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computed dtw signals for matchings and bottleneck
1 parent b8e7330 commit 3bcae79

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+5266
-46
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‎.gitignore

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@@ -12,6 +12,8 @@ build/*
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# Jupyter notebook files
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.ipynb_checkpoints
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*.egg-info
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# Saved matrices
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*.npy
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# Output and experiments
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*.h5
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*.yaml
@@ -20,5 +22,10 @@ build/*
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experiments/*
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notebooks/experiment_*
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notebooks/output
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# Strange files
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*Zone.Identifier
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# Python files
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__pycache__
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# Other
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*/time-warping-corridor/
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‎notebooks/Corridor/Corridor-behaviour-illustration.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "10174b23-25f5-45d2-85a2-43985ce76950",
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"metadata": {},
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"source": [
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"In this notebook, we explore basic analysis of the positions and speeds to detect similarities and differences between behaviouirs.\n",
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"\n",
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"We start importing some libraries."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "35f7c19a-b478-4fa9-9451-2722d6275e7c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib as mpl\n",
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"import os\n",
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"\n",
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"import perdiver.perdiver as perdiver\n",
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"\n",
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"from navground import core, sim\n",
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"\n",
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"plots_dir = os.path.join(\"plots\", \"visualisation\")\n",
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"experiment_dir = \"experiments\"\n",
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"os.makedirs(plots_dir, exist_ok=True)\n",
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"os.makedirs(\"experiments\", exist_ok=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f88c00d1-7647-4543-8357-965de185f7ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Small Corridor\n",
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"length = 8\n",
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"width=1.0\n",
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"# Large Corridor\n",
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"# width=1.5\n",
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"# length = 15.0\n",
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"num_steps = 400\n",
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"\n",
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"num_agents = 11 # 31\n",
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"num_runs = 1\n",
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"behaviour_list = [\"ORCA\", \"HL\", \"HRVO\", \"Dummy\"]\n",
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"marker_behaviour = {\"ORCA\": \"o\", \"HL\": \"X\", \"HRVO\": \"+\", \"Dummy\": \"*\"}\n",
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"color_behaviour = {}\n",
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"for i, behaviour in enumerate(behaviour_list):\n",
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" color_behaviour[behaviour] = mpl.colormaps[\"Set1\"](i / (len(behaviour_list) +1))\n",
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"for behaviour in behaviour_list:\n",
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" path = os.path.join(experiment_dir, f\"visualisation_{behaviour}.h5\")\n",
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" yaml = f\"\"\"\n",
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" steps: {num_steps}\n",
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" time_step: 0.1\n",
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" record_pose: true\n",
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" record_twist: true\n",
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" runs: {num_runs}\n",
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" record_collisions: true\n",
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" record_deadlocks: true\n",
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" record_safety_violation: true\n",
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" record_efficacy: true\n",
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" terminated_when_idle_or_stuck: false\n",
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" scenario:\n",
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" type: Corridor\n",
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" length: {length}\n",
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" width: {width} \n",
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" groups:\n",
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" -\n",
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" type: thymio\n",
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" number: {num_agents}\n",
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" radius: 0.08\n",
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" control_period: 0.1\n",
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" speed_tolerance: 0.02\n",
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" kinematics:\n",
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" type: 2WDiff\n",
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" wheel_axis: 0.094\n",
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" max_speed: 0.166\n",
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" behavior:\n",
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" type: {behaviour}\n",
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" optimal_speed: \n",
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" sampler: normal\n",
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" mean: 0.2\n",
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" std_dev: 0.05\n",
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" horizon: 5.0\n",
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" safety_margin: \n",
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" sampler: normal\n",
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" mean: 0.2\n",
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" std_dev: 0.05\n",
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" state_estimation:\n",
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" type: Bounded\n",
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" range: 5.0\n",
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" \"\"\"\n",
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" experiment = sim.load_experiment(yaml)\n",
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" experiment.run(keep=False, data_path=path)\n",
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" del experiment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d84b1524-0407-4828-b2ab-8b8485e41a97",
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"metadata": {},
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"outputs": [],
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"source": [
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"runs = {}\n",
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"# Reload simulations\n",
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"for behaviour in behaviour_list:\n",
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" path = os.path.join(experiment_dir, f\"visualisation_{behaviour}.h5\")\n",
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" recorded_experiment = sim.RecordedExperiment(path)\n",
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" runs[behaviour] = recorded_experiment.runs"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec5913e6-73bc-4a7e-b5c0-1a0fe3f11ee4",
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"metadata": {},
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"source": [
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"Now, we illustrate movements for each behaviour in a single image."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "73b1f80f-61aa-4a96-b574-c4d361661a5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(nrows=len(behaviour_list), figsize=(10, 1.5*len(behaviour_list)))\n",
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"timestep_list = list(range(0,100,10))\n",
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"for i, behaviour in enumerate(behaviour_list):\n",
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" run = runs[behaviour][0]\n",
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" perdiver.plot_timesteps_corridor(run, timestep_list, length, width, ax[i])\n",
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" ax[i].set_title(behaviour, fontsize=20)\n",
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"plt.tight_layout()\n",
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"plt.savefig(os.path.join(plots_dir, \"behaviours_simulations.png\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6ed906db-afe1-4713-adb4-cff59c65d427",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎notebooks/Corridor/Corridor-matchings-PCA.ipynb

