scafet_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "13932aa7",
   "metadata": {},
   "source": [
    "# Import all the modules\n",
    "#### Note: As this is a barebone example, dask is not used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "929606a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modules required for computation\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import time\n",
    "from glob import glob\n",
    "from importlib import reload\n",
    "import sys\n",
    "import dask\n",
    "from tqdm import tqdm\n",
    "import metpy.calc as mpcalc\n",
    "\n",
    "# Modules required for plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import matplotlib.ticker as mticker\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.colors as colors\n",
    "import cmaps as allcmaps\n",
    "\n",
    "# Modules for feature extraction\n",
    "scafet_folder = 'src/'\n",
    "sys.path.append(scafet_folder)\n",
    "import object_properties as obp\n",
    "import ridge_detection as rd\n",
    "import object_filtering as obf\n",
    "import object_tracking as obt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c3027b",
   "metadata": {},
   "source": [
    "# Overall Schematic for SCAFET"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34738ef0",
   "metadata": {},
   "source": [
    "![alternative text](summaryfig.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd7cf6d",
   "metadata": {},
   "source": [
    "# Detection of Cyclones from daily ERA5 reanalysis dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68568ed5",
   "metadata": {},
   "source": [
    "## Inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "005c4096",
   "metadata": {},
   "source": [
    "### Primary Field: Cyclonic relative vorticity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "903f4130",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wind data from ERA5 reanalysis\n",
    "ufile='http://apdrc.soest.hawaii.edu:80/dods/public_data/Reanalysis_Data/ERA5/hourly/U_wind_component_10m'\n",
    "vfile='http://apdrc.soest.hawaii.edu:80/dods/public_data/Reanalysis_Data/ERA5/hourly/V_wind_component_10m'\n",
    "# Load last 5 timesteps\n",
    "tslice = slice(-120,-1)\n",
    "u=xr.open_dataset(ufile).isel(time=tslice)\n",
    "v=xr.open_dataset(vfile).isel(time=tslice)\n",
    "# Calculate absolute value of relative vorticity\n",
    "u['u10'] = u['u10'].assign_attrs(units=\"m s**-1\")\n",
    "v['v10'] = v['v10'].assign_attrs(units=\"m s**-1\")\n",
    "rv_unf = mpcalc.vorticity(u['u10'], v['v10'])\n",
    "rv = rv_unf.metpy.dequantify().to_dataset(name='rv')\n",
    "cyc = rv*np.sign(rv.lat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5be7c7",
   "metadata": {},
   "source": [
    "### Grid Properties: Area and coastlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88e923c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_area = xr.open_dataset('resources/grid_area_era5.nc')\n",
    "            # .sel(latitude=latslice,longitude=lonslice)\n",
    "grid_area = grid_area.rename({'longitude':'lon'})\n",
    "grid_area = grid_area.rename({'latitude':'lat'})\n",
    "grid_area = grid_area.reindex\\\n",
    "    (lat=list(reversed(grid_area.lat)))\n",
    "land_mask = xr.open_dataset('resources/land_sea_mask_era5.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71ceebbc",
   "metadata": {},
   "source": [
    "### Feature Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0b6e3e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "smooth_scale = 1.5e6\n",
    "angle_threshold = 45\n",
    "shape_index = [0.625,1]\n",
    "min_length = 20e3\n",
    "min_area = 1e11\n",
    "min_duration = 6\n",
    "max_distance_per_tstep = 1000e3\n",
    "shape_eccentricity = [0.0,1.0]\n",
    "lat_mask = [-0,0]\n",
    "lon_mask = [360,0]\n",
    "\n",
    "properties = obp.object_properties2D(grid_area,land_mask,min_length,min_area,\\\n",
    "                    smooth_scale,angle_threshold,min_duration,max_distance_per_tstep,\\\n",
    "                    shape_index,shape_eccentricity,lon_mask,lat_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ead81f",
   "metadata": {},
   "source": [
    "#### All feature properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73658db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Object Properties</th>\n",
       "      <th>Values</th>\n",
       "      <th>Units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Smooth_Scale</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angle_Coherence</td>\n",
       "      <td>45</td>\n",
       "      <td>degree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shape_Index</td>\n",
       "      <td>[0.625, 1]</td>\n",
       "      <td>unitless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Min_Length</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Min_Area</td>\n",
       "      <td>100000000000.0</td>\n",
       "      <td>m$^2$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Min_Duration</td>\n",
       "      <td>6</td>\n",
       "      <td>counts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Max_Distance</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Min_Lon_Points</td>\n",
       "      <td>&lt;scipy.interpolate._interpolate.interp1d objec...</td>\n",
       "      <td>counts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Min_Lat_Points</td>\n",
       "      <td>&lt;scipy.interpolate._interpolate.interp1d objec...</td>\n",
       "      <td>counts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Map_Lons</td>\n",
       "      <td>&lt;scipy.interpolate._interpolate.interp1d objec...</td>\n",
       "      <td>degrees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Map_Lats</td>\n",
       "      <td>&lt;scipy.interpolate._interpolate.interp1d objec...</td>\n",
       "      <td>degrees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Eccentricity</td>\n",
       "      <td>[0.0, 1.0]</td>\n",
       "      <td>unitless</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>isglobal</td>\n",
       "      <td>True</td>\n",
       "      <td>0-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lon_Mask</td>\n",
       "      <td>[360, 0]</td>\n",
       "      <td>degrees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Lat_Mask</td>\n",
       "      <td>[0, 0]</td>\n",
       "      <td>degrees</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Object Properties                                             Values  \\\n",
       "0       Smooth_Scale                                          1500000.0   \n",
       "1    Angle_Coherence                                                 45   \n",
       "2        Shape_Index                                         [0.625, 1]   \n",
       "3         Min_Length                                            20000.0   \n",
       "4           Min_Area                                     100000000000.0   \n",
       "5       Min_Duration                                                  6   \n",
       "6       Max_Distance                                          1000000.0   \n",
       "7     Min_Lon_Points  <scipy.interpolate._interpolate.interp1d objec...   \n",
       "8     Min_Lat_Points  <scipy.interpolate._interpolate.interp1d objec...   \n",
       "9           Map_Lons  <scipy.interpolate._interpolate.interp1d objec...   \n",
       "10          Map_Lats  <scipy.interpolate._interpolate.interp1d objec...   \n",
       "11      Eccentricity                                         [0.0, 1.0]   \n",
       "12          isglobal                                               True   \n",
       "13          Lon_Mask                                           [360, 0]   \n",
       "14          Lat_Mask                                             [0, 0]   \n",
       "\n",
       "       Units  \n",
       "0          m  \n",
       "1     degree  \n",
       "2   unitless  \n",
       "3          m  \n",
       "4      m$^2$  \n",
       "5     counts  \n",
       "6          m  \n",
       "7     counts  \n",
       "8     counts  \n",
       "9    degrees  \n",
       "10   degrees  \n",
       "11  unitless  \n",
       "12       0-1  \n",
       "13   degrees  \n",
       "14   degrees  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "properties.print_properties()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7a92a2",
   "metadata": {},
   "source": [
    "#### All grid properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9c392a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "properties.plot_properties('grid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3583be1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "properties.plot_properties('land')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30372c28",
   "metadata": {},
   "source": [
    "#### Smoothing scale calculated from grid area and input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "59a80ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "properties.plot_properties('smooth')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d76f281",
   "metadata": {},
   "source": [
    "## Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7626e652",
   "metadata": {},
   "source": [
    "### Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c05fc0bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished smoothing in 29.058950185775757 seconds\n"
     ]
    }
   ],
   "source": [
    "stime = time.time()\n",
    "rdetect = rd.ridgeDetector(cyc)\n",
    "vor = rdetect.apply_smoother(cyc,properties)\n",
    "print('Finished smoothing in {} seconds'.format(time.time()-stime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "749a5eb2",
   "metadata": {},
   "source": [
    "### Shape Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9073b6c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mac/miniconda3/envs/iccp/lib/python3.10/site-packages/pint/facets/plain/quantity.py:1119: RuntimeWarning: invalid value encountered in divide\n",
      "  magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n",
      "/Users/mac/miniconda3/envs/iccp/lib/python3.10/site-packages/pint/facets/plain/quantity.py:1119: RuntimeWarning: divide by zero encountered in divide\n",
      "  magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n",
      "/Users/mac/miniconda3/envs/iccp/lib/python3.10/site-packages/pint/facets/plain/quantity.py:879: RuntimeWarning: invalid value encountered in subtract\n",
      "  magnitude = op(self._magnitude, other._magnitude)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished ridge extraction in 264.48995304107666 seconds\n"
     ]
    }
   ],
   "source": [
    "stime = time.time()\n",
    "vor = vor.rename({'rv':'mag'})\n",
    "cyc = cyc.rename({'rv':'mag'})\n",
    "# Select only positive values of cyclonic vorticity\n",
    "cyc_sm = vor.where((vor.mag>0)).fillna(0)\n",
    "# Detect Ridges\n",
    "ridges = rdetect.apply_ridge_detection(cyc_sm,properties)\n",
    "print('Finished ridge extraction in {} seconds'.format(time.time()-stime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac182ddb",
   "metadata": {},
   "source": [
    "### Secondary Field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c26594bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error:DAP DATADDS packet is apparently too short\n"
     ]
    }
   ],
   "source": [
    "# Use unsmoothed vorticity as primary field \n",
    "cyc_us = cyc.where((cyc.mag>0)).fillna(0)\n",
    "# Define the secondary field as wind speed\n",
    "ws = np.sqrt(u['u10']**2+v['v10']**2)\n",
    "props_mag = xr.concat([ws.expand_dims('Channel'),\\\n",
    "                    cyc_us.mag.expand_dims('Channel')], dim='Channel')\n",
    "props_mag = props_mag.to_dataset(name='mag')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ece8b12c",
   "metadata": {},
   "source": [
    "## Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "149dfe84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished Filtering in 861.4567539691925 seconds\n"
     ]
    }
   ],
   "source": [
    "stime = time.time()\n",
    "obfilter = obf.filterObjects(ridges)\n",
    "filtered = obfilter.apply_filter(ridges,\\\n",
    "            props_mag,['max_intensity','mean_intensity'],\n",
    "            [10,2e-5],properties,'ridges')\n",
    "print('Finished Filtering in {} seconds'.format(time.time()-stime))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16c320e",
   "metadata": {},
   "source": [
    "## Tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "75521a0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                              | 0/118 [00:00<?, ?it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  1%|▋                                                                                     | 1/118 [00:00<00:49,  2.36it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  2%|█▍                                                                                    | 2/118 [00:00<00:50,  2.30it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  3%|██▏                                                                                   | 3/118 [00:01<00:48,  2.35it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  3%|██▉                                                                                   | 4/118 [00:01<00:46,  2.45it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  4%|███▋                                                                                  | 5/118 [00:02<00:46,  2.45it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  5%|████▎                                                                                 | 6/118 [00:02<00:46,  2.43it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  6%|█████                                                                                 | 7/118 [00:02<00:45,  2.44it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  7%|█████▊                                                                                | 8/118 [00:03<00:43,  2.52it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  8%|██████▌                                                                               | 9/118 [00:03<00:42,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  8%|███████▏                                                                             | 10/118 [00:04<00:42,  2.55it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "  9%|███████▉                                                                             | 11/118 [00:04<00:42,  2.52it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 10%|████████▋                                                                            | 12/118 [00:04<00:42,  2.50it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 11%|█████████▎                                                                           | 13/118 [00:05<00:42,  2.45it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 12%|██████████                                                                           | 14/118 [00:05<00:45,  2.30it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|██████████▊                                                                          | 15/118 [00:06<00:47,  2.16it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 14%|███████████▌                                                                         | 16/118 [00:06<00:48,  2.09it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 14%|████████████▏                                                                        | 17/118 [00:07<00:50,  2.00it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 15%|████████████▉                                                                        | 18/118 [00:07<00:52,  1.91it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 16%|█████████████▋                                                                       | 19/118 [00:08<00:52,  1.89it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 17%|██████████████▍                                                                      | 20/118 [00:08<00:50,  1.94it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 18%|███████████████▏                                                                     | 21/118 [00:09<00:47,  2.03it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 19%|███████████████▊                                                                     | 22/118 [00:09<00:44,  2.13it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 19%|████████████████▌                                                                    | 23/118 [00:10<00:44,  2.16it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 20%|█████████████████▎                                                                   | 24/118 [00:10<00:43,  2.16it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 21%|██████████████████                                                                   | 25/118 [00:11<00:44,  2.11it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 22%|██████████████████▋                                                                  | 26/118 [00:11<00:46,  1.99it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 23%|███████████████████▍                                                                 | 27/118 [00:12<00:46,  1.94it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 24%|████████████████████▏                                                                | 28/118 [00:12<00:46,  1.94it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 25%|████████████████████▉                                                                | 29/118 [00:13<00:45,  1.98it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|█████████████████████▌                                                               | 30/118 [00:13<00:43,  2.00it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 26%|██████████████████████▎                                                              | 31/118 [00:14<00:43,  1.98it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 27%|███████████████████████                                                              | 32/118 [00:14<00:44,  1.92it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 28%|███████████████████████▊                                                             | 33/118 [00:15<00:45,  1.87it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 29%|████████████████████████▍                                                            | 34/118 [00:16<00:45,  1.85it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 30%|█████████████████████████▏                                                           | 35/118 [00:16<00:45,  1.81it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 31%|█████████████████████████▉                                                           | 36/118 [00:17<00:45,  1.82it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 31%|██████████████████████████▋                                                          | 37/118 [00:17<00:43,  1.88it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 32%|███████████████████████████▎                                                         | 38/118 [00:18<00:41,  1.92it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 33%|████████████████████████████                                                         | 39/118 [00:18<00:39,  1.98it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 34%|████████████████████████████▊                                                        | 40/118 [00:19<00:38,  