coherence_lifetime_functions.ipynb

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   "source": [
    "# How to calculate coherence lifetime of eddies\n",
    "\n",
    "In this notebook I have collected the functions that are needed to go from ocean parcels trajectory dataset to **(1)** calculate LAVD cumulatevly for all time steps in the trajectory dataset, **(2)** identify RCLVs in all these LAVD fields.\n",
    "\n",
    "The wast majority of functions are taken from or hevily inspired by RCLVatlas by Lexi Jones-Kellet from MIT."
   ]
  },
  {
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   "id": "c8879fd4-3c3c-4574-937f-440be70a68ec",
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   "source": [
    "import os,sys\n",
    "import trajan\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import cmocean\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "from shapely.geometry import Polygon\n",
    "import geopandas as gpd\n",
    "import pyproj\n",
    "\n",
    "from config import *\n",
    "\n",
    "sys.path.append('./RCLVatlas')\n",
    "from subfunctions_for_RCLV_atlas import read_RCLV_CSV_untracked,read_RCLV_CSV_tracked,save_RCLV_CSV\n",
    "from mainfunctions_for_RCLV_atlas import *\n",
    "from functions_for_parcels import *\n",
    "from scipy import integrate"
   ]
  },
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   "source": [
    "## Input data \n",
    "\n",
    "Here we use ocean parcels .zarr dataset of 32 days as input. \n",
    "\n",
    "30x30 degree box around Hawaii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adcb7f4b-1cd6-40c8-a6bf-ba1d5c1af03b",
   "metadata": {},
   "outputs": [],
   "source": [
    "traj_path= \"/work/bk1377/b382618/RCLVs/f32day/lang_traj/20100423_32days_runtime_20min_timestep_particle_start_lat_10.0_40.0_lon_180.0_210.0_spatial_step_0.03125_6hr_output_freq.zarr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c690bbfd-5183-4fbc-b8a3-29feb94f4f18",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = \"/work/bk1377/b382618/LAVD_days_function/\""
   ]
  },
  {
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   "source": [
    "## Calculate LAVD for all days in the trajectory dataset\n",
    "\n",
    "The function calculates LAVD for all trajectory time stepas in the dataset. For example, LAVD for day 7 out of 32 will integrate from time=0 to time=7 days. The original LAVD function (like in RCLV) usually integrate only the full trajectory lifetime. Here daily snapshots are available.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8aed3eca-27a0-4b43-a6f8-f371be9035ff",
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   "source": [
    "# a bit weirf function to deal with different date formats in different datasets\n",
    "#this function is from chatgpt\n",
    "def cleanup_nc_time_format(time_str):\n",
    "    \"\"\"\n",
    "    Removes the last three zeros (microseconds) from a .nc file time string.\n",
    "    \n",
    "    Parameters:\n",
    "    time_str (str): Time string in the format \"2010-05-24T18:00:00.000000000\"\n",
    "    \n",
    "    Returns:\n",
    "    str: Time string with the last three zeros removed, in the format \"2010-05-24T18:00:00\"\n",
    "    \"\"\"\n",
    "    if '.' in time_str:\n",
    "        time_str = time_str[:time_str.index('.')]\n",
    "    time_obj = datetime.datetime.strptime(time_str, \"%Y-%m-%dT%H:%M:%S\")\n",
    "    \n",
    "    return time_obj.strftime(\"%Y-%m-%dT%H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0cdd140-79a9-46ea-9304-bc552520e8d1",
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   "source": [
    "# this is the real function, pls don't delete it\n",
    "def calc_LAVD_spec_time(vort,output_freq,runtime, LAVD_days):\n",
    "    time_select=int(LAVD_days*(24/output_freq))\n",
    "\n",
    "\n",
    "    \n",
    "    vort_avg_t = np.nanmean(vort,axis=0)[0:] #Find average vorticity over the entire spatial domain at each time step\n",
    "    \n",
    "    if LAVD_days==0:\n",
    "        LAVD = np.absolute(vort[:,0]-vort_avg_t[0]) #this is just vorticity field\n",
    "        # LAVD = np.trapezoid(np.absolute(vort[:,0:time_select] - vort_avg_t[0:time_select]), dx=output_freq*60*60, axis=1)/(time_select*6*60*60-output_freq*60*60) #trapz does the integration; convert data to be in se\n",
    "        # LAVD = np.trapezoid(np.absolute(vort[:,0:time_select] - vort_avg_t[0:time_select]), dx=output_freq*60*60, axis=1) #trapz does the integration; convert data to be in se\n",
    "\n",
    "    else:\n",
    "        # LAVD = integrate.cumulative_trapezoid(np.absolute(vort[:,0:time_select] - vort_avg_t[0:time_select]), dx=output_freq*60*60, axis=1)/(LAVD_days*24*60*60-output_freq*60*60)\n",
    "        ### if i do the above then the values at 0 are small and correct at the end.\n",
    "        LVD = integrate.cumulative_trapezoid(np.absolute(vort[:,0:time_select] - vort_avg_t[0:time_select]), dx=output_freq*60*60, axis=1)   \n",
    "        # LAVD = np.trapezoid(np.absolute(vort[:,0:time_select] - vort_avg_t[0:time_select]), dx=output_freq*60*60, axis=1)/(LAVD_days*24*60*60-output_freq*60*60) #trapz does the integration; convert data to be in se\n",
    "    return LVD, time_select\n"
   ]
  },
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   "cell_type": "code",
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   "id": "e274be63-c6d4-4a16-af3f-a6cd7665870a",
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   "source": [
    "def trajectories_to_specific_day_LAVD(traj_path, LAVD_days):\n",
    "    print(\"Calculating the LAVD ...\")\n",
    "    traj_ds = xr.open_dataset(traj_path) # open the Lagrangian trajectory dataset that was just produced\n",
    "    date = str(traj_ds.time.values[0][0])[0:10]\n",
    "    vort_premask = traj_ds.variables[\"vort\"]\n",
    "    vort = np.array(vort_premask.where(vort_premask != 0)) #filters out land values\n",
    "    LVD, time_select = calc_LAVD_spec_time(vort,sim_params['output_freq'],sim_params['runtime'], LAVD_days)\n",
    "\n",
    "\n",
    "    # z=sim_params['output_freq']*60*60\n",
    "    divide_by_list = []\n",
    "    for i in np.arange(2, time_select+1):\n",
    "        val = (i*sim_params['output_freq']*60*60-sim_params['output_freq']*60*60)\n",
    "        divide_by_list.append(val)\n",
    "    LAVD = LVD/divide_by_list\n",
    "\n",
    "    # Create an xarray Dataset with the necessary dimensions\n",
    "    one_lat = np.unique(traj_ds.lat[:,0])\n",
    "    one_lon = np.unique(traj_ds.lon[:,0])\n",
    "    time = traj_ds.time[0][1:time_select].values\n",
    "    # cleaned_time_strings = np.vectorize(cleanup_nc_time_format)(time_strings)\n",
    "    time2= np.vectorize(cleanup_nc_time_format)(time.astype(str))\n",
    "    print(time2)\n",
    "\n",
    "    # Check the shape of LAVD and reshape it    \n",
    "    # Reshape LAVD from (921600, 17) to (17, 960, 960)\n",
    "    num_lat = len(one_lat)\n",
    "    num_lon = len(one_lon)\n",
    "    num_time = len(time2)\n",
    "    \n",
    "    if LAVD.shape == (num_lat * num_lon, num_time):\n",
    "        LAVD = np.reshape(LAVD, (num_lon, num_lat, num_time))\n",
    "    else:\n",
    "        raise ValueError(f\"Unexpected LAVD shape {LAVD.shape}. Cannot reshape.\")\n",
    "    \n",
    "    LAVD_ds = xr.Dataset(\n",
    "        {\n",
    "            'LAVD': (['longitude', 'latitude', 'time'], LAVD)\n",
    "        },\n",
    "        coords={\n",
    "            'time': time2,\n",
    "            'latitude': one_lat,\n",
    "            'longitude': one_lon\n",
    "        }\n",
    "    )\n",
    "\n",
    "    date_good=str(date.replace(\"-\", \"\"))\n",
    "    output_file_name = str(date_good) + \"_\" + str(LAVD_days) + \"days_LAVD.nc\"\n",
    "    # Save the dataset as a NetCDF file\n",
    "    LAVD_output_file_path = os.path.join(base_path, output_file_name)\n",
    "    LAVD_ds.to_netcdf(LAVD_output_file_path)\n",
    "    \n",
    "    print('LAVD NetCDF output file: %s' % (LAVD_output_file_path))\n",
    "    return LAVD_output_file_path\n"
   ]
  },
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   "id": "d02562f2-d4e2-4e01-9906-88c81db63b17",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating the LAVD ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/b/b382618/.conda/envs/parcels2/lib/python3.11/site-packages/xarray/backends/plugins.py:159: RuntimeWarning: 'netcdf4' fails while guessing\n",
      "  warnings.warn(f\"{engine!r} fails while guessing\", RuntimeWarning)\n",
      "/home/b/b382618/.conda/envs/parcels2/lib/python3.11/site-packages/xarray/backends/plugins.py:159: RuntimeWarning: 'scipy' fails while guessing\n",
      "  warnings.warn(f\"{engine!r} fails while guessing\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2010-04-23T06:00:00' '2010-04-23T12:00:00' '2010-04-23T18:00:00'\n",
      " '2010-04-24T00:00:00' '2010-04-24T06:00:00' '2010-04-24T12:00:00'\n",
      " '2010-04-24T18:00:00' '2010-04-25T00:00:00' '2010-04-25T06:00:00'\n",
      " '2010-04-25T12:00:00' '2010-04-25T18:00:00' '2010-04-26T00:00:00'\n",
      " '2010-04-26T06:00:00' '2010-04-26T12:00:00' '2010-04-26T18:00:00'\n",
      " '2010-04-27T00:00:00' '2010-04-27T06:00:00' '2010-04-27T12:00:00'\n",
      " '2010-04-27T18:00:00' '2010-04-28T00:00:00' '2010-04-28T06:00:00'\n",
      " '2010-04-28T12:00:00' '2010-04-28T18:00:00' '2010-04-29T00:00:00'\n",
      " '2010-04-29T06:00:00' '2010-04-29T12:00:00' '2010-04-29T18:00:00'\n",
      " '2010-04-30T00:00:00' '2010-04-30T06:00:00' '2010-04-30T12:00:00'\n",
      " '2010-04-30T18:00:00' '2010-05-01T00:00:00' '2010-05-01T06:00:00'\n",
      " '2010-05-01T12:00:00' '2010-05-01T18:00:00' '2010-05-02T00:00:00'\n",
      " '2010-05-02T06:00:00' '2010-05-02T12:00:00' '2010-05-02T18:00:00'\n",
      " '2010-05-03T00:00:00' '2010-05-03T06:00:00' '2010-05-03T12:00:00'\n",
      " '2010-05-03T18:00:00' '2010-05-04T00:00:00' '2010-05-04T06:00:00'\n",
      " '2010-05-04T12:00:00' '2010-05-04T18:00:00' '2010-05-05T00:00:00'\n",
      " '2010-05-05T06:00:00' '2010-05-05T12:00:00' '2010-05-05T18:00:00'\n",
      " '2010-05-06T00:00:00' '2010-05-06T06:00:00' '2010-05-06T12:00:00'\n",
      " '2010-05-06T18:00:00' '2010-05-07T00:00:00' '2010-05-07T06:00:00'\n",
      " '2010-05-07T12:00:00' '2010-05-07T18:00:00' '2010-05-08T00:00:00'\n",
      " '2010-05-08T06:00:00' '2010-05-08T12:00:00' '2010-05-08T18:00:00'\n",
      " '2010-05-09T00:00:00' '2010-05-09T06:00:00' '2010-05-09T12:00:00'\n",
      " '2010-05-09T18:00:00' '2010-05-10T00:00:00' '2010-05-10T06:00:00'\n",
      " '2010-05-10T12:00:00' '2010-05-10T18:00:00' '2010-05-11T00:00:00'\n",
      " '2010-05-11T06:00:00' '2010-05-11T12:00:00' '2010-05-11T18:00:00'\n",
      " '2010-05-12T00:00:00' '2010-05-12T06:00:00' '2010-05-12T12:00:00'\n",
      " '2010-05-12T18:00:00' '2010-05-13T00:00:00' '2010-05-13T06:00:00'\n",
      " '2010-05-13T12:00:00' '2010-05-13T18:00:00' '2010-05-14T00:00:00'\n",
      " '2010-05-14T06:00:00' '2010-05-14T12:00:00' '2010-05-14T18:00:00'\n",
      " '2010-05-15T00:00:00' '2010-05-15T06:00:00' '2010-05-15T12:00:00'\n",
      " '2010-05-15T18:00:00' '2010-05-16T00:00:00' '2010-05-16T06:00:00'\n",
      " '2010-05-16T12:00:00' '2010-05-16T18:00:00' '2010-05-17T00:00:00'\n",
      " '2010-05-17T06:00:00' '2010-05-17T12:00:00' '2010-05-17T18:00:00'\n",
      " '2010-05-18T00:00:00' '2010-05-18T06:00:00' '2010-05-18T12:00:00'\n",
      " '2010-05-18T18:00:00' '2010-05-19T00:00:00' '2010-05-19T06:00:00'\n",
      " '2010-05-19T12:00:00' '2010-05-19T18:00:00' '2010-05-20T00:00:00'\n",
      " '2010-05-20T06:00:00' '2010-05-20T12:00:00' '2010-05-20T18:00:00'\n",
      " '2010-05-21T00:00:00' '2010-05-21T06:00:00' '2010-05-21T12:00:00'\n",
      " '2010-05-21T18:00:00' '2010-05-22T00:00:00' '2010-05-22T06:00:00'\n",
      " '2010-05-22T12:00:00' '2010-05-22T18:00:00' '2010-05-23T00:00:00'\n",
      " '2010-05-23T06:00:00' '2010-05-23T12:00:00' '2010-05-23T18:00:00'\n",
      " '2010-05-24T00:00:00' '2010-05-24T06:00:00' '2010-05-24T12:00:00'\n",
      " '2010-05-24T18:00:00']\n",
      "Original LAVD shape: (921600, 127)\n",
      "Reshaped LAVD shape: (960, 960, 127)\n",
      "LAVD NetCDF output file: /work/bk1377/b382618/LAVD_days_function/20100423_32days_LAVD.nc\n"
     ]
    }
   ],
   "source": [
    "out_p = trajectories_to_specific_day_LAVD(traj_path, 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6023c90d-29ab-4e75-8bc9-c6bbc442d625",
   "metadata": {},
   "source": [
    "## Identify RCLVs in the created LAVD daily fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30b092aa-f4ec-4c57-8d2b-4a9dc5186b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_file_path = \"/work/bk1377/b382618/LAVD_days_function/20100423_32days_LAVD.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7329cecc-0ef3-4eeb-9129-70fcfc79bf6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "traj_path= \"/work/bk1377/b382618/RCLVs/f32day/lang_traj/20100423_32days_runtime_20min_timestep_particle_start_lat_10.0_40.0_lon_180.0_210.0_spatial_step_0.03125_6hr_output_freq.zarr\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68dc85de-e4f9-4cc8-9d72-9caa23a13d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_particles_after_time_for_LAVD(traj,x_mask,y_mask,traj_lat_array,sim_params,obs_to_select):\n",
    "    \"\"\"\n",
    "    Get the lat/lons of particles from a ploygon after some number of days along the Lagrangian trajectory.\n",
    "    \n",
    "    Input\n",
    "        traj: trajectory file \n",
    "        x_mask,y_mask: indeces from the longitude & latitude arrays that are inside of the polygon\n",
    "        days: number of days from the initialization time to retreive particle locations (back trajectories will be back in time)\n",
    "    Output\n",
    "        eddy_xdays_lons,eddy_xday_lats: lon/lat coordinates of the particles of interest on day x\n",
    "    \n",
    "    \"\"\"\n",
    "    particle_lon,particle_lat,particle_vort = traj.variables[\"lon\"],traj.variables[\"lat\"],traj.variables[\"vort\"] #read in particle location lat, lons, vorts\n",
    "    particle_nums = x_mask*len(traj_lat_array) + y_mask #formula to get the integer ID of the particles inside a polygon\n",
    "    if obs_to_select==0: ### not sure if this if statement is needed\n",
    "        eddy_xday_lons = [float(particle_lon[p,int(((24/sim_params['output_freq'])*obs_to_select))]) for p in particle_nums]   \n",
    "        eddy_xday_lats = [float(particle_lat[p,int(((24/sim_params['output_freq'])*obs_to_select))]) for p in particle_nums]\n",
    "        eddy_xday_vorts = [float(particle_vort[p,int(((24/sim_params['output_freq'])*obs_to_select))]) for p in particle_nums]\n",
    "\n",
    "    else:\n",
    "######Stella- added -1 to the int bc i though that the indexing does not work bc of that. i think it should be like that because it is indexing         \n",
    "        eddy_xday_lons = [float(particle_lon[p,int(obs_to_select)]) for p in particle_nums]   \n",
    "        eddy_xday_lats = [float(particle_lat[p,int(obs_to_select)]) for p in particle_nums]\n",
    "        eddy_xday_vorts = [float(particle_vort[p,int(obs_to_select)]) for p in particle_nums]\n",
    "    return eddy_xday_lons,eddy_xday_lats,eddy_xday_vorts"
   ]
  },
  {
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   "source": [
    "### Without saving rejected ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "35ca35cf-8eb0-4d42-bd36-c5b7038648ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def LAVD_days_to_RCLV_loop(traj_ds_path, LAVD_ds_path):\n",
