Astable_Multivibrator_-_Simulation.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Astable Multivibrator Simulation\n",
    "\n",
    "A notebook to play a bit with the code to simulate the astable multivibrator behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df94af200fa04b1cab01583bf5981820",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10000, description='R1 (Ω):', max=50000, min=1000, readout_format='.2d',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a65408dc1c24e3395213dba65cb79cd",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "from typing import Sequence\n",
    "from ipywidgets import IntSlider, FloatSlider, Textarea, interact\n",
    "from numpy import linspace, pi, sign, sin, ndarray\n",
    "from scipy.signal import square\n",
    "from matplotlib.pyplot import figure\n",
    "from matplotlib.lines import Line2D\n",
    "# from matplotlib.pyplot import figure\n",
    "\n",
    "def ThresholdCalculator(Vdd:float, R1: int, R2: int, Rf: int)-> tuple[float, float]:\n",
    "    \"\"\" Calculates the threshold voltage on a Schmitt trigger (single rail,\n",
    "    biased with a voltage divider, ideal rail to rail opamp)\n",
    "    Parameters:\n",
    "    -----------\n",
    "        VoutH: float\n",
    "            The value of VoutH when the opamp is saturated high\n",
    "        VoutL: float\n",
    "            The value of VoutH when the opamp is saturated low\n",
    "        Vdd: floag\n",
    "            the power supply voltage\n",
    "        R1: int\n",
    "            top resistor of the voltage divider\n",
    "        R2: int\n",
    "            bottom resistor of the voltage divider\n",
    "        Rf: int\n",
    "            feedback resistor\n",
    "    Return\n",
    "    ------\n",
    "        VthL: float\n",
    "            the threshold value when the output is LOW\n",
    "        VthH: float\n",
    "            the thershokd value when the output is HIGH\n",
    "    \"\"\"\n",
    "    parallelR1Rf:float= (R1 * Rf) / (R1 + Rf)\n",
    "    parallelR2Rf:float= (R2 * Rf) / (R2 + Rf)\n",
    "    ratioH:float = (parallelR1Rf / R2) + 1\n",
    "    ratioL:float = (R1 / parallelR2Rf) + 1\n",
    "    VthH:float = Vdd / ratioH\n",
    "    VthL:float = Vdd / ratioL\n",
    "    return VthL, VthH\n",
    "\n",
    "def ThresholdSignal(Vout: Line2D, VthL: float, VthH:float, Vdd: float)-> Sequence[float]:\n",
    "    \"\"\" Calculates the threshold voltage signanl on a Schmitt trigger (single rail,\n",
    "    biased with a voltage divider)\n",
    "    Parameters:\n",
    "    -----------\n",
    "        Vout: float\n",
    "            a line 2D with the values of the output signal of the opAmp, as\n",
    "            they determine the threshold value\n",
    "        VthL: float\n",
    "            the threshold value when the output is LOW\n",
    "        VthH: float\n",
    "            the thershokd value when the output is HIGH\n",
    "        Vdd: int\n",
    "            the power supply voltage\n",
    "    Return\n",
    "    ------\n",
    "        returnArray: Sequence[float]\n",
    "            a sequence with the values of the Schmitt Trigger thresholds, depending\n",
    "            on the values of the resistor network, Vdd and the current output of\n",
    "            the OpAmp\n",
    "    \"\"\"\n",
    "    returnArray:Sequence[float] = []\n",
    "    # extrac data from Vout (line2D) to array\n",
    "    voltages:Sequence[float] = Vout.get_ydata()\n",
    "    for i in range(len(voltages)):\n",
    "        if voltages[i] > (Vdd / 2):\n",
    "            returnArray.append(VthH)\n",
    "        else:\n",
    "            returnArray.append(VthL)\n",
    "    return returnArray\n",
    "\n",
    "# Resistor Values and VDD\n",
    "R_1:int = 10000\n",
    "R_2:int = 10000\n",
    "R_3:int = 10000\n",
    "Vdd:float = 3\n",
    "# x axis is 0 to 4s, 100 values\n",
    "x = linspace(0, 4, 100)\n",
    "fig = figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "# Generate a square signal, simulating the switching output of the opamp, .5Hz\n",
    "a = 3/2 * (square(2 * pi * 1/2 * x) + 1)\n",
    "out_signal, = ax.plot(x, a)\n",
    "# Calculate the threshold values for the resistor network and the output signal\n",
    "thresholdl, thresholdh = ThresholdCalculator(Vdd, R_1, R_2, R_3)\n",
    "threshold_signal, = ax.plot(x, ThresholdSignal(out_signal, thresholdl, thresholdh, Vdd))\n",
    "\n",
    "def update(R1:int=1, R2:int=1, R3:int=1):\n",
    "    thresholdl, thresholdh = ThresholdCalculator(Vdd, R1, R2, R3)\n",
    "    print(f\"Threshold low is {thresholdl:.2f}V and threshold high is {thresholdh:.2f}V. Ratio is {100 * (thresholdh - thresholdl) / Vdd:.0f}% of VDD\")\n",
    "    threshold_signal.set_ydata(ThresholdSignal(out_signal, thresholdl, thresholdh, Vdd))\n",
    "    fig.canvas.draw_idle()\n",
    "\n",
    "# Create a wiget for\n",
    "R1 = IntSlider(value=10000, min=1000, max=50000, step=1000, description='R1 (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2d')\n",
    "R2 = IntSlider(value=10000, min=1000, max=50000, step=1000, description='R2 (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2d')\n",
    "Rf = IntSlider(value=10000, min=1000, max=50000, step=1000, description='Rf (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2d')\n",