2.04it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 35%|█████████████████████████████▌                                                       | 41/118 [00:19<00:37,  2.06it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 36%|██████████████████████████████▎                                                      | 42/118 [00:20<00:36,  2.07it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 36%|██████████████████████████████▉                                                      | 43/118 [00:20<00:35,  2.09it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 37%|███████████████████████████████▋                                                     | 44/118 [00:20<00:34,  2.13it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|████████████████████████████████▍                                                    | 45/118 [00:21<00:33,  2.16it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 39%|█████████████████████████████████▏                                                   | 46/118 [00:21<00:32,  2.23it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 40%|█████████████████████████████████▊                                                   | 47/118 [00:22<00:30,  2.35it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 41%|██████████████████████████████████▌                                                  | 48/118 [00:22<00:27,  2.50it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 42%|███████████████████████████████████▎                                                 | 49/118 [00:22<00:26,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 42%|████████████████████████████████████                                                 | 50/118 [00:23<00:26,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 43%|████████████████████████████████████▋                                                | 51/118 [00:23<00:26,  2.56it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 44%|█████████████████████████████████████▍                                               | 52/118 [00:24<00:25,  2.54it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 45%|██████████████████████████████████████▏                                              | 53/118 [00:24<00:25,  2.56it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 46%|██████████████████████████████████████▉                                              | 54/118 [00:24<00:24,  2.62it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 47%|███████████████████████████████████████▌                                             | 55/118 [00:25<00:23,  2.70it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 47%|████████████████████████████████████████▎                                            | 56/118 [00:25<00:23,  2.64it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 48%|█████████████████████████████████████████                                            | 57/118 [00:25<00:23,  2.55it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 49%|█████████████████████████████████████████▊                                           | 58/118 [00:26<00:23,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 50%|██████████████████████████████████████████▌                                          | 59/118 [00:26<00:22,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 51%|███████████████████████████████████████████▏                                         | 60/118 [00:27<00:22,  2.52it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 52%|███████████████████████████████████████████▉                                         | 61/118 [00:27<00:23,  2.47it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 53%|████████████████████████████████████████████▋                                        | 62/118 [00:27<00:22,  2.46it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 53%|█████████████████████████████████████████████▍                                       | 63/118 [00:28<00:22,  2.48it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 54%|██████████████████████████████████████████████                                       | 64/118 [00:28<00:21,  2.51it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 55%|██████████████████████████████████████████████▊                                      | 65/118 [00:29<00:20,  2.56it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 56%|███████████████████████████████████████████████▌                                     | 66/118 [00:29<00:20,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 57%|████████████████████████████████████████████████▎                                    | 67/118 [00:29<00:19,  2.58it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 58%|████████████████████████████████████████████████▉                                    | 68/118 [00:30<00:19,  2.58it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 58%|█████████████████████████████████████████████████▋                                   | 69/118 [00:30<00:19,  2.54it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 59%|██████████████████████████████████████████████████▍                                  | 70/118 [00:31<00:19,  2.48it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 60%|███████████████████████████████████████████████████▏                                 | 71/118 [00:31<00:19,  2.45it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 61%|███████████████████████████████████████████████████▊                                 | 72/118 [00:31<00:19,  2.41it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 62%|████████████████████████████████████████████████████▌                                | 73/118 [00:32<00:18,  2.39it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 63%|█████████████████████████████████████████████████████▎                               | 74/118 [00:32<00:18,  2.39it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|██████████████████████████████████████████████████████                               | 75/118 [00:33<00:18,  2.39it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 64%|██████████████████████████████████████████████████████▋                              | 76/118 [00:33<00:17,  2.37it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 65%|███████████████████████████████████████████████████████▍                             | 77/118 [00:34<00:16,  2.45it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 66%|████████████████████████████████████████████████████████▏                            | 78/118 [00:34<00:15,  2.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 67%|████████████████████████████████████████████████████████▉                            | 79/118 [00:34<00:14,  2.62it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 68%|█████████████████████████████████████████████████████████▋                           | 80/118 [00:35<00:14,  2.68it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 69%|██████████████████████████████████████████████████████████▎                          | 81/118 [00:35<00:13,  2.71it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 69%|███████████████████████████████████████████████████████████                          | 82/118 [00:35<00:12,  2.82it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 70%|███████████████████████████████████████████████████████████▊                         | 83/118 [00:36<00:11,  3.00it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 71%|████████████████████████████████████████████████████████████▌                        | 84/118 [00:36<00:10,  3.11it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 72%|█████████████████████████████████████████████████████████████▏                       | 85/118 [00:36<00:10,  3.22it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 73%|█████████████████████████████████████████████████████████████▉                       | 86/118 [00:36<00:10,  3.17it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 74%|██████████████████████████████████████████████████████████████▋                      | 87/118 [00:37<00:10,  2.96it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 75%|███████████████████████████████████████████████████████████████▍                     | 88/118 [00:37<00:10,  2.76it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 75%|████████████████████████████████████████████████████████████████                     | 89/118 [00:38<00:11,  2.62it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|████████████████████████████████████████████████████████████████▊                    | 90/118 [00:38<00:11,  2.44it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 77%|█████████████████████████████████████████████████████████████████▌                   | 91/118 [00:39<00:12,  2.24it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 78%|██████████████████████████████████████████████████████████████████▎                  | 92/118 [00:39<00:12,  2.09it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 79%|██████████████████████████████████████████████████████████████████▉                  | 93/118 [00:40<00:12,  1.99it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 80%|███████████████████████████████████████████████████████████████████▋                 | 94/118 [00:40<00:12,  1.90it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 81%|████████████████████████████████████████████████████████████████████▍                | 95/118 [00:41<00:12,  1.83it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 81%|█████████████████████████████████████████████████████████████████████▏               | 96/118 [00:42<00:12,  1.83it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 82%|█████████████████████████████████████████████████████████████████████▊               | 97/118 [00:42<00:11,  1.85it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 83%|██████████████████████████████████████████████████████████████████████▌              | 98/118 [00:43<00:10,  1.85it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 84%|███████████████████████████████████████████████████████████████████████▎             | 99/118 [00:43<00:10,  1.87it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 85%|███████████████████████████████████████████████████████████████████████▏            | 100/118 [00:44<00:09,  1.88it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 86%|███████████████████████████████████████████████████████████████████████▉            | 101/118 [00:44<00:09,  1.84it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 86%|████████████████████████████████████████████████████████████████████████▌           | 102/118 [00:45<00:08,  1.86it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 87%|█████████████████████████████████████████████████████████████████████████▎          | 103/118 [00:45<00:07,  1.90it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 88%|██████████████████████████████████████████████████████████████████████████          | 104/118 [00:46<00:07,  1.93it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 89%|██████████████████████████████████████████████████████████████████████████▋         | 105/118 [00:46<00:06,  1.94it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 90%|███████████████████████████████████████████████████████████████████████████▍        | 106/118 [00:47<00:06,  1.85it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 91%|████████████████████████████████████████████████████████████████████████████▏       | 107/118 [00:48<00:06,  1.74it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 92%|████████████████████████████████████████████████████████████████████████████▉       | 108/118 [00:48<00:05,  1.68it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 92%|█████████████████████████████████████████████████████████████████████████████▌      | 109/118 [00:49<00:05,  1.65it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 93%|██████████████████████████████████████████████████████████████████████████████▎     | 110/118 [00:49<00:04,  1.64it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 94%|███████████████████████████████████████████████████████████████████████████████     | 111/118 [00:50<00:04,  1.66it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 95%|███████████████████████████████████████████████████████████████████████████████▋    | 112/118 [00:51<00:03,  1.66it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 96%|████████████████████████████████████████████████████████████████████████████████▍   | 113/118 [00:51<00:03,  1.61it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 97%|█████████████████████████████████████████████████████████████████████████████████▏  | 114/118 [00:52<00:02,  1.57it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 97%|█████████████████████████████████████████████████████████████████████████████████▊  | 115/118 [00:53<00:01,  1.54it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 98%|██████████████████████████████████████████████████████████████████████████████████▌ | 116/118 [00:53<00:01,  1.51it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      " 99%|███████████████████████████████████████████████████████████████████████████████████▎| 117/118 [00:54<00:00,  1.50it/s]/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:323: FutureWarning: Behavior when concatenating bool-dtype and numeric-dtype arrays is deprecated; in a future version these will cast to object dtype (instead of coercing bools to numeric values). To retain the old behavior, explicitly cast bool-dtype arrays to numeric dtype.\n",
      "  df = pd.concat([df,nandframe])\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 118/118 [00:55<00:00,  2.14it/s]\n",
      "/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:40: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  obj_counts = pd.DataFrame(tracked_ids,columns=['track_ids','Objects'])\n",
      "/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:62: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  mindex=pd.MultiIndex.from_frame(tracks_dframe[['timestamp','label']])\n",
      "/Users/mac/부산대학교산학협력단 Dropbox/Arjun N.B/Dropbox Personal/GitHub/SCAFET/Git/src/object_tracking.py:62: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n",
      "  mindex=pd.MultiIndex.from_frame(tracks_dframe[['timestamp','label']])\n"
     ]
    }
   ],
   "source": [
    "ar_objects = filtered[1]\n",
    "ar_props = filtered[0]\n",
    "\n",
    "# Tracking\n",
    "properties.obj['Min_Duration']=48\n",
    "properties.obj['Max_Distance']=500\n",
    "\n",
    "# Tracking based on centroid of each object\n",
    "latlon = ['wclat','wclon']\n",
    "\n",
    "tracker = obt.Tracker(latlon,properties)\n",
    "tracked = tracker.apply_tracking(ar_props,ar_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "820275e6",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (id: 291)\n",
       "Coordinates:\n",
       "  * id                  (id) object 1.0 1.0 1.0 1.0 ... 139.0 145.0 145.0 145.0\n",
       "Data variables: (12/39)\n",
       "    mean_intensity-0    (id) object 120142092.14731053 ... 542315917.2031018\n",
       "    mean_intensity-1    (id) object 6.262796303505374 ... 6.502689395163887\n",
       "    mean_intensity-2    (id) object 3.44651544531623e-05 ... 5.65995641052689...\n",
       "    min_intensity-0     (id) object 90828959.45741372 ... 522074558.18083715\n",
       "    min_intensity-1     (id) object 0.6751485466957092 ... 0.5878952145576477\n",
       "    min_intensity-2     (id) object 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    ...                  ...\n",
       "    track_ids           (id) object 1.0 1.0 1.0 1.0 ... 139.0 145.0 145.0 145.0\n",
       "    travelled_distance  (id) object nan 2.4314333840434945 ... 83.87956501121813\n",
       "    tstep               (id) object 1 2 3 4 1 2 3 4 1 2 ... 4 2 3 4 2 3 4 2 3 4\n",
       "    label               (id) object 1 1 1 1 2 2 2 2 ... 57 56 59 59 58 65 65 64\n",
       "    timestamp           (id) object 1696100399999997000 ... 1696111199999997000\n",
       "    old_label           (id) object 5.0 5.0 7.0 5.0 ... 724.0 829.0 838.0 816.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fdf030cd-a2c1-4524-b16a-6388fdc606fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fdf030cd-a2c1-4524-b16a-6388fdc606fa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>id</span>: 291</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d03b3f3d-6c84-45f8-aa75-13ea24c49ef0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d03b3f3d-6c84-45f8-aa75-13ea24c49ef0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>id</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 ... 145.0 145.0 145.0</div><input id='attrs-a754c474-08b1-4df7-9756-c449a397e82c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a754c474-08b1-4df7-9756-c449a397e82c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf7f606f-7097-4017-a103-71a36a71e569' class='xr-var-data-in' type='checkbox'><label for='data-cf7f606f-7097-4017-a103-71a36a71e569' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.0, 1.0, 1.0, ..., 145.0, 145.0, 145.0], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-52d32225-9af6-4280-9e3a-2e1867b468eb' class='xr-section-summary-in' type='checkbox'  ><label for='section-52d32225-9af6-4280-9e3a-2e1867b468eb' class='xr-section-summary' >Data variables: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mean_intensity-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>120142092.14731053 ... 542315917...</div><input id='attrs-c54b2adb-20af-4aa6-aba6-2680e6986db4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c54b2adb-20af-4aa6-aba6-2680e6986db4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f155f5f-d576-4911-b6a9-70522325a7d3' class='xr-var-data-in' type='checkbox'><label for='data-3f155f5f-d576-4911-b6a9-70522325a7d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([120142092.14731053, 120041581.20547415, 120080717.96839054,\n",
       "       120659763.34736802, 211865842.3235185, 211728147.5021513,\n",
       "       211473252.47879392, 210973408.52543697, 224881135.56211728,\n",
       "       225119004.04616904, 225217373.54402307, 226403248.2882716,\n",
       "       247164073.5020472, 245194622.2137839, 243770524.33202052,\n",
       "       242271996.49567682, 271561111.1482758, 271737319.02679485,\n",
       "       273185126.8415249, 252739089.14114866, 280180556.25196916,\n",
       "       276527569.72303224, 272111148.8363925, 271093672.85348845,\n",
       "       266787204.0147081, 263062023.49916548, 259859147.720747,\n",
       "       260726317.531402, 270037577.892552, 270778276.9211118,\n",
       "       271425953.4009166, 262938906.97834286, 277830506.05914336,\n",
       "       276376805.51650256, 274681112.4152921, 274584566.234354,\n",
       "       359569906.7763073, 359262467.3997306, 358514385.75258535,\n",
       "       357290296.7607651, 362634023.5493014, 363615346.3907776,\n",
       "       364534611.6825831, 379650846.9212118, 381525768.7484554,\n",
       "       383256450.1867036, 384942829.93711615, 378595744.4482675,\n",
       "       379298353.9768916, 379986879.3679291, 379633619.17935556,\n",
       "       403690625.67532974, 399620815.699861, 397437420.9500083,\n",
       "       400900340.124838, 403002142.78750235, 398687522.4850923,\n",
       "       393854739.0008278, 415808891.5156397, 417520208.23702073,\n",
       "...\n",
       "       457855129.6707004, 456109221.8386688, 455303477.05763006,\n",
       "       454537060.916516, 450044198.0321714, 443123291.9101885,\n",
