Kill me
This commit is contained in:
Maximilian Schmeller
parent
b456645621
commit
9848c39b61
1 changed files with 105 additions and 160 deletions
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@ -7,6 +7,18 @@
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"source": [
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"import os\n",
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"import sys\n",
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"import re\n",
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"import math\n",
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"import graphviz as gv\n",
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"from tqdm import tqdm\n",
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"from bisect import bisect\n",
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"from termcolor import colored\n",
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"\n",
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"import matplotlib.patches as mpatch\n",
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"from scipy import stats\n",
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"from cycler import cycler\n",
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"\n",
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"import glob\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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@ -14,7 +26,6 @@
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"\n",
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"from misc.utils import cached, parse_as\n",
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"\n",
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"%load_ext pyinstrument\n",
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"%matplotlib inline"
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],
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"metadata": {
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@ -50,14 +61,14 @@
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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 = \"data/awsim-trace/ust\"\n",
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"TR_PATH = \"/home/max/Projects/optimization_framework/artifacts/iteration0_worker1/aw_replay/tracing/scenario-trace/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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"OUT_PATH = \"out/\"\n",
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"\n",
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"# Whether to cache the results of long computations per set of inputs\n",
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"CACHING_ENABLED = True\n",
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"CACHING_ENABLED = False\n",
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"\n",
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"# Whether to annotate topics/publications with bandwidth/message size\n",
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"BW_ENABLED = False\n",
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@ -89,7 +100,7 @@
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"DFG_INPUT_NODE_PATTERNS = [r\"^/sensing\"]\n",
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"# RegEx pattern for nodes that shall be marked as system outputs\n",
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"# These will be plotted with a double border\n",
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"DFG_OUTPUT_NODE_PATTERNS = [r\"^/awapi\", r\"^/control/external_cmd_converter\"]\n",
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"DFG_OUTPUT_NODE_PATTERNS = [r\"^/awapi\", r\"^/control/external_cmd_converter\", \"emergency\"]\n",
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"# RegEx for nodes which shall not be plotted in the DFG\n",
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"DFG_EXCL_NODE_PATTERNS = [r\"^/rviz2\", r\"transform_listener_impl\"]\n",
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"\n",
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@ -108,11 +119,10 @@
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"\n",
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"# All topics containing any of these RegEx patterns are considered output topics in E2E latency calculations\n",
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"# E.g. r\"^/control/\" will cover all control topics\n",
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"E2E_OUTPUT_TOPIC_PATTERNS = [r\"^/control/trajectory_follower/control_cmd\"]\n",
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"E2E_OUTPUT_TOPIC_PATTERNS = [r\"^/control/\", r\"^/vehicle\"]\n",
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"# All topics containing any of these RegEx patterns are considered input topics in E2E latency calculations\n",
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"# E.g. r\"^/sensing/\" will cover all sensing topics\n",
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"E2E_INPUT_TOPIC_PATTERNS = [\"/vehicle/status/\", \"/sensing/imu\"]\n",
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"\n",
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"E2E_INPUT_TOPIC_PATTERNS = [r\"^/vehicle/status/\", r\"^/sensing/imu\"]\n",
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"\n",
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"# This code overrides the above constants with environment variables, do not edit.\n",
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"for env_key, env_value in os.environ.items():\n",
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@ -123,10 +133,12 @@
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" value = parse_as(type(globals()[key]), env_value)\n",
