main_ntb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import toml\n",
    "import time\n",
    "from ipywidgets import *\n",
    "import cv2\n",
    "import uuid\n",
    "from absl import logging\n",
    "logging.set_verbosity(0)\n",
    "\n",
    "import copy\n",
    "import os\n",
    "import json\n",
    "\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/home/anujraaj/mule\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lc_utils import load_imu_data\n",
    "from submap import Submap\n",
    "from lc_utils import combine_yelli_poses, get_inverse_yelli_pose, imu_pose_estimate, normalize_pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data [0.4 0.3 0.1 0.4 0.8 0.7 0.8 0.4 0.3 0.6 0.8 0.9 0.9 0.1 0.4]\n",
      "unique [0.1 0.3 0.4 0.6 0.7 0.8 0.9]\n",
      "sorted [0.1 0.1 0.3 0.3 0.4 0.4 0.4 0.4 0.6 0.7 0.8 0.8 0.8 0.9 0.9]\n",
      "indices of unique values [ 0  2  4  8  9 10 13]\n",
      "argsort [ 2 13  1  8  0  3  7 14  9  5  4  6 10 11 12]\n"
     ]
    }
   ],
   "source": [
    "from ati.perception.lidar import lidar_pb, lidar_utils\n",
    "from ati.perception.lidar.lidar_voxel import voxel_filter\n",
    "from ati.perception.utils.voxel_filter_utils import voxel_filter_fast\n",
    "from ati.perception.lidar.lidar_frame import *\n",
    "from ati.slam.yelli import utils, grid\n",
    "from utils.imu_tracker import ImuTracker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submap Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_frames = 32000 \n",
    "# data_dir = \"/atidata/demo/SLLumax/2021-07/21-07-2021/map_raw_data/2021-07-21-10-50-46-mini20-02-manual-sllmax_map/\"\n",
    "data_dir =r\"/atidata/customer/Bosch/2021-08-30-16-24-40-minimule-11-3-manual-Bosch-32beam_capture_2\"\n",
    "data_dir=r\"/Users/anujraaj/robotics/SLAM-loop-closure/data/indoor-run/2021-12-29-11-52-57-minimule-11-2-manual-bosch_nashik_indoor\"\n",
    "data_dir=r\"/Users/anujraaj/robotics/SLAM-loop-closure/data/2021-11-11-15-31-42-minimule-11-2-manual-floor_1__mapping\"\n",
    "data_dir=r\"/atidata/customer/Flipkart_Malur_Impl/floor-Gplus1-repeat-32beams/2021-11-11-15-31-42-minimule-11-2-manual-floor_1__mapping/\"\n",
    "data_dir = \"/atidata/customer/Flipkart_Malur_Impl/floor-Gplus1-repeat-32beams/2021-11-11-15-31-42-minimule-11-2-manual-floor_1__mapping/\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bootstrap_frames =20\n",
    "\n",
    "grid_res = 0.05 \n",
    "grid_alpha =1\n",
    "num_submap_range_data = 90\n",
    "alpha= 1.05\n",
    "start_frame = 50\n",
    "\n",
    "grid_params = {\n",
    "    \"x\": (-0.1, 0.1),\n",
    "#    \"x\": (-0.2, 0.2),\n",
    "#    \"x\": (-0.4, 0.4),\n",
    "#    \"y\": (-0.02, 0.30),\n",
    "    \"y\": (-0.20, 0.30),\n",
    "#    \"y\": (-0.40, 0.60),\n",
    "    \"theta\": (-0.2, 0.2),\n",
    "#    \"num_x\": 11,\n",
    "#    \"num_x\": 22,\n",
    "#    \"num_x\": 44,\n",
    "#    \"num_y\": 17,\n",
    "#    \"num_y\" : 26,\n",
    "#    \"num_y\" : 52,\n",
    "#    \"num_t\": 41,\n",
    "    \"num_t\": 61,\n",
    "    \n",
    "}\n",
    "\n",
    "grid_params['num_x'] = int((grid_params['x'][1] - grid_params['x'][0])/grid_res + 1)\n",
    "grid_params['num_y'] = int((grid_params['y'][1] - grid_params['y'][0])/grid_res + 1)\n",
    "\n",
    "\n",
    "\n",
    "zmin, zmax = 0.3, 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Z slices: [(0.3, 2.0)]\n"
     ]
    }
   ],
   "source": [
    "z_slices = [(zmin,zmax)]\n",
    "print(\"Using Z slices:\", z_slices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "imu_data = load_imu_data(data_dir)\n",
    "imu_tracker = ImuTracker()\n",
    "imu_bias_initialized = False\n",
    "i = 0\n",
    "while not imu_bias_initialized:\n",
    "    imu_bias_initialized = imu_tracker.get_initial_gyro_bias(imu_data.iloc[i])\n",
    "    i += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pose = np.array([0.,0., np.pi/2])\n",
    "prev_pose = pose.copy()\n",
    "spose = pose #local submap pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "submaps, active_submaps, finished_submaps = [], [], [] #active_submaps contain two two at a time -- implementation for overlapping submaps\n",
    "best_scores = []\n",
    "node_poses = [] #? poses of inserted frames in the global frame\n",
    "inserted_frame_ids=[] #frame_id for inserted frame\n",
    "submap = Submap(spose.copy(), grid_res, grid_alpha)\n",
    "active_submaps.append(submap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_once = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total frames 27638\n",
      "Num Bootstrap insertions 0\n",
      "Num Bootstrap insertions 1\n",
      "Num Bootstrap insertions 2\n",
      "Num Bootstrap insertions 3\n",
      "Num Bootstrap insertions 4\n",
      "Num Bootstrap insertions 5\n",
      "Num Bootstrap insertions 6\n",
      "Num Bootstrap insertions 7\n",
      "Num Bootstrap insertions 8\n",
      "Num Bootstrap insertions 9\n",
      "Num Bootstrap insertions 10\n",
      "Num Bootstrap insertions 11\n",
      "Num Bootstrap insertions 12\n",
      "Num Bootstrap insertions 13\n",
      "Num Bootstrap insertions 14\n",
      "Num Bootstrap insertions 15\n",
      "Num Bootstrap insertions 16\n",
      "Num Bootstrap insertions 17\n",
      "Num Bootstrap insertions 18\n",
      "Num Bootstrap insertions 19\n",
      "frame:250 scores:0.788163358554579 best pose:[0.         0.         1.57082695]  \n",
      "frame:500 scores:0.7849897318652183 best pose:[0.        0.        1.5711034]  \n",
      "frame:750 scores:0.7781111293589882 best pose:[0.         0.         1.57110896]  \n",
      "frame:1000 scores:0.7847013314694867 best pose:[0.         0.         1.57120154]  \n",
      "frame:1250 scores:0.7172158868537609 best pose:[ 0.31501047 15.94082645  1.65829522]  \n",
      "frame:1500 scores:0.7029622284487335 best pose:[ 0.95651883 34.93116512  1.56080465]  \n",
      "frame:1750 scores:0.7637636094262247 best pose:[ 1.42794194 54.43728918  1.61824442]  \n",
      "frame:2000 scores:0.6215052775804439 best pose:[ 2.03149026 73.77058466  1.51353235]  \n",
      "frame:2250 scores:0.7482358081039112 best pose:[ 2.29373692 92.72193042  1.64884545]  \n",
      "frame:2500 scores:0.7045082639280935 best pose:[  2.79030723 111.91906775   1.50719096]  \n",
      "frame:2750 scores:0.5197484942380135 best pose:[ 7.69525622e+00  1.25859937e+02 -1.75611350e-02]  \n",
      "frame:3000 scores:0.6087256369850521 best pose:[2.67422593e+01 1.25326738e+02 1.41596578e-02]  \n",
      "frame:3250 scores:0.6764352655296821 best pose:[ 4.44348869e+01  1.25038935e+02 -1.62553857e-02]  \n",
      "frame:3500 scores:0.6310002661957967 best pose:[6.26286799e+01 1.24303175e+02 1.58099793e-02]  \n",
      "frame:3750 scores:0.6181164836789361 best pose:[ 8.20281766e+01  1.23978525e+02 -3.07555252e-03]  \n",
      "frame:4000 scores:0.5689919562517842 best pose:[1.01386916e+02 1.23425337e+02 1.90614202e-02]  \n",
      "frame:4250 scores:0.6427455586854532 best pose:[109.3428821  115.76057609  -1.52676108]  \n",
      "frame:4500 scores:0.676436610228276 best pose:[107.99895489  97.77604333  -2.71411685]  \n",
      "frame:4750 scores:0.6576355891211346 best pose:[89.58277143 98.86553208 -3.07564808]  \n",
      "frame:5000 scores:0.7274369115022186 best pose:[70.58283656 99.11241387  3.10947016]  \n",
      "frame:5250 scores:0.6750389388794001 best pose:[51.640498   99.68089407 -3.10123232]  \n",
      "frame:5500 scores:0.6480594258844411 best pose:[32.87859487 99.87618229  3.13285693]  \n",
      "frame:5750 scores:0.7368682056892162 best pose:[ 13.75061742 101.05577795   3.09880134]  \n",
      "frame:6000 scores:0.7309790026993352 best pose:[ 2.18239835 96.38739958 -1.63210257]  \n",
      "frame:6250 scores:0.6457384529395771 best pose:[ 1.61920399 77.83802088 -1.59648029]  \n",
      "frame:6500 scores:0.6708177440422259 best pose:[16.24064282 75.23938988  0.09473827]  \n",
      "frame:6750 scores:0.6424331194478913 best pose:[3.52058935e+01 7.47263822e+01 3.22623700e-02]  \n",
      "frame:7000 scores:0.6646351060264175 best pose:[ 5.39931505e+01  7.28613654e+01 -3.45929527e-02]  \n",
      "frame:7250 scores:0.6776964673520011 best pose:[72.81408895 72.47616435 -0.08900769]  \n",
      "frame:7500 scores:0.6147177351525325 best pose:[ 9.05188903e+01  7.06469324e+01 -7.31187733e-02]  \n",
      "frame:7750 scores:0.5901800801602125 best pose:[107.4555493   68.96934946  -1.05627882]  \n",
      "frame:8000 scores:0.6132783315885586 best pose:[106.23138746  50.42118703  -1.56748377]  \n",
      "frame:8250 scores:0.6548651342298476 best pose:[97.26148764 44.72003892 -3.13613527]  \n",
      "frame:8500 scores:0.6599411553667921 best pose:[78.43569723 45.71976069 -3.09226818]  \n",
      "frame:8750 scores:0.6290462511596387 best pose:[59.90186293 46.62691384 -3.129392  ]  \n",
      "frame:9000 scores:0.5870776828120685 best pose:[40.95879024 47.12127564  3.13378759]  \n",
      "frame:9250 scores:0.66556622558844 best pose:[21.9649621  47.83322888 -3.13172459]  \n",
      "frame:9500 scores:0.611503152193732 best pose:[ 2.7674823  48.54930524 -3.1371413 ]  \n",
      "frame:9750 scores:0.7547944286001574 best pose:[-1.06816317 34.10426434 -1.55248658]  \n",
      "frame:10000 scores:0.5726688488707823 best pose:[ 3.10331948 23.46196639 -0.10965839]  \n",
      "frame:10250 scores:0.657608658386078 best pose:[2.02832229e+01 2.24043716e+01 2.48949065e-03]  \n",
      "frame:10500 scores:0.7210881351544093 best pose:[ 3.82258600e+01  2.16117744e+01 -1.59543109e-02]  \n",
      "frame:10750 scores:0.6689680596856222 best pose:[ 5.57557715e+01  2.06304064e+01 -4.37504012e-04]  \n",
      "frame:11000 scores:0.5282587564816492 best pose:[ 7.34217278e+01  1.92682843e+01 -3.32644263e-02]  \n",
      "frame:11250 scores:0.5630372215515221 best pose:[ 9.27627868e+01  1.86042716e+01 -2.72215947e-02]  \n",
      "frame:11500 scores:0.6484051642395497 best pose:[104.41176238  12.06483329  -1.62799945]  \n",
      "frame:11750 scores:0.6424410180869561 best pose:[103.47535635  -4.80844178  -1.49286205]  \n",
      "frame:12000 scores:0.5855660084971116 best pose:[93.86566712 -6.59573461  3.0918577 ]  \n",
      "frame:12250 scores:0.5386694514573409 best pose:[78.04121839 -5.85625826  3.09509648]  \n",
      "frame:12500 scores:0.6640881272217345 best pose:[58.93329559 -4.70330298  3.05723517]  \n",
      "frame:12750 scores:0.6481177754455969 best pose:[43.5510448  -3.73827996 -3.11318952]  \n",
      "frame:13000 scores:0.6898416119736445 best pose:[28.00295458 -3.211617   -3.09613803]  \n",
      "frame:13250 scores:0.6342779609734199 best pose:[ 8.71553365 -2.55435532 -3.06992073]  \n",
      "frame:13500 scores:0.5596657202529659 best pose:[-2.61804005 -1.13592205  1.73557153]  \n",
      "frame:13750 scores:0.7279222183157104 best pose:[-2.31719568 17.51402984  1.5874778 ]  \n",
      "frame:14000 scores:0.6846283869653645 best pose:[-1.80121886 33.89991381  1.60310515]  \n",
      "frame:14250 scores:0.7400550788089449 best pose:[-0.98786367 53.37914347  1.58295274]  \n",
      "frame:14500 scores:0.590882907507144 best pose:[-0.38023447 72.66118833  1.52299935]  \n",
      "frame:14750 scores:0.7495751907813121 best pose:[-0.39915338 91.67291085  1.51288827]  \n",
      "frame:15000 scores:0.7091970586466857 best pose:[  0.46769989 110.81679399   1.5917946 ]  \n",
      "frame:15250 scores:0.6660661709175818 best pose:[2.54192790e+00 1.25110256e+02 1.54416905e-02]  \n",
      "frame:15500 scores:0.6299122126458736 best pose:[ 2.08235060e+01  1.24603722e+02 -2.03698124e-02]  \n",
      "frame:15750 scores:0.5652178516682422 best pose:[ 3.97763747e+01  1.24181137e+02 -2.45774684e-02]  \n",
      "frame:16000 scores:0.6659124810346438 best pose:[5.80792600e+01 1.23503665e+02 6.81104856e-02]  \n",
      "frame:16250 scores:0.5748478896376914 best pose:[7.74781033e+01 1.23549826e+02 8.24590166e-02]  \n",
      "frame:16500 scores:0.6143807598972593 best pose:[9.67511002e+01 1.23109070e+02 1.01571255e-02]  \n",
      "frame:16750 scores:0.6548146446226811 best pose:[107.11086772 114.30852823  -1.56285572]  \n",
      "frame:17000 scores:0.7454995969042939 best pose:[106.48662951  95.00402106  -1.54728123]  \n",
      "frame:17250 scores:0.7242417472579905 best pose:[105.9509603   75.7460709   -1.52479462]  \n",
      "frame:17500 scores:0.7009306596427385 best pose:[105.72539472  56.4892051   -1.55087538]  \n",
      "frame:17750 scores:0.7806121309765542 best pose:[105.71743678  37.21631658  -1.6750612 ]  \n",
      "frame:18000 scores:0.7073397169546524 best pose:[104.8971314   17.78700816  -1.54282801]  \n",
      "frame:18250 scores:0.614932798084601 best pose:[104.58272617  -1.56389059  -1.5357928 ]  \n",
      "frame:18500 scores:0.6502930250616773 best pose:[91.42235753 -4.01104264 -3.0759646 ]  \n",
      "frame:18750 scores:0.7715592820128578 best pose:[79.53789112  2.66075033  1.57712021]  \n",
      "frame:19000 scores:0.6305389769884181 best pose:[79.82081004 21.87383338  1.77017232]  \n",
      "frame:19250 scores:0.746634177517839 best pose:[80.04933694 40.97910452  1.60012557]  \n",
      "frame:19500 scores:0.7051333901506988 best pose:[80.51664848 60.33282963  1.53513441]  \n",
      "frame:19750 scores:0.666076277359347 best pose:[80.22217082 78.38446272  1.56686733]  \n",
      "frame:20000 scores:0.5777164403133619 best pose:[81.00887296 97.72034629  1.56946376]  \n",
      "frame:20250 scores:0.6703555722244547 best pose:[ 81.72156021 116.70811583   1.62403787]  \n",
      "frame:20500 scores:0.697078081403438 best pose:[ 82.32094449 135.9053104    1.68880825]  \n",
      "frame:20750 scores:0.6903408412345341 best pose:[ 80.17096086 150.84611686   3.03591099]  \n",
      "frame:21000 scores:0.6115126086523299 best pose:[ 61.48049303 151.4832039   -3.05914786]  \n",
      "frame:21250 scores:0.6142262284909911 best pose:[ 54.84787024 142.58057014  -1.61444386]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frame:21500 scores:0.5425153318674731 best pose:[ 53.85488167 123.24689097  -1.60530539]  \n",
      "frame:21750 scores:0.69365064612321 best pose:[ 52.85619768 104.53163374  -1.62017917]  \n",
      "frame:22000 scores:0.7018812513930078 best pose:[52.61864601 85.64037797 -1.50755722]  \n",
      "frame:22250 scores:0.775217058365646 best pose:[52.24298609 66.79425186 -1.57791233]  \n",
      "frame:22500 scores:0.6159125440449069 best pose:[51.62219484 47.94737777 -1.51362331]  \n",
      "frame:22750 scores:0.7579196273608196 best pose:[50.71696646 29.1606479  -1.58777359]  \n",
      "frame:23000 scores:0.7693279762548859 best pose:[51.01035993 10.36779125 -1.53003734]  \n",
      "frame:23250 scores:0.7512921328120509 best pose:[50.54991549 -0.93199199 -1.53860525]  \n",
      "frame:23500 scores:0.5344877769899848 best pose:[38.12558299 -2.60204384  3.13106897]  \n",
      "frame:23750 scores:0.7297531503421824 best pose:[23.10483637  0.72063149  1.60595203]  \n",
      "frame:24000 scores:0.7347041587634306 best pose:[23.76669187 20.01171783  1.61993805]  \n",
      "frame:24250 scores:0.7746504045142756 best pose:[24.06410529 39.00553197  1.55331864]  \n",
      "frame:24500 scores:0.7255606268147512 best pose:[24.60689646 58.13865288  1.59334967]  \n",
      "frame:24750 scores:0.7593224132870853 best pose:[24.91715718 77.44621067  1.61688008]  \n",
      "frame:25000 scores:0.7127557065467305 best pose:[25.45687982 96.79293784  1.57499851]  \n",
      "frame:25250 scores:0.7024115252062909 best pose:[ 26.41850472 116.13380073   1.54355108]  \n",
      "frame:25500 scores:0.5382320793857603 best pose:[ 18.40620839 125.41346633  -3.09707582]  \n",
      "frame:25750 scores:0.5269605963901487 best pose:[  1.67865857 125.85890739  -2.58534336]  \n",
      "frame:26000 scores:0.6562113912233085 best pose:[  0.14443042 109.07137051  -1.5320259 ]  \n",
      "frame:26250 scores:0.7586103200189973 best pose:[-0.56565079 89.9867203  -1.51963719]  \n",
      "frame:26500 scores:0.6974067775106285 best pose:[-1.17888317 72.44437972 -1.54746273]  \n",
      "frame:26750 scores:0.7741139450625341 best pose:[-1.90827026 63.37368919 -1.57278395]  \n",
      "frame:27000 scores:0.7309600311082823 best pose:[-3.02245831 44.04059429 -1.58878547]  \n",
      "frame:27250 scores:0.5541053505898407 best pose:[-3.86124226 24.74998324 -1.54578578]  \n",
      "frame:27500 scores:0.8095429842074436 best pose:[-5.04553653  5.96306739 -1.60317668]  \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    lpb = lidar_pb.LidarSmallPb(data_dir)\n",
    "except:\n",
    "    lpb = lidar_pb.LidarPb(data_dir)\n",
    "prev_frame_time, prev_frame = lpb.get_frame(start_frame - 2)\n",
    "num_frames = np.minimum(len(lpb.list_frames()), max_frames)\n",
    "print(f\"total frames {num_frames}\")\n",
    "for frame_id in range(start_frame, num_frames - 1):\n",
    "    #if frame_id % 2 != 0:\n",
    "        #continue\n",
    "    try:\n",
    "        frame_time, frame = lpb.get_frame(frame_id)\n",
    "    except:\n",
    "        print(f\"skipping {frame_id}. divide-by-zero error\")\n",
    "        continue\n",
    "\n",
    "    frame = np.copy(frame[:, :6]).astype(np.float64)\n",
    "    frame = frame[(frame[:,4]>0) & (frame[:,4] < 60)]\n",
    "    frame = frame[(frame[:,2] > zmin) & (frame[:,2] < zmax)]\n",
    "    #filtered_frame = voxel_filter(frame, voxel_size=0.05)\n",
    "    filtered_frame,_ = voxel_filter_fast(voxel_filter(frame, voxel_size=0.05), trunc=False, voxel = 0.25)\n",
    "    frame_z = voxel_filter(frame[:,:3], voxel_size=0.05)\n",
    "    \n",
    "    if frame_id < start_frame + bootstrap_frames:\n",
    "        print(\"Num Bootstrap insertions\", active_submaps[0].num_insertions)\n",
    "        spose = combine_yelli_poses(pose, get_inverse_yelli_pose(active_submaps[0].pose)) # pose wrt submap origin\n",
    "        node_poses.append(pose)\n",
    "        inserted_frame_ids.append(frame_id)\n",
    "        active_submaps[0].insert_points(frame_z, spose, frame_id, node_id = len(node_poses)-1 )\n",
    "#         frame_list.append(frame_id)\n",
    "#         poses.append(pose)\n",
    "        prev_insert_ts = frame_time\n",
    "        prev_frame_time, prev_frame = frame_time, frame\n",
    "        last_inserted_pose = pose.copy()\n",
    "        continue\n",
    "    \n",
    "    #HACK\n",
    "    #skip if vehicle is not moving. Skip?\n",
    "    \n",
    "    relevant_imu_data = imu_data[imu_data[\"time\"].between(prev_frame_time, frame_time)]\n",
    "    for imu_idx in range(relevant_imu_data.shape[0]):\n",
    "        imu_tracker.add_imu_data(relevant_imu_data.iloc[imu_idx])\n",
    "    \n",
    "    pose_estimate = imu_pose_estimate(imu_tracker, frame_time, pose) #only theta around which we need to search changes\n",
    "    spose_estimate = combine_yelli_poses(get_inverse_yelli_pose(active_submaps[0].pose),pose_estimate)\n",
    "    search_space = utils.grid_space(center=spose_estimate, **grid_params)\n",
    "    \n",
    "    if count_once is True:\n",
    "        scores,n_unique_visited_points = active_submaps[0].search(filtered_frame, search_space, count_once = True)\n",
    "        scores = scores/n_unique_visited_points\n",
    "    else:\n",
    "        scores = active_submaps[0].search(filtered_frame, search_space, count_once = False)\n",
    "    \n",
    "    best = np.argmax(scores)\n",
    "    best_scores.append(scores[best])\n",
    "    \n",
    "    spose = search_space[best]\n",
    "    pose = combine_yelli_poses(active_submaps[0].pose, spose)\n",
    "    \n",
    "    #HACK\n",
    "    if len(frame_z) < 500:\n",
    "        continue\n",
    "    #Check all quadrants have points\n",
    "    if (np.sum((frame_z[:,0] < 0) & (frame_z[:,1] > 0)) <10) or \\\n",
    "    (np.sum((frame_z[:,0] < 0) & (frame_z[:,1] < 0)) <10) or \\\n",
    "    (np.sum((frame_z[:,0] > 0) & (frame_z[:,1] > 0)) <10) or \\\n",
    "    (np.sum((frame_z[:,0] > 0) & (frame_z[:,1] < 0)) <10):\n",
    "        continue\n",
    "    \n",
    "    if (np.linalg.norm(pose[:2]-last_inserted_pose[:2])> 0.15 or (prev_insert_ts - frame_time)>1) :\n",
    "        node_poses.append(pose)\n",
    "        inserted_frame_ids.append(frame_id)\n",
    "        active_submaps[0].insert_points(frame_z, spose, frame_id, node_id=len(node_poses)-1)\n",
    "        if len(active_submaps) ==1:\n",
    "        \n",
    "            if active_submaps[0].num_insertions >= int(num_submap_range_data/2):\n",
    "                active_submaps.append(Submap(pose.copy(), grid_res, grid_alpha))\n",
    "                s2pose = combine_yelli_poses(get_inverse_yelli_pose(active_submaps[1].pose),pose)\n",
    "                active_submaps[1].insert_points(frame_z,s2pose, frame_id, node_id=len(node_poses)-1)\n",
    "                #print(f\"spose {spose}, s2pose {s2pose}\")\n",
    "                \n",
    "        else:\n",
    "            \n",
    "            s2pose = combine_yelli_poses(get_inverse_yelli_pose(active_submaps[1].pose),pose)\n",
    "            active_submaps[1].insert_points(frame_z,s2pose, frame_id, node_id=len(node_poses)-1)\n",
    "            #print(f\"len else spose {spose}, s2pose {s2pose}\")\n",
    "            \n",
    "        if active_submaps[0].num_insertions >= num_submap_range_data - 1:\n",
    "            active_submaps[0].finish()\n",
    "            finished_submaps.append(active_submaps.pop(0))\n",
    "            active_submaps.append(Submap(pose.copy(), grid_res, grid_alpha))\n",
    "            s2pose = combine_yelli_poses(get_inverse_yelli_pose(active_submaps[1].pose), pose)\n",
    "            active_submaps[1].insert_points(frame_z,s2pose, frame_id, node_id=len(node_poses)-1)\n",
    "        \n",
    "        #print(\"num active submaps\", len(active_submaps))\n",
    "        prev_insert_ts = frame_time\n",
    "        last_inserted_pose = pose.copy()\n",
    "    if frame_id % 250 == 0:\n",
    "        print(f\"frame:{frame_id} scores:{best_scores[-1]} best pose:{pose}  \")\n",
    "    prev_frame_time, prev_frame = frame_time, frame\n",
    "node_poses = np.array(node_poses)\n",
    "inserted_frame_ids = np.array(inserted_frame_ids)\n",
    "# print(f\"final pose {poses[-1]}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving and Plotting generated Submaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# node_poses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"maps/5/submaps-node_poses-inserted_frame_ids_countonce.pickle\", 'wb') as f:\n",
    "    pickle.dump((finished_submaps, node_poses, inserted_frame_ids),f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open(\"maps/2/submaps-node_poses-inserted_frame_ids.pkl\", 'rb') as f:\n",
    "#     random = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(-11.225767128892368,\n",
       " 115.45215801249205,\n",
       " -15.179834279163876,\n",
       " 159.60958386537877)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_poses = np.array(node_poses)\n",
    "st = 0; stp = -1\n",
    "plt.figure()\n",
    "plt.plot(node_poses[st:stp,0], node_poses[st:stp,1], )\n",
    "plt.axis('equal')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# node_poses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA+gAAAPoCAYAAABNo9TkAAAAAXNSR0IArs4c6QAAIABJREFUeF7s3QuwZdOBP/5F2qNbG68m6G6huiNeUyE0GvEIQ7yG8lYlmPJ+DIqgWgxS/GhEhKI8Y4gi0mRGeUsmCY20V9KpSaKR9hrxCG1Ie2US0f9aO7n3f/v2Ofeec/e+9+x11mdXpUbfu/c+a32+q3+/+vbeZ+/FFixYsCDYCBAgQIAAAQIECBAgQIAAgY4KLKagd9TfhxMgQIAAAQIECBAgQIAAgUJAQbcQCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQIAAAQIECBAgQIBADQQU9BqEYAgECBAgQIAAAQIECBAgQEBBtwYIECBAgAABAgQIECBAgEANBBT0GoRgCAQIECBAgAABAgQIECBAQEG3BggQIECAAAECBAgQIECAQA0EFPQahGAIBAgQIECAAAECBAgQIEBAQbcGCBAgQKAWAq+88kq4/PLLw7333hteffXVsNRSS4VJkyaF/fbbLxx33HFhzJgxtRinQRAgQIAAAQIEhktAQR8uWeclQIAAgZYF7r777nDQQQeF+fPnNzxm7bXXLor75MmTWz6nHQkQIECAAAECqQko6KklZrwECBDoMoHZs2eHLbfcMnz88cdh7NixYdq0aWG77bYr/nzbbbeF6667rphxLOlPP/10WHbZZbtMwHQIECBAgAABAn8TUNCtBAIECBDoqMDWW28dHnnkkTBq1Kgwc+bMMHXq1IXGc/HFF4fTTjut+NnZZ58dzjnnnI6O14cTIECAAAECBIZLQEEfLlnnJUCAAIFBBZ588smw2WabFfsdddRR4eqrr17kmE8//TRssMEGYc6cOWH55ZcPb731VlhiiSUGPbcdCBAgQIAAAQKpCSjoqSVmvAQIEOgigTPOOCNccMEFxYwef/zx3rLef4rTp08vbn2P24MPPhh23HHHLlIwFQIECBAgQIDA3wQUdCuBAAECBDom0HN7+zLLLBPee++94jb3RtusWbPCFltsUfzqrLPOCt/85jc7NmYfTIAAAQIECBAYLgEFfbhknZcAAQIEBhVYeeWVw7x588IXv/jF8Ktf/arp/u+++25YccUVi9/vu+++YcaMGYOe2w4ECBAgQIAAgdQEFPTUEjNeAgQIdInAn/70pzB69OhiNrvuumu45557BpxZfML7hx9+GDbffPMQr6i3sv3+978fcLc4hmeffTZ89rOfDfEfC5pdwW/ls+xTb4FPPvkkvP3228Ug//Ef/zEsvfTS9R6w0REgQIBAlgIKepaxmzQBAgQ6LxDL0iqrrFIMZP/99y9eqTbQFkt0fEBcfGDcr3/965YmsNhii7W0n53yEogPJ5wyZUpekzZbAgQIEEhCQEFPIiaDJECAQPcJvPrqq2GNNdYoJva1r30tfO973xtwknHfeMykSZPC3LlzWwJR0Ftiym4nBT27yE2YAAECyQgo6MlEZaAECBDoLoGRuII+2C3usfD3PHwulrbVVlutu5DNplfgjTfeCJtuumnx55deeimsueaadAgQIECAQO0EFPTaRWJABAgQyENgJL6DPphkLPATJ04sdotlfcKECYMd4veJCsg60eAMmwABApkJKOiZBW66BAgQqJPAuHHjwjvvvNOxp7grbXVaDcM7FlkPr6+zEyBAgEA1Agp6NY7OQoAAAQJDEOj0e9CVtiGElughsk40OMMmQIBAZgIKemaBmy4BAgTqJHDGGWeECy64oBjS448/HjbbbLOGw5s+fXqYNm1a8bsHH3ww7LjjjpVMQ2mrhDGJk8g6iZgMkgABAtkLKOjZLwEABAgQ6JxAfDBbTyk/6qijwtVXX73IYD799NPi1Wpz5swJyy+/fPGqtSWWWKKSQSttlTAmcRJZJxGTQRIgQCB7AQU9+yUAgAABAp0V6LnNfdSoUWHmzJlh6tSpCw3o4osvDqeddlrxs7PPPjucc845lQ1YaauMsvYnknXtIzJAAgQIEAghKOiWAQECBAh0VGD27Nlhyy23DB9//HEYO3ZsiLe9b7fddsWfb7vttnDttdcW41t77bXD008/HZZddtnKxqu0VUZZ+xPJuvYRGSABAgQIKOjWAAECBAjUQeDuu+8OBx10UJg/f37D4cRyfu+994bJkydXOlylrVLOWp9M1rWOx+AIECBA4O8CrqBbCgQIECBQC4FXXnklXHbZZUURj2VqySWXLAr5vvvuG44//vgwZsyYyseptFVOWtsTyrq20RgYAQIECPQRUNAtBwIECBDIVkBpyyd6WeeTtZkSIEAgZQEFPeX0jJ0AAQIESgkobaX4kjpY1knFZbAECBDIVkBBzzZ6EydAgAABpS2fNSDrfLI2UwIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIKC05bMGZJ1P1mZKgACBlAUU9JTTM3YCBAgQKCWgtJXiS+pgWScVl8ESIEAgWwEFPdvoTZwAAQIElLZ81oCs88naTAkQIJCygIKecnrGToAAAQKlBJS2UnxJHSzrpOIyWAIECGQroKBnG72JEyBAgIDSls8akHU+WZspAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQJKWz5rQNb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgobfmsAVnnk7WZEiBAIGUBBT3l9IydAAECBEoJKG2l+JI6WNZJxWWwBAgQyFZAQc82ehMnQIAAAaUtnzUg63yyNlMCBAikLKCgp5yesRMgQIBAKQGlrRRfUgfLOqm4DJYAAQLZCijo2UZv4gQIECCgtOWzBmSdT9ZmSoAAgZQFFPSU0zN2AgQIECgloLSV4kvqYFknFZfBEiBAIFsBBT3b6E2cAAECBJS2fNaArPPJ2kwJECCQsoCCnnJ6xk6AAAECpQSUtlJ8SR0s66TiMlgCBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUgNJWii+pg2WdVFwGS4AAgWwFFPRsozdxAgQIEFDa8lkDss4nazMlQIBAygIKesrpGTsBAgQIlBJQ2krxJXWwrJOKy2AJECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCkBpa0UX1IHyzqpuAyWAAEC2Qoo6NlGb+IECBAgoLTlswZknU/WZkqAAIGUBRT0lNMzdgIECBAoJaC0leJL6mBZJxWXwRIgQCBbAQU92+hNnAABAgSUtnzWgKzzydpMCRAgkLKAgp5yesZOgAABAqUElLZSfEkdLOuk4jJYAgQIZCugoGcbvYkTIECAgNKWzxqQdT5ZmykBAgRSFlDQU07P2AkQIECglIDSVoovqYNlnVRcBkuAAIFsBRT0bKM3cQIECBBQ2vJZA7LOJ2szJUCAQMoCCnrK6Rk7AQIECJQSUNpK8SV1sKyTistgCRAgkK2Agp5t9CZOgAABAkpbPmtA1vlkbaYECBBIWUBBTzk9YydAgACBUgJKWym+pA6WdVJxGSwBAgSyFVDQs43exAkQIEBAactnDcg6n6zNlAABAikLKOgpp2fsBAgQIFBKQGkrxZfUwbJOKi6DJUCAQLYCCnq20Zs4AQIECCht+awBWeeTtZkSIEAgZQEFPeX0jJ0AAQIESgkobaX4kjpY1knFZbAECBDIVkBBzzZ6EydAgAABpS2fNSDrfLI2UwIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIKC05bMGZJ1P1mZKgACBlAUU9JTTM3YCBAgQKCWgtJXiS+pgWScVl8ESIEAgWwEFPdvoTZwAAQIElLZ81oCs88naTAkQIJCygIKecnrGToAAAQKlBJS2UnxJHSzrpOIyWAIECGQroKBnG72JEyBAgIDSls8akHU+WZspAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQJKWz5rQNb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgobfmsAVnnk7WZEiBAIGUBBT3l9IydAAECBEoJKG2l+JI6WNZJxWWwBAgQyFZAQc82ehMnQIAAAaUtnzUg63yyNlMCBAikLKCgp5yesRMgQIBAKQGlrRRfUgfLOqm4DJYAAQLZCijo2UZv4gQIECCgtOWzBmSdT9ZmSoAAgZQFFPSU0zN2AgQIECgloLSV4kvqYFknFZfBEiBAIFsBBT3b6E2cAAECBJS2fNaArPPJ2kwJECCQsoCCnnJ6xk6AAAECpQSUtlJ8SR0s66TiMlgCBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUgNJWii+pg2WdVFwGS4AAgWwFFPRsozdxAgQIEFDa8lkDss4nazMlQIBAygIKesrpGTsBAgQIlBJQ2krxJXWwrJOKy2AJECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCkBpa0UX1IHyzqpuAyWAAEC2Qoo6NlGb+IECBAgoLTlswZknU/WZkqAAIGUBRT0lNMzdgIECBAoJaC0leJL6mBZJxWXwRIgQCBbAQU92+hNnACBXAUWW2yxlqa+zTbbhIceemjAfe+///5w7bXXhqeeeiq8/fbbYeWVVw5TpkwJRx55ZNh5551b+pxPPvkkXH/99eGWW24Jzz77bPjggw/C6quvHnbYYYdwwgknhPXXX7+l8wxlJ6VtKGppHiPrNHMzagIECOQmoKDnlrj5EiCQvUAVBf3TTz8tSvh3v/vdpp6HH354uOaaa8Liiy/edJ958+aFXXbZpSj4jballloqXHHFFSGeazg2pW04VOt5TlnXMxejIkCAAIGFBRR0K4IAAQKZCfQU9GOOOSYce+yxTWe/zDLLhLXWWqvh76dNmxamT59e/G6jjTYKp512Wpg0aVJ44YUXwkUXXRRmz55d/C7ud/755zc8x1//+tew7bbbhkcffbT4/V577RWOOOKIsOKKK4YnnnginHfeeeGtt94qCv4999zT8hX5duJU2trRSntfWaedn9ETIEAgFwEFPZekzZMAAQJ/F+gp6GeffXY455xz2nZ5/vnni9vO463pm2yySZg5c2YYPXp073k++uijEG+Pf/rpp8OoUaPCnDlzwuTJkxf5nBtuuCEcdthhxc/jPxRceeWVC+0zd+7csPHGG4f58+cXx8fzxPNVuSltVWrW+1yyrnc+RkeAAAECfxNQ0K0EAgQIZCZQtqDHMn3VVVcVarNmzQqbb775IoKPP/54mDp1atPyHX+x3nrrFaU7XjF/9dVXw5gxYxY5T7xKH6/Cx23GjBlh3333rTQtpa1SzlqfTNa1jsfgCBAgQODvAgq6pUCAAIHMBMoU9AULFoQJEyaE119/PayzzjpFwW62xd8/99xzYfz48UUB7/vd93gV/gtf+EJx6NFHH91b+Puf68033wyrrbZa8eMDDzww3HrrrZWmpbRVylnrk8m61vEYHAECBAgo6NYAAQIE8hQoU9BffPHF4rvmcTvqqKPC1Vdf3RQx/j4+4T1u8bi+32fve3v797///XDAAQc0PU8s8rHQr7HGGuGVV16pNDSlrVLOWp9M1rWOx+AIECBAQEG3BggQIJCnQE9Bj7eYxyviL7/8cvjMZz4TVl111bDFFluEQw89NGy33XYNceLD2nbffffid5deemk46aSTmiLG35988snF7++9997iae0929e//vVwySWXFH+MD5TbcMMNm55njz32CHfddVdxBf79998P8eF1VW1KW1WS9T+PrOufkRESIECAgO+gWwMECBDITqCV16ztueee4cYbbwzLLbfcQj7xinl8+nvcbr/99rDPPvs09bvjjjt6vzMej4tX1Hu2eMX8Bz/4QfHH+P70cePGNT3P8ccf3/sAufie9J5b41sJLpaygbY33ngjbLrppsUu8Tb8ePu+rTsFFPTuzNWsCBAg0G0CvoPebYmaDwECBAYRiFeg//mf/zlsv/32xffIx44dW5Tkhx9+uLhl/Z133inOEJ/E/uMf/zgsscQSvWe8+OKLi1eqxe3+++8PX/3qV5t+Wvx9z1Xzb33rW+GUU07p3XfXXXcN9913X/Hnjz/+OCy99NJNz3P66acXr26LW3wyfHyye6tbK/8Y0XMuBb1V1TT3U9DTzM2oCRAgkJuAgp5b4uZLgED2Au+9915YfvnlGzr84Q9/KN433vMe88suuyyccMIJvfuee+654ayzzir+/JOf/CR85Stfaer505/+tPhHgLjF484888zefePP4+/jFt+HHt913myLnxePj9sjjzwSttpqq5YzVNBbpur6HRX0ro/YBAkQINAVAgp6V8RoEgQIEKhOID7QLV5Z/8tf/lK8f/x3v/td78lTu4LuFvfq1kXqZ1LQU0/Q+AkQIJCHgIKeR85mSYAAgbYE+t6C/tprr4XVV1+9OD6176APNmmlbTCh7vm9rLsnSzMhQIBANwso6N2crrkRIEBgiAKnnnpqiN8bj9uTTz4ZpkyZUvy3p7gPEdRhHRdQ0DsegQEQIECAQAsCCnoLSHYhQIBAbgLxQXDxdvb+Bd170HNbCd0zXwW9e7I0EwIECHSzgILezemaGwECBIYosNtuuxXvLo9bLDbjx48v/ju+Nz2+iuz1118vvqc+Z86cpp+w7rrrhvhatHhsfEJ63we2Pf/8872vSzv66KPDVVdd1fA8b775ZlhttdWK3x144IHh1ltvHeKMGh+mtFXKWeuTybrW8RgcAQIECPxdQEG3FAgQIEBgIYGXXnqpKN9//vOfw6RJk8LcuXMX+v2xxx7bW6hnzZoVNt9880UEH3/88TB16tTi53H/K6+8cpF91ltvvaLgr7jiikWBHzNmzCL7TJ8+PUybNq34+YwZM3rfq15VZEpbVZL1P4+s65+RERIgQIBACAq6VUCAAIGMBO6+++7iNWqjRo1qOOv+r1m75JJLwsknn7zQvvHqdyzX8fVom2yySZg5c2YYPXp07z7xveZbb7118c7y+DnPPPNM+PznP7/I591www3hsMMOK35+3HHHhSuuuGKhfV544YXwpS99KcyfP794mnws883GPdQIlbahyqV3nKzTy8yICRAgkKOAgp5j6uZMgEC2AmuuuWbx+rS99967uMId/xzL9bx588JDDz0UrrnmmuK/4xbfN/5f//VfYamlllrEK17Vjle347bRRhuF008/vbjaHkv1hRde2Pse9bjf+eef39A7FvxtttkmPPbYY8Xv45iOOOKIsMIKKxQPpovvPn/rrbeKd6THh9PFf1ioelPaqhat7/lkXd9sjIwAAQIE/n8BBd1qIECgmefpAAAgAElEQVSAQEYCsZC/8sorg844luXrr78+LL/88g33/fTTT4syHa+CN9vi1fFrr722KNjNtviPAbvsskt46qmnGu4S/3EgXlk//PDDBx3zUHZQ2oailuYxsk4zN6MmQIBAbgIKem6Jmy8BAlkLPPzwwyH+L353PD6RPRbkeAv52LFjw8SJE8MWW2wRDjnkkN7vjw+Gdd999xUlPBbseK5x48YVr2Q76qijWr7i/cknn4TrrruueABcvI39ww8/LN67vv3224cTTzwxrL/++oMNY8i/V9qGTJfcgbJOLjIDJkCAQJYCCnqWsZs0AQIECEQBpS2fdSDrfLI2UwIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIKC05bMGZJ1P1mZKgACBlAUU9JTTM3YCBAgQKCWgtJXiS+pgWScVl8ESIEAgWwEFPdvoTZwAAQIElLZ81oCs88naTAkQIJCygIKecnrGToAAAQKlBJS2UnxJHSzrpOIyWAIECGQroKBnG72JEyBAgIDSls8akHU+WZspAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQJKWz5rQNb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgobfmsAVnnk7WZEiBAIGUBBT3l9IydAAECBEoJKG2l+JI6WNZJxWWwBAgQyFZAQc82ehMnQIAAAaUtnzUg63yyNlMCBAikLKCgp5yesRMgQIBAKQGlrRRfUgfLOqm4DJYAAQLZCijo2UZv4gQIECCgtOWzBmSdT9ZmSoAAgZQFFPSU0zN2AgQIECgloLSV4kvqYFknFZfBEiBAIFsBBT3b6E2cAAECBJS2fNaArPPJ2kwJECCQsoCCnnJ6xk6AAAECpQSUtlJ8SR0s66TiMlgCBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUgNJWii+pg2WdVFwGS4AAgWwFFPRsozdxAgQIEFDa8lkDss4nazMlQIBAygIKesrpGTsBAgQIlBJQ2krxJXWwrJOKy2AJECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCkBpa0UX1IHyzqpuAyWAAEC2Qoo6NlGb+IECBAgoLTlswZknU/WZkqAAIGUBRT0lNMzdgIECBAoJaC0leJL6mBZJxWXwRIgQCBbAQU92+hNnAABAgSUtnzWgKzzydpMCRAgkLKAgp5yesZOgAABAqUElLZSfEkdLOuk4jJYAgQIZCugoGcbvYkTIECAgNKWzxqQdT5ZmykBAgRSFlDQU07P2AkQIECglIDSVoovqYNlnVRcBkuAAIFsBRT0bKM3cQIECBBQ2vJZA7LOJ2szJUCAQMoCCnrK6Rk7AQIECJQSUNpK8SV1sKyTistgCRAgkK2Agp5t9CZOgAABAkpbPmtA1vlkbaYECBBIWUBBTzk9YydAgACBUgJKWym+pA6WdVJxGSwBAgSyFVDQs43exAkQIEBAactnDcg6n6zNlAABAikLKOgpp2fsBAgQIFBKQGkrxZfUwbJOKi6DJUCAQLYCCnq20Zs4AQIECCht+awBWeeTtZkSIEAgZQEFPeX0jJ0AAQIESgkobaX4kjpY1knFZbAECBDIVkBBzzZ6EydAgAABpS2fNSDrfLI2UwIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIKC05bMGZJ1P1mZKgACBlAUU9JTTM3YCBAgQKCWgtJXiS+pgWScVl8ESIEAgWwEFPdvoTZwAAQIElLZ81oCs88naTAkQIJCygIKecnrGToAAAQKlBJS2UnxJHSzrpOIyWAIECGQroKBnG72JEyBAgIDSls8akHU+WZspAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQJKWz5rQNb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgobfmsAVnnk7WZEiBAIGUBBT3l9IydAAECBEoJKG2l+JI6WNZJxWWwBAgQyFZAQc82ehMnQIAAAaUtnzUg63yyNlMCBAikLKCgp5yesRMgQIBAKQGlrRRfUgfLOqm4DJYAAQLZCijo2UZv4gQIECCgtOWzBmSdT9ZmSoAAgZQFFPSU0zN2AgQIECgloLSV4kvqYFknFZfBEiBAIFsBBT3b6E2cAAECBJS2fNaArPPJ2kwJECCQsoCCnnJ6xk6AAAECpQSUtlJ8SR0s66TiMlgCBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUgNJWii+pg2WdVFwGS4AAgWwFFPRsozdxAgQIEFDa8lkDss4nazMlQIBAygIKesrpGTsBAgQIlBJQ2krxJXWwrJOKy2AJECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCkBpa0UX1IHyzqpuAyWAAEC2Qoo6NlGb+IECBAgoLTlswZknU/WZkqAAIGUBRT0lNMzdgIECBAoJaC0leJL6mBZJxWXwRIgQCBbAQU92+hNnAABAgSUtnzWgKzzydpMCRAgkLKAgp5yesZOgAABAqUElLZSfEkdLOuk4jJYAgQIZCugoGcbvYkTIJCawFtvvRWefPLJ4n9PPfVU8b933nmnmMYhhxwSbrzxxramdP/994drr722OM/bb78dVl555TBlypRw5JFHhp133rmlc33yySfh+uuvD7fcckt49tlnwwcffBBWX331sMMOO4QTTjghrL/++i2dZ968eeHyyy8Pd955Z3j55ZeLY9Zcc82w5557hhNPPDGstNJKLZ2n3Z2UtnbF0t1f1ulmZ+QECBDISUBBzyltcyVAIGmBxRZbrOn42ynon376aVHCv/vd7zY93+GHHx6uueaasPjiizfdJ5bqXXbZpSj4jballloqXHHFFSGea6DtiSeeKIr4m2++2XC31VZbrSjum266aeX5KW2Vk9b2hLKubTQGRoAAAQJ9BBR0y4EAAQKJCPQt6GussUZYZ511wo9+9KNi9O0U9GnTpoXp06cXx2200UbhtNNOC5MmTQovvPBCuOiii8Ls2bOL38X9zj///IY6f/3rX8O2224bHn300eL3e+21VzjiiCPCiiuuGGLhPu+880K84h8L/j333NP0ivyrr74aNt544+IK/qhRo8LJJ58cdtttt+Kc8bhvf/vbIV6lX2WVVcIvfvGLMGHChErTUtoq5az1yWRd63gMjgABAgT+LqCgWwoECBBIRODss88ubkGP//vsZz9b3Aq+1lprtVXQn3/++eK281h6N9lkkzBz5swwevToXoGPPvoobLPNNuHpp58uCvOcOXPC5MmTFxG64YYbwmGHHVb8/Nhjjw1XXnnlQvvMnTu3KN7z588vjo/niefrvx188MHh5ptvLn48Y8aMsO+++y60S/zZ/vvv39Yc24lTaWtHK+19ZZ12fkZPgACBXAQU9FySNk8CBLpOYCgFPZbpq666qrCYNWtW2HzzzRdxefzxx8PUqVOblu/4i/XWW68o3fGKebwKPmbMmEXOE6/Sx6vwzcp3vKV9/PjxId5yv9NOO4UHHnigYUZf/epXw4MPPlhcjX/ttdfCqquuWlmWSltllLU/kaxrH5EBEiBAgEAIQUG3DAgQIJCoQLsFfcGCBcUt4q+//npxe3ws2M22+PvnnnuuKNCxgPe9vT5ehf/CF75QHHr00Uf3Fv7+54oFPH5/PG4HHnhguPXWWxfaJT6g7qijjip+dtttt/VeKe9/nvi7eHzc4vfi4/fnq9qUtqok638eWdc/IyMkQIAAAQXdGiBAgECyAu0W9BdffLH4rnncYjG++uqrm849/j4W6LjF43pupY9/7nt7+/e///1wwAEHND1PLPKx0MfvzL/yyisL7df39vY33nij6ZXx+Lv4ZPi4xWNuuummyjJT2iqjrP2JZF37iAyQAAECBFxBtwYIECCQrkC7BT0+dG333XcvJnzppZeGk046qenk4+/jA9vidu+99xZPa+/Zvv71r4dLLrmk+GN8oNyGG27Y9Dx77LFHuOuuu4or8O+//35YZplleveN34GPD35bbrnlwnvvvTdgEHGf+H32+P37+Jq5qjalrSrJ+p9H1vXPyAgJECBAwBV0a4AAAQLJCrRb0OMV82OOOaaY7+233x722WefpnO/4447eh/YFo/ruRU9HhCvmP/gBz8ojo1PXx83blzT8xx//PG9D5CL70nvuTU+HhC/S/6HP/yheGjdb37zmwFz2GCDDcJvf/vb4ph4Rb3VLZaygbZ4rp7Xt8Vb+at+Snyr47Tf8Aso6MNv7BMIECBAoLyA76CXN3QGAgQIdESg3YJ+8cUXF69Ui9v9998f4sPXmm3x9z1Xzb/1rW+FU045pXfXXXfdNdx3333Fnz/++OOw9NJLNz3P6aefXry6LW7xyfDxye49W7yaHp8av9lmm4X4YLqBtrhPvHI+duzY4kp8q9tA747vfw4FvVXVNPdT0NPMzagJECCQm4CCnlvi5kuAQNcItFvQzz333HDWWWcV8//JT34SvvKVrzS1+OlPfxq233774vfxuDPPPLN33/jz+Pu4xfehx6erN9vi58Xj4/bII4+ErbbaqnfXz3zmM8UT3L/85S8Xr3sbaNt6662L4+Mx8RVxrW4KeqtS3b+fgt79GZshAQIEukFAQe+GFM2BAIEsBdot6DleQXeLe5Z/NRpOWkG3FggQIEAgBQEFPYWUjJEAAQINBNot6Dl+B32whaO0DSbUPb+XdfdkaSYECBDoZgEFvZvTNTcCBLpaoN2C7inuiy4Hpa2r/4osNDlZ55O1mRIgQCBlAQU95fSMnQCBrAXaLejeg66g5/wXRkHPOX1zJ0CAQDoCCno6WRkpAQIEFhJot6AvWLCgeI3Y66+/HtZZZ50wZ86cpqLrrrtuiK9FGz9+fIhPN+/7sLXnn3++93VpRx99dLjqqqsanufNN98Mq622WvG7Aw88MNx6660L7Xfttdf2vr7ttttuC/vvv3/D88TfxePjds0114QjjzyyspWgtFVGWfsTybr2ERkgAQIECATvQbcICBAgkKxAuwU9TvTYY4/tLdSzZs0Km2+++SLzj688mzp1avHzuP+VV165yD7rrbdeUfBXXHHFosCPGTNmkX2mT58epk2bVvx8xowZve9V79kxFvj4DwDxSe477bRTeOCBBxpmEV8H9+CDDxZPi3/ttdeKd6FXtSltVUnW/zyyrn9GRkiAAAECCro1QIAAgWQFhlLQ49XvWK7j69E22WST4vVmo0eP7jWI7zWPrzSL7ywfNWpUeOaZZ8LnP//5RYxuuOGGcNhhhxU/P+6448IVV1yx0D4vvPBC+NKXvhTmz58fJk+eXJT5eL7+28EHHxxuvvnm4se333572GeffRbaJf5sv/32K352yCGHhBtvvLHSvJS2SjlrfTJZ1zoegyNAgACBvwu4xd1SIECAQCICjz76aJg7d27vaOfNmxdOPfXU4s9bbrllOPzwwxeayaGHHtpwZvGqdry6HbeNNtoonH766WHSpEkhluoLL7wwzJ49u/hd3O/8889veI5Y8LfZZpvw2GOPFb/fe++9wxFHHBFWWGGF8OSTTxbvPn/rrbeKq97x4XQ777xzw/PEq+8bb7xxePvtt4sCf8opp4Tddtut2Dced8kllxTvPV955ZXDL3/5y+IW/So3pa1KzXqfS9b1zsfoCBAgQOBvAgq6lUCAAIFEBGLhvummm1oebfzOeaMt3lIey3S8Ct5si1fH43fEY8FutsV/INhll13CU0891XCXpZZaqriy3v8fDvrv/MQTT4Q999wzxFveG23xlvY777wzbLbZZi3PvdUdlbZWpdLfT9bpZ2gGBAgQyEFAQc8hZXMkQKArBKoq6D0Y9913X1HCY8GOZXvcuHFhypQpxYPbml3x7g8Zr25fd911xQPg4m3sH374YVh99dXD9ttvH0488cSw/vrrt2QfP/+yyy4rini8dT9ua621Vthjjz3CSSedFFZaaaWWztPuTkpbu2Lp7i/rdLMzcgIECOQkoKDnlLa5EiBAgMBCAkpbPgtC1vlkbaYECBBIWUBBTzk9YydAgACBUgJKWym+pA6WdVJxGSwBAgSyFVDQs43exAkQIEBAactnDcg6n6zNlAABAikLKOgpp2fsBAgQIFBKQGkrxZfUwbJOKi6DJUCAQLYCCnq20Zs4AQIECCht+awBWeeTtZkSIEAgZQEFPeX0jJ0AAQIESgkobaX4kjpY1knFZbAECBDIVkBBzzZ6EydAgAABpS2fNSDrfLI2UwIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIKC05bMGZJ1P1mZKgACBlAUU9JTTM3YCBAgQKCWgtJXiS+pgWScVl8ESIEAgWwEFPdvoTZwAAQIElLZ81oCs88naTAkQIJCygIKecnrGToAAAQKlBJS2UnxJHSzrpOIyWAIECGQroKBnG72JEyBAgIDSls8akHU+WZspAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQJKWz5rQNb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgobfmsAVnnk7WZEiBAIGUBBT3l9IydAAECBEoJKG2l+JI6WNZJxWWwBAgQyFZAQc82ehMnQIAAAaUtnzUg63yyNlMCBAikLKCgp5yesRMgQIBAKQGlrRRfUgfLOqm4DJYAAQLZCijo2UZv4gQIECCgtOWzBmSdT9ZmSoAAgZQFFPSU0zN2AgQIECgloLSV4kvqYFknFZfBEiBAIFsBBT3b6E2cAAECBJS2fNaArPPJ2kwJECCQsoCCnnJ6xk6AAAECpQSUtlJ8SR0s66TiMlgCBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUgNJWii+pg2WdVFwGS4AAgWwFFPRsozdxAgQIEFDa8lkDss4nazMlQIBAygIKesrpGTsBAgQIlBJQ2krxJXWwrJOKy2AJECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCkBpa0UX1IHyzqpuAyWAAEC2Qoo6NlGb+IECBAgoLTlswZknU/WZkqAAIGUBRT0lNMzdgIECBAoJaC0leJL6mBZJxWXwRIgQCBbAQU92+hNnAABAgSUtnzWgKzzydpMCRAgkLKAgp5yesZOgAABAqUElLZSfEkdLOuk4jJYAgQIZCugoGcbvYkTIECAgNKWzxqQdT5ZmykBAgRSFlDQU07P2AkQIECglIDSVoovqYNlnVRcBkuAAIFsBRT0bKM3cQIECBBQ2vJZA7LOJ2szJUCAQMoCCnrK6Rk7AQIECJQSUNpK8SV1sKyTistgCRAgkK2Agp5t9CZOgAABAkpbPmtA1vlkbaYECBBIWUBBTzk9YydAgACBUgJKWym+pA6WdVJxGSwBAgSyFVDQs43exAkQIEBAactnDcg6n6zNlAABAikLKOgpp2fsBAgQIFBKQGkrxZfUwbJOKi6DJUCAQLYCCnq20Zs4AQIECCht+awBWeeTtZkSIEAgZQEFPeX0jJ0AAQIESgkobaX4kjpY1knFZbAECBDIVkBBzzZ6EydAgAABpS2fNSDrfLI2UwIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIKC05bMGZJ1P1mZKgACBlAUU9JTTM3YCBAgQKCWgtJXiS+pgWScVl8ESIEAgWwEFPdvoTZwAAQIElLZ81oCs88naTAkQIJCygIKecnrGToAAAQKlBJS2UnxJHSzrpOIyWAIECGQroKBnG72JEyBAgIDSls8akHU+WZspAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQJKWz5rQNb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgMtbQttthiYcGCBQATEhhq1glN0VAJECBAoAsEFPQuCNEUCBAgQGBoAkMtbQr60Lw7edRQs+7kmH02AQIECOQnoKDnl7kZEyBAgMDfBYartCnw9Vtiw5V1/WZqRAQIECCQsoCCnnJ6xk6AAAECpQSUtlJ8SR0s66TiMlgCBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUgNJWii+pg2WdVFwGS4AAgWwFFPRsozdxAgQIEFDa8lkDss4nazMlQIBAygIKesrpGTsBAgQIlBJQ2krxJXWwrJOKy2AJECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCkBpa0UX1IHyzqpuAyWAAEC2Qoo6NlGb+IECBAgoLTlswZknU/WZkqAAIGUBRT0lNMzdgIECBAoJaC0leJL6mBZJxWXwRIgQCBbAQU92+hNnACB1ATeeuut8OSTTxb/e+qpp4r/vfPOO8U0DjnkkHDjjTcOOqW4z7/8y78Mul/c4d///d/DoYceOuC+H330UbjiiivC7bffHl544YXwf//3f2HixIlh1113DSeccEL43Oc+19JnvfLKK+Hyyy8P9957b3j11VfDUkstFSZNmhT222+/cNxxx4UxY8a0dJ52d1La2hVLd39Zp5udkRMgQCAnAQU9p7TNlQCBpAUWW2yxpuPvREGfO3du2GWXXcLvfve7huP6h3/4h3DLLbeE3XbbbUD3u+++Oxx00EFh/vz5Dfdbe+21i+I+efLkyvMrU9piHgsWLKh8TE44PAJlsh6eETkrAQIECBBYVEBBtyoIECCQiEDfgr7GGmuEddZZJ/zoRz8qRj+Ugv7ggw+G1VdfvensJ0yYEJZffvmGv3///ffDJptsEp5//vni90cccUQ44IADwujRo8PPfvazcMEFF4QPPviguPL92GOPhQ033LDheWbPnh223HLL8PHHH4exY8eGadOmhe22267482233Rauu+664rhY0p9++umw7LLLVpqW0lYpZ61PJutax2NwBAgQIPB3AQXdUiBAgEAiAmeffXaYMmVK8b/Pfvaz4eWXXw5rrbXWkAv6Sy+9FNZcc80hzf6ss84K5557bnHsRRddFE499dSFzvPzn/88bLPNNuGTTz4p/u9DDz3U8HO23nrr8Mgjj4RRo0aFmTNnhqlTpy6038UXXxxOOzFmiMcAACAASURBVO204mdx/uecc86QxtvsIKWtUs5an0zWtY7H4AgQIEBAQbcGCBAgkLZApwr6X/7yl7DyyiuHP/7xj2HdddcNv/nNb8Liiy++CObRRx8drrnmmuLn8Xvz8R8W+m7xZ5tttlnxo6OOOipcffXVi5zj008/DRtssEGYM2dOcTU/fg9/iSWWqCy4MqXNLe6VxTAiJyqT9YgM0IcQIECAAIEQgivolgEBAgQSFehUQY+31e+0006F2vTp08Ppp5/eUPDxxx/vvSIeb10///zzF9rvjDPOKG6Fj1vct6es9z9Z/Ix4fNzibfk77rhjZYkpbZVR1v5Esq59RAZIgAABAgq6NUCAAIF0BTpV0Pve3j5r1qyw+eabN0SMt7cvt9xyIT7pPd7K/vDDDy+0X8/t7csss0x47733itvcG23xM7bYYoviV/Gzv/nNb1YWmtJWGWXtTyTr2kdkgAQIECCgoFsDBAgQSFegbEHfdtttw3PPPRfmzZsX4hPX41PSd9hhh3DMMceE8ePHN4XZZ599wg9/+MPi9++++27TB8nF33/xi18M//3f/13cEh9vT++7xZ/Fz477/OpXv2r6efEzVlxxxeL3++67b5gxY0ZloSltlVHW/kSyrn1EBkiAAAECCro1QIAAgXQFyhb0ZjNfeumlw3e+853ie+GNtnjF/Iknngjxynd8UvtAW3zFWnxFWtz+9Kc/Fe837/nv+MT3uMV3pt9zzz0Dnic+4f3DDz8srtbHK+qtbrGUDbS98cYbYdNNNy12ie9fj0+ut3WngILenbmaFQECBLpNwHfQuy1R8yFAIBuBoRb0+PT1vfbaq/h++MSJEwuvF198sbgqfscdd/S+2zs+4O3II49cxHP99dcPzzzzTPEk+TfffHNA7/3337/3ine8Wr7SSisV+7/99tthlVVWKf477hNfqTbQFj8rXoGPD4z79a9/3XLGA707vv9J2i3oHhLXcgy12FFBr0UMBkGAAAECgwgo6JYIAQIEEhUYSkGPT16Pt7M3K67xSnYs7/FJ7fEd5i+88EJYddVVFxKaNGlSUehjuf+f//mfAfUOPvjgcPPNNxf79C3A8b/ju9zj9rWvfS1873vfG/A8cd94TPzsuXPntpzYcBb0lgdhx1oIKOi1iMEgCBAgQEBBtwYIECDQnQJDKeitSJx33nnh3/7t34pd439/4xvfWOiwlK6gu8W9lcTz2EdBzyNnsyRAgEDqAq6gp56g8RMgkK3AcBX0eCt5vGq+YMGC8E//9E8hvlat75bSd9AHWxxK22BC3fN7WXdPlmZCgACBbhZQ0Ls5XXMjQKCrBYaroEe0niesr7feeuG3v/3tQo5VPcV93Lhx4Z133vEU965epfWZnIJenyyMhAABAgSaCyjoVgcBAgQSFRjOgh4f4BYf5NaooLfzHvTll1++ePq696Anusi6aNgKeheFaSoECBDoYgEFvYvDNTUCBLpbYLgKeizm8anp8Rb3+F70H//4xwtBxlved9ppp+Jn06dPD6effnpD6Mcff7x4Unzcpk2bFs4///yF9jvjjDPCBRdcUPws7rvZZps1PE/8jHh83B588MGw4447Vhas0lYZZe1PJOvaR2SABAgQIOA96NYAAQIE0hUYroL+//7f/wtnnnlmARNfydbz3z1Sf/7zn4tXpMUnwq+77rrFLfCNnpZ+9NFHh/iqtrg9+eSTYcqUKQthx5/1lPL4zvWrr756kTA+/fTT4tVqc+bMCfFqfPx+/BJLLFFZaEpbZZS1P5Gsax+RARIgQICAgm4NECBAIF2Bdgt63P/dd98NG220UdNJx9es7b333iGW8NGjR4ff/e53Yfz48Yvs3/c294suuiiceuqpC+0za9as4rb2Tz75JGyzzTbhoYceaviZcZ9HHnkkjBo1KsycObP3invPzhdffHE47bTTij+effbZ4Zxzzqk0MKWtUs5an0zWtY7H4AgQIEDg7wJucbcUCBAgkIjAo48+utA7wOfNm9dbjLfccstw+OGHLzSTQw89dKE/x5K83XbbFSV49913Lx7OFq+Exy2+1/yOO+4o/hdvbY/blVdeGY499tiGOu+//37YZJNNwvPPP1/8/sgjjwwHHHBAUep/9rOfFbezf/DBB8Wff/7zn4cNN9yw4Xlmz54d4tg//vjjMHbs2BBve49jjH++7bbbwrXXXlsct/baa4enn346LLvsspWmNZylLd5V0GNZ6aCdbEgCw5n1kAbkIAIECBAg0EBAQbcsCBAgkIhALNw33XRTy6PtXw57CvpgJxgzZky49NJLi9I90DZ37tywyy67FFfZG23/8A//EG655Zaw2267DXieu+++Oxx00EFh/vz5DfeL5fzee+8NkydPHmzobf9eaWubLNkDZJ1sdAZOgACBrAQU9KziNlkCBFIWKFvQ41Xvu+66K8Tbz+PV6DfeeCPEq/DxNvQVVlghrL/++mH77bcvrsT3XFkfzCs+oT1eab/99tuLq/vx1viJEycWxf3EE08Mn/vc5wY7RfH7V155JVx22WVFEY9FaskllywK+b777huOP/74EP/RYDg2pW04VOt5TlnXMxejIkCAAIGFBRR0K4IAAQIEshVQ2vKJXtb5ZG2mBAgQSFlAQU85PWMnQIAAgVICSlspvqQOlnVScRksAQIEshVQ0LON3sQJECBAQGnLZw3IOp+szZQAAQIpCyjoKadn7AQIECBQSkBpK8WX1MGyTiougyVAgEC2Agp6ttGbOAECBAgobfmsAVnnk7WZEiBAIGUBBT3l9IydAAECBEoJKG2t8XXDO91l3VrW9iJAgACBzgoo6J319+kECBAg0EEBpa01fAW9NSd7ESBAgACBsgIKellBxxMgQIBAsgKdKOjdUHZTDLwTWafoZMwECBAg0FkBBb2z/j6dAAECBDookHtpi/9YELcFCxZ0MIWR+ejcsx4ZZZ9CgAABAmUFFPSygo4nQIAAgWQFlLZko2t74LJum8wBBAgQINABAQW9A+g+kgABAgTqIaC01SOHkRiFrEdC2WcQIECAQFkBBb2soOMJECBAIFkBpS3Z6NoeuKzbJnMAAQIECHRAQEHvALqPJECAAIF6CIx0afOAuM7lPtJZd26mPpkAAQIEUhZQ0FNOz9gJECBAoJSA0laKL6mDZZ1UXAZLgACBbAUU9GyjN3ECBAgQUNryWQOyzidrMyVAgEDKAgp6yukZOwECBAiUElDaSvEldbCsk4rLYAkQIJCtgIKebfQmToAAAQIjXdp8B71za26ks+7cTH0yAQIECKQsoKCnnJ6xEyBAgEApgZEubQp6qbhKHTzSWZcarIMJECBAIFsBBT3b6E2cAAECBJS2fNaArPPJ2kwJECCQsoCCnnJ6xk6AAAECpQSqLG2tXB1vZZ9SE3JwU4Eqs8ZMgAABAgSGS0BBHy5Z5yVAgACB2guULW0Kd+0j7h1g2azTmamREiBAgEDKAgp6yukZOwECBAiUEqiitCnppSIYsYOryHrEBuuDCBAgQCBbAQU92+hNnAABAgSUtnzWgKzzydpMCRAgkLKAgp5yesZOgAABAqUEypY2V89L8Y/owWWzHtHB+jACBAgQyFZAQc82ehMnQIAAgbKlTUFPZw2VzTqdmRopAQIECKQsoKCnnJ6xEyBAgEApAaWtFF9SB8s6qbgMlgABAtkKKOjZRm/iBAgQIFBVaXMlvf5rqaqs6z9TIyRAgACBlAUU9JTTM3YCBAgQKCVQtrQp5qX4R/TgslmP6GB9GAECBAhkK6CgZxu9iRMgQICA0pbPGpB1PlmbKQECBFIWUNBTTs/YCRAgQKCUQFWlrdUr6a3uV2pSDm4oUFXWeAkQIECAwHAKKOjDqevcBAgQIFBrgZEubQp655bDSGfduZn6ZAIECBBIWUBBTzk9YydAgACBUgIjXdoU9FJxlTp4pLMuNVgHEyBAgEC2Agp6ttGbOAECBAiMdGmLBb1nW7BgQVDYR24NjnTWIzczn0SAAAEC3SSgoHdTmuZCgAABAm0JjHRp61vQ40CV9LbiKrXzSGddarAOJkCAAIFsBRT0bKM3cQIECBBQ2vJZA7LOJ2szJUCAQMoCCnrK6Rk7AQIECJQSGOnS5pb2UnGVOniksy41WAcTIECAQLYCCnq20Zs4AQIECCht+awBWeeTtZkSIEAgZQEFPeX0jJ0AAQIESgmMdGlzBb1UXKUOHumsSw3WwQQIECCQrYCCnm30Jk6AAAECSls+a0DW+WRtpgQIEEhZQEFPOT1jJ0CAAIFSAkpbKb6kDpZ1UnEZLAECBLIVUNCzjd7ECRAgQEBpy2cNyDqfrM2UAAECKQso6CmnZ+wECBAgUEpAaSvFl9TBsk4qLoMlQIBAtgIKerbRmzgBAgQIKG35rAFZ55O1mRIgQCBlAQU95fSMnQABAgRKCShtpfiSOljWScVlsAQIEMhWQEHPNnoTJ0CAAAGlLZ81IOt8sjZTAgQIpCygoKecnrETIECAQCmBkS5t8T3ocVuwYEGpcTu4fYGRzrr9ETqCAAECBAiEoKBbBQQIECCQrYDSlk/0ss4nazMlQIBAygIKesrpGTsBAgQIlBKoorTFq+I5XhFPbd5VZF1qsTmYAAECBAi0IKCgt4BkFwIECBDoToEqSlsKRbXn1vq+Kbb6jwp9j+17TArz7jvfKrLuzr8FZkWAAAECdRJQ0OuUhrEQIECAwIgKdKK0lS22jcp2/+Ld6LvufX/WrHT3PU/POPv/3559ys5jRIMOIXQi65Geo88jQIAAgfQFFPT0MzQDAgQIEBiiQB1K22BFt1FR7pluvKLdv3j3/Kz/FfJWSnn/gt6Itf9nDpF+xA+rQ9YjPmkfSIAAAQLJCSjoyUVmwAQIECBQlUCKpa3/FfS+hbxvmY9GA93G3qjYD+Y62D8mDHZ8J3+fYtad9PLZBAgQINAZAQW9M+4+lQABAgRqIJBiaRvsFve+rD0FvdFt6s1uXW8USze8Hi7FrGvwV8QQCBAgQGCEBRT0EQb3cQQIECBQH4EUS1urBX2wq+eNboUf7Lvr9Umu/ZGkmHX7s3QEAQIECKQuoKCnnqDxEyBAgMCQBVItbf2/Tz7YU9obff+82VX1iNnqE96HDN+BA1PNugNUPpIAAQIEOiigoHcQ30cTIECAQGcFOlHayn6Pu9l30PtKln1AXGdTGZ5P70TWwzMTZyVAgACBbhZQ0Ls5XXMjQIAAgQEFUi1tg11B78Yr4GWXcqpZl5234wkQIEAgLQEFPa28jJYAAQIEKhQYrtJW9ir5YFNs9LT2dl+jNthndNvvhyvrbnMyHwIECBDorICC3ll/n06AAAECHRSoorQNdxlvxNOsoDe7ct4NT2Evu0yqyLrsGBxPgAABAgQGE1DQBxPyewIECBDoWoHUS1uzd5l34h8N6r5IUs+67r7GR4AAAQLVCCjo1Tg6CwECBAgkKFBFaetkGW5W0GMUrpovvCCryDrBJW7IBAgQIJCYgIKeWGCGS4AAAQLVCaRc2ho9KK7nFvdGr1CrTi3NM6WcdZriRk2AAAECQxFQ0Iei5hgCBAgQ6AqBKkpbu1fQ292/P3Sjd5737NO3oPf9mQfIhVBF1l2x6E2CAAECBGotoKDXOh6DI0CAAIHhFKiitLVbuNvdv9H82ynpzUr7cLrW8dxVZF3HeRkTAQIECHSXgILeXXmaDQECBAi0IVBFaWu3cLe7f7PpNCvp/Qt5/HP/fXN8T3oVWbextOxKgAABAgSGJKCgD4nNQQQIECDQDQKdKG1VFPRGhXuwh8Llfpt7J7Luhr8j5kCAAAECIyugoI+st08jQIAAgRoJVFHa2i3c7e5fxRX0eA4F/fdh4sSJBeerr74aJkyYUKOVaCgECBAgQOBvAgq6lUCAAAEC2QqkXND7l+6eEHtuae9/G/tgV9i7fRFUkXW3G5kfAQIECHReQEHvfAZGQIAAAQIdEqiitLV7Rbzd/ZvRtPMd9L5lPsfvn8f5V5F1h5apjyVAgACBjAQU9IzCNlUCBAgQWFigitLW7pVpBb0zq7CKrDszcp9KgAABAjkJKOg5pW2uBAgQILCQQBWlrd2CXkUErb5mre/t7p0YZxVzreocVWRd1VichwABAgQINBNQ0K0NAgQIEMhWoIrSVtUV8XZCaFS2+4+j0Z/jZ7jF3UPi2llr9iVAgACBkRVQ0EfW26cRIECAQI0EUi7ojR4C1/dnzQp6riW9iqxrtHQNhQABAgS6VEBB79JgTYsAAQIEBheoorR18gp6/xm2WtBzLOlVZD34irIHAQIECBAoJ6Cgl/NzNAECBAgkLFBFaevEd7sH+g56T/nu+YeDvv+3b1S53epeRdYJL3VDJ0CAAIFEBBT0RIIyTAIECBCoXqCK0langt6odDcq6LmV87hyqsi6+hXojAQIECBAYGEBBd2KIECAAIFsBaoobUMp6GVvi+/7mf2vpveU74GuoCvor4YJEyZku+5NnAABAgTqK6Cg1zcbIyNAgACBYRbotoLe6MFxkbDndWs9nAq6gj7Mf7WcngABAgSGKKCgDxHOYQQIECCQvkCqBT3KN/oeeqOHxLnF/W/rtIqs01/xZkCAAAECdRdQ0OuekPERIECAwLAJVFHaOnGLezsFvQevb6F3Bd0V9GH7S+XEBAgQIFBKQEEvxedgAgQIEEhZoFMFvaxZs6e4t3qLe/z83Ep6FVmXzc3xBAgQIEBgMAEFfTAhvydAgACBrhWoorQN5Qp6FaCt3rruNWt/064i6ypycw4CBAgQIDCQgIJufRAgQIBAtgJVlLZ2C3q7+zcLp/+T4Bs9Gb7vz9zi/vswceLEgvPVV93inu1fehMnQIBAzQUU9JoHZHgECBAgMHwCnSroVdxePthD4nrUWr3SPnzK9ThzFVnXYyZGQYAAAQLdLKCgd3O65kaAAAECAwpUUdqGckW87HvQ+5fv+Odm4+hf0Nv5x4GqxlmHZVhF1nWYhzEQIECAQHcLKOjdna/ZESBAgMAAAlWUtnYLelWlt9kV9L7jafUqew6LpIqsc3AyRwIECBDorICC3ll/n06AAAECHRSoorS1W9B7rna3cyW7P1Gzp7j33y9+Rpkr6I0+t8y4Oxi1h8R1Et9nEyBAgEDLAgp6y1R2JECAQGcFnn766XDfffeFRx99NDzzzDPh7bffDksssURYffXVw5ZbbhkOO+ywsNVWW7U8yPvvvz9ce+214amnnirOtfLKK4cpU6aEI488Muy8884tneeTTz4J119/fbjlllvCs88+Gz744INiPDvssEM44YQTwvrrr9/SeebNmxcuv/zycOedd4aXX365OGbNNdcMe+65ZzjxxBPDSiut1NJ52t2pEwV9KIW+0bz6Fu+eIh7361ugG11NT7Vgt5tt//2ryLrsGBxPgAABAgQGE1DQBxPyewIECNRAYOuttw6PPPLIoCM5+OCDw3XXXReWXHLJpvt++umnRQn/7ne/23Sfww8/PFxzzTVh8cUXb7pPLNW77LJLUfAbbUsttVS44oorQjzXQNsTTzxRFPE333yz4W6rrbZaUdw33XTTQeff7g5VlLahFO4qbnNv5Snu0aPKK+jt+tZp/yqyrtN8jIUAAQIEulNAQe/OXM2KAIEuE5g8eXJ44YUXiqvT++67b/jyl78c1lhjjfDXv/41zJo1K1xyySXhtddeK2Z94IEHhltvvbWpwLRp08L06dOL32+00UbhtNNOC5MmTSrOf9FFF4XZs2cXv4v7nX/++Q3PEz932223La7mx22vvfYKRxxxRFhxxRVDLNznnXdeeOutt4qCf8899zS9Ih9fd7XxxhsXV/BHjRoVTj755LDbbrsV54zHffvb3w7xKv0qq6wSfvGLX4QJEyZUmmwVpa3TBX2wp7QP9vtKQWt8siqyrvH0DI0AAQIEukRAQe+SIE2DAIHuFoilNV4d33vvvcNnPvOZRSYbr2bH29yff/754ncPP/xwiFfd+2/x9/G281h6N9lkkzBz5swwevTo3t0++uijsM0224R4O30szHPmzAnxHwf6bzfccENxS33cjj322HDllVcutMvcuXOL4j1//vzi+HieeL7+W5zTzTffXPx4xowZxT8+9N3iz/bff//iR4cccki48cYbKw26itLWyYIeMfrf0t7/FnYF/W9LpoqsK118TkaAAAECBBoIKOiWBQECBLpEIF5x3n333YvZ/Ou//mvxne7+WyzTV111VfHjeOV98803X2Sfxx9/PEydOrX4eaPyHX++3nrrFaU7XjGPV8HHjBmzyHniVfp4FT5ujcp3vKV9/PjxId5yv9NOO4UHHnigYRJf/epXw4MPPlhcjY93Cay66qqVJVZVaWv3lvV292804Ua3uMf9mhX2vg+WG+x76FWMr7KQKjpRVVlXNBynIUCAAAECDQUUdAuDAAECXSLw4YcfhrFjxxazid8Nv/feexeaWSxl8Rbx119/PayzzjpFwW62xd8/99xzRYGOBbxvuYtX4b/whS8Uhx599NG9hb//uWIBj98fj1uj2+7jA+qOOuqo4ve33XZb75Xy/ueJv4vHxy1+Lz5+f76qrarS1m6hbXf/RvNtdGW8Z79GJb2Vgj6UuwGqymK4z1NV1sM9TucnQIAAgbwFFPS88zd7AgS6SOB///d/e592Hq+k33XXXQvN7sUXXyy+ax63WIyvvvrqprOPv48FOm7xuLXWWqt33763t3//+98PBxxwQNPzxCIfC338vvwrr7yy0H59b29/4403ml4Zj7+L372PWzzmpptuqiy1KkrbUEptVQW9byFv9dVrjUp8ZaA1PlEVWdd4eoZGgAABAl0ioKB3SZCmQYAAgf/8z/8sHtYWt/jgtwsvvHAhlL63wF966aXhpJNOaooWfx8f2Ba3eCU+XpHv2b7+9a8XD6WLW3yg3IYbbtj0PHvssUfxDwWxPL7//vthmWWW6d03fgc+PvhtueWWC++9996AAcZ94vfZ42vgnnzyycrCrqq0tVu4292/0YT7XxHvX9D7vnqtGdhgt7pXBl2DE1WVdQ2mYggECBAg0MUCCnoXh2tqBAjkIxC/xx2/N95TXuND3uJD2vpu8Yr5McccU/zo9ttvD/vss09ToDvuuKP3gW3xuJ5b0eMB8Yr5D37wg+LY+PT1cePGNT3P8ccf3/sAufie9J5b4+MB8bvkf/jDH4qH1v3mN78ZMKwNNtgg/Pa3vy2OiVfUW91iKRtoi+fqeX1bvJV/qE+Jb7dwD+Wqe/95NLti3qh0N7sdXkFvdSXZjwABAgQIjIyAgj4yzj6FAAECwyoQr2jHK9txi1fRf/jDHy7yeRdffHFxZT1u999/f4gPX2u2xd/3XDX/1re+FU455ZTeXXfddddw3333FX/++OOPw9JLL930PKeffnrx6ra49f9Hg3g1PT41frPNNgvxwXQDbXGf+I8P8Tv28Up8q1s7t32XKeg942l0FbvRWNst9M3O0ejnPaV7sCvs8VgFvdWVZD8CBAgQIDAyAgr6yDj7FAIECAybQHyl2g477ND7vvBf//rXxXvD+2/nnntuOOuss4of/+QnPwlf+cpXmo7ppz/9adh+++2L38fjzjzzzN5948/j7+MW34cen67ebIufF4+P2yOPPBK22mqr3l3j6+Lilf/4Tvf4ureBtvjKuHh8PCa+Iq7VbaQKertXxIezoDezaXTLu4Le6kqyHwECBAgQGBkBBX1knH0KAQIEhkUg3vYdC+67775bXMmOryNr9P7z+OE5XkGv8y3uZctxO//4MFBpH5aFWcOT+g56DUMxJAIECBBYREBBtygIECCQqMBLL71UXJGOr02LV5bjbe3xoWzNthy/gz5YtFWVtnaviLe7f6N5DFTQW3lAXDznQN9XH8wutd9XlXVq8zZeAgQIEEhLQEFPKy+jJUCAQCEQS3m8ch5fgRaL2o033li8gmygzVPcF9WpqrS1W7jb3b//yAe7et7/Peg9ZbzV78h341+zqrLuRhtzIkCAAIH6CCjo9cnCSAgQINCSwLx588I222wTnnnmmWL/K664Ihx33HGDHus96MNT0Nv9/nkcRdmC3nOOgULvX9J9B/33YeLEiQVZFQ8EHPQvnB0IECBAgMAQBBT0IaA5hAABAp0S+OMf/1g83O2Xv/xlMYTp06eH+KT0VrZY0OJrxOLV93XWWSfMmTOn6WHrrrtuiK9FGz9+fFFm+l55ff7553tfl3b00UeHq666quF53nzzzbDaaqsVvzvwwAPDrbfeutB+1157be/r22677baw//77NzxP/F08Pm7XXHNNOPLII1uZbkv7VHVVtd2SXteCXsW4WoLvwE5VZd2BoftIAgQIEMhIQEHPKGxTJUAgbYH4SrIdd9wxPPbYY8VEvvGNb4TzzjuvrUkde+yxvYV61qxZYfPNN1/k+PjKs/hO9bjF/a+88spF9llvvfWKgr/iiisWBX7MmDGL7BP/8WDatGnFz2fMmNH7XvWeHWOBj/8AEJ/kvtNOO4UHHnig4Vzi6+Diw+/i0+Jfe+214l3oVW1VlbZ2i227+zea72DfQY/H9P2Hg0b7+w56VSvJeQgQIECAQDUCCno1js5CgACBYRX485//HHbffffwox/9qPicE088MXznO99p+zPj1e9YruPr0TbZZJPi9WajR4/uPU98r3l8Cnx8Z/moUaOK2+g///nPL/I5N9xwQzjssMOKn8fb6+Nt9n23F154IXzpS18K8+fPD5MnTy7KfDxf/y1+b/7mm28ufnz77beHffbZZ6Fd4s/222+/4meHHHJI8V37KrfUC3o7D4NT0N3iXuXfHeciQIAAgeERUNCHx9VZCRAgUKnA3nvvHf7jP/6jOGe8xT2W84GuoC655JJh7bXXbjiGeFU7Xt2O20YbbVTcIj9p0qQQS/WFF14YZs+eXfwu7nf++ec3fHep1QAAIABJREFUPEcs+PF78D1X8+P4jjjiiLDCCiuEJ598snj3+VtvvVVc9Y4Pp9t5550bnidefd94443D22+/XRT4U045Jey2227FvvG4Sy65pHjv+corr1zc1h9v0a9yq7Kgx3G1+uq04byC3sp3z9sZa5XenTxXVVl3cg4+mwABAgS6X0BB7/6MzZAAgS4QGOyp3f2n+LnPfS68/PLLDWcebymPZTpeBW+2xavj8TvisWA32+LD6nbZZZfw1FNPNdxlqaWWKq6sH3744QMm8MQTT4Q999wzxFveG23xlvY777wzbLbZZpUnWVVpq+t30Fu5Db4vahX/cFB5SBWdsKqsKxqO0xAgQIAAgYYCCrqFQYAAgQQEqizoPdO97777ihIeC3Ys2+PGjQtTpkwpHtzW7Ip3f6p4dfu6664rHgAXb2P/8MMPw+qrrx6233774jb89ddfvyXd+PmXXXZZUcR7/mFhrbXWKt7rftJJJ4WVVlqppfO0u1NVpW0oBT2OtdUr7o3m1e6aaHSOMp/frnWn968q607Pw+cTIECAQHcLKOjdna/ZESBAgMAAAlWVtnYLehxS2avVfY8faln3kDh/PQgQIECAQL0EFPR65WE0BAgQIDCCAp0s6GWn2W5B7/9AuZyunkfrqrIum5vjCRAgQIDAQAIKuvVBgAABAtkKVFXaOn0FveeKfLtB5lTSq8q6XWP7EyBAgACBdgQU9Ha07EuAAAECXSVQVWlrt6CXvb29J4Se8/S9xb3vlfKBXsOWUzmPXlVl3VV/AUyGAAECBGonoKDXLhIDIkCAAIGREqiqtA2loMc5VlWS+xf0Zn7tjnOkchiJz6kq65EYq88gQIAAgXwFFPR8szdzAgQIZC9QVWkbSvGt4ip6o4fDDfTgt1aLfDcujKqy7kYbcyJAgACB+ggo6PXJwkgIECBAYIQFqiptQynoVUy12dPb+5f0ZrfCVzGGVM5RVdapzNc4CRAgQCBNAQU9zdyMmgABAgQqEKiqtA2loA/XFfQelr4lvX9Br+rW+goiGLFTVJX1iA3YBxEgQIBAlgIKepaxmzQBAgQIRIGqSlsnCvpAV88bXTHvm7iC/mqYMGGCvwQECBAgQKB2Agp67SIxIAIECBAYKYFOFfShFPpGJoMV8f6lvH+pz6moV5X1SK1Nn0OAAAECeQoo6HnmbtYECBAg0AVX0Pvfxt4/1J7XrPXsp6BPLIhefdUVdP8PAAECBAjUU0BBr2cuRkWAAAECIyBQ1VXVoVwRr+o76M3edT7QO9AjbU5Xz+N8q8p6BJaljyBAgACBjAUU9IzDN3UCBAjkLlBVaet0QY859h9D/1eq5Xz1XEHP/W+6+RMgQCAdAQU9nayMlAABAgQqFuimgt6/pPf9fnqjW9xdQfeQuIr/OjkdAQIECFQgoKBXgOgUBAgQIJCmQKcK+lCuuDcS7nub/GBPde9/vIKuoKf5t9aoCRAg0N0CCnp352t2BAgQIDCAQKcKes/V7rIludFt633Lf7PS3kNS9vNTWlxVZZ3SnI2VAAECBNITUNDTy8yICRAgQKAigapK21CuiA/nQ+Ka3dLeqLDnUtKryrqipec0BAgQIECgoYCCbmEQIECAQLYCVZW2dgt6o6vc/V+J1kooA10h7/8U99y/h15V1q3kYh8CBAgQIDBUAQV9qHKOI0CAAIHkBaosbe1eEW93/0bYzW5xH+iqeLv/mJB8yH+fQJVZd4uJeRAgQIBA/QQU9PplYkQECBAgMEICVZa2dgt3u/s3K+h9y3j/V6v1PabvU917fp7L7e1xvlVmPULL08cQIECAQIYCCnqGoZsyAQIECPxNoKrSNpSr0sNd0AfLOKdyXmXWg7n6PQECBAgQKCOgoJfRcywBAgQIJC1QVUGPCO0W7nb3bwTd6Kp4z359v9Oe+9VzBT3pv6YGT4AAgawEFPSs4jZZAgQIEOgrUFVBb/cKerv7N0utf8nv//C5vg+Ka/TfOV1Fryprf4MIECBAgMBwCijow6nr3AQIECBQa4EqS1u7V8Tb3X+gK+h9f9fovDm/Xq3Hpsqsa72oDY4AAQIEkhZQ0JOOz+AJECBAoIxAlaWt3cLd7v4DFfRmr1vr/6q1vufI6ep5nHeVWZdZc44lQIAAAQIDCSjo1gcBAgQIZCtQZWnrW7hbKd+t7DNYMP0/s//+jd593rOPgj5hMF6/J0CAAAECIy6goI84uQ8kQIAAgboIVF3Q47z6v/asWREuW9AbfY+92ZX0vt65FfOeuVeZdV3Wr3EQIECAQPcJKOjdl6kZESBAgECLAp0qbcPxkLj+70DvX9b73+6eW1HvVNYtLkW7ESBAgACBQkBBtxAIECBAIFuBTpa2slfQi/9PfLHFeq/YNyrkjYKt4nNTXDCdzDpFL2MmQIAAgc4IKOidcfepBAgQIFADgapLWztXxqsoygM9sd0D4hZeYFVnXYPlawgECBAg0IUCCnoXhmpKBAgQINCaQJWlracst1q8W91voJkM9B70Zse1O87WJOu/V5VZ13+2RkiAAAECqQoo6KkmZ9wECBAgUFqgytLWbvHtZEH//9q7DyBLqnp/4IeccxLYFRRECf5BSZJBchIko4ggUUCwkCD4CBYKK8gjPBAJIk8FVkC0iCI+omQUFQGBRUAecUFykPivXz9nnN29E/vOTJ97Pl31SnZuh3M+355X9Z3u2x1wvoPuKe61f4HsgAABAgTaLqCgt53UDgkQIEAgF4F2FvSuOQ+0eA90vb4se7vFvev29lYlvB3HzSXfnuMcjqxzdDBmAgQIEGi2gILe7HyMjgABAgSGUaCdpW2wxXew67di6O896F3bTP4+9NKunodDO7MexlPSrgkQIECgcAEFvfATwPQJECBQskA7S9vkrznrz3U4CnpfD4abfDyllfR2Zt1ftj4nQIAAAQJDFVDQhypnOwIECBDIXqCdpa0J30GPQHqOI/7dW2lX0H0HPftfYBMgQIBABwoo6B0YqikRIECAwMAERqug9/bO8sFeVW+1n95+1vM299LKeZwN7cx6YGeXtQgQIECAwOAFFPTBm9mCAAECBDpEoJ2lbSjlum5RbnVb/eQFvWdUfT08rkMi7XUa7cy6063MjwABAgRGT0BBHz17RyZAgACBURZoZ2kbrVvcg7Bn8e6roHdx1/3DwCjHNqTDtzPrIQ3ARgQIECBAYAACCvoAkKxCgAABAp0p0M7SNhoPiWuVStc4Wn333BX0sRXZE088kcaM8R30zvytNisCBAjkLaCg552f0RMgQIBADYF2F/TRLMCTX8HvWdS7iFr9rAZfVpu2M+usJm6wBAgQIJCVgIKeVVwGS4AAAQLtFGhnaRvMFfR2FeXJn9jeZdPXLeyD/a58O71Hc1/tzHo05+HYBAgQINDZAgp6Z+drdgQIECDQh0A7S9tgS3c7inKrffS33/4+79QTpp1Zd6qReREgQIDA6Aso6KOfgREQIECAwCgJtLO0DeYKerumq6APXLKdWQ/8qNYkQIAAAQKDE1DQB+dlbQIECBDoIIF2lrbBXpke7PrtYh+t47Zr/EPdTzuzHuoYbEeAAAECBPoTUND7E/I5AQIECHSsQDtLWxOK7+SvWOv6LnpvP+/YYFtMrJ1Zl+RmrgQIECAwsgIK+sh6OxoBAgQINEignaVtsAV9sN9ZHyhbb+9jH+x72gd6vFzWa2fWuczZOAkQIEAgPwEFPb/MjJgAAQIE2iTQztI22IIeUxjKNkOZes/jjNQxhzLO4dymnVkP5zjtmwABAgTKFlDQy87f7AkQIFC0QDtL23BdER9KQK1ev9b1jvbYX1+vYRvK8XLYpp1Z5zBfYyRAgACBPAUU9DxzM2oCBAgQaINAO0tbkwp60DRtPG2Iq9Yu2pl1rYHYmAABAgQI9CGgoDs9CBAgQKBYgXaWttEsxJM/BK6/QF1BfyKNGTOmPyafEyBAgACBERdQ0Eec3AEJECBAoCkC7S7ogym+vZXqnrei93Tqbd997afn9qV+97zLoJ1ZN+X8NQ4CBAgQ6DwBBb3zMjUjAgQIEBigQCeUtlbFu9VD4TzF/X/T2LFjqzPjiSdcQR/gr4jVCBAgQGCEBRT0EQZ3OAIECBBojsBwFfSRvFo9ksdqTnKDH8lwZT34kdiCAAECBAj0LqCgOzsIECBAoFiBTihtCvrATt9OyHpgM7UWAQIECOQsoKDnnJ6xEyBAgEAtgeEqbSNZmgdzrNF8kF2toNqw8XBl3Yah2QUBAgQIEOgWUNCdDAQIECBQrMBol7bBlOveQurtO+hd63c9XK4dx8r5RBntrHO2M3YCBAgQGDkBBX3krB2JAAECBBom0Amlra+C3vOJ8F3/PZgnzTcsrlrD6YSsawHYmAABAgSyEFDQs4jJIAkQIEBgOASGq7QN9Gr1QNfra+79PcV9ONxy3OdwZZ2jhTETIECAQHMFFPTmZmNkBAgQIDDMAp1Q2hT0gZ0knZD1wGZqLQIECBDIWUBBzzk9YydAgACBWgKjXdq6HtoWk+h563nd0t3qnec9jzX58WohZrLxaGedCZNhEiBAgMAoCyjooxyAwxMgQIDA6Al0UmlrVcB7Fv2epV1BfyKNGTNm9E48RyZAgAABAr0IKOhODQIECBAoVqDdBb3VleuRxu3ve+2lvmqt3VmPdK6OR4AAAQJlCCjoZeRslgQIECDQQmA4SttoFuCB/oGgvxLfiSfLcGTdiU7mRIAAAQKjK6Cgj66/oxMgQIDAKAoMV2mb/NbynlMcidec9VbAB1rgRzGSYTv0cGU9bAO2YwIECBAoUkBBLzJ2kyZAgACBEBiu0jZ5QR7Mw+CGK5meV/ZdQfcd9OE6z+yXAAECBOoJKOj1/GxNgAABAhkLDFdBHy2S3q6QT/4HAgVdQR+tc9RxCRAgQKBvAQXdGUKAAAECxQp0QkEf6CvZSizlPU/sTsi62F9UEydAgEBBAgp6QWGbKgECBAhMKlBSaVPQ/zeNHTu2OgGeeMIVdP+/gAABAgSaKaCgNzMXoyJAgACBERDolILe20Ppej6QbjSfLj8CUfZ7iE7Jut+JWoEAAQIEshZQ0LOOz+AJECBAoI5AJ5a2VkW8t4fU1bHLbdtOzDq3DIyXAAECBPoXUND7N7IGAQIECHSoQCeXtsmvqo/E692afJp0ctZNdjc2AgQIEBicgII+OC9rEyBAgEAHCZRS2lq99q20wl5K1h3062kqBAgQKFJAQS8ydpMmQIAAgRAY7dI2Wg9uG63jjuZZN9pZj+bcHZsAAQIE8hFQ0PPJykgJECBAoM0CnVra+ivg/X3eZuZG7K5Ts24ErkEQIECAQNsEFPS2UdoRAQIECOQm0O7SlkvxzWWc7Tyf2p11O8dmXwQIECBAoEtAQXcuECBAgECxAqNd2oarKA/XfnM+UUY765ztjJ0AAQIERk5AQR85a0ciQIAAgYYJdGpp6+ud56W+D71Ts27Yr5ThECBAgEBNAQW9JqDNCRAgQCBfgU4tbb2V8JKvrHdq1vn+9hk5AQIECLQSUNCdFwQIECBQrECnlra+CnqEXdor1mLOnZp1sb+8Jk6AAIEOFVDQOzRY0yJAgACB/gU6tbT1daW81KvonZp1/2e5NQgQIEAgJwEFPae0jJUAAQIE2iqgtLWVs9E7k3Wj4zE4AgQIEPiXgILuVCBAgACBYgVKLW0lXkUvNetif7lNnAABApkKKOiZBmfYBAgQIFBfQGmrb5jLHmSdS1LGSYAAgbIFFPSy8zd7AgQIFC1QYmnzmrWUnnjiiTRmzJiiz32TJ0CAAIFmCijozczFqAgQIEBgBARKKuhdt7Ur6Ar6CPxqOQQBAgQIDFFAQR8inM0IECAw0gJ33313uuqqq9Lvfve7dP/996eJEyem6aabLi200EJptdVWS7vttltaffXV+xzWeeedl3bdddcBDf3HP/5x2mWXXfpc94033kinnXZauvjii9MjjzyS/vnPf6axY8emTTfdNO2///5pkUUWGdCxHn/88XTqqaemK6+8srq6OcMMM6TFFlssbbfddmnfffdNM88884D2M9iVOrWgd5Xw3jy8Zs0V9MH+rlifAAECBEZGQEEfGWdHIUCAQC2BNddcM91888397mPnnXdOZ599dpp++ulbrtvOgj5hwoS0ySabpIcffrjlsWafffZ0/vnnp80226zPcV9++eVpp512Sq+88krL9ZZYYomquC+++OL9zn+wK3RqQe/NoedV9NJKemlZD/Z3wfoECBAg0AwBBb0ZORgFAQIE+hSIchpXqONq+bbbbpvWWGON9OEPfzi999576bbbbksnnnhievLJJ6t97LjjjumCCy7ot6Bfc8011f56W+I7unPOOWfLj1999dW0wgorpIceeqj6fI899kg77LBDmmmmmdL111+fjjvuuPTaa69VV75vueWWtNxyy7Xczz333FNd/X/zzTfTrLPOmg477LC0zjrrVP8eP3589ceGWKKkxx0Es802W1vPFKWtrZyN3pmsGx2PwREgQIDAvwQUdKcCAQIEMhCIq9BxdXzrrbdO00wzzRQjfv7556ui21WYb7zxxhRX3Sdfel5Bf/TRR9Oiiy46pNkfeeSR6Zhjjqm2Pf7449PBBx88yX5uvfXWtNZaa6V33323+t8bbrih5XG67gyYdtpp00033ZRWWWWVSdY74YQT0iGHHFL97KijjkpHH330kMbb20ZKW1s5G70zWTc6HoMjQIAAAQXdOUCAAIHOErjiiivS5ptvXk3qa1/7WvWd7uEo6O+8806ab7750ssvv5yWXHLJ9Je//CVNPfXUUxxr7733TmeeeWb18zvvvDOtuOKKk6wTP1t55ZWrn+21117phz/84RT7eP/999MyyyyTHnjggepq/nPPPVd9775dS0mlrcR3n/c8T0rKul2/H/ZDgAABAiMv4Ar6yJs7IgECBIZF4PXXX69uE48lvhse39sejoL+m9/8Jm244YbVrseNG5cOPfTQlvO5/fbbu6+Ix63rxx577CTrHX744dWt8LHEul1lffKdxTFi+1jitvwNNtigbX6llrYSy3qpWbftl8WOCBAgQGBEBBT0EWF2EAIECAy/wD/+8Y80zzzzVAeKK+mXXXbZsBT0nre3x/ffP/OZz7ScXNzePsccc6R40nvcyh633fdcum5vn2WWWdJLL72U4jb3VkscY9VVV60+imN/+9vfbhum0tY2ysbvSNaNj8gACRAgQCClpKA7DQgQINAhAr/85S/TVlttVc0mvrf9ve99r8+Cvvbaa6cHH3wwxffX44nr8SC69dZbL331q19NCy+8cK8q22yzTfrFL35Rff7iiy/2+iC5+HzZZZdNf/7zn6tb4uP29J5L/CyOHev88Y9/7PV4cYy55567+jwekHfRRRe1LTGlrW2Ujd+RrBsfkQESIECAgILuHCBAgEBnCMR3teMBa/G97ljiiefLL798nwW9t5nPOOOM6eSTT66+F95qiSvmd9xxR4or3/Gk9r6WeLhd1632b731VvV+81jiv+OJ77HEO9Pj+/N9LXHrftzCH8eOK+oDXaKU9bU8/fTTaaWVVqpWifevx5PrO30p8fb2yFRB7/Qz2/wIECDQGQKuoHdGjmZBgEDhAvGatYMOOqhSiKvoXVe4J2eJp7jH09djnSj0Y8eOrVb529/+Vm1zySWXpK73Y8cD3vbcc88pZJdeeul0//33pwUWWCA988wzfcpvv/323Ve842p51y34EydOTPPPP3+1bawTr1Tra4ljxRX4eGDcvffeO+C0o4wOdCmloA/Uo9PWU9A7LVHzIUCAQGcKKOidmatZESBQkEB8tztuTY/vfEfpjQLbVX4nZ4gnr8ft7L0V17iSHeU9ntQe7zCPd69/6EMfmmQ3iy22WFXoo9z//e9/71M6Xg3305/+tFqnZwGO/473uMfypS99Kf3kJz/pcz+xbmwTx54wYcKA01XQJ6Uq9ep5KCjoA/61sSIBAgQIjKKAgj6K+A5NgACBugL33XdfWmONNarvgset6fGU81bvPx/Mcb7zne+kI444otok/vtb3/rWJJvndAXdLe6DSb6z11XQOztfsyNAgECnCCjonZKkeRAgUJzAo48+mlZfffX01FNPpWmmmaa6RX2LLbao7RC3ksdV87jVff3110/xWrWeS07fQe8Po5NLW193D3R9jaE/n076vJOz7qSczIUAAQKlCyjopZ8B5k+AQJYCUcrjynncah5FLL5bHreTt2vpesL6UkstleIqfc+lXU9xn3feedMLL7zgKe7tCs1++hRQ0J0gBAgQIJCDgIKeQ0rGSIAAgR4C8bC1tdZaq3pQWyynnXZa2nfffdtqFN9hjwe5tSrog3kP+pxzzlk9fd170Nsaz5B2VvL3zwNMQR/SaWMjAgQIEBhhAQV9hMEdjgABAnUE4iFvn/3sZ9Mf/vCHajfjxo1Lhx56aJ1dTrFtFPN4anrcBh0Pn7v22msnWSdued9www37Pf7tt99ePSk+lsMOOywde+yxk+zn8MMPT8cdd1z1s1h35ZVXbjmPmGNsH0t8x36DDTZo23w7ubR13eI++e3spRb1Ts66bb8QdkSAAAECoy6goI96BAZAgACBgQm88cYbVTm95ZZbqg3i4W3xELd2L9/97nfTf/zHf1S7jVeydf1313Hefvvt6inx8ceCJZdcsroFvtX3nffee+8Ur2qLJd7PvuKKK04y1PhZVymPd67/8Ic/nGIq8X73eLXaAw88kOJqfHw/frrppmvblJW2tlE2fkeybnxEBkiAAAECKSUF3WlAgACBDASiFG+++ebdD2w74IAD0sknnzyokT/22GPV094/9alP9bpdvGZt6623TnG8mWaaKT388MNp4YUXnmL9nre5H3/88enggw+eZJ3bbrutuq09Xv0Wt+PfcMMNLY8Z69x8881p2mmnTTfddFP3FfeulU844YR0yCGHVP886qij0tFHHz2oOfe3cqeXtp5Xy0u9ct51DnR61v2d6z4nQIAAgTwEFPQ8cjJKAgQKF4jSfOmll1YKcYt7lPO+ntI9/fTTpyWWWGIStSjJ66yzTlWCo+wvu+yy3e9Lj4fNXXLJJdX/dd0Sffrpp6d99tmnpfyrr76aVlhhhfTQQw9Vn++5555phx12qEr99ddfX93O/tprr1X/vvXWW9Nyyy3Xcj/33HNPWm211dKbb76ZZp111hS3vccY49/jx49PZ511VrVdzOXuu+9Os802W1vPhNJKW8klvbSs2/qLYmcECBAgMGICCvqIUTsQAQIEhi7QVxlvtddFFlkkxRXznktXQe9vFDPPPHM66aSTqtLd1zJhwoS0ySabVFfZWy2zzz57Ov/889Nmm23W534uv/zytNNOO6VXXnml5XpRzq+88sq0+OKL9zf0QX+utA2aLNsNZJ1tdAZOgACBogQU9KLiNlkCBHIVaEdBj6vel112WYrbz+Nq9NNPP53iifBxG/pcc82Vll566bTuuuum3XffvfvKen9e8YT2uNJ+8cUXpyjscWv82LFjq+Iet+HHHwoGsjz++OPplFNOqYp4FKm4AyAK+bbbbpv222+/FH80GI6l00tbyVfMJz9fOj3r4fj9sE8CBAgQGHkBBX3kzR2RAAECBBoioLQ1JIgRGIasRwDZIQgQIECgtoCCXpvQDggQIEAgVwGlLdfkBj9uWQ/ezBYECBAgMPICCvrImzsiAQIECDREQGlrSBAjMAxZjwCyQxAgQIBAbQEFvTahHRAgQIBArgKdXtr6enZB19P6c81usOPu9KwH62F9AgQIEGimgILezFyMigABAgRGQEBpGwHkhhxC1g0JwjAIECBAoE8BBd0JQoAAAQLFCiht5UQv63KyNlMCBAjkLKCg55yesRMgQIBALYFOL21es/bv06PTs671i2BjAgQIEGiMgILemCgMhAABAgRGWkBpG2nx0TuerEfP3pEJECBAYOACCvrAraxJgAABAh0moLR1WKB9TEfW5WRtpgQIEMhZQEHPOT1jJ0CAAIFaAkpbLb6sNpZ1VnEZLAECBIoVUNCLjd7ECRAgQEBpK+cckHU5WZspAQIEchZQ0HNOz9gJECBAoJZAp5c2D4n79+nR6VnX+kWwMQECBAg0RkBBb0wUBkKAAAECIy2gtI20+OgdT9ajZ+/IBAgQIDBwAQV94FbWJECAAIEOE1DaOizQPqYj63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECDQ6aUtvoPetXzwwQdp8n+XdAZ0etYlZWmuBAgQ6GQBBb2T0zU3AgQIEOhTQGkr5wSRdTlZmykBAgRyFlDQc07P2AkQIECgloDSVosvq41lnVVcBkuAAIFiBRT0YqM3cQIECBBQ2so5B2RdTtZmSoAAgZwFFPSc0zN2AgQIEKgloLTV4stqY1lnFZfBEiBAoFgBBb3Y6E2cAAECBJS2cs4BWZeTtZkSIEAgZwEFPef0jJ0AAQIEagl0emmLp7bH09stKXV61jImQIAAgc4QUNA7I0ezIECAAIEhCChtQ0DLdBNZZxqcYRMgQKAwAQW9sMBNlwABAgT+LaC0lXM2yLqcrM2UAAECOQso6DmnZ+wECBAgUEtAaavFl9XGss4qLoMlQIBAsQIKerHRmzgBAgQIKG3lnAOyLidrMyVAgEDOAgp6zukZOwECBAjUElDaavFltbGss4rLYAkQIFCsgIJebPQmToAAAQKdXtq6nuIe/9tqKekJ752etd9mAgQIEOgMAQW9M3I0CwIECBAYgoDSNgS0TDeRdabBGTYBAgQKE1DQCwvcdAkQIEDg3wJKWzlng6zLydpMCRAgkLOAgp5zesZOgAABArUElLZafFltLOus4jJYAgQIFCugoBcbvYkTIECAgNJWzjkg63KyNlMCBAhKxK8AAAAgAElEQVTkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLoNNLW9dr1mohdcjGnZ51h8RkGgQIECheQEEv/hQAQIAAgXIFOr20Kej/Prc7Petyf4vNnAABAp0loKB3Vp5mQ4AAAQKDEFDaBoGV+aqyzjxAwydAgEAhAgp6IUGbJgECBAhMKaC0lXNWyLqcrM2UAAECOQso6DmnZ+wECBAgUEtAaavFl9XGss4qLoMlQIBAsQIKerHRmzgBAgQIKG3lnAOyLidrMyVAgEDOAgp6zukZOwECBAjUElDaavFltbGss4rLYAkQIFCsgIJebPQmToAAAQJKWznngKzLydpMCRAgkLOAgp5zesZOgAABArUElLZafFltLOus4jJYAgQIFCugoBcbvYkTIECAgNJWzjkg63KyNlMCBAjkLKCg55yesRMgQIBALYESSttUU02VPvjgg1pOnbBxCVl3Qk7mQIAAgdIFFPTSzwDzJ0CAQMECSls54cu6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECSls554Csy8naTAkQIJCzgIKec3rGToAAAQK1BJS2WnxZbSzrrOIyWAIECBQroKAXG72JEyBAgIDSVs45IOtysjZTAgQI5CygoOecnrETIECAQC0Bpa0WX1YbyzqruAyWAAECxQoo6MVGb+IECBAgoLSVcw7IupyszZQAAQI5CyjoOadn7AQIECBQS0Bpq8WX1cayziougyVAgECxAgp6sdGbOAECBAgobeWcA7IuJ2szJUCAQM4CCnrO6Rk7AQIECNQSUNpq8WW1sayzistgCRAgUKyAgl5s9CZOgAABAkpbOeeArMvJ2kwJECCQs4CCnnN6xk6AAAECtQRKLW1TTTVV+uCDD2rZ5bZxqVnnlpPxEiBAoHQBBb30M8D8CRAgULCA0lZO+LIuJ2szJUCAQM4CCnrO6Rk7AQIECNQSUNpq8WW1sayzistgCRAgUKyAgl5s9CZOgAABAkpbOeeArMvJ2kwJECCQs4CCnnN6xk6AAAECtQSUtlp8WW0s66ziMlgCBAgUK6CgFxu9iRMgQICA0lbOOSDrcrI2UwIECOQsoKDnnJ6xEyBAgEAtAaWtFl9WG8s6q7gMlgABAsUKKOjFRm/iBAgQIKC0lXMOyLqcrM2UAAECOQso6DmnZ+wECBAgUEtAaavFl9XGss4qLoMlQIBAsQIKerHRmzgBAgQIKG3lnAOyLidrMyVAgEDOAgp6zukZOwECBAjUElDaavFltbGss4rLYAkQIFCsgIJebPQmToAAAQJKWznngKzLydpMCRAgkLOAgp5zesZOgAABArUElLZafFltLOus4jJYAgQIFCugoBcbvYkTIECAgNJWzjkg63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECJZW2qaaaKn3wwQfFhl5S1sWGbOIECBDoAAEFvQNCNAUCBAgQGJqA0jY0txy3knWOqRkzAQIEyhNQ0MvL3IwJECBA4F8CSls5p4Ksy8naTAkQIJCzgIKec3rGToAAAQK1BJS2WnxZbSzrrOIyWAIECBQroKAXG72JEyBAgIDSVs45IOtysjZTAgQI5CygoOecnrETIECAQC0Bpa0WX1YbyzqruAyWAAECxQoo6MVGb+IECBAgoLSVcw7IupyszZQAAQI5CyjoOadn7AQIECBQS0Bpq8WX1cayziougyVAgECxAgp6sdGbOAECBAgobeWcA7IuJ2szJUCAQM4CCnrO6Rk7AQLFCLzyyivpqquuSnfddVe6++6705NPPpkmTpyY3nzzzTTnnHOmpZZaKm2yySZpt912S/PMM0+/Lrfeemv6wQ9+kG6++eb07LPPVvtYdtll0y677JJ23HHHfrfvWuHCCy9MP/7xj9Of//zn9NJLL6UFFlggrbHGGmnfffdNq6yyyoD288Ybb6TTTjstXXzxxemRRx5J//znP9PYsWPTpptumvbff/+0yCKLDGg/Q1lJaRuKWp7byDrP3IyaAAECpQko6KUlbr4ECGQp8Nvf/jatv/76/Y593nnnTT/72c/Shhtu2Ou6Rx99dDrmmGPS+++/33KdKMaXXHJJmnHGGXvdR/xhYJtttqn+aNBqmXrqqdORRx6ZjjrqqD7HPGHChOoPCw8//HDL9WafffZ0/vnnp80226zfuQ9lBaVtKGp5biPrPHMzagIECJQmoKCXlrj5EiCQpUAU9K985StpnXXWScsvv3x1hXnBBResSnYUjyjUl156aXrvvffS9NNPn+68887qivjky5lnnpn23nvv6seLLbZYOvzww9MnP/nJ9NRTT6VTTjklXX/99dVncRX9ggsu6NUqPh8/fnz1eYzpgAMOSAsttFC6995707HHHltdCY8ljrfnnnu23M+rr76aVlhhhfTQQw9Vn++xxx5phx12SDPNNFM1juOOOy699tpraeaZZ0633HJLWm655dqendLWdtLG7lDWjY3GwAgQIECgh4CC7nQgQIBABgJRvKeZZpo+R/qrX/0qff7zn6/Wif+Nwt5z+cc//pE++tGPppdffjl9+MMfTr///e9TXHHvWuIYsd3ll19e/ShK8tprrz3FMa+77rq07rrrVj/ffPPN0y9/+ctJxvb8889Xf0T4+9//Xt06/7e//S3NNddcU+wnrrDHlfxYjj/++HTwwQdPsk7chr/WWmuld999t/rfG264oe1JlVLapppqqsrugw8+mMQwfj75z9qO3JAdlpJ1Q7gNgwABAgSGKKCgDxHOZgQIEGiiwCc+8Yn04IMPVsU7vqPec4kSfOihh1Y/iu+Ox9XqyZcoMYsuumh1JT5uPb/yyiunWCd+fvXVV6dpp502Pfroo2nMmDFTrBNX17u+y96qfL/zzjtpvvnmq/5YsOSSS6a//OUvKW6Ln3yJq/1xFT6WuCtgxRVXbCu70tZWzkbvTNaNjsfgCBAgQOBfAgq6U4EAAQIdJBAFNh4iN+uss6a4hbznsuqqq6bbbrstxfe6o7zHrfCtlo022ihdc801aYYZZqjWm2222bpXi31G+X/77bdTrBdFvdUSn0cBj4fbxcPi4mp4z+U3v/lN9/fkx40b1/2Hg8n3dfvtt3c/bO6www6rbp9v56K0tVOz2fuSdbPzMToCBAgQ+D8BBd2ZQIAAgQ4RiCvnyyyzTHVLeHy3O5743rVEYY7vcseV8XiA3K9//eteZx3f/Y7vpscSt7PHd8y7lp63t8d63/zmN3vdTxwninhcaY8ntU833XTd6/a8vT3+aPCZz3ym5X5iLnPMMUe1/ZprrpluvPHGtqaltLWVs9E7k3Wj4zE4AgQIEPiXgILuVCBAgEDGAlFc45Vr8b3xuJU8XpkWSzzJ/Ytf/GL3zOIW8ngYXCzxQLeTTz6511nHd8q32mqr6vPTTz897bPPPt3rxuvQvva1r1X/jvW23HLLXvcTxzn11FOrz++7777qVXBdSzwB/he/+EX1zxdffLH6rnpvSzzsLl7jFlfkn3vuubampbS1lbPRO5N1o+MxOAIECBD4l4CC7lQgQIBAZgLnnXde2nXXXXsddVzVjlvBux4MFivGFfONN9642uaEE05IBx10UK/bxy3yXd/1jn3FlfKuJf79ve99r/pnXKGPK/W9Ld///ve7H/wWx+/56re4Yn7HHXekWWaZpXpSe19LvGKt67vwb731VnXr/UCXKGV9LU8//XRaaaWVqlWeeOKJlt+nH+ixrNdsAQW92fkYHQECBAj8n4CC7kwgQIBAZgK9FfR4DdlZZ53V8kFqF198cdpuu+2qmZ5xxhndr1prNfUHHnig+2r3fvvtl/7rv/6re7V99903/eAHP6j+HevFQ+l6W+I4XVff4zVwW2+9dfeqSy+9dLr//vvTAgsskJ555pk+E9h+++3TRRddVK0TT4ifZ555BpxYzz9S9LeRgt6fUN6fK+h552f0BAgQKEVAQS8lafMkQKBjBF566aXq3eexvPnmm9U7x6PAxi3n8W7zuH09rjr3XH7605+mnXfeufrRj370o+qd6r0t8Vq02E8su+22WzrnnHO6V41/n3vuudW/47jx2rbellgv1o8ljr/TTjt1rxr7j+PE+9zjdWx9LTHu2D6WwZZoBb1jTvvaE1HQaxPaAQECBAiMgICCPgLIDkGAAIGREIgS++Uvf7m6tT1K+C677NJ92FKvoLvFfSTOvDyOoaDnkZNREiBAoHQBBb30M8D8CRDoKIGu28Hju91xZXruueeu5lfqd9D7C1dp60+ocz6XdedkaSYECBDoZAEFvZPTNTcCBIoTuOCCC7qf3n7++eenL3zhC5WBp7i3PhWUtnJ+RWRdTtZmSoAAgZwFFPSc0zN2AgQITCZw7bXXpg022KD6aTzJ/bDDDqv+O+f3oMcr2F5//XXvQXe21xJQ0Gvx2ZgAAQIERkhAQR8haIchQIDASAj0fMJ7vIO8653lcexVV1013XbbbWn22WdPEydOTNNPP33LIW200UbpmmuuqV5nFuvNNtts3eu9+uqrad55560Kf6x39dVXt9xHfB7vLX/llVfSKquskm699dZJ1vvNb37T/dq1cePGpUMPPbTlfm6//fZq+1jijw3xR4d2LkpbOzWbvS9ZNzsfoyNAgACB/xNQ0J0JBAgQ6CCBTTfdNF111VXVjK6//vq09tprd8/u+OOP7y7CF154Ydphhx2mmHmUmEUXXTS99957aZNNNul+/3jPFePnUcynnXba9Oijj7Z8d/j48ePTjjvuWG0Wxz344IMnOVYU+Pnnnz+9/PLLackll0z33XffJO9t71p57733TmeeeWb1zzvvvLPlK+TqxKe01dHLa1tZ55WX0RIgQKBUAQW91OTNmwCBrATiyngU6hlnnLHXcZ900knpwAMPrD7/yEc+kh5++OE0zTTTdK//j3/8o3otWpTiRRZZJP3+97+f5J3iUco///nPp8svv7xlwe/a0XXXXZfWXXfd6p+f+9zn0qWXXjrJceJd5csvv3z1kLq4PT1epzbXXHNNMe4jjzwyHXPMMb2W+Ljav+aaa6Z33303rbXWWumGG25oe2ZKW9tJG7tDWTc2GgMjQIAAgR4CCrrTgQABAhkIxFXtuL186623Tquvvnr1nvJZZ521+tm9996b4oFwt9xySzWTuHX9yiuvTOutt94UM4ur0XFVOpbYx7e+9a30yU9+Mj311FPV+9PjqnsscfU7HjjX2xKfx1XyWNZZZ5309a9/PS200ELVWL773e9W70iPJY635557ttxNjH2FFVZIDz30UPV5rBd/hJhpppmqccTt7K+99lr177hFfrnllmt7Ukpb20kbu0NZNzYaAyNAgAABBd05QIAAgbwEoqA//vjj/Q56zJgx6dxzz03rr79+r+seddRR1ZXrDz74oOU6cQv7L37xiz6v1r/55ptpm2226b6dfvIdTT311OmII45IRx99dJ9jnjBhQnUrfVztb7XE9+Xjjw+bbbZZv3MfygpK21DU8txG1nnmZtQECBAoTcAV9NISN18CBLIUePDBB6ur4nGVPErts88+m1544YXq6nJ8lzuuLkeJ3W677dLMM8/c7xzjivTpp5+ebr755mpfcSv6sssum3bdddfu7473u5OUqqvscfv9n/70p/TSSy+lBRZYIK2xxhppv/326364W3/7iSe0x1guvvjiam7x/fSxY8dWxf2AAw6obscfrkVpGy7Z5u1X1s3LxIgIECBAYEoBBd1ZQYAAAQLFCiht5UQv63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECSls554Csy8naTAkQIJCzgIKec3rGToAAAQK1BJS2WnxZbSzrrOIyWAIECBQroKAXG72JEyBAgIDSVs45IOtysjZTAgQI5CygoOecnrETIECAQC0Bpa0WX1YbyzqruAyWAAECxQoo6MVGb+IECBAgoLSVcw7IupyszZQAAQI5CyjoOadn7AQIECBQS0Bpq8WX1cayziougyVAgECxAgp6sdGbOAECBAgobeWcA7IuJ2szJUCAQM4CCnrO6Rk7AQIECNQSUNpq8WW1sayzistgCRAgUKyAgl5s9CZOgAABAkpbOeeArMvJ2kwJECCQs4CCnnN6xk6AAAECtQSUtlp8WW0s66ziMlgCBAgUK6CgFxu9iRMgQICA0lbOOSDrcrI2UwIECOQsoKDnnJ6xEyBAgEAtAaWtFl9WG8s6q7gMlgABAsUKKOjFRm/iBAgQIKC0lXMOyLqcrM2UAAECOQso6DmnZ+wECBAgUEtAaavFl9XGss4qLoMlQIBAsQIKerHRmzgBAgQIKG3lnAOyLidrMyVAgEDOAgp6zukZOwECBAjUElDaavFltbGss4rLYAkQIFCsgIJebPQmToAAAQJKWznngKzLydpMCRAgkLOAgp5zesZOgAABArUElLZafFltLOus4jJYAgQIFCugoBcbvYkTIECAgNJWzjkg63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECSls554Csy8naTAkQIJCzgIKec3rGToAAAQK1BJS2WnxZbSzrrOIyWAIECBQroKAXG72JEyBAgIDSVs45IOtysjZTAgQI5CygoOecnrETIECAQC0Bpa0WX1YbyzqruAyWAAECxQoo6MVGb+IECBAgUEppm2qqqdIHH3xQdOClZF10yCZPgACBDhBQ0DsgRFMgQIAAgaEJKG1Dc8txK1nnmJoxEyBAoDwBBb28zM2YAAECBP4loLSVcyrIupyszZQAAQI5CyjoOadn7AQIECBQS0Bpq8WX1cayziougyVAgECxAgp6sdGbOAECBAgobeWcA7IuJ2szJUCAQM4CCnrO6Rk7AQIECNQSUNpq8WW1sayzistgCRAgUKyAgl5s9CZOgAABAkpbOeeArMvJ2kwJECCQs4CCnnN6xk6AAAECtQSUtlp8WW0s66ziMlgCBAgUK6CgFxu9iRMgQICA0lbOOSDrcrI2UwIECOQsoKDnnJ6xEyBAgEAtAaWtFl9WG8s6q7gMlgABAsUKKOjFRm/iBAgQIKC0lXMOyLqcrM2UAAECOQso6DmnZ+wECBAgUEtAaavFl9XGss4qLoMlQIBAsQIKerHRmzgBAgQIKG3lnAOyLidrMyVAgEDOAgp6zukZOwECBAjUElDaavFltbGss4rLYAkQIFCsgIJebPQmToAAAQJKWznngKzLydpMCRAgkLOAgp5zesZOgAABArUElLZafFltLOus4jJYAgQIFCugoBcbvYkTIECAgNJWzjkg63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECSls554Csy8naTAkQIJCzgIKec3rGToAAAQK1BJS2WnxZbSzrrOIyWAIECBQroKAXG72JEyBAgIDSVs45IOtysjZTAgQI5CygoOecnrETIECAQC0Bpa0WX1YbyzqruAyWAAECxQoo6MVGb+IECBAgoLSVcw7IupyszZQAAQI5CyjoOadn7AQIECBQS0Bpq8WX1cayziougyVAgECxAgp6sdGbOAECBAgobeWcA7IuJ2szJUCAQM4CCnrO6Rk7AQIECNQSUNpq8WW1