parcels/examples/tutorial_output.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial on how to analyse Parcels output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial covers the format of the trajectory output exported by Parcels. **Parcels does not include advanced analysis or plotting functionality**, which users are suggested to write themselves to suit their research goals. Here we provide some starting points to explore the parcels output files yourself.\n",
    "\n",
    "* [**Reading the output file**](#Reading-the-output-file)\n",
    "* [**Trajectory data structure**](#Trajectory-data-structure)\n",
    "* [**Analysis**](#Analysis)\n",
    "* [**Plotting**](#Plotting)\n",
    "* [**Animations**](#Animations)\n",
    "\n",
    "First we need to create some parcels output to analyse. We simulate a set of particles using the setup described in the [Delay start tutorial](https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_delaystart.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from parcels import FieldSet, ParticleSet, JITParticle\n",
    "from parcels import AdvectionRK4\n",
    "import numpy as np\n",
    "from datetime import timedelta as delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Compiled JITParticleAdvectionRK4 ==> C:\\Users\\GEBRUI~1\\AppData\\Local\\Temp\\parcels-tmp\\41e0d83670ae127d5c88fa24315b7a08_0.dll\n"
     ]
    }
   ],
   "source": [
    "fieldset = FieldSet.from_parcels(\"Peninsula_data/peninsula\", allow_time_extrapolation=True)\n",
    "\n",
    "npart = 10  # number of particles to be released\n",
    "lon = 3e3 * np.ones(npart)\n",
    "lat = np.linspace(3e3 , 45e3, npart, dtype=np.float32)\n",
    "time = np.arange(0, npart) * delta(hours=2).total_seconds()  # release every particle two hours later\n",
    "\n",
    "pset = ParticleSet(fieldset=fieldset, pclass=JITParticle, lon=lon, lat=lat, time=time)\n",
    "\n",
    "output_file = pset.ParticleFile(name=\"Output.nc\", outputdt=delta(hours=2))\n",
    "pset.execute(AdvectionRK4, runtime=delta(hours=24), dt=delta(minutes=5),\n",
    "             output_file=output_file)\n",
    "output_file.close()  # export the trajectory data to a netcdf file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the output file\n",
    "### Using the netCDF4 package\n",
    "The [`parcels.particlefile.ParticleFile`](https://oceanparcels.org/gh-pages/html/#parcels.particlefile.ParticleFile) class creates a netCDF file to store the particle trajectories. It uses the [**`netCDF4` package**](https://unidata.github.io/netcdf4-python/netCDF4/index.html), which is also suitable to open and read the files for analysis. The [`Dataset` class](https://unidata.github.io/netcdf4-python/netCDF4/index.html#netCDF4.Dataset) opens a netCDF file in reading mode by default. Data can be accessed with the `Dataset.variables` dictionary which can return (masked) numpy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'netCDF4._netCDF4.Dataset'>\n",
      "root group (NETCDF4 data model, file format HDF5):\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.dev2+ga8340d9b\n",
      "    parcels_mesh: flat\n",
      "    dimensions(sizes): obs(13), traj(10)\n",
      "    variables(dimensions): int32 trajectory(traj, obs), float64 time(traj, obs), float32 lat(traj, obs), float32 lon(traj, obs), float32 z(traj, obs)\n",
      "    groups: \n"
     ]
    }
   ],
   "source": [
    "import netCDF4\n",
    "\n",
    "data_netcdf4 = netCDF4.Dataset('Output.nc')\n",
    "print(data_netcdf4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      " [1 1 1 1 1 1 1 1 1 1 1 1 --]\n",
      " [2 2 2 2 2 2 2 2 2 2 2 -- --]\n",
      " [3 3 3 3 3 3 3 3 3 3 -- -- --]\n",
      " [4 4 4 4 4 4 4 4 4 -- -- -- --]\n",
      " [5 5 5 5 5 5 5 5 -- -- -- -- --]\n",
      " [6 6 6 6 6 6 6 -- -- -- -- -- --]\n",
      " [7 7 7 7 7 7 -- -- -- -- -- -- --]\n",
      " [8 8 8 8 8 -- -- -- -- -- -- -- --]\n",
      " [9 9 9 9 -- -- -- -- -- -- -- -- --]]\n"
     ]
    }
   ],
   "source": [
    "trajectory_netcdf4 = data_netcdf4.variables['trajectory'][:]\n",
    "time_netcdf4 = data_netcdf4.variables['time'][:]\n",
    "lon_netcdf4 = data_netcdf4.variables['lon'][:]\n",
    "lat_netcdf4 = data_netcdf4.variables['lat'][:]\n",
    "print(trajectory_netcdf4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the xarray package\n",
    "An often-used alternative to netCDF4, which also comes with the parcels installation, is [**xarray**](http://xarray.pydata.org/en/stable/index.html). Its labelled arrays allow for intuitive and accessible handling of data stored in the netCDF format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:     (obs: 13, traj: 10)\n",
      "Dimensions without coordinates: obs, traj\n",
      "Data variables:\n",
      "    trajectory  (traj, obs) float64 ...\n",
      "    time        (traj, obs) timedelta64[ns] ...\n",
      "    lat         (traj, obs) float32 ...\n",
      "    lon         (traj, obs) float32 ...\n",
      "    z           (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.dev2+ga8340d9b\n",
      "    parcels_mesh:           flat\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "data_xarray = xr.open_dataset('Output.nc')\n",
    "print(data_xarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'trajectory' (traj: 10, obs: 13)>\n",
      "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
      "       [ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1., nan],\n",
      "       [ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2., nan, nan],\n",
      "       [ 3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3.,  3., nan, nan, nan],\n",
      "       [ 4.,  4.,  4.,  4.,  4.,  4.,  4.,  4.,  4., nan, nan, nan, nan],\n",
      "       [ 5.,  5.,  5.,  5.,  5.,  5.,  5.,  5., nan, nan, nan, nan, nan],\n",
      "       [ 6.,  6.,  6.,  6.,  6.,  6.,  6., nan, nan, nan, nan, nan, nan],\n",
      "       [ 7.,  7.,  7.,  7.,  7.,  7., nan, nan, nan, nan, nan, nan, nan],\n",
      "       [ 8.,  8.,  8.,  8.,  8., nan, nan, nan, nan, nan, nan, nan, nan],\n",
      "       [ 9.,  9.,  9.,  9., nan, nan, nan, nan, nan, nan, nan, nan, nan]])\n",
      "Dimensions without coordinates: traj, obs\n",
      "Attributes:\n",
      "    long_name:  Unique identifier for each particle\n",
      "    cf_role:    trajectory_id\n"
     ]
    }
   ],
   "source": [
    "print(data_xarray['trajectory'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trajectory data structure\n",
    "The data in the netCDF file are organised according to the [CF-conventions](http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#discrete-sampling-geometries) implemented with the [NCEI trajectory template](http://www.nodc.noaa.gov/data/formats/netcdf/v2.0/trajectoryIncomplete.cdl). The data is stored in a **two-dimensional array** with the dimensions **`traj`** and **`obs`**. Each particle trajectory is essentially stored as a time series where the coordinate data (**`lon`**, **`lat`**, **`time`**) are a function of the observation (`obs`).\n",
    "\n",
    "The output dataset used here contains **10 particles** and **13 observations**. Not every particle has 13 observations however; since we released particles at different times some particle trajectories are shorter than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  2.  4.  6.  8. 10. 12. 14. 16. 18. 20. 22. 24.]\n",
      " [ 2.  4.  6.  8. 10. 12. 14. 16. 18. 20. 22. 24. nan]\n",
      " [ 4.  6.  8. 10. 12. 14. 16. 18. 20. 22. 24. nan nan]\n",
      " [ 6.  8. 10. 12. 14. 16. 18. 20. 22. 24. nan nan nan]\n",
      " [ 8. 10. 12. 14. 16. 18. 20. 22. 24. nan nan nan nan]\n",
      " [10. 12. 14. 16. 18. 20. 22. 24. nan nan nan nan nan]\n",
      " [12. 14. 16. 18. 20. 22. 24. nan nan nan nan nan nan]\n",
      " [14. 16. 18. 20. 22. 24. nan nan nan nan nan nan nan]\n",
      " [16. 18. 20. 22. 24. nan nan nan nan nan nan nan nan]\n",
      " [18. 20. 22. 24. nan nan nan nan nan nan nan nan nan]]\n"
     ]
    }
   ],
   "source": [
    "np.set_printoptions(linewidth=160)\n",
    "ns_per_hour = np.timedelta64(1, 'h') # nanoseconds in an hour\n",
    "\n",
    "print(data_xarray['time'].data/ns_per_hour) # time is stored in nanoseconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the first observation occurs at a different time for each trajectory. **`obs` != `time`**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "Sometimes, trajectories are analysed as they are stored: as individual time series. If we want to study the distance travelled as a function of time, the time we are interested in is the time relative to the start of the each particular trajectory: the array operations are simple since each trajectory is analysed as a function of **`obs`**. The time variable is only needed to express the results in the correct units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = data_xarray['lon'].values\n",
