doc/source/examples/02CreatePrograms.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Instantiating Pulses: Obtaining Pulse Instances From Pulse Templates\n",
    "\n",
    "In the previous examples, we have modelled pulses using the basic members of qupulse's `PulseTemplate` class hierarchy. However, these are only templates (or classes) of pulses and may contain parameters so that they cannot be run directly on hardware (this is also the reason why we always have to provide some parameters during plotting). First, we have to instantiate a concrete pulse in a process we call *instantiating*. We achieve this by making use of the `create_program()` method and will need to provide concrete parameter values.\n",
    "\n",
    "The example should be mostly self-contained and easy to follow, however, if you started here and don't know what pulse templates are and how to create them, maybe it's best to have a look at [Modelling a Simple TablePulseTemplate](00SimpleTablePulse.ipynb) first.\n",
    "\n",
    "To start, let us first create a pulse template with a few parameters and two channels.\n",
    "## Instantiating a TablePulse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qupulse.plotting import plot\n",
    "from qupulse.pulses import TablePT\n",
    "\n",
    "template = TablePT(entries={'A': [(0, 0),\n",
    "                                  ('ta', 'va', 'hold'),\n",
    "                                  ('tb', 'vb', 'linear'),\n",
    "                                  ('tend', 0, 'jump')],\n",
    "                            'B': [(0, 0),\n",
    "                                  ('ta', '-va', 'hold'),\n",
    "                                  ('tb', '-vb', 'linear'),\n",
    "                                  ('tend', 0, 'jump')]}, measurements=[('m', 0, 'ta'),\n",
    "                                                                       ('n', 'tb', 'tend-tb')])\n",
    "\n",
    "parameters = {'ta': 2,\n",
    "              'va': 2,\n",
    "              'tb': 4,\n",
    "              'vb': 3,\n",
    "              'tc': 5,\n",
    "              'td': 11,\n",
    "              'tend': 6}\n",
    "_ = plot(template, parameters, sample_rate=100, show=False, plot_measurements={'m', 'n'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `HardwareSetup` class represents the actual hardware and interfaces to the devices in qupulse. It is thus responsible for uploading to and executing pulses on the hardware. To do so it currently expects an instantiated pulse which is represented by `Loop` objects. These can be obtained by plugging the desired parameters into the `create_program` method of your `PulseTemplate` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOOP 1 times:\n",
      "  ->EXEC MultiChannelWaveform((TableWaveform(channel='A', waveform_table=(TableWaveformEntry(t=0.0, v=0, interp=<Hold Interpolation>), TableWaveformEntry(t=2, v=2, interp=<Hold Interpolation>), TableWaveformEntry(t=4, v=3, interp=<Linear Interpolation>), TableWaveformEntry(t=6, v=0, interp=<Jump Interpolation>))), TableWaveform(channel='B', waveform_table=(TableWaveformEntry(t=0.0, v=0, interp=<Hold Interpolation>), TableWaveformEntry(t=2, v=-2, interp=<Hold Interpolation>), TableWaveformEntry(t=4, v=-3, interp=<Linear Interpolation>), TableWaveformEntry(t=6, v=0, interp=<Jump Interpolation>))))) 1 times\n",
      "Defined on frozenset({'B', 'A'})\n",
      "{'m': (array([0.]), array([2.])), 'n': (array([4.]), array([2.]))}\n"
     ]
    }
   ],
   "source": [
    "program = template.create_program(parameters=parameters,\n",
    "                                  channel_mapping={'A': 'A', 'B': 'B'})\n",
    "\n",
    "print(program)\n",
    "print('Defined on', program[0].waveform.defined_channels)\n",
    "print(program.get_measurement_windows())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output shows us that a simple `Loop` object was created which just executes a single waveform without repetitions, just as our `PulseTemplate` specifies. In the `Loop` object all parameter references from the template have been resolved and replaced by the values provided in the `parameters` dictionary, so this is our pulse ready to be executed on the hardware.\n",
    "\n",
    "### Mapping Channels and Measurements During Instantiation\n",
    "\n",
    "The `channel_mapping` keyword argument allows us to rename channels or to drop them by mapping them to `None`. We can do the same to measurements using the `measurement_mapping` keyword argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOOP 1 times:\n",
