{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QCoDeS Example with Keysight E4980A LCR meter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example is following the \"Capacitor Measurements\" on P215 of the user's guide: https://literature.cdn.keysight.com/litweb/pdf/E4980-90230.pdf?id=789356\n",
    "\n",
    "A 8400 pF (8E-9 F) leaded ceramic capacitor is connected to the LCR meter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from qcodes.dataset import initialise_database, Measurement, new_experiment\n",
    "from qcodes.dataset.plotting import plot_by_id\n",
    "\n",
    "from qcodes.instrument_drivers.Keysight.keysight_e4980a import KeysightE4980A, E4980AMeasurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to: Keysight Technologies E4980A (serial:MY46618801, firmware:B.07.05) in 0.07s\n"
     ]
    }
   ],
   "source": [
    "meter = KeysightE4980A(\"lcr_e4980a\", 'USB0::0x2A8D::0x2F01::MY46618801::INSTR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'vendor': 'Keysight Technologies',\n",
       " 'model': 'E4980A',\n",
       " 'serial': 'MY46618801',\n",
       " 'firmware': 'B.07.05'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meter.IDN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "meter.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Set up the E4980A’s measurement conditions:\n",
    "\n",
    "1. set frequency to be 1MHz (system default is 1kHz)\n",
    "\n",
    "2. set the voltage level to be 1.5 V (system default is 1V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The frequency for normal measurement is set to be 1.0 MHz.\n"
     ]
    }
   ],
   "source": [
    "meter.frequency(1e6)\n",
    "freq = meter.frequency()\n",
    "print(f'The frequency for normal measurement is set to be {freq/1e6} MHz.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the voltage for measurement signal is set to be 1.5 V.\n"
     ]
    }
   ],
   "source": [
    "meter.voltage_level(1.5)\n",
    "volt_lv = meter.voltage_level()\n",
    "print(f'the voltage for measurement signal is set to be {volt_lv} V.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. (optional) Set up the corrections for the unit\n",
    "\n",
    "In the \"Capacitor Measurements\" example, a Keysight 16047E Direct Couple Test Fixture (general purpose) was used. To compensate for its residuals and strays, an OPEN/SHORT correction is required.\n",
    "\n",
    "However, for our setup with a leaded ceramic capacitor, this step may not be necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "meter.correction.open()\n",
    "meter.correction.open_state('on')\n",
    "meter.correction.short()\n",
    "meter.correction.short_state('on')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step 3. Set the meausurement function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User should chose one function from the follow list, for example \"**E4980AMeasurements.CPD**\".\n",
    "\n",
    "    \"CPD\": \"Capacitance - Dissipation factor\",  (by default)\n",
    "    \"CPQ\": \"Capacitance - Quality factor\",\n",
    "    \"CPG\": \"Capacitance - Conductance\",\n",
    "    \"CPRP\": \"Capacitance - Resistance\",\n",
    "    \"CSD\": \"Capacitance - Dissipation factor\",\n",
    "    \"CSQ\": \"Capacitance - Quality factor\",\n",
    "    \"CSRS\": \"Capacitance - Resistance\",\n",
    "    \"LPD\": \"Inductance - Dissipation factor\",\n",
    "    \"LPQ\": \"Inductance - Quality factor\",\n",
    "    \"LPG\": \"Inductance - Conductance\",\n",
