q2sIjK2rBeq6hVVH44xxtQcCbUiuLdLBI9d1plnf1jNPZ8sIbVtfS5OqP6nmsp6zuEcYBfwObAQ1zgOxhhjyqlPC9eppo/mbeeVqZuYsymfPWEbeSC1JRGh1bOgX1mnlRoATwGdgBG4Cu0dVtWZqjrTF8EZY0xNEep0cNeAFkx7fBA9E528Om0TF748ixnrD/o7tBKV2jmoaoGqTlTV24C+wGYgTUQe8ll0xhhTwyTWiuC+rhF8dlcfQp3C7R8u4p6PF3P4VPU61VRm+Qx3cb1LgRuBZOBV4Dvvh2WMMTVbv1bxTHh0IO/N2cpr0zaTtr6Ag5Gbuat/C8JC/D8CYakRiMjHwHxcA/38RVV7qerfVHWPz6IzxpgaLCzEwQOprZj6+CA613fyn4kbuHjELOZtOezv0Mq85nAL0BrXcw7zROSEezopIid8E54xxtR8SbUjebh7BKOG9yS3oJCb3l3IOyuy/VoWvKxrDg5VjXVPtYpMsapay5dBGmNMMDivXSJTfjOIR85vzaL9BZz/wkw+mredAj+UBfdkPAdjjDE+EhHq5LcXtKFR7m5+2h/Nn8au4cvFu7imaQGpPozD/1c9jDHGnKFBtIOP7+jNGzf14HBGDn9bkM1T363ieJZvBuC0zsEYY6opEeHSLg2Z9ngqFyaH8MWiXZz3Yhpz9uR5vQyHdQ7GGFPNxYSHcGO7cH58qD/N6kXx3qpcrn9nARsPnPTaMf3SOYjI8yKyXkRWish3IlK7yLo/ishmEdkgIhf5Iz5jjKmOOjSqxdf39eP2TmFsPHiSS0bMZsI275xm8tcvhylAJ1XtAmwE/gggIh2AG4COwFDgTRGpnoVHjDHGDxwOYVDjUKY/nso1PRrTMNo7Ze/80jmo6mRVzXc3FwCN3fPDgDGqmqOq23CV7OjtjxiNMaY6qxsdxr+v7UK3BO/cdCr+ri0uIj8CX6jqaBF5HVigqqPd694HJqjq1yW87h7gHoDExMSUMWPGkJGRQUzMfwepK6t9er74NpXh6b7Otl1p60ta7kmOxdcFS85l/S0qqjz7sZw9X1+ez27xdiDmXN73uHi7qnIePHjwElXtWeJKVfXKBEwFVpcwDSuyzdO4ajWd7qReB24psv594NqzHSslJUVVVWfMmKFFldU+PV98m8rwdF9n26609SUt9yTH4u1gybmsv0VFlWc/lrPn68vz2S3eDsScy/seF29XVc7AYi3le9VrD8Gp6pCy1ovIcOAy4Hx3kAB7gCZFNmvsXlamJUuWHBaRHUAccLzIqrLap+fjgaoqZFL8eBXdrrT1JS33JMfi64Il56LzVZWzp/l6sq3lXPryinyWIXByLu97XLxdVTk3K3VNab2GNydcF5vXAvWLLe8IrADCgebAVsBZjv2O9LR9ep4yes4K5DWyKrYrbX1Jyz3JMVhzLjZfJTl7mq/lXLmcK/JZDqScy/se+yLn4pO/yme8jqsDmCIi4LrOcJ+qrhGRL3F1HPnAg6panlG5fyxHu/i6quDpPs+2XWnrS1penhyDLWd/5uvJtpZz6csD5bPsybaevJ8lLfN1zv/D7xek/U1EFmtpF2RqKMs5OFjOwcFbOdsT0jDS3wH4geUcHCzn4OCVnIP+l4Mxxpgz2S8HY4wxZ7DOwRhjzBmsczDGGHMG6xyMMcacwTqHYkTkShF5V0S+EJEL/R2PL4hIexF5W0S+FpH7/R2Pr4hItIgsFpHL/B2LL4hIqojMdr/Xqf6Ox9tExCEi/xCR10TkNn/H4wsiMsD9/r4nIvMqs6+g6BxEZJSIHBSR1cWWD3WPG7FZRJ4EUNXvVfVu4D7gen/EWxXKmfM6Vb0P+BVwrj/irQrlydntCeBL30ZZtcqZswIZQASw29exVoVy5jsMVwmePAI0Xyj3Z3m2+7M8DvioUgf2xmPX1W0CBgI9gNVFljmBLUALIAxX2Y4ORda/CPTwd+y+yhm4ApgA3OTv2H2RM3ABrrFDhgOX+Tt2H+XscK9PBD71d+w+yPdJ4F73Nl/7O3Zf5Fxk/ZdAbGWOGxS/HFR1FnC02OLewGZV3aqqucAYYJi4/BtXqfClvo61qpQnZ/f2Y1X1YuBm30ZadcqZcyrQF7gJuFtEAvKzUJ6cVbXQvf4YrvI1Aaec7/FuXLkClKcMT7VS3s+yiDQFjqtqpcYQ9VdtpeogCdhVpL0b6AM8DAwB4kSklaq+7Y/gvKTEnN3nn6/G9YUx3g9xeVOJOavqQ/BLdeDDRb44a4LS3uergYuA2rjqm9UUpX2WRwCvicgAYJY/AvOi0nIGuBP4oLIHCObOoUSq+irwqr/j8CVVTQPS/ByGX6jqh/6OwVdU9VvgW3/H4SuqmoXrizKoqOqfqmI/AflTuopUaOyIAEqc26cAACAASURBVGc5W841UbDlCz7IOZg7h0VAaxFpLiJhuC5OjvVzTN5mOVvONVGw5Qs+yDkoOgcR+RyYD7QVkd0icqeq5gMPAZOAdcCXqrrGn3FWJcvZcqYG5hxs+YL/craqrMYYY85QIy5Ix8fHa3JyMpmZmURHR/+yvKz26fni21SGp/s623alrS9puSc5Fl8XLDmX9beoqPLsx3L2fH15PrvF24GYc3nf4+Ltqsp5yZIlh1W1fknrArpzEJHLgcuTkpJ44YUXyMjIICYm5pf1ZbVPzxffpjI83dfZtittfUnLPcmx+Lpgybmsv0VFlWc/lrPn68vz2S3eDsScy/seF29XVc6DBw/eUepKfz/9VxVTSkqKqqrOmDFDiyqrfXq++DaV4em+zrZdaetLWu5JjsXbwZJzWX+LiirPfixnz9eX57NbvB2IOZf3PS7erqqcgcVayvdqQP9yMCaQ5OQXkJ1X6P7wwYlc5XBGDoWq1IkKI9QZFPeHmABhnYMxXrT1UAbT1x9k+vqDLNp+lLyCYjeATJ8KQFSYk5Rmdejboh5h6QX0yy8kLMQ6C+M/1jkYU4VUlXlbjjB13QHGL8viwMSZALRJjOGOc5uTUCsCh4AAmzdvpm2b1gBsOZTJgq1HeH7SBgBeWjqZnsl1SG2bwHU9G/srHRPErHMwpoqs3nOcP49dw+IdxwgPcdC2toOHLmhHatsEmtSNOmP7tLwdpJ6T/D/LjmbmMurHWWRENWTB1iP8bdxaXp6ykf4NoU23UzSqHemjbEyws87BmEo6lpnLC5M38NnPO6kbFcZzV3fmym5JLJw3+4wv/7OpGx1GzwYhpKZ2BFwdzruzt/Ljir1M/c8MLuvSkLsGtPBCFsb8L+scjKmggkLls5938uLkDZzMzue2c5L5zQVtiIsMrbJjdEqKY8QN3RkYd4x1BQ34/OedfL98Lx3rOUhoc4IOjWpV2bGMKco6B2MqYO3eE/zuqxWs3XeCvi3q8pcrOtG2QazXjlcv0sEzqR14+PzWjPl5J69PXc9lr83m1+ck0yvSqhyYqmedgzHlNGP9QR76bCnR4SG8flN3Lu3cEBHxybHjIkO5d1BLknJ2sjCrPh/P3863oZBVZxfX9LAL16bq2L1yxpTD9J153PnRIpLjo/nx4f5c1qWRzzqGomLChL9d2YmxD/UnIcrB779eybVvz2PHiYAd8MxUMwFdeK9I+Yy7R48ebeUzKPux+mDLuSrLKhSq8uWGPCZuz6NrfSf3dw0nIqTsTsFXOZ84mcGK4+F8uTGXjFzl4uZhXNU6lJyszIAoJVHSciuf4bPyGUtUtWeJK0t7dDqQJiufUXLbymeUPF9ep3Lz9f7Ri7XZE+P0jjcmal5+gUev83XO6Vm5eutrE7XZE+P0opdn6sdjp3kU59lY+YzKbRuo5TPstJIxZTiSkcON7y5gwur9PHNpe25pH0ZINS1zERcZyh2dwnnv1z05nJHDX+ad4p2ZWygoDNyzA8Z/quf/5cZUA+lZudz47gLW7TvBWzencNeAFn65vlBeQzokMumxgXRNcPLchPXcOHIBu45m+TssE2CsczCmBKdyC7jjw0VsP5zFqOG9GNqpgb9DKpd6MeE81C2cF67rytp9Jxj6yiy+WbLb32GZAGKdgzHF5BUU8sCnS1i+K51Xb+xGv5bx/g6pQkSEa1MaM/GxAXRMiuPxr1bwu69WkJWb7+/QTACwzsGYIgoLlT98vZIZGw7x9ys7M7RTQ3+HVGmN60Tx2V19eOS8VnyzdDfDXp/LxgMn/R2WqeasczDGTVX5x/h1fLdsD7+7sA039Wnq75CqTIjTwW8vbMsnd/ThWFYeV7w+h68W7/J3WKYas87BGLe3Z27l/TnbGN4vmQcHt/J3OF7Rv3U84x/tT4+mdfj91yv57ZfLycyx00zmTNW2cxCRoSKyQUQ2i8iT/o7H1GxfLtrFvyeu54qujfi/yzoExF1JFZUQG8End/bhN0Pa8N2yPVz5xly2HMrwd1immimzcxCRxiLyOxH5QUQWicgsEXlTRC4VEa91LCLiBN4ALgY6ADeKSAdvHc8Et5+3HeWp71YxoHU8L1zXFYej5nYMpzkdwqNDWvPpnX04kpnLsNfnMmnNfn+HZaqRUr/gReQDYBSQC/wbuBF4AJgKDAXmiMhAL8XVG9isqltVNRcYAwzz0rFMENt3/BQPfLqEpnWjeOPmHkE3NGe/VvGMe7g/LetHc+8nS3hh0gZ7aM4AZdRWEpFOqrq61BeKhAFNVXVzlQclci0wVFXvcrdvBfqo6kNFtrkHuAcgMTExZcyYMVZbibJrrgRbzmerP5NXqDy3MJu9GYU8e04kSTFn7xj8UWeopGVVXU8qt0AZvS6XWbvz6RTv5L4u4cSESbn3Y7WVPFtvtZUqOAHXAu8Vad8KvF7a9lZbqeS21VYqef60J75eoc2eGKcTVu0tMzZP4qzIttWxntRnC3do66fGa/9/T9NVu9PLvR+rreTZ+kCorXTW8RxEZBVQ/OfFcWAx8HdVPVKhLqtse4AmRdqN3cuMqRKfLdzJmEW7eHBwyxrxLENVubF3U9o1iOX+0Uu59u15vHhdN6L9HZTxC09OsE4AfgJudk8/4uoY9gMfeimuRUBrEWnuPn11AzDWS8cyQWbJjmP8aexqBrWpz28vaOvvcKqd7k3r8OPD/enUKI4HP1vKd5tyKbTrEEHHk5HghqhqjyLtVSKyVFV7iMgt3ghKVfNF5CFgEuAERqnqGm8cywSXgyeyuX/0EhrGRfLqDd1xBsGdSRVRPzacT+/uw9PfrebrJbvJ+3wpL1zXlagwGzwyWHjyTjtFpLeq/gwgIr1wfWEDeO3pGVUdD4z31v5N8HHVTFrKyex8Pr6zN3FRof4OqVoLD3Hy/LVdcGYc5KvV+9lxJIt3f92TRrUj/R2a8QFPTivdBbwvIttEZBvwPnC3iEQDz3k1OmOq0POTNrB4xzH+c20X2jWo5e9wAoKIcHHzUN6/rRc7j2RxxetzWbLjmL/DMj5w1s5BVRepamegG9BNVbuo6s+qmqmqX3o/RGMqb/nBfEbO2sqtfZtxeddG/g4n4Axul8C3D/QjOtzJjSMX8OOKvf4OyXjZWTsHEUkUkfeBMap6XEQ6iMidPojNmCqxN/0U767KoUPDWjx9aXt/hxOwWifG8v0D59KtSW0e/nwZb8/ccvpWc1MDeXJa6UNcF4ZP/3NrI/CYtwIypirlFRTy8OfLKCiEN27uQUSo8+wvMqWqEx3Gx3f25vKujfjXhPU8+8Nq8gsK/R2W8QJPOod49+mjQnDdSQQUeDUqY6rIi5M3smTHMYZ3Cqd5vN2xXxUiQp2MuL4b9w1qyegFO7n3kyU2gFAN5EnnkCki9XA/CCcifXE9BGdMtbbiUD5vz9zCjb2b0reh3YJZlRwO4cmL2/H3KzsxY8NBrn9nAQdPZvs7LFOFSq2t9MsGIj2A14BOwGqgPnCtqq70fnhlE5HLgcuTkpLuHj16tNVWouyaK8GU89HsQp6dk0XdSCfP9o0g91RmQNTcKW25t2srVSaP5QfzeXNFDrGhwv0dCmmVYLWVzra+xtRWwvU8REdcHUSoJ6/x5WS1lUpuB2ttpbz8Ar32rbna5qlxuvngyXLFeTY1vbaSp/EUt3JXuqb8bbJ2fGacrth1rNz7stpK1a+2Ulklu68+PQFXAG2BNsDl7mXGVEsvT93Iou3HuK1jOC3rV80vJFO2zo3j+Pq+fkSECDeOXMC8zYf9HZKppLKuOVzunu7E9eDb6dpK7wF3eD80Y8pv9eF83kzbwvU9m9CvkV1n8KXk+Gie7hNB4zpRDP9gERNX7/N3SKYSSu0cVPV2Vb0dCAU6qOo1qnoNrtNLVnfAVDsHTmTzzsoc2iTE8ucrOvo7nKBUJ8LBF/f2pVNSLR74dClfLNrp75BMBXlyt1ITVS36T4ADQFMvxWNMhRQUKo+OWUZOAbxxc3ciw+x5Bn+pHRXG6Lv6MKB1fZ74ZhVvz9zi75BMBXjSOUwTkUkiMlxEhuMq3z3Vu2EZUz4jpm1iwdaj/LpDGK0SYv0dTtCLCgvh3V/3/OVhuX9PXG9PUweYs56UVdWHROQq4PR40SNV9TvvhmWM59YeKeC1xZu4pkdj+idYUbjqIizEwSvXdyM2IoS30rZwKreAQbHWQQSKUjsHERH3rU64O4MzOoSi2xjjDwdPZvP2ihxa1o/hb1d25Od5c/wdkinC6RD+cWUnIkKcjJq7je2NQxg0SHHYOBrVXlmnlWaIyMMi8j/XF0QkTETOE5GPgNu8G54xpSsoVH7zxXKy85U3buphA9FUUyLCs5e154HUlqTtzud3X62wekwBoKxP01Bct6x+LiLNgXQgEleHMhl4RVWXeT9EY0r2+vTNzN18hNs7hdG2gV1nqM5EhD8Mbcf+PTv5dtkecvILeeWGboQ6Pbnsafyh1M5BVbOBN4E3RSQUiAdOqWq6r4IzpjQzNhzklWkbubp7EgPtOkPAuKJlGO1at+Sf49eTk1/IGzd3JzzE7iyrjs5aW6k6s9pKwVlbafvhDP6zXKgX6eCZvhHkFambFOg1d0pbXp1rK5Vn29Prp+7IY/S6XDrFO3mke/gZta+stlKA1Faq7pPVViq5XRNrK2Xl5Gv/v4/XLn+epDsOZ56xTaDX3ClteXWvreTptkXXf75whyY/OU5veW+BTpw6vcz9WG0l39dWsit4JmCoKk9/t4rdJwsZdXsKTetF+TskUwk39G6K0yH84ZuVHD7iYMCAfLupoBrx6GqQiDQTkSHu+UgRsat/xuc+WbCDb5ft4cpWoQxum+DvcEwVuK5nE176VVfWHy1k+AeLyMyxQYOqC0/GkL4b+Bp4x72oMfC9N4MyprglO47y1x/Xcn67BC5vaaW9apKrujfm3q7hLNlxjNtG/UyGdRDVgie/HB4EzgVOAKjqJsD+2WZ85uDJbO4fvZSkOpG8dH03HGIPUNU0fRuG8OoN3Vm2K51fv7+QrLzAvVGmpvDkBF+OquaK+wMpIiG4hww1xttyC5T7Ry/lRHYeH93Rm7hI+9VQU13apSFOBzz02TJeOC6ce24ecVH2fvuLJ78cZorIU0CkiFwAfAX86N2wjIHCQuW9VTks2XGMF6/rRvuGtfwdkvGyoZ0a8tYtKew8UchN7y3gaGauv0MKWp50Dk8Ch4BVwL3AeOCZqji4iDwuIioi8e52qogcF5Hl7un/quI4JjA9P3kDP+8v4I8Xt+PSLg39HY7xkQs6JPJIj3A2H8zgxpELOHQyx98hBaWzdg6qWqiq76rqdcA9wEL3/bGVIiJNgAuB4qOBzFbVbu7pr5U9jglMny3cyVtpWxjcJIR7BrbwdzjGx7rUD+GD4b3YeTSL60fO51i21WLyNU/uVkoTkVoiUhdYArwrIi9XwbFfBv6AXb8wxaRtOMizP6wmtW19bmkfhtgF6KDUr1U8H9/Zm4Mncvjnwmx2H8vyd0hBxZPTSnGqegK4GvhYVfsA51fmoCIyDNijqitKWH2OiKwQkQkiYmM9Bpm1e0/w4KdLaZsYy+s39cBppZ2DWq/kuoy+qw+Zecqv3p7P9sOZ/g4paJy1tpKIrMJ1+ucj4GlVXSQiK1W1y1leNxVoUMKqp4GngAtV9biIbAd6quphEakFFKpqhohcAoxQ1dal7P8eXKe5SExMTBkzZozVVqLsmivVPee8kCj+Oj8bEXi2bwR1IhxBUWfIk+XBlnPx9rr9GbyxRghxCL/vGUGcZAV0zjWithJwHbASeNPdbgF8c7bXlbG/zsBBYLt7ysd13aFBCdtuB+LPtk+rrVRyO5BqK/04abpe9PJM7fh/E3Xt3uNlvq4m1hk62/Jgy7mk9ob9J7TX36dolz9P0pHfTi1x20DJORBqK3lyQforVe2iqg+421tV9ZoKdVOu169S1QRVTVbVZGA30ENV94tIA3GfYBaR3rhOex2p6LFMYEjPyuX5xdlsPZzJ27ek2C2rpkRtEmP55v5+1I0O4/lF2Uxbd8DfIdVoZ30ITkQigDuBjkDE6eWqeocX4rkWuF9E8oFTwA3u3s3UUMez8rjl/YXsySjkvdt60b91vL9DMtVYk7pRfHXfOVz76nTu+WQJ/7q6M/X9HVQN5ckF6U9wXTu4CJiJq7bSyaoKwP0L4rB7/nVV7aiqXVW1r6rOq6rjmOrndMewcX8Gj3QPJ9WK6RkPxMeE80TvCM5pUY/ff72S8dvsQTlv8KRzaKWqzwKZqvoRcCnQx7thmZru+Kk8bh21kA37T/LOrSl0qW+lmo3nIkOE94f35LIuDflyQx7/+GkthYV2kqEqefKJzHP/N11EOgH7scJ7phJOZOfx6/cXsm7fCd6+JYXB7RJI27/W32GZABMe4uTVG7pzKv0Q787ext70bK5ItA6iqnjSOYwUkTrAs8BYIAawshamQo5l5jL8w0Ws3XeCt25O4fz2if4OyQQwh0O4pX0YfTq14rkJ61m9w0GnnqdIqh3p79ACnid3K72nqsdUdaaqtnDfafS2L4IzNcvWQxlc9eZc1u07wZs3pzCkg3UMpvJEhHsGtmTUbb04mFXIsNfnsHj7UX+HFfA8uVspHLgGSC66vVrdI1MO644U8Oib83A6hM/v7kNKs7r+DsnUMIPbJfBs30hGrhNufHcB/7iyM7/q1cTfYQUsTy5I/wAMw/WwWmaRyRiPfLl4Fy8szqZ+bDjfP3CudQzGaxrFOPjhwf70aV6PP3yzkr/+uJb8AivaVxGeXHNorKpDvR6JqXEKC5UXJm/gzbQtdKzn4LP7+9lgPcbr4qJC+fD2Xvxj/DpGzd3Gmr3Hefn6bjSy6xDl4kltpZHAa6q6yjcheU5ELgcuT0pKunv06NFWW4mya674MuecAtdAPYv2FzCocQhXN80jrpbVGfJkveVcdXWG5u7J4+O1uYQ4YHjHcHo18OyWaautROm1lXAN7rMSWIvrdtYN7vYqYGVpr/PHZLWVSm77q7bSyl3pOviFGZr85Dh9Z+ZmLSwstDpD5VhvOVdtnaFthzL0itdma7Mnxunvv1quGdl5ZcZYVpwV2TZQayuV1Y1eVqGuyAStgkLl7ZlbeHnKRuJjwhl9Zx/ObWXlMIx/JcdH8/X9/Xhl6kbeTNvCz9uOMuKG7nRtUtvfoVVrZV2QPgBcBfweGIpr/IUdpyefRGcCxq6jWdw4cgHPT9rARR0bMPGxAdYxmGoj1Ong9xe14/O7+5KbX8g1b83jtWmbyM23i9WlKatz+Ajoies00sXAiz6JyAQUVeW7Zbu5ZMRs1u47wYvXdeX1m7pTOyrM36EZc4a+Leox4dGBDO3UgBenbGToiFnM2XTY32FVS2WdVuqgqp0BROR94GffhGQCxfbDmby6LIdlB1fQs1kdXr6+G03qRvk7LGPKFBcVyus39eCalIP8eewabnl/IZd2acgzl7anYZzd0XRaWZ3D6ZpKqGq+jeNrTjuZncfr0zczau42HChPXtyOu/o3J8TpyWMzxlQPg9smcM5j9Rg5aytvzNjMjPUHefT81tx+bnN/h1YtlNU5dBWRE+55ASLdbQFUVW1EliBTUKh8tXgXL0zewOGMXK5NaUz/2CNcOailv0MzpkIiQp08cn5rruqexF9+XMtzE9bz5eJdXNgonwGFGtRjmJf6Tz1VdapqLfcUq6ohReatYwgiqsrczYe5/LU5PPntKpLrRTP2oXN54bqu1I6wXwsm8DWpG8V7t/Xk/dt6IiK8tSKHi16ZxdgVeykI0lLgVkTflKqgUJm4ej/vzNrCyt3HSaodyWs3dueyLg2x04ymJjq/fSKpbRN44YtpTN0Hj3y+jBFTN/LI+a25rEujoPolYZ2DOUN2XgFfL9nNu7O3suNIFsn1ovjnVZ25ukcSEaFOf4dnjFc5HUKfhiH8/vqBTFi9nxHTNvLomOWMmLaJ4f2SubJ7ErUian4ZGOsczC/2pp/i6yW7+Xj+dg5n5NK1cRxP3tyDCzs2CKp/MRkDrrEiLu3SkIs7NWDimv28mbaZ//thDc+NX8+wbo24qU9TujSuuQ/SnbW2UnVmtZUqX1spO19ZciCfuXvzWXekEAU6xzu5pHko7eo6znr6qDrnXFPqDJW0rKbn7I86Q57Eue14ATN25bNgXz65BZBcy0G/hAL6N4smKrTkz0qNq60USJPVViq5Xdq6qdOm69xNh/S3XyzX9s9O0GZPjNMB/56uL0/ZoDsOZ5YdvIexlnc7qzNU9vJgy9kfdYbKOl5xx0/l6sfztulFL8/UZk+M09ZPjdfbP/hZv1i0U49l5px1X4FeW8nUIMcyc5m3N59vP1/GtLVZZOYtJDY8hCu6NuKalMb0bFbHLjIb46FaEaHcek4yt/RtxqgfprM/tCHjV+1n+vqD/NEh9GtZj6GdGjAkgIfBtc6hhsrNL2RLegFr3A/3LN15jEKF+JjDdE8I4ebULgxul2AXmI2pBBGhZW0nd6Z24KlL2rN6zwnGr97HhFX7ePq71Tz93WoaRQtDjq+mX8t4zmlRj7iowLiYbZ1DDXEyO49lO9P5ZlMub2+cz/Jd6WTnFQIb6JRUi4fOa03tzF0Mv+I8Zs2aSWrnhv4O2ZgaRUTo3DiOzo3j+MNFbVm//ySzNh5i3KJNfLV4Nx/P34FDoFNSHI1Ccsiou5eujWvTuE5ktfzVbp1DADqckcO6fSdYu/cEaSuy+eeymWw+mEGhuh5f75RUwI29mxKVuY/bLu1PQmwEAGlpe3HYXUfGeJ2I0L5hLdo3rEVb3UW//gNZviudeVsOM2/zEabuyGfi9mUA1I0Oo3NSHHEFueQlHKBtYiyN6/i/xpN1DtVUfkEhe9JPse1w5i/T0o3Z/GHuVA6ezPllu7oRQvfkKIZ2akjv5Lqc3LGKi4f0ByAt7dAvHYMxxn/CQhz0bl6X3s3r8tgQmDp9BolterBidzord6ezcvdxZu3PY+yWxQBEhDpIjITu+5fROjGWVgkxHDpZSFZuPlFhvvnats7BD1SVjJx8Dp3MYV/6Kfakn2JvejZ700+x9/gpNu3J4siUieQV/Pc249jwEOLDlf6t4unQqBYd3P8qWbFoHqmpvX7ZLm2P/TIwproLcfz3FBQ0A2DS1BnEt+7K5oMZbDyQwcL1O/l521G+X773l9c9M3cS8TFh1HLm882+ZTStG0lMRgGp3ojRC/s8KxHpCrwNxADbgZtV9YR73R+BO4EC4BFVneSPGMsjN7+QjJx89mcWsnTnMdKzcknPyuNYVh7pWbkczczlcEYOh07mcDgjl/3Hs8iddGZa9WPDaVQ7ksaxDq7slUyL+Gia148muV408TFhzJw5k9TUbn7I0BjjbeEhQkqzuqQ0qwtAWsxBUlNTOZmdx5ZDmUycs5jYBsnsPpbFii17WLErnfGr9nFpc+98jfvrl8N7wO9UdaaI3IFrtLlnRaQDcAPQEWgETBWRNqpa4I0gjmTksO5IAbrhIDl5BWTnFXIqr4Ds0/O5+WTmFpCVm09mTgFZ7vmMnHxOZp+e8sgpOprU7Hn/cwwRqB0ZSv3YcOrHhtO9aW2y03Pp3r4V9WNcnUFS7UgS48IJD3HdOZSWlkZqajtvpGyMCTCxEaF0a1Kb9IYhpKa2AiAt7SipqankFxQyLW2mV47rr86hDTDLPT8FmAQ8CwwDxqhqDrBNRDYDvYH53ghi/tYj/HtRNixaVOJ6EYgOCyEyzEl0mJPIsBCiwpzUiQqjad0oYiNCqRURQkx4CLERIezZvpl+KV2pHRVKnagwakeFUisi9IyLwGlpaaRamWtjTCWFOB2EO71zKtkv5TNEZB7wH1X9XkR+C/xFVWNF5HVggaqOdm/3PjBBVb8uYR/3APcAJCYmpowZM6bcj58XhEaz5VAmtWIiCXNAmFMIc0KYQwh1QpiDct1iVp1LSRRtB0vJkJpSSqKkZTU95+paPqMi21r5jGITMBVYXcI0DGgHTAaWAH8Cjrhf8zpwS5F9vA9ce7ZjWfmMktulrQuWnGtKKYmSltX0nKt7+YzybGvlM87sdIacZZMLAUSkDXCpe9keoEmRbRq7lxljjPEhvwzjJSIJ7v86gGdw3bkEMBa4QUTCRaQ50Br42R8xGmNMMPPXBekbReRB9/y3wAcAqrpGRL4E1gL5wIPqwZ1KS5YsOSwiO4A44HiRVWW1T8/HA4crkUtRxY9X0e1KW1/Sck9yLL4uWHIuOl9VOXuaryfbWs6lL6/IZxkCJ+fyvsfF21WVc7NS15R2vikQJ2Ckp+3T85Rxzq2yx6/odqWtL2m5JzkGa87F5qskZ0/ztZwrl3NFPsuBlHN532Nf5Fx8qmmjw/9Yjnbxdd44fkW3K219ScvLk2Ow5ezPfD3Z1nIufXmgfJY92daT97OkZb7O+X8E9EhwVUFEFmtpt3LVUJZzcLCcg4O3cq5pvxwqYqS/A/ADyzk4WM7BwSs5B/0vB2OMMWeyXw7GGGPOYJ2DMcaYM1jnYIwx5gzWORQjIleKyLsi8oWIXOjveHxBRNqLyNsi8rWI3O/veHxFRKJFZLGIXObvWHxBRFJFZLb7vU71dzzeJiIOEfmHiLwmIrf5Ox5fEJEB7vf3PXeB0woLis5BREaJyEERWV1s+VAR2SAim0XkSQBV/V5V7wbuA673R7xVoZw5r1PV+4BfAef6I96qUJ6c3Z4AvvRtlFWrnDkrkAFEALt9HWtVKGe+w3DVZ8sjQPOFcn+WZ7s/y+OAjyp1YG88WVfdJmAg0ANYXWSZE9gCtADCgBVAhyLrXwR6+Dt2X+UMXAFMAG7yd+y+yBm4ANfAUsOBy/wdu49ydrjXJwKf+jt2H+T7JHCve5uv/R27L3Iusv5LILYyxw2KXw6qOgs4Wmxxb2Czqm5V1VxgDDBMXP6NaxyJpb6OtaqUJ2f39mNV9WLgNM0rQQAABftJREFUZt9GWnXKmXMq0Be4CbjbXQQy4JQnZ1U9PWThMSDch2FWmXK+x7tx5QquYYcDUnk/yyLSFDiuqicrc1x/Fd6rDpKAXUXau4E+wMPAECBORFqp6tslvThAlZiz+/zz1bi+MMb7IS5vKjFnVX0IQESGA4eLfHHWBKW9z1cDFwG1cY2dUlOU9lkeAbwmIgP478iTNUVpOQPcibuYaWUEc+dQIlV9FXjV33H4kqqmAWl+DsMvVPVDf8fgK6r6La4qyEFBVbNwfVEGFVX9U1XsJyB/SleRYBxYyHK2nGuiYMsXfJBzMHcOi4DWItJcRMJwXZwc6+eYvM1ytpxromDLF3yQc1B0DiLyOTAfaCsiu0XkTlXNBx4CJgHrgC9VdY0/46xKlrPlTA3MOdjyBf/lbIX3jDHGnCEofjkYY4wpH+scjDHGnME6B2OMMWewzsEYY8wZrHMwxhhzBuscjPn/9s4txKoqDMDfV0ZaTUKFYREI5WQFqZQ9dLGMtAsERRd7KAuLIkrQqJdMMCgqkp6kCMaIJMSuEATdiC5Ol5mcnMpuL1mIURrRdTQa/x72stnOmRkjDzXH+T/YzNprr/Wvy5yz/nU5+/+TJGkglUMyqlH71Q21a8r/Xadmoc5UV+2ljMfVy2v3V6lL9752oN6qLmyGrKT1SNtKyWinLyJmDPVAlepdnVY1mncncM/gSHVcecnp33AhzbMN9hjQWf4mY4xcOSQthTqlODh5AvgEOEa9Q+1WP1LvrqVdqn6prlPXqLeX+DfUU0v4CHVTCe+vPliTdVOJP6fkeUb9XH2yKCbUWeo7aq/apbapb6kzavVYp04f1I424OSI6C33y9XVaiewurTzbbWnXKeXdKorSx+8BkyqyRSYAfSoZ9dWWx+W8hihrxaUuF51NfxtuG6TelpT/nlJS5Erh2S0M0HdUMJfAUuAqcC1EfGelSvXqVT27QVeUGcDv1HZm5lB9TnvAdbvoazrqezgz1IPBDrVV8qzmcBJwBaq2fQZahewFpgfEd3qoUAfsIrKidBitR0Yv0sJ1DiVSrnVORE4MyL61IOAuRGxXZ0KrCl5LgWOL2mPBD5lYGY/E+iNiCiK8JaI6FQPAbaP0Fc/AHcBp0fENvWwWp0+AM4CuvbQd8k+RiqHZLSz27ZSOXP4OiLeK1HzyvVhuT+EagBsA54vs1/Uf2KUbB5wcm0Pf2KR9QfQFRGbi6wNwBTgJ+DbiOgGiIify/OngWXqHcBC4PEhypoMbB0U90JE9JXwAcDKsgLpB9pL/GxgTUT0A1vU12v5L6Dy5geVAntIfRJ4LiI2F+UwVF9NB56OiG2lHXXHMt8D04burmRfJpVD0or8VgsL3BcRj9YTqItHyP8nA1uq4wfJWhQRLw+SdQ6woxbVzwjfnYj4XX2VyjPXlcApQyTrG1Q27N6uJcB