diff --git a/batch_analysis_analysis.py b/batch_analysis_analysis.py new file mode 100644 index 0000000..7968a08 --- /dev/null +++ b/batch_analysis_analysis.py @@ -0,0 +1,76 @@ +import pandas as pd +import numpy as np +import argparse +import seaborn as sns +import matplotlib.pyplot as plt + + +def parse_arguments(): + parser = argparse.ArgumentParser(description='Analyze chain data from CSV file.') + parser.add_argument('--input', '-i', required=True, help='Path to the input CSV file') + return parser.parse_args() + + +def main(): + args = parse_arguments() + + # Load the CSV file from the input argument + df = pd.read_csv(args.input) + + # Extract the experiment_name which should be the same across all rows + if 'experiment_name' not in df.columns: + raise ValueError("Input CSV must contain 'experiment_name' column.") + experiment_name = df['experiment_name'].iloc[0] + + # Strip timestamp from experiment_name if it exists + experiment_name = experiment_name.split('-')[0] if '-' in experiment_name else experiment_name + + # Group data by chain + chain_groups = df.groupby('chain') + + # For each chain, create a plot with four boxplots (mean, std, min, max) + for chain_name, chain_data in chain_groups: + # Create a figure for this chain + plt.figure(figsize=(12, 8)) + + # Normalize chain name for filename + chain_name_fs = str(chain_name).replace('--> /', '-').replace('/', '_').replace(' ', '') + + # Create a DataFrame with the columns we want to plot + plot_data = pd.DataFrame({ + 'Mean': chain_data['mean'], + 'Std': chain_data['std'], + 'Min': chain_data['min'], + 'Max': chain_data['max'] + }) + + # Create boxplots + ax = sns.boxplot(data=plot_data, palette='Set3') + + # Add individual data points + sns.stripplot(data=plot_data, color='black', alpha=0.5, size=4, jitter=True) + + # Set labels and title + plt.title(f'Statistics for Chain: {chain_name}\nAcross {len(chain_data)} Experiment Runs\n{experiment_name}', fontsize=14) + plt.ylabel('Latency (ms)', fontsize=12) + plt.xlabel('Statistic Type', fontsize=12) + + # Add grid for better readability + plt.grid(axis='y', linestyle='--', alpha=0.7) + + # Tighten layout and save the figure + plt.tight_layout() + output_file = args.input.replace('.csv', f'_chain_{chain_name_fs}_analysis.png') + plt.savefig(output_file, dpi=300) + plt.close() + + # Also calculate and print summary statistics for this chain + summary = chain_data.describe() + print(f"\nSummary for chain: {chain_name}") + print(summary[['mean', 'std', 'min', 'max']]) + + print(f"\nAnalysis complete. Plots saved with base name: {args.input.replace('.csv', '_chain_*_analysis.png')}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/batch_analyze.py b/batch_analyze.py index 976a8a5..4d77a76 100755 --- a/batch_analyze.py +++ b/batch_analyze.py @@ -35,14 +35,17 @@ def main(base_dir, name_filter): print(f"Found {len(unprocessed)} unprocessed and {len(unprocessable)} unprocessable artifacts.") current_artifact = unprocessed.pop() - print(f"Now working on {current_artifact}.") + experiment_name = os.path.basename(current_artifact) + print(f"Now working on {current_artifact} --> {experiment_name}.") out_dir = os.path.join(current_artifact, 'output') + shutil.rmtree(out_dir, ignore_errors=True) os.makedirs(out_dir, exist_ok=False) os.environ["ANA_NB_OUT_PATH"] = f"'{out_dir}'" - os.environ["ANA_NB_TR_PATH"] = f"'{os.path.join(current_artifact, 'tracing/max-ma-trace/ust')}'" + os.environ["ANA_NB_EXPERIMENT_NAME"] = f"'{experiment_name}'" + os.environ["ANA_NB_TR_PATH"] = f"'/home/niklas/dataflow-analysis/{current_artifact}/ust'" try: pm.execute_notebook( diff --git a/requirements.txt b/requirements.txt index cf482cb..677a569 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,3 +11,5 @@ pyvis ruamel.yaml termcolor tqdm +seaborn +papermill \ No newline at end of file diff --git a/trace-analysis.ipynb b/trace-analysis.ipynb index cc30642..20b2319 100644 --- a/trace-analysis.ipynb +++ b/trace-analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, @@ -37,45 +37,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "User Settings:\n", - " TRACING_WS_BUILD_PATH................... := /home/niklas/dataflow-analysis/dependencies/build\n", - " EXPERIMENT_NAME......................... := ros_single_timed_20-20250609154040\n", - " TR_PATH................................. := /home/niklas/dataflow-analysis/traces/ros_single_timed_20-20250609154040/ust\n", - " OUT_PATH................................ := /home/niklas/dataflow-analysis/out/ros_single_timed_20-20250609154040\n", - " CACHING_ENABLED......................... := False\n", - " BW_ENABLED.............................. := False\n", - " BW_PATH................................. := /home/niklas/dataflow-analysis/path/to/messages.h5\n", - " CL_ENABLED.............................. := False\n", - " CL_PATH................................. := /path/to/code_analysis/output\n", - " DFG_ENABLED............................. := True\n", - " DFG_PLOT................................ := True\n", - " DFG_MAX_HIER_LEVEL...................... := 100\n", - " DFG_INPUT_NODE_PATTERNS................. := ^/input_\n", - " DFG_OUTPUT_NODE_PATTERNS................ := ^/output_\n", - " DFG_EXCL_NODE_PATTERNS.................. := ^/rviz2\n", - " E2E_ENABLED............................. := True\n", - " E2E_PLOT................................ := True\n", - " E2E_PLOT_TIMESTAMP...................... := 5\n", - " E2E_TIME_LIMIT_S........................ := 1000\n", - " E2E_OUTPUT_TOPIC_PATTERNS............... := ^/output/\n", - " E2E_INPUT_TOPIC_PATTERNS................ := ^/input/\n", - " E2E_EXCL_PATH_PATTERNS.................. := ^/parameter_events\n", - " E2E_INCL_PATH_PATTERNS.................. := \n", - " E2E_EXACT_PATH.......................... := \n", - " DEBUG................................... := False\n", - " MANUAL_CACHE............................ := False\n" - ] - } - ], + "outputs": [], "source": [ "##################################################\n", "# User Settings\n", @@ -97,7 +63,7 @@ "\n", "# The name of the experiment that is being analyzed.\n", "# This will be used to name output directory.\n", - "EXPERIMENT_NAME = \"ros_single_timed_20-20250609154040\"\n", + "EXPERIMENT_NAME = \"edf_multi_timed_20-20250610091402\"\n", "\n", "# Path to trace directory (e.g. ~/.ros/my-trace/ust) or to a converted trace file.\n", "# Using the path \"/ust\" at the end is optional but greatly reduces processing time\n", @@ -224,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, @@ -256,65 +222,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CACHE] Cache disabled for tr_objects.\n", - "found converted file: /home/niklas/dataflow-analysis/traces/ros_single_timed_20-20250609154040/ust/converted\n", - " [100%] [Ros2Handler]\n", - "[TrContext] Processing ROS 2 objects from traces...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " ├─ Processing TrNodes: 100%|██████████| 23/23 [00:00<00:00, 259325.25it/s]\n", - " ├─ Processing TrPublishers: 100%|██████████| 69/69 [00:00<00:00, 404765.00it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DEBUG] Duplicate Indices in id\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " ├─ Processing TrSubscriptions: 100%|██████████| 43/43 [00:00<00:00, 191256.70it/s]\n", - " ├─ Processing TrTimers: 100%|██████████| 14/14 [00:00<00:00, 229376.00it/s]\n", - " ├─ Processing TrTimerNodeLinks: 100%|██████████| 14/14 [00:00<00:00, 216679.91it/s]\n", - " ├─ Processing TrSubscriptionObjects: 100%|██████████| 43/43 [00:00<00:00, 516776.71it/s]\n", - " ├─ Processing TrCallbackObjects: 100%|██████████| 195/195 [00:00<00:00, 357781.84it/s]\n", - " ├─ Processing TrCallbackSymbols: 100%|██████████| 195/195 [00:00<00:00, 700847.71it/s]\n", - " ├─ Processing TrPublishInstances: 100%|██████████| 2688/2688 [00:00<00:00, 849351.30it/s]\n", - " ├─ Processing TrCallbackInstances: 100%|██████████| 4208/4208 [00:00<00:00, 781302.84it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "def _load_traces():\n", " file = load_file(TR_PATH)\n", @@ -347,61 +259,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/cameraA/debayered................................................................................................................ | 97 msgs\n", - "/cameraA/geometric................................................................................................................ | 96 msgs\n", - "/cameraA/radiometric.............................................................................................................. | 97 msgs\n", - "/cameraB/debayered................................................................................................................ | 115 msgs\n", - "/cameraB/radiometric.............................................................................................................. | 114 msgs\n", - "/flight/plan...................................................................................................................... | 127 msgs\n", - "/input/baroA/alt.................................................................................................................. | 119 msgs\n", - "/input/baroB/alt.................................................................................................................. | 121 msgs\n", - "/input/cameraA/raw................................................................................................................ | 98 msgs\n", - "/input/cameraB/raw................................................................................................................ | 116 msgs\n", - "/input/gpsA/fix................................................................................................................... | 121 msgs\n", - "/input/gpsB/fix................................................................................................................... | 121 msgs\n", - "/input/imuA/data.................................................................................................................. | 119 msgs\n", - "/input/imuB/data.................................................................................................................. | 121 msgs\n", - "/input/lidar/scan................................................................................................................. | 120 msgs\n", - "/input/operator/commands.......................................................................................................... | 120 msgs\n", - "/output/cameraA/mapped............................................................................................................ | 95 