Basic_oil_model_2022-01-24.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from parcels import FieldSet, ParticleSet, JITParticle, ScipyParticle, AdvectionRK4, ErrorCode, Variable, ParcelsRandom,   DiffusionUniformKh, AdvectionDiffusionM1, AdvectionDiffusionEM\n",
    "from datetime import timedelta\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Particle set\n",
    "\n",
    "class OilParticle(JITParticle):\n",
    "    \"\"\"\n",
    "    This sets up the oil particle with different variables used in the kernels.\n",
    "\n",
    "    The water fraction is the amount of water in the oil droplets from emulsification, and affects the density and the viscosity.\n",
    "    The evaporation fraction affects the viscosity of the oil.\n",
    "    \"\"\"\n",
    "    viscosity = Variable('viscosity', dtype=np.float32, initial=0.053)\n",
    "    density = Variable('density', dtype=np.float32, initial=902)\n",
    "    original_density = Variable('original_density', dtype=np.float32, initial=902)\n",
    "    water_fraction = Variable('water_fraction', dtype=np.float32, initial=0)\n",
    "    evaporation_fraction = Variable('evaporation_fraction', dtype=np.float32, initial=0)\n",
    "    is_emulsified = Variable('is_emulsified', dtype=np.float32, initial=0)\n",
    "    emuls_start_time = Variable('emuls_start_time', dtype=np.float32, initial=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filedir = '/Users/Gomez023/Postdoc/tutorials/parcels_examples/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Casting lon data to np.float32\n",
      "WARNING: Casting lat data to np.float32\n",
      "WARNING: Casting depth data to np.float32\n",
      "WARNING: Trying to initialize a shared grid with different chunking sizes - action prohibited. Replacing requested field_chunksize with grid's master chunksize.\n"
     ]
    }
   ],
   "source": [
    "# Field set\n",
    "\n",
    "fname = filedir + 'GlobCurrent_example_data/*.nc'\n",
    "filenames = {'U': fname, 'V': fname}\n",
    "variables = {'U': 'eastward_eulerian_current_velocity', 'V': 'northward_eulerian_current_velocity'}\n",
    "dimensions = {'U': {'lat': 'lat', 'lon': 'lon', 'time': 'time'}, # In the GlobCurrent data the dimensions are also called 'lon', 'lat' and 'time'\n",
    "              'V': {'lat': 'lat', 'lon': 'lon', 'time': 'time'}}\n",
    "fieldset = FieldSet.from_netcdf(filenames, variables, dimensions)\n",
    "\n",
    "fieldset.add_constant('density_water', 1028)\n",
    "fieldset.add_constant('viscosity_water', 0.0014)\n",
    "fieldset.add_constant('g', 9.81)\n",
    "fieldset.add_constant('wind_threshold', 5)\n",
    "fieldset.add_constant('wind_speed', 6)\n",
    "fieldset.add_constant('sea_surface_temperature', 10)\n",
    "fieldset.add_constant('MixedLayerDepth', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Field set WOC\n",
    "\n",
    "# data_path_v01 = '/data/oceanparcels/input_data/ESA_WorldOceanCirculation/NorthAtlantic/'\n",
    "# fname_v01 = 'runWOC_NATL_4lb_v1_201109*.nc' #runWOC_NATL_4lb_v1_20110901.nc\n",
    "\n",
    "# filenames = {'U': data_path_v01 + fname_v01, 'V': data_path_v01 + fname_v01}\n",
    "# variables = {'U': 'utot', 'V': 'vtot'}\n",
    "# dimensions = {'U': {'lat': 'lat', 'lon': 'lon', 'time': 'time', 'depth': 'depth'},\n",
    "#               'V': {'lat': 'lat', 'lon': 'lon', 'time': 'time', 'depth': 'depth'}}\n",
    "# indices = {'depth': [0]} # choosing 00m depth\n",
    "# fieldset = FieldSet.from_netcdf(filenames, variables, dimensions, indices)\n",
    "\n",
    "# fieldset.add_constant('density_water', 1028)\n",
    "# fieldset.add_constant('viscosity_water', 0.0014)\n",
    "# fieldset.add_constant('g', 9.81)\n",
    "# fieldset.add_constant('wind_threshold', 5)\n",
    "# fieldset.add_constant('wind_speed', 6)\n",
    "# fieldset.add_constant('sea_surface_temperature', 10)\n",
    "# fieldset.add_constant('MixedLayerDepth', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Field set MOI\n",
    "\n",
    "# data_path = '/storage/shared/oceanparcels/input_data/MOi/'\n",
    "# ufiles = sorted(glob(data_path+'psy4v3r1/psy4v3r1-daily_U_*.nc'))\n",
    "# vfiles = [f.replace('_U_', '_V_') for f in ufiles]\n",
    "# wfiles = [f.replace('_U_', '_W_') for f in ufiles]\n",
    "# mesh_mask = data_path + 'domain_ORCA0083-N006/coordinates.nc'\n",
    "\n",
    "# filenames = {'U': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': ufiles},\n",
    "#              'V': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': vfiles},\n",
    "#              'W': {'lon': mesh_mask, 'lat': mesh_mask, 'depth': wfiles[0], 'data': wfiles}}\n",
    "# variables = {'U': 'vozocrtx', 'V': 'vomecrty', 'W': 'vovecrtz'}\n",
    "# dimensions = {'U': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},\n",
    "#               'V': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'},\n",
    "#               'W': {'lon': 'glamf', 'lat': 'gphif', 'depth': 'depthw', 'time': 'time_counter'}}\n",
    "\n",
    "# fieldset = FieldSet.from_nemo(filenames, variables, dimensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DeleteParticle(particle, fieldset, time):\n",
    "    particle.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled OilParticleAdvectionRK4 ==> /var/folders/54/mb8qg4_j53d9_q2jnv7n8sjc0000gn/T/parcels-501/850ef885b38eca68022c7e37ea08137a_0.so\n",
      "WARNING: Casting field data to np.float32\n"
     ]
    }
   ],
   "source": [
    "number_particles = 20\n",
    "\n",
    "lonp = 32 * np.ones(number_particles)\n",
    "latp = [i for i in np.linspace(-30, -32, number_particles)]\n",
    "\n",
    "pset = ParticleSet.from_list(fieldset=fieldset, pclass=OilParticle, lon=lonp, lat=latp, depth=np.zeros(number_particles))\n",
    "\n",
    "# kernels = pset.Kernel(Emulsification) + pset.Kernel(ViscosityChanges) + pset.Kernel(AdvectionRK4)\n",
    "kernels = pset.Kernel(AdvectionRK4) \n",
    "\n",
    "output_file = pset.ParticleFile(name=\"basic_model_output_ARK4_test01\", outputdt=timedelta(seconds=600))\n",
    "\n",
    "pset.execute(kernels, runtime=timedelta(days=2), dt=timedelta(seconds=600), recovery={ErrorCode.ErrorOutOfBounds: DeleteParticle}, output_file=output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# --> just advection kernel is ok"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining the emulsification kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Emulsification(particle, fieldset, time):\n",
    "    \"\"\"\n",
    "    Process: Emulsification\n",
    "    \n",
    "    Breaking waves mixes water into oil droplets. This increases the amount of water in the oil, which increases \n",
    "    density as the density of water is higher than the density of oil.\n",
    "    \n",
    "    Emulsification does not occur immediately. From OpenDrift for this oil type, the emulsification mostly begins at \n",
    "    time steps 9, 10, and 11 (93% of the oil). This kernel then gives the chance of emulsification beginning in these\n",
    "    time steps as 60.6%, so approximatelt 93% of the oil particles should emulsify in these time steps. After these \n",
    "    time steps, there is a small chance per time step (3.5%) of emulsification beginning for the remaining particles.\n",
    "    \n",
    "    Once emulsification begins, the water fraction changes based on the equation a + b*x + c*x**2 + d*x**3, with\n",
    "    x = time in hours (total time of simulation minus the time at which emulsification began for the particle), and\n",
    "    the coefficients (a, b, c, d) found using an optimizer. The density is then updated using the water fraction, \n",
    "    the original oil density, and the density of sea water.\n",
    "    \n",
    "    The water fraction cannot go below 0, and cannot exceed 90% (0.9).\n",
    "    \n",
    "    \"\"\"\n",
    "    if particle.is_emulsified == 0:\n",
    "        if time == 9*600 or time == 10*600 or time == 11*600:\n",
    "            if ParcelsRandom.random() < 0.606:\n",
    "                particle.is_emulsified = 1\n",
    "                particle.emuls_start_time = time\n",
    "        elif time > 11*600 and time < 144*600:\n",
    "            if ParcelsRandom.random() < 0.035:\n",
    "                particle.is_emulsified = 1\n",
    "                particle.emuls_start_time = time\n",
    "        else:\n",
    "            print('error not emulsified time')\n",
    "            \n",
    "    elif particle.is_emulsified == 1:\n",
    "    \n",
    "        a = 1.65438927e-02\n",
    "        b = 7.78843895e-02\n",
    "        c = -2.51646082e-03\n",
    "        d = 2.60225731e-05\n",
    "        x = (time - particle.emuls_start_time)/3600\n",
    "        particle.water_fraction = a + b * x + c * x**2 + d * x**3\n",
    "        if particle.water_fraction < 0:\n",
    "            particle.water_fraction = 0\n",
    "        elif particle.water_fraction > 0.9:\n",
    "            particle.water_fraction = 0.9\n",
    "        else:\n",
    "            print('error in water frac')\n",
    "    else:\n",
    "        print('error in is_emulsified')\n",
    "\n",
    "    particle.density = particle.water_fraction * fieldset.density_water + (1 - particle.water_fraction) * particle.original_density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled OilParticleEmulsificationAdvectionRK4 ==> /var/folders/54/mb8qg4_j53d9_q2jnv7n8sjc0000gn/T/parcels-501/cdb521af9921e9ec226cc1e905de9f09_0.so\n"
     ]
    }
   ],
   "source": [
    "number_particles = 20\n",
    "\n",
    "lonp = 32 * np.ones(number_particles)\n",
    "latp = [i for i in np.linspace(-30, -32, number_particles)]\n",
    "\n",
    "pset = ParticleSet.from_list(fieldset=fieldset, pclass=OilParticle, lon=lonp, lat=latp, depth=np.zeros(number_particles))\n",
    "\n",
    "kernels = pset.Kernel(Emulsification) + pset.Kernel(AdvectionRK4) #+ pset.Kernel(ViscosityChanges)\n",
    "\n",
    "output_file = pset.ParticleFile(name=\"basic_model_output_ARK4_and_Emulsification_test01\", outputdt=timedelta(seconds=600))\n",
    "\n",
    "pset.execute(kernels, runtime=timedelta(days=2), dt=timedelta(seconds=600), recovery={ErrorCode.ErrorOutOfBounds: DeleteParticle}, output_file=output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# --> works, trying below with the oil_age variable:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding oil age particle equivalent to :\n",
    "cycle_age = Variable('cycle_age', dtype=np.float32, initial=0.)\n",
    "<br>\n",
    "from : https://nbviewer.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_Argofloats.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Particle set\n",
    "\"\"\"\n",
    "This sets up the oil particle with different variables used in the kernels.\n",
    "\n",
    "The water fraction is the amount of water in the oil droplets from emulsification, and affects the density and the viscosity.\n",
    "The evaporation fraction affects the viscosity of the oil.\n",
    "\"\"\"\n",
    "\n",
    "class OilParticle(JITParticle):\n",
    "    viscosity = Variable('viscosity', dtype=np.float32, initial=0.053)\n",
    "    density = Variable('density', dtype=np.float32, initial=902)\n",
    "    original_density = Variable('original_density', dtype=np.float32, initial=902)\n",
    "    water_fraction = Variable('water_fraction', dtype=np.float32, initial=0)\n",
    "    evaporation_fraction = Variable('evaporation_fraction', dtype=np.float32, initial=0)\n",
    "    is_emulsified = Variable('is_emulsified', dtype=np.float32, initial=0)\n",
    "    emuls_start_time = Variable('emuls_start_time', dtype=np.float32, initial=0)\n",
    "    oil_age = Variable('oil_age', dtype=np.float32, initial=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Emulsification(particle, fieldset, time):\n",
    "    \"\"\"\n",
    "    Process: Emulsification\n",
    "    \n",
    "    Breaking waves mixes water into oil droplets. This increases the amount of water in the oil, which increases \n",
    "    density as the density of water is higher than the density of oil.\n",
    "    \n",
    "    Emulsification does not occur immediately. From OpenDrift for this oil type, the emulsification mostly begins at \n",
    "    time steps 9, 10, and 11 (93% of the oil). This kernel then gives the chance of emulsification beginning in these\n",
    "    time steps as 60.6%, so approximatelt 93% of the oil particles should emulsify in these time steps. After these \n",
    "    time steps, there is a small chance per time step (3.5%) of emulsification beginning for the remaining particles.\n",
    "    \n",
    "    Once emulsification begins, the water fraction changes based on the equation a + b*x + c*x**2 + d*x**3, with\n",
    "    x = time in hours (total time of simulation minus the time at which emulsification began for the particle), and\n",
    "    the coefficients (a, b, c, d) found using an optimizer. The density is then updated using the water fraction, \n",
    "    the original oil density, and the density of sea water.\n",
    "    \n",
    "    The water fraction cannot go below 0, and cannot exceed 90% (0.9).\n",
    "    \n",
    "    \"\"\"\n",
    "    if particle.is_emulsified == 0:\n",
    "        # Checking if timestep = 9 or 10 or 11 (*600, which is the particle.dt, could be substituted by that!)\n",
    "        if particle.oil_age == 9*600 or particle.oil_age == 10*600 or particle.oil_age == 11*600: \n",
    "            if ParcelsRandom.random() < 0.606:\n",
    "                particle.is_emulsified = 1\n",
    "                particle.emuls_start_time = particle.oil_age\n",
    "        elif particle.oil_age > 11*600 and particle.oil_age < 144*600:\n",
    "            if ParcelsRandom.random() < 0.035:\n",
    "                particle.is_emulsified = 1\n",
    "                particle.emuls_start_time = particle.oil_age\n",
    "        else:\n",
    "            print('error not emulsified time')\n",
    "            \n",
    "    elif particle.is_emulsified == 1:\n",
    "    \n",
    "        a = 1.65438927e-02\n",
    "        b = 7.78843895e-02\n",
    "        c = -2.51646082e-03\n",
    "        d = 2.60225731e-05\n",
    "        x = (time - particle.emuls_start_time)/3600\n",
    "        particle.water_fraction = a + b * x + c * x**2 + d * x**3\n",
    "        if particle.water_fraction < 0:\n",
    "            particle.water_fraction = 0\n",
    "        elif particle.water_fraction > 0.9:\n",
    "            particle.water_fraction = 0.9\n",
    "        else:\n",
    "            print('error in water frac')\n",
    "    else:\n",
    "        print('error in is_emulsified')\n",
    "\n",
    "    particle.density = particle.water_fraction * fieldset.density_water + (1 - particle.water_fraction) * particle.original_density\n",
    "    \n",
    "    if particle.state == ErrorCode.Evaluate:\n",
    "        particle.oil_age += particle.dt  # update oil_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled OilParticleEmulsificationAdvectionRK4 ==> /var/folders/54/mb8qg4_j53d9_q2jnv7n8sjc0000gn/T/parcels-501/cf994081563e5dc954f1d772b928fa56_0.so\n"
     ]
    }
   ],
   "source": [
    "number_particles = 20\n",
    "\n",
    "lonp = 32 * np.ones(number_particles)\n",
    "latp = [i for i in np.linspace(-30, -32, number_particles)]\n",
    "\n",
    "pset = ParticleSet.from_list(fieldset=fieldset, pclass=OilParticle, lon=lonp, lat=latp, depth=np.zeros(number_particles))\n",
    "\n",
    "kernels = pset.Kernel(Emulsification) + pset.Kernel(AdvectionRK4) #+ pset.Kernel(ViscosityChanges)\n",
    "\n",
    "output_file = pset.ParticleFile(name=\"basic_model_output_ARK4_and_Emulsification_test02\", outputdt=timedelta(seconds=600))\n",
    "\n",
    "pset.execute(kernels, runtime=timedelta(days=2), dt=timedelta(seconds=600), recovery={ErrorCode.ErrorOutOfBounds: DeleteParticle}, output_file=output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now checking with the viscosity kernel too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ViscosityChanges(particle, fieldset, time):\n",
