{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using `datasaver_builder` and `dond_into` to streamline measurements\n",
    "\n",
    "This example notebook shows examples of how to use the `datasaver_builder` and `dond_into` extensions together. It showcases these as an intermediate abstraction layer between the low-level `Measurement` object and the high-level `doNd` routines.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Here, we call necessary imports for running this notebook, as well as setting up a database, dummy parameters, and creating an experiment object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Module Imports\n",
    "These are the main components we'll be looking at in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcodes.dataset import DataSetDefinition, LinSweeper, datasaver_builder, dond_into"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import qcodes as qc\n",
    "from qcodes.dataset import LinSweep, Measurement, plot_dataset\n",
    "from qcodes.parameters import Parameter, ParameterWithSetpoints\n",
    "from qcodes.validators import Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up database and experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_path = \"measurement_extensions.db\"\n",
    "qc.initialise_or_create_database_at(db_path)\n",
    "experiment = qc.load_or_create_experiment(\"Examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Parameter Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "set1 = Parameter(\"set1\", get_cmd = None, set_cmd=None, initial_value=0)\n",
    "set2 = Parameter(\"set2\",  get_cmd = None, set_cmd=None, initial_value=0)\n",
    "set3 = Parameter(\"set3\",  get_cmd = None, set_cmd=None, initial_value=0)\n",
    "\n",
    "\n",
    "def get_set1():\n",
    "    return set1()\n",
    "\n",
    "\n",
    "def get_sum12():\n",
    "    return set1() + set2()\n",
    "\n",
    "\n",
    "def get_diff13():\n",
    "    return set1() - set3()\n",
    "\n",
    "\n",
    "meas1 = Parameter(\"meas1\", get_cmd=get_set1, set_cmd=False)\n",
    "meas2 = Parameter(\"meas2\", get_cmd=get_sum12, set_cmd=False)\n",
    "meas3 = Parameter(\"meas3\", get_cmd=get_diff13, set_cmd=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ParameterWithSetpoints Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_setpoints_array():\n",
    "    return np.linspace(-5, 5, 11)\n",
    "\n",
    "\n",
    "def get_pws_results():\n",
    "    setpoints_arr = get_setpoints_array()\n",
    "    return setpoints_arr**2 * set1()\n",
    "\n",
    "\n",
    "setpoint_array = Parameter(\"setpoints\",\n",
    "                            get_cmd = get_setpoints_array,\n",
    "                            vals=Arrays(shape=(11,))\n",
    "                            )\n",
    "\n",
    "pws = ParameterWithSetpoints(\"pws\",\n",
    "                             setpoints = (setpoint_array,),\n",
    "                             get_cmd = get_pws_results,\n",
    "                             vals=Arrays(shape=(11,))\n",
