OpenMC/examples/jupyter/post-processing.ipynb

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248 KiB
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook demonstrates some basic post-processing tasks that can be performed with the Python API, such as plotting a 2D mesh tally and plotting neutron source sites from an eigenvalue calculation. The problem we will use is a simple reflected pin-cell."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from IPython.display import Image\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import openmc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Input Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we need to define materials that will be used in the problem. We'll create three materials for the fuel, water, and cladding of the fuel pin."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# 1.6 enriched fuel\n",
"fuel = openmc.Material(name='1.6% Fuel')\n",
"fuel.set_density('g/cm3', 10.31341)\n",
"fuel.add_nuclide('U235', 3.7503e-4)\n",
"fuel.add_nuclide('U238', 2.2625e-2)\n",
"fuel.add_nuclide('O16', 4.6007e-2)\n",
"\n",
"# borated water\n",
"water = openmc.Material(name='Borated Water')\n",
"water.set_density('g/cm3', 0.740582)\n",
"water.add_nuclide('H1', 4.9457e-2)\n",
"water.add_nuclide('O16', 2.4732e-2)\n",
"water.add_nuclide('B10', 8.0042e-6)\n",
"\n",
"# zircaloy\n",
"zircaloy = openmc.Material(name='Zircaloy')\n",
"zircaloy.set_density('g/cm3', 6.55)\n",
"zircaloy.add_nuclide('Zr90', 7.2758e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our three materials, we can now create a materials file object that can be exported to an actual XML file."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate a Materials collection\n",
"materials_file = openmc.Materials([fuel, water, zircaloy])\n",
"\n",
"# Export to \"materials.xml\"\n",
"materials_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's move on to the geometry. Our problem will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces -- in this case two cylinders and six reflective planes."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create cylinders for the fuel and clad\n",
"fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.39218)\n",
"clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, R=0.45720)\n",
"\n",
"# Create boundary planes to surround the geometry\n",
"min_x = openmc.XPlane(x0=-0.63, boundary_type='reflective')\n",
"max_x = openmc.XPlane(x0=+0.63, boundary_type='reflective')\n",
"min_y = openmc.YPlane(y0=-0.63, boundary_type='reflective')\n",
"max_y = openmc.YPlane(y0=+0.63, boundary_type='reflective')\n",
"min_z = openmc.ZPlane(z0=-0.63, boundary_type='reflective')\n",
"max_z = openmc.ZPlane(z0=+0.63, boundary_type='reflective')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the surfaces defined, we can now create cells that are defined by intersections of half-spaces created by the surfaces."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a Universe to encapsulate a fuel pin\n",
"pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin')\n",
"\n",
"# Create fuel Cell\n",
"fuel_cell = openmc.Cell(name='1.6% Fuel')\n",
"fuel_cell.fill = fuel\n",
"fuel_cell.region = -fuel_outer_radius\n",
"pin_cell_universe.add_cell(fuel_cell)\n",
"\n",
"# Create a clad Cell\n",
"clad_cell = openmc.Cell(name='1.6% Clad')\n",
"clad_cell.fill = zircaloy\n",
"clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
"pin_cell_universe.add_cell(clad_cell)\n",
"\n",
"# Create a moderator Cell\n",
"moderator_cell = openmc.Cell(name='1.6% Moderator')\n",
"moderator_cell.fill = water\n",
"moderator_cell.region = +clad_outer_radius\n",
"pin_cell_universe.add_cell(moderator_cell)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create root Cell\n",
"root_cell = openmc.Cell(name='root cell')\n",
"root_cell.fill = pin_cell_universe\n",
"\n",
"# Add boundary planes\n",
"root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
"\n",
"# Create root Universe\n",
"root_universe = openmc.Universe(universe_id=0, name='root universe')\n",
"root_universe.add_cell(root_cell)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now must create a geometry that is assigned a root universe, put the geometry into a geometry file, and export it to XML."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create Geometry and set root Universe\n",
"geometry = openmc.Geometry(root_universe)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Export to \"geometry.xml\"\n",
"geometry.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 10 inactive batches and 90 active batches each with 5000 particles."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# OpenMC simulation parameters\n",
"batches = 100\n",
"inactive = 10\n",
"particles = 5000\n",
"\n",
"# Instantiate a Settings object\n",
"settings_file = openmc.Settings()\n",
"settings_file.batches = batches\n",
"settings_file.inactive = inactive\n",
"settings_file.particles = particles\n",
"\n",
"# Create an initial uniform spatial source distribution over fissionable zones\n",
"bounds = [-0.63, -0.63, -0.63, 0.63, 0.63, 0.63]\n",
"uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
"settings_file.source = openmc.source.Source(space=uniform_dist)\n",
"\n",
"# Export to \"settings.xml\"\n",
"settings_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Instantiate a Plot\n",
"plot = openmc.Plot(plot_id=1)\n",
"plot.filename = 'materials-xy'\n",
"plot.origin = [0, 0, 0]\n",
"plot.width = [1.26, 1.26]\n",
"plot.pixels = [250, 250]\n",
"plot.color_by = 'material'\n",
"\n",
"# Instantiate a Plots collection and export to \"plots.xml\"\n",
"plot_file = openmc.Plots([plot])\n",
"plot_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the plots.xml file, we can now generate and view the plot. OpenMC outputs plots in .ppm format, which can be converted into a compressed format like .png with the convert utility."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run openmc in plotting mode\n",
"openmc.plot_geometry(output=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD6AgMAAAD1grKuAAAABGdBTUEAALGPC/xhBQAAACBjSFJN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\nN/GQ0AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert OpenMC's funky ppm to png\n",
"!convert materials-xy.ppm materials-xy.png\n",
"\n",
"# Display the materials plot inline\n",
"Image(filename='materials-xy.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see from the plot, we have a nice pin cell with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a 2D mesh tally."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Instantiate an empty Tallies object\n",
"tallies_file = openmc.Tallies()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create mesh which will be used for tally\n",
"mesh = openmc.Mesh()\n",
"mesh.dimension = [100, 100]\n",
"mesh.lower_left = [-0.63, -0.63]\n",
"mesh.upper_right = [0.63, 0.63]\n",
"\n",
"# Create mesh filter for tally\n",
"mesh_filter = openmc.MeshFilter(mesh)\n",
"\n",
"# Create mesh tally to score flux and fission rate\n",
"tally = openmc.Tally(name='flux')\n",
"tally.filters = [mesh_filter]\n",
"tally.scores = ['flux', 'fission']\n",
"tallies_file.append(tally)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Export to \"tallies.xml\"\n",
"tallies_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we a have a complete set of inputs, so we can go ahead and run our simulation."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" %%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
" ####################### %%%%%%%%%%%%%%%%%%\n",
" ####################### %%%%%%%%%%%%%%%%%\n",
" ###################### %%%%%%%%%%%%%%%%%\n",
" #################### %%%%%%%%%%%%%%%%%\n",
" ################# %%%%%%%%%%%%%%%%%\n",
" ############### %%%%%%%%%%%%%%%%\n",
" ############ %%%%%%%%%%%%%%%\n",
" ######## %%%%%%%%%%%%%%\n",
" %%%%%%%%%%%\n",
"\n",
" | The OpenMC Monte Carlo Code\n",
" Copyright | 2011-2017 Massachusetts Institute of Technology\n",
" License | http://openmc.readthedocs.io/en/latest/license.html\n",
" Version | 0.9.0\n",
" Git SHA1 | 9b7cebf7bc34d60e0f1750c3d6cb103df11e8dc4\n",
" Date/Time | 2017-12-04 20:47:36\n",
" OpenMP Threads | 4\n",
"\n",
" Reading settings XML file...\n",
" Reading cross sections XML file...\n",
" Reading materials XML file...\n",
" Reading geometry XML file...\n",
" Building neighboring cells lists for each surface...\n",
" Reading U235 from /home/romano/openmc/scripts/nndc_hdf5/U235.h5\n",
" Reading U238 from /home/romano/openmc/scripts/nndc_hdf5/U238.h5\n",
" Reading O16 from /home/romano/openmc/scripts/nndc_hdf5/O16.h5\n",
" Reading H1 from /home/romano/openmc/scripts/nndc_hdf5/H1.h5\n",
" Reading B10 from /home/romano/openmc/scripts/nndc_hdf5/B10.h5\n",
" Reading Zr90 from /home/romano/openmc/scripts/nndc_hdf5/Zr90.h5\n",
" Maximum neutron transport energy: 2.00000E+07 eV for U235\n",
" Reading tallies XML file...\n",
" Writing summary.h5 file...\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k \n",
" ========= ======== ==================== \n",
" 1/1 1.04359 \n",
" 2/1 1.04323 \n",
" 3/1 1.04711 \n",
" 4/1 1.03892 \n",
" 5/1 1.02442 \n",
" 6/1 1.02046 \n",
" 7/1 1.05998 \n",
" 8/1 1.04184 \n",
" 9/1 1.04786 \n",
" 10/1 1.06636 \n",
" 11/1 1.07229 \n",
" 12/1 1.04413 1.05821 +/- 0.01408\n",
" 13/1 1.06376 1.06006 +/- 0.00834\n",
" 14/1 1.06898 1.06229 +/- 0.00630\n",
" 15/1 1.05095 1.06002 +/- 0.00538\n",
" 16/1 1.04453 1.05744 +/- 0.00510\n",
" 17/1 1.05626 1.05727 +/- 0.00431\n",
" 18/1 1.03423 1.05439 +/- 0.00472\n",
" 19/1 1.04240 1.05306 +/- 0.00437\n",
" 20/1 1.03719 1.05147 +/- 0.00422\n",
" 21/1 1.04308 1.05071 +/- 0.00389\n",
" 22/1 1.03956 1.04978 +/- 0.00367\n",
" 23/1 1.05824 1.05043 +/- 0.00344\n",
" 24/1 1.03151 1.04908 +/- 0.00346\n",
" 25/1 1.02695 1.04760 +/- 0.00354\n",
" 26/1 1.02581 1.04624 +/- 0.00358\n",
" 27/1 1.09932 1.04936 +/- 0.00459\n",
" 28/1 1.05983 1.04995 +/- 0.00437\n",
" 29/1 1.03381 1.04910 +/- 0.00422\n",
" 30/1 1.06727 1.05001 +/- 0.00410\n",
" 31/1 1.03180 1.04914 +/- 0.00400\n",
" 32/1 1.04520 1.04896 +/- 0.00382\n",
" 33/1 1.07158 1.04994 +/- 0.00378\n",
" 34/1 1.04283 1.04965 +/- 0.00363\n",
" 35/1 1.03272 1.04897 +/- 0.00354\n",
" 36/1 1.02730 1.04814 +/- 0.00351\n",
" 37/1 1.01975 1.04709 +/- 0.00353\n",
" 38/1 1.04815 1.04712 +/- 0.00341\n",
" 39/1 1.02642 1.04641 +/- 0.00336\n",
" 40/1 1.04063 1.04622 +/- 0.00325\n",
" 41/1 0.97384 1.04388 +/- 0.00392\n",
" 42/1 1.06049 1.04440 +/- 0.00383\n",
" 43/1 1.04467 1.04441 +/- 0.00371\n",
" 44/1 1.04454 1.04441 +/- 0.00360\n",
" 45/1 1.06529 1.04501 +/- 0.00355\n",
" 46/1 1.05496 1.04529 +/- 0.00346\n",
" 47/1 1.03717 1.04507 +/- 0.00337\n",
" 48/1 1.03874 1.04490 +/- 0.00328\n",
" 49/1 1.02083 1.04428 +/- 0.00326\n",
" 50/1 1.04847 1.04439 +/- 0.00318\n",
" 51/1 1.03789 1.04423 +/- 0.00310\n",
" 52/1 1.04447 1.04423 +/- 0.00303\n",
" 53/1 1.01942 1.04366 +/- 0.00301\n",
" 54/1 1.04639 1.04372 +/- 0.00294\n",
" 55/1 1.02539 1.04331 +/- 0.00291\n",
" 56/1 1.06312 1.04374 +/- 0.00288\n",
" 57/1 1.05854 1.04406 +/- 0.00283\n",
" 58/1 1.05150 1.04421 +/- 0.00278\n",
" 59/1 1.04321 1.04419 +/- 0.00272\n",
" 60/1 1.04762 1.04426 +/- 0.00266\n",
" 61/1 0.99442 1.04328 +/- 0.00279\n",
" 62/1 1.06907 1.04378 +/- 0.00278\n",
" 63/1 1.03170 1.04355 +/- 0.00274\n",
" 64/1 1.04308 1.04354 +/- 0.00268\n",
" 65/1 1.01439 1.04301 +/- 0.00269\n",
" 66/1 1.04581 1.04306 +/- 0.00264\n",
" 67/1 1.04404 1.04308 +/- 0.00259\n",
" 68/1 1.03158 1.04288 +/- 0.00256\n",
" 69/1 1.04953 1.04299 +/- 0.00251\n",
" 70/1 1.06338 1.04333 +/- 0.00250\n",
" 71/1 1.03768 1.04324 +/- 0.00246\n",
" 72/1 1.02531 1.04295 +/- 0.00243\n",
" 73/1 1.04552 1.04299 +/- 0.00240\n",
" 74/1 1.04293 1.04299 +/- 0.00236\n",
" 75/1 1.05928 1.04324 +/- 0.00233\n",
" 76/1 1.05057 1.04335 +/- 0.00230\n",
" 77/1 1.01846 1.04298 +/- 0.00230\n",
" 78/1 1.05755 1.04320 +/- 0.00227\n",
" 79/1 1.05222 1.04333 +/- 0.00224\n",
" 80/1 1.04860 1.04340 +/- 0.00221\n",
" 81/1 1.03026 1.04322 +/- 0.00219\n",
" 82/1 1.02360 1.04294 +/- 0.00218\n",
" 83/1 1.06679 1.04327 +/- 0.00217\n",
" 84/1 1.06297 1.04354 +/- 0.00216\n",
" 85/1 1.04426 1.04355 +/- 0.00213\n",
" 86/1 1.00337 1.04302 +/- 0.00217\n",
" 87/1 1.04787 1.04308 +/- 0.00214\n",
" 88/1 1.04332 1.04308 +/- 0.00211\n",
" 89/1 1.04369 1.04309 +/- 0.00208\n",
" 90/1 1.05006 1.04318 +/- 0.00206\n",
" 91/1 1.05394 1.04331 +/- 0.00204\n",
" 92/1 1.06017 1.04352 +/- 0.00202\n",
" 93/1 1.02032 1.04324 +/- 0.00202\n",
" 94/1 1.04816 1.04330 +/- 0.00200\n",
" 95/1 1.06601 1.04356 +/- 0.00199\n",
" 96/1 1.02876 1.04339 +/- 0.00197\n",
" 97/1 1.03929 1.04334 +/- 0.00195\n",
" 98/1 1.01958 1.04307 +/- 0.00195\n",
" 99/1 1.01899 1.04280 +/- 0.00195\n",
" 100/1 1.05150 1.04290 +/- 0.00193\n",
" Creating state point statepoint.100.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 6.5927E-01 seconds\n",
" Reading cross sections = 6.1679E-01 seconds\n",
" Total time in simulation = 1.2768E+02 seconds\n",
" Time in transport only = 1.2740E+02 seconds\n",
" Time in inactive batches = 2.7221E+00 seconds\n",
" Time in active batches = 1.2496E+02 seconds\n",
" Time synchronizing fission bank = 2.4179E-02 seconds\n",
" Sampling source sites = 1.7261E-02 seconds\n",
" SEND/RECV source sites = 6.7268E-03 seconds\n",
" Time accumulating tallies = 1.5442E-02 seconds\n",
" Total time for finalization = 1.6664E-01 seconds\n",
" Total time elapsed = 1.2854E+02 seconds\n",
" Calculation Rate (inactive) = 18368.1 neutrons/second\n",
" Calculation Rate (active) = 3601.09 neutrons/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 1.04331 +/- 0.00192\n",
" k-effective (Track-length) = 1.04290 +/- 0.00193\n",
" k-effective (Absorption) = 1.04136 +/- 0.00151\n",
" Combined k-effective = 1.04183 +/- 0.00122\n",
" Leakage Fraction = 0.00000 +/- 0.00000\n",
"\n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run OpenMC!\n",
"openmc.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tally Data Processing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our simulation ran successfully and created a statepoint file with all the tally data in it. We begin our analysis here loading the statepoint file and 'reading' the results. By default, data from the statepoint file is only read into memory when it is requested. This helps keep the memory use to a minimum even when a statepoint file may be huge."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Load the statepoint file\n",
