From 0ebb09f06b2aaa828c8ac0d271b740802166d5f5 Mon Sep 17 00:00:00 2001
From: Henry <ehdykhne@uwaterloo.ca>
Date: Fri, 8 Dec 2023 22:14:19 -0500
Subject: [PATCH] a
---
experiments/bulk_running_experiments.ipynb | 275 ++++++---------------
1 file changed, 75 insertions(+), 200 deletions(-)
diff --git a/experiments/bulk_running_experiments.ipynb b/experiments/bulk_running_experiments.ipynb
index 3557913..6307f84 100644
--- a/experiments/bulk_running_experiments.ipynb
+++ b/experiments/bulk_running_experiments.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"id": "3e31b233",
"metadata": {
"scrolled": true
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"id": "5a4f8ab4",
"metadata": {
"scrolled": true
@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -61,7 +61,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 4,
"id": "64d027e9",
"metadata": {
"scrolled": true
@@ -100,7 +100,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 5,
"id": "ec762c52",
"metadata": {
"scrolled": true
@@ -111,7 +111,7 @@
"output_type": "stream",
"text": [
"default_simulation\n",
- "../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.20.26.52\n"
+ "../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.22.08.14\n"
]
}
],
@@ -150,6 +150,7 @@
"# 'planner=pdm_hybrid_planner',\n",
"# f\"planner.pdm_hybrid_planner.checkpoint_path={ckpt_dir}\" ,\n",
" 'planner=log_future_planner', \n",
+ " 'ego_controller=perfect_tracking_controller',\n",
" #'planner.ml_planner.model_config=${model}',\n",
" #f'planner.ml_planner.checkpoint_path={ckpt_dir}',\n",
" #f'observation=idm_agents_observation',\n",
@@ -176,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 6,
"id": "c221b637",
"metadata": {
"scrolled": false
@@ -186,118 +187,77 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Global seed set to 0\n"
+ "Global seed set to 0\n",
+ "INFO:nuplan.planning.script.builders.main_callback_builder:Building MultiMainCallback...\n",
+ "INFO:nuplan.planning.script.builders.main_callback_builder:Building MultiMainCallback: 4...DONE!\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-08 20:26:55,563 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/main_callback_builder.py:20} Building MultiMainCallback...\n",
- "2023-12-08 20:26:55,563 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/main_callback_builder.py:20} Building MultiMainCallback...\n",
- "2023-12-08 20:26:55,582 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/main_callback_builder.py:35} Building MultiMainCallback: 4...DONE!\n",
- "2023-12-08 20:26:55,582 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/main_callback_builder.py:35} Building MultiMainCallback: 4...DONE!\n",
- "2023-12-08 20:26:55,734 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:19} Building WorkerPool...\n",
- "2023-12-08 20:26:55,734 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:19} Building WorkerPool...\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:101} Worker: Sequential\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:101} Worker: Sequential\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:102} Number of nodes: 1\n",
+ "2023-12-08 22:08:15,778 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:19} Building WorkerPool...\n",
+ "2023-12-08 22:08:15,779 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:101} Worker: Sequential\n",
+ "2023-12-08 22:08:15,779 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:102} Number of nodes: 1\n",
"Number of CPUs per node: 1\n",
"Number of GPUs per node: 0\n",
"Number of threads across all nodes: 1\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/utils/multithreading/worker_pool.py:102} Number of nodes: 1\n",
- "Number of CPUs per node: 1\n",
- "Number of GPUs per node: 0\n",
- "Number of threads across all nodes: 1\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:27} Building WorkerPool...DONE!\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:27} Building WorkerPool...DONE!\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:32} Building experiment folders...\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:32} Building experiment folders...\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:35} \n",
- "\n",
- "\tFolder where all results are stored: ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.20.26.52\n",
- "\n",
- "2023-12-08 20:26:55,735 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:35} \n",
+ "2023-12-08 22:08:15,779 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/worker_pool_builder.py:27} Building WorkerPool...DONE!\n",
+ "2023-12-08 22:08:15,779 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:32} Building experiment folders...\n",
+ "2023-12-08 22:08:15,779 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:35} \n",
"\n",
- "\tFolder where all results are stored: ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.20.26.52\n",
+ "\tFolder where all results are stored: ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.22.08.14\n",
"\n",
- "2023-12-08 20:26:55,736 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:70} Building experiment folders...DONE!\n",
- "2023-12-08 20:26:55,736 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:70} Building experiment folders...DONE!\n",
- "2023-12-08 20:26:55,736 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:52} Building AbstractCallback...\n",
- "2023-12-08 20:26:55,736 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:52} Building AbstractCallback...\n",
- "2023-12-08 20:26:55,736 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:68} Building AbstractCallback: 0...DONE!\n",
- "2023-12-08 20:26:55,736 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:68} Building AbstractCallback: 0...DONE!\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:49} Building simulations...\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:49} Building simulations...\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:55} Extracting scenarios...\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:55} Extracting scenarios...\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/common/utils/distributed_scenario_filter.py:83} Building Scenarios in mode DistributedMode.SINGLE_NODE\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/common/utils/distributed_scenario_filter.py:83} Building Scenarios in mode DistributedMode.SINGLE_NODE\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