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@@ -46,21 +46,22 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "786083bb-b76e-4198-9ff8-e2045b119dbc",
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"id": "e39169b8-b6e2-45f7-930c-3743eab4d13a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Small Corridor\n",
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"# length = 8\n",
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"# width=1.0\n",
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"# Large Corridor\n",
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"width=1.5\n",
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"width=2\n",
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"length = 15.0\n",
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"num_steps = 300\n",
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"num_steps = 400\n",
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"\n",
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"num_agents = 20 # 31\n",
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"num_runs = 20\n",
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"behaviour_list = [\"ORCA\", \"HL\", \"HRVO\", \"Dummy\"]\n",
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"num_agents = 12 # 31\n",
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"num_runs = 15\n",
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"behaviour_list = [\"ORCA\", \"HL\", \"HRVO\", \"Dummy\", \"SocialForce\"]\n",
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"marker_behaviour = {\"ORCA\": \"o\", \"HL\": \"X\", \"HRVO\": \"+\", \"Dummy\": \"*\", \"SocialForce\": \"x\"}\n",
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"color_behaviour = {}\n",
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"for i, behaviour in enumerate(behaviour_list):\n",
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" color_behaviour[behaviour] = mpl.colormaps[\"Set1\"](i / (len(behaviour_list) +1))\n",
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" -\n",
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" type: thymio\n",
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" number: {num_agents}\n",
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" radius: 0.08\n",
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" radius: 0.35\n",
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" control_period: 0.1\n",
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" speed_tolerance: 0.02\n",
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" kinematics:\n",
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" type: 2WDiff\n",
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" wheel_axis: 0.094\n",
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" max_speed: 0.166\n",
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" max_speed: 0.2\n",
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" behavior:\n",
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" type: {behaviour}\n",
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" optimal_speed: \n",
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" sampler: normal\n",
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" mean: 0.2\n",
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" std_dev: 0.05\n",
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" mean: 0.16\n",
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" std_dev: 0.04\n",
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" horizon: 5.0\n",
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" safety_margin: \n",
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" sampler: normal\n",
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" mean: 0.2\n",
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" std_dev: 0.05\n",
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" safety_margin: 0.1\n",
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" state_estimation:\n",
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" type: Bounded\n",
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" range: 5.0\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(ncols=4, figsize=(20,5))\n",
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"fig, ax = plt.subplots(ncols=len(behaviour_list), figsize=(20,5))\n",
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"for i, behaviour in enumerate(behaviour_list):\n",
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" image = perim_arr_dict[behaviour][1].reshape((npixels,npixels))\n",
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" image = image * np.array(list(range(npixels)))\n",
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"pca = PCA(n_components=2)\n",
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"# scaled_all_perim = minmax_scale(all_perim_transformed)\n",
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"# Y = pca.fit_transform(scaled_all_perim)\n",
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"Y = pca.fit_transform(all_perim_transformed)\n",
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"Y = pca.fit_transform(all_perim_transformed[:-num_runs])\n",
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"\n",
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"Y_dict = {}\n",
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"for i, behaviour in enumerate(behaviour_list):\n",

‎notebooks/Corridor/Corridor-matchings-old.ipynb

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"gs_kw = dict(width_ratios=[5, 2, 2], height_ratios=[1,1])\n",
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"fig, axd = plt.subplot_mosaic([['points_HL', 'Diag_HL', 'Diag_ORCA'],\n",
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" ['points_ORCA', 'Diag_HL', 'Diag_ORCA']],\n",
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" gridspec_kw=gs_kw, figsize=(16, 4),\n",
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" gridspec_kw=gs_kw, figsize=(14, 4),\n",
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" layout=\"constrained\")\n",
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"for behaviour in [\"HL\", \"ORCA\"]:\n",
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" ps = np.array(runs[behaviour][0].poses)[:,:]\n",
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" twists = np.array(runs[behaviour][0].twists)[:,:]\n",
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" run = runs[behaviour][0]\n",
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" ps = np.array(run.poses)[:,:]\n",
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" twists = np.array(run.twists)[:,:]\n",
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" X = ps[start_step]\n",
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" Y = ps[start_step + shift_time]\n",
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" vel_X = twists[start_step]\n",
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" # Plot two timesteps\n",
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" # fig, ax = plt.subplots(figsize=(8, 1))\n",
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" ax = axd[f\"points_{behaviour}\"]\n",
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" perdiver.plot_two_timesteps_with_velocities(X[:,:2], Y[:,:2], vel_X[:,:2], vel_Y[:,:2], ax, arrow_width=0.03)\n",
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" # perdiver.plot_two_timesteps_with_velocities(X[:,:2], Y[:,:2], vel_X[:,:2], vel_Y[:,:2], ax, arrow_width=0.03)\n",
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" perdiver.plot_timesteps_corridor(run, [start_step, start_step + shift_time], length, width, ax) \n",
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" ax.set_title(f\"points_{behaviour}\", fontsize=20)\n",
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" # Plot matching diagram\n",
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" ax = axd[f\"Diag_{behaviour}\"]\n",
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" perdiver.plot_matching_diagram(match_diagram, ax, color=\"blue\")\n",
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" ax.set_title(behaviour, fontsize=20)\n",
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"\n",
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"perdiver.same_diagram_scale(axd[\"Diag_HL\"], axd[\"Diag_ORCA\"])\n",
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"perdiver.same_diagram_scale([axd[\"Diag_HL\"], axd[\"Diag_ORCA\"]])\n",
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"plt.savefig(os.path.join(plots_dir, f\"two_timesteps_points_matching_diags_ORCA_HL.png\"))"
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]
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},

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