    "                             )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the `datasaver_builder`\n",
    "\n",
    "The `datasaver_builder` is a shortcut to creating multiple qcodes `Measurement` objects, registering the relevant parameters and dependencies, and entering the datasaver context managers.\n",
    "\n",
    "This process begins with the `DataSetDefinition`, a dataclass specifying the name of the dataset, the independent parameters, the dependent parameters, and optionally the experiment to write the dataset to. If the experiment kwarg is omitted, Qcodes will write to the default experiment. Note that the `datasaver_builder` assumes that all dependent parameters depend on all independent parameters. This is less flexible than the raw `Measurement` object, but covers the most common measurement types.\n",
    "\n",
    "Once the datasets have been defined, we enter the `datasaver_builder` by passing the list or tuple of `DataSetDefinition` objects.\n",
    "\n",
    "When we enter the context manager, we get a list of the respective datasavers, in an order which matches the order passed. These can be accessed like any other `DataSaver`, and arbitrary code can be executed in whatever order is needed. When the measurement is complete, these datasavers can then be assigned to datasets which can be plotted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 1. \n",
      "Starting experimental run with id: 2. \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ukHSrpGWSjp6A8F0DN7N2KrkXylrgMxFxk6QNgBslXR4Rt5a1gaE4gZtZa5XVhBIRDwEPpcerJN0GzAacwM3MytbhPTFnSVqSm++NiN6hXihpHrALcP34IhydE7iZtVIAa4vXwFdExILRXiRpJnA+cExEPDWO8ApxAjez1iqzF4qkqWTJ+6yIuKC0gkfgBG5m7RQdNaGMSJKA04DbIuLkUgotwN0IzayVBm7oUEY3QmAv4BBgH0lL07So0h3ANXAza7GyauAR8RuY+Nv7OIGbWSv5hg4jkDQNuAroSds5LyK+WNX2zMw6EYi1/c1uRa6yBv4csE9ErE5nZ38j6dKIuK7CbZqZFeabGg8jIgIYuBnf1DRFVdszM+tINL8JpdL/D5KmSFoKPAJcHhGVX5lkZlbEQBt4kalbVZrAI6IvIuYDc4DdJe04+DWSjpC0RNKS59c+U2U4ZmYv4QReQESsBK4AFg6xrDciFkTEgvXWXX8iwjEzIxB9/esUmrpVZZFJ2lTSxunxdGBf4Paqtmdm1qkSL+SpRZW9UDYHzpA0heyH4tyIuKjC7ZmZFRaT4CRmlb1QbiYbUtHMrCuFE7iZWRN19wnKIpzAzay1XAM3M2ugCOjrdwI3M2ukbu5hUoQTuJm1UuAmFDOzhvJJTDOzxoqGD6/nBG5mrdX0JpTuvcjfzKxCWS+UcsZCkfR9SY9IumUCQn+BE7iZtVZEsamA0xlisL6quQnFzFqrrCaUiLhK0rxSCuuAE7iZtVKgThL4LElLcvO9EdFbQVgdcQI3s9bqoBPKiohYUF0kY+MEbmbtFBC+lN7MrJncjdDMrKHK6oUi6WzgWmA7ScslHVZ17NBlNfC+nnVZve3GdYdRqifnTak7hEo8vWV/3SGUrmfu6rpDqMTus++vO4TSlXFvxjLHQomID5RSUIe6KoGbmU2YABrehOIEbmat5bFQzMwaSe6FYmbWWK6Bm5k1UDS/G6ETuJm1l2vgZmZN5Rq4mVkzNfxyBidwM2unmvuBSxLw31Mk5wH7AAeSXaf0nYgY9efFCdzMWqvmfuCLgVcC65El7h7gQmB/YDvg6NEKcAI3s/aqN4G/OSJ2kjQVeBjYPCKeT+Oq3FSkACdwM2uversRrgWIiDWSboiI59P8WkmFWuedwM2stVRvDfxhSTMjYnVEvHA/TUmbAc8XKcAJ3MzaKQQ1XkofEfsNs+gp4IAiZYw4Hrik9SUdJ+lvJU2TdKikCyV9TdLMTgM2M+sqUXCqkKS9JM1Ijz8MnAhML7LuaDd0OB14FbAVcDGwAPg6We/3/z3GeM3MukMXJHCyXPqMpJ2BzwB3AWcWWXG0JpTXRsT7Un/Fh4C3R0RI+g3wu/FEbGZWu+64lH5tyqsHAt+KiNOK3tGnUBt4KvySiKzXZJrvjl03MxuL7rmhwypJxwMfBv6rpHWAqUVWHK0JZclAW3dEfGzgSUlbA6tGWlHSXElXSLpV0jJJo3ZKNzObSIpiU6GypIWS/iDpTkmf7SCM9wPPAYdFxMPAHLKm6lGNWAOPiMNTYD0R8Vzu+bsk7TtK2WuBz0TETZI2AG6UdHlE3FokMDOzypXUjiBpCtmVlfsCy4EbJF1YJN+lpH1ybv5+CraBF70r/bVDPHf1KEE9FBE3pcergNuA2QW3Z2ZWuRJr4LsDd0bE3emCnHPILo8fPQZpT0k3SFot6XlJfZKeLLLuiDXw1KF8NjBd0i68OPbihsD6RTaQypkH7AJcP8SyI4AjAHqmb1y0SDOz8SveBj5L0pLcfG9E9ObmZwMP5OaXA3sULPtbwMHAv5P19PsI8NoiK452EvMdwKFkbTIn555/CvhckQ2kNvTzgWMi4qnBy9NB6AWY+Yq5PjFqZhOjsy6CKyJiQWWhRNwpaUpE9AE/kPRb4PjR1hutDfwM4AxJ74mI8zsNKg3Scj5wVkRc0On6ZmaVKq/K+CAwNzc/Jz1XxDOS1gOWSvoaWZftQs3bRdvAr5Z0mqRLASTtMFo/xdR3/DTgtog4eaTXmpnVQf3FpgJuALaVtFVKxgeTDQ1bxCFkufgo4GmyH4L3FFmxaAL/AXAZsEWavwM4ZpR19kqB7SNpaZoWFdyemVn1SroSMyLWkiXgy8g6bJwbEcsKhRBxH9n5xc0j4ksR8emIuLPIukUHs5oVEeemzuYDwx32jRLUb2j6DefMbNLqpI93ERFxCXBJx3FI7wS+QXZjh60kzQf+MSLeNdq6RWvgT0vahPRbJGlPoFA3FzOzrhUqNlXrBLJuiCsBImIp2fhToypaA/80WXvO1pKuBjYF3tthkGZm3aU7+r2tiYgns9OGLygUWdEEvjWwHy82ru/RwbpmZl2pS0Z0Wibpg8AUSdsCnwKuKbJi0SaUz6c+3K8A3gp8Gw8na2ZNFqX2QhmPTwKvJxsP5UdkzdOFxo4qmsAHTljuD3wvIi4ma3A3M2uu7hgPfIc0rQtMI7sE/4YiKxZtBnlQ0nfJBmo5SVIPxZO/mVl36o4mlLOAY4FbgI7q+0UT+PuAhcA3ImKlpM2Bv+0oRDOzLtMlbeCPRsTPxrJi0Rs6PANckJt/iOxyTzMzG58vSjoV+CVZOzgARYYfcU8SM2uv7qiBfxTYnuwuPANNKEGu0jwcJ3Aza6eYkB4mRewWEduNZUWfiDSz9uqOXijXSNphLCu6Bm5mrSS65iTmnmRDyd5D1gYusnvHv2G0FZ3Azay9uiOBLxzrik7gZtZOJY9GOOYwsuFkx8QJ3MzaqztOYo6ZE7iZtVY31MDHo6sSeF8PPDlvSt1hlOrpLRv+Ez+Mnrmr6w6hdLvPvr/uECpx4CY31R1C6c4sqyAncDOzBpqYLoKVcj9wM2utgduqjTZ1VKa0vaRrJT0n6dhqIs+4Bm5m7VVNDfxxspsyHFRJ6TmugZtZa1VxQ4eIeCQibgDWVBJ0jmvgZtZOnbWBz5K0JDffGxG9pcfUISdwM2slpamgFRGxoLJgxshNKGbWXiUNZiXpSElL07RFZfEO4hq4mbVWWRfyRMRiYHE5pRXnBG5m7VVBLxRJmwFLgA2BfknHADtExFNlb8sJ3MzaqaIbOkTEw8Cc8kt+OSdwM2uvhl+J6QRuZq3lwazMzJrKCdzMrJlcAzcza6LAN3QwM2uiLrqp8Zg5gZtZezU8gVd2Kb2k70t6RNItVW3DzGw8FFFo6lZVjoVyOrCwwvLNzMau6Dgo3Zu/q2tCiYirJM2rqnwzs/FyG7iZWUNVcSn9RKo9gUs6AjgCYOoGr6g5GjNrlYbXwGsfDzwieiNiQUQsmLL+jLrDMbO2KHhD425uZqm9Bm5mVpsuTs5FVNmN8GzgWmA7ScslHVbVtszMOjVwIY9r4EOIiA9UVbaZWRnU38XZuQA3oZhZO3V5H+8inMDNrLWa3o2w9l4oZma1qeBKTEkfknSzpN9LukbSzqXGnOMauJm1VkUnKO8B3hIRT0jaD+gF9qhiQ07gZtZOAVQwUFVEXJObvY4Kb3DsBG5mrdVBG/gsSUty870R0VtgvcOASzuNqygncDNrpQ5v6LAiIhZ0VL70VrIE/pedRVacE7iZtVNEaU0oko4EPp5mFwGzgFOB/SLisVI2MgT3QjGz1irrSsyIWBwR8yNiPlnF+ALgkIi4o8r4XQM3s/aqphfKF4BNgG9LAljbafNLUU7gZtZaVXQjjIjDgcPLL/nlnMDNrJ0C6Gv2tfRO4GbWWt080mARTuBm1l5dfMf5IpzAzay1XAM3M2siDydbrv714OktGz6+4yA9c1fXHUIldp99f90hlO7ATW6qO4RKHDTj6bpD6EoC5JOYZmbNJLeBm5k1kJtQzMyaqryxUOriBG5mreVeKGZmTeUauJlZA4V7oZiZNVez87cTuJm1l7sRmpk1lRO4mVkDBdDwC7+dwM2slUS4CcXMrLH6m10F902NzaydBppQikwdkHSgpJslLZW0RNJflhl2nmvgZtZaFTWh/BK4MCJC0huAc4Htq9iQE7iZtVcFCTwi8mNIz6DC3uZO4GbWUtUNZiXpvwFfBV4J7F/JRnAbuJm11cBd6YtMMCu1Zw9MR4xYdMSPI2J74CDgy1XtgmvgZtZaHbSBr4iIBcOWIx0JfDzNLoqIPwJExFWSXiNpVkSsGF+0L1dpDVzSQkl/kHSnpM9WuS0zs45FFJtGLSYWR8T8iJgPrC9JAJJ2BXqAx6oIv7IauKQpwGJgX2A5cIOkCyPi1qq2aWZWWAD9lbSBvwf4iKQ1wLPA+yOqaWyvsglld+DOiLgbQNI5wIGAE7iZdYFqTmJGxEnASaUXPIQqm1BmAw/k5pen515C0hEDJwb6Vvvu2WY2gUpqQqlL7ScxI6IX6AXoefXc7j1SZja5BNDX7Evpq0zgDwJzc/Nz0nNmZl0gIJqdwKtsQrkB2FbSVpLWAw4GLqxwe2ZmnXETytAiYq2ko4DLgCnA9yNiWVXbMzPrSHW9UCZMpW3gEXEJcEmV2zAzG7Murl0XUftJTDOz2jiBm5k1UAT09dUdxbg4gZtZe7kGbmbWUE7gZmZNFO6FYmbWSAHR8At5nMDNrL18Kb2ZWQNFQL8TuJlZM/kkpplZM4Vr4GZmTdTdA1UV4QRuZu3kwazMzJopgGj4pfSV3pXezKxrRbqhQ5FpDCTtJmmtpPeWHPkLXAM3s9aKippQJE0hu7HxzyvZQOIauJm1V3U18E8C5wOPlBvwSym66CyspEeB+yZgU7OAFROwnYk2GfdrMu4TeL/Ga8uI2HQ8BUj6v2TxFjEN+HNuvjfdkH2ocmcDPwLeCnwfuCgizhtPrMPpqiaU8b4hRUlaEhELJmJbE2ky7tdk3CfwfnWDiFhYUdGnAH8XEf2SKtpEpqsSuJlZE0k6Evh4mt0IOCcl71nAIklrI+InZW/XCdzMbJwiYjGwePDzkk4na0L5SRXbbetJzCHbriaBybhfk3GfwPtlJeiqk5hmZlZcW2vgZmaN5wRuZtZQrUvgkhZK+oOkOyV9tu54yiDp+5IekXRL3bGURdJcSVdIulXSMklH1x1TGSRNk/Sfkn6X9utLdcdUFklTJP1W0kV1x9IWrUrg6fLWxcB+wA7AByTtUG9UpTgdqKpPa13WAp+JiB2APYEjJ8l79RywT0TsDMwHFkras96QSnM0cFvdQbRJqxI4sDtwZ0TcHRHPA+cAB9Yc07hFxFXA43XHUaaIeCgibkqPV5Elhtn1RjV+kVmdZqemqfE9CSTNAfYHTq07ljZpWwKfDTyQm1/OJEgKk52kecAuwPU1h1KK1NSwlGycjMsjYjLs1ynAcUCzb3HTMG1L4NYwkmaSDQp0TEQ8VXc8ZYiIvoiYD8wBdpe0Y80hjYukA4BHIuLGumNpm7Yl8AeBubn5Oek560KSppIl77Mi4oK64ylbRKwErqD55y/2At4l6V6yZsl9JP2w3pDaoW0J/AZgW0lbSVoPOBi4sOaYbAjKBpI4DbgtIk6uO56ySNpU0sbp8XRgX+D2WoMap4g4PiLmRMQ8su/UryLiwzWH1QqtSuARsRY4CriM7KTYuRGxrN6oxk/S2cC1wHaSlks6rO6YSrAXcAhZbW5pmhbVHVQJNgeukHQzWYXi8ohwtzsbE19Kb2bWUK2qgZuZTSZO4GZmDeUEbmbWUE7gZmYN5QRuZtZQTuDW9SQdKmmL3PxRaTTJkFT0ruJmk44TuDXBocAWufmrgbcD99USjVmX8E2NrRaSZgDnkg1nMAX4MnAncDIwE1hBlrj3AhYAZ0l6FnhTRPw2lTHxgZt1ESdwq8tC4I8RsT+ApI2AS4EDI+JRSe8HvhIRH5N0FHBsRCypMV6zruMEbnX5PfAvkk4CLgKeAHYELk816ynAQ/WFZ9b9nMCtFhFxh6RdgUXAicCvgGUR8aZ6IzNrDp/EtFqkXiXPRMQPga8DewCbSnpTWj5V0uvTy1cBG9QTqVn3cg3c6rIT8HVJ/cAa4BNk98H8X6k9fF2yu7wsI7vn53cGTmICHye7+8tmwM2SLomIwyd8D8xq5tEIzcwayk0oZmYN5QRuZtZQTuBmZg3lBG5m1lBO4GZmDeUEbmbWUE7gZmYN9f8B9Xj69AcosH8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_definition = [DataSetDefinition(name=\"dataset_1\",\n",
    "                                        independent = [set1, set2],\n",
    "                                        dependent= [meas2],\n",
    "                                        experiment=experiment),\n",
    "                      DataSetDefinition(name=\"dataset_2\",\n",
    "                                        independent = [set1, set3],\n",
    "                                        dependent= [meas3],\n",
    "                                        experiment=experiment)]\n",
    "with datasaver_builder(dataset_definition) as datasavers:\n",
    "    for val1, val2, val3 in product(range(5), repeat=3):\n",
    "        set1(val1)\n",
    "        set2(val2)\n",
    "        set3(val3)\n",
    "        meas2_val = meas2()\n",
    "        meas3_val = meas3()\n",
    "        datasavers[0].add_result((set1, val1), (set2, val2), (meas2, meas2_val))\n",
    "        datasavers[1].add_result((set1, val1), (set3, val3), (meas3, meas3_val))\n",
    "    datasets =[datasaver.dataset for datasaver in datasavers]\n",
    "\n",
    "plot_dataset(datasets[0])\n",
    "plot_dataset(datasets[1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `dond_into`\n",
    "`dond_into` is a dond-like utility function which performs gridded measurements and writes them to a specified datasaver. Unlike `dond` however, `dond_into` can write to the same datasaver -- and thus the same dataset -- multiple times. There are caveats, though.\n",
    "1. `dond_into` will not stop a user from measuring the same data point more than once, which may lead to unexpected output shapes, especially when converted to an xarray.\n",
