Adapt to paper requirements
This commit is contained in:
Maximilian Schmeller
parent
3ae2d5595a
commit
ca1fc0ea50
2 changed files with 128 additions and 138 deletions
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@ -3,6 +3,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import os\n",
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@ -21,23 +24,23 @@
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"plt.rcParams['figure.dpi'] = 300\n",
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"\n",
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"%matplotlib inline"
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],
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"metadata": {
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"collapsed": false
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# User Settings"
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],
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"metadata": {
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"collapsed": false
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}
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},
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"source": [
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"# User Settings"
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"##################################################\n",
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@ -56,13 +59,12 @@
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"\n",
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"# The path to the build folder of a ROS2 workspace that contains the\n",
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"# tracetools_read and tracetools_analysis folders.\n",
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"TRACING_WS_BUILD_PATH = \"~/Projects/autoware/build\"\n",
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"TRACING_WS_BUILD_PATH = \"/path/to/tracing_ws/build\"\n",
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"\n",
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"# Path to trace directory (e.g. ~/.ros/my-trace/ust) or to a converted trace file.\n",
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"# Using the path \"/ust\" at the end is optional but greatly reduces processing time\n",
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"# if kernel traces are also present.\n",
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"# TR_PATH = \"~/Downloads/iteration1_worker1/aw_replay/tracing/scenario-trace/ust\"\n",
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"TR_PATH = \"/home/max/Projects/ma-measurements/artifacts_20221223_155956/tracing/max-ma-trace/ust\"\n",
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"TR_PATH = \"/path/to/tracing/session-name/ust\"\n",
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"\n",
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"# Path to the folder all artifacts from this notebook are saved to.\n",
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"# This entails plots as well as data tables.\n",
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@ -75,7 +77,7 @@
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"BW_ENABLED = False\n",
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"# Path to a HDF5 file as output by ma-hw-perf-tools/messages/record.bash\n",
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"# Used to annotate message sizes in E2E latency calculations\n",
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"BW_PATH = \"../ma-hw-perf-tools/data/messages-x86.h5\"\n",
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"BW_PATH = \"path/to/messages.h5\"\n",
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"\n",
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"# Whether to use dependencies extracted by the Clang-tools to supplement\n",
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"# automatic node-internal data flow annotations.\n",
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@ -83,7 +85,7 @@
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"CL_ENABLED = False\n",
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"# Path to the output directory of the ROS2 dependency checker.\n",
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"# Will only be used if CL_ENABLED is True.\n",
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"CL_PATH = \"~/Projects/llvm-project/clang-tools-extra/ros2-internal-dependency-checker/output\"\n",
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"CL_PATH = \"/path/to/code_analysis/output\"\n",
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"\n",
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"# Whether to compute data flow graphs.\n",
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"# If you are only interested in E2E latencies, set this to False\n",
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@ -209,14 +211,14 @@
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" if isinstance(v, Iterable) and not isinstance(v, str):\n",
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" v = (\"\\n \" + (\" \" * 44)).join(list(map(str, v)))\n",
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" print(f\" {k:.<40s} := {v}\")"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# The last few imports can only be resolved using the user-specified paths above\n",
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@ -230,25 +232,25 @@
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"from tracetools_analysis.processor.ros2 import Ros2Handler\n",
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"\n",
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"from tracing_interop.tr_types import *"
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],
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"metadata": {
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"collapsed": false
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# Load Trace Data\n",
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"\n",
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"Load (and, if necessary, convert) tracing data obtained through ros2_tracing. Build indices for fast analysis."
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def _load_traces():\n",
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@ -278,14 +280,14 @@
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" globals()[name] = getattr(_tracing_context, name)\n",
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"\n",
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"print(\"Done.\")"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"##################################################\n",
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@ -320,14 +322,14 @@
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"publishers.rebuild()\n",
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"print(\"Adding new topics and rebuilding topic index\")\n",
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"topics.append(tf_split_topics)"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"print(\"Artificial TF topics (/tf[...]):\")\n",
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@ -339,14 +341,14 @@
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" #for node in nodes:\n",
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" # if node.path.startswith(prefix) and node.path.removeprefix(prefix).count(\"/\") <= 1 and \"transform_listener_impl\" not in node.path:\n",
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" # print(f\" -> {' ' * (len(prefix) - 3)}{node.path.removeprefix(prefix)}\")"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"##################################################\n",
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@ -368,24 +370,24 @@
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" for f in E2E_OUTPUT_TOPIC_PATTERNS:\n",
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" if re.search(f, t.name):\n",
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" print(f\"--[DEBUG] {f:<30s}:{t.name:.<89s} | {sum(map(lambda p: len(p.instances), t.publishers)):>5d} msgs\")"
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],
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"metadata": {
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"collapsed": false
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# Analyze ROS Graph\n",
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"Reconstruct namespace hierarchy, data flow graph between callbacks."
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import latency_graph.latency_graph_structure as lg\n",
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@ -396,24 +398,24 @@
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"\n",
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"\n",
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"lat_graph = cached(\"lat_graph\", _make_latency_graph, [TR_PATH], not CACHING_ENABLED)"
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],
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"metadata": {
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"collapsed": false
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Plot ROS Graph (full)\n",
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"Plot the DFG hierarchically by node namespace. Plot each internal and external dependency between callbacks as one arrow."