msgs\n", - "/output/classifier/classification................................................................................................. | 112 msgs\n", - "/output/flight/cmd................................................................................................................ | 125 msgs\n", - "/output/telemetry/radio........................................................................................................... | 127 msgs\n", - "/parameter_events................................................................................................................. | 23 msgs\n", - "/rosout........................................................................................................................... | 0 msgs\n", - "/sensorsA/fused................................................................................................................... | 127 msgs\n", - "/sensorsB/fused................................................................................................................... | 128 msgs\n", - "/telemetry/data................................................................................................................... | 129 msgs\n", - "\n", - "[DEBUG] INPUT TOPICS\n", - "--[DEBUG] ^/input/ :/input/baroA/alt......................................................................... | 119 msgs\n", - "--[DEBUG] ^/input/ :/input/baroB/alt......................................................................... | 121 msgs\n", - "--[DEBUG] ^/input/ :/input/cameraA/raw....................................................................... | 98 msgs\n", - "--[DEBUG] ^/input/ :/input/cameraB/raw....................................................................... | 116 msgs\n", - "--[DEBUG] ^/input/ :/input/gpsA/fix.......................................................................... | 121 msgs\n", - "--[DEBUG] ^/input/ :/input/gpsB/fix.......................................................................... | 121 msgs\n", - "--[DEBUG] ^/input/ :/input/imuA/data......................................................................... | 119 msgs\n", - "--[DEBUG] ^/input/ :/input/imuB/data......................................................................... | 121 msgs\n", - "--[DEBUG] ^/input/ :/input/lidar/scan........................................................................ | 120 msgs\n", - "--[DEBUG] ^/input/ :/input/operator/commands................................................................. | 120 msgs\n", - "\n", - "[DEBUG] OUTPUT TOPICS\n", - "--[DEBUG] ^/output/ :/output/cameraA/mapped................................................................... | 95 msgs\n", - "--[DEBUG] ^/output/ :/output/classifier/classification........................................................ | 112 msgs\n", - "--[DEBUG] ^/output/ :/output/flight/cmd....................................................................... | 125 msgs\n", - "--[DEBUG] ^/output/ :/output/telemetry/radio.................................................................. | 127 msgs\n" - ] - } - ], + "outputs": [], "source": [ "##################################################\n", "# Print (All/Input/Output) Topic Message Counts\n", @@ -436,43 +298,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CACHE] Cache disabled for lat_graph.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Finding CB nodes: 100%|██████████| 195/195 [00:00<00:00, 288192.14it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DEBUG] 138 callbacks have no owner, filtering them out.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing node publications: 100%|██████████| 23/23 [00:00<00:00, 3026.19it/s]\n", - "Processing CB subscriptions: 100%|██████████| 57/57 [00:00<00:00, 5516.15it/s]\n", - "Building graph edges: 100%|██████████| 25/25 [00:00<00:00, 205603.14it/s]\n", - "Building graph nodes: 100%|██████████| 57/57 [00:00<00:00, 2461.40it/s]\n" - ] - } - ], + "outputs": [], "source": [ "import latency_graph.latency_graph_structure as lg\n", "\n", @@ -496,785 +326,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Processing INPUT: 1\n", - " Processing OUTPUT: 1\n", - " Processing input_cameraA_node: 2\n", - " Processing debayerA_node: 2\n", - " Processing radiometricA_node: 2\n", - " Processing geometric_node: 2\n", - " Processing output_mapping_node: 2\n", - " Processing input_cameraB_node: 2\n", - " Processing debayerB_node: 2\n", - " Processing radiometricB_node: 2\n", - " Processing output_smoke_classifier_node: 2\n", - " Processing input_gpsA_node: 2\n", - " Processing input_imuA_node: 2\n", - " Processing input_baroA_node: 2\n", - " Processing fusionA_node: 5\n", - " Processing input_lidar_node: 2\n", - " Processing input_cmd_node: 2\n", - " Processing mgmt_node: 5\n", - " Processing output_control_node: 2\n", - " Processing input_gpsB_node: 2\n", - " Processing input_imuB_node: 2\n", - " Processing input_baroB_node: 2\n", - " Processing fusionB_node: 5\n", - " Processing telemetry_node: 4\n", - " Processing output_radio_node: 2\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "cluster___INPUT\n", - "\n", - "INPUT\n", - "\n", - "\n", - "cluster___OUTPUT\n", - "\n", - "OUTPUT\n", - "\n", - "\n", - "cluster___input_cameraA_node\n", - "\n", - "input_cameraA_node\n", - "\n", - "\n", - "cluster___debayerA_node\n", - "\n", - "debayerA_node\n", - "\n", - "\n", - "cluster___radiometricA_node\n", - "\n", - "radiometricA_node\n", - "\n", - "\n", - "cluster___geometric_node\n", - "\n", - "geometric_node\n", - "\n", - "\n", - "cluster___output_mapping_node\n", - "\n", - "output_mapping_node\n", - "\n", - "\n", - "cluster___input_gpsB_node\n", - "\n", - "input_gpsB_node\n", - "\n", - "\n", - "cluster___input_imuB_node\n", - "\n", - "input_imuB_node\n", - "\n", - "\n", - "cluster___input_baroB_node\n", - "\n", - "input_baroB_node\n", - "\n", - "\n", - "cluster___fusionB_node\n", - "\n", - "fusionB_node\n", - "\n", - "\n", - "cluster___telemetry_node\n", - "\n", - "telemetry_node\n", - "\n", - "\n", - "cluster___output_radio_node\n", - "\n", - "output_radio_node\n", - "\n", - "\n", - "cluster___input_cameraB_node\n", - "\n", - "input_cameraB_node\n", - "\n", - "\n", - "cluster___debayerB_node\n", - "\n", - "debayerB_node\n", - "\n", - "\n", - "cluster___radiometricB_node\n", - "\n", - "radiometricB_node\n", - "\n", - "\n", - "cluster___output_smoke_classifier_node\n", - "\n", - "output_smoke_classifier_node\n", - "\n", - "\n", - "cluster___input_gpsA_node\n", - "\n", - "input_gpsA_node\n", - "\n", - "\n", - "cluster___input_imuA_node\n", - "\n", - "input_imuA_node\n", - "\n", - "\n", - "cluster___input_baroA_node\n", - "\n", - "input_baroA_node\n", - "\n", - "\n", - "cluster___fusionA_node\n", - "\n", - "fusionA_node\n", - "\n", - "\n", - "cluster___input_lidar_node\n", - "\n", - "input_lidar_node\n", - "\n", - "\n", - "cluster___input_cmd_node\n", - "\n", - "input_cmd_node\n", - "\n", - "\n", - "cluster___mgmt_node\n", - "\n", - "mgmt_node\n", - "\n", - "\n", - "cluster___output_control_node\n", - "\n", - "output_control_node\n", - "\n", - "\n", - "\n", - "INPUT\n", - "\n", - "\n", - "INPUT\n", - "\n", - "\n", - "\n", - "\n", - "OUTPUT\n", - "\n", - "\n", - "OUTPUT\n", - "\n", - "\n", - "\n", - "\n", - "187651297581776\n", - "\n", - "\n", - "void(CameraNode::CameraNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651297813008\n", - "\n", - "\n", - "void(DebayerNode::DebayerNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651297581776:out->187651297813008:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651297579760\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651297803888\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651297997136\n", - "\n", - "\n", - "void(RadiometricNode::RadiometricNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651297813008:out->187651297997136:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651297978064\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651298203760\n", - "\n", - "\n", - "void(GeometricNode::GeometricNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651297997136:out->187651298203760:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651298399984\n", - "\n", - "\n", - "void(MappingNode::MappingNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651298203760:out->187651298399984:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651298185168\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651301889184\n", - "\n", - "\n", - "void(TelemetryNode::TelemetryNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651298399984:out->187651301889184:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651298374800\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651298585440\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651298608000\n", - "\n", - "\n", - "void(CameraNode::CameraNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651298776400\n", - "\n", - "\n", - "void(DebayerNode::DebayerNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651298608000:out->187651298776400:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651299008832\n", - "\n", - "\n", - "void(RadiometricNode::RadiometricNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651298776400:out->187651299008832:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651298782000\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299013232\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299234352\n", - "\n", - "\n", - "void(SmokeClassifierNode::SmokeClassifierNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651299008832:out->187651299234352:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651299220704\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299395024\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299420688\n", - "\n", - "\n", - "void(GPSNode::GPSNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651299976736\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651299420688:out->187651299976736:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651299196848\n", - "\n", - "\n", - "void(IMUNode::IMUNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651300001904\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651299196848:out->187651300001904:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651299594752\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299808512\n", - "\n", - "\n", - "void(BaroNode::BaroNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651300018672\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651299808512:out->187651300018672:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651299801984\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299999568\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651299954176\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651300614704\n", - "\n", - "\n", - "void(FlightManagementNode::FlightManagementNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651299954176:out->187651300614704:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651300193360\n", - "\n", - "\n", - "void(LidarNode::LidarNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651300629424\n", - "\n", - "\n", - "void(FlightManagementNode::FlightManagementNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651300193360:out->187651300629424:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651300222176\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651300401584\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651300020784\n", - "\n", - "\n", - "void(CommandNode::CommandNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651300635248\n", - "\n", - "\n", - "void(FlightManagementNode::FlightManagementNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651300020784:out->187651300635248:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651300599024\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651300655552\n", - "\n", - "\n", - "void(FlightManagementNode::FlightManagementNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651300874016\n", - "\n", - "\n", - "void(ControlNode::ControlNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651300655552:out->187651300874016:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651300821520\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651301038384\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651301095136\n", - "\n", - "\n", - "void(GPSNode::GPSNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651301657264\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651301095136:out->187651301657264:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651301229376\n", - "\n", - "\n", - "void(IMUNode::IMUNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651301630272\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651301229376:out->187651301630272:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651301237840\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651301432256\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651301442864\n", - "\n", - "\n", - "void(BaroNode::BaroNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651301667504\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651301442864:out->187651301667504:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651301655312\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651301623936\n", - "\n", - "\n", - "void(FusionNode::FusionNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651301873008\n", - "\n", - "\n", - "void(TelemetryNode::TelemetryNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651301623936:out->187651301873008:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651301854592\n", - "\n", - "\n", - "void(TelemetryNode::TelemetryNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::vector<std::__cxx11::basic_string<char,std::char_traits<char>,>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))()\n", - "\n", - "\n", - "\n", - "\n", - "187651302065632\n", - "\n", - "\n", - "void(RadioNode::RadioNode(std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,std::__cxx11::basic_string<char,std::char_traits<char>,>,int,double))(std_msgs::msg::String)\n", - "\n", - "\n", - "\n", - "\n", - "187651301854592:out->187651302065632:in\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "187651301845808\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n", - "187651302090656\n", - "\n", - "\n", - "void(rclcpp::TimeSource)(rcl_interfaces::msg::ParameterEvent)\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%skip_if_false DFG_ENABLED\n", "%%skip_if_false DFG_PLOT\n", @@ -1300,447 +356,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%%%skip_if_false DFG_ENABLED\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting DFG overview with max hierarchy level 100...\n", - "Input Node Patterns: ['^/input_']\n", - "Output Node Patterns: ['^/output_']\n", - "Excluded Node Patterns: ['^/rviz2']\n", - "Input Nodes: /input_cameraA_node, /input_cameraB_node, /input_gpsA_node, /input_imuA_node, /input_baroA_node, /input_lidar_node, /input_cmd_node, /input_gpsB_node, /input_imuB_node, /input_baroB_node\n", - "Output Nodes: /output_mapping_node, /output_smoke_classifier_node, /output_control_node, /output_radio_node\n", - "Intermediate Nodes: /INPUT, /OUTPUT, /input_cameraA_node, /debayerA_node, /radiometricA_node, /geometric_node, /output_mapping_node, /input_cameraB_node, /debayerB_node, /radiometricB_node, /output_smoke_classifier_node, /input_gpsA_node, /input_imuA_node, /input_baroA_node, /fusionA_node, /input_lidar_node, /input_cmd_node, /mgmt_node, /output_control_node, /input_gpsB_node, /input_imuB_node, /input_baroB_node, /fusionB_node, /telemetry_node, /output_radio_node\n", - "/input_baroA_node /fusionA_node 1\n", - "/telemetry_node /output_radio_node 1\n", - "/input_gpsB_node /fusionB_node 1\n", - "/input_lidar_node /mgmt_node 1\n", - "/radiometricA_node /geometric_node 1\n", - "/geometric_node /output_mapping_node 