examples/SpectrumAnalyser_carrier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7904f229-36c5-421d-9f07-f9d41b0e1854",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class MockAgilentE44nnInstrument():\n",
    "    \n",
    "    def write(self, input_):\n",
    "        print(f'{input_=}')\n",
    "    \n",
    "    def read(self):\n",
    "        return input('... : ')\n",
    "    \n",
    "    def query(self, query):\n",
    "        if query == '*OPC?':\n",
    "            return True\n",
    "        elif query == ':SWEep:POINts?':\n",
    "            return '8192'\n",
    "        elif query == ':INST:SEL?':\n",
    "            return 'SA'\n",
    "        elif query == ':FREQuency:SPAN?':\n",
    "            return '10e6'\n",
    "        elif query == ':FREQuency:STARt?':\n",
    "            return '995e6'\n",
    "        elif query == ':FREQuency:STOP?':\n",
    "            return '1005e6'\n",
    "        elif query == ':UNIT:POWer?':\n",
    "            return 'DBM'\n",
    "        elif query == ':CORRection:OFFS:MAGN?':\n",
    "            return '0'\n",
    "        elif query == ':FREQuency:CENT?':\n",
    "            return '1e9'\n",
    "        elif query == ':POWer:RF:ATTenuation?':\n",
    "            return '10'\n",
    "        elif query == ':POWer:ATTenuation:AUTO?':\n",
    "            return '+1'\n",
    "        elif query == ':DISP:WIND:TRACE:Y:RLEV?':\n",
    "            return '0'\n",
    "        elif query == ':BANDwidth:RESolution?':\n",
    "            return '1e3'\n",
    "        elif query == ':SWEep:TIME?':\n",
    "            return '3.141'\n",
    "        elif query == ':TRACe1:MODE?':\n",
    "            return 'MAXH'\n",
    "        elif query == ':BANDwidth:VIDeo?':\n",
    "            return '3e3'\n",
    "        elif query == '*IDN?':\n",
    "            return 'HP, E4404A, Mock, Mock'\n",
    "        else:\n",
    "            return input(f'{query} :')\n",
    "    \n",
    "    def control_ren(self, input_):\n",
    "        print(f'CTRL {input_}')\n",
    "    \n",
    "    def query_binary_values(self, query, container):\n",
    "        print(f'{query=}')\n",
    "        x = np.linspace(-3, 3, 8192)\n",
    "        y = 10*np.log10((np.sinc(x)-1.00025)**-2) -75\n",
    "        # carrier like simulation\n",
    "        return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "007a7d8e-598a-4eab-b0ee-7ad91ddf7a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_=':SYST:ERR:VERB 1'\n",
      "input_=':FREQuency:CENT 1000000000.0'\n",
      "input_=':FREQuency:SPAN 10000000.0'\n",
      "input_=':SWEep:POINts 8192'\n",
      "input_='FORM:DATA REAL,32'\n",
      "input_='FORM:BORD SWAP'\n",
      "query='TRAC:DATA? TRACE1'\n",
      "CTRL 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'external_gain': 0.0,\n",
       " 'frequency': 1000000000.0,\n",
       " 'frequency_span': 10000000.0,\n",
       " 'frequency_start': 995000000.0,\n",
       " 'frequency_stop': 1005000000.0,\n",
       " 'input_attenuator': 10.0,\n",
       " 'input_attenuator_auto': True,\n",