sayzistgCRAgUKyAgl5s9CZOgAABAkpbOeeArMvJ2kwJECCQs4CCnnN6xk6AAAECtQSUtlp8WW0s66ziMlgCBAgUK6CgFxu9iRMgQICA0lbOOSDrcrI2UwIECOQsoKDnnJ6xEyBAgEAtAaWtFl9WG8s6q7gMlgABAsUKKOjFRm/iBAgQIKC0lXMOyLqcrM2UAAECOQso6DmnZ+wECBAgUEtAaavFl9XGss4qLoMlQIBAsQIKerHRmzgBAgQIKG3lnAOyLidrMyVAgEDOAgp6zukZOwECBAjUElDaavFltbGss4rLYAkQIFCsgIJebPQmToAAAQJKWznngKzLydpMCRAgkLOAgp5zesZOgAABArUElLZafFltLOus4jJYAgQIFCugoBcbvYkTIECAgNJWzjkg63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECSls554Csy8naTAkQIJCzgIKec3rGToAAAQK1BJS2WnxZbSzrrOIyWAIECBQroKAXG72JEyBAgIDSVs45IOtysjZTAgQI5CygoOecnrETIECAQC0Bpa0WX1YbyzqruAyWAAECxQoo6MVGb+IECBAgoLSVcw7IupyszZQAAQI5CyjoOadn7AQIECBQS0Bpq8WX1cayziougyVAgECxAgp6sdGbOAECOQm88sor6aqrrkp33XVXuvvuu9OTTz6ZJk6cmN58880055xzpqWWWiptsskmabfddkvzzDNPy6ndcMMNaZ111hnQtI866qh09NFH97nuu+++m84555x0/vnnp7/+9a/ptddeSwsttFBab7310v7775+WXnrpAR3r+eefT6eeemr61a9+lR577LFqm0UXXTRtueWW6YADDuh1PgPaeT8rKW3tUMxjH7LOIyejJECAQOkCCnrpZ4D5EyCQhcBvf/vbtP766/c71nnnnTf97Gc/SxtuuOEU67azoEepjj8IxB8MWi0zzDBDOu2009Luu+/e55jvuOOOqog/88wzLddbcMEFq+K+0kor9Tv3oaygtA1FLc9tZJ1nbkZNgACB0gQU9NISN18CBLIUiIL+la98pboCvvzyy6exY8emKK/vv/9+iuJxySWXpEsvvTS99957afrpp0933nlnWnbZZSeZa8+Cfu6556YVV1yxV4v5558/xf+1WuIYa6+9dvrd735XfbzVVlulPfbYI80999wpCvd3vvOd9Nxzz6Wpp546XXHFFWnjjTduuZ8nnniimkvcCTDttNOmAw88MG222WbVurHdf/7nf6a4Sh/j+P3vf5/GjBnT9uyUtraTNnaHsm5sNAZGgAABAj0EFHSnAwECBDIQiFI8zTTT9DnSuNL8+c9/vlon/jcKe8+lZ0G//vrrq5I9lCXKfdxKH8s+++yTTj/99El2M2HChKp4x235iy++eHrggQeqAj75svPOO6ef/vSn1Y8vuuiitO22206ySvxs++23r3725S9/OZ133nlDGW6f2yhtbSdt7A5l3dhoDIwAAQIEeggo6E4HAgQIdJDAJz7xifTggw+muNU9rkwPR0GP77tH6Y4r5nEVfOaZZ55CcNy4cemwww7rtXzHLe0LL7xwdQdA3I7/61//umUKG220Ubrmmmuqq/HxvfsPfehDbU1LaWsrZ6N3JutGx2NwBAgQIPAvAQXdqUCAAIEOEojb1uMhcrPOOmt69dVX217QH3roofTxj3+82u/ee++dzjjjjJZ6UcDjFvxYdtxxx3TBBRdMst5ZZ52V9tprr+pn48eP775SPvnO4rPYPpYzzzwz7bnnnm1NS2lrK2ejdybrRsdjcAQIECCgoDsHCBAg0FkCceV8mWWWqb63vcIKK0zxALd23OLe8/b2Cy+8MO2www69IkaRj0L/4Q9/OD3++OOTrNfz9vann3661yvj8Vk8GT6W2Oa///u/2xqa0tZWzkbvTNaNjsfgCBAgQEBBdw4QIEAgf4E33nijuvX78ssvT8cff3x69tlnq0nFk9y/+MUvTjLBngV91VVXrR4uF1e64xb1eK1ZfCf9q1/9alpiiSV6hTnooIPSiSeeWH1+zz33pOWWW67XdbfYYot02WWXpammmqq6mj/LLLN0rxt/QIgHv80xxxzppZde6jOIWCe+zx53B8TD79q5KG3t1Gz2vmTd7HyMjgABAgT+T8At7s4EAgQIZCYQD0vbddddex31N7/5zXTsscdWxbjnMpDXrMV3vY844ogU70GffPvYV1wx//nPf17tNr7jHt91723Zb7/9uh8gF+9J77o1PtaP75LHHxPiXel/+ctf+kwg7gq478/VkeUAABm/SURBVL77qm3iivpglihlfS2xv65XuMX36YfjSfGDGa91h09AQR8+W3smQIAAgfYJKOjts7QnAgQIjIhAbwU9rmbHd7t7e31aFPQvfOEL1WvRVl999fTRj360err63//+9+q1Zj/5yU/SO++8U80hHvAWJX/yZdNNN01XXXVV9eM333wzzTjjjL3O+dBDD62u6scS34uPJ7t3LXE1Pa7+r7zyyun222/v0y3WiSvnrb5X3x94qz8y9LaNgt6fZt6fK+h552f0BAgQKEVAQS8lafMkQKBjBOKW8K4rw1GSH3nkkeo1Zb/85S/TYostlk4++eTu94n3nPTrr79evSN9uumma2kRJXiDDTZIL7/8cnX1PG5hn/xd6uuuu2667rrrqu3j1W9xxb235cgjj0zHHHNM9fHNN99c/VGga4lXxsUT3NdYY41000039ZnNmmuuWW0f28T36wezKOiD0ersdRX0zs7X7AgQINApAgp6pyRpHgQIFC8Q7xSP94VHKf3Rj36Udtlll0GbxHfXv/SlL1Xb7b777unss8+eZB+5XUF3i/ugT4GO3UBB79hoTYwAAQIdJaCgd1ScJkOAQOkC22+/fXU1PW4hj1vX413lg1niCvU888xTPZTtYx/7WPUU9p5Lbt9B72/uSlt/Qp3zuaw7J0szIUCAQCcLKOidnK65ESBQnEC8b7zr6e3nn39+9Z3zwS5d71KPp7vHbfE9F09xH6ym9ZsioKA3JQnjIECAAIG+BBR05wcBAgQ6SODaa6+tvkceSzzkLR72Ntglnmp+1113Va9fm7ygew/6YDWt3xQBBb0pSRgHAQIECCjozgECBAgUItDzCe+nnnpq+trXvjaomcct7vHqtHhQ3OKLL54efvjhSbaPW967Xpe29957pzPOOKPl/uP96gsuuGD12Y477pjiyn7PJZ42v9dee1U/Gj9+fIpb81st8VlsH8uZZ56Z9txzz0HNp7+Vlbb+hDrnc1l3TpZmQoAAgU4WcAW9k9M1NwIEihPo+RC366+/Pq299tqDMojb4nfaaadqm9122y2dc845U2y/1FJLpQceeKD6fnu8miyutE++jBs3rvvqfXwnftttt51klSjwCy+8cPUk9w033DD9+te/bjnOjTbaKF1zzTXV0+KffPLJ6l3o7VyUtnZqNntfsm52PkZHgAABAv8noKA7EwgQIJCBQFwZjwe09fXe8ZNOOikdeOCB1Ww+8pGPVFe/49Vksbz44ovpT3/6U5+FPV6zFmU5XuMWT4KP29x7vru8i6nnbe777rtvOu200yYRjNe+ffrTn64eNBdX4aPMx/vWJ1923nnnFE+ej+Xiiy9O22yzzSSrxM+222676mfxdPowaPeitLVbtLn7k3VzszEyAgQIEPi3gILubCBAgEAGAosuumh69dVX09Zbb129Tzzedz7rrLNWP7v33ntTXPm+5ZZbqpnEu86vvPLKtN5663XP7LHHHqtK+//7f/8vbbnlllXxjlvQo8DH096vuOKKqiy//fbb1TYHH3xwOv7441vKxPvP11prre7jxZj22GOPNNdcc6Uo+fHu8+eee6666h373XjjjVvuJ66+xzgmTpxYFfhvfOMb3e9vj+1OPPHE6r3n8803X/rDH/6QxowZ0/aklLa2kzZ2h7JubDQGRoAAAQI9BBR0pwMBAgQyEIiC/vjjj/c70iixcYV7/fXXn2TdroLe3w6isB9xxBHpyCOPrK6i97Y8//zzaZNNNqmusrdaZphhhurKerxLva/ljjvuqP5gELe8t1rilvZf/epXaeWVV+5v6EP6XGkbEluWG8k6y9gMmgABAsUJKOjFRW7CBAjkKPDggw9WV8XjKvmECRPSs88+m1544YU000wzpfnnnz8tt9xy1dXnuCW81XfC48r4ZZddlm677bbqKnd8nztK9ltvvZXmmGOO6sFv8X31KNTxx4CBLHF1++yzz64eABe3sccT3xdaaKG07rrrpgMOOCAtvfTSA9lNNY5TTjmlKuLxh4RY4mr/Fltskb7+9a9X72UfrkVpGy7Z5u1X1s3LxIgIECBAYEoBBd1ZQYAAAQLFCiht5UQv63KyNlMCBAjkLKCg55yesRMgQIBALQGlrRZfVhvLOqu4DJYAAQLFCijoxUZv4gQIECCgtJVzDsi6nKzNlAABAjkLKOg5p2fsBAgQIFBLQGmrxZfVxrLOKi6DJUCAQLECCnqx0Zs4AQIECCht5ZwDsi4nazMlQIBAzgIKes7pGTsBAgQI1BJQ2mrxZbWxrLOKy2AJECBQrICCXmz0Jk6AAAECSls554Csy8naTAkQIJCzgIKec3rGToAAAQK1BOK96/HO9Vji/fALLrhgrf3ZuLkCTz/9dFpppZWqAT766KNp0UUXbe5gjYwAAQIEihVQ0IuN3sQJECBA4K677uoubTTKEYg/xqy44orlTNhMCRAgQCAbAQU9m6gMlAABAgTaLXDFFVekzTffvN27tb+GCyjoDQ/I8AgQIFCwgIJecPimToAAgdIFJkyYkD72sY9VDLfeemsaO3Zs6SSNnX/PW9SH8nWEd999N02cOLGa3yc/+ck044wzNnauBkaAAAEC5Qoo6OVmb+YECBAoXsCDw/I5BWSVT1ZGSoAAAQJDF1DQh25nSwIECBDIXEDpyydAWeWTlZESIECAwNAFFPSh29mSAAECBDIXUPryCVBW+WRlpAQIECAwdAEFfeh2tiRAgACBzAWUvnwClFU+WRkpAQIECAxdQEEfup0tCRAgQCBzAaUvnwBllU9WRkqAAAECQxdQ0IduZ0sCBAgQyFxA6csnQFnlk5WREiBAgMDQBRT0odvZkgABAgQyF1D68glQVvlkZaQECBAgMHQBBX3odrYkQIAAgcwFlL58ApRVPlkZKQECBAgMXUBBH7qdLQkQIECAAAECBAgQIECAQNsEFPS2UdoRAQIECBAgQIAAAQIECBAYuoCCPnQ7WxIgQIAAAQIECBAgQIAAgbYJKOhto7QjAgQIECBAgAABAgQIECAwdAEFfeh2tiRAgAABAgQIECBAgAABAm0TUNDbRmlHBAgQIECAAAECBAgQIEBg6AIK+tDtbEmAAAECBAgQIECAAAECBNomoKC3jdKOCBAgQIAAAQIECBAgQIDA0AUU9KHb2ZIAAQIECBAgQIAAAQIECLRNQEFvG6UdESBAgAABAgQIECBAgACBoQso6EO3syUBAgQIZCzw+OOPp1NPPTVdeeWV6YknnkgzzDBDWmyxxdJ2222X9t133zTzzDNnPLvRHfpUU001oAGstdZa6YYbbuhz3auvvjqdddZZ6a677koTJ05M8803X1pxxRXTnnvumTbeeOMBHefdd99N55xzTjr//PPTX//61/Taa6+lhRZaKK233npp//33T0svvfSA9mMlAgQIECAw3AIK+nAL2z8BAgQINE7g8ssvTzvttFN65ZVXWo5tiSWWqIr74osv3rix5zCgdhT0999/vyrhP/rRj3qd8u67757OPPPMNPXUU/e6zvPPP5822WSTquC3WuIPM6eddlqKfVkIECBAgMBoCyjoo52A4xMgQIDAiArcc889abXVVktvvvlmmnXWWdNhhx2W1llnnerf48ePT2effXY1nijpd999d5ptttlGdHydcLCugv7Vr3417bPPPr1OaZZZZkkf+chHWn4euYwbN6767FOf+lQ65JBDqjscHnnkkXT88cenyDGWWO/YY49tuY/33nsvrb322ul3v/td9flWW22V9thjjzT33HOnO+64I33nO99Jzz33XFXwr7jiigFfke+EjMyBAAECBJopoKA3MxejIkCAAIFhElhzzTXTzTffnKaddtp00003pVVWWWWSI51wwglVGYzlqKOOSkcfffQwjaRzd9tV0Ifq99BDD1W3ncet6SussEKV00wzzdQN9sYbb6S4PT7+gBI5PvDAAy3vdjj33HPTbrvtVm0Xfyg4/fTTJ0GfMGFCWn755as7KeJuidhP7M9CgAABAgRGS0BBHy15xyVAgACBERe4884708orr1wdd6+99ko//OEPpxhD3Fq9zDLLVGVtzjnnrK6wTjfddCM+1pwPWLegR5k+44wzKoLbbrstfeYzn5mC4/bbb+/+40qr8h0bLLXUUlWOccU8njPQ6rkCcZU+rsLHctFFF6Vtt902Z3pjJ0CAAIHMBRT0zAM0fAIECBAYuMDhhx+ejjvuuGqDKHhdZX3yPfQsbddcc03aYIMNBn4Qa6Y6Bf2DDz5IY8aMSU899VT6xCc+URXs3pb4/MEHH0wLL7xwVcB7fvc9rsJ//OMfrzbde++9uwv/5Pt65pln0oILLlj9eMcdd0wXXHCBBAkQIECAwKgJKOijRu/ABAgQIDDSAl23t8d3n1966aVeb2eOq7arrrpqNbwjjzwyffvb3x7poWZ9vDoF/W9/+1v1XfNYervLoQsnPo8nvMcS2/X8PnvP29svvPDCtMMOO/RqGkU+Cv2HP/zhFE/3txAgQIAAgdESUNBHS95xCRAgQGDEBeIVXfFU72WXXTb98Y9/7PX4L774YnVbdCxxy3Pc+mwZuEBXQY9bzOOK+GOPPZammWaa9KEPfaj6w8cuu+xSPZiv1RIPa9t8882rj0466aT09a9/vdcDx+cHHnhg9Xk8dT+e1t61HHTQQenEE0+s/hkPlFtuueV63c8WW2yRLrvssuoK/KuvvpriDzgWAgQIECAwGgIK+mioOyYBAgQIjLjAW2+91f2gsU033bR6andfSzzh/fXXX6++/xxX1C0DFxjIa9a23HLLdN5556U55phjkh3HcwHi6e+xXHzxxWmbbbbp9cCXXHJJ93fGY7u4ot61xBXzn//859U/4/3p8847b6/72W+//bofIBfvSe+6NX7gM7YmAQIECBBoj4CC3h5HeyFAgACBhgtESZt//vmrUW6//fbVK9X6WhZYYIHqAXHxwLh777234bNr1vDiCvTnPve5tO6661bfI48/doT/jTfeWD2Y74UXXqgGHE9iv/baayd5CF/Pp+hfffXVaaONNup1cvF511Xz73//++kb3/hG97rxR5irrrqq+ne8Qm/GGWfsdT+HHnpo9eq2WOLJ8PFkdwsBAgQIEBgNAQV9NNQdkwABAgRGXCAeIhbfMY7lS1/6UvrJT37S5xhi3dgmvg8dr+OyDFwgvt8fT8BvtTz77LPV+8a73mN+yimnpP3337971WOOOab63n8s//M//5M++9nP9nrg6667rvojQCyx3X/8x390rxs/j89jifehx7vOe1vieLF9LPEKvtVXX33gk7UmAQIECBBoo4CC3kZMuyJAgACB5gq4gt6cbOKBbnFl/Z133qneP/7www93D84V9ObkZCQECBAgMPICCvrImzsiAQIECIyCgO+gjwJ6H4fseQv6k08+mRZaaKFqbd9Bb1ZORkOAAAECIyugoI+st6MRIECAwCgKxIPC4vvPnuI+iiH869AHH3xwiu+Nx3LnnXemFVdcsfpvT3Ef/WyMgAABAgRGT0BBHz17RyZAgACBERbwHvQRBu/jcIccckiK29knL+jeg96cjIyEAAECBEZeQEEfeXNHJECAAIFREjj88MPTcccdVx399ttvTyuvvHLLkYwbNy4ddthh1WfXXHNN2mCDDUZpxJ172M0226x6d3ks//u//5sWXnjh6r/jveljxoxJTz31VPU99QceeKBXhCWXXDLFa9Fi23igX8/Xuz300EPdr0vbe++90xlnnNFyP88880xacMEFq8923HHHdMEFF3QuupkRIECAQOMFFPTGR2SABAgQINAugbiVuquUxzuz4/vOky/vv/9+9Wq1KIbxJPJ41dp0003XriHYT0rp0Ucfrcr322+/3fIp+fvss093oY530Me76Cdf4g8sq6yySvXjWP/000+fYp2lllqqynHuueeuCvzMM888xTo9/xhz0UUXdb9XXVAECBAgQGA0BBT00VB3TAIECBAYNYGu29ynnXbadNNNN3WXvK4B9XyK+FFHHZWOPvroURtrjge+/PLLq9eohW+rZfLXrJ144onpwAMPnGTVuPod5Tpej7bCCitUOc0000zd68R7zSPHeGd5HOf+++9PH/vYx6Y43Lnnnpt222236uf77rtvOu200yZZ55FHHkmf/vSn0yuvvFI9TT7KfG/jzjELYyZAgACB/AQU9PwyM2ICBAgQqCEQ799ebbXVUpS8WWedNcVt7+uss0717/Hjx6ezzjqr2vsSSyxRFcDZZputxtHK23TRRRetXp+29dZbV3/8iH9HuX7++efTDTfckM4888zqv2OJ943/9re/TTPMMMMUUPEVg7i6HcunPvWpdOihh1ZX26NUf+973+t+j3qsd+yxx7aEjoK/1lprpVtuuaX6PMa0xx57pLnmmqt6MF28+zzukIh3pMfD6eIPCxYCBAgQIDCaAgr6aOo7NgECBAiMikBc5d1pp52qK6etlijn8f3ouKpqGZxAFPLHH3+8342iLJ9zzjnV1whaLfFVgyjTcRW8tyWujscfVKJg97bEHwM22WSTdNddd7VcJf44EFfWd999937HbAUCBAgQIDDcAgr6cAvbPwECBAg0UiBK5CmnnFIV8XhI2fTTT18V8m233Tbtt99+Lb+v3MiJNGxQN954Y4r/i++OxxPZoyDHH0LiboWxY8emVVddNX35y1+e4qsFvU3jqquuqkp4FOzYV7wqL17JFs8QGOgV73fffTedffbZ1QPg4jb2119/vXrv+rrrrpsOOOCAtPTSSzdM0XAIECBAoFQBBb3U5M2bAAECBAgQIECAAAECBBoloKA3Kg6DIUCAAAECBAgQIECAAIFSBRT0UpM3bwIECBAgQIAAAQIECBBolICC3qg4DIYAAQIECBAgQIAAAQIEShVQ0EtN3rwJECBAgAABAgQIECBAoFECCnqj4jAYAgQIECBAgAABAgQIEChVQEEvNXnzJkCAAAECBAgQIECAAIFGCSjojYrDYAgQIECAAAECBAgQIECgVAEFvdTkzZsAAQIECBAgQIAAAQIEGiWgoDcqDoMhQIAAAQIECBAgQIAAgVIFFPRSkzdvAgQIECBAgAABAgQIEGiUgILeqDgMhgABAgQIECBAgAABAgRKFVDQS03evAkQIECAAAECBAgQIECgUQIKeqPiMBgCBAgQIECAAAECBAgQKFVAQS81efMmQIAAAQIECBAgQIAAgUYJKOiNisNgCBAgQIAAAQIECBAgQKBUAQW91OTNmwABAgQIECBAgAABAgQaJaCgNyoOgyFAgAABAgQIECBAgACBUgUU9FKTN28CBAgQIECAAAECBAgQaJSAgt6oOAyGAAECBAgQIECAAAECBEoVUNBLTd68CRAgQIAAAQIECBAgQKBRAgp6o+IwGAIECBAgQIAAAQIECBAoVUBBLzV58yZAgAABAgQIECBAgACBRgko6I2Kw2AIECBAgAABAgQIECBAoFQBBb3U5M2bAAECBAgQIECAAAECBBoloKA3Kg6DIUCAAAECBAgQIECAAIFSBRT0UpM3bwIECBAgQIAAAQIECBBolICC3qg4DIYAAQIECBAgQIAAAQIEShVQ0EtN3rwJECBAgAABAgQIECBAoFECCnqj4jAYAgQIECBAgAABAgQIEChVQEEvNXnzJkCAAAECBAgQIECAAIFGCSjojYrDYAgQIECAAAECBAgQIECgVAEFvdTkzZsAAQIECBAgQIAAAQIEGiWgoDcqDoMhQIAAAQIECBAgQIAAgVIFFPRSkzdvAgQIECBAgAABAgQIEGiUgILeqDgMhgABAgQIECBAgAABAgRKFVDQS03evAkQIECAAAECBAgQIECgUQIKeqPiMBgCBAgQIECAAAECBAgQKFVAQS81efMmQIAAAQIECBAgQIAAgUYJKOiNisNgCBAgQIAAAQIECBAgQKBUAQW91OTNmwABAgQIECBAgAABAgQaJaCgNyoOgyFAgAABAgQIECBAgACBUgUU9FKTN28CBAgQIECAAAECBAgQaJSAgt6oOAyGAAECBAgQIECAAAECBEoVUNBLTd68CRAgQIAAAQIECBAgQKBRAgp6o+IwGAIECBAgQIAAAQIECBAoVUBBLzV58yZAgAABAgQIECBAgACBRgko6I2Kw2AIECBAgAABAgQIECBAoFQBBb3U5M2bAAECBAgQIECAAAECBBoloKA3Kg6DIUCAAAECBAgQIECAAIFSBRT0UpM3bwIECBAgQIAAAQIECBBolICC3qg4DIYAAQIECBAgQIAAAQIEShVQ0EtN3rwJECBAgAABAgQIECBAoFECCnqj4jAYAgQIECBAgAABAgQIEChVQEEvNXnzJkCAAAECBAgQIECAAIFGCSjojYrDYAgQIECAAAECBAgQIECgVAEFvdTkzZsAAQIECBAgQIAAAQIEGiWgoDcqDoMhQIAAAQIECBAgQIAAgVIFFPRSkzdvAgQIECBAgAABAgQIEGiUgILeqDgMhgABAgQIECBAgAABAgRKFVDQS03evAkQIECAAAECBAgQIECgUQIKeqPiMBgCBAgQIECAAAECBAgQKFVAQS81efMmQIAAAQIECBAgQIAAgUYJKOiNisNgCBAgQIAAAQIECBAgQKBUAQW91OTNmwABAgQIECBAgAABAgQaJaCgNyoOgyFAgAABAgQIECBAgACBUgUU9FKTN28CBAgQIECAAAECBAgQaJSAgt6oOAyGAAECBAgQIECAAAECBEoVUNBLTd68CRAgQIAAAQIECBAgQKBRAv8fg/6Tj+eYbKEAAAAASUVORK5CYII=\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(574, 3594)\n"
     ]
    }
   ],
   "source": [
    "# %matplotlib inline\n",
    "%matplotlib notebook\n",
    "# %matplotlib notebook\n",
    "plt.figure(figsize = (5,5))\n",
    "#plt.imshow(1-(finished_submaps[2].grid.grid.T> 0), cmap='gray') \n",
    "idx = 0\n",
    "if finished_submaps[idx].iscompressed:\n",
    "        finished_submaps[idx].decompress_data()\n",
    "plt.imshow(1 - (finished_submaps[idx].grid.grid.T>0), cmap='gray') \n",
    "print(finished_submaps[idx].grid.grid.shape)\n",
    "#print(np.sum(finished_submaps[1].grid.grid >0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204 9064 9064\n"
     ]
    }
   ],
   "source": [
    "print( len(finished_submaps), len(node_poses), len(inserted_frame_ids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pg' from '/home/anujraaj/robo/SLAM-loop-closure/pg.py'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import branch_bound as bb\n",
    "from importlib import reload\n",
    "from constraint import Constraint\n",
    "import pg\n",
    "\n",
    "reload(bb)\n",
    "reload(pg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Version-1: Find constraints altogether at the end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9064 9064\n"
     ]
    }
   ],
   "source": [
    "x_search_window = (-2,2)\n",
    "y_search_window = (-2,2)\n",
    "angle_search_window = (-np.pi/6, np.pi/6)\n",
    "score_th=0.8\n",
    "dist_th = 5\n",
    "node_dists = np.cumsum(np.linalg.norm(node_poses[1:,:2]-node_poses[:-1,:2], axis = 1))\n",
    "node_dists = np.concatenate(([0],node_dists))\n",
    "print(len(node_dists), len(node_poses))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sid, nids, pose, score:  [0, 4244] [0.11675523 1.88263737 1.57570846] 0.8003867224616429\n",
      "Submap 0 done..\n",
      "Submap 1 done..\n",
      "Submap 2 done..\n",
      "sid, nids, pose, score:  [132, 4366] [-0.13370811  3.51458869  1.53921433] 0.8420666615287044\n",
      "Submap 3 done..\n",
      "sid, nids, pose, score:  [176, 3020] [ 0.14752503  3.94444127 -1.6074135 ] 0.8249418794774028\n",
      "Submap 4 done..\n",
      "sid, nids, pose, score:  [220, 2975] [-0.00544996  4.32302664 -1.48760225] 0.8302904402044937\n",
      "sid, nids, pose, score:  [220, 2976] [ 0.00743633  4.10238027 -1.50465106] 0.8116781840866157\n",
      "sid, nids, pose, score:  [220, 2977] [ 0.02288849  4.00692948 -1.53121291] 0.8092506091932996\n",
      "sid, nids, pose, score:  [220, 2978] [ 0.03121738  3.72336649 -1.55637098] 0.8213658641827961\n",
      "Submap 5 done..\n",
      "Submap 6 done..\n",
      "Submap 7 done..\n",
      "Submap 8 done..\n",
      "sid, nids, pose, score:  [396, 1786] [ 0.04918543  6.51738355 -1.52092724] 0.8288240503311017\n",
      "sid, nids, pose, score:  [396, 4631] [-0.425526    2.54973991  1.66513321] 0.804923255630837\n",
      "Submap 9 done..\n",
      "Submap 10 done..\n",
      "sid, nids, pose, score:  [484, 4704] [0.06717233 0.80797272 1.52000472] 0.8170722248502743\n",
      "sid, nids, pose, score:  [484, 4706] [0.10159778 1.29233386 1.51655873] 0.8138396344110121\n",
      "sid, nids, pose, score:  [484, 4708] [0.183306   1.72000141 1.505282  ] 0.8441199070949328\n",
      "sid, nids, pose, score:  [484, 4709] [0.18135575 1.94181321 1.51460016] 0.8267815648785207\n",
      "sid, nids, pose, score:  [484, 4711] [0.20517265 2.34938206 1.51796664] 0.8063911608025854\n",
      "sid, nids, pose, score:  [484, 4712] [0.20492837 2.61521374 1.52813386] 0.8098064870233981\n",
      "sid, nids, pose, score:  [484, 4714] [0.27137212 2.98359265 1.53068643] 0.851342576592447\n",
      "sid, nids, pose, score:  [484, 4715] [0.27473523 3.22565314 1.53368119] 0.8424714890627925\n",
      "sid, nids, pose, score:  [484, 4716] [0.2897298  3.33991567 1.53349799] 0.8512089360159701\n",
      "sid, nids, pose, score:  [484, 4717] [0.29863882 3.6854837  1.5352485 ] 0.8359172512953296\n",
      "sid, nids, pose, score:  [484, 4718] [0.35457365 3.78888705 1.53348127] 0.856573574969974\n",
      "sid, nids, pose, score:  [484, 4719] [0.44636652 4.06168971 1.53113988] 0.8252144533602104\n",
      "sid, nids, pose, score:  [484, 4720] [0.35837664 4.09173991 1.53145612] 0.8594824559739687\n",
      "sid, nids, pose, score:  [484, 4721] [0.37353099 4.28878667 1.5303916 ] 0.8358064482687609\n",
      "sid, nids, pose, score:  [484, 4722] [0.38314051 4.66592783 1.52775647] 0.8395731435734543\n",
      "sid, nids, pose, score:  [484, 4723] [0.40136852 4.8654342  1.52490451] 0.8441847618805017\n",
      "sid, nids, pose, score:  [484, 4724] [0.40583416 5.05025459 1.5246286 ] 0.831400155678796\n",
      "sid, nids, pose, score:  [484, 4725] [0.47398901 5.18656066 1.52017043] 0.8694259516814262\n",
      "sid, nids, pose, score:  [484, 4726] [0.43243007 5.34079428 1.52086235] 0.8312446352851018\n",
      "Submap 11 done..\n",
      "sid, nids, pose, score:  [528, 4766] [0.26608941 4.16352953 1.58970224] 0.8664361294198126\n",
      "sid, nids, pose, score:  [528, 4771] [0.42405243 5.35723321 1.58244446] 0.8535954330650555\n",
      "Submap 12 done..\n",
      "sid, nids, pose, score:  [572, 4812] [-0.03493981  4.89571348  1.56789628] 0.8222915634279323\n",
      "Submap 13 done..\n",
      "sid, nids, pose, score:  [616, 4856] [-0.10065854  4.33722954  1.5766629 ] 0.8115636211044989\n",
      "sid, nids, pose, score:  [616, 4857] [-0.12718271  4.53530781  1.58662948] 0.8204348464571035\n",
      "sid, nids, pose, score:  [616, 4859] [-0.02512238  4.83779703  1.57678712] 0.8098817324918525\n",
      "sid, nids, pose, score:  [616, 4861] [0.00801303 5.29119765 1.57237524] 0.8150994796434875\n",
      "sid, nids, pose, score:  [616, 4862] [3.73231043e-03 5.53146349e+00 1.58617908e+00] 0.8607359407518268\n",
      "Submap 14 done..\n",
      "sid, nids, pose, score:  [660, 4892] [0.1696331  2.62821695 1.55062399] 0.8194332687667398\n",
      "sid, nids, pose, score:  [660, 4893] [0.21964088 2.77607686 1.55183594] 0.8259910193775083\n",
      "sid, nids, pose, score:  [660, 4894] [0.15263492 3.01161306 1.55670389] 0.834949519011852\n",
      "sid, nids, pose, score:  [660, 4895] [0.14688825 3.21835895 1.55417999] 0.8543245189179552\n",