    "    RCLV_data = [['date', 'RCLV_id', 'orientation', 'age_days', 'area_km2', 'center_lon', 'center_lat', 'CD', 'flag', 'boundary_coords']]\n",
    "\n",
    "    traj_ds = xr.open_zarr(traj_ds_path)\n",
    "    lavd_ds = xr.open_dataset(LAVD_ds_path)\n",
    "    lavd_ds = lavd_ds.LAVD\n",
    "    \n",
    "    time_list = traj_ds.time[0].values\n",
    "    midnight_list = time_list[::4]\n",
    "\n",
    "    # midnight_list = midnight_list[9:]\n",
    "    # midnight_list = midnight_list[1:] #this removes the first date when particles start, so the first RCLVS will be identified at 1 day later\n",
    "    print(midnight_list)\n",
    "\n",
    "    # ## now i need to itterate the midnight list\n",
    "    # plms_cum,cons_cum,areas_cum,cds_cum,pols_cum = [],[],[],[],[]\n",
    "    for sk in np.arange(25, 30):\n",
    "        ## select the right LAVD field\n",
    "        right_date = midnight_list[sk]\n",
    "        print(right_date)\n",
    "        short_date=str(right_date)[0:19]\n",
    "        print(short_date)\n",
    "        lavd_subselect = lavd_ds.sel(time=short_date)\n",
    "        #select subselect the lavd field \n",
    "        traj_subselect = traj_ds.isel(obs = slice(0, sk*4))\n",
    "        \n",
    "        # LAVD = np.load('%s%s_LAVD_%s.npy'%(LAVD_dir,initial_date,filename_str)) #Load LAVD data\n",
    "        LAVD = np.ma.masked_where(np.isnan(lavd_subselect),lavd_subselect) #Land mask required for the peak_local_max function to work\n",
    "        LAVD_reshape = np.transpose(np.reshape(LAVD,(len(traj_lon_array),len(traj_lat_array))))\n",
    "        \n",
    "        # Iterate through all of the local maxima to find structures that meet our RCLV criteria \n",
    "        plms,cons,areas,cds,pols = [],[],[],[],[] # arrays to store the output\n",
    "        plm = peak_local_max(LAVD_reshape,min_distance=RCLV_params['min_dist']) # grid indices of the local maxima in the LAVD field\n",
    "        for ji in plm:\n",
    "            args = {'lon0':traj_lon_array[ji[1]],\n",
    "                    'lat0':traj_lat_array[ji[0]],\n",
    "                    'dlon':np.abs(traj_lon_array[1]-traj_lon_array[0]),\n",
    "                    'dlat':np.abs(traj_lat_array[1]-traj_lat_array[0])}\n",
    "            \n",
    "            for target_CD in [0.03,0.02,0.01]: #target CDs to test, will iterate through each of these unless if a successful contour is identified\n",
    "                con,area,cd = rclv.convex_contour_around_maximum(LAVD_reshape,ji,\n",
    "                                                                 init_contour_step_frac=RCLV_params['init_contour_step_frac'],\n",
    "                                                                 convex_def_tol=RCLV_params['convex_def_tol'],\n",
    "                                                                 convex_def=target_CD)\n",
    "            \n",
    "                try: # Code errors if the local maximum is too close to the land where there are missing values\n",
    "                    if (area >= RCLV_params['min_area']) and (cd <= 0.03): # check area and convex deficiency meet thresholds; actual cd identified will be different from target CD\n",
    "                        lon_inds = [round(i) for i in con[:,1]]\n",
    "                        lat_inds = [round(i) for i in con[:,0]]\n",
    "    \n",
    "                        # Reformat points of the polygon with parentheses\n",
    "                        poly_pts = [(traj_lon_array[lon_inds][pt],traj_lat_array[lat_inds][pt]) for pt in np.arange(0,len(lon_inds))]\n",
    "    \n",
    "                        # Get the CI of the particles after simulation run time to make sure it meets threshold set\n",
    "                        x_mask,y_mask = find_polygon_pts(poly_pts,traj_lon_array,traj_lat_array)                    \n",
    "                        eddy_day0_lons,eddy_day0_lats,_ = extract_particles_after_time_for_LAVD(traj_subselect,x_mask,y_mask,traj_lat_array,sim_params,0) #day 0 data\n",
    "                        eddy_dayx_lons,eddy_dayx_lats,eddy_dayx_vorts = extract_particles_after_time_for_LAVD(traj_subselect,x_mask,y_mask,traj_lat_array,sim_params,sk) # last day\n",
    "                        contour_CI = CI(eddy_day0_lons,eddy_day0_lats,eddy_dayx_lons,eddy_dayx_lats) #calculate the coherency index (measure of dispersal)\n",
    "                        \n",
    "                        if (contour_CI >= -0.5): # Coherency index criteria, minimal dispersal required\n",
    "                            # Check that the vorticty is consistent within the vortex over the course of the simulation (filters out some weird saddle point features,etc)\n",
    "                            # Timestep to get initial vorticity needs to be day 1 since at day 0 all particles are initialized with vort=0\n",
    "                            eddy_day1_lons,eddy_day1_lats,eddy_day1_vorts = extract_particles_after_time(traj_subselect,x_mask,y_mask,traj_lat_array,sim_params,1) \n",
    "                            day1_vort_signs = [np.sign(v) for v in eddy_day1_vorts]\n",
    "                            day1_anti_count,day1_cyc_count = day1_vort_signs.count(-1.0),day1_vort_signs.count(1.0)  \n",
    "                            day1_vort_freq = np.max((day1_anti_count,day1_cyc_count))/(day1_anti_count+day1_cyc_count) #percentage of particles with the same vorticity\n",
    "    \n",
    "                            if (day1_vort_freq >= 0.85): # 85% or more have the same vort sign on day 1\n",
    "                                dayx_vort_signs = [np.sign(v) for v in eddy_dayx_vorts]\n",
    "                                dayx_anti_count,dayx_cyc_count = dayx_vort_signs.count(-1.0),dayx_vort_signs.count(1.0)  \n",
    "                                dayx_vort_freq = np.max((dayx_anti_count,dayx_cyc_count))/(dayx_anti_count+dayx_cyc_count)\n",
    "    \n",
    "                                if day1_anti_count > day1_cyc_count: #get the polarity of the vortex on day 1\n",
    "                                    day1_vort_pol = 'anti'\n",
    "                                else:\n",
    "                                    day1_vort_pol = 'cyc'\n",
    "                                if dayx_anti_count > dayx_cyc_count: #get the polarity of the vortex on last day\n",
    "                                    dayx_vort_pol = 'anti'\n",
    "                                else:\n",
    "                                    dayx_vort_pol = 'cyc'\n",
    "\n",
    "                                # If all criteria are passed, add values to output arrays & move on to next local maxima\n",
    "                                if (dayx_vort_freq >= 0.85) and (day1_vort_pol == dayx_vort_pol): # Final criteria: vort is consistent for 85% of particles on the last day\n",
    "                                    region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                                    plms.append(ji)\n",
    "                                    cons.append(con)\n",
    "                                    areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                                    cds.append(cd)\n",
    "                                    pols.append(day1_vort_pol)\n",
    "                                    break # do not need to look at other CDs if the criteria to this point has been met \n",
    "                except TypeError: # too close to land to define an RCLV\n",
    "                    break\n",
    "#### save the rclvs in a file\n",
    "                for i in np.arange(0,len(cons)):\n",
    "                    temp = [short_date,np.nan,pols[i],np.nan,areas[i],traj_lon_array[plms[i][1]],traj_lat_array[plms[i][0]],cds[i],0] \n",
    "                    # Convert from grid indices to lat/lon coordinates for the output\n",
    "                    lon_bounds = traj_lon_array[[round(j) for j in cons[i][:, 1]]]\n",
    "                    lat_bounds = traj_lat_array[[round(j) for j in cons[i][:, 0]]]\n",
    "                    for j in np.arange(0,len(lon_bounds)):\n",
    "                        temp.extend([lon_bounds[j],lat_bounds[j]])\n",
    "                    RCLV_data.append(temp) # Add RCLV data to main array\n",
    "                    # temp = []\n",
    "                plms,cons,areas,cds,pols = [],[],[],[],[] # if yoou don't do this then \n",
    "    return RCLV_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d3b2183-0408-4330-828d-ffa84d572d39",
   "metadata": {},
   "source": [
    "### This function saves all identified and rejected contours for more interesting analysis. \n",
    "\n",
    "\n",
    "Filter for \"accepted\" to get the \"real\" RCLVs.\n",
    "So the function ooutputs all RCLVs from all LAVD_days LAVD fields.\n",
    "THe basis for this is lexiland function that is made to take in .nc fles and alsp output the rejected RCLVs so we see why the coherence ends\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f932951e-4522-4524-8689-b7fd91cd060c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def LAVD_days_to_RCLV_loop(traj_ds_path, LAVD_ds_path):\n",
    "    RCLV_data = [['date', 'reject_accept', 'RCLV_id', 'orientation', 'age_days', 'area_km2', 'center_lon', 'center_lat', 'CD', 'CI', 'flag', 'boundary_coords']]\n",
    "    # RCLV_data_rejected = [['date', 'reason', 'RCLV_id', 'orientation', 'age_days', 'area_km2', 'center_lon', 'center_lat', 'CD', 'boundary_coords']]\n",
    "\n",
    "    traj_ds = xr.open_zarr(traj_ds_path)\n",
    "    lavd_ds = xr.open_dataset(LAVD_ds_path)\n",
    "    lavd_ds = lavd_ds.LAVD\n",
    "    \n",
    "    time_list = traj_ds.time[0].values\n",
    "    midnight_list = time_list[::4]\n",
    "\n",
    "    # midnight_list = midnight_list[9:]\n",
    "    # midnight_list = midnight_list[1:] #this removes the first date when particles start, so the first RCLVS will be identified at 1 day later\n",
    "    print(midnight_list)\n",
    "\n",
    "    # ## now i need to itterate the midnight list\n",
    "    # plms_cum,cons_cum,areas_cum,cds_cum,pols_cum = [],[],[],[],[]\n",
    "    for sk in np.arange(1, 32):\n",
    "        ## select the right LAVD field\n",
    "        right_date = midnight_list[sk]\n",
    "        print(right_date)\n",
    "        short_date=str(right_date)[0:19]\n",
    "        print(short_date)\n",
    "        lavd_subselect = lavd_ds.sel(time=short_date)\n",
    "        #select subselect the lavd field \n",
    "        traj_subselect = traj_ds.isel(obs = slice(0, sk*4))\n",
    "        \n",
    "        # LAVD = np.load('%s%s_LAVD_%s.npy'%(LAVD_dir,initial_date,filename_str)) #Load LAVD data\n",
    "        LAVD = np.ma.masked_where(np.isnan(lavd_subselect),lavd_subselect) #Land mask required for the peak_local_max function to work\n",
    "        LAVD_reshape = np.transpose(np.reshape(LAVD,(len(traj_lon_array),len(traj_lat_array))))\n",
    "        \n",
    "        # Iterate through all of the local maxima to find structures that meet our RCLV criteria \n",
    "        plms,cons,areas,cds,pols,cis, rejacc = [],[],[],[],[],[],[] # arrays to store the output\n",
    "        plm = peak_local_max(LAVD_reshape,min_distance=RCLV_params['min_dist']) # grid indices of the local maxima in the LAVD field\n",
    "        for ji in plm:\n",
    "            args = {'lon0':traj_lon_array[ji[1]],\n",
    "                    'lat0':traj_lat_array[ji[0]],\n",
    "                    'dlon':np.abs(traj_lon_array[1]-traj_lon_array[0]),\n",
    "                    'dlat':np.abs(traj_lat_array[1]-traj_lat_array[0])}\n",
    "            \n",
    "            for target_CD in [0.03,0.02,0.01]: #target CDs to test, will iterate through each of these unless if a successful contour is identified\n",
    "                con,area,cd = rclv.convex_contour_around_maximum(LAVD_reshape,ji,\n",
    "                                                                 init_contour_step_frac=RCLV_params['init_contour_step_frac'], ##might be sensitive to the eddies i ID\n",
    "                                                                 convex_def_tol=RCLV_params['convex_def_tol'],\n",
    "                                                                 convex_def=target_CD)\n",
    "            \n",
    "                try: # Code errors if the local maximum is too close to the land where there are missing values\n",
    "                    if (area < RCLV_params['min_area']):\n",
    "                        rejected_reason = \"rejected_min_area\"\n",
    "                        region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                        plms.append(ji)\n",
    "                        cons.append(con)\n",
    "                        areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                        cds.append(cd)\n",
    "                        pols.append(None)\n",
    "                        cis.append(None)\n",
    "                        rejacc.append(rejected_reason)\n",
    "                        # print(type(con))\n",
    "                        continue\n",
    "\n",
    "                    if (cd > 0.031): ### this is changed from 0.03 because it misses the contours that are above the cd threshold bu within the tolerance. \n",
    "                        rejected_reason = \"rejected_cd\"\n",
    "                        region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                        plms.append(ji)\n",
    "                        cons.append(con)\n",
    "                        areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                        cds.append(cd)\n",
    "                        pols.append(None)\n",
    "                        cis.append(None)\n",
    "                        rejacc.append(rejected_reason)\n",
    "                        # print(type(con))\n",
    "                        continue\n",
    "\n",
    "                    \n",
    "\n",
    "                    else:\n",
    "                    # if (area >= RCLV_params['min_area']) and (cd <= 0.03): # check area and convex deficiency meet thresholds; actual cd identified will be different from target CD\n",
    "                        lon_inds = [round(i) for i in con[:,1]]\n",
    "                        lat_inds = [round(i) for i in con[:,0]]\n",
    "   \n",
    "    \n",
    "                        # Reformat points of the polygon with parentheses\n",
    "                        poly_pts = [(traj_lon_array[lon_inds][pt],traj_lat_array[lat_inds][pt]) for pt in np.arange(0,len(lon_inds))]\n",
    "    \n",
    "                        # Get the CI of the particles after simulation run time to make sure it meets threshold set\n",
    "                        x_mask,y_mask = find_polygon_pts(poly_pts,traj_lon_array,traj_lat_array)                    \n",
    "                        eddy_day0_lons,eddy_day0_lats,_ = extract_particles_after_time_for_LAVD(traj_subselect,x_mask,y_mask,traj_lat_array,sim_params,0) #day 0 data\n",
    "                        eddy_dayx_lons,eddy_dayx_lats,eddy_dayx_vorts = extract_particles_after_time_for_LAVD(traj_subselect,x_mask,y_mask,traj_lat_array,sim_params,sk) # last day\n",
    "                        contour_CI = CI(eddy_day0_lons,eddy_day0_lats,eddy_dayx_lons,eddy_dayx_lats) #calculate the coherency index (measure of dispersal)\n",
    "\n",
    "                        if contour_CI < -0.5:\n",
