{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3082d18-56df-4834-8963-586a5500bced",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.00385,
     "end_time": "2025-01-30T07:33:26.503739",
     "exception": false,
     "start_time": "2025-01-30T07:33:26.499889",
     "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": 0.011374,
     "end_time": "2025-01-30T07:33:26.517654",
     "exception": false,
     "start_time": "2025-01-30T07:33:26.506280",
     "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": "6bc9d69f",
   "metadata": {
    "papermill": {
     "duration": 0.006633,
     "end_time": "2025-01-30T07:33:26.526409",
     "exception": false,
     "start_time": "2025-01-30T07:33:26.519776",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "decoder = \"bplsd(max_iter=5)\"\n",
    "json_filename = \"speed-rsc-bplsd-it5.json\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e4bf8f4-0e7c-453e-aacd-18c9061a999c",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.015115,
     "end_time": "2025-01-30T07:33:26.543397",
     "exception": false,
     "start_time": "2025-01-30T07:33:26.528282",
     "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": 0.009904,
     "end_time": "2025-01-30T07:33:26.554807",
     "exception": false,
     "start_time": "2025-01-30T07:33:26.544903",
     "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": 1.168433,
     "end_time": "2025-01-30T07:33:27.724733",
     "exception": false,
     "start_time": "2025-01-30T07:33:26.556300",
     "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": 70.921702,
     "end_time": "2025-01-30T07:34:38.647958",
     "exception": false,
     "start_time": "2025-01-30T07:33:27.726256",
     "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: 0.011155s, average: 2.789e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 8 shots, elapsed: 0.021314s, average: 2.664e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 16 shots, elapsed: 0.043030s, average: 2.689e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 32 shots, elapsed: 0.087016s, average: 2.719e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 64 shots, elapsed: 0.273866s, average: 4.279e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "initialization time: 4.279e-03s\n",
      "\r\n",
      "\u001b[KEvaluating decoding of 50 shots, elapsed: -0.000131s, average: -2.624e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 100 shots, elapsed: 0.000644s, average: 6.438e-06s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 200 shots, elapsed: 0.002444s, average: 1.222e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 400 shots, elapsed: 0.006183s, average: 1.546e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 800 shots, elapsed: 0.013238s, average: 1.655e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 1600 shots, elapsed: 0.028325s, average: 1.770e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3200 shots, elapsed: 0.057645s, average: 1.801e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 6400 shots, elapsed: 0.117827s, average: 1.841e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 12800 shots, elapsed: 0.234562s, average: 1.833e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 25600 shots, elapsed: 0.465597s, average: 1.819e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 51200 shots, elapsed: 0.933953s, average: 1.824e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 102400 shots, elapsed: 1.863604s, average: 1.820e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3626521 shots, elapsed: 66.079240s, average: 1.822e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "decoding time: 1.822e-05s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecodingSpeedResult(elapsed=66.07923996301179, shots=3626521)"
      ]
     },
     "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": 0.002031,
     "end_time": "2025-01-30T07:34:38.652170",
     "exception": false,
     "start_time": "2025-01-30T07:34:38.650139",
     "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": 0.02928,
     "end_time": "2025-01-30T07:34:38.683537",
     "exception": false,
     "start_time": "2025-01-30T07:34:38.654257",
     "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": 0.420439,
     "end_time": "2025-01-30T07:34:39.106114",
     "exception": false,
     "start_time": "2025-01-30T07:34:38.685675",
     "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": 0.001955,
     "end_time": "2025-01-30T07:34:39.110400",
     "exception": false,
     "start_time": "2025-01-30T07:34:39.108445",
     "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": 73.986517,
   "end_time": "2025-01-30T07:34:39.529512",
   "environment_variables": {},
   "exception": null,
   "input_path": "decoding_speed_mwpm.ipynb",
   "output_path": "decoding_speed_bplsd_it5.ipynb",
   "parameters": {
    "decoder": "bplsd(max_iter=5)",
    "json_filename": "speed-rsc-bplsd-it5.json"
   },
   "start_time": "2025-01-30T07:33:25.542995",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}