{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "551a4cf5",
   "metadata": {},
   "source": [
    "# Example Gate Characterization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "celtic-google",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging hadn't been started.\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : /Users/jana/.qcodes/logs/command_history.log\n",
      "Mode           : append\n",
      "Output logging : True\n",
      "Raw input log  : False\n",
      "Timestamping   : True\n",
      "State          : active\n",
      "Qcodes Logfile : /Users/jana/.qcodes/logs/210816-13797-qcodes.log\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from qcodes import Station, load_or_create_experiment\n",
    "from qcodes.dataset.plotting import plot_dataset\n",
    "from qcodes.dataset.data_set import load_by_run_spec\n",
    " \n",
    "import nanotune as nt\n",
    "from nanotune.tuningstages.gatecharacterization1d import GateCharacterization1D\n",
    "from nanotune.classification.classifier import Classifier\n",
    "from nanotune.tuningstages.settings import DataSettings, SetpointSettings, Classifiers\n",
    "from nanotune.device.device import Readout, NormalizationConstants\n",
    "\n",
    "from sim.data_providers import QcodesDataProvider\n",
    "from sim.qcodes_mocks import MockDoubleQuantumDotInstrument\n",
    "\n",
    "nt_path = os.path.dirname(os.path.dirname(os.path.abspath(nt.__file__)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "binary-arnold",
   "metadata": {},
   "source": [
    "Define databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "handmade-gospel",
   "metadata": {},
   "outputs": [],
   "source": [
    "db_name_original_data = \"device_characterization.db\"\n",
    "db_folder_original_data = os.path.join(nt_path, \"data\", \"tuning\")\n",
    "char_db_path = os.path.join(db_folder_original_data, db_name_original_data)\n",
    "nt.set_database(db_name_original_data, db_folder_original_data)\n",
    "\n",
    "db_name_replay = 'qdsim_test.db'\n",
    "db_folder_replay = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "direct-density",
   "metadata": {},
   "source": [
    "Create qc.Station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "conventional-concept",
   "metadata": {},
   "outputs": [],
   "source": [
    "station = Station()\n",
    "\n",
    "qd_mock_instrument = MockDoubleQuantumDotInstrument()\n",
    "station.add_component(qd_mock_instrument, name=\"qdmock\")\n",
    "\n",
    "qdsim = qd_mock_instrument.mock_device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neural-methodology",
   "metadata": {},
   "source": [
    " Create the data provider to use for the right plunger pinch-off measurement. \n",
    " Binding the sim.r_plunger pin as the input data provider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "liked-czech",
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = \"GB_Newtown_Dev_3_2\"\n",
    "captured_run_id = 1206\n",
    "\n",
    "# load original dataset to get device normalization constant\n",
    "ds = nt.Dataset(1206, db_name_original_data, db_folder_original_data)\n",
    "\n",
    "pinchoff_right_plunger = QcodesDataProvider(\n",
    "    [qdsim.right_plunger], \n",
    "    char_db_path, \n",
    "    \"GB_Newtown_Dev_3_2\", \n",
    "    captured_run_id,\n",
    ")\n",
    "qd_mock_instrument.drain.set_data_provider(pinchoff_right_plunger)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "played-financing",
   "metadata": {},
   "source": [
    "Train pinchoff classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "middle-basket",
   "metadata": {},
   "outputs": [],
   "source": [
    "pinchoff_classifier = Classifier(\n",
    "    ['pinchoff.npy'],\n",
    "    'pinchoff',\n",
    "    data_types=[\"signal\"],\n",
    "    classifier_type=\"SVC\",\n",
    "    folder_path=os.path.join(nt_path, 'data', 'training_data'),\n",
    ")\n",
    "pinchoff_classifier.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legal-transaction",
   "metadata": {},
   "source": [
    "Instantiate nanotune GateCharacterization1D tuning stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "unknown-jewelry",
   "metadata": {},
   "outputs": [],
   "source": [
    "nt.set_database(db_name_replay, db_folder_replay)\n",
    "exp = load_or_create_experiment(\"simtest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "jewish-apparatus",
   "metadata": {},
   "outputs": [],
   "source": [
    "gatech = GateCharacterization1D(\n",
    "    data_settings=DataSettings(\n",
    "        db_name=db_name_replay,\n",
    "        db_folder=db_folder_replay,\n",
    "        normalization_constants=NormalizationConstants(**ds.normalization_constants),\n",
    "    ),\n",
    "    setpoint_settings=SetpointSettings(\n",
    "        voltage_precision=0.01,\n",
    "        parameters_to_sweep=[qd_mock_instrument.right_plunger],\n",
    "        safety_voltage_ranges=[(-3, 0)],\n",
    "        ranges_to_sweep=[(-1, 0)],\n",
    "    ),\n",
    "    readout=Readout(transport=qd_mock_instrument.drain),\n",
    "    classifier=pinchoff_classifier,\n",
    "    voltage_interval_to_track=0.3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acute-better",
   "metadata": {},
   "source": [
    "Run gate characterization stage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "connected-wales",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 5. \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gatecharacterization1d: Good result measured. Regime: pinchoff. Termination reasons: None. \n"
     ]
    }
   ],
   "source": [
    "tuning_result = gatech.run_stage()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "radio-greensboro",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_result.success"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "hindu-trailer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'features': {'transport': {'amplitude': 0.446805496793312,\n",
       "   'slope': 3.7184570057045274,\n",
       "   'offset': -1.657143286292184,\n",
       "   'tanh_sign': 0.9999999999999999,\n",
       "   'residuals': 0.13731377761110516,\n",
       "   'low_voltage': -0.767676767676768,\n",
       "   'low_signal': 0.0017014191789815178,\n",
       "   'high_voltage': -0.0909090909090909,\n",
       "   'high_signal': 0.9507964203562004,\n",
       "   'transition_voltage': -0.707070707070707,\n",
       "   'transition_signal': 0.2791761004636656,\n",
       "   'max_signal': 0.9972517232633776,\n",
       "   'min_signal': -0.023107398869144757}},\n",
       " 'quality': True,\n",
       " 'regime': 'pinchoff'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_result.ml_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "exceptional-vault",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_result.data_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d8d2e38",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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