    "                            rejected_reason = \"rejected_ci\"\n",
    "                            region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                            plms.append(ji)\n",
    "                            cons.append(con)\n",
    "                            areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                            cds.append(cd)\n",
    "                            pols.append(None)\n",
    "                            cis.append(contour_CI)\n",
    "                            rejacc.append(rejected_reason)\n",
    "\n",
    "                            continue\n",
    "                        \n",
    "                        else:\n",
    "                        # if (contour_CI >= -0.5): # Coherency index criteria, minimal dispersal required\n",
    "                            # Check that the vorticty is consistent within the vortex over the course of the simulation (filters out some weird saddle point features,etc)\n",
    "                            # Timestep to get initial vorticity needs to be day 1 since at day 0 all particles are initialized with vort=0\n",
    "                            eddy_day1_lons,eddy_day1_lats,eddy_day1_vorts = extract_particles_after_time(traj_subselect,x_mask,y_mask,traj_lat_array,sim_params,1) \n",
    "                            day1_vort_signs = [np.sign(v) for v in eddy_day1_vorts]\n",
    "                            day1_anti_count,day1_cyc_count = day1_vort_signs.count(-1.0),day1_vort_signs.count(1.0)  \n",
    "                            day1_vort_freq = np.max((day1_anti_count,day1_cyc_count))/(day1_anti_count+day1_cyc_count) #percentage of particles with the same vorticity\n",
    "\n",
    "\n",
    "                            if day1_vort_freq < 0.85:\n",
    "                                rejected_reason = \"rejected_vort_consistency_day1\"\n",
    "                                region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                                plms.append(ji)\n",
    "                                cons.append(con)\n",
    "                                areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                                cds.append(cd)\n",
    "                                pols.append(None)\n",
    "                                cis.append(contour_CI)\n",
    "                                rejacc.append(rejected_reason)\n",
    "\n",
    "                                continue\n",
    "                            else:\n",
    "\n",
    "                            # if (day1_vort_freq >= 0.85): # 85% or more have the same vort sign on day 1\n",
    "                                dayx_vort_signs = [np.sign(v) for v in eddy_dayx_vorts]\n",
    "                                dayx_anti_count,dayx_cyc_count = dayx_vort_signs.count(-1.0),dayx_vort_signs.count(1.0)  \n",
    "                                dayx_vort_freq = np.max((dayx_anti_count,dayx_cyc_count))/(dayx_anti_count+dayx_cyc_count)\n",
    "    \n",
    "                                if day1_anti_count > day1_cyc_count: #get the polarity of the vortex on day 1\n",
    "                                    day1_vort_pol = 'anti'\n",
    "                                else:\n",
    "                                    day1_vort_pol = 'cyc'\n",
    "                                if dayx_anti_count > dayx_cyc_count: #get the polarity of the vortex on last day\n",
    "                                    dayx_vort_pol = 'anti'\n",
    "                                else:\n",
    "                                    dayx_vort_pol = 'cyc'\n",
    "\n",
    "                                if dayx_vort_freq < 0.85:\n",
    "                                    rejected_reason = \"rejected_vort_consistency_lastday\"\n",
    "                                    region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                                    plms.append(ji)\n",
    "                                    cons.append(con)\n",
    "                                    areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                                    cds.append(cd)\n",
    "                                    pols.append(None)\n",
    "                                    cis.append(contour_CI)\n",
    "                                    rejacc.append(rejected_reason)\n",
    "\n",
    "                                    continue\n",
    "\n",
    "                                if day1_vort_pol != dayx_vort_pol:\n",
    "                                    rejected_reason = \"rejected_polarity_mismach\"\n",
    "                                    region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                                    plms.append(ji)\n",
    "                                    cons.append(con)\n",
    "                                    areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                                    cds.append(cd)\n",
    "                                    pols.append(day1_vort_pol)\n",
    "                                    cis.append(contour_CI)\n",
    "                                    rejacc.append(rejected_reason)\n",
    "\n",
    "                                    continue\n",
    "\n",
    "                                else:\n",
    "                                # If all criteria are passed, add values to output arrays & move on to next local maxima\n",
    "                                # if (dayx_vort_freq >= 0.85) and (day1_vort_pol == dayx_vort_pol): # Final criteria: vort is consistent for 85% of particles on the last day\n",
    "\n",
    "                                    ### for all the ones that passed\n",
    "                                    rejected_reason = 'accepted'\n",
    "                                    region_area = rclv.polygon_area(rclv.project_vertices(con, **args)) #Convert area units from pixels to m^2\n",
    "                                    plms.append(ji)\n",
    "                                    cons.append(con)\n",
    "                                    areas.append(region_area/(10**6)) #convert from m^2 -> km^2\n",
    "                                    cds.append(cd)\n",
    "                                    pols.append(day1_vort_pol)\n",
    "                                    cis.append(contour_CI)\n",
    "                                    rejacc.append(rejected_reason)\n",
    "\n",
    "                                    break # do not need to look at other CDs if the criteria to this point has been met \n",
    "                except TypeError: # too close to land to define an RCLV\n",
    "                    break\n",
    "    # RCLV_data_rejected = [['date', 'reason', 'RCLV_id', 'orientation', 'age_days', 'area_km2', 'center_lon', 'center_lat', 'CD', 'boundary_coords']]\n",
    "\n",
    "                # if rejected_reason:\n",
    "                #     rejected_entry = [short_date, rejected_reason, np.nan, pols,np.nan,areas,traj_lon_array[[1]],traj_lat_array[[0]],cds,]\n",
    "                #     # lon_bounds = traj_lon_array[[round(j) for j in cons[i][:, 1]]]\n",
    "                #     # lat_bounds = traj_lat_array[[round(j) for j in cons[i][:, 0]]]\n",
    "                #     # for j in np.arange(0,len(lon_bounds)):\n",
    "                #     #     rejected_entry.extend([lon_bounds[j],lat_bounds[j]])\n",
    "                #     RCLV_data_rejected.append(rejected_entry)\n",
    "#### save the rclvs in a file\n",
    "    # RCLV_data = [['date', 'RCLV_id', 'orientation', 'age_days', 'area_km2', 'center_lon', 'center_lat', 'CD', 'flag', 'boundary_coords']]\n",
    "\n",
    "            for i in np.arange(0,len(cons)):\n",
    "                # print(len(cons))\n",
    "                temp = [short_date,rejacc[i], np.nan,pols[i],np.nan,areas[i],traj_lon_array[plms[i][1]],traj_lat_array[plms[i][0]],cds[i],cis[i],0] \n",
    "                # Convert from grid indices to lat/lon coordinates for the output\n",
    "                lon_bounds = traj_lon_array[[round(j) for j in cons[i][:, 1]]]\n",
    "                lat_bounds = traj_lat_array[[round(j) for j in cons[i][:, 0]]]\n",
    "                for j in np.arange(0,len(lon_bounds)):\n",
    "                    temp.extend([lon_bounds[j],lat_bounds[j]])\n",
    "                RCLV_data.append(temp) # Add RCLV data to main array\n",
    "                # temp = []\n",
    "            plms,cons,areas,cds,pols,cis, rejacc = [],[],[],[],[],[],[] # arrays to store the output\n",
    "    return RCLV_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0e1c48-d8f3-40d0-adf4-5a5883e6c684",
   "metadata": {},
   "source": [
    "### try"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d23c8527-c617-4a4c-ae9f-fde356412095",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2010-04-23T00:00:00.000000000' '2010-04-24T00:00:00.000000000'\n",
      " '2010-04-25T00:00:00.000000000' '2010-04-26T00:00:00.000000000'\n",
      " '2010-04-27T00:00:00.000000000' '2010-04-28T00:00:00.000000000'\n",
      " '2010-04-29T00:00:00.000000000' '2010-04-30T00:00:00.000000000'\n",
      " '2010-05-01T00:00:00.000000000' '2010-05-02T00:00:00.000000000'\n",
      " '2010-05-03T00:00:00.000000000' '2010-05-04T00:00:00.000000000'\n",
      " '2010-05-05T00:00:00.000000000' '2010-05-06T00:00:00.000000000'\n",
      " '2010-05-07T00:00:00.000000000' '2010-05-08T00:00:00.000000000'\n",
      " '2010-05-09T00:00:00.000000000' '2010-05-10T00:00:00.000000000'\n",
      " '2010-05-11T00:00:00.000000000' '2010-05-12T00:00:00.000000000'\n",
      " '2010-05-13T00:00:00.000000000' '2010-05-14T00:00:00.000000000'\n",
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      "2010-04-27T00:00:00.000000000\n",
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      "2010-04-28T00:00:00.000000000\n",
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      "2010-04-29T00:00:00.000000000\n",
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      "2010-04-30T00:00:00.000000000\n",
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      "2010-05-01T00:00:00.000000000\n",
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      "2010-05-02T00:00:00.000000000\n",
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      "2010-05-03T00:00:00.000000000\n",
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      "variance_t0:  617.2232908894456 variance_tx: 530.9326293734947\n",
      "variance_t0:  572.2193066192054 variance_tx: 488.9639772567743\n",
      "variance_t0:  402.83910189760763 variance_tx: 441.228678204162\n",
      "variance_t0:  239.13451354643752 variance_tx: 221.54291305969295\n",
      "variance_t0:  280.99557363669544 variance_tx: 222.3581516776952\n",
      "variance_t0:  548.0017018010635 variance_tx: 537.3191115468157\n",
      "variance_t0:  528.5328029149173 variance_tx: 549.5827355216111\n",
      "variance_t0:  441.1881916944083 variance_tx: 395.52261559042324\n",
      "variance_t0:  376.2259055041444 variance_tx: 381.35128764070134\n",
      "variance_t0:  590.1889263817897 variance_tx: 636.5021726743355\n",
      "variance_t0:  335.1887238117917 variance_tx: 366.7739411302219\n",
      "variance_t0:  341.4591105093713 variance_tx: 332.93215546180994\n",
      "variance_t0:  306.0121295152508 variance_tx: 274.529689340207\n",
      "variance_t0:  294.864870400149 variance_tx: 274.72985455579544\n",
      "variance_t0:  474.7376873313125 variance_tx: 452.4657861485011\n",
      "variance_t0:  273.60095674456767 variance_tx: 301.72485436951314\n",
      "2010-05-04T00:00:00.000000000\n",
      "2010-05-04T00:00:00\n",
      "variance_t0:  206.829910480062 variance_tx: 563.828716684565\n",
      "variance_t0:  185.11093392673274 variance_tx: 499.18391749445067\n",
      "variance_t0:  3283.2697734511403 variance_tx: 2880.935809925414\n",
      "variance_t0:  3231.4810022064503 variance_tx: 3201.7252256627735\n",
      "variance_t0:  1263.3855539178767 variance_tx: 1180.7895409568127\n",
      "variance_t0:  254.25134910521965 variance_tx: 260.9485813355674\n",
      "variance_t0:  388.43673055462654 variance_tx: 353.1283204355573\n",
      "variance_t0:  501.12765839022796 variance_tx: 554.6977766218006\n",
      "variance_t0:  214.002602205352 variance_tx: 197.7474731119218\n",
      "variance_t0:  288.2280416396503 variance_tx: 211.91008621516409\n",
      "variance_t0:  505.3217122633927 variance_tx: 452.7852170743999\n",
      "variance_t0:  508.76712836690484 variance_tx: 532.119344534926\n",
      "variance_t0:  432.35418170355166 variance_tx: 415.4652668151248\n",
      "variance_t0:  373.833245929384 variance_tx: 322.43977720123064\n",
      "variance_t0:  581.2414915650131 variance_tx: 397.1012781761581\n",
      "variance_t0:  438.2229441711489 variance_tx: 467.506535195954\n",
      "variance_t0:  438.2229441711489 variance_tx: 467.506535195954\n",
      "variance_t0:  332.1192767445143 variance_tx: 358.6724249680907\n",
      "2010-05-05T00:00:00.000000000\n",
      "2010-05-05T00:00:00\n",
      "variance_t0:  3380.671492490309 variance_tx: 3019.0927704361648\n",
      "variance_t0:  2075.3204980540727 variance_tx: 2094.424568391149\n",
      "variance_t0:  269.71889855255995 variance_tx: 294.92929219685385\n",
      "variance_t0:  548.2446104881739 variance_tx: 510.6903216731046\n",
      "variance_t0:  3505.0950389261866 variance_tx: 3319.1358894183927\n",
      "variance_t0:  395.13720732345035 variance_tx: 354.60080415774706\n",
      "variance_t0:  194.61597192468943 variance_tx: 108.0733046097772\n",
      "variance_t0:  316.77034432835404 variance_tx: 365.3916640144222\n",
      "variance_t0:  187.21649029207856 variance_tx: 174.70149722473195\n",
      "variance_t0:  653.253718458547 variance_tx: 551.4077791112634\n",
      "variance_t0:  458.5300647401033 variance_tx: 488.24234535764936\n",
      "variance_t0:  458.5300647401033 variance_tx: 488.24234535764936\n",
      "variance_t0:  184.5427904176397 variance_tx: 184.21740807332543\n",
      "variance_t0:  542.9868892800561 variance_tx: 351.19271445353735\n",
      "variance_t0:  343.0341197209224 variance_tx: 282.8235285299313\n",
      "variance_t0:  235.4746599495912 variance_tx: 246.0895979311472\n",
      "variance_t0:  195.91107379967735 variance_tx: 158.72498065220577\n",
      "variance_t0:  376.51444684923416 variance_tx: 382.8507668453743\n",
      "variance_t0:  468.25462036275053 variance_tx: 501.4619749387911\n",
      "variance_t0:  176.22166560281096 variance_tx: 159.75347960337947\n",
      "variance_t0:  242.09594933850266 variance_tx: 253.00218588086264\n",
      "2010-05-06T00:00:00.000000000\n",
      "2010-05-06T00:00:00\n",
      "variance_t0:  542.5663847031747 variance_tx: 198.19619867879743\n",
      "variance_t0:  4438.640645199116 variance_tx: 4272.709256729306\n",
      "variance_t0:  2104.534767998968 variance_tx: 2155.4372018188656\n",
      "variance_t0:  539.9392680352539 variance_tx: 498.935591537215\n",
      "variance_t0:  261.02857679605125 variance_tx: 292.73290420762123\n",
      "variance_t0:  393.91416059603995 variance_tx: 351.145765994814\n",
      "variance_t0:  347.3100970010883 variance_tx: 400.43840089596824\n",
      "variance_t0:  864.0305272170945 variance_tx: 727.2748674191156\n",
      "variance_t0:  179.13863676607073 variance_tx: 139.20979396297028\n",
      "variance_t0:  671.1902044239265 variance_tx: 583.0972215072086\n",
      "variance_t0:  464.8575081353982 variance_tx: 487.7929329179964\n",
      "variance_t0:  464.8575081353982 variance_tx: 487.7929329179964\n",