    "2. `dond_into` does not support `TogetherSweeps` or multiple datasets via grouping parameters. These arguments will raise Exceptions if passed.\n",
    "\n",
    "In this example, we take two slices from the same measurement function: one at low resolution, and another at high resolution. Both sets of data are then written to the same dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 3. \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "core_test_measurement = Measurement(name=\"core_test_1\", exp=experiment)\n",
    "core_test_measurement.register_parameter(set1)\n",
    "core_test_measurement.register_parameter(meas1, setpoints=[set1])\n",
    "with core_test_measurement.run() as datasaver:\n",
    "    sweep1 = LinSweep(set1, 0, 5, 11, 0.001)\n",
    "    dond_into(datasaver, sweep1, meas1)\n",
    "\n",
    "    sweep2 = LinSweep(set1, 10, 20, 100, 0.001)\n",
    "    dond_into(datasaver, sweep2, meas1)\n",
    "\n",
    "    dataset = datasaver.dataset\n",
    "plot_dataset(dataset, marker=\".\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `datasaver_builder` with `dond_into` and `LinSweeper`\n",
    "\n",
    "Put together, the `datasaver_builder` and `dond_into` extensions provide a clean way of writing flexible measurement code that too complicated for `dond` yet too simple for the full power of the `Measurement` object.\n",
    "\n",
    "In the example below, we also introduce a new class, the `LinSweeper`. This is an iterable that sets the given swept parameter according to the inputs. It is useful for building outer-loops, within which more detailed measurements or functions are called. Once again, arbitrary code blocks can be included, simplifying the equivalent of dond's `enter_actions`, `exit_actions`, and `break_conditions` kwargs. Instead of being arguments, they can be explicitly included in the measurement, and can be nested precisely where the user wants them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 4. \n",
      "Starting experimental run with id: 5. \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NLhufl4ymUVjSJODvgNeNGEfEgvR3saQfAzsBlSQGVyWZmbURFKtGGmNV0l7AnRExf7iFkqZImjr4GHgrcOtYDtiOE4OZWQerWKPQ1Imkc4BrgW0lzZd0eFp0IC3VSJJmSBocp2ZT4GpJNwO/BX4WEZeWdoItXJVkZtZGBKVdrhoRB40wf84w8x4C9k2P7wV2LCWIApwYzMzayBqf3SWGmZnleKAeMzNbLZAH6jEzs6FcYjAzs9UCWOWBeszM7Hny0J5mZva8AF+V1PcmDXhM5g6qHo8Zqh+TuerxmKH5YzJXPR4zVD8m86T1nqt0/2WIkKuSzMxsqBLHY2gEJwYzszay8RjcxmBmZquVOoJbIzgxmJm1kV2u6hKDmZkl7ivJzMxeoKwxn5ui1rOVdLCk30u6RdKvJfWsW1kzsyKybrd7MuZz36i7xPAn4I0R8bikfYC5wM41x2RmNoTbGHooIn6de3odsGVdsZiZDSfrXXViVSXVXWLIOxy4ZLgFko4EjgSYPGn9XsZkZhNc1iWGE0PPSdqTLDHsPtzyiJhLVs3EBpM3ix6GZmYT3sQrMfT8bCUdJWlemmZIeg1wGrB/RDza63jMzDpZhQpNnUg6Q9JiSbfm5n1B0oLc9+K+lZ5MAT1PDBFxSkTMjIiZZCWW84FDIuKPvY7FzKyTkq9KOhPYe5j5Xx/8XoyIi0s9gVGouyrpc8CLgG9KAlgREbPqDcnMbKiyqpIi4ipJW5eyswrVfVXSEcARdcZgZtZOl2M+T5d0Q+753NRG2snRkj4I3AAcGxGPdxtnmeouMZiZ9bUAVhQvMSwZRa3Ht4Dj06GOB04EPtTlPkrlxGBm1kGVVyVFxMODjyV9G7iosoMV5MRgZtZOdFWV1DVJm0fEwvT0b4Fb263fC04MZmZtlDlQj6RzgNlkbRHzgc8DsyXNTIe6D/j7Ug42Bs1LDANreEzmTvuveDzm7BjV9h1T9XjJvThG1WMyVz0eM1Q/JvPU9f5c6f7LUlaJISIOGmb26aXsvETNSwxmZj3kgXrMzGyIQKxYNbG6xHBiMDProKw2hqZwYjAzaydclWRmZjluYzAzsxdwYjAzs9UCsdKNz2ZmlufGZzMzWy3c+GxmZq3CicHMzJ5XbSd6/ciJwcysA5cYzMxstQhYucqJwczMcnxVkpmZrRa4KsnMzIZw47OZmbWIqDuC3nJiMDPrwFVJfS4G1vDQmx33X/2HuOphMXsxtGfTh96sethNqH7ozY2nLKt0/2XIrkoq539W0hnAfsDiiNghzTsBeAewHLgHOCwinhhm2/uApcBKYEVEzColqGFMrJ6hzMxGIaLYVMCZwN4t864AdoiI1wB/BD7bZvs9I2JmlUkBnBjMzDqKUKGp837iKuCxlnmXR8SK9PQ6YMvyz6A7TgxmZm0ExZJCSgzTJd2Qm47s8nAfAi4ZMRS4XNKNo9hvVxrXxmBm1mtdXJS0ZLTVPJL+CVgBnD3CKrtHxAJJmwBXSLozlUBK5xKDmVk7AbFKhabRkjSHrFH64IjhWysiYkH6uxj4MbDTqA/YgRODmVkHZbUxDEfS3sCngHdGxDMjrDNF0tTBx8BbgVtHeTod9UVikPR6SSskvafuWMzMWpV1VZKkc4BrgW0lzZd0OHAyMJWsemiepFPTujMkXZw23RS4WtLNwG+Bn0XEpRWcKtAHbQySBoCvApfXHYuZWasy+0qKiIOGmX36COs+BOybHt8L7FhKEAX0Q4nho8B5wOK6AzEze4EAQsWmcaLWEoOkLYC/BfYEXt9mvSOBIwHWnjytJ7GZmQ1yX0m99Q3g0xGxSho520bEXGAuwPrrbznB3iIzq9fYrjhqop4nBklHAR9OTzcAfpCSwnRgX0krIuKCXsdlZjaiCfZztOeJISJOAU5pnS/pTOAiJwUz6yvh3lXNzKyVSwz1iIg5dcdgZjY8lxjMzCyv2qE7+o4Tg5lZO4P3MTSEsqt53ksW+Y+ANwH7A3cCp0ZExzTnxGBm1kHD7mM4BdgEWIssIawNXAi8HdgWOKbTDpwYzMw6aVZi2CMiXi1pTWARsHlELE/9NN1UZAeNSwyrBuQxmTvoxXjJVR+j6vGYofljMlc9HjNUPybzjHWfqnT/pWlQVRLZmA5ExHOSro+I5en5CkmF/rEalxjMzHpNzSoxLJK0XkQsi4jV40tL2gxYXmQHTgxmZu2EoEFdYkTEPiMseopsMKCO2taZSFpX0qckfVLSZElzJF0o6d8l9aDCwsysD0TBqY9I2i0N6oOkDwBfBNYpsm2nyvQzyQaIeCnwM2AWcALZ3R7fGmW8ZmbN0sDEQPYd/YykHYFjgXuA7xbZsFNV0isj4n3putiFwF4REZKuBm4eS8RmZo3Rf1/6RaxI39f7AydHxOlpxLiOCrUxpJ1fPDhIdXrezJfKzKwbDbvBLWeppM8CHwD+RtIawJpFNuxUlXTDYFtCRHxocKaklwNLRxmsmVmjKIpNHfcjnSFpsaRbc/M2knSFpLvS3w1LCvsA4C/A4RGxCNiSrCmgo7aJISKOiIhlktZumX8P8JZRBmtm1izltTGcCezdMu8zwJURsQ1wZXo+9pAjFkXESRHxq/T8gYgo1MZQ9E6ua4eZd03RAM3MmqysEkNEXAU81jJ7f+Cs9Pgs4F2lxCztIul6ScskLZe0UtKTRbZt28aQbojYAlhH0mt5vu/Z9YF1xxS1mVlTFG9jmC7phtzzuWlo4nY2jYiF6fEisitBy3AycCDw32RXlH4QeGWRDTs1Pr8NmENWN3VSbv5TwHHdRmlm1jjdXYq6JCJmjfpQJV/YExF3SxqIiJXAdyT9Dvhsp+3aJoaIOAs4S9K7I+K8kmI1M2uWaq/BfFjS5hGxUNLmwOKS9vuMpLWAeZL+neyWg0LNB0XbGK6RdLqkSwAkbV/0elgzs6bTqmLTKF0IHJoeHwr8pIyYgUPIvuOPBp4GtgLeXWTDoonhO8BlwIz0/I/Ax7sK0cysqUq6Kil1fX0tsK2k+ekH9leAt0i6C9grPR97yBH3k7ULbx4R/xoR/xgRdxfZtmgnetMj4tx0s8Rg963V9llsZtYHil5xVEREHDTCojeXc4TnSXoH8DWyAXteKmkm8G8R8c5O2xYtMTwt6UWknChpF6DQZU9mZo0XKjb1ly8AOwFPAETEPLJ+7zoqWmL4R7J6sJdLugbYGHhPl0GamTVTMzsAei4insy6ulut0JkUTQwvB/bh+caLnbvY1sys0RraM9xtkt4PDEjaBvgY8OsiGxatSvqXiHgK2BDYE/gm7nbbzCaCqPyqpKp8FHgVWX9J3yer/j+myIZFf/UPNjS/Hfh2RPxM0he7jbIMMeAxmeveP1Q/JnPV4zFD88dkrno8Zqh+TOYtJz9e6f5L08wSw/ZpmpSm/YF3Aq/ptGHRxLBA0n+SdZz31dSpXrXfzmZm/aKZieFs4BPArUBXv+SKJob3kfUI+LWIeCLdnffJrkI0M2uohrYxPBIRPx3NhkUH6nkGOD/3fCHZ7dVmZtafPi/pNLKuvP8yODMizh95k4yvLDIz66SZJYbDgO3IRm0brEoKcj/yR+LEYGbWTvTlFUdFvD4ith3Nhm5ANjPrpLwR3Hrp15K2H82GtZcYJM0GvkFW3FkSEW+sMx4zszzR2MbnXci63P4TWRuDyIZ8KO1y1UpImkZ2s9zeEfGApE3qjMfMbFjNTAytY0sXVneJ4f3A+RHxAEBElDVAhZlZOUrsXbWXUrfbo1J3G8MrgQ0l/ULSjZI+ONxKko6UdIOkG1b8+ekeh2hmE96qgtM4UXeJYRLwOrK+yNcBrpV0XUT8Mb9SGkx7LsCU6Vs1MHebWZM1scQwFj0vMUg6StI8SfOAh4DLIuLpiFgCXAXs2OuYzMzaauZVSaPW88QQEadExMyImAn8GNhd0iRJ65J1531Hr2MyMxtR0aTgxFCOiLgDuBT4PfBb4LSIuLXOmMzMWg0O79lp6rgfadvBGpM0PSXp4y3rzJb0ZG6dz1V0WiOqu42BiDgBOKHuOMzMRlTemM9/AGYCSBoAFpDVnLT6VUTsV85Ru1d7