docs/examples/driver_examples/QCodes example with Tektronix DPO 72004C.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QCoDeS example with Textronix DPO 7200xx scopes \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcodes.dataset.measurements import Measurement\n",
    "from qcodes.dataset.plotting import plot_by_id\n",
    "from qcodes.dataset.experiment_container import new_experiment\n",
    "\n",
    "from qcodes.instrument_drivers.tektronix.DPO7200xx import TektronixDPO7000xx, TektronixDPOMeasurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to: TEKTRONIX DPO72004C (serial:C600862, firmware:CF:91.1CT FV:10.9.1 Build 16) in 0.66s\n"
     ]
    }
   ],
   "source": [
    "tek = TektronixDPO7000xx(\"tek3\", \"TCPIP0::169.254.158.44::inst0::INSTR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = new_experiment(name='DPO_72000_example', sample_name=\"no sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aqcuiring traces "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, determine the number of samples we wish to acquire\n",
    "tek.channel[0].set_trace_length(1000)\n",
    "# alternatively, we can set the time over which we \n",
    "# wish to acquire a trace (uncomment the following line): \n",
    "# tek.channel[0].set_trace_time(4E-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axes._subplots.AxesSubplot at 0x2725f9cde10>,\n",
       "  <matplotlib.axes._subplots.AxesSubplot at 0x2725fc95c88>],\n",
       " [None, None])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2725f9a8518>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2725fc95240>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meas = Measurement(exp=experiment)\n",
    "meas.register_parameter(tek.channel[0].waveform.trace)\n",
    "meas.register_parameter(tek.channel[1].waveform.trace)\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for i in [0, 1]:\n",
    "        datasaver.add_result(\n",
    "            (tek.channel[i].waveform.trace_axis, tek.channel[i].waveform.trace_axis()),\n",
    "            (tek.channel[i].waveform.trace, tek.channel[i].waveform.trace())\n",
    "        )\n",
    "\n",
    "    dataid = datasaver.run_id\n",
    "\n",
    "plot_by_id(dataid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be something wrong with the `plot_by_id` method. Fixing this is beyond the scope of this PR. Below we show that the driver works properly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2725fe33f60>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2725f9a8e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "plt.plot(\n",
    "    tek.channel[i].waveform.trace_axis(), \n",
    "    tek.channel[i].waveform.trace()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the waveform format \n",
    "\n",
    "If we wish, we can change the way in which data is retrieved from the instrument, which can enhance the precision of the data and the speed to retrieval. \n",
    "\n",
    "We do this through the 'waveform' module on the main driver (e.g. `tek.waveform`) as opposed to the 'waveform' module on a channel (e.g. `tek.channel[0].waveform`). We have this distinction because the waveform formatting parameters effect all waveform sources (e.g. channel 0 or channel 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'signed_integer'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.waveform.data_format()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Enum: {'unsigned_integer', 'floating_point', 'signed_integer'}>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The available formats \n",
    "tek.waveform.data_format.vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.waveform.is_big_endian()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.waveform.bytes_per_sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Enum: {8, 1, 2, 4}>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.waveform.bytes_per_sample.vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.waveform.is_binary()\n",
    "# Setting is_binary to false will transfer data in ascii mode. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trigger setup \n",
    "\n",
    "The `tek.trigger` module is the 'main' trigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CH1'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.trigger.source()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'edge'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.trigger.type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fall'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.trigger.edge_slope()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tek.trigger.edge_slope(\"fall\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delayed trigger \n",
    "\n",
    "You can trigger with the Main trigger system alone or combine the Main trigger with the Delayed trigger to trigger on sequential events. When using sequential triggering, the Main trigger event arms the trigger system, and the Delayed trigger event triggers the instrument when the Delayed trigger conditions are met.\n",
    "\n",
    "Main and Delayed triggers can (and typically do) have separate sources. The Delayed trigger condition is based on a time delay or a speciﬁed number of events.\n",
    "\n",
    "See page75, Using Main and Delayed triggers of the [manual](https://download.tek.com/manual/MSO70000C-DX-DPO70000C-DX-MSO-DPO7000C-MSO-DPO5000B-Oscilloscope-Quick-Start-User-Manual-071298006.pdf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CH1'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.delayed_trigger.source()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'edge'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tek.delayed_trigger.type()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etc... The main and delayed triggers have the same parameters. However, the accepted values of these parameters might differ. Please see the above manual for details. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measurements\n",
    "\n",
    "The scope also has a measurement module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tek.measurement[0].source1(\"CH1\")\n",
    "tek.measurement[0].source2(\"CH2\")\n",
    "tek.measurement[1].source1(\"CH2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of signal at channel CH1: 9.96E+09 Hz\n"
     ]
    }
   ],
   "source": [