    "    \"\"\"\n",
    "    Process: Viscosity changes\n",
    "    \n",
    "    Viscosity of the oil generally changes due to emulsification and evaporation.\n",
    "    \n",
    "    This kernel simulates how viscosity changes over the simulation time. The coefficients (e, f, g, h) were found\n",
    "    using an optimizer function. y is time in hours.\n",
    "    \n",
    "    The limit of the viscosity has been set to 0.001 as a minimum, and 377 as a maximum.\n",
    "    \"\"\"\n",
    "    \n",
    "    e = 3.07642086e+01 \n",
    "    f = -1.16058678e+01  \n",
    "    g = 9.71084673e-01 \n",
    "    h = -1.19897826e-02\n",
    "    y = time/3600\n",
    "    \n",
    "    particle.viscosity = e + f * y + g * y**2 + h * y**3\n",
    "    \n",
    "    if particle.viscosity < 0.001:\n",
    "        particle.viscosity = 0.001\n",
    "    elif particle.viscosity > 377:\n",
    "        particle.viscosity = 377"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled OilParticleAdvectionRK4EmulsificationViscosityChanges ==> /var/folders/54/mb8qg4_j53d9_q2jnv7n8sjc0000gn/T/parcels-501/7cec83a526ae0aaca1daf895d00e5fea_0.so\n",
      "WARNING: Casting field data to np.float32\n"
     ]
    }
   ],
   "source": [
    "number_particles = 20\n",
    "\n",
    "lonp = 32 * np.ones(number_particles)\n",
    "latp = [i for i in np.linspace(-30, -32, number_particles)]\n",
    "\n",
    "pset = ParticleSet.from_list(fieldset=fieldset, pclass=OilParticle, lon=lonp, lat=latp, depth=np.zeros(number_particles))\n",
    "\n",
    "kernels = pset.Kernel(AdvectionRK4) + pset.Kernel(Emulsification) + pset.Kernel(ViscosityChanges)\n",
    "\n",
    "output_file = pset.ParticleFile(name=\"basic_model_output_ARK4_and_Emulsification_ViscosityChanges_test01\", outputdt=timedelta(seconds=600))\n",
    "\n",
    "pset.execute(kernels, runtime=timedelta(days=2), dt=timedelta(seconds=600), recovery={ErrorCode.ErrorOutOfBounds: DeleteParticle}, output_file=output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# --> works!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking out the outputs:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Emulsification kernel outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "ds_E_01 = xr.open_dataset(\"basic_model_output_ARK4_and_Emulsification_test01.nc\")\n",
    "ds_E_02 = xr.open_dataset(\"basic_model_output_ARK4_and_Emulsification_test02.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (obs: 289, traj: 20)\n",
       "Dimensions without coordinates: obs, traj\n",
       "Data variables:\n",
       "    trajectory            (traj, obs) float64 20.0 20.0 nan ... 39.0 39.0 39.0\n",
       "    time                  (traj, obs) datetime64[ns] 2002-01-01 ... 2002-01-03\n",
       "    lat                   (traj, obs) float32 -30.0 -30.0 nan ... -32.17 -32.17\n",
       "    lon                   (traj, obs) float32 32.0 32.0 nan ... 32.29 32.29\n",
       "    z                     (traj, obs) float32 0.0 0.0 nan nan ... 0.0 0.0 0.0\n",
       "    viscosity             (traj, obs) float32 0.053 0.053 nan ... 0.053 0.053\n",
       "    density               (traj, obs) float32 902.0 902.0 ... 1.004e+03\n",
       "    original_density      (traj, obs) float32 902.0 902.0 nan ... 902.0 902.0\n",
       "    water_fraction        (traj, obs) float32 0.0 0.0 nan ... 0.8093 0.8113\n",
       "    evaporation_fraction  (traj, obs) float32 0.0 0.0 nan nan ... 0.0 0.0 0.0\n",
       "    is_emulsified         (traj, obs) float32 0.0 0.0 nan nan ... 1.0 1.0 1.0\n",
       "    emuls_start_time      (traj, obs) float32 0.0 0.0 nan ... 5.4e+03 5.4e+03\n",
       "Attributes:\n",
       "    feature_type:           trajectory\n",
       "    Conventions:            CF-1.6/CF-1.7\n",
       "    ncei_template_version:  NCEI_NetCDF_Trajectory_Template_v2.0\n",
       "    parcels_version:        2.2.1\n",
       "    parcels_mesh:           spherical</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0d987897-8929-4c9b-8e10-d5a5b1c14961' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d987897-8929-4c9b-8e10-d5a5b1c14961' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 289</li><li><span>traj</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c1615a9d-15f8-4d63-afb6-1edbc7dd766d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c1615a9d-15f8-4d63-afb6-1edbc7dd766d' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e2e90d2d-7e33-46c4-b787-35fcc264429e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2e90d2d-7e33-46c4-b787-35fcc264429e' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>trajectory</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a5a35d48-fc3e-4aeb-bfd8-c8890eca6266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5a35d48-fc3e-4aeb-bfd8-c8890eca6266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5101098-8546-4a60-b51f-73279db2c348' class='xr-var-data-in' type='checkbox'><label for='data-f5101098-8546-4a60-b51f-73279db2c348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Unique identifier for each particle</dd><dt><span>cf_role :</span></dt><dd>trajectory_id</dd></dl></div><div class='xr-var-data'><pre>array([[20., 20., nan, ..., nan, nan, nan],\n",
       "       [21., 21., nan, ..., nan, nan, nan],\n",
       "       [22., 22., 22., ..., 22., 22., 22.],\n",
       "       ...,\n",
       "       [37., 37., 37., ..., 37., 37., 37.],\n",
       "       [38., 38., 38., ..., 38., 38., 38.],\n",
       "       [39., 39., 39., ..., 39., 39., 39.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3baf4e1f-d82e-458c-a929-dc17f4a261bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3baf4e1f-d82e-458c-a929-dc17f4a261bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d62986f0-e96f-4e0b-8936-40ece4deb640' class='xr-var-data-in' type='checkbox'><label for='data-d62986f0-e96f-4e0b-8936-40ece4deb640' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a7282f7-c606-4c61-9056-67c362ddc8c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a7282f7-c606-4c61-9056-67c362ddc8c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a298c046-f365-4ed4-89df-d8ba6d718374' class='xr-var-data-in' type='checkbox'><label for='data-a298c046-f365-4ed4-89df-d8ba6d718374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-30.      , -30.      ,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [-30.105263, -30.105263,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [-30.210526, -30.213636, -30.216728, ..., -30.738686, -30.740742,\n",
       "        -30.742796],\n",
       "       ...,\n",
       "       [-31.789474, -31.791996, -31.794502, ..., -32.057014, -32.057674,\n",
       "        -32.058334],\n",
       "       [-31.894737, -31.897154, -31.899555, ..., -32.122482, -32.122936,\n",
       "        -32.12339 ],\n",
       "       [-32.      , -32.002304, -32.004593, ..., -32.173954, -32.174156,\n",
       "        -32.17436 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b54d8058-ae47-4ca6-bd0c-8b1ff1e21660' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b54d8058-ae47-4ca6-bd0c-8b1ff1e21660' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dc73315-6044-43fb-9335-bc0f4fc99640' class='xr-var-data-in' type='checkbox'><label for='data-4dc73315-6044-43fb-9335-bc0f4fc99640' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[32.      , 32.      ,       nan, ...,       nan,       nan,       nan],\n",
       "       [32.      , 32.      ,       nan, ...,       nan,       nan,       nan],\n",
       "       [32.      , 31.996878, 31.993744, ..., 31.014305, 31.010906, 31.00751 ],\n",
       "       ...,\n",
       "       [32.      , 32.000126, 32.00026 , ..., 32.07842 , 32.07866 , 32.0789  ],\n",
       "       [32.      , 32.000626, 32.001255, ..., 32.18494 , 32.1856  , 32.18626 ],\n",
       "       [32.      , 32.00091 , 32.001827, ..., 32.285065, 32.28606 , 32.287056]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45c5c17d-279d-44e2-9a7f-77a1fca1fcdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45c5c17d-279d-44e2-9a7f-77a1fca1fcdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c4f2b50-687e-4924-a3e2-c1592f9e230f' class='xr-var-data-in' type='checkbox'><label for='data-1c4f2b50-687e-4924-a3e2-c1592f9e230f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viscosity</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-45152025-6b33-44e3-b450-0c5dd94bfcd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45152025-6b33-44e3-b450-0c5dd94bfcd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a353dc1-3fea-4f0d-8e5f-147bc0387f6b' class='xr-var-data-in' type='checkbox'><label for='data-4a353dc1-3fea-4f0d-8e5f-147bc0387f6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>viscosity</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[0.053, 0.053,   nan, ...,   nan,   nan,   nan],\n",
       "       [0.053, 0.053,   nan, ...,   nan,   nan,   nan],\n",
       "       [0.053, 0.053, 0.053, ..., 0.053, 0.053, 0.053],\n",
       "       ...,\n",
       "       [0.053, 0.053, 0.053, ..., 0.053, 0.053, 0.053],\n",
       "       [0.053, 0.053, 0.053, ..., 0.053, 0.053, 0.053],\n",
       "       [0.053, 0.053, 0.053, ..., 0.053, 0.053, 0.053]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-02ab8191-0f50-4cc1-abc6-db1b885d2fa7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02ab8191-0f50-4cc1-abc6-db1b885d2fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e42b572-5b04-44d3-81fa-3ce7d3ea2f73' class='xr-var-data-in' type='checkbox'><label for='data-3e42b572-5b04-44d3-81fa-3ce7d3ea2f73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>density</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[ 902.     ,  902.     ,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [ 902.     ,  902.     ,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.4843 , 1003.72327,\n",
       "        1003.96985],\n",
       "       ...,\n",
       "       [ 902.     ,  902.     ,  902.     , ...,  999.7673 ,  999.8531 ,\n",
       "         999.9442 ],\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.4843 , 1003.72327,\n",
       "        1003.96985],\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.72327, 1003.96985,\n",
       "        1004.224  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>original_density</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f7c44f13-0187-4ad6-bdaf-83a48c21f743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c44f13-0187-4ad6-bdaf-83a48c21f743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae5b7c5f-b070-4bb4-bddf-6d1cfbddaedf' class='xr-var-data-in' type='checkbox'><label for='data-ae5b7c5f-b070-4bb4-bddf-6d1cfbddaedf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>original_density</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[902., 902.,  nan, ...,  nan,  nan,  nan],\n",
       "       [902., 902.,  nan, ...,  nan,  nan,  nan],\n",
       "       [902., 902., 902., ..., 902., 902., 902.],\n",
       "       ...,\n",
       "       [902., 902., 902., ..., 902., 902., 902.],\n",
       "       [902., 902., 902., ..., 902., 902., 902.],\n",
       "       [902., 902., 902., ..., 902., 902., 902.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_fraction</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-06acfa8a-f6a0-47f0-86a7-4f73c914f3e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06acfa8a-f6a0-47f0-86a7-4f73c914f3e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fda020b3-378a-46b2-8ee4-17f7a42998bf' class='xr-var-data-in' type='checkbox'><label for='data-fda020b3-378a-46b2-8ee4-17f7a42998bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>water_fraction</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[0.      , 0.      ,      nan, ...,      nan,      nan,      nan],\n",
       "       [0.      , 0.      ,      nan, ...,      nan,      nan,      nan],\n",
       "       [0.      , 0.      , 0.      , ..., 0.805431, 0.807328, 0.809285],\n",
       "       ...,\n",
       "       [0.      , 0.      , 0.      , ..., 0.775931, 0.776612, 0.777335],\n",
       "       [0.      , 0.      , 0.      , ..., 0.805431, 0.807328, 0.809285],\n",
       "       [0.      , 0.      , 0.      , ..., 0.807328, 0.809285, 0.811302]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>evaporation_fraction</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29e5a005-54a6-4249-bfb4-58010ffadfc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29e5a005-54a6-4249-bfb4-58010ffadfc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aad6384-f51b-41ed-833e-3492e4619c34' class='xr-var-data-in' type='checkbox'><label for='data-0aad6384-f51b-41ed-833e-3492e4619c34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>evaporation_fraction</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>is_emulsified</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-00d58268-18c4-40a8-a461-7d466a21f521' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00d58268-18c4-40a8-a461-7d466a21f521' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d84f8c8c-b522-4057-b3d0-d8b4220dc116' class='xr-var-data-in' type='checkbox'><label for='data-d84f8c8c-b522-4057-b3d0-d8b4220dc116' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>is_emulsified</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>emuls_start_time</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-68bdab92-85c2-4ff4-9b1f-75440d17b0ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68bdab92-85c2-4ff4-9b1f-75440d17b0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-646ca40b-7e2e-4eea-8496-19ac986bbf89' class='xr-var-data-in' type='checkbox'><label for='data-646ca40b-7e2e-4eea-8496-19ac986bbf89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>emuls_start_time</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[    0.,     0.,    nan, ...,    nan,    nan,    nan],\n",
       "       [    0.,     0.,    nan, ...,    nan,    nan,    nan],\n",
       "       [    0.,     0.,     0., ...,  6000.,  6000.,  6000.],\n",
       "       ...,\n",
       "       [    0.,     0.,     0., ..., 20400., 20400., 20400.],\n",
       "       [    0.,     0.,     0., ...,  6000.,  6000.,  6000.],\n",
       "       [    0.,     0.,     0., ...,  5400.,  5400.,  5400.]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76b10101-b53e-4e87-9556-68557beb376c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76b10101-b53e-4e87-9556-68557beb376c' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>feature_type :</span></dt><dd>trajectory</dd><dt><span>Conventions :</span></dt><dd>CF-1.6/CF-1.7</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Trajectory_Template_v2.0</dd><dt><span>parcels_version :</span></dt><dd>2.2.1</dd><dt><span>parcels_mesh :</span></dt><dd>spherical</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (obs: 289, traj: 20)\n",
       "Dimensions without coordinates: obs, traj\n",
       "Data variables:\n",
       "    trajectory            (traj, obs) float64 ...\n",
       "    time                  (traj, obs) datetime64[ns] ...\n",
       "    lat                   (traj, obs) float32 ...\n",
       "    lon                   (traj, obs) float32 ...\n",
       "    z                     (traj, obs) float32 ...\n",
       "    viscosity             (traj, obs) float32 ...\n",
       "    density               (traj, obs) float32 ...\n",
       "    original_density      (traj, obs) float32 ...\n",
       "    water_fraction        (traj, obs) float32 ...\n",
       "    evaporation_fraction  (traj, obs) float32 ...\n",
       "    is_emulsified         (traj, obs) float32 ...\n",
       "    emuls_start_time      (traj, obs) float32 ...\n",
       "Attributes:\n",
       "    feature_type:           trajectory\n",
       "    Conventions:            CF-1.6/CF-1.7\n",
       "    ncei_template_version:  NCEI_NetCDF_Trajectory_Template_v2.0\n",
       "    parcels_version:        2.2.1\n",