    "y = data_xarray['lat'].values\n",
    "distance = np.cumsum(np.sqrt(np.square(np.diff(x))+np.square(np.diff(y))),axis=1)  # d = (dx^2 + dy^2)^(1/2)\n",
    "\n",
    "real_time = data_xarray['time']/ns_per_hour # convert time to hours\n",
    "time_since_release = (real_time.values.transpose() - real_time.values[:,0]) # substract the initial time from each timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,4),constrained_layout=True)\n",
    "\n",
    "ax1.set_ylabel('Distance travelled [m]')\n",
    "ax1.set_xlabel('observation',weight='bold')\n",
    "d_plot = ax1.plot(distance.transpose())\n",
    "\n",
    "ax2.set_ylabel('Distance travelled [m]')\n",
    "ax2.set_xlabel('time since release [hours]',weight='bold')\n",
    "d_plot_t = ax2.plot(time_since_release[1:],distance.transpose())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two figures above show the same graph. Time is not needed to create the first figure. The time variable minus the first value of each trajectory gives the x-axis the correct units in the second figure. \n",
    "\n",
    "We can also plot the distance travelled as a function of the absolute time easily, since the `time` variable matches up with the data for each individual trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8DF9vD8IoJV/0rMvg5r7/XVDr9mHYOBL0BdB7HtTrW+Z5s3XZTD41md3hu2lZqSXftv+Wig7K+z1yL1wg9sOP0Ccn4/nRh7gOG/ZIioUJIc5LKUu8NdIy9DU0NP4xJGbm8+mmqxy8kUjLqm7MHNAIX7diUVlGI/wxCw5PgQq1YdAq8Ago87zXk6/z8bGPicuOY2yTsYysP1Lx3BVpNJKy5GeSfvwR68qV8f/1V+wb1Fd0DiXRjIuGhsY/gu2X45iw9Rp5OgMTetbl1Tb+WFgUe6PPTYXNoyFkHzQYCM/PBptyZZrTKI2sClrF7Auz8bD3YFnXZTT1bFq2G7kH+uRk4j4ZR86ff+LUozuVJk3CsnzpZGceFZpx0dDQeKJJzdExYes1dl6Jp5GvC7MGNKJGRcf/vij2AqwbbspdeW4WBI4stdDk/8+bn8oXx7/gj9g/eNb3WSa3nYyzrXOZxrwXOX/+Sewn4zBmZVFp8iRcBgx4JMdgZUUzLhoaGk8sB4ISGL/pKhl5Oj7uWovRT1XDyrJYCLGUcH457B4Hjp4wYi/4NCvzvGfvnGX8sfGkF6TzWcvPGFxrsOIPfKnXkzR3HimLF2NTvRp+y5ZiV1P5jH610IyLhobGE0dmfiFfbw9i/fkYalcqz8oRLahb2em/L9Llws4P4PJvUP1Z6PczlHMv07x6o55FVxax6PIiqjhV4adOP1HbrXaZxrwXhfHxxH74EXkXLuDc/wUqffYZFg7KZvSrjWZcNDQ0nihOhCbz8frL3MnM5+1navBOxwBsrO5KeEy5DWtfhsQgeHo8PP0JlNHBfifnDuOOjeNC4gV6V+/NZy0/U1zCBSDr0CHiPv0MCgupPGMGzs/3VHyOR4FmXDQ0NJ4IcnV6pu2+wcqTkVSrUI6Nb7Shid89cjuCt8OWN03GZOgGCOhU5rkPRx1mwp8TKDQUMrX9VHpWU/6Bb9TpSJw5k7SVq7CrWxfv72dh4++v+DyPCs24aGhoPPaci0jlw/WXiUrNZWS7qnzctRZ21nftRAx6OPgV/DkXKjeFgSvApWxCtTqDjlnnZvHrjV+p41aHGU/PoIpTlTKNec95IiNNBb2CgnB9+WUqfvwRFjalV2B+HNCMi4aGxmNL8UJe3i72/PZ6K1pVu4ffJOsObBgBkSeg+WumYl5Wtv973UMQkRHBJ8c+ITg1mJfqvMT7zd7HxlL5B37Gjp3cmTgRrKzw+Wke5Tt2VHyOv4P7GhchREkCtgullFcVWo+GhoYGAIlZ+bz88xluJmTxYgs/Pn/urkJeRUQch/Wvgi7bVM++4cAyz7399na+PvU1tpa2zHt2Hk/7Pl3mMe/GmJfHnSlTyNiwEfsmTUwFvSqXXdfsryjUGbC2eXSClg/auRwFznJvafsiqmIqWayhoaGhCBl5hQxbeobotFyWv9KcZ2rfQ0ZFSvjzRzgwCdyqwrCt4Fm3TPPmFuYy5fQUtt3eRqBnINPaT8OznGeZxrwXhXfuED3mDQpu3sR99GgqvP2WqgW9kqKy2LXgCm1eqEFAoPL3cy8eZFzOSimfvd8FQohDCq5HQ0PjX05+oYHXV5zjdlI2y19pQbsAj3tclGFy2t/YAXV7m2Ty7Zz+97qHIDQtlPeOvEd0VjRvNnqTUQ1HKS7hApAfFET0mDcw5uTgu3gRju2VF7YsTuj5RA7+EoRdeWtcKz26cOb7GpcHGZaSXqOhoaFREgoNRt7+9QJnI1OZ92LTexuWO9dg3cuQHgVdp0KrN8qcbX8p8RJvHXwLW0tbfu7yM80rlb1I2L3IOnyY2A8/wtLZmSq//opdLfWSIqWUnNsVwZnt4VSq5kT3MQ1xcHp0QQIldugLIRpiOv76/z5Syk0qrElDQ+NfiNEoGbfxCgeCE/mmT32ea+j1vxdd+hV2fAD2LvDKTvBrVeZ5j8Uc48MjH+JZzpNFnRfh7ej94E6lIHX1GhK+/Ra7OnXwWTBf1UqRhToDh1YEE3o+kVqtKvHM0NpYWpet+NnDUiLjIoRYBjQErgNGc7MENOOioaFRZqSUfLsrmE0XYvmgc01eanVXuG9hPuwZB+d/Af/20H8ZOJb94bwjbAcTjk8gwDWABZ0W4G5ftgz+eyENBhK/+47UFStxfPZZvGfOUDXbPjutgN0Lr5AYlUXrvtVp0sXvb9EiK+nOpZWUsmyeMg0NDY2/YMHR2/x8PJxX2vgz9tka//3LtAhYNwziL0O79+GZL8Cy7FkUq4NWM/3sdFpWasnsZ2bjaOP44E4PiTE3l9iPPyH74EFch72M57hxqpYgTozMZNf8K+jyDfQY04CqjSqoNteDKOlf6KQQoq6UMkjV1WhoaPzr+O1MFN/tuUmfxpX5smfd/37LvrUPNr1uigwb/BvU7lHm+aSUzL04lyVXl9DJrxPTnpqGrWXZcmLuhT4pieg33iQ/KAjPzz/H7eWXFJ+jOCHnEji4IhiH8jb0+7gxHj7KG8uHoaSHcCswGZibQogrQoirQogrD+okhHARQmwQQtwQQgQLIVoLIdyEEPuFECHm767Frv9UCBFqnqdrsfZm5jlDhRA/CvO/PiGErRBirbn9tBDC/yHvX0ND429kz7V4Pt98lQ61KjBjQKP/rr9yZR38OhBcfGH0EUUMi8FoYPKpySy5uoT+Nfsz8+mZqhiW/Fu3CB80iILbt/GZN09VwyKNkjPbw9j383Uq+pWn//jAv92wQMl3LsuAl4Gr/MfnUhLmAHuklP2FEDaAA/AZcFBKOU0IMR4YD4wTQtQFBgP1gMrAASFETSmlAVgAjAJOAbuAbsBuYCSQJqWsIYQYDEwHBj3E+jQ0NP4m/gxN5p3fLtHY14X5Q5tiXVwqP2grbB4D/u1gyDqwKbuPosBQwPhj4zkQdYDXG7zO2CZjVfFFZJ84Qey772Fhb0+V1auwr1dP8TmKKNQZOPhLMLcvJFK7dSU6DHn0jvu/oqTGJUpKue1hBhZCOAFPAa8ASCl1gE4I0RvoYL5sBXAEGAf0Bn6XUhYA4UKIUKCFECICcJJSnjSPuxLog8m49Aa+Mo+1AZgnhBBSSvkwa9XQ0Hi0XI3J4PWV5/D3cGDZK81xsCn2KLq1FzaMBJ9AePF3RQxLti6bdw+/y5k7ZxjXfBwv1VVnJ5G+YQPxX03Ctlo1fBctxNrrHhFvCpGdls+uBVdJis6iTb8aNO7s+1gVESupcbkhhPgV2A4UFDU+IBS5GpAELBdCNALOA+8CnlLKeHP/eCFEUciHN6adSREx5rZC8+e724v6RJvH0gshMgB3ILmE96WhofGIuZ2UzfDlZ3AtZ8PKES1xcSiWexF2xCSV71kPhq4H27If76TkpfDGgTcISQvh23bf8nz158s85t1Io5Gk2XNIWbyYcu3a4T37Bywd1TuaSgjPZNfCKxTmG3jujYb4N7xHPtDfTEmNiz0mo9KlWNuDQpGtgKbAWCnlaSHEHExHYH/FvUyuvE/7/fr898BCjMJ0rIafX9lUUjU0NEpPfEYew5aewULAqpEtqeRs959fRp6E314E9xrw8mawK3vJ4NjsWEbvH01CTgJznp3DUz5PlXnMuzEWFBD/6adk7tqNy8CBVJrwhapSLiFnEzi4MhgHJxt6fdIYd++/379yL0pkXKSUr5Zi7BggRkp52vzzBkzGJUEI4WXetXgBicWu9y3W3weIM7f73KO9eJ8YIYQV4Ayk3mP9i4HFAIGBgdqRmYbG30Bajo5hS8+QkVfI76NaUdWj3H9+GXse1gwAJ28YtgUc3Mo8X0haCKP3jybfkM+SLktoXLFxmce8G31qKjFvvU3exYtU/Pgj3EaMUO1oSholZ3aEc25XBF41nOk+ugH25R9fWf77en7Mb/z35a+ukVLeAaKFELXMTR2BIGAbMNzcNhzYav68DRhsjgCrCgQAZ8xHaFlCiFbmKLFhd/UpGqs/cEjzt2hoPH7k6vSMWHGWyNRclgwLpL53sV3Jnauwqp+pBPHwbYokR15KvMTwPcMRCFZ0W6GKYSkICydi8IvkBwXhPXs27iNHqmZYCgsM7F1yjXO7IqjTxove7zV5rA0LPHjnMl4IcT//hcDkR1n8F78fC6wxR4qFAa9iMmjrhBAjgShgAICU8roQYh0mA6QH3jJHigG8AfyC6Xhut/kLYCmwyuz8T8UUbaahofEYodMbGbP6Apej01nwUjNaVy+WBZ90E1b2AZtyMGwbOJVdcv5RyLnknj1L9NtjEZaWVFnxC/aNGys+RxFZqfnsWnCFlJhs2vavQaOOj5fj/q8Q93vRF0IsL8EYGVLK9xRbkcoEBgbKc+fO/d3L0ND4V2A0St5de4ntl+OY/kIDBjUv5vNMuQ3LewASXt0N7tXLPN/229uZcGICNV1rqibnkrF9O/GffY61ry++ixZi4+v74E6l5E54BrsWXEWvM9BlZD38G/x9jnshxHkpZWBJr3+QKnJpfC0aGhoaSCn5avt1tl+OY3z32v9tWNKjYWVvMOjg1V2KGJZVQav47ux3qsm5SClJnj+f5LnzcGjRAp+5P2LpXPagg7/i1pk7HFp5g3IuNvR+rzHulR9Px/1foZU51tDQUIU5B0NYeTKSUU9VY8zTxYxHZjyseB7yM+GV7VCxTpnmeRRyLlKnI/7LiWRs2YJz7954fT0ZoVKNe2mUnN4Wxvk9kVQOcKHb6PrYOz7e/pV7oRkXDQ0NxVl5MoLZB0Lo38yHT7vX/s8vspNgZS/ISYKXt4BXozLNYzAa+PrU12wM2Uj/mv35ouUXihf4MmRkEDP2HXLPnMFj7Nt4vPmmaj4PXb6eg78EE3YpibptvXjqxVpYWj0eGfcPi2ZcNDQ0FGXrpVgmbrtOpzqeTOvX4D8P4txUWNXXdCT20kbwLVtBrkch56KLjiZ69BgKo6Op/N10nHv1UnT84mSl5rNz/hVSY7NpNyCAhs/6PBGO+7/ivsZFCPHB/X4vpfxe2eVoaGg8yRy9lcSH6y7T3N+NeUOaYFWkF5afCatfgOSbMGQt+Lct0zyPQs4l79Ilot98C2kw4LdsKQ7N1alOCXAnLINdC69i0Bl47q1GVKmvfCDCo+ZBO5fy5u+1gOaY8koAngeOqbUoDQ2NJ48LUWmMWXWeAM/y/Dw8EDtr8/GULsekbnznCgxaDdXLVhn9Uci5ZO7ZS9y4cVhVrIjvokXYVquq+BxF3DwVz6HVN3B0taPP+01w8yr34E5PAA+KFpsEIITYBzSVUmaZf/4KWK/66jQ0NJ4IbiVkMeKXs1R0smXliBY42ZnlTwrzTZIu0adN1SNrdS/TPLHZsYzaN4rE3ERV5FyklKQuW0bijJnYN2mCz0/zsHIru1rAPecySk5tDePC3ki8a7rQbVQD7BzVk4151JTU5+IH6Ir9rAP8FV+NhobGE0dMWi7Dlp7B2tKCVSNaUqG8OVJLrzNVkAw/Bn0XQr2+ZZrnVtotxuwfo5qci9TrufP1N6SvXUv57t2oPG0aFrbK13oB0OsM7F8WZHLct6/MU4NrYmmpnuNer9NxZNVSmj/fF+eKlVSbpzglNS6rgDNCiM2YhCH7AitVW5WGhsYTQUp2AcOWniFHp2fd6Nb4uZvl8Q162DgSQvZCz9nQqGziGRcTL/LWwbewt7RnRbcVBLgGlH3xxTDqdMS+/wHZBw/iPmoUFd57F2GhzsM+P6eQnT9d4U54xiNx3OdmZrB1xjfE3Qqmon81GnZ8jIyLlHKKEGI30N7c9KqU8qJ6y9LQ0Hjcycov5JXlZ4lNz2P1ay2p4+Vk+oXRAFvegOBt0HUqBJYtF1ttORdjbi4xb48l588/8ZzwBW5Dhyo6fnEyU/LYMfcyGcl5dH2tPjWalV1H7X6kxESzefpX5KSl8fz746nZqp2q8xXnYUKRHYBMKeVyIUQFIURVKWW4WgvT0NB4fMkvNDBq5XmC4jNZMqwZzf3NfgkpYcd7cHUddPwSWr9ZpnnUlnMxZGURPXoMeZcu4TV1Ki59+yg6fnGSY7LYPvcyep2RXu80xrum64M7lYHIq5fY/v1ULK2tGfjVVLxq1HpwJwUpkXERQkwEAjFFjS0HrIHVQNniCTU0NJ44DEbJe79f4mRYCj8MasSztT1Nv5AS9oyHCyvhqY+h/YdlmmdzyGa+/PNL1eRc9GlpRI98jfyQELy//x6nbl0VHb84MTdS2b3wKtZ2VvT7qKnqNViuHNzDwaULcKvsQ99xE3GqoO4O6V6UdOfSF2gCXACQUsYJIcrfv4uGhsY/DSkln2++yp7rd5jQsy59m/gU/QIOfAWnF0Lrt+GZz8s0z7bb25j450TaVG7Dj8/+qLicS2FCIlEjR1AYHYPvT/NwfEr5ImJFhJxN4MAvQbh4OtDz7UaUd7N7cKdSIo1Gjv36C+e2b8K/cTN6vjsOW4eyl4kuDSU1LjoppRRCSAAhxD8jEFtDQ+OhmLXvFr+fjeatZ6ozsl2x3I9jM+DEbAgcAV2+gTI4qHeG7WTCiQm0qNSCOc/MUdyw6GJiiXr1VQwpKfguXky5li0UHb84lw5EcWJDKJUDXOg+pgF25dQLNS7Mz2fXvFmEnj1Joy7P8ewro7CwVFYK52EoqXFZJ4RYBLgIIV4HRgBL1FuWhobG48a6s9HMOxzK4Oa+fNSl2Pn9iR/h8BRoPBR6zCqTYdkbsZfPj39Ok4pN+PHZH7GzUvYtvyAsjKhXR2DMz8dv+TLsG5VN2+yvkEbJiU2hXD4QTfUmFeg0oi5W1uo96LNTU9gy42sSwm/zzPDXadK9198uHVPSaLGZQojOQCYmv8uXUsr9qq5MQ0PjseHP28l8tvkq7QM8+LpP/f88uM4sgf0ToF4/6DUXyhC+ezDqIOOPjadhhYbM7zgfB2tlj3Pyb9wgasRIEIIqK1dgV0sdB7eh0MjBFUGEnEukQQcf2g0MwMJCvQd9YkQYm7+bTEF2Nn0+/oLqzVqqNtfDUOJoMbMx0QyKhsa/jNtJ2YxZdR5/j3LMG9IU66JkvwurYNdHUOs56LcYyqBGfDT6KB8d/Yi6HnVVMSx5ly4RNWo0FuXK4bdsKbZV1ZFzKcjTs3vhFWJvptO6b3WadPFTdQcRduEsO+Z8h62DA4Mnf0dF/2qqzfWw3Pc1QwiRJYTIvMdXlhAi80GDCyEihBBXhRCXhBDnzG1uQoj9QogQ83fXYtd/KoQIFULcFEJ0LdbezDxOqBDiR2H+awkhbIUQa83tp4UQ/qX+L6GhofE/pOboGPHLWawtLVj+SnOc7c0+g6sbYNtYqN4RBiwHy9L7Eo7HHuf9I+9Ty7UWCzotUDwqLOfUKSJHjMTSxQX/1atUMyw56QVsnnmB+JAMOr1Sh6Zdq6hqWC7s3s6W777G1asyQ6d8/1gZFniAcZFSlpdSOt3jq7yU0qmEczwjpWxcrDzmeOCglDIAOGj+GSFEXWAwUA/oBswXQhS9Ci0ARgEB