       " 'reference_level': 0.0,\n",
       " 'resolution_bandwidth': 1000,\n",
       " 'sweep_points': 8192,\n",
       " 'sweep_time': 3.141,\n",
       " 'trace_type': 'Max Hold',\n",
       " 'unit_power': 'dBm',\n",
       " 'video_bandwidth': 3000,\n",
       " 'points per RBW': 1.220703,\n",
       " 'points per RBW_': 1.220703125,\n",
       " 'IDN': 'HP, E4404A, Mock, Mock',\n",
       " 'ISO8601': '2021-12-20T22:46:12.718998',\n",
       " 'YYYYMMDD': '2021-12-20',\n",
       " 'YYYYMMDDHHMMSS': '2021-12-20--22-46-12'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q1TegUzRRiUg80UxiIkmqraun99YqgIy04K8DdXGLxAcFtEiSauvsIatPQKf5fZgpoEXihQJaJEm1dfb0DgwDMDMyU/0KaJE4oYAWSVKtXT1kpp06DCUj1a9BYiJxQgEtkqTaTxskBoRa0BokJhIPFNAiSaq1q7v3EZNh6ak+taBF4oQCWiRJtXUGzgjozFQ/HQpokbiggBZJUm2d3acMEoPgNWh1cYvEBwW0SBJyztHadeptVhCcj1td3CLxQQEtkoQ6ugM4xylTfQK6zUokjiigRZJQW2cwhM9sQes2K5F4oYAWSULhED49oLPS/L3hLSLeUkCLJKHWUAif3sWdnZ5Cc0e3F0USkdMooEWSUHtvC/rUmcSy01Jo7ezBOedFsUSkDwW0SBIKt6BPv80qK91PT8DR0a1brUS8poAWSULha9CnT1SSkx5sUbeom1vEcwpokSTU1hkM4DNa0KEu71YNFBPxnAJaJAkNNIo7O/ReA8VEvKeAFklCvdegTw/o9HALWgEt4jUFtEgSam4PBnD4mnNYdnowsFs61MUt4jUFtEgSaunoxqyfLm4NEhOJGwpokSTU1NFNTloKZnbK8uzQILEWDRIT8ZwCWiQJtXR097aW+wq3qNWCFvGeAlokCTV3dJOTcWZA93Zxa5CYiOcU0CJJqLmjp98WdHqKD7/PaNUgMRHPKaBFklBzexe5/QS0mZGV5td90CJxQAEtkoRaOnp6b6k6XU56iu6DFokDCmiRJNTc0U1Oemq/67LS/LoPWiQOKKBFklAwoAduQWuQmIj3FNAiScY5N+Aobgg+MEODxES8p4AWSTLtXQF6Aq7fUdwQvNWqsb1rhEslIqeLKKDN7ItmttHMNpjZn8xsQmi5mdk3zKwmtP6C6BRXRCIVHqHd3yhugLzMFJra1cUt4rVIW9Bfds7Nd84tBH4H/Gto+duAGaGv5cADEZ5HRKIkHNADtaDzMlLVghaJAxEFtHOusc/bbMCFXt8I/NgFrQEKzGx8JOcSkegIT+N5+pOswvIyU2nu6CYQcP2uF5GR0f9P6DCY2f3ArUADcEVo8URgX5/NakPLDvaz/3KCrWxKS0uprq6OtEgJpbm5Oek+c7SpDofnzfrgALCaLZtJq9sCnFqHdfu7cA7+8OdqslJtoMNIP/S9GDnV4UmDBrSZPQ2M62fVPc65x5xz9wD3mNndwCeAe4dTAOfcCmAFQGVlpauqqhrO7gmvurqaZPvM0aY6HJ6uNw7Dupe57KILmV9WAJxah0fW7ePnWzYyv/IiygqzvCtoAtL3YuRUhycNGtDOuauHeKyHgCcIBvR+oLzPurLQMhHxWENb8Ppyfmb/E5XkZQZ/LTS2dUPhiBVLRE4T6SjuGX3e3ghsCb1+HLg1NJp7KdDgnDuje1tERl44oPMyBgjo0HINFBPxVqTXoL9kZrOAALAH+Fho+RPA24EaoBX4UITnEZEoaQwH9IAt6NRTthMRb0QU0M65vx5guQPujOTYIhIbDW3BJ1n5ff0PADvZgta90CJe0kxiIkmmsa1rwNYzQG5GSu92IuIdBbRIkmlo6xpwgBj0CWhdgxbxlAJaJMk0tnf1jtTuT4rfR3aaX9N9inhMAS2SZAZrQUNwoJi6uEW8pYAWSTJDCmjNxy3iOQW0SJIZSkDnZqQEJyoREc8ooEWSSEd3D+1dgSF1cTeoi1vEUwpokSQSbhUPFtAFCmgRzymgRZJIwyCziIUVZqdxvLVzJIokIgNQQIskkSEHdFYqrZ09tHf1jESxRKQfCmiRJNI4yJOswgqz0wA40apubhGvKKBFksixlmC3dVEogAcyJivtlO1FZOQpoEWSSDhwCwcJ6IJQQOs6tIh3FNAiSaS+pZNUv5GbfvYH2Y3JVkCLeE0BLZJEjrd0UpiVhln/j5oMK8xO7d1eRLyhgBZJIvUtnb2t47Mp7L0GrUFiIl5RQIskkeOtQwvoVL+P3PQUdXGLeEgBLZJEjg2xBQ2arETEawpokSQy3IDWbVYi3lFAiySJrp4ADW1dQw7oMVmpakGLeEgBLZIkwrOCDbkFnZXGcQ0SE/GMAlokSYS7q9XFLZIYFNAiSaK+pQM4OY3nYMbmpNPW1UNLR3csiyUiA1BAiySJcHf1mJyhBXRxbjoAR5s7YlYmERmYAlokSYSDtig7fUjbhwO6rkkBLeIFBbRIkjjS1I7fZ0O+Bl2co4AW8ZICWiRJ1DV1UJSdht939nm4w3pb0OriFvGEAlokSRxp6qAkb2jd2xAc7e0ztaBFvKKAFkkSRxo7KMnNGPL2fp9RlJOugBbxiAJaJEnUNXf0XlceqmIFtIhnFNAiSaAn4KhvHl4XN0BJXrquQYt4JCoBbWb/aGbOzMaG3puZfcPMasxso5ldEI3ziMi5qW/uIOCgJHf4LegjjQpoES9EHNBmVg5cA+zts/htwIzQ13LggUjPIyLn7kiom7p4GNegg9unc7S5g0DAxaJYInIW0WhBfw34LND3J/hG4McuaA1QYGbjo3AuETkHdb0BPcwWdG463QHHiTY9NENkpKVEsrOZ3Qjsd869ZnbKvZUTgX193teGlh3s5xjLCbayKS0tpbq6OpIiJZzm5uak+8zRpjoc3HO1wYDduflVmnad+Xf5QHVYdzA4D/fvn1lFea6GrAxG34uRUx2eNGhAm9nTwLh+Vt0D/BPB7u1z5pxbAawAqKysdFVVVZEcLuFUV1eTbJ852lSHg9v0zHbYtI0b3voWMlL9Z6wfqA6zdx/j26+9yKRZ5/OWmcUjUNLEpu/FyKkOTxo0oJ1zV/e33MzOB6YC4dZzGfCKmS0B9gPlfTYvCy0TEQ8cbuwgPzO133A+m/H5wWvWB0+0xaJYInIW59xn5Zx73TlX4pyb4pybQrAb+wLn3CHgceDW0GjupUCDc+6M7m0RGRkHG9p6w3Y4SvMyMIMDDe0xKJWInE1E16DP4gng7UAN0Ap8KEbnEZEhOHCinQkFmcPeL9XvozgnnUMNakGLjLSoBXSoFR1+7YA7o3VsEYnMgYY2Fk0qOKd9xxdkclAtaJERp2GZIqNcW2cPJ1q7zqkFDTAhP4MDugYtMuIU0CKj3IFQ9/SEguFfgwYYnx9sQQc7xkRkpCigRUa5gyeC3dPj88+tBT0+P4PWzh4a27ujWSwRGYQCWmSU621Bn2tAh1reBzVQTGREKaBFRrnw9ePS/OFN8xkWbnmHW+IiMjIU0CKj3MET7RTnppOeMrxJSsLC164PqAUtMqIU0CKj3IGGNiacwyQlYSW5Gfh9pha0yAhTQIuMcgdOtJ3zADEAv88Yn59B7fHWKJZKRAajgBYZxQIBR+3xNsrHnHtAA5QXZrH3mAJaZCQpoEVGsbrmDjq6A0wqyo7oOJPGZLH3mK5Bi4wkBbTIKBZu9U4akxXRcSYVZXG0uYPWTt0LLTJSFNAio9ie+ugEdHlo/9rjakWLjBQFtMgotvdYKz6Diec4D3dYOOD31us6tMhIUUCLjGL7jrUyPj+TtJTIftR7A1oDxURGjAJaZBTbe6w14u5tgMKsVHLSUxTQIiNIAS0yiu2pj05AmxnlY7LYp4AWGTEKaJFRqrWzm6PNHUwqijygASaNyVQLWmQEKaBFRql9ofuWo9GCDh9n77FWAgE9F1pkJCigRUapXUebAZgcpRb0lLHZdHQHONioOblFRoICWmSU2lHXAkBFcU5UjjctdJwdR5qjcjwROTsFtMgotaOumXF5GeSkp0TleBXFwelCd9YpoEVGggJaZJTaUdfCtJLI5uDuqzgnndyMlN6WuYjElgJaZBRyzrHzSHNvt3Q0mBkVxTnsPKoWtMhIUECLjEJ1zR00dXRTMTZ6LWiAacXZ7DiiFrTISFBAi4xC4RCdVhK9FjQEB4odamynuUNPtRKJNQW0yCi0IzSQK5pd3MHjBVvku3QdWiTmFNAio9COumYyU/2My8uI6nF7b7XSSG6RmFNAi4xCO+taqCjOxuezqB53UlEWfp8poEVGgAJaZBTadriJGVG+/gyQnuJnSlEWWw41Rf3YInIqBbTIKHOitZODDe3MHp8Xk+PPHp/HVgW0SMwpoEVGmXDrdva43Jgcf3ZpLnuPtWokt0iMRRTQZnafme03sw2hr7f3WXe3mdWY2VYzuzbyoorIUIRbt3Ni2ILuex4RiY1oTNL7NefcV/ouMLO5wC3APGAC8LSZzXTO9UThfCJyFlsONVKQlUpJbnpMjh9umW891MSFkwtjcg4RiV0X943ASudch3NuF1ADLInRuUSkjy2Hmpg9Lhez6I7gDisrzCQnPYUthxpjcnwRCYpGQH/CzDaa2Q/MLPzn9ERgX59takPLRCSGAgHH1kNNzB4Xm+5tCM7JPWtcrkZyi8TYoF3cZvY0MK6fVfcADwBfBFzo368Ctw+nAGa2HFgOUFpaSnV19XB2T3jNzc1J95mjTXV40pHWAK2dPVjDAaqr64a833DrMC/Qwdr93Tz77LMxa6knIn0vRk51eNKgAe2cu3ooBzKz7wK/C73dD5T3WV0WWtbf8VcAKwAqKytdVVXVUE43alRXV5NsnznaVIcn/XHzIWA9N1YtZmF5wZD3G24d7kvfzbOPbWbWoqVMKMgcdjlHK30vRk51eFKko7jH93n7TmBT6PXjwC1mlm5mU4EZwEuRnEtEBrd5fwM+g5ml0Z+kpK+5E4Jd6JsP6Dq0SKxEOor7v8xsIcEu7t3ARwGcc5vN7BfAG0A3cKdGcIvE3sb9DcwszSUrLRo3aAxs7vh8fAav157grXNLY3oukWQV0U+xc+4DZ1l3P3B/JMcXkaFzzvF6bQNXzi6J+bky0/zMLM1l4/6GmJ9LJFlpJjGRUeJAQzv1LZ3ML8sfkfOdPzGfjbUNOOdG5HwiyUYBLTJKbNx3AoD5ZQUjcr755QUca+lk/4m2ETmfSLJRQIuMEhv3N5DqN2aPj80c3KebPzHYUt9Yq25ukVhQQIuMEq/XNjBrXC7pKf4ROd/s8bmk+k0BLRIjCmiRUcA5x8baE5w/sWDEzpme4mfWuFxe339ixM4pkkwU0CKjwO76Vhrbu0dsgFjY/LICNtY2EAhooJhItCmgRUaBl3cfAxjxp0stKi+gqb2bmrrmET2vSDJQQIuMAuv3HCcvI4XpxbGdQex0lVPGALAu9AeCiESPAlpkFHh5z3EunFyIzzeyD66YUpTF2Jw01u8+PqLnFUkGCmiRBHe8pZOaI829rdmRZGZUTh7Duj1qQYtEmwJaJMGt3xNsvVaO8PXnsMophew71saRxnZPzi8yWimgRRLcy3uOk+o3Fgzj8ZLRFG65v7xH3dwi0aSAFklw6/ccY96EfDJSR2aCktPNm5BHRqpPA8VEokwBLZLA2rt6eK22wbPubYBUv4+F5QW8rIFiIlGlgBZJYOv3HKezO8Al04s8LcdFU4vYfKCBhrYuT8shMpoooEUS2OodR/H7jMUejODu69LpYwk4WLOz3tNyiIwmCmiRBPZCTT0LyvLJzUj1tBwLywvITPWzuuaop+UQGU0U0CIJqrG9i421J7h0+livi0Jaio8lU8ewSgEtEjUKaJEEtXbnMQIOLpnmfUADXDZ9LDvqWjjUoPuhRaJBAS2SoF6oOUp6io9Fkwq8LgpA70C11TvUihaJBgW0SIJaveMoi6eM8ez+59PNGZfHmOw0dXOLRIkCWiQB7T/RxrbDzSybER/d2wA+n3HxtCJWbT+Kc3o+tEikFNAiCejZLUcAuHJ2icclOdUVs0o40tTB5gONXhdFJOEpoEUSUPXWI5QVZjK9ZGSf/zyYqlnFmMEzoT8gROTcKaBFEkx7Vw8v1NRz5ewSzEb2+c+DGZuTzoKyAv6sgBaJmAJaJMGs2VlPW1cPV8RZ93bYVbNL2Fh7grqmDq+LIpLQFNAiCebZLUfISPVxcYW3828P5IrZJTgX7IYXkXOngBZJIM45ntl6hEunjY2b26tON29CHqV56boOLRIhBbRIAnnjYCP7jrVx9dxSr4syIDPjytmlPLetjvauHq+LI5KwFNAiCeTJ1w/hM7gmjgMa4LrzxtHS2cPz2zVpici5UkCLJAjnHE9sOsjSiiKKctK9Ls5ZXTKtiIKsVJ58/aDXRRFJWBEHtJl90sy2mNlmM/uvPsvvNrMaM9tqZtdGeh6RZLf9SDM761p423njvC7KoFL9Pq6ZW8pTbxymo1vd3CLnIqKANrMrgBuBBc65ecBXQsvnArcA84DrgG+bWXyOaBFJEE++fggzuHZe/Ac0wNvOH09TRzer1M0tck4ibUF/HPiSc64DwDkXHrZ5I7DSOdfhnNsF1ABLIjyXSFJ7ctNBFk8eQ0lehtdFGZJLp40lLyOFJ14/5HVRRBJSpAE9E1hmZmvN7C9mtji0fCKwr892taFlInIOao40seVQE9clQPd2WFqKj7fOHcdTbxyiszvgdXFEEk7KYBuY2dNAf78V7gntPwZYCiwGfmFmFcMpgJktB5YDlJaWUl1dPZzdE15zc3PSfeZoS4Y6fGRbJz6DopbdVFfvifrxY1WHk62bxvZuvvmrZ7iwdNBfNwkvGb4XY011eNKgPzHOuasHWmdmHwd+7YLPlnvJzALAWGA/UN5n07LQsv6OvwJYAVBZWemqqqqGXPjRoLq6mmT7zNE22uswEHD804vPcPnMAm68NjZXimJVh5f1BPjJtmfY1lnAP1ZVRv348Wa0fy+OBNXhSZF2cf8GuALAzGYCacBR4HHgFjNLN7OpwAzgpQjPJZKU1uyq50BDO+9clHhXiVL8Pm5aOIFnthzheEun18URSSiRBvQPgAoz2wSsBD7ogjYDvwDeAP4A3Omc070WIufg0Vf2k5OewjVzE+f6c1/vuqCMrh7Hbzce8LooIgklootCzrlO4G8GWHc/cH8kxxdJdm2dPTy56RDXnTeOzLTEvFNxzvg85ozP41fra7n14ileF0ckYWgmMZE49sfNh2ju6OZdCdi93ddfXzCR12obqDnS5HVRRBKGAlokjv1s7V4mF2WxNE4fLTlUNy6cSIrPeHjdvsE3FhFAAS0St7YfbuKl3cd435JJ+HzmdXEiUpybzjXzSvnl+lo94UpkiBTQInHqobV7SfUb77mwzOuiRMXfXDSZE61d/H6jHqAhMhQKaJE41N7Vw69fqeW688bH/ZOrhuriaUVUjM3mobXRn2hFZDRSQIvEod9tPEhjezfvXzLJ66JEjZnx/osm8creE7xxoNHr4ojEPQW0SJxxzvGj1buYVpzN0ooxXhcnqt59YRnpKT5+qla0yKAU0CJxZu2uY2za38gdl1VgltiDw05XkJXGjQsn8OtXajmmmcVEzkoBLRJnvvf8TsZkp/GuCxL73ueBfHhZBe1dAX66Rq1okbNRQIvEkZ11zTz95hH+ZulkMlITc+awwcwszeWKWcU8uHq3brkSOQsFtEgc+cELu0jz+/jA0sleFyWmPnJ5BfUtnTz6ar8PuRMRFNAicaOuqYNH1tdy06IJFOeOjlurBnJxRRHnTczju8/vJBBwXhdHJC4poEXixPee30lnd4CPvWWa10WJOTNj+eXT2FnXwh83H/K6OCJxSQEtEgfqmzv48Yt7eMeCCVQU53hdnBFx/fnjqRibzdf/vF2taJF+KKBF4sD3V+2ivbuHT1w53euijBi/z/i7q2aw5VCTWtEi/VBAi3jsRGsnD67ezfXnj2d6Sa7XxRlRNyyYQEWxWtEi/VFAi3jsO8/tpKWzh09eOcProow4v8/4+1Ar+g9qRYucQgEt4qGDDW38YNUublo4gVnjkqv1HPZX8ycwrTibr/5pK909Aa+LIxI3FNAiHvraU9twDv7xmlleF8Uzfp/x2etms6OuhYdf3ud1cUTihgJaxCPbDjfxyPpaPnDxZMrHZHldHE9dM7eUJVPG8LWnttHc0e11cUTiggJaxCP/+eQWstNT+MQVyTNyeyBmxj9dP4ejzZ185y87vC6OSFxQQIt44C/b6vjzliP8bdV0CrPTvC5OXFhYXsANCybw3ed3crChzeviiHhOAS0ywjq6e7jv8c1MHZvN7ZdN8bo4ceWz184i4OD+37/pdVFEPKeAFhlh33t+F7uOtnDvDXNJTxmdT6w6V+Vjsrizajq/23iQ57fXeV0cEU8poEVG0P4TbXzrmRqunVdK1awSr4sTlz76lgqmjs3mX36zSY+jlKSmgBYZIc457nt8Mw7Hv/zVXK+LE7cyUv184cZ57K5v5Tt/2el1cUQ8o4AWGSGPv3aAp944zKffOpOywuS+rWowy2YU81fzx/M/1TXUHGnyujginlBAi4yAuqYO7nt8MwvLC7jjsgqvi5MQ7r1hHtlpfv7xF69phjFJSgpokRFw7+ObaOno4cvvno/fZ14XJyEU56bzhRvP47XaBr7znLq6JfkooEVi7PHXDvDE64f41FtnMKM0OefbPlc3LJjA9eeP5/89vY03DzZ6XRyREaWAFomhfcdauefXr3PBpAKWL1PX9rn44k3nkZ+Zyj88vEGjuiWpKKBFYqSrJ8Anf/4qGHz9lkWk+PXjdi7GZKfx5XcvYMuhJr74uze8Lo7IiInoN4aZPWxmG0Jfu81sQ591d5tZjZltNbNrIy6pSIL52lPb2LDvBF961/ykfxhGpK6YXcJHL6/gobV7+d