thor_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aff9595e-a7ab-453b-a3ef-529898fd3061",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14c12caa-757a-4678-94e7-6375cd9eb81f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Let us install timing extensions\n",
    "try:\n",
    "    %load_ext autotime\n",
    "except:\n",
    "    !pip install ipython-autotime\n",
    "    %load_ext autotime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c7d4fb-8349-47af-b979-e5f9957ebe41",
   "metadata": {},
   "source": [
    "### Dependencies: \n",
    "graph_tool, networkx, spot, toytree, toyplot, cvxopt, skunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "573dfc69-6b64-41ee-aee2-1e0d05830dff",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "(ipykernel_launcher.py:44497): Gtk-WARNING **: 13:04:38.627: Locale not supported by C library.\n",
      "\tUsing the fallback 'C' locale.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 5.04 s\n"
     ]
    }
   ],
   "source": [
    "from src.workspace import *\n",
    "from src.high_level_monitor import *\n",
    "from src.low_level_monitor import *\n",
    "from src.predictive_intent_tree import *\n",
    "from src.buchi import *\n",
    "from src.himo import *\n",
    "from src.buchi_with_AP import *\n",
    "from src.processing_thor import *\n",
    "\n",
    "import numpy as np\n",
    "import skunk\n",
    "import graph_tool.all as gt\n",
    "import time\n",
    "import pandas as pd\n",
    "import matplotlib as mpl \n",
    "# mpl.use('cairo') \n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.optimize as sp\n",
    "from collections import deque \n",
    "import toytree\n",
    "import toyplot\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe6164f-d7f6-4cb9-94c5-8e5fabb1099f",
   "metadata": {},
   "source": [
    "### Load Thor trajectory data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6702325c-f0d5-4535-96d9-0ebb6189c73c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 461 µs\n"
     ]
    }
   ],
   "source": [
    "exp_id = 1\n",
    "run_id = 1\n",
    "agent = 'lab'\n",
    "region_accept_dist = REGION_ACCEPT_DIST_LAB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f129a8fd-fafe-456e-905c-249d22d02fdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 834 µs\n"
     ]
    }
   ],
   "source": [
    "# Reference\n",
    "if exp_id == 1:\n",
    "    agent_names = ['2','utility','4','5','6','7','8','lab','inspector','lidar','robot']\n",
    "elif exp_id == 2:\n",
    "    agent_names = ['utility','3','4','5','6','7','lab','9','inspector','lidar','robot']\n",
    "elif exp_id == 3:\n",
    "    agent_names = ['2','3','lab','utility','6','7','8','9','inspector','lidar','robot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d1879ed0-7681-44ee-a894-c172b443588d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 10.1 ms\n"
     ]
    }
   ],
   "source": [
    "traj_data = pd.read_csv(f'./data/Thor/exp{exp_id}-run{run_id}-{agent}_with_goal.csv', usecols=['X', 'Y'])\n",
    "traj_data = list(traj_data.itertuples(index=False, name=None))\n",
    "goal_data = pd.read_csv(f'./data/Thor/exp{exp_id}-run{run_id}-{agent}_with_goal.csv', usecols=['G1', 'G2', 'G3']).values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa55f91-ac6b-47c4-9dcc-f10aa5d80e32",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Map setting\n",
    "- In this version, MAP_SIZE 'x' and 'y' should be the same.\n",
    "- Starting position (0,0) is the left bottom of the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "474687f2-6bcd-4041-a9ea-b5a39b1086b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(46, 5)\n",
      "time: 821 µs\n"
     ]
    }
   ],
   "source": [
    "NUM_REGIONS = 6\n",
    "MAP_SIZE, start_position = {'x': 100, 'y': 100}, traj_data[0]\n",
    "print(start_position)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3131e733-9aae-4638-b0bb-751619cba554",
   "metadata": {},
   "source": [
    "### Workspace\n",
