diff --git a/Examples/Physics_applications/pulsar/Pulsar_scale_RstarPhysical.ipynb b/Examples/Physics_applications/pulsar/Pulsar_scale_RstarPhysical.ipynb
new file mode 100644
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+++ b/Examples/Physics_applications/pulsar/Pulsar_scale_RstarPhysical.ipynb
@@ -0,0 +1,1392 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Overview\n",
+ "\n",
+ "This a notebook that inspects the results of a WarpX simulation.\n",
+ "\n",
+ "# Instruction\n",
+ "\n",
+ "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import statements\n",
+ "import yt ; yt.funcs.mylog.setLevel(50)\n",
+ "import numpy as np\n",
+ "import scipy.constants as scc\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#Define the Physical Constants, normalizations, and the scale-down parameter, r_scale, for the pulsar. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "######################\n",
+ "# physical constants #\n",
+ "######################\n",
+ "c = 299792458.0 # speed of light \n",
+ "q_e = 1.602176634e-19 # elementary charge\n",
+ "me=9.10938356*np.power(10.,-31) # electron mass\n",
+ "epsilon=8.8541878128*np.power(10.,-12) # permittivity of free space\n",
+ "mu_o = 4*3.14*1e-7 # permeability\n",
+ "pi = 3.14159265358979323846\n",
+ "SolarMass = 2e30\n",
+ "G = 6.674e-11 # gravitational constant\n",
+ "\n",
+ "#############################################################################\n",
+ "# Parameters for a real Pulsar #\n",
+ "# (Table 7 of J.Petri's article on Theory of Pulsar Magnetosphere and Wind) #\n",
+ "#############################################################################\n",
+ "Omega_real = 6283\n",
+ "B_real = 7.4E4\n",
+ "R_real = 12000\n",
+ "n_real = 6.9e16\n",
+ "omega_pe_real = (n_real*q_e*q_e/(me*epsilon))**0.5 #plasma frequency\n",
+ "SkinDepth_real = c/omega_pe_real \n",
+ "Mstar = 1.4*SolarMass\n",
+ "Rstar_skinDepth_real = 6e5\n",
+ "\n",
+ "##################\n",
+ "# Normalizations #\n",
+ "##################\n",
+ "Rstar_skinDepth = 6e0 # Ratio of radius of star to the skin Depth \n",
+ " # For a real star, this ratio is 6e5\n",
+ "exponent = np.arange(0,6,1) \n",
+ "Factor = np.array(10**exponent)\n",
+ "Rstar_skinDepth = np.array(6*Factor) \n",
+ "\n",
+ "RLC_Rstar = 4 # Ratio of light cylinder (where the particle speed ~ c) to Rstar\n",
+ " # For a pulsar with 1ms period, this ratio is 4. \n",
+ " # i.e., This ratio sets the omega value, since Omega*RLC = c\n",
+ "\n",
+ "# Choose skindepth as the free parameter for a choice of normalizations given above\n",
+ "# The skin depth below is computed from the number density for a real pulsar\n",
+ "# Keeping the SkinDepth constant across all the scales in our scaling study\n",
+ "#SkinDepth = 0.02\n",
+ "\n",
+ "# Since skin depth is maintained across the scales, and RLC/Rstar is also maintained \n",
+ "# lets define the decrease in the scale by comparing the value of \n",
+ "# (Rstar/skinDepth)/(Rstar_real/skinDepth_real)\n",
+ "#r_scale = Rstar_skinDepth/Rstar_skinDepth_real\n",
+ "\n",
+ "Rstar = np.ones(6)*12000\n",
+ "SkinDepth = Rstar/Rstar_skinDepth\n",
+ "r_scale = Rstar_skinDepth_real/Rstar_skinDepth"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "##################################################################\n",
+ "# Derive other physical parameters from the above normalizations #\n",
+ "##################################################################\n",
+ "\n",
+ "# 1. Lorentz Boost (dimensionless) #\n",
+ "# Note that in Alex Chen's paper, gamma_o ~ (1/4)*normalized_values.\n",
+ "# Instead here we have pi/2, since that leads to the closes gamma_o \n",
+ "# value for a real 1ms pulsar. \n",
+ "gamma_o = (pi/2)*(Rstar_skinDepth)**2/RLC_Rstar \n",
+ "\n",
+ "\n",
+ "gamma_real = (pi/2)*6e5**2/RLC_Rstar;\n",
+ "gamma_scaling = gamma_o/(gamma_real) # This is to see how the gamma value \n",
+ " # decreases due to decrease in the ratio of R_star to skin depth\n",
+ "\n",
+ "\n",
+ "\n",
+ "# 3. Light cylinder location (m)\n",
+ "RLC = Rstar*RLC_Rstar\n",
+ "# 4. Angular Frequency (rad/s)\n",
+ "# Omega remains constant\n",
+ "Omega = c/RLC\n",
+ "# 5. Period (s)\n",
+ "# Period remains constant\n",
+ "Period = 2*3.14/Omega\n",
+ "# Moment of inertia for a sphere = (2/5)MR^2 (kg.m^2)\n",
+ "# Note that when the Rstar is scaled by r_scale, \n",
+ "# Mstar decreases as r_scale^3. Thus Mstar*r_scale**3 is the \n",
+ "# mass of the scaled down star.\n",
+ "# I remains constant across all scales\n",
+ "I = (2/5)*(Mstar)*Rstar**2\n",
+ "# 6. Rotation induced potential drop from pote to equator (V)\n",
+ "# Reference: Alex Chen's 2014 paper\n",
+ "# Scales as 1/r_scale**2\n",
+ "phi_o = gamma_o * (me/q_e) * c**2\n",
+ "\n",
+ "# Braking is the rate of slow-down of the spin. \n",
+ "# It is not relevant for the scaling. \n",
+ "# However, 1e-15 is the P_dot for a pulsar with P=1s\n",
+ "# and the rate of slowdown decreases proportional to the period. \n",
+ "# So we were to compare two stars with same radius, but different Omega\n",
+ "# then we can use Braking to determine the magnetic field strength at the surface as follows\n",
+ "# Bo = (3*mu_o*c*c*c/(32*pi*pi*pi))**0.5 * (I*Braking*Period)**0.5/(Rstar**3) \n",
+ "# (Reference : Table 7 of Jetri's article)\n",
+ "# Remains constant across all scales\n",
+ "Braking = 1e-15*Period\n",
+ "\n",
+ "# 7. Magnetic field strength (Tesla)\n",
+ "# Bo decreases as ~ 1/r_scale**2\n",
+ "Bo = phi_o/(Rstar*Rstar*Omega)\n",
+ "\n",
+ "# 8. Volume charge density (C/m^3) for uniform magnetization insize the star \n",
+ "# Refer to Table 7 of Petri's article \n",
+ "# Since Omega increases as r_scale and Bo decreases as r_scale\n",
+ "# The product of Omega*Bo remains constant across all scales. \n",
+ "# Thus, rho_e decreases as (1/r_scale**2)\n",
+ "rho_e = 2*epsilon*Omega*Bo #(Not adding the negative sign here)\n",
+ "\n",
+ "# 9. Electron number density (#/m^3)\n",
+ "# ne decreases as (1/r_scale**2)\n",
+ "ne = rho_e/q_e\n",
+ "# 9a. plasma frequency \n",
+ "# decreases as (1/r_scale)\n",
+ "omega_pe = (ne*q_e*q_e/(me*epsilon))**0.5 #plasma frequency\n",
+ "# 10. Magnetic moment (Am^2)\n",
+ "# decreases as (1/r_scale**2)\n",
+ "magnetic_moment = Bo*Rstar*Rstar*Rstar*4*pi/(mu_o)\n",
+ "# 11. E-field (V/m)\n",
+ "# Efield decreases as (1/r_scale**2)\n",
+ "E = Omega*Bo*Rstar\n",
+ "\n",
+ "\n",
+ "#########################################\n",
+ "# How do the energies and forces scale? #\n",
+ "#########################################\n",
+ "# 12. Magnetic Energy (J)\n",
+ "# Magnetic energy decreases as (1/r_scale**4)\n",
+ "magnetic_energy = (4*pi/3)*Bo**2*Rstar**3/(2*mu_o)\n",
+ "\n",
+ "# 13. Gravitational Potential Energy (J)\n",
+ "# G.pot energy remains constant \n",
+ "GP_energy = (3/5)*G*Mstar*Mstar/(Rstar)\n",
+ "\n",
+ "# 14. Gravitational to Electric force (dimensionless)\n",
+ "# This ratio increases as r_scale**2\n",
+ "G_EForce = G*Mstar*me/(Rstar*Rstar*q_e*E)\n",
+ "\n",
+ "# 15. Rotational kinetic energy \n",
+ "# Rot. KE remains constant\n",
+ "rotational_KE = (1/2)*I*Omega**2\n",
+ "\n",
+ "# 16. From (12) and (13), we know the B.energy scales as (1/r_scale**4) and GP energy is constant \n",
+ "# Thus the ratio of GP_energy and B_energy increases as r_scale**4\n",
+ "GB_energy_ratio = GP_energy/magnetic_energy\n",
+ "\n",
+ "# 17. Rate of change of Omega, or angular acceleration decreases as 1/r_scale**4\n",
+ "Omega_dot = Bo*Bo*Rstar**6*Omega**3/(I) * (32*pi/(3*mu_o*c**3))* (1/(4*pi*pi))\n",
+ "\n",
+ "# 18. Torque decreases as 1/r_scale**4\n",
+ "Torque = I * Omega_dot\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Input parameters for WarpX for geometry scaled down by r_scale = [1.e-05 1.e-04 1.e-03 1.e-02 1.e-01 1.e+00]\n",
+ "Lorentz factor, (gamma_o) = [1.41371669e+01 1.41371669e+03 1.41371669e+05 1.41371669e+07\n",
+ " 1.41371669e+09 1.41371669e+11]\n",
+ "Rstar (m) [12000. 12000. 12000. 12000. 12000. 12000.]\n",
+ "Omega (rad/s) [6245.67620833 6245.67620833 6245.67620833 6245.67620833 6245.67620833\n",
+ " 6245.67620833]\n",
+ "Bo (Tesla) [8.03230942e-06 8.03230942e-04 8.03230942e-02 8.03230942e+00\n",
+ " 8.03230942e+02 8.03230942e+04]\n",
+ "ne (/m^3) [5.54482989e+06 5.54482989e+08 5.54482989e+10 5.54482989e+12\n",
+ " 5.54482989e+14 5.54482989e+16]\n",
+ "Size of the domain, (m) [360000. 360000. 360000. 360000. 360000. 360000.]\n",
+ "Minimum cell size (m) [2.e+03 2.e+02 2.e+01 2.e+00 2.e-01 2.e-02]\n",
+ "timestep (s) [3.76386776e-06 3.76386776e-07 3.76386776e-08 3.76386776e-09\n",
+ " 3.76386776e-10 3.76386776e-11]\n",
+ "Numver of cells assuming unif. grid, [1.8e+02 1.8e+03 1.8e+04 1.8e+05 1.8e+06 1.8e+07]\n"
+ ]
+ }
+ ],
+ "source": [
+ "############################################################\n",
+ "# Print all the values that will be used as input in WarpX #\n",
+ "############################################################\n",
+ "print(\"Input parameters for WarpX for geometry scaled down by r_scale = \", Rstar_skinDepth/Rstar_skinDepth_real)\n",
+ "print(\"Lorentz factor, (gamma_o) = \", gamma_o)\n",
+ "print(\"Rstar (m)\", Rstar)\n",
+ "print(\"Omega (rad/s)\", Omega)\n",
+ "print(\"Bo (Tesla)\", Bo)\n",
+ "print(\"ne (/m^3)\", ne)\n",
+ "print(\"Size of the domain, (m)\", 30*Rstar)\n",
+ "print(\"Minimum cell size (m)\", SkinDepth)\n",
+ "print(\"timestep (s)\", 0.5*omega_pe**-1) # Or may be cfl criterion\n",
+ "print(\"Numver of cells assuming unif. grid, \", 30*Rstar/SkinDepth)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Gravitational force to E-force : [1.22562947e-02 1.22562947e-04 1.22562947e-06 1.22562947e-08\n",
+ " 1.22562947e-10 1.22562947e-12]\n",
+ "Gravitational energy to B-energy : [1.40727411e+38 1.40727411e+34 1.40727411e+30 1.40727411e+26\n",
+ " 1.40727411e+22 1.40727411e+18]\n",
+ "B energy, (J) [1.85906071e+08 1.85906071e+12 1.85906071e+16 1.85906071e+20\n",
+ " 1.85906071e+24 1.85906071e+28]\n",
+ "Gravitational potential energy, (J) [2.616208e+46 2.616208e+46 2.616208e+46 2.616208e+46 2.616208e+46\n",
+ " 2.616208e+46]\n",
+ "Rotational Kinetic Energy (J) [3.14564313e+45 3.14564313e+45 3.14564313e+45 3.14564313e+45\n",
+ " 3.14564313e+45 3.14564313e+45]\n"
+ ]
+ }
+ ],
+ "source": [
+ "#############################################################\n",
+ "# Print ratios of G.Pot.Energy/B_energy and G.Force/E_force #\n",
+ "#############################################################\n",
+ "print(\"Gravitational force to E-force : \", G_EForce)\n",
+ "print(\"Gravitational energy to B-energy : \",GB_energy_ratio)\n",
+ "\n",
+ "\n",
+ "# Print dimensional values of energies\n",
+ "print(\"B energy, (J)\",magnetic_energy);\n",
+ "print(\"Gravitational potential energy, (J)\", GP_energy)\n",
+ "print(\"Rotational Kinetic Energy (J)\", rotational_KE )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
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+ {
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+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('');\n",
+ "\n",
+ " var fmt_picker = $('');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option);\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('![](\"')
');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('');\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('');\n",
+ " var button = $('');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
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+ "output_type": "display_data"
+ },
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+ "data": {
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plot Gravitational force to electric force -- should be constant across all scales\n",
+ "\n",
+ "plt.plot(Rstar_skinDepth/6e5,G_EForce,'ro')\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"G.Force/E.Force\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Eforce_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot Gravitational to magnetic energy -- should be constant across all scales\n",
+ "\n",
+ "plt.plot(Rstar_skinDepth/6e5,GB_energy_ratio,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Trends of all energies\n",
+ "plt.plot(Rstar_skinDepth/6e5,magnetic_energy,color=\"red\",ls='--',lw=2,marker=\"^\",markersize=8)\n",
+ "plt.plot(Rstar_skinDepth/6e5,GP_energy,color=\"blue\",lw=2,marker='o',markersize=8,markerfacecolor='blue')\n",
+ "plt.plot(Rstar_skinDepth/6e5,rotational_KE,color=\"purple\",lw=2,marker='s',markersize=8,markeredgecolor='purple')\n",
+ "plt.plot(Rstar_skinDepth/6e5,Torque,color=\"cyan\",lw=2,marker='>',markersize=8,markeredgecolor='purple')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Energy (J)\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.legend([\"Magnetic\",\"G. Potential\",\"Rotational KE\",\"Torque\"],loc=2,fontsize='small',frameon=\"false\",borderpad=0.1)\n",
+ "plt.ylim([1e0,1e90])\n",
+ "plt.savefig(\"Energy_scaling.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot Gravitational to magnetic energy -- should be constant across all scales\n",
+ "plt.plot(Rstar_skinDepth/6e5,Bo/Bo[5],color=\"blue\",lw=2,marker='s',markersize=8)\n",
+ "plt.ylabel(\"Normalized B. field\",color=\"blue\")\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.yticks(color=\"blue\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.twinx()\n",
+ "plt.plot(Rstar_skinDepth/6e5,Omega/Omega[5],color=\"red\",lw=2,marker='o',markersize=8)\n",
+ "plt.ylabel(\"Normalized \\u03A9\",color=\"red\")\n",
+ "plt.yticks(color=\"red\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.ylim([0,2])\n",
+ "plt.savefig(\"Normalized_B_Omega.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,gamma_o,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Normalized G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,Omega_dot,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Omega_dot\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"G_Benergy_scaling.png\",bbox_inches='tight')\n",
+ "#plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,Torque,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Torque\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"Torque.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(Rstar_skinDepth/6e5,phi_o,'ro')\n",
+ "plt.rcParams.update({'font.size': 18, 'font.family': 'serif'})\n",
+ "plt.xlabel(\"Log( Normalized (Rstar/Lambda) ) Scaling\")\n",
+ "plt.ylabel(\"Normalized G.Energy/B.energy\")\n",
+ "plt.xscale(\"log\")\n",
+ "plt.yscale(\"log\")\n",
+ "plt.savefig(\"phi_o.png\",bbox_inches='tight')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.020230407144897423"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "SkinDepth_real"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([48000., 48000., 48000., 48000., 48000., 48000.])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RLC\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "140.625"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "36000/256"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "particle_weight = 140.625**3*5.544e6/64"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "240896701812.74414"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "particle_weight"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.0010055, 0.0010055, 0.0010055, 0.0010055, 0.0010055, 0.0010055])"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Period"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([48000., 48000., 48000., 48000., 48000., 48000.])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "RLC\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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": "3.7.9"
+ },
+ "widgets": {
+ "state": {
+ "11d243e9f5074fe1b115949d174d59de": {
+ "views": [
+ {
+ "cell_index": 6
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM b/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM
index 4785f42cc0c..c7368c4421d 100644
--- a/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM
+++ b/Examples/Physics_applications/pulsar/inputs.corotating.3d.PM
@@ -15,8 +15,8 @@
#################################
max_step = 50000
amr.n_cell = 128 128 128
-amr.max_grid_size = 64
-amr.blocking_factor = 64
+amr.max_grid_size = 128
+amr.blocking_factor = 128
amr.max_level = 0
geometry.coord_sys = 0 # 0: Cartesian
geometry.is_periodic = 0 0 0 # Is periodic?
@@ -26,11 +26,12 @@ geometry.prob_hi = 180000 180000 180000
#################################
############ NUMERICS ###########
#################################
-algo.maxwell_fdtd_solver = yee
+#algo.maxwell_fdtd_solver = yee
warpx.verbose = 1
warpx.do_dive_cleaning = 0
warpx.use_filter = 1
-warpx.cfl = .5
+warpx.cfl = .95
+warpx.do_pml = 1
my_constants.pi = 3.141592653589793
my_constants.dens = 5.544e6
my_constants.xc = 90000
@@ -40,20 +41,12 @@ my_constants.r_star = 12000
my_constants.omega = 6245.676
my_constants.c = 299792458.
my_constants.B_star = 8.0323e-6 # magnetic field of NS (T)
-my_constants.dR = 1.4e3
-my_constants.to = 2e-4
+my_constants.dR = 1400
+my_constants.to = 2.e-4
interpolation.nox = 3
interpolation.noy = 3
interpolation.noz = 3
-#warpx.E_ext_grid_init_style = "parse_E_ext_grid_function"
-#warpx.Ex_external_grid_function(x,y,z) = "((x-xc)*(x-xc)+(y-yc)*(y-yc) + (z-zc)*(z-zc))^0.5"
-#warpx.Ey_external_grid_function(x,y,z) = "((x-xc)*(x-xc)+(y-yc)*(y-yc) + (z-zc)*(z-zc))^0.5"
-#warpx.Ez_external_grid_function(x,y,z) = "((x-xc)*(x-xc)+(y-yc)*(y-yc) + (z-zc)*(z-zc))^0.5"
-
-
-
-
#################################
############ PLASMA #############
@@ -65,30 +58,15 @@ plasma_e.charge = -q_e
plasma_e.mass = m_e
plasma_e.injection_style = "NUniformPerCell"
plasma_e.profile = parse_density_function
-plasma_e.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=(r_star+dR)) * ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=(r_star)) )*dens"
-plasma_e.num_particles_per_cell_each_dim = 3 3 3
+plasma_e.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=(r_star)) * ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=(r_star-dR)) )*dens"
+plasma_e.num_particles_per_cell_each_dim = 4 4 4
plasma_e.momentum_distribution_type = parse_momentum_function
-## Inject pairs rotating at omega(t) x r velocity
-#plasma_e.momentum_function_ux(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=r_star)*(-( (t=to)*omega )*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-( ( (t=to)*omega) *(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (y-yc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-#plasma_e.momentum_function_uy(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=r_star)*( ( (t=to)*omega ) *(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-( ( (t=to)*omega )*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (x-xc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-
-## Inject pairs rotating at omega x r velocity
-#plasma_e.momentum_function_ux(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=r_star)*(-omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-(omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (y-yc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-#plasma_e.momentum_function_uy(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=r_star)*(omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-(omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (x-xc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-
## Inject stationary pairs
plasma_e.momentum_function_ux(x,y,z) = "0.0"
plasma_e.momentum_function_uy(x,y,z) = "0.0"
-
## uz is always 0 for the injection methods above
plasma_e.momentum_function_uz(x,y,z) = "0.0"
-
-#plasma_e.momentum_distribution_type = "constant"
-#plasma_e.ux = 0.0
-#plasma_e.uy = 0.0
-#plasma_e.uz = 0.0
-
plasma_e.do_continuous_injection = 0
plasma_e.density_min = 1E0
@@ -96,62 +74,67 @@ plasma_p.charge = q_e
plasma_p.mass = m_e
plasma_p.injection_style = "NUniformPerCell"
plasma_p.profile = parse_density_function
-#plasma_p.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=r_star)* ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<(r_star+dR)))*dens"
-plasma_p.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=(r_star+dR)) * ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=(r_star)) )*dens"
-plasma_p.num_particles_per_cell_each_dim = 3 3 3
+plasma_p.density_function(x,y,z) = "( ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=(r_star)) * ((( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=(r_star-dR)) )*dens"
+plasma_p.num_particles_per_cell_each_dim = 4 4 4
plasma_p.momentum_distribution_type = parse_momentum_function
-## Inject pairs rotating at omega(t) x r velocity
-#plasma_p.momentum_function_ux(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=r_star)*(-( (t=to)*omega )*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-( ( (t=to)*omega) *(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (y-yc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-#plasma_p.momentum_function_uy(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))>=r_star)*( ( (t=to)*omega ) *(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-( ( (t=to)*omega )*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (x-xc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-
-## Inject pairs rotating at omega x r velocity
-#plasma_p.momentum_function_ux(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=r_star)*(-omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-(omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (y-yc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-#plasma_p.momentum_function_uy(x,y,z) = "( (( (z-zc)*(z-zc) + (y-yc)*(y-yc) + (x-xc)*(x-xc) )^(0.5))<=r_star)*(omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/((1.0-(omega*(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5))/c)^2)^(0.5)) * (x-xc)/(((x-xc)*(x-xc) + (y-yc)*(y-yc))^(0.5)))"
-
## Inject stationary pairs
plasma_p.momentum_function_ux(x,y,z) = "0.0"
plasma_p.momentum_function_uy(x,y,z) = "0.0"
-
## uz is always 0 for the injection methods above
plasma_p.momentum_function_uz(x,y,z) = "0.0"
-
-#plasma_p.momentum_distribution_type = "constant"
-#plasma_p.ux = 0.0
-#plasma_p.uy = 0.0
-#plasma_p.uz = 0.0
-
plasma_p.do_continuous_injection = 0
plasma_p.density_min = 1E0
-diagnostics.diags_names = plt
+diagnostics.diags_names = plt chk
plt.diag_type = Full
-plt.period = 200
+plt.period = 20
plt.fields_to_plot = Ex Ey Ez Bx By Bz jx jy jz part_per_cell rho divE
+chk.format = checkpoint
+chk.diag_type = Full
+chk.period = 1000
#################################
######### PULSAR SETUP ##########
#################################
pulsar.pulsarType = "dead" # [dead/active]: sets particle injection type
pulsar.omega_star = 6245.676 # angular velocity of NS (rad/s)
-<<<<<<< HEAD
-pulsar.ramp_omega_time = 0.0 # time over which to ramp up NS angular velocity (s)
-#pulsar.ramp_omega_time = 2e-4 # time over which to ramp up NS angular velocity (s)
-=======
-pulsar.ramp_omega_time = 0.e-5 # time over which to ramp up NS angular velocity (s)
->>>>>>> 2756eb86 (recent input file)
+pulsar.ramp_omega_time = 0.0 # time over which to ramp up NS angular velocity (s)
# if ramp_omega_time < 0, omega = omega_star for any t
# consistency requires ramp_omega_time = my_constants.to
pulsar.center_star = 90000 90000 90000
pulsar.R_star = 12032 # radius of NS (m)
pulsar.B_star = 8.0323e-6 # magnetic field of NS (T)
-pulsar.dR = 1075
+pulsar.dR = 1400 # thickness on the surface of the pulsar
+ # consistency requires dR = my_constants.dR
pulsar.verbose = 0 # [0/1]: turn on verbosity for debugging print statements
pulsar.EB_external = 1 # [0/1]: to apply external E and B
pulsar.E_external_monopole = 0 # [0/1]
+pulsar.EB_corotating_maxradius = 12032 # The radius where E-field changes from corotating
+ # inside the star to quadrapole outside.
+ # Default is R_star. (r<=cor_radius) i.e. the includes
+ # the r specified in the input
pulsar.max_ndens = 5.54e6 # max ndens == ndens used in initializing density
-pulsar.Ninj_fraction = 0.1 # fraction of sigma injected
+pulsar.Ninj_fraction = 0.2 # fraction of sigma injected
+pulsar.ModifyParticleWeight = 1 # (0/1) the fractional injection is achieved
+ # by modifying particle weight if 1
+ # by modifying num_ppc if 0
+pulsar.particle_inj_rmin = 10632 # default is Rstar-dR (consistent with density function above)
+pulsar.particle_inj_rmax = 12032 # default is Rstar (consistent with density function)
+pulsar.max_particle_absorption_radius = 12032 # Maximum radius within which particles are
+ # deleted every timestep.
+ # Default is Rstar
pulsar.rhoGJ_scale = 1e0 # scaling down of rho_GJ
pulsar.damp_EB_internal = 1 # Damp internal electric field
-pulsar.damping_scale = 10.0 # Damping scale factor for internal electric field
+pulsar.damp_EB_radius = 12032 # The radius within which EB should be damped.
+ # default is r_star (damping will include this radius)
+pulsar.damping_scale = 1000.0 # Damping scale factor for internal electric field
+pulsar.turnoffdeposition = 0 # (0/1) 0 for depositing (default)
+ # 1 for no deposition
+pulsar.max_nodepos_radius = 0. # radius within which particle deposition for j/rho
+ # is turned off. (r<=max_radius)
+pulsar.turnoff_plasmaEB_gather = 0 # (0/1) 0 is default. always gather
+ # 1 for no gather of self-consistent fields
+pulsar.max_nogather_radius = 0. # radius within which self-consistent field gather
+ # is turned off
diff --git a/Examples/Physics_applications/pulsar/pulsar_viz.ipynb b/Examples/Physics_applications/pulsar/pulsar_viz.ipynb
new file mode 100644
index 00000000000..57ef79c1599
--- /dev/null
+++ b/Examples/Physics_applications/pulsar/pulsar_viz.ipynb
@@ -0,0 +1,690 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Overview\n",
+ "\n",
+ "This a notebook that inspects the results of a WarpX simulation.\n",
+ "\n",
+ "# Instruction\n",
+ "\n",
+ "Enter the path of the data you wish to visualize below. Then execute the cells one by one, by selecting them with your mouse and typing `Shift + Enter`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import statements\n",
+ "import yt ; yt.funcs.mylog.setLevel(50)\n",
+ "import numpy as np\n",
+ "import scipy.constants as scc\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib notebook"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data in the simulation frame"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot data with yt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 149,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "ds = yt.load( './plotfiles/plt00090/' ) # Create a dataset object\n",
+ "filenum = 10\n",
+ "#ds.field_list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 150,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.5/dist-packages/yt-3.6.dev0-py3.5-linux-x86_64.egg/yt/units/yt_array.py:1400: RuntimeWarning: invalid value encountered in true_divide\n",