1\n", - "/output_mapping_node /telemetry_node 1\n", - "/input_gpsA_node /fusionA_node 1\n", - "/input_imuA_node /fusionA_node 1\n", - "/mgmt_node /output_control_node 1\n", - "/input_imuB_node /fusionB_node 1\n", - "/debayerB_node /radiometricB_node 1\n", - "/input_baroB_node /fusionB_node 1\n", - "/input_cmd_node /mgmt_node 1\n", - "/input_cameraB_node /debayerB_node 1\n", - "/fusionB_node /telemetry_node 1\n", - "/fusionA_node /mgmt_node 1\n", - "/debayerA_node /radiometricA_node 1\n", - "/radiometricB_node /output_smoke_classifier_node 1\n", - "/input_cameraA_node /debayerA_node 1\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "G\n", - "\n", - "\n", - "\n", - "/INPUT\n", - "\n", - "/INPUT\n", - "\n", - "\n", - "\n", - "/OUTPUT\n", - "\n", - "/OUTPUT\n", - "\n", - "\n", - "\n", - "/input_cameraA_node\n", - "\n", - "/input_cameraA_node\n", - "\n", - "\n", - "\n", - "/debayerA_node\n", - "\n", - "/debayerA_node\n", - "\n", - "\n", - "\n", - "/input_cameraA_node->/debayerA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_cameraA_node__before\n", - "\n", - "\n", - "\n", - "/input_cameraA_node__before->/input_cameraA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/radiometricA_node\n", - "\n", - "/radiometricA_node\n", - "\n", - "\n", - "\n", - "/debayerA_node->/radiometricA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/geometric_node\n", - "\n", - "/geometric_node\n", - "\n", - "\n", - "\n", - "/radiometricA_node->/geometric_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/output_mapping_node\n", - "\n", - "\n", - "/output_mapping_node\n", - "\n", - "\n", - "\n", - "/geometric_node->/output_mapping_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/telemetry_node\n", - "\n", - "/telemetry_node\n", - "\n", - "\n", - "\n", - "/output_mapping_node->/telemetry_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_cameraB_node\n", - "\n", - "/input_cameraB_node\n", - "\n", - "\n", - "\n", - "/debayerB_node\n", - "\n", - "/debayerB_node\n", - "\n", - "\n", - "\n", - "/input_cameraB_node->/debayerB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_cameraB_node__before\n", - "\n", - "\n", - "\n", - "/input_cameraB_node__before->/input_cameraB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/radiometricB_node\n", - "\n", - "/radiometricB_node\n", - "\n", - "\n", - "\n", - "/debayerB_node->/radiometricB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/output_smoke_classifier_node\n", - "\n", - "\n", - "/output_smoke_classifier_node\n", - "\n", - "\n", - "\n", - "/radiometricB_node->/output_smoke_classifier_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_gpsA_node\n", - "\n", - "/input_gpsA_node\n", - "\n", - "\n", - "\n", - "/fusionA_node\n", - "\n", - "/fusionA_node\n", - "\n", - "\n", - "\n", - "/input_gpsA_node->/fusionA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_gpsA_node__before\n", - "\n", - "\n", - "\n", - "/input_gpsA_node__before->/input_gpsA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_imuA_node\n", - "\n", - "/input_imuA_node\n", - "\n", - "\n", - "\n", - "/input_imuA_node->/fusionA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_imuA_node__before\n", - "\n", - "\n", - "\n", - "/input_imuA_node__before->/input_imuA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_baroA_node\n", - "\n", - "/input_baroA_node\n", - "\n", - "\n", - "\n", - "/input_baroA_node->/fusionA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_baroA_node__before\n", - "\n", - "\n", - "\n", - "/input_baroA_node__before->/input_baroA_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/mgmt_node\n", - "\n", - "/mgmt_node\n", - "\n", - "\n", - "\n", - "/fusionA_node->/mgmt_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_lidar_node\n", - "\n", - "/input_lidar_node\n", - "\n", - "\n", - "\n", - "/input_lidar_node->/mgmt_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_lidar_node__before\n", - "\n", - "\n", - "\n", - "/input_lidar_node__before->/input_lidar_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_cmd_node\n", - "\n", - "/input_cmd_node\n", - "\n", - "\n", - "\n", - "/input_cmd_node->/mgmt_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_cmd_node__before\n", - "\n", - "\n", - "\n", - "/input_cmd_node__before->/input_cmd_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/output_control_node\n", - "\n", - "\n", - "/output_control_node\n", - "\n", - "\n", - "\n", - "/mgmt_node->/output_control_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_gpsB_node\n", - "\n", - "/input_gpsB_node\n", - "\n", - "\n", - "\n", - "/fusionB_node\n", - "\n", - "/fusionB_node\n", - "\n", - "\n", - "\n", - "/input_gpsB_node->/fusionB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_gpsB_node__before\n", - "\n", - "\n", - "\n", - "/input_gpsB_node__before->/input_gpsB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_imuB_node\n", - "\n", - "/input_imuB_node\n", - "\n", - "\n", - "\n", - "/input_imuB_node->/fusionB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_imuB_node__before\n", - "\n", - "\n", - "\n", - "/input_imuB_node__before->/input_imuB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_baroB_node\n", - "\n", - "/input_baroB_node\n", - "\n", - "\n", - "\n", - "/input_baroB_node->/fusionB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/input_baroB_node__before\n", - "\n", - "\n", - "\n", - "/input_baroB_node__before->/input_baroB_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/fusionB_node->/telemetry_node\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "/output_radio_node\n", - "\n", - "\n", - "/output_radio_node\n", - "\n", - "\n", - "\n", - "/telemetry_node->/output_radio_node\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%skip_if_false