{ "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": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab61935f394e43aeaa093dc378e11680", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " Figure\n", "
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\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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", 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