    "interact(update, R1=R1, R2=R2, R3=Rf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "035c4c05a701485b895a71d303ff0b37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10000, description='R (Ω):', max=500000, min=1, step=10000), FloatSlider…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4efa2000e35c41b490635366e256a974",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMIUlEQVR4nO3deXwU9eH/8ffm2hwkgQBJIAn3fYVwBw9QUEQ84ol+VaiiVb+oUPrTqvWrbW2NrUe1LRWtVbwoeAEWD0QQEIjKkUg45SZATo5szs2x8/tjIRohmA1JJrP7ej4e+9jJZiZ5j+Nm38zxGZthGIYAAADgM/zMDgAAAIDmRQEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMRRAAAAAH0MBBAAA8DEUQAAAAB9DAQQAAPAxFEAAAAAfQwEEAADwMQFmB7Ayl8ulI0eOKDw8XDabzew4AACgHgzDUFFRkTp27Cg/P9/cF0YBPAdHjhxRQkKC2TEAAEADZGVlKT4+3uwYpqAAnoPw8HBJ7v+BIiIiTE4DAADqw+FwKCEhoeZz3BdRAM/BqcO+ERERFEAAACzGl0/f8s0D3wAAAD6MAggAAOBjKIAAAAA+xisK4EsvvaRBgwbVnIuXnJysTz/99KzLvPfee+rTp4+Cg4M1cOBAffLJJ82UFgAAwFxeUQDj4+P19NNPa+PGjdqwYYMuvvhiXX311dq6desZ51+3bp1uvvlmTZs2Tenp6UpJSVFKSoq2bNnSzMkBAACan80wDMPsEE0hKipKzzzzjKZNm3ba9yZPnqySkhItWbKk5rVRo0Zp8ODBmjNnTr1/h8PhUGRkpAoLC7kKGAAAi+Dz20v2AP5YdXW15s+fr5KSEiUnJ59xnrS0NI0fP77WaxMmTFBaWtpZf7bT6ZTD4aj1AAAAsBqvKYCZmZlq1aqV7Ha77rnnHi1cuFD9+vU747w5OTmKiYmp9VpMTIxycnLO+jtSU1MVGRlZ8+AuIAAAwIq8pgD27t1bGRkZ+uabb3Tvvfdq6tSp2rZtW6P+jkceeUSFhYU1j6ysrEb9+QAAAM3Ba+4EEhQUpB49ekiShg4dqvXr1+vFF1/Uyy+/fNq8sbGxys3NrfVabm6uYmNjz/o77Ha77HZ744UGAAAwgdfsAfwpl8slp9N5xu8lJydr+fLltV5btmxZnecMAgAAeBOv2AP4yCOPaOLEierUqZOKioo0b948rVy5UkuXLpUkTZkyRXFxcUpNTZUkzZgxQ2PGjNFzzz2nSZMmaf78+dqwYYNeeeUVM1cDAACgWXhFAczLy9OUKVOUnZ2tyMhIDRo0SEuXLtUll1wiSTp48KD8/H7Y2Tl69GjNmzdPjz32mB599FH17NlTixYt0oABA8xaBQAAfIJhGCqrrFZReZUcZZVylFfJUV4pR1ml+7XyyprvFZVXadKgDprQ/+ynaMFzXjsOYHNgHCEAgC9yuQw5yit1orRSJ8oqdby0QoWllTpRWqETZbULnKO88rRSV+Wqf/V4YFxPzbqkV6Pm5/PbS/YAAgAAzxmGoZKKah0rrtCx0orTityJH00fL61U4cnpwrJKnevuI38/m8KDAxQRHFjzHBESoPDgwB9eCwnUkE6tG2VdURsFEAAAL2EYhkorqnW0uEJHS5w6VlJxcrpCR4tPfl1y8nvFFSooqVBFlavBvy8syF+tQ4PUOjTQ/QgJUmRo7QIXUUe5Cw3yl81ma8S1hycogAAAtHDlldXKL3Iqr8ip/KLyk89O5TmcyisqV0FxhY6VVKig2ClnAwpdcKCfokKD1Do0SG3CfihybUJ/PH2y6IUEqnVokCJDAhUU4LWDiXg9CiAAACYprahSdmG5cgvdpS6vqLym6OU5nMovdirPUS5HeZVHPzc40E9tw+xq2ypIUWHuR7tW9h9NBykqzK62YUFq2ypIoUHUAV/DFgcAoAmcKnfZJ8qVXVimnMJyHSksV05hmfv1wnIVllXW++cFBfgpOtyu9uF2RYfbFR0eXPN1+3C72rZyF7qosCAOr+JnUQABAPCQy2WooNiprONlOnS8VIeOl+nQ8bIfit6JsnrvtQsL8ldsZLC70EXY1b6VXdERtQtedHiwIkICKHVoNBRAAAB+oq6Cd+h4qQ4fL9OhE2X1ungi3B6g2MhgxUYGq2NkiGIjg9Xh1Net3V+H2yl2aH4UQACAT6qqdim7sFz7Ckp04GiJ9h8trXk+eKz0Zwuen03qEBmi+DYhim8Tqrg2IYpvXbvkhQcHNtPaAJ6hAAIAvFZVtUuHjpdp/9ESHThaqv1HS7S/wD2ddbxUldV1D2Z3quDFtfmh5MW3CVHCyefYyGAF+nMVLKyJAggAsLyi8krtzS/R7rxi7ckvrnk+cLT0rHedCArwU+eoUHVuG6YubUPVud3J56gwdWhNwYP3ogACACzBMAzlFzlrlbzd+cXak1eiHEd5ncsFB/qpc1SYurQLVZe2YbXKXoeIYPn5cf4dfA8FEADQ4hQ7q7Qzp0g7c4q0I8ehHSenzzZsSvtwu3q0b6Xu0WEnn1upe/tWiqXkAaehAAIATFNV7dL+oyU1BW97dpF25jqUdazsjPP72aROUaHqEf1DwesR3Urd27VSZCgXXAD1RQEEADSL8spqbc92aMsRh7YeLtSWI4X6Pre4zqttYyLs6h0boT6x4eoTG67eseHq3r6VggP9mzk54H0ogACARlfirHKXvcOFyjzs0NYjhdqVV6zqM1yQERrkr14x4T8qeu7S1yYsyITkgG+gAAIAzklFlUvbsh1KP3hc32Wd0JYjDu3JL5Zxhotv24YFaUBcpAbGRap/xwj16xihhDahnKMHNDMKIACg3gzD0KHjZUrPOqGMgyeUnnVcWw87VFF9+mHcmAj7yaIXWVP6YiLs3PUCaAEogACAOpU4q/TdoRNKP+h+ZGSdUEGx87T52oQGKqlTGw1OaK2B8e69e9HhwSYkBlAfFEAAQI1jJRVav/+Y1u87pm/3H9PWI47TztsL8LOpf8cIDU5oraRObZTUqbU6RYWyZw+wEAogAPiwwyfKasret/uOaXde8WnzdIwMVlLnNkpKaK2kTq3Vv2MkV+ICFkcBBAAfkl1YprW7j2rd7gJ9s++YDp84fby9ntGtNLxrlEZ0idLwrlGKax1iQlIATYkCCABe7ERphdL2HNXaPQVat/uo9haU1Pq+v59NAzpGaPjJsje8S5SiGH4F8HoUQADwIqUVVVq//7jW7S7Q2j0F2nrEUWs4Fj+bNDC+tc7r3lbJ3dtqSKc2CrPzUQD4Gt71AGBhhmFoT36xVu7M18qd+fp237HThmTpGd1K5/Vop9Hd22pkt7aKDOGWaYCvowACgMWUOKu0bs9RrdyZp5U78087jy+udYhGd29bU/qiIxiOBUBtFEAAsID9BSX6YnuuvtyZp/X7jtfayxcU4KeRXaM0tne0xvRqr+7twxiSBcBZUQABoAVyuQxlHDqhZdty9cW2XO36yfAsnaJCNbZ3e43t3V6jurVVaBB/zgHUH38xAKCFKK+s1ro9Be7Stz1P+UU/3HEjwM+mUd3a6uI+0Rrbu726tmMvH4CGowACgIlKnFVasSNPn2Rma9X3+SqtqK75Xit7gMb2bq9L+sVobO9oLt4A0GgogADQzEor3KXv483Z+nJnnsorfzifLzYiWOP7ReuSfrEa1S1K9gDuuAGg8VEAAaAZlFZU6csd+fo484hW7Khd+jq3DdWkgR102YBYDYyL5NAugCZHAQSAJlJR5dKq7/O1KOOwVmzPU1nlD4d3O0WFatKgDpo0sIP6d4yg9AFoVhRAAGhEhmEoPeuEFqUf1n+/O6LjpZU130uICtGkgR01aWAHDYij9AEwDwUQABrBwaOlWph+WIsyDmvfj+632z7crqsTO+qqwR05vAugxaAAAkADOcor9d/vjmjhpsPacOB4zeshgf66bECsUpLidF73tgrw9zMxJQCcjgIIAB4wDEPr9x/X/PUH9Ulmds3FHH426bwe7XRNUpwm9I9VmJ0/rwBaLv5CAUA95Bc59eGmQ1qwPkt7f3SIt2d0K90wLF5XD45TDPfcBWARFEAAqIPLZWj1rnwtWJ+lZdtyVeUyJEmhQf66clBHTR6RoKSE1pzXB8ByKIAA8BMnSiv07oYsvf31QR08Vlrz+uCE1rppeIKuSOyoVhziBWBh/AUDgJO2HC7Um2n7tTjjiJxV7nP7IoIDdO2QeN00IkF9YiNMTggAjYMCCMCnOauq9Wlmjt5I26/0gydqXu/XIUJTkjvr6sFxCgnidmwAvAsFEIBPOlZSobfSDuitr/eroLhCkhTob9PEAR00JbmzhnZuw7l9ALwWBRCAT9mTX6x/r9mnDzYeqjnMGxsRrP8Z2Uk3jUhQdDhX8gLwfhRAAF7PMAx9u++Y/vXVPi3fkSvDfTGvBsZF6s4LuurygR0UyGDNAHwIBRCA16p2GfokM1v/+mqvNh8qrHl9XJ9o3XVhN43sGsVhXgA+iQIIwOtUVru0MP2wXlq5p+a+vPYAP107JF7Tzu+qHtGtTE4IAObyimMeqampGj58uMLDwxUdHa2UlBTt3LnzrMvMnTtXNput1iM4mHN/ACsrr6zWW2n7NfaZlXro/c3aV1Ci1qGBmjGup9Y9fLFSrx1I+QMAeckewFWrVmn69OkaPny4qqqq9Oijj+rSSy/Vtm3bFBYWVudyERERtYoih4IAaypxVumdbw7oX1/tU36RU5LUrpVdv7ywq/5nZGcGbQaAn/CKv4qfffZZra/nzp2r6Ohobdy4URdeeGGdy9lsNsXGxjZ1PABNpNhZpblr9+nVNft0orRSktQxMlj3jO2uG4clKDiQ8fsA4Ey8ogD+VGGh+2TvqKios85XXFyszp07y+VyaciQIXrqqafUv3//Oud3Op1yOp01XzscjsYJDMAj7kO9B/TSqj06VuIew69z21D979juuiYpXkEBXnF2CwA0GZthnBoQwTu4XC5dddVVOnHihNasWVPnfGlpadq1a5cGDRqkwsJCPfvss1q9erW2bt2q+Pj4My7zu9/9Tr///e9Pe72wsFAREdwiCmhqFVUuLVh/UH9fsVt5Jw/1dm0XphnjeuqKQR0UwFAuAOrB4XAoMjLSpz+/va4A3nvvvfr000+1Zs2aOovcmVRWVqpv3766+eab9eSTT55xnjPtAUxISPDp/4GA5lBV7dKH6Yf14he7dPhEmSQprnWIZozrqWuHxFH8AHiEAuhlh4Dvu+8+LVmyRKtXr/ao/ElSYGCgkpKStHv37jrnsdvtstvt5xoTQD0ZhqGPM7P1/Offa+/J4Vzah9t1/8U9NHl4guwBnOMHAA3hFQXQMAzdf//9WrhwoVauXKmuXbt6/DOqq6uVmZmpyy+/vAkSAvDUt/uO6U+fbNd3WSckSW1CA3Xv2O66bVQXhQRR/ADgXHhFAZw+fbrmzZunxYsXKzw8XDk5OZKkyMhIhYSESJKmTJmiuLg4paamSpL+8Ic/aNSoUerRo4dOnDihZ555RgcOHNCdd95p2noAcN+r98+f7tDn23IlSaFB/rr7wu6adkFXhnMBgEbiFX9NX3rpJUnS2LFja73++uuv6xe/+IUk6eDBg/Lz++E8oePHj+uuu+5STk6O2rRpo6FDh2rdunXq169fc8UG8CNHi516cfkuvfPNQVW7DPnZpJtGdNLM8T0VHc4g7QDQmLzuIpDmxEmkwLlzVlXrtTX7NfvL3Sp2Vkly36v34Yl91DMm3OR0ALwRn99esgcQgDWt2JGrP/x3m/YfLZUkDYiL0KOX99Xo7u1MTgYA3o0CCKDZ7Sso0ZNLtmnFjjxJ7it7f3NZH12bFCc/P27JCABNjQIIoNkUO6v0jxW79e81e1VZbSjQ36Y7zuuq+y7uofDgQLPjAYDPoAACaHKGYei/m7P1xyXbau7gMaZXez1+ZT91b9/K5HQA4HsogACa1IGjJXps0RZ9tatAktQpKlSPX9FP4/pGy2bjcC8AmIECCKBJVFS59K+v9upvy3fJWeVSkL+fpl/UQ3eP6abgQAZyBgAzUQABNLr1+4/p0Q8ztSuvWJI0untb/TFlgLpxuBcAWgQKIIBGU1haqdRPt2v++ixJUtuwIP12Ul9dkxTH4V4AaEEogAAaxfLtuXp0YaZyHe6LPG4anqCHJ/ZR69Agk5MBAH6KAgjgnJwordAf/rtNH6YfliR1axemp68bpBFdo0xOBgCoCwUQQIMt3ZqjxxZtUX6RU3426c4LumnWJb24yAMAWjgKIACPHSup0O8+2qqPvjsiSerePkzP3JCoIZ3amJwMAFAfFEAAHlmxI1cPvb9ZBcUV8rNJd4/prhnjerLXDwAshAIIoF7KKqr1p0+26e2vD0qSeka30rM3JCoxobW5wQAAHqMAAvhZmw+d0MwFGdqbXyJJmnZ+Vz04oTd7/QDAoiiAAOpU7TL00srdeuGLXapyGYqJsOu5Gwbr/J7tzI4GADgHFEAAZ5R1rFS/WpChDQeOS5IuHxirp64ZyLh+AOAFKIAATvNJZrZ+8/5mFTmr1MoeoN9f1V/XDuFuHgDgLSiAAGqUV1brTx9v11tfH5AkDe3cRi9MHqyEqFCTkwEAGhMFEIAkaV9Bie6bt0lbjzgkSfeM6a5fX9pLgf5+JicDADQ2CiAAffTdET36YaaKnVWKCgvSczcm6qLe0WbHAgA0EQog4MPKK6v1hyXbNO8b99h+I7pE6W83Jyk2MtjkZACApkQBBHxU1rFS3fP2Rm094pDNJk0f20Mzx/dUAId8AcDrUQABH7RmV4Hu/88mHS+tVFRYkF6YPFgX9mpvdiwAQDOhAAI+xDAMvbx6r/7y2Q65DGlQfKReunWo4lqHmB0NANCMKICAjyhxVumh9zfr48xsSdINQ+P1ZMoAbucGAD6IAgj4gL35xbr7rY3alVesQH+bnriyv24Z2YmBnQHAR1EAAS/35c48PTAvXUXOKkWH2/XSrUM1tHMbs2MBAExEAQS8lGEYmrtuv55csk0uQxrWuY3+ecsQRUcwxAsA+DoKIOCFKqtd+t1HW/XOyfH9bhwWrz+mDFRQAEO8AAAogIDXKSyr1PR3NmnN7gLZbNIjE/vorgu6cb4fAKAGBRDwIvsLSnTHG+u1N79EoUH+evGmJF3SL8bsWACAFoYCCHiJr/ce1T1vb9SJ0kp1iAzWq1OHqX/HSLNjAQBaIAog4AUWpR/Wg+9/p8pqQ4nxkfrXlGFc7AEAqBMFELCwU3f2ePrTHZKkSQM76LkbExncGQBwVhRAwKKqXYaeXLJNc9ftlyTdeX5XPXp5X/n5cbEHAODsKICABZVXVutXCzL06ZYcSdJjk/rqzgu6mZwKAGAVFEDAYk6UVuiXb27Ut/uPKcjfT8/dmKgrEzuaHQsAYCEUQMBCch3luu3f3+j73GKFBwfolduGKbl7W7NjAQAshgIIWMSBoyW69d/fKOtYmWIi7HrzjpHqHRtudiwAgAVRAAEL2JlTpFv//Y3yi5zq3DZUb08bqYSoULNjAQAsigIItHDpB4/rF6+vV2FZpfrEhuvNaSMUHc4YfwCAhqMAAi3Y2t0FuuvNDSqtqNaQTq31+i9GKDI00OxYAACLowACLdSybbma/s4mVVS7dEHPdnr5tqEKDeItCwA4d3yaAC3QZ1tydN+8TapyGbqsf6xevHmw7AHc3QMA0DgogEAL8/HmbD0wP13VLkNXJXbU8zcmKsDfz+xYAAAv4hWfKqmpqRo+fLjCw8MVHR2tlJQU7dy582eXe++999SnTx8FBwdr4MCB+uSTT5ohLVC3xRmHa8rftUlx+uvkwZQ/AECj84pPllWrVmn69On6+uuvtWzZMlVWVurSSy9VSUlJncusW7dON998s6ZNm6b09HSlpKQoJSVFW7ZsacbkwA8Wph/SrxZkqNpl6Pqh8XrmhkT5c19fAEATsBmGYZgdorHl5+crOjpaq1at0oUXXnjGeSZPnqySkhItWbKk5rVRo0Zp8ODBmjNnTr1+j8PhUGRkpAoLCxUREdEo2eGb3t94SA++/50MQ7ppeIKeumag/Ch/ANAk+Pz2kj2AP1VYWChJioqKqnOetLQ0jR8/vtZrEyZMUFpaWp3LOJ1OORyOWg/gXH246Yfy9z8jO1H+AABNzusKoMvl0syZM3XeeedpwIABdc6Xk5OjmJiYWq/FxMQoJyenzmVSU1MVGRlZ80hISGi03PBNSzYf0f97z13+bh3VSX9KGUD5AwA0Oa8rgNOnT9eWLVs0f/78Rv/ZjzzyiAoLC2seWVlZjf474DuWbs3RjPkZchnS5GEJ+sNVA2SzUf4AAE3Pq4aBue+++7RkyRKtXr1a8fHxZ503NjZWubm5tV7Lzc1VbGxsncvY7XbZ7fZGyQrf9uXOPN03b5OqXYauSYrTU9dy2BcA0Hy8Yg+gYRi67777tHDhQq1YsUJdu3b92WWSk5O1fPnyWq8tW7ZMycnJTRUTkOS+vds9b21UZbWhSQM76JnrB3G1LwCgWXnFHsDp06dr3rx5Wrx4scLDw2vO44uMjFRISIgkacqUKYqLi1NqaqokacaMGRozZoyee+45TZo0SfPnz9eGDRv0yiuvmLYe8H7r9x/TnW9skLPKpfF9Y/TCTYzzBwBofl7xyfPSSy+psLBQY8eOVYcOHWoeCxYsqJnn4MGDys7Orvl69OjRmjdvnl555RUlJibq/fff16JFi8564QhwLrYcLtQdr69XWWW1xvRqr9m3JCmQ8gcAMIFXjgPYXBhHCPW1r6BEN8xZp4LiCo3oGqU37xih4EDu7QsAZuDz20v2AAItWa6jXLf9+xsVFFeoX4cIvTp1GOUPAGAqCiDQhApLKzXl39/q0PEydWkbqjfuGKGI4ECzYwEAfBwFEGgipRVVuuON9dqZW6TocLvemjZS7cMZRggAYD4KINAEKqtd+t93NmnjgeOKCA7QW9NGKiEq1OxYAGAdhiHlZJqdwmtRAIFGZhiGfvPBZq3cma/gQD+9fvtw9Y4NNzsWAFjLur9Jcy6QvmF4tqbgFeMAAi3JX7/YpQ83HZa/n00v3TJUQztHmR0JAKxl87vSssfd09UV5mbxUuwBBBrRu+uz9LfluyRJf0wZoIv6RJucCAAsZu8qadH/uqeT75NG32duHi9FAQQayarv8/XIQvf5Kvdd1EM3j+hkciIAsJjcbdKCWyVXpdT/GumSJ81O5LUogEAj2HqkUP/79kZVuwxdmxSnX1/ay+xIAGAtjmzpnRskp0PqNFpKmSP5UVOaCv9lgXN05ESZ7pi7XiUV1Uru1lZPXzdINpvN7FgAYB0VJdJ/JkuOQ1LbntJN70iBwWan8moUQOAcFJVX6vbX1yvX4VSvmFaac9tQBQXwtgKAenNVSx/cKWV/J4W2lW55Twrl4rmmxicV0EDVLkMP/Ce9ZqDn128focgQ7vIBAB5Z9ri08xPJ3y7dPF+K6mp2Ip9AAQQaKPWT7fry5Fh/r04dprjWIWZHAgBr2ThXSvuHe/qal6SEEabG8SUUQKABFqw/qFfX7JMkPXtDogbFtzY3EABYzb7V0se/dk9f9FtpwHXm5vExFEDAQ9/sParHFm2RJM0Y11NXDOpociIAsJhje6V3p0iuKmnA9dKFD5qdyOdQAAEPZB0r1b3vbFJltaFJAztoxrieZkcCAGspL5Tm3SSVHZfihkpX/0Ni5IRmRwEE6qmovFLT3livYyUVGhgXqWdvSJSfH3+0AKDeXNXSB3dJBTul8I7STfOkQM6fNgMFEKgHl8vQrxZk6PvcYkWH2/WvKcMUEuRvdiwAsJYVf5R2LZUCgt1j/YXHmp3IZ1EAgXr424pd+mJ7noIC/PSvKcMUG8kApQDgkS0fSmued09f9Xcpboi5eXwcBRD4Gcu35+qFL3ZJkp66ZqASE1qbGwgArCZni7R4unt69P3SoBvNzQMKIHA2+wpKNHNBhiTptlGddf3QeHMDAYDVlB2XFtwiVZZK3S6Sxv/e7EQQBRCoU4mzSne/tUFF5VUa1rmN/u+KfmZHAgBrOXWbt+P7pdadpetfk/w4f7oloAACZ2AYhh76YLO+zy1W+3C7/nnLEO7xCwCeWvVnafcXUkCI+6IP7vHbYvCJBpzBK6v36uPN2Qrws+mlW4YoOoKLPgDAI98vdRdASbryRSl2oLl5UAsFEPiJdbsL9OfPdkiSnriyn4Z14V+sAOCR4/ulD+9yTw+/S0qcbGocnI4CCPxIXlG5HpifIZchXTckXreO6mx2JACwlspy923eygul+OHShKfMToQzoAACJ1W7DM34T4YKip3qHROuP6YMkI3bEwGAZz57WMr+TgptK90wVwoIMjsRzoACCJz04vJdStt7VKFB/pp9yxDu9AEAntr8rrTxdUk26dpXpEiGzmqpKICApK925evvK34Y7LlHdCuTEwGAxeR/L/13pnt6zENSj/GmxsHZUQDh83Id5Zo5P0OGId08IkEpSXFmRwIAa6ksk977hVRZInW5QBrzG7MT4WdQAOHTqqpduv8/6TpaUqE+seF64sr+ZkcCAOv59DdS3lYpLFq67t8M9mwBFED4tBe+2KVv9x1TWJC//nnLEAUH8kcLADyy5QNp0xuSbNJ1/5LCY8xOhHqgAMJnrd1doNkrd0uSUq8bpG7tOe8PADxybJ/00Qz39AW/lrqNNTUO6o8CCJ90vKRCs9794by/qxI7mh0JAKylqkJ6/w6pokhKGCWNfcTsRPAABRA+xzAM/eaDzcp1ONWtfZj+74p+ZkcCAOtZ+ZR0ZJMUHCld96rkH2B2IniAAgif859vs/T5tlwF+tv0t5uSFBrEHy0A8MjeVdKaF9zTV/1dap1gahx4jgIIn7I7r1h/WLJVkvTQhD4aEBdpciIAsJjSY9LCuyUZ0pCpUr+rzU6EBqAAwmc4q6r1wH/SVV7p0gU922na+V3NjgQA1mIY0kf3S0XZUtue0mWpZidCA1EA4TOeXbpT27IdahMaqGdvSJSfH/f5BQCPpL8l7Vgi+QW6z/sLCjM7ERqIAgif8NWufP3rq32