      "variance_t0:  363.092738761471 variance_tx: 317.93328603370617\n",
      "variance_t0:  338.8754806034226 variance_tx: 364.34584247638793\n",
      "variance_t0:  215.48458490741896 variance_tx: 168.47521384885565\n",
      "variance_t0:  325.75839240340656 variance_tx: 331.02066716987053\n",
      "variance_t0:  347.4351478631338 variance_tx: 364.289692205022\n",
      "variance_t0:  568.8071309698615 variance_tx: 615.2793316672276\n",
      "variance_t0:  179.72226747868547 variance_tx: 186.22102853707767\n",
      "variance_t0:  185.37595301155014 variance_tx: 166.65675961357852\n",
      "2010-05-07T00:00:00.000000000\n",
      "2010-05-07T00:00:00\n",
      "variance_t0:  3411.459371765124 variance_tx: 3217.0439263889766\n",
      "variance_t0:  1897.923344228724 variance_tx: 1962.2849804262173\n",
      "variance_t0:  527.9413707335285 variance_tx: 468.59801126430364\n",
      "variance_t0:  282.20170347649747 variance_tx: 314.21886777522957\n",
      "variance_t0:  343.78412316642306 variance_tx: 306.797881063855\n",
      "variance_t0:  203.57200455635189 variance_tx: 241.51624448301342\n",
      "variance_t0:  173.5548908086763 variance_tx: 139.89109952630636\n",
      "variance_t0:  427.26411579931334 variance_tx: 439.68996287872324\n",
      "variance_t0:  481.7559254051475 variance_tx: 502.7252652622411\n",
      "variance_t0:  230.79885770956574 variance_tx: 228.3452790511276\n",
      "variance_t0:  325.38253160759115 variance_tx: 350.5729798637942\n",
      "variance_t0:  355.26132843313485 variance_tx: 326.4618580715257\n",
      "variance_t0:  317.17754025717596 variance_tx: 233.52510018681642\n",
      "variance_t0:  338.0022788574931 variance_tx: 333.1839058585617\n",
      "variance_t0:  318.4165138372393 variance_tx: 335.7454436434151\n",
      "variance_t0:  426.79968127623056 variance_tx: 461.70844364682586\n",
      "variance_t0:  635.6390161838317 variance_tx: 703.3660837990071\n",
      "variance_t0:  282.5725163586476 variance_tx: 301.8791501023476\n",
      "2010-05-08T00:00:00.000000000\n",
      "2010-05-08T00:00:00\n",
      "variance_t0:  3546.276800505529 variance_tx: 3372.139768012622\n",
      "variance_t0:  1686.2467318027716 variance_tx: 1732.8224540773322\n",
      "variance_t0:  512.6015726362617 variance_tx: 450.84843132956246\n",
      "variance_t0:  2539.1804190558373 variance_tx: 2460.0866518495195\n",
      "variance_t0:  372.932078106711 variance_tx: 339.12207733782293\n",
      "variance_t0:  298.1165374214246 variance_tx: 307.53590260779896\n",
      "variance_t0:  175.12636943763067 variance_tx: 142.06064649019882\n",
      "variance_t0:  167.53896068305525 variance_tx: 176.53815772383516\n",
      "variance_t0:  400.77282466580425 variance_tx: 398.7440585944002\n",
      "variance_t0:  299.6892338487842 variance_tx: 326.3469947836116\n",
      "variance_t0:  353.58830765379844 variance_tx: 188.0506367671108\n",
      "variance_t0:  190.2861376949936 variance_tx: 166.97859495386012\n",
      "variance_t0:  219.43052542351793 variance_tx: 202.894712706917\n",
      "variance_t0:  244.95020178023125 variance_tx: 171.45467556714746\n",
      "variance_t0:  400.4512323048617 variance_tx: 435.2887296266732\n",
      "variance_t0:  427.2735829795122 variance_tx: 472.09922031642054\n",
      "variance_t0:  684.1561703350627 variance_tx: 770.4689531939988\n",
      "variance_t0:  684.1561703350627 variance_tx: 770.4689531939988\n",
      "2010-05-09T00:00:00.000000000\n",
      "2010-05-09T00:00:00\n",
      "variance_t0:  3725.244013387666 variance_tx: 3502.7526623507592\n",
      "variance_t0:  3341.403645816323 variance_tx: 3228.2172182580766\n",
      "variance_t0:  279.5555198050815 variance_tx: 269.3809514697711\n",
      "variance_t0:  1058.0133079087725 variance_tx: 1015.0662746070536\n",
      "variance_t0:  312.33379447113305 variance_tx: 272.9536644136815\n",
      "variance_t0:  844.4643196474125 variance_tx: 708.1884675149188\n",
      "variance_t0:  310.55267951068885 variance_tx: 304.2842436318268\n",
      "variance_t0:  246.59320560857998 variance_tx: 249.88185186118542\n",
      "variance_t0:  310.4304563025014 variance_tx: 341.2681210013851\n",
      "variance_t0:  389.0524697697421 variance_tx: 200.5993406704809\n",
      "variance_t0:  230.51784615914772 variance_tx: 214.33981810244862\n",
      "variance_t0:  457.26806396941316 variance_tx: 503.9644048455638\n",
      "variance_t0:  375.38459387464127 variance_tx: 426.9478324176824\n",
      "variance_t0:  371.80805303828697 variance_tx: 287.5323248186301\n",
      "variance_t0:  247.56027959785644 variance_tx: 195.1753063863086\n",
      "variance_t0:  672.0968268224245 variance_tx: 773.5670253222996\n",
      "variance_t0:  381.5427759440643 variance_tx: 442.9603129893247\n",
      "variance_t0:  165.8755642355625 variance_tx: 190.17115535928332\n",
      "2010-05-10T00:00:00.000000000\n",
      "2010-05-10T00:00:00\n",
      "variance_t0:  960.7532132572688 variance_tx: 444.9998198530996\n",
      "variance_t0:  266.5869089540946 variance_tx: 256.24266475097545\n",
      "variance_t0:  1147.1888580217176 variance_tx: 1085.1456198812946\n",
      "variance_t0:  312.55033510798944 variance_tx: 277.42688654852657\n",
      "variance_t0:  770.8159715712142 variance_tx: 647.2413205031957\n",
      "variance_t0:  374.0880689301325 variance_tx: 371.6383958476484\n",
      "variance_t0:  310.4468769722346 variance_tx: 304.3107127069608\n",
      "variance_t0:  241.0440010515114 variance_tx: 257.0028434729576\n",
      "variance_t0:  330.5429676315068 variance_tx: 371.3430412627025\n",
      "variance_t0:  384.81962803866895 variance_tx: 239.81329437603145\n",
      "variance_t0:  365.85329850232637 variance_tx: 288.335167992618\n",
      "variance_t0:  647.5502045203657 variance_tx: 753.1124173580247\n",
      "variance_t0:  349.8380738497635 variance_tx: 410.49201724937456\n",
      "variance_t0:  178.52788694898618 variance_tx: 212.11798306457263\n",
      "2010-05-11T00:00:00.000000000\n",
      "2010-05-11T00:00:00\n",
      "variance_t0:  241.46038242443706 variance_tx: 234.4523243805793\n",
      "variance_t0:  644.2568846224294 variance_tx: 532.1573681524987\n",
      "variance_t0:  1086.6979967755365 variance_tx: 995.698182226461\n",
      "variance_t0:  218.2145043686097 variance_tx: 196.83449638432313\n",
      "variance_t0:  315.93697597462005 variance_tx: 282.83653246151016\n",
      "variance_t0:  200.4794554060328 variance_tx: 193.318457020287\n",
      "variance_t0:  384.02734840201714 variance_tx: 375.49130307443505\n",
      "variance_t0:  232.3291116242668 variance_tx: 253.67408500799937\n",
      "variance_t0:  334.5439325600841 variance_tx: 380.9342835788332\n",
      "variance_t0:  365.0216939470484 variance_tx: 215.96682194178067\n",
      "variance_t0:  329.90150955291637 variance_tx: 255.20966286934967\n",
      "variance_t0:  641.2699614388641 variance_tx: 748.4265358254474\n",
      "variance_t0:  347.67311503973946 variance_tx: 419.0232876675985\n",
      "2010-05-12T00:00:00.000000000\n",
      "2010-05-12T00:00:00\n",
      "variance_t0:  924.5559935939926 variance_tx: 440.12849926891323\n",
      "variance_t0:  258.94980898513404 variance_tx: 252.41256826617638\n",
      "variance_t0:  656.4391832210001 variance_tx: 546.6276399230145\n",
      "variance_t0:  1344.5904601467576 variance_tx: 1259.5707365221108\n",
      "variance_t0:  283.06135444537875 variance_tx: 226.6664870601532\n",
      "variance_t0:  339.134123676766 variance_tx: 389.22223055926975\n",
      "variance_t0:  740.8997395539958 variance_tx: 602.2639002842135\n",
      "variance_t0:  317.80873256576706 variance_tx: 311.6096321425924\n",
      "variance_t0:  387.68928578087565 variance_tx: 371.146957927346\n",
      "variance_t0:  362.39683243527844 variance_tx: 208.34544298492983\n",
      "variance_t0:  318.9188870358387 variance_tx: 242.5196878358179\n",
      "variance_t0:  704.8988156083443 variance_tx: 825.1240101411158\n",
      "variance_t0:  321.03252580691975 variance_tx: 394.7375403273304\n",
      "2010-05-13T00:00:00.000000000\n",
      "2010-05-13T00:00:00\n",
      "variance_t0:  949.1930892736282 variance_tx: 441.86050655492625\n",
      "variance_t0:  251.70088993667213 variance_tx: 252.76221869690016\n",
      "variance_t0:  651.9779812217892 variance_tx: 539.1730311296946\n",
      "variance_t0:  1334.7795865305472 variance_tx: 1265.81348154799\n",
      "variance_t0:  286.59308834070936 variance_tx: 226.65148709309508\n",
      "variance_t0:  315.2199124813309 variance_tx: 305.72992748865363\n",
      "variance_t0:  266.3854087760861 variance_tx: 230.36224738489886\n",
      "variance_t0:  221.9019804154193 variance_tx: 200.57812205235444\n",
      "variance_t0:  269.13367094941987 variance_tx: 194.12108137147922\n",
      "variance_t0:  398.98451042281584 variance_tx: 488.567641559822\n",
      "variance_t0:  326.4075644200882 variance_tx: 405.6474765003945\n",
      "2010-05-14T00:00:00.000000000\n",
      "2010-05-14T00:00:00\n",
      "variance_t0:  219.50364631531028 variance_tx: 226.36380383091384\n",
      "variance_t0:  661.2214283694029 variance_tx: 545.5592457823788\n",
      "variance_t0:  1792.7436327331534 variance_tx: 1719.7866880029428\n",
      "variance_t0:  379.24294027827375 variance_tx: 313.44146150134407\n",
      "variance_t0:  395.78976246092407 variance_tx: 410.1542768392731\n",
      "variance_t0:  189.61571589301775 variance_tx: 175.52920450447877\n",
      "variance_t0:  855.1502056395914 variance_tx: 479.9654101363668\n",
      "variance_t0:  295.5462726780756 variance_tx: 261.5100834344125\n",
      "variance_t0:  246.0429302700285 variance_tx: 175.2977341109074\n",
      "variance_t0:  308.7200590033279 variance_tx: 383.05820423711754\n",
      "variance_t0:  265.77675190931825 variance_tx: 322.49978317960813\n",
      "2010-05-15T00:00:00.000000000\n",
      "2010-05-15T00:00:00\n",
      "variance_t0:  220.75021209612862 variance_tx: 228.215294021705\n",
      "variance_t0:  662.7802148916694 variance_tx: 551.2341025958702\n",
      "variance_t0:  1414.0918714909612 variance_tx: 1348.9383854276834\n",
      "variance_t0:  356.45749523216597 variance_tx: 287.5103426726768\n",
      "variance_t0:  559.6569693474983 variance_tx: 611.7159321685023\n",
      "variance_t0:  350.19260003133195 variance_tx: 334.2693120394545\n",
      "variance_t0:  180.4972629748224 variance_tx: 166.8432634958199\n",
      "variance_t0:  189.79845649602933 variance_tx: 201.32013766603808\n",
      "variance_t0:  213.014128066756 variance_tx: 190.78273226174565\n",
      "variance_t0:  276.53881541568484 variance_tx: 246.31518718374267\n",
      "variance_t0:  287.597436882897 variance_tx: 344.02688773923484\n",
      "2010-05-16T00:00:00.000000000\n",
      "2010-05-16T00:00:00\n",
      "variance_t0:  2398.850874444559 variance_tx: 2365.2250116401838\n",
      "variance_t0:  288.57870373334174 variance_tx: 167.25455524272706\n",
      "variance_t0:  1571.038405641008 variance_tx: 1728.2968143185421\n",
      "variance_t0:  260.2319207787467 variance_tx: 225.48906084893852\n",
      "variance_t0:  410.7221940687806 variance_tx: 383.1539803500475\n",
      "variance_t0:  240.00013398316833 variance_tx: 241.0308357290304\n",
      "variance_t0:  307.5352845334357 variance_tx: 231.8854677935229\n",
      "variance_t0:  672.9122806750617 variance_tx: 737.093434064946\n",
      "variance_t0:  336.808177593934 variance_tx: 327.24837469289594\n",
      "variance_t0:  193.77795783109264 variance_tx: 207.46158874311632\n",
      "variance_t0:  222.08461619087413 variance_tx: 222.59020993739244\n",
      "variance_t0:  195.45751220855723 variance_tx: 219.50286658903153\n",
      "2010-05-17T00:00:00.000000000\n",
      "2010-05-17T00:00:00\n",
      "variance_t0:  2335.1498143609415 variance_tx: 2309.3564601203047\n",
      "variance_t0:  1604.0785569987195 variance_tx: 1717.7338821425374\n",
      "variance_t0:  1345.3577379759834 variance_tx: 1240.2402691447571\n",
      "variance_t0:  456.3851721411784 variance_tx: 331.1299736560833\n",
      "variance_t0:  296.48371857261833 variance_tx: 216.98302216356026\n",
      "variance_t0:  639.7139631107127 variance_tx: 696.1071431638984\n",
      "variance_t0:  353.5978908030866 variance_tx: 343.8109852786525\n",
      "variance_t0:  251.65951679230594 variance_tx: 197.981984670311\n",
      "2010-05-18T00:00:00.000000000\n",
      "2010-05-18T00:00:00\n",
      "variance_t0:  2091.825464719943 variance_tx: 2091.7973126536886\n",
      "variance_t0:  1561.4554737404037 variance_tx: 1625.2101208586823\n",
      "variance_t0:  1418.859776870629 variance_tx: 1268.6265237624482\n",
      "variance_t0:  464.6898869086712 variance_tx: 348.46560433037934\n",
      "variance_t0:  192.69599214479229 variance_tx: 122.21790221182277\n",
      "variance_t0:  408.502247540698 variance_tx: 409.30465276379744\n",
      "variance_t0:  279.4708919866368 variance_tx: 259.9147619760937\n",
      "variance_t0:  240.89474006663374 variance_tx: 264.2175430327782\n",
      "2010-05-19T00:00:00.000000000\n",
      "2010-05-19T00:00:00\n",
      "variance_t0:  581.4569583016486 variance_tx: 341.14964797058616\n",
      "variance_t0:  358.15014035735334 variance_tx: 317.39708181212865\n",
      "variance_t0:  455.63680093431645 variance_tx: 366.5021450189403\n",
      "variance_t0:  199.50598847970173 variance_tx: 129.36511021773177\n",
      "variance_t0:  410.4159625540199 variance_tx: 404.06413278613564\n",
      "variance_t0:  275.92826668949823 variance_tx: 251.20863513406442\n",
      "variance_t0:  264.0898408289813 variance_tx: 313.3385275092905\n",
      "variance_t0:  199.12996052834404 variance_tx: 154.97137636803285\n",
      "2010-05-20T00:00:00.000000000\n",
      "2010-05-20T00:00:00\n",
      "variance_t0:  1961.7971324560122 variance_tx: 1976.2866383109217\n",
      "variance_t0:  466.42441766258537 variance_tx: 297.9944151423321\n",
      "variance_t0:  616.4559696742787 variance_tx: 570.3652197357197\n",
      "variance_t0:  194.93669251062488 variance_tx: 126.9783374262714\n",
      "variance_t0:  280.04145683014565 variance_tx: 261.9120569779058\n",
      "variance_t0:  252.57336393428295 variance_tx: 282.6777008392391\n",
      "variance_t0:  184.40202292064424 variance_tx: 149.12858582405227\n",
      "2010-05-21T00:00:00.000000000\n",
      "2010-05-21T00:00:00\n",
      "variance_t0:  1909.7638542944144 variance_tx: 1939.6915652568798\n",
      "variance_t0:  427.3950016544495 variance_tx: 268.2657645005456\n",
      "variance_t0:  617.7446050953562 variance_tx: 599.4270191077198\n",
      "variance_t0:  321.24195721150437 variance_tx: 369.50148455115385\n",
      "variance_t0:  205.82890458260493 variance_tx: 131.84915974126994\n",
      "variance_t0:  220.30050984475804 variance_tx: 224.62696723826704\n",
      "variance_t0:  338.8117444138495 variance_tx: 268.4755229099353\n",
      "2010-05-22T00:00:00.000000000\n",
      "2010-05-22T00:00:00\n",
      "variance_t0:  1914.227325051566 variance_tx: 1958.1364762122957\n",
      "variance_t0:  376.87775691252494 variance_tx: 228.33800515245375\n",
      "variance_t0:  574.0832652437717 variance_tx: 581.388360107998\n",
      "variance_t0:  293.33969760010905 variance_tx: 342.9374748291158\n",
      "variance_t0:  356.592239765683 variance_tx: 351.76630581774486\n",
      "variance_t0:  202.0662129064429 variance_tx: 128.05500838904484\n",
      "2010-05-23T00:00:00.000000000\n",
      "2010-05-23T00:00:00\n",
      "variance_t0:  1915.251697912814 variance_tx: 1964.423339186817\n",
