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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import babeltrace\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"\n",
"from IPython.display import display, clear_output\n",
"\n",
"sys.path.append(\"/home/adlink/ros2-workspace/install/tracetools_read/lib/python3.8/site-packages/\")\n",
"sys.path.append(\"/home/adlink/ros2-workspace/src/tracetools_analysis/tracetools_analysis/\")\n",
"from tracetools_read.trace import *\n",
"from tracetools_analysis.loading import load_file\n",
"from tracetools_analysis.processor.ros2 import Ros2Handler\n",
"from tracetools_analysis.utils.ros2 import Ros2DataModelUtil"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"found converted file: /home/adlink/.ros/tracing/autoware-trace/converted\n"
]
}
],
"source": [
"path = os.path.expanduser(\"~/.ros/tracing/autoware-trace/\")\n",
"file = load_file(path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [100%] [Ros2Handler]\n"
]
}
],
"source": [
"handler = Ros2Handler.process(file)\n"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================ROS 2 DATA MODEL===================\n",
"Contexts:\n",
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" timestamp pid version\n",
"context_handle \n",
"578889920 1652795531158728529 9807 2.3.0\n",
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"Nodes:\n",
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" timestamp tid rmw_handle name namespace\n",
"node_handle \n",
"578605264 1652795531205345494 9807 578658512 launch_ros_9807 /\n",
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"Publishers (rmw):\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Publishers (rcl):\n",
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" timestamp node_handle rmw_handle topic_name depth\n",
"publisher_handle \n",
"281474154018016 1652795531205338094 578605264 580193376 /rosout 1000\n",
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"Subscriptions (rmw):\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Subscriptions (rcl):\n",
" timestamp node_handle rmw_handle topic_name depth\n",
"subscription_handle \n",
"187651141000448 1652795532311067501 187651140946384 187651141210960 /parameter_events 1000\n",
"Subscription objects:\n",
" timestamp subscription_handle\n",
"subscription \n",
"187651141131344 1652795532311098621 187651141000448\n",
"Services:\n",
" timestamp node_handle rmw_handle service_name\n",
"service_handle \n",
"1652795531227060891 1652795531227060891 578605264 578509024 /launch_ros_9807/describe_parameters\n",
"Clients:\n",
" timestamp node_handle rmw_handle service_name\n",
"client_handle \n",
"281473303560864 1652795531234252437 578605264 580853584 /system/system_monitor/system_monitor/system_m...\n",
"Timers:\n",
" timestamp period tid\n",
"timer_handle \n",
"281472359468464 1652795531230470704 1000000000 9832\n",
"Timer-node links:\n",
" timestamp node_handle\n",
"timer_handle \n",
"187650864529200 1652795532337464355 187650863948976\n",
"Callback objects:\n",
" timestamp callback_object\n",
"reference \n",
"187651141131344 1652795532311101741 187651141131568\n",
"Callback symbols:\n",
" timestamp symbol\n",
"callback_object \n",
"187651141131568 1652795532311126901 std::_Bind<void (rclcpp::TimeSource::*(rclcpp:...\n",
"Callback instances:\n",
" callback_object timestamp duration intra_process\n",
"0 187650864342544 2022-05-17 13:52:12.337689915 00:00:00.000010 False\n",
"Publish instances (rclcpp):\n",
" timestamp message\n",
"0 1652795532321318177 281474880694112\n",
"Publish instances (rcl):\n",
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" publisher_handle timestamp message\n",
"0 580257728 1652795531226289729 580728240\n",
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"Publish instances (rmw):\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Take instances (rmw):\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Take instances (rcl):\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Take instances (rclcpp):\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Lifecycle state machines:\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"Lifecycle transitions:\n",