       "    parcels_mesh:           spherical"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_E_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/py3_parcels/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:307: UserWarning: The .xlabels_top attribute is deprecated. Please use .top_labels to toggle visibility instead.\n",
      "  warnings.warn('The .xlabels_top attribute is deprecated. Please '\n",
      "/opt/anaconda3/envs/py3_parcels/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:343: UserWarning: The .ylabels_right attribute is deprecated. Please use .right_labels to toggle visibility instead.\n",
      "  warnings.warn('The .ylabels_right attribute is deprecated. Please '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8c32b27908>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "\n",
    "plt.figure(figsize=(12,10)) #(17,5)\n",
    "\n",
    "ax1 = plt.subplot(121, projection=ccrs.PlateCarree())\n",
    "ax1.coastlines(resolution='50m')\n",
    "cc = ax1.scatter(ds_E_01['lon'], ds_E_01['lat'], c = ds_E_01['density'], transform=ccrs.PlateCarree()) #marker='o'\n",
    "# ax1.set_xlim([4, 10])\n",
    "# ax1.set_ylim([52, 59])\n",
    "gl = ax1.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)\n",
    "gl.xlabels_top = False\n",
    "gl.ylabels_right = False\n",
    "gl.xformatter = LONGITUDE_FORMATTER\n",
    "gl.yformatter = LATITUDE_FORMATTER\n",
    "ax1.set_title('ARK4 + Emulsification (time)')\n",
    "plt.colorbar(cc)\n",
    "\n",
    "ax1 = plt.subplot(122, projection=ccrs.PlateCarree())\n",
    "ax1.coastlines(resolution='50m')\n",
    "cc = ax1.scatter(ds_E_02['lon'], ds_E_02['lat'], c = ds_E_02['density'], transform=ccrs.PlateCarree()) #marker='o'\n",
    "# ax1.set_xlim([4, 10])\n",
    "# ax1.set_ylim([52, 59])\n",
    "gl = ax1.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)\n",
    "gl.xlabels_top = False\n",
    "gl.ylabels_right = False\n",
    "gl.xformatter = LONGITUDE_FORMATTER\n",
    "gl.yformatter = LATITUDE_FORMATTER\n",
    "ax1.set_title('ARK4 + Emulsification (oil age)')\n",
    "plt.colorbar(cc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAALJCAYAAAAEWg8/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAADeSklEQVR4nOzdZ3gU1/n38e/Zrt4lQBKo0HsRvRhww733mrjEiZPYqX+nVydxiePEJbbjXnG3sTG2aQLTDZgu0REgod531Xb3PC924ZGFkISNmJF0f65rr92dmZ35Ha1We+vMzBmltUYIIYQQQpiPxegAQgghhBCidVKoCSGEEEKYlBRqQgghhBAmJYWaEEIIIYRJSaEmhBBCCGFSUqgJIYQQQpiUFGpCiB5PKXWrUmpFB5Z7Sin1u2bPv6+UKlJK1Sql4oL3GZ2Qb7tSauapXm9w3d9TSj3axvwblFKfd9K21ymlhnXGuoXoLpSMoyZE96WUygZGAb201g3Npr8IXA80Bm8bgB9prXOD828FbtdaTws+jwQWAIXAtVrrpuB0B7AFCNdap5zi7DOBJYCnxayztdarT/G2bqVZezv4GjtQDUzSWm8+hVleBA5rrX97qtbZxrYcwF4CbchXSqUB+wG71tp7GrZ/NXCN1vqKzt6WEF2V9KgJ0U0Fv3SnAxq4uJVFHtRahwPJQD7w3AnWEwMsAvIIfKk2NZv9C6D4JHNln0TvUIHWOrzF7ZQWad9CEuACthsd5Fu4BMjVWucbtP15wCylVG+Dti+E6UmhJkT3dTOwBngRuOVEC2mt64C3gNEt5yml4gn0am0Hbmzey6KUSgduBP5+KkN3VLDg+6tSalVwl+NHwd2PrymlqpVSXwaLVZRSaUoprZSytXj97a2sVyml/qWUKlZKVSmltiilhgfnvRjc5kBgZ/AllUqpJcH5WinVP/g4RCn1T6VUXnA9K5RSIcF5byulCoPTlx/d/aeUuhO4Afjl0TYFpx9QSp0VfOxUSj2qlCoI3h5VSjmD82YqpQ4rpX4WzH9EKfWdNn6M5wHLmj1f3qxNtUqpyS13Cwfb+AOl1G6lVI1S6i9KqUyl1Orgz/2tYE/d0eUvVEptUkpVBt+rkUfnaa3rCfTmntNGRiF6NCnUhOi+bgZeC97OVUoltbaQUioMuA7Y02JWLIEv8bXAd7XW/hbzHwN+DdSdytAn6VrgJgK9gpnAauAFAtlzgD98g3WeA8wABgLRwDVAWfMFtNa7gKPHVkVrrWe3sp6HgXHAlGCeXwJHf4YLgAFAIrCRwHuE1vqZ4OMHg72HF7Wy3t8AkwgU1qOACUDz3aS9gCgCP5PbgCeCvaKtGcH/LzgJtvtom9rqvZwTbNukYLueIVBgpgLDCfw+oZQaCzwPfA+IA54G5h0tLINygu0QQrRCCjUhuiGl1DSgH/CW1noDgeOQrm+x2M+VUpVADTCNQMHTXCqBYuUF3eJgVqXUZYBNa/1+J8Rvrk+wJ6b5LazZ/Be01nu11lUEip+9WutFwZ6/t4Ex32CbTUAEMJjAcbw5WusjJ7MCpZQF+C5wj9Y6X2vt01qvOnqcoNb6ea11TfD5H4FRSqmoDq7+BuDPWutirXUJ8Ce+/t41Bec3aa0/AWqBQSdYVzSB9/9kPaC1rtZabwe2AZ9rrfc1ex+O/tzvAJ7WWq8N/gxeAhoIFHhH1QRzCCFaIYWaEN3TLQS+PEuDz1/n+N2fD2uto4E0Ar1iLb/MNwM/BxYopY4VPMFC6UHgRx0N07zQIlAUftxs2n1tvLRAax3d4uZuNr+o2eO6Vp6HdzTjUVrrJcDjwBNAkVLqmeDJFCcjnsDxa3tbzlBKWZVS/1BK7VVKVQMHmr2mI/oQOF7wqLzgtKPKWpwI4OHEP4cKAkXpyeroz70f8LMW739qi7wRQOU3yCBEjyCFmhDdTPA4qKuBM4LHQRUCPyHQa3PcLiat9UHgHuDfR4+hajbv38A/gIVHj9MisMsuDfgiuO73gN7BbaW1lql5oQWsAC5sNu0f377V7Tpa3IU2m9brRAtrrf+jtR5HYPfmQAInTZyMUqCewO7Ylq4ncBD/WQR2UaYFp6ujm29n3QUECqCj+ganfRNbCLTvqFM9DMAh4P4WhXao1vqNZssMIfBPgRCiFVKoCdH9XAr4gKEEjmMaTeDL8AsCx60dR2u9kMCX/Z2tzHsQ+DewSCk1iMCurtRm676dQI/KaAJfzKYT3EWYD9wY7NH6Lq0XUSilxiulJqrA8BtuAgWX7yS35ydwbNYjSqk+wW1ODh6bFUFg918ZgcLxby1eXgS0NRbbG8BvlVIJKnCyx++BV08mXzOfAGc0e15C4Di6UzUW3P+Au4I/T6WUClNKXaCUioDAiREEjnVbeIq2J0S3I4WaEN3PLQSO3TqotS48eiOwO+8G1ezMxxYeInC2obPlDK31X4BngcVAvxbrLQf8wecnVdB0QJ/g2YfNb990zK07CPSMlRHoKVt1guUiCRQYFQR2K5YRODHgZP0c2Ap8SeBn9ACBv7kvB9ebD+wgcGZuc88BQ4O7Cj9oZb1/BdYT6A3bSuBkhL9+g3wAHwGDlVJ9ALTWHuB+YGVw+5PafHU7tNbrCfzcHyfw89wD3NpskYuBbK31N+0RFKLbkwFvhRCiBwsOCTJUa32vAdteC9ymtd52urctRFchhZoQQgghhEnJrk8hhBBCCJOSQk0IIYQQwqSkUBNCCCGEMKkTnf3V5cXHx+u0tLRO3Ybb7SYsLKz9Bbuhntx26Nnt78lth57d/p7cdujZ7Ze2d37bN2zYUKq1Tmg5vdsWamlpaaxfv75Tt5Gdnc3MmTM7dRtm1ZPbDj27/T257dCz29+T2w49u/3S9pmdvh2lVF5r02XXpxBCCCGESUmhJoQQQghhUlKoCSGEEEKYlBRqQgghhBAmJYWaEEIIIYRJSaEmhBBCCGFSUqgJIYQQQpiUFGpCCCGEECYlhZoQQgghhElJoSaEEEIIYVJSqAkhhBBCnEBDfYOh2++21/oUQgghhPim/vfUU6wnnU0pUaTnbGfgkGGG5JBCTQghhBAC+OCdN/ki38b6tN7sHDQJi/Yxmo2sWV0rhZoQQgghxOm2dsUXzFt7gE2p6XwVPwB/nIV+eh/X+Vcw5sgGfLk+zrjgz4blk0JNCCGEED3Kgo/eY/nuBral9GFTXDhNY0cQp0u4kM8YX74B545KrAfiGTTiIqonDqTf5NmGZZVCTQghTMDjdlNSUsiBffsoLS6gorqc+voGGvw+vEqj8aMtfjQKAL8CtEJbFDq4Dgt+LHix+PxYtB+LHyxaY0FjRWFTEGazExMeQUpCConJ6UTEp+CKjcUWEoLFcnLnl/l8PpqammhqasLr9X7tXmvd5g3AYrEcu1mt1q/dWywWbDYbdrv92O1k87XU5PNT3+SjvunoffCx14fPr/FrjdagNfj1/39+9H5HiRfb7lKsFoXdqrBZLdgsCptVYbNYcFgthDishDishNqtWCzqW+UVp9bc115ifUkYW/omsi06Df8YC3G6hDP5hDGVW4nZeQhbXhzpfScx9IYbiMrIBCA7O9vQ3FKoCSHEKeRxu1n1RTa79uynqqkJt8OBx+XE47LhcVjxOGy4bQ7cVgduq5Nai4sG5aABB1pZQCVAUgIkdX5WR0UDzop9OMnFqRtw6iYcuhGHbiLEV4/LX4/L24jT24DD24Td6+WTFz/H1uTD1uDH1gjWBvB77fh8dqBzC5OjhZvNbsditaEtNvzKildb8CorTdho0FbqfBbcPis1XkV1I9Q0KWq9Fur8FhqxHSt2v5ENazu8qNNmIdRhJdRhCxRvDish9kAhF+GyE+myERliJyrETqTLTmSIrdnjwPQIlw27VQZo+CbyDuzl9bc/50BUP7b1CWdvn1HQB/roQ1zE54wq2Ub4rmIchxPpP3gaQ25+lLA+fYyOfRwp1IQQ4iTk5+3l8ce2UdGkqAoNoTI8hLIwF6WuECrsIVRYwvA5k2FY8tdep7SPMDyE4iZM1xHmryHGV0poYz0urw+714/d58Ph82Lz+rD5fNi9//+x8oPyW4IlhsYS7JVSGhQatMKnrGgs+C0WfCrQ2+azWGiyKrwWC41WCw02G/U2K412K41WC41WK01WG00WG40WO03KjtsSQqE9AY8KpU6F4FNtf1WE6RoiqCZS1xLh8xDu9RDeVE94Yz2h9XW46hsIqW8gtL6JWIuT3rFpRPfKJDwpGXt0NFopfD4ffr+fusYmSqvrKa2tp7SmjoqaOqpq66ipq6e+vpHG2kYs+LDhx0YDNuXHhg87PlwWHw58uNDENA9oARzNntoc2Jwu7M4QnK4QnCGhuEICjx2uEFwhoTiD966QUOz2QPu/XL+BkaPH0OTz4/NrvD597HGTX9Po9VPX5KOu0Yun0Uddow9P8Fbf5MMTnF7ubuRgmYequiaq6prw+jVtiXTZiAt3EhvmIDbMQVzwPjbMQVy4g9gwJ3FhDmKC81x2a4d+l7uj5555mlxPLDuT49gaG0Fd1mSs2ssAdnK1dwvDinYQtrsSV0kfBow+h8F3X4szJtbo2G2SQk0IIVooKsxn3gcfU1ALFZHhFEeFUBAeSoEzkuq+4762rF03Ek8JcbqYod5aIj11RNbXEV7nIbSujlC3h6gmP8mhiSSnjSA+fShRaek4IiJOWV6fz0dlZSVlZWWUlpZ+7ebxeLADdiAEsFqtREVFERUVTmRkJOGh4YSHf/0WFhaGy+XCYrHgcbvZvnUTe3fvIr+oCK/NSoNV0WB3UO+wU+d0UOtwUONwUmN3UWKLZq8zlGpXKH7VesEQqmuJppLo8lyii2uJanIT1eAhvM6Ds7Ye3A1U1VrZVxdHuS8RZ0QCcYnp9OoXQUKEk4Rw59fu48OdRIXYsVgUWmuampqor6+nrq6O+vr6r93q6upwu914PJ5j9zVFpRR5PPj9/lbzOp1OwsPD8fl85HkrCA8PJyIigtjgzysiIoLw8HBCQkJQ6uR667TW1DX5qK7zUl0fKNyq65oCjz1NVNY1UeFupMzdSLm7kUPlHjYdqqTC3XjCAi/CaSMhMvDzSYx0kRgR+Dn9//vAtOhQ+0nnNROP282LL7zEAW8sB3pFkxMbQcmAiQAk6CKmspih1bvos28/zoNOEsOGM/icn9H7+hnfejf66SSFmhCiR/v8k3l8uT2fkvBY8uPCyIsMo8ARiXfQxGPLROkKelPAOG8uCbXVxNTWEFldQ2RVLb38IfTuM5xeA8aSMHZYp/937vF4KCws/NqtrKwMn893bJnQ0FDi4+MZPHgwMTExREdHH7uFhYWd1JdUaFgY4ydNZfykqWRnZzNz5swOva6mqpK1q1eyc88BKhoaabBZqXc5qHPZqQ5xUe10UumIZK+tFxWOcBrDHcetw64biaKSaF2F3bcHS1Mt9fUeqio9HPF4CHV7iKirp48KpU+fgSRmjCI2cxCuhAQiIyOJjIzscDu11tTX1x9XxLndbmpra6mtrSU/P5/8/Hxqa2tpamo6bh1Wq/VrBe/RAq7l4/DwcKzWQBGrlCLUYSPUYaNXlOuk8lbXeSlzN1DhaaSsNlDIlbkbKalpOHbberiS4poGPI2+49Zht6pAsRvpChZ1zmP3R4u5xMhAIWwGGzeuZdHS9RxxJbI3KYrc6Eiqh00BIFpXMIjNnFe3l4yC3UTuryHc05f+Y2aSeevDpu81a0unFWpKqeeBC4FirfXw4LRY4E0gDTgAXK21rgjO+xVwG+ADfqy1/iw4fRzwIoF/Bj8B7tFHj0QVQoiTsHzJZyzfcICCqCQOxbvYHx5GaUhfyOoLQKwupR97GdlQTFJNJbEV5cSWVtDLG0VS8mhSRk9hu6pi1qVnn5a89fX15Ofnc+jQIY4cOUJhYSFVVVXH5kdERNCrVy8GDBhAfHw88fHxxMXFERoaelrytVTb4GXzoUo25lWwraCK7QUhHK5IOzY/McLJkN6RDA6PIDMhjMyEcDISwnHRxOb169i6PZfyhnrcdgdul5OaUCdVLieVTifF9mR2usKoCQ07brtK+4mgmuj6cmK2fEa0101Uo5vIeg/hHjdhHg/hnjoSvFZSEzJJyhhJbOZgwpNTsNgCX4NKKUJCQggJCTlh+5oXqg0NDdTU1Bwr4lo+rqio4NChQ3g8nlbXFRoaeqx4a+vebre3+nqlFFGhdqJCW5/fkrvBS3FNA8XV9ZTUNlBc3XDsvrimnsMVHr46WEGZu7GVbUG4HZI3LT+uhy4xwhUs7AKPQxynZrfrlk0b+WzRGgpdseQnhLMvMozDzij8Y6cCkKSPMIZVDKjJI7kgj6i8WqL96WSOmUm/y36NKy7ulOQwg87sUXsReBx4udm0+4DFWut/KKXuCz7/P6XUUOBaYBjQB1iklBqotfYB/wXuBNYQKNTmAAs6MbcQohvwuN289sqr7POEcTgxhl1xYRx0JqKzklDaR28KGKg3cZa7iN7lxcQfKSSuykli3Ej6jZ9O0syJ2Fr50t7RSWeAaa2PfbkfvRUVFR2bHxcXR2pqKuPHj6dXr1706tWL8PDwTsnS0byHK+rYkFdx7JZbWI1fB77Y0+PCGJUazfUT+zK0dyTD+kSREHGinhkHk8+YxeQzZrW73bwDe1m5fAUFpeXUWm14ggVdtctBpctFpT2BQ/Y0Kp1h+COPLxoCu10riNm9kuic4G7Xeg8RdR7Cgr10MY0+ksOTSckYRXz/YUSmpWN1fj270+nE6XQSHx/fZl6v13usV+5oMdfyvri4mNraWlrrg3A6nR0q6JxOZ5u7McOcNtKdNtLjjy90m2vy+SmrbaS4pj5YxAUKuU0792MLD6Wkpp7dRTWU1DS0uuv16G7XxIiv98o1f5wQ4SLSZUMpxa6c7Xz6+VKK/SGURkdQGBXCobBwjtij8I8L9JZF6QrS2cu4xnxSKgtJLCggrgTiwobSb9w5pNw8E1srBXx30WmFmtZ6uVIqrcXkS4CZwccvAdnA/wWnz9VaNwD7lVJ7gAlKqQNApNZ6NYBS6mXgUqRQE0K04pUXn2N7eSh7+8SzNS6CyuDuy1Dtpj87ubxpH2mlBSQcPExEeTi9E8eQMeNCEs+fYMgxK1VVVezbt499+/axf/9+amtrAXA4HKSmpjJkyBBSU1NJTk7G5er4brHOUlHv550Nh1m5p5RVe0spqg5cAzHMYWVM3xh+OHsA4/rFMDo1mqiQjvX0nKx+aZn0S8tsd7nSsmJWLF3KgfxCajR4nE5qQ51Uu5xUupxUOqLZbetDhSOcpvDjszp1PdFUEH14E9EHVhDd5Caywc3CVxcT5nET4qknvN5Loi2MfqmDSeg7nOh+6YQkJh7rpYPAmaqBYwKj2szr9/vxeDxtFnSHDh2itrYWr9d73OttNlu7BV1oaCihoaHYbCf+6rdbLfSKch23GzbbVsDMmVnN8moqPI3BQi7QU1cc3N16tMjbdKiS4pp6IhsqGOOoIircSlO0k8pIFyVhLgpCIii0ReMfOQ0InHDTi0KS9W7G1xeTWllAQtERYop8xIcPJX3cdPpcMOOUHt/ZmqamJqqqqqioqKCyspK9e/cyffr0Y7urTzfVmXsRg4Xax812fVZqraObza/QWscopR4H1mitXw1Of45AMXYA+IfW+qzg9OnA/2mtLzzB9u4k0PtGUlLSuLlz53ZW0wCora019D9aI/XktkPPbr+Z2p63dyd7y+0cSOzDtqRwCpyBXNG6nGFsZWDdfpILDhO9rxxHTQJhCYNxjRiJNfab7xb5Nu1vamqisrKSiooKKioqqKurA8ButxMTE3PsCz0sLMwUB3nXeTW55T62l/rYUeajwB34vohwwJBYK4NirQyItpASYcFigrzfREN9A4X5ByirqMGjrNS5nNSGuqhxOagKcVLhcFFpD6HCGk6dar1YdmkPEdQQqWuI8HmI8NUdO+s1rKGO0Pp6XPUN2OubcDT6CPEpwh2RhEf1xhGVhCU6CovrxLtcm9Na4/V6aWxsPHZraGho9Xnz4xabs1qtXxuf7kQ3m8127FZXV0dEiwKptqaSooJ8Kqrc1NnseFwhgWI4xEWFy0mF00WJLYJK69df59R1JFFEor+E5IZietWUEVlWQXVxE/vcqeQ19aXUHofHGgpK4bJCuEMRYVeBe4ciws6xx+H2wH2oXRFig1CbwmXjuN9Jv99/bJy/oz+jlvf19fU0Nn59969SiokTJ3b6P0uzZs3aoLXOajndLCcTtPYJ121Mb5XW+hngGYCsrCzd0YNev6mTObC2u+nJbYee3X6j2/6/p55me2MSW/vGkpM+Hn+GBZeuYyjbmN2QQ0bhfiL3VBBem0LmyDPIvPhXhCadukHJTrb95eXl7Ny5k507d5KXl4fWGrvdTlpaGhkZGWRkZJCYmGiKwgzgULmHhTuKWJRTxLr95Xj9GpfdwsT0eKarKm45dyKDe0X0yMFc5772Mu7aOkrddXjsVuqcgV2vtU47tQ47NQ4n1bZ48u0hVDtDaVAnPghfaT8heAjFQ5iuI6SpmlB/A6G+Blw+Lw6vD7vPj8PnPTZki93nxeY9evMHh21RKB0oSqwoHErhcigsTgvKogArDX5Fo1b4lJ9GwIcft6UeHw34LODVFhr8Nrw+K14s+LQFr99Ck9eK12KlyWqnweqgrq6OOmsIHksIbksodeFpMDDtuLaF6RpiqCDaX8YI737iPRXEuquJrK4hurKW+KYQ4hIGkdR/FNHDz8EZEYHFYqHBq6lwN1JSW09ZTQOVnkaq6hqo8jRS5Wmkuq6RmrpGqmobKaxvwOttwoYfe3CIFhv+4HAtfpzKS6jVT4jy4VRN2LUXqz6+JxJA2RzYXaE4XOFExvQiLCKKyKhooqKiiI6JITc3l7POnoXNoPHsTnehVqSU6q21PqKU6g0UB6cfBlKbLZcCFASnp7QyXQjRQ5SWFfPCC++wJyKdjamRHAruzuyr93Ox/ozBpXuJ2ZWPqyiOtLTJDLzwTmJuHWxYXr/fT0FBATt37iQ3N5eSkhIAEhMTmTZtGv379yc5ObnN3U+nk9+v2ZpfxaKcIhbuKCK3sAaAAYnh3D49g5mDEhjTNxqnzUp2djZD+3T8TMrupldy35Mq0rds2sjWzZspqazEjabJZqHebqXBYafeYcNjd1Bnt+Gx2fFYXZRbIznkcNKg7DQoO41HB0E2kF034KSBUOoI17VE+ivp5T1CqK8el7chcGusD4wH6PFir9PoegdNTSFo3Tx7KHWEUkcSRwCO1MCRFfDFig7liAjejmkxNt5RymJFWaxoqx1tdeBVITQSQTU2PD4rtT4LtV4r1T4bFU1W3NqODyvUtlxTZfCWB8CcWU2Gnf16uv9SzANuAf4RvP+w2fTXlVKPEDiZYACwTmvtU0rVKKUmAWuBm4HHTnNmIcRpVlNVyX+ffY0d0YNZkxpB5bgpWLWXIWxnlncjg/JzceyqJ7wqlcGTzyfzJ1fgCDPuYGKtNYcPH2br1q3s2LGD2tpalFL069ePsWPHMmjQIGJjzTM8gNaaLYermLe5gPlbjlBYXY9FQVZaLL+9YAhnDUkirZ2DzkX7Ro4ey8jRY7/x6z1uNwWHD7F//25KywqpqamhvqGBBu3Hi6LJagGl0VaNXyn8yoJW4FcW/BaFVgqtgpcW0z6U9mPxa5TWgXsCz61+H3Z/IzavFwd+QrEQGRJJcq++JKdmEhGfysacXGaee1mbx3Jqrb92WbGWlxdramrC7/ejtcbv97f6WGuNUuprlxdr+fzorluHw3Hc/ckca6q1ptHnP3ZJsbpGH/XewH1dk4+GJj+NPj+bt2wjwmXcP1adOTzHGwROHIhXSh0G/kCgQHtLKXUbcBC4CkBrvV0p9RawA/ACdwfP+AT4Pv9/eI4FyIkEQnRLHrebZ599ge3OAaxJi6Jo7FTsupGxrGdc4wb67d2D2mMlSQ1h8Jyfknrz2YYPWllcXMzWrVvZunUrlZWVWK1WBg4cyJAhQ+jfv79hw2ScyJ7iWuZtymfe5gIOlHlwWC2cMSiBXwwbxOzBicSEtdJFIQwTGhZG/0GD6T/IuB7ioywH8tr9vCmljh3T1tYwJ2ahlMJps+K0Wds8+cVZkovTZtzVHjrzrM/rTjDrzBMsfz9wfyvT1wPDT2E0IYSJvP7yS6ypjmVtWjx5I6dh0T5GsJnLvKvovy8H704nCU2ZjLjkN6TcdZbhxVlDQwMrVqxg69atFBUVoZQiIyODM844gyFDhpji7Mzmimvq+eCrfD7cVMD2gmqUgimZcXx/ZiZzhvXu8DhcQghjmOMgCSFEj5J3YC/Pv72cDX0HsiFlJFopBuocbvGtYNihbfh3QlRlX4affQ+Zt13+teEOjODz+di9ezcbN25k165dAKSkpHDeeecxbNgw05wFe5TX52f57hLmrjvE4txifH7NqJQofnfhUC4c2ZukSHMVk0KIE5NCTQhx2vzvqadZpzL4Ij2ayqwxxOoyLuETJpWvRm2rx7Y/gWGTrmb47281xQCW5eXlbNy4kU2bNh0blqNv375ccsklxJlw5PPDFR7mrjvE2xsOUVTdQHy4g9unpXNVVir9E81VTAohOkYKNSFEp6qpquRfz7zF2r7D2TBoIlbtYwxfckbTUlJ27sOzI4rk8BGMu/V2YoeNMDoufr+fffv2sW7dOnbt2oVSigEDBjB27FgGDBjAF198YaoiTWvN6r1lvLjqAItyAlcyOGNgAn+6uC9nDknEbtCQAkKIU0MKNSFEp1ixdDHvba1iWWYK+VkTiNaVXK7nMbViBY1bNPb9CYw88y4G/+cmrHbjD2JvaGhg06ZNrFu3jrKyMsLCwpgxYwZZWVkndXHv08XT6OW9jfm8vPoAu4pqiQ1zcNcZmdwwqR/J0eY/kFsI0TFSqAkhTqm5r73EYncfFmXEUjcijgE6l7v9Cxi4bzvVWyOJ9A5i/C130GviJKOjAlBTU8OaNWtYv349DQ0NJCcnc9lllzFs2DDTjHXWXElNAy+tOsAra/KoqmtieHIkD105kotG9cFlN+7MNCFE5zDfXyEhRJf05GNP8kXkcJanjEArzSRWMqfxE8K3l1G7JZbk9PO4+Hf3EJLQ9kWsT5fS0lJWrlzJli1b8Pv9DB06lMmTJ5OSktL+iw2wv9TN/77YxzsbDtPk83Pu0F7cMSOdsX1jTHNVAyHEqSeFmhDiW3n4kSdY3nss64ZPwakbOIsFnO35FP8WL007Yhg88QZGPf19rE5jRvVuqaCggOXLl5Obm4vNZmPs2LFMnjzZVAPSNrejoJrHl+5mwbZC7FYLV4xN4Y7p6WQkyMkBQvQEUqgJIb6Rhx95guw+Y1k/ZioR/lqu1G9wRlU2tV/Z8e+NY8KFVzHwvusNH/fsqIKCArKzs9m1axcul4sZM2YwceJEwgy8okFbthdU8Z/Fu/lsexERThvfPyOTW6emkRghQ2sI0ZNIoSaEOClr1m7i4ULnsQLtWv0KM6qWUrE+jPoDfZhx7c1k/vkyo2Me07JAmzVrFhMnTjTdwLRHtSzQ7jlzAN+dmi4D0wrRQ0mhJoTokMcfe5LP40azbsLM4wo094F+zLz2ZjL/Zp4CraSkhCVLlpCTk9MlCrT9pW4e/nwn87ccIcIlBZoQIkAKNSFEm15/+SXm+9NYOmwyLl3P1fo1ZtUsovzLcGr2pTLzultNVaBVV1eTnZ3NV199hd1uZ+bMmUyaNMm0BVpJTQP/WbybN9YdxGGz8KPZ/bl9ekab1x4UQvQcUqgJIVq16NP5vJVn59P+I/ArP3OYxwX1H1G7wUHp9j5MveBKBv/lFtMcg1ZfX8+KFStYs2YNfr+fCRMmMH36dNNd3umo2gYvzyzfx7Nf7KPR6+e6CX350Zn95Rg0IcTXSKEmhPiaosJ8Hp67mg+HZFAzQDFNL+PyprfwbfZR/FU8ySlDuODVB0wxSC0EriSwceNGlixZgsfjYcSIEcyaNcu0Z3H6/Zp3Nx7mwc92UlLTwAUje/PzcwaRHm/OkxqEEMaSQk0Iccz9Dz/FRwOyODCqP8N827lBP0d4ThXF6+IZljqKqc/8jlUbNpimSDtw4ACffvophYWF9O3blzlz5tCnTx+jY53QhrwK/vTRdrYcrmJM32j+d3MWo1OjjY4lhDAxKdSEELz47P/4MGQYq8dNItFXyr36OYYUbSd/ZSLRjUO56Q9/ICqzv9Exj6msrGThwoVs376dqKgorrzySoYNG2bagV+PVNXxwIJcPthUQFKkk0evGc0lo/uYNq8QwjykUBOiB9uzM5dHF+1l3qAsLKqJq/WrnO1ZSPHqKMr39eP8736ftHPPNzrmMT6fj1WrVrFs2TIAZs6cyZQpU3A4zNHD15LX5+fFVQd4ZOEuvH7Nj2b3564zMglzyp9eIUTHyF8LIXqovz/0X94bPJ5DQ5OZ5FvN9f4X8G6CvA29mTjxTMbd/zPTnCgAkJeXx8cff0xJSQlDhgzh3HPPJTo62uhYJ/TVwQp+/f42co5Uc+bgRP548TBSY0ONjiWE6GKkUBOih/n4w/d4rTKBpVmTSfSV8Uv9JAMK95D3RSIp/j5c8s/7CUs2z/UuPR4PCxcu5KuvviIqKorrrruOQYMGGR3rhKrqmnjos1xeW3uQpAgXT904lnOH9ZLdnEKIb0QKNSF6CI/bzf1Pvc/bw4fiToWL/e9xUeMHlK6O5EhuCufdeBuZl1xudMxjtNZs376dTz75hLq6OqZMmcLMmTNNu5sT4NNtR/jtB9spdzdw65Q0fnbOIMJlN6cQ4luQvyBC9ABvv/Ear5DBurHDGejdy208RtiuGvav6s2IlJFMf+mP2ELNMzxETU0N8+fPJzc3lz59+nDzzTfTq1cvo2OdUFltA3+Yt52PtxxhWJ9IXrh1PCNSooyOJYToBqRQE6Ib87jd/OXpD3h7+BCarH5u9L/AmfWLOLQsAf+hZK7+ya/oNWmy0TGP0VqzdetWFixYQGNjI2eddRaTJ0/GarUaHe2EPtl6hN99sI3q+iZ+dvZA7pqZid1qnmP7hBBdmxRqQnRTb772Cq9a+vPlmGEM9u7mDvUozpwGdq1KZUzmWKa++kfTjIcGX+9FS0lJ4ZJLLiEhIcHoWCdUVtvA7z/czvytRxiRHMVrV01kcK9Io2MJIboZKdSE6IZ+//ALvDFiFI02Hzf7n2N23VIOLoun6XA81/zs1/SaMNHoiF+Tm5vLvHnzaGxs5JxzzmHSpEmmOuO0paU7i/nF21uormviF+cO4nszMrBJL5oQohNIoSZEN7J62VIe229lybgx9Pce5PvqQUJ21ZO7IoVR/UYy49W/mqoXrbGxkU8//ZSNGzfSq1cvrrjiClP3otU3+XhlRwOLP/2SQUkRvHr7BOlFE0J0KinUhOgmHnr4SV4fMpGivhYu873Lxb73KFgaR+2uJC6/6x76nnWO0RG/Jj8/n3fffZfy8nKmTp3KrFmzsNnM+ydpe0EV98zdxJ5iL9+dms4v5wzCZTfvsXNCiO7BvH8VhRAdUlpWzB/eXM/7YycT76vitzxI38Ij7F6SQpqjD+c++y+cJhoY1u/3s2LFCrKzswkPD+eWW24hPT3d6Fgn5Pdrnluxnwc/yyUm1MHPs5z88KKhRscSQvQQUqgJ0YW98+br/E9lsnlIH6Y1reZmy5NUrwln/1e9mH3BFQy/9XajI35NbW0t77//Pnv37mXYsGFceOGFhISEGB3rhCrcjfz87c0szi3mnKFJ/OOKkWz5cpXRsYQQPYgUakJ0Ufc//BSvDZ+A2+7nTv8TTKtfzd6FSYSWRHDjX/9BzKDBRkf8mgMHDvDuu+/i8Xi48MILGTdunKlH6994sIIfvraRktoG/nTxMG6e3M/UeYUQ3ZMUakJ0MaVlxfxh7nreGzuRZG8p/6fuJyavhh1LUxkSl8lZL/0Tq9NpdMxj/H4/K1euZMmSJcTExHD77bfTu3dvo2OdkNaaZ7/YzwOf5tI72sW735/CyJRoo2MJIXooKdSE6ELmvfcOT3pT2TQ0sKvzVssTVK6O4MBXiZxz6bUMvfEWoyN+jcfj4b333mPPnj0MGzaMiy66CJfLZXSsE6qqa+Lnb29m4Y4izh2WxINXjiIqxG50LCFEDyaFmhBdxCP/eoIXB02m0qm53fdfZjSuZO/niYQURXDjX/9O7GBzHeBeWFjI3Llzqamp4YILLiArK8vUuw53F9Vw5ysbOFTu4XcXDuW7U9NMnVcI0TNIoSZEF/Drf77Gq6OmEOWv5ffqz/QuLCPn8xQyQpM574X/mOo6nQBbt27lww8/JCQkhO985zukpKQYHalNn24r5GdvbSLEYeX1OyYxIT3W6EhCCAFIoSaEqR06mMcfPt/DJ2OHMaxxLz+0/xXfVge7VvVi6vgZTPj5fUZH/Bqfz8fixYtZtWoVqampXH311URERBgd64R8fs2ji3bx2JI9jEqN5qkbx9I7yrxnoQoheh4p1IQwqXnvvcNjvr5szYzjvKbPuNbyAkeWxOPOjeSy2+4m7bwLjI74NR6Ph3feeYd9+/aRlZXFnDlzTD2AbVVdE/fO/YqlO0u4OiuFP18yXAawFUKYjnn/igrRgz352JM8kzGRchfc5X2MqQ3r2P1pMmFl4dz44CNEZWQaHfFrSktLef3116mqquLiiy9m7NixRkdq08EyD9996UsOlLr5y6XDuXFiXzkeTQhhSlKoCWEyf33oaV4YNRmHque36g+klpeyY0Eyfe2JXPT8f013PNr+/ft58803sVgs3HLLLfTt29foSG368kA5d768Hr+GV26byOTMOKMjCSHECUmhJoSJ/OLRd3ht3AT6NpXwU9tvce2FnCVJjMscyfQ//x2LxWJ0xK/ZsGED8+fPJy4ujuuvv56YmBijI7XpvY2Hue/drSTHhPD8reNJjzdX0SuEEC1JoSaECRQV5vOrj3P5ZFR/xjbs4AeO+3FviGTfumjOPvcSRtx+l9ERv8bv97No0SJWrVpFZmYmV111lanHR/P7NY8s3MXjS/cwOSOO/944luhQh9GxhBCiXVKoCWGwjRvX8oedfr7MjOO8xoVcb32W/MVJuHeGc/ld99L3rHOMjvg1Pp+Pt956i9zcXMaPH8+cOXOwWs17EH6D18fP397CR5sLuCYrlb9cOhyHzVw9k0IIcSJSqAlhoI8/fI+HfansSnLwncbnOcu/mN3zk7EeCeX6+x823fU6PR4Pmzdvprq6mjlz5jBp0iSjI7Wpur6J7728gdX7yvi/OYO564wMOWlACNGlSKEmhEFefPZ/PJ40luJwuMf3MOMat5LzcR8iq8O58vGnCE1KMjri11RWVvLqq69SU1PDVVddxbBhw4yO1Kai6npueX4de4pr+dc1o7hsjLkH3RVCiNZIoSaEAR791xM8M2QyTVYv9+k/kVldwPaPe9NHx3LJs8/iCDPXQe5FRUW8+uqrNDY2MnLkSNMXaXuKa7jl+S+p9DTy/K3jmTEwwehIQgjxjUihJsRpdv9DT/Hc6CmEaQ/3WX9F/BEPOz7txaCovsx59AksJhsk9sCBA7zxxhs4HA6++93vkpOTY3SkNm3IK+e2l9Zjsyje/N5khidHGR1JCCG+MTmiVojT6PcPv8h/x04k0VfGH2z3ELOvgdyPExmbPprzH3/adEXazp07eeWVV4iIiOC2224jyWS7Y1vK3lnMDc+uJTrEznvfnypFmhCiyzOkUFNK3aOU2qaU2q6Uujc4bbRSao1SapNSar1SakKz5X+llNqjlNqplDrXiMxCfFu/euQNnh07iv6NBfza8ROs2+3s/jyOGRNnc8af/250vONs3bqVuXPnkpSUxHe/+12io6ONjtSmBVuPcMfL68mID+ed70+hb1yo0ZGEEOJbO+3/viulhgN3ABOARuBTpdR84EHgT1rrBUqp84PPZyqlhgLXAsOAPsAipdRArbXvdGcX4pv62b/f5bUxQxhVt48fu35L3VcxHFoTxZnnXMSoO75vdLzjrF+/no8//ph+/fpx/fXX43Q6jY7Upnc2HOaX72xmTN8Ynr91PFEhdqMjCSHEKWHEfpYhwBqttQdAKbUMuAzQQGRwmSigIPj4EmCu1roB2K+U2kOgyFt9WlML8Q143G7ue2Epb43MZHzdDu52/YmqtfEUbIxizmXXMeT6m4yOeJxVq1bx+eefM2DAAK6++mrsdnMXPS+tOsAf5m1nWv94nrl5HKEOc+0+FkKIb0NprU/vBpUaAnwITAbqgMXAeuBJ4DNAEdglO0VrnaeUepxAYfdq8PXPAQu01u+0su47gTsBkpKSxs2dO7dT21JbW0t4eHinbsOsenLboWPtb6hv4N08xbyBiUyv28Adrn9Q8kUvSrdGMHDiGYSMn3ia0naM1poDBw6Ql5dHQkICQ4YMafWSVWZ67z/e28g7u5sYk2jl+6OcOKydP0aamdp/uvXktkPPbr+0vfPbPmvWrA1a66yW00/7v55a6xyl1APAQqAW2Ax4ge8DP9Fav6uUuhp4DjiLQOF23GpOsO5ngGcAsrKy9MyZM099A5rJzs6ms7dhVj257dB++z1uNz98fQ2fDIzjrLqV3OJ8lMIlvanIDecyE15tQGvNokWLyMvLY/To0Vx88cUnvK6oWd77fy3cxTu7d3PJ6D48fNUo7NbTc8itWdpvhJ7cdujZ7Ze2zzRs+4acTKC1fk5rPVZrPQMoB3YDtwDvBRd5m8DuTYDDQGqzl6fw/3eLCmE6HrebH76xhk/6x3Fe3TJudTxKwaI+VOaGc8U9/2fKIm3hwoWsXLmSrKysNos0M9A6cN3Ofy/ezVXjUnjk6tGnrUgTQojTzaizPhOD932By4E3CBRfZwQXmU2geAOYB1yrlHIqpdKBAcC605tYiI7xuN3c/cZaPsmM4/y6pdzgeJz8RX2o3h3GFffeR/L0M9pfyWl0tEhbtWoV48eP54ILLjB9kfavhbv4z+LdXJ2VwgNXjMRqkUtCCSG6L6OOun1XKRUHNAF3a60rlFJ3AP9WStmAeoLHmmmttyul3gJ2ENhFerec8SnMyON284M31vJpZiwXeJZwnfO/gSJtTxhX/vTX9J4yzeiIX6O15vPPP2f16tWMHz+e888/39TXwTzak/bYkj1ck5XK3y8fgUWKNCFEN2dIoaa1nt7KtBXAuBMsfz9wf2fnEuKbOlGRVtMFirQJEyZw3nnndZki7drxqfztMinShBA9g5zHLsS3dPSYtE8z47jQs5hrnU+RvzCZmr2hXPGz39B78lSjI37N0d2dXaVIA/jXot08tmQP101I5f5LpUgTQvQc5j0YRYguwON285NXVgaOSfMs41rXfylY1IdqkxZpAMuWLTt2TFpXKNKeWrb32DFpUqQJIXoaKdSE+Bbue2EpHw5K5Cz3Wq53PUbhkmQq94Rx2Q9/bsoibdWqVWRnZzN69OguUaS9svoA/1iQy0Wj+vD3y0dKkSaE6HGkUBPiG/rpf97jrWEpTHdv5pbQhyj5IpnSXWFc/N0fkDJzttHxjvPll1/y+eefM2zYMNMPwQGBy0L97sPtnDUkiUeuHiVndwoheiRz/6UWwqQWbCzk9REZTHTnckfoX6lY3YfC7eFccO2tpJ1/odHxjrN582bmz5/PwIEDueyyy0xfpH28pYBfvrOZ6QPiefz6MTJOmhCix5K/fkKcpN/88xVeHj2IMZ79/CD0D1Sv78XhzRGce8nVDLjiaqPjHWfHjh188MEHpKenc9VVV2GzmfscosU5Rdw7dxPj+sXw9E3jcNmtRkcSQgjDSKEmxEn480PP8OLo4QzzHOHHIb/GsyWBvPURnHnWBQy98Raj4x1n3759vPPOO6SkpHDttdea/gLrXx4o5wevbWRI70ieu3W8XGBdCNHjSaEmRAf9859P8Pzo8fRrqOSe0F/i3R3HvtVRTB8/nVF3/sDoeMcpKChg7ty5xMXFcf311+N0Oo2O1KadhTXc9uKXJEeH8OJ3xhPpMndRKYQQp4MUakJ0wFOPP8nTwycT463jZ86fYTscwa6lUYzNGMqEX/zK6HjHKS8v57XXXiMkJISbbrqJkJAQoyO16XCFh5ufX0uIw8rLt00gLtzcRaUQQpwuUqgJ0Y65r73EkxmTsOLl5/ZfElFqJ+ezGAbF9uGM+x8wOt5xampqeOWVV/D7/dx4441ERkYaHalN5e5Gbn5+HZ5GHy99dwIpMaFGRxJCCNOQQk2INiz6dD7/ihhGrV3zM/V7kmrq2TE/lgS/k/P/81/TnT1ZX1/Pa6+9Rm1tLTfccAMJCQlGR2qTp9HLd178ksMVdTx3y3gG9zJ3USmEEKebub5lhDCRnK2b+Ls7gYIwKz/2/4P0xkJy5sUS1+Qi+da7sJjs7Emv18ubb75JcXExV199NSkpKUZHalOTz88PXtvI1sOVPH7dGCakxxodSQghTEcKNSFaUVNVyW83u9kRY+d7Df9lhN7Brg/jCXU7ufI//8NisgPztdbMmzeP/fv3c8kllzBgwACjI7VJa83vPthG9s4S7r9sBOcM62V0JCGEMCUp1IRoxc/eWMfK5DCudX/IZHs2Bz5JQlc4uOL+fxKSEG90vONkZ2ezZcsWZs2axahRo4yO067/LtvL3C8PcfesTK6b0NfoOEIIYVpSqAnRwi//9SbzBiVyds16Lgh7hcLsPtQWuLjknv8jesBAo+MdZ9OmTSxbtozRo0czY8YMo+O066PNBTz46U4uGtWHn509yOg4QghhalKoCdHMXx56htdGDmRs7UFuCn+A6g0pFO0OY86VN5A8/Qyj4x1n//79zJs3j/T0dC688ELTX2R9/YFyfvb2ZrL6xfDQlXKRdSGEaI8UakIE/effj/PC6PH0q6/mB2G/onFPL/avD2fa+GkMuvYGo+Mdp6Sk5NiAtldffbXpLw11oNTNHS+vp0+Ui2duzpJLQwkhRAdIoSYE8PYbr/HsgCmE+hq51/lL7EWR7FoayfA+6aYc0La2tpbXXnsNm83G9ddfb/oBbSvcjXznxS8BeOE7E4gNcxicSAghugYp1ESPl7N1E4+7BlJjh3stvyfW7SPnk2hSnZGc/dCjRsc7ztFhOGpra7n++uuJiYkxOlKbjg7DkV9RxzM3Z5EeH2Z0JCGE6DKkUBM9msft5g9f1bIrysb3Gh8nw5/Pzg9jiGywc8l/njLdWGlaaz7++GMOHTrEpZdeSnJystGR2vXnj3awel8Zf798BOPTZKw0IYQ4GVKoiR7tvheWsDw1nKtqFjDe+QV5C5Kg2s5lf30IR2SU0fGOs2bNGjZt2sSMGTMYPny40XHa9eqaPF5Zk8edMzK4Ypy5B+AVQggzkkJN9Fh/fOg53h6awrTqXC6OeI6yVSlUFri44I4fEjPQfMNG7Nmzh88//5zBgwczc+ZMo+O0a/XeMv44bzuzBiXwf3MGGx1HCCG6JCnURI/0+GNP8PLosfT3VHJbxJ+o25nCoe1hzJh+Fmnnnm90vOOUlpby9ttvk5iYyGWXXWa6a4y2dLDMw/df20BafBj/vm4MVhmGQwghvhFz/7UXohMs+Og9ns2YRIi/iR+57sNaHMOu5eEM79OPcT/+idHxjlNXV8cbb7yB1Wrluuuuw2myy1e1VFPfxO0vf4nW8OzNWUS67EZHEkKILksKNdGjFBXm82h9MuVOCz/y/5W4uiZy5kfSxxrK2Q/92+h4x/H7/bz77rtUVFRwzTXXEB0dbXSkNvn9mp++tZm9JW6evGEsaXKGpxBCfCtSqIke5bfzdrA53sl3PC8zwLqLvR/H4qqzcum//mu6MzwhcA3PPXv2cP7559OvXz+j47Tryew9LNxRxK/PH8LU/ua7JqoQQnQ1UqiJHuN3D7/IRwMSOKdyIzPC51Gc3Ye6cgeX/Py3przQem5uLsuXL2fMmDGMGzfO6DjtWrarhH8u3MUlo/vw3alpRscRQohu4YRdCEqpyzvw+nqt9SenMI8QneLJx57k1VGTGVJbzg1RD+DZkUrB3lDOOvt8ek2abHS845SWlvL+++/Tu3dvzj//fNNfw/NQuYcfv/EVg5Ii+PvlI0yfVwghuoq29vX8D/gQaOsv7gxACjVhaiuWLub5tIm4/F5+4Po1qiSOXSvDGNa7H6Pu/IHR8Y7T0NDAm2++icVi4ZprrsFuN/fB+PVNPr73ygb8WvPUjeMIdZhvF7IQQnRVbf1FXaC1/m5bL1ZKvXqK8whxStVUVfLwkRCOJFm4r+EfxHrr2TE/iUQcnP3Qv4yOdxytNfPmzaO0tJQbb7zR9CcPaK359ftb2XGkmudvzZKTB4QQ4hQ74TFqWusb23txR5YRwki/eWUFa3qHcl3Nhwx1bObAgjisHiuXPvhvrHbzXRh89erVbN++ndmzZ5OZmWl0nHa9uiaP9zbmc8+ZA5g9OMnoOEII0e2csFBTSsUqpX6tlPqpUirydIYS4lT420NP8fbQZKZV7WFO5CuUr06husjFBXf8mIjUvkbHO05eXh4LFy5k8ODBTJs2zeg47dp8qJI/f7yDWYMSuOfMAUbHEUKIbqmtsz7fBcKBFGC1Uirj9EQS4tt77+25vDpiAql1dXwn4g805aVycFsIk8ZOot+5c4yOdxy3280777xDdHQ0l156qekPxq/yNHH36xtJjHDxr2tGY5ErDwghRKdoq1CL01r/Wmv9U+CnwDKl1Fal1DlKqbdOUz4hTlppWTFP63TcNrjL+mdc7hByF4bQNySSif/3G6PjHcfv9/P+++/j8Xi46qqrcLlcRkdqk9aaX7yzmcKqeh67fgzRoebbhSyEEN1FWycT1Cil0rTWB7TWnyml+gJ9gApg6+mJJ8TJ+/2bG9g8pDd3VL9Gv9AD7JufQkijhQv//Zgpr5G5cuVK9uzZwwUXXECfPn2MjtOuhXlePs8t4rcXDGFs3xij4wghRLfW1rfWd4Fj/yrrgHyttUdrfaTzowlx8u5/6CneH9yLMyp3MjPiPcpXJlNb7uTCH/7MlIPa5uXlsWTJEoYNG0ZWVpbRcdq16VAlb+5s5OyhSdw2Ld3oOEII0e21ddbnTq31LqVUplLKCaCUmqmU+rFSKvq0JRSig9558/Vjx6XdHPlnGg+kcjDHxeSsyaTMnG10vOMcPS4tJiaGiy66qGscl/baRqKdioevHGX6vEII0R10ZD/Qu4BPKdUfeA5IB17v1FRCnKSiwnyesfSnzgY/sP4RV20ouYtC6BcaxYRf/NroeMfx+/289957Xeq4tJ+/s5nimnruHu0kKtTcg/AKIUR30ZFCza+19gKXAY9qrX8C9O7cWEKcnD+/u5kt8Q5uqn2dVOtB9n0SRWijhQsfNudxaatWrWLv3r3MmTOH3r3N/3F6adUBFu4o4r7zhpARbTU6jhBC9Bgd+QZrUkpdB9wCfBycJv9OC9N48J9P8P6Q3syo2MPMyPepWJNCbbmT83/wE1xxcUbHO05+fj5LlixhyJAhXeK4tJwj1fxtQS5nDk6Ui60LIcRp1pFC7TvAZOB+rfV+pVQ6IJeOEqawfMlnvD54Mkn1jdwU+We8+SnkbXOSNWw0qbPPMjrecRoaGnj33XcJDw/n4osvNv1xXvVNPn78xldEhdh58MqRps8rhBDdTUcKtbO11j/WWr8BoLXeD9R1biwh2udxu3ksP4wSl+JO/QBhDRZ2fhZCL6uLqb/9k9HxWrVgwQLKy8u5/PLLCQkJMTpOu+6fn8Pu4loeuXoUceFOo+MIIUSP05FC7ZZWpt16inMIcdL+8vSHfJESzhVVSxjo2sLhhfGoegsX/vVBLLa2hgg0xrZt29i0aRMzZswgLS3N6Djt+nx7Ia+syePOGRlMH5BgdBwhhOiR2rrW53VKqY+AdKXUvGa3pUDZt9moUuoepdQ2pdR2pdS9zab/SCm1Mzj9wWbTf6WU2hOcd+632bboHv731NPMHTGEEdVlXBT9FJ7taZQccXHmpdcQlWG+i5lXVFTw0UcfkZKSwhlnnGF0nHYVVtXzy3e3MDw5kp+fM8joOEII0WO11e2wCjgCxAP/bDa9BtjyTTeolBoO3AFMABqBT5VS8wlcU/QSYKTWukEplRhcfihwLTCMwJURFimlBmqtfd80g+ja8g7s5dWksdi0n9tdv0dVJLBrtZPBCb0ZemNrHcDG8vl8vPfee2itueKKK7BazX3WpN+v+elbm2ho8vPva8fgsJnvrFkhhOgpTlioaa3zgDwCJxKcSkOANVprD4BSahmBoT+ygH9orRuC2y8OLn8JMDc4fb9Sag+BIm/1Kc4luoi/f7qHnYOS+FH1MySEVLBrfjJRXsXZDzxqdLRWffHFFxw6dIjLL7+cmBjzX3Lpf1/sY9XeMh64YgSZCeFGxxFCiB6trV2fK4L3NUqp6ma3GqVU9bfY5jZghlIqTikVCpwPpAIDgelKqbVKqWVKqfHB5ZOBQ81efzg4TfRAD/7zCeYNTGR2+U4mRXxG+cpk6mocXPCT+3CEhRkd7zj5+fksW7aMESNGMHLkSKPjtCvnSDX//HwXc4b14uqsVKPjCCFEj6e01qd/o0rdBtwN1AI7CJxFejawBLgHGA+8CWQAjwOrtdavBl/7HPCJ1vrdVtZ7J3AnQFJS0ri5c+d2ajtqa2sJD++ZPQ5GtD3/4AH+GTsCv8XHXx23YzsSw/aPw8mITyHmqutOa5aOtN/n87Fhwwa8Xi/jx4/Hbjf38INNfs2fV9dT1aD567QQIh2tD8XRk3/voWe3vye3HXp2+6Xtnd/2WbNmbdBaHze4ZodOjVNKWYGk5strrQ9+0zBa6+cIXI4KpdTfCPSSDQHe04HKcZ1Syk/g+LjDBHrcjkoBCk6w3meAZwCysrL0zJkzv2nEDsnOzqazt2FWRrT95uezORJq4dfuhwltgu2fu0hQdi759xOn/SzPjrR/wYIFeDwebrrpJjIzzXeCQ0sPfJrLoZq9PHdLFmcOSTrhcj359x56dvt7ctuhZ7df2j7TsO23e5SwUupHQBGwEJgfvH3c5ovaX+fREwX6ApcDbwAfALOD0wcCDqAUmAdcq5RyBgfbHQCs+zbbF13Pnx96hs/To7mgfANDw9ZRlJ2Ev97KBb/5symH4ti3bx9r165lwoQJXaJIW3+gnKeX7eXa8altFmlCCCFOr458w90DDNJaf6shOVp4VykVBzQBd2utK5RSzwPPK6W2ETgb9JZg79p2pdRbBHaReoPLyxmfPcjnn8zjzeFZpLk9XBHzTxr3p1FwwMn0SdOJG2G+477q6ur44IMPiIuL46yzzHd1hJbcDV5++tZm+kSH8NsLhxodRwghRDMdKdQOAVWncqNa6+mtTGsEbjzB8vcD95/KDKJr8LjdPFMeR1Vvxb0N/8BZF8rWxXaSHaFk/eQXRsdr1aeffkpNTQ233XYbDofD6Djtuv+THA5VeHjzzsmEO83XOymEED1ZR/4q7wOyg2OdNRydqLV+pNNSCRH0t6feY8XYEVxdvpiM6B0cnpeGpdHC+Q8/gMVivvG9duzYwebNm5kxYwYpKSlGx2nX0txiXl97kO/NyGBCeqzRcYQQQrTQkULtYPDmCN6EOC3eefN13hw+jME1VVwY8xT1uWmUFLo4Z86FRKalGx3vOG63m48//pjevXt3iasPVHma+L93tzAoKYKfnD3Q6DhCCCFa0W6hprU259WtRbfmcbt51ZdGg1XxXdv9WGuj2f6FnX5hUYy4/S6j47Xqk08+ob6+nltuucX0Vx8A+Mv8HZS5G3n+1vG47ObPK4QQPdEJC7XgdT6bD7KmCZyFufTomGZCdJa//fdd1owbyXVlC0mO2Uf+gnTsTYrzHn7A6Git2rFjB9u3b2fWrFkkJZn/rMmlO4t5Z8Nh7p6VyfDkKKPjCCGEOIG2etQebmVaLHCjUmq41vq+TsokergP3nmTt0YMZ3BNNefHPkP9znRKip2cM+dCwpLNd9yXx+Nh/vz59OrVi2nTphkdp13V9U386t2tDEgM58dnDjA6jhBCiDa0da3PZa1NV0rNAzYAUqiJTvFyfQr10YpbrX/D6o5i1xc2+oZEmHaX54IFC6irq+Omm27qErs8/zY/h+Kaep66aSpOm/nzCiFET3bSp83JGGaiM/3+4RdZlRzGpRVLSXXtIX9RLLZGxZy/mHOX586dO9m6dSvTp0+nV69eRsdp1xe7S5j75SHumJ7B6NRoo+MIIYRoR1vHqLV2rn4McDOwvdMSiR7r4w/f463hIxlQU8uFcf+lYXcaxYUOzjxzDhGpfY2Od5y6ujo++ugjEhMTmT79uKEBTae2wct9724lIz5MzvIUQoguoq1j1DYQOIHg6JWZj55MkA18v3NjiZ7o5epEavsofun7OzZPJDuy7aQ4wxj9/R8aHa1Vn332GW63m+uvvx6bCS9j1dI/FuRQUFXHO3dNlrM8hRCii2jrGDXzDVQluq0/PfQsy7OyuLL0C/rF5XLko0wsTYo59//N6GitKi8vZ8uWLUybNo0+ffoYHadda/eV8eqag9w2LZ1x/WRgWyGE6CpOeIyaUmpsey/uyDJCtGf1sqW8O2wsae46Lop5gsb9GRQWOJgybTZRGea7oHljYyO7du0iLi6uSwxsW9/k41fvbyUlJoSfnSO7PIUQoitp62SCF5RSMUqp2BPdgOdOV1DRff1vt58Sl+Jm/6PYm5zsWWIlyeJg7I9/anS0VmVnZ1NfX89FF12E3W43Ok67nszey74SN/dfNoJQh/l30QohhPj/2vqrHUXgODXVxjIlpzaO6Gn++c8nWDBmCueWbWdQ3HrKlvTH22jh3N/81pTX8jxy5AirV6+md+/epKWlGR2nXbuLavhv9h4uHd2HMwYmGB1HCCHESWrrGLW005hD9ECHDubxTv+JxDd4uTL6YXyFqRzcbWPcoOEkjDbfXnW/38+8efMIDQ0lIyPD6Djt8vs1v3pvK2FOG7+9cKjRcYQQQnwD5uuyED3GQ/O2sD/SxvX1LxKqG9n3uYMov2Lab/5odLRWrV27liNHjnDeeed1iV2er687yPq8Cn5z/hDiw51GxxFCCPENSKEmDPG/p57mg8EpTC7PZ0LUp9R81Re3x85Zt/8AW0iI0fGOU1FRwZIlSxgwYADDhg0zOk67iqrreWBBLlMy47hynPkuuyWEEKJjpFATp53H7ead2NE4/Jrrw/4OlUns+crK4MQ+pJ17vtHxjqO1Zv78+QBccMEFKNXWYZvm8Md522n0+fnbZSO6RF4hhBCta7dQU0q9q5S6QCklRZ04Jf7y9IdsTnByZdVHxNqKOPh5OCFezZl/+ofR0Vq1fft29uzZw+zZs4mOjjY6TrsW7ihiwbZCfnzmANLiw4yOI4QQ4lvoSPH1X+B6YLdS6h9KqcGdnEl0Y4s+nc/7wwYzpLqaM+NeoWF3BuUVDmacfzmuuDij4x2nvr6eTz/9lN69ezNx4kSj47TL0+jlj/O2MzApnDtnmP+EByGEEG1rt1DTWi/SWt8AjAUOAAuVUquUUt9RSpn/iGphKi8XhFFjU1xveQRrXSR7llvo4whh+HdvNzpaq7Kzs6mtreWCCy4w5XAhLT2+ZA/5lXX89dIR2K3mzyuEEKJtHfpLrpSKA24Fbge+Av5NoHBb2GnJRLfzz0eeYGFaFOeWbSMjbCtlqxLxeRXn/PJ3Rkdr1ZEjR1i7di1ZWVmkpJj/gPw9xTX874t9XDE2hQnpcpkoIYToDtodplwp9R4wGHgFuEhrfSQ4602l1PrODCe6j9KyYj5In0hcg4/Lov+FvziVg3usjB0wlLgRI42Odxy/38/8+fMJCQnhzDPPNDpOu7TW/O6D7YTYrfzqfDk6QQghuouO9Kg9q7UeqrX++9EiTSnlBNBaZ3VqOtFtPPDKcnZH27ja/QZhykPe5w7CfTD9t380OlqrNm3axOHDhznnnHMIMeFwIS3N21zA6n1l/HLOYBkzTQghupGOFGp/bWXa6lMdRHRf7709l/eHZjK6spwpsR/g2ZFGldvBrGtuxhZqvrMS3W43CxcupG/fvowaNcroOO2qrm/iLx/nMColiusm9DU6jhBCiFPohLs+lVK9gGQgRCk1hv9/zc9IIPQ0ZBPdxFueZBpi4Tr7g1jcsexZregXGsXAq64xOlqrFi9eTH19fZcZM+2Rz3dR5m7ghVvHY7WYP68QQoiOa+sYtXMJnECQAjzSbHoN8OtOzCS6kb8/9F+ysyZzack6UuJ3U/TJAJRXcfZv/2R0tFYdOnSIjRs3MnnyZJKSkoyO065t+VW8vPoAN07sx4iUKKPjCCGEOMXauij7S8BLSqkrtNbvnsZMops4dDCPDweOp1ddExfHPIavII2CQzYmjhhDVGZ/o+Md5+gJBBEREcycOdPoOO3y+zW//3AbsWEOfn7OIKPjCCGE6ARt7fq8UWv9KpCmlPppy/la60daeZkQxzz6/noOjMzkx2XP44pqYs8iG5F+mHzfb42O1qqNGzdSWFjIFVdcgdNp/gPyP9iUz8aDlTx45UiiQmVIQyGE6I7a2vV59Cjv8NMRRHQvH3/4Hh8OyWBMRTkTYufj2TqQmjorF11zM1YTFkF1dXUsXryYvn37Mnz4cKPjtKu2wcvfF+QyKiWKK8eaf4w3IYQQ30xbuz6fDt6b82AiYWpvlsVS1w+udjyCxRPDntWQ6gpn4JVXGx2tVUuXLqW+vp7zzjuvS5xA8NiS3ZTUNPDMTeOwyAkEQgjRbXXkouwPKqUilVJ2pdRipVSpUurG0xFOdE2P/utxFqVFMad0G31DcyhbnYD2Kc765W+MjtaqoqIivvzyS8aNG0fv3r2NjtOu/aVunl+xnyvHpTCmb4zRcYQQQnSijoyjdo7Wuhq4EDgMDAR+0ampRJflcbuZ13ci0Y1+Lo1+FH9JKgf3WBiZMYjYYSOMjnccrTWffvopTqeT2bNnGx2nQ/7y8Q6cNiu/nCMnEAghRHfXkULt6FHK5wNvaK3LOzGP6OLuf+p9dsTaubz6Q0KtNRxe7CTUp5n+mz8aHa1VOTk57N+/n9mzZxMaav7hAZfkFrEkt5h7zhxAYoTL6DhCCCE6WUcKtY+UUrlAFrBYKZUA1HduLNEVrV3xBR8MHcrAajcz496gcV8G5VV2ppx7EY5I843x1djYyGeffUZSUhLjxo0zOk67Grw+/vJxDhkJYdwyJc3oOEIIIU6Ddgs1rfV9wGQgS2vdBLiBSzo7mOh6nt9WR5nLwjX6MayNoezJhkSLnRG3fc/oaK1atWoVVVVVnHfeeVitVqPjtOuFlQfYX+rm9xcOxWHryP9YQgghurq2hudobgiB8dSaL/9yJ+QRXdTz/3uG+QPGM7P0AIPjvqR69SCavIqz7v0ZFov5ioqqqipWrFjBsGHDSEtLMzpOu4pr6nls8W7OGpLIzEGJRscRQghxmrRbqCmlXgEygU2ALzhZI4WaaObjkGHY/XB52L+gOom9m2FgXC96T5lmdLRWLV68GK01Z599ttFROuRfC3fR4PXzmwuGGh1FCCHEadSRHrUsYKjWWnd2GNE1PfDwk6waN4Uri1YTl3iYkiUDsflh5q//YHS0VuXn57NlyxamTZtGdHS00XHalXOkmje/PMStU9JJjw9r/wVCCCG6jY7sk9oG9OrsIKJrqqmq5JPM8STUezk/9in8hWnkH7IyeugoIlL7Gh3vOFprPvvsM8LCwpg2zZy9fc1prbl/fg4RLjs/PtN810cVQgjRuTpSqMUDO5RSnyml5h29dXYw0TX849mP2Rlt57Lq93Fa6ji8xEaYTzP5/8x5Pc+cnBwOHjzI7NmzcbnMP7zF0p3FrNhTyr1nDSA61GF0HCGEEKdZR3Z9/rGzQ4iuacumjXw0ZCgDqj1Mj3+Lxr2ZlNfYOOuc83GEmW8XndfrZeHChSQmJjJmzBij47Sryefn/vk5ZMSHceOkfkbHEUIIYYCODM+xDDgA2IOPvwQ2dnIu0QU8s/IIxSEWrvQ/jdXrYt8yiFdWRtx+l9HRWrV27VoqKio499xzTXkmakuvrz3I3hI3vzp/CHar+fMKIYQ49Tpyrc87gHeAp4OTkoEPOjGT6AJytm/m44F9mFxWyPCoFdRu7Uu918bM73zflEWQ2+1m+fLlDBgwgMzMTKPjtKvK08Sji3YxOSOOs4bIcBxCCNFTdeQb9W5gKlANoLXeDcg3Rw+3InQ0Xgtc7voXyh3Lvi81aWFR9Dt3jtHRWpWdnU1jY2OXGY7j8aW7qaxr4jcXDEEpZXQcIYQQBulIodagtW48+iQ46O23GqpDKXWPUmqbUmq7UureFvN+rpTSSqn4ZtN+pZTao5TaqZQ699tsW3x7//n3EyzpF8m5pdvoE7qH8jUJaD/M+uVvjI7WqpKSEtavX09WVhaJieb/HyOvzM2Lqw5w5dgUhieb79JbQgghTp+OFGrLlFK/BkKUUmcDbwMffdMNKqWGA3cAE4BRwIVKqQHBeanA2cDBZssPBa4FhgFzgCeVUua/3k839mmvcUQ0aS6OegxdnkLeHsWwvpnEDjbnYKyLFi3Cbrczc+ZMo6N0yEOf7cRqUfz83EFGRxFCCGGwjhRq9wElwFbge8AnwLcZe2EIsEZr7dFae4FlwGXBef8CfsnXe+wuAeZqrRu01vuBPQSKPGGA+x96io2JLi6sWEa4o4zi5SE4fJpp9/3O6GitOnjwIDt37mTatGmEmfBM1JY2H6rk4y1HuH1aBkmR5h8+RAghROdSHbnggFIqAUBrXfKtN6jUEOBDAhd6rwMWA+uBhcCZWut7lFIHCFwEvlQp9TiBwu7V4OufAxZord9pZd13AncCJCUljZs7d+63jdum2tpawsPDO3UbZuKpc/P3+iSqnfCA7Tuo4kS2fRRCRmIqMVdcY3S842it+eqrr6ivr2fixImn9MLrnfHea6158Mt6Dtf4efCMUEJs5jw2raf93rfUk9vfk9sOPbv90vbOb/usWbM2aK2zWk4/4ThqKnAE8x+AHwIqOMkHPKa1/vM3DaK1zlFKPUCgMKsFNgNe4DfAOa1FaW01J1j3M8AzAFlZWbqzd3VlZ2d3md1pp8JvH36Z3eOSubPkXeyxDRxcZiPUp7nogYexhZqvtyonJ4fq6mouuugixo0bd0rX3RnvffbOYnLKv+QPFw3lvKnpp3Tdp1JP+71vqSe3vye3HXp2+6XtMw3bflu7Pu8lcLbneK11nNY6FpgITFVK/eTbbFRr/ZzWeqzWegZQTmCctnRgc7A3LQXYqJTqBRwGUpu9PAUo+DbbFydvz85c5g8ZRkZNHdNj36Rhfxrl1XYmzjzblEWaz+dj8eLFxMfHM3r0aKPjtMvn1/xjQS59Y0O5YaIMbiuEECKgrULtZuC64HFhAGit9wE3Bud9Y0qpxOB9X+By4GWtdaLWOk1rnUagOBurtS4E5gHXKqWcSql0YACw7ttsX5y8/366gyOhVi5tfBmr38bBZYporRh91w+Njtaqr776itLSUs4666xTusuzs3zwVT65hTX8/NxBOGzmG4dOCCGEMdq6hJRda13acqLWukQpZf+W231XKRUHNAF3a60rTrSg1nq7UuotYAeBXaR3a61933L74iSsWLqYjwenMaqikrGxn1O3YxDuJgsXXXk9FltHrkJ2ejU2NpKdnU1qaiqDBpn/zMn6Jh+PLNzFiOQoLhzR2+g4QgghTKStb9nGbzivXVrr6e3MT2vx/H7g/m+zTfHNvZrbRNVgC5fV/RdLQzj7V/iJ81sZePV1Rkdr1erVq6mtreXqq6/uEoPFvrz6APmVdTx45UgsFvPnFUIIcfq0VaiNUkpVtzJdATJuQA/x5muvsGDAcKaX5jMgbj216wfT4IcBM1s778N4breblStXMnjwYPr27Wt0nHZVeZp4YuleZgxMYGr/+PZfIIQQokc5YaGmtTb/gT2i081vSMWn4BLXf1DuOPZu0KSFx+AcNsLoaK1avnw5TU1NnHnmmUZH6ZD/LttLdX0T980ZbHQUIYQQJiRHLYsTeu6Zp1ncL4qzSnbRO2wPVRsS0Vpxxr2/NDpaqyorK1m/fj1jxowhISHB6DjtKq6u58VV+7lkVB+G9ok0Oo4QQggTkkJNnNDnrqHYNFwQ8SSqOon9O/wMiOtF/MhRRkdr1bJlywA444wzDE7SMY8v3YPXp7n3rIFGRxFCCGFSUqiJVj3+2BMsTwnn3JItxIYcpnxdDErDjJ//yuhorSotLWXTpk2MHz+eqCjzX8j8ULmHN9Yd5OrxqaTFm28cOiGEEOYghZpo1aK4MYR5NedHPQUVyeTt0QxJTicqs7/R0VqVnZ2NzWZj2rRpRkfpkH8v3o1Sih/NNufPUwghhDlIoSaO8/AjT7Cmdyjnla0lwlVM2aoIbH6Y/gtz9qYVFhaybds2Jk2a1CWuRbenuIb3Nh7m5kn96B0VYnQcIYQQJiaFmjjOopQsoht9zIl5Fl3al0OHFSP6DyEsOcXoaK1asmQJLpeLKVOmGB2lQx5ZuIsQu5Xvz8w0OooQQgiTk0JNfM3fHnqKTQlOLihfhstRSfEXITh8fqb84tdGR2vVwYMH2bVrF1OnTiUkxPy9U9vyq/hkayG3Tc8gLtxpdBwhhBAmJ4WaOMbjdrMwYxwJ9V7OinsJf2E6R4otjB4+BldcnNHxjqO1ZvHixYSFhTFx4kSj43TIw5/vJDrUzu3T042OIoQQoguQQk0c89CTb5ATa+fCqk9xWN0cWWbD5fMz8Wf3GR2tVfv27SMvL48ZM2bgcDiMjtOudfvLyd5Zwl1nZBLp+raXyxVCCNETSKEmAKipquSzgaNJcTcyM+51fPmZlFTaGDdhKo6ICKPjHedob1pUVBTjxo0zOk67tNY8/PlOEiKc3DI5zeg4Qgghuggp1AQAD/3vffZF2ji/dh421UTBckWoT5P1o58aHa1Vu3btoqCggBkzZmCztXXJWnNYtbeMdfvLuXtmJiEOuTqbEEKIjpFCTVBTVcnCASPo625ketw7ePMzKauxMW7yDGwmPEBfa012djbR0dGMHj3a6Djt0lrzr4W76BXp4toJ5r9QvBBCCPOQQk3w4P8+YH+kjfNrP8Si/BQshzCfZtwP7zU6Wqt27tzJkSNHmDFjBlar+XunVuwpZX1eBXfPysRlN39eIYQQ5iGFWg9XWlbM5wOH09fdyNT4d/AezqC81sa4qTOxOs03fMTR3rSYmBhGjTLnNUebO9qb1ifKxdXjU42OI4QQoouRQq2He/SFT8mLsHFB7ftY0BR8EehNG/uDHxsdrVW5ubkUFhZ2md605btL2Xiwkh/M6o/TZv68QgghzEUKtR6stKyYzwcNI622gSnx7x/rTcuaPtuUvWl+v5/s7GxiY2MZOXKk0XHadbQ3LTk6hKuzpDdNCCHEyZNCrQd79MXPOBhu5Xz3e1jwk78Mwn2aMT/4kdHRWpWbm0tRUVGX6U3L3lXCpkOV3D2rPw6bfNSEEEKcPPn26KFKy4r5bOBQ0msamBz/AU2HMqnw2MiacRZWu/kGj23emzZixAij47RLa82jwd60K8eZ8xqpQgghzE8KtR7qXy9+zqFwK+fXvYsFyF+mifDD6O/fbXS0VuXk5FBcXMwZZ5zRJXrTlu4sZvPhKn40W3rThBBCfHPyDdIDFRXm89nAIWTU1DMp7gOaDmZQWWcj6wzz9qYtW7aMuLi4rtObtmg3qbEhXCG9aUIIIb4FKdR6oMdeW8LhcCvne97FgiJ/eaA3bdT3fmB0tFbl5uYe602zWMz/K5u9q4Qth6v40awB2K3mzyuEEMK85FukhwlchWAIaTUNTIz/iKbDgd60cdPPNGVvmtaa5cuXExsby/Dhw42O0y6tNY8t3k1ydAiXjU02Oo4QQoguTgq1HubhZ94nL8LGHM88LPgoDI6bNtqkvWm7d++msLCQ6dOnd4netNX7yth4sJK7ZmZKb5oQQohvTb5JehCP283iASNIcTcxNe49fEcyKau1MmbiVFOOm3a0Ny0qKqpLjJsG8PiSPSRGOLlKjk0TQghxCkih1oP888nX2RNl49zqT7FYmij8QhHi8zPu7nuMjtaqAwcOcPjwYaZOndolzvTckFfBqr1l3DkjQ67pKYQQ4pSQQq2H8LjdLM0YTVKdlxnxb+ErzKCkysqo0eOxhYYZHa9Vy5cvJzw8nDFjxhgdpUOeWLqHmFA710/sa3QUIYQQ3YQUaj3Ev598mR2xds6tXIrN5qF0lQ2nz8/4H//U6GitOnjwIPv372fKlCnY7Xaj47RrW34VS3KLuX16BqEOm9FxhBBCdBNSqPUQy/qNJa7ex6y41/EXp3Ok1MKIISNxREYZHa1VX3zxBSEhIWRlZRkdpUOeWLqHCJeNmyb3MzqKEEKIbkQKtR7goX8+waYEJ+dWrMRhr6ZstRO7z8/Ee39hdLRWHTlyhN27dzN58mQcDvMNGdLS7qIaPt1eyK1T0oh0mb/3TwghRNchhVoPsLzPWKIa/ZwZ8zK6tB/5hYphmYNxxcUZHa1Vy5cvx+l0MmHCBKOjdMiT2XsJsVv5ztR0o6MIIYToZqRQ6+YeffQJvuwVwjllX+JyVlC+LgSbz8+kn5izN624uJicnBwmTJiAy+UyOk678srcfLgpnxsm9iU2zPy9f0IIIboWKdS6ueXxowlv8nN29AtQkcKhgzA4NYOwXr2NjtaqL774ArvdzqRJk4yO0iH/zd6LzWrhjukZRkcRQgjRDUmh1o09+fiTrO4TylmlmwlzlVC5PhKr1ky+9+dGR2tVeXk527ZtIysri7Awcw4Z0lxBZR3vbjzMteNTSYw0f++fEEKIrkcKtW5secQIXD44J+p5qOpN3l4fA5NSieyXZnS0Vq1YsQKLxcKUKVOMjtIhzyzfh9bwvTMyjY4ihBCim5JCrZt68dn/sTw1nNmlOUSFFFD9VQxKKyabdNy0qqoqNm3axNixY4mIiDA6TruqGjRvrDvI5WOTSY4OMTqOEEKIbkoKtW4qm0ysGs4Jfx5Vm8iBHB8ZMQnEDBxkdLRWrVmzBq11l+lNW5TXRKPPz13SmyaEEKITSaHWDX384XssTYthRslB4sP2U7s9Ab9STL7zB0ZHa1VdXR0bNmxg+PDhxMTEGB2nXe4GL4sPNnHu0F5kJIQbHUcIIUQ3JoVaN/RJfggNVsWZjpdQ9VEc+KqJFFc4iVnmHJds/fr1NDY2dpnetLlfHsLjhe+dIWd6CiGE6FxSqHUzGzeuZVFmEhNLy+gXvZm63Sk0KSsTb7jF6GitampqYs2aNWRmZtK7tzmHDGmuyefnuS/2MSjGwpi+5u/9E0II0bVJodbNvP7FYaodFs7iFVRTCAfXNJKgbKSde77R0Vq1ZcsW3G43U6dONTpKh3y8pYCCqnrOz5BLRQkhhOh8Uqh1I4cO5rGofzrDKt0MjV1Bw4E0PH4bWRdcanS0Vvn9flauXEnv3r1JTzf/5Ze01jy9bB+DkiIYGW81Oo4QQogeQAq1buTpt5dTGGrhrIb3QFspWOkj0g+Db7jZ6Gitys3Npby8nKlTp6KUMjpOu7J3lZBbWMOdMzK6RF4hhBBdnyGFmlLqHqXUNqXUdqXUvcFpDymlcpVSW5RS7yulopst/yul1B6l1E6l1LlGZDY7j9vN0swh9K1tJCtuPt78DCobrIyZOhOLxXz1uNaalStXEhMTw5AhQ4yO0yFPL9tL7ygXF43qY3QUIYQQPcRp/wZXSg0H7gAmAKOAC5VSA4CFwHCt9UhgF/Cr4PJDgWuBYcAc4EmllOx3auGRJ19lb5SNs2s+x2LxUrzKQojPz+jv3W10tFbl5eWRn5/PlClTsFrN/3ZuOlTJmn3l3DYtHYfNfIWvEEKI7smIb5whwBqttUdr7QWWAZdprT8PPgdYA6QEH18CzNVaN2it9wN7CBR5opkV/UYTX+9jetzb+IvTKa5UjBg+BluIOUfNX7lyJaGhoYwePdroKB3yzPK9RLhsXDuhr9FRhBBC9CBKa316N6jUEOBDYDJQBywG1mutf9RsmY+AN7XWryqlHidQ2L0anPccsEBr/U4r674TuBMgKSlp3Ny5czu1LbW1tYSHGz/g6bq1X/HIhFlce2QlF/V6hOL3B1JyRDH8lruwRER2yja/Tdtra2tZv349aWlppKWlndpgnaDI7ee+L+q4IMPOlQMdgHneeyP05LZDz25/T2479Oz2S9s7v+2zZs3aoLXOajnd1ulbbkFrnaOUeoDArs5aYDNwtCcNpdRvgs9fOzqptdWcYN3PAM8AZGVl6ZkzZ5664K3Izs6ms7fREY8fshPW5Gd29CtQkUJBkYXhfdOZfdHFnbbNb9P2999/H7vdztVXX01oaOipDdYJfv3+Vuy2w/z+uhkkRrgA87z3RujJbYee3f6e3Hbo2e2Xts80bPuGHGyjtX5Oaz1Waz0DKAd2AyilbgEuBG7Q/7+r7zCQ2uzlKUDB6cxrZs//7xlWJYdyZukOwlwlVG2KwqJh0g9/YnS0VlVVVbF161bGjh3bJYq0kpoG3tlwmCvGphwr0oQQQojTxaizPhOD932By4E3lFJzgP8DLtZae5otPg+4VinlVEqlAwOAdac7s1ktV/2xajgz4kVUbSJ5uV4yYhOJyjDnxcKPXnx98uTJRkfpkJdWHaDJ5+eO6eYf500IIUT3c9p3fQa9q5SKA5qAu7XWFcFj0ZzAwuAYVWu01ndprbcrpd4CdhDYJXq31tpnUG5TCVx8PT1w8fXE/dSuHY5febvExdejo6ONjtMud4OXl1cfkIuvCyGEMIwhhZrWenor0/q3sfz9wP2dGqoL+vSwk4ahitnBi6/nfdVIiiuCxHHjjY7Wqg0bNnSpi6+/vf4Q1fVe7pghF18XQghhDBkQqovaszOXJZm9GVdaSVr0Zur3pdKorGRdeZ3R0Vrl8/lYt24daWlpXeLi636/5oVVBxjTN5px/eTi60IIIYwhhVoX9dz8DZQ7Lcz0v4PyOTi8ppEYbSH9wkuMjtaqnJwcqquru8yxaYtzi8kr83DbNDk2TQghhHGkUOuCPG43yzMGkVbTyJjYhTTlZ1DTZGX09NmmvFwUwOrVq4mNjWXAgAFGR+mQ51bso0+UiznDehkdRQghRA9mzm910aZ/P/kye6NszK5ZjLJ6KV0DLp+fkbd/z+horTp06BD5+flMnDjRtIVkc9sLqlizr5xbpqRhs5o/rxBCiO5LvoW6oDXJo4lu8DM97h10aTqFFYqhA4eZ9nJRq1evxuVydZnLRT23Yj+hDqtcLkoIIYThpFDrYv731NOs6+VidtlmHI5Kqr4Kw+r3M+Hue4yO1qrKykpycnIYO3YsTqfT6DjtKq6u56PNBVw1LoWoELvRcYQQQvRwUqh1MSttA7BpmBnxKqo2iYN7msiISyIsOaX9Fxtg7dq1AEycONHgJB3zypo8vH7Nd6bKSQRCCCGMJ4VaF7Lks0/I7hfD1JIC4sIP4M5JwG+xMOE75jw2raGhgY0bNzJ06FCioqKMjtOu+iYfr609yJmDk0iLDzM6jhBCCCGFWlcyb1cT9TbFLPsrqIZw8jY20NvmotfESUZHa9VXX31FQ0NDlxmS4/2v8il3N8qQHEIIIUxDCrUu4tDBPJZkpjKyvIb+MV/SkNePBmyMu/hyo6O1yu/3s3btWlJSUkhJMedu2ea01jy/Yj9De0cyKSPW6DhCCCEEIIVal/HMW9kUh1iY1fgB+G0UrGoi0g8DrrrW6Git2rVrFxUVFV2mN+2L3aXsLq7ltmnpBK81K4QQQhhOCrUu4ouMoaS4mxgftwDfkQwqG6yMmjDVtOOSrV69mqioKAYPHmx0lA55bsV+4sOdXDjK/Je3EkII0XOY81tefM1D/3yC3Bg7s6uWo2wNlK2z4vD5GX3XD42O1qojR46Ql5fHhAkTsFqtRsdp157iGpbtKuHmyf1w2syfVwghRM8hhVoXsDZpNBFNfs6IeQvK+1JQDIPTBuCIiDA6WqtWr16N3W5n7NixRkfpkOdWHMBhs3DDRBngVgghhLlIoWZyr7z4HKv6hDKzJBeXq5SqLZFY/JoJP/ix0dFaVVNTw7Zt2xgzZgwhJr1SQnMV7kbe23iYy8ckExdu/gF5hRBC9CxSqJncivpUAGaFvYryxHEwp5F+kTFEZWQanKx169evx+/3d5kBbud+eYgGr18GuBVCCGFKUqiZWM7WTWSnxzOxpIykyJ3U7e2Nz2Jl3LU3GR2tVV6vlw0bNtC/f3/i4uKMjtMun1/z6po8JmXEMqiXOXcjCyGE6NmkUDOxlxfuoMphYTrvorxO8tc1EouFfmefa3S0VuXk5FBbW9tletMW5xSRX1nHrVPSjI4ihBBCtEoKNZPyuN2sSB9IWk0jI2OX0nQknRqvlZFTZxkd7YTWrVtHTEwMmZnm3C3b0sur8+gd5eKsIUlGRxFCCCFaJYWaSf33qRfZHW1jZvVylLWJii8tOH1+Rt1+p9HRWnXkyBEOHTrEhAkTTDu2W3N7imtYsaeUGyf1w2Y1f14hhBA9k3xDmdS6hJGENWmmxbwLFX3JL/YzqF9/bKHmvFj4unXrsNvtjB492ugoHfLK6jwcVgvXjE81OooQQghxQlKomdAH77zJyuRwZpTsISSkmNrtUSgg684fGB2tVR6Ph61btzJy5MguMSRHTX0T72w4zIUjexMvQ3IIIYQwMSnUTGhxURhei2KG6w0s9VHkbW0gJSSCmEHmvBzTV199hdfrZfz48UZH6ZD3v8rH3ejjZjmJQAghhMlJoWYyhw7mkZ3eh7GlVfSN2UzdgVSaLFbGXnKl0dFa5ff7+fLLL+nXrx+9evUyOk67tNa8tOoAo1KiGJ0abXQcIYQQok1SqJnM/95aQkmIhWneeeC3cWRNE5F+yLj0cqOjtWr37t1UVlYyYcIEo6N0yKq9ZewtcXPz5DSjowghhBDtkkLNZNb0G0Zvj5cJsZ/hK8qgssHC8HGTTHsm5bp164iIiGDwYHPulm3ppVUHiA1zcMHI3kZHEUIIIdplzm//Hurxx55gS7yDMyrWoex1VG2wY/P5Gfs9c55EUFpayt69e8nKysJqtRodp12HKzwsyini2vGpuOzmzyuEEEJIoWYi68KG4fRpzoh6E1Xdh0OHvfTvlYIzJtboaK368ssvsVgsjB071ugoHfLa2oMA3DCpn8FJhBBCiI6RQs0klnz2CctTo5hWcpjIsMO4d8ahlYXx37nD6Git8nq9bNq0iWHDhhERYf7rZNY3+Zi77iBnD00iOdr8Q4gIIYQQIIWaaXy8s4F6m2KabS6qMYyDG+vpZXOSOM6cQ14UFRXR0NDQZU4i+HjLESo8TdwiJxEIIYToQqRQM4GaqkqWZfRlaIWHgbFraTzUj3psjDn3QqOjtUprTX5+Pr179yYlJcXoOO06OiRH/8RwJmfGGR1HCCGE6DAp1Ezg8WfeID/MynTPQgDK1kGoTzP4+psMTta6w4cP4/F4GD9+PEopo+O0K+dIDVvzq7h5cr8ukVcIIYQ4Sgo1Eyh0JQMwMmQtypNEaYWP5PgkLDabwclal5+fD8CAAQMMTtIxmw9XAjBrUKKxQYQQQoiTJIWaCRRGxZBU5yc2pAifJxav1UJCv3SjY51QUVERdrud8PBwo6N0SM6RasKdNjmJQAghRJcjhZoJHIwKoW+1B7+rEl9VoJhIGjHS4FQnVlRURFhYWJfZjZh7pIZBvSKwWLpGXiGEEOIoKdQMtmdnLgfDrSTXB3YneosDb0nviVONjHVCfr+f4uLiLtObprUmp7Cawb3MP4SIEEII0ZIUagb75NPF+CyKXmo3AJ5DTYT6NCEJ8QYna115eTler5ewsDCjo3RIQVU9NfVeBveONDqKEEIIcdKkUDNYkTcKgGRnLpbGcCrK6ogJNW8RVFRUBNBletRyj1QDMLS39KgJIYToeqRQM1hxeBJOn6af6zC6LoFaiyIu0bwXDC8qKkIpRWhoqNFROiS3sAaAgUlSqAkhhOh6pFAzWH50BP1qvNjCivDWRoJSJA4cZHSsEyoqKiI+Pr5LXIQdAmd8psaGEOGyGx1FCCGEOGnmHKirC3jgkd9RZ1Ms27L0W61n76ALGFmVh7Y2UlhowRsSzt6oFI6s2XqKkp5aeYfziUnow85yH6H7y42O066t+VUM7iXHpwkhhOiapFD7hpYMGcRm1/BTsq7M+CUAFOpk6tJi2JKzEXI2npJ1d4Z5lXVs99XDutVGR+mQK8ea/zJXQgghRGukUPuGzsjJZaJj77dej0X7SPJZyVNX0TskHUdqIiEpad8+YCdRFgtnxvdi67ZtjBo1yug47bIoxZi+0UbHEEIIIb4RKdS+oV//9K9kZ2czc+ZMo6MYwnckh6n9zTmEiBBCCNFdGHIygVLqHqXUNqXUdqXUvcFpsUqphUqp3cH7mGbL/0optUcptVMpda4RmYUQQgghTrfTXqgppYYDdwATgFHAhUqpAcB9wGKt9QBgcfA5SqmhwLXAMGAO8KRSqmucciiEEEII8S0Y0aM2BFijtfZorb3AMuAy4BLgpeAyLwGXBh9fAszVWjdorfcDewgUeUIIIYQQ3ZrSWp/eDSo1BPgQmAzUEeg9Ww/cpLWObrZchdY6Rin1OIHC7tXg9OeABVrrd1pZ953AnQBJSUnj5s6d26ltqa2t7TIj9J9qPbnt0LPb35PbDj27/T257dCz2y9t7/y2z5o1a4PWOqvl9NN+MoHWOkcp9QCwEKgFNgPeNl6iWlvNCdb9DPAMQFZWlu7sA/178skEPbnt0LPb35PbDj27/T257dCz2y9tn2nY9g05mUBr/ZzWeqzWegZQDuwGipRSvQGC98XBxQ8Dqc1engIUnM68QgghhBBGMOqsz8TgfV/gcuANYB5wS3CRWwjsHiU4/VqllFMplQ4MANad3sRCCCGEEKefUeOovauUigOagLu11hVKqX8AbymlbgMOAlcBaK23K6XeAnYQ2EV6t9baZ1BuIYQQQojTxpBCTWs9vZVpZcCZJ1j+fuD+zs4lhBBCCGEmhuz6FEIIIYQQ7ZNCTQghhBDCpE77OGqni1KqBMjr5M3EA6WdvA2z6slth57d/p7cdujZ7e/JbYee3X5pe+frp7VOaDmx2xZqp4NSan1rg9P1BD257dCz29+T2w49u/09ue3Qs9svbTeu7bLrUwghhBDCpKRQE0IIIYQwKSnUvp1njA5goJ7cdujZ7e/JbYee3f6e3Hbo2e2XthtEjlETQgghhDAp6VETQgghhDApKdSEEEIIIUyqxxRqSqk5SqmdSqk9Sqn7mk2/Sim1XSnlV0qd8PRbpVSsUmqhUmp38D4mOD1OKbVUKVWrlHq8jdenK6XWBl//plLKEZyulFL/CebaopQaeyrbHdyGKdsenDdTKbUpmGPZqWpzs/Ub3fYfBretlVLxzabfEHy/tyilVimlRp2qNrfYvlnbH6WU+kgptTmY4zunqs3NttFZbT9bKbVBKbU1eD/7BK837DMf3I4p2x+c11U/9x1tu2GfexO3vdM/88HtdFb7JwR/ZzcF23DZCV5/6j/3WutufwOswF4gA3AAm4GhwXlDgEFANpDVxjoeBO4LPr4PeCD4OAyYBtwFPN7G698Crg0+fgr4fvDx+cACQAGTgLU9qO3RwA6gb/B5Yjds+xggDTgAxDebPgWICT4+71S/712g/b9utq4EoBxwdJG2jwH6BB8PB/JP8ve+Uz/zXaD90XTdz31H227I597kbe/Uz/xpaH8oYAs+7g0UH33ewd/7b/y5P6V/HMx6AyYDnzV7/ivgVy2Wae/N2wn0bvYm7Wwx/1ZO8IUVfGNKm73Jx/IATwPXtbadHtD2HwB/7a7ve4vlDtDsj1aLeTGc4I9ed21/MMuTwd+PdGAPYOlKbQ9OV0AZ4GxluiGf+S7Q/i7/uW+r7W393reYd8o/92Zue2d/5k9z+9OBIloUap31ue8puz6TgUPNnh8OTjsZSVrrIwDB+8STeG0cUKm19ray/VORrS1mbvtAIEYplR3sSr/5JHO1x+i2d9RtBP7TOtXM3P7HCfyHWwBsBe7RWvtP0brh9LX9CuArrXVDi+lGfuZP1TY6q/3d5XN/orZ3VGd87s3c9s7+zEMnt18pNVEptZ1A/rua/X4f1Smfe9tJNqCrUq1M0ybZfmdnM3PbbcA44EwgBFitlFqjtd51GrZtCkqpWQT+YE/rjNW3Ms0s7T8X2ATMBjKBhUqpL7TW1ado/Z3edqXUMOAB4JyT3P7peF/M3P4u/7lvp+0deX1nfe7N3PbO/sxDJ7dfa70WGKaUGgK8pJRaoLWu7+D2v3G2ntKjdhhIbfY8hUBVf0JKqReCBw1+EpxUpJTqHZx3dP90R5UC0Uqpo4Vx8+2fdLaTZPa2f6q1dmutS4HlwKiTWHd7jG57m5RSI4FngUu01mWnar3NmLn93wHe0wF7gP3A4FO0bujktiulUoD3gZu11ntbWZ2Rn/lvtI3T3P4u+7nvQNvb1MmfezO3vbM/83Ca/uZprXMAN4Fj9ZrrlM99TynUvgQGBM/GcADXAvPaeoHW+jta69Fa6/ODk+YBtwQf3wJ82NGN68AO6aXAla28fh5wc/CMkElA1dFu11PEzG3/EJiulLIppUKBiUBOR9fdAYa2vS1Kqb7Ae8BNp7AnoSXTth84SKBHBaVUEoGDfPedonVDJ7ZdKRUNzCdw7MvKE6zLyM88mLv9XfZz35G2t+U0fO5N23Y6/zMPndv+9KMFmFKqXzD/gRbr6pzPfUcOZOsONwJnXOwicEbIb5pNv4xApdtA4ODAz07w+jhgMbA7eB/bbN4BAmew1AbXNbSV12cA6wgcQPk2wYMwCXSHPhHMtZU2DnLsbm0PzvsFgTPAtgH3dsO2/zg4z0vgv6dng9OfBSoI7ArYBKzvpr/3J2p/H+Dz4O/8NuDGrtJ24LcE/pve1Ox23JmLJ/q95zR85s3c/uC8Lvm5P4m2G/a5N3HbO/0z38ntvwnYHmz3RuDSE7z+lH/u5RJSQgghhBAm1VN2fQohhBBCdDlSqAkhhBBCmJQUakIIIYQQJiWFmhBCCCGESUmhJoQQQghhUlKoCSGEEEKYlBRqQgghhBAmJYWaEEIIIYRJSaEmhBBCCGFSUqgJIYQQQpiUFGpCCCGEECYlhZoQQgghhElJoSaEEEIIYVJSqAkhhBBCmJQUakIIIYQQJiWFmhBCCCGESUmhJoQQQghhUlKoCSGEEEKYlBRqQgghhBAmJYWaEEIIIYRJSaEmhBBCCGFSUqgJIYQQQpiUFGpCCCGEECYlhZoQQgghhElJoSaEEEIIYVJSqAkhhBBCmJQUakIIIYQQJiWFmhCix1BK3aqUWtGB5Z5SSv2u2fPvK6WKlFK1Sqm44H1GJ+TbrpSaearXG1z395RSj55sDqXUH5VSr3ZGpnYyvKeUmnO6tyuE2UihJkQ3oJTKVkpVKKWcLaa/qJRqDBYW5UqphUqpwc3mf61wUUpFKqVWKqXeVUrZm013KKVylVKHOyH7TKWUP5ix+W3yqd5WR2mt79Ja/yWYzw48ApyjtQ7XWpcF7/d9m20E35u/ttjuMK119rdZ7wm25QB+CzzUkeU7K8dJ+gdwv8EZhDCcFGpCdHFKqTRgOqCBi1tZ5EGtdTiQDOQDz51gPTHAIiAPuEZr3dRs9i+A4pPMlX0SvUMFweKn+W31yWyvEyUBLmC70UG+hUuAXK11vtFBOkprvQ6IVEplGZ1FCCNJoSZE13czsAZ4EbjlRAtpreuAt4DRLecppeKBJQSKkRu11t5m89KBG4G/n8rQHRUs+P6qlFoV7Gn7KLj78TWlVLVS6stgsYpSKk0ppZVSthavv72V9Sql1L+UUsVKqSql1Bal1PDgvBeD2xwI7Ay+pFIptSQ4Xyul+gcfhyil/qmUyguuZ4VSKiQ4722lVGFw+nKl1LDg9DuBG4BfHm1TcPoBpdRZwcdOpdSjSqmC4O3Roz2mwV7Iw0qpnwXzH1FKfaeNH+N5wLIW7b84uIuzMvgzGtJs3rEc7bw3MUqpj5VSJcEe3Y+VUinN5qcH212jlFqklHqi+W5UpdSk4PtaqZTa3Ephnw1c0F4OIbozKdSE6PpuBl4L3s5VSiW1tpBSKgy4DtjTYlYsgS/xtcB3tdb+FvMfA34N1J3K0CfpWuAmAr2CmcBq4AUC2XOAP3yDdZ4DzAAGAtHANUBZ8wW01ruAYcGn0Vrr2a2s52FgHDAlmOeXwNGf4QJgAJAIbCTwHqG1fib4+MFg7+FFraz3N8AkAoX1KGACgd2XR/UCogj8TG4Dngj2irZmBP+/4CRYgL4B3AskAJ8AHwV3kZ4MC4H3oR/Ql8DvyOPN5r8OrAPigD8SeA+PZkgG5gN/JfBz+znwrlIqodnrcwi0XYgeSwo1IbowpdQ0Al+Sb2mtNwB7getbLPZzpVQlUANMo9mXZVAqgWLlBa21brH+ywCb1vr9TojfXJ9gr0rzW1iz+S9orfdqrasIFD97tdaLgj1/bwNjvsE2m4AIYDCgtNY5WusjJ7MCpZQF+C5wj9Y6X2vt01qv0lo3AGitn9da1wSf/xEYpZSK6uDqbwD+rLUu1lqXAH/i6+9dU3B+k9b6E6AWGHSCdUUTeP+PugaYr7VeGNzF/TAQQqDY7LDg8Xrvaq09WusaAseUnQGglOoLjAd+r7Vu1FqvAOY1e/mNwCda60+01n6t9UJgPXB+s2VqgtmF6LGkUBOia7sF+FxrXRp8/jrH7/58WGsdDaQR6PFo+WW+mUBvxgKl1LGCJ1goPQj8qKNhmhdaBIrCj5tNu6+NlxZoraNb3NzN5hc1e1zXyvPwjmY8Smu9hEDvzxNAkVLqGaVU5EmuJp7A8Wt7W85QSlmVUv9QSv2/9u47vK3y7OP499G0hveKHWfvvRwgzIQZKLvQQqFAS6EFWiid0LeDjlDKaJkdbCiFQAkjjISEhAQCJJC9d5zEGY63rWWt5/1DCjUhw7FiH8m6P9fly/LRkc7vjiXrznPOec4WpVQTUNHqMW1RSux4wf22x5ftV9t6FzXg49D/DvXEmtKDPnd8FHUnsdG5NlNKOZVS/4rv9m0CPgRylFLm+DbqtNa+Vg/Z2ep2L+Dyg7xmSlqtkwk0HE0mIboaadSESFHx46C+AZwWPw5qL3A7sVGbr+wu0lrvAG4DHtp/DFWr+x4idpbd7P3HaRHbZdcb+Cj+3K8BJfFt9T5YptaNFrAAOL/VsnsSr/qI9jd3zlbLuh1qZa31w1rrccR2bw4kdtLE0agBAsR2xx7oW8QO4j+T2C7K3vHlav/mj/Dcu4k1M/v1jC9rj5XE6jvocyulFLGR1aM92eCnxBr/47XWWcR2JUOsxj1AnlKq9e+iR6vbO4F/H9Ccuw54nQwh9h8JIdKWNGpCpK6LgQgwlNhxTKOJfbB9ROy4ta+I717aDdx4kPvuBR4C3ldKDQJWE/tg3f/c3yM2kjWaL4+MJI34LsJdwNXxEa3vcvAmCqXUeKXU8So2/YaXWMMVOcrtRYGngb8qpUrj25wQP+g/E2ghdtybE7j7gIdXAYebi+0l4NdKqUIVO9njt0B75zN7l/guybhXgK8ppc6I1//TeNZPjvJ5M4mNaDYopfJodayg1no7sV2Zd6nY9C4TgNbH4r0AXKCUOif+75YRP0mirNU6pxHb1S1E2pJGTYjUdS2xY7d2aK337v8itjvvKtXqzMcD3EfsbEP7gXfE5w57EpgD9DrgeeuAaPzno2po2qBUfXUeta+387luIDYyVktspOxQzUcW8ASx3YLb4+vf347t/QxYBXxO7N/oL8T+tj4ff95dwFpiZ+a29hQwNL7b742DPO+fiDU6K+PPvzS+rD3eAgYrpUoBtNYbiB0j9gixUcELgAu01sGjfN4HiR3bVkOsvpkH3H8VMIHYv+2fgJeJNYRorXcSG3H8FVBNrPn/OfHPJaXUeMAbn6ZDiLSlDjh2WAghRBekYlOCDNVa/9jADC8Tm8/tiGfpKqWmAU/FT5QQIm1JoyaEEKJDxEfF6oBtxKZDeQOYoLVeZmQuIVLJoXaNCCGEEInqRuwklHygErhJmjQhjo6MqAkhhBBCJCk5mUAIIYQQIkl12V2fBQUFunfv3h26Da/Xi8vlOvKKXVA61w7pXX861w7pXX861w7pXb/U3vG1L1mypEZrXXjg8i7bqPXu3ZvFixd36DbmzZvHxIkTO3QbySqda4f0rj+da4f0rj+da4f0rl9qn9jh21FKbT/Yctn1KYQQQgiRpKRRE0IIIYRIUtKoCSGEEEIkKWnUhBBCCCGSlDRqQgghhBBJSho1IYQQQogkJY2aEEIIIUSSkkZNCCGEECJJSaMmhBBCCJGkpFETQgghhEhS0qgJIYQQQhxCw5rlhm6/y17rUwghhBCiPbYuWcSyOY/j77uHLYOK6fH6VMZdcoUhWaRRE0IIIUTaq6+q4oMnf0+o5y7Wd8vj0/KTWMVoIspC74b5jDMolzRqQgghhEhLPq+XWQ/+hmDxdip62Pl8wng+59sElIOslmYyfDOwhTYx9ILfG5ZRGjUhhBBCpI3ayh3Mf+5eWkr3srkki2UTRrOMy/ArJxnhAI7mz7GHlzEyrw/fO/5i7JtqGV/Qx7C80qgJIYRBdq1dxeZFC6hrqKY+3IIPTcCsCZvNhK0mghYTYYuZkMVMyBT70ihQEI1/10oRBVAKrRUahSmqMesoKqoxRzXmaOy2KapRUY2KRDGFwyitsVgUVoeD7OIiykp7Mqh7H0rc+eTanVhM5nbVpbUmFIjQ4g8TDkYIh6JEQlFCwQiRYJRwKEo4FCEcjBIJR0HHHqOjoNFf/llrlAKT2YTJrFAmhTn+ff8yk1lhsZqw2M1YbWYsNjNWuyn+PfazyaSO6e9OpJbtK5ez+O1/4O9Zy4aiPJZNGMcKriGkbLhCPrKbVmJlFUOzirhqzGQu6XXzF6//eZvmGZpdGjUhhDgGfF4vq2e8yZ6ta6jVfpqcZprcNpqdNprtdpqsDpotTvymDHwmB17lxI8Lb69yIr2tRsePqQfqG7Doamy0YNdBHNEAzkgLjkgLjnCQf7z6JBmhEPZQEGtLEFMoRCQaxmOyYNU55DZkk1ObRbbPgSmJJhYwW02xJs5uwu6wYnda/vflsGJr/bPTit1hwe6y4HDbyHBbpdFLMT6vlw8e+zPejAoqyyxszO7OqhPOYCv90cpEVrCZnOaPCbOJMXm9uXbCeZxVciMmU/K8ZveTRk0IIdrA5/Wy6D9PsG/vJv6x6g1qs+zUuzOoz3BQZ8ui3pxDTV4hdXmXotVXR6IytJ+saDPOSABHqIXcSB0Zob044k2PIxTEEg5jjkQxR8KYolFMWmMxK6w2G26bDbfNgdvmwG6xYXfYcdhcZDic2NxObGYr0aYmAo1NeBrq8Hma8AX8+IMBgpEIYSKEo5qIihAxK0IWRchmIWg1EbRYCFrMBC1mQmYzQbOFFrOZoNlMwGzHb8mg0epgj8rDhwufch3238qpvbgjXtwRD+5wAFeoBWdLCxktAWwtASKRIC1ZNrp3L2Vst4EMzSujlysfq9mMUgqlQCkFJlCABqIRjY5oohFNJBJFR2O39/8cCUUJByOEWqKEWiLx2xFCwdjIXTh+e/9IX9AfpqnGT4svTIsvTKglcuiCFGQ4rUTNUeoWL8GRacPhtuLIjDVxjkwrDpcNR5YVZ5Ydh9uKksau03326ovs2DyXhh5+Nhfks6Z8CGuYjE+5UTpKmXcvOYF3sZpqOal4ANeMvZAJhf2Mjn1E0qgJIUQr21cuZ9W7U2mwNFKVn8G+7Az2ObPYZ81nT/9+VPU/iYj63wiY0lFyIw3kBZroG6hitH8LjhY/KugnlBEhu6SI0WX9GVsykP6ZRbisdsNq8zcHqd/ro36vl/oqHw17fdTv8NJUG4h1Q3EmsyIzL4PM/PhXXgbOLBsbNqyiLKeOmt2fUO+vo1lF8GWY8DnM+DMseDOseGx2PFY7XosDj8VJvc3NDoppJpOIOuAjR8Mzu6O4d20mK7KcrJCPrKAfV8CPI+BHBX2ErGFcpfmMLRvA2JKBDMgqJtOaccz/baKRKEF/hIAvRNAfpsUbJuAN4fcE8TeH8HtC7Ny6C6UU9Xt97PEECXhCaP3V51ImhTPTijPbjjPbhivLFrudZcMVX+bMsuHMtmGxtm/3soD3/3E/9Z61VHU3sS03h615vdhw3BXUqQIAsoPNlDRsoNqylv4