Scope Capture-Transfer Function.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RIGOL TECHNOLOGIES,DS1054Z,DS1ZA192409533,00.04.04.SP3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from ds1054z import DS1054Z\n",
    "#%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy.fft import fft\n",
    "from numpy.fft import fftshift\n",
    "from scipy.signal import blackmanharris\n",
    "from matplotlib.ticker import EngFormatter\n",
    "\n",
    "scope = DS1054Z('192.168.1.252')\n",
    "print(scope.idn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_transfer(Ts,V1,V2):\n",
    "    N=2**14\n",
    "    X1A = 1/N*fftshift(fft(V1,N)) #N-point complex DFT\n",
    "    X2A = 1/N*fftshift(fft(V2,N)) #N-point complex DFT\n",
    "\n",
    "    mag1 = abs(X1A)\n",
    "    mag2 = abs(X2A)\n",
    "\n",
    "    Fs=1/(Ts[1]-Ts[0])\n",
    "    df=Fs/N #%frequency resolution\n",
    "    sampleIndex = np.arange(-N/2,N/2)\n",
    "    f=sampleIndex*df\n",
    "\n",
    "    thresh = 1/10\n",
    "\n",
    "    X1B = np.copy(X1A)\n",
    "    X1B[mag1 < max(mag1)*thresh] = 0\n",
    "    phase1=np.arctan2(np.imag(X1B),np.real(X1B))*180/np.pi\n",
    "\n",
    "    X2B = np.copy(X2A)\n",
    "    X2B[mag2 < max(mag2)*thresh] = 0\n",
    "    phase2=np.arctan2(np.imag(X2B),np.real(X2B))*180/np.pi\n",
    "    \n",
    "    fig, (ax1,ax2) = plt.subplots(2)\n",
    "    ax1.plot(f,mag1)\n",
    "    ax1.plot(f,mag2)\n",
    "    ax2.plot(f,phase1)\n",
    "    ax2.plot(f,phase2)\n",
    "    ax1.set_xscale('log')\n",
    "    ax1.set_yscale('log')\n",
    "    ax2.set_xscale('log')\n",
    "    ax2.set_ylim([-180, 180])\n",
    "    ax1.set_xlim([1e1,1e5])\n",
    "    ax2.set_xlim([1e1,1e5])\n",
    "    plt.show()\n",
    "    \n",
    "    f_single=f[int(N/2):N]\n",
    "    mag1_single=mag1[int(N/2):N]\n",
    "    index_of_peak1=mag1_single.argmax()\n",
    "    #print('Peak Mag.',mag1[int(N/2)+index_of_peak1])\n",
    "    print('at Freq.',f[int(N/2)+index_of_peak1])\n",
    "    #print('with phase',phase1[int(N/2)+index_of_peak1],'deg')\n",
    "\n",
    "\n",
    "    #print('Peak Mag.',mag2[int(N/2)+index_of_peak1])\n",
    "    #print('at Freq.',f[int(N/2)+index_of_peak1])\n",
    "    #print('with phase',phase2[int(N/2)+index_of_peak1],'deg')\n",
    "\n",
    "    gain = (mag2[int(N/2)+index_of_peak1]) / (mag1[int(N/2)+index_of_peak1])\n",
    "    phase_shift = (((phase2[int(N/2)+index_of_peak1]) - (phase1[int(N/2)+index_of_peak1])) + 180) % 360 - 180\n",
    "\n",
    "    #print()\n",
    "    print('gain:', gain)\n",
    "    print('phase shift:', phase_shift)\n",
    "    print()\n",
    "    return (f[int(N/2)+index_of_peak1], gain, phase_shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Press Enter to continue...\n"
     ]
    },
    {
     "data": {
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bkNS+rrNWcw4+eR7yjoeXfghL/92w7Kwfwcm3tn2fk77iJQiAu4fDhEsgkARn/wT6DopG1I0oQYh0QGVNLWUHathfWcu+ymrKDtRQVhnxOFDD/soa9h2oIli2lfSyDfSt2MhhB4oZUF1MVa3j0arTeKV2IqGIGzwGzPsGPqBvGoP796Ygvz8DMtIY0DeN3L6pLNuyl1/NXcmCtTs5eWR2u2IvKtnH2u37ue7k/FZvk5WeSlZ6arver7XSgvCL4UvhjV9490cA+PDvcNo3G6+44D5I7cvUL91OsOgl+O9C72KzQcdC2TZYcK83FjHouIZtPngE3rgThpzYcCFaZ1k1F5645tDy6+ZA/int2+cR0+BHu+CuYVC1ryHp9M6Gab+AxY/DsJO9JBiF3y8hEsSW0gp+9J+lLa7X2u9RHZ5U65D9tXK9VkbY+v1FcV+tXLHVNRfHddLa/VXVhCirrA0f6KvDSaCGsnAi2F9ZWz+bp8eRQynD7VPyA58y3LxHvn3KMNtGL6tq2LelsD05j3TKOD15IWV9B7F19DVUH30N2bkDyEpPbbZbZvKILB58ex2z3l7f7gQxd9k2AM4dN7Bd20dVqBaq9nsT1r32c9j+CQw8Gq7+Hcz7FXz0Tzj1fxr+cPt3wvL/QMEN5GTnQMZ0eOVbXnJISoX+w7z1Sjc2ThArnvN+7ljlHUhrKhtfnNac9W/Dhvlw+ncbl+9aCztWe6eq1tm6GJ6e4T1PyYB+gyFnjDfxXnuTQ51AEL6/ybvj3B/CV1i/ex9snA9bPmxY790HOvY+JEiCKK2o5rnFzc/c0drJmlp7f+/W3gi81ZNEtfZ9W7u7KMbX6jppZXSt318rxSi+pKCRnppMRloS6aneI69fGgOTaxnKTgbVbmFgzWayKzeRWbGR9PKNJNU0nK3jgim4zGFY1ngsa7o3GHnYSMgaSUrGIPICAaitgZUvkP7eTEYvuRuW3wNHXQYnfgUGHtVkbGnh/vl7Xy9iw879DMvq4/+Lbv4ANrwN/fMhZywcNgKC3r/93GWfcsyQTAb2i/Ks+6Fa75tzXddHc4Own8yBZ2+GA3sayrJGw2V/g7EXepPU7dsCs2/zpsMecoK3zuLHoLYKJl7rvU5Nh+n3Qsbh3uvM8NhF5D2e92z25jwCLwG9/CNY+wZ87b3W/V4L/+rNjVRwY8OppqEQ3H+SNz33N5Z63TyFs+DlH3uT7n19CfTKBKz1iag1zLy/6Tc+9pLe3P9tnBwAXG2H3yYhEsS4nBQKb47CjK5RazlEYT/dMpYoiKd6Kd8Fu9Z4p0vuWgM7i2DrWqiMOJhZ0Pu2mjsSss6ArFHeQThrJNZvCBZooQ8+mATjpnuPbcu8fvPFT3hdKkNP9s6BH/s572BzkGsmD+OBN9bw8Pz1/Phz4xsWlJV4XQ0fPeodCBu9XwpkH0F55mhO3ZrMhONOhNKhkDmEqCmcBSvneN09K1/0BoqnfOfQA2RlGbzwTe+gPvmr3kBsvyFw5AX1SQzwLhSb813v9xlygpf4PvgbDJ4EA8Y1rHfUpQ3P0zIhta/XgijdBA+eC33DySPjcG96i/07YMfKQ8cpmrItPDnExvkw4kzvrm+rX2q4d8OsqV4r6ECp93rCFxtaMp0lcyhM/BK8fqfX5RRl1tpvorFUMCjoCmd00iRZIi0y7wAa/vZf/zNrlPcP6nPw7pCK3fDho7DwL7B7vXdAK7gBjr8O0hvPJPqNxz/klRUlLPjuqWRsfM3bbvVL3rfHwZPguKu9CeD2boGSFd6Vu9s/oWzTx6Qf2NqwozN/CFO+3fEEvW8b3Fvg3fTm83/2Tsn86FHokwMXPeDdTrPOKz+Bt38HN77S0DJoytNf8ZLNt1d635QfOg+m3+/9fk25/2Tv7zPhYnj6Jq8sdzwMPh6WPtNwQJ3xZsN1CK/8BGqrYeodjfdVfQB+cTi4EBx/Pax93bt3Q84RsPK/cMwVUPgg5BwJp3zD6/6Z+KVOGTj2VVUOz97iXVB3+u1e0nrn99hP9y5yzhW0d7cJ0YKg/3C49O4O7iRKiTCeEmpUYomneomnWPC+8R420mvKJ3fhzQ979ffOcJl8i3ePgPf/DK/f4fXFj/88TJoBg73/+a+Oq2TC0odI+v1XoHo3pA/wtj32Gq/Pu056bqOLsWb85V3K9u5m9heyw/v/OVTs8k697Mi9B+b+r9evf/5vvcnmLrofTrgRZn8d/nm59/qYK7xW2fx74ZirWk4OAMde5V1D8OhlXqsgtZ9XF83JHOqtW7LC6+766rvet/5lzzb+tr2zyKub2mp4/6/esmOu8Lr4Pn7K61qa+gsvOQSSYdFDDdtuWwojzoDzfwNHXw65Y6PbldRaKb3hC39reL3smajsNjESRK9M71uASE8SCHpnrRwxzRsEff8v3mDtkidg0ERwIcZs/YjhSUm8EzqB0674BsHRZzfunvGxe38V763bxc2nj4AhR3qnYfbOhnfvh/KdMP2+9rWK1rzmTVt9+u2Nxx3yjofr58ATV8MzX4H922HdW5CU5o1RtEb+aTDsVO++zf2Hedu1NC9R5lBvYHn7J16izw5fiZ1TN/13uLX06RJYMRvyChoSx8MXeAli/Vve640LvJ/T74W3fuPtr0+21xWYO95reQ1t8h5pXW/kZ6Kym8RIECI9XfZo72ras/7PG18ofMhLINPu4vXg6cz49zr+EjqOc1pIDgCvflJCbcgxdXz47KVAAKbd6Q28vvZzqCiFyx5u28Rw1QfghW974y+n/s+hy9P6wtVPeWf2vBS+Kc65P4eMAYeu6ycQgOtfaH084CWIqn2w6T1vuoo6ddNm547zbu35wd+91tPy/3hjSuf/2mtlrHuzYZvNi7xlR13mtS6c806l3b3e6/qLN2n94GsL4acdmyJcCUIkkaRmeAPXk26qL/pMbYhBr2xl1tvrOGdcywfcucs+5fB+aRwVec9iM28guddh8MK34B8Xw5WPh8/AaYV3fu8N5H/xmaa745JS4dJZ8PJgr2tm0ldat+/2qjuTqXynlwzq9BvsdeMNOwl2b4Cil72Dv6uFIZPC4z3XewPbxe972+xa521Td9KBmdeFdt3znfs7dERkF2M7KUGIJLikYIAvnZzPXS9+woqtexl7eNN94OVVNcxbtZ0rThjif+3LCTd6B8KnZ3jdLFf+0+t3r9jtnZ1TUeo937sF9oTPENqzybsWYMIlLXdtBIKHDgB3lsyIMx9zIu4qZ+YNjKfnwBt3eQliwiVel1FdS8PM62p7/Q5v4Ld0g1cvPYwShEg3cMUJQ/jDK6t56J113H1p07ennLdqO5U1oYbuJT8TLva6KJ64Bn7f9LUY9MnxDsIDJnin6Z7UjqkjOlNkgsgd23hZ3RxIWeGfYy/wfodIOWO8M7uWP+u1Qg4b0XmxxiklCJFuILN3ChdPzONfi4r53rQjm5wOY+6ybWT2Tm50m05fo87yZgwtetlLFmmZXndT3c+Mw70zguJZr/7eVcw1B5o+uI+bDnuKYcw0/+VpEd1waa3sbutGlCBEuonrT8nn0fc28s/3NnLbWaMPWV5dG+LVFds4Z9zA1t00Z+AE75GozMKtCNf0WVl9suHsHze9j8gE0drxmG4kDm6tJCLRMCo3gyljcnjk3Q1U1YQOWf7e2l3sPVDD1PGtPHOoO5jyrUPnTmqLlAyw8GGyB45BKEGIdCM3nJLP9n2VzPl46yHL5i77lF7JQaaM8bn3cXc14ZKWL6hrTiDgTdkBPbKLSQlCpBuZMjqHkTl9mPXOukYTOoZCjpeWf8rpY3JIS27bPRp6vLpuJrUguoaZBczsDjP7o5ldG4sYRLqjQMC47pThLCnewwcbd9eXLy4uZdveSs7tSd1L0VKfINSCaJGZzTKzEjNbelD5NDNbaWZFZnZ7C7uZDuQB1UBxW2MQkaZdMjGPvmlJzHp7fX3Z3GXbSAoYZx2pBNFmakG0ycNAo3PCzCwI3AecB4wDrjSzcWZ2lJk9f9AjFzgCWOCc+yYQJzeNFekeeqckceWJQ/nvsk/ZXFqBc46XwrcW7dc7yjPP9gR1CUJjEC1zzs0Ddh1UPAkocs6tdc5VAY8D051zHzvnLjjoUYLXaqhr/3b8rhYi0siXTsoH4JEF6ykqKWPtjv096+ylaKpLDD2wiyla10HkARG3bqIYaG5qw6eBP5rZacA8vxXMbAYwA2Do0CjcLEikB8nL7MW08QN57L2NJIVvXXpOPNxaNBHVJYYe2MUUrQThd5eRJifld86VAzc2t0Pn3ExgJkBBQUEc3YRBJDFcf0o+L3y8lT+/uZZjO+PWoj1Feq53Jz51MbVbMRB5z8LBQPM3kRaRTnX8sP4cPbgfNZFTe0vbFdwAN/y3a28aFSeilSAWAqPNbLiZpQBXALOjtG8RaQcz45bTR5KSFOCzRylBtFtqhnfTox6ozV1MZvYYcAaQbWbFwI+dcw+a2a3AXCAIzHLOLYtqpCLSZucddThnHJFLrxRdHCdt1+YE4Zy7sonyOcCcDkckIlGl5CDtpak2RETElxKEiIj4UoIQERFfShAiIuJLCUJERHwpQYiIiC8lCBER8aUEISIivpQgRETElxKEiIj4UoIQERFfShAiIuJLCUJERHwpQYiIiC8lCBER8aUEISIivpQgRETElxKEiIj4UoIQERFfShAiIuJLCUJERHwpQYiIiC9zzsU6hhaZ2T5gZRe8VT9gTydv19K6zS33W9aasoNfZwM7Woy0Y9pbl23dtr312ZbynlKf0f5s+pW3pn7juT4T7X/9COdcRsuhNsE5F/cPoLCL3mdmZ2/X0rrNLfdb1poyn9edXp/trcuuqs+2lPeU+oz2Z7M1dZdo9dnT/tfVxdTYc12wXUvrNrfcb1lrytr7e3VER96zK+qzLeU9pT6j/dn0K2/tZ7iz6X+9FRKli6nQOVcQ6zi6C9VndKk+o0v1GT0drctEaUHMjHUA3YzqM7pUn9Gl+oyeDtVlQrQgRESk6yVKC0JERLqYEoSIiPhSghAREV8JmSDMbISZPWhmT8U6lu7AzC4ys7+Y2X/M7NxYx5PIzGysmf3JzJ4ys1tiHU93YGZ9zGyRmV0Q61gSnZmdYWZvhT+jZ7S0ftwkCDObZWYlZrb0oPJpZrbSzIrM7HYA59xa59yNsYk0MbSxPp91zt0EXAdcHoNw41ob63KFc+5m4AuATtX00Zb6DPse8GTXRpk42lifDigD0oDiFnfe2VcstuGqwynARGBpRFkQWAOMAFKAxcC4iOVPxTrueH20sz5/A0yMdezx9mhrXQIXAvOBq2Idezw+2lKfwNnAFXhfXi6Idezx+GhjfQbCywcAj7a077hpQTjn5gG7DiqeBBQ5r8VQBTwOTO/y4BJQW+rTPL8EXnTOfdDVsca7tn42nXOznXMnA1d3baSJoY31eSYwGbgKuMnM4uaYFS/aUp/OuVB4+W4gtaV9J0U10ujLAzZFvC4GTjSzLOAO4Dgz+75z7s6YRJd4fOsTuA3vm1o/MxvlnPtTLIJLME19Ns8ALsb755sTg7gSlW99OuduBTCz64AdEQc4aV5Tn8+LgalAJnBvSzuJ9wRhPmXOObcTuLmrg+kGmqrPe4B7ujqYBNdUXb4BvNG1oXQLvvVZ/8S5h7sulG6hqc/n08DTrd1JvDfXioEhEa8HA1tiFEt3oPqMHtVldKk+oysq9RnvCWIhMNrMhptZCt5g1ewYx5TIVJ/Ro7qMLtVndEWlPuMmQZjZY8AC4AgzKzazG51zNcCtwFxgBfCkc25ZLONMFKrP6FFdRpfqM7o6sz41WZ+IiPiKmxaEiIjEl3g/iwmA7Oxsl5+fH+swREQSyqJFi3Y453Lau31CJIj8/HwKCwtjHYaISEIxsw0d2V5dTCIi4ksJQkREfClBiIiILyUIERHxpQQhIiK+lCBERMSXEoSIiPhSghAREV9KECIi4ksJQkREfClBiIiILyUIERHxpQQhIhIP9u+El34IB/bEOpJ6ShAiIvFgzWsw/4/w6zGwu0OTsEaNEoSISDwIBL2fNQdgwX2xjSVMCUJEJB64UMPzmorYxREhKgnCzGaZWYmZLY0oO8zMXjaz1eGf/cPlZmb3mFmRmS05kwC4AAAKH0lEQVQxs4nRiEFEJKGFaiKeh5perwtFqwXxMDDtoLLbgVedc6OBV8OvAc4DRocfM4AHohSDiEjiCtU2PHfdKEE45+YBuw4qng78Lfz8b8BFEeWPOM+7QKaZHR6NOEREElZdCyKQ3L0SRBMGOOe2AoR/5obL84BNEesVh8tERHquugQRTAFX2/y6XSQWg9TmU+YOWclshpkVmlnh9u3buyAsEZEYCrcaaiyJNz7Zxk9mL4txQJ2bILbVdR2Ff5aEy4uBIRHrDQa2HLyxc26mc67AOVeQk5PTiWGKiMSBcAuiygUpO1DJw/PXxzYeOjdBzAauDT+/FvhPRPmXwmczTQb21HVFiYj0WOFB6lAgmcChnSoxkRSNnZjZY8AZQLaZFQM/Bu4CnjSzG4GNwGXh1ecAnwWKgHLg+mjEICKS0MItiFpL6l4Jwjl3ZROLzvJZ1wFfi8b7ioh0G+GB6VpLJog3HhEKOQIBv2HbrqErqUVE4kGoIUFYOEHUhGLbklCCEBGJB3VjEBFdTLVKECIiQqgGLEjIAvVdTDUxnnJDCUJEJB6EaiAQJEQAC7cgamrVghAREVcLgSScGQGNQYiISL1QrdfFRICgxiBERKReqLa+iylgGoMQEZE64TEIh2kMQkREIoTHILwuJo1BiIhInVBNfYLQdRAiItIgFALzupgCug5CRETqRVwHUZ8gNAYhIiL1CcIaupg0BiEiIg2D1K6hi0ljECIi0nChXKMWhMYgRESk/kI5a0gQGoMQEZH601zVxSQiIo24uhZEUJP1iYhIhPoL5SziQrkeOgZhZtPMbKWZFZnZ7bGKQ0QkLoQvlAthBMOT9VX3xDEIMwsC9wHnAeOAK81sXCxiERGJC+HrIGrjaKqNpBi97ySgyDm3FsDMHgemA8v9Vq46UM76FYVdGJ6ISNc6vHwPSf0G1ncx5bCb8uKPWZ+2NWYxxSpB5AGbIl4XAyc2tXLKrpXkP3FWpwclIhJLi6tyCGH0t33MT/1/JC+qhUWxiydWCcJ8yhq1pcxsBjADID8vl0WT7uqKuEREYuL5JVvYmX4Mny97jFSqwWD9iKvZmX1CB/Z6Q4diilWCKAaGRLweDGyJXME5NxOYCVBQUOCO/+z1XRediEgX++WmBQQMQq7h+3P+xLPIn3BJB/basQQRq7OYFgKjzWy4maUAVwCzYxSLiEjMJQeN6lpHbeRhOZgau4CIUQvCOVdjZrcCc4EgMMs5tywWsYiIxIOkQICa2hpCkQkiqQcmCADn3BxgTqzeX0QkniQHA1TVOmojupgIpsQuIHQltYhIXEgOGjW1IWojz+GJcQtCCUJEJA4kBwPUhByu0RiEWhAiIj1eUtCoqgmpi0lERBpLDgSoCamLSUREDpKc5J3mGlIXk4iIREoKBKiuPaiLSS0IERFJSQpQU+sadzGpBSEiIkkBUwtCREQOVXeaa6jRWUxKECIiPV5y0EsMVaHIBBGzyS4AJQgRkbiQFPQOx9WxvQ11I0oQIiJxIDmcIN52R7O8zyQ4+bYYR6QEISISF+q6mJbVDmXmkLvh3J/HOCIlCBGRuFDXgqisqSVgfjfd7HpKECIicSAp4CWF6lpHIKAEISIiYXUtCIA4yQ9KECIi8aBxgoiPDKEEISISB4Yc1qu+m2loVu8YR+PpUIIws8vMbJmZhcys4KBl3zezIjNbaWZTI8qnhcuKzOz2jry/iEh3cfTgTJb+dCpLfnIuXz1jVKzDATreglgKXAzMiyw0s3HAFcB4YBpwv5kFzSwI3AecB4wDrgyvKyLS46UlB+mblhzrMOp16Dpu59wKADu0v2w68LhzrhJYZ2ZFwKTwsiLn3Nrwdo+H113ekThERCT6OmsMIg/YFPG6OFzWVLmIiMSZFlsQZvYKMNBn0Q+cc/9pajOfMod/QnJNvO8MYAbA0KFDWwpTRESirMUE4Zw7ux37LQaGRLweDGwJP2+q/OD3nQnMBCgoKPBNIiIi0nk6q4tpNnCFmaWa2XBgNPA+sBAYbWbDzSwFbyB7difFICIiHdChQWoz+zzwRyAHeMHMPnLOTXXOLTOzJ/EGn2uArznnasPb3ArMBYLALOfcsg79BiIi0inMufjvvSkoKHCFhYWxDkNEJKGY2SLnXEHLa/rTldQiIuJLCUJERHwpQYiIiC8lCBER8aUEISIivpQgRETElxKEiIj4UoIQERFfShAiIuJLCUJERHwpQYiIiC8lCBER8aUEISIivpQgRETElxKEiIj4UoIQERFfShAiIuIrIe4oZ2b7gJVd8Fb9gD2dvF1L6za33G9Za8oOfp0N7Ggx0o5pb122ddv21mdbyntKfUb7s+lX3pr6jef6TLT/9SOccxkth9oE51zcP4DCLnqfmZ29XUvrNrfcb1lrynxed3p9trcuu6o+21LeU+oz2p/N1tRdotVnT/tfVxdTY891wXYtrdvccr9lrSlr7+/VER15z66oz7aU95T6jPZn06+8tZ/hzqb/9VZIlC6mQteBG29LY6rP6FJ9RpfqM3o6WpeJ0oKYGesAuhnVZ3SpPqNL9Rk9HarLhGhBiIhI10uUFoSIiHQxJQgREfGlBCEiIr4SMkGY2Qgze9DMnop1LN2BmV1kZn8xs/+Y2bmxjieRmdlYM/uTmT1lZrfEOp7uwMz6mNkiM7sg1rEkOjM7w8zeCn9Gz2hp/bhJEGY2y8xKzGzpQeXTzGylmRWZ2e0Azrm1zrkbYxNpYmhjfT7rnLsJuA64PAbhxrU21uUK59zNwBcAnarpoy31GfY94MmujTJxtLE+HVAGpAHFLe68s69YbMNVh1OAicDSiLIgsAYYAaQAi4FxEcufinXc8fpoZ33+BpgY69jj7dHWugQuBOYDV8U69nh8tKU+gbOBK/C+vFwQ69jj8dHG+gyElw8AHm1p33HTgnDOzQN2HVQ8CShyXouhCngcmN7lwSWgttSneX4JvOic+6CrY413bf1sOudmO+dOBq7u2kgTQxvr80xgMnAVcJOZxc0xK160pT6dc6Hw8t1Aakv7TopqpNGXB2yKeF0MnGhmWcAdwHFm9n3n3J0xiS7x+NYncBveN7V+ZjbKOfenWASXYJr6bJ4BXIz3zzcnBnElKt/6dM7dCmBm1wE7Ig5w0rymPp8XA1OBTODelnYS7wnCfMqcc24ncHNXB9MNNFWf9wD3dHUwCa6punwDeKNrQ+kWfOuz/olzD3ddKN1CU5/Pp4GnW7uTeG+uFQNDIl4PBrbEKJbuQPUZParL6FJ9RldU6jPeE8RCYLSZDTezFLzBqtkxjimRqT6jR3UZXarP6IpKfcZNgjCzx4AFwBFmVmxmNzrnaoBbgbnACuBJ59yyWMaZKFSf0aO6jC7VZ3R1Zn1qsj4REfEVNy0IERGJL0oQIiLiSwlCRER8KUGIiIgvJQgREfGlBCEiIr6UIERExJcShIiI+FKCEBERX/8flT/C6ftOvOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at Freq. 60272.216796875\n",
      "gain: 0.06798109274276914\n",
      "phase shift: -156.28375585810528\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "for n in range(100):\n",
    "    inn = input(\"Press Enter to continue...\")\n",
    "    if inn == 'q':\n",
    "        break\n",
    "    scale = scope.timebase_scale\n",
    "    scope.timebase_scale = scale*100\n",
    "    if(not scope.running):\n",
    "        scope.run()\n",
    "    time.sleep(1)\n",
    "    scope.memory_depth = 120e3\n",
    "    time.sleep(1)\n",
    "    scope.single()\n",
    "    time.sleep(1+scope.timebase_scale*12)\n",
    "    V1 = scope.get_waveform_samples(1,mode='MAX')\n",
    "    V2 = scope.get_waveform_samples(2,mode='MAX')\n",
    "    Ts = scope.waveform_time_values\n",
    "    scope.timebase_scale = scale\n",
    "    scope.run()\n",
    "    \n",
    "    data.append(get_transfer(Ts,V1,V2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1,ax2) = plt.subplots(2)\n",
    "f=[i[0] for i in data]\n",
    "gain=[i[1] for i in data]\n",
    "phase=[i[2] for i in data]\n",
    "ax1.plot(f,20*np.log10(gain),'-o')\n",
    "ax2.plot(f,phase,'-o')\n",
    "ax1.set_xscale('log')\n",
    "ax2.set_xscale('log')\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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}