"Empty DataFrame\n",
"Columns: []\n",
"Index: []\n",
"==================================================\n"
]
}
],
"source": [
"\n",
"self = handler.data\n",
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"n=1\n",
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"print('====================ROS 2 DATA MODEL===================')\n",
"print('Contexts:')\n",
"print(self.contexts[:n].to_string())\n",
"print('Nodes:')\n",
"print(self.nodes[:n].to_string())\n",
"print('Publishers (rmw):')\n",
"print(self.rmw_publishers[:n].to_string())\n",
"print('Publishers (rcl):')\n",
"print(self.rcl_publishers[:n].to_string())\n",
"print('Subscriptions (rmw):')\n",
"print(self.rmw_subscriptions[:n].to_string())\n",
"print('Subscriptions (rcl):')\n",
"print(self.rcl_subscriptions[:n].to_string())\n",
"print('Subscription objects:')\n",
"print(self.subscription_objects[:n].to_string())\n",
"print('Services:')\n",
"print(self.services[:n].to_string())\n",
"print('Clients:')\n",
"print(self.clients[:n].to_string())\n",
"print('Timers:')\n",
"print(self.timers[:n].to_string())\n",
"print('Timer-node links:')\n",
"print(self.timer_node_links[:n].to_string())\n",
"print('Callback objects:')\n",
"print(self.callback_objects[:n].to_string())\n",
"print('Callback symbols:')\n",
"print(self.callback_symbols[:n].to_string())\n",
"print('Callback instances:')\n",
"print(self.callback_instances[:n].to_string())\n",
"print('Publish instances (rclcpp):')\n",
"print(self.rclcpp_publish_instances[:n].to_string())\n",
"print('Publish instances (rcl):')\n",
"print(self.rcl_publish_instances[:n].to_string())\n",
"print('Publish instances (rmw):')\n",
"print(self.rmw_publish_instances[:n].to_string())\n",
"print('Take instances (rmw):')\n",
"print(self.rmw_take_instances[:n].to_string())\n",
"print('Take instances (rcl):')\n",
"print(self.rcl_take_instances[:n].to_string())\n",
"print('Take instances (rclcpp):')\n",
"print(self.rclcpp_take_instances[:n].to_string())\n",
"print('Lifecycle state machines:')\n",
"print(self.lifecycle_state_machines[:n].to_string())\n",
"print('Lifecycle transitions:')\n",
"print(self.lifecycle_transitions[:n].to_string())\n",
"print('==================================================')\n"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Structures"
]
},
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{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed 61 nodes\n",
"Processed 264 publishers\n",
"Processed 212 subscriptions\n",
"Processed 67 timers\n",
"Processed 31 timer-node links\n",
"Processed 201 subscription objects\n",
"Processed 483 callback objects\n",
"Processed 183512 publish instances\n",
"Processed 390810 callback instances\n",
"Processed 161 topics\n"
]
}
],
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"source": [
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"from dataclasses import dataclass\n",
"from typing import List, Dict\n",
"\n",
"def str_to_cls(classname):\n",
" return getattr(sys.modules[__name__], classname)\n",
"\n",
"def row_to_type(row, type, has_idx):\n",
" return type(id=row.name, **row) if has_idx else type(**row)\n",
"\n",
"def df_to_type_list(df, type):\n",
" if isinstance(type, str):\n",
" type = str_to_cls(type)\n",
" \n",
" has_idx = not isinstance(df.index, pd.RangeIndex)\n",
" return [row_to_type(row, type, has_idx) for _, row in df.iterrows()]\n",
"\n",
"def by_index(df, index, type):\n",
" return df_to_type_list(df.loc[index], type)\n",
"\n",
"def by_column(df, column_name, column_val, type):\n",
" return df_to_type_list(df[df[column_name] == column_val], type)\n",
"\n",
"def list_to_dict(ls, key='id'):\n",
" return {getattr(item, key): item for item in ls}\n",
"\n",
"#################################\n",
"# Predefined (from ROS2DataModel)\n",
"#################################\n",
"\n",
"@dataclass\n",
"class Node:\n",
" id: int\n",
" timestamp: int\n",
" tid: int\n",
" rmw_handle: int\n",
" name: str\n",
" namespace: str\n",
"\n",
" @property\n",
" def path(self) -> str:\n",
" return os.path.join(self.namespace, self.name)\n",
"\n",