- "2023-12-08 20:26:55,737 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
- "2023-12-08 20:26:55,747 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
- "2023-12-08 20:26:55,747 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
- "2023-12-08 20:26:55,747 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:35} Building ScenarioFilter...\n",
- "2023-12-08 20:26:55,747 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:35} Building ScenarioFilter...\n",
- "2023-12-08 20:26:55,748 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:44} Building ScenarioFilter...DONE!\n",
- "2023-12-08 20:26:55,748 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:44} Building ScenarioFilter...DONE!\n",
- "2023-12-08 20:26:56,405 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:76} Building metric engines...\n",
- "2023-12-08 20:26:56,405 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:76} Building metric engines...\n",
- "2023-12-08 20:26:56,421 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:78} Building metric engines...DONE\n",
- "2023-12-08 20:26:56,421 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:78} Building metric engines...DONE\n",
- "2023-12-08 20:26:56,421 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:82} Building simulations from 4 scenarios...\n",
- "2023-12-08 20:26:56,421 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:82} Building simulations from 4 scenarios...\n",
- "2023-12-08 20:26:56,436 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:142} Building simulations...DONE!\n",
- "2023-12-08 20:26:56,436 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:142} Building simulations...DONE!\n",
- "2023-12-08 20:26:56,436 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:114} Running simulation...\n",
- "2023-12-08 20:26:56,436 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:114} Running simulation...\n",
- "2023-12-08 20:26:56,436 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:138} Executing runners...\n",
- "2023-12-08 20:26:56,436 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:138} Executing runners...\n",
- "2023-12-08 20:26:56,437 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:82} Starting 4 simulations using Sequential!\n",
- "2023-12-08 20:26:56,437 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:82} Starting 4 simulations using Sequential!\n",
+ "2023-12-08 22:08:15,780 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/folder_builder.py:70} Building experiment folders...DONE!\n",
+ "2023-12-08 22:08:15,781 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:52} Building AbstractCallback...\n",
+ "2023-12-08 22:08:15,781 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_callback_builder.py:68} Building AbstractCallback: 0...DONE!\n",
+ "2023-12-08 22:08:15,781 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:49} Building simulations...\n",
+ "2023-12-08 22:08:15,781 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:55} Extracting scenarios...\n",
+ "2023-12-08 22:08:15,781 INFO {/home/ehdykhne/nuplan-devkit/nuplan/common/utils/distributed_scenario_filter.py:83} Building Scenarios in mode DistributedMode.SINGLE_NODE\n",
+ "2023-12-08 22:08:15,781 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
+ "2023-12-08 22:08:15,791 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
+ "2023-12-08 22:08:15,791 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:35} Building ScenarioFilter...\n",
+ "2023-12-08 22:08:15,792 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_filter_builder.py:44} Building ScenarioFilter...DONE!\n",
+ "2023-12-08 22:08:16,438 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:76} Building metric engines...\n",
+ "2023-12-08 22:08:16,456 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:78} Building metric engines...DONE\n",
+ "2023-12-08 22:08:16,456 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:82} Building simulations from 4 scenarios...\n",
+ "2023-12-08 22:08:16,728 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/simulation_builder.py:142} Building simulations...DONE!\n",
+ "2023-12-08 22:08:16,728 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:114} Running simulation...\n",
+ "2023-12-08 22:08:16,728 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:138} Executing runners...\n",
+ "2023-12-08 22:08:16,728 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:82} Starting 4 simulations using Sequential!\n",
"occlusion_window_timesteps 50\n",
"intersection_delta_timesteps 30\n",
"numrel agents 2\n",
"scenario 1f151e15c9cf5c81 1f151e15c9cf5c81\n",
- "time elapsed false 0.23780369758605957\n",
+ "time elapsed false 0.24103260040283203\n",
"occlusion_window_timesteps 50\n",
"intersection_delta_timesteps 30\n",
"numrel agents 1\n",
"scenario 3318937ef0c65127 3318937ef0c65127\n",
- "time elapsed false 0.25443482398986816\n",
- "2023-12-08 20:27:19,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:127} Number of successful simulations: 4\n",
- "2023-12-08 20:27:19,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:127} Number of successful simulations: 4\n",
- "2023-12-08 20:27:19,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:128} Number of failed simulations: 0\n",
- "2023-12-08 20:27:19,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:128} Number of failed simulations: 0\n",
- "2023-12-08 20:27:19,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:147} Finished executing runners!\n",
- "2023-12-08 20:27:19,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:147} Finished executing runners!\n",
- "2023-12-08 20:27:19,607 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:74} Saved runner reports to ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.20.26.52/runner_report.parquet\n",
- "2023-12-08 20:27:19,607 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:74} Saved runner reports to ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.20.26.52/runner_report.parquet\n",