    "- Regions are randomly generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9d3ebed6-8518-44a7-ae10-91b1c9f93bb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 94.9 ms\n"
     ]
    }
   ],
   "source": [
    "ws = Workspace(MAP_SIZE, NUM_REGIONS, start_position)\n",
    "# ------------------------------------------------------------------\n",
    "# Useful vars\n",
    "#     ws.region: coordinates of regions\n",
    "#     ws.region_idx: cell indices of regions\n",
    "# ------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc4bd22-b6fd-4770-900c-6aaaeaa9970c",
   "metadata": {},
   "source": [
    "- Or the user can manually define regions: **Workspace(MAP_SIZE, NUM_REGIONS, start_position, region = [(a, b), (c, d), ...])**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ab2799f-f8d5-4214-899c-ea1a40224cb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 100 ms\n"
     ]
    }
   ],
   "source": [
    "region = REGION\n",
    "NUM_REGIONS = len(region)\n",
    "ws = Workspace(MAP_SIZE, NUM_REGIONS, start_position, region = region)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f244563-7a59-49a9-bd9c-2b8b9920f521",
   "metadata": {},
   "source": [
    "- Remove wall and an obstacle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acc30f33-8e7f-4c42-9690-9ab8ae4dad84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 2.12 ms\n"
     ]
    }
   ],
   "source": [
    "pre_obstacle = [(52, 23), (51, 23), (50, 24), (51, 24), (52, 24), (50, 25), (51, 25), (52, 25), (49, 23), (49, 24), (49, 25), (49, 22), (50, 22), (51, 22), (52, 22)]\n",
    "ws.set_pre_defined_obstacle(pre_obstacle)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bc3edb-cefb-4e2f-9000-e70f0b983384",
   "metadata": {},
   "source": [
    "### Hypothetical missions\n",
    "- LTL formulas for 'patrol', 'avoid', and 'patrol_while_avoid' missions can be automatically generated using region indices in each list. For example, if **['patrol', 0, 1]** is given to the monitor, **G (F p0 & F p1)** will be added to the intent set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd4b30d2-d01c-452c-80d3-cd9b25a43fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 1.1 ms\n"
     ]
    }
   ],
   "source": [
    "patrol = ['patrol', 0,1,2,3]\n",
    "sequenced_patrol = ['seq_patrol', 1, 0]\n",
    "ordered_patrol = ['ord_patrol', 1, 0]\n",
    "avoid = ['avoid', 2]\n",
    "patrol_while_avoid = ['patrol_while_avoid', patrol, avoid]\n",
    "sequenced_patrol_while_avoid = ['seq_patrol_while_avoid', sequenced_patrol, avoid]\n",
    "ordered_patrol_while_avoid = ['ord_patrol_while_avoid', ordered_patrol, avoid]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25fcf46c-61f9-4a54-a47a-8badff5356bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 3.51 ms\n"
     ]
    }
   ],
   "source": [
    "# ------------------------Three ground truth intents---------------------\n",
    "# LTL_formula = [\n",
    "#               ['patrol_while_avoid', ['patrol', 0, 1, 2, 3], ['avoid', 4, 5]], # visitor\n",
    "#               ['seq_patrol_while_avoid', ['seq_patrol', 0, 2, 4], ['avoid', 1, 3, 5]], # lab\n",
    "#               ['patrol_while_avoid', ['patrol', 1, 4], ['avoid', 0, 2, 3, 5]]] # utility\n",
    "\n",
    "# ------------------------Three ground truth intents and ten arbitrary intents---------------------\n",
    "LTL_formula = [\n",
    "              ['patrol_while_avoid', ['patrol', 0, 1, 2, 3], ['avoid', 4, 5]], # visitor\n",
    "              ['seq_patrol_while_avoid', ['seq_patrol', 0, 2, 4], ['avoid', 1, 3, 5]], # lab\n",
    "              ['patrol_while_avoid', ['patrol', 1, 4], ['avoid', 0, 2, 3, 5]], # utility\n",
    "              ['ord_patrol_while_avoid', ['ord_patrol', 0, 2], ['avoid', 1, 3, 4, 5]], # random1\n",
    "              ['seq_patrol_while_avoid', ['seq_patrol', 3, 2], ['avoid', 0, 1, 4, 5]], # random2\n",
    "              'G(p0 -> G(!p2)) & G(F(p0 & Fp3))', # random3\n",
    "              ['patrol_while_avoid', ['patrol', 0, 1, 2, 3, 5], ['avoid', 4]], # random4\n",