" @property\n",
" def publishers(self) -> List['Publisher']:\n",
" return list(filter(lambda pub: pub.node_handle == self.id, publishers.values()))\n",
"\n",
" @property\n",
" def subscriptions(self) -> List['Subscription']:\n",
" return list(filter(lambda sub: sub.node_handle == self.id, subscriptions.values()))\n",
" \n",
" @property\n",
" def timers(self) -> List['Timer']:\n",
" links = [link.id for link in timer_node_links.values() if link.node_handle == self.id]\n",
" return list(filter(lambda timer: timer.id in links, timers.values()))\n",
"\n",
"@dataclass\n",
"class Publisher:\n",
" id: int\n",
" timestamp: int\n",
" node_handle: int\n",
" rmw_handle: int\n",
" topic_name: str\n",
" depth: int\n",
"\n",
" @property\n",
" def node(self) -> 'Node':\n",
" return nodes[self.node_handle]\n",
"\n",
" @property\n",
" def subscriptions(self) -> List['Subscription']:\n",
" return list(filter(lambda sub: sub.topic_name == self.topic_name, subscriptions.values()))\n",
"\n",
" @property\n",
" def instances(self) -> List['PublishInstance']:\n",
" return list(filter(lambda inst: inst.publisher_handle == self.id, publish_instances))\n",
" \n",
" @property\n",
" def topic(self) -> 'Topic':\n",
" return topics[self.topic_name]\n",
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"\n",
"\n",
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"@dataclass\n",
"class Subscription:\n",
" id: int\n",
" timestamp: int\n",
" node_handle: int\n",
" rmw_handle: int\n",
" topic_name: str\n",
" depth: int\n",
"\n",
" @property\n",
" def node(self) -> 'Node':\n",
" return nodes[self.node_handle]\n",
"\n",
" @property\n",
" def publishers(self) -> List['Publisher']:\n",
" return list(filter(lambda pub: pub.topic_name == self.topic_name, publishers.values()))\n",
"\n",
" @property\n",
" def topic(self) -> 'Topic':\n",
" return topics[self.topic_name]\n",
" \n",
"@dataclass\n",
"class Timer:\n",
" id: int\n",
" timestamp: int\n",
" period: int\n",
" tid: int\n",
"\n",
" @property\n",
" def nodes(self) -> List['Node']:\n",
" links = [link.node_handle for link in timer_node_links.values() if link.id == self.id]\n",
" return list(filter(lambda node: node.id in links, nodes.values()))\n",
"\n",
"@dataclass\n",
"class TimerNodeLink:\n",
" id: int\n",
" timestamp: int\n",
" node_handle: int\n",
"\n",
"@dataclass\n",
"class SubscriptionObject:\n",
" id: int # subscription\n",
" timestamp: int\n",
" subscription_handle: int\n",
"\n",
" @property\n",
" def subscription(self) -> 'Subscription':\n",
" return subscriptions[self.subscription_handle]\n",
"\n",
"@dataclass\n",
"class CallbackObject:\n",
" id: int # (reference) = subscription_object.id\n",
" timestamp: int\n",
" callback_object: int\n",
"\n",
" @property\n",
" def subscription_object(self) -> 'SubscriptionObject':\n",
" return subscription_objects[self.id]\n",
"\n",
" @property\n",
" def callback_instances(self) -> List['CallbackInstance']:\n",
" return list(filter(lambda inst: inst.callback_object == self.id, callback_instances))\n",
"\n",
"@dataclass\n",
"class PublishInstance:\n",
" publisher_handle: int\n",
" timestamp: int\n",
" message: int\n",
"\n",
" @property\n",
" def publisher(self) -> 'Publisher':\n",
" return publishers[self.publisher_handle]\n",
"\n",
"@dataclass\n",
"class CallbackInstance:\n",
" callback_object: int\n",
" timestamp: int\n",
" duration: int\n",
" intra_process: bool\n",
"\n",
" @property\n",
" def callback_obj(self) -> 'CallbackObject':\n",
" return callback_objects[self.callback_object]\n",
"\n",
"#######################################\n",
"# Self-defined (not from ROS2DataModel)\n",
"#######################################\n",
"\n",
"@dataclass\n",
"class Topic:\n",
" name: str\n",
"\n",
" @property\n",
" def publishers(self) -> List['Publisher']:\n",
" return list(filter(lambda pub: pub.topic_name == self.name, publishers.values()))\n",
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" \n",
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" @property\n",
" def subscriptions(self) -> List['Subscription']:\n",
" return list(filter(lambda sub: sub.topic_name == self.name, subscriptions.values()))\n",
