{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The ConstantPulseTemplate: Efficient constant voltage description.\n",
    "\n",
    "The `ConstantPulseTemplate`(or short `ConstantPT`) can be used to define pulse templates with all channels a constant value. The template is easy to define and allows backends to optimize the waveforms on an AWG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'B', 'A'}\n",
      "{'A': ExpressionScalar('10.0000000000000'), 'B': ExpressionScalar('1.0*b')}\n"
     ]
    }
   ],
   "source": [
    "from qupulse.pulses import ConstantPT\n",
    "\n",
    "constant_template = ConstantPT(10, {'A': 1., 'B': 'b * 0.1'})\n",
    "\n",
    "print(constant_template.defined_channels)\n",
    "print(constant_template.integral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pulse template has two channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from qupulse.pulses.plotting import plot\n",
    "\n",
    "_ = plot(constant_template, parameters={'b': 2.2}, sample_rate=100)"
   ]
  }
 ],
 "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}