YjAz63cVdYnxZuCesVxWWpW6L1c1M+tv3bUxTB+8tD5NR7bZ84HAOSMs21XSzZIukfSqck6kOJcYzMzaUJoKWhIRszruU1qLbDS1zw6z+CbgJRGxTNK+wAXANsVDGDuXGMzMOin/qqR9gJsi4uEXHCriqYhYlh5fDKwpafpYwu9W40oMsYbHZO6k6vGYofoxmasejxmaPyZz1eMxQ/VjMr908iOV7r8sFdzgdhAjVCNJ2gx4OCJC0k5kP+AfLT2CNhqXGMzMeq7ExCBpCvAW4O9z8/4BICJOBd4DfETSCuBZ4MCI6OldEk4MZmbtlDxQT0Q8DbyoZd6puccnAyeXd8TuOTGYmXUyju5qLsKJwcysg4nWiZ4Tg5lZJ04MZmaW5xKDmZk9LxhXg/AU4cRgZtaGcInBzMxaOTGYmVmeent/We2cGMzM2hlno7MV4cRgZtaB2xjMzGyIigbq6VtODGZmnbjEYGZmq4WrkszMrJUTg5mZDfINbmZm9gJaNbEyQ+MSQwx46M1Oqh52E6oferPqYTeh+UNvVj3sJlQ/9ObL1lpc6f5L4fsYzMyslS9XNTOzocod8/k+YCmwElgREbPK23s5nBjMzDqooPF5z4hYUvpeS+LEYGbWTgATrBO9NeoOwMys32lVsQmYLumG3HTkMLsL4HJJN46wvHYuMZiZtdHlfQxLCrQZ7B4RCyRtAlwh6c6IuGosMZat1hKDpA0k/VTSzZJuk3RYnfGYmb1ARPGp0O5iQfq7GPgxsFOF0Y9K3VVJRwG3R8SOwGzgRElr1RuSmdlQimJTx/1IUyRNHXwMvBW4tdrou1d3VVIAUyUJWA94DFhRb0hmZi3Ka3veFPhx9pXHJOD7EXFpaXsvSd2J4WTgQuAhYCpwQES84FaS1EBzJMCa62/Y0wDNzMq6XDUi7gV2LGdv1am7KultwDxgBjATOFnS+q0rRcTciJgVEbMG1p3S2wjNbGILYGUUm8aJnpcYJB0FfDg9fRz4XEQEcLekPwHbAb/tdVxmZiOZaL2r9rzEEBGnRMTMiJgJ3Am8GUDSpsC2wL29jsnMrK0Sr0pqgrrbGI4HzpR0C9nlwp/u59vEzWximmglhloTQ0Q8RHa5lplZf3K322ZmlidA46hhuQgnBjOzDjSO2g+KcGIwM2vHVUlmZjbU+LriqIjGJYZYw2Myd1L1eMxQ/ZjMVY/HDM0fk7nq8Zih+jGZXzap2vegLL4qyczMhnKJwczMVgtflWRmZq0mVl5wYjAz68SXq5qZ2VBODGZmtloA1V6o2HecGMzM2hDhqiQzM2uxamIVGeoewc3MrL8NViUVmTqQtJWkn0u6XdJtko4ZZp3Zkp6UNC9NnyvvZIpxicHMrIMSq5JWAMdGxE2SpgI3SroiIm5vWe9XEbFfWQftlhODmVknJSWGiFgILEyPl0q6A9gCaE0MtXJVkplZWwWH9ewyeUjaGngt8JthFu8q6WZJl0h6VQkn0RWXGMzM2gmgeJcY0yXdkHs+NyLmtq4kaT3gPODjEdHak+BNwEsiYpmkfYELgG26jnsMnBjMzDrooo1hSUTMarsvaU2ypHB2RJzfujyfKCLiYknflDQ9IpZ0E/NYuCrJzKyTkqqSJAk4HbgjIk4aYZ3N0npI2onse/rREs+mI5cYzMzaCWBVaVcl7QYcAtwiaV6adxzwYoCIOBV4D/ARSSuAZ4EDI3p7h50Tg5lZW+WN4BYRVwPqsM7JwMmlHHCUnBjMzDpxlxhmZrZaACsnVpcYjUsMMeAxmTupejxmqH5M5qrHY4bmj8lc9XjMUP2YzC+eNLXS/cOiEvYREE4MZmaW56okMzNbrdyrkhrBicHMrBOXGMzMbAgnBjMzWy0CVlZ7QUq/cWIwM+tkgpUYetJXkqTtJF0r6S+SPtGybG9Jf5B0t6TP9CIeM7OuVNDtdj/rVYnhMeBjwLvyMyUNAKcAbwHmA9dLunCY0YzMzGoSE+6qpJ6UGCJicURcD7Te2bUTcHdE3BsRy4EfAPv3IiYzs0ICIlYVmsaLutsYtgAezD2fD+zcupKkI4EjAQY23LA3kZmZDXKXGP0njYA0F2DtF281scp0ZlavCFg1sRJDZVVJko6SNC9NM0ZYbQGwVe75lmmemVn/cONzOSLiFLKG5XauB7aR9FKyhHAg8P6qYjIzG42YYCWGnlQlSdoMuAFYH1gl6ePA9hHxlKSjgcuAAeCMiLitFzGZmRUzvkoDRfQkMUTEIrJqouGWXQxc3Is4zMy65k70zMwsL4CYYF1i9OQ+BjOzxoo0UE+RqYAm9PbgEoOZWQdRUlVSU3p7cInBzKyT8koMjejtQdGw1nZJjwD3d7nZdGBJBeH0StPjB59DP2h6/ND9ObwkIjYeywElXZqOW8RkID/o+tx0g+7gvt4D7B0RR6TnhwA7R8TRY4mxbI2rShrNmyzphoiYVUU8vdD0+MHn0A+aHj/Ucw4RsXcvj9cPXJVkZtY7jejtwYnBzKx3Vvf2IGktst4eLqw5phdoXFXSKM3tvEpfa3r84HPoB02PHxp+DhGxogm9PTSu8dnMzKrlqiQzMxvCicHMzIYY14mhCbeetyNpK0k/l3S7pNskHVN3TKMhaUDS7yRdVHcsoyFpmqQfSbpT0h2Sdq07pm5J+r/pM3SrpHMkTa47pk4knSFpsaRbc/M2knSFpLvSXw/pWIFxmxhyt57vA2wPHCRp+3qj6toK4NiI2B7YBTiqgecAcAxwR91BjMF/AJdGxHbAjjTsXCRtAXwMmBURO5A1eh5Yb1SFnAm03kPwGeDKiNgGuDI9t5KN28RAQ249byciFkbETenxUrIvpC3qjao7krYE3g6cVncsoyFpA+BvgNMBImJ5RDxRa1CjMwlYR9IkYF3goZrj6SgirgIea5m9P3BWenwW8K5exjRRjOfEsAXwYO75fBr2pZonaWvgtcBvag6lW98APgU0dQislwKPAN9J1WGnSZpSd1DdiIgFwNeAB4CFwJMRcXm9UY3aphGxMD1eBGxaZzDj1XhODOOGpPWA84CPR8RTdcdTlKT9gMURcWPdsYzBJOCvgW9FxGuBp2lY9UWqh9+fLMnNAKZI+kC9UY1dZNfa+3r7CoznxNCIW887kbQmWVI4OyLOrzueLu0GvFPSfWRVeW+S9L16Q+rafGB+RAyW1H5EliiaZC/gTxHxSEQ8B5wPvKHmmEbrYUmbA6S/i2uOZ1waz4mhEbeetyNJZHXbd0TESXXH062I+GxEbBkRW5O9/v8bEY36pZqGpX1Q0rZp1puBvuo7v4AHgF0krZs+U2+mYQ3oORcCh6bHhwI/qTGWcWvcdonRlFvPO9gNOAS4RdK8NO+4NE629c5HgbPTD4x7gcNqjqcrEfEbST8CbiK70u13NKBrCUnnALOB6ZLmA58HvgKcK+lwsu7331dfhOOXu8QwM7MhxnNVkpmZjYITg5mZDeHEYGZmQzgxmJnZEE4MZmY2hBODjRuS5kiakXt+dOpZNyRNrzM2syZxYrDxZA5Zlw+DriG76/f+WqIxa6hxe4ObjQ+pw7pzybo0GQCOB+4GTgLWA5aQJYTdgFlkN6I9C+waEb9L++h94GYN5sRg/W5v4KGIeDus7gb7EmD/iHhE0gHAlyLiQ+lO909ExA01xmvWeE4M1u9uAU6U9FXgIuBxYAfgilQSGCDrStrMSuLEYH0tIv4o6a+BfYEvAv8L3BYRjRte06wp3PhsfS1dZfRMRHwPOAHYGdh4cNxlSWtKelVafSkwtZ5IzcYPlxis370aOEHSKuA54CNkPYT+v9TeMIlslLjbyMYIPnWw8Rn4MNnocZsBv5d0cUQc0fMzMGsY965qZmZDuCrJzMyGcGIwM7MhnBjMzGwIJwYzMxvCicHMzIZwYjAzsyGcGMzMbIj/D3uzP2JjuztXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_definition = [DataSetDefinition(name=\"dataset_1\",\n",
    "                                        independent = [set1, set2],\n",
    "                                        dependent= [meas2]),\n",
    "                      DataSetDefinition(name=\"dataset_2\",\n",
    "                                        independent = [set1, set3],\n",
    "                                        dependent= [meas3])]\n",
    "with datasaver_builder(dataset_definition) as datasavers:\n",
    "    for _ in LinSweeper(set1, 0, 10, 11, 0.001):\n",
    "        sweep1 = LinSweep(set2, 0, 10, 11, 0.001)\n",
    "        sweep2 = LinSweep(set3, -10, 0, 11, 0.001)\n",
    "        dond_into(datasavers[0], sweep1, meas2, additional_setpoints=(set1,))\n",
    "        dond_into(datasavers[1], sweep2, meas3, additional_setpoints=(set1,))\n",
    "    datasets =[datasaver.dataset for datasaver in datasavers]\n",
    "\n",
    "plot_dataset(datasets[0])\n",
    "plot_dataset(datasets[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `datasaver_builder` and `dond_into` with `ParameterWithSetpoints`\n",
    "\n",
    "One final use case includes support for the `ParameterWithSetpoints`. As seen below, the `datasaver_builder` and `dond_into` behave intuitively with these special parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 6. \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_definition = [DataSetDefinition(name=\"dataset_1\",\n",
    "                                        independent= [set1],\n",
    "                                        dependent =  [pws])]\n",
    "with datasaver_builder(dataset_definition) as datasavers:\n",
    "    for _ in LinSweeper(set1, 0, 10, 11, 0.001):\n",
    "        dond_into(datasavers[0], pws, additional_setpoints=(set1,))\n",
    "\n",
    "    datasets =[datasaver.dataset for datasaver in datasavers]\n",
    "\n",
    "plot_dataset(datasets[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bringup-dev",
   "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": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "58ba35545862df1654293636174b4c99ecf11cb4e666dc0cc0b7e9739510814a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}