- "2023-12-08 20:27:19,608 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/time_callback.py:27} Simulation duration: 00:00:24 [HH:MM:SS]\n",
- "2023-12-08 20:27:19,608 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/time_callback.py:27} Simulation duration: 00:00:24 [HH:MM:SS]\n",
- "2023-12-08 20:27:19,648 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_file_callback.py:79} Metric files integration: 00:00:00 [HH:MM:SS]\n",
- "2023-12-08 20:27:19,648 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_file_callback.py:79} Metric files integration: 00:00:00 [HH:MM:SS]\n",
- "2023-12-08 20:27:19,672 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:58} Running metric aggregator: open_loop_boxes_weighted_average\n",
- "2023-12-08 20:27:19,672 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:58} Running metric aggregator: open_loop_boxes_weighted_average\n",
- "2023-12-08 20:27:19,678 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:69} Metric aggregator: 00:00:00 [HH:MM:SS]\n",
- "2023-12-08 20:27:19,678 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:69} Metric aggregator: 00:00:00 [HH:MM:SS]\n"
+ "time elapsed false 0.25691843032836914\n",
+ "2023-12-08 22:08:40,077 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:127} Number of successful simulations: 4\n",
+ "2023-12-08 22:08:40,078 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/runner/executor.py:128} Number of failed simulations: 0\n",
+ "2023-12-08 22:08:40,078 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:147} Finished executing runners!\n",
+ "2023-12-08 22:08:40,083 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/utils.py:74} Saved runner reports to ../../data/nuplan/exp/exp/simulation/open_loop_boxes/2023.12.08.22.08.14/runner_report.parquet\n",
+ "2023-12-08 22:08:40,083 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/time_callback.py:27} Simulation duration: 00:00:24 [HH:MM:SS]\n",
+ "2023-12-08 22:08:40,123 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_file_callback.py:79} Metric files integration: 00:00:00 [HH:MM:SS]\n",
+ "2023-12-08 22:08:40,154 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:58} Running metric aggregator: open_loop_boxes_weighted_average\n",
+ "2023-12-08 22:08:40,161 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_aggregator_callback.py:69} Metric aggregator: 00:00:00 [HH:MM:SS]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "Rendering histograms: 100%|██████████| 10/10 [00:01<00:00, 6.98it/s]\n"
+ "Rendering histograms: 100%|██████████| 10/10 [00:01<00:00, 7.24it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-08 20:27:22,441 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_summary_callback.py:344} Metric summary: 00:00:02 [HH:MM:SS]\n",
- "2023-12-08 20:27:22,441 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_summary_callback.py:344} Metric summary: 00:00:02 [HH:MM:SS]\n",
- "2023-12-08 20:27:22,442 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:116} Finished running simulation!\n",
- "2023-12-08 20:27:22,442 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:116} Finished running simulation!\n"
+ "2023-12-08 22:08:42,889 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/simulation/main_callback/metric_summary_callback.py:344} Metric summary: 00:00:02 [HH:MM:SS]\n",
+ "2023-12-08 22:08:42,889 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/run_simulation.py:116} Finished running simulation!\n"
]
},
{
@@ -312,7 +272,7 @@
},
{
"data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAJRCAYAAABY7oO4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/SrBM8AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAutElEQVR4nO3dfZzNdf7/8ecZY0YuZnIRNWaQ0o5cNhlfuRqKUGnniy9pUJuLJIPGRWhaF2FysXLVropNLdqlWluhK5GwylrFICvEDCkNnZlxMRfm/fujm/Nr1th9FWfm4HG/3eaW8znnfD6vz6dmevicM5/jcc45AQAA4D8KKukBAAAALgdEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAl1C3bt20aNGikh4DgB8QTQAAAAZEE3AF27Jli+688041adJEt912m5YvXy5JeuGFF3TLLbcoJiZGzzzzjDwej+857733nmJiYtSwYUPFxcVp165d/3EbDz/8sAYMGKB27drpxhtv1COPPKLPPvtMbdq0Ue3atZWUlOR77NGjR9W9e3c1bdpUDRo0UHJysu++ESNGKDY2Vo0bN1br1q21Z88e330ej0dTpkxR06ZNdeONN+rll1/+jzPt3btXLVq0UKNGjQptZ/z48eratavuvPNORUdHq3PnzsrIyJAk5eXlafTo0WratKkaN26s7t2768SJE5KkrKws9e/fX02bNlXDhg01YMAA5ebmSpK+/PJLNW/eXPXq1VN8fLwyMzP/678XAJcpB+CKdOLECde4cWN35MgR55xzx44dc1FRUW7dunXu+uuvd998841zzrnf/va37tyPgm+//dZVqlTJbd++3Tnn3OLFi13dunVdQUHBBbfz0EMPuWbNmrnTp0+7nJwcd9NNN7n4+HiXm5vrsrOzXdWqVV1qaqpzzrm7777brVu3zjnnXF5enuvQoYNbtmyZc8657777zrfO1157zXXo0MF3W5KbMWOGc8653bt3u/Lly7u8vLwLzjRkyBA3ZcoU3+2MjAznnHPjxo1z1113nW/fH3vsMde/f3/nnHOTJ092EydO9D1n4sSJbtCgQc455/r37+9eeeUV55xzBQUFrm/fvm7atGnOOeeaNGniFixY4Jxzbvv27S4kJMS9/PLLF5wNwOUruKSjDYB/bNq0Sfv371enTp0KLf/iiy/UsWNHXX/99ZKk/v37a+LEiZKkTz/9VA0aNFCDBg0kSQkJCXr88cd1+PBhRUZGXnBbv/71r1WmTBlJUoMGDdShQweVLl1apUuX1q233qq9e/eqVq1aWrNmjb799lvf87Kzs31nlD744APNnTtXWVlZKigo0PHjxwttIyEhQZIUHR2t4OBgHT169IIztW7dWiNHjlR2drbi4uLUrl0733333nuvb98HDBigLl26SJJWrFghr9erN954Q5KUm5urWrVq+e77+9//rpkzZ0qSTp8+rVKlSikzM1Off/65Hn74Yd++t2zZ8oLHCcDljWgCrlDOOdWrV0+bNm0qtHzOnDmFbv/0pblf6lwwSVKpUqXOu52fny/nnCRp8+bNhe6XpEOHDmnw4MHasmWLbrrpJm3fvl2tW7f+j9vIz8+/4Dxdu3ZV8+bN9cEHH2jevHmaNWuWVq1aVeRjz+2/c05z587V3Xfffd5jnHN64403dMsttxRaXtRLcZfieAIITLynCbhCNW/eXAcOHNCHH37oW/b5558rLi5O7733nr777jtJ0sKFC333N2vWTDt27FBqaqok6c9//rOqV6+u6tWrX/Q85cuXV9u2bfXss8/6lh05ckTp6enyer0qXbq0brjhBjnnNG/evIva1t69e1WtWjX16dNH06ZN0+bNm333rVq1yne2a8GCBb6zUPHx8Xruued06tQpSdKpU6e0c+dO331Tp071hdqJEyf01VdfKSwsTLfddpteffVVSdLOnTu1YcOGi5odQODiTBNwhapYsaJWrlypESNGaPjw4crLy1ONGjW0YsUKJScnq0WLFqpQoYI6duyo8PBwSdJ1112nJUuWqE+fPsrPz1fFihW1fPnyS3b2ZMmSJUpKSlL9+vXl8XhUrlw5vfDCC2rUqJEeeOAB1atXT5UrV1Z8fPxFbef111/X4sWLFRISooKCAs2fP993X6tWrfTggw/q8OHDqlOnju/yAE8++aRycnL0P//zP779ffLJJ1WvXj0999xzGj16tBo3bqygoCAFBwdr2rRpuvnmm/Xqq6/qN7/5jX73u9+pTp06550hA3Dl8Lhz58wBXDWysrJUoUIFSdLs2bP17rvvavXq1SU8lf+NHz9eP/zwg2bNmlXSowC4DHGmCbgKjR49Whs3blReXp4iIiL0wgsvlPRIABDwONME4L9atWqVxo4de97yMWPGqEePHiUw0Y+aNGly3hvC69WrpyVLlpTQRACuZEQTAACAAb89BwAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEGDVp0uSinv/111+rW7dul2gaAEBxI5oAAAAMiCYU4pxTYmKi2rZtq3bt2ik9PV1TpkzRHXfcoSFDhigmJkaStGPHDrVs2VItWrRQSkpKketat26dOnTooP/93/9Vo0aN9Je//EUdOnRQ06ZNlZGRIUmaMmWK4uLi1Lp1a+3YsUOSlJSUpLi4ODVt2lSff/65JKlNmzZKSkpS69atNXjw4CK3t2/fPjVv3lxt27bVo48+6nteYmKiWrduraFDh0qSzpw5o169eunOO+/U/fffr8zMzAvO8qc//UlNmjRRz549lZ2dXeR2Bw8erE8//VSStGbNGo0ePVrffvut2rZtq1atWqlbt246e/Zsoee0adPGt75u3brp66+/vuBcAIAA4YCfePvtt93TTz/tnHNu8+bNrm/fvq5169auoKDA7dmzx9WqVcs559x9993ndu3a5QoKClz79u3dgQMHzlvX2rVrXbt27Zxzzr3wwgsuPj7eOefcrFmz3MKFC92OHTtcnz59nHPOHT582N1///3OOedOnjzpnHPun//8p3vwwQedc87FxcW5jz/+2DnnXLNmzdwPP/xw3vYWLFjgnn/+eeecc2fPnvU977333nPOOdejRw+3detWN3fuXLdw4ULnnHN//vOf3fTp04ucJT8/3zVu3NidOXPGHTt2zJUrV67IY/bJJ5+4YcOGOeec69+/v9u2bZvLyclxeXl5zjnnhgwZ4t5//3134MAB17VrV99cWVlZzjnnunbt6g4cOFDkXACAwBFc0tGGwLJr1y799a9/1fr16+WcU+nSpdWwYUN5PB7dcsstKl++vCTp6NGjqlu3riQpJiZG+/btU61atc5bX8OGDSVJERERvj9Xr15dBw8e1K5du7Rp0ya1adNGklSqVClJ0vTp0/Xhhx9KkoKD//9/orfddpvv+T/88IPCw8MLbat79+6aOHGiEhIS1KFDB/Xp00eSdPvtt0uSYmNjtXfvXu3atUtbtmzRq6++qry8PLVq1arIWY4dO6bIyEiFhoYqNDRUN954Y5HHrEWLFho5cqRyc3O1a9cuNW7cWN98840ee+wxnThxQkeOHFFMTIzq1Knje47H4/H92TnnO/b/PhcAIHAQTSgkOjpa3bt319NPPy1JSktLU58+feSc01dffeV7SalatWravXu3oqOj9c9//lMDBw4scn0/jYN/D4Xo6GjFxcVpwYIFkqS8vDxlZGTogw8+0IYNG7R161YNHz78gs//d8HBwZo+fbokqV69eurVq5ckadu2bWrXrp3+8Y9/qE2bNvr22291xx13qHfv3r7t7t69+7xZgoKClJ6ertzcXGVnZ+vAgQMX3McWLVpowoQJateunSRp6dKluu+++9SvXz8lJiaeN2/FihWVnp6um2++WTt37vQd+3+fCwAQOIgmFNK5c2d99NFHatu2rTwejxISEtS+fXvdcccduv3221WpUiVJ0uTJk9WvXz8553TvvfcWeZbpv2nYsKHq1KmjuLg4BQUFqX379ho9erQqVaqkNm3aqFmzZj9rfW+99ZbmzZsnSerQoYOCgn58y97q1as1ceJENWrUSLfffrvq1aunAQMG6OWXX5YkDR8+XPfee+95s4wdO1bDhg1T8+bNFR0drRo1alxw2w888ICaNWum1NRUSdJdd92l3r176+2339Y111xz3uMHDRqk//u//1PDhg1VrVo1SdKAAQOKnAsAEBg8rqi/sgM/kZeXp9KlS+tf//qXhg0bplWrVpX0SGZt2rTRO++843tZEQCAX4ozTfivxo0bp40bN+r06dP6/e9/X+RjPv74Y40bN67QsjVr1vjep3Sp7dmzx/cbcucsWbJE1atX98v2zinu/QQABA7ONAEAABhwnSYAAAADogkAAMCAaAIAADAgmgAAAAyuyN+eKygo0JEjR1ShQoVCF0QEcOVzzikrK0sRERG+a3UBwKVwRUbTkSNHFBUVVdJjAChBaWlpioyMLOkxAFxBrshoqlChgqQff2iGhYWV8DQAilNmZqaioqJ8PwcA4FK5IqPp3EtyYWFhRBOuWAUFBcrNzS3pMQJGSEhIoZfjeGkewKV2RUYTcKUrKCjQwYMHdebMmZIeJWCUKVNGNWvWLOkxAFzBiCbgMpSbm6szZ87o+uuvL/IDga82p0+f1tGjRznzBsCviCbgMnbNNdco46Y65sdXP5x23rLx48crOTlZwcEX/nHwyCOPqGHDhoqNjVWLFi18yxctWqSWLVvq5ptv/q/bXrdunZYtW6a8vDxNnTpVM2fO1KRJk8yzA0BJI5oASJJmz56toKAgnT17Vm3atNHrr7+ujIwM9ejRw/eYypUrKzk5WZMmTVJycrKCgoL03Xff6ZZbblH58uUVHBys/Px8LV68WO3bt1d2drZOnTql/Px8xcbGqkuXLjp+/Lj27NkjSfr888+1fv16ffvtt5o8ebI6duyou+66S3Xr1tU//vEPRUVFaevWrZo5c6aeeuopVa5cWa1atdKyZcvUrFkz3XPPPapYsWJJHTIAVxkuYgJAknTo0CElJiYqLS1N7733noYOHaq2bdtKkiIiIjRs2DBFR0f7Hn/27FnVrl1bXbp0UcWKFeWc09mzZ32PT0hI0Pr161WxYkVlZWVJkt58803t2bNHsbGxkqTs7GyFhob6Iuq2227TiBEj9Omnn0qSevXqpeuuu05ffPGFcnJyFBERoa+//loVKlRQQkICwQSgWBFNADRnzhzVqFFDc+fOVY0aNXT33Xdr9uzZ+uijj8572c7j8Wjx4sXyer2qXbu2Fi9erFtuuUWrVq3Su+++K0m+32K78847lZGRobp160qSunTpoqefftq3zm3btql8+fIqKCiQJJUqVUoej0fOuUK3b731VpUrV07Z2dmqX78+F60EUCI87txPpytIZmamwsPD5fV6ueQArkhnzpzRgQMHdOONN6pMmTKXfP1ZWVlaunSpvvzyS02YMCHgv49+ejxyc3P5/gfgF7ynCcB5KlSooEcffbSkxwCAgEI0AZex06dPl/QIAYHjAKA4EE3AZSgkJERlypTR0aNHS3qUgFGmTBmFhIRwrSYAfkM0AZehoKAg1axZk0D4iX//GBUAuNSIJuAyFRQU5Jc3gQMAisZfywAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAwK/XaVqxYoVWrlypzMxM9e3bV8nJyYqJiVHNmjU1ZswY3+NOnjypQYMGKSQkRG3atNEDDzygpKQkSdLEiRO1c+dO7du3T7179/bnuAAAABfk12iKj49XfHy8Tpw4oREjRqhcuXLKzc1VREREoce9+eab6tatmzp37qwePXqoffv2qlWrlmrUqKHU1FQtW7ZMs2fPvuB2cnJylJOT47udmZnpt30CAABXp2K5IvikSZP0+OOPq3HjxgoKClKPHj3UuXNnVapUSZKUnp6uBg0aSJJKlSqlqlWrSpK+/PJL7d+/X3FxcUpJSVHLli3VqlWr89afkpKiCRMmXNSMh6tHmR9b/XDaRW3rUvo5c0uBNXug4BgWP445gMuRX9/T5JzTk08+qU6dOikmJsb3uVAVK1bUmTNnfI+LjIxUenq6JKmgoECS9MQTT6hHjx4KCgrSli1bNHr0aK1cubLI7YwZM0Zer9f3lZbGD1gAAHBp+fVM09y5c/Xhhx/K6/Vqy5Yt2r17t8qUKaNKlSopIiJCM2fOVNu2bdWlSxcNHjxYK1euVOfOnQs9f/r06VqxYoV++9vfqlatWkVuJzQ0VKGhof7cFQAAcJXzOOdcSQ9xqWVmZio8PFxer1dhYWGm5/Dy3NWLY1j8/HnMf8n3PwBYcMkBAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAACDYH+ufMWKFVq5cqUyMzPVt29f7dixQwcOHFBeXp7mz58vj8cjSXLOaeDAgQoODlbt2rU1fPhwjR07Vrm5uRo0aJAk6Y033tDIkSP9OS4AAMAF+TWa4uPjFR8frxMnTigpKUm5ublasmSJ5s2bpw0bNqhVq1aSpA0bNqh+/fpKTExU7969lZubK+ecevbsqa1bt2rjxo2aNm3aBbeTk5OjnJwc3+3MzEx/7hYAALgKFcvLc5MmTVK/fv103XXXSZJq1qyp9PR03/3p6emKioqSJFWtWlUZGRmqU6eO3nnnHR0/flxNmzbVjBkz9Pbbbxe5/pSUFIWHh/u+zq0LAADgUvFrNDnn9OSTT6pTp06KjY3V999/L0k6dOiQIiMjfY+LjIz0RdSxY8dUuXJlPfLIIxoyZIgOHTqk9PR0DRw4UBs3bixyO2PGjJHX6/V9paWl+XO3AADAVcivL8/NnTtXH374obxer7766ivFxMRo6NChysnJ0aBBg7R8+XKFhoaqc+fOWrp0qYYOHapGjRopJCREkjR9+nSNGDFCu3bt0pQpU1S5cuUitxMaGqrQ0FB/7goAALjKeZxzrqSHuNQyMzMVHh4ur9ersLAw03MOV7e/pFf9cOCcyfo5c0uBNXug4BgWP38e81/y/Q8AFlxyAAAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAINifK9+/f78mT54sr9er119/XZ06dVLNmjVVvnx5zZgxw/c455wGDhyo4OBg1a5dW8OHD9fYsWOVm5urQYMGSZLeeOMNjRw50p/jAgAAXJBfzzTVrl1bCxcu9N0uW7asCgoKVK1atUKP27Bhg+rXr6/nn39en3/+uXJzc+WcU8+ePbV161bNmTNHQ4cO9eeoAAAA/1Gxvjy3fPlyvfjii/rmm2+0fft23/L09HRFRUVJkqpWraqMjAzVqVNH77zzjo4fP66mTZtqxowZevvtt4tcb05OjjIzMwt9AQAAXErFGk1BQT9urmrVqsrOzvYtj4yMVHp6uiTp2LFjqly5sh555BENGTJEhw4dUnp6ugYOHKiNGzcWud6UlBSFh4f7vs4FGAAAwKXi1/c0ZWRk6KmnntK2bduUkpKiL7/8UmXLllV+fr5GjRql5cuXKzQ0VJ07d9bSpUs1dOhQNWrUSCEhIZKk6dOna8SIEdq1a5emTJmiypUrF7mdMWPGKCkpyXc7MzOTcAIAAJeUxznnSnqISy0zM1Ph4eHyer0KCwszPedwdXtkVT+c9ktHu+R+ztxSYM0eKDiGxc+fx/yXfP8DgAWXHAAAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwMH2Myu7duzV//nx9/fXXCgoKUnBwsDp06KCHH35YwcF+/SQWAACAgGAqnvfff1/jx49XxYoVfcs2bNigTz75RG3btvXbcAAAAIHCFE1Dhw6VJJ06dUqrV69WTEyMWrZs6dfBAAAAAonpPU3Hjx+XJM2dO1c33nijZs+e7dehAAAAAo3pTNNzzz2nunXrKiIiQsuWLVNISIi/5wIAAAgopmh65plntGnTJi1fvlyDBw/WTTfd5O+5AAAAAoopmmbMmKHjx4+rRYsW+tvf/iaPx6MnnnjC37MBAAAEDNN7mrxer0aNGqXU1FQlJSXp/vvv9/dcAAAAAcUUTffcc48WLFigfv36SRIvzwEAgKuO6eW5gwcPatiwYYUuZLlv3z4dO3ZMzZo189twAAAAgcIUTTfccIMeffRRZWRk+K4Ifscdd2jAgAH+ng8AACAgmKIpLi5OcXFx/p4FAAAgYPGBvQAAAAZEEwAAgIE5mvr06aPVq1f7cxYAAICAZY6mBQsW6NixY3rggQc0e/ZsnTx50p9zAQAABBRzNGVkZGj//v0KCwvT9ddfr759+/pzLgAAgIBi+u056cePUnn88cdVu3ZtSVJUVJTfhgIAAAg05jNNbdq08QXT6tWr1bx5c78NBQAAEGjM0bR+/Xrfnz/55BO/DAMAABCozC/PHTt2TGvWrJHH49E333zjz5kAAAACjvlM05w5c/Svf/1Le/bs0ezZs/05EwAAQMAxR9OhQ4fk9Xr13XffadasWX4cCQAAIPCYX56bOXOmkpKSVLp0aX/OAwAAEJDM0VS/fn3Vr1/fn7MAAAAELHM0rV27VuvWrVNoaKg8Ho+WLVvmz7kAAAACijmaXnvtNe3evVuxsbFKT0/350wAAAABx/xG8CeeeEKLFi2SJKWkpPhrHgAAgIBkPtNUoUIFXXvttZKkMmXK+GseAACAgGQ+01SlShVt2rRJI0aMUFCQ+WkAAABXBPOZprFjx6pLly5yzqlu3br+nAkAACDgmKOpZ8+e8ng8OnXqlCRpxYoV/poJAAAg4Pys3547Z+bMmX4ZBgAAIFCZo2nXrl2SpPz8fN+fAQAArhbmaFq+fLk8Ho9CQ0OVmJjoz5kAAAACjjmamjRpIo/HI0k6fPiwDh8+rHvuucdvgwEAAAQSczQtWLBALVu2lCRt3LhR8fHx/poJAAAg4JijKTo6WsOHD5ckHTt2TH369PHbUAAAAIHGHE0ej0f9+vWTx+NRtWrV/DkTAABAwDFH06RJk5Senq5rr72Wj1EBAABXHfPnoQwbNkwTJkxQWFgYvz0HAACuOuZoKlWqlGrWrClJCg8P99tAAAAAgcgcTaGhodq9e7fmzZunEydO+HMmAACAgGN6T5NzTl27dlVGRoYKCgr02GOP+XsuAACAgGKKJo/Ho7Vr12rUqFH+ngcAACAgmX977q233tKaNWsUFhYmj8ejZcuW+XMuAACAgGKKprfeeksbNmzQ9u3b1bBhQ3/PBAAAEHBMbwR/9913JUnz58/36zAAAACByhRNBw8e1KpVq3z/XLVqlb/nAgAACCiml+e6d++uY8eO+f7p8Xj8PRcAAEBAMUXTQw895O85AAAAApr54pYAAABXM6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMCAaAIAADAgmgAAAAyIJgAAAAOiCQAAwIBoAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMAg2J8r379/vyZPniyv16vXX39dS5cu1dq1a5WTk6M//OEPKleunO+xo0eP1qlTp1S2bFk9++yzevbZZ5Wdna377rtP0dHRmjp1qlJSUvw5LgAAwAX59UxT7dq1tXDhQt/tv/71r3rppZfUvXt3vfnmm77lhw4dUl5enubMmaOzZ88qLS1NXq9XTzzxhD755BPNmDFDI0aMuOB2cnJylJmZWegLAADgUirWl+c8Ho8kqWbNmkpPT/ctP3z4sKKioiRJNWrUUHp6umJjYzVnzhzVqFFDEREReuGFF7R48eIi15uSkqLw8HDf17l1AQAAXCol8p6mQ4cOKTIy0ne7evXqvohKS0tTZGSkunTpouTkZG3atElVqlRRx44dlZqaWuT6xowZI6/X6/tKS0srlv0AAABXD7++pykjI0NPPfWUtm3bppSUFMXHx+uxxx7T6dOn9fzzz+vvf/+7tm3bpkGDBql06dJKSkpSaGio70zRnDlzlJiYKOec5syZo7Jlyxa5ndDQUIWGhvpzVwAAwFXO45xzJT3EpZaZmanw8HB5vV6FhYWZnnO4uv0lveqHA+dM1s+ZWwqs2QMFx7D4+fOY/5LvfwCw4JIDAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGxRpN69atU6tWrTRw4ECtW7fOt3zt2rV66KGHlJCQoCNHjmjTpk1KTk7WtGnTJEnJycn6/vvvi3NUAACAQoKLc2Mej0fly5fXmTNnFBkZ6Vs+f/58vfbaa9q1a5cWLlyo0NBQDR8+XFOnTtXGjRsVHR2tKlWqFOeoAAAAhRTrmaZWrVpp9erVmjp1qsaNG+db7pxTUFCQatasqfT0dPXs2VOzZs1STEyMli1bpoKCAqWkpCg7O7vI9ebk5CgzM7PQFwAAwKVUrNEUFPTj5ipWrKicnJxCywsKCnTo0CFFRkYqKipKEyZMUFpamgYPHqydO3fq7rvv1urVq4tcb0pKisLDw31fUVFRxbI/AADg6lGs0fTmm2/q0UcfVe/evTV48GD17t1bkjRgwAD169dPzz77rPr27StJ+uqrryRJderUUUFBgV555RXFxMQUud4xY8bI6/X6vtLS0opnhwAAwFXD45xzJT3EpZaZmanw8HB5vV6FhYWZnnO4uv3sVPXDgRNlP2duKbBmDxQcw+Lnz2P+S77/AcCCSw4AAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEEwAAgAHRBAAAYEA0AQAAGBBNAAAABkQTAACAAdEEAABgQDQBAAAYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgUazSdPHlSDz30kPr3768lS5b4lqempiohIUEJCQlKTU3Vvn37NHz4cCUnJ0uSnnvuOe3Zs6c4RwUAACikWKPpzTffVLdu3fTSSy/prbfe8i2fPXu2nn/+ef3+97/X3LlztXXrVvXq1Uv5+fnav3+/8vPz9atf/ao4RwUAACgkuDg3lp6ergYNGkiSSpUq5Vvu9Xp17bXXSpKysrLUoUMHzZo1S7/61a80e/ZstWjRQlOmTNHDDz+siIiI89abk5OjnJycQuuTpMzMTPNsWQUF5sf+nPX628+ZWwqs2QMFx7D4+fOYn3usc+5nbQMA/ptijabIyEilp6ercePGKvjJD83w8HB5vV55PB5VqFBB4eHhGjdunJYuXapu3brpnXfe0ahRo/THP/5RI0eOPG+9KSkpmjBhwnnLo6Ki/LMj4eH+WW9xuJxnDxQcw+L3C455VlaWwvl3BeAS8rhi/OvYyZMnNXjwYJUpU0YtW7bUu+++qz/96U9KTU3VtGnTJEmjRo1S/fr1deLECc2YMUOTJ0/W5MmTdeLECXXp0kXNmzc/b73/fqapoKBAx48fV+XKleXxeP7rXJmZmYqKilJaWprCwsIu3Q6XgCtlX66U/ZDYl+LmnFNWVpYiIiIUFMTvugC4dIo1mgJVZmam72xXoP6PwOpK2ZcrZT8k9gUArhT8NQwAAMCAaAIAADAgmiSFhoZq3LhxCg0NLelRLtqVsi9Xyn5I7AsAXCl4TxMAAIABZ5oAAAAMiCYAAAADogkAAMDgqo+mC32I8OVoxYoV6t+/v3r06KH333+/pMe5aCdPnlSTJk30zjvvlPQov1hBQYGeeuopJSYm6pVXXinpcS7KoUOHFB8fr0ceeUTPPvtsSY8DAMXuqo+mC32I8OUoPj5eL730kubPn6+//OUvJT3ORZs6daq6d+9e0mNclL/97W9KT09X6dKlFRkZWdLjXJQdO3aoW7du+uMf/6ht27aV9DgAUOyu+mhKT0/3fUbdTz9E+HI2adIkPf744yU9xkX54IMPdOutt6pq1aolPcpF2bNnj5o3b66ZM2fqD3/4Q0mPc1GaNWumhQsX6s4771THjh1LehwAKHZXfTSd+xBhSYU+RPhy5JzTk08+qU6dOikmJqakx7ko69at0+bNm7V06VK99NJLl+2/m8jISFWsWFHS5R/lL7/8siZMmKCPPvpIK1euLOlxAKDYXfXXafr3DxFOSEgo6ZF+sTlz5uiVV15RbGysGjdurIEDB5b0SBdt0aJFqlKliu67776SHuUXOXXqlBITE1W2bFlFR0df1mcAU1NTNX78eFWpUkXly5fXjBkzSnokAChWV300AQAAWFz1L88BAABYEE0AAAAGRBMAAIAB0QQAAGBANAEAABgQTQAAAAZEE0rMokWL/uvnyl3Ki1oW5wUyLfsGALi8BJf0ALjyLFq0SGvXrtU111yjG264QXl5eUpNTdVjjz2m1atX6/Tp0+ratas2bNigU6dOSZJCQ0O1cuVK331HjhzRhx9+qCZNmujs2bM6ePCgwsPD9cwzzxS5rVtvvVWlSpXSiBEjNHnyZH3//ffKysrSc889p549e6p58+aKjY1V+/btfc9dunSp1q1bpwoVKmjKlClasWKFNm/erMzMTCUmJqqgoECzZs1SlSpV1K9fP+3du1dvv/22zpw5o/Hjx2vDhg36+OOPVbt2bQUFBempp57S6NGj5ZzTnj171K9fv2I97gAA/yKa4BcdOnTQgw8+qLvuuktr1qzRlClTNG/ePN1+++269tpr9dlnn6lly5a+q3137ty50H2RkZHq1KmTEhISlJiYqNjYWHXq1KnIbbVv3169evXSAw88oJ07d2r9+vW64447dObMGe3evVsFBQUaNWqUgoML/+e+b98+NWzYUL/+9a8VGhqqefPm6a677lKZMmX02Wefaf369XrxxRdVtmxZSdLTTz+tN954QwcPHtTcuXNVv359dezYUT169FDPnj3l9Xp19OhRLVq0SFOnTvX7MQYAFC+iCX4RFhYmSbruuuskSSEhITp16pSSk5N98fLqq6/6Hl9QUFDovkWLFik8PFySNHv2bG3ZskW/+c1vtHTpUt+6z8nPz5ck5eXlSZLq1aun8ePH++4vW7bsecEk/RhBX3zxhUaOHKlnnnlG11xzTaHnffzxx/J4POc9z+Px6NyF9MuVKydJvtshISGSfjxzBgC4shBNKDatW7dWv379VKlSJTVp0kSNGjXS5MmTlZ+fryFDhhS676emTZum77//XpUqVfKd9fmp999/X9u3b1fTpk1Vr149BQUFKSkpSadPn9bYsWMvOM+LL76ovXv3KigoSJUrV1avXr00YMAAXXPNNbr33nv1xBNPaNCgQapWrZoefvhh9erVS48++qhOnTqlp59+Wps2bSq0vvDwcN1www363e9+p40bN+rmm2++NAcOABAQ+Ow5XNYu9w/0BQBcPjjThMvKu+++q82bN/tujx49WmXKlPH7cwEA4EwTAACAAddpAgAAMCCaAAAADIgmAAAAA6IJAADAgGgCAAAwIJoAAAAMiCYAAAADogkAAMDg/wH4aqWDBdHMVwAAAABJRU5ErkJggg==",
+ "image/png": "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",
"text/plain": [
"<Figure size 600x600 with 2 Axes>"
]
@@ -352,7 +312,7 @@
},
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
"<Figure size 600x600 with 4 Axes>"
]
@@ -362,7 +322,7 @@
},
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
"<Figure size 600x600 with 4 Axes>"
]
@@ -408,7 +368,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 7,
"id": "1bdb7bef",
"metadata": {
"scrolled": true
@@ -440,7 +400,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 8,
"id": "e1f01dcc",
"metadata": {
"scrolled": false
@@ -450,149 +410,64 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-08 20:23:16,606 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
- "2023-12-08 20:23:16,606 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
- "2023-12-08 20:23:16,616 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
- "2023-12-08 20:23:16,616 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
- "2023-12-08 20:23:16,617 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:84} Opening Bokeh application on http://localhost:5006/\n",
- "2023-12-08 20:23:16,617 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:84} Opening Bokeh application on http://localhost:5006/\n",
- "2023-12-08 20:23:16,617 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:85} Async rendering is set to: True\n",
- "2023-12-08 20:23:16,617 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:85} Async rendering is set to: True\n",
- "2023-12-08 20:23:16,617 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/server.py:403} Starting Bokeh server version 2.4.3 (running on Tornado 6.3.3)\n",