       "       436910646.17378604, 425398020.6556488, 399438407.79079247,\n",
       "       399812554.2793642, 400366952.8300925, 403499717.6563181,\n",
       "       425822457.6418088, 423767970.96171385, 421093176.18240917,\n",
       "       423434536.71185476, 403404087.15470195, 403015460.98884356,\n",
       "       403078054.6122264, 403107373.85216, 398526849.22296625,\n",
       "       401391511.75665456, 404886918.94178444, 406093747.43320835,\n",
       "       341668822.16326994, 340632996.48849875, 339181723.882338,\n",
       "       363280444.9774279, 326503752.5227377, 326756589.28697485,\n",
       "       327536185.3131089, 327477866.8306221, 271252011.5502474,\n",
       "       271559774.5446767, 271945349.6884645, 271562361.1559536,\n",
       "       238762428.61579397, 238662618.98771068, 238673869.34620175,\n",
       "       240046769.40373638, 213352980.3746324, 209098526.26324403,\n",
       "       204422707.47669157, 200157639.42277145, 168014754.12258103,\n",
       "       168917016.55517748, 169246849.9949688, 170873452.6478001,\n",
       "       662641362.407773, 663229805.9864507, 662285987.4819349,\n",
       "       628964006.9136673, 628109861.5769945, 625388819.1201142,\n",
       "       549035999.4611086, 546604418.7432435, 542315917.2031018],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_intensity-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>6.262796303505374 ... 6.50268939...</div><input id='attrs-84250dc4-6f96-48a2-a4ad-3161a2dec7fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84250dc4-6f96-48a2-a4ad-3161a2dec7fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc927789-be9a-4785-81f5-456ad8bcbbf4' class='xr-var-data-in' type='checkbox'><label for='data-fc927789-be9a-4785-81f5-456ad8bcbbf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6.262796303505374, 6.400451546030203, 6.502944733434596,\n",
       "       6.523708769324189, 11.219461960262722, 11.255811139138578,\n",
       "       11.314349276929637, 11.275556049965045, 8.74106132790342,\n",
       "       8.372901507598513, 8.056189651069584, 7.875939149460845,\n",
       "       10.489990016357234, 10.534633968317682, 10.564443351303762,\n",
       "       10.154568991742094, 6.765321847424503, 7.007210318337787,\n",
       "       7.583329518532391, 9.900319048190186, 8.876639154702184,\n",
       "       8.797419437141148, 8.817087913345038, 9.208271072473346,\n",
       "       8.691085774844485, 8.579514878581426, 8.525715027443963,\n",
       "       8.153229984106279, 6.672866233261177, 6.58586788666686,\n",
       "       6.527642644496045, 6.0245949157427265, 8.830240740205548,\n",
       "       8.844410909467669, 8.813711313491172, 8.579676696284466,\n",
       "       11.81043267638787, 12.213607143590794, 12.461568744492467,\n",
       "       12.686924680903717, 7.075039709674432, 7.085787307530547,\n",
       "       6.995776527597825, 7.653777187059649, 7.775005942713605,\n",
       "       7.992647563610818, 7.945005974402794, 8.059042649178565,\n",
       "       8.196109365281604, 8.329680670004386, 8.97901911170859,\n",
       "       9.023111643080126, 9.829167529940605, 10.2472172343978,\n",
       "       11.676558599194552, 7.652178612169346, 7.424536026186413,\n",
       "       6.996151793834775, 11.291916442945427, 11.242368950759094,\n",
       "...\n",
       "       10.692466783240791, 10.580617005895512, 10.682629314763695,\n",
       "       10.710098200039456, 11.356446731541165, 11.157782014471586,\n",
       "       11.192095540091396, 10.956577918117809, 12.906621425582758,\n",
       "       12.974918654157682, 12.998121197451276, 13.747888690342485,\n",
       "       11.57143577890533, 11.720742497467189, 11.806600119707719,\n",
       "       11.016585425376892, 6.366761039005467, 6.110879269715782,\n",
       "       5.860535328146778, 5.825445075001982, 5.74124013704319,\n",
       "       5.569700670409036, 5.319829503545221, 5.43380903583212,\n",
       "       4.226943510563967, 4.3235223979680235, 4.50028683025656,\n",
       "       5.295693124862427, 9.080533045836917, 9.179413770091829,\n",
       "       9.256060087532731, 9.146299721012886, 7.465577205510671,\n",
       "       7.4212106274794944, 7.300715698457476, 7.350041908605565,\n",
       "       5.566781883395237, 5.530458505033972, 5.540408949552419,\n",
       "       5.195046001659184, 11.309165969855384, 11.326767322340734,\n",
       "       11.319988243633109, 11.650210480018986, 6.166558665230991,\n",
       "       6.14158663171309, 6.1214144429154995, 6.725482747413383,\n",
       "       7.273601822364024, 7.658902430381531, 7.834639187017198,\n",
       "       6.91581609411147, 6.9582557832663845, 6.929978727773808,\n",
       "       6.965816013756755, 7.472177364755033, 6.502689395163887],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_intensity-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>3.44651544531623e-05 ... 5.65995...</div><input id='attrs-11daeb45-9d0a-4cd9-9586-333757ef2eda' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11daeb45-9d0a-4cd9-9586-333757ef2eda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c3810ed-cabc-485f-9975-96a7026d963c' class='xr-var-data-in' type='checkbox'><label for='data-9c3810ed-cabc-485f-9975-96a7026d963c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3.44651544531623e-05, 3.4411213091618935e-05,\n",
       "       3.467665406219503e-05, 3.6252378677232025e-05,\n",
       "       5.7093251414034906e-05, 5.8162983243970316e-05,\n",
       "       5.939384020451594e-05, 5.9433457081155794e-05,\n",
       "       7.557596724221715e-05, 7.577905411171692e-05,\n",
       "       7.593234506053676e-05, 7.507927362527117e-05,\n",
       "       8.680616656635532e-05, 8.704002494946538e-05,\n",
       "       8.609810244431907e-05, 8.03880665001765e-05, 3.554857785695186e-05,\n",
       "       3.541436184623993e-05, 3.674392161313308e-05,\n",
       "       4.0766269745795866e-05, 6.0997311843904126e-05,\n",
       "       6.044242597601676e-05, 6.050058454777581e-05, 6.31991127393773e-05,\n",
       "       4.8546046884840166e-05, 4.768527402703946e-05,\n",
       "       4.789609101269562e-05, 4.226362634477422e-05,\n",
       "       3.3048304671864864e-05, 3.270280409482562e-05,\n",
       "       3.225976294815737e-05, 3.080286820224227e-05,\n",
       "       5.128018130571931e-05, 5.265905889416179e-05,\n",
       "       5.392031255316183e-05, 5.421858305210018e-05,\n",
       "       8.675113928623647e-05, 8.890383704764495e-05,\n",
       "       9.225396213368442e-05, 9.983261492481365e-05,\n",
       "       5.5384562401563854e-05, 5.6532260738586816e-05,\n",
       "...\n",
       "       3.300026362438464e-05, 3.244495351035763e-05,\n",
       "       3.207231797453162e-05, 4.1417006011611666e-05,\n",
       "       4.287149192270564e-05, 4.122903273207302e-05,\n",
       "       3.504937978566589e-05, 3.0119647715416456e-05,\n",
       "       3.0036847534102455e-05, 3.138650610840572e-05,\n",
       "       3.298349723840481e-05, 5.150137444587875e-05,\n",
       "       5.255766553399047e-05, 5.2267588026035734e-05,\n",
       "       5.431582278960794e-05, 4.7146390713665115e-05,\n",
       "       4.643659904062686e-05, 4.598378504801305e-05,\n",
       "       4.720268061788936e-05, 4.54583259672964e-05,\n",
       "       4.5539436933600614e-05, 4.5810565596629234e-05,\n",
       "       4.211120216865753e-05, 8.720975961339578e-05,\n",
       "       9.044371593938508e-05, 9.169331239975798e-05,\n",
       "       9.946268186975537e-05, 5.093207978112006e-05,\n",
       "       5.042175818744172e-05, 5.0209074428714744e-05,\n",
       "       5.327299475606082e-05, 3.8158670117559766e-05,\n",
       "       3.620708523033612e-05, 3.580037512303957e-05,\n",
       "       2.228794646336051e-05, 2.4931781406794412e-05,\n",
       "       2.1757879525719052e-05, 5.1538814316101034e-05,\n",
       "       5.4884929599972484e-05, 5.659956410526897e-05], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_intensity-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>90828959.45741372 ... 522074558....</div><input id='attrs-0d494d7f-1c08-4403-8f47-522cc055b3ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d494d7f-1c08-4403-8f47-522cc055b3ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0466a0e4-4306-4612-b390-df5bbbe3a511' class='xr-var-data-in' type='checkbox'><label for='data-0466a0e4-4306-4612-b390-df5bbbe3a511' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([90828959.45741372, 87479647.22053932, 90828959.45741372,\n",
       "       87479647.2205589, 183675724.74912184, 183675724.74912184,\n",
       "       183675724.74912184, 183675724.74912184, 200006729.87770587,\n",
       "       200006729.87770587, 200006729.87770587, 200006729.87770587,\n",
       "       216242545.10998967, 213003368.8433974, 213003368.8433974,\n",
       "       213003368.84339604, 232375443.23383301, 232375443.23383301,\n",
       "       232375443.23383155, 235588996.21727148, 232375443.23381945,\n",
       "       229157466.26799983, 225935126.58434418, 219477604.53555027,\n",
       "       235588996.21726012, 232375443.23381832, 229157466.26799655,\n",
       "       225935126.5843358, 242002585.6011944, 242002585.60119066,\n",
       "       242002585.60119066, 242002585.60119066, 245202499.89830947,\n",
       "       242002585.60119897, 242002585.6011954, 242002585.60119897,\n",
       "       335725006.4985079, 335725006.4985079, 335725006.4985051,\n",
       "       335725006.4985051, 341786164.6024449, 341786164.602446,\n",
       "       344807012.0046447, 347821294.91609824, 347821294.91609824,\n",
       "       347821294.91609824, 344807012.0046735, 362792237.9913438,\n",
       "       362792237.9913438, 362792237.991347, 362792237.9913438,\n",
       "       380528829.3113143, 377590515.0907755, 371692376.810172,\n",
       "       374645012.23102695, 383459898.95146245, 380528829.3113093,\n",
       "       377590515.0907804, 386383668.20792496, 389300081.4163573,\n",
       "...\n",
       "       426518984.9205159, 423703200.6455086, 420879349.8103572,\n",
       "       420879349.8103572, 423703200.64550954, 418047486.1773803,\n",
       "       412359936.32900554, 398004630.21674675, 341786164.60244393,\n",
       "       338758810.2217135, 338758810.2217135, 338758810.2217142,\n",
       "       403769868.3469458, 403769868.3469458, 400891065.40597165,\n",
       "       400891065.40597004, 383459898.9514799, 383459898.9514799,\n",
       "       383459898.9514869, 386383668.20794743, 383459898.9514638,\n",
       "       386383668.20792836, 392209083.05244887, 389300081.4163572,\n",
       "       298837456.90515625, 295725110.963958, 295725110.963958,\n",
       "       320461273.5408753, 286354530.3879873, 286354530.3879862,\n",
       "       286354530.3879851, 286354530.3879851, 251588263.1005409,\n",
       "       251588263.1005409, 251588263.1005409, 251588263.1005409,\n",
       "       216242545.1100026, 216242545.11000302, 219477604.53555623,\n",
       "       219477604.53555644, 190219053.11927894, 183675724.749115,\n",
       "       180398784.16083044, 177118409.13069847, 137508816.76963863,\n",
       "       140825516.32579234, 140825516.32579234, 140825516.3257937,\n",
       "       648098619.4268311, 648098619.4268941, 648098619.4268024,\n",
       "       613078654.7374505, 613078654.7374505, 611020166.9691898,\n",
       "       529487587.5052659, 527026595.7663374, 522074558.18083715],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_intensity-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.6751485466957092 ... 0.5878952...</div><input id='attrs-4edf0c90-c2ab-4898-8aec-78a0b2e70399' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4edf0c90-c2ab-4898-8aec-78a0b2e70399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abed82df-2dc8-4652-aca8-d5d510350855' class='xr-var-data-in' type='checkbox'><label for='data-abed82df-2dc8-4652-aca8-d5d510350855' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.6751485466957092, 0.19340430200099945, 0.3430732786655426,\n",
       "       0.5248144865036011, 1.1031570434570312, 0.95167475938797,\n",
       "       0.8933326601982117, 0.7676515579223633, 0.23542587459087372,\n",
       "       0.21963730454444885, 0.1215188130736351, 0.25887322425842285,\n",
       "       0.2626877725124359, 0.4517812132835388, 0.21070604026317596,\n",
       "       0.05473894998431206, 0.10801462829113007, 0.17054913938045502,\n",
       "       0.08336737751960754, 1.8087223768234253, 0.4967890977859497,\n",
       "       0.7961123585700989, 0.8302701711654663, 0.6439337134361267,\n",
       "       0.5066907405853271, 0.28045952320098877, 0.15064893662929535,\n",
       "       0.44360265135765076, 1.3347485065460205, 1.1757937669754028,\n",
       "       1.4417593479156494, 0.9043184518814087, 2.455829381942749,\n",
       "       2.715822219848633, 3.03232741355896, 2.3680927753448486,\n",
       "       4.181614398956299, 3.545548439025879, 1.01515531539917,\n",
       "       2.880052089691162, 0.44036874175071716, 0.6557313203811646,\n",
       "       1.0494027137756348, 4.862456798553467, 4.87794828414917,\n",
       "       4.728339195251465, 4.015822410583496, 1.448232889175415,\n",
       "       0.8991024494171143, 1.0317851305007935, 0.8795671463012695,\n",
       "       0.2783939838409424, 0.5816711783409119, 0.6413775682449341,\n",
       "       0.5832082033157349, 1.416309118270874, 1.5525550842285156,\n",
       "       2.0195934772491455, 1.798258662223816, 0.6084924340248108,\n",
       "...\n",
       "       0.40986329317092896, 0.09227871894836426, 0.5243004560470581,\n",
       "       1.8388837575912476, 3.6432433128356934, 1.3581287860870361,\n",
       "       0.7745957374572754, 0.94142085313797, 1.61285400390625,\n",
       "       1.1975480318069458, 1.1517596244812012, 1.655401587486267,\n",
       "       4.446273326873779, 4.925250053405762, 5.241636276245117,\n",
       "       4.134402751922607, 1.6794673204421997, 1.7474987506866455,\n",
       "       1.7451450824737549, 2.047947406768799, 1.6443986892700195,\n",
       "       2.093174934387207, 2.6986515522003174, 1.870199203491211,\n",
       "       0.546673595905304, 0.49527406692504883, 0.5232982635498047,\n",
       "       1.742357850074768, 1.072553277015686, 0.9813233613967896,\n",
       "       0.5382696986198425, 0.2566301226615906, 0.07939037680625916,\n",
       "       0.04866983741521835, 0.22616036236286163, 0.02879820205271244,\n",
       "       0.37064117193222046, 0.31861090660095215, 0.32330429553985596,\n",
       "       0.10757461935281754, 0.7118397355079651, 1.0069047212600708,\n",
       "       2.038719892501831, 1.1399726867675781, 0.2265031635761261,\n",
       "       0.31918519735336304, 0.2603169083595276, 0.08134022355079651,\n",
       "       1.0236711502075195, 1.1096651554107666, 2.2417376041412354,\n",
       "       3.5047097206115723, 3.5961008071899414, 3.980242967605591,\n",
       "       0.8170110583305359, 0.8825781941413879, 0.5878952145576477],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_intensity-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-1b54ed61-fc79-4c8b-8097-c726a5bead20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b54ed61-fc79-4c8b-8097-c726a5bead20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ce164ab-502b-48c7-bb38-0cdf42a45919' class='xr-var-data-in' type='checkbox'><label for='data-8ce164ab-502b-48c7-bb38-0cdf42a45919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 8.882360930158522e-06,\n",
       "       5.4404497669289604e-06, 3.3553981829527186e-06, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 5.487848521182676e-06,\n",
       "       4.952815743552337e-06, 6.041071138804295e-06,\n",
       "       1.1937469914022127e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 3.845069909983013e-06, 2.6229672342041706e-06,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 5.9683715943080095e-06, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.622257451147414e-06,\n",
       "       8.087568724574666e-06, 6.0472922921232425e-06,\n",
       "       9.301922497669543e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       3.6180051217471485e-06, 7.385200442004136e-06, 0.0,\n",
       "       2.022560412107474e-06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_intensity-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>150759276.42625695 ... 567461230...</div><input id='attrs-ad9770d0-e1c4-411c-b182-456c377acc25' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad9770d0-e1c4-411c-b182-456c377acc25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-427b7099-6b29-4923-8507-e9b4356b6636' class='xr-var-data-in' type='checkbox'><label for='data-427b7099-6b29-4923-8507-e9b4356b6636' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([150759276.42625695, 150759276.42625695, 150759276.42625695,\n",
       "       154064873.470435, 238798064.03806114, 238798064.03806114,\n",
       "       238798064.03805795, 238798064.03805795, 257954867.35230216,\n",
       "       257954867.35230216, 257954867.35230216, 261130833.30318388,\n",
       "       286354530.3880254, 286354530.3880178, 283220020.83441997,\n",
       "       283220020.83441997, 320461273.54088783, 320461273.540888,\n",
       "       323526455.1767425, 280080119.30623007, 320461273.5409268,\n",
       "       314312665.78216755, 305045021.72895104, 308140122.4297497,\n",
       "       314312665.7821621, 314312665.78211063, 311229356.71876556,\n",
       "       311229356.7188279, 332684811.1917618, 335725006.49854714,\n",
       "       338758810.2217569, 305045021.7289154, 314312665.78213495,\n",
       "       311229356.71879566, 308140122.4297173, 308140122.429715,\n",
       "       380528829.311356, 380528829.311356, 380528829.31135243,\n",
       "       377590515.0908228, 377590515.0908311, 383459898.95150065,\n",
       "       386383668.20796615, 415207663.661508, 418047486.17740303,\n",
       "       420879349.81040716, 423703200.6455588, 398004630.21678984,\n",
       "       398004630.21678984, 398004630.21678984, 400891065.40601397,\n",
       "       429326649.02657264, 423703200.64560443, 420879349.8104481,\n",
       "       443241301.59173095, 426518984.920572, 420879349.8104094,\n",
       "       415207663.6615076, 443241301.5917294, 443241301.5917294,\n",
       "...\n",
       "       486318580.39066696, 483693540.8055591, 486318580.39066654,\n",
       "       486318580.39066654, 475763370.6239354, 470431218.01858413,\n",
       "       465063240.7720955, 454221450.2435872, 459659847.6824173,\n",
       "       462365945.5521553, 462365945.5521553, 459659847.6824173,\n",
       "       448748462.6154783, 445999127.6264417, 443241301.5916772,\n",
       "       454221450.24354845, 423703200.64555025, 423703200.64555025,\n",
       "       423703200.64555025, 420879349.81039774, 418047486.17742944,\n",
       "       420879349.8104027, 423703200.64556444, 426518984.92056817,\n",
       "       398004630.2167783, 398004630.2167783, 395110617.73296136,\n",
       "       398004630.2167796, 365765933.2750844, 365765933.2750844,\n",
       "       368732665.04206353, 368732665.0420663, 301944113.5419918,\n",
       "       301944113.5419918, 301944113.54199207, 301944113.54199207,\n",
       "       267467790.53180546, 267467790.53180546, 267467790.53180546,\n",
       "       270628661.1648787, 238798064.0380588, 232375443.23384953,\n",