      "variance_t0:  340.7272530440395 variance_tx: 199.99172011269135\n",
      "variance_t0:  545.0381885921494 variance_tx: 564.9507919873652\n",
      "variance_t0:  233.91099088827903 variance_tx: 275.00398020098754\n",
      "variance_t0:  261.03811982349526 variance_tx: 197.93905620682082\n",
      "2010-05-24T00:00:00.000000000\n",
      "2010-05-24T00:00:00\n",
      "variance_t0:  1296.8598943446877 variance_tx: 1264.7131995414145\n",
      "variance_t0:  534.5521051351959 variance_tx: 553.7210255786403\n",
      "variance_t0:  242.44399169847225 variance_tx: 285.7824679424785\n",
      "variance_t0:  171.77705565959147 variance_tx: 139.5175300612029\n",
      "variance_t0:  291.42402440990156 variance_tx: 220.04887054706828\n",
      "variance_t0:  183.37552752930938 variance_tx: 211.27665514593437\n"
     ]
    }
   ],
   "source": [
    "outputs = LAVD_days_to_RCLV_loop(traj_path, nc_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1b2c253f-a539-4474-855b-1c51f3e257b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "directory_of_LAVD_days = nc_file_path.rsplit('/', 1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6ac8c016-9738-4533-a996-850e836789c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_RCLV_CSV(outputs, directory_of_LAVD_days + '32_day_RCLV_full_newcd.csv') #save RCLV as CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe9aff5-bcac-4571-bdfc-2316c715982c",
   "metadata": {},
   "source": [
    "## post-processing daily RCLVs (adding them into the same file and deleting duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd4253ee-7ca9-4257-ac54-28029c3ea39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/work/bk1377/b382618/LAVD_days_function32_day_RCLV_full_newcd.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4a3d3bc-2ea6-48ea-b1a5-90e4faa9af28",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_file_path = \"/work/bk1377/b382618/LAVD_days_function/20100423_32days_LAVD.nc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c6fec8b-ff40-482e-91be-eb811f07b035",
   "metadata": {},
   "outputs": [],
   "source": [
    "# path = \"/home/b/b382618/try_rclv_fun/32_day_LAVD_eddies.csv\" "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f902133f-c49b-402d-a9ac-fef121970ea6",
   "metadata": {},
   "source": [
    "Open csv file as a pandas df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b277ed0-e9c2-4a03-b2ce-5820862e94d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "### \n",
    "# Step 1: Open and process the file\n",
    "processed_data = []\n",
    "with open(path, 'r') as file:\n",
    "    for line in file:\n",
    "        # Split the line into entries and process\n",
    "        row = line.strip().split(',')\n",
    "        first_9 = row[:11]  # First 9 entries\n",
    "        rest = row[11:]     # Remaining entries\n",
    "        processed_data.append(first_9 + [rest])  # Combine first 9 and rest\n",
    "\n",
    "# Step 4: Create DataFrame with transformed data\n",
    "columns = [f\"Col_{i+1}\" for i in range(11)] + ['Extra_Entries']\n",
    "df = pd.DataFrame(processed_data, columns=columns)\n",
    "\n",
    "# Set first row as column headers\n",
    "df.columns = df.iloc[0].astype(str)\n",
    "df = df[1:].reset_index(drop=True)\n",
    "df.columns.values[-1] = 'Points'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05edd972-8ab1-4045-806a-d9d530b4ecee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>date</th>\n",
       "      <th>reject_accept</th>\n",
       "      <th>RCLV_id</th>\n",
       "      <th>orientation</th>\n",
       "      <th>age_days</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>CD</th>\n",
       "      <th>CI</th>\n",
       "      <th>flag</th>\n",
       "      <th>Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>64.76424653211522</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.02332902563005996</td>\n",
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       "      <td>[200.0, 17.5, 200.03125, 17.46875, 200.03125, ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>61.55094419308734</td>\n",
       "      <td>199.96875</td>\n",
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       "      <td>0.020607925807497065</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>[200.0, 17.5, 200.03125, 17.46875, 200.0, 17.4...</td>\n",
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       "      <th>2</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
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       "      <td>17.46875</td>\n",
       "      <td>0.010057660578586227</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
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       "      <td>0.4613749905198093</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>304.63808417735385</td>\n",
       "      <td>203.78125</td>\n",
       "      <td>20.03125</td>\n",
       "      <td>0.4613749905198093</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>[203.84375, 20.09375, 203.84375, 20.09375, 203...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>16918</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>188.02238814495635</td>\n",
       "      <td>198.34375</td>\n",
       "      <td>32.59375</td>\n",
       "      <td>0.09145865310326246</td>\n",
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       "      <td>0</td>\n",
       "      <td>[198.375, 32.6875, 198.40625, 32.65625, 198.40...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16919</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>176.18544047032546</td>\n",
       "      <td>198.34375</td>\n",
       "      <td>32.59375</td>\n",
       "      <td>0.009769225745521077</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>[198.375, 32.6875, 198.40625, 32.65625, 198.40...</td>\n",
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       "    <tr>\n",
       "      <th>16920</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>50.57951908659911</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.029644482634797093</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>[199.84375, 31.53125, 199.875, 31.5, 199.875, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16921</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>47.980191969730434</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.01938025695657512</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>[199.84375, 31.53125, 199.875, 31.5, 199.875, ...</td>\n",
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       "    <tr>\n",
       "      <th>16922</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>45.92359203205484</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.010314945770268997</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>[199.84375, 31.53125, 199.875, 31.5, 199.875, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16923 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0                     date      reject_accept RCLV_id orientation age_days  \\\n",
       "0      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "1      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "2      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "3      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "4      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "...                    ...                ...     ...         ...      ...   \n",
       "16918  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16919  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16920  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16921  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16922  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "\n",
       "0                area_km2 center_lon center_lat                    CD CI flag  \\\n",
       "0       64.76424653211522  199.96875   17.46875   0.02332902563005996       0   \n",
       "1       61.55094419308734  199.96875   17.46875  0.020607925807497065       0   \n",
       "2      52.650777603291154  199.96875   17.46875  0.010057660578586227       0   \n",
       "3      304.63808417735385  203.78125   20.03125    0.4613749905198093       0   \n",
       "4      304.63808417735385  203.78125   20.03125    0.4613749905198093       0   \n",
       "...                   ...        ...        ...                   ... ..  ...   \n",
       "16918  188.02238814495635  198.34375   32.59375   0.09145865310326246       0   \n",
       "16919  176.18544047032546  198.34375   32.59375  0.009769225745521077       0   \n",
       "16920   50.57951908659911    199.875   31.46875  0.029644482634797093       0   \n",
       "16921  47.980191969730434    199.875   31.46875   0.01938025695657512       0   \n",
       "16922   45.92359203205484    199.875   31.46875  0.010314945770268997       0   \n",
       "\n",
       "0                                                 Points  \n",
       "0      [200.0, 17.5, 200.03125, 17.46875, 200.03125, ...  \n",
       "1      [200.0, 17.5, 200.03125, 17.46875, 200.0, 17.4...  \n",
       "2      [200.0, 17.5, 200.0, 17.5, 200.03125, 17.46875...  \n",
       "3      [203.84375, 20.09375, 203.84375, 20.09375, 203...  \n",
       "4      [203.84375, 20.09375, 203.84375, 20.09375, 203...  \n",
       "...                                                  ...  \n",
       "16918  [198.375, 32.6875, 198.40625, 32.65625, 198.40...  \n",
       "16919  [198.375, 32.6875, 198.40625, 32.65625, 198.40...  \n",
       "16920  [199.84375, 31.53125, 199.875, 31.5, 199.875, ...  \n",
       "16921  [199.84375, 31.53125, 199.875, 31.5, 199.875, ...  \n",
       "16922  [199.84375, 31.53125, 199.875, 31.5, 199.875, ...  \n",
       "\n",
       "[16923 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "041c9d74-f6b6-49b2-a365-cf38badb0f81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25aaaff2-1c30-47f3-8f99-3396b5d1fa68",
   "metadata": {},
   "outputs": [],
   "source": [
    "### open lavd file\n",
    "\n",
    "lavd = xr.open_dataset(nc_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a77332-e1d7-464b-8527-c7a2e378e9f1",
   "metadata": {},
   "source": [
    "### fix datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97345112-a586-4353-b298-bf636deda85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def points_to_polygon(points_list):\n",
    "    # Organize as coordinate pairs\n",
    "    coordinates = [(points_list[i], points_list[i+1]) for i in range(0, len(points_list), 2)]\n",
    "    # Close the polygon if needed\n",
    "    if coordinates[0] != coordinates[-1]:\n",
    "        coordinates.append(coordinates[0])\n",
    "    return Polygon(coordinates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c054cd5-867d-4bd7-abef-f8bad9beadce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Polygon'] = df['Points'].apply(points_to_polygon)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c175a517-15d6-4109-ba9e-8337d09efc8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_194283/127318340.py:2: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  df = df.applymap(lambda x: str(x) if isinstance(x, list) else x) #because there is a list of polygins\n"
     ]
    }
   ],
   "source": [
    "### i currently need to drop duplicates bc my function did not clear the temporary list and soo many of the same eddies are saved\n",
    "df = df.applymap(lambda x: str(x) if isinstance(x, list) else x) #because there is a list of polygins\n",
    "# Then remove duplicates\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "909263f8-38b2-487b-95ba-a3d4a3a907e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "### save as a csv or pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81ec0a62-72b5-4868-b1ee-c18c7d960be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>date</th>\n",
       "      <th>reject_accept</th>\n",
       "      <th>RCLV_id</th>\n",
       "      <th>orientation</th>\n",
       "      <th>age_days</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>CD</th>\n",
       "      <th>CI</th>\n",
       "      <th>flag</th>\n",
       "      <th>Points</th>\n",
       "      <th>Polygon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>64.76424653211522</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.02332902563005996</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['200.0', '17.5', '200.03125', '17.46875', '20...</td>\n",
       "      <td>POLYGON ((200 17.5, 200.03125 17.46875, 200.03...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>61.55094419308734</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.020607925807497065</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['200.0', '17.5', '200.03125', '17.46875', '20...</td>\n",
       "      <td>POLYGON ((200 17.5, 200.03125 17.46875, 200 17...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>52.650777603291154</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.010057660578586227</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['200.0', '17.5', '200.0', '17.5', '200.03125'...</td>\n",
       "      <td>POLYGON ((200 17.5, 200 17.5, 200.03125 17.468...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>304.63808417735385</td>\n",
       "      <td>203.78125</td>\n",
       "      <td>20.03125</td>\n",
       "      <td>0.4613749905198093</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.84375', '20.09375', '203.84375', '20.093...</td>\n",
       "      <td>POLYGON ((203.84375 20.09375, 203.84375 20.093...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2010-04-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>137.28216024949646</td>\n",
       "      <td>199.6875</td>\n",
       "      <td>20.71875</td>\n",
       "      <td>0.03031260508700064</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.75', '20.8125', '199.75', '20.78125', '1...</td>\n",
       "      <td>POLYGON ((199.75 20.8125, 199.75 20.78125, 199...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16918</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>188.02238814495635</td>\n",
       "      <td>198.34375</td>\n",
       "      <td>32.59375</td>\n",
       "      <td>0.09145865310326246</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['198.375', '32.6875', '198.40625', '32.65625'...</td>\n",
       "      <td>POLYGON ((198.375 32.6875, 198.40625 32.65625,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16919</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>176.18544047032546</td>\n",
       "      <td>198.34375</td>\n",
       "      <td>32.59375</td>\n",
       "      <td>0.009769225745521077</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['198.375', '32.6875', '198.40625', '32.65625'...</td>\n",
       "      <td>POLYGON ((198.375 32.6875, 198.40625 32.65625,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16920</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>50.57951908659911</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.029644482634797093</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.84375', '31.53125', '199.875', '31.5', '...</td>\n",
       "      <td>POLYGON ((199.84375 31.53125, 199.875 31.5, 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16921</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>47.980191969730434</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.01938025695657512</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.84375', '31.53125', '199.875', '31.5', '...</td>\n",
       "      <td>POLYGON ((199.84375 31.53125, 199.875 31.5, 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16922</th>\n",
       "      <td>2010-05-24T00:00:00</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>45.92359203205484</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.010314945770268997</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.84375', '31.53125', '199.875', '31.5', '...</td>\n",
       "      <td>POLYGON ((199.84375 31.53125, 199.875 31.5, 19...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13414 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0                     date      reject_accept RCLV_id orientation age_days  \\\n",
       "0      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "1      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "2      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "3      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "6      2010-04-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "...                    ...                ...     ...         ...      ...   \n",
       "16918  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16919  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16920  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16921  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "16922  2010-05-24T00:00:00  rejected_min_area     nan                  nan   \n",
       "\n",
       "0                area_km2 center_lon center_lat                    CD CI flag  \\\n",
       "0       64.76424653211522  199.96875   17.46875   0.02332902563005996       0   \n",
       "1       61.55094419308734  199.96875   17.46875  0.020607925807497065       0   \n",
       "2      52.650777603291154  199.96875   17.46875  0.010057660578586227       0   \n",
       "3      304.63808417735385  203.78125   20.03125    0.4613749905198093       0   \n",
       "6      137.28216024949646   199.6875   20.71875   0.03031260508700064       0   \n",
       "...                   ...        ...        ...                   ... ..  ...   \n",
       "16918  188.02238814495635  198.34375   32.59375   0.09145865310326246       0   \n",
       "16919  176.18544047032546  198.34375   32.59375  0.009769225745521077       0   \n",
       "16920   50.57951908659911    199.875   31.46875  0.029644482634797093       0   \n",
       "16921  47.980191969730434    199.875   31.46875   0.01938025695657512       0   \n",
       "16922   45.92359203205484    199.875   31.46875  0.010314945770268997       0   \n",
       "\n",
       "0                                                 Points  \\\n",
       "0      ['200.0', '17.5', '200.03125', '17.46875', '20...   \n",
       "1      ['200.0', '17.5', '200.03125', '17.46875', '20...   \n",
       "2      ['200.0', '17.5', '200.0', '17.5', '200.03125'...   \n",
       "3      ['203.84375', '20.09375', '203.84375', '20.093...   \n",
       "6      ['199.75', '20.8125', '199.75', '20.78125', '1...   \n",
       "...                                                  ...   \n",
       "16918  ['198.375', '32.6875', '198.40625', '32.65625'...   \n",
       "16919  ['198.375', '32.6875', '198.40625', '32.65625'...   \n",
       "16920  ['199.84375', '31.53125', '199.875', '31.5', '...   \n",
       "16921  ['199.84375', '31.53125', '199.875', '31.5', '...   \n",
       "16922  ['199.84375', '31.53125', '199.875', '31.5', '...   \n",
       "\n",
       "0                                                Polygon  \n",
       "0      POLYGON ((200 17.5, 200.03125 17.46875, 200.03...  \n",
       "1      POLYGON ((200 17.5, 200.03125 17.46875, 200 17...  \n",
       "2      POLYGON ((200 17.5, 200 17.5, 200.03125 17.468...  \n",
       "3      POLYGON ((203.84375 20.09375, 203.84375 20.093...  \n",
       "6      POLYGON ((199.75 20.8125, 199.75 20.78125, 199...  \n",
       "...                                                  ...  \n",
       "16918  POLYGON ((198.375 32.6875, 198.40625 32.65625,...  \n",
       "16919  POLYGON ((198.375 32.6875, 198.40625 32.65625,...  \n",
       "16920  POLYGON ((199.84375 31.53125, 199.875 31.5, 19...  \n",
       "16921  POLYGON ((199.84375 31.53125, 199.875 31.5, 19...  \n",
       "16922  POLYGON ((199.84375 31.53125, 199.875 31.5, 19...  \n",
       "\n",
       "[13414 rows x 13 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "94c8ab1d-22c6-495d-818d-659be5b645f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=df[df['reject_accept']=='accepted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7ec41a4-36cc-4ed1-9dd6-8adfb3fe9c67",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as mcolors\n",
    "\n",
    "gdf = gpd.GeoDataFrame(df, geometry='Polygon')\n",
    "categories = gdf['reject_accept'].unique()\n",
    "color_map = {category: color for category, color in zip(categories, mcolors.TABLEAU_COLORS)}\n",
    "gdf['edge_color'] = gdf['reject_accept'].map(color_map)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "# plt.xlim([198, 205])  \n",
    "# plt.ylim([17, 22])\n",
    "\n",
    "\n",
    "gdf.plot(ax=ax, edgecolor=gdf['edge_color'], facecolor='none', legend = True)\n",
    "for category, color in color_map.items():\n",
    "    ax.plot([], [], color=color, label=category)\n",
    "ax.legend(title=\"Categories\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f685d3bd-8200-4d8e-9636-c614bded8007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf = gpd.GeoDataFrame(df, geometry='Polygon')\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "# plt.xlim([198, 205])  \n",
    "# plt.ylim([17, 22])\n",
    "gdf.plot(ax=ax, edgecolor='black', facecolor='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d28e237-5754-470e-af70-65c98f55864c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3d24476c-c617-416c-a91b-8103d4eb0b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle('/work/bk1377/b382618/LAVD_days_32_days_RCLVs_full.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "15816a15-e30e-43a9-b76d-642f430987ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('/work/bk1377/b382618/LAVD_days_32_days_RCLVs_full.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f52c75-1c59-49ac-8f9a-21b68eaecdbd",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "45aad4f5-a8a9-4784-8559-b640ff92b5f9",
   "metadata": {},
   "source": [
    "## Add group_id and age order for overlapping eddies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "89298eec-d279-422c-8653-0ef394a30cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from shapely.geometry import Polygon\n",
    "# from shapely.strtree import STRtree\n",
    "# import networkx as nx\n",
    "\n",
    "# not using these but should"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5ba5df20-1683-4190-86b1-05126a5e17a4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "group_ids = [-1] * len(df)\n",
    "current_id = 0\n",
    "\n",
    "# Iterate through polygons to assign group IDs\n",
    "for i, poly1 in enumerate(df[\"Polygon\"]):\n",
    "    if group_ids[i] == -1:# If not yet assigned, create a new group\n",
    "        print(i)\n",
    "        group_ids[i] = current_id\n",
    "        for j, poly2 in enumerate(df[\"Polygon\"]):\n",
    "            if i != j and poly1.intersects(poly2):  # Check for overlap\n",
    "                group_ids[j] = current_id\n",
    "        current_id += 1\n",
    "\n",
    "# Add group IDs to the DataFrame\n",
    "df.loc[:, \"group_id\"] = group_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fc342504-db31-4471-a47a-654dd4fc7f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])\n",
    "# Rank polygons within each group based on the 'Date' column\n",
    "df['age_order'] = df.groupby('group_id')[\"date\"].rank(method='dense', ascending=True).astype(int) ## ascending=False for it being backwards trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d565cf5-eb7c-4f9a-bcbd-a96c6dccbe02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>reject_accept</th>\n",
       "      <th>RCLV_id</th>\n",
       "      <th>orientation</th>\n",
       "      <th>age_days</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>CD</th>\n",
       "      <th>CI</th>\n",
       "      <th>flag</th>\n",
       "      <th>Points</th>\n",
       "      <th>Polygon</th>\n",
       "      <th>group_id</th>\n",
       "      <th>age_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>64.76424653211522</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.02332902563005996</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['200.0', '17.5', '200.03125', '17.46875', '20...</td>\n",
       "      <td>POLYGON ((200 17.5, 200.03125 17.46875, 200.03...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>61.55094419308734</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.020607925807497065</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['200.0', '17.5', '200.03125', '17.46875', '20...</td>\n",
       "      <td>POLYGON ((200 17.5, 200.03125 17.46875, 200 17...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>52.650777603291154</td>\n",
       "      <td>199.96875</td>\n",
       "      <td>17.46875</td>\n",
       "      <td>0.010057660578586227</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['200.0', '17.5', '200.0', '17.5', '200.03125'...</td>\n",
       "      <td>POLYGON ((200 17.5, 200 17.5, 200.03125 17.468...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>304.63808417735385</td>\n",
       "      <td>203.78125</td>\n",
       "      <td>20.03125</td>\n",
       "      <td>0.4613749905198093</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.84375', '20.09375', '203.84375', '20.093...</td>\n",
       "      <td>POLYGON ((203.84375 20.09375, 203.84375 20.093...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>137.28216024949646</td>\n",
       "      <td>199.6875</td>\n",
       "      <td>20.71875</td>\n",
       "      <td>0.03031260508700064</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.75', '20.8125', '199.75', '20.78125', '1...</td>\n",
       "      <td>POLYGON ((199.75 20.8125, 199.75 20.78125, 199...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16918</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>188.02238814495635</td>\n",
       "      <td>198.34375</td>\n",
       "      <td>32.59375</td>\n",
       "      <td>0.09145865310326246</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['198.375', '32.6875', '198.40625', '32.65625'...</td>\n",
       "      <td>POLYGON ((198.375 32.6875, 198.40625 32.65625,...</td>\n",
       "      <td>792</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16919</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>176.18544047032546</td>\n",
       "      <td>198.34375</td>\n",
       "      <td>32.59375</td>\n",
       "      <td>0.009769225745521077</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['198.375', '32.6875', '198.40625', '32.65625'...</td>\n",
       "      <td>POLYGON ((198.375 32.6875, 198.40625 32.65625,...</td>\n",
       "      <td>792</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16920</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>50.57951908659911</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.029644482634797093</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.84375', '31.53125', '199.875', '31.5', '...</td>\n",
       "      <td>POLYGON ((199.84375 31.53125, 199.875 31.5, 19...</td>\n",
       "      <td>217</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16921</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>47.980191969730434</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.01938025695657512</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.84375', '31.53125', '199.875', '31.5', '...</td>\n",
       "      <td>POLYGON ((199.84375 31.53125, 199.875 31.5, 19...</td>\n",
       "      <td>217</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16922</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>45.92359203205484</td>\n",
       "      <td>199.875</td>\n",
       "      <td>31.46875</td>\n",
       "      <td>0.010314945770268997</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['199.84375', '31.53125', '199.875', '31.5', '...</td>\n",
       "      <td>POLYGON ((199.84375 31.53125, 199.875 31.5, 19...</td>\n",
       "      <td>217</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13414 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0           date      reject_accept RCLV_id orientation age_days  \\\n",
       "0     2010-04-24  rejected_min_area     nan                  nan   \n",
       "1     2010-04-24  rejected_min_area     nan                  nan   \n",
       "2     2010-04-24  rejected_min_area     nan                  nan   \n",
       "3     2010-04-24  rejected_min_area     nan                  nan   \n",
       "6     2010-04-24  rejected_min_area     nan                  nan   \n",
       "...          ...                ...     ...         ...      ...   \n",
       "16918 2010-05-24  rejected_min_area     nan                  nan   \n",
       "16919 2010-05-24  rejected_min_area     nan                  nan   \n",
       "16920 2010-05-24  rejected_min_area     nan                  nan   \n",
       "16921 2010-05-24  rejected_min_area     nan                  nan   \n",
       "16922 2010-05-24  rejected_min_area     nan                  nan   \n",
       "\n",
       "0                area_km2 center_lon center_lat                    CD CI flag  \\\n",
       "0       64.76424653211522  199.96875   17.46875   0.02332902563005996       0   \n",
       "1       61.55094419308734  199.96875   17.46875  0.020607925807497065       0   \n",
       "2      52.650777603291154  199.96875   17.46875  0.010057660578586227       0   \n",
       "3      304.63808417735385  203.78125   20.03125    0.4613749905198093       0   \n",
       "6      137.28216024949646   199.6875   20.71875   0.03031260508700064       0   \n",
       "...                   ...        ...        ...                   ... ..  ...   \n",
       "16918  188.02238814495635  198.34375   32.59375   0.09145865310326246       0   \n",
       "16919  176.18544047032546  198.34375   32.59375  0.009769225745521077       0   \n",
       "16920   50.57951908659911    199.875   31.46875  0.029644482634797093       0   \n",
       "16921  47.980191969730434    199.875   31.46875   0.01938025695657512       0   \n",
       "16922   45.92359203205484    199.875   31.46875  0.010314945770268997       0   \n",
       "\n",
       "0                                                 Points  \\\n",
       "0      ['200.0', '17.5', '200.03125', '17.46875', '20...   \n",
       "1      ['200.0', '17.5', '200.03125', '17.46875', '20...   \n",
       "2      ['200.0', '17.5', '200.0', '17.5', '200.03125'...   \n",
       "3      ['203.84375', '20.09375', '203.84375', '20.093...   \n",
       "6      ['199.75', '20.8125', '199.75', '20.78125', '1...   \n",
       "...                                                  ...   \n",
       "16918  ['198.375', '32.6875', '198.40625', '32.65625'...   \n",
       "16919  ['198.375', '32.6875', '198.40625', '32.65625'...   \n",
       "16920  ['199.84375', '31.53125', '199.875', '31.5', '...   \n",
       "16921  ['199.84375', '31.53125', '199.875', '31.5', '...   \n",
       "16922  ['199.84375', '31.53125', '199.875', '31.5', '...   \n",
       "\n",
       "0                                                Polygon  group_id  age_order  \n",
       "0      POLYGON ((200 17.5, 200.03125 17.46875, 200.03...         0          1  \n",
       "1      POLYGON ((200 17.5, 200.03125 17.46875, 200 17...         0          1  \n",
       "2      POLYGON ((200 17.5, 200 17.5, 200.03125 17.468...         0          1  \n",
       "3      POLYGON ((203.84375 20.09375, 203.84375 20.093...         1          1  \n",
       "6      POLYGON ((199.75 20.8125, 199.75 20.78125, 199...         2          1  \n",
       "...                                                  ...       ...        ...  \n",
       "16918  POLYGON ((198.375 32.6875, 198.40625 32.65625,...       792         14  \n",
       "16919  POLYGON ((198.375 32.6875, 198.40625 32.65625,...       792         14  \n",
       "16920  POLYGON ((199.84375 31.53125, 199.875 31.5, 19...       217         20  \n",
       "16921  POLYGON ((199.84375 31.53125, 199.875 31.5, 19...       217         20  \n",
       "16922  POLYGON ((199.84375 31.53125, 199.875 31.5, 19...       217         20  \n",
       "\n",
       "[13414 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c77a01c5-e495-4fd9-98de-42a2c8d633c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle('/work/bk1377/b382618/LAVD_days_32_days_RCLVs_full_rejects_newcd.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ad567566-298e-4817-b034-db3feb1c1d1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>reject_accept</th>\n",
       "      <th>RCLV_id</th>\n",
       "      <th>orientation</th>\n",
       "      <th>age_days</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>CD</th>\n",
       "      <th>CI</th>\n",
       "      <th>flag</th>\n",
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       "      <th>Polygon</th>\n",
       "      <th>group_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>rejected_min_area</td>\n",
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       "      <th>7840</th>\n",
       "      <td>2010-05-08</td>\n",
       "      <td>rejected_min_area</td>\n",
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       "      <td>['203.875', '19.96875', '203.875', '19.9375', ...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7841</th>\n",
       "      <td>2010-05-08</td>\n",
       "      <td>rejected_min_area</td>\n",
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       "      <td>nan</td>\n",
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       "      <th>8409</th>\n",
       "      <td>2010-05-09</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>36.84013760170746</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030428492135175816</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.9375', ...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.9375, 2...</td>\n",
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       "    <tr>\n",
       "      <th>8410</th>\n",
       "      <td>2010-05-09</td>\n",
       "      <td>rejected_min_area</td>\n",
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       "      <td>rejected_min_area</td>\n",
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       "      <th>8965</th>\n",
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       "      <td>rejected_min_area</td>\n",
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       "    <tr>\n",
       "      <th>8966</th>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>rejected_min_area</td>\n",
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       "    <tr>\n",
       "      <th>8967</th>\n",
       "      <td>2010-05-10</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
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       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.009057400297559267</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9512</th>\n",
       "      <td>2010-05-11</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>35.101454836569786</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030244454837172016</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.9375', ...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.9375, 2...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9513</th>\n",
       "      <td>2010-05-11</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>32.599209208485604</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.01978963976974978</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9514</th>\n",
       "      <td>2010-05-11</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>21.920417063377382</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.010312951991287523</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10065</th>\n",
       "      <td>2010-05-12</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>34.58580986962128</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02960844465986504</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10066</th>\n",
       "      <td>2010-05-12</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>32.13662597423267</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.019881571847903123</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10067</th>\n",
       "      <td>2010-05-12</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>30.224623127322673</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.010153669369377153</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10603</th>\n",
       "      <td>2010-05-13</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>32.96350590383387</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02958405205199544</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10604</th>\n",
       "      <td>2010-05-13</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>31.037549126550196</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.020755036625079205</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10605</th>\n",
       "      <td>2010-05-13</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>29.15080914355421</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.01009204076270937</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11140</th>\n",
       "      <td>2010-05-14</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>31.633921619269373</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030125210672132467</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11141</th>\n",
       "      <td>2010-05-14</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>29.521131982640505</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02007758326928624</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>2010-05-14</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>20.43105314877987</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.01020068504803029</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11699</th>\n",
       "      <td>2010-05-15</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>30.285879086524726</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.029288830573098425</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11700</th>\n",
       "      <td>2010-05-15</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>28.52896380659175</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.0204080588375169</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11701</th>\n",
       "      <td>2010-05-15</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>26.804704472355365</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.009810521467590882</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12258</th>\n",
       "      <td>2010-05-16</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>31.136696973345042</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.029693769144160914</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12259</th>\n",
       "      <td>2010-05-16</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>28.570278758745193</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.019514024436831572</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12260</th>\n",
       "      <td>2010-05-16</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>26.689481395917177</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.009950151436676723</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12817</th>\n",
       "      <td>2010-05-17</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>30.547485697069167</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030775143774549752</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12818</th>\n",
       "      <td>2010-05-17</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>28.02088673219013</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.0209048301375906</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12819</th>\n",
       "      <td>2010-05-17</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>25.96430069656062</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.010524403576790912</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13376</th>\n",
       "      <td>2010-05-18</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>29.35902557221341</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.029254401179382244</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13377</th>\n",
       "      <td>2010-05-18</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>27.280467792750834</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.020552297928881406</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13378</th>\n",
       "      <td>2010-05-18</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>25.245031491189717</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.009819049450836042</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13929</th>\n",
       "      <td>2010-05-19</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>30.511879688855885</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030541054605835553</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13930</th>\n",
       "      <td>2010-05-19</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>27.450812685198546</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02099856005309837</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13931</th>\n",
       "      <td>2010-05-19</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>24.849853668070793</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.009735558074087106</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14484</th>\n",
       "      <td>2010-05-20</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>32.93876164742422</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.029720043462092293</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14485</th>\n",
       "      <td>2010-05-20</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>28.437962956083535</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.020382391568962984</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14486</th>\n",
       "      <td>2010-05-20</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>25.19113789677</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.00978379745214744</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15030</th>\n",
       "      <td>2010-05-21</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>34.574755543420075</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.03059105785948564</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15031</th>\n",
       "      <td>2010-05-21</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>28.78190170144248</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02019787387170177</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15032</th>\n",
       "      <td>2010-05-21</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>25.16954811752844</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.009592573851640996</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15586</th>\n",
       "      <td>2010-05-22</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>34.87367672750163</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.029631923457886452</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15587</th>\n",
       "      <td>2010-05-22</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>29.385600901595353</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02099188603633882</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15588</th>\n",
       "      <td>2010-05-22</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>25.09683714949584</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.010259259358208084</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16134</th>\n",
       "      <td>2010-05-23</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>37.47337497770929</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030014616388945075</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16135</th>\n",
       "      <td>2010-05-23</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>30.263478536855697</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.02068750519875754</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16136</th>\n",
       "      <td>2010-05-23</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>25.301499180501224</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.010816745928083814</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16703</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>43.50400978008413</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.030606437841181753</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16704</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>31.908124739841224</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.01973278249665755</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16705</th>\n",
       "      <td>2010-05-24</td>\n",
       "      <td>rejected_min_area</td>\n",
       "      <td>nan</td>\n",
       "      <td></td>\n",
       "      <td>nan</td>\n",
       "      <td>26.131399632631542</td>\n",
       "      <td>203.875</td>\n",
       "      <td>19.96875</td>\n",
       "      <td>0.010692040592843637</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>['203.875', '19.96875', '203.875', '19.96875',...</td>\n",
       "      <td>POLYGON ((203.875 19.96875, 203.875 19.96875, ...</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0           date      reject_accept RCLV_id orientation age_days  \\\n",
       "3     2010-04-24  rejected_min_area     nan                  nan   \n",
       "7840  2010-05-08  rejected_min_area     nan                  nan   \n",
       "7841  2010-05-08  rejected_min_area     nan                  nan   \n",
       "8409  2010-05-09  rejected_min_area     nan                  nan   \n",
       "8410  2010-05-09  rejected_min_area     nan                  nan   \n",
       "8411  2010-05-09  rejected_min_area     nan                  nan   \n",
       "8965  2010-05-10  rejected_min_area     nan                  nan   \n",
       "8966  2010-05-10  rejected_min_area     nan                  nan   \n",
       "8967  2010-05-10  rejected_min_area     nan                  nan   \n",
       "9512  2010-05-11  rejected_min_area     nan                  nan   \n",
       "9513  2010-05-11  rejected_min_area     nan                  nan   \n",
       "9514  2010-05-11  rejected_min_area     nan                  nan   \n",
       "10065 2010-05-12  rejected_min_area     nan                  nan   \n",
       "10066 2010-05-12  rejected_min_area     nan                  nan   \n",
       "10067 2010-05-12  rejected_min_area     nan                  nan   \n",
       "10603 2010-05-13  rejected_min_area     nan                  nan   \n",
       "10604 2010-05-13  rejected_min_area     nan                  nan   \n",