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" globals()[key] = value\n",
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"\n",
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"\n",
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"# Convert input paths to absolute paths\n",
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"def _expand_path(path):\n",
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" return os.path.realpath(os.path.expandvars(os.path.expanduser(path)))\n",
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"\n",
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"\n",
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"TRACING_WS_BUILD_PATH = _expand_path(TRACING_WS_BUILD_PATH)\n",
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"TR_PATH = _expand_path(TR_PATH)\n",
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"OUT_PATH = _expand_path(OUT_PATH)\n",
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@ -153,7 +165,9 @@
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"from tracetools_analysis.loading import load_file\n",
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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 TrTimer, TrTopic, TrPublisher, TrPublishInstance, TrCallbackInstance, TrContext"
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"from tracing_interop.tr_types import *\n",
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"from latency_graph.message_tree import DepTree\n",
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"from matching.subscriptions import sanitize"
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],
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"metadata": {
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"collapsed": false,
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@ -185,7 +199,7 @@
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" return TrContext(handler)\n",
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"\n",
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"\n",
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"_tracing_context = cached(\"tr_objects\", _load_traces, [TR_PATH], CACHING_ENABLED)\n",
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"_tracing_context = cached(\"tr_objects\", _load_traces, [TR_PATH], not CACHING_ENABLED)\n",
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"_tr_globals = [\"nodes\", \"publishers\", \"subscriptions\", \"timers\", \"timer_node_links\", \"subscription_objects\",\n",
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" \"callback_objects\", \"callback_symbols\", \"publish_instances\", \"callback_instances\", \"topics\"]\n",
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"\n",
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@ -223,10 +237,12 @@
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"source": [
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"from latency_graph import latency_graph as lg\n",
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"\n",
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"\n",
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"def _make_latency_graph():\n",
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" return lg.LatencyGraph(_tracing_context)\n",
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"\n",
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"lat_graph = cached(\"lat_graph\", _make_latency_graph, [TR_PATH], CACHING_ENABLED)"
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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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@ -243,8 +259,6 @@
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"%%skip_if_false DFG_ENABLED\n",
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"%%skip_if_false DFG_PLOT\n",
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"\n",
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"from tracing_interop.tr_types import TrNode, TrCallbackObject, TrSubscriptionObject\n",
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"\n",
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"#################################################\n",
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"# Plot DFG\n",
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"#################################################\n",
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@ -276,13 +290,13 @@
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" 'pointcloud_container': 'sensing',\n",
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"}\n",
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"\n",
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"import graphviz as gv\n",
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"\n",
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"g = gv.Digraph('G', filename=\"latency_graph.gv\",\n",
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" node_attr={'shape': 'plain'},\n",
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" graph_attr={'pack': '1'})\n",
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"g.graph_attr['rankdir'] = 'LR'\n",
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"\n",
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"\n",
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"def plot_hierarchy(gv_parent, lg_node: lg.LGHierarchyLevel, **subgraph_kwargs):\n",
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" if lg_node.name == \"[NONE]\":\n",
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" return\n",
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@ -311,6 +325,7 @@
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" for ch in lg_node.children:\n",
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" plot_hierarchy(c, ch, **subgraph_kwargs)\n",
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"\n",
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"\n",
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"def plot_lg(graph: lg.LatencyGraph):\n",
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" for top_level_node in graph.top_node.children:\n",
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" colors = node_colors[node_namespace_mapping.get(top_level_node.name)]\n",
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@ -319,6 +334,7 @@
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" for edge in graph.edges:\n",
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" g.edge(f\"{edge.start.id()}:out\", f\"{edge.end.id()}:in\")\n",
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"\n",
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"\n",
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"plot_lg(lat_graph)\n",
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"\n",
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"g.save(\"latency_graph.gv\")\n",
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@ -341,8 +357,6 @@
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"%%skip_if_false DFG_ENABLED\n",
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"%%skip_if_false DFG_PLOT\n",
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"\n",
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"import re\n",
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"import math\n",
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"\n",
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"##################################################\n",
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"# Compute in/out topics for hierarchy level X\n",
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@ -363,6 +377,7 @@
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"\n",
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" return _traverse_node(lat_graph.top_node)\n",
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"\n",
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"\n",
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"lvl_nodes = get_nodes_on_level(lat_graph)\n",
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"lvl_nodes = [n for n in lvl_nodes if not any(re.search(p, n.full_name) for p in DFG_EXCL_NODE_PATTERNS)]\n",
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"\n",
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@ -373,6 +388,7 @@
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"print(', '.join(map(lambda n: n, output_nodes)))\n",
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"print(', '.join(map(lambda n: n.full_name, lvl_nodes)))\n",
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"\n",
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"\n",
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"def _collect_callbacks(n: lg.LGHierarchyLevel):\n",
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" callbacks = []\n",
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" callbacks += n.callbacks\n",
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@ -387,7 +403,6 @@
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" for cb in cbs:\n",
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" cb_to_node_map[cb.id()] = n\n",
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"\n",
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"\n",
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"edges_between_nodes = {}\n",
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"for edge in lat_graph.edges:\n",
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" from_node = cb_to_node_map.get(edge.start.id())\n",
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@ -422,6 +437,7 @@
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" g.node(helper_node_name, label=\"\", shape=\"none\", height=\"0\", width=\"0\")\n",
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" g.edge(helper_node_name, n.full_name)\n",
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"\n",
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"\n",
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"def compute_e2e_paths(start_nodes, end_nodes, edges):\n",
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" frontier_paths = [[n] for n in start_nodes]\n",
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" final_paths = []\n",
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@ -446,6 +462,7 @@
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" for path in final_paths]\n",
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" return final_paths\n",
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"\n",
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"\n",
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"e2e_paths = compute_e2e_paths(input_nodes, output_nodes, edges_between_nodes)\n",
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"\n",
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"for (src_name, dst_name), edges in edges_between_nodes.items():\n",
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@ -472,9 +489,6 @@
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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"\n",
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"from tqdm import tqdm\n",
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"from bisect import bisect\n",
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"\n",
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"\n",
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"def inst_get_dep_msg(inst: TrCallbackInstance):\n",
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" if inst.callback_object not in _tracing_context.callback_objects.by_callback_object:\n",
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@ -512,6 +526,7 @@
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" # print(f\"No messages found for topic {sub_obj.subscription.topic}\")\n",
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" return None\n",
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"\n",
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"\n",
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"def inst_get_dep_insts(inst: TrCallbackInstance):\n",
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" if inst.callback_object not in _tracing_context.callback_objects.by_callback_object:\n",
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" # print(\"Callback not found\")\n",
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@ -531,6 +546,7 @@
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" insts = [inst for inst in insts if inst is not None]\n",
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" return insts\n",
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"\n",
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"\n",
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"def get_cb_dep_cbs(cb: TrCallbackObject):\n",
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" match cb.owner:\n",
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" case TrSubscriptionObject() as sub_obj:\n",
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@ -591,23 +607,24 @@
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" return dep_inst\n",
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"\n",
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"\n",
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"def get_dep_tree(inst: TrPublishInstance | TrCallbackInstance, lvl=0, visited_topics=None, is_dep_cb=False, start_time=None):\n",
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"def get_dep_tree(inst: TrPublishInstance | TrCallbackInstance, lvl=0, visited_topics=None, is_dep_cb=False,\n",
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" start_time=None):\n",
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" if visited_topics is None:\n",
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" visited_topics = set()\n",
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" \n",
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"\n",
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" if start_time is None:\n",
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" start_time = inst.timestamp\n",
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" \n",
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"\n",
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" if inst.timestamp - start_time > E2E_TIME_LIMIT_S:\n",
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" return None\n",
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" \n",
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"\n",
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" children_are_dep_cbs = False\n",
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"\n",
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" match inst:\n",
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" case TrPublishInstance(publisher=pub):\n",
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" if pub.topic_name in visited_topics:\n",
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" return None\n",
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" \n",
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"\n",
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" visited_topics.add(pub.topic_name)\n",
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" deps = [get_msg_dep_cb(inst)]\n",
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" case TrCallbackInstance() as cb_inst:\n",
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@ -616,7 +633,8 @@
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" deps += inst_get_dep_insts(cb_inst)\n",
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" children_are_dep_cbs = True\n",
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" case _:\n",
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" print(f\"[WARN] Expected inst to be of type TrPublishInstance or TrCallbackInstance, got {type(inst).__name__}\")\n",
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" print(\n",
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" f\"[WARN] Expected inst to be of type TrPublishInstance or TrCallbackInstance, got {type(inst).__name__}\")\n",
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" return None\n",
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" #print(\"Rec level\", lvl)\n",
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" deps = [dep for dep in deps if dep is not None]\n",
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@ -640,21 +658,26 @@
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"\n",
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"end_topics = [t for t in _tracing_context.topics if any(re.search(f, t.name) for f in E2E_OUTPUT_TOPIC_PATTERNS)]\n",
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"\n",
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"\n",
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"def build_dep_trees():\n",
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" all_trees = []\n",
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" for end_topic in end_topics:\n",
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" end_topic: TrTopic\n",
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" print(f\"====={end_topic.name}\")\n",