    "    \"LPRP\": \"Inductance - Resistance\",\n",
    "    \"LPRD\": \"Inductance - DC resistance\",\n",
    "    \"LSD\": \"Inductance - Dissipation factor\",\n",
    "    \"LSQ\": \"Inductance - Quality factor\",\n",
    "    \"LSRS\": \"Inductance - Resistance\",\n",
    "    \"LSRD\": \"Inductance - DC resistance\",\n",
    "    \"RX\": \"Resistance - Reactance\",\n",
    "    \"ZTD\": \"Absolute value of impedance - Theta in degree\",\n",
    "    \"ZTR\": \"Absolute value of impedance - Theta in radiant\",\n",
    "    \"GB\": \"Conductance - Susceptance\",\n",
    "    \"YTD\": \"Absolute value of admittance - Theta in degree\",\n",
    "    \"YTR\": \"Absolute value of admittance - Theta in radiant\",\n",
    "    \"VDID\": \"DC voltage - DC current\"\n",
    "\n",
    "Note: \n",
    "1. CP vs CS: ***P*** means measured using parallel equivalent circuit model, and ***S*** means measured using series equivalent circuit model.\n",
    "2. Same for LP and LS\n",
    "3. RP vs RS: Equivalent ***p***arallel/***s***eries resistance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the measurement function is \"CPD\", which mean \"Capacitance - Dissipation factor\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CPD'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meter.measurement_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"measurement\" property will return a \"MeasurementPair\" class (sub class of \"MultiParameter\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "qcodes.instrument_drivers.Keysight.keysight_e4980a.MeasurementPair"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement = meter.measurement\n",
    "type(measurement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which has the same name as the ***current*** \"measurement_function\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CPD'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User can view the parameters to be measured, and the corresponding units as following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The parameters to be measured are ('capacitance', 'dissipation_factor'), with units ('F', '')\n"
     ]
    }
   ],
   "source": [
    "print(f'The parameters to be measured are {measurement.names}, with units {measurement.units}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and take measurements by calling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-8.35219e-09, -0.00183439)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User may also directly call the name of the physics parameters: (can't call the unit this way though)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capacitance is measured to be -8.35219e-09\n",
      "The dissipation_factor is measured to be -0.00183439\n"
     ]
    }
   ],
   "source": [
    "print(f'The capacitance is measured to be {measurement.capacitance}')\n",
    "print(f'The dissipation_factor is measured to be {measurement.dissipation_factor}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change to another measurement function, for example, \"LPD\" for Inductance measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'LPD'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meter.measurement_function(E4980AMeasurements.LPD)\n",
    "meter.measurement_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The measurement \"LPD\" returns values (3.03277e-06, -0.00183439)\n",
      "for parameters ('inductance', 'dissipation_factor')\n",
      "with units ('H', '')\n"
     ]
    }
   ],
   "source": [