5q9u5vaRQJqUsgbwAzC9hGvS0NB4AAV6A2NWnSc+I5/Fw5rh62beTQRvh02jwL+dSYjSqvQO95NxJ3n30LtUd6nOos6LcLIp6WOlZGQdOUL0qNHYeFemyupVWHsrm4BZRGp8Dhu+O0dmch7Pvd2QWq28VJkHwGgwcHDZQg7/sohqzVow+KvpOLo9firKD5Lcv69im5QytRRz9gY6mD+vAI4A48ztv0spC4BwIUQo0EIIEQE4SSlPmte0EugD7Db3+co81gZgnhBCSCllKdaloaFhRkrJpxuvciYilTmDG9OsivlRELIf1r8K3s3gxd/ApvTHV2fvnOWdQ+9QxbkKSzovwdlW2ZLBmXv2EPvRx9jVro3vksVYuaqTtBgfms7O+VewsBT0/bApFfzUy9IoyM1l55zphF86T+Dz/Wg/ZDgWChdHU4oH+VzOY9ISu9feTgIP2odJYJ85hHmRlHIx4CmljAeQUsYLIYqye7yBU8X6xpjbCs2f724v6hNtHksvhMgA3IHkB6xLQ0PjPsw7FMqmi7F80LkmvRub/3eL/BPWvgSedWHoerAt/UP0fMJ53jr4Ft6O3izpvAQXOxdlFm4mfeMm4idMwL5JE3wXLcTSUZ2kxbBLSexbeh1HV1ueH9sY5wr2qswDkJmcyObpk0mJiaLz62/TsFO3B3f6G3mQ5H5ZDyfbmhMuKwL7hRA37nPtXxmwv2q/X5//HliIUZiO1fDz87v/ijU0/uVsvxzHrP236NvEm7HP1jA1xl+BXweBsy+8tAnsXUo9/qXES7x54E08HTz5uevPiku6pK5aTcKUKZRr2xafuT9ioVIS4bVjsRz77SYVqjjR862G2JdXr879ndBbbP5uMobCQvp9Ogn/hk1Um0spShQ3KEy8JISYYP7ZTwjxwMwjKWWc+XsisBloASQIIbzM43gBiebLYwDfYt19gDhzu8892v+rjxDCCnAG/ueoTkq5WEoZKKUMrFChQkluWUPjX8n5yDQ+XH+Z5v6uTHvBXKI45Tas7ge2TjBsC5TzKPX4V5Ou8saBN/Cw92Bp16V42Jd+rHuRvHARCVOmUL5zJ3wWzFfFsEgpObX1Nkd/vYlffXf6vN9EVcNy6/QJ1k76FCsbW178esYTYVighMYFmA+0BoaYf84CfrpfByFEuSKJGHNGfxfgGqZqlsPNlw0Htpo/bwMGmyPAqmJy3J8xH6FlCSFamaPEht3Vp2is/sAhzd+ioVE6olNzGbXyHJWc7Fj0ciC2VpaQEQsr+5jkXYZtAWefBw3zlwSlBDH6wGicbZ1Z2nUpFR2U07uSUpI463uSZs/GqdfzeP/wAxY2yj/wDQYjh1bd4PzuSOq09aLHmAZY26rj85BScmbrBrZ/P5UK/lUZOmUW7j5PzslLSfNcWkopmwohLgJIKdOEEA/6y3kCm82heFbAr1LKPUKIs5gy/kcCUcAA85jXhRDrgCBAD7wlpTSYx3oD+AWwx+TI321uXwqsMjv/UzFFm2loaDwkmfmFjPjlLIUGI8teaY5bORvISYFVfSEvDV7ZAR6lr6FyM/Umr+97nfLW5VnWdRmVyilXU0QajSR8M4W0X3/FZfAgKn35pSq1WHT5evYuuU7U9RSaP+dP855VVQs1NugLOfDzAq4d3ketNk/R7Y33sFLBWKpJSY1LoTksuEhbrAJgvF8HKWUY8D/aClLKFKDjX/SZAky5R/s5oP492vMxGycNDY3SoTcYeWvNBcKTc1g5ogU1KjpCQRas6Q/pkfDSRqjcuNTjh6SF8Pq+17G3sufnrj9T2bGyYmuXej3xX0wgY8sW3EaMoOLHH6nywM/N1LHzp8skRWXRYWgt6rVXJ6QZID87m23ff0v09Su0emEwbfoPUa1wmZqU1Lj8iMlnUlEIMQXTEdQXqq1KQ0PjkSClZOK26/wRksz0FxrQpoaHqe7970Mg/jIMXmPKZyklYelhvLbvNawsrFjWdRm+5X0f3Kmka9fpiP3oY7L27cPjnbF4vPGGKoYlIymXbT9eJje9gO5jGlC1kXp+2/Q78WyaPomMhDt0f+sD6j71rGpzqU1JtcXWCCHOY9pxCKCPlDJY1ZVpaGiozrITEaw5HcXop6sxqLnff8oThx+DvouhVvdSjx2REcHIfSMRCJZ2XYqfk3L+AmNeHjHvvEvOH3/g+el43IYPf3CnUpAYmcmOeZeRRuj9fhMqVVM2F6c4MTeus3XmFJCSAV98g0/d/zmseaIoabGwOcBaKeV9nfgaGhpPDgeCEvhmZxDd6lViXNfaJqf99nfhxg7oNh0aDSr12NGZ0YzcNxKjNLKs6zKqOisnuWLIziHmjTfIPXeOSl9PxnWAOifjkddS2LPkGvblrHn+nUa4VlKv0kjwH4fZu3AOThU86Tt+Iq6VlDs6/Lso6bHYBeALIURNTMdja81+EA0NjSeQa7EZvPP7RRp4O/PDoMZYCGDfF3BpNTw9HlqNKfXYsdmxjNg3Ap1Bx9KuS6nuUl2xdRvS04kaNZr869epPGMGzj2fU2zs4gT/Gc/h1Tdw9y5Hz7cbUc5Z2ZoyRUgpObnhV05u+A3fug14/sPPsHf8Z9RhLOmx2ApghVkO5gVguhDCz6wPpqGh8QRxJyOf11acw9nemp+HBWJvYwnHZsLJedBiNHQYX+qx47PjGbl3JLmFuSztupSarjUVW7c+OZmoESPRhYfjM/dHyj+rvD9CSsn5PZGc3hqGT21Xuo9ugI19icXjHwq9TsfehXO4ceIo9Tp0ovPrb2FppWIxseQ8rNztHlmdl4f9r1YDqA34YwoZ1tDQeILI1el5beVZsvILWT+mDRWd7ODsUjj0NTQcBN2mlbrYV0JOAiP3jSSzIJMlXZZQ2622YusujIsj6tURFCYm4rtoIeXatFFs7CKMRskfa29x7WgsAc096Ti8DpZW6kRp5Waks3XmFOJuBdPuxeG06N1f1Yd+7qVEUjeE4NzNn/Lt1It0K05JfS7TgX7AbWAd8LWUMl3FdWloaCiMwSh59/dLBMVl8vPwQOpWdjJJ5+/8EGp2g94/lbrYV1JuEq/te43U/FQWd15MPY96iq1bFxlJ5KuvYszKxm/pUhyaKp+hXqgzsH/pdcIvJ9Oksx+t+1ZHqFTgKzk6ks3TJ5ObnkbP98ZTq3Xpo/EehDRKMvZGkH00Bpuqzjg0fnQKJSXduYQDraWUmiCkhsYTyvQ9N9gflMDE5+vybG1PCDkAm0dDlTYw4JdS12RJzkvmtX2vkZCbwKLOi2hYoaFia86/dYuokSNBb8Dvl+XY11POaBWRm6lj5/wrJEZm0m5gAI2eVS5c+m7CL51nx+xpWNvaMeiraVSqodyx4d0Y8/Sk/n6D/JtplGvlhcvz1RCWjy5fpqQ+l4VCCFeznphdsfZjqq1MQ0NDMX49HcXiY2EMa12FV9r4Q9Qpk8Jxxbom6Xzr0qn5puWn8fq+14nLjmNBpwU0qajcriLv8mWiR41G2Nrit2o5tjVqKDZ2EekJuWyfe4mcDB3dRzWgWhP13uwv7tnO4V+W4FHFnz4fT8DJQ725CpNySVkZhD4lH5e+NXBsqV59mb+ipMdirwHvYhKNvAS0Ak4CT26Gj4bGv4TjIclM2HqNp2tW4MuedREJ12DNQHD2Nikc25UudyOjIIPX971OdFY0P3X8icBKgQ/uVEKyjx0j5t33sPLwwG/ZUmx8ld9NxN/OYNf8KyCgj4o5LEaDgcMrFnNp706qB7akx9iPsLFTT5o/72Yqqb/dQFgKKrzWAFsVc3PuR0mPxd4FmgOnpJTPCCFqA5PUW5aGhoYShCZm8caa89So4Mi8IU2wSg+HVf3A1hFe3gyOpXt7ztRl8vq+1wnPCGfus3Np6dVSsTVnbN1K3OdfYFszAL/Fi7HyUFY5GeD2hUT2Lw/C0cWWnmMb4VJRHVn+gtwctv8wjcgrF1Uv7iWlJPtYLBl7wrGuVA73YXWxcrV7cEeVKKlxyZdS5gshEELYSilvCCFqqboyDQ2NMpGSXcCrv5zF1sqCpa8EUl6XBKv6gFFvEqJ0KV3GfJYuizH7xxCaHsrsZ2bTxlu5yK2UZctJ/O47HFq1wmfeXFWKfF0+GM3xDSFUqupEjzcbYu+ojiBkesIdNk+fRPqdOLqMfocGz3ZRZR4AWWggbWMIuZeSsG/ggeuAmljY/L0VKktqXGKEEC7AFkxFv9L4T00VDQ2Nx4z8QgOjVp0nMbOA30e1wsc2H5b3g9xUGL4dKpTu3TCnMIc3DrxBcEow33f4nqd8nlJkvdJoJHHmLFKXLaN8165UnvGd4pL5RqPkxIYQrhyKoVrjCnQeURcrlR7AMTeus23mFKTRSP/Pv8a3nnJBDndjyCggeVUQhTHZOHWpQvlnfB9ZLsv9KKlDv6/541dCiMOYinLtUW1VGhoapUZKyScbrnA+Mo2fhjSliac1rOwNqWHw0gbwblqqcTMKMhh7aCzXkq8x4+kZPOP3jDLrLSw0KRtv3YrrkBfx/PxzhKWyD329zsD+ZUGEXUqi0bO+tOlfAwuVQo2vHz3I/sVzTVIu477E1Uu9vJKCyExSVgchC4y4D6uLfV1lq3qWhQcaFyGEBXBFSlkfQEp5VPVVaWholJrZB0LYdjmOj7vW4rm6bvDrQIi7CINWQdXS7TQScxMZc2AM4RnhfPfUd3Su0lmRtRpzc4l5/31yjh5TTdk4L8sUapwQkUm7AQE06qhOqLE0GjmxbjWnN6/Dt15Dnv/gU1WlXHLO3SFtcyiWLrZUeK0B1p7qaZ+VhgcaFymlUQhx2Sz3EvUoFqWhoVE6tlyMZc7BEF5o6sObT/nDhlch7Aj0WQC1S6fDFZkZyej9o0nLT2N+x/m0rtxakbXq09KIGfMGeVevUmnSJFwHDVRk3OKkJ+ayY+5lstML6PZ6fao3Va76ZXEKC/LZ/dP3hJz+kwbPdqHjyDextFJHNkYaJBm7wsg+EYdtDRfch9TGwkE92ZjSUtK79wKuCyHOADlFjVLKXqqsSkND46E5F5HKJxuu0LKqG1P71kfseBeCt0HXqdB4yIMHuAfBKcGMOTAGKSXLui5TLPO+MD6eqNdepzA6Gu/ZP+DURXln952wDHbOvwISer/XBK/q6oTkZqemsGXGNySEh/L0yyNp9lwf9SpU5hSS+tsNCkLTcWxbGece1RCWf79/5V6U1LhoYccaGo8xkSk5jFp1Hm9Xexa+1Aybw1/BxVXw1MfQ+s1SjXn2zlnGHhqLk40TizovUkw2vyA0lKjXXseYnY3vz0so16KFIuMWJ+xiEvuWXaeciy3Pv90IF091Qo0Twm+z5bvJFOTk0OfjL6jeTLmQ7LspTMgheUUQhowCXPsHUC5QuVLRalBSLYAeUsqjxb+AHiXpKISwFEJcFELsMP/sJoTYL4QIMX93LXbtp0KIUCHETSFE12LtzYQQV82/+1GYXwuEELZCiLXm9tNCCP8S37mGxj+EjLxCRvxyFqOULHulOa4Xf4I/f4Tmr8Ezn5dqzIORBxmzfwyVHCqxsvtKxQxL7sWLRAx9CWnQU2X1KlUMy5XD0exefBUPH0f6f9JMNcMScvYkv0/8BCEsGDz5O1UNS971FBJ/uowsNFBhVMPH3rBAyY3Lvbx3JS1R9y5QvGrleOCgWa7/oPlnhBB1gcFAPaAbMF8IURQysgAYBQSYv7qZ20cCaVLKGsAPwPQSrklD4x9BocHIm2vOE5Way8KXmlE1Yh0c+Arq94fuM0qlcLzx1kY+OPoBtd1r80u3X6hUTpkHWdaRI0S9OgJLF2f8f/sNu9rKqSaDSaTx+IYQ/lgbQtWGHvR+vwn25ZXPYZFScnbbRrbN+hYPHz+GTJlFRf9qis9TNFfmwShSVgVhVdEez7ebYFvFSZW5lOa+x2JCiDeAN4FqQogrxX5VHjjxoMGFED7Ac8AU4ANzc2+gg/nzCuAIMM7c/ruUsgAIF0KEAi2EEBGAk5TypHnMlUAfYLe5z1fmsTYA84QQQkopH7Q2DY0nnfRcHe/8fokToSnMHNCIVrlHYcf7UKMz9F340ArHUkqWXlvKnAtzaOvdlu+f/h4Ha2Xe+tM3byH+iy+wq10b38WLsHJXNmRWrzNwYHkQty8m0eAZH9oNCFAl1NigL+TAz/O5dng/NVu3p9ub72Fto04hMaPOQNr6W+RdTcahSUVc+9VAWP+9iZEPw4N8Lr9ieohPxbzDMJMlpUwtwfizgU8wGaMiPKWU8QBSynghRFH4hjdwqth1Mea2QvPnu9uL+kSbx9ILITIAd0BTb9b4R3PjTiajVp4nPiOPaf0a0N/5Bvw6CnxbwsCVD61wbJRGZp2bxcqglXSv2p0pbadgXUqV5OJIKUldupTEmbNwaN0Kn7nzsHRUNmQ2L1vHrvlXuROeQdv+NWjUUZ0kwrysTLZ9/y0xQddo9cJg2vQfgihliYIHoU/LJ2VlEIV3cnDuURXH9t6PRWLkw3Bf4yKlzAAygBcfdmAhRE8gUUp5XgjRoSRd7rWE+7Tfr8/daxmF6VgNP7/SSV5oaDwu7Loaz0frL+Noa8Xvo1rTzCIEVr4MFWrDkLVg83C7jUJjIRNPTGR72HaG1B7CuBbjsBBlf2hKo5HE72aQ+ssvOPXojte0aYpn3Wck5bJ97mWyUwvo+lp9ajRTJ9Q4NS6GzdMnkZWcRI+3P6ROe2USSO9FQVgGKWuCkAaJxyv1sKvlptpcaqJOILaJtkAvIUQPTDL9TkKI1UCCEMLLvGvxAhLN18cAxbObfDBJzMSYP9/dXrxPjBDCCpNywP/sqKSUi4HFAIGBgdqRmcYTicEombXvJvOP3KapnwsLX2pGxewbsLI/lK8EL28Ce5eHGjNPn8dHRz/iWMwx3m78NqMajlLkDVnqdMR9/gWZ27fj+tJLeH72qeJv+QnhmeycfxmjUdL7vcZ41XBRdPwiIq9eYvsPU7GwtGLAl1PxrlVHlXkAsk/Fk77tNlbudrgPq4t1BXWCER4FqlWOkVJ+KqX0kVL6Y3LUH5JSvgRsA4abLxsObDV/3gYMNkeAVcXkuD9jPkLLEkK0MkeJDburT9FY/c1zaMZD4x9HRq4pImz+kdu82MKP30a1omLMXljeHWzKw8tbwPHh3tozCjIYvX80f8T8wYRWExjdaLQihsWYk0P0m2+RuX07Fd57D8/PP1PcsIRfTmLL9xewtrWk/yeBqhmWKwf2sGnqRBxd3Rk65XvVDIvUG0nbHEL6llDsAlyo+FbjJ9qwwEPsXIQQVYAAKeUBIYQ9YCWlzCrFnNOAdUKIkUAUMABASnldCLEOCAL0wFtSSoO5zxvAL4A9Jh/QbnP7UmCV2fmfismIaWj8o7h5J4tRq84Rl57Ht30bMKSFLxz9Do58Cz7NYdAaKO/5UGMWyblEZEQw8+mZdPFXJolRn5ZG9Ogx5F+7RqWvJ+M6YIAi4xbn6pEY/lh7iwpVnHjuzYY4OCkfEWY0Gji2ehnnd27Fv3Ezer47DlsHdR72hmwdKauD0UVkUv5pH5y6+qtWYvlRIkryoi+EeB2Tz8JNSlldCBEALJRSdlR7gUoTGBgoz50793cvQ0OjROy+Gs+H6y9TztaKhS81pZmXHWx9E65vhkYvQs/ZYP1wNTuKy7nMeXYOrbxaKbLWwthYU9Z9XBze38+ifEdlHw/SKPlz820u7Y+iaiMPOo+sh7UKqsa6vFx2/jiDsAtnadLteToMew0LhYU0/3+uuGxSVgZhyC7ErX8ADo3V8RkBFBYWYm1d+iANIcR5KWWJK8KVdOfyFtACOA0gpQwpFuWloaGhMAaj5Pv9N/np8G2amP0rnjIFlveF+MvQeTK0eeeh81jUknPJv3WL6Ndex5iXh9/Sn3EIVK4qJYC+0MDBX4IJPZ9Ig6e9aTeopiqhxpnJiWyePpmUmCg6jniDxl1Lp8dWEnKvJJG2/hYW9lZUHNMQGx/1RC5jYmJYv3493bp1o04d9XxGxSmpcSmQUuqKzmPNznPNt6GhoQIZeYW8+/tFjtxMYnBzXyb1roftnYvw+xDQ5cCLv0Otbg8e6C7UknPJPX+e6DfexMLWliqrV2NXq6Yi4xaRn1PIrgVXiA/NoM0LNWjcSZ1Q47hbwWydOQW9Tke/cRPxb9xM8TnALDy5J5zsP2KxqeKE+0t1sFQh2RPMCZ9nz7Jnzx7Kly+Pk9OjS8AsqXE5KoT4DLAXQnTGlFi5Xb1laWj8O7mVkMWoleeITc9jSt/6DG1ZBa6sg61vmyPCtoBn3Yce92DkQT4+9jF+5f1Y2Hmhcln3hw4R+/4HWHt54fvzz9j4KFu7JDM5j+1zL5OVkk+X1+oREPhwvqWSIKXkyoHdHFq+mPIeHgz88lvcfdRJWTBk6Uj59Qa68AzKtfbC5blqCCt14qoKCgrYsWMHV69eJSAggL59++Kgkt/oXpTUuIzHJLVyFRgN7AJ+VmtRGhr/RvZci+fDdZext7Hit9dbEejnAgcmwfHvoUpbGLgKyj18ZvvGWxuZfGoy9T3qM7/jfJxtlVEHTt+wgfgvJ2JXrx6+ixZi5aZsPkZcaDp7Fl3FaJT0eq8xlVWICNPrdBxctoBrh/fj37gZPcZ+pFoNloLITFLWBCPz9LgOrEm5psobyiKSkpJYt24dSUlJPPvss7Rr1w4LlRI+/4qSGhd7YJmUcgmYxCjNbblqLUxD49+C0Sj54cAt5h4KpbGvyb9Sya4Q1r4EN3dC02HQYxZYPdzRiVpyLlJKUhYvIemHHyjXti0+P87BopxyWfdSSq4djeX4uhDKe9jx3JsNca2kfCGszOQktn//LXduh9Cy7yDaDByChYXyjnspJTkn40nfEYaliy0ebzTCprKj4vMUce3aNbZt24aVlRUvv/wy1atXV22u+1FS43IQ6ARkm3+2B/YBbdRYlIbGv4WMvELeX3uJQzcSGRToy+Q+9bDNioGlL0JSMHSbDi1HP7Tj3iiNzDw3k1VBq+hRtQfftP1GGTkXo5GEadNIW7kKp549qfztFISCWff6QgNHf7vFjT/j8W/gTqcR9bC1Vz7XO/r6FbbPno6hUEevjz4noLkyBdDuxqgzkL4phNxLSdjVdsNtYE3VCnvp9Xr279/P6dOn8fHxYcCAATg7q1PDpiSU9K9mJ6UsMixIKbOFEE92ho+Gxt9MSEIWo1adJzo1l6/71Oelln6IqFOwdigY9DB0A9R4+HDe4nIuQ+sM5ZPmnygi52LMySHu8y/I2rMHt+HDqDhunKLJkVmp+exZdJXEyCyaP+dP8+eqKp7vIaXkwq6tHF29DNdKlen10ee4e6tT9lifnEfK6iAKE3Jx6lyF8s/4qpa/kpGRwfr164mJiaFly5Z07twZK5UqYZaUks6eI4RoKqW8AKb6KkCeesvS0Phns/f6HT5Ye8nkXxnViub+bnBhJez4AFyrmCLCPAIeely15Fx0ERHEjB1Lwe0wKn78MW4jXlU0Yiv2Vhp7l1xDX2ikxxsNqNqogmJjF1GYn8++xXO5ceIoAS3a0O3N97CxV+cdOS8ohdR1NxEWAo9X62NX0/XBnUrJ7du32bhxI3q9ngEDBlCvnjLh5WWlpMblPWC9EKJI08sLGKTKijQ0/sEYjZLZB27x46FQGvm6sPClpng5WsOez+DUT1DtGRiwHOwf/mGUUZDB2ENjuZR4iQmtJjCwljI16bMOHSLuk3EIKyt8lyzGsW1bRcYFc6TW4RhObAjFpaI93cc0UMW/kn4nnm2zppAUHUm7wcNo0WeAKuHM0ijJPBBJ1qForL0dcR9aByu3h0tyLSlGo5E//viDw4cPU6FCBQYNGoSHh4cqc5WGEhkXKeVZIURtoBYmJeIbUspCVVemofEPIzO/kPd/v8TBG4kMaObD133qY2fIht9egtAD0HIMdJkClg9/nJGYm8jo/aOJzIxUTM5FGgwkzZ1LysJF2NWrh8+Pc7D2Vi7UWK8zcGTNTW6evkPVRh50eqUuNir4V8IvnmPn3BkIBC+M/0q1/BVDTiGpv9+gICQdh0BPXHvXQFirE6GVm5vLpk2bCA0NpUGDBjz//PPYKKw4XVYe5i/ZHPA392kihEBKuVKVVWlo/MMITcxm1KpzRKXkMrl3PV5uVQWRGga/DYbUMJOMS+CrpRq7uJzL/E7zFZFz0aelEffxJ+QcP47zC/2o9OWXWNgqVxQrMyWP3QuvkhyTTcteVWnWTXk9LWk0cnrzOk6sX0MFP396ffg5Lp7qlAfWxWSRsjoYQ5YO134BlGuhXhni2NhY1q1bR3Z2Ns899xyBgYGPZa2XEhkXIcQqoDpwCSgSk5SAZlw0NB7A/qAE3l97CTtrC9a81pKW1dwh7AisGw7CwpQYWbV9qca+nHSZdw69o6icS97168S+8y76xEQqTZqEy0Blj5BibqSyd8l1jEbJc282xL+B8kc5Bbm57P7pe26fO0Xttk/TZfRYrG3VOZ7KOXuHtK2hWDraUHFMI2x81cmTkVJy7tw59uzZg6OjIyNGjMBbwZ2k0pR05xII1NXk7DU0So7RKJlzMIQ5B0No6OPMwpeaUdnFHs4sgd3jwKMmvPgbuD28DItRGll+bTnzLs7Ds5wnCzotUETOJX3TZu5MmoSlqytV1qzGvmHDMo9ZhJSSywej+XNjKC6VytFjTANcPJV3qKfERLN11hTS78TxzPDXadK9lzr+lUIj6dtuk3P2DrYBLrgNro1lOXXCjHU6HTt27ODKlSvUqFGDfv36PdJs+9JQUuNyDagExKu4Fg2NfwyZ+YV8sPYyB4IT6N/Mh2/61MfOwmiKBju3FGp2g35LwO7htZ6S85L5/Pjn/Bn3J12qdGFim4k42ZRNM8qo05Hw7bek/74Wh5Yt8f5+lqJ17gt1Bg6vukHI2QSqN6nAs8PrYGOnvH8l5Myf7P7pB6xtbRkwYQq+dRsoPgeYyxCvDqYwNpvyz/ji1LmKamHGycnJrF27lqSkJJ555hnat2//yLPtS0NJ/7oeQJAQ4gxQUNQopeylyqo0NJ5givwrkSm5TOpVj2GtqyDy0mD9cAg/Bm3fhY4ToRTZ4H/G/clnf3xGdmE2X7b+kv4B/cv8Vl545w4x775L/uUruI0cQcX330comCORmZzHroVXSYnNplWfajTtWkXxnYTRaODPdWs4vXkdlWrUpNcHn1HeXZ3IqfxbaaT+fgNpkLgPq4t9XeWM8N1cv36drVu3/u3Z9qWhpP+CvlJzERoa/wSSswtYejycFX9GYG9tyZrXWtKqmjsk3YRfB0FmLPRZCI1ffOixC42F/HTxJ5ZdW0Y152os6bKEANeHz4O5m5zTZ4j94ANkXh7es2fj1K1rmccsTnRQKnuXXgMJPd9uRJV6yj+I87Kz2PXjDCIuX6DBs114dsQbWJWhbslfIY2SrCPRZO6PxKqiA+4v18Xaw17xeQAMBgP79+/n1KlTj0W2fWkoaSjyUbUXoqHxpJKQmc+io2H8eiaSAr2RHg28+LxHHZN/JWQ/bBgBVrbwyk7wbfHQ48dmxzLu2DguJ13mhYAXGNdiHPZWZXuoSSlJ/WUFiTNnYuPnh8/KFdgq+FYspeTi/ihObb6Nq1c5erzRAGcVyvYmRoSxbdYUslNT6DzqbRp2fPhSBCXBmKcndd1N8oNTsW9cAdd+AVioUKgMIDMzk/Xr1xMdHf3YZNuXhpJGi7UC5gJ1ABvAEsiRUv7lQa8Qwg44Btia59kgpZwohHAD1mIKa44ABkop08x9PsWkvmwA3pFS7jW3N+M/ZY53Ae9KKaUQwhZTxFozIAUYJKWMKPnta2iUnpi0XBYevc26szEYpKRPY2/efKY61Ss4gpTw5zzYPwE868Hg38Dl4WVG9kfuZ+KJiUgkM56aQbeqZX94GnNyiJ8wgcxduynfuRNeU6di6aickGJhgYFDK02FvWo0q8gzL9dWxb8SfPwI+xbNxc7RkUFfTccroJbicwDo4nNIWR2EIa0Al+erUa5NZdVCf8PCwtiwYQN6vZ7+/ftTv359VeZ5FJT0Lz4PU3369Zgix4YBD9qTFwDPmnXIrIHjQojdQD/goJRymhBiPCY5/3FCiLrmOeoBlYEDQoiaUkoDsABTmeVTmIxLN2A3JkOUJqWsIYQYDExHUw7QUJnw5BzmHw5l88VYhID+zXx54+nq+Lmb38z1BSbH/aXVUKcX9F0INg+XdZ6vz2fG2Rmsu7WOBh4NmP7UdHzLl10DqyA8nNh33qHgdhgVPvwA99deU/RBmZGUy+6FV0mNy6F13+o06eKn+IPYoNdzbM1yLuzaik+d+vR8bxzlXNSRV8m9mEjaphCEnRUVRjXA1l+doymj0cjx48c5fPgwHh4eDBw4kAoVlJfAeZSU+HVCShkqhLA0P+yXCyH+fMD1kv+oKFubvyTQG+hgbl8BHAHGmdt/l1IWAOFCiFCghRAiAnCSUp4EEEKsBPpgMi69+Y8/aAMwTwghtJBpDTW4lZDFT4dD2X45DmtLC15qVYXRT1fDy7nYEVXKbdjyBkSfhqfHwdPj4SEje8LSw/jo2EeEpIXwar1XGdtkrCKKxlkHDxI3bjzCygq/n5dQro2youaR11PYv/Q6AD3HNsJPBUd3TnoaO+ZMJyboGk279+Kpl0ZgqcKRkdQbSd8ZRs7JeGyqOuE+RL1qkbm5uWzevJmQkBAaNGhAz549sVUwYfXvoqR/lVwhhA1wSQjxHaaQ5Ae+ipnrvpwHagA/SSlPCyE8pZTxAFLKeCFERfPl3ph2JkXEmNsKzZ/vbi/qE20eSy+EyADcgeQS3peGxgO5FpvBT4dD2X3tDg42lrzevhoj21elYvliSXmFeXD8B9OXlR30Xwb1X3ioeaSUbA7dzNTTU3GwdmBBpwW0825X5vVLg4GkH+eSsmgRdvXrm2RcKlcu87j/P76UXNgbyamtYbhXdqT7mAY4V1De0R0fepNts74lPzub7m9/SN32zyg+B4Ahs4CUNTfQRWbi2M4b5+7+CEt1Qn/j4uJYt24dmZmZ9OjRg+bNm6t25FZYmIG19aMLCiipcXkZsADeBt4HfDEdb90X8y6nsRDCBdgshLjfAeK9/ovK+7Tfr89/DyzEKEzHavj5qVO+VOOfx4WoNOYdCuXQjUTK21ox9tkajGhbFddyd73BhuyHXR9DWjg0GABdvjGVJH4IsnXZTD45md0Ru2lZqSVT20+lgkPZj0X0aWnEffQxOSdO4DKgP55ffKGojIsuX8+hFcHcvphEQHNPnnmpNta2yju6rxzcy6FlCyjn6s6LX8+gon81xecAKAhLJ+XXG0idAbchtXFoqM7RlJSS8+fPs3v37v/Ptvfx8VFlLoDk5MMEBX9CQI1P8fJ64KNbEUpqXPpIKecA+cAkACHEu8CcknSWUqYLIY5g8pUkCCG8zLsWLyDRfFkMJqNVhA8QZ273uUd78T4xQggrwBlIvcf8i4HFAIGBgdqRmcZ9ORWWwrxDoRwPTcbVwZqPutTk5db+ONvfdTSVEQN7PoXgbeAeAMO2QrUODz3fteRrfHz0Y+Jz4nmnyTuMqD8CSwUqIuZdv07s2HfQJyVRafIkXAcqo5JcRHpCLrsWXiX9Tg5t+9egUUdfxd+69YWFHF6+iCsH91ClYROee/cTVcoQS6Mk+48YMvZGYOVuj/vrDbD2VF6dGSA/P5/t27dz/fp1qlevTr9+/SinYCXP4hiNOm7fnklU9FIcHWvj5NRIlXnuRUmNy3D+15C8co+2/0cIUQEoNBsWe0yVLKcD28zjTTN/32rusg34VQjxPSaHfgBwRkppEEJkmSPWTmMKJphbrM9w4CTQHzik+Vs0SoOUkj9Ckpl3KJQzEal4ONryWY/aDG1ZhXK2d/1vYiiEUwvgyDSQBnh2ArQZawo3fgiM0siqoFXMPj+bCg4VWN5tOU0qNlHkftI3bjLJuLi7Ky7jAhBxNZn9y4KwsBA8/25jfGu7KTo+QGZSIjtmTyc+9CYt+gyg7aCXVClDbMjWkbruFgW30rBv4IHrCwFYqBDdBibRyQ0bNpCenk6nTp1o06aNatn2ubmRXLv+LllZV/H2fomAGp9iaamOvtq9uO9/QSHEi8AQoKoQYluxXzlhCv29H17ACrPfxQJYJ6XcIYQ4CawTQowEooABAFLK60KIdUAQoAfeMh+rAbzBf0KRd5u/AJYCq8zO/1RM0WYaGiVGSsmB4ETmHQrhckwGXs52TOpVj0HNfbGzvseDLPIk7PwAEoNMEi7dp4Or/0PPm5qfyufHP+d47HE6+nVkUptJONuW/TzcqNORMOVb0teuxaFVK5OMi5tyD35plJzfE8Hp7eF4+Jj8K07uyvtXbp0+wb5FPyKNkl4ffEZAS3UqqueHppO69gbGPAMufWtQrkUlVXweRqORU6dOceDAAcqXL8+IESPw9VWnAiZAQsIOgm98jhAWNKg/n4oVlU2OLQnifi/6QogqQFVgKqaQ4SKygCtSSr26y1OewMBAee7cub97GRp/MwajZM+1O8w9FMKNO1n4utnzZoca9Gvqja3VPYxKTjLs/xIurQFnX5NRqdXjoWvbA5yOP82nf3xKRkEGHzf/mEG1BinyQCsu4+L+2kgqvPeeojIuedk6Dq28QcSVZGq29OSZobWxUjiRsFBXwNGVS7m8fxeVatTkuXc+UUUmXxokmQcjyTocjZWHPe5D62CtQpEygJycHLZs2UJISAi1a9emd+/e2Nurldmfx61bk4mLX4ezUxPq1ZuNvb0yvhwhxHkpZWBJr7/vvzwpZSQQKYToBORJKY1CiJpAbeBq2ZaqofHo0RuMbL8Sx7xDodxOyqFahXLMGtCIXo0rY32viCCjES6sgANfgS4b2r0PT3380HkrAHqjngWXF7DkyhKqOFVhQacF1HJTJvEv59Rpk4xLfj7ec+bg1LXsxcKKE30jlQPLg8jPKaT9oAAadPBR/A0/JSaaHXOmkxwVQeDz/Wg3+GUsrZSXcdFnFJD62w10EZk4NPPEpXd11bLtIyIi2LhxI7m5uapHg2Vn3+Ta9XfJyQmlSpU3qFb1XSws1FFpLgklfa05BrQXQrgCB4FzmJIVh6q1MA0NJdHpjWy6EMP8I7eJSs2ldqXyzBvShO71vbD8KzXb+Muw432IPQ/+7aHHTKhYu1Tzx2fHM/6P8VxIvECfGn34tMWnOFiXXQ5FSknqsuUkfv89NlWq4DP3R0VlXAx6I6e3hnHxQBSung70fLsRFRSuVyKl5OqhfRz+ZTHWdna88Okk1apF5gWnkLb+FlIvcR1Ui3JNKj64UykwGo0cO3aMo0eP4ubmxpAhQ/Dy8lJlLiklcXG/cyvkaywtHWnc+Bfc3coewl5WSmpchJQy1+wnmSul/E4IcVHNhWloKEFGXiEbz8fw8x9hxGXk09DHmS+ea0anOp5Y/JVRyc+AQ1Pg7BJwcIe+i6HhwFIdgQEcjDrIlye+RG/UM7X9VHpW61mGO/oP+qQk4id8SfaRI5Tv0gWvb7/F0lG5o530hFz2Lb1OUlQW9Z7ypm3/Glgr/IZfkJvD/sXzuHnyD/zqN6L72x/i6Kp8cIDUG8nYHU72iTisvcrhNqQ21iponYFJG2zTpk1ERETQsGFDnnvuOdWSIvX6LIJvfEZi4i7cXNtRt+5MbG0fj8z+EhsXIURrTDuVkQ/ZV0PjkRMUl8mqU5FsuRhLXqGBwCqufNuvAU/XrPDXxxJSwrWNsPczyE6E5iNNkWD2LqVaQ4GhgFnnZvHbjd+o41aHmU/PxM9JmTyrzD17uPPVJIx5eXh+9imuL7+s2HGLlJLgP+P5Y+0tLK0t6D6mAdUaK//Aig+9yc4535GZnES7wcNo0bs/QoXIKX1KHim/3aAwJptyrb1w6VFNtdr2ISEhbN68mcLCQvr06UPjxo1VmQcgI/My1669S0FBHNWrfUyVKqMQ4vGp81JSA/Ee8Cmw2RzVVQ04rNqqNDRKgU5vZPe1eFadjORcZBq2Vhb0blyZl1v508DnAZFYSbdg14emeiuVm8CLv4N301KvJTwjnI+PfszNtJu8XPdl3mv6HjaWZZcPMWRkcOfrb8jcsQO7Bg2oPH0attWUSyjMzynkyJob3L6QhHctVzq9UhdHV2XfuqXRyLkdmzn++0oc3dwZ9NV0vGvVUXSOInIvJ5G2KQSEwP2lOtjXV6fGi8Fg4ODBg/z55594enrSv39/1bTBpDQSFb2U27dnYmtTkWZNf8fZufT/VtXiYST3jxb7OQx4R61FaWg8DHHpefx6Oorfz0aRnK2jirsDn/eow4BAH1wcHvBA1+XCHzPhxI9g7QDPzYJmr5aqkBeY3vq33t7Kt6e/xdbSlnnPzuNp36dLNdbdZP9xnPjPP0efmorHO2PxGDVK0WiwuJA09i8LIjdDZxKd7OyneHXFnPQ09sz/gYjLFwho2YYuo9/BrpxyisxFGHUGMnaEkXPmDjZ+5XF7sTZWrurkeKSlpbFhwwZiY2MJDAyka9euWKtQTwZAp0smKOhjUlKPUaFCF+rUnvZIJV0ehgflucyWUr4nhNjOPWRVtEqUGn8XUkpOhKaw8mQEB4ITkEDH2hV5qVUVngqo8Nf+lOLc3AO7P4b0KGg4GLp8DY6ld/DGZMXwzelvOBF7gkDPQKa1n4ZnOc9Sj1eEMSeHhBkzSP99LTY1quO/YD729eqVedwiDAYjZ3eEc35PJM4e9vT7pBme/mUrm3wvIq9cYte8mehyc+n02ls07NRNlcipwoQcUn69gT4hl/IdfEwliFXSBgsKCmLrVlMe+IABA6in4N/lblJT/+R60Ifo9enUqjkJb++hqkWeKcGDXntWmb/PVHshGholochBv/pUJGHJObg6WDPqqeoMbemHr1sJHbTpUbB7PNzcCR61TEW8/EsfXVNoLGRV0CoWXFqAhbBgXPNxvFj7RUUkXHIvXCBu/KcURkfj9uqrVHjvXUW1wTKS8ti/7DoJ4ZnUaeNFu4EBitdeMej1/Ll+DWe2bsCtsg/9v/iGCn7+is4BpheO3HMJpG+7jbC1xGNEfexqqiPFX1hYyN69ezl37hze3t70798fV1d15jIa9YRH/EhExHwcHKrSuNEyypdX5xhRSR6U53Le/P2oWc4FKWXSo1iYhkZxTA76CLZcjCOv0EBjXxe+H9iIHg287p1Jfy/0Ojj1Exz9zvRzp0nQ6k2wKr0v5ErSFSadnMSttFt08O3A5y0/p1K5sif9GXU6kn/8kZSly7D29qbKyhU4NG9e5nGLc/P0HY7+dhMhBF1eq0dAYNl3WXeTkZjAzrkziL91gwYdu/LM8NextlX+eMqYrydtcyh5l5OwreGC26BaqknkJyUlsWHDBhISEmjTpg3PPvusapUi8/PjuHb9fTIyzuFV6QVq1foKS0t1otyU5kHHYgKYiEkNWQAWQgg9pnDkyY9gfRr/YsrkoL+b8D9g54eQfBNq94RuU8Gl9JFb2bps5lyYw9qba6ngUIHZHWbTsUrHUo9XnPzgYOI+GUdBSAguAwZQcdw4RUOMC/L0HP31JiFnE/Cq4UynV+uqI+Fy6jj7Fs1FSslz735C7TZPKT4HgC4mi5TfbmBIzcepSxXKd/BV3FdUxKVLl9i5cyfW1tYMHTqUgIAH1UwsPUlJBwgKHoeUhdStOwuvSn1Um0sNHmRu3wPaAs2llOEA5kixBUKI96WUP6i8Po1/IbHpefx2l