    "              ['patrol', 0, 1, 2], # random5\n",
    "              ['patrol_while_avoid', ['patrol', 0, 2, 5], ['avoid', 1]], # random6\n",
    "              'G(p0 -> G(!p2)) & G(F(p0 & Fp3)) & ( ! p2 U p0 )', # random7\n",
    "              'G(p2 -> G(!p1 | !p0)) & G (F ( p2 & F p4)) & ( ! p4 U p2 ) & G ( p4 -> X ( ! p4 U p2)) & G ( p2 -> X ( ! p2 U p4))', # random8\n",
    "              'G(p3 -> F(p0)) & G(F(p3 & F(p2 & Fp0))) & ( ! p3 U p2 )& ( ! p2 U p0 )', # random9\n",
    "              ['seq_patrol', 0, 1, 2, 3, 4, 5]] # random10\n",
    "\n",
    "# ------------------------Three ground truth intents and three arbitrary intents---------------------\n",
    "LTL_formula = [\n",
    "              ['patrol_while_avoid', ['patrol', 0, 1, 2, 3], ['avoid', 4, 5]], # visitor\n",
    "              ['seq_patrol_while_avoid', ['seq_patrol', 0, 2, 4], ['avoid', 1, 3, 5]], # lab\n",
    "              ['patrol_while_avoid', ['patrol', 1, 4], ['avoid', 0, 2, 3, 5]], # utility\n",
    "              ['patrol', 0, 3], # random1\n",
    "              ['ord_patrol_while_avoid', ['ord_patrol', 1, 2], ['avoid', 0, 3, 4, 5]], # random2\n",
    "              'G(p1 -> F(p3)) & G!p5 & G(F(p1 & Fp0)) & ( ! p3 U p0 )'] # random3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ca0b23-e466-4399-92db-93b785aca00a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Monitor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "96524629-0768-4c53-ba5b-2caf5e74c2b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Regions:  [(95, 40), (87, 18), (42, 3), (3, 20), (55, 40), (15, 36)]\n",
      "-------------------------------------------------------------------\n",
      "LTL: probability\n",
      "\n",
      "0- 1: 0.06433636913747938\n",
      "1- (F p0 & F p1 & F p2 & F p3) & G ! p4 & G ! p5: 0.04585382491051007\n",
      "2- (F ( p0 & F ( p2 & F p4))) & G ! p1 & G ! p3 & G ! p5: 0.7648242852297781\n",
      "3- (F p1 & F p4) & G ! p0 & G ! p2 & G ! p3 & G ! p5: 0.029854768241723367\n",
      "4- (F p0 & F p3): 0.03479387160795858\n",
      "5- G (F ( p1 & F p2)) & ( ! p2 U p1 ) & G ( p2 -> X ( ! p2 U p1)) & G ( p1 -> X ( ! p1 U p2)) & G ! p0 & G ! p3 & G ! p4 & G ! p5: 0.029854768241723367\n",
      "6- G(p1 -> F(p3)) & G!p5 & G(F(p1 & Fp0)) & ( ! p3 U p0 ): 0.03048211263082694\n",
      "-------------------------------------------------------------------\n",
      "AP(immediate): probability\n",
      "\n",
      "0: 0.12645419551715695\n",
      "1: 0.09462473736283822\n",
      "2: 0.010722728189579898\n",
      "3: 0.053823442854152\n",
      "4: 0.6939040027611021\n",
      "5: 0.02047089331517087\n",
      "-------------------------------------------------------------------\n",
      "(47, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"toyplot\" id=\"t39856e2b8d8f4bc4b0e7a31a57e859a7\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns=\"http://www.w3.org/2000/svg\" width=\"500.0px\" height=\"500.0px\" viewBox=\"0 0 500.0 500.0\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" id=\"tc9005cbb81e24815913161ee9cecaa0c\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t5d885fc905b14dc3ac8c1e6079a5ca24\"><clipPath id=\"ta2a6067238cb43b99e1667a1dfb2b4c8\"><rect x=\"30.0\" y=\"30.0\" width=\"440.0\" height=\"440.0\"></rect></clipPath><g clip-path=\"url(#ta2a6067238cb43b99e1667a1dfb2b4c8)\"><g class=\"toytree-mark-Toytree\" id=\"ta8595e1ff71740dab9c3ccfa6fc047b5\"><g class=\"toytree-Edges\" style=\"fill:none;stroke:rgb(14.9%,14.9%,14.9%);stroke-linecap:round;stroke-opacity:1;stroke-width:2\"><path d=\"M 70.8 203.3 L 70.8 343.6 L 163.0 343.6\" id=\"5,4\" style=\"stroke-width:4.469520013805511\"></path><path d=\"M 163.0 343.6 L 163.0 343.6 L 255.2 343.6\" id=\"4,3\" style=\"stroke-width:4.119595867470082\"></path><path d=\"M 70.8 203.3 L 70.8 62.9 L 163.0 62.9\" id=\"5,2\" style=\"stroke-width:1.6322709775857849\"></path><path d=\"M 255.2 343.6 L 255.2 250.1 L 347.4 250.1\" id=\"3,1\" style=\"stroke-width:3.4979250734272362\"></path><path