"sp = openmc.StatePoint('statepoint.100.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we need to get the tally, which can be done with the ``StatePoint.get_tally(...)`` method."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t1\n",
"\tName =\tflux\n",
"\tFilters =\tMeshFilter\n",
"\tNuclides =\ttotal \n",
"\tScores =\t['flux', 'fission']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"tally = sp.get_tally(scores=['flux'])\n",
"print(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The statepoint file actually stores the sum and sum-of-squares for each tally bin from which the mean and variance can be calculated as described [here](http://openmc.readthedocs.io/en/latest/methods/tallies.html#variance). The sum and sum-of-squares can be accessed using the ``sum`` and ``sum_sq`` properties:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 0.40981574, 0. ]],\n",
"\n",
" [[ 0.40963388, 0. ]],\n",
"\n",
" [[ 0.41117481, 0. ]],\n",
"\n",
" ..., \n",
" [[ 0.41179009, 0. ]],\n",
"\n",
" [[ 0.41329412, 0. ]],\n",
"\n",
" [[ 0.41494587, 0. ]]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tally.sum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, the mean and standard deviation of the mean are usually what you are more interested in. The Tally class also has properties ``mean`` and ``std_dev`` which automatically calculate these statistics on-the-fly."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10000, 1, 2)\n"
]
},
{
"data": {
"text/plain": [
"(array([[[ 0.00455351, 0. ]],\n",
" \n",
" [[ 0.00455149, 0. ]],\n",
" \n",
" [[ 0.00456861, 0. ]],\n",
" \n",
" ..., \n",
" [[ 0.00457545, 0. ]],\n",
" \n",
" [[ 0.00459216, 0. ]],\n",
" \n",
" [[ 0.00461051, 0. ]]]),\n",
" array([[[ 2.00748004e-05, 0.00000000e+00]],\n",
" \n",
" [[ 1.75039529e-05, 0.00000000e+00]],\n",
" \n",
" [[ 1.96093103e-05, 0.00000000e+00]],\n",
" \n",
" ..., \n",
" [[ 1.69721143e-05, 0.00000000e+00]],\n",
" \n",
" [[ 1.58964240e-05, 0.00000000e+00]],\n",
" \n",
" [[ 1.81009205e-05, 0.00000000e+00]]]))"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(tally.mean.shape)\n",
"(tally.mean, tally.std_dev)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The tally data has three dimensions: one for filter combinations, one for nuclides, and one for scores. We see that there are 10000 filter combinations (corresponding to the 100 x 100 mesh bins), a single nuclide (since none was specified), and two scores. If we only want to look at a single score, we can use the ``get_slice(...)`` method as follows."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t2\n",
"\tName =\tflux\n",
"\tFilters =\tMeshFilter\n",
"\tNuclides =\ttotal \n",
"\tScores =\t['flux']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"flux = tally.get_slice(scores=['flux'])\n",
"fission = tally.get_slice(scores=['fission'])\n",
"print(flux)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get the bins into a form that we can plot, we can simply change the shape of the array since it is a numpy array."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"flux.std_dev.shape = (100, 100)\n",
"flux.mean.shape = (100, 100)\n",
"fission.std_dev.shape = (100, 100)\n",
"fission.mean.shape = (100, 100)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f4e6fcf8358>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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dFOPivZOX+IeFlwA4EWtgm0AyqwMYCJF3IUBzw5Pn/Ic7D/PxW/LOBS/nGX1K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\nRNgj+L+7omDDDxCT/gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4e73071358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.subplot(121)\n",
"fig.imshow(flux.mean)\n",
"fig2 = plt.subplot(122)\n",
"fig2.imshow(fission.mean)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's say we want to look at the distribution of relative errors of our tally bins for flux. First we create a new variable called ``relative_error`` and set it to the ratio of the standard deviation and the mean, being careful not to divide by zero in case some bins were never scored to."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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5CewHLkxpe/fkrERaHx7S2dimFfRbgSsDz68Cf3tK2zLQpQ1q3A+ASf2/vBE/YGb5YTiz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4e6fc83cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Determine relative error\n",
"relative_error = np.zeros_like(flux.std_dev)\n",
"nonzero = flux.mean > 0\n",
"relative_error[nonzero] = flux.std_dev[nonzero] / flux.mean[nonzero]\n",
"\n",
"# distribution of relative errors\n",
"ret = plt.hist(relative_error[nonzero], bins=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Source Sites"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Source sites can be accessed from the ``source`` property. As shown below, the source sites are represented as a numpy array with a structured datatype."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ (1.0, [0.08865821621194064, 0.3784631548839458, 0.3904972254761878], [-0.1326194120629367, 0.6232164386612264, 0.7707226233389667], 1192346.6320091304, 0),\n",
" (1.0, [0.08865821621194064, 0.3784631548839458, 0.3904972254761878], [-0.6644604177986176, -0.6182443857504494, 0.41984072297352965], 356616.8122252148, 0),\n",
" (1.0, [-0.0524358478826145, -0.2989344511695743, -0.17188682459930016], [0.26140031911472983, 0.8973965547376798, 0.35545646246996265], 836460.5302888667, 0),\n",
" ...,\n",
" (1.0, [-0.1061743912824093, 0.2687838889464804, 0.3209783107825243], [0.04074864852534854, 0.6722488179526406, -0.7392029994559244], 4419569.703536596, 0),\n",
" (1.0, [-0.1061743912824093, 0.2687838889464804, 0.3209783107825243], [0.2921501228867853, 0.9089796683155593, 0.2973285527596903], 2716068.3317314633, 0),\n",
" (1.0, [0.24742871049665383, 0.2887788071034633, 0.12388141872813332], [0.9088435703078486, -0.040848021273558396, -0.4151322727373983], 1005076.8690394862, 0)], \n",
" dtype=[('wgt', '<f8'), ('xyz', '<f8', (3,)), ('uvw', '<f8', (3,)), ('E', '<f8'), ('delayed_group', '<i4')])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.source"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want, say, only the energies from the source sites, we can simply index the source array with the name of the field:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1192346.63200913, 356616.81222521, 836460.53028887, ...,\n",
" 4419569.7035366 , 2716068.33173146, 1005076.86903949])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sp.source['E']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can look at things like the energy distribution of source sites. Note that we don't directly use the ``matplotlib.pyplot.hist`` method since our binning is logarithmic."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f4e6fa81400>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f4e6fbcaf98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create log-spaced energy bins from 1 keV to 10 MeV\n",
"energy_bins = np.logspace(3,7)\n",
"\n",
"# Calculate pdf for source energies\n",
"probability, bin_edges = np.histogram(sp.source['E'], energy_bins, density=True)\n",
"\n",
"# Make sure integrating the PDF gives us unity\n",
"print(sum(probability*np.diff(energy_bins)))\n",
"\n",
"# Plot source energy PDF\n",
"plt.semilogx(energy_bins[:-1], probability*np.diff(energy_bins), linestyle='steps')\n",
"plt.xlabel('Energy (eV)')\n",
"plt.ylabel('Probability/eV')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also look at the spatial distribution of the sites. To make the plot a little more interesting, we can also include the direction of the particle emitted from the source and color each source by the logarithm of its energy."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-0.5, 0.5)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JwZmMxXQxcuPttNdMocXtZpdtGM0pViy6E/DG5hEXM4YKW5CKgiI63bFE0m2QGAsz5yBE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\nkj6gH+Hf/oba4z6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XogaiwNMAjXfhiKtB7qzjN5Pf4gT/Es57eTZrTjifs2s7YOMqAmou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MPFoWvLCbpuIau9kOTmAwhlI9ClPkO3fSl6ehBDNnyHv8WbqWA9E4aWBEIhO75IL7p8DxrnEYTK\nbkibBgteRCFCQNiO0PEibm07ut5KJFGDadCIcNYC3gGC5CIk9RF+zI7csgzcIxCaN0HpZNRH/kTe\ntEdpOrWS+sV34uAUYw+vJcN6FXz+DP7bP0J1+tf8+M2FfLd/CeWLjzBj6jcMVAfI39yESgkSLjTQ\nUzYR7cEqLjhyDE2Hi3DTViK2GmRtGlgkCA0SnJqEJA0gVJ0lZtwcYk58BTFPY5S9nFH3IAzYOTdt\nHIKSylmplU5dI6NjD6A/a8KoViAjHaRp5491R4+G9kY48yi0HGffjHvYG5fN52IeYYeZTlMdEzTt\nyIqAJ7uJg+lJOHueZ4Z7AYYMCzSknH/bybsU1jwK5V4wTgD15xAYAM15f29lsIJQ823YJxSgEYah\nMcugUSBkgLF7sbT8yLGMBDIbh5HQ8iEaQyvKsE/RpRuJsA39lvfR1FcizAyhOhAi19tIWsMHdJXN\nIuXkQUIX3oyOCdQ7PyQo1nAuKZuM9QOcve5lhp1uZCDwKeo1T1M/5UOMGSvIEF9GTJY5MsvJsD3r\nSX+7CV/pARw2kfjaz4k09iJ0CFT9ZipjhEWcZg0yqQT4jB6M2I1/wpLhweowYKh4C0lfAElbYcgD\nnUaY/jscsU9iPTkO7bS30BrMaLfOIeBuxpM0HFP5u9T1/JLc6o8g67G/jPmYi86XjSOhtAyH9hQu\ndR3uyIXIynqOTHiY7JAaf+XziCkioVAl0jYtoaMf4H9qFkZ5ElJkIQHPI8jqInj1AojP+5cT5/P8\nvMTxT8WHnwZayCUT1f/XX9fozoch9bpBZ4D6HQjJcwkuamT8MYFwwtfUGu9mzZxbuMTxCrLVStLG\nLM7FPkzum5sIjVHQtqtBW4Ty8Tokb4hvH7mCPm00dx//FmWCC19aLEPaZ1DbmzC0ViCFPHi1WqI+\nduK/qwBpixrJHk1kmAlEPWFLAl75NKqEdQhxv0YomAA7H4HR1yI2byaTRLZzFkkJIoRl6PPjtYVo\nUh3m0Zd/w8irT7Lh15OQj/pJ/4Od4KhstGXjEQQfJLRjTstiQJdCyqY9NC0s5PuUVMpMJ0g5EA1+\nF7jOIWviUfZ0QlSE1MHhRIwq2DuX0TOOMtnXSVdSAo2pCtKpSsa5TqKr+xElNwdB20HErkUQJiDU\nvwK5l0NKKRx/CUXrwn/VMO4KvcwiTQK36N5knvcb+vbEcuyq+QwKpSSHmzCGdxFnWszQwVcRJ0Sj\nPRpAaKiA/HIwpULvZoi9CAxp4G75T4GWfApSRxVCOJ9E6XUoOQoNn4J2IigC6jObGSwvxWFsxKqo\n0eJGaHge4ayViFSJzueAy0Og/QRX1g/Iv/8cjRwkJZiFyr8P1c6tEL0btXYQL0bGH/YhtPYgPDuP\ngCYaMV/FZ9MW02WxctfHl9OYMwp7XgFqqRdfsQ51yM/xgmwm7TuKHFHol00o1+rB18lp7WtInmgi\n2hFYP/8ErnyGbvsREk9akR95G+HAwxDTDdYSCC2Dj28lXLmS8KUj0embwZoMp1egTroYqRX643Jo\nVldhTMhB1O2EOh/E/pdxH/ajWHLxjbuTXvfzTORjBqUkIiEV5aHf0DN5OursUaje/AjLlkzk08n4\nvojFK5jRKG7kzt+jsd4MG5+CmfefT431L8hP0c3u30qgtWjooJvP+IZLmE05I//fF42ZC0c3w+RF\nkDWJQN5E9ob+yJyED5FirmFY8gKGCWV4eZcNNNA+rYyuoIPLr05Cn1jMkM/IqIdXI4YEmDabr4uu\nJivkxV66BX1bNZqVjxEpTiY0IpNIlwrXiEl4LQ14xmrxJ7Vj7I/HVZKFkp+BubMav+AlbNQQEeyE\nBicg6ichlU0k3PUZom0ecm8VCQGFYH8LWmeYYNtqGm+y8v3nAW555TXELDVpT3fTlVSCf7abUKkT\nsbsA9aavEPpyCDg2o/dHIc14jLjDT1FpNLE392auWngHuB1Qsw9h3a/xj41FffU2PMrviEgiavVY\nAr2/p/hcDwfnpDBtrw+rp41Imh2lSAF9KkJnJpGiTMKD3yHHBcAaQpC1KK5EglMHULSn0R2AxBEd\nTAltw5cA453HmBpMov+Ui+bqKtqm5PFyshXnnKeY5dhD+UP7GPvxL9E8/BWGkqUoR1cgGfeCfQ84\nuiC2ASQNsuU67MP/SJRjKlK4EXY/AebrQNUL1asQ6tYz1bQM17jP8eklpD9dhipqNRGDDne5CiUn\njGB8ikAkiXavnZMPXMGY19dj/e5NBlOMKJ4eGtPNhAwqLAMBei2NJJxrRhEFQrNicPaF6bDEcHHr\nMCyWSVi/X0t29mYUnYQylI0nxk6qQ6C+MA+l10ZZgwGh5Alyq5eQcLgPofI4qmwFhdPIh1fwfPmz\nfGl8HkF0gH03TN0B+nwI+lAmHMZRuIso29fnQ6hWXwTDvgQpCsl+EMFrpp1zzNF1gf1iqPwRssaj\nJCQhCCa87GZgZCsB8TpMmjQIhEEt0R3JocS3CpXhS8IJZhTtMSIHvAjLLkQWmklkBQysBE0JbF0P\ntmyY9K+dG/pngf4nM4OJuPFQTT16dBSR/9cXjL0I3r7/vEDLarpq3+N0TifTogQ0wRTYfwNMPYgU\nkkhd/RrOnHjmdGxk3/gJDBPPkayXCS1fjibnQcIrljC1p4HS1Ggs+5oIRmTCo0ei/2wfNMoISZ1o\nNGU4xyajzPfiUiah63sHl2Ai2jsZ/KtwhuaA92qk+vuRInkocbUI1g0oMSEGLVsxaoMIQzuRzSKh\nM82sOjeVrLERrh39ATafj+6ADSXPS5yxGyGvFOMxL5KpDIYngL8Df2grOusIemwNnF06l2E7N9O/\n3klPho+hiJ/EVa8TNLhxG2IIHVuIqzCMqTYeR2o2MY4XCcYXMUf9DrrAg2AMIJgyoKsCwbEdVKVI\nlocQt0lEBmoJdlUg3rwU9SWfod5wC/YcHX2jB/CIAnHWQU6ER6D2Ktj7j5K0oRbbPIkR6gOM7j+I\nX5TIIEhv4xCixcdgw/3osz7Aq92G0T0fGj8CfxdEj4GgF/ASMPmJa7CD5xNoPYaSoicoOZE1x1Fi\nEhiasIP2/nwKttUiqxoJ+zMJqVowHAkR3OpDnrYKdczzlO+0wQ0LCcSNRQ78gKk3SMUiG9b+fhKr\nFbw5E4g+a4fYXgStj8q8QlpsKi5r/IGUuh/wHutBlyCCXUSY8iBCjAFDywsUndhG48R7idRUsmNK\nN6OOriW62wGxY+ifeQztxdNQD2xF+KyK7RPUbHnkBeZU3we5t0FHFeTkg0qLLyWEvk+FlBcN+b+D\nc6OhfQVK2m+IqCT2JrZxkWsySF+i9JQgTFsGj19C8NmJhKNdgIaowRKUtVXIWfMZGOkhTt5MlXoH\nXvEalMCr8OL3hOO9KNpkIjPq0fMQeI+B5wCcSIHotH95cf55Bv0TQEBgAbNQUNjBAWo4y3xmoPvz\ncWiMVvC6IBQEWYWQMxWb8wyeMzFoSrdBdxue1SOYfclKXBebWb/pVtLcNWQdKEZp2k84aKW7fApG\ntUB0Wi6X/XYFiRcECTeJCOnRSKPDYDBBoBrFAd6KI0RsItJaEyn9mxE61SQdTSKQVovAAHJXFcb8\nWQijTkDr1QgnnWA2I5t9RMVdQYt5FcaBARKrO1jZewu3zj9NfJ+MdGKA0EUGYqv7WZWziKtP70I1\nsATGXQ6nXoOsSwjrLfh7dxLVtokmIY6A2olqpofET7uI+dMjaGwmwlf4cQetaNRe4to6kHYFiIRV\nCPbfc2RqHkUHa9CNWQA9Hii/H2HY7XjbTYR1e9GN+ZyQyYbmqpFIrzyKuKedcPV9+NMF5Iz5uHV/\nIjYkc6fpAS5duYFR2koG+0wYm+7A1PYWbBnCX+4nxqDw8rAJ3Nj1FYVBB2GzhOWN74gsKidSVgwx\n00BXAnIvpE0GQwoDPI+x0oS9oJDojbvh4nJgkP6EUsJNnxMusaESfkdiww7cUhuNJZ2Ygj4MkQhC\nioD8f9h77+g4qmxR/zvV1VmtbrVyzlawLNtyzhlHDNjkOOQ4DDAwAwMTGAaThjzkOORsgsEYbGMb\nG+cgy7IsWzmrJXXOoer3h7m/N++uux537uNOeHe+Xmf1Oqd2ne5VtfZe1bt3aE2gHNlLtNiEPPs4\nctf96MYIYmYbI1o9uSNhMgeX4hfNSMcdMO8PoFPZPPI0oSGFxaZd7LPlYo4NU+xyQ0RHJJRDfN2z\niJI8jDlxsBnI++QN5IJlZLe2s2v8HgwlBUzrOkxgShrpzk9Qh4y4U/TMHz5Oiq0GtfYlxJYb4fAN\nULaaOL2Ekx3YjqfAyOcna2Ek3Q3dT9OXezZxuYfp9UnokhYTSZ+P5DqM9hsZ2gfQfSbDFe+iqgGU\nrP2Q3ozS9irW8inEtL9nOhMxigAoF+O6ZR0pfecT3TGAsGvRJGww+FNorIXkHJh19X+obyoq7YQo\nwfQ30/H/KiqCyL9Svf/+iO9fC5hBLwO8xSfMYQplFAEQnjkHTdcBtCVTyNeUY7LVsLW6iNOO3sum\nzBm8XLmK813vkRWpwerqIRGV8LVtxzspj4HRqewVJs7ffApK+gi56Q6oD4Mk0CgxEp+FCRuTEaUJ\njMkRDC+50c/Jx/i7RnjgdFidCcNrEfJCQrlWEi4X+N+DjLMgtQJOfRSOPAwtryCdeIiQPYc8Wwex\nZAs3rB4kvW0Z7LoHrn0NTfASAlEDX6Yv5ExjF9rC009WHOvdCLrjaI7swVNp5YuaRWSmnsoMtRWX\nL5umsgHc7jIU2cu3g2Oo143nHft5TMncw33pzzLNuJPGzLFkDvSQ6vTDJ3lQFERtup+18iBHxley\nMMlOgUZDUH0eX8pR8v5wAZnu+5GtKUhtW4lvupWsjQ6a4mXcZnuM0nEdBNZVk5xo4rmZDVy9NAVl\n2wiauIGRijHss9Qw51AvGbvWIU+6HNT7kQYGMB4VqAOzUWKDaAptsONy4vNfIC4P4slMRT7wW8xy\njK7icyj97k509i5GqnPxazJw0UTSpBz6J88mjwj+hELZum1Y3g8SzDUij0tDl2tAKG0oTUZ6Sqvo\nWmFh4vs7MRzwQduL6E7JI+6KoBZU85m+Hmv21Sz/82KUfD35ZYWoObMQxlQI9qOXDiLnP0Rgxz6c\nX6xF9h/HkuQkmv02hpiJ1uGHmNH/JtuKC6n8YzNcZKAlvwx/pIwFzW/j0rlpDw9QktCDCKLG3Di1\n92K33oJwXQgjL4DtWhL7ziCcVkaX93XMScmketyopioo+g1K+Cpo2QJ33Amd60l8NJXQ7Fkk+ebh\nmzQJddCB5XMf6uwqTNYGorpXUbUjmNU8nMVbMEoS5tD1qC1LES2FkJJ7siDYf0Azfh6lg9PJ/Kcx\n0P96gv4HI5csLmY1G9hKEy0sYS6H5/kY/8EnUDIFABmJ5zLHs97wFKVt2/n95t9T4upkYNwv0GVJ\nRLPuwL7zaZKSf044bRPX73sfKdGMEg4RKxfo9oBbZ6Nt2QQycxVsUSeWLgUCWpTVESxfdKF+l4LI\nk4B0esbdSm7pbwkNLEYeaKRb00pO35skvAJLdClYrofTjhPbcx8DtnpCCrRPzOGsjWtRX38OceFs\nSEtHNKfSrVRRt+Eg5onLoflliH0HkXYoeITw9itYP2YRz/iuROsVTA0JYsPl2ILdyBMTZIf6yVYd\n1I5PwhPbxmmp3ZSod7PbdRf6pHEU+VaC4Veo3jYalEXsG5NEwuGlZ3c5Bw0yZbpF2Epeo3O6iyE2\norVZSfFYEcOXE1og8/llp2Hb4GPZ19uIbjGSpHQQjcocT02Gee+jbshFlRWMQTcLzfmU5e/Ca0gi\nqTwHyawg3tGQmH8D8he/Z2jFRPShOCkcwnd8CeFRV9OTupvcsIPoCR/DcicFRgPmphjSqFJi6RbS\nxFekiXPRMxa9EiH//RaMGzy4l9mw75AR34YJrzIgTv+Advk+GqozmXfciCFPA24LLL4K7eZH6VuV\nyXrPGkqVNsaqh4hNyEPT7KCE3XjCFQTdn2BMuFEqryKRegzr6jXEJlxF6OO1uDX8hh4AACAASURB\nVEZ2Yh/5GrVwPKv1D9MTncaYJj3br08l5bU+WrSlrDxrL16lkE2WObgc+yhpPwhJEQJ7itAbtcju\nXtCOO9lcImMxXTOfx6t8y+j6dzBZZuGzHaUrv4CCbXcTV5pgUg7ka1BqlxJ773GM3lkkjlyNf2qI\nLFc5YuIixLZbkVICaDS5MG0zxJ2I4TUECzqwNJ2L2hEA25mIudeDqkDHRjDYIftkH1UVlT/RiY84\np5D2d9Tw/zz/MtD/iOz6DO36F1hRNp7W01fxhuVFKoIxtB8/BqffTUiS+didhcXby88OP0n1hm7U\n8RZETRk5zm8I5MzgRF0ORn0eZdtuo8CRhVIQBF8EpU9BI9nxjQ7TXlJM0ZrjJB74gCTjd2BYS0w6\nghwfJrTEQvgLQfRYEg0/m8qmomxWR74m2ZZPfryJ+LDCcV7CHqomqSQPkXIpRPwMO1s4PKOE3GAP\nYSkZ07YQyoCAfCsx5SZkk4N0GW6O7QJqoCkGNdfCmBWw/1MMsTCnp09jkdqBTSknbe0NRG1avpk1\ng6BBobqnlp5cQb52FJmJtWT2dnFAfhNvpoWq7z5hn2sG35Y+hW7mJtzWccw0Xkil9BLLetZgDg6j\nmz8eY+EkqpgGQFfoRhxiG9ZsI+flPcpPxdtMn+XGPWLE1O+h5dRs8taGuG/D3fjSNmLOSeaYVU+a\nMZccuQ9/rg9z5gDR4O3oMq9CMm1C/8itqF4dUT08ZL6ONXkNqO7HSf92Da5x2eR0exCTkqhNWAnm\nvY6v62fkSqvZ7dvJdPPN+DXrqGpfjfuZ8zCPDuC8vhBvRpSUr7LgJxcQmroTaftr+MYWM3/dZlIH\nSkDNg9ml4HITKzmdr0fnc4q6mkrteGKJu4ilfoBSdQHyrg2k2L4kJIJElAiqHbTSyfCzcJFM+Gcz\nSB/JQ2wZAN1OAiUPsME6movfPxfNoYl8d/UUZp3YSsrlzeTNi+G9YgmHU2tZeWIbaqaNuElCH5AI\n0YwuZEfTOBrywyT3bmPTqBDZqkrEUE9HncLoPTGEM4HGEKNl/jmUGnNIOG5COyUNTcPZRC1x0g9p\nEFoXhA5Bciaog4j8y/DbJyEFOlHWt5E0XYsaCSKKNQTHKJj9A4Q+modRleDcD0EJg2TgFXqYjZ25\n2E9Wu/sn4R8tDvpfxZKmngpTV8LXr1L6zhvURD30h0xsWzWXHa1vcYanFV9fPmW2MfSOTUH1GxGK\nE6pmgKsX89F9lPTnkO8cQQpqMcUHYb8fr9eAUOIQcPDG2WcRrbCiu+h8xF2XoB7fBAPdyN4kpDCE\nKwtJtVSSc+dX1L3nwSVr6TfYGDCm803ZfGKqTFJ4GJO0CW/XViKNF+PddBrOU3SYMFI66Ge6K5PN\nK+eA1oC7NoTQLkRoCkhR/WjSVLDZQdVD3y6wVKFuf5hDl11OU/wD6p66m9JX52Gte4IUpZfUgS60\ncoLCohLmaucxgY84Y+tetMOr6JBWMtAwmm9q53BgkQft1O3Uan38cuNtVI2UoT98L1kJPdZKCb2m\nGiXxPhJa1Ph35AVfwqK7iK3ZNVzU/yr5HMEc1qGbXYss4pRv6MaUZ0PyKXjuGkCyxsjZESKa8GGK\nncDYBIb2XHTDFxELvEa8pg9FUUjU2DDE3Vgig/jVdKxZT2FQreiDAlOKjzDZtItKvkz/ipDGhGbQ\ni2qaj3/gedKebyDxxul0XpeHa9ZzaHvdiJhA+tMOmLARw2ffoOvfT9lgE2p5gpHx7Xguu4pgVSWh\n1td58dwJlGkbSNMNIoRAJ9+LUfstcvR84mUq8ewuJI+eEcmEOP4qmvY9DNPEZuU2VO/PSHx5DdHs\nTvbNWsWnua24DUfZtnwVsz/ZwS+ff5LRycfw3ZpEaW830WYDc48lYZzeiHnMS9hs52MonY9BGoPk\nbYWmP8Gro7AeeR9zKMxIRjH+7Bpq35MwtfRCVoLwrGkENG0ExBdIGgMi6TherQ1pG8jlW6HwBWhQ\nYdY6WNgJ9QeRP7yONd4tHI4XYsgfICplEMkYSyL4Nsr2RawbV4trpgPV8ywIDW/Qiw6Js8gm/R/M\np/t/4t9Svf8z44cQQrwshHAIIY78xdo9QojDQohDQoivhBA5P7TPvww0wNIr4Mn9hBeeitT0HTN2\nHOLtOUvZmZGL9tMCgoft1GgCtA+nEZxVC5PPgehXkG+DARfmr88mXLKQ+LzriXRpkAJxjJ4IatyI\ne9KDBItOw6IpxpI7BdNv3sQrHSA0dixS3QmUpAyseyViM8xEc3aQdIOdiWo+7RSi4iA36XSqOyN4\nLZUYCp9hUD+BkPwJqv0YlUeDLDl2lNpDu5i64UGy7FM5fOdNmLTzkAYfJ555Cdq01xBhLQSmQc5i\nGDkBL5+NKmXjHNiIrS+KMHfCed9A+UpOzH2L8uFOip0yTrZi8Tix7XoEg7qZYVOCeUk3kFWix5Fk\np3bDYSpDMdpKo7yz4hQCEQOBS+LEJk5GFF2EWPc5ica7oPt2hOd6onoz2zwNpMd7mbNuL4RijJh2\nYvC7iItkRMAMx4fQLLiZTFGFmllFSo9CyGKhz5NOlj+Cpi2B1HAM/fZMNH0KpAjCS1ykm/U0ZV1B\nSvZPiDiaCFpGEIEgeydN4NiYUbTFPySh8ZMcXUC060WyP/sI4x93o8k+gf6cQvL3OznW+yJG02Ts\n+xPQsgshe9CcloxxyQZsbzST9pyTFOclGMVCcKYTnZDPCuVrKjlEiCtxcTdhXOwV7Yw430Q30IX4\nRoc8rZtsTT9yQyd893O0789izrZ1JO07iKILEMu4jHJ1GtNik/ETYVA1sOOnswn25DHkzmBoto3o\nbdmomSaU/RsQF4+Fhy8g0N0H4SMILIjFD8EFDXBZK3LJm8z+sA9b21HyX96O3iND8gqY+j6ajCLs\nQ3vR9e0lnnIJAZuetuzRSO0y3D0X/nQROE3w2t3w5hVgHUKnfsOvtvyRulg9DY2VPFdzOkMpLgyO\no3hm+qnT1aPxRfHZXudt9X0iapwLyf17a/Vfzb+5OH6kanavAkv+3dpDqqrWqqo6DlgH/OaHNvmX\niwMgEQZrGq2meopb9USKt/CL15ooKq+jM5Yg3TJIlWkWbxqW40l6HLNrC/Q4TlZMm7mCbruTiKaN\njpx6xooEkdwCJKUfZ6mF3nEHWepswRTrRzLrSDZ04UjP4ZBhBfOFhGKvwdBej+6SrYQTv0NHA4Pi\nDNbi5iVkJrS/Dt5O4hmn0GxZg30gQkdfPtacMobSjpLz3DBSrR6R7mGSxssnC6rIDn1Cht+Er7gb\nrfFqRM4SCGqgcT/01aPac+k8qxivHEaOy7B0KiiPgCdGktqIqcDK5KY3EXYJ0f8nEilGJM08VrX9\nhg7pG9xZeoqindR2RXBc2cPYhYuIzdDyXXoGEw99xUD1AZKP2bDtHUC3MYE67gHiJiMGEWG55UvU\n9Pkk0i2kXrmd7t/m4Y6qFE9fDtveBVshwjWIfM8dKGuXEtWFMWzsQB2fw5HqOmJJi8nylpPx1cvI\nLkFs/3rcRSZ66ga5KrCUPUE/OXIYa9o88sMRTI2H6bKW4dAlmJg/TKZ5IuKBZ6iM1vPnJy9hpbqF\n7McHSJ0ew+tUodWBxRmA1utRC81oVCfqwKkkkmVk1YgkBOKFSzB4hzCd9yusIxtxRCpI1V5AQjXx\nTeR6zLEw6fGDiIJb0ZRMgt6zUKN6ooVaOgv/QEH9PZiCcYQcQU0yo9l6iKH5+ykfgjOTJzOKEM6U\nhaw9J5m8kR40cRN9KXlUqCcYPDWbjROBvgQjeQnIKqfq+AEqe6rRLSiBiBfVux5Tygk6S0ZhnH4R\ntkO/x7nzdcxZbxHIyWZ3YQ114hWGB35FqaxjVEcqQmMCSYWLn4CaZfBvjVH7P0XaeyHGoRiB5xOM\nvlQi6eh3vGVfxQrbdoqGurAkdKwvvg299hLcQuFaCv+e2vxf5mQUx49Ti0NV1W1CiKJ/t+b9i6mZ\nk9nl/0f+ZxtoXz10PwyBJuKJKL5SC5XBo0ixTERlGv3aNMRwL8ut79M1UkxZsBVj/hGicjXafi/+\nxADumukMFx8iMz5AVlsQMWYpxvwL4cvzyJCcbCqfxPKhepTQNoYye0l2+pDzLiZVjHCUTyiJ9+MV\nFh7qjrHKaMJiWoImUc817gWUJKkggWIoIkmXwOEyoBMuDpZO59LYTLpSVE6c2k/Z640YJ/wcadE9\nLBExPtLtZ1TOxUzsuQ71yYUQ7YVJcTDGUM98DOfRu2kwB/jWtoClXbsh5xFIDEDgXfSOE5hb46iN\nMu7kTLw5Et7KUkY7i5HdIYJJFk4//jXJDQcIh8aR/8IzCG0HjqTPqI2vJylRS9LOBjwzJUIXykQt\nSeizRqGRZdQeJ47UsZSGr2PQdx+mPC2jb+zEcVYG7pUVZLbPhNqroe8AInYQ36lZmO5qw+ZxM3Xy\ncUzWMpKPZmN+5wpiaoL60lzcd08kJ+ggq7eHYPJifDmrKei8Hma+R4BeYsfHEfPtItdnpay0FNFy\nL+GFqfhmZyJFImTc2IqSk422ZYjiLA+RhInOG3Kx70vBsuwJpF1ziAkP7XMWUPHJPpTKIcjpROov\ngw3vgkFHunwcZUaUE+JbpjXuJVGzgIRWi6wEkBwPoR5MI1HoRc6/jO7knTjrljC16zOkRhmRYkYa\nl0si9SCxoJPSwQ48GVD43nMkj6rCUNeC/YSKUn4HxvrnyR7cTf6BPlg4l5YDhxChOBnWHLSLf06I\nFgxDL+DKXIe3JI3KniHklBshkY77jBtp7/mSnYM1hEr1RDSvsCdnFrcq3aTmZ8Ef98CRl6F2IQye\nB3ImaCvBmIAFhyDYhxw/BbWoi6IDUW4d1UlAMkO2m7ayAvboF1JAnBuVzH/a3+V/ZTW7NCHEvr+Y\nP6+q6vM/dJIQ4l7gYsADzPsh+X/SS/kjEA+AZxeEvRD00jn6EnLsFQhDJUL3MF2J07jGcS8bIjNI\nlnuZvn41Z397P33WUXB4P2pOOn2l1zLkPUj2HidtvvG0lt+MW/UTOPYS/vLz8dQtZWr313SdsJJ4\nUY/6QIjEIS/WZ/sY9WEjxh2/JRE7gXZsKneGX6QysYOmoRsYCBegk67H176bUH8EacLTVL26A000\nie60lWQX1dBcMYIy0MqY35zAN3YGfdZ9RF49D9nlQB8aYoc9B3WwFXHOfXDRc6ihI7iyTSS+vZrG\nqaUU9hmp6Ghn3JG9sHY5rL8LPnyWzpYMNJtGiBplIj6F3ukT6bfokft3EbTtwd6+mYyCu2HyTzDP\nE2j2z4TvlpK69UVy9oSwxi9FL5eQ9tkxwlWZmNQbkBv60Y1MQpGHydNaENVzUMbPQlsaIdZuInXr\nBDTbP2O4So+SnAutTShXXY9lWTOKCkZtkAxpCNtQGOt7F6HVhjAEXNiDHiKaPDK9MsZCcFVpmdR+\nIeqmMN3qJlz3X4j0gURvcg51uU7kyEeIU87FKI3BFOzFZh9h112XEsdBYtCAoyaXD6avIuJJpX/e\nCCMja4gba9Hqhyg6uB0yRhM5uht5WAPlCbj4ZRivwt5hYk/dSvG3h0muPQWL/100mgwI7IBADkKb\nRqJ4LFFnHzN2fUlt/VrECTNi0Scw4TmEbCPJcidmzSQipeMZPpaEa3GCLG0M/UAMVedH7thOQ+V1\n6CQv5EXA6aQs2UdpbRBLpAdx9DJcB6+jr+V9TD6JjK9cGIY6CasmQlnnkJuRRPlUP+dm7OLaIy9j\nat/Nllg1n7WNIrj1I/B7wNMBkgmy34eUu+gJtbDG6WPV0AC3Oj3sX/E+31VNQikpRwoGMBVdglDK\nqHEcIqzuozjsQIz89mTRqn9S/goXx7CqqhP/YvygcQZQVfVOVVXzgTeBG35I/n+mgVZVOHwrHL4P\nUs8kNnsfA0lHyBrwINmvgZYHyRQHeH4l5ObZKUk5gX1wiEPVE3FZbfTmFuMrnEZF2xPUfVSPL28F\n1YNBjF0WBlU/w4lm3p2jwez9mu8K5rJ1xQqOXjWb2K9eRLv4RsQ5NrTFFwBnMhApJr5ei3X/vaSs\n7+Ssa/KZcrSF07027MkdyOVj8B97muDslaRlX0Z7xgQWSDeSwlTiyXHi2gSWj5tpmH8pfQvKUe4b\nz6rHP2TapiP0DX4FuRUw7gxi+jgh13dsrprDkeL5xKLZ1OScR1rOZMj1QZUNZs6ieCCInJTAZSyk\n5exKQkYVq2KnT9uNiSBZroOIyACGjNW4q4th/mGkiR9B3InR7UK771pE2Y0MCgtiay6BkSeIZpuJ\nVF2OZPJjMOgg/DVpmosx5p2G9rHnSDQcpmfupbj7WjjiuRL3jG46nslGrKik+548vOUmdLcPY75n\nIwlFQyBZQU1SKbRXMpg5E93IDGRNOVO7duA5HMVp1mA75+fkfrCdwap0qsevQbZlwFuA+3WwSajW\nM5l1bCfy/kO0/Oly9q2ZQ8Y+G0v27CZ5v47h2GKG0w6xpcLOx7WnEjSNJW7uwrD1GKJxBmR9CP0L\nCeXOZvcFZ6HMWIZh4gNEvn4HqdWN6N0JHS4Y/hJKQugHDmKKfIA7w4BbayVk1p9s0pB/GnFJRXQ8\nDQEbtq8O4zpDJhYYw8g0Pa6y0XRnVxBzbSHftZkXJt8LnUtO1iXPTQJhhWQDpGQSKepEMuvRn+hE\ne8SDmqmi7imhXbOLSN/tdFmm0qcV9KWlsSCwn7f3/IKFji1oep10f/4yeAfglenw3ipo20dW9nnM\nzU0lTashLsl8nCbxiuF87qv6CWvH/BqaXsc0YMA4YOFm70MsCB+GY4+A48O/t4b/l/iRfdA/xJvA\n6h8S+p/p4mh7DhJBqLwD8lazi58jKW7k/h5wrwX9FPT5KWT2jCGReJat2ikUFoUIzFtHWvdS8jUQ\n0jTClE9RH5yMtbiDtIvWM7hlNXk9beybeBbpsoaupBpO1c8nibHsyvyOgEVlt2E8Fs27mCyP4tHf\nxIgoIhb+lEWOEyiBGzDVPsJy05MYAqVIRQ8SDbyPwXAeztG9ZDGDMO+hRWaYTWQMJBH+3WkcTfQx\n5a5HSLnjM9RpmxFHh5g8kofSsBale4jmpRUU+IexXLsDN59yzDuK5jG53BgOg8l7sitzYBf0pRGM\nq1hd5Rz/RRaqPh+3GKLEtJ94/hrEtmcgZxhSpiCSKoGddHGUzlQN6sxzqWt2obOehkSAR6Y/xXUf\n/JLCP/sYedaIJ/hrikKz0cS+hIF96AaKQIqiWbIKQ+1kav0Kg6eMJ/OVZTjOsGDeGyY03YPBZufE\ntWUkxRNUHkiDE1FiSXvR1ksIZ4QZG58gmGnEPDpEPGsJltxDxCNxTKkpBE/PwT7Oie74SiIGFWYl\nodomw8x+tB3fEZVTiV6oJ8/cQIsmhY03FJM+2E/t/QGy/K/iWK5FY2tkU+58Hksex1mD7zI6V4Fp\nyyH6Pq6UK1FaXmSCpoTEkjt4VzrI7ISJbF8VFLwMO24FMRN14hoCvhkYegwM6/NpS08lyzCJYmmI\nDI8Tj/gUeciHun0dXQsuwBmuZjhzLzWHLPhy9tFZlIxa4afqq3foNM2ERXEY8y4cWg7606BoCm5L\nOyZfgPTUKtRRbXgm6Uk5oaI7oqMoZRT62AksPX1sS6+muDhGSWeAlO56Mto6kZI15MY+Ja7XI4RC\nZEoySu4bgKBad4jHByI0156OgqD4aAv7JmbTo82BvI1w+HE0Iz7SunuJVqxBU52MfngLYuhrqHgA\ntLa/r67/Ffx3x0ELIcpVVT3x/fQ04NgPnfM/00CXXnNyAIragY8OJjUOoMo1KLkWYj0fotdK0L+K\nrKNO6heOIdv4LcuaZuOQtYRyR6MUtEC4jNilZ5C+4WvEmM+o8QswpLOwt4vt2RLfZY1Hlr7C3Pci\nnRl65uglapiCTbccNfomavhjJMNjuOKtSHxGt+ZhCm0W2ixTKIrnkbbvNaLeHDRLLkbhdppxMI9T\n6GczqupENxIgedKVNOo2k5+Wh3zPXJSbb8W2bDV8ej6eW1+kSbOBUV98ganFQ0/zOsZm21i+921e\nmTyaL/p9TDxQzYQhGd34r/DaL6WlPEIkepAs49lERp5FsoNOTaEg/Vs492dQvxb0ed9fyGUcYQMt\nQoPFWEGgwEfcrNKtRHnPUMp8YyF5y5uxPNhP+KEsIi0NSJ/aEaEYIvwF4lQB3xQhOvIRA0VkaxV6\nlllI21WMtUmLOsaB6Ohjt20OpdG97MwWyGVF5O4YJHL/EKm9M8g48hHC3o+iupG2PYcWHdrjEu7f\nZ5CsW0HI/RwjditpPcuQm7+F/QcQ9Xbi2lzeveOXXNt4E01TJhAzVlDp+IKswVbkK84i2iCQ07sw\nBdJZZN6Etc+LUFTQCjhxKx7dNLxtAXIqHkRK20midzV1iVJSoz6k8l+Btw/6j8JPvibmPRVdTxXS\nvo1E8i0UpaSiK5DR1i9GDPdiS52E19dPbJGdsaFPCB8bIJo6QiArSiAnGWPAgNLWi93k5Ar3AGiA\nPRdARhF0HYb4CLoDuzB5FJTCELEJd2MJ/AFN1VwipYeI9zViOhAjt+M4M60e7IkAGn2YcDCF8Pwc\n9EfCiEAQddxqJP1UDHlnom4ahac6jVC8GE3WVMZqf40aczHcWkN4ch0zQgrR+jOJTn0A08j5BI+d\nT/LRPtTEMtxfbMYw8VSMkYsh/2rIWP730vS/ih8z1VsI8TYwl5O+6h7gt8AyIUQFoACdwDU/tM+P\n4uIQQiwRQjQLIVqEELf/B8eFEOKJ748fFkL8Q/RjjyubCPhWM7o5QbqmiCNVFXSbU3EEtLR6s1D/\n+BLFpUFubH+Rct9hhqvXINnPIGDvQ1bHwbuXESpMRiRUeOJCcPjhvKME5zxAlreFZH0tFS2fsuLY\nW+RFgtT6O0khHYFA0l2IkBeieM4iZXAdOlbSkXUTX2bOo0U1Euu2M3BoIh8n34hovQZbezuH4zuo\noZhWXkP2tKJRYyDXkUAhJ+snmG96D+2fXiD43nhijt202Y5ToFqRi84g1m8m/OHz5O37Na0TDczt\nb+bqP9/O2KZ3eeOsWXw06hz2je+g8KE36TrrVv7s72fIrKUq0Ez2SAgSjWC4EFImg7segn4ymMw4\nOjiNds5TL2WJ5SZOrf8zF+/4EwvadrLQ0Yh/oZm2K+xk3zlCaJaWwV/3EbytAPXmzRBeCXOOwNlX\nwhUhWHEQOdmOb3I1Mf0IpGVhLN9LXqsfrTdORUs9U275gqTpz2P9SEFpewRrIoqQFGi0YOo/A7lh\nLPoHO0jN+BatI4o++Q4ICqLVpyDO/BohRuBsiaNjcpipayCuyJQejzCu7Uvi6WGiaYU4x9zC++fM\nYJ2yHEevBYGZmN5AWJMCeSqK04t19wbs2WUEyvNxJXXiT9FSojQgx6tArobPr4BF95DwXEtQchDf\nuR0lS6ZmsI2x+tUcE9344hrEUAxNQxBNbiYxk0o82YZSlk1Gq5NWm5bBLhPJiR702RGClXPA6ITt\nARgagi3boHcvhFyYLH40UhCOOTHs24QmbAf5csLOICO1dtQpMaTBHk7kF+Arkdk+q4KD59QwKNLR\nqDloNJlI/X56E7s5JO6mZ9yN2D5ykRuox2JSwXM/cektnpp1O6XR1YTECYy9xUSVQwylbuToqFPQ\nfKPgnfwxUssEWGQgNDYbxbsDGq6EmOvvre4/yI/p4lBV9TxVVbNVVdWqqpqnqupLqqquVlW15vtQ\nu1NVVe39oX3+r5+ghRAa4ClgEdAD7BVCfKqq6tG/EFsKlH8/pgDPfP/+tyEegI6nwZANljFgqUE9\nfDvB6qfQ+eoojMch/w5G77+YQY1E5n4tG35ioCCgcEr9WwQuX44vuJtHdZn8KlFGUHVhOpQN/Vvo\nr8zB8NOlFD34Hrg1EAogy61kSymU7XocTHE6Rz9N+tAb4H0HJlxw8juFG5Eca1GFQEnPQE26h/lt\nu3g8SybNn8B96GVWxTZytWSHisVEg5uIhT6gp+dcbLkyGU49crCfrtdvZ+GrawkWHkNq2oEomwLb\n6on6FMp2PItiVjCmjUe1zCS7vwGNZRpdSSXMCXxO/NfXkTBmsYyjDPUdxb6lB/fdNuqMt1Gp70Do\nFSIhM1KoFlJ+A0KCwsvg6F3wsQOuOwc104RB5KOPWwnvXYRhrR/J0cqC09Yh27roLaogLMtEL4PU\n34Y59sss0sMbkeR8IHEyPdl88cnReDZZ5ijxwQP0/qQCTUodmaEE1XI94dBiPFmfkzlFInX778Ab\nRslIQEUbSe/lIdLGoObrify0FDnRDko2eBvQpxeS1e0gFL+FkO0y/KtvpsdoZqRgB2LoCCdspZSn\n3UIidw+6UAPGoW1synsWi6Jj8p9byPZZUQdT6Cl3k2P5I/hXgxeiVgnh3Y9r4CN60tOpM5yHJ22Q\ntFAPgS2TOTKqmNoDNzJ4phZNMEFmMMIx3Rgqs8dAVgVY29ledxbpmmeJapyo2Vbkg1ECYQMTOo9T\nnz6LuFzNvhIP1za/jWQpoL20FJvmYbSTSkE8AceD0BwAfyOE8xE2B4H5s0i2dKBps8DaC7DNC5Mc\n9hHNOAf9aA8z6rfQnjGDaTnX84K0n3j8XYYTCkbFh8/Sgc1bSR6/Q6RpUPIeJ7FWRbnqECL0MX5t\nATVJL1Et0nBEdiJKJ2FrzSZYbiTy3f00b8qn/JH5mC64DaGpIKE2Ei7+LVr/ZOT61QjLFMi7FMyj\n/maq/9fy/2Kq92SgRVXVNgAhxDuc9K/8pYE+DXhNVVUV2CWEsAkhslVV7f8RPv+Hkc2QsRh2LQZj\nIYgKYtI76Pynow9XQduD0PcL1JIMHKYExpCDySY/rblFjPUcJCbfQWJggIusSXxo1zErbkW0NkBR\nP1a0HJl9E4Xr24lOOYi84adIeZsxJIJQ9BKR7MkcdTzJOHU8PPclPNEIjodAkwLZa0hoY4T7L0Tn\nrMajnYDRPJb2nEyk839N8tt2RilPQl+MZqWGkjQnaxLzGb+1jbPS30PzhygWMgAAIABJREFUWgSN\neQMjdWnkx75EYwmRuLoRKXY1vPgRwYkakmtzEH2vgzoXvb8G//jXcUp7sPifAlkB4y1YgKxHV6Bu\nPUTTbxZgl/fRW1dKoaafrfLD5MTWMybsxG4IgCETlChILpSfX4nuvFSSvo0hXB8jZzahBLw4C7NZ\nGviacJkGeTjBmONtGAzl8LtdlP1hFY4bbif303rID5y8P/E4fPEuvLIDzrwdWayn0PoWIdpoM/6C\naL6G0q+aieVGGKpUSN++H+EKo5kA6pEcJN1k8MTA1Ih8ogfiQ1CWjRr8llj8INr0KIZAM7usa+lI\nzWZa13FGb4oRnnERTfIntOjuoTdYzGh3N7pojNX++1F/NweRpIPybRBLkHVkIv2L/0xW8iyUnB5C\nRRocARCNaxkT8OCfCab9i9iVW05guoYaTyOhMiOpzuUYDgUQ9g/ZkzaJ0lG/w/jNlSwt8vJxQTaR\nchPefdnEtClkxVtI2uli8DwbuZb5fO7uY44xQSRtNhbbHyhqfB3l8IOoxrGIqfkw+SoYvgyOuGHW\nzSiRnfhc35HIOp+U5BfhDCOS62KULz5D2L4EXQKDV6YwsReZXdjatRRtayWSK3PngnsZbU/nDO8z\nRHwrwJSJmGXHmF+Hdv0gnnOvJR57m1XuJmh7jYzad6AqHfWTq4k/Zqcs28aed6aQbzZj/r6Er0aM\nxii9R8RyN6GaPVh2HkEMfgTTdoE25W+i+n8N/4hdvX8MF0cu0P0X857v1/5amf9ekmthxnYo/z1o\nXGgNKzEoNyL2PQnhFJj5Npq1ftK+HmLT7c+RpJHY80gFjuJknL0JYrYgue7fsdIMQasNr89JKDUL\nW0hLKB5n4Po/ot2YQSL2BvGcIRLV16HmzcV96E6+zrXgbXbxlcFO+/CXkL0Gch9F9ToQm69E7ygB\npQhryj5UJGraIjiU0ZxTa2aZdRLdD7zOl6Gd5G8b5g+7HyOiTcWVlgyP/4LG++biuHMS8txaqElG\n46/Cm2vHddloDFXnIY7lQ/ItMO0OcEc42Ppbxh/5iLiYSEw34eS1icegbTvqNZks8L5PNK5i1Ngw\nuu2cZzmDwpwn6HDcS8+hG2D7K/DgFti6B9UdJmWtG0PEgTq+m+BSA+GJZkKnmQicr8M9IxlLbBhv\nRhrDBQMMB+fg/bUWEXiW4dP9DJUewP/qeJSrJkPUB9esgMjLUDTzZNW9RAs6/y5Sj/tIJHZhbo4y\nVGEjlizB0jro1CIqp8GtD4HFiuipR/pkCFrikDgb0ZqGbN9JxHwenbbRpPYPc8qjW8h+tInh4mEO\n5+/DoIlhUnJZ1qVQ0j1C3FYH3zyGUIdhQgwy9CSOpxFWB/Gp2eyvLCOkjqC0u6mvXU6Brw6j2YJm\nOIzLvJPelBHiPj1ptgexJ1KxNPehbXyL2Cgti0p7MfTdCbYuTD2dnLn7IMa2RRQOx3DqjAxmpKEk\nyQx0raRRu49ep5am4M9oTupD1WYij74N57QziEgu2NMKn98J3iCq04sa3AjpRZgLnYT8b6GERlBt\nN8P0Z5GrZyNiITjoQXPcgwE/ytPfML3lDeL+JPwrqlijrCXhiXOz9SO2D08jrWsFqfa9mCa8gJy1\nmP1bdGj7ctB89zM4egBULbHDzbgf2oHxrBXk3fUcp5kvQsFPkHX/v9oJIaETV2PQPUl41qkoo38P\n7t1/U9X/z/Jjpnr/WPzD/UkohLgKuAqgoKDgx93cXHpyZJ2CCPfDoZshowryLkBpboXicWjnqxgC\nT+HVHkVk5PHpdedz/tMfc/T+XKxhB1nqJgwxP9qBBhquKMLi0bF0+8N8PGYcZ8j56D4fQh0ykDjT\nT8RTiDErhdGhCEfm+HmZm6iTMjgjtJu63esRWgua6W/hNcRoH/oF5dFqJoZ6mNhdzCXWXi5LeYVb\nWqu5pkKDMz+DzIKnUFsu5gLdRwRy3DzsLiJbcjHKMp54cBuyOULAMgp1/ZNYLJMJn52GtnQMbHuK\n+OtPIlxxKjd3YRvyEB+nJbrjKoZn3IJFZ8R4F6itw8QKNXSXpzPK4CfpaAYkb6Qo6iA/mIPU8go0\nfQElDqgG1/mjSXtPEFsYReTG0MsalLiH5E4fqV97iY9PxelVSEu7AclZBI9cBONKGRyVgv2hjah2\nM4FV3YR+tgSjyYbkrYIjb4G9CuWzqUjDbeQbnGj6EiTSdYisEgoOdtG90kapZx6MfR3MWZBkh7Ez\n4LuPwQS0tcK+P0L15UiaNAy2pyi+uwK1r4vgQh0bV85Fb4KqE9nkBJIIpPcha44i9F6U4UOowX1w\n5kx8mVMZ0n6Bd2ou5lgOLlWhQDkdXeZmYgOprMi6E8MiN7GPS1EUO3unncuSZ1+jZcoEoiYTxhMN\nkHwMygoxWLvI2/sNis6EZL8O4XgGXdFSEuPuI+g/nUrZxKA3mz01tZz4soORWAkTcxu5bTDE+hQz\nDYanOS4OkFZUzqz0X8Hun4PVAIYS0DbA8DY0xXuRJSvZx0ZQqjYQcl2P7vBOtOxFHgoRyc1Df+FT\n7Nv2DlNKGhEfy8QXVJA98gBkTOTaw1dRZ13F9dZlOEOfcelgBomsWXy16EzK3vsV5rQRsGrBbSD+\n4U0oW9uw/mI1krERLUuBZcAyVML/m9pJIgeduBSkSyH1x1XpH5v/F10cvUD+X8zzvl/7a2UA+D7g\n+3mAiRMn/vdFvBuyYeo7EOgksLkWV1Y+XQtmopWHmNm3DVOml9qeOEbbftzJDrLqqxmtUaD6TJz7\n3yRYAqnxKEmJMIFiE8sO72D7yirmvKogDjiRzZ8gz3+JRH450+M/QauGmVGwFV+SkbeHMqiovR1T\ncjGNbGNQbWK23oSm4wPGOpMJZ/hR993CiYqvqRPf8uDcd7hSqyVVk0NvvoOUrwYJZaWw+g+/J5Aw\ncvCXbqZ81UKo1krwq7dJHdShmRtDrz8NysyQiKLRa4m1NhMuyCccbsT0TS+GaBzLPj9xqx6RI5Di\nCRIjMln5fvQDfUgePUQdoM9Go6sASzZcNI9E8jL8nTdiNC2DmtcQaUbiugCS9SnCdZfjfSuFzPHl\nyEN7MPn1SL2PwlUnQDWCLZWM4yUMnhEic4cDS6+L4O6P6Jv3NQbMyLWTMbtvQksXarIWEVQhLR+N\n0gcDPZhty+izBrFJQ6RmVv+v+3na1TC4DfzrUYYGEC0nEAXng5KALx8CBJy3Bl3FeuYdS0ZKPYyk\nZKMe+hB9myCmZKItSCGWCDJcI2OKdRPxdpKak0aJ6W2QrLTFLiGt4WpM2ofZfurnjPMfgMYHiejL\n6a6o4PTGDDS2xdQ+9xKJ+WlQ9hhK+8tIS0oRn06A0XtBmk88+iEaWy0xfxMOriBWvQTDzq8wFgc4\nOnoJB/Kmc+eOn3IsOYspg5v4KGMGNem7yWU8Ewdq0TRcD1MeBVMKyu5PSCyIovEch40ORFaUeDQT\nJboEU6qHoXw7xsFpmMr7aJ5eSoV9KcdFKxPuX0fSgmzUhAM2fAgzMiARZcp7l/CnUfP4zfiL2BPY\nzpgg1BuTeWL6VOh6D4YmQkYO8vS7kEMPwraXId0OdT8F7ckICPFvzS/+yVARRH+kVO8fix/DxbEX\nKBdCFAshdMC5wKf/TuZT4OLvozmmAp6/mf/534gFYO9D8Mlp8OES6NgAjkP0qq18Nf0UjhbWUjcQ\nYcLhHQRHqYxYbBhtNRR5QqRMGabq8d0o3m4Sm35Gyi43nrRySnzPoEu+DUdBAU0zjIwONNFzaiWk\nyNDeCe/fykDrW8Qi5Ty672bO4V3u0t/LT1Of5gp9kHuUz5GUCPMDbWjVPoSmENEbYoN7Bg80rmKq\nV6Yq3UqVt5kv+vW4goUQDOMvS8LYG2f4tjI2/2ohWQ4HBxfUonZPoO2n5+JcmQJ114LeChoZqpcj\nLn4fXdEKjo5fzb03X01iWjqJYhu4YSCSRuOsIrqvWk7s3DKSMyqwpLwMkoVw62ccDB+kaeAbhoZL\nIZpGVHecoboa9MaZxGdeyXBEEEpZjmg+AGENGoeBeKodpcWMRpOKGgnDF7WwyAsj3yJKtxK+IIHn\nzCSEXsa8p4q8T4Yxu+rw2XvpyYricyWhiwokI4iKBYiF3+IrKSa49A/oU++mx95DvPf1/3V/VRW1\noA66vajpQRRTMuzvgafOgpxqWHA5jNqGfDwDc/cejBnPoS+7H40jTGJUCfHpCqrejRI0kHLASNLh\nbtIc3diOHET0Po4adhOJjKCXphGfewZqogu16Vb2j78c1etn/Dc6NDt2QO1yen/9JEHlMOGi44zU\nqfTFjIT2H2CofwIx9VO6k6GrpIWwtwVNcznm1DPIHEyQGfEx0VLBotGP07yygJn1DVT4j/PAxgs5\ncfBOcva9iabtQ5j9JsRN0NODtOUPtJXpaDNNJP5sAl3cg0h0oN3lJj5iQHEYcOqjRKY0Y+49wnHP\nlZyy7jE8RXas2n5MlT+B5j1wyXzw2+DEeqakj6VYa/j/2Dvv6LjKa9H/vjO9N/XeJUtyl9x7wQZM\nNb3jUEMLhJIQEiCUwKUmJJRQDAQMpmNsbGNw75ZtWZJlyeq9ayRNb+e8P5y7bt59uQn3XS6P9S6/\ntWatmTN7nW9m1tl79tnfLkwxzmW1WiK3dxtqTyVkTIWt+6C+Fj6/B6ZfAr8fhoWroGv/qQKwnm++\nV7X+LvnXGPS3eXxf/Jc9aEVRokKIW4HNnMrQfENRlONCiJv++v7LwJecuv9pBPzAtf/Vdf/TaExQ\nuupUbNPdAL5eGDhGqr+P87xq6N1JLHESJ8pzMChBEqN1JOx+B/n0HWzLeAx7914MwQtQjX6C7DaR\n9s4JlPRXke64gqMsQ6v7hrjCENQ2EEzJIjJ7MgNiD832ffR6Emk2pJEk52NQf43GfJDHvDewMXIz\nT+rO5H7TIgqFmkhsHarPbyShdj/mi0wYJk5lx6GNLAhtoMK9g5cD93Nr73t480ZwhrtxbQtSc/F4\nblj/DgeWXEpL4SIm3vcucl47nQUDpPT0IJKSEEKc8iRNepZsO8TUSfvR9PWAVaLx7IWsyVlI07gS\n7lW/xIjOQWLoMGHHSgZm3EGzeyt9lgMsbKskLv+PMO58VO0LyNQ/iuSaTL+yFmd1HJHq3UhVfvy3\npuJJX0LI8iGhRUm4pRhK0qNYdz4Gi1+EP/0LSnw7qf0GTqw8G/3hdnRHGhFyMqqmRhKG+ggV6wkk\naIiFzZjHv4M6ezEAIWUFHV2XkZn1Gpst51M4/DCqTz9GHAbMreCSCMwwYxjwQuI0+Ho1zL8TMjNR\ndtwDXd2IRZtg1p/AGA8DLZA4FYN7AEQZNDYxcskZJGx5ATmSSChJhdFsRoy2wP5S0spSkAKtKPUv\nU+qTORlOIeeFpzEFvESvnoKcnkGME8TYTXvGMEnqtwgm6bG+20jnuYvIlvbiNcRxMpyI1u8kOXGY\n0MhWIspUrHF2olITJ8VmLO2zaamqZ0JaPfeIzSwvq6D9uAldWEGK7oGDt4I1DixxEHQTUxI4efoA\ncf1mlGo1ynWpKGW9WI6MkagfwDe+ArXpYbJVEso79xBrNeI7w47vQAKD5bVkXHQ6KpcfsX8XzDwL\nkVrOHQq8ET3Czzo381bCdE4YynlefhDd7KXQUAGJYeg/DPvfgIRsGHsY+ptANwsS54L0w/JEvw3/\nyV4c3wvfyadRFOVLThnhvz328t88V4Bbvou1/ksYXDD1rr//Xv3rdKtrCTk2kduXhcpaixwz4FPf\nRFRkcnLeDKbc/zQDz15BZMYsgn1b0PdtZ9S3j2zTbCR/Lzr3SWKT8wlVnEDRHWHv+Ln4e9WwycS1\n1W9i8B9AJJ5FbMFcMoue4xZTCxf1r+R3qW9gUoxc/+m9pMYGWJv1DLMmTiDMGxSOV9jTYeC6zl7q\nB3rpHHORPVjH0bLxpE+fzs3frCViNFNq8LIpX0ex20aDMxX9a39mdPNT6GcuQf/U04hnZyP7hvGc\nVYojqkUZJ4gFBUnhaqZ59TT2ppDcuYjgjG1E2i7BM/or9KMGZlebiCxfjsFeA8ouUM5Bk/oZdFzJ\nQFYqTvEAmvGvohx8ESkYQmsex9h5EurNMupJcTQlqvCZM7HHvUDcxnswpk4DfQSl4GxkuZGguxa1\nYxRVQgztODVKvwVNMeiH4nHbewmpbsb1ShHq8puxTboLad9a+jIbGdc4jGeHGmn6ZvxBM5bixwmM\nn4SqaSnSmzJIDXDNlfD7h1Cqw3DaCBzTgD33lHGW/SjxWaDPRZS9A1/PwzfzZgxH/8hY1lJizfsx\neQ0gJcIbQwxfLhjfUYOsyFD5HMaGYXIn2pDO0RDdHyTq7EVNKRouJIGbUW2YikWdgaalHaVdTZI4\nxM5p15CihyVbn6V9YRY1SbkEhInJg80cmg9CN4GsYylMevRxmq+/naDBiaNHZsrQVuImXUriaD7Y\nkiHtHEhchHL8c+S+BJJONGHd30P71CwK9ugR1Uvwn3GS8FADAUcr+vAo0ZPPER0V6GpkpNNckHYz\nhrguEvwfEE4bwvizL+HNZSimDiKeLym2z8ASqWFysJXpTVFW5F/CTknF6aPfoJmSdsrBKT0TFj8E\nTW9D/UsQCYJmLnQ+A2n3gacZTrwG/hYouR0SZ39/uv5/yQ8tBi2UH3Bjk7KyMqWiouKfC34XyBF8\n69JoX5aOYXAEe3wMX/04+tXZ5GZcRX3gFcrf2cugUYfx2rVIWgO1B+6ib5qdHAxoh+vRW45gwYr4\nTRGHbouhUU+jpHES14b0fLj3OdSuFjgcgG7l1GZWaS5E98JUA/v803i+bBXnmddRstHG+Fnng7oe\nuedN+scpqL1anB/k8djsm5jT+iQTkyJ4A/XoRhUsrmKa58VTGUhi2UmZmKEH4/i7GXztOUz1wzgG\nunHfWcbolP14htRojydRVXQ6BZoE0kY6cXZuZIvJhaKFFce24gtqUcIFmOMCiLQq+GgClI1Anxcm\nTEMutNEbtxuj8efYuRUiHcR+Px5F1jF0rxUNf8DR6CIgnc1Y/O10WYo4yudM7LYz6ZPPUKcECC8e\nxVeTwpBeTX5NI8OLL8Ppj0e5/TWUO25CKlEx6PwLAY0eSeMi6asLUR3djTLOyMjQYUKyjc+uGk/i\nmI/la9cTvVyPCIbRHpVQhwNInqVg8kPBNSivPQ72IkShHuWStfiU1UT6HmQkPp+0twyEihYigg/T\nm1JE9qFKYm2pvHXz5aza+BzCLiPinRzNuYDkzp0kRjyQ/0fYdi2iawiKlxPt349U6UTKuBCuvgeP\nWY3++SR2LruZvOEPSGkZQRn3O9jwEs0FQdqnZpJmMZHp+4raxJsZDR3GZehHM6ghu3IYw6gd6dJD\nyAfmEWhsQ++TUGkiMDYBxppQxukYrkhH17+L0QGZaNiEiBuj/4w8dJHxOJ+tQnX+RYjm1xGZA6hH\nJcwZajRKCHpANklIp91BLPwWUe0ous4cRPF9KJKMPHAHkfEPolVnMtz2MPdP/oRX1EXIVX+hIf5J\n9vfOYEFMQ5a6+FQvkZS5MNYCb08F5yRwlUD1O2CaAENVkKWHOS9C1nn/reorhDisKErZf+UcCWXp\nyoUVd34r2RfFz//L630b/mc2S/p7SBoMOiMFVcO4wiN8oruIEyklTDj+GdbKX1F+aDVc+zb6g33o\nX7kW/3AXnycuZ0nXPPLdi4l33kintJLhwxPwpNahtXtodjTy9sndzLScRH327lOFn/c54FEjXK5G\nCdfDSQk+DzNp7ABrvriWGT0HSJq9E9ZcA7XvIzmXkFRdg80p8P8yyi8m1LPOdSXrCx+ivSAb8uYi\nR7tI3F/BlKr1fDJlmO6sVnrV7+CaEY9vyQi9955LZGIMJSZwVsRI6xmiOOEgqrhqOvPiGdCYyB7s\n5v2MMziYk0XfJBve8R1ErC3I7ZPhFgk6+uCwCZQDiJPvkvjTfswjp8Y3UXMSqUUibJ2F0q9Fz15E\n3jRUA5NwNn3CVH8854WvJDXpevxzrYjDasSggqOpFZcvjY7Zv0F1rBnFkwVTg4iRDgg0YdHejzYU\nxEc/vZNbYOk5iG0bsA+3MKBTkRUq57SxuWhrLYxKGhRNBGmqh/ay+fTlZhMbl4ZSkIOypAg+3cyg\n1UI1lzPmfxSNfwRHQwdeWxd+65sMZ6Zh7w0hau10ZZdy/kgRSmUQRsLQP8jEQy8TN2KCxFRE90OI\nyb+EJU9AQze48hHX38OOzHbqfn0aNatXcWzB3aSbysjobcObew7VvjfYeo8TzVCIGc/uJv3dveiO\nJtIr9WC1+8kfkkmuTMaUkIU0NYbceSVDGX6i1+xDVXobyBNgpgmu+DVCtuFI3YEpFsE2TUV6io+k\nmS7Gh5sJXjEOwx9/jbX7OOYVEuZkCaMBhvpdROtMiFwb0dkqvAlvIE5koT1eivD3wN5H8eTE6Cqx\nER1YD8cuw6SMsUiVyGeMoRrZQpbOSaEU5MspEfq6joGjFGIhOHInXPAlaC2QFoD0KLTthZAA7QpI\n+fd963+Y/H8Zg/7/hTAhRGIxUtNGhCaHa/bVEnZMpDmzAOdwO5bJb6G1lBK6SIVxpAHL1nn8JOs2\n1IEqfOnpeF23o5XmEmx4Es+CBcj0oNKGaD8/HqM/iYjhWTQHP0DJhWjWAORHUc2Owt5iov4RNFt6\nkCt1pL43iOpGBS5/GDbugPgDMOF9NKNvo26diTL8Sx6rNXGr7ndsiLuF36T9GU/+PSRXbcWaNYuh\n6Ag+TyGOvk+JOMrQ5ZYTHFpNWOiIqlNwLL4I6xdbyN80SP0ZJ7F3djHaHaaxpJw7P34DxZJF7txn\nYKiamOculN526DeDXYEHfoZi24CyphLJPoR4fBZMuR9efQBsak7c10bpR1aCN20iKCWjm/oRga5k\nNAEDLvdlgApUIWAc6poGkDtx+NNxaKLsmziDmZX3wpkxREYOBFrQaq5BH/oT+qp25OhfCO/+hmDJ\n6TSXj+EIx5H96msYiyJgyUfyDuOxR9F6xkjVbkNd9Q1BnZ0mbT3+M/LQT5hHyhd1ZA6fj3lARupf\ni2weIDzVhCdzAmPNUdQNRwhfcDUtyZ0s/PQtlNMXQO22U63V9emovuiEn3ggdxsYJp66cAZiqFu2\nQ9tfSJ+8nKYL9FjbZFLe2Etk2TZ2LJhDs8lCSchIwt4EbPvq6WpLY+Nty0keieDaGaawTI+5cTbm\n+atQ7KV4+ZCA910cPbehGdLTYksku28fnL0bBtagGBSCWVoMHQFM1z2BSL0dv+o6ItUhbIZFNMz6\niElvr0dyxRO7aAO+pj+R8MRe9s0ZT5mxDUmvxnxChUjphJ0O6A3A8iJ09V+R1A/SaB1yxIFBF+Ui\n7NygdDMzXInd3cX0PTJJ2oU0FLuJ+/NVqLK0MP1noLOAWQX9XeC8BGYYYcaNkFT8H+rcD41TWRw/\nrBFdP3rQgDtcz4fhx2nXxZA0ArPXiTTpCfTjHiUyeRrakUQOJE8AlQGVkNk/aQIbzlnG8XHHOZjr\no9XSR0AZIoXJtF45kdxv7Mz5aDmjciInWqdybdxD+Ewv45vaALHdSNF+JJIRujRY9CKe5RLhn1qI\n3Ogi8rtE5PlhZN0OuOppOGCHSAFojQihR5LU6LPgUvU7bBHL2D54OXlfv4qpdR/JnU1Yx4YY6TqC\nrB3Fsv7PGNmH7UQY08FiukUq5sE4VGM1mBUtxW8Ooh9opO5MDcPj+onNUIi5u4gNHIHYZlS5z6HK\nuwsGu2jJz2dNxkE+i1+C++5qRm+5kkhiAkrVWygTHQycXUCKexT1Zc8ighGCymOg1qNV7iby1oMo\n8pWgqMFghSXFSJEYcn4GVLUiandT3rIWJSMKeidKax8xqQtv5GqMa1uwrAug2OPYece1fH3DcvLG\n/YwMXyXaKY14Z9yDIiXj7OsHsZD6xOmIdpnY1CTEFEF+pJXyyg8Z7zXiWZTCC2GZZ8R83KZLiGSE\nUUXzSVNW49y+h6E8PSLlQw4nXYV8y0ZiE1YiZ6ehdGkQw82IjHY4qqDoi05dOKNdULcB5v0CJeAj\n59P7KesYYbqyg4S7b6F1RialsRquHXyRab4HmFJzENdT20nNn8p1AzHsziCLT9QgyVEUfSmxVx5n\ncPAyFMVLvPs2NG098NKNfJWZxeays6BhN+i0yCl1hBQzsVk6REAGSaBWirGOux330Odk11VT//wE\nlNcCeJV9eHIGENfex6ztB+HQEJoDakROKaQH4DQZrDJsrSFYtYOWWDYxRYdaHwSjASEEycFGVs56\nDm3yYaS+k2TX1TBn51dIS5ZCaxesvgmqX4R5fwJzGvQPwznP/33jHAvB4LZTrRfkyPeq5/+M77nd\n6Lfif7wHHWx7GXXtA8x35pNkPY4y5wbEgb0Q6yNEIhptMiJzKaqWHfijTyJ8XgL6y1ioXkRQM0rC\n2lWMrRjEfOSPqEjD5uii7kIbxQmNqKIRHvY+Tpr0a1TMRbRdh+IZIPxUiMAVA/RNX4Jm5BHaSUNO\nKyHfuonULjUkOFGie8CaDZc+A6uvhhVqiDsEOhtSRT9Lm+tpvPxdXvUmEhmpR5SXE53YzkWKin2J\nE6GqhZaLZmNHkLikGdtTZ9M5YxonNPVM3DcBZf9uNEXlxLdFcLaq0Z0t0VmQRUnDQZqqnibfHEbY\nZ4ArHXpCZFu0xJQSNqi8bOz/DamdQ7h+piZFuhhV53SqdM9QXpOCVPkoBtGL0uFnaPZKfMXFtF81\nhse6n5R+E+N689FZtoO+F+G3oRQNIGQX6pRCZHeQQOFcRLAWbUsP5l3J0KomOjOdgNGHg4+YHHgA\nU+UKUNlRBybTqF1PoX8zZMzBongg3EcsaESxB9GGDAhvL62uxXh0Ho4n/Bz0Ln4qnYVKk4i2YwaR\npFHkA2ditRhpLrMjYvPIVXVTwd2U1cxGGZeBrBIocVZUDb0oZSOIkT+A4x7c+hCOlp18rawhP93G\nYPmtiH07aTBOpDxiIcE1iZ7Afly7JyBqzgV/HCK5AO3Pf0FF1R04H7okAAAgAElEQVTMnpCBUq5D\n6oni97yL52I3zl0L0O79IzgSwNMLv9nNDdEhXszJJabZyukHN1A1vhTn0ltx7HsIah4BXyd6myCU\ne4jxo0Gi4RMIcy4Dv7gV+5+OY7h5EnLew/DIkwzvfBvD9l4cPZVQApj7Uaab2RS/lPeWz8DUtZDf\n5+RB+68gtA0hB7nH/SD1jntp1JgpmHwaZC2A4w2IEy/AzHPBvhw2/AGUDVD5Jdx4HMTfmeQtR2Hf\nIvDUwOxdIGm+X2X/FvzQNgn/Zxvo7i/Q9HyNLnUVltyrkEcX0m0/SnLWAlS+ATyWE1gYh3ZqOSmf\nLeDoRBPFMRV5hkJsUg42YCQnj6DtGOrEO9DvPYDeHiStax2q2BIWSCX0Z8i8L06Q1bSBqR37GPIl\nU3fHcig9E7XQIgdeok2fhVYbhzvpSsrjT5Jp+Rdkz1Tk6keQ2tVwug3atoAIg6UERr2QPgVHy0Pc\nG3YTm7MSUfIgA+IIHbGXWf7QSTpu96DXq3jPciFXiyDOWVcz9bCawLSZKM/fhnTfIuTC+WyK97Js\n9160J7cTzBRsXz6d7GNdxD5pQ523DlKTIawBVT954Wu46aUH0DpSaLtkEYeiW6mUWsh31VBqeArb\n/GLkwS/oN3cQ2+fEXn0IS1UtmjOXozrwPgnBRIR+DqNZZVirHkBZMJGw+hjhsTDa/hii9DLCvr2E\njSoSnD9DaD9AmVuOb8kKVD3DpI+uRe2+FlKMYItD+Moo/Ho1IQ0YzI8gaW1o3cdQRS6hylZKhfkK\nUv2jTAk1kpXxIqV99xEQb0HMhNazBMk7gvbQN4QLFJRpd5Ix8CFVtk4SOEgFN1O+92uUKYnIqgCK\npgrVkIFoKEas/wn01fuxp51DaNwidDnLCMoZ5G19lq2ll+Bqqif25V/IXNlChSuByPRX0VbMg8gk\n+OjP3LTgTM4eKsW0dx9KyQKIfoqId+M64EUE65DVIDqOQrYJ74dn4E30cPFokPdSV/BpYRaWksMU\n9+2CzLnQfAiankelVyFbl2EZaqY+8zROKkYSSg4yo68WxadBejaMdImWpJwZfFhu4/wvfo/GEyHW\nq+fxGRXsKu7B0urkpcIctGoBgROnrEPzVVjZzdv6uwlW/5qYrZXWlBdId4fQupZD4kKwj4db18IT\n02CJBiIHQbf0fzfS7oPQ8ChkrAJj9qnWCz8wfoi9OH7M4vhXwlUoxGjVXo+Vy3BxF838mSSWYySD\no18vwGjXkdHVgiHtNoj0g3knw2oPofQurKqJGAI6jpkFUsxAvHcRzi8+Ye8Eien7TShLDhBTBGbX\nalR7X4DSn0DOmXSFp2Lyv4zdMZ1hBnhdeYIpvmEW9rciWw4hfRlCGjsLko9CSit4yqAucioG6qiG\n8aUQbUWxL6cuxU3aW+vwlg8RK5iIVvsOH/AhZ3EOGUo6DSdvIkEzC3vONcgn1rBNV0MsO52MoVaK\n6gdQWj6nf5GLo9o0Ut8ZZHyvDLY2mGiEDSFQF8Cdb0PWX2/xlSjB4d+y31RBqzqHxDE1U5s3Y5xo\nwxxaAdFOqKwAcxSMx6Be4LMbCZkWYKSLtrJRknr6MZgfR/vCE3BLBYrNSau/AHvMi6YyGXXAgpyx\nHE20jcGcLxAqEwmGKoQMYvAFAkPNaG//C96Lr2bbdb+HxsdZvvdZYgUKrepCBnPimOSeiE1pR7Z6\nQDcb6YvXTm247QtBShRlQSvekTMIq3bRYVyBWlNAvXI5i+69B+tvAtDeiCJUqLamoozWMXqfD12r\nHsOhAEKbDWfugNYKeO10lGkuetKv4KmOm7il4Xz6r8pErzYw+Rs3In4Grf5G2mMB5vqaUUJNBNNU\nqOyzUex69P0LUT58AcU4TKg8yMCceGyKF8NJP+rDEfwtFh55/nfMiBznPM9UGPkGMp6DvReBbycR\nowtpxma6VM8iAt/gGA2y07SEQm8uue+shbIMlKQEtrlMrEksY3h1EkXSMMYFXSwPB5kSfyGSWcDg\np+A9DKpG6GsCqw2ipdCwFwomETIH6MjMw8o84ocSECMV0PIVWNNAOwAJc8BfAc5zIO4n0PAYSDoo\nfBDUlv8W9f0usjgcZTnK4opHvpXsx+KKH7M4vle0ExDayWSwhRDVyPgI0ImBNHBvoXbyTGIJQ8hm\nB2iGYfBx6N6Jo/4ouh4fus1DSOsPIw2HEL4oSTv60Dd+RTBFj2mujNk4Hp1UQF38RnrOuhr/wOeE\ntpxJIKTCuvFBooP7ULGH2YqeIvdWImP7kcJOYnOL8LceQCnKPjUDuKoDHHNg/UFoHIOer8EUwK/+\ngtyjHxKc6UYpWkSqdhPxJHENq9jPXhCC9MxfM1LxEHu7rmRDYQMm31Gm17sodD6OMut15KUXk7Cl\nh7kfNNF6fTo9aWMoU71wPALFS+CCn8Kn90LlhlNVY9Fq9KqdLPBs5nzfPsZ3f8LBrAK2S9NQ9GeB\n9QmUOXsIdvTgUy8j3Gsj2gvhlJMMl2aS7BmHOZqJJvAqnHcnrLkD4T9JWq+RcI9ApxuG3FLUHavp\nSxGomguRAiP0xC5idOR2hsRWmsf5IX0i8uAuCg4+wtm7XkPXBv5aI13FDnySQnfOJcSCW5AGqpDM\nt0EkAIFemJ6DkhAj1mJHHjhKlzGVQlGOkTymtWyisiAZwh2giYFPi/DvRuQuxLA1AdmZDWlLYdqj\nMLgP4ksgBCGzk0TfLn7pupmXC/8Fq2LHtaOCvTPS8dRVsHLiA0yQDiMnNSCKIujHtGi3NxFx7yfy\n+UvQO0jVdbdxcOoF2NrOwHp8ImrDrQh7LpsenstFldvp0l7KN2IY1DYwJKLYi5ELHkQyxZAjq0iL\nxBM/akFzJJMlm5qpESo+vuEuYqo0GHOyQKrEFskm77whrLOauMRxAVmVuxBPLYCWD6H7JVB6wdoG\numwQdvAZYXkFTPoaXVEtucZ1SMZCmtIrCSrZYL0Bpr8GjsmgmQyDo3D017A/DyxhiNODyvT/WMn/\nMf9a6v1tHt8XPxrof4cKBw5uZYhnAAXh7YfaKwg7poMzE8WUABMeRMnfSKQuDmpBGpVRYpVwtJfc\nTW5MkQyk6UshOYnshmpikVrQGtBLZRQqv8Yn1VExvYru5OPkvFeJkL+CT5fS7/45UbGLwZSz0awN\nMexWo7IM47lR4N5YR/SIAY7r4cMt8Ju7YYYFNBEU7RFGrDrcES3RlEKSGsMIz1Ei+DnOm1hoZxO/\noVq/DvKWkrnjCFXMYZp3NrY9axGPzEOMDULkOCSOw9Q2xrKW82ifk4iSICAhCOFKcMbBT9dCbwO8\ncAF8/B7I74C+DuvhRBIs5zPVFcc0Yoz4nkPu+5K2oYX4baAZc9K2IofReQVIFgMp2jex6u5ECg8h\nyz2QlQnlF8Ket9C0nsS5O0Znrgpt9Ztos/5IWquahJxXcPUvxTByGDl8lP6ID9vu3SjJIRxaC8XV\nzyAMMk23nk/3jGRmv3SAuR+nM+B+jy8dy5HHtChjO1G0U1BiZxPu3E2UPvx6LV0pNmzhM9GIIrKU\nKxlteosU/QAB9QBK2pxTHmDGKpRIN1p9JprdEUKFKdCyGuKmg7ub2CQrHnOAvYsuptfo5obcRwm7\nB8kwF2EarGVHVMsvh17CNDyIT+siOm0P0sVuRMltmF4eo3qWnQhhMj0q5stXYqvfhohfjBRtJ2YZ\nIqu1m+KPurhFNZsjipf1ajUoUZS6RuTkVxDKCHLsJGKkBX30LlQrD6G9eAfnZD/OrFFBm+8w9SUr\niDrGk6gcxRDrZWVnHcOtqxADJxHWVNh+DNRxkP/mKYMaH4K+Hki9AlxTQOMEIRAI4jibzI4z6LF8\nQfd8K3KoAZzzwDkHTHlQ+keYNwrGVBh8GjovAzn4TzTw/x0/xDS7H0Mc/wGd3Ic/pqFgwxow2lmz\n5EkW1/2Z+ME4JMWAHNYS7nwbjSdA36XZJOxPQS0fgpiRUCwNjbuLgGoQbShAwGFASclBtnsZslgx\naCcwmiTjFVUkVRhJ211NxATe8alYlTQkbSbSkc+pnJxFjqkdqTGAT23H3qhB326Am96Bmoeg6Qsi\nWhuByWHkHoH5ay3R2V40xgvwljupjOsnYIgnS1pGBW4u5jIUGdZV/5YJIwfILH8J3Zf3wO7PUOwJ\nUB5AqMrBPw0y6+GT7XDGCDTnQuMAmAJQrgIlAgm3wMvvgN4Kc3Jh6ZtgTCY89AoD/BZT/xi+HBvd\nOifJoxOJBXZg6o3gGtAzPNWDK7AA9o6BsZWosRvVUQ1k3IHY/Tq4u8EeRzjeStSsJWRPIlpSRLz3\nJdpLlhPfsZ3++Dz0bdmoLDNRF1yK9mQF0sbLqF1RQOLgMK5IjLBQMPeqGW6bzGFdiLLiPsyFragq\ngkTnOtG0zSGYewZtxqfIGxugNf6XVGkEI9Iol170JvVvrkAjDlMUnYWo/gx17yoURSZ42ueot9cT\nMVrQS5ch9e6EhS8T3HweTXMm4zwRxFFXT/skEzFXFG/JfDbUTWJh3ftMjJ3E2t/PaOJMrKYsFMWG\ntP5LRuaq8SQvwaYqwJ6RAc1roHE9XHgUfCeI1d6CNBRG+DRUzXmU6sGdDEyYQY7jHCZtv4e0smRE\nzQv4k1So2xLRzqwmqpXRxExw8GHo/AplrJstk05Hzuvja+NSVp48CDkuio7H4bjvtzBrEUybAp69\nEIlDyf8agQli46HPBis/+N/jymO9sOZqWPUpI9pD9AeeJLXdiDHkQhQ+Crr4f5NVZIj2gtCe+gP4\njvkuQhy2sjxlRsWz30r2K3HO9xLi+J+9SfgPUDMXFR+Dw0drTya2uvXYjh1B8rth2ecMpnyJ65Ni\nIuPPJSq9D2f+iQAG3M0/x9W2mUBQgsQ4tk/IRCUJph9oRds9RCxfBucQKd3liNYKlP0uWmZMwmBP\nInnHN4hcAxTJkFvGpJEg3pATQ5oTg7mV0eM+QiUhYtmtCDmGd1keMEzy4ShKpoJ88yzkguNENUbM\nqguY3+aGlmdh0Z/Q0MhedjMmpTFu4u1kf34v9b2PUBIeQMw4Fzq2QV301Nius94g1pUOo8OIDkDX\niihyIppGoMoECQ6o+wqePglbf0owwYxk6kEjx9NgTgXFCDlRlGgYl3qEgOUwGbv16Ho6YdqtaJve\nQ17fjXT8MORrkCZNQhk+iGx+DGlcDKldD4/2opUkeniEJo6RoFyOZ6CbMWsTncp0MrpbMH9Uha7q\nG9wzN9BXZqP7xumUnKwlvmEEf4KO/tMWYvqqEUfiDqyaqdj3QDhdQZUmoXWn4C9YTod+B3mdbdTa\nJtEa/hpDIINpfheGlcswCSMDWolaHJRE/RB1I+Q0dIbVyPorMXxRQyz5RcSURxHH7oYUDRqpmahj\niCGrlqTtg/RcPZ/6Ji3GoSESE9xUTcoivq+UUdftHPCPsPKZx0lrb8d20IV9bB1S/hw4cBhU9TAs\nwW/LwZyAFFcKg/1EZRtvl6RxZbSFkroOtpi/YY8zmUt7ExjR2NHGRtEW/4Ve+TV0Yxacu9bDhBsh\n8BdEXiLzE8p52DPIQvUGKktSOY2l2FLTIfs12LQbbn4VJfE+5GNpiG0gbO5TXeqcTqh8HSZf929K\nImlPVQ/KMnbmYwn46Uxfh2xMxMR2Erjw32SFBJqU71eJ/5N8l0NjhRBvACuAfkVRSv967CngLCAM\nNAHXKooy8g/P86MH/X8Sxk0Dz2LqzyRr4yvIajcDmSVYu05gMBfjSR5GUTRYBwpQ1u+i8w4tttaJ\nHJo/gqxVMc1zPbY9lyLbbbSPm8Fwax0lXjsajRHp+CB4mwkHJdyTdVTOmkL5Bhnnot/CezPAFkYp\nuhHSbkLUbUCZcwf8KQG0fnzjXIxGVHgWGNAgEJowiUfAoEQQZhOkvA6WhYjeKtj1L+DIhjk/B8Op\n6RUf8yELmEOMFhweG7x3BbHCVnTHFeSyPMRnnYhjg4hzEyElBcXdRMQ1j76CfAZTZExBJ4nW87Gq\nihA7XoKRHcTyLYwVVSNhwyo+Zoc4SYQXmKBMROX5iGHTeBxeFXGfv4gybECc/zQR0zBBpwbzqIJ0\n7DXGCi+G+jfQD/USy5XQ1yxDRE6DHV8yNh2abuyEYRuSKUSIKMW1A/QaDfgG7EzYGKbt0gzGUgMU\n1BjQ9+9GieqJRdx4Yi6M0xci1O8zeKIQOfd6Un0OQtobUQ/KNIzLQtUlMZDuID+cTsjcjd6SihBa\n7P0LUStqvIffolMZIKUwjLXCTLjFSzRRj1y8EEPLx0SVIeRUI2FNHGZHIwdyp5HfOROXbwHRdb/g\nkflXs8G2jN0HzoG4fg6cNZMxnQ6j10FBY5B9yfPIOnqEAmkzpqPg+8V7GLZvxND0EQg/RLyQeTmy\n0o7Yt5296nI+mnUZD2asxtKnoXfyKg4e38SCzi0YUgKgUhMuvhF35yYym1NgznRQGqD9EGjS+bjo\ndyzY9imxgrU0pPyBaeIsdg4/yfzB7ag9KmK1EwjPeZOBeImUinGoC5+DxIlw6DboaITTXj+1Gfgf\noKDQykMM8QX5/BEbs74Xnf0uPGhLWYFSVvGHbyW7XZz+D9cTQswDvJyaJPWvBvo0YOtfG8w9CaAo\nyn3/aJ0fPei/i4J84AMOxpYRck2j0L2BBM8X9JacgZT7a3z61SR+3AE7N4LUjXrAgWF7F4tq5iFc\n/WC5FDJvQlKvJbMjntBwDRpfJlJwA7EWhUPJN/Fxgo17q5/EdrAbEbGi5D2LsAAJNxCbdDchqQL9\nvioktYqAzoBnpgFReBbarh6SfnUQ+e5FGBLvR9V4OoOLV6EfXoel9mxE6yyUpFQ44y4wlSCiKvjF\nUlArrMi00a88h9PbyoAlhiUajykYIGiVUfpHELEIencM2rVgNCGsi9EGY6TLp5NuWITX0E6fsoPm\n6Fuk2utwNu8hpB5HVFFhlK7CLWTa6OEMZjDMX/BackiSFxP/zRNEvTaicUF88UfR6+biEXdh8ueB\n3Ym+dwtHpiUxbq+MOnWAkLwVXagDkarF2tlHzqdOTpZFiOg1lI3dy9D46fRwOUXhBtovSyeut57M\neh1irBUSnAhdKarB7YxmOejfeoyt5dfjX2JlxeHdREbd+LUWVGYfroZh+mbEkRNpRAy3oelX46yp\nR0QiEPwMxStj6nBhynLQf0KNdcSORnHB4lUE1E1Ewmq0wxHG8iKo9mgQNhPlo/ehkSsIT5mPetd4\n1qTdzqXutWgGelGpY5TX1dAy4Rc4LQtQl0aZMfAOtQsG6T7mIHVSL93KUYpbX0OxT0T07gT16dAe\nQYpm4x9s5HeLfskHY1dg2BwAnZ2UdbdxbkqUgTlTCOefxCgH6Y2sIxcZpqrAsAI0s0DMIEgPsdAg\nTu9JaJ5D/NA2RgvHkTf0JWpFDSUvETWfzrBXQ+LQeNQFP4fkyadUIv8a8N4DX98D5635+3nOgECQ\nzcOkcydeTmVGiR9Y6to/4ruKLyuKslMIkfXvjn31Ny/3Axf8s/P8aKD/DmqsZFaMIbmOsuSi1Xze\nGoei7qXJEGGO73wSh36JUPqhuRsxczrGeD3iogJEsBVCTvCPA/Eq9KQQOPQe8RGFt0oVpupTeCv3\nTvKlNtKTPXxadCHnbFyHVVuDu8GBPjUFbde7KMPJjMW9gvs0FSr3YgwODXGbdajSb0JJDRAcOpvw\nmn4Mt1rBPUr8N7ugsw+mTkCZuRVMAsLvQPRalJ25iBEtfL0F+eEb0Jv6MThV9C24gNG4QtKqnkcb\nbmPYO0bC9ASEexhmdMOAE3btgbRM8N4LKR9h9rdgVCYwuv8BHAPtdJ12JcYv9lCXmEOG7SRb2cG5\nLMSKzLDYQgQP8dtfhUFQ+2ZCqIJIzzqU1GY0koIY84LKgLZ7hLzhUcInVcgFBuROPX1TRzH3aTA4\nZtGWmIDG1UlKRREjNRW8f90IqwamYI3VElJS0c/diqduEw3yy9RnX8dAaIhZdQFs4T4s41M4e0SF\n/fN36brQSO8Q6EMSaqOBXnc8CYemED+5kIAmnlhsIypbO2jaoM2EcmwE8gQJuRJyTROxlfNRyReh\n9TSife8hKC2F+W9j1sjEWInPOov+lHoi+t00KMMUW5t52XSEpfFzUb6wsXXlL5nc9DCp295CuL5k\nNDcTdcpk5vbNQdr5B2LnqYg/fpCQ2UrN6QuJDWoxJp+Nc/82nHOf4jfrLufB0XcxDgShqBw8B6Cg\nFPo0aFydaGuy6ZkWJN3ThZSyAMVwK6hLEXIEZdBGT46Xcw5+gyj/NUp8EdTm4W+oQCp8HULdMPgA\nGucGUro3IdRjkHLavymFfTKEm0HTBIefhrJ7aOdB0nkAwf9ZcKLGjp1535vOfhfISP+ZUu84IcTf\n3t7/+a/DRr4tq4C1/0zoRwP9d5D2PorFPULqZAOnhzaxzxLg2k/qkM/R4Hhfiyp7DfR1wBkLIacY\ntbIXVBrwaUD+jFhKCVVbCmjTz+CoczJXSJ8wX3uIdGsTj00yoe9TeONoIkfdqZTTQ+L9n9Eln+DY\nvgc5PXAC8561SMtD6N0R9G0VqCcXIxcPIvvOR1FZkZ72YtiyG89X12JxhKmfOEjKNDsW00Sw3AdD\nl0M4glCnQtFclNW/YuAnSxmp3kZWyijStYfJMcUTG91OSDOCKiGG4rQyos7FtVsPe1Vwy5sozrcQ\nzdshZIatV6GYugknuYjpZORlm0hNW8bomU8zZc3b1NzQxPhYHg6tBYUYen8OpQc3oR1OhGv3wvuP\noo45SfzyA2LLGwjLRShj3dDUC+1DuNrUyEXz6Ne5cVkrMbekUu8M4zU1E+euJOqPoo0msfr66ayq\nuw+LaQF7xm9nUu1prB/7A/0uCwWjWpZYzyJOsiLib6Ov5yeYqmtQ6z5gdKFA1SOIq3WgjcbYNLOQ\nctdJ4vd0gjEL89RbwVEOQ19A42vgOh8ReRnM/WhtFxPMtuMNnYUtexFEp6OkvUtYMdAWv5uo8JGv\n06NPfoZcSiFWxnH/ZkbGlVDe+gly11HklAkkWbaj5MPo4BQSHReTdWIL+HaAL4bbE8bcexoqw0ZE\nSRplx4PgmUZoSM2A1kSD9y1u6v+aPE8XtNoh+9ipgp3wGQSNL6MaVTHo8uOQrsJgvwxFJIL3NJAy\nwPwulcn52CMp6PwnIa4A0XozAbuMb12I+Iz94N0OKWuQYjE4sRlmb/x3SqGGmeth13xo/TMUXobe\nnIVf1GJi4v8LNf1v4T8Rgx78vw2pCCF+BUSBd/+p7I8x6H/H0efhizshYQLcdAz5zxdz0RWruH/9\nA+Tle7C+GYV5EyHWAccPw/xCGCuD/dvgYh+I+Sijgp7EaewZ0nFuehbhXT9H4xCQlM7OcZOYVt2C\nNfU63q2vZOfGWYwmLWblTAVteQ3N6i6uX/smKtFDLNaKWXcVrHwE9r4LY1XIpk/ALcOOLCILVqBJ\n7mF/6W5K1fdhDXVA5BDIXgiFIHaCqM9P9YFSTC0tOOcI4nIvhto1yPYIoqcTuSAboQ4RHQjjs1gw\nr5+CZmwAOdxD6Mnf4PX9AttHo0i6LFSGXpSgldEJ5egnP4whkAgGO97avxCo+xVxE9IQmduh9huU\nI1fjKZCxSMsRSgw+3QdzTNB1AlKL4KwqCO6EwcXwngl8ZlCPIRtDBM814N+lo+KCCWQc6UNTYiVu\nRx394TgihTqUcXoSDPfRyTTGjR3D2PYoeBRIuQmy/m0Tqz+2hhblOXKZjPA0MVApkesN4I91Ul08\nnmJrCiZ9PZoXokgTF8GkJFh3P1y2Bzz+U0moqZNRdt5AYKqCZm89vrwz6M2NQiRExpeHUE3/OWpz\nEtLGOYjQVCg4H8rv4JOhW/BLaVz+yoeIjGKUHe/BL/ehJGxEvLIacfUucGWcKn9+7TReL03ncmUX\nYqgNTfcMRFsfwj+EIgyM6mXsOh2KaCdU4iDo1WHdGELkRVDmTmdgdjXSESN9BUYctvOI66hH2+yG\nuDHQ1IBmMf2hXhLqu6A4gFetZdDswOMQGIIaXDsdOFdu/eug3jC03AfBRog7DxIuA+lvRljV/g56\nnoCTCfhXPs5YwjBJ3Pj96ujf4buIQRvLximFFau/lWylmPlP1/triGP9v8ag/3rsGuBGYLGiKP5/\nts6PHvTfUr8OZf9qxIo3oPEgBMaQtCZ+1+zmkTPv42FvBda5qyG0Axg+deG6LZB6DIa6of0ucJ5A\nRPZwwHI1ckMd7bNfITkyhmKagKZjO5NFiIPlZzJe5FF86A0uuNRCrGQxH+8TfPD8eOIS83lsoYlf\np00k6LkK/8nxJABMPQ9e30AwU0Xr/Cdwzksk6cGnCV5UyIRdrZgL3JB566nvER0EbwttfMU2az2z\n5+9hZNEZ5B98A1qfQXZFUdwSsaxrUcc2oKhz0Eb3E4uWMJhQxcDdmeT+sQvN5i/Qn3Et6p4XkWae\nSbh4DW3DkOQ5hLflRQxHY5A4j6+clZyX1omoG4Y1C0DXhyhMJZzYjM9Vgtl2H+y8BJxusI3A8TAE\nxmBgPbyTCR0dKKk+YnlqfKlWZEsApU3L4kd3E525hN5QhI0lp3P+N58Ra1Sh2aWgJNyFK38B6oQF\n4G0FyQyhMCFiHGKI8qiOgZ4vOZGWQ62YhttxB/WzB4kfOszZFe8zrUWLoupEztpFZJIZ3cMHoDgK\nj98FzhJwnvopZSWCN24fxJqJzhGoB1pJlxeCKoBSfgCxeQW1l82ksOB2tF0D0F8Jn6zkLL1MnbMd\nEQxD1nzE/s8gfRIi8DbDZwewXzUPKXcuaHUExBiU6FHtmkxkcQ+U7UPz4SKEaxXCFUOz43nkSJRI\n1IwufQT9sELUoqH+4tPJe3kjUZ8DJT6Iz2hnxCioLpCYubEewx4HmqKz8EzLozddw0iWjYiqH3N/\nlKD1Qvaowtyk+QoylsMnj0GxAXnwaURsFBF/HvR/Ap1/AH3mqQpAWyFEX6bJFkeuRsv/Yu+9o+So\nzn3tp6pzDjM9OSdNlEY554iEkARIIHKSyDknA8Y2yeRgMFTxmwsAACAASURBVDlKILBACCQhCYFy\nDqMwM5qcY/d0T+dUdf+Qfc499/O9n79lbOt+x89atbq6ulbVXrvq/fWuXXv/Xt2pKnqSAhCLQDwG\nGv2/Imp/MWR+uT7ov4YgCPOA+4Cpf4s4w78FGgCZCN74t+jXXkY0Jwu39R3k4naIuokv6EXWvcsQ\n7S1siZm5vDuMNs1Dv34GynHdWHefgm0amHgb7NoI09th3Ea2OJVcm/4NOvJQ+44hFqxAPrkTASep\nkaMc0RxGVerC4/CQqIly+TQVl0+DmnYtL20dxfgeN9eNXsL3A+cwLauWYY5GPOekobOPI6dnI2Zn\nFO5+Au2TK2BqD5jXQXIheF4gHKliszwcQQyzvGMLB3JGkaRWEE6ZhPKLvYSteQhzNYh9HyMlqYgn\nGtH1CqhOm0gZKEfSnkv46qsxPPUH1DPfAvX7yB3NnBpTQK7zELr4IJHqD6FjCJ32XiZ3tCGpDChq\n9TBqApgaYdyHWNvupkd7EKO7HYoyIOMIZO4mrnwGxVsTiA4bj9gZIJJrgJEhwhYtJM1G01SFySAi\nLv2I1pQPSX13PeeLMdRK6F1qJTlYhfDa3dBdDSk1QBQ5T2STTuAP0U34lRpm+k6RlHIxGuFPlJKN\n4dQqztfmo9B9iWqEh8DBBsyHIapXEZvqQ84TiMkODO/tQri9i6jFRBPrMApp6A03oPvpj4SmDUN1\n4CDubB0qoRyNowS5/H1yv+1APV4Fw945k1pMEFHVriNr99UQ0kLj1+CRoaeGgcR8OtNzsV2qBFs3\nTFtHS6yOnM7HUC6dSFBQoP1cgzQnTDyrGkXnxQS+16NJ6iWQOY6At55QVhHmOw+QvbcLtAIpgV5i\nTTbqKqYyVqpEjv0Ozy3Xof21C9eEWbTa1yPHm0g7MIjJMQMpM5+NnYcZU3wjRF6G0lL44Bbon8f3\n425khmoPeu1mUI2GeBL4W6H3GPQpQJkA+iTiFSAGVxGQipH66xBXPQRXvg72//0Ij7OfXy7llSAI\nqzjjAJ8oCEI78BjwIKABNgtnXrLulWX5hv/Tcf4t0ECUHhTVuwhXDCM2+y6SD3YjuvuhSaB/Qi4Y\nhzPdvYsjx8N4jQqcpkocnpMot/TgsxgJ/LqWJOdTcCIOow4Tshjpbt5JwbB7EPtWIzp9BExvoE2a\ngDXzSQa895KgDuA2W9lr3U0yTzOWRwEYkiFz45W1ZIa+49h3uWz81ETtvjQmXZrM20Iczb6PobwU\nNAHoXwMLlsKm01CsB0FFnWEGJ7wGxq/eRVLlCraXZpO94Wcy69cSmNNL+KL5WH7wMthai3EbsCiC\nWteBoBRQdu1DFicQEg6Slvgy3DoMXlhBtKiQA4vcpPXUoz/ip2d2Gsnt3cidx0nq7UBhtRHr1iAu\n+RCh5SXIeAA2P48yoIZpdcT0IZSGVSCdA1/dQ2CcC9UhJd6EboxDBbR5AvJgAUJpKwS/RXNEhmmv\nUlWmpyOeiXvZRVR4uxA2b8fcPwt0++CGc6DZD65vYUCD0NHJPM9Opvg9tHl/JLPkXnyqDzHxCD7+\niJipojb6BhmRNlSW8aimXYCU5UQKfYpq/WEaHyxEN+R1jI++AA/OQPXAp1izitjHoyTmpJOXMQrt\n8dOIC9aQLJWAoASlQHhYALn9S+j5LRhmgfnPL8Z+PMjJtGGM1ZxEYRkDB37Eu/tmWidbqYgXIxj2\nQtoADC6mST+XId5MBOVmtMZTSKPNKGJziNVqCUsXopihQbBL6LssqMxl2E4egUgunvxWQpe8gurY\n/eiPeLEF61BKuxCtn5KqWIZw4XdoXrqcBGEQ8jIhZQY0diD98AUTgyqM4xNh7MMgSvDAd/DOjUQl\nPWLSRciKmbjXufBt70WZOoKU5YUIiQuh6z5qClagFSqx9t1JUAB/ihfjVW8gfHATnPsAFI7/1wXz\n38EvOQ5aluXlf2Xzu/9fj/NvgQbUZKLOewTK//xcm3EQSlPh/kxUqbkkdIiknjqEhX7WlDzE0g1P\nERmMUDvuQg4OWkjd8wAd+XfTev5dWH7YRfnIHeTLCzErShlQ9xPLihA1H0IbHIGYMJm82CZs2+bS\nkaimz+GjXzjEYfH3lLMSJz68eJmgHccl5m3c/PT1xOrnUh2ZzZWKybx/6g50sS9BIYEyA6ashZ0f\nEa7V03b0t+jdHhYd2YNgl4jteZySlMdJzMvn9BUpJAou7O0XITvuRLO/H2HRLfD2y8jnNSAUmyDQ\nx8DI4yS4KhHUW88Y1Wda6aIDn85ES2o2zbPSKWhvIxxVox0XRmjvw5XuR8yJ4Om+mWS7Et2WF6Hl\nIFz6PtawnsHeudhtfdBbAYtXILY9jGfFcRLWtBEdmUtcakJKr0bZAOEaA4/kPMbD6buJBd8kbUBP\nSfwEyqTloNyOsq8JIX0AFDZIWwiDg7DzNBTOAPE0eucWhpgvx+d+HPugAqXmZSRxP2KkkWSVCVWj\nhCFLJKp5g4hPh8YhIliXodjVTdbw6XCfFV66Au5fiu2Oxxgz9tc0CO/Spsqh2DQF1bHNMH4YfPA0\nXHk/AcWlxCd/jrFeCa2PIdlGEksrQzkxn3FLf8vgPDuWmho6HrThs8qU6V9EbNwExu/hYBhmH6LV\nMIRZigHo1iDl61CYu2hNPE4wdTg51OFOWYa9aw/0NUN3PWSHkLpTaBuZRtlJmYH+LKR4NSZvPUpp\nJAODj2JIK0DriMPcufDdz7B8MyAj+XtoqboWVYuIad+X0FIIjlNnRuymWUmorSfcPYe+L7YTrq9H\nP8xK8iUSQuq1YJ8Lvs/RhpoRuj+krziNQSFGMw9RbtsEN6+Gj2+FrhqY8s/PC/33IiMQ/if6bPwt\n/PcU6HAAjn4D+z6BsrlnRmCEvGf6RUODZz6lGLLWgH6rE0H5DMoUPcUHod+5nnjASNuyHNIysphe\ns5cdtXk8rMhglD3Ee1vvpbGmHKFiPL/aDzeWnMTSGcH0vhVFzwnYX44w7BLszaOxF11I+PRcaobc\nQYQD7KeJfHkZk4SpZ8o5fTL9teUkqDoYXbefOfPux+OW6QifS0HiDgh7oeoBnJOTUL/3JZmFRaiz\nhuB75TUOtD/G6GPdKI58g5heSJ4vEVEzFcUHDyNNNeNeMJ/kt3edsYf0RJC2JiC4fAxaQ2S7QhA/\nH1qyoa+e8IhRGDvT6HbIjDu+n5SqGP5FF9PLepLTgsQEFfv8Y1BpZbK7D0FBL3SkQtsq9LW7kRb0\nIZ+oRJ6iQDx8G+rNP6G+PoyQFERh8CN7AiiaU/g46WI2ZI9k4cit1Jv0+BSVjDXPx6t/hUT/Nci+\ndxAOR6D0cjj1FeQugklXgeVNMBZA/8PQpIHKlRj3nQT3MTh3NQFpJXFNLw5vAmF9M6pdVWiKBhAk\nF4hDkVWfk9NkQAjtApUDHCfpn/UAuteewNE2ieAEkZSU66ku/BBDbB8Fn96HeHAj0nwRp00LJg1W\no4TYlEw0+QRizavEtQ4C79uIWgxE60EK2jAlSUg6JYgnwZBxpuuAR4kpVKgc74KnCzkcpDnPjqPn\nJA7/1aiNyfSpyjD2+xCXKLBssCMMJCKaDJR91wYDt2HRASoZ82A/H2RMYcRgHzXi80yv2Uy05D6M\ntuVYP/0j3PQkDcndfJt+PVd7OxBb5TNeIou3EzpZDZ9eSM3M6ZQ1tZP20ksgexGrhyKoLzojzgC5\nq0itnc3WtOlMUfTQjxc7LoRIBBo2QnYi1G6E07thxJIzcTVuKYhnv+3P2ZjV++yvtX8E3dXQ1wDx\nKKi0kF4B5fNg8nWw8Few6DFISiBWWUJo7AxwmuC0jCJFYELjER6//G7qvCZuqZ3Fc471LBD3skz1\nNkW6ozgS8tB0dvDIuHY+2yRx8QtPEi/MhtGXIIy8COY9C+NvI+5shIypaKw3MUx8mhGhWyn01NIs\nv84+7iHMICjVaCUboXAI2qpI+e5BEu5YQz2VPD+iEzl3Cvgb0epcqFdWoAkn0nzTLXxheZHCml5M\nFa30X1RKvyCi+OJVlK/dDAUjiadnIGSPQF7xLvSNhREJSElBIphIqEtAUBRC7gnwDAe1kTRFHQWb\nd7Jo6xFStE6EoTJBcyK9RQlE3AI+m5XURCdz9csQjPmw9QgY7TDmQoTrNmJqHw6abnyx3+PTrUKY\nKaJv0hLNT0FZ088e8zIWj1yLZZeH1bVXM8I0nEzFUqa4qlCfvgLL0d3I+26AeBwxojtzDQc7oa/6\nzLohDYINUPw6jH8fDr4A6fOJl99OZ3ARodBxdL6lGFWPk2i8GYXxAYTDBuhUIvvP52TS9cTSL4Su\nKHR9QXTcA2ye4qfn9bfwNQ6i6neh3XYTw3c3Y7VM5MCobZy6G362r0UY3EAkOBJBkwgKJ9q6JaiU\n96Dq7UGfeQu+rwqpz5VI8I7HVe7B4/kALAXgaQCdjL/6GHrJAukHgRxUqeUkqqdhyKlB796Fu+8K\nkqI/4isJI1Y30DB8FjFrEuQpYekTSGUghE0IShlFWGJE72E8jlcIyzp2jZvK21mFrJzoY7+2kcjK\nqTRL+5mjKcAWaSGWdjuDHYk0nj+NyKq7EGfcSiQjh4Trr0dhtaIYeAsh6QrI+d1/xk7/12TG41is\nKhJYRhpzSZIvh7r18N1K8HXArPth5xp4aRmkFPxfIc5/4d8ZVc4GskeeWf5nZBk2PQQDzaAywKS7\niH72HcYth2HqfLBVwPEXeHfaK1Q3FqGJxbmq+B3SC7pxBrTc63mGjxXP8eiw+6h0bmJ4egGvT7uT\n+rCR9xR3sfLQG3DOO2e8cp0d+BNCSEeWYk2+FsInULRPw5/0GBFxNhHWsZMrKRuchUM1BU/FUXSn\neqFqL6rL3mReQjrdYS3XWL7kga7JFIX2cyKvmJzZVRwKPE/FqT7sggyOtRRpnQyMWIGiyw3dBigN\nEI0cxUsCjuxrYfktCB33E01KISzX0pa1kNz27zFmX03Y2kcoz4agMWEvfgDxhZXEijQobSESm15H\n69VTW7qIPYm/Ynr7j4gtL4NuPDQ2whNd4L0KBtRQsBihuxnT9gywtxHXt6IMKenQX8Ij5RMol518\n/NISLP1JCHcUkFrbhbrtO4SwEVmphXQN0phZSLFmlC0e+P4pyBkCvScga8IZgfY0gGkUmICe/YQb\n7qd7zmg0AyNxbN6G6tIXINAI1rmQ9Ci8V0xEk0Qw70MMnWZ02bNh9Tlw5Vf4TVGE0NcozUk03TcX\nNXHSuRn8nSTVfkKsp4/eIiOaQYl0ywfIfVUoju9FemUXlP8MIQE5loyY8UcM7jgZL/tQFQfJ2WGj\nv3A1iQOZCBVvgPg1dYGTZJ9oBKsL1EsQg0VoY9fSaThFyDGGBP96LHpoiwzFP81BaJ0T8ad6pFsW\nI4UfRuFV0jJhAVnVn6LwW6js2w/6fkZ1mtlXnMxNPX/keOo4kmYvRFj/EGX7VQTGrqdzWzPao5dh\nnBNBOj8T8x49TFwALZ//Zzx0nITJa5FlENq2gq+KkLATV+YyymI7MKgeYWTzJjQHv4G8hTD5Ndj7\nLXhXwS0fg8kOax+Di5+DxOyzfpTHL9kH/Uvx31Og/1ckCdbdCqc3wPArYPbjsONTYg4zwsKHYe86\nmDEWKjScY5e5/sBbCL2rGTxehlPxGMaYggT/IPfa38e/v4pduXOI/fFCiiq9zM7oY0tdJV+1XMSF\nPz0PtX+CLDOezHZ6CgKM0A5F7L+RxtQ3Wa/X0c9RbuQuHLKeeM1yFMM/Ia64HTl6ECEtE1wdYE/n\nKsAdUzPZtI7Dg4tJ8zaza+Iw5v34PYZaG76bBggZ/oSmaxG2z/qRAyDfOhxF3Qm0tU4c+jIE4x2Q\nkEy8LZHew80kCuBwvoj7Mju6zs2oVTOJuVvwTgkRVXyP8j4r2k+dYJuPkJSBIvYBAVM3XZKfIZ3f\nQW09HBNgziTQaMD2ONQ+AxUriNedpm5IK0OaoMWSxVvZ1+GPmPld8NekHzsBAyL09iHVVaIrFWmY\nfR5lrl2ENjrQHNmBIusUkYosVIG0My5qkTSoXQ+jVp4RaH/nf17PkgWoah4h49BehJ4apCljoeu3\nIMdhYBdxdzWBC4Io9gdw7y1Bq+yF+k/hUAzMrxE3ushPjZA5+Bqti23kKicDENGpGHT+kTRnC6nb\nYLD0XI4VXIPdoyI30U3oEiMKr5foEiWxeJAWUw5Jp/2IPjfRuAZP5WwSfjxCKKsJn+JnukY3sdM8\nnEmH99IYeo6WsTnYByQEfTIOtR+b/B5hhQ6VdzgpxgNoNhdi+mE3RxZNIyGrk8ydKg4MX0xa1IA/\nrZJwVu6ZekiehKHpC8aue40di2/ALVRhy4px+ItFzDtoJ0Y6niXHSNb68KfECQxWQ8klRI7/CpWp\n7Ewdtq6Bnj10tq/jK9d53FxUwmDvDawrG83UxnXo0xMRNl+KLmUk5FwL2z6B7GFw7Wtnnp7+QsYn\n8O61EHDDPRtBefalufoLMgJx6d8CffYRj8A5z8Di1/9jU2TydILDTmG+6wMoHk/c8hPioclkTdmI\nMOIc2P81JsUUwkNmEGh5icSqbuLnPsXn5nfQxuFm4+Vc2/8ZGeEWZud8yssf3c2PRxXMML4CDVbM\nlyxA0lVR7byZvdI4MhS5XO4bAuEgtsAA9HyKMlQETjdGy3yw/ghhHZIx8h/9UrelwsI1v8fiqEHZ\nn8nMEy14xtioHcinRz2BATwsW3UVyvoI/otHc1g7g0lFnyEPWjEd2Iqs2UIwch2xfUfRfyMhVSrx\nLsvHPZCP3vwgUrkB7QE/Ce3Posq9lKhwD+7LP8YSmonKu4VqJqMON3OT+0Lk4i8RfkpkcFgTEcsA\nMcGPom0nCamPI/rSEdcOkrzQyYph73Palst5Yg0XrVpFmuwmbtQhaoLUPZdPjkGJRrEdWRoOygx6\nCraQcliLTv0UatV4KHXAQCYMNEHHvjMVoU9CDnTzF3cIWZeHZ+hwrAeP4BubSzThLmKCDc+JVxDK\nHWgVBtxH8giXj6HRtZ7coBU5v4GUn3WI5zxJs/wquaFihKxcosq96OVkPF2vEq/9DXK9js/GXMxY\n7TGs+6uw6ZJxGUSSdCJGcz+DxYn0ZqTTI6spcntJ1jSAPgaDJpIDE4gPbSMeqUaR6MftL6FVzsdv\n6yezfg8TqmtQK+K45llAkBB6dBg6o5AQxhsaQtK2OmTZRX77jzTsnYAuIlJun4ju0CqO55Wjcwwl\nKfbjGZ+MMc9jyDiH3MaPqc+zkuzahVqfgXraxaTEq2jXDSDlq/AoP8cii1DzIN6hH2EOdp2pREsZ\nccHLGx01zCr+kUhrO0dLluAINWPsP4W+wQzBKbBrN5Rr4eYPQftXDPkNVhi3HNY/BeufhsWP/qOi\n+O9GlgTCobMrq/d/e4GOEKJOdQQtRqwkEiZIiAAufkSK6ql99TJ8yuNEFBbiZSIJmi4mBm5AEESi\n4S9A1Y411088HMV94lruWL6DP3VcisHSzxMtb/Pq+AV01Gdw+8rneOPbx5mQtAH10HNonLGS/YH3\nKfi5nktfeBztjGVgMINWD9Y60PwMXA/WZnRbnwEphjR0KcHARJD0CKpRKGOzyPYeoXNeKYmNVag0\nt2Dv+5TDo0T8ChmTu45YXxOB4XbWjboAT8yFHMqgorsene9n5D49knoXA5cnYlyxmLB7B9knm7H3\n5mLbfxqUYYLZBrzqJ7HJy1DtbUQzbQ5i94MEC/fgjn/Aia4CbjF8Bh0PI6uHYJqwjPjBJ+mOpCAF\nD1PVL+H4032cWnkefxTuR6/XcnPgJNPanmVf8Xg2ZFRSmjufipLlJCQLdFnmkNOzhaLG95HTXkIY\nWYNqcxqypYhYbTIK5R4YPQPqHWcsLKMhUKpozvCTK0sgiPTyEdb2buIBkcGGNrZmuVGJJ8gZUs1x\n3QLUrEQT+hBV/iS6DIe54K1POKodSfLwAXhpPNlZBSTkFiF7X8Ws1yPWX0rYYUMIhjAWBZipOo5d\nVYBqbB72A4cJpXvoKzAhlIXo6DUTF/1YhGHY1ZnENM8j/hxDXqJD2P4HNGVJBBuzaUjowaa9jh7R\nS6Y6giUnjDJSgLT7EHKWhMYto+v101dZRsDmwrypm7Yrc7AeiqPtDzB870YOLF5Mom8NjhQ1HqmT\nQfO5FHb/+W/q1HpQ/ES22E6fL46+00VaYjI90v2otE7s6nG488OE2gwUNJ+E0Z/wUyRIp1FECrsR\nax/nmGklP7bfw4OaIqpzbRiDHiZ8s4OoVY12axjGKqAwBrHdRPf1EnWUoCy5AJWgQ+B/MlMacyGM\nvgBajpyZ0KI4O2VHlgXisbOrBf3feqq3Bye7+JYWqkkikwwK0KBDgw4vn5HG9ahdByG4ASE9D10s\nh2ORvRzyqsj5uZVp3+3AXAScW4zccpi+n+1cfM8avva/ihDYw2njSwRTnsN6KEx57DQxX5Sn5Tex\nDz/NCKOT3G5QpJzEGL8ZrW4x6I3Q8g0cuAN0Jpi7B9xNxPY/gdLlgmlPweszkCrSiS4cS9y7G5wB\nAnkhVCo/6tUimpBM/LtEAs+HMToDsEuJLBqRIyH8NUHkYBjDFUC2GqVLhVDlI2rSohyTg8/cimlT\ngFiCCoWiEDHzSXj9ApizAtJSkO3dyIMf4k0z02S5llVCJvc2FGL/4ToQvAgrpkI4CWH3ahizkkBS\nL6FrT6PpOcG+l8Yw47NtDMy6AkX1MbSNdbhNBnTxCI3TR1KVVYhK4aKytok8ZxOq0y4YbYQsP8KX\n05CurQTnG4gtQ6FCDWoJdjXAvMPI/kvZY/Qx8uBSxCN1xGq3ou1sIbo0kUiCl46hJfQEDIzxR/Ha\nIliwo66pp7lkBFb/KawtnbAlGWJWyDkF855HDioJNzxIc6ENR88krI5vEE45EQfTYf5wSHwMen5P\nd24hycLjOBsnEFCX8uRVKdz40gaKu/Ro1hxB8VMI2WREnjoNMXYUEtqJxzV4cgpRqUr5cqSVy8On\nET07EPvUxGQTcr8TtyWbXfMnkOUrJcX9Fgn7w8j6IAcm/x5r+mSSDk9HuzdCyJZGdaad4sZu9Ffu\nxVx9IxR/Ap5O+OlZuqYmMuhfB1EPRe1tyAEIZCsR1FacGAlo8yg+vhU0s/jEOJzEyAnmDdbTa7uc\nPQ2DLDTtJRY6Rp89ATm9GLe6nsTTblKGbIDsCoInP0K76lpQymxfMo7e4fOZzHJSyP+Hxe1f45eY\n6i1WDpdVP/78N+0bSbD827D/H42FBOZzFTIyEUJoODNCwMN3uOgiJaxEWfsljF2DhA+/8kEmyncy\nMX4TDYk9rLn5cjDBlJ82kbdexlri4Q8f30jrohuw6JeiGnyFNE0+dqsDuqw0DB/NAkcqw47+CnHE\nMSL5MRR3zmLwqrtQWXwoWjZC+lwovgRKf3VmlMnOBxmY/zSh9ZdhavoDllQBsUmFJvxb5KvyiV4X\nQzdEJO7SotDriSsjCLf2oTZlUJVcQoniOPKGbqJHQLuwGOWINhDCiDVjCdGDkNCDZrSPdm0Unz0J\nzcQkkltOY0yeCfpPICMdZCdEbQgqGeQI3riSOv0pippyaFvzO6ytAvJDkxEVqQiKd/GXp+BT9zDo\n9eC6SEdFfyJTXcuRc2R0p+No3z4GM5Kx330C6c3LyZtsJAcV4ZZWjpal0G21UH78OOEh95FGFmS8\njnzyQ0KSFcOUzyG6A7xPIFf2IvwwCj/FZE9uJahah+/cNMxTL6U75xR65RZEg5Jnm+4gW9XDlLxF\n9BsaaYp/QOFP+Wyr9LCkaRAUMgzpQkpPQahcjXT4egYSRNorxtCbF8ORfyFUfUJMnIjSUYkoNcOx\nlTDmR1TCZ4SpwarI4UjGNOJZ6RzZd5yKKS0oVo4mlqdB2rmP3muq0WsnoN3/DSp1AUqlgVDZJi44\nGSFqtYPZhi7RhqK9n2BLOg5zArPe+xYpupG+q4w0n1NA/LCE1n6cktBU5JI1DDpuRruxninbjrPn\n/Emkdb2MIrAfdfPtqHJfg8FuXJZppNouJ9bzBdGG1ahVUfwj8jC17SScnIMp3INsFhBkNwsPvIHJ\n5kEOWmho2cvM8nG0D5nP0YQCzhm4C/GJCQSes9GeVMLg0duwbnczOGQ0uqtfwZQxj+zQEaZIixDE\ns7ef+f+ELAvEomdXC/q/tUD/BQHhP8QZIEILshxBUfUoVDwPohoRO6BEUiUhpnxHXv8SJKOVFVlL\n0DX2svHtOQyTqtgemsCITd+S5uqHEpHTjgrSk0s5WPZbisJHGOF6mah3GaKrG7WjGFbej/XgowTz\nHkMzZi8xixmNbIPt6yC0Dib8GkfTCUIdbWwcW8SM4bswOz0M/nw+piQNqhMxFIGFyCfXE1oQQ5Mu\nQ7cRMeUT8lp+w/6C+Qw7bwDziE3Io0Yi9LQgtGUQG/8rXIqXSO9zIXRcg6mwh1DwBIpkJ8pDcRh1\nO8SqYcwxGNwMIx9Bqn6WsGUCx4wmVMEYcyO/wzXDwH6Dg2iRDp10FJ32BhKEg6CYiVpey+l5D1G2\n63E80acxnTLiL+5EfvQedMp3ULr2A1ZU0sdExNUoU1qZ1SoR0hwn5FDhd3+JNxRFb6hGPhxHW2hC\nrr+PUGsVQcsg1h4BQejCmDKHGiGFnMoWjPFjBJLySdnfxu4Rc/m85gleXTedpuWlQBb26t8Q0YeJ\naGaz6L6NWPpi8ND5oNtIRNFCyPcAntkzcWxIwJhbQK/4BYZNdyHaBWKeaqQFhYgtuyAwFpRWdIwl\nwH7s6kIEBB560Iui9xThxGJUBetR2hug92HSmobyiipGNGc4FxXsRmnYTU88g6+cj7E470mKXX0Q\nNiPk6VF2NxBNHorhwEHabnPQluRAE1RSEKlBaojSJuwmWVlKXHBCukhIoSHLWIfpgB9q+4hJnxCz\nNaGLanDXr6a45gQK4SNIToHZW0g6+jKy83ucmSDok4h0NxN11WGwB6BOQdgVY6RyO67ERqrKhzPv\nxFJCX04ifJ4Jx0YnGf5cQqPm0jVxgFzhJtQ4UGLEhaWQfQAAIABJREFUrPvntpp/eQSk+NkliWdX\nac4SRElF2oE6BOtlZyZA/BktlxPiY/TiXTSX3sT3Che//uxrRvfuRSn3892wmTQU5GFNGEBzzEr2\ntHeol4O8IA9QFt1Cqedtfp38JLLYyMR9D5GYnEKlOpvg5ZuoEu8kFruEkt6LiYTcmL7fAUtmIO97\nCCF9Hqq8xYzNeIht8vfMP/0U4ZCM7+4CrPpU4kWJaKpvw/Cnj5F7ncSus+GPvQa1h6mcfA49JaC1\nVaJqXgUDAmRNINq4grQEESF5DQTeIRraiT4uYO7zowkEYW0JZJfBgAvCEdgxGlmwEAiO44ilnK54\nKuN7Mynp+RJ/noAQHoVS4UVLCQr97RCz41n1COdNWoVpcxjZ6Sf46yfw219iUNpGxo9RVL5GyKpA\naD2BJucyVJrpxNRTkCUlcjiJ7gSJQrEUaep1sO4BwgdE9jwl4BBLGVJXiqD9GrnjJK7SH5DtqfSJ\nDopaFoPmReomDwVJxzP5VxCZEsBiqcbNI8QsUSxiPxHNJyjHCQyOT0Hr/BZ1JI2wEMJd1EfCSQn9\nyXr8Q4NkHvOg1rchmwSCw8JYnKsg5TzYtx9qV6PL9OMWnwaFxDDvShSa5/lTeD57PZcR9opUHDhN\n1vBKdqQPYZywlom5J/GqkznhmsOX8hKsZf2cDo+iKL6G5vRh5DSdiyLlCryu47Q8Ogqdvp+Muh7S\n+ruJpGaTUD6JqOChni4SDikxno4SXnojqfpriHRegDz5IrQZpQhJN0D9bmT/kyg2vAD5cZi/Auoe\nga7VoBUx+YNoAzKCKg2F5TTd5fm0lr2Hq3cnw0vb6G7Zxew3t6N2bUKlCqJrDOI9JwW1YjH61Fsw\n4aaRVxjkJOX8Hh2Z/8Ko/QWQgbOsD/rfAv1XsPQYUQ4MQuG0/7JdyViCvIREnKByO7eevAjx2L1w\nwVAYuJ+5dfej0Ml023MRRjrZIrk4jIU3hELS1YWQvIAKSWLt4A8U9NZRO+o8atJkYnyMmXGYOUhA\n/Qr+9gDemiC6Ti/heQIK7xcY/X3oO+uZGjhGVAyiClpxV1jpUo1lWHgeYuI9DN49AdX3h9C95yEy\n7gD2OjWqvAUYBp4mrG1GTk1Eo+xFjn2GKluJrJtEXLcBIVNJ3Cyj7o8i7tTTXpBLRncngqcNsm+H\nnU8ga0YQnjtIgnwDefoW7qtToP7pGCx8DF+8CoN/E/pYChJPI/E9YlCN+bsO2P4Z3L0YobECvTyZ\ndM6jQ1yJu6IXhb0ac2wyyppdkFOJSDqk3IXS/TbmU41kTk1BNpXTv7ODxF4/+loPU3svQnne+Tj5\nE9GmOsw/tyGYyon53RTu70ccPMLXk95mQuwxZojXIfii8EkrepeTwMsJCKEJWH/3CUI8TnRkKp4K\nJVGnkuiUUiQpF61zNcGMWiSfl351jMrAfISGl8BoRjkkDZwuyH4dWm+D/uMIfTLy+NHIYpzEvjfZ\n6ChnbGo/RaonmJhwD4o5JmKtLzFep8WQCcp4GnF5kLJYC8tiazgVGY2p2UfIqOGHhFQSg19TZMlh\nSEM1yTED4ZiMPyeTzkYLkkcgnu/GmjSTHCrQ755Ed46D9PdfRqg8hOLqQwR23Exo1xto504g0vkO\nqvQAnGuBBAksGciqSqK9a1BGlNicHpTBXtQDHYTsWnTOXhL6biIjV8TpCpLX5EE5ehI09yKUzURs\n34H58xrE2tuJL96Lau4HpCnO5B2s5TeU8SwqLP/8gP2lkAQInV2SeHaV5ixBGYnA9COgSYBwCGoO\nw/E9CNPPR5U+g37ewO4bhvj2r2FxDKzHYVsanDeaUbWH+f3Ie9humM4K4Wau402Ugh0JPyF+QM0o\nFhRcy6DzIENCO7GFbsSiWYEsQKytDtdTC1A2m7E88C7KGQuIHViGzJcoZSXquiZEaxz5HRsnn1hE\nItegCz3K6egX9DsuZqx4Axr7/UizPyTpywGESTlw5CLiFcOIti9B59uGbBUJyUbEzlQEuQ2Rowyk\n6hG1MdSOAlTOegw6H+2ZNtL7lYjCTshWI3lq0H2hQVbdxDLHAHJGBCpVyP5XSLE0cyg8l+JYKebq\nj5DqNxBqHkZgfg5267lQ8gwcmgd7P0Sx4FfYuYXQ4AnMqil4Cw6j2/YNQXowcQ1x8SQqXy5CtAdb\npJ2+tS9jOSogZmYhKyVk1+N0131EX2YbZiERXXAE2pNHKFbGEI+ruHfa7RQMeDDpPOB7BnrjUASq\nU1H0r3QTHHIAwSQimOOoc3pI/DCBhoVm4oNVFK5RgpRO/6JGYsVxXGmFqDszkU0i6EzIA/0I3mvg\nyMPQ/zWkzYeohG37PqTsNBSaMWQZbyac205azcMooo8QFqs5klqIXqfGpr2LsHMLifbXIHknQv9m\nEtUpWLsfIpodI913lEGHBuvJCEeyhpIa6UI76KDRIjA+/3fUnHM76WM9aF5MJiB/gUqrQ7KClCcQ\n7d1HT+8KUhwRNNF6qHmEAc8p7IpkEMtA24Rs3kuPp4GqicMZ62+nURApqvLQVJpBqreHWEgkQdVP\nrXE59oPV2Ga+Ad428H0LOUHEypehNwJdNShEJfQ3Y0ouYQi/QkYizt/koHl2E/tXF+C/8m+B/mtk\nX/mf6w0nYP0HsHk1nNiLRglh6WcsR1WQpkU+mEFgoYvwfUqCIS+n8/IpVb/DFdIAcaVEF3PRU4GA\nlSDrUYjphKPZMGwuNG3CeKiB2P7f46u1Icsi1gffJ+C/BjJMhGsvQVb3oqhTIUkapGIFkW1R1P1m\nsuUviXq+w0whwYSrkAUbX7GF6IhyFlU7sJSnIU9rRFaoUL1/hMFLR2JIcCO5DIi+bCRdCoPFD4Mg\nEB24E1U8gNF1PifG+Clt/CNVw4tptE1nSp0HioYghT9B3KNEmHYbwr4nEdui4I+DtwFhbAmFx6tZ\nW1rKlWuLoPgQXY8ImEwT4P3+My874z1w5BmYdTsGzURcljjKqjU4pz1AXuQkTtbjj3+OvttJVyyB\n3EAP5no1WOMIOVH6JqXSkWsgw3ASi3MSKW23QetamDYXYd2F6GbAijmfMz9jGhcofmLA3YN0fwsK\nSyaMdSMttRGIqPCOUWNJyiEW8aAIuPANb8fersBao0RcdDlxQxK0zUEaGifT2UG05H3C/gixIR34\ntDno3l+PUq4BkxbyVoK1DGlwDV2RP5CheRB1tIH01hsxKX10+xxEhpiRfTq8GgnPrvvJuKMbYfrg\nn28uGavhFIHRoJLizLinjT13DiNVHEtq51oGIxb8BVHCOjWx+DKGfplA/Pb1yDdvwDRGjWBSkCI6\nieoUhK1WTEe2c6KwkNioJTh6lPiGDCW9px6fMw1dh4tQ1hrUKgvjnL2oxRA5igTiI6/C3fUtOaF2\nIm4rPZNyENoySTn3cRA0sPYWuPJT6LkK1EMgU4DMof+PkBEQUWL8x8fmP5IzhtBnFf8W6P83Sked\nWe5+CSSJQfkQ0c2NyK4IwtFTMO1J9O796HwCP0tzGdb3E8PTb0Fz9FpODk/lJ91iZlBPIhdi5HY8\n1KAJ30qXHryZvfQ1voA4xoZyVi+iyYQqYw+KWjue+JOIZh0aqQZBkURirAOhV4+8D7znxNC2Z2Ip\nfYmjGj8iIikcolxuYoA+XAeS6F4+mvxoG9HAPehvWkKiPQCqV2HwapRl78J7D6J/cSlS8QT67nQS\nVpSAkE5w7DpUGicTD7XgtrxN3FyKPOwWZH8+wp4PwG1ClEvxTXOj6ylCsfNbFO3V2GN65mq/JvqQ\nB+WuGJr6fsydfZB9I6xeCSNugy23QM2d9A97HKFPpld/mMxOaF44h5zTDuIHrsQ91IR5dz99gh1H\nm4uII4Om6fMQU76iCyPKrmR0UpyCg3eDKwQtX+HXp7LM/Cn3yN8yXVcImukYgyMZ+O0AiTtkqANh\njwdEPaYTbfhMCTQvfoPyj95BmlqEbe8rRG0qpKPL8RZr0XjiKLvD5KZ0YN6Uy/sj72RhdC3JhysQ\nh1ihJQ4d3XDKBzPTienHEJZ+gg1PU+BIYGvpCqz+H/DtSOTD7ud5cWYdzcLLdA6bScbvZsOUBaDV\nwan3EErOpfvQVNKiLTiHRLFmLaAlECD3+3UIhTpSjV2c1iYh6eLENUYIdCMmxojoBdRRP6G4A8UQ\nH4bNJuKBbkZ+tR1Jr6Z/6TwUzmoUfS62X5DGpM+CBJWPkWy7FRkVLlc+hmNl2Jo2kGxpZqDQQWrR\ne2AM0VFygA9oJdJdxbkzz6NMdIMq/X+bKPb/N/xboP8vRnvGR0B++g4STgYJPq1At/lmRJcbjh+l\nO62cWTEnpt59kC0THP4WKsUrJFPNV6RwgfwgQtyHSylh01SQ8V0TrPejt2ahvesjFHVrkT99lOjY\nIURM59FYFkWQKjDKvWhyWhFTZaQqFYFZU2m5Sk1YEjEIB0gKG3CGT1Ej9mDXn6YoNovU3hq67Rfy\nTcROY46ecw99S6nqMkjQQodA7PPFKB2VMPNGPMvrEMVxIBtQpl+FiwXsyQ0w7ssfME3Ws78wSnn8\ndxi1G6C4B1p2QlIuupxLiEUeQqHIAVUxiG2kfOmmdZGFxGG5qJU+whM6UdR1oDi6Bnp3ESyaR2jH\neuqGzmZMXyHKLa2ELzhGW8Z+8g4GiDkqsRw6jhQy0J5kxjnRjqAdQKv9DPMpG1GTG4/eSJnTBYNt\n0B+H5R8jHX+MeweeYhrp4HoMYu2oonVEywTk3EkIiW6EmAKd2IJb0NF2SkPucy8QPX4Ay+cbYYKI\nemgUfB5kWURhTSUw1IZl4wn8F5Zx3b6NSLk9CJW/AXUPoAbNFuhphgeW439oFEqLGWnms4iflDK5\nQaZ1zq0801DKszNfJCQkk9i9nGjT1whz3jpzP8kyHHsRwZ5GttNLOE+gaZiV7rqt6G0+ctLHEJsU\nxpv0PXLoViRRgdzZh7hAiWgLo8gOQYOENhLE7dOgP7eRXapzsZ1sI8eeg+Pw13ixYeryUn6sm6Bk\nYJf5J4bgoCRkwa3LJDdrKCj0iN1TiO1azdHK5xhVn4x4gZrhvi5UngQ2FFlIbb0Eu/Gif1Xk/fOQ\ngei/uhD/lb/LZkoQBLsgCJsFQaj786ftr+yTKQjCNkEQTgmCcFIQhNv/nnP+K/GGD2Ha3oQ0U00w\nrZ3YVefCbc/C0MVIa/sx9O9DUmmISm+i1Y7FoL6TVtlEQjQVs68HndxHCgZs8jOoZmUjvXYFjO6k\nI/FVmHwjwpMdqFudGLPmYeZetokjyE3eSprgAfdw0M5Hf+EYLN5Octxt5DdvJbFhG0Z1EX2YcYTO\nQ328DrmsiJSuTcyIllDJMKpy1TSvnQd1L8LQQY7cUwnXPkP0suVEFUeJS1Y0wnAAFnQeQOoU8Ewa\niqpOwjjopEn04u8Yjlx+CHn2AmSli4C0FPXuIMKi5yBqhsLrEGbdgO5UOvF3ThFv8zKg1+As+Qn/\n0sc4PXoSR+ddiMULE05/jTLcDqNFalSfU9pRApu2E0ptJKzSEcnKITOngwxVC/IJDW5VAvqWWgpr\nT5Bf18LpkiLQRGCEHfRxjOkXkpyhhawAVF4LxRqoyEevLiYw/mKY1wzpE+gfew5NhYVkZ7TSe4+X\nnpus+K/IRMgFITkVcgWi1jjeiiKk9HyUCEg93+Aq60MgiHDiGWj7LejXQqQPFl4Id7+Av/1bYu5j\n9Esvg8VCvXoKN6xfyRTPXtS+3TiOt5G+5kk8dg2xgQaIx6HuZ+htR3zzIVQ/dEOhzJhTgywtfxVL\nJMbXF8yiSpdCn7idNkM6qi9E1N0hRDmEYNAgmOciS2Zip4NYOzxwRGZ0w490T7PS23KU+FYRY60a\nweIgqzWXBJeGMb7rCMmN1Lhfx7K/DfqOIGnuIBYoxPanATKe3UHY20HK79tQ/2ox4gcrWfDZh1i6\n91MltiFx9k5q+0WQgfDfuPyT+Htb0A8AW2VZfloQhAf+/P3+/2WfGHC3LMuHBUEwAYcEQdgsy/Kp\nv/Pc/1Rk4vS3vUXGbx5GFXoEbWQRkqbnzI/Z56JJtNKii5J1/E3kl1chXXM5BY7ZSLKf1MD9xCIx\nTLXnEk6uQTy8BMuBIOTuQxgAwyYPzA2dsT51haGgkhxE2ggSF3UodFeBsQLFyacRq9YSGlqGXjmU\ngKoLba8Vd+AbZoqpJA9eBK0ByHQSc76B3vQUc7gSdMkw+0GIrgbzT4SE1YTCrfjkizAJtxIQ7kXd\nlQnVz0A0QoklkRAurKYQxUcM/DQjHbctQn77KWy9N6HUSaj6ZiL0bIBYEGaugF3vQtVXKKZezaFb\nwiQlONF3G/Ck2xCH/ImCVQGKxv8OKpbCD38ETQxJ0pIkd5J4YDNxPyg0OuQl5yPEu+BYCkZ3G1kx\nGGiL8T/Ye+/wOqprf//dM3N6lY56L5ZkWbbl3o0LBtvYgGkOHRJICEkIcBOSkASSSwgdcoEQSkIA\n02wggE01GNyrcK9qVu/99Dazf3+Ym+TW5H5J4ZfwPs95zsyeLe2RtPfnLK1Ze60tZ8wlvcuGaPSj\nDL/HYJmV1PpWaFuDkb4C++BhKFwMLfPBPQrUs3F2HKHf/hQO67nICU8wFJhBT8Ec8nQLIxkuKqLZ\nWJYlCYZ9RPzdxFyzyKndRSiyAeEqIjxnFJae4wxPl+hDBro9ghqwIHM7wQ00V4B9IlSdhrsthPLJ\nagjGac8bR0ePg7PUt9m38WecNbIKTBayVzegD05FG0iDpANS09HnjsfQ/Kh+ie3q9xGhfqpb3dSP\nKsAR6GR3/H00NCKFVTi6ggh3GzJ9LGJoL0rIj3X6fEKxTzCEwDYUZ96xPahz4gRcZaR8eBI5lITg\nFrTBdHLvfY/s+vUYk9sJdjsYKThOUnsGT8CPNncq6RPmoC78Jpx4Go5sB70XhBe1JpeCmgPsrb6J\niZN/gmb1nirt9TlOfPT/xOfQxfFZE7WeCzz36fFzwIr/3EFK2SWl3PfpcQA4DuR+xnH/5jTxC8Kj\nzGi+YajagMPyAirFpy6mzsDXfZhDWXmIcfNR5xSQOPYErbXLcbTfit4exhW5F81WhCNzM5blRxHZ\nlyCPJ0hahpCHnoEH0uCF5cTitQw2nkr5OCVpZa/wg16Mse27CC2B2TOfssYOei0n8B48SF90P2MS\n++h2pDH85lV0njuJoVIbwVSN9pR30P2rGRz5OkbK+ej7UhgYeRp392t018/FtbsB68F1KEoSW4eO\nzF2EMfOHeLIEDWkT0M0q1DZT3ebCbfjwburjULSYQ9Wjie7fB14gej+khtAjNYycMRutdwMRsYSe\nthxSt3eQ1+jC1T+CyNVgyyzwbQCTBknocPiI9NowbBZi1ztIlGjEzGvRtx/AvK0VI2LFPwccRddT\npi0gU9bywmnzaQgWUVdQQDJNh+430Pe+jL29EZwXgesrMNwAaj4qGchkBwZhgoqC4U5SFXmPwSKN\nKtPz2APpqKkPoGOlZfIYaqs0/D0pDOd4iI3uIlrUgMWmk3pwhLi0EBtYC4N9yO1ZKH2QPCppGizA\nSgG+nHtwpmYgM3poPJLBJ85vUrDyQSLOJEqpH73iFlpvvoORDDMy0QTOJhB9iN52ktZS7A1TEK48\n2PYLvGO+jZ9uyhy3cUb3RhYe38uRbA+tRpiOETey5SjEKsHiQzHFEFaJEBECMSfxvNtBPx1raQt9\n41zoigIVVvAmEdedjboyQH/VmRg3voF7xRrc9lp6l2j0XKYSKv4NweM3IAsWwgU1cGUXXPor+PoO\nvNetJc8xlpYXzkd/6nK4e/6pCi//SPy7QP85rz+BEOK3QoheIcSRP2q76FMvgiGE+LO2iX9WCzpT\nSvlp+iu6gcz/rfOnZcgnArs/47h/c4bZQxbnIXLPAs2DAEzMgd5dsOd7iMg+Zm/zU5unERubgmLN\nIcN8FkGxjkzuRjMUaFgBNc8hZl0Hl34LHngTZUs/kaVWLGO/jNoXJpI1gPOt75Os/C3Lio8zdDgD\nZBbH5j5Iet3DZDa+h9lsIc9sIWxSGMhSGUm/BqNhD8n0TNK9ywgbHdib3Vj1a8B4Et10gq5IF3Kh\nm0HHL9GCJiJDLlSnBVo2o+UaCG8SPc8PsbuQab3kS52tlkvxevdTvXEL3qIRjNPvorLiKE1yG4o5\nyPFFl1NS9A2sihdl2XSsmTcykrCzaOOlJLPmY2s/hEjMJHLhJfiVN3A9sRFF9MEZII87OXH2OBZ8\n4KdrnIOUXDOm9h62NU1kwUgbpFhJ5jhJ2MoYjj6GdyCNtKEGvrvtBdoyPFjSvQy7vKTGzJi8u/Ac\nGKF+1IOkmw2UzJdwaSUIZxJHPM5I8lFe0cYyU6QStyuMOdSOVtUO0TZ4+2o89uuZ0pIkWPAY9vwQ\nIcVNrHEU0bJOrHGDloxsUhr8BEY7SBluYnfJfHL21VGd2kjK+OkoVKOM7MMcC0NTEd+o6oecizCq\nxnGe/05i1g6Utw6R2n82Oy5YxAxXE1nXvgObzkfmJrHt+AT1zQA4C6F7BNoVCq5aREdyDRl1JZhK\nLiCn7y2M6AidKTPocneT17YdKi8GTy4DZgeyv5WUUBhj14NEJt7CU+5ZnJb/Gr6GQcTQMPhC0LMF\n2spxXfcwzi0vQNsm1CIfuXGNZEqQuqLrSaoWMtBJ9QcwK3mnUh5pp5Z19qSv0j3pXD46/Asmf9yJ\nb8334II7Ibv877gy/4L8ZS3oZ4FfAqv+qO0IcD7w5J/7Tf6kQAshNgBZ/82lH/3xiZRSCiH+RyeV\nEMIJ/A64SUrp/1/6fQ34GkBBQcGfur2/GkkSNLCPdmrxM0AOU0gygW5tEBMhVFRUPY5qdDIwfQ6W\n1kH8ZROpd9tZHHHQHtqCd9V1pKfNQx23H/QN0JMHn9wJPR+hp5rQ59lRR03HfDBC57n95DSPpXPB\nPLKDazBNteB4bTxmfSNJdwVt6WM5lP0Elw4OEx54mQHTPnz9I2SlD9Hv30zlyycInedB9t6MJ7mI\n5JEwId9uAt4IvZ7TCfb2cFrzBLJb3kUZqIekQV9GJV6zgL6xJN7MxXplJv3KOIy8Cwmqy+n1PoOe\nYkE/9iKqOgfR+CSOnLtx2A6i5xYypvhucJ76Z0ho38USfI2M1J/D1O9gbroacjR45nasb3vQXAGC\nlXYcoSDqPo22nBzyDjeh1fXjGEklMieflLTXSA4+gK0/Tnx6mJAziS96Ofb6X+OzHiaSOpvU8AFS\nDkxAGb0Ev3KQmsICMjo0vOPqqXtmK+8nxzF1YRrTq/aBZyrSCQPaI1RyDsgcRieuIZy7EvvuaoyS\n0dROm0qKfwhfbymOvvng2YSm9qH4YqQODSJVndFHTtLXlc1Idga+9nbGZfsp1GpR8014+36EUCbC\noQ5EnRNm/xDa9yCrziUR/inKwFG0MTeiztVI/cVq2u+YyIHFBos/fBRhy0Id/a8I27WQdhJiJjho\ng8HDFK93s3XBYYrUBcjtdxKoHovJkkmutwfOeg7eWApZQ8QsNmIOnZLV7ajDUUbyHXz3NAv5gUGs\naoKe7HSyMyzQkArNcSicjfOXC6G4HM68GZl9Jkb8GjTLKqrUKgx0FFR6Yr9gqP8mypPXo5Rd9PsI\njiwyaBl3IfePs3IlFzOGfxBxhr+oQEspt3xqkP5x23EA8X+IhvmTAi2lXPQ/XRNC9AghsqWUXUKI\nbKD3f+hn4pQ4vyilfP1PjPcU8BScymb3p+7vr4WGiRxGEWSIBDFSmUiAQRLEiNJFiLVIVUVmOdEp\nYFyTi/xNTxFf/g4nrSpF8XUMVhSS/cFqOPQ7qLLBqDtg+kwSzk6i1f04OQgyAXuWkPfbzcT6ThI7\n72b06CDe11VE9my2zX6Msfu+xoDeynHRxyfmfTiUbvIPRHH4DdKMKeS8txYhYiSOKwyFNhLO2oFa\nZcPT8TG62SBD2qnoOsahziwm+eZihHMI2GtJH2xBejxEItnUXJzH7L4nGCxYSo52MVW4eYSr+bbj\nPiJzXLh3aXCiDPLWUpzIp7MMUpw5f0gqaZkII3eDEYSMK6GrB/xPwDfakJU30lhRS9HmDagtmdDV\nwNF5xUzbGYbrV+J5fzNbPJMoqtlInr0MhWFM+dVE1dV4t1+CvacM5qzA1rcaUTKbtw7NYPLHd7Ez\n5XKobkM91kxh5zBLdrwHVQOodR2ghaBoEoMDY4iU2EhLHqCyPwdhb8HmuBdp/z5qoWC0I86wew8t\nBRuIJbpwqsWkH+5F0eshRaPRPIMxic14bWE0awm2me9g3lBCrW0Mox0/Y8T0LVK7d0KtA656G35z\nFvr4BUSNLxFVknhP24DqnQ19P0ebolKxaROh08ZiHHkNdcoEVEsZJFy0V52BWvMO8jv3khMIoTXd\nhS2+nID2Jo6hINH6TiwNVhg5iNj3Zbj2PobGFpDcfwXFfhfBJW7c68potlspbW1h1pFapGZBN9sx\ngjkoA33Q+ijMWQlf3Q6eU8aPEb0dYboIqZbTEngIacQp9vyAzPSbsaTO5aj/fjL2PENGYiZi7JXg\nLWI6kykgj8McpZxRaJ+zKiSfic+ZD/qzujjWAVcB93z6vvY/dxCnPi6eBo5LKR/6jOP9TXHjYwpL\n/kNbnGFaeQUfC3BTSWZ4NDQ8CwM6WHKpUGbwFr8my/0wJyu+hTc6A/v4e+H9y+DY+8SKBlG2HsGp\n34awvgCVVyELT0ekbsFUM0DO8J14X2lCSDPM+Cmz+1o45DYYNXg/IdMcJh1pRxkW0OuEqvOgTkdJ\nzUOvaGR4ZhYiYUK1Z5DfdJjmMTaUzg4KQ6sI2xysci/DMv1KUucd4qPYfSw4Wklu8xo8fp3ZjWuI\nGTYyZBYHHM8z4Lbhppo69Xz08lbKe1KxHWlGOr+NdvJOiIfQw4fR7OMxurqI3f49jKajaFnTMI8p\nRYybj7xqO327z8dQXqB8TwBa4sj9TRxbVEVm6yANNg+JLZvJKpHkn4hQG97E9OyLwXycIbEGayCK\nyP8GcuBpjNAzkJyMltXKssF7EN09nPfuao6tbbPSAAAgAElEQVSRyVClBeWNJFylQqwRAmPhndeI\nO/YRm6bhMhRycr6KyL0aABWITuhF7TqK1lWCL/sSfL0fI0/cwv5ZFxHK7mfMzk/o92YzJrgZGbOg\nDYSJiz04lX0ISzWF8UM0DN1OcWsbRudolEUa8qmlJM4qRRfvEdevwGadj9IfIKGsRs9pYSD3KOkt\nLqq2vkrv3GyyDx6HxUDGl0iv28LO02fRmfk2U61ZlJ6YyJg1LxC3unDuB9/2QUTOORiuDpTSQyQL\ncunSdlAZryaR/AgyDdp/pvGuehNfvfsF0qvz0QPlcGwt0ErC8KJaFYwpEs2dB4CR+B0AwnQe+7mT\nfud+FmxLwpQesGXiVacQT/kaR6ffhvHhKrJfegYu2wyeArLJJPt/92j+/w8DiP7ZvdOEEH+cC/mp\nT43LvyifVaDvAV4RQlwDtAArAYQQOcBvpJRnAbOBK4DDQogDn37dD6WU737Gsf8umPEy6pQHBowE\nbF4EgwdhxVHoeAUVlWKqGBAqlZlP0lf5BIX1t8LK3xJv34S643lU7xLE0XUQ3Act78H465CpbiJ3\nZ+H7zQmSHheaosKHV2NJU9BdpzN991YOrViJkhphKGsKKfEXILIGOmcjDpkQVhNpZW4GJ96Mqf1O\n6kqz0aIllHY1oecWcGTcUv6l7iDfPt7L6smVqJoDV1MdNA0Sv/Bh9iz6mBmrTmA78Qpzjq1CZp5B\n8VQXjemrMKvHqJl3GrP3pCH3fwVVLSfFfRtDvd8hvf8ilFg1lvgHRINBjKCDaP1E5LE9GDtfZWDa\nMCldNkTDCCeWXECapYY954xn/EkXVX3b6ZQGwbZ0io/VoCbb8ezcBpF2RpZm4YmPQRx9BRwqajKC\nvn43RpEZkRFFxIoQU6oYu2Y3bWeZiS+bgNkYB8bzkL8W6fSxbdxkKjNbyBoZjyg569SfLPkxRvRf\nUGUbkcwSXDs2Q6wbim5GlHyfSsdcBjlJW66d3QWFLOh9DznKRGrPMJYhHaPtShyjn4KPb6VM2UOg\nzYl7RgUM+dHPCUBcondGEf7fYu14EjEYRs3MRtdC2HI1VDUEURWvdyUE7wN/K/guwsLTzMr4HcFV\ny4jJJkZOdCHiPlwTliIWP42YeAtkngODi0nW/ADt0EOMmbWa5MD99CzKwhQaYlA4WXH0d6RNqYa6\nJOqiZbDndfS5N8L+Z9g7pRp3QxOFvq9j8XwVmXgNxfYivexGk1YmxM/CNMYLNd+COatBUUlnIRNJ\no2fRe/gmzMU83Ph76/sfjv+bi6P/c58PWko5AJz+37R3Amd9erwN+MfcgtS9Gcq/Bvlng9kNZd8G\noIoZvMMznMO1uNK+xbvqj3EPvcjoUR58JccQmx+E3bvgq3shvQJjeAO6O4FtfR0Jm53u6sso8s2G\nodth3GqyP36QY8pkpCmHcHwL4sAekL0wYxJy4XOImgtRP+pD3QgFmz+gZWEcxWnHUnolB9J7KO46\nSk53B3nlL7Fsx7PM3HMdb/cP4Ax1IId6CD17MdWlM9D0bGicQPLbdxGK72VM40E64hnE8i5jrBjP\nsZueo/Kn6xBuHVfNN+j/0TzSumKIuq+jXH0r5L5BslDiTJxF0uqhr/nrFLySg7L9BK2FhRwrGmTR\niRzK6vsJlzfRGbfgfnYvnXPG8n52AdnjEzibykjt7MQ60E7qsRQIKQjDhNGqYkyfgDI8AMFmjL5O\nxPF2MJLk7VcR566EzHpQ5yF7j2P4Bpnl2Iw1GIUPTDA1AwBFmYuwf4ARvxubkkkk8DCGdzXB/u3U\nOidjYjdx23JGFUrmn2xG8Y2jq2oxQ8mXMIcHiQnoMK8ltbADpSuFoRkZdGWYyOwFj/cOYjxBNFsh\ndfcIIhNkjoJiL0Z0p5KcOIbIwT5iWe0kIlEKp5wPry1BFt+OeLkO828W4CmO0JuXwj133MC/ds/F\nHN4L8a9A8ytQ9xRqwY9RG7qITMvD+voMjKwomd0JHs37CTMPvkFOYROM3wyhb0J/DMrKUXevQU13\n4iy00zz+GlqNPcwJLcTi2E2v2E2P8SHj2h5BmEohaxMUKXD0Hhj3IwQCL9V4RTUyXfKPupSBz2WY\n3T91RZW/FhLJep5HJ8nSATNGx09oNLs5KiZgDdlwJs1M+uQQDqFjpKnEyg9jHVmGMu1hDkS2U+wc\ng6flRqgNwxlvMrBuJWtFCoHZpVy67XG0eBkp+UXI0X5kvBCltwhOvIex+F7ak+8Tb/wto55pg1Fz\nGV52nA5bBjl97VgCczkc8XKu7X4aPxqFY/YCYh9tIDl+KQ4jA865BX5+FZQV0jB5gOYJMXYMz0WG\nJnG7eyz1vsexdT9L3i+LEKVldFc14/KtxO6aiuh8iFh+FvG0Opw9grjTieyox1pzlLApi3VLvsyK\nrd0k+20ke95g5PxyCuozMUYi+CN7aLDkYr9gGG+PH1trAR6LD7VvE7SaIKRgJINEq83YomaEPRPZ\nOwJbeiEV5CVpCO9KRNdWDHs7RjiMqsdAZmMkB1HDEtLywJwKIgpZt0D6SrpbdqE/dy17bpnM0pE3\nGEpz40laMcsMEAtQN65Gn/MbklYF/Z3niWZtRoY14mkqemuI2tQKtGIFc3qAnLYBvKEY8eIwvroA\nivQilAFkxARDVoKZDvSCObgbWvEXFeJpeBWRUY7xZDvSJFC/FsLAws68KynYHeKo2kdu1RIqTh7E\nPOVZiPUiuz9ET9ajHmtjpKoOS2cH1nA/NdZyBrRsTsv/MkL5mISpFk80H1YPQ0UADg8iCx3EixJY\n5u4kHp6GjokhqTJgncnY/qMI8xhwfwe0T+MBPrkJ8s+DzHl/x5X05/OXqKgiCqdIfvRn6s11//t4\nQoiXgflAGtAD/AQYBB4F0oFh4ICUcvH/NsxnjYP+gv8GgSCfEqKso8V+KxHPAGXKlzn/k99xxsm3\nOV4epz4tQMyznfCo7Vg/iiJPNsLGVYytH8Tz4nLIugtyL4TNP8Q35kpmpYYwxwNogzrOGVcjs6aQ\nsHwEPe9Cdy+xWTezn9uIUcsobQAm55BccAXOrhBj7gpi7EonmnqAtEnN7NKuBsVGk3OYaHkpjtKl\nkBuCk+/BdQ9A3ENp6RNkKfNZ7F+Pc3gD4mezKT7Qj63JRKDcINm1kZSOFAaiT7M78ABUPMVedwFa\nchpy8y7M967DcrgPo9DJu7Omc+Ybz2G1hnEW1eBNTVJYsxFcx1Emvo9nko6eo+M6EcYiw9Ql7aiK\nCdLmQtmVMLYaxWNg1g1k7qXATYi4H7HCB+ebkKYwScfz6EGFSF8uAZeVeP5YRMXpDE45m6juhF4z\nnByGXj+0XQ1bU8jcv4Cc85s5a+QturNSUeQo7CdvRDVvQ5WnQ3IA3b8ca+8PcHTswJmbi0d0kdWr\n0VpxMzX5lzOh9Tgz6zrJbejBFImQ4t6Dqt2O8N0DGwvBOREK4kT1ONaat1BM6XjS72C/9Uyiz3SQ\nzKlCKVCgBQYC+Uyx/Jxc13SWbP+QrK33EWnqhMgIWNIRafMZzNuNTMvCuakNLdhFNGQw5cR+zqzb\njSMwjD37XpKJ/STogyvuhZpauPxZ9J46jodsDCXnM2Rvo9/UQL+iMbb7Q0T6Gki9/w/iDDDxHjj2\nAIQ7QBp/l3X0d+EvFActpbxESpktpTRJKfOklE9LKd/49Ngipcz8U+IMX+Ti+KvQz27ivMXohAUd\nwWChBYkFt+0nmHbezte2foBMDBBcYMF5cxLOKCC6SMNRX4P2/ntwYBjqvgGnL4F9j4HVxuh9bxLz\nn0ZkxvWkBE+i56cglQhCZmD0bqNxSTaBuE55ewCUGBTmECxdh72nDHWaxIgPkb5jGOcZhRwb34fa\np1E/RmOG91wIGDB1AWy9+1TMq2sL7HifYocV264jlJa00D/ThKN+HY64pHmigtmjkrWzhtiKsyGa\nif72T4jP07CeLMLo0TCmedHOauSjvm8y8cRmUjOGQb4JngugYiHSYgLvneDIIdSk0F5RStGJCqxH\n32R4mUbHSQ+5k9eCUDCafgaZyxG9W5FHTqL3HIH+VBIX3oBl6DHIP8ogX8GeqtCn1VA0lIIIDYDz\nXRJ5Cl0FVlI6u/CY/YQyz0LxGmhdSZSSBErCQ0gLYu8EekfDB99DbHmU0JQJKGM8mLqywbwALL3E\nCpehuQOYNj+DI+tqfjD0M0TaMGSvJTl0KUoijDrSBL5JEB+Ga3cg3vsx0igkVrwdJduKOXEEvXEK\nuW+aaV18AaPm/hvi0UUY5h7SfDMRh+4jmttDZLaHtDYfYuQIrBoLGWWQPRmjupsB38s4zsxAbfKz\nKX06s2xdeKavgppHQLHhMlUw7K0jLbUQkZoKD12FOHM8mYHDyGQqCXIYMjTGKXchUtph6AfgvuFU\ntrp/R7XC2B/Ce9Ng7mrImPt3W09/Mz6HLo4vBPovSJwh6nkSG9lUcyf6yGT88STJxCxi7l3E8q/H\nktmD2LgYOT0f18ZWRNdJONKCnDGE3rQD9czL4Dv/CtEhOP4C2E1Q90twFFPW2cLJM+8ip+V9cNrQ\nOjOhbjeJeA4muZ05PdeifXIlTL4SvasFbeh9tISCcdE0Or3nkx5YjLnxBxSEsxlymcgd9qOnH8Fo\n70fxPAKz5sHGJ2CoHpobseaNgewSfBMXELd3QXwT5nCcNFsL8ZJpONbtI+NYAeFxQUJnXE7Vq1dj\n9JmJfn0s0jOXk+ox7IF8Sj8sglmpkFIIFh0ib4M/CiMLEJ0JLJadjD3STdZZH5F8diLFa/cSFFeR\nnNREkMfRQr9CEfnYLHVQDoZmxVh8NqbQFvC7SdbOwOEcRW+VmcJ1BShldigph5FfY4lNJewborci\nl9CgnQzHu0R1KwfViYTGXYhXe50oxYwMh2nO18isvp05XMKvaj7iu6VDWHemAT5oWY3V/zXUI+vg\nrLVM6P8WtLXBxBp0VwaaZymmwfeh+W4oexGOPADFlyJj3cjIAWRE55PxU5mj1NK3eh6Hb40Tdw4w\n1HkOrBRU741jDXwMaV2IVgWHG/yL8rCmPYD5cC/i0C1wvAaXP5fwtGxMB3ajtQnmZx7FVPwNOPIj\nmPccbc0P4nXfhIOjBLpW4uhNkrywkXB+Kt1tk0mN1uO35zDO8ipCfFrmLbIBumZCXgOoqX+Y0K4y\nSJ0IJ5//5xBoA4j8vW/iP/KFi+MvgETSyXsc50GKuJQiuQRdrkMYGin6V4hqkMbjBHmRkPkjmPIY\nyo5diJzJsEyBUWHsLR6MDBsceQ1emQGt70DrRsgthWl3QyhGLGcyA0YPcmgzRvhXgEBaJmNKzaZw\n79sIRxKECsxgyFePNa4i8wtRvOtPPdtxTyVZcBtKUw/2dD8lXSGcbCeqr4OfXgkD7eycE+WD637M\nwa+/S88176ObZiEqf4O54A3wzCJ6eAWBTh+25mLaH34Rd4sL9/ENaGtvpeucawjdsg4lNMD6SB1H\n2l9l5po2uHAqZM2AARVGPwdaNaK3FZGmQPYwKJKyI8dhxxkoxWW4lZ9hLn6d4L4WtM507LUCy3Md\niKOg16WgvqOimeejBA9jCBtSBRk+QtHxBtTsLdC7DRJVYF2Bz3sPpqCdosQ9KMkgyYUC50tJpgfb\nGe//BUVGNXPkc8ztMLGYBEticerr3uUZ7SJc6scINRX5ySpIRDB1bEbJSMLxqWD6MoQmg2ssKhmY\njFyoWg2GAxSJlEfQG1aQzDlObIIFUjQU01QsN1fhTh+kikbcidlMSV3N9C6BtTkNcm1gtqJl6IRb\nM3DXLkJ96Sckd92K7DRDWgmOIx1otYcQzTZElxXbkXaGLfeTmLCAaNcUKN6E6LwP+weHSAR34v9B\nAdFRZjzPGLgm9dLmqGBszwrE8GGQ+qkJnHInOK+Ckf8UBWtNg/nrIG0GGJ8z0/KvgQT0P/P1N+IL\nC/ozIJFE6KSBJ0llCuO5A4FCRN5IUr6P0/UoIhnAbPQQo51UfsEI9+BPacE19T7Eid/Amf8Gh15D\nmV5E0PYKaqsdZbAPIhtBbYc0HYwdkJdNcspkhowupH0AoY9FMV4ncWQYRuUiCy9EOXYfjJ6LblIR\nvhDCnI0SHo1wDZEVFKdyZ5zcgtHZQ7y6Ej3SgVUPog7EYKkf4ncz0f1T+lyF9LmHOZhopm9iBtHa\nh5CxbuymItKVRuxdHjZMSmKzvs0lo97C7T6N1quWIo8/TujNB9HscPKSDBZu2QSFjVAfObUhJ5YH\nJ16EvjA0JWDYjxz0o+bpqNO+CqUtKCdNZF7yZbq1EG2JOxh1x17qlhThPCdJ9v42hD9CYGkeWo4f\nS8BHT0khES2dQnk3SqQfQgfg3e+id/2U/uLxmHrvwxBDRA/+GFtHPZ3bs8lt/C6BtAfQTZK0DhUy\nenGJDNSkE3XzAxyS16Nme+hM7sGVYcIR70KJmSHyG4zsGIr2JOK1m5HnvYhk8FRB4dgQhrMLnAFk\n7MvgOIY85CA6twOjw4K1tQBiDShXfYemmR1k+r9HhroVNfElksMelK1HUBxToboRJSCxD4cQBQ60\nc17AsDgJ8TDJwCs4m8B9f4Lg3eV4Nh9FdoPvmWFixY8QLUuQ4T4AqQrxqUewpAvi+lZc++IIJUjx\nTf1kpKUgp+1C+DKh8LpT+brVdPD9ApLdp9Kg/vFON6HAqK/8vZbY357P2efQFwL9/8gAn9DBWyiY\nqeDbWEgDQMoguvwQq3IfwnYmDLyIL3Iafc7V5PJjvPyQoLKKocrjpBS+g1AsMPEG0Huw9rqIBVdh\n26ZB5hBklcJwLcx4Gsbb8AqD4fhadHcj2tCFCE8mWkoNh8+eToZswDvRi6lF0OP8KSn9OkrPPMTC\n70DnTWQmssELWn8zyfG52B1VJJsfR0biiM58ZPEHyPhKjLW1uHavwtHXR5HFgivzCPqKR5CTHiBe\ne5DeWf/CMUczdc4UXEMBPh4ey7yM73Mi+hOyUuYSmdUMw3Gu3vgOKfFepClOHDNSzUSxOVE234E6\nPIiQCljbSM6RJMwW7LPugs5LOekbR3r4GMdcJygxupE3305+7cfo5h2wJAkJE+aCHyB6fkqyu5eg\nz0th4pvQuxYajkDjDoh5UTf2kxleQSI/k5DrEXoqGrAa5VhrJb0Vr2JpS+ALn0vMFiYeupNY9iCW\n3dtQ+xXGu/fyleST2BMOtPp6kvYUzGe0Qv5ChPIGjb4f4ZnlxjF8BZaUZ5CWqcjYVozEU4j0C1C6\nypGtTYj+JI4TNxBa/yLuj2oRL1ShHNxD6SWPYjkjhuvMXpBXoIZ2IkMKftmLy2cnLnW0PD/0fROM\n+Sjps3E2fIw80U14lAf7ml701maSJZWo579E4OAPsW7+GM/+GIwrJ24NIywe7H4TUu+Hxh6MSon0\np3DQNxZLxplMm/EdMJn/46TW/ruMDv9EfOGD/scgSBOHuB0PlVTzc1Ssv78m6caubEYR6acajBhm\nCtE5gE4QFSdOriTCxwzY7yCVh/DzDiG1BjW7BDWjgthwEltzPUl/JpotH9XoRxW5wACX1Pwa8lIQ\nIhVSbkI5t57xrsnUy+fp15+lINvPcIqKp8eOcNbDO7dBTg84S0/dT+8JUsq+Trd4GVubIFJlRneH\n0A7kYIptx1J8CMuNj6COmn/q53nmUmIz78XUfRC3ugBb4GO8liKWdvyS2owxxDwVJF65Fab34Hz9\nA/rGp1HlmURQeZtgnop31FuY3/8dsnYduvUE4VINqy2BGhMITzemRgMq5pyy2oSgNTWFS10Jro17\nyDTBcOrLZPVbeFK7gRUTXsB3pAvz02sxLojgz0jH15JJqPdWwtNj+Hw/xSoFcv1DGCVWxNRKTMo8\nUlo6CPYdJC3j5/j1qxgy2gmUOIn3rMc64MIc6KTPNBGZDJFWMIcuYxnlDbWk1LfCst+gn5xB4PVs\nnAt/jjA0HE2HiBd1oXkHsL16O2QVI0QmqvM1eH8rIrEG4VkMvgHkth4sv+pBHQMM9GK8fh8eWy/S\nayVk64ZmJ0IRiKUeHKeN0GKbgm/kEGpHNqQ2w6GTsKMWJhskSnJIVpdiXNOCiHfQtyRMTGnhw4U+\nlszbRv5L98Inr2PGRqwoijr/dsxZHRjt95AocyJWDlDVexRjax9sehZ8i2D+ZVA581T6UPWfXA6+\nEOh/DJIEmM2LmPkv9QlQxKg/nEgJRgwUCymsYIg3SeNyAGwsRCWTfr5OKvfhYi5JRogavZj0x4hO\nmUQ8RSGaZqAnF0JARYoxWCbuwbsriu77CVa2QsHzCFTKuIpOcZRG7wZyQoVY0ifD8FawfQgfBWBG\nM7iXQaAX00Aa2W/WkRgF3WnZpE2uJl6ymVD/haS322HLtbDRCwEN2dWAcjiMFo8gixcj3/o+KeU1\naEVBdMPLpMpHOVj5Gsndv8Td48dsjiPbQyhTCxhpCtCX9j1G5YZRAhUogV5kxwCi9CLEjCSKOBO5\n82I0dyaGjNJr0UlYX+H73Rqt1kIOOi5k6Xu/I2BrIFYwj5Mj4/CpCsa1BnQM49mThqKWoZefh996\nNx22G8k4vQp7eQbKA90kN15Ncu5ZWDLGMhA+jtb/Ml59DO6nF6JfWEC8/Xka83zo4VQyBntxxux0\nLDbjOV5DiW8v8UgxJm82qv10QtEk7vVvIKtGyKzxEittJzQhi8EpCqkfPgnebMTDv4YP18B3imDj\n48gz7sc42ov28lrEmn9BmNMJXRdEulbg7j2J4c2DoQEIAHMSqL2LyS29jibjCop8Tcg+iXijFZZW\nIFfcwXDmbbiML6PO2ofngaeJn1XFYOhlLnv5GNahlejmPoyFbpg7k4TcjeaOooUzwTidRDIAseOE\niu4i3tZM2sjvwNsFRzfDGw9CIgblU+FLPwb1Hyi3xv+F/9tW778JXwj0/wNe/mvRzP8eCU2PQupc\nnL7HGeAlfFyGkEDLesydW0kN9uKf/X1SjLuwtO7DtGszoeWleFLfhr3XYhT+lpHQ7SRiWzBG9iMz\nFMJ5o7CGzCSVIFrfg5BxKzL5DhmRh1DtMzlhz8Ui6hlTeATX+jjK2AxoDcDwWXDMj/zwG8jLIlg3\nQ0YJdE6oIyMWxpP2PJ9U3YNH/IpRre9A7SPIYCq2FjvJjAimn16BqUsgkkmYk8vQl8IodZcxMdjJ\nrpwooRInByZXMm/rYRjzAFlHn0I+sZXjZ0+ifIGGonZidj8KjjxkZBNSm0giPRNTzlVs1b+Lw9lB\nMJhGQ0cJX3EPox5+m71ZRVQXdmDrTzIzuZiEeQMkm1H8JvigB1m0DdH9b6S7bIyUpxIxl6CU34bj\nuwlMj30Daj5BvzoXw9FO0v8oet4Mhgf30ayWgneYktrj+Nb1QNhLcnQUc7KansZyJvftw5geIdg3\nH3naEJakQdKUx+a0QnrOyeFsVya+AxOJp/WQrLKhdnYhUp6Ha70wsvNU/kdXJ2JyALpvR6TkYZq0\nnPbedVQoU6E7iPVkE7J1M0Jzg02FGjemmWaS1iJQ+sEURD/bh7r8IozMubj4V2zKxdBwK0rpMqwd\neVRknol1RQwhChBHjqCedirzXPjADIwJSzBbc5CVFbi0NvS+H7JRvErlaT+C0+4Ewzj18E81wW+/\nB4c2QTgAV/wMLLa/2vr5XPOFBf1PhFDAXQ2psxAoOJnBAC+SJi6HrOkwXIvWHiD1wEz4ZClYLYj8\nUqTcT/hwMfYjEZTnxpAyPYzsuATDtBu8MaRrObHMNxixJ9HNq2DwWcw2O5bo9aSsOsrs596h+TIf\nO2+YTPYZAcb/8CQiLxuGToAGxowqxMg20CWOvhbyDpgYrHARcqdQPPQw76dWM5CbxzT3m6gD1yCz\nqlCNEHzz2/S3PktadwwxNo/0gAm6giTLvsSA+y3iN0wnLNvwW+x477oa2aeheVQKt/ax/9qZ2JPZ\njBafwOCv6cpdzavibewTvkKB6QSBoTbi/ZWUeyJ4itvwPn4YZbKLucHN7B89msb+0fDij1EXJNFd\nKtI9C3H3XvBaET0XYu80oyQOoTk7MCUePbW5YkEYWk+g7diPZUoqxshylL6tWKb0Mu3gJlDSYGQE\n2pIgQ5iaLZTdsYm7qhZyeeNJFH8Ca6wRmcwj6hxm//l1pNtaKLKk0K/nYZl0GiY5mrYML6nhhzFE\nCZ54IYQWw6vPIx77BWJSGcmHhmHaaJLE6UifQ2XLdmSyG1N9C/hBXuTHMGZA7y4SpKKHR9DeD8I8\nSFjDdATDWBgg01gJLY+B3QsXfAfXW8/j/+pz2NzzkbIWZoxgxL4LcgS7uxM9dAW6GANqLzL5MaSM\nY76eSYN6hE00MFO5GKEIzJjhmvv/3qvl788XLo5/QjKXQeby35928jNSWIFqTYHxN0DYDA0b4bK1\nkFaKMHSUoWXERu3HbnwLll8PzZWI8CqMGdlo6+dCuhXbQDb2gIKo+Bf0+n8jLo4Ty+rApc5CzohT\nFotjjV+GKHie/R9cTOGLh/A1lyNz6yBzPUKZiEirg7RMtP5UTMUN2Pf10zfOwfKuDdTaC+hs/Zhc\nxYkYOIIYKobKBL6uHuLKAKK2AW9eLrHlazEdbafg8CvEJjpoyHAylK7hisZQ58WgTMMe6qbq1bUc\nnF/JxtxMlPQxzBNZnCE7WG2ys2/AR1UghyuU1wgM++jNshO99hZk6wOYTqQQD5r4fvn3qKs8l9EH\nn8VosyHEJvDbIZGCcIyDsYVYrFfSb76LdNMrCBRoOg8SXeBI4t6bJDn0CnZvPu5aO9gzELXdGN5S\nRN4worwQVr4I3kLiR+tQdBMEemAoDznczdOzriVvcy/n5I+ht+ITWs1WPIlLkMoIKW0Sy74E8mg9\nwYxm7N25KLPPg9nL4PSFiOg3MQoO4A31M2guJJg5FXv7c5hDkJxmQR2xkExJQ0s0o3QEqKxphUvP\nhcQGLCW/wrntDnalHGVevw9H/WoYyoCuy1EOuXDrj4PSh6AShAchPKfe2x9nJHM7afYXkUYTYEaI\nHNwCJtDGMIINPAFIlnIzyhcRt5/LouclDGsAACAASURBVLFfCPRfm9xLPo1NBh+X4udjkr07UPUc\nWH8bjL8Qrljzh9AmRcXhe4dgbDHS9iLBbR8xvGk0kUO15JhBG+uEoz9D5JyHWHw37Pg+6vIabMPP\nY6u9AULvInMkMt1Efl0X+PeQlfYozWc8T/vQekqsKdhiftQ39sJMMxQrmFw56EVWtKLHyO/fQav9\nLtyWMFkDzST8JuJGMY62YwieRY0Moy74N5JlixGHZnMy9lWyXm6kaP5ogpFKuuOC/K1LCI1fgzvX\ngZ4YwTCbsXmGmN6whwgWImYLDeFqNvmGyeq+jhvtv2Wvo45XtXOoVOfi8Z6kQ96BK2Ci9UvLiUa7\ncSYPcZgmRtncqHIcJ00hsjPN2I65YGQXzLKhiDVYNRvh0K9xbHsNhjbCfAUsAufviiGuQGcXBCXM\ns8L9TSiOVNhxH4zsgJRTD1INaz76nIeQL92Eroywc9FplObFqXylnqFODXOgl755BWTXW7DJfDaI\nctLnOJnT9xLJCWm0nV9Cpv1WrEY57P8qSs48kil5BIx9LOr/LTSsQ/rBKLYRq5qHs2ECqrcLMbwB\nbe3ziItXInpfhOoaUFJwz3Ry+jtXoBb3YSQESuYcmHYXWDegfNKNHDcVsf0jZO1rcM3tCGc2ImcS\nIrQB3TGIqhT/fjoKQKWAVCSlTOMEW6jhDaZzwd96ZXz++Pc46M8RXwj0XxvxhwcuAhMFI99HfXIR\nZFSdEmZ76n/onhweZuSDDxhen0P3UBJn1TBp52lYv7cAuvYgt/4KkdARy24DdzGUng8HHoKyL8O2\nt+E0FWlZQzIWxhzqhaI70ezVjErJIHbglzTMK0AGSim3xzCH2yGhgmkfvkN28OxEO6YhFk7BFdxL\n95hCcuq6MfWeQHeaCOZLPHIMouJiNOBk5VVM2/wqdCTRDq3HsW2E6ZMWYLKtxZ43k0TtdoZn5GA3\ndxEZbUOaQd2m45FRtMi/Mu+Kmxg3MYuuD0aYwDBzZl5BrSPM1sRefP0TObNlMyOjGnAbUQbUHNpt\nM/hgTAWLWj8ku78LWZuP7FuFPHMCik8Hx7O4FI1IYhVU9kGyChIBaEqHjxtgugI/fhax72aY8jw4\nPv3dJxWk/wAytouEOkhKXxvi1etJOE28tvgyRquQUX428awGnAv7kK06lSMdnEyZysStNqqqBYdE\nNnd9cw03f/gtctfsw3/lbqxvvwyuVxCpxWjyFjL2LCWUHIVD60MssqJ6f4zofgU642iNHmS3QeKm\nJKbQY8jIDETjy9C0GzVtPD0zZiIzdpM9EMHIOA9ObEf0dcMz9xKZANapCxBXZIHNDoB0pCEDzQyk\nfIUM05v/dVoiGM0cRjOHEMO/r6TyT80XLo5/chJRtE1PwaxvQcVisKcSra9nYM0aFJuN4J49qC4X\nnjPPpPDBR9C83lORIDKBbB+HdAcQ1ToMzYKdX4PFG2DUSuS7y2H7LxEr34bcfMT7XaiWHTB+DqRf\ndGrsxm1YpKCytoVAy6Vs+NIImd5KJkeWwY6foExNoOmvEW8OkmzyUJy3Abl+MiQjCB+ofoH5kwH0\nSCP+1qV4TdeTVVOD5df1UCVhiSDuPwPH849T++ASCj/YiGbVMZuj6CYnjqZ+tFIrRBIcmHUBE95Z\nT+XurSR6DjJQHfz/2rvv8Kiq9IHj33OnT5KZSU9IbxAgdAi9KEhTBCwoYu9t177Ydl17+a2ru66w\n6upiAbHTBJSO9N6SUEJIgPSeSTJ9zu+PiYoKEhbEoPfzPHmYcnLvOTOXN/eee857cPYMJbjiD6Rv\nKMfazcpuUxamDu1JdRlo0NVQZdLSwb+RlOJsyre5iQr2o+1RiAybhDPYR1DIH4DAGWKhbEenpnzQ\nxsKWC2DUY9DtERhaCrm3ILKeRVrDkbvW4dJ9hXbnSyhaDx73JKo0I7hx+x4abJ3473WjuGPRRoyD\npkDtxzQaK9Dvn4LQNmDZNpMDg3SYY3bTdcVBOmiz2BOZwBt9r+S2LbMJmzkfHOFQ1x2mf4Bw/43I\nB3vSGO1BhI1Cv2UO2rA3MR0ogZ0e8BqgfxK6SgUZBUJbiT8oiS19+7NSFpFeFUU/Ry6ioRwxZwq1\nQ0OwdA7H28+KNjwUpWkT0p+K0KQGPof6YsybDtGQFH/SwzII2y91xJ9bJG1uqrcaoM8mnRHGvwqA\n9Pspf/11jj7+OEKjIePTT4m5917Ej4c4CYFEC4ZEhGEOQvc+JK6FxnxYMBz6TceRYsVUXAlRsbBj\nNiJkBJqQKIi6EprKoWQzHPgA3/n/YL+yhK14CLPGkmrPCSRl0kbi80YgS6MpGHmI0KIymP0MYnME\nTPGARkFE9MZcU4Ys9mCekUet5R5En0H44nvjy85FkS40rid5VnmdKz/ahXeAHo9ej/lQFeKIDhF/\nOZ6sScw5uImxK1+nOisILFU0DetIaUU5li21fJkxknYZRbQv3k9VzxA+sgzAKNLZWddED2sDnXas\nRNfwLiUjrcyMuYzBa9Yy4J0dmHteA9cCzbmgmAmpfhbCJkPZcOgZguvoX6FxEXp9FCJrFhzdBh/2\nwVWYxtZ3nXSsHYDV2hWtaRih36xmTs8BzOt+MffVT8Job4JKK4Q9hXfHm0jn54ik7oTXpBLf5ShV\nUalo+6+CxjIyG/IodS1jY3xnBuctRlsSjiwoBRGKeDARi7uE5lRo0K/AOCADvBWIQVp0KUZyxHl0\n//xLdDskMknHesN4VkcV03PtF9y74nPsncJQekbj3dsJXf8afKkhNJi60miMJ5GXkSV3wd4FEHQ/\ndHgAOozCtKIfXmX82T7Kz11tsItDzQd9rmh4HXSdwTgUcm4AdGD2IyuW4PI3Y0j/GrH3U3D7YPgL\nsHsyxD4Ks0dD5iSqhj/KEvke1Q0VXLRmNtagcEJrk2BQMr7Dq3EmG/HkG7G784j7xoGSqgAm0PmR\ntWX44jSIPeEodgei7x34x/wV+5MjCS7bCBEafEVOnNPC2F2cRVa8hqC9h9AmPoer9jlYs5emlCCK\nunTi01cuhgIv3fW7iIuu5eW8J7gk6g3M0s6XYbcTPKSRu/3PEBZ6hN2uwVh0E0ivfopgQzMVBZFE\nhwfjDIM1WX3ZGeXjEmcpJmsi1sIVmDxWMClsCO9MX8s/EQsfhb63wuI78WxdQuUj8YRvs2PYHoJ3\n7F/wPHwvrhevxlKegBKdBSmD+OyzW3jnqqv5U3M00fZHyZyxB/x62FyPIyGU8mQzyTcug8Ymau0P\ncKDZTrZyH1iCYfN71EfmUb/GTW2ZQruoRkKzg6nv0YHwLauQmSZIfwtPyR/RxXwFnqW4jjzJ4WYL\nljQ3OllCk8vMvopOdN5whFhnFSKiL76IrVToNIQ1JuMYeREWZTyeinH8O/ZqbvJvwaR5EsW9DZwf\nIsR02Pd3UAwQORGZNAzxOzgPOyP5oG29JUNaGW/mn/7+WkO9ddtWeZ1QkwO+ltvKppHg+DpwMzHz\nn9B0EAoL8c7tgHZ5MGLlzYExrMNfCJQJ2Ya3cDibL7iM+QP78I2yif4eB8NMX2AKg9Clh6EmCoLu\nQ5gK8Ni2U5YYQ8z79Sgxt8HR88CSAW4DwtUNRZ+B845uNN4ditezA8+hJQRXWfC/mo+/JozmF0Ox\nKzaSNNWENN2IoumIxzSNug79cZutNA9Kpn3hLp4d+BLlg5K5OHklfTo38dHIywm+tZqJGXN5NP1O\nkrIK+Czmn+Rtz2bDjqFoKz7G3MeBv6+V8lts0LUfxvCOjHj1AJ1K22GyPUd1vov3qs6Hxp1QqaM8\nZCheTyFUrgP/DChajy7MR/T6MnxHmqm7oAf7u+7AGzuAyrDOKEueh9Wv4tjwKZ7MWD6Ydjubir+g\nwN0eyqsAP/LZzzHc4MU/3Mvh3X+B1X/G721HTFoDfPogfPUviB6L+b1DREuF0vtvZfDTn7A0vDMe\nz1YojWRV8FPMaLahC3oTgYkVwRdRIYLQJvbiKcNdzPdNIEpr5zyzlXZplyFC68C0AuVgJaHNZvRj\nl+MyFuHW19EQkc2FZYsJUmbi9c3A7/kXUlggSAvZ/4EO90HlZ4hNd0LD/l/xQD6HfNsHfQbyQZ8p\nv/0/reca6Ye8/8D6qYEkQ5720AQMHweJn8N2CcOnwLRDyJrD2J8fRWjn/bDlJki/4bvRIF6tiVnd\n7iZXX8v1zU4ymUKR4y9Yau0Q3YfSx3oQ9tpHGHgWscuH3qQnacYO6BsN814DxQjB3SHSBBHtUfJL\nMQ/8gGbvCBovykP37zvYd34IpqAbCZ+WhblwI6VbQ4kd0IHmuj8hksETch3SE43GdIQ4OQsRtgS2\nXc3UsOeosFtIqDiAzxhCTVIk/pQOJNeUMnnXG0yP0ZJ9x+O0X3M1fk00ipKBN3cLneLzQbMWjujh\n7nvoHHKYQ3sfYV7ppTyZ9GdY2ww9Xeibl+Oxr0BnPwj7dVCTDf2b0OwrwOQs4/C1Ask+6sUOotq9\nCdn54CnDZF/GyB569J2/oFYUoHNJCJ8FaT2R+x4CVyXRA3S80SmS2yr91FKAxe1GWnWIxcth3Rp8\nHcOof2ASI9cs59W4IczNfpgu8kXo1oWh21aydHA8sxqPUuI6gM1ZR7/6MuaHNjCmejOZBzbhGOxF\n1/g1OutWDHsiwKrBnhCBfuBbCK0JDeE08iV2o5Hc0GtIL78XfeTf8cnOuPSlmBwvIELegZB06D0d\nmg7DvpfB0xCY7p92Cxh/Y4u9niltcJidegbd1ggFOt0K1xTB5a9D5kWgMcPm1ZB/CFb/H1zUDdYV\nIftDyPKViIfHwiIHzBsHL1wNrz1ASYOO3s52PCrvJdNRiSy9jaCSMip9aZSnHkTXvAv9oG4wbyKu\nsBFs2tkZ721BKCIEuveBcbdD9n1QVwKVa6GyCfHvBwl624f1pS4Ytd1JCdMSte8w0pvHTtMoXvj8\neTziZsxrytEdSeNoQx0HCufQWJrL9oo/sz29CXdYDIlNhbwe+wKkmvEY7Qys2owSXIS0+Qg9epQr\n3PNw7n6UZsWFuaEAv70K7XYHjn9YYKtE9krBHzKbENcCcpvCeTD9aRxKKs7wUbhXHKbcB7ud1TjC\nk3APnAnuMGgGb7sh1A3pRNoyF2W+rphsTpwV9yM7joCJb+EfMZYQ32wM/gFMFJ8yPKgO/6WJyMQS\nnLYSSsvC8dVY6FWzjpnDdZgbmrBNL8NrdsC9j4MFtA0NaGq2oxgjGK2LYppMRAgzcuADiNpKuoW2\n588pvYiKH8sthkbMGgNX1AQxrnIjGZYSRFkqDkcErkNOKLQhtVl4RC1G7ytQPRmTPReH90OiHYOo\nMCaBeQgUZqLxjsRgWID07wV//ffHU1Ai9PwHZD4EeS/B0oHQeOjXO77bOjXdqKpV9BbodRP0IjCS\nw3UYaq6DBAe0exdfajL5njEkrK9AV3cE7pgBhjrIew7S7ibReQgslwW2Ff4wwrGBYPdMCLuGLuJG\nxOGp4F+GDD6EZ4nCvivuoFvdF2iiQqBeCzc9F+hm2RcKPh8MT4DNX0KPOLyZfmpjzUS6spBNDbg8\n+5j/8mjuH38fIXVO8s4fi9s2mCh3Mh2/XI2SX0LkqCZc+e/g7SjwehSeqp9MRYiNENlAZImXhiQ9\ntYXhxDuddCjKZ8v1N9B9vg1drxsRubfgbdJiLGjGNyEMb8xeKuw9eOibp3gx8kE0hR7KJk2hXfVy\njAXNeLQ2jtYrZAT5CXH7oXw//oomCm9KJ9GyglXeT2i/8WMs1kvwlPTgSOFjWO3RWGO7UBN1CQ3O\nFOz5DQixF6qLcLkVvBFerJN86CI6ke73ENJcRXVDPWKUgqFHJmKpn9Cb36YhUmL54GYYeAd8MR5R\nvpPw1DSkchmNumpS51zLCp2No74GPHU70HXUQ/Lj+JUCPLXXIg1ZBG8uorG9FvQX4apajS40EiI/\nAqDcPwEFBZ0MRngLwb0aRC3UzELjKQDTaHC8HbhZeCxrJ7ikBpoOgasKglNQ/cgZzMUhhHgHuAio\nkFJmtbwWBnwEJAOFwCQpZe3PbUc9g27raubDrn6wLR0adRBXjEc+zWLPVejrijE35MH5Zqh9GFn2\nAk6LA6qng7XnD7ej70VdyAAS7AtpKpgMea/Crt5459twXDEejbUOXjkMmZWQtSvwO1ojdL4ACALr\nHUjRg9y0EJalgzbkKUTEbFwJ6SiGheQWjKHbkP9itG2is20eWeIu0EfSHObEcWkojY5S6poVGutC\nCaaJcn0XHEcseGx90NV0odjZnrjwasjugM9eQ+c9B0AoKEnjcRtC8KZbWDvjbvw9fPjsYez4bxxv\n5txAkiON4Mt2s1McITj7DbRWQajnEGsSUwh1W9G9/gwUb6cyO4qUskR2sYo4fS/CeydRoyvifXcl\nL456iOCtR/B50wk+uBtf1Wb6xfVDiCxEUBAbtDdRNqcDzvIkPOZrMHn6EaPRYsuv4233GHK8TeRW\nr4JP5hM0aykemwXWbQSvgEFP4z7/dpzj7sU3eQ6ayBiSenelPUeZ0+V2/AY9TrOFXcaVNMXMJ9Q3\nHOPYRXi694HRf8RTWorBGThlc3EYn5KLUAx4zaNQ9D0h6jMIjwXrDWB7GYLuBe+q75PxH0vRBlZJ\nCe/zyx6z56pvuzha83NyM4DRP3rtYWCZlDIDWNby/GepZ9BtXdg4MHeFuq8hcjK4VlNgqCfF/1eM\nOdnQ5X7w1UP6VJzk0sAqjFvngz0bQggkxNm5FdYsx3WVj5yI7gzN+4SHx75Aal4+/RJ70qX7G1hL\nptFYnIjNW42IroUD51EXfDtBB7ehs5uhuZrSKybRrF9F2p4Swrp0wkcuAg0L5qUwfoIE/ff5hHUE\nEVedhqNSQ3W2AVdNE/WpOtLsNTjcwcRk7mFvRAeiGnbAUQ9JpWaODh1CreUoUR3DsOYu4nDYebi8\nN6KzStIKGui99nMIDmLF7sH0yFlPSO9qGPE2bo2HSorIFZ+R0eFPVJQcolf4Zui5B/97Vbiioojo\n+wmNm64n3boA6/bufB3n4t0HruPm6e9w4cZ5FNa3I/W/d6DxaPj8+iuYumYmBOUhLXZivItIazyI\nq9wKs18mdKdAVhczM68H5i42wjZWkBKSg9ybjtJvIM0jIwlq9yEseQn63I9gPXuZxxHlMKOCDsE+\nO5GVPkIvvplvdm3AIl+ho3gAo4iEyGwEIPGCMQiP3kNwUyAiuDmChcF4OUg9jyJaMiNiehxhvP37\nY0aTDvbJYPn47B2nvwVncJidlHK1ECL5Ry+PJ7DSN8C7wEpg6s9tRw3Q5wJjEsTcEnhc1wXt1hsx\nj3UQfv5UkFnQsAOABlZgYTikN8CeBRAyBZZvh/ffRG7bQHCBmS71LoLsHh7TvcyWkExSfc2w6GYG\nycPomqvxvR+O5sJmmm25rJFLudDtwGe1sLf5WQwHoukV9xgcmQnOF3H1zsEo/sbHH7uZMSPop/We\n8yw1116CO/xTYr8qJ+3rI/gPhqP0dYDpfAoaJxAR+SThverR2T0kmi/Aymyk1o9xg5No0zKcei3e\n3Qa0O73IzlXkrevIiC8Xo0tyISLS8JV8hiCDjpYwQnQvo9mcwajMWuL2rIKgUERXDYaLn6ZUb0fj\nrSU6cj07hr3KWn0j3WtyaF4SxexuY8gOWodX3wt7oomsoA5QugPyG3AN15FRX4hiFehn1OOz+BEW\nC16blvn+85maeg/vx0SSXR7FJSO+QmN9CHvTnUTqypDjX2Afu8mRm8iuz6Vn8XrQ9oF9n+KPvwyL\n/wlMzlLmOi8jxGTlmES1CLR4aMDdoR3i4EHw+whRBuJnM5Le2PkEhZb0AIZbf/i5a3uC/d8/XR1F\ndXK/7AiNaCllacvjMuCkd2vVAH2ukBJWLoYP3yLmpckITRx6ugSmzll7AOBkH1HcCRRB2n2wsicM\nXop9/OscqZ3O9hwTF898B4/dh/PmcfTsdyPBzasguAcfazeDfQg3XrsQY5CV7Xd2o4emHw4+Jfei\nSFKa7iC8zAALnkF2GoTPsRrdoUhenWtBp/Oh/9HiHBzNxaWrxhUTT4kznhT/N0hzEkpaLdhBVi4j\nPa6SWXGP8cdqI3LLffi6fESwUk+9fwCKcx5eazts20qoikrFcdkTFG58i4icYoTBReGNEdhSKtF5\nnibk8CMk9fJQHuYn4fonSNO1g62vQHJviJ6BJ3kWjroZJHv0zG58gzU2A1nuGm6SuSjxOXS420RM\nvYXgzQv41/n3cfW66TDsespu/YQn7WVM3/gKMnwGvpcS8JfVwdxiChrDib/Mw2ZfDHl7hhPWewcu\n9/0YKv6CN1hDueMNtnkSyKh3MLF8A57mpdDgB99h8MZQbtxGZGUfYt06KJnPf9NSeJbw7z4+HfGU\ns5ig2E6wtQgqt0F0H0K4BYEJB0e+C9BC/Kin0jgFfHtB1oAIR9VKpzbVO0IIceyg6TellG+2eldS\nSiHESSehqAH6XLBmKcx4DdpnwbSPMWkdKIT8oIiHKrSEIxDgrwuslhE7AXfR3ykKS8MY+ij7ur2H\nf48GSroT8XUdom4n9Pwa9LsZKi/hgLEIuy6KqhgbfXeMpbpPJHkDOpK1Mx5jv4n4MkKp66dQpX0T\nRZYSuy6GNfNzic7MRKP54Zmab+5jlE+JIIVHCCt3UdXPhkVrx7CgC8QuoSAymlUJPbipaS7+hOE0\nDomkSpZQq2ST5UtCXJKEPmwYDTvWElrlo6DQTOSmaILbH8DXV0fd2FAsQXZM/3XBzfcQZ1/Aobp9\nsP08aLKCuwastXilgUW+ixmVX0qN7yBbjQ5iPS6uL8tH82kx/s+Kab59H+ZIiSc8jgZdV0KHj4bF\nD/K8ER4ofQsadkJlE9qNThTZF1La88pX8Vw8fgsfV7v5qHc/tjTNYE79Gno02jEf3Y2fzYwIuQJd\ncBek/mPqk3oRvkFBc/482LeUmKVPIfxmMFr4Y+Vi7k+7kwb8WFpuC2mIp4BpdG+KBl07OLgAovug\nELhSUfgjfg7iwYfueDk0zE8C7l/kcPzNOrWbhFX/w0SVciFErJSyVAgRC1Sc7BfUm4RtXWU53HsN\nuN1w1yOg1f4kOAPYWU0IQwJn2gA6C/W9p1IaI+kgH2Etb5IUtBWnOQLtZZew+Wkrfs0S5KZvYM0S\nuq19kP4VVohoIDrmZg46plF39CMyDmupb9/EEeNjlPEUQhdMEEnEMoqgngt5fPBj/OP/vN/vF/Du\nX0h91BFibS8i0GJLeoYgJYXGEB/+sX/kUGwM6yIGMvLwVxy2Oiirfo+qVDOmMgfBB7ZS4VlEQUIz\nlealVPb1YdBH0blgFTG33YxWF0TexETi/CXoHUNRElJASoS00WS0IhJugjE7oPsYPi94kvf23cGY\nTVU4jjTwf0Pu5IpNH/PIV09hXPEFwrADjB7Ct9WjlFxP7rDH6KLdjNz1L+RhDc9sXE56+A3Q0AQe\nkO1BjHwJrz6IakcS0Xs1PNVlB4fKL6bT/iVIofBpQgcOd3iEWJMPXegkiBqJV3Mt9pAS7MMmgikc\nul+BGPhHqM6HlDuwGPW8JSN/cLakJx2Tw05QvRt0UfDhWz/4vtdwlLUcOPFxIxQQxhO/r/qpX36i\nyjzgupbH1wFzT/YL6hl0W5efB5+shpSMny3WyHrieQocbyE9W2jwLeOoZiXJsS+yhYeJoB9zGMBV\ntdcj97+OrtMI7N02Ydk/EfYdRISWEm94B/8FHurl3UTGxuCr34rPFEtoSQY6zWWI5L4AWEhBI7IR\nZkHqNS+g//pK6HYnpI3DJQ/j/HIK4rZp6AgDXw00fIRZSeBr3UCajZ/gjrqEy0NvpsJYiFZXRlOE\nICS/M46Go6QU5qC9txn7zTH4s01U1NlQCkugaAfoPsHTw0t4gkTZGoRvaC/IqsBLMwstazARQnPG\nw5gqnwZvKReG7uLS3L8zIfsrnh8+hZ77dpK1tgypV8DkR/gE3mtC2XvlE8Rrx1JWvpIBBwsDK4MP\nKiW42of8YAFCG40YPwiZZYCvZrPoiBXXwHgijcE4tQpRSPb1mEKIUkRPxtCLIZA2ADZPB1M0um2v\nERySgi6mx/dfWP/boGgFtJsIvg0IVwFm4/e90DqSia4PQ3NgHnR8GPKWgqcZdIFsdV1JoIS64589\nq/53Z6gPWgjxIYEbghFCiKPAE8ALwMdCiJuAImDSybajBui2rv+wkxbx40biQ8GEHwNOjYcizRzC\n6Mpm/sZSxvI4o4hUauD69/Ftv5xI4aK8OR5r/79B1ACIWA0zn0BJP4LpqB9taRjazgMgfzaIPKjf\nAdEzwRSFVvT9bt+2+ATYUg87Xoe0cVTzGeLq9gSbv6CJ5Wjq16F1h6BEr6Sm6Z/kmhPoZA7DRDSR\n/mh86w6xryaar3rHMzRtGAZLHeLaf+HbI/DrIwi7qB5n1p8xVt2P+2Ad3uv0xK7w4T5Ug5Cb8fW6\nkM31U6mwuBm3JYc91vtI9e0kvDEGvTeJm0f34fGODzGssZlxxoEYrr8Dz9LRaIp70NBBYe9NfyZe\nnwDTkhmtdyEvsFIX0kyF5iKk9wBhSRoij0YhdmxD2ViGs87A7PnnM/rNlRw5qCX70OPo0j4gGxuH\n/B9QonxOLjlkGC8lePA0WPMQhKVis76EoqT+8IsLjoamGrCNgsbVcEyAllQibRJ/WArK2Kfh8F4o\nWgjpgbHtaUQyhA5n5BBTtTiDMwmllJNP8NbwU9mOGqB/A5rYQjCBsa1V2nLKgqy42E0N5SzibmwE\noSDIJgq/1Yqjkxa/mE2VtTft614DxyDYOgluehtZ0EDzgxMxjdqAtl052JJBawAioXgRJFwKW9+C\nnrfA1g9h7b+hsQJ6DEQWriEosSuWyHsRCKRjNb7mr/FGnsfuxts4rIvlvPqduEONLPLl0ak6lpR5\nJSRk2YjeNoFn7JE8etliejb1JyJkP9UH9tC8eyjN/XNwXNhMc0osTfvCCS3ahTYE5PpF+N1+XN00\njKzvQ5B1G7Uxocw2P0GaiGPkdY7SkAAAEq5JREFUsjcY38PKrM+GM1y/GFPtQqTnRZS0bJyjb8EZ\nUUmBqKDPotcoTsggv78O/H5sriCiwq4iZNtctLqF1EZUsm7kzQx55Z/URCXjNZu4rmQReksDlYsi\n8eoHE6zVYhk2kbrw1SRqhnHQ9C4e4SK962hsqeMxrHgG3A5IuxQ63wIaIzRXw+KHYOI/oOg2sFwA\n+gQAdLTDZSzDM/RBDEKBHp1h+Q3fBWgFhW4k/EpH3G9UG8xmp/ZBn+MkEjsrCWEYfjw4dJFEGv5E\nOk+TzrtkEslUeqJvuRQWR5ejczmwrhEYXLU4C+dB4k0Q3h0M4Yjs4QS/MxfXrh6wLxZqNoB+GdQv\nh7SrQR8E0V3gi2tA64JLX4Vul0LCxYidX2J9723Ep/cE9oUebbt9GAyvoa2O4U9VVobZBpMq3RxV\nmlid0R9f/9uJ2ZPH5E4unhiexeOf38bi+Az8wQcIj62mQ95SNL6R+OMUdoZ0JNkehLzwAzwXX0Bz\n70wqbVVoooeTbJiHKdpK3+Jk7vrwecJXPcmr6TYO7Xydx1Zcx9OLPHDe84gbD6OJmYBh4XU0Hcon\nZfl/cctmNCMeYnBFPEO3lNJt7x6cR95gS0Yhh61Wao1GOuZ+QnCpnVnzo7ky6yDmuCrAQ5jOwH8i\nbmNW9C2E2UNJKozFmL+EbrslWTl+iu0fstEwnZ09I2ls3A7rH4GctwM5V6I7g6MWHLug7lNwH/3u\ne9VgJZRr0SZfGXih882B2aWqX04bTJakphs9x9WzhDL+QTLTMJD4g/d8SDTfjpX12GHrw1DwDrKb\nkcbqjjSlNtNk6UlayNvgKIFtD8GgWQC4V6xAP2QQvJECnS+Hhj0w+AmwDQpsT0rYNx+2/QcyJ0C3\na0Fz/AsyiaTOnUeNZj/5Si5J3r0kanpxUBnNrsKZXPblx2iCjGiv3Ua1U+HuuWX8OWoMKbqDuO1J\niORqGrUSeTAYQ18rUpoI+mY3Ov8gijtWEuPvjCFsF66IXhj1bwR2WriA5i+v5bPeY0CjY/XGqTxw\nYTyZYQ4a9MsQxRvQb8qn0ufEevU7OCjHQTGl9auJ2bAQjbaJnCEZ9NuwDcr6Ydu/l0athZCHL+P1\n2zdwR1cN/ppGNDEJeG54n1W1YNVCL6uXZgoIpv137W+mgtVyKj6cDCgdQajDAmmXg7sJVr8II56C\ngkkQ+xcwZR3z/dWjYAmMzAHY9z50uOb0D5rfoDOSblT0lmhbGW+8ZyfdqBqgz3G1zKGSt0nnMxR+\nPBj5ODwbwbMOuXQV/n6ZbIoMpb9omcyU+zJY2kP8uO/L7/0MEodD4Qbo9OOZqwRmKuZ8hNw1C0+P\nK9FnTg5MjhACds5DhieyNnYJ+coO+jVuIsMXhlAMKLqrwNeOwqUzWT5uAuOXvkhYymMIWxneVfO5\n3/gs3c3v0adXGVXuGgwuB/3LNuKrNOGp8aCtlNR364VvUH9C80Db7EdmLEETvue7yRlyzgSEo5iD\nLj3vdRjGhkXD+XDsdBoidhPzYRWNEcEcunYcocYeaJQqFLEOE7cQtu4I/pyZSHMOrgQFS+zn4Kyi\neM5U/jB3MjM/2I5oKMLwX4HwFsEfFgRWrznZR08zTmoI4ZhVTlyNYAgG92EQetDFnHgD6sSTEzpj\nAVq0Mt5INUCrAboValmAgWTMZJ28MIDjTZBxsPwjuPBdtoppZHE1Bqzg98CqS2DAu2BoWa/v26Bw\nguDQSB3rmUuRzCHULknOP4rJq8XY6TpMlZVUyXwaU1OJkqnEuytRfK+h1U9H+Etg80ygmooOZvIK\ni+m/aT36SCPUCEgfwI6KcA40uEhN2Ut6QSUWezGeS6PQWgfiVa6h3P06MriAuOVdEUd24h0bgt72\nEFiuQu7+D+yYhrCng1KCr+wQN/Zeiy5jPX8of5tOS/azd0Rn0hKS0TlXomzxojSEIoakw45PIDoa\np9mIR5oIaWwPWbdQtfQ5FFsmYWlX43OuwRUzC+NrB5BhXdGMfB6yR525L1Z1Ss5YgKa18ebsBOjT\nukl4KtmZhBAaAq0vllJedDr7VX3PynAUTK0rLD3g3QnlQYGzYiGIox/FrCeZESiKDqKGwIIsmHAI\nNIbvg/IJztyCsTGMyRwU2xAWDaGpLhyrH8FZ/Rcasm/EWV6Bk3QOsId9ulwSdZeTpaQD6ZD7L7h+\nJlE1ewnxzqU07BBWTyy2IX8AaxopXWewJu8S3qrQMHVwLwZUjUefp6dh+BQMza8SbptAqeFfeMe9\ng/boEpQ1jyK7PI5j4aOYdpbCiHuh13VgTUWz522e+WoS52/7FFd3SHvSQGftu0ThJIIVaHI+hgsn\nQfVBUIrAV4+faoL6rQQlHLa+QESUFRybwDYeTcqfMbnvxtM/A/d5dZjDe6k3dFRn3OkeU6eSneke\nIO8096f6kVYHZwDvNnDNhMqPIHVky+9r2cV71FMUKJM4ETQmqNrQ6s3q0JNJPzrQhyjbIJIu/oYO\nI5bRvSqJfnPXc15xF0Z48hnh3kBncW3gl6oOgbUd6AwQ1QlTtwzaDbZQ2UEhr2g+G+3/ZH2EifaD\nSxjSp46/l+SyuP9wVo26gjWaHGaGplLq/oqo+mDy/S9CbBaKNQoREo8yzEHdFWMQDdWw8jHIvxIS\nU7CdfxPJdQdQCm101q6jnuFE8x4a4uDSP0BELFQshcgMfFlTccZ3QzEmgT4Y+j8Dw6aBXwsLJ8H6\nRxA6G7qqPpjmROOVH9GWr0ZV56bTHWbXquxMQoh44ELgWeD+H7+vOkuUePBpIO8gZAaWLw6jPXqC\n8X87ADQkHUathaqNp7cvnQnCUuHoDtAZ8fvmomhvQQgjuJrgjYkw6o/Q+Aq4V0JDJrp1cWRMnBVY\nsfyoDjr8DYBhSTl8nfQWHbmdNDJppBo9ZvQhJvyedaTX3oabrzD1HItcNg/9MD95aaHYDhkQE+YA\nPvxlzxNkWcUnkyp45atJXFh8HiIu8EcKvxfWPgz1BdDhSmj/JN6to9B0vemHbbIkw/C34YthsP1l\naH81ImUImtIcNMpdp/d5qVTHcbpn0K3NzvQq8CcCs91VvxYlBhrHg0+AJXCjSouJvtyPPParMcVA\nwhlYDToyDbKvRoYa0egeR6t7AGQz2PdAx90QMxt0XSB0DuxphOoCqHsQojOhoTsSiZ966niOUfyV\nbWyggTqCCUffcuWgaPujNz2IR7hxmSqRiTWIzb2I2VdL8cSLQFGQio+GduuoT9qPJSqZv9xixhzU\nERMtU6FXPQA7p0PmVdBhEngb8MpSgvXH6YmL6gnXHITwVDjwHgy9CxJ7nf5npWoD/ICjlT9nx0kD\ntBBiqRBiz3F+fvA/WAau735yjSeE+HZVga2tqZAQ4lYhxBYhxJbKysrWtkPVGkID7pEw5i3QfD/i\nI5xMwn+pWWkTXgBtHBrNVdAwFaovAOeLEPcYxH8NhhGB/m1nLUzsAqEXQuI9UFGIi/WUMwEbj6DD\nxigmsJgv8B07m0AIhPk6lNCFbAouoja8E2LvGmIynqdS2YSbBpzMQs8FBBu+QKTPQWNOh/IHwL4e\nGg5A8mi4sxraByaB+I/OoikuHQ3Bx2+TzgTW9lC8FEwWGHLnL/PZqc6yM5ux/0w4aReHlHLEid4T\nQrQmO9NA4GIhxFjACFiEEB9IKa8+wf7eBN6EwCiO1jRCdQrSxgUmm/yI+KVyOlhjENILjU8GblAa\nRkLQfRB3TMInfxMMaIbwe8EwDAwSGmtpYjYKwShYAbBgozcDWcFCRjDuB7sxudcQX1yFsrkJJs9C\nLJlA2qULOKh5n478qPshbDxYh0Px01D7BSS+Elg9BqB2K57S/6Dt96cTt0kXDKPmwu5XoD4frOkn\nLqs6h5xavtGz4XS7OE6anUlK+YiUMl5KmQxcCSw/UXBWnQXHCc6/OKGFkKchbDGEPBE46/x2VIi/\nGuqmQOQTgeAMIAQSDzoyieILtMdMaU6lPXoM7GU38pgLNqV4JsnLDuOe9AL+hLHgi8Hy2WQkPuwc\n/GmdNMEQcw9YR8HRv0Dj5sDrFSvQVO/E58w/SZsEdL0fLKk/X051Dml7Z9CnG6BfAC4QQhwARrQ8\nRwjRTgix8HQrp/oN89vBWwR114Hl/0DX4wdvC7RY5B2I41zkDWIEOWxnFV+BlMi1r8OyvTAll2jj\nRBRhgA43wv4c0utt5PPuD4L5d/TtIPk16LIVTJkASE8tpb26Em6+r3Xt+HGyfNU5rO0F6NMaxSGl\nrOY42ZmklCXA2OO8vpLASA/V713jk+BaBGFfgybup++HhIO9Biw/XRFEIIggmnWsoF9eI8Yv7kZe\n8xHCHPV9oZ43gtGG/vB0IjInkK/9DyliClrMx6+PJtDlIhOvIMxyOcqJyql+wyRn8wZga6jZ7FRn\nnycXnJ8H+qKVEyzJFJUMlUUnDNBDGUWCTMaR8yjGO1Yh0ob8dBudLgFXOsHVY8iJaY+NbkSS/bNV\nUyxd+RU6gVRtQtvrg1YDtOpX4IfIvYHcEycSmQRlByG1xwlnMab605CXzgZFd+Lt6DsSariVrLqV\n1Np2nTRAq37PzmBC6DNEDdCqs0/Xirwhe1bCjiUw8PITl9FoOWnqIKGD0CeId42hyW9RE+yqfoZ6\nBq1StU6vsXD0DGYGMGSrXReqk1DPoFWq1uk5OtAHrVKdNWf2DFoIcQ9wCyCAt6SUr57qNtQArWqb\nFAVG3vpr10L1u/LtVO/TJ4TIIhCcswE3sFgIsUBKeZIB9j+k9sip2i6NumK16mw6o+OgOwIbpZTN\nUkovsAq45FRrpAZolUql+s4ZW5RwDzBYCBEuhDATmBdyyqv8ql0cKpVKBZziTcIIIX6wPtabLXmE\nAluSMk8I8SLwNdAE7OB/WDNcDdAqlUoFnGKArjrZkldSyreBtwGEEM8BR3+u/PGoAVqlUqmAX2AU\nR5SUskIIkUig/7nfqW5DDdAqlUoFnMlRHC0+E0KEEzgtv0tKWXeqG1ADtEqlUgFneqKKlHLw6W5D\nDdAqlUoFqFO9VSqVqs1Sp3qrVCpVG6WeQatUKlUbdcZvEp42EViMu20SQlQCZyNjTgRQdRb2c7b9\nFtv1W2wTqO06XUlSysjT2YAQYjGB+rZGlZRy9OnsrzXadIA+W4QQW0426Pxc9Fts12+xTaC2S3V8\nai4OlUqlaqPUAK1SqVRtlBqgA948eZFz0m+xXb/FNoHaLtVxqH3QKpVK1UapZ9AqlUrVRv0uA7QQ\nIkwIsUQIcaDl39CfKasRQmwXQiw4m3X8X7SmXUKIBCHECiFErhAip2XdtDZHCDFaCLFPCJEvhHj4\nOO8LIcQ/W97fJYTo+WvU81S1ol1TWtqzWwixTgjR7deo56k4WZuOKddHCOEVQlx2Nut3LvtdBmjg\nYWCZlDIDWNby/ETuAc7g8tK/qNa0yws8IKXsRCD94V1CiE5nsY4nJYTQAK8DY4BOwOTj1HEMkNHy\ncysw/axW8n/QynYdAoZKKbsAT9PG+3Bb2aZvy32bwF7VSr/XAD0eeLfl8bvAhOMVEkLEAxcC/zlL\n9TpdJ22XlLJUSrmt5bGdwB+fuLNWw9bJBvKllAVSSjcwm0DbjjUeeE8GbABsQojYs13RU3TSdkkp\n10kpa1uebgDiz3IdT1VrviuAPwCfARVns3Lnut9rgI6WUpa2PC4Dok9Q7lXgTwTmgJ4LWtsuAIQQ\nyUAPYOMvW61TFgccOeb5UX76R6Q1ZdqaU63zTcCiX7RGp++kbRJCxAETOQeuctqa32wuDiHEUiDm\nOG89duwTKaUUQvxkKIsQ4iKgQkq5VQgx7Jep5ak73XYds51gAmc090opG85sLVWnSwhxHoEAPejX\nrssZ8CowVUrpF0L82nU5p/xmA7SUcsSJ3hNClAshYqWUpS2Xxce77BoIXCyEGAsYAYsQ4gMp5dW/\nUJVb5Qy0CyGEjkBwniml/PwXqurpKOaHKyDHt7x2qmXamlbVWQjRlUC32hgpZfVZqtv/qjVt6g3M\nbgnOEcBYIYRXSjnn7FTx3PV77eKYB1zX8vg6YO6PC0gpH5FSxkspk4ErgeW/dnBuhZO2SwT+l7wN\n5Ekp/34W63YqNgMZQogUIYSewOc/70dl5gHXtozm6AfUH9O901adtF0t69d9Dlwjpdz/K9TxVJ20\nTVLKFCllcsv/pU+BO9Xg3Dq/1wD9AnCBEOIAMKLlOUKIdkKIhb9qzU5Pa9o1ELgGOF8IsaPlZ+yv\nU93jk1J6gbuBrwjcxPxYSpkjhLhdCHF7S7GFQAGQD7wF3PmrVPYUtLJdfwHCgWkt382WX6m6rdLK\nNqn+R+pMQpVKpWqjfq9n0CqVStXmqQFapVKp2ig1QKtUKlUbpQZolUqlaqPUAK1SqVRtlBqgVSqV\nqo1SA7RKpVK1UWqAVqlUqjbq/wF79oRnTvb9OAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4e6fc58320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.quiver(sp.source['xyz'][:,0], sp.source['xyz'][:,1],\n",
" sp.source['uvw'][:,0], sp.source['uvw'][:,1],\n",
" np.log(sp.source['E']), cmap='jet', scale=20.0)\n",
"plt.colorbar()\n",
"plt.xlim((-0.5,0.5))\n",
"plt.ylim((-0.5,0.5))"
]
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}