4.taskset_evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. evaluate taskset\n",
    "a sample of evaluate scheduling algorithm\n",
    "\n",
    "implementation is refered in src/simulate.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 1. load RD-Gen from directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.dag_rd_gen import RDG_DAG\n",
    "\n",
    "dags: list[RDG_DAG] = RDG_DAG.load_from_rd_gen_dir('./sample_input')\n",
    "# print([d.filepath for d in dags])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 2. evaluate taskset\n",
    "scheduling algorithm is implemented in src.simulate.simulate function\n",
    "\n",
    "src.simulate.simulate return evaluated value (ex. WCRT)\n",
    "\n",
    "src.simulate.simulate_taskset return list of evaluated values for all DAG (dags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 / 20 : 100%\n",
      "[155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242]\n"
     ]
    }
   ],
   "source": [
    "from src.simulate import simulate_taskset\n",
    "\n",
    "results = simulate_taskset(dags)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 2'. evaluate taskset\n",
    "Instead of using the simulate_taskset function, the unit tests are written in .py files and all loops are done in .ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 / 20 : 100%\n",
      "[155, 186, 198, 169, 125, 173, 172, 285, 256, 280, 237, 138, 152, 223, 144, 214, 169, 140, 206, 242]\n"
     ]
    }
   ],
   "source": [
    "from src.simulate import simulate\n",
    "\n",
    "loop_len = len(dags)\n",
    "results: list = []\n",
    "\n",
    "for idx, dag in enumerate(dags):\n",
    "    results.append(simulate(dag))\n",
    "    print(\"\\r\" + f'{idx+1} / {loop_len} : {100*(idx+1)/loop_len:.0f}%', end=\"\")\n",
    "print()\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 3. visualize\n",
    "ex) case of histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3., 3., 3., 2., 1., 2., 1., 2., 1., 2.]),\n",
       " array([125., 141., 157., 173., 189., 205., 221., 237., 253., 269., 285.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import hist\n",
    "\n",
    "hist(results, bins=10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}