3GA14XR2REpESys3Pu5vBrM/sq0BB6PRe4BZgHTACeNrOZzjn1T0lSeHbLER74yw5uWVzO9fPHe12cUeEz185i3e5jfO5Xr3PehHymjM32ukgiMRWVPjczM+C9wM9Di24EVjrnOpxzu4AaYEk0ziUS73bWNfN3K19lzrg87r1hntfFGTVS/T6++f4L8PuMj/10PS16LKWMcuaci/wgZpcD/+2cqwy9/xawxjn309D77wNPOuce6Wff5cBygNLS0gtXrlwZcXkSSXNzMzk5OV4XI6HFUx22dTu++GIbTZ2Oey/JZGxmYlx3jqc6HMymo9189eUOFpX4+cSidHwWP7etJVI9xqtkrMMrrrhifTg/+xq0i9vMngbG9bPqHufcY6HX7+Nk63lYnHMrgBUAlZWVrqqq6lwOk7Cqq6tJts8cbfFShz0Bx8d+up7DbW385I6LuGTaWK+LNGTxUodDUQVkjdvFF373Bq90TuAz187yuki9Eqke45Xq8KRBA9o5d/XZ1ptZCvAu4MI+i/cD5X3el4WWiYxKzjm++Ls3eOqNw9x3w9yECudE9KFLp7D1UBPferaG6SU53LRootdFEom6aPS/XQ1scc7V9ln2OHCLmaWb2VRgBvBSFM4lEpdWPLeTH63ezYcvm8ptl071ujijnpnxxZvO46KpY/jML1/jL9v0aEoZfaIR0LdwWve2c24z8AvgDeAPwJ0awS2j1WMb9vMfT27hr+aP55/ePsfr4iSNtBQf3/1gJTNKc/n4T9ezYd8Jr4skElURB7Rz7jbn3P/2s/x+59w059ws59yTkZ5HJB79YdMh/vEXr3HR1DF89b0L8Gme7RGVl5HKg7cvZmxOOh/64UtsP6wnX8nokRhDTEXi0FNvHOYTP3uF88vy+d4HK0lP8XtdpKRUkpvBT+5Ygt/n433fXauQllFDAS1yDp7dcoS/fWg98ybk8eDtS8jNSPW6SEltclE2K5dfhBncsmINWw8ppCXxKaBFhunx1w6w/CcvM3tcHj++4yLyFM5xYXpJLiuXL8XvM9733TVsPtDgdZFEIqKAFhmGB1fv5u9XvsqiSYX89MMXkZ+pcI4n04pzePijF5Oe4uPm76zhhZqjXhdJ5JwpoEWGIBBwfPVPW7n38c1cPaeUH9++ROEcp6aOzebXf3sJEwsyue2HL/GbVzUFgyQmBbTIIFo6urnzZ6/wzWdquLmynAf+zwVkpGpAWDwbn5/JLz52MRdOLuRTD2/g609vJxCIfFpjkZGkgBY5i33HWvnrB1bzx82H+Ofr5/Clvz5fz3VOEPmZqTx4+xLetWgiX3t6Gx/96Xoa27u8LpbIkOk3jcgAnn7jMO/41ioOnGjjhx9awoeXVWBx9GAGGVx6ip+vvncB990wl2e3HOGmb73ANt2GJQkioudBy6laO7s52tRJU0cXTe3dNLd309TRRXtXgIBzBFxwzmbnID3FR2aanx2Hu0nZfpT8zFTG5qZRlJ1OWor+bvJSe1cPX3pyCz9avZt5E/L41vsvYKqePZywzIzbLp3KnPF53PmzV7nhm6u45/o5fGDpZP3B5bHmjm6ONXfS2N5FY1sXje1drKvt4tBLe+lxjp5A8CvF7yMz1U9GavDfzDQ/Y7LTgl9ZaaO2V0sBPQyd3QF217ew7XAT2w43U3u8lSONHRxqbOdwYztN7ef4fNpX157ytjArlZLcDMrHZDG5KIspRVlMLsqmojibiQWZ+qUSQ5sPNPCZX27kzYONfOjSKXzubbM1AckocVFFEU/8/WV89pGN/Otjm3lmyxH+693zKcnN8Lpoo1Yg4Kg93kZNXRM1R5rZdbSVAyfaONjQxsGGs/zO3PT6sM6Tn5lKcW46EwsyKSvMpKwwK/RvJlOKsinMTovCpxl5Cuh+dPUE2H20hW2Hm9l2uIntR4KBvPtoC92hgSY+g3F5GZTmZzCjJIfLpo+lJC+dsTnp5GWkkpeRQk5GCrkZqaSn+PD7DDN6n13b2R2gtbOHVS+uZc75C2lo6+Jocyd1TR0cbQ6G/r5jrayqqaO9K9BbtryMFOaMz2PuhDzmjM/jvAn5zBqXi19TTEakvauHr/95Oyue20lhVirfu7WSq+eWel0sibKS3Ax+eNtifrpmD//2+zd5638/x91vm817K8s1TWuEOrsDbDnUyGu1Dbxee4JN+xvZUddMR/fJ319F2WlMKAiG5iXTxjI+P4OinHTyM1PJzUghLyOVTRte5rJLLibFZ/h8ht+MrkCAjq4AbV09tHf10NLRw/HWTuqbO6hv6eRYSydHGjvYf6KNjbUnON566liDsTlpTCvOYVpJDtOLc5heksOM0hzG5WXEdYMnqQO6qyfAnvo+QRz6d1efIDaDyWOymFGay7XzSplZmsuMklwqirOjMpK3Nt/PRRVFA653znGkqYNdR1uoOdLMmwcbeeNgIytf2kdbV/D5I9lpfhZOKuCCSYVcMLmQC8oLyc/SLUBDVb31CF/47RvsPNrCuy8s45+vn0NBVmL+xS2DMzM+cPEULp42ln969HU+9+vXeWR9Lfe/83xmjcv1ungJ42hzB2t3HmPtrnpe23eCNw820dkTDOPCrFTOLyvgkmlFTC/J6f0ays/VkW0+JhRkRlS25o5u9h9vY9+x1t7fnTV1zfx+40Ea2k6Gd25GCnPG5TFrXC6zx+cyO/Q6Jz0+ojE+ShFj3T0Bdte3sj3UNb3tSBM1h5vZebSZrp6TQTxpTBYzSnJ569xQEJfmMK04x9NbasyM0rwMSvMyWNonyHsCjj31LWysbeCVvcdZv+c4//NsDeE7SWaW5nBxRREXTxvL0ooxCpx+bDvcxP2/f5O/bKtjclEWP7ljCctmFHtdLBkh00tyeHj5Un65vpb/eOJN3v6N57l5cTmfumoGJXnq9j7dsZZO1u6s58Wd9azZWc+2w80AZKX5mV+Wz4cuncL8sgLml+VTVujtpbic9BRmjcs94w8u5xxHmzt7A3vroUa2HmriN6/up2nNye728jGZzB6Xx+xxJ0N76tjsEe+pHLUB/ebBRr5dvYPth5vYWdfS+5edGZQXZjGzNIcr55QwszSHGSW5TCvOITMtca41+n1GRXEOFcUnH1bf0tHNa7UnWL/7OC/tPsbDL+/jwRf3YAZzx+eFAruIJVPHJPXc0buPtvDt6hoeWV9LdnoK/3z9HG69eIoG5yUhM+O9leVcPaeUb/x5Oz9ds4dHX9nPh5dN5cOXVSR1T9Txlk7W7jrGmlAgbwnNb56Z6qdySiE3LZrI0ooizp+YT2qCDNIyM4pz0ynOTefiaScbPM459p9oY8vBJrYebuLNg41sOdTEM1uO0BNq9aSn+JhZGgz9keplG7UB3dUTYMO+48wsyaVqVgkzSnKYWZrL9JLECuLhyE5P4ZJpY7lk2lggeE3otdoTvLijntU7jvLjNXv43qpd+H3GeRPzubiiiKUVY1g8ZQzZcdKlE0s1R5r4n2d38NiG/aT6fXzwkil88soZjEnQASQSPWOy07jvHfP40KVT+PIft/LNZ2r4wapdvP+iSdxxWQXj8kd/i7qhtYu1u8It5GNsOdSIc5CR6qNy8hjuunYCSyvGcP7EglH3x6yZhQaWZZ0y9qS9q4eaI81sOdTE1kPB0H6h5ihZaSPz+3LU/laeX1bA85+90utieCotxcfiKcEA/rurZtDe1cMre4+zZkc9q3fU8/1VO/nfv+zA7zPml+WztKKIiyuKuHBy4agJ7O6eAE+/eZifrNnDCzX1ZKb6ueOyqXzk8gqN3pUzTC7K5lvvv4C/rWrkO8/t4Acv7OZHq3dzw/wJ3LJkEounFMb1oKLhaGjt4qXdJ1vIbxxs7L0F9MLJhXz66pksnVbEgrLRF8hDlZHq57yJ+Zw3Md+T84+O38IyJBmp/t4W9qcJ3rf9yp4TvLjzKGt2HuO7z+3kgeodpPQN7GnBwB6pvxijwTnH5gON/HbjAR579QCHGtuZkJ/BXdfO4pbF5RTlpHtdRIlzcyfk8fVbFvGZa2bxved38qtX9vPrV/czrTibmxeXc/38CUyMcCDTSBsokNNSfFw4qZC/v2oGF1cUsXBSgW4tjBOJ81tXoi4rLYXLZozlshnBLvHWzm7W7znOizuCP8ArntvJt0OBPW9CHgvLC1hQXsDC8gKmFGXH1W0pXT0BXt17gr9sO8KTmw6xs66FFJ+xbMZYvnDjPK6cXTJqJzOQ2Ckfk8XnbzyP//u22fx+40FWrtvHvz+xhX9/YgsLyvK57rzxXDUneAktnlrW3T0Bth5u4tW9J9iw7wSv7j3OjroW4GQgf+qqmSytGMOC8gLNLR+nFNDSKysthWUzintHMrd0dPPynuOs2VnPq3uP88v1tTz44h4geD/2gvIC5k3IZ2bpyev7I/WD3tbZw6YDDWzYe4I/vNrOJ599iqaObvw+Y/GUQu64bCpvO2+8ri9LVGSlpfCeynLeU1nO7qMtPLnpEH/YdJD//MMW/vMPWyjOTeeSaUWM6eqi5EAjM0pzRmzgVFN7F9sON7HlUBNbDjax5VAjm/Y39t6GWZSdxqJJBbxz0UQWT1EgJxIFtAwoOz2Ft8ws5i0zg4HdE3DUHGnmtX0neHVf8C/z76/a2Xurmi90q9q04hwmFmaGZvXJYmJhJmNz0ijISiM7zT/klkZLRzd1TR3UNXew/3gbO+ua2VHXwo66ZrYfae4dXVmcafzVgjIun1HMJdPH6jGQElNTxmbz8appfLxqGvtPtLFqex2rd9TzQk09R5s7+eHm50lL8TEndJvP5KJsJo3JYtKYLErzMijISh1yQDrnaGjror6lk/rmzt4JjPbWt7L3WPBr/4m23u1zQ7cX3by4nEWTClhUXkj5GM0+mKgU0DJkfp/13lv43sXlwMnJXrYeag5NgdrE7vpWXtp9rN9p/FJ8RkFWKjnpKaT4faT4jFS/D59Be1eA9u4e2jp7aO7oprWz55R9fRbscpxWnMOVs0tYNKmQheUFbF7/IlVV80ekDkT6mliQyc2LJ3Hz4kk453j4iWfJnDiLzQcaeb22gWe31lHXVHvGfllpfgqz0khP9ZHq85HiN1L8Pjq7A3R099DRFaC9q4eGtq7eSZP6Ks5NZ9KYLBZPKeT9pZOYVRqcaENTAY8uCmiJSKrfx/SSXKaX5HI9409Z19jexf7jbew/3saxlk5OtHVyorWL461dNHd00xMI0NXj6O4JEAjdzpGR6icz1U9WWkrv/YrFuemMz89gclGWBq9I3DIzxmX7qFo4kRsXTuxd3trZHWzt1rdytLmT462dHG/p5HhrFx3dPXT3OLp6AnQHHKl+H+mpPjJS/KSn+ijITGVMdhpjc9IpykkLzdGfmVCDNuXc6X9ZYiYvI5W88anMGZ/ndVFEPJOVlhKalUo/BzI8GtYqIiIShxTQIiIicUgBLSIiEocU0CIiInFIAS0iIhKHFNAiIiJxSAEtIiIShxTQIiIicSiigDazhWa2xsw2mNnLZrYktNzM7BtmVmNmG83sgugUV0REJDlE2oL+L+DzzrmFwL+G3gO8DZgR+loOPBDheURERJJKpAHtgPD8dfnAgdDrG4Efu6A1QIGZje/vACIiInImc+7MJ6UMeWezOcAfASMY9pc45/aY2e+ALznnVoW2+zPwf51zL/dzjOUEW9mUlpZeuHLlynMuTyJqbm4mJyfH62IkNNVh5FSH0aF6jFwy1uEVV1yx3jlXefryQR+WYWZPA+P6WXUPcBXwD865X5nZe4HvA1cPp2DOuRXACoDKykpXVVU1nN0TXnV1Ncn2maNNdRg51WF0qB4jpzo8KdIWdANQ4JxzFnwIaYNzLs/MvgNUO+d+HtpuK1DlnDs4yPHqgD3nXKDENBY46nUhEpzqMHKqw+hQPUYuGetwsnOu+PSFkT5u8gDwFqAauBLYHlr+OPAJM1sJXEQwuM8azgD9FXC0M7OX++vakKFTHUZOdRgdqsfIqQ5PijSgPwJ83cxSgHZC15KBJ4C3AzVAK/ChCM8jIiKSVCIK6NAgsAv7We6AOyM5toiISDLTTGLeW+F1AUYB1WHkVIfRoXqMnOowJKJBYiIiIhIbakGLiIjEIQV0FJnZdWa2NTQH+ef6WT/ZzP4cmp+82szK+qz7TzPbFPq6uc9yM7P7zWybmb1pZn83Up/HKzGqx6vM7JXQvPGrzGz6SH2ekWZmPzCzI2a2aYD1A86Vb2YfNLPtoa8P9ll+oZm9HtrnG6HbKketaNehmWWZ2e/NbIuZbTazL43UZ/FSLL4X+6x/fKDjjhrOOX1F4QvwAzuACiANeA2Ye9o2vwQ+GHp9JfCT0OvrgacIDtrLBtYBeaF1HwJ+DPhC70u8/qwJWo/bgDmh138L/MjrzxrDOrwcuADYNMD6twNPEpwBcCmwNrR8DLAz9G9h6HVhaN1LoW0ttO/bvP6ciVSHQBZwRWibNOD50V6HsfpeDK1/F/CzgY47Wr7Ugo6eJUCNc26nc64TWElwTvK+5gLPhF4/22f9XOA551y3c64F2AhcF1r3ceALzrkAgHPuSAw/QzyIVT0ONG/8qOOcew44dpZNBpor/1rgKefcMefccYJ/7FwXWpfnnFvjgr8dfwzcFNtP4a1o16FzrtU592zo2J3AK0DZQAcfLaJdjwBmlgN8Gvi32Jbeewro6JkI7Ovzvja0rK/XCP7lB/BOINfMikLLrwt1g40FrgDKQ9tNA2624OM8nzSzGTH7BPEhVvX4YeAJM6sFPgAkRRfjAAaq47Mtr+1neTIbbh32MrMC4Abgz7EtYkI4l3r8IvBVgnNsjGoK6JH1GeAtZvYqwRnY9gM9zrk/EZzcZTXwc+BFoCe0TzrQ7oIz63wX+MGIlzr+nEs9/gPwdudcGfBD4L9HvNSS9EKTOv0c+IZzbqfX5Uk0ZrYQmOace9TrsowEBXT07Odkaw2C3Vf7+27gnDvgnHuXc24RwYeN4Jw7Efr3fufcQufcWwlej9kW2q0W+HXo9aPA/Jh9gvgQ9Xo0s2JggXNubegQDwOXxPZjxLWB6vhsy8v6WZ7MhluHYSuA7c65/xfrAiaI4dbjxUClme0GVgEzzax6RErqAQV09KwDZpjZVDNLA24hOCd5LzMba2bhOr+bUGvYzPyhLlrMbD7BEP5TaLvfEOyqhWBrcRujWyzq8TiQb2YzQ/u8FXgz5p8kfj0O3BoaQbuUk3Pl/xG4xswKzawQuAb4Y2hdo5ktDY3evhV4zLPSx4dh1SGAmf0bwfEPn/KozPFouN+LDzjnJjjnpgCXAducc1VeFT7mvB6lNpq+CI5I3EZwFPI9oWVfAN4Rev1ugg8U2QZ8D0gPLc8A3gh9rQEW9jlmAfB74HWCXbYLvP6cCVqP7wzV4WsEH+5S4fXnjGH9/Rw4CHQR7IG5A/gY8LHQegP+J1S/rwOVffa9neAc+jXAh/osrwQ2hfb5FqFJjkbrV7TrkGAL0BH8w3BD6OvDXn/ORKvH0449hVE+ilsziYmIiMQhdXGLiIjEIQW0iIhIHFJAi4iIxCEFtIiISBxSQIuIiJyDwR4Gctq2Az7kZyAKaBEPmFmPBZ+sFf6a4nWZosXMFpnZ90OvbzOzb522vtrMKs+y/8okmNJWRocfcXK+/8F8heC84/MJ3jb6H4PtoIAW8UabC854Fv7aHV4RmrQhkX82/wn4RgT7PwB8NkplEYkZ18/DQMxsmpn9wczWm9nzZjY7tGqgh/wMKJF/CYiMGmY2xYLPwP4xwQlBys3sLjNbF+oS+3yfbe+x4PPBV5nZz83sM6HlvS3T0Gxru0Ov/Wb25T7H+mhoeVVon0cs+Jzih0IzhWFmi81stZm9ZmYvmVmumT0Xmgs5XI5VZrbgtM+RC8x3zr02hM/8jj49CFvNbFdo1fPA1aF5q0USzQrgk865Cwk+N+DboeUDPeRnQPoBEPFGppltCL3eRfBhHjMIPud6jZldE3q/hOBsS4+b2eVAC8HpTxcS/Pl9BVg/yLnuIDiF4mIzSwdeMLPwVLKLgHkEH7/5AnCpmb1EcL7ym51z68wsD2gDvg/cBnwqNG1qRj9BHJ5xrK+bzeyyPu+nAzjnHic0jauZ/QL4S2h5wMxqgAVD+GwiccOCj8K8BPhl6G9dCD7wCIJh/S0zuw14jtBDfs52PAW0iDfanHMLw29C16D3uOAzcSE49/A1wKuh9zkEAzsXeNQ51xra75R5ygdwDTDfzN4dep8fOlYn8JJzrjZ0rA0Ep09sAA4659YBOOcaQ+t/CfyLmd1FcBrGH/VzrvFA3WnLHnbOfaLPZ63uu9LMPkuwPv6nz+IjwAQU0JJYfMCJvj/bYc65A4Ra0KEg/2sXesjPQBTQIvGjpc9rA/7DOfedvhuY2afOsn83Jy9bZZx2rE865/542rGqgI4+i3o4y+8E51yrmT1F8NrZe4EL+9ms7bRzn5WZXQ28B7j8tFUZoWOJJAznXKOZ7TKz9zjnfhm6ZDTfOfeaBZ9Rf8w5F6DPQ37ORtegReLTH4HbQ39pY2YTzayEYNfYTWaWGbree0OffXZzMjTffdqxPm5mqaFjzTSz7LOceysw3swWh7bP7XM9+HsEB4Ctc84d72ffNwl1YQ/GzCYTfFDCe5xzp4fxTM7sKheJK2YWfu78LDOrNbM7gP8D3GFmrwGbOTkYrArYambbgFLg/sGOrxa0SBxyzv3JzOYAL4auZTUDf+Oce8XMHiY44OQIwcdzhn0F+IWZLSf4BLSw7xHsun4l9Bd9HXDTWc7daWY3A980s0yCLdmrgWbn3HozawR+OMC+W8ws38xynXNNg3zM24Ai4Dehz3jAOfd2Mysl2OV9aJD9RTzlnHvfAKvOuPXKOfcI8Mhwjq+nWYkkMDO7j2BwfmWEzjeB4OM6Z4e66vrb5h+AJufc987xHP8ANDrnvn/OBRUZBdTFLSJDYma3AmsJPqO733AOeYBTr20P1wngwQj2FxkV1IIWERGJQ2pBi4iIxCEFtIiISBxSQIuIiMQhBbSIiEgcUkCLiIjEIQW0iIhIHPr/k8mDufbEgWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import labtoolkit.SpectrumAnalyser.AgilentE44nn\n",
    "\n",
    "\n",
    "sa = labtoolkit.SpectrumAnalyser.AgilentE44nn.AgilentE44nn(\n",
    "    MockAgilentE44nnInstrument()\n",
