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   "source": [
    "# An overview of Gate Set tomography the function-based way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial shows you how to run gate set tomography using the older-style function-centric API.  This can be useful in certain scenarios, but most of the time it's easier to use the newer, object-oriented approach outlined in the main [GST overview tutorial](GST-Overview.ipynb).  For more details and options for running GST, see the [GST circuits tutorial](../objects/advanced/GSTCircuitConstruction.ipynb) and the [tutorial covering the different pyGSTi functions for running GST](GST-Drivers.ipynb).\n",
    "\n",
    "To run GST, we need three inputs:\n",
    "1. a \"**target model**\" which describes the desired, or ideal, operations we want our experimental hardware to perform.  In the example below, we use one of pyGSTi's build-in \"model packs\" (see the [tutorial on model packs](objects/advanced/ModelPacks.ipynb)) - which acts on a single qubit with the following operations:\n",
    "    - three gates: the identity, and $\\pi/2$ rotations around the $x$- and $y$-axes.\n",
    "    - a single state preparation in the $|0\\rangle$ state.\n",
    "    - a 2-outcome measurement with the label \"0\" associated with measuring $|0\\rangle$ and \"1\" with measuring $|1\\rangle$.\n",
    "    \n",
    "2. a list of circuits tailored to the target model; essentially a list of what experiments we need to run.  Using a standard model makes things especially straightforward here, since the building blocks, called *germ* and *fiducial* circuits, needed to make good GST circuits have already been computed (see the [tutorial on GST circuits](../objects/advanced/GSTCircuitConstruction.ipynb)).\n",
    "\n",
    "3. data, in the form of experimental outcome counts, for each of the required sequences.  In this example we'll generate \"fake\" or \"simulated\" data from a depolarized version of our ideal model.  For more information about `DataSet` objects, see the [tutorial on DataSets](../objects/DataSet.ipynb).\n"
   ]
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   "source": [
    "#Import the pygsti module (always do this) and the XYI model pack\n",
    "import pygsti\n",
    "from pygsti.modelpacks import smq1Q_XYI\n",
    "\n",
    "# 1) get the target Model\n",
    "target_model = smq1Q_XYI.target_model()\n",
    "\n",
    "# 2) get the building blocks needed to specify which operation sequences are needed\n",
    "prep_fiducials, meas_fiducials = smq1Q_XYI.prep_fiducials(), smq1Q_XYI.meas_fiducials()\n",
    "germs = smq1Q_XYI.germs()\n",
    "maxLengths = [1,2,4,8,16,32] # roughly gives the length of the sequences used by GST\n",
    "\n",
    "# 3) generate \"fake\" data from a depolarized version of target_model\n",
    "mdl_datagen = target_model.depolarize(op_noise=0.01, spam_noise=0.001)\n",
    "listOfExperiments = pygsti.construction.make_lsgst_experiment_list(\n",
    "    target_model, prep_fiducials, meas_fiducials, germs, maxLengths)\n",
    "ds = pygsti.construction.generate_fake_data(mdl_datagen, listOfExperiments, nSamples=1000,\n",
    "                                            sampleError=\"binomial\", seed=1234)\n",
    "\n",
    "#Note: from listOfExperiments we can also create an empty dataset file\n",
    "# which has columns of zeros where actual data should go. \n",
    "pygsti.io.write_empty_dataset(\"../tutorial_files/GettingStartedDataTemplate.txt\", listOfExperiments,\n",
    "                              \"## Columns = 0 count, 1 count\")\n",
    "# After replacing the zeros with actual data, the data set can be \n",
    "# loaded back into pyGSTi using the line below and used in the rest\n",
    "# of this tutorial. \n",
    "#ds = pygsti.io.load_dataset(\"tutorial_files/GettingStartedDataTemplate.txt\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have all of the inputs, we can run GST in a standard way using the `do_stdpractice_gst` function.  For more information about this and related functions, see the [GST methods tutorial](GST-Drivers.ipynb).  This returns a `pygsti.report.Results` object (see the [Results tutorial](../objects/advanced/Results.ipynb)), from which we can generate a report giving us a summary of the analysis.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Run GST and create a report\n",
    "results = pygsti.do_stdpractice_gst(ds, target_model, prep_fiducials, meas_fiducials, \n",
    "                                    germs, maxLengths, verbosity=3)\n",
    "\n",
    "pygsti.report.construct_standard_report(\n",
    "    results, title=\"GST Overview Tutorial Example Report\", verbosity=2\n",
    ").write_html(\"../tutorial_files/gettingStartedReport\", verbosity=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now open the file [../tutorial_files/gettingStartedReport/main.html](../tutorial_files/gettingStartedReport/main.html) in your browser (Firefox works best) to view the report.  **That's it!  You've just run GST!** \n",
    "\n",
    "In the cell above, `results` is a `Results` object, which is used to generate a HTML report.  For more information see the [Results object tutorial](../objects/advanced/Results.ipynb) and [report generation tutorial](../reporting/ReportGeneration.ipynb)."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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