{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Advanced Customization\n",
    "\n",
    "The core principle of `TriPoDPy` is that we can change anything easily. Not only the initial conditions as shown in the previous chapter, but also the physics behind the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tripodpy import Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Grids\n",
    "\n",
    "By default, the radial grid will be created when calling `Simulation.initialize()`. However, there can be situations where one needs to know the grid sizes before completely initializing the Simulation object. For example, if we want to create custom fields and need to initialize them with the correct shape.\n",
    "\n",
    "In that case, we can call `Simulation.makegrids()` to only create the grids without initializing the simulation objects. In fact, `Simulation.makegrids()` is by default called within `Simulation.initialize()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : NoneType\n",
       "    Nr           : NoneType\n",
       "    OmegaK       : NoneType\n",
       "    r            : NoneType\n",
       "    ri           : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "  -----\n",
       "    OmegaK       : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The Keplerian frequency has not been initialized at this point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Radial Grid\n",
    "\n",
    "By default the radial grid is a regular logarithmic grid, i.e. the ratio of adjacent grid cells is constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum ratio= 1.0715193052376093 ; minimum ratio= 1.0715193052376046\n"
     ]
    }
   ],
   "source": [
    "radius_ratio = sim.grid.r[1:]/sim.grid.r[:-1]\n",
    "print('maximum ratio=', max(radius_ratio),'; minimum ratio=', min(radius_ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained in the previous chapter, the location of the grid boundaries and the number of grid cells can be controlled via `Simulation.ini.grid.rmin`, `Simulation.ini.grid.rmax`, and `Simulation.ini.grid.Nr`.  \n",
    "`Simulation.makegrids()` will use these parameters to create the radial grid.\n",
    "\n",
    "But it is also possible to completely customize the radial grid. To do so, we have to set the locations of the radial grid cell interfaces `Simulation.grid.ri` before calling either `Simulation.makegrids()` or `Simulation.initialize()`.\n",
    "\n",
    "In this example, we simply want to refine the grid at a given location. We use this helper function, which takes an existing grid `ri` and  doubles the number of grid cells in a region `num` grid cells on both sides around location `r0`. We also recursively call this function with reduced `num` to even further refine the grid and to have a smooth transition between the high and low resolution regions. Note that this and many othr useful functions for setting up your simulations ad adding additional physics can be found in the [Dustpylib](https://github.com/stammler/dustpylib) repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def refinegrid(ri, r0, num=3):\n",
    "    \"\"\"Function to refine the radial grid\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ri : array\n",
    "        Radial interfaces\n",
    "    r0 : float\n",
    "        Radial location around which grid should be refined\n",
    "    num : int, option, default : 3\n",
    "        Number of refinement iterations\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    ri : array\n",
    "        New refined radial grid\"\"\"\n",
    "    if num == 0:\n",
    "        return ri\n",
    "    for _ in range(num):\n",
    "        ind = np.argmin(r0 > ri) - 1\n",
    "        ind_left  = ind-num\n",
    "        ind_right = ind+num+1\n",
    "        ri_left  = ri[:ind_left]\n",
    "        ri_right = ri[ind_right:]\n",
    "        number_refined_cells = (2*num+1)*2\n",
    "        ri_mid = np.empty(number_refined_cells)\n",
    "        for i in range(0, number_refined_cells, 2):\n",
    "            j = ind-num+int(i/2)\n",
    "            ri_mid[i] = ri[j]\n",
    "            ri_mid[i+1] = 0.5*(ri[j]+ri[j+1])\n",
    "        ri = np.concatenate((ri_left, ri_mid, ri_right))\n",
    "    return ri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a regular logarithmic grid and feed it to our function. We want to refine the grid in a location around $4.5\\,\\mathrm{AU}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dustpy.constants as c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri_unrefined = np.logspace(0., 3., num=100, base=10.) * c.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ri = refinegrid(ri_unrefined, 4.5*c.au, num=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1,1, figsize=(8,1))\n",
    "ax.plot(ri/c.au, ri*0, 'rx', label='local refined')\n",
    "ax.plot(ri_unrefined/c.au, ri_unrefined*0, 'b+', label='even log-spaced')\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlabel('cell faces')\n",
    "ax.legend(ncol=2)\n",
    "ax.set_xlim(3,8)\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a new empty Simulation object, assign the grid cell interfaces and initialize the grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim = Simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.ri = ri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : NoneType\n",
       "    Nr           : NoneType\n",
       "    OmegaK       : NoneType\n",
       "    r            : NoneType\n",
       "    ri           : ndarray\n",
       "  -----"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** It is sufficient to assign a `numpy.ndarray` to `Simulation.grid.ri` and not a `simframe.Field`.\n",
    "\n",
    "We can now make the grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.makegrids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "  -----\n",
       "    OmegaK       : NoneType\n",
       "  -----"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, `Simulation.grid.ri` was automatically converted to a `simframe.Field` and the other fields were created. The number of radial grid cells is greater than $100$ as we added more grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.Nr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see that we actually refined the grid at the correct location, we can plot the location of the radial grid cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogy(sim.grid.r/c.au)\n",
    "ax.axhline(4.5, c=\"gray\", lw=1)\n",
    "ax.set_xlabel(\"# of radial grid cell\")\n",
    "ax.set_ylabel(\"Location of radial grid cell [AU]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The position of the radial grid cells have to be exactly in the center between their grid cell interfaces and are automatically calculated by `Simulation.makegrids()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True\n",
       "  True  True  True  True  True  True  True  True  True  True  True  True]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.r == 0.5 * (sim.grid.ri[1:] + sim.grid.ri[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Physics of a Field\n",
    "\n",
    "In this example, we want to have a fragmentation velocity that depends on the temperature in the disk. If the temperature is below 150 K, we want to have a fragmentation velocity of 10 m/s, otherwise it shall be 1 m/s. The idea behind this approach is that particles coated in water ice are stickier than pure silicate particles and can consequently withstand higher collision velocities (see e.g. [Pinilla et al. (2017)](https://doi.org/10.3847/1538-4357/aa7edb)). Keep in mind, however, that newer experiments suggest that particles covered in water ice do not have a beneficial collisional behavior (see e.g. [Musiolik & Wurm (2019)](https://doi.org/10.3847/1538-4357/ab0428)).\n",
    "\n",
    "First, we initialize our simulation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.initialize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now lets look at the fields we want to modify, `sim.dust.v.frag`. Each field has an updater that is called to update the values of the field in each timestep:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Heartbeat\n",
       "---------\n",
       "\n",
       "\u001b[91mSystole: \u001b[0m None\n",
       "\u001b[91mUpdater: \u001b[0m None\n",
       "\u001b[91mDiastole:\u001b[0m None\n",
       "\n",
       "Docstrings\n",
       "----------\n",
       "\n",
       "\u001b[91mSystole:\u001b[0m\n",
       "The type of the None singleton.\n",
       "\n",
       "\u001b[91mUpdater:\u001b[0m\n",
       "The type of the None singleton.\n",
       "\n",
       "\u001b[91mDiastole:\u001b[0m\n",
       "The type of the None singleton."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.frag.updater"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the updater is empty (None) for the details of updaters and fields work beyond what we explain in the documentation i refere the reader to [Simframe](https://simframe.readthedocs.io/en/latest/) the underlying simulation framework we use.\n",
    "\n",
    "The fragmentation velocity is of shape `(Nr,)`, i.e. there is one value at every location in the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.frag.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the moment it is constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(sim.grid.r/c.au, sim.dust.v.frag)\n",
    "ax.set_xlabel(\"Distance from star [AU]\")\n",
    "ax.set_ylabel(\"Fragmentation velocity [cm/s]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to write a function that takes the simulation object as input parameter and returns our desired fragmentation velocities. To this end, we can use the fact that the gas temperature has the same shape. Keep in mind that everything has to be in cgs units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def v_frag(sim):\n",
    "    return np.where(sim.gas.T<150., 1000., 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now assign this function to the updater of the dust fragmentation velocities. For details of this process, please have a look at the [Simframe documentation](https://simframe.rtfd.io)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.frag.updater = v_frag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The updater of a group/field stores a `simframe.Heartbeat` object. When calling the `update()` function, the heartbeat which consists of a `systole`, the actual `updater`, and a `diastole` will be executed. The `systole` is executed before the actual update functions, the `diastole` afterwards.\n",
    "\n",
    "When assigning a function (or `None`) to the updater of a group/field, a new `Heartbeat` object will be created with empty systoles and diastoles only executing the update function. If the existing updater already has systoles/diastoles, those would be overwritten with an empty function.\n",
    "\n",
    "To prevent this, we can directly assign the function only to the updater leaving the systoles/diastoles as they are."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The systoles/diastoles can be set with the following command. (Only for demonstration here, since we assign `None`. Read more about this in the section about systoles and diastoles.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.updater.systole = None\n",
    "sim.dust.v.updater.diastole = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of now, the simulation object still holds the old data for the fragmentation velocity and needs to be instructed to update itself. We can either update the whole simulation frame with `Simulation.update()`, or we merely update the fragmentation velocities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.dust.v.frag.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fragmentation velocities should now show our desired behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(sim.grid.r/c.au, sim.dust.v.frag)\n",
    "ax.set_xlabel(\"Distance from star [AU]\")\n",
    "ax.set_ylabel(\"Fragmentation velocity [cm/s]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Upon customizing a quantity on which other quantities depend, one also has to update these quantities. In our case, this would be the sticking/fragmentation probabilites. Therefore, it is always better to update the entire simulation frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding Custom Fields\n",
    "\n",
    "We can not only modify existing fields, we can also create our own fields.\n",
    "\n",
    "In this example, we want to add another field `rsnow` to `Simulation.grid` that gives us the location of the so called snowline, i.e. the location in the disk where water ice starts to sublime.\n",
    "\n",
    "First, we add the field and initialize it with zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.addfield(\"rsnow\", 0., description=\"Snowline location [cm]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grid group now has a new member."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Group (Grid quantities)\n",
       "-----------------------\n",
       "    A            : Field (Radial grid annulus area [cm²]), \u001b[95mconstant\u001b[0m\n",
       "    Nr           : Field (# of radial grid cells), \u001b[95mconstant\u001b[0m\n",
       "    OmegaK       : Field (Keplerian frequency [1/s])\n",
       "    r            : Field (Radial grid cell centers [cm]), \u001b[95mconstant\u001b[0m\n",
       "    ri           : Field (Radial grid cell interfaces [cm]), \u001b[95mconstant\u001b[0m\n",
       "    rsnow        : Field (Snowline location [cm])\n",
       "  -----"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a next step, we have to write a function that returns us the location of the snowline. Here, we simply use the first grid cell where the temperature is smaller than $150\\,\\mathrm{K}$ and return the value of the inner interface of that grid cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rsnow(sim):\n",
    "    isnow = np.argmax(sim.gas.T<150.)\n",
    "    return sim.grid.ri[isnow]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assign this function to the updater of our snowline field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.rsnow.updater.updater = rsnow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And update the field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The snowline is located at 2.15 AU.\n"
     ]
    }
   ],
   "source": [
    "print(\"The snowline is located at {:4.2f} AU.\".format(sim.grid.rsnow/c.au))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right now, the temperature is constant throughout the simulation, because the stellar parameters do not change. To see an effect in our snowline location, we need to have a changing temperature profile.\n",
    "\n",
    "To achieve this, we let the stellar radius decrease from a value of $3\\,M_\\odot$ to $2\\,M_\\odot$ within the first $10,000\\,\\mathrm{yrs}$. As a consequence, the disk temperature decreases. This is only for demonstration purposes and is not necessarily physical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Rstar(sim):\n",
    "    dR = -1.*c.R_sun\n",
    "    dt = 1.e4 * c.year\n",