4P+i+fq0L9ZCRz0d5MWCQcnw7UNJmPy4lqoVfpSwVJKDkYdZOrpqSTlJfFi7RcZ22QsjjZld0pLvZ6Un38m6af5WLo447toIY5PKxMMUMSdsAz2Lb1OdloBLXtVpWk3/5L5px6CQl0BR1Ys4cqBPepKuEhJ9ok4MnaHY+loTYXRDVWrFFlQUMDOnTu5cuUK/v7+9OvXDycn5f1SAEZjASGh04mJWUH58vWoX28ODg5VVZlLTR5kXIYBnaWU/198S0oZJoR4CdgHaMZFQxGMRsmJ28msOhn5Xw76l1v7076Gx8M/AHNT4dhMUyKksDBJtrT7AGxKf6QQnx3Pt6e/5UjMEWq51mL2M7NpUKFBqccrTkF4OHHjx5N/+QpOPbrjOWECVgqe4RuNkvO7Izi7MwJHV1v6fdSUStWUfxAnR0eyc853JEdH0rzXC7Qd9JIqEi6GnELSNtwiPzgVuzpuuPaviWU5dSK04uPj2bBhA6mpqXTo0IGnnnpKRSXjcK5de5es7Ov4+rxCjRqfYGHxZFalfJBxsS5uWIqQUiYJIf4+0RqNfwx3O+jdytkw+unqDGnxEA764hTmwemF8McPoMuCxkOgw2fg7P3gvn+B3qjntxu/MfeiqdLDh80+5KW6L2FlUfZzdmk0kvbrbyTOnImwtaXyrJk4P/dcmcctTmZKHgeWBxEfmkHNFp489WItbO2V9REUl3CxsbdXVcKlICKD1N9uYMguxLlnNRzbVlYlakpKydmzZ9m7dy8ODg4MHz4cf39/xecpIv7OFm7e/BIhrGnYYBEVKnRSba5HwYP+helK+TsNjftyt4O+iV8pHPTFMRrg8m9w+FvIjIWArtDpK/CsW6Z1Xk+5zqQ/JxGcGkw773Z80eoLvB1Lb6iKUxgfT9xnn5F78hTlnmqP19ffYO2prNZVyLkEjqy5iZSSTq/WpVZL5Y+nCnJz2Ld4HrdO/oFfg8b0ePtDyrkoHzkljZKsI9FkHojE0tWOim80wsZH+cJhAHl5eWzbto3g4GACAgLo06ePagW99Pocbt36ivg7m3Bxbk69ej9gZ6eODtmj5EHGpZEQIvMe7QJ4dFVnNP4RSCk5eiuJRUfDOBmW8v8O+mGt/anvXcojGilNZYYPTDTVWKncFPotLlNoMUBuYS5zL87l1xu/4mrryoynZ9C1SldF3pCllGRs3UrClG+RBgOVJk3CZeAARd++dfl6/lh7ixsn7+BZ1YnOI+qpU9c+5CY7fzRLuLw4nBa9XlBFwsWQpSN17U0KQtOxb1QB1741VCvoFR0dzYYNG8jKyqJLly60atVKtWOwrKxgrl1/h9zccKr6j8Xf/20sFNgR34sCoxFble7jXjwoFFkdHWqNfxV6g5GdV+NZeDSM4PhMKjnZ8Wn32gxq7vvwDvrixF4w1ViJ+ANcq0L/5VCvb6kFJos4En2EKaencCfnDgNqDuC9Zu/hZKOM81afkkL8xIlkHziIfbNmVJ42FRuFi0YlRGSyf+l1MpLzCOzhT+Bz/lgqnEQojUbObt/EibWrcHRzZ/Ck6VSuqU7uRf6tNFLX3UQWGHB9IQCHQE/VCnr9+eefHDx4EGdnZ1Xr2kspiYldTWjot1hZudCkySrcXFurMhfA2Ywc3gqK5JsAb7p4PJqMfk18UkM18nQG1p2LZskfYcSk5VG9Qjm+69+QPo29sbEqw8MuNRwOfW0SmXRwh+7fmSRbypCvApCQk8C0M9M4EHWAGi41WNV9FY0rNi7TmMXJOnCA+C8nYszKouLHH+P2ynCEpXLvb0aj5OK+SM5sC8fB2Ya+HzShcoDyx1M56Wns/ul7Iq9cVFXCRRqMZO6PJOtIDFaeDqrWtc/MzGTLli2EhYVRt25dnn/+edUKeul0yQQHf0pyyiHc3Z+mbp0Z2Ni4qzKX3iiZHZnAD5F3qGxrg5v1o3vkqzaTEMIXWAlUAozAYinlHCGEG7AW8AcigIFSyjRzn08xqS4bgHeklHvN7c34T5njXcC7UkophLA1z9EMU9nlQVLKCLXuSaNkpOXoWHEyghV/RpCWW0hTPxe+7Fn3/jL3JSEnGY7NgLNLwdLaFAHW5h2wK9uuwmA0sO7WOuZcmIPeqOedJu/wSr1XsLZUJmbFkJVFwpRvydiyBdu6daj8y3LsatZUZOwistMKOPDLdWJvplO9aQU6DK2NnQrRUxGXL7D7p+/Vl3BJyiV17U2TknHzSjg/X021gl7BwcFs27YNvV5Pz549adasmWqyKikpxwgK/hi9PpOaARPw8Rmu2lxReQW8HRzFmYwcXvB0ZWpNH5zuVWVVJdQ0Y3rgQynlBSFEeeC8EGI/8ApwUEo5TQgxHlP55HFCiLrAYKAeUBk4IISoKaU0AAuAUcApTMalG7AbkyFKk1LWEEIMBqZjKmKm8TcQk5bLz3+Es/ZsNHmFBjrWrsiYDtVp7l/G+hy6XDg1H47PhsIcaPIydPgUnMru9LyZepPJJydzJfkKrbxaMaHVBMVk8QFyTp4k7rPP0Scm4vHmG3iMGYOwUTZzPOxiEodWB2MoNPLMy7Wp08ZL8QdWYUE+x9b8wqW9O3D38WPAF9/goZKES87pO2TsDENYW+A2tA4ODdRRMi4oKGDv3r1cuHABLy8vXnjhBTw81FJNLuB22Ayio5dTrlwATRqvxNFRmSqk92JTQhrjbkYjgZ/q+PFCJeVr5DwI1YyLlDIeiDd/zhJCBAPeQG+gg/myFcARYJy5/XcpZQEQLoQIBVoIISIAJynlSQAhxEqgDybj0hv4yjzWBmCeEEJIKf9HZFNDPYLjM1l09Dbbr8QjgF6NKzP6qerUqlTGSB6DHi7/aooAy4o31azvOLHU1SCLk1uYy8LLC1kZtBJnW2emtp/Kc1WfU+yhbMzLI3HW96StXo1N1ar4//Yr9g0bKjJ2EQV5ek6sDyH4z3gq+JWny8h6uHgqLw1yJ/QWu376nrS4GJp270W7IcOxtlE+98KQpSNtYwj5N1KxDXDBbUBNLJ3UyfGIjY1l48aNpKam0q5dOzp06KCahEt29i2uB71PdvYNfHyGUaP6OCwt1YmHytIb+PRWDBsS0gh0cuCnulWoYv/35Mk8kgM4IYQ/0AQ4DXiaDQ9SynghRFHspTemnUkRMea2QvPnu9uL+kSbx9ILITIAd+B/cnM0lEVKyenwVBYevc2Rm0k42FgyvLU/I9tXxduljGfVUsKtPaa69Uk3wKc59F8GVdoosvbjscf55tQ3xGbH0rdGXz5o9gEudi6KjA2Qd/kycePGo4uIwHXYy1R8/30sFD6/j7qewuHVN8hJL6Bp1yq0eL4qlmXxY90Dg17P6c1rObVpLeVc3ej/xTdUadBY0TmKyAtKIW1jCMYCPc7PV8OxdWVVJFyMRiMnTpzg8OHDODo6Mnz4cKpWVSf73eS0X0Vo6DQsLR1p1PBnPDyeUWUugHMZObwZFElMvo4P/T15v0olrFSSwSkJqhsXIYQjsBF4T0qZeZ83w3v9Qt6n/X597l7DKEzHavj5KXfk8W/EaJTsC0pg4dHbXIpOx72cDR92rsnLrauULfKriJhzsG8CRP0JbtVh4Eqo06vMEWAAyXnJTD8znT0Re/B38mdZ12U0r6ScGKQxN5ekufNIXbECq0qe+P2ynHKtWik2PoAuT8+JDSEEnYjHtZIDL3wSiGdV5WVIUmKj2T3vexLCQqjT/hmefXW0enVXdoaRc/oO1l7lqDBYPad9eno6mzdvJjIyknr16tGzZ09VnfZBweNJSTmMu/vT1KnzHbY2Kh25ScmcyARmRdzBy9aaLU1q0MJF+b/Vw6KqcTFn8W8E1kgpN5mbE4QQXuZdixeQaG6PAYrHZPoAceZ2n3u0F+8TI4SwApyB1LvXIaVcDCwGCAwM1I7MSkGB3sCWi7EsOhZGWFIOvm72fN27HgMCfUuX9Hg3Kbfh4CQI2grlKpiKdjUdbnLclxGjNLIxZCM/nP+BfH0+bzZ6k5ENRipSGbKI7BMnuDPxKwpjYnAZNIiKH32IZXllE/yig1I5tCqYnPQCmnTxo8XzVbFS4r99MaTRyMW9O/hjzS9Y2dnx/PvjqdmqbDlDf4UuOovUtTfRp+Th+JQPzl2qIBTefRVx7do1tm/fjpSSPn360KhRIxWd9kcJCv7E7LT/Eh+fYarNFZ2v4+2gSE5n5NDP05Vpj9hpfz/UjBYTwFIgWEr5fbFfbQOGA9PM37cWa/9VCPE9Jod+AHBGSmkQQmQJIVphOlYbBsy9a6yTQH/gkOZvUZbM/EJ+PR3FsuPhJGYVUK+yEz++2IQe9SthpUTuRHYSHJ0O55eDpS08PR7avA22yjyYb6TeYMqpKVxKukTzSs2Z0GoCVZ2VOwbRp6WROG06GVu3YuPvT5VVKxWXxtfl6/lzYyjX/4jDxdOBfh83U0UXLDM5ib0LZhN17TJVmwTSZfQ7OLoq7wiWBnOm/cEoLMtb4/FaA+yquyg+D0B+fj67du3iypUr+Pj40K9fP9zc1HFuGwwF3L79HdExv1CuXE3VnfZbEtL45FY0Rgnz6vjR/29w2t8PNXcubYGXgatCiEvmts8wGZV1QoiRQBQwAEBKeV0IsQ4IwhRp9pY5UgzgDf4Tirzb/AUm47XK7PxPxRRtpqEAiZn5LDsRwZpTkWQV6Glbw51ZAxvRroaHMm9huhw4+ROcmGPSA2s23GRYyitTUyRTl8m8i/NYe3MtzjbOTG4zmT41+ij2BimlJHPHThKmTsWQmYn7G2PwGDNG0UJeANE3Ujm88gZZafk07uRLy17VsFI4JFdKSfDxIxxathCjwUDn19+mQUdl1AjuRp+SR+q6W+giM02Z9r2rY+GgjkxhVFQUmzZtIiMjg6effpqnnnoKSwXzioqTnX2T69ffJzvnJj4+w81Oe3Uc6dl6A5+GxLD+ThrNnByY/zc67e+H+Le96AcGBspz58793ct4bAlLymbJH2FsPB+L3mike30vRj9djYY+LspMYNDDpdWmCLDsBFNtlU5fgYcydTGM0sjW0K3MvjCb9IJ0BtYcyNtN3sbZVrk3/cLYWOInTSLn2B/YNWqI1+SvsaulbN6KLl/Pyc23uXY0FueK9nQcVgevGi6KzgGQl5XJgSU/cev0CSrXrEP3tz7ApZLyulZSSnIvJJK+7TYArn1r4NBYWR21IgwGA8eOHePYsWM4OzvTr18/1Xyt/3HaT8XSsjx1636Hh3sHVeYCOG922kfn63jP35MPHqHTXghxXkoZWNLrtQx9DQAuR6ez4Mht9gbdwdrSgv6BPoxqXw1/D4Wcq1JC6AGTsz4pGHxbwsBV4NdSmfGBoJQgvj39LZeTLtOoQiMWdlpIHXflJEmkwUDamjUkzp4DgOdnn+E6dIiiWfYAsTfTOLQqmMyUfBp19KVl72pYq5BAGH7xHHsXziEvK4t2Lw6nea9+WChQmvluDDmFpG8JJe9qMjZVnXAbWAsrV3VCcVNTU9m0aRMxMTE0atSI7t27Y2enzlwmp/04UlKO4O7egbp1pmPziJz2m5vUoOVj4LS/H5px+ZcTFJfJrH03OXgjESc7K97sUJ1X2lSlQnkFt9nxV2D/BAg7YtIAUzACDCCjIIO5F+ey/tZ6XGxd+Lrt1/Sq3gsLoZxzOP/mLeInTCD/yhWTgvHEiVh7K6OOXERhgYGTm29z9UgMThXs6ftBUyoHuCg6B4AuP4+jq5Zy5cAePHyr0O/TSVT0r6b4PAD5IWmkrr+FMacQ5+7+OLb3USXEWErJ5cuX2bVrF0IIXnjhBRo0UKbWzr1ITjlCUNAnGAxZ1Kw5ER/vlx+J075vRRem1fTB+RHKuJSWx3+FGqoQlpTN9/tvseNKPE52VnzctRbD2/jjaKvgP4nMODj0DVz6FexdoNs0CBxZZg2wIoqOwH44/wMZugwG1RrE203eVkxkEsBYUEDyggWk/LwUy/LlqTxjBk49lUu2LCIuJI2DK4LJTM6n4TM+tOpTHWtb5XcRsTeD2fPT96Qn3iHw+X60HfgSVgorBgDIQiMZe8LJPhGHVUV7PIbXw8ZbnTftvLw8duzYwfXr16lSpQp9+/bFxcVFlbkMhgJCb5uqRDqWq0W9eqsemdN+bh0/+nu6qmbElEYzLv8yYtJy+fFgCBsvxGJrZcFbz1RnVPvqOCvpVC3IMjnq/5wH0mCK/mr/IdgrJ6IYlBLElNNTuJJ0hSYVm/BZy8+o7Vb2zP3i5J49S/yEL9FFRODcuzcVx49TtDokQKHOwKktt7lyOAYndzv6fNAE75rKi00a9IX8uf5Xzm7dSHkPDwZ++S2+ddV5s9fFZZtCjBNyKdfaC5ceVREKh0wXER4ezubNm8nOzqZjx460bdtWNXn87OybXLv+Hjk5t/D1eYXq1T9R1Wn/WUgM6x5zp/390IzLv4SkrAJ+OhzKr6ejABjWugpvdqih7PGXQQ8XV5qc9TlJUP8F6PgluPorNkXREdi6m+twtXNlSrspPF/teUXf5gxZWSTOnEX62rVYe3vj+/PPOLZrq9j4RcSFpnNoRTAZSXk06OBD677q7FaSoyLY9dP3JEWEUf+ZznQY9jq2DsrLxEijJPt4LBl7I7BwsML91XrY11InPFav13PkyBGOHz+Om5sbI0eOxFvhY8oipJTExKwk9PY0rKycaNxoGe7uT6syF8CFjBzeDI4kKk/H+1U8+cC/EtZ/Y6Z9adGMyz+cjNxCFh27zfITEegMRgY082Fsx4CyS7QUp6hg1/4JJrkWv9bw4u/gU+LAkgdilEY2h2xmzoU5ZOgyGFJnCG82flPRIzCAzP37SZj8NfqUFNxefZUKY9/GQuEHcaHOwOmtYVw+FE15Nzt6v98En1rK71aMRgMXdm7l+O8rsXEoR++PvqBGc2UVA4rQpxeQtu4mBWEZ2NV1x7VfDSwdlT9uA0hOTmbjxo3Ex8fTtGlTunbtiq3CIeBFFOiSCf5/p/0z1K0zTVWn/dzIBGZE3KGSjTWbmtSg1WPutL8fmnH5h5JToGf5iXAWHQsjK19Pr0aVeb9zTaoqFf1VRPxlUwRY+FFwqwaDVpvCixXcSVxPuc63p77lSvIVmlZsymctP6OWm7Ln3IUJiSR88w1Z+/djW7s2PvPnY9+gvqJzAMTfzuDgiiAyEvOo/5Q3rftVx0aFiooZiQnsmf8DMcHXqB7Yii6j3sbB2UXxeQByLyeRtjkUjEZVi3lJKblw4QJ79uzBysqKQYMGUaeOOgXKAJKTDxMUPA6DIZuaNb/Cx/sl1fwdMWan/amMHPpUdGH6E+K0vx9P9uo1/of8QgNrTkcx/3AoKTk6OtWpyAeda1G3ssL6UxmxpoJdl383+VIUKtj1X1MUZPDjhR9Zf2s9bnZufNvuW3pW66no/+DSaCR9/QYSZ85E6nRU+PAD3F95BWGtbGKfXmfg9PZwLh2IwtHVll7vNca3tgrZ71Jy/cgBDq9YDEDXN96j3tMd1ancmK8nfettci8mYuNbHrdBtbDyUEerKycnh+3bt3Pjxg2qVatGnz59cHJSXlMNipz204iJWWl22q/G0VHZPKbibE1M45ObMeil5Mc6fgx4gpz290MzLv8QCg1GNp6PYc7BEOIz8mlT3Z2PutaiqZ/Cxy0FWaa6KifngTRC23eg3QemaDCFMEojm0I2MefCHLJ0WQytM5Q3G79JeRtltboKwsK58+WX5J47h0PLlnhN+gobf39F5wC4E5bBwRXBpCfkUrd9Zdr2q4GNvfL/6+VmpLNv8TxunzuFT536dHvzfZwrKqN4cDcFYRmkrruJIbMAp05+lH/GD2GpzgMxNDSULVu2kJeXp3pN+/9y2vu+SvVqH6vqtP88JJa1d1Jpanba+z9hTvv7oRmXJxyjUbL9Shw/7L9FREoujX1dmDmgEW1rKHwubNDDhRVwZKrJWd9gADw7AVyrKDrN9eTrTDk9havJV1U7ApM6HSnLlpE8fwHCzg6vKd/g3K+f4m+L+kIDZ7aHc2l/FOVcbHn+nUb41VWnnG3o2VPsWzwXXW4OT780gmbP9UGo8ACWeiOZByLJOhqDpZsdFcY0wtZPnR1EYWEhBw8e5NSpU3h4eDB06FC8vJRXDwCQ0kh0zApu3/7ukTjtz2Xk8HZwJJF5Ot6r4smHT6jT/n5oxuUJRUrJgeBEZu27yY07WdSuVJ6fhwXSsU5FZR+SUsKtvSZnffIt8GsDQ9aCdzPl5gDS89OZc3EOG29txN3eXZUjMDDVWomf8CUFt25Rvls3Kn3+GVYVKig6B0BCRCYHfwki7U4uddt60aZ/ALYq7FYKcnM5snIJ1w7vp4J/NXpMmKJKhUiAwjs5pK67SWFcjqn0cM9qWKgQ3QYQHx/Ppk2bSEpKonnz5nTp0gVrhY8qi8jPjyc4eBypaSfw8OhIndrfqua0LzRKvo+4w5zIBCrbmZz2rZ9gp/390IzLE8iJ0GS+23uTy9Hp+Ls78OOLTejZwKts9envRdwl2PcFRPxhqq0yaA3Ufk5RZ73BaGBTqOkILFuXzUt1X+LNRm/iaKPs/3DGnBwS58whbdVqrCpWxGf+T5R/9llF5wDTbuXszggu7o3EwdmWnmMbUaWeOruViCsX2bfoR7JTUmjZdyCt+7+IpZXyD2BplGT/EUPGvkgs7Kxwf7kO9vXUKgds4MSJExw5cgQHBweGDh1KQIAyunP34k7Cdm7e/BIp9dSu/S2VvQaq5u+4nZvPW0FRXMrKZUAlV6YEPD7y+GqgGZcniPORaczce5OTYSlUdrZjWr8GvNDMB2slpO+LkxEDB7+GK7+Dgzt0nwGBrypSW6U415Kv8c2pb7iecp1mns34rOVn1HRV3nGafewY8V99hT4uHtchL1Lhgw+wdFT+bTH+dgaHVwWTdieX2q0r0W5AALYqKP4W5OZybPUyrhzcg2tlHwZPnk7lmupETemT80hdb1YxrueOS1/1QoxTUlLYvHkzMTEx1KtXj+eeew4HFfJxAAoLM7h5ayIJCdtxdmpC3bozcXDwV2UuKSUr41L4KjQOWwvB4nr+9KroospcjxOacXkCCI436X8dCE7EvZwNX/asy5CWfsoU6SpOfiYc/wFOzTcdh7V9D9p/AHbK1g5Jz09n9oXZbArZhLu9O9PaT6NH1R7K+zySkkiYOpXMXbuxqV6dKr+uwaFpU0XnAJOC8emtYVw5EoOjqy3Pj22En0q7lcgrl9i7aA5ZKckEPt+PNgOHqlLPXholOafjydgVDpYWuA2qhX3jCqqFGJ87d459+/ZhaWmpui5YauoJgoI/QadLplq1D6jiNxoLC3UehUm6Qt6/Ec2BlEyedi3P7Dq+eNmqY5wfNzTj8hgTlpTNDwdC2H45jvJm/a9X2vhTTkn9LzA763+Bw1MhNxkaDISOE8BFWZnyooqQRUdgL9d9mTcavaH4Edj/hxfPmoXMy8Nj7Nu4v/46FipoaEUHpXJ4zQ2yUvJp8LQ3rfqqk7eiy8vl6OplXDmwB1cvb16c/J16u5X0fNI2hFAQmo5tTVfcXgjA0lmdKKbMzEy2bdtGaGgo1apVo3fv3jg7K18IDcBgyOd22Eyio5fj4FCdwGYLcXJSz4jtTc7ggxvRZBsMfBPgzQhvDyz+ASHGJUUzLo8hiVn5/LD/FuvOxWBjqZL+VxFhR2HPeEgMgirtoMvX4K382/31lOtMOWWKAmvm2YzPW35OgKvyZ+kFISHET/yKvAsXcGjRgkpffYVtNeUqTxaRn1PIiY2h3PgzHhdPB/p+qI6CMUDk1UvsW/QjmclJNOvZl7aDXlJntyIluecTSd9+G6TEpW8NyrWopJoP4tq1a+zcuZPCwkJ69OhBYGCgaiHGWVnXuR70ITk5Ifj4DKNG9U+wtFQpJ0dvYGJoHKvjU6jvaM+8utWpXU6duR5nNOPyGJFfaGDp8XDmHw6lQG/k5VZVeOsZhfW/ikiLgL2fw40dph3KwFVQ53lFnfXw31pgaiVCAhjz80leuJCUpcuwdHDA69tvce6rXOXJ4oRdTOLobzfJyy6kadcqNO/pr3gtezDtVo6tWc7l/btx9fJm8KTv8K6lzm7FkKUjbVMI+cGp2Pg74TagJlbu6jwQ8/Ly2LVrF1evXsXb25u+ffvi4aFOgICUBiIjlxAWPhsbazcaN/oFd/f2qswFpmJebwdHEpGn422/inxctRK2KhnMxx3NuDwGSCnZfiWe6btvEJueR+e6nnzavTbVKqgQoliQDce/NykWW1jCs19A67FgrWxBpbvl8IfUGcJbjd9SPBESIOfkSeK/+orCyCice/ei4rhxWKlQJz03U8ex329x+0IiHr6O9Hy7ERX8lL8fgKhrl9m78EcykxNp9lwf2g5+WZXdCkDu1STSN4di1Blwfq4ajm0rq1JzBeD27dts2bKF7OxsOnToQPv27VUrPZyXF831oI/IyDhHxYo9qF3ra6ytXVSZq9AomR15h9mRCf+vC/ZPDTEuKaoZFyHEMqAnkCilrG9ucwPWAv5ABDBQSplm/t2nwEjAALwjpdxrbm8G/ALYA7uAd6WUUghhC6wEmgEpwCApZYRa96MWF6PS+HpHEBei0qnj5cSM/g1po3QCJJgc9FfXw/4vISve5FfpPAmcKis+1Y3UG0w5NYVLSZdoXKExX7T6QvFESAB9aiqJ06eTsXUb1lX88Fu+jHKtWys+j5SSW6fv8Mf6EAoLDLTsXY0mXfywVDpKD1Mhr2NrfuHyvp24elVm8FfT8a5dV/F5AIy5haRtvU3e5SSsfRypMLAW1hXVic7S6XQcOHCAM2fO4OHhweDBg1VVMY6P38itkMmAoG7dWVTy7K3a8V5YbgFvBUVyMSuX/p6ufFvznx1iXFLU3Ln8AszDZACKGA8clFJOE0KMN/88TghRFxgM1AMqAweEEDWllAZgATAKOIXJuHQDdmMyRGlSyhpCiMHAdGCQivejKLHpeXy35wZbL8Xh4WjL9Bca0L+ZL5ZqvDHGXjD5VaJPg1djGLBC0fLCRWTpsvjp0k/8duM31SpCgunhkbF5C4nffYchOxv3MaPxGDMGCxXK2Wal5nNkzU2irqdQqZoTz7xcBzcvhcU/zURdu8LehXPMu5XetB30Mta26pTozbuRStrGEIw5hTh1rkL5Dr6qybfExsayadMmUlJSaNmyJZ06dVItIVKnS+HGzS9IStqHi0tL6tWdiZ2d8i9QYPp3uDo+hS9D/l0hxiVFNeMipTwmhPC/q7k30MH8eQVwBBhnbv9dSlkAhAshQoEWQogIwElKeRJACLES6IPJuPQGvjKPtQGYJ4QQUkqpzh0pQ06BngVHbrPkjzAA3n6mBmM6VFe2AmQR2YlwcBJcXAPlPKDXPGg8FBQ+A5ZSsiNsB7POzSI1P5WBtQYytslYnG2Vj/opCA/nzsSvyD1zBvsmTfCaPAlbFZLspFFy7VgsJzffRgLtBwVQ/2kf5RNV+e/dikslLwZ9NQ2f2vUUnwfAWKAnY0c4OWfvYOXpgMcr6lWINBgMHDt2jGPHjlG+fHmGDRtGtWrqlFMGk4px8I3xFBZmUqPGp/j5jkAo/GJTRJKukA9uRLM/JZOnXB2ZU8fvXxNiXFIetc/FU0oZDyCljBdCVDS3e2PamRQRY24rNH++u72oT7R5LL0QIgNwB5LVW37pMRglG8/HMGPfTZKyCujduDKfdKutbF2VIvQ6OL0Qjn4H+nxTJcinPlY8XwUgJC2EKaencD7hPA08GvBTp5+o5678g9Go05GyZAkpCxch7OyoNGkSLgP6q6KflZ6Qy6FVwcSHZuBbx5UOQ2vjpJLab/R1024lIymRpj16026weruV/NvppG24hSG9gPJP++DUuQrCSqWHb1ISmzdvJi4ujoYNG9K9e3fs7dX5b2gw5BISOpXY2F9xdKxNk8YrVS09vC85g/fNIcZf1/BmpM+/K8S4pDwuDv17/WXkfdrv1+d/BxdiFKajNfz8lM3dKAknb6fw9Y4gguIzaeLnwqKXmymvVlzErb2w51NIvQ0BXaHrt+BRQ/FpcgpzmH9pPmuC1+Bo48jE1hPpF9BP8SMwgNxz54j/ciK6sDCcenTH89NPVdEDMxqMXDoQzZkd4VhZW/DssNrUbu2lyll9YX4+x379hUt7d5h2KxOn4lNH+foxALLQQMaeCFM9e3ez2GQVdcQmjUYjZ86c4cCBA1hbWzNgwADq1VNnFwaQkXGJ60EfkJcXhZ/f61Sv9j4WFuoEPuQYDHwVGsequBTqOdrx0780xLikPGrjkiCE8DLvWryARHN7DOBb7DofIM7c7nOP9uJ9YoQQVoAzkHqvSaWUi4HFAIGBgY/s2Cw8OYepu4LZF5SAt4s9P77YhOcbqvOwIjnEZFRC94N7DRi6AQI6Kz6NlJI9EXuYeXYmSXlJ9Avox3tN38PFzkXxuQzp6STMnEnGho1YV66M7+JFOD71lOLzACTHZHFo5Q2SorKo1rgCT71Yk3IqJQ5GB1017VYSE2javRftXhym2m6lICqTtPW30CflUa61F87dq2Jho46zOSMjgy1bthAeHk5AQAC9evWifHl1oumMxkIiIn4iInI+tjaeNG2yBldX5f2IRVzIzOHtoCjC8wp4y68in/yLQ4xLyqM2LtuA4cA08/etxdp/FUJ8j8mhHwCckVIahBBZQohWwGlgGDD3rrFOAv2BQ4+LvyUjt5AfD4Ww8mQENpYWfNz1/9q79+iqqjuB499fHuatSYQkItEoCAbRUchyKC+FKr5iC6PlpQ5ZY0fboS5h6KhtraCrlEerwEzV0uqAWAbHF1PeoJYOyihToCxBUh5FEghJIBBIyMub3N/8cU5shjQhF87hxtzfZ62se3Jyc87OXjv3d/Y++/x2Xx4eepX36VoA6k85w19bfgmxiTBqJtz8iKeLdjU7cPIAP93yU7aUbSE3PZd5I+ZxQ/cbPD+PqlK1ajXls2fTdPIk6Q//A90nT/Z8uWGApkCQrWsPsn1dEXFJMdzxj/3pNcCfNCeB+no+XPYaf1y3ktTMyxj3zCx69vOpt9IYpOqDYqp/f4joi+Po9u3+xPf2p7esquzcuZPVq1cTDAbJz89n4MCBvs3Oqqk5wO7d06iq/pSsrDH07TOdmBh/glhjUJlfVM68Imfp4Xdu7M3gtMieYtxRfk5FXoZz876biBwGpuMElTdF5GGgGPgWgKp+JiJvAruBRmCyO1MM4Lv8ZSryWvcL4FXgdffm/wmc2WZhFWgKsvSTIuZ/sI9TdQHG5WXzz6P6kJHiw1VpsAl2LIUPnoOaChjwkLO+SnLG2X83RLWBWhZ+upAlu5eQEJPA03/7NPf3uZ/oKB8eHDx0iLIZz1KzeTPx11/PFa/8mniflrItO3CK3y1xEk32HZTF0PuvIT7Zn1lMh3fvYv0vF3CyvJSb7rqXYeMnEevD7DaAL0prqHxzD4HSGhIHZpJ679VE+ZCSBqC2tpZVq1axe/dusrOzGTNmDOk+PGMEThArKVnKvv2ziIqKp3//X5CZcZcv5wL4vLaByYVFbK+yKcbnQjrJxf4Fk5eXp1u3bvX0mKrKxj1Hmbm6kD8fq2Fwr0t5+p5+3i8t3Kz4E1j7hLN+ffYguGs29LjJ89OoKu8Xv8/cP8ylrKaM0b1HM3XgVNLjfVieNxDg+OLFVLz4EhIdTfepU0mbMB7x4QG7QEMTn/z2z3y68TDJqXHc+sC1XNnfn0STgfp6PnzjNf64diWXZGZx53em+NdbaVKqNx2m6v0iohJiSPu7a0jwaXEygL1797JixQpqa2sZMWIEQ4YM8S19S0PDUQoLn+T4iU1cmj6c3Nw5xMV5fyEFTrtfWnqCZ/aXECvCnD49GZ3p0z3SrxAR2aaqeR19f2e5of+V9aeyKmauLuTDfRVc3S3JnwW7mp0qcR6C3PU2pPSA+16F/vd5nrIFoKiqiFlbZrH5yGb6pvVl7vC53JThfQADqNuxg9JnpjsLeN1+G5k/+hGxWVm+nOtQ4Qk2/sb/RJMAhwt3sf5lt7dy570Mm+BfbyVwrJbKt/byRXE1Cdd3I3V0b6KT/OmFNTQ0sGHDBrZt20ZGRoavK0QCHD26jj/teZqmpjr69nmWyy9/wLcht2NfBPj+nkOsr6hiWFoyC669gh7xNsX4XFhwOUcVpxt4fsNe/vMPxaTEx/JMfj8eHHQlF/kxtTNQ56Rr+egFZzhs+BMwdApc5P3DfHWNdbyy8xUW7VpEXHQcT938FOP6jiPGh5TkTdXVHJs3j8plbxCTmUnPF39Byte/7vl5ABpqA2x+ez+F/1PKJRkJviaaDDTU89GyJWxft5JLMjIZO30W2f38yb6rQeX0x0eoWncQiY0ifcK1JP6N9zPpmhUXF7N8+XIqKysZPHgwI0eOJCbGn4+RxsZq9ux9lrKy5aSkXM91/V4gKcm/52Q2Hq/iscJiqpuaeK53D77ds7tNMT4PFlxCVB9oYtHmg7y4cT/1gSb+/ms5TLntGlITfbi6UYXCFc5qkCeLIfcbMOonnq9b75xK2XhoI3P+dw5Hao6Qf3U+0/Km0S3B+1Q0qkr1+g2Uz5xJY0UFaQ8+SPfHHyc62Z8n3w/scBNNVruJJu/JIcanGVOVpSW8O3sGJ8tKufGOfIZPLPCttwJQ/btiqt4vJr5vGmn39SH6Yv+usoPBIGvWrEFVKSgoICcnx7dzgRNcjh//PVflPEZOzmSiovzpiX15PlUy42J4K7cXuck2xfh8WXDpIFVlzc4yZq0t5HBlHbflZvCDu3Pp5UdySYDyz2Dtk84SwxnXwaSVcJU/03DLa8p57pPn2HR4E71Te7PojkXkZXV4aDVkxxcu5Nj8BcTl5tLzpRdJ8HFhKA0q29cXkZByEff80w1k+PR8R7PkS7uRltWDUY88RvZ13s+kO1PSoMuITo0ncaBPQ7EtREVFMXbsWBITE4n3MWA2i4/vweCvbfRtJtiZbu92CSMvvZho6614wm7od9C89/ay4IN9XJuVwo/z+zHEj+SSzYJBeGkQ1Bx1shYPKIBo/64DKusrGb9qPBNzJzIxdyKxPl8hBkpKqNrwHukPPYj4NKTSUs2pBuKTY31JNGlMpAj1hr4Flw46dKKWj/ZXMDbPp+SSZyrb5WQsTvRnWueZAsGA70HFGPPVZbPFfJKdnsiEmy9g6pgsf6artsUCizHGSzZOYIwxxnMWXIwxxnjOgosxxhjPWXAxxhjjOQsuxhhjPGfBxRhjjOcsuBhjjPGcBRdjjDGei7gn9EXkGFAU7nL4rBtQEe5CdDJWJ61ZnbRmddJac51cqaodTrkdccElEojI1lDSNEQCq5PWrE5aszpp7VzrxIbFjDHGeM6CizHGGM9ZcOmafhXuAnRCVietWZ20ZnXS2jnVid1zMcYY4znruRhjjPGcBZcuREQOishOEdkhIqGviNZFiMi/i8hREdnVYl+6iLwnIvvc17RwlvFCa6NOZohIidtedojI3eEs44UmItkislFECkXkMxF53N0fsW2lnToJua3YsFgXIiIHgTxVjeh5+iIyHDgNLFHV/u6+ucAJVZ0tIk8Baar6ZDjLeSG1USczgNOq+vNwli1cROQy4DJV3S4iKcA2YDRQQIS2lXbqZCwhthXruZguR1U3ASfO2P1N4DV3+zWcf5iI0UadRDRVLVXV7e52NVAIXE4Et5V26iRkFly6FgU2iMg2EXkk3IXpZDJVtRScfyAgI8zl6Sy+JyKfusNmETP8cyYRyQFuArZgbQVoVScQYlux4NK1DFHVAcBdwGR3KMSYtrwM9AJuBEqB58NamjARkWTgHWCKqlaFuzydwV+pk5DbigWXLkRVj7ivR4HlwM3hLVGnUu6OJzePKx8Nc3nCTlXLVbVJVYPAr4nA9iIisTgfoktV9V13d0S3lb9WJ+fSViy4dBEikuTegENEkoBRwK72fyuirAAmuduTgN+GsSydQvMHqGsMEdZeRESAV4FCVX2hxY8itq20VSfn0lZstlgXISJX4/RWAGKA/1DVmWEsUtiIyDLgVpxsruXAdOC/gDeBK4Bi4FuqGjE3uNuok1txhjkUOAg82nyvIRKIyFDgQ2AnEHR3/xDnHkNEtpV26mQCIbYVCy7GGGM8Z8NixhhjPGfBxRhjjOcsuBhjjPGcBRdjjDGes+BijDHGcxZcTMQTkUQ362tBi30FIqIi8n2fzpnjHl/dhKOIyGL3+7Cs4d6iPDaF1Jw3Cy7GQCLOcx8FLfb9N87c/pU+n/sd4DGfz/ElEYlp58cTgE0Xqiyma7PgYgw0r31zi3vlPgO4BVgG3AtfrpVzWkSeF5FTIvKuiIwSkUMiUioid7rvu0hEfu6ufXFSRN4Ske7tnHuXqp4ZwMaISLF77GHucdPcns0x92tJc/LA5rK523nu37DY/b65N/SyiJTgJB981D12g3ueaQCq+gbw+flXpzEWXIwB5wlkcNKLTwDebuN9SUA88DFOCoxfAT/DyZo7233PD4BpOD2e+ThJRF8OsTwj3GP3BGa4+xbgpCJZDCwCHnL3ddQwnN7Zx8BcoBL4DvAS0Bhi+Yw5KwsuxsAG9/Woqr6hqm3lTQoCU3GGsgBeV9V/BY4AV7n78t3XR3E+zJvzvIVihqr+BGgActx9dwMlqvovqvoEUIITuDrqh6r6iqpuAfbhBK5bgBrg9RDLZ8xZtTf+akyk6OgN7DpV/UJEAu73p9zXJiDa3RacnkC+ux9Cv4hrzmPV2OK47Wl5/tQ23nOkxfZI4D5gADALGAcMDbGMxrTLei7GQBVOr6S3iDwgIleex7FW4ly0TcJJfHgnTi/mfK0GLheROSIyB2d1wDXuzw4C8SLyXaAjy/HOx5nEsB0nQPbwoHzG/D/WczERT1UDIvIz4HvAb3DuZ5yrWThDYRNwlsf9HFh4vmUEprivD7uvr7fYNwPoA/wYeBe47SzHSgWeBVLc8kXE+vDmwrKsyMaEgbuE7Oc4EwJmqWpleEsEItIN+DdgvKpKuMtjvtosuBgTBi2CC0CRquaErzSOlg9PWnAx58uGxYwJjzLgdne7LpwFaeH2s7/FmI6xnosxxhjP2WwxY4wxnrPgYowxxnMWXIwxxnjOgosxxhjPWXAxxhjjuf8DPQdHRL7P7XQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "ax= plt.axes()\n",
    "ax.set_ylabel('Distance travelled [m]')\n",
    "ax.set_xlabel('time [hours]',weight='bold')\n",
    "d_plot_t = ax.plot(real_time.T[1:],distance.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional selection\n",