       "10605 2010-05-13  rejected_min_area     nan                  nan   \n",
       "11140 2010-05-14  rejected_min_area     nan                  nan   \n",
       "11141 2010-05-14  rejected_min_area     nan                  nan   \n",
       "11142 2010-05-14  rejected_min_area     nan                  nan   \n",
       "11699 2010-05-15  rejected_min_area     nan                  nan   \n",
       "11700 2010-05-15  rejected_min_area     nan                  nan   \n",
       "11701 2010-05-15  rejected_min_area     nan                  nan   \n",
       "12258 2010-05-16  rejected_min_area     nan                  nan   \n",
       "12259 2010-05-16  rejected_min_area     nan                  nan   \n",
       "12260 2010-05-16  rejected_min_area     nan                  nan   \n",
       "12817 2010-05-17  rejected_min_area     nan                  nan   \n",
       "12818 2010-05-17  rejected_min_area     nan                  nan   \n",
       "12819 2010-05-17  rejected_min_area     nan                  nan   \n",
       "13376 2010-05-18  rejected_min_area     nan                  nan   \n",
       "13377 2010-05-18  rejected_min_area     nan                  nan   \n",
       "13378 2010-05-18  rejected_min_area     nan                  nan   \n",
       "13929 2010-05-19  rejected_min_area     nan                  nan   \n",
       "13930 2010-05-19  rejected_min_area     nan                  nan   \n",
       "13931 2010-05-19  rejected_min_area     nan                  nan   \n",
       "14484 2010-05-20  rejected_min_area     nan                  nan   \n",
       "14485 2010-05-20  rejected_min_area     nan                  nan   \n",
       "14486 2010-05-20  rejected_min_area     nan                  nan   \n",
       "15030 2010-05-21  rejected_min_area     nan                  nan   \n",
       "15031 2010-05-21  rejected_min_area     nan                  nan   \n",
       "15032 2010-05-21  rejected_min_area     nan                  nan   \n",
       "15586 2010-05-22  rejected_min_area     nan                  nan   \n",
       "15587 2010-05-22  rejected_min_area     nan                  nan   \n",
       "15588 2010-05-22  rejected_min_area     nan                  nan   \n",
       "16134 2010-05-23  rejected_min_area     nan                  nan   \n",
       "16135 2010-05-23  rejected_min_area     nan                  nan   \n",
       "16136 2010-05-23  rejected_min_area     nan                  nan   \n",
       "16703 2010-05-24  rejected_min_area     nan                  nan   \n",
       "16704 2010-05-24  rejected_min_area     nan                  nan   \n",
       "16705 2010-05-24  rejected_min_area     nan                  nan   \n",
       "\n",
       "0                area_km2 center_lon center_lat                    CD CI flag  \\\n",
       "3      304.63808417735385  203.78125   20.03125    0.4613749905198093       0   \n",
       "7840    36.44801163980055    203.875   19.96875  0.019798446968822218       0   \n",
       "7841   33.778669663143155    203.875   19.96875  0.009226720499665287       0   \n",
       "8409    36.84013760170746    203.875   19.96875  0.030428492135175816       0   \n",
       "8410    34.16142430203819    203.875   19.96875   0.01971062444392273       0   \n",
       "8411    32.34302096718454    203.875   19.96875   0.01005781326610629       0   \n",
       "8965   35.203524389986995    203.875   19.96875   0.03037169543437752       0   \n",
       "8966   33.111271486583234    203.875   19.96875  0.020629572451361714       0   \n",
       "8967   23.317446839458942    203.875   19.96875  0.009057400297559267       0   \n",
       "9512   35.101454836569786    203.875   19.96875  0.030244454837172016       0   \n",
       "9513   32.599209208485604    203.875   19.96875   0.01978963976974978       0   \n",
       "9514   21.920417063377382    203.875   19.96875  0.010312951991287523       0   \n",
       "10065   34.58580986962128    203.875   19.96875   0.02960844465986504       0   \n",
       "10066   32.13662597423267    203.875   19.96875  0.019881571847903123       0   \n",
       "10067  30.224623127322673    203.875   19.96875  0.010153669369377153       0   \n",
       "10603   32.96350590383387    203.875   19.96875   0.02958405205199544       0   \n",
       "10604  31.037549126550196    203.875   19.96875  0.020755036625079205       0   \n",
       "10605   29.15080914355421    203.875   19.96875   0.01009204076270937       0   \n",
       "11140  31.633921619269373    203.875   19.96875  0.030125210672132467       0   \n",
       "11141  29.521131982640505    203.875   19.96875   0.02007758326928624       0   \n",
       "11142   20.43105314877987    203.875   19.96875   0.01020068504803029       0   \n",
       "11699  30.285879086524726    203.875   19.96875  0.029288830573098425       0   \n",
       "11700   28.52896380659175    203.875   19.96875    0.0204080588375169       0   \n",
       "11701  26.804704472355365    203.875   19.96875  0.009810521467590882       0   \n",
       "12258  31.136696973345042    203.875   19.96875  0.029693769144160914       0   \n",
       "12259  28.570278758745193    203.875   19.96875  0.019514024436831572       0   \n",
       "12260  26.689481395917177    203.875   19.96875  0.009950151436676723       0   \n",
       "12817  30.547485697069167    203.875   19.96875  0.030775143774549752       0   \n",
       "12818   28.02088673219013    203.875   19.96875    0.0209048301375906       0   \n",
       "12819   25.96430069656062    203.875   19.96875  0.010524403576790912       0   \n",
       "13376   29.35902557221341    203.875   19.96875  0.029254401179382244       0   \n",
       "13377  27.280467792750834    203.875   19.96875  0.020552297928881406       0   \n",
       "13378  25.245031491189717    203.875   19.96875  0.009819049450836042       0   \n",
       "13929  30.511879688855885    203.875   19.96875  0.030541054605835553       0   \n",
       "13930  27.450812685198546    203.875   19.96875   0.02099856005309837       0   \n",
       "13931  24.849853668070793    203.875   19.96875  0.009735558074087106       0   \n",
       "14484   32.93876164742422    203.875   19.96875  0.029720043462092293       0   \n",
       "14485  28.437962956083535    203.875   19.96875  0.020382391568962984       0   \n",
       "14486      25.19113789677    203.875   19.96875   0.00978379745214744       0   \n",
       "15030  34.574755543420075    203.875   19.96875   0.03059105785948564       0   \n",
       "15031   28.78190170144248    203.875   19.96875   0.02019787387170177       0   \n",
       "15032   25.16954811752844    203.875   19.96875  0.009592573851640996       0   \n",
       "15586   34.87367672750163    203.875   19.96875  0.029631923457886452       0   \n",
       "15587  29.385600901595353    203.875   19.96875   0.02099188603633882       0   \n",
       "15588   25.09683714949584    203.875   19.96875  0.010259259358208084       0   \n",
       "16134   37.47337497770929    203.875   19.96875  0.030014616388945075       0   \n",
       "16135  30.263478536855697    203.875   19.96875   0.02068750519875754       0   \n",
       "16136  25.301499180501224    203.875   19.96875  0.010816745928083814       0   \n",
       "16703   43.50400978008413    203.875   19.96875  0.030606437841181753       0   \n",
       "16704  31.908124739841224    203.875   19.96875   0.01973278249665755       0   \n",
       "16705  26.131399632631542    203.875   19.96875  0.010692040592843637       0   \n",
       "\n",
       "0                                                 Points  \\\n",
       "3      ['203.84375', '20.09375', '203.84375', '20.093...   \n",
       "7840   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "7841   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "8409   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "8410   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "8411   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "8965   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "8966   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "8967   ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "9512   ['203.875', '19.96875', '203.875', '19.9375', ...   \n",
       "9513   ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "9514   ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "10065  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "10066  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "10067  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "10603  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "10604  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "10605  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "11140  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "11141  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "11142  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "11699  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "11700  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "11701  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "12258  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "12259  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "12260  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "12817  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "12818  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "12819  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "13376  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "13377  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "13378  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "13929  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "13930  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "13931  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "14484  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "14485  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "14486  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "15030  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "15031  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "15032  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "15586  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "15587  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "15588  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "16134  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "16135  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "16136  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "16703  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "16704  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "16705  ['203.875', '19.96875', '203.875', '19.96875',...   \n",
       "\n",
       "0                                                Polygon  group_id  age_order  \n",
       "3      POLYGON ((203.84375 20.09375, 203.84375 20.093...         1          1  \n",
       "7840   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          2  \n",
       "7841   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          2  \n",
       "8409   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          3  \n",
       "8410   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          3  \n",
       "8411   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          3  \n",
       "8965   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          4  \n",
       "8966   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          4  \n",
       "8967   POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          4  \n",
       "9512   POLYGON ((203.875 19.96875, 203.875 19.9375, 2...         1          5  \n",
       "9513   POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          5  \n",
       "9514   POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          5  \n",
       "10065  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          6  \n",
       "10066  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          6  \n",
       "10067  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          6  \n",
       "10603  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          7  \n",
       "10604  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          7  \n",
       "10605  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          7  \n",
       "11140  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          8  \n",
       "11141  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          8  \n",
       "11142  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          8  \n",
       "11699  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          9  \n",
       "11700  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          9  \n",
       "11701  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1          9  \n",
       "12258  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         10  \n",
       "12259  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         10  \n",
       "12260  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         10  \n",
       "12817  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         11  \n",
       "12818  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         11  \n",
       "12819  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         11  \n",
       "13376  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         12  \n",
       "13377  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         12  \n",
       "13378  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         12  \n",
       "13929  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         13  \n",
       "13930  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         13  \n",
       "13931  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         13  \n",
       "14484  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         14  \n",
       "14485  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         14  \n",
       "14486  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         14  \n",
       "15030  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         15  \n",
       "15031  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         15  \n",
       "15032  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         15  \n",
       "15586  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         16  \n",
       "15587  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         16  \n",
       "15588  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         16  \n",
       "16134  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         17  \n",
       "16135  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         17  \n",
       "16136  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         17  \n",
       "16703  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         18  \n",
       "16704  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         18  \n",
       "16705  POLYGON ((203.875 19.96875, 203.875 19.96875, ...         1         18  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eddy = df[df['group_id']==1]\n",
    "eddy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "013f3c20-41eb-4fb5-9494-9320a911dd06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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