    "measurement = meter.measurement\n",
    "print(f'The measurement \"{measurement.name}\" returns values {measurement.get()}')\n",
    "print(f'for parameters {measurement.names}')\n",
    "print(f'with units {measurement.units}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any \"MeasurementPair\" object, when first initialized, will dynamically generate the corresponding attributes. For the LPD measurement above, the \"measurement\" object will have attributes \"inductance\" instead of \"capacitance\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.03277e-06"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurement.inductance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'MeasurementPair' object has no attribute 'capacitance'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    measurement.capacitance\n",
    "except AttributeError as err:\n",
    "    print(err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To validate the measurement, we can measure the impedance, and calculate the capacitance.\n",
    "\n",
    "For a capacitor, we have Zc = - j / (2 * Pi * f * C), where Zc is the impedance of the capacitor, f is the frequency, and C is the capacitance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are actually two methods to measure the impedance:\n",
    "1. to use the \"measurement_function\" method as above, and choose \"RX\";\n",
    "2. to use the \"measure_impedance()\" call, which will always return impedance in complex format, Z = R + iX, where Z is impedance, R is resistance, and X is the reactance.\n",
    "\n",
    "(The results from the two methods should be the same.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('resistance', 'reactance')\n",
      "(-0.0349551, 19.0554)\n",
      "('Ohm', 'Ohm')\n"
     ]
    }
   ],
   "source": [
    "meter.measurement_function(E4980AMeasurements.RX)\n",
    "measurement = meter.measurement\n",
    "print(measurement.names)\n",
    "print(measurement())\n",
    "print(measurement.units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('resistance', 'reactance')\n",
      "(-0.0349551, 19.0554)\n",
      "('Ohm', 'Ohm')\n"
     ]
    }
   ],
   "source": [
    "imp = meter.measure_impedance\n",
    "print(imp.names)\n",
    "print(imp())\n",
    "print(imp.units)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the impedance: (\"imp\" here is also a \"MeasurementPair\", so user can call the resistance/reactance directly.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The impedance is 19.0554320606754 Ohm.\n"
     ]
    }
   ],
   "source": [
    "Zc = np.sqrt(imp.resistance**2 + imp.reactance**2)\n",
    "print(f\"The impedance is {Zc} Ohm.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the capacitance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capacitance is -8.352208576804858e-09F.\n"
     ]
    }
   ],
   "source": [
    "C = -1/(2*np.pi*freq*Zc)\n",
    "print(f\"The capacitance is {C}F.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is the same as what we got previously using the \"CPD\" function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The capacitance  is -8.35219e-09 F.\n"
     ]
    }
   ],
   "source": [
    "meter.measurement_function(E4980AMeasurements.CPD)\n",
    "measurement = meter.measurement\n",
    "print(f\"The capacitance  is {measurement.capacitance} F.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To work with QCoDeS \"Measurement\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialise_database()\n",
    "exp = new_experiment(\n",