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false DFG_ENABLED\n",
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@ -426,24 +428,27 @@
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"from latency_graph.latency_graph_plots import plot_latency_graph_full\n",
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"\n",
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"plot_latency_graph_full(lat_graph, _tracing_context, os.path.join(OUT_PATH, \"latency_graph_full\"))"
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],
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"metadata": {
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"collapsed": false
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Plot Latency Graph (overview)\n",
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"Plot the DFG down to a certain hierarchy level in a flattened manner. Aggregate dependencies from multiple callbacks into corresponding node-node dependencies."
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%%%skip_if_false DFG_ENABLED\n"
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}
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},
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"outputs": [],
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"source": [
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"%%skip_if_false DFG_ENABLED\n",
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@ -457,27 +462,24 @@
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"from latency_graph.latency_graph_plots import plot_latency_graph_overview\n",
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"\n",
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"plot_latency_graph_overview(lat_graph, DFG_EXCL_NODE_PATTERNS, DFG_INPUT_NODE_PATTERNS, DFG_OUTPUT_NODE_PATTERNS, DFG_MAX_HIER_LEVEL, os.path.join(OUT_PATH, \"latency_graph_overview\"))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%%%skip_if_false DFG_ENABLED\n"
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# Analyze Message Flow\n",
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"Build dependency trees ending in the specified output topics."
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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@ -495,14 +497,14 @@
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" import traceback\n",
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" print(e)\n",
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" traceback.print_exc()"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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@ -518,14 +520,14 @@
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"\n",
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"from bw_interop.bw_plots import dds_lat_msg_size_scatter\n",
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"plot_topic = \"\""
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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@ -540,14 +542,14 @@
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"trees_paths = [e2e_paths_sorted_desc(tree, E2E_INPUT_TOPIC_PATTERNS) for tree in tqdm(trees, mininterval=10.0,\n",
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" desc=\"Extracting E2E paths\")]\n",
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"all_paths = [p for paths in trees_paths for p in paths]"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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@ -575,14 +577,14 @@
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"df_print = df_print.sort_values((\"e2e_latency\", \"count\"), ascending=False)\n",
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"df_print.to_csv(os.path.join(OUT_PATH, \"e2e_overview.csv\"), sep=\"\\t\", index=False)\n",
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"df_print"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pickle\n",
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@ -590,14 +592,14 @@
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"# pickle.dump((trees_paths, all_paths, cohorts), f)\n",
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"with open(os.path.join(OUT_PATH, \"state.pkl\"), \"wb\") as f:\n",
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" pickle.dump((trees_paths, all_paths, cohorts), f)"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"##################################################\n",
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@ -623,14 +625,14 @@
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" print(f\"[WARN] Expected exactly one cohort to remain, got {len(cohorts_filt)}. Only the first one will be used.\")\n",
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"\n",
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"relevant_path, relevant_dataflows = next(iter(cohorts_filt.items()))"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"from message_tree.message_tree_algorithms import e2e_latency_breakdown\n",
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@ -650,14 +652,14 @@
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"#e2e_breakdowns_orig = e2e_breakdowns\n",
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"\n",
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"#relevant_dataflows, e2e_breakdowns = zip(*filt)"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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@ -713,14 +715,14 @@
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"plt.savefig(os.path.join(OUT_PATH, \"plot_e2e_portions.png\"))\n",
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"\n",
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"None\n"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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@ -747,14 +749,14 @@
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"ax.text(np.mean(e2es) * 1.02, max_ylim * 0.98, 'Mean: {:.3f}s'.format(np.mean(e2es)))\n",
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"plt.savefig(os.path.join(OUT_PATH, \"plot_e2es.png\"))\n",
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"None"
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],
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"metadata": {
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"collapsed": false
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}
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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": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
|
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|
|
@ -806,14 +808,14 @@
|
|||
"df_types.to_csv(os.path.join(OUT_PATH, \"plot_e2es_violin_types.csv\"), index=False, header=[\"type\"])\n",
|
||||
"\n",
|
||||
"None"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for concat_pc_items in component_latency_items:\n",
|
||||
|
|
@ -822,14 +824,14 @@
|
|||
" dur_ts_records = [(item.location[0].timestamp, item.duration) for item in concat_pc_items]\n",
|
||||
" df_dur_ts = pd.DataFrame(dur_ts_records, columns=(\"timestamp\", \"duration\"))\n",
|
||||
" df_dur_ts.to_csv(os.path.join(OUT_PATH, f\"dur_ts_{concat_pc_items[0].location[0].publisher.topic_name.replace('/', '__')}.csv\"), index=False)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"indices = [i for i, t in enumerate(types) if t == \"cpu\"]\n",
|
||||
|
|
@ -839,14 +841,14 @@
|
|||
"records = [(lbl, x.mean(), x.std(), x.min(), x.max()) for x, lbl in zip(xs, lbls)]\n",
|
||||
"df_cpu = pd.DataFrame(records, columns=[\"callback\", \"mean\", \"std\", \"min\", \"max\"])\n",
|
||||
"df_cpu.to_csv(os.path.join(OUT_PATH, \"calc_times.csv\"), index=False)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%skip_if_false E2E_ENABLED\n",
|
||||
|
|
@ -858,30 +860,18 @@
|
|||
"fig.set_size_inches(16, 9)\n",
|
||||
"fig.set_dpi(300)\n",
|
||||
"None"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"Done.\")"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue