{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QCoDeS Example with QDevil_QDAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qcodes as qc\n",
    "import numpy as np\n",
    "from time import sleep\n",
    "import qcodes.instrument_drivers.QDevil.QDevil_QDAC as QDac\n",
    "from qcodes.instrument_drivers.QDevil.QDevil_QDAC import Mode"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialisation\n",
    "When initialised, the driver will read in the state of the QDAC to the extend spossible. This means that initialisation does not change any outputs, implying that any ongoing ramping is continuing undisturbed. The initialisation is, however, **not** able to figure out if any **slopes** have been assigned to channels, see later. So these will have to be set again by the user. \n",
    "\n",
    "During the initialisation all channels are queried in order to update the parmeter cache. However, as the current query is very slow due to the long current sensor integration time, it is optional to update currents. They are only read at startup if the **update_currents** flag is set to True.  \n",
    "\n",
    "NOTE: After switching the QDAC off and back on the driver has to be re-initialised, OR the reset() command has to be executed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to the instrument\n",
    "# By default the initialisation skips reading the current sensors on all channels\n",
    "# as this takes some 0.2-0.5 secs per channel due to the sensor settling time. \n",
    "# You can force reading the current sensors at startup by specifiying \"update_currents=True\" in the call.\n",
    "\n",
    "qdac = QDac.QDac(name='qdac', address='ASRL2::INSTR', update_currents=False)\n",
    "print(\"Number of channels: \",qdac.num_chans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic QDAC Usage\n",
    "\n",
    "The QDevil_QDAC driver supports controlling each individual BNC output channel. Each output channel \"qdac.ch##.\" has seven attributes:\n",
    "  * v: DC voltage\n",
    "  * i: Current out (read-only)\n",
    "  * mode: the combined voltage output and current sensor range: Mode.vhigh_ihigh, Mode.vhigh_ilow, Mode.vlow_ilow\n",
    "  * slope: Maximum ramp rate for an output channel when changing teh DC voltage,v.\n",
    "  * sync: Sync output assigned to a channel \n",
    "  * sync_delay: Sync pulse delay  \n",
    "  * sync_duration: Sync pulse duration\n",
    "  \n",
    "The slope is the (maximal) slope in V/s that the channel will allow its voltage to change by. By default, all channels have a slope of \"Inf\". The slope can be changed dynamically, but no more than 8 channels can be ramped simultaneously.\n",
    "\n",
    "In addition this driver supports:\n",
    "   * Reset the QDAC to start up conditions. Allows continuing operation after the QDAC has been \n",
    "     powered off/on without restarting teh driver\n",
    "   * Simultaneous ramping of up to 8 channels\n",
    "   * 2D ramping of two groups of channels (slow and fast channels, up to 8 in total)\n",
    "   * Override of protection against mode change for non-zero output voltages\n",
    "   * Reading the internal temperature sensors \n",
    "   * Pretty printing the state of all channels, of assigned sync outputs and of assigned slopes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting voltages and reading currents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the output voltage of a channel using \"set\"\n",
    "qdac.ch01.v.set(1)\n",
    "# Reading the output voltage of a channel using \"get\"\n",
    "print('Channel 1 voltage: {} {}'.format(qdac.ch01.v.get(), qdac.ch01.v.unit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the output voltage of a channel using short-hand notation, which is used hereafter\n",
    "qdac.ch01.v(-1)\n",
    "# Reading the output voltage of a channel using short hand notion \"qdac.ch01.v()\", which is used hereafter\n",
    "print('Channel 1 voltage: {} {}'.format(qdac.ch01.v(), qdac.ch01.v.unit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the current output of a channel  \n",
    "print(qdac.ch01.i(), qdac.ch01.i.unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smooth ramping between voltages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For smooth voltage changes the maximal voltage change (in V/s) may be set for each channel\n",
    "qdac.ch01.slope(1)\n",
    "qdac.ch02.slope(2)\n",
    "# An overview may be printed (all other channels have 'Inf' slope)\n",
    "qdac.print_slopes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now setting channel 1 and 2 voltages will cause slow ramping to 0V (1 V/s and 2 V/s, respectively)\n",
    "# Note that ch02 is already at 0 V, so the ramping function will complain bacause a ramp time\n",
    "# less than 2 ms is not possible.\n",
    "qdac.ch01.v(0)\n",
    "qdac.ch02.v(0)\n",
    "sleep(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that only 8 (or fewer) channels can be slow ramped at a time\n",
    "# To disable slow ramping of a channel, set its slope to 'Inf':\n",
    "qdac.ch01.slope('Inf')\n",
    "qdac.print_slopes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Addressing multiple channels\n",
    "Multiple channels can be addressed simultaneously via the 'channels' list, by use of slicing.\n",