    "    name='capacitance_measurement',\n",
    "    sample_name=\"no sample\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x1f356cfd3c8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement()\n",
    "meas.register_parameter(meter.frequency)\n",
    "meas.register_parameter(meter.measurement, setpoints=(meter.frequency,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 73. \n"
     ]
    }
   ],
   "source": [
    "with meas.run() as datasaver:\n",
    "    for freq in np.linspace(1.0E6, 1.5E6, 5):\n",
    "        meter.frequency(freq)\n",
    "        time.sleep(1)\n",
    "        value_pair = meter.measurement()\n",
    "        datasaver.add_result((meter.frequency, freq),\n",
    "                             (meter.measurement, value_pair))\n",
    "    run_id = datasaver.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s1BTFm3IFhca23cHFP29nPlt3FT+cF5cUYokiNpwXvt6VX5jQNuvXqUVmRjqZddOD/xnptG9a/7txDcJxWXXTaVAnGJeVEY6PG/5m0w5u+OdXXPXyl7w5YxV3D+tNi4Z1k3zEnHNVVaoSjwFZCr4KZxI8zbHo8+SJSzoieKpkpe4628zYvrsgSACxEsSuuEQRK0GUUE2VF7fMtt0FCW0zIz1tr2TRICOdAxrW/S5RZGX8d3xJiSIzTCS10iqmZNK1ZRav/vxInv14KX96bwEnPDCR20/tyVn92njpxzn3PSl5LEL4vPY3gO4Ezwo/z8zeKmHe5wgenToH+KGZbQ+r2l4leAzwKuDXZja7mGWvAK4AaN++ff/ly/e9y6FFa/P4YO6a/1Y7FZMoit7fKEt6mvYqVcSXKOITRVbdvZPGXvOH4+ukV+72IEty87jxn18xdflGju2Wzf+ddTCtGiX6RGDnXNQkTTOznKRuI0WJ5xxgIHAdcBDwPtAnVsIpZv5awCPA52b2nKSGQGF4v2co8JCZdSltmzk5ObY/nYSOm7maX7z0BRJk1tm7ZLFXYohVQRVTHRWfXDIz0slIT6tR3/wLCo0XPl3GH96ZT3qa+M0Pe3DegHY16hg4V1VV6cQjaTjws/DlRuB2M5sUTvsIuNnMppSy/GDghuLu64TPB88xs3UlLb+/iWdXfgH5BUb9OrX8QllOy9dv46ZXv+KzJRs4unNz7jnrYNo1rR91WM65UqQi8SSt3sbMHjWzvmbWF5gHHA8gqSXQDVgSP78CnWPDwGnhckg6IByHpMPCuNcnI+6M9Fo0yEj3pFMBOjRrwF8vP4KRw3rz5YqNnPzgRP7y6TIKE6mfdM5VW6m6YTASOErSTOBD4KZYaUXS25JaEzShfj6cZybQCog1mT4HmCVpBvAwcL75M7urhLQ0cdERHXj32kH069CE28bM5oKnP2P5+m1Rh+aci0hK7vFEYX+r2lzymBmvTF3JXWPnkl9o3HBSNy4+qmOFta5zzpVfla5qc64oSZw3oD3vXTeIIzo15c6xc/ifJz9lcW5e1KE551LIE49LuVaN6vHsJQO4/3/6sGhtHkMfmsSTExaTX5DYD1+dc1WbJx4XCUmc1a8t7187iMFds7ln3DzOfvwTFqzZGnVozrkk88TjItWiYV2evKg/j/zoUFZu3MGpD09m1EcL2eOlH+eqLU88LnKSOK1Pa96/dhAn9GrJH99bwLBHP2bOqmJ/X+ycq+I88bhKo1lmBo9e0I8nLuzHmi27OH3UZO5/fwG7E+z01DlXNXjicZXOyb1b8f61gzitT2se/nAhp4+azMyvN0cdlnOugnjicZVSkwZ1eOC8vjxzcQ4bt+9m2GMf8/t35rFzT2K9eDvnKi9PPK5SO75HS967djBn92vD4+MXc+ojk/lixcaow3LOlYMnHlfpNapXmz+c04cXLjuMHbsLOPvxT7hr7Bx2JPgMI+dc5eKJx1UZg7pm8841x3DBYe15evJSTnloIlOWbog6LOfcPvLE46qUrLq1ufvMg/nr5YdTYMZ5T33KiDdms23X9x5o65yrpDzxuCrpqM7NeefqQVx8ZEee/3QZJz80kU8Wlfh4JudcJeKJx1VZDTLSGXF6L1753yNJT0vjgqf/w63/msnWnXuiDs05VwpPPK7KG9CxKW9fdQxXDOrE36as4KQHJjJhQW7UYTnnSuCJx1UL9erU4tahPXj1F0dRPyOdi5+dwg3/mMHmHV76ca6y8cTjqpVD2zdh7JVH88shB/Hal99w4gMT+HDumqjDcs7F8cTjqp26tWtx48ndef2XA2lSvw4/fX4q1/ztSzZu2x11aM45PPG4auzgto1441dHc/XxXRj71WpOeGAi78xaHXVYztV4nnhctVYnPY1rT+jKG786mpYNM/j5i18w/KUvWJe3K+rQnKuxPPG4GqFn64a8PnwgN5zUjffnrOHEBybyxoxVmFnUoTlX43jicTVG7VppDD+2M2OvOpp2Tetz1ctfcsVfprF2y86oQ3OuRvHE42qcri2zeO0XR3Hr0O5MXJDLCQ9M5NVpX3vpx7kU8cTjaqRaaeKKQQcx7upj6NIik+v/MYPLRn/O6s07og7NuWrPE4+r0TplZ/L3/z2SO07ryWdLNnDi/RP525QVXvpxLokiTTySGkl6U9IMSbMlXVrG/I9IyktVfK5mqJUmLh14IO9ccwy92jTk5tdmctEzU1i5YXvUoTlXLUVd4hkOzDGzPsAQ4E+S6hQ3o6QcoHEKY3M1TIdmDfjr5Udw17DefLliIyc/OJG/fLqMwkIv/ThXkaJOPAZkSRKQCWwAvvdgFUm1gPuAG1Mbnqtp0tLEhUd04N1rB9GvQxNuGzObC57+jOXrt0UdmnPVRtSJZxTQA1gFzASuNrPCYub7FfCGmZX6s3NJV0iaKmlqbq73Tuz2X9sm9XnhssP4/dkHM/ubLZz84CSenbyUAi/9OFduUSeek4DpQGugLzBKUsP4GSS1Bs4FHilrZWb2lJnlmFlOdnZ2MuJ1NYgkzhvQnveuG8QRnZpy59g5/M+Tn7I4128zOlceKU88koZLmi5pOsE9ntcssAhYCnQvssihQGdgkaRlQH1Ji1IatKvRWjWqx7OXDOD+/+nDorV5DH1oEk9OWEx+QXGFc+dcWRRls1FJjwNrzGyEpJbAF0AfMyvxGcaS8swss6x15+Tk2NSpUyswWudg7Zad/Pb1Wbw3Zw192jbivnP70LVlVtRhOVdhJE0zs5xkbiPqqraRwFGSZgIfAjfFko6kt8NqNucqjRYN6/LkRf155EeHsnLjDk59eDKjPlrIHi/9OJewSEs8yeQlHpds6/N2cccbsxn71Wp6tW7Ifef0oWfrhmUv6FwlVhNKPM5VWc0yMxh1QT+euLA/a7bs4vRRk7n//QXszvfSj3Ol8cTjXDmd3PsA3r92EKf1ac3DHy7k9FGTmfn15qjDcq7S8sTjXAVo0qAOD5zXl2cuzmHj9t0Me+xjfv/OPHbuKYg6NOcqHU88zlWg43u05L1rB3N2vzY8Pn4xpz4ymS9WbIw6LOcqFU88zlWwRvVq84dz+vDCZYexY3cBZz/+CXeNncOO3V76cQ488TiXNIO6ZvPONcdwwWHteXryUk55aCJTlm6IOiznIueJx7kkyqpbm7vPPJi/Xn44BWac99Sn3DFmFivW+yMXXM3lv+NxLkW27crnvnfn8/ynyzCDA5s3YHDXbAZ3y+bITs2oW7tW1CE6l5Lf8XjicS7Flq/fxkfz1jJhQS6fLl7PrvxCMtLTOLxTM4aEiahT8wYETwtxLrU88ZSDJx5XFezcU8B/lm5g/PwgES3JDZ77065pPQZ3zWZI1xYceVAzGmSkRxypqyk88ZSDJx5XFa1Yv50JC3OZMH8tnyxez/bdBdSplcaAA5sE1XJdW9C1ZaaXhlzSeOIpB088rqrblV/A1GUbmbAgl/Hz17JgTfAcoFaN6galoW7ZHNW5OQ3r1o44UledeOIpB088rrpZtWkHExfkMn5+Lh8vWsfWXfmkp4l+HZp8l4h6tmropSFXLp54ysETj6vO9hQU8sXyWGkolzmrtwCQnZURVsllc0yX5jSuXyfiSF1V44mnHDzxuJpk7ZadTFiQy4QFuUxauI7NO/aQJujbrjGDu7ZgSLdsDm7TiLQ0Lw250nniKQdPPK6mKig0pq/cFCSi+Wv56pvNmEHTBnUY1KU5g7tlM6hLNs0yM6IO1VVCnnjKwROPc4H1ebuYtHAdExbkMnFBLuu37UaCg9s0+u53Q33aNia9lndk4ipJ4pGUAxwDtAZ2ALOAD8ysUnc65YnHue8rLDRmrdrM+PlBtdyXKzZSaEHHpkd3aR4koq7ZtGhYN+pQXUQiTTySLgGuApYC04C1QF2gKzCQIAHdZmYrkhng/vLE41zZNm3fzeRF65gQJqK1W3cB0LNVQwZ3y2ZI12z6dWhCbS8N1RhRJ57hwLNmtqOE6X2BZmb2YRLj22+eeJzbN2bG3NVbGb9gLRPm5zJt+UbyC43MjHQGdm7GkG4tGNw1m9aN60UdqkuiSlHVVlV54nGufLbs3MMni9YzYcFaxs/PZfXmnQB0bZkZ/m6oBTkdm5CR7p2bVidRl3jeM7MTw+FbzOyeZAZS0TzxOFdxzIyFa/OYMD+X8QvW8vnSjewuKKR+nVocdVCz77rzad+sftShunKKOvF8aWaHhsNfmFm/ZAZS0TzxOJc823bl89mS9YwPE9HKDUGNfKfmDRgU9qJwhD/qoUpKReIprcvb6lkH55wrtwYZ6RzfoyXH92iJmbF03bbvelF4ecoKRn+yjIz0NI7o1Oy77nwO9Ec9uFBpJZ5NwERABM2pJ8ZPN7PTkx5dOXiJx7lo7NxTwGdL1oc/YM1lybr/PuphSNeggYI/6qHyirqqbXBpC5rZhIQ3IjUCXgTaE5Sy/mhmz5Uy/yPApWaWGb4eAowhaNoN8JqZ3VnaNj3xOFc5rFi/nQkLgucNFX3Uw5CuLRjcLZsuLfxRD5VFtWnVJulWoJGZ3SQpG5gPHGBmu4uZNwe4GjizSOL5tZmdmug2PfE4V/mU9KiH1o3qMrhb0EBhYOdmZPmjHiIT9T2eWBADgRFAh3B+AWZmnfZhOwZkKfhKkwlsAPKL2VYt4D7gAuDMfVi/c64KyEivxcDOzRnYuTm3Du3Bqk07vquSGztjNS9PWUl6mujfoUmYiPxRD9VRIl3mzAOuJei9oCA23szWJ7wRKQt4A+gOZAHnmdlbxcx3NZBmZg9IyitS4nkV+BpYRVD6mV3M8lcAVwC0b9++//LlyxMN0TkXsdijHsaHiSj2qIcWWRk8eF5fjurcPOIIa4ZKUdUm6T9mdni5NiKdQ9DNznXAQcD7QB8z2xI3T2vgFWCImeUXSTwNgUIzy5M0FHjIzLqUtk2vanOuaos96uHRfy9iT4HxwXWDqVfHm2cnWyoSTyIdMP1b0n2SjpTUL/ZX1kKShkuaLmk6MJygQYCZ2SKCRgLdiyxyKNAZWCRpGVBf0iIAM9tiZnnh8NtAbUn+9ce5aqxFw7qcm9OOe88+hG827eCx8YuiDslVkETaM8ZKO/EZ0IDjSlvIzB4FHgWQ9DhwPDBJUkugG7CkyPxvAQfEXoclns7h8AHAGjMzSYcRJMyEq/qcc1XXEZ2aceahbXhywhLO6teWA5s3iDokV05llnjM7Nhi/kpNOsUYCRwlaSbwIXCTma0DkPR2WM1WmnOAWZJmAA8D51t17WTOOfc9twztTkZ6GrePmYV/9Ku+EhOPpAsllTb9IElHJ7IRM1tlZiea2cFm1tvMXoybNtTMVhWzTGbc8Cgz62VmfczsCDP7JJHtOueqhxZZdbnuxK5MWriOd2Z9G3U4rpxKq2prBnwpaRpBi7ZcgufxdAYGA+uAm5MeoXPOARcd0YFXpn7NnWPnMKhrtvd8UIWVWKIxs4eAfsDLQDbBPZp+wDfARWZ2tpktTEmUzrkaL71WGncN68XqzTt55CNvaFCVlfqVwcwKCJo+v5+acJxzrmT9OzTl3P5teXrSEs7p34bOLbKiDsntB3+erXOuSrn5lO7Ur1OL216f7Q0NqihPPM65KqVZZgY3nNydT5es582vVkcdjtsPnnicc1XOBYe15+A2jbhr7By27twTdThuH5WZeCS1lPSMpHHh656Sfpr80Jxzrni10sTIYb3JzdvFgx94G6eqJpESz2jgXSD2I88FwDXJCsg55xLRt11jzh/QntGfLGPet1vKXsBVGokknuZm9gpQCGBm+cT1Uu2cc1G58aRuNKybzu3e0KBKSSTxbJPUjKB/NiQdAWxOalTOOZeAJg3qcNPJ3ZmybAP/+vKbqMNxCUok8VxH8CydgyR9DLwAXJnUqJxzLkH/k9OOQ9s35v/ensvmHd7QoCpIpJPQLwi6yDkK+F+gl5l9lezAnHMuEWlpYuQZvdmwbTf3vzc/6nBcAhJp1TYcyDSz2WY2C8iU9Mvkh+acc4np3aYRFx7Rgb98tpxZ3/idgMoukaq2n5nZptgLM9sI/Cx5ITnn3L67/sRuNKlfh9vGzKKw0BsaVGaJJJ40SYq9kFQLqJO8kJxzbkrmB0wAABgdSURBVN81qlebW4b24MsVm/jHtJVRh+NKkUjieRd4RdLxko4j6K36neSG5Zxz++7sfm0Y0LEJ946bx6btu6MOx5UgkcRzE/AR8