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"\n",
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" pubs = end_topic.publishers\n",
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" print(len(pubs))\n",
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" for pub in pubs:\n",
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" msgs = pub.instances\n",
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" print(len(msgs))\n",
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" for msg in tqdm(msgs, desc=f\"Building message chains for topic {end_topic.name}\"):\n",
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" msg: TrPublishInstance\n",
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" tree = get_dep_tree(msg)\n",
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" all_trees.append(tree)\n",
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" return all_trees\n",
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"\n",
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"trees = cached(\"trees\", build_dep_trees, [TR_PATH], CACHING_ENABLED)"
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"\n",
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"trees = cached(\"trees\", build_dep_trees, [TR_PATH], not CACHING_ENABLED)"
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],
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"metadata": {
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"collapsed": false,
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@ -671,7 +694,6 @@
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"%%skip_if_false E2E_ENABLED\n",
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"%%skip_if_false BW_ENABLED\n",
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"\n",
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"import glob\n",
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"\n",
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"\n",
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"def parse_bytes(string):\n",
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@ -688,10 +710,10 @@
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"\n",
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"\n",
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"def bytes_str(bytes):\n",
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" if bytes >= 1024**2:\n",
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" return f\"{bytes/(1024**2):.2f} MiB\"\n",
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" if bytes >= 1024 ** 2:\n",
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" return f\"{bytes / (1024 ** 2):.2f} MiB\"\n",
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" if bytes >= 1024:\n",
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" return f\"{bytes/1024:.2f} KiB\"\n",
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" return f\"{bytes / 1024:.2f} KiB\"\n",
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" return f\"{bytes:.0f} B\"\n",
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"\n",
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"\n",
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@ -706,7 +728,8 @@
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" #print(f\"No data for {topic}\")\n",
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" continue\n",
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"\n",
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" line_pattern = re.compile(r\"(?P<bw>[0-9.]+ [KM]?)B/s from (?P<n_msgs>[0-9.]+) messages --- Message size mean: (?P<mean>[0-9.]+ [KM]?)B min: (?P<min>[0-9.]+ [KM]?)B max: (?P<max>[0-9.]+ [KM]?)B\\n\")\n",
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" line_pattern = re.compile(\n",
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" r\"(?P<bw>[0-9.]+ [KM]?)B/s from (?P<n_msgs>[0-9.]+) messages --- Message size mean: (?P<mean>[0-9.]+ [KM]?)B min: (?P<min>[0-9.]+ [KM]?)B max: (?P<max>[0-9.]+ [KM]?)B\\n\")\n",
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" m = re.fullmatch(line_pattern, lines[-1])\n",
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" if m is None:\n",
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" print(f\"Line could not be parsed in {topic}: '{lines[-1]}'\")\n",
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@ -731,9 +754,6 @@
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"source": [
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"%%skip_if_false E2E_ENABLED\n",
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"\n",
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"\n",
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"from latency_graph.message_tree import DepTree\n",
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"\n",
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"def leaf_topics(tree: DepTree, lvl=0):\n",
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" ret_list = []\n",
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" match tree.head:\n",
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@ -743,7 +763,7 @@
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" ret_list += [(lvl, None)]\n",
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"\n",
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" for dep in tree.deps:\n",
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" ret_list += leaf_topics(dep, lvl+1)\n",
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" ret_list += leaf_topics(dep, lvl + 1)\n",
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" return ret_list\n",
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"\n",
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"\n",
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@ -777,7 +797,7 @@