    "    m = dR/dt\n",
    "    R = m*sim.t + 3.*c.R_sun\n",
    "    R = np.maximum(R, c.R_sun)\n",
    "    return R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And assign this to the updater of the stellar radius."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.star.R.updater.updater = Rstar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modifying the Update Order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we are still not done, yet. We have given `TwoPopPy` instructions how to update the snowline location, but we have not yet told it to actually update it regularily.\n",
    "\n",
    "`TwoPopPy` calls `Simulation.update()`, the updater of the simulation object, once per timestep after the integration step and just before writing the data files. The updater of a group/field is basically a list of groups/fields, whose updater is called in that order.\n",
    "\n",
    "For the main simulation object this is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['star', 'grid', 'components', 'gas', 'dust']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that if we call `Simulation.update()`, we basically call `Simulation.star.update()`, `Simulation.grid.update()`, `Simulation.gas.update()`, and `Simulation.dust.update()` in that order.\n",
    "\n",
    "The updaters of the sub-groups and fields look as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OmegaK']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sigma',\n",
       " 'mu',\n",
       " 'T',\n",
       " 'alpha',\n",
       " 'cs',\n",
       " 'Hp',\n",
       " 'nu',\n",
       " 'rho',\n",
       " 'n',\n",
       " 'mfp',\n",
       " 'P',\n",
       " 'eta',\n",
       " 'torque',\n",
       " 'S']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ext', 'tot']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.S.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['visc', 'rad']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.gas.v.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['delta',\n",
       " 'rhos',\n",
       " 'fill',\n",
       " 'backreaction',\n",
       " 'f',\n",
       " 'qrec',\n",
       " 'a',\n",
       " 'm',\n",
       " 'St',\n",
       " 'H',\n",
       " 'rho',\n",
       " 'D',\n",
       " 'eps',\n",
       " 'v',\n",
       " 'p',\n",
       " 'q',\n",
       " 'SigmaFloor',\n",
       " 'S']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'B']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.backreaction.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rad', 'turb', 'vert']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.delta.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adv', 'diff', 'tot']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.Fi.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['frag', 'stick', 'fragtrans', 'driftfrag']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.p.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['compo', 'ext', 'tot', 'smax_hyd']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.S.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['frag', 'driftmax', 'rel']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.updateorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['azi', 'brown', 'rad', 'turb', 'vert', 'tot']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.rel.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The gas updater does not contain the updaters of `Simulation.gas.v` and `Simulation.gas.Fi` and the updater of the gas sources does not contain the updater of `Simulation.gas.S.hyd`. These are quantities that are calculated in the finalization step of the integrator, since they are derived from the result of the implicit gas integration.\n",
    "\n",
    "The same is true for `Simulation.dust.v.rad`, `Simulation.dust.Fi`, and `Simulation.dust.S.hyd` in the dust updater, which are also calculated from the implicit integration in the finalization step of the integrator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the grid updater is only updating the Keplerian frequency, but not our snowline location. So we can simply add it to the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\", \"rsnow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we assign lists to updaters, their systoles and diastoles will always be overwritten with `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OmegaK', 'rsnow']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.grid.updateorder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Systoles and Diastoles\n",
    "\n",
    "However, the previous solution has a conceptional problem. As we previously saw from the update order the grid is updated before the gas. The snowline location, however, needs the gas temperature and, therefore, has to be updated after the gas. On the other hand, we also cannot update the grid as a whole after the gas, because the gas updaters need the Keplerian frequency. We need another solution.\n",
    "\n",
    "But first, we revert the grid updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.grid.updater = [\"OmegaK\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every updater has a systole and a diastole. These are functions that are called before and after the actual updater respectively. Since no other quantity depends on our snowline location, we can simply update it at the end and put it in the diastole of the main updater. Alternatively, we could assign it to the diastole of the gas temperature updater, since it only requires the updated gas temperature.\n",
    "\n",
    "We therefore write a diastole function that is updating the snowline location separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def diastole(sim):\n",
    "    sim.grid.rsnow.update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then assign this function to the diastole of the gas temperature updater."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.gas.T.updater.diastole = diastole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now every time `Simulation.gas.T.update()` is called, `Simulation.grid.rsnow.update()` will be called at the end of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing the Snapshots\n",
    "\n",
    "As has been already explained in a previous chapter, the snapshots can be customized by simply setting `Simulation.t.snapshots`. In this example, we only want to run the simulation for $10,000\\,\\mathrm{yrs}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.t.snapshots = np.logspace(3., 4., num=21, base=10.) * c.year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now change the data directory to avoid an overwrite error and start the simulation with our modifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.writer.datadir = \"3_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "tripodpy v1.0.0\n",
      "\n",
      "Creating data directory 3_data.\n",
      "Writing file \u001b[94m3_data/data0000.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0001.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0002.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0003.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0004.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0005.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0006.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0007.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0008.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0009.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0010.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0011.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0012.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0013.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0014.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0015.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0016.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0017.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0018.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0019.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Writing file \u001b[94m3_data/data0020.hdf5\u001b[0m\n",
      "Writing dump file \u001b[94m3_data/frame.dmp\u001b[0m\n",
      "Execution time: \u001b[94m0:00:02\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "sim.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now have a look at the result of our modifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tripodpy import plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9.89573147e+01 9.99700269e+01 9.99816380e+01 9.99795476e+01\n",
       " 9.99793393e+01 9.99784526e+01 9.99755832e+01 9.99729235e+01\n",
       " 1.00062050e+02 1.02801513e+02 1.66129999e+02 9.94209033e+02\n",
       " 9.99064410e+02 9.98508517e+02 9.97316291e+02 9.95912211e+02\n",
       " 9.94586883e+02 9.94388105e+02 9.93159217e+02 9.91990969e+02\n",
       " 9.91810650e+02 9.92189259e+02 9.91472052e+02 9.90759690e+02\n",
       " 9.91007207e+02 9.90579368e+02 9.90026175e+02 9.89886066e+02\n",
       " 9.89765992e+02 9.89620537e+02 9.89471329e+02 9.89316184e+02\n",
       " 9.89153082e+02 9.88979790e+02 9.88768627e+02 9.88588472e+02\n",
       " 9.88396302e+02 9.88148220e+02 9.87500185e+02 9.87598115e+02\n",
       " 9.87589396e+02 9.87167335e+02 9.85846687e+02 9.86131466e+02\n",
       " 9.86006149e+02 9.83087789e+02 9.83956664e+02 9.83020641e+02\n",
       " 9.80871611e+02 9.78475302e+02 9.75475351e+02 9.71412769e+02\n",
       " 9.65484095e+02 9.56281980e+02 9.41445651e+02 9.17181625e+02\n",
       " 8.77656420e+02 8.14612987e+02 7.18125446e+02 5.82398472e+02\n",
       " 4.26173835e+02 2.89905167e+02 1.89920778e+02 1.21401378e+02\n",
       " 7.00025016e+01 3.06967233e+01 8.22527489e+00 6.17477792e+00\n",
       " 4.80836892e+00 3.87983125e+00 3.43761907e+00 2.90657122e+00\n",
       " 2.55028645e+00 2.30944635e+00 2.14906082e+00 2.04775325e+00\n",
       " 1.99226007e+00 1.97442200e+00 1.98948036e+00 2.03510348e+00\n",
       " 2.11084532e+00 2.21787855e+00 2.35891932e+00 2.53830480e+00\n",
       " 2.76221229e+00 3.03902974e+00 3.37990768e+00 3.79954576e+00\n",
       " 4.31730106e+00 4.95876021e+00 5.75801536e+00 6.76106858e+00\n",
       " 8.03113020e+00 9.65655090e+00 1.12968943e+01 1.39588282e+01\n",
       " 1.75675389e+01 2.25791417e+01 2.90941472e+01 3.92961547e+01\n",
       " 5.52673998e+01 8.35954126e+01 1.33788306e+02 2.10997169e+02\n",
       " 3.35328346e+02 5.48781762e+02 9.32635389e+02 1.64510219e+03\n",
       " 2.97594296e+03 5.28541001e+03 8.00115238e+03 8.00982059e+03\n",
       " 9.02852515e+03 8.59801065e+03 4.90500343e+03 2.27115662e+03\n",
       " 9.62479116e+02 3.81478456e+02 1.31810488e+02 1.32071602e+01]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.dust.v.rel.tot[:,-2,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qjQX7zQV7ORJtLwAAgDv7R0UDJtw9uy85OVmSkpLKfF3rSxcdkK4IXV3f5et9b0X/GwAAuIsGNXz66afSunVrue6664r1kbR/9McffzjWaUBzTUuS+EPfCwAAAAgUlCOpYj30kSNHOtJq22shqmnTppkLEa11zoUIAADACTrQ62zRgAvVokULx7ryAjCUvl7W/pYtW2a2Of/88x3r4L/oewMAgEDma31V+l4AAACA6wR1OZLFixfLpEmTikV8q+7duzvWPfbYYzJo0CDH87Fjx8rSpUtl1apV0qpVK+ndu7fs2LFD1qxZI/Xr15eZM2e6rb2kQwYAAMFkxowZZrn88svlqaee8nZzEGR9b0X/GwDgSQSRBhbNBleVjHCuLkfij30vAAAAIFAEdRDGgQMHzEVESUXX6TYlU/fpLEu9ETBnzhyZP3++1K1bV4YPH24ubBo3buy29o4aNcp8TtFyJAAAAIHq4MGDJg3z3r17vd0UBGHfW9H/BgC4m95sP3TokKSlpTlukiNwaHaJuLg4qVevnoSGhlb6fbrt1q1bHc+rU4rEH/teAAAAQKCw2Hwt9x08Vp8dAADAm32OlJQUM8NPy5EUHWS209l+EydOlJtvvlnefvvtCve3fPlyOe+880w5Ep3BB7gS/W8AgDsCMHbu3MnYTpD0I5o0aVIsEGPdunWycOFCad26tVx33XXFtlWM+QEAAAD+K6gzYfib1NRUsyhiZwAAgL9LSkoqt0+jQRhVSbvct29f+khwKfrfAAB30gwYeqNdb8yfdNJJEhMTIyEhIRz0AGG1WiUjI0P2799vzrOe7wYNGpTazlnJEu0ja8By0UwY1cmGAQAAAMA7CMLwI9SkBgAAAOh/AwACg5YgURqAER8f7+3mwMU0oMZ+Xvfs2WPOt7MgjLKypGjGOLvx48dXKTgZAAAAgHcRXu9HtCZ1cnKyWbSmZFVqSQIA4IsWLBAZMULk+++93RIAKI3+NwDAXTTTQW5urvlZM2AgcNnPr57vyma21TE/+xigLtonAQAAAOA/yIThR4qmHnSWqhAAAH+h1bXuvlvkP/8pfL5okciOHTrY6O2WAcAJ9L8BAO5S9GY8JUgCW9Hzq+fdPqZXXkCGbqOl+wAAAAD4J4Iw/Ag1qQEAgWDZMpGbbxb5668T63bvFjlyRCQx0ZstA4Di6H8DAABv0ACNlJQUp4GhAAAAAHwf5Uj8yPTp0009SF00haHWhwQAwF/k5Ig8+KDI+ecXD8CwO3bMG60CgLLR/wYAAN6gY372MUBdtE8CAAAAwH+QCcOPaP3H4cOHm59PO+00bzcHAIBK27tX5KKLRDZuPLEuLExneOkAY+Hzo0c5oAB8C/1vAADgDaGhobJ161bHc7JgAAAAAP6FIAw/Qk1qAIC/eu654gEYbdqIvP++yGWXnciKQRAGAF9D/xsAAHiDxWKRpKQkDj4AAADgpyhHAgAA3G737hM/Dxkism6dSJcuIvHxJ9YThAEAAADALjMzU1588UUZOHCgNGzYUCIjIyUuLk7atWtnMsV++umnAVGqVwMuAAAAAAQWMmEAAAC3S0s78XO/fiLR0YU/E4QBAAAAoKSVK1fKsGHDZO/evRIVFSXdunWTRo0aSU5Ojmzbtk1mz55tFg3I2Lx5MwcQAAAAgE8hCMOPpKammkXZbDZvNwcAgGoFYcTFnfiZIAwAvoz+NwAAnrd+/Xo5//zzTcDFgw8+KGPHjpXatWsX2+avv/6SadOmyauvvhqQp0jH/VJSUpyWSAMAAADg+yhH4kemT58uzZo1M0tubm5ApFwEAAR3EEbRsVTKkQDwNfS/AQDwLKvVKjfccIMJwJg0aZI8/fTTpQIw1KmnnirPP/+8fPfdd357isqbYKVjfvYxQF20TwIAAADAfxCE4UdGjRolycnJZomIiJDQ0FBvNwkAAJdlwjh2jIMJwLfQ/wYAwLM+++wz+e2336RJkyby8MMPV7h9165diz3/9ttvZeTIkdKhQwepU6eO1KpVS9q0aSMPPfSQI7tsSatWrZLLLrtMmjZtKpGRkXLyySfLWWedZd6Tnp4u3qBjfvYxQF20TwIAAADAf1COxI8UTT1osVi83RwAACqNciQA/BH9bwAAPOvzzz83j8OGDavW5CMtX7Jx40YThKElTbKzs015k6lTp8qiRYtk9erVEhsb69h+4cKFJgBDs1Jo4MU555xjgjW2bNli3jNixIhi23uKjvslJSV5/HMBAAAAuAZBGAAAwO0IwgAAAACqRgMDjmXn+9Vhqx0VVqOJQxpAoTp37lyt948fP94EUsQXSbmnpU3++c9/yuuvvy7Tpk2TcePGOV579tlnTQmUefPmyRVXXFFsX2vXrpV69epV+3cBAAAAELwIwgAAAG5VUCCSmVlxOZKjRzkRAAAAgJ0GYHSc+JVfHZCN4wdKfK3war//0KFD5jExMdHp67fddpsU6AVGEbfffrv06tXL/HzRRReVeo+WGJk+fbrMnDlTFixYUCwI48CBA+axf//+pd7XrVs38QSy3QIAAACBhyAMAADgViXLKBOEAQAAAKA6Zs+eXSoIo2/fvo4gDLV7925TZuT333+XY8eOmUwXKiIiwpQZKapr167y22+/yY033iiPPfaYeR4SEsLJAQAAAFAjBGH4Ea1JqYs9JSUAAP5WikQRhAHAX9D/BgDAs+zlPw4ePOj09fz8E+VZRowYIa+99lqx17XcyEMPPSR5eXmV+rwnn3xSfv75ZxO0oUudOnVMQMell14qN9xwg0RFRYk36LhfSkqK43lCQoJZAAAAAPgHgjD8iKZOnDhxouN5aGioV9sDAEBVgzAiIgoXu9q1T/xMORIAvob+NwDAm2pHhZnyHv7W5pro2LGjrFy5Un766Se5/vrrq/Te1atXy+jRoyU+Pl5eeOEFkyHj5JNPNuVIVKNGjWTv3r3F3nPqqafKjz/+KN98840sWrRIVqxY4QjIePrpp+X77793BIa4WnkTrDTbR7NmzRzPx48fLxMmTHBLOwAAAAC4HkEYfmTUqFEyfPhw8/Npp53m7eYAAFDlIIyiWTBUfPyJn48d44AC8C30vwEA3mSxWCS+VnhQnYSLLrpIXnnlFZk7d65MnTq1ShOQPvnkE/P4xBNPyM0331zstaysLNm3b5/T94WFhcnAgQPNonbs2CG33nqrCczQNmgwhqfp771161bHc7JgAAAAAP6FIod+RC+4kpKSzKIX4roAABAoQRi6XYnyzgDgVfS/AQDwrIsvvljatm0rO3fulKeeeqpK7z1y5Ih5bNy4canXNKijsqV9mzZtKmPGjDE///LLL+INOuZnHwPUhSAMAAAAwL8QhAEAAHwiCKPktgAAAACCS0hIiLz77rumhMhjjz0m//rXv+Sok7qFhw4dkj/++KPYutatW5vHt956S/Ly8hzrf/31V0dQRUnPP/+80wwZn332maNcCQAAAABUFeVIAACA14Iwatcu/lzHVxMSOCEAAABAsOratassXbpUhg0bJs8884y8+OKLcvbZZ0ujRo0kOztbdu3aJRs3bjSBFm3atJEzzzzTvO+WW26R5557ThYuXGjK+Hbr1k0OHz4sK1askMsuu0x++OEHU2qkqIkTJ8oDDzwgHTt2lFatWplsGbrvP//8U+rWrWteczcy3QIAAACBh0wYAADAa0EYYWEiMTEnnjuZ5AYAAAAgyPTq1Uu2bdsmL7zwgvlZs1589NFHJjgjLS1NrrrqKvnkk0/k559/ltNPP928p169erJ27Vq57rrrJDc3Vz799FPZvXu3TJo0Sf7zn/84/ZyXXnpJrrnmGsnMzJTPP/9cvvjiCwkLC5P7779fNm3aZAIzAAAAAKCqyIQBAAC8FoRhL0mSkVH4M0EYAAAAAFR0dLT885//NEtlNW7cWN5//32nr6WkpJRad+ONN5oFAAAAAFyJTBgAAMCrQRhFS5IQhAEAAAAgGGjpEwAAAACBiSAMAADg9UwYdgRhAAAAAAAAAAAAf0Y5Ej+SmppqFkW0PAAgEIMwjh3zTJsAoDLofwMAAG/Qcb+i5VMSEhLMAgAAAMA/kAnDj0yfPl2aNWtmltzcXCkoKPB2kwAAqBCZMAD4K/rfAADAG3TMzz4GqIv2SQAAAAD4DzJh+JFRo0bJ8OHDzc+nnXaat5sDAEClEIQBwF/R/wYAAO5msVhKrQsNDZWtW7c6npMFAwAAAPAvBGH4kaKpB51doAEA4IsIwgDgr+h/AwAAb9Bxv6SkJA4+AAAA4KcoRwIAANyKIAwAAAAAAAAAABAsCMIAAABeDcKoXfvEz0ePcjIAAAAAAAAAAID/IggDAAC4FZkwAAAAAKA4m83GIQEAAAACFEEYHrBhwwbp3bu31KpVS5o1ayYzZszwxMcCAOB3QRjHjnmmTQAC0+zZs+XMM8+UhIQEiYmJkS5dusgHH3zg7WYBAAAELMY9AQAAgNLCnKyDCx04cEAGDBggZ511lixatEjWr18vo0aNkvj4eLnxxhs51gCAgJabW7hUNgiDciQAauLIkSNy2WWXSadOnSQqKkrmz58v1157rflZ1wMAAMB1GPcEAAAAnCMIw81effVVsVgsMnfuXImOjpbzzz9fkpOTZdKkSQRhAACCKguGIggDgDtpsHNR/fv3N7Mz33//fYIwAAAAXIxxTwAAAMA5ypG42ZdffikXX3yxCcCwGzZsmGzZskW2b9/u7o8HAMCngjBiYysuR0JpZACuVK9ePcnLy+OgAgAAn6STt/wV454AAACAc0EdhLFu3TqZMmWKDB06VBo3bmwueipz4ZOVlSXjxo2T1q1bm9TGjRo1kltvvVV2795dats///xT2rRpU2yd/fkff/zhwt8GAADfDsLQeMTQ0PKDMKxWkfR0z7QN3nXo0CFp0KCB6Xu1bNmySu9NTU2VOXPmmDITzZo1k4iICImLi5Ozzz5bXnjhBW64B3Hf2y4/P1+OHTsmH374oSxZskTuvPNOF/82AAAAvo1xTwAAAMB7grociZYEWbBgQZXek52dLf369ZPVq1dLw4YNZciQIZKSkiKzZs2SRYsWmfXNmzcvVpc6ISGh2D7q1KnjeA0AgECmmS3satd2vk3J9UePOi9bgsAyevRoOXjwYLXe++yzz8oTTzxhbuB36tTJBF9oPeqVK1fKDz/8IPPmzTOz8opmIkNw9L3Vvn37zLYqNDRUXnnlFbnoootc+rsAAAD3KRmkGRYWJvHx8ebve9euXWXw4MGmT6DrUTbGPQEAAADvCepMGD169JDHHntMPv30U9m7d69ERkZW+J7JkyebwV59r2a50Nl1a9askeeee84M/uusPAAAUDoTRlmBFfrnt+ifYA3CQGD7+uuvZfbs2XLHHXdU6/0xMTHyr3/9y9yMX79+vXzwwQdmnz///LM0adJEvvvuO9NnQ3D2vRMTE2Xt2rXyzTffmGCfkSNHykcffeSS3yGvwCrzf9otqZm5LtkfAAAo280332wWzX7Ws2dPk+nqnXfekSuvvFLatm1rgm89TfufGiTSt2/fGu/L5uY6jIx7AgAAAN5jsbm7x+9HNL1xTk5OmRdBubm5Jm320aNHzYB/586di73esWNH2bRpk/z4448mMl/p9jr4O2bMmFKz8z777LNqz8rTttpnBwIA4KvmzhW56qrCn7t00ZS4zrc76SSRv/8u/HnVKh0w9Fwb4dk+h5aWOOOMM8wN+Pnz55sSEy1atJCtW7e6ZP//+c9/5LrrrpOkpCRJTk52yT7hP31vZzTYZ8WKFSaIo8btzbdK0wc+kdAQi3RLqiP9255klqTEmBrtGwAQfKxWq6NM7WmnnSYhIUE9T8ppJgxnfYRt27bJI488Iv/9739N1jPNhKaZ0TwZhKHl8Pr06SPLly+v0bn+/vvvTfa2008/3QSWuHvMz5/GPQEAAAB/xxVeFeiFnV6I6I2Ckhciyn7BtHDhQsc6vbHw+++/F9vO/lwvvAAACPZMGCo+/sTPZMIIbBMnTpTt27fLq6++KuHh4S7fvw4Oqz179rh83/D9vrczemNGv3OuVGC1yerth2Xy4t+k77PLZcC0FTL1i99l/c4jYrUS4w4AgLtov0AzY912222SmZlJRloXYtwTAAAAcB2CMKpg48aN5rGLTuV1wr5eo8LtLrjgAhP5rbM+7bROeatWrUrVr3amffv2TheNTgcAwNcRhIGitI+kZSRuueUW6d27t1sOjv1m+8knn8zBD8K+tzOrVq0ymVEqq7z+d6hFJDG2dBmVLX+ny7+Xb5Ohr6ySs578WsbM2yRLf90v2XkFlf5cAABQedqn1BJ1P/30kylFZ6fZKTSTxvDhw52+T9fr6yWzWOzYsUPuuusuM5lKM2zUrVvX/P2/8847HVksJkyYYLJgKM2ypfuxL2V9XnVpv6OsPkkgjXsCAAAAgSrM2w3wJzt37jSPjRs3dvq6fb1euNmNGDFCXnzxRbnqqqtk1KhR5uLwtddek5kzZ3qo1QAAeA9BGIHBPgjszObNmyudhvn222+XhIQEefrpp8VdXnjhBfM4ZMgQt30GfLfvfd5558kVV1whbdq0MSm8FyxYIHPmzJHXX3/dJW0KCw2RHx45XzbuSpWlv+2XJb/ulz/3pxfb5mB6jnz4419miQoPkd6t6suAtidJv7YNnAZwAACAqouPjzelLvSG/7Jly6RXr17VPox//fWXCTA4fPiwCR64+OKLpaCgwPQx3njjDenRo4fJZqvZtbSf8dFHH8lJJ50kF154oWMfNfl8e/kVb2PcEwAAAHAdgjCqID29cIBVI+Kd0Qh8lVbkjlP9+vVlyZIlMnLkSBk0aJC5SJs2bZrceOONlfrMsm5s2OtDAgDgywjCgN1LL70ka9eulVmzZkm9evXccmC0xMnSpUtNoMdDDz3EwQ/CvreWo9Hvmt5M0dfbtWtnypVccskllf7civrfISEW6dykjlkevKCN7DyUKUt+228yX/yQctiUKrHLzrOaQA1d9P5KlyZ1pH/bk2RAu5OkZYPYSrcJABCkbDaR7KPiV6LiNarAIx+lQREahPHbb7/VaD9vvvmmCcDQsTvtR5QMTMjLyzM/X3bZZeYzNQhDAz7ffvttcZeIiIhKBzv787gnAAAAEKgIwvAAvUArmhqxulJTU82ibHohDgCAjyMIIzDUdBBYB6/Hjh0rffr0cXmqZrtvv/1W7r33XjOTUDOONWrUyC2fA982ffp0s7hKZfrfTepFy229mpklNTNXlv9xwARdrPjzgKTn5Du207ev23HELFO/+F2aJ8aYYIz+7U4ywRmhIb4xCxYA4EM0AGNqU/ErY3aI1ErwyEclJiaaxyNHjtRoPwcOHDCP/fv3L/VakyZNxBu035GSkuJ4rkHGugTyuCcAAAAQSAjCqILY2MLZapmZmU5fz8jIMI9xcXHiDjqgPHHiRMfz0NBQt3wOAACeDsKoXfvEz0f9bLIfKnbPPfeYkiaaqcIdfvnlF1N+RD9Dy8BdfvnlnJYA4O2+d3X63wnREXJZ51PMkpNfIKu3HzYZMrR0yd6j2cW23X4wQ17733az1IuJkH5tGpigDC1fUiuCfj4AABWxB0jWtJxH165dzeMjjzxi/tZrMIYnMtCWN8FKy6E0a9bM8Xz8+PEyYcKEgO97AQAAAIGCIIwqsEe/79q1y+nr9vVNm7pnlsKoUaMcs0e1FiUAAL6OTBhQixYtMjP3RowYUeyAZGcX3pTevXu39O3b1/z8wQcfyMknn1zpA5ecnCwDBw40MyB1YPof//gHBz1AeLvvXdP+d2RYqPRpXd8sjw9pL7/sPmbKlmiWjN/2Hiu27aGMXJm7bpdZosJDpFfL+jKw3UlyftsGUi820qW/EwAAgeLgwYPmsW7dujXaj/6t/+qrr+S///2vDB482ARgdOvWTS688EK59dZbq9Q3dRUNBtm6davjuSeyYPhC3wsAAAAIFARhVIHWmFbr1693+rp9fYcOHVxxbgAACMogjGPF700iQGhJhxUrVjh9TYMx7K/ZAzMqY+/evTJgwADzqKVIdIYgAkcg9b11hu4ZjePNcv+A1rLrSKbJkKFBGWu2H5Z864mZsNl5VpM5Qxed2Htm0zomQ8aAdidLs8TCWuwAgCARFV9Y3sPf2uwhP/30k3ls165dpd9jtVqdBjx8+OGH8tBDD8mCBQvkm2++kTVr1phyd1OmTJEvvvhCzjnnHAl0gdT3AgAAALyNIIwq6Nmzp8THx8u2bdtkw4YNpuZhUfPmzTOPGjXvDpQjAQD4GzJhoLxUy1rnWtMst2jRothMv8rQzBcXXHCB6Zfdcsst8vzzz3OwA4y3+97u7H83rhMtw3s2M8vRrDxZ/sff8tWv+2XFHwckPSffsZ3+01mbcsQsT372u7RsEGsyZAxsf7J0OCVeQkJqln4dAODjNBqvlvszIPijo0ePypdffml+Pu+88xzrIyIizGN6errT9/31119l7rNz585m0exqx44dM4/ax9TMWD/88IN4kjfKkfhC3wsAAAAIFCHeboA/0Qu5kSNHOmqb22shqmnTpsmmTZukT58+jlqSrqYXfZpyWxdti6sGgfH/7N0HdFRFFwfwf3ovkEIJhITee1c6SJeOigqCIghKsX4oCNgrolhBiiCKiALSe++9J9TQEiAB0nv5zp1lNwkkISSbbPv/zpmT996+bCZvU2bn3bkXRERURBiEQYXx/fffo3r16pgwYUK241Knunv37jhx4gQGDhyIWbNmFboWOBkfQ4+9i2v87eFkh171/fDDoIY4NKkjfhvWFM8190dp9wdr0Z+/FYsft15A7x92ofmnm/Du0hMqgCMpNU3v/SIiIjJmb7zxhhobSNmQFi1a6I6XKVNGfTx79uwDn3Pnzp1cszzcz93dHZ9++qkaY548efKBII/U1MygycLKaRwrYw7tGESajEksYexFRERERGQuLDoTxqpVq/Dhhx/q9pOTk9XH5s2b645NmjRJTfJrTZw4ERs3bsTu3btRpUoVtGrVCpcvX1ZpCn18fDBnzpxi/i6IiIiMF4MwqLB1voODg1W5kazee+897NmzR01O29ra4sUXX8zx8+fNm8cXwIhw7P1wDrY2aFPVR7UPe2XgxPUobJCyJadvIuhGlvpOAG7FJOGPfVdUc3WwRZtqPipLRrvqvnB3tCuy15GIiMiQLl68qAJ0Fy9eDBcXF8yePTvb45I9wt/fXwXrSmmRXr16qeMSUPDyyy+rDBf3W7BggcqAUbt27WzH16xZozK6lS9fXnfM29sbdnZ2KluEZKsw5gVSHHsRERERERmORQdhhIeHq+CJ+2U9Judk5ejoiC1btqho+D/++APLli1DyZIl8cILL6iAjnLlyhVZf1mOhIiITA2DMKgoSCkSIRPfMh7LDYMwjIupjb0NPf6WVbF1y3mq9sYT1XDldjzWn76hAjIOhNxBepYqP1LCZNXxMNXsbKzQvKKXCsjoWLMUyng4FVufiYiI9En+34v09HQVPCHZLYKCglRghCyMkrFBnTp1Hvg8Kd0hQbr9+vVD69at4erqqsqJSHYLCcqQ4Iys/vnnHwwePFiVyJPnc3JyUtknZIxibW2Njz76KFu2iC5dumDFihWoV68eGjZsqI5JKQ8pkWdM5UhMcexFRERERGQurDJyK9JNRicyMlI1Ua1aNfUxKSnJwL0iIiLKmYwwbG1l0lSzf+4cULlyzucGBQE1ami25XMkORWrSxgHmYgViYmJhu4KUbEz1vH3nbhkbA66hfWnbmD7uXAkptz7Q5uDeuU88ESt0iooo7KvK0v3EBEZCQkskIxf2v8xcrOfci7PIZnPJICibNmyqhSGBFI8+eSTeQZHSjDu119/rQI3SpQogZ49e+Kzzz5TZUx+++03FWjQtm1bde727dtVZo1du3bh6tWrKmuGfK2mTZuq8xs3bpztuW/duoU333wTGzZsUEEMEjAxZMiQXAOAc3utJcvu+vXrUbduXfTt2zfb+Fuma7WfIzw9PVUjIiIiIiLTwCAME2Ksk8BEREQ5kRLCrq6Z+zduAKVK5XytpNpE2bLZP9fZmdfVGDAIgyyZKYy/E5LTsPN8BDacvoGNZ26pAI3cBHq7qGCMJ2qVQoPyJWBt/WANeiIiKh4MwrAcub3WEvQhgRwMwiAiIiIiMj8WXY7E1LAcCRERmWopEuHmlvu5Hh7Z96VUM4MwiMjQTGH87WRvg041S6mWlp6BQ5fvqgwZ60/fxJU78dnOvRQRh1+2X1TN29UBnWr6qiwZLSt5wcHW+L43IiIiS6WPciRERERERGQ4zIRhQkxhJR4REZGWlB+pWlWzLYu9UlNzLzEipUvs7TXnaMuT3PtXRwbGTBhkyUx5/C1pzM/ejNUFZJy4HpXruS72Nmhb3VdlyWhX3RfujnbF2lciIkvETBiWg5kwiIiIiIgsDzNhmJCs9R/vr49JRERkzJkwJAtGXv+65DHJhnH7tmY/Kvd7hURExcaUx9/S32ql3VR7rUMVXI9MwMbTN7H+9A3svXhHZc3QiktOw6rjYarZ2VihRSVvdK5VCp1qlIKvu6NBvw8iIiJzl9MYQ44FBAQYpD9ERERERFR4DMIgIiKiYgnCeBgGYRARFR0/TycMaRmgWmR8MrYE38K6kzex7Ww4ElLSdOelpGVg+9lw1d5behIN/D3RuVZp1QK9XfgSERERERERERERPQSDMEw0HbKkFyYiIjK3IAwtZsIgImNgruNvT2d79GlQTrXElDTsOBehypZsPHMTd+NTsp175Eqkap+tCULVUq54oqYmIKO2n7vJZQchIiIyFTLuCAkJyTE7FxERERERGT8GYZiQ6dOnY+rUqbp9Gxsbg/aHiIhIn0EY7u6Z2wzCICJjYAnjb0c7G3SqWUq11LR0HLx8F+tO3cD6UzdVCZOszt6Mxdmb5/H9lvMqs4Z8jgRkNAkoAVsba4N9D0REROYmLS0NgYGBuv3JkydjypQpBu0TERERERHlH4MwTMi4cePwwgsvqO1q1aoZujtERER5YiYMIjJ1ljb+lkCK5hW9VHu/R02cCo1WGTLWn76JoBtZIusAFaAxb3eIaiWc7dCxhiYg4/Eq3iqwg4iI8pY1m1B6ejqsrRnMZq7k9c3pdc8ry5YEfp4/f163zywYRERERESmhUEYJiRr6kGm/iUiImPHIAwiMnWWPP6W77e2n4dqrz9RDSERcdhw+qbKknHoyl1kvW8kJUz+PnRNNRd7G7St5osnapVC++q+cHO0M+S3QURk1H9n7e3tkZycjLi4OHhkrc1HZkVeXyGvd37HE3JeQEBAEfeMiIiIiIiKCoMwiIiIyOiCMKKji6ZPRERUMAHeLhjeuqJqt2ISsfH0LRWQsftCBFLSMiMy4pLTsOpEmGp2NlZ4rLK3ypAhpUu8XR14+YmIsnBzc8Pt27dx8+ZNte/i4sKMGGaWAUMCMLSvr7zeRERERERkGRiEYUIiIyNVe1jKQiIiImPATBhEZOo4/s6Zr5sjBjXzVy06MQVbgjQBGVuDwxGfnKY7T4Iz5Ji095aeQOMKJdG5dml0rlUK5Uo4F9vrSERkrLy8vNRN+sTERISGhhq6O1SEHB0d1eudXzLvFxISkmN2LiIiIiIiMn4MwjAh06dPx9SpU7PVhyQiIjJWDMIgIlPH8ffDuTvaoVd9P9USU9Kw81yECsjYcOYmIuNTdOelZwD7Q+6o9uHK06jt544utUqjS+3SqOzLlcFEZJlkXsff319lw4iJiVGlSci8SAkSyYAhARi5zePlVKIkLS0NgYGBuv3JkydjypQpRdpXIiIiIiLSHwZhmJBx48bhhRdeUNvVqlUzdHeIiIjyxCAMIjJ1HH8/Gkc7G3SsWUq11LR0FXCx/tRNFZQRFpWY7dyT16NV+2r9WVTycVElSyQgo46fR443o4iIzJXcmPf19VVNsh8w86n5kP9nBf2fJj8X58+f1+0zCwYRERERkWlhEIYJyZp6kBOTRERkbkEY7u6Z21FRRdMnIqJHwfF3wdnaWKNlJW/VJvesiePXolQwxtpTN3AxPC7buRfC4/Dj1guq+Xk66QIyGlUoARtrBmQQkeUozE17Mi/ycxAQEGDobhARERERUQExCIOIiIiKBDNhEBGR9kZSvfKeqr3dpTrO3YzRBWRINoysrkcmYM6uS6p5u9qjU81SKihDgjnsba15QYmIyGww6wkRERERkfliEAYREREVCQZhEBFRTqqUclPt1fZVcPVOPNafvom1J8Nw8PJdZGRknhcRm4w/919Vzc3RFh1rlFIZMtpU9VGlT4iIiIi0bt68iTlz5mDHjh24fv26Oubn54fWrVtj6NChKFWqFC8WERERERUbBmEQERGR0QVhRGdfGE1ERGaqfElnvPh4oGrhMUlYf/oG1p68gT0XbiM1PTMiIyYxFUuPXFfNyc4G7ar7oEvtMmhf3ReuDnxbS0REZMn++ecfDBs2DLGxsdkyjJw4cQLr1q3DZ599htmzZ6Nfv34G7ScRERERWQ7OVpmQyMhI1QRTFhIRkTkHYSQmAsnJgL190fSNiCg/OP4uXj5uDni2WQXVouJTsCnoJtacvIHtZ8ORlJquOy8hJQ2rT9xQTUqUtKrsrTJkSOkST2f+4yAiItMn834hISG6fU9PT9XoQQcPHsQzzzyD9PR09OnTB88//zwCAgJUOTS5hgsWLMDSpUsxaNAg7Nq1C40bN+ZlJCIiIqIixyAMEzJ9+nRMnTpVt29jwxS8RERknkEYIioK8PHRf7+IiPKL42/D8XC2Q9+G5VSLS0rF1uBwrD11A5vP3ERccpruvOTUdGwKuqWarbUVWlTyUgEZT9QsrYI6iIiIjJ0EC9wvLS0NgYGBuv3JkydjypQpxdwz0/Dpp5+q67VkyRIVhJFV3bp18eSTT6ogDMmCIRkx5DwiIiIioqJmlcGUCia5Eq9atWrqY1JSkoF7RURE9KC0NMA2S6jnpUtAQEDeV0qyxkp8oTZ77LlzQOXKvLqG5ujoqD4mSnoSIgvD8bfxSUxJw67zEapkyYYzNxEZn5LjedZWQOOAkuhWu7QqW1LaQ/O3jIiIyFjs2LEDmzZtQoMGDdCrV69s42+Zrg0ODtYdYyaM3JUqVQpVq1ZV1zMvrVq1wtmzZ3Hz5k29vH5ERERERHlhJgwTkvUNV05R8kRERMYiNjb7fn4yYci/Nnd3TQYMof1IRGQoHH8bH0c7G3SoUUq1lLR07Lt4B2tOhmHdqZuIiM0MUE/PAPZfuqPalBWn0cDfE91ql1FZMsqXdDbo90BERPQwMu8nJTXo4aKiouDv7//Q8+ScAwcO8JISERERUbFgEAYREREVaSmS/AZhaEuSMAiDiIjyw87GGo9X8Vbtg161cejyXRWQIVkywqKyZ+85ciVStY9Xn0FtP3d0rV0GXWuXRkUfV15sIiIiE1a6dGkcOXLkoecdPXpUnUtEREREVBysi+WrEBERkcUGYdjba1p+gzC0mAmDiIjyy8baCk0DS2Jyz1rY/b/2WDb6MYxoUxEVvB7MenHyejS+XBeM9l9vQ5fp2/HtxnM4d/O+6EEiIqIixgrR+tG5c2dVuuXdd99FmtTFzOE6T5w4EUFBQejSpYuevioRERERUd6sMjjiN0msz05ERMZs/36gWTPNtpcXEBGRv89r1QrYuVOzPW8eMGRI0fWR8odjDiL+Lpgyebt7JixGZchYfSIMF8Ljcj23sq8rutUujW51y6BaKTeWgCQioiK1fft2bN68GQ0aNECvXr10xzn+fjTXrl1T1/DOnTuq5MjAgQN1pVwuX76Mv//+GyEhIfDy8sLhw4dRrlw5Pb+SREREREQPYjkSIiIiKtJMGPktRSKYCYOIiPTJysoKNcu6q/bGE9VUxovVJ26ooIygG9mzX5y/FYvvNp9XraK3C7rWKa3KltQq686ADCIiIiMlQRUSzPLss8/i5MmT+PLLL3X/t7VrD+vUqYOFCxcyAIOIiIiIig2DMMhgUsLCcGPqB8hIS0OZKZNh5+fHV4OIyEwwCIOIiIxRlVJuGCutYxVcDI/FmpOagAwpUZLVxYg4/LDlgmpS0kSCMbrVKY06fh4MyCAiIr3SBgxQwUmQxfHjx7F161bs2LEDoaGh6njZsmXRqlUrtG3blpeXiIiIiIoVgzDIIFJv38aVocOQHBKi9q+OfhUBi/6EtaMjXxEiIjMQneVelrt7/j+PmTCIiKi4VPRxxeh2lVW7eidelSuRduxaVLbzLt+Ox8/bLqhWvqQTutUug651yqBeOQZkEBERGRMJtmDABREREREZAwZhmJDIyEjVdOn00tIQf+QInGrXhpWdHUxFWmwsrg5/WReAIZKCglRWjDKffMwVAEREZoCZMIjILMffZLbKl3TGiDaVVLt2Nx5rT95QARmHr2hef62rdxLwy/aLqvl5OqF7XcmQwYAMIiLSLxl3hGSZN/P09FSNiIiIiIhMA4MwTMj06dMxdepU3b6dlRUuPzMI1s7OcGrSGC5Nm8G5eTM4Vq8OKxsbGKP0xERce2UUEk+ffuCxqKVL4dSwAUoMGGCQvhERkeGDMLJmzYjKvhCZiMjg428bIx1jk36VK+GMl1pVVC0sKgFrTmgCMg5evpvtvOuRCZi5/aJqEpAh5Uq61y3LDBlERFRoaWlpCAwM1O1PnjwZU6ZM4ZUFsH37dnUdmjZtCkdHR91+frVu3ZrXkYiIiIiKnFUGl3SZ5Eq8atWqISMlBceqVnvgPGs3Nzg3bAinxo3g3KgxnGrXgpW9PQxN+nttzFjEbtmiO1Zi8PNIPHkKCYcPq33pZ4U//4BTrVoG7CkRERXWe+8Bn3yi2R42DJg9O3+f9+OPwOjRmu0nngDWreNrYWgysSkSExMN3RUig4+/RVJSEl8JC3UjKhFrT0rJkhs4cPkOckuOwgwZRESUH9u2bcOWLVvQsGFDPPnkk9nG3zJdGxwcrDvGTBiZrK2tVRbdM2fOoGrVqrr9RwlwISIiIiIqasyEYUKyvuFSby6srFQZEgluyCo9Jgax27apps51cIBT3bpwatRQE5xRrx5sPDyKte8Z6ekImzgxWwCGR+/eKPW//yE1PByX+vZD2u3byEhOxvUxYxH4zxLYMM0iEZHJYjkSIjLL8TdZtNIejnjhsUDVbkUnYu2pG1h5PAwHQrIHZGTNkFGuhBO61ymjypbU8fPgzxEREeWLjDsCAgJ4tXIwePBgdX087s1taveJiIiIiIwJM2GY+KrU+MhIJBw5gri9+xC/bx8STpyQkO6Hfr5DlcpwatAQTvXrw6leXdgHBsLK2rpI+ppy/TpufT0N0atX6465duiAct9Oh5WtJg5I+n9Flkqnp2seb9MG5X76scj6RERERWvoUGDePM32xInAhx/m7/NWrgR69tRs16gB5FC9iooZM2EQ8XeB8qYNyFh1PAz77wvIyKp8SQnIKIsedcugVll33jAiIrJweWXCEMxER0RERERkupgJw8RZOzrCpUUL1UR6XBzijx5F/MGDSDh4CAnHjyMjh5TJSefOqxa5eLHmedzc4FSnDhzr1VVZMxxr1IBtqVKFmhiU4IuIX2YiculSIEu2DudmzeA37WtdAIZwad4MPmPHIvybb9S+ZPG4PXMmvEeOLPDXJyIiw2EmDCIishS+7o4Y3CJAtbwyZFy9k4Cft11QLcDLWWXH6FG3LKqXdmNABhGRBWMWByIiIiIi88NMGCYqv1Hx6cnJSDx5EvGHDiHh8BGVNSPtXl3rh5GSJQ7Vq8OhWlU4VqsO+4qBsCtVCrY+PqoMSo5fLyEBKaGhuPPb/AeCL1S/69WF/+zZsHF1zbFkybXRr+pKlljZ26PKzh2wcXfPV3+JiMh4dO4MrF+v2f7xR+CVV/L3ecePA/XqabZdXIDY2KLrI+UPV+IR8XeBCkYCMtaclICMUBy8fDfXDBmVfFxUMEbPemVQ2deNl5uIyMIyYTRq1Ag9tekAOf5+ZBUrVsSAAQPw+eef53nehAkTsHjxYly4cKEgLxcRERER0SNhJgwzZ21vD+eGDVUTGRkZSL4UgoQjhxF/5AgSjx1D0vkL8sADn5sWFaVKnEjLxsoKNiVLwraUL2w9SyAtJgZpd+4g9e5dZMTH59gP27Jl4D1iJDz79FbBFTmR0iNlP/sU5zt0RHpsLDIkgORMEFyaNdXHpSAiIhPLhBEXB6SmAlkSJxEREZlUhowhLQNUuxGViNUnwrDqRBgOXb6b7bwL4XH4dtM51SQrhpQrkaCMAG8Xg/WdiIjIVISEhCA8PPyh50VERKhziYiIiIiKg3WxfBULd/DgQQwePBiVK1dWKQYnTpxosL7I13eoGAjPfv1Q9qOPUHHFClQ9sB/+8+bCZ/x4uHbooMqQ5CkjA2m3byPp9BnE7d6NxBMnVOmRnAIwJPii9NSpqLx2LUo8NTDXAIys2TekFIpW0tmzBf9miYjI5IIw7k9+FB2tvz4RkWWQFY7du3dHmTJl4OHhgdatW2Pnzp2G7hZZuNIejhj2eCD+eaUldv2vPSZ2r4F65T0fOC/oRgy+Wn8Wbb/aip4zdmLm9gu4HplgkD4TERGZ0zxpXFwc7HLJ7EtEREREpG9cW1oMdu3ahb179+Lxxx9XUdfGRkqDuDRvrpqWZLVICj6LpOAgJAYFIyk4WJUZyW8pE/uAAJQcOjTPzBe5cahaFfEHDqjtpLPBj/jdEBGROQVhREUBJUvqr19EZP6mT5+OKlWq4IcffoCrqyvmzp2LDh06YP/+/ainrXdEZEB+nk54qVVF1a7eicfK42GqZMmp0OyRhyeuR6n2yeogNKpQAj3rlkG3umXg66YpTUlERKZNstVaouKeJ01PT0dwcLAq/eLv71/kX4+IiIiISDAIoxi89tprGDt2rNoOCAgwiZ882xIlYNu8GVyaN8t2PD0pCam3bmnazZtIjYyEjZs7bL1KqhIlNiVKwraE5yMHXmTlUK2qbjsxmJkwiIgsKQjDxgZwdQViYzODMMg83b59GzVq1FCpgytVqoTz588/8nPcvXsXU6ZMwbJly3Djxg2ULl0affr0Ucc8PR9cYU6WYcWKFfDy8tLtd+zYEXXq1FFBGTNnzjRo34juV76kM15pW0m1SxFxWHksVAVlBN/M8o8UUCVMpH2w8jRaVPJCz7pl0bV2GXg4c0UvERFZ3jypjbxxzOK3335T7WFBLy+//HKBvh4RERER0aNiEEYxsLY2n6ov1g4OsC9fXrWi4litmm476dw5ZKSnw8qMriERkSUoaBCG8PDIDMJgORLz9cYbbxRq5Zt8bosWLVTwRsWKFdG7d2+cOnUK3377LdasWYM9e/agJNOoWKSsARjasXjt2rVx6dIlg/WJKD8CvV3wWocqqp29GaMCMv47FoqQ25llH9MzgF3nb6s2aflJtK7igyfrl0XHGqXg4sC390REZBnzpOXLl1elTMSVK1fg7OwMb2/vHM+1t7dH2bJl8eSTT2LMmDGF/tpERERERPlhVne2Dx06hM8++wx9+/ZFuXLl1GBcOyDPS0JCAt5//31UrVoVjo6OamA+bNgwXL9+vVj6Tdk5VK6s285ISEDK1au8REREJiQpCUhJKVwQhhYzYZinTZs2qZVqw4cPL/BzjBs3TgVgyLhP0gv/9ddfOHnypFpZd/bsWbz++ut67TOZ7ng9LS0NBw4cUHXHiUxF1VJueP2JatjyZlusfO1xjGhdEWU9spchSUnLwKagWxi76Cgaf7QRr/5xGOtP3UBSaprB+k1ERMbDnOdJQ0JCVICtNMlwMWDAAN3+/U1bimT8+PEPZNAgIiIiIioqVhlmVIBQVkAuX778geN5fYuJiYlo166dqkVYpkwZtGrVSg3kpWa0j4+POi6rK/VF0uw999xz+Oijjwr1PPImSNt/c3T+ic5IuXJFbfvN+A7unToZuktERJRPktzAxyd7IIW7e/4vX8uWwJ49mu0FC4DnnuOlNyR9jzlkUldKQzg4OKgyIjK5+6jlSMLCwtREsq2trVr5VqpUKd1jSUlJamXcnTt3EBoaCl9fX730m0x3vC7ZUSTzypEjR9TPXkGZ+/ibjF96egaOXL2LFcfCVMmSiNikHM9zd7RVpUp61S+LZhW9YGP98BtuRERU/LZu3apa48aN0aNHD72POSxlnnTbtm2qLGG1LJl1iYiIiIgMzazylUpK6rp166JJkyaqyUBeJuLzIoN8eQMhn7t+/Xq4SiF6ANOmTVOTtRLpLW+ItCIjI1XN8bxICjx/f389fVeWyaFqFV0QRlLwWYBBGEREJlmKRNz715pvzIRh3qZOnYqLFy+qyVI7O7sCPcfatWuRnp6uJoWzBmAICe7o2bMn5syZg9WrV+OFF17QU8/JVMbrWe3btw//+9//MHHixEIFYBAZA2trKzSqUFK1id1rYO/FO1hxLBRrToYhOjFVd55s/3Xwqmq+bg7oWa8snqxXFnXLeeRrBTQREZkHS5knbdOmTZE9NxERERFRQZlVEMY777zzSOcnJyfj+++/V9s//PCD7o2FkBTWkiZbbhBI+r5GjRqp44sWLcIrr7zy0MF/bhPBlD+OVashduMmtZ0UHMzLRkRkokEYLi5y0+jRPj9r1ow5c+Qmqv76Ro8uJcUXdna39HLpjh8/jq+//hpDhw7VraoriGPHjqmPDRs2zPFxOS5BGPL1yPLG61ry89WrVy8VlDN58mQ9fQdExsHWxhqPV/FW7YPetbD9bAT+OxaKDadvIDElXXferZgkzN55SbUAL2c8Wd8PveuXRUWfR4yQJCIik2OJ86SnTp3CuXPnEBMTk2vGj8GDBxdLX4iIiIjIsplVEMaj2rVrF6KiolQK7AYNGjzweP/+/dXk/YoVK3RvLkaOHKkaFS2HLCkEk86e5eUmIjLRIAw3t0f//KyZMA4f1jQyJDckJ19DrVq1cp3ozA/JXPHSSy/B09MTX3zxRaF6JCVIhJQkyYn2+OXLlwv1dcg0x+vaVZndu3dXKz7lhgFX/5M5c7C1QaeapVSLS0rFxjM38d/RUGw7G47U9MwbUCG34/HdpnOqSVaMXvX90LNeGfi6adLeExGRZTPledKNGzdi1KhRuHDhQq7nSFCGjAkZhEFERERExcGigzDys4pSGHIVZW43PCQ63d7eHuZcjkQr+coVpMfHw9rZ2aB9IiKi4gnCaN8emDWLV9vczJgxAwcOHMDcuXPh5eVVqOeKjY3VpTbOiYukYFE/i/fVxiGLGK/LOLlv376Ij4/H5s2b4eTk9Ehf01LH32QeXBxsVXCFtLtxyVh9MgzLj4Zi/6U72c47fi1KtY9XncZjlb3V+V1ql4arg0VPERARFavcMjVoxx2FDYI2x3nSnBw8eFAF30qAxaBBg3DixAnVpCSdBGVIgMbdu3dVNj6WjyYiIiKi4mLRMyzFtYoyPDxcpesTMhkcFBSEJUuWqBsEXbt2LdRzmyt7f39YOToiIzFR3pUi6cIFOLGONxGRRQRhDBwISOZbZsAwDh98cAe2tvaFmuyVMdfEiRNVKuIXXnhBr/0j81aQ8bqsgpSx96xZs3Dp0iXVhIODQ46rOonMVQkXezzbrIJq1yMTsOJYKJYduY6gG5n/qCVRxo5zEapNXHYCnWqWRp8GZdGqig/sbB6xnhgREZk0U50n/fTTT5Gamoq1a9eiU6dOKthCgjA+/vhjXYa0ESNGYOXKlSpgg4iIiIioOFh0EEZxraKUmxYDBgzQ7f/zzz+qVahQ4aG10LPe8JA3DdJEtWrV8oyYN3VWNjZwqFwZiSdPqv2k4GAGYRARWUgQhrU10KOHppHhffLJ3UI/x+jRo9Vqvp9//lkvfdLWp5ZJ25zExcWpj24F+QEkkx+vy2pHKX/z4osvZjs3P2NvSx5/k3nz83TCyDaVVAu+EYNlR6+rkiUSnKGVmJKuAjWklXSxR4+6ZdCngR/ql/dkSR8iomJmZ2eHVatW6falpJ80S58nzcnu3btVoK0EYORErtv8+fNVmToJDJdSdURERERERc2igzCKS9u2bfUyYTt9+nRMnTpVt29jYwNz5lCtqi4II/HsWUN3h4iIiikIg8yPrDqTyc/760UnSsYrANevX1fjJbFo0SKULl06z+fTphG+du1ajo9rj8tELlmegkze58bSxt9kGaqVdsM7XarjrSeq4dCVu1h65DpWHQ9DVEKK7pw7ccmYv+eyahW9XdC7gZ8KyChfkiUiiYj0Tcpo3C8tLQ2BgYG6/cmTJ2PKlClmcfH1NU+qdefOHd17CaEtHyeB2drAEcmI1qpVK2zYsEFvX5eIiIiIKC8WHYRhaqsox40bp0vhLSvxzJ1j1aqIuredFMwgDCIiU8EgDMqJZBPQph2+nwRjaB/TBmbkpV69eurj4Vxq1miP161bly+GiTP0eN3Sxt9kWaytrdAkoKRqk3vWxNbgcFWuZFPQLSSnpuvOuxgRh2kbzqrWJKAE+jYsh251ysDDyc6g/SciMmcS+Hn+/HndflFnwTCGcVdB+fj4IDo6Otu+uHjxIupkKW2ckJCAqCjtTCMRERERkZEHYUg6N30ZPHgwihNXURo3hywT3VKORKLkc1odQERExoVBGHS/3Fa6ScYCWeFXqVKlbJPMD9OlSxdYW1tjx44duHXrFnx9fXWPJSUlYcWKFWriulu3bnwxTBzH60TFw8HWBp1rlVZNMmKsORGmMmTsu3Qn23kHQu6qNvm/U+hUoxT6NvRD66o+sLOx5ktFRGTiTHXcVblyZVy6dEm337RpU/X+45dffsH333+vjsl7jc2bN6NixYoG7CkRERERWZJCB2HIyjB93Rgv7iAMU1tFaWnpkB2qVtVtp0VGIjU8HHZZbrIQEZFxYhAG6YtMmkrr06cPPv30U93xMmXK4JlnnsHChQsxatQoVcLE1lYzrH377bcRHh6OIUOGZAvOINNk6PG6pY2/iYRkuHi6qb9q1+7GY/nRUPx7+BouhGtWQAvJlLHqRJhqXi726FmvLPo3KodaZd0ZOE9EpAeGKEdi6HFXQUng9YQJE3DmzBnUqFFDBWxLoMhPP/2EAwcOoFy5cioAQzLuvfjii4buLhERERFZCFt9DdJ79epV4M9ftmwZjh8/juL22GOPwcPDAxcuXMDRo0dRv379bI8vWbJEfezZsyeMgaWlQ7YtWRI2Pt5IC4/QlSRhEAYRkfFjEAbpS0REBIKDgxEWFpbjzfG9e/fin3/+QfXq1dG4cWOcOnUKJ0+eRJUqVTBt2jS+EGbA0ON1Sxt/E92vXAlnjG5XGaPaVsKJ61H49/B1/HcsFHfiknXn3I5LxrzdIapVK+WGfo380Lu+H3zdHXlBiYgKkC3OUOVIDD3uKsyiPul3erqmlJa9vT3+++8/DBw4UAVhSJMsei+99BLGjh1r6O4SERERkYXQSxCGDMolIrugJBW1IYIwZFD+6quv4uOPP8bo0aOxfv16uLi4qMdk4l761KZNGzRq1KjY+0YajlWrIU4bhHH2LFxbPc5LQ0Rk5BiEQcXB29sb+/fvVysCJaB36dKlKFWqFMaMGaMyFxTHRDUVPY7XiYyDZL+sW85Ttfe618C24HBVrmTD6ZtITtPc9BLBN2PwyeogfLYmCG2q+qB/o/LoUMMXjnbMIkNEZOxMddxVunRpjBgxItuxOnXqqMwYQUFBuHv3ripZ4uPjY7A+EhEREZHlKXQQhru7O5ydnQv1HE5OTup5CmvVqlX48MMPdfvJyZrVOc2bN9cdmzRpErp3767bnzhxIjZu3Ijdu3erVZOtWrXC5cuXsW/fPjU4nzNnDoyFJaZDlpIkcbt2qe2ks8GG7g4REeUDgzAovwICAvJcASgBFnmlXS5ZsiS+++471cg0mNp43RLH30QPY2djjY41S6kWFZ+ClSdC8c+hazh8JVJ3TnoGsCU4XDUpb/JkvbIY0Lgc6vh5sFwJEVExlSMxtXFXQcl7AZmblkwX95OMeUREREREhmCVkdfMt4mZN28ehg4dmuc5c+fO1aUU1kpISFB1xv/44w9cvXpVTehL/UB5oyJ1A41FZGSkalnTISclJcGcRS5bhrD/TVDbDjVqoOLSfw3dJSIieoimTYEDBzTbf/4JPP00L5kpc3TUpJOXGspkHObPn6/X9M3FydTG65Y4/iYqqIvhsapcyb+HryE0Kuf/GVVLuaJ/o3Lo06AcfNwceLGJyKJt3rwZ27dvR9OmTdGtW7ds42+ZrpWyfFqS5e1RM72Z2riroOzs7NT1W758uaG7QkRERERknkEYlsRSbogknjmDS336qm0rOztUO3xIfSQiIuNVowYQFKTZXrkSyLKwikyQpYw5TInUtJbSAPpaZUn5w98FovxJT8/A3ou3seTQNaw+GYbElMxyJVo21lZoV80XAxuXQ7vqviq7BhGRpckrCENw/J0/5cuXR8uWLfHXX38V0StFRERERGSAciRUfLKuxLOU2Bn7SpUk77PcIUBGSgqSQ0LgUKWKobtFRER5YDkSoqJXr1499OrVq8Cfv2zZMlXXm/JmieNvosKytrZCy8reqk3tVQtrTtxQARn7Q+7ozklLz8DGMzdV83a1R58GfhjYuDyqlHLjC0BEdG/cERISUqhMGJaic+fOWLNmjSq3Ym9vb+juEBEREREVTxDG9evX4efnV9RfxiJYYk1qa3t72AcGIPn8BbWfePYsgzCIiIwcgzCIil79+vVVbfCCkkl9BmE8nCWOv4n0yc3RDgOblFctJCJOBWP8c/gawrKUK4mITcasHZdUa+Dviacal0ePemXh6sA1I0RkuSRbWWBgoG5fxn1TpkwxaJ+M1ccff4z169fj2WefxXfffYcyZcoYuktERERERIUPwoiKisLw4cOxceNGNG7cGF9++aVamafVoEED3Lp1i5daD8aNG6er06itSW0JHKtW1QVhJAWfZV57IiOUnpiI1Bs3kBoejpRbt9RHaWl37iIjNRVIS0VGahoyJO19aiqs7O1g7eYOGzc3WLu5wcbdDdbu7rArUxb25cvBtlQpWPFGl0mSheKxsZn7blzQSqR37u7ucHZ2LtRzODk5qeehvFnq+JuoKAR4u+DNztUwvlNV7DofgcUHr2L9qZtITsssV3LkSqRqH6w8je51yuDppuXR0L+E3kowEREZk7yybEng5/nz53X7zIKRuwkTJqi56H///RerVq1Cw4YN4e/vryvrkpX8P5k9e3ahXzsiIiIiooexyihkXt2RI0di7dq16Nu3rxroXrlyBTNnzsTzzz+vHvfx8UF4eHhhvgTlwJLqQ0b8/AvCp09X265t26L8zz8ZuktEFi317l0kBQUh8fQZJJ7RtORLl6QAuP6+iJ0d7MuWhV358rAPDIRjzZqqOVSqCCtbroo0ZnFxgKtr5v6NG0CpUobsERWWJY05iPLC3wUi/YuMT8byo6EqIONUaHSO51TxdcVTTcqjb8NyKOnCNPNEZD42bdqEHTt2oGnTpujWrZvuOMccj8ba2jrf50oQhmQZISIiIiIqaoW+kyWBF1u2bEHlypXx6aef6laLxcTEYNSoUfrpJVl0TWqHqlV124lngw3aFyJLJJksEo4eRey2bYjdug1J584V/RdNSUHy5cuqxe3cqTts5egIx2rV4FirFpwbN4Jz8+awLVmy6PtDBSpFIpgJg4hMmaWOv4mKi6ezPYa0DFDt5PUoFYyx7Mh1RCem6s45dysWH606gy/WBuOJWqXwTFN/tKjoBWtrZscgIvMl4w4pH5c1EwazYeRM5qWJiIiIiMwuCENWRUoAhnBwcMBPP/2ESpUqYcyYMSp1HtOG6o+l1qR2rJYZhJEaGoa0mBhVwoCIira8SMzGTYjdsgWxO3ciPSrq4Z9kbQ1bLy/Y+vhomq8vbLxKwtrBAbCxVeVFrGxtAGsbZKSkIC0mGunRMbqPqXfvIOV6KNIiInL9EhmJiUg4dky1u3/8oY45VKsGl+bN4dyiOZwbN4GNq4s+LwUVIghDFiQ5OfESEhW369evw8/PjxdeDyx1/E1kCLX9PFR7t1sNrDt1A4v2X8Wei7d1j0vZkpXHw1Sr4OWMp5v4o3+jcvBxc+ALRkQmLae5U8nWEBgYqNufPHkypkyZUsw9Mw1t2rQxdBeIiIiIiPRfjkRqI69btw4BAQHZjkswhgRiyBuJ5OTkwnwJymElnrYmdVJSktlfH/kRPdukKdJjY9V+hYW/w7lRI0N3i8gsJV26hMhFfyFy2bI8Ay9sPD3hWLMGHGrUgGMNKRVSA/b+/nopFZIeH4/ka9eQcu0akq9cQdLZc0g8dQpJUg/3IWlDrezs4NyyBdw7dYJr+/bMkmEAR44ADRtqtj085H+XIXpB+sR0yMYnKioKw4cPx8aNG9G4cWN8+eWXqg62lq+vL27dumXQPpoLSx1/ExmLkIg4/HXwKv4+eA0RsQ/+7tlaW+myYzxWyZvZMYjIJMuRNGvWDF27ds02/pa5sODgzGywzIRBRERERGRhQRgjR45EhQoVMGHChAcemzVrlnqctfb0z9JuiIQMehYJhw+r7VLvT0LJQYMM3SUisyFZKWI2bcbdRYsQv3dvzifZ2cGlSRO4tm0D19atYVehQrFnOpLsHEnBwUg4dQqJx44hbs9epOZ1k9HaGs6NG8OtUye4dX4Cdr6+xdldi7VjB9C6tWZbFuJfu2boHlFhWdqYwxTI+Hrt2rXo27evKg145coVzJw5E88//7x63MfHB+Hh4YbuptmxtN+Fmzdv4siRI2jUqJH6mSIypJS0dGwOuoVF+69g29lwpOcwi+Ff0lkFYwxoXA7ersyOQUSmHYRhSWMOIiIiIiJzVOggDDIMS3tDFjZ1KiL/XKS2nZs0gf9v82Alee6JqMAy0tMRvWo1wr/9VmWduJ+VszPcO3eGa/t2cGnR0ujKfMi/r+RLlxC3Z48KHonbtx/p0dE5n2xjA9fHH4dH375wa9cWVvb2xd1di7F6NdC9u2ZbFo0HBRm6R1RYljbmMAXly5dXta+lJKBkZRg3bpwKwpgxYwZGjRrFIIwiYim/CxJAv3PnTmzbtg3p6enq50kCf1iKhYzF9cgELD5wFX8duIob0Q/+PtrZWKFL7TJ4tpk/mgWWZIlUIjJaDMIgIiIiIjJfhc8bT1QMXFu10gVhxB84gNuzZ8N7+HBee6ICit25C7e+/hpJZ8488JhDlSrwfOZpePTsCRs3N6O9xpKJw6FiRdVKPvssMlJTEX/wEGI2bFAtW5aMtDTEbtummpRSce/ZE579+sKxenVDfgtm6V7lKMWIf3yITJoEAUgAhnBwcFBlACtVqqRKAcqN8uLOVETmIywsDMuXL8eNGzfUvrW1tcqqsm/fPrRs2dLQ3SNS/DydML5TVbzWvrLKivHn/isqS4Y2O0ZKWgZWHAtVrZKPC55rXgF9G5aDh5MdryARERERERERmXYmjJCQEGzfvl1N5OVWN1kmiCdNmlQUX94sWXJNavkxvT52HGLWr9ccsLVFwB8L4VS3rqG7RmRSEk6eQvi0rxG3e0/2B2xt4d61K0o88zScGjQw+Rt4kuUj8fhxRG/YgOjVa5AaFpbjeU4NG6Lk4Ofh1rEjrGwZl6gPc+YAL76o2W7bFtiyRS9PSwZkKav/TYmMA9etW4eAgIBsxyUYQwIx5G94cnKywfpnTixl/J2amqrSoUuT7BdOTk4qLboc/++//2Bvb49XX30V7u7uhu4qUY5CIxOw6MBVVa7kVsyDv6NOdjbo3aAsnm1WAbX9PHgVicgobNy4UWWfyqkcicyDBQcH6455enqqRkREREREFhqEIRP0w4cPxx9//KH283p6mSCWdLeUP1OmTMHUqVN1+7LSUSZGLUVaVBQu9u6ju5lq5++PwH//gY2rq6G7RmT00uPjceurr3H33t/mrNy6doHvuHGwr1AB5kgCMqRcSeS/S1WGjIwcbp7ZlimDks8Ogmf//ipTBhXcd98BY8dqtnv0AFas4NU0dQzCMD5SGqJChQqYMGHCA4/NmjVLPc4xtn5Ywvhb3q/99ttvKohe1KhRA927d4erq6sKyJgzZw6uXbuG2rVro3///obuLlGeUtLSsenMLSzcdxk7zkXkeE4Df08837wCutctAwdbG15RIjLKIAwZb2Qdz02ePFmNS4iIiIiIyEKDMMaOHavqUfv6+uLZZ59FxYoV1QReboYMGaLPL2/WLGUlXl7iDx3C5ecHA+npat/9yZ7w++ILQ3eLyKjFHz6M0P9NQMqVK9mOOzdrBt8334BTnTqwFGnR0SozRuQ//yDxxIkHHrdydIRn377weulF2JUta5A+mrqPPwYmTtRsP/MMkEPcD5kYBmGQJbOE8bcEWPz666+wtbVF7969UatWrWwZsUJDQ1Vwj7xtlPdugYGBBu0vUX6FRMSpUiV/HbyKyPiUBx73crHH003Lq+wYZT2deGGJqNgxEwYRERERkfnSexBG6dKl1Yqp48ePq20qGpZ8QyR8xveI+OEH3X7ZLz6Hx5NPGrRPRMYoPSkJ4d99hztz5soyV91xhyqV4fv2O3B5/DGTLztSGAnHjuHO/AWIXrdO8rBnf9DODp69e8Pr5eGwL1/eUF00SbIw/7PPNNvDhwMzZxq6R1RYljzmILKE34U1a9Zg3759eWa6WLVqFQ4cOABvb2+88sorKiMIkalITEnDquNh+H3fZRy5ogmqysraCuhUsxSGtAhAi0peFj0+JiLDBGE0b94cXbp0MfsxhyFERUXh3LlzKFeuHOepiYiIiKhYWev7CWNjY9G6dWsObKnIeL8yEk4NG+r2b0yZiuT7VvgTWbqEk6dwqV8/3Jk9JzMAw9oaXiNGIOCff+Da6nGLn2B2qlcPfl9/hcqbNsJr5AjYlCiReQFTUhD599+40KWryiKSdOmSwV5LUxMbm7nNalFERMZNgudPnTqltuvkkRmrffv2cHZ2RkREBPbu3VuMPSQqPEc7G/RrVA5LRz2Gla89jqcal4ejXeZUSHoGsO7UTQz6dR86T9+uSpnEJ5tX2SEiInO2fv16DBs2DEeOHMl2XDI1ywJBKfciQRjjx483WB+JiIiIyPLoPROGRG+7u7urATAVHUuPik+5fh0X+/RFenS02nesUwcBf/4BK1tbQ3eNyOCk1IYEJ2WkZKZdtg8MRNnPPlWBB5Sz9MRERC1dioiZs5AaFpb9QWtrePTpDZ/XXoMdszzl6YUXgN9+02xPmgR88AF/4kydpY85TElISAi2b9+OsLCwXEtmyArvSfLLSY/MHH8XLl68iPnz58PJyQlvvPGGKkmSG7mxsXz5ctjZ2eHVV1+Fh4dHsfaVSJ8i45Px98FrWLD3Mq7ciX/gcXdHWzzd1B/PN6+A8iWdefGJqEgwE4Z+DBw4UGX2kjGwtiT2iRMnUL9+fZW9q0mTJjhz5ozKivHvv/+iV69eevrKRERERES50/sda5m8e/bZZ9UkXYMGDfT99ESKnZ8fynzwAa6PG6f2E0+cwN0//kDJwYN5hchiSdDFzS++xN0FC7IdLzlkMHzGj4f1vZtHlDO5PiWeeQae/fohcvly3J45CylXr2oeTE9H1D//InrlKvV3xmv4S7Bxd+elfEgmDDc3XiKi4iBBAcOHD8cff/yh9vOKsWYQBmUlNyhEzZo18wzAEPXq1cPhw4dx9epVFXA/YMAAXkwyWZ7O9hjeuiKGPR6IbWdv4bfdl7HtbLju8ejEVMzcfhG/7riIjjVKqfOaBZa0+ExyRETGSMYnEnChDcAQ8+bN030cNGgQLl26pMY7P/74I4MwiIiIiMg0gzBkMu7atWvo1KmTWiElH/38/GBtnXPlE39/f313wWxFRkaqJvScwMQkuXfpjNhevRC1fLnaD5/+Ldw6d4ZdqVKG7hpRsUu9exfXx7+O+Cwpwm28vOD39ddwad6Mr8gjsLK3R4kBA+DZpw+iVq7E7Z9+RvLly+qxjKQk3J41C5GLF8Nr5EiUeHYQrO3teX2zYDkSouL3zjvvYOHChfD19VXB0BUrVsw2CU0FZ87j79TUVJw+ffqhpUi05P1ct27dMHPmTFXCpGnTpqhQoUIx9JSo6NhYW6F99VKqXQyPxfw9l/H3wauIS07TlSpZf/qmarXKumPYY4HoUa8MHGxt+LIQUaHlNbaQxyTLmZanp6dq9KCbN2+qbBdZbdq0SV2vp59+Wu0HBgaiTZs2urEPEREREVFRK5LaDXXr1kXJkiXx4YcfqpbXSjyZ/KP8mT59OqZOnarbl5R6ls73nbcRu3Ur0qKikB4fj5uffoZy078xdLeIilVi8FlcGz0aKdeu6Y451qyJct/PgF3Zsnw1CkjKG3n27g2P7t1ViZfw739AWkSEekz+5tz6/HPcXbgQpd59F27t2/E638MgDKLi99dff8Hb2xtHjx5Vda9Jf8x5/H3u3DlVtsbNzS3fgfFlypRBw4YNcejQIZUN46WXXmJmADIbFX1cMeXJWnjjiapYcugaftsdgpDbmaVKToVG442/j+HTNUGqTMmzzf3h7epg0D4TkflKS0tTgQNakydPxpQpUwzaJ2Ml47Os5eLu3LmDkydPokePHtkWBfr4+CA8PDPrERERERGRSQVhrFy5En379lXBFTIZLKujuBJPP8aNG4cXXnhBbVerVk1Pz2rabEuWhM+bb+DGpPfVfszatYjd0ReurVoZumtExSJ2+3ZcGzceGfGZE8Tu3bujzEcfwtrJia+CHljZ2aHE00/Do2dP3P7tN9z5dbYK+hIS+HJt1Ci4tmmDUu9OgD1XBCMmJvPacSE+UfGIjY1Fly5dGIBRBMx5/C03J0Tt2rVzzVqYk7Zt2+L48eO4fv26yoghn09kTtwc7TD0sUAMaRGArWdvYc7OEOw8rwnEFRGxSfhm41n8sPU8+jX0w4uPV0RlX2YfIiL9BxacP39et88sGLkLCAjA7t27kZKSAjs7O/z7778qk4hkZ87q9u3b8PLy4o8qEREREZlmEIZEZstAd+7cuRg8eDBXRulR1tSDkkWE7l2Xfv0Q9e9SJBw5ovZvfPAhKq74D9aOjrxEZNZiNm1SARhISdEcsLKCz+vj4cVVqUXC2sUFPqNGocRTTyHix59wd9EiWZ6kHovdtg1xu3ej5IvD4D1ihEUHwGTNhOHmZsieEFkOuQkeHR1t6G6YJXMdf0sGjODg4HyXIslKMmc89thj2Lp1KzZu3Ijq1avD1rZIEiwSGZR1llIlQTeiMXdnCJYevY7k1HT1uHz8c/9V1dpX98VLrQLRoqKXWf2tIKLikdPfDTkmwQX0cE899RTee+89tG7dGi1btlRz0hKM0bt3b905MlctmbzMLaiWiIiIiIxX/pc85dOZM2fUoHfIkCGcfKBiYWVtjdJTJssyAbWfcvUqbs+cyatPZi167VpcGztOF4AhAQLlfvoR3sOH829vEbP18kLpSRNRcdlSODdrpjuekZKC2z//ggvduyNm61ZYKpYjISp+b7zxhrohfuReQCrRwwQFBanMhbIaVEqMPCq5wSHZDiMjI3HgwAFecDJ71Uu74/P+dbHnf+3xeqeqD5Qh2Rx0C4Nm7UOPGTux/Oh1pKZpAjWIiKjojRkzBk2bNsW+ffvwzTffqODkTz/9FH5+fpl/pzdvVqVI2rVjKVEiIiIiMtEgDClBIo2oODlWq4aSQ4bo9iNm/Yqki5f4IpBZilqxEtdffwNITVX71lLLfe4cuLVta+iuWRSHKlXgP28u/KZ9DdtSpXTHU0PDcG3kK7j+1ttIvXsXloblSIiK34ABA/D555+rlMtSK3zXrl0ICQnBlStXcmxEJ06c0GVRKciqfXt7e7Rv315tb9u2DQkJCbyoZBG8XB0wpkMV7HynHb7oVxdVS2UvQ3IqNBpjFx1F26+2Yu6uS4hP1ozXiYio6Li4uKhyJBJo8ddff6kFguPHj3+gvIsEaMiiQSIiIiKi4mCVIfnY9Oj111/Hn3/+iUuXLsGR5SCKjPbaJiYmFt0XMTHpcXG40KMnUsPC1L5z8+bqxjTTwZI5iVy6DGHvvQeka1bX2Xh4oPyc2XCqVcvQXYOl//2J+Pln3J73W2Z5GHl9SpRAqYnvwb1bN4v4WyTVWbJmpA8JASpUMGSPSB845jANmzZtwiuvvIILFy7keZ78LZIMCGS5vwtxcXH46quvVFruV199NecA+vCzwMklwI2TQO2+QJ3+D5ySnp6On3/+Gbdu3UKLFi3QuXPn4vkGiIyI/B5tPxeBX3dcxI5zEQ887uFkh8EtKmBIy4AHsmcQEW3YsEEFz97/f9RcxhxERERERJZM70EY8fHx6NChg6oV/NNPP6FSpUr6fHq6h2/IchazcSOuvfqabr/Mxx/Ds19f/tyQWYhcsgRhk96X2V7dDX4JNHKsXt3QXaN7ks6fR9h7E5Fw7Fi2a+Lavj1KT34fdlkyZpij6GjAwyNzPyIC8PIyZI9IHzjmMH4rV65E3759VXCF3FCvUKGCKhWRmy1bthRr/8yFufwuSPmQVatWqTIkI0aMyHwg6hpw8h/gxN/ADU2mDJ0RO4AydR94rnPnzmHhwoVqdakEdJQoUaIYvgMi43Q6NBqzdlzEimOhSE3PPs3iYGuNp5qUx/BWFVG+pLPB+khExmX9+vUqgwODMIiIiIiIzI/egzAkLW1ycjL27NkDa2trBAQEqBp8sv3AF7eyUqv2yHIngfVNfpyvvTIKsVu3qn0rBwdU+P13ONWpbeiuERVK9OrVmhIk99jITba5c1RJDDIuGWlpuPv777g1/VtkZEnPLmVjSk+ZDI/u3WGuQkOBLGV3If+iHLjo0+RxzGH8GjVqhOPHj+PXX3/F4MGDLSLzjiGYy+/CnDlzVFmaJ554Ai1btgTibgP/vgRc2Jz7J1XvATy9MMex9/z581UWRClt0r//gxkziCzN9cgEzNl5CYv2X0Fcclq2x2ysrfBkvbIY2aYSqpV2M1gficg4MAhDv2RhoAQbS5BoTEyMGqfcT8bJkyZN0vNXJiIiIiIqhiCMnIItciMD3zTJXU75EhkZqZqoVq2a+piUlMSrd5+U0FBc6tMXaVFRat+2VCkE/L0Ydr6+vFZkkuL27sPV4cORca/Mha2vL/znzYNDxUBDd43ykHz1KsLefx/xe/ZmO+7esydKvz8JNm7mN/EeHAxoE7NIWZLkZPlfb+heUWGZy41nc+bs7KxWUDK4Wf/Mbfwt38v06dPVttRK95D0ReveA/Z8/+DJJSsCdy5m7r+8DShb/4HTwsLC8Msvv6jt4cOHqwB8IgKi4lPw+77LmLvrEiJikx+4JB2q+2J0+8po6M8MMkSWKq8gDJmuDZY3WPd4enqqRjmbN2+eGttES3rGe+QaZg1O1u5zLpqIiIiIikP+IybySVZB5bddvJhlUo8eSiZMAwMDVZNsI3zTkDO7smXh9+10wMZG7afevInrr41ButwNJDIxiUFBuPbqq7oADBsPD/jPm8sADBNgX748/OfMQekPP4C1c2ba6egVK3CxVy/EHzgAcxMbm7ktMSYMwCAqHlKCRBrpn7mNvw8fPqw+SskaFYCRmgQc/SPzhBKBQJt3gNEHgNcOA6WzlCDZ+lmOzyllTerVq6e2//vvP6TcG7MQWToPZzuMblcZO99pj49610b5kk7ZHt8UdAt9f9yNQbP2YveFiBxXbBORZcgpi5mMObRjEGnaIEp60MaNG/Hiiy+q6/juu++qoBYhQaJvvfUWKleurP7GSuk0yQhGRERERGSSmTCo6JjbSryidmfhQtz88CPdvkefPijzycdM0U0mI+X6dYQ8/QxSw8PVvpWjI/znzoFzgwaG7hoVICtG6NvvIOHIkcyDVlbwGj4cPq+OhpW9vVlc023bgLZtNdvlywNXrhi6R6QPzIRh/F5//XX8+eefKshZ+3qRfpjT+Pv27dv48ccf1U0dKRsi5UNw8l9gyVDNCbZOwBtBgFOWVbZBq4FFz2TuD98C+DV84Lkl5fdPP/2k0oA3a9YMXbt2LY5vicikpKalY9WJMPy09QKCbsQ88HhDf0+82r4y2lXz5XtWIgvLhCHlwaRMmBYzYTwaGXds2LABhw4dUoGhQ4cOVeXStMGzqampePvttzFz5kzs3btXMwYiIiIiIjK1TBixsbGqJnVERESu58hjck5cXJy+v7xZk7SDAQEBqkl0N+t9563EoEHwHDBAtx+1dCnuzp9f5K8TkT6k3r2LK8Nf1gVgwNoaftO+ZgCGCWfFqLBgPnzGjdXU6RAZGbg9cyZCnn9elVEyBzFZ7ie4uhqyJ0SW5aOPPlLjwyeffBIXLlwwdHfMirmMvyXufvXq1epmRMWKFVGrVi3NA4d/yzypVu/sARiiWlegTP2HZsNwc3ND79691fa+ffuypU8nIg1bG2v0qu+HNWNbYe4LTVTQRVaHr0Ri2LyD6DFjJ9adusHMGEQWTsYc2jGINJYiyd2BAwfQvHlzXWau+9na2uKrr76Cr68vJk+eXGSvGRERERFRkQZhTJs2DQ0aNMhzAlgek3O+/fZbfX95omxvWEtPmginRo10x25+/gVid+7iVSKjlp6YiGujRiM5S8mm0lMmw619e4P2iwrHytYW3iNHIuDPP2BfoYLueOKx47jUpy9iJY2EmZUjIaLi0aNHD9jY2GDTpk2oXr06qlSpgrZt26J9+/YPtA4dOvBlsUCnTp1S78Hk56R79+6aYJK7IcDFrZknNRz84CfKee3ezdw/tw64djDHr1G1alV1A0QsX748W012Isr6a2WFdtV98c8rLfHH8GZ4rLJX9t/X0GiMWHAI3b7bibUnw5CezuSlREQPWxDo7++v23dwcNBl6tKytrZW2bp27NjBi0lEREREphmEsWLFClVrTwa2uZHHKlWqhGXLlun7yxNlIyn+y333LWzLlNEcSE/HtTFjEMmfPTJiN6ZMzVa2wnv0aJQYONCgfSL9capTB4FL/4VH/366Y2lRUbg6YiRuTfsGGampZhGEwUwYRMVn69atKpW1ZDuQTAdys3379u3qeE6NLEtiYiLWrl2rtlu1agUvr3s3fA8vyDzJqwrgr6mf/oAqTwB+mUHN2Ppprl+rY8eOKF26tCpLsnTpUqSnp+vpuyAyz2CMlpW8sfCl5vh3VEt0qO6b7fEzYdEY+fthdPtuB1YdZzAGEVFuZOxx584d3X6Ze3OAZ8+ezXaenJOQkMALSURERETF4l5OdP25ePEiHn/88YeeV6NGDTVZTFTUbL28UP6H7xEy6FlkJCYiIz4eYf+bgPg9e1D6/fdh7eJiti9CelIS0iIikHr7NlIjpIUjTbZv34G1szMcKlWEfcVKcKgYaNbXwZTE7tqFqCxBQlJSx/vV0QbtE+mf/P6V/egjODdqjBtTp6q/TULKkyQcPYqyX30JO9/sE/GmgEEYRIZx6dIlXnrK1ZYtW9QK0ZIlS+Kxxx7THExLBY4uzJ4FI7dSK3K87QRgYX/N/vmNwNX9QPmmOab77t+/P3755Rf1c7lr1y4V+EFEeWvoXwKzX2iCk9ej8N2mc1h/+qbusaAbMRj9x2FUL+2GcR2ronOtUiZbGomIspMAWio8yQR37tw53X7Lli3Vtf3iiy+waNEi9TdT5qA3b96ca8kSIiIiIiKjD8KQiGInJ6eHnifnyGQgUXFwrFkT5WZ8h+tvvIn0e6mRo5b/h4Sjx+D3zTT1uLHKSEtDStgNpIaFIvXuXaTHxyPt9h2khocj+dpVpFy+gtRbt2Dt5gabEiVgZWd3L9DiNtIf4XdMsoU4VK4Ml5Yt4dapI+zLlSvS74selJ6QoLJgaDnWro3Sk9/nJKsZ8+zTG461auL6uPG68jPx+/fjUt9+KPfdd3Bu2ACmJEu2V2bCICpGFbKUOCLKKjQ0FPv371fbUobEzs5O88D5DUBMmGbb2g6o90zeF65yR6BcE+DaAc3+lk+AwTlnNfT29ka3bt1USRK52SF17MuXL88Xhigfavt5YObgxjgVGoXvN5/HmpM3sgVjjPz9EGqVdcfrnaqifXVfvk8gIro3xhk7dqwa8zRt2lSV36tbty6WLFkCPz8/lRnj5MmTKkPXuHHjeM2IiIiIyDTLkcgE24ED9ybn8iDnlC1bFpZg8eLF6g2BDPo9PDzQunVr7Ny509DdsjiurVqh4tJ/4dQg86Zm8uXLCHnqadyZPx8ZKSkG61taTAwSTpxA1IqVCP/+B4S+8z9cfu55nO/cGcENGuJCx464/PxgXB8zVmXxuPXll7gzbx5iN25C0rlzqpRByrVrSDxxAgmHD6vv61ECMERqWBjiduzArc8/x4WOnXCxV2+Ez/geiUFBXJ1RTCJ+/AkpV69qdmxsUOaDqbCy1XusHBkZx6pVEfj3Yrj36KE7JhlsrgwZgsh//oEpyfpnx83NkD0hYzVt2jT07dsXVapUUWMiqdcsAQSDBw/GiRMnHvn5NmzYoMZYPj4+6uaylFl44oknVBkESyKBzcePH0dERESu58hjck5cXBzM3cGDB9XPlJRIlJWPEydOhCWSGw0rV65U47jatWurcpA6h+dnblfvBrj65P1k2mwYWhe3AFf25np6/fr11deUry03QCIjIwv1vRBZmlplPfDTc42wdlwrdKtTOttjp0Kj8eJvB9H7h13Yfjac79WIzIClZbfR9zypjPvWrFmDUqVKqX1ra2usWrUKnTp1wq1bt3DkyBE4Ozvjo48+wnPPPafH74SIiIiIqBiDMDp37oyQkBB88803uZ7z7bffqvS0Xbp0gSWYPn26WhH2ww8/4O+//1ZR2BKVfezYMUN3zeLY+fmhwoL58Bo5QpdyWYIvbn7yKc493gphkyapchAZqal6+5oZyckqKCJu715Er1+POwt+x41PPsG1115TASBnH3scZ5s0RciAgQh96y1EfP89opYvR/zBgyrLhXx+YVl7eMC+UiU4N20K925dUeK559QNX4eaNWDl6Jjj5yQFByPihx9wqXcfXOzWHVErVyGDdb2LTGLwWdyeO1e3X3LIEKPO0EL6JeWAyn75BUpPmQzcW6Usf5vC3puo/l7o829SUWI5EnqYTz75RE2QSlkEGQvJ5KujoyMWLFiARo0aqRvGjzK+koALeb6qVauiX79+KhXxxo0bVaDHe++9Z1HBLQ0aNMCFCxdyPUcek3NkHG7upATG3r17VYlEmdi3VBKMIpkwJNhJ3qPpRIcBZ9dl7jcckr8nrNQeKN8sc3/b53neTOrRowdKlCiBqKgo/Prrr6ovRPRoqpd2x4/PNsLqMa3wRE3NzUWtY9eiMHjOfjw9cy8OhtzhpSUii50nlfGejHWyZoeT51y7di2io6Nx/fp1FZA8YUKWgFIiIiIioiJmlaHnAoTXrl1DnTp11CC3a9euePnll3WrrmTyd+bMmWqy3M3NTQ2uLSF98u3bt9XKzKyr0uQaSU1muR4FITcsRGJiot76aWkkKOL6W28hLfzBVaNS1sOtUyfYenurLBVSwkR9jIlRN0at3d1g4+4BG3d32JQsCYcqVeBYvZr6PCkNkhIWhvhDhxC/Zy8STp4E0tIK3V8bb2/VH2snJ/U1bb28VFCJfYUKsCtdCmlxcUi7G6n6Z+tVEjYlvWDr7QXbkiVhZW+f6/NKYEVKaBiSL15A/OHDiNm4Ecnnc76J41C1KnzGjYVru3YWt1KjqEvOhAwahMRjx9W+vK4VV/wHa2dnQ3eNDED+dlwbM1aVFdJybtEcftOmwbZECaN+TYYMAebfW1w9eTIwZYqhe0T6oM8xh9wcl2AL7XNq/fjjjxg9erRavSZjSduHZAEKDw9X2ddkTCXZMNq0aaN7bPv27So4Izk5GefPn0fFihVh7po0aaLG3sHBwXmeJ8Eqnp6euvIU5kp+LmQFpJBSGLLiUVY+FpapjL/l7d3Ro0fVCtDU1FRVGkRSc+ts/wrY/KFm28MfGHtMlozm78kvbAYW9MncH7EdKJN7bXUJwFi4cKFahSrZavr3749q1aoV+HsjsnQnrkXhm41nsTno1gOPSXmSN56oqrJoEJFpWLduHfbs2aPmByVrg6mNOYxpnpSIiIiIyNjoPc99uXLl8N9//6nViKtXr1YBF/dPCkq0s0Q6W0IAhsj6xkLIpLCk55VsIGQ4Ls2bo+KyZbj52eeIWbcuW8aJtLt3Ebl4cbH2x8rJSQVUqObvr27E25YuBbsyZWBfvnyR3ZC3sraGfTk/1Vxbt4bvuHFIunRJBWNI0wYGiKSzZ3Ft1Gg41q0L3/Hj4NKiRZH0ydLcXbQo23WWbAgMwLBczo0aqfIkV199FUmnz6hjEtAVMvAplP/pRzhUrgxjFROTue3qasiekLGSidWcjBo1SmVzkIDd06dPqxrOedm3bx+SkpLUiresARhC0hnLcRmPSiYASwjCuHjxosr68DA1atTA7t27Ye60ARiWSH4vJPhCSs8IKf3TuHHjzBMkq9mRBZn7DZ/PfwCGqNgOKFMfCDuq2d85HRiQmckrp5Wpw4YNU2nH5ed00aJFKhtis2ZZMmoQUb7VKeeBOS80wZErd/H1+rPYeT5zQYEEZkjrXrcM3nyiGgK9XXhlicgocZ6UiIiIiCyB3oMwRKtWrdRKvFmzZmHTpk24evWqOi4rFjt27IiXXnpJpabVt0OHDqnVkLK6T5qkmxMPS/aRkJCATz/9VE0KXrlyRaXIlsnBDz/8UKWv07e0tDQcOHAge1pgMgjJJuH35RdIm/w+YrdsQfSatYjbsUNlk9ArKyvY+vpqMliU8IRd+XtBFr4+sCtTFvYV/GFbqpTRZJdwCAyEw/Dh8B4+HEkXLyHi+xmIXp0ZUJV4/DiuDB0Gty5dUPaTjxkwUAgpN28ifFpm+Sb37t3h2qpV4V5AMnl2ZcsiYOFChL33nu53L+XqVYQ8MwjlZsyAS/NmRl+OxM3NkD0hUySr5IV9HtmbtKS8QkEmeM2VjGWdnJweep6cE5v1F9UATGW8bopu3LiBJUuWqHTbMqZs166dCs7JFpQSsh24G6LZtrIG6j/7aF9ExqqtXgcWD9bsn14G3J4IeGkyH+ZEVvM+++yzKjjk8OHDKkj/7t27KmONJQfMEBVGA/8S+P2lZth9PgJfrg/GkSuRusdWHQ/DupM38ExTf7zWoTJ83XIuP0lE5s9S5knbt2+fr/PkfYa8P6hfvz6efvppNU9NRERERGQy5UgMqXfv3li+fPkDx/P6FiW1n0xQSt3oMmXKqACSkJAQ9ebEx8dHHdf3Ckqpxf3GG2/gyJEjKt1eQZh7akJDSouORszmzYjfs0d+RWAtJUfc3DQlSNzcYWVrg7ToGKRFR6kyJSlhN5AYHISUy1c0T2BnBzsfH9hXqqSybTg3bwbHKlXyLAliChLPnEH4t98hduvWbMcdqldH+R++V0El9OiuvTYGMRs2qG35Wau0epUqO0Ok/f91+5eZCP/2W9nRXBQ7OxX85NGzp9FdpJYtAfWnE8DvvwPPPuK9PTJOxTHmWLBgAYYMGYLKlSvjzJkzsLGxyfN8KXEgZSbi4uJyLUfi7++PkydP5iuow9RJeQcpOyGZRPKStUSgpYzX9V6OJCMDiUlJMCZy7eQmi9Q9l58DKfsoWQnle3/AkmHAyX8021U6A88WIPNbehrwQ1Pg9nnNfqMXgJ7f5qufO3fuVEH6omzZsup3V8rkGEsgMpEpkt8tyYDx5bpgBN2IyZ5hzd4GL7WqiJdbV4SrQ5GswSGiQpD/3TKOKapyJJYyT6oN6pTxRG7f2/2PSQD4559/jnHjxhWi50REREREuSv0u3AZeD/55JPo0aMH2rZt+9Aa3kWpRYsWKn211MWWJhOPkpI3LzIhK28g5HPXr18P13v50yUltrwBkPS5W7PcdI6MjFSrzPLi7OysJv5zS5/9v//9DxMnTixwAAYVLRt3d3j27q3ao0iPi0N6UhJsPD1ViQ9z41ijBsr//BPijxzBra+/RsLBQ+p4UlAQLg0YiHLfz4Bzw4aG7qZJiT90SBeAIXzfepMBGPTARJH3yBGwr1QRoW++hQz5n5aSgtC33kbK9VB4jXjZqG5csRwJ5deXX36JU6dOqSAKCbqQbbkh++effz40AENb4mD27NkYNGiQmiRu2bKlKol37do1VW5DJrLnz59vEQEYQlYN/vDDD/jmm28wfvz4XCe3pRTeK6+8AkMqjvF6kUpLBmZ3Bqp3A6p1B7wNUyJKaqdLtsGgoCD1OyTvUYQEMvXp0wcuLjmUIbi0Azi1NHO/4b1sFo/K2gZ4bCzw32ua/aN/AG0nAG6l8/w0+X8lN3I8PT1VuaDQ0FD1Oy83eCQYQ4KJjOl/GpGpkN+bDjVKoV01X6w4Hoqv1gfj6p0E9Vh8chq+23QOC/dexpgOVTComT/sbMzvvSoRWfY8qYxxp0+fjh9//BEDBw7EU089pft6Ml7666+/VBs5cqR6TIK2JdOHfD81a9ZUAdxEREREREaXCUNWLp0/f1698ZdVV5KeToIyunbtWiQlRx6FRI7Lm4vcvsXk5GT4+vqq1ZSSGrdBgwbZHq9Xr56qpyz1xBs1aqSO/fzzzw+dvJZJxJwmgiVyvHnz5qpWuQz+CzPJyEwYZEgZaWm49fU03JkzJ/OgnR3KTJkMz379DNk1kxL2/mRELtasQHWqXx8V/lholgE8pB8JR4/i6iujkHb3ru6Y54ABKD35fVgZMAAyq8BA+V+n2ZaFzvnMCktGTsYcMmaqUaNGjo9LAMWjkvJ02tXwokKFCipoQsZIj2LLli1qolXKL2i5u7vjzTffxDvvvFOoIAzpT2EMHlzAG9wFIMEnMmkdHR2txuAvv/xytqwXM2fOVCUgZKx+7Ngxdb2NRVGM14s0E0ZqEhInumce9KpyLyCjG1CuiSZAoQhIWnAp4SHt4sWLKvhCgpi0JBBeAuIlICnHEh8xN4CfWwFxtzT7JQKBVw8ANpoyQI8sNQn4th4QE6bZbzkGeOLDfH+6lMXZs2ePWlWbcq8MYKlSpVT5lCpVquje6xDRo0tOTccf+y7ju83ncScuOdtjFb1d8L+u1dGppvGUwiSyZEWdCcNS5knlc6X0mYx3s17HrCR7Xrdu3dQY/5lnnlHvIzp06IDu3btjxYoVBfq6RERERERFXo4kODhYpbeTQau8eZCVWbKKUSYBJSCjZ8+eajKtuD3szYUMuKVuoExSSyDJ/aTW4fvvv4/JkydjypQpheqLRIbLmyqZ/Javm5+63XlhEAYZg8ily3Dj/feRcW/yXJQcMgS+b78Fq3ysZLZkGcnJONuqNdKjotR+mc8+feTsK2R5ki9fxpWXX84sfwTApXUrlJs+HdbOzjA0Hx9Aey983z6gaVND94iMNQgj6/joxIkT+OCDD7Bx40Z1o/y9997L1+d+/fXXePvtt1WaZRmnSXY2uTktY7dly5apCdWVK1cWuG9yI7sgE8Ey7pTPk9rWxWnHjh2qBIUEpNzfb+mTt7c3/v7772ylW4xBUY/X9R2EYWOVjuPvVIMVsvdX7Tt4AOWbAuWbAWUbALYOuu9LPmq35WdD3i9pP0qTQAS5DvK7Jh+lyY0X+R2RwIucVq06ODiogHj53ZQMGLkGHaWlAvN7AZd3avZt7IEX12v6WBi7vwfW3/t9tXcFxp8EnB4tCF8CSbTBGPK9C/n5lcw28ppLkyw5+cmQQ1RQWX8/c3rMVMUmpeLXnZcwZ+cllREjqyYBJTChaw3ULecBU1JUr8ejPO/Dzs3vz9LDnkf7v1z+tucYXEdmFYQhQYgSpGzoIAxTnSeVLB+SsUOeJy+SQS8mJkYFkQgJMpHMXDdv3izw1yYiIiIiyo1els5K6liZBJd2+/ZtrFq1SqWYlShjSfH21ltvqSCMXr16qbIl8ubCGFZdyEpA0TCXEgra4xLlXRgyodi3b1/Ex8dj8+bNj/TGolatWrk+p6Wk2Cbj5dmnN+wrVMC1115D2u3b6tid336DlZ0tfN9809DdM2qxu3bpAjCsHBzglmXChSg38vsWsGgRrr0ySmXGEHHbd+DKiy+h/C8/q3JKhhQbm7l9L2stmQkZcxQm2CI3UpZAyhOsXr1apTyeNGmSSgcsE6l5kZV0ku1CxmoSWKC9OSHZIJYsWYLGjRur8aishpPMEAUhE8wS1PH777+rFMrSL7mhLy5fvqzSM8sNZLnBr806YUhyHSUwetasWSrLiKReFuXLl1eT+i+99JLBs9QZ83g9v+NvCWZZgFyyfkmchNyvOB8sYerQNykxIq9h6dKlUb16dfXzmK9SkFs+zgzAEF0+K3wAhmg0BNj+JZAYCSTHAgdmA63ffOTvSX4+JXhfbkLJ3xl5Pyk/v9Lkd12CTSQoQ8qWyPcuH+U68KakZUlNTVUBSXfu3FErtCU7jNyclI/abQlmkvOkSZCT9qMEOmkDLbJuW4oB8i/y/uQyN4DlczdguYH6RPknfwMlGM3Pz0/9LZSPctOazJ+MO3IbkxTFuNxU5klzIqXZZM75YeR3SRYRaslc9enTpwv1tYmIiIiIcqP3/OVeXl4q/bI0mQSRwbRkyJCViFL/+6uvvkLJkiVVCjjJkiE1rLX1BYvblSualcTyRjYn2uMy0V4Yo0aNwrZt29SkuNQplKZ9M31/aj8iU+PcsAEC/16Mq6NfRdKZM+rY7V9nw7FOXbh3Zl3N3ESvXKXbdm3bFja8Y035ZFuiBPznzUXo2+8gZv16dSzhyBFcHjwE/r/Ogq23t0GuZWqqrNTK3OePND0KOzs7VZ/50KFDatz4sCCMBQsWqI99+vR54EasrJiXSd2jR4+qYOCCBmFISuOmTZuqIAupMS3j16zkZuC4ceN0meAkI4GhyY1pbWC0uSjIeD08PFyNvYVM7kvpDgnOkRv+Bf150JIbupKmW7OTAiTFICMpBkiJ152T9dauJuw8A1a2jrBycAcc3WDj4KJ+buVnVftRginkvYE0CXrSbkugkrYVKAA7eC2wc1rmfp0BQONh0AsHN6Dpy8D2LzT7e38CWowG7B79RooEOsnKW2nakitSSkc+ys112ZaWlbye0uS6yPWTvyOyLU1W+kqTbe11lnO0QSuyn1OThQLam/PazxFyTF57+ah9Pu350uTGvvaGvzQ5pn1dtR+lafvHzB4PL1cjKerl91+y+2gDLywpcIJIS7IWZJ1H0v6/f/rpp1UZJyJ9MtV5UhlHSHYLbUa6nMhjco6cqyVjDCllSERERERUFIq0iLxMhEmQhbTvv/9eRUpLxLEEZMiqQplAl0moGTNmYPjw4TDE5I7IOgDPSib1hKSqKwxJry0Tcy+++GK241KPWyaX8pJbdDvrJJMxsStbFhUWLMDlZ55G0jlNysqwCRPgULkSHIxgdbCxSY+PR8zmzbp99x7dDdofMj3Wjo7w+2YabkyZisi//1bHkoKCcPnZ5+A/d476nTRkFgzBBXr0qCTDgPYG+sNcu3ZNffTwyDmNuva43MwtqAkTJqibHHPnzs3xhqk8Nnv2bBV8Ief+888/KC5SekWCmSXDXNu2bfOXDcFEFWS8LuPnAQMG6PbltZGWn7G39vNzG39L9hO5cfCAhEjg/EYgaJXmY1J09sdT77U4+cfvB1TuCFR5AqjYRhPMUBTuXgaWjsjc964G9JguOe719zWajQR2zwBSE4D4CODI70DTwr2vk98tqTMvTd5D3bhxQ6UKDwsLU01Shkugg7wW0kyR/E3JGjQiTbuv/agNJJEbUtptWSmsDT6RfXPJBiKZLOQGnPx+ysfc/g/IdZGAOAlKkmuRtWUNutEGvUi7P8hG2v3lpnLbfhhjyO5ZEHsuRODr9Wdx7lb2wVspdweM7VAV3eqUNrnvraj6m5/nzemc3D7v/uN53SzWfpTsQNevX9e1W7duqfGNrPxnEIZ5K6pMdKY+T5oTyar1119/4bXXXsMXX3zxQP/l/8w777yjSqw888wzuuPnzp1TWeOIiIiIiIpCsc7Y1q1bVzVJNS2TabJyUJqsajFnBXkDkVu9RGmCq4DI2Ni4uqDcjBm41H8A0mNjVaDBtVdfQ8Dfi5nl4T4xW7YgIyFBbVu7usK1dWtDvGRk4qxsbFD6g6mw8XBX2WdE8uXLCJFAjNmz4VAx0KBBGPfm54jyTZu5ID+lPaQkgdDWc77fgQMH1Edt+ZCCkDIIUoIkrxXrcoOvefPmqjRJcZKv+91336lAZklJ3qVLFxWUIVkeTLHkiL5JYIq+xsr5Hn87eQJ1+mtaajIQsgM4u1aTiSJKs6pUJ/o6cPg3TbO2Ayq01ARlBLYGStcBrHP/mcu3uAjg7xc0pUKEnTMwcD7goOcMhC5emrIk+37W7O/6Dmg4BLDVT9lEuVkuqcOlaclNG20AhjTJvqhtkt5cmqxslSbb2nIU2kwV2pIU9zftce1NUdmW80XWG/fa49qSFtKyZjSRj3Isa0kMbZPPFdqMGdLHgpK+aAMyZBWvBJ9lbfK3QP4+GOvNdLkhJllqJA28ZDnRXhstyTgjf8Pl771k25TgC8mgaazfjylpX7s82tQsh38OXcMX64IRESu1lICr0Wl4c+kZLDwUhsk9a6F+eU9Dd5XujXmkSWCakJJjO3bsMNkgNMour7GFPJZ1PlGbGcsc6GueVOvTTz9VgR0//fQT/vzzTzU21gZXSHmzdevWqeAlHx8ffPzxx+q4BDJJKT8poU1EREREVBQMtmxO3kRK9gtDZMDQ0pZBkTTFOdG+qTWWepuSCnvq1Km6faaxJWNjHxCAsl98gWv3VogmX7qkMmL4ffcdJ0xzKUXi9sQTsHZwKP4Xi8yC3IjwffNNWLt7IHyaJt18algYLj/3nCpN4lizZrH1JetiKMmYX5Cs+WTedu3apVbNSWBD1tXbcuP0559/VhnSZDWzlCXRkkxq0qTsiEyuavXu3Rvz58/HwoULVcYDyQihJVnX/vjjD/U15PMKc4NQVt0/jAQWF+ZGakGcPXtWTRrL9yoBzZLl4e+//1Zjw5YtW6qAjJ49e6o616bO0OP1Ao2/JQChcgdN6/oFcOs0ELwaCF4DXD+cvWBJegpwaZumCUdPIOBxILANENhKk73iUbId3DgB7P0ZOPE3kKa5sar0/BbwrY4iISVIDvwKpKdqAk72/wK0fK1ovta9wAx5vY3lPdqjkECMrMEi2pb1mHZbG0gipQi02/J3SX7mZVtuzsmKZWmSHSS3VdQSwCBNsg1Jk+AG2TfEe0npq/z9ksALKTWTNfBC+hYYGKiarITWrramomFjbYWBTcqja53S+GHLBczZeQnJaZrX48iVSPT+YReealweb3epBi9Xvlcxxv+LDMIwfxKsJ38TtSZPnowpU6aY9biroOT/xp49ezBixAhVFlsCMe7XoUMHFaQh52ozy8lYP7fMekREREREhWW+uYvzwd/fP1tK6/tpj2sH6IY2dOhQlWJPSL1krgIiY+TWvh28R72CiB9/UvsxGzbi9qxf4f2y4QKujElaZCRid+7U7bt372bQ/pB5kN8vGzdX3PjgQ1kyhbQ7d3B56DD4z5kNp1q1ij0ThpHNyZGRkHS/MpaRG22ymlNuAkZERODEiRNqAlRSyc+bNy9bSmB5XIIN7g+GkCAMCb6QwAMJNmjcuLGapJY09trsGLLKrVq1agXur2Rvk5WmsqpOO/66n6xG3b59O5o2bYriJt/b22+/rZqkKl+1ahX+++8/bNiwQfVJVvVJEEavXr1UkMrjjz9ukmNHQ4/XCz3+lvNL1dK01m8BseHAhU3AufXA+U2ZmSq0ZD9opaZpM1j41rz3HLU1H900mWCQka7+5quPEWeBfb8AlzPHGDqNhgJ1B6LIePoDDQcDB+do9rd+DtQZkNlP0tGWyJCAs8IGc8gNMrkZJoEN0dHRKruktkn2FvkowRzaMi5ZSQCGBGNIKQNpskBCPuaWfr6gJFBESifI33EJvrj/91i+Zs2aNVWT1clU/Nwc7fC/rtXxTNPy+GT1Gaw7lRnQ89fBq1hzMgxvdq6GQU39YWtjHuVvTJ02QElbNoLMl/ytlsxsWsVRNsPQ467CkGx6Mm6X7EoS/K3931emTBkVpFy5cuVs50u5L5b0ISIiIiKTCsKQVYn5oV2VU69ePTUBZAjytcXhw7Ii7UHa4zIJbwykJjkzYZAp8B49GgknTiJuxw61Hz59Ohxr1YTrY4/B0kVLyvqUFLVt4+0Nl2bNDN0lMhMlnnlGlbcJ/d8EWTaF9KgoXBn2YrEFYmSdB763gIoomzZt2uDdd99VZUeOHz+uAixkPCjp5vv3748xY8Y8MDmaG7kRLnWfJdXwb7/9pp7v6NGjKkVzt27dVD1oeawwJkyYoII9JIBh0KBBKkOHdsL58uXLWLx4scrEITcZ//e//xn01ZYx9eDBg1WTVfSyAlAyZKxcuRJffvklvvrqK5XKX66NZMno3LmzbqWjsTP0eF3v429XH6De05qWlgpcPwic26DJhCFZMjI05S90UuI150h75K9VCmj+CtCi6LJS6LSfBJz8VxNEkhwDbJgM9P2l6L+uhZJADilBIi2vQA1JvS5/ayVQSz5Kk6CI3IIz5Pm0ARnyUUqayN9VCRrJTwCSBIZIVg5tk8A4bTkfLbkZVqNGDRV4IUF5ZBwqeLngl+cbY/f5CExZcQpnb2oGdtGJqXh/+Sn8se8KPuhVG00DSxq6qxZPG4TBTBjmJae/sZIJo1WrVsWaCcPQ4y59BWPkp7whEREREVFRs8rQV8Hke7S1cvPdASsrtbpMalrrO2WyrKiU9K25fYsy+SQBILJK6MiRI6hfv/4Dbz5kUl9WVGrrbxpS1prU2pWd8v0RGWvGh0v9ByDl3koJGy8vVFq3Djaulp1a+PKQFxC/b5/aLvHccyg98T1Dd4nMMNDn+utvyN0XtW/t4VEsgRgrVgBPPqnZli918mSRfjkqRjKeEsVdcsMYSJmU119/XX3v949vZXwpK+i+/vprjLpXhssYyVhWypZIQIaMaaXfEvwiY29DlgU0lfF6sY6/E6OBK3vvlSfZriktkrV0SX6VbQA0ewWo1UdTGqW47J8FrH4zc3/YesCfwabGRkqAyM+0lFLSBkvI9v3BElnJ3wwJxtAGZMhzZG0S/BUeHq5KTuUUMCKZiuT3p2rVqnkGjpBxSElLx4I9l/HNhrOISdKMJ7X6NvTDu91qwJslSgxGftd++OEH9f/T0EGgVHhr1qzBvn37VLCFlMvQktdXxkaSSUhL+3fYnMdd+rR7926cP39eBSkTEREREZlFEIZEZYeEhKiMGLLCTup+a9PZXb16FevXr1eTM88//7yauJZBsdSElUH+oUOH4Ofnp7e+POzNhZg4caJKVy2p6aRv2lUF06ZNwxtvvKFWbWZN/2csLPmGCJmOxKAghDz1NDLu3azwGTsG3q+8AkuVcvMmzrdtp0kdDiBg0Z9wum9Sg8hUAzH++AN49lnNtiR42bu3yL4UFTNLH3NcuXIFs2fPxs6dOxEaGqpbyS2T5VKqQjJ5mAq50SoZMqS1bt0ab76Z5Ya5gZjSeL3YfxeS44BbQcDNE8DNU5ktKVrexgFW1ppyJ/LR1gGo1F4TfFG+qeZ4cUtPA2a2uRc8AqB0XeDlrYB1IbOHULGQn2ttQIY2OEMCMx51tb3cINRm0yhbtqwKwJAgDjI94TFJ+HxtEJYcyl6WwMPJDu90qY6nm5SHtbXplbkydZJx5osvvtD9f5RAJzLPIIyiGHOY0rirsGScLnPTklGEiIiIiMgsgjCk9p7Uxe7Tp49aGejh4ZHtcalXKysKly5dqt5oVKxYUdWs/uabbzB69Gi1Kq+gpBb2hx9+qNvfv3+/emPRLEu6/0mTJqF79+66fXlD07ZtW9UX7YS6pJiWfalLu3fvXtVHY8BMGGSKbn0zHbd/0aSjllIJlTdugE0hV2+Yqtvz5uHWZ5+rbbty5VBpw/pHry1PlE/RGzbg+vjXiy0QY+ZMYMQIzXbHjsCGDXypzIWlB2GQfpnaeJ3j70d0eQ8wN0spoB7fAI2H6flVoeIkWS5kRbT2d0F+H6Usj2TA1DbZl5JHsrBC+z+DzMehy3fx/vKTOBUqAWCZGvp74qPedVCzLLObFCfJPvPRRx+pj+PHj39gzo1MS1FnwjC1cZc+MQiDiIiIiAzNWt9PKPWzpXbszJkzc3wzKOlH5TE5R+qCy6TNp59+qgb2a9euLXRaRnlToG3a+JKsx+ScrOSNzZYtW9SbDmdnZyxbtky9uXjhhRdUrUNjemMxffp0tZJImqQIZDQ3mQKvYUNhfS/tcHpsLG7PngNLFb1ylW7bvXt3BmBQkXLv1Al+30yTPOBqPz0qCleGDkPi6dNF8vViNaXDFVfXIvkSRGQGTG28zvH3I6rQAqgzMHN/04dA/B09vypUnOzs7ODt7Y3KlSujcePGePzxx9GiRQt1A69JkyYqHb2kq5fslwzAME+NKpTA8tGPYXLPmnB1yMy6cPhKJHp+vxMfrTyN+OTsZUuo6MgcmjYzwaNmqiHTInN+2jlAaTImMfdxFxERERGROdF7JgyJipYSJAsXLszzvEGDBqm0dhEREWq/W7duKp2dpFaknHElHpmqiJ9/Qfi9CQMrR0dU3rAetj4+sCTJISG40KWrbj/wv+VwrFrVoH0iy8yIYVOiBCr8vgAOlSrp9et88AEwebJm+7nngAUL9Pr0ZEDMhGH8JNVyfkhJAC8vL1XPW1as08Nx/F0A0WHA942B5HvReU1eArp/zR83IjNwMzoRH6w8jVXHw7IdL1fCCR/3qYM2VS3rPZ6h/Pzzz6p0kMyrVeV7SpO2evVqlZ2iqDJhWDJmwiAiIiIiQ9N78UgJopA3gw8jNWazprWWDBmsZUlknko+/xzuLFiAtNu3kZGYqIIySk+aCEsStSozC4ZD1aoMwKBizYiBb6bh+rjxspwKaXfv4soLQ1Fh4e+w9/fX29eJicncZiYMouIlKxMfpbyVnNuxY0dVBrBKlSpF2jeyQO5lgDZvAxve1+wfnAM0HAyUqWfonhFRIZVyd8QPgxriqcbhmLT8JC7f1iyiuXY3AUPm7Efv+mUxqUdNeLk68FoXIWbCIHo4CWLR87pDIiIiIiLDliOpU6cOtm/frlpuduzYgW3btqlzta5evaqyaFDumA6ZTJW1iwu8R4zQ7d9dvBgp16/DksRkKbfk3qOHQftClhmIUfbzz+XOq9pPDQ9XgRgpYdlXMeqrHImbm96elojy4f3338fzzz+vJprlxkyfPn0wduxY1fr27QtXV1f12HPPPYcXX3wR1atXVxnpZNXldQv7f/yoOP4uoGavAF73Anwy0oG/h7IsCZEZaV3VB+vGtcbodpVga50ZBLjsaCg6TtuGfw9f483PIiT/1wXLkZiPnIJp9VGOxJJ9/fXXuHTpkqG7QUREREQWTO9BGG+//bZ6o9C5c2eMGDECGzZsQFBQkGqyPXLkSPWYTATLuSIqKgqHDh1C8+bN9d0dszJu3Dj1BkKapJO2sbExdJeI8s3z6adgW6aMZiclBeE//GgxVy/5yhUknTuv23fv0tmg/SHL5NGjO8p8+IFuPyU0VAViSECGvoMwmAmDqHhJAMaKFSswbNgwFdi8ZMkSTJs2TbW///5bHZPHVq5cqcbfx48fx/jx43Hr1i189tlnfLnywPF3AdnaA92+yNy/cwFYPBhITebPG5GZcLSzwVudq2PFa4+jXvnMEgl341Pw+uJjGDrvAEIjEwzaR3PPhBGbdQBOZkfm/LRzgNJkTEL5JyX4KlSowEtGRERERAZjlVEEudm+/fZbvPPOO0hOTn4gmlu+nAQQfPHFFxgzZow6dvHiRTVBLPUPGzdurO/umCXWZydTFLlkCcImTtLsWFuj4soVcKhYEebu9rx5uPXZ52rboUoVVFzxn6G7RBbszvwFuPnJJ7p9+Zn0n/8bbEuUKNTz9u4NLF+u2f76a+D11wvbUzIWHHMYv4EDB+Lw4cM4e/YsrK1zjrFOT09XdeMbNmyIxYsXq3G6rKp0dnbGuXPnir3Ppoi/CwWw7Utgy0eZ+w2eB56cocvMRETmIS09A/P3hODLdcGIT07THXdzsMW73Wvg6SblH6lsFuVt165dapGTZJft168fL5cJW716Nfbv34/WrVujffv2uuMccxScjHGPHj2qy/bm5+eH+vXrq7loIiIiIiKTzoQhJPXxmTNn8O6776JNmzZqwleabL/33ns4ffq0LgBDVKxYUQVtMACDyLx59O4Ne+1KhPR0hM+YAUsQu2mzbts1y8QKkSGUHPw8fMaP1+0nnTuHqyNGIj1eU9O7oFiOhMhwtmzZgmbNmuUagCHksaZNm2LzZs3/JJmIrlevHsuRUNFq/SZQ96nM/SMLgN2WMf4jsiQ21lYY+lggNrzeBm2rZZaZjUlKxYR/T2DwnP24drdwY03KxHIkRA9KTExUGd+k1HWLFi3Qv39/1WRbjsm8s5xDRERERFRcbIvqiWVl3UcfZVn1RIUWGRmpmiiCBCZERc7K1hbeY15D6Btvqv2YNWuROHw4HGvWNNurn3r3LuIPH9btu7VvZ9D+EAnvES+roIvbv/yi9hOPH8f1N95EuRnfqd/TgmA5EiLDiY+Px40bNx563s2bN7NNPru7u8O2gL/zloLj70KSle+S+eLuZeDqXs2xDe8DXpWA6t318AoRkTHx83TC3Bea4J/D1/HBilOITkxVx3eci0Dnb7ZjQrcaeLaZP7NiFBLLkVgGmfcLCQnR7Xt6eqpGD0pKSkLHjh2xZ88etV+3bl0EBASovzVyDY8dO4avvvpKZZHZtGkTHBwceBmJiIiIyDQzYVDRmD59ugpukSbp9dLSMtN8EpkK965d4VCtmm7/zoLfYc7iduwA7v2u2vh4w7FOHUN3iUjxGTcWns88rbsasVu24MbHHxc4yI9BGESGI+nIt2/frlpuduzYgW3btqlzta5evapWBlLuOP7WA1sH4OmFgKe2LnsG8M9LQNgx/ugRmSG56dm/UTmVFaNjDV/d8bjkNExcdhJD5h7AjSiuRtdHEEZcXFyhXy8yrLzee8mcn3YOUJqMSShn33zzDXbv3o3HHntMlSI5cuQIli5din///VeV7JMgjFatWqkgDV5HIiIiIjKZIIw7d+6o1XeFIZ8vz0N5GzduHC5duqSapJC2sbHhJSOTY2VtDa/hw3X7MevXI92MU0LGZClF4ta2nfr+iYxlgrz0xInZSuRE/rkIt3/9tUDPFxOTue3qqo8eElF+Seplmajv3LkzRowYoerEBwUFqSbbI0eOVI/JRL+cK6KionDo0CE0b96cFzoPHH/riYs3MOgvwMFds58SDywcAFw/xJ8/IjNVyt0RswY3xvSn6sPT2U53fPvZcDzxzTYsP3qdGT4LWY5E5tLS09P184KR0ZE5P+0coDQZk1DO/vzzTxVYvGrVqmwBx1q1a9fGypUr4e3tjYULF/IyEhEREVGxKPTdQBnkvvbaa4V6jtGjR8PXN3OFBOVM0g5KOj1tSj1pRKbIrUN7WDs7q+30uDjEbt0Kc5SenKzJhHGPK0uRkJGxsrGB39dfwbFeXd2x8K+nIWrFykJlwnBz01cPiSg/+vbtq1YASpDFrFmz0KVLF9SqVUs12Z45c6a6SSPn9OnTR33O7du3MXXqVIwfP54XOQ8cf+uRbw1gwFyJyNXsx94E5nQFjv3Fn0EiMyVzFr0b+GH9+NbZsmJImZKxi47i1T+P4G5cskH7aIqc772Xlv/7hV0URcYhp/k9OaadA5TGUiS5O3/+PNq2bQu3PN6ISvCSnHPhwgU9vWpEREREREUchCFv+gqauvz+5yEiy2Dt5AS3Tp10+wW54WsK4vftQ/q9STErJye4tGhh6C4R5fj7WP6nn2BXwV93LPTddxG3d98jXS2WIyEyrLFjx+LMmTN499130aZNG1StWlU12X7vvfdw+vRpjBkzRnd+xYoV8c4776Bx48YG7TdZmModgSdnSBSgZj8tCVj6MrB+IpDOUotE5srXTZMV44t+deFin5nRc9XxMDwxfTu2nQ03aP9MMUOCNhCDJUmIAFtb23wFJMk5ci4RERERUXHQy8hz586dGDZsWKE+nx4uMjJSNcGgFTJ17j17Imr5crUdu3070iIjYePpCXMSszmzFInLYy1h7eho0P4Q5ca2ZEn4z5yJkKefQdrdu0BKCq69+ioCFv0Jh8qVH3rhkpM1TYvlSIgMQ+qFf/TRR7z8esTxdxFo8BzgWQFYPBhIuFeScvcM4OYpoP8cwKlEUXxVIjIwWdU/sEl5tKjkhTf+Pob9lzS//+ExSRgyZz+GPRaIt7tUg6Mdy67mh4uLi7qhzCAM8yXzfiEhIbp9yYTBbBg5kxIkmzdvxsWLF1WgcU6kpIuc07BhwyJ6xYiIiIiIiiAIQ9K+SSsMltZ4uOnTp6u00VlXPxCZKpfmzWDj7Y20iAh1wzd67TqUePopmNOESezmLbp9t/YdDNofooexr1AB5X/+CZeHvICMxESkx8bi6qjRCFz810MDpLJmwRAsR0JE5oLj7yIS2Ap4eSuwaBBw86Tm2IXNwKz2QL9fAb9GRfWVicjAypd0xqLhzTFn1yV8sS4Yyanp6rjs774Qge+eaYCqpVjbLj9BGOHh4Yi9fyBOZiMtLU0F2GpNnjwZU6ZMMWifjNWIESMwZMgQVW5ErtOgQYPg5OSkHktISMCiRYvUtUtMTMTIkSMN3V0iIiIishBWGYVMqbBt2za9dUbSJVP+VuJVq1ZNfUxKSuIlI5N145NPcHf+ArXt1LgRAn7/HeYi4eQphPTvr9mxskKVXTtVtgEiYxezcSOuvTZGIonUvnOL5ipLhpWdXa6fc+UKUKFC5n5KiqSELY7eUnFwvJfFRyYtyTjcuXNHvS7aVOQFIatn5TUtyf9NeeL4u4glxQLLXgHO/JfloJUmW0bHKYCLd1H3gIgMKPhGDMb8eQTBN2N0xxxsrfFe9xp4vnkFLtbJw5IlS3Dy5El07twZLVj20mStWrUKBw4cUPOh7dq10x2XcZ5M1wYHB+uOMRPGwwMxZs2apfu74e2tGUNEyMKfewtl5JyffvqpSF5LIiIiIqL7FfoWCQMnik/WN1zMHEL6ICUzjx8HDh3StAsXNMclyYo0a2vNx/LlgQ4dNM3LS3/X3qPnk7ogjISDh5By/Trs/Pz09wUMKDZLKRKnBg0YgEEmw61jR/iMHYvw6dPVfvyevbj5+RcoPfG9XD8n6wI8uV/PAAyiouXj44MXXngBs2fPLvBzjB49GgsWLEBqaqpe+2ZuOP4uYg6uwMD5wPavgC3aUjoZwJEFmsCMdhOBxsMAG0b2EZmjaqXdsPzVx/DZmiDM260pu5CUmo73l5/C9rPh+LJ/PZRwsTd0N402E4ZgJgzTlte6OJn3CwgIKNb+mLJffvkFTzzxBL777jvs27dPZYoR9vb2aN68OV577TX069fP0N0kIiIiIgvC2SwiC3PyJDBjBrB7N3DmjKS4zN/nzZypEjqgUSOgUyfgiSeAVq00QRoF5Vi7FuwDApB8r85p1KrV8H55OMxBTJYgDLcO7Q3aF6JH5TXiZSSdPYvo1avV/t3ff4dD1SooMXBgjufHZC5ehKsrrzdRcUzYFzKZne55iAxOBpht3gIqtABWvwXcOq05nhgFrHkLODQP6Pq5poQJEZkdRzsbTHmyFtpU88Fbfx9DRGyyOr7xzC10+24HZjzTAI0DmFEwtyCMuLi4Yn/NiIyVBFlIkyDj27dvq2NeXl6w5SoBIiIiIjIABmEQWVDwxQcfAH//XfDnkHs1Bw9q2qefAg0aAHPmAPXrF+z5ZGWHe88eiJjxvdqPXvEfvIa/ZPKZXiSjR1JQkG7ftR2DMMi0yO9gmY8/QvLly0g8dUodu/HhR3CoWBHOjRvnmQnDjSW8iYrFzp07MWzYsEJ9PpFRCXgcGLEDOPArsOVjIClac/zWKeC3HkDFdkD7SUC5RobuKREVgXbVfLFmbGu8veQYtgRrVrCHRSXiqZl78XqnqnilTSVYW5v2+0R9cr0X+cwgDCJg+/btKF26NKpWraouhwRdlCpV6oFLc+7cOYSFhaF169a8bERERERU5BiEQWQBwRcffqgJvshpwauUk5cgCslwUacOYGenyY4hLT0dSE4GDhwANmwAbt7M/rlHjgBNmgDvvANMmgQ4ODx6/zx6ZAZhJJ07j6TgYDhWrw5TFrN5i25bMn04VAw0aH+ICsLayQnlfvgelwYMQFp4BJCSgmuvjUHgkr8fKBuUNQiDmTCIisf58+dVKwxTD3okMyRlR5qPBGr3BTZOBY7+nvnYxS2aVq0b0HYCUKauIXtKREXAx80Bc15ogtk7L6kSJanpGUhLz8CX64Kx9+JtfPNUfXi7FuBNpxliORLzwjFZ4bRt2xZDhw59aKm+L774AnPmzEFaflPCEhEREREVAoMwTEhkZKRqgumjKS8SbLF9O/Dtt8CyZQ8GX9SuDYwbB7RoAVSrlr+SIvIcJ05ogjHWrdN8FFJK/uOPgX//1WTFaN780V4b+woV4FivLhKPHVf7UStWmHwQRuyWzFIkrixFQibMrnRplJ8xA5efH4yMlBSk3b2La2PGImDRn7CSiK17WI6EqHht2ZIZ7EdFi+NvA3H1BXr/ADR6AdgwCbiyJ/Ox4NWa5t8CaPwiUPNJwJY3ZYnM6Wb0S60qolGFEnj1jyO4Hpmgju84F4Gu32rKkzSv6AVLx3Ik5k/m/ULulW4Vnp6eqlHu1ys/15SIiIiIqLgwCMOETJ8+HVOnTtXt2+TnzjlZlIQE4I8/gO++A45rYhoeCL6YPBno2xewtn6055bFsnXratobbwAbNwLDhwPaOYEzZ4CWLTXBHZ9/rsmokV8ePZ/UBWFEr1wF3zfegNWjdtBIpEVHI27/Ad2+W3uWIiHT5lS/Pkp/8AHCJkxQ+1KeJGLWLPiMGqU7h5kwiIpXmzZteMmLCcffBla+CTB0DXBhE7D5IyD0SOZjEpghbbUHUKsPUGegJjDDRMeQRJRdA/8SWD2mFd7+5xjWndKkZAyPScKzv+7DO12qYXirihadPSBrORK5sWzJ18JcSbaGwMDMrJqTJ0/GlClTDNonUxcaGqr73SEiIiIiKmoMwjAh48aNwwsvvKC2q0n6AiIAp08Df/6pyXyxbx+QlPTgZalVSxN80a+f/ualO3bUZMZ47z1gxgxNpgxp33yjWRU/c6YmcCM/3Lt2wc1PP1U1UFJv3kT8/gNwad7MJF/f2G3bNOlBJFCqRAl1A5vI1Hn26Y2Ew4cQ+fcStR/x088qwEibtSZrEIabm6F6SUSkfxx/GwEZUFbuCFTqAAStArZ/CYQdzXw8MQo4NE/TPCsA9Z4B6j8DlAgwZK+JSA88nO3w83ONMH/PZXy86gyS09JVeZJPVgfhyJVIfNG/LtwcHyH63wwzYciN+sTERDg5ORm6S6RnsvAqa+k5ZsHIbv78+dn25Vrdf0wrNTUVwcHB2LhxI5o/avpWIiIiIqICYhCGCcmaepCrHOjqVU1gxW+/AenpOV+PTp2AMWOAbt2KZlGgLCCQkicDBwIvvggEB2uO//qrJmPGa6/l73lsvbzg8lhLxG3fofajVq4w2SCM6NVrdNuu7dvBihlryEz4vvMOYnfuQmpYGJCSgtAJ7yJw8V+qLAkzYRCRueL428iCMWr00LTrh4ADs4GT/wKpmlIFSuRlYNtnmhbYGmgwGKjeDbDX3KwkItMjcx9DWgagfnlPjFp4WFeeZM3JGwi+EYOfn2+EqqUsLwrYzs4O9vb2SE5OVtkwGIRhnj/7AQEMKMyNLFLTzo3Kx127dqmWG8kY4+joiPfff79IXi8iIiIiovsxCIPIxJw8qck8IcEXOWW9kAUxzz+vCYCoWbN4+vTYY8CRI0CHDsCeeyW7x48HatTQZMzID4+ePXVBGDHr1iPj/fdhZW8PU5IWFYXYnTt1+x7duxu0P0T6ZOPqijIffYirL76k9pPOnEHELzPh8+polf1Gi9ldiYioyPk10rSun2uyYxz/C7i4FcjIEpl8abum2bkA1boCtftpMmrYmtb4kog06pX3xIrXHsfYRUew41yEOnYxIg69vt+Fz/rVQa/6fhZ3qaSswp07d1QQhre3t6G7QwUggQFUMBJMIcEXcg0/+OAD1K9fH7169crxXAlYKlu2LJ544gmUKVOGl5yIiIiITD8IQ2oXPkrGhosXLxZld4hMVnIysHChJsPE7t0PPi7VaYYOBVq3Bho1kjeYxd9HyX76779AkybAtWuqsojKkCElUqpUefjnu3XoACsHB2QkJSE9JgYJx47BWZ7MhMRs2KAyBAgbLy84N21q6C4R6ZXrY4/Bc+BARC5erPYjfv4Zbh3aIza2hu4cliMhIqJi4+AG1Hta06JDNcEYRxYCt89lnpMSB5xcomlOJTWZNGr2BgLbADZck0BkSkq62GPe0Kb4dtM5fLdJ83uekJKGsYuO4lRoNN7pUh021vmfgzKHkiQShBGbNS0dkYWYMmWKbnvevHno2LEjJku6WCIiIiIiI1Gks07PPvusLggjPT1dDYolQrlz587w8fFBeHg41q1bp84ZKneQiSgbCWRYsEDeXAKXLz94cSSAf+pUTQCGrRHMIZcuDSxfDjz+OJCQANy9C8hCBMmO4eGR9+daOzvDuVFDxO3WpNKI27PX5IIwolev1m27d+4MK2N4UYj0zPfttxG3cydSQkOluC5C/zcBsc7/SOiRepyZMIiIyCDcywKPjwceGwdc3QccWQCc/g9Iis48J+EOcHi+pjl7AzV7aTJk+Lcomtp9RKR3EmTxeqeqaFDeE+P+OoqoBE0Q/MztF3EmLBrfP9MQHs52FhOEISQTBpk2lhwunJCQED29EkRERERE+lOkM00fffQRPvzwQ9Uk+KJRo0a4fPky5s6diy+++EJ9lIFyw4YNkZqaWpRdITIpEsDw00+ach4SYHF/AEaDBsCcOcCFC8Dw4cYRgKHVsKGsQsjcP3MGGDRIE1DyMM7NW+i24ySFhglJjYhA3N7MPrv3YCkSMk82ri4o8/FHuv2k4GDcOZn5R4pBGEREZFCyCMC/OdDrB+DNc8BTvwO1+gA2DtnPi48ADs4G5nUDvqkFrJ0AXDsoueEN1XMiegTtqvti5WuPo3ppN90xKVPy5A87cfZmllp5Zl6ORDAIg+hBs2fPxrBhw3hpiIiIiMhgim25z6xZszB69GjY2dk9UJdPjv8qdRaILJwEX0yfLqV8gFGjgHNZMilLoMVLLwGHDwOHDmmCM6QEiDGSMiSTJmXuS4KI9957+Oe5tGiu25ZyJOkmtKIneu06Sfmjtm3LlIFT/fqG7hJRkXFp0QKeTz+l2797KVy3zSAMIiIyGnaOQI2ewIB5wFvngT4zgWrdAJv7avfFhAJ7fwR+7QB8UxtY8w5wZR8DMoiMXPmSzvjnlZboVqe07tjl2/Ho88MurD91A5aSCYPlSIgetHPnTvz222+8NERERERk/kEYaWlpOHbsWI6PHT16VJUrIbJUycnAzz8DlSsD48cDN29mPiaZkZ99FggOlmAmTRaMe1V+jJqUUOnTJ3P/yy+Bs2fz/hzHmjVh7e6u2UlNRbxEm5hiKZKuXWHFlNZk5nzffAt2fn5qOz4tMyLMLXMxIhEVEVnVN0dSYj2izz//HO3bty+SPhEZPUd3oN5TwDN/ajJk9PoRqNQBsNKU09KJvgbs+xmY8wTwbV1g4xTg+mEGZBAZKRcHW/wwqCHefKKq7n1yXHIaXl5wCDM2nVNZWc0Vy5EQEREREREZr2IrYvDSSy9h0qRJiIiIQM+ePeHj44Pw8HD8999/+PbbbzFu3Lji6orJioyMVE2Y80SCJZESHX/+CUyeDFy8mP0xR0dNqRH51ahYESZHYhDmz9cEjZw/r0kS8ckn2UuV3M/KxgbOTZsgduMmtR+3Zy9cW7eGsUsJDUWCpCi5x71bN4P2h6i4ypK4tm+PuwsWIC7dWXecmTCIit68e/9MHzXFclBQELZt21ZEvTJPHH+bKSdPoMGzmhYXAZxeDpz8F7i8S95pZZ4XeQXY+Y2muftpsmjU7geUb6YZ7BKRUbCyssKr7augeml3jPvrKGKTNOVuv95wFhcj4vBZvzpwsL0v4MoMsByJeZN5PynhrOXp6akaERERERGZhmKbOZKVdx9++CHmz5+PNm3aoFatWurjggUL1PHPPvusuLpisqZPn47AwEDVkpOTVXYRMt2yI5LVolYt4PnnswdgODgAY8Zojn33nWkGYGS9Gfv++5n7v/8OXLiQ9+e4NMssSRK3dy9MQfSatbpt+woV4FirpkH7Q1RcbEuWUB/j0zWpkAWDMIgM686dO3wJ9Ijjbwvg4g00eREYugp4Ixjo8Q0Q0Epu62Y/L/o6cGAWMLcLML0OsOF9IPQoM2QQGZGONUth2eiWCPTOHJsuPXIdz87ah9uxSTA3LEdi+vJaYCVzfto5QGkyJiEiIiIiIgsOwhg4cCA+/fTTB95IyMqEt99+G9evX8fFixexe/du9VH233nnHVhzJdFDSbaQS5cuqWZvbw8bG/NbyWHukpKAH34AKlUCXn5ZU2JES15OOSZZI779FihTBmbhmWc036+QuKFPP837fJcWmUEYSWfOIPXuXRi76FWrdNvu3bupv3dElsCmREn1MZ6ZMIiMwldffQU/Pz8V4MxSf/rB8beFcSsFNB4GvLASeP0M0PlTwL9FDgEZ14Bd3wIz2wAzGgIbpwJXD2hSvxGRQVX2dcPSUS3RLFAzThUHL99Fnx934/ytGJgTliMxbzLnp50DlMYMwo/Gy8sL/v7+RfTqEBEREREZIAhjyZIlmDhxIlJSUnJ9ExEQEIBmzZqpjwwkyD9JOyjXTJrc5OWNXtMRHg588IEmGOHVV4GwsMzHJP5o0CBJEQ788gtQrhzMiq0t8N57mfu//QZkyaj5APtKlWDr46Pbj9+3D8Ys6dIlJJ4+rdtnKRKyJDZeJSExl1mDMNzcDNolIoskKyWHDx+uApuTkpIwZcoUtG7dOlsKayoYjr8tmHsZoMUoYNha4M2zwJMzgIrtAKv73kLfuQjsnAbM7gh8XQ347zXg3AYgTVMOgYiKn6ezPRa82AwDGmW+ub5yJ14FYuw4F2525UgkU6o0Ml05ze/JMe0coDSWInn04GQJXiEiIiIiMrtyJF988QW6du2K5s2bo2fPnpg6dSoOHTpUVF+OyCjFxQEffaQJvpg8Gbh+PXvZkVdeAc6cARYuBCpXhtl67jkgIECznZoK5FV9SCYanJtnKUmyx7hLkkSvWaPbdqhaFQ7m/EIS3ce2ZEmkZNghFXa6YyxHQlT85Uc6deqE2bNno3Llyvjvv/9Qp04dlXWuXr16+E2iH4mocFx9gYaDgcHLgNeDgC6fA+WbPXhe3C3g8HxgYX9gWnVgzf+A0CMsWUJkAPa21viif138r2t1aO9vxySm4oW5B7D44FWzeE0cHBx0C5viZPKByEINGzYMc+bMeeh58+bNU+cSEREREZl0EMbkyZOxbt067N+/H6tWrVJBGE2bNlWr8k6ePFlUX5bIaOzaBdSrB0yaBMRkyXpqZweMGqUpO/Ljj0DVqjB78j2/+27mvrw3vprHvJdL1iCMvXtgrKTsUvSq1bp99+7dDdofouIm5Uji0jNrbgsGYRAVn7Nnz6qA561bt6J9+/bYt28fevTogQMHDuD1119HbGysmmgeMGAA7ppAeS8ikylZ0nwk8OJ6YNxJTcmSgFaA1X2lIuPCgX0/ATPbAj80A3Z8DURlicgmoiInAf4j21TCT882gqOdZvorLT0Dby85jm83nnugjK4pfn8sSUKkCa7YuXPnQy/Frl27GKBMRERERKYfhCEBF7/88gu2b9+uVuRNmjQJNWvWVIPiFi1aYOPGjUX1pYkMShag/O9/QKtWwIULmcdLlAA+/BC4cgX44QfzKzvyMEOGAOXLa7alWtHnn+d+rkuLzCCMlMtXkBIaCmOUdPYskrO8yO7duhq0P0TFzbZkCcRnZJYiES7ZYzKIspk2bRr69u2LKlWqwMPDQ63grFChAgYPHowTJ04U6GpJyY2RI0ciMDBQPZ+3t7caa3755ZdmffWPHDmivs/z589jxIgRKvhZm6bazs5OpWBev349ypQpg3///Vdlxzh27Jihu01kXjzLa0qWvLASeOs80GcmUL0HYGOf/byIYGDTB8D02sDv/YFTy4DUJEP1msjidKldGn+PaAlvVwfdsW82nsW7S08gNS0d5lCShJkwiB5OyvawLDYRERERmXQQRrly5dSKPKlN/fjjj6sVeZIJQybXf/31V1hbW+Opp57C9ay1GcyYpIFu3LixmhiXVQoNGzbEokWLDN0t0rOICGDqVMDfXxNgoF1UY20NvPkmcPEiMHEiULq0ZV56e3tgwoTM/VmzspdnycqubFnYVfA3+pIkWbNgONatC3ttlAmRhbB2d0e8lbtu39kpHfcyIhPl6JNPPsGaNWtQsmRJdOjQAd27d4ejoyMWLFiARo0aYeXKlY905eS5atWqhZkzZ8LLy0sFeMg4SwIzJBjYnB0/fhzR0dGYPn06fvrppxwnlOUay/i7T58+CA0NtZggjKNHj6JVq1ZwcnJSwTnff/+9obtElsC5JFDvKeDphcCbZ4Ee3wDlMwOLlYx04PwG4O8hwNf3ypXcYJZIouJQp5wHlo5qiYremRHDf+6/ipcXHEJ8cqrJvgjaTBiS/YrIVBhinlQy3xw+fBg+Pj5F+nWIiIiIiLRsoWcSYPHYY4+plYg5kZTIEqTRpUsXtULvm2++gbmT9M+9e/dG/fr11Y2GZcuW4ZlnnlHbcpxMU0IC8McfcgNIJvuzZ73QqlIFmD8fyFJdw6JJ6c2PP9YEXyQnA7JIefr0nM91ad4CkZevqO24vXvh2a8vjElGWhqis9ws9OjezaD9ITIEK2trJLiU0u27OKUVZZItMgPLly9XwRYyBsrqxx9/xOjRo/HSSy/h2rVrsLV9+BA1KChIBV24ublhw4YNaNmype6x9PR0NclqzuT7/uuvv9C5c+c8zytRogSWLFmi6mSPHTsW8fHxMGfh4eHo1KmTysonQT3yczBu3DiVeeX55583dPfIUjiVABoP07Q7l4Dji4FjfwB3QzLPSbijKVcirWwDoMFzQO3+gJMmow0R6V/5ks5Y8kpLvPTbARy+EqmObQ66hadn7sWcF5pky5RhKpgJw7SZekkcQ86TSim+rNauXfvAMa3U1FRcuHABN27c4HiQiIiIiIqNVYaeR/wSUSzRzLIyMS8yUR4REaFqWVsiyRAi6aH//vvvAn2+9uZFYmKinntGDxMeLjeLNCVFZDsnshhl9Gjg/feZmv9+M2YAY8ZotuXH+NKlnLODRK9di+vjxqttGx9vVNm+XdW8NRax27fj6ssjNDs2Nqi8eTPsSvkaultExW5+i/cxZO8HajugTDwuhWYvT0Kmr7jGHJUrV1aTo5KtoW7dug89v1u3bmq8uWrVKrVNJD788EPMmDFDZUNxdtb8PRo1apQqhVjY9x0cf1OhpKcDl3cBR34HTi8HUhMePMfWEajxpCYgI6CVJqUeEeldYkoaxvx5BOtP39QdC/R2wYIXm6JcCdMay8r/Nyn7K8GHHA+ZZoCylJiT4IHWrVtb9JjjUedJZRGglswVPWx6W8r1yYLA2bNnq/KFRERERERFTe+zOvXq1cO2bdtw5YpmBXtuJDXw1atXYakkZXZKSoqhu0GPIDoaeO01TbmRKVNyDsCQrIby2OXLmpIk9zKDUhYvvZQZdCHzCRLMkhPnZs1022nhEUjOKdWIAd39a7Fu27VdWwZgkMVKcMxM5+pqz/r2VHAyMSrspX7VQ8gYct26dahYsSJvOFA28nMhN6G0ARhiwIABOHfuHC5KbTgiQ5GbRYGtgL6/AG8Ga8qVlG2Y/ZzURODEYmD+k8CMhsDOb4DYXKK+iajAHO1s8NNzjTC4RQXdsUsRcej/0x6cuxljkuVI4uLiDN0VomKdJ7106ZJqMr6TAIz+/fvrjt3fpBy2lOyRoBcGYBARERGRyQZhDB06VEVqS43vM2fO5HiODI4l0lv7ZlFfDh06hM8++0ylppaSJxIJnZ+V8wkJCXj//fdRtWpVFW1etmxZVTZFBun6JOnvpHa3pI6WtNkjRtxbRU9Gb+tWQBblSknxrAsRnJwAeRn/+UdTjuTGDWDyZHnzaMjeGje5ZmPHZu7LtcuJbYkScKhRQ7cft2cvjEXKzZuIlR+Ke0o89ZRB+0NkSIkOmX/wnG0tZ6UW6deCBQsQHByMKlWqqPYwW7duVSVHJLOajK8WL16sSm28+uqr+Pnnn1WKYzI+xTFWl2wX1atXz3ZMuy8/Y0RGwdFDU6rk5S3AK3uA5qMB5/veQNy9BGycAkyrAfw9FLi0Xd5IG6rHRGbHxtoKU5+shbc6V9MduxGdiIG/7MGxq5pSJaaA5UjMQ1Fl/TTnedIKFSqoFhAQgMmTJ+PZZ5/VHbu/SYYNbcA3EREREVFxeXjB7Uckg97//vtPpY9r2LAhhgwZgj59+qjUiO7u7jh//rxKEyyToD169NDr15bnlajmRyEBI5L2b+/evWpQ3qtXL5W+eO7cuaqOtByXVZaFJXUH5fmFjY2Nqn3etWvXQj8vFa2EBOC994Bvvsl+vFQp4NVXgZEjAWYxfHQDBwITJmi2JVZL7olUy5z70nFp3hxJ94K54vbuRcnnn4MxiJTIkbQ0tW1XtixcWrY0dJeIDCbBrqRu29k6nq+EmUpOTkatWrVyfOzUqVOP/Hxffvml+jxZtSlBu7Itk7t//vmnGic9zOnTp3U3Hlq1aqXGa1m99957WLJkCdq1a/fIfaOiUxxjdQnA8fT0zPYcJUqU0D1GZHRK1QS6fAJ0nAKcXQscWQCc3whkpGseT08BTv2raV5VgKYvA/WfARzcDN1zIpMnN6NHt6sMDyc7TFp+UsU53Y1PwaBZezFrSGO0rGT8JQu0i5tklT+Rpc6TShAGEREREZHZB2GIRYsWoWbNmiraeubMmZg1a9YDmTAkRfAHH2hqyOtLixYtVA3xJk2aqCbR0ElJeadG/+ijj9QbCPnc9evX61YRTJs2DW+88YaK9JbVllqRkZHqjUJe5Hvzl5oVWUi6uwMHDiAmJgZr165VKzUl1V6/fv0K9T1T0Tl0CHj+eU2QgJatLTBpEvD221Kjk1e/oOT9er16wLFjmv2lS4H//e/B81xaNMeduXPVdvz+/chITYWVvAgGlJGWhsi/l+j2PQcOgFU+bhgSmat4W3fdtgs4+Uv5LxmxadMm3b6sUJs/fz4aNWqUr8/X3kz/9ddf1djtjz/+UDWew8PD1WTz77//roKAJbjDz8+PL4uRKI6xOpHJsrUHaj6paZFXNcEYh+cDMWGZ59w+B6x5C9j0AdDgOaDpcMCrkiF7TWQWnmteQQVijP/rKFLTMxCXnIYX5h7AjGcaoHOte7U0jRTLkVBeLGWeVMrO7dmzRwVnS/lrLfk+xo0bh5MnT6qvL9+bZAUhIiIiIioOVhkSEVFEbt26pSbUZZL9xIkTiIiIUNkwWrdujSlTpqB27dooSpIyT95c5PYtyqpOX19fREVF4fDhw2jQoEG2x+vVq4fjx4/j4MGDupsCkuL6lVdeyfPrtmnT5qGTwcOHD8e2bdtUyuSCfm/aCHXSr0uXgK+/Bn75RVIjZh6XyhgLFgD5vD9EDzF1KjBlima7aVNg374Hz0mPi0Nws+a6FyJg8V9wkrowBhSzdSuujbz3N8DWFpU3b4Kdr69B+0RkSG/1O4Wv/tVkSOhfeQf+PteKL4iZKcoxh0zayhhRAnM3btyoJkYli8XDvPzyy7ogX0lfPFBSLGUhGdhkUvfdd9/Fxx9/rPd+k/GO1eV8uUHwzjvvPLDScvXq1YXKRMfxNxW7tFTg3Drg4FxNdgzc/7tiBVR5AmgxGghsLcv6+SIRFcLW4FsY+fshJKak60qWTBtYD73qG29Ap2QVkwxjYtKkSfnKKEbGQ7JUSLnmDh06qACCoh5zmOs86ciRI1VwtmTskLIr4ubNm6qcigR5SNYb+Z7l92Pfvn0qczMRERERUVEr0iXlMnB/8803VTNGu3btUm8sKlWq9MAbC9G/f3/15mLFihW6NxcysJdWWPXr11ep/ApD3jNFRwPx8fLGW9NkW+5Xa99PZf2Ynq6poCAftdtZ2/3H5Hly2peP92/f33J6P2dvD3ToAHTvLqkGYXSOHwc+/1xu5ugqTSgylzl+vKwGAJycDNlD89KnT2YQxv79wLVrwL33yjrWLi5wqlcPCZKWRCaY9uw1eBBG5F+Lddtu7doxAIMsXgI0KZCFU1q0xV8PejRSNkImnOXmuKy2k5sHTzzxhFqplxftijz5OGDAgAceHzp0qArCkIlcMl0FGavLZHtQUFC287T71XKqfUZkzGxsgerdNe1uCLB/FnB4AZAUde+EDE2QhrT/t3cXYFKVXxzHf+wuS3cJ0mUgJSAICAiKgRgoYmIXFqJ/uwVbBLtBUSwElLBBQASkQUFEJKSUbtj8P+cdJhZ2YWN2p76f57nPnRtz5507w/LOe889p1pzqV1f6ZjuUlwY/tgCIkCnoyrrw2ta6+qhM7V9b4pS09LV99N5Sk5N1wUtDvixGiaKFSvmu8BsARl24xMQa+OkP//8s3u+NwDDvPfeey4Ao1+/fnryySfd7w3LsmEZPSxrHgAAAJDfQpvXP8Tm76+FkFUEtHe9/cAItl9++cWlATycrOqvW3R6evoxKlNGEeWll6QaNaQbbpCuuUY6IsSZPS1YZPJk6dlnpfHjD95eq5Y0dKjUqVMoWhfdGjf2lCX5+2/P8ujR0i23HLxfidatfUEYVpJEN1yvUElev147Ay7ole3VK2RtAcLFrtSAIIxkT4kIIKcKFy6sXr16afbs2W5Q93BBGFa+xFhaYbvwcCBvH8uysiG2+uqnnXaaXnnlFe3Zs8ddmDIjRoxQgwYNsl2//FD970SLKgZCoVxt6bQBUqf7pAWfSDPelDYG3C28dq70+RVS+XpSu9ukJhdJhamfCORUy9rl9cn1J+ryd2do0y4bd5H+N2K+klPTdPEJGcsphIO4uDhX6sECMAjCiDyHSk5s/Y6s+iRWci9Wx0kzs27dOnU6YODOSpwUKVLEZWK2/tu5556r1q1bu0wYAAAAQEGIUwxbtWqVmwdGSgfyrl+5cmWeXufkk092g8GWZnvs2LEuxZ7VLg9MkxxL/vlHevBBTzDGRRfZD62Cb4NldLQAe/v9aL/TDgzAsDH611+XFi8mACO/2DUzy4bhNWpU5vsVt1ol++2eO1fpyckKla0jvvCkjLELhtWrq0TbE0PWFiBc7Er1pwgqlrTpkAOJwKFYXWizYcOGw54o7515W7ZkHvizefPmDBkzEDt9dbsTMy0tzZWosZKIzz//vN58802XZQWICkVKSq2ulW7+VbpspFSvc8btm5dJY26XBjeVpr8uJe8JVUuBiHVstdL65Po2qlSqiFu27u19Ixfqg2krFI68/R0LwgBicZzUyrYEluKxkiuWFc+CLgJ/D9SpU0dr167NU9sBAACA7IrpTBg7d+50c7trIDMlSnju7rX0dXlhNRNffvll/fPPP+6Yxx57rLvL86yzzjrsc7OKbvfUccx4QdvehjW5cGH/usC5/R6Ji/PPvY+9U+ByQkLGbd513vWBj+31vMvedXasAy1bZvUuPeVKjM2t9IdNn30mZZJNPOjWrJFee0166y1p48aDtzdrJt17r3T++Z73gvxlQRgvvOB5bAkmNm2SKlTIuE+xpk08X7LkZKXv3q29ixeHpCRJekqKto4Y4Vsu27OnCmX2RQdizO5kz+C0Ka6dStu+XfGRlqYJYcFbOsTSHx9O27ZtVaFCBa1fv15Lliw5qMyE91iZpVFGdPfVK1WqpO+//1633HKLunXrpipVqri005dffnm2X/dQ/W8gbNiPvPpdPNPaedLUwdKi0VK6J2BYO9dL39wr/TxIOqmfdPwVZMYAcqBBlVL69Po2uuTtGVq/fa9b9/CXvyspJU3XnpS9zEoFxfv/off/TUSezDK7WfaG/Mp4EcnjpFkFhwRm57DgDgvM6Nw5Y6CiZUrzvgcAAAAgv3GZuQAMGjTITXm1detWNxm707hw4ZWyAG77/WBjwpn8Zgs769ZJ77wjvfmmJyDCy7JS5GcQhlWzGDjQE+zhDQIJ1KWLdPfd0qmnRsZ5jBYnnugpSbN+vZSaKo0dK11xRcZ94ooVU7HGjbVnzhy3vHvmzJAEYeycMkUp1lCTkKCyPQLSeAAxbOdef1eieNxupWzeTBAGsqwxbQO2Xbt2damzvZKTk/XGG29o2LBhrnyElSXxsjvkbDrvvPP01FNP+dYnJCS4+s4PPPCAbr75Zo0cOdJXA90GXYcOHeoGs2+w+meIOVYT3GqDB8OB/W8gLFVrJvUcIm1+SPrlFWneR1LKXn8wxtd3BwRj9JYS/AGUALJWt1JJfXqDJxBjzVZPVpn+4xYrOTVdN3U6fNBoQfFeVCYTRnSxfseKFf7sK2XLlnVTNAjWOKmXBVu89dZb6tu3r7p06aL77rvP/RY455xzMuy3cOFC1bC0vAAAAEABiOnbuL0p6Xbv3p3pdu8P2FKlSikc2A8US51nk9WGTEvbK8vcbeWuIyVwoGpVybJB2+/Il1/2r//pJ0sXGNzXsqoRdlHfyo20bCkNH54xAMPOm12b+e03u2Ajde0aOecxWtg1uMDfxFmWJGnVyvd4968zFQpbP/3M97hUly5KqFQpJO0Aws3Onf4/nMUL7VZqFuUhgKVLl+qMM85wmQlOP/10XXrppTrttNNUq1Yt3Xbbbe5uPwueCBwY3bhxo8t0YXWeD/S///1Pp5xyiis50bBhQ1fnuX379u7YFtjRv39/nRBQ0gqRJxz66gf2v1MtahQIV+XrSmcNlG6fL7W+UYoPCLbYsVYaf5f0cgtp3sdSGt9lIDtqVSjhAjFqlvdnBnjmmz/0zpS/w+YEUo4kOlmfw9sHsSmYQQvh3PfKDQu6sAAVy65hvwkWLVrkStNZxg0vyyqybNkytWvXLqRtBQAAQOyI6SCMmjVruvnq1asz3e5dbxcHwoFFdC9fvtxNdqEisN5hpLFSH9dd5wmEMHv2SL/8EpxjWzDH229Lxx4rde/uKXMRyD72Z5+1z1d64w2pUaPgvC5yX5LE69tv7Uf9YYIw5sxRegFfAElet047J0/2LZfrdWGBvj4QzgKzHpeI26UUqysEZKJjx466//77XekQSxf8+eefu+wY5cuX16233uruTLPB0uwqXLiwxo8fr2eeeUYVK1bUt99+645hr2PpjO21ENnCoa8eTf1vxJBSR0hnPCPdPk864XopPtG/bds/0ugbpTfaS39+a7dah7KlQESoXq64C8SoU9FfxsAyYgyZulzhgHIk0cn6HN4+iE3WJ4mFvldu2z1//nw99NBDuvHGG/XOO+/oo48+yrDP3LlzXWaMnPzeAAAgFsyePVtPP/20evTo4Up8WTapzEql5cSWLVt0++23uz5DkSJF3Nz6Mt5Mo1kFoL744otq3Lixy5Rr5Wbt/