{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ca7eee9",
   "metadata": {},
   "source": [
    "<a href=\"figures/Banner.jpeg\">\n",
    "    <img src=\"figures/Banner.jpeg\" alt=\"image0\" width=\"100%\">\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80dde2f8",
   "metadata": {},
   "source": [
    "# Pulsed ODMR for Color Centers\n",
    "### Introducing Color Centers\n",
    "One of the commonly known color centers is Nitrogen-Vacancy (NV) centers in diamond. They are unique quantum systems with applications in quantum computing, sensing, and communications.\n",
    "In this notebook, we are going to use the qblox hardware to tune-up NV center qubit.\n",
    "\n",
    "To use a NV-center as a quantum device, it must first be tuned and characterized. The process depends on the specific color center and involves sequential experiments, where each step builds on the results of the previous one. The workflow for tuning a color center is illustrated in the accompanying figure.\n",
    "<p align=\"center\">\n",
    "    <a href=\"figures/experiments.png\">\n",
    "        <img src=\"figures/experiments.png\" alt=\"image1\" width=\"68%\" style=\"display: block; margin: 0 auto;\">\n",
    "    </a>\n",
    "</p>\n",
    "\n",
    "In this diagram, arrows indicate dependencies, showing that experiments are typically conducted from left to right.\n",
    "\n",
    "One key experiment in characterizing color centers is Optically Detected Magnetic Resonance (ODMR), which is analogous to qubit spectroscopy. This notebook demonstrate two crucial methods, **Pulsed ODMR** and **Pulsed ODMR with power**, to measure the energy levels of an NV center."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e7ea503",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:08.555612Z",
     "iopub.status.busy": "2025-03-12T18:00:08.555446Z",
     "iopub.status.idle": "2025-03-12T18:00:12.790071Z",
     "shell.execute_reply": "2025-03-12T18:00:12.788706Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations  # noqa: I001\n",
    "import numpy as np\n",
    "from nv_analysis import odmr_curve_fit\n",
    "import pickle\n",
    "from qcodes.instrument.parameter import ManualParameter\n",
    "from quantify_core.data import handling as dh\n",
    "from quantify_scheduler import QuantumDevice, Schedule, ScheduleGettable\n",
    "from quantify_scheduler.enums import BinMode\n",
    "from quantify_scheduler.operations import (\n",
    "    Measure,\n",
    "    SetClockFrequency,\n",
    "    SquarePulse,\n",
    ")\n",
    "from quantify_scheduler.operations.shared_native_library import SpectroscopyOperation\n",
    "from utils import initialize_hardware"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f7696c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Quantum device settings\n",
    "\n",
    "Here we initialize our `QuantumDevice` and our qubit parameters,  checkout this [tutorial](https://quantify-os.org/docs/quantify-scheduler/tutorials/Operations%20and%20Qubits.html) for further details.\n",
    "\n",
    "In short, a `QuantumDevice` contains device elements where we save our found parameters. Here we are loading a template for 1 qubit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c97a657a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:12.795308Z",
     "iopub.status.busy": "2025-03-12T18:00:12.793984Z",
     "iopub.status.idle": "2025-03-12T18:00:12.800878Z",
     "shell.execute_reply": "2025-03-12T18:00:12.799884Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "device_path = \"devices/nv_center1q.json\"\n",
    "config_path = \"configs/nv_center_qrm.json\"\n",
    "dh.set_datadir(dh.default_datadir())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b2726f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "We define the `QuantumDevice` for NV center as  `nv_center`. Next, we import the device and hardware config json file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "932c0670",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:12.803484Z",
     "iopub.status.busy": "2025-03-12T18:00:12.803197Z",
     "iopub.status.idle": "2025-03-12T18:00:13.496421Z",
     "shell.execute_reply": "2025-03-12T18:00:13.495418Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "nv_center = QuantumDevice.from_json_file(device_path)\n",
    "qe0 = nv_center.get_element(\"qe0\")\n",
    "nv_center.hardware_config.load_from_json_file(config_path)\n",
    "cluster_ip = None  # Fill in the ip to run this tutorial on hardware,\n",
    "\n",
    "meas_ctrl, ic, cluster = initialize_hardware(\n",