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" match tree.head:\n",
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" case TrPublishInstance(publisher=pub):\n",
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" if pub and any(re.search(f, pub.topic_name) for f in input_topic_patterns):\n",
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" return [(latency,new_path)]\n",
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" return [(latency, new_path)]\n",
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"\n",
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" ret_list = []\n",
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" for dep in tree.deps:\n",
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@ -860,7 +880,7 @@
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"\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(18, 9), num=\"e2e_plot\")\n",
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"DS=1\n",
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"DS = 1\n",
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"times = [tree.head.timestamp - trees[0].head.timestamp for tree in trees[::DS]]\n",
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"\n",
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"ax2 = ax.twinx()\n",
|
||||
|
|
@ -873,16 +893,19 @@
|
|||
"ax.plot(times, [np.mean(e2es) for e2es in e2ess[::DS]])\n",
|
||||
"ax.fill_between(times, [np.min(e2es) for e2es in e2ess[::DS]], [np.max(e2es) for e2es in e2ess[::DS]], alpha=.3)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def scatter_topic(topic_name, y=0, **scatter_kwargs):\n",
|
||||
" for pub in topics.by_name[topic_name].publishers:\n",
|
||||
" if not pub:\n",
|
||||
" continue\n",
|
||||
"\n",
|
||||
" inst_timestamps = [inst.timestamp - trees[0].head.timestamp for inst in pub.instances if inst.timestamp >= trees[0].head.timestamp]\n",
|
||||
" inst_timestamps = [inst.timestamp - trees[0].head.timestamp for inst in pub.instances if\n",
|
||||
" inst.timestamp >= trees[0].head.timestamp]\n",
|
||||
" scatter_kwargs_default = {\"marker\": \"x\", \"color\": \"indianred\"}\n",
|
||||
" scatter_kwargs_default.update(scatter_kwargs)\n",
|
||||
" ax.scatter(inst_timestamps, np.full(len(inst_timestamps), fill_value=y), **scatter_kwargs_default)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"scatter_topic(\"/autoware/engage\")\n",
|
||||
"scatter_topic(\"/planning/scenario_planning/parking/trajectory\", y=-.04, color=\"cadetblue\")\n",
|
||||
"scatter_topic(\"/planning/scenario_planning/lane_driving/trajectory\", y=-.08, color=\"darkgreen\")\n",
|
||||
|
|
@ -944,6 +967,7 @@
|
|||
" sort_subtree(dep, sort_func)\n",
|
||||
" return subtree\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _leaf_filter(inst, root):\n",
|
||||
" if root.timestamp - inst.timestamp > E2E_TIME_LIMIT_S:\n",
|
||||
" return False\n",
|
||||
|
|
@ -972,7 +996,8 @@
|
|||
"%%skip_if_false E2E_ENABLED\n",
|
||||
"%%skip_if_false E2E_PLOT\n",
|
||||
"\n",
|
||||
"from cycler import cycler\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def dict_safe_append(dictionary, key, value):\n",
|
||||
" if key not in dictionary:\n",
|
||||
|
|
@ -981,8 +1006,8 @@
|
|||
"\n",
|
||||
"\n",
|
||||
"fig, (ax, ax_rel) = plt.subplots(2, 1, sharex=True, figsize=(60, 30), num=\"crit_plot\")\n",
|
||||
"ax.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i/4) for i in range(5)]))\n",
|
||||
"ax_rel.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i/4) for i in range(5)]))\n",
|
||||
"ax.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i / 4) for i in range(5)]))\n",
|
||||
"ax_rel.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i / 4) for i in range(5)]))\n",
|
||||
"\n",
|
||||
"critical_paths = [critical_path(tree) for tree in relevant_trees[::DS]]\n",
|
||||
"\n",
|
||||
|
|
@ -1010,7 +1035,7 @@
|
|||
" last_cb_time = None\n",
|
||||
" case TrCallbackInstance(callback_obj=cb, timestamp=t, duration=d):\n",
|
||||
" if last_pub_time is not None:\n",
|
||||
" assert last_pub_time <= t+d, \"Publication out of CB instance timeframe\"\n",
|
||||
" assert last_pub_time <= t + d, \"Publication out of CB instance timeframe\"\n",
|
||||
"\n",
|
||||
" match cb.owner:\n",
|
||||
" case TrTimer():\n",
|
||||
|
|
@ -1031,7 +1056,9 @@
|
|||
"\n",
|
||||
"time_breakdown = {k: np.array(v) for k, v in time_breakdown.items()}\n",
|
||||
"\n",
|
||||
"timer_cb_times = [sum(inst.duration for inst in path if isinstance(inst, TrCallbackInstance) and isinstance(inst.callback_obj, TrTimer)) for path in critical_paths]\n",
|
||||
"timer_cb_times = [sum(inst.duration for inst in path if\n",
|
||||
" isinstance(inst, TrCallbackInstance) and isinstance(inst.callback_obj, TrTimer)) for path in\n",
|
||||
" critical_paths]\n",
|
||||
"sub_cb_times = [sum(inst.duration for inst in path if isinstance(inst, TrCallbackInstance)) for path in critical_paths]\n",
|
||||
"\n",
|
||||
"labels, values = list(zip(*time_breakdown.items()))\n",
|
||||
|
|
@ -1066,10 +1093,8 @@
|
|||
"%%skip_if_false E2E_ENABLED\n",
|
||||
"%%skip_if_false E2E_PLOT\n",
|
||||
"\n",
|
||||
"from scipy import stats\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(figsize=(60, 15), num=\"crit_pdf\")\n",
|
||||
"ax.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i/4) for i in range(5)]))\n",
|
||||
"ax.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i / 4) for i in range(5)]))\n",
|
||||
"\n",
|
||||
"kde = stats.gaussian_kde(timestep_mags)\n",
|
||||
"xs = np.linspace(timestep_mags.min(), timestep_mags.max(), 1000)\n",
|
||||
|
|
@ -1105,22 +1130,19 @@
|
|||
"%%skip_if_false E2E_ENABLED\n",
|
||||
"%%skip_if_false E2E_PLOT\n",
|
||||
"\n",
|
||||
"from tracing_interop.tr_types import TrSubscription\n",
|