SpL9cn6iYiGCTEwGAxRzbK336sHv64sekjoNNjYNzQwGE1yssrdT/e+87SdKtozrpkn4MUgoAHqmukj68232rt87nSaPvNzsRzhEFEF7viY+2uId8aRem317OkC8A4LG1f5UOfSvZI6Rr5nCrNy9AAYRX+zQzW4syjsjPJj17Y6JCgvijBQAeyf5OWvm0e3riX6TWnczNg0ZBAYTXKih26reLtkiS7h3bXUM6tTE5EQBYTJVTWniP5KqS+l4pJd5kdiI0EgogvJJhGHr0w0wdK6lQn9hwPTCup9mRAMB6vnxKytsmhbWXrnhBsjF6gregAMIrLUw/XHO3j+dvHCx7AId+AcAjB7+R1v3NPX3li1JYO3PzoFF5TQFcvXq1rrzySnXs2FE2m02LFi066/wrV66UzWY77ZGTk9M8gdFksgvL9MRH7rt9zBzfS/06RpicCAAspqJUWnSvZLikQTdJfSaZnQiNzGsKYElJiRITEzV79myPltu5c6eys7NrHtHR0U2UEM3BMAw99P5mFZVXaXBCa919YTezIwGA9az4o3RsjxTeQZr4tNlp0AQCzA7QWCZOnKiJEyd6vFx0dLRat27d+IFgivnrs/TVrgLZA/z03I2JCvD3mn/jAEDzOPi19PU/3dNX/k0K4QI6b+Tzn46DBw9Whw4ddMkll2jt2rVnndfpdMrhcNR6oOXIKSzXUx9vlyQ9OKG3urdvZXIiALCYyjJp8XRJhjT4VqnXpWYnQhPx2QLYoUMHzZkzRx988IE++OADJSQkaOzYsdq0aVOdy6SmpioyMrLmkZCQ0IyJcTaGYeixRZkqcroP/d5+XlezIwGA9Xz5lHR0t9QqVprwJ7PToAnZDMMwzA7R2Gw2mxYuXKiUlBSPlhszZow6deqkt95664zfdzqdcjqdNV87HA4lJCSosLBQERFcaGCm/353RPf/J12B/jZ9/MAF6hUTbnYkALCWwxulV8e7L/y4eb7U2/PTqqzC4XAoMjLSpz+/veYcwMYwYsQIrVmzps7v2+122e32ZkyE+jhWUqHfnbzq976LelL+AMBTVRXS4vvd5W/gDV5d/uDms4eAzyQjI0MdOnQwOwY89OSSbTpaUqHeMeG6d2x3s+MAgPWsfUHK2yqFtpUu46pfX+A1ewCLi4u1e/fumq/37dunjIwMRUVFqVOnTnrkkUd0+PBhvfnmm5KkF154QV27dlX//v1VXl6uV199VStWrNDnn39u1iqgAb7ckaeF6YflZ5P+fP0gBQXwbxoA8Ej+Tmn1M+7piX9hwGcf4TUFcMOGDbroootqvp41a5YkaerUqZo7d66ys7N18ODBmu9XVFTo17/+tQ4fPqzQ0FANGjRIX3zxRa2fgZat2Fml3y7MlCTdcV5XDU5obW4gALAal0v66AGpukLqOUEacJ3ZidBMvPIikObCSaTmenzxFr2ZdkCdokL12cwLFBrkNf+eAYDmsf7f0sezpKBW0v9+LbX2jdEt+PzmHEBY1MYDx/Vm2gFJ0tPXDqT8AYCnHNnSF79zT1/8mM+UP7hRAGE5VdWumkO/1w2J1+genK8CAB777GHJ6ZA6DpFG/NLsNGhmFEBYztx1+7Ujp0itQwP16OV9zI4DANbz/VJp2yLJ5i9d+aLk5292IjQzCiAs5ciJMj2/7HtJ0sOX9VHbVozLCAAeqSiVPv5/7ulR90odBpmbB6agAMJS/vDfbSqtqNbQzm104zDOVwEAj63+i1R4UIpMkC561Ow0MAkFEJaxfHuuPtuaI38/m/50zQD5+dnMjgQA1pK3XVr3d/f0xL9IQWHm5oFpKICwhLKKaj2+2H27tzvP76o+sb552T4ANJhhSB//WnJVSb0nSX0uNzsRTEQBhCX8bcUuHT5Rpo6RwXpgXE+z4wCA9WxeIB1YKwWGShO53ZuvowCixfs+t0j/Wr1XkvS7q/orzM6YfwDgkbIT0uf/556+8EGpdSdT48B8FEC0aIZh6P8WbVGVy9D4vjG6tH+s2ZEAwHpWpkoleVK7XlLyfWanQQtAAUSL9nFmtr7Zd0z2AD89cWU/s+MAgPXkZErfvuKevvwZKSDI3DxoESiAaLFKK6r01MfbJUn3ju2uhKhQkxMBgMUYhvTJg5LhkvqlSN3Gmp0ILQQFEC3WnJV7dKSwXHGtQ3TPmO5mxwEA69n8rnQwzX3hx4Q/mZ0GLQgFEC1S1rFSzTl54cdjk/oqOJDbFAGAR5xF0rLH3dMX/FqKjDc3D1oUCiBapD9+vE0VVS6N7t5Wlw3gwg8A8NjqZ6XiHKlNVy78wGkogGhxvtqVr6Vbc+XvZ9MTV/aXzcYdPwDAI0f3SGmz3dMTnpICg83NgxaHAogWpbLapd//d5sk6bZRndU7NtzkRABgQUt/K7kqpe7jpN4TzU6DFogCiBblzbQD2p1XrKiwIP1qfC+z4wCA9exZIX3/qeQXIF2WKnEUBWdAAUSLUVDs1AvLvpckPTihtyJDA01OBAAWU10lffaIe3r4XVL73ubmQYtFAUSL8ddl36vIWaUBcRG6cViC2XEAwHo2vi7l75BCoqSxvzE7DVowCiBahF25RfrPtwclSf83qZ/8/ThkAQAeKTsuffmUe/qiR6WQNubmQYtGAUSLkPrpDrkM6dJ+MRrZra3ZcQDAelY/K5Udk9r3kYbebnYatHAUQJhu7e4CrdiRpwA/mx6e2MfsOABgPcf2St+87J6+9E+Sf4C5edDiUQBhqmqXoT+evN/vraM6q1v7ViYnAgALWvbED8O+9BxvdhpYAAUQpvpw0yFtz3YoPDhAD4zraXYcALCeg19L2z+SbH7SpX80Ow0sggII05RWVOnZz3dKku6/uIeiwoJMTgQAFmMY7kGfJSnpVimmn7l5YBkUQJjmX6v3KdfhVHybEE0d3cXsOABgPVsXSoc3SIFh0kW/NTsNLIQCCFPkOcr18uo9kqTfXNZH9gB/kxMBgMVUVUjLf++ePu8BKTzW3DywFAogTPH8su9VWlGtpE6tdcWgDmbHAQDr2fCadHy/1CpGSr7P7DSwGAogmt2u3CK9uyFLkvTYpL6ycZ9KAPBMuUNa/Rf39NiHJTsjKMAzFEA0u2c/3ymXIU3oH6OhnaPMjgMA1rPub1LpUaltTylpitlpYEEUQDSrjKwTWro1V3426f9dyk3KAcBjRTlS2mz39PgnGPQZDUIBRLMxDEN//nSHJOnaIfHqGRNuciIAsKBVf5EqS6X4EVKfK8xOA4uiAKLZrNldoLS9RxXk76eZ4xn0GQA8dnSPtOkN9/T430mcQ40GogCiWRiGob985h70+dZRnRXfJtTkRABgQV/+SXJVST0vlbqcZ3YaWBgFEM3i0y05yjxcqLAgf02/qLvZcQDAerI3S1s+cE+Pe9zcLLA8CiCaXFW1S88ude/9u/OCbmrbym5yIgCwoBVPup8HXC/FDjQ3CyyPAogm9/7GQ9pbUKKosCDdeUFXs+MAgPUc/Fra9blk85cuetTsNPACFEA0qfLKar3wxS5J0vSLeig8ONDkRABgMYYhLT+59y/pVqktp9Hg3FEA0aTeSjugHEe5OkYG65aRncyOAwDWs/dL6cAayd8ujXnI7DTwEhRANJkSZ5VeWrVHkjRzfC8FB/qbnAgALMYwpBV/dE8PnyZFxpubB16DAogm82baAR0rqVDntqG6dkic2XEAwHq+/0w6vFEKDJXO/5XZaeBFKIBoEsXOKr2y2r3374GLeyrAn//VAMAjLpd73D9JGvFLqVW0uXngVfhURpN4Y91+HS+tVNd2Ybp6cEez4wCA9exYIuVkSkHh0nkzzE4DL0MBRKMrKq/Uv77aK0l6YFwP9v4BgKdcLmllqnt61D1SaJS5eeB1+GRGo3tj3X6dKK1Ut/ZhuiqRc/8AwGPbF0t52yR7pJQ83ew08EJeUwBXr16tK6+8Uh07dpTNZtOiRYt+dpmVK1dqyJAhstvt6tGjh+bOndvkOb2do7xS//pqnyRpxrie8vfjRuUA4BGXS1r5Z/f0qHulkDbm5oFX8poCWFJSosTERM2ePbte8+/bt0+TJk3SRRddpIyMDM2cOVN33nmnli5d2sRJvdvctftVWFapHtGtdMUgzv0DAI9tXyzlb3fv/Rt1r9lp4KUCzA7QWCZOnKiJEyfWe/45c+aoa9eueu655yRJffv21Zo1a/TXv/5VEyZMaKqYXq2wrFKv1pz7x94/APDYj/f+Jf+vFNLa1DjwXl6zB9BTaWlpGj9+fK3XJkyYoLS0tDqXcTqdcjgctR74wetr98lRXqVeMa00aWAHs+MAgPVs/+iHvX8j7zE7DbyYzxbAnJwcxcTE1HotJiZGDodDZWVlZ1wmNTVVkZGRNY+EhITmiGoJhWWV+veaU+f+9WLvHwB4yuWSVj/jnh51D3v/0KR8tgA2xCOPPKLCwsKaR1ZWltmRWow31u1XUXmVeseEa+KAWLPjAID17PxEyt3iHvePc//QxLzmHEBPxcbGKjc3t9Zrubm5ioiIUEhIyBmXsdvtstvtzRHPUkqcVXptrXvv3/SLe8iPvX8A4BnDkFb/xT098pdc+Ysm57N7AJOTk7V8+fJary1btkzJyckmJbKued8c1ImTd/3g3D8AaIBdy6Ts76TAMGkU4/6h6XlNASwuLlZGRoYyMjIkuYd5ycjI0MGDByW5D99OmTKlZv577rlHe/fu1UMPPaQdO3bon//8p95991396lfcbNsT5ZXVNXf9uHdMd879AwBP/Xjv3/A7pLC25uaBT/CaArhhwwYlJSUpKSlJkjRr1iwlJSXp8ccflyRlZ2fXlEFJ6tq1qz7++GMtW7ZMiYmJeu655/Tqq68yBIyH3t94SHlFTnWMDFZKEnf9AACP7VslHVovBQRLyfebnQY+wmvOARw7dqwMw6jz+2e6y8fYsWOVnp7ehKm8W2W1S3NW7ZEk/fLCbgoK8Jp/TwBA81n9rPt5yBQpPObs8wKNhE9sNNhHGUd06HiZ2rUK0k0jOpkdBwCsJ+tbaf9Xkl+ANPoBs9PAh1AA0SAul6F/rtwtSZp2fjcFB/qbnAgALOir593Pg26SWjO2LJoPBRANsnRrjvbklygiOEC3jmLvHwB4LGeL9P2nkmzS+VyAiOZFAYTHDMPQP7507/37xeguCg8ONDkRAFjQ2hfcz/2ultr1MDUKfA8FEB5b+X2+th5xKDTIX7ef19XsOABgPcf2SVs+cE9fMMvcLPBJFEB47J8n9/7dMrKT2oQFmZwGACxo3d8lwyV1Hyd1SDQ7DXwQBRAe2XjgmNbvP64gfz/deUE3s+MAgPUU50kZ77inOfcPJqEAwiMvr3Lf9eOapDjFRASbnAYALOibOVJVuRQ3TOpyvtlp4KMogKi3vfnFWrY9V5J014Wc+wcAHnMWSetfdU+fN0OycftMmIMCiHp7dc0+GYY0rk+0ekSHmx0HAKxn05tSeaHUtofUZ5LZaeDDKICol4Jip97feEiS+7ZvAAAPVVdKaf90T4++X/JjAH2YhwKIenlz3X5VVLmUmNBaI7pGmR0HAKxny4eS45AUFu2+8