d=\"M 255.2 343.6 L 255.2 437.2 L 347.4 437.2\" id=\"3,0\" style=\"stroke-width:1.6115723337190668\"></path></g><g class=\"toytree-AlignEdges\" style=\"stroke:rgb(66.3%,66.3%,66.3%);stroke-dasharray:2, 4;stroke-linecap:round;stroke-opacity:1.0;stroke-width:2\"><path d=\"M 347.4 437.2 L 347.4 437.2\"></path><path d=\"M 347.4 250.1 L 347.4 250.1\"></path><path d=\"M 347.4 62.9 L 163.0 62.9\"></path></g><g class=\"toytree-Nodes\" style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;stroke:None;stroke-width:1\"><g fill=\"rgb(12.5%,69.8%,66.7%)\" fill-opacity=\"1.0\" id=\"node-0\" transform=\"translate(347.444,437.209)\"><circle r=\"6.8347170011572\"></circle></g><g fill=\"rgb(67.8%,84.7%,90.2%)\" fill-opacity=\"1.0\" id=\"node-1\" transform=\"translate(347.444,250.058)\"><circle r=\"12.493775220281709\"></circle></g><g fill=\"rgb(82.7%,82.7%,82.7%)\" fill-opacity=\"1.0\" id=\"node-2\" transform=\"translate(162.983,62.907)\"><circle r=\"6.896812932757355\"></circle></g><g fill=\"rgb(82.7%,82.7%,82.7%)\" fill-opacity=\"1.0\" id=\"node-3\" transform=\"translate(255.213,343.633)\"><circle r=\"14.358787602410244\"></circle></g><g fill=\"rgb(12.5%,69.8%,66.7%)\" fill-opacity=\"1.0\" id=\"node-4\" transform=\"translate(162.983,343.633)\"><circle r=\"15.408560041416532\"></circle></g><g fill=\"rgb(100%,75.3%,79.6%)\" fill-opacity=\"1.0\" id=\"node-5\" transform=\"translate(70.752,203.270)\"><circle r=\"15.0\"></circle></g></g><g class=\"toytree-NodeLabels\" style=\"fill:rgb(0%,0%,0%);fill-opacity:1.0;font-size:20px;stroke:none\"><g transform=\"translate(341.88,442.32)\"><title>idx: 0\n",
       "dist: 1.0000\n",
       "support: 0.0000\n",
       "height: 0.0000\n",
       "name: 191</title><text>4</text></g><g transform=\"translate(341.88,255.17)\"><title>idx: 1\n",
       "dist: 1.0000\n",
       "support: 0.0000\n",
       "height: 0.0000\n",
       "name: 189</title><text>2</text></g><g transform=\"translate(157.42,68.02)\"><title>idx: 2\n",
       "dist: 1.0000\n",
       "support: 0.0000\n",
       "height: 2.0000\n",
       "name: 1</title><text>0</text></g><g transform=\"translate(249.65,348.74)\"><title>idx: 3\n",
       "dist: 1.0000\n",
       "support: 0.0000\n",
       "height: 1.0000\n",
       "name: 31</title><text>0</text></g><g transform=\"translate(157.42,348.74)\"><title>idx: 4\n",
       "dist: 1.0000\n",
       "support: 0.0000\n",
       "height: 2.0000\n",
       "name: 5</title><text>4</text></g></g><g class=\"toytree-TipLabels\" style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:11px;font-weight:normal;stroke:none;white-space:pre\"><g transform=\"translate(347.44,437.21)rotate(0)\"><text x=\"15.00\" y=\"2.81\" style=\"\">(4, 0, 4): 0.1223</text></g><g transform=\"translate(347.44,250.06)rotate(0)\"><text x=\"15.00\" y=\"2.81\" style=\"\">(4, 0, 2): 0.4996</text></g><g transform=\"translate(347.44,62.91)rotate(0)\"><text x=\"15.00\" y=\"2.81\" style=\"\">(0,): 0.1265</text></g></g></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
       "{\n",
       "var modules={};\n",
       "})();</script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 4.33 s\n"
     ]
    }
   ],
   "source": [
    "print(\"Regions: \", ws.region)\n",
    "print(\"-------------------------------------------------------------------\")\n",
    "monitor = HIMO(LTL_formula, ws, alpha=5, beta_high=0.01, beta_low=3, epsilon=0.3, tree_depth=4)\n",
    "n =110\n",
    "monitor.add_observations_thor(traj_data[1:n], print_probab = True, accept_dist=region_accept_dist)\n",
    "\n",
    "monitor.prediction(300,5)\n",
    "monitor.plot_thor(traj_data[1:n+5], region_accept_dist, tree_prob_threshold=0.1, tree_height=500, tree_width=500)\n",
    "print(traj_data[n-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9f5e4c-f03a-4dbe-afeb-1f736ed7a86d",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "notebook",
   "language": "python",
   "name": "notebook"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}