Frequency Response.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.rcParams['figure.figsize'] = [5.333, 3]\n",
    "plt.rcParams['font.sans-serif'] = ['Tahoma', 'DejaVu Sans','Lucida Grande', 'Verdana']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurements = pd.read_csv('frequency measurements.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inter = interpolate.interp1d(measurements['Frequency'], measurements['Amplitude'], kind = 'cubic')\n",
    "inter_freq = np.logspace(1, 5)\n",
    "inter_amp = inter(inter_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measured Frequency Response\n",
    "\n",
    "In practice, the amplifier exhibits mild roll-off of sub-bass frequencies below 30 Hz. This is well below the necessary bandwidth of a guitar amplifier. The high-end frequency response extends well above the audio band, ensuring accurate phase response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAADAocsd6HS4//77o6GhoWN87LHHxle/+tWU8jwiYsKECfH1r389xowZ0zFX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\nA4Aea2tPxu1Pr487n92417W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n0yPlu/fn3cddddcejQoazXAgAAAAB0Pp5AJ0NBQUGcddZZcdZZZ8XUqVOjR48eGZ+//fbb8eMf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\nULesDwAABB4S6AAAAID+K5vR+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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb1d8112e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.semilogx(inter_freq, 20*np.log10(inter_amp),\n",
    "             color='tab:blue')\n",
    "plt.grid(which='minor')\n",
    "\n",
    "plt.xlabel(r'Frequency $[Hz]$')\n",
    "plt.ylabel(r'$A_v$ $[dB]$')\n",
    "plt.xlim((10,20000))\n",
    "plt.ylim((-3, 0.5))\n",
    "plt.title('Gain')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}