    "In other cases, the processing of the data itself however depends on the absolute time at which the observations are made, e.g. studying seasonal phenomena. In that case the array structure is not as simple: the data must be selected by their `time` value. Here we show how the mean location of the particles evolves through time. This also requires the trajectory data to be aligned in time. The data are selected using [`xr.DataArray.where()`](http://xarray.pydata.org/en/stable/generated/xarray.DataArray.where.html#xarray.DataArray.where) which compares the time variable to a specific time. This type of selecting data with a condition (`data_xarray['time']==time`) is a powerful tool to analyze trajectory data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using xarray\n",
    "mean_lon_x = []\n",
    "mean_lat_x = []\n",
    "\n",
    "timerange = np.arange(np.nanmin(data_xarray['time'].values), \n",
    "                      np.nanmax(data_xarray['time'].values)+np.timedelta64(delta(hours=2)), \n",
    "                      delta(hours=2))  # timerange in nanoseconds\n",
    "\n",
    "for time in timerange:\n",
    "    if np.all(np.any(data_xarray['time']==time,axis=1)):  # if all trajectories share an observation at time\n",
    "        mean_lon_x += [np.nanmean(data_xarray['lon'].where(data_xarray['time']==time).values)]  # find the data that share the time\n",
    "        mean_lat_x += [np.nanmean(data_xarray['lat'].where(data_xarray['time']==time).values)]  # find the data that share the time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conditional selection is even easier in numpy arrays without the xarray formatting since it accepts the 2D boolean array that results from `time_netcdf4 == time` as a mask that you can use to directly select the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using netCDF4\n",
    "mean_lon_n = []\n",
    "mean_lat_n = []\n",
    "\n",
    "timerange = np.arange(np.nanmin(time_netcdf4), \n",
    "                      np.nanmax(time_netcdf4)+delta(hours=2).total_seconds(), \n",
    "                      delta(hours=2).total_seconds()) \n",
    "\n",
    "for time in timerange:\n",
    "    if np.all(np.any(time_netcdf4 == time, axis=1)):  # if all trajectories share an observation at time\n",
    "        mean_lon_n += [np.mean(lon_netcdf4[time_netcdf4 == time])]  # find the data that share the time\n",
    "        mean_lat_n += [np.mean(lat_netcdf4[time_netcdf4 == time])]  # find the data that share the time"
   ]
  },
  {
   "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",
    "ax = plt.axes()\n",
    "ax.set_ylabel('Meridional distance [m]')\n",
    "ax.set_xlabel('Zonal distance [m]')\n",
    "ax.grid()\n",
    "ax.scatter(mean_lon_x,mean_lat_x,marker='^',label='xarray',s = 80)\n",
    "ax.scatter(mean_lon_n,mean_lat_n,marker='o',label='netcdf')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "Parcels output consists of particle trajectories through time and space. An important way to explore patterns in this information is to draw the trajectories in space. Parcels provides the [`ParticleSet.show()`](https://oceanparcels.org/gh-pages/html/#parcels.particleset.ParticleSet.show) method and the [`plotTrajectoryFile`](https://oceanparcels.org/gh-pages/html/#module-scripts.plottrajectoriesfile) script to quickly plot at results, but users are encouraged to create their own figures, for example by using the comprehensive [**matplotlib**](https://matplotlib.org/) library. Here we show a basic setup on how to process the parcels output into trajectory plots and animations.\n",
    "\n",
    "Some other packages to help you make beautiful figures are:\n",
    "* [**cartopy**](https://scitools.org.uk/cartopy/docs/latest/), a map-drawing tool especially compatible with matplotlib\n",
    "* [**cmocean**](https://matplotlib.org/cmocean/), a set of beautiful colormaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To draw the trajectory data in space usually it is informative to draw points at the observed coordinates to see the resolution of the output and draw a line through them to separate the different trajectories. The coordinates to draw are in `lon` and `lat` and can be passed to either `matplotlib.pyplot.plot` or `matplotlib.pyplot.scatter`. Note however, that the default way matplotlib plots 2D arrays is to plot a separate set for each column. In the parcels 2D output, the columns correspond to the `obs` dimension, so to separate the different trajectories we need to transpose the 2D array using `.T`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x252 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1,ax2,ax3,ax4) = plt.subplots(1,4,figsize=(16,3.5),constrained_layout=True)\n",
    "\n",
    "###-Points-###\n",
    "ax1.set_title('Points')\n",
    "ax1.scatter(data_xarray['lon'].T,data_xarray['lat'].T)\n",
    "###-Lines-###\n",
    "ax2.set_title('Lines')\n",
    "ax2.plot(data_xarray['lon'].T,data_xarray['lat'].T)\n",
    "###-Points + Lines-###\n",
    "ax3.set_title('Points + Lines')\n",
    "ax3.plot(data_xarray['lon'].T,data_xarray['lat'].T,marker='o')\n",
    "###-Not Transposed-###\n",
    "ax4.set_title('Not transposed')\n",
    "ax4.plot(data_xarray['lon'],data_xarray['lat'],marker='o')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Animations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trajectory plots like the ones above can become very cluttered for large sets of particles. To better see patterns, it's a good idea to create an animation in time and space. To do this, matplotlib offers an [animation package](https://matplotlib.org/3.3.2/api/animation_api.html). Here we show how to use the [`FuncAnimation`](https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.animation.FuncAnimation.html#matplotlib.animation.FuncAnimation) class to animate parcels trajectory data.\n",
    "\n",
    "To correctly reveal the patterns in time we must remember that [the `obs` dimension does not necessarily correspond to the `time` variable](#Trajectory-data-structure). In the animation of the particles, we usually want to draw the points at each consecutive moment in time, not necessarily at each moment since the start of the trajectory. To do this we must [select the correct data](#Conditional-selection) in each rendering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.animation import FuncAnimation\n",
    "from IPython.display import HTML\n",
    "\n",
    "outputdt = delta(hours=2)\n",
    "timerange = np.arange(np.nanmin(data_xarray['time'].values),\n",
    "                      np.nanmax(data_xarray['time'].values)+np.timedelta64(outputdt), \n",
    "                      outputdt)  # timerange in nanoseconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "fig = plt.figure(figsize=(5,5),constrained_layout=True)\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "ax.set_ylabel('Meridional distance [m]')\n",
    "ax.set_xlabel('Zonal distance [m]')\n",
    "ax.set_xlim(0, 90000)\n",
    "ax.set_ylim(0, 50000)\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "time_id = np.where(data_xarray['time'] == timerange[0]) # Indices of the data where time = 0\n",
    "\n",
    "scatter = ax.scatter(data_xarray['lon'].values[time_id], data_xarray['lat'].values[time_id])\n",
    "\n",
    "t = str(timerange[0].astype('timedelta64[h]'))\n",
    "title = ax.set_title('Particles at t = '+t)\n",
    "\n",
    "def animate(i):\n",
    "    t = str(timerange[i].astype('timedelta64[h]'))\n",
    "    title.set_text('Particles at t = '+t)\n",
    "    \n",
    "    time_id = np.where(data_xarray['time'] == timerange[i])\n",
    "    scatter.set_offsets(np.c_[data_xarray['lon'].values[time_id], data_xarray['lat'].values[time_id]])\n",
    "    \n",
    "anim = FuncAnimation(fig, animate, frames = len(timerange), interval=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\"\n",
       "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
       "<script language=\"javascript\">\n",
       "  function isInternetExplorer() {\n",
       "    ua = navigator.userAgent;\n",
       "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
       "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
       "  }\n",
       "\n",
       "  /* Define the Animation class */\n",
       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
       "    this.img_id = img_id;\n",
       "    this.slider_id = slider_id;\n",
       "    this.loop_select_id = loop_select_id;\n",
       "    this.interval = interval;\n",
       "    this.current_frame = 0;\n",
       "    this.direction = 0;\n",
       "    this.timer = null;\n",
       "    this.frames = new Array(frames.length);\n",
       "\n",
       "    for (var i=0; i<frames.length; i++)\n",
       "    {\n",
       "     this.frames[i] = new Image();\n",
       "     this.frames[i].src = frames[i];\n",
       "    }\n",
       "    var slider = document.getElementById(this.slider_id);\n",
       "    slider.max = this.frames.length - 1;\n",
       "    if (isInternetExplorer()) {\n",