"\n",
"#######################################\n",
"# Instantiate collections\n",
"#######################################\n",
"\n",
"nodes: Dict[int, 'Node'] = list_to_dict(df_to_type_list(handler.data.nodes, 'Node')); print(f\"Processed {len(nodes):<8d} nodes\")\n",
"publishers: Dict[int, 'Publisher'] = list_to_dict(df_to_type_list(handler.data.rcl_publishers, 'Publisher')); print(f\"Processed {len(publishers):<8d} publishers\")\n",
"subscriptions: Dict[int, 'Subscription'] = list_to_dict(df_to_type_list(handler.data.rcl_subscriptions, 'Subscription')); print(f\"Processed {len(subscriptions):<8d} subscriptions\")\n",
"timers: Dict[int, 'Timer'] = list_to_dict(df_to_type_list(handler.data.timers, 'Timer')); print(f\"Processed {len(timers):<8d} timers\")\n",
"timer_node_links: Dict[int, 'TimerNodeLink'] = list_to_dict(df_to_type_list(handler.data.timer_node_links, 'TimerNodeLink')); print(f\"Processed {len(timer_node_links):<8d} timer-node links\")\n",
"subscription_objects: Dict[int, 'SubscriptionObject'] = list_to_dict(df_to_type_list(handler.data.subscription_objects, 'SubscriptionObject')); print(f\"Processed {len(subscription_objects):<8d} subscription objects\")\n",
"callback_objects: Dict[int, 'CallbackObject'] = list_to_dict(df_to_type_list(handler.data.callback_objects, 'CallbackObject')); print(f\"Processed {len(callback_objects):<8d} callback objects\")\n",
"publish_instances: List['PublishInstance'] = df_to_type_list(handler.data.rcl_publish_instances, 'PublishInstance'); print(f\"Processed {len(publish_instances):<8d} publish instances\")\n",
"callback_instances: List['CallbackInstance'] = df_to_type_list(handler.data.callback_instances, 'CallbackInstance'); print(f\"Processed {len(callback_instances):<8d} callback instances\")\n",
"\n",
"_unique_topic_names = {*(pub.topic_name for pub in publishers.values()), *(sub.topic_name for sub in subscriptions.values())}\n",
"topics: Dict[str, 'Topic'] = list_to_dict(map(Topic, _unique_topic_names), key=\"name\"); print(f\"Processed {len(topics):<8d} topics\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PublishInstance(publisher_handle=580257728, timestamp=1652795531226289729, message=580728240)]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Topic-Node Mapping"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"161 topics were aggregated into 78 cohorts\n"
]
}
],
"source": [
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"# Aggregate topics that have the same pubs and subs\n",
"topic_cohorts = {}\n",
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"for topic in topics.values():\n",
" key = (frozenset({*(pub.node_handle for pub in topic.publishers)}), frozenset({*(sub.node_handle for sub in topic.subscriptions)}))\n",
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" if key not in topic_cohorts:\n",
" topic_cohorts[key] = []\n",
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" topic_cohorts[key].append(topic)\n",
"\n",
"print(f\"{len(topics)} topics were aggregated into {len(topic_cohorts)} cohorts\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Timer-Node Mapping"
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]
},
{
"cell_type": "code",
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"execution_count": 24,
"metadata": {},
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"outputs": [
{
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"name": "stdout",
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"output_type": "stream",
"text": [
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"/system/system_error_monitor : 10.00Hz\n",
"/system/emergency_handler : 10.00Hz\n",
"/system/ad_service_state_monitor : 1.00Hz\n",
"/planning/scenario_planning/scenario_selector : 10.00Hz\n",
"/planning/planning_diagnostics/planning_error_monitor : 1.00Hz\n",
"/planning/planning_diagnostics/planning_error_monitor : 10.00Hz\n",
"/system/system_monitor/cpu_monitor : 1.00Hz\n",
"/system/system_monitor/hdd_monitor : 1.00Hz\n",
"/control/trajectory_follower/lateral_controller_node_exe : 33.33Hz\n",
"/system/system_monitor/hdd_monitor : 1.00Hz\n",