    "Note that numbering goes from 0 to N-1, where N is the number of channels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will query voltages of all channels of a 24 channel QDAC \n",
    "# Note that index 0 refer to channel 01, and so on \n",
    "print(qdac.channels[0:8].v())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similarly, we may set them. The outputs will not change simultaneously but witin some milliseconds.\n",
    "qdac.channels[0:8].v(-0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output a SYNC pulse\n",
    "The QDAC can output a puls one one of the SYNC outputs when a channel is ramped, either using the \"slope\" functionality, see above, or when using one of the ramping functions below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To each channel one may assign a SYNC output\n",
    "# SYNC output 1 will fire a 10 ms 5 V pulse when ch02 initiates a ramp\n",
    "# ch will ramp when setting a voltage while a slope is assinged, or when using \"ramp_voltages\"\n",
    "qdac.ch02.sync(1)  \n",
    "# note that a pulse is still fired even if no visible ramp is performed\n",
    "# e.g if ramping from 1 V to 1 V\n",
    "\n",
    "# The sync pulse settings can be modified\n",
    "qdac.ch02.sync_delay(0)  # The sync pulse delay (s)\n",
    "qdac.ch02.sync_duration(25e-3)  # The sync pulse duration (secs). Default is 10 ms.\n",
    "\n",
    "# Print an overview of assigned SYNC ports\n",
    "qdac.print_syncs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plug in an oscilloscope to CH02 and SYNC1 and observe the ramping and the sync pulse\n",
    "qdac.ch02.slope(1)\n",
    "qdac.ch02.v(-0.5)\n",
    "sleep(3)\n",
    "qdac.ch02.v(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# syncs are unassigned by assigning sync 0\n",
    "qdac.ch02.sync(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ramp one or more channels simultaneously \n",
    "Setting several channels simutaneously is only possible using \"ramp_voltages\". Note that "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here we ramp channels 1, 2, 3, and 7 from there current values to zero, in 0.2 seconds\n",
    "duration = qdac.ramp_voltages([1,2,3,7],[],[0,0,0,0],0.2)\n",
    "sleep(duration+0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As it takes tens of milliseconds to read the channels' current voltage, it is faster \n",
    "# if their previous voltages are known:\n",
    "duration = qdac.ramp_voltages([1,2,3,7],[0,0,0,0],[1,2,3,4],0.2)\n",
    "sleep(duration+0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ramp a single channel step by step and record and plot the current sensor reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform a 1D scan of the QDAC ch01 and record the current on\n",
    "# the same channel also using the QDAC. \n",
    "# Replace the QDAC current measurement by a DMM to do a typical physical measurement\n",
    "\n",
    "from qcodes.dataset.plotting import plot_by_id\n",
    "from qcodes.dataset.measurements import Measurement\n",
    "from time import ctime\n",
    "STATION = qc.station.Station(qdac)\n",
    "qc.new_experiment(\"QDAC\", \"TestIV\"+ctime())\n",
    "meas = Measurement()\n",
    "meas.register_parameter(qdac.ch01.v)  # register the independent parameter\n",
    "meas.register_parameter(qdac.ch01.i, setpoints=(qdac.ch01.v,))  # now register the dependent one\n",
    "meas.write_period = 2\n",
    "with meas.run() as datasaver:\n",
    "    for set_v in np.linspace(-1, 1, 10):\n",
    "        qdac.ch01.v(set_v)\n",
    "        sleep(0.1)\n",
    "        get_i = qdac.ch01.i()\n",
    "        datasaver.add_result((qdac.ch01.v, set_v),\n",
    "                            (qdac.ch01.i, get_i))\n",
    "        print(set_v, get_i)\n",
    "    dataset = datasaver.dataset\n",
    "myplot = plot_by_id(dataset.run_id)\n",
    "qc.dataset.plotting.plt.show()      # Sometimes it is necessasry to out-comment this line in Jupyter...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D scan \n",
    "It is possible to ramp two groups of channels simultaneously. This is useful for a 2D data acquisition setup\n",
    "Note! The slope definitions are not used during the 2D scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set outputs to zero\n",
    "qdac.ch01.v(0)\n",
    "qdac.ch02.v(0)\n",
    "qdac.ch03.v(0)\n",
    "\n",
    "# enable sync on one of the fast channels (sync signal is output at every start of a staircase ramp.)\n",
    "# for example for triggering a digitizer\n",
    "qdac.ch02.sync(1)\n",
    "# enable a 10ms sync delay which allows for stabilizing of the device\n",
    "qdac.ch02.sync_delay(0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note! The slope definitions are not used during the 2D scan\n",
    "duration = qdac.ramp_voltages_2d( slow_chans=[1], slow_vstart=[0], slow_vend=[1],\n",
    "                                  fast_chans=[2,3], fast_vstart=[0,0], fast_vend=[1,-1],\n",
    "                                  slow_steps = 10, fast_steps = 10,\n",
    "                                  step_length=0.02)\n",
    "# wait for the ramp to finish\n",
    "sleep(duration+0.1)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"attachment:image.png\" width=\"400\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qdac.print_syncs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set outputs back to zero\n",