    "    nv_center, ip=cluster_ip\n",
    ")  # to initialize the cluster and other instruments in hw config"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e99940e",
   "metadata": {},
   "source": [
    "### Hardware Configuration Overview\n",
    "\n",
    "Here, the NV center setup uses the Qubit Control Module (QCM) baseband module to control the acousto-optic modulator (AOM) for the readout laser. The QCM-RF II controls the qubit states $|0\\rangle$ and $|1\\rangle$ as the computational basis. Photon detectors (PD) are connected to the Qubit Readout Module (QRM) for counting fluorescent photons.\n",
    "<p align=\"center\">\n",
    "    <a href=\"figures/nv_center_1.png\">\n",
    "            <img src=\"figures/nv_center_1.png\" alt=\"image0\" width=\"50%\">\n",
    "    </a>\n",
    "</p>\n",
    "In the above figure, we show how a conventional NV center integrates qualitatively with the Qblox hardware. The hardware configuration (`nv_center.hardware_config`) translates this qualitative understanding into the specific backend setup required for the Qblox cluster.\n",
    "\n",
    "- The readout laser is connected to the optical control port of the quantum device (`qe0:optical_control`). It is responsible for exciting the NV center to its fluorescent state.\n",
    "- The acousto-optic modulator (AOM) precisely controls the readout laser, enabling modulation for optimal readout performance.\n",
    "- To manipulate the $|0\\rangle$-$|1\\rangle$ level, the microwave port of the qubit (`qe0:mw`) is connected to QCM-RF II.\n",
    "- Finally, to measure photon counts, the optical readout port of the quantum device (`qe0:optical_readout`) is connected to QRM (or QTM).\n",
    "\n",
    "More information about ports for NV center can be found [here](https://quantify-os.org/docs/quantify-scheduler/v0.21.2/autoapi/quantify_scheduler/device_under_test/nv_element/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b229ba",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Pulsed ODMR Schedule\n",
    "\n",
    "Pulsed Optically Detected Magnetic Resonance (ODMR) probes the $|0\\rangle$-$|1\\rangle$ qubit transition by sweeping a microwave frequency using the **QCM-RF II**, which drives the transition when it matches the resonance frequency. The readout process involves activating the laser with the **QCM** to excite the color center, causing it to fluoresce, and detecting emitted photons with the **QRM**.\n",
    "\n",
    "For NV centers:\n",
    "- The $|0\\rangle$ state is the **bright state**, producing photon emissions when excited.\n",
    "- The $|1\\rangle$ state is the **dark state**, resulting in negligible photon emissions.\n",
    "\n",
    "The `pulsed_odmr_freq` consists for following steps:\n",
    "1. **Initialization**\n",
    "   - The readout laser is turned on for a fixed duration using a `SquarePulse` to initialize the qubit to $|0\\rangle$.\n",
    "\n",
    "2. **Microwave Frequency Sweep**\n",
    "   - The microwave frequency is set using `SetClockFrequency` at the qubit's microwave port.\n",
    "   - A `SpectroscopyOperation` applies the microwave tone, sweeping through frequencies to probe the $|0\\rangle$-$|1\\rangle$ transition.\n",
    "\n",
    "3. **Readout**\n",
    "   - The `Measure` operation:\n",
    "     - Activates the readout laser for a set duration, followed by acquisition with a time-of-flight delay.\n",
    "     - Accumulates photon counts using `bin_mode=BinMode.SUM`, grouping results into a single bin per frequency across multiple repetitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "654fcab7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.499303Z",
     "iopub.status.busy": "2025-03-12T18:00:13.499113Z",
     "iopub.status.idle": "2025-03-12T18:00:13.505328Z",
     "shell.execute_reply": "2025-03-12T18:00:13.504563Z"
    }
   },
   "outputs": [],
   "source": [
    "def pulsed_odmr_freq(\n",
    "    qubit: QuantumDevice,\n",
    "    spec_frequencies: np.ndarray,\n",
    "    repetitions: int = 1,\n",
    "):\n",
    "    \"\"\"\n",
    "    Construct a pulsed ODMR schedule.\n",
    "\n",
    "    Args:\n",
    "        qubit: The target qubit.\n",
    "        spec_frequencies: Frequency or sweepable range of frequencies\n",
    "        repetitions: Number of repetitions (default: 1).\n",
    "\n",
    "    Returns:\n",
    "        Schedule: A pulsed ODMR schedule.\n",
    "\n",
    "    \"\"\"\n",
    "    # Initialize the schedule with the specified number of repetitions\n",
    "    sched = Schedule(\"Pulsed ODMR\", repetitions=repetitions)\n",
    "\n",
    "    # Define control parameters\n",
    "\n",
    "    clock_op = f\"{qubit.name}.ge0\"\n",
    "    spec_clock = f\"{qubit.name}.spec\"\n",
    "\n",
    "    port_op = f\"{qubit.name}:optical_control\"\n",
    "    amp_op = qubit.measure.pulse_amplitude()\n",
    "    duration_op = qubit.measure.pulse_duration()\n",
    "\n",
    "    # Add an initial optical reset pulse\n",
    "    sched.add(SquarePulse(amp=amp_op, duration=duration_op, port=port_op, clock=clock_op))\n",
    "\n",
    "    # Add operations for each frequency\n",
    "    for idx, spec_freq in enumerate(spec_frequencies):\n",
    "        sched.add(SetClockFrequency(clock=spec_clock, clock_freq_new=spec_freq))\n",
    "        sched.add(SpectroscopyOperation(qubit.name))  # MW pulse\n",
    "        sched.add(Measure(qubit.name, acq_index=idx, bin_mode=BinMode.SUM))  # Measure\n",
    "\n",
    "    return sched"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a47931",
   "metadata": {},
   "source": [
    "Generate a range of frequencies with the defined steps for Pulsed ODMR spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0137725b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.519768Z",
     "iopub.status.busy": "2025-03-12T18:00:13.519245Z",
     "iopub.status.idle": "2025-03-12T18:00:13.528428Z",
     "shell.execute_reply": "2025-03-12T18:00:13.527617Z"
    }
   },
   "outputs": [],
   "source": [
    "spec_freq_param = ManualParameter(\"spec_freq_param\")\n",
    "spec_freq_param.batched = True\n",
    "frequency_steps = 10  # Set the number of frequency steps for the spectroscopy sweep\n",
    "frequency_setpoints = np.linspace(2.52 * 1e9, 2.92 * 1e9, frequency_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d288e89c",
   "metadata": {},
   "source": [
    "Setting the number of repetitions for the schedule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7658e637",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.531134Z",
     "iopub.status.busy": "2025-03-12T18:00:13.530789Z",
     "iopub.status.idle": "2025-03-12T18:00:13.537893Z",
     "shell.execute_reply": "2025-03-12T18:00:13.537035Z"
    }
   },
   "outputs": [],
   "source": [
    "rep = 1\n",
    "nv_center.cfg_sched_repetitions(rep)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a078ec",
   "metadata": {},
   "source": [
    "Create a dictionary of parameters to pass to the pulsed ODMR function and initialize a `ScheduleGettable` to handle data acquisition with the schedule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7cdbc9e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.540437Z",
     "iopub.status.busy": "2025-03-12T18:00:13.540105Z",
     "iopub.status.idle": "2025-03-12T18:00:13.545643Z",
     "shell.execute_reply": "2025-03-12T18:00:13.544872Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "schedule_kwargs = {\n",
    "    \"qubit\": qe0,  # Target qubit for ODMR\n",
    "    \"spec_frequencies\": spec_freq_param,  # Frequencies for the sweep\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb6946b0",
   "metadata": {},
   "source": [
    "Initialize the `ScheduleGettable` to handle data acquisition with the schedule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "efc6e4d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.548575Z",
     "iopub.status.busy": "2025-03-12T18:00:13.548186Z",
     "iopub.status.idle": "2025-03-12T18:00:13.553363Z",
     "shell.execute_reply": "2025-03-12T18:00:13.552678Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "pulsed_odmr_gettable = ScheduleGettable(\n",
    "    quantum_device=nv_center,  # Quantum device object (NV center)\n",
    "    schedule_function=pulsed_odmr_freq,  # Function defining the experiment sequence\n",
    "    schedule_kwargs=schedule_kwargs,  # Parameters for the experiment schedule\n",
    "    batched=True,  # Enable batched data acquisition\n",
    "    data_labels=[\"Accumulative Counts\"],  # Label for data output\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b178c8e9",
   "metadata": {},
   "source": [
    "Running the Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39e413a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.555745Z",
     "iopub.status.busy": "2025-03-12T18:00:13.555514Z",
     "iopub.status.idle": "2025-03-12T18:00:13.910783Z",
     "shell.execute_reply": "2025-03-12T18:00:13.910048Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t spec_freq_param \n",
      "Batch size limit: 10\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69b2878d65ef4fbbbbd3510f88c01674",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meas_ctrl.settables([spec_freq_param])\n",
    "meas_ctrl.setpoints_grid([frequency_setpoints])\n",
    "meas_ctrl.gettables(pulsed_odmr_gettable)\n",
    "dataset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd8bdfb",
   "metadata": {},
   "source": [
    "Looking at the Pulse Schedule Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c1cae977",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:13.914344Z",
     "iopub.status.busy": "2025-03-12T18:00:13.914027Z",
     "iopub.status.idle": "2025-03-12T18:00:14.359790Z",
     "shell.execute_reply": "2025-03-12T18:00:14.359268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x621 with 1 Axes>,\n",
       " <Axes: title={'center': 'Pulsed ODMR'}, xlabel='Time [μs]', ylabel='Amplitude [mV]'>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pulsed_odmr_gettable.compiled_schedule.plot_pulse_diagram()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bce7729",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Analysis\n",
    "Once measured the pulsed ODMR, the acquired data can be fitted to find the qubit transition frequency.\n",
    "\n",
    "Here we analyse the experiment data taken for NV center at Fraunhofer IAF, Freiburg Germany with our modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d2e908b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.362658Z",
     "iopub.status.busy": "2025-03-12T18:00:14.362487Z",
     "iopub.status.idle": "2025-03-12T18:00:14.576432Z",
     "shell.execute_reply": "2025-03-12T18:00:14.575695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transition Frequency: 2.87220 GHz with Contrast: 9.32%\n"
     ]
    }
   ],
   "source": [
    "# If using a dummy cluster, load the dataset from the example data\n",
    "if cluster_ip is None:\n",
    "    with open(\"pulsedodmr_one_amp_.pkl\", \"rb\") as file:\n",
    "        dataset = pickle.load(file)\n",
    "\n",
    "transition_freq = odmr_curve_fit(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2d2e5c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Pulsed ODMR versus Power Schedule\n",
    "To resolve the hyperfine structure of the qubit energy levels, pulsed ODMR is often performed across varying power levels. Additionally, after identifying the qubit’s transition frequency, optimizing the microwave power is crucial for better precision.\n",
    "\n",
    "In Quantify, it’s easy to combine frequency and power sweeps within a single schedule. It only requires to add a few modifications to the existing `pulsed_odmr_freq` schedule.\n",
    "\n",
    "In the newly defined `pulsed_odmr` schedule, the amplitudes to sweep over are provided via the `spec_amps` parameter. In the loop, for each frequency, the amplitude of the spectroscopy operation is adjusted, enabling efficient frequency and power optimization in one seamless schedule. It's that simple!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0056c805",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.578312Z",
     "iopub.status.busy": "2025-03-12T18:00:14.578139Z",
     "iopub.status.idle": "2025-03-12T18:00:14.583543Z",
     "shell.execute_reply": "2025-03-12T18:00:14.582994Z"
    }
   },
   "outputs": [],
   "source": [
    "def pulsed_odmr(\n",
    "    qubit: QuantumDevice,\n",
    "    spec_frequencies: np.ndarray,\n",
    "    spec_amps: np.ndarray,\n",
    "    repetitions: int = 1,\n",
    "):\n",
    "    \"\"\"\n",
    "    Construct a pulsed ODMR schedule.\n",
    "\n",
    "    Args:\n",
    "        qubit: Name of the target qubit.\n",
    "        spec_frequencies: Frequency or sweepable range of frequencies.\n",
    "        spec_amps: Amplitude or sweepable range of amplitudes.\n",
    "        repetitions: Number of repetitions (default: 1).\n",
    "\n",
    "    Returns:\n",
    "        Schedule: A pulsed ODMR schedule.\n",
    "\n",
    "    \"\"\"\n",
    "    # Initialize the schedule with the specified number of repetitions\n",
    "    sched = Schedule(\"Pulsed ODMR\", repetitions=repetitions)\n",
    "\n",
    "    # Define control parameters\n",
    "    port_op = f\"{qubit.name}:optical_control\"\n",
    "    clock_op = f\"{qubit.name}.ge0\"\n",
    "    spec_clock = f\"{qubit.name}.spec\"\n",
    "    amp_op = qubit.measure.pulse_amplitude()\n",
    "    duration_op = qubit.measure.pulse_duration()\n",
    "    # Add an initial optical reset pulse\n",
    "    sched.add(SquarePulse(amp=amp_op, duration=duration_op, port=port_op, clock=clock_op))\n",
    "\n",
    "    # Add operations for each frequency and amplitude\n",
    "    for idx, (spec_freq, spec_amp) in enumerate(zip(spec_frequencies, spec_amps)):\n",
    "        sched.add(SetClockFrequency(clock=spec_clock, clock_freq_new=spec_freq))\n",
    "        sched.add(SpectroscopyOperation(qubit.name, amplitude=spec_amp))  # MW pulse\n",
    "        sched.add(Measure(qubit.name, acq_index=idx, bin_mode=BinMode.SUM))  # Measure\n",
    "\n",
    "    return sched"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa645ad0",
   "metadata": {},
   "source": [
    "In addition with defining the frequency setpoints, we define the amplitude setpoints as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1440763",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.585121Z",
     "iopub.status.busy": "2025-03-12T18:00:14.584959Z",
     "iopub.status.idle": "2025-03-12T18:00:14.588788Z",
     "shell.execute_reply": "2025-03-12T18:00:14.588373Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "spec_freq_param = ManualParameter(\"spec_freq_param\")\n",
    "spec_freq_param.batched = True\n",
    "frequency_steps = 4  # Set the number of frequency steps for the spectroscopy sweep\n",
    "frequency_setpoints = np.linspace(\n",
    "    2.52 * 1e9, 2.92 * 1e9, frequency_steps\n",
    ")  # Generate a range of frequencies with the defined steps\n",
    "\n",
    "spec_amp_param = ManualParameter(\"spec_amp_param\")\n",
    "spec_amp_param.batched = True\n",
    "amplitude_steps = 4  # Set the numb3r of amplitude steps for the spectroscopy sweep\n",
    "amplitude_setpoints = np.linspace(\n",
    "    0.1, 1, amplitude_steps\n",
    ")  # Generate a range of amplitude with the defined steps\n",
    "\n",
    "rep = 1\n",
    "nv_center.cfg_sched_repetitions(rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0765e36e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.590075Z",
     "iopub.status.busy": "2025-03-12T18:00:14.589916Z",
     "iopub.status.idle": "2025-03-12T18:00:14.592301Z",
     "shell.execute_reply": "2025-03-12T18:00:14.591912Z"
    }
   },
   "outputs": [],
   "source": [
    "schedule_kwargs = {\n",
    "    \"qubit\": qe0,  # Target qubit for ODMR\n",
    "    \"spec_frequencies\": spec_freq_param,  # Frequencies for the sweep\n",
    "    \"spec_amps\": spec_amp_param,  # amplitude for the sweep\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b571b8de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.593628Z",
     "iopub.status.busy": "2025-03-12T18:00:14.593475Z",
     "iopub.status.idle": "2025-03-12T18:00:14.596072Z",
     "shell.execute_reply": "2025-03-12T18:00:14.595676Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "pulsed_odmr_gettable = ScheduleGettable(\n",
    "    quantum_device=nv_center,  # Quantum device object (NV center)\n",
    "    schedule_function=pulsed_odmr,  # Function defining the experiment sequence\n",
    "    schedule_kwargs=schedule_kwargs,  # Parameters for the experiment schedule\n",
    "    batched=True,  # Enable batched data acquisition\n",
    "    data_labels=[\"Accumulative Counts\"],  # Label for data output\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e4b07f43",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.597392Z",
     "iopub.status.busy": "2025-03-12T18:00:14.597237Z",
     "iopub.status.idle": "2025-03-12T18:00:14.850018Z",
     "shell.execute_reply": "2025-03-12T18:00:14.849320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t spec_freq_param, spec_amp_param \n",
      "Batch size limit: 16\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4383dfab2984485bae87c98c0140ad79",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meas_ctrl.settables(\n",
    "    [spec_freq_param, spec_amp_param]\n",
    ")  # Set parameters to vary: acquisition index and spectroscopy frequency\n",
    "meas_ctrl.setpoints_grid([frequency_setpoints, amplitude_setpoints])\n",
    "meas_ctrl.gettables(pulsed_odmr_gettable)\n",
    "dataset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "352ba561",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-12T18:00:14.853077Z",
     "iopub.status.busy": "2025-03-12T18:00:14.852794Z",
     "iopub.status.idle": "2025-03-12T18:00:15.164327Z",
     "shell.execute_reply": "2025-03-12T18:00:15.163201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1000x621 with 1 Axes>,\n",
       " <Axes: title={'center': 'Pulsed ODMR'}, xlabel='Time [μs]', ylabel='Amplitude [V]'>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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