       "       229157466.26803017, 225935126.58437, 206512912.53632432,\n",
       "       209760137.40377483, 206512912.53632432, 206512912.53632432,\n",
       "       672584077.5768534, 674238056.0762318, 672584077.5768534,\n",
       "       649928892.6315556, 648098619.4268316, 649928892.6315713,\n",
       "       574278873.5482472, 572017201.864147, 567461230.8660405],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_intensity-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>12.71552562713623 ... 12.4214363...</div><input id='attrs-ae8f14bc-41c9-4a00-8f8a-4ec1ba919ba9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae8f14bc-41c9-4a00-8f8a-4ec1ba919ba9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-498ae08a-0d1a-4602-a3cd-5a52bc2afb9a' class='xr-var-data-in' type='checkbox'><label for='data-498ae08a-0d1a-4602-a3cd-5a52bc2afb9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([12.71552562713623, 12.916648864746094, 13.062923431396484,\n",
       "       12.990326881408691, 15.31355094909668, 15.473597526550293,\n",
       "       15.639409065246582, 15.6596040725708, 18.757829666137695,\n",
       "       18.54732894897461, 18.653017044067383, 18.786256790161133,\n",
       "       19.975034713745117, 19.92694854736328, 20.395553588867188,\n",
       "       19.36324119567871, 15.891728401184082, 16.19060707092285,\n",
       "       16.298263549804688, 16.565126419067383, 15.144295692443848,\n",
       "       14.867974281311035, 16.193187713623047, 21.872060775756836,\n",
       "       13.726594924926758, 13.35848331451416, 13.405755043029785,\n",
       "       12.4762601852417, 10.738884925842285, 11.070155143737793,\n",
       "       11.37685775756836, 10.362202644348145, 14.197004318237305,\n",
       "       13.787090301513672, 13.397665977478027, 12.90455436706543,\n",
       "       14.803756713867188, 15.345556259155273, 15.931687355041504,\n",
       "       16.137422561645508, 10.400568962097168, 10.748393058776855,\n",
       "       10.537457466125488, 11.38623332977295, 11.81324291229248,\n",
       "       12.303969383239746, 12.782204627990723, 10.547408103942871,\n",
       "       10.341395378112793, 10.273778915405273, 11.512372016906738,\n",
       "       18.969200134277344, 18.947824478149414, 18.64460563659668,\n",
       "       19.015548706054688, 13.174626350402832, 12.429778099060059,\n",
       "       11.746902465820312, 18.425142288208008, 18.108713150024414,\n",
       "...\n",
       "       18.56768798828125, 18.370155334472656, 18.106332778930664,\n",
       "       18.338476181030273, 17.05195426940918, 17.29546546936035,\n",
       "       18.026609420776367, 16.78628158569336, 19.61948585510254,\n",
       "       19.599185943603516, 19.808774948120117, 21.0169734954834,\n",
       "       16.353282928466797, 16.791963577270508, 16.645008087158203,\n",
       "       14.714006423950195, 13.760592460632324, 13.54490852355957,\n",
       "       13.209822654724121, 12.946063041687012, 13.05945110321045,\n",
       "       12.18547534942627, 11.256011009216309, 11.623970031738281,\n",
       "       12.558880805969238, 12.215376853942871, 11.766539573669434,\n",
       "       13.099310874938965, 14.941354751586914, 15.183076858520508,\n",
       "       15.509475708007812, 16.127716064453125, 12.773529052734375,\n",
       "       12.517017364501953, 12.010346412658691, 12.64455795288086,\n",
       "       11.661396980285645, 12.248019218444824, 12.477884292602539,\n",
       "       12.188323020935059, 14.197325706481934, 14.000564575195312,\n",
       "       14.650347709655762, 15.340311050415039, 12.176239967346191,\n",
       "       12.14764404296875, 12.020005226135254, 12.062057495117188,\n",
       "       11.874637603759766, 11.854778289794922, 11.552735328674316,\n",
       "       11.786481857299805, 12.14553165435791, 11.435341835021973,\n",
       "       10.055511474609375, 10.71857738494873, 12.421436309814453],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_intensity-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0001658675475725849 ... 0.0003...</div><input id='attrs-d3a974a8-91cc-42d6-b224-ce71ad08fb9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3a974a8-91cc-42d6-b224-ce71ad08fb9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4555c8d-fe5a-4737-bf8e-67d6a5691be8' class='xr-var-data-in' type='checkbox'><label for='data-d4555c8d-fe5a-4737-bf8e-67d6a5691be8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.0001658675475725849, 0.00015829897894793318,\n",
       "       0.00015618544427963363, 0.00016264968793848182,\n",
       "       0.00028738018136807085, 0.00028626959352003826,\n",
       "       0.000287267262005565, 0.0002817513565067884,\n",
       "       0.00026483352613629877, 0.0002618280436892434,\n",
       "       0.0002531277227382598, 0.0002542010221664197,\n",
       "       0.00020850576192184416, 0.0002105031595457373,\n",
       "       0.0002173113527439847, 0.0002182099394251236,\n",
       "       0.0002417875900442677, 0.00024738935552201746,\n",
       "       0.0002486134731242382, 0.00024827754319597993,\n",
       "       0.000275875509626649, 0.0002852909096034353, 0.0002957240020275251,\n",
       "       0.0002775163260469643, 0.00014378519280688055,\n",
       "       0.00014594987998257285, 0.00016345612338591252,\n",
       "       0.00016655325976857886, 0.00013780413849274586,\n",
       "       0.00015282215655671854, 0.00016666879610005353,\n",
       "       0.0001690813399354846, 0.00016145682797594435,\n",
       "       0.0001576859207663033, 0.00016075884378481588,\n",
       "       0.00014985208727304666, 0.00043128916901510417,\n",
       "       0.0004886327067939475, 0.0005525744103808604,\n",
       "       0.0005410809550208758, 0.00027467181634403055,\n",
       "...\n",
       "       0.00016348076503204786, 0.00013896952005198272,\n",
       "       0.0001331495912072982, 0.0001416379601074759,\n",
       "       0.00018411335616666104, 0.00016588213207487454,\n",
       "       0.0001716824832799195, 0.0001380614324720719,\n",
       "       0.0001771988949490531, 0.00016851237417899503,\n",
       "       0.00019094072525614435, 0.00016237741298687756,\n",
       "       0.00019316980404627778, 0.0002147271506251736,\n",
       "       0.00022268582588015818, 0.00022830913221546422,\n",
       "       0.00024129493630727764, 0.0002342833433350769,\n",
       "       0.00022229326010493638, 0.0003004895916720135,\n",
       "       0.00016997096921744472, 0.0001750541170225502,\n",
       "       0.00018310658989400286, 0.00018805632615576703,\n",
       "       0.0004773558390709787, 0.0004752627133950061, 0.000432224777014024,\n",
       "       0.000465304131458282, 0.00012662026665437662,\n",
       "       0.00012929619664184418, 0.00014144591005017934,\n",
       "       0.0001479351801386963, 0.00022879667924706834,\n",
       "       0.00023241407271560693, 0.00023853190827943506,\n",
       "       0.00010208762170039651, 0.00010661962938738703,\n",
       "       0.00011833915040761439, 0.0004454790582653278,\n",
       "       0.0004345057788787892, 0.0003059261665676384], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perimeter</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>247.42640687119288 ... 73.083261...</div><input id='attrs-c8de1a60-35f8-4f6c-b979-d87d1957fe2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8de1a60-35f8-4f6c-b979-d87d1957fe2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d07aa0-9c18-4a1e-9ad2-6ecd8e55b7cc' class='xr-var-data-in' type='checkbox'><label for='data-c4d07aa0-9c18-4a1e-9ad2-6ecd8e55b7cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([247.42640687119288, 247.66904755831217, 251.25483399593907,\n",
       "       258.49747468305833, 145.25483399593904, 145.01219330881975,\n",
       "       144.42640687119285, 147.01219330881975, 125.25483399593904,\n",
       "       125.87615433949868, 124.08326112068522, 124.66904755831214,\n",
       "       176.04772721475248, 183.49747468305833, 191.84062043356596,\n",
       "       210.01219330881977, 286.0243866176395, 293.5746391493337,\n",
       "       246.60407640085654, 244.94722215136414, 155.29646455628165,\n",
       "       153.29646455628165, 151.63961030678928, 150.46803743153546,\n",
       "       181.29646455628165, 191.29646455628165, 198.71067811865476,\n",
       "       265.33809511662423, 197.43250352560273, 196.71067811865476,\n",
       "       199.33199846221441, 161.74011537017762, 116.5685424949238,\n",
       "       116.5685424949238, 116.15432893255073, 119.7401153701776,\n",
       "       87.4558441227157, 87.4558441227157, 88.8700576850888,\n",
       "       86.45584412271572, 98.5269119345812, 106.35533905932738,\n",
       "       108.76955262170046, 91.49747468305833, 93.49747468305833,\n",
       "       96.42640687119285, 101.84062043356595, 78.2842712474619,\n",
       "       76.8700576850888, 76.2842712474619, 77.698484809835,\n",
       "       66.76955262170047, 72.18376618407356, 93.84062043356595,\n",
       "       109.39696961966999, 70.18376618407356, 67.35533905932738,\n",
       "       65.698484809835, 97.01219330881975, 95.59797974644665,\n",
       "...\n",
       "       110.76955262170047, 114.18376618407356, 117.35533905932738,\n",
       "       115.59797974644665, 69.55634918610404, 70.38477631085023,\n",
       "       71.79898987322333, 81.11269837220809, 227.0243866176395,\n",
       "       226.47413408594534, 226.47413408594537, 238.61626970967632,\n",
       "       85.11269837220809, 87.698484809835, 92.35533905932738,\n",
       "       112.42640687119285, 83.24873734152916, 80.8700576850888,\n",
       "       77.83452377915606, 71.00609665440987, 90.2842712474619,\n",
       "       89.07716446627535, 90.00609665440987, 72.04163056034261,\n",
       "       191.48023074035524, 193.65180361560903, 202.06601717798213,\n",
       "       106.98275605729691, 154.22539674441617, 149.39696961967,\n",
       "       146.81118318204307, 137.1543289325507, 115.01219330881975,\n",
       "       116.42640687119285, 117.01219330881975, 119.01219330881975,\n",
       "       212.2193000900063, 216.2193000900063, 215.39087296526012,\n",
       "       211.42640687119285, 135.35533905932738, 135.94112549695427,\n",
       "       137.698484809835, 139.35533905932738, 196.32590180780448,\n",
       "       199.1543289325507, 200.32590180780448, 185.49747468305833,\n",
       "       50.9350288425444, 51.59188309203678, 55.55634918610404,\n",
       "       66.66295090390226, 66.62741699796952, 72.38477631085023,\n",
       "       86.11879502661796, 81.70458146424487, 73.08326112068522],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eccentricity</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.9873694621462599 ... 0.7719856...</div><input id='attrs-1e626110-85ce-4b17-94d3-00d90e3767bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e626110-85ce-4b17-94d3-00d90e3767bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d56a6aa2-33b7-46ab-9179-b62ccb0bb491' class='xr-var-data-in' type='checkbox'><label for='data-d56a6aa2-33b7-46ab-9179-b62ccb0bb491' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.9873694621462599, 0.9875317508244154, 0.988220011565396,\n",
       "       0.9894085347897406, 0.9642764677870496, 0.9648286585030745,\n",
       "       0.9657410619822439, 0.9662888140757091, 0.9379910742974449,\n",
       "       0.9357481793004625, 0.9332541300655727, 0.9268179548215072,\n",
       "       0.9343392640650457, 0.944011752887527, 0.9530873230011326,\n",
       "       0.9649847731449117, 0.97284846291586, 0.9723680973031538,\n",
       "       0.957806273575442, 0.9919771492995064, 0.9085165750223022,\n",
       "       0.9113214122493429, 0.9197811583952846, 0.895838562895326,\n",
       "       0.9596856181747739, 0.9616212843728209, 0.9639198181251112,\n",
       "       0.9723103238100945, 0.9432531799548078, 0.9413741045985945,\n",
       "       0.9350970652084978, 0.9775076249759554, 0.9067459093527118,\n",
       "       0.9113579665653163, 0.9151647649321644, 0.9276058953214239,\n",
       "       0.909633860549074, 0.9121510115835606, 0.9133187945011276,\n",
       "       0.9176436102054115, 0.9799774881907866, 0.9848668150178751,\n",
       "       0.9860999803463754, 0.8582078595714246, 0.8213168720233589,\n",
       "       0.7871347434418576, 0.7727430692095157, 0.9235629860880571,\n",
       "       0.9215005438201616, 0.9191446904048528, 0.8977764768056367,\n",
       "       0.7845211724859059, 0.8332119826028217, 0.9036220198130915,\n",
       "       0.9378832420390085, 0.8273075594320155, 0.8439849436932829,\n",
       "       0.8628605143096407, 0.8479580985512859, 0.8458248916693848,\n",
       "...\n",
       "       0.8367094918286481, 0.8493177728302231, 0.8534562913580948,\n",
       "       0.8488743726405137, 0.5810352407225948, 0.3612373796549533,\n",
       "       0.3790129303548469, 0.753610804805945, 0.4384552757000761,\n",
       "       0.48221276022772624, 0.5403428322593281, 0.5771409180953646,\n",
       "       0.8576904537966324, 0.885077155273515, 0.9270390254138989,\n",
       "       0.9595820513894374, 0.8897914520457627, 0.8799134091943955,\n",
       "       0.8751420516188375, 0.8922037418787288, 0.9546165433424587,\n",
       "       0.9515410032071845, 0.9524815431909098, 0.8869649861353307,\n",
       "       0.9410138859292769, 0.9417353629538195, 0.9436667152625606,\n",
       "       0.4907374113157985, 0.9356342240910602, 0.9278964393710196,\n",
       "       0.9158094555411549, 0.9007458118345822, 0.9418848780296359,\n",
       "       0.9416284971247075, 0.9420642085015287, 0.946144662599356,\n",
       "       0.984969887790148, 0.9863612502823608, 0.9876158344310839,\n",
       "       0.9848499763820467, 0.9648092747267568, 0.9662811881852721,\n",
       "       0.9644699337203941, 0.966934412066608, 0.9686659432492142,\n",
       "       0.9705628302015875, 0.9714041164503165, 0.9664120720024258,\n",
       "       0.6899374034641501, 0.7141769128797888, 0.7192686609349608,\n",
       "       0.9161623192548712, 0.8457254642443864, 0.7695308561441414,\n",
       "       0.841861731646382, 0.8244033047687119, 0.7719856989088697],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bbox_area</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2204.0 2300.0 ... 700.0 580.0</div><input id='attrs-2499182c-a5be-4b44-9255-872fcc357c7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2499182c-a5be-4b44-9255-872fcc357c7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efe19863-7419-4357-9155-a09b7fa5d4a1' class='xr-var-data-in' type='checkbox'><label for='data-efe19863-7419-4357-9155-a09b7fa5d4a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([2204.0, 2300.0, 2242.0, 2562.0, 1188.0, 1170.0, 1170.0, 1188.0,\n",
       "       1045.0, 1064.0, 1045.0, 1080.0, 1748.0, 1920.0, 1932.0, 2116.0,\n",
       "       3132.0, 3103.0, 2940.0, 1545.0, 1885.0, 1820.0, 1690.0, 1798.0,\n",
       "       2106.0, 2295.0, 2403.0, 2744.0, 2250.0, 2325.0, 2432.0, 1512.0,\n",
       "       1127.0, 1127.0, 1100.0, 1122.0, 560.0, 560.0, 576.0, 540.0, 585.0,\n",
       "       720.0, 735.0, 816.0, 850.0, 858.0, 952.0, 442.0, 429.0, 429.0,\n",
       "       462.0, 450.0, 493.0, 702.0, 1025.0, 464.0, 420.0, 378.0, 798.0,\n",
       "       760.0, 777.0, 720.0, 1485.0, 1395.0, 1242.0, 1128.0, 608.0, 560.0,\n",
       "       578.0, 544.0, 608.0, 640.0, 627.0, 576.0, 720.0, 702.0, 646.0,\n",
       "       720.0, 451.0, 405.0, 520.0, 560.0, 580.0, 609.0, 588.0, 660.0,\n",
       "       616.0, 660.0, 713.0, 558.0, 1406.0, 1480.0, 1628.0, 903.0, 561.0,\n",
       "       561.0, 578.0, 561.0, 434.0, 434.0, 434.0, 434.0, 600.0, 648.0,\n",
       "       696.0, 696.0, 391.0, 336.0, 448.0, 665.0, 252.0, 252.0, 285.0,\n",
       "       285.0, 390.0, 464.0, 486.0, 448.0, 902.0, 902.0, 902.0, 924.0,\n",
       "       624.0, 624.0, 621.0, 624.0, 552.0, 552.0, 552.0, 600.0, 476.0,\n",
       "       448.0, 459.0, 580.0, 273.0, 242.0, 253.0, 360.0, 304.0, 304.0,\n",
       "       357.0, 399.0, 380.0, 380.0, 399.0, 936.0, 962.0, 504.0, 1080.0,\n",
       "       418.0, 418.0, 440.0, 484.0, 323.0, 336.0, 357.0, 414.0, 672.0,\n",
       "       644.0, 700.0, 675.0, 224.0, 224.0, 224.0, 224.0, 285.0, 270.0,\n",
       "       270.0, 675.0, 650.0, 625.0, 352.0, 608.0, 589.0, 589.0, 506.0,\n",
       "       462.0, 400.0, 288.0, 437.0, 475.0, 520.0, 520.0, 340.0, 340.0,\n",
       "       340.0, 323.0, 324.0, 306.0, 306.0, 306.0, 289.0, 306.0, 306.0,\n",
       "       288.0, 324.0, 306.0, 306.0, 306.0, 528.0, 624.0, 648.0, 810.0,\n",
       "       728.0, 702.0, 440.0, 288.0, 288.0, 288.0, 285.0, 693.0, 420.0,\n",
       "       399.0, 416.0, 494.0, 540.0, 588.0, 736.0, 540.0, 558.0, 589.0,\n",
       "       551.0, 480.0, 480.0, 483.0, 483.0, 459.0, 459.0, 442.0, 442.0,\n",
       "       609.0, 660.0, 1056.0, 594.0, 966.0, 989.0, 1075.0, 1075.0, 380.0,\n",
       "       400.0, 440.0, 588.0, 2184.0, 2200.0, 2200.0, 2408.0, 578.0, 576.0,\n",
       "       640.0, 940.0, 540.0, 495.0, 480.0, 377.0, 533.0, 520.0, 480.0,\n",
       "       406.0, 2278.0, 2345.0, 2380.0, 918.0, 1755.0, 1674.0, 1680.0,\n",
       "       1512.0, 867.0, 884.0, 884.0, 901.0, 1751.0, 1785.0, 1680.0, 1717.0,\n",
       "       976.0, 976.0, 976.0, 1008.0, 1958.0, 2002.0, 1932.0, 1764.0, 225.0,\n",
       "       240.0, 270.0, 420.0, 399.0, 420.0, 760.0, 700.0, 580.0],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1532.0 1539.0 ... 335.0 291.0</div><input id='attrs-0fda6636-1a9f-4b99-968c-12da0268b36b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fda6636-1a9f-4b99-968c-12da0268b36b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f17205c9-a040-4640-9af5-f97027d7d8be' class='xr-var-data-in' type='checkbox'><label for='data-f17205c9-a040-4640-9af5-f97027d7d8be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1532.0, 1539.0, 1547.0, 1562.0, 855.0, 854.0, 849.0, 864.0, 715.0,\n",
       "       714.0, 708.0, 726.0, 1287.0, 1313.0, 1332.0, 1315.0, 1258.0,\n",
       "       1243.0, 1054.0, 698.0, 1242.0, 1197.0, 1123.0, 1119.0, 1147.0,\n",
       "       1216.0, 1263.0, 1304.0, 892.0, 926.0, 936.0, 776.0, 677.0, 670.0,\n",
       "       658.0, 654.0, 368.0, 374.0, 377.0, 364.0, 298.0, 313.0, 331.0,\n",
       "       517.0, 548.0, 566.0, 650.0, 316.0, 315.0, 308.0, 342.0, 285.0,\n",
       "       316.0, 386.0, 421.0, 311.0, 288.0, 258.0, 603.0, 591.0, 583.0,\n",
       "       562.0, 750.0, 728.0, 687.0, 644.0, 374.0, 358.0, 344.0, 345.0,\n",
       "       396.0, 392.0, 391.0, 387.0, 473.0, 470.0, 468.0, 504.0, 293.0,\n",
       "       302.0, 325.0, 349.0, 374.0, 393.0, 395.0, 422.0, 368.0, 380.0,\n",
       "       391.0, 381.0, 731.0, 738.0, 733.0, 554.0, 404.0, 406.0, 410.0,\n",
       "       400.0, 345.0, 353.0, 351.0, 368.0, 374.0, 389.0, 409.0, 429.0,\n",
       "       201.0, 234.0, 306.0, 417.0, 201.0, 204.0, 209.0, 214.0, 303.0,\n",
       "       327.0, 317.0, 304.0, 454.0, 451.0, 459.0, 492.0, 355.0, 353.0,\n",
       "       357.0, 425.0, 434.0, 432.0, 433.0, 459.0, 281.0, 288.0, 299.0,\n",
       "       331.0, 163.0, 162.0, 163.0, 180.0, 168.0, 162.0, 200.0, 223.0,\n",
       "       218.0, 214.0, 206.0, 451.0, 460.0, 261.0, 492.0, 309.0, 324.0,\n",
       "       345.0, 351.0, 206.0, 216.0, 234.0, 251.0, 403.0, 392.0, 386.0,\n",
       "       390.0, 145.0, 148.0, 151.0, 156.0, 170.0, 163.0, 160.0, 303.0,\n",
       "       284.0, 262.0, 192.0, 389.0, 391.0, 386.0, 290.0, 286.0, 272.0,\n",
