{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3082d18-56df-4834-8963-586a5500bced",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.002395,
     "end_time": "2025-01-30T07:34:40.758847",
     "exception": false,
     "start_time": "2025-01-30T07:34:40.756452",
     "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": 0.008431,
     "end_time": "2025-01-30T07:34:40.769042",
     "exception": false,
     "start_time": "2025-01-30T07:34:40.760611",
     "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": "8053b9c7",
   "metadata": {
    "papermill": {
     "duration": 0.005036,
     "end_time": "2025-01-30T07:34:40.775698",
     "exception": false,
     "start_time": "2025-01-30T07:34:40.770662",
     "status": "completed"
    },
    "tags": [
     "injected-parameters"
    ]
   },
   "outputs": [],
   "source": [
    "# Parameters\n",
    "decoder = \"bpuf(max_iter=5)\"\n",
    "json_filename = \"speed-rsc-bpuf-it5.json\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e4bf8f4-0e7c-453e-aacd-18c9061a999c",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.013377,
     "end_time": "2025-01-30T07:34:40.790570",
     "exception": false,
     "start_time": "2025-01-30T07:34:40.777193",
     "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": 0.009889,
     "end_time": "2025-01-30T07:34:40.801891",
     "exception": false,
     "start_time": "2025-01-30T07:34:40.792002",
     "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": 1.25758,
     "end_time": "2025-01-30T07:34:42.060842",
     "exception": false,
     "start_time": "2025-01-30T07:34:40.803262",
     "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": 70.582583,
     "end_time": "2025-01-30T07:35:52.644919",
     "exception": false,
     "start_time": "2025-01-30T07:34:42.062336",
     "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: 0.011176s, average: 2.794e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 8 shots, elapsed: 0.022337s, average: 2.792e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 16 shots, elapsed: 0.041786s, average: 2.612e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 32 shots, elapsed: 0.082961s, average: 2.593e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating initialization of 64 shots, elapsed: 0.171224s, average: 2.675e-03s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "initialization time: 2.675e-03s\n",
      "\r\n",
      "\u001b[KEvaluating decoding of 50 shots, elapsed: 0.001667s, average: 3.335e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 100 shots, elapsed: 0.002448s, average: 2.448e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 200 shots, elapsed: 0.004375s, average: 2.188e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 400 shots, elapsed: 0.007961s, average: 1.990e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 800 shots, elapsed: 0.015046s, average: 1.881e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 1600 shots, elapsed: 0.029438s, average: 1.840e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3200 shots, elapsed: 0.058349s, average: 1.823e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 6400 shots, elapsed: 0.118499s, average: 1.852e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 12800 shots, elapsed: 0.235643s, average: 1.841e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 25600 shots, elapsed: 0.465970s, average: 1.820e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 51200 shots, elapsed: 0.935718s, average: 1.828e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 102400 shots, elapsed: 1.862696s, average: 1.819e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\u001b[KEvaluating decoding of 3628290 shots, elapsed: 65.868940s, average: 1.815e-05s per shot\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "decoding time: 1.815e-05s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecodingSpeedResult(elapsed=65.86894034010402, shots=3628290)"
      ]
     },
     "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": 0.001923,
     "end_time": "2025-01-30T07:35:52.648865",
     "exception": false,
     "start_time": "2025-01-30T07:35:52.646942",
     "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": 0.020777,
     "end_time": "2025-01-30T07:35:52.671524",
     "exception": false,
     "start_time": "2025-01-30T07:35:52.650747",
     "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": 0.409292,
     "end_time": "2025-01-30T07:35:53.082853",
     "exception": false,
     "start_time": "2025-01-30T07:35:52.673561",
     "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": 0.002007,
     "end_time": "2025-01-30T07:35:53.087316",
     "exception": false,
     "start_time": "2025-01-30T07:35:53.085309",
     "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": 73.519235,
   "end_time": "2025-01-30T07:35:53.507271",
   "environment_variables": {},
   "exception": null,
   "input_path": "decoding_speed_mwpm.ipynb",
   "output_path": "decoding_speed_bpuf_it5.ipynb",
   "parameters": {
    "decoder": "bpuf(max_iter=5)",
    "json_filename": "speed-rsc-bpuf-it5.json"
   },
   "start_time": "2025-01-30T07:34:39.988036",
   "version": "2.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}