OFxcOPI0r+/yUbJvD4ORHRxo1IUTa+uzVF9m54SPqCqLsynex253NLlsJFcedQa0q/GI9k45QFKql0N9IP+9yrP46mh0tdC/txkk9hjC+cAj9s4rafUzXsaajmsZqP9U7m6nZ6aFmZzPVlR78TcEv1jFbTeQUOynqncXA47uRU+Qkq8BBVn6sKTvYiFB1ZBPHTZwMXNDmLD6vl51LFrFt2WxqfLU02sHjsuBxWml27N8t7KTZ7KLRkcleRylN2dlfaepeqwb7vj1kRzaSFfaSGfSR2eLHGfBjCfiJEiBS6GRw914c130ow/K6U2TPbPOuLJPZRIbbRIb70Luh583bw8SJY7/4ORrVtPhC+JtDBDxBfE0hfE1BfI0teJuC+BqDeBta2Le9GX9z8EvN8H52p+WLps2ZZccV/+7MtsVuZ8eW2RyW2Chjmtq6ZBErZ06lKbuZXcUZVGZnsXVQXzbyHbwqE4DsUDOljZWgPyLHFmJi9+Fcedw5jMi93uD0iemwRk0p9TRwPrBPaz08viwPeBnoDVQA39Ba18fvuxO4HogAt2qt34svHwc8CziAd4HbtD7Y/2GEEOLQti5ZxMoZL9Cc38z2YjcV7iK25fVm+4Rv4Yn/oVc6SlGojjJPDYOb11FnqaOgIJNTyoZyWulwRuR058MPP2TixInGFnMAb0MLe7Y0sndrI/sqmqip9HyxK89kUuSWuug1LI+CskxyuznJ6eYkMzejU3bPOV0uBp16OoNOPf2I6/q8XjZ9NJcdK+dRF/XS4DLT5LbS7LTRZLfTbHPQaHHRZHOzx57Dusw+BNSXR0fe84J5g4+s6FKywx6yQv9r6Ox+HzoaIJhjpXfPHhxfOphh+T3o5cxr17FJJpPC4bbhcNuAw+8Ojkai+D2hWPPW2PJFQ+drDH7R1FVta8TbGCQSin7l8Rar6cvNXPZXm7mustt188IFrJ4zjaYsD7uKMqjMzmGnrYRtEyZTrYq/WK+opY7eDVvZY9pIicPMWT3HctkJZ9A/60oD0x97HTmi9izwKPB8q2V3AHO01vcope6I//xLpdRQ4ApgGFAKvK+UGqi1jgD/AG4EFhJr1CYDMzowtxCiC5j/1GNUVy9jX3fYnpdFRUYpm048h92qDIiNkpUGqhnUtA1/aA8tWQGOK+jJ5EHlnFJ8Kk6LzeAKDi4a1dRWer5ozPZuaaS5LgDERsmKemYy+MQSCsrcFPbIJK/EhdmafAdIH4zT5WLU5AsYNfnII3a71q5i/Qcz2de0h/oMaHLbaHLaaHLYabJn0Gxx0mhxU2fLZqur9Itm/AsBeHJLlMzNa77U0LkCPjICfgj7aXFouvXqztiS/owu6ks4+tUGqq1MZhOubDuubDuFZB5yPa01QX8Yb2N8ZK4xPjLXFGvqfI0t1O72snNdHcHAV4+rM5lUq12r9i83cq0aOmeWFZPZ2NfFho/ns37+dBocXvYWOKjKclLlyGGPpZjdEy6kQeV+sW5+qJ6S5mpKvGuocdQyqqAbkwcdx9mll5NrdxpYRcfrsEZNa/2hUqr3AYsvAibGbz8HzAN+GV8+VWvdAmxTSm0GjlNKVQBZWutPAZRSzwMXI42aEOIAK2a+xebl02ks9bOpMJeNffqzps9VX/yxd0Z89GjcQ3//bHzOesrzunPBiBM5q+QMMixJctblQWitadznZ+e6OirX11O5oZ6gPwyAK9tGt345jDqjB936ZlPQw43ZkhpNWaK6Dx1B96EjjrhefVUVq96ZRtXebdTbIjS6rTS5rDQ57DTbM2iyOmmyuGiyZrHLWUYj2V8+GSQKL+wC185tZEeCZL07jcwWP+4WP86AD1swAJEgOC3k9yhlRNkARhX1oac7v13NvlIqdtap00peyeFH6ULByJeaOV9TyxcNnq8xSHNtgKptjfibQwfZEDjc1i83cfHmzpFpxeG2kuGOnTSR4Wrf+8Pn9bJ02otU7VxDrTtKbY6dWncGtQ43ddYsasz57DvhQppV9hePsUdb6BaooZ93F1H/cmqcjYzIz+fsAeM5u/Rr5NkP/2/SFamO3IsYb9TebrXrs0FrndPq/nqtda5S6lFgodb6hfjyp4g1YxXAPVrrM+PLTwF+qbU+/xDbu5HY6BvFxcXjpk6d2lGlAeDxeHC73R26jWSVzrVDetefLLX7amrwrZ1DsFsjW0oy2ejqzWpGskd1B8Ad9lLWsJ3a6Fpc5hZGZZQxwd6b3tbsIzzz4XVG/eGAxrMXvFUabxWEfLHlVie4uoGrSOEsjP3cmcctJcvvviOEfAE8W1YTrNuNNyNKo8tKU6aNJoeN5gz7Fw1doymTRpVDA7mE1cEbGJtuITPqwRXx4474cYUCOIMtOIMBMoJBLKEQ5lAIHQ2jLRpTjpvizEKK7dkUm1w41LGdDkRHNeEAhP0Qin8P+w+yLMBBj6MDUBaNxa7QpiAZpkrMaidBhw+/Q+NzW/C5zDQ7rDRlZNBoddJkcdNozqKeXBrJIdqq+TXpCHmRBvJbmsj2ebD46qiy1ZCfYWdYRgkjbd0osXR+Q6ajmkiQ/321xP5NfE0tlIy0YzJ37Htt0qRJS7TW5QcuT5aTCQ5WvT7M8oPSWj8OPA5QXl6uO/o4knnz5iXdsSqdJZ1rh/Su38jaV896lw3Lp9FU1sy6wmKWnzyetYwgrKzYokF6NlZSEH4dh83H6aUj+ObY0xib/61jmqGj6q/f62XbyhoqVtSwZ2sjaLC7LPQcnEvZ4Dx6DMklq8Bh6AHlXf91P/mw9+6vf+3cWWxf+R61/gYa7QqP04LHYcWXYcVrs+G12vFYMvCYHXgtLmoyCmkii4A6/C46paM48OOI+nFEAtijQaw6jC0a+7JEI9giYayRSOwrHMEaDWONRmITGevYl1nHJzaOahQas4qiNGiTIrp/kmSXiahboU0KbYKoGcJmRdCyf2oWK0GzhaDJQtBkpUVZCZqs+JQTDwPxMuag09AAuKPNZEc8ZPu9DGzZhdO3GVOgmVqTlwyLgwEt3Rmoy+hh7o/NZsHiMMcmLLaZsVhMWMzmVpMaK5RSmEyxn5WJL26jY4cC6KiOfdfxKVyifLEsHIz8b9qWYCQ2Tcv+aVvitwPe0BfTtBycjYuuOxF3rjFnbHd2o1allCrRWu9RSpUA++LLK4EerdYrA3bHl5cdZLkQIk188PiD1PqWUNUzzNrsXiwrP5etqj8A+YF6Cps/pMVUwbj8Xnzr+LM5p+TipJy08kDRqKZqayPbVtawbUUNDVWxYbOCHm7Gn9eb3iMLKOiRKROtJqGhp5/N0NPPbvP6tZU72PTRXKr2bKcu5Mdj1fhsZlrsZgI2CwG7Bb/Vit9iJWCx4jfZ8ZvsBE1WQsqKV7kIYiOkrASx04KNkOq4psGsw9gIYtct2HQQuw5hi4awh8LkhutwhPbG5sULBrEEg6hwEK/TRHZxEQMcZfTLKKVbqCdaQ0swTNAUpkXF5q4L75/XLhihJuj54soVX1ylooOYLAprqytVxL7HzvLNLnKSEd/FG/uyxL7H58r7fNmnhjVp0PmN2nTgWuCe+Pc3Wy1/USn1V2InEwwAPtNaR5RSzUqpE4BFwDXAI52cWQjRyT554Sn21H5AXS8Py/oP4ROuZK8qRWlNqWcPWS3/xWVuYmLpWG6YcCVDc0qNjtwmWmv2bW9m02dVbFpSha8xiMmk6D4ohxETy+gzqoDMvGM/R5gwVn5ZT/KvvO6YPJfP66V6yyaqtmxgX9UuvKEWmiMhgtEoYSJEYhfhIgJE0USViahSRFFoNGYFCoVJmTCbwGKyYM2wkVGQT0HPvnTvVkr3rCLybK4vHbvZGaOp0aj+YuLiSDj6xWiZjv5v5Gz/pcWiEQ3qf6Nrpv2jbftH4OIjcvsvLWZO4MSJjt7leSQdOT3HS8ROHChQSlUCvyPWoL2ilLoe2AFcDqC1XqOUegVYC4SBW+JnfALcxP+m55iBnEggRJe0eta7bFj5Cs2961lZ0pdPSi+hQvVDaU1JUwXu4LMU2qJc3O8Mvtv/Dgodhz5zLtnU7/Wy8bMqNn1eRWO1H5NF0WtYPv3Li+g1vAC7I1mOQhHJzuly0WvkaHqNHG10lGPOZFKY7LHRLvE/HXnW56EmMjnjEOtPAaYcZPliYPgxjCaESBL1VVXMfeq3hPpUsqqwjE/Gnsl6hqKViSLPblyhF8nVjXytz+n8cMgfU6o58zcH2bBoLxsW7aVmpweloPugXMZO7kW/MYXYncl7pqkQInnIf+OEEJ1u9iN/psm2lL09Qyw44SQ+5ToCykGevxaHfxpuXcUZPU7kh0N/Tr/MwiM/YZLQUc3O9XWsXbCHbSuqiUY0Rb0yOfnyAfQvL8KVbdxxLkKI1CSNmhCiU9RW7mDeC38g1H8HS4cN5AOuZqfqhTUSIsOzEHvLZwwrGMSPy7/DSUX9jY57VLyNLaxdsJt1H++huS5AhsvKiIllDDmphPzSrjmdhRCic0ijJoToUAtfepZdDTNo6N3IvONO40Oup0U5yPVU4m55mgJTgG8MuoibB/0gqSeePZDWmqptTaz8oJItS/cRjWh6DMnlxK/3p8/IgpS5GoAQIrlJoyaE6BBv3v1TIj3XsalbFrOLz2O1GoU5Gsbm+ZRs/wJGFI7gjhN/ytj8XkZHPSrRiGb9p3tY+UEl1TuasWWYGXFaGcMndienqGtfykYI0fmkURNCHDM+r5eZD/4MPWg9y08YwNvcwh7VHVdLE07/K7iCWzm/3/n8YsQzKXd9vhZ/mDUf7mLTDM26wDpyS1ycduVABh7fDVuG/CkVQnQM+esihEhYbeUO5r14F5FBm/lkQjkz+S0NKheXv5JM72MUWiL8YOR3ubrPiSkxGW1r3oYWVszdyZoPdxEMRHAVw7k3jqLHkDxDrxIghEgP0qgJIdpt98YNfPrOFCIDNzOn/HRm8T38yonDu5Ys75P0dBdxx8m3c2bJUKOjHrWmWj9LZm5n/ad70BFNv3FFjDmrJ2u3LaXn0Hyj4wkh0oQ0akKIo/ZFgzZoM3NGncFMbiaIHZv3M3I8MxlSMJK7TryHUXk9jvxkSeaLBu2TPaBgyImljDmrB9mFsV21a7cZHFAIkVakURNCtNnujRuoXfY0C3xfbtCs3kVke95leGE5d5/2TwZmFRsd9ah90aB9vAdMMOzkUsZO7oU7Vy7pJIQwjjRqQogj8tTX894TP8M0bBXzR53KW9xEgIwDGrQnUrJB83uCLHl3O6s+rARg2CnSoAkhkoc0akKIw3ptys2Yhy9jaflIXtX30qhysHmXkd30KoMKxnBfijZooZYIK+bsZOms7YRbIgw5sYTyr/WRi6ILIZKKNGpCiIN68+6foQd8zqYJpbykf8NeVYq1ZTM5jY/Q3VXK3Wc9wvGFfY2OedSikShrP97D529vw9cUpM+oAk64uB95JS6jowkhxFdIoyaE+JJPXniKquhr1B/v59/cyDo1DFuwiqymv5Fn0VyS+3V+ec63jI7ZLpXr6/jolU3U7fZS0i+byd8fQUm/bKNjCSHEIUmjJoQAYicKLJz1e6IDN/G6+evMVWdjjgRwNzyLM7iRH4y+nZuHnMW8efOMjnrUGqv9fDJtM1uXV5OZn8HkG4fTd0yhzIMmhEh60qgJIXjtzz/AMmIxC4eewKv6IfwqA7vnfZxNMzi93xXcO/5enBab0TGPWqglwpIZFSx/fyfKrDj+or6MPrMHFqvZ6GhCCNEm0qgJkcY+ePxBGjPfpuZ4M09Ef8NO1Qtryzqy65+nb+5IHr7wZfpnFRkds122rajmw5c34qlrYdDx3Tjh4n64c+1GxxJCiKMijZoQaai2cgfzXrsT0+C1/Nd8BR9wNmbdRFb1Q7ho4A+n3M0FPcYYHbNdmusCfPTyRratqCGv1MWlPxtGSf8co2MJIUS7SKMmRJp5Y8pPMA9fwLrhg3k++iDNKhN782ycTW9x1oBruW/8ddjMqfenIRqJsvKDSha9tQ2imgmX9GPUmT0wm1Pr2qJCCNFa6v01FkK0y+aFC1i15gGCJ1TylL6FlWo0lvAOsuvup5u7B3+/4GWG5pQaHbNdaio9zH1+HdU7muk1PJ9TrxhIVoHD6FhCCJEwadSESAOvT7kV66iPWNqnnH/rXxJSJlz1L+DwL+GmsXdyy5CzjY7YLpFIlKUzt7P43QrsTgvn3DCcfmPlbE4hRNchjZoQXdiGj+ezdsNfaTlhL4/pn7BajcAa3Ex27T/onTeOp899g26O1JxHrHpnM3OfX0fNTg8DxhdzyjcH4HCn3pmpQghxONKoCdFFvT7lVmyjP2JJ7/G8oO+MjaLVPYcjsIJby3/NjYNONzpiu0QiUZa8W8GSGduxu62c+4MR9B1daHQsIYToENKoCdHF7KvYyoJZv0CdsIVHoz9imRobG0Wr+Tu988fzzHlvUOzIMjpmuzTs8zH76bXsq2hi4HHFnPLNgWS4rEbHEkKIDiONmhBdyIwH7iLcbybb+3fnH9GH8ZgcuOpfwuH7hJvG/SZlj0XTWrPukz189MomzGbFOTcMp/+41JzfTQghjoY0akJ0AT6vlxmP34R95FJeUVczW03GEq0iu+Ye8p3dePbCaSk7cW3AE+KDF9azdXk13QflcuZ1Q3DnZhgdSwghOoU0akKkuCVvvEpl4B94RoWZErmX3eZSMprn4Gp8lXMH/YB7y7+NyZSac4lVbqhn9tNrCHhDnPj1/ow+owfKJGd0CiHShzRqQqSwN6bcjm3MXJZmlvOM/j5RFSR737048fLQ2c9yavEgoyO2SzSqWTKjgs/f3kZ2kZPzfziKwh6ZRscSQohOJ42aECnI5/Uy46kbsR+/nKf191lgOhVryxayqv9Gv6JTefH035JpTc3dg76mIO8/s4ad6+oZeHwxp105CFuG/KkSQqQn+esnRIrZv6vTOzzElMj97DZ3w9H4Ns6m6Xxr1J3836hLjI7Ybrs21jPrqTW0+MJM+vZghpxYIpPXCiHSmjRqQqSQN+/+GdZRs1mROZon9S1EVIjs6ntx4uHRyS9yUlF/oyO2i9aaZbN2sPCNLWQXObngR6MpKHMbHUsIIQwnjZoQKWLafd/FOf4TXtbXMNN0HrbgdrKr76d3/gReOuP3ZNtS89qWwUCYuc+vY8vSavqXFzHp6sGyq1MIIeLkr6EQSa62cgfz37sV87gK/hK+i/XWwWR4PsJV9xznD72Vv4y/xuiI7dawz8eMf66ifo83dlbnmT1kV6cQQrQijZoQSWzhS8+yz/YU9X3sPBD+Gw0WN+66p3H4l/PbUx/j8j7HGx2x3bavrmX202tAwQW3jqbHkDyjIwkhRNKRRk2IJPXm3T/DNnoWy2zjeFLfjDb5yar6I5mWDP59/ssMzikxOmK7aK1Z+t52Fr65lYIyN+d+fwRZBam521YIITqaNGpCJKFp99yAe/yHTNNXMN18CbbgdrL23UvvwlN45Yzf47LajY7YLpFQlA9eWM+GRXsZML6YSd8ejNVmNjqWEEIkLWnUhEgiPq+XGc/cgGP8Uh4N/4zPbePJ8H2Oq+bvnD7wJh6ZcKPREdvN7wky45+r2LO5keMu6EP5eb3leDQhhDgCadSESBL7Krby8Yc/Qg3dwx9Df2GbrRfOxjdwNL3Dzcf9JWUvqA5Qt8fLO4+twNsY5OzvDWNAebHRkYQQIiVIoyZEEljyxqvsCj9EY5mZe8N/pdHqJLPmMRzBLdx3xjOc03240RHbbee6OmY+vhqz1cTFPxlDtz7ZRkcSQoiUIY2aEAab89h9BHq+xFZ3Lx6M3kHEHCSz6k+4zTZevOAlBmal7ujTuk/28MEL68krcXLezSPJypeTBoQQ4mhIoyaEgabf80uso95hsWU8j6sfYonWkbn3TxRkDuatcx9M2UlstdYsmbGdRdO30mNILpNvHIHNIX9uhBDiaMlfTiEM8tqUW3CNn8MMLmCq5Srswe24q+6mb/EZTDvzD9jMqfn2jEY1H728kdXzdzHwuGJOv2YIZovJ6FhCCJGSUvOTQIgUN+3e63Ef/xH/iVzPLNs5ZATW4N73AMf3uYYnTr4Vkyk1G5twMMLsZ9aydVk1Y87qyYRL+qFMcmanEEK0lzRqQnSyaQ9fhbt8MY8Ff85n9vE4vJ/grH2Ci4b/nCnjvmV0vHYLeEO8+4+V7NnSyMmXD2DUGT2MjiSEECnPkP+2K6VuU0qtVkqtUUr9OL5stFJqoVJquVJqsVLquFbr36mU2qyU2qCUOseIzEIkyuf18tq/Lsc5fCn3Bu/iM/t4XI1v46x7mpuP+0tKN2m+piBv/HUZVRVNnH39MGnShBDiGOn0ETWl1HDgBuA4IAjMVEq9A9wL/F5rPUMpdV7854lKqaHAFcAwoBR4Xyk1UGsd6ezsQrSXz+tl5tSrsA7YzN0tU9hk7427fioZ3g/4zSmP8c0UvmZnc12A6Q8tx1Mf4PybR9FjqFyzUwghjhUjdn0OARZqrX0ASqn5wCWABrLi62QDu+O3LwKmaq1bgG1Kqc3EmrxPOzW1EO1UX1XFvPe+i6lPJX8K3sMOewmZdc9iDyzhvtOf4NzuI42O2G4NVT7efGgZQV+YC28dTUn/HKMjCSFEl2JEo7YamKKUygf8wHnAYuDHwHtKqfuJ7ZI9Mb5+d2Bhq8dXxpcJkfR2b9zAZ4t/RKSslruD91Fly8Vd808yQht59KynOK14sNER2612l4c3H1qOjmou/slYCntmGh1JCCG6HKW17vyNKnU9cAvgAdYSa9jMwHyt9TSl1DeAG7XWZyqlHgM+1Vq/EH/sU8C7WutpB3neG4EbAYqLi8dNnTq1Q+vweDy43e4O3UaySufaoW31e3ZsI2J/Hn+hnynBu6m3uXBWP4I9vJdbi25hgDW3k9IeWx6PB3OLi+3zNcoMvScq7Nnpc2ZnOr/207l2SO/6pfaOr33SpElLtNblBy43pFH7UgCl7iY2SvZnIEdrrVXsSs2NWusspdSdAFrrP8fXfw+4S2t92F2f5eXlevHixR2afd68eUycOLFDt5Gs0rl2OHL9W5csYtXWX+DLa+GP4b/QbLXhrPorDuVl6uSnGJxT0nlhj7F3X/2AXQtMZLitXPTjMWQVpOakvO2Vzq/9dK4d0rt+qX1ih29HKXXQRs2osz6L4t97ApcCLxE7Ju20+CqnA5vit6cDVyil7EqpPsAA4LPOTSxE221euIBVW3+ONy/IH8L34rFace29F6fyM+1rz6V0k7Z3ayPb52kyMm1c8tOxadekCSFEZzNqHrVp8WPUQsAtWut6pdQNwENKKQsQIL4LU2u9Rin1CrFdpOH4+nLGp0hKmxcuYG3lr/Dmhflj+C94rRace+/FYQrz2vnP0sddYHTEdtu7tZHpDy/HkgGX/GQM7twMoyMJIUSXZ0ijprU+5SDLFgDjDrH+FGBKR+cSIhH7mzRPbpg/hu/pUk3ani2NvPXIcpyZNoonBKRJE0KITpKa16kRIsnEmrQ7v9qkmSNdo0l7ONakXfyTsVid6XPigBBCGE0uISVEgravXM6anb/Cnxfij6F78dr2N2lRXvvaMyndpFVta4qNpGXbuPj2sbhz7UZHEkKItCKNmhAJ2FexleXrfkKo0MuUlvvx2q04q+4lwxTita89l9JNWk1lM289shyH2ypNmhBCGEQaNSHaqb6qik8++QF0q2NKy/3U25249v0Nu/by8nnPp3STVr/Xy/SHlmOxmbnox2OkSRNCCIPIMWpCtEOLp5l5730Xc7c9TAncwz57Fs6ax7BH9vH85KcYlN3N6Ijt1lTj580HlwNw0Y9HyxQcQghhIGnUhDhKPq8X774nsZVt457A3ezKKMBV+yT2YAX/OvsJRuX1MDpiu3nqW3jzwWWEgxEuvG0Mud1cRkcSQoi0Jo2aEEdp5n+uxdl7Pff5/8jWjO646p7HFljNQ2f8iwmF/YyO127+5iDTH1qG3xPigltHU1CWnpeLEUKIZCKNmhBHYdrDV5HZfwUP+3/Demc/3A0vY/cv4p6Jf+f0kiFGx2u3YCDM24+uoKk2wPm3jKS4d5bRkYQQQiCNmhBtNu2+75I9fCFP+m5nqXM47qZ3sDfP5hcnPsD5PUYbHa/dIuEoMx9fTfVOD+fcMJzSAal5sXghhOiKpFETog1eu/smssZ8yCv+7zDfdSJuz8dkNLzMtWPv4pp+Jxsdr910VDPnuXXsXFvHxKsG0Wdk6p6pKoQQXZE0akIcwRtTfoJ7/FxmBS9kuvN83P6VZNT9k8lDfsrPR1xodLx201rz8aub2fR5FSdc3JehJ5UaHUkIIcQBpFET4jBmPHAXGeNmsihyEi9kXI27ZQsZ1X+lT/aF3H/ctUbHS8iyWTtYMXcnIyeVMfacXkbHEUIIcRAy4a0Qh/DRs/9ED36djWow/7LcjCu8h4x9f6Z38bncbjvT6HgJWb9wD5++voX+5UWcfPkAlJLrdwohRDKSETUhDmLDx/NpyHmGalsuD6mfY9cN2KqmkJc9jtfO+iMmU+o2NpXr6/jg+fV0H5TLmdcORaVwLUII0dVJoybEAeqrqlhf+QdCmSHuC99F1BLBVnUfDnsJb07+GzZz6g5E1+32MuNfq8kudnLu94djtsqfACGESGbyV1qIA8x750Zshbu43/9H6u0u7NX/wKrCvDT57+TanUbHazdfU5C3H1uB2Wri/B+OxO60Gh1JCCHEEUijJkQr0x66GnfvNTzi/TVbXd1x1b+ENbiFhyY9xsCsYqPjtVsoGOGdx1bgbw5y/i0jycqX63cKIUQqkEZNiLhpf76R7OEL+Y/vJpa6h5PZNAubZy4/O+FeJpUMNjpeu0WjmvefXsu+Hc2c9d1hFPWSqw4IIUSqkEZNCODNu39GZvl8ZgUuYqbrDDJ9S7E3/JtLR9zBdQNONTpeQj59bTNbl1dz8mUD6Du60Og4QgghjkLqHhUtxDHy0bP/xDZ6FmsiI3nB8S2yglux1TzE6F7X8IexVxgdLyFrP97N8vd3MmJiGaPO6GF0HCGEEEdJGjWR1ravXE5D9nM0WbN4VN+OM1qPpepeCnJP5PlTf2p0vITs2dzA/Bc30GNILidf3t/oOEIIIdpBdn2KtOXzelm28k5UdhMPBH9H2ALWfX/Dbi/mtXPux2RK3bdHc12AGf9aRWZ+Bmd/bzgmc+rWIoQQ6Uz+eou0NeOZG3CVbuJhz2/Z68gjo+55LNFGnjnrEfLsLqPjtVuoJcI7f19JJKz52s0jyXDJNBxCCJGqpFETaWna3d8ne8giXvT8gJWZg3A3z8LqXcSvT3qA0Xk9jY7XbjqqmfPsWup2eTj7e8PI7Za6DacQQghp1EQaeufeX+Mun8+HgcnMyDyTLP8KbA0vcuGwn/PNPscbHS8hn7+zjS3Lqjnx6/3pNSzf6DhCCCESJCcTiLSyYuZbMPQdKnRfnsm4luzQTqzVf2Ng6de5u/xbRsdLyNbl1Xz+TgWDT+gmZ3gKIUQXISNqIm146uupaHyQYIbm4egvseHDXPUAbvdwpp7+a6PjJaR+r5f3n11LUa9MTrtqEErJhdaFEKIrOOSImlLq0jY8PqC1fvcY5hGiw7w39QdkDdrOnz330uB24a5+GLNJ8Z+zU/tC68FAmBn/Wo3ZYmLy90dgsZqNjiSEEOIYOdyn0xPAm8Dh/mt+KiCNmkh6r025iewTFvOS5ybWZvYlu/FtrIHV/HHSE/TLTN3Z+rXWzH1+PQ17vVx422gy8zKMjiSEEOIYOlyjNkNr/d3DPVgp9cIxziPEMTf7kT/jKp/HwsBE3sk8kxz/KiyNL3P+sF9ycc9xRsdLyPLZO9mydB8TLu1H2eA8o+MIIYQ4xg7ZqGmtrz7Sg9uyjhBG2rpkES09X6eOYp6y3UB2eA+Wmr/Rs+hc7ilP7Zdv5YZ6Pn19M/3GFjLmrNSdUkQIIcShHfJkAqVUnlLqV0qpnyilsjozlBDHgs/rZcXau8Dt4W/hX6PNYUz7HsSe0ZuXz/yD0fES0lwXYNaTq8kpdnL6NUPk5AEhhOiiDnfW5zTADZQBnyql+nZOJCGOjRn/upnM7ht5ovmO+JUHXsIc9fD0mQ+RaU3dY7kikSiznlxDOBjl3B+MwJaRuidCCCGEOLzD/YXP11r/CkApdQ4wXynVAPwU+J7W+hudkE+Idnnrz78ks/xTZnkuZVH2SHI8C7B4P+KHJ/yNUXmpPcfYoje2sndro1x5QAgh0sDhGrVmpVRvrXWF1vo9pVRPoBSoB1Z1Tjwhjt7aubMwjZjN9khfXnJdTm6oAnPd44zs8S1+MOgMo+MlpGJlDctm72D4qd0ZUF5sdBwhhBAd7HCN2ncB2/4ftNYa2BX/0deRoYRoL5/Xy8Y9D2DpFuTRwM+xWgOofQ/hdA3j2dN+ZnS8hDTXBXj/ubUU9HBz0uX9jY4jhBCiExzyGDWt9Qat9UalVD+llB1AKTVRKXWrUiqn0xIKcRRmPH4T7pLN/Kv5DvY5crHVvYSJIM+e+UBKT2obiUR574nVRCOac24YLpPaCiFEmmjLJaSmARGlVH/gKaAP8GKHphKiHab/+RdkjfiU9zyX8Xn2cLK9H2Pxfcxt4//E0JxSo+MlZOHrW6ja1sTp3x5CTpHT6DhCCCE6SVsatajWOgxcAjyotb4dKOnYWEIcndWz3sU8YjbbwgOY6rqM3OA2LHVPMKrnVdwwaJLR8RKybUU1y9/fyYjTutN/XJHRcYQQQnSitjRqIaXUlcC1wNvxZdaOiyTE0du87xFwtPD3yE+x4UNVP4zTNYxnTv2p0dES4qlvYc7z6yjo4ebEy+S4NCGESDdtadS+A0wApmittyml+gBy6SiRNKbd910ySzfybNNP2evMx1o/DZMO8NQZ96X0cWnRqOb9Z9cQCUU553tyXJoQQqSjtnyKnaW1vnX/D/Fmzd+BmYRos1kPTcE96hMWeM/mo5xx5Pk+x+z9gGvH/ZkRuWVGx0vI8tk72LWhgUnfHkxOsRyXJoQQ6agtI2rXHmTZdcc4hxBHrbZyBy0936aOXJ7P+Da54d2Yah6jV/H5/Gz4+UbHS0hVRROL3txKv7FFDDlRDgkVQoh0dcgRtfhxad8C+iilpre6KxOo7ehgQhzJ/Hd+SuaAGu5rfoBgphlb1ROYbSX8e9JvjI6WkGAgzOyn1uDMtjHxqkFyHU8hhEhjh9v1+QmwBygAHmi1vBlYmchGlVK3ATcACnhCa/1gfPmPgB8CYeAdrfUv4svvBK4HIsCtWuv3Etm+SH2vT/kRWScs49Wm77Exuye5jW9hDm7nnjOfJdee2rsJP5q6kaYaPxf/ZCwZLjlvRwgh0tkhGzWt9XZgO7ETCY4ZpdRwYk3acUAQmKmUeofYxd8vAkZqrVuUUkXx9YcCVwDDiF3C6n2l1ECtdeRY5hKpY8kbr5IxZj7rWkYyPess8gPrUY2vMHHgDzm3+0ij4yVk4+d7Wb9wL+Vf603pgByj4wghhDDYIY9RU0otiH9vVko1tfpqVko1JbDNIcBCrbUvPj/bfGJztN0E3KO1bgHQWu+Lr38RMFVr3aK13gZsJtbkiTS10/c4YVuEx/kRLt2Irn6UrKxyHj7+BqOjJaS5LsD8FzfSrW8W48/rbXQcIYQQSUDFLuHZiRtUagjwJrGROj8wB1gMnBJfPhkIAD/TWn+ulHqUWGP3QvzxTwEztNavHuS5bwRuBCguLh43derUDq3F4/Hgdrs7dBvJyqjaaz97npzy+fyz4f9YkDOa7NonsQSWc2fJr+hu6bw8x7p+rTXbP9D466DfZIXNnbzHpaXz6x7Su/50rh3Su36pveNrnzRp0hKtdfmBy9s0yZRSygwUt15fa72jPUG01uuUUn8BZgMeYAWxY9IsQC5wAjAeeEUp1ZfYcWxfeZpDPPfjwOMA5eXleuLEie2J2Gbz5s2jo7eRrIyoff6TD+Ee9SmfeSfyUe5YCnyLUb6PuHbcn7mqk8/yPNb1r5i7k7X7NjHp6sEMPTm5L3eVzq97SO/607l2SO/6pfaJhm3/iI1a/AD/3wFVQDS+WAPtPhhIa/0UseuGopS6G6gktkv0NR0b4vtMKRUldiJDJdCj1cPLgN3t3bZITT6vl4bs14iYHDxju46cSBW65h+UFJyZ8lNx1O3x8unrW+g9Ip8hJ8lUHEIIIf6nLfOo3QYM0loP01qPiH8ldMR2qxMFegKXAi8BbwCnx5cPBGxADTAduEIpZY9fFWEA8Fki2xepZ8YTP8CdX8lTzT/HY3VA7X8wWbJ4btLvjI6WkEgkypxn12K1mZl49WCZikMIIcSXtGXX506g8Rhvd5pSKh8IAbdoreuVUk8DTyulVhM7G/Ta+OjaGqXUK8BaYrtIb5EzPtPLjL/+nswRi/ig+QKW5A4h3/MRpsAybj/xUUqdOUbHS8iSdyvYt72ZyTcOx5VtNzqOEEKIJNOWRm0rMC8+hUbL/oVa67+2d6Na61MOsiwIXH2I9acAU9q7PZG6ait3EO4zk4ZoAS86v0FeuBJV9yT9Sy7huwNOMzpeQqoqmlg8YzuDju9Gv7FFRscRQgiRhNrSqO2If9niX0J0mnlv/ZzsQfv4a9MDhLLM2Pc9jdlWwtOn3WF0tISEgxHef2Ytrmwbp3xzgNFxhBBCJKkjNmpa6993RhAhDvT2PXeQVb6UGY1Xsi6nN/nNs1EtW/j9pKdT/uoDC6dvpaHKx4W3jcbulKsPCCGEOLjDXevzLb48DYYmdnD/B/vnNBOio9RW7kAP/oB94W5MyzyfglAF1P+bET2u5OKe44yOl5C9WxtZMWcnw07tTo8heUbHEUIIkcQON6J2/0GW5QFXK6WGa61Te9+TSGrzp/+CrME1/K3xAaLZikj1s9jtZTx+yu1GR0tIOBRh7vPrcOfaOfHSfkbHEUIIkeQOd63P+QdbrpSaDiwBpFETHeKd+35N5pglzGr6BmtzepPfPAcV3MpvJj5NpjXD6HgJ+fztbdTv9XHBraOwZbRpvmkhhBBprC3zqH2JTI0hOlJt5Q6i/d+nJlLIf90XUBDajqp/jqFl3+Drvb5yZY2UUlXRxLJZOxh6Ugk9h+YbHUcIIUQKONwxagc7eCYXuAZY02GJRFqb98YvyR5azUMN9xLOsRCp/jc2WylPnPJTo6MlJBKKMue5dbhy7Jx4mZzlKYQQom0Ot+9lCbETCPZPlb7/ZIJ5wE0dG0uko3fv/x1Zoxczt+lSVuf2o6B5HgQ38qtTnyDb5jA6XkI+f3cb9Xu8nP/DUdgdsstTCCFE2xzuGLU+nRlEpLf6qioifWfjieTzsusSCsI7of5pBpZeyjf7HG90vIRU72hm6Xs7GHxCN3oNl12eQggh2u6Qx6gppcYe6cFtWUeItpg79ac4c6p41ns7LWYrkbr/YLJ246lTf2F0tIREI1Hm/nsdDreVky6XXZ5CCCGOzuH2wTyjlJrI/3Z9HsxTwJhjGUiknzn//CuZQz7nk6ZzWJo7iCLPx+jAGn584qMpP7HtijmV1Oz0MPnG4WS4ZGJbIYQQR+dwjVo2sePUDteoVR/bOCLd+LxePLnvgLLzYsY3yY3sJVL3FGWFZ6f8tTwbq/189tZW+owqoO+YQqPjCCGESEGHO0atdyfmEGlqxqO3kTO+gifr7qQhL5Pcuqkos4PHT/2V0dESorVm/ksbUCbFqVcMRKnD/X9HCCGEOLijnkdNiGNl2Tuv4xy5kDXecczLG0tRYCVm7wK+Mfx2erlT+6D7jZ9VsXNtHSdc3A93bmpP0iuEEMI40qgJw1TUPI2yhXhWfQ+3biRU+ySZWeP49chLjY6WEL8nyIL/bqK4TxbDT+tudBwhhBApTBo1YYjXp9xKVo+1vFF/A7tdRdia5mDSAR4+9feYTKn9svzk1c0EfWEmXT0Yk0l2eQohhGi/I34iKqWmKaW+ppRK7U9PkTR2rV2FbeQCdrT05Z3cUylq2YCp6U1O6fddyvN7GR0vITvX1bF+4V7GnN2T/O5uo+MIIYRIcW1pvv4BfAvYpJS6Ryk1uIMziS5u0fw/YXc18lzgh5hVmFDt81gz+vHQCTcaHS0h4VCEeS9uILvIQfnXehsdRwghRBdwxEZNa/2+1voqYCxQAcxWSn2ilPqOUkomhhJH5b0H/0TmgKXMa7iY9Tm9yPIswhzeyW8n/AabObUvrbT0vR00Vfs57cpBWKxmo+MIIYToAtq0O1MplQ9cB3wPWAY8RKxxm91hyUSX4/N6CZTOwRd18Yr7IvLDu4nUP0ufbudzcc9xRsdLSMM+H0tnbmdAeRE9huQZHUcIIUQX0ZZj1F4DPgKcwAVa6wu11i9rrX8EyEE4os1mPPJj3AU7eLnxFpotbnTjW5jMmfzj5J8bHS0hWms+enkjJovipMvkMlFCCCGOnbbsa3pSa/1u6wVKKbvWukVrXd5BuUQXs+Hj+ThHLmKjbwTz8sdSHFhFxLuAS0f8mjJXrtHxErJ1eTU71tRx8uUDcOXYjY4jhBCiC2nLrs8/HWTZp8c6iOja1qx9GEuGj+cjN+LAR7DuORyu4fxu9OVGR0tIMBBmwSubyC9zM2KizJkmhBDi2DrkiJpSqhvQHXAopcbwv2t+ZhHbDSpEm7xz36/JHLOCWfVXsi2vlMKmmehIHfec9FjKz5m2+J0KPPUtnHPDcEzm1K5FCCFE8jncrs9ziJ1AUAb8tdXyZiC1L8QoOo3P6yXSdx6NkTxey5pMYWg7uuFFhpZ9g9NLhhgdLyG1uzysmLOToSeV0K1vttFxhBBCdEGHuyj7c8BzSqmva62ndWIm0YXM+McPyRm7h+dqf4s/PwN7zWuYLfn8/eQfGx0tIVprPpy6EZvDwoRL+hsdRwghRBd1uF2fV2utXwB6K6V+cuD9Wuu/HuRhQnxh9ax3cQ77nLWecXySP4Ju/mWE/Uu5ZuwUCuypfcJw43bYvamBSVcPJsMt0wkKIYToGIfb9emKf0/tT1RhmI07nsTdJ8B/gtfhopmWuufJzhzDT4edb3S0hAQDYapWaIp6ZTLkxBKj4wghhOjCDrfr81/x77/vvDiiq3j3/t+ROXoVc+q/SUV+KcVNM4lEG7l7wq9S/gSCJTO3E/bDKd8ciJKLrgshhOhAbZnw9l6lVJZSyqqUmqOUqlFKXd0Z4URq8nm9hHrOxxt18VrWORSGdxJueIkBpRcxqSS1LxXbsM/H8vd3kN0bOYFACCFEh2vL0MbZWusm4HygEhgIpPZU8qJDzXz4J7gLdjKt4SaarFlEG2egzG4eO+l2o6Ml7ONXN2M2mygeJSNpQgghOl5bGrX9R0qfB7ykta7rwDwixe1au4qMkZ9R4RvE3IJxlPhXYfJ+xPmDb6LUmWN0vIRsX11Lxcoayr/WG6tDGjUhhBAdry2N2ltKqfVAOTBHKVUIBDo2lkhVi+bdjd3RxH+CN2AlSEvDS1gd/fnT2CuNjpaQSDjKgv9uIqfYyajTexgdRwghRJo4YqOmtb4DmACUa61DgBe4qKODidQz/8mHcA9YyqcN57I2tw853sWYQzu5c/wdWExmo+MlZOXcShqqfJx8+QDMltQ+GUIIIUTqaMtF2QGGEJtPrfX6z3dAHpHC6jNmYFMmXnZcSl6kilDdc5QWnMnlfY43OlpCvI0tfP7uNnqPyKfX8Hyj4wghhEgjR2zUlFL/BvoBy4FIfLFGGjXRyhtTbiNzwiZerbmJ6sI8ChteRisTj5z0C6OjJWzRm1uJhKKcdNkAo6MIIYRIM20ZUSsHhmqtdUeHEanJ5/ViHvoZNS1FzMg/iZLgZsJNb3N8n+sZnJPaE8JW72xm3ad7GH1GD3KKnUbHEUIIkWbacrDNaqBbRwcRqWvGY7fizN7HtObvE1Q2go1vg6WAv57wfaOjJURrzcevbibDaaX8vN5GxxFCCJGG2jKiVgCsVUp9BrTsX6i1vrDDUomUsX3lchzDF7PFO5wFBSPoFlhFyL+Eb4z6Hdk2h9HxElKxsoZdG+o59YqB2J1yPU8hhBCdry2N2l0dHUKkriUL/kL2YA9TG67DToBAw1QcjoH838hLjY6WkEg4ysfTNpPbzcnQU0qNjiOEECJNtWV6jvlABWCN3/4cWNrBuUQKWPD8E7j6L+OzxrPj03EsxRzaxS/G/yLlr+e5+sNdNO7zc+LX+2M2p3YtQgghUldbrvV5A/Aq8K/4ou7AGx2YSaSIaj0dTFFesV1KTrSGYP0LFOSdyjdTfDqOgDfE529vo2xwrkzHIYQQwlBtGSq4BTgJaALQWm8CijoylEh+NfOnk1m2jvfqr2aPsxB78ycoHeK+E1N/Oo7F71YQ9Ic56bIBKCWXihJCCGGctjRqLVrr4P4f4pPeylQdaczn9WIdugRPOIu3ciZRHNpOpPG/DCu7lPL8XkbHS0hDlY9V8yoZclIpBWVuo+MIIYRIc21p1OYrpX4FOJRSZwH/Bd5KZKNKqduUUquVUmuUUj8+4L6fKaW0Uqqg1bI7lVKblVIblFLnJLJtkbgZD/4Ud34lrzfcQLMlk0jTLDBn8dCJPzI6WsI+fX0LZouJ4y7oY3QUIYQQok2N2h1ANbAK+D7wLvDr9m5QKTUcuAE4DhgFnK+UGhC/rwdwFrCj1fpDgSuAYcBk4O9KqdS+cGQKq6+qwj5iCXsDPfigcCzdA2tR3g85c8B36ebINjpeQvZsaWTr8mrGnN0TV7bd6DhCCCFEm876jBI7eeBmrfVlWusnErxKwRBgodbap7UOA/OBS+L3/Q34BV/etXoRMFVr3aK13gZsJtbkCQPM/fcvcbjrmOa5nghmAk3TMVlLuHvct42OlhCtNZ++thlnlo3RZ/Y0Oo4QQggBgDpUz6ViR1H/DvghoOJfEeARrfUf2r1BpYYAbwITAD8wB1gMzAbO0FrfppSqAMq11jVKqUeJNXYvxB//FDBDa/3qQZ77RuBGgOLi4nFTp05tb8w28Xg8uN3pcxyTp7ICU95f2Rnuy13u31LqW06w9m+cWnAjl7tGGB0vIU2Vmp0LNCXlirz+Rz6BIN1+962lc+2Q3vWnc+2Q3vVL7R1f+6RJk5ZorcsPXH64CW9/TOxsz/HxkSyUUn2Bfyilbtda/609QbTW65RSfyHWmHmAFUAY+D/g7IM85GCfmgftLrXWjwOPA5SXl+uJEye2J2KbzZs3j47eRjKZ9ui3yCn18mr9t2OT2zb9lwzHQB4595aUnjctGoky9Y+fkVMMF3/nOExtmDct3X73raVz7ZDe9adz7ZDe9UvtEw3b/uE+ka4BrtzfpAForbcCV8fvazet9VNa67Fa61OBOmIT6vYBVsRH08qApUqpbkAl0KPVw8uA3YlsXxy9JW+8invAMpY3nMqqvH7kepZhClVy27jbU7pJA1j/6V7q9/qYcHG/NjVpQgghRGc53KeSVWtdc+BCrXU1kNCFD5VSRfHvPYFLgee11kVa695a697EmrOxWuu9wHTgCqWUXSnVBxgAfJbI9sXR217zIiZLkP9aLiMrWk+w8SWyssZzTb+TjY6WkFAwwmdvbaVb32z6jC448gOEEEKITnS4XZ/Bdt7XFtOUUvlACLhFa11/qBW11muUUq8Aa4ntIr1Fax1JcPviKMz551/J7L+KBfUXU5HfnZLmOYSiTdx1/E+MjpawFXN24m0McvYNw2VyWyGEEEnncI3aKKVU00GWKyAjkY1qrU85wv29D/h5CjAlkW2K9mtyvY8DE284zyU/UkWw/kXyXCdxVulwo6MlxO8Jsuy97fQeWUBp/xyj4wghhBBfcchGTWstc5UJ3r7nDjLHb+Cd2u9QVVhAceMbRJTmO9mpP+/w4ncrCLVEmHBxP6OjCCGEEAclR06Lw4r0+QxfxMW7OafQLbydcONrDO1+KX2tOUZHS0hTrZ/V83cx5MQS8kpdRscRQgghDkoaNXFIb0y5DXfhdt6tu45GazZRzwKUycEDJ9xsdLSEff5OBUopxp8vl4oSQgiRvKRREwfl83oxDV5KYzCX2QXH0T24Gd00k/E9r6CHK8/oeAmp3+tlw6d7GH5ad9y5CR1uKYQQQnQoadTEQc188Oe4cnfzVsN38ZrdBD3z0OYs7j3ue0ZHS9ii6duw2MyMm9zL6ChCCCHEYUmjJr7C5/ViHbqU2kAxcwvHUtayDjzzmdjvGgodmUbHS0j1jma2LN3HqDN64Mi0GR1HCCGEOCxp1MRXzHjkxzizq3mj6bsElZVA82yw5PPn8muNjpawRdO3YndaGH2WXHhdCCFE8pNGTXxJfVUV9qHL2O3vzYdFI+nesg7l+5zzBl1PpjW1j+favbmB7atrGXtOL+yOw00hKIQQQiQHadTEl8x9/k4c7nre8FyLxkSg6R1M1hJ+P/oKo6MlRGvNwje24MyyMWJimdFxhBBCiDaRRk18YV/FVhxDl1LhGczCwqGUBFahAqu5fNj3ybAkdHlXw+1cW8eezY2Un9cbq13mchZCCJEapFETX/jojbuwO5p5I/BtzETwN76Jxd6LO0ZcbHS0hGitWfjmVjLzMhh6cqnRcYQQQog2k0ZNALB95XKcg5axuWkUiwsGUuRfhSm4hWtH3IzFlNojUNuW11C9o5nx5/fGbJGXvBBCiNQhn1oCgMUf3I/N7uON0BXYCeBvfA2bYwC3DplsdLSE6Kjms7e3kV3kYNDx3YyOI4QQQhwVadREbDRt4Ao2No1leX5/Cn2rMYV2cv2ImzCZUvslsnV5NbW7PIz/Wh9M5tSuRQghRPqRTy7B4nn3YbP7eDP0DewE8DW+jt0xiB8MOsPoaAnZP5qWU+xkwPhio+MIIYQQR00atTS3dckinANWsKGpnOUFA2KjaeFKvjcq9UfTtiyrpm63l/Ff643JpIyOI4QQQhy11P4kFglb9vHD2Ox+3gxfjkN78TVOw+4czI0DJhkdLSE6qvn8nW3kdnPSv1xG04QQQqQmadTS2OaFC3ANXMH6xvGsyO9PgW81pvBuvj/y5pQfTdu8dF98NK2PjKYJIYRIWan9aSwSsuLzv2O1+Xkzcll8NO01MpxDuX7AaUZHS0g0qvn87W3klrjoN67I6DhCCCFEu0mjlqY2fDwf14AVrG04npX5/cn3rcIU3s0PRqf+sWmbl1RRv9cnx6YJIYRIean9iSzabfWyf2G1BXgz+vX4aNqrZLiGcf2AiUZHS0g0qln8TgV5pS76j5XRNCGEEKlNGrU0tOHj+bj6r2BNwwmszu9Hvm8V5nAVt4y62ehoCdv0eWw07bjz+6BkNE0IIUSKk0YtDa1eGhtNezt6CQ7txd84jQzXMK4bcKrR0RKio5olMyrI7+6i7+hCo+MIIYQQCZNGLc1sX7kcZ/+VbGwcx8r8/hT61mAK7+XGEd83OlrCti6vpn6vj3Hn9pbRNCGEEF2CNGppZvHcB7DZ/bwd+jp27cfb9CY2x8CUP9NTa83iGRXkFDvpJ8emCSGE6CKkUUsj+yq24hi4gq3NI1ha0J9i/xpMoR18e9j1KX+m5/bVtdTs9DD2nF5ypqcQQoguI7U/ncVR+Wja77FneHkncDkWIvia38Fi78WtQyYbHS0hWmsWv1tBZl4GA4+XqxAIIYToOqRRSxP1VVVkDFpJpWcAnxcOosS/BtWymcuHpP5o2q4N9VRta2Ls5F6YzaldixBCCNGafKqlibnP/YoMZxNv+64EwOeZicnWnV+MuNDgZIlbPKMCV7aNwRO6GR1FCCGEOKakUUsDnvp67INXsMfXm0+LhtA9sBYVWMuFg67FYjIbHS8hezY3sGtDA6PP6onFmtq1CCGEEAeSRi0NvPfPX+Bw1/Ou51tEMePzvo+yFvF/Iy8zOlrCFs/YTobbyrBTuhsdRQghhDjmpFHr4nxeL7Yhq6jxl7KgaBhlLetQvmWc3f8aMixWo+MlpHpHMzvW1DL6zB5Y7TKaJoQQouuRRq2Lm/ngz3BmVfNu01WEsOH3fAiWPH435ptGR0vY4hkV2J0WRpxWZnQUIYQQokNIo9aF+bxeLENW0hAo5MOikZQFN4DvU07r+y0yrRlGx0tI7W4PW5dVM2JiGTaHxeg4QgghRIeQRq0Lm/ngL3Hl7GVG49X4TU5avB+jzVn8YezVRkdL2JIZ27HYzYw6vYfRUYQQQogOI41aF2YeuIrmYA4fFI6mR3Aj2jOfE3p9kzy7y+hoCWnY52Pz4ipGnNqdDHdqH2cnhBBCHI40al3UG1N+gju/kln1V+E1uwl5P0ObXEwpv87oaAlb9t52TBYTo86U0TQhhBBdmzRqXVXfVQTCGcwtGENZaCuR5vcY2f1iih1ZRidLiLexhfWL9jJkQgmubLvRcYQQQogOJY1aFzTzb3/AXbSV+XXfoMGSi/atRCsrd5V/x+hoCVs5txId0TKaJoQQIi1Io9YF+XM/J6LNzM46gaLIbgKNb9K76BwGZqX2BcuD/jCrP9xF3zFF5BQ5jY4jhBBCdDhp1LqYBc8/gbtsPYtqz2ePoxi7bw2KCP837ntGR0vYmgW7CfrDjD2np9FRhBBCiE4hjVoXsy/wHkpFmZUxiZxoLb6G1yjIPYkJhf2MjpaQSDjKijk76T4ol6JeqX2cnRBCCNFW0qh1IRs+nIur1xpW1p/G5qweZPnWYtIebh2V+qNpGz+rwtvQwtizZTRNCCFE+pBGrQtZvfwpLNYg7+lzcWoPvqY3cbpHcGmvcUZHS4iOapbN3kF+dzc9huYZHUcIIYToNIY0akqp25RSq5VSa5RSP44vu08ptV4ptVIp9bpSKqfV+ncqpTYrpTYopc4xInOy21exFWf/VWxpHMXK/H4U+taiwlVcNyz1z/TcvrqW+j1expzdE6WU0XGEEEKITtPpjZpSajhwA3AcMAo4Xyk1AJgNDNdajwQ2AnfG1x8KXAEMAyYDf1dKmTs7d7L76NU/YsvwMrPlEsyE8XpmYrH34vsDzzA6WsKWztqOO89O//Iio6MIIYQQncqIEbUhwEKttU9rHQbmA5dorWfFfwZYCJTFb18ETNVat2ittwGbiTV5Is7n9WIfuJq93l58VjSY7oG1qJZNXDToGkym1N67vXdrI3s2NzL6jJ6YzaldixBCCHG0lNa6czeo1BDgTWAC4AfmAIu11j9qtc5bwMta6xeUUo8Sa+xeiN/3FDBDa/3qQZ77RuBGgOLi4nFTp07t0Fo8Hg9ut7tDt9EWNQteJPfEOTy395fM7lZOfvUjEKrggdJfYzd1zOBjZ9W+46MovmoYeKHCZEme3Z7J8rs3QjrXDuldfzrXDuldv9Te8bVPmjRpida6/MDllg7f8gG01uuUUn8htqvTA6wA9o+koZT6v/jP/9m/6GBPc4jnfhx4HKC8vFxPnDjx2AU/iHnz5tHR22iL6bW/oTGQz0fFw+gR3IA/sJjTBv6QcyZ03G7Pzqi9fq+XNS8vovzc3hx/Zt8O3dbRSpbfvRHSuXZI7/rTuXZI7/ql9omGbd+QfUla66e01mO11qcCdcAmAKXUtcD5wFX6f0N9lUDr6wWVAbs7M28ye3PK7bhydzO74Ur8JhdB32K0yc1dY68yOlrCls/egdliYsTEsiOvLIQQQnRBRp31WRT/3hO4FHhJKTUZ+CVwodba12r16cAVSim7UqoPMAD4rLMzJyvddzWBcAYfFI6hLLSFSPMsRpVdTIE9tYeoW1983ZllMzqOEEIIYYhO3/UZN00plQ+EgFu01vXxY9HswOz4FAwLtdY/0FqvUUq9Aqwltkv0Fq11xKDcSWXm3/6Ae+RWZlVfQ0NxDq7GeWhl5ffjUn9KDrn4uhBCCGFQo6a1PuUgy/ofZv0pwJQODZWCfDlLcGsT72cdH7/4+nR6FZ1F/6zUnsYi1BJhzUe76Du6UC6+LoQQIq3JfAcpatk7r+MuW8+yurPZ7ehGhm8DJkL8cmzqj6at/3QPLb4wo86Q0TQhhBDpTRq1FLV12yuYzWFmm0/HrZvwNr1BZtY4TisebHS0hOioZsXcnRT1yqRbv2yj4wghhBCGkkYtBdVW7sDZdw1bG0exJrcPBb71mCJ1XDf0GqOjJWz7mloa9/kZdWYPuVyUEEKItCeNWgqa9+LvsWV4mRW4CDMRPN73Mdt78r0BE42OlrAVc3bizrXTb2xqH2cnhBBCHAvSqKUg24C11Pq6sah4EGUt61GBdZzT/4qUv1xUTaWHyvX1jJhYJpeLEkIIIZBGLeW8MeV2nNn7mNv0DYIqgxbfYrQpi1+NvMzoaAlbMXcnFpuJoSeXGh1FCCGESArSqKWaPmsIhBzMKxxJWWgLYc9cRpVdSLbNYXSyhPiagmz8bC+DJ5SQ4bIaHUcIIYRICtKopZD3/3E/7uKtfFx3MQ2WXPCvQ2Pht2OvNTpawlbPryQa1ow6XabkEEIIIfaTRi2FNFk/BmCO+wTyo1X4G9+kpGAig7K7GZwsMeFQhNUf7qL3iHxyimWCWyGEEGI/adRSxIYP5+LquY6VdRPZ7i7D5duASQe4fdR1RkdL2MbPqvA3h2SCWyGEEOIA0qiliNXLnsZiCTGLs3FqL76m6TjdIzivbJTR0RKitWbl3J3kd3fTfVCu0XGEEEKIpCKNWgqor6rC0X8NO5uGsCK/H0X+dahwFVcOvtroaAmrXF9P7S4vo86QCW6FEEKIA0mjlgLmPvNr7I4mZvkuQQE+7zxM1hJ+OOQco6MlbMWcnTiybAwcX2x0FCGEECLpSKOWAqwD1tEQKODTbkPo2bIB7V/BxL7fwGIyGx0tIfV7vWxfXcuI07pjtspLUQghhDiQfDomuel//gWu3D3Ma7gMv3ISDCxHm1z8ZswVRkdL2Mq5lZgtJoad0t3oKEIIIURSkkYtyUW6ryEUsTI/bxSl4e0Em95jULfJFNjdRkdLSIs/zPpFexkwvghnls3oOEIIIURSkkYtiS15fSqukk0srZvMPnsRFv8mQPOL0dcYHS1h6z/dQ7glwshJMiWHEEIIcSjSqCWxil3TMZsjzDWfQqZuxNv0FtlZ5Rxf2NfoaAnRUc3q+bvo1jeLwp6ZRscRQgghkpY0akmqvqoKR5+1bG8cxpq8PhT4NmCK1HH1kCuNjpawnevraKjyMWJimdFRhBBCiKQmjVqS+uDp32J3NDPXdyEKjc+3AJO1hBsGnm50tIStmrcLR5aNfmOLjI4ihBBCJDVp1JKUuf96PC3ZfNptED2DG4j6l3FKn6+n/JQcTTV+KlbVMOzkUswWefkJIYQQhyOflEno3ft/hzu/kg/rL8VryiQUWIdWdn416ptGR0vY6vm7UErJlBxCCCFEG0ijloQCecuJRE3Myx5HcWQ3gcZ36FV0JqXOHKOjJSQUjLD24930HV2IO9dudBwhhBAi6UmjlmTWzp2Fq2wDq+omsctZgsO/CRMhfjwy9a/ruenzKlp8YUZOktE0IYQQoi2kUUsy61b+G4slxFzOwKG9eJrfweEazlmlw42OlhCtNavmVZLf3UVJ/xyj4wghhBApQRq1JOLzesnot5bdzX1ZXtCPbv4NqNAevj4o9S8XtXdrEzU7PYyYWIZSyug4QgghREqQRi2JzHzwF2S4GpjnuYSIsuAPfAaWPG4bcp7R0RK2al4lNoeFgcd1MzqKEEIIkTKkUUsipr4b8IecLCgeSs/gRiKejykvu5gMi9XoaAnxNrawZck+hpxYgtWe2tOLCCGEEJ1JGrUk8f4/7sddVMGndRfTaM5BBzaisXDnmG8ZHS1haxfsJhrVDD9NTiIQQgghjoY0akmiybwQgHnO8eRHq/E1Tqdb/ikMzCo2OFliIpEoqz/cRc9h+eQUOY2OI4QQQqQUadSSwPaVy3H2XMv6+hPYktWTTP9GTNrP94en/pQcW5dV42sMMmKijKYJIYQQR0satSSw+N3HsNpa+KTldKw6iN+3AGtGXy7vfZzR0RK2YdFeMvMz6DUs3+goQgghRMqRRi0JqNwGtFZU5mRSGtlFJLCB3nljjI51TNTsaKZ0QA7KJFNyCCGEEEdLGrUkYMqqJ+DLYrejG65QI0qHGJI30OhYCfN7gngbg+R3dxsdRQghhEhJ0qglAVtWLbXe3nhUJkR9AEwoGmpwqsTV7vICUCCNmhBCCNEu0qgZbMOHc7E7mtgZjI2ghaMNaEyc1m2wwckSV1vpASC/TBo1IYQQoj2kUTPYuo/eAmBntAyAQLgCi707mdYMI2MdEzW7PDgyrTizbEZHEUIIIVKSNGoGizhrAdjlzCU3Wovfv4k8Vx+DUx0btZUeOT5NCCGESIA0agYz5TQQDlvZk5tJYXgfhGvokz3A6FgJi0ai1O3xym5PIYQQIgHSqBnMnF2L11PEXnMp1nDsmK5RBal/fFpjtZ9IKConEgghhBAJsBgdIFX93z/+SEOWg//+Z3FCz+MoLqe2uTcRZaEquBcFdN9VwMqqnccmaAep3aRZqQ+dsXZ37IxP2fUphBBCtJ80au20uHcfVmQMPzZPlgPoEFHvB5hMReye1shemo7Nc3egvUs3HfZ+R6aV3BK5vqcQQgjRXtKotdMtzWG2r51OTm5uQs+jMuwUjhhBpiOT/OH/ocCWiesS+zFK2XEWfLyAk086+bDrWOwmLFZzJyUSQgghuh5DGjWl1G3ADYACntBaP6iUygNeBnoDFcA3tNb18fXvBK4HIsCtWuv3jMjd2oXfuI558+YxceJEo6MYwmJXZLitRscQQgghurROP5lAKTWcWJN2HDAKOF8pNQC4A5ijtR4AzIn/jFJqKHAFMAyYDPxdKSXDNEIIIYTo8ow463MIsFBr7dNah4H5wCXARcBz8XWeAy6O374ImKq1btFabwM2E2vyhBBCCCG6NCMatdXAqUqpfKWUEzgP6AEUa633AMS/F8XX7w60Pr2wMr5MCCGEEKJLU1rrzt+oUtcDtwAeYC3gB76jtc5ptU691jpXKfUY8KnW+oX48qeAd7XW0w7yvDcCNwIUFxePmzp1aofW4fF4cLvTc/qJdK4d0rv+dK4d0rv+dK4d0rt+qb3ja580adISrXX5gcsNOZlAa/0U8BSAUupuYqNkVUqpEq31HqVUCbAvvnolsRG3/cqA3Yd43seBxwHKy8t1Rx/on84nE6Rz7ZDe9adz7ZDe9adz7ZDe9UvtEw3bviFXJlBKFcW/9wQuBV4CpgPXxle5Fngzfns6cIVSyq6U6gMMAD7r3MRCCCGEEJ3PqHnUpiml8oEQcIvWul4pdQ/wSny36A7gcgCt9Rql1CvEdpGG4+tHDMothBBCCNFpjNr1ecpBltUCZxxi/SnAlI7OJYQQQgiRTOSi7EIIIYQQSUoaNSGEEEKIJCWNmhBCCCFEkjJkHrXOoJSqBrZ38GYKgJoO3kaySufaIb3rT+faIb3rT+faIb3rl9o7Xi+tdeGBC7tso9YZlFKLDzY5XTpI59ohvetP59ohvetP59ohveuX2o2rXXZ9CiGEEEIkKWnUhBBCCCGSlDRqiXnc6AAGSufaIb3rT+faIb3rT+faIb3rl9oNIseoCSGEEEIkKRlRE0IIIYRIUtKoCSGEEEIkqbRp1JRSk5VSG5RSm5VSd7RafrlSao1SKqqUOuTpt0qpPKXUbKXUpvj33PjyfKXUB0opj1Lq0cM8vo9SalH88S8rpWzx5Uop9XA810ql1NhjWXd8G0lZe/y+iUqp5fEc849Vza2e3+jafxjftlZKFbRaflX8971SKfWJUmrUsar5gO0na/3ZSqm3lFIr4jm+c6xqbrWNjqr9LKXUEqXUqvj30w/xeMPe8/HtJGX98ftS9X3f1toNe98nce0d/p6Pb6ej6j8u/ppdHq/hkkM8/ti/77XWXf4LMANbgL6ADVgBDI3fNwQYBMwDyg/zHPcCd8Rv3wH8JX7bBZwM/AB49DCPfwW4In77n8BN8dvnATMABZwALEqj2nOAtUDP+M9FXbD2MUBvoAIoaLX8RCA3fvvcY/17T4H6f9XquQqBOsCWIrWPAUrjt4cDu47ydd+h7/kUqD+H1H3ft7V2Q973SV57h77nO6F+J2CJ3y4B9u3/uY2v+3a/74/pH4dk/QImAO+1+vlO4M4D1jnSL28DUNLql7ThgPuv4xAfWPFfTE2rX/IXeYB/AVcebDtpUPvNwJ+66u/9gPUqaPVH64D7cjnEH72uWn88y9/jr48+wGbAlEq1x5croBawH2S5Ie/5FKg/5d/3h6v9cK/7A+475u/7ZK69o9/znVx/H6CKAxq1jnrfp8uuz+7AzlY/V8aXHY1irfUegPj3oqN4bD7QoLUOH2T7xyLb4SRz7QOBXKXUvPhQ+jVHmetIjK69ra4n9j+tYy2Z63+U2P9wdwOrgNu01tFj9NzQebV/HVimtW45YLmR7/ljtY2Oqr+rvO8PVXtbdcT7Pplr7+j3PHRw/Uqp45VSa4jl/0Gr1/d+HfK+txxlAalKHWSZTpLtd3S2ZK7dAowDzgAcwKdKqYVa642dsO2koJSaROwP9skd8fQHWZYs9Z8DLAdOB/oBs5VSH2mtm47R83d47UqpYcBfgLOPcvud8XtJ5vpT/n1/hNrb8viOet8nc+0d/Z6HDq5fa70IGKaUGgI8p5SaobUOtHH77c6WLiNqlUCPVj+XEevqD0kp9Uz8oMF344uqlFIl8fv2759uqxogRym1vzFuvf2jznaUkr32mVprr9a6BvgQGHUUz30kRtd+WEqpkcCTwEVa69pj9bytJHP93wFe0zGbgW3A4GP03NDBtSulyoDXgWu01lsO8nRGvufbtY1Orj9l3/dtqP2wOvh9n8y1d/R7Hjrpb57Weh3gJXasXmsd8r5Pl0btc2BA/GwMG3AFMP1wD9Baf0drPVprfV580XTg2vjta4E327pxHdsh/QFw2UEePx24Jn5GyAlA4/5h12MkmWt/EzhFKWVRSjmB44F1bX3uNjC09sNRSvUEXgO+fQxHEg6UtPUDO4iNqKCUKiZ2kO/WY/Tc0IG1K6VygHeIHfvy8SGey8j3PCR3/Sn7vm9L7YfTCe/7pK2djn/PQ8fW32d/A6aU6hXPX3HAc3XM+74tB7J1hS9iZ1xsJHZGyP+1Wn4JsU63hdjBge8d4vH5wBxgU/x7Xqv7KoidweKJP9fQgzy+L/AZsQMo/0v8IExiw6GPxXOt4jAHOXa12uP3/ZzYGWCrgR93wdpvjd8XJva/pyfjy58E6ontClgOLO6ir/tD1V8KzIq/5lcDV6dK7cCvif1venmrr6+cuXio1z2d8J5P5vrj96Xk+/4oajfsfZ/EtXf4e76D6/82sCZe91Lg4kM8/pi/7+USUkIIIYQQSSpddn0KIYQQQqQcadSEEEIIIZKUNGpCCCGEEElKGjUhhBBCiCQljZoQQgghRJKSRk0IkbaUUvnxyS6XK6X2KqV2xW97lFJ/NzqfEELI9BxCCAEope4CPFrr+43OIoQQ+8mImhBCHEApNVEp9Xb89l1KqeeUUrOUUhVKqUuVUvcqpVYppWYqpazx9cYppear2IXG39t/GRohhEiENGpCCHFk/YCvARcBLwAfaK1HAH7ga/Fm7RHgMq31OOBpYIpRYYUQXYflyKsIIUTam6G1DimlVgFmYGZ8+SqgN7Hr/g0HZiuliK9zrK/fKYRIQ9KoCSHEkbUAaK2jSqmQ/t/BvVFif0cVsEZrPcGogEKIrkl2fQohROI2AIVKqQkASimrUmqYwZmEEF2ANGpCCJEgrXUQuAz4i1JqBbAcONHQUEKILkGm5xBCCCGESFIyoiaEEEIIkaSkURNCCCGESFLSqAkhhBBCJClp1IQQQgghkpQ0akIIIYQQSUoaNSGEEEKIJCWNmhBCCCFEkvp/JLm48eODTaIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,12))\n",
    "\n",
    "ax1 = plt.subplot(211)\n",
    "for i in range(20):\n",
    "    plt.plot(ds_E_01['time'][i], ds_E_01['density'][i])\n",
    "ax1.set_title('ARK4 + Emulsification (time)')\n",
    "ax1.set_ylabel('Density [Units?]')\n",
    "plt.grid()\n",
    "\n",
    "ax1 = plt.subplot(212)\n",
    "for i in range(20):\n",
    "    plt.plot(ds_E_02['time'][i], ds_E_02['density'][i])\n",
    "ax1.set_title('ARK4 + Emulsification (oil age)')\n",
    "ax1.set_xlabel('Time')\n",
    "ax1.set_ylabel('Density [Units?]')\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Viscosity simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "ds_V = xr.open_dataset(\"basic_model_output_ARK4_and_Emulsification_ViscosityChanges_test01.nc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:               (obs: 289, traj: 20)\n",
       "Dimensions without coordinates: obs, traj\n",
       "Data variables:\n",
       "    trajectory            (traj, obs) float64 60.0 60.0 nan ... 79.0 79.0 79.0\n",
       "    time                  (traj, obs) datetime64[ns] 2002-01-01 ... 2002-01-03\n",
       "    lat                   (traj, obs) float32 -30.0 -30.0 nan ... -32.17 -32.17\n",
       "    lon                   (traj, obs) float32 32.0 32.0 nan ... 32.29 32.29\n",
       "    z                     (traj, obs) float32 0.0 0.0 nan nan ... 0.0 0.0 0.0\n",
       "    viscosity             (traj, obs) float32 0.053 0.053 nan ... 377.0 377.0\n",
       "    density               (traj, obs) float32 902.0 902.0 ... 1.004e+03\n",
       "    original_density      (traj, obs) float32 902.0 902.0 nan ... 902.0 902.0\n",
       "    water_fraction        (traj, obs) float32 0.0 0.0 nan ... 0.8054 0.8073\n",
       "    evaporation_fraction  (traj, obs) float32 0.0 0.0 nan nan ... 0.0 0.0 0.0\n",
       "    is_emulsified         (traj, obs) float32 0.0 0.0 nan nan ... 1.0 1.0 1.0\n",
       "    emuls_start_time      (traj, obs) float32 0.0 0.0 nan ... 6.6e+03 6.6e+03\n",
       "    oil_age               (traj, obs) float32 0.0 0.0 ... 1.722e+05 1.728e+05\n",
       "Attributes:\n",
       "    feature_type:           trajectory\n",
       "    Conventions:            CF-1.6/CF-1.7\n",
       "    ncei_template_version:  NCEI_NetCDF_Trajectory_Template_v2.0\n",
