302 lines
58 KiB
Text
302 lines
58 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import json\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from dataclasses import dataclass\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"this_dir = os.path.dirname(os.path.abspath(''))\n",
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"# results is in \"../results\"\n",
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"results_dir = os.path.join(this_dir, \"results\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"experiment_folder = \"casestudy_example\"\n",
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"experiment_name = \"cs_example_edf\"\n",
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"\n",
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"experiment_file = os.path.join(results_dir, experiment_folder, experiment_name + \".json\")\n",
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"if not os.path.exists(experiment_file):\n",
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" print(\"Experiment file not found: \", experiment_file)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(experiment_file) as f:\n",
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" experiment_data_raw = json.load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of records: 12035\n",
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"First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99436}, 'time': 0.000101}\n",
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"Operation types: ['next_deadline', 'wait_for_work', 'get_next_executable', 'start_work', 'end_work']\n"
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]
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}
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],
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"source": [
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"def pre_process_data(data):\n",
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" for record in data:\n",
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" record[\"time\"] = int(record[\"time\"])\n",
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"\n",
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" min_time = min([record[\"time\"] for record in data])\n",
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" for record in data:\n",
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" record[\"time\"] -= min_time\n",
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" record[\"time\"] /= (1000 * 1000)\n",
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"\n",
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" if record[\"entry\"][\"operation\"] == \"next_deadline\":\n",
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" #print(\"Record: \", record)\n",
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" record[\"entry\"][\"deadline\"] = int(record[\"entry\"][\"deadline\"])\n",
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" record[\"entry\"][\"deadline\"] -= min_time\n",
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" record[\"entry\"][\"deadline\"] /= (1000 * 1000)\n",
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"\n",
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" # data = sorted(data, key=lambda x: x[\"time\"])\n",
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" return data\n",
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"\n",
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"experiment_data = pre_process_data(experiment_data_raw)\n",
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"\n",
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"print(\"Number of records: \", len(experiment_data))\n",
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"print(\"First record: \", experiment_data[0])\n",
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"operation_types = list(set([record[\"entry\"][\"operation\"] for record in experiment_data]))\n",
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"print(\"Operation types: \", operation_types)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"@dataclass\n",
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"class Record:\n",
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" start_time: float\n",
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" end_time: float\n",
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" node_name: str\n",
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"\n",
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"@dataclass\n",
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"class RecordLine:\n",
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" node_name: str\n",
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" count: int\n",
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"\n",
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" def __eq__(self, other):\n",
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" return self.node_name == other.node_name and self.count == other.count\n",
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"\n",
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" def __hash__(self):\n",
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" return hash((self.node_name, self.count))\n",
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"\n",
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"def get_records(data) -> list[Record]:\n",
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" # used to match start_work and end_work records\n",
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" current_records: dict[RecordLine, Record] = {}\n",
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" records = []\n",
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" for record in data:\n",
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" if record[\"entry\"][\"operation\"] == \"start_work\":\n",
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" current_record = Record(start_time=record[\"time\"], node_name=record[\"entry\"][\"node\"], end_time=None)\n",
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" current_record_line = RecordLine(node_name=record[\"entry\"][\"node\"], count=record[\"entry\"][\"count\"])\n",
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" if current_record_line in current_records:\n",
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" raise Exception(\"Overlapping records\")\n",
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" current_records[current_record_line] = current_record\n",
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" elif record[\"entry\"][\"operation\"] == \"end_work\":\n",
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" current_record_line = RecordLine(node_name=record[\"entry\"][\"node\"], count=record[\"entry\"][\"count\"])\n",
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" if current_record_line not in current_records:\n",
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" raise Exception(\"No start record\")\n",
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" current_record = current_records[current_record_line]\n",
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" current_record.end_time = record[\"time\"]\n",
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" records.append(current_record)\n",
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" del current_records[current_record_line]\n",
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" return records\n",
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"\n",
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"records = get_records(experiment_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of nodes: 4\n"
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]
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}
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],
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"source": [
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"num_nodes = len(set([record.node_name for record in records]))\n",
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"print(\"Number of nodes: \", num_nodes)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "5f9cc11846554ad4968929054ae2ba3c",
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"version_major": 2,
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"version_minor": 0
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},
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"image/png": 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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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' width=640.0/>\n",
|
|
" </div>\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": "6bb50c1987ec45a2b25dc1cdee9e794c",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"image/png": 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",
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"\n",
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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' width=640.0/>\n",
|
|
" </div>\n",
|
|
" "
|
|
],
|
|
"text/plain": [
|
|
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# plot with lines for deadlines\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"for i, record in enumerate(records):\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",
|
|
"\n",
|
|
"# draw a vertical line for each deadline\n",
|
|
"for deadline in deadlines:\n",
|
|
" # may have to adjust the y value depending on your chain layout\n",
|
|
" ax.plot([deadline.deadline, deadline.deadline], [0, num_nodes], color='red')\n",
|
|
"\n",
|
|
"ax.set_yticks(range(num_nodes))\n",
|
|
"ax.set_yticklabels(name_to_id.keys())\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.16"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|