+3FixerIFl/56qrrlK1atVcWVq78euRRx5xpc4OZIGro0eP1jXXXOPGO21/65tbIOjjjz+e43KBlgXNe/6tL7gmsJzAAZ599lnfvp3sTvQQuvLKK107frK77SPM0KFD3U18FoxsY9RnnnmmfgnWxepYL0fijYies7/MwYG865uEoPRBLChSxJOl4uuvPcvffSedfHLuj2eBHBZ8YQEWmf1tat1auvNOz0V/CwJBeLDPvEwZads2yf4fs0CMHj0y7lOsWTMbgXA1S9J27NC+JUtU1KJsCsjmoUM9qVXsol/Nmirepk2BvTYQ7gL7klaOJHUzmTCQObuDb8CAATk6PY8++qibDhWIcffdd7sJ0Ye+OpBHpatJZz4ntesr/TxQmv2+lJbs2fbfImn4hVLNttKpj0k1yBwEHErVMsU0/LrW6vXmdK3a7MkS8NiYRUqIj9PlbUJ7QZpyJAiWSO572UWjQ/1uuOyyy9wEAAAyeuKJJ/Tll18G7bRYVtsTTzxRf/31l+rWreuyVFlGqsGDB+vrr7/WtGnT3MXuQGlpaerZs6dGjRrlslt169bNHWfEiBEaN26cJk6cWCDZbq3N1nZ77eOOO04nnXSSZs2a5QIqLBOvTRZU4jV8+HBdZ3ebSzrmmGN09tlna/v27e4ivgVufPzxx5o0aZIqV66c47bYObHn33XXXZlu//DDDxXuateurZUrV4ZteV8LDLLvpQX9WPlsC7T5/vvv9d1337nvnn138yqmM2FYCroyZcq4dHTz5s07aLudZNPd0imEgWhMh2wlQLwsCCO3FwCff94u7ki3354xAMMC9izowsqCT5sm9exJAEa4SUyUunU7dEmS+JIlVDQgZcnumQVXkiRlwwZt+eRT33L5yy5VIaujAsDZseOAIIwtmzkzAKKmrx6N/W/EoDJHSt1ekG6ZKTXumXHbql+kd0+VPrlU2rg0VC0EIioQ48iy+1N6Snpo9G/65NdVIW0X5Ugi16EGpENRjiQc+l4AAKBgWdCBZZP66quvXDngwCCD3F7YtmAGy6xhJYY//fRT/fbbby4L7p9//ql+/fod9Jz33nvPBWA0aNBAf/zxh+tzWFYFy6Jr2SaspHFKSooKIqODBWBY2WTLtmttt/dw3nnnuWy+Tz311EE3h11//fWuFJpNn332mb755hv3nObNm7v3kptsZpblq0KFClkGWliGYWvf8ccfr3Bg58UylkRSWegffvjBBWDYebaMapbRxD67yZMnuywklg3lUJlbsiumryRaSuFbbrnFPb755pt9tQ3NwIED3RfZUlq3aNFC4SAa0yEHBmFYQP2GDdl/rv3NHTzYoqmsLrz0778ZL+zfcIO0ZIk0cqT9kPQEZCD8S5KMGSMlJR28T/FWLX2Pd8+aVUAtkza9+57SrcaNBYNUqqiypK4EfGzM8OByJARhAIievno09r8Rw8rXkc5/R7phslSvS8Ztf4yVXm0tjekr7VgfqhYCEVGa5JPr26hamaK+dfeNWqgRszMv31AQKEcSnUJRjiQc+l65YYPlOZkAAIDfPffc4zI9WJDlEUcckadTY0Eclr3B+hSvvfaaEgJS0j/33HOuxIgFFvz3338Znmf9DG+JjSpVqvjWn3/++S67hAV1BDNbR2Z+/fVXF2hhWSusHV72Hl5//XUXcPHSSy9lCAa54oor9Oabb7osGIGqVq2qV1991T0eOXKku6knJ+y1LDOIBQdYFpEDeYMzwiXLl73fo48+WsWLF1ekGLj/O/fggw+64J/AoCQrb2cBGO+++26eXyeqgjAsLU2bNm18k/eLHbjO9glkJ7h169YuPYyd6F69ern97rzzTvcHwSKwkH/sb1O1av7lH37I3vOmT5datbKBcWnTJv/6okWl226Tli2T3nhDCvi3gzB2+ume8jTGypJkVjqqeMuAIIyZs5S+vzxIfkrZuFFbPvnEt1zhmmsUZ18yAI6VEAr8p+gpR0IQBoDM0VcHwkTVptLlI6UrxkjVAu6cSU+VZg+RXmouTRgg7QtIdwXAp0b54hp+XRtVKV3EF5j8vxHzNXbB2pAGYdhdguGa6heHlte667He97I66CeffHK2JwAAkD8sk4CV0bAyHoHBFMYybFigh2X7Gj9+vG+9BZtaFgUrCWFlSA50wQUXuPkYu3v3ABYQYQESduG8dOnS7hjNmjVzGcRymjnD2yeyNh6YDcTei72nLVu26GdLu5+DMm/79u3TpsCLmNnkDbA4MBuGt0xJvXr13Ps+0PPPP+/6lvfff3+Wx7bSG7aPlXnxsmUrH2L9RQvKsaAKOw/ZKclhGUTs+Za9xNjclleuXOk7tney1wi1PXv2aMKECRm+X9n9zsV0EMaGDRs0Y8YM3+T98Rm4zvYJVLRoUfdFs3Q7FqVjKUfsi2FfGqt1aDWLwkU0pkO235k5KUli1/Ysw0XbtlJgZkQbc7BsGCtWeLJjVK+ef21G8JUsKZ122qFLkhS3Oy32D0ykbt2qfX/9VTBZMOwqs92FUrGiyvXqle+vCURqKRJDORIA0dZXj8b+N+BTp4N03QTpgiFSuTr+9cm7pcnPSi8eJ/30jLQn7yk4gWhTu2IJfXxdG1Uq5Q/EuOPTefppSca7+gqCDTZ7B2RzepcdwlcwypFEYt8rN3r37p3pZBcvLHNHqVKl3Hu3O2ltPQAAyB+WucFkVSbDu96yax34nOOOO85lgMjOc7wX0i2YoE+fPq7MiQWNnnrqqS4bxx133OGyaFj/OD/bfih///23m9t7Kl++vHKqbdu2rg84fPjwDIHWkyZN0urVq3XJJZdk+jzrs1nwxJAhQzINRLGgFyvFYcG2Bwan2vmyoAvLBGJBHuecc47LcpFTllHFsoSU2B8sbo+9U2ZBDwXNysVYcIwFGFfP5GJyTj/rQ/HngokC9uWyKTc/WC2yx6ZwZqkHve/vqKOOUrSwIIyhQz2Pv//eM3iR2U0AFvB1xx3Sxo0Z19speeYZqXLlgmkv8q8kyVdfeR6PHi1Ztqa4gDCx+NKlVeSYo7Vv0WJfSZKiDRvm28eRsmmTtnz8sW+5wtVXK27/4BYAj8BSJKZYoT1K2byF0wMgavrq0dr/Bnzsh9dxPaSjz5JmD5UmPSPt3v+Da+9W6acnpWmvSCdcJ7W5WSpRgZMH7Fe3UkkNv7a1LnxzmrbsTlZyarpu/HC2PrymtVrWzvlAa27ZwK6VrrCL9jYYndc63ggP9pla6m2vsmXLxkTfKzeGegcVs2B3rV533XWuHv20adMKrF0AAMSaVatWuXlmF7YD13szJOT2Oeauu+5ygaOWtctKgpQpU8at37Fjhy666CJ99dVXeuutt1xpifxq+6EMtrvFXRb403PVP7esERZoMWDAAE2ZMkUdOnQ4qBTJ5kwyUlesWNEFoFjwxtixYw/KZGElNiyo49prrz3ouf/8849rqwUpHHnkkcoty6Jh/bOffvrJlbc7XF8tq0xnFnCSExZ4kp2+7+E+awsesb639SHt+2QBvbkVVUEY0c4+dO+PrvxKVRgKp5zif7xmjbR4sXTssRn3GT5cuvzyjOsaNZJef1066aSCaSfyV/funqALC05cv97zPbDPOFCJVq38QRgzZ6p8FtF+wbDpvYAsGOXLq9xFZMEADhWEUbzQLsUVSldqLtKrAUC4itb+N3CQhESp9fVS04ukX16Wpr8uJe1PebVvuzTlBc+6FldJzS+VqhzQUQdiVIMqpTT0qhN0ydvTtSspVXuT03TV0Jn69PoTdWy10gXSBvv/ybIX2ACnBWHk5mI9wk+4pGuOBuXKldMHH3zg7ui877779IbVLwYAAEG3c/9gsWXTyow3M4Jd2M7Lc/777z+9/fbbqlGjhrvw7s0MZ+yCuQUa1KpVy5UqyW4QRm7akRUrt2JtsGDpJ554Qrl16aWXuiCMjz76yAVh7N27V1988YVatWqlhg0bavr06Zk+z96zBWHYOQoMwrCgbQuIsHZlFazw1FNP5SkAI1hOP/30HPeF69evH5TP2vt5b926lSCMWGIfuE0mmup8Vqpk6V2kOXP8JUkCgzDsOvi99/qX7d/Fo4/anYl2x0fBtxf5o0IFqXFjS/vkWbabEw4MwijWsqX0/gfu8e6Zs9y/g/y4IJKyebO2DA/IgnHN1Yo7xB9kIFZlCMKI2+3mKVu35tu/TQAoaNHa/wayVLS01PkBqc1N0ow3pRmvS3u3+cuUTH/VM1U+Vmp8gXTc+VI5LhIitjWtUVZvX9FSVw6ZqaSUNO3Ym6Le783Q5ze2VZ2KnsHa/GYDzxaEYQOziA7W71hhNXczCQxFztkg+wknnODuiiUIAwCAyGYZFpKTk92F+sAAjMByGFZuY+HChS5IObN98ssff/zhslRYX+65555T06ZNc32sY445xpXG+Pzzz/Xyyy9rzJgx2rZtmzv+oZx00klq1KiRvvnmG5fdwoJVvMEha9ascSVBKmdSWsDG87vb3dJh4N7Ai8IRLCDZP8JdNNektpIkXhaEEciyXfzzj+exBV3Mni39738EYESjE0/0P/7ll4O3F7cgjP1SN25UUsCARDBtHjJE6Xv2uMfx5cqp3MUX58vrAJEuMPDXG4Sh5GSlZSMiGAAiQTT3v4FDKl5eOvk+qe9vUpeHpeIHlCH5b5H04+PS4KbSO6dKE5+UFo+Vtq7y1JcEYkzbehX1ysXNFR/nCUTeuDNJl70zQ+u2eX5X5jfvwLINMiNyHCrA0/oc3j6ITdYnQd7YXY+WVhoAAOSPkiVLuvnu3fvHiQ9gQcMmsLxDbp7jDVS1TA8WOJDZ9Pvvv7u+lrdkx9NPP+0r1eadbF1e2nEgC3CwwBDrb/Tr10+333678soCLux4FkBhpUgSEhJcuZXDueGGG5SWlqb33nvPt87Ol7EybZmxwIxYKG1Y8jCfdXY/7+ygHEkEieaa1BaE4f1799NP0r59kv1b37ZNGjDAv99NN1k9oZA1E/msbVvJmxUyszKdCeXKqUiDBtq3dKmvJEmROnWC2oaULVu0+aPhvuXyV19FFgwgG5kwSsTv8v872rRJ8aULJv0yAOSnaO5/A9nOjHHSnVLrG6W5H0rzP5HW7k9h6LX6V8/ke05Z6YjGUqWjpRIVpWLlPUEdxcp55kVKS4klpSIlpcLF7XYbPgxEha6NjtCz5zfRnZ970juu2bpHvd/9VSNubKsyxfM3jaeVIzFkwoge8fHx+uuvv3zLZMHIG7tzdPLkyTr2wPrHAAAgaGrWrOnmq1evznS7d72VCsnLcyy4wDRr1uywmSa8QQWWFWLSpEkZtnXs2NGXccHaMXfu3By1I5AFe3Tt2lUrV67UVVddpeeff17BYAEX//vf/1wmjClTpujUU0/NNIvFgXr37u3emwVhPPTQQ1q/fr0L5LASH3aMQ/2mCAcWIGNZRXLi2muvVfv27Q+73+G+cxaAYVlxraQdQRgxJJprUtvFd6v2YIFHduPG1KlS587SCy9ImzZ59rHgpAceCHVLUVCZMOzvqwUpli+fcZ/irVoGBGHMUrkLLwxqGzYPGar0/RFw8WXLqvwllwT1+EDUBmEkJvkep9rdRUEOkAKAUIjm/jeQI4klpNY3eKZNy6TfvpAWfCZt8vTLM9i7VVoxxTMdTqE4T0BGsbKesiYHTpWOkRIpC4jIcX6L6tq+N1mPjVnklpf+t1PXDZulD64+QUULx+fb65IJI7Jl1sewdTmtgx2rrr766kNmv/jzzz9dOnK7G/bOO+8s0LYBABBLvAERc+YcELi/n3d9kyZNDnrOb7/95kqMFLZ0+Id5TvXq1d3cLrhbcEJ2S5gcru1ffvlljtoe2N8444wztGjRIvXo0cOXoSMYqlatqs6dO+v77793y4crReJVpkwZF8BhQRjffvuta79lWrNAhUgY3/omk6CZw+nUqVO2gjDsJisLztmwYYPLXnLkkUdm+7POKTJhRJBorkltwWidOllNIs+y/T1p1EgaONC/j/1OykaAFyJYvXpSpUrShg2e5enTpTPPzLhP8VattGX4x75MGPZvIVj/aST98482v/++b7n8VVcprkTB1PAFIlFg1ZESRVJ8j1P3p3kDgEgXzf1vINcq1JM63i11+J+0foH053fSunmex1aOJCfS06R92z2TPXf55Izb4wpL1VtKtdtLtdpJNVoTlIGwd1W7Otq8K0kvT/BkMfh1+WaXHePli5orbn+5kmDz3rVGOZLoYf0Ob6rtAwNDkdHQoUOzdcfjI4884u4KBQAA+cNKccTFxbmMDf/991+GjA379u1zmaks29eZARd9rOzaMccco8WLF2vcuHE699xzMxxzxIgRbt69e3ffupNPPtkdZ+zYsRo4cOBBgRu50a1bNz3++OOujdbWwLIc//77r3tPlhmhXbt2GZ5n+55zzjn69ddfddppp+njjz92bQsmy9BqgQEWeH3g+TmUG2+80QVhvPnmm1qwYIFrl2XpKCiJiYlunpKS4sqo5MThgmbyws6jBbZ8/fXX+vzzz10W3MN953IrLs9HQIGJ9prUgRlwvvtO6t/f0r54litWlPr1C1nTUEAsliIwG8Yvvxy8T/GWLX2PU9avV/KaNUEb4Fj/2ONKt1o4lgWjYkWVu/TSoBwbiIVMGCWLe9LAmRSCMABEiWjvfwN57rxXbSp1/J900UdS34XSPSukK8ZIXQd4Spg0vlCqf4p0ZAupXB2paBlP9ovsSkuWVk2TJj8nDTtXerqm9N7p0i8v5zzgAyhA/U5tqAtaeO7QM+MWrNOT4xfneyYMypFED+tzePsgNlmfBJmbOHFiltPUqVP1999/u4CWgrzoAABANHvllVd09NFH67777jsoa8PFF1/sxk/69OnjLr573X333S7zgGVyOLCcRr/9F/9sHwve8Bo5cqS++uor1a9f3wU6eFnmAsuEZf+/2+tZkMSBrKzbF198ke33dMIJJ7gAC3v9e+65x7fe3oO9F8vScdttt2UI+LD+mr3+hAkTdNJJJ7n2egMPgumSSy7Rxo0b9c8//6i4lRTIplatWun44493GT6WL1/uAk2qVaumglJt/2stWbJE4cb7nevfv7+W7s+8b6ZNm+aCViz4+Zprrsnz65AJI4JEe03qrl39jy3by8KF/mUrQ1K6dEiahRCUpvnqK8/jadMO3p5QqZIS7ULI8uW+kiSJ+9NP5cX28eO16+effctV7rtX8SXJggFkOwijZLq0v19NJgwA0SLa+99A0BUrJ9Xp4JmyYlllkvdISbukpB3Svp3Szv+krSukLSukzculLSulzcukZE+ZwIOCMmz67kGp2vFSo3OlY8/xlC8BwoRla3yqR2P9t2OfJv/pSfX4zs/LVbVsMV3TPvhl+yhHEn3sTkW7eOBFFoysWT13AACQO5Z94oknnvAtWwCFadOmjW/dQw895C7ge1lAgF1YX7du3UHHs8DR6dOnuwAIC9Ro2bKlfv/9d1dupEGDBi5zxYEsoGL8+PEaNWqUe06XLl3ca1g5CuvnfvjhhwdlUhg8eLALwrDXsdIVzZo1c5mvdu3a5cqCWD/KAjfOP//8bJ+LIUOG6MQTT3THtsCKY489VjNnznQBnW3btj0o6MSCUazNpmLFii5YIzPPP/+82x4Klg3j+uuvd4+984Jy9tlnu8/QPk/LXlKiRAl3Hp5++mmF2imnnKLbb7/dfdb23Tn11FPdd9/KvtgN2/ZdCEb/myCMCBLtNamPOcYi2CRvYoPkZM+8Zk37QxHSpqGAgzC8ZsywSEPpwExFlg3DH4QxU2XPy34Kpsykbtumf598yrdcon17lT6wDgoQQnatwvq/NtnfxsDHdlO2TWlpnskeezPme/+rsLlNFsxmgcY5CJjNkiWN2bTJv1yyVCFpi+cxmTAARIto738DIWH/lhKL7y8rUinr/VJTpPXzpRU/e6aV0zxBG4HWzvFM3z/sybbR7BLpuPM9wSBAiBWOj9Nrlx6vi96apt/WbHfr+o9bpCNKF1W3JlXzpRwJmTCih/U7atcmuAwAAOQvy04xwy7EHCBwne2TXXaR3UpzPProoxo9erQLUqhSpYrLIvHYY49lemHbSphYWQi7IG7lM6zMiF2wtwAKe44FQxzIgjOsnMRHH32k999/X/PmzXOvW6lSJdWqVUuXX365LrroohydCwsSmTt3rh5++GEX2GFtt8AOC0K5//77M5QoMVu27B8Ml3zBGJmxcxGqIAwru2GqV6/uysUUpNtuu82dIyvRYsEylk3EPptwCMLwBgxZAIYF01jwhWUxseAM+7wt6CYYCqVT3DgiResP7KuvtmizjOustOMVV4SqRShoe/Z4LhR7M1XNnSs1a5Zxn21jxmjt/+52jwvXqKH633+Xp9dc9/Aj2vrZZ+5xoaJFVXfsmKBk1wC87Pu8caNk2dS8k/Vdbd22bf5p+3bP3Eox2b+F3bs9kz32BlYEQ8mSUpUqnoAM6/fa6+3Y4Xl979xez7KrWQY179yumVj2C9vHGyjn1bfrLF2/8nL3uPRZZ+nI55/jCxAlorXPAeQU/xaAEPMGZfwxXlo0Wtrkv0M8g/gi0jFneQIy6p4sxQW3Hi+QU//t2Kser/2i1Vv2uOXE+DgNv661WtYuH7STaXci2uCmpfwt6DvckHuWstrqc3ft2jXDQC99jpz55ZdfXOkRqyVvA/0WwFK+fHl3wcbuumzdujVfUwAAEJOeeuopF0DyyCOPuGAQFCwyYUSQrVu3uslEa+yMlSQJDMKwALfLLgtli1DQrJRt8+bSzJme5V9+OTgIwzJheCX/84/2LvlTRY9qmKvX2z1nji8Aw1S8uQ8BGMgxy0CxapW0eLH099/SypWeZe+0dm1wgyjyygIpbFq27PDZLrKrqt3Mt9LzOHVzQIoMAIhgsdD/BiJGfIIn24VNnR+U/lsk/T7aE5Cx8U//fqn7pN++8EylqknNL5OO7y2VrRHK1iOGVS5VVO9ffYLOf/0Xbd2drKTUNF0/bLZG92mnmhWCkKIu4KL9HoveRsQ4VN/CtlmK7cyyc8HDAlgsfbndsZrZ+fRmMbMa7++++26md9ECAABEq+3bt+vll192GR4I1A4NgjAiiKVGsdQ7gfUho80pp3jutPb+bnrySXufoW4VCtqJJ/qDMKZNkw4spVW4alUVOfYY7Vu02C1vfu9dVXvmmRy/TnpSktY/8ohvuUiDBqqwv+47kBUrdTdrljRnjifo4o8/7M4zyxJQ8OfM/l5auZ64OM/fSpt7J+/fUZvbZIEilvEiP1gJ3gvP2q490z3LKZv9qdgAIJLFQv8biEjWCarSyDN1fkBav1CaN1xa8Km0OyAYdMdaafKz0pTnpfqnSi2vkhp0JTsGCly9SiX1Tu+WuuTtGS4IY/OuJF3z/kx90aetShctnOfjWzpmQ+ay6JGamqo6der4lrl7MSOrz27pta3uu6UrP+OMM1w6aUv1bcEYVkfegjO+/fZbl07d6rv/9NNPam53/QAAAESxIUOGaNKkSZo8ebLWrVunvn37uox5KHiUI4nQO/GOOuooN9+Xk9uUI8TDD0vPPuvJgPH2257xNcSWTz+VvOW66tbN/G79bWPHae1dd3kW4uNV/7tvVfjII3P0OhvffEsbXnzRt1zr4+Eqzg9yBLBSIJaNxYKBLPDCJstqkVs2NmplQCpV8pQCsVJwZcocPFm5kOLF/ZM9z25us7JzgSVCcnotMCnJUwLl3389JVFsbqVH7PVKlfKUArK5TXZs29/Kjthkj1NT/ft69y9RwhP0sXvmTK28vLd7nYTKldVg8iS+S1GCdMiIZbHS/waiRkqS9Nf30tyPpKXfSmn7axwGKn2kdPwVUosrpFJHhKKViGGj5q7WHZ/O9y13bFhJ717RUgnxcXk67o4dO/TCCy+4O/+thrHV1Ub4s9rYCxcu1GmnneaCBAL73xZIYGVmvMiEkTFA5ZhjjtFff/2la665xn33S9uP0yzuAO3Xr5+rL299uUWLFvkyZAAAAESjK6+8Uu+//74qVaqkCy+80PWVitiFBRQ4gjAiVLRfELE7thkziF1WvqFWLf/y+vWeC9eB0lNStOz0M5S8erVbLnf55Trigfuz/Rr7/l6u5eedp/T9F1LKXtRLVamJFfMs2ODXX6UJE6Qff/QEX1jwQXbVrCk1bCjVru157J1q1PCU67CAhWi1b9ky/d3tLM9C4cI6esF8BreiRLT3OYDs4t8CEGF2bpDmfSTNHiptWX7w9rgE6ZizpVbXSrXaEv2PAvP8t0v0ysS/fMtXtq2tR89ulKdjJicna8CAAe7xPffc48uMgcgNwjD0vzM3cuRIXXDBBerVq5c+/vjjbJ1r23fEiBEaNWqUzj777KB8fgAAAMChUI4EYYkAjNhmF6wtqcWaNZ5luxB+7rkZ9ymUkKDyV1+lfx9/wi1v/fxzVexzkxLKlTvs8VO3bdPqPn18ARjxlSqqcr9++fBOEAl27JDGjbMBMOmbb6SdOw//nOrVpZYtpSZNpKOP9kwWfBHNQRaHE1++vH8hOVlpO3YoPou7kQAAAPJdyUpS+75S29uk5ZOk2UOkP8b5s2PY/PeRnqlyI6nVNVKTXlKRknw4yFf9Tm2ovzfu1PiF693y0F9WqF6lErr8xNq5PmbhwoWVkJCglJQUd+GeIAxEszFjxrhsL09aDeNseuqpp1wQxujRownCAAAAQIEgCANA2LHMkHYTyIgRnmUrB3FgEIYp26OHNr7yqlI3b1b63r3a8uFHqnTrLYc8tmXQWHPHHUpascK3zjJgcLE4tmzZIn35pSfw4vvvLbV81vta+Y327aXWrT2BFzYdQebqg8RbHRWLoLNURhbstHkz/64AAEDoWf+k3smeace/0twPpFlDpe2ejHrOf79L4/pJPzwmHX+5JztG+TqhbDWiWFxcIb3Qs5lWb5mmBau3uXWPjlmkWhVKqEPDSrk+rgVeWFmSPXv2qFw2bk4AItXs2bNdaZE6dbL/d7pu3bo6+uij3XMBAACAgkCRSABhqW1b/2PLhJGZuKJFVb735b7lLR9+qLTduw953H+felq7fvEfsOKtt6hUly5BaDHCXXq6NHGidMklniCKq66Sxo49OADDyqOdfLLUv78nAGjzZunrryWrVnPWWQRgZKVQXJziAwZ7UzZvyc+PEwAAIOdKVZE6/E+6fb7U6yOpbqeM2/dtk6a9Ir3UXBp+kbRsoqcTCQRZscR4vd27pY4o7Sk7kZqWrluGz9HKTbtyfUxKWCBWrFu3Tg0tFWUO2XPWrl2bL20CAAAADkQmjAiydetWN5l0BoIQ5QLKoWrmTCkpSUpMPHi/chdfrE1vve2CL6zMyNYRI1S+d+9Mj7nlk0+05aOPfMulzzxDFfv0yZf2I3z8+680dKj0zjvSX/7SyxlY7MA550jnny9ZTA4llHMnoXw5pW7a5B6nbvbMASCS0f8GolR8gnTMWZ5p41Jp5jvSvOHSvu37d0iX/vzaM1U6Wmp9o6dUSWLxEDcc0aRK6aJ654qW6vnGNO1JTtX2vSm6/oPZGtmnrUoUyflwnbcEiWXCQGQ41NiebVsRkMGzbNmyboK0bds2lbFMjDlUunRpbd/u/TsPAAAA5C8yYUSQQYMGuVR7NiUlJSk1NTXUTQLyTfPmnowExjIVzJuXdQmEsr16+ZY3DRmqdIvYOMCu6dO1/on+vuWixx2nqk8+qUJW+wRR6fffpcsvl6pXl+699+AAjMqVpRtv9JQjsUCNIUM8mS4IwMi9+PIVfI9TLIUIAEQ4+t9ADKjYQDrjGanfIunM56UK9TNu3/CHNLav9OKxnnIl27mLGsFz3JFl9FzPJr7lJf/u0N0jFuTqxhtvJgyCMKKDjfl5xwBtsj4JPFJSUhRnpaZyyJ5jzwUAAAAKAkEYEaRv375avny5mxITExUfHx/qJgH5xgIwWrb0L1tZiKyUv/IKqXBh9zhl3TptGzfety09LU2758zV6tv72iiGW5dQqZKqv/qKK2eC6DN9uierxXHHSR9+aAM0/m0Wc3PGGdLIkdLq1dLrr0unnOL7+iCP4sv7y5GkUo4EQBSg/w3EkCKlpBOuk26eKV32hdSga8bte7ZIPw+UBjWWRlwjrZkdqpYiypzVpJpu6FjXtzxu4Tq9MenvXGfC2Lt3b1Dbh/yX2c0hNubnHQO0yfokAAAAACIH5UgiSGDqQe7eR6yUJJk61fN42jS7EJL5foWrVFGZs7tr2xcj3fKmd95R+t492jV9hnbPmKHU/WV8TKEiRVT9tVfdcxA97EaxCROkAQOkiRMP3m7ZMK65Rrr6aqlmzVC0MDYklCvve5xCORIAUYD+NxCD7O7q+qd4po1/Sb++Kc39SEre5dmeliL9NsIz1Wwrtb1FaniG53lALt192tFatHa7pizd6Jaf/fYPHVuttDo2rJTtY1COJLrYuF/t2rVD3Yyw9f7777sJAAAACFeMEgAIW23bZi8ThqlgV9j33z2StGyZ1j/2uHZ8+22GAAxT7aknVaxx43xpL0JjwQLptNM8GS0ODMCwj3r4cGn5cunRRwnAyG/x5f1BGGTCAAAAEa9ifenM5zylSrr2l8ocEM276hfpk0ukV1pKM9+RknaHqqWIcPFxhfTyxc1Vo3wxX5D5bR/P1cpN+4N/clCOhEwYiAVWsic3EwAAAFBQCMIAENaZMLysdMQ//2S9b5G6dVXqlC6Zb4yLU9GmTXTkoEEqfeaZwW8oQmLtWk92i2bNpO+/PziAZ8wYaf586eKLpQTyPhWIhAqBQRibC+ZFAQAA8luxslLbW6Xb5ko935eqn5Bx++Zl0rg7pRcbSROflHZ5shkAOVG2eKLevKylihb2DNVt25OsG4bN1u6klByVI9mzZw8nHlEtLS0t11Pq/jK1AAAAQH4jCANA2DriCKlOHf+ylSQ5lEr9+im+QgX3uEiDBirX+3JVf+01NZwxXXU+/VSlTz8tn1uMgrBrlyerRYMG0nvvee4S8zr5ZGnSJOnnn6WzzvIlR0EBiQ8sR7JlC+cdAABEl/gEqdG50rXfS9d8Lx17jlQoYFhlz2Zp0jOeYIyxd0ibloWytYhAVoLkmfOb+Jb/WL9DD476LVt38HszYRCEAQAAAAChx73BAMI+G4aVkvAGYVx4Ydb7FqlTRw0mT1Lanr2KL1miwNqIgvPNN9INN0irVmVcf/TR0nPPSd26EXgRSvHly/kep27aFNK2AAAA5KsaJ0g1PpA2L5dmvCHNGSYl7y8dkbJXmvWeNGuIdEx3qX1f6cgWfCDIlnOaHanf1mzT21M8P4RHzl2j1nXLq1erA8rhZJEJg3IkkYPyGAAAAED0IhMGgLDWpo3/8cyZh9+/UHw8ARhRaONG6bLLpDPOyBiAUamS9Npr0sKFZL4IBwn7M9F4M2EwqAgAAKJe+TrSGc9I/X6XOj8klagcsDFdWvyV9HZn6f3u0rKJGdO4AVm4+/Sj1aq2P8D54S9/16K12w95vihHAgAAAADhgyAMAGGtZUv/47lzpZTslcNFlLAx6o8+ko45xjP3SkiQ7rlH+usv6aabPMsIvfjy/nIkSk5W2s6doWwOAABAwSlWTupwl9R3odR9sFShfsbtyydLw86V3uok/T5aSkvl00GWCsfH6eWLj1f5EolueV9Kmm4ePkc79iYfthwJmTAiTyHqaAIAAABRhyCMCLJ161atWLHCTXZ3MXcYIxY0bSrFx3se794t/fFHqFuEgrJ2rae8iGXAsEwYXiecIM2ZIz39tFS6NJ9HOIkvU0aK83ctKEkCINLR/waQY4WLSi2ulG6eKfX6SDoyIKrcrJsnfX6F9OoJ0tyPpNSsL6ojth1RpqgG9Wom7/X55Rt36d4vFmY5FhRYjiQtLa0gm4p8YJ+zdwzQJuuTAAAAAIgcBGFEkEGDBqlOnTpuSkpKUmoqd84g+hUvLh13nH951qxQtgYFZexYqUkT6euvM34XBg2SfvlFatyYzyIcFYqLU3zZsr7llM1bQtoeAMgr+t8Acs0CU485S7r2B+nKcVL9UzJu3/SX9GUf6aXjpZnvSMl7Odk4SIeGlXRr5wa+5XEL12nY9JWHzIRhyIYR+WzMzzsGaJP1SQAAAABEDoIwIkjfvn21fPlyNyUmJiremx4AiKGSJARhRLe9e6XbbpO6d5c2bfKvP+006fffpdtv92dGQXhKqOAvSZK6ZXNI2wIAeUX/G0CeWRqD2u2ly76QbpgsNephkav+7dtWSePulAY3lX55WUraxUlHBrd3aaC29Sr4lp8Yu0jz/zk4K0JCQoIKFy7sHhOEEflszM87BmiT9UkAAAAARA6CMCJI2bJlVbvBB+3/AABWkklEQVR2bTdZvUhqRiJWEIQRGxYtklq3ll5+2b/OMuq+9ZYnI0bt2qFsHbIrvpw/CCNlM0EYACIb/W8AQVW1qdRziHTLLKn55VJcgn/bzvXSdw9KgxpLP78o7dvJyYcTH1dIgy9qrkqlirjl5NR03fLxHO3Ym5xlSZI9e/Zw9iKcjfl5xwBtsj4JAAAAgMhBEAaAiArCmDdPSqZsctR57z3P57xggX9d06bS7NnSddd5biBEZIgvH5AJYxNBGAAAAAepUE865xXptnnSCddL8Z6L687uTdIPj3qCMSY/L+3dzgmEC8B4+eLmitv/u+ifzXv08Je/Z1mShCAMAAAAAAgtgjAAhL3GjaX9WVW1b5+nLAWiQ1KSdOON0jXX2EChf72VJJk+XTrmmFC2DrmREBiEQTkSHGDgwIHq0aOHGjRooDJlyqhIkSKqVauWevfurYULF+bpfC1dutTd/Wl3DZ5yyimcewBA+CtbQzrzOanvQqntrVLh4v5tezZLE57wBGNMepZgDKhN3Qq6vUtD35kYNXeNRs9dk2kmDMqRRIb09PRQNwEAAABAPiEIA0DYK1JEatLEvzxrVihbg2BZv17q3Fl6803/uooVpTFjpMGD7S4uznWkZ8JI2bwlpG1B+HnyySf19ddfq3z58urSpYu6devm7tgcNmyYWrRoobFjx+b62Ndff732WaQeAACRplQVqWt/TzBG+zukxJL+bXu3ShMHSIObSFNeoExJjLv55HpqVbucb/nB0b9p1abdvmXKkUQmyg0DAAAA0YcgDAARV5Jk5sxQtgTBMGOG1KKFNHWqf12rVtLcudJZZ3GOI1l8ef+gcOqmTSFtC8LPl19+qS1btmjGjBkaOXKkm5YsWaJXX31VycnJuvbaa5WSkpLj47777rv66aefdJ3VLwIAIFKVqCid8qgnGOOkO6XEUv5te7ZIPz7uCcaYOlhK2hXKliJEEuLj