{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": 2, "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": 3, "metadata": {}, "outputs": [], "source": [ "experiment_folder = \"casestudy_example\"\n", "experiment_name = \"cs_example_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": 4, "metadata": {}, "outputs": [], "source": [ "with open(experiment_file) as f:\n", " experiment_data_raw = json.load(f)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of records: 11236\n", "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99799}, 'time': 0.0001}\n", "Operation types: ['start_work', 'get_next_executable', 'next_deadline', 'end_work', 'wait_for_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": 6, "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": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of nodes: 4\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": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f7b2b16a21a439c849b44f675f73fbc", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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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": 9, "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": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f6f1486922045bfacd302ecc325e4f1", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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