||||
"import matplotlib.patches as mpatch\n",
|
||||
"import math\n",
|
||||
"\n",
|
||||
"tree = trees[E2E_PLOT_TIMESTAMP]\n",
|
||||
"e2es = e2ess[E2E_PLOT_TIMESTAMP]\n",
|
||||
"e2e_paths = e2e_pathss[E2E_PLOT_TIMESTAMP]\n",
|
||||
"margin_y = .2\n",
|
||||
"margin_x=0\n",
|
||||
"arr_width= 1 - margin_y\n",
|
||||
"margin_x = 0\n",
|
||||
"arr_width = 1 - margin_y\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def cb_str(inst: TrCallbackInstance):\n",
|
||||
" cb: TrCallbackObject = inst.callback_obj\n",
|
||||
" if not cb:\n",
|
||||
" return None\n",
|
||||
" ret_str = f\"- {inst.duration*1e3:07.3f}ms \"\n",
|
||||
" ret_str = f\"- {inst.duration * 1e3:07.3f}ms \"\n",
|
||||
" #if cb.callback_symbol:\n",
|
||||
" # ret_str = repr(sanitize(cb.callback_symbol.symbol))\n",
|
||||
"\n",
|
||||
|
|
@ -1131,16 +1153,18 @@
|
|||
" case TrTimer(period=p, node=node):\n",
|
||||
" p: int\n",
|
||||
" node: TrNode\n",
|
||||
" ret_str = f\"{ret_str}{node.path if node else None} <- @{1/(p*1e-9):.2f}Hz\"\n",
|
||||
" ret_str = f\"{ret_str}{node.path if node else None} <- @{1 / (p * 1e-9):.2f}Hz\"\n",
|
||||
" return ret_str\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def ts_str(inst, prev):\n",
|
||||
" return f\"{(inst.timestamp - prev)*1e3:+09.3f}ms\"\n",
|
||||
" return f\"{(inst.timestamp - prev) * 1e3:+09.3f}ms\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def bw_str(bw):\n",
|
||||
" return bytes_str(bw['mean'])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def trunc_chars(string, w, e2e):\n",
|
||||
" if w < e2e * .005:\n",
|
||||
" return \"\"\n",
|
||||
|
|
@ -1150,6 +1174,7 @@
|
|||
"\n",
|
||||
" return \"...\" + string[-n_chars:] if n_chars < len(string) else string\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#e2e_paths = sorted(e2e_paths, key=lambda path: path[-1].timestamp - path[0].timestamp, reverse=True)\n",
|
||||
"#for y, e2e_path in reversed(list(enumerate(e2e_paths))):\n",
|
||||
"# last_pub_ts = None\n",
|
||||
|
|
@ -1165,6 +1190,7 @@
|
|||
"\n",
|
||||
"legend_entries = {}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def plot_subtree(subtree: DepTree, ax: plt.Axes, y_labels, y=0, next_cb_start=0):\n",
|
||||
" height = fanout(subtree)\n",
|
||||
" inst = subtree.head\n",
|
||||
|
|
@ -1179,13 +1205,16 @@
|
|||
" r_h = height - margin_y\n",
|
||||
"\n",
|
||||
" r = mpatch.Rectangle((r_x, r_y), r_w, r_h,\n",
|
||||
" ec=\"cadetblue\" if is_sub else \"indianred\", fc=\"lightblue\" if is_sub else \"lightcoral\", zorder=9000)\n",
|
||||
" ec=\"cadetblue\" if is_sub else \"indianred\", fc=\"lightblue\" if is_sub else \"lightcoral\",\n",
|
||||
" zorder=9000)\n",
|
||||
" ax.add_artist(r)\n",
|
||||
"\n",
|
||||
" text = repr(sanitize(inst.callback_obj.callback_symbol.symbol)) if inst.callback_obj and inst.callback_obj.callback_symbol else \"??\"\n",
|
||||
" text = repr(sanitize(\n",
|
||||
" inst.callback_obj.callback_symbol.symbol)) if inst.callback_obj and inst.callback_obj.callback_symbol else \"??\"\n",
|
||||
" text = trunc_chars(text, r_w, t_e2e)\n",
|
||||
" if text:\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + r_h / 2, text, ha=\"center\", va=\"center\", backgroundcolor=(1,1,1,.5), zorder=11000)\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + r_h / 2, text, ha=\"center\", va=\"center\", backgroundcolor=(1, 1, 1, .5),\n",
|
||||
" zorder=11000)\n",
|
||||
"\n",
|
||||
" if is_sub and \"Subscription CB\" not in legend_entries:\n",
|
||||
" legend_entries[\"Subscription CB\"] = r\n",
|
||||
|
|
@ -1194,7 +1223,7 @@
|
|||
"\n",
|
||||
" if next_cb_start is not None:\n",
|
||||
" r_x = t_cb - t_start + d_cb - margin_x / 2\n",
|
||||
" r_y = y + .5 - arr_width/2\n",
|
||||
" r_y = y + .5 - arr_width / 2\n",
|
||||
" r_w = next_cb_start - (t_cb + d_cb) + margin_x\n",
|
||||
" r_h = arr_width\n",
|
||||
" r = mpatch.Rectangle((r_x, r_y), r_w, r_h, color=\"orange\")\n",
|
||||
|
|
@ -1208,7 +1237,8 @@
|
|||
"\n",
|
||||
" text = trunc_chars(text, r_w, t_e2e)\n",
|
||||
" if text:\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + r_h / 2, text, ha=\"center\", va=\"center\", backgroundcolor=(1,1,1,.5), zorder=11000)\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + r_h / 2, text, ha=\"center\", va=\"center\", backgroundcolor=(1, 1, 1, .5),\n",
|
||||
" zorder=11000)\n",
|
||||
"\n",
|
||||
" if \"Idle\" not in legend_entries:\n",
|
||||
" legend_entries[\"Idle\"] = r\n",
|
||||
|
|
@ -1218,14 +1248,14 @@
|
|||
" if not subtree.deps:\n",
|
||||
" y_labels.append(pub.topic_name if pub else None)\n",
|
||||
"\n",
|
||||
" scatter = ax.scatter(t_pub - t_start, y+.5, color=\"cyan\", marker=\".\", zorder=10000)\n",
|
||||
" scatter = ax.scatter(t_pub - t_start, y + .5, color=\"cyan\", marker=\".\", zorder=10000)\n",
|
||||
"\n",
|
||||
" if \"Publication\" not in legend_entries:\n",
|
||||
" legend_entries[\"Publication\"] = scatter\n",
|
||||
"\n",
|
||||
" if next_cb_start is not None:\n",
|
||||
" r_x = t_pub - t_start\n",
|
||||
" r_y = y + .5 - arr_width/2\n",
|
||||
" r_y = y + .5 - arr_width / 2\n",
|
||||
" r_w = max(next_cb_start - t_pub + margin_x / 2, 0)\n",
|
||||
" r = mpatch.Rectangle((r_x, r_y), r_w, arr_width, color=\"lightgreen\")\n",
|
||||
" ax.add_artist(r)\n",
|
||||
|
|
@ -1233,7 +1263,8 @@
|
|||
" text = pub.topic_name\n",
|
||||
" text = trunc_chars(text, r_w, t_e2e)\n",
|
||||
" if text:\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + arr_width / 2, text, ha=\"center\", va=\"center\", backgroundcolor=(1,1,1,.5), zorder=11000)\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + arr_width / 2, text, ha=\"center\", va=\"center\",\n",
|
||||
" backgroundcolor=(1, 1, 1, .5), zorder=11000)\n",
|
||||
"\n",
|
||||
" if \"DDS\" not in legend_entries:\n",
|
||||
" legend_entries[\"DDS\"] = r\n",
|
||||
|
|
@ -1241,7 +1272,8 @@
|
|||
" topic_stats = msg_sizes.get(pub.topic_name) if BW_ENABLED else None\n",
|
||||
" if topic_stats:\n",
|
||||
" size_str = bw_str(topic_stats)\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + arr_width + margin_y, size_str, ha=\"center\", backgroundcolor=(1,1,1,.5), zorder=11000)\n",
|
||||
" ax.text(r_x + r_w / 2, r_y + arr_width + margin_y, size_str, ha=\"center\",\n",
|
||||
" backgroundcolor=(1, 1, 1, .5), zorder=11000)\n",
|
||||
" else:\n",
|
||||
" print(\"[WARN] Tried to publish to another PublishInstance\")\n",
|
||||
" next_cb_start = None\n",
|
||||
|
|
@ -1252,7 +1284,6 @@
|
|||
" return height\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"fig, ax = plt.subplots(figsize=(36, 20), num=\"path_viz\")\n",
|
||||
"ax.set_ylim(0, len(e2es))\n",
|
||||
"\n",
|
||||
|
|
@ -1300,14 +1331,12 @@
|
|||
"source": [
|
||||
"%%skip_if_false E2E_ENABLED\n",
|
||||
"\n",
|
||||
"from termcolor import colored\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"\n",
|
||||
"critical_paths = {}\n",
|
||||
"print(len(relevant_trees))\n",
|
||||
"for tree in tqdm(trees):\n",
|
||||
" crit = critical_path(tree)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" def _owner(inst):\n",
|
||||
" match inst:\n",
|
||||
" case TrCallbackInstance(callback_obj=cb_obj):\n",
|
||||
|
|
@ -1324,6 +1353,7 @@
|
|||
" case _:\n",
|
||||
" raise ValueError()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" key = tuple(map(_owner, crit[::-1]))\n",
|
||||
" if key not in critical_paths:\n",
|
||||
" critical_paths[key] = []\n",
|
||||
|
|
@ -1336,7 +1366,8 @@
|
|||
"for key, paths in items:\n",
|
||||
" path_records = [(\" -> \".join(key), p[0].timestamp, p[0].timestamp - p[-1].timestamp) for p in paths]\n",
|
||||
" out_df.append(path_records)\n",
|
||||
" print(f\"======== {len(paths)}x: {sum(map(lambda p: p[0].timestamp - p[-1].timestamp, paths))/len(paths)*1000:.3f}ms\")\n",
|
||||
" print(\n",
|
||||
" f\"======== {len(paths)}x: {sum(map(lambda p: p[0].timestamp - p[-1].timestamp, paths)) / len(paths) * 1000:.3f}ms\")\n",
|
||||
" paths_durations = []\n",
|
||||
" for path in paths:\n",
|
||||
" next_inst = None\n",
|
||||
|
|
@ -1371,7 +1402,7 @@
|
|||
" dur_str = colored(f\"{duration * 1000 :>.3f}ms\", colors[E2E_PLOT_TIMESTAMP])\n",
|
||||
" print(f\" -> {dur_str} {part}\")\n",
|
||||
"\n",
|
||||
"out_df.to_csv(os.path.join(OUT_PATH, \"e2e.csv\"), sep=\"\\t\")"
|
||||
"out_df.to_csv(os.path.join(OUT_PATH, \"e2e.csv\"), sep=\"\\t\", index=False)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
|
|
@ -1383,100 +1414,14 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%skip_if_false E2E_ENABLED\n",
|
||||
"\n",
|
||||
"from termcolor import colored\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"from matching.subscriptions import sanitize\n",
|
||||
"\n",
|
||||
"critical_paths = {}\n",
|
||||
"print(len(relevant_trees))\n",
|
||||
"for tree in tqdm(trees):\n",
|
||||
" crit = critical_path(tree)\n",
|
||||
"\n",
|
||||
" def _owner(inst):\n",
|
||||
" match inst:\n",
|
||||
" case TrCallbackInstance(callback_obj=cb_obj):\n",
|
||||
" cb_obj: TrCallbackObject\n",
|
||||
" if cb_obj and cb_obj.callback_symbol:\n",
|
||||
" sym = repr(sanitize(cb_obj.callback_symbol.symbol))\n",
|
||||
" else:\n",
|
||||
" sym = str(cb_obj.id)\n",
|
||||
" return sym\n",
|
||||
" case TrPublishInstance(publisher=pub):\n",
|
||||
" pub: TrPublisher\n",
|
||||
" topic = pub.topic_name\n",
|
||||
" return topic\n",
|
||||
" case _:\n",
|
||||
" raise ValueError()\n",
|
||||
"\n",
|
||||
" key = tuple(map(_owner, crit[::-1]))\n",
|
||||
" if key not in critical_paths:\n",
|
||||
" critical_paths[key] = []\n",
|
||||
" critical_paths[key].append(crit)\n",
|
||||
"\n",
|
||||
"items = list(critical_paths.items())\n",
|
||||
"items.sort(key=lambda pair: len(pair[1]), reverse=True)\n",
|
||||
"\n",
|
||||
"out_df = pd.DataFrame(columns=[\"path\", \"timestamp\", \"e2e_latency\"])\n",
|
||||
"for key, paths in items:\n",
|
||||
" path_records = [(\" -> \".join(key), p[0].timestamp, p[0].timestamp - p[-1].timestamp) for p in paths]\n",
|
||||
" out_df.append(path_records)\n",
|
||||
" print(f\"======== {len(paths)}x: {sum(map(lambda p: p[0].timestamp - p[-1].timestamp, paths))/len(paths)*1000:.3f}ms\")\n",
|
||||
" paths_durations = []\n",
|
||||
" for path in paths:\n",
|
||||
" next_inst = None\n",
|
||||
" durations = []\n",
|
||||
" for inst in path:\n",
|
||||
" match inst:\n",
|
||||
" case TrCallbackInstance(timestamp=t, duration=d):\n",
|
||||
" if not next_inst:\n",
|
||||
" durations.append(d)\n",
|
||||
" else:\n",
|
||||
" durations.append(min(d, next_inst.timestamp - t))\n",
|
||||
" case TrPublishInstance(timestamp=t):\n",
|
||||
" if not next_inst:\n",
|
||||
" durations.append(0.0)\n",
|
||||
" else:\n",
|
||||
" durations.append(next_inst.timestamp - t)\n",
|
||||
" case _:\n",
|
||||
" raise ValueError()\n",
|
||||
" next_inst = inst\n",
|
||||
" paths_durations.append(durations)\n",
|
||||
"\n",
|
||||
" durations = list(map(lambda l: sum(l) / len(l), zip(*paths_durations)))[::-1]\n",
|
||||
" assert len(durations) == len(key)\n",
|
||||
" perc = np.percentile(durations, [70, 90, 95, 100])\n",
|
||||
" colors = [\"green\", \"yellow\", \"red\", \"magenta\"]\n",
|
||||
" for part, duration in zip(key, durations):\n",
|
||||
" E2E_PLOT_TIMESTAMP = 0\n",
|
||||
" for j, p in enumerate(perc):\n",
|
||||
" if duration < p:\n",
|
||||
" break\n",
|
||||
" E2E_PLOT_TIMESTAMP = j\n",
|
||||
" dur_str = colored(f\"{duration * 1000 :>.3f}ms\", colors[E2E_PLOT_TIMESTAMP])\n",
|
||||
" print(f\" -> {dur_str} {part}\")\n",
|
||||
"\n",
|
||||
"out_df.to_csv(os.path.join(OUT_PATH, \"e2e.csv\"), sep=\"\\t\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue