{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03cc92e0",
   "metadata": {},
   "source": [
    "# Real Time Predistortions\n",
    "This tutorial demonstrates the use of real time predistortions feature available on a Qblox QCM.\n",
    "Please familiarize yourself with the feature by going through our user guide on [Real Time Predistortions](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/cluster/real_time_predistortions.html).\n",
    "\n",
    "## Setup\n",
    "First, we are going to import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5176aa15",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:18.524104Z",
     "iopub.status.busy": "2024-10-17T13:20:18.522623Z",
     "iopub.status.idle": "2024-10-17T13:20:19.437501Z",
     "shell.execute_reply": "2024-10-17T13:20:19.436684Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d98d11",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc14615f",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23643193",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:19.440442Z",
     "iopub.status.busy": "2024-10-17T13:20:19.440014Z",
     "iopub.status.idle": "2024-10-17T13:20:19.444415Z",
     "shell.execute_reply": "2024-10-17T13:20:19.443732Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e76800",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b61bf2c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:19.446632Z",
     "iopub.status.busy": "2024-10-17T13:20:19.446404Z",
     "iopub.status.idle": "2024-10-17T13:20:20.269213Z",
     "shell.execute_reply": "2024-10-17T13:20:20.268416Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971045db",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "06bf20b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:20.271729Z",
     "iopub.status.busy": "2024-10-17T13:20:20.271471Z",
     "iopub.status.idle": "2024-10-17T13:20:20.276423Z",
     "shell.execute_reply": "2024-10-17T13:20:20.275709Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, Module]:\n",
    "    def checked_filter_fn(mod: ClusterType) -> bool:\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87349b7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:20.278851Z",
     "iopub.status.busy": "2024-10-17T13:20:20.278589Z",
     "iopub.status.idle": "2024-10-17T13:20:20.305032Z",
     "shell.execute_reply": "2024-10-17T13:20:20.304255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <Module: cluster0_module2 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QCM baseband modules\n",
    "modules = get_connected_modules(cluster, lambda mod: mod.is_qcm_type and not mod.is_rf_type)\n",
    "modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7d0574a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:20.307308Z",
     "iopub.status.busy": "2024-10-17T13:20:20.307081Z",
     "iopub.status.idle": "2024-10-17T13:20:20.310870Z",
     "shell.execute_reply": "2024-10-17T13:20:20.310191Z"
    }
   },
   "outputs": [],
   "source": [
    "module = modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00f106e",
   "metadata": {},
   "source": [
    "### Reset the Cluster\n",
    "\n",
    "We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters, so resetting between experiments is usually not desirable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c754ef27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:20.313253Z",
     "iopub.status.busy": "2024-10-17T13:20:20.313029Z",
     "iopub.status.idle": "2024-10-17T13:20:22.761856Z",
     "shell.execute_reply": "2024-10-17T13:20:22.760648Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07cd4538",
   "metadata": {},
   "source": [
    "### Upload a sequence\n",
    "\n",
    "Note that we are using a sequence that plays a square pulse once and uses no waveform memory, only voltage offsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12c75b23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.766384Z",
     "iopub.status.busy": "2024-10-17T13:20:22.765640Z",
     "iopub.status.idle": "2024-10-17T13:20:22.804630Z",
     "shell.execute_reply": "2024-10-17T13:20:22.803342Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sequence program.\n",
    "seq_prog = f\"\"\"\n",
    "set_mrk       {0b1111}          #Set all marker outputs to 1.\n",
    "set_awg_offs  1638, 1638      #Set the offset of the AWG to about 0.3 of the max.\n",
    "upd_param     1000              #Update parameters and wait 1 us.\n",
    "set_mrk       {0b0000}          #Set all marker outputs to 0.\n",
    "set_awg_offs  0, 0              #Set the offset of the AWG to zero.\n",
    "upd_param     1000              #Update parameters and wait 1 us.\n",
    "stop                            #Stop the sequence.\n",
    "\"\"\"\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": {},\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "module.sequencer0.sequence(sequence)\n",
    "module.disconnect_outputs()\n",
    "module.sequencer0.connect_sequencer(\"out0_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5287fab",
   "metadata": {},
   "source": [
    "Now let's start the sequence. If you want to observe the sequence, this is the time to connect an oscilloscope to marker output 1 and one or more\n",
    "of the four outputs. Configure the oscilloscope to trigger a *single* time on the marker output 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eea43d24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.808782Z",
     "iopub.status.busy": "2024-10-17T13:20:22.808341Z",
     "iopub.status.idle": "2024-10-17T13:20:22.822377Z",
     "shell.execute_reply": "2024-10-17T13:20:22.821166Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e499f897",
   "metadata": {},
   "source": [
    "The following is data acquired from a scope.\n",
    "\n",
    "<img src=\"./rtp_figs/before_rtp.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5713a074",
   "metadata": {},
   "source": [
    "## Apply Predistortions\n",
    "Each output of a QCM module has the following filters:\n",
    "\n",
    "1. 4 x Exponential overshoot filter\n",
    "    This filter corrects for an exponential overshoot or undershoot.\n",
    "    It is characterized by two coefficients, the time constant and the relative amplitude of the overshoot/undershoot.\n",
    "\n",
    "2. 1 x FIR filter\n",
    "    This filter may be used to correct for arbitrary distortions.\n",
    "    It provides 32 configurable coefficients corresponding to 32 ns.\n",
    "\n",
    "Each of the above filters can be in one of three states in a QCM:\n",
    "\n",
    "1. `enabled`: The filter is enabled. Note that the playback is delayed in this case.\n",
    "\n",
    "2. `bypassed`: The filter is disabled.\n",
    "\n",
    "3. `delay_comp`: The filter is disabled but is delayed as if it were enabled.\n",
    "\n",
    "In a QRM, QCM-RF or QRM-RF there are no filters, and so the `enabled` mode doesn't exist."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f10d14",
   "metadata": {},
   "source": [
    "### Exponential overshoot filter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b9ebae",
   "metadata": {},
   "source": [
    "In the scope data shown previously, if we zoom in to the maximum value of the square pulse, we see that there is an exponential overshoot.\n",
    "The fit shows the same, characterized by a relative amplitude and a time constant which we can use to correct the overshoot.\n",
    "\n",
    "The fit function used is:\n",
    "$$ f(t) =\n",
    "\\begin{cases}\n",
    "A_p(1 + Ae^{-t/\\tau}) \\text{ if } t > 0\\\\ % # noqa: ERA001\n",
    "0 \\text{ if } t < 0\n",
    "\\end{cases}$$\n",
    "where,\n",
    "\n",
    "$A_p$ is the pulse amplitude\n",
    "\n",
    "$A$ is the amplitude of the exponential overshoot\n",
    "\n",
    "$\\tau$ is the time constant of the exponential overshoot\n",
    "\n",
    "<img src=\"./rtp_figs/before_rtp_zoom.svg\" alt=\"drawing\" width=\"50%\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f68f751",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.827012Z",
     "iopub.status.busy": "2024-10-17T13:20:22.826201Z",
     "iopub.status.idle": "2024-10-17T13:20:22.878474Z",
     "shell.execute_reply": "2024-10-17T13:20:22.877165Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out0_exp0_time_constant(2019)  # ~2 us time constant\n",
    "module.out0_exp0_amplitude(0.01044)  # relative amplitude of 0.01\n",
    "# Note that a negative amplitude corrects for an undershoot.\n",
    "module.out0_exp0_config(\"enabled\")  # enable the exponential overshoot correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6492c76b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.882916Z",
     "iopub.status.busy": "2024-10-17T13:20:22.882006Z",
     "iopub.status.idle": "2024-10-17T13:20:22.896533Z",
     "shell.execute_reply": "2024-10-17T13:20:22.895260Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea84fc22",
   "metadata": {},
   "source": [
    "The following is data acquired after correction with one exponential overshoot filter.\n",
    "\n",
    "<img src=\"./rtp_figs/after_one_exp.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68e604d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.901307Z",
     "iopub.status.busy": "2024-10-17T13:20:22.900332Z",
     "iopub.status.idle": "2024-10-17T13:20:22.952542Z",
     "shell.execute_reply": "2024-10-17T13:20:22.951199Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out0_exp1_time_constant(313)  # 313 ns time constant\n",
    "module.out0_exp1_amplitude(0.00617)  # relative amplitude of 0.006\n",
    "# Note that a negative amplitude corrects for an undershoot\n",
    "module.out0_exp1_config(\"enabled\")  # enable the exponential overshoot correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85978454",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.957213Z",
     "iopub.status.busy": "2024-10-17T13:20:22.956263Z",
     "iopub.status.idle": "2024-10-17T13:20:22.970656Z",
     "shell.execute_reply": "2024-10-17T13:20:22.969505Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b9b97cd",
   "metadata": {},
   "source": [
    "The following is data acquired after correction with two exponential overshoot filters.\n",
    "\n",
    "<img src=\"./rtp_figs/after_two_exp.svg\" alt=\"drawing\" width=\"50%\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce78f8f1",
   "metadata": {},
   "source": [
    "### FIR filter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f8342d",
   "metadata": {},
   "source": [
    "We can zoom in even more to see that there are short timescale distortions.\n",
    "The prediction is a convolution of the data using a best fit of 32 FIR coefficients.\n",
    "\n",
    "<img src=\"./rtp_figs/after_two_exp_zoom.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cfbe7575",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:22.975130Z",
     "iopub.status.busy": "2024-10-17T13:20:22.974511Z",
     "iopub.status.idle": "2024-10-17T13:20:23.011563Z",
     "shell.execute_reply": "2024-10-17T13:20:23.010876Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out0_fir_coeffs(\n",
    "    [  # 32 FIR coefficients (corresponding to 32 samples or ns)\n",
    "        # may be used. Each with a range of [-2,2).\n",
    "        7.48915982e-01,\n",
    "        9.69579219e-02,\n",
    "        9.52478332e-02,\n",
    "        9.81260837e-02,\n",
    "        -3.27428212e-02,\n",
    "        6.12058070e-03,\n",
    "        -1.29196458e-02,\n",
    "        6.58390575e-03,\n",
    "        -3.33153314e-03,\n",
    "        -3.51480767e-03,\n",
    "        9.60311013e-04,\n",
    "        -2.87579262e-03,\n",
    "        6.52713771e-03,\n",
    "        -2.56164552e-03,\n",
    "        -3.57635616e-04,\n",
    "        6.04179726e-04,\n",
    "        -1.12007036e-03,\n",
    "        6.47927968e-04,\n",
    "        -7.30273082e-04,\n",
    "        1.47880116e-04,\n",
    "        8.68155082e-05,\n",
    "        -3.04216668e-04,\n",
    "        -2.92916380e-04,\n",
    "        4.34019812e-04,\n",
    "        -8.44964139e-04,\n",
    "        8.17907868e-04,\n",
    "        -8.49444212e-04,\n",
    "        7.95641409e-04,\n",
    "        6.26446369e-05,\n",
    "        -6.65652499e-04,\n",
    "        1.23404188e-03,\n",
    "        -1.01509375e-03,\n",
    "    ]\n",
    ")\n",
    "module.out0_fir_config(\"enabled\")  # enable the exponential overshoot correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b0ccffa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.016048Z",
     "iopub.status.busy": "2024-10-17T13:20:23.014937Z",
     "iopub.status.idle": "2024-10-17T13:20:23.029591Z",
     "shell.execute_reply": "2024-10-17T13:20:23.028547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23dbebd5",
   "metadata": {},
   "source": [
    "Here is the scope acquisition after the FIR coefficients are applied to the device.\n",
    "\n",
    "<img src=\"./rtp_figs/after_fir.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c3d777",
   "metadata": {},
   "source": [
    "## Filter latency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94c71391",
   "metadata": {},
   "source": [
    "Looking at output 2 on the scope we see that the distortion corrected pulse is delayed.\n",
    "\n",
    "<img src=\"./rtp_figs/nd_9.svg\" alt=\"drawing\" width=\"50%\"/>\n",
    "\n",
    "Note that the two pulses have been plotted with offsets to make them both visible.\n",
    "\n",
    "We can also get the delay caused by the filters using `module.out{x}_latency()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6efadea5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.034577Z",
     "iopub.status.busy": "2024-10-17T13:20:23.033506Z",
     "iopub.status.idle": "2024-10-17T13:20:23.045812Z",
     "shell.execute_reply": "2024-10-17T13:20:23.044784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4e-07 0.0\n"
     ]
    }
   ],
   "source": [
    "print(module.out0_latency(), module.out1_latency())  # Compare latencies of the two outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe37cfc5",
   "metadata": {},
   "source": [
    "### Delay compensation\n",
    "In order to have the two outputs be synchronized in time, we can enable delay compensation on output 2 for the 3 filters that we enabled.\n",
    "\n",
    "**Note**: Using delay compensation increases the output path latency, and so also increases the time of flight. This means you may haev to adjust your acquisition delay accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7977ea86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.050025Z",
     "iopub.status.busy": "2024-10-17T13:20:23.049278Z",
     "iopub.status.idle": "2024-10-17T13:20:23.102913Z",
     "shell.execute_reply": "2024-10-17T13:20:23.101771Z"
    }
   },
   "outputs": [],
   "source": [
    "module.out1_exp0_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 2 for the exponential overshoot correction 0\n",
    "module.out1_exp1_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 2 for the exponential overshoot correction 1\n",
    "module.out1_fir_config(\"delay_comp\")  # enable delay compensation on output 2 for the FIR filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91029356",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.107544Z",
     "iopub.status.busy": "2024-10-17T13:20:23.106427Z",
     "iopub.status.idle": "2024-10-17T13:20:23.121188Z",
     "shell.execute_reply": "2024-10-17T13:20:23.120118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "375525a4",
   "metadata": {},
   "source": [
    "After delay compensation, here are the two waveforms.\n",
    "\n",
    "<img src=\"./rtp_figs/nd_10.svg\" alt=\"drawing\" width=\"50%\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b87cffd",
   "metadata": {},
   "source": [
    "## Marker and non-QCM outputs\n",
    "\n",
    "All marker channels on the Cluster and outputs that do not belong to a QCM (i.e. QRM, QCM-RF and QRM-RF outputs), do not have predistortion filters. However, they might need to be synchronized with QCM outputs.\n",
    "\n",
    "To do this on a non-QCM output, you may use the same function as in a QCM output, i.e. `module.out{x}_exp0_config(\"delay_comp\")`.\n",
    "To do this on a marker output, you may use the `module.marker{x}_exp0_config(\"delay_comp\")`.\n",
    "\n",
    "For the above example, to ensure that pulses from output 1 of a QCM-RF in slot 6 and marker 3 of the QCM coincide with output from QCM, use the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba636a02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.126202Z",
     "iopub.status.busy": "2024-10-17T13:20:23.124987Z",
     "iopub.status.idle": "2024-10-17T13:20:23.228367Z",
     "shell.execute_reply": "2024-10-17T13:20:23.227123Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster.module4.out0_exp0_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the exponential overshoot correction 0\n",
    "cluster.module4.out0_exp1_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the exponential overshoot correction 1\n",
    "cluster.module4.out0_exp2_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the exponential overshoot correction 2\n",
    "cluster.module4.out0_fir_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on output 1 for the FIR filter\n",
    "\n",
    "module.marker2_exp0_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on marker 3 for the exponential overshoot correction 0\n",
    "module.marker2_exp1_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on marker 3 for the exponential overshoot correction 1\n",
    "module.marker2_exp2_config(\n",
    "    \"delay_comp\"\n",
    ")  # enable delay compensation on marker 3 for the exponential overshoot correction 2\n",
    "module.marker2_fir_config(\"delay_comp\")  # enable delay compensation on marker 3 for the FIR filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87750e1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.232545Z",
     "iopub.status.busy": "2024-10-17T13:20:23.231885Z",
     "iopub.status.idle": "2024-10-17T13:20:23.246874Z",
     "shell.execute_reply": "2024-10-17T13:20:23.245835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: NONE, Warning Flags: NONE, Error Flags: NONE, Log: []"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Arm and start both sequencers.\n",
    "module.arm_sequencer(0)\n",
    "module.start_sequencer()\n",
    "\n",
    "# Print status of both sequencers.\n",
    "print(module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da94118",
   "metadata": {},
   "source": [
    "## Simulator Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb793a5",
   "metadata": {},
   "source": [
    "The filters mentioned above may be simulated using functions from `qblox_instruments.simulations.predistortions`. The functions listed here allow you to predict the behaviour of the filters given the coefficients that are applied to them, and can be used to determine the correct coefficients.\n",
    "\n",
    "This can be done by measuring the impulse response $h$ of the path to the DUT perhaps using an oscilloscope, or a technique that is specific to your system (like the Cryoscope). The impulse response can then be inverted to get $h_{inv}$.  The simulation functions can then be used to validate these filter coefficients and, together with an iterative optimization scheme, to find the values that best match the inverse impulse response.\n",
    "\n",
    "We demonstrate the utility of these functions through a straightforward example where we apply the simulator function to visualize the effects of a filter designed to correct for exponential overshoot. We follow these steps:\n",
    "\n",
    "1. Play a square waveform as done in the previous sections without applying any predistortions.\n",
    "2. Obtain the waveform samples from the oscilloscope (or any equivalent measurement device such as a Cluster QRM).\n",
    "3. Fit the waveform for an exponential overshoot/undershoot to the form $f(t) =A_p(1 + Ae^{-t/\\tau})$ and thereby obtain the filter coefficients $AA_p$ and $\\tau$.\n",
    "4. Pass these filter coefficients and the waveform samples obtain to the simulator function to see the corrected waveform as seen by the measurement device (oscilloscope/QRM)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ad6433",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Plotting the Obtained Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a36e2d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.251355Z",
     "iopub.status.busy": "2024-10-17T13:20:23.250615Z",
     "iopub.status.idle": "2024-10-17T13:20:23.257979Z",
     "shell.execute_reply": "2024-10-17T13:20:23.256887Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Define t(time samples) and waveform_captured\n",
    "# Here we use a simulated trace\n",
    "def func(x: np.ndarray, a_p: float, a: float, tau: float) -> np.ndarray:\n",
    "    return a_p * (1 + a * np.exp(-x / tau))\n",
    "\n",
    "\n",
    "t = np.arange(1000)\n",
    "noise = np.random.normal(0, 2e-4, size=(t.size,))\n",
    "waveform_captured = func(t, a_p=9.4e-2, a=-3.5e-2, tau=200) + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b9cdfddd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.262374Z",
     "iopub.status.busy": "2024-10-17T13:20:23.261334Z",
     "iopub.status.idle": "2024-10-17T13:20:23.581729Z",
     "shell.execute_reply": "2024-10-17T13:20:23.580580Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, waveform_captured, \"ko\", label=\"Original Captured Waveform\")\n",
    "plt.xlabel(\"Time (in ns)\")\n",
    "plt.ylabel(\"Amplitude (in V)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f67bee8",
   "metadata": {},
   "source": [
    "We can see that there is an exponential undershoot as captured by the oscilloscope. We will now try to fit these samples to the form $ f(t) =A_p(1 + Ae^{-t/\\tau})$ using the `scipy.optimize.curve_fit` module."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379537fc",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "### Curve Fitting to an Exponential Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cdc90910",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.601278Z",
     "iopub.status.busy": "2024-10-17T13:20:23.600762Z",
     "iopub.status.idle": "2024-10-17T13:20:23.610502Z",
     "shell.execute_reply": "2024-10-17T13:20:23.609319Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_p, a, tau are : [ 9.40002195e-02 -3.48428985e-02  2.01082205e+02]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Fit curve using initial guess p0\n",
    "popt, pcov = curve_fit(func, t, waveform_captured, p0=(1, 0.4, 400))\n",
    "\n",
    "print(f\"a_p, a, tau are : {popt}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4d211a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.626238Z",
     "iopub.status.busy": "2024-10-17T13:20:23.625480Z",
     "iopub.status.idle": "2024-10-17T13:20:23.864481Z",
     "shell.execute_reply": "2024-10-17T13:20:23.863422Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, waveform_captured, \"ko\", label=\"Original Data\")\n",
    "plt.plot(t, func(t, *popt), \"r-\", label=\"Fitted Curve\")\n",
    "plt.xlabel(\"Time (in ns)\")\n",
    "plt.ylabel(\"Amplitude (in V)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6c13f70",
   "metadata": {},
   "source": [
    "The `a` and `tau` values correspond to the $A$ and $\\tau$ filter coefficients for the exponential overshoot filter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d40bbcb",
   "metadata": {},
   "source": [
    "### Simulate the Corrections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facc4df5",
   "metadata": {},
   "source": [
    "We now import the utility functions from the qblox-instruments driver and pass these filter coefficients to validate the distortion corrections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e14fd76e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:23.868335Z",
     "iopub.status.busy": "2024-10-17T13:20:23.867639Z",
     "iopub.status.idle": "2024-10-17T13:20:24.272511Z",
     "shell.execute_reply": "2024-10-17T13:20:24.271130Z"
    }
   },
   "outputs": [],
   "source": [
    "from qblox_instruments.simulations.predistortions import exponential_overshoot_correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8d22b851",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:24.276715Z",
     "iopub.status.busy": "2024-10-17T13:20:24.275797Z",
     "iopub.status.idle": "2024-10-17T13:20:24.281962Z",
     "shell.execute_reply": "2024-10-17T13:20:24.280764Z"
    }
   },
   "outputs": [],
   "source": [
    "amp = popt[1]\n",
    "tau = popt[2]\n",
    "\n",
    "# Pass the captured waveform and filter coefficients to the simulator functions\n",
    "corrected_waveform = exponential_overshoot_correction(sig=waveform_captured, amp=amp, tau=tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2e453b3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:20:24.286058Z",
     "iopub.status.busy": "2024-10-17T13:20:24.285319Z",
     "iopub.status.idle": "2024-10-17T13:20:24.562413Z",
     "shell.execute_reply": "2024-10-17T13:20:24.561259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t, corrected_waveform, \"r-\", label=\"Corrected Waveform\")\n",
    "plt.plot(t, waveform_captured, \"g-\", label=\"Original Waveform\")\n",
    "plt.xlabel(\"Time (in ns)\")\n",
    "plt.ylabel(\"Amplitude (in V)\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "289b03d6",
   "metadata": {},
   "source": [
    "**PLEASE NOTE** : The argument `sig` of the simulator function takes in waveform samples only at 1 GSPS (1 Gigasample per second) owing to the sampling rate of the Digital-to-Analog converters present in the Cluster QCM modules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff26bff",
   "metadata": {},
   "source": [
    "Following the procedure above, you can also simulate the behaviour for FIR coefficients using `qblox_instruments.simulations.predistortions.fir_correction`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb266fe",
   "metadata": {},
   "source": [
    "## Things to note:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "356ba5f6",
   "metadata": {},
   "source": [
    "When the filters are enabled, they are applied on a continuous data stream.\n",
    "When filter parameters are changed, the sequencer is force stopped, and the data stream is changed to all zeros.\n",
    "This means that when changing filter parameters, there might be transient effects.\n",
    "These can be avoided by stopping the sequencer connected to the output before changing the filter parameters.\n",
    "All active sequencers of a certain can be stopped using `module.stop_sequencer()`"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}