{
"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": null,
"metadata": {},
"outputs": [],
"source": [
"#experiment_folder = \"casestudy_example\"\n",
"experiment_folder = \"casestudy_wildfire_drone\"\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: 12055\n",
"First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99890}, 'time': 0.0001}\n",
"Operation types: ['wait_for_work', 'get_next_executable', 'next_deadline', 'end_work', 'start_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": "eabdbc0b05594f0bbbb97b9b63edf92e",
"version_major": 2,
"version_minor": 0
},
"image/png": 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",
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