{
"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: 12118\n",
"First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99886}, 'time': 0.0001}\n",
"Operation types: ['get_next_executable', 'next_deadline', 'wait_for_work', '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": "3c172c35015c4ffd92a176ce80ffd18d",
"version_major": 2,
"version_minor": 0
},
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",
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