{ "cells": [ { "attachments": {}, "cell_type": "markdown", "id": "202f4da1", "metadata": {}, "source": [ "# Particle-Field interaction\n", "\n", "This notebook illustrates a simple way to make particles interact with a ` Field` object and modify it. The ` Field` will thus change at each step of the simulation, and will be written using the same time resolution as the particle outputs, in as many `netCDF` files.\n", "\n", "The concept is similar to that of [Field sampling](https://docs.oceanparcels.org/en/latest/examples/tutorial_sampling.html): here instead, on top of reading the field value at their location, particles are able to alter it as defined in the `Kernel`. To do this, it is important to keep in mind that:\n", "\n", "- Particles have to be defined as `ScipyParticles`\n", "- `Field` writing at each `outputdt` is not default and has to be enabled\n", "- The time of the `Field` to be saved has to be updated within a `Kernel`\n", "\n", "In this example, particles will carry a tracer and release it into a clean `Field` during their advection by surface currents. To show how can particles interact with a `Field` and alter it, the exchange of such tracer is modelled here with a discretized version of the mass transfer equation, defined as follows:\n", "\n", "\\begin{equation}\n", "\\Delta c*{particle}(t) = aC*{field}(t-1) - bc\\_{particle}(t-1)\n", "\\end{equation}\n", "\n", "In Eq.1, $c_{particle}$ is the tracer concentration associated with the particle, $C_{field}$ is the tracer concentration in seawater at particle location, and $a$ and $b$ are weights that modulate the sorption of tracer from seawater and its desorption, respectively.\n", "\n", "Additionally to a relevant `Kernel`, we will define a suitable particle class to store $c_{particle}$, as it is needed to solve Eq.1.\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "c6d39d45", "metadata": {}, "source": [ "## Particle altering a Field during advection\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "b5f35c63", "metadata": {}, "outputs": [], "source": [ "from datetime import timedelta as delta\n", "\n", "import matplotlib.pyplot as plt\n", "import netCDF4\n", "import numpy as np\n", "import xarray as xr\n", "\n", "from parcels import (\n", " AdvectionRK4,\n", " Field,\n", " FieldSet,\n", " ParticleSet,\n", " ScipyParticle,\n", " download_example_dataset,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "068921eb", "metadata": {}, "source": [ "In this specific example, particles will be advected by surface ocean velocities stored in netCDF files in the folder `GlobCurrent_example_data`. We will store these in a `FieldSet` object, and then add a `Field` to it to represent the tracer field. This latter field will be initialized with zeroes, as we assume that this tracer is absent on the ocean surface and released by particles only. Note that, in order to conserve mass, it is important to set `interp_method='nearest'` for the tracer Field.\n", "\n", "As we are interested in storing this new field during the simulation, we will set its `.to_write` method to `True`. Note that this only works for Fields that consist of only _one_ snapshot in time.\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "a64ed4f0", "metadata": {}, "outputs": [], "source": [ "# Velocity fields\n", "example_dataset_folder = download_example_dataset(\"GlobCurrent_example_data\")\n", "fname = f\"{example_dataset_folder}/*.nc\"\n", "filenames = {\"U\": fname, \"V\": fname}\n", "variables = {\n", " \"U\": \"eastward_eulerian_current_velocity\",\n", " \"V\": \"northward_eulerian_current_velocity\",\n", "}\n", "dimensions = {\n", " \"U\": {\"lat\": \"lat\", \"lon\": \"lon\", \"time\": \"time\"},\n", " \"V\": {\"lat\": \"lat\", \"lon\": \"lon\", \"time\": \"time\"},\n", "}\n", "fieldset = FieldSet.from_netcdf(filenames, variables, dimensions)\n", "\n", "# In order to assign the same grid to the tracer field,\n", "# it is convenient to load a single velocity file\n", "fname1 = f\"{example_dataset_folder}/20030101000000-GLOBCURRENT-L4-CUReul_hs-ALT_SUM-v02.0-fv01.0.nc\"\n", "filenames1 = {\"U\": fname1, \"V\": fname1}\n", "\n", "# a field with the same variables and dimensions\n", "# as the other velocity fields\n", "field_for_size = FieldSet.from_netcdf(filenames1, variables, dimensions)\n", "\n", "# Adding the tracer field to the FieldSet\n", "# with same dimensions as the velocity fields\n", "dimsC = [len(field_for_size.U.lat), len(field_for_size.U.lon)]\n", "dataC = np.zeros([dimsC[0], dimsC[1]])\n", "\n", "# the new Field will be called C, for tracer Concentration.\n", "# For mass conservation, interp_method='nearest'\n", "fieldC = Field(\"C\", dataC, grid=field_for_size.U.grid, interp_method=\"nearest\")\n", "\n", "# aad C field to the velocity FieldSet\n", "fieldset.add_field(fieldC)\n", "\n", "# enable the writing of Field C during execution\n", "fieldset.C.to_write = True" ] }, { "attachments": {}, "cell_type": "markdown", "id": "6caf2eb7", "metadata": {}, "source": [ "Some global parameters have to be defined, such as $a$ and $b$ of Eq.1, and a weight that works as a conversion factor from $\\Delta c_{particle}$ to $C_{field}$.\n", "We will add these parameters to the `FieldSet`.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "54ec0e6f", "metadata": {}, "outputs": [], "source": [ "fieldset.add_constant(\"a\", 10)\n", "fieldset.add_constant(\"b\", 0.2)\n", "fieldset.add_constant(\"weight\", 0.01)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "470c2e80", "metadata": {}, "source": [ "We will now define a new particle class. A `VectorParticle` is a `ScipyParticle` having a `Variable` to store the current tracer concentration `c` associated with it. As in this case we want our particles to release a tracer into a clean field, we will initialize `c` with an arbitrary value of `100`.\n", "\n", "We also need to define the `Kernel` that performs the particle-field interaction. In this Kernel, we will implement Eq.1, so that $\\Delta c_{particle}$ can be used to update $c_{particle}$ and $C_{field}$ at the particle location, and thus get their values at the current time $t$.\n", "\n", "Additionally, the time of `fieldset.C` is updated within the `Interaction` Kernel, which is an important step to properly write its value.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "203a82eb", "metadata": {}, "outputs": [], "source": [ "VectorParticle = ScipyParticle.add_variable(\"c\", dtype=np.float32, initial=100.0)\n", "\n", "\n", "def Interaction(particle, fieldset, time):\n", " \"\"\"define the interaction between the particle and the fieldset.C field.\n", " the exchange is obtained as a discretized mass transfer equation,\n", " with a and b as the rate constants\"\"\"\n", " deltaC = fieldset.a * fieldset.C[particle] - fieldset.b * particle.c\n", "\n", " xi, yi = (\n", " particle.xi[fieldset.C.igrid],\n", " particle.yi[fieldset.C.igrid],\n", " )\n", " if abs(particle.lon - fieldset.C.grid.lon[xi + 1]) < abs(\n", " particle.lon - fieldset.C.grid.lon[xi]\n", " ):\n", " xi += 1\n", " if abs(particle.lat - fieldset.C.grid.lat[yi + 1]) < abs(\n", " particle.lat - fieldset.C.grid.lat[yi]\n", " ):\n", " yi += 1\n", "\n", " particle.c += deltaC\n", "\n", " # weight, defined as a constant for the FieldSet, acts here as a conversion factor between c_particle and C_field\n", " fieldset.C.data[0, yi, xi] += -deltaC * fieldset.weight\n", "\n", " # update Field C time\n", " fieldset.C.grid.time[0] = time\n", "\n", "\n", "def WriteInitial(particle, fieldset, time):\n", " \"\"\"store the initial conditions of fieldset.C\"\"\"\n", " fieldset.C.grid.time[0] = time\n", "\n", "\n", "# for simplicity, we'll track a single particle here\n", "pset = ParticleSet(fieldset=fieldset, pclass=VectorParticle, lon=[24.5], lat=[-34.8])" ] }, { "attachments": {}, "cell_type": "markdown", "id": "2a6783bf", "metadata": {}, "source": [ "Three things are worth noticing in the code above:\n", "\n", "- The use of `fieldset.C[particle]`\n", "- The computation of the relevant grid cell (`xi`, `yi`)\n", "- Writing $C_{field}$ through `fieldset.C.data[0, yi, xi]`\n", "\n", "Because `fieldset.C[particle]` interpolates the $C_{field}$ value from the nearest grid cell to the particle, it is important to also write to that same grid cell. That is not completely trivial to do in Parcels, which is why lines 7-11 in the cell above calculate which `xi` and `yi` are closest to the particle longitude and latitude (this extends trivially to depth too). Our first guess is `particle.xi[fieldset.C.igrid]`, the location in the particular grid, but it could be that `xi+1` is closer, which is what the `if`-statements are for.\n", "\n", "The new indices are then used in `fieldset.C.data[0, yi, xi]` to access the field value in the cell where the particle is found, that can be different from the result of the interpolation at the particle's coordinates. Note that here we need to use `fieldset.C.data[0, yi, xi]` both for calculating `deltaC` and for the consequent update of the cell value for consistency between the forcing (the seawater-particle gradient) and its effect (the exchange, and consequent alteration of the field).\n", "\n", "Remember that reading and writing `Fields` at particle location through particle indices is only possible for `ScipyParticles` (and returns an error if `JITParticles` are used).\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "93994a91", "metadata": {}, "source": [ "Now we are going to execute the advection of the particle and the simultaneous release of the tracer it carries. We will thus add the `interactionKernel` defined above to the built-in Kernel `AdvectionRK4`.\n", "\n", "Before running the advection, we will execute the `pset` with the `WriteInitial` for `dt=0`: this will write the initial condition of fieldset.C to a `netCDF` file.\n", "\n", "While particle outputs will be written in a file named `interaction.zarr` at every `outputdt`, the field will be automatically written in `netCDF` files named `interaction_wxyzC.nc`, with `wxyz` being the number of the output and `C` the `FieldSet` variable of our interest. Note that you can use tools like [ncrcat](https://linux.die.net/man/1/ncrcat) (on linux/macOS) to combine these separate files into one large `netCDF` file after the simulation.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "d20fcba8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: Output files are stored in interaction.zarr.\n", "100%|██████████| 2160000.0/2160000.0 [00:00<00:00, 2741732.73it/s]\n" ] } ], "source": [ "output_file = pset.ParticleFile(name=r\"interaction.zarr\", outputdt=delta(days=1))\n", "\n", "pset.execute(\n", " # the particle will FIRST be transported by currents\n", " # and THEN interact with the field\n", " AdvectionRK4 + pset.Kernel(Interaction),\n", " dt=delta(days=1),\n", " # we are going to track the particle and save\n", " # its trajectory and tracer concentration for 24 days\n", " runtime=delta(days=25),\n", " output_file=output_file,\n", ")\n", "\n", "# # Also write the last location of the particle to the output file\n", "# output_file.write_latest_locations(pset, time=pset[0].time)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "42182927", "metadata": {}, "source": [ "We can see that $c_{particle}$ has been saved along with particle trajectory, as expected.\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "a6d869e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[80. 64. 51.2 40.96 32.767998 26.214397\n", " 20.971518 16.777214 13.421771 10.737417 8.858369 7.5430355\n", " 6.3699727 5.0959783 4.0767827 3.2614262 2.6906767 2.1525414\n", " 1.7220331 1.4206773 1.1365418 0.93764704 0.75011766 0.60009414\n", " 0.49882826]]\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pset_traj = xr.open_zarr(r\"interaction.zarr\")\n", "\n", "print(pset_traj[\"c\"].values)\n", "\n", "plt.plot(pset_traj[\"lon\"].T, pset_traj[\"lat\"].T, \".-\")\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "id": "3fcc6dca", "metadata": {}, "source": [ "But what about `fieldset.C`? We can see that it has been accordingly modified during particle motion. Using `fieldset.C` we can access the field as resulting at the end of the run, with no information about the previous time steps.\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "184d6860", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Copy the final field data in a new array\n", "c_results = fieldset.C.data[0, :, :].copy()\n", "\n", "# using a mask for fieldset.C.data on land\n", "c_results[[field_for_size.U.data == 0][0][0]] = np.nan\n", "\n", "# masking the field where its value is zero:\n", "# areas that have not been modified by the particle,\n", "# for clearer plotting\n", "c_results[c_results == 0] = np.nan\n", "\n", "try: # Works if Cartopy is installed\n", " import cartopy\n", " import cartopy.crs as ccrs\n", "\n", " extent = [10, 33, -37, -29]\n", "\n", " X = fieldset.U.lon\n", " Y = fieldset.U.lat\n", "\n", " plt.figure(figsize=(12, 6))\n", " ax = plt.axes(projection=ccrs.Mercator())\n", " ax.set_extent(extent)\n", "\n", " ax.add_feature(cartopy.feature.OCEAN, facecolor=\"lightgrey\")\n", " ax.add_feature(cartopy.feature.LAND, edgecolor=\"black\", facecolor=\"floralwhite\")\n", " gl = ax.gridlines(\n", " xlocs=np.linspace(10, 34, 13), ylocs=np.linspace(-29, -37, 9), draw_labels=True\n", " )\n", " gl.right_labels = False\n", " gl.bottom_labels = False\n", "\n", " xx, yy = np.meshgrid(X, Y)\n", "\n", " results = ax.pcolormesh(\n", " xx,\n", " yy,\n", " (c_results),\n", " transform=ccrs.PlateCarree(),\n", " vmin=0,\n", " )\n", " cbar = plt.colorbar(mappable=results, ax=ax)\n", " cbar.ax.text(0.8, 0.070, \"$C_{field}$ concentration\", rotation=270, fontsize=12)\n", "\n", "except:\n", " print(\"Please install the Cartopy package.\")" ] }, { "attachments": {}, "cell_type": "markdown", "id": "52a42fbe", "metadata": {}, "source": [ "When looking at tracer concentrations, we see that $c_{particle}$ decreases along its trajectory (right to left), as it is releasing the tracer it carries. Accordingly, values of $C_{field}$ provided by particle interaction progressively reduce along the particle's route.\n", "\n", "Notice that the first particle-field interaction occurs at time $t = 1$ day, and namely after the execution of the first step of `AdvectionRK4`, as shown by the unaltered field value at the particle's starting location.\n", "In order to let the particle interact before being advected, we would have to change the order in which the two Kernels are added together in `pset.execute`, i.e. `pset.execute(interactionKernel + AdvectionRK4, ...)`. In this latter case, the interaction would not occur at the particle's final position instead.\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "e2ce3270", "metadata": {}, "outputs": [ { "data": { "image/png": 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evXrZnuMeOM4LL7xgtGzZMsfz3AvnefbZZ41KlSoZZrOZ++Ag3bt3N4YMGWJVdu+99xoPPfSQYRj8fYBzuNUMe1pamrZt26ZOnTpZlXfq1Enr1693UlTu7ejRo4qNjbW6Jz4+PmrTpg33xI4uXrwoSSpWrJgk7oMzZGZmav78+bp8+bIiIiK4B07w5JNPqnv37urQoYNVOffCcQ4dOqRSpUqpYsWK6tevn44cOSKJe+BIv/zyixo3bqz7779fJUqUUIMGDfTZZ59ZznMvnCMtLU1z587VkCFDZDKZuA8O0rJlS61YsUIHDx6UJO3cuVPr1q1Tt27dJPH3Ac5RyNkBOFJcXJwyMzMVFhZmVR4WFqbY2FgnReXern3u2d2T48ePOyOk255hGBo1apRatmyp2rVrS+I+ONLu3bsVERGhlJQUFS5cWD/++KNq1qxp+UHPPXCM+fPna/v27dqyZUuWc/x9cIymTZtqzpw5qlq1qs6cOaNXXnlFzZs31969e7kHDnTkyBHNnDlTo0aN0osvvqjNmzfrmWeekY+PjwYNGsS9cJKffvpJFy5c0H/+8x9J/LvkKC+88IIuXryo6tWry9PTU5mZmXr11VfVv39/SdwHOIdbJezXmEwmq+8Nw8hSBsfinjjOU089pV27dmndunVZznEf7K9atWrasWOHLly4oIULF2rw4MFas2aN5Tz3wP5OnjypZ599VsuWLZOvr2+O9bgX9tW1a1fL13Xq1FFERIQqVaqkr776Ss2aNZPEPXAEs9msxo0b67XXXpMkNWjQQHv37tXMmTM1aNAgSz3uhWPNmjVLXbt2ValSpazKuQ/2tWDBAs2dO1fffvutatWqpR07dmjEiBEqVaqUBg8ebKnHfYAjudUj8aGhofL09Mwym3727NksvymDY1xbEZh74hhPP/20fvnlF61atUplypSxlHMfHMfb21uVK1dW48aNNXXqVNWrV0/Tp0/nHjjQtm3bdPbsWTVq1EiFChVSoUKFtGbNGr3//vsqVKiQ5fPmXjhWQECA6tSpo0OHDvH3wYFKliypmjVrWpXVqFHDshgv98Lxjh8/ruXLl2vYsGGWMu6DYzz33HMaO3as+vXrpzp16mjgwIEaOXKkpk6dKon7AOdwq4Td29tbjRo1sqy6eU1kZKSaN2/upKjcW8WKFRUeHm51T9LS0rRmzRruSQEyDENPPfWUFi1apJUrV6pixYpW57kPzmMYhlJTU7kHDtS+fXvt3r1bO3bssByNGzfWgw8+qB07duiOO+7gXjhBamqq9u/fr5IlS/L3wYFatGiRZZvPgwcPqnz58pL4+eAMX375pUqUKKHu3btbyrgPjpGcnCwPD+v0yNPT07KtG/cBTuGcte6cZ/78+YaXl5cxa9YsY9++fcaIESOMgIAA49ixY84O7baVlJRkREVFGVFRUYYkY9q0aUZUVJRx/PhxwzAM4/XXXzeCgoKMRYsWGbt37zb69+9vlCxZ0khMTHRy5LePJ554wggKCjJWr15txMTEWI7k5GRLHe6D/Y0bN874888/jaNHjxq7du0yXnzxRcPDw8NYtmyZYRjcA2e6fpV4w+BeOMLo0aON1atXG0eOHDE2btxo3H333UaRIkUsP4+5B46xefNmo1ChQsarr75qHDp0yPjmm28Mf39/Y+7cuZY63AvHyczMNMqVK2e88MILWc5xH+xv8ODBRunSpY1ff/3VOHr0qLFo0SIjNDTUeP755y11uA9wNLdL2A3DMD766COjfPnyhre3t9GwYUPL1lawj1WrVhmSshyDBw82DOPqFhmTJk0ywsPDDR8fH6N169bG7t27nRv0bSa7z1+S8eWXX1rqcB/sb8iQIZZ/e4oXL260b9/ekqwbBvfAmf6dsHMv7K9v375GyZIlDS8vL6NUqVLGvffea+zdu9dynnvgOIsXLzZq165t+Pj4GNWrVzc+/fRTq/PcC8dZunSpIck4cOBAlnPcB/tLTEw0nn32WaNcuXKGr6+vcccddxjjx483UlNTLXW4D3A0k2EYhlOm9gEAAAAAQI7c6h12AAAAAABuFSTsAAAAAAC4IBJ2AAAAAABcEAk7AAAAAAAuiIQdAAAAAAAX5JYJe2pqqiZPnqzU1FRnh+LWuA+ugfvgGrgProH74Bq4D66B++AauA+ugfsAZ3HLbd0SExMVFBSkixcvKjAw0NnhuC3ug2vgPrgG7oNr4D64Bu6Da+A+uAbug2vgPsBZ3HKGHQAAAAAAV0fCDgAAAACACyrk7ACcKTEx0dkhuLVrnz/3wbm4D66B++AauA+ugfvgGrgProH74Br4/OEsbvkO+8WLFxVSNESZynR2KAAAAABuAYULF9apU6cUFBTk7FDgRtxyht1kMilTmWqpbiokL8n0/28GmDxM/6qcwznTdV/nUG7y8Mihfg7l17e9btyc+repT0m6/lsb4pYtYytvn4VhVSeHeHL62ub2OdTP4fptqpPDWEZOn2lOMfwr7pzGzqlfq9+qXV/HI6eYbOgzj/VzbeORQ3kufd24/s2U53Bvc2lTYHHIhjrXl+dQ35a2uZ6zpf1NXLNNMdha56auwbChji395NCncpDrfTCyr2fDeDnHmn0dU17HVQ51JJlM2V+tKY9jm6y+vnF8VvVzGDfHH33/itlkQxweOdTxkC1xXFffhnKrPnOq869ryOmcyYY6OX6tnOqYcxgrp7b/X98zh/6z9vv/5Z5Wn0f2Y1v3m0OdnMqv6/P6a/C0Guu6a8gh5n/HanU9NsRkNV5OcVzfj9W4OcWa02ef/bhZx8g+Ds8c7nVOdUw5XXMOf8auL7euc32c18dgzdPqz48p23qepuvLb/y1dX2PHOpkLU9MMqt8o2My5fT/GcBO3DJhv6aQvFTI9K+EPUuym8M5Ux6TblvaeuTQ9qYT9hz/p3Pj+GwZ22US9ryNd0sl7Dn95/sWTdgLLjHPqTyHe5tLG3vEcVsn7DbGZ1Odm4rDzgl7Tv8vy/U+3AYJu8mW8uzHzjnRvpmE/cbJd37a5Jiw21LfwQl7zkl3wSfsttS3PWHPKWGzb8KeY6KcU7JqY8Ke49g3kbB7WvXz/3/4PK77g3h9ufVndH259V9q6wTXyLaedcJ+fUzKvo5VrLbUuXHC7mmnhN26fvafk20JO0t+wbn4EwgAAAAAgAsiYQcAAAAAwAWRsAMAAAAA4IJI2AEAAAAAcEEk7AAAAAAAuCASdgAAAAAAXBAJOwAAAAAALoiEHQAAAAAAF0TCDgAAAACACyJhBwAAAADABZGwAwAAAADggkjYAQAAAABwQSTsAAAAAAC4IBJ2AAAAAABcEAk7AAAAAAAuiIQdAAAAAAAXRMIOAAAAAIALImEHAAAAAMAFkbADAAAAAOCCSNgBAAAAAHBBJOwAAAAAALggEnYAAAAAAFwQCTsAAAAAAC6IhB0AAAAAABdEwg4AAAAAgAsiYQcAAAAAwAWRsAMAAAAA4IJI2AEAAAAAcEEk7AAAAAAAuCASdgAAAAAAXBAJOwAAAAAALoiEHQAAAAAAF0TCDgAAAACACyJhBwAAAADABZGwAwAAAADgggo5OwBnylC6ZEjX/97CZJj+VSunc9d9nUO5yfDItlw5lZuva2vKoY4ph6+v/92L6V/XYBVeDtdg1a8NY+dUbmRfbljVySGeHK/N1vY51DdnX9/Ioa1VHY8cynP6THOKQbK6RTmNnVO/11+yVaweOcVkQ595rJ9rm5z+qOfS143r30x5Dvc2lzYFFodsqJPDH+e8xpPrOVva38Q12xSDrXVu6hoMG+rY0k8OfSoHud4HI/t6NoyXc6zZ1zHldVzlUEeSyZT91ZryOLbJ6usbx2dVP4dxc/rxYPwrZpMNcRg51DGUfRzmHOp72FDuIRvq/OsacjpnsqFOjl8rpzrmbMtNyqnt/9f3zKH/rP3+f7mn1eeR/djW/eZQJ6fy6/q8/ho8rca67hpyiPnfsVpdjw0xWY2XUxzX92M1bk6x5vTZZz9u1jGyj8Mzh3udUx1TTtecw5+x68ut61wf5/UxWPO0+vNjyraep+n68ht/bV1fOdTJGl9ikvXnCziKWybs3t7eCg8P17rYJVcLrv85k+mUkAAAAAC4sPDwcHl7ezs7DLgZk2EYOU4g3M5SUlKUlpbm7DAAAAAA3AK8vb3l6+vr7DDgZtw2YQcAAAAAwJWx6BwAAAAAAC6IhB0AAAAAABdEwg4AAAAAgAsiYQcAAAAAwAWRsAMAAAAA4IJI2AEAAAAAcEEk7AAAAAAAuCASdgAAAAAAXBAJOwAAAAAALoiEHQAAAAAAF0TCDgAAAACACyJhBwAAAADABZGwAwAAAADggkjYAQAAAABwQSTsAAAAAAC4IBJ2AAAAAABcEAk7AAAAAAAuiIQdAAAAAAAXRMIOAAAAAIALImEHAAAAAMAFkbADAAAAAOCCSNgBAAAAAHBBJOwAAAAAALggEnYAAAAAAFwQCTsAAAAAAC6IhB0AAAAAABdEwg4AAAAAgAuya8J+zz33qFy5cvL19VXJkiU1cOBAnT59Otu68fHxKlOmjEwmky5cuJBrv23btpXJZLI6+vXrZ4crAAAAAADY24wZM1SxYkX5+vqqUaNGWrt2bY51Fy1apI4dO6p48eIKDAxURESEli5dmqXewoULVbNmTfn4+KhmzZr68ccfb2pcZ7Brwt6uXTt99913OnDggBYuXKjDhw+rT58+2dYdOnSo6tata3PfjzzyiGJiYizHJ598UlBhAwAAAAAcZMGCBRoxYoTGjx+vqKgotWrVSl27dtWJEyeyrf/nn3+qY8eOWrJkibZt26Z27dqpR48eioqKstTZsGGD+vbtq4EDB2rnzp0aOHCgHnjgAW3atCnf4zqDyTAMw1GD/fLLL+rVq5dSU1Pl5eVlKZ85c6YWLFigiRMnqn379jp//ryKFi2aYz9t27ZV/fr19d5779k0bmpqqlJTUy3fm81mJSQkKCQkRCaTKb+XAwAAAMANGIahpKQklSpVSh4et+ZbxSkpKUpLS3PYeIZhZMm1fHx85OPjk6Vu06ZN1bBhQ82cOdNSVqNGDfXq1UtTp061abxatWqpb9++mjhxoiSpb9++SkxM1O+//26p06VLFwUHB2vevHkFNq7dGQ4SHx9vPPDAA0aLFi2syvfu3WuEh4cbx48fN1atWmVIMs6fP59rX23atDFCQ0ONkJAQo2bNmsbo0aONxMTEHOtPmjTJkMTBwcHBwcHBwcHBwZHv4+TJkwWRGjnclStXjPASng79rAoXLpylbNKkSVliS01NNTw9PY1FixZZlT/zzDNG69atbbq+zMxMo2zZssYHH3xgKStbtqwxbdo0q3rTpk0zypUrV2DjOkIh2dkLL7ygDz/8UMnJyWrWrJl+/fVXy7nU1FT1799fb731lsqVK6cjR47Y1OeDDz6oihUrKjw8XHv27NG4ceO0c+dORUZGZlt/3LhxGjVqlOX7ixcvqly5clq+fLkCAgJu7gIBAAAA3NYuX76sDh06qEiRIs4OJV/S0tIUezZTx7dVUGAR+z8hkJhkVvlGx3Ty5EkFBgZayrObXY+Li1NmZqbCwsKsysPCwhQbG2vTeO+8844uX76sBx54wFIWGxuba58FMa4j5Dlhnzx5sl566aVc62zZskWNGzeWJD333HMaOnSojh8/rpdeekmDBg3Sr7/+KpPJpHHjxqlGjRp66KGH8hTDI488Yvm6du3aqlKliho3bqzt27erYcOGWern9OhFQECAChcunKexAQAAALinW/112sJFTCpcxP7XYNbVMQIDA60S9tz8+7M1snmkPjvz5s3T5MmT9fPPP6tEiRJ57jO/4zpKnhP2p5566oYrsleoUMHydWhoqEJDQ1W1alXVqFFDZcuW1caNGxUREaGVK1dq9+7d+uGHHyRd/XCutRk/fvwNfzFwTcOGDeXl5aVDhw5lm7ADAAAAAFxPaGioPD09s8xqnz17Nsvs978tWLBAQ4cO1ffff68OHTpYnQsPD8+1z5sZ15HynLBfS8Dz41pCfm0BuIULF+rKlSuW81u2bNGQIUO0du1aVapUyeZ+9+7dq/T0dJUsWTJfcQEAAADA7S7TMCvTcMw4tvL29lajRo0UGRmp3r17W8ojIyPVs2fPHNvNmzdPQ4YM0bx589S9e/cs5yMiIhQZGamRI0daypYtW6bmzZvf1LiOZrd32Ddv3qzNmzerZcuWCg4O1pEjRzRx4kRVqlRJERERkpQlKY+Li5N0dWW+a6vER0dHq3379pozZ46aNGmiw4cP65tvvlG3bt0UGhqqffv2afTo0WrQoIFatGhhr8sBAAAAANjBqFGjNHDgQDVu3FgRERH69NNPdeLECT3++OOSrq5JFh0drTlz5ki6mqwPGjRI06dPV7NmzSyz5H5+fgoKCpIkPfvss2rdurXeeOMN9ezZUz///LOWL1+udevW2TyuK7Bbwu7n56dFixZp0qRJunz5skqWLKkuXbpo/vz52b5PnpP09HQdOHBAycnJkq7+JmTFihWaPn26Ll26pLJly6p79+6aNGmSPD097XU5AAAAAAA76Nu3r+Lj4zVlyhTFxMSodu3aWrJkicqXLy9JiomJsdob/ZNPPlFGRoaefPJJPfnkk5bywYMHa/bs2ZKk5s2ba/78+ZowYYL++9//qlKlSlqwYIGaNm1q87iuwKH7sLuKxMREBQUFacOGDSw6BwAAACBXly5dUkREhC5evGjzImqu5Fr+E3ugnMNWiQ+vduKW/bxcif3vFgAAAAAAyDO778MOAAAAAHA+s8yyfTm4mxsHBYMZdgAAAAAAXBAz7AAAAADgBjINQ5kOWMLMEWO4C2bYAQAAAABwQcywAwAAAIAbMMuQWfaf/XbEGO6CGXYAAAAAAFwQM+wAAAAA4AbMMpTJDPsthRl2AAAAAABcEDPsAAAAAOAGeIf91sMMOwAAAAAALogZdgAAAABwA+zDfuthhh0AAAAAABfEDDsAAAAAuAHz/w5HjIOCwQw7AAAAAAAuiIQdAAAAAAAXxCPxAAAAAOAGMmUo0wFbrjliDHfBDDsAAAAAAC6IGXYAAAAAcAOZxtXDEeOgYDDDDgAAAACAC2KGHQAAAADcANu63XqYYQcAAAAAwAUxww4AAAAAbsAskzJlcsg4KBjMsAMAAAAA4IKYYQcAAAAAN2A2rh6OGAcFgxl2AAAAAABcEDPsAAAAAOAGMh30DrsjxnAXzLADAAAAAOCCmGEHAAAAADfADPuthxl2AAAAAABcEDPsAAAAAOAGzIZJZsMB+7A7YAx3wQw7AAAAAAAuiIQdAAAAAAAXxCPxAAAAAOAGWHTu1sMMOwAAAAAALogZdgAAAABwA5nyUKYD5mwz7T6C+2CGHQAAAAAAF8QMOwAAAAC4AcNB27oZbOtWYJhhBwAAAADABTHDDgAAAABugFXibz3MsAMAAAAA4IKYYQcAAAAAN5BpeCjTcMAq8Ybdh3AbzLADAAAAAOCCmGEHAAAAADdglklmB8zZmsUUe0Fhhh0AAAAAABfEDDsAAAAAuAFWib/1MMMOAAAAAIALYoYdAAAAuE3UDL3X2SHkyb64Rc4Owa04bpV43mEvKMywAwAAAADggphhBwAAAOBYHiGSKUheXl7KyMiQwYwskC0SdgAAAAAO4Cv5dZfJf4BMXnUkSdWKS5mZaTp/PlEJCQlKS0tzcoy3t6vbutl/QThHjOEuSNgBAAAA2FehqjIFfyp5hEupf8p8eYSUeVYy+cjDu7lCgvsqJKSKzpw5o7i4OGdHC7gMEnYAAAAA9uN5h0zF5kqZp2UkDJIyT1idNtL+ki69L1PhJxQePlwmk0nnzp1zUrC3N7M8lOmAZczM4hWHgkLCDgAAAMBuTEXflTLPykgYKBlJOdRKlXHpPRlGqsLCRurSpUu6cuWKQ+MEXBEJOwAAAAD78Gosk1cNmRMG5ZKsX+fyxzL73aeQkGI6dSra/vG5GbZ1u/WwrRsAAAAAuzD5D5CRcURK22hjC0NKnqugoEB5enraNTbgVkDCDgAAAMA+vOtLKcvz1iY1UiaTp/z8/OwSkjszy8NhBwoGnyQAAAAA+zAFyLDlUfjrma/W9/AgVXEnM2bMUMWKFeXr66tGjRpp7dq1OdaNiYnRgAEDVK1aNXl4eGjEiBFZ6rRt21YmkynL0b17d0udyZMnZzkfHh5uj8vLN/4WAAAAALAP8yWZPILy1uZ/9c1msx0Ccm+ZhslhR14sWLBAI0aM0Pjx4xUVFaVWrVqpa9euOnHiRLb1U1NTVbx4cY0fP1716tXLts6iRYsUExNjOfbs2SNPT0/df//9VvVq1aplVW/37t15it3eWHQOAAAAgH2kb5F8OktJb0uyMQH37SazOYNV4m8DiYmJVt/7+PjIx8cnS71p06Zp6NChGjZsmCTpvffe09KlSzVz5kxNnTo1S/0KFSpo+vTpkqQvvvgi27GLFStm9f38+fPl7++fJWEvVKiQy82qX48ZdgAAAAB2YSR/K1OhspJPaxtbeMjk96AuXkxUZmamXWNzR5n/24fdEYcklS1bVkFBQZYju+Q7LS1N27ZtU6dOnazKO3XqpPXr1xfYtc+aNUv9+vVTQECAVfmhQ4dUqlQpVaxYUf369dORI0cKbMyCwAw7AAAAAPtI3yUjLUqmIv+Vkb5bMsfnWt1U5HnJM0zx8YcdFCDs6eTJkwoMDLR8n93selxcnDIzMxUWFmZVHhYWptjY2AKJY/PmzdqzZ49mzZplVd60aVPNmTNHVatW1ZkzZ/TKK6+oefPm2rt3r0JCQgpk7JtFwg4AAADAbowLI2UKWSBTsXkyLoySMvZkrWQKlAqPkilggE6fPq2UlBTHB+oGzIaHzA7Yh938v33YAwMDrRL23JhM1u+9G4aRpSy/Zs2apdq1a6tJkyZW5V27drV8XadOHUVERKhSpUr66quvNGrUqAIZ+2aRsAMAAACwH/NpGQkDZAr+RB6hi2Sk7ZBx5SfJfEYy+UreLWTy7SGZCik6Olrnz593dsRwoNDQUHl6emaZTT979myWWff8SE5O1vz58zVlypQb1g0ICFCdOnV06NChmx63oJCwAwAAALCvzJMy4nrI8Gkrk/8AeQRNtpxKT7+ihHMXlJCQwHvrdnb9++X2Hcewua63t7caNWqkyMhI9e7d21IeGRmpnj173nQs3333nVJTU/XQQw/dsG5qaqr279+vVq1a3fS4BYWEHQAAAIADZEqpK2SkrpAhL8mjiP6O+4Lt26BRo0Zp4MCBaty4sSIiIvTpp5/qxIkTevzxxyVJ48aNU3R0tObMmWNps2PHDknSpUuXdO7cOe3YsUPe3t6qWbOmVd+zZs1Sr169sn0nfcyYMerRo4fKlSuns2fP6pVXXlFiYqIGDx5sv4vNIxJ2AAAAAA6WLpkTSNYhSerbt6/i4+M1ZcoUxcTEqHbt2lqyZInKly8vSYqJicmyJ3uDBg0sX2/btk3ffvutypcvr2PHjlnKDx48qHXr1mnZsmXZjnvq1Cn1799fcXFxKl68uJo1a6aNGzdaxnUFJOwAAAAA4AbMkjKNglnI7Ubj5NXw4cM1fPjwbM/Nnj07S5lh3Pix+6pVq+Zab/78+TbH5yzsww4AAAAAgAtihh0AAAAA3IBZHjI7YM7WEWO4Cz5JAAAAAABcEDPsAAAAAOAGMg0PZRoO2NbNAWO4Cz5JAAAAAABcEDPsAAAAAOAGzDLJLEesEm//MdwFM+wAAAAAALggZtgBAAAAwA3wDvuth08SAAAAAAAXxAw7AAAAALiBTHko0wFzto4Yw13wSQIAAAAA4IKYYQcAAAAAN2A2TDIbDlgl3gFjuAtm2AEAAAAAcEHMsAMAAACAGzA76B12M/PCBYZPEgAAAAAAF0TCDgAAAACAC+KReAAAAABwA2bDQ2bDAY/EO2AMd8EnCQAAAACAC2KGHQAAAADcQKZMypT9t1xzxBjughl2AAAAAABcEDPsAAAAAOAGeIf91sMnCQAAAACAC2KGHQAAAADcQKYc8355pt1HcB/MsAMAAAAA4IKYYQcAAAAAN8A77LcePkkAAAAAAFwQM+wAAAAA4AYyDQ9lOmD22xFjuAs+SQAAAAAAXBAz7AAAAADgBgyZZHbAKvGGA8ZwF3adYb/nnntUrlw5+fr6qmTJkho4cKBOnz5tVcdkMmU5Pv7441z7TU1N1dNPP63Q0FAFBATonnvu0alTp+x5KQAAAAAAOJRdE/Z27drpu+++04EDB7Rw4UIdPnxYffr0yVLvyy+/VExMjOUYPHhwrv2OGDFCP/74o+bPn69169bp0qVLuvvuu5WZyY5/AAAAAJCda++wO+JAwbDrI/EjR460fF2+fHmNHTtWvXr1Unp6ury8vCznihYtqvDwcJv6vHjxombNmqWvv/5aHTp0kCTNnTtXZcuW1fLly9W5c+eCvQgAAAAAAJzAYb/6SEhI0DfffKPmzZtbJeuS9NRTTyk0NFR33nmnPv74Y5nN5hz72bZtm9LT09WpUydLWalSpVS7dm2tX78+2zapqalKTEy0OgAAAAAAcGV2X3TuhRde0Icffqjk5GQ1a9ZMv/76q9X5l19+We3bt5efn59WrFih0aNHKy4uThMmTMi2v9jYWHl7eys4ONiqPCwsTLGxsdm2mTp1ql566aWCuSAAAADkiclkUtGiRVU0OEi+vr6SyaQrmZe0/+JmbU5YqtNXjjg7xFz195ji7BBsti9ukbNDgAszGyaZDfsvCOeIMdxFnmfYJ0+enO1CcdcfW7dutdR/7rnnFBUVpWXLlsnT01ODBg2SYRiW8xMmTFBERITq16+v0aNHa8qUKXrrrbfyfCGGYchkyv4Pxrhx43Tx4kXLcfLkyTz3DwAAgLwLDAxUtepVVbJUSZ1I368/Yufo1+jPtTU+UlWK1NeTVd7Wfyr+V36ehZ0dKgC4nDzPsD/11FPq169frnUqVKhg+To0NFShoaGqWrWqatSoobJly2rjxo2KiIjItm2zZs2UmJioM2fOKCwsLMv58PBwpaWl6fz581az7GfPnlXz5s2z7dPHx0c+Pj42XB0AAAAKStGiRVWmTBntPL9Wy2Ln6kL6OavzK87MV43AJupZ5nENu+NlfXZ4vFLMyU6KFrj9ZcpDmQ54K9oRY7iLPCfs1xLw/Lg2s56amppjnaioKPn6+qpo0aLZnm/UqJG8vLwUGRmpBx54QJIUExOjPXv26M0338xXXAAAAChYvr6+KlW6lDbHL9PP0dlv2WuWWXsTN+rs4ZN6tNJrurfsU/r2OP+fA4Br7PYO++bNm7V582a1bNlSwcHBOnLkiCZOnKhKlSpZZtcXL16s2NhYRUREyM/PT6tWrdL48eP16KOPWmbEo6Oj1b59e82ZM0dNmjRRUFCQhg4dqtGjRyskJETFihXTmDFjVKdOHcuq8QAAAHCuYsWKKSn9vBZHf3rDuudSo/VHzFe6t+xTKuYdpoS0Mw6IEHA/vMN+67Hbswp+fn5atGiR2rdvr2rVqmnIkCGqXbu21qxZY0nGvby8NGPGDEVERKhu3bqaPn26pkyZonfeecfST3p6ug4cOKDk5P9/POrdd99Vr1699MADD6hFixby9/fX4sWL5enpaa/LAQAAgI08PT0VVDRIG+N/l1k57/5zvZ0X1ik5I0lNQtiiFwCusdsMe506dbRy5cpc63Tp0kVdunTJtU6FChWsFqmTrj5i9cEHH+iDDz646TgBAABQsPz9/eXp4amdF/60uU2Gkaa9FzeqcuH6kubYLTbAnZnlIbMD3i93xBjugk8SAAAABeraU4+XMi7kqd2ljAvy9QywQ0QAcGuy+z7sAAAAcC9m89XH4L09fHUl85LN7bw9fJVuTrFXWIDbyzRMynTA++WOGMNdMMMOAACAApWScjXprlqkYZ7aVQ1sqJiUY3aICABuTSTsAAAAKFBpaWlKSkpUREg3m9tUKlxXxX1Ka3P8UjtGBri3a6vEO+JAwSBhBwAAQIFLSDivsgFVVa9o6xvW9fbwVZeSgxR75biOXd7ngOgA4NbAO+wAAAAocElJSUpISNB9ZZ9WIVMhbTuf/e5BAZ5BGlDheRXzDtdnhyc4OErAvRiGh8yG/edsDQeM4S5I2AEAAGAXp0+flmTo3rJPqVXx3toYv0RHLu1RhpGmIK9QNQxupzpFWyrNfEWzj05RLO+vA4AVEnYAAADYzenTMbp4MVHBxYLVvdRQeZj+f+YtIe2MVpxZoG0Jy5WcmeTEKAH3kCmTMuWAVeIdMIa7IGEHAACAXV2+fFmXL1+Wp6envL29tVpf6UrmJcWmnJAhs7PDAwCXRcIOAAAAh8jMzNSVK1d0xLzH2aEAwC2BhB0AAAAA3IDZkEO2XDMbdh/CbbB8HwAAAAAALogZdgAAAABwA2YHbevmiDHcBZ8kAAAAAAAuiBl2AAAAAHADZplkdsCWa44Yw10www4AAAAAgAtihh0AAAAA3ECmYVKmA1aJd8QY7oIZdgAAAACAU82YMUMVK1aUr6+vGjVqpLVr1+ZYNyYmRgMGDFC1atXk4eGhESNGZKkze/ZsmUymLEdKSkq+x3UGEnYAAAAAcAPXVol3xJEXCxYs0IgRIzR+/HhFRUWpVatW6tq1q06cOJFt/dTUVBUvXlzjx49XvXr1cuw3MDBQMTExVoevr2++x3UGEnYAAAAAQIFLTEy0OlJTU7OtN23aNA0dOlTDhg1TjRo19N5776ls2bKaOXNmtvUrVKig6dOna9CgQQoKCspxfJPJpPDwcKvjZsZ1BhJ2AAAAAHADZplkNhxw/G+V+LJlyyooKMhyTJ06NUtMaWlp2rZtmzp16mRV3qlTJ61fv/6mrvfSpUsqX768ypQpo7vvvltRUVEOGbcgsegcAAAAAKDAnTx5UoGBgZbvfXx8stSJi4tTZmamwsLCrMrDwsIUGxub77GrV6+u2bNnq06dOkpMTNT06dPVokUL7dy5U1WqVLHbuAWNhB0AAAAA3IDhoH3Yjf+NERgYaJWw58Zkso7LMIwsZXnRrFkzNWvWzPJ9ixYt1LBhQ33wwQd6//337TZuQeOReAAAAACAU4SGhsrT0zPLrPbZs2ezzH7fDA8PD9155506dOiQQ8e9WSTsAAAAAOAGHPL++v8OW3l7e6tRo0aKjIy0Ko+MjFTz5s0L7NoNw9COHTtUsmRJh457s3gkHgAAAADgNKNGjdLAgQPVuHFjRURE6NNPP9WJEyf0+OOPS5LGjRun6OhozZkzx9Jmx44dkq4uLHfu3Dnt2LFD3t7eqlmzpiTppZdeUrNmzVSlShUlJibq/fff144dO/TRRx/ZPK4rIGEHAAAAADhN3759FR8frylTpigmJka1a9fWkiVLVL58eUlSTExMlr3RGzRoYPl627Zt+vbbb1W+fHkdO3ZMknThwgU9+uijio2NVVBQkBo0aKA///xTTZo0sXlcV2AyDMNwdhCOlpiYqKCgIG3YsEGFCxd2djgAAABuZZ55orNDyJP+HlOcHQKc7NKlS4qIiNDFixdtXkTNlVzLf3pHPiyvAG+7j5d+OU0/dvzylv28XAnvsAMAAAAA4IJ4JB4AAAAA3EBeF4S7mXFQMJhhBwAAAADABTHDDgAAAABuwCyTzHLADLsDxnAXzLADAAAAAOCCmGEHAAAAADfAO+y3HmbYAQAAAABwQcywAwAAAIAbYIb91sMMOwAAAAAALogZdgAAAABwA8yw33qYYQcAAAAAwAUxww4AAAAAboAZ9lsPM+wAAAAAALggZtgBAAAAwA0Yksyy/+y3YfcR3Acz7AAAAAAAuCASdgAAAAAAXBCPxAMAAACAG2DRuVsPM+wAAAAAALggZtgBAAAAwA0ww37rIWEHAOA6PVYvdXYIebK4bWdnh2Cz/ke+d3YIeTLvjvudHcJtq7/HFGeHAAC3BBJ2AAAAAHADzLDfeniHHQAAAAAAF8QMOwAAAAC4AWbYbz3MsAMAAAAA4IKYYQcAAAAAN2AYJhkOmP12xBjughl2AAAAAABcEDPsAAAAAOAGzDLJLAe8w+6AMdwFM+wAAAAAALggZtgBAAAAwA2wSvythxl2AAAAAABcEDPsAAAAAOAGWCX+1sMMOwAAAAAALoiEHQAAAAAAF8Qj8QAAAADgBlh07tbDDDsAALhlFDJ5KtjbXwGFfJwdCgAAdscMOwAAcGkmmdSyRGX1rXCnWoZVkafp6nzDmSuJWnhimxYe36azKUlOjhIAXB+Lzt16SNgBAIDLCvMN1PtN+qtm0VLad+G03tzzh2KvXJSXh6fuDK2o/1RqrkertNa0fZH6+sgGZ4cLAECBImEHAAAuKcSnsGa3GCJPk0mD1s1SVMIJq/NLT+/Vu/si9XjVNnq+dhf5eBbS54fWOilaAHB9hoPeYWeGveCQsAMAAJf0aoPe8vLw1EPrPlfslYvZ1rmckap39i3T5Yw0PVujg6LiT2hbwnEHRwoAgH2w6BwAAHA5dxQurhYlKuudfUtzTNav9/HB1fon8awG3NHUAdEBwK3JkGQYDjicfaG3ERJ2AADgcvpWuFPxqZcUeXq/zW0WHNuiu8Krq4RvETtGBgCA45CwAwAAl9MopLxWxvytDCPT5jbLTu9VIQ9P1Qsua8fIAODWZZbJYQcKBgk7AABwOQGFvHUx/Uqe2lxIS/5fW/ZoBwDcHlh0DgAAuJwrmekqnMfEu7CX7//aptkjJAC45bEP+62HGXYAAOBydp4/pbbh1eRpsv2/Ku1L1pDZMGv3+Wg7RgYAgOOQsAMAAJfz3bEtCvcLUpuwqja36VfhTq09c0inr1ywX2AAcAsz/28fdkccKBgk7AAAwOXsvxij7fHHNbpWJwV7+9+w/oCKTVWzaCl9e3STA6IDAMAxSNgBAIBLejFqkfw9vfVli4d1R+Hi2dYpZPLU0MotNa5ON83+5y+tP3fYwVECwK3DIXuw/+9AwWDROQAA4JKiky9o8F9faEbTh/TzXU9p/dl/9PPJHYq9clHeHoV0Z2gF3Ve+kUJ8CuvTg2v0wd8rnR0yAAAFioQdAAC4rBOXE3Tv6hnqVKqm+la4U2806mM5l5Seol9O7tB3x7bqyKVzTowSAAD7IGEHAAAuLc2coV9P7dKvp3apmHeAgrz9lGbO0LmUS0ozZzg7PAC4ZbCt262HhB0AANwyEtIuKyHtsrPDAADAIUjYAQAAAMANMMN+62GVeAAAAAAAXBAz7AAA3AR/Ly/dVeEOhQUUliTFXkrSimNHlJLBu9UAANdiNkwyOWD228wMe4EhYQcAIB9KFS6iRxo01r3VaynQx0dJaakyyaTC3t66kHJFP+zfq892bNXZy7xvDQAA8oeEHQCAPKpbIkxf9LhXhiF9vTtK8/buUnRSkiSpbGCQBtSqq7616qhH1ep6+JdF2h/PlmMAAOczjKuHI8ZBweAddgAA8qBCUFHNvuc+Hb94QZ2+na23N/5lSdYl6WTiRb2xYa06fTtb5y5f1lf33KfSRYo4MWIAAFzfjBkzVLFiRfn6+qpRo0Zau3ZtjnVjYmI0YMAAVatWTR4eHhoxYkSWOp999platWql4OBgBQcHq0OHDtq8ebNVncmTJ8tkMlkd4eHhBX1pN4WEHQCAPHi+eSslpqbq4cWLdD7lSo714pKT9Z/FC5VhmDW6aUsHRggAQPauzrCbHHDkLa4FCxZoxIgRGj9+vKKiotSqVSt17dpVJ06cyLZ+amqqihcvrvHjx6tevXrZ1lm9erX69++vVatWacOGDSpXrpw6deqk6Ohoq3q1atVSTEyM5di9e3fegrczEnYAAGwUHlBYHStW1udRW5WYmnrD+vFXrmj2zu3qVqWqgn39HBAhAACuIzEx0epIzeFn57Rp0zR06FANGzZMNWrU0HvvvaeyZctq5syZ2davUKGCpk+frkGDBikoKCjbOt98842GDx+u+vXrq3r16vrss89kNpu1YsUKq3qFChVSeHi45ShevPjNXbSk3bt3KzIyUlu2bFFycvJN9UXCDgCAje6rXkspGRn66cB+m9t8v3+vZEi9qtWwY2QAANyYY2bX/3+v97JlyyooKMhyTJ06NUtMaWlp2rZtmzp16mRV3qlTJ61fv77Arj05OVnp6ekqVqyYVfmhQ4dUqlQpVaxYUf369dORI0fyPcaqVatUuXJl1atXT507d1bTpk1VrFgx9e7dW9u2bctXnyw6BwCAjcoXLaoD8XG6lJ5mc5vzKVd09MJ5lQ8qar/AAABwQSdPnlRgYKDlex8fnyx14uLilJmZqbCwMKvysLAwxcbGFlgsY8eOVenSpdWhQwdLWdOmTTVnzhxVrVpVZ86c0SuvvKLmzZtr7969CgkJyVP/+/btU/fu3RUQEKAnnnhC5cuXV0xMjHbs2KHFixfr119/1fjx4zV58uQ89UvCDgCAjbw8PJRpNue5XbrZLC8PHmoDADiX8b/DEeNIUmBgoFXCnhuTyXrvdsMwspTl15tvvql58+Zp9erV8vX1tZR37drV8nWdOnUUERGhSpUq6auvvtKoUaPyNMbrr7+u4sWLa+vWrVkeq//nn380YcIETZkyRampqdk+aZAT/vcAAICN4q9cUenAQOXlvw+eJpNKFi6i+Cs39w4bAAC3o9DQUHl6emaZTT979myWWff8ePvtt/Xaa69p2bJlqlu3bq51AwICVKdOHR06dCjP46xatUrPPfdctu/AV65cWfPnz9eUKVP05ptvZlmtPjck7AAA2GjJPwdVukigWpUrb3ObDhUrKdTfX7//k/cf/gAAFCRHv8NuC29vbzVq1EiRkZFW5ZGRkWrevPlNXe9bb72ll19+WX/88YcaN258w/qpqanav3+/SpYsmeexzp07p5o1a+ZaZ8KECWrZsqU+/vhjm/slYQcAwEbbY09r37mzGlKvkU31TZKG1GukrTHR2h9/zr7BAQBwixo1apQ+//xzffHFF9q/f79GjhypEydO6PHHH5ckjRs3ToMGDbJqs2PHDu3YsUOXLl3SuXPntGPHDu3bt89y/s0339SECRP0xRdfqEKFCoqNjVVsbKwuXbpkqTNmzBitWbNGR48e1aZNm9SnTx8lJiZq8ODBeb6GokWLKjMz84b1Bg8erDVr1tjcL++wAwCQB+9v2aCPu/XU6KYt9M6mv3Kt+2LLNmpcqrSGLF7koOgAALj19O3bV/Hx8ZoyZYpiYmJUu3ZtLVmyROXLX32iLSYmJsue7A0aNLB8vW3bNn377bcqX768jh07JkmaMWOG0tLS1KdPH6t2kyZNsiz8durUKfXv319xcXEqXry4mjVrpo0bN1rGzYty5cpp9+7d6tixY671KlWqpJiYGJv7JWEHACAPlh75R6//9afGtmitCkWD9fH2zdp77qxVnXolwvVEoybqXKmKJq1ZoTUnjjknWAAArufoVefyYPjw4Ro+fHi252bPnp11CCP3Qa4l7rmZP3++LaHZpF27dlqwYMENF6tLSkqSp6enzf2SsAMAkEefRG3R2eRLGt2spX7tO1A7z8TqUEKcJKl6SHHVLhGmk4kX9eTvi7Xk8EEnRwsAAOxtwIAB2rx5s1auXKm77rorx3pLly694bvu1yNhBwAgH348sF+/HPxbd1W4Q72r1VTFosVkyNCxixf0zqa/9OeJYzLf4Lf/AAA4VB4XhLuZcdxNvXr1tGrVqlzrHDhwQF988YUmTpxoc792XXTunnvuUbly5eTr66uSJUtq4MCBOn36tFUdk8mU5bjRqnlt27bN0qZfv372vBQAALLINAxFHj2s4X8sVp+F83T/wvl6eumvWn38KMk6AABu4NSpU9mWX758Oduy8ePH68knn7S5f7vOsLdr104vvviiSpYsqejoaI0ZM0Z9+vTR+vXrrep9+eWX6tKli+X7oKCgG/b9yCOPaMqUKZbv/fz8Ci5wAAAAALjNGMbVwxHjuIv69evLbDarTp06qlOnjurWrau6deuqY8eOSkpKsqrbsGFDNWzYME/92zVhHzlypOXr8uXLa+zYserVq5fS09Pl5eVlOVe0aFGFh4fnqW9/f/88twEAAAAAoKDExcXp9OnT2r17t3bt2qW1a9dqxowZunLliurWravp06erXbt2+e7fYfuwJyQk6JtvvlHz5s2tknVJeuqppxQaGqo777xTH3/8scxm8w37++abbxQaGqpatWppzJgxWX57cb3U1FQlJiZaHQAAAADgToz/vcPuiMOdlCpVSp07d9Zzzz2nr7/+Wjt27JC/v79eeumlHFe+t5XdF5174YUX9OGHHyo5OVnNmjXTr7/+anX+5ZdfVvv27eXn56cVK1Zo9OjRiouL04QJE3Ls88EHH1TFihUVHh6uPXv2aNy4cdq5c6ciIyOzrT916lS99NJLBXpdyNmohq85O4Q8MTIznR1Cnry787/ODgEuYNAH2f9756pSQp0dge0W9+/s7BBuW/PuuN/ZIQAAUKAmT55seRS+cuXKMpmu/rKiQ4cO6t27t4YMGXJT/ed5hn3y5MnZLhR3/bF161ZL/eeee05RUVFatmyZPD09NWjQIKs98yZMmKCIiAjVr19fo0eP1pQpU/TWW2/lGsMjjzyiDh06qHbt2urXr59++OEHLV++XNu3b8+2/rhx43Tx4kXLcfLkybxeNgAAAADc2gyT4w43kZKSolmzZqldu3YqUqSImjRpomHDhqlt27aSruaiNyPPM+xPPfXUDVdkr1ChguXr0NBQhYaGqmrVqqpRo4bKli2rjRs3KiIiItu2zZo1U2Jios6cOaOwsDCbYmrYsKG8vLx06NChbF/i9/HxkY+Pj019AQAAAABgi9dff93y9fnz57Vr1y7LIUnvvfeenn/++Xz3n+eE/VoCnh/XZtZTU1NzrBMVFSVfX18VLVrU5n737t2r9PR0lSxZMl9xAQAAAMDtjlXi7WPdunXavn27ihUrpg4dOqhNmzaWcwcOHLipvu32DvvmzZu1efNmtWzZUsHBwTpy5IgmTpyoSpUqWWbXFy9erNjYWEVERMjPz0+rVq3S+PHj9eijj1pmxKOjo9W+fXvNmTNHTZo00eHDh/XNN9+oW7duCg0N1b59+zR69Gg1aNBALVq0sNflAADsyNerkDrXrqrqJYvLz6uQElNSte7gMW0+mv3epgAAAK7gnXfe0bvvvqvOnTtr7ty58vHx0TPPPKOXX35ZJpNJRYoUuan+7Zaw+/n5adGiRZo0aZIuX76skiVLqkuXLpo/f74lGffy8tKMGTM0atQomc1m3XHHHZoyZYrVRvLp6ek6cOCAkpOTJUne3t5asWKFpk+frkuXLqls2bLq3r27Jk2aJE9PT3tdDgDADgJ8vDW8XVPd26i2ivj66GhcglLSMxRS2F/DWt+pw2fj9eW6bVq0ba+zQwUA4NZn/O9wxDhuYvr06Vq+fLmqV6+uH3/8Ufv27VP//v317rvvatSoUTfdv90S9jp16mjlypW51unSpYu6dOmSa50KFSpYLVJXtmxZrVmzpkBiBAA4T7EAP33+8L0qExyk+Zt36bvNu3Xq/EXL+TsrltGApvX0yr2dVKt0mF7+JfefKQAAAI524cIFVa9eXdLVV8DDw8M1b948tWnTxrUTdgAAcuLl6akZA3sqtHCABnyyQP+cjc9SZ8vRU9py9JTu++e4Xu7dUQmXkvXRyo1OiBYAgNuDo/ZId6d92MPCwhQfH6+QkBBLWWBgoOLi4gqk/zxv6wYAwM3qWqeq6pYtqeFf/5xtsn69hVv3aMbKjXqkzZ0qFuDnoAgBAABu7LHHHrNsa242mzVr1iz17NlT99xzT4H0T8IOAHC4/k3rad2hY9oTfcam+l+v3y6zId3bqLadIwMAALDdmDFj1LlzZ0nSsGHDtGHDBt1777367LPPCqR/HonHba1inbIKr1BCnoU8dOFsovZtPCRzptnZYQFurXxIUdUrV1JPf/OLzW0uXknV77sPqGeDGvr8zy12jA4AgNucGy0I52jvvPNOgfdJwo7bjpd3Id3Vv4V6PN5RVRvdYXXu7Ik4/fb5Ci35fKUuxiU5KULAvZUMurq9yYGYvL3bdSDmnDrXqmKPkAAAAPJlypQpatCggRo0aKAyZcoUeP8k7LitFClWWC8tHKWaEVW15Y8dmnDPGzqw+R9lZmSqdJWS6jr0LvUf20s9HuuoCT3f1JFdJ5wdMuB2TKarC9EY+fkVv8l9FrEBAKCgsehcwbt06ZLef/99RUVFycPDw5K8N2jQQA0bNlSVKjc32UDCjtuGj5+3Xvn5OZW8I0wjWk7U/k2HrM7/vfkf/b35H3016Tu9/MsLev33FzWizWRFH4h2UsSAezqbeEmSVLlEiKLPJ9rcrlKJEJ37X1sAAABX8Oabb1q+PnHihKKiohQVFaVvvvlGY8aM0cWLF5WYaPv/d/6NhB23jXuf7ao76pbXqDaTdHDbkRzrJcRe0NjOr+iDDa/qqfcGa1zX1xwYJYDD5xK07/QZPdCkrtYcOGpTm8I+3upet5pmrd1q5+gAALiNGXLMO+xu9J68YRj6448/dODAAYWGhqp9+/bq2bOn5fzNbu/GKvG4LXh4eqj7sPZa8c3aXJP1a5LOX9bcVxepcad6Kl053AERArjevE271KZqRVUNC7Wpfv+m9eRVyFM/bN1j58gAAABsN3ToUD388MPatGmT5syZo+rVq2v06NHKzMyUJIWG2vZ/nZyQsOO2cGfneipRLlSLP460uc2a7zYoMT5J3R5pb8fIAGTn1x37dfBMnGYO6qlyxYJyrdulTlU907G5vl4fpXNJlx0UIQAAtyOTAw/3sGjRIm3evFnz5s3TsmXLdOTIEe3fv1+TJk0qkP5J2HFbqFCrjBLjk/RPlG2P10pSemq6dq/7W+VrFvxqjgByl5qRqSfm/KSU9AzNf6K/HmvbRKGF/a3q1CxVQq/c21HT+nXXbzsPaNrSdU6KFgAAIHtlypSRv////x8mJCREc+bM0Zdfflkg/fMOO24L3r7eSktJz3O7tCtp8i+S++weAPs4k3hJAz5ZoJGdW+rRNk00/K5m2n/6rK6kZyi0sL8qlQhRzIVEvf7ban29IUqGG70PBwCAXfAOe4Fr3769HnroIc2ZM0clSpSQJKWnpysgIKBA+idhx20h6fxlBYYUlpePl9JTbU/cQ0oVU1JCklr3aaYyVUvJy9tTF+OStP6XrTp74uYWiABwYxevpGjyT8v1zh9r1bNBDVUND5Wvl5eOnEvQu8vWac2Bo8o0u9FPfQAAcEtZu3at9u3bpwoVKqhVq1YqXry4/vrrL73++usF0j8JO24Lm//YoeHTBqnN/c20fO5am9qUvCNMtVtW05VLKWrRq4nOn7mgjPRMBYUW0RPvDtamJVFa8MbP2rv+gJ2jB5CUkqq5G3Y4OwwAAG5vzLAXuO3btys9PV179+7V9u3bFRUVpdKlS2vYsGF6+umn1bhxYy1ZsiTf/ZOw47Zw+p9YbV22U/cM72xzwn7PE51kzjRr+dd/6uePlurkgdOSJF9/H7Xr30I9n+yid1ZN0ruPfaqls1fbMXoAAAAAtyovLy/Vr19f9evXtyo/cOCAoqKibqpvFp3DbeP7ab+pRtMqGvpa/xvWjejRSPc+202/frJcHz7zpSVZl6SU5FT9Pmulht85Vn98uUqjPntMEfc0tmfoAAAAgP0ZJscdULVq1dSvX7+b6oOEHbeNqJV79Mlzc9XvhV567osnFF6xRJY6hYsGqN8LvTR54RhtX75bM0fOzrE/c6ZZ05/4XJuWRGn4u4Pl4cE/PAAAAAAch0ficVtZOH2JEhMu6bG3HlKHga21ddlOHdh8WJkZmSpVOVyt+zSTt4+XTuyP1vger8t8g8WsDMPQ3JcX6qNNr+nOrg206bftDroSAAAAoGAZhhyy6wo7uxQcZthx24n8+k89WPEpTXv0M/kV9lOXIe10z/DOqlS/glYv2CAPTw+9PWymzJlmm/o7uPWwDm49rC4Pt7Nz5AAAAADw/5hhx20p9Uqals1Zo2Vz1liVdx3STp0Gt9bBrUfy1N/fWw6rZrMqBRkiAAAAAOSKGXa4Fc9CnjLMhow8PqdjzjTLs5CnnaICAAAAHMBw4OHGUlJSdPr0aV25cuWm+yJhh1s5f+aCPAt5ZrsgXW5KVwnX+TMX7RQVAAAAgFvdrl271KFDBxUpUkRlypRRYGCg2rdvr507d+a7TxJ2uJVtkbt16cJldRvW3uY2YeWLq1HHulr93Xo7RgYAAADYGdu62c2BAwfUqlUr7dixQ8OGDdPLL7+sRx55RDt27FCrVq104MCBfPVLwg63kpKcqmVz/lTXoXcpIMjfpjb3PttNyYlXtGreX3aODgAAAMCtaNKkSSpevLgOHjyomTNnavz48ZoxY4YOHTqk4sWLa/Lkyfnql4QdbufHD36Xh6eHJi8cI98An1zrdn64re59tpvmv/GzUpJTHRMgAAAAYAcmw3GHu1m5cqXGjh2rYsWKWZUXK1ZM48aN06pVq/LVLwk73M6Z43GadO87qtKooqave1ltHohQIS/rBeUq1CqrER8/ojGfP6HFHy/Tgjd/dlK0AAAAAFxdUlKSKleunO25ypUr68KFC/nql23d4Jb2rj+gUe1e0vBpgzVh3gglxF7Q/k2HlJ6arrDyxVWjaRXFx5zXzFFfadH0Jc4OFwAAALh5jlrB3Q1n2MPDwxUbG5vtuZiYGIWFheWrXxJ2uK2ju0/quY6vqFy1kuo2rL1KVwmXb4CPYo+d0w/v/qr1P21RRnqms8MEHCYowFdVSofKz8dLl1PS9PeJs0pOTXd2WAAAAC6vffv2Wrx4sfr165fl3C+//KJ27drlq18Sdri94/tOaeaor5wdBuA0tSqEqW/b+urYuKp8vP7/x8KlK6n6beN+fbd6p47GJjgxQgAAUCActYK7G64SP3PmTKWlpWV77vPPP5e3t3e++iVhx23NZDLJP9BPkpSceEWG4YbP5wC5GNqtiZ7s2UKnzl3UzF/Wa83OI7p8JU1BhX3VqXFV9W5ZR/e2rqNXvl6uxRv2OTtcAAAAl+Tl5SUvLy9JUnJyss6fP6/g4GD5+/srICAg3/2SsOO2VLFOWd39aEe1H9BC/kX+l7AnXdHKeX9p8SeROrr7pJMjBJzvP53v1JM9W2jmL+v1+ZJNuv73WXGJlzXzlw367LdNGtv/Lr30n85Ky8jU0i3520MUAAC4AN5ht6s///xT48aN06ZNm2Q2m2UymdSkSRO9/vrratOmTb76JGHHbcXLu5BGzBymjgNbK/50gn58/3cd3XNCklSxdjl1ebit7n60gyLn/qn3Hv9c6WkZzg0YcJIKYcF65t6W+uy3jfrst0051svINOuVucvlXchTEwd21Pq9x5TEFocAAABW1q9fr06dOikkJERPPPGESpYsqdjYWH3//ffq1KmTVqxYoZYtW+a5XxJ23DY8PD303/nPqmGHOpr26CeK/PpPZWb8/6Jxf/6wUXNfWaiOA1vrqfcfVpGiAZp8/7vKzGRhObifPm3qKSExWZ8v2WxT/emL1qrzndV0d7Oamrcyys7RAQAAu2CG3W4mTZqkBg0aaPXq1fLx8bGUv/HGG2rbtq0mTZqkFStW5Llf9mHHbeOB0Xfrzi71Nfm+d/THl6uskvVrMjMy9ceXqzT5vnd0Z5f6emBMDydECjiXj5enejSvqZ/+2qP0bP6eZCc+MVkrth9SnzZ17RwdAADArWfTpk0aM2aMVbIuSX5+fnr++ee1ZcuWfPVLwo7bgmchT93zRCf98eUqbV2284b1ty7bqd+/WKl7Hu8oz0KeDogQcB3hxQJVxM9H6/cey1O7DfuOq2J4MRXy5EcHAAC3JMOBh5sxm82WRef+zdvbO99P9fK/LtwWIno0VGjpYvpl5jKb2/z6SaRCSxdT856N7RgZ4Hqubd2Wksc1HFLTr9b39eJtKgAAgOvVq1dPM2bMyLIrldls1owZM1S/fv189cv/unBbqNuqhk7+Ha2ju0/Y3ObIrhM6+Xe06rSqobULc150C7jdJCWnSJJCg/K2xUhIYIAyzWZdTs1+j1EAAAB3NWHCBPXo0UP16tXTQw89pNKlS+v06dP6+uuvtXfvXi1evDhf/ZKw47bgH+inxPOX89wuMeGSAoL87RAR4LpiEpJ0NCZe3ZrW0J+7jtjcrlvT6lq/95gMN3zMDQCA24Jhuno4Yhw307VrV82fP1+jR4/WuHHjLOVlypTR/Pnz1a1bt3z1S8KO28KVS6kK+N9+63kREOivK0lX7BAR4Nq+W7NLo+5vrRJFC+vshUs3rF+7QrhqVQjXMx/8ZP/gAAAAbkF9+vRRnz59dOjQIcXFxSkkJERVq1a9qT55hx23hb+3/KMKtcuqVOVwm9uUrlJSFWqX1d9bDtsxMsA1/bZhnxIvp+iNR7vL1zv3390WK+KvV4Z00T/RcXleqA4AALgOk+G4w51VqVJFERERN52sS8yw4zbx5w+b9MTbA9XjsY765LmvbWpz96MdlBifpDXfbbBzdIDruZSSphEf/ayPR/bRZ6Pv15vzV2n30VirOiaT1LR6OY17sL18vb305JsLZOZ5eAAAgCxeeumlXM8bhqHJkyfnuV8SdtwW0lPTtWTWSvV+uqvWfL9Bf2/+J9f61ZtUVvdHO+inj5YqPTXdQVECrmXvsTMa9vZ3euOR7vpqbH/tP3FGf+48okspaSoa4KuOjaqqXFiw9h6L1ePvLlRMfKKzQwYAADfDUVuuueHv96dMmZLjuWsrx5Oww619/fIi1W5RTa/9Nk6vD/pQm3+PyrbenV3qa9zXT+ufqKP6espCB0cJuJYDJ8+p96TZiqhZXve3qaf7WteVv4+XLqekacuBk5o4e6l2HYlxdpgAAAAu7dy5c1nK4uPjtXjxYn3++ef64Ycf8tUvCTtuG+mp6Zpwz1sa/+0zeuWXF3Rk93H99ulyHd19UpJUsU5ZdX+0g+6oU15bl+3UK/3fZ3YdkGQY0vq9x7V+73FnhwIAAHBLKlasWLZlo0aNUlJSkl555RV9++23ee6XRedwW0lOuqIJ97ypsV1fU8zhsxr+3sOatnqypq2erOHvPazYI2c1rttUje/xppJZHR4AAABwCTNmzFDFihXl6+urRo0aae3atTnWjYmJ0YABA1StWjV5eHhoxIgR2dZbuHChatasKR8fH9WsWVM//vjjTY2bX61atdKSJUvy1ZYZdtx2DMPQ9hV7tH3FHgUE+atYeFFJUkLsBV2+mOzc4AAAAAAnMckxK7jndRf2BQsWaMSIEZoxY4ZatGihTz75RF27dtW+fftUrly5LPVTU1NVvHhxjR8/Xu+++262fW7YsEF9+/bVyy+/rN69e+vHH3/UAw88oHXr1qlp06b5Gje/du/eLV9f33y1JWHHbe3yxWSSdAAAAMAJEhOtF6z18fGRj49PlnrTpk3T0KFDNWzYMEnSe++9p6VLl2rmzJmaOnVqlvoVKlTQ9OnTJUlffPFFtmO/99576tixo8aNGydJGjdunNasWaP33ntP8+bNy9e4ufnqq6+ylKWmpmrXrl2aNWuWBgwYkKf+riFhR4Ezed1af6ymbX/R2SHARTw9+jdnh2Az36Bb6+/ZnKc7OzsEAABgmK4ejhhHUtmyZa2KJ02alGWl9LS0NG3btk1jx461Ku/UqZPWr1+f7xA2bNigkSNHWpV17txZ7733nl3GHTJkSLblvr6+evDBB3N8EuBGbq3/8QEAAAAAbgknT55UYGCg5fvsZtfj4uKUmZmpsLAwq/KwsDDFxsbme+zY2Nhc+yzocY8ePZqlzM/PT8WLF89zX9cjYQcAAAAAd+DgfdgDAwOtEvbcmEzWM/+GYWQpyytb+iyocQvynffrkbDDJRUJDlDLXneqeOmr2yPEnT6vtT9tUVLCJSdHBgAAAKCghIaGytPTM8us9tmzZ7PMfudFeHh4rn3aa1xJSklJUUJCgoKDg+Xn53dTfbGtG1xKyYolNHLGUH1zaLqefm+wOg1spU4DW+nJaQP17aH3NPrjR1S6crizwwQAAABuPYYDDxt5e3urUaNGioyMtCqPjIxU8+bN83WZkhQREZGlz2XLlln6tMe4u3btUocOHVSkSBGVKVNGgYGBat++vXbu3Jm/ixAz7HAhNZpU1pSFo5R6OVXfvLpIf8xerQtnr64sGRRaRJ3/01Y9Huug5j0aatL972rP+oNOjhgAAADAzRo1apQGDhyoxo0bKyIiQp9++qlOnDihxx9/XNLVFd6jo6M1Z84cS5sdO3ZIki5duqRz585px44d8vb2Vs2aNSVJzz77rFq3bq033nhDPXv21M8//6zly5dr3bp1No+bFwcOHFCrVq3k5eWlYcOGqUyZMoqOjtaCBQvUqlUrbdmyRdWqVctzvyTscAllqoTr5UWjdWzPCU28950sW7FdjEvSd28v1q+fLtfk70dpyg+jNLL9yzq+P9pJEQMAAAAoCH379lV8fLymTJmimJgY1a5dW0uWLFH58uUlSTExMTpx4oRVmwYNGli+3rZtm7799luVL19ex44dkyQ1b95c8+fP14QJE/Tf//5XlSpV0oIFCyx7sNsybl5MmjRJxYsX1+bNm1WsWDFL+SuvvKI777xTkydPtmwnlxck7HAJ/5nUR0kJl7JN1q+XnHhFk+57Rx9ufEX/mdxHL/Wd7sAoAQAAgFuXybh6OGKcvBo+fLiGDx+e7bnZs2dnKTOMGw/Sp08f9enTJ9/j5sXKlSv12muvWSXrklSsWDGNGzdOEyZMyFe/vMMOpysWXlTNezTSovd/zzVZv+bKpRQtfHeJmnZtoBJlQxwQIQAAAADkLCkpSZUrV872XOXKlXXhwoV89UvCDqfrMKCF0lLStXzuWpvbrJz/l1IupajDgJZ2jAwAAAC4jbjgonO3i+xWpb8mJiYm3yvPk7DD6cLKherUoRglJ12xuU3K5VSd+DtaYeVD7RgZcOuoU62UOrWsrq5taqppvQry9vJ0dkgAAABuo3379lq8eHG253755Re1a9cuX/3yDjuczrOQp8yZ5jy3M2ea5eHJ75zgvnx9CqnHXXV0b6f6Kl/a+n2p84nJWrxit374Y4fizl9yUoQAAMClOGr22w1n2GfOnKm0tLRsz33++efy9vbOV78k7HC6hDMXFXF3Q3kW8lRmRqZNbTw8PRResYT2bjxk5+gA11QsyF9vj7tXlcuFavWmQ3rj00jtPxyrzEyzypYMVo/2ddS7Uz31aF9Hz73+o/b9k/0jWgAAALh5Xl5e8vLykiRduXJF58+fV0hIiHx8fBQQEJDvfpmehNOtWbhRRYsHqkWvxja3ada9oUJKBmvNwk12jAxwTX5+3po2/j6FFA3QkHHfaOL037Rj/ymlpmUoI9Oso6fi9f5Xq3X/07N08vR5TXvxPlX41ww8AABwP9dWiXfE4Y7279+vTp06KTAwUGXKlFGRIkXUpUsX7du3L999krDD6Y7vi9budX/r3me6ycPDdMP6JpNJ9z7TVfs2HdI/O447IELAtfS7v4nKhgdr5KsL9c/xcznWS7yUotFTFyn+wmWNHtregRECAAC4lxMnTqhVq1aKi4vToEGDZDKZNHz4cO3fv18tWrTQP//8k69+SdjhEr5+7UdVa3yHnv5gSK5Ju8lk0vB3B6tWi2qa+9pPjgsQcBGenh7q3rWefl+zV0dOxt2w/uUraZq9cKMa1S6X5T13AADgZgyT4w438/LLL6ty5cravHmznnrqKRmGoXfeeUf79u1T+fLlNXHixHz1S8IOl7BzzX6999QX6jKknV77bZwatq8tk8n6L3r9drX06uIX1OPxDvrg2dnatny3k6IFnCeiaSWFFCusRct22txm9aZDOn8xWT3b17FjZAAAAO5r6dKleuKJJ1SoUCEZxv+/ExAQEKDnn39eK1asyFe/LDoHl7Hs67VKOHNRw17uq9d/f1HRh2J1bN9JSVK5GqVVtmopHdt3SpMfeE+bft/h3GABJylfLkQXEpNtml2/Jj0jU7sPnla5UsywAwDg1lgl3m7Onj2rChUqZHsuLCxMFy9ezFe/JOxwKVuX7dLWZbtUK6KqOgxoodD/PcK7Z/1BTX/6S+1ed8DJEQLO5eVVSOnptu2mcL20tAwFBPnbISIAAAAULVpUSUlJ2Z5bunSp7rjjjnz1S8IOl7R3w0Ht3XDQ2WEALicx8YqCAv3k411IqWkZNrcLCw3U2YTsf4gAAAD34KgV3N1xlfgqVaro0CHrLaeXLVum33//XTNmzND777+fr355hx0AbiEbNv2jQp6e6tCius1typcqpjrVSmntlvytTgoAAIDcde/eXb/++qvle5PJpO7du2vWrFmaNGmShg8fnq9+mWEHgFtITOxFbdpyRH0619fvq/fKbNz4V9j3damv84nJWrXx0A3rAgCA2xjvsNvN2LFjNXbsWElSxYoVNWfOHIWGhqpZs2YKCgrKd7/MsAPALWbB95tUuUJxPfuftjes26F5NfXp0kDzF29Tekbe330HAABA3gQHB+vBBx9U586dbypZl5hhB4Bbzs7dJ/XOrBV6/pGOCikaoC9+2KAjJ+Ot6hQL8tf9XRtoYK+m+n3NXs39ZbOTogUAALj9vfTSS7meNwxDkydPznO/JOwAcAv6KXKXEi+laMR/2mnuO//Rjn2ntP9wrDLNZpUOL6pWjSopPcOs2Ys2atb362XDk/MAAOB256BF59zxkfgpU6bkeO7avuwk7ADgRlZuOKg1m/9R6zsr6+52tdW84R3y9DQp4UKyPvh6jX5fs0+XklOdHSYAAMBt79y5c1nK4uPjtXjxYn3++ef64Ycf8tUvCTsA3MIyM81atfGgVm1kG0QAAHADLDpnN8WKFcu2bNSoUUpKStIrr7yib7/9Ns/9sugcAAAAAAB20qpVKy1ZsiRfbZlhBwAAAAB3wAy7U+zevVu+vr75akvCDrvxL+KrhnfVVmBIEWWkZyj6nzPau4HHdgEAAADcXr766qssZampqdq1a5dmzZqlAQMG5KtfEnYUuDJVwtX7yc5q37+F/Ar7KjPTLE/Pq29fnPg7Wos/W6nfv1yt9NR0J0cKAAAAuA+Tg1aJd8hK9C5myJAh2Zb7+vrqwQcf1LRp0/LVLwk7ClThwoX10V8v6/LFZP3w7m/6Y/ZqnTsZL89CnqrdoprufqyDHnu9v9r2aapJ97+rpPOXnR0yAAAAANyUo0ePZinz8/NT8eLFb6pfEnYUGH9/f5UtU1ab/9ih1x78QKlX0iznMjMytXPNPu1cs0/VGlfSyz8/p5d+GKUXur3OTDsAAACAW1q5cuXs0i+rxKPAhIeF68DWI3ql33SrZP3fDmw9rP/2fEvVGlVU96HtHBghAAAAADjW6dOn9dJLL+WrLTPsKBD+/v7yD/DX7IkLlJ6WccP6B7Ye1roft+juYXfppxnLHBAhAAAA4OZYJd6u1qxZo927dys1NdWq/MSJE/rggw8UEBAgk8mk5s2bKyIiwqY+SdhRIIKDg3XqUIx2rN5nc5vFH0fqnZUTVadlNe1ed8CO0QGuy8PDpPLlQlSksK9S0zIUffq8Ll1KvXFDAAAAuIwJEybotddek8lkyva8yWTSCy+8IEmaOHEiCTscy8vTS7vWROWpzZ6/DshsNqtM5ZIk7HA7QUF+6ta5ru7p3kDh4UGW8rS0DK1as18/LY7S3wdinBghAAC43bBKvP189dVXeuyxxzRixAj5+flZnduxY4d69eqlY8eOSZKKFi1qc78k7CgQJpPJpkfhr2cYhjLSM1XI29NOUQGuqU7tMnpl8r3y8S6klav3a9m0vYqPvyQfn0Jq3LCC7rm7gTp3rKP532/Sp7NWy3DDH3oAAAC3ktjYWA0ePFjVqlXL9pyUv4XpSNhRIDINs8LKh+apTXBYkLx9vJSYcMlOUQGup3q1knrz1Qe0/8BpvfTqz7p48YrV+X8On9V3C7fo3p6N9MSjd8nDw6SZn65yUrQAAOC2w0SAXZjNZnl5eeV4PqdH5W+EhB0F4tKlJN3ZuZ6KhRdVQuwFm9p0/k9bpSSnatvy3fYNDnARHh4mTRjbQ/8cOauxE35QWg5PpZjNhn74cavMZkNPD++g9Rv/0c5dJx0cLQAAAGy1atWqbGfXJalmzZpatSp/EzAk7CgQFy5cUIniJdTj8Y76avL3N6zv5eOl7sPu0urvN+rShWQHRAg4352NKqp0qWC9+vriHJP16y36eZt6dK+vXj0akrADAAC4sFWrVuWalBuGodatW+e5XxJ2FAiz2azzF86r3ws9dWDLYW38bXuOdT0Leer5L59Q0RJBWvThHw6MEnCuHt3r6+ChWO3Pw2JyP/8apaceb69ixQKUkHDZjtEBAIDbHtu62c2UKVOyLTeuW4xo8uTJee7XI78BAf925swZJSUlatL3IzXwv/epaInALHVqRlTV1N/GqkXPxnr94Zk6vi/aCZECzlG1Srg2bj6cpzYbNv0jT08P3VGhuJ2iAgAAwM06d+6c1XH27Fnt3LlTkydPVoUKFbRt27Z89csMOwrUqVOntG/Zcd0/+m71e6GnNv8epTPHzqmQdyHValFNleqWV/Q/sRrf6+087dkO3A58fbx0JSU9T22uXLla39c350VMAAAAbMG2bvZTrFixLGUhISGqXbu2UlJS9Nprr+n772/86vC/kbCjwM367wIteGexOj7YSi16NlaZqqWUkZ6h6H/O6PMJCxS1cq/VoyGAu7h8OVVFg/zz1Ca4qL+lLQAAAG49HTp00Mcff5yvtiTssItLF5L140dL9eNHS50dCuAytkYdU7s21fXprNUym237pVWHu2rq0uXUPL33DgAAkC3eYXeKy5cvy8/PT1euXJGfn1+e2vIOOwA4yC+/RqlE8UA1b1bZpvpeXp7q1qWelkXuVkoeH6UHAACAa+jRo4eio6PznKxLJOwA4DCH/jmj7TuO69mnOiosm0UZr2cySaOf7azCAT76aXGUgyIEAAC3s2vvsDvicDeJiYn6z3/+o9DQUIWEhOiZZ55RZmamJGnt2rU6fDhvCw9fQ8IOAA70yuuLlZaWqQ+mPaQ7G1WUyZS1TlhYoCaN76WO7WvrjXeW6OSpBMcHCgAAAJuNGTNG3333nR544AH17t1bM2fO1BtvvCFJWrx4sV599dV89cs77ADcRuHCheXj4yMPDw9lZGTo0qVLSk937KPm589f1tOj5uql//bWm689oJOnErR85V7FJ1yWj3chNWpYQc2aVNLly6ma9PKPWrf+kEPjAwAAtzHeYbebxYsXa+rUqXr22WclSSVLltQ333yjF198Uc2bN9fzzz+fr35J2AHc1kwmk0JDQxUUWFS+fj66kpyq9LQMBYT4ylTSQ0mJiYpPiFdycrLDYkpIuKynR85V7Zql1bNHA91/750qXNhXaWkZOnL0nN557w+tXL1fKam8tw4AAHAruHTpkurWrWv5vnXr1nrzzTclSaGhoYqOjs5Xv3ZN2O+55x7t2LFDZ8+eVXBwsDp06KA33nhDpUqVsqo3e/ZsTZs2TQcPHlTRokXVp08fffjhhzn2m5qaqjFjxmjevHm6cuWK2rdvrxkzZqhMmTL2vBwAtxhPT0+VLVtOPt4+Wvnzdv32zQYd2nNKkuTr7612PRronsEtVaFyRcXEnHZ4fHv2RWvPvqv/eJtMErsdAgAAu2KG3W4aNmyorVu3ql27dpKk4OBgpaeny2w2KyEhQcHBwfnq167vsLdr107fffedDhw4oIULF+rw4cPq06ePVZ1p06Zp/PjxGjt2rPbu3asVK1aoc+fOufY7YsQI/fjjj5o/f77WrVunS5cu6e6777a81A8AJpNJZcuUlTldGnn/h3pv3PeWZF2SUpLT9PuCTXry7mn69Zv1Kl26tNq2ru60eEnWAQAAbl1Tp07V22+/rd9++02S5OFxNdU2m8366quv1LBhw3z1a9cZ9pEjR1q+Ll++vMaOHatevXopPT1dXl5eOn/+vCZMmKDFixerffv2lrq1atXKsc+LFy9q1qxZ+vrrr9WhQwdJ0ty5c1W2bFktX778hsk+APdQtGhR+Qf4a+R9H+jw3pwfQTKbDc186ScVCy2ip59or3XrDyojw+zASAEAABzDUSu4u+Mq8RMmTJCvr6969Oih0qVLKzQ0VCaTSbVq1VJ0dLQ2btyYr34dtkp8QkKCvvnmGzVv3lxeXl6SpMjISJnNZkVHR6tGjRoqU6aMHnjgAZ08eTLHfrZt26b09HR16tTJUlaqVCnVrl1b69evz7ZNamqqEhMTrQ4At7eiRYO1acU+Hdx96saVJX39/jIVK1ZYrVpUtXNkAAAAuN2YTCZVqlRJbdu2VZUqVRQcHKwuXbqoR48eioqKUu3atfPVr90XnXvhhRf04YcfKjk5Wc2aNdOvv/5qOXfkyBGZzWa99tprmj59uoKCgjRhwgR17NhRu3btkre3d5b+YmNj5e3tneUdgLCwMMXGxmYbw9SpU/XSSy8V7IU52JgW05wdgkXleuV0V9/mKl766j2Ij7mgld9t0MHtx5wbGFzOkLeWOWXcGhXD9NXk2vr1mw02tzlx6Ix2bTqsrl3qasVfB+wYXcH4dCJPEwEAgDziHXa7WbFihV36zfMM++TJk2UymXI9tm7daqn/3HPPKSoqSsuWLZOnp6cGDRok438va5rNZqWnp+v9999X586d1axZM82bN0+HDh3SqlWr8hSXYRgyZbehsaRx48bp4sWLliO3GXzkrG7LapoW+aI+/HOyWvduLP/CvvIv7KsWPRrq/VUTNX3lBDVoW9PZYQIqXTxIkvT3juN5andgxwmVCguyR0gAAABAnuV5hv2pp55Sv379cq1ToUIFy9ehoaEKDQ1V1apVVaNGDZUtW1YbN25URESESpYsKUmqWfP/k7zixYsrNDRUJ06cyLbv8PBwpaWl6fz581az7GfPnlXz5s2zbePj4yMfHx9bLxHZaH3vnXr+00d0cOsRTb5/mjYtiZI58+p7vh4eJt3Zpb7uH3W3Xlk4Uu88/plWzMv+9QTAETw9r/4uMjOP76JnZGTKs5CnPUICAACAmzp9+rQ+++wzTZo0Kc9t85ywX0vA8+PazHpqaqokqUWLFpKkAwcOWLZkS0hIUFxcnMqXL59tH40aNZKXl5ciIyP1wAMPSJJiYmK0Z88eyz53KFi1mlXRc588ojU/bNQ7j3yqzAzr1fjNZkOblkRpyx879OyMYRr98SOKiz6vnX/ud1LEcDXlwooqNLiwDMPQmfgknY6z7zoS5xOv7qleslyIjh3M/lWZ7JQsH6oLFxy3HzsAAIBD8Ui8Xa1Zs0a7d++25LvXnDhxQh988IECAgJkMpnUvHlzRURE2NSn3d5h37x5szZv3qyWLVsqODhYR44c0cSJE1WpUiVLcFWrVlXPnj317LPP6tNPP1VgYKDGjRun6tWrW/avi46OVvv27TVnzhw1adJEQUFBGjp0qEaPHq2QkBAVK1ZMY8aMUZ06dSyrxqNgPTT2Hh3dfSLbZP16ZrOh6U/OUrlqpTRowr0a3elVB0YJV2MymXR3q1rq066ealYKtzq3ff9J/bByp1Zt+0eZmQW/InvUgWglXLyszg800Sev/GJTm8BgfzXvVFuzvl5X4PEAAADg9jZhwgS99tprOb6mbTKZ9MILL0iSJk6c6PyE3c/PT4sWLdKkSZN0+fJllSxZUl26dNH8+fOtHk+fM2eORo4cqe7du8vDw0Nt2rTRH3/8YVlJPj09XQcOHFBy8v/Per377rsqVKiQHnjgAV25ckXt27fX7Nmz5enJo6wFrUyVcDVoV0uv/+ejXJP1a8yZZi18/3f9d96zqlCrjI7ttW2FbtxeChUqpLJly2lCjZrauu6gprwbqROHz0qSKtcspa59m+q1J+9W1N8n9dz7i5V4OaVAx0/PyNTPf+7RA/fdqW8/XK4kG2bN736wuWRIvy/fXaCxAAAAuAq2dbOfr776So899phGjBghPz8/q3M7duxQr169dOzYMUlXtx+2ld0S9jp16mjlypU3rBcYGKhZs2Zp1qxZ2Z6vUKGC5VH6a3x9ffXBBx/ogw8+KJBYkbO29zVVUsIlrVu02eY2GxZv0/kzF9XugQh9Oel7O0YHV+Tp6any5SrocmKqnnvwMx09YP1IevSxOK1Zsku1GlXQfz98SNNH99YTr3+vlLSMAo3j++U71Kt1HU385D+aNHSWki+l5lg3okMtDXiqg777aasuJl4p0DgAAABw+4uNjdXgwYNVrVq1bM9JUrly5fLcr8P2YcetqVh4kGKOnlV6HpKpzIxMnT5yRsXCi9ovMLissLAwZaSb9fygT7Mk69fbu+2Yxg/9QneUCtHDPZoWeBxxFy5r9PSfVb56Sb3z/VNq1a2uCnlZP4UTViZYw8berQkfDdL+Y2d08J9Y3VE+f2t0AAAAuDzDgYebMZvNlqfEs5PTo/I3Yvd92HFrM5sNmTzy/ofLZDLJMLvh31Q35+npqaDAIH01PVIxJxNuWP/wvtNa+v0W9exZX5//vFHpNrx2kRd7DsfokVcX6PmBd+nF9wcq/lyi9m45qrTUDBUvWVR1mtwhs9ksD08P1a5cSrXH9pQk7d0frR9/i9LKNfuVyZ9jAAAA3MCqVauynV2Xru6Kltdty68hYUeuzkUn6K4Hmsk3wEcpl3N+pPh63r5eKlutlLav3GPn6OBqihYtKsOQli3aanOb3+ZvUq9BLdSucWUt23igwGM6Eh2vx1//XneUDlHvtnVVoVwxlQ7yV/mSxXTlSpp++3G7liyO0rkzifLy9lS9huXV497GmjDmbt3Vurpeen2xUlLTCzwuAAAAR+Mddvs5evSojh49mmud1q1bKz4+Xr/++qsGDx5sU788Eo9crVywQT7+Prqrfwub27S5P0IBQX5a/u1fdowMrsjX11eH9p7SxYTLNrc5dfScTp9MUNVyJVSxVDH1bltHD3ZtpHvb1VWVsgX3ePqR6Hi9880qffHLRpUND9bmjYfVr8d7+mzGCkWfTFBaWoYuX0rV+j8PatyIbzVu5LdqULucJo+9R575eMoEAAAAtpsxY4YqVqwoX19fNWrUSGvXrs21/po1a9SoUSP5+vrqjjvu0Mcff2x1vm3btjKZTFmO7t27W+pMnjw5y/nw8PB/D2WTIUOG5Ho8/PDDkqTDhw9ryJAhNvfLDDtyde5UgjYuidK9z3TVynl/3XCW3cfPW/c9201bI3cr5uhZB0UJV+Hh4aErl9Py3C41JU1dIqprUPc7lZlp1pUrafLz9ZZnIQ/tPBit75bvUOSmm599N5mkCUM6af/uaL384g/KyMh5S7mtm47opRd/0OvvDVCHtjW1dOXemx4fAADAqVx0H/YFCxZoxIgRmjFjhlq0aKFPPvlEXbt21b59+7JdqO3o0aPq1q2bHnnkEc2dO1d//fWXhg8fruLFi+u+++6TJC1atEhpaf///9L4+HjVq1dP999/v1VftWrV0vLlyy3f53fnsRvNrl/ToEEDm+tKJOywwZxXf9K0ZS9q0ncjNaXve7pyKfstuHz8fTT+m6dVsmIJvfXopw6OEq7AbDarWPEieW5XLLSIUlPS9drz32n9yv3KyMiUh6eHmrWuprv7NtGrw7vrzppl9frsFTIb+f8p07R2BZUND9ZbE3/KNVm/ZtvmI9qy8bB6dm9Awg4AAJBHiYmJVt/7+PhYbfF9zbRp0zR06FANGzZMkvTee+9p6dKlmjlzpqZOnZql/scff6xy5crpvffekyTVqFFDW7du1dtvv21J2IsVK2bVZv78+fL398+SsBcqVCjfs+rXs3UFeC8vrzytFs8j8bih4/uj9dKAD1TtzsqaselV9XqyswKC/C3n/QP9dM/jHfXRxldUt1UNvdR/ug7vPO7EiOEsSUlJqlA1XOUql7C5Tf1mlRQUHKC3JizSn8v2KON/C8+ZM81av2q/Xnz8K73930Xq0aq2RvRvc1Px9W5bR4cOxmr/nmib2yxetFW1qpdSpYrFb2psAAAAp3PwKvFly5ZVUFCQ5cgu+U5LS9O2bdvUqVMnq/JOnTpp/fr12V7Ghg0bstTv3Lmztm7dqvT07NcemjVrlvr166eAgACr8kOHDqlUqVKqWLGi+vXrpyNHjmTbPi+Sk5MVHR2t5OTkm+6LhB022bX2b43s8KoO7jiuR14foHnHPtLnu97W57ve1rxjH+nxtwfq6N5ojer0mravYLE5d5WUlKTU1DT1GBBhc5seAyJ07J8z2r3tWI51li/eoU/f/l39OjdU5Zt4r71iyWLauT3ncbKzY/vVXz6VKxOS73EBAADc0cmTJ3Xx4kXLMW7cuCx14uLilJmZqbCwMKvysLAwy/7l/xYbG5tt/YyMDMXFxWWpv3nzZu3Zs8cyg39N06ZNNWfOHC1dulSfffaZYmNj1bx5c8XHx+f1UiVJf/75p1q0aKHAwECVLVtWRYoUUUREhNasWZOv/iQeiUcenDhwWq8P+UTBJQLV5r6mCi0dLJNMio85r9ULNysh9oKzQ4STGYah8+cT1K1vE21de1CbVu3PtX7nPo3VvGMtvTPpxxv2/ev3W3T/w63U5656ev2rFfmKz6uQp9JT87Z1XHpahiTJx4d/LgEAwK3N9L/DEeNIUmBgoAIDA21r8699yg3DyHXv8uzqZ1cuXZ1dr127tpo0aWJV3rVrV8vXderUUUREhCpVqqSvvvpKo0aNsinua9avX69OnTopJCRETzzxhEqWLKnY2Fh9//336tSpk1asWKGWLVvmqU+JhB35cP5son6aGensMOCi4uLi5Ovrq/++/6Bmvf2Hfv9+s1KSrReiKxzoq96DW2rA8PZavGCTIn+OumG/mRlm/bFom+4d3ELvfrtGqekZeY4t8XKKiofZ9kPjmtASV+snJl7J83gAAADIXWhoqDw9PbPMpp89ezbLLPo14eHh2dYvVKiQQkKsn4pMTk7W/PnzNWXKlBvGEhAQoDp16ujQoUN5vApp0qRJatCggVavXm31nv4bb7yhtm3batKkSVqxIu+TTiTsAArcqVOnVLJkSQ17vpsefKq9VvwcpZNHzsrDZNIdNUqqTff68vYupD+X7tZHU3+zud+9O07oocfbKTjQT7HxSXmOa+3OIxrYpbH8A3yUfIMdD67p1LWukpNTFbX7ZJ7HAwAAcCkuuEq8t7e3GjVqpMjISPXu3dtSHhkZqZ49e2bbJiIiQosXL7YqW7ZsmRo3biwvLy+r8u+++06pqal66KGHbhhLamqq9u/fr1atWtl+Af+zadMmffnll1kW1fPz89Pzzz9v2dYtr3iHHYBdxMTEqNeYz/Xdyp26s2NNPTr2bg15vptqNq+s+ZFR8vAwacVvu/LUZ+b/FqTzKpS/7TZ+Wr1bXl6e6ty9nk31vb0LqVvPhlq6cq+uXMn7dnUAAAC4sVGjRunzzz/XF198of3792vkyJE6ceKEHn/8cUnSuHHjNGjQIEv9xx9/XMePH9eoUaO0f/9+ffHFF5o1a5bGjBmTpe9Zs2apV69eWWbeJWnMmDFas2aNjh49qk2bNqlPnz5KTEzU4MGD83wNZrM5yy8LrvH29lZmZt5ey7yGGXYAdhMbn6SZC//SzIV/WZV7FfLUwG6NFV4mOE/9hZe+Wv9CUv4eT4+7cFm/rdunoU+008G/Y7R3V86z5p6eHnphUk8FFPbRwl+252s8AAAA3Fjfvn0VHx+vKVOmKCYmRrVr19aSJUtUvnx5SVcngk6cOGGpX7FiRS1ZskQjR47URx99pFKlSun999+3bOl2zcGDB7Vu3TotW7Ys23FPnTql/v37Ky4uTsWLF1ezZs20ceNGy7h5Ua9ePc2YMUM9evSweo/ebDZrxowZql+/fp77lEjYkU8+ft4KLRUsb18vJSZcUnzMBWeHhFtIekam1kYdVufeDfXztxttbte5dyNt3nNcScm2Pc6enbfmrlSZsKJ6Y/oAzZq5Skt/3aHkf71jX61GKQ15vJ3q1C+nSa//rJPRCfkeDwAAwFWYjKuHI8bJq+HDh2v48OHZnps9e3aWsjZt2mj79twnVapWrWpZjC478+fPz1OMuZkwYYJ69OihevXq6aGHHlLp0qV1+vRpff3119q7d2+WR/htRcKOPKlYq4y6D22n9n0j5FfY11J+KOqYfvtilVb9sEkpiXlfDAzu54eVO/Xh831Uv8kd2rH5xvtdVq1dWrXql9Pz7/9yU+OmpWfq2XcW6fmBd+nRpzvo4cfaau2qv3XubKK8vDxVr1F5VateSjGxF/TcxO8VtevEjTsFAACAW+vatavmz5+v0aNHW21fV6ZMGc2fP1/dunXLV78k7LCJyWTSfybeq76juis+5rx+eG+Jdq7Zr7TUdBUvHaxOA1vrmemD1W9MD0287x0d3x/t7JDh4rbsO6GdB6P1/NQ+eu7hWYo+kfN+lyVKBmnC2/108PhZrY06fNNjp6Vn6pUvIvXJjxvUs01tta5fSXWbVFB6SoZOnIrXF5N/0OZtR2U2O2JVFgAAAAdxwUXnbid9+vRRnz59dOjQIcXFxSkkJERVq1a9qT5J2GGTYS8/oN5PdtTn4+dr0ft/WBb/kqQDW6R1P21VqTtK6L/zntVbf7yoEXdN0enDZ5wYMVydYUjPvf+LPhn7gKbNeURfz1ihlb/tslq93cfXS+261tHAJ9srJTNTo979SZkFmESfO39Jn/+0UZ//dPWxfL84ng4BAADAzalSpYqqVKlSIH2RsOOGGt5VS/c93VkfjZqjX3LZf/30kbN6vstrmrbyvxr7xeN6ps1LDowSt6ILSVc07NX5emFwez3+QncNGdFJ2/46pKSLVxRQxFeNmleRf4CP1kYd1mtfRup8PhebKwg+PoVUqUJx+fv76EpKmo4dj9PlZFaOBwAAtxg3nf22t5deyj33Mf6vvTuPr/nK/zj+vrJdQmKJSpSgi61oiZYgVGlQRk0XVJvqVLVmqorhh25UO6WbqtqGUjXTUWppVQ2S1lohRaw1agixJKOxRRKy3e/vD+NOr+xxV/f1fDzO4yHfe77nnO/JaWY+9/P9nq9haMKECWVul4AdJer9fBcdTjxWbLB+zaXzmZo7bpHeWjFKjSJu06GdJT+bDO92KStbr81arVu+3KQ+97fQPXfWVsjtIcq8kqOlG/dqxfq9SklLd9n4bq1dTX0eukc9ujZTlSoVrccvX85R3MaDWrFql44k/eqy8QEAAMD1Jk6cWORn1za+I2CH3d1St4bu69ZCH784v9Tn7Fi3V6nHflWvwV0I2FFqZ85naM6Kra4eho2u9zfV2OE9lJVxRWv+vlUbv03UpQtZqhQYoMhuzdXjqXb6Xfe7NXPeei1e/pOrhwsAAFAsd94l3tP9+mvBBM7Zs2f17bff6tNPP9XSpUvL1S4BO4p1Z8v6qlChguJXlf491BaLofjvdqnVA80cODLAsTp3aKTXR/fSuiXbNf3Vr5Sbbft8+7FDKVo8I04xI7vrTy9Fy2Ix9NXXO1w0WgAAALhS9erVCz02cuRIXbp0SW+//bb+8Y9/lLldAnYUy1wpQJKUlV62Z4ez0i+rYuUARwwJcLjKgQEaM7yHflixQx+NWlRkPUu+RZ+/v1o+vj760wsPaOv2f+tUygXnDRQAAKAs2CXeJaKiovTRRx+V69wKdh4LbjJZ/93kq0qNymU6L6h6ZWWlX3HEkACH6961mXx9K+jTt78pVf2/TfmnMi5m6eGHWjp4ZAAAAPA0+/btk9lsLte5ZNhRrIMJR5Sbk6dOj7bRiulrS3WOr5+P2j/cWlu+4fZgeKaHe9yjH1fv1flfL5Wqfm52ntYu3qYeA9rp04WblJObX/JJAAAATsYz7I7z+eefFziWnZ2tvXv3at68eRowYEC52iVgR7Eu/JquLSt3qNfzXfT1jHXWHQ6L077PvaoeWlWr5n7vhBEC9uXv56PwujX01UdrynTezg3/0uNDuqhmSBVuiwcAAPAyzz77bKHHzWaznnzyyXLfEk/AjhKtnP29Pop7Vc+8+bg+e2NJsXVvCa+hF94doJ1x+3T84CknjRCwH/+Aq38Wr5TxHevX6gcE+Nl9TAAAAHbBM+wOk5SUVOBYxYoVVbNmzRtql4AdJTr40xHNeeVLPf9Of1WqYtbCict06XxmgXrNOzTWmM+GKOdKrt5/fo4LRgrcuKysHFkshqqGVCnTeVVrXt3nITMz2xHDAgAAgBsLDw93SLsE7CiV5TPWKftKjl6Y9IS6DeykDUvitWfjQeVk56pmnep68Kko3dY8XId2JWnC4x/p/H8uunrIQLlYLIZ27T6mzr+P0KqFW0p9Xuc+rXU8OU3/+TXdgaMDAAAoP55h9zwE7Ci17+Zt0JZvdqr701Hq8cz96jawkyQpPy9f2/65W3NfX6LdGw7Kkpvr4pECN2bFd4n6y+uP6PZmdXRk/8kS61evFaT23Vtoxrz1ThgdAAAAvAUBO8rkYtolLZ6yWounrJZfgK/8zX7KSr9Sqs3oAE8Rn3BEySfOavTHT2n0o9N06UJWkXUDzH4aN+MZZWRe0drv9ztxlAAAALjZ8R52lFtudp4yL14mWMdNJ99iaNzE5QoKqaIPvx6u5m3vKLTeHc3raNLiF3V78zp65a0VyizjRnUAAABOZTixwC7IsANAIU6ePq8XR3+ht17po/eWDNXxX1K16dtEpZ/PVKUqZkV2a67G99RTSsoFvTz2Sx36d6qrhwwAAICbDAE7ABThVMoFPfvSArVsEa4+PVuq93OdFBgYoMuXc/TzoRSNm7hc2386onwLXyMDAAAPwGvdPA4BOwCUIHFvshL3Jrt6GAAAAPAyBOwAAAAA4AV4rZvnIWCH3fgF+Krj7+/T3R0aqlKVirqSla1DO44qbtGPykq/7OrhAQAAAIBHIWDHDfPx9dFTY3ur56