- "2023-12-08 20:23:16,617 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/server.py:403} Starting Bokeh server version 2.4.3 (running on Tornado 6.3.3)\n",
- "2023-12-08 20:23:16,618 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/util.py:145} Host wildcard '*' will allow connections originating from multiple (or possibly all) hostnames or IPs. Use non-wildcard values to restrict access explicitly\n",
- "2023-12-08 20:23:16,618 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/util.py:145} Host wildcard '*' will allow connections originating from multiple (or possibly all) hostnames or IPs. Use non-wildcard values to restrict access explicitly\n",
- "2023-12-08 20:23:16,618 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/tornado.py:360} User authentication hooks NOT provided (default user enabled)\n",
- "2023-12-08 20:23:16,618 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/tornado.py:360} User authentication hooks NOT provided (default user enabled)\n",
- "2023-12-08 20:23:17,296 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
- "2023-12-08 20:23:17,296 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
- "2023-12-08 20:23:17,300 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
- "2023-12-08 20:23:17,300 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
- "2023-12-08 20:23:17,415 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 180.53ms\n",
- "2023-12-08 20:23:17,415 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 180.53ms\n",
- "2023-12-08 20:23:17,487 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre.min.css (127.0.0.1) 2.75ms\n",
- "2023-12-08 20:23:17,487 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre.min.css (127.0.0.1) 2.75ms\n"
+ "2023-12-08 22:08:45,633 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:18} Building AbstractScenarioBuilder...\n",
+ "2023-12-08 22:08:45,644 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/script/builders/scenario_building_builder.py:21} Building AbstractScenarioBuilder...DONE!\n",
+ "2023-12-08 22:08:45,645 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:84} Opening Bokeh application on http://localhost:5006/\n",
+ "2023-12-08 22:08:45,645 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:85} Async rendering is set to: True\n",
+ "2023-12-08 22:08:45,645 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/server.py:403} Starting Bokeh server version 2.4.3 (running on Tornado 6.3.3)\n",
+ "2023-12-08 22:08:45,645 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/util.py:145} Host wildcard '*' will allow connections originating from multiple (or possibly all) hostnames or IPs. Use non-wildcard values to restrict access explicitly\n",
+ "2023-12-08 22:08:45,645 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/tornado.py:360} User authentication hooks NOT provided (default user enabled)\n",
+ "2023-12-08 22:08:46,347 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
+ "2023-12-08 22:08:46,352 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
+ "2023-12-08 22:08:46,448 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 163.11ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:tornado.access:200 GET / (127.0.0.1) 180.53ms\n",
- "INFO:tornado.access:200 GET /resource/spectre.min.css (127.0.0.1) 2.75ms\n",
- "INFO:tornado.access:200 GET /resource/spectre-icons.min.css (127.0.0.1) 0.27ms\n",
- "INFO:tornado.access:200 GET /resource/spectre-exp.min.css (127.0.0.1) 0.34ms\n",
- "INFO:tornado.access:200 GET /resource/style.css (127.0.0.1) 0.28ms\n",
- "INFO:tornado.access:200 GET /resource/css/overview.css (127.0.0.1) 0.48ms\n",
- "INFO:tornado.access:200 GET /resource/css/cloud.css (127.0.0.1) 0.44ms\n",
- "INFO:tornado.access:200 GET /resource/css/histogram.css (127.0.0.1) 0.36ms\n",
- "INFO:tornado.access:200 GET /resource/css/scenario.css (127.0.0.1) 0.26ms\n"
+ "INFO:tornado.access:200 GET / (127.0.0.1) 163.11ms\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-08 20:23:17,503 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre-icons.min.css (127.0.0.1) 0.27ms\n",
- "2023-12-08 20:23:17,503 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre-icons.min.css (127.0.0.1) 0.27ms\n",
- "2023-12-08 20:23:17,512 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre-exp.min.css (127.0.0.1) 0.34ms\n",
- "2023-12-08 20:23:17,512 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/spectre-exp.min.css (127.0.0.1) 0.34ms\n",
- "2023-12-08 20:23:17,516 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/style.css (127.0.0.1) 0.28ms\n",
- "2023-12-08 20:23:17,516 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/style.css (127.0.0.1) 0.28ms\n",
- "2023-12-08 20:23:17,571 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/overview.css (127.0.0.1) 0.48ms\n",
- "2023-12-08 20:23:17,571 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/overview.css (127.0.0.1) 0.48ms\n",
- "2023-12-08 20:23:17,572 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/cloud.css (127.0.0.1) 0.44ms\n",
- "2023-12-08 20:23:17,572 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/cloud.css (127.0.0.1) 0.44ms\n",
- "2023-12-08 20:23:17,605 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/histogram.css (127.0.0.1) 0.36ms\n",
- "2023-12-08 20:23:17,605 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/histogram.css (127.0.0.1) 0.36ms\n",
- "2023-12-08 20:23:17,606 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/scenario.css (127.0.0.1) 0.26ms\n",
- "2023-12-08 20:23:17,606 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/css/scenario.css (127.0.0.1) 0.26ms\n",
- "2023-12-08 20:23:17,616 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh.min.js?v=3c61e952b808bb7e346ce828a565a5f23aaf7708d034fa9d0906403813355d45bb4e8d8b0b23a93f032c76831d4f0221846f28699c7f5147caa62e0d31668314 (127.0.0.1) 1.14ms\n",
- "2023-12-08 20:23:17,616 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh.min.js?v=3c61e952b808bb7e346ce828a565a5f23aaf7708d034fa9d0906403813355d45bb4e8d8b0b23a93f032c76831d4f0221846f28699c7f5147caa62e0d31668314 (127.0.0.1) 1.14ms\n",