"/system/system_monitor/mem_monitor : 1.00Hz\n",
"/aggregator_node : 10.00Hz\n",
"/system/system_monitor/net_monitor : 1.00Hz\n",
"/system/system_monitor/ntp_monitor : 1.00Hz\n",
"/system/system_monitor/process_monitor : 1.00Hz\n",
"/control/trajectory_follower/longitudinal_controller_node_exe : 33.33Hz\n",
"/system/system_monitor/gpu_monitor : 1.00Hz\n",
"/control/external_cmd_selector : 1.00Hz\n",
"/control/external_cmd_selector : 10.00Hz\n",
"/control/external_cmd_converter : 1.00Hz\n",
"/control/external_cmd_converter : 10.00Hz\n",
"/control/trajectory_follower/lane_departure_checker_node : 1.00Hz\n",
"/system/ad_service_state_monitor : 10.00Hz\n",
"/planning/scenario_planning/lane_driving/motion_planning/surround_obstacle_checker : 10.00Hz\n",
"/planning/scenario_planning/parking/costmap_generator : 10.00Hz\n",
"/planning/scenario_planning/parking/freespace_planner : 10.00Hz\n",
"/control/trajectory_follower/lane_departure_checker_node : 10.00Hz\n",
"/control/shift_decider : 10.00Hz\n",
"/control/vehicle_cmd_gate : 1.00Hz\n",
"/control/vehicle_cmd_gate : 10.00Hz\n",
"/planning/scenario_planning/lane_driving/behavior_planning/behavior_path_planner : 10.00Hz\n",
"UNKNOWN (x34) : 1.00Hz\n",
"UNKNOWN (x1) : 2.00Hz\n",
"UNKNOWN (x1) : 10.00Hz\n",
"Found 31 timers with a recorded node, 36 without.\n"
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]
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}
],
"source": [
"unknowns = {}\n",
"\n",
"print_node_timer = lambda node_path, period: print(f\"{node_path:<90s}: {1/(period*1e-9):8.2f}Hz\")\n",
"\n",
"for timer in timers.values():\n",
" timer_nodes = timer.nodes\n",
" if not timer_nodes:\n",
" if timer.period not in unknowns:\n",
" unknowns[timer.period] = 0\n",
" unknowns[timer.period] += 1\n",
"\n",
" for node in timer_nodes: print_node_timer(node.path, timer.period)\n",
" \n",
"for period, count in unknowns.items():\n",
" print_node_timer(f\"UNKNOWN (x{count})\", period)\n",
"\n",
"n_unknown = sum(unknowns.values()) # Values are counts per period\n",
"print(f\"Found {len(timers) - n_unknown} timers with a recorded node, {n_unknown} without.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Measure Frequency Deviations"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
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"219 unknown publisher handles (176 known ones)\n"
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]
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}
],
"source": [
"# Get Publisher frequencies\n",
"df_publications = handler.data.rcl_publish_instances\n",
"pub_stats = {}\n",
"unknown = 0\n",
"for pi in publish_instances:\n",
" try:\n",
" pub = pi.publisher\n",
" except KeyError:\n",
" unknown += 1\n",
" continue\n",
" if pub.id not in pub_stats:\n",
" pub_stats[pub.id] = {'times': []}\n",
" pub_stats[pub.id]['times'].append(pi.timestamp*1e-9) # Nanoseconds to seconds float\n",
"\n",
"print(f\"{unknown} unknown publisher handles ({len(pub_stats)} known ones)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Frequency Deviations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"fig_dirname = \"fig_frequency\"\n",
"os.makedirs(fig_dirname, exist_ok=True)\n",
"for i, (k, v) in enumerate(sorted(pub_stats.items(), key=lambda kv: len(kv[1]['times']), reverse=True)):\n",
" pub_time_diff = np.diff(np.array(v['times']))\n",
" v['period'] = pub_time_diff.mean()\n",
" v['period_std'] = pub_time_diff.std()\n",
" v['frequency'] = 1 / v['period']\n",
" v['frequency_std'] = (1/pub_time_diff).std()\n",
"\n",
" try:\n",
" publisher = publishers[k]\n",
" publisher_node = publisher.node\n",
" topic_name = publisher.topic_name\n",
" node_path = publisher_node.path\n",
" except Exception:\n",
" topic_name=\"UNKNOWN\"\n",
" node_path=\"UNKNOWN\"\n",
" \n",
" fig = plt.figure(figsize=(15,5))\n",
" ax = fig.add_subplot()\n",
" ax.hist(1/pub_time_diff)\n",
" ax.set_xlabel(\"Publication Frequency [Hz]\")\n",
" ax.set_ylabel(\"#Publications\")\n",
" ax.set_title(f\"{node_path} =({v['frequency']:.2f}Hz)=> {topic_name}\")\n",