DFG_ENABLED\n", "%%skip_if_false DFG_PLOT\n", @@ -1772,88 +395,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 4 end topics for E2E latency calculations: /output/flight/cmd, /output/telemetry/radio, /output/cameraA/mapped, /output/classifier/classification\n", - "Using 195 callback objects, 4208 callback instances, and 2688 publish instances for E2E latency calculations.\n", - "[CACHE] Cache disabled for trees.\n", - "=====/output/flight/cmd\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing output messages: 100%|██████████| 125/125 [00:01<00:00, 73.48it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 125 trees for topic /output/flight/cmd\n", - "=====/output/telemetry/radio\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing output messages: 100%|██████████| 127/127 [00:01<00:00, 78.03it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 127 trees for topic /output/telemetry/radio\n", - "=====/output/cameraA/mapped\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing output messages: 100%|██████████| 95/95 [00:00<00:00, 1460.40it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 95 trees for topic /output/cameraA/mapped\n", - "=====/output/classifier/classification\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing output messages: 100%|██████████| 112/112 [00:00<00:00, 281.79it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 112 trees for topic /output/classifier/classification\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "%%skip_if_false E2E_ENABLED\n", "\n", @@ -1879,22 +425,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "'Skipped (evaluated BW_ENABLED to False)'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%skip_if_false E2E_ENABLED\n", "%%skip_if_false BW_ENABLED\n", @@ -1913,33 +448,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Extracting E2E paths: 100%|██████████| 459/459 [00:00<00:00, 24623.78it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 1298 E2E paths in total.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "%%skip_if_false E2E_ENABLED\n", "\n", @@ -2024,193 +537,11 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Aggregating E2E path cohorts: 100%|██████████| 1298/1298 [00:00<00:00, 256306.51it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 11 cohorts of E2E paths.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
pathe2e_latency
countmeanminmax
6void(GPSNode::GPSNode(std::__cxx11::basic_stri...1230.4727540.1556790.589257
10void(LidarNode::LidarNode(std::__cxx11::basic_...1230.3055070.1343260.384916
0void(BaroNode::BaroNode(std::__cxx11::basic_st...1220.4752700.1801330.629801
8void(IMUNode::IMUNode(std::__cxx11::basic_stri...1220.4776900.1556170.636953
5void(CommandNode::CommandNode(std::__cxx11::ba...1220.3045120.1391730.382762
7void(GPSNode::GPSNode(std::__cxx11::basic_stri...1210.4583830.1725210.567640
1void(BaroNode::BaroNode(std::__cxx11::basic_st...1200.4562180.2052010.565442
9void(IMUNode::IMUNode(std::__cxx11::basic_stri...1200.4582160.2052630.565517
3void(CameraNode::CameraNode(std::__cxx11::basi...1180.8310550.3204651.448288
4void(CameraNode::CameraNode(std::__cxx11::basi...1120.1743140.1177730.584865
2void(CameraNode::CameraNode(std::__cxx11::basi...950.3799840.0746860.810526
\n", - "
" - ], - "text/plain": [ - " path e2e_latency \\\n", - " count mean \n", - "6 void(GPSNode::GPSNode(std::__cxx11::basic_stri... 123 0.472754 \n", - "10 void(LidarNode::LidarNode(std::__cxx11::basic_... 123 0.305507 \n", - "0 void(BaroNode::BaroNode(std::__cxx11::basic_st... 122 0.475270 \n", - "8 void(IMUNode::IMUNode(std::__cxx11::basic_stri... 122 0.477690 \n", - "5 void(CommandNode::CommandNode(std::__cxx11::ba... 122 0.304512 \n", - "7 void(GPSNode::GPSNode(std::__cxx11::basic_stri... 121 0.458383 \n", - "1 void(BaroNode::BaroNode(std::__cxx11::basic_st... 120 0.456218 \n", - "9 void(IMUNode::IMUNode(std::__cxx11::basic_stri... 120 0.458216 \n", - "3 void(CameraNode::CameraNode(std::__cxx11::basi... 118 0.831055 \n", - "4 void(CameraNode::CameraNode(std::__cxx11::basi... 112 0.174314 \n", - "2 void(CameraNode::CameraNode(std::__cxx11::basi... 95 0.379984 \n", - "\n", - " \n", - " min max \n", - "6 0.155679 0.589257 \n", - "10 0.134326 0.384916 \n", - "0 0.180133 0.629801 \n", - "8 0.155617 0.636953 \n", - "5 0.139173 0.382762 \n", - "7 0.172521 0.567640 \n", - "1 0.205201 0.565442 \n", - "9 0.205263 0.565517 \n", - "3 0.320465 1.448288 \n", - "4 0.117773 0.584865 \n", - "2 0.074686 0.810526 " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%skip_if_false E2E_ENABLED\n", "\n", @@ -2242,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, @@ -2261,175 +592,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 123/123 [00:00<00:00, 91439.10it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['GPSNode', '/input/gpsA/fix', 'FusionNode', '/fusionA_node', 'FusionNode', '/sensorsA/fused', 