ROS-Dynamic-Executor-Experiments/analysis/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from dataclasses import dataclass\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "this_dir = os.path.dirname(os.path.abspath(''))\n",
    "# results is in \"../results\"\n",
    "results_dir = os.path.join(this_dir, \"results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#experiment_folder = \"casestudy_example\"\n",
    "#experiment_name = \"cs_example_edf\"\n",
    "experiment_folder = \"existing_system_monitor\"\n",
    "experiment_name = \"uas_edf\"\n",
    "\n",
    "experiment_file = os.path.join(results_dir, experiment_folder, experiment_name + \".json\")\n",
    "if not os.path.exists(experiment_file):\n",
    "    print(\"Experiment file not found: \", experiment_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(experiment_file) as f:\n",
    "    experiment_data_raw = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records:  42\n",
      "First record:  {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'CustomListenerNode/CustomListenerNode-wildfire_talk', 'count': 0}, 'time': 0.0}\n",
      "Operation types:  ['start_work', 'end_work']\n"
     ]
    }
   ],
   "source": [
    "def pre_process_data(data):\n",
    "  for record in data:\n",
    "    record[\"time\"] = int(record[\"time\"])\n",
    "\n",
    "  min_time = min([record[\"time\"] for record in data])\n",
    "  for record in data:\n",
    "    record[\"time\"] -= min_time\n",
    "    record[\"time\"] /= (1000 * 1000)\n",
    "\n",
    "    if record[\"entry\"][\"operation\"] == \"next_deadline\":\n",
    "      #print(\"Record: \", record)\n",
    "      record[\"entry\"][\"deadline\"] = int(record[\"entry\"][\"deadline\"])\n",
    "      record[\"entry\"][\"deadline\"] -= min_time\n",
    "      record[\"entry\"][\"deadline\"] /= (1000 * 1000)\n",
    "\n",
    "  # data = sorted(data, key=lambda x: x[\"time\"])\n",
    "  return data\n",
    "\n",
    "experiment_data = pre_process_data(experiment_data_raw)\n",
    "\n",
    "print(\"Number of records: \", len(experiment_data))\n",
    "print(\"First record: \", experiment_data[0])\n",
    "operation_types = list(set([record[\"entry\"][\"operation\"] for record in experiment_data]))\n",
    "print(\"Operation types: \", operation_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Record:\n",
    "  start_time: float\n",
    "  end_time: float\n",
    "  node_name: str\n",
    "\n",
    "@dataclass\n",
    "class RecordLine:\n",
    "  node_name: str\n",
    "  count: int\n",
    "\n",
    "  def __eq__(self, other):\n",
    "    return self.node_name == other.node_name and self.count == other.count\n",
    "\n",
    "  def __hash__(self):\n",
    "    return hash((self.node_name, self.count))\n",
    "\n",
    "def get_records(data) -> list[Record]:\n",
    "  # used to match start_work and end_work records\n",
    "  current_records: dict[RecordLine, Record] = {}\n",
    "  records = []\n",
    "  for record in data:\n",
    "    if record[\"entry\"][\"operation\"] == \"start_work\":\n",
    "      current_record = Record(start_time=record[\"time\"], node_name=record[\"entry\"][\"node\"], end_time=None)\n",
    "      current_record_line = RecordLine(node_name=record[\"entry\"][\"node\"], count=record[\"entry\"][\"count\"])\n",
    "      if current_record_line in current_records:\n",
    "        raise Exception(\"Overlapping records\")\n",
    "      current_records[current_record_line] = current_record\n",
    "    elif record[\"entry\"][\"operation\"] == \"end_work\":\n",
    "      current_record_line = RecordLine(node_name=record[\"entry\"][\"node\"], count=record[\"entry\"][\"count\"])\n",
    "      if current_record_line not in current_records:\n",
    "        raise Exception(\"No start record\")\n",
    "      current_record = current_records[current_record_line]\n",
    "      current_record.end_time = record[\"time\"]\n",
    "      records.append(current_record)\n",
    "      del current_records[current_record_line]\n",
    "  return records\n",
    "\n",
    "records = get_records(experiment_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes:  2\n"
     ]
    }
   ],
   "source": [
    "num_nodes = len(set([record.node_name for record in records]))\n",
    "print(\"Number of nodes: \", num_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "version_major": 2,
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib widget\n",
    "\n",
    "# swimlane plot\n",
    "name_to_id = {name: i for i, name in enumerate(set([record.node_name for record in records]))}\n",
    "fig, ax = plt.subplots()\n",
    "for i, record in enumerate(records):\n",
    "  # ax.plot([record.start_time, record.end_time], [name_to_id[record.node_name], name_to_id[record.node_name]], label=record.node_name)\n",
    "  ax.broken_barh([(record.start_time, record.end_time - record.start_time)], (name_to_id[record.node_name] - 0.4, 0.8), facecolors='blue')\n",
    "ax.set_yticks(range(num_nodes))\n",
    "ax.set_yticklabels(name_to_id.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class Deadline:\n",
    "    chain_id: int\n",
    "    deadline: float\n",
    "    on_time: bool\n",
    "\n",
    "def get_deadlines(data) -> list[Deadline]:\n",
    "    deadlines = []\n",
    "    for record in data:\n",
    "        if record[\"entry\"][\"operation\"] == \"next_deadline\" and \"on_time\" in record[\"entry\"]:\n",
    "            deadlines.append(Deadline(chain_id=record[\"entry\"][\"chain_id\"], deadline=record[\"entry\"][\"deadline\"], on_time=record[\"entry\"][\"on_time\"]))\n",
    "    return deadlines\n",
    "\n",
    "deadlines = get_deadlines(experiment_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4461f17363834d11a2f9b35f79bbb477",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot with lines for deadlines\n",
    "fig, ax = plt.subplots()\n",
    "for i, record in enumerate(records):\n",
    "  ax.broken_barh([(record.start_time, record.end_time - record.start_time)], (name_to_id[record.node_name] - 0.4, 0.8), facecolors='blue')\n",
    "\n",
    "# draw a vertical line for each deadline\n",
    "for deadline in deadlines:\n",
    "    # may have to adjust the y value depending on your chain layout\n",
    "    if deadline.on_time:\n",
    "       print(\"On time deadline: \", deadline)\n",
    "    ax.plot([deadline.deadline, deadline.deadline], [0, num_nodes], color=('green' if deadline.on_time else 'red'))\n",
    "\n",
    "ax.set_yticks(range(num_nodes))\n",
    "ax.set_yticklabels(name_to_id.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv310",
   "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": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}