" plt.savefig(os.path.join(fig_dirname, f\"{i:06}{node_path}__{topic_name}\".replace('/','-')))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Flow Graph"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": false
},
"outputs": [
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{
"data": {
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"text/html": [
"\n",
" <iframe\n",
" width=\"100%\"\n",
" height=\"750px\"\n",
" src=\"graph.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
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"text/plain": [
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"<IPython.lib.display.IFrame at 0xfffc086ae5e0>"
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]
},
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"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"node_filters = [\"transform_listener_impl\", \"_monitor\"]\n",
"topic_filters = [\"/rosout\", \"/parameter_events\", \"/diagnostics\"]\n",
"\n",
"from pyvis.network import Network\n",
"net = Network(notebook=True, height='750px', width='100%', bgcolor='#ffffff', font_color='#000000')\n",
"\n",
"net.add_node(\"INPUT\", label=\"Input\", size=100, color=\"green\", physics=False, x=0, y=0)\n",
"net.add_node(\"OUTPUT\", label=\"Output\", size=100, color=\"red\", physics=False, x=6000, y=0)\n",
"\n",
"\n",
"for node in nodes.values():\n",
" if any(f in node.name for f in node_filters): \n",
" continue\n",
" net.add_node(node.id, label=node.name, size=20, color=\"#333\")\n",
"\n",
"for cohort_key, cohort_topics in topic_cohorts.items():\n",
" cohort_topic_names = [topic.name for topic in cohort_topics if not any(f in topic.name for f in topic_filters)]\n",
" if not cohort_topic_names: \n",
" continue\n",
" cohort_id=\"\\n\".join(cohort_topic_names)\n",
" cohort_weight=len(cohort_topic_names)\n",
" net.add_node(cohort_id, label=\" \", title=cohort_id, size=5, color=\"#333\")\n",
" \n",
" pubs = cohort_key[0]\n",
" subs = cohort_key[1]\n",
" n_pubs = len(pubs)\n",
" n_subs = len(subs)\n",
" \n",
" try:\n",
" if not n_pubs:\n",
" net.add_edge(\"INPUT\", cohort_id, arrows=\"to\", color=\"green\", weight=cohort_weight)\n",
" if not n_subs:\n",
" net.add_edge(cohort_id, \"OUTPUT\", arrows=\"to\", color=\"red\", weight=cohort_weight)\n",
"\n",
" for pub in pubs:\n",
" net.add_edge(pub, cohort_id, arrows=\"to\", color=\"green\", weight=cohort_weight)\n",
" for sub in subs:\n",
" net.add_edge(cohort_id, sub, arrows=\"to\", color=\"red\", weight=cohort_weight)\n",
" except:\n",
" continue\n",
"\n",
"net.toggle_physics(True)\n",
"net.show_buttons()\n",
"net.show(\"graph.html\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pub-Use Latencies\n",
"Compute for each node and its data dependencies the list of pub-use delays (per-topic-per-node list of pub-use delays)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pub_use_delays = {node.path: {'pubs': {}, 'n_unknown_invocations': 0} for node in nodes.values()}\n",
"\n",
"for node in nodes.values():\n",
" node_path = node.path\n",
"\n",
" timestamp_min = np.inf; timestamp_max = 0\n",
"\n",
" for sub in node.subscriptions:\n",
" pub_use_delays[node_path]['pubs'][sub.topic_name] = {}\n",
" for pub in sub.publishers:\n",
" pub_timestamps = [inst.timestamp for inst in pub.instances]\n",
"\n",
" try:\n",
" pub_t_min = min(pub_timestamps); pub_t_max = max(pub_timestamps)\n",
" except ValueError:\n",
" pub_t_min = np.inf; pub_t_max = 0\n",
" \n",
" if pub_t_min < timestamp_min: timestamp_min = pub_t_min\n",
" if pub_t_max > timestamp_max: timestamp_max = pub_t_max\n",
"\n",
" pub_use_delays[node_path]['pubs'][sub.topic_name][pub.node.path] = pub_timestamps\n",
" \n",
" t = node.timers\n",
" node_invocations = []\n",
" if t:\n",
" if len(t) == 1:\n",
" trigger_str = \"single timer \"\n",
" else:\n",
" trigger_str = f\"{len(t)} timers\"\n",
"\n",
" for timer in t:\n",
" timer_invocations: np.ndarray = np.arange(timer.timestamp, timestamp_max, timer.period)\n",
" node_invocations += timer_invocations.tolist()\n",
" else:\n",
" trigger_str = f\"{len(node.subscriptions)} subscriptions\"\n",
" for sub in node.subscriptions:\n",
" try:\n",
" for cb_inst in filter(lambda inst: inst.callback_obj.subscription_object.id == sub.id, callback_instances):\n",