3VAP3fsD24cqrMQ+4rNThfvVF4CKqFdD5DN9Xi0aQOb7UNRlj5JlD0uq8DCwsWyeoR6uTqDx/XaJOKPvtF9fybGJgwL58kKyb1QOKrHb14BHK/gKYrM4q6dvUXUqjg+pguDsifhwi72fAcSPInki1KtkJXEPlM5jSrvnlfGQyMKeUPREYFxE/lPtjI+LjiHiAyrzzNIbvq9eBK9TDS3x9W6mdxu2vZAyQK4ekpYmIV9QTgHfLGfGvwNUR0aOupXK8/j3VALmLFcBT6o3Ai7X4Dqrtop5yuLsVuGSEsv9Q51O5opxANcM+D/g1ItarPzOMu8aI+FydqLYN4+v3YeBZdQHwEgOriueBc6nOGr6hMuUMMBd4rZZ/sToH2AlspPKJvmOYvtqo3gu8qfZTbTtdV+ScASwfrg+SfZc02Z2MCdTlVIP2iv+ovKOoXK9OG+6ntuoS4JeI6GhCeR1AR+0sZq9RZwK3RcQ1zZKZtA65rZQkTabM9t8Hlu7hHYxH2P0s418TETc0UzEUjgCWNVlm0iLkyiFJkiRpIFcOSZIkSQOpHJIkSZIGUjkkSZIkDaRySJIkSRpI5ZAkSZI0kMohSZIkaeAvpvtiGDLZD1sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "pwPj_5q3OAp-",
        "outputId": "56ddf771-9ffd-4000-dc59-af200549a6e0"
      },
      "source": [
        "#Ganancia de lazo\r\n",
        "k= (R_f1+R_f2)/R_f2\r\n",
        "#k= R_f2/(R_f1+R_f2)\r\n",
        "\r\n",
        "\r\n",
        "M = series(1/k,G_c,G)\r\n",
        "\r\n",
        "#bode_M = bode(M)\r\n",
        "bode_M_delay = bode(M*delay)\r\n",
        "print(M*delay)\r\n",
        "\r\n",
        "#gm, pm, wg, wp = margin(M)\r\n",
        "#fl = wp/(2*np.pi)\r\n",
        "#print(\"Margen de ganancia: {0} en w = {1}\".format(round(gm,3),round(wg,2)))\r\n",
        "#print(\"Margen de fase: {0} en w = {1}\".format(round(pm,3),round(wp,2)))\r\n",
        "\r\n",
        "gm, pm, wg, wp = margin(M*delay)\r\n",
        "fl = wp/(2*np.pi)\r\n",
        "print(\"Margen de ganancia: {0} en w = {1}\".format(round(gm,3),round(wg,2)))\r\n",
        "print(\"Margen de fase: {0} en w = {1}\".format(round(pm,3),round(wp,2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-e11f8300bfb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Ganancia de lazo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mR_f1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mR_f2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mR_f2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#k= R_f2/(R_f1+R_f2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'R_f1' is not defined"
          ]
        }
      ]
    }
  ]
}