- "2023-12-08 20:23:17,626 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-gl.min.js?v=e5df31fd9010eacff0aa72d315264604b5e34972ba445acea6fce98080eecf33acf2d2986126360faaa5852813cffa16f6f6f4889923318300f062497c02da4e (127.0.0.1) 0.39ms\n",
- "2023-12-08 20:23:17,626 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-gl.min.js?v=e5df31fd9010eacff0aa72d315264604b5e34972ba445acea6fce98080eecf33acf2d2986126360faaa5852813cffa16f6f6f4889923318300f062497c02da4e (127.0.0.1) 0.39ms\n"
+ "2023-12-08 22:08:47,604 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
+ "2023-12-08 22:08:47,609 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
+ "2023-12-08 22:08:47,700 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 152.33ms\n",
+ "2023-12-08 22:08:47,709 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 101 GET /ws (127.0.0.1) 0.43ms\n",
+ "2023-12-08 22:08:47,709 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:132} WebSocket connection opened\n",
+ "2023-12-08 22:08:47,709 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:213} ServerConnection created\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:tornado.access:200 GET /static/js/bokeh.min.js?v=3c61e952b808bb7e346ce828a565a5f23aaf7708d034fa9d0906403813355d45bb4e8d8b0b23a93f032c76831d4f0221846f28699c7f5147caa62e0d31668314 (127.0.0.1) 1.14ms\n",
- "INFO:tornado.access:200 GET /static/js/bokeh-gl.min.js?v=e5df31fd9010eacff0aa72d315264604b5e34972ba445acea6fce98080eecf33acf2d2986126360faaa5852813cffa16f6f6f4889923318300f062497c02da4e (127.0.0.1) 0.39ms\n",
- "INFO:tornado.access:200 GET /static/js/bokeh-widgets.min.js?v=8a1ff6f5aa0d967f4998d275803bbb111d928fd9f605ef9e1f30cfd021df0e77224ee3d13f83edb3a942f6e4ccc569ee5dd8951a8aa6cb600602463b90c65a87 (127.0.0.1) 0.46ms\n",
- "INFO:tornado.access:200 GET /static/js/bokeh-tables.min.js?v=ae2903e57cf57f52819fdf4d938c648982b51c34f73b6e653a0f3bb3c8ab44f338505931ace43eafc1636e215492e2314acf54c54baffb47813b86b4923a7fe0 (127.0.0.1) 0.68ms\n"
+ "INFO:tornado.access:200 GET / (127.0.0.1) 152.33ms\n",
+ "INFO:tornado.access:101 GET /ws (127.0.0.1) 0.43ms\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-08 20:23:17,748 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-widgets.min.js?v=8a1ff6f5aa0d967f4998d275803bbb111d928fd9f605ef9e1f30cfd021df0e77224ee3d13f83edb3a942f6e4ccc569ee5dd8951a8aa6cb600602463b90c65a87 (127.0.0.1) 0.46ms\n",
- "2023-12-08 20:23:17,748 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-widgets.min.js?v=8a1ff6f5aa0d967f4998d275803bbb111d928fd9f605ef9e1f30cfd021df0e77224ee3d13f83edb3a942f6e4ccc569ee5dd8951a8aa6cb600602463b90c65a87 (127.0.0.1) 0.46ms\n",
- "2023-12-08 20:23:17,784 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-tables.min.js?v=ae2903e57cf57f52819fdf4d938c648982b51c34f73b6e653a0f3bb3c8ab44f338505931ace43eafc1636e215492e2314acf54c54baffb47813b86b4923a7fe0 (127.0.0.1) 0.68ms\n",
- "2023-12-08 20:23:17,784 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-tables.min.js?v=ae2903e57cf57f52819fdf4d938c648982b51c34f73b6e653a0f3bb3c8ab44f338505931ace43eafc1636e215492e2314acf54c54baffb47813b86b4923a7fe0 (127.0.0.1) 0.68ms\n",
- "2023-12-08 20:23:17,884 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/scripts/utils.js (127.0.0.1) 0.72ms\n",
- "2023-12-08 20:23:17,884 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/scripts/utils.js (127.0.0.1) 0.72ms\n",
- "2023-12-08 20:23:17,886 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-mathjax.min.js?v=176c36fdbcd8fc1019fc828101a2804081a35baf4018d7f2633cd263156b593aa73112f400112b662daa0590138b74851bc91f1f2a5fbf5416ee8c876c3e0d0c (127.0.0.1) 2.06ms\n",
- "2023-12-08 20:23:17,886 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /static/js/bokeh-mathjax.min.js?v=176c36fdbcd8fc1019fc828101a2804081a35baf4018d7f2633cd263156b593aa73112f400112b662daa0590138b74851bc91f1f2a5fbf5416ee8c876c3e0d0c (127.0.0.1) 2.06ms\n",
- "2023-12-08 20:23:17,886 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/motional_logo.png (127.0.0.1) 2.27ms\n",
- "2023-12-08 20:23:17,886 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET /resource/motional_logo.png (127.0.0.1) 2.27ms\n"
+ "2023-12-08 22:08:58,874 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0128 seconds.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "INFO:tornado.access:200 GET /resource/scripts/utils.js (127.0.0.1) 0.72ms\n",
- "INFO:tornado.access:200 GET /static/js/bokeh-mathjax.min.js?v=176c36fdbcd8fc1019fc828101a2804081a35baf4018d7f2633cd263156b593aa73112f400112b662daa0590138b74851bc91f1f2a5fbf5416ee8c876c3e0d0c (127.0.0.1) 2.06ms\n",
- "INFO:tornado.access:200 GET /resource/motional_logo.png (127.0.0.1) 2.27ms\n"
+ "Rendering a scenario: 100%|██████████| 1/1 [00:00<00:00, 32.03it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "2023-12-08 20:23:18,678 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
- "2023-12-08 20:23:18,678 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/base/simulation_tile.py:172} Minimum frame time=0.017 s\n",
- "2023-12-08 20:23:18,683 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
- "2023-12-08 20:23:18,683 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 0.0005 seconds.\n",
- "2023-12-08 20:23:18,774 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 153.62ms\n",
- "2023-12-08 20:23:18,774 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 200 GET / (127.0.0.1) 153.62ms\n",
- "2023-12-08 20:23:18,783 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 404 GET /favicon.ico (127.0.0.1) 0.25ms\n",
- "2023-12-08 20:23:18,783 WARNING {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 404 GET /favicon.ico (127.0.0.1) 0.25ms\n",
- "2023-12-08 20:23:18,835 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 101 GET /ws (127.0.0.1) 0.31ms\n",
- "2023-12-08 20:23:18,835 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/web.py:2344} 101 GET /ws (127.0.0.1) 0.31ms\n",
- "2023-12-08 20:23:18,835 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:132} WebSocket connection opened\n",
- "2023-12-08 20:23:18,835 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:132} WebSocket connection opened\n",
- "2023-12-08 20:23:18,836 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:213} ServerConnection created\n",