       "    parcels_version:        2.2.1\n",
       "    parcels_mesh:           spherical</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f05a44af-b10e-4e1c-a4bd-47bca809fb96' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f05a44af-b10e-4e1c-a4bd-47bca809fb96' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 289</li><li><span>traj</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aa4b25f2-f9cd-4895-a3ae-4887e7276e0e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aa4b25f2-f9cd-4895-a3ae-4887e7276e0e' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-edf0c150-d1e6-4de8-9548-ec09fdfc23a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-edf0c150-d1e6-4de8-9548-ec09fdfc23a1' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>trajectory</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-213d266f-f6c4-4efb-bf6f-6ba694305536' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-213d266f-f6c4-4efb-bf6f-6ba694305536' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-497cc7f9-3258-4e7b-bec8-4d804fbabe57' class='xr-var-data-in' type='checkbox'><label for='data-497cc7f9-3258-4e7b-bec8-4d804fbabe57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Unique identifier for each particle</dd><dt><span>cf_role :</span></dt><dd>trajectory_id</dd></dl></div><div class='xr-var-data'><pre>array([[60., 60., nan, ..., nan, nan, nan],\n",
       "       [61., 61., nan, ..., nan, nan, nan],\n",
       "       [62., 62., 62., ..., 62., 62., 62.],\n",
       "       ...,\n",
       "       [77., 77., 77., ..., 77., 77., 77.],\n",
       "       [78., 78., 78., ..., 78., 78., 78.],\n",
       "       [79., 79., 79., ..., 79., 79., 79.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2c9dd897-878c-4d4a-b63b-07f1885b89ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c9dd897-878c-4d4a-b63b-07f1885b89ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30bd3a6c-9909-427e-812a-2099e780a462' class='xr-var-data-in' type='checkbox'><label for='data-30bd3a6c-9909-427e-812a-2099e780a462' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:00:00.000000000&#x27;,\n",
       "                                  &#x27;NaT&#x27;, ...,                           &#x27;NaT&#x27;,\n",
       "                                  &#x27;NaT&#x27;,                           &#x27;NaT&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;],\n",
       "       [&#x27;2002-01-01T00:00:00.000000000&#x27;, &#x27;2002-01-01T00:10:00.000000000&#x27;,\n",
       "        &#x27;2002-01-01T00:20:00.000000000&#x27;, ..., &#x27;2002-01-02T23:40:00.000000000&#x27;,\n",
       "        &#x27;2002-01-02T23:50:00.000000000&#x27;, &#x27;2002-01-03T00:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4ce31963-de06-4c91-97af-b3dc4b1408fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ce31963-de06-4c91-97af-b3dc4b1408fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44cc8bbd-7dfa-411b-b1f1-cdd65360bd60' class='xr-var-data-in' type='checkbox'><label for='data-44cc8bbd-7dfa-411b-b1f1-cdd65360bd60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-30.      , -30.      ,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [-30.105263, -30.105263,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [-30.210526, -30.213636, -30.216728, ..., -30.738686, -30.740742,\n",
       "        -30.742796],\n",
       "       ...,\n",
       "       [-31.789474, -31.791996, -31.794502, ..., -32.057014, -32.057674,\n",
       "        -32.058334],\n",
       "       [-31.894737, -31.897154, -31.899555, ..., -32.122482, -32.122936,\n",
       "        -32.12339 ],\n",
       "       [-32.      , -32.002304, -32.004593, ..., -32.173954, -32.174156,\n",
       "        -32.17436 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3d0f2b00-48a8-4a09-b662-c37fb4693888' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d0f2b00-48a8-4a09-b662-c37fb4693888' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-220a097b-0c8a-4d27-b836-d4293682c979' class='xr-var-data-in' type='checkbox'><label for='data-220a097b-0c8a-4d27-b836-d4293682c979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[32.      , 32.      ,       nan, ...,       nan,       nan,       nan],\n",
       "       [32.      , 32.      ,       nan, ...,       nan,       nan,       nan],\n",
       "       [32.      , 31.996878, 31.993744, ..., 31.014305, 31.010906, 31.00751 ],\n",
       "       ...,\n",
       "       [32.      , 32.000126, 32.00026 , ..., 32.07842 , 32.07866 , 32.0789  ],\n",
       "       [32.      , 32.000626, 32.001255, ..., 32.18494 , 32.1856  , 32.18626 ],\n",
       "       [32.      , 32.00091 , 32.001827, ..., 32.285065, 32.28606 , 32.287056]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d3471d7-620b-424b-99b1-574347a9f135' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d3471d7-620b-424b-99b1-574347a9f135' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f790ec-cea2-4332-b60f-d636007b65a9' class='xr-var-data-in' type='checkbox'><label for='data-83f790ec-cea2-4332-b60f-d636007b65a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>viscosity</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb6b880c-9133-4c51-ac7f-7db854c3d649' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb6b880c-9133-4c51-ac7f-7db854c3d649' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5ad1379-b0eb-44b4-bbde-ab7a206dc228' class='xr-var-data-in' type='checkbox'><label for='data-a5ad1379-b0eb-44b4-bbde-ab7a206dc228' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>viscosity</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[5.300000e-02, 5.300000e-02,          nan, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [5.300000e-02, 5.300000e-02,          nan, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [5.300000e-02, 3.076421e+01, 2.885682e+01, ..., 3.770000e+02,\n",
       "        3.770000e+02, 3.770000e+02],\n",
       "       ...,\n",
       "       [5.300000e-02, 3.076421e+01, 2.885682e+01, ..., 3.770000e+02,\n",
       "        3.770000e+02, 3.770000e+02],\n",
       "       [5.300000e-02, 3.076421e+01, 2.885682e+01, ..., 3.770000e+02,\n",
       "        3.770000e+02, 3.770000e+02],\n",
       "       [5.300000e-02, 3.076421e+01, 2.885682e+01, ..., 3.770000e+02,\n",
       "        3.770000e+02, 3.770000e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-674b431a-3d9c-40c7-b1ad-d38a01e806d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-674b431a-3d9c-40c7-b1ad-d38a01e806d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22c60df6-e1c9-436c-9236-23658a60a8b4' class='xr-var-data-in' type='checkbox'><label for='data-22c60df6-e1c9-436c-9236-23658a60a8b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>density</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[ 902.     ,  902.     ,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [ 902.     ,  902.     ,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.2527 , 1003.4843 ,\n",
       "        1003.72327],\n",
       "       ...,\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.72327, 1003.96985,\n",
       "        1004.224  ],\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.72327, 1003.96985,\n",
       "        1004.224  ],\n",
       "       [ 902.     ,  902.     ,  902.     , ..., 1003.2527 , 1003.4843 ,\n",
       "        1003.72327]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>original_density</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-03544cae-7f8a-4478-81a2-cab57427fcc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03544cae-7f8a-4478-81a2-cab57427fcc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1298429-0b3b-4f65-858c-67c281792538' class='xr-var-data-in' type='checkbox'><label for='data-d1298429-0b3b-4f65-858c-67c281792538' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>original_density</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[902., 902.,  nan, ...,  nan,  nan,  nan],\n",
       "       [902., 902.,  nan, ...,  nan,  nan,  nan],\n",
       "       [902., 902., 902., ..., 902., 902., 902.],\n",
       "       ...,\n",
       "       [902., 902., 902., ..., 902., 902., 902.],\n",
       "       [902., 902., 902., ..., 902., 902., 902.],\n",
       "       [902., 902., 902., ..., 902., 902., 902.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_fraction</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f557892f-eeb4-465d-9d76-477d44c036c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f557892f-eeb4-465d-9d76-477d44c036c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-615324e9-80e9-41e4-bb62-88cdf59d4fc7' class='xr-var-data-in' type='checkbox'><label for='data-615324e9-80e9-41e4-bb62-88cdf59d4fc7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>water_fraction</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[0.      , 0.      ,      nan, ...,      nan,      nan,      nan],\n",
       "       [0.      , 0.      ,      nan, ...,      nan,      nan,      nan],\n",
       "       [0.      , 0.      , 0.      , ..., 0.803593, 0.805431, 0.807328],\n",
       "       ...,\n",
       "       [0.      , 0.      , 0.      , ..., 0.807328, 0.809285, 0.811302],\n",
       "       [0.      , 0.      , 0.      , ..., 0.807328, 0.809285, 0.811302],\n",
       "       [0.      , 0.      , 0.      , ..., 0.803593, 0.805431, 0.807328]],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>evaporation_fraction</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8582567a-770d-4576-b045-63182094f1a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8582567a-770d-4576-b045-63182094f1a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a360fe2-c350-4795-a469-b95d6c14518d' class='xr-var-data-in' type='checkbox'><label for='data-6a360fe2-c350-4795-a469-b95d6c14518d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>evaporation_fraction</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>is_emulsified</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d53a6e7-778c-45fd-8c65-bbfeb30e0320' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d53a6e7-778c-45fd-8c65-bbfeb30e0320' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d2aac59-0ac1-4e5c-b8a7-168267a6ef58' class='xr-var-data-in' type='checkbox'><label for='data-6d2aac59-0ac1-4e5c-b8a7-168267a6ef58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>is_emulsified</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0., nan, ..., nan, nan, nan],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  1.,  1.,  1.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>emuls_start_time</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bad27356-f24c-41f4-85f1-16f090ea3585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bad27356-f24c-41f4-85f1-16f090ea3585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00df4ba1-f214-4783-a3f9-64a276942888' class='xr-var-data-in' type='checkbox'><label for='data-00df4ba1-f214-4783-a3f9-64a276942888' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>emuls_start_time</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[   0.,    0.,   nan, ...,   nan,   nan,   nan],\n",
       "       [   0.,    0.,   nan, ...,   nan,   nan,   nan],\n",
       "       [   0.,    0.,    0., ..., 6600., 6600., 6600.],\n",
       "       ...,\n",
       "       [   0.,    0.,    0., ..., 5400., 5400., 5400.],\n",
       "       [   0.,    0.,    0., ..., 5400., 5400., 5400.],\n",
       "       [   0.,    0.,    0., ..., 6600., 6600., 6600.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oil_age</span></div><div class='xr-var-dims'>(traj, obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-542b40cc-7c3b-4f8c-92a7-42f84f445b42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-542b40cc-7c3b-4f8c-92a7-42f84f445b42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94aae18-f0fd-48e0-8cde-abef3b4fe434' class='xr-var-data-in' type='checkbox'><label for='data-b94aae18-f0fd-48e0-8cde-abef3b4fe434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd></dd><dt><span>standard_name :</span></dt><dd>oil_age</dd><dt><span>units :</span></dt><dd>unknown</dd></dl></div><div class='xr-var-data'><pre>array([[     0.,      0.,     nan, ...,     nan,     nan,     nan],\n",
       "       [     0.,      0.,     nan, ...,     nan,     nan,     nan],\n",
       "       [     0.,    600.,   1200., ..., 171600., 172200., 172800.],\n",
       "       ...,\n",
       "       [     0.,    600.,   1200., ..., 171600., 172200., 172800.],\n",
       "       [     0.,    600.,   1200., ..., 171600., 172200., 172800.],\n",
       "       [     0.,    600.,   1200., ..., 171600., 172200., 172800.]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6749cca9-53f0-44a3-a6b5-30244277b056' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6749cca9-53f0-44a3-a6b5-30244277b056' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>feature_type :</span></dt><dd>trajectory</dd><dt><span>Conventions :</span></dt><dd>CF-1.6/CF-1.7</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Trajectory_Template_v2.0</dd><dt><span>parcels_version :</span></dt><dd>2.2.1</dd><dt><span>parcels_mesh :</span></dt><dd>spherical</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:               (obs: 289, traj: 20)\n",
       "Dimensions without coordinates: obs, traj\n",
       "Data variables:\n",
       "    trajectory            (traj, obs) float64 ...\n",
       "    time                  (traj, obs) datetime64[ns] ...\n",
       "    lat                   (traj, obs) float32 ...\n",
       "    lon                   (traj, obs) float32 ...\n",
       "    z                     (traj, obs) float32 ...\n",
       "    viscosity             (traj, obs) float32 ...\n",
       "    density               (traj, obs) float32 ...\n",
       "    original_density      (traj, obs) float32 ...\n",
       "    water_fraction        (traj, obs) float32 ...\n",
       "    evaporation_fraction  (traj, obs) float32 ...\n",
       "    is_emulsified         (traj, obs) float32 ...\n",
       "    emuls_start_time      (traj, obs) float32 ...\n",
       "    oil_age               (traj, obs) float32 ...\n",
       "Attributes:\n",
       "    feature_type:           trajectory\n",
       "    Conventions:            CF-1.6/CF-1.7\n",
       "    ncei_template_version:  NCEI_NetCDF_Trajectory_Template_v2.0\n",
       "    parcels_version:        2.2.1\n",
       "    parcels_mesh:           spherical"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/py3_parcels/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:307: UserWarning: The .xlabels_top attribute is deprecated. Please use .top_labels to toggle visibility instead.\n",
      "  warnings.warn('The .xlabels_top attribute is deprecated. Please '\n",
      "/opt/anaconda3/envs/py3_parcels/lib/python3.6/site-packages/cartopy/mpl/gridliner.py:343: UserWarning: The .ylabels_right attribute is deprecated. Please use .right_labels to toggle visibility instead.\n",
      "  warnings.warn('The .ylabels_right attribute is deprecated. Please '\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fd7bfb45b00>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "\n",
    "plt.figure(figsize=(6,10)) #(17,5)\n",
    "\n",
    "ax1 = plt.subplot(111, projection=ccrs.PlateCarree())\n",
    "ax1.coastlines(resolution='50m')\n",
    "cc = ax1.scatter(ds_V['lon'], ds_V['lat'], c = ds_V['viscosity'], transform=ccrs.PlateCarree()) #marker='o'\n",
    "# ax1.set_xlim([4, 10])\n",
    "# ax1.set_ylim([52, 59])\n",
    "gl = ax1.gridlines(crs=ccrs.PlateCarree(), draw_labels=True)\n",
    "gl.xlabels_top = False\n",
    "gl.ylabels_right = False\n",
    "gl.xformatter = LONGITUDE_FORMATTER\n",
    "gl.yformatter = LATITUDE_FORMATTER\n",
    "ax1.set_title('ARK4 + Emulsification + Viscosity')\n",
    "plt.colorbar(cc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "for i in range(20):\n",
    "    plt.plot(ds_V['time'][i], ds_V['viscosity'][i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (py3_parcels)",
   "language": "python",
   "name": "py3_parcels"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}