       "       221.0, 212.0, 203.0, 202.0, 194.0, 262.0, 263.0, 261.0, 241.0,\n",
       "       230.0, 226.0, 229.0, 228.0, 233.0, 233.0, 233.0, 218.0, 252.0,\n",
       "       245.0, 250.0, 250.0, 406.0, 440.0, 401.0, 256.0, 238.0, 222.0,\n",
       "       207.0, 202.0, 198.0, 204.0, 190.0, 354.0, 285.0, 278.0, 284.0,\n",
       "       357.0, 372.0, 381.0, 369.0, 402.0, 399.0, 408.0, 373.0, 315.0,\n",
       "       313.0, 296.0, 268.0, 297.0, 297.0, 295.0, 298.0, 440.0, 455.0,\n",
       "       546.0, 428.0, 801.0, 812.0, 836.0, 831.0, 291.0, 305.0, 320.0,\n",
       "       388.0, 1018.0, 1027.0, 1037.0, 982.0, 418.0, 414.0, 424.0, 500.0,\n",
       "       326.0, 321.0, 316.0, 288.0, 301.0, 286.0, 265.0, 291.0, 951.0,\n",
       "       937.0, 896.0, 470.0, 1147.0, 1132.0, 1127.0, 1041.0, 605.0, 605.0,\n",
       "       603.0, 620.0, 1035.0, 1006.0, 963.0, 989.0, 777.0, 779.0, 806.0,\n",
       "       803.0, 1353.0, 1350.0, 1361.0, 1311.0, 156.0, 156.0, 181.0, 206.0,\n",
       "       247.0, 229.0, 339.0, 335.0, 291.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_loc-0-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>18.0 19.0 18.0 20.0 ... 0.0 0.0 0.0</div><input id='attrs-b2bbdc7d-d981-4b55-9c05-3d6f69f1cfde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2bbdc7d-d981-4b55-9c05-3d6f69f1cfde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-715067af-685e-4980-a204-37725ee28636' class='xr-var-data-in' type='checkbox'><label for='data-715067af-685e-4980-a204-37725ee28636' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([18.0, 19.0, 18.0, 20.0, 17.0, 17.0, 17.0, 17.0, 18.0, 18.0, 18.0,\n",
       "       19.0, 22.0, 23.0, 22.0, 22.0, 28.0, 28.0, 29.0, 14.0, 28.0, 27.0,\n",
       "       25.0, 28.0, 25.0, 26.0, 26.0, 27.0, 29.0, 30.0, 31.0, 20.0, 22.0,\n",
       "       22.0, 21.0, 21.0, 15.0, 15.0, 15.0, 14.0, 12.0, 14.0, 14.0, 23.0,\n",
       "       24.0, 25.0, 27.0, 12.0, 12.0, 12.0, 13.0, 17.0, 16.0, 17.0, 24.0,\n",
       "       15.0, 14.0, 13.0, 20.0, 19.0, 20.0, 19.0, 32.0, 30.0, 26.0, 23.0,\n",
       "       15.0, 15.0, 16.0, 16.0, 18.0, 19.0, 18.0, 17.0, 17.0, 17.0, 16.0,\n",
       "       17.0, 10.0, 8.0, 9.0, 9.0, 19.0, 20.0, 20.0, 21.0, 21.0, 21.0,\n",
       "       22.0, 17.0, 36.0, 36.0, 36.0, 20.0, 16.0, 16.0, 16.0, 16.0, 13.0,\n",
       "       13.0, 13.0, 13.0, 23.0, 23.0, 23.0, 23.0, 16.0, 15.0, 15.0, 18.0,\n",
       "       13.0, 13.0, 14.0, 14.0, 14.0, 15.0, 17.0, 15.0, 21.0, 21.0, 21.0,\n",
       "       21.0, 23.0, 23.0, 22.0, 23.0, 22.0, 22.0, 22.0, 24.0, 16.0, 15.0,\n",
       "       16.0, 19.0, 20.0, 21.0, 22.0, 17.0, 15.0, 15.0, 16.0, 20.0, 19.0,\n",
       "       19.0, 20.0, 35.0, 36.0, 23.0, 39.0, 18.0, 18.0, 19.0, 21.0, 16.0,\n",
       "       15.0, 16.0, 17.0, 27.0, 27.0, 27.0, 26.0, 2.0, 2.0, 2.0, 2.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_loc-0-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>8.0 9.0 8.0 9.0 ... 12.0 9.0 6.0</div><input id='attrs-fc919e56-af9e-4aed-b55d-bfedb97ca45a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc919e56-af9e-4aed-b55d-bfedb97ca45a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78d773b2-7a9a-468f-8d62-ce07bf44c194' class='xr-var-data-in' type='checkbox'><label for='data-78d773b2-7a9a-468f-8d62-ce07bf44c194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([8.0, 9.0, 8.0, 9.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 22.0,\n",
       "       23.0, 22.0, 22.0, 0.0, 0.0, 2.0, 1.0, 25.0, 23.0, 0.0, 0.0, 25.0,\n",
       "       25.0, 26.0, 27.0, 8.0, 9.0, 10.0, 7.0, 4.0, 4.0, 4.0, 4.0, 12.0,\n",
       "       12.0, 10.0, 8.0, 5.0, 5.0, 4.0, 21.0, 22.0, 23.0, 25.0, 12.0, 12.0,\n",
       "       12.0, 6.0, 13.0, 12.0, 12.0, 14.0, 15.0, 14.0, 13.0, 17.0, 15.0,\n",
       "       17.0, 17.0, 6.0, 7.0, 6.0, 7.0, 14.0, 12.0, 15.0, 7.0, 12.0, 12.0,\n",
       "       12.0, 13.0, 16.0, 16.0, 12.0, 6.0, 4.0, 4.0, 3.0, 5.0, 8.0, 8.0,\n",
       "       8.0, 9.0, 10.0, 8.0, 7.0, 8.0, 20.0, 17.0, 22.0, 9.0, 14.0, 14.0,\n",
       "       14.0, 14.0, 10.0, 10.0, 8.0, 9.0, 7.0, 8.0, 8.0, 23.0, 16.0, 15.0,\n",
       "       14.0, 16.0, 8.0, 8.0, 9.0, 9.0, 13.0, 5.0, 7.0, 15.0, 4.0, 3.0,\n",
       "       6.0, 5.0, 17.0, 17.0, 17.0, 17.0, 16.0, 15.0, 15.0, 14.0, 8.0, 7.0,\n",
       "       8.0, 11.0, 9.0, 10.0, 12.0, 12.0, 12.0, 12.0, 10.0, 7.0, 7.0, 7.0,\n",
       "       10.0, 21.0, 20.0, 21.0, 19.0, 5.0, 5.0, 5.0, 4.0, 7.0, 6.0, 7.0,\n",
       "       3.0, 2.0, 2.0, 2.0, 3.0, 14.0, 14.0, 14.0, 14.0, 9.0, 10.0, 10.0,\n",
       "       21.0, 21.0, 19.0, 14.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 15.0,\n",
       "       13.0, 14.0, 13.0, 19.0, 19.0, 17.0, 18.0, 10.0, 10.0, 10.0, 11.0,\n",
       "       7.0, 7.0, 7.0, 8.0, 5.0, 4.0, 4.0, 2.0, 21.0, 22.0, 25.0, 24.0,\n",
       "       23.0, 24.0, 18.0, 9.0, 9.0, 10.0, 5.0, 8.0, 8.0, 6.0, 5.0, 5.0,\n",
       "       5.0, 11.0, 4.0, 13.0, 12.0, 12.0, 11.0, 6.0, 6.0, 8.0, 10.0, 10.0,\n",
       "       10.0, 10.0, 10.0, 0.0, 0.0, 0.0, 1.0, 5.0, 5.0, 7.0, 9.0, 6.0, 6.0,\n",
       "       5.0, 3.0, 16.0, 25.0, 27.0, 28.0, 12.0, 13.0, 13.0, 19.0, 4.0, 4.0,\n",
       "       4.0, 3.0, 11.0, 12.0, 11.0, 13.0, 4.0, 4.0, 3.0, 10.0, 0.0, 0.0,\n",
       "       0.0, 0.0, 6.0, 6.0, 6.0, 3.0, 10.0, 10.0, 10.0, 11.0, 6.0, 4.0,\n",
       "       3.0, 2.0, 2.0, 3.0, 1.0, 2.0, 7.0, 7.0, 4.0, 12.0, 11.0, 12.0,\n",
       "       12.0, 9.0, 6.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_loc-0-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>15.0 15.0 14.0 ... 12.0 12.0 10.0</div><input id='attrs-55d2a763-1519-4fb7-8f6b-ffa35d29b136' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-55d2a763-1519-4fb7-8f6b-ffa35d29b136' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89d1f495-ef79-4b68-8287-bd85baa31a68' class='xr-var-data-in' type='checkbox'><label for='data-89d1f495-ef79-4b68-8287-bd85baa31a68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15.0, 15.0, 14.0, 16.0, 11.0, 13.0, 13.0, 13.0, 2.0, 2.0, 2.0, 1.0,\n",
       "       7.0, 8.0, 8.0, 8.0, 9.0, 9.0, 9.0, 8.0, 7.0, 8.0, 9.0, 10.0, 10.0,\n",
       "       9.0, 9.0, 9.0, 8.0, 13.0, 13.0, 12.0, 10.0, 11.0, 11.0, 10.0, 4.0,\n",
       "       4.0, 4.0, 4.0, 7.0, 7.0, 6.0, 12.0, 12.0, 13.0, 14.0, 5.0, 4.0,\n",
       "       5.0, 5.0, 10.0, 10.0, 8.0, 5.0, 5.0, 4.0, 4.0, 10.0, 10.0, 10.0,\n",
       "       8.0, 5.0, 5.0, 6.0, 6.0, 9.0, 9.0, 8.0, 8.0, 10.0, 11.0, 10.0, 9.0,\n",
       "       4.0, 3.0, 3.0, 2.0, 7.0, 6.0, 7.0, 6.0, 5.0, 5.0, 5.0, 6.0, 6.0,\n",
       "       6.0, 7.0, 7.0, 18.0, 19.0, 20.0, 6.0, 3.0, 3.0, 2.0, 2.0, 7.0, 7.0,\n",
       "       7.0, 7.0, 23.0, 20.0, 20.0, 10.0, 3.0, 2.0, 13.0, 12.0, 1.0, 0.0,\n",
       "       0.0, 0.0, 1.0, 3.0, 5.0, 11.0, 13.0, 12.0, 13.0, 8.0, 13.0, 14.0,\n",
       "       8.0, 9.0, 11.0, 13.0, 13.0, 15.0, 6.0, 5.0, 6.0, 8.0, 10.0, 11.0,\n",
       "       12.0, 5.0, 5.0, 3.0, 3.0, 8.0, 8.0, 8.0, 10.0, 25.0, 25.0, 17.0,\n",
       "       26.0, 6.0, 6.0, 4.0, 5.0, 9.0, 8.0, 9.0, 8.0, 9.0, 8.0, 8.0, 6.0,\n",
       "       13.0, 13.0, 13.0, 13.0, 10.0, 10.0, 10.0, 19.0, 19.0, 17.0, 8.0,\n",
       "       5.0, 4.0, 4.0, 6.0, 8.0, 7.0, 5.0, 3.0, 3.0, 0.0, 0.0, 14.0, 14.0,\n",
       "       14.0, 14.0, 9.0, 8.0, 8.0, 9.0, 7.0, 7.0, 7.0, 7.0, 8.0, 7.0, 7.0,\n",
       "       8.0, 19.0, 23.0, 23.0, 23.0, 22.0, 14.0, 8.0, 6.0, 6.0, 7.0, 6.0,\n",
       "       8.0, 9.0, 9.0, 7.0, 8.0, 8.0, 8.0, 8.0, 10.0, 11.0, 12.0, 11.0,\n",
       "       13.0, 9.0, 10.0, 6.0, 11.0, 11.0, 11.0, 11.0, 7.0, 9.0, 9.0, 10.0,\n",
       "       10.0, 10.0, 12.0, 13.0, 8.0, 8.0, 8.0, 14.0, 10.0, 11.0, 11.0,\n",
       "       27.0, 11.0, 10.0, 10.0, 15.0, 5.0, 9.0, 9.0, 5.0, 9.0, 9.0, 10.0,\n",
       "       10.0, 25.0, 27.0, 26.0, 4.0, 17.0, 17.0, 18.0, 19.0, 8.0, 8.0, 7.0,\n",
       "       7.0, 9.0, 9.0, 9.0, 10.0, 13.0, 12.0, 11.0, 11.0, 11.0, 12.0, 11.0,\n",
       "       15.0, 5.0, 7.0, 5.0, 14.0, 13.0, 18.0, 12.0, 12.0, 10.0],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_loc-1-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>55.0 54.0 56.0 ... 17.0 15.0 13.0</div><input id='attrs-1e9c7224-c6f2-4fb7-9217-05f009294793' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e9c7224-c6f2-4fb7-9217-05f009294793' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4dae519-a92f-454c-aa9e-4d5a41fb563b' class='xr-var-data-in' type='checkbox'><label for='data-b4dae519-a92f-454c-aa9e-4d5a41fb563b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([55.0, 54.0, 56.0, 53.0, 27.0, 26.0, 13.0, 13.0, 24.0, 25.0, 25.0,\n",
       "       24.0, 52.0, 48.0, 54.0, 58.0, 72.0, 77.0, 63.0, 31.0, 37.0, 34.0,\n",
       "       17.0, 37.0, 56.0, 57.0, 59.0, 64.0, 30.0, 29.0, 32.0, 16.0, 34.0,\n",
       "       35.0, 37.0, 41.0, 5.0, 3.0, 6.0, 6.0, 28.0, 46.0, 47.0, 15.0, 15.0,\n",
       "       17.0, 20.0, 16.0, 13.0, 11.0, 12.0, 7.0, 8.0, 8.0, 39.0, 13.0,\n",
       "       11.0, 15.0, 22.0, 21.0, 22.0, 18.0, 19.0, 22.0, 32.0, 36.0, 8.0,\n",
       "       8.0, 8.0, 9.0, 11.0, 15.0, 15.0, 14.0, 18.0, 17.0, 14.0, 15.0,\n",
       "       25.0, 33.0, 50.0, 49.0, 27.0, 23.0, 22.0, 27.0, 20.0, 22.0, 24.0,\n",
       "       22.0, 35.0, 38.0, 42.0, 39.0, 13.0, 19.0, 19.0, 14.0, 10.0, 7.0,\n",
       "       10.0, 10.0, 10.0, 12.0, 13.0, 4.0, 2.0, 2.0, 7.0, 22.0, 7.0, 9.0,\n",
       "       6.0, 5.0, 14.0, 11.0, 11.0, 11.0, 12.0, 14.0, 15.0, 15.0, 4.0, 5.0,\n",
       "       6.0, 9.0, 7.0, 12.0, 8.0, 9.0, 19.0, 20.0, 17.0, 22.0, 9.0, 7.0,\n",
       "       8.0, 10.0, 12.0, 11.0, 12.0, 15.0, 12.0, 12.0, 12.0, 23.0, 23.0,\n",
       "       5.0, 25.0, 6.0, 6.0, 8.0, 4.0, 11.0, 13.0, 13.0, 14.0, 9.0, 9.0,\n",
       "       9.0, 10.0, 8.0, 8.0, 8.0, 9.0, 1.0, 0.0, 0.0, 10.0, 11.0, 12.0,\n",
       "       4.0, 10.0, 10.0, 9.0, 12.0, 12.0, 10.0, 2.0, 5.0, 6.0, 7.0, 6.0,\n",
       "       4.0, 2.0, 2.0, 9.0, 9.0, 9.0, 6.0, 7.0, 12.0, 12.0, 12.0, 12.0,\n",
       "       8.0, 9.0, 9.0, 9.0, 5.0, 6.0, 6.0, 3.0, 3.0, 1.0, 21.0, 10.0, 10.0,\n",
       "       10.0, 13.0, 28.0, 16.0, 16.0, 20.0, 18.0, 18.0, 18.0, 19.0, 17.0,\n",
       "       17.0, 17.0, 16.0, 10.0, 10.0, 10.0, 12.0, 13.0, 11.0, 9.0, 8.0,\n",
       "       17.0, 9.0, 18.0, 13.0, 25.0, 32.0, 9.0, 8.0, 2.0, 3.0, 4.0, 12.0,\n",
       "       26.0, 26.0, 26.0, 27.0, 13.0, 17.0, 15.0, 42.0, 26.0, 24.0, 23.0,\n",
       "       22.0, 22.0, 21.0, 17.0, 5.0, 14.0, 12.0, 15.0, 18.0, 21.0, 18.0,\n",
       "       19.0, 24.0, 24.0, 24.0, 28.0, 28.0, 59.0, 59.0, 58.0, 55.0, 30.0,\n",
       "       21.0, 36.0, 22.0, 47.0, 43.0, 44.0, 37.0, 9.0, 4.0, 12.0, 20.0,\n",
       "       19.0, 18.0, 17.0, 15.0, 13.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_loc-1-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>113.0 112.0 115.0 ... 14.0 16.0</div><input id='attrs-0a8907d1-b78a-4712-8357-4f13bbf1cb14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a8907d1-b78a-4712-8357-4f13bbf1cb14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3070fe8a-818d-48f9-82af-1fb6ac7b0f1a' class='xr-var-data-in' type='checkbox'><label for='data-3070fe8a-818d-48f9-82af-1fb6ac7b0f1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([113.0, 112.0, 115.0, 115.0, 30.0, 29.0, 30.0, 28.0, 46.0, 49.0,\n",
       "       48.0, 44.0, 50.0, 48.0, 49.0, 65.0, 26.0, 26.0, 61.0, 68.0, 52.0,\n",
       "       54.0, 17.0, 16.0, 52.0, 54.0, 59.0, 63.0, 2.0, 3.0, 5.0, 1.0, 7.0,\n",
       "       9.0, 9.0, 8.0, 24.0, 25.0, 25.0, 25.0, 13.0, 11.0, 9.0, 4.0, 4.0,\n",
       "       3.0, 2.0, 10.0, 9.0, 10.0, 21.0, 14.0, 13.0, 12.0, 12.0, 14.0,\n",
       "       14.0, 15.0, 6.0, 4.0, 4.0, 5.0, 5.0, 4.0, 5.0, 4.0, 5.0, 4.0, 11.0,\n",
       "       9.0, 7.0, 6.0, 5.0, 6.0, 15.0, 15.0, 12.0, 5.0, 11.0, 10.0, 30.0,\n",
       "       1.0, 11.0, 9.0, 7.0, 6.0, 24.0, 24.0, 21.0, 24.0, 2.0, 2.0, 12.0,\n",
       "       10.0, 8.0, 8.0, 9.0, 8.0, 28.0, 28.0, 29.0, 29.0, 7.0, 4.0, 3.0,\n",
       "       11.0, 2.0, 2.0, 19.0, 16.0, 15.0, 14.0, 14.0, 15.0, 7.0, 8.0, 6.0,\n",
       "       12.0, 28.0, 28.0, 31.0, 31.0, 5.0, 5.0, 5.0, 4.0, 4.0, 3.0, 4.0,\n",
       "       4.0, 24.0, 24.0, 23.0, 26.0, 4.0, 4.0, 4.0, 19.0, 17.0, 17.0, 19.0,\n",
       "       8.0, 8.0, 8.0, 9.0, 4.0, 4.0, 3.0, 3.0, 16.0, 16.0, 16.0, 14.0,\n",
       "       12.0, 15.0, 16.0, 13.0, 0.0, 0.0, 1.0, 5.0, 6.0, 6.0, 6.0, 7.0,\n",
       "       12.0, 13.0, 15.0, 23.0, 23.0, 22.0, 14.0, 10.0, 10.0, 8.0, 8.0,\n",
       "       8.0, 9.0, 10.0, 19.0, 21.0, 23.0, 20.0, 10.0, 11.0, 2.0, 12.0,\n",
       "       13.0, 13.0, 13.0, 13.0, 7.0, 8.0, 8.0, 8.0, 13.0, 14.0, 15.0, 13.0,\n",
       "       5.0, 5.0, 11.0, 17.0, 16.0, 15.0, 8.0, 15.0, 15.0, 13.0, 16.0,\n",
       "       29.0, 17.0, 17.0, 22.0, 14.0, 15.0, 0.0, 3.0, 2.0, 3.0, 3.0, 0.0,\n",
       "       10.0, 10.0, 9.0, 7.0, 15.0, 18.0, 20.0, 20.0, 10.0, 12.0, 15.0,\n",
       "       16.0, 34.0, 36.0, 35.0, 35.0, 2.0, 2.0, 4.0, 9.0, 3.0, 2.0, 4.0,\n",
       "       5.0, 24.0, 18.0, 22.0, 23.0, 21.0, 19.0, 18.0, 14.0, 17.0, 17.0,\n",
       "       15.0, 4.0, 23.0, 22.0, 24.0, 1.0, 28.0, 29.0, 22.0, 17.0, 18.0,\n",
       "       19.0, 19.0, 23.0, 102.0, 104.0, 104.0, 100.0, 13.0, 24.0, 28.0,\n",
       "       25.0, 32.0, 31.0, 41.0, 28.0, 8.0, 7.0, 5.0, 16.0, 16.0, 15.0,\n",
       "       34.0, 14.0, 16.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_loc-1-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>43.0 38.0 40.0 ... 14.0 11.0 6.0</div><input id='attrs-8a19baa5-4cf6-4eb6-abb2-785ef67043de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a19baa5-4cf6-4eb6-abb2-785ef67043de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4736e230-d325-4fc5-953f-596867266f51' class='xr-var-data-in' type='checkbox'><label for='data-4736e230-d325-4fc5-953f-596867266f51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([43.0, 38.0, 40.0, 46.0, 25.0, 26.0, 26.0, 26.0, 33.0, 34.0, 34.0,\n",
       "       35.0, 20.0, 21.0, 22.0, 25.0, 87.0, 85.0, 76.0, 83.0, 40.0, 37.0,\n",
       "       36.0, 33.0, 54.0, 53.0, 56.0, 61.0, 60.0, 12.0, 13.0, 13.0, 36.0,\n",
       "       35.0, 38.0, 44.0, 11.0, 14.0, 14.0, 12.0, 12.0, 14.0, 13.0, 25.0,\n",
       "       25.0, 23.0, 27.0, 27.0, 24.0, 25.0, 24.0, 11.0, 10.0, 11.0, 12.0,\n",
       "       7.0, 9.0, 10.0, 27.0, 26.0, 24.0, 25.0, 10.0, 9.0, 8.0, 7.0, 26.0,\n",
       "       24.0, 18.0, 17.0, 15.0, 15.0, 16.0, 16.0, 23.0, 21.0, 19.0, 21.0,\n",
       "       13.0, 17.0, 17.0, 26.0, 14.0, 12.0, 11.0, 11.0, 11.0, 12.0, 12.0,\n",
       "       11.0, 15.0, 16.0, 18.0, 15.0, 22.0, 22.0, 16.0, 14.0, 12.0, 11.0,\n",
       "       11.0, 11.0, 13.0, 14.0, 14.0, 19.0, 16.0, 16.0, 24.0, 25.0, 16.0,\n",
       "       15.0, 15.0, 15.0, 11.0, 10.0, 8.0, 9.0, 13.0, 13.0, 13.0, 29.0,\n",
       "       7.0, 7.0, 10.0, 9.0, 7.0, 6.0, 6.0, 6.0, 19.0, 19.0, 18.0, 20.0,\n",
       "       1.0, 0.0, 0.0, 4.0, 3.0, 5.0, 6.0, 10.0, 11.0, 11.0, 11.0, 20.0,\n",
       "       21.0, 4.0, 22.0, 14.0, 14.0, 14.0, 13.0, 16.0, 18.0, 16.0, 11.0,\n",
       "       12.0, 12.0, 13.0, 15.0, 6.0, 6.0, 6.0, 7.0, 10.0, 9.0, 9.0, 23.0,\n",
       "       24.0, 24.0, 20.0, 9.0, 9.0, 9.0, 6.0, 9.0, 9.0, 9.0, 6.0, 7.0, 5.0,\n",
       "       4.0, 16.0, 15.0, 15.0, 14.0, 11.0, 12.0, 12.0, 12.0, 9.0, 10.0,\n",
       "       10.0, 10.0, 8.0, 8.0, 8.0, 7.0, 15.0, 16.0, 16.0, 18.0, 18.0, 20.0,\n",
       "       13.0, 10.0, 10.0, 10.0, 13.0, 27.0, 15.0, 14.0, 20.0, 11.0, 18.0,\n",
       "       18.0, 13.0, 7.0, 7.0, 8.0, 7.0, 11.0, 13.0, 13.0, 15.0, 14.0, 13.0,\n",
       "       14.0, 14.0, 16.0, 17.0, 18.0, 17.0, 28.0, 29.0, 28.0, 29.0, 1.0,\n",
       "       2.0, 2.0, 10.0, 29.0, 31.0, 32.0, 10.0, 9.0, 11.0, 11.0, 6.0, 6.0,\n",
       "       17.0, 16.0, 16.0, 22.0, 19.0, 2.0, 6.0, 55.0, 55.0, 57.0, 9.0,\n",
       "       47.0, 45.0, 43.0, 39.0, 24.0, 24.0, 26.0, 26.0, 44.0, 44.0, 43.0,\n",
       "       40.0, 20.0, 15.0, 10.0, 11.0, 74.0, 74.0, 74.0, 54.0, 7.0, 11.0,\n",
       "       11.0, 0.0, 0.0, 14.0, 14.0, 11.0, 6.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wclat</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-80.8041775456919 ... 45.6709621...</div><input id='attrs-632fb01d-de38-4af6-9981-77b9821acca7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-632fb01d-de38-4af6-9981-77b9821acca7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d048ba3-1070-420a-9482-0f5e15f4e7a2' class='xr-var-data-in' type='checkbox'><label for='data-2d048ba3-1070-420a-9482-0f5e15f4e7a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-80.8041775456919, -80.81172839506172, -80.80882352941177,\n",
       "       -80.76536491677336, -73.83479532163743, -73.84543325526931,\n",
       "       -73.86513545347468, -73.90364583333333, -72.82797202797202,\n",
       "       -72.80952380952381, -72.80190677966101, -72.70971074380165,\n",
       "       -71.08896658896658, -71.24333587204875, -71.35491741741743,\n",
       "       -71.47205323193916, -69.1635532591415, -69.14923572003218,\n",
       "       -69.03249525616698, -70.65723495702005, -68.48289049919485,\n",
       "       -68.77380952380952, -69.12511130899377, -69.20464700625558,\n",
       "       -69.54686137750653, -69.84066611842105, -70.09303246239114,\n",
       "       -70.02338957055215, -69.28699551569507, -69.22786177105831,\n",
       "       -69.17574786324786, -69.85405927835052, -68.6731905465288,\n",
       "       -68.78880597014926, -68.92363221884499, -68.9315749235474,\n",
       "       -62.016304347826086, -62.042112299465245, -62.104774535809014,\n",
       "       -62.207417582417584, -61.76006711409396, -61.677316293929714,\n",
       "       -61.59969788519638, -60.31431334622824, -60.153284671532845,\n",
       "       -60.00397526501767, -59.857692307692304, -60.411392405063296,\n",
       "       -60.3515873015873, -60.293019480519476, -60.32236842105263,\n",
       "       -58.251754385964915, -58.60522151898734, -58.7940414507772,\n",
       "       -58.49049881235155, -58.31189710610933, -58.68663194444444,\n",
       "       -59.1046511627907, -57.18822553897181, -57.037648054145514,\n",
       "...\n",
       "       40.68571428571428, 40.835164835164846, 40.3586448598131,\n",
       "       53.901997503121095, 54.06280788177341, 54.136363636363626,\n",
       "       54.206077015643814, 54.62113402061857, 55.25081967213114,\n",
       "       55.81171875000001, 56.8382731958763, 59.0687622789784,\n",
       "       59.03383641674782, 58.984570877531354, 58.71614052953157,\n",
       "       56.80502392344496, 56.98792270531402, 57.22523584905662, 57.0145,\n",
       "       58.776840490797554, 58.81074766355141, 58.805379746835456,\n",
       "       58.80381944444446, 59.20182724252493, 58.953671328671334,\n",
       "       58.650000000000006, 58.54381443298968, 63.99290220820188,\n",
       "       64.07764140875133, 64.19810267857142, 62.19521276595745,\n",
       "       65.24651264167395, 65.22526501766785, 65.16060337178351,\n",
       "       65.16522574447646, 69.69710743801653, 69.67272727272729,\n",
       "       69.64220563847431, 69.6725806451613, 72.25, 72.25795228628232,\n",
       "       72.25726895119419, 72.14989888776543, 74.22039897039897,\n",
       "       74.54845956354299, 74.90818858560795, 75.2359900373599,\n",
       "       77.6890243902439, 77.62055555555557, 77.59551800146951,\n",
       "       77.47177726926012, 31.20192307692308, 31.116987179487182,\n",
       "       31.254143646408835, 35.756067961165044, 35.86538461538461,\n",
       "       36.20524017467248, 44.96681415929203, 45.2223880597015,\n",
       "       45.670962199312726], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wclon</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>221.46768929503915 ... 140.20704...</div><input id='attrs-73ce57e3-e90a-4516-abad-5dd05e31164f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73ce57e3-e90a-4516-abad-5dd05e31164f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fc86349-bde1-4d3c-a30f-52034ed76ce3' class='xr-var-data-in' type='checkbox'><label for='data-4fc86349-bde1-4d3c-a30f-52034ed76ce3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([221.46768929503915, 221.59551656920078, 221.70071105365224,\n",
       "       222.16309218950065, 68.18567251461988, 68.23507025761124,\n",
       "       68.28121319199057, 68.30179398148148, 256.3881118881119,\n",
       "       256.3830532212885, 256.3714689265537, 256.52685950413223,\n",
       "       206.7882672882673, 207.3349200304646, 207.9204204204204,\n",
       "       208.7083650190114, 32.70906200317965, 33.21279163314561,\n",
       "       35.64065464895636, 29.272922636103154, 279.609500805153,\n",
       "       279.62385129490394, 279.5618878005343, 279.7899910634495,\n",
       "       313.8565823888405, 314.6761924342105, 315.5726444972288,\n",
       "       316.69056748466255, 151.14209641255604, 151.22732181425485,\n",
       "       151.11832264957266, 151.3263530927835, 181.76403249630724,\n",
       "       181.96305970149254, 182.1363981762918, 182.1085626911315,\n",
       "       171.0305706521739, 171.63636363636363, 172.24535809018568,\n",
       "       172.79532967032966, 137.1258389261745, 137.9608626198083,\n",
       "       138.65936555891238, 91.23500967117988, 91.36040145985402,\n",
       "       91.50220848056537, 91.16230769230769, 40.59493670886076,\n",
       "       41.17063492063492, 41.76948051948052, 42.46052631578947,\n",
       "       288.94473684210527, 290.01661392405066, 291.44559585492226,\n",
       "       292.7066508313539, 203.83038585209005, 204.265625,\n",
       "       204.6327519379845, 347.86567164179104, 348.05795262267344,\n",
       "...\n",
       "       173.42640186915887, 196.46847690387017, 196.85837438423644,\n",
       "       197.20604066985646, 197.53850782190133, 342.0094501718213,\n",
       "       342.65901639344264, 343.0984375, 343.3034793814433,\n",
       "       327.889489194499, 327.8853456669912, 327.93731918997105,\n",
       "       327.95366598778, 213.563995215311, 214.03200483091788,\n",
       "       214.58549528301887, 215.9265, 10.173312883435583,\n",
       "       10.544392523364486, 10.844936708860759, 11.369791666666666,\n",
       "       173.80897009966776, 174.11625874125875, 174.26792452830188,\n",
       "       175.0962199312715, 28.801787592008413, 28.970384204909283,\n",
       "       29.083705357142858, 24.84627659574468, 273.87728857890147,\n",
       "       274.1687279151943, 274.4427684117125, 274.947646493756,\n",
       "       13.892148760330578, 14.035537190082644, 14.222222222222221,\n",
       "       14.388306451612904, 53.364975845410626, 53.690357852882705,\n",
       "       53.99688473520249, 54.22244691607685, 197.86615186615185,\n",
       "       198.8100128369705, 199.76271712158808, 200.72820672478207,\n",
       "       158.26718403547673, 158.8162962962963, 159.39290962527554,\n",
       "       160.21777269260107, 127.19871794871794, 127.27083333333333,\n",
       "       127.85635359116021, 246.3131067961165, 246.22874493927125,\n",
       "       246.42139737991266, 139.03687315634218, 139.34477611940298,\n",
       "       140.2070446735395], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx_lat-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-78.5 -78.5 -78.5 ... 42.5 43.0</div><input id='attrs-94eccbbc-9c0d-4c91-bc36-0996561ccfa3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94eccbbc-9c0d-4c91-bc36-0996561ccfa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f66c566-9dbf-4915-b068-3b2f8b9e51a5' class='xr-var-data-in' type='checkbox'><label for='data-7f66c566-9dbf-4915-b068-3b2f8b9e51a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-78.5, -78.5, -78.5, -78.25, -71.75, -71.75, -71.75, -71.75,\n",
       "       -70.25, -70.25, -70.25, -70.0, -68.0, -68.0, -68.25, -68.25,\n",
       "       -65.25, -65.25, -65.0, -68.5, -65.25, -65.75, -66.5, -66.25,\n",
       "       -65.75, -65.75, -66.0, -66.0, -64.25, -64.0, -63.75, -66.5, -65.75,\n",
       "       -66.0, -66.25, -66.25, -60.25, -60.25, -60.25, -60.5, -60.5, -60.0,\n",
       "       -59.75, -57.25, -57.0, -56.75, -56.5, -58.75, -58.75, -58.75,\n",
       "       -58.5, -56.0, -56.5, -56.75, -54.75, -56.25, -56.75, -57.25,\n",
       "       -54.75, -54.75, -54.5, -54.25, -51.0, -51.5, -52.75, -53.5, -55.25,\n",
       "       -55.0, -54.75, -54.5, -53.0, -52.75, -52.75, -52.5, -53.0, -53.0,\n",
       "       -53.25, -53.0, -52.75, -53.25, -53.25, -53.25, -49.5, -49.0,\n",
       "       -48.75, -48.5, -48.25, -48.75, -49.0, -50.75, -43.5, -43.25, -43.0,\n",
       "       -43.0, -48.0, -48.0, -48.0, -48.0, -48.5, -49.0, -49.25, -49.75,\n",
       "       -44.75, -44.75, -44.75, -45.0, -45.0, -45.25, -45.25, -44.5, -40.5,\n",
       "       -40.5, -40.5, -40.25, -39.5, -39.75, -39.75, -39.75, -37.75, -37.5,\n",
       "       -37.5, -37.25, -36.25, -36.25, -36.5, -36.5, -35.25, -35.5, -35.75,\n",
       "       -35.75, -35.75, -36.25, -36.5, -36.75, -32.0, -32.0, -32.25,\n",
       "       -31.25, -31.75, -32.0, -31.75, -23.5, -23.75, -23.75, -24.0,\n",
       "       -17.25, -17.0, -20.5, -16.0, -21.25, -21.25, -21.0, -20.75, -21.0,\n",
       "       -21.0, -21.0, -21.0, -3.5, -3.5, -3.5, -4.25, 0.25, 0.25, 0.25,\n",
       "       0.25, 5.25, 5.25, 5.25, 6.0, 6.0, 6.25, 8.5, 6.75, 6.75, 6.75, 8.5,\n",
       "       8.5, 8.75, 9.25, 8.75, 8.75, 8.75, 8.75, 9.25, 9.25, 9.25, 9.5,\n",
       "       14.5, 14.5, 14.5, 14.5, 15.0, 15.0, 15.0, 15.0, 15.0, 15.25, 15.25,\n",
       "       15.25, 15.0, 14.75, 14.75, 15.5, 15.5, 15.25, 16.5, 20.5, 20.75,\n",
       "       20.75, 21.25, 21.0, 21.25, 21.5, 22.5, 34.5, 34.5, 34.5, 34.5,\n",
       "       36.5, 36.5, 36.5, 36.75, 37.0, 37.0, 36.75, 36.75, 37.5, 37.75,\n",
       "       38.0, 38.25, 38.25, 38.0, 38.0, 37.75, 51.25, 51.5, 51.25, 51.25,\n",
       "       52.25, 52.75, 53.25, 54.25, 53.75, 53.5, 53.5, 53.75, 54.75, 55.0,\n",
       "       55.25, 54.25, 57.0, 57.0, 57.0, 57.25, 57.5, 57.25, 57.0, 56.75,\n",
       "       59.25, 59.25, 59.5, 59.25, 62.0, 62.0, 61.75, 61.75, 67.25, 67.25,\n",
       "       67.25, 67.25, 70.0, 70.0, 70.0, 69.75, 72.25, 72.75, 73.0, 73.25,\n",
       "       74.75, 74.5, 74.75, 74.75, 29.75, 29.5, 29.75, 33.0, 33.25, 33.0,\n",
       "       42.25, 42.5, 43.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx_lat-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-81.0 -81.0 -81.0 ... 44.75 44.5</div><input id='attrs-05f72151-1ca9-4068-acb7-07e48b25fdd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05f72151-1ca9-4068-acb7-07e48b25fdd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae815a85-295d-4811-9a43-cbe67d8e097e' class='xr-var-data-in' type='checkbox'><label for='data-ae815a85-295d-4811-9a43-cbe67d8e097e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-81.0, -81.0, -81.0, -81.0, -76.0, -76.0, -76.0, -76.0, -74.75,\n",
       "       -74.75, -74.75, -74.75, -68.0, -68.0, -68.25, -68.25, -72.25,\n",
       "       -72.25, -71.75, -71.75, -66.0, -66.75, -72.75, -73.25, -65.75,\n",
       "       -66.0, -66.0, -66.0, -69.5, -69.25, -69.0, -69.75, -70.25, -70.5,\n",
       "       -70.5, -70.5, -61.0, -61.0, -61.5, -62.0, -62.25, -62.25, -62.25,\n",
       "       -57.75, -57.5, -57.25, -57.0, -58.75, -58.75, -58.75, -60.25,\n",
       "       -57.0, -57.5, -58.0, -57.25, -56.25, -56.75, -57.25, -55.5, -55.75,\n",
       "       -55.25, -54.75, -57.5, -57.25, -57.75, -57.5, -55.5, -55.75, -55.0,\n",
       "       -56.75, -54.5, -54.5, -54.25, -53.5, -53.25, -53.25, -54.25,\n",
       "       -55.75, -54.25, -54.25, -54.75, -54.25, -52.25, -52.0, -51.75,\n",
       "       -51.5, -51.0, -52.0, -52.75, -53.0, -47.5, -48.0, -46.5, -45.75,\n",
       "       -48.5, -48.5, -48.5, -48.5, -49.25, -49.75, -50.5, -50.75, -48.75,\n",
       "       -48.5, -48.5, -45.0, -45.0, -45.25, -45.5, -45.0, -41.75, -41.75,\n",
       "       -41.75, -41.5, -39.75, -42.25, -42.25, -39.75, -42.0, -42.0,\n",
       "       -41.25, -41.25, -37.75, -37.75, -37.75, -38.0, -36.75, -37.25,\n",
       "       -37.5, -38.25, -37.75, -38.25, -38.5, -38.75, -34.75, -34.75,\n",
       "       -34.75, -32.5, -32.5, -32.75, -33.25, -26.75, -26.75, -26.75,\n",
       "       -26.5, -20.75, -21.0, -21.0, -21.0, -24.5, -24.5, -24.5, -25.0,\n",
       "       -23.25, -23.25, -23.25, -24.5, -9.75, -9.75, -9.75, -10.0, 3.25,\n",
       "       3.25, 3.25, 3.25, 7.5, 7.75, 7.75, 11.25, 11.25, 11.0, 12.0, 6.75,\n",
       "       6.75, 7.0, 8.75, 8.75, 8.75, 9.25, 12.5, 12.0, 12.25, 12.0, 14.0,\n",
       "       14.0, 13.5, 14.0, 17.0, 17.0, 17.0, 17.25, 16.75, 16.75, 16.75,\n",
       "       17.0, 16.25, 16.25, 16.25, 15.75, 20.25, 20.25, 21.0, 21.5, 21.25,\n",
       "       21.25, 21.0, 22.75, 23.0, 23.25, 22.5, 23.0, 23.25, 23.0, 23.75,\n",
       "       35.75, 35.75, 37.25, 35.5, 39.75, 39.5, 39.5, 39.5, 38.5, 38.5,\n",
       "       38.75, 39.25, 40.0, 40.25, 40.5, 40.75, 38.25, 38.0, 38.0, 38.0,\n",
       "       52.5, 52.75, 53.0, 53.5, 53.75, 54.25, 54.5, 55.0, 57.75, 59.75,\n",
       "       60.25, 60.75, 57.75, 58.25, 58.5, 59.0, 58.0, 58.0, 58.0, 58.0,\n",
       "       60.25, 60.25, 59.75, 60.0, 60.25, 60.25, 60.25, 61.75, 62.0, 62.0,\n",
       "       61.75, 61.75, 68.75, 68.75, 68.75, 68.0, 72.5, 72.5, 72.5, 72.5,\n",
       "       73.75, 73.75, 73.75, 73.75, 75.25, 75.25, 75.0, 75.25, 31.5, 31.25,\n",
       "       30.75, 36.0, 36.0, 36.0, 45.25, 44.75, 44.5], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx_lat-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-79.25 -79.5 -79.5 ... 45.5 45.5</div><input id='attrs-a90d1b1c-4775-4071-9d0c-af30dc00791f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a90d1b1c-4775-4071-9d0c-af30dc00791f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96bfbc5d-d79b-47fe-98eb-d376c2a77715' class='xr-var-data-in' type='checkbox'><label for='data-96bfbc5d-d79b-47fe-98eb-d376c2a77715' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-79.25, -79.5, -79.5, -79.25, -73.25, -72.75, -72.75, -72.75,\n",
       "       -74.25, -74.25, -74.25, -74.5, -71.75, -71.75, -71.75, -71.75,\n",
       "       -70.0, -70.0, -70.0, -70.0, -70.5, -70.5, -70.5, -70.75, -69.5,\n",
       "       -70.0, -70.25, -70.5, -69.5, -68.25, -68.25, -68.5, -68.75, -68.75,\n",
       "       -68.75, -69.0, -63.0, -63.0, -63.0, -63.0, -61.75, -61.75, -61.75,\n",
       "       -60.0, -60.0, -59.75, -59.75, -60.5, -60.75, -60.5, -60.5, -57.75,\n",
       "       -58.0, -59.0, -59.5, -58.75, -59.25, -59.5, -57.25, -57.0, -57.0,\n",
       "       -57.0, -57.75, -57.75, -57.75, -57.75, -56.75, -56.5, -56.75,\n",
       "       -56.5, -55.0, -54.75, -54.75, -54.5, -56.25, -56.5, -56.5, -56.75,\n",
       "       -53.5, -53.75, -53.75, -54.0, -53.0, -52.75, -52.5, -52.25, -52.0,\n",
       "       -52.5, -52.75, -53.25, -48.0, -47.5, -47.0, -46.5, -51.25, -51.25,\n",
       "       -51.5, -51.5, -50.0, -50.5, -50.75, -51.25, -44.75, -45.5, -45.5,\n",
       "       -48.25, -48.25, -48.5, -45.75, -46.0, -43.5, -43.75, -44.0, -43.75,\n",
       "       -42.75, -42.75, -42.75, -40.75, -39.75, -39.75, -39.5, -40.5,\n",
       "       -38.75, -38.5, -40.0, -40.0, -38.0, -37.75, -38.0, -38.0, -38.25,\n",
       "       -38.75, -39.0, -39.5, -34.5, -34.5, -34.75, -34.25, -34.25, -35.0,\n",
       "       -35.0, -26.5, -26.5, -26.5, -26.5, -19.75, -19.75, -22.0, -19.25,\n",
       "       -24.25, -24.25, -24.75, -24.75, -22.75, -22.75, -22.75, -23.25,\n",
       "       -8.0, -8.25, -8.25, -9.25, 3.0, 3.0, 3.0, 3.0, 7.75, 7.75, 7.75,\n",
       "       10.75, 10.75, 10.5, 10.5, 8.0, 7.75, 7.75, 10.0, 10.5, 10.5, 10.5,\n",
       "       9.5, 9.5, 8.75, 8.75, 12.75, 12.75, 12.75, 13.0, 16.75, 16.5, 16.5,\n",
       "       16.75, 16.75, 16.75, 16.75, 16.75, 17.0, 17.0, 17.0, 17.25, 19.75,\n",
       "       20.5, 20.5, 21.25, 21.0, 18.75, 18.5, 22.0, 22.25, 22.5, 22.75,\n",
       "       23.0, 23.5, 23.75, 24.25, 36.5, 36.5, 36.5, 36.5, 39.0, 39.25,\n",
       "       39.5, 39.5, 40.25, 39.25, 39.25, 38.25, 40.25, 40.5, 40.75, 41.0,\n",
       "       40.0, 40.25, 40.25, 40.25, 53.75, 54.0, 54.25, 54.5, 54.25, 54.75,\n",
       "       55.25, 57.75, 56.25, 56.25, 56.25, 60.5, 57.5, 57.5, 57.75, 58.0,\n",
       "       58.25, 59.25, 59.25, 58.5, 59.75, 59.5, 59.5, 59.25, 65.5, 66.0,\n",
       "       66.0, 60.25, 66.25, 66.25, 66.25, 66.5, 69.25, 69.25, 69.0, 69.0,\n",
       "       72.25, 72.25, 72.25, 72.25, 75.5, 75.75, 75.75, 76.0, 77.5, 77.5,\n",
       "       77.5, 78.5, 31.0, 31.25, 31.0, 36.5, 36.5, 37.5, 45.25, 45.5, 45.5],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx_lon-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>220.75 220.75 ... 139.25 140.25</div><input id='attrs-ec1d32b4-261c-4d8a-996a-e5912f982108' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ec1d32b4-261c-4d8a-996a-e5912f982108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93abec32-3467-46b7-8b3c-fe885f3d3dd5' class='xr-var-data-in' type='checkbox'><label for='data-93abec32-3467-46b7-8b3c-fe885f3d3dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([220.75, 220.75, 220.75, 219.5, 67.25, 67.25, 64.0, 64.0, 256.0,\n",
       "       256.0, 256.0, 256.0, 210.0, 208.75, 210.0, 210.0, 35.0, 36.75,\n",
       "       35.5, 25.75, 281.25, 280.5, 276.5, 282.25, 317.25, 317.75, 318.75,\n",
       "       319.75, 150.25, 150.25, 150.75, 147.0, 183.75, 184.25, 184.75,\n",
       "       185.75, 169.0, 169.0, 170.25, 170.75, 138.75, 143.75, 144.5, 91.0,\n",
       "       91.25, 92.0, 92.0, 40.25, 40.25, 40.25, 41.25, 287.75, 288.75,\n",
       "       289.5, 297.75, 203.75, 203.75, 205.0, 348.5, 348.5, 349.25, 348.5,\n",
       "       189.5, 190.5, 193.25, 194.5, 266.75, 267.5, 268.0, 269.0, 103.25,\n",
       "       104.5, 104.5, 104.5, 156.5, 157.5, 157.75, 158.5, 57.25, 59.5,\n",
       "       64.0, 64.0, 182.25, 182.25, 183.0, 184.75, 124.5, 125.25, 126.25,\n",
       "       126.5, 99.75, 101.0, 102.25, 103.25, 272.25, 274.75, 275.75,\n",
       "       275.75, 210.5, 210.5, 212.0, 212.75, 39.0, 40.25, 41.25, 40.25,\n",
       "       139.75, 141.25, 143.25, 147.75, 163.75, 165.0, 165.0, 165.0,\n",
       "       144.25, 144.5, 145.75, 146.5, 305.25, 305.75, 306.0, 306.0, 188.0,\n",
       "       188.75, 189.25, 190.75, 350.0, 351.75, 351.0, 351.75, 214.5, 215.0,\n",
       "       215.0, 216.25, 314.0, 314.0, 314.5, 209.5, 210.5, 210.5, 210.5,\n",
       "       16.5, 15.5, 15.5, 15.5, 118.5, 118.5, 114.0, 119.0, 45.25, 45.25,\n",
       "       45.75, 45.0, 315.75, 315.75, 315.75, 315.75, 170.0, 170.0, 169.5,\n",
       "       169.5, 36.75, 36.75, 36.75, 36.75, 124.5, 124.5, 124.5, 203.75,\n",
       "       203.75, 203.75, 202.25, 113.25, 113.25, 113.25, 51.5, 51.5, 51.0,\n",
       "       49.0, 78.5, 78.5, 78.5, 78.5, 215.0, 214.5, 214.5, 216.25, 303.5,\n",
       "       303.5, 302.5, 302.5, 74.0, 74.0, 74.0, 74.0, 129.5, 129.5, 129.5,\n",
       "       129.5, 341.0, 341.0, 341.0, 20.75, 20.75, 20.5, 27.25, 308.5,\n",
       "       308.25, 308.25, 308.25, 138.0, 138.0, 138.0, 137.5, 31.25, 31.25,\n",
       "       31.25, 31.25, 293.5, 293.5, 293.5, 293.75, 235.5, 235.5, 235.5,\n",
       "       236.25, 146.25, 146.25, 146.25, 146.25, 172.75, 171.25, 174.0,\n",
       "       173.5, 197.75, 199.75, 194.5, 194.5, 340.25, 341.0, 341.5, 342.75,\n",
       "       328.75, 328.75, 328.75, 328.75, 212.75, 213.75, 213.25, 219.75,\n",
       "       12.25, 12.25, 12.25, 13.0, 173.5, 174.0, 173.25, 173.5, 24.5, 24.5,\n",
       "       25.0, 25.5, 270.75, 270.75, 271.5, 274.0, 13.75, 13.75, 15.0, 15.0,\n",
       "       54.75, 54.75, 54.75, 54.75, 198.25, 197.0, 201.75, 199.0, 159.75,\n",
       "       159.0, 159.75, 159.75, 127.75, 126.5, 128.5, 248.5, 248.25, 248.25,\n",
       "       139.0, 139.25, 140.25], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx_lon-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>235.25 235.25 235.5 ... 139.0 141.0</div><input id='attrs-2bb01f76-821c-4e1e-8b9c-cc21a31473b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2bb01f76-821c-4e1e-8b9c-cc21a31473b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e848b94-9194-4fa4-8df1-7df371367721' class='xr-var-data-in' type='checkbox'><label for='data-2e848b94-9194-4fa4-8df1-7df371367721' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([235.25, 235.25, 235.5, 235.0, 68.0, 68.0, 68.25, 67.75, 261.5,\n",
       "       262.0, 261.75, 261.0, 209.5, 208.75, 208.75, 211.75, 23.5, 24.0,\n",
       "       35.0, 35.0, 285.0, 285.5, 276.5, 277.0, 316.25, 317.0, 318.75,\n",
       "       319.5, 143.25, 143.75, 144.0, 143.25, 177.0, 177.75, 177.75, 177.5,\n",
       "       173.75, 174.5, 175.0, 175.5, 135.0, 135.0, 135.0, 88.25, 88.5,\n",
       "       88.5, 87.5, 38.75, 39.25, 40.0, 43.5, 289.5, 290.0, 290.5, 291.0,\n",
       "       204.0, 204.5, 205.0, 344.5, 344.25, 344.75, 345.25, 186.0, 186.0,\n",
       "       186.5, 186.5, 266.0, 266.5, 268.75, 269.0, 102.25, 102.25, 102.0,\n",
       "       102.5, 155.75, 157.0, 157.25, 156.0, 53.75, 53.75, 59.0, 52.0,\n",
       "       178.25, 178.75, 179.25, 179.5, 125.5, 125.75, 125.5, 127.0, 91.5,\n",
       "       92.0, 94.75, 96.0, 271.0, 272.0, 273.25, 274.25, 215.0, 215.75,\n",
       "       216.75, 217.5, 38.25, 38.25, 38.75, 42.0, 139.75, 141.25, 146.25,\n",
       "       146.25, 165.75, 166.25, 167.0, 167.5, 142.5, 143.75, 144.5, 146.75,\n",
       "       309.25, 309.25, 310.0, 310.0, 188.25, 188.75, 189.0, 189.5, 349.25,\n",
       "       349.5, 350.0, 350.5, 215.75, 216.0, 216.5, 217.25, 312.75, 313.25,\n",
       "       313.5, 211.75, 211.75, 212.0, 212.25, 14.75, 14.5, 14.5, 14.75,\n",
       "       113.75, 113.75, 113.5, 113.5, 47.75, 47.75, 47.75, 47.5, 316.0,\n",
       "       316.25, 316.5, 315.5, 167.75, 167.75, 167.5, 168.25, 36.25, 36.25,\n",
       "       36.25, 36.25, 127.25, 127.75, 128.25, 207.0, 206.75, 206.25,\n",
       "       204.75, 113.25, 113.25, 113.0, 50.5, 50.5, 50.75, 51.0, 82.0,\n",
       "       82.25, 82.5, 82.0, 216.5, 216.75, 214.5, 217.0, 304.5, 304.5,\n",
       "       304.25, 304.0, 72.75, 73.0, 73.0, 73.0, 130.75, 130.75, 131.0,\n",
       "       130.5, 341.0, 340.75, 342.25, 24.25, 24.0, 24.0, 24.0, 309.75,\n",
       "       309.5, 309.0, 309.0, 138.25, 138.25, 138.25, 138.0, 30.25, 30.5,\n",
       "       26.75, 27.25, 289.75, 290.0, 290.0, 289.75, 235.5, 235.5, 235.25,\n",
       "       235.0, 146.75, 148.0, 149.0, 149.25, 171.0, 172.0, 173.25, 174.25,\n",
       "       200.0, 200.75, 201.0, 201.25, 340.25, 340.75, 341.5, 342.0, 323.0,\n",
       "       322.75, 323.25, 323.25, 215.5, 214.0, 215.0, 215.0, 11.0, 11.0,\n",
       "       11.0, 11.0, 172.25, 173.0, 172.75, 173.25, 26.75, 27.0, 27.25,\n",
       "       21.25, 272.5, 273.5, 272.25, 272.25, 12.25, 12.5, 12.75, 13.75,\n",
       "       65.5, 66.0, 66.25, 66.0, 194.0, 197.75, 199.75, 199.75, 156.0,\n",
       "       156.0, 159.0, 157.5, 127.5, 127.25, 126.75, 247.5, 247.5, 247.5,\n",