- "2023-12-08 20:23:18,836 INFO {/home/ehdykhne/miniconda3/envs/nuplan/lib/python3.9/site-packages/bokeh/server/views/ws.py:213} ServerConnection created\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:tornado.access:200 GET / (127.0.0.1) 153.62ms\n",
- "WARNING:tornado.access:404 GET /favicon.ico (127.0.0.1) 0.25ms\n",
- "INFO:tornado.access:101 GET /ws (127.0.0.1) 0.31ms\n"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
- "\u001b[1;32m/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb Cell 11\u001b[0m line \u001b[0;36m4\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bwisedave.eng.uwaterloo.ca/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnuplan\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mplanning\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mscript\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrun_nuboard\u001b[39;00m \u001b[39mimport\u001b[39;00m main \u001b[39mas\u001b[39;00m main_nuboard\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Bwisedave.eng.uwaterloo.ca/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# Run nuBoard\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bwisedave.eng.uwaterloo.ca/home/ehdykhne/nuplan-devkit/experiments/bulk_running_experiments.ipynb#X13sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m main_nuboard(cfg)\n",
- "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/hydra/main.py:44\u001b[0m, in \u001b[0;36mmain.<locals>.main_decorator.<locals>.decorated_main\u001b[0;34m(cfg_passthrough)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(task_function)\n\u001b[1;32m 42\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorated_main\u001b[39m(cfg_passthrough: Optional[DictConfig] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m 43\u001b[0m \u001b[39mif\u001b[39;00m cfg_passthrough \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m---> 44\u001b[0m \u001b[39mreturn\u001b[39;00m task_function(cfg_passthrough)\n\u001b[1;32m 45\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 46\u001b[0m args \u001b[39m=\u001b[39m get_args_parser()\n",
- "File \u001b[0;32m~/nuplan-devkit/nuplan/planning/script/run_nuboard.py:74\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(cfg)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[39mExecute all available challenges simultaneously on the same scenario.\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[39m:param cfg: DictConfig. Configuration that is used to run the experiment.\u001b[39;00m\n\u001b[1;32m 72\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 73\u001b[0m nuboard \u001b[39m=\u001b[39m initialize_nuboard(cfg)\n\u001b[0;32m---> 74\u001b[0m nuboard\u001b[39m.\u001b[39;49mrun()\n",
- "File \u001b[0;32m~/nuplan-devkit/nuplan/planning/nuboard/nuboard.py:112\u001b[0m, in \u001b[0;36mNuBoard.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[39m# Catch RuntimeError in jupyter notebook\u001b[39;00m\n\u001b[1;32m 111\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m io_loop\u001b[39m.\u001b[39;49mstart()\n\u001b[1;32m 113\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 114\u001b[0m logger\u001b[39m.\u001b[39mwarning(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00me\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
- "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/tornado/platform/asyncio.py:195\u001b[0m, in \u001b[0;36mBaseAsyncIOLoop.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mstart\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 195\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49masyncio_loop\u001b[39m.\u001b[39;49mrun_forever()\n",
- "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/nest_asyncio.py:82\u001b[0m, in \u001b[0;36m_patch_loop.<locals>.run_forever\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[39mwith\u001b[39;00m manage_run(\u001b[39mself\u001b[39m), manage_asyncgens(\u001b[39mself\u001b[39m):\n\u001b[1;32m 81\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m---> 82\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_once()\n\u001b[1;32m 83\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stopping:\n\u001b[1;32m 84\u001b[0m \u001b[39mbreak\u001b[39;00m\n",
- "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/site-packages/nest_asyncio.py:116\u001b[0m, in \u001b[0;36m_patch_loop.<locals>._run_once\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 109\u001b[0m heappop(scheduled)\n\u001b[1;32m 111\u001b[0m timeout \u001b[39m=\u001b[39m (\n\u001b[1;32m 112\u001b[0m \u001b[39m0\u001b[39m \u001b[39mif\u001b[39;00m ready \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_stopping\n\u001b[1;32m 113\u001b[0m \u001b[39melse\u001b[39;00m \u001b[39mmin\u001b[39m(\u001b[39mmax\u001b[39m(\n\u001b[1;32m 114\u001b[0m scheduled[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39m_when \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtime(), \u001b[39m0\u001b[39m), \u001b[39m86400\u001b[39m) \u001b[39mif\u001b[39;00m scheduled\n\u001b[1;32m 115\u001b[0m \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m)\n\u001b[0;32m--> 116\u001b[0m event_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_selector\u001b[39m.\u001b[39;49mselect(timeout)\n\u001b[1;32m 117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_process_events(event_list)\n\u001b[1;32m 119\u001b[0m end_time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtime() \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_clock_resolution\n",
- "File \u001b[0;32m~/miniconda3/envs/nuplan/lib/python3.9/selectors.py:469\u001b[0m, in \u001b[0;36mEpollSelector.select\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 467\u001b[0m ready \u001b[39m=\u001b[39m []\n\u001b[1;32m 468\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 469\u001b[0m fd_event_list \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_selector\u001b[39m.\u001b[39;49mpoll(timeout, max_ev)\n\u001b[1;32m 470\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mInterruptedError\u001b[39;00m:\n\u001b[1;32m 471\u001b[0m \u001b[39mreturn\u001b[39;00m ready\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ "2023-12-08 22:09:07,821 INFO {/home/ehdykhne/nuplan-devkit/nuplan/planning/nuboard/tabs/scenario_tab.py:485} Rending scenario plot takes 2.2702 seconds.\n"
]
}
],
--
GitLab