wdgIgogflZpRZXe/PqAJOnuC7vJxjkrAOAZw3AP/SJJI++WArmIDuaiAOJnvb/xkE6UVqpTVKgm9I81Ow4AWM/eL6XcTCkwTBp2h9lpAAogzq7aZejVr/ZJku68oKv8/dj7BwAeO7X3b8htUiin0cB8FECc1WdbcnTwWKnahAbqhqEJZscBAOvJ3SrtWSHZ/KRR95qdBpBEAcRZGIahV1bvkSTdltxFIUFcsQYAHjt15W/fK6U2XUyNApxCAUSdvtl3TN8dKpQ9wE9TkzubHQcArKcoV8p81z2dfL+5WYAfoQCiTqfO/bt+aLzatrKbnAYALGj9q1J1hRQ/QkoYbnYaoAYFEGd04GiJlu9w3/btjvO57RsAeKyy7IfbviVPNzcL8BMUQJzRG+sOyDCkMb3aq3v7VmbHAQDr2bxAKjsmRXaS+lxhdhqgFgogTlPsrNJ7G7IkSbef18XcMABgRYYhff2Se3rUPZJ/gLl5gJ+gAOI072/IUpGzSt3ah+nCnu3NjgMA1rNnhZS/QwpqJSXdanYa4DQUQNTichl6I81927fbR3eRHwM/A4DnTu39S7pNCo40NwtwBhRA1LLy+zztKyhReHCArh0Sb3YcALCegl3S7mWSbNLIX5qdBjgjCiBqeX3tfknSTcMTFGbnnBUA8Ng3L7ufe0+UorqZmwWoAwUQNXblFumrXQXys0lTkruYHQcArKe8UMqY554eebe5WYCzoACixuvr9kuSLukXo4SoUHPDAIAVpb8jVZZI7ftKXceYnQaoEwUQkqQTpRX6cNMhSdLt5zHwMwB4zOWSvn3FPT3yl5KNi+jQclEAIUmavz5L5ZUu9e0QoZFdo8yOAwDWs3uZdHyf+6rfQZPNTgOcFQUQqqp26c2Th39vP6+LbPyrFQA8d+rij6TbpKAwc7MAP4MCCH2xPVdHCsvVNixIVyV2NDsOAFhPwS5pz3JJNmn4nWanAX4WBRB662v3wM83jUhQcKC/yWkAwILWv+p+7jVBiuI8arR8FEAftye/WGt3H5XNJt08opPZcQDAepzFPwz9MuIuc7MA9UQB9HHvfH1QkjSuT7Ti2zD0CwB4bPMCyelwD/rc7WKz0wD1QgH0YaUVVXpvY5Yk6dZRnU1OAwAWZBg/HP4dfqfkx8cqrIH/U33Yf787oqLyKnWKCtWFPdubHQcArOdgmpS3TQoIkQb/j9lpgHqjAPoowzBqLv64ZWQn+fkx9AsAeOzU3r9BN0ghbczNAniAAuijvjtUqC2HHQoK8NMNwxLMjgMA1lOcJ237yD09bJq5WQAPUQB91Ftp7r1/VwzsoKiwIJPTAIAFbXpTclVKccOkjoPNTgN4hALog46XVGjJ5iOSpFuTufgDADzmqpY2vuGeHs7eP1gPBdAHvb/xkJxVLvXvGKGkhNZmxwEA69m9XCo8KAW3lvpfY3YawGMUQB/jchl6+xv34d9bR3Xmvr8A0BAbXnM/D75FCgwxNwvQABRAH7Nmd4EOHC1VuD1AVw/mvr8A4LETWdKupe7pYbebmwVoIAqgjzk19Mt1Q+MVGhRgchoAsKBNb0qGS+p6odSup9lpgAahAPqQnMJyLd+eK0m6dRT3/QUAj1VXSelvuaeHsvcP1uVVBXD27Nnq0qWLgoODNXLkSH377bd1zjt37lzZbLZaj+Dg4GZM2/ze35gllyEN79JGPaLDzY4DANaza6lUlC2FtpP6XGF2GqDBvKYALliwQLNmzdITTzyhTZs2KTExURMmTFBeXl6dy0RERCg7O7vmceDAgWZM3LxcLkMLNrjv+3vTcPb+AUCDbJzrfk66RQpgDFVYl9cUwOeff1533XWXbr/9dvXr109z5sxRaGioXnvttTqXsdlsio2NrXnExMQ0Y+LmtW7PUWUdK1N4cIAuH9jB7DgAYD0nsqRdy9zTQ6aamwU4R15RACsqKrRx40aNHz++5jU/Pz+NHz9eaWlpdS5XXFyszp07KyEhQVdffbW2bt161t/jdDrlcDhqPaxi/vqDkqSUwXEKCfI3OQ0AWFD6W5IM98UfbbubnQY4J15RAAsKClRdXX3aHryYmBjl5OSccZnevXvrtdde0+LFi/X222/L5XJp9OjROnToUJ2/JzU1VZGRkTWPhARr3EP3WEmFPt/qvvhj8nBrZAaAFsVVLaW/7Z5m7x+8gFcUwIZITk7WlClTNHjwYI0ZM0Yffvih2rdvr5dffrnOZR555BEVFhbWPLKyspoxccN9uOmQKqpdGhAXoQFxkWbHAQDr2f2F5DgshURJfa80Ow1wzrxiILh27drJ399fubm5tV7Pzc1VbGxsvX5GYGCgkpKStHv37jrnsdvtstvt55S1uRmGoQXrufgDAM7Jqfv+Jt4sBVjrcwA4E6/YAxgUFKShQ4dq+fLlNa+5XC4tX75cycnJ9foZ1dXVyszMVIcO3nWBxKaDx7Urr1ghgf66ijt/AIDninKk7z9zTw+ZYm4WoJF4xR5ASZo1a5amTp2qYcOGacSIEXrhhRdUUlKi2293D9Q5ZcoUxcXFKTU1VZL0hz/8QaNGjVKPHj104sQJPfPMMzpw4IDuvPNOM1ej0c3/1r33b9KgDooIDjQ5DQBYUMY8yaiW4kdI0X3MTgM0Cq8pgJMnT1Z+fr4ef/xx5eTkaPDgwfrss89qLgw5ePCg/Px+2OF5/Phx3XXXXcrJyVGbNm00dOhQrVu3Tv369TNrFRpdUXmllmzOliTdxMUfAOA5w/jhzh/s/YMXsRmGYZgdwqocDociIyNVWFioiIgIs+Oc5p1vDui3C7eoR3QrLfvVhbLZbGZHAgBr2b9Wmnu5FNRK+vVOyd7K7ERoBC3987s5eMU5gDizU4d/bxqeQPkDgIY4NfRL/2sof/AqFEAvteVwoTIPFyrQ36Zrh8SbHQcArKfcIW1b5J7m8C+8DAXQS7138r6/l/aPVVQY96sEAI9t/VCqLJXa9ZLih5udBmhUFEAv5Kyq1uLvjkiSbhzGxR8A0CDp77ifk26TOI0GXoYC6IW+3JGnE6WViomw6/we7cyOAwDWk79TOvStZPOXEm8yOw3Q6CiAXuj9je77GV+TFC9/P/7VCgAeyzi596/XBKlVtLlZgCZAAfQy+UVOfbkzX5J0/dA4k9MAgAVVV0nfzXdPD77F3CxAE6EAepnFGYdV7TKUmNBaPaLDzY4DANazZ7lUnCuFtpV6Xmp2GqBJUAC9zKnDv9cPZegXAGiQU4d/B94oBTCKArwTBdCLbD1SqB05RQry99NVgzqaHQcArKf0mLTzU/d0Eod/4b0ogF7k1N6/S/rFKDI00OQ0AGBBWz6QqiukmIFS7ECz0wBNhgLoJSqqXFqc4R77j8O/ANBAGfPcz4NvNjcH0MQogF5i5c48HSupUPtwuy7oydh/AOCx/J3SkU3usf8G3mh2GqBJUQC9xA9j/8UpwJ/NCgAe++4/7ueel0it2pubBWhiNAUvcLTYqRU78iRJ1w3h8C8AeMxVLW1+1z2dyOFfeD8KoBf46LsjqnIZGhgXqd6xjP0HAB7b/5XkOCwFR0q9LjM7DdDkKIBe4INN7sO/1w3hzh8A0CDfLXA/979WCgw2NwvQDCiAFrczp0hbDjsU6G/TVYMpgADgsYpSaftH7unEm8zNAjQTCqDFLco4LEka2ztaUWGMWA8AHtvxsVRRLLXpIiWMNDsN0CwogBbmchn66OTYfyns/QOAhtk83/08aLJks5mbBWgmFEAL23DguA6fKFMre4DG9Y02Ow4AWE9xnrRnhXt60GRzswDNiAJoYacO/142IFbBgf4mpwEAC9rygWS4pLhhUtvuZqcBmg0F0KIqqlz6JDNbEod/AaDBNp+8+peLP+BjKIAWter7fJ0orVR0uF3J3duaHQcArCf/e+lIuuQXIPW/xuw0QLOiAFrUqcO/VyZ2lL8fJy0DgMcyT975o/s4KYx7qMO3UAAtqKi8Ul9sy5XE4V8AaBDDkDLfc08PutHcLIAJKIAWtHRrrpxVLnVrH6YBcRFmxwEA6zm0Xjq+XwoMk3pPNDsN0OwogBa0+OTh35TBcbIxZhUAeO7U3r8+k6SgMHOzACagAFpMXlG51u4ukCRdldjR5DQAYEHVVdKWD93THP6Fj6IAWsyS77LlMqTBCa3VpR3/agUAj+1bKZUWSKFtpW5jzU4DmIICaDE/HP5l7x8ANEjm++7nfimSf6CpUQCzUAAtZF9Bib47VCh/P5uu4PAvAHiuskzavsQ9PfAGc7MAJqIAWsiidPfev/N7tFO7VnaT0wCABe1aJlUUSRHxUsJIs9MApqEAWoRhGProuyOSpJQk9v4BQINsOXn4d8C1kh8fgfBd/N9vEduyHdpXUCJ7gJ8u6RdrdhwAsB5nkfT9Uvf0wOvNzQKYjAJoEUs2Z0uSLuodrVb2AJPTAIAF7fhEqiqX2vaQYgeZnQYwFQXQAgzD0McnC+AViR1MTgMAFrXlA/fzgOslBtGHj6MAWkDm4UIdPFaqkEB/Xdwn2uw4AGA9pcekPcvd0wOuNTcL0AJQAC3g1N6/i/tGKzSIw78A4LEdSyRXlRQzQGrf2+w0gOkogC2cYRg15/9dMZDDvwDQIKdu/db/GnNzAC0EBbCFy8g6ocMnyhQa5K+xvTn8CwAeKymQ9q1yT1MAAUkUwBbv1OHf8X1jFBLkb3IaALCgbYslwyV1GCy17W52GqBFoAC2YC6XoY8z3QVw0iAO/wJAg2xd6H5m7x9QgwLYgqVnHVd2Ybla2QM0pld7s+MAgPUU5UoH1rqnKYBADQpgC3bq4o9L+sUoOJDDvwDgse0fuQ//xg2V2nQ2Ow3QYlAAWyiXy9AnJw//XsHhXwBomG2L3c/9UkyNAbQ0XlUAZ8+erS5duig4OFgjR47Ut99+e9b533vvPfXp00fBwcEaOHCgPvnkk2ZK+vM2HDiuXIdT4cEBOr9nO7PjAID1FOf96PBviqlRgJbGawrgggULNGvWLD3xxBPatGmTEhMTNWHCBOXl5Z1x/nXr1unmm2/WtGnTlJ6erpSUFKWkpGjLli3NnPzMlmw+Ikma0D9W9gAO/wKAx05d/Rs3VGrdyew0QIviNQXw+eef11133aXbb79d/fr105w5cxQaGqrXXnvtjPO/+OKLuuyyy/Tggw+qb9++evLJJzVkyBD94x//aObkp6t2GfokM0cSV/8CQINx+Beok1cUwIqKCm3cuFHjx4+vec3Pz0/jx49XWlraGZdJS0urNb8kTZgwoc75JcnpdMrhcNR6NIVv9x1TQbFTkSGBOq87h38BwGPF+T8c/u13lblZgBbIKwpgQUGBqqurFRMTU+v1mJgY5eTknHGZnJwcj+aXpNTUVEVGRtY8EhISzj38GZw6/HtZ/1gFBXjFJgKA5nXq6t+OSVKbLmanAVqcALMDWMkjjzyiWbNm1XztcDiapATeeUE3dYgMVnL3to3+swHAJ/S5QnJVS2H8HQXOxCsKYLt27eTv76/c3Nxar+fm5io2NvaMy8TGxno0vyTZ7XbZ7fZzD/wzurYL030X92zy3wMAXis8Rhr5S7NTAC2WVxxfDAoK0tChQ7V8+fKa11wul5YvX67k5OQzLpOcnFxrfklatmxZnfMDAAB4C6/YAyhJs2bN0tSpUzVs2DCNGDFCL7zwgkpKSnT77bdLkqZMmaK4uDilpqZKkmbMmKExY8boueee06RJkzR//nxt2LBBr7zyipmrAQAA0OS8pgBOnjxZ+fn5evzxx5WTk6PBgwfrs88+q7nQ4+DBg/Lz+2GH5+jRozVv3jw99thjevTRR9WzZ08tWrRIAwYMMGsVAAAAmoXNMAzD7BBW5XA4FBkZqcLCQkVERJgdBwAA1AOf315yDiAAAADqjwIIAADgYyiAAAAAPoYCCAAA4GMogAAAAD6GAggAAOBjKIAAAAA+hgIIAADgYyiAAAAAPsZrbgVnhlM3UXE4HCYnAQAA9XXqc9uXb4ZGATwHRUVFkqSEhASTkwAAAE8VFRUpMjLS7Bim4F7A58DlcunIkSMKDw+XzWZr1J/tcDiUkJCgrKwsr7xPIetnfd6+jqyf9Xn7OrJ+DWcYhoqKitSxY0f5+fnm2XDsATwHfn5+io+Pb9LfERER4ZVv7FNYP+vz9nVk/azP29eR9WsYX93zd4pv1l4AAAAfRgEEAADwMRTAFsput+uJJ56Q3W43O0qTYP2sz9vXkfWzPm9fR9YP54KLQAAAAHwMewABAAB8DAUQAADAx1AAAQAAfAwFEAAAwMdQAE0ye/ZsdenSRcHBwRo5cqS+/fbbs87/3nvvqU+fPgoODtbAgQP1ySefNFPShvNkHefOnSubzVbrERwc3IxpPbN69WpdeeWV6tixo2w2mxYtWvSzy6xcuVJDhgyR3W5Xjx49NHfu3CbP2VCert/KlStP2342m005OTnNE9hDqampGj58uMLDwxUdHa2UlBTt3LnzZ5ezyvuwIetntffgSy+9pEGDBtUMEpycnKxPP/30rMtYZftJnq+f1bbfTz399NOy2WyaOXPmWeez0jZs6SiAJliwYIFmzZqlJ554Qps2bVJiYqImTJigvLy8M86/bt063XzzzZo2bZrS09OVkpKilJQUbdmypZmT15+n6yi5R3vPzs6ueRw4cKAZE3umpKREiYmJmj17dr3m37dvnyZNmqSLLrpIGRkZmjlzpu68804tXbq0iZM2jKfrd8rOnTtrbcPo6OgmSnhuVq1apenTp+vrr7/WsmXLVFlZqUsvvVQlJSV1LmOl92FD1k+y1nswPj5eTz/9tDZu3KgNGzbo4osv1tVXX62tW7eecX4rbT/J8/WTrLX9fmz9+vV6+eWXNWjQoLPOZ7Vt2OIZaHYjRowwpk+fXvN1dXW10bFjRyM1NfWM8994443GpEmTar02cuRI4+67727SnOfC03V8/fXXjcjIyGZK17gkGQsXLjzrPA899JDRv3//Wq9NnjzZmDBhQhMmaxz1Wb8vv/zSkGQcP368WTI1try8PEOSsWrVqjrnseL78JT6rJ+V34OntGnTxnj11VfP+D0rb79TzrZ+Vt1+RUVFRs+ePY1ly5YZY8aMMWbMmFHnvN6wDVsS9gA2s4qKCm3cuFHjx4+vec3Pz0/jx49XWlraGZdJS0urNb8kTZgwoc75zdaQdZSk4uJide7cWQkJCT/7L12rsdo2bKjBgwerQ4cOuuSSS7R27Vqz49RbYWGhJCkqKqrOeay8DeuzfpJ134PV1dWaP3++SkpKlJycfMZ5rLz96rN+kjW33/Tp0zVp0qTTts2ZWHkbtkQUwGZWUFCg6upqxcTE1Ho9JiamzvOlcnJyPJrfbA1Zx969e+u1117T4sWL9fbbb8vlcmn06NE6dOhQc0RucnVtQ4fDobKyMpNSNZ4OHTpozpw5+uCDD/TBBx8oISFBY8eO1aZNm8yO9rNcLpdmzpyp8847TwMGDKhzPqu9D0+p7/pZ8T2YmZmpVq1ayW6365577tHChQvVr1+/M85rxe3nyfpZcfvNnz9fmzZtUmpqar3mt+I2bMkCzA4ASFJycnKtf9mOHj1affv21csvv6wnn3zSxGSoj969e6t37941X48ePVp79uzRX//6V7311lsmJvt506dP15YtW7RmzRqzozSJ+q6fFd+DvXv3VkZGhgoLC/X+++9r6tSpWrVqVZ0lyWo8WT+rbb+srCzNmDFDy5Yts9TFKt6EAtjM2rVrJ39/f+Xm5tZ6PTc3V7GxsWdcJjY21qP5zdaQdfypwMBAJSUlaffu3U0RsdnVtQ0jIiIUEhJiUqqmNWLEiBZfqu677z4tWbJEq1evVnx8/Fnntdr7UPJs/X7KCu/BoKAg9ejRQ5I0dOhQrV+/Xi+++KJefvnl0+a14vbzZP1+qqVvv40bNyovL09Dhgypea26ulqrV6/WP/7xDzmdTvn7+9daxorbsCXjEHAzCwoK0tChQ7V8+fKa11wul5YvX17nuR3Jycm15pekZcuWnfVcEDM1ZB1/qrq6WpmZmerQoUNTxWxWVtuGjSEjI6PFbj/DMHTfffdp4cKFWrFihbp27fqzy1hpGzZk/X7Kiu9Bl8slp9N5xu9ZafvV5Wzr91MtffuNGzdOmZmZysjIqHkMGzZMt9xyizIyMk4rf5J3bMMWxeyrUHzR/PnzDbvdbsydO9fYtm2b8ctf/tJo3bq1kZOTYxiGYdx2223Gww8/XDP/2rVrjYCAAOPZZ581tm/fbjzxxBNGYGCgkZmZadYq/CxP1/H3v/+9sXTpUmPPnj3Gxo0bjZtuuskIDg42tm7datYqnFVRUZGRnp5upKenG5KM559/3khPTzcOHDhgGIZhPPzww8Ztt91WM//evXuN0NBQ48EHHzS2b99uzJ492/D39zc+++wzs1bhrDxdv7/+9a/GokWLjF27dhmZmZnGjBkzDD8/P+OLL74waxXO6t577zUiIyONlStXGtnZ2TWP0tLSmnms/D5syPpZ7T348MMPG6tWrTL27dtnbN682Xj44YcNm81mfP7554ZhWHv7GYbn62e17XcmP70K2OrbsKWjAJrk73//u9GpUycjKCjIGDFihPH111/XfG/MmDHG1KlTa83/7rvvGr169TKCgoKM/v37Gx9//HEzJ/acJ+s4c+bMmnljYmKMyy+/3Ni0aZMJqevn1LAnP32cWqepU6caY8aMOW2ZwYMHG0FBQUa3bt2M119/vdlz15en6/fnP//Z6N69uxEcHGxERUUZY8eONVasWGFO+Ho407pJqrVNrPw+bMj6We09eMcddxidO3c2goKCjPbt2xvjxo2rKUeGYe3tZxier5/Vtt+Z/LQAWn0btnQ2wzCM5tvfCAAAALNxDiAAAICPoQACAAD4GAogAACAj6EAAgAA+BgKIAAAgI+hAAIAAPgYCiAAAICPoQACAAD4GAogAACAj6EAAgAA+BgKIAAAgI+hAAIAAPgYCiAAAICPoQACAAD4GAogAACAj6EAAgAA+BgKIAAAgI+hAAIAAPgYCiAAAICPoQACAAD4GAogAACAj6EAAgAA+BgKIAAAgI+hAAIAAPgYCiAAAICPoQACAAD4GAogAACAj6EAAgAA+BgKIAAAgI+hAAIAAPgYCiAAAICP+f9V9XM6rpHwwAAAAABJRU5ErkJggg==",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Cap Calculations - Shows an interactive plot for the charging of a capacitor based\n",
    "on different parameters: the values of R and C, the instant that a voltage is applied\n",
    "to it and the initial voltage of the capacitor\n",
    "\"\"\"\n",
    "from math import ceil, exp\n",
    "from numpy import float64\n",
    "from numpy.typing import NDArray\n",
    "\n",
    "def capacitor_voltage(t:float, R: int, C: float, Vapplied:float)->float:\n",
    "    \"\"\" Calculates the voltage of a capacitor (Vout) in an RC filter after\n",
    "    time = t seconds, when Vapplied volts are applied at the input\n",
    "    Parameters:\n",
    "    -----------\n",
    "        t: float\n",
    "            time elapsed in seconds since Vapplied was applied at the input of\n",
    "            the RC filter\n",
    "        R: int\n",
    "            The value of the resistor (in Ω) of the RC filter\n",
    "        C: float\n",
    "            The value of the capacitor (in Farads) of the RC filter\n",
    "        Vapplied: float\n",
    "            The voltage (in Volts) applied at the input\n",
    "    Retunr\n",
    "    --------\n",
    "        cap_Volt: float\n",
    "            The voltage in the capacitor in Volts (Vout of the RC filter)\n",
    "    \"\"\"\n",
    "    cap_volt = Vapplied * (1 - exp (-t/(R*C)))\n",
    "    return cap_volt\n",
    "\n",
    "def cap_voltage_signal(times:NDArray[float64], startV:float, Vapplied:float, R: int, C: float)->Sequence[float]:\n",
    "    \"\"\" Calculates the values of the voltage at the capacitor of an RC filter when a certain\n",
    "    fixed voltage is applied for a range of time values. The signal returned is limited in time between [start_t, stop_t]\n",
    "    Parameters:\n",
    "    -----------\n",
    "        times: NDArray[float64]\n",
    "            A sequence of values for the time at wich the voltage of the capacitor has t o be calculated\n",
    "        startV:float\n",
    "            the voltage of the capacitor at t = times[0]\n",
    "        Vapplied: float\n",
    "            the voltage applied to the capacitor. It's constant for all the values in times\n",
    "        R: int\n",
    "            The value of the resistor (in Ω) connected to the capacitor\n",
    "        C: float\n",
    "            The value of the capacitor (in Farads) in the RC filter\n",
    "    Return\n",
    "    ------\n",
    "        returnArray: Sequence[float]\n",
    "            a sequence with the values of voltage of the capacitor for the different times\n",
    "    \"\"\"\n",
    "    start_t:float = times[0]\n",
    "    returnArray:Sequence[float] = []\n",
    "\n",
    "    for t in times:\n",
    "        returnArray.append(capacitor_voltage(t-start_t, R, C, Vapplied-startV) + startV)\n",
    "    return returnArray\n",
    "\n",
    "# Resistor Values and VDD\n",
    "r = 100000\n",
    "c = 0.00001\n",
    "Vdd = 3\n",
    "\n",
    "# x axis is 0 to 4s, 100 values\n",
    "start_t = 0\n",
    "stop_t = 4\n",
    "delta_t = .01\n",
    "times = linspace(start_t, stop_t, ceil((stop_t - start_t) / delta_t))\n",
    "\n",
    "# Set plots\n",
    "fig = figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Get signal for the RC filter with resistance r and capacitance c\n",
    "# that starts at t=0 with V=0 and VDD is applied to it\n",
    "# Plot the signal twice, and we will swap out_signal2 in the update() loop\n",
    "signal = cap_voltage_signal(times, 0, Vdd, r, c)\n",
    "out_signal, = ax.plot(times, signal)\n",
    "out_signal2, = ax.plot(times, signal)\n",
    "\n",
    "def update(r:int=10000, c:float=0.000001, start_v:float=0, vdd:float=3, start_t:float=0):\n",
    "    # Because one of the parameters is start_t, we need to build a new timebase for\n",
    "    # the signal and set it\n",
    "    new_times = linspace(start_t, stop_t, ceil((stop_t - start_t) / delta_t))\n",
    "    out_signal2.set_xdata(new_times)\n",
    "    # and generate the Y values and plot them\n",
    "    out_signal2.set_ydata(cap_voltage_signal(new_times, start_v, vdd, r, c / 100000))\n",
    "    fig.canvas.draw_idle()\n",
    "\n",
    "# This experiment shows the capacitor voltage curve for an RC filter wit res resistance\n",
    "# and cap capacitance where v_applied is the voltage applied. v_start is the voltage at\n",
    "# t = t_start\n",
    "# We create widgets for res, cap, v_start, v_applied and t_start to see how the voltage\n",
    "# changes as they change.\n",
    "res = IntSlider(value=10000, min=1, max=500000, step=10000, description='R (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='d')\n",
    "cap = FloatSlider(value=10, min=.01, max=100, step=.01, description='C (μF):',\n",
    "    orientation='horizontal', readout=True, readout_format='.3f')\n",
    "v_start = FloatSlider(value=0, min=0, max=5, step=.01, description='Start V (V):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2f')\n",
    "v_applied = FloatSlider(value=3, min=0, max=5, step=.01, description='Applied V (V):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2f')\n",
    "t_start = FloatSlider(value=3, min=0, max=5, step=.01, description='start t (s):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2f')\n",
    "interact(update, r=res, c=cap, start_v=v_start, vdd=v_applied, start_t=t_start);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d28c17146eb419a86380671a3c60c51",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=10000, description='Rd1 (Ω):', max=500000, min=1, step=10000), IntSlider…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "575d5a0a0fdf4cce9fc381c9a8fbbc38",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Astable Multivibrator Graphs\n",
    "Putting it all together, shows an interactive plot where we can see the impact of the different\n",
    "variables of the circuit have on the output.\n",
    "\"\"\"\n",
    "from numpy import array, greater\n",
    "from scipy.signal import argrelextrema\n",
    "\n",
    "def astable_vibrator_signals(times:NDArray[float64], Rd1:int, Rd2:int, Rf:int, Rc:int, C:float, Vdd:float)->tuple[Sequence[float], Sequence[float], Sequence[float]]:\n",
    "    \"\"\" Calculates the values of the signals of interest on an Astable Multivibrator: the\n",
    "    voltages at the inverting and non inverting pins and the output signal. The amplifier\n",
    "    is ideal, powered with a single rail and biased with a voltage divider.\n",
    "    The calculations are based on the values of the feedback resistor Rf, the values of the\n",
    "    resistors on the voltage divider and the values of the RC filter in the non-inverting pin\n",
    "    as well as the Vdd voltage.\n",
    "    Parameters:\n",
    "    -----------\n",
    "        times: NDArray[float64]\n",
    "            A sequence of values for the time at wich the values of the signals has to be calculated\n",
    "        Rd1:int\n",
    "            The value of the voltage divider resistor connected to VDD\n",
    "        Rd2:int\n",
    "            The value of the voltage divider resistor connected to GND\n",
    "        Rf:int\n",
    "            The value of the feedback resistor connected from out to v+.\n",
    "        Rc: int\n",
    "            The value of the resistor (in Ω) connected to the RC filter\n",
    "        C: float\n",
    "            The value of the capacitor (in Farads) in the RC filter\n",
    "        Vdd: float\n",
    "            the voltage applied to the capacitor. It's constant for all the values in times\n",
    "    Return\n",
    "    ------\n",
    "        [vi_signal, vo_signal, vf_signal]: tuple[Sequence[float],Sequence[float],Sequence[float]\n",
    "            Sequences with the values of voltage at the inverting, output and non-inverting\n",
    "            pins of the OpAmp, respectively.\n",
    "    \"\"\"\n",
    "    vi_signal:Sequence[float] = [0.0] * len(times)\n",
    "    vo_signal:Sequence[float] = [0.0] * len(times)\n",
    "    vf_signal:Sequence[float] = [0.0] * len(times)\n",
    "\n",
    "    # Get the schimtt trigger thresholds for this config\n",
    "    thresholdl, thresholdh = ThresholdCalculator(Vdd, Rd1, Rd2, Rf)\n",
    "\n",
    "    #initial conditions\n",
    "    vi_signal[0] = 0 # capacitor starts discharged\n",
    "    # vi below threshold, so vo is HIGH\n",
    "    vo_signal[0] = Vdd\n",
    "    # vo is HIGH so Vthreshold is VthH\n",
    "    vf_signal[0] = thresholdh\n",
    "    start_t = times[0]\n",
    "    start_v = 0\n",
    "    rising = False # an aux variable to keep track if we are charging or discharging C\n",
    "    if vi_signal[0] < vf_signal[0]:\n",
    "        rising = True\n",
    "\n",
    "    for i in range(1, len(times)): #skip times[0] as we have entered the initial conditions by hand\n",
    "        if vi_signal[i - 1] < vf_signal[i-1]:\n",
    "            # If v- < v+, the voltage at the cap is below trheshold\n",
    "            # We will charge the capacitor\n",
    "            time = times[i]\n",
    "            vi_signal[i] = capacitor_voltage(time-start_t, Rc, C, Vdd - start_v) + start_v\n",
    "            vf_signal[i] = thresholdh\n",
    "            vo_signal[i] = Vdd\n",
    "        else: # vi_signal[i - 1] >= vf_signal[i-1]:\n",
    "            # Else, the voltage at the cap is above the trheshold\n",
    "            # We will discharge the capacitor\n",
    "            time = times[i]\n",
    "            vi_signal[i] = capacitor_voltage(time-start_t, Rc, C, 0 - start_v) + start_v\n",
    "            vf_signal[i] = thresholdl\n",
    "            vo_signal[i] = 0\n",
    "        # after updating the values, check if we switched the thresholds\n",
    "        if (vi_signal[i] >= vf_signal[i]) and rising:\n",
    "            start_v = thresholdh\n",
    "            start_t = times[i]\n",
    "            rising = False\n",
    "        elif (vi_signal[i] <= vf_signal[i]) and not rising:\n",
    "            start_v = thresholdl\n",
    "            start_t = times[i]\n",
    "            rising = True\n",
    "    return vi_signal, vo_signal, vf_signal\n",
    "\n",
    "Rd1:int = 10000\n",
    "Rd2:int = 10000\n",
    "Rf:int = 10000\n",
    "Rcap:int = 100000\n",
    "cap:float = 0.00001\n",
    "Vdd:float = 3\n",
    "\n",
    "start_t = 0\n",
    "stop_t = 10\n",
    "delta_t = .01\n",
    "times = linspace(start_t, stop_t, ceil((stop_t - start_t) / delta_t))\n",
    "\n",
    "# Prepare the plot\n",
    "fig = figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# Generate the three signals to print\n",
    "vi_values, vo_values, vf_values = astable_vibrator_signals(times, Rd1, Rd2, Rf, Rcap, cap, Vdd)\n",
    "vf_signal, = ax.plot(times, vf_values)\n",
    "vi_signal, = ax.plot(times, vi_values)\n",
    "vo_signal, = ax.plot(times, vo_values)\n",
    "\n",
    "def update(Rd1, Rd2, Rf, Rcap, cap, Vdd):\n",
    "    vi_upd_values, vo_upd_values, vf_upd_values = astable_vibrator_signals(times, Rd1, Rd2, Rf, Rcap, cap/100000, Vdd)\n",
    "    vf_signal.set_ydata(vf_upd_values)\n",
    "    vo_signal.set_ydata(vo_upd_values)\n",
    "    vi_signal.set_ydata(vi_upd_values)\n",
    "    # get the indices of the local maxima of vi\n",
    "    vi_local_maxes=(argrelextrema(array(vi_upd_values), greater, mode='wrap'))[0]\n",
    "    clk_period = times[vi_local_maxes[1]] - times[vi_local_maxes[0]]\n",
    "    print(f\"Clk Freq is {1/clk_period:.2f} Period {clk_period}\")\n",
    "    fig.canvas.draw_idle()\n",
    "\n",
    "# Create a widgets for variables\n",
    "Rd1_slider = IntSlider(value=10000, min=1, max=500000, step=10000, description='Rd1 (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='d')\n",
    "Rd2_slider = IntSlider(value=10000, min=1, max=500000, step=10000, description='Rd2 (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='d')\n",
    "Rf_slider = IntSlider(value=10000, min=1, max=500000, step=10000, description='Rf (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='d')\n",
    "Rcap_slider = IntSlider(value=10000, min=5000, max=50000, step=5000, description='Rcap (Ω):',\n",
    "    orientation='horizontal', readout=True, readout_format='d')\n",
    "cap_slider = FloatSlider(value=10, min=.01, max=100, step=.01, description='C (μF):',\n",
    "    orientation='horizontal', readout=True, readout_format='.3f')\n",
    "Vdd_slider = FloatSlider(value=3, min=0, max=5, step=.1, description='Vdd (V):',\n",
    "    orientation='horizontal', readout=True, readout_format='.2f')\n",
    "\n",
    "interact(update, Rd1=Rd1_slider, Rd2=Rd2_slider, Rf= Rf_slider, Rcap=Rcap_slider, cap=cap_slider, Vdd=Vdd_slider);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('docs': venv)",
   "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": "3.12.0"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1daec58c924830f75dc3e9a8aa4e82cc8d807c64d162de405d7cd5538c04c525"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}