    "channel = tek.measurement[0].source1()\n",
    "value = tek.measurement[0].frequency()\n",
    "unit = tek.measurement[0].frequency.unit\n",
    "\n",
    "print(f\"Frequency of signal at channel {channel}: {value:.2E} {unit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of signal at channel CH2: 9.98E+09 Hz\n"
     ]
    }
   ],
   "source": [
    "channel = tek.measurement[1].source1()\n",
    "value = tek.measurement[1].frequency()\n",
    "unit = tek.measurement[1].frequency.unit\n",
    "\n",
    "print(f\"Frequency of signal at channel {channel}: {value:.2E} {unit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amplitude of signal at channel CH1: 2.84E-01 V\n"
     ]
    }
   ],
   "source": [
    "channel = tek.measurement[0].source1()\n",
    "value = tek.measurement[0].amplitude()\n",
    "unit = tek.measurement[0].amplitude.unit\n",
    "\n",
    "print(f\"Amplitude of signal at channel {channel}: {value:.2E} {unit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amplitude of signal at channel CH2: 1.88E-01 V\n"
     ]
    }
   ],
   "source": [
    "channel = tek.measurement[1].source1()\n",
    "value = tek.measurement[1].amplitude()\n",
    "unit = tek.measurement[1].amplitude.unit\n",
    "\n",
    "print(f\"Amplitude of signal at channel {channel}: {value:.2E} {unit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phase of signal at channel CH1 wrt channel CH2: 25.50738557245 °\n"
     ]
    }
   ],
   "source": [
    "channel1 = tek.measurement[0].source1()\n",
    "channel2 = tek.measurement[0].source2()\n",
    "value = tek.measurement[0].phase()\n",
    "unit = tek.measurement[0].phase.unit\n",
    "\n",
    "print(f\"Phase of signal at channel {channel1} wrt channel {channel2}: {value} {unit}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all the availble measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('amplitude, area, burst, carea, cmean, crms, delay, distduty, extinctdb, '\n",
      " 'extinctpct, extinctratio, eyeheight, eyewidth, fall, frequency, high, hits, '\n",
      " 'low, maximum, mean, median, minimum, ncross, nduty, novershoot, nwidth, '\n",
      " 'pbase, pcross, pctcross, pduty, peakhits, period, phase, pk2pk, pkpkjitter, '\n",
      " 'pkpknoise, povershoot, ptop, pwidth, qfactor, rise, rms, rmsjitter, '\n",
      " 'rmsnoise, sigma1, sigma2, sigma3, sixsigmajit, snratio, stddev, undefined, '\n",
      " 'waveforms')\n"
     ]
    }
   ],
   "source": [
    "from pprint import pprint\n",
    "print_string = \", \".join([i[0] for i in TektronixDPOMeasurement.measurements])\n",
    "pprint(print_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measurement statistics\n",
    "\n",
    "We can measure basic measurement statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean amplitude of signal at channel CH2: 0.284 V\n"
     ]
    }
   ],
   "source": [
    "channel = tek.measurement[0].source1()\n",
    "value = tek.measurement[0].amplitude.mean()\n",
    "unit = tek.measurement[0].amplitude.unit\n",
    "\n",
    "print(f\"The mean amplitude of signal at channel {channel}: {value} {unit}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same with all the measurement which are supported by the instrument. The following statistics are available: `mean`, `max`, `min`, `stdev`\n",
    "\n",
    "### Statistics control\n",
    "\n",
    "A seperate module controls statistics gathring: `tek.statistics`. For instance, The oscilloscope gathers statistics over a set of measurement values which are stored in a buffer. We can reset this buffer like so... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tek.statistics.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following parameters are available for staistics control: \n",
    "\n",
    "1. `mode`: This command controls the operation and display of measurement statistics and accepts the following arguments: `OFF` turns off all measurements. This is the default value. `ALL` turns on statistics and displays all statistics for each measurement. `VALUEMean` turns on statistics and displays the value and the mean (μ) of each measurement. `MINMax` turns on statistics and displays the min and max of each measurement. `MEANSTDdev` turns on statistics and displays the mean and standard deviation\n",
    "of each measurement.\n",
    "2. `time_constant`: This command sets or queries the time constant for mean and standard deviation statistical accumulations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future work \n",
    "\n",
    "The DPO7200xx scopes have support for mathematical operations. An example of a math operation is a spectral analysis. Although the current QCoDeS driver does not (fully) support these operations, the way the driver code has been factored should make it simple to add support if future need arrises. \n",
    "\n",
    "An example: we can manually add a spectrum analysis by selecting \"math\" -> \"advanced spectral\" from the oscilloscope menu in the front display of the instrument. After manual creation, we can retrieve spectral data with the driver as follows: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcodes.instrument_drivers.tektronix.DPO7200xx import TekronixDPOWaveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "math_channel = TekronixDPOWaveform(tek, \"math\", \"MATH1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axes._subplots.AxesSubplot at 0x2725fa022b0>], [None])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2725fe4f780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meas = Measurement(exp=experiment)\n",
    "meas.register_parameter(math_channel.trace)\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    datasaver.add_result(\n",
    "        (math_channel.trace_axis, math_channel.trace_axis()),\n",
    "        (math_channel.trace, math_channel.trace())\n",
    "    )\n",
    "\n",
    "    dataid = datasaver.run_id\n",
    "\n",
    "plot_by_id(dataid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to fully support math operations and spectral analysis, we need code to add a math function through the QCoDeS driver, rather than manually. Additionally, we need to be able to adjust the frequency ranges and possibly other relevant parameters.\n",
    "\n",
    "As always, contributions are more then welcome! :-)"
   ]
  },
  {
   "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": 2
}