      "sid, nids, pose, score:  [660, 4900] [0.27317841 4.33367009 1.61942515] 0.8008459343496355\n",
      "sid, nids, pose, score:  [660, 4903] [0.26318898 5.02069646 1.63505769] 0.8252867031063265\n",
      "sid, nids, pose, score:  [660, 4912] [0.23015835 6.86805046 1.6163851 ] 0.8205511068366583\n",
      "Submap 15 done..\n",
      "sid, nids, pose, score:  [704, 4937] [0.14797839 2.51089259 1.63440972] 0.8131786992935875\n",
      "sid, nids, pose, score:  [704, 4943] [0.1064871  3.78599758 1.59066083] 0.8096986482218308\n",
      "sid, nids, pose, score:  [704, 4944] [0.14307654 4.03634571 1.59362783] 0.8236363185120498\n",
      "Submap 16 done..\n",
      "Submap 17 done..\n",
      "Submap 18 done..\n",
      "sid, nids, pose, score:  [836, 5074] [0.23954183 3.71102211 1.57709925] 0.8063006209045221\n",
      "sid, nids, pose, score:  [836, 5084] [0.30239692 5.71849325 1.57330646] 0.803448343602525\n",
      "sid, nids, pose, score:  [836, 5085] [0.35724677 5.99717331 1.57262935] 0.816547087149252\n",
      "sid, nids, pose, score:  [836, 5086] [0.36303408 6.20672603 1.56755013] 0.8119414380413741\n",
      "Submap 19 done..\n",
      "sid, nids, pose, score:  [880, 5127] [0.1717676  5.07389123 1.55847938] 0.8090873597101906\n",
      "Submap 20 done..\n",
      "sid, nids, pose, score:  [924, 5171] [0.16949887 4.83633001 1.62625372] 0.8083554893792096\n",
      "sid, nids, pose, score:  [924, 5172] [0.13253035 5.15586288 1.62444227] 0.8003698600772331\n",
      "Submap 21 done..\n",
      "sid, nids, pose, score:  [968, 5219] [0.26415146 5.65522039 1.51732762] 0.8328647982861823\n",
      "sid, nids, pose, score:  [968, 5224] [0.41121693 6.76642542 1.55487761] 0.8372063479562285\n",
      "Submap 22 done..\n",
      "sid, nids, pose, score:  [1012, 5250] [-0.00625829  2.59467345  1.55513972] 0.8101531519659085\n",
      "sid, nids, pose, score:  [1012, 5251] [0.02571525 2.84982823 1.54321873] 0.8109651437551749\n",
      "sid, nids, pose, score:  [1012, 5270] [0.23013814 6.94863535 1.54153558] 0.819529554052317\n",
      "sid, nids, pose, score:  [1012, 5271] [0.24034295 7.19915292 1.54002443] 0.8239642814637345\n",
      "Submap 23 done..\n",
      "Submap 24 done..\n",
      "sid, nids, pose, score:  [1100, 5329] [0.06271004 1.37539049 1.60159515] 0.8058820235777416\n",
      "sid, nids, pose, score:  [1100, 5331] [0.09433881 1.86132147 1.58035979] 0.816642965626809\n",
      "sid, nids, pose, score:  [1100, 5332] [0.09211362 1.98390738 1.58410247] 0.8245028554196794\n",
      "sid, nids, pose, score:  [1100, 5333] [0.2233508  2.2844378  1.59294003] 0.8073368178624868\n",
      "sid, nids, pose, score:  [1100, 5334] [0.08621009 2.48742442 1.61550495] 0.8297439043201496\n",
      "sid, nids, pose, score:  [1100, 5335] [0.12962848 2.70178422 1.63219944] 0.8444345987122903\n",
      "sid, nids, pose, score:  [1100, 5336] [0.12908642 2.93737854 1.64852399] 0.8331318295790104\n",
      "sid, nids, pose, score:  [1100, 5337] [0.11128236 3.15839415 1.65383695] 0.8429270139391669\n",
      "sid, nids, pose, score:  [1100, 5338] [0.07812235 3.45723434 1.67081939] 0.8310656909660853\n",
      "sid, nids, pose, score:  [1100, 5340] [0.08741741 3.80625012 1.67529271] 0.850661967437098\n",
      "Submap 25 done..\n",
      "sid, nids, pose, score:  [1144, 5367] [0.07739196 0.16470778 1.54165296] 0.8319346364279288\n",
      "sid, nids, pose, score:  [1144, 5368] [0.13466519 0.28978987 1.54580507] 0.8026202099161713\n",
      "sid, nids, pose, score:  [1144, 5369] [0.13922288 0.48500319 1.54795427] 0.8136064974532945\n",
      "sid, nids, pose, score:  [1144, 5372] [0.19810554 1.11170625 1.55508075] 0.8226580659379427\n",
      "sid, nids, pose, score:  [1144, 5373] [0.19840545 1.37522964 1.55541188] 0.8397651300184125\n",
      "sid, nids, pose, score:  [1144, 5374] [0.1981932  1.59216015 1.5510757 ] 0.8547140525604949\n",
      "sid, nids, pose, score:  [1144, 5375] [0.20575879 1.8188399  1.558487  ] 0.8624488392489555\n",
      "sid, nids, pose, score:  [1144, 5376] [0.26207869 2.04610265 1.56533742] 0.8461855172898668\n",
      "sid, nids, pose, score:  [1144, 5377] [0.2565093  2.28787003 1.57995393] 0.8765229853012654\n",
      "sid, nids, pose, score:  [1144, 5378] [0.25789544 2.51711384 1.58916212] 0.8667217012566198\n",
      "sid, nids, pose, score:  [1144, 5379] [0.26566506 2.72139967 1.58644177] 0.8829141162287871\n",
      "sid, nids, pose, score:  [1144, 5380] [0.17280812 2.98546779 1.58474378] 0.852006257506485\n",
      "sid, nids, pose, score:  [1144, 5382] [0.2988435  3.30432992 1.58768608] 0.8550395592252856\n",
      "sid, nids, pose, score:  [1144, 5383] [0.29195059 3.64333835 1.58194638] 0.8596545014931977\n",
      "Submap 26 done..\n",
      "sid, nids, pose, score:  [1188, 5411] [0.20101382 0.50579584 1.63560714] 0.827372950261459\n",
      "sid, nids, pose, score:  [1188, 5413] [0.17392241 0.95768039 1.63445666] 0.8278300149975928\n",
      "sid, nids, pose, score:  [1188, 5414] [0.19644662 1.22911672 1.62800969] 0.8267350535351345\n",
      "sid, nids, pose, score:  [1188, 5415] [0.18144983 1.44809827 1.61584198] 0.833127336714282\n",
      "sid, nids, pose, score:  [1188, 5416] [0.16696885 1.71192044 1.60939505] 0.8257321681864797\n",
      "sid, nids, pose, score:  [1188, 5417] [0.15781113 1.91207869 1.60922832] 0.8213076756652353\n",
      "sid, nids, pose, score:  [1188, 5418] [0.14581968 2.16769956 1.60321872] 0.8206286263648608\n",
      "sid, nids, pose, score:  [1188, 5419] [0.18878296 2.32400625 1.60453853] 0.8513804017717563\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sid, nids, pose, score:  [1188, 5420] [0.17795226 2.57960564 1.60189552] 0.8334381453122937\n",
      "sid, nids, pose, score:  [1188, 5421] [0.16814708 2.83644789 1.60263923] 0.8229485800248194\n",
      "sid, nids, pose, score:  [1188, 5422] [0.1695352  3.02172771 1.60634683] 0.8497685175093412\n",
      "sid, nids, pose, score:  [1188, 5423] [0.15934042 3.2769838  1.60386139] 0.830787673505\n",
      "sid, nids, pose, score:  [1188, 5424] [0.20479195 3.46821092 1.60549671] 0.847547942182071\n",
      "sid, nids, pose, score:  [1188, 5425] [0.18725333 3.67405099 1.60485571] 0.8345853451526285\n",
      "sid, nids, pose, score:  [1188, 5426] [0.18099366 3.87484967 1.60192129] 0.8312018834389227\n",
      "Submap 27 done..\n",
      "Submap 28 done..\n",
      "Submap 29 done..\n",
      "Submap 30 done..\n",
      "Submap 31 done..\n",
      "Submap 32 done..\n",
      "Submap 33 done..\n",
      "Submap 34 done..\n",
      "Submap 35 done..\n",
      "Submap 36 done..\n",
      "Submap 37 done..\n",
      "Submap 38 done..\n",
      "sid, nids, pose, score:  [1716, 8607] [-0.52823312  2.21549152  1.8048449 ] 0.8253968819657068\n",
      "sid, nids, pose, score:  [1716, 8609] [-0.62221769  2.67064542  1.81759383] 0.8351141979126112\n",
      "sid, nids, pose, score:  [1716, 8611] [-0.76105738  3.1381598   1.81070207] 0.8195279664924457\n",
      "sid, nids, pose, score:  [1716, 8612] [-0.78052529  3.27322618  1.8096888 ] 0.8418584441419154\n",
      "Submap 39 done..\n",
      "sid, nids, pose, score:  [1760, 421] [ 0.22603457  3.90610952 -1.61983189] 0.8648371762241093\n",
      "sid, nids, pose, score:  [1760, 422] [ 0.22564637  3.70838319 -1.61450137] 0.8450940454634281\n",
      "sid, nids, pose, score:  [1760, 423] [ 0.21571786  3.50182386 -1.61021071] 0.8428639767412924\n",
      "sid, nids, pose, score:  [1760, 424] [ 0.21611373  3.30327147 -1.60873892] 0.8389060722919797\n",
      "sid, nids, pose, score:  [1760, 8639] [0.04886715 0.05242014 1.57377384] 0.8390277959883861\n",
      "sid, nids, pose, score:  [1760, 8641] [0.05305716 0.53163268 1.57971144] 0.8613516364748917\n",
      "sid, nids, pose, score:  [1760, 8642] [0.05133685 0.72840516 1.58094864] 0.8522736184657316\n",
      "sid, nids, pose, score:  [1760, 8643] [0.04728299 0.98286617 1.58272684] 0.8569112169199024\n",
      "sid, nids, pose, score:  [1760, 8644] [0.04135125 1.19791838 1.57692804] 0.845054550529311\n",
      "sid, nids, pose, score:  [1760, 8645] [0.05084399 1.53074637 1.56734479] 0.8372450636667671\n",
      "sid, nids, pose, score:  [1760, 8646] [0.16205542 1.74432942 1.56883069] 0.8487693087829447\n",
      "sid, nids, pose, score:  [1760, 8647] [0.09984576 1.76855502 1.5621152 ] 0.8700974247843473\n",
      "sid, nids, pose, score:  [1760, 8648] [0.08756108 2.0079815  1.56955832] 0.870378897274596\n",
      "sid, nids, pose, score:  [1760, 8649] [0.0985909  2.14902039 1.56655863] 0.8785413266314833\n",
      "sid, nids, pose, score:  [1760, 8650] [0.08839372 2.4174834  1.56297844] 0.8601763758753848\n",
      "sid, nids, pose, score:  [1760, 8651] [0.07959697 2.68233199 1.53453943] 0.8623551576703681\n",
      "sid, nids, pose, score:  [1760, 8652] [0.19269764 2.86748309 1.50577254] 0.8687936546112275\n",
      "sid, nids, pose, score:  [1760, 8653] [0.23770393 3.09993931 1.49762277] 0.8582952050747592\n",
      "sid, nids, pose, score:  [1760, 8654] [0.26533108 3.21429221 1.50850487] 0.8502528509105823\n",
      "Submap 40 done..\n",
      "Submap 41 done..\n",
      "Submap 42 done..\n",
      "Submap 43 done..\n",
      "Submap 44 done..\n",
      "Submap 45 done..\n",
      "Submap 46 done..\n",
      "Submap 47 done..\n",
      "Submap 48 done..\n",
      "Submap 49 done..\n",
      "Submap 50 done..\n",
      "Submap 51 done..\n",
      "Submap 52 done..\n",
      "sid, nids, pose, score:  [2332, 5559] [ 1.55216812  1.91609752 -0.0677648 ] 0.8355592706988181\n",
      "sid, nids, pose, score:  [2332, 5560] [ 1.81495508  1.84028093 -0.08357622] 0.8088637876639948\n",
      "sid, nids, pose, score:  [2332, 5561] [ 2.01524385  1.83789233 -0.08874562] 0.8137200874484931\n",
      "sid, nids, pose, score:  [2332, 5562] [ 2.19369178  1.79730029 -0.03529146] 0.8319185472110774\n",
      "sid, nids, pose, score:  [2332, 5574] [4.82227091 1.7533054  0.00888459] 0.859177212796613\n",
      "Submap 53 done..\n",
      "sid, nids, pose, score:  [2376, 5593] [0.09831313 1.53793729 1.56578837] 0.8217527715201683\n",
      "sid, nids, pose, score:  [2376, 5594] [0.13023736 1.78289324 1.56779455] 0.8215515221167897\n",
      "sid, nids, pose, score:  [2376, 5596] [0.12207591 2.23612087 1.56806098] 0.8105428432865958\n",
      "sid, nids, pose, score:  [2376, 5597] [0.06890881 2.41127626 1.5895132 ] 0.8039944328232447\n",
      "sid, nids, pose, score:  [2376, 5609] [0.14275208 2.100078   1.6036632 ] 0.8030852815220146\n",
      "sid, nids, pose, score:  [2376, 5612] [0.14686917 2.71495301 1.59482153] 0.8232986122064478\n",
      "Submap 54 done..\n",
      "sid, nids, pose, score:  [2420, 5632] [-0.03007238  0.65829824  1.57220036] 0.8079717372036799\n",
      "sid, nids, pose, score:  [2420, 5633] [-0.05301232  0.76959163  1.57851283] 0.8052784148644944\n",
      "sid, nids, pose, score:  [2420, 5634] [0.0326608  1.13125037 1.59965591] 0.8152368911704784\n",
      "sid, nids, pose, score:  [2420, 5635] [-0.01930961  1.32162143  1.62086131] 0.8586255422208233\n",
      "sid, nids, pose, score:  [2420, 5636] [-0.01522175  1.5652166   1.63290408] 0.8188090955422003\n",
      "sid, nids, pose, score:  [2420, 5637] [-0.04755762  1.81076269  1.63710755] 0.8059355058908917\n",
      "sid, nids, pose, score:  [2420, 5638] [0.06496561 2.01872187 1.64102481] 0.8209141952707132\n",
      "sid, nids, pose, score:  [2420, 5639] [-0.08984854  2.26057256  1.62969053] 0.856224179122046\n",
      "sid, nids, pose, score:  [2420, 5640] [-0.11125319  2.51037199  1.62850866] 0.8362215998546206\n",
      "sid, nids, pose, score:  [2420, 5641] [-0.08580927  2.76369964  1.62889599] 0.8578115670658586\n",
      "sid, nids, pose, score:  [2420, 5642] [-0.10015916  2.96451582  1.62674925] 0.854729474973309\n",
      "sid, nids, pose, score:  [2420, 5643] [-0.1021741   3.08483289  1.62117597] 0.8305380035487774\n",
      "sid, nids, pose, score:  [2420, 5644] [-0.1194751   3.33615474  1.61812354] 0.8344369792708205\n",
      "sid, nids, pose, score:  [2420, 5645] [1.43440317e-03 3.67875651e+00 1.61612184e+00] 0.8470787235947997\n",
      "sid, nids, pose, score:  [2420, 5646] [-0.08710705  3.93057444  1.60621045] 0.8629193371234344\n",
      "sid, nids, pose, score:  [2420, 5647] [-0.08252763  4.05238803  1.59861836] 0.8594941282098584\n",
      "sid, nids, pose, score:  [2420, 5648] [-0.0845571   4.39081547  1.59087872] 0.8511043071733737\n",
      "sid, nids, pose, score:  [2420, 5649] [-0.07064562  4.51502819  1.58306486] 0.8471945950942291\n",
      "sid, nids, pose, score:  [2420, 5650] [-0.06641076  4.77047348  1.57138488] 0.8553346519762584\n",
      "sid, nids, pose, score:  [2420, 5651] [-0.01250604  5.05715321  1.57358119] 0.8393191330380931\n",
      "sid, nids, pose, score:  [2420, 5652] [-0.00736986  5.17883726  1.56114244] 0.86018205171971\n",
      "sid, nids, pose, score:  [2420, 5653] [-0.02649308  5.50692149  1.57360213] 0.8316347918656438\n",
      "sid, nids, pose, score:  [2420, 5654] [-0.04926452  5.6196248   1.57460016] 0.824850113130303\n",
      "sid, nids, pose, score:  [2420, 5655] [-0.0171619   5.97956595  1.62177844] 0.8441223733340105\n",
      "sid, nids, pose, score:  [2420, 5656] [-0.01640786  5.98999676  1.63107692] 0.8384373603530688\n",
      "sid, nids, pose, score:  [2420, 5657] [-0.04499467  6.32191207  1.63132873] 0.8403255843307738\n",
      "sid, nids, pose, score:  [2420, 5658] [-0.0551346   6.57229685  1.62918815] 0.8301483395183503\n",
      "sid, nids, pose, score:  [2420, 5659] [0.05787423 6.83027763 1.63421437] 0.8102004268544843\n",
      "Submap 55 done..\n",
      "Submap 56 done..\n",
      "Submap 57 done..\n",
      "Submap 58 done..\n",
      "Submap 59 done..\n",
      "Submap 60 done..\n",
      "Submap 61 done..\n",
      "Submap 62 done..\n",
      "Submap 63 done..\n",
      "Submap 64 done..\n",
      "Submap 65 done..\n",
      "Submap 66 done..\n",
      "sid, nids, pose, score:  [2948, 225] [-4.53470697  4.1522447  -0.00797812] 0.8657036293724977\n",
      "sid, nids, pose, score:  [2948, 226] [-4.19361976  4.15030816 -0.0170547 ] 0.8676326984571556\n",
      "sid, nids, pose, score:  [2948, 227] [-4.06333742  4.15601358 -0.01358054] 0.8297889724622457\n",
      "sid, nids, pose, score:  [2948, 228] [-3.8497453   4.15484003 -0.03225259] 0.8545623419713179\n",
      "sid, nids, pose, score:  [2948, 229] [-3.58639695  4.10149054 -0.03782811] 0.8739750011778034\n",
      "sid, nids, pose, score:  [2948, 230] [-3.25715185  4.09046997 -0.03963572] 0.875041537993781\n",
      "sid, nids, pose, score:  [2948, 231] [-3.18728635  4.09416664 -0.03400356] 0.8646970938730101\n",
      "sid, nids, pose, score:  [2948, 232] [-2.99948628  4.09076401 -0.02258087] 0.8291084128890729\n",
      "sid, nids, pose, score:  [2948, 233] [-2.80995876  4.08808607 -0.0163229 ] 0.8423351311921203\n",
      "sid, nids, pose, score:  [2948, 234] [-2.50158343  4.08334574 -0.00884801] 0.851142445125498\n",
      "sid, nids, pose, score:  [2948, 235] [-2.37733943e+00  4.03409398e+00  3.99758216e-03] 0.869959415414792\n",
      "sid, nids, pose, score:  [2948, 236] [-2.16864612  4.0420833   0.02868705] 0.8655453100759825\n",
      "sid, nids, pose, score:  [2948, 237] [-1.90636895  3.92242233  0.03952246] 0.8262178177313356\n",
      "sid, nids, pose, score:  [2948, 238] [-1.73225168  4.01190875  0.04116867] 0.836409498158217\n",
      "sid, nids, pose, score:  [2948, 239] [-1.51231551  3.93677501  0.04301508] 0.8321907465283426\n",
      "sid, nids, pose, score:  [2948, 240] [-1.38924282  4.02968796  0.04077057] 0.8523160413006645\n",
      "sid, nids, pose, score:  [2948, 241] [-1.14133695  4.04677514  0.03943274] 0.8401202983854109\n",
      "sid, nids, pose, score:  [2948, 243] [-0.66477647  3.94613061  0.04505282] 0.8263336535277408\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sid, nids, pose, score:  [2948, 244] [-0.53401169  4.0394981   0.03824114] 0.8269515021327645\n",
      "Submap 67 done..\n",
      "sid, nids, pose, score:  [2992, 205] [-0.11393328  3.65449034 -1.55195467] 0.8111621086104355\n",
      "sid, nids, pose, score:  [2992, 4423] [-0.09563981  3.22642185 -1.59935789] 0.8343758362000072\n",
      "sid, nids, pose, score:  [2992, 4425] [-0.17015413  2.82040973 -1.60376925] 0.8551617945480989\n",
      "sid, nids, pose, score:  [2992, 4428] [-0.17485059  2.1033511  -1.56985786] 0.8065209118709429\n",
      "sid, nids, pose, score:  [2992, 4430] [-0.23580913  1.77555325 -1.50318922] 0.8307993895630086\n",
      "sid, nids, pose, score:  [2992, 8866] [0.25319153 2.27734754 1.62789572] 0.8092651654322799\n",
      "Submap 68 done..\n",
      "Submap 69 done..\n",
      "Submap 70 done..\n",
      "Submap 71 done..\n",
      "Submap 72 done..\n",
      "Submap 73 done..\n",
      "Submap 74 done..\n",
      "Submap 75 done..\n",
      "Submap 76 done..\n",
      "Submap 77 done..\n",
      "Submap 78 done..\n",
      "Submap 79 done..\n",
      "Submap 80 done..\n",
      "Submap 81 done..\n",
      "sid, nids, pose, score:  [3608, 5835] [0.86462559 7.31558969 1.5773943 ] 0.8582752375183328\n",
      "sid, nids, pose, score:  [3608, 5837] [0.94120132 7.85043229 1.52596887] 0.832636633327331\n",
      "Submap 82 done..\n",
      "sid, nids, pose, score:  [3652, 5853] [0.12830778 2.39595232 1.5780489 ] 0.8038595196167341\n",
      "sid, nids, pose, score:  [3652, 5872] [0.2811277  6.48306863 1.64552002] 0.8321546559268412\n",
      "sid, nids, pose, score:  [3652, 5874] [0.35536819 7.0703327  1.6434721 ] 0.8281069861321397\n",
      "sid, nids, pose, score:  [3652, 5875] [0.3080745  7.29894961 1.67195201] 0.8124863059069136\n",
      "Submap 83 done..\n",
      "sid, nids, pose, score:  [3696, 5906] [1.28554919 4.11845387 0.19711524] 0.8228625434402357\n",
      "Submap 84 done..\n",
      "sid, nids, pose, score:  [3740, 5928] [0.18460306 0.68827252 1.63597082] 0.8086347275641552\n",
      "sid, nids, pose, score:  [3740, 5936] [0.15014353 2.45290304 1.47509517] 0.8226555356966326\n",
      "sid, nids, pose, score:  [3740, 5944] [0.3774291  4.15238184 1.55779825] 0.8215823005379033\n",
      "sid, nids, pose, score:  [3740, 5945] [0.37427983 4.54154172 1.55878931] 0.8174085322126385\n",
      "sid, nids, pose, score:  [3740, 5946] [0.37763066 4.7280278  1.56712133] 0.8406293144305902\n",
      "Submap 85 done..\n",
      "sid, nids, pose, score:  [3784, 5985] [0.39702131 4.34916363 1.51767022] 0.8214023478900047\n",
      "sid, nids, pose, score:  [3784, 5987] [0.40628638 4.812632   1.51585488] 0.833986493057586\n",
      "Submap 86 done..\n",
      "Submap 87 done..\n",
      "Submap 88 done..\n",
      "Submap 89 done..\n",
      "Submap 90 done..\n",
      "sid, nids, pose, score:  [4004, 7649] [-0.29234186  1.93685911  1.60133801] 0.8062141448679374\n",
      "sid, nids, pose, score:  [4004, 7657] [-0.14266819  3.59858343  1.45027801] 0.8117447340566082\n",
      "sid, nids, pose, score:  [4004, 7658] [-0.10892212  3.86843085  1.42953486] 0.8212132709118104\n",
      "Submap 91 done..\n",
      "sid, nids, pose, score:  [4048, 7689] [-0.05825839  2.05612729  1.60416789] 0.8250175468420274\n",
      "sid, nids, pose, score:  [4048, 7690] [-0.04548937  2.28486625  1.60139171] 0.8419686847756862\n",
      "sid, nids, pose, score:  [4048, 7698] [0.01328912 3.87877929 1.57950444] 0.8089087012652307\n",
      "sid, nids, pose, score:  [4048, 7699] [0.0161402  4.2263169  1.56849048] 0.8165031094156573\n",
      "sid, nids, pose, score:  [4048, 7700] [0.06722622 4.29379419 1.55761351] 0.8189171903834714\n",
      "sid, nids, pose, score:  [4048, 7701] [0.07224138 4.50262118 1.54818569] 0.8381792275677511\n",
      "sid, nids, pose, score:  [4048, 7702] [0.08079965 4.76419206 1.53637619] 0.8306665264151818\n",
      "sid, nids, pose, score:  [4048, 7703] [0.13690259 5.01031139 1.54209797] 0.8226656184910537\n",
      "Submap 92 done..\n",
      "Submap 93 done..\n",
      "Submap 94 done..\n",
      "Submap 95 done..\n",
      "sid, nids, pose, score:  [4224, 27] [2.47767248 1.76596988 0.21520266] 0.8207107148569356\n",
      "sid, nids, pose, score:  [4224, 29] [2.98286351 1.86312097 0.23848753] 0.8219579033781003\n",
      "sid, nids, pose, score:  [4224, 30] [3.2563105  1.88478143 0.23369493] 0.8319602955073061\n",
      "Submap 96 done..\n",
      "sid, nids, pose, score:  [4268, 59] [-0.00274145  1.53445509  1.63660172] 0.8252933023238909\n",
      "sid, nids, pose, score:  [4268, 62] [0.06342966 2.2216079  1.61109629] 0.8387741621327341\n",
      "sid, nids, pose, score:  [4268, 65] [0.05980734 2.74181467 1.5751363 ] 0.8652957105798558\n",
      "sid, nids, pose, score:  [4268, 66] [0.04647839 2.96457124 1.56348528] 0.8739456418002011\n",
      "sid, nids, pose, score:  [4268, 67] [0.03882578 3.24085293 1.55622662] 0.8638832598170578\n",
      "Submap 97 done..\n",
      "Submap 98 done..\n",
      "sid, nids, pose, score:  [4356, 146] [0.2948239  1.6698622  1.56794156] 0.8099125478982457\n",
      "sid, nids, pose, score:  [4356, 150] [0.31902713 2.40654583 1.46250842] 0.8580771057431315\n",
      "sid, nids, pose, score:  [4356, 151] [0.47295354 2.71235489 1.43631852] 0.8411670213931757\n",
      "sid, nids, pose, score:  [4356, 154] [0.614549   3.30259888 1.43165555] 0.8422571522463598\n",
      "sid, nids, pose, score:  [4356, 8924] [ 1.11084779  7.80535647 -1.62998916] 0.8024235860641986\n",
      "Submap 99 done..\n",
      "sid, nids, pose, score:  [4400, 186] [0.42511523 3.28763597 1.52144361] 0.8040578055009989\n",
      "sid, nids, pose, score:  [4400, 189] [0.26931106 1.05929957 1.55362735] 0.8083240273327031\n",
      "sid, nids, pose, score:  [4400, 191] [0.41874107 1.60528987 1.56223016] 0.8147730817875528\n",
      "sid, nids, pose, score:  [4400, 192] [0.41446444 1.87594644 1.56331909] 0.8405595867017347\n",
      "sid, nids, pose, score:  [4400, 193] [0.3304755  1.99488702 1.5623913 ] 0.8343270374933287\n",
      "sid, nids, pose, score:  [4400, 194] [0.33326301 2.32747926 1.55933853] 0.8442542117083552\n",
      "sid, nids, pose, score:  [4400, 195] [0.45188848 2.46265834 1.5652497 ] 0.8454034643712709\n",
      "sid, nids, pose, score:  [4400, 196] [0.46656061 2.78157575 1.56183948] 0.8577013892620271\n",
      "sid, nids, pose, score:  [4400, 197] [0.48192054 3.05017432 1.55764547] 0.8417662307978615\n",
      "sid, nids, pose, score:  [4400, 8879] [ 0.13665751  3.53654648 -1.61026075] 0.8321159601326511\n",
      "sid, nids, pose, score:  [4400, 8880] [ 0.13781979  3.22444512 -1.60178918] 0.821219299227873\n",
      "sid, nids, pose, score:  [4400, 8881] [ 0.1289732   3.10740372 -1.59014706] 0.8153585766032828\n",
      "sid, nids, pose, score:  [4400, 8882] [ 0.09193873  2.99348321 -1.57680405] 0.8189489965349585\n",
      "sid, nids, pose, score:  [4400, 8884] [ 0.10931565  2.4441779  -1.54946522] 0.8188893002989719\n",
      "sid, nids, pose, score:  [4400, 8886] [ 0.0788716   2.07385055 -1.5381335 ] 0.8152047761922728\n",
      "sid, nids, pose, score:  [4400, 8888] [ 0.09328878  1.63557854 -1.53425359] 0.8003946880358093\n",
      "Submap 100 done..\n",
      "sid, nids, pose, score:  [4444, 228] [-0.00491931  0.15116178  1.51003038] 0.8038109199469288\n",
      "sid, nids, pose, score:  [4444, 229] [0.05462193 0.41808013 1.4977882 ] 0.8067598601121542\n",
      "sid, nids, pose, score:  [4444, 231] [0.07938117 0.81565058 1.50494608] 0.8259999119693794\n",
      "sid, nids, pose, score:  [4444, 233] [0.11268692 1.17858191 1.52596007] 0.8363852590508992\n",
      "sid, nids, pose, score:  [4444, 234] [0.13540848 1.47906927 1.53010163] 0.8480748636182235\n",
      "sid, nids, pose, score:  [4444, 235] [0.18844113 1.65273038 1.54628055] 0.8258804569654821\n",
      "sid, nids, pose, score:  [4444, 236] [0.20756255 1.84101244 1.57430335] 0.833041670476888\n",
      "sid, nids, pose, score:  [4444, 237] [0.20612784 2.0893958  1.57828754] 0.8363489798005284\n",
      "sid, nids, pose, score:  [4444, 239] [0.22506631 2.47335468 1.58196472] 0.8425945386334418\n",
      "sid, nids, pose, score:  [4444, 240] [0.25839286 2.61356256 1.58305354] 0.8304550839624683\n",
      "sid, nids, pose, score:  [4444, 241] [0.26962879 2.79213589 1.58838238] 0.8104311812817802\n",
      "sid, nids, pose, score:  [4444, 242] [0.26393134 3.12373949 1.58573162] 0.8154950076158582\n",
      "sid, nids, pose, score:  [4444, 243] [0.24581157 3.25141668 1.58733579] 0.8409009744520372\n",
      "Submap 101 done..\n",
      "sid, nids, pose, score:  [4488, 274] [-0.02565336  0.41861955  1.594844  ] 0.8020557177639942\n",
      "sid, nids, pose, score:  [4488, 277] [0.01476466 1.02678707 1.58460851] 0.8189145300753775\n",
      "sid, nids, pose, score:  [4488, 285] [0.04164745 2.8319085  1.56549048] 0.8214146511549002\n",
      "Submap 102 done..\n",
      "sid, nids, pose, score:  [4532, 321] [0.17961037 1.18916562 1.49146437] 0.8104692628395946\n",
      "sid, nids, pose, score:  [4532, 322] [0.1971861  1.37084538 1.50233108] 0.8288766512973041\n",
      "sid, nids, pose, score:  [4532, 324] [0.2735464  1.75908643 1.50504475] 0.8348917645878087\n",
      "sid, nids, pose, score:  [4532, 325] [0.28230586 1.87096288 1.50696066] 0.8249212820912292\n",
      "sid, nids, pose, score:  [4532, 326] [0.30113768 2.2076688  1.50862251] 0.8499708314600413\n",
      "sid, nids, pose, score:  [4532, 327] [0.31695493 2.45587976 1.50584116] 0.8487605240355875\n",
      "sid, nids, pose, score:  [4532, 328] [0.33078047 2.71184857 1.50312334] 0.8476353738977349\n",
      "sid, nids, pose, score:  [4532, 329] [0.39162726 2.85984924 1.50302042] 0.8512729226655255\n",
      "sid, nids, pose, score:  [4532, 330] [0.4001873  3.06908804 1.49901062] 0.8638398440740913\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sid, nids, pose, score:  [4532, 331] [0.41510638 3.3262577  1.49518321] 0.8574095283067351\n",
      "sid, nids, pose, score:  [4532, 8755] [ 0.40303302  2.1089437  -1.66530171] 0.8171855275557572\n",
      "Submap 103 done..\n",
      "Submap 104 done..\n",
      "sid, nids, pose, score:  [4620, 401] [ 0.17601456 -0.22871076  1.43673704] 0.8008683111076742\n",
      "sid, nids, pose, score:  [4620, 402] [0.25921555 0.01968723 1.43346653] 0.8080913453977521\n",
      "sid, nids, pose, score:  [4620, 404] [0.34657483 0.48860853 1.47424863] 0.8118716208588213\n",
      "sid, nids, pose, score:  [4620, 411] [0.50567807 1.85923885 1.48340401] 0.8400894006302416\n",
      "sid, nids, pose, score:  [4620, 413] [1.06673342 5.13770752 1.47660424] 0.8227278729925118\n",
      "sid, nids, pose, score:  [4620, 417] [0.64374781 3.17173782 1.42170678] 0.8894656100289853\n",
      "sid, nids, pose, score:  [4620, 418] [0.66078352 3.34318474 1.39519477] 0.8887348819325632\n",
      "sid, nids, pose, score:  [4620, 1785] [ 0.40163399  2.32718572 -1.68061882] 0.8405300868306257\n",
      "sid, nids, pose, score:  [4620, 1786] [ 0.3737319   2.12655831 -1.68460539] 0.8221821223649225\n",
      "sid, nids, pose, score:  [4620, 1792] [ 0.19627746  0.85999028 -1.7251122 ] 0.8096792637235933\n",
      "Submap 105 done..\n",
      "sid, nids, pose, score:  [4664, 450] [0.43482487 0.62780542 1.64844606] 0.8185043193787883\n",
      "sid, nids, pose, score:  [4664, 455] [0.36639048 1.59719391 1.64234482] 0.8438138499670121\n",
      "sid, nids, pose, score:  [4664, 456] [0.40020375 1.73994659 1.63975284] 0.8489063778767877\n",
      "sid, nids, pose, score:  [4664, 457] [0.37505276 1.87067657 1.63599874] 0.8401032604183609\n",
      "sid, nids, pose, score:  [4664, 459] [0.35893229 2.44361667 1.63770682] 0.8615989877004301\n",
      "sid, nids, pose, score:  [4664, 460] [0.33463441 2.60648198 1.62415267] 0.8644080347953257\n",
      "sid, nids, pose, score:  [4664, 463] [0.34978546 3.21924192 1.61909332] 0.8741725686122012\n",
      "sid, nids, pose, score:  [4664, 8613] [ 0.60650533  3.88313735 -1.51151175] 0.8453882412414586\n",
      "Submap 106 done..\n",
      "sid, nids, pose, score:  [4708, 8572] [ 0.0691236   3.3902677  -1.50716424] 0.8064062324632287\n",
      "sid, nids, pose, score:  [4708, 8574] [ 0.08501554  2.93966038 -1.50830998] 0.8254191768166989\n",
      "sid, nids, pose, score:  [4708, 8575] [ 0.17786337  2.6958671  -1.51235636] 0.8131037259324551\n",
      "Submap 107 done..\n",
      "sid, nids, pose, score:  [4752, 537] [0.01341109 0.70326121 1.49282788] 0.8149594652820892\n",
      "sid, nids, pose, score:  [4752, 538] [-0.03903464  0.86991548  1.4907262 ] 0.8065882926892888\n",
      "sid, nids, pose, score:  [4752, 540] [0.04967176 1.31072817 1.48885812] 0.8394461725507332\n",
      "sid, nids, pose, score:  [4752, 541] [0.07364477 1.54623644 1.49177778] 0.853077339559009\n",
      "sid, nids, pose, score:  [4752, 542] [0.16128069 1.75852873 1.51271768] 0.8497136424356156\n",
      "sid, nids, pose, score:  [4752, 544] [0.20792396 2.2379439  1.55956606] 0.8451171247417286\n",
      "sid, nids, pose, score:  [4752, 546] [0.26759256 2.5298166  1.57507881] 0.8313592248415983\n",
      "sid, nids, pose, score:  [4752, 547] [0.23523416 2.7377025  1.58511114] 0.8480002059008317\n",
      "Submap 108 done..\n",
      "sid, nids, pose, score:  [4796, 583] [0.15305984 0.80494396 1.56350452] 0.8142683862666041\n",
      "sid, nids, pose, score:  [4796, 584] [0.17053385 1.01228872 1.56063269] 0.8051976810503692\n",
      "sid, nids, pose, score:  [4796, 585] [0.16988012 1.22338514 1.55938587] 0.8169830905902821\n",
      "sid, nids, pose, score:  [4796, 586] [0.1491179  1.43791339 1.55617235] 0.8150579333187822\n",
      "sid, nids, pose, score:  [4796, 587] [0.17184108 1.68722896 1.54991047] 0.8086506891535395\n",
      "sid, nids, pose, score:  [4796, 588] [0.17481463 1.89192316 1.55473834] 0.8019679657781754\n",
      "sid, nids, pose, score:  [4796, 590] [0.23072519 2.41208919 1.54563377] 0.8122666286236033\n",
      "sid, nids, pose, score:  [4796, 594] [0.35622795 3.25875546 1.51075159] 0.8243994958360883\n",
      "Submap 109 done..\n",
      "Submap 110 done..\n",
      "Submap 111 done..\n",
      "sid, nids, pose, score:  [4928, 713] [-0.00710308  1.6787698   1.57247199] 0.8235756177744543\n",
      "sid, nids, pose, score:  [4928, 714] [0.07481919 1.89320518 1.58906825] 0.8251967633318926\n",
      "sid, nids, pose, score:  [4928, 715] [0.08710987 2.01665972 1.60271152] 0.8172822979820917\n",
      "sid, nids, pose, score:  [4928, 717] [-0.01088316  2.49702412  1.59218916] 0.8172059800649755\n",
      "Submap 112 done..\n",
      "sid, nids, pose, score:  [4972, 762] [0.12418691 2.33531743 1.59851139] 0.8104350734005558\n",
      "sid, nids, pose, score:  [4972, 763] [0.14114133 2.5352119  1.60649896] 0.8114645026793023\n",
      "Submap 113 done..\n",
      "Submap 114 done..\n",
      "Submap 115 done..\n",
      "Submap 116 done..\n",
      "sid, nids, pose, score:  [5148, 928] [0.32024459 0.91779701 1.66022437] 0.8180048593265018\n",
      "Submap 117 done..\n",
      "Submap 118 done..\n",
      "Submap 119 done..\n",
      "sid, nids, pose, score:  [5280, 1064] [0.11737214 2.21384381 1.63776207] 0.8146681870332642\n",
      "sid, nids, pose, score:  [5280, 1065] [0.1113377  2.41283299 1.63920204] 0.8316967762804484\n",
      "sid, nids, pose, score:  [5280, 1066] [0.12625508 2.66094597 1.63063517] 0.8428413912053591\n",
      "Submap 120 done..\n",
      "Submap 121 done..\n",
      "Submap 122 done..\n",
      "Submap 123 done..\n",
      "Submap 124 done..\n",
      "Submap 125 done..\n",
      "Submap 126 done..\n",
      "sid, nids, pose, score:  [5588, 2379] [-0.0042919   0.44798049  1.54246862] 0.8038413826642623\n",
      "sid, nids, pose, score:  [5588, 2381] [0.07346551 0.8921378  1.53928281] 0.8383084393499938\n",
      "sid, nids, pose, score:  [5588, 2383] [0.09901732 1.2908197  1.53888502] 0.8427288383243874\n",
      "sid, nids, pose, score:  [5588, 2384] [0.11534696 1.42669812 1.54066491] 0.8501093525635329\n",
      "sid, nids, pose, score:  [5588, 2385] [0.05154163 1.58247778 1.53895919] 0.8235650882002745\n",
      "sid, nids, pose, score:  [5588, 2387] [0.07242029 2.02761972 1.54172393] 0.846346713084844\n",
      "sid, nids, pose, score:  [5588, 2388] [0.10215528 2.28449052 1.5489655 ] 0.8688500620208793\n",
      "sid, nids, pose, score:  [5588, 2389] [0.10529583 2.46320529 1.54109488] 0.8696721218925517\n",
      "sid, nids, pose, score:  [5588, 2390] [0.11434448 2.77399949 1.55479707] 0.8711959336467442\n",
      "sid, nids, pose, score:  [5588, 2391] [0.1933976  2.8352459  1.55361275] 0.8775370366253497\n",
      "Submap 127 done..\n",
      "sid, nids, pose, score:  [5632, 2422] [ 0.04918743 -0.15381308  1.56594833] 0.827526927190896\n",
      "sid, nids, pose, score:  [5632, 2423] [0.05455667 0.09451346 1.56344885] 0.8422242093059943\n",
      "sid, nids, pose, score:  [5632, 2425] [0.1097344  0.52770732 1.56028507] 0.8160342327307845\n",
      "sid, nids, pose, score:  [5632, 2426] [0.11322491 0.77339496 1.55493466] 0.8294248544624178\n",
      "sid, nids, pose, score:  [5632, 2427] [0.1246407  1.02876607 1.54434149] 0.8721278241015619\n",
      "sid, nids, pose, score:  [5632, 2428] [0.14897964 1.35874797 1.55881164] 0.8308924210767881\n",
      "sid, nids, pose, score:  [5632, 2429] [0.23381859 1.4808351  1.57001966] 0.8199371184244647\n",
      "sid, nids, pose, score:  [5632, 2430] [0.25952702 1.82138435 1.57968328] 0.8301179905067793\n",
      "sid, nids, pose, score:  [5632, 2431] [0.14100952 1.89769105 1.57231246] 0.864849900080306\n",
      "sid, nids, pose, score:  [5632, 2432] [0.16370273 2.10383185 1.58440936] 0.8703319308940523\n",
      "sid, nids, pose, score:  [5632, 2433] [0.17313014 2.35541569 1.5868445 ] 0.8706478088898202\n",
      "sid, nids, pose, score:  [5632, 2434] [0.17667769 2.5057411  1.58734457] 0.8694669591130936\n",
      "sid, nids, pose, score:  [5632, 2435] [0.23450244 2.65677285 1.59007958] 0.8821189830883269\n",
      "sid, nids, pose, score:  [5632, 2436] [0.24232755 2.90832356 1.59572807] 0.8844016626755905\n",
      "Submap 128 done..\n",
      "Submap 129 done..\n",
      "Submap 130 done..\n",
      "Submap 131 done..\n",
      "Submap 132 done..\n",
      "Submap 133 done..\n",
      "sid, nids, pose, score:  [5896, 3714] [-0.05340547  1.16041363  0.88861258] 0.8015802550080032\n",
      "sid, nids, pose, score:  [5896, 3716] [0.17027975 1.38217577 0.55885678] 0.8061118220670896\n",
      "Submap 134 done..\n",
      "Submap 135 done..\n",
      "Submap 136 done..\n",
      "Submap 137 done..\n",
      "Submap 138 done..\n",
      "Submap 139 done..\n",
      "Submap 140 done..\n",
      "Submap 141 done..\n",
      "Submap 142 done..\n",
      "Submap 143 done..\n",
      "Submap 144 done..\n",
      "Submap 145 done..\n",
      "Submap 146 done..\n",
      "Submap 147 done..\n",
      "Submap 148 done..\n",
      "Submap 149 done..\n",
      "Submap 150 done..\n",
      "Submap 151 done..\n",
      "Submap 152 done..\n",
      "Submap 153 done..\n",
      "Submap 154 done..\n",
      "Submap 155 done..\n",
      "Submap 156 done..\n",
      "Submap 157 done..\n",
      "Submap 158 done..\n",
      "Submap 159 done..\n",
      "Submap 160 done..\n",
      "Submap 161 done..\n",
      "Submap 162 done..\n",
      "Submap 163 done..\n",
      "Submap 164 done..\n",
      "Submap 165 done..\n",
      "Submap 166 done..\n",
      "Submap 167 done..\n",
      "Submap 168 done..\n",
      "Submap 169 done..\n",
      "Submap 170 done..\n",
      "Submap 171 done..\n",
      "Submap 172 done..\n",
      "sid, nids, pose, score:  [7612, 3996] [2.0422551  3.25826801 0.91485955] 0.8211506460456998\n",
      "sid, nids, pose, score:  [7612, 3997] [2.10877284 3.46036599 0.89732208] 0.8043233505960828\n",
      "sid, nids, pose, score:  [7612, 3998] [2.28010175 3.55879631 0.89463984] 0.8333509256850878\n",
      "sid, nids, pose, score:  [7612, 3999] [2.44681391 3.76081567 0.90009043] 0.8000603305600383\n",
      "Submap 173 done..\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sid, nids, pose, score:  [7656, 4031] [0.37193696 2.07588824 1.64383013] 0.8333251718355703\n",
      "sid, nids, pose, score:  [7656, 4032] [0.3923349  2.34387355 1.66867358] 0.8116555060666661\n",
      "sid, nids, pose, score:  [7656, 4039] [0.22827083 3.62955448 1.67399281] 0.8049614431938216\n",
      "sid, nids, pose, score:  [7656, 4040] [0.24390151 3.95650684 1.66911659] 0.8277531949285178\n",
      "sid, nids, pose, score:  [7656, 4042] [0.28308868 4.32928223 1.65867908] 0.8317837692979617\n",
      "sid, nids, pose, score:  [7656, 4043] [0.27134361 4.52239461 1.63797651] 0.8311736822428796\n",
      "Submap 174 done..\n",
      "Submap 175 done..\n",
      "Submap 176 done..\n",
      "Submap 177 done..\n",
      "Submap 178 done..\n",
      "Submap 179 done..\n",
      "Submap 180 done..\n",
      "Submap 181 done..\n",
      "Submap 182 done..\n",
      "Submap 183 done..\n",
      "Submap 184 done..\n",
      "Submap 185 done..\n",
      "Submap 186 done..\n",
      "Submap 187 done..\n",
      "Submap 188 done..\n",
      "Submap 189 done..\n",
      "Submap 190 done..\n",
      "Submap 191 done..\n",
      "Submap 192 done..\n",
      "Submap 193 done..\n",
      "Submap 194 done..\n",
      "Submap 195 done..\n",
      "Submap 196 done..\n",
      "Submap 197 done..\n",
      "sid, nids, pose, score:  [8712, 4565] [ 0.3394909   3.88702526 -1.62441394] 0.8457201821836325\n",
      "Submap 198 done..\n",
      "sid, nids, pose, score:  [8756, 4529] [ 0.02465027  5.36299092 -1.48368855] 0.8176620866504546\n",
      "sid, nids, pose, score:  [8756, 4532] [ 0.16804598  1.96060149 -1.48928801] 0.8046940876241085\n",
      "Submap 199 done..\n",
      "Submap 200 done..\n",
      "sid, nids, pose, score:  [8844, 4442] [-0.33626227  2.05255227 -1.52412536] 0.8167057867912961\n",
      "Submap 201 done..\n",
      "Submap 202 done..\n",
      "Submap 203 done..\n"
     ]
    }
   ],
   "source": [
    "constraints=[]\n",
    "for sid in range(len(finished_submaps)):\n",
    "    submap = finished_submaps[sid]\n",
    "    if submap.iscompressed:\n",
    "        submap.decompress_data()\n",
    "    hgrids =  bb.compute_hierarchical_grid(submap.grid, num_levels=4)\n",
    "    for nid in range(len(node_poses)):\n",
    "        npose = node_poses[nid]\n",
    "        dist = np.linalg.norm(npose[:2] - submap.pose[:2])\n",
    "        node_dist = abs(node_dists[nid] - node_dists[submap.node_ids[0]])\n",
    "        if node_dist < 20:\n",
    "            continue\n",
    "        if nid not in submap.node_ids and (dist < dist_th):\n",
    "            fr_id = inserted_frame_ids[nid]\n",
    "            ts, frame = lpb.get_frame(fr_id)\n",
    "            frame = np.copy(frame[:, :6]).astype(np.float64)\n",
    "            frame = frame[(frame[:,4]>0) & (frame[:,4] < 60)]\n",
    "            frame = frame[(frame[:,2] > zmin) & (frame[:,2] < zmax)]\n",
    "            frame_z = voxel_filter(frame[:,:3], voxel_size=0.05)\n",
    "            if len(frame_z) < 500:\n",
    "                continue\n",
    "            #Check all quadrants have points\n",
    "            if (np.sum((frame_z[:,0] < 0) & (frame_z[:,1] > 0)) <10) or \\\n",
    "            (np.sum((frame_z[:,0] < 0) & (frame_z[:,1] < 0)) <10) or \\\n",
    "            (np.sum((frame_z[:,0] > 0) & (frame_z[:,1] > 0)) <10) or \\\n",
    "            (np.sum((frame_z[:,0] > 0) & (frame_z[:,1] < 0)) <10):\n",
    "                continue\n",
    "            frame_z,_ = voxel_filter_fast(frame_z, trunc=False, voxel = 0.25)  \n",
    "            \n",
    "            spose_estimate = combine_yelli_poses(get_inverse_yelli_pose(submap.pose), npose)\n",
    "            hpose, hscore = bb.hierarchical_search(hgrids, spose_estimate, frame_z,\n",
    "                                        x_search_window,y_search_window,angle_search_window\n",
    "                                        ,count_once=True, max_range = 60, score_th = score_th, debug=False)\n",
    "            if hpose is not None:    \n",
    "                print(\"sid, nids, pose, score: \",[submap.node_ids[0], nid], hpose, hscore)\n",
    "                constraints.append(Constraint([submap.node_ids[0], nid], hpose))\n",
    "    print(f\"Submap {sid} done..\")\n",
    "    submap.compress_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"./maps/5/constraints_oneshot_3.pickle\", 'wb') as f:\n",
    "    pickle.dump(constraints,f)\n",
    "#pickle.dump(constraints,\"./cons.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(204, 2340)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(finished_submaps), len(constraints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### linear pose graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2o_nps = node_poses.copy()\n",
    "for i in range(len(node_poses)):\n",
    "    g2o_nps[i,2] = normalize_pose(node_poses[i,2]- np.pi/2)\n",
    "\n",
    "g2o_cons = constraints\n",
    "for i in range(len(constraints)):\n",
    "    g2o_cons[i].relative_pose[2] = normalize_pose(constraints[i].relative_pose[2] - np.pi/2)\n",
    "ps = pg.PoseGraphOptimization()\n",
    "for i in range(len(g2o_nps)):\n",
    "    if i == 0:\n",
    "        ps.add_vertex(i, g2o_nps[i], fixed = True)\n",
    "    else:\n",
    "        ps.add_vertex(i, g2o_nps[i])\n",
    "for i in range(len(g2o_nps)-1):\n",
    "    ps.add_edge_from_state([i,i+1], information=np.identity(3))\n",
    "for i in range(len(g2o_cons)-1):\n",
    "    #print(i, g2o_cons[i].relative_pose)\n",
    "    constraint = g2o_cons[i]\n",
    "    ps.add_edge(constraint.node_ids, constraint.relative_pose, information= 1 *np.identity(3))\n",
    "    \n",
    "ps.optimize()\n",
    "oposes=[]\n",
    "for i in range(len(g2o_nps)):\n",
    "    oposes.append(ps.get_pose(i).vector())\n",
    "oposes = np.array(oposes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### flower pose-graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy\n",
    "g2o_nps = deepcopy(node_poses)\n",
    "for i in range(len(node_poses)):\n",
    "    g2o_nps[i,2] = normalize_pose(node_poses[i,2]- np.pi/2)\n",
    "\n",
    "g2o_cons = deepcopy(constraints)\n",
    "for i in range(len(constraints)):\n",
    "    g2o_cons[i].relative_pose[2] = normalize_pose(constraints[i].relative_pose[2] - np.pi/2)\n",
    "\n",
    "last_nid = finished_submaps[-1].node_ids[-1]\n",
    "ps = pg.PoseGraphOptimization()\n",
    "cdist_th = 1\n",
    "#for i in range(len(g2o_nps)):\n",
    "for i in range(last_nid+1):\n",
    "    if i == 0:\n",
    "        ps.add_vertex(i, g2o_nps[i], fixed = True)\n",
    "    else:\n",
    "        ps.add_vertex(i, g2o_nps[i])\n",
    "\n",
    "for si in range(len(finished_submaps)):\n",
    "    s = finished_submaps[si]\n",
    "    snid = s.node_ids[0]\n",
    "    for ni in s.node_ids[1:]:\n",
    "        ps.add_edge_from_state([snid, ni], information = np.identity(3))\n",
    "for i in range(len(g2o_cons)):\n",
    "    #print(i, g2o_cons[i].relative_pose)\n",
    "    constraint = g2o_cons[i]\n",
    "    if np.linalg.norm(constraint.relative_pose[:2]) < cdist_th:\n",
    "        ps.add_edge(constraint.node_ids, constraint.relative_pose, information= 1 * np.identity(3))\n",
    "    \n",
    "ps.optimize()\n",
    "oposes=[]\n",
    "#for i in range(len(g2o_nps)):\n",
    "for i in range(last_nid+1):\n",
    "    oposes.append(ps.get_pose(i).vector())\n",
    "oposes = np.array(oposes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-0.96839012,  9.20483968, -3.11859376],\n",
       "       [-0.96842159,  9.00485712,  3.12119933],\n",
       "       [-0.97880445,  8.80513531,  3.08043995]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_poses = np.array(node_poses)\n",
    "st = 0; stp = -1\n",
    "plt.figure()\n",
    "plt.plot(node_poses[st:stp,0], node_poses[st:stp,1], label=\"w/o optimization\" )\n",
    "plt.plot(oposes[st:stp,0], oposes[st:stp,1], label=\"w one-shot optimization and flower-graph\")\n",
    "plt.axis('equal')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"./maps/5/oposes_3.pickle\",'wb') as f:\n",
    "    pickle.dump(oposes,f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Version-2: Run Optimization iteratively as you find constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_poses = copy.deepcopy(node_poses)\n",
    "dist_th = 10\n",
    "x_search_window = (-5,5)\n",
    "y_search_window = (-5,5)\n",
    "angle_search_window = (-np.pi/6, np.pi/6)\n",
    "score_th = 0.7\n",
    "cdist_th = 1.5\n",
    "constraints = []\n",
    "use_flower_pg = True\n",
    "cscores = []\n",
    "prev_node_dist = 0\n",
    "\n",
    "for sid in range(0,len(finished_submaps)):\n",
    "    submap = finished_submaps[sid]\n",
    "    if submap.iscompressed:\n",
    "        submap.decompress_data()\n",
    "    hgrids = bb.compute_hierarchical_grid(submap.grid, num_levels = 4)\n",
    "    \n",
    "    for nid in range(0, submap.node_ids[-1]):\n",
    "        npose = temp_poses[nid]\n",
    "        spose = temp_poses[submap.node_ids[0]]\n",
    "        dist = np.linalg.norm(npose[:2] - spose[:2])\n",
    "        node_dist = abs(node_dists[nid] - node_dists[submap.node_ids[0]])\n",
    "        if node_dist < 20:\n",
    "            continue\n",
    "        if abs(node_dists[nid]-prev_node_dist) < 0.25:\n",
    "            continue\n",
    "        prev_node_dist = node_dists[nid]\n",
    "        if nid not in submap.node_ids and (dist < dist_th):\n",
    "            print(nid)\n",
    "            fr_id = insertion_fr_ids[nid]\n",
    "            ts, frame = lpb.get_frame(fr_id)\n",
    "            frame = np.copy(frame[:, :6]).astype(np.float64)\n",
    "            frame = frame[(frame[:,4]>0) & (frame[:,4] < 60)]\n",
    "            frame = frame[(frame[:,2] > zmin) & (frame[:,2] < zmax)]\n",
    "            frame_z = voxel_filter(frame[:,:3], voxel_size=0.05)\n",
    "            if len(frame_z) < 500:\n",
    "                continue\n",
    "            #Check all quadrants have points\n",
    "            if (np.sum((frame_z[:,0] < 0) & (frame_z[:,1] > 0)) <10) or \\\n",
    "            (np.sum((frame_z[:,0] < 0) & (frame_z[:,1] < 0)) <10) or \\\n",
    "            (np.sum((frame_z[:,0] > 0) & (frame_z[:,1] > 0)) <10) or \\\n",
    "            (np.sum((frame_z[:,0] > 0) & (frame_z[:,1] < 0)) <10):\n",
    "                continue\n",
    "            frame_z,_ = voxel_filter_fast(frame_z, trunc=False, voxel = 0.25)  \n",
    "            \n",
    "            spose_estimate = combine_yelli_poses(get_inverse_yelli_pose(spose), npose)\n",
    "            hpose, cscore = hierarchical_search(hgrids, spose_estimate, frame_z,\n",
    "                                        x_search_window,y_search_window,angle_search_window\n",
    "                                        , max_range = 60, score_th = score_th)\n",
    "\n",
    "            if hpose is not None: \n",
    "                thpose = combine_yelli_poses(spose, hpose)\n",
    "                mid_pose = temp_poses[submap.node_ids[45]]\n",
    "                dthpose = thpose[:2] - mid_pose[:2]\n",
    "                #print(dthpose, spose, mid_pose)\n",
    "                if np.linalg.norm(dthpose[:2]) > cdist_th:\n",
    "                    print(\"Crossed cdist_th .Skipping...\",np.linalg.norm(dthpose[:2]))\n",
    "                    continue\n",
    "                print(\"sid, nids, pose, cscore\",[submap.node_ids[0], nid], hpose, cscore)\n",
    "                print(\"Doing pose graph optimization\")\n",
    "                ps = PoseGraphOptimization()\n",
    "                for i in range(0,submap.node_ids[-1] + 1 ):\n",
    "                #for i in range(0,len(temp_poses)):\n",
    "                    pgpose = temp_poses[i].copy()\n",
    "    \n",
    "                    pgpose[2] = normalize_pose(pgpose[2]- np.pi/2)\n",
    "                    if i == 0:                        \n",
    "                        ps.add_vertex(i, pgpose, fixed = True)\n",
    "                    else:\n",
    "                        ps.add_vertex(i, pgpose)\n",
    "                    \n",
    "                #for i in range(0,submap.node_ids[-1] + 1):\n",
    "                #for i in range(0,nid+1):\n",
    "                #for i in range(0,len(temp_poses)- 1):\n",
    "                #    ps.add_edge_from_state([i,i+1], information=np.identity(3))\n",
    "                for si in range(0, sid+1):\n",
    "                    si_nid = finished_submaps[si].node_ids[0]\n",
    "                    for ni in finished_submaps[si].node_ids[1:]:\n",
    "                        ps.add_edge_from_state([si_nid, ni], information= np.identity(3))\n",
    "                \n",
    "                con_pose = hpose.copy()\n",
    "                con_pose[2] = normalize_pose(con_pose[2] - np.pi/2)\n",
    "                ps.add_edge([submap.node_ids[0], nid], con_pose, information= np.identity(3))\n",
    "                ps.optimize()\n",
    "                \n",
    "                tpose = temp_poses[submap.node_ids[-1]].copy()\n",
    "                for ti in range(0, submap.node_ids[-1] + 1):\n",
    "                #for ti in range(0, len(temp_poses[i])):\n",
    "                    temp_poses[ti] = ps.get_pose(ti).vector()\n",
    "                    temp_poses[ti,2] = normalize_pose(temp_poses[ti,2] + np.pi/2)\n",
    "                \n",
    "                apose = combine_yelli_poses(temp_poses[submap.node_ids[-1]], get_inverse_yelli_pose(tpose) )\n",
    "                for ti in range(submap.node_ids[-1]+1, len(temp_poses)):\n",
    "                    temp_poses[ti] = combine_yelli_poses(apose, temp_poses[ti])\n",
    "                \n",
    "                constraints.append(Constraint([submap.node_ids[0], nid], hpose))\n",
    "                cscores.append(cscore)\n",
    "  \n",
    "    print(f\"Submap {sid} done..\")\n",
    "    submap.compress_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.08580927,  2.76369964,  1.62889599])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constraints[141].relative_pose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2420, 5641]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constraints[141].node_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2420, 5642]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constraints[142].node_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2420, 5645]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constraints[145].node_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9021, 3)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oposes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/anujraaj/ati/data/dataman/maps/map/bosch_16beam_BAU/yelli_poses.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-101-c30ce6ee63c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mr\"/Users/anujraaj/ati/data/dataman/maps/map/bosch_16beam_BAU/yelli_poses.npy\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/robo/.venv_3.7/lib/python3.7/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/anujraaj/ati/data/dataman/maps/map/bosch_16beam_BAU/yelli_poses.npy'"
     ]
    }
   ],
   "source": [
    "path = r\"/Users/anujraaj/ati/data/dataman/maps/map/bosch_16beam_BAU/yelli_poses.npy\"\n",
    "b = np.load(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(node_poses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"yelli_poses.npy\", \"wb\") as f:\n",
    "    np.save(f, oposes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"yelli_poses.npy\", \"rb\") as f:\n",
    "    blah = np.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9021"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(oposes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True],\n",
       "       [ True,  True,  True],\n",
       "       [ True,  True,  True],\n",
       "       ...,\n",
       "       [ True,  True,  True],\n",
       "       [ True,  True,  True],\n",
       "       [ True,  True,  True]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blah == oposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "64-43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}