       "        // switch from oninput to onchange because IE <= 11 does not conform\n",
       "        // with W3C specification. It ignores oninput and onchange behaves\n",
       "        // like oninput. In contrast, Mircosoft Edge behaves correctly.\n",
       "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
       "        slider.setAttribute('oninput', null);\n",
       "    }\n",
       "    this.set_frame(this.current_frame);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.get_loop_state = function(){\n",
       "    var button_group = document[this.loop_select_id].state;\n",
       "    for (var i = 0; i < button_group.length; i++) {\n",
       "        var button = button_group[i];\n",
       "        if (button.checked) {\n",
       "            return button.value;\n",
       "        }\n",
       "    }\n",
       "    return undefined;\n",
       "  }\n",
       "\n",
       "  Animation.prototype.set_frame = function(frame){\n",
       "    this.current_frame = frame;\n",
       "    document.getElementById(this.img_id).src =\n",
       "            this.frames[this.current_frame].src;\n",
       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
       "  }\n",
       "\n",
       "  Animation.prototype.next_frame = function()\n",
       "  {\n",
       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
       "  }\n",
       "\n",
       "  Animation.prototype.previous_frame = function()\n",
       "  {\n",
       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
       "  }\n",
       "\n",
       "  Animation.prototype.first_frame = function()\n",
       "  {\n",
       "    this.set_frame(0);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.last_frame = function()\n",
       "  {\n",
       "    this.set_frame(this.frames.length - 1);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.slower = function()\n",
       "  {\n",
       "    this.interval /= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "  }\n",
       "\n",
       "  Animation.prototype.faster = function()\n",
       "  {\n",
       "    this.interval *= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "  }\n",
       "\n",
       "  Animation.prototype.anim_step_forward = function()\n",
       "  {\n",
       "    this.current_frame += 1;\n",
       "    if(this.current_frame < this.frames.length){\n",
       "      this.set_frame(this.current_frame);\n",
       "    }else{\n",
       "      var loop_state = this.get_loop_state();\n",
       "      if(loop_state == \"loop\"){\n",
       "        this.first_frame();\n",
       "      }else if(loop_state == \"reflect\"){\n",
       "        this.last_frame();\n",
       "        this.reverse_animation();\n",
       "      }else{\n",
       "        this.pause_animation();\n",
       "        this.last_frame();\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.anim_step_reverse = function()\n",
       "  {\n",
       "    this.current_frame -= 1;\n",
       "    if(this.current_frame >= 0){\n",
       "      this.set_frame(this.current_frame);\n",
       "    }else{\n",
       "      var loop_state = this.get_loop_state();\n",
       "      if(loop_state == \"loop\"){\n",
       "        this.last_frame();\n",
       "      }else if(loop_state == \"reflect\"){\n",
       "        this.first_frame();\n",
       "        this.play_animation();\n",
       "      }else{\n",
       "        this.pause_animation();\n",
       "        this.first_frame();\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.pause_animation = function()\n",
       "  {\n",
       "    this.direction = 0;\n",
       "    if (this.timer){\n",
       "      clearInterval(this.timer);\n",
       "      this.timer = null;\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.play_animation = function()\n",
       "  {\n",
       "    this.pause_animation();\n",
       "    this.direction = 1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function() {\n",
       "        t.anim_step_forward();\n",
       "    }, this.interval);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.reverse_animation = function()\n",
       "  {\n",
       "    this.pause_animation();\n",
       "    this.direction = -1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function() {\n",
       "        t.anim_step_reverse();\n",
       "    }, this.interval);\n",
       "  }\n",
       "</script>\n",
       "\n",
       "<style>\n",
       ".animation {\n",
       "    display: inline-block;\n",
       "    text-align: center;\n",
       "}\n",
       "input[type=range].anim-slider {\n",
       "    width: 374px;\n",
       "    margin-left: auto;\n",
       "    margin-right: auto;\n",
       "}\n",
       ".anim-buttons {\n",
       "    margin: 8px 0px;\n",
       "}\n",
       ".anim-buttons button {\n",
       "    padding: 0;\n",
       "    width: 36px;\n",
       "}\n",
       ".anim-state label {\n",
       "    margin-right: 8px;\n",
       "}\n",
       ".anim-state input {\n",
       "    margin: 0;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "</style>\n",
       "\n",
       "<div class=\"animation\">\n",
       "  <img id=\"_anim_img5ae60c9f876e49b69030ff8e0f875d14\">\n",
       "  <div class=\"anim-controls\">\n",
       "    <input id=\"_anim_slider5ae60c9f876e49b69030ff8e0f875d14\" type=\"range\" class=\"anim-slider\"\n",
       "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
       "           oninput=\"anim5ae60c9f876e49b69030ff8e0f875d14.set_frame(parseInt(this.value));\"></input>\n",
       "    <div class=\"anim-buttons\">\n",
       "      <button title=\"Decrease speed\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.slower()\">\n",
       "          <i class=\"fa fa-minus\"></i></button>\n",
       "      <button title=\"First frame\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.first_frame()\">\n",
       "        <i class=\"fa fa-fast-backward\"></i></button>\n",
       "      <button title=\"Previous frame\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.previous_frame()\">\n",
       "          <i class=\"fa fa-step-backward\"></i></button>\n",
       "      <button title=\"Play backwards\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.reverse_animation()\">\n",
       "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
       "      <button title=\"Pause\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.pause_animation()\">\n",
       "          <i class=\"fa fa-pause\"></i></button>\n",
       "      <button title=\"Play\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.play_animation()\">\n",
       "          <i class=\"fa fa-play\"></i></button>\n",
       "      <button title=\"Next frame\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.next_frame()\">\n",
       "          <i class=\"fa fa-step-forward\"></i></button>\n",
       "      <button title=\"Last frame\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.last_frame()\">\n",
       "          <i class=\"fa fa-fast-forward\"></i></button>\n",
       "      <button title=\"Increase speed\" onclick=\"anim5ae60c9f876e49b69030ff8e0f875d14.faster()\">\n",
       "          <i class=\"fa fa-plus\"></i></button>\n",
       "    </div>\n",
       "    <form title=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select5ae60c9f876e49b69030ff8e0f875d14\"\n",
       "          class=\"anim-state\">\n",
       "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_5ae60c9f876e49b69030ff8e0f875d14\"\n",
       "             >\n",
       "      <label for=\"_anim_radio1_5ae60c9f876e49b69030ff8e0f875d14\">Once</label>\n",
       "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_5ae60c9f876e49b69030ff8e0f875d14\"\n",
       "             checked>\n",
       "      <label for=\"_anim_radio2_5ae60c9f876e49b69030ff8e0f875d14\">Loop</label>\n",
       "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_5ae60c9f876e49b69030ff8e0f875d14\"\n",
       "             >\n",
       "      <label for=\"_anim_radio3_5ae60c9f876e49b69030ff8e0f875d14\">Reflect</label>\n",
       "    </form>\n",
       "  </div>\n",
       "</div>\n",
       "\n",
       "\n",
       "<script language=\"javascript\">\n",
       "  /* Instantiate the Animation class. */\n",
       "  /* The IDs given should match those used in the template above. */\n",
       "  (function() {\n",
       "    var img_id = \"_anim_img5ae60c9f876e49b69030ff8e0f875d14\";\n",
       "    var slider_id = \"_anim_slider5ae60c9f876e49b69030ff8e0f875d14\";\n",
       "    var loop_select_id = \"_anim_loop_select5ae60c9f876e49b69030ff8e0f875d14\";\n",
       "    var frames = new Array(13);\n",
       "    \n",
       "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWgAAAFoCAYAAAB65WHVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
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