{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3082d18-56df-4834-8963-586a5500bced",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002743,
     "end_time": "2025-01-30T07:30:59.479453",
     "exception": false,
     "start_time": "2025-01-30T07:30:59.476710",
     "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": 0.007266,
     "end_time": "2025-01-30T07:30:59.488685",
     "exception": false,
     "start_time": "2025-01-30T07:30:59.481419",
     "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": "4c56efdd",
   "metadata": {
    "papermill": {
     "duration": 0.004222,
     "end_time": "2025-01-30T07:30:59.494075",
     "exception": false,
     "start_time": "2025-01-30T07:30:59.489853",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "decoder = \"mwpf\"\n",
    "json_filename = \"speed-rsc-mwpf.json\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e4bf8f4-0e7c-453e-aacd-18c9061a999c",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.012077,
     "end_time": "2025-01-30T07:30:59.507403",
     "exception": false,
     "start_time": "2025-01-30T07:30:59.495326",
     "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": 0.008583,
     "end_time": "2025-01-30T07:30:59.517282",
     "exception": false,
     "start_time": "2025-01-30T07:30:59.508699",
     "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": 1.112265,
     "end_time": "2025-01-30T07:31:00.630845",
     "exception": false,
     "start_time": "2025-01-30T07:30:59.518580",
     "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": 71.125079,
     "end_time": "2025-01-30T07:32:11.757511",
     "exception": false,
     "start_time": "2025-01-30T07:31:00.632432",
     "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: 0.008887s, average: 2.222e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 8 shots, elapsed: 0.017232s, average: 2.154e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 16 shots, elapsed: 0.035996s, average: 2.250e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 32 shots, elapsed: 0.069661s, average: 2.177e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 64 shots, elapsed: 0.140707s, average: 2.199e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "initialization time: 2.199e-03s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 50 shots, elapsed: 0.019517s, average: 3.903e-04s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 100 shots, elapsed: 0.004250s, average: 4.250e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 200 shots, elapsed: 0.004647s, average: 2.324e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 400 shots, elapsed: 0.007703s, average: 1.926e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 800 shots, elapsed: 0.016693s, average: 2.087e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 1600 shots, elapsed: 0.033325s, average: 2.083e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3200 shots, elapsed: 0.064646s, average: 2.020e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 6400 shots, elapsed: 0.123402s, average: 1.928e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 12800 shots, elapsed: 0.235163s, average: 1.837e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 25600 shots, elapsed: 0.466897s, average: 1.824e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 51200 shots, elapsed: 0.933903s, average: 1.824e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 102400 shots, elapsed: 1.857947s, average: 1.814e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3637563 shots, elapsed: 66.421162s, average: 1.826e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "decoding time: 1.826e-05s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecodingSpeedResult(elapsed=66.42116174319199, shots=3637563)"
      ]
     },
     "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": 0.001695,
     "end_time": "2025-01-30T07:32:11.761123",
     "exception": false,
     "start_time": "2025-01-30T07:32:11.759428",
     "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": 0.021027,
     "end_time": "2025-01-30T07:32:11.783845",
     "exception": false,
     "start_time": "2025-01-30T07:32:11.762818",
     "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": 0.363029,
     "end_time": "2025-01-30T07:32:12.148953",
     "exception": false,
     "start_time": "2025-01-30T07:32:11.785924",
     "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": 0.002018,
     "end_time": "2025-01-30T07:32:12.153279",
     "exception": false,
     "start_time": "2025-01-30T07:32:12.151261",
     "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": 73.848904,
   "end_time": "2025-01-30T07:32:12.574026",
   "environment_variables": {},
   "exception": null,
   "input_path": "decoding_speed_mwpm.ipynb",
   "output_path": "decoding_speed_mwpf.ipynb",
   "parameters": {
    "decoder": "mwpf",
    "json_filename": "speed-rsc-mwpf.json"
   },
   "start_time": "2025-01-30T07:30:58.725122",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}