      "  ->EXEC TableWaveform(channel='Y', waveform_table=(TableWaveformEntry(t=0.0, v=0, interp=<Hold Interpolation>), TableWaveformEntry(t=2, v=-2, interp=<Hold Interpolation>), TableWaveformEntry(t=4, v=-3, interp=<Linear Interpolation>), TableWaveformEntry(t=6, v=0, interp=<Jump Interpolation>))) 1 times\n",
      "Defined on {'Y'}\n",
      "{'foo': (array([0.]), array([2.]))}\n"
     ]
    }
   ],
   "source": [
    "program = template.create_program(parameters=parameters,\n",
    "                                  channel_mapping={'A': None, 'B': 'Y'},\n",
    "                                  measurement_mapping={'m': 'foo', 'n': None})\n",
    "print(program)\n",
    "print('Defined on', program[0].waveform.defined_channels)\n",
    "print(program.get_measurement_windows())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiating Composed Pulses\n",
    "\n",
    "Let's have a brief look at a slightly more complex pulse. Say we want to repeat our previous pulse a few times and follow it up with a brief sine wave on each channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOOP 1 times:\n",
      "  ->LOOP 4 times:\n",
      "      ->EXEC MultiChannelWaveform((TableWaveform(channel='A', waveform_table=(TableWaveformEntry(t=0.0, v=0, interp=<Hold Interpolation>), TableWaveformEntry(t=2, v=2, interp=<Hold Interpolation>), TableWaveformEntry(t=4, v=3, interp=<Linear Interpolation>), TableWaveformEntry(t=6, v=0, interp=<Jump Interpolation>))), TableWaveform(channel='B', waveform_table=(TableWaveformEntry(t=0.0, v=0, interp=<Hold Interpolation>), TableWaveformEntry(t=2, v=-2, interp=<Hold Interpolation>), TableWaveformEntry(t=4, v=-3, interp=<Linear Interpolation>), TableWaveformEntry(t=6, v=0, interp=<Jump Interpolation>))))) 1 times\n",
      "  ->EXEC MultiChannelWaveform((FunctionWaveform(duration=TimeType(6283, 1000), expression=ExpressionScalar('sin(t)'), channel='A'), FunctionWaveform(duration=TimeType(6283, 1000), expression=ExpressionScalar('2*sin(t)'), channel='B'))) 1 times\n",
      "{'m': (array([ 0.,  6., 12., 18.]), array([2., 2., 2., 2.])), 'n': (array([ 4., 10., 16., 22.]), array([2., 2., 2., 2.]))}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Simon\\Documents\\git\\qupulse\\qupulse\\plotting.py:186: UserWarning: Sample count 30293/10 is not an integer. Will be rounded (this changes the sample rate).\n",
      "  times, voltages, measurements = render(program,\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qupulse.pulses import FunctionPT, SequencePT, RepetitionPT, AtomicMultiChannelPT\n",
    "\n",
    "repeated_template = RepetitionPT(template, 'n_rep')\n",
    "sine_template = FunctionPT('sin_a*sin(t)', '2*3.1415')\n",
    "two_channel_sine_template = AtomicMultiChannelPT(\n",
    "                                (sine_template, {'default': 'A'}), \n",
    "                                (sine_template, {'default': 'B'}, {'sin_a': 'sin_b'})\n",
    ")\n",
    "sequence_template = SequencePT(repeated_template, two_channel_sine_template)\n",
    "\n",
    "sequence_parameters = dict(parameters) # we just copy our parameter dict from before\n",
    "sequence_parameters['n_rep'] = 4       # and add a few new values for the new params from the sine wave\n",
    "sequence_parameters['sin_a'] = 1\n",
    "sequence_parameters['sin_b'] = 2\n",
    "\n",
    "_ = plot(sequence_template, parameters=sequence_parameters, sample_rate=100, show=False)\n",
    "sequence_program = sequence_template.create_program(parameters=sequence_parameters, \n",
    "                                                    channel_mapping={'A': 'A', 'B': 'B'})\n",
    "print(sequence_program)\n",
    "print(sequence_program.get_measurement_windows())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, our `Loop` now contains an inner `Loop` object which repeats a waveform four times and additionally executes another waveform. This reflects the structure of our pulse template. Note also that the single measurement window defined by our pulse template `template` is repeated four times as well in the `Loop` object, according to the number of repetitions of the corresponding pulse.\n",
    "\n",
    "Don't worry too much about the inner workings of the `Loop` objects, though. We were just taking a short look at them here. In practice it will be sufficient to just obtain them using the `create_program` method of `PulseTemplate` and pass them on to `HardwareSetup` when required."
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}