    ")\n",
    "\n",
    "sa.frequency_center = 1e9\n",
    "sa.frequency_span = 10e6\n",
    "sa.sweep_points = 8192\n",
    "sa.OPC\n",
    "df = sa.trace\n",
    "# returns a DataFrame of the trace data\n",
    "df.plot(grid=True, figsize=(8, 6))\n",
    "df.attrs\n",
    "# df.attrs are used to store metadata (sweep_time, resolution_bandwidth, etc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4dd850cb-efaa-4f26-9832-858142f410e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power (dBm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frequency (Hz)</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9.950000e+08</th>\n",
       "      <td>-75.002171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.950012e+08</th>\n",
       "      <td>-75.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.950024e+08</th>\n",
       "      <td>-74.997927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.950037e+08</th>\n",
       "      <td>-74.995803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.950049e+08</th>\n",
       "      <td>-74.993678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.004995e+09</th>\n",
       "      <td>-74.993678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.004996e+09</th>\n",
       "      <td>-74.995803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.004998e+09</th>\n",
       "      <td>-74.997927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.004999e+09</th>\n",
       "      <td>-75.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.005000e+09</th>\n",
       "      <td>-75.002171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8192 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Power (dBm)\n",
       "Frequency (Hz)             \n",
       "9.950000e+08     -75.002171\n",
       "9.950012e+08     -75.000050\n",
       "9.950024e+08     -74.997927\n",
       "9.950037e+08     -74.995803\n",
       "9.950049e+08     -74.993678\n",
       "...                     ...\n",
       "1.004995e+09     -74.993678\n",
       "1.004996e+09     -74.995803\n",
       "1.004998e+09     -74.997927\n",
       "1.004999e+09     -75.000050\n",
       "1.005000e+09     -75.002171\n",
       "\n",
       "[8192 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6134937d-e82e-47b0-a707-4fafd3ae1944",
   "metadata": {},
   "source": [
    "``` Python\n",
    "# Communicate with Hardware with PyVISA\n",
    "import pyvisa\n",
    "import labtoolkit.SpectrumAnalyser.AgilentE44nn\n",
    "\n",
    "\n",
    "rm = pyvisa.ResourceManager()\n",
    "sa = labtookit.SpectrumAnalyser.AgilentE44nn.AgilentE44nn(\n",
    "    rm.open_resource('GPIB0::18::INSTR')\n",
    ")\n",
    "\n",
    "sa.frequency_center = 1e9\n",
    "sa.frequency_span = 10e6\n",
    "sa.sweep_points = 8192\n",
    "sa.OPC\n",
    "df = sa.trace\n",
    "# returns a DataFrame of the trace data\n",
    "df.plot(grid=True, figsize=(8, 6))\n",
    "df.attrs\n",
    "# df.attrs are used to store metadata (sweep_time, resolution_bandwidth, etc)\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}