{ "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: 11799\n", "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99881}, 'time': 0.0001}\n", "Operation types: ['get_next_executable', 'next_deadline', 'wait_for_work', '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": 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": "020847cef8dc4d5dbb6e2e7ce53ffb31", "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": "5d212f9c6001419fb629d834907dc863", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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