9GKvZipVNMEt79yXots/nauU1LQM5UjIhAEAAAAAoUUQRgGyNNsJCQmqXr16Qb4sEHVBGGTCiGzvvit16CCtXetfd9VV0uTJEn8eo6scScoWMmEgo3bt2vkuDgTq06eP6tWrp3///VeLFi3K0Wmz5/zvf//TqaeeqosvvphTDs2aNcuVuKlfv767s/TBBx/krACILMXLS10elvoukDr8L2Mwxu5N0vcPS4ObStNfl5L3hrKlCIHq5YprwHmNfctzV23VSz8udY/JhIFIw1gpAAAAohVBGAWob9++qlChQkG+JBCVQRgLF9qdPaFsDXIjLU26807p2mulpCTPuoQE6dVXPYEZlB+JvnIkqZs3h7QtiCyFCxd288TExBw97/bbb9eePXv02muv5VPLEGmmTp2q6dOnq3379ipTpkyomwMAeQvG6PygJxjDypQULuHftmuD9M290sstpDkfSKk5zySFyHV202o6/3j/DT6vTPxLvy7f7At2tb4REAkYKwUAAEC0IgijgIwePVp///23rr766oJ6SSCqNGokFSnieZyc7AnEQOTYvVvq2VMaONC/rnJlacIEuwPeauCGsnXIt0wYmzcrPT2dE4zDGjZsmCtL0qBBAzdl1/jx4/Xpp5/q/vvvd1kPAHPrrbfqzz//1NChQ1W2bFlOCoDoCMawMiW3z5fa3iolFPNv275a+upW6dUTpN++8EQ+IyY8dk4j1apQ3D1OS5fu+HSeCiV4glkpR4JIwFgpAAAAopmniCTyVVJSku666y49/fTTWrx4MWcbyAW7QbpZM2nGDH9JklatOJWR4L//pO7dpV9/9a9r3lz66ivKj0Sj+MALnsnJSt+7V4WKBVwoQMT2ZRpZNFwmfv/99xwf77nnnnPP27Vrl+sb2eNq1arp448/Vnx8fLaOYc+1MiZHHXWU7rnnnhy3AdErLo44cwBRqmQlqWt/6cRbpSkvSLPek9KSPds2L5NGXC1VedETsFG/C5HOUa5kkQQNvqi5Lnj9F6WkpWvN1j0a+/tuWVgGmTAQ7hgrBQAAQLSLmhHK2bNnuyCHHj16qHr16q7+s02HYz9MH374YTVs2NClbbQLAJatYs2aNUFr26BBg1SpUiX16tUraMcEYr0kiQVhIPz98YfUpk3GAIxu3aTJkwnAiFZxpQJqlltJku07QtYWhK9vv/1W77//vkaMGOECMGrVquUCMFq0aJHtYzz44INauXKl3njjjRyXMEH0978BIKqVqiKd+ax02xyp2WVSoYChnX8XSh+dL73fXVozO5StRAFoVqOs7ji1oW956ortbk4mjMiQ31kDw7mvxlgpAAAAol3UZMJ44okn9OWXX+boOfajtHPnzq5mdNWqVXXOOedoxYoVGjJkiMaOHevW161bN0/t+vfffzVgwAB98803eToOAIIwIs2kSdK550pbt/rX3XST9NJLUkLU/O+DA8UVKaJCRYoofd8+t5y2fZtUpTInKsJZkENuMl5k5YcffnDzrVu3auHChXr88cfVsWNH9e/fXw888MBhnz9r1iy99NJL6t27tzp16hS0diE6+t8AEDPK1pTOfVVqd7s0cYC0aLR/24op0tudpWPPlbo8LFWoF8qWIh/d2LGeflj8r+au2qp96Z6MYrt27eacI2z7aoyVAgAAIBZEzWWwE088UU2aNFGrVq3cVLt2be3bfwEoKzbQbz8e7LnfffedSpYs6dYPHDhQd955p4vy/umnn3z724WC9evXH/KYxYsXV82aNX3LVqP89NNPd68BIG8Cy4/YtcDdu+3fHGc1HI0cKV18saUY9SzbzTbPPSf160dW5FgQV7qUUjd4/g9O3UEmDGStbNmyOumkkzR+/HjXV3rooYfUtWtX15fLSkpKiq677jr33Oeff57TG0Lh2v8GgJhTqaF04fvS2nnSD49If/v/jrrAjMVjpJZXSR3v9ZQ0QVSJjyukF3o21ZkvTdG+FM8w37adu12WhexkPUDo5dfnFK59NcZKAQAAEAuiJggjp7XArfbgK6+84h6/+uqrvh8Vpl+/fi5F9qRJk1zqPm9q7E8++UQ32W3ch2B3cXp/jPz222/68MMP3Y8X+1HijSi3H8K2bD9CSJ8NZN/RR3uCLiz4IjVVmj/fBhU4g+Hmrbc8GS/S0jzLRYtKH34onX9+qFuGghJfqrRSN2x0j1O3e1IiA4dSuHBhV7bN+l1jxow5ZBDG6tWrNW/ePB1xxBHq2bNnhm3e/pYdx5shI3CQGNHf/waAmFatmdT7S2nZBOmHR6V18z3r01Olme9I8z+VTrpDatNHKlws1K1FENWtVFL3nH60nh7j+czj0lP05dzVOvf4GpznGBaOfTXGSgEAABArAgqHxpapU6dq27Ztqlevnpo3b37Q9gsuuMDN7UKA14033ugCKA41BQ4A//XXX+4HzPHHH69y5cq56ZlnntHatWvd4/fee6+A3i0QHeLjpeOP9y/PmhXK1uBAVs52wADphhv8ARjlykk//kgARqyJL1XK9ziNTBjIpooVK7r5hg0bsrW/3XFng8CB03yLztsfjOFdh9jqfwMAJNXrLF33k3T+u1K52v5TkrRD+vFx6eWWnoAMb6cdUeGKE2ureR1/ppP+X83Xv9v3hrRNiCyMlQIAAADBEzWZMHLKO0hvARKZ8a5fsGBBrl+jffv2mjhxYoZ1Q4cO1bhx4/T555+rYcOGhz1Go0aNMl1vwR1k0UAsatlS+vlnz+OZM0PdGnjZ+K2VGhk82H9OqlWTvvvO/o5xnmJNXOnSvsdkwkB2eQMm7AL9oVgaZbvwnhm7GH/yySerS5cu+uGHHzj5Mdj/Dgb63wCiQlyc1PgC6ZizPVkwJj0j7fVkjNL21dKo66Xpr0mnPyXVahvq1iII4uIK6bmezfXG4AkqXChVSfv26Z4vFmjIla0oSxKBbNwvqz7J71afNYbHSgEAAIBIELNBGKtWrXLz6tWrZ7rdu37lypV5uqPTmwo78OJAkSJFDloPIPtBGF5kwggPSUnSVVdJw4f719m4iQVg1KoVypYhVMiEgazurNuxY4e6du2qOLswtF9ycrLeeOMNDRs2TMWKFXNlSbwsHbJN5513np566ilObIQriP63N5uKN6hn9+7d+uOPPzRixAiVKFFCZ5xxRp6ODQARJyFROrGP1OxiafLz0ow3pbRkz7Z186QhZ0iNzpNOfVwqWzPUrUUe1ShfXMWKF1PKnp1KVIp+WrJBn836R71a8dni8BgrBQAAAIInZoMwdu7c6ebFixfPdLsN0hq7WBBKgdHtllrbW+v8qKOOyvIuUCBWgjD++MP+jUoBlQ9QwPbssfTx0vjxGT8jW67kz4SLGBNX2v+PMnV7aP8fRfhYunSprrrqKhekajWkK1SooI0bN2rhwoVat26dihYt6u6Cq1HDX7vcti9ZssRtR+QrqP639Z979uzpW/7iiy/cVKtWLa1YsSJbz/ei/w0gahQrJ502QGp1rfTjY9Lvo/zb7PGSr6W2t0rt+kpFSoaypcijimVKav2enSpSKFVKl54Yu1gdGlZS1TLFOLcRpHDhwi47hFfZsmXdlJ8iZawUAAAAiAT+2xBRIB599FGtXr06V88dNGiQ6tSp4yZLS5iamhr09gHhrkEDf9CFxSHNnRvqFsUuG3exG4oDAzC6dJEmTCAAI9bFly7je5y2Y3tI24Lw0bFjR91///0ukNRSGFu6YcuOUb58ed16660uGOPCCy8MdTMRBSzjnAUrHzhlJwDjQPS/AUSd8nWknkOlq7+TqjX3r0/ZK01+TnqlpTT/U8+PLUQkC2w1ZRI9n+HOfSl6aPRv3MgThg51c5WN+XnHAG2yPkm0ystYKQAAABCuYjYTRsmSJX0pijOza9cuNy8VRrfY9+3bV1deeaV7bBcwgFhkGexbtLDSPv6SJB06hLpVsWfTJk8AxsyZ/nU9enhKkhQpEsqWIRzEkwkDmbDB4wEDBuR4QNamnF58R3ii/w0AYaRma+naCdKCT6QfHpN2rves37FOGnW9NHuIdMazUtUmoW4pcsjKu5mzG1XUotkp7vEPi//TuIXrdFaTapzPMFSoUKGD1sXHx+uvv/7yLed3FoxI7asBAAAA4SpmM2HUrOmph5lVpLV3vaUtBhC+JUksCAMFy6oCdOqUMQDjiiukTz8lAAMecaVK+04FmTAAeNH/BoAwjHBvdol062zppDul+IBo6lXTpLc6SuPuknZvDmUrkctMGMdULqq29Sr41j/61e/aujuJ84ks0VcDAAAAgidmgzCaNm3q5nPmzMl0u3d9kybhc9cH6ZABD4IwQmflSk/mkd9+86+75RbpvfekhJjNrYRDZsLYRjkSAB70vwEgTBUpKXV5WLrlV+nos/zr09OkmW9LL7eQZg+V0tJC2UrkMBPG3r179VSPxiqS4Bn627gzSf3HLeY8RohQlCOJxL4aAAAAEK5iNgijXbt2KlOmjJYtW6Z58+YdtH3EiBFu3r17d4VTOZLly5e7KTEx0aUmBGI9CGPpUk9pDOS/JUuk9u2lgIyouv9+6aWXPDfRAZllwkjdsYMTA8Ch/w0AYa5cbemij6TLvpAq1Pev37NZGnO79O6p0roFoWwhchCEsWfPHtWqUEL9Tm3o2zZi9mr9vHQj5zEC2JifdwzQJhsTzG+R2FcDAAAAwlXMXjazIIZb7PZtSTfffLOvrqEZOHCgFixYoI4dO6pFixYhbCWAzNStKx1xhH959GjOU36bP9+TASOwgtMzz0gDBlj9Ws4/ss6EkbadTBgAPOh/A0CEqH+KdNM06ZTHpMIl/OvXzPKUKPnmPmkfgbbhXo7EMmGYa9rX0XFH+oOk7xu1QHuSUkPWPoQv+moAAABA8ERNEMa4cePUpk0b35SU5KlzGbjO9gn04IMPqnXr1vrll1/UoEED9erVy+135513qlKlSnrP8uuHEcqRAB520f+CC/xn45NPODP5afp0qVMn6b///Of/tdeku+/mvCNzcaUCypHs2KH09HROFRCF6H8DQBRLSJTa95VunSU16pGxRMn016RXWkm/j5bo54V1JgyTEB+nZ85vovg4T/T8P5v3aOD3S0LaRhRMOZJY6KsBAAAA4SpBUWLDhg2aMWPGQesD19k+B94dMHHiRD311FMaPny4Ro8erfLly+vKK6/UE088oerVqyucWOpBa5s56qijQt0cIKQuukh65RXP4wkTpH//lapU4UMJNju3Z58teZMFWRWkoUOlyy7jXCNr8aX9d9opNVXpu3erUImAuygBRAX63wAQA0pXk3oOkZpfJo2/S9r8t2f9jnXS51dI9U+Vzhoola0Z6pYiiyAM06haGV3foa5e/2mZW3735+Xq3rSamlQvy3kLoUMFq1s5kr8CaoGWLZvzzyoW+moAAABAuCqUzu2pUZFeEog1aWlS7drSP/94li0g4+abQ92q6GI3xJx/vrRvn2c5MdGTdeS880LdMoS79KQk/dGkqW+5/k8TVTiwhhAiCn0OgH8LAOAk75V+flH6eaCU6rmj3rGSJZ0flFrfIMXFc7JCbM2aNXr77bdVunRp9evXz7d+b3KqTh80WSs27XbLTaqX0ag+7XwZMlDwPv74Yy1ZskTdu3fPUA6Z/jcAAAAQ+aKmHEks2Lp1q1asWOEmi50hfgaxLC5O6tXLv0xJkuCy83nuuf4ADLuZaswYAjCQPYUSE1Vo/x14JnX7dk4dgIhE/xsAAhQuKp18n9RnulT3ZP/65F3St/dJ75wirV/IKQvDTBimaOF4PdmjsW95weptGv7rqgJvH7LHxvy8Y4A2WZ8EAAAAQOQgCCOCWP1Hby1Iq+No9SGBWC9J4vXzz/6sGMib11+XLrlESknxLFtlie++k7p25cwi++JLlfI9Ttuxg1MHICLR/waATFSoJ10+SjrvLalYef/6tXOktzpJPzzqyZqBkPBmUUhOTlaK90fdfm3rVdQ5zar5lp/75g9t3Lk/8h5hxcb8vGOANlmfBAAAAEDkIAgjgvTt21fLly93U2JioqsPCcSy44+X6tf3L3/2WShbE/msHG3//lKfPp7HpkIFacIEqX37ULcOkSautD8Ig0wYACIV/W8AyEKhQlLTXtIts6QmAdHxaSmekiVvtJf++ZXTF8IgjKxK2D5w5jEqVSTBPd6+N0VPf/1HgbYP2WNjft4xQJusTwIAAAAgchCEEUHKli2r2rVru6lQoUJuAmKZ/RMIzIZBSZLcS0uTrFzwQw/511Wv7skwElCaFsi2+FKl/d8vMmEAiFD0vwHgMEpUkHq8KV02Uipby79+01Lp3a7Stw9ISbs5jQUoLi7OF4hxYEkSU7l0UfXr2tC3PGL2as1csZnPKMzYmJ93DNAm65MAAAAAiBwEYQCIaIFBGLNmSX/9FcrWRCbLUHv11ZZy3b/uqKOkqVOlo48OZcsQNZkwtm0PaVsAAACQz+p3kfpMk068xS4f71+ZLk17RXqjnbRiKh9BAfIGYWSWCcNc3qaWjq3qD5p+aPRvSklNK7D2AQAAAEC0IwgjgmzdulUrVqxwU3p6upuAWNeokXTccf7lTz8NZWsiz+7d0vnnS++/n7HMy5QpUs2aoWwZIl1gJozUHQRhAIhM9L8BIAcSS0inDZCu+U6q0MC/fvPf0tAzpfF3kxWjgBQrVizLTBgmIT5OT5zr/yH9x/odGvrLioJqHvZLs5SUWbAxP+8YoE3WJwEAAAAQOQjCiCCDBg1SnTp13JSUlKTU1NRQNwkIC5QkyZ0NG6TOnaWvvvKv69RJmjhRqlQpWJ8OYlV8QCaMtO07QtoWAMgt+t8AkAs1TpBunCK1u10qFDDs9Oub0psnSatncVrz2aHKkXi1qFVOvVrW8C2/+P2fWr8t88wZyB8bN2508zJlyhy0zcb8vGOANlmfBAAAAEDkIAgjgvTt21fLly93U2JiouLj40PdJCAs9Orlf/zbb9Lvv4eyNZHByra0bSvNmOFfd8450tdfS6X9CQyAXIsjEwaAKED/GwByqXAx6dTHpWt+kCoF1Djc9Jf07qnShP5SShKnN58zYWRVjsTrnjOOVtnihd3jXUmp6j9uEZ9JAdm9e7e2bNniHh955JEHbbcxP+8YoE3WJwEAAAAQOQjCiCBly5ZV7dq13VSoUCE3AZDq15datvSfCUqSHJoFXpx4oicQw+umm6QRI+yOKb5RCA4yYQCIBvS/ASCPqreQbpjsyYqh/WMY6WnS5Oekd7pI/y3mFIegHIlX+RKJuud0f5DM2AXrNOPvTXwmBWDNmjWez6B8ed/nFcjG/LxjgDZZnwQAAABA5CAIA0DUZcP45BOrnxrK1oSvL7+UTj7Z0p761z31lPTqq1JCQihbhmgTF5BSJXUH5UgAAABiVkIRT1aMq8ZLZWv5169fIL3ZQZr2qpSWFsoWRm05ksNlwjBWkqRpdX85jMfHLlJqGj+oCyoII7MsGAAAAAAiH0EYEWTr1q1asWKFm9LT090EwOPCC/1nYulSac4czsyBXn9d6tHD7obyLBcuLH34oXTvvXaXDecLwRUfUI4kbft2Ti+AiET/GwCCqFZb6aapUosr/etSk6Rv75c+ukDa8S+nu4AzYZi4uEJ6uHsj3/Lva7drxOx/+Czy2dq1aw8ZhGFjft4xQJusTwIAAAAgchCEEUEGDRqkOnXquCkpKUmpqamhbhIQNmrWlNq18y+fcYb00kvSvn2hbFV4sHit+++X+vTx32BmSQq++Ua69NJQtw6xUI6ETBgAIhX9bwAIsiKlpO6DpUs+l0pW8a9f9qP0eltpyTec8gIOwjAtapXTOc2q+Zaf+3aJduxN5rPIJxZgcbhMGDbm5x0DtMn6JAAAAAAiB0EYEaRv375avny5mxITExUfHx/qJgFh5dpr/Y83bJBuv1066ihp6FAbwFBMSkqSrrjCU3LEq3p16eefpc6dQ9kyRLs4MmEAiAL0vwEgnzTsKt00TTrqTP+63Rulj3tJ4+6SkrMXPIC8lyPxuuf0o1W0sGeYcOPOJL06cRmnN59s27ZNu3btUlxcnI444ohM97ExP+8YoE3WJwEAAAAQORJC3QBkX9myZd1kClE7ADiIBRvYzSRPPint3u1Zt3KldNVV0rPPekqW2E0mFoTgnZcrF72lOKwCxPnnSz/84F933HHS11973jtQkJkw7G4v/u8CEGnofwNAPipRQbpouDTrPenbB6SU/YEXM9+WVvws9RwqVT6aj6AAMmGYamWL6YYO9TT4x6Vu+b2fl+viE2qoVoUSfAZB5s2CUaVKFRW2OqGZsN9OtWvX5twDAAAAEYpMGACihgVTPPCAtGyZdPPNUuBYxuLF0mOPSddfL515ptS0qVShgmQ3nfTv7wlYiCZWXrZjx4wBGJ06SVOmEICBghFnNW+80tKUtmsXpx4AAAAH/4hrdY10wySpSmP/+g2Lpbc6SXM/9NRXRL5nwjA3dqynqmU8z01KTdOT4xdz5vPB4UqRAAAAAIh8BGEAiDoWWPHKK9Iff0iXXXboTBf//Sc99JBUp470zDNSNFwntiCUtm2lefP86y66SPrmG7ujN5QtQyyJL1kyw3JatEU6AQAAIHgqHSVd96N04i3+dZYZ48ubpVE3Svt2crbzOROGe15ivO49w5995Nvf/9UvyzZy7oOMIAwAAAAg+hGEASBq1a0rDRsmzZ8v3XefdPHFUocOnvVFimTcd/Nm6d57PcEYAwf6y5kEy/jxUvfu0qOPSsnJyjcWeGLv0cqweN11l/TRRwe/ZyA/FSpcWIWKF89QkgQAAADIUkIR6bQB0iWfScXK+9cv+MSTFWP9b5y8HAZhpKSkKDmHP0DPblpNzWv6o/cfH7NIqWlkIwmWtLQ0rbXUlVYCplq1oB0XAAAAQHghCCOCbN26VStWrHBTenq6mwAcXuPG0pNPSsOHS5MmeTJF2A1BNu5h5UsCb9jfsEG6807pmGOksWPzfnYtmMNKo3Tr5jmelUS58EJp377gf3ILF3pKkOwfz3EsoOS556Q4/tojBOJLlfI9JhMGgEhE/xsAQqDhadKNP0s1T/Sv27RUeruzNOcDPpJsSExMVKH9KSFzWpLEnvdI90a+5T/W79CnM//hvAfJhg0bXGBM4cKFValSpSz3szE/7xigTdYnAQAAABA5uCwXQQYNGqQ6deq4KSkpSampqaFuEhCxbDyqalWpf39p+XLp7rulgJv2tWqVJ3NFz57SunW5ew0rB9KypfTaaxnXjx4t9ehhg2EKmtmzpU6dPOVVvO/vrbekO+4I3msAORVfurTvMZkwAEQi+t8AECJljpSuGCuddKf9uvGsS90nfXWrp0RJcs7KbMSauLg4FS1aNFclSUyzGmXVo/mRvuVBP/yp3UkpQW1jrArMgmGfU1ZszM87BmiT9UkAAAAARA6CMCJI3759tXz5cjfZXQ3x8fGhbhIQFSpWlJ55Rvr7b0/QQuHC/m0jRkhHHy29/rqlDc3e8Wy/F16QTjhBWrzYvz4gKYArT3L22cEpezJtmtSli6ekirFxnPffl667Lu/HBvIiLjAIY/t2TiaAiEP/GwBCKD5B6vKwdNkXUvGK/vVzP5Te7SptXh7K1oU9bxBGTjNheN112lEqkuAZNvxvxz699zPnOxjWrFnj5kce6Q9yyYyN+XnHAG2yPgkAAACAyEEQRgQpW7asateu7SZLD+lNLQkgOKpU8ZTvsAwW7dr519u14z59pLZtPVktliyx1KAZn2vLixZ5Sn+0aSPddZcUWHrXlu2Glwsu8K/7/nvpzDOlnTtz3+Yff5S6dpW2bfMsJyRIH38sXX557o8J5E85kh2cWAARh/43AISB+l2kG6dI1U/wr1u/QHqro7Tkm1C2LKwVK1Ys15kwTLWyxXRlu9q+5Tcm/a3Nu5KC1r5Yld0gDBvz844B2mR9EgAAAACRgyAMADjAscdKkydLb74plSnjXz9jhnTzzZ7MGDVqSL17S2+8Id1yi1S3rtSokaesycyZ/udUqyb98IMnOKNkSU+AxCWX+LdPmiSddpo/iCInrH2nn+4P4khM9GTuuPBCPlKEh7jS/iCM1B1kwgAAAEAula4mXTlOan2jf93ebdLHvaQJ/bOftjCG5DUIw/TpWF9linlSRe7cl6KXJywNWvtiUXJysv79999sBWEAAAAAiGwEYQBAZn8c46Trr5f++EPq1evg7XbzyrBh0k03Sa++Kq1YcfA+558vLVjgKRXiZZkqPvhAuvJK/7pffpG6dct+aZKUFEuPLt14o+exsUyzX34pnXMOHyfCR3wpfzkSMmEAAAAgTxISpTOekc5/Vypcwr9+8nPSJxd7gjIQtHIkpkzxwrr55Hq+5Q+nr9Q/m4NQUzNGrV+/XmlpaSpRooTKBN7xAQAAACDqEIQBAIdwxBHSJ594MmPccYfUpMmhT1fz5tKDD0q//urJSlGhwsH7xMdL774r3XCDf93UqZ5gj8ASJpmxjBlnny0NHuxfV7Wqp32WFQMI30wYlCMBAABAEDS+QLruR6lCA/+6P7+R3u4ibSRTQzAzYZjeJ9ZWtTKegI7k1HS98N2SPB0vlgWWIqHEMAAAABDdCMIAgGw46SRp4EBp/nzJsod++qknU0bLltK550pvv+3JjjFnjvTEE1KrVof54xsnvf66dO21/nVjx3qOmZ6e+XP+/ltq21b6+uuMQR8W8HG41wNCnQkjdTt3JgIAACBIKh8jXTdBOupM/7pNS6W3O0t/fsdpllSkSBF3Hvbt25en81G0cLz6dT3Ktzx63lr9toa+fV6CMKpZ3VIAAAAAUY0gDADIocqVpQsvlN58U5o5Uxo1yhNMkdNxlEKFPIEYFsThNXSodO+9GffbulV66CGpaVNp0SL/+h49pClTpOrV+QgRnuIDMmFQjgQAAABBVbS01OsjqeM9/nX7tkvDL5SmDMw6uj1GBCsIw5zX/EgdfYS/b//MN3/k+ZixngkDAAAAQHQjCAMAQighQfr4Y6ljR/+6Z5+Vnn9esuoN/ftLdep45jt3+vd54AHp88+lEgGlkIFwExeYCYNyJAAAAAh6hzNOOvl+6cJhUmHvj6N06cfHpJHXScl7Y/acBzMIIz6ukO45/Wjf8pSlG/Xz0o15Pm4ssbIwmzdvdo8JwgAAAACiX0KoG4Ds27p1q5tMeozf0QFEk6JFpS+/9ARiWLkT87//SU8+KW3ZknHf8uWll1+WLrkkJE0FciS+jD8II237ds4egIhD/xsAIsSxZ0sV6kkfXyxtXelZt/BzactK6aLhUslKijVF7YdmkIIwTKejKql1nfKasdwTSPD0N4v1Vb32iosrFJTjR7u1a9e6ebly5VS8ePHD7m/jfitWrPAtly1b1k0AAAAAIgOZMCLIoEGDVKdOHTclJSUpNTU11E0CECRlykjffCPVretfFxiAUbq09Nhj0vLlBGAgcsSV8qcsJhMGgEhE/xsAIkiVRtL1P0m1T/KvW/2r9E5n6d+Auo4xIpiZMEyhQoV035nH+JZ/W7Nd3y1aH5Rjx4KcliKxMT/vGKBN1icBAAAAEDkIwoggffv21fLly92UmJio+Pj4UDcJQBAdcYT03XdS5cr+dVZu5P77PcEXDz/sCcYAIkV8wBc2bccOpaelhbQ9AJBT9L8BIMIULy9dPko6vrd/3dZV0rtdpaU/KJYEOwjDNKtRVqc1quJbfvH7pUpLI1NrfgRh2JifdwzQJuuTAAAAAIgcBGFEEEs7WLt2bTfZHQg2AYgu9epJkydLV14pPfSQ9Pff0oABnjIkQKSJD8iEofR0pe3aFcrmIAwMHDhQPXr0UIMGDVSmTBl3caBWrVrq3bu3Fi5cmKMSEcOHD9fFF1/s7gy04NRSpUqpdevWGjx4sJKTk/P1fSB20P8GgAgUX1jq/pLUtb/lb/CsS9ohDe8p/fq2YkV+BGGYO05tKO9w1JJ/d2jswnVBPX40stIiOQ3CsDE/7xigTZQiAQAAACILQRgAEGaOOkoaMkR6/PGMWTGASBNXsmSG5bTt20PWFoSHJ598Ul9//bXKly+vLl26qFu3bq5e+bBhw9SiRQuNHTs2W8d5/vnndemll+rTTz91dbUtsOOEE07Q/Pnz3V2CnTt31u7du/P9/QAAgDBlUQJtb5UuGi4VLuFZl54mjb9L+u5BKQYytHmDMPbu3RvU4x59RGl1a1zVtzzohz+VSjaMQ9q+fbt27tzpAiuOsBSYAAAAAKIeQRgAACBfFEpIUJzV1NkvdccOznSM+/LLL7VlyxbNmDFDI0eOdNOSJUv06quvuuwV1157rVJSUg57nBIlSujuu+/WihUrNGfOHH3yySf68ccfXTaNmjVr6ueff1b//nb3KwAAiGlHnyld/bVUqpp/3S8vSyOvlVKCmyEiXIMwkpKSlBbkoJO+pzRQ3P5sGH9v2KUv53myPCBzq1evdvMqVaq4DG4AAAAAoh9BGAAAIP86GqVL+x6nkgkj5rVr185lvjhQnz59VK9ePf37779atGjRYc/Tfffdp2eeecYFXASyMidPP/20e/zxxx/H/PkGAACSqjaVrpsgVWnsPx2/fSF9eL60Z2vUB2F4AzGCqX7lUjqnmb+sxuAflyo5Nfqzi+TWP//84+Y1atQIdVMAAAAAFBCCMArA0KFDXcrBA6effvqpIF4eAICQiS9VyveYciQ4lMKFC7t5Xu8ObNq0qZuvXbuWEx6jPvvsM1fqpmrVqipTpow6dOjgsqMAAGJY6arSVeOlup3861ZMkYacIW3zZCmINgmWlS7OM+y3b1/ws37c1qWB4venw1i5abdGzSEbRlYIwsiIcVIAAADEgoRQNyCW2OBvfHy8b/nYY48NaXsAAMhvcaX9QRip2ylHgswNGzbMlSWxTBY25cXff//t5tTbjl2DBg1y3yMrc1OyZEkNGTJEXbp00a+//uoL0gEAxKCipaVLPpfG3CbN358x679F0junSpeNkKo0UjSxm38sG8aePXvyJQijTsUS6tH8SH0+e7UvG8a5zY9UYgL3ewWyknvr1q1zj8mEkRHjpAAAAIhmBGEUoNatW7s7EQAAiBXxpcv4Hqft2B7StiD3LIV1o0aZX5j4/fffc3y85557zj1v165dWrx4sXtcrVo1V0IkMGA1NwYPHuzm55xzTp6Og8g1ZswYVahQwbd8yimnqHHjxi4o46233gpp2wAAIZaQKJ37ulS6mjTlBc+6HWs9GTEuHSHVOEHRxMrA5VcQhjcbxqi5a5SSlq41W/fo89n/6NLWtfLltSKVZWdLS0tzgaFly5YNdXPCCuOkAAAAiGaEpwMAgAIpR0ImDHh9++23ev/99zVixAgXgFGrVi0XgNGiRYs8naQ33nhDP/zwgxvgvvfeeznhMSowAMNYKvbjjjtOy5cvD1mbAABhpFAhqcvDUreBUqH9w2J7t0kfnCP99YOiiWXCMPkVhFGjfHH1bFnDt/zKhL+0Nzk1X14rGkqRWHYSAAAAALEhqoIwZs+eraefflo9evRQ9erV3Y+b7PzAsbsCHn74YTVs2NDdJWB3Yl599dVasya49SyPPPJIlwmjSZMm7qIDAADRLq50ad/jVDJhRKzExEQXLJHZlBsWKJGenq4tW7Zo8uTJrnREx44dNWDAgFy3ccqUKbr99ttd3++9995z/TnEdt/bKzU1VTNnzlT9+vXz5fgAgAjV6hrpwg+k+ETPcvJuafhF0m8jFS3yOwjD3NK5vhLjPcOL67bt1aczPUEHODgIo6CFe1+NcVIAAABEs6iqjfHEE0/oyy+/zNFz9u7dq86dO2v69OmqWrWqS129YsUKVzt67Nixbn3dunXz1C47rl1UsDR79kPm3XffVc+ePTV69GhSZQMAYiYTRtr2HSFtC8KPZaw46aSTNH78eJ144ol66KGH1LVrV7Vq1SpHx/ntt99cn8rKprz00ks677zz8q3NCP++94FeeeUVrVq1Sn369OHjAwBkdEx36dLPpU8ulZJ2SmnJ0oirPZkxWl4VNUEY9v9vfjmybDFddEINfTBtpVt+Y9IyXXxCTSUmRNV9X7liQcehDMI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      "text/plain": [
       "<Figure size 2160x889.969 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot.panel(sim,show_limits=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The change in fragmentation velocity has an obvious effect on the particle sizes.\n",
    "\n",
    "To check the time evolution of the snowline, we have to read the data. The gray lines are the positions of the radial grid cell interfaces and snapshots, which explains the discrete behavior of the snowline location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = sim.writer.read.sequence(\"t\") / c.year\n",
    "ri = sim.writer.read.sequence(\"grid.ri\") / c.au\n",
    "rsnow = sim.writer.read.sequence(\"grid.rsnow\") / c.au"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(t, rsnow)\n",
    "ax.hlines(ri, 1.e3, 1e4, lw=1, color=\"gray\", alpha=0.25)\n",
    "ax.vlines(t, 2.5, 5.5, lw=1, color=\"gray\", alpha=0.25)\n",
    "ax.set_xlim(1.e3, 1.e4)\n",
    "ax.set_ylim(2.5, 5.5)\n",
    "ax.set_xlabel(\"Time [yr]\")\n",
    "ax.set_ylabel(\"Snowline location [AU]\")\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv (3.14.0)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}