    "# First remove sync output so that we do not trigger an acquisition\n",
    "qdac.ch02.sync(0)\n",
    "qdac.ch01.v.set(0)\n",
    "qdac.ch02.v.set(0)\n",
    "qdac.ch03.v.set(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The \"mode\" parameter: controlling voltage and current ranges: \n",
    "The \"mode\" parameter is controlling the output voltage range (by an attenuator) and the current sensor range. Only certain combinations of the two are allowed, which is why they are conrolled by a single parameter. The mode parameter is allowed values are:\n",
    "\n",
    "Mode.vhigh_ihigh : high voltage output range / high current sensing range\n",
    "\n",
    "Mode.vhigh_ilow  : high voltage output range / low current sensing range\n",
    "\n",
    "Mode.vlow_ilow   : low voltage output range / low current sensing range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The \"QDac.Mode\" enum class is used for setting and reading the mode. \n",
    "\n",
    "# This will set the voltage output range to low, and the current sensor range to low\n",
    "qdac.ch01.mode(Mode.vlow_ilow)\n",
    "print(qdac.ch01.mode.cache().get_label())\n",
    "\n",
    "# This will return ch01 to the default mode: high voltage range, high current sensing range\n",
    "qdac.ch01.mode(Mode.vhigh_ihigh)\n",
    "print(qdac.ch01.mode.cache().get_label())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### When \"mode\" change results in change of voltage range\n",
    "\n",
    "When changing \"mode\" so that the voltage range is changed the attenuator is switched immidiately. The driver will re-adjust the output voltage in order to keep it constant, but a spike will always occur if the voltage is non-zero. If the set voltage is outside the range of the low range and the transition is from high to low range, the output will be clipped. \n",
    "\n",
    "To avoid spikes, the driver by default **does not allow changing the voltage range (mode)** when the output is non-zero. To over-ride this protection, set qdac.mode_force(True)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is a small example showing demonstrating the behavior - if posible hook up an oscilloscope on ch01\n",
    "#\n",
    "qdac.ch01.slope('Inf')                # Make sure that we are not fooled by a slow changing ch01   \n",
    "qdac.ch01.mode(Mode.vhigh_ihigh)      # Attenuation OFF (the default), high voltage range\n",
    "qdac.ch01.v(1.5)                      # Set the voltage to outside the low voltage range (but inside present range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qdac.mode_force(True)            # Enable changing voltage range eventhough the output is non-zero\n",
    "qdac.ch01.mode(Mode.vlow_ilow)   # Attenuation ON, low voltage range - signal is clipped, and a dip occurred\n",
    "print(qdac.ch01.v())            # Returns approximately 1.1V as the output is clipped to the low range limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qdac.ch01.mode(Mode.vhigh_ihigh)      # Attenuation off, high voltage range\n",
    "print(qdac.ch01.v())                  # Returns approximately 1.1V, unchanged - but a spike occured"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return to protected mode\n",
    "qdac.mode_force(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now provoke an error\n",
    "print(qdac.ch01.v())\n",
    "print(qdac.ch01.mode.cache().get_label())     # Pretty printing the mode parameter\n",
    "try:\n",
    "    qdac.ch01.mode(Mode.vlow_ilow)\n",
    "except ValueError as ve:\n",
    "    print(\"ERROR: \", ve)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of channels\n",
    "\n",
    "The driver provides a method for pretty-printing the state of all channels\n",
    "\n",
    "The pretty-print method may or may not **update** the values for the currents, depending on the value of the `update_currents` flag. Each current reading takes some 200-500 ms, so updating all current values takes about 14-24 seconds depending on the number of channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qdac.print_overview(update_currents=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temperature sensors\n",
    "Physically, the QDac consists of either three or six boards each hosting eight channels. For diagnostics purposes temperature sensors are placed at three locations on each board,. Read-only parameters for these sensors are provided, named tempX_Y where X is the board number (0-2, or 0-5) and Y the sensor number (0-2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(qdac.temp0_0.get(), qdac.temp0_0.unit)\n",
    "print(qdac.temp2_1.get(), qdac.temp0_0.unit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resetting the driver and the QDAC\n",
    "To get to a well defined state, the QDAC should be powered off and then on before starting this driver.\n",
    "\n",
    "Alternatively the reset command can be executed. The reset command can also be used to recover an off/on situation of the QDAC without having to restart the driver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qdac.reset(update_currents=False)\n",
    "# Then print the overview gain\n",
    "qdac.print_overview(update_currents=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shut down the VISA connection\n",
    "qdac.close()"
   ]
  }
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