'FlightManagementNode', '/mgmt_node', 'FlightManagementNode', '/flight/plan', 'ControlNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 123/123 [00:00<00:00, 134769.96it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['LidarNode', '/input/lidar/scan', 'FlightManagementNode', '/mgmt_node', 'FlightManagementNode', '/flight/plan', 'ControlNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 122/122 [00:00<00:00, 90263.73it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['IMUNode', '/input/imuA/data', 'FusionNode', '/fusionA_node', 'FusionNode', '/sensorsA/fused', 'FlightManagementNode', '/mgmt_node', 'FlightManagementNode', '/flight/plan', 'ControlNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 122/122 [00:00<00:00, 65368.56it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['BaroNode', '/input/baroA/alt', 'FusionNode', '/fusionA_node', 'FusionNode', '/sensorsA/fused', 'FlightManagementNode', '/mgmt_node', 'FlightManagementNode', '/flight/plan', 'ControlNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 122/122 [00:00<00:00, 130271.15it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['CommandNode', '/input/operator/commands', 'FlightManagementNode', '/mgmt_node', 'FlightManagementNode', '/flight/plan', 'ControlNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_256141/3060490163.py:48: 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`). Consider using `matplotlib.pyplot.close()`.\n", - " fig, axes = plt.subplots(1, 3, num=f\"E2E type breakdown histograms {name}\\n{EXPERIMENT_NAME}\", dpi=300, figsize=(16, 9))\n", - "Calculating breakdowns: 100%|██████████| 121/121 [00:00<00:00, 98126.60it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['GPSNode', '/input/gpsB/fix', 'FusionNode', '/fusionB_node', 'FusionNode', '/sensorsB/fused', 'TelemetryNode', '/telemetry_node', 'TelemetryNode', '/telemetry/data', 'RadioNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 120/120 [00:00<00:00, 98342.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['IMUNode', '/input/imuB/data', 'FusionNode', '/fusionB_node', 'FusionNode', '/sensorsB/fused', 'TelemetryNode', '/telemetry_node', 'TelemetryNode', '/telemetry/data', 'RadioNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 120/120 [00:00<00:00, 98074.14it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['BaroNode', '/input/baroB/alt', 'FusionNode', '/fusionB_node', 'FusionNode', '/sensorsB/fused', 'TelemetryNode', '/telemetry_node', 'TelemetryNode', '/telemetry/data', 'RadioNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 118/118 [00:00<00:00, 53662.35it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['CameraNode', '/input/cameraA/raw', 'DebayerNode', '/cameraA/debayered', 'RadiometricNode', '/cameraA/radiometric', 'GeometricNode', '/cameraA/geometric', 'MappingNode', '/output/cameraA/mapped', 'TelemetryNode', '/telemetry_node', 'TelemetryNode', '/telemetry/data', 'RadioNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 95/95 [00:00<00:00, 113715.43it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['CameraNode', '/input/cameraA/raw', 'DebayerNode', '/cameraA/debayered', 'RadiometricNode', '/cameraA/radiometric', 'GeometricNode', '/cameraA/geometric', 'MappingNode']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Calculating breakdowns: 100%|██████████| 112/112 [00:00<00:00, 137357.32it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Labels: ['CameraNode', '/input/cameraB/raw', 'DebayerNode', '/cameraB/debayered', 'RadiometricNode', '/cameraB/radiometric', 'SmokeClassifierNode']\n", - "Done.\n" - ] - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.patches as mpatches\n", "from collections import Counter\n", @@ -2450,6 +613,9 @@ "# 2. Iterate through filtered DFG paths\n", "##################################################\n", "\n", + "result_strings = []\n", + "result_strings_csv = []\n", + "\n", "for relevant_path, relevant_dataflows in cohorts_filt.items():\n", " # Construct a filesystem-safe name for output files\n", " topics = [topic for topic in relevant_path.split(\" -> \") if topic.startswith(\"/\") and not topic.startswith(\"/void\")]\n", @@ -2537,6 +703,11 @@ " bbox=dict(facecolor='white', alpha=0.7, boxstyle='round,pad=0.3')\n", " )\n", " plt.savefig(os.path.join(OUT_PATH, f\"plot_e2es_{name}.png\"))\n", + " result_strings.append(f\"Chain {topics[0]} --> {topics[-1]} E2E stats: Mean: {mean_latency:.2f} ms, Std: {std_latency:.2f} ms, Min: {min_latency:.2f} ms, Max: {max_latency:.2f} ms\")\n", + " # also do it as csv of order: exepriment_name, chain, mean, std, min, max\n", + " result_strings_csv.append(\n", + " f\"{EXPERIMENT_NAME},{topics[0]} --> {topics[-1]},{mean_latency:.2f},{std_latency:.2f},{min_latency:.2f},{max_latency:.2f}\"\n", + " )\n", "\n", " ##################################################\n", " # 5. Violin plot of per-component/stage latencies on the DFG path (across all dataflows)\n", @@ -2632,6 +803,13 @@ " fig.set_size_inches(16, 9)\n", " fig.set_dpi(300)\n", "\n", + "for result_string in result_strings:\n", + " print(result_string)\n", + "\n", + "# append csv results to results file one dir up\n", + "with open(os.path.join(OUT_PATH, \"..\", \"..\", \"results.csv\"), \"a\") as f:\n", + " f.write(\"\\n\".join(result_strings_csv) + \"\\n\")\n", + "\n", "print(\"Done.\")" ] }