{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3082d18-56df-4834-8963-586a5500bced",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.003289,
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     "exception": false,
     "start_time": "2025-01-30T07:32:13.995890",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Decoding Speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "afceb91f-5b83-487f-96f9-5015002f5daf",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.011568,
     "end_time": "2025-01-30T07:32:14.013323",
     "exception": false,
     "start_time": "2025-01-30T07:32:14.001755",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# This cell is tagged `parameters` and will be override by `papermill`\n",
    "decoder: str = \"mwpm\"\n",
    "json_filename: str = \"speed-rsc-mwpm.json\"  # where to save the result, must be provided\n",
    "\n",
    "rounds: int = 200\n",
    "kwargs = dict(\n",
    "    min_time = 60,\n",
    "    min_shots = 50,\n",
    "    min_init_time = 0.1,\n",
    "    min_init_shots = 1,\n",
    ")\n",
    "max_per_round_time: float = 0.1  # if each round takes more than 100ms to decode, then do not run the next `d`, since it is not meaningful anymore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a85bdcb",
   "metadata": {
    "papermill": {
     "duration": 0.006747,
     "end_time": "2025-01-30T07:32:14.022254",
     "exception": false,
     "start_time": "2025-01-30T07:32:14.015507",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "decoder = \"bposd(max_iter=5)\"\n",
    "json_filename = \"speed-rsc-bposd-it5.json\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e4bf8f4-0e7c-453e-aacd-18c9061a999c",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.015552,
     "end_time": "2025-01-30T07:32:14.039721",
     "exception": false,
     "start_time": "2025-01-30T07:32:14.024169",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "552dcc10-a319-487b-a20b-09221382176b",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.010501,
     "end_time": "2025-01-30T07:32:14.051958",
     "exception": false,
     "start_time": "2025-01-30T07:32:14.041457",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import psutil\n",
    "# psutil.Process().nice(0)# if on *ux\n",
    "# psutil.Process().nice(psutil.IDLE_PRIORITY_CLASS)# if on win"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4ce38b6-65b8-4ec5-b097-aab9f6943e10",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 1.193746,
     "end_time": "2025-01-30T07:32:15.247200",
     "exception": false,
     "start_time": "2025-01-30T07:32:14.053454",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from qec_lego_bench.cli.decoding_speed import decoding_speed, DecodingSpeedResult\n",
    "from qec_lego_bench.hpc.job_store import Job, JobStore\n",
    "from qec_lego_bench.hpc.plotter.logical_error_rate_plotter import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2eb79028-afab-46a7-a36e-f567fd486716",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 68.932249,
     "end_time": "2025-01-30T07:33:24.180940",
     "exception": false,
     "start_time": "2025-01-30T07:32:15.248691",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d_vec: [3, 5, 7, 9, 11, 13, 15, 17, 19, 23, 27, 31, 37, 43, 51]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 4 shots, elapsed: 0.011366s, average: 2.842e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 8 shots, elapsed: 0.022194s, average: 2.774e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 16 shots, elapsed: 0.042255s, average: 2.641e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 32 shots, elapsed: 0.086256s, average: 2.696e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 64 shots, elapsed: 0.173953s, average: 2.718e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "initialization time: 2.718e-03s\n",
      "\r\n",
      "\u001b[KEvaluating decoding of 50 shots, elapsed: 0.000955s, average: 1.910e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 100 shots, elapsed: 0.001208s, average: 1.208e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 200 shots, elapsed: 0.001823s, average: 9.117e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 400 shots, elapsed: 0.003245s, average: 8.113e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 800 shots, elapsed: 0.006151s, average: 7.689e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 1600 shots, elapsed: 0.011835s, average: 7.397e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3200 shots, elapsed: 0.022817s, average: 7.130e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 6400 shots, elapsed: 0.045404s, average: 7.094e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 12800 shots, elapsed: 0.091847s, average: 7.176e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 25600 shots, elapsed: 0.181618s, average: 7.094e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 51200 shots, elapsed: 0.354046s, average: 6.915e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 102400 shots, elapsed: 0.713566s, average: 6.968e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 204800 shots, elapsed: 1.466339s, average: 7.160e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 9218056 shots, elapsed: 64.380725s, average: 6.984e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "decoding time: 6.984e-06s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecodingSpeedResult(elapsed=64.38072477282367, shots=9218056)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_d_vec(ratio: float = 1.18, max_d: int = 51) -> list[int]:\n",
    "    d = 3\n",
    "    d_vec = [d]\n",
    "    while d <= max_d:\n",
    "        int_d = 2 * round(d / 2) + 1\n",
    "        if int_d != d_vec[-1]:\n",
    "            d_vec.append(int_d)\n",
    "        d *= ratio\n",
    "    return d_vec\n",
    "d_vec = generate_d_vec()\n",
    "print(\"d_vec:\", d_vec)\n",
    "p_vec = [0.00001, 0.0001, 0.001, 0.003, 0.01]\n",
    "\n",
    "def evaluation_function(d: int, p: float, rounds: int, verbose: bool = True) -> DecodingSpeedResult:\n",
    "    if verbose:\n",
    "        print(f\"d: {d}, p: {p}\")\n",
    "    return decoding_speed(decoder=decoder, code=f\"rsc(d={d},p={p})\", **kwargs, no_print=not verbose)\n",
    "\n",
    "jobs = [Job(d=d, p=p, rounds=rounds) for p in p_vec for d in d_vec[:1]]  # only add the smallest d and dynamically add others\n",
    "# evaluation_function(min(d_vec), min(p_vec), rounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd460d0-efa9-4075-9a1d-a62791230623",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.001777,
     "end_time": "2025-01-30T07:33:24.184867",
     "exception": false,
     "start_time": "2025-01-30T07:33:24.183090",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Define the plot functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aea5f255-0cca-4a5b-a43d-f5d4b460d502",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.027425,
     "end_time": "2025-01-30T07:33:24.214059",
     "exception": false,
     "start_time": "2025-01-30T07:33:24.186634",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from decoding_time_plotter import DecodingTimePlotter\n",
    "\n",
    "plotter = DecodingTimePlotter(p_vec=p_vec, d_vec=d_vec, rounds=rounds, max_per_round_time=max_per_round_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1cfa5901-2d84-4321-90e2-220ac77b9da1",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.383622,
     "end_time": "2025-01-30T07:33:24.599851",
     "exception": false,
     "start_time": "2025-01-30T07:33:24.216229",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "job_store = JobStore(evaluation_function, jobs, result_type=DecodingSpeedResult, filename=json_filename)\n",
    "job_store.execute(loop_callback=plotter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2edb553-f1b5-41ba-9643-86978a83b3e8",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002069,
     "end_time": "2025-01-30T07:33:24.604372",
     "exception": false,
     "start_time": "2025-01-30T07:33:24.602303",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.11.6"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 71.980959,
   "end_time": "2025-01-30T07:33:25.023493",
   "environment_variables": {},
   "exception": null,
   "input_path": "decoding_speed_mwpm.ipynb",
   "output_path": "decoding_speed_bposd_it5.ipynb",
   "parameters": {
    "decoder": "bposd(max_iter=5)",
    "json_filename": "speed-rsc-bposd-it5.json"
   },
   "start_time": "2025-01-30T07:32:13.042534",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}