       "       143.25, 139.0, 141.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mx_lon-2</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>217.75 216.75 ... 138.25 138.5</div><input id='attrs-65465df0-551f-4099-829d-513d3d5618e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65465df0-551f-4099-829d-513d3d5618e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88808d2e-09f1-4e7b-b878-eb5e0fdce412' class='xr-var-data-in' type='checkbox'><label for='data-88808d2e-09f1-4e7b-b878-eb5e0fdce412' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([217.75, 216.75, 216.75, 217.75, 66.75, 67.25, 67.25, 67.25, 258.25,\n",
       "       258.25, 258.25, 258.75, 202.0, 202.0, 202.0, 201.75, 38.75, 38.75,\n",
       "       38.75, 38.75, 282.0, 281.25, 281.25, 281.25, 316.75, 316.75, 318.0,\n",
       "       319.0, 157.75, 146.0, 146.0, 146.25, 184.25, 184.25, 185.0, 186.5,\n",
       "       170.5, 171.75, 172.25, 172.25, 134.75, 135.75, 136.0, 93.5, 93.75,\n",
       "       93.5, 93.75, 43.0, 43.0, 43.75, 44.25, 288.75, 289.25, 290.25,\n",
       "       291.0, 202.25, 203.25, 203.75, 349.75, 349.75, 349.75, 350.25,\n",
       "       187.25, 187.25, 187.25, 187.25, 271.25, 271.5, 270.5, 271.0,\n",
       "       104.25, 104.5, 104.75, 105.0, 157.75, 158.5, 159.0, 160.0, 54.25,\n",
       "       55.5, 55.75, 58.25, 179.0, 179.5, 180.25, 180.75, 122.25, 122.75,\n",
       "       123.25, 123.75, 94.75, 95.5, 96.25, 97.25, 274.5, 275.5, 275.0,\n",
       "       275.75, 211.0, 211.5, 212.25, 213.0, 39.75, 40.75, 41.5, 44.0,\n",
       "       143.25, 144.75, 147.5, 148.5, 166.0, 166.5, 167.25, 167.5, 143.5,\n",
       "       144.25, 145.0, 146.0, 305.5, 305.5, 305.5, 309.5, 188.75, 189.25,\n",
       "       190.25, 190.75, 350.0, 350.25, 350.5, 351.0, 214.5, 214.75, 215.25,\n",
       "       215.75, 312.0, 312.25, 312.5, 208.0, 208.25, 209.0, 209.0, 15.25,\n",
       "       15.25, 15.25, 15.25, 117.75, 118.0, 113.75, 118.25, 47.25, 47.25,\n",
       "       47.25, 47.25, 317.0, 317.0, 316.5, 315.0, 170.75, 170.75, 170.5,\n",
       "       170.75, 36.25, 36.25, 36.25, 36.25, 126.75, 126.75, 126.75, 207.0,\n",
       "       207.0, 206.75, 206.25, 113.0, 113.0, 113.25, 50.0, 50.75, 50.75,\n",
       "       50.75, 78.75, 78.75, 78.0, 78.0, 218.0, 217.75, 217.75, 217.5,\n",
       "       304.0, 304.25, 304.0, 303.75, 73.25, 73.5, 73.5, 73.5, 129.5,\n",
       "       129.25, 129.25, 129.0, 343.5, 343.5, 343.5, 24.5, 24.5, 25.25,\n",
       "       25.25, 308.5, 308.25, 308.25, 308.25, 137.75, 137.75, 137.5, 137.5,\n",
       "       29.5, 31.25, 31.25, 29.75, 291.0, 291.0, 291.25, 291.5, 235.75,\n",
       "       236.25, 236.25, 237.0, 146.5, 146.75, 147.5, 147.75, 172.5, 173.25,\n",
       "       174.0, 174.5, 198.5, 199.0, 199.25, 199.75, 340.0, 340.75, 341.0,\n",
       "       342.25, 329.5, 330.0, 330.25, 324.5, 211.75, 212.25, 212.25,\n",
       "       210.75, 7.25, 10.5, 10.5, 11.5, 173.5, 173.5, 169.5, 173.75, 34.75,\n",
       "       35.25, 35.5, 23.25, 277.25, 277.5, 277.5, 277.75, 13.75, 13.75,\n",
       "       14.5, 14.5, 51.0, 51.0, 51.0, 51.0, 195.75, 195.5, 195.25, 196.25,\n",
       "       166.5, 166.75, 167.25, 164.0, 127.25, 128.25, 128.25, 243.5, 243.5,\n",
       "       247.25, 138.25, 138.25, 138.5], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-83.0 -83.25 -83.0 ... 42.5 43.0</div><input id='attrs-1269cfd7-7836-4b27-b3a6-72862d638030' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1269cfd7-7836-4b27-b3a6-72862d638030' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb73f17c-5312-449d-90b7-cde2282578b5' class='xr-var-data-in' type='checkbox'><label for='data-cb73f17c-5312-449d-90b7-cde2282578b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-83.0, -83.25, -83.0, -83.25, -76.0, -76.0, -76.0, -76.0, -74.75,\n",
       "       -74.75, -74.75, -74.75, -73.5, -73.75, -73.75, -73.75, -72.25,\n",
       "       -72.25, -72.25, -72.0, -72.25, -72.5, -72.75, -73.25, -72.0,\n",
       "       -72.25, -72.5, -72.75, -71.5, -71.5, -71.5, -71.5, -71.25, -71.5,\n",
       "       -71.5, -71.5, -64.0, -64.0, -64.0, -64.0, -63.5, -63.5, -63.25,\n",
       "       -63.0, -63.0, -63.0, -63.25, -61.75, -61.75, -61.75, -61.75,\n",
       "       -60.25, -60.5, -61.0, -60.75, -60.0, -60.25, -60.5, -59.75, -59.5,\n",
       "       -59.5, -59.0, -59.0, -59.0, -59.25, -59.25, -59.0, -58.75, -58.75,\n",
       "       -58.5, -57.5, -57.5, -57.25, -56.75, -57.25, -57.25, -57.25,\n",
       "       -57.25, -55.25, -55.25, -55.5, -55.5, -54.25, -54.0, -53.75,\n",
       "       -53.75, -53.5, -54.0, -54.5, -55.0, -52.5, -52.25, -52.0, -48.0,\n",
       "       -52.0, -52.0, -52.0, -52.0, -51.75, -52.25, -52.5, -53.0, -50.5,\n",
       "       -50.5, -50.5, -50.75, -49.0, -49.0, -49.0, -49.0, -43.75, -43.75,\n",
       "       -44.0, -43.75, -43.0, -43.5, -44.0, -43.5, -43.0, -42.75, -42.75,\n",
       "       -42.5, -42.0, -42.0, -42.0, -42.25, -40.75, -41.0, -41.25, -41.75,\n",
       "       -39.75, -40.0, -40.5, -41.5, -37.0, -37.25, -37.75, -35.5, -35.5,\n",
       "       -35.75, -35.75, -28.5, -28.5, -28.5, -29.0, -26.0, -26.0, -26.25,\n",
       "       -25.75, -25.75, -25.75, -25.75, -26.0, -25.0, -24.75, -25.0,\n",
       "       -25.25, -10.25, -10.25, -10.25, -10.75, -0.25, -0.25, -0.25, -0.25,\n",
       "       5.25, 5.25, 5.25, 6.0, 6.0, 6.25, 8.5, 6.75, 6.75, 6.75, 8.5, 8.5,\n",
       "       8.75, 9.25, 8.75, 8.75, 8.75, 8.75, 9.25, 9.25, 9.25, 9.5, 14.5,\n",
       "       14.5, 14.5, 14.5, 15.0, 15.0, 15.0, 15.0, 15.0, 15.25, 15.25,\n",
       "       15.25, 15.0, 14.75, 14.75, 15.5, 15.5, 15.25, 16.5, 20.5, 20.75,\n",
       "       20.75, 21.25, 21.0, 21.25, 21.5, 22.5, 34.5, 34.5, 34.5, 34.5,\n",
       "       36.5, 36.5, 36.5, 36.75, 37.0, 37.0, 36.75, 36.75, 37.5, 37.75,\n",
       "       38.0, 38.25, 38.25, 38.0, 38.0, 37.75, 51.25, 51.5, 51.25, 51.25,\n",
       "       52.25, 52.75, 53.25, 54.25, 53.75, 53.5, 53.5, 53.75, 54.75, 55.0,\n",
       "       55.25, 54.25, 57.0, 57.0, 57.0, 57.25, 57.5, 57.25, 57.0, 56.75,\n",
       "       59.25, 59.25, 59.5, 59.25, 62.0, 62.0, 61.75, 61.75, 67.25, 67.25,\n",
       "       67.25, 67.25, 70.0, 70.0, 70.0, 69.75, 72.25, 72.75, 73.0, 73.25,\n",
       "       74.75, 74.5, 74.75, 74.75, 29.75, 29.5, 29.75, 33.0, 33.25, 33.0,\n",
       "       42.25, 42.5, 43.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-78.25 -78.25 -78.25 ... 47.5 48.0</div><input id='attrs-e94d2974-e965-4af4-ae83-49a60773fb37' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e94d2974-e965-4af4-ae83-49a60773fb37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a73d4beb-ef0f-47ed-b475-2b262a5a1be3' class='xr-var-data-in' type='checkbox'><label for='data-a73d4beb-ef0f-47ed-b475-2b262a5a1be3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-78.25, -78.25, -78.25, -78.0, -71.5, -71.5, -71.5, -71.5, -70.0,\n",
       "       -70.0, -70.0, -69.75, -67.75, -67.75, -68.0, -68.0, -65.0, -65.0,\n",
       "       -64.75, -68.25, -65.0, -65.5, -66.25, -66.0, -65.5, -65.5, -65.75,\n",
       "       -65.75, -64.0, -63.75, -63.5, -66.25, -65.5, -65.75, -66.0, -66.0,\n",
       "       -60.0, -60.0, -60.0, -60.25, -60.25, -59.75, -59.5, -57.0, -56.75,\n",
       "       -56.5, -56.25, -58.5, -58.5, -58.5, -58.25, -55.75, -56.25, -56.5,\n",
       "       -54.5, -56.0, -56.5, -57.0, -54.5, -54.5, -54.25, -54.0, -50.75,\n",
       "       -51.25, -52.5, -53.25, -55.0, -54.75, -54.5, -54.25, -52.75, -52.5,\n",
       "       -52.5, -52.25, -52.75, -52.75, -53.0, -52.75, -52.5, -53.0, -53.0,\n",
       "       -53.0, -49.25, -48.75, -48.5, -48.25, -48.0, -48.5, -48.75, -50.5,\n",
       "       -43.25, -43.0, -42.75, -42.75, -47.75, -47.75, -47.75, -47.75,\n",
       "       -48.25, -48.75, -49.0, -49.5, -44.5, -44.5, -44.5, -44.75, -44.75,\n",
       "       -45.0, -45.0, -44.25, -40.25, -40.25, -40.25, -40.0, -39.25, -39.5,\n",
       "       -39.5, -39.5, -37.5, -37.25, -37.25, -37.0, -36.0, -36.0, -36.25,\n",
       "       -36.25, -35.0, -35.25, -35.5, -35.5, -35.5, -36.0, -36.25, -36.5,\n",
       "       -31.75, -31.75, -32.0, -31.0, -31.5, -31.75, -31.5, -23.25, -23.5,\n",
       "       -23.5, -23.75, -17.0, -16.75, -20.25, -15.75, -21.0, -21.0, -20.75,\n",
       "       -20.5, -20.75, -20.75, -20.75, -20.75, -3.25, -3.25, -3.25, -4.0,\n",
       "       3.75, 3.75, 3.75, 3.75, 9.0, 9.0, 9.0, 12.75, 12.5, 12.5, 12.5,\n",
       "       14.75, 14.5, 14.5, 14.0, 14.0, 13.75, 13.25, 13.5, 13.5, 13.75,\n",
       "       13.75, 14.25, 14.25, 14.25, 14.25, 19.0, 19.0, 18.75, 18.75, 19.25,\n",
       "       19.25, 19.25, 19.0, 19.5, 19.5, 19.5, 19.5, 21.0, 21.25, 21.5,\n",
       "       22.25, 22.0, 21.75, 21.5, 24.5, 24.75, 24.75, 25.0, 26.25, 26.25,\n",
       "       26.25, 26.5, 39.25, 39.5, 39.75, 40.25, 41.0, 41.0, 41.25, 41.5,\n",
       "       42.0, 42.0, 42.0, 42.0, 41.75, 42.0, 42.25, 42.5, 43.5, 43.5, 43.5,\n",
       "       43.25, 57.0, 57.25, 57.5, 57.5, 57.25, 57.75, 58.25, 59.5, 64.25,\n",
       "       64.5, 64.5, 64.5, 59.0, 59.0, 59.25, 59.25, 60.75, 60.75, 60.75,\n",
       "       60.5, 60.75, 60.5, 60.0, 60.25, 67.75, 68.0, 68.0, 66.0, 68.75,\n",
       "       68.75, 68.75, 68.75, 71.5, 71.5, 71.5, 71.5, 74.25, 74.25, 74.0,\n",
       "       74.0, 76.25, 76.75, 77.0, 77.25, 80.25, 80.0, 80.0, 80.0, 33.5,\n",
       "       33.5, 33.5, 38.0, 38.0, 38.25, 47.25, 47.5, 48.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon-0</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>207.0 207.25 206.75 ... 135.5 137.0</div><input id='attrs-376a05f3-5e97-4ab4-8d02-f7171773dba9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-376a05f3-5e97-4ab4-8d02-f7171773dba9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b434724-849d-4486-8623-bf6cbfbca416' class='xr-var-data-in' type='checkbox'><label for='data-7b434724-849d-4486-8623-bf6cbfbca416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([207.0, 207.25, 206.75, 206.25, 60.5, 60.75, 60.75, 60.75, 250.0,\n",
       "       249.75, 249.75, 250.0, 197.0, 196.75, 196.5, 195.5, 17.0, 17.5,\n",
       "       19.75, 18.0, 272.0, 272.0, 272.25, 273.0, 303.25, 303.5, 304.0,\n",
       "       303.75, 142.75, 143.0, 142.75, 143.0, 175.25, 175.5, 175.5, 175.5,\n",
       "       167.75, 168.25, 168.75, 169.25, 131.75, 132.25, 132.75, 87.25,\n",
       "       87.5, 87.75, 87.0, 36.25, 37.0, 37.5, 38.25, 286.0, 286.75, 287.5,\n",
       "       288.0, 200.5, 201.0, 201.25, 343.0, 343.25, 343.75, 344.0, 184.75,\n",
       "       185.0, 185.25, 185.5, 264.75, 265.5, 266.0, 266.75, 100.5, 100.75,\n",
       "       100.75, 101.0, 152.0, 153.25, 154.25, 154.75, 51.0, 51.25, 51.5,\n",
       "       51.75, 175.5, 176.5, 177.5, 178.0, 119.5, 119.75, 120.25, 121.0,\n",
       "       91.0, 91.5, 91.75, 93.5, 269.0, 270.0, 271.0, 272.25, 208.0,\n",
       "       208.75, 209.5, 210.25, 36.5, 37.25, 38.0, 39.25, 139.25, 140.75,\n",
       "       141.5, 142.25, 162.0, 162.75, 163.5, 163.75, 140.75, 141.75, 143.0,\n",
       "       143.75, 302.25, 302.25, 302.25, 302.25, 187.0, 187.5, 187.75,\n",
       "       188.5, 348.25, 348.75, 349.0, 349.5, 209.75, 210.0, 210.75, 210.75,\n",
       "       311.75, 312.25, 312.5, 207.0, 207.5, 207.75, 207.5, 12.75, 12.5,\n",
       "       12.5, 12.5, 112.75, 112.75, 112.75, 112.75, 43.75, 43.75, 43.75,\n",
       "       44.0, 313.0, 312.5, 312.5, 312.25, 167.75, 167.75, 167.25, 167.0,\n",
       "       34.75, 34.75, 34.75, 34.5, 124.25, 124.5, 124.5, 201.25, 201.0,\n",
       "       200.75, 201.25, 110.75, 110.75, 111.0, 48.5, 48.5, 48.5, 48.5,\n",
       "       77.25, 77.0, 76.75, 77.0, 214.0, 214.0, 214.0, 214.0, 301.25,\n",
       "       301.25, 301.0, 300.75, 71.0, 71.0, 71.0, 71.0, 127.5, 127.25,\n",
       "       127.25, 127.25, 339.75, 339.5, 339.5, 20.0, 20.0, 20.25, 22.0,\n",
       "       306.0, 305.75, 305.75, 305.0, 131.0, 134.0, 134.0, 132.5, 26.75,\n",
       "       26.75, 26.75, 26.5, 289.25, 289.25, 289.25, 289.75, 233.0, 233.0,\n",
       "       233.0, 233.25, 143.0, 143.5, 144.0, 144.25, 168.5, 169.0, 169.5,\n",
       "       170.25, 191.5, 191.75, 192.25, 192.5, 339.75, 340.25, 340.5,\n",
       "       339.75, 322.25, 322.25, 322.25, 322.0, 209.5, 209.5, 209.5, 209.25,\n",
       "       5.75, 6.25, 6.5, 7.5, 168.0, 168.75, 169.0, 172.25, 21.0, 21.5,\n",
       "       21.25, 21.0, 265.5, 266.25, 266.75, 268.0, 7.75, 7.75, 8.0, 8.0,\n",
       "       40.0, 40.0, 40.25, 41.0, 190.75, 191.75, 192.75, 193.5, 148.0,\n",
       "       148.25, 148.75, 150.5, 125.5, 125.5, 125.5, 243.5, 243.5, 243.75,\n",
       "       134.75, 135.5, 137.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon-1</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>236.0 236.0 ... 144.25 144.25</div><input id='attrs-8d4c51da-1ed3-47ec-8b3d-a408c71fecc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d4c51da-1ed3-47ec-8b3d-a408c71fecc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0f51bc2-7e5f-4700-8c06-09f17336a1a0' class='xr-var-data-in' type='checkbox'><label for='data-c0f51bc2-7e5f-4700-8c06-09f17336a1a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([236.0, 236.0, 236.25, 236.75, 77.0, 77.0, 77.0, 77.25, 263.75,\n",
       "       263.75, 263.5, 263.5, 216.0, 216.75, 217.5, 218.5, 44.0, 44.25,\n",
       "       44.25, 43.75, 288.25, 288.25, 288.5, 288.5, 323.5, 324.75, 326.25,\n",
       "       328.25, 161.5, 161.75, 161.75, 161.0, 187.5, 187.75, 188.0, 188.25,\n",
       "       176.5, 177.0, 177.75, 178.25, 143.0, 144.25, 145.0, 95.75, 96.0,\n",
       "       96.0, 95.5, 44.75, 45.25, 45.75, 46.5, 292.25, 294.0, 297.25,\n",
       "       298.25, 207.75, 208.0, 208.0, 352.5, 352.75, 353.0, 353.0, 196.0,\n",
       "       196.25, 196.75, 197.25, 274.25, 274.25, 274.5, 274.75, 108.5,\n",
       "       108.75, 109.0, 109.0, 162.0, 163.0, 163.75, 164.75, 61.25, 62.5,\n",
       "       64.5, 65.75, 182.75, 183.75, 184.5, 185.5, 126.5, 127.25, 128.0,\n",
       "       128.75, 100.5, 101.5, 102.75, 104.25, 277.25, 278.25, 279.5, 280.5,\n",
       "       215.75, 216.5, 217.25, 218.0, 42.75, 44.0, 45.25, 46.5, 145.0,\n",
       "       146.0, 148.5, 151.0, 166.5, 167.25, 168.25, 168.5, 147.25, 149.0,\n",
       "       149.75, 150.75, 312.5, 312.5, 312.5, 312.75, 193.5, 194.0, 194.5,\n",
       "       195.0, 354.25, 354.75, 355.0, 355.5, 216.75, 217.0, 217.5, 218.0,\n",
       "       315.0, 315.0, 315.25, 212.0, 212.25, 212.5, 212.75, 17.5, 17.25,\n",
       "       17.25, 17.25, 119.25, 119.25, 118.0, 119.5, 49.25, 49.25, 49.25,\n",
       "       49.5, 317.75, 317.75, 317.75, 318.0, 173.75, 173.5, 173.5, 173.25,\n",
       "       38.25, 38.25, 38.25, 38.0, 129.0, 129.0, 129.0, 207.5, 207.25,\n",
       "       207.0, 206.75, 115.5, 115.5, 115.75, 54.25, 53.75, 53.5, 53.0,\n",
       "       83.0, 83.25, 83.25, 83.5, 218.25, 218.25, 218.25, 218.25, 305.75,\n",
       "       305.5, 305.5, 305.25, 75.25, 75.5, 75.5, 75.5, 132.0, 131.75,\n",
       "       131.75, 131.75, 345.25, 345.5, 345.5, 27.5, 27.0, 27.0, 27.5,\n",
       "       310.5, 310.25, 310.25, 309.75, 139.25, 139.25, 139.25, 139.0,\n",
       "       33.25, 33.5, 33.75, 34.5, 296.75, 297.0, 297.0, 297.0, 239.0,\n",
       "       239.0, 238.75, 239.0, 149.75, 150.25, 150.5, 150.75, 175.75, 176.5,\n",
       "       181.5, 177.0, 202.0, 202.5, 203.0, 203.25, 344.5, 345.25, 346.0,\n",
       "       346.75, 335.25, 334.75, 334.75, 336.0, 218.0, 218.5, 219.5, 221.0,\n",
       "       14.75, 14.5, 14.5, 14.75, 178.25, 178.75, 179.0, 179.5, 37.75,\n",
       "       38.25, 38.75, 29.5, 281.75, 281.75, 281.75, 281.5, 20.5, 20.75,\n",
       "       21.0, 21.25, 65.75, 66.25, 66.5, 66.25, 206.0, 207.0, 208.0,\n",
       "       209.25, 170.25, 171.0, 171.75, 171.5, 129.25, 129.25, 130.0,\n",
       "       248.75, 248.75, 248.75, 144.25, 144.25, 144.25], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Obj_Area</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>184057685169.67972 ... 157813931...</div><input id='attrs-aaf10c5d-0f5d-4ce0-9358-e34cd8a392b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaf10c5d-0f5d-4ce0-9358-e34cd8a392b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe6aaea-05f4-47b8-a6b4-fa396425b782' class='xr-var-data-in' type='checkbox'><label for='data-dbe6aaea-05f4-47b8-a6b4-fa396425b782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([184057685169.67972, 184743993475.22473, 185764870697.10016,\n",
       "       188470550348.58884, 181145295186.60834, 180815837966.83722,\n",
       "       179540791354.49603, 182281024965.97754, 160790011926.91385,\n",
       "       160734968888.9647, 159453900469.16833, 164368758257.2852,\n",
       "       318100162597.13477, 321940538966.69824, 324702338410.25134,\n",
       "       318587675391.815, 341623877824.53094, 337769487550.30597,\n",
       "       287937123690.9672, 176411884220.52176, 347984250864.9457,\n",
       "       331003500958.4696, 305580820143.2688, 303353819923.0536,\n",
       "       306004923004.8702, 319883420574.9852, 328202103571.30347,\n",
       "       339987118060.9482, 240873519480.1564, 250740684428.94955,\n",
       "       254054692383.2579, 204040591815.19406, 188091252602.04007,\n",
       "       185172459696.0567, 180740171969.26218, 179578306317.26752,\n",
       "       132321725693.68108, 134364162807.49925, 135159923428.72467,\n",
       "       130053668020.91849, 108064939017.69182, 113811603420.31339,\n",
       "       120660956466.935, 196279487858.26648, 209076121274.15356,\n",
       "       216923150805.67426, 250212839459.1255, 119636255245.65254,\n",
       "       119478981502.72084, 117035958845.32216, 129834697759.3396,\n",
       "       115051828317.46898, 126280177761.15607, 153410844486.7032,\n",
       "       168779043192.5568, 125333666406.91322, 114822006475.70657,\n",
       "       101614522662.21356, 250732761583.93076, 246754443068.07925,\n",
       "...\n",
       "       366741958866.231, 370360688132.9991, 380633706820.1787,\n",
       "       377720297621.6248, 130962861627.3619, 135152604032.6075,\n",
       "       139811406775.61154, 165054432014.39175, 406628299131.02673,\n",
       "       410607493244.90704, 415180530084.8059, 396236722738.5044,\n",
       "       177993787294.2761, 175439939978.14954, 178543506701.3415,\n",
       "       211717268355.92737, 131509732412.43283, 129367962977.41878,\n",
       "       127372665257.46355, 116094923669.42207, 119956581616.11284,\n",
       "       114797972362.4032, 107295033519.57288, 118173280503.06363,\n",
       "       324927049877.2697, 319173117709.7233, 303906824598.5748,\n",
       "       170741809139.3911, 374499804143.58014, 369888459072.8555,\n",
       "       369133280847.8738, 340904459370.6776, 164107466987.89966,\n",
       "       164293663599.52942, 163983045862.1441, 168368663916.69122,\n",
       "       247119113617.34677, 240094594701.63696, 229842936180.39227,\n",
       "       237406254940.2953, 165775265751.08936, 162887751959.0671,\n",
       "       164764702226.2134, 160726584456.48547, 227323962327.85214,\n",
       "       228037972349.4896, 230344962843.15253, 224015096421.26593,\n",
       "       103372052535.6126, 103463849733.8863, 119873763734.23022,\n",
       "       129566585424.21547, 155143135809.51764, 143214039578.50616,\n",
       "       186123203817.3158, 183112480278.98657, 157813931906.1026],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>length</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>740973.7747088755 ... 794331.040...</div><input id='attrs-662629aa-abac-4513-991b-b4fc17b55643' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-662629aa-abac-4513-991b-b4fc17b55643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-472c62cd-7ea2-4515-850f-d06838076b3b' class='xr-var-data-in' type='checkbox'><label for='data-472c62cd-7ea2-4515-850f-d06838076b3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([740973.7747088755, 756311.0592722734, 746204.0177975949,\n",
       "       797387.3499662951, 720144.5411547094, 714817.4546972087,\n",
       "       714218.884084136, 719543.1996948945, 701962.2184516988,\n",
       "       707365.939380963, 701777.849801787, 719494.294305363,\n",
       "       944815.580973225, 986271.5878647219, 991503.1302392401,\n",
       "       1047698.1928101114, 1344974.59073601, 1337187.0888476497,\n",
       "       1293375.0946581666, 1054712.934549082, 1050333.8055602522,\n",
       "       1023575.0621772062, 979205.7206185098, 1026309.8322116833,\n",
       "       1084889.5856496077, 1129368.0352369775, 1150615.9264405004,\n",
       "       1229276.8748295903, 1143995.4172192393, 1166599.0999649654,\n",
       "       1196760.1021144479, 926288.7470842595, 812558.385798242,\n",
       "       809437.0698818371, 792148.6433034746, 798746.2739193838,\n",
       "       647826.2638565872, 645901.4509931316, 653894.9520571572,\n",
       "       633285.8709027099, 696426.8920917872, 761422.9562911021,\n",
       "       775224.9616917938, 818655.4573780308, 843284.051782205,\n",
       "       859953.2578776248, 914110.0855623706, 595329.6858187919,\n",
       "       583868.9828249615, 583774.9383335649, 603508.0338697035,\n",
       "       622204.6980592931, 635373.2227586989, 760651.6233501052,\n",
       "       925780.739409205, 617618.2936979383, 584453.5276560569,\n",
       "       552649.1952735833, 819151.4018284085, 801257.9366242221,\n",
       "...\n",
       "       938731.1301682551, 948894.0592435354, 985307.48825639,\n",
       "       985124.6957896207, 635047.9547024298, 640742.2971207058,\n",
       "       654638.7952120041, 724186.9946871547, 1382232.7136202147,\n",
       "       1415603.514541934, 1416028.957946475, 1436225.102311594,\n",
       "       701466.7935854074, 705354.94447521, 750484.7327714162,\n",
       "       909582.9386812876, 666738.9258432696, 634509.1449596204,\n",
       "       625096.7017962998, 555022.2556261637, 691173.7708119755,\n",
       "       681158.7703767031, 671275.8504439227, 576341.2754355115,\n",
       "       1254283.489642586, 1272624.6747888245, 1273307.1550610494,\n",
       "       870164.2300979949, 1069591.1737315431, 1044896.4676612122,\n",
       "       1050851.0521477677, 1005399.8909874542, 687666.5597122603,\n",
       "       694667.876169066, 694655.668800269, 701808.8309518173,\n",
       "       998104.4583956399, 1013141.4256008652, 1003284.5981235589,\n",
       "       990693.0239669061, 640633.4100258953, 632663.179986205,\n",
       "       625810.8845846253, 629774.3503857001, 808078.39974999,\n",
       "       821452.9964421906, 802865.7546398664, 772472.9785273336,\n",
       "       548865.5543372685, 570624.6932603645, 601487.6169497834,\n",
       "       730915.3094010008, 709652.1527613659, 738679.6865925533,\n",
       "       935232.8903145421, 886770.3961039404, 794331.040314808],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>track_ids</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 ... 145.0 145.0 145.0</div><input id='attrs-312f9cdd-5a59-4cdf-8052-0ca06d52f13a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-312f9cdd-5a59-4cdf-8052-0ca06d52f13a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a5576e4-f9e8-4cf8-a805-187bc8719ded' class='xr-var-data-in' type='checkbox'><label for='data-6a5576e4-f9e8-4cf8-a805-187bc8719ded' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0,\n",
       "       4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 6.0, 6.0, 6.0, 6.0, 7.0, 7.0,\n",
       "       7.0, 7.0, 8.0, 8.0, 8.0, 8.0, 9.0, 9.0, 9.0, 9.0, 10.0, 10.0, 10.0,\n",
       "       10.0, 11.0, 11.0, 11.0, 12.0, 12.0, 12.0, 12.0, 13.0, 13.0, 13.0,\n",
       "       13.0, 14.0, 14.0, 14.0, 14.0, 15.0, 15.0, 15.0, 16.0, 16.0, 16.0,\n",
       "       16.0, 17.0, 17.0, 17.0, 17.0, 18.0, 18.0, 18.0, 18.0, 19.0, 19.0,\n",
       "       19.0, 19.0, 20.0, 20.0, 20.0, 20.0, 21.0, 21.0, 21.0, 21.0, 22.0,\n",
       "       22.0, 22.0, 22.0, 23.0, 23.0, 23.0, 23.0, 24.0, 24.0, 24.0, 24.0,\n",
       "       25.0, 25.0, 25.0, 25.0, 26.0, 26.0, 26.0, 26.0, 27.0, 27.0, 27.0,\n",
       "       27.0, 28.0, 28.0, 28.0, 28.0, 29.0, 29.0, 29.0, 29.0, 30.0, 30.0,\n",
       "       30.0, 30.0, 31.0, 31.0, 31.0, 31.0, 32.0, 32.0, 32.0, 32.0, 33.0,\n",
       "       33.0, 33.0, 33.0, 34.0, 34.0, 34.0, 34.0, 36.0, 36.0, 36.0, 37.0,\n",
       "       37.0, 37.0, 37.0, 39.0, 39.0, 39.0, 39.0, 40.0, 40.0, 40.0, 40.0,\n",
       "       41.0, 41.0, 41.0, 41.0, 42.0, 42.0, 42.0, 42.0, 43.0, 43.0, 43.0,\n",
       "       43.0, 45.0, 45.0, 45.0, 45.0, 46.0, 46.0, 46.0, 47.0, 47.0, 47.0,\n",
       "       47.0, 48.0, 48.0, 48.0, 49.0, 49.0, 49.0, 49.0, 50.0, 50.0, 50.0,\n",
       "       50.0, 51.0, 51.0, 51.0, 51.0, 52.0, 52.0, 52.0, 52.0, 53.0, 53.0,\n",
       "       53.0, 53.0, 54.0, 54.0, 54.0, 54.0, 55.0, 55.0, 55.0, 56.0, 56.0,\n",
       "       56.0, 56.0, 58.0, 58.0, 58.0, 58.0, 59.0, 59.0, 59.0, 59.0, 62.0,\n",
       "       62.0, 62.0, 62.0, 63.0, 63.0, 63.0, 63.0, 64.0, 64.0, 64.0, 64.0,\n",
       "       65.0, 65.0, 65.0, 65.0, 66.0, 66.0, 66.0, 66.0, 68.0, 68.0, 68.0,\n",
       "       68.0, 70.0, 70.0, 70.0, 70.0, 71.0, 71.0, 71.0, 71.0, 72.0, 72.0,\n",
       "       72.0, 72.0, 73.0, 73.0, 73.0, 73.0, 74.0, 74.0, 74.0, 74.0, 75.0,\n",
       "       75.0, 75.0, 75.0, 76.0, 76.0, 76.0, 76.0, 77.0, 77.0, 77.0, 77.0,\n",
       "       78.0, 78.0, 78.0, 78.0, 79.0, 79.0, 79.0, 79.0, 80.0, 80.0, 80.0,\n",
       "       80.0, 138.0, 138.0, 138.0, 139.0, 139.0, 139.0, 145.0, 145.0,\n",
       "       145.0], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>travelled_distance</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan ... 83.87956501121813</div><input id='attrs-ac817c99-2178-4f08-b52b-bd4c281495ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac817c99-2178-4f08-b52b-bd4c281495ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f03e70e-f3d8-4ff5-888d-3dcd75abd4df' class='xr-var-data-in' type='checkbox'><label for='data-9f03e70e-f3d8-4ff5-888d-3dcd75abd4df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([nan, 2.4314333840434945, 1.9041289249466646, 9.586791273827385,\n",
       "       nan, 1.9407327112835673, 2.6244797117273855, 4.345110972023458,\n",
       "       nan, 2.065489200011489, 0.9320714642966936, 11.502696219292131,\n",
       "       nan, 26.17071685915425, 24.379300864309787, 30.93793401887898, nan,\n",
       "       20.074649766754145, 97.6042163969463, 304.4916531149898, nan,\n",
       "       32.455580470718154, 39.26586995179234, 12.679356316905874, nan,\n",
       "       45.636812306885204, 44.36425051084022, 43.27307638833475, nan,\n",
       "       7.407479218703737, 7.243427894483478, 76.10478591032829, nan,\n",
       "       15.208072869982704, 16.580545923153817, 1.4260057987749264, nan,\n",
       "       31.84214305581421, 32.590275371182535, 30.867793143849557, nan,\n",
       "       45.10900235565416, 38.02977617360479, nan, 19.23937543749705,\n",
       "       18.413099105951535, 25.03671033001026, nan, 32.444906424924184,\n",
       "       33.72718810425226, 38.341243699272724, nan, 73.97076951876846,\n",
       "       85.47164766750775, 80.6569574869487, nan, 48.849406716927085,\n",
       "       51.14856198143718, nan, 20.418265084089267, 26.183307448744568,\n",
       "       35.62683899783095, nan, 53.53119789870498, 55.19830298273256,\n",
       "       46.25560668251992, nan, 33.324943589350454, 33.43655562024587,\n",
       "       43.12074840514407, nan, 22.661850034188824, 25.18410388670184,\n",
       "       28.51911633714811, nan, 62.39013253578617, 55.88402281766315,\n",
       "       47.15721080779214, nan, 37.063069960354156, 49.86504967034428,\n",
       "...\n",
       "       10.078442297019135, 11.701527071445216, 41.09519186938672, nan,\n",
       "       5.687194291038993, 14.994234686908083, 3.0903522189342256, nan,\n",
       "       52.82258137008566, 44.463333208021346, 27.57703778953265, nan,\n",
       "       47.80260898608307, 137.85069235608438, 67.34296662435264, nan,\n",
       "       31.21911454104741, 24.17273103936359, 23.057147123012314, nan,\n",
       "       81.52940599266645, 68.33356225750434, 115.00611262038129, nan,\n",
       "       3.897862218361847, 6.247890953592022, 29.91602066711954, nan,\n",
       "       35.04650583466919, 42.70095912096084, 84.55393662709827, nan,\n",
       "       21.78517425438171, 17.380176285025968, 30.337350637095287, nan,\n",
       "       32.782496826214796, 34.9440885730302, 49.59249182648833, nan,\n",
       "       12.534892286863423, 14.518312009694208, 308.6301665412009, nan,\n",
       "       13.831818063786427, 14.720914337749713, 23.678280951723597, nan,\n",
       "       6.187862622914506, 8.005872069300644, 7.2825509587796935, nan,\n",
       "       11.109363547414766, 10.430374914314887, 14.240983277770576, nan,\n",
       "       46.319746745050836, 48.96154042696837, 45.93486719520703, nan,\n",
       "       15.17648402786642, 14.09870834951386, 24.212488299423153,\n",
       "       1187.5557136291957, 11.660086454137522, 57.845644804163214,\n",
       "       1029.3983621381274, 14.326640554649964, 41.515270530282386,\n",
       "       843.290451350497, 37.338437958066486, 83.87956501121813],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tstep</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1 2 3 4 1 2 3 4 ... 3 4 2 3 4 2 3 4</div><input id='attrs-eaae4e9f-04a9-43de-9227-aed362967ab2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eaae4e9f-04a9-43de-9227-aed362967ab2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8a2fade-a043-48e0-8da6-a7e8843d4533' class='xr-var-data-in' type='checkbox'><label for='data-d8a2fade-a043-48e0-8da6-a7e8843d4533' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,\n",
       "       3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 1,\n",
       "       2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4,\n",
       "       1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,\n",
       "       3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,\n",
       "       1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,\n",
       "       3, 4, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,\n",
       "       2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 1,\n",
       "       2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,\n",
       "       4, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,\n",
       "       3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,\n",
       "       1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,\n",
       "       3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 2,\n",
       "       3, 4, 2, 3, 4], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>label</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1 1 1 1 2 2 2 ... 59 59 58 65 65 64</div><input id='attrs-f81b1801-9fde-4104-becf-8a5ecb9e3f07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f81b1801-9fde-4104-becf-8a5ecb9e3f07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3e51446-6c0f-4d79-926b-68f74e206038' class='xr-var-data-in' type='checkbox'><label for='data-d3e51446-6c0f-4d79-926b-68f74e206038' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 6, 7, 7, 6, 5,\n",
       "       5, 5, 7, 7, 6, 6, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 11, 11, 11,\n",
       "       11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16,\n",
       "       16, 16, 16, 17, 17, 17, 15, 18, 18, 18, 17, 19, 19, 19, 19, 20, 20,\n",
       "       20, 18, 21, 21, 21, 20, 22, 23, 23, 22, 23, 22, 22, 21, 24, 24, 25,\n",
       "       27, 25, 26, 26, 24, 26, 25, 24, 23, 27, 27, 27, 25, 28, 28, 28, 26,\n",
       "       29, 29, 30, 29, 30, 30, 29, 30, 31, 31, 31, 31, 32, 32, 32, 32, 33,\n",
       "       33, 33, 34, 34, 34, 34, 35, 36, 35, 35, 37, 36, 37, 37, 39, 37, 38,\n",
       "       38, 40, 38, 39, 40, 41, 39, 40, 39, 42, 40, 41, 41, 43, 41, 42, 43,\n",
       "       45, 42, 43, 44, 46, 43, 44, 47, 44, 45, 45, 48, 45, 46, 49, 46, 47,\n",
       "       47, 50, 47, 48, 46, 51, 48, 49, 48, 52, 49, 50, 49, 53, 51, 52, 50,\n",
       "       54, 52, 54, 51, 55, 50, 51, 56, 53, 53, 52, 58, 55, 55, 54, 59, 56,\n",
       "       56, 55, 62, 60, 60, 59, 63, 61, 61, 61, 64, 62, 62, 60, 65, 63, 63,\n",
       "       63, 66, 64, 64, 62, 68, 66, 66, 65, 70, 67, 68, 69, 71, 68, 69, 67,\n",
       "       72, 69, 70, 68, 73, 70, 71, 71, 74, 71, 72, 70, 75, 72, 73, 72, 76,\n",
       "       73, 74, 73, 77, 74, 75, 74, 78, 75, 76, 75, 79, 76, 77, 76, 80, 77,\n",
       "       78, 77, 58, 57, 56, 59, 59, 58, 65, 65, 64], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>timestamp</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1696100399999997000 ... 16961111...</div><input id='attrs-7e6e5d90-d3a6-4853-9d69-189bda63eab0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e6e5d90-d3a6-4853-9d69-189bda63eab0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de3dad8b-3617-4dc4-9742-1aebf2ea439e' class='xr-var-data-in' type='checkbox'><label for='data-de3dad8b-3617-4dc4-9742-1aebf2ea439e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696100399999997000, 1696104000000003000,\n",
       "...\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696100399999997000, 1696104000000003000, 1696107600000000000,\n",
       "       1696111199999997000, 1696100399999997000, 1696104000000003000,\n",
       "       1696107600000000000, 1696111199999997000, 1696100399999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000,\n",
       "       1696104000000003000, 1696107600000000000, 1696111199999997000],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>old_label</span></div><div class='xr-var-dims'>(id)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>5.0 5.0 7.0 ... 829.0 838.0 816.0</div><input id='attrs-b7bebdd2-95ed-4f72-8970-09841f2161eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7bebdd2-95ed-4f72-8970-09841f2161eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd10d1e5-c666-4ed3-80e2-546c0638e29d' class='xr-var-data-in' type='checkbox'><label for='data-dd10d1e5-c666-4ed3-80e2-546c0638e29d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5.0, 5.0, 7.0, 5.0, 27.0, 23.0, 23.0, 21.0, 29.0, 25.0, 25.0, 23.0,\n",
       "       31.0, 27.0, 27.0, 29.0, 33.0, 31.0, 33.0, 35.0, 34.0, 29.0, 29.0,\n",
       "       31.0, 40.0, 32.0, 31.0, 33.0, 45.0, 39.0, 37.0, 37.0, 47.0, 40.0,\n",
       "       38.0, 38.0, 63.0, 57.0, 58.0, 57.0, 65.0, 59.0, 62.0, 69.0, 63.0,\n",
       "       65.0, 63.0, 71.0, 65.0, 67.0, 71.0, 79.0, 71.0, 71.0, 78.0, 81.0,\n",
       "       74.0, 74.0, 85.0, 79.0, 79.0, 87.0, 89.0, 81.0, 81.0, 85.0, 90.0,\n",
       "       83.0, 83.0, 91.0, 95.0, 89.0, 87.0, 99.0, 97.0, 92.0, 88.0, 95.0,\n",
       "       107.0, 101.0, 95.0, 103.0, 109.0, 104.0, 99.0, 107.0, 113.0, 103.0,\n",
       "       97.0, 105.0, 117.0, 107.0, 103.0, 136.0, 119.0, 111.0, 104.0,\n",
       "       112.0, 122.0, 108.0, 101.0, 109.0, 127.0, 117.0, 111.0, 123.0,\n",
       "       129.0, 123.0, 119.0, 133.0, 145.0, 139.0, 132.0, 149.0, 149.0,\n",
       "       141.0, 131.0, 151.0, 150.0, 144.0, 140.0, 161.0, 162.0, 155.0,\n",
       "       144.0, 163.0, 173.0, 166.0, 149.0, 171.0, 177.0, 169.0, 154.0,\n",
       "       175.0, 186.0, 175.0, 162.0, 195.0, 189.0, 174.0, 195.0, 235.0,\n",
       "       235.0, 217.0, 231.0, 251.0, 253.0, 227.0, 247.0, 256.0, 257.0,\n",
       "       232.0, 245.0, 269.0, 266.0, 242.0, 252.0, 371.0, 375.0, 369.0,\n",
       "       373.0, 472.0, 474.0, 472.0, 472.0, 499.0, 507.0, 509.0, 505.0,\n",
       "       516.0, 519.0, 531.0, 517.0, 531.0, 529.0, 531.0, 545.0, 541.0,\n",
       "       541.0, 537.0, 549.0, 542.0, 533.0, 543.0, 557.0, 550.0, 547.0,\n",
       "       590.0, 605.0, 595.0, 580.0, 597.0, 615.0, 607.0, 589.0, 598.0,\n",
       "       617.0, 612.0, 595.0, 599.0, 610.0, 603.0, 603.0, 621.0, 611.0,\n",
       "       601.0, 636.0, 649.0, 640.0, 621.0, 640.0, 655.0, 649.0, 629.0,\n",
       "       764.0, 758.0, 764.0, 740.0, 780.0, 771.0, 783.0, 756.0, 786.0,\n",
       "       776.0, 786.0, 755.0, 793.0, 787.0, 798.0, 777.0, 801.0, 790.0,\n",
       "       799.0, 770.0, 944.0, 907.0, 906.0, 886.0, 951.0, 912.0, 920.0,\n",
       "       900.0, 965.0, 918.0, 922.0, 894.0, 972.0, 923.0, 932.0, 899.0,\n",
       "       980.0, 931.0, 936.0, 919.0, 982.0, 933.0, 938.0, 914.0, 988.0,\n",
       "       941.0, 952.0, 925.0, 1001.0, 956.0, 961.0, 934.0, 1025.0, 980.0,\n",
       "       988.0, 971.0, 1029.0, 990.0, 996.0, 977.0, 1033.0, 995.0, 1004.0,\n",
       "       985.0, 1055.0, 1012.0, 1022.0, 995.0, 726.0, 716.0, 690.0, 747.0,\n",
       "       752.0, 724.0, 829.0, 838.0, 816.0], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16b6e3b9-dd05-4da9-9e07-80b84a738587' class='xr-section-summary-in' type='checkbox'  ><label for='section-16b6e3b9-dd05-4da9-9e07-80b84a738587' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-94d68cd4-c6ae-4e17-af85-2a566e9f20b1' class='xr-index-data-in' type='checkbox'/><label for='index-94d68cd4-c6ae-4e17-af85-2a566e9f20b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1.0,   1.0,   1.0,   1.0,   2.0,   2.0,   2.0,   2.0,   3.0,   3.0,\n",
       "       ...\n",
       "        80.0, 138.0, 138.0, 138.0, 139.0, 139.0, 139.0, 145.0, 145.0, 145.0],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;id&#x27;, length=291))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b11a0d1-298f-47f2-b48a-3751f2dad9c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1b11a0d1-298f-47f2-b48a-3751f2dad9c4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (id: 291)\n",
       "Coordinates:\n",
       "  * id                  (id) object 1.0 1.0 1.0 1.0 ... 139.0 145.0 145.0 145.0\n",
       "Data variables: (12/39)\n",
       "    mean_intensity-0    (id) object 120142092.14731053 ... 542315917.2031018\n",
       "    mean_intensity-1    (id) object 6.262796303505374 ... 6.502689395163887\n",
       "    mean_intensity-2    (id) object 3.44651544531623e-05 ... 5.65995641052689...\n",
       "    min_intensity-0     (id) object 90828959.45741372 ... 522074558.18083715\n",
       "    min_intensity-1     (id) object 0.6751485466957092 ... 0.5878952145576477\n",
       "    min_intensity-2     (id) object 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    ...                  ...\n",
       "    track_ids           (id) object 1.0 1.0 1.0 1.0 ... 139.0 145.0 145.0 145.0\n",
       "    travelled_distance  (id) object nan 2.4314333840434945 ... 83.87956501121813\n",
       "    tstep               (id) object 1 2 3 4 1 2 3 4 1 2 ... 4 2 3 4 2 3 4 2 3 4\n",
       "    label               (id) object 1 1 1 1 2 2 2 2 ... 57 56 59 59 58 65 65 64\n",
       "    timestamp           (id) object 1696100399999997000 ... 1696111199999997000\n",
       "    old_label           (id) object 5.0 5.0 7.0 5.0 ... 724.0 829.0 838.0 816.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracked[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "38286407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14df630a0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tracked[1].isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b512e83d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}