" node_invocations.append(cb_inst.timestamp)\n",
" except KeyError:\n",
" pub_use_delays[node_path]['n_unknown_invocations'] += 1\n",
" continue\n",
" \n",
" pub_use_delays[node_path]['invocations'] = node_invocations\n"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
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{
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"name": "stdout",
"output_type": "stream",
"text": [
"Skipping /planning/scenario_planning/lane_driving/behavior_planning/transform_listener_impl_ffff24598628, no publications\n"
]
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},
{
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"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-54-7f29df84f629>:8: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
" fig = plt.figure(figsize=(15,5))\n"
]
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},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Skipping /planning/scenario_planning/lane_driving/behavior_planning/transform_listener_impl_ffff24598628, no publications\n",
"Skipping /planning/scenario_planning/lane_driving/behavior_planning/transform_listener_impl_ffff24598628, no publications\n"
]
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},
{
"data": {
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"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAABC8AAAFNCAYAAAAgtUXLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOzdedxd0/n//9dbag5iSEvUVK2paHATaorSopSqISqtBqX0Y+jgq6ooqj5a/VWrqqYaWqmp1Ac1xBRKCUEGQqNFS02JIRI1pMn798e6juycnHNPuXPfd+R6Ph555Jy911p77bX3OffZ115rbdkmpZRSSimllFJKqbdaqKcrkFJKKaWUUkoppdSaDF6klFJKKaWUUkqpV8vgRUoppZRSSimllHq1DF6klFJKKaWUUkqpV8vgRUoppZRSSimllHq1DF6klFJKKaWUUkqpV8vgRUoptUHSYEnPd8N2VpU0TVKfeb2t3qY79l3Ss5J2mFflV7Zzs6SvzYNyV5dkSR/q6rI7K+rz8VbWnyvphHaUM8+Ov6STJF3W3XnbKHdrSX/r6nLbsd1Wj1cXbudxSYPn9Xa6m6QtJT0V5+oXe7AeIyV9PV4PlTSip+oytyQdJ+nCeVT2++3URrpTJU2W9FK830PSc3GcN5K0tqRHJU2VdKSkxSXdIGmKpKvnRd3nR5LWkzS6C8tr19+PeU3StZJ26ul6pCKDFymlBZakAd0RlGgv2/+y3df2jK4qU9IISZ/rqvLmlXmx7z3F9s62L+2q8iTtJ+kPXVVed7J9qO0ftSPdAnXu2/6L7bV7uh7ziu1P2h7ZVeXFBe5p3RVIbsUpwNlxrl7Xg/V4n+3htts8zyVdIunU7qhTR9g+zXabAYZ5RdIqwHeB9WyvGIt/Bhwex/lR4BhgpO2lbJ8F7AV8BFje9t6SdpF0r6Q3JL0k6QJJSzXY1nKSJkm6t265Jb0VwZJp1WCOpEUlnSnpBUmvSzpH0sKV9atLuinWvSTp7GqAW9ISkWdyBFvuqaz7f5Iei6DMM5L+X129Bkr6S+R7XtKJbTTnj6LtavX+raR/RvmPStq5leMwrL5d2vv3oxucDvy4pyuRigxepJQWZJ8HbunpSswrkpYENgHu7um6tKY39STopT4P3NTTleionuxBNL+c+53Rk+3ag9r9GZjH3yerAY93JuOC/D3Xy/d9NeBV26/ULXu8jfcTbf833i8DnAoMANYFPgqc0WBbPwGeaFKPT0WwpG9dMOdYoAVYH1gL2Bg4vrL+HOAVYCVgILAt8M3K+vOB5aJeywHfrqwTsD+wLLATcLikfSvr/wDcE/m2BQ6TtFujyktaCdgOqAX1PgQ8F/mWAU4ArpK0epP97zFtnZ+2HwSWltTSTVVKrbGd//Jf/st/C+Q/4FrgS/H6WeD7wATgdeBiYLFYNxh4vpLvWOAfwNRIv0dl3TDgXsrdh9eBZ4CdK+tHUu5O3Bf5RwArxLrVAQMfaittrN8f+CfwKuWHwbPADpX1uwHXx+vNgNHAm8DLwM8r6bYC/gq8QfmxMSyWLxr78a/Icy6weLVNKHesXgFeBA6olLkL8Ghs7zngpMq62n4eFGXf02DfBwDXA68BfwcObsfxPAn4I3BltNcjlB+EtfXvt0+0x/2xzy8CZwOLVNIaOBR4Ko7jrwF14Bh/vZ1p14j9nwrcHtu5rLJ+oWj7FRq00QGUH8JTgaeBb1TytXV8mh7bNtr4/0VZLwAHRn0+HusuAX5Duch8C9ghlp0a658Adq2U9SFgMuXHeP2+jaTrzv2TaP28GABcA0yK43Nk3Tl1FfC7yPs40NLWd0G07xvA+pW0/YG3gQ8z53fKurHPb8Q2dqusm6NdWzk+l8SxvC3qdDewWt15XTte7fmMfi3OkcnADzrQLu8fj3ak3TjqMRW4Oo7TqZX1y1LO4SWj/WYC0+LfgMrxvSz25evM3ef749FuU2K/r4zl/4htvx3bXpRWvqea1Ouk2MfLYn/HUy5Ivx/7+BzwuXZ8DkdS9x0TrwWcGWVNAcZRLnoPAaYD70Xdb+iCc7+tvLPtexvf25fVnXcHRFu8Hsdp09iXNyg9X6p/b+8DfhX7+ySwfaN2arLtHZj9nLo8/jfls/YP4E5gBvBOJc170Z7TgIMalPslYHzdsi0o5+QBtePV6HPZoKzRwN6V9/sBz1XePwF8vvL+DOC8eL12tP/SbZ1Tkf4s4FeV9/+h9Eipvb8a+H6TvPsDt7dR/jhgzwbL1432nRFt+kbl+6z292Mw5W/aMcz6m/ZFSmBzIuUzeFylzIWY9f38KuVcXq7uPKv+BlmMcr6+GufZQ8BHKuVdAPywPe2Y/+btv+x5kVJaIEW3y20oP/JrhgI7AmtSflAe3yArlD+GW1PuJpwMXBZ3HWoGAX+jXHD+FPitJFXW70f5AfNhYBHg6Faq2jCtpPUod1yGUu64LAOsXJf388Cf4/UvgV/aXjr276ooZ1XgZsqPv/6UOzdjIs9Poh0GUn7QrwxUu42uWNnuQcCvJS0b696i/JjpR7lIOqzBGPFtKT9admyw35dTfqgMoHTRPU3S9o2baDa7U35gLUe5a3RdtYttxQzKHagVKD8qt2f2u1UAu1J+NH8K2Keunm0dY9qZ9g/Ag8DylB/xX63LuxnwtO3JDcp9Jeq4NOUcOVPSxpX1rR2fto7tHGLM79HAZ4FPUH7419uP0r12KUrQpupy4MuV9zsCk20/0mSTXXXuQ5PzQtJCwA3A2Chje+BbkqrHejfgCsq5fD3lQrim4XeB7XcpwdHq/u4D3O3Z7/DWvotuoARoPgwcAQyXVB1W0lq71htKCfysQPksD2+Srj2f0a0oF0DbAydKWreyrrV2qdcwraRFgD9RLlKWo5wje9Tl3RG4w/ZbwM7AC551h/qFSLM75WK5X+zv3Hy+f0Q5FstS7qD/CsD2mpQLnS/Ett+l7e+p+noBfAH4fZT/KHAr5UJrZcqwlPNaace2fI7yd22t2OYQSq+C82P7P426f2Fuzv125m207+01iPIdMwT4BfADyvfNJ4F9JG1bl/ZpyrH+IXCtpOXasxHbtzP7OfVl231j9adsr2n7M8BfmDWM5MvAaZSgVl/bv21Q9DZUempEb6lfA4dTLpobuSeGfVxb1ztB8a/6/qOSlon3vwT2jeEhK8f+1HqUDqIEeU+OYSPjJe3ZaOPxN2lrZu9h8gtg//iuXJvyWbq9Sf03oPyda0jSRyjn5Rw9l2w/QQlS3R9t2q9JMStSggy1v1cXAF+h9LLbmvId9bFIeyQluLEt5fNZC1JWVX+DfI3yPb4K5e/xoZTAVs0TlO+K1MMyeJFSWlBtA4y1PbWy7Gzbz9l+jXKh8OVGGW1fbfsF2zNtX0m5e7dZJck/bV/gMn7/UsoF1kcq6y+2PdH225QgwsBW6tks7V6Uu2f32n6P8oe8/kfRzszqaj0d+LikFWxPs/1ALB9KuVtyue3ptl+1PSZ+yBwMfNv2a9FOpwHVLqXTgVMi302UOyZrRxuNtD0+2mgc5Ud+9QcnlDu9b8W+vS/GIG8FfM/2O7bHABcy54V9Iw/b/qPt6cDPKT90Nq9PZPth2w/Y/q/tZykXDPX1O932G7b/BdzF7MeprWNMW2kjcLQpcKLt92zfS7lAqNqFJt3lbf/Z9j9c3E254Nq6kqTh8WnnsW1kH8r5+FhcSJ7UIM3/2b4vjvs7dev+AOwmaYl4v18sa6arzn1ofl5sCvS3fUocg6cpP4irbXGv7Zvi+P2eyg/YNr4L/sDs3yHN9ndzoC/lfHvP9p3AjXV5W2vXen+2fU9cXP8A2CI+U7Np52f0ZNtv2x5LuVCt/nhv2i4NNEu7OaUHzllxnl5LCeZVNf0MVNxv+7rYl7fn8vM9nTIsYEB8/zQMFrXze2q2esWyv9i+1WXIwdWUoPHpcW5eAawuqdnFW1umUwJc61B6kjxh+8Umaefm3G9P3kb73l4/ijYdQQmyXW77Fdv/pgQSNqqkfQX4RZw/V1IuoHfp4Pa6jKTPUi6Eq8HgI4FRth9ukm1bSm+AdSi
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
"image/png": "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
2022-05-23 13:03:38 +02:00
"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
2022-05-23 21:28:40 +02:00
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"text/plain": [
"<Figure size 1080x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"fig_dirname = \"fig_pub_use\"\n",
"os.makedirs(fig_dirname, exist_ok=True)\n",
"for node_i, (node, node_path, node_pub_use_dict) in enumerate(map(lambda node: (node, node_path, pub_use_delays[node.path]), nodes.values())):\n",
" if len(node_pub_use_dict['pubs']) == 0:\n",
" print(f\"Skipping {node_path}, no publications\")\n",
" continue\n",
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"\n",
" fig = plt.figure(figsize=(15,5))\n",
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" ax: plt.Axes = fig.add_subplot()\n",
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"\n",
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" max_pubs_per_topic = max(len(pubs) for pubs in node_pub_use_dict['pubs'].values())\n",
" topic_names, topic_pubs = (zip(*node_pub_use_dict['pubs'].items()))\n",
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"\n",
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" vmin = 0; vmax = max_pubs_per_topic * len(topic_names)\n",
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"\n",
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" ax.scatter(np.array(node_pub_use_dict[\"invocations\"]) * 1e-9, [0] * len(node_pub_use_dict[\"invocations\"]))#, c=0, vmin=vmin, vmax=vmax)\n",
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"\n",
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" for topic_i, (topic_name, pubs) in enumerate(zip(topic_names, topic_pubs)):\n",
" for pub_i, (pub_name, timestamps) in enumerate(pubs.items()):\n",
" ax.scatter(np.array(timestamps) * 1e-9, [topic_i + 1] * len(timestamps))#, c=topic_i * max_pubs_per_topic, vmin=vmin, vmax=vmax)\n",
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" \n",
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" t = node.timers\n",
" if t:\n",
" if len(t) == 1:\n",
" trigger_str = \"single timer\"\n",
" else:\n",
" trigger_str = f\"{len(t)} timers\"\n",
" else:\n",
" n_subs = len(node.subscriptions)\n",
" trigger_str = f\"{n_subs} subscription{'s' if n_subs != 1 else ''}\"\n",
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"\n",
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" ax.set_xlabel(\"Publication / Invocation Timestamp [s]\")\n",
" ax.set_ylabel(\"Topic\")\n",
" ax.set_yticks(range(1+len(topic_names)))\n",
" ax.set_yticklabels((\"Invocations\", *topic_names))\n",
" ax.set_title(f\"{node_path} ({trigger_str})\")\n",
" plt.savefig(os.path.join(fig_dirname, f\"{node_i:06}{node_path}\".replace('/','-')))"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