DOCqpeWb/sSlL6uQxVD62q+x9vq2cn9lXcP37U/DcWK+vSFVcPFwAAAPBOPMPucQjYcUP8Anw14cthatGhsVbOjtWqud/r9NEz1s+rh1ZVj2c66dFhPXRX5J0a03Oy0s9muHDEAAAAAOAZeA87bsjIGc/qrrZ36tWH39eccYtsgnVJOpd6QV9M/kYjur6larWCNWHxcFXwYdkBAAAAzmYyDKcV2AeRE8qtQbO66vx4W00f8bn2bDpYbN3jP5/S209N112RDdWmxz3OGSAAAAAAeDACdpRbr0H3K+30Of3wZXyp6u//8ZAOJvxbv3u+i4NHBgAAAKAAw4kFdkHAjnK7/7G2Wrtws/Lz8kt9zur56xXRpbmCa1Zx4MgAAAAAwPMRsKNczIEBCgyqqBOHTpfpvOR/Xa1fvVZVB4wKAAAAQFGuvYfdGQX2QcCOcjEs//2v0FS280ymqydYLBY7jwgAAAAAbi4E7CiX7Ms5unj2ku64u16Zzrv97nrKz7foXMoFxwwMAAAAQOF4ht3jELCj3OL+sVXRT3WUv9mv1Of0GvyAtn23S5fOZzpwZAAAAADg+QjYUW7fzV+voBqV1fO5B0pVv+1DLdXgrrpaNfd7B48MAAAAADyfr6sHgNL54MeRrh5CodLS0jT4nSd09vR5bVqeUGS9Zu0aasz8IdoWu097Ek+pQtVgJ46yeKM7T3f1EMrG18fVIyi1+bF/dPUQAAAA8F/O2hCOTefsh4AdNyQ1NVU+Pj569W9D1aHPNq2cHaf9W3+xfn5b87rqNbiLHnwqSj//dFTv/mmBDIP/ggEAAACgJATsuGGnTp3S5cuXdW+Pu9Xp0bZKO31e6ecyVKlKRYXWC1FaygUt+nitls6MU15u6d/ZDgAAAMCOnLUhHPk5uyFgh12cO3dO586dU2BgoLZ/+4sqVTbrcma2ftmTrO2x+5Sfx2vcAAAAAKAsCNhhV5mZmfr83VWuHgYAAACA6/AMu+chYIdbaB55h+o1DJNfgK8uXcjSzg0Hdf5MuquHBQAAAAAuQ8AOl/Hz91XPpzuoZ0wH1bmjlvJy85RzJU+VqpiVl5uvH/+5W8v/ul6/7D7u6qECAAAAno9n2D0OATtcIjCoot6YP1hNWzfQlm926KMX5+lA/OGrnwVXVNcn2qvXcw/ow69H6OP/W6S4JdtdPGIAAAAAcC4Cdjidr5+PXv/0OTVoXFuje7yrgwn/tvk88+JlfTM7Tt/O/UFDp8RoxIcDlJV+WVvX7HXRiAEAAICbA8+Xe5YKrh4AvM8Dj9yr5pF3aEK/jwsE679lybfok+ELFf9dol58p698fFmuAAAAALwHERCcrtczUdoRu08Hth0usa5hGPrbX75W9VrBiuzewgmjAwAAAG5ShuG8ArsgYIdT3XbXrbqzRbhWzVtf6nOO/XxS+348pO5PtHPgyAAAAADAvRCww6lq168pSfq5FNn13/p5+78VVj/EEUPCDQio6Keat1ZT9VrB8vXzcfVwAAAAUIxr72F3RoF9sOkcnMrH92pQl5ebX6bz8vPy5efHcnUHJpNJLTs2Us9nOqrNg83l43P1e7/Lmdn6YVmCVi3YpGMHT7t4lAAAAIDnIwKCU6Wfy5AkhdarqWM/nyz1ebXCQ3Tx3CVHDQulVKVaoF6f/7yat71DRw+c1Jw3lurk0TPyqVBBDVvWU/cn26vn01H67vPNmvnqElcPFwAAAL/Fe9g9DgE7nGp/whFdOHtJ0TEdNGfcl6U6p3LVSmrfO0JfTlvn4NGhOJUqmzX5q2GqERqscX2naffmQzaf//TDAX358Rp1f7K9hrz1uMyBAS4aKQAAAHBz4Bl2OFVudp7W/iNe0U9FqXLVSqU6p8cz98vH10drF8U7eHQozgsTH9Mtdarr/x75uECwfk1+nkXffb5Z7w9doC6P3adq1ao5eZQAAADAzYOAHU636vPNsuRb9MYXL5WYhY3o2kxPv/Z7rf7bFl1I45Z4VwmqHqjOj7TWlx+vVfIvKSXW37Ryl7au2aOqwQTsAAAA7sJkcV6BfXBLPJwuLeWCJj47VxP//kd9sGac/v7O10pYu0cWy/8edqkeWlU9B92vfiN7aseGg5o7cUWp2m7Q9Fb1GthBkd1aKKhaoHJz8nXy6H+05h/x+mFpgi5nZjvqsm5q0f0jZRhS7OLS3+Xw3YJNatf9blWqVElZWVkOHB0AAABwcyJgh0sc+OmoRj8yVS9N7q8Ji1/Wf5LTtHfLIeVeyVXIrdUU0aWZcrJz9fWnG/TZpG9lyS/+a7pKVcwa/cnTavtgc6WdPq91X/yoMyfOys/fV80i79QfJz6qP4z7nWa9tlTfL01w0lXePJree7v2xh9W+rnMUp+TuOmQLmdeIWAHAABwF2w653EI2OEyRw+c0ojffag7W9RV9yfbq17DUPkF+OrShSzNmbBccUsTlHXpSontVAwM0OQlLyk0vIYmDfqrtqzcpfy8/7027uvZcQqpXU0DX+2jUR/HyBwYoO8+3+zIS7vpVAwMKPMjCYZhKOvSFVWowJM3AAAAQHkQsMPlDu89ocN7S7djfGGGTu6n2vVralTPd5W0v/BXxaWdPq8PX/xMGRez9Me3HtO/9ybrUOLxcvfpbS5nZpd6k8BrTCaTKlUx62IpvnQBAACA45mMq8UZ/cA+SH3Bo4WEVVWnhyO04K3lRQbrvzXn1SVKOXpGfZ673/GDu4n8/NMRtYi8U0HVA0t9TsuOjVQx0Mzt8AAAAEA5EbDDo/V4sp2yL+co7sutpapvGIZWzd+g9g/do2o1qzh0bCaTSeENw3RXm9t1593hqhxctgy1O1n35dXN5h7sF1nqc3o+01GZGVkE7AAAAO7CMJxXYBcE7PBorTo10bbVu3U5o/S7v6//apv8/H3VPPJOh4ypSrVAPfrig5q3faL++uN4fbBqtKbFvaIv9r+rP09/Ro0jGjikX0dKP5epDSt2qP/L3RTeMKzE+h17t1K77nfrwsXzThgdAAAAPN3MmTPVoEEDmc1mRUREaPPm4vec2rhxoyIiImQ2m3Xbbbdp9uzZNp8vWLBAJpOpQLlyxfZxzbL262wE7PBogUFmnT+TXqZzLp7NkMViUWBQRbuPp3FEA83dOkEDx/XWgYQjGvfoVD3fboKGPvAXLZy0Unfdd7s+WjNGz7/1uCpUMNm9f0f66xtLdebkOb23/GW17Ni40Do+vhXUc2CU/m/GM/p+aYLOnydgBwAAcBfXnmF3RimLxYsXa/jw4Xr11VeVmJioqKgo9ejRQ8nJyYXWT0pK0kMPPaSoqCglJibqlVde0bBhw7Rs2TKbekFBQUpJSbEpZrO53P26ApvOwaNlX86VOdC/TOf4m/1UoUIFXcmy7zvZb29eV+8se1lH9p3QX56dowu/2u6qfmTfCS2fFaffDeqk59/uqwo+Js1+ZYldx+BIWRlXNPbxaXp9/vN6Z/FLSvr5lNZ88aNOJf2qChVMatSyvro/2U41Qqtq1YJNmvXaV3p3zQuuHjYAAABcJD3dNrEWEBCggICAAvWmTJmiQYMG6bnnnpMkTZ06VWvXrtWsWbM0adKkAvVnz56t8PBwTZ06VZLUpEkT7dixQx988IEeffRRaz2TyaTQ0NAix1fWfl2BgB0e7ciBk2rdpZkqVDDJYindV3n3PthckpT08ym7jcNkMun/Zj+rE7+k6rV+nyg7K6fQeoZhaOWnG5SXm6+XPnhSCev2adeGg3Ybh6NdOp+pMY9M1T1RjdTrmSg9P/Ex+fhcvVHncma2fliaoFWfb9Kxg6ddPFIAAAAU4OT3sNetW9fm8Pjx4zVhwgSbYzk5Odq5c6fGjh1rczw6Olpbtxa+T1V8fLyio6NtjnXr1k3z5s1Tbm6u/Pz8JEkZGRmqV6+e8vPzdc899+itt95Sy5Yty92vKxCww6OtXrhF3fpHqvWDzZWwdm+pzuk1qLMOJBzRsX+l2G0cd0c1UnjDMI363QdFBuu/tfrzzXromY7q9ez9HhWwS1e/dEjc9C8lbvqXAir6qUq1QOXnWXTpfKbycvNdPTwAAAC4iRMnTigoKMj6c2HZ9bS0NOXn56tWrVo2x2vVqqXU1NRC201NTS20fl5entLS0hQWFqbGjRtrwYIFat68udLT0/Xxxx+rffv22rNnj+68885y9esKPMMOj/bLnmQdSjymZ8c/qkqleCa9/e9aqWWnJvp2gX03k+j5h45K+vmUDmz7d6nP+W7+Rt0X3Vw1b61m17E4U/blXKWdvqDzZ9IJ1gEAANycs59hDwoKsimFBezWsZls93cyDKPAsZLq//Z427Zt9dRTT+nuu+9WVFSUlixZooYNG+qTTz65oX6djYAdHm/KiC9UI6yaJi0fqeqhwUXW6/x4G42ZO1jrV+zQppW77DqGO5qH66e4fWU6Z/u6ffLxqaAGTevYdSwAAACApwgJCZGPj0+BrPaZM2cKZL+vCQ0NLbS+r6+vatSoUeg5FSpU0L333qvDhw+Xu19XIGCHx0s+nKpx/T5RzTrVtWD3ZP3fX5/T3VGNFVa/psIbhemhP3TSrC0TNGbOYG3+NlFTRvzd+g2cvQRU9NflzLJtYnflv/XLumkeAAAAcLPw9/dXRESEYmNjbY7HxsaqXbt2hZ4TGRlZoP66devUunVr6/Pr1zMMQ7t371ZYWFi5+3UFnmHHTeHI/pN64YF3FN2vrXrGdNADfdtaP8vPt2jb2r2a+9bXStx8yCH9Z166rKohVcp0TnDNq/Uz0y87YkgAAACALcO4WpzRTxmMHDlSMTExat26tSIjIzVnzhwlJydryJAhkqRx48bp1KlTWrhwoSRpyJAhmj59ukaOHKnBgwcrPj5e8+bN06JFi6xtvvnmm2rbtq3uvPNOpaena9q0adq9e7dmzJhR6n7dAQE7bhoZF7K0/K8/aMWc9arXOExB1QKVl5OnlONpOn/dK9bsbffGfymqd4TmvrFU+XmWUp3T+ZF7dSUzW//akeTQsQEAAADurF+/fjp79qwmTpyolJQUNWvWTKtXr1a9evUkSSkpKTbvRm/QoIFWr16tESNGaMaMGapdu7amTZtm80q3Cxcu6Pnnn1dqaqqCg4PVsmVLbdq0Sffdd1+p+3UHJsPe9wZ7gPT0dAUHBys+Pl6VK1d29XBuOqO7/dXVQyibnNwbbqJ+k9qatekNTRo8V5u+3llifR/fClqw6x0lrNurT0b9o2yd+fqUc5TO937sH109BAAAgBuWkZGhyMhIXbx40WbXc09xLf6J7DFRvn5mh/eXl3tF8f98w2Pny53wDDtgB8cOntau9T/rhb/0VWi9kBLrv/jeAFUNqaJv521w/OAAAAAAeCQCdsBO3vvTZ7qcka0Pvxute7s2K/R1ECG1q2nMXwepR0wHfTzy7zp28LQLRgoAAACvZDixwC54hh2wk4tplzSq1/t6df7zmrhoqE4nndH3i7cpLfWC/AP81LJjE7Xp3kJXMrP1znNztfmbkm+dBwAAAOC9CNgBO7rw6yWN/t2HanLvber1h0565E8PKjCoonJz8pR86LRmjlmkH5YmWF/pBgAAADiLybhanNEP7IOAHXCAgz8d1cGfjkqSKlQwyWLhrxYAAACAsiFgBxyMYB0AAABuwWJcLc7oB3bBpnMAAAAAALghMuwAAAAA4A2ctYM7CXa7IcMOAAAAAIAbIsMOAAAAAF7AJCftEu/4LrwGGXYAAAAAANwQGXYAAAAA8AaGcbU4ox/YBRl2AAAAAADcEAE7AAAAAABuiFviAQAAAMALmAwnbTrHHfF2Q4YdAAAAAAA35NCAvXfv3goPD5fZbFZYWJhiYmJ0+vTpAvUWLFigFi1ayGw2KzQ0VEOHDi223fvvv18mk8mm9O/f31GXAQAAAACez3BigV049Jb4zp0765VXXlFYWJhOnTqlUaNG6bHHHtPWrVutdaZMmaIPP/xQ77//vtq0aaMrV67o6NGjJbY9ePBgTZw40fpzxYoVHXINAAAAAAC4gkMD9hEjRlj/Xa9ePY0dO1Z9+vRRbm6u/Pz8dP78eb322mv69ttv1aVLF2vdu+66q8S2K1WqpNDQ0FKNIzs7W9nZ2daf09PTy3AVAAAAAOD5TIYhkxNeueaMPryF0zadO3funL744gu1a9dOfn5+kqTY2FhZLBadOnVKTZo00aVLl9SuXTt9+OGHqlu3brHtffHFF/r73/+uWrVqqUePHho/fryqVKlSaN1JkybpzTfftPs1oXDvr33B1UMAAAAAAI/n8E3nxowZo8DAQNWoUUPJycn65ptvrJ8dPXpUFotF77zzjqZOnaqlS5fq3LlzevDBB5WTk1Nkm08++aQWLVqkDRs26PXXX9eyZcv0yCOPFFl/3LhxunjxorWcOHHCrtcIAAAAAG7P4sQCuyhzwD5hwoQCG75dX3bs2GGtP3r0aCUmJmrdunXy8fHR008/LeO/t0hYLBbl5uZq2rRp6tatm9q2batFixbp8OHDWr9+fZFjGDx4sLp27apmzZqpf//+Wrp0qeLi4rRr165C6wcEBCgoKMimAAAAAADgzsp8S/zQoUNL3JG9fv361n+HhIQoJCREDRs2VJMmTVS3bl1t27ZNkZGRCgsLkyQ1bdrUWr9mzZoKCQlRcnJyqcfUqlUr+fn56fDhw2rVqlXZLggAAAAAvADPsHueMgfs1wLw8riWWb+2AVz79u0lSYcOHVKdOnUkXX3WPS0tTfXq1St1uwcOHFBubq71CwAAAAAAADydw55hT0hI0PTp07V7924dP35c69ev14ABA3T77bcrMjJSktSwYUM9/PDDevnll7V161bt379fAwcOVOPGjdW5c2dJ0qlTp9S4cWMlJCRIko4cOaKJEydqx44dOnbsmFavXq3HH39cLVu2tH4BAAAAAAC4Du9h9zgOC9grVqyo5cuXq0uXLmrUqJGeffZZNWvWTBs3blRAQIC13sKFC9WmTRv17NlTnTp1kp+fn9asWWPdST43N1eHDh1SVlaWJMnf31/ff/+9unXrpkaNGmnYsGGKjo5WXFycfHx8HHU5AAAAAAA4lckwvO8Bg/T0dAUHBys+Pl6VK1d29XAAAAAAuLGMjAxFRkbq4sWLHrmB9bX4p2P71+Xra3Z4f3l5V7Tpx7c8dr7cicNf6wYAAAAAAMquzJvOAQAAAAA8j8m4WpzRD+yDDDsAAAAAAG6IgB0AAAAAADfELfEAAAAA4A0M42pxRj+wCzLsAAAAAAC4ITLsAAAAAOAFTJarxRn9wD7IsAMAAAAA4IbIsAMAAACAN+AZdo9Dhh0AAAAAADdEhh0AAAAAvIHx3+KMfmAXZNgBAAAAAHBDZNgBAAAAwAuYDEMmJzxf7ow+vAUZdgAAAAAA3BAZdgAAAADwBuwS73HIsAMAAAAA4IbIsAMAAACANzAkWZzUD+yCDDsAAAAAAG6IDDsAAAAAeAF2ifc8ZNgBAAAAAHBDBOwAAAAAALghbokHAAAAAG9gyEmvdXN8F96CDDsAAAAAAG6IDDsAAAAAeAPDcFKGnRS7vZBhBwAAAADADZFhBwAAAABvYJFkclI/sAsy7AAAAAAAuCEy7AAAAADgBUyGIZMTni93Rh/eggw7AAAAAABuiAw7AAAAAHgDdon3OGTYAQAAAAAuNXPmTDVo0EBms1kRERHavHlzsfU3btyoiIgImc1m3XbbbZo9e7bN53PnzlVUVJSqVaumatWqqWvXrkpISLCpM2HCBJlMJpsSGhpq92u7EQTsAAAAAOANrmXYnVHKYPHixRo+fLheffVVJSYmKioqSj169FBycnKh9ZOSkvTQQw8pKipKiYmJeuWVVzRs2DAtW7bMWmfDhg164okntH79esXHxys8PFzR0dE6deqUTVt33XWXUlJSrGXfvn1ln1cH4pZ4AAAAAIDdpaen2/wcEBCggICAAvWmTJmiQYMG6bnnnpMkTZ06VWvXrtWsWbM0adKkAvVnz56t8PBwTZ06VZLUpEkT7dixQx988IEeffRRSdIXX3xhc87cuXO1dOlSff/993r66aetx319fd0uq/5bZNgBAAAAwBs4OcNet25dBQcHW0thwXdOTo527typ6Ohom+PR0dHaunVroZcRHx9foH63bt20Y8cO5ebmFnpOVlaWcnNzVb16dZvjhw8fVu3atdWgQQP1799fR48eLfV0OgMZdgAAAACA3Z04cUJBQUHWnwvLrqelpSk/P1+1atWyOV6rVi2lpqYW2m5qamqh9fPy8pSWlqawsLAC54wdO1a33nqrunbtaj3Wpk0bLVy4UA0bNtR//vMfvf3222rXrp0OHDigGjVqlOlaHYWAHQAAAAC8gUWSyUn9SAoKCrIJ2ItjMtkOzDCMAsdKql/YcUl67733tGjRIm3YsEFms9l6vEePHtZ/N2/eXJGRkbr99tv1+eefa+TIkaUat6MRsAMAAAAAXCIkJEQ+Pj4FsulnzpwpkEW/JjQ0tND6vr6+BTLjH3zwgd555x3FxcWpRYsWxY4lMDBQzZs31+HDh8txJY7BM+wAAAAAAJfw9/dXRESEYmNjbY7HxsaqXbt2hZ4TGRlZoP66devUunVr+fn5WY+9//77euutt7RmzRq1bt26xLFkZ2fr4MGDhd5S7yoE7AAAAADgBUyG4bRSFiNHjtSnn36q+fPn6+DBgxoxYoSSk5M1ZMgQSdK4ceNsdnYfMmSIjh8/rpEjR+rgwYOaP3++5s2bp1GjRlnrvPfee3rttdc0f/581a9fX6mpqUpNTVVGRoa1zqhRo7Rx40YlJSVp+/bteuyxx5Senq6BAwfe4EzbD7fEAwAAAABcpl+/fjp79qwmTpyolJQUNWvWTKtXr1a9evUkSSkpKTbvZG/QoIFWr16tESNGaMaMGapdu7amTZtmfaWbJM2cOVM5OTl67LHHbPoaP368JkyYIEk6efKknnjiCaWlpalmzZpq27attm3bZu3XHZgMo4xff9wE0tPTFRwcrPj4eFWuXNnVwwEAAADgxjIyMhQZGamLFy+WehM1d3It/ul65wj5+hTcqd3e8vKzFXf4I4+dL3fCLfEAAAAAALghbokHAAAAAG9gMSSTE26wtnjdTdwOQ4YdAAAAAAA3RIYdAAAAALyBYVwtzugHdkGGHQAAAAAAN0SGHQAAAAC8gpMy7CLDbi9k2AEAAAAAcENk2AEAAADAG/AMu8chww4AAAAAgBsiww4AAAAA3sBiyCnPl/Medrshww4AAAAAgBsiww4AAAAA3sCwXC3O6Ad2QYYdAAAAAAA3RMAOAAAAAIAb4pZ4AAAAAPAGvNbN45BhBwAAAADADZFhBwAAAABvwGvdPA4ZdgAAAAAA3BAZdgAAAADwBjzD7nHIsAMAAAAA4IbIsAMAAACANzDkpAy747vwFmTYAQAAAABwQ2TYAQAAAMAb8Ay7xyHDDgAAAACAGyLDDgAAAADewGKRZHFSP7AHMuwAAAAAALghMuwAAAAA4A14ht3jkGEHAAAAAMANkWEHAAAAAG9Aht3jkGEHAAAAAMANEbADAAAAAOCGuCUeAAAAALyBxZDkhNvVLdwSby9k2AEAAAAAcENk2AEAAADACxiGRYZhcUo/sA8y7AAAAAAAuCEy7AAAAADgDQzDOc+X81o3uyHDDgAAAACAGyLDDgAAAADewHDSLvFk2O2GDDsAAAAAAG6IDDsAAAAAeAOLRTI5YQd3dom3GzLsAAAAAAC4ITLsAAAAAOANeIbd45BhBwAAAADADZFhBwAAAAAvYFgsMpzwDLvBM+x2Q4YdAAAAAAA3RIYdAAAAALwBz7B7HDLsAAAAAAC4IQJ2AAAAAADcELfEAwAAAIA3sBiSiVviPQkZdgAAAAAA3BAZdgAAAADwBoYhyQmvXCPDbjdk2AEAAAAAcENk2AEAAADACxgWQ4YTnmE3yLDbDRl2AAAAAADcEBl2AAAAAPAGhkXOeYbdCX14CTLsAAAAAAC4ITLsAAAAAOAFeIbd8zg0w967d2+Fh4fLbDYrLCxMMTExOn36tPXzBQsWyGQyFVrOnDlTZLvZ2dl66aWXFBISosDAQPXu3VsnT5505KUAAAAAABxk5syZatCggcxmsyIiIrR58+Zi62/cuFEREREym8267bbbNHv27AJ1li1bpqZNmyogIEBNmzbVihUrbrhfZ3NowN65c2ctWbJEhw4d0rJly3TkyBE99thj1s/79eunlJQUm9KtWzd16tRJt9xyS5HtDh8+XCtWrNCXX36pLVu2KCMjQ7169VJ+fr4jLwcAAAAAPJdhcV4pg8WLF2v48OF69dVXlZiYqKioKPXo0UPJycmF1k9KStJDDz2kqKgoJSYm6pVXXtGwYcO0bNkya534+Hj169dPMTEx2rNnj2JiYtS3b19t37693P26gslw4v0KK1euVJ8+fZSdnS0/P78Cn//666+69dZbNW/ePMXExBTaxsWLF1WzZk397W9/U79+/SRJp0+fVt26dbV69Wp169atwDnZ2dnKzs62aSM8PFxxcXEKDAy009UBAAAAuBllZmaqa9euunDhgoKDg109nDJLT09XcHCwOugh+apgHGZvecrVFq3WiRMnFBQUZD0eEBCggICAAvXbtGmjVq1aadasWdZjTZo0UZ8+fTRp0qQC9ceMGaOVK1fq4MGD1mNDhgzRnj17FB8fL+lqcjg9PV3//Oc/rXW6d++uatWqadGiReXq1yUMJzl79qzRt29fo3379kXW+eCDD4zg4GAjKyuryDrff/+9Ick4d+6czfEWLVoYb7zxRqHnjB8/3pBEoVAoFAqFQqFQKOUuJ06cKF8w5GKXL182QkNDnTpXlStXLnBs/PjxBcaWnZ1t+Pj4GMuXL7c5PmzYMKNjx46FXk9UVJQxbNgwm2PLly83fH19jZycHMMwDKNu3brGlClTbOpMmTLFCA8PL3e/ruDwTefGjBmj6dOnKysrS23bttWqVauKrDt//nwNGDBAFStWLLJOamqq/P39Va1aNZvjtWrVUmpqaqHnjBs3TiNHjrT+bLFYdO7cOdWoUUMmk6mMV+R86enpqlu3boFvqHDjmFvHYW4dh7l1HObWcZhbx2J+HYe5dRxPmlvDMHTp0iXVrl3b1UMpF7PZrKSkJOXk5DitT8MwCsRahWXX09LSlJ+fr1q1atkcLy6+S01NLbR+Xl6e0tLSFBYWVmSda22Wp19XKHPAPmHCBL355pvF1vnpp5/UunVrSdLo0aM1aNAgHT9+XG+++aaefvpprVq1qsAvLz4+Xj///LMWLlxY1iFJKnxBXFPYrRdVq1YtVz+uFBQU5PZ/zDwVc+s4zK3jMLeOw9w6DnPrWMyv4zC3juMpc+uJt8L/ltlsltlsdvUwinR9LFdcfFdU/euPl6bNsvbrbGUO2IcOHar+/fsXW6d+/frWf4eEhCgkJEQNGzZUkyZNVLduXW3btk2RkZE253z66ae65557FBERUWzboaGhysnJ0fnz522y7GfOnFG7du3KejkAAAAAABcJCQmRj49Pgaz2mTNnCmS/rwkNDS20vq+vr2rUqFFsnWttlqdfVyjzLvEhISFq3LhxsaWob26ufevx2w3gJCkjI0NLlizRoEGDSuw/IiJCfn5+io2NtR5LSUnR/v37CdgBAAAAwIP4+/srIiLCJr6TpNjY2CLju8jIyAL1161bp9atW1s3Ny+qzrU2y9OvKzjsGfaEhAQlJCSoQ4cOqlatmo4ePao33nhDt99+e4Hs+uLFi5WXl6cnn3yyQDunTp1Sly5dtHDhQt13330KDg7WoEGD9Oc//1k1atRQ9erVNWrUKDVv3lxdu3Z11OW4VEBAgMaPH1/oMx+4Mcyt4zC3jsPcOg5z6zjMrWMxv47D3DoOc4trRo4cqZiYGLVu3VqRkZGaM2eOkpOTNWTIEElX9yQ7deqU9fHpIUOGaPr06Ro5cqQGDx6s+Ph4zZs3z7r7uyS9/PLL6tixo9599109/PDD+uabbxQXF6ctW7aUul+34Kjd7Pbu3Wt07tzZqF69uhEQEGDUr1/fGDJkiHHy5MkCdSMjI40BAwYU2k5SUpIhyVi/fr312OXLl42hQ4ca1atXNypWrGj06tXLSE5OdtSlAAAAAAAcaMaMGUa9evUMf39/o1WrVsbGjRutnw0cONDo1KmTTf0NGzYYLVu2NPz9/Y369esbs2bNKtDmV199ZTRq1Mjw8/MzGjdubCxbtqxM/boDp76HHQAAAAAAlE6Zn2EHAAAAAACOR8AOAAAAAIAbImAHAAAAAMANEbADAAAAAOCGCNidaNKkSbr33ntVpUoV3XLLLerTp48OHTpkU8cwDE2YMEG1a9dWxYoVdf/99+vAgQPFtrtgwQKZTKYC5cqVK468HLdSmrldvny5unXrppCQEJlMJu3evbtUbS9btkxNmzZVQECAmjZtqhUrVjjgCtybo+aXtVvy3Obm5mrMmDFq3ry5AgMDVbt2bT399NM6ffp0iW17+9p11Nyybkv3N2HChAlq3LixAgMDVa1aNXXt2lXbt28vsW3WrWPmlnVburn9rRdeeEEmk0lTp04tsW3WrWPmlnULELA71caNG/Xiiy9q27Ztio2NVV5enqKjo5WZmWmt895772nKlCmaPn26fvrpJ4WGhurBBx/UpUuXim07KChIKSkpNsVsNjv6ktxGaeY2MzNT7du31+TJk0vdbnx8vPr166eYmBjt2bNHMTEx6tu3b6n+T+fNxFHzK7F2S5rbrKws7dq1S6+//rp27dql5cuX65dfflHv3r2LbZe167i5lVi3pfmb0LBhQ02fPl379u3Tli1bVL9+fUVHR+vXX38tsl3WrePmVmLdlmZur/n666+1fft21a5du8R2WbeOm1uJdQs47D3sKNmZM2cMSdZ3/VksFiM0NNSYPHmytc6VK1eM4OBgY/bs2UW289lnnxnBwcGOHq5HuX5ufyspKcmQZCQmJpbYTt++fY3u3bvbHOvWrZvRv39/ew3VI9lrflm7BRU3t9ckJCQYkozjx48XWYe1W5C95pZ1W1Bp5vbixYuGJCMuLq7IOqzbguw1t6zbgoqa25MnTxq33nqrsX//fqNevXrGRx99VGw7rNuC7DW3rFvAMMiwu9DFixclSdWrV5ckJSUlKTU1VdHR0dY6AQEB6tSpk7Zu3VpsWxkZGapXr57q1KmjXr16KTEx0XED9wDXz215xcfH2/w+JKlbt24l/j5udvaaX4m1e73SzO3FixdlMplUtWrVIuuwdguy19xKrNvrlTS3OTk5mjNnjoKDg3X33XcX2Q7rtiB7za3Eur1eYXNrsVgUExOj0aNH66677ipVO6zbguw1txLrFiBgdxHDMDRy5Eh16NBBzZo1kySlpqZKkmrVqmVTt1atWtbPCtO4cWMtWLBAK1eu1KJFi2Q2m9W+fXsdPnzYcRfgxgqb2/JKTU0t8+/jZmfP+WXt2irN3F65ckVjx47VgAEDFBQUVGRbrF1b9pxb1q2t4uZ21apVqly5ssxmsz766CPFxsYqJCSkyLZYt7bsObesW1tFze27774rX19fDRs2rNRtsW5t2XNuWbeA5OvqAXiroUOHau/evdqyZUuBz0wmk83PhmEUOPZbbdu2Vdu2ba0/t2/fXq1atdInn3yiadOm2W/QHqK4uS2Psv4+bnb2nF/Wrq2S5jY3N1f9+/eXxWLRzJkzS2yPtfs/9pxb1q2t4ua2c+fO2r17t9LS0jR37lzrc7233HJLke2xbv/HnnPLurVV2Nzu3LlTH3/8sXbt2lXmNce6/R97zi3rFiDD7hIvvfSSVq5cqfXr16tOnTrW46GhoZJU4BvZM2fOFPjmtjgVKlTQvffe65XfPhY1t+UVGhp6w7+Pm4m95/d6rN2i5zY3N1d9+/ZVUlKSYmNji80AS6zd37L33F6PdVv03AYGBuqOO+5Q27ZtNW/ePPn6+mrevHlFtse6/R97z+31WLcF53bz5s06c+aMwsPD5evrK19fXx0/flx//vOfVb9+/SLbY93+j73n9nrevG7hvQjYncgwDA0dOlTLly/XDz/8oAYNGth83qBBA4WGhio2NtZ6LCcnRxs3blS7du3K1M/u3bsVFhZmt7G7u5LmtrwiIyNtfh+StG7dujL9Pm4Gjprfwvph7Rac22sB5eHDhxUXF6caNWqU2C5r13FzW1g/rNvS/U0wDEPZ2dlFfs66ddzcFlafdWs7tzExMdq7d692795tLbVr19bo0aO1du3aIttl3Tpubgvrx9vWLcAu8U70xz/+0QgODjY2bNhgpKSkWEtWVpa1zuTJk43g4GBj+fLlxr59+4wnnnjCCAsLM9LT0611YmJijLFjx1p/njBhgrFmzRrjyJEjRmJiovGHP/zB8PX1NbZv3+7U63Ol0szt2bNnjcTEROO7774zJBlffvmlkZiYaKSkpFjrXD+3P/74o+Hj42NMnjzZOHjwoDF58mTD19fX2LZtm1Ovz9UcNb+s3ZLnNjc31+jdu7dRp04dY/fu3TZ1srOzre2wdgty1Nyybkue24yMDGPcuHFGfHy8cezYMWPnzp3GoEGDjICAAGP//v3Wdli3BTlqblm3pfvfsusVtpM567YgR80t6xYwDAJ2J5JUaPnss8+sdSwWizF+/HgjNDTUCAgIMDp27Gjs27fPpp1OnToZAwcOtP48fPhwIzw83PD39zdq1qxpREdHG1u3bnXSVbmH0sztZ599Vmid8ePHW+tcP7eGYRhfffWV0ahRI8PPz89o3LixsWzZMudclBtx1Pyydkue22uvySusrF+/3toOa7cgR80t67bkub18+bLx+9//3qhdu7bh7+9vhIWFGb179zYSEhJs2mHdFuSouWXdlu5/y65XWFDJui3IUXPLugUMw2QYhlGezDwAAAAAAHAcnmEHAAAAAMANEbADAAAAAOCGCNgBAAAAAHBDBOwAAAAAALghAnYAAAAAANwQATsAAAAAAG6IgB0AAAAAADdEwA4AAAAAgBsiYAcAAAAAwA0RsAMAAAAA4IYI2AEAAAAAcEP/D3kdaU+iTirmAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_centers, y_centers = np.meshgrid(\n", " fieldset.U.lon - np.diff(fieldset.U.lon[:2]) / 2,\n", " fieldset.U.lat - np.diff(fieldset.U.lat[:2]) / 2,\n", ")\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(10, 7), constrained_layout=True)\n", "ax.set_facecolor(\"lightgrey\") # For visual coherence with the plot above\n", "\n", "fieldplot = ax.pcolormesh(\n", " x_centers[-28:-17, 22:41],\n", " y_centers[-28:-17, 22:41],\n", " c_results[-28:-18, 22:40],\n", " vmin=0,\n", " vmax=0.2,\n", " cmap=\"viridis\",\n", ")\n", "# Zoom on the area of interest\n", "field_cbar = plt.colorbar(fieldplot, ax=ax)\n", "field_cbar.ax.text(3, 0.070, \"$C_{field}$ concentration\", rotation=270, fontsize=12)\n", "\n", "particle = plt.scatter(\n", " pset_traj[\"lon\"][:].values[0, :],\n", " pset_traj[\"lat\"][:].values[0, :],\n", " c=pset_traj[\"c\"][:].values[0, :],\n", " vmin=0,\n", " s=100,\n", " edgecolor=\"white\",\n", ")\n", "particle_cbar = plt.colorbar(particle, ax=ax, location=\"top\")\n", "particle_cbar.ax.text(42, 1.8, \"$c_{particle}$ concentration\", fontsize=12);" ] }, { "attachments": {}, "cell_type": "markdown", "id": "ad657e8f", "metadata": {}, "source": [ "Finally, to see the `C` field in time we have to load the `.nc` files produced during the run. In the following plots, particle location and field values are shown at each time step.\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "53b8979a", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(5, 5, figsize=(30, 20))\n", "\n", "daycounter = 1\n", "\n", "for i in range(len(ax)):\n", " for j in range(len(ax)):\n", " data = netCDF4.Dataset(r\"interaction_00\" + f\"{daycounter:02}\" + \"C.nc\")\n", "\n", " # copying the final field data in a new array\n", " c_results = data[\"C\"][0, 0, :, :].data.copy()\n", "\n", " # using a mask for fieldset.C.data on land\n", " c_results[[field_for_size.U.data == 0][0][0]] = np.nan\n", "\n", " # masking the field where its value is zero:\n", " # areas that have not been modified by the particle,\n", " # for clearer plotting\n", " c_results[c_results == 0] = np.nan\n", "\n", " # visual coherence with the plots above\n", " ax[i, j].set_facecolor(\"lightgrey\")\n", "\n", " fieldplot = ax[i, j].pcolormesh(\n", " x_centers[-28:-17, 22:41],\n", " y_centers[-28:-17, 22:41],\n", " c_results[-28:-18, 22:40],\n", " vmin=0,\n", " vmax=0.2,\n", " cmap=\"viridis\",\n", " )\n", "\n", " particle = ax[i, j].scatter(\n", " pset_traj[\"lon\"][:].values[0, daycounter - 1],\n", " pset_traj[\"lat\"][:].values[0, daycounter - 1],\n", " c=pset_traj[\"c\"][:].values[0, daycounter - 1],\n", " vmin=0,\n", " vmax=100,\n", " s=100,\n", " edgecolor=\"white\",\n", " )\n", "\n", " # plotting particle location at current time step:\n", " # daycounter-1 due to different indexing\n", " ax[i, j].set_title(\"Day \" + str(daycounter - 1))\n", "\n", " daycounter += 1 # next day\n", "\n", "fig.subplots_adjust(right=0.8)\n", "fig.subplots_adjust(top=0.8)\n", "\n", "cbar_ax = fig.add_axes([0.82, 0.12, 0.03, 0.7])\n", "fig.colorbar(fieldplot, cax=cbar_ax)\n", "cbar_ax.tick_params(labelsize=18)\n", "cbar_ax.text(3, 0.08, \"$C_{field}$ concentration\", fontsize=25, rotation=270)\n", "\n", "cbar_ax1 = fig.add_axes([0.1, 0.85, 0.7, 0.04])\n", "fig.colorbar(particle, cax=cbar_ax1, orientation=\"horizontal\")\n", "cbar_ax1.tick_params(labelsize=18)\n", "cbar_ax1.text(42, 1.2, \"$c_{particle}$ concentration\", fontsize=25);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.11.6" } }, "nbformat": 4, "nbformat_minor": 5 }