AtgOMETRG9MZlDOObc/JHHnGb3ZsjOfP7zrDQ0qq0RatRWa2eNmdk74DJ4nw8clOOdcpdOjVUMuPrIjL09ZwYyVm8pewKVcIq3aBkp6X9ICSUskLZW0JBXBOefc/rj2hC5kZ2Zw25hZFHhDg0onkaq2Z4D7gaOBAUBO+N855yqlrLq1+c0Pe/DV15t5ecqKqMNxRSSSeDab2TgzW2tm62N/SY/MOefK4fQ+rTmyUzPue3c+6/N2RR2Oi5NI4vm3pPskHSmpX+wv6ZE551w5BA0NerFtVz6/f2de1OG4OOkJzHN4+D8nbpwBx1V8OM45V3G6tMzip0cfyJMTl3DegPb079Ak6pAcibVqO7aYP086zrkq4arju9CqUV1ue30W+QWFUYfjSPDR15J+KOlGSbfH/pIdmHPOVYQGGencdmpP5qzewoufLY86HEdizamfAM4jeBSCgHOBDhWxcUmNJL0paYak2ZIuLWG+0WEz7unhX9+K2L5zrmY4pfcBHNOlOX96bwG5W72hQdQSKfEcZWY/ATaa2e+AI4F2FbT94cAcM+sDDAH+JKmkfuBuMLO+4d/0Ctq+c64GkMTvTu/FzvwC7nl7btTh1HiJJJ4d4f/tkloDe4ADK2j7BmSFnZBmAhuA/Apat3POfadTdiZXDOrEa19+w3+W+C9CopRI4hkrqTFwH/AFsAz4WwVtfxTQA1gFzASuNrOS7v7dLekrSQ9IyihuBklXSJoqaWpubm4Fheicqy5+dWwX2jSux+1jZrPHGxpEJpHE8wcz22RmrxLc2+kO3FVB2z8JmA60BvoCoyQ1LGa+W8LtDgCaEnRc+j1m9pSZ5ZhZTnZ2dgWF6JyrLurVqcUdp/Vk/pqtPP/JsqjDqbESSTyfxgbMbJeZbY4ft68kDY81EiC4x/OaBRYBSwkSzF7MbHU4zy7gOeCw/d2+c65mO6FnS47tls0D7y9gzZadUYdTI5WYeCQdIKk/UE/SoXG9FgwB6u/vBs3s0VgjAWAecHy4vZZAN+B7HZBKahX+FzAMmLW/23fO1WySGHF6L/YUGne95Q0NolBazwUnAZcAbQk6CY3ZCtxaQdsfCYyWNJOgqfZNZrYOQNLbwOVmtgp4SVJ2OM904OcVtH3nXA3UoVkDfjnkIB78YCHnD2jHwM7Now6pRpFZ6V2GSzo7vL9TpeTk5NjUqVOjDsM5V0nt3FPAiQ9MpHYtMe7qQdRJT+j39NWepGlmllP2nPuvtKq2C8PBjpKuK/qXzKCccy7Z6tauxYjTe7I4dxvPTF4adTg1SmkpvkH4PxPIKubPOeeqtOO6t+SEni15+MOFrNq0o+wFXIUos6qtqvKqNudcIr7euJ0f3D+BY7u14PEL+0cdTuQirWqLC+L58AeksddNJD2bzKCccy5V2japz5XHdWHcrG+ZsMB/eJ4KidxNO8TMNsVemNlG4NDkheScc6l1+TEH0ql5A+4YM4td+QVRh1PtJZJ40iR99/QkSU1J7AFyzjlXJWSk12LE6b1Ytn47T0343k8JXQVLJPH8CfhE0khJI4FPgD8kNyznnEutQV2zGXrwAYz69yJWbtgedTjVWiJPIH0BOAdYA6wFzjKzvyQ7MOecS7XbTu1JrTTxuzfnRB1KtZbQL6bMbDbwCjAGyJPUPqlROedcBFo1qsfVx3fhg7lr+HDumqjDqbYSadV2uqSFBB14TiB4LMK4JMflnHORuHTggXRukcmIN2ezc483NEiGREo8I4EjgAVmdiBBp54fJzUq55yLSJ30NEae0ZuVG3bw2PjFUYdTLSWSePaY2XqC1m1pZvZvgmfnOOdctXTkQc04o29rnpiwmGXrtkUdTrWTSOLZJCkTmETQS/RD+OOpnXPV3G+G9qBOrTRGvDmb6trDS1QSSTxnADuAa4B3gMXAackMyjnnotaiYV2uPaEr4+fn8u5sb2hQkRJpTr0NyAaGAhuAV8KqN+ecq9YuPrID3Q/IYuTYOWzf7RU9FSWRVm2XA1OAswh+z/OZpMuSHZhzzkUtvVYaI4f15ptNOxj10aKow6k2Eun65gbg0FgpR1Izgt4LvKNQ51y1N6BjU87u15Y/T1rCWf3a0rlFZtQhVXmJ3OP5muBx1zFbgZXJCcc55yqfm0/pHjw47g1vaFAREkk83wD/kTRC0h3AZ8AifxKpc66myM7K4IaTujF50Tremrk66nCqvEQSz2LgdSCW5scAq/EnkTrnapAfH96BXq0bMnLsHPJ2eUOD8ijzHo+Z/S4VgTjnXGVWK02MHNabsx77hIc/XMitQ3tEHVKVVWbikZQN3Aj0AurGxpvZcUmMyznnKp1+7Ztw/oB2PDt5Kef0b0vXll7psz8SqWp7CZgHHAj8jqCT0M+TGJNzzlVaN57cncy66dz2+ixvaLCfEkk8zczsGYI+2yaY2WUEnYY651yN07RBHW48qTv/WbqBMdNXRR1OlZRQJ6Hh/9WSfijpUKBtEmNyzrlK7fwB7ejTrjF3vTWXLTv3lL2A20siiecuSY2A64FfA08D1+7LRiQ1kvSmpBmSZku6tIT5RktaKml6+Nc3HD9E0ua48bfvy/adc64ipaWJkWf0Yv22XTzw/oKow6lyEmnVNjYc3Awcu5/bGQ7MMbPTwsYK8yW9ZGa7i5n3BjP7ZzHjJ5nZqfu5feecq1CHtG3Mjw9vz/OfLOPc/u3o2bph1CFVGYn01fa8pMZxr5tI2tfucgzIkiQgk6CzUW8I75yr0n59Yjca16/DbWNmUVjoDQ0SlUhV2yFmtin2wsw2Aofu43ZGAT2AVcBM4GozKyxh3rslfSXpAUkZceOPDKvqxknqVdyCkq6QNFXS1Nzc3H0M0Tnn9k3j+nW4+ZTuTFu+kVe/+DrqcKqMRBJPmqQmsReSmpJY56LxTgKmA60Jnl46SlJx5dJbgO7AAKApcFM4/gugg5n1AR4h6Enhe8zsKTPLMbOc7OzsfQzROef23Tn92tK/QxPuHTePzdu9oUEiEkk8fwI+kTRS0p0EPVP/oayFJA2PNQYguMfzmgUWAUsJEsxezGx1OM8u4DngsHD8FjPLC4ffBmpLap7gPjrnXNKkpYk7z+jFxu27+eN786MOp0pI5EFwLwBnA2uAXOAsM/tLAss9amZ9zawvwQ9QjweQ1BLoBiwpuoykVuF/AcOAWeHrA8JxSDosjNsfRuecqxR6tW7ET47syIv/Wc7MrzdHHU6ll0iJBzObY2ajzOwRM5uzH9sZCRwlaSbwIXCTma0DkPS2pNbhfC+F88wEmgN3hePPAWZJmgE8DJxv/pNh51wlct2JXWnWIIPfekODMqm6Xr9zcnJs6tSpUYfhnKtB/vXl11z79xncc9bB/Oiw9lGHs18kTTOznGRuI6ESj3POubIN69uGww5syu/fmceGbcX9TNGBJx7nnKswkhh5Rm+27sznvnfnRR1OpeWJxznnKlC3A7K4bGBH/vb5Sr5csTHqcColTzzOOVfBrv5BV1pkZXDbmFkUeEOD7/HE45xzFSwzI53f/rAns77Zwl//szzqcCodTzzOOZcEpx7SioGdm3Hfu/NZl7cr6nAqFU88zjmXBJL43em92bGngHve9oYG8TzxOOdcknRukcnlx3Ti1S++5vNlG6IOp9LwxOOcc0l05XGdad2oLre9Pov8gpI65a9ZPPE451wS1a+Tzu2n9WTet1t54VNvaACeeJxzLulO6nUAg7tmc//7C1i7ZWfU4UTOE49zziWZJEac3ovd+YXc/fbcqMOJnCce55xLgQObN+DngzsxZvoqPl1cs5/q4onHOedS5JfHdqZtk3rcPmYWe2pwQwNPPM45lyJ1a9dixGm9WLg2j+c+Xhp1OJHxxOOccyn0g54t+UGPFjz4wUJWb94RdTiR8MTjnHMpdsdpvSgoNO4aWzMbGnjicc65FGvXtD7Dj+3MWzNXM2lhbtThpJwnHueci8AVgzrRsVl97hgzm135BVGHk1KeeJxzLgJ1a9dixOm9WLJuG09PqlkNDTzxOOdcRIZ0a8HJvQ7gkY8W8vXG7VGHkzKeeJxzLkK3ndYTIe58c07UoaSMJx7nnItQm8b1uPL4zrw3Zw3/nrc26nBSwhOPc85F7PKjO9EpuwEj3pzNzj3Vv6GBJx7nnItYnfQ0Rp7Rm+Xrt/PEhMVRh5N0kSYeSY0kvSlphqTZki4tYT5JulvSAklzJV2V6lidcy6ZBnZuzqmHtOKx8YtZsb56NzSIusQzHJhjZn2AIcCfJNUpZr5LgHZAdzPrAfwtZRE651yK/PaHPamdJka8ORszizqcpIk68RiQJUlAJrAByC9mvl8Ad5pZIYCZ1Yw7cM65GuWARnW55gdd+WjeWj6YW30vc1EnnlFAD2AVMBO4OpZcijgIOE/SVEnjJHUpbmWSrgjnmZqbW/O6oXDOVX2XDOxI15aZjHhjNjt2V8+GBlEnnpOA6UBroC8wSlLDYubLAHaaWQ7wZ+DZ4lZmZk+ZWY6Z5WRnZycrZuecS5ratYKGBt9s2sGj/14UdThJkfLEI2m4pOmSphPc43nNAouApUD3Yhb7Gng1HP4XcEhqonXOudQ7vFMzzjy0DU9NXMKS3Lyow6lwKU88ZvaomfU1s77APOB4AEktgW7AkmIWex04LhweDCxIRazOOReVW4Z2JyM9jTveqH4NDaKuahsJHCVpJvAhcJOZrQOQ9Lak1uF89wJnh/PdA1weSbTOOZciLbLqct2JXZm0cB3jZn0bdTgVStUtk8bk5OTY1KlTow7DOef2W35BIaeP+piN23fzwXWDaZCRnvRtSpoW3k9PmqhLPM4550qQXiuNkcN6sXrzTh7+aGHU4VQYTzzOOVeJ9e/QlHP7t+WZSUtZtHZr1OFUCE88zjlXyd18Snfq16nFba9Xj4YGnnicc66Sa5aZwQ0nd+fTJet5Y8aqqMMpN088zjlXBVxwWHsOaduIu9+ay9ade6IOp1w88TjnXBVQK02MPKM3uXm7ePCDqt3QwBOPc85VEX3aNeb8Ae0Z/cky5n27Jepw9psnHuecq0JuPKkbDeumc9vrs6psQwNPPM45V4U0aVCHm0/pzufLNvLaF99EHc5+8cTjnHNVzLn923Fo+8bcM24um3dUvYYGnnicc66KSQsbGmzYtpv735sfdTj7zBOPc85VQb3bNOLCIzrwl8+WM+ubzVGHs0888TjnXBV1/YndaNqgDreNmUVhYdVpaOCJxznnqqhG9Wpzyyk9+HLFJv4xbWXU4STME49zzlVhZ/Vrw4COTbh33Dw2bd8ddTgJ8cTjnHNVmCTuPKM3W3bm84d3q0ZDA088zjlXxfVo1ZCLj+zIy1NWMGPlpqjDKZMnHuecqwauPaEL2ZkZ3DZmFgWVvKGBJx7nnKsGsurW5jc/7MFXX2/m5Skrog6nVMl/gLdzzrmUOL1Paz6cu5Ym9etEHUqpPPE451w1IYmHf3Ro1GGUyavanHPOpZQnHueccynlicc551xKeeJxzjmXUilJPJIaSXpT0gxJsyVdWsJ8knS3pAWS5kq6Khw/RNJmSdPDv9tTEbdzzrmKl6pWbcOBOWZ2mqRsYL6kl8ysaMdClwDtgO5mViipRdy0SWZ2aoridc45lySpSjwGZEkSkAlsAPKLme8XwAVmVghgZmtTFJ9zzrkUSdU9nlFAD2AVMBO4OpZcijgIOE/SVEnjJHWJm3ZkWFU3TlKv4jYi6Ypw2am5ubkVvhPOOefKL1UlnpOA6cBxBMnlfUmTzGxLkfkygJ1mliPpLOBZ4BjgC6CDmeVJGgq8DnQpsixm9hTwFICkXEnL9zPe5sC6/Vy2qvJ9rhl8n2uG8uxzh4oMpDgyS05ncpKGAz8LX24EbjezSeG0j4CbzWxKkWXmASeb2bKwWm6TmTUqZt3LgBwzS8rJJGmqmeUkY92Vle9zzeD7XDNU9n1OWlWbmT1qZn3NrC8wDzgeQFJLoBuwpJjFXicoFQEMBhaEyxwQJiIkHRbGvT5ZsTvnnEueVFW1jQRGS5oJCLgpVlqR9DZwuZmtAu4FXpJ0LZAHXB4ufw7wC0n5wA7gfEtWUc0551xSpSTxhEnlxBKmDY0b3gT8sJh5RhE0UEiVp1K4rcrC97lm8H2uGSr1PiftHo9zzjlXHO8yxznnXEp54nHOOZdSnnjiSHpW0lpJs6KOJRnK2j9J3SV9KmmXpF+nOr5kSGCffyzpq/DvE0l9Uh1jRUtgn88I93d6+IPro1MdY0VL9LMraYCkAknnpCq2ZEngfa60fVx64tnbaODkqINIotGUvn8bgKuAP6YkmtQYTen7vBQYbGaHELS+rNQ3ZRM0mtL3+UOgT/hTh8uAp1MRVJKNpozPrqRawO+Bd1MRUAqMpuzr1aTYz1rM7M4UxJQQTzxxzGwiwcW3Wipr/8xsrZl9DuxJXVTJlcA+f2JmG8OXnwFtUxJYEiWwz3lxP0doQNCXYpWW4Gf3SuBVoFr0AVmVr1eeeJz7r58C46IOIhUknRn2FPIWQamnWpPUBjgTeCLqWFKszD4uo+CJxzlA0rEEieemqGNJBTP7l5l1B4YRVDFWdw8S/HC9IOpAUijWx2Uf4BGCnmEqBU88rsaTdAjBfY4zzKxGdcUUVtccJKl51LEkWQ7wt7Cfx3OAxyQNizak5DKzLWaWFw6/DdSuLO9zqrrMca5SktQeeA24yMwWRB1PKkjqDCw2M5PUD6hDNe/70MwOjA1LGg2MNbNKUwJIBkkHAGvC97lS9XHpiSeOpJeBIUBzSV8Dd5jZM9FGVXGK2z+gNoCZPRGeqFOBhkChpGuAnsU8vqLKKGufgduBZgTfgAHyK3OvvolIYJ/PBn4iaQ9B34fnVfW+DxPY52ongX2utH1cepc5zjnnUsrv8TjnnEspTzzOOedSyhOPc865lPLE45xzLqU88TjnnEspTzyu2pF0laS5kl6KOpaKJOlBSYPC4fGSVihsAx6Oe11SXjjcsWivxZJGlNbruKRfSbo0WfE7F+OJx1VHvwSGmtmP40dKqrK/W5PUFDgi7GkgZhMwMJzeGGhVzs08S9A7uXNJ5YnHVSuSngA6AW9Iujb8lv+UpPeAFyRlS3pV0ufhX+zC3UzSe5K+lPSkpOWSmhctOUj6taQR4fBBkt6RNE3SJEndw/GjJT0cPt9nSfyzXyTdKGlm2HHjveE6voib3kXStGJ27RzgnSLj/gacHw6fRdADQyLHqHXcM1qmh8+n6WBm24Fl4a/cnUsaTzyuWjGznwOrgGPN7IFwdH+CftguAB4CHjCzAQS/4I89i+YOYLKZHQq8AbRPYHNPAVeaWX/g18BjcdNaAUcDpwL3Akg6haBTzsPDjhv/YGaLgc2S+obLXUrwnJWiBgJFE9KHwKDwOTPnA38vMv2g+AQD/BzAzFbFntEC/Bl41cyWh8tMBY5JYN+d229VturBuX3whpntCId/APSMuzXSUFIWMIig1ICZvSVp4/dX81+SMoGjgH/ErSsjbpbXzawQmCOpZdy2nwtLFphZ7FkqTwOXSroOOA8orsTRCsgtMq4AmBwuU8/MlsXFAkF/bLGERqykFvd6IHA5eyeatUD3EnbbuQrhicfVBNvihtOAI+MSEQDhBbu4/qPy2btmoG7cejbFX9iL2BW/+rj/xW3jVYIS10fAtBJ6yN4Rt+14fwP+BYwoIY5iSWoFPAOcHuvBOFQ33JZzSeNVba6meQ/4VexFXBXXRODH4bhTgCbh+DVAi/AeUAZB1Rlhx6lLJZ0bLiNJfRLY9mWS6ofLNA3XtZPgccyPA8+VsOxcoHMx4ycB9wAvl7Ht70iqDbxC8Hyaoj1ydwVmfX8p5yqOJx5X01wF5Ej6StIcwvsewO8I7pd8AZwIrAAwsz3AncB/gLHAvLh1/Rj4qaQZwGzgjNI2bGbvENw/mhrec4lv2vwSQWnovRIWf4ugJ+Ki6zQz+6OZrStt20UcBQwAfhd3D6h1OG0g8ME+rMu5fea9UztXDAUPDMvZxwt6ebb3a6CRmd1WyjyTgVPNbFOSYjgUuM7MLkrG+p2L8Xs8zkVM0r+Ag4Djypj1eoLWdklJPEBzoMTE51xF8RKPc865lPJ7PM4551LKE49zzrmU8sTjnHMupTzxOOecSylPPM4551Lq/wGjgpLMP6KYlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plot_by_id(run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset will have two items, one for \"capacitance\", and the other for \"dissipation_factor\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'capacitance': {'capacitance': array([-8.35219e-09, -8.34623e-09, -8.41993e-09, -8.45158e-09,\n",
       "         -8.63672e-09]),\n",
       "  'lcr_e4980a_frequency': array([1000000, 1125000, 1250000, 1375000, 1500000])},\n",
       " 'dissipation_factor': {'dissipation_factor': array([-0.00183439, -0.00214259, -0.00246672, -0.00283031, -0.00317048]),\n",
       "  'lcr_e4980a_frequency': array([1000000, 1125000, 1250000, 1375000, 1500000])}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = datasaver.dataset\n",
    "dataset.get_parameter_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To switch to another measurement function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RX'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meter.measurement_function(E4980AMeasurements.RX)\n",
    "meter.measurement_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which will measure the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('resistance', 'reactance')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E4980AMeasurements.RX.names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to re-set the measurement, and re-register parameters so that qcodes knows the correct units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x1f35934db88>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement()\n",
    "meas.register_parameter(meter.frequency)\n",
    "meas.register_parameter(meter.measurement, setpoints=(meter.frequency,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 74. \n"
     ]
    }
   ],
   "source": [
    "with meas.run() as datasaver:\n",
    "    for freq in np.linspace(1.0E6, 1.5E6, 5):\n",
    "        meter.frequency(freq)\n",
    "        time.sleep(1)\n",
    "        value_pair = meter.measurement()\n",
    "        datasaver.add_result((meter.frequency, freq),\n",
    "                             (meter.measurement, value_pair))\n",
    "    run_id = datasaver.run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = plot_by_id(run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "meter.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.5"
  },
  "nbsphinx": {
   "execute": "never"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}