+ " out=out, **kwargs)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ 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yz7dU7PmGQ589zuHlT+IbP5bgkbMITDnXYQfzu4fEkX7jqzQW5pC/4ln2v38f5ZkrSFrwqPidFoR+TLQ02IloaRDORO2hH9nzyrW/eEYBSISfdRlJlz0qV1l/Wnt1ITUHNlJzYDONx/cgWS2o3bwJSJqEf9Jk/BInOOyJUH+y7q5UdMExjLvvM9F600e0lh+hYs83VO75FkN9GUonZ0JGzyE4fQa+CeMc9iq+ZLVQ8uOnHFn5HJLFQtzM24masrBPTjkr9IzyJcms7cGWhnWrW1i3upXMbb4cPXq0x/Yr/HEiNNiJCA3C6XQ213Lk6+cp3/EFAM5uPnhGDsPJ1QO11p3gETPxiR0pc5W/z2I2dnU72kjNgU3dsxy5h8TjP2wSAcMy8IpKFieuvWzt31LxDB/C8JuXiJDWx0iSRFNRDpW7V9FWXUj94a24h8QTmXEVISNnO+z6Ih1NVRxa9hjVORtwD0sgacETeEUly12WIIOeDg0/mZWhEd2TZCa6JwlCL7KYjBRvep9ja17DajISfc71xEy/uc+d2LVVF1C27XPq8rbRWpaHwkmN7+AxRE2+Ev+kSX1mYGd/pUBBZ0sdls72k362LMYO2qqOYWxrwqRvwtTeBJJEZ0sdVosZyWLCajZitZiQzCaUag2gQKnWoFJrUHbdVE6ak55TOmlQOXfdn+I5hZMzKidxBfp0FAoF3tGpeEenYjEZqdzzDUXfv0vuR/8gf+UiIiYuIGLiAocb++DiFUT6ja9RtW8dh5Y9xo5nLyFy0hUMnn2nGCg9AFkQ16P7IxEaBKEXSJJEzYFN5H3xFPraYgKGZZBw4YPd06v2BRajgaqsNZRu/5zGY3tQKJ0IGDaZ2Bm34pc4AScXsSCA45DQ1xQhWU+eK729ppDtT8874TnPqFRaSg6gUDmhVKlROKlRqtQonZxRqJwwd7RhNXXabmbjn67KK2Y47dUFuHgFofUOxsUrCBfvIFy8g3++9wp02KvpvU2ldiZs7DxCx8yl/sgOir5/l2PfLqZg7ZuEjDqfqIyFDrdac1DqufjGjyV/5SKKN39A9b51DJn/sJhFUBD6AREaBMHOWiuPkbf8SeoOb0UXFMOI297Gf8hZcpd1xlpKD1G67TMqdq/CbGjF1T+SwXPuJWz0XDSe/nKXJ5yC1WLGxSf0lN8fV78I0m96HbWrJ846L9Q6L5xcPVA5OZ/RviWr1dYSYe7EYurEaurAYjJ2hYqOruds4cJi7MBqtv3bYuoEQF8bR0djJYaGchqP78Wkbz7pGM5uPieECJfugNEVLryCUKnPrN7+QKFQdC8i11Z1nKJNH1C+8yvKti/HL3ECURkL8RtylsOMe1Br3Rl66SOEjLqA3I8fIuuNmwhKn0Hixf/ExTNA7vIEO5MAi9xFCHYhxjTYiRjTIJj0zRz99hVKtnyESqMjbtYdREy8rE8MEDQZ2qjc8w2l2z6jpSQXpZMzQWnTCRt/MT5xoxzm5EQ4tTV3JuGfOJHhN70mdym/y9ypp6Opio7GrltTFR2NlV23agyNlZgNLSdt5+zui/egdLS+oXhGJeMVlYzWN3zA/Gwa2xop3fopxVs+prO5Bl1QDNEZCwkZdQEqZxe5y+tmNRspWP82x1e/ilKtIX7ufYSPuwSFUqzu21+VLUnmmy0xPb7feRkuYkyDzERosBMRGgYuyWqhdOtn5K96AZO+hfAJ84mbdScad8deZEuSJJoKsind9hlVWauxGA24h8QTNv5iQkdfgNrVU+4ShTO05vaheEYOY+y9n8pdSo8wd7TbwsQvwoWhvpy26gJaSg9i7WrFULt54xWZjGd0iu0+cli/X7XYajZSuXc1RRvfpaX0EGo3byLOWkDkxAUO1RLYXl1I7icP05C/E++YdIYueAL34J4fLCvIr3RJMivtMBD6EjEQWnaie5Ig9KDWiqMcX/sGlbtX4RM3isSL/4lHWKLcZZ2Wsa2B8syVlG3/nLbKY6g0OkJGziZs/CV4Rg4bMFdu+xtja73cJfQYJxcdbkExuAWdfPXSajHRWnGU5sIcmor301yUQ+2hH6Drepirf2RXS0QKnlHJeIQl9qsxE0onZ0JHX0DIqPNpOLqboo3vcnzNaxSsX0LIiNlEZSzEIyxB7jLRBUYz6s73Kd/5JXlfPM22Jy8gZtoNDJp284DqaiYIfZkIDYDVamXRokVUVVXh5eXF4cOHmThxIjfddNMJr/vss8/YsGEDcXFx7Nq1i+uuu45p06bJVLXgSCSrlaJN75G/8nmcXNxIvfYlgtKnO+wJt2S1Up+/g7Jtn1OVsx7JbMIrOpWky58kOH2GGNTcxykUSryiUuQuo1coVWo8w4fgGT6ECC4DbN3rWkoO0FS0n+ai/TTkZ1K5exUACpUaj9D4n1sjolPQ+Uf1+e4yCoUC38Gj8B08ivaaIoo3fUDZji8o3/klIaPmEHHWfLxjhsteY9jYC/FPmsTh5U9y7LtXqdz7HSkLF+EWMphDnz1GcPoM/BLHy1qnIAinJkID8Nhjj5Gdnc3KlSsBMJlMJCYm4u/vz4UXXgjA22+/zSOPPEJeXh46nY6ysjKGDh3Kt99+y4QJE+QsX5CZob6c/R88SMPRTAKSp5D0f0843HSIP7FaTFTs+prag1uoylqDWudFxFkLCB9/scPNwtJXSJKEuaMds6EFk74Fs6EVk74Zk6EFs74VU/fztnsnFx0pC5+ze004aGDtDWqtG77xY/GNH9v9XEdTFU2FOTQX76epcD/lO1dQsuVjAJy07gQPn4lbUAz+SZPQBUTKVXqP0AVEMWT+v4mbfSelWz+j9tCP7Fx0GYEp5zB4zj24BQ6StT6Nuy+pVy8idPQcCje8w47n5uMeMpiW0oPUHd7G2Y+uR6kSpyd9mRgI3T+J30pgxYoVZGT8PB2cWq1m+PDhbNq0iQsvvJDW1lbuvfdebr75ZnQ62xXYsLAwZsyYwe233052drZcpQsykiSJiswVHPrscSRJYtgVTxE6Zp5Dti78FBaOrX4NQ10pXtGpJC98jqC0af2qq0ZPsZqNGOrL0deV0F5TjLlTj6GuBJOhFbO+GVNXGLAFhBaQTp7a9JecXNy6F+5z9Yuwe/2SxYRa62734/QlLl5BBKUFEZRmax2WrBbaqgpoKsqhuWg/zSW5lG79lMPL/4NrQJRtFfOhZ+MdO7LPdp9Ru3oy6NzriTj7/yja+B4F65ZS8/hGwifMJ/a829B4+Mlan/+Qs/CKTmXnostoKT0IQEdDOZV7viV09AWy1iYIwslEaAD8/f35/PPP+de//oW3tzeSJJGTk8MDDzwAwNq1a2lqamLMmDEnbDdmzBiWLVtGbm4uSUlJcpQuyMTY1kDu//5N9b51eMeOIPnK/+LqFy53WSexWsxU7Pqa42teQ19bgkdEEkNufgj/pEkOGW56k8VoQF9Xir6m2BYOakvQ1xajry3B0FBxQhDwjh2BvrYEtasnald3NJ7+uAXF4OTqbntO646T1gO1q+1me9z1nNa991fEVippKRMTMZyOQqnCPSQO95A4wsddBEB7bQl1B7dQk7uZkh/+R9HG91BpXPGNH4t/0tn4DzkbrU+wzJX/cU4aV2Jn3EL4hPkc+3YxpVuXUZ65kkHnXEfUlKtx0rjKVpupvQl9bckJzx356hlCRs7u813GBioJBWYG9t+X/kqEBuDxxx9n8uTJTJw4kbfffpv33nuP+fPns3DhQoDuloSIiBOvEEZG2pqwd+/eLULDAFJzYBMHPv4nJn0z8XPuI3rqNb1/Uvg7TgoL4UNJv+kNAoZNHlBhwWRoRd8dBorR15bS3vW4s7nmhNeqdV64+kfgNSiNkFEXoPOPwNU/Elf/CJzdffvW/5skYTK0yV1Fn6Pzj0A36QoiJ12BxWig/shOag9uoTZ3CzX7vwfAPSTeFiCSzsYrOq1PdaPRuPsy9NJHiJx8Jfkrn+foNy9R8uMnxM26g9Ax82R5L2ZDKyq1Bqupo/u5zpZa9r37N9KufanX6xEE4bf1nU87OxozZgzr1q1j7ty5jB49miuuuIKXX365+yShrq4OAHf3E5v73dzcAKiurj5pn5Ikse0Xq66GT7iEiAmX2ustCL3A3NFO3pdPU7p1Ge4h8Yy87R2HmJXkl6wWMxW7v+b46oEXFjpb62kqyKapMJv2mmIaju3G1NZ4wms0ngG4+oXjlzihKxCEo+sKBv1rSlkJ70GpchfRp6mctQQMm0zAsMlIkkRb1XFqczdTm7uFwg3vULBuCU5aD/yHTMB/6Nn4DZ3osGOZfs0tcBDpN7xC4/G95H35DLkfP0TRxveIn3Nfr7dCeoQPYcqzuzA0lNNScpDaQz9SkbmSqr2rOeDixpBL/i26UPYBJVs/pXTrZwCE+YAZ0UrUH4nQ0KWgoIC5c+eSn5/Phx9+SFlZGatWrUKn06HR2D6wfv1BquxqOnV2Prm/q0KhYPyDX9q/cKFXNB7PYv8H96OvKyX6nOuJm3WnQ/VzPjksDCH9ptcJGJbRL8OC1WKmrSKfxq6Q0FS4r7uLg0KlxiN8CAHJU3ELjMbVPwJXvwhc/cIHzKxQCqUT5k6D3GX0GwqFAvfgWNyDYxl0znWYDK3UHd5ma4U4uIXKvd+BQoFnRBL+SZMISp+Oe3Cc3GX/Lu+Y4Yy591Oq963jyMpF7H39RnziRpMw7348I4f1Wh0KhQJX3zBcfcMISpvG0Esf4eg3L1Gw9k1aSg6Sdv3LvTIWSPjzIiZc2n1htHjJwJi5bSASoQF4/fXX+eijj/jxxx9RKBQ8+uijPProozz44IMsXryYoKAgANraTmzu/+nfoaGhvV6z0DusZiNHv11MwbqlaH1CGH3XR/jEjZS7rG5Wi5nK3as4tvo19LXFuIclkn7jawQkT+lXYcHY1khT4T6aCvfRWJBNc/F+LJ16AJw9/PCOTiN8wqV4RafiGZHkUCviysXS0Sp3Cf2WWutOcPp0gtOnI1mttJQdtrVCHNxCwbqlFK5/C13gIMLGXUTIyFkO3YqlUCgISptGQHIGpVuXcezbV9j+3wsJHjGTweffLctYLaXKifgL7sErOo3979/PtqfmkXzVMwQmZ/z+xoLsJMAiWhr6JREagCeeeIL777+/u+XgkUce4fDhwyxbtozFixeTnp4OQGlpKcnJyd3blZTYrmympopuAP1Ra8VRct67l9ayw4SNvYiEi/6BWusmd1lA/w4LktVKW9UxGguyaCrYZ+tuVF0IdA1eDU0gdMxcvAel4xWditY3rM+/5x4nSaKloZcolEo8I4biGTGU2PNupbO5lqrsNZRuX86hZY+S9+XTBKaeS9jYC/EdPMZhB/cqVWoiz76ckFFzKNzwFoUb3qEqex2RZ/8fMdNvlmVl7cDkDMb//Uuyl95J1hs3MejcG4ibfVefGkciCP2J+M0DOjs7sVhOnFX47LPPZsuWLQBMnToVX19fMjMzmTlzZvdrdu7cyciRI4mPj+/VegX7+vVCbek3vkZgylS5ywLAarFQuacrLNQU4R6WQPoNrxKQMrVPnzi3VxdSf2QnVfvW0VyUg7nD1oqndvPGOzqN0NFz8RqUjmdkkqwzvfQZCgVe0eJihhw0nv5Edg2mbi49RNn2z6nYtYrK3avQ+oYROnYeYWPmofUJkbvUU1Jr3Rg8+y4izrqUo9+8TFHXInEx024iMuMqVE692y3T1S+CMfd+yuHPn6Bg3RKaCnNIveZ5NJ7+vVqHIAigkCRJkrsIud11111kZmaydetWVCrbLDhXXXUVfn5+LFq0CICXXnqJF154gcOHD6PVaqmsrGTw4MF88sknzJo166R9KlVOTH9FTHnY13S21HH0m5co3brM4RZqayrK4dCyxzAZ2lCpnYmdeTuByVMd9srl72mvKaYq6zsqs9bQWnYYJ607Lt7BeMcMxzs6Fa9Babj6R/bpMCQHSZJYc2s8sefdStysO+UuRwAsxg6qczZQtv1z6o/sAIUCv8QJhI29kIDkqQ41PurXWivyOfLVsxjbGrEY9SRf9QyeEfLMFli+8ytyP3kYtdad1GtfdKiuosLPipak8sGWnp8k5KYMBXv27Onx/QpnToQGbCtAP/XUU+zfv5/ExETa2trw9fXlgQceQK1Wd7/ujTfeYNu2bQzLvv35AAAgAElEQVQbNow9e/Zw2WWXMXfu3FPuU4SGvqepKIesJbdh6dSTMO/vhI270CFOWE36FvJXPk/J1k/QePiTeNE/CEqb3ifDQnttCVVZq6nKWk1L6SEAvKLTCBo+g6C06Wi9g2SusO8z6lv5/r4RDJ3/MBETF8hdjvAr+voyynd8QdmOL+lorESt8yJk5PmEjbvI4WZj+6WaA5vI/d+/MLY2EDPjZmKm34RSpf79DXtYa/kRspbejqGulMEX3EP01Gsd4nNa+JkIDf2XCA12IkJD31K6fTkHP30YF89A0m94BY/wIXKXZFtxevcq8r58GmNrA5GTriBu1p0OM67iTOnrSqjMWkPV3tXdq756RacSlN4VFPrgYlmOrK2qgK1PzCR54bOEjDi5FVRwDJLVQl3eDsp3LKcqZz2S2YRHRJJt8PSIWahdPeQu8SQmfTOHlj1Oxe6v8QgfSvJVz+Ae0vuzRJkMbRz46O9UZ68ldOw8hlz8EE4ufetzsT8rXJLKu1t6/m/obRmSCA0yE6HBTkRo6BusZiOHP3+Skh//h2/COFKveUGWAX+/1lZdwKFPH6H+yE48o5IZetljeDpAkDlT+rpSqrLWUJW1muaSXAA8o1IITp9uCwq+YsYxe2k8vpediy5jxG3v4D9kwp/ej2S1YDF1gmRFqdbIclV5oDC2NVKxexVlO5bTWpaHUq0hKHUaERMX4DUozeGupFdlryX3k39j7mhj8Oy/ET3l6l5f4FKSJAo3vkfZts9QqTUMv2UJLp4BvVqDcGoiNPRfYiC0MGB1NNeQvfQOmgqyiD7negaf/zfZZ+WwGDs4vvYNCtYvRaV2YeiljxI+4RKHW3H6VAz15bYWhazvaC4+AIBn5DDi5z5AUPo0XH3DZK5wYLBazKB0QqXRnvLrjcf30nBsD53NtXS22G4ad1+aig9gNXViMXVgNRmRLCYAdEGDaK8qQKFUoVS7oFJrUDlrUao1qJxdzuA5F1TOGpRqF9SunqhdPdH6hqD1DhFT43ZxdvMmavKVRE66gpafBk/vXkVrZT5OGldiZ96Ob/w4hwkPQWnT8I4dwcH//YsjXz1Ddc4Gkq/8L7qAyF6rQaFQMGjK1bgHDSL7rTvZ+ex8Rty6FLfg2F6rQTg1CQVm7PE3y2yHfQp/hGhpsBPR0uDYGo9nkf3WHZgNrQy74imCh58nd0nUHvyBg8sexVBXSsio80mY9yAaDz+5yzqtztZ6Kvd8S8XuVTQX5QDgGZFk63qUPl2WOd4Hurq87ex+eSGj7/4Yn9iTB4oeWbmIgrVvonLR4eIRgMbTH7eQwVg621E6/XTSr7EFALULSpUac6ceq6kDi9GAxdTZ9dh2s5o6fvFc19eNHVi6XoNk7T62W0g8bRVHuv/t7OGH1icErU+o7d43tPuxi09on+uK15PMHXrKd37J8XVv0tlUjXdMusOFB0mSqNj1NYc+ewzJYiZ+7n1EnLWg18dbNZfksue1G7CajAy/6XUxQFpmBUvSeGtLzw+WvyvDLFoaZCZCg52I0OCYJEmidOsyDn32OFqfYNJveBX3UHmnzO1oquLw8qeoylqNLiCaoZc9gm/8WFlr+j2t5Uco2vg+Fbu/xj0sEcliJmj4DILTp4uVW2XWWLiPnc/OZ/gtbxKQNOmkr5sMbSiUyl6ZulaSJCSLCYup0xYoOvR0ttZiqK/A0FBuu3U97miowGo2nrC92tUTF58QXH1DcTlFsFDrvBzmBNpeLCYjZds/5/jaNxw2PBgaq8j9+J/UHfoR3/ixDLviqV6fUlZfX8aeV65DX19KypXPEDxi5u9vJNjF8SVpLNmS/Psv/IPuyTCK0CAzERrsRIQGx2MxdXJo2aOUbV+O/9CJpFy9SNaVWq0WMyVbPiZ/1YtIVjMx028meup1Djv9omS1UnvoB4o2vkd93naUahfCxswj4uzLcQ8RXQIcRVt1AT8+Op2UqxcRMnK23OWcMclqxdhaj6GhAkN9me2+oRxDfXnX4wosne0nbOMdOxK1q4dtmt6Y4XhGDEXZy+sI9BaLyUjZjuUUrHmDjqYqhwsPkiRRum0ZeV88jUKhJPHifxI6Zl6v1mZsbyLrjZtpPL6X+LkPED31Gof4vxloRGjov8SYBmFAMDRWkb30NpqL9hMz/WbiZt0h6ziBpsIccj/5N61lh/EbchZD5j+Mzt8xr9CbO/WUZ66geNP7tFcXovEKZPAF9xA+YT7OOi+5yxN+xUlrm3XH2NYkcyV/jEKpROPpj8bTH6/olJO+LkkSpvamn8NEQwVtFUdpPLaHmv3fA6BUa/CMTMYndgTeMcPxGpSGWuve22/FLlRqZyInLiBs7EXd4WH3y1fjNSiduJm345sgb3hQKBRETLgUv4Rx7P/g7xz48O9U71tP0oLHe20hNmedFyPveI/979/Pka/+S0djBYkX/aNPjAkThL5AhAah32s4upvst+7AYuwg7YZXCEo9V7ZaTPpmjqx8ntKtn6Lx8Cf1upcJSpvmkFfDOpqqKN78EaVbl2HSN+MZkUTK1YsISp8uZtJxYD+1nllMBpkr6VkKhQJnN2+c3bzxjBh6wtc6W+tpPL7Xdju2h4J1S5CsFlAocA+NxztmOD4xI/COHY6LV99eC+SU4WGx44QHV78IRt/1IUWbPyB/5SJ+fGImQy99pNfGjanUGlKveYE87yCKvn+XjsYqUq5eJAbd9zIzfW8dIeH3ie5JdiK6J8lPkiSKN39I3hdP4+oXTtqNr+Iu48wa1fu/p3jLx9TnbSdq8pXEzrzDIQd6NhXtp2jje1RlrUGSrASmnkN0xkK8BqU7ZLgRTqSvL2PLvzIYdsXThI2dJ3c5sjB36mkuyqGhK0Q0Fe7D0qkHQOsbhndMOt6xI/COGYFb4KA+uVDiT37dbclRwgNAW9Vx9n/wAM1F+wkefh5D5j+Cs1vvtU4WbfqAw8v/g1dUCsNvfgNnN59eO/ZAdnxJGq9tSe3x/T6Q0SG6J8lMhAY7UShVhI+7CP9hGQQmZ8hdzoBjMXZw8JN/U565goBhGSQvfFa2bgrmjjYOL3+Ssu3L8Y4bSeJF/3S4NRckq4XqnA0UbXyXxuNZqFx0hI+7mMhJV4gZkP4EyWrF3NGKsb0Zs74ZY3szJn0TJn0LCqWSiAmX2u3YNbmb2fvaDYx78KuTrsgPVFaLmdbyPBqO7elukTC21AGg1nnhPSidgOSp+CaM6bNTA1tMRsp3fMHxtW/Q0VjpMOHBajFTsG4p5ZlfoVAoSbn6OTwjen5mnd9Slb2WnPfuxcU7mBG3vuWw3UD7g+r9G6k9sBGX9u9YvCW9x/f/z4x2ERpkJkKDnYiWBvlIksTuxVdTn7ed2Fl3EDv9FtmuJDYWZJPz3r0Y6ssYdM71xM26w6EGapoMrZRt/5zizR9hqC9D6xtG1OSrCB17oUO2gsjBYuzA0FCBsa0ek74F0y8CgKm9CZO++eTH+mb4jY9WjWcAGU9ttVu9xVs+4tCyx5j89HZcHHzKXrlIkoS+tqS7O1PDsT1YTAY6m6rxjBzWtVr5tD4ZmE8KDzHDSZj3AN7RPX/l949oKsohe+kdGFvrGXrZY73aCtZ4PIu9b9yEQqFk+C1v4hV18pgZoeccW5IuQkM/JUKDnYjQIK/agz9gtZhla+WxWkwc++5Vjq95A61PCMlX/feUc+bLpaOpmuItH1G85SMsHe14x44gKmMhgclTBuygQWN7E+1Vx2nrutkeF2BoKMcrOo2mgqwTN1AoUbt6oNZ52e5dPX/x2Au1zrP7sbPOEydXT5xdbff2nCGrdNtn5H78EJP+8wNa777df783tdeWUJW9hqqsNbR0rWLuEZFEUPr0PjmV8E/hoXr/BuoO/UjYuIuIn3OfrCved7Y2kPPOXdQf2UnEWQtIvPgfvXYRpb26kN2vXkdncy2p175AYPKUXjnuQHR0STovbhnR4/t9OKNVhAaZidBgJyI0DFxtVcfJee8+WkpyCR0zj8SLH3KYq/YWo4HCDe9QsH4pHuFD0XoHEzVlYa92F5CTJEl0NFWfIhwcx9ha3/06pVqDLjAat6AYdEEx6AIH4ezq2RUEbOHASaNzyL7wJVs/5eAnjzD5P1tw8QqUu5w+SV9XQlX2OqqyVnevbu4RPpSgtGkEpc/o1ZWP/ypzp55j375C0cZ3cXL1IHHeg4SMniNblyWrxUz+yucp3PAWXtFppF3/cq/9nHa21rP3tRtoLjnIkEv+ReTZ/9crxx1oRGjov0RosBMRGgYeSZIo2fIReV89g8pZS9KCxwlKmyZ3WYCtj33Fnm/IX/EcHU1VBKZNI37Off22f6/VbEZfX0J75S/CQXUBbdUFWDp+nuvfSeuBW1BMdzhwCxqEW3AMWp/QPtvicnzdEvJXPMc5L+zrlQXc+jt9fRlVWWupyl5Nc9F+ANzDEglOn05Q2nR0gdEyV3hmWsryOPjJv2kq3IfP4DEMvewR3AIHyVZPZdZqDnz4d1QaV9Kue6nXWmLNnXr2vf03anM3ET/3fqKnXiv7gPH+Jn/JcJ7fPKrH9/vYlCYRGmQmQoOdiNAwsHQ0VXPgo39Qd+hH/IacxbArnsLFM0DusgBbf97DXzxJc9F+PMKHknjRP/CJc5yuUj3BajHRXHKQhqO7aDy6i+aSg7Z1CiQLABqvwF+FA9vN2d23350w5H/zMsfXvM70xYf63XuTm6G+nKp9a6nKWkNT4T4A3EPjbWMg0qfLehJ+JiSrldJtn3FkxXNYTAYGnXMDMdNvQqXWyFJPa8VRspbciqGujIQLHyRy0hW98jNra+1YRPHmD4mcdCXxc+8Tvys9SISG/kus0yAIf1FV9lpy//cvLMYOhsx/mIiJCxziD5ChvpwjK56lcu93aDwDGHbl04SOmuOQXWr+KKvZSHNxLg1HM2k4upvGgqzuKTV1QTEEpp6LV1QKbsEx6AJjHKZ7WG/obKoCyeoQP4P9jdY3lOgp1xA95RoMDZXdAeLoqhc5uupF3EIGE5Q2ndCx83D1CZG73JMolEoizrqUwJSpHP7iKY6vfpXKPd8w9LJH8EsY3+v1uIfEMe6BL9j//v0c/vwJmov3k7TgcVTOWrseV6lyIn7u/V3dNd8ChYL4OfeK3xlB+B0iNAjCn2QytHL4s8cpz1yBZ0QSyVc/5xBXGs0dbRxf+yZF378LCiWx591K9DnX9+muKhaTkebiHBryd9laEwqysZo6AHALGUzomLn4xI3CJ3YkmgE/Y5ACFH2za1VfovUJJjpjIdEZCzE0VlGdvdY2kDp7Dce+e4XAlKlEZSzEO2a4w52Majz8SL16EWFj5nHw00fY/fLVhIw8n4QLH+z13x+11p30G17l+No3OPrNS7RW5JN+wyt2H3iuUCgYMv9hkKBw/VIUCgWDL7jH4b5XfZVY3K1/EqFBEP6EhmO72f/e/RgaK4mZcSux590i+yrJktVC2Y4vyF/1IsaWOkJGnc/g8+9B6xMsa11/hsXUSVPhPhqO7qIhfxdNRfuwmjq7V/gNH38JPnEj8YkbKRZs+hVTeyMKRK/T3qT1DiIq4yqiMq7CUF9OyY+fULp1GdX71uERkUTU5KsIHj7DoaZbBvBLHM+Eh76hYO0bHF+3hJrczcTPvZfwcZf0aoukQqkkdsYteIQPIefde9n+9IWkXL0I/6ET7XtchYIh8/+NJFkpWLcEFAoGn3+3CA6C8BvEmAY7EWMa+ieLycixb1+iYP1buPqFk3zVs3gPSpO7LOqP7ODwF0/RWpaHd0w6CRf+vU/NRS5JEi2lh6nOWU/DUVtIkMwmUCjxCEvsCgij8I4dgbOu91aU7Yv2vXsPVVlrmb44V+5SBjSL0UB55kqKNr5He3UBGs8AIib+HxFnzXfIoNtWdZyDnzxCw9FMvKLTSFrwGO6h8b1eR3ttCdlv3kprZT6DZ9/FoHNvtHuAkaxWDn76MKVblxEz/WbiZt8lgsNfcGTJCJ7aPK7H9/vMlFoxpkFmoqVBEM6Qvq6Eg8sep+7gFsLHX0LChX/HyUUna03t1YXkfflfag5sROsbRuq1LxKUPqPP/MEzNFRQnrmS8syv0NeWoHb1xNUvjKhJV3aHBLlW8u6r2iqPgmhpkJ3KWUvEWZcSPv4S6g5vpWjT+xxd9QLH17xG6KgLiJx8Fe4hcXKX2c0tKIZRd31AReYKDn/5NNuemkNUxtXEzrytV7s26vwjGHPfMnL/9y/yv36B5uIDDLvyGbuOS1IolQy99FEkycrxNa+DQkHcrDv7zOeoIPQWERoE4Qw0FeWw97UbcfEOJv2m12VfGMjY3sSx716lZMvHKJ01DL7gHqIyFso2C8ofYe5op3rfOsozv6I+PxMkCZ+4UcRMuxH/YVPQuImWhL9CqXJGoRD9iR2FQqnEf+hE/IdOpLXyGMWb3qc8cwWl2z7DN2E8URlX4T9kokNMUKBQKAgdMxf/pEkcWfEshRveoiprNUMufZiApEm9VoeTxpWUhc/hFZlM3pdPs+OZC0m/4VXcgmPtdkyFUknSZY+DJHF89WsouoKD8MdJKDAjxlX1R6J7kp2I7kn9R3XOBva9czcaDz9G3PaWrIOdJUmiYtfXFH7/Dq3lRwgffzFxs+50+MG/ktVKw9FdlGd+RVX2WiyderR+4YSOmUvoqAtw9QuXu8R+Y8P9o7F06pn20oHTvk6SJIxtjRjqSulsrcdsaMHc0Y65ow1LRzvmznbMHe04u/lgbG9E5eyCSq213Tvb7pXOWpyctSidT3z+xHstCpVaXLX9BWNbI6Vbl1G85SM6m2vQBUYTOfkqQkfPcagJCxqO7ebgJw+j1nnhEZpAwoUP9vq4jPr8XWS/dQceYYnEnneL3ddzkKxWcj9+iLIdy4k97zbiZt1h1+P1R3lLRvLE5gk9vt/np1SJ7kkyE6HBTkRo6B+KNn3A4eX/wTMiieE3vynrybmxvYmDnzxMVdZqgtKmEzPjFjzCEmSr50y01xRTnvkVFZkrMTSUo3LREZw+g9Axc/GOGSFOJO1g498nYNI3nzI0dDRVceizJ9DXlaKvK+le6E7t6olJ3/zzCxVKnFx0OGl0uIcl0Fp+BIvRgMXY0T1r1R+hUKpQOrug8QhApXbGNSAKnX8Erv5R6AKicA2IROPhP+B+HqwWE1VZayj6/l2aS3JRu3oSPuESIiZe7jATGFhMnRxZ8RzFm97HKzqV1OteRusd1Ks1tNeVsffV6zA0VJB+wyt2HyAtWa0c+OgflO/8ktiZtxM383a7Hq+/ObxkJI9u7vnv0ctTKkRokJnoniQIpyBZreR99V+Kvn+XgOQppFy9SNYrgHV529j//gMYWxsYfP7dDDr3eoddsdhkaKVq73eUZ35F4/EsUCjwSxjP4AvuJjBlqt3nYB/oPMKHUH9k5ym/plS70FZ5FFe/CHxiR+DqF4HWLxyNpx9qrQdOLm44uehQql1+8wReslqxmDpsAcJosIUJU0d3qOgOFyf823azWswY6stoK8+nJud7JKu5e78qjSuu/hHo/KNw9Y+wBYuASHT+UTh7+J02UFTv/x6tT6jDh+hfU6rUhIycTfCIWTQVZFO08T0K1r9N4YZ3CB55PoPOuU72cQ8qtYYhF/8T75h0Dnz4D7Y9dQGp17yAX0LPD3T9LTq/MEbf/TG7F1/D3jduJvWa5wlKm2a34ymUSoZd/h9A4ti3i21jHM67zW7HE4S+QoQGQfgVi7GDnPfvozp7LZGTriDxon/IdoJuMXZwZOUiije9jy5wEMNvfhPPiKGy1PJLLWV5qLXuaH1DAdt0r3WHt1Ge+RXVORuwmjrRBcUweM69hI46Hxev3r0yOZDpa4pBsp7ya846LyY+vPYv7V+hVOKkcf3LIdpqMdPRWEF7TTH6miLaa0vQ1xTRUp5Hdc6GXwUKnS1QBET94j4St+AYFEonst60TXmceu2LBKZM/Ut1yUGhUOAdk453TDqG+nKKt3xEc8khtv5nNhFnXUrcrDtkn3EpOH0G7sFxZC29nd2Lr+ma2eiGXhuLoXH3ZfRdH7Ln1evJfutOkq94itAxc+12PIVSxbDLnwRJ4tg3L6NQ2KaFFYSBTHRPshOFUkX4uIvwH5ZBYHKG3OUIZ6iztYGsN26mqWgfCfMeJCpjoWxdJlpKD5Hz3r20VR4j8uzLiZ97n0NcpW+tOMr2p+fi4hPC6Ls+pHLvdxRueJvO5hrUOi+CR8wkdPRcPCOHDbjuJo5g6xOzaaspYPrLB+Uu5U+zWswYGirQ1xShry22BYtaW7Aw1JV1BwrPqBQMDRUYW2q7tlSQePE/iZp8pXzF9xBjWyPHvl1MyY+foNLoiJ15G5ETF8i+1oO5o53cj/9J5d7vCEieQvKV/0Xt6tGrx8968xbqj+xgyPx/E3n25XY9nmS1sP+DB6nYtZK42X8jdsbNdj1eX1a9fyO1BzaibF/Hw5sm9fj+X51aKronyUyEBjsRYxr6nvaaYva8eh0dTVWkLHzOrs3fpyNZLRRseJujq17C2c2bYVc8hf+Qs2Sp5dfMnXq2PzWX9ppC2xMKFVqfYNyC4wgbOw//pMmo1I61gNVAs++du6net55pL59+IHRfZbWYMNRXoK8tpqXsMIXfv4upreGE12h9wxl62SN4xwx3qIHFf0Zr5THyvniKukM/oguIJuHCB/FPmiRrIJckieLNH5L3xdO4+ASTfv1iPMKH9NrxLaZO9r19FzX7v2fw+XcTM/0mux7PFhweoGLX1ww+/2/ETBfB4XQOLRklQkM/JbonCQLQWJDN3tdvBGDUnR/ItmCbob6cnPfvp/HYbgLTppF02WM4u3nLUsup5Lx798+BAVCqVIy4dSluQTEyViX8kr62GOk3uif1B0qV2jbWISAS/6ETqdi96qTQYKgvZc8r16JQOuEVnYJv/Bh8Bo/FKzq1z4Va9+BYRtz6FrUHt5D3xVPsff1G/BInkHDRP3C34xSkp6NQKIiafCWeEUlkv3UHO56bz9DLHiVszLxeOb5KrSHt+pfZ/8GD5H/9POaONgZfcI/dgpRCqSL5yv8iSRL5X7+As7sv4eMvscux+gsz8k8hLPQ8ERqEAa8qey05792Li1cQI25dii4gqtdrsE2lupJDyx5DQmLYlU8TOnquw3Tv6WypI2vJ7TQV7D3heavZSHt1oQgNgmy8B6Xj4hWIV1QKXtEpeEYmo1RraDyeRUP+DurzMzm2+nX47lWUag3eg9JtISJ+LJ4RSShVjv9nUKFQEJA0Cb/E8ZRs+R9Hv13Mtv/MJuKsy4idebtsFxa8Y9IZ/4+V7Hv7bxz44EGaCrJJvPihXlkvRqlSk3LVszhpdBSsW4K5o40hl/zbbmMsfgoOap0XuR8/hJPWg+D06XY5liA4Ksf/tBQEO5EkiaKN75H35dN4RaWSftPraNx7f7DhL6dS9Y5JJ/mqZx1m3YLO1noK179F8ZaPsZo6cHJxwz00oXvqWYXKCV2QfOtW2FNTYQ5qnRe6gMi/vC/JasFkaMXU3ozV3Il7yOAeqPDUFH3gJLgnJS147JTP+w+ZgP8Q21zxJkMrDUd305C/k/ojO8j/+gXgBVQuOnxiR+IbPwb/oZNwc/CfZaVKTVTGVYSMms3RrvEO5bu+Jm7m7UScvQClSt3rNWncfRl5+zscXfUCBeuW0lxykLTrX8bVN8zux1YolQy97FGcXHQUbngbS6eepMuftFsQVKqcSJh7P62lB9n//n24eAfhHZ1ql2P1ZWJxt/5LjGmwEzGmwbFJVguHlz9J8eYPCUybRspVz6Jydun1OuoOb2P/B7apVONm3eEwU6n+FBZKfvgfFlMnISNnEzvjFnSB0XKXZndWi4n8lS9QuOEtfAaPYfRdH3R/TbJaMOlbMLU3YdQ3Y2pvwtTejEnfjKm9EVN788/P//Lrhhbo+qjV+oQy6YlNdqv/x8fPo722hOkv59rtGH1dZ2sDDUczqT+yk4YjO2mvKUQXHItS5UTo6LmEjJzt8Asmgm1SgsPLn6Q+bxu6wGgSLvx7r67c/GvV+9az/4MHUKicSFn4nN3XU/iJJEkcX/M6R1e9SGDquaRc/bxdu6F1tjaw87lLMHe0M/b+z3slIPUlB5eM5sFNPT+L2dtTC8SYBpmJ0GAnIjQ4LovRwL537qZm//dETbmahLkP9Nq0gT/X0GFbMGnzB+iCYkhZ+JxDTKU6kMMCgKGxiuylt9NclAPYuiT4JIxHpXahIX8nZkPLb2+sUKDWeqDWeaJ29USt80Kt88LZ1bPrOS/UOk+c3f26r4Dbw+5Xr6MhfxfTXtpvt2P0N/qGSmpy1lOxayXNxQdQKFX4JU4gdPQcApKnyHJB4UxJkkRt7mbyvnia9ppC/IacRcKFf5dtvEN7TTHZS2+jtSKf2PNuI3bGrb32+Vq08X0OL/8PfkPOIv2GV+w621xbdQE7n52PxtOfMfd82qszSDm63CWjuW9jz08k8v45R0VokJkIDXYiQoNjMhnaOPz545RnriDxot6bmlFfV0r+1y8QP+c+LJ3t7HvvXlpLDxE56Qri59wn+0mJLSy8TckPHw+osGDuaKe1/Agt5XnU5m6mNnfzSa/R+kfiP/QsFCh+DgOunji7eXUHAbWrJ2pXD9lbicwd7ay/Ow2Vs5ZzX8yRtZa+qq3yGOW7VlKRuZKOpiqctO4Ep88gZPQcvGOGO8w4o1+zmo0Ub/mYY9+9gqVTT8TEBbbxDjqvXq/FYjRw8JOHKc9cgd+Qs0he+ByaXhp3Ubp9ObkfP4T3oDSG37IEtdbdbseqz89k9+Jr8IkbyYhbl8rSPcwRidDQf4nQYCciNDgei9HA7leupbloPynXPE9Q6rm9duz9H/6d8h1foPEKxNjegk/scKKnXC37VKonh4VZxMy4BbdAx+7b/UdJkkRHYyUtZXm0lufRUnaY1rI89HUl3d2GlAgdcAwAACAASURBVGpXrOaOkxZGG37zmwQMmyxH2X9YU9F+djxzEU4ubpzzfJbc5fRpktVCfX4mFZkrqNq3DkunHq1fOKGj5xA6+gJc/SLkLvGUjG0NHP3mZUp+/BTfhHGEjZ1HyIhZvV6HJEmUbl1G4ffvoHZxI+36xd2LQdpb5d7vyHn3XtxD4xl5+9t2XRivbOeXHPjgQcLGX0zSgiccNlT2JhEa+q+BNWJOGLAsJiNZS26j8XgWqVcv6tXA0NlaT8Wur22Pm6pRqjUkLXgCV9+QXqvh16wWE0Ub36dwwzsY2xr6XVhory2h4dhuWsvybLfyPEz65u6vu/pH4h6WQOjoObiHJeIRloCLdzAKhQJDQwW1B3+gNnczjYXZDrGg3pkyd7TiNSiNtsrjcpfS5ymUKvwSxuGXMI4h8x+met86yjNXcOy7Vzj27WK8Y4YTOnoOQcPPs+vV7D/K2c2Hof/P3nmHN1l+DfhO07136aC7UEoXHVD2XspwoCB7LxkqiKKCgICIICJDpgzZ4ELZU3ZpS+lkdEAX3SPdK8n3R6GfqD8VyJu0kPu63ivJm+Sc06bN+5znrCELcOw0lFsHlxL1MBWz5ZAFaOmbKM0OkUiEY8chGDRx48aGKVxdOYTgad9hZOchuG7bwJcQa+sRuWUGkVtm4j9utWBNLhxCXqM8N5WkY+sxsHLGtdcEQfQ0JuSIkKpbrj6XqCMNAqGONDQcZNJabm6ZSXbUKbyHL6Vpu0FK1X/n55Ukn9z42DnP1z7Apcc4pdrxCElqLDHfz6Uk4w6OnYfh1GVEo3cWpNUV5N8NJS/uArnxFyjPTUXHrAk1pUUY2TfH2MGzzjmw98TIvhmauoaqNlkQcuMuEL5uPJr6xvRcod6RE4KKggc8uH6YjNCfKMu+h4amNjZ+PbBr8wqWLTo0qBauMmkt905tJuG3NWgbW+A7YhmWLdor3Y7i9NuErxuPrKaKwLc3K63jUN7tq8Tu+QQdI0taz9whWBqoXC4natt7ZIYfodWENSobDNpQiNkUwqyzim9Hu6vnHcEiDT/++CP3799HLBYjFouZNm2aIHoaOw3n202NGgGQy2TEfD+X7KhTtBj0sdIdhvKCzL84DGJdAzSU0Mf8z0irK0k8uoZ7p79D28iCgEnrsfFTfIcLZSCXyynPuV8XEYi/QEHCdWQ1VWho6WLRPATnbqMx92iDYRNXldcZKBPNR8WY6r0gwdAzt8Otz2Rce09CkhJDRuhPZIYfITPiKNrGljh2fAuHkNeUlorzT2iINXHrMwVLr05EbZ9N2JoxD+uoZis1gmbs4EnIrL2ErRlD2OpRtJqwRimdlSw92+L56hwit8wgescc/Md9LUhRtkgkwmfEMioKMutn/pi6+ClcT2OiMbVcTUhI4PPPPycsLAyAgQMHEhISQlBQkIota3ioIw0CoY40qB65XE78voWkXtyDR/93cO87Van6i9PiCVs/iWpJNgZN3HDuOgoztwAMm7grvVtTQWI4sbs+piznHg5tB+H5+gdKTVVQBLVV5RTcDSU3/gK5cReoyEsDwMDGBauWnbH06oS5R7BSBks1VEqzk7m4sA9iXUN6qWsalIastprc2N/JCP2Z6rJCiu5F0bT9G7j1mYyuaRNVmwc87Nj2y0pSzu3AwMYVvzErMHH0VqoNVcV5hK0dR+mDBHxHLccuWDm1FvdOb+X2j1/g0nMCnq++L5ieqpICrn75BtKq8he6FWvMphBmnn1Z4XL39YwXJNIwb948SktLWbVqFQDffPMNsbGxbNq0SeG6GjvqSIOa5xK5XM6dn1eQenEPLj0n4NZnilL1Z908SfT299HSN6Hdhz+prJ1qbWUZd39ZScqF3eiZ2RE8fZtK0hOeBrlcTll2cl00Ie4ChYlhyGqrEWvrYd4sBJfuY7Fq2anBDMJrCIi1dDF1DaDkwV1Vm/JCoaGpjY1/T2z8e1JRkEnS8W9Ju3SA9CuHcOw0FNdeE1U+90GsrYvXGx9j7dOFmJ1zubr8TdxfnoZrr4lKS6nSMbakzbu7uLFhClHbZ1FTVohTlxGC63XuPpbyvDTundqMvoUDjp3eEkSPjpE5QVM3cfXLwUSsn0jI7P0Nqt5FWcjlImrljaemISYmho4d/78pSdOmTdm7d68KLWq4qJ0GNc8lScc3cO/UZhw7DqX5K7OV1tGibsjQBhJ+XYWJsx8Bk9aha2KtFN1/Ju/WZWJ3f0JF4QOcOg+n2YD30NQ1UIkt/5XaqnLy71yrr02oyE8HwMDGFcdOQ7Fq2Rkz96AXOprwT0iryilKvoG4EdRsZN88haGt+3PX1lfP3BbvoYtw7TWBxGPrSTn/PWmX9uPYeTiuPccJ2snnv2Dp2Z4On/xK3P5FJPz6Nbmx5/EdtRwDa2el6NfSMyJo2laivnuX+AOfUV1agPvLMwT9jhaJRLR44xMq8jOI278QPQs7rFp2FkSXYRM3AiauJWzNWCI3zyDo7U3qVqxKIDs7m6+++gpbW1veeeedvzx/4MABTp8+jYeHB9evX2f8+PH07l1Xe1JWVoah4f9/ZxoZGZGbm6s02xsTaqdBzXPH/bM7SPh1FXatB+I1eL7SHAZpdSUxuz4iM/w37IIH4D18iUoWtzXlEm79sIyMqz9gYONCyHt7MHMLVLodT0LRvSjun99JWXYyxalxiLX1sGjeFpee47Hy6qiOJvxHasrrhs/91795uVxOlSSXisIH1JZL6qZd/+lWJBJRUyZBrKOHWFv/sVtNbT3EOvqItfXqjkf3dfQfPqeHWMcADU3tx2yqKsnnxqa30dDSoeWQBdiHvPbctarUt2yK74jPces9icSja7l3um5oonPXkbj0GKvS9EAtfRP8x6zExqcbsXs/5fLSgXi+PpemHQYr5XMQa+ngP/4b4vbOJ/HoOqpLCvEaPE/Q+iMNsSb+474mdNUwIre8Q8h7uzFu6iWILovmIXgPW0zM9x8Sv28hLYd+9tz9ff8TcpRb03Ds2DH27NnDrl27+PTTT//y/NatW1mwYAG3b9/GwMCA9PR0WrZsyZEjR+jQoQPm5uaUlZXVv76srAwLCwul2d+YUDsNAiGXy4nd/QlWPt2w8e2manNeGNKuHOLWoSXY+PXEZ8TnSqsdqCzK5sbGqUhSY2k2cBauvSaq5CKRffMUcfsWUF1agGuvibi/PL3B7spLa6rJunGMlPM7kaTEINY1qC/SNHMLRqylrWoTGx3VZUWYuQVS8iDxb58vyUwk/9ZlSjLvUvogkdKsRGoryxCJRMhl0sdeK9bWQ1PfGF1TW6TV5UirypFWV9QdVeVPZphI46GToY95s9aU5aYCIKupIub7ueTEnMdnxOdo6TX8CMmTYmDtjN/oFbj1nkzCkTUkHf+WlN934dx9DM5dR6v0Z7YNehkz90Cid84lbu98cqLP4D18iVKioxpiTbyHLUHb0Izkk5upLivEd9SXgv7fa+oaEDhlI1eXv0H4+om0nXMIPTNhak4c2r5Gec59kk5sQN/aGdee4wXR05DIjj5LbsxZpevt27cvzZo1Y9euXX95rqSkhNmzZzNlyhQMDOoi7Q4ODvTt25fp06cTGRmJv78/GRkZ9e9JT0/Hz+/FLmT/X6idBoEQiUR4D1usajNeKDIjjhK7+xMsvTriN3aV0vJ0JSkxRGyYQm1lKQET16mkI1FVST7x+xeRdeMYRg6eBE7dpLI6in+jUpJD2sW9pF7aT3VxHgY2LngNno99m1ee21aoyqI8L5XCpAi0DP5++m7+7SvcOrQELQNTjOyaYRfcH0NbD/TMbdEyMENL37h+urWG5v9evMllMqQ1lfUORN1tBdLqcmof3tY9rviLsyEHynPTHpOXffME2TdPYuXbDRMHTwxtPTC09cDA2ukf7WhMGNq602r8aorTJ5Pw2zck/vYNKed24tJjHE5dRqCpo68Su3RNmxA8rW7A4+2flnNpcT+8h36mlLahIpGI5q+8j7ahObd//IKacgkBE9cJmkapa2pD4NRNXPvqLSLWT6DNe3sFc9w8+r9DeV4q989up2mHIc+lU/xHbHzrNkmjNp5Rum6x+O8jGydOnKCoqIiQkJDHzoeEhLB//35iY2N54403GDp0aP1zp0+f5r333hPU3saK2mlQ81yQE3OOqG2zMXMLIGDiWqXtUmeGHyH6+w/RMbIkZNY+jB08laL3EXK5nAdhv3Lr4GJqq8rw6P8Orr0mNMgc2kcpSFkRx5DLpVi17IxTl5FYerZTejep55WKvDTEugbwP6Jc9m0GYhv4EtpGFs8UCRNpaKCpo1+30DV68jC+SKRB2qV9f5AnRqxrSFlmErkx5+qncos0NDGwca5zIuw8MLL1wNDWHX0rpwY1D+FJMHZoQeDkb5GkxpLw62ru/rKS+2e24dp7Io4dhwo2S+CfEGlo4NRlBBae7Yje/j6Rm6djH/IqLd74RCmFvC49xqFlaEbsro+5vnokQW9vFrT2w9jBk1bj1xCxfgI3t84kcMoGQb4zRRoa+IxYRnVp4XPvMPwROSKkCiqEDt9xm8troqgsrsZQbPzE74+MjATA0fHxCe5OTk4AhIWFMWbMGGbMmMHy5cvR19enffv2jxVGq/l/Gue3rho1fyD/zjUiN0/H2MGTwCmblNJ/XC6TkXh0DYlH12HmFkiriWvReYrF07NQUZhF3N755Maex8TZD58Rn2Nk665UG/6NP6cgaeoa4tRlOI6dhmFg7aRq855DRJi6+FOSfvtvn20obXb1LZti0MQN64fpm6Yu/vX57NKaKsqykynNTKTkwV1KMxMpTosjK/J4/fwJkaYWhtauWHl3xqCJK1Ytuwg28VcoTBy9CXp7M4XJkST8tprbPyzj3unvcOs9CYf2g1WSnmfYxI2Q9/eTdGw9Scc3UJGfgefrc5UStXQIeQ0tfVNubp3JtZVDCZ7+HXrmdoLps/LqQMu3FhK7+xPi9y+i5VuLBEkpFWvromduq3C5LwpBozwJGlW3Gfdr78gnfn9eXh5QV9z8Rx4VPmdnZwMwfPjwZzHzhUHtNKhp1BTeu0nEhsnoWzkRNG2rUnZzZNJa4g8sIv/2Vezbvk7LIQuVfoHPjj7L/TPfUXQ/Gs/X5+LcdWSDGmJWn4J0cR/VJfnqFCQlUZ6bQk2ZRNVm/CuuvSbg2mvC3z4n1tLB2KEFxg4tHjsvra6gNCuZ0swESh4kUJqZgCQ1juSTm0Akwsy1VV0NmU83DJq4NZrCUzPXVrSesZ38u9dJ+O1r4g98RvKpLYTM2ivoovl/oSHWwqPfTKxadiH+4GJCVw2j1fjVgnUb+iM2vt0Inv4dEd9O5tqKIQRP/w5DATdCmrZ/k/K8NJJPbETf0vF//k2qeXIaynA3HZ26mr4/fx9oPIxua2s/H+mPykLtNKhpdFQUPEDP3I6SzETC101Ax9iS4Bnb0Db8+zxuRSKT1hC9/X0yI47iMeBd3HpPVuriRCatJeHXr0k+uQnjpl60n/szhk1clab/n5DL5RTdjyLl3E6ybhxXpyCpAA1NbeS1Nao2QxDE2nqYOLZ8bNdbLpdTnBZHTvRZcmLOcvfnFdz9eQX6Vk5Y+3TF2rc7Zm4BDTJd789YNGuN+bu7yb9zhczwI+iaqXZ32tTFj4CJa4n4dhIR306m5ZAFNO0wWHC95u7BtHl3F+Frx3P7xy9o/uocChLDKEm/Tcu3Fir8+7ZZ/3epyEvjzs9fomdhj23gSwqVr0a1NGlSV+heWlr62PlHj+3tVT+5vTGhdhrUNCqyo05zY+NU7NsNIjf+EiZOPvgM/Uwp3T5ktdXc3Pou2VGnaP7qHKV3w6iS5HLzu/coSAilafs3afHmvAbTGelB+G/cP7NNnYKkYjQ0tZBJq1VthtIQiUSYOHpj4uiNR78ZVBRkkhN7jpzoM6Rc2M39s9vR1DPGyrsz1j5dsfLqhJb+k+dFKwuRSISlZ3ssPRvGAEZdUxvavLuLyC0zid0zj4r8DDwGvCv4RomxQwtav/M911YN4/KyV5HX1v1NO3cfjaGNYjdJRBoa+Iz8gsqiLKJ3zEHX1EawFtWlWUnE7pmP76gvnutp0YqsaXhWAgICAEhLS8PX17f+fGpqXQc3f39/ldjVWFE7DWoaFXm3LwOQceUQAC4jPkfPQvidAmlNFZGbZ5Abe44Wgz7GudsowXX+kYKEMG5ufYeaihJ8R36BfcirStX/b+REn6W2sgyvwZ9i32agOgVJRYjEWsie00jDf0HP3BanTkNx6jSU2spS8m5dJifmHDmx58gM+xWRhibmHkFY+3TH2rcr+paO/y70BUdT15DAKRuJ2/cpSSc2UFHwAJ8RSwXvamVg7YxFszZkRRytP5cbe17hTgPUpcQFTFrP1RVDiNgwhbbvHxRkw0OsrUdxWjxxe+YTNG1ro0mha8z06NEDCwsLQkNDefnll+vPX7t2jeDgYJo3b65C6xofDcMVVKPmP1KYGP7Y49jdn1BbWfo/Xq0YpNWV3NgwhdzYc7QcslCpDoNcJiP55Gaurx6Jpq4h7eYcbHAOA0DLtxbScd5RnDoPUzsMKkTXzBZDWzdVm9Eg0NQ1pEmr3viOXEb3ZVcImbUPlx5jqJTkcuvQEn6f34OLn73MnV9WUpgc+Zc5FWr+Hw2xJt5DF9NswLs8CDtM2Nrx9YMEhSIr8vhjDgNAZsQxwfRpG5oTNHUzAHd+Wk5NheKvK3rmdjR/ZRZ5ty7x4PovCpffUHg03E3Rxz9RVVUFgEwme+y8pqYm8+bNY+fOnVRUVACQmZnJ4cOHmT9/viA///OMOtKgptFQW1lGScadx86JNDSQ1QqXjlFbVU7Et5MpSAjFe/hSmrYbJJiuP1NTLiF654fkRJ+hSUBfvIctabBt+5TRlrEhIpdJ6yYnl0moLiui5uFRXS6pv19TVkR12cPH5XWTlSsLs9HSN0ZTzwgtPaO6W31jNHWN0NI3QlPPGPuQV9E2MH0ie2oriim6F9WgiuIbAiINMWZuAZi5BdD8lfcpy0khJ+YsOTHnuHdqC8knNmLl0xVTZ3+adhjc6DoxKQORSIRbnynomtkRs+sjrq0cQtDbWwQr1rb06oRL97Gkh/5ETWkhAJL7UZQXPEBfIJ0G1k4ETFrH9a9HEbfnE/zGrlJ4NMCx41AehP3GrUNLsfTqqPSue88jZ86cYf369QAcPHiQFi1a8PLLL2NsXJeKOHPmTHR0dJg4cSI+Pj6Eh4ezc+dO+vXrp0qzGyUiufxhDzs19VRXVxMREUF6ejqDBg16qi8NDbEmfdbeEsC6F5c7v3xF8okNAJg4++Haczw2fj0EWyDVVpYSvn4ihUk36lKC2gwURM/fIUmNI3LzdCoLs/B8/UOcuoxQh7KVjLSmipKMOxSnx1NTWkRJZsL/OwLlEqpLi6it+IfdVpFG3bA0A1O0DUzrhqYZmKJlaAYyKTXlxdRWltbdVhRTW1FKTUUxtRUlAHT+7OwT5z3fOrSUtMsHEWvr0v2Lq8/y478w1JRLyIk5z4Oww+TFX0RDUxu71gNw7joKI3t16sLfkX/nGjc2vY1YW69ukGRTL8F0yaS15N26xK1Dn1Oecw8TF3/avLNL0I51Scc3cPfwV3gP/UyQ4u/SzEQufT6QJv698B+7SuHyVc2Nje0ZeVrxv7ezfa4SHh7+7y9UIxjqSMMfyMzMZMGCBSQlJTF79mwGDBjw2ELtwIEDnD59Gg8PD65fv8748ePp3Vv4qZlq6ibdplzYhYaWLv7jvsbGt5ug+mrKiwlfNx5JSgz+Y1ZiG/Tyv79JAcjlctIu7+fWgcVoG5rT5r3dmLm2UoruF5naylKK029RnHaL4rQ4ilPjKc1KrE9ZMfdoQ0Xhg7rFv4Ep+tbOaD9yAh4e2gYPH+ubom1oiqau0VN1jJLLZNRWlT3VhGANTW1ktdUqGRDWWNHSN8G+zUDs2wykJDORlHM7yQj9mfQrhzBvFoJz15FY+3RVR2/+gEXzEEJm7SN83XhCvxoqaEtWDbEm1t5dsPbuQvrVH4j5fi5R22fRatzXgn0mrr0mkn83lPiDizF1bYWRXTOFyje0dce9z1QSfluNXfAArH26KlS+6lFsIXTSiWSST96jQlKlMJlqng51pOEh4eHh9O/fn7Fjx7Jo0aK/jCTfunUrCxYs4Pbt2xgYGJCenk7Lli05cuQIHTp0+Is8daRBcdRWlnL1y8FUSXJo+8EPGFgJW7xYXVZE2JqxlGTcwX/cKpr49xJU3yNqq8qJ2/spD67/gmWLDviNWSHoVNQXlerSQorT4ylOja9zENJuUZZ7v35wmLaxJSZNvTB28MLYsSXGTb3Qs3BoFJGeu7+uJunYOrQMzemx/JqqzWm0VJcVkX75ICm/76KyMBM9y6Y4dR6OQ7tBL2wq3t9RWZRNxLeTKMm4o7SWrPfObOP2D5/j0G4Q3sOWCPZ/WVWcx+WlA9HUN6bdBz88lRP/T8hqq7m87DVqy4vpMO9og009fRpubOzAsFNDFC73976X1ZEGFaN2GqhrxRUYGEivXr3YtWvXX54vKSnB0dGRKVOmsHTp0vrzQ4YM4c6dO/Vjyv+I2mlQDHKZjBub3iY39jxB07Zi6dlOUH1VJQWErRlNaVYSARPWKm0HqDQ7mcjNMyjNTMD95em495mi3tlUANXlEoqSbiBJi6MkLR5JWjyVBQ/qn9czt8e4qdf/H44tldK+Vyhu/bCMB2GHkUml9PwyVNXmNHpk0lqyo06Tcm4HhUkRiHUMcGj7Gk5dRmBg7axq8xoEtZWlRG6ZSV78Rdx6T1ZKS9a7v35N0rH1uHQfS/PXPhBMX97tK4StGYN9yGv4jvhc4fKL7kdx9cvBOHYYQsu3FihcvqqI2NiBIaeGKVzu5b4X1E6DilGnJwEffvghBQUFLFu27G+fP3HiBEVFRYSEhDx2PiQkhP379xMbG4u3t7cyTH3hSPhtNTnRZ/B6c57wDoMkl+vfjKY8L5XAyRux8vprBEkIMiOOErPrYzS0tAme9h2WLRpGj/bGSlVxHtlRp8mKPE7RvZvIamuQy2oxsHbGzDUA487D6xwEhxZKGQioTGrKCusGman3ghSChlgT24A+2Ab0QZIay/2zO0i9uI+U33dh1bIzzl1HYeHZrlFEoYSiriXrBuL2LVBaS1aPfjOpKZdw78x3aBmY4NZniiB6LD3b4dZnKknH1mHRLEThdW2mzn44dx3J/bPbsQ3uh7l7kELlq1GjaF54p0EikXDgwAEcHR1Zvnw5N2/eJDo6mo4dO/LNN9/g4uJSH0lwdHw8LcbJqa6Pc1hYmNppEIDM8CMkHf+Wpu3fxLHzcEF1VRZlcX31KCoLswiaugmL5m0F1QcPpzsfXkXyqc2YurTCf9zX6JmrdgpsY6WyKJvsqFNk3ThBQWIYyGXoWzvj1GUElp7tMXHyQVPXQNVmCk5VcR46xpZU/CGaokYxmDh64zf6SzxfnUPqxb2kXtxL2JoxGNq649RlJPZtBiLW1lO1mSpBQ6yF99DF6Fs4cPfwKiolOQRMXCvYID2RSITXG/OoLS/h7uFVaOqb4NRpqCC63F96m4KEUOL2fYqpsy8GNi4Kle/R/x2yo04Tu/tj2n90uMEM7HwWGtJwNzWK5YV3Gq5fv05tbS2enp4sW7YMfX197t+/T9euXenfvz83b94kLy8PACOjx3NZDQ3rchCzs7P/Ilcul3N52Wv1j5t2eBPHDorP8XtekaTGEv39XMzcAvEaPF/QnbyKggdcXz2SquJ8gqZtwdw9WDBdj5DVVnNz2ywqCx7g2GUkLV6bI/iwpOeNioJMsm6eIDvyOIXJkSCX1xUY9p1Ck1Z9MLRr9sLtAFdJctAzt1c7DQKiY2KFR78ZuPaeTFbEEe6f20nc3vnc/WUlDu3fxLnbqEad4va0/Lkla9z+hXi98Qn5t69QXVaIk4I3fuomOX9OTUUJ8fsXoqVnjF2w4ltoaog18RvzFZeXDiBy60zavn9QoQt7TR19Wr61iPC1Y0k6tp5mA95VmGxlknppH2mXDgCg83wFcNX8gRfeaXjkEEycOBF9/bpCJ2dnZ+bMmcPUqVM5fvw4Ojp1XxB/XoBoPOyMoq3918WeSCSi/Yc/Cmn6c0uVJJcbG6aibWROq4lrBV1Mlxdkcn3VcGrKJQTP2IaZi/Aj5aU11dzcMoOcmLN4vj4Xl+5jBNf5vFCel0ZW5EmyIo8juR8FgJF9czxenoFNq94Y2bqr2ELVoqlrhJ6FPUUPfzdqhEOspY19yKvYtXmFwqQI7p/bwb3TW7ENfOmFdBoeYd9mINpGFoR/O4m8W5eoKS1EpCHGrvUrCi/21RBr0Wr8asLXjSd6xxw0dQ0EqUPTM2uC78gviPh2Erd/+JyWQxYoVL6VVwfsQ14l+eRmmgT0xdjBU6HylYFjhyH1G6NhGzpSq440PJe88E6DmVmdS/znbkmBgYEA3LlzhyZNmgBQWvr4hMhHj+3t7YU284VBWlPNjU3TqCmXEDJrr6CDb2rKi4lYOw59q6Y0f+UbTByFTzGTVldyY9Pb5MVfxGvwpzh1Vnyx2PNGWc59sm4cJ+vmSYpTYwEwdvSm2cBZNGnVW12Q+geKM25h4qROlVQmIpEIc/cgzN2DqJTkvNAOwyMsPdth5dme3LjzQN0QxPw7VwTpRCfW1iVg8gaufz2CyC0zCJ7+nSDRYmufrrh0H8u9M99h0bwtTVoptt265+tzyY27QOyujwh5/wAa4hd+eaamAfLCu4LNm9cN7/lzipGJiUn9bUBAAFDXZemPpKamAuDvL/zu9IuAXC4nbu98iu5F4jtqOcaCDgyqIXLLDMpyU3Dr87ZSHIbaqnLC108k79YlvIctUTsM/0BJVhKJR9dxaUl/Lizoxd3DXyHSENP81Q/ovOgM7T/8Ebfek9QOw5+QS2sRaagXG6pC7TDUwUN1JwAAIABJREFUkXblQL3D8IicmHOC6dPSMyRo2lb0zO25sXkGJZkJguhpNvA9TJx9idn1EeV5af/+hidA28AUrzfnIUmNJeXcDoXKVqNGUbzwToOLiwtt27bl7Nmzj53PyMgAoHXr1vTo0QMLCwtCQx9vYXjt2jWCg4PrHQ81z0bK77vJuPYj7i9NU/guzh+Ry+XE7/+M/NtX8B66CItmrQXT9YiailLC146jIOE6viOX07T9G4LrbGzI5XLybl3m+uqRxO2ZT8KRb9DUNcBz0Ed0WXyednMO4tpzHPqWTVVtaoNFLq1V71AqALlMpmoTGjUWzdth7dMNRP+/xMgI/RmZgL9XHSNzgqdvw9DGlYj1k6guK1K4Dg1N7foJzje/exdZbbVC5TcJ6Iu1b3fu/rqa8rxUhcpWLiKkiBV+qFE9L7zTALBgwQIOHz5McnJy/bmdO3cybNgwfH190dTUZN68eezcuZOKigqgbnr04cOHmT9/vqrMfq6QpMaSfGIjDu3ewP2laYLqSjm3k7RL+3DtNQGHtq8Lqgvq0qDC1oyh6F4U/uNWKbxtX2NHJq0lM/wIV5a9StiaMZRmJWPj14OuSy4QMmsfLt1Go2dup2ozGzxyuRy5TIpI7TQ8E+V5qfy+oCcpv+9CWl2panMaJQZWjgRO2UC3pRdp/uoHaGjpgEzKnR+/EFSvnrktnq/OoVKSTdS2WfUT3RWJvmVTvIctQXI/mruHVylUtkgkouWQT9EQaxK7Zz7qMVpqGhrqqwvUD3WbOnUqrVq1QiKRYG1tzWeffVb/mpkzZ6Kjo8PEiRPx8fEhPDycnTt30q+f4rs1vGhIqyuI2jYbkUiE52tzEGkI58vmxJzj1g+fY+PXk2YDZgmm5xHVpYV106Uf3KXVhG+w8eshuM7/QkVhFgmHv6LFm/NVNolUWl1JxrWfSD69hYq8NAxsXPAetgS71gMRa6k7ST0pclktACKxlootadzUVpahY2RJ/P5FJB5bj0u3MTTt+Fajmdh77+x2jB28lBJB/Td0TKxw7TkO5+5jiNn5IffPbkPX3BaXbqMF02nq4ofXm/MeRivX0Kz/OwrXYRvQh4KOQ7l3eivmHq0VWnyta9qE5q+8z+2flpMVeQLbgD4Kk60s5KDQlqupJxNIO5mATFL67y9WIyjqidACoZ4I/d+J37+IlN93ETxju6AD3IrTb3Nt5VsYWDvT5r3daOroC6YLoKokn7DVoynLuUeriWux9u4iqL7/SlbkCWJ3f4JMWkPglE1KX1zUlBeTemE398/tpLokHxNnX1x7TcTGt4egDuPzTnV5MRcX9ML95RkkHl1D9y+uqtqkRotcLqcg4TpJxzeQf/symnrGOHUZgXPXkQ16IKC0upLLy16lPCcFr8HzcezYcNp8y2VSIrfMJPvmSXxHLce+zSvC6ZLLid39MelXDhEwab0gmzXSmiquLh9EZVE27T86jJ5ZE4XJlstkD6OuSXRacFLwa5Wiub6hEwNOjFW43Oh+J9QToVWM+gqtRqXkxl8k5fddOHcbLajDUCXJJWLDZDR1DQic8q3gX8KVkhxCVw2nLDeFwKkbG4TDUFtVTszuj4ncPB19y6a0n/uzUh2GyqIsbv/4Bec+7szdw6swbupF63e+p+37B2ni30vtMDwjteUSqksLEGvrqtqURo9IJMKiWRtaz9hG2zmHsGjWhqRj6zg/ryu3fvicyqK/zuZpCIi1dWn7/gEsPNsSt3c+cfsXIpPWqNosAEQaYvzGfIVF87bEfD+X7Oiz//6mp9UlEuE1+FNMHL2J2vE+pdnJ//6mJ0SspYP/+NVoGZiSeOQbhaYSiTQ08Og3gypJDvfPbleYXGUhR0StXEPhhxrVo/4U1KiM6tJCYr6fi6GtB80GCpcqJK2uJGLjVGpKCwmcshFdU8XtCP0dFQWZhH41jMrCLIKnbcXSs72g+v4LktQ4rnz+KulXDuHaayIhs/cprfNQaVYSMd9/xPl53bl3ZhvWPl1pP/dngqdtxaJZmxduAJtQ1JRLAASbwvuiYursS8CkdXT45Ag2fj1IObeT8/O7EbtnXoMsVtXSMyJo6iZcuo8l9ffdhK8dR3VpoarNAupmWwRMWoexQwtubp1ZN71dMF069XN+IjdNo7ZS8akthjauOHUeQfqVQ2RFHFWobDO3QGz8epJ8cjNVxXkKla1GzdOidhrUqAS5XE7s3vlUlxbiN/pLhU7YfEyPTEb0zg+QpETjN3oFJo4tBdHziPK8NEJXDaO6JL+uX7iH8NOl/wm5TEbyqa1c/fJNaqvLaT1jB81fma2U6dNF96K4sfFtLn72Eg/Cf6Vp+zfovPAk/mO/ErSd7otKvdNgYKJiS55PjOw88Bu9gk4LTuAQ8jrp137k9097EbVtNiUP7qravMcQaYjxfP1DfEYuoyApgivLB1HyQJg2pE+Kpq4hQdO2oGduR8T6SRSnC5fGq2duh//YVZRmJRPz/UeCFBY7dR6KsaM3tw4tpaaiRKGym78yG1lNFQlH1ihUrjKQoqHwQ43qUX8KalTCg9CfyY48gUf/mYIuIBOOrCHrxjGaD5yNjX9PwfRA3RCy0FXDqa0oofXMHZi5thJU379RWZRN2Nqx3PnpC6x9utLho8NYNA8RVKdcLic37ndCVw3n6pdvUJBwHbc+U+iy+DwthyxA39JRUP0vMjVljyINaqdBSPQtHfEeuogui87i0m002dGnubS4Hzc2TqXofrSqzXsMh5DXaPPubqTVFVxd8aagsxKeBG3DuvaomrqGhK0dR1mucBEbS892NH9lNlmRx7l3eqvC5Ys0xHi/tZCqkjwSfv1aobINbFxw7DiE9MsHKM1MVKhsNWqeBrXToEbplOenE3dgEWbuQbj2GCeYnozrh0k6tg6HtoNw6TleMD0ApZmJhK4ajrSmktYzd2Di5COovn8jO/oMl5b0pyg5Eu+hn9FqwhrBCziL028TtX024esmUJabgufrH9Jl8Tma9X9H0MneaupQldMgl8mQVldSU15MVXEe1aUFL8ScA11Tm7q/8c/O4f7S2+Tfvc7V5YO4/s1o8u9cq9/VlsvlVJcWqMxOMxd/2n3wIwZWzkRsmEzSyU0NopWnnrkdwdO/Qy6tJeyb0VRKcgTT5dJjHE1a9eHOzyvIu31F4fJNnHxw7DSUlN93I3k4tV5RuL00DbG2Hnd+XqFQuWrUPA3qlqtqlIpcJiV6xxwAfEctR6QhzMCWwqQIYnbNxdyjNS3fWiBo3nxFfga3f/wCENHmnV0Y2XkIpuvfkFZXcOuHZaRd3ItxUy/8xqzEsImbsDprqkg6tp7kk5vR0jfGe9gS7NsMVEoKlKqoKS+mIj8daXUl0ppKZDVVdbfVlUhrqpBWVyCrqbsve/iauvsVaGjrI5fWoGduh665LXpmduia26FnZoumrsFT2ySrrUasa4hYW++J3yuXy6kszEKSEo0kJQaZtIaS9NvIaqv/dNQ8/rimur7V6yPM3IOQ3I9Gz9wePUuH+lv9R48tHNA2NH9ualm0Dc3w6DcTlx7jSL24j3tnvuP66pHYBg/ApfsYMsN/4/65nbT74AeMHTxVYqOeWRNCZu0h5vu53P15BSUZd/AZtkTlRfOGtu4Evb2Z66tHEb5mHG3e2yWI0ysSifAZsZTSrERubn2H9h/+hJ6FvUJ1NOv/LlmRJ4jbM5+2cw4q7NqmY2SOa+9J3P1lJfl3Q7Fo1kYhcoVEjgipXD2M7XlE7TSoUSrJp7dSmBiO78gv0LdwEERHeV4aNza+jZ6ZHa0mrBF08VpTXkz4+glUFmXT5r09KnUYitNvcfO79yjLSsKlxzg8+r8r+LyDgsQwYnd/Qln2PezbvILn63MbdEvKp6FSkkNxahzFafEUp8UhSY2jSpIDIg3k/9aZRiRCrKWLhpZO3a22LtpGFlQVZpFZlPWX4VNa+iZ4D1v8VBPRZbXVSCtL0dD873Ma7p3eSkFCGJKUGKqKc+tM1tDEyrsL0upKNLR00NLRR0NT+/8PLe3HH//pADB19qciP52K/HQkKTHU/Gk6r1hbDz0Le/QsHNCzsEffwgEj+xYYN23RaP9+NHUNce05HqfOw0m/9iMPQn/myrLXqOtaD7cOLaH1zJ0qc5bE2nr4jV2FoV1zEn5dRXnOfQImrRO8McS/YersR8CkdYSvm0jEhrcJmrYFTQGcGU1dQ1pNXMvVLwZxY/N0QmbtVWgtnZa+MS1e/4iobe+RenEfTp2HKUy2c9dRpF7Yw+0fv6DdnEPqTnNqVIbaaVCjNCRp8ST8uhqbVr2xE6hHd01FCRHfTkImrSVw6kZBFyCy2moiN0+jLCeF4GlbMLZvLpiuf0Iuk3H/3A7u/LICbQMzgqdvw7KFsB2baipKufvzClIv7kHPwoGgad9h5dVBUJ1CU7fbnklxahyShw5CcWpc/WIakQgDaxfM3IMwaeqFrpktmrqGaGjpItauOzS0dBFr6dTf19DU/p+LRLlMSmVRDpWFD6goyKSiIIPKgkz0ntKZrh/u9gQ7nDkx56gqzsPCsx2mzr6YOPli5OCp8MYEtZWlVORnUJ6fUe9MVOSnU56fQWFSBLUVJRjYuFKWnYyhrQfm7sGYeQRh7h6k8kXtkyLW1sWp01BsfLvz+/zuyGqrASi4G0pO9BmVDngUiUS4952CkZ0HUdtnc+WLQQRMWoeps5/KbAKw9GyP3+gVJJ3YQPy+BfiM+FwQ58rQxhXfUcu5sXEq8fsW4j18iUL12Aa9TPqVg9w9/BVN/HuhY2KlELlibV2aDXiX6B1zyIw4gl1wf4XIFRJFDndT03BQOw0CUTdc5hOsfLph49tN1eaoHGlNFdHbZ6NtaIb3W4sEuSDIZVKit8+hLPs+wdO3YmjjqnAd9brkcmL3zCP/zjV8R36BRfO2gun6J6okuUR//yF58Rex9u2Oz/AlaBuaC6ozO/oscfs+pUqSi3O30Xj0f6fRDR+CurkVhck3KLhzjeK0eCRpcdQ8bE0p0hBj2MQdS68OGDdtiXFTL4wdWjxT+tCfEWmI0TO3Rc/cFjMFZJDJHkYtnsRpCJ6xHQ2x8JcBTV1DjOybY/Q/HOua8mKK0+MpSo6kIDGcjLBfSL24BwA9y6Z1ToR7EObuwehbOTaK1KaM67/UOwyPuLn1Hbp9cRUtPSMVWVWHjV8P2s7eT8SGKYR+Naw+pVCV2Ab2pTTzLolH12Hq6o9jB2EG09n49cCt71SSjq3HxMVXoXpEIhEthyzg4pJ+3PpxGf5jVipMtl3wAO6f3c6dX1Zi499LsI6Dz0J29FlyY4Sbv6FG9aidBoEQiUR4D1usajMaDHd/WUlpZiJB07YKtvufdGIjZdnJeA1ZIPgiPvHYOjKu/YT7y9OxD3lVUF3/C0laPIlHviH/bigthyykacchgi6mqorziD/wGVk3jmFo14yAiWtVvkP5pFSV5JMTfZbsqNPk37mCjrE1lUVZGNk1w8avB8ZNW2LStCVG9s1Vnu/9pMilD1OdRP99h08ZDsN/QUvfGItmIVg0C8ENkElrKcm4TUFCGIWJ4eTEnCXj2o8A6BhbPXQggjBzD8bIrlmDTNcwdwvCxr8X5XlpVOSlUVtZiqy2mkuL+xMwaZ3g7Z//DSP75rT74BCRm2cQveN9Sh7cpfnA9wSrM/svuL80jaJ7UcQf+AxjBy9MnX0F0ePx8nQkKTHE7/8MI3tPzFz8FSbbwMYF154THzbheF1hQ0tFGho0f3UOYd+MJuX897gK3NzjabDxrdskvbqhi7pF6nOKSN4Q2ig8h2iINemzVrj+042JvNuXCftmDE6dh+M1eL4gOgrv3SR05Vs0CXxJobs7f0fGtZ+I3vkB9m1ewWfkFyrZ9cy/c42IjVPQ0jMmYPK3mAjYtlYul5Nx7Sdu//A5tdXluPedhmvPcY2m0LksJ4XsqNPkRJ+iMDkS5HL0LByw9u2OtU83TF0D0NRueLt2T8rdX74i+dQW+qyN58wHben+xVVVm6Qw5HI5pVlJFCaGU5AYRmFCGJVFWQBo6hlj5haIuUcw5h6tMXFsqdKF7/+itrKUtKs/ce/kRqpL8nHtPRn3vlNU/n8kq60m/sBi0i7tw67NK3i/tfCpiukVRXVpIZeX1W3EtP/wJ8E2marLiriy7DVk0hraf/gTOsaWCpMtra7k0pJ+iERi2n/8q0Jry8LXTaAw+QadF55usPU/Vzd0oevRtxUmL/PULbJO3UbzehEJCQ1j3siLitppEAi101BHdVkRl5b0R1PHgPZzfxLkYlRTUcrlzweCXE77j34RNPSff+caYWvHYeYWSPC0LSq54GfeOE7U9lkYWDkTNG0rembC5XyX56USu2c++bevYOYWiPewxYJ3Y3pW5DIZktRYcqJOkx19ur6/uZFDC2z8emDj1wMje89GkeLyJNz+6UtSzu+k9+qY585p+DNyuZyKggwKE8LqnIjECMpy7mHc1IuaMgn27V7Hoe0gQf83npaacgm3Di4hI/RnjOya4zNymcqjDgD3z39Pyvnv0TVtQtDUjSp1HIruR3Ptq7ewaBZC0NRNgjmBxem3iN+/CD1ze3xHf6nQ74Tc+IuErx2HR7+ZuL+kuAV0yYMELi3pj1OXEXi98bHC5CqSK992pfPRaQqXm/HKIcLDwxUuV81/p2HEptU8MRX5GQppGSdJi0dDrCVY15+4/YuoLs4ncM63gl2Ebh1YREV+BiHv7RbUYSjJTOTGprcxsHYiYOJalTgMKRf2EL9/IWaurQicslGwnvwyaS0p53eS8Otq0NDAa8gCHDsMaZBpIFC3W5p/93q9o1AlyUGkIcbMPZgWHYZg7dtNsG5dDQWRhgb6Vk6qNkMpiEQi9C0c0LdwqE8PrJLkknf3GhlXfyDxt29IPLIWq5adaNr+Tay8uzSgVCwTfEctx6ZVH+L2zufq8kENIurg3GUEWnrGRO/8gPD1kwicskFltUqmzr54vTmPuD3zSTy6Fo9+MwXRY+zQAmvfHtz56Qts/Hs+Vdey/4WVV0eaBPQl6fi32Ab3x8BKMYMtjew8aNpuEKkX9uDUeTgG1i/G/7yahkHD+BZV80SUZNzh0tIB+I1eiV1wv6eWI5fLid+/iCpJDp0XnlL4bk5u/EXKMhPw6P8OJo7eCpX9iAfhv5ER+jPuL72NmVugIDqgbkESsW4CGlo6BE7dhJa+sWC6/g65XE7ikW9IPLoOa59u+I9bJZgTVpx+m9hdHyFJjcXKuysthyxAz9xWEF3PglwmJf9OKOlXD5Ib+zu1laWItfWw9OqIjV8PrLy7oG1gqmozlUZtecn/d3p6AdExscI+uD/2wf0pz0sl/fIh0q/9yI2NU9ExscYh5DUc2r+BvmVTVZsK1OV/m7sHcuvgEpKOrSMn6rTKow72bQaCSET0jjlEfDtZpY5D0/aDKUq+SeLRdZg4+2Ht3UUQPc7dRvEg7DDxBz7DwrOdQjeeWgyaS27cBeL3LyTo7S0Ki2S495vBg/DfuPvLSlpN+EYhMtWo+S80zG1DNf9I8qnNDxdHz9biMv/2FYqSb+Daa6LCHQaZtJbbP3yOtLoC526jFSr7ERX5GcTt/RRTl1a49VVc+PfP1FaVE/HtJKpLCwiaslHpO9ZymZS4vZ+SeHQd9m1fp9XEtYI4DDJpDYnH1nNl2WtUFDzAf+wqAqdsaHAOQ21VOSm/7+bCwj6ErxuHJDWWJgF9CJi8ge7LQwmYuBb7Nq+8UA4D1EVbVJ0f31DQt3Sk2cD36LL4PAGT1mPc1Iukk5v4fX53rn8zmsyIo3/pbKQKHkUdAiZvoLqskKvLB3H319Uqtc2+9QB8Ry2nIOE6EesnUVtVrhI7HnUiMnLwJHr7+5TnpQmiR0Osic+wxVQV53H3Z8XWw+maNqFZ/3fIi79IVuRxxck1scal+1iyIo/X1Wk1QKRyDYUfalSPOtLQyKjIzyAz/AhOXUY806JILpeTcGQNuma2OLR9TYEW1pF2+QClmYl1C1wBBozJpLVEbZ+NXC7Db8wKwVIP5DIpUdtmIUmLI2DSekycfATR87+Q1lQRtW0W2TdP4tprIs0GzhIkF7+mvJjIzdORVpVjG9yPFg1wSFulJIfU87tIvbSPmrIiTJx8aNZ/Bdb+vRA/wUCz5xW10/BXNMSa9XUsFYVZpF89RPqVQ9zc+g5ahmY4tHkVhw5vCtqe+b/Q0KIO9q0HIBKJiNr+PhHrJxE4daNKIg5ibV1aTVjLlWWvErl5OiGz9gnS1czEyQfnriO5f24Hdq0HYOYWoDDZjp2HkX7tR24dXIJli45o6RkqRK5Lj3GkXdrPvZNbMJ209rmr0VLTMFG7bo2Me2e2ASKcu415JjmPogxuvScrfKFRU15Mwq9fY+7RGhu/ngqV/YjkExspTIqg5eAFgqYb3PphGTnRZ2gx6GNsfLsLpufvqKkoIXzteLJvnsRz0Ec0f2W2IBeG8rw0rq4YTEFiOE07vYXfqOUNymEoTr9N9M4POT+vK0knN2LuHkSb9/bQds4hbINeVjsMD5HWVjXI3u0NBT2zJni8NI0ui84Q9PYWzN2DuX9uJxcX9uHaV0PJCP0ZaXWlyuz7c9Qh4ttJpF7cqzJ77IL74zf6SwoSw1QacTCwcsRv9AqK0+KJP7BIMD0e/Waia2ZL7J55Co30aIg18X5rEVXFuSQeWaMwuZq6Bnj0n0l2zBkK7oYqTK4ikCNSRxqeU9SfQiOiurSQ9CsHsWvd/5lSRv4YZbBv+7oCLawj6dh6asoleA76SJBFbmFyJIlH12Ib3F/QgUT3z+4g5dwOnLuNxrnrSMH0/B2VkhxCVw2jMCkCvzErcREoxavoXhRXv3yT6uI8gqd/h0OI4qNOT4NcLic3/iLXvxnD5aUDyLpxjKbt36TTpycJmLQec/cg9c7an5DVqCMN/wWRhhirlp0ImLiWrksv0OyV2XVDEnfM4ezcDsTvX0Rx+m2V2Wfj242O845g2aIDcXs/5faPy5HLZCqx5XHHYaLKHAdrn6649ZlC+pVDpF0+KIgOTV0DWg7+lNLMBJJPbVWobFMXP1x6jCP75knKclMVJtcueADahmYkn9qsMJlq1PwT6vSkRkTKhd1Iqytw6fFsQ10eRRlaDlmo8NShspwU7p//HvuQ1wSZHVBTUUrUtlnomtnScsgChct/RHbUaW79sBQbvx54vvaBYHr+jrKcFMLWjKG6tIDAqZuwesbalf9F5o3jRO94Hx0Ta4Le3qzyFA0AaU01D8IOc//MNkozE9AxsabZgPdo2nHIC1ej8KSo05OeHB1jS9x6TcS15wQK7oaSdvkAqZf3kx76C5ae7fDoN0OwznL/hJa+CT7DlyDW1uXe6S1USXLwGbFUJZ+vXXB/QETU9tlErJ9I4NRNKklV8ug3g6L70cTvX4hx0xaCNNew9ula1/Ho2DpsA/pgYOOiMNnO3eqGsiUdXYvvqOUKkSnW1sW56yjuHv4KSVq8oPN6ngQ5qCMDzynqT7WRIK2uIOX891h5d32mi5hcLifxqHBRhjs/LUdDU4tmA95VuGyA+P0LqSh4gN/oLwVrrypJieHmd+9h4uiN35iVSh0UJUmN5dqKwdRWltJ65g5BHAa5XE7SyU3c3DID46Ytafv+QZU7DNWlBSQeXcf5eV2I3fURIg0NfEYuo/Ois7j1max2GP4Daqfh6RGJRFg0D8F/7Fd0W3oJj37Tyb9zlctLBxB/4DOqy4qUb5OGGK/Bn9JswLs8CDtM+PpJ1FaWKt0OALvgfviNXkFBYjjh6yaoJOIg0hDjP3Yl2kYWRG6eIdhn4vXGJ2ho6RC7Zz6KHGOla2KNU6dhZFw/TGlWksLkOnYailjXgHvqaIMaJaB2GhoJ6VcOUVNaiGuvCc8kJ//OFQqTbuDae5LCowz5d0PJjjqFa69J6JpYK1Q2wIOw33hw/Rfc+04VrL1qdWkhcfsWYuLkTeAU5Q44yrt9mdBVI9DQ1iVk9j5Mnf0UrkMmrSF298fc/XkFtkEv03rmDnSMzBWu579Smp1M7N5POfdxFxJ+W42xQwuCZ2yj/UeHcQh5TZAi+ucVWW0VGuqahmdG29AMl26j6bTgJA7t36zr1LWgFym/70YmrVWqLSKRCLc+U/AZsYyCu9cIXTWcKolq2uraBffDb8wKCpMi6hyHyjKl26BtaE6rCd9QWZRN9PbZgqRt6ZhY4fnqHAoSQsm49qNCZbv0mohYW1ehtQ1a+sY4dniLzIhjCk19elZkaCj8UKN61J9CI0AmreXemW2YugZg7h701HLqev2vQde0CQ5tBynQwrouQ7cOLUXX3A6X7s9WpP13lOenE7d3PqauAbj1napw+VD3+4nd/THF6bdoMegjdIwtBdHzd2RGHCV83UT0LOxpO3ufIDv/NeXFhK8dT/qVQ7j1nYrf6JUqK5wtzrhLxLeTubiwD+lXD2EX3I8OnxwheNpWLD3bq+sVngJtQ3N0BHDWX1R0jMzxfmsh7ef+hJFdc+L3L+Ty56+Qf0f5k7Yd2r5G4JQNlGXf4+qKwZRl31O6DQB2QX9wHNZPVInjYOrsR4s3PiY37gKJx9cLosOh3RuYuQdx+4dlVJXkK0yujpE5zl1GkhlxVKF1M87dRiESi7l/+juFyVSj5u9QOw2NgKwbx6jIT1dclKHPZIXv4KZf+4mS9Fs0Hzhb4S3xZNJaorfPBsBv9JeCtVdNvbCH7KjTNH9llmDD6P6OzBvHiD+4GFNnX0Le242uaROF6yjPS+PayiEUJIbjM3IZzfq/o5LpzjUVJcTvX8Tlpf2pKZfg1vdtui7+HZ/hS1WSO/48UZGfQW1FiarNeO4wdmhB63d20mrCGqSVZVxfPYobm6YJNjfgf2HVsjOt3/keaVU5V1cOoehelFL1P8IuqB/+Y1Y+dBxUE3Fw7PgWdq0HkhNJoDmWAAAgAElEQVRzjoLEcIXLF2lo4P3WImqry7l1aKlCZbv0HIemnhGJRxQ3lE3X1Ab7Nq+QfvUQVcV5CpOrRs2fUTsNDRy5XE7yqc0Y2rpj7d31meQIFWWorSwl4fAqTF38sQ16WaGyAZJObKAw6QZeQ4Rrr1qScYfbP3yOpVdHnLuOFkTH35F/5xpR22Zj6uJP8PRtaOmbKFzHow5JlUU5KuuQJJfLeRD+GxcX9iHlwh6cOg8ncOommvWfqdSIzvOMXFormEP9oiMSiWjSqjcd5x/D4+GwrouL+nL3l6+Uumg2dfYlZPZ+NHUNub56JDkx55Sm+4/YBr2M/5iVyGqqiN3zidK7O4lEIrwGz6emtEDhLVIfYWjrjluvSWSG/Upu3AWFydXSN8G5+xiyo04jSYlRmFzXHuORSWtIObdTYTKfFnXL1ecX9acgEHWpLp+QHX32meTkxV+kJP02Lj3GP9POsJBRhuSTm6kqzhWkxaokNZako+uwCx6AfesBCpX9CGl1BTe/exdNfWN8R36htB340qwkbmyahoG1E74jlgkytCjzxnFCvx6OWEeftnMOYNGsjcJ1/Bt13aDGEvXde+iY2tBuzkG83pwnWCH7i4pMVotI7TQIilhbF/e+U+n46QmatOpD0okNXFjYm4zQX5S2cDawdqLt7P0YNHHjxsappF05pBS9f8Y26GVsg/qRGX6E5JMbla5fS88IrzfnU5aVxD2B0nJce0/GwMaFuH0LFFr87dx1NFoGpiT8tlphMg1sXLDx70XKhd3UVKimYD47+iyxuz9RuNy807HcmbsPiUSicNlqngz1FUYgRCIR3sMWP7Oc5JOb0TVtgl1wv6eWURdlWCtIlKGi4AH3znyHbXB/zFz8FSpbJq0lZudcLFq0x2vIpwqV/UduHVpKaWYiwdO3KW3Xu6qkgPB1E9DQ1CJw6ia09I0VKv9RhOruzyswdQ0gYNJ6pRc8S2uqST61ieTjG9DQ1MZr8HwcO76l1G5UikIuk1JZlEN5XhrlealU5KU9vF+XolKecx+xth5iHT3E2voPb/UQ6+ijqf34OW0jCwysnTFzC1SooyiX1iLSEO4rvTwvjeK0W5RlJ9edEInqDkDEo/ui+qf++FjbwAxNfWOM7JuhZ27f6GtW9Mya4DdmBY6dhnLr4GKid7xP6oXdtHjjY0EaGPwZHWNL2rzzPZFbZhC76yOqJDm49Zmi9N+rc7fRSFJiuPvr1xg7emPl1VGp+q19umLj34vEY+uwDXoJfUtHhcoXa2njPXQxoauGkXhkLZ6vzVGIXC09Q1x7TuDOz19SmHRDYROoXXtOIDvyBGmX9uPac5xCZD4JNr7dsPHtxsX13ZEpMDJg3t0X8+6+lL2xXWEy1TwdaqehAVN0P4qChFA8X//wmVop5t+5SmFSBF6DP1V4lOHOzysAaD5wlkLlAqT+vpuSB3dw7zddsF3pzBvHSbu0H5eeE/6PvfMOb7r8+vCdpHvvvQctXWwoe8gQlA2KIIgIiCKiIg5AcCKIiCgoAioqgiIoeyp7t9AW2tKW7r33bpPv+0eFFxV+QvskDHNf1/cqTZvPOSRp8pznOQOb1t3VYuPvKOtrubjmOerKC+jy8kaMrF2E6kuSxNXdK8m79AeOHR8heOISjRc8F8adJvand6jKT8GxwxD8R7+JgYW9Rn1oDpIkUZlzlfKMWEpTo6guSKemMJPq4kykxobrvyeTKzCwdMTIxhVDG1cs3INprK9BWVfddNXX0FBZQk1xNsprt9fXoGqoQ9/CnrrSPOS6Blj7dcUuqA+2Qb0xtHJqme+C05NqinMoTjhLUcI5ihPOUVOcBTIFSMo71jJzC6I8PRoAhYExpo6tMHX2w9S5FaZOTV/VkZqnbiy929P1ta1knfuN+B3LOfPRWNx6PoHv0JfUPlVdx8CYDs+tIXrjfK7u+pTa0jwCH1+o0aD82uZYZfZVor6dQ7fXt6kthfRWtB67gMIrp4j9+V06PL9OeOBk5dsJ1+6PkXr4Wxw7PSpsFoJb7wmk/PENV3d/SufZYlKKLDxCsPYLJfXwt7j3majtPqdFONqg4R4m+dB6dAzNcO3+WLM1rtUy6FvY49JtrEDvmiYz54Tvxnvw8y1e8Pyd2rJ8ru5eiU1AT+zbDBCqfY2aoiyif1yAuXswrYbOVouNvyOpVFz6/nVKUyNpN/UztexKJh9YQ9K+L3DrNYGAx97SaMFzXXkhcduWkB22EyNbNzq+8LXGdx/vlPqqUoqunKLgykkKr5ykrjQPM9dAqgvTMbJxw9TZD7s2/TGycW26bF0xsHRErtC9Y1uSSkl9dQXl6ZcpiD5KfvRRCqKb8tJNnFrh+8gsHNoNatb/o6XpSZJKRXFiGNlhOylOOEf1n+0bdY0tsPLtjGf/KVj6dsHY1r3pAOF6D3upqZ/9X75v+tr0rYSqoZ7qogwqsuOpyIqnIiuB3Iv7yDj503X7BhYOmDq3wsTJrymgcGqFiYPXPT97QiaX49J1NPZtB5G0/0tKUyI49eEI2j7zKZZe7dRqW67QJXjSUvTN7Ug+uBYkiYDH32rWa7O56Ogb0W76Kk4vHcXFtS/Q9dWf1ZJqeSsMLR3wHTqbuK2LyY04gGP7h4Xb8Bs5l7zLh0na/yXtnlkp5D1VR98I70EzuLL1A4riz2LtFyrAU/Aa+Cxhnz9N9vntLVo7tISm4W7392milpujDRruUaryUsiLPIj3oBnoGJg0W0ddpwySSsWVrYvRN7fDa0DLujrdjPjfPkLZWNe06FXDkbtK2UjUhjlIkpI2U1ZobGGSsGsFuRf34TfytWYvDv8XaUd/IGHnCpw6D9NowCCpVGSc/Jn4HR+jrK/Fe/BMvAc9q9HFw9/9USkbbnrColI2UpZ6icIrJyiIPdlUjCip0DE0w6Z1d2xa98DavxuGVk7CX3syuQJ9EwtsA3piG9CT1mMXUJWfQkH0UQqij7boddiS9KTyjFhifnqHhuoy6soLsPLphFuvCVj7hWLq5CfkdaRvbvOXRbQkSdSV5V0PIq4FFIVxZ5CUTac6MrkOjp0ewdTJD8cOj2Bo5dhiP9SFrqEJ/iPnUpZ2mYj1szn3yQT8RszB46Epak0bkslk+I14FX0LOxL3rEJZX0PIUx9pNFXJ2M6dNpM/5sKXzxKzeSHBk5Zq1L577yfJOvsbV355H5vWPdA1bP5n5s3QNTLHf+TrXPpuLnlRh4S9d7v2HEfy7+u5uvtTrFptFvKYWft3w8w1gJRDX+PSdfR9mQ6q5d5FGzTco6T8/jVyhS7ufSa2SEddpww5F/ZQlhpF8MQP0TEwFqpdfDWM7PM78R78PMZ2HkK1r5G4dzUlSRdp8/RyjG3F5sHeiozTW0k+8BWuPR7Hs7/4fNOss78Ru+U97EIeInjiEo0FDOUZsURvXkRZahRWrUIJHLcIEwdvjdi+GcqGOs6vnERjbRU95u1EJpdTU5xNYewJCq6cpCjuDI015SCTY+ERgs/g57EJ6Im5e7DGuw/JZDJM7L0wsffC86EpLdJqTnpSQ00FV3d9StqxH9EztqDV8Fdx7jIcuY76d6plMhkGFg4YWDhgG9j7+u0qZQNVealUZMdTnhlHSdIFss/tIP63j7D06YRTp6E4tBuk9vSf5mLuHkz3N7dz+Yc3ift1KcWJ4YRMWqL29CuPPpNorKnk6q5PMbCww2/EXLXa+zt2wX3xGfICiXtXYe7RBvfeEzRmW67QIeiJdznz8WNc3b2SgLHzhdtw7PgISfu+IHHPKuzbDBDy/qrQ1cfn4eeJ+WkRhbEnsA3s1WJNmUyG18DpRH79EnlRv6tlc+o2vBBa06Dl3kEbNNyD1FUUUZmThHOXUS0qzC1JjqCxphLvwTOFnjKolI2kHfsR+3aDcO4yUphuk3YDMT+/g6GVM96DnhWqfY2ihHMk7f8C59CROHUaqhYbf6cw7jQxmxZi07oHAY8vFL4Llxt5kMsb52Ht15W2z3yqkcVvY20lV3d/RuqR79EzsSRk8sc4dRp6V4tcJUkietNblCZHAHBx3YtU5SZeL941sHDAod1AbAJ6Yu3XFT1ji7vmq2hUyttPT5IkiezzO4n7bSn1FUW49XiCVsNfvifqCuQKXUydfDF18sWpY1MDiKr8NHLCd5MdtpOYzQuJ3fIetgE9cez0KHbB/dDRN7rLXv8VXSMz2k1fRdqR74n77SNOLR5B26mfqr1I2vvh56gtzSP54Dr0ze3x6DtJrfb+js+QFyhLu8yVrYsxc2ktrMD3drDwbINbjydIO/oDzl1GYO4WKFRfrtDBe8hMLm14ldzIg8LSoFy6jSb50Dqu7voUm4CeQt4/HdoNwsjWjeSDa7FvO/C+bzyg5d5BGwreg+Re2EtJ8gXcej3RIp20YxupKcnGWfDCOPfiPkqTL+LcaZjw3ey0Yz9SmZ1A6zHzUOgZCtUGqK8sIWrDqxjZuhPw2ELh+jejIieRiHWzMHbwou3UlcLzjQuvnCLym5cxdw+m/bNfaKToOTfyEMffHUzqke9w7fE4vRbtx7nzsLv+4ZR8cC3Z57Zf/74o7iSGVk74j36DHm/tpc8HTYPkHNsPfqACBgBDa+fb/puJWP8il76bi6GlI91e20rgE2/fEwHDrTC2c8dnyEx6LtxPtze349F3EmUZMUR98wqH3+hG1Levkh99FJWy4d/FNIRMJsOj31OEztkEwNnl40k5vKGp/kONNgMfX4h9m/5c2foBORf2qs3WTe3L5bR5+mMMLB2IWP8idWUFGrXfavgr6JlaEbPpLSTVnRfs/xtOHR/B2N6TxL2rhLXYlevo4TNkJmXp0eRf+kOIpkyuwLP/VMrSLlOccFaIphYtoA0a7kmyzu/E1NkPM9fWzdaoKysg9+J+XLqOFpo+JKlUJB34ChNHX+xCHhKmC/9f/Gwb2Au7Nv2FakPT7urljfOoryim7ZQVwtOqbkZdeSEXvpiOQteAjs+vFd4FqiTpIhe/eh4Tey86zlyn9v+TStlI7Jb3ST3yHXomloS++jNBT7xz1xecDTWVRKyfTcKO5X+5XVlXjd/IuXg+NAVTR5+7HtSok5rCTJQNtbf1uw5tBxL4xLt0nbsFc/dgNXsmDplMhrlrAP6jXqfv+8fo/NJGnDoNpSDmGBe+mM7hN7oTs/ltihPDNT5w7FZYeLSh+7zt2Ab2Im7rYiLWvkBDdbna7MnkCto8/QmWXu2I+m4uRQnn1GbrZugamdN++moaqsuJ+Hq2RgM5XSMzWo+eR1l6NOknNgvXl8kV+Ax5gcrsBHIjDwjTdeo8HCM7D67uXinsdescOhI9MxuSD64TonenqCS58EvL3Uf7LNxjVOWnUZYahVOnlg0yyzi1BUnZgFuv8YI8ayI/+giV2Ql4DXpW+ClD/K8foWqsp/VY9RQ/px//kfxLf+A34lXhR9c3Q1lfw4U1M6grL6TDc2uEd5gqz7xC+BfT0De3U9s06RtpqKnk4poZpB39HjMXf7q+tk34bI47pbookytbF3Nkfk9yL+4DmRyFvjG6RubIdfSQyXWoryi+qz5qCrmu3m1PxnXqNBS3nuPu6yJJmVyOdavOBI1/j35LTtF+xhps/LuRefZXzn0yngtfPkvOhb33RPCga2RO+2e/wH/0G+RfPsKpD0cInQb8dxR6BrSfsQZjW3curnmO8sw4tdm6GWYu/gQ/+QElieHE/fqRRm07dnwEa/9uJOz4hNqyfPH6HYZg7OBN4h6Bpw0KHXwfmUVFVjy5EfuFaCp09XHvM4nasnwqsq8K0dSiRRs03GNkh+8CmQzHjs0f5qZSNpB+YjM2AT2FFhJLkkTS/i8xtHHFscMQYboARQnnyQ7bideA6RjbuQvVhqYFdty2JdgG9saj32Th+n9HUqmI+u41ytIu0/bp5cJ3c6vyUgj7fAo6BiZ0fnGD2ofSVRdlcnb5OAqvnCLwiXcJGLsAhQaKZW+GJElNJyzrZnFsYX/Sjm7ELrgf3V7fxuDVcQxcEUH/j8MY9Fk0gz6L1tj8jbuNXOf2g4YHDbmOHvYh/Wj7zKc8tPQ0wRM/pKY4m8ivX+LUkpHkRx9Va1rQ7SCTyfB8aApdXvkRSaXkzPJxpB39QW1+6Rlb0HHm1ygMjAlfPZWaoiy12LkVTp2G4t73KdKOfEd22C6N2ZXJZASOextVYz1x2z4Ury9X4DNkJpU5V4Ut8KEpGDFzDyEv6g9hrwnX7mOpyksh8/QvQvRuF+nPQmjtScODh/ZZuIe4Vpxo5dO5Ra0F8yIPUVeWj3vvJwV619S+tSz1El4DpwkttFUpG4i9Xvw8XZju/+s3EvXtHHSNzAmetEQjKSrxO5aTF3EA/1GvY99W7JyJmuJszn82GSSJTi9+i6G1s1D9v1OSHMGZpWOoLcml4wvrces5Tq32boVK2UB2+G7OLBvL2eXjKIo7g9eAZ+j93uFbBmaanFFxt5Hr6KFqqLvbbtx1dAxMcOk6mh7zdxLy1Ec01lRw4YvpnPtkPMWJYXfbPSy92tH9ze3Y+Hcndst7RH79Eg01FWqxZWjlSKcXvkZZX0PYqmeoryxRi51b4T/qNSx9OnJ543yNnnYY23ng9fAMcsL3UBB7Uri+Y/vBTacNe1cJq52QyRW49xpPTvguiq+eF6Kpb2qNXXBfssN2oVI2CtHU8t/mv/OJeh9QlnaZ6vxUnDq3LDUp7dhGDG1chbRvu5HkA2vQN7fDucsoobppRzdSmXOV1mPnq6X4Wa7QofWY+bR5ejn6ptbC9aEp4Lu2O5QbcYCUQ+tw6zkej35PC7VTV17I+c8m01hbSadZ32Bi7yVU/+9kh+3i/KcT0TE0oevcn7Hx1/yufUN1OcmH1nNsYX+ivnmFhupyAh5fRN/Fx/EbMRdDSweN+3Qv8l8+abgZMrkC5y4j6LVoPwHj3qa6MINzn0wgbNUzlGXE3lXf9Ews6TBjDX4jXyMv8iCnl4xUm0+mTq3oMGMNNUWZXPhyBsr6GrXYuRlyhS7tnlmJrpEZEeteoKG6TGO2m06tPYn9+W2U9bdX63O7yOQKfIe8QGVOIjkX9wnTdewwBF0jc9KPbxKm6dxlBPUVRRReER883RIJVJJM+KXl7qMNGu4hss/vRKaj26K+yuWZVyhJDMe99wSh+colKZEUxZ/Fs/8Uoe1ba0vzuLrnM2wDewsvrL4Rm9bdhU3cvBl5UYc49HI74nd8QuSG13DsNJTWjy0QeqrRUF1G2OdPU1eaR8fn12HmGiBM++9IksTV3SuJ+nYO5h5t6Dr3F43PXpBUSlIPf0fk1y8T/9tHGNm6037GGnot3I977wn3XJvNu01T0HDvdA+6V5Dr6OHeazy93zmE34i5lKVe4vSHI4hYP5vKP1vx3g1kcjleA6bS5eWNKBvqOLvsMdJPblGLLSvfTrR5ejmlqZFEfvOKRned9c1taTf1M2qKc0jYtVJjaWIKXT0Cxi2iuiCdpANfCdd3aD8YE0cfEveuFnbaoNAzwKXraPIiD1FbmidE0zawF7rGFn/pKqdFS3PRBg33CCplIzkX9mAX1BddI7Nm66Qd24hct+mNRyTJ+9ega2yBa/fHherG/fYRUmMDrdU0+VlTlCZHoqyvJvnAGqSGGly6jRGawtVYW0X46mlU5iXT/tkv1Nr/XFlfS9Q3L5O4dzXOXUfT+cVvNT5IqzwzjjMfP86VrR+g0Dek+5vb6fLS99iH9PtPpRzdCXdSCP1fRKFniNfAafR+7zDeg2dSEHOME+8O4fIP86gpzr5rfll6d6DHvB1YtepC1pmtxP22TC0La4d2gwh47C3yL/1B7M/vaLTGw9K7Pf6j3yT92EZywvdozK6NfzecOg0j+eBa4QGiTC7HZ8gLVOUmkXNB3GmDa88nkFSNZAiqQ5Dr6OHU8VHyon5Xa9euG5HQdk96UNE+C/cIRfFnqK8oalFqUn1VKdnnd+HceZjQTjrlmXHkXz6MR9+nhLb0LEo4R07YLrwGTtPYVGZ1UVOcBfx/0BO+aqqwdANlQx0Xv3q+qah6ygq1FvbWlRVw7tMnybm4D78Rcwl+cjFyHXEnS/+GsqGOhB2fcHrJKGoKM2nz9Ce0m/a5Wk9VHhTkOvramobbQNfQlFZDZ9P73T/w6DuJ7LCdHHt7ALG/fEBdRdFd8UnPxIoOz63FzDWAlEPriN60QC1zBtx7P4n3oBlknPyZxL2rhOv/T9u9nsDcPZgrWz+gvqpUY3b9R7+BlU9HkvZ9IVzbod3DmDi1ElrbYGznjk1ATzJO/izsRMipywhUjfVCC7e1/DfRBg1qQpIkon9cQN6lw7f1+9nnd6BjaIZtYJ9m28w6sw1VQy1uvSc0W+NmJB/8CoWBMe59xBVWXy9+tnbBa6B6Jj9rkoqseJr2V5owsnVrUf1EU5u8BCRJImHXSorizxD85GIc2g4U4O3NKc+M4/RHY6jMvkq7aZ/jNXCaRk9/ihLOc/KDoSQdWINT56H0XLgPp06P3tcnUJpEW9NwZ+ibWtN6zDx6vX0I5y4jSD+2kVMfjiT1yA93pU2rXKEg4PFFeA9+nsxTvxD59csoG8Q/n77DXsY5dBSJez4n4+TPwvVvhUyuIGj8ezRUlRK/fZnG7Oqb2WDp3Z7s8zv/fJ8Wx/XThrxkoYP03Ho+QV1pHvmXb2/98G+YuwdjbO9FlppTlPIuHSb6xwVqtaHl7qINGtSETCYjaML72If0+9ffbayrJi/qdxzbP9zsegFJpSTt+CYsfTpi5tL8oXB/pyo/lZwL+3DvNV7o6UV22C4qc1P+LH42EKZ7N5Akiaq8FAAU+kYEjHubHvN3YWBh32zN2J/e4eT7Qzn36URSf19P6zHzcA4dKcrlf5B/+Qhnlz+BpFLS5ZVNag1O/k5DdRmXf5zP+U+fRFI20mnWt4RMWqrxlCjR1JTkkrDjE04tGUlNcY7a7WmDhuZhaOVI8IQP6PnWXmwDenLll/cIWzVFWE75nSCTyWg19CX8R79BbsR+Lq6ZQWNdtXAbQRPewzawF9GbF5EnaArx7WDmGoBHv8lknvpFo52s3Ps+hcLAmER1nDa0HYipk5/Q0wbboD4YWDoKK4iWyWQ4dxlBSWI41YXpQjRvhn1IP4ImvA/IkCS58EvL3Udc0rWWZpN/6Q+UddU4tmCgW0HMcWoKM/AbPkegZ5B8aB1yHV2hXYCUDfUk7PgEu6De2AX/e1B1r5N5ZhsgYezoQ9c5m4UEV6WpUYBEydXzKPSNcOjwSIs1b6QyNwlJ2Yipsx+pR3/gyi/vY+YSQIfnvsTAQjPdiCRJIjfiAFe2vEddRRGe/afi++gstXTQagnKhnpqCtOpKkijuiANSaWirrwAHUNTdA1M0TFq+opMTmNdFcqGWgpjT5IXeRCkph3r6sL0FrVRvh3kOno0VGm2peaDhLG9J0ET3sfCI4QrWxdz8oOhBE/4QHjL5NvB86Ep6BiaEf3jAsI+e5qOM9cK3bSRK3RpO/Uzzn86kdKUSOzV2ITi7/g8Movci/uJ/vEtus/bKbSxxq3QM7bAo/dEkg5+RUVOIqaOPsK0ZXI5Po+8QMS6WWSH78G5hd0Poanjn2uPcVzdtYKqvBSM7T1brOnUeRgJu1aQdX4nvkNeaLGeJik7Ekn5kUgsyzTXfUvLzdEGDfcA2ed3YmDpiJVPx2ZrpB3biL65ndAPuJriHLLObse1x2NCh4dln9/RNEeiz8T7PvWkpiSXuG0fYuXbmc6zvxdSpFtfVUrdDZNMlXXVnPtkPL3ePijs8br41fNUF2bg2OERqvJSsA8ZQMjkjzTWkai2NJeYn94h/9IfmLkG0OH5tRqZ0v2/aKyrpjwtmrLMWKrzU6nKbwoSaoqzry/+ASy82lOReeWOWlfqGpqqw+W/oNDVR6mtaWgRMpkM1x6PY+nbiahv5nBx7UxcezyO/+g3Nd6ty7XbGHQNTYn89hXOffIkHWd9jYG5nTB9HX0jury8UeNBuo6+EYFPvE346mkkH1qrsQWsx0OTST36PUn7v6Tt08uFatu3GYCpsx9Je1fj2GGIkCYYrt3GkLh3FeknNtN6zLwW6xlaOWHdqgvZ53bgM3im2j97VYjTN+3bDtO+7dB98nNhmlqahzZouMvUVRRTeOUknv2nNHvBWZWXQmHsCXwfnY1cIW5Kb8of34Ak4dV/qjBNSaUk5dB6zFwDsPbvJkz3biBJEjGb3kJSNhL05GJhXX3KM6785XuFniGuPcYJe5OvLsy4nk6VfX4H5u7BtJu2UmiL3lshqVSkn/yJ+O3LkJSN+I18DY9+k4V2mroTakpyKbh8hPzLf1AUfxZzjxBKEsPRMTTF2NYdC8+2OHcZjpGtB8Z2HhjZuaNnbAE01eU01lTSWFtBQ00l1flplKVdpijhLOXpMdxY45Idvgc9U+sWpaz9GybOrWio0Ux3FHVRkhKJsrbqrk/xNrH3ouvcn7m6eyXJh9ZTnHCeNlOWY+4WpFE/HNoNoqPhWi5+NZNzn4yn06xvMbJxFaZ/t071bAN749hhCEn7v8SxwxC1z5uBpmJzt57jSfnjG3yHvCBk9/4a12obItbNIid8D85dhrdYU9/cFoe2A8g88yuthr0s5Lly6jKCy9+/QWlKJJZe7Vqsp+W/hzZouMvkXtiDpFLi1ILUpLTjm5ApdHHt/pgwv+oqisk4+TNOnYcJnTicF/U7VfkptH3m0/v+lCH73HYKYo7Resx8od2fEnauAJoKB70GPYtHv8nXF6oiKIg++pfvy9Iuk3NhL06dhgqzcTMqchKJ2bSAkqSLWPt1JXD8exrvmiWpVJRnxJB/+TD5l49Q/meHK0MbV1x7PoFtYC/MXAPRM7H819enXKGLnonl9doLc9cAHDsMBprSkWK3vH/9sU45tI6U379uagHZZQQO7R9GIbgrlY6BCUVxp6ktzRWq21JUyqcoDV8AACAASURBVAbqyguRy3XQM7X6n8Fp1LdzqCnMoPWY+Xj0e0qDXv4TuY4efiPmYtO6J1HfzeXMssdpNfQlPPs/o9G2vzb+3en84neEr57K2eVP0GnWt5g6+WrMvrpoPWY+BbEnidm0iM4vfa+RzwPP/lNIO7aRpANrCJm0VKi2fZsB2AX3Iy/yIE6dhwn5/7j1mkDOhb1kh+/BtduYFus5tB1I7E/vkHVuu9qDBm0NwoOJNmi4y2SH7cLU2Q9TZ79m3b+xtoqsM9twaP8w+ua2wvxKO7wBVWMdXoOmC9OUJInkQ+swsnVr0QC7e4Ha0jxif/kAS+8OuPeZKEy3JOkiZalR6Fs60v3N7eiroRg49cj3f/lerqOHSqneoWBZ53aQdX47lTlJBE9cgnPoSI0Fjcr6Woriz1wPFOrK8kEmx9KrLa1GvIpdcD9MHLyF+mNk40bH59dSFH+WytwkbFp3J+vcDrLPbSdu24ekH/+R4CcXCx2Yd20RUJIcKUzzdpFUSmpKcqjOT6MqP6UptevPrzVFmZh7hFCaHAEyOXqmVuib2aBvZvvn1fRvgJrCDIA/23KW4Pvo7Lu+uWDtF0qP+buI3vQW8duXURB7gpCnPtLoJHILzzZ0eeVHwj6fwrkVE+j4/DosPNtozL460De3xW/kq8RsWkjW2V+Fzxa6qU0zG9x6jiPt6A94D54pdNNCJpdjG9yHmE0LKU2NwtKzbYs1LX06YuLoS8bxTUKCBh0DE+zbDiAnfA+tx8zXSD2JlgcLbdBwF6nKT6M0JRK/EXObrZF1fgeNtZW49xbXDrWxtpLM89ubelALPDYuvnqestRLBI57RyOpMOpCkiRifnobVWMdwRM/FLbrWF9ZTOTXL2Fo40L3N7erJQ++JDmC6oI0AMzcgnDrNR6Hdg+ja2gi3BY0LSbjd3xCyqF1WPl2ofuC3RiqMUXnRsrSo8m9uJ/UI9+jaqhFoW+MTUAP7IL7YhvYB31TK7X7YO0Xen0Seauhs/F9ZBa5kQeJ+WkRpz8aQ8jEJcICaDOX1sh19SlNiRCidzvUFGeTengD1YUZ5N/QhUehb4SRrTtmrgE4dhiCobUzTp2GUVde0HSVFVJfXkBFdgL15UVIqn/2o0/a9wUpv39Dh5nrsPbtfFeDBz1jC9pN/YzMM9u48sv7nPpgKEET3tfo5oepUytC52zi/GdPc/6zp2j/7BfY3Ocpnq7dHiP73A7ifl2KbVBfjfxNeg6YSvrxTSQfWEPwk4uFajt1fJS4bUvIPLVFSNAgk8lw6zWe2J/foTT1EhYeIS3WdO4ykuzzOymIPqK216+EDEm6vzMJtNwcbdBwF8kO2wUyGY4dH73j+0qShEwmoyDmGA7th2Ah4A3qul/nd9FYVYZn/ynCNAGSD65Fz8wG566jhOpqmpzw3eRf+gP/0W9gbOchRFNSqbj03WvUVRbR9dUtagkYyjPjCF89DT1Ta9pM/ljteeMNNZVEffsKBdFHces5ntaPzRdac3MrSpIjSNq3moKY4+hb2OPcdST2IQOw8u1813fWZHI5ju0fxsKjDRHrZxGxbhZeA6fhO/TlFtd1yHX0MHcLbNrRVzMVWfEkH1p/fbqvU6ehBI1/DyM7T4zt3NE3t7vtRb6kUtFQXcqFNc9TmnzxLz9TNdQS9ulE9M3tsA3ohU1gL2xad9dIYfnfkclkuHYbg5VPR6K+nUPEulm4dB1D67HzhQ69/F8Y2bgROmczYZ9PIfyLabSf/gV2Qb01YlsdyORygsa/x8nFw4nbtpg2kz9Wu00Dcztcuz9G+omf8B78PEbWLsK0dQxMcGw/hJwLe2k9Zh46Bi3fjHHqPJz47ctIP75JSNBg7ReKvrkdWee23/cn/lo0jzZouAtIKiUyuYLCKydw7Dj0jlsxVuYlc2rxcEwcfCjPiMH7kReF7sJlnP4FI1s3zN1b/gZ1jfKMWApjT9Bq2CsodPWF6WqaurICYre8h4VnOzz6isu5Tv59PQUxxwl4fJFaughVFaQTvuoZFPqGhM7ZLPSD8lb2Lq6ZQVVeCgHj3sa913i12gMovhpG4r7VFMWdRtfEklbDXsGt95NqO0VpCYZWjnR5eRNXfnmf5IPrKEu7TJspn7Z4p9XCsz2pR79Dx0A9i+qaklxiNr1FQcwxFPpGuPd5Eo9+kzG0cmq2pkwuR8/ECtvAXugYmGDm4o+pi3/TvBlJojTtEgXRx8iNPEDmma3I5DpYerfHNrA3tkG9MXH01egphLGdB6Gv/kTi7s9JOvgVxYlhtHl6uZAF3e1gYG5Hl5c3cmnDXGJ+WoQ0Zj5JB9di5dMR/1Gva8QHkZg4+uA1cDpJ+1bj3GWkRorgPQdOJ/3UzyQfWEvQ+HeFart0H0vmma3khO/BtcfjLdbTNTTBqfNwss7+iv/oN1pc3yaTK3DqNIzUwxuoqyjWyOmOlgcHbdCgYerKCzm6oA/65nbUFGVibO+NStlwRzuwqoY6VA11lGfEAJC0dxW1xVmETPywxf6VZ8RSnh5N67ELhH4QJx9aj8LAGDcNLB7VxbW0JGVdNcETFwtLsSpODOfqzhU4tB+slsentjSPsM8mo1I20GX2JrUHDEXxZ4hYPxuATrO+vZ6eow4kSaIo/jSJe7+gJDEMPTMb/Ee9jmvPJzTeIvNOUejqETT+XSw82xCzeRGnl4yk3bTPsPBofq66hVdb5Cc2IamhRqW+qpTwL6ahrK3C99HZuPWeILRA32fw8ze93cTRB5fQUaiUjZSmRFIQc4yCmGPEb19G/PZlOLQfgqV3e1y6jdHYcy5X6NJq+CtYt+7Bpe/mErbqGQLGzse5ywiN2NcztiDkqaUcWzSQi2tnAlBXlndfBg0A3g/PIPfCXmJ+WkSP+bvVPvDT0NIBl65jyDy9Fe+HnxM6Q8XCs21THcLpX4QEDdA0ITrjxGayzv6K50MtzwBwDh1Byu/ryQnfjUffSQI8/BuSthD6QUX7rN6ERYsWMXny5H/cvmXLFqZPn86yZcsYO3YsBw4cuGNtmUIHVWM9NUWZAGSd2crRBf3uqL+6ofXfWu5Jqr/09W8Jmae3ItfRw0nAgJprVBemk3NhL249xqFrZCZM92YoG+qJ+3Up1X8+viLJvbCXvKhD+D46W1gBa13Fn3UM1i4ETfhA+I5pfVUpYZ9Pob6yhI4z1wsdanQz0o41FWvqm9nQ9bWtagsYJEmiIOYYZz9+nLDPnqa6II3WYxfQ593DePZ/5p4PGG7EpetoQl/9GZlMztlPxpN+8ickSfr3O94E28Be6JpYoayvabbGrYj7dSlVOYm0m74KnyEzhQYMt4NcoYOVT0f8hs+hx7yd9F18nMDx79FQWcyVX97n6II+XN39GfWVxRrzybpVZ3rM34l9cD8uffcayYe+1pjtrHPbabyhxW5tSa5G/+8iUejqE/jEO1QXpJO4b7VGbHoPnA6SRMqhdUJ1ZTIZLt3HUpZ6ifLMOCGaZi7+WHp3IP34ZiSV6t/v8C+YOrXCzDWA7LDdArzT8l9CGzT8jfPnz7NkyZJ/3P71118zZ84cVqxYwdy5c1mxYgWPPfYYJ0+evCN9PWOL651CrmHi6I38Dtov6hqa/KVns6VPJ9pNa/nQE2V9LVnnd2LfdqDQBUHK798gUyjw6DdZmOatyA7bScrvX1OVlypUt66imJgt72LuHozHQ2KmYzfVMcyloaqUdlNXCk+jaaytInz1NKoL0ugw40u1pk+olA1Eb15E7M/vYBPQi9BXt6ilnaokSeRF/c6ZpaMJXz2N2rJ8Asa9Te93/8Cj7yS171CqC3O3QLq98SvWrUKJ2bSQ6I3zmzWoTaFrgNeAqUjKxqaJ1IIoij9D1pltePZ/BnPXAGG6LcHAwgG3Ho/T+aXvCZ3zE5beHUjcu4qjC/oSu+U9aoqyNOKHrpE5QU++j2OHIcT/tpSrez4XHrDdjPzLR/5xW1latNrtqgtrv1CcQ0eRcuhrKrLi1W7P0NoZ59ARZJzaQm1pnlBt587DkOnoknn6F2Garj2fAJmMkr/V/TQXl66jqcpLVtvfiSTJhF9a7j7aoOEGqqurWbVqFZ07d/7L7RUVFbz66qtMnDgRY+OmgjcXFxcGDx7MrFmz7tiOyQ3tVc3dg2k/fXWzd5jNPdrQ6YX1QnZW86IO0VhTjmv3sS3WukZdRRGZZ7bh3Hm4WgdbwZ+D4w6uw8w1UHhebOKezzFx8CboyQ+FDSJLOvAVhbEnaD12AWaCF2LKhnourp1JeXo0bZ/5VK0pQvWVJYR9NoWME5vxGjiNDjO+UEsdQWHcaU59OJyLXz1PfXUZQRM+oPfbB3HvNf6+rpO5hp6JJR2fX4v34JlkntnKpe9fp6og/Y513Ho8jkyuIG7bEpT1tUJ8y798BCNbN3w0NL33TrH0bk+HGV/SY8EeHNo/TPrxzRxb1J+oDa9qZAEqV+jS5unlOIeOInHP58T/tkztgUOnF74meOISjGzdr9+WdHCtWm2qG/9Rr6NjZEr0poVCdtT/Da9BM5o+N34Xe0KkZ2KFQ9uBZJ3bIexv0L7NAOrK8sk+t12InrV/NxprysmPOSZET8t/A23QcAMLFy7krbfeQqH4a676gQMHKC0tJTT0rwuv0NBQIiMjiY6+s90dSdnUXlDX2IKOM9ffcecNSZKQkJDrGdLl5Y3CpnpmnPoFQ2sXrHy7CNEDSDv6A6rGejwHiJsqfSuuDY7zGjRdaJpPVX4qGSd/wtTZDzPnVkI0ixLOcXX3Shw7Pios7/UaKmUjUd++QlHcaYKe/AD7Nv2F6t9IRfZVTi8dTWlKBCFPLcNvxFzh7XTrK0uI/OZlLn33OqrGBkImLaXXogO4dh97Ryd09wMyuYJWQ2fT4bm1lCSGc/rDEeRc3HfHGgp9Y2qKs4QthlqPmUfXuVvu+ZMcUydfQiYtpfe7f+DeZyJ5Ub9z8oOhhK+eRvHVMLUu5GVyBcFPLsat9wRSfl9P7M/vqHXhK9fRw6XrKHot2k/IU8tArqA0+SKVeclqs6lu9Ews8R/1BjKFgtyIO3vdNwdjWzecOg8j/cRm6soLhWq7dBtLY005eVGHhOjp6BtiF9SH3KhDqJT/bFF8pxjbe2Fo40pBtHqCBu1Jw4OJNmj4k3379uHt7Y2v7z8nbUZENLUwdHP7a7qFu3vTDk9YWNgd2ZL/+cHb+aUfrk+TvRPKUi+hqq8l8PFFwnZYqwrSKU44i0u3scLmDjTWVpJ2dCP2If2Fznu4GZIkkXTgK4zsPHBoO1Co9tXdK5Hr6OHz8M0LNe+UuvJCor55BWNbd4LGvys0wJEkiZhNC8mLPEjrMfNxCVVfe9u8S4c5s+wxVA11dHn5R5y7DBduIz/6KCfff5Tciwdw6zWe7vN24hw6Uthpz72KXXAfur76MyaOPkSun03M5rfvKF1JrqOLQ7uHSTrwFTXFOUJ80jO5f7qsGFo50nrMPPq8fxTfR2dTmnaJcysmcPbjx8mLPKS2xbxMLifgsYV49m+aBXB545tCFnj/26YC5y7D6fPuH+gYmhKx9gUaa6vUalOdOHceTkN1OYl7V2vktMF70AxUjQ2k/P6NUF3rVqEYWruQcUpcipJ9u4dpqCyhJDG8xVoymQy7wN4UxZ8Rdhqi5cFHGzQARUVFbNmyheeee+6mPy8sbNqBMDX9axtDE5OmFIy8vH/mQ0qSxKklo65f6Sd/uv6zmvw0bAJ6YtbMKdA5F/Yg09HFvu2AZt3/ZmSd3goyOS5dRwrTzDi1hcaacrwGipsqfSuK4k9Tnh6N14CpQne6yzJiyQnfg0e/ycImbifuXYW+hT1tp64U0sf7RpL2f0nmma34DJmJRz9xLWFvRJIkkg6u5eJXz2Fs70nX17cJn07bWFvJ5R/nc+GL6eiaWNL19a34DH4OhY765zzcKxhaO9PllR+bFqAnNnFm2dg72kX2G/UaBhb2xP26VO0Tv+9V9Iwt8Bkyk77vHyXg8YXUlRdyce1MLv3wJqWpl9RiUyaT4TdyLj6PvkjW2d+I+nYOqsZ6tdi6EUMrJ9pOWUFlbjKXN87TSF2FOpDJ5XgPmkFlTiJ5l35Xuz1je0+cOj5KQcwx6qvKhOnK5HJcu4+lOOEsVflpQjRtA3sh1zUgN/LOm7DcVC+oD6qGWoqvnm+xVvrJn66vd0AGklz8peWuo30WgDfffJMPPvjglj/X12/azf/7jrD8zx15Pb1/pkjIZDK6v/Hr9cutxzigaQp0VX4KdkF9muWrpFKSc2EvdoG9hQ04UikbyTz7K7aBvTCwcBCj2VhPyh/fYuXbRfiC8mYkH/gKfXM7nDqLbXmYsGM5usYWwtKrihPDSD++CbuQfpi5+AvRvEZuxAFSDq3Hc8BUfB55Uaj2NZQNdVzaMJeE7R/j2GEIoa9swtBSzGvmGkUJ5zn5wTAyT2/Da+A0ur3+6z1TfKtp5Apd/Ee9Rofn11JbksvpJaPIOrfjtu5rZO2C95AXyL24l8s/zNPIru29ikLPEPfeT9Lr7YMET1pCSeJ5zn78OAm7VqplQS+TyfAd8gL+o14n9+I+Lq6d1azC9jvFxr8brYa/Qu7FfaQe/lbt9tSFQ/uHMbJ1I3n/Go0EP+4PTaYy5yo54buE6jqHjgKZXFhBtI6+EbaBvYSdlln5dkaua0BB9NEWa7n1GHd9vaPlweU/HzR899139OvXDyenWw8ncnBoWhRVVlb+5fZr3zs7O9+2vYI/i45smznFszgxnLqyfBw7PNKs+9/cp+PUleXj2k1cAXR22G7qSvPwGjhNmOatKE2Noij+LJ4PPS104m9RwnkKY0/gNXC6kABNUqm4svVDDCwc8Or/jAAP/5+aoiwub5yPsb0nrYa+pLZhV5c3ziM7bCe+Q1+mzdOfCM1xV9bXcmXrYs6vnIhMLif0lR/xGzH3rk9xvhewC+pD93k7MXMN4NJ3c7n8wzyU9TX/ej+XLsPxHfoS2ed3EL9d/dN273XkCh1cQkfR/c0dOHUeRtK+1Zz+aKzaiqU9+z9D4Lh3KIg+woUvptNYV60WOzfiNWAa9m0HEv/bMooSzqndnjqQK3TwGjidsvRoCq+cUrs9C7cgTF1ak3VG7ILXwMIeu6A+ZJ39Tdhpn0O7QdSV5VOa0vLJ7wo9A6z9upIfc+y+PZnSoln+80HDhg0bmDp1KiYmJtevEydO8OOPP2JiYsLixYtp3749ABkZGX+5b3p6U2eTtm3b3ra9gphjGNt7YWTTvHaUOeF7UOgbYRvct1n3vxmZp39Bz8wG2+A+wjSL4k5j4d0Bm4CewjRvRfKBtegamQstKJYkiYQdy9E3t8O995NCNLPP76A8PZpWw+cIK16HPwufN8xBkpS0mbJCrcXBPkNm0v7ZL/AZ/JzQwKQs7TKnlowk9fAG3Ho+Qfc3d2Dp3UGY/oOAoaUDnWd/j/fg58k8u40zyx6/rT7w3g8/d704N+WP+3f3WSS6RmaETFpK+xlfUldewKklo0jav0Yt9QduvZ4gZNJSihLOEb5qCg01FcJt3IhMJrveVSny65eoKclVqz114dR5BPoW9iQfWKMRey5dR1OWHk1FdoJY3e6PUVdeQMHlo0L0bIP6ItPRJTdCVIpSb2oKM6jKTxGidx1JJv7Sctf5zwcNGzduJDIy8i9Xx44dGTZsGJGRkcyYMYP+/ftjbW3NuXN/3bU5e/YsnTp1ws/v9moTGuuqKU441+xTBpWygdyI/dgF9xM2vKq2LJ+C6KM4dxl5R1Op/xcVWfFkh+3EqdNQte14X6MyJ5G8qEO49X5SaH1A/uXDlKZE4PvILCG76Y111cTvWI65ezBOnYYK8PD/Sdy7ipKkiwQ98a5aZiPciIm9l9BuTCplA1d3r+TMssdorK2k4wvfEDju7TvuKPZfQa7QodXQl+g06xsMLB04vXT0vy52ZTIZAWMX4NDuYeK2fUh2mNgUjPsZ+5CH6LlgN/Zt+pOw8xPOLh9HZW6ScDvOoSNp+8wKSlMuEbbyKeorS4TbuBFdQxPaT1+Fsr6WyPUvomxQf02FaBS6enj1n0rx1fOUJF1Quz2nTo8iU+gKP22wDeyFvrkdGYJSlHQNTbDx70Fu5EEhpwN2gU3rEREpSloefP7zQYOzszM+Pj5/uQwNDTE1NcXHxwcrKyt0dHR46623+P7776mpaUoJyMnJYefOnSxcuPC2bRXFn0XVWI9tYPOChqK40zRUleLYUVxqUta57UgqJa7dxgjTzA7bhUyuwKHdw8I0b0XyofXIdQ1w7zNRmKakUpKw4xOM7Txx7jpaiGbKofXUleXjP/pNYd2poOk1lbT/S5xDRwkPRtRNTXEOZz56jMS9q3Hs+Cg9F+zBNqDH3XbrvsDGvzshk5ZgH/LQn4vdJ/5nkbRMriBk8jKsfDuTcWYrJUliBkQ9COiZWNFu6kraTllBdUE6pz4cQcrhDcJrQBzbD6b9s6upyE7g3KcTqSsrEKr/d0wcfQie+CGlKZHEbV2sVlvqwqX7WHRNLEna/6XabemZWDWlEoXtFNo44FpKXEHMcWGnPg7tBlFbnE1Z2uUWaxlaO2Pi6Cu29aqkbbn6oPKfDxpul9mzZ/PGG28wffp0PvroI2bPns3333/Po48+etsaBTHHUOgbY+XTsVk+5ITvQcfQDJvWYlJ+JEki89QvWPp0xNjeU5hmTvgerP27oW+q3haNNcU5ZJ/fiWv3x4Tayj6/k8qcq/gOe0lIa8+aklySD63Hof3gZj/3N6O+spioDa9ibOdBwGNvCdPVFHomlij0jWg37XPaTF6GrpHZ3XbpvkLPxIq2U1fSZsonVOencmrx8P+52FXo6tN22ipqi7KIWP8idRVFGvb43sax4yP0XLAHa79uxG1dzPmVk6guzPj3O94BdsF96fD8WmoKMzi7YgJ1FcVUF6arrfbAsf3D17tvZZ69/wpUdfSN8Ow3mYKY45RlxKrdnnPXUdSXF1IQc0Korkv3MSCpyDqzTYieXUg/ZHIdcSlKgb0pTgynsbby339Zy38abdBwE44ePcqGDRv+cfuMGTP44YcfeO2119iyZQsjR95+e1JJkiiIPoqNf7dm5ZwrG+rIjTqEQ9uBwgpDi6+GUV2QJrQAujQ5gpriLI3seqf80dRX27P/FGGayoZ6ru7+DDPXQBzaDhKimbBjOZKkxG/Eq0L0oOn1dOn7N6mvKqHtlBX3ZTqPQs+ALi9vxKGdmMf5v4hMJsOp46P0eGsP1n5dry92JZXypr+vb2JBu6mf0VBdRuTXL6t9hsD9hr65LR2eW0PwxCWUZ8Ry8oOhpJ/8SWiRqI1/NzrN+gZ9czvOLh/HifceJeyzybdV2N4cWg1/BatWocRsXqSRhbdo3HpNQMfAhOT96q9tsA3shZ6pNVmCAywjGzfsQh6iIiteyGtJz9gCa79Q8iIPCNGzDeqDpGygMO50i7Wa0LZcfVDRPgsaojLnKrUlOdg2s9VqQfRRlLVVQlOTMk//go6BCQ7txaUR5YTvRq6rr9YpxNC0y555agtOnYdiaHXrzld3SsbJn6gpzqLV8DlC0ohKUy+RfX4Hnv0mY2TjKsDDJtKO/kBB9BH8R76O2X3cjlTdNS//FQzM7ejw3FcET/yQsowYGqpKb7nYNXMNIHDcOxQnnOXq7pV3wdt7G5lMhkvXUfRYsBsLz7bEbFoopI/9jZh7hKCjb0h1fiqqhloklZKKLLEFuNeQK3Ro+8wK9ExtSD6w9r4b5KVrZIZ77yfJjTyglnqTG5ErdHHqPIz8y0eorywWqm0b1JvciP1U5iQK0XNoN4jqgnQqsv69GcK/YendDh1DU21dg5Z/5cEeq3oPkf/nH6NtYK9m3T/nwl70TK2xatVFiD8N1eXkRuzHOXSUsE4+KmUjORf3NRVqCx5a9nfSjv6Asr4GzwHiWro21laStO8LrFqFYtO6e4v1JEkibtti9Eyt8Rp088GBzaEsI5a435ZiG9RXaC3Hg44kSdQUZ1GZnUh1YTqSqhFJqUSlbPzz301fVcqm22/8XqFvhLK+BoWeAQpdw6avek1f5Xp//f7Gn9/4M7mOnlqDpKbF7misW4Vy7O2Bf04GP0Twkx/8Y/6KS9dRlCZfJPnAV1h4tsU+5CG1+XW/YmjlRKdZ31J45RTWgt53r5Gw89N/5JCXZcSobaaNvqk1QRPeI/zzKZg6+eIzZKZa7KgL936TSTm8geSDawmZtFSttlxCR5H6x7dkh+3Go+8kYbp2QX2IoWkD0NTJt+V6bQbA5kXkRhzAzKV1i7TkCl1sWvegIOY4kiSJeZ/S1iA8kGiDBg1REH0MM9cADCzs7/i+jXU1VBWkYd9uoJAce4DC2JMY23vh1GW4ED1oKsqtrygSehpyMxprq0g7uhG7kIcwdfQRppt6+DvqK4vxGz5HyJtmbsR+SpIuEjj+XXQNxQRRjbVVRH39MnrGloRM+lC7U38LJEmiriyPsrRoytKjKUu7TFnaZRqqSkEmB+kmef8yOXKFDjKFDjK5DjKFArlCF5lcgYGlI7UlOSjra1DW16JqaMZurUyGQtcAI1s3esxXXwcjQ2tndAxN8X1kFol7V92yc07rx96iLD2GS9+9Rrc3flN75637EZlMppbifMcOQyiKO0X5DelC+VG/495rvHBb17Bt3QOH9oNJOrAGpy7DMbJ2UZst0eibWuHa43HSj23E55FZavXd1NkPM7cgss7+KjRoMLBwwMw1gPzoI0LmF+mbWmHl25nciAO0GvpSi/VsA3uTe3Ef5ZlX/rPDNLX8O9qgQQM01FRSnnkFj2buCpemRFCREYuvwCm/BVdOUFucjaWHuJ2tnPDd6BiYNLs71O2ScepnGqrL8Br0rDDN+spikn9fj32b/kJ2+5QNdcT/tgxTJz+hNSOxW96jqiCVzi9+h56JegvN7zeUDfXkX/qd7PM7kxkoSQAAIABJREFUKUu7TF15U3camVyBiZMv9m36Y+4WjLl7EPoW9ij0DJHJFU2BglznjtLRJJUKVWPd9SDi719V9TUoG25+u0Jf3IyOWyGTyXDvPQGXrqNv2TJYoatPu2mfcXrJKCLWvUDXV7cIHdan5daYuwXS7Y3fKLxyivjfllKRFU9h3Gkaa6vUWp/kP+p1CqKPErdtCe2nr1KbHXXg2f8Z0o9vIuXQegLHva1WWy6hI4nd8h7lmVdavIt/I7ZBfUjav4b6qlL0jC1arOfQbiCxP79LRU5iizfQbP+cqVQQfUwbNGi5JdqgQQOUJIWjqq/FqlVo8+6fGA4yGVa+nYT4I0kSRXGnsfILRSZXCNFUNtSRF3kQ+3aDUOjqC9G8GZJKRUnSRWyD+mDpeftD9f6NjBNbUNZW4zv0ZSF6qUe+o6Yok04vfivsMc4O20XW2V/xHvw81n7Ney09iCjra0g/+fP1trbmHm2wad0dM7cgzN2DMXPxFzpMD0Aml/+ZjqT+AKAl/FsQYGTjSshTy7i653MyTm0RurOq5X9z7RTDNqAH8Ts/IXn/GqI3L6TN5I/VdoJoaOWE98MzSNi5gsIrp4SkYWoKQ0sHnENHkHl6Kz6DZ6Jvbqs2W44dH+XKtiVknf0NszHigga7oL4k7fuCwtgTQpqF2LcZQOyW98iLONDioEHf3BZztyAKYo7hM1hAOq02PemBRFsIrQFKU6KQJBUWHiHNun9JUjimzv7oGpoK8ac6P5Xakhxs/LoJ0YOmPM3G2kqcOt5+C9rmUJJ0gbzIgzh1GiZMU1lfQ/Lv63DpPlZIrmldeSFJ+7/ENqgvNv5iPpSrCtKJ3rwQS+/2+Ax5QYjm/U5DTSVJB77i6IK+xG1djLGdJ51e3EDXuVsIeeojPPpOwtKr3T2/sL/b2AX3Qd/UkuQDX6FqvP+GgD0I+A17Bd+hL5ETtov045vUasvjoWcwsnUndst7993z7TVgOiplo9onm+uZWGIX0o/s82JnNpi7B6NnYnW9xrGlGFjYY+nVjtxIcdOhS1Mi1T58UMv9izZo0AClKZGYOrdq1rGzStlAaUqU0P7+hfFnALD2Fxc0ZIfvFlqofStywveg0DPELqSfOM0Le2msqcC5s5j6jqu7V6Kqr8N/9OtC9FSN9UR9/RIymYI2k5cLq2u5X6mvKuXq7s84uqAPCTuWY+4eROiczXR5+Qds/Ltp6zyagXvfp6grLyDn4v677cp/Fu9BM7AN7M2VrYspTY1Smx2Frh6tx8yjKi+Z1CM/qM2OOjC2c8exwxDST2ymvqpUrbZcQkdRX1ksdOiZTC7HNqg3hTHHhbU7tm87iIrMOKry01qsZRvUB5lCR0y3MEkm7Ko4FkbO+19QVlbWcr+0tAht0KBmJJWKstQoLDyal0pTnh6Lsr4GS4FBQ1HcaQysnDASVPjYUFNJweUjOHYYotYFrUrZQE7EPuyC+6KjbyRMN/3ETxg7eAt5jCuy4sk49QtuvcZjYu8lwDtI2LmCsvRogp/8AENrZyGa9yOSJJFxeiuXvptL4t5VWLfqQrc3fqXjzPVYene42+7d19j4d8fY3pO0I98JnUmg5faRyeWETF6GgbkdEetnq3W31y64L7ZBfUncu4ra0jy12VEH3oOeRVlXRfqJn9RqxyagJ3pmNsJnNtgG9aWhuozSlAghenZtBmDp3YGiPzcDW4KZaxA6+sYUXjlFfWWJ2maH3CmmvTrjOH8m5ubmd9uV/zzaoEFNSJJE9I8LSDu+icbaSiy8mhc0FCeGA2DlLSZokFRKiuLPYuMnbkc2P+oQqsZ6HNWcmlQUf5aGyhIcO4jrzlSeEUtZahRuPca1+PGQJIkr2z5E19AUn0fEpBCVpEaRduxHXHs+8Z8eglZXVsDFNc8RvXEeqsYGus/fRftnV2PuFnS3XXsgkMnluPeZRFnaZbXucmv53+gZW9Bu2mfUlRcQteHVW073FkHrsfNRKeuJ375MbTaq8lKoLkwXqmnq7Idz19Fknv4FZX0N5ZlX1PI4yRU6OHceTv7lo0Knp9u07oFMrkP+5aNC9IxtXKgrL6AgpmUnIo111WSe2oKkUpJxagt/vNaFsM+fuSONvEuHif5xAUggU8mEX1ruPtqgQU3IZDKCJrx/vSjYoplFuyVJ4RjZugsr+ipLj6Gxplx4apKhtUuz/4+3S074HnQMTbER2J0p/eTPyHX1cQ4d0WKtkqQLqJSNeA+ZKaQzhiRJXNnyHiYOXrQe/WaL9e5XciMOcOL9Ryi8chL/MfPo9MI3mDn73W23Hjicu4zAzCWA0mQxO6Bamoe5ezABYxZQGHuCxP1fqM2Osa0bXgOmkX1+J8WJYcL1lfW1nF46msS9q4XqSioVhpaO1BRm8PvcLpxaPJySpAtCbVzDOXQUkqqR7DBxLZJ1DU2w8u0odJCapU8nSpIutCh4yjy9lZifFtFYU369JbWBleMdadiH9CNowvvN9kHLvY82aFAzpamR6BqZY2zrccf3beoUdEFsatK1egZB3XfqKoooijuNY8dH1JpLfr07U5sBKHT1hGg21laRHbYTx/aD0TVq+bFn+rGNVGTG4dZjnADvoPDKKcpSL+Hac9x/shVmQ3U5URteJWLdLAytXej25nY8+00WMqlbyz/RMTBGkpTCpx9ruXNce47DqfMwEvd8TkHsSbXZ8R70LAaWjsT+/K6wHPtrKPQMcOo8jJzwPdRViJuuHLZ6Kol7m9rFXpuXoq42xqZOvpi7B5N19jehurZBfanMuUp1YYYQPSvfTjRUlVKZc7XZGs6hIzGydQf+/3Pc2rdzM9XE1TP85dLyf+ydd3wc1bXHf7O9V/VeLbnJTS5yAwzY2MYBTMAEMO0R6qNj8kgeJIGE8oCYAKFDIDTTsTEYUwzuRXKTm3qXVn1X2l5m5v2xWlkryUjaubOS7f1+mM+HXc+ce7do5557zu+cUSdy9+UZS/VhaNPyQlro2Jor4bVbiIqgO0p2QZ2QA6kmioi95gObwDI071WT2o9tg89lI5oCZSraCNplRzKBRb67ux3Nh35AUsFKIgt8lmVRueklyHRxSJy9krO90432kp3Y8beLYSr6BlnL70LBmo+JNvKLMDhipd7fAC/CqEJRFCb+7jGo4rJw+N/3w9lp4mUcoUSO3Msf9muxdpDXCKSccy0YnwcNuz4lZlM3SEqi3JBAzH5/kgouh9duIZpmFTP5PAAgFm0w9CzuuTj8YrkaM+54Lej+xXdhkwinHxGngUe8ThtspvLQU5N69Ax6QnoG2uOCuXI/jLkFROwB/oW3Kj4bap7TRZqKNkKs0hPtT1C342OoEsZBlzGNs62GXZ+Cpb1IXkAmytBZthfmygPIWPx7YpGV0wHa48TxTx5H4Qs3QihVoGDNJ8hefhcEQvFoT+2sQKzU8l6VJsLwEEkVmHbLi2BoLw6+eTdv5VHjpi2BMacAZRueJxoRAAB1fBaMOXNQt/0jsAxNxGb2b+5D5pLbeh9TAiHESj0R24NhyJ4Fl9mEjhLuQuMAypg0KGLSiJVeVRiTIDMkoLOcW5qZKjYD037/gv8BRfVEHiJEOEnEaeCRrtpigGVDdho6Kwoh1UQTq3JkrjoAxuchpmdwmpthrjyAhJn8Rhl8Ljtaj/yM+GlLiVVn6qo7iu66o0iZv4q7AJqhUbfjYxhz5hCrmFSx6WVItTFImnclEXunAx6bGQdevxMth39E2qIbMO/hr6BNnTxq8xlJfjDtccHZ0QhHex3sbXWwt9bC3loDe0s1bM2VsJkqYDVVwNpUDltz5ZhdmEuUOngdkbKGfWFZdtQqSqliMzD52ifRVXMYtVvf52UMiqIw/spHQLsdKN/wD+L2UxZeA1dnE1qP/EzEHkVRGHfJ/chafhcAgO15ji+UcZmQamOIVCfqS8ykc9FRtgc+l52IPUNWPjorCjl/V6MnngNVfDYoIbfNKoqliB8RRp+zu+A7z1iq/VVIQmnqxrIszBVF0GflE/tB7CjZBUoggiGLTGfp9mNbITcmIibvAiL2TkXrkZ/BeF2In0mualL99nUQiGVIINCboe3YNrg6m5C78n8IzMxfMauzbA9yL3+Y1+7aYwlXVysKX7wJjtYaTLlpLeKmXjhqc6E9Lhx847/hsVtQsObToL8/lmXh7GyEtaEE3Y0lsDaWwtpQAmdnE0AJwPrcQ9oXK7TwOrog08dDkzwBmpSJ0CZPhCZ5AmS6WD5f2vDmZrOAZdlIvwv4P+/yDWtBCYXIvvieUZlD/PSLYD7vOpR++Yx/QReXSXwMdXwWUs9bjZot7yB5/iqiznpM3vmQ6eJQu/V9xE4hd6/IXn4XWg7/CGtjCbyOLiK6tMGgKArGnAK0Hd8GlmGIaapiJp+Hmi3voKN0N5H3xZA9C037NsDeUsX5O5J23nU4+uEjcLTWQBmbznluEc4cIk4Dj1iqD0EZlxnSj5mzsxEuSzPR+vMdJbugS58SUpO5wTBX7gftcUHFc665qWgjZLo46DPIvBdepw1NRRuRkH8xxAoNZ3t12z6EVBuD2CnnE5gdULnpZUhUBqQQSnUa6zg7GrHvhRvg7m5D/p1vwJhDLn1upNBeN/a/ehs6SnYBAJr2rQfjdZ90EBpL4XNa/Sf3hO/VSblInH0pJOooCMRiUKAAigJA9SwwqJ7//M8ztA/urjZ01x9Dd/0xtB7ZAvTsDko10b2OhDF3LvQZ08KamiVSaKDLmArG44ZQevaJ7wfAsnBb29Gw6zMIRBJkXnT7qEwj86Lb0bD7c5R++Qxm3P4qL2NkLbsLTYVf49jHj6HgwY+JLY4FQhFSFv4OZRvWwmaqIHq/mHzt37HrqZVoPrgZyTxGZaNy56Jp33pYm0qhSRpPxKY+cwaEMiVaj/5MzGkAgM7yQs5OQyBl11x9MHSnIRIZOCOJOA08Yqk5hJjJoXUuDugZSImgvY4udNUfQ9YyMv0DAKCzcj/0mTN43ZH0OrrQdnw70s5dTewmZir8GrTbgeQFqzjbcrTXo+34NmRedAeRxZ2l+jDaT+xAzqVrIJTwUxFkLGFvqca+F26Az2XHzLvfgZ7nsr2/Bu31YO/zq9FVfaj3ueJ3HwIACKVKaBJzkJC/AprEXKgTc6FOGAeRdHAHnBUMP0XA57Khu6EE3fXH/Y5E3XG0n9iBtiO/wG1tR/K8K5A0bxXk+jhuL3AYONvrYWsqh0BydkS4hoISCDDp6sfB+Dwo27AWApEE6ReMrHY9CaRqIzKX3Iay9c+ho3QPUW1XALFchdzLHkLxuw+h+cAmxOeTi+wmzbsS5d++hLptH2LCqkeJ2dUkT4QyNh1N+zbw6jQEdIAdJbuIOQ0CkQTR4xeg7ehWIpE9f2n2GHSW7+O84aSKy4JIpoKl+jCS5px9hTginJqI08AjXpsZuvQpIV1rriiCSK4mJjDuKN0DsCyiCOkZXJYWONvrkXrOtUTsnYrmg9+Dpb3EbmAsy6Juxzqok8ZDmzrytLH+1O/4GABF7IZVselfECt1SFn4OyL2xjLdDSUofPFGgGUx+773iN2MR4qrqxXNB75D6frnwPTrgCpW6lHwwCdQRqXwVupVJFPBkJUftEFAe1xoO74NDTs/QcWml1H53auImbwIKQuvhjGngLe5WBtLoU7MiaQm9YESCDF59VNgfF6UfPE0BCIJUs9dHfZ5pC26AXXbP0LJF09h7h++4OU7kDDrErQc/gnl376EuBnLiH0PpGoj4qcvRcPeLzHukvshkqmI2KUoCgkzf4Pyjf+Es9ME+Qj7CgwXmS4OytgMdJTuJuo0Rk8+F80Hv0N3/XFoUyZyskVRVJCugctnRwkE0KZNCb1rNQtQbEQyeyYS+VR5IiBG0qWF3glanzEdlEBIZD4dJbshlCqhDUFfMRjmqgMAQDR9ajBM+7+BIjoFGkKdf7tqi2FtOEGkAzTt9aBh12eImXwekZtVV90xtB39BWmLbiR2Ux2rWKoPY+/aayEQijH7gQ/D7jB4bJ2o274Oe9euxs9/XIATn/4NIokcEpUBMn0CBCK/CNBrN0OqNoa9N4RQIkPc1MXIv/NNnPPXH5B+wU3orCxC4Ys34uhHj8LeWkt8TJZhYG0q470S2umIQCjClBufReyUC3D8k8dRx0N50qEQiqXIueQBdNcfR+O+r3gZg6IoxOYtgr25Ep1le4jaTj1nNWiXHY171xO1Gz9zBQB/GiufGHML0FleSLSKVdSEhdBn5cPSJ7rJBX32LLgtLUT6P+gzpsLaWAafy0ZgZhHOFCKRBt5gIZQqoE7IHvGVbmsn7C1VSJxzGbHZdJTuhiF7JrH8aHPFfgglcmiS+Vvsubvb0VG6B5kX3UZsx6tu+zoIpYreGw0XWg59D4+tEykLryYwM7+WQSTXIPVcfqM3o01H6R7sf/U2SDVRmHn3O1AYk8IyrtfRheZDP6B5/7foKN0NlqGhjM1A1tI7ET9jWW+uNcVQYFkW7q5WsAwNsVwdlvmdCkVUCnIuXYOs5fegce+XKFv/D7Qc/gEzbn+NaDqXo6MBtNsRcRpOgUAoxpSbnsfB1+/EsY/+DIFIEvbUjfgZy1Gz5R2UbViL+OlLeUlhjJuxDCc+fxJ12z4iqi/SpuVBmzIJtVvfR8rCq4n9piujU6BLn4amwg3IWPx7IjYHIypnLuq2fgBLzWFixURkmij/fa5kJ1LPuYazvb79GpQcqy7q0qcBLIOu2iMhfA8izdjOVCKRBr5gWWhT80KKFJgryeoZnJ0m2FuriZVaBfwiaG3aFF5Fms0HNgEsg/gZZFKTvI5umIq+8Qug5dx38uu2fwhFdAqicudxttXdUIKWwz8g7bzVo75I5ZPWIz+j6OXfQ25IxOz7P+TdYWBZFh1le1H08q346Q9zcfT9P8LeVov0C2/GvD+ux4JHNyH74rsHiDMpioJMF8tr06iRIhRLkDJ/FQrWfAyxXI19z69G88HNxOxbG0sAIOI0/ApCsQTTbnkJxpwCHHnvj2gq5Hd3uz+UQIDcyx+G29KC6h/f5mUMoViKpILL0XL4R7i6WonZpSgKKedey0sUI2HWit5CBXxhGDcboAS9RRJIoUvLg6WmmIgtVVwmxCo9zAS6ugdSq81VZKIgEc4MIk4Dj4SqZ7A1VUCk0EKTQqbsnaX6EBTRKcR2R/zCzRNhSU1SJ+SEFK0ZDH8lHBeRBmzWpnKYK4qQPP8qIqkrld+9AqFMidTzrudsayzRUbYPRS/fAqupAqb93+LAa3dCFZ+N2fe9B5k2htexrY2lKHzhehS+eCOcHfVIO3c15q75Auf++SfkrngQ2oQJELACUAwVdJCgv81Q7bIUO+BQxKZizpqPoU4aj4Nv3o3qLf8ecM6prv21w9pYClAUlAlZI7rubEMolmLGba/AkJWP4nfXEHXchoMhKx+xUxej6oc3iC7q+5I8/yqwjA8NO8l1cgb8kRKxUofarR8QtRs3fRkogRBNhRuI2u2LWKGBNnUS8X4N2tQ8uLta4TQ3c7bl1zXMRGdPIRUuiBVaKGMzQtc1RDgjiTgNPKJLD63TcGf5HiijU4l1Au6uPwZnp4nY4ttSfRhgGV6dBpe5Beaqg4jPX0bEHsuyaNj7FTQpk6AloI+o3/6RPz2hgHt6gs1UgeaD3yH1nGshUeo42wtAe91gaC8xe6FgKtqItqO/YOfff4NDb90HbfoUzLrnP5CoDLyN6bGZcWzdX7DjiUvQXX8C43/7R8z74wbkXv4/0KVOPiNEvhK1AbPufRexUxej5LMnUbeNe469rbkC8TNXQCRVEJjhmY1QIseM21+DNm0KDr11H1qKt4R1/JxL14DxeVG+8Z+82FfGpCJq/HzU7VgHhvYRsysUS5E87wq0HP7R39uEEFK1AVHj56OpcOOImjKOFGNOASzVxUTz/AObi101h4nYM2TPgrOjgcj7q8uYBkv1oZAaxkWau52ZRJwGnqAEQhjHzQ7pWqupAipCC3wA6G44AVV8Zq+4kyvmyiKAEoTc6Xo4tB77GVJtDLHGcY7WGjhaqpE87wrOtnwuOxr3foW46RcRWfxWfvcqhBI50s+/kbOtvtRtfR9bH71gVLsP20zlAACW8QFgIVHqIJTwU/+foX2o/eU9bPvLYtTv+BipC6/Gwr98j9RzriXWSXwsIZTIMPXm55E497co+fwpONpCFz+6LC1oPvAdFFHh0ZecCYhkSuTf+QY0yeNx8M270HZ8e9jGVsakIvWca9Cw63PeUnJSFvwObksL2gh1cg6QvMBfGa5+O1kxecKsS+Aym2Cu3E/Ubl+MOQVgGR86ywuJ2VQnjgclEhNLUTJk+zMKOgmkKOnTp8Jrt8DRRr7wQoTTk4jTwBMsy6Lk8ydHvAPlsZnh6W6HmmADHGtDCdHqNObK/dAk5RLRBZxyjIr9YGkfsUZAbce3w+eyIWo8d/2BqWgjfC4bUhZwL4tqb61BU9FGpCz4HdHdd4b2oebn/0ARlUI0ejESWJZFd8OJoOfajm2Dx9pOfKz2kp3Y+cQlOP7J49Akj8e8P67HhFWPQqLSEx9rLEEJBMhecTdYnxd12z4M2U7T3vUAyyJh9qUEZ3fmI5arMfOut6GOz8aB1+7wl7YOE5lL74BYrkbJF0/zYj968nmQ6eJQt/0jonYVxiTETD4P9Ts/Ae0lV4koJm8RhFIFmvaRrc7UF33mDAjEUqIpSkKxBJqk8egi5DSoE3IgkmuIOA2BJm+WquGlKLUUb8HRD/4XFACKJX9EGH0iTgNPUBSFSdf8DbF5I2vuZjNVAABU8WQiDe6uNri726Am5DQwtBeW6sO86xnMhBvHtZ/YAUV0KhRR3CpKsCyLuu0fQZ2QA13GdM7zqvzuVQhEYqSffxNnW31pObgZLrMJaYSjFyPBaW4C7Xb4H1AUEuesxMK/bIZMR65Jmb2tDvtfvR2FL9wI2uPE9Fv+hZl3vwt1wrgB55LSKwwXVsAGHcO6JgS9gEwXi9hpi9Gw81P4XPZT2jqVbZZl0bjnK+gyp0MZkzqyF8kzfOhNSCNWaDHzrrehSZmE0vXPwWXhnps+HCRKHTKX3elvBHhsG3H7AqEISfOvRPuJHcRL/Kaecy08tk5/sQtCiKQKxE65AKYD3xF1RvoiFEuhz5iOdtJi6NQ8dNUdBcvQnG1RAkFPvwbuuoZAk7eu+uPDOj82bxEmXfM3zuNGGLtEnIYxhrUnnYOU09DdUxGFVKShu/4EaI+TV6fBaW6Gs6MBekLVo2ivB51le4lEGWzNlWAZGinnXsPZoXF3t8NlNiFl4bWQaqM5zy0Ay7Ko3vJvKGLSEDPpXGJ2R0rlt/8C4O9UuuB/v0HedU8Rq5bkc9lQ+tWz2P74UnSU7sa439yPBY9uQuzUC3s/l9NhwTmUkzBcMXLK+dfC57SiqXA9WEFPTvcQ23aBa7vqjsDeXImEOZeEJHoOp1h6MIH5WPicJSoD8lY/BVtTGY68/6eQcsBDIXXh1VBEp6Lki6eJag8CJM+9ApRAiHrCfSmMOQVQxGagvWQnUbvxMy6GOj6rtwIhHxhz58LWVAZ3Vxsxm9q0KaDddtiaK4nYM+bOhVRt5JyaSgkE0GVMg7VfxHhYsBT5I8KoE3Eaxhg2UzlEMhVkejK7sYH0EE1SLhF7gXxRfSb3XfZTj0G25Kylaj9ojxNRExZwttV25GdYG0sRPfFczraaD2xCR+luImLqvliqDqKrphhp510f9qZkATrK9qJh9+eIn30ZzvnrD8TSzFiGQeOeL7Htrxeh6vvXkTBjORb+eTMyL7oNQrGUyBinI7qMqdCkTEDtz++PeMHatOcrCEQSxM1YytPszg6UManIufRBtB/fjoZdZKsOnQqBSIKcSx+EzVSOhl2fEbcv08UiZsoFaNj9OWivm5hdSiBA/PSlaNq3nqjmypgzG5aaYuIVjoLH8Pcs6CBYNlbX03TVUk1GDC03JsFcuR8OAhEiiUoPZ0cjgVlFOBOIOA1jDFtTBVTxWcTScqwNJZAbEiFWaInYM1fuh9yYRDTFZMAYFUUQSpVQJ5JxdNqO7wAlEIUsTO9L69GfoUmeADkBp66pcCPRkrIBqrf8G2KFlmhzwJHgsZlx+J0HoYxOw6RVjxKza6k9gt3PrkLxf/4AmT4Oc9Z8grzr/w8yXSyxMU5XKIpCyqLVYHxeuC3DL8PJ+DwwFX6DmKkXQKzQ8DjDs4OUhdfAMG4OTnz+ZNgWWrFTF0OflY+mwq9Be5zE7acs+B28dgvRVCIA/sgvyxLJvQ8glMihScqFhcfeAtqUiRDJNegoIeeYKGLSIJJr0FVLRtcQaOxmJyBglhuT4LI0jyySxUaqJ52pRJyGMYbNVE5sVxbwRxrUhKIMLMv2ag34xFyxH7r0qcQq3rSf2A595nSIZNyE2x6bGebKA4iedB7nOTna62GpPoj4mRdzttXfbsuhH5A8f9WolM5kWRZHP/gTPNZOTLnpHxDJlETs1u/4GCc+/TucHY3Iu+5pFDz4CdFuyGcC8bMuxoLHNkGmH74T1bRvI1SJ45A477c8zuzsgRIIMHn1EwALHHn/YV7Lf/aOSVHIWnonzBWFMBFe2AP+nXVlTDrqtpEVROvS8iCUyNFJWDyuy5iGrtpiXtK1gJOVEUlFBQD/Z+hv8kYo0hCVAlAUHK013G0ZEsAydNi0OhHGNhGnYQzhtnbCY+skpmegPU7YW6qJ6RkcbbXwWDugz+LPafA6umA1lRFLTXJ3tcHaUIKo8fM522o7vh1gGcRM5u40mIq+AQDE55Ppdh2g9pf3QFECpJ57LVG7w6V++zq0HP4ROZc+AG3KRM72WJZF+cZ/4uiHj0AkU2LBIxuROOcyCCAkktvO127XYMLnYekTBEzQAYoFK2SGPBghA0gEYMUAI2TAggXT8/ypDh/jROXGF0G77TBMmDWo3VPpIEaqYQgTbr7AAAAgAElEQVRnQ7jR1j0ojEnIvfwP6Cjdg7rtoVe0GgnG3LlQxqSjfvvHxG1TFIXkBVfBUn1wQDU0LghEEuiz8omm+QD+/ki0x8lzd+hZsLVUwN1NrhKcNm0KbE3l8AWKR3BAKJZArk8gFGlIBAA4O8j11Yhw+hJxGsYQNsIiaGtTGcAyxJyGk3oGMgv6wcc4ALAssWhGQGhHRs+wBVJNNJHmcKaib6DLmE5MGAwAXqcV9Ts/RXz+Ml7Tx06FtakMJz5/AlETFiDtvBs422NoL46+/ydUfPsvJBZcjhm3v8prU7izjYZf1sHVaUL2yvtGTftyppI8bxWiJixA6ZfPEK88NBgURSF5/pWwVB/kZbGcVLASArGMeLTBmFMAm6mCqKhYHygTymMnY3VCDsCy6G4oIWZTl5YHlqHRXX+MiD1FTCqR/gpyg/8e5exsGNF1kfSkM5PInWIMEXAa1Alk0pMCu0Kk0pPMFfshVuqgis0gYm/QMSqLQAnFvV0yudJ+fAckKgNnx4mhvWg7vh3Rk87hvMCyNpXB2lSKBMJRhoadn4B225G2KPxlVmmPC4fevh8imQp51z3N+T3yuew48OrtaNj9GTKX3onJ1z4BgVBMaLYRvA4rqr59DcYJc2GcMHe0p3PG4S+5/XdQQhGOvPc/REppDkXinMtAicSo30E+2iBWaBGfvxxNhRvgdZLrhmzMmQPAXziBFDJDAqTaGJiH2VsgFAL3VGsjOadB2yOGJtWvQRGdSkQILdPHAwDRDt4RTl8iTsMYwtZUAZFcDamWjLDTWl8CkUwFOaHd7HGXPogZt7/G665kZ8V+aFMmQiiRc7bFMgzaT+yAcfw8znM2V+yHz2lFDAE9g6lwIyiBEHHTyVWrYWgfan55D4bsWUTSgkZKyZdPw9ZUhrzrnoZUE8XJlru7HXufX4224zsw8erHMG7FPcQKA0TwU7v5bXhtFmSvvH+0p3LGItfHYfwVf4K5cj9qfn6X9/EkKgPipl2Exn3reRNE024HmvZ9RcymJmm8X1RcRk5UTFEUdOlTh92QLBQkSh1kujhYCUYapGoj5MYkYp2hlTGp8Dq6OFenEoolkGpjRijsJ1tqtWvnHtQ/uxZdXV2cXksE7kSchjGE1VQOVXw2sQWSXwQ9npg9qdrQG/rlA9rjQlftEWLpT90NJ+CxdSKagJ6h9ejPEIgkMHLs9cCyLJqKNsKYU8B5cd2XlkPfw9XZNCrN3DrLC9Fy6AeknX8joicu5GTL3lqD3c+ugs1Ugem3voyU+VcBCK3nAl/h7sGuG04jt0Fz//trBvrZYYQMGEHwQQvpAUf/c0BhwHOBw9ndgtof3kPsrKVQpecG/dtAjUW/13WGtGkNl8YhcfZliJm8CGXr/0GsBv+vkTxvFXxOK0z7yQuidWl5MOQUoKv2CDGbflHxLOKdtPUZ0+DsaCCa9tQfdVJubx8kUujS8ghGGtIAgEi0QW5IHNWyq9q5BUh+4H5otWSqQEYInYjTMEZgWRY2UznUxOrZ07A2lRLTM4SDrtojYGkvMaF1+/HtAEBEBN165GcYc+ZwrkhkqTkMZ0cD8apJ1T/9G4roVCKRkJHAMgyOf/o4RDIVsldw27W2VB/G7mdXweeyYfa97424m/pYIxTxbyiN0U7VA+lUfZGqv34NLO1FxmX/PWCcIQXNhDpb8y2GHitQFIVJVz8OoVSB4v/8gbeKPgEM2TOhjE3nJUUJAKJyCtC450uiAmDDuDlwttfD0TGynPlfQ5fu39wy86hr0CTmwt5cSbR/hTYtD87ORiLvb2/ZVRIVlIyJcHaOzGmIaBrOTCJOwxjBY+2A124hJoJ2tNWBdjugST59nIZA23t9BpnGce0ndkCdlMu527K9pRqO1hoipVZNhRshEEkQO2UxZ1sBzFUH0VVzGGmLwt/Mrfngd7A2lCBz6R0QSUJvrtZSvAV7n18NkUyFggc/JqZpIQHtcYatwy/fOFpq0bT1MyQsvByK2JTRns5ZgVQbjQmrHkVXTTGqf3iT17H8guhVvAmio3oirSSbp/U2SyMYbdCkTAQlFPPar0GdlAuWoWEzVRCzqUvz/+6RSFHqLbtKqIKSq9MUFm1OhLFNxGkYI/RWTiIugj59nAZzZRFU8dmQqPScbflcNpgrDyBqPPeqSa1HtgAA51KrDO2D6cC3iJ58HsRybj0j+lLzU6CZG9nO0kPB0D6Ub/wnVPHZnETdddvX4cBrd0Adn4WCBz+GMiaN3CQ5Yqktxg9/nI3K718Z7akQofKLFyEQS5D+m9tGeypnFfEzliNu2kUo/+ZFXkuBAv6UKL4E0ZrkCRArdWg/sZOYTVV8FiRqI9F+DUKxFNqUifxWUOJBDK1JngBd2hQ4CURdiJZdNSSCZXxwjSDdKxJpODOJOA1jBGtTj9MQR85poAQiYvb4hmVomKsOQE+oP0NH2V6wjA/RE0ikJv0CdWIO5IYETnY6y/fC092OhHxyqUmO9no0H/oeyfOvDHszt6Z9G2BvqUb2intACYQh2WjY/QWOffQooifMx6x73yOq8+CKva0G+16+CbTbgepf3gFDewEAHrsF5uqDaC7+CY371qO9dBdspgp4ndYxHZGw1pfCbWlF8uLrINWOnff5bICiKEy46i/Qpk5G1Y9v8TqWRKXnTRBNCYQw5hSgvWQnse86RVEw5sxBR9keon8/uvSp6Ko9AsbnIWazL8roVAjEMqJlV4USOVzdbcR0I8TKrvb2aiCXQhbh9IRMy90InLGZKiCSayDVxhCxZ204AVV8JoRiCRF7fNPdWAraZSfXn+H4dgglcugyuNnzOrpgrixCxuJbOM+pqXAjRDIVoieew9lWgEAzt5RzVhOzORwYnwcV374EbcokxE65MCQbrUd/wZH3H0bygqsx4co/9ZZUDUWYOtgulIDDzpTb2oE9L66G1+mv1uGxdWLrE8vgtVvgtZsB+KvVeGydwWOKZZDpYqCITYcxZzYSCi7rjZyxwoHdgRkB0+9x8KJpONecClp8Mn+eYinUbH4btvpSTLz/edBiGgI6eM+IBRssfvCPFvSIYimwwuA59rfjNzb0e99f1zCaO4mDfeeGq98YLlK1AXHTFqPk86eQsvBqXjuap8y/CqbCr2HavwlJBWQjkMbcAjQf2AR7cyVUhDR4xnFzYCr6BvaWKqjiMonY1GVMQ82Wd9DdcKI37YcklEAIdeI4ohWUAH8HZlLlTZXRaTDt/4azncCGmbOjESC0sRfh9CTiNPAEy7I4+sH/InryomEJOm2mcqgTsohVOqIEIhhzTp/66+YePQOpTtDtJ3bAkD2Ls9PUdnw7WIZGNMfUJNrrRsuh7xE79UIIJTJOtgL4XA60Fm9B/IxlkOvD28ytfuencHY0YOLv/hrSd9bZ2YTidx+COjEH4y//nzHTg4H2ulHx4yso3/wKWCZYtOqxdSBh2nIoY9KgiEmDIioZlEAId1cbXNYWuLta4epqhburDV6HBaWf/x8qNr6E5IVXIe2CGyAxjN7uvqvdhNY9m5G0+HeQqCIVSEaL5HmrUPndq6ja/Bpm3MZfyps+Kx/K2AzU71hH3GmIyvXrGtpLdpFzGnJP6hqIOQ09YmhL1SFenAbAL4ZuPrgZLMsSu3fL9QkwVx0gYksRk9JbdlWi1IU+J0NPpGEIZ6aleAvajmzpKawWSSc6E4k4DTzhb+7zt2Gfn3f90/C57ETGZmgfWo/+gswltxKxFw7MlfshMyRwTgEC/HX+3V1tSD33Os62Wo/8DInKAF1qHic7bce2wue0Ij5/Bec5BWg/sR2OjgZMvPpxYjaHA+1xonLTy9Bn5YdUmYrxeXDorXvB0F5Mu/kFYk4UF2wtVajdvQ71e7/oiSRQkKqjQQmF8Lls8LlskGliMfkq/3vdd5dcFZsxYFeapVh0N5Wi+rvXUfPjO6j7+X0kzF+J9CX/BXlUYjhfGgCgYfMHoCgKSUuuCfvYEU4ikimRes61qPj2JVibyqBOGMfLOAFBdMnnT6K7oQQaQg0+AUARlQxFdAo6SnYh7Tzuv7EAIDcmQ25IREfZHqSeQ+Y7KtfHQaaPh7nqANIWXU/EZn/USbmo3/kJXOZmyA3xRGzKDQkw7f8WLEOHnPYZoG/ZVUl66E6DUCKDRBM1ZFfo2Dz/JunWtWSrA0YYO0SchjGCIopcJRN3VxvAMr2dHE8HnB2NiJ7ArcZ/AKkmCuc/Uzhgp3ikMLQP7ce2ISbvfM5ViUxF30CiNvZ2QCVB29GtEMmUMGTPJGZzONRu/QDu7jZMvfn5kHbXSr96FpbqQ5h68wujKnqmvW40F29G7c516KjYC0ogQtzkC5A673eIGje39zNnKLZHzzCy16pOHIe8/3oWWb+5G9Wb30Dj9s/QuO1TxM2+GLlXPQyxQsPDqxqI196Npp8/R8ycJZBFnR6/CbTXDa/dDK+1C167BZRIArk+HjJd3Gnf6C/13NWo/ultVH3/Oqbc8Cxv4yTOvhRl659D/c6PMXHVn4naNubOQ1PhBjC0l0iUkKIoGHLmoLX4J7AMQ6wKnC59KizV/FVQCpQ0tzaWEHMaZIYEv+jY0srZZt+yq1wr0skNCXB2jCBtKhJpOCOJOA1nIC6zCQAgC2PKSt32dXB21CPn0jXDvoZlWRx68x7QPg+6aoshkmvQVXcU2pRJnOfjT0vilppkqToIr6OLc9Ukr9OG1iNbkDz3CgiEZP7kWJZF27GtiBo/n5jN4eB12lD1/euImrAAhqyROyvNBzejZss7SD33OsRPvyjkeXAJfTO0Dw1F63Hiq6fgsXdCYUxG7sUPInnObyHTDF6ed6QLo76RCEV0CiZe+zgyVtyOmh/eQcfRHdj39LXIf/BtiHTBlcL6axgG7XMw3L4HPc+ZfvoUjNuJ5KXBu8L9oyN8Njnrj89pQ0fZHjja69FVcwRemxleuwUeuwVeuwW02xF0viZ5Irrrj0Gs0EGTNB7qxFxoEnOhScqFKi4LAtHJv/X+342x1hNCotIjed6VqP3lPWRffA8UUcm8jRM37SI07V2PnEvXEC2UEJU7F/XbP4Kl+jCxlFLjuDlo3P05rI0l0CRPIGJTnzEdzQc2wWlu5iWFU52YAwDobijhfJ8IEIi2u8xNnJ0GomVXDYm9VRkjnL1EnAYe8TptcLbXQRmbDqFEHrZxA06DPIyRhtbin+Dqah2Z08DQaDu+HbTbn5bVUbIDu57agTkPrIM+k0yvBi50lO4BJRL31iYP3c5ugBIinmDVpO7643B3tyF6EjlR9XCo2fJveO0WjPvNfSO+1t5WhyPvPQxtWh5yVz4EgNxCdTDR82COhdPSjP3/uQedVUWIz7sIKXNXITpnHuedzeEs7MXR0ci+eg1iqpbgwNM34dDLd2PK/7wOYZ/+FrQoODrGCgY6DYMtggd7rYyQAeNxo+H7j6CfPAeKjCwwOFlnnWKCXzNFDSxryPQrsDeYBnswwTBF97ND+9BVU4z2EzvRcWInumqKwTI0tGl58Nq7IFbpIdXGQJ0wDmKlDhKVHmKlHhKFDhKlAQztg721CtbGEnQ3lqBux4dgeppqUUIxVLGZficiMRexeYt7d1hP9d4Mx5HgUxyddv5NqN36Aap/fAsTr/oLEZuDkbxgFZoKN6D5wCYkFVxOzK4xZw5ACdBRsouc05AzGwDQUbaHmNOgy/CLzS3VByHXLyVisy8imQryqGSiZVdlAdFxZxPnwiCky662FP847EjQGPPVIxAi4jTwSFftYRS+cCNm3/9BSLuyoeI0NwM4+eMTDhztdVCNMD9XIBQhNu98NBV+DYAFQEEZm07shsGVjtJdMGTmQyTj1lOh7dgvEIol0BKsltJ2bCsAEK3ENBQemxnVP72N2KmLRxwNor1uHHrzblACIab+1/NBO8PhovXENhx4737QXhemXfMckmdeOiq70NqMPEy4+e84+sqDKH37Lxh/6xO8pdy07tgEr6UDSXcMX19FApZl4WitRceJXeg4sROdpXvhc9kASgBt6iSkL/49oibMhy59Su93YbDFfd+Fe3Qf552hfXC01cDaUIruxhPobjiB9tJdaCz8CpU/vQlDxgzkXHw/MVEtaeT6OCTOvgQNuz9H1rL/5q3UsD4zH8q4TDQf3EzUaRArtNCmTkJ7yS5kX3w3EZsyXRw0KZPgaKsjYg/wpw9pUibxWipUk5hLdAde3sdpIIEmZRJYZngV134NZWw69OnT4LZ2QhYp2XzWEnEaeMRj85dmFCu5NysbCS6zCUKZEmK5OizjsQwDR0cDYiafP+Jr46YtQVPhBgAAJRBg6n89PyaEsSzLwtpURiQ60FV7BNqUSRAQ7NbcdnQrtKmTIVUbidkciqrv3wDtdiD74ntGfO2JT/+O7vrjmHH7a1AYk3iY3alhaB9Kv12L8h9fgTo+B/k3vgR17OguJmNnLoGjpRZVX7wIRXw60i7hXtK3PyzLomHj+1Cm5UA3MXybFs2Fm1D22XNw9Sx65MZExOUvR9T4uTDkzOFUxSWAQOjvQaOOzUbCjJN/o46ORjTu+wKVP72J5uIfkDzntxi37G7IdOGtLjYcMhbfgobdX6BmyzvIufRBXsagKAoJM3+D8o3/5FxBpz9RufNQ9f3r8DqtxO41qtgMtB79Bdl2C8CynBt9CkQSMF4XLFX8NnlrKf4RPreDSAqYSKqAWKkj5jSIpHJ0lhdytiOUyNFZvg8+hwUY0mngpxlbJHgx+kScBh7x2gL13MPvNIQzNcllaQHr80IRPXIxd9SEkx2bcy5bQ7TKBxdclhb4nFaoE7I52fG5HbA1lSNmydBld4eLx9YJS80hZC29k5jNofA6bbA2lSJxzsoRvyeN+zagfsc6ZCz+PbG83+HitJiw/11/OlJqwVWYtPLRMeGUAkDqxb+HvbkK1Z//C+qMSTBOJlsi2dFQBUogQMLiK8IqHhardNCkTET6kt/DOH4uFNEpEISpj6jCmIjspXchZf7VqNj8Mmp3fIjGovVIO+d6ZF14K8SKk+VmfW4HwDKcI4mhooxJQ9y0Jajd9gEyFt/CmzDekJUPsAzMlQeGVf57uBhz56Hyu1fQWb4PsXkj3zDqi8fWiYpvXkJ7yS54rO34ac0sCCVyXLj2EOfvrtyYBAfPkQb0bDKR6r0hNyTA1dlIxJZYpR/QTyYkOz3fT6/TytlWhNOXiNPAIx67BQCCblThwGk2hbVykqPdH05WRI9c0CeUyCCUKMAwNNLOu4HwzELH1lQKAFAn5HCy011/3J+7zbFka1/aju8AWDaseobW4h/Rfnw75jywbkTXOdrrcOyjR6HPykf2ivt4a9w22Dktx3/BwfcfAO3zYPrqtUjKv2RYtvsjHERYO1SwnxEyA1KfaDEd9JilWGTe8Ud4vTaUvf8kpj/7Mdh+0qfB7PSHYqmB7yvForv2GBwNVVBOnARaRA94rf2X8SwG6hz6n9Nf4xB4ti+GCXMQ1a9HTP9Ov3zXcJeqjZj420eQfu4NKPv2n6j66Q3U7VyHrAtvRdo518FjN2Pns7+FIioZc+/7eNhpav3fZ64ah4wlt6L5wCbUbfsAmRfdzsnWqdCm5YESiWGuKCLqNOgzpkIokaPjxE7OToO9tRa1W98Pek4RnUrE2VUYk2CuKCLaS6Ev6p6NLmtDCVGnwd7KXYcAABKlHrTHCdrj5KStDKxjvI4uIvOKcHoSnu2fsxSvzQyRXBPW6jaAP9IQzspJjvZ6AAipCgjLshCIxEgqWEmszB4JrI1lAPxlM7nQVVsMANClTuY8pwBtx7ZCojJAm0LO5lCYir6B3JAIXca0EV0nNyQia+mdmHrTP8L6d1Czax32vnYTZNpYnPPA+kEdhrGAQCxB3KJL4TLVoX33D0Rt26tLIZDKII8nV875dEMRlYyp1z2LBX/4GoaMGSjZ8Ay2/HURdj6zEu7uVpir9qO7Z4NgNNAmT0DUhAWo2fIuaI+TlzGEYil0qZNhriwialcgksCQPRPtJTs529JnTEPC7MuCnovjUF2tL/KoJH+vFWc3EXsD7BuToE2fCnd3GzGbMr2/K3R/ZzsUApkOHpuFk53eSINjmO8jS5E/Iow6Y2eVdgbisZvDnppEez3wWDvCGmlwttWBEggh049ceO2xmeF1dEEZm87DzELH2lQGqS6Wc5Soq/YIZIYESLWDl/IcKSxDo/34dkRPXBg2J8tjM6P9xE7EzVg24p06SiBExuLfhzWnvGbXRzjy6SNInXc1Ftz3BVSxGWEbOxT0U+dCnpiOxo3vE1kkBLBXl0GZlg1KyK1B1JmAJjEXM297A7PufAe02wG3tb3nXyjU7/pkVOeWueQ2eGydaNj1GW9j6DPz0VV3jLhjYsydB3tLNZydJs62Jq56JOi+FTvlQs42gZPdjB3t/KQoURQFj7UD9tYaYjblxgTQbjsRR0eiMgDwr0e4IOpxGnyRSMNZTcRpAOD1evG3v/0N2dnZUKlUyM/Px8aNGwec98knn+CWW27BM888gyuuuAKbN2/+VbsemyX8egaLv3KSPIQFfKg42usgMySEtJNsb6kCAKjGoNNAolurpaYYWoJRBktNMbx2S1irJjUf2gyW8SE+f3nYxgyV9vLdOPLpnxGdMx+TL//LmNEv/BqUQIDEi6+BvaYM3Uf2E7HJsizs1aVQpvPTcfh0hXY7e0s8+2FRt3Md6J7yraOBPisfuozpqPrxrZ4mgjyMkTkDLO2FpaaYqF1jzhxoUyf3RlS5IJKpMO3mFwD4NxtU8VmcbQKAvCcCzmcFJakmGh7CkQbA3/SUK+JApMHKTdcwIk0D25M2SfiIMPpEnAYADz30EMxmM55++mk89thjaGhowCWXXIJt27b1nvPWW2/hgQcewNq1a7FmzRqsXbsWV155JXbs2HFKu167GWKC1SqGw2g0dnO014fc0dreUg0AYyrSwNA+2JsrOTsNHlsnnO310JHUMxz9BZRAiKgJ84nZHApT0bdQxqT3dj/lm8FuFAKWCjoGO8fRWofCt++EMjoN+atfgJASg2KoXz0EtCDoEPqEA47+5whowYD5BJqwBQ5GwIARsEEHLfYFHT6JF16ZB16ZB/oLFkGk1aNh4/twy929h0fmhlfqCTp8Em/Q4ZV4B9hmGQa00wF5VhZosQ+MmAbd/xAFH4yQASsIPph+Bysc5BjstQuDj9Em8DnFT7oAM258EckFV0Cq9kf/GJ8bWx5ZCDAY8QKl//cpFCiKQuaSW+HqbIKpcOBmFQn0mTMAioK5gmyKkio+G92NJcScEV36FChi0iBW6YnpDwLV2viKNACAVBMFd3f70CcOE5JlVwMbl16OkQaBUAyhVBHRNJzlnPVOQ1NTE+RyOZ577jmsXLkS999/P77++mswDIO3334bAGC1WvHggw9i9erVUCqVAICkpCQsXboUd9111ylte2zhT08ajcZujvb6kConAX6nQSCS9IaQxwKO1howPg9np6Gr9ggAvxCRFG3HtkKXMS1s4npXVys6y/ciPn95SDdxEouq4eCzW7HnzZsBAHNufhNi+fAq0QzYzRrMuQh1x4tig46BC3DW37xNwIKSSRC74nJ0F+6Fq6Wxz8KdHbAAH2BnEAeFZfyia0XmuJML+n4OQf/5+c/79RTiwQTDYz3tuK9onhIIkDBtGfKufhLn/30X5v/ha1ACIby2ThR/+DAY2vcrlvgjetK5UCfkoPL714nU1O+PWKGBOmEcOgk7DQKhCMro1N6IMQlip1wIr6OL2GchVmggkqvh7Dy7nYZACXguiOWaYWsahtqwCeWIMPqc9U5DS0sLHnrooaDnZs6cCYPBALPZ/0e2efNmWCwWzJkzJ+i8OXPm4NChQzh69Oigtr12CySj0KMBCF+kwevohtduCUkEDQD21mp/lQzB2Mm7tprKAWDEzer6Y6k9AlAUtCkTSUwLLksLuuuPhzc1af8mgGURP2NZ2MYcKSxDo+i9e2Bvq8HMG16GMip1tKcUErHLViLnlTcgjefu8LM0DUoohDx17ETwxhoURUGbNB5Lny9B9tK70LDnM+x/43beBMlDzSVjyS2wN1eitfgnXsbQZ+XDUn2IuGOkjM0g6jSo4jLA+rxEUnMCyI1JcPIaaYiG124B4/MQsSdRGyEQS3t7nXBBrNAClICI0yBSaCKahrOcs95pmDZtGnS6gSlELpcL8+b5O5AePOhvDJOSErybnprqX5wUFg7eOIX2OMOenuQ0N0Os1HEqrTYSuFROAvyahrGUmgT0VE6iBJy7yXbVFEMVl0msDnzb8e0AwtsF2rT/G6iTconlF/PBsfVPofXEL8i7/K+Izi4Y7emEjEitgSKTUNM52gd5SjoE4vB33j7doCgK45bdg0mrHkPr8a3Y8+JqzqLRUIibvhTyqGTUbP2AF/uGzHzQbjusBLsXA/6GbI62emJ6DGXP766tuZKIPcCfosRnr4ZAR283R91AAIqiIO+poMTZlkAIsVLb2zeKC2KFBl7HMPs0sDwcEUads95pGIw9e/ZAqVTi5pv96Q7t7f6wo1od3PVSpfIvBltaWgbYYFl/iLluxzrsfGol6naMrL59qLhGrUfDyNOTGNoLR1v9mHMabE2lUMakchLRsiyLrppisv0Zjv4CmS4O6kRuvSOGi6O9HpbqQ0S6YvNF7e6PUfXLW0hfcD3S5l492tMZE7AsC5amociMiKBHQur8qzHjv15Cd8Nx7PrHKjgJNdcaLgKhCAmzLoG5cj8Ovnk39vzjaritHcTs67NmAAA6K8mI7QMoY9PBMj442uqI2AtUOyMZvZAbE+HsaCRanawvEo1fH0NUDG2Ih9PMvSoV4O/VQKbBmxZe5+CRhsBaZ+dTKzmPE2HsEmnuNghPPPEE3njjDRgM/lJlUqkUAAbkdAt6Sl5KJIPs5lECgKUx/rd/RNzUxfxOuA8us5qN4IoAACAASURBVAnyHuFXOAjcKEKJNDjbG8AyvjHnNFibyjmLfp2djfDYOonpGRifB+0lO5GQf3HYuvua9n8LACGnJg0nB3U4jdsGO49iKDg6G2A69B2ixs3DpEv+d+hrBmmCJvGIhjynv5CXHkzYKw1OS6DFNBhhcDM3jyK4Qg8jZOBRuIKe80mGTm8QesUD5izwnZwT7XQCFAVxZjI8Mv+YAkYwQHAgFAkH2BH6gp8T9nt/BLRg4PvaT+dAsRTY4Jc+8BqfcNDPdCiGoycZ7PsznHMYikXclMWYfee7KHz9Vux6bhVm3fE2ND0NHofTAI5iqJAavjG0F3uevapXB9V84DsAgM/RDanaOGJ7gyHTxUHe0+gsfdENRGwCgDIusMiv5hydBfy72VJNNNFIg9yYBMbrgsfa0RsVIIlU4/+MSOoaZLo4Yr01JCo9IU2DGt2n0DSkzL8KKfOvAgBs/ccKzmNFGJtEIg39+Ne//oXzzz8fl1xyshlUXJxfH2Cz2YLODTxOTBxExNuzoxFuTYOzM7yN3Zzt9ZCoDCGl4Nh6KyeNnTr6PrcDjvY6qBKyOdnp6qkmoiPkNJgrD4B22cOemqRLn9pbfWSsUf7jK2gv341pV/1f2BsojmVYjwdgGAhEkdSkUDBkzcTce9eBooDCV2/u1TjxCSUQDdgFF8lUUMSkER3HkJXf2x2ZFIHfbxvByIAyNgP2ZnL2AptafKUoSXsiDSQbvAnFsuE3UhsCiUpPJOVOpNAOq3oShUjJ1TOViNPQh6+++grd3d245557gp6fPn06AKC+vj7o+bo6/y771Kmnbh0vDmP1JK/LAZFcHfb0JHnIega/0zCWejTYTBUAyxKpnCQQSTjbMVcfwtEPH0X1T29DEZ0KoVQZFqGmzVQBa0MJ4meMzd4MTosJdfs+Q8rsK6AIY0+S0wHW488tF0jEQ5wZ4VSoE8Zh7n2fQqqJQdEbt8EX1NuBPBRFIf/214KaIGrTphCPKuqz8uGxdcLeWk3MpliuhlQTTXSRr4rLhK25kphzE4i+89WrQaLu0TQQjDSI5Epi3zuJykBM00C77KNWZSzC6BPZnuvh+++/R3FxMR599NGg54uLi3HBBRfAaDRi7969WL785CJqz549mDlzJnJyTp1jHs6Sqz5HF1ydjWGNbjja6qHLnB7StfbWKkhUhrCVDx0Otp5dRa66AUtNMdRJ4znv9nac2In6PnqYwheuR/TEc5B/5xuc7A6Faf+3AEUhbsZSXscJlYotr4NlGWSff9toT2XMwXj9KU5UGEXQHqsZHksHlIkZoKjBK6F5Hd3orjmKruoj6K46AkoggqX6EIQSOYQSGYQSRc//+x/r0qdBnzYF2pS8sHU/74vckIDxlzyEPS9eixNfPolJv3uc1/Gk2mjk3/U2dv59BViG5iWNRp+VDwAwVxQR7ZTur6BEzhFRxmXA5+wmlk7UW8KUpwpKQrEEYoUWHoJOg1CqAuvzgvZ6IOT4tyzuSU9iWZaTIxq4V/uc1l9f20QiA2csEacBwPbt2/HYY4/h3nvvxWeffQbALybctWsXlixZgry8PDzyyCNYu3YtHn74YcjlcphMJmzYsAEfffTRKaz6d0jCuSAO7EqIpIqwjMf4PHCaTUgIsbEbJRDCOH4e4Vlxw9pYCoFYFnI1KMDfHK677iiS5v6W83xip1yA8o3/DHouehL/KUqO9gbETl0MmTZm2NeEq462q7sNNXs+QnL+ZVAYTp06NdhNS0gHLz4F/R73//dT4etnu38ee6DHQl/6ayMY4UDdA9vvsf/J/hqL4HP6v06a7olEyUVgRD1jDmK2PwJmoF5BIAieM8sI0L+MCQWg/N2n0LZ3M0RKLfQTZ0M/bia8Ngu81k74nDZ0Vx2Fo/nkolIZmw5dxnRETzrH36XZ4wTjdsHnccBrM8PrtqP16M+g3Q7IdHGIm7IYcVOWwJCZz1t55v46B4ZiYcyejczzf4/KH19HzMRzEZN3/pB2+v8djETjoI7PQs5lf0DJ50+AEpKPFClj0iFRGWCuKELyvCvJ2Y1NR/OBTZwXpQFUfSookXAaRDIlJCoDr12hJYR7NYhk/p5QtNsGodjAyZZEpQfL+OBzWns7O4eCWO4vBuN1dId1Q9S8bwfMhTug6YqUex1tznqn4dChQ1i+fDmsVit27twZ9G+pqal47rnnAAD33HMPpFIpbrnlFkyePBlFRUX4z3/+g4svPnVlGZFcE9Zca7rHaRD2/NjwjdPSCnV8dsjpSZaqg2EVbQ8Ha1MZVPFZnBYm9uZK0B4nkcpJ6sScoDrohnGzkbKA3ypBPrcDTYUbkLX0dl7HORWDLfb7LsSqtrwJxudF9gV3DPsawO8QiPsJe+WO4B08ES0A1U/n7JEGr7hpIQOfuN9iv59I1tfThTnITj/RMyOkQSuC0w8E4mCxNMsM/B4y3n67jiwFus95Xspvk1Gy8Mqdvef0R+DrJ3JmBBC5f32hKhQAFD1QYC41+p1Ln70Lbfu+R9u+73v/XaI1Qpueh/g5K6BNnwxN+iTIRcYBn4+IDn6tHkcXmo/9BNPh71C362PUbP0PJCoDYvMuRNyUxYjKmReW39dxy+9FW8l2FH/4MBakfdObv84XaYuuR9UPb4DxOIjbpigK+swZ5CsoxWXA6+iCx2aGVM1tgQuc1EnYm6tgHDebsz0AkEclwUGw90N/pJpoopqGgE7Q57JDouLoNChPNnjj4jSIeiMNQ2stSEYaDDMXwDBzATyP/zcxmxFC46x3GqZOnYru7uGJjW677Tbcdtsw0yHY8KYmAQDt9t9kRNLwOA1eWwesTaWQhvg6vU4rNBx+wPjA1lSOqAkLONnobiwFQE4EHZN3Pqp/qAIoAfJWP8V7qoa1sRRgGWiSyTSl+zUa9n4BTeL4YVercts6Ub3zAyRNXwFVdBq/kztNYTw96UnS8KUnKZP6p7pQMEyei7RLb4UmKw/ifhWfMIweWBKFFsmzLkPyrMvgc9vRenwrTIc3o2n/RjQWroc6PhsZi25G/PRlvFYUE4gkmHrdP7DjmUtR/P7DyL/9DV7HoygKUbkFaD+xk9jOfV/0WfloOfwDXJYWyHSxRGwGdGn2lkoiToNMHwehVEG2gpIhEd31x4nZ649UE9Vb/YoEgUiDz2Ub4syhkfR8Jh67GUqE3vwy4HCQEmhHOP2ICKH5ggI0KZPCOqTPFZ5Ig7OjET/9oQD7nr8OAHBs3V9Q9Mqt8DpH9uPmdXSNKT2D12mDVBcDdVIuJzt2UwWEMiXkIaZt9UebOhkAEDN5EeTGQSp1ESZwY+XbafA6rTiy7n9Rv+fTYV9TufUt0F4nxl0Y2XE6FQEhNCXmXwjN0j40/fgZKj9c2/ucSKnBtD+9jSlrXoY2m4yYVyRVImHaMky78Xlc+OQ+TFn9DBifFwffuQe71l4Jc/UBzmP8Gur4bIy/5A9oO74Vdds/5HUsADDmFMBj7fAXZiCMLn0atGlTYG0qI2azb2SABBRFEe80rYhKgrOzCSwzjFy9EJAST086GWngijgQaeDYfC5wv/YOI9IAliJ/RBh1Ik4DX7As0UYvw8HXG2ngV9MglCrgdXb3VvFxmU3oKN2NkbRsZGgfaJcdIrl66JPDhM/Rhe66Y5w7OHvsZgjFMgiEZHKvAykY4UoX6q4/BrFKz3vp3pbiH8H4PEiYMbzmcR5HF6q3/wcJeUuhjh27HapHG9bt38bnu3qS+dBuHFxzDcr//QQUCRmQGuOgzpyI/Kc+gS4ntOIIw0EoliJh2lIs+MN65F39JJwdDdj1jytx4N/39Hao54PUhasRPWEhTnz5JGzN5BfzfTH0pOT4f1fJooxJQVfNYaLVjuSGBAjEUqJiaHXCOCIL5gBSXRyEEvmwSoaGgkQTBdptJzZnYa+mgbs9kUIDbepkMF7X0Cf/2pwkMlBCUW9WQ4Szj7M+PYlPBGJpWMejXQEhNL+RBolKj6Q5K1G/4+Pe57Iuur1XJDUcfE5/K/qxFGnw9NxMJBzn5LF2cs5B7Uugtni49B/d9cehTZ7ISwpG3zxXU9HXkBuSYEid/qv5r4Hc96pt78DntmHc4oFRhsGu79+4TUALBmgYDO3BjyUuQOgLdn6dqmA7HhkLbz+9AtNPMOyTeuGTBJcl9KqswXMWeaFVWIKeU4oGltMV9hNZuOjgOTOsAD72pINKS1oAkQCaxC4oo04t/LR7gjcXaEYIpzP4t4O2Bf9NSx1SMF121L//Blq//QLSuESMv///YJx5HlifF5RIDAErAPpVZBwgsKYFEDLBe1aSfnoKhmIHiIh94j6GKRGSC65A/PRlqPrxDVT+9CZair9H2rk3Imvxyd+j4TQLHKxx20BhNJB3zdPY/uQyHHr3Qcy7/9NhRXNCEUYrjEmQG5PQWbYHaeddN+T5I0Gs0IESiOC2ktsVpwRCKKPTiPZqEAhFRIXLIqkSPmc3fC4b0d/nAAHBttva0ZtaxIXAfdw3wgj+oLYkMnTVHhlehOBXEIilYGkfaK976JMjnJFEIg08Igyz0xCINAjDUD0p9bzre/9fojYibdGNI7reG3AaxlCkwdvT/Eas1HGy47GRdRqc7fUQypSc5zUcaK/H3xE7mVtH7KFwWzvQXroLiTNWDNs5MaRNQ/YFd0CbwO/cTnfcjR2Aj4E0hvz3hXY6UPrXB2Ep3InkG+5A3r/eR9SsRaAoCgKxJGzdyvsikioxbvm9OPeRH5AwfQWqfnoDvzy2CDXb3gNDe4mOJdNEY/JVT6C7/hjKvv3n0BdwwDhuDjrLC4mn01ACASRqA9FUGsAvhiYZaRDJNUSjAr0aAQKL8MEICORJlV3tTU8iEGkQiGUAAMbDbbEfKCHO+IYWJVEs+SPC6BNxGngk7JEGt//HUCiR8z6WOj6rdxGbveJeCCWyEV3v67kZiMZQpMFr98+JjNNATgTvaG+AIio5LAsym6kcLO3lXc9gOvgtWIZG4owVw74mJnchJixfw+Oszgzc9e0ABYi0ZDcPGI8HFY//CbbyE0i9+R4krLwGgjD2ghgKuT4eU1b/H+av+RKq+HE49ulf0VL8I/Fx4qZciOSCK1H54+vorNhH3H4AQ84ceB1d6G4oIW5bqokm2lMA8JdddbTXg/YOQ+U+DMQKDRifh9iutkhObhE+GBK1AaqEccREwieF0NznG9jApH3hcxoinJlEnAYeEYjCH2kQSpVha4RkHFcAAEiae8WIrw2EScWKsRNp8Nj9qSJiJcf0JJuZbKShox6KsKUmHQPAvwi6qehrqOPHQZPArYlehIG46zsAwr8BLE2j7vEn0H14PzLuehiGAv57hYSKNnkS5tz1Pmbf+S7ipizhZYwJl/8JckMSqrb8m7dUjUCp0c6yPcRtSzVG8pGG2AyAZeBoqyViT9RTqcdHbBHe4zTwFGkQydSwNZX1Rqy52yNXPSmwFmE83DQNI3IaIkLoM5KIpoFHhOKR7b5zhXbZw9bYDQCk2ih/L4oQFiiB3ZixpGnojTQoQo80sAwNr6MLEjWZSAPLsnB2NCJq/Hwi9oaiu+44RDLVkM3tuDRyc3Q0wFx9ADkrHiBuO8hOv5uMkBZA5gz+ydO2Bt/8pLYuUHTwjVUpDS4L7JXJ4RMG/521JQT/DTAiGrQ4OLFfqghe/MhEbsRIgxcYCZLgxwqBG6J+ndnsbPDvipsRwcqcjC4eb2yGQEghR/nrouBGkTHosZcRwSwMTudxUyxYhoHpLy+he8dOJPz3HdAuPx8enFwoi539og0UBap/0zxv8PsudUkGNNKTDqJp6N8Pgxb1S9Wh2AF6BIqlesqW+htHMmAH1TX0v6Y//e0KWApMz3MiqRITLnsY+9+8A80Hv0PirEt+1X7QWAw1LF2DTBcLZWw6Okr3IP2C/2fvvMPjqM7v/5mZ7ateXWTJHcu44AbYGJsYYnqHhBYCgUAINQkh9BDKjwAhtFBCM1+agUAILRQTenM37rhbtiVbsrq2T/n9MdqVZiVZZe+uBOg8zzz2jO7cudtm7nvf95xzQZf77wocGfkxaWhR8BYOQ1LsBGp2kT5oVML9tch71uPMTNwbQ6QaUXuIujYnupofhSQrKA53jKuYWF8yss2RcIArKzaQZAxB2aR+fP/QHzQkEalO3ashf8qM3cC8+faU8BVdPbK5+45PQ8Rfh+L0xG7+PUHYVweGEZO4SxThxmq0cCAhh+ruoGHnWjKGjE1qtqp86dsADJpsqiaJMgGKDzbi92VdwhZnTGYLWR+icqQRNOtDuj0NLHtk399bQ2rrAO2Mm5DbJA2HbA0sPLJ1PBmyv03QoBjWfkNSy2Tb0A38O+uQFYkMuS2pujWqZeuDX5F0HHFjDCph9vztGRr++yl5vz2dnJ+dgB6Jc4mOn7TTtv443nlbNqQ2QYOiWvcl2WjzHvYlFI4/Am/BMLZ9+my3gobuIHf0dHYtegNdiyALdIh2pucSbqjG0HVhv3Vneh6GFiFYu1tIf/bmZ0OU/5YoWoKG5GQaYqv5AifUissrrJxKtjvRBWTFFLuz80yDIdbcrR99B/3lSUlE9YZv2LPyo5RdT01xpkEN9TxoaMk09KGgwVeXcOYj3GSuFDsEGBwBMQnJVCgn6ZpKw871ZAwZm9TrlC99m6xhk1IWCP2Y4K9sQguqSLKYB3bNs29T99L7ZP/iWHJ/fYqQPn8okGSZktm/oH77Suq2rUjKNXL2Owgt5KO+bI3Qfp0Z+Ri6KphobD57tARlPaOIeQIIGqPIcp/2EOUwiqz3t7nShI1XsbsSllwFs0Spo9e4Z+VHrH7hxoSv0Y++i/6gIYkYcMCRFE6Yk7Lrac2chpRdL+jr8fUigQYk2ZYS0nZXEfHVJ06CbjbPEcVpiEoOulMwwfbt2YoeCSaVz9BYsYHG8vUM6gYBuh9dR1OZOcGSlMSDhsbFG6ie9yZZP59Lwe/P6RVlpL6OooNOxubysu3T55LSf/bIA8kadgBNAo3YABwZZmmaSF5Di0KPmKDB1sx3UwVlGhSHByQpaeVJsXp/gRwXm9MrbLyy3SmEf7OvoKFwwhzGnX07EpJZJih460fvoz9oSCJSrZ6khlKcaUigPCnib8DuyehTE5Gwrw5HwiToaNAgpjzJv9cMGjwpcYKOkqCTl2koX/o2SDKDJh2TtGv8mNGw3STzJxo06MEwW294DsXrpuCqs/rU77QvweZKY/BBp1Cx7F1CSTDzdGXk4q/eSd1WsZmMmDyoQK8G2eYASRJGDBedaZBkuXkSnqRMg2JDkm1igwZXmtCgQcTY9hU09OOHj35OQxKRap8GLeTDll+csuupIV/M0Kbb5wYaYuoYfQURXx2uwYmp+USi5UmiMg17d+DMyE9JRibcVEv2yKl4C4cJ67P16pBhGJQveYu80TNwp+cnVPcaTyQ1DKPTJRBFk3H5rdez131r2dcim9GNuIe031pXL8lehjRNtRzbsl+hZX+vTSXisq64DndbJ5UeOUyxw3qsQOl8grQhaA0g/bqDMr95/fKt67HlpmOgsVttyZqV2Kva8iXcVldXFYWG5hKTrx/6nNC2Sma8eB4FRS0GW9tdAwiGrAsFqs/63VQiBkqT9VhGvfWc7GoP9kg8p8H62UTsOg1Z1jH706zvqa7o7RKhW6N88dtsXfAkGAYDJh3NoEnHkJ4/bJ/ndIQ2hm/A0Fnnsv3T59jxxcuMPKat8WCi8OYX468qE9qnM118pkGSJGSbEz28by5NVxH18In4xWQaQGy5T3uQ7U5hRGgwS6oCtRVC+lJSkGnoxw8f/ZmGJCLlmYagLyXGblFoiWYa+pCxG5grWolzGsRmGmSHi+yRUztvKABNuzfhr9xuKmQkAb7KbbizB/VnGZKIpmWbsWWlJZQZqFq1hxWPLGbMmeMoOHSkwNGlHsG6PXw77/cYmoorawDbP3uOL+89jb0bFwq7RlrBMPJLZ7H9ixeTMpny5Jfgq9wmtM+oGpFo2VXF4RLGaZBtDhSHO+bpIwI2d7KDBofQTIM7bwiePDELgXIKOA0WGEnY+tHr6A8akohU+zRoIX9MISIVUEO+Hqs1meVJfUdu1TCMZk5D4kGDzZUWq29NFP6q7QRqyoX01RnUQFPMACkZaKzYQPWmhWQm2QPixwpD02latpn0KSN63Ieu6nz8uw9w53qYcUvf9WLoKhzpuSBJDJg4l2m/eYLpV72EIz2Xbx75JeXL/yvsOkNnn0uooYrdK94X1mcUnvyhhOorUUP+zht3ETZ3BpLNLjxoEFU3H4XNnR7z9BEBxelNmk8DgGJzClVPivjqYmWjiUJcpsHen2n4EaM/aEgiuuuSnAgMw2g2d0sxp6GHRGjV34CtD2Ua1KAPQ1dxJOwGXStMOSkGIzWyk2qwKamfSaAmys/oV01KBgIby9EaA6RN7blG/opHl7B3VSWH/vVwXFmp9ZlJBmTFxoAD5hJqrAbAm1/CIb9/hYLSWaz9z/9jz2ox6nb5pbPw5JckhRDtbS45FVmiJEkSzvQ8oZwGAMXhFrKaHYXdkyFMchW+f+VJkiyDIWaJXRinwd61TEM/EfqHif6gIYkQtdrcFehqiJwRU4Q6Ee8LWiSMoUV6nNmIBPpWpiHq4pmwelJTjdDPQJLEPTQ6gxpoxOZKXtDgr96JzZUW01/vKgzZaLO1adNs8mXZ4s7RZQNdxrK16ceItNl0I2jZDCMAWo1lk3TJsml2DcMRsmweOWzZ3M3Gba031ZAtW73uoUZLs2xVkQzLVh3OoNGfyd6vzKBM2n8SADb02Jav1DNIqbFso2wVlk1ftpad765j7PElzDwhl4FKDQ5ZtWw2WUOSdMumK5plM2S9zWcR//60Bz32GbV8VppNt26ydWv3c4/bZLuLiuXvYuim34Xdk8nk8x7AkZbL0v+7isbdm7r1fWwPkiwzdNYvqNu6nLrtqxLurzU8BUMBhDktR+HMyBNfnmR3oglSTwIzaBBdnqQJ8j1oD7JNbHkSkowhaNFIsYspHevnNPy40U+ETiJSymkwDGo2LSZvXGpKCqKrNT3hNBiG0Rw09J1MQ1ShQ0TQ4MoaKGJIJiTJJPmmAJFAI66sAUnrP1C9E0/ukE7r7TsKClpDj3frbafPiMNqnCbpEmHXvt9L3fCh69aVTV2zTlp0yYkkWYm+sj7Osh/2+sjMsJKc40nPNjQyZGvJSYNuzRSuCxXh06wr/pXrDoy7tswg3Yn9+TUMTytiqHMmO4vG8PARb7BOHcYL50/hoN/uIE+qsZyX3+qtWLM6wOU/20J6np07/j2cNJsp8blTtQodVCghwnaryVjEYTWEw5DQZetEJ97IzQwerKdF7NbPJuzSCDmtfatxLtu6TWvz3VBUqx1f/oTDKF/0BtXbl5LTzA+yOdwceNHjrHjhWpY9cyWH/uF1lLhy0viVzfjrxKPooFP57q2/s/2z58j6xd37bBsfOO3LIdrTnGnwCQ4aHOl5BOvEGLFFYdbNiyxPyiBUL06VyuZKS2p5kqjV/CgkSRIWNIjiW8g2R5few/7MwA8T/UFDEqHYU1ueBCCRmh+qFjJvGj3lNMiyLeWcj30h4msOGgSYu4n0OZAkmVQxwNRAY1I5Df7qHaQViFNm+rEiy26jNCOdMRnplGamMTYjnQKHnXzfRk46/SSePnwSLF8CwPTmzTCuJ6htY1tZFdmDPqWuaRNlFVUUF29my+YQPztlGx6vzF//PY60THHOw30BufvPRJJtVK3+OBY0ALgyCxk2+1wW/fNCNn7wCGOO+V1C17G70xlyyM/Zu/5LDF1DktvzEu9Zv460HOGZBkdGLo271gvtU9RqdhR2TwZNFYlngqJIdnmSYnegCyxPQpKEZZoVQQGdmWmIdN6wH+2ioqKC9evXs337dmprawkEAtjtdtLS0hg8eDDDhw9n7NixyIKc2kWjP2hIIlKdaQDMm0wKoKsRsoYdgM3RfQ6FJElo4QB9SQ4hLKg8yZmRF9NAFwKBK02dIZmcBsMwCNTsomDMrKT0/0OGU7Ix21PK+Z9KZE4ZgzSnpE2bhZ99RumIERw1aRKsXs1qVWWcw4HhcoHbDXl5uO3DKR0+HDiIAS4wcnS2f/QRp515FrphY/7bQzBK+h6PYfU3/2Ldf37H6X/d2XnjdmB3p1M48XCCdZVt/la4/08oOvBkNi54hIET5pJZlFjAnzlkLNs+nkdjxUYyBo9JqK/W8BQMxV8pNmiQFZvwMhPF4YwZXIqA3Z0hzNwNzMy4GvJh6LrJFxAM2SaWCC6yPFVOZXmSAfRnGgDz2ffuu+/y2muv8f7771NeXo7X6yUrK4vs7GwyMzPx+/3U1dVRW1tLQ0MDLpeLgw46iBNPPJEzzjiDgoKC3n4ZMfQHDUmEbEvlil30xpK6H2rd1hXomtp5w/YgySkru+kKdDWCp2Aotm7W28ejqWITuaMPFjQqM8BCT/77pGuqqb6VpKAh3FSDFg7gyS1KSv8/REw43c6FJ4ym+Ly5SNnZcErzH3w+WL4cY8kSWLoUaelS/rduHUuBOUvMDMPhwJ7m5hKAwwHjx8PUqRhTpiBNmcJntbVcfNll5AwayLx571GSOwrPX+/mH6tqmPTCt23G01tY95/EMgAA3oJhbFnwBOPOvgPFbuWa7X/KTVSt/5KN/3ucKb+4L6HJZNYwk09St3W50KDBm1/M3u++FtYfmPc8WRH7jDInpgLLkzyZRAINwib5NlcaGAZaODlKg7LdScQvTu1JJKeh39wttTAMg8cee4x77rmHQYMGcdRRRzF//nzGjRtHdnbHkuzhcJj169ezaNEiPvzwQ2699VaOP/54brvtNoqKev/52R80JBGi0tNdQYoTDbHXFiUXdvt8SQY9NSvoXYEeDuKv3Jbw+6c43M1ZFEGQZIwUZGSiKXt7B0TojsirXYW/egcA7pzu3/QMyWjDYdBs+03/awAAIABJREFU1u+dKrf9LoWd1geblC7TlGnlENRnWT0wBm+biaspwqsLbqRe38Flh/2d8z8+Apse5OUDnqfBU0zEncnWsdYs4voZn1v2pxUtYYCt1nJskGJdgfUZrjacgc+rJzE7fQh3bFPwfNySlTG+/hrpqafgq68wvvsOSddjywM68BCwH9C6NwMzYDAAwmGkpUsxli5FA/4f8BdghNPJC2eeyf4ZGUgl6XDnbVwdDrMx8g1rtAWs/PpYy/jc9V4KI9bHRkN+nWVfMiSC6db3efcQa136nqKW79PorSu59aHfUpVdwFU3/B8hp8kX0RWTZF103Lk0bl5DWcYaHJnW90tu53tphOxxbWTSivbD0DWaKjeTUTTG8n2yeTMYc+zv+Xb+tRQfdBoFYw5t02f0dXXGa/DkFeNIy6F263KKZ565z7bdgSe/hNDC/6CFA8KMHvVwAMUp1jRSsbuE3v8c3iyyhk9CDfuxC5jk2z1ZZI2YihoKJC1oEDmhFslpsDk92NNy0HU9odIXZ2YBaYN6rtD2Y8C6des477zzOPjgg1mwYAEjRnRdBtvhcDBhwgQmTJjAhRdeiM/n4/nnn+fII4/k4osv5oorrkjiyDtH3yya+oFg5bPXsmLeH4SuvHSM1EYN0VUfw+hZ0IAs9fzcJCAqj5uo8ofi9AgOGqSUSK5GSwCSlWkIVEflVnt/paQzbK/+hm99DzN+5BxKqj4nJ7iNjPBuDtzxRNKuKSExwzWDxelHcF/RT/DMnIXh98MTT8DkyTBjBsZTT2GsWwe6bvE7koAtwLv77N9EPTAX+DNwJrA0FGLcM8/A6NFw5JHwxhugKIz2zuLkjNv4criHgwqSq3J2zluPkN1Yw+iy9YzYsaHN3wccdhL165ex6735Pb5GerPTe0c1/IOnnoAjLZdtXzzf42uAOcnLGjaJ2i3LE+onHp4CsyRNpOyqGhIXgEQhO8QSgbVIkLrNy4RlRHQ1SN3mJUkpTYJmnwaRkqsCy5MMQydUtzvhWoRwUw0NO9Z22i5eMU3E9n3Am2++yTXXXMPLL7/MAw880K2AoT14vV4uvvhiVqxYwd69ezn33HPRtN6bO/UHDUlE/bZv2bNiQY9X47sFI7XlSZIUzTT0bEIrSQpGCspuugq5+eGpJzjhVxxutJC4oEFKURlXS9CQHCK0v48HDeFgA5tXvsIrn/yc91bfSHH2wRTlTmLDoGPZmHsEFWnj+GbopUm59skHbuCeVcfz84zTUYoGwXffwZVXIg0aBBddBMuXI0Fso9X/o5sbGAgscpfw+NRTCboU3h8/ghDWO0I6kAY8DTzXvA8gGQZ88AGcdBIMHw533AGVlUgTJnDfjFI+rVrF3Nv/lZTX/8+fXcPmov3476EnsnbkxDZ/9wwqYcjx57H7o9cJ7NnRo2t4CkqQbQ4ad37X7t8Vu5OSg3/G7tX/w1+zq0fXiCJ72CT8Vdti7vAi4M03gwaRCkp6JCDcS0g0EVoLB0GShMmXR7MAyZJDl+0OsYuEktzjZ2w8DF0DSU44YDJ0HUnpL1JpDx988AELFy7kzTffZOjQoUL7ttvt3HrrrVxwwQVcemlynkVdQf8nn2SUzD4HW0oM11KcaVCiQUPPOA2maU3fKU+yNafpE840iC5PgpS8T5FmCT17sjINNTtxpOX02AwwGfDX7KJyyatU7VrK1jX/RtcjjBlyIvsPOokDh52Pw+ZFB94c/0iSRiBx9KfjmL36NqS0NKiqgssug1deibWIZhJa7+u0BAs6LSs/GnDZv8yVcv38d1j1yXS2rhnDxbPeifUhA6+3Oqd1CVMsICkrgxtvhFtvxfjd75D+8hfsv/41fz5qB39aq/LtDnGTQoBdA4bz2z+/3kZOtTUGzf0Z5QteYeMTdzD+hkc7le2Nh6zYSBs4isZdbTMZUZQcchYb//dPtn81n9Ljru5W/62RPdzkNdRuXUHh+Dk97qc1PM1Bg0gytBYOCi/RURxigwa9uRyru593h/01T+iTFzSIzbSIzDQbWiT23E6oH11tVvXbF36cZmzFxcXccccdSb3G7Nmze5UY3R80JBVSymr/Ui25mmimIZWqQF2BbI8GDQIyDQKDBjO4EtZdh1CDyS1P8lfv6LITdHueDPHHjDgOg2pX24h1aHH+AbqhUb5tNTVLPqVm2Rf4y0wpR8egIgpO/RlZ02fCQYPY7lTZzsLYedlZJqX4sr+8zJnPfYoOXPGbK9g4sTjWpiTOdGB90xBWRKy//bylB8T+P1pr4pEhAeTTDzdfzyuvIF16KcZe02wrxkXA+vHvpIB/Tf+39YVKdnSX+RD52d/NQ9eHPLzz3u8BePmF29AP/oKBLuvKd5Pm4o3ia9t9CER5ENx1F8abb8IzzyAdeCB3D4GNT3zEG/O+JuI3J0dK4W+s/eZl8O1BVg5DfW6L18Wp771IVV4mH84+3NJGjfd7gBYFlow0in51Cdsf/htVaz4me9oh5rXDtjaTEyWOc6GjI0sG9owcajcu6ZCX4M4dROG4OWz/+iVGHXUZNlvPVuEzi8cjyTbqBAYNdk8GOaMORBeYtdZCAZwZeZ037AZkuwtDjQiTnNXCQaHS5bFMg2ACeBR2TybpAgnwkiQJ47QZmibkde8r07Bn5UdUrRLjst4ae5d9SvXyT/HWizP6SwbGjBH32e8LpaWlKblOe+gPGpKInNEHUnTwyam5WKqZ0NEHQgJEaFFpVxFo4TQkNuG3Od1CJQchNcFVrDwpSY7QgepdpA/u+Y0u3vyqzb4EhmJ9n/TmfUPXqV7yKWWvPY7ktNO06TvSx06g+PzLsM8dh6N4cGwlM7dwC+l2K4l3P1c5AD9/8TMkQAGu+uurPPrGabE2uyJWF/BttcUoTVYlrqmfmbWtQwY08dtrhiOnN2cXfvtbpFdfBaxZBQn4jPEsss2kqWAoUmMtntFDIbzO0q+EghTZbDlmiygc8HVL0LJM0dmcaXXC9dSlMWf+T7n8jut4ftMx/Dt4FdFpniX7sG6dyan4wx+Qbr2VUb+ew+VHjuG1a55k07dbcNRaVX2y/CNwjbeuhNXlm59Fyc4tXP78PaiywntHHGq5X8WbxEmGhNTKtjvn2GOo+fR/7PzXPLIOmgGAoRhmiqUV2hilGRJIBpJsQw8HaCzfQFZBads2wNCZ57Bn1YdUrHiPIVNPoidQHG7SB4+hduuyHp3fEXx7tuItFOdzokUCsbJMUYhO8LVISEiGXYuI5V3oahjJZk8apyHcVEtTecfZrO5CJKdBFxTIGbrWYaahcMIcCifM4Yt7TqITzYBuIX/SbPInzabh/gvEddqHoChKr/IUuoN+TkMSMXjG6Sm8Wmr5ATEidE85DbLSp8qTog+mRFPrisODKjLTkCJzt2QToW2utKQ9qDuCoevsXfQRy689m3V//yNaOEj+kScy5bl3GHvHPxh40pk4S4q6XPrw3D+OwADCCrz6t85XkHNq63j8qut57qLfMevzr8EwOG32Li6/rRAlPc0sQxo7Fl591fIJG4AKrGEM/xrxJ4yJE/EOzDQDhiTgoRvupHbeofzxgIcwMOfgDXit2Q5NQ7r7bpg0CRYtwlk8iDOfvZYxP53accftYMegEl48/pc8+KvLu73AIckyWVOm49uwluDu8m6dC+DMyAVg838f67BN/n6H4M0fyrbPn+t2/62RPWwS9dtX9lySuh0Yhg6dloV0HVo4iE04EdpFzuiDMQRNgLRQAFkg70JXI0krTTL7D4v1ZxLKaVDFBA2a2s9p6CHKy9u/b/Ul+fnO0P/JJxHurAEpu1ZLoiFV6km25uv28OHQx8qTYpmGBEnMisOFniAvwgIpgRKwbqCF05AcIrRsdxJq3JuUvuNh6DpVSz9i6xuP4i/bhHtgMftdeiu5M49A8/T85rzy1FL+cGrXsyWHLFzKjCUrAHjk+tsx/p6DdM5V5oTq4ovhqac6LEPaxmDePuA8hsi1QGr8XraffSL32szsyLBv3+GUyCuWcRkQyzpw771IV17J6fddQvCiK3hkVdfKcHRZ4Z9nXkHIG6RNiqALyJ05hx3/9wg1X/yPQaf9olvnRnymNOyeJe9SM/NsckYe2KaNJMsMnXkOGz54mGBDFa4eGjVmD5/E9s+eo6liIxlFYkoJDEMXWn6qhcVnGkJ1u6ndvFSYoIJIiVkAPRJObtAQCQntX5JFcho0IZN9w9A7Dz76zd0wDIO1a9dSXV0dO/bwww/z8ssvt2mbqnmbCPQHDUlET0nCPbya+U+qJVd7uKKUKlWgriKWaUiY0+BBDfs7b9hFdE44EwTDwJFZkLQHqjtrIHVlK5PSd2sE95az9T//ZPdn/8E9qIT9LruN/BlzkWQFXdaBdurmk4T3f3Io05Z9y9EffobroYeQLroIQiGkM8+E11+PtYsGDucNmM9IdQnh2jBp4wenbJztYevEY7m36VAatr3LxOAeTmFpbKxoGlx1FdTUIP3lL7ifephxl7/F6oXJN3xyFg7EM6qUygVvdytoMHSd2o2LY/vLnrqSQ69/G2d6bpu2xYecQcnMs3rMaQBw5wzGkZYjNlA2DJAFBg0h8epJwbpKXJkFwiZBWjgodIy6moKgQXSmQZTkqiYw05BCD6rvKy655BLq6upwu1uC3m+/7TummT1Ff9CQRKS0Zj/VkqtCzN36Tg2fOJ8GsZKrqcrIGHqEcEMVuibeJRbAlT2A4MoPMAzDMqFoj5Qab+RmtEOE1hTrd0ezqwSqd/LtnRdjRMKM/s0tZM2dg6QoRFABFV3Rm1e4W+DNs8prjk/bTo7caDlWattp2Q9iZ6taaDkmP3ORZf/M529ADe9mBXYG/O0tZlw0F/x+jBNPRPrwQ4t/uwH8ld+QG7yUJnchFEBNs52zzZ6NpFgnIYrSdhVXkuIMzcINZC24MLY/54O23yFda7LsG3qEGs362rXsBhYBoytGMY6N1g5uvRXq6+H++znyoeMZc+7veOLz5Si2DCa9bHVF3/GTyyz7m8eUE4z7LAJxnAtDMmK8lNiYZR1JkghV7GTna/9H8UnnE19lG/9dkSSD+h2riPiiJEqDcONe1rxyC5MveIh4xEp22lAjOp+8RQ3gJMVOuKkGXRUYpBqGsEUEXYtg6CqKQ6yyX6i+EmemOGUXLRLA7hHnE5L0oEENo4jMNCAw06CryHKKMg39YNasWZx11lmWY2+++eY+z3n66adZtGgRN910E2eddRZr167l8ccf5+STU8SN7QJ6ndMQiUTYsGEDCxcuZMWKFZSXl39vCCGdIaWZhhQToVvM3Xp4Q5PlPlWeJMcIfImrJ+mRoLCA0ebOxOHt2HJeFJyZhWAYhBqqO2/cA7iyBqKrYaHa9a0RrCrn29suQvM1sv/V9zFg1vFC5AUTxYkXnMqMy+dihMNwyinw4YfttqvKeqXd430Fzw9sLkeM23jgAYxrrgGg+Ol7OGPi+KSPJWvGbADKX3iapk3rOmltom7zCsu+K2sAaQNGCh9bDM33NpE8HpGchujiiPBMQ30lziyBQUMS1JOSGzSEkO0C+xf4nNR1QeVJXcw0SIYkfPs+QZZlgkHrwkhdXd0+z/H5fJx//vncd999vPjii3z33Xds27YtiaPsPnol01BeXs5TTz3Ff/7zH1atWoUkSeTm5qKqKrW1tdhsNqZOncrJJ5/M+eefT3Z28idNyUAqMw1RWbbUS67+MMqTZMWGZLMLMXcDk1AtQj1EsTtoLN+ArqnISSSfOTPN2u1QfSXubPFcHFczvydYt7vdkpBEENi7ixV3X4jmb2L89Y+SPrz35Ohao+CIc5hy3Vmgaaw8+zZGvf9+zIgtmmG4XRpOTUZt7w60i7g+ez/SgzaWBn7DL3mVE/gUAOmeeyArC+n669n/pXv493H3kFFRR5MtDV3AymY8pOYSHUNVWXPzZez/53+QPmr/fZ4zYNrR2FxeNr/9KGlF+3Hgrx8XPi4rmu/HAssLDUMXWPZj3udEO0KH6ivJGzNDWH/COQ1JDhq0SBhFYHmSJEniHKFFlSfp/ZmGriAjI4PRo0czbNgwFEXBMAzWr1/Pueee2+E5AwYMoLy8nEsuuYTBgwezbNkyRo5M4uJGD5DSTEM4HOaPf/wjJ5xwAoZhcO+991JdXU0oFKK8vJzKykoikQi7du3iuuuuY8+ePUyfPp0777zz+5l9SGX5TW9kGiQpsfKkPpRpAFDsbiHlSZA4NyIKb+EwDC1CoKb7ajHdgau5pCBUX5mU/t3ZAwEI1FUI7TdQtZNld52PFvAx/oa+EzAE0kZy0e3HAmBcdRUTX70dDxBdM/Xh4hN5DDWZdamTSU4QukumPjNCYf7LHEpLba4BcMMNppeDy8XFd57LJd/dxwnbk5M9CVW0lJTpAT9rbrmUwK59m545M/MZPPNUXDkD0YK+fbYVgRa3e4GfrYGw70q0hFJkpkEN+VEDjULLk/TvG6dBcP+SbMdbMAxdwAKkISrT0EUVph97puHDDz/krbfe4tlnn2XevHnMmzePE044YZ/nTJ8+HVVVGTHClOd++eWXe9XIrT2kLNNQWVnJRRddxC9/+UvuueeefbbNycnhmGOO4ZhjjuG2227jmWee4YwzzuCZZ57B6+07jrKdQaQRT+eIBg2pu6IkKT0OGhIJOJIFxeESQoQG0EJ+ELCi7i0YCoBvzxa8+cX7bpwAWjINVZ207BlczUFDsG63sD7DjbUsu+dC1KCPCdc9Stqwzo11JN36AwlrVi5Ao+bGFqfqU21YZWjrdQ9bwtZszP6LzWCxskHi+W/S2PLsVBx5mbBgAfzjH5a2BvCK9FPWZC9FxjohiucmSLIdibgHtGTdl5BBjuehSHF9tZ10SFLc7V+WkQ1rDb6hxJVYGhoZcgM1ZJFFS6rdALjiCpgzh8JpJeh/vJqmfy7A0E1RgKxq65idQSeazfo+h1VrG0MyaE/sPRQntypJEqq/qU279uDwZuGvKmtzXJcMZIGTklhJiciA0NCFZS6SkWmILji4RHIaBCs8aYLVjeIhmgitBpsI1JYjCyhzE2W4Z+g6sr0/09AZpk2bxsSJEy3Hrrjiin2eU1RUxOmnt0j133XXXUkZWyJIWdBw991389RTT5Gb272JlMPh4KKLLuLwww/nnnvu4ZZbbknOAJOB3pgUp7DiR5J7riEtyUqfKk+CqJuzqEyDGNlVT4Fp5uSr3Cakv47gSM8DSSKYpEyDMy0XSbETqO1ZpqGNI7RhsPbJ63Fk5jHh0vtwDxvZhkAdD8mQkDXrwzfQYL0fbbCFcMjWifMmuzVAqApm41k8zXKsdOONAHxd/nP+fnwdWaceDw0NcOGFbQzb3mEm6/JXI0seZMVawqbYrYZwsuJpM7mXZes5kmRv0w9I2Gwtbr+G0ZaQGx+gGGjINivJ2hY3/zH0EBgaz46E6zbtwNUqwDIaG5EuvBA++ADpllt4adEBZO42ORy5G6wGcCWZB1Gdb1UZq7Bb75e6ohH2hOKO6aAbIMsobg8jL7+ZnAnTzTryLtyK7GlZRLZ2TcWrK8Tnjs+JlicJDkREBQ2RKKdB3IQ8WGfeO5xZhZ207DqSoZ5kcyVHVjrav8igQWTmwtAiyEKCBrVLXJ3vW2ZANCKRCA8++CDjx49HaebXdSS5ui98/fXX2O12pk6dyvvvv8+mTZs455xzyMwUJxDQHaSsPOlvf/tbtwOG1hgxYsT3K2AgAZJwTyDbkGQbhkAzoc4gyUpCjtB9rjzJ6U480xAlVAuSXXWkZWNzZ+Dfs01Ifx1BVmw40nMJNSQnaJBkGU9+CYF6MZmGqmUfUb3qCwqnzSW9JPklSaovxKpr/0396l37bHdy6Uf88h/TzZ2rr4ayslgcrwNBHHyW1Xal+/uI+7wnW0jREmAsWIDx+ONITidX3T8dSRH/iBlzy71kTJiKa9AQcg6c1S3iqT0tm3BTbfIXLGKZBtGcBjF96d+DTEOLwpNYToNIzkGb/gVnMkxitZjx6pqGJEAZz+Q09Atvdoabb76ZxYsXW8qTeiK5+tprr+H1enn11Ve5/fbbmT17No8/nmxOVsfos5/8ww8/zKWXXtrbw0gIqZzA2xwuDF1Fi4Q6bywIZragZxP/tMGjcXhzBI8oMYjhNETLk8RwGiRJwls4DF/lViH97QuuzAJCdckJGgByRx5I+dK3UMOBhJxo1ZCfDfP/SlrRKIoOP6vzEwRg58tL2PHiIvZ8sJbS12+iI4r7ybeciJSXh/HBB/DEE5YsQxgHNxf0rv+CSAQHhriF4/n9pk/JoCF2XLr6aowjj6RofAknHHsIb7z5udDrSorNLNXpQX22PS0TQ1PRQr6krjhHOQ1CTZsMhAUhapTT4BQZNJg6waIyDclQeEo6EVoNocSn5xKA0EyDrglRlOtSpsGQQP9xZxoeeOABTjzxRMuxt99+u9v9zJw5k9LSUv70pz9x9dVXM27cOFauTL7nUUfo9aChoqKCLVu2WIjOhmHw2GOPff+DhhSupLeYk4kzFusMjvQcekqiCNZWpISQ2B0oDhe6AMlVEEeEBpPXULNxkbD+OoIzsyBpRGiAQVOOo+yLFylf9g7FB5/W4362vPsYoZrdjLvorqQqSkVhaDpbn/wcJAjX+Nj4m4eZfPWhbSYzo4+YROlPD8BoaDDLdDDneW9IQ6h32VmT1rcya6Jwd/GByDiYXLUDN/Uc1Vhmvv4FCxh/y8/59NtNRAQLRBmqimTr/mfv8GaTNWoyapKDhqSYbQrkNIQbq0kbOBK7N0tIf2DKrSoOt7D3Nca7sH9/1JPM8iSBmYaIuCDEnOyLIEL3LGD/sSE+YAA47rjjut1PbW0tO3bsYOHChTz//PMEg0H27hVoGtlN9Oon/+CDD/K73/2u3VTx98lWuyOkkugryQqyzSGslr4r0MJB1FDXCIjxsLsziAQaOm+YQigONxF/fecN9wG7N4vMYQcQCTR23riL8BYOo3zRG6ghvxAZ147gzMynvmx1u38z5DjDrB6sIuWMmIY7ezDr376XIQee0uFqVRv+Qqtr1+9YR813ixh4yAlkjZ7cajwyclxRu6Jab2+GZLQZtyMY90Dek0e8r3HZx98Q2NlM+jUM/GvKWHvFLzn/2M9xOswJktM1niPPbJaavP56jB07ALMkafNUk8yWXfBTS7+yqmILWnW79aYvrGPWfRhxxOz2iNGSFE+ofgGna1Krjtrei9r0KzmQFesqceX+My37QY+Bqlg/n+FLNwOwtRicagPBb2bj+vBDjGeeQTrvPE4/diLP/9sqGygbEq6gtVTCEbBOtlS72oYsTTPvwVA1FKcLJaK0qZ2W4wjVktHyfdXDIeo2Lmt38hTPiUmEGB1dMBItuSoqCPFXbce3Z1tMClkEosZuIt2g4fuVadAjYvsXy2nQkAUEIIauCv1e92PfOPjgg7nnnnv417/+RX19Pffffz8eT/LmAZ2hVz/5zz77jM2bN6NpGrquxzZN05g1a1ZvDk0IUq1lrDg9Kc002FxpqIEeBg2eDCL+vhU0eAeMwJGWWMmUK7OA+q0rCFTv7LxxFxFVUPJX7VtWMlG4MgsIN1ajJ6mszle5lVBjFaGGSrZ98Xy3z1dDflY9+XuCNbsYdcY1SRhhO9dsbKDsrWfaHK9p3MhDr47iy5V34w/upbA0j8yDSqGmBp5+GoA6MnjGfUlKxtlXcPg3FxKb4vztbwDkn3sUdrfYRSBDjfQo0xB1aE6G63lrxAQihGYaDGETcl/lNjz5Q4Rm6oJ1Yt2go7wLkepJyQwaDF03ycYCORNaRJxZnKFFkIWUJ3Ut0/Bjl1wVhdLSUh588EFmzZrFkCFDuPfee7ntttt6bTy9GjRMmzaNoUOHtrkRSpLEDTfc0EujEocdn7/EnpUfpex6it2V0kyDzZ2GGuxZ0GBzZ6D2saABw6B648KESJKKw4UzIz8pQUOyFZScGQVgGIQbxac+w746Fj96QSwg2bTgn93uY/3Ld+Cr3Mb48+/B7k2+ckTd0m9Yefk5hGr34swbQOaYyQyYfSKjTv4jB4w6n4G5k/l0xa089/5RrGS5+b2ZNw8CASQgmwbODjyV9HH2JezHRmQgBLBmDXz6KUq6l7FHiCsF0kJBwjVVZIyd1HnjOBhac9BgS27QEKjegbdgmOm0LgCRQCOu7EE4BWUG/JXb8eQPFdJXFKH6SlwC3aDDvjo8+SU40sSZuzrScmLy0qKhq2aOUmhQYhgJL2RF4c4rxp1XknA/jvScDse0Z+VHrH7hxoSv8UPC66+/Tjgcn7/+/qJXy5N0XScYDOJytU0/rl27liOOOKIXRiUO+ePnUDhhTsqupzjcCTsadwc2VxpqD3kJdk8GkUAjhsDVs0Thyh6AoUYIN9Uk5Frszh0sNGjwxLwatgnrsz04swpAUghU7xJatgCw4v9+j796R2w/WL+b2u3fkl0ycR9ntaBi8dvs+uo1hh/9G3LHHIxKWwlRUdACfsrmPUzle//BPWQopRffQfqwFoUmd4OXyekmobmqbi1fvXcp9z5/B/WuPdz38cf4yKeAKiTA0abY6YeNIE5chHBiVvXXPvoi2bNnc8AJ6Xz7lpiSvdpFnxGprSatExfo9hANWkWoyOwLtVuWE/HX4ckRQ3z3V24jWFsuZAJt6Dq+qu3klopzbjYMI1aeJAqh+ir8VdtxCHKQ18IBfLs3J60eX1dNERKRmYawry6hZ1FrNOxYQ2bJuIT78VduJ31w+544hRPMOc+Xd538o80MtEYkEuHPf/4zEyZMiBm29RSvv/46xx57LA5H8srruoJeDRouu+wyrrnmGqZOncrQoUNjxw3D4IknnujUCKOvQ4+kbtUfTCUMVZBqT1dgc6URaujZqrTNk4GhRdDCgaTW6XcHrmxzohys3Z1g0FBE3bbuS6t1BJvTgytrAP4kKyhlFI1FkmUadq4byVHNAAAgAElEQVQne8QUoX23Rwxf/OQlzLn1806Ni2q3rWTNC38ma/gkRhx3GQCybj3H0AyMuIeUEo7LGEkGRpwxmhJX/24L2Pnu+X9Qu/QzSuaex8iTriSnIR92tGqjKtTkm5NPJX80D+x/I58oi7n99tupBSJXLeT1+w9CwpQJeOMPowEIuzdYruUIOMktz7Mcm/TVzyz7npoaJNVacihp7fzG4zkLkg3Z2WpSLbd90GgOa7ZGdbioGWw1z1w1xSox68tuRHVYy9fWTmkpH5mzaiwT65fH9q+Y+BceatLIH+5A+1kuG7eZ5/rTmtCUfRPD2/PVkAyJqv/9F2feQHJGTTM/v3hOQxxvRTKkWBtDjYAk9bh0tDMvEDCfX9UbvyFn1EFd0rPvCnyVZmmit9m3JREE6/agR4J4BWYa1EAjWjgg1NgtWGeqMbkEqTGFm0z+kEMg+bs19Ii5QCBSPUkL+VDyhojpKxwQIl+rRUJJla39IcFutwtROhIZfCSKXg0annnmGR555JF2LdL7yupzIhCpoNMViFD/6Q7MTEPPOQ0AaqChzwQN7qhrcW0FmcXdX8WM9ZM7mN3L3hPmwAngLRxK057kBg2u7AG4MguoXv8VJbPPFtr39KvmE2qspnbLUmq2LKXsq5cJ1e/h09t+yqzr/9vuQyhQW8HmDx6j7KtXyN//UEp/fmPHNdiGhBR3G2kzvzOkNjXmSsTaX+Wn77Dzk/mMPfkGRvzkV6CCx2cdm2RIhJ3mJF2SYP+rjuXwvBMY9f7/uHjrdsKPzKbk1xfwyFeruP6xa1Adu2LntYY94MYWtq54Vw62pv1zHHnYg9aAwOGz/sYlQ0dW4zMaErqzJfDVbQ6MOPJiINMaIERcMjV51n4C6daAxZfVhOq0ZnlC3pbxXPrsHWTXhrj5j7fw0HnXsruwmrc3KZxzQD4zD1VYVmtmG3RF79IEPP49C+7dQ92qhRSffCGSpIDetta5vf3oMcOQcGYNSOrzxb+3jGBtBbk/vVhYn77KrSBJeAS4wvv2bAZJxls4NPGBNSNqCinS2C1UvwfF6RGmxhTxmRJedoHlTq2hRYI4M/KFcjBM8Qtv5w27AC3kR3Ek/qzVI6GYH9G+0J9pEAdRwYcI9BOhk4hUeiYAKA5PyjMNPSZCu82goS+RoaMlOcG6xAzIPLlFGLoaWykTAU/BMHx7tibVlEqSJHLHTKd6wzdJUf5ypucyYOJcxp58HXPvWkbemJn4927nq/vOoGr9FzTu3oR/bxkNO9ex+uU/88kth7Pjq38xZPpplJ5xM+7c5HochOurWf/aneSMOJDhs8/r0jljhtkYkKewpynMc5f9k8nXP0N26YHsfOIpfmbU0lS2bzO4HxqCLjdVufnc8NcX2D1mPAD//a4GXTc4aD83GZ7EHjmVn78DhkHBrGN7dL4eDhBpEqz/GoeoPHLu6IOE9emv3I47e5CQFd767auRbY4OS0x6gqhHg0sQhwPMjK8rq1BYgBduDhoc3uQEDWrQR6ihCptA7wst5I95/yQCwzDMTEOCY9M1FUNXkQUqWv1Q4fP1LUl5UejVoGHq1KmUlJS0S4TuTXa4KKS8PMnhQu8FInRPJrJ2j1ka0ZeCBkd6LpJiJ1hbkVA/7twiAPx7xfEasoZOIG3QKIJ1iY2tM+SOmYEaaOxQelUUZFnmoEufYcqFj6AG6ln08Hl8dsdRfPyXOXz73DXs+OpfFB18Kof9eQHjzroVd87ApI4HoGr5x7hyBjPxjDu6XFYyZoSZKfh4az26Aa6cAUy46iGKb7qeSEUlu+98mN33/BO1OrkT1b6MKp/KmrIwNkViv6Ke1+OqvkaqvvmQAUechruwZyUbhqELKxnqCNUbv8GRnoe3UFwZga9yK56CxEmsYAY1nvxioQTjQHU5zswCoZkGw9DJ2W+6sP7CTXVkDZ+MMyOv88Y9QFSuO/psSxSGYeDMzBdC3NYjIbKGT0q4fEwL+0kfMrbzwMvoV0+64IILUFVrKeeqVauYPn06s2fPZvHixb00ssTQq+VJNpuN+vp6MjPb/sg++eQTZs6c2c5Z3x+kUskImjMNKZZcxdB7xEuwudMBsxa2r0CSZUpPv4GMBFfgskdMYc6dX+IQ+HDKKCqlbvNSqtd/Q9H0U4T1G4/c5od09fqvyBraNZJyIhgwcS55pYeyd93n6GoYXQ1jS8smY9Bo3DmDADBIXnalNaqWfAgYpBUO7/I5JYPM8rP1e1t+d5IkkTXnJzhmllLzwuvUvPAG9W//j7zzTif75KNRMtNFD73PY3NFmPFDnQwvtLN4Q8/ui1vm34+vbCOjLrqpx+OQZSV270kWskom4C0YJrQEauic84X4FehahNrNSymafqqAUbWgbsty9EgIj6BsoK6pVK35lOJZ4hzf/VXbqduyLMZdE42Iz+RMiFJ2U4M+fLu3CJHFjQQaqN20hEFTj0+oHy3kp3HHWpD71gT+lVde4cMPP2TUqFEsWrSICy+8kCOPPLJXxzR58mROOeUUtmzZwtFHH82dd97JXXfdxYMPPsi0adO4/fbbmTZtWo/67k0BmV4NGkpLSznzzDOZPn06JSUtqyiGYTBv3jxuvPH7LN0lYU/ywykeqc40FE48Am/hsB7d1NIHj+Gw2z/BkZ6cVZ+eokTAQ0pxuIQaEgGkF5XizMhn79rPkho0ONNzSS8aw971XzPiqMQ9BuKN2tpbLVKcLgoP+Gmb4/s6L54g28HFO21iD7d8dyUNsgeOx+W3loDEm5BF7BpN6ebvrKTInCB8G6nGn9VSjqgrGvaMLAovOZ/s446k6okXaFzwDdXPvk7+6afjHrMf9sGjMSTrKuLOodWW/aoBDpS415rWaP1uKaqEx2edqOhfyewe2dJ3wKuhxxn01eVYSwvDDhVfHIch7LXeTzS7imZrWT3zb9yMr7EaKTeHnBFDAZBVG3ocyfnbUBonkU5RCezdVI096GhDZo+HrMvYQuZ7X7t+MRUfv07xUeeTO3AiBFradGbuBq2+P4YUm9x1hi6RnttpM/SwX3Z+nty9QHjQtO47ybaH+m2r0MIBcgSWThm6TtWaT8krPUQYh8tXuQ09EiKjqLTzxl1EYO9OnBn5QsjA7aEl0yCGaB3xR4OQxPuL+JrHlmBAEy1HtjmT6abePTz11FPccsstrF+/Hq/Xy86dO9l///155513enXhuby8nBtuuIHc3Fy+++47nn76aTZt2sTkyaYpaWvxn+7i5ptv7rVqnF4NGs4880waGxt577332vzt+0+ENpJOXI2H7HCnlHztLRga8xDoLhS7I7aS3I/OIUkSeWNnUrnyI6EE6/Yw9mc3J01hpC+jftsqMieP6nL73AyFDI9CY0Bnd6Bj/pJjyCAG3/pH9FW7qXr1VSpfmo8Ram4/80wOu/hPiQ69V2CoGlt/dVlsP+frtzpsu6HavC+Nyu3+hE0LB1n3zC24C4Yw/MTEAlm7Jx09EkINB7H9COuyazaZJRHZI3u2wtkeGnauI9RQRf7+s4X12bhzHQAZQ8YK69NfvTNWOpoUSDLOrMKYyEeiiE303Yn3JyoLoobMOn2bq3NydrwwRTLQ2NjI1VdfzSWXXILXa46pqKiIo48+mssvv5zly5d30kPyMHr0aA46yAzOhw8fzpNPPonf70dpNtiLRLovG75ixQpefPFFnnjiiR9n0DBmzBjee+89srOt9XGGYTBnTur8DZIDCTWY2tKbvNJDUp7d6EfqkDf2UHZ98zp121eRPeyApF0nZ+TUpPXdl5E3bhY7133G5OO6lvodMcBcBd+yu2teDO7hwym+5hoGXnghgbIy7OkF7Pq275TndRf+9RtAURh86x/JmHPIPtvuborQEFLJdtso8Nqp7UZCVPU14MzKZ9iJlyRM5MweeSDu3CKq139B4YTvtw9QTzDsiAsoGHcYznQxhmEAVas/AUkib39x4iUNO9Yh2xx4CxOXmI0iUL2TrOHdNwTsKobMOI0hM04T1l8scyEk0xANGhLjsUR9mRRBik6J4v3336euro6DDz7Ycvzggw/m5ZdfZvXq1Ywbl7g3RU+Qnp7OlClT8Hg8NDY2cvjhh5Odnc3DDz/MlClTqKjoGj/xu+++46WXXuKll16iqamJo48+mrFjxQXT3UWvBg133nlnm4ABzFXVm2++uRdGJBY9VRbqKQrGHUbBuMNSes1+pA75Yw9l0q8fIn3gyN4eyg8ShVOOZPfyj9G1cJe01oc1Bw1bd3dvxciek4M9Jwdb2M5UQauSvQH/8pUggZKXw44/3kbh7y/CMbBjIuzG6gBTBqUzKtfNouquRw3O7AIm/2mekOxz1rCJjDruSvL3Pyzhvr6PkBUb6YP3E9rn4Omn4i0cJsyEDKBk9tnk7ncwskATvgnn3iVEiShVyBo6kRnX/rvH2fzWyB9/GHPu/DLhTEPGkLFMvexpMoo6+w6lhrgczSQUF1uliKPl7osXL+61oOEXv/gFxxxzDNu2bWPs2LG43eaCxwsvvMAbb7zBdddd1+G5ZWVlvPTSS8yfP5+KigrOPvtsfvrTn/Lggw8C8NVXX6XkNbSHXg0a9pVN6G0Di/bQLbKNRI89DPrRj/Zg92QyYFLvkrsSRXs14D3syLLb3gOqS5dqdV7hhCMoOuIaZIfDQr6O2K3ys5qiY0gGw5uDhs27wyiatVzMHnQgxdXsx3syKBGlDTcjvtZdb75Wa4TjzNUke9vXbkgmjyGKoFttY6YW349m09BtcVK77XBJFNV8bKgVVSjeNJyZuTR9sQjvxInknXEKimpD0tuO6bvmoGF0rptFUqDtdyH+M9VBaiZcSkgxRZb48XTpu9A8HkWyUzTtpDZ/70fP4c4egHvKMWL7zB0sXGI5Z5S4kqxUwObyklksZsIrK/aYCpOh6xiG3ikXUdd1ZFlGDYdAktHVICCRPWISajiMHA6hhpqwuTNQ/fUoDpcwT43WKPviJTZ/8CRqoJEsj3XMe/ea5rLp6dYKi7Q0cxx79oiTPe8ufD4fubm55Oa2BNMrVqzg7LM790C65ppr+OSTT7j33nv5+c9/js1m4+677479fcYMcW7u3UWvBg033ngjt99+e7t/+/Wvf83777+f4hF1jO6TbSTSBo7qVZZ7P/qRLLRH5Gxvoiis705WraT2yK9dIEtbybgyk48+F1WzTpyjpOfY+CQD1a4xKNe8fW6pDeHGmq63hRxtyMDx7tOyJmMPWQMJVYkLUNxtC4ODHit/oj3nZE0xKB/SIvOq2bQ2xN6Iy1pWpcs6kTjjtvj33RF0oofNMWnle3HmDyArbTTuYSPxffQ1xUefi6zLbUzzDNlgS535PhZnOtEVvY3ed3yQhSFhDylxbdoGCHLcGLtCYO4pRAS93SVB96Pn0CJhQvWVGIaO3Z2B3ZsZex6roQCh+krUYBN2bxYObxZqOIAaaEIN1KNHQkiKHWd6Ho70HCL+eoJ1lUT8dSavTJKJ+BuwudNAklH9DaghP4rTizM9CySFcMNe1JAP2eZAkmUivga0SNA0rbO7UEN+c+Lt9GBzZYAkEW6oQg0FkCRz/GBgaCpIMpIsY+gahqGjODwYmooeCZpjtTlMbyhDa553yMiKDV3XkCXJDBaQCNZWINsd5O13MEPnnB+TIK7fsZZN7zyEJNvQQn5kuwNdjYAEuqoiyQqyoqCrEWS7Ey3YhOJMI+KvQ3Gl487Kp/T0G7E5PUiIM3crOeRMSg45E4DdT59h+ZvTaWaE4+dYcvNrcjh6LvGcKC677DLmzZsX26+treXSSy/lyy+/7PTcl156iYaGBj744ANeffVVhgwZYpG2r6qqIj8/cSnenqBXg4Ynn3ySm266KfbBgxnd3nTTTXz44Ye9ODIrekq2qd20mIi//kdJKu1HP37ocDav8PvDOvTes6nXEKrcjavILAPImTGbXS88RXDXDjwD2/cT8EXMgMhp6/pkQgsHUEMBoTX4/fhhQFfDfPXXU/EUDKWhbDX2tGxsLi++PVtJGzgSkKjZuAhvwVAcaTloagg9HETXIkQCjXjziwnW7iZQvRNnZiGyzYHNk46i2JHsTmhlcKnrGhgGofpKwr5aMwiQZNw5RdhcHnQ1ghYJogYakRQbjrRsJNkGGCbxPuQDScHhzUJXwxi6js3pIeKvR5JkFJfXFDHRdRSX15zIS3ILR7F5UmyabkpIsoKuhjA0HcXpxubyYoSDKA4HGHYMo1kswzBQFBsYBpJNwtAiZI+YhK9yGxVL38WdP5QBEw8HTOW8rKETzUDBZkMPB7G5vGiREM60PMxUn4Rsc6CFTafqiL8BxemmafdWdn75MpklEyg+1DqxTyYGDDDlc5uarFUd0f3Bg5NrCLovvPLKK/zqV7/i0EMP5ZVXXuGmm25C07pumpqRkcFpp5kcmb1797J9+3bmz5+P1+vl/vvv56OPPkrW0PeJXg0aKisrufLKK3nssccAWLduHeeeey5lZWV4PH2n9jARsk24sbo/aOhHP36AsCvmgzyiGj+6oMEwDBy5BXiGm/ya3MOPon7pQrY9/DfG3HIfSjuPlrBmrpQ5lK6Zqxm6zsL7z8VftZ1Zf35fqBnZjxm6FqFhxzrqy1YjK3ZkmwO7NwtnRi7egqFJKTFJBiRZwZM/BHfOQGxONzZPpkmezi9BstlRHB4yi8fiKRyBYncQ8dURbqymqWIz9rRs0gqHY/NkYHenE2rYCxJIkozNnYHNnYbicKOHg4R9tWjhIJKs4EjLweHNQlIUJMWBzeUlEmgEXcfmTsfmTkMLBQg37sUwNJzp+biyCon4G9DUEM6MPBSbAy0cJBJowObwYHOnoWsRArV7kCQJd86gpFcn6JrKovvPpX77yljQ4MoqZMRRv+lRf4ZhULvxGxrLNzQfEJdp2Bei8qU7duxgwoQJseNlZWUAHHBA8gRDOsOqVatYsWIFxxxzDJmZmXz22Wc97isvL4+zzjLl4MvKyqiqqhI1zG6jV4OG22+/ndLSUu69914Mw+Cmm27i7LPP5qOPPmLJkiW9OTQLekq2yR41jVBDFWkD+h4/ox/96EdicDSvmIfVH1+5ieZromHFYjKnHAiAs2AA+Ucdz9b772TbY/cy8qLr20x8wppZ1uRQujaZ2PHlK9Rv+xaA7/7zN8afc4fAV/Djga6phBr24qvcQtWqTyhf/BYRXz2G3sJpyR4xhdrNSwEYMPlockYfRNHBJyfN00AEJFlh8sWP9PYw2iItG0+uVU7cmWElicd7+ciKHW9eEuVg4yArNgxdo36rGElSSZKQFBu+FMvMH3HEEeTm5rJw4UKOPfbY2PFvvvmGadOmsd9+Ykn/+4KmaWzevNlybNy4cRx44IH85Cc/QdM0brvtNh599NGErlNcXNxhWX8qIBmtC6V6Ca+99hoXX3wxzz77LMccYxKq3nzzTU444YReHpmJiy++mMcff5xNmzZZCNoLFixg7ty53HnnnVx77bWWc2IPTEkyawvt4g2/+tGP3kDLLcMwa2eRoLVrs9GaRmzEjplLedHjcbXp0JKCx1zxS3i1rSd3tnYu2X43BsG6KmRZxpWVj6G30ypZ12+vrj6er9BYjz29lVKK0UHnPYShqai+BhS3F9neUl6qBf3o4SCy091GHnXalCl8+dEHLF66jEPmtDXza70yaeg6EV+t5e82dyZKF9R02r477b1fnXbT69B1vfl3ET0S95uJ/l4MHUPXoflfQ9di/0cyPyvzBAUMDcnmaK6xt0V/crHzdV1Dj4Sb29mxOdNiNe/9+GFBDTZh6LowX4mwrx50k/uQMbiUQ//0HyH9tkbFvJ+3WVB+4IEHuO+++1i3bh1ut5uKigpGjx7N/PnzOe44MaaIXUFDQwNDhgyJZT/aw/r169uVWlUUpVulS72JlGUaQqFQh0z2adOm8Zvf/IaGhgZqa2vRdZ277rqrzwQNPSHbSLKC4kqj+NAz2O/EPyR/kP34UUALBwjUlOOr3I6/ahtaJEywemebdubE3mieRKixyYShazgzCwjWlMfa0PyvM2sAgb1l6GoYPRJGVyPoahh37mAadq1Hj4TMTW0h0KYXlcaMmPaF7JHTqG02luoMGcXjaNixFmdmPq6sAbizB5JXOov8MTNw55g1ql1JfccTZNs7L57Yqsl6G6KqZreqDEXPC+vgkmHuA58RCVgJu4qqtLl+V1ys1Tj1ItWuYsQRqkPuOCM5yUB1WAnMiy48imlPt5hmymo7zsldcdWOh2FKKVav/JJV91zKhKv/QfaIKS1/1nXWPnkjvootpBePYeRZf8DmTsOQDCYWmHwwR9FoDn3wkzaqR85AS/Cx9pXb2f7Js9Zr6yo/ufJ90nL2Xacc/x4aktGGlN4eWTrVJGfDMNBCfiL+BkJNNfgrt9K4cz2Nu9bTsHM96YXDqN3UccZdkhW8hcNoqthkPW6z484ehDtnEN6BI7G7M3BlFeLJLyZjyNhOy2UNw2D38vdY9dx1ODPzmfGn11DsnUsQ9+P7hfWv/ZWyz+dz+F1fC+lvzfxbqFj+LkfcvZCv7jxFmDBGZ7jyyitxOp1cdNFFjB8/niVLlvDss8+mNGAAk4Nw33338atf/arDNs8991y7x/vA2n2XkbKg4YsvvmDu3Lkd/j36pkmS1OcUh3pKtskaOoHqDYvYteQd0CIYBtjcXrSgH6lZTSGrZALunIHJfQH96BXoapiKpe/SsGMNNZuW4PBmUbt5KbquYmia+SCWZGSbHdnuJLNkAr49W8x92WauCCp2DF0j1FBFuKkGV/ZAmqJ1o0Dm0IkEa9uuXBi6jiTLSIrNXLVv9a+s2AnUVjT/xiQzG4a5kqurYZMQ6EpDbl6RdGYW4s4djGxzItudKHZH7P82V1oz6c6GLCvmdWTF3GL/t8XS4VHlj5aVUVPpg+Z/DV1D///snXecG9W1x79T1He13WXXvTfcsLHBmGqKwwsEEkMgwSGBYGqSR0l5CQnkQRrFeSTUBBJICCUhBky3qQGMbWzj3heXXXt7VZdm5v0xK+2OtPYWjaT1Wt/PZz62Zu/cuZJGM/fcc37nhIP4Gw8TaKoi0HiYlsqdNO5dx5Znf0bp7IuYvOgOrPbM1zcIRVTssohVEul5bc9jm1CjHlNrKzTWZRBEkUnX/C97//0HDrz2Vxq2rGLid+8if/JJMS1DVNtwNIrGz6Vx91paq/YiWWxEgh4iQS9v/uZkZi26j+GzFvWpZ0Q8kYAHf8Mh/PWV+r8Nhwg0VCJaHTSVryfsayHsa0FT9Csnt2wCrZU7ECQLOYNGUTT+ZHLLxjN45sI4w9/4r2RzIogyjqLSmKFgzS1KyjsgCAKDZy7EmlPA9n/+ij2vPcj4r9xu1keTpY9gzS1qSzTgQzahfoUlp4Cwt6lNrJ1errvuOq67rnd6DDPpqibElVde2el+Ve1Z+ey1a9cye3ZmUginzWgoLCzkvPPO449//GOsjPaRUFWVyy67LE0j65reim3GLLyBPa8/zP73nkKNhLC6ClCVEGgCktWGFolgu6AQQRTx1VXEMi6o4QCi1Y4gW4n4mxElCxa7m2BrHYIoY3UX4as9QMjbgC2nCNmZR6ChEiUSxuLKRw36UJUwttxiIkEPIU+Dnk4u4Itld7DnDSDQUoMaDiFa7FjsLlQlgqqEUUM+VFXV3dqaiqqEEQQJUbaiaSpoGmokpIddyVaUSIhIoBU1HERDQ5JtSFYHgiCghAJtIjIR2e5CVdW21bUmlJCeKi7Y2oA9fwD2/EGgqUSCXv24oBfP4T1YcwqxFwxGCfpo3r+ZSNBL3vAT8Nbsx543ANfAkSjhAJ7De5j/s1dTf0F0A3/DYXYv/z2Vq5dRNP5krDkFuAaNJrdsfFtmDCn22eor+CFkl1tPkRcJoSlhfaVfiWDNLcReMBBrTlHbpKAQ54ARuEqGHzfi0EDdYQ589Cy12//D2oe+zck/eL7LPOOpJjr5tckC3oyOJP3oGVbasszEIYgSoy/9PsUzzmD74z/j899cS9k5lzPntjuAdm3D0Rg4bQFqIEj5yic46VsP4Soaxr5PnmH7G/fz2T9vpWLz68xadC/23MykHeyIGgnR+MUG6nZ8jL/pMLWb34tV840iSBYchYMpGH0iuWUTsDjzsDjd+r+uPKzuEhwFg8kZNApR7huq+qJxc3ENHMmB/zzH6POvP2YE0lm6h7UtI1motd4Uo8FRMIiC0bP0MCXSI4Tuayxbtow9e/bERMup4LnnnmP79u3932gYPnw4t956K6NGjepW+1tv7TshPb0V2xSMPpETrryHQFMNrsFjCDbXoCoKFodbNwZkG6JsYfu/fk3z/k3Y8/W0bwhCW15kAURJz8UcCemaCE1FCYeQLLrREQ606noJi01P2aapyPYcIgGvbqjkFqIpEfwNh7EXDkIJBRBlK2ok0pZzWZ/8S1YHqhLWU6qJEr76CkSLXU91KIgoAQ9hXyuibMGSU4AgiigBLyFfi545wu7C6i6OTXpVJaIbGhYLomTR97XldxZFGdnpRrLYESQLmhKmcc9n2PMHITty9RRyVicWVwHOgSNRgn4EUUJTFYonnapnt7A5cRQNwd8WmiO0fd59wUulKhH2vP4HqjauYOq37qV09pezccFJIKh6RpHxF95KbtkENj59OzWfr6B0ZntBqc5CbcT4vP8k5vlX4kJZQtZwQjhQwOU3vI4+C+uCIYpdFgpLoL7ZuHru9NqxhIy3V2vca0VSCTiMtRJCcXUSItYwqmwcTzjuGFVSEuoraAIEcnyx11JYTniIWwJx+gBNSKglYQkYJ7DWgA05IuEM5kAkjKveikM11qiIyApBZ4D80dM56Zf/ZO+/HqT5wCb+dtMlXPP6cupEt14jIqEGhPFcvpZGWiq3kGsvRQ5bGDf7KiZOuYpdq59gx6d/ZuOLP2fe1x+DOGF1fOhRZyFD8UXsOuJliiEAACAASURBVAtNOlKokaZpeA7voW7Hx9Rv/5iGPWtRgj4EUaJgzCwGn3gB9iJ91d9RWIajsBSbu+SYvAeMXHA1VRveonrjSsrmZIvi9ScsOUXklI4j5G3CWTw06f4E2ULjnrUoweO3qO2vf/1r7rzzTr75zW9y//33M3DgwK4P6iZVVVXcfvvtTJw4kbvuusu0fntKWj0NZ599drfb9yVPgyzL3HHHHSxdupSf/OQnMbHNK6+8wrPPPnvUY+35g/QVdMDSsRx8vr5CVr3xHXwNFYw6/0YsNgdIEggiWiQEmoZosaKpqh42YnWgRcKxsBNHYZluiESCWHOLdWMgHNBDXiQ9z7KqadjzBxHy1MXS0QWaqlCCPiw5BVic+QSbqlGUEIIgIVn1ibwoyzFjQrI4UCMhXTilKcg2F5I9BzUSIuxtQrTYsDjzYhoPTdNQQj5A1PvrkGNaCQeQbcZJRn/j8NrlVHzyL6Yu/h1lcy7K9HD6BdEJb9nM/2Lfe39l77t/omzGBZ22iSJ1Ykh0ZTQgaKiievQ2be12NnmZUOhiXLGd3XuMhoWkiFjCxgm4LWi83YYtCmFrXEXoTia88eNR4wrAqZKasA8047g1IcGIijdGBFVEi+sm/hhLWEaKSDhcgygcMYtIUytyTuJjJKq6kGwOxn3jRxwqX0nlg//LrFmzuPDO36GWzehyJXLXsj9CwanYJBe0jcuCzOQ512O3F/HJSzdxYPNyRpxgnMwmFHtTIb6SXHwbpQs9Qzjgo2bjCup3fEzdjk8INtcA4BwwgrI5X6F4wjwKx89tz6vfT8gfMRX30ElUrn45azT0MyyOHDyHdhHxt5rSX/S5Hgl4AeG49DQA3HnnnaxYsYJzzjmH+fPnc/XVVx9VHN0VmzZt4k9/+hMfffQRDzzwAGeeeaaJo+05aTEafvWrX3H11Vf32uqqr6/n7rvvZunSpSaPrPukSmwTbKlBUDVKZy3snYtw6MTutRvYXnApPluC1ZUX3zoBUZaR7cbxiVY7snVQQltBEDo1DARR6vcGA0DDns8oGn8ypVmDISWUTDyN3W/+gZCvGauz62s3Vexs1IOSxhc4eQ1/F611fJ4aKg98wJpPX+LSJU93fUAfxZE7gIZ9n+Gr30fBkMldti+YcTJ/+s8n/OCqb/K3/17CkK9cxfDLrtMLUB2BC/6whtyWzu+Jo6Z+jW2fPMSmd37LsEkXIHYjq1JvUMIh9r//N/Z/8DdCnkYki42i8SdTPHEexRPm4SjKXPGodJE/chqVn76EqkQyHhKYxTyi6XSVUKCLlt0jGr4WCRy/noYo55xzDuvXr+eJJ55g8eLFBINBzjzzTObMmcOkSZMYPHgw+fn5uN1uRFFE0zRaWlpobm6mqqqKbdu2sWbNGt59912sVis333wzS5cuRZYz//tLywi+973vce2113L++efzjW98o0tNQ0f+9a9/8cwzz8QKwGWSVIht/A2HaKnYbkpMYZbME/Z7qFj1ImO+dGPGw6T6K8UT5rH7jQep272K0mnnZ2wcMaMh3wXUd9m+Yt/7fPDW9/G07AfGpXZwKSZnwGgQBFqr93TdGHDKEqdOmcC7H3zI+IuvoHbVCtRwiJFX/qBXvxNBEJl+1k94/7nFlG94njGzvtnjPo6GpmkcXv8GO5fdi7++gpLJpzPynGspHHPiMRlmlAwFo0/kwAfP0Fq5g7xhRxd6Zjl2iKaA16MCTOjP3u5pELREr+7xhizLLFmyhCVLlrB+/XpeffVVnn/+edavX09DQ0Psvme32wkEdMNN0zQKCwuZOXMm8+fP59lnn2XGjBmZfBsJpMVoyMnJ4W9/+xv33nsvU6ZMYdGiRZx66qnMmTOHvDzjSqHX62XdunW8//77LFu2jAULFvCPf/wDh6PvFplJBjUcNOQ5z3Js07xvI2gqBaN6747McnTyh0/F4iqkuWJrRo2G8hY/YVVlaK4dp0XEFz6ywLexZgdvLvs6Q4afwQVfW0Ze4QgivSrk0DeQrQ6c+WV4asu71X5cnr4oUhlSGXnDj5GffpyKZX/BVjyIsi9d3qsxDBl3HsVDZ7F3/bOMnnmFaZP5pn2fs/3fv6axfD25peOZffNfKJ44z5S+j0UKRs8CdA9q1mjoP5juabA5yRsxLZYRLEs7M2fONIQotbS0UFlZSWtrK8FgEJvNRm5uLmVlZbjdmc8MeDTS5uuQJIkf//jHLFmyhGeeeYalS5fywQcfAJCXl4cgCDQ2NqIoCnPnzuVLX/oSy5cvZ8iQ9FVJzAR6isvUuNazpJ/GvetAEMkbMS3TQ+kT9Cb3fVexsKJkIb9sEjVb32PSwtuAzusixAuRBVVIjGWPz98vqQk5/X35noQ2wRzdy7DT38IUVz4lk/2sb2jPmGMNWWLibCUS4oNl1zC85FQunPEIrlAJVEHACQG78QHrjTtXwO1N0FSE7UYhtF6HIFHYG3L5EvZ1RAob7ztiRMbRavR45jQaY/SdnnaBt0V2YlHseN3G3FFBZ4CGIbWGfecO18N4NoXraCytYcA1i/FU7uaLpx6gYcsqxv/sd1jChYZj5LCM3W8UR8frQkaPv4TVK/+HYE0l7sKRAIk1GUQtQdQc3ybKgY9fYM9bD6OGg0z5xt0MOfmrhhCq+M/weIjbdhQMomTKGQQaqzI9lCwmEvU0qKHuhVZ2hShZaN63kUjQnP76M263u88bB0ci7QFSBQUF3HTTTdx0000oikJ1dTVVVVUoikJJSQmDBg3Cbj9+KifrKU+znob+QmP5BnLLxmFxZNMTppL8YVPZ8+5jKKFARiutb/U2McWVzwR3jsFo6MiOTx6luWYn551xNy575lOEmoWv5TCDRnbvOp/cppva6m0C9HoOo39wB+uuOI/mdavYeMPljD/9eobMvrhHhcTyiscC4PdUx4yG3lK18W22PPtTBs34ElO/cQ+Ss//rr3pC/faPMz2ELCZitqdBaAs711S9GGZnxTWzHPtkNDhTkiRKS0uZOXMms2fPZsSIEceVwQDEimll6SdoKsXjT8n0KPo9BcOmoakKzYe2ZXQcmz36JHheSef1MkL+JqrKP2LIhPMZO2RhOoeWUoL+ZsLBVmzOoi7bWgSBuW7dWNrcZjQAyK4c3NNP0vs7XMGm537Kip/P4/Cmt7s9DodrAKAbDcnQUrGNjU/dRt7wqUy78rfZmgRx5Awajbfmi4wU7sqSGkRLVNNgjmdAEHXPpaZETOkvS9/k+FJ09UGyRkP/QY2EqN+5Cikrak85+UP1IouNBzZmdBwrGg/jVyKcVFzAcFei7urwnvepLv+QCfOuz8DoUseW/+iZ7A7vfb/LtguKB1JssbHT18IOX4vhb0XzFxheh72NeKq6J64GcOboGfl8SRgNgZZaPnvsOixONyde+0hGPVd9Fdeg0aiREL66ikwPJYtJCIKAZHWY5mmIZtZS24wGQRNM37JknsznbzrOUcLBHrnjs/Rdgq0NANjyBmR4JP0fZ95g7O4BNB3YjKDpWoX4bB3dqdMQ30ZUxIS6CPEPK1VSwaavzrXg57WmA3ytaBQvrPuIyC3/TSv5eOVcvve7F9hXtRLZkYtw0ijq9hhX4AL2MK35Rj1AIE4f4M/1ocrG41SLUdOgpyrREvc52vsSBBUhro0SsSS8ji+SF8gxrkKqkoLi87Frg54uNmJTaCirM7S5+oePsmTLP9DQiy5qH34IwD+q94KkvxcpIiEHLeSP7pAZRBA44YKfMea0axCCAtaQnFDrQooYvwuHvRhBlAl4ahDbvtvEycWRdTVKJMxnf76esLeJuf/9LPYOv93e6HH6KzkD9aKsnqq9uAYM76J1lmMF0Wo3LXuS0GY0ZD0N/Zus0ZBh+ounQQmHCDZXYXOXxGIljzeiBZ9sef0nbt1sujMRi5/0aaKGFBET2uQPnUbTgU260aCIsUljFDluwimqQoKREC+QlUOWhErF8a9VOcIgd7sodPm+jXytaBTit6/C+rOfUuhroijSxMVP/Zo3qz/GcfI4Kk/ZiK8gbgIuqwSdxlU+y+jthteDLV4sYpzRoCUaQ0rcvnJRYWTB/thrhxhCEtSjHhNSZWryjWFWdVJ8NWo/Vfc9EsvF3rh3HROHPItzgB7OM/yvh7hmyz+IfmLCCScgzJ+P1tLCS+/VYD1ZLyRl8w7H1ZiLyzIFBBEEgf/6yj8ZNvxsONx2rCpg98d/XwlvnTPPf5jCoolYg/r3HbIlNoocIf3j3nceBwSmfvM3uIdPQuthRqvjRRidM3g0AN6qvTD1rAyPJotZmOlpEKRoeFJY9wyo2UCW/kj2W00RmqYRaOo624QaCSLK6fc07P/gGdY8eJVp/Xmr9/LBzxdQu/VD0/o81gi26BljbO6s0dATQp5Gqj9fwc4X79W3l++nef9mNO3oE7hBkxagKmF8jZVpGmnnHCg8QP3qHZCfD1//OqCvbf/XxlcorGrAMW1sRsdnJr7Pt9H0zzfbd2gae5btiL388Y+fQQaipoZ2vR6W9fG/dsDJiffDqvVv4ho4kgmX/Eg3GHrBuImXUjzghB4fF2iuZs+KR7DnDWDwzC/16tzHCxZnHoXj5hIxKf49S99AsthN0zSIbUJoNat76ddkjYZUoalsevrHVG9696jN1EgI0ZJ+T4O3Zh/N+zeb1l90teJ4jucPNrcZDdnwpG4TaKpmzf1XsmvZAxx4/xkOvP939r33FJ/87musffBbeKr2HvHYkjEn42s4QMWG5Wkccee8MG4rANqNN8b2eYHDaAjO/hN+GNi+O2HfvjeNGoRYWFJuLlx5JQD3n34o4biWih1s/ftPkW1Ohp9pbnG27rDj1fvRFIWJF/4w7efuLVv+cQefP3lLRs4d9jTiqdzRdcMsxwzmehr0wJWq9fqiQlbT0D/JGg0pZPgZixnYhSs3U+FJZheVi8ZFHs8iwpinIbewi5ZZAHy1B1lz3zcINBxi8hV3cs4fPuecP2zk3Ac2MOmyX9B8YBsf3XMhO1++n0gwMe42p3g4BcNmULHh5QyM3shq/zZaFS/CzJm8fPP/ADAUcAOhvYkT5mOVvEsWIpcNwnHiFAb/7w8o+d5iZnx/DgCDt+vheUJ0W7wYISeHvaFdlIeM6WiDdVWsf3gJFqebGdc9ZKiFkA6aDm7h4Jp/M/L0xbhKjp0Y/ZCnkdYMTdxlp5uQt/O0wlmOTSSbAzVsVvYk3WgYMOV0U/rL0jfJahpSSDSbwNFQw8HMGA2RIJKJYVExT8NxqmcA3dNgySlI6vtUQn7USAjZnmtahdtMcTT9ghLys+ul+4n4W5n930+RN6JDeIkkMuz0yxk441x2LruX8rceI2/wJEpPvCChnyEzLmTzy3fRWrWHgqLxhr/Fi2hFVcDuN+5zeo2/UZfHhhqnc3D4jIaw1+2lOq4w2uzJL7HGu4qz3Qv4yjcuhId/C4pCDuB77X1+92gY29DESqlK3LpNBOP4AliIxLUJaonXl4wxJGCNEOI77ndir+2EE9rEnwvAg/GekF9iNNb+ffcOdlfW8ucHrZyxYAUANk0AdlB8Vgc9RkkJwi9+AUB5+GVKPm1PN9uybyu777uJUnkwXxn5A8qWW4CGtq0DkotQW3ak2PiKjLUTVFFEkeJ0BXH6BUGI08hoGluX3YPVlc/o829EFfTib/HXa2fXb6ZXO625RTTsWZuZc7vy8NUezMi5s6QGyeogHJfRrLcIgoAgyrHsSVn6J8f2rKSP051Kz2oknEFPg3nnjcZFSpbj29NgdycXmnRozSusvG02wZYak0bVN2nYtZbqdW8yftFPjAZDB2zuIqZ+6zec8uNlR4w5HzL9AhBEDmxalsrhdov3g28SOlgFc+ag3XorGvoNtqk1zJn575M3yttVF32aPRs9PLG0lksuz+OMBe2VoqvHCZTkb0HSdKNEA7SHHoKSEio/2coX4U9jbQ999BLrfrUYVQtz4YjvUeYal+63wcENL6FGwoy74L+xOHK7PqAPYcstJOxtysjETHbmEfZlPQ39CcliRzUpPAn0EKVo9iRRFUzfsmSerNGQQoRuehoykXJVMT08KetpCDbXJJ05KZqVpr8Xl2rYsQpRtjJo5vldts0bNjlhtTiK3T2QkjEns3/jS10Kp1NNkABP574IgPDLXyJMnEj013ARMKvhc8bnZ2aVOFl8ngj337CT/CKZn/9qUGx/7U0OptZsRqJDWNKiRQiLFuEPhvlgyh8AiAR87Pjb3Wx/8g7yxs7ghtPeY2jOhPS/j8ZKNi67A0EQGHryZWk/f7JYc4tA0wh3KJKXLixOd9Zo6AVKOMTBT/5F66FEPVCmkawOFJPCk0CvCp0tANi/yajR0NJidIvV1dVRXV2NovSPi06Uul7Jz5imIRIyNWuT2uZpEI9no6GlzhyjQRCQrP1bUF6/81PyR88wRQMzZMZFeBr20XDocxNGlhy7wtvx//lpsNnwPPww+9r2rwSWAEMIMTq/PHMD7AWbP27mxvkbcOTK3P1QGfmFbYLHkXZm/e2zWDsNiJSUwEMPAfD67tfxqLV8sa6BtXdfQev+bQw7/9tMv+VRXLbitL8PTVX47Ln/RtMUpi++L5btJVPsWv5/VK5+qUfHWHP1Ctyh1vpUDOmoWJz5KCE/SjjUdeMsMdSwny1//x/qd3yS6aEkIFntpgmhAUTJgqroYZhZIXTnHOvz3oxqGoYPH86jjz7KZZfpKz6VlZWsXLmS5cuXs3XrVmprazM5vKTplqchg0JoMz0c0RuPbDs+jQZN03SjwZ3cZCgS8CLbXGnTM1SsepEt//g5p9+1Ekfh4LScM+RppPXgdsZc+P1ute8strzjvsHTzmPjiz9j3+ZlFA6dfsR+BBXkkPHB4/QY+y4MCcSVM2Do7nzD60BOMQcPGo3DNwe0i52fuvW/qDrvIG83NNBRxfAnwAL8gcMMyq+jpjkxNOcwxt9PvZZDAGOYo10whjnZCZMnGFcLJVQmCu0pTmU05B7WIPB5Vf7vzhr+/Hg9w0dY+dXPijh3/gAkZOS8jxlGe9m06L9SW1jS1ncOcXddM/UPNnLwiQ/Iswzikol3MiFyBjwPYtNyIqrx4SlJRcYBRBzIstF4FtVEYzo+HXx87Q1N1DULuz54nLryT5l52X04i4cZ23SzkJuZdRkOrXmZgtEnUjbnK90+xpqjf0bB1nrSHVhlcboBiPiakfpQLZqw34O3upycQaP6vYfWbESrw7SUq2AMT8rSOcf6vDejRsOoUaP41re+xRtvvMEf//hHpk2bxrRp07jllls45ZRTMjk0U+iOENrsMKHuooSDsYeAKf1FPQ0ZqDnRFwh7G9GUMLYkNQ0RvyetDz5NVdCURIFuKmnYuRqAoglz9R3xAtS49oImHHVSZ3XkMWr2FTQc2kTQW4/NVdRpu85iYuWQ0UKwegOIcQ89KWScpNs8LiDPsG99Q/v3XvLaGpY8VEa4Wk8FK7a9Jw14GPADjxLG6VXBabxHxBdgk1GQ4xzCdozfl40wLoyrvyLgor2vzu5E0lEmvJ985OWWmyrYvy/MDdcN5K6fD8XlkrDhRlWFWCiSgUWLYNEi1FYPV+59ga33/I7wgcPkX3wOtx34Lg7ZBa2bAIgojWiacYVTE4wiZ0GUEJTgEccIerG3eCNB7UTQ3HhoC9veuI/SqQsZetJXCQuZn9gIkoym9mwc0cxsodaGLlqaj8WlX/NhX3OfKmDZvH8jax/8NnNueYbCMbMzPZwEMh02eTRs7iJyh5gXJii2CaEFLTEhQTJU7nibyh1vIzQf++Fxx/q8N6PhSZdeeimrVq3i008/Zfr06axZswbQVfjz58/P5NDMoQujQVUioKkZ8TTY8wdhzx/UdcNuIshW8oafcMxn/OktwZY6IPlq0JGAB9mRxtWy6APtCJqBVNCw81MkmxP38Cmm9TnqpG9Qf3Ad6167w7Q+k2Hij36DcqCJwYMHc878+XzfmcNfgB8BLwPzgf+UbkdV+9aE4uD+EDd89wC/+MlhBEHg7dcnct9vR+BytYfyiKKGgmA07mbNgieeAODxO3ZSXXcQIipDH76LQf9zvW4wZIhI2M+6v/8Am6uA6Yt+dUR9TLoRpZ5nmomFJ3kyYDQ4o0aDOdl2TCN2C+vjz54+ct11JOL30FRuXlin7mkwfxGqbMK5nPSV+8jLy+u6cR/nWJ/3ZvRXFolEmDFjBhs2bOCss85i/vz53HPPPWiaht1+7Gfh6erhpIb1VbRMeBp8dftNffBEfM1pE3q1HtrFruX/RzADq21Hor2wW/JGg2RL3wRLa3vipvN5Fva3Ujh+LqLUdXax7pI/eBKTT/8B+zcto2LbG6b1mwxffVFlzxsrefPDD3lg1cd8Kz+fXwMvoCcX/SpwbuEW3svfnHHjob4+ws9/cphTZ+/mjeWtXHixm3c+GsOp84zeyNa8FQh5K5DarhsNYNIktDffhNxcDj37HkPvuo3cc+Yx8vnf45rd80rNZrPxzXtord7NzMvvw+oqyPRwYgiSBS3SswmWxZmHIEoZ0jTo10JfE0NrWtSj1vcm5UD7wkwfRNM0Uxf6dO+ZAgiImvlbf+BYn/dm1Gj4/HPdwnU4HDz++OM899xzLF26lNNPP539+/dncmjmoKpH/7MSxjV4TEbSlAqCiNbF+HrWoQiaif0dBc/hPex946GMPDiPRLBZT5Fqc5vgaUhnXG7seZa+G3Jz+edYnOZHZE86/WbyB09h7Ss/IuhrNL3/nhIR/Lw95Jdo27cjTJ2K8PrrCLm5nA1sB/4OBIHLgOlDKlj2YhOKkt4Jhs+n8uADtZw8YxdPPFbPVy/N4+N147j5lgE4Xe2Ph79/dy6VW1zk0iFLEiCMGgVvv41QVIT26qtUnqYXthMEAdFuXAzJCdZwVvkfERXzhJdHQ1NVNq78NQ2VGxlzxrUMGH9aWs7bXXRPQ8+MBkEUseQUZEwIDX3PaMiEt7TfoCqmemh64z073jjW570Z1TQsW7aMH/3oRyxYsICzzz6biy++mDlz5rB48WL+/ve/89RTT2VyeEnTVSyjIMp4D+9BVdKfjUIweZIviGLaYjej1WP7Umo3VVXJHzk95sLvLZGAB1v+wK4bmkUGHriibEWNZmDphgBVayu+1RFVUhNeCzaR2YvuZ+UfL+CzN37Gmf/1mLGNICFFjO9TFY2vRSWCGIn7PWrG60wKecmvND4Yv/KY0a38zpyx+PJ0LUTdfX/n6leuRTj5ZHjnHVi4EKm+niuAr6N7Hu72NbHk6iasd2kMWHw5eWedgSBL5B8uwuo3Tr5rR1QZXofdzcwetNGwr0Xbyw9aLo+93r3vRJzN7caopkTwLPuA2nXvUL95G8XTz2DsFTewea6bb9UDbXPSwcO28O3xq7gm+H/6QDsyZQra228jDB6M9v77nPN9D84fPENeYw7f+TDOMLA0cOVnl+NQWhjWuo2/znwu4XNFNno1NMmOP99ohLfEFVv35IRRZOO1ELZGCAdaWP3c9zi8YyUjT7qciRfdjiq2t0sQS3dTCG0mgmTplWjUllNEMJOehj5XFTrqLe395Hfrs3dicboZd9EtZg2qA33Z06CCiZ4G2ZHbLS3n8cyxPu/N6Le7d+/e2P/Ftgu3tLSUlStX8vTTT2dqWObRxaQ8Gr6kZSI0QRQ7uHWTR/dcpGcSH304mDn+ZFGCXpq+SD42NO1C6OgDN42eBtFiQwkfXeDaW/JLJzPxzJvZ9s5SDo67hKHjzkvJeXrCP267jHfX+3gmshdh9mz48EOEc86BQ4eQ0A2HS4F/A/97oILNd99L871P4L7lKvKmfd3UsWiaRtPqj6h46jECB/fjHj2NmT/+KwXjTiTkCBFCD7M74ZndPPrYT8jBY7gyBNqmQCedBG+8gVBYyOa9rdy9YzjOHwzr5Izt1DtHUdb6OYdzzNOydEZrbTkfP/0dPPVfMOOiuxl1ymLCYt9b/eyYnrInWHMLMyOE7qvhSVGPeRK3sKZ9n5uq8esMs7Q0u5b/npaD25h1w+NJ96WpqqmeBiXkj2VSNFMI3Z841ue9GU+5eiQWL16cxpGkhq4m0dFYQi1+xS0N6J4G84wVQUxfeBJtngZMMlK2vXA3xRPnMeCEM3vdRzS9brLp5tIfnpQhT0P8ar6JTDzzZiq3vsmat+4gr3gc7sKRKTtXd6nyhDl/4zqWBSI4J0+CTz+Fq6+GFSsQAAlYhK5zeAW4K9hAwxPPof1ukanj2Pd/v6Zu5RvYhwxn6k2/p3jGWYbJzH1X/JSJFdvJQcaNB43EuZhw7bVw//2Qk8PKvVU88pafcDfCql6Y9pf2F2qAktadBGUXLY4h5rw5oGLvO3yw7LsIosRpVz/LgNGnZMSL0B0ESUYN9tx4tuYW0bx/UwpGdHQEUUJ25PY9IXSM3t/D1Ei4WynS+wK+uoN4q02q9aKpMc+9Od2Za4T0Nzwej2He+8gjj6BpGhMmTGDChAnHxLw3++2mkC7DdYTo5DcDK+ZmewZiq/+pf0DHjC2TPrcDHz1L4951SfURdckmE8+pqSqRoDfNucYzYDRYrLEkACnpX7Yy5/KHsTrcvPbEuVTsXpGyc/WE5qkeLo+8g/rxJzB0KLz9Njz2GFpubiyAQUSvHr0e2FBziP9cdbKpYyicfzYjbrqdKQ/9lZKZZ8cMhtqNOSxd/C3OrVjHMHwUYpwYaoA2bBisWAGPPQY5OXzy6mZ+qrzWLYMhntH17/PNLVdz9edfxxZJfhKqaRpbVv2Rd567HGd+GQtuep0Bo/t2+sLexn9bczMTngS6EDvsS3816qMRfeYkI+jVlEjKwmrMfiRqSsS0ib6mqqbe+zVVQZBkBI2sELqNSy65hLlz53LNNdewbNkyw9+uv/56Fi5cyPbt25k1axb5+flH6KXvcGyY1scqXYUntcVTZyKPs9kahFjIUNtNI5WYrWkwEp8K8wAAIABJREFUY+VbENs8DT3Mu94RJeQDTcuMpyGd4UmylUjA23XDHqAKmiE01z14DPMvfZwPn7+ad567ghNOv4Xpp/4Q2W6cWPjcxmtVE91IEePvVogLH9REIUELIUWMbQZVFhJsMIq9d+dNpRqYL8BpP4vwqzsUfdX+vPPgmmtg5Ur9fG3tC9En6yuvmhrr4/eF1/P8nddjyY0QLTjvb3WyI8cYrqIoFnbvnxl7fcqrcxm+XQMm6+/hz2A/vJzrP/sWzrbzRM8bf1fQAK69Fu67D3Jz0erqeOCTRlZUWSg4NI+ZHxl1B3JIQVSMISy+4hHG1+Hoa4HWwVMJyzkJn7Mqi/jcxslR0Gb8fQXwsG/LK+xc+xdaG/cxdOIFzLziPmSrMxZ6p0hqgrehL3gfRLnn2ZNAr9WgBLwooYApFdV7gm409DFPgwnZk1QljGBiNrdOMWlyrj9jzRmrZrqnQTlu064fiVdeeYVVq1Yxe3bnNURGjhzJjTfeyIIFC5gyJbWhm2aQNRpSyJ43HmbIKYsYOPWsTv8ec+NlIDY/FUJoIC3vxeywLlOMBhPCkyIBDwCyI40pV6OrdGlcxBEtNlTPUbIbdVEBGkAR464zayQhhtZSVsoZN/+bDct+xuYPHqCxficjZn6NQePOjKV7bSgy9hu2RhLOFbTHFU5TRKwh40N79rtGQ2/CyjdRVeN7nPTvjqvDK7h+2XQe/GsZ1tmz2lfwb78dWltjreK/lu83PML4763jBMrZKU6nSs1jOBVMYwM/Hf8wwa98FYBtLbdy6Q1bsct2Rq5/nWHcwmR28Qkj8DGASRxCohUnJBRqM4QkDRsGf/4znHMOAK/9q5Wff+8AFzh/zknRz8NqLGgoSHkIuRcb9v3nAuPvYoVrHM81vYo3J5eg7TAAzhbjdS9GJBw+46RYDusTnJaaPZSv/jv71v+TsL+ZnJJRTP/qPZSesBDFphDqUABPE7SE6yXBaOiOEWHySqfQS02DxVVA/qgZhP0tGTAa3H3QaIjew5IzGsxMAW3EXAPVVE+DZm72pEjAQ/P+zTjkgqymoY1Zs2Yd0WDoyPjx4znppJO6bJdpskZDChl13hIKRk4/coNMCnoFMalV8c76gzSFJwlRT4M5n5sZRoMZ4UkRf5vRkNbwpCgZyp6UYiSLg1mX3s+AMaey84PH+Ojpb2PLKWb49EsYPv2r5JVN6nKyMbDmILm+RvaMmHrUdj1lhGUTS6/bzO3nvohw550IS5agfelL8PvfI/z1r2gNuvegowdABBaiFwMaqq40TPAf3HkNS/gqkbAPr+Lhnk0/5XIKuJ3qWJtzKAfKY4Lmjn0bPoWRI2HJErQbbkBo8y5oN97I6nWtXOA0Z5LRUKRnCZMiEaZtX8/B4hPxuo5cpV6JBDm8+V32rnqK2vJVCJKF0innMWruNykefXKfKdrWXXpTERogEvDSVL4ByepMwaiOjsWZR+DQrrSf92jEnp9JfP+aEkmDpsEsT4OJoVSqZqrRIMk2CkbNJHDgC9P6PNaJ1zC8+OKLvPfee5x66qmceuqp3HPPPZ227atkjYZU0sWkNvaQy4inQUA1UUvRMTwp1fTJ8CQp+fCkaMhOvxdCW2yokdRpGjpj2MyLGXrCl6ne+QH71r/AnlV/oaFiI96G/QwYM5/BE85iwOhTEK3GlLnTt3/ALx6/CgFY+s27eXueucJkTdH43RtOinf9hYV3XUTZlKFw//1od98Nzz0HDz+M9tln7e07HttJf4/9toAH0KcnfyTENVR3ft7O+hNFOP98uOEGtIULEUQRAdjy+gcs//lSvPVNiJL5E9Xf/O4W5m5cRUSU+PoD/6HVZYzrDbTUsvc/T7JvzfO4i0fjb6nihPN/zIgTL8OSX2T6eNKFKFlQexGeFGypQbI6kO3pr7JtKxjUp1JdG0nOaBDlFIcnmYSqRGLhsMmiaYqpKVf18CR9bGLW0wCAy9X+O73++uu59tprmTx5Mu+9914se1KUY6G4W9ZoSCHd8iAI6atvYDytdOyHJ5npaUhy5TumaTAjPCkTKVczVachjYiShdKJ51A68RyC3gYO73qfQ9vforZ8Ffs3vAiaytiFP2bSuUtixwgdKh+35KSumnDd3lr+vvgJgpPOZO43ZnHuQgd8+9v69tln8PDDuhHh9xuO63jnEAA/cDdgA77byXk6/ZaLi/VMTtddByNGAKAGgrz0fBM7nnsQ746PTXiHR6Yxr0AXWoPhDWmqwher/sHml+/CWTSMohGzGDP7mwwce3rsHqCQmvtN2N9K2NuMs9i8zE7xiBYbNndxj48LttRhdRdnxLMSqK/EW9u3ClCZIYTWNQ0pmg6Z/HzXFPN0g2ZnO8pqGrpGkiRmzZqVYDAcK2SNhlTSjZtFOusbxJ3Y3LCoNIYnxVKu9iFNgynhSRkwGlIphBY0oVPBqf5598zToMXFpCtxdy5VUxNiaEM24yquJmhErNF9Au6RZyJ8YyaDFQXf3p1svf277G5+B/nk9qw7u04uYsVX/4TPYcPvcgKbEYN2cuuMBkTVFycYXg+vtiPEJaeLhA/FvSsVVfEZ9gjr9vDpuj+x9748rjt/CHznOwizZsGTT8If/gCffw7r1umGxLp1CDt2GDya7wJTgY6l3gyhRzYbTJ0KJ54Y27QpUxAs+iqrVl6O+sgj3PPGKnwNTcg246RWU8Ox33oUUTR6H0Qpl1D+BMO+1iJjuEJLSSOKTb8GbrnvWu5svAyNHHzOZkAXUTf/+21qP3qBgnlnMmTR1TgGD8Ha5KKRdpG1HJYR1K4fvj3VMGz56//grf6CU+98tcu+e4u/vqJX9/5gcy32vOQqz/eWsK856QKWpmOSEDrlRclME0KHEWWrOX1pqqmTfE1V2p/PWQBoaEisqWK1dv79NTYeRevXR8gaDSmkO5PytNY36HheQTQ11Wtaw5OEFHga+oAQWgNyyyYgWmxdtjWP9IcnSRZbr8IyUoUoSeSMm4Sw+CYKzpyY8Pf64tR5GI5E7aFm/vfJZmzP/5ZJ58/mxEvnUDp1GMybp29taF6vbkisXw/19Sx/+WU2bN3Kku98B83hALsdHA600lLdSOhgIMRQVba+tZKXHlvN3g9XM8BqUg74HtBSkIcUNF73gbo6glWVzHz4xbSPx1FUSv22j9E0LWUr+q2VOymeOK/rhnEEm2vIKR2bghF1TdjbjLNkaEbOfUSSDLHUNK1N05Ci8CSTF9JURUGymTRWTY1pBE3pLmqEaN3LLXA88MorryBJiZ9xX6/8fCSyRkMq6W54UiYqQgsmV4ROa3hSH9Q0mJByVQBaK3eghvxdtjWNDKRcdQ0ejbNkGGFfCxZXbtcHpImTvno53oJWFNIfOnUkIsEwm5evZ/Py9djcTpqcJ6KdeCrnnNjKlBOdFI0oMBgSHzz/PKedey73Pvpop/2pqsberV7Wrwuzdl0tDeu2M/DgrYS8PgRBZIC176wit1R3rsdIB/bCUpSQn7C3CWsKwtKCrQ0Em2vILRvf82Nb6iiekJkaFCFfE3nOE7pumEaSDbHUVAU0LYXZk3QEs4TQfb1Og0l6i/6C3W5n0aJFXYYjqarKiy+mf4Gkp2S/3RTSpz0NorlGQ3qzJ0XrW5gzfslii4UG9RYzwpNsbj3kINhSl9RYeoI1rwTXwNFE/K1YHOkJi3INGEFrxQ4adq5m4MwFaTlnfyDY4sPR8h/ktVNZvRZW48Xh9pN3YgHh071c/uaD1B88yOWjRvHUo4+yOBCAQICAX+EdMZ/KTZ9zeOsuQj7dKNV8lRRAHzKRjEz80Q9xXvfLjJzbUTgYgEDDoZQYDZ5DOwF6bDQooQARfwu2TIUneZuxuPqOYQkk72lou2cfKxWhNdXMlKupqdMgIGSF0G0sWLCg216Fpqa+VTixM46NX8kxSrfCZ8zWFnQTPTwpNcXdUk27p6F/hSdFJwLpNBpsOUV4q/fiqzsQmyilmrxR05BsTuq3f9yp0aCJiddlvF6h02Jd8Qs5cW1USSViNX4/isV4vapyJEE/QcS4AilGZOSAcV98WL0g5SHGTcfjY/81LQwYNQ1KXFE0VWltrxwf7bum3YvgqQFfeS6W107nV4EZ1Hq9bNk5jd+9fRtVU+5A9yDJ+DxvoSgeQEKUdePQ6jKGmYiiHTmu5oIoGQ3JzkIZZEup4bUk5hN/F3DXxFU6FTTCcZqTiMX4WlSkRC2LJfH+IsZ9+EIv6yl0vMbsRWUA+BsO4x42uVf9HY2WyqjRMKGLlkaCrfq9wepOv9GghAKo4UCf0zS015rpXWx+dKEnVZoGJRLEWTIcyaRsV5qqmOcVUc2t06CpiqnhTv2B7363s5QUnXPNNdekcCTmkDUaUkl3PA0Zyp6EyZ6GdIYnxYRWJoYnKeHkUoCKZhgNbZlUgi21SY2lJ7gGjgLAW1VO0bg5aTmnKFkoHD+Xum3dz8oTb0gI3bjMVMnYSBXVhEmo2lnRr7jOBcX4gBZUMWGimoDgAIxiN0Ew9qN1lvknTtyvaonaD0VpiGsTQArtotm3B4BcIdC2v0PVbUFCQCKsKkiCiCgISJIxNEwQbUiycVVdjGvTWfIBUTBOhgTBoadv7YAtaPws5KAlwfCL/y5EVeyykrMmJO7rrdHQEau7iJzSMUn9no9Ga8UOrO5ibLk9SxkbbNbvDZkQQod9+iqoJSe/i5ZpJkkhtKboxn2qNA0Rvwdf7X7TxMuqEkHoJEa+N2iaZnLK1XZhdba4m86Xv/zllLTNFMdmzqdjhO6shJtdmbm7mJ61KeZpSEN4UqwidN/zNKhJaBr03Os5sYlBOrDnD0SyOfHWpLcYT/GkefjrDuKrPZDW8/ZnWsL6pC7X0vmkriLQxAMHPuRgoO+7wDNNsOEwnkN7Uhay0lq5k9zS3ugZ9HtDJjwNYa/uBbM4+5bRoKkqBaNm9npS3u5pSJXRoFd4tzjN0W9pZtZpMN3TkI4ieVkySdZoSCHd8iAIQkZSrupaChPDk2IT+XRkT+qDQmgTPA2g6xrS6WkQRBHXgBF4qvam7ZwARW1ZY3ribchydMJqiGLbQBxy5wXYBlhz0dDY5s2cwPhYofnANgDyhk8xvW9VieA5vBt3b0TQUU9DBoyGUJvRYO1jmoZgcw2N5et7rT1JtaYhHDUaHEeudt4TzKxeLTtyTcuGpWkaaG0VpjW9uJvZW5bMkzUaUko36jSIUkbCk+z5g7AXlXbdsJvI9lzcQyejhlJf6Tclxd1MqtOQrNFgdRenVdMAeoiStzq9ngbngOE4isqozxoNphFSg9QFq5GFzldMraLEWGcx273VqBnwbh5LtOzfgjW3CFv+QNP79tXsQ42Eep05CUHEmlto+ri6Ihae1Mc0DcHWeiS7C8nau2q60fTPqaoIHfG3APoE3Qx0TYM5RoO/vjLmQUqW6CKemcLqLH2PrB8phUT8XWfkcRYPTXNefh0l5KflwFbT+pOsNloObiXcdoNMJbI9B9mVR9hnzs1OtJiXcjWZ7Emg6xpaK3Yk1UdPcQ0axeF1r6GEAr1+8HaXaLy5gEDRxFOp+uy1tsJKPXtgdyaWTmjTSfw7cfHuNr/xtydFpAQBtSVgbCMHZfLqjCEaTq9x/UWzFCDEyYEl2bg6LKqBBI2AFqdh0BQfxGkfQgFjSJcgWoiEDuP37wIg6P0QtDAB3+ZYGzXiQdPCTHIWs91bwz5/HaNzyhL6iSehcJvggjijRJKN4mmwYms5bNhzwtpRhtcVI3PxO42/ubpBRq2GJqkosvH3lKBXELQEEXr859XZcV3pHlr2b8E9fIpeoK/D5dDTfjqjNSqCHtIzETRAsKUGW25RRiZmsfAkV98KTwq11vVYG9IRTdF/cynzNPiiRoM5ngZVCZv2/StBr2nZwaKLeFmjoX+T9TSkEKUb+fZDngZCrfVpGI0R2ZFLxO8xzcsRfZCEvamPl7Y481D8XkKexEqLvUGULSZ4GizIjtyk+7G5Swi01CTVR0/JGTASNA1vzb60nrd44jwiAQ/NX2zuunGWLlHbJsviUWKURzsKsQgi27zpC4E71ogEfXgOl5M33PysSaAbDYIo4xo4usfHBpvrsLqLu26YAqKLNH3O09BSjzW395+JEgnhLB6GZO08rC9Zoum8zUppramKaaLtSNCHbDMrq1NbmJcoIQCiJpi+Zck8WaMhhSghX5dtZHtOtzwSZiM7ctHUCGo4YEp/UXGcWav/R0MQRWzuYtMEw6JsRYuEkzKgLK48IgEvodbkQotseSUoAS+RYNfXjlm4BrVlUKpObyXgogkngyBSt+2jtJ63v6K2Xb/iUfLVW0SJcc5idvpqUbIhSp3SenAHaCruFOgZQE+36ho0CsnSc+FusKU2I5mTQF8QEiQLki01k+vekqynIextwld3ANmklKjxRHwtSFaHadmTnCXDTDPclKDPtO9TUyIUjJnV9+p4ZDGVrNGQQg6tXU71pneP2ka25xIJtKZpRO1Y7Hp8ZVSklXR/bTeKUBo8DaCH8ZglGBZlG4JkQQn13oASZSv2gkH4ag8mNZZogbdkjY+e4BowAgQBT5qNBovTTf7IqVldg0lomoaE2GXIzCRnCQWygy986fdwHgu07N8CkJL6DACth3b2Ss8AutGQicxJoC8IWVx5va68nCqCLcl5XwJNVYCu80sFYX+LaXoGNRKi5cDW9hTnSaIEfcgmGQ2RgJfGPZ9R+elLgICgmr9lyTxZoyGFFE84hYFTzzpqG9mRk3Q14t4QvYlFzDIaHG4QBNNEVV1hyxtAsNmcMB5b3gA0JUzY25hUP66SYXhr9yc3lmithub0GQ2S1YGjoDQlYmhBEwxbPEUT59G8bwshT3M06X7KHhSCljgeQRUNm6iIiIpk3CKiYWtv174lIFpAsBo2gbhNsCIIFuMm2gybGPdaEG2IktOwCaINQbCgIKCgIklWQDjicWNyy2iMBNjkOWxsEz8WwYKAcUOwICAZNuI3QdK1Gh02ayBuC8kJW0K2lIjY5fVj2rXR4Tpr3r8FW14J9hSIoIMtdWiq0iujQVNVQi31GcmcBG3VoPtYaJKqhAl7m5LyNASb9Gxiqfi+Qdc2mmU0RBflzNCVaKqCEvKb5mmILn4OP/NKU/rL0jfJCqFTiBLsWtOQufAkPb7SrHMLoojFmZf0xLu72PJKaNq30ZS+7AX6CpOv7iCOwt5nlHIWD6Nq44qkxpKJAm+ghyh5q9LraQAomXomDbvXUr3+LYbOv6zTNoIqdCl8jhc9H4n4Cb4lrrKz1En+c0sgrjBZRMISNraLF+MqVhfxckAxZFwN1TQ/khangYmvpiorCUXghE7WejRUBFEXbEtyHiAiy+0ZdrQORdoswNS8saxr2klEKsQhtQm9BUuC8FkQjcJ4UXQhxBWt0wvZdXgpSKAYEyI4643Xc1HOIPw5xs+wucDYT0RW4nXrCSL1xAbJ07J/a8pCk2o2vUOwqZqSSaf1+FhvzX7shYPJ6YWA2gzCvqY+l2411Krr2pLyNDRWY3HlpywJRNjfYlq61bBHf76aIV6Oai4lkzQNUcG3xeFuS7lqSrcAfFH+Bl+Uv0EokJ5FySxHJutpSCGRbgihLY5c01b7e4LFYW54EuirH+nQNIAexhPyNCSdrQj0DFZA0oXGnCXDCXsak/pMo+FJaTcaBo7CW11uWhrb7uIeNhkl6KP8jceSFpEf77RPp7ueSE/PH0tEU9jSvCeVQzrmaK3YSSToY9CJ56ek/8rVL+EaOKpXnobGvZ/hrzuIu3RcCkbWNSFvc58r7BZsSyKSjKch0FydMi8D6JoGswq7RT0NVlfyRkNUN2daeFLbc082SfDdkZGjFnLWggfJy+tbRuvxSNZoSCFKN8Sssl0PT0p3rYZYeJKJegrd05C+8CQ0zZTYf3vBYARRxl+XnB7BWTIMSM74sOYWgiBmoFbDSJSQn0BTegt/CYLAmC9/j0DDISo/+Xdaz93/0O8h3Vl7L7UXM8BWwIamnakd0jFG+euPEvG3UnLC6ab37a09QOPedZTNvbhXuoCG3Wuw5hTiGtTzrEtmENU09CWimQdtSXkaqlJqNIT9raalWw2Z6Wlom5+YF56kRy3Ijlw9e1K2uFu/JGs0pJDupFzVsxgp3WprJrLdXE0D6JVC0ymEBkzJoCRKMo6iUnz1ZhkNvdc1CKKELbco7Z6GnIFtGZRq0h+iVDx5PvmjprP39UdQwqkvDtj/6frhKggCM/LHc9BfTV0wPb/Zvo6nqpyq9W8x7IxvpGRyfGj1SyAIlM6+sFfHN+xZS8HY2RkRImuahihZ9MWaPkSobXElmZSrgaaqlImgQX/GmqVpiIYnmaFpMNvTYAhPytJvyWoaUkh3NQ3QdmNJYyo7s4XQoN/IPGmKi7e1pR0MNNdixuPdUTQUX7KeBpPCnKzu4rQKoQFySsdSOHYOzQe2UTxhXtrOK2gCCDDmwu/z2e+/TcV/nmf42YlCunhBdLzGoTsiWUETEj16mlHTIHfi8JPj9AuCKqJIxjCulgJjUTZbIA9LwDhRcIfjYtGVFgTBqA8QlDhPj6aixRWJExLUEoAgIorbAbBYykAQkS1DEtt14MTiAbxdvZpNrVWclzMNBBEhTp8gisYJgCgNBNEYfhAoMr4vVZYI242fmSIbv5/WPJWQ3fi+Et6SKiDFaVDUbgRKd3YtJBRl62TV8os3HkeUbYw4+ypdkG+iXkLTNCpXv0TRuLk4Cgf3+Hh/fSWBhkOMPPs7po2pJwQaD+Or3Z/SyXVviIUnuXsXnqRGQoRa67EVpOZ9aZpG2N8aCwdOlpA3ajSY4WnwAiDZzQ5PMue9ZumbZD0NKUPoXp2GWJhQesXQss2pZzsyUYRtceanpbgbgM2tr3iFTFqRdxYPTXqyL9tzsLqL8dUl14/NXWza++r2OXOLiAQ81GxcmdbzRikcP5eCsbMpf/PxtHvd+gtaLDype5Ndt8XNuNxxrG9Yj3qc12zw1R7k8JpXGXraZXqIoMk07l2Hv76CsrkX9+r4ht1rACgce5KZw+o2LQe2ApA3LDUC8d4SaqlDtNh7LeYNtGXgs+elJjxJDQfQlDCy0yQhtLcJye7qVY2PeNrDk0wSQvtbEWUrksUGGgiq+VuWzJM1GlJIt8KT2grKpDuDkiCKbZmbTPQ05OQTCXhQlXDXjZMkurIUMMtoKBlG2NuUtDDcWTwsaePD5i4hYFLhup4wcNoCmvZ9HnuQphNBEBh74fcJtdRx8IPn0n7+/kRPwldmFs5ERGRny/GtbfjizT+BKDLinNSs5FeuXoZkczJw2jm9Or5h91oszjxyMySCbj64FUGUel1fIsoX7/6VvW8+atKoINhah81d3OuQrVi61RR5GqLPE7NCdkKeBlNE0JAaIXTWy9D/yRoNKUPDU7W3y+w+mfI0aJpGJODl0GevmtantS2Hd9iX+mxQomzFklNgWlXoaGhRsmJoV0nyRoN76CRCnoZYjGi6GDhtAWgaNZuPXpAwVRSMnUXRxFMof/NPRALejIzhWEaj58kUJudNRhZl3jz8Jop29HCh/oq/4TCVq15iyClfS4kgVgkFqFr3BoOmn9vrqsMNe9ZQMGaWaUW9ekrLgS3kDBqTdFrSQ6tfon7XapNGBcGWeqzJZE5qjBZ2S1GNhrZ7uFkZhUKeRqwm6BkgFUJoo9GQFUL3T7KahlSiaVSuepGhp3aefx5Sk/q0OzTsWg2aStjTQPOBreSZUP00Ks4KexuxpcDFH4/NXWJagTdHVI9QdxD30Em976dkGIHVL6GEAr1+wOYMHoOmhGn64nNKJvc8n3tvySkdh6N4KDUb32HYqV9PyTni48Q1QTPsG/vlH/Dp7y7lwHvPMOr8JR0PNP1cAJLSnfh3MeHv8bH1fqcxXWxTkYglbDxODhmz3lj8ASytRg0BIaNeQdP8QHcm8xICdgQERLEAARFJOrpoVUBCkmBh2aU8Xf4QnzVsZ97AC4xtLMMMr/3F4wk5jTqQ8onGMMyIHCLgMH4ecsT4vlRBS9ClRGTj++xMv9AbnUFXx+x7+0nQNEaed03SfXVGzaZ3iAQ8vQ5NCjRV4as9wLDTrujV8cmiaRrNB7ZSMjm5jFJqJETrod2MOOsqcwYGhFrrcBQdXbtzNALN0cJux4anIexpxGJC5iRoNxrMFEKbpd3I0nfJehqAcDjM3XffzdixY8nJyWHWrFm8+mriCvwLL7zAtddey7333suiRYt46623uux75ysPHHXVtKMQOp3sf/9vsf9ve+F/TUn52m40pK9Wg1lZhpwdjIZkcJUM1/upr+h1H/kjpoIg0vTFhqTG0lMEQWDgtAXU7fwkI1XKAfJHTqPkhNP54u0nj2pI96ZqdEI16CMIZrvqu7N9iqQatqBNSdhCTtm4uRxolnzDJogu4yY4EreEytL6fgR9DUgXMwuIouuomyDaEUQ7JxSewujcSbx1aBl+RdGLtbVtmrXIsPndVjz5gmFrdfsMmzfXjy/XZ9i8OX7DFrSHCFnDhk0VVcPWlaGoRsJ49u/o9HuN/46P9J0Gm2up+OiflM69CEdRWbeuo55SuXoZ9oLBFI6d06vjG3Z/BmROzxBoqibUWp/0wpLn8B40JYx76ESTRqYLoZOq0dBYhWRzpiysJuxrIXfIRFOyHYFep8GMdKvQHp5klqahY+Vrgaymob+SNRqAH/7whzQ2NvLb3/6WX/7yl1RUVHDRRRfx4Ycfxto88cQT3HrrrSxdupTbb7+dpUuXcumll/LRRx8dte+wp5EvVv75iH/PRHiSr76C6k3tgtem8vVUrX8j6X4tsfCk9BgN9rwS0+oZWBy5WFz5JmRQSj7tqmzPIbdsHI3l6TUaQA9R0iJharf9J+3njjLmy98j4mtm/ztPZWwMxyKapnVbBN0RQRC4cNg38Uc8rKh8PgUjMx9NVdn25M/7EZQ8AAAgAElEQVRY85tvEmio6nU/+1b+FTUSZuR53zVxdO0Em2up3fYRZSdd1OvQoobda5DtObiHmDfZ7gktB7YA4E5SBN1SoWf3SsaT2xFNVQi1NiRXDbqpGnvewJSlsQ00HKK1YrtpqWpD3kasOeZ48ZWgT8+6JicvqgZzK19n6bsc90bDoUOHcDgc3H///VxyySXccsstLF++HFVVefLJJwFobW3ltttu48orr8Tl0q3yIUOGsHDhQm6++eYuz7HvvaeP+Ld2IXT6PA2HVr8McZ6Fjp6H3hJdTYmmhUs1tjzd02BWYTyrqyC2qtdbzCjwBlAwciZN+zaiqemNMy8YNRNLTgHVGcqiBHqV6NKTL6Zhx6dpS+HbP9Cgl5OfMucITio5i49qXqXWX2nyuMxnz4u/p2r1a4z6r+uwF/YutMRXW0HDrrUMnv0lXANHmDvANg59thw0ldI5F/W6j4bdaygYfSKC2Emq3TTQfGArCCLuIRO6bnwUWg5uQ7I5cZWMMGVcwZZ6HIWlSYmYA03V2AtSV9jNV3cA0WLDboLRoIRDKAGvKelWQfc0yDanaQaTLoRu025oIKrmb1kyz3FvNFRXV/PDH/7QsG/27NkUFhbS2KhPft966y2ampqYO3euod3cuXP5/PPP2bJlyxH7H73wRqZddd8R/y6IEpLNlVZPQ8nk0xh22hU4B4xEkC1MuuwXTP76nUn3a017eNIAtEjYtDSvvvoKvFW78VT3fqJqceUjO3KT8jQA5I+ajhLw4jm8J6l+eoogSgw84Wxqt7yflixYR2Lshd/HW13OhkduSLsg/FhFo3vVoI/E+WWXYhGsLD/4F7OGlBIOvvMP9r/5JEPOuIwRC7vWIXSGEgrw+WPfw1dzgHFf+2HXB/QCNRKiZtO7lM65iJxeVnEOttThrS7PWGgStImgB49Bsjq6bny0fg5uI7dsgmlibm/NF/jrK3AUlPa6j1QXdvPV7sdZPNSU9xxuW4wzSwgNun7OLCIBTzZ70nHAcS+EnjFjRqf7A4EA8+bpRa42bNDDRIYNM4oChw/X49fXrl3LlClxrltBxJZXQtncr8Ti3I+EbHelNeVq3vATyBt+Artf+yOH1r7CsNOuMGW1Qbbn4CweFouVTDWxqtAttUnHefrqDqK1TZI3/uU2TvnhP3u1sicIAs6S4UmHOeWP1K/LxvINSac57CkDpp1Nxap/0bB7TcoLvR1JWOrIH8z07/6Btb//FpueuI2ZN/4/e+cd3lZ1/vHPlazlveS9neEsZ4dMEkKgQIAwyi4FWqBAoUAps6zS/soqUKBlzzDKHiGMQIDsPRwndjzjvbcsW5Y17u8P2U7sDNu6x7IT9Hme+8RSrt5zPKR733Pe9/t9sffvo0+9+0D7Gg4bv+9t9gCMwQCcfczd+jb+ypKMqo8xmazq3Rjv06kiIDC+13NBNb1LDzTtjUiOjt6DO47SI6UKBFSoNEkgqZE0fW5UpT5N1j69a5n9VRpOGXMX3+X8jSysjDYuojax95xrYlqx6nonky3hvRcJZJUTm773OerO3pcaH5sPUp+fT18jtyNRt+NHcv/3KMYpixl32f1IqOgrGtXf34Isy2S/9zCtZfuZ9seX0AdGDMgQbrCUrHufxvxtzPjjq27HaCzYDkDI6JmK5uIusizTUpaFcfwCZXGcTkzlOcTOPk/QzKC1PAeAADd3QGSnE2tzLbohUk4Cl/9Hd8mqUjq73aAF9TS015XgtHf2f+IAcDpsOKzt3vKkXwC/+J2GI7Flyxb8/Py49lrXKlZ9vatuPiCgdxbt7+/aiqup6ePiCiDLWJtr2PnCH9j42AWUbji69ryPIQBbh2cboQE0voG01xb3fBgpRVKpkJ0O2hSs1A8GQ1gsIaNmYmlyv6a5m6pDejpMpfs4sPp1t2O5jOKU7TT4GhPQ+od6vBkaIDxtHmqtYVhLlABCR89g3KUPUJ+1jvwv/z2sczk+cK+n4VDmJl1HqG8SK7P/itXumeR/oFRt+4a9r91FUHI66dc+6Xa5Ttma96nc8iWpZ9+McdIisZPsorOtmcJv/ktY2jzCx7uvgNaYvw21zleIup07WFtq6DTVExivbPz2+lIc1jbFcQ7FVJGD1j8UXaDRrdd3tjYgO+1DttMgyzLt9aU9JatK6d5RF9UI3dFUJex77170LN34ERsfuwDwSq6eqJyQSUN5eTnFxcX9Hjbbkcsv/vnPf/Lqq68SGupa9dPpdMDhpkmqri1HrfYIjURd/zf2vL8w757PjilhGZw8RVgz0mAwhLq2dTsaxdUw+0Yk0l5bLCzeMccyJtJUsB1zZZ7iWJXbvur1OP+rf7u9W+BrTMDSUKmovEeSJIJTpg5LM7Raqyd83Hxq9qwW1i/iLgkLLiV+wSUUrXqVqu3iPEVORET8pnzUOs6f9DRqyYe3t19KR0ejgKjKMFcUsOvfN7DvlTuJmnUWU29+AbXOvVKZpoJd5Hz0GMZJi0g96ybBMz1I4bcvYLO0Mu7Ce9zexZWdDsxVhcTOuRCVWtP/C4aA7I/+DwCVRqcojqksGxDXBA2unYaAuHFu/3wPyq0OzU6D1VSHo9MiLGno7hUUljQ01whLGrqV7saecxvz7vlMSEwvI5MTMmmYP38+ycnJ/R6FhYWHvfa///0vp556KsuWHWxci4pyvbHM5t4lRN2PY2MPl+rr/hgbiPmYs9NCS3HmQL89YXQnDe0N4pIGP2MSbbUlHrnZ1PqHoAuOpLUiR1Ech82KufKgI67KR0tQYrrbK5lBiZPxj0pV3I8QnDK1ayfI8zdukZOXYG2u6VFOGU7GXXw/waOms2/5/ZhKs4d7OiMXWRZSZpgavoBFo26jrHknH31+Oi2mEjo6PCNucCjWlnqylz/M5ofPp6Uwg9EX/YVxVzyINsC9myZrSy0ZL9+KITyGSdc8MWRGaW21JZSsfY+4uRcqKi2sy1pHY94WQlOnC5zdwGkp2UtNxvfAwK5jx8JUloWk1hAgqIbe6bBhrspX1JzdXltKYMJEYeVDh8XvEsMQXp4koKfB0WnB1tYsrAnc1mNi1yW5KnslV09UTsiehtzc3AHdtHbvIHTzxRdfYDKZuPfee3s9P23aNADKyspIT0/veb601PWhMGXKlCMPIElYTf2bj+lDYlyruk6nRx0/u01xRO802C0mbAKl4Y5FYGwareW5/Z94DNQaHfPu+xLZ6WTT478mafHVjD3vL27HC0qYQGtlLg25WxTJJIb09DVkEJm+2O047mCcuAhJpaZmz2qCEid5dOy+qHy0TLn+WbY8ehG7X7qZ2fd8jC5o6P+2joaskqGfC5hTJdP3JHufPgi0YNP2fr/bdb13HFU2f1Sq3qvMR/uEkFU+rvIktR8ggfrgarwsqUHq/Uqnj2+fx1psBtf441Iv4beBcby/5TLe//hkrNYmpky/hdEX3nVYP8dhJnqyhNTHNK+v5YLklI5qw+CwWij54W2Kv30dp72ThMWXk7L0RreTBXA1JWe8fDuOjjZm3PY6Gt+hq73O/eJJVD4axpx9m6I4JWvfRRcUQeSU0wTNbOB0mpvY9fIf6d6/UlomaSrbj3/0KGE76m01RTjtnQTEup80mCr201qeg29EkpA59aW9vitpEFae1EJwyjQhSUNHs1hTu87WelRaX7QB7svfejk+OCGThr7JwED4/vvvyczM5MEHH+z1fGZmJkuWLCEsLIytW7eydOlBx9QtW7Ywc+ZMxo498mqSNiCMjgE4FutDonDaO+k0N/Y093oCjW8gPnp/LCJ3Gro+gNtqSzySNATEjqV+/0Yctk7UGvcvSN0391HTzlBsGGcIjcYvIpmG3M0kn3qN23GCEiciqXxoLtrt8aRB6x9CyKiZNBbscOn/D5GO+UDRBxiZ+of/sPmJS1h770Jm3/0RgQmHlDq44RgNHLHxeSA4+uj/ST6HS+P2bciV/K29HqsdqsMSCYdP7wTB1xyC2t77e9OZD39fqRwO9P6JaH0CsATGIEsqrIHRPf/vVKuRVb3n0+HfeyyHD7QFHJyPf/R8lkxawapXzgJkMnY+R7O+ifEXP9BrceOwhuYj1B6r+rpqO49grueQqdryFflfPIu1qZqIqUsYc/4dB+VQ3V1plCVyP36C5sJdpF/7NAExYxU3OR+Nxvzt1GR8z+hzbkMX5F6tPUBbbTH12esZffatw1KalPn2XXQ0H+wVa8zbisNmRe1GmZIsy5jKsjFOOkXY/ExdTdBKdhpMZdn4x4xSdN04Fu11ZUgqdc+OvlLM1QVYW2pRC0i8Orr6APUh0f2cOTCszTU4O9t7lXp5JVJPTE7I8qTBsn79eh555BHGjx/PJ598wieffMLHH3/M7bffTmVlJT4+PjzwwAMsX74ci8UCQFVVFStWrDgsyTgUfVDEgLZ1e3oLmqrEfEODwBAWi6WxUlg834guR2SFjcADJSAuDdlpp63m8FIzd1BrdNTu/VlxeVXY2Dk05m9X1Neg1hoIjEujeRj6GgAS5l9KU8F2GvO2DMv4fQlKnEjoqOnIDjub/3kBu1/8I00Fu4a972KkoFEbMHfW0d4prpwt0DgGjf7gqnzxxnfY8eqNOGzKVVdkWaatupjKjV+yf/nf2PnsH9j35r3ogozMvHM5U256Toh/QsXmzyld8x5JS64hesZZiuMdDdnpJOfTR9EFRypaLAAoWfseklpD/LyLBc1u4MiyfJi4hNPe6fbnkLWlhk5zo1BzutbyHCQfDX5RKW7HMJXvH1LDvPa6EvQh0cJ2V7rlW0XQfa+hxOPiUCxNVT2KkV5ObE7InYbBkJGRwdKlS2ltbWXjxo29/i8xMZGnnnoKgFtvvRWdTsf111/PpEmT2LFjB8uXL+fss88+amxdoBHrQHYaupIGS2Olx0tBDKGxQncafMPiQFLRVuuZpCGwa3u6tTxHyAUgJHU6FVs+o63mgNva6gBhaXMoXf8+LcWZhCioSQ5OmUb5po9xOmweX3GMmHwqukAjB354jbCxczw69tGY+Nt/su7+UwGo3fMTtXt+JGTUDKbf+ipqrb6fV5/Y+OtcF2xzp7KdskNprtmPxdR7MaN234989+fxJC74DVHpp2GYMgmVT/9/m/aOdloP7KWlIBNTXiamA5nYzC5FGLXBn/AJC0i/4Rkip54mpExTlmVKf36Pik2fY5y0iNHn36E45rGo3LGSltJ9pP/2cUWeBvaONio2f0rUtDOG5SZMkiTm/3UFRT++QdGPbxAxYRFOuxXffqTDj4apTKwTNEBrRQ4B0aPd/ky0ttS5VKHixM2pLyKVk8DVIxE19Qwhsbp3kUQ1gXc0VaEPikClPnhL6e1BODH5xScNU6ZMwWQamHnUDTfcwA033DDg2LrgiB7ViGNh6NoitAzTTkNj/jZh8VQ+WgyhMbTXFQuLeSx8I5JQ+WgxVeRweDv64AkZ5brBbyrcqShpCB1zEkgS9TmbFCYNUyhZs5zWilyCEib2/wKBqDU6EhddSd6KpzGV5yh2hBWBITQWjV9Il9GRa4eho6XWo71AIxV/nct11mwVlzS0m1y7kGofPUERY9BHxNNckYXD2k7Zlk8oWf8uPr4BhE6ch8Ho6pGSZSeyyonsdAIystNJS/YOzGX5ILvuJHyjUzBOOYWg1MkEpU7BLzoFtcDLkb2jjax3H6J6+9cY009h0jVP9rqhEY2js4O8L58iMH4CMbPcd38GqNy2AnuHmcRFVwqa3eCRJInGvG2oVD5MvPzvit5fprJskCShnx+mihyMCqRsTeXi1Zz6IvIm39begq2t2e3ErS8dTTVo/IIVG/YdjFclrNTJy8jmF580DCW6wAisrQ04HfZjXrC637wdAsuEBoohNAZ7hxlbu0lYc6BfRJLHdhpUah/8Y8bQWqGsGbobv4hkNP4hNBXuVFQaoPULJih+Ag25mxm99Ba344SkuJrwmw/s9njSAJBw8mUUrnqZotWvHdPZXCRHqjeXu3oWJEkiZNR0ag/xkNAFGrFb2tH6K5OFPNrYA6Fvj8NAPllVsoS9Ty+EXeN6XF6wmoKM91l4+vP4+vRufNRZe6+uSk5Q27XYfeNgC1T7tSCrJBpiD94QOHwOL+Gy+Nl7PXaqZCy+vcuOwhJO5uzRmWgMQUiShE1nw9n1u3B0WqjL2UDVvu/pbG2g9Ns3Xc3WEkiSCiQJSZIIHTcXrV8oKUv/QHDqFIKS09Ea+jRzOnG7v6Qv5uoDZLz8J9qqDzD6vNtJPv06VJL6mJq0Snscin96k46mKtKvelLRDbYsy5SsfZfAhIkEJ01WNCcl2NpN1O/fQOKi3yhOyE1l2fgaE/HR+wuZW/cugbumbq45uXY/lMQ4FrZ2k9Cb/B4lJqOg8qTmaqFSs5am6iEt9fIycvAmDUOILsgIspPO1oZjvkElSUIfEt3TnORJuhWULA3laHzFrLr4RiTSvDXDYw20gbFp1O79Sch4kiQRmjqdpsKdiucVljaHotVvYu9ow0fv1/8LjoA+JJqIyUtoGcCO1VCg8Q0ift5FlKx5hzHn/llYU58SQsecRO2e1SSffi1+Ualkvf8gWx6/hGk3vYh/tPu7QyMFq6WZTStvR6cPxsdn4CVXvgbXTkN7e/8lkYNB63tktRa11kBU+mmET+/T4CrJLoWpYzFEpQvVO79j3/L7UGv1zLj1dcLSusrqhrDtxWqqp3DVK0Skn0rYmFmKYjXmbcVclc+kKx8bVvGBmszVyA4b0dOU94CYyvcTnJTe/4kDjdclsR2oRDmpfD+G8Hg0hoD+T3aDg8pJYm7yu5MGP1E7Dc3VwpSTZFmmo6mKyPRTe56TZFA5vb1mJyLePf0hRB/kuogPRI1HHxKNpWl4dhoAoc3QfsYk7B1mj/kLBMSOpdPcSKepXki8kNTptNeVDkj56liEpc1FdtppKtzhdgxJkvAzJlG59UvFWunukrT4akCi+Ke3hmX8viQuuoL5D33L2PPvJG7OBcy6bTmOjja2Pn4J9Vnrh3t6itm26j46zHXMX/Yf1D4D3z3R60JQqTS0WcQmDccDToeN3E8eZ8+rt+EfM5o59312MGEYYgq++Q9Om5W08+9SHKtk7bto/IKJnj50DdsDoXrntxhCYwlSeLNva2/B0lBOgGBTN1C2S2Aqyx7iJmixHg1tXfEMwhqhq4WVE3Wam3DarN7ypF8I3qRhCOluYhuYglI0HY2e72nQh7o6AYQ2Q3crKHnIGbr74mFSaPLWTUjqDACaCncpi5MyHZWPloaczYrixM29ENlpp2LrF4riuIshNIboGUsp2/gRtvaWYZnDoUgqNf6HqKaEpE5j9j0foQ+LZed//kDJj8uPW0Wl0pxvOLD3Y9IX/Jmw6MGVp0iShK/BSPsvLGlorcxj27+upHj1m8QvvJxZd7wjTBWmP0rWvkvpuvdJ+/W9+EUmK4plaaykZs9q4uddNKxN/bb2Fur3byRq2pmKdzuaDmQQMmomIclH8TJyA1NFDvqQaLRu+hXYO8y015UMeT8DiPNoaK8rQRcUgY/Ot/+T+8Fhs9JpbhT2HjmaEpPX3O3ExFueNIToAl07Dd129cdCHxrjsp1X6DcwWLT+Iai1BiwCDd4O9WpQ0gQ8ULpdV1vLczCOX6A4XmD8OFQaPc0HdhE9zf1GNrVWT3DKNOpzNymaj39UKiGp0ynf9AnJp107LGULyUt+T+W2Lyld9z6pZ9zo8fEPMxDr48vgGxrH7L/8j8y37iTn439iripg3KX3C5M7HCx95wfg6NO/4JSlnv4AgA5zA5u+uYOQ6ImMXXIzVrUNh4/zML+HTl1vp3LJKaHq+vno/Y2Y7NU4VWAKOSj36zxCyVCnrk9PgyT39FT0PNfXkO4I9PVgkCX5sAv8kX4e/f1Oj0ZzUSZN+dsxlWbRmLetZ4dx0jVPEnPSOa64/YQS4dNQu/dnsj/6BxHpp5J48uWK45Wt/wCAhAXKYynBZTRqJ3r6mQJifY+pPJsggf0ZreU5ykzdejwehnanQRdoFHKTD2KVmEQbu3UnDQbvTsMvAu9OwxCiC3IZtQ1opyGky6uh2bN9DZIkoQ+NEbrTYAiLRVKpPbbToPULRh8cJawZWuWjJThpMk0F7pcVdROeNofW8hysrcpKteLmXkRbbZGiUiclBMalET5+AcVr3sFhs/b/gmHAR+/H1Ov/Q/Kvrqd8w0fsffNuzJX5QzKWrJJ7H1LvwzmAG2BZOvj6lpocNrxyOb4hscy85BlkrRqH2jmgw65x9Hyt9zfS3u76vDnaOUc7nEc4+n5fA+FIN+SSLB12uHNOR3MNWx+/mLzPnqR657c9CUPsvF8Tc9I5A5qfCFrKssl443YC48cz+ZqnkFTq/l90DBw2K6UbPyQyfTGGMBE6cO5TtfMbDGFxBCoUXnA67NRk/EDEpMVumcIdCYfNSlvNAUVKTK3l3RKwQ5g01JcK62cAVxIiTjmp29hNnNyqK94hSUNXT4Pow8vw400ahhCVWoPWP3TA5UkwPAZvvoIN3lRqDYawONo8ZPAGrhIlUeVJ4JJeNZXvx97RpihO2Ni5ABT9+IaiOFHTzkCt96N848eK4igh5bRr6TTVs/ede4ZtDv0hqVSMPe8Oplz3HM1FmWz8+zKy3nsQq6B+F9HIskzh+jf5+d/nYDXXk37mXwmKcv9mxuBnxNJ24pYnqTR6/GPGuB50laAZwuOZcMXfPDYHS1M1O1+4Ho1vENNvfEnIanL1zm+wmZtIWPgbATN0n05zEw05m4merrw0qTF/G7a2ZqKm/krQ7MBclY/sdCjqZ2jI24aPbxC6IHHqQX1prysV1s9gt7ZjbakVFu+gR4MoY7dqVD5atP6HO9WLIrfiG77adgstLcNfHvtLx5s0DDG6oAEavHVl6cMhu6oPjRVangTgF5FIm4d2GsClpNFWfUCIUy24mqFlp4Pm4j2K4mi7+lqKfniN9voyt+P46HyJmXEOVbu+w2ZpVTQndwkdMxuQqNrxNfkrn+vS4R+ZRE37FXPu+4SERVdQsfEz1j9wOoXfvISjs2O4p9aDtbWeza9fQ+YXD2McPY9T7/iOiNS5imLq/Yx0tNUftz0dR8Nqqifv86dY99fFmCvzeunLp551o+KV/oFis5jZ+cJ12K1tzLjplR6xC6WUrH0Xv6jUYTdR7C5NihKgmlS96zvUOl9Ffgp96SktcrM8ydraQE3GKhydliEr8+xsa8be2U5gohjFKEvXdcNPWHmS2KTBVJ6N1j/0MGlekb0MadFnce6M5wkKChIyZy/u400ahghZltn33v1IKp+BJQ1db+DhMniztTUrXlU/FF9jEu11JR67eQmIHYvstNNWXSAkXnDyVJBUiqVXW0uzXF/ITrY//zs6zU1ux4qb+2uctg6qtq9UNCd3kSSpx/m34Jv/sPvVm7F3mIdlLgNB6x/CuEv+yryHviIsbS4FK/7NhofOoHLLl8Oe8NTsX8PPT5xBXf4mJp//CHN+/wa6gHDFcQ1+ETidNoZUY9SDWBoqyP7fI6z766kUff8axgknM+e+z5h936eotHp0wZFEe6gsyemwk/H6rZirCph67fM9vVRKaS7eQ0vJXhIX/mZYZVYBqnd9i68xQXGTsOx0UJPxPcYJC4U2dZvKslHrfN2q75edDjJeu9X1tb1T6PXuUFqKM7G3mwgQJP+c9/XzANhtYhY8rE3V+BgC3ZYBPxSbxUzD/k09SoM1mT+x7737Fcf1MnLxNkIPEZIkMfGKf5D5zr3U79/Q7/lqrR5tQNiwGbyBS70jIGa0kJh+EYk4rO1YTXXCVuOOxaEKSiJUMTQGfwJixypOGppLMnu+bq8rZeeLNzDr1rfdupAGJU4iIHYsZZs+JuHkyxTNy22kg+sMNXtWs/lflzL/vi89ttLbM41BNLL6R6Qw7Yb/0pi3jZxPH2PvW3dT8tNy0i68h1CFuvrH4rC+BrUTh83K/hWPU7T2bQJixjLn5vcIjB6Ds8u4oG/fgF3jOLwRWt/78aH/L4W7VuKcOGgNtPQ6r28jdN/5dfdmHOs1R6Lv/IABLUcdq0fCXFXIge9fpWrbVyBJxJ60zOXLcYhC0dx7PkVSqVGrtL1yJBFNzofNVZbJ/vAR6rPXM/Hyv2McP19Y7LL1H6LW+xF7kjInaaVYWxtpyN0sRGyhsWAHneZGohQISRyJpsJdREw61a3Pm4Jv/ktj/raex43524iYdMoxXuEezSWZLgfshEmKY7XXl/YYWZrKsmHOhYpjWpqqhCkn5X7+BCCDLGMq309k+mIi0xeT8ddLhMT3MvLwJg1DjD7QSKepHtnp6PeDzhASPUw7DQcN3kQlDb5dCkrttSUeSRp8jYmoNDqX0+dsMTFDU6dTvuWzfh29j0VLceYhj2Sai3ZTn72eyCmnDTqWJEnEzb2I/R//w6UzPoSSgUdDdth6PTaExrpM9Tw+k8ETOmYWc+7+hKrtX5H35TNkvHYr2kAjxokLMY5fQHDKlCFRW3J0WqjP3UhN5o80FGzD0lhJ8sKrGXfuXYMybxsIOn9XORzyyC0dOxKOTgstxXtpOrAbc2U+VTtWovLRkbDwcpKW/K6n5+tQ/KNHeWx+RavfoGzDB6Scfh3x88XdEDUd2E355k9Iu+AeYY7J7lKz53tkp4PoacpVk6p3f4dKoxdamtTRXE1reTYxM5YO+rVNhbso+Oa/vZ6ry1o7JElDS9Ee/KNS0RiU/T5lp4M9b9/V816uz1onxMDU3mEmdPRMRTHA1RtStuGDnscVW7/oUaSSAMnbuHxC4k0ahhj/6FEEJkzA0lSNbz+qGPrQGNpqDnhoZgcJiB7FtBteEiqLFxAzBv/o0bSUZQn5gOoPldqHyMmn0Xxgt7CYIanTKVn7Lq0VOQS5qSRysMFcQhccwbgL7iHiEOfMwRIz61xyP3+C8k2fMP6SB92O4w6y0+naaebA0A8AACAASURBVJCcqHV+qHw0TL76CbcTquFAUqmIOWkZkVN/RU3Gaso2fEDxD29QtOoV1Ho/wsbOJnz8AsLHz8dXgZGS1VRPTdZP1GSupj53I06bFR+9PxHjFzHjd/8lqFvuUfB1VR9gJDz5JJorh8dBfKBYGqtoKtpF84FdNB3YTWtZDrLTJQEbmDCRUWffSsKCS9AGDF1z5UCp3r2K3M8fJ2ramYw59w5hcZ0OO1kfPIQ+OEpoIuIu1Tu/xTciiQCFUqSy00nN7u8xTjhZSAlMN3VZ6wAwTlw4+DnJTnTBEVibD8qfN+RtFTa3g+PINBfvUfQZ303xT2/RfIhXUHtdCa0VOYqkYh2dFhrzt/d4EbmL0955mCBG5dYvGXvencfV9cDL4PH+docYfWgMLcWZmCvz+k0a/CJTsDRWCFlNGAw+en8i0xcLjakPjsRubXN96C2+WmjsoxEQM5qqHSuxttT1GOspIbjLY6KpYKfbScOsP72FLDvIX/k85qp8oqadcVjD2GDQ+gUTOeV0KratYOz5d3nUBEpSqTjp9nfRBYZhazex+YmLyfrwEaZc85TH5iAKtVZPzKyziZl1NnaLmYbcLdRnr6MuawO1e34EXLtlxgkLCB11ErogI5KkAomufyVAcr1PJQlJVmPvMNNUuIOazNU0l+wBWcYQGkvC3EuInLSE8JSZQ+4bofePoL5oK2qtuJu1/nDYrNTu+xlJpUZSqVw7qmqV62tJjaRS47R3Yq4qoOnALpoP7O5pxlRrDQQlTSL59GsJTplKcPJktP4hHpt7fzTkb2PPW38hOHkq6b99XNF7ty8la96htTyHqdc9L/Tm2h2srQ005G0l9YwbBBi67cJqqhNemlS3by36kGj8owe/Gx46agaL/7mejY//GltbMwkLLsMwBAaA7fVl2NqaCRawANd4BMnv2syfFCUNrRV5IDsJUrhL7bBZD+tX7DQ3Yq7Kd81Plr07DSco3qRhiOk2oWmtyOl3K9QQGoOpNAtLYwW+XSVDxzOhqTOoz93ksSTIOGEheSueoS57PXFzLlAczxAShSE0lqbCnSQtvsq9GF2JYtT0pex68XrqstYp3hKPn3cRVTtWUrPnB2Jmek6bHiAkZWrP16ln3UTByueInLxESEnDcOFj8CdyyhIipyxBlmXaaoqoz15PffYGyjZ8REtJFs0H+ncHD06aQnNxBkEJkxhz1q1ETlpCQMzYnr99lZu19of1IvSpPDr0PlbjF4ik1oDs7PW6I/lG9I07GC+GQ7FbTOx+/ZZjnhOUlE5LcSb60BhCRk0nOHkqwalTCYgbi0qtGfSYnqBy+0r2/e9BjOMXMuHyR4Qm6JamavJXPodxwkIip5wuLK671Oz+HmSnoNKkVah8tERMXKR8Yl047Z3U52wiZta5bl9LnA47Gp0vERMXknLa74XN7VBautT2gpOUKydNve45zFUFbPznMkJGzSAwfoJbuyy95lfmEuYITJigKI7GEMAp//iZ8i2fs3f53Uy47G/oAo0eLRn0Mjx4k4YhRmPwxxAW1yMVdyy6zWZMZdknRNIQMnoGldtX0F5b3KuBcagIiBuHLiiCun1rhCQN4CpRasjdrDjxMY6fhy7QSOn6DxQnDaGjT8IQFkfZxo89njQcSuqv/kDd3p/J+t9DhKRO90jvymAYSENs35tkSZLwj0rBPyqFpMVX4ejsoK2qiM7WekDuUV1yrbLJ4HQi43JA9vENwj8i6TApQ7mrBulIXQZ9162P5MB8WKNxnxc56P296gPCsZobsR/iQH2kZGAgTc59X3ek5EPjF8z8e79CdjpcPx/Z9W/3Y1l2oNLp0IfEHHl1Vz76HPsyFE3OfXE67OR98S+KfnyDkFEzmHDZw+gEl0nt/+T/kJ12xl/ywLArJgFU7foWv8iUgx4YbuIqTVpF+PgFQns0mgp34rC2YZzg/k1zc1EGDXlbSFh4hbB5HT7Gnt5eIgpQqTWoVBqCEtNJWPgbYqYrl8E1lWWh8QvubcSmAEdHGyGjZxE9fSka38Be/+c1Yzsx8SYNHiAgduyA3IoDYsaApMJUtp+oEbD6pJTQrrrJxoIdHkkaJEnCOHEh1Tu/xemwCVnBNE46hfa6UsW1pCq1hri5v6Zw1ctYGit7FKvcQVKpiJt7EflfPUNbXakw/e7BolJrSL/qCTY+eh773ruf6Te+PCJugESi1uoJSuh/K/+ICkLDhC4ggo5Wz5nZqdSafnXzB5KgjAQ6zU1kvH4bDbmbSVh4BeMuvFd4SVld1lpqdq9i9Dm3CzPsUoK1pY7G/G2MOvNGxe/f5uI9dDRXM2aZuN4PgNp9a5B8NISNdV/loj5rHZJKTViaMi+UY9FSkklQwgRhdf0tpXtpKckkQNAKvqnUJaAh6nO6pSST9triwxIGLycuXp8GDxAYl0ZbbXG/xlJqrQH/qJQem/vjHb+oVDT+ITQVHl6bOVRETFjUVVvefznJQAhPm0NL6T4qt32lOFb8vIsBmTIBrs5xc84HSUXFpk8Ux1KCf1QqY5f9hbp9aygf5rl4caEPNB536kkjAVP5fjY9fiGNhTuYdOWjTLjkIeEJg6Ozg6wPH8EvMoXkJUNTIjNYyjZ/SujomURNH7wqUV+qd3+HpNYQIbhHrm7fWsJGn6TIfbsuez3BKVPRGAIEzuwgTnsnprJsIf0M3TQX7UGt98M/Srnng9PeSWtlHoHxykqTDsVUkXtkvxIZJKdT+OFl+PEmDR4gIDYNZCetlXn9nhsYN96lx3wCIEkSoakzaMzf7rExw9LmIqk11O1bIySe1j8U44STqdy+Atnp6P8Fx8AQFotx/ALKN32M02FXFEsfHIVxwsk9krDDSeKiKwkdM5v9n/xTkeu1FzHoAozI3qRhUFTuWMnmJy/B6bAx+8/vEydAD/9IFK56CUt9GRMufRi1Zmib4geCrd1E0Q+vodb6Kl7NlmWZ6t2rCB83T+iNeXt9KW01BxSVJllN9ZjKsjCOXyBsXn0xlefgtHcSlCwuaWgq2k1w0mQhPjitVQXIDpviJuhunA475uoCAmLEmBx6OT7wJg0eoFvCrrWi/76GgLhxdDRX02luHOppeYSQUTOwNJT3qKUMNT56P0JHz6Qua62wmDGzzsXaUktD3hbFseLnX4q1pZa6vT8rjzX3IlesLinC4UJSqUi/8lGQIHP5PcPutjwYJFnq9xgIskrudRzxnK5G40MPZ5/DoXL2OmRJxql29ns4fBw9hzYoDGQZu6qz57kjveZI8+k7ft/5Hek17v48hup3MRicDjs5nz3Bnjf+TFDCBObd/ZnQleJDMVUVUPjdy8TMOldRmY1Iin58A7vFxJhzblUcq6VkLx2NlURNFaya1CO1usjtGN0Gq+ECfSP6crAJWszfj72jjdaKXIKTp/Z/8gDoXoxU2gTdTVttMbLdRkCs8v4NL8cP3qTBA/iGxaHW+dE6qGboE6NEKXTUwb4GT2GcuAhzVQHtDeVC4kVMWoyP3p/KbSsUxzJOXIQuOJLSQ0xx3I41aRGBCROpzvhecSylGMJiGX/RAzQVbKf457eGezq/aPSBroZ0a2vDUc+p3b+O1qr+dz5PNJz2TpqL91D883J2vnQTq/40kaLVr5Gw4HJm3fq2EKnmI1Gft5WN/zgbZCdpF9zT/ws8gLW1keKf3iZq2plCjCKrd69CUvkQOVm5R8Gh1O1bg29EEn4Rie7HyF6PNiBMUV9afzQX70EXaBTWZNxSshdkJ8GCdi5MZVmo9X7C+mjMXX2aRypPkpCRZPGHl+HHmzR4AEmlIiBmDKYBNEN3f3ibTpC+hoC4cah1fjTle7KvwbWNLapESa3VEzXtDKp3f4+j06IolkrtQ/zci6jfv0FxKY9KrSF6+lIqt3xGfc4mRbFEEDv7fCLSTyXvy6dprcwf7un8YtEFhgNgNdUd8f/tnRYy3ruLHW/cgt3a7smpDSuy08maB09l8xMuV/XazNUgOwmIG8eEyx4eEg+NTnMTmcvvYfu/rwTZiS7I2PP7GW6KfngVR6eF0UuPLZc7EFylSd8RljYHjW+QgNm5cHRaaMjbqqg0SXY6qM9eT/i4+UJ9NvrSXJxJUFK6sCbj5qIMAIKTpwiJZyrNJjBuvLCfQWtFLpJKjV+k8n4LL8cP3qTBQwTEpdFakXOYIUpftH7B6ENjTpi+BpXah5CUqTR6sBnaLzIZX2MidftEliidh8PaRs2e1Ypjxc27GJAo2/iR4liJi36DPjSG3M+fHPayIEmSmHj531Hr/ch8+y6cDtuwzueXii7QtVre0XrkpMFHa2DqlU9hri1k3ycPe3Bmw4tr8WZs3yeZcdMrQzJeS+k+1j18OhVbPu95LnzcvCEZa7B0tNRSsvZdYmaeI0Rbv6VkL5b6MuGlSQ15W3HarIqShpbSLGxtzYpi9EdnWzPttcVC/Bm6aS7KwC8yGa1fsOJYstOBqXy/kB2lblor8/CLTDlqb463EfrExJs0eIiA2LHYLa10NFb2e25g3LgTJmkAV1+DuTKPzrZmj41pnLCQhrwtincGugkdNQN9aAyV275UHMsQEkXEpFMo3/wpTnunolhqjY4x59yOqSyLqp1fK56bUnSB4Uy8/O+YyrIo+OaF4Z7OiKe//oC+PQUDObRdK9kdrXVHPScsbS6jfvVHyrZ+SsnWT3qeH+z8jhfsHW0UfPsCTYU7Dz4pScTOPh99cOSQjGlrb8FubaPHiIKDCd1wc+C7l5AddkYJ2GUAKF33Pn5RKcKlwuuy1qLWGggdPdP9GNnrQJIIHzeUUqt7AQgS1M8gy7KrCVpQP4O5pginrUNYEzS4kgZvP8MvD2/S4CG6dcxNA2iGDowbR1tt0QlTOhA6yvWB3+uCPcQYJy7CabPSkLdVSDxJpSJm5jnU79+I1aRcAz9+/iV0muqpyfxRcayYmecQEDeOvC+fxmFTloSIIGrK6SQs/A21e3+kasfwJzLDgbu+BCJu0rtvTI9WntTNmDNvIXT0SWR9/DCtVfnCxh9JngxOeycla95h7UNLyP/q34SnzWHajS+j1hpAlkkZQtnT8LR5jDnn9l7PaQPChmy8gWJpqKB0w4fEzblQiMeLpbGSym0rCE+bh8ZPXGmSLMsuqdW0uag1Orfj1GetJyhxElp/sQZ9h9JcvAckiaBEMTsN7fVl2MxNAkuTxDhBd2OzmLE0lLu8pY6EDJJTFn54GX68ScMQIcsy+967n5rMnwB6MvKBmLwFxo8HWR6QROvRsLbUUbbpk2GX4wRcdZ4+Gpo82AwdOnoWaq1BWF8DQOysZchOB5U7ViqOZRy/AH1oDGXrlTdESyoVaRfchaWxgtJ17yqOJ4K08+/ER+/Pnrfu7HkPePEMaq0ekPpNGiSVmqlXPYNa58uuE6y/QXY6qdi2gnV/O4Psj/6Of1Qqs+/8iGl/eIHISacw809vMenKR4WU5hyN5uI95K98lpBRs0i/+kkCEyYQOnrWkI03UAq+ewEkSD3zJiHxila/ASDcd6KtuhBLQ7misqLOtmaai/cMqdQqQEvRHvyjUtEYxLhgNx/YDQjsZyjPRqXR4ReZIiSeueveJKCPqWNN5k/se+9+IWN4GZl4k4YhQpIkJl7xDyK7TG589P74GhMGqKDU1QytoESpMX8b+969r2eFYThRa3QEJ07yqIKSWqMlLG0udfvW9ttHMlD8o0cRmDCRyq3KS5QklZr4eRfTkLuZttpixfHC0+YRPn4Bhd++iK29RXE8pai1Bqbf+AqBcWlkvPanEdGo/YtC1X/SAKAPimDKb5/C1t7C7rduPabi0vGALMvU7v2ZjY8uI/Otv+BjCGDGza8z67Z3CDnkBiwkZeqQeTGAq2dg18t/RBdoZNr1zxE7axnz7vmcIEErve7SVltMxebPSJh/mSJX+m46zY2UbfyImFnnCIl3KN2y2cYJ7sukNuRsAtk5pFKrsizTWpErrDQJoLloN2qdHwExo4XEM5VmExCbJsypunvxs+9OQ2T6YiZe8Q9AdhlMij68DDvepMGDBMSMHVB5kj4kGo1vEK0KkobQsXMAaMgdGTdrIaNmYirN8uhqpnHiQiyNFZirC4XFjJl1LqayLFqrChTHipv7aySVmrINHwqYGYw97y/YLCYKv3tZSDylaAz+zLj5dXwjktj18k3CXLq99I8kqWCAKi7GtPlMuPhv1OdsZMNjy2gs8JwZo0iaCney9enL2fniH3B0Wpj8u6eZd8/nGMcvEKZoMxActk52v3Izdksr0254YUjLYgZLwdf/QVJrSP3VH4TEK/75HZx2KymnXSck3qG0lO0nLG2eomSkLns9Gt8gghInCZxZb0xlWXS01ArdzWgu2kNw0iQhpm6y04mpLEtwE3QuPnp/9IITRS8jH2/S4EEC4tJoryvp98ZZkiQC45U1Q+sCQgmIG0d9zma3Y4gkdNQMZKe9R0bOExjHi5VeBYiZcTaSSi2kIVofFEFE+qmUb/lMSC9CYNw4Yk86j5I1y7E0VCiOJwKtfwizbnkTXaCRHS9cR8sI2PnyFO4YnB0W4yiGav0dyDJ2a9uAz4+achpz//Ixaq2BLc/+hoJVLw1YjUvE9+ku1tYGStf9jx0vXM+Wpy6jva6UCZf+jQUPftv1XvXsJU6WZbI+eIjmogzSr3piSH0BBktrZT6VO74icdGVQvwo7B1mSta8Q2T6EuFlXqaybKq2r1CkNiXLMvXZ6wlLmytshf1I1GT8AEBY10KdUuzWdlorcoQ1Qbc3lGPvMIttgq7IJSB2jEeTcS8jA2/S4EECY9NAljEPQMM+MG48rZV5inoSwtPm0nRgpzAFISUEp0wDSfJoX4MhNJqA2LFCkwZdYDjh4+ZRuf0rIRKn8fMvxWZuokaQQdvos28DIO+rfwuJJwJdkJFZt76NjyGA7f/5nZBdGi/9I0mDW6UMjBvPvLs+I3rqmeR99RTbX7x2RJYrWVsbKV3/Adue/S0/3TOPrA8eor22hIlX/B8n/+0HEk6+DJVaMyxzK1nzDhWbPyX1zD8SNfVXwzKHo5H/9XOodb6knHatkHil6z/EbjGRImjX4lCK17yDWmsgft5FbsdorcjF2lKrqLxpINTsWU3oqJlo/UOExGsp2YfsdIjrZygT2wQtd/VbBsSkHfM8r+TqiYk3afAgAXHdCkr9G7cFxI/Hae+kreaA2+OFjZ2DbLeNiLIQjSGAwLhxHi99ME5YiKWpCmtro7CYMbOW0dFYKaRHIzxtLobweMoEOESDK1FKWnwVldtX0DKCZHsNoTHM+tNbqFQ+bH/uatrrS4d7Sic87qyyawwBTLnmGSZe+giN+VvZ8NgyYQpkSrC2NlK64QO2PXsVP90zl6z/PUhHUw2pZ9zAvPtWsOCh74ifdxE+Ot9hm2N9ziZyPn2UiPRThRimiaSlNIua3atIXny1kJtbh62T4p/eJGzsHKHeBODaParcvoLY2ecrMoqry14HQPi4+aKmdhhttcWYq/KJnHKasJjNRd1N0GJ6JMyV+YSlzcU/Wow8akdTFXZLK/5eudVfJN6kwYMYQmOJnrEUfXBUv+d2b2srKVEKGTUDSa0ZMU2o0dOX0l5fis3S6rExY046H0t9GZVbP+//5AESOXkJap0fldu+UBxLUqmIn38JTQd2YxpAk/xASDn9D2h8g8j9/Ekh8UThF5HEzD+9idPeybZnr8bSVD3cUzqxcbN0QJIkEuZf1lWupCdnxb/Y+OSFFP7wikeTvU5zI2UbPmTbc1fz873zyHr/QSxNVaT+6g/Mu+9LFjz0HWPOuY3AuLRhL5Nory8l4/Xb8ItMYfLVT3q8LKo/8lc+i8Y3iKRTfyckXsXWz7C21A7JLkPZ+g+Q7TYST/mtojj1WesJiB07ZD4cQI/ZZ2T6qcJiNhdl4BuRJKwXpibjB5CdRzVhGyzdTdCBsWOPfpIMkuwUfngZfkbWJ9sJjqRSMeV3zxAxcVG/5/pFJqPS6DCV9b8rcTR8dL6EpEx1KUiMAEJHz6KjsYqaPT94bMyA6FRCRs2gZO17yE6HkJhqrYGoqadTves7HJ0diuPFz70IQ2g0BV8/L2B2oPENJPXMm2jI2Uhd9nohMUUREDOGmbe8ga2tme3PXTUiy19OFCRJ2cd7YNx45t/9BfGzXSpDuV8+yZqHT2XDY8soWPWSENUvcNVwt5RlU7l9Jfkrn2X3a7ey/u9ns/7vS9n3/gNYGipIOe065t33JSc/tIox595OYNy4YU8UurF3mNn54o0gy0y74UV89GJkN0XRdGA3dfvWkHzatWgMAYrjOR12in54jaDEScLq+Hti2zspXfc+4eMX4K9AHtTeYaapcOeQqiaB64Y8MH4ChrBYIfFkWaa5KKOX0pcSOlpqaa3MJXycuCbtbil4/6N5NHg5oRm67iAvilCpfQgfN5/G/G2K4oSNnUP+18/RaW4SVnPpLkFJ6RjC4qja8TVxsy/w2LiJi35Lxmt/onbfGmErQjGzllGx5XNq9/5E9PSzFMXS+ocQO/tC8r96hsb87YrcT7tJWHA5JT8vJ/fzJwhPmytEhUMUQQkTmX7TK2x//ndsf/4aTrrtHUVlCMcLA2kSlpxiboRlEPI7V/v6EX/yJcSffAntDeXU7F5F9e5V5H31FHlfPUVA7Fgi0peg8Q3ExxCAxhCAj8EfH333165D5aPFaqqjrfoA5poDtFUfoK3G9XVHY+XBASUVvuFx+EWmkLDgEkJSpxMwghKEvshOJ5lv34W5upCZN78uxCxNJLLTScma5WgDwkhcdKWQmNW7v6O9rpSp1z0v/PdStfNbrKY6Ji1+TFEcSeVD+tVPEBBzjNVwhXS01NJcnMHopX8SFtPSUEZna4OwJuj6/RsAFDWU96W1Ihd9aMwxE1AJGUkWs0jnZWThTRpGMIHx46nd+5OiG/6wtLnkr3yWhrytRE87Q/AMB4ckSURPP4ui1a9jbW1EF+AZKcLIyUvQB0d1KX2ISRrCxpyEf/QoOgWtlCefejVl6/9HzqePMueuTxSXN6g1WsYuu4OMN26nYtuXHk3SBkLoqBlMv+FFdr50I5nv3Mu4C+/FNzx+uKd14iDLbpcnHQ3fsDiSl/ye5CW/x9JYSXXG99Ts/o6G3E09ZlRHIzB+Qk9DJoBa54tfZAqhqTPwm5eCX2QK/lEp+BoTFbn/epr8lc9Rs2c1ab++T+iNmShK1r5L1Y6vmfy7Z4T0e8iyzIHvX8EvMoXIyeLq+Ltjl/z8Nn6RKYr7ENRaPTEzzhY0syNTm/kTyLLYfoYDGfgaEwlOEbPTUJ+9AW1g+GEmbEqwW9sISZkuLN5AyK5bTXb9aloMw+9B9EvHmzSMYMLT5lHw9fM05G0hetqZbsUISpyEWu9HQ86mYU8aAKJnnM2B71+hZvcqEk6+zCNjqtQ+JJx8OXkrnqa1qoAAAfKAkkrN/Pu/FrbSptYaGLPsz2S+fReVO1YSO+tcxTGjpp1J0I9vkL/i30RPO6vLKXjkED5uHjNveZ2dL97ApscuYPI1/1Lk/uqlN0rLk46FITSG5MVXk7z4amSnA3tHGzaLCbulFbvFjM3S6vq6w/WvSutL3JwL8ItKwS8yFX1w5IjdPRgIsiyT/9W/qc74nsRFV5J0ylXDPaXDMFcXkvvFkxgnLFS8G9pNXdZaWstzmHTlY8L7NpoP7KKldB8TLv3bcfG3UbPnB3yNCfhHizFgA6jO+B6HtV3IDonsdFKfs9HlUyLod2U11VO392fGnHt7/ycLVDsaH7aY8WGL+aDl18JienEPb0/DCCYoKR0fvT8N+ze6HUOl9iFs9EkjxuQtIHYsflGpVO382qPjxs+/GJWPlpI17wiLKfrCFjPzXAITJpL35VNCeiUklYqx59+NrzGR/JXPCpiheEJHzWTu3Z+hD4lixwvXk7/yWWG9J794hjBp6D2MGo1vIL5hcQTGjSN09Ewi0xcTe9IyEhf+htQzbiR58VUkLrqS8LR5GEKijoubwqPhtHeyd/ndFH73IiEpU0m78J4R9/04HTb2vHUnaq2Bib/5P2HzO/D9K+hDoomZKX4Vv/jn5fgYAok5aZnw2KKxWVppyN1C5OTThP1s7R1t1GWtJXLq6UJu8k3l2djMTUL7GRpyXb5PYWkjb1fNi2fwJg0jGJXah9AxJ1GfswlZdt80KSxtLu11pbQ3lAucnXtIkkT0jKU0Fmyno9lz6jla/1CiZ55D5dYvsLWbPDbuYJBUKtIuuIeOpiqKf3pTSMywMbPwi0ym6Mc3ej7wRxp+EYnMufMjYk86j4Jv/suO/15Hp1mcRO7xRF+jtIEcR6PvToPI2L9UbBYzO164noqtXzDq7D8x8Yr/GzZPiGNR+O0LmEr3MfHyv6MPihASs6lwJ00FO0he8ntUPmKUeLqxNFZSk/E98fMvGVbZ3IFSt28tssNG5OQlwmLW7vsZp81K9DQxu0L12eL7Ger3b0TjF0yQIM8HL8cf3qRhhBM+bh6WhnLa69yXOgxLmwtAwwhxh46ZvhRkmaqd33p03MRFV+LotFC++ROPjjsYwsbMIiL9VApXvYLVVC8k5rhf34tfRDJ73rqTTnOTkJiiUWsNTLryMSZc/ggN+VvZ+NgFNBdnDve0jmtGmuzn8U5HczVbn76MxrxtTLryMUafdfOI22EAaC7eQ+F3LxEza5lQg7nCVS+j8Q9RZLh2NErWvgdA4sIrhMceCmr2/IA2MFyYARtA9a7v0AUaCUmdJiRe/f4NBMSNQxcYLiSeLMvU52wkbOycAYgsyF7J1RMU71VlhBPetQ1Yn+N+iZJ/VCq6oIgRU6LkF5lMYPwEj5coBcWPJyR1ulD51aEg7fy7cNqs5K98Tkg8tdbA5N89Tae5iX3v3a9o12oocfkDXMqcOz5AQmLL05dRuu5/I3a+Ix4PlSf9EmitzGPzExfTNpK4vAAAIABJREFUXl/O9JteIW7OyBIW6MZubWfPW3eiC4pg/CUPCotrKs+hbt8akk65CrXWICwuuOZctuFDIqecjiE0RmjsocBhs1KXtY7ISYuFqdL1Lk1SHtPeYabpwG6M48UZ27VVF2JtriG8axHSyy8T71VlhOMbkYQ+NEZRX4MkSYSNnU1DzmbkEWLFHj1jKS3FmR53Bk485bdY6suo3bfWo+MOBr/IZBJOvoyyjR/RWpkvJGZQ/HjGLvszNXt+oHzjR0JiDhVBiZOYe89nhI2ZTdYHD7F3+d04Oi3DPa3jDHlIG6F/STTkbWXLU5chy05m//k9oTdiosn9/Anaa4tJ/+3jQjwZuild9z76kKgh2Qmo3PoldouJJIVmbp6iIXczDmubUNWkun1rcNqsRE11T/CkLw1525AdNqFu2N0msWEDKXeSAdkh/vAy7HivKkOELMvse+9+ajJ/UhRHkiTC0+bSkLdF0ep4WNpcOs2NPcYsw023GlTVjm88Ou5B+dXlHh13sIw664/46P3I/fxxYTGTFl9DWNpc9n/yT8w1B4TFHQq0/iHMuOlVRi29hYptX7L5yYuFmYmdaBy1F0ElefsTFFK5fSXb//M7dEERzPnLhwTGjx/uKR2Vuuz1lK57n6TFVxM2drawuNUZ31O24QMST7lKuJ+K7HRS/PPbBCZMJDhFTFnOUFOz5wfUej9Cx4gztqva9S26QCOho8RImTbs34BaayBYoDRq/f6N+BoT8Q2LO+o5NZk/se+9+4WN6WXk4U0ahghJkph4xT+ITF+sOFZY2lzsllZaSva5H2NsV1/DCClRMoTFEpwyjaodni1RUqk1JJx8GQ05m2itKvDo2INB6x9K6hk3UZe1jnoFu0yHIqlUpP/2cVQaHXveuAOnvVNI3KFCUqkYvfQWZtz0Kh3NNWx67AJq9qwe7mkdJ0gjugRvpOPyI3iVPW/+meCkKcz5ywfCXH+Hgk5zE3vfuRf/6FGMWXaHsLi29hayP/gbAXHjhmQnoD5nI201B0g65aoR2R/SF9npoDbzJyImLEStEdMMLro0CaBu/wZCR88SNkenw0Zj/raecumjEZm+mIlX/AOQQXaKP7wMO96k4Tig+4ZfSV+DISQKv8hkGnJGRtIAYEybT2tlLiVr3/fouHHzLkHlo6V0zbseHXewJC66EkNYHDmfPibsBlAfHMmk3/wTU1kWeSv+LSTmUGOccDLz7vkcv4ikEasANRJx2JTL9v4ScTrsZH/0CLlfPEn09LOYecubI9qxXJZlsj54mE5zE+lX/0uoOd7+Tx+j09zIpCsfHRKVqJKf30YXaCR6upiynKGm6cBuOlsbiBComiS6NKm9voz22mLCx4uTWm0uysBhbSNsnLef4ZeON2k4DtAFhBIYP17xinPUtDNx2DpHTH24JsDlcp390SM0F+3x2Li6gFCiZ5xNxdbPR6z8KnS5Op9/J62VuZRv/kxY3MjJS4iffylFq1/rqVMd6RjCYjnpjv+RdsHdwz2V4wJJknAK8Pr4pWEqy2bzvy6hqWAnyUuuZfI1TwtbrR0qqnaspHrXt4xeejNBAsun6vdvpGLzpySfdq3QuN2Yqwupy1pHwsmXC5dwHSpq96xGUmswTlgkLGbV7u/QBoYLK03qvk8QK7W6CSQVYWMGXvYmyQ7hh5fhx5s0HCeEpc2juSgDe0eb+zHGzKYxbwu1e38WODP36Vk9l51se/a3NBXu9NjYB+VXP/XYmO4QNfUMglOmkf/VvxX97vsy7tf34heZQubbd41YGda+qDW64+bmYiTg3WkYOPaONvZ/+iibHr+QjoYKUs/6I2kX3DXiZWstTdVkffA3gpOnknzadcLi2jva2Pf+A/hFJjPqrJuFxT2Usg0fEpgwkfgFlw5JfNHITifm2iKip5+JxuAvJKa9o426fWuJmvorYaVJ9dnr0YfG4BeZIiQeQEPORoKT0tH4BgqL6eX4ZGR/InrpIXzcXOSuukJ3CR09E11QBJXbvxI4M/dpry/r+drRaWHbc9d4rFE7KGFCl/zquyO69luSJNIuvAerqY4DP7wmLG63DKvVVM+6h39Fe0OlsNheRgCShNNmHe5ZHBfUZPzA+kfOpPjHN4mb+2tOfngV0QL9DYYK2elk7/K7kZ120q96ApXaR1jsvBXPYGmsYNJv/im03KkbU3kOxT+/TeioGegCwoTHHwrq96+nbu/PREw8RVhMV2lSh7DSJKfDTkPuZozj5gvrEbG1m2guzvS6QHsBvEnDcUNI6gxUGp2ichJJpSZ6xlLqstZha28RODv36Cu3KqnU2C1mj42fvOT36IMjqdzpWQWnwRKSPIXo6WdRtPp1LE3iXLSD4sfjF5mMrb2ZtQ+eQk5X/bKXEwHJu9PQD5bGSna+dCO7XvkjGt8gZt/xARMv//uI7l84lJK179KQu5m0C+/BLyJRWNymwl2UrH2HxJOvICRVnPpON7Isk/PZY2h8gxh11h+Fxx8qSta+jzYwXKjUqujSpJbiPdg7zIQJlFptyNsCspPwQfUzyF7J1RMUb9JwnKDW6AhJnUGDgmZogJiZ5yA7bFTvXiVoZgqQZVQaHT6GQHwMAZz62GZhbpgDIWLSKdg7zOSveAaHbWQrCY1ZdgdBSenkfPJ/Qs3Okk/9vesLWaboxzdYc/8plG0auY7ZXgaIBM5O707DkXA6bBz44XXWP3ImDTmbGHv+Xcy99zOPfvYopSFvK5XbvyJ6xlLi510iLK7DZmXvu/ehD4lmzLI/C4t7KHVZa2nI2cSoM/943CRo7Q3l1GWtIX7excJKJO3Wdldp0hRxqkn1+zeApCI8TZwcbMP+Tah1fkLdr70cv3iThuOI8HHzMFcV0NHs/mpzYPwE/CKSqdw2/CVK0/7wAkue3M74Sx/CEBZHU/Fuj44vqdSMPe8uLA3llK73rILTYPENjydqyulU715F8U9vCosbEDu612NHp4X67PXC4nsZHiTvTsMRaSrKYNNjF5D7+eOEjpnNgge+IeW0a4dEGWioMJVls+ulG7F3mBl/8YNCpUoLv32BtpoDTLz87/joxdTtH4rTYSfns8fxjUgi4eTLhMcfKsrW/w9JUhE/X1z/RU9p0rSzhMVsKtxF3JwLhCZj9TkbCR09a/DvEa/k6gmJN2k4jui2b1dUoiRJRM88m8aCbUJLXdxBpfZBrdUTNeV0rM01FAms2R8oxvHzCUubR+G3L4xoJSVwNW9HTl5C7uf/orlYjNqUX2Rqr8dBSZOZfM2/hMT2MoxI0ohRSRsJdDTXsve9B9jyr0uwtbUw9fr/MP3Gl0a098KRaK8vZft/r8VH78/Mm99A6x8iLLapLJsD379K7OzzMQqU6zyUso0f0VZdSNp5dx43ogYOm5WyTZ8QMWkxhpAoYXGrd4ktTTKV59CQu5nAuHFC4kGXfGtdiVAlJi/HN96k4TgiIDYNbUA4zQcyFMWJmXkOyDLVOz1rrHY01BodSYuvpj57PS1l2R4ff+z5d2Jrb+HA9y97fOzBIEkSk658FH1wJBmv3y4kydEY/PE1JqILMmKctBhkJ5XbR8bfhRdl/NIboWVZprFgOxlv3M6aB06hrbaIpFOuYsGD37hKQo4DM7FDsZrq2f7875Addmbc8gaG0GhhsZ0OO3vf/Ssa/2DSLrxXWNxDsVlaKVj5HKGjZwn1ORhqqnd9h83cRMLCy4XFtFvbqd23RmhpUuW2FUgqH6KmLxUSDw56Q3UvWA4cGXAMweFluPEmDUfgoYce4uqrrz7s+Y8++ojrr7+eJ598kosuuohVqzzbFyCpVERN+xWVO77CoUCD3S8iiaDESVRuXylwdspIWHgFPnp/Dqzy/I17UPx4YmaeS/FPb2NpHNkqQhrfICb//hk6mqrZ++59Qvob5tz5ISc//ANTr3sOtT6Ave/cQ8XWLwTM1svw8cstT7J3mCld9z82/t+5bH36Cuqy1pO48AomXvH/7J13eFN1F8c/SZruvQctbemAltECLWVvEAQUlCWiIoICMpSNIqCiiCiiLEU2KiLI3rJ3KZ1QaGnpXkBpS/dI8v5RQPAFoe29San5PM99SENyzmkz7j2/3znfM59Gr84SpexGbMqLCwhZNoqS3Ju0GPMjJg4egtpPOLKGuylX8B08B10jc0Ft3+fGwZWUFdyh4SsznquELfnkrxjZumHlJVyfgNClSSqlgvSQ3Vj7tEfPxFIQm1DZz6Bvbo+RfYOnP1jLfwJt0vAPgoODWbBgwf/dv3r1aiZPnszixYuZOnUqixcvZtCgQZw+fVqt8dn79UBRUsjNqKM1suMQ0Je7KVcoyIwXKLKaITcwwaXjMDLDDlCQdUPt/r36TgLg+p4lavddVSzc/PB6aTJZ4YdIPvlLje3pGluio2eITEeXlmNWYOUVSOSG6aSd3y5AtFo0wj3JVSGb5ms7+enXufL7PI7Oas+VzXOQyGQ0HjafLl+eotGrszAWUGFInSjKywj7aRz5adfwf2cJFu7+gtovyLpB3J7vsfPrgb1IUrNF2akkHl2PU6uXMXNpLIoPMchLiSY3IQyXDq8JOrPD3M2fhgOmC1aalB17gdLcLJxavSSIPagUDCgvvoudf8+qJ3kqlXa4Wx1FmzQ8RFFREUuXLiUwMPCR+/Pz85kyZQrDhw/HyMgIgHr16tGrVy/Gjx+v1hgtPQPRM7cj7cLOGtlxaNEbJBIyQmrPboNrl7eQ6uhy49Aqtfs2sHKifqc3SLuwg7upV9Xuv6q4dR2BTeNOXN32JXnJVwSzK9M1oMWYH7HyCiJy4wxSzws3ifpJZITsrRUSwHUJCZUn+dpSolR0O4X4Qz9RmJUgqN2KkiIyQvZyYfFwTn/+Iqln/sC+WXdaT/2DNjO249x2IDJdA0F9qhOVUkHk+qlkx5yjyetfYNtEuBkBlfaVXN70EVK5Pr6D5whq+2Fid34DEime/cRRZBKL5JO/IJXr4xTUX1C7BpYOuHUbKWhpko6+MbZNughiDyD72lmyr53FyvvZp0Brqftok4aH+OSTT5g9ezYy2aMf5IMHD5Kbm0tQ0KMfnqCgIMLDw7l8+bLaYpRIZTgG9ON29ClK86uvqa9vZouVVxDpF3fXmtVIPRMrnNsMJP3CTo2UCTV44T3khmbE7Kj9jcASqZSmbyxA19iS8NWTKBdwvsWDxMG7DVEbZ4o6NbskN5OIdVM5Mac7ySd/q9WD9p4r7i0M1pYSpezYC8TuWMTJeT059dmLxO5eQl5KdJW/e0rzbpEZdpCrW7/g7MKBHJ7Sktg9Syi+k4b3y1Pp/MVJmr65EHO3Zs9VCczjUKlURP/xOZmh+/HuP13wC1eA5FO/kRN/iUavzkLPzEZw+1CpWJURshe3biMFbSQWm/Kiu6QH78YxsG+tnoSsKCsmM+wg9v4vINPVF8xu+sU96BiYYt1InKZ4Lc8nwo2QfM7Zv38/DRo0wNPT8//+LyysUgrUxcXlkfvr16/c7r548SKNG6tvy9UpsB8Jh1eReWkv9TsNr7Ydh4C+XN40i7ykSMxdmwkYYfVx6zaS5FObSfhrDT6DPlarb7mhKQ1eGMO1bV9y++qZWq8YoWtsid/b3xL83Rtc+W02zUZ8K9iFkkxXnxbvrSD0x7GVvRNKJc5tBwpi+2H0ze1pM30rV7d+wZXNc0g+9SuNXv1Iu7pVUyQS9M3tKxWURKpRrwrObV7FumFbsiIOkxV+mPgDK4jfvwwDq3rY+XXH2MHjiTsCZQW55CWGkxMfSnF2KgBSuR5m9Zvg3u1tbBp3xsLdT7BV29pC/IEVJJ/4Bbeub+PefaTg9gtvJRGzcxHWjdqJkpDAvUFuW79Ez9QG9+7viOJDLNLOb0dZXkL9DsM0Hcq/khV5BEVpIY6B/QSzqSgrISviLxxa9EImr6bKlbacqE6iTRqA7OxstmzZwtq1j9e/v337NgAmJiaP3G9sXNlQl5WV9X/PUalUnFkw4MHPzu0G4SKQxrOJkzcm9RqSFryrRkmDvX9PojfPJf3inlqTNBhYOeEY2I+UM1to0GusoE1dz4JLh2EkHd/Ite0Laeu9XdA6VjGw9AjAs88EYnctxsorCOd2wg16kunq0/xe4nD5l49ApRTU/n1MnX0InLSRzLCDxPz5FcFL3sDOvycN+0/D0NpZcH//DSSU5Gb+X3lSxqV9xO1bRpsZfyKT66k1IgNLB1w7v4Fr5zcozb/DzaijZIUfIvXsNpQVZSifsCti7u5P8e1UzBs0p36n17Fwb4Gpc6PnRrKzOiSf3sz13d/hGPgS3v2nCW6/orSIsFUTMHX2xfe1T0XblckMO0BuQhiNh81HR99IFB9ioFIqST75K+Zu/pg6+2g6nH8l/cJO9M3tsfQMfPqDn5Gbl4+jKC3EoWWfZ35O8unNpJzeAkDt3ZfRUlPqZNKQmppKRUXFUx/n5OSEXC5n5syZzJ8//4mP09OrPLn+84tVeu+CUlf3/09eEomEtjPEqwd3DHiJmO1fUXgzESNb12rZkBuYYNO4ExmX9tLolRm1ZqXOvcco0i5sJ+nYerz6faBW3zK5Ll79PiRi7YekX9yFU6uX1eq/Orj3eJfs2GCi//gcczc/TJy8BbMtk+vR/N3lhP00jsu/zkaFSrDk92EkEgkOzV/AtnEnEo6s4cbBHzkVdQy3biNx7zH6ubrgqA3c/6b6p8qajoEpBRnXuXX5uGhNr8+Cnoklzm1exbnNqyjKyyi+kwpPKFWSG5iia2r93JcbPSuZ4Ye48ttcbHw70GT4F4IvXKiUSqI2zCA/LYaWY3/E0KqeoPbvoygvI2b715g4eVOv9YCnP6EWkR17jsKbCTR982tNh/KvlOZnc/vqady6vS3o+yQjZA96pjZYebV65ue4tBvy4NwQNbUvWonUukntXkatJu3atcPNze2pR3x8POvXr6dLly44Ojo+0Z69fWUdZkHBo3Xj9392clL/gCDHgBdBIiE9uGYN0Y4BfSm7e5vs2PMCRVZzjO0bYOfXg6QTmygvzle7f4cWvTF1aUzsrsUoakkj6b8hkUpp9tbXyA1MCft5IhUlhYLal8n18B+9HBvfjlz59ROST20W1P4jvnT18eg1lvZzKmt04w+s4OS8nqRd2IlKqZ0I+sxIHt8IbeUdhK6JFem1SABBJtfF2M4dY/sGjz30zGz+MwlDdmwwEWs+xNy1KX7vfC/KpOq4/cvIDDtAw/5TsfHtKLj9+yQd30hxdmqlxGotWZB6VpJP/Irc2AL75r00Hcq/khGyF5VSgWOgcItb5cX5lYsKzXs9d6+bFvGpk0lDTEwMxcXFTz28vb1Zt24d77zzDsbGxg+OU6dO8csvv2BsbMwXX3xB8+bNAUhJSXnET3JyMgB+fn5q/x31ze2x8m5NWvCuGjUy2/h2REffuFbNbABo0PNdKorzST75q9p9S6RSGvafRklOBknHN6rdf3XQM7Wm2YhFFN5MIHrLp4Lbl8l18R+9DJvGnbjy2yckify6GFjY02zEIoImb0bfzJbI9VM5/80QwSZh130qL7L/2Qgtleng0KI3t6KOCdo8r6Xm5NwIJXzNJAysnWkx5kd09AwF95EZdpC4vT/gFDQA165vC27/PsW5WaRd2I6Nb0esG9bu3rB/UpyTSVbkEZzbDKx+Pb+aSA/eiUm9Rpg4/n8vZnXJijiMsqIMh4BnL016LCqF8IcWjVMnkwY9PT309fWfekgkEjZt2kR4ePgjR8uWLenXrx/h4eG89957dOvWDSsrKy5cuPCIn/PnzxMQEIC3t3DlIFXBMbAfxbdTyL0RVm0bMl197Px6kBV2sFatqpu5NMbapz2JR9fVaJBddbHyDsLGtyPxB1ZQVpCjdv/Vwcq7NR69xpJ2frsoUqkyuS7+o5Zi26QL0ZvnknRik+A+/olFg+a0nraVJm8soDg7jXMLBxK5fholuf/fR6TlIe4tzD+uT8AhoC/KijKyIg6pOSgtTyLj0j6Cl7yJmUsTAt5fg66xheA+7qZEE7l+GuZu/vgOFa+PQaVUcnnjTIrvZOAz+BNRfIhJyunNgArn9sKXYQpJQdYN8pKiBJ3NAJW7FwZW9WpNn6OW2kWdTBqqgpOTEx4eHo8cBgYGmJiY4OHhgaWlJTo6OsyePZsNGzZQXFwMQEZGBrt27eKTTzT3pWjv1wOpXJ/04F01suMY0JeKkgJuRtZsYJzQuPd4l7L8bFLPbdWIf+/+U6koKST+4EqN+K8OHr3fx9KzFdGb51GQESe4/crE4Xtsm3Yl+vdP1bITI5FKqRc0gA5zD+LeYzTpl/Zycl5P4g+srFWJbu3i3k7DYxJuc9dmGFjVI6OW7S7+F1GpVMTtX0746kmYOvvSZPiXGFg6CO6n9O5tLq0cg9zQjOajl4q6gp54bD23r56mYf+pz52QgbKijJQzW7Dx7SRar4dQpAfvAom0Ss3KT6M0/w7Z187i0PLFGiaVKip7GoQ5rtw5xZb4heTlaef5aJr/fNLwrEycOJEZM2YwevRoFi5cyMSJE9mwYQN9+gj3ga0qOvrG2DXrRkboPpQVZdW2Y+UdhIVHS5JP/SZgdDXH0jMAc/fm3Dj8M0pFudr9mzh6US9oAEknNlF0O+XpT6gFSKQymo1YhEzXgIh1UygrEr4nRKqji/87S7Br1o3oLZ+RcGSNWmZ96Ogb4/3yFNrP3o91wzbE7vqWU5/2IjP8UK2ZNVJbuH/Cf1zSIJFIcGzZh9vXzlJ697a6Q9NyD0V5GZHrp95TSepH4MT1oqjFKcrLCP3pfcoKcmg+ZqVo8xigcjcjZucibJt2FUVpTWwywg6ho29M/Y61W2ZVpVSSHrwL64Zt0DezFcxuZuj+yh4JARMRIfC1bMOgBpMxMzPTdCj/ebRJw2M4fvw469at+7/733vvPTZu3Mi0adPYsmUL/fuLo21dFRwD+1FemMutKyerbUMilWHbpCt3Ys+TlxItYHQ1QyKR0KDnu5TcSdfYqqhn3wmYOjUkds+S5+bCVN/cDr+R36EsLyNk6QjKCnMF9yHV0cXvnSW4dn2ba39+Tciyd9RWMmRk40Lzd5cTMGEtMl0Dwn+eSPCSN0g+vVkjjfO1mSfJmDoE9AWVkvSQvWqOSAtUruhe/P4N0oN34dl3Ek3f/FoUCVyVSsWVzXPIvRFK0ze/wkxE+VBFWTHhayeja2hOk9fnP3fN60pFOXF7liCV62PVqJ2mw/lXcm5UziwRcjYDVKomGTt4CqrAp6VuoU0annOsG7VD19iyxiVKzm0HItMzIvHIGoEiEwabxp0wcfIm/uCPGlHP0Te3xzGgHxnBu0gRUTVIaKy8g/B6eTJ3U68S/N1wUVaUpTI5DQdMx2fwbO5cv8jp+X3JCN0vuJ8nYd2wLW1n7aTpiG8oy7/DlV8/4eiMtkSsm0J2zPn/ttrS/Z2GJ5RvmTh64hDQj8xL+56bZLiukJ8Rx7mFr5KXfAW/kd/h0WusaBfYiUfXknZuGx69x+EgshLQtT+/ojAznqZvLkTXWL3zdYQg7dyfFN1KwrvfBw/k1Gsr6cG7kOkaYNesu2A2i++kkxN/SaByJxUqlULwQ4vmqd2fDC1PRSrTwaFlH25GHaW86G617cgNTXFuO5CMkH2U5GYKGGHNkEgkuPd8l8KsG2RFHNZIDPU7Dcfapz1Xt30pSp+AWNg17UrLMT9RdCuZC4uHUZwj/OsqkUio3+E12s7agaG1M+E/TyRi3ZQavRerglSmg2OL3rT7eA+tp23FKag/NyOPErzkDU7M6UbcvmUU30lXSyy1i8erJz2MlWcAuQlh5MSHqCuo/zy3ok9x/utBKMpLaPXBJhxa9BbP15UTXPtzIXb+PfHoPV40PwBZkUdJPvkrrl1HYN3o+VJLgsrk+vq+pZi7+WPTuJOmw/lXFOVlZIbux65Zd0Hn12Rc2geAQ8sXBbOppe6hTRrqAI6B/VBWlJEZdqBGdup3Go5KpSTpuPiqOFXBoXkv7Px7cuOvNRrpbZBIpTQZ/iUyPQPC105GUV79/hF1Y92oLS3fX01J3k0ufPsaRbeTRfFjbOdO0JTNePR+n4yQvZye35fsGPXN/pBIJJi7NqXx0Hl0WXCGpm8twtDamet7lnB8dmeCvx9Besiex668q5R1dwVL+S/KY46B/ZAbmpF07PmQFX7eSTrxC5eWj8bAyok207aKqk5TkBlP+OoPMHHyoukbX4k62b407xZRm2ZiUq8hXv0mi+ZHTJJP/kppbhZeL31Y68uqbkYdQ9fUBkfBVZP2YObaFCMbF4EsCtcI/fehRdNok4Y6gFn9JhjZudW4RMnQ2hl7vx4kn9os+ICwmiCRynBq9TJ5CWEam5ugb2ZLk9e/ID/1Ktd3f6eRGKqLpUdLWk3cQEVJAee/HUZBZrwofqQyOZ59JhA0eTNSuR7BS97g6rYv1a5wJNM1wOleY2nHT4/g0WschTcTiFjzIX9NbcVfU1tx6rMXOTnvBQ5PbsGB8b5khNYs4a5tSCQSpDq6/ypXLNM1oF6bgWRFHKb4ToYao/tvoVRUEL3lc6J/n4e1T3uCJv+GgeWTh4nWlPKiPC6tHINUR5cW764QZd7DfVRKJZEbZ6AoLaLZiG9r/VyDx1FRUkD8wZVYNWxTpQnImkClUpHw18+oFGVYebcRzG5B1g3upkQLqsSkpW6iTRrqABKJBMfAl7hzPZji7LQa2XLtOoKK4rukiaDzXxNsm3TBxrcD1/f+QGneLY3EYNe0K87th5Lw18/cvnZWIzFUF7P6TWg1aSMqpYIL3w7jbupV0XyZuzWj7cwduLR/jcQjazm7YAB3NdRgb2jtjGefCXT69CgBE9ZhZOdKeWEOBRnXKcy6QUVxPqiU6BqZayQ+RXmZaApGUrn+Exuh7+PS8TVUKhUptUw5ra5QXlxA6Mr3SDq+Adcub9HivRXo6Btgry3kAAAgAElEQVSL5k+pqCDs54kUZ6fR/N1lGFg5ieYLIPH4Bm5Hn6LhKzMxcfAQ1ZdYJB5dT3lBDl79PtB0KE8lJz6EvMQIXLuMQCoTblpzxsU9IJEI2PeiApVS+EOLxtEmDXUEx8CXsPQMrLFsqoW7P+Zu/iQeW1+ryjYkEgmNXv0IZXkZMTu+1lgcjV6ZgZGdG5Ebposy9K34TgYXl44Upf/AxMmboA9+Qaqjy4XFw8lNEG+6so6eIb5D59Jy3CrKC3M5u3Ag8Yd+0th7SiKVYt2wDe1m7vj/SbgSKdd2fsuV3+eRcnYreSnRNZIwfhqKshKywg8TsXYKR6cHcW3bAlH8yHT1/7WnAcDQqh71O77OzSsnNZbY1VVy4kOJWDuZ21fP4jtkHo1enYVEKtyF3uO4tm0B2dfO4jt0HhYNWojq627qNWJ2fI1tky64tB8qqi+xKCvMJeGv1dg16/ZcDDNLOLwaubEF9VoPEMymSqUiI2Qvlp6t0De3E8yulrqJNmmoIxhaOaFnZkPSiV9qLLHp2nUERbeSyYo8IlB0wmBk54Zb1xGkXdhBTnyoRmKQ6RrQbMS3lOXf4fJvnwiuPFNWcIecG6Fc/P4tSvPvCGobKv+GrSb/iq6xOcHfv0V2bLDgPh7Gxrcj7T7ejW2TzsTuWMSFxa9rfOZFwwHTqd/5jQc/Wzdqh0wmI+38Di5vmsXZL1/m0Af+nP7iJaI2ziLp+EZy4i/VqGSvorSIjNADhK+exJHpQYT+NI5b0Sex938BJwEvAB6mMml4emmYW/d3KC+4Q8jy0TXeqdRSubtw5fd5nP92KPnpMbSa/CsuHcS/qE4+/fuDHQ3nNq+K6ktRVkLE2g/RNTSn8etf1Po+gCeRcHgVFaWFePadpOlQnkp+Rhw3o45Sv8MwZLoGgtnNS46i8GYCjoI3QGt7GuoiOpoOQItwNOj5Hhkhe0k6tgHPPhOqbceuWTcMrOqReGQt9n49BIyw5jR4YQxpwTuJ3vIpbaZvE33l7nGYufji1e8DYrYvJPXcNkFP0GYuvrQc8xMXl75NyNKRBE7agNzARDD7ULm63OqDXwj+/i1Clo2k+ehl2Ph2ENTHw+gaW+I/6gfSL+zgypZPOT2/Hz6DPsYpaIBGLjYkEgmNXplFeUEumeEHafzapxhYOqJSKim6lcTdlGjupkZzN+UqWVFH/p5ILpFgZOuKqbMPJg5e6BiaoqwoRVleiqK88t/K2yUoy8tQVJRWNiJLIDvmAsryEnSNLXEM6Id9855YegYilclF+z2N7NyRP0PZlYGFPS3fX835b4Zwcdk7BE3+TWPlWs87N6OOceW3OZTkZVG/43C8+n0gqMLNk0g5u5Wr277Eud0QvPtPE93fte1fUZARR8v314gykE4dlOTdJPHYRhwD+mLi6KXpcJ5K4l9rkMr1cOn4uqB2U07/gZlrU+z8XxDUrpa6iUSlFekWBYlUhnObV7Fp0gW7pl3U5jf0x3Fkx16g0+fHkRtUv3Y28eh6rm6dT+tpf9S6bdv0kD1ErPkQ36Gf4tJ+iEZiUCmVXPzhLXITI2k7cwdGtq6C2r95+TihK8di7u5HwPurBV1Zuk9p/h1Clr5Nfvp1/EYuVkuCWJydRsT6aeTEXcSuWTd8X/tcYxcdKpWK8qK8f71AVqlUlORmPUgk8lOiyUuJRm5oRv7DfSESCTK5PlK5HlK5HjK5HlK5PjIdXfQtHdE3t8fOrzuWHi3Vkugemd4aHX0jzFyb4Tfim2d6TnZsMBeXjsC8flMCJqwTZdhYXaU0P5urf3xORshejB08afz6fCzc/NTiO+nEJqJ//xRrn/Y0H71UlO+Kh7kZdYxLK97FtctbNHp1lqi+xOTK7/NIOfU77eccEFAxSBxK8m5yfHZnnNsMxHfIXMHslhXmcmxWBxxb9qHJ8C9qbC8r8ii3oo6iCg/lg8aLBYjwUX6tmEtIiFYiWpNokwaRkMp0eGGpeM2mTyIv+TJnFwzAq9+HNHjhvWrbqSgp4NisDtj4dsBvZO1SC1KpVAR/N5z89Fg6zDmIrrGFRuIozsnkzPy+6Jnb0Wb6VmRyfUHtZ4TsJXzth9j4tKf5u8uR6givTFJedJeQZaPIS4qk6ZsLcQzoK7iPf6JSKkg4so7Y3d8iNzDFd/Ac7Px7PlclDhXFBSgqSpHq6CHT1UMi1alV8R+Z3hpUKuz8e9J46Lxnfl7GpX2Er56ETZPONH3jK+2Ow1NQqVSkX9jB1W1fUlFaiMcLY3HvMUqUz+rjuHF4NTHbv8K2aVf8Ri4RXb2oNO8Wp+f3Rc/MhtbTtj63iWXR7RROznuBem1erdLnQ1PE7FjEjcM/02HuIUETnBuHVhGz42vaztqFab2GgtmNmtKLDxo/22JFVfhV8Zk2adAw2p6GOoaZS2NsfDuScHQtFaVF1bajo2+Mc7vBZIYdrHV1zhKJBJ9Bs6kozidWg/KnBhb2NHz1IwrSY/lrSiA3o44J2uPg0PJFGg/9lFtXThKxbqooTcRyQ1MCxq/BwiOAiHVTSDn9u+A+/olEKsO9+0jaTNuGiZM3ERtncHbBALIijzw304l1DIzRM7FCbmCMVCavVQnDfSpKC9HRq1ppjEOL3vgM/oSK4spFg6hfPuJu6jVUKhWFN5NEivTpqJRK8tNiSDr5K1kRf2ksjocpup3CxR/eJnLDdIzs3Gk7cycevcepJWFQqVRc37eUmO1fYd+8F/6jvhc9Ybgvr1pRWnhPXvX5TBgA4vYtRSKV4tFrjKZDeSoVJQUkn/oNe78egiYMSkUFSSc2YekVJGjCoKVuo00a6iANeo2lvCCHlNOba2SnfqfKZlFNzUb4N0ycvHHpMIyU05vJS76isTicAitX5pXlJVxa8S7nFw3hTpxwKyHO7Qbj3X86maH7RWm8BtDRN6Ll2J+w8enA5V9nc+PIGrVcvJvWa0jLcavwHTSb8uK7hK4cw9kF/cmK+Ou5SR5qKyqVCmV5KTrVKFGs3/F1fAZ9jGNgX9KDd3Pmi36c+qw3J+d259LKMZTkZokQ8aMoykq4c/0i8QdWErJsFH9NDeT0/L5Eb577YHKtpqjcKVvL6c/7kJsQjs/gOQR9+KvaJEdVKhWxO78hbs/3OAX1x+/tb0XtjbnPjcM/czf5Cg0HTMfE0VN0f2KRnxFH2oWd1O/4Ovrm9poO56mknNlCRXE+bt1HCmo3K+IvSnIycH1IFEJYtI3QdRFtI3QdxMLdHyvvIBIOr8alw7BqrwgZWDpg37wXKWe20KD3+zXqkRADzz4TyAjZQ/SWzwia/JtmmmqlMozs3CjMSgAgNyGMC9++RtuZOzB19hHEh3v3kVQU3yX+wArk+iZ4D5gu+O8q09Wn+bvLiN29hJhtC8i5fpGmbyxAbmgmqJ9/IpXJqdf6FRwD+5EevJv4A8sJ/XEsps4+ePQah22zbrVyFb/2U5l0VXWn4T6m9RrRZNh8vF+eSurZrcQfWAnAzcgj3LpyEq+XPsStywjBJg1XqoaFkRN3iZz4S+QlX0Z1b/q7sYMHDi16YeHeHAuPFhhYOQviszrcTb3G5V8+Ii8pCpvGnfEdMhcDSwe1+VepVFz9Yz5Jxzfg3H4ovoPniDrt+T7JpzYTu3MRLh2G4dJhmOj+xOT6niXI9Axw7zFK06E8FaWinMSj67HwCBC8tzDp2HoMrOph26SzoHa11G20SUMdpUGvcQR/N5zUs39QvwZqC25dR5ARsofUs3/g1nWEgBHWHLmhKd79pxK1cSbpwTtxavWyRuIwcWpIYVYi9y/U7Jv3xshe2FVHz76TKC++S8KRNegYmomyrS7V0cX75Snom9lybftCznzxMn7vfKeWRvjK5GFAZfJwcRfx+1cQ+tM4TOo1wqP3OOyadlPLxVGd4d5OTXV2Gh5G18gc9+7vUHQriZQzW0ClQqUoJ+bPr4jZ/jW6xhZYerbCxNETfQt79Mzs7v1riwQVZQW5lBfmUlaYc+/fyp/LC/6+TyqTcyv6JAASmRyz+k1w7fIWFg1aYOHur7GepYdRlJcSv385Nw6tQm5oit/bi7Fv0VutCa1KqeTKb5+QcmYLrl3eouErM9XiPz1kD1c2z8GmcScaDZz1XCfxeUlRZIUdxOPF8ega137Vp4yQfZTkZAja/AyVvY858Zcq30OiCDOoUKm0OwN1EW3SUEex9AzEokFzbhxahXPbQdWuszWr3wQLjwCSjm2gfqfhSGW16y3j1Ko/yac2c237QmybdtPIboiRnTugQiKVoVIqsGncSfD6YolEgs/Ayj6O67sXIzcwpn6n4YL6uO/HtcubmLv7Ef7zJM5/81rlXINOw9VysSCV6VAvaACOAf3IuLibuAMrCPvpfUzqNcSj1zjsmnXXJg/PwP3yLlk1dxr+SWnercpERCIFlRKJTI5UpkNFaRG3r54mM/TRkiFdE2vK8p8w6VoiRW5oiq6RBXIjc0wa+GLh0RILjxaYuTRBpiusoEBNuRN3kcu/fExhVgJOQf1pOGCG2hOZwuw0wn96n7spV2jQ8z08+32gls/jzcvHiVw3DYsGLfF/53u1lEGJSezu75AbmePapXYtgD0OlUpFwl8/Y+zggY1vR0FtJx5dj0zPiHoiz/PQUveoXVeAWgRDIpHQoNc4QpaOJO3CDpzbDqq2LbeuI4hYO5nMsIMiDICpGRKpFJ9Bszn39UDi9v1Ao1dmqj0Gx8C+lOZl4d5jNFe3fknK6c2YOnkJVp50H4lUSpPhC6goKSR6y2fo6BvjFNRfUB/3MXdtRpuZ24naMIOrf3zOnbiLNHn9C8FnRjwJqUwHp6D+OAT0JSNkD3H7lxO2ajwmTt549H5fmzw8jfs7DfrCJNEqZQVIpNj4dqRe6wHYNO78SGKsKC+jNC+LktwsSnIzqSgtQlFSiK6ROXIjc+TGFn/fNjB9Ll67/PRYEo+uJ/XcNgwsHQkYvxbrRm3V5r/wZiJZEX+RGbqfvKQoABwC+uH10odq8X8n7uKDz1yLMStrXTJXVW7HnOd29Cm8+0+vdaW2j+P21TPkp8XQZPiXgn5eSvNukXFpHy7tBov4fa5CyB6EK7mXiM4NJc8sTzCbWqqHVnJVJDQlufowKpWKM1/2pyQvi86fn6j26rdKqeDCkjcpuZNO+0/210rVjMu/fEzquT9p+9EutTUkPo6SvJucWzgQlVJB62lbMbAQvtFOUV7KpeWjuHP9Iv6jfsCuWTfBfdyncrVrDbE7F2Fg5YTfyCWYufiK5u9JKBUVZITsJX7/cgpvJmDi6E2D3uOw9+vxXFyAqpvDk1tQUZxP0JTfsXD3r7G98uICVIqy56KkoyaoVCruXA8m4fDP3LpyApmuAZ59J+Lcbgg6eoZqiyMz7CBhq8Y/cp9EJqfnkki1zPnIS4kmePHr6JnZ0OrD357bAW73UZSVcOaLlzCwcqT5uyueiwQoeMmbFGTG0/HTo4LuXF/f8z1x+5bSYc5BjOzcBLP7MFFTejLJt+ZzH/7Jb8pFWslVDaM929ZhJBIJNj7tKc/P5sScbpQV3KmeHakMj15jKc5OJfHYeoGjFAbPfh8i0zPk6pbPNKq8o29mS8uxP1FRWsil5aMoLy4Q3IdMrkfzd5dj6tKYsNUTuX3trOA+7iORSHDvPpJWH2xCUV7KuUWDSD75m9r/xlKZDk6tXqL9J/to+tYilIoywn+ewOkv+pERuh+VUqnWeGo7KoF3GuQGxnU6YVApFWSEHuDcwlcJ/m44eUlRePadRKfPj+PW9W21JgwAps4+/zfNu37HYWpJGAqybhDyw9voGJgQMH7dc58wAFzf/R2FNxNw6/7Oc5Ew5CVfITvmHPU7vyFowqAoLyP51G/Y+HYULWHQUrfRJg11HEuvVgCU5mZyen4/chMiqmXHumEbbJt2Jf7ACkrvPqFWWYPomVji1W8SBZnxpF/co9FYTJy8aT5qKQUZ8YSvnojyngqMkOjoG9Ny3CqMbN0I/XFstV/XZ8WiQQvazdqJlVcQVzbPIWLtZCpKhE+InoZEKsMpsB/tZ++j2YhvUCkqCP95IheXvk3i0fWU5N1Ue0y1kgdJgzA9DXUVRVkJySd/4+S8noT/PIHyorv4DplHp8+P49FrrMaasMuL85HpGT6SJDi07CO63+I7GVz8vrLeP3DCOrUqQ4lFTnwoCUfX4txuCNYNxSsvSz75GwlH1giygJHw12pkeka4tBsiQGR/k3lpL2X52dTv/Kagdh+PVnK1LqJNGuo4DzfLlebd4vw3Q0i/uLtathoOmI6yvIzrGhyo9m84txuCiaMXVzZ/QuGtZI3GYt2oLb5D53E7+hRXNs8VZWVe18icgPFr0DO1JmbH19yJuyi4j0f8GVvScuwqvPp9QMalfZxd8Ar5aTGi+nwSEqkMx4C+tJ+9l2YjvkXX1JqrW+dzbFZ7Lnz3Bimnf6esIEcjsdUOhN1pqGuUFeYSt385xz/uxJXNc5AbmuE/6gc6zDmAS4ehGluNVimVJBxZy7mvB6JSVOAzeA4yfSMMbepjVr+JqL5L87O5+MNbVBTn03L8mjqxEq0oKyFq40wMLBxpOGCaaH5KcrO4tmMh2dfOQQ0b1IuyU8kM3Y9zu0HIDU0FirBy9zHx2HqM7BuotTdHS91CmzTUcR6dIqxCpVSQE3+pWraMbF2p3+l1Us7+wd2UaGECfAwqlYrk05spvpNepedJZTo0HvY5UqkO4asnoSgvEynCZ8O57UAavDCG1DN/cOPQT6L40DezJXDiRsoKcgn+/i3SLuwUxc99JFIpDV4YQ+DE9VSUFHB24auknN2qsZKwyuShD35vLaL97H149BpHaW4Wl3+dzdEZbQlZNoq0Czs1siuiUR6oJ6m3rKa2U5SdSvSWzzn+UUeu7/4Os/pNCJy0idbTtmLv31Mt5T9PovTubUKWj+Lati+x8elAu4924dJ+CO0/3kvghHWiqSWpVCoyIw5zcm4PCm+n0WLsT5gJLOKgKe6XJTV+fb6oCfS1P79CVVFOo0Gza/w6JZ34BTM3P1w7vyVMcPfIiQ/hbko0rp3fVIPyVqXkqtCHFs2jVU+q46hUj26VNuj9Pp4vjn/Co5+OR69xpJ3fztWtXxI4aYMoXz6leVlc27aAzNCDBIxfUyUfBpaONBn+JaE/jiV25yIavTpL8PiqgmffSRTdTiF25zcYWtXDQQT1KQNLB1p9uImwn8YTuX4qRbcS8XhxgqgnBiuvVrSdtZOItZO5vGkWOdeDaTR4DnINlsMYO3jg2WcCHi+O527qVTJC9pJxaS+31k9FKtfDpnEnHFv2wca343NR11wTVICVd+taJ5GsKe6mRHPjr9VkXtoHSHAM7Itbt5GYOHppOjQAbkWfInLDdCqK7uIzeA4uHV578Pk1sHQUxWdxTibpF3aQcmYLxdmpAHi9NBlLj5ai+FM3j5YltRHNT3bMeTJC9uDRexxGNi41slWQGU/S0XW4tB8qeGlY4rENyA3NcGr1kqB2tfy30J5R6jgWDVpQv+Pr2DTpwuXfPiE/Jfqe3nr1LijlhqZ49p1E9Oa53Iz4Czu/7gJHDPrm9nj3n0b05rmknNqMS4ehVXq+XbNu1O/8JolH12Hp1Qq7pl0Fj/FZkUgkNBm+gJLcTCI3TEfP3E6Uk3JlqdJqLv/6CXH7llF4K5kmr38hqtKVnqk1AePXELd/Oaln/+Dsly/j2WciDi1f1OgAKIlEgpmzD2bOPni/NJnchHAyQvaQEbqfrLCDyPSNsGvaDYeWL2LdqO1zrz3/WFRKjZWO1RaUFWXcvnqGxGMbyL52BpmeEa6d36R+5zdrTa2+oryM2F3fkHhkLcYOngROWKeWRObO9YtcWPw698vYAKRyPdy71/4pyc+CusqSlIpyrvw+DwOrerj3eLfG9mJ2LEKqq0+D3u8LEN3fFGenkRV+GLduI5HpGghq+/EIK7mqpfagTRrqOHIDE3wGfwJU9iSErxpPWvAO6gUNqLZN57aDSD7xC9e2f4W1b0fBB5kBuLQfSlb4oUofPm0xtK7aCo73y1PJiQshasMMTGftFG217lmQyXVpPnoZ5xcNIfTHsbSeugUjW1fB/Uh1dGky/EuMbF2J3fUtJXfS8B+9XFT1E4lUhueL47Hx6XCvQfpDEo+tp9ErM7Fo0Fw0v88enxSLBs2xaNCchq/O4s71YDJC9pAZdoj04J3IjcyxbdoNq4ZBmDh4YmTnXislhauMSvWfLE2qKCng1pWTZEUc5tblE0jlekh19PB6eQou7YYIWiNeUwqybhCx5kPupkTj0uE1Gg6YobYdMEMbF/TN7SjJzXxwn2NA3zojX3y/LClgwjpRy5ISj26gMDOeFmN+rPFrdyfuIjcjj+DV7wPBv7OTTvyCRCKhfsdhgtrV8t9DO6dBJCRSGc5tXsWmSRfsmnbRdDhAZZPd+W+GUnQ7mQ5zDtboBHr76hku/jAC7/7Tce8+UsAo/6b4TjqnP++DqbMPgRM3VPmEVngziTMLXsbE0ZtWH2zU+Ipy4a1kzn09ELmBCUFTtoh6MZ9xaR+R66ehZ25Hy7E/YWzfQDRf91EplaQF7yB257eU5t3EvnkvvF+egqG1s+i+q8r9Vej0kD0UZ6eSeyOs8j8kUgytnTF28MDYvkHlvw4eGNm5q112syYcmNAYI5v6tJ+9918fp1KpNLorJASl+dncjDxKVsRhsq+dRVlRhtzYArsmXbHz6451o3ZIdWrPbpJKpSL13DaubvkMqVyPJq9/IeqslcfGoFQQ9cts0s5tfXCf/+il2Pv1UGscYpATH8r5b4fi3G4IjYfOE81PcU4mpz59ASuvIFqMWVkjWyqVinNfD6QkN4uOcw8JuhtQXlLAidldsfJqhf+o7wWz+ziyIo9yK+ooqrALTGw0W3D7m1mhndOgYbRJg0jUhuFujyMv+QpnvxpA/U5v4DPwoxrZCln+LjlxF+kw7zB6JlYCRfgoKWe3cnnTLBoN/BjXzm9U+fnpIXuIWPMh7j3fxfulySJEWDVyboQR/N1wTJ19CZy4XtSVxZyEcEJXvIdSUUHz0Uux8g4SzdfDVJQWkXD4Z24c/hmVSoFr57do8MJ7apsmXVUU5aUU3UykICOOgsw4CjLiKciMp/BmIqqH5HINLJ0wdmiAsb0HRveSCWP7BlX6vZSKcipKCqkoKUBRUlh5u7QQqUwu6OtzYLwvps6NaDNt6xMfczf1KhHrpuL54njs/Ho8V8lDUXYqWeGHyYo4TE58KKiUGFg6YefXHbtm3TF396+V/RzlRXe5/OtsMkP3Y+kVRLO3FqJvLvwAyH+joqSAiHVTyUuMxKn1K9y+eorCrAS6fHn6uVfbuj/ETVlRRruPd4v6+4SvnkRW5BHaz95X44WRjEv7CF89iSbDF1CvdfWrAB5H/MEfSTq+iRbvLRddges+UVO6a5OGOkrt+1bVIipmLr44txtC8olNOLd5FRMn72rbavjKdE5/1ofru5fQ+LVPBYzyb+q1foWssIPE7FiEjU/7KssAOrbsw52Y89w4+COWnoHY+LQXJc5nxcLdn6ZvLSL85wlEbpiO39uLRSsJsHDzo/W0PwhZPpqLP7xN42GfUa/1K6L4ehgdPUM8+0zAud0gYnctJuHwKtLObcOzz0TqtR1Y6y7mZHI9TJy8/++zoFSUU3Qr+V4yEf8gqciOvYCyvPTB4/TMbDF28MDUqRHlxXlUlBSiKP07IagoLqCitBBFSSHKiscrepk4edPuo+pJIT8eFTL5v69WVpQUoFKUE7ZqPKbOPnj2mYhN4061MnlQqVQUpMeSGXGYrPDD5KdWLsiYOHrj0WsMds26Y1KvUa2M/T458ZeIWDuFktxMvPp9iHuPUWpXayrOTuPSyvfIT7+Oz8CPcOn4Oh6936e8MOe5TxhAfWVJt6+dJePSPjz6TKhxwqAoLyNm5zeYOHoL3qRcXpTHjUOrsGjQQm0Jg5a6jXanQSRq604DQFlBDifn9cTE0YvASRtrdKK9uvULEo9toO3MHZjWayhglH9TkpvFqc9exNi+AUGTf63yiVZRVszZhQMpu3ubth/tQt/MVpQ4q8KNw6uJ2f4V7j1G4f3yVFF9lRfdJeznCWRfO4t7z3fx6vuBWmuX85Ivc3Xrl+TEXcTYwYOGA2Zg49tBbf6FRqVUUJSd9iCJKMyIoyAjDn0Le3ITI9HRN0JHzwiZvnHlbX0jdPSMkd2/fe9+mZ7Rg8fKjc0xtnMXLMYD7zfC2qc9Lcf+u9SvUlFBxsXdXN/7A8XZqZi7+eHRexzWjdppTH5UpVRSnJNe+ffNiCc/PZa7yZcpyLgOEgkW7v7YNeuObbPuNVarUQcqpYK4AyuI27sUAysn/EZ8i7lbM7XHkXMjjNAfx6IsL8XvnSUaX0ARGnWVJSkryjg9vx9KRTntZ++rcQ9U4tH1XN06n5bvrxb8NYnZ+Q03Dv1E25k7RTs/P47IKd2Y2KhmlQyP43d+0u40aJjateSnRS3oGlvg1e9Drvz2CRmX9uJYg0mjHr3HkXZhB9e2fUmASFri+uZ2+AyaTeT6qSQcWYt793eq9HyZrgF+I7/j3FevELluCgHj12pUjx3ArdvbFN9O4cahVRhYOePSXtjJnw8jNzSl5bhVRG+ex42DP1J0K5mmb3yltqZLM5fGtPpgE1kRh4nZvpCQZe9g7dOehgNmYOLoqZYYhEQilWFk44KRjUut6Vf6JyqV6pnqoqUyHZyC+uMQ0IfUc9uI37ecuH3LCPt5EmYujTFzbYq5a1PMXJthYCFsGY1KqaT4TmplOVhGHAUZ1yv/zbqBorToweP0zGyx8+tB/c7DsWvSFT0zG0HjEJP89Fhidizi1uXjOAb2w2fwXOQG6l/RT7uwk6hfZqFvbk+rSRsxdvBQewxi8ohaUn9xF2ESj66nMOtGZfNzDROG8qK7xO1fhsA+ip0AACAASURBVFXDtoInDCV5N0k8uh6Hln3UmjBoqdtok4b/KM5tB5Jy+neubVuAbePO6FRTX19uaIZnnwlE//4pN6OOiiZv6hjYj6zwg1zf/R02jTthUsWTnomDBz6DPyFq40ziD6zEo/c4UeJ8ViQSCY0GfUzxnTSif5+Hvrkdtk06i+ZPKpPj+9pnGNq6EbNjIcV30mnx3gr0TK1F8/kwEokEe78e2DbuRNKJX4jbt4zT8/vi3G4wHr3GoW+u+d2fukZV1JOkMjku7Ybg1Ko/2THnuHXlBHmJkSQeXfegr0PPzPZBAmFk745Mro9EKkUikYFEWnlbKkNy7zZSGRKJBIlUhlKhoOTB7kHcg5IvZXnJgxgqy7w8qddmICb3e0YcPJAbmgn+txGbouxU4vZ8T1rwTiw9Amj65kKcWr2s9jhUSiXXd39H/MGVWHoG4j/qB3SNLdQeh9ioqyyp+E4GcfuWYtu0qyDf1/EHf6S8KE+URCd+/3JUigo8+0wU3PazIZzkanRuJNF5UeSZ5wlmU0v10JYniURtLk+6T86NMM4vGlzjEhmlooIzX/RDWVFO+9l7keoIL8EKlRNTT33WG0OregRN3VLl2niVSkXk+qmkX9xD4MQNWHkFihJnVagoKSRq0yxuXztLszcXipo43Ccz7CAR66aiZ2pNizE/amS1v6wgh7h9S0k58wd6JtZYeLTApf1QzN39a3Vd+vPC/rHe1O847IHccnVRlJeSn3qN3MQI8hIjyU2MoOhWEqbOPlWaCm/m0oS85Cigcg6L8UNJgbGDZ2VDeS2SQ60upfnZxB9YSfKpX5FIpNTvOBz3nqPRNTJXeywVpUVErp9GVvgh6rUdiO/gOaJ9N2sSdZUlAYStmsDNy8dp/8k+DK3q1chWcXYaJ+f1xKFFb5q+uVCgCCspup3MybkvUK/tQNH/Jo+jsjxphuB2f2e1tjxJw2h3Gv7DWLj74xQ0gIQj63Bq/Uq1a6qlMh0avjKTkKUjSTqxCbeubwscaSV6ptb4Dp1H+M8TuXFoFR69xlTp+RKJBN8hc8lLjCJi7Ye0nbVLVNnTZ0FH3wjfIXMJWTqS0B/H0WzEIhxa9BbVp71/T/QtHAhdOYbziwbjN/I7tfcY6Bpb4DNoNvW7jCDxyGrSLuwgPXgnJk7eOLcfilNgvzrRmKkpJDIZemZ2NbYjk+th7tbskRr8soIcinMyUZYVo1IpUClVoFKgUipRqZSolJW3uX9bpUSma4DcyLzKalPPCxUlBST8tYaEI2tRlBVTr80rePQeL3hJ17NSnJNJ6Mr3uJt6jYavzMS1y1t1MhkvLylQW1nSrejTZIYdwLPvBzVOGFQqFaGrxqNUqfDs+4FAEf7N9T3fI5Hp4NFrrOC2n43K7wTBqXtv4ecObdLwH8f75SlkhR/i6pbPafn+6mqfWGx82mPj25HUs9uwb/EiBuY1v2B5HA7Ne5HV4mDlFnGTTpjWa1Sl5+voG+M3cjHnvh5E1IZptBjzk8YHGukaWxAwcQOXVowmfM2HlRcdIqscmbs2pfW0PwhdOZbYXYvJjr2AZ58Jah9sZmRdD9/Bc/B+aQoZIXtIPvUb0ZvnErP9axwD+uLSYWiVX+P/OsqKMlSKClQqpSj2dY0t6mSJS3VQlJeRcuo34g4sp7wgBzv/nnj1naSWuShPIjcxgtCVY6koK6LFeyvUsnupCVRKJZc3fYSOgTHe/aeJusigKC/j6pbPMLSpj1u3ms0lKr17m4tLR5KfehVDW1fBp5Pnp8WQfnE37t3fQV+k87CW/y51Y/yjlmqjZ2qNZ5+J3L56mpsRf9XIls+gTyjJu8nljTMrVxpFwmfwHORGZkRumP5ECct/w9TZh4avzOTWlZMkHFkrQoRVR25gTMD7q7Fu2IaojTNJPLZBdJ8Glo60mvwrZi6+JBxexdkFA8hLviy638eho2+Ec7vBtJmxndbT/sDevydpF7Zz5ouXOPf1INLOb0dRVvJ0Q1qouNdE/DwNo3veUCkVpJ3fzql5Pbm6dT6mTt60nraV5qN+0GjCkBGylwuLX0cq16P1lN/rbMIAELfvBzJD92PfvBdWXq1E9ZV4ZA2FNxPwGTQbmbz6JV6Z4Yc4+WmvB5LB9v4vCBXiA2J3LUZH3xj3HqMFt10VVCgEP7RoHm3SoAWXjsMwdvTi6tYvanRhZmjjjHe/D7l99TRJxzcKGOGj6Bpb0Hjop+SnXiNu/4pq2XDp8BoOAX3JijhMVuRRgSOsHjJdA5q/txK7Zt25+sfnxB+o2ZTRZ0FHz5DGwz6n5bhVlBflcW7hIK7v+R7lQ0PN1IlEIsHctRlN31hA5y9O0fDVWZQX5RG5YTrHPurA1W1fUpiVoJHYnhfuKw9VpRFay7OhUqnIijzC6fn9iNwwHbmROQHj1xI4cQPmrk01Gtf1Pd8TvuYDzFwa03raVkwcvTQWj9ikX9xD3L5lOLV+pcYr/0+j8FYycfuXY+fXo0ZlnInHNhD20/tUFP3dzCu0AEROfCg3o47i3v2d51JAQEvtR5s0aEEq08Fn0GyK76Rx49C/67o/Def2Q7Bt0oWYHV+TnxYjUIT/j12zbjgGvsSNgyurtToukUhoPPRTVBVlhP88gezYCyJEWXVkcl383lmCY0A/Ynd9S8zOb1CHVoGNb0faz96LQ8vexO1byrmFg8hPjxXd77+ha2SOW5e3aP/JgcrGde/WJB3byMl5PQle8iYZoQc0ltzUZrQ7DeJwJ+4i578ZSujKMSgV5fi9s4Q207dh3aitRuNSlJUQvnoScfuW4hTUn4AJ6zXeqyUmuQkRRG2cgYVHS3yHzBO1V0OpqCBy/TQsGrSg0auzamTLyNYVnX/08zyLLPKzolKpiN31Lbqm1tTv/KZgdrVoeRht0qAFACuvVji0fJEbh36i6HZyte1IJBIavz4fHQMTItZOQfHQ5Fyh8Rn0MbomVlzfvYSKkoIqP19H34iW7/+MoY0Ll1a8R25ipAhRVh2pTIemby7Eud0Qbhz8kat/fC5qudd95IZmNHtrEf6jfqAkJ4MzC/pz49AqVErNbgtLJBKsvIPwf2cJneefwLPvBxTeSiL85wkc/6gTsbu/o/hOukZjrE0oSgsB7U6DUNxNvUbIslFc+HYYxdmp+L72aWWC3byXxvuhSnKzuLB4GJlhB/B+eSpNhi+oUflMdSi8lcz5b4dRnJ0muq/iO+lc+nEMema2NB+9VPTf9fru78i9EYpTUH8MLB1rZMvGtwOefSZV/iCpfN8ImTTcvnqaO9eD8XhhTC1YMFABShEOLZpGNnfu3LmaDqIuMu/TT/Ho/b6mw6gSZq5+JJ/6lcKbiTUa+KajZ4iJoyeJRyuVRMSaPCqT62Pm6kfsniUUpMXg0KIXEknVTuIyXQPsmnUl49I+Us9txaZJ51qxSieRSLBp3AlFaRGJx9ZTnJOBbZPOVf79qoOxgwdOQf0puplI0vGN3L56FkvPAI3IRv4THX0jLD0DcO00HLP6TSnJyyL13FYSj22g6HYyxdkpyOR66JpY1Um1mH+jvLiAG4d+5NaVExSkxyI3MEVRXoyxXYP/3N9CCO6mxXB125dEb/mUsvw7ePQZj9+IRVi4+Wt8OCRU1sdf3/M9d1Ou4D9yCc5tB6r9dc5NjOTikjcoy8/Grlk39M3FU4uqKCnk4g8jKM+/Q+CkDRhaOYnmC+Dm5eNEb56Lc9tBVVbqexzlRXdJOLwKPTNbWk3aiJ6ZDY4BfQWRwVUplYSv/gCpXI+mbyzQ+Psz69B6Wlm3FtzuFUkUo0drtlfjv442aRCJ5zFpkBsYg1RK8olfMHdtipGta7VtGdm6Ul6QQ9LxDZi7+WNk4yJcoA9hYOmArpEZiUfXoSgtxtqnXZVt6OgbY9OkM+nnt5MevBM7v+61oh5UIpFg1agtSCQkHV1HQeYN7Jp2VcsJQUfPEPvmvTC0dSXtwg4Sjm2gICMWQ+v66OgbIdWRix7DvyGRSDGyc8MxoC9OQf3R0TMkNz6UtPPbSTm9maQTm8hLiqK8MBcdfWPkRuZ1/sK5MDOOiDUfUnCvrCwvOYrM0APU7/yG2qZ/P+9UlBaRfnEXVzbP4/qub9EztsShRS/8R32PdcO2SGWafd9D5e5C5PrpxO1bilTXkMDxa7H0aKH2OG5GHePSineRG5oROGmDqCpnlRfFk8iJu0Tzd5dh4e4vmi+oHOIWsnQkRnau+I/6ocozgR5H9JZPyQw9QPPRP2Di4IFlgxaCzc3IDDtA0vEN+Az6GDMXX0Fs1oSsg+tpZR1I5Y6DcMcVyWVt0qBhtJKrWh7BtfNbpJ37k+RTm7HwCKjRNqd3/2lkx54nauMM2n20C11jcVbw63d8ncLMGyQcWYORvTvObQdV2YaRjQsBE9Zy4dthBH//FkGTf0PfTPNTiiUSCZ4vjkdHz5Brf36FoqwY/3e+V8tFoEQiwSmwH1ZegYQsG0XmpX1kXtoHgNzIAiMbFzz7TtJ4TbehVT28+k7Cq+8kinMyuRNzjuzY82RfO0dm2AGgcqCYlXcQVt6tsfRurTH9fDExqdcIk3qNyE+9RuVJVoJ9i161YoeotpOXfIWUM1vIuLibipICjOzcafjKDJxavSza91ZVUSmVpJz+nZgdX6NUlOP18hTcuo7QSCKTcuYPrvz2CSZO3rQcuwo9MxtR/cXu+oabkUdoNPBj0WfKKBXlhK+ehLKiHP+RSwT5rr197QypZ/7AvccozFwaCxDl3ygVFVzf9R3GDp44BvQV1LYWLf9EOxFaJCRSGc5tXsWmSRfsmnbRdDhVIvv6RYIXD8Ol4zB8B8+pka27KdGc/Xogtr4d8R+9TLTVXqWigksr3iX72jkCxq/ByjuoWnZyEyII/v5NDKzq0eqDTbXqgiv51GaubJ6DpWcrWry3Ah19I7X5VlSUc3R6EBXF+Y/c7ztkHi4dhqotjqqgUqkoupVE9rV7SUTMOcoLcwEwsnXD8l4SYeUVWGsuDGtKesgeItZ8CFR+B3WYexBDa3F2+Z53yosLyAjZQ8qZLdxNvoxUroe9/ws4txuMRYMWtWpnqiAjjsu/ziYn/hJW3kH4Dv0MI9v6ao9DpVIRt/cH4vYtxdqnPf7vLBF9CGPq+T+J2jAD53ZD8B0qbuMzwLU/F5Lw1880e/vbGpXp3qeipJDT8/silclpO2un4As+KWe2cPmXj2n+7nLsmnUT1HZVyYo8yq2ooyhDzzC+4XjB7f/xP/bOO7ypso3Dd9K99960paW00ELZG5SNIqIgw8kWRMAB6qc4URQVlCGIIEuGoiiIyN6r0Am0dO+9d9b5/ghWcYGQNGk593XlapJz8r5P0/Tk/b3Pku4SO0LrGFE0aAmpgSFDP7+mazPumGvfvkfGkY1Ezv4KpzsI+fkj6YfWk7j7A0InvnNHXoDbRV5fzbmPxtFYWUyPF3di4eJ3R+OUJp0lauVUrDzb0fW5jc26OL8Vuef3EL95ITY+HYh8dh1G5tbNNnfJtdNc/OyppseGZtb0eGE7lm4BzWbD3SCoVFTnJVGadJbSpHOUJV9sShq28mz3uyciILLFdqNWKRUcerEryoYaPHuPI2zC27o2Sa8QBIHKjDiyT+0g/9I+lLJ6dRfyXo/i3vUBvQhL/CNKuYy0X78g9cAaDI3NCX54ER7dH9KJoFEpFVz55nVyznyLR/eHCJ34jta9HGUpUVxY/gT2AZ2JnL1e6/P9FnLl1ecxQh97UyNjXt35NpnHt9Bt3lbsAyI1MuZvKOWNnHjjfkztXOn+wg69EbpxCwZoVDRcrbxKYmUiZXZlJCcna2xckf+OKBq0REsXDUpZA6eXjEbRWEuf1/be1ZepoFJx8bOnqEiPodeiH+54MX871JVkc2bpWIzMbejx4s479hQUxh4iet0c7AO70nnW2mbvlPxvFMT8SuyGBTi260XQ6BebtZnUxc+nUHL1BBKpATa+4VSkR+MeORL/oTNajHj4DZVSTmVmQpOIKE+7hKCQI5EaYtsmAiMzK0ztXDG1c8PUzg0zOzf1Y1sXjcUia4Nf53dC2VBDn9f367TRmD4hr6sk9/weck7vojovCQMTc9w6D8er9zhsfDrozWLrj5SnXiJ+62vUFqTiFjmSdo+8iomVg05sUTTWEfPlXIqvHMd/2CwCR87V+numqWv57VJfmsvpJaMxc/Cg+ws7NHLNL0uJ4vwnE/HpN4mQR/+nAStvRimrJ+3QeuwDuuLQtqvGx79T4hYMYE7Qsxofd5fBd6KnQceIokFLtHTRAFCZGc/ZD8fh2nkY4U8tu6uxGioKOPXuA5g7etH9he1a3TEqT73E+eWPY9emM11mf3nHC7zc8z8Q9/VLOHcYpLFkOE1RnnaZS2tmopI30n78Yjy6jW6WeavzUzj30XjaPjgf1/DBpB/6iqwTW1HKG3DrNAz/YbNabFMppayB8rTLlKVEUZ1zjfrSHBrKC5D/oRkTABIJJlaOTYLCzN79T+LCFRNrp39MWBcEAZW8EaW8AZW8AaWsEZWiEUGpwNor5K5/j6s73yHz2CaGfp6o85KgukQQBMpTosg+vYOC6AOo5I3YeIfi2ftR3CNH6q03SV5fzfUflpF1chum9u60H78Y59D+OrOnsbqUS6umUZl1hfbjF+PdZ7zW55TX13Duo3E0VBTS86VdWt1oAlApZJz7eCI1BSn0WviDRkK/lLIGTr/3ICqlnN6v/qRXHmttI4qG1ov+rIJE9A4bnzD8h88iZe8KXDoMwq3z8Dsey9TWldAJbxO9bg4p+z6n7QPzNGjpzdj5dyZs4nvEff0iV7YvJnTiu3e0K+bRbTSK+mqu7nyb+M2L6PD4B3qzCLNr04ner+wh5qsFxH39EqXXz9N+3Osarfv9d1i5BTBo6dkm0Rc85iX87p9CxpENZB7bQv6ln3GJGELA0JkaWQA3JwbGpjgG98QxuOdNzysaammoKKChvID68nwayvKa7tfkp1By7VRTF+bfkEgNMbF1xsjMCkVjnVokyNQiQaWQ/e38RuY23PfRRQ38HiY3bNCPz2pzIq+vpiLtMqXXL1By7RTVOdcwNLXEs/vDePZ+FBs9/0wWxhzkyo43aawqwXfgkwSOnKvTxWZtUSZRK5+hoaKITtNX4tJhkNbnFFRKYr96ntrCNCJnr9e6YABI2rOMyoxYwqcs11iuSMrPn1FblE6X5zbcU4JBpHUjigaRf8V/yHSK449yZfti7AIi76qikGvEEDx7jCX1wBocQ3pjH9BFg5bejEe3B6ktTCP1l9VYuvrjd98zdzSOT//JyOurSf7pU4zMrWn3yGt6E8pgautK17lfk/Lz56T+sprKjDjCpyzHSsthQn/2EplY2RP04AL8Bj1NxtFNZB79msLoAzh3GETA8Gc1Xi2kuTE0tcDS1f8fQ30EQUBRX0V9WT4N5fk3BEUejRWFqJRKJBIJUmMTDAxNkBqZYmB846eR6e/PG5tqrCGTUtYI6MdnVNs0VpdRnnKRspQoylOiqMq5BoIKidQQj55j8B3wBK6dhupBs6t/p6GikKs736Yw5lesPILoNH0Vtr4ddGpTRUYsUaumgSDQ9flN2PmFN8u8ibs/oPjKCdqPf/MvAl4bFMYeIuPwBrz7TcSt0zCNjFmZGU/awfV49hyLY7Buq8vpBgEB3TYFFdEOomgQ+VekBkZ0ePJDTr/3IAlbXqHzrHV3tWhu98irlKVcIG7jS/R69UeMzKw0aO3NBI6cS21hOonfL8Xc2feOd8n8h85EUVdF+uGvMDSzpu2ouRq29M6RGhjSdtTz2Ad0IXbDAs5+8DAh497As8eYZrfF2NKOtqPm4jfoKTKObSLj8EbOvD8Gp9D+BAx/Flvfjs1uU3MgkUgwMrfByNwGa89gXZuDStHYajVDfVk+ZSkXm4RCbUEqAFIjE2z9IggYNgv7wC7Y+HbUe6EAN8qont6pLqOqkNH2wQX43fe0zvtBFMUfJWb98xhbOdClmXb7QV0hLuPIRnz6P94sVdnqSrKJ27QQa+9Qgscs0siYKoWM+M2vYGLtSPCYhRoZU0REXxBFg8gtsXRpQ9DoF7m26x2yT+/Au/edx7QamlrQ8cllnFs2nqvb36TjUx9p0NKbkUildHjiA+pLc4jdsIDu87fdUciMRCIhaMzLyOurSN2/EiNzK/wGPa0Fi+8cx3a96PXKHmI3vED85oWUJZ8nZNwbOlk4GZlbEzh8Nr4DniTr+GbSD2/g7NJHcAzpQ8CwZ7Hz79TsNt1LGJrZIJG2/Eu7IAjUFWXcEAlRlKVEUV+aA6gbMtr5d8aj+0PYB3TBxru9Xien/x2V2Ve5tutdylMuYt+2O6ET3rqrhpqaIvvUDhK+eQNrrxAiZ63FxNqxeeY9vYvE3R/g1Xs8wQ9rf7GtUsiIWf88IBAx5VMMjDTz+Uk98AXVeUl0mrG6Wavb6RcCCKKnoTXS8r9ZRJoFn36TKIo7TOJ37+MQ1POuOjzb+nUkYPhs0g9vIOfsbq3uihsYm9Fp5hrOfjCWS6tn0OPlb+8oxEoikRA64W0UDbVkHtuCV+/xereTaWrrQte5G0n5+XNS9q+iIiOOiCnLdZaYbGRmif/Qmfj0n0zWiW9IP7Sec8vG4xDUg4Dhs7EP1F542r2MrLoYQXX7X9iCIOhFyJ1SLqOmIIWK1EuUpURRlnIRWVUJAMaW9tgFdsF34JPYB0Ri5RHULJ3RtUFNYRope1eQf+lnHNv3I2zyEjy6j9H530ClUpGy7zNS96/EqX1fwp9Z3myx+JnHt3J1x5s4hvSh3dhXmqXoROL3H1KZGU/E1M801sukOjeJ1F/W4BY5slnyP0REmhuxepKWaA3Vk/5MfVk+p94diZV7IN3mbb2rL22VUkHcxhcpiPmVLrPX33EzttulKvsq5z6egKWrP93mbbnjhGGVQoa8tlLrHVDvlpLEM8RufAFFfQ0hj/4Pz55jdb4oUTTWkX1qO2kHv0RWVYJ9YFe1eGjbTee2tSbiN79CzrndDFuZeMtzVQoZx14bgKV7W2x9O2Dj2wEbnzCNd0MXVCoaq0toKMtXJ5OX30gmL8ujoVz9nKyqBAtXf2oLUjG1d8c+IBL7gC7YBXbBwtmvxX9G6kpzSPl5JbnnvsfA2BTf/o/jd/8zetEbQlZbQdymhchrK7Bw8SN0wlvNFiKVfmQjid++h3PYQMKnrNDYjv+/URB9gOh1c/Dp/zghj76mkTFVSgXnPnyU+rI8+rz+c6tpGHknxC3ox+y2UzU+7reG+8TqSTpG9DT8DTKZjEuXLpGTk8PYsbpfbOkLZvZuhDz6OnFfv0jaofX4D552x2NJDQxp/9ibVOclcXnts3R/YYdWE3itvULo+ORHXF77LHFfv0z4M5/eUXUZqaGx3gsGAMfgnvRetIfYjQtI2PoqZcnnaT/+TZ1W8TA0Mcdv0NN495lA9umdpP26lgvLH8cptD+Owb1w7TxM44vVexGJgQFwe3tBisY6nMMGUJEZT9qva5s8FA5BPagvy0VqaIzU0OTGT2OkRsa/3zc0xsDoj8fU9yWGRijqq9Vi4Lfk8IpCBKX8prkNTMwxtXXFzN4dK48gTO3dsfZsh7VHMGYOHpp+W3RGQ0Uhqb+sJvv0LiQSCb4DHqfNkOk667nwZ8pSoojdsIDGqhKCx7yMT//Jzfadl/rrWq7/8BEuEUMIf2pZs4SYVRekkrp/FTY+YQSPeUlj42Yc2UhlVgLhT39yTwsGkdaN6Gn4A/n5+SxevJjU1FReeOEFBgwYgInJ7w1edu7cyaFDhwgMDOTChQtMmTKFIUOG/O1YrdHTAOpQhugvn6Mo7gg9X/7urhM/60tzOfPhIxgYmdLjxZ1aj59NO7ie6z8uw3/oTAKGz271glBQKUn5ZTUp+z7DwtmX8GeW60WyLqi7mRZc+pn0IxupzrkGEikObbvhFjkS14jBerED2xK58s1isk59w7CVSf/pdUpZPVXZV6nIjEdRX01tUQYqhQyVvFH987db02P5TceU8kYQVJjautFYVXyjd4XrHxrj/d7TwszeHUMz61b9/yerKSPt13VkHt+CoFTi2XMs/sNmYWbnqmvTAPW1IfXAFyTvXYG5oyfhT3+CjU9Ys82f8vNKkvcuxy1yBB2e+LBZQpIaq0o4++GjCIJA9/lbMbN318i4tYXpnHrvARzb9abT9FWt+nN9O8Qt6Muzbe+sYuG/8Z3hL6KnQceIouEGUVFRjBo1iqeffpq33noLA4ObQ2/Wr1/P4sWLSUxMxMLCgpycHNq3b8++ffvo3bv3X8ZrraIB1F+GJ98ZiYmVAz1e+u6u3cmVmfGc/2QSlm4BdxU6dDsIgkDyvs9I/flz2gyZTtsH5t8TF/jSpHPEbliAvL6KkEdew7PXo3r1e9cUpJIftZe8qH3UFWUgMTDCqX1f3CNH4txhoNb7T7Qmru58m8xjWxi26r+JBk2gUipQKeQYGBm32JyDu0VeX036oa/IOLIRpawO9y4PEDBizl3lgWmahopCYje+SNn1c7hFjqT9Y29hZNY8ze4EQSD5p09J/WU1Ht1GEzZ5SbN8VhSNdVz4ZBI1Bal0fX6zxkraCioV5z+ZSHVeMn3+tw9TWxeNjNuS0bRouFZ5ncSqZMrta0hOTtbYuCL/HVE0ANnZ2XTu3JnBgwezZcuWvxyvrq7G29ubmTNn8t577zU9P378eJKSkoiOjv7La1qzaAB1Sb5Lq6fTZvBUgka/eNfjFcYe4vLaZ3G50X1Zm18igkrFle2LyT61Hf8hMwh8YJ5eLaC1RWNVCbEbX6Q08TRukSMJefR/GFva6dqsmxAEgaqsBPKi9pIftY/GyiIMTMxx6XAfbpEjcAzprfNylPrOtW/fI+PI1zoRDfcyisY6Mo9tJv3gl8jrKnGNGErAyOe03jflv1J85TixX7+EStZAyLjXSGJjaAAAIABJREFUmzUJWxAEkr5fSvqh9Xj2eoTQx95uliaEKqWCy1/MovjKCTrPWI1z2ACNjZ154huubn+DsMlL8OzxsMbGbcm0Jk/D7t27ycjIwMDAAAMDA2bPnt2s8+sbYk4DsHDhQsrKynj//ff/9viBAweoqKige/ebk3W7d+/Ojh07SEhIIDS0ZTew+q84hw3Aq9ejpB38EqfQAdgHRN7VeC4d76Pd2Fe5tusdEnd/QLuxr2jI0r8ikUppP34xCCpSD6wBqZTAkXNbvXAwsXaky+z1pB74gqK4w5x4axhBD8xTJ0nrya6wRCLBxidMHW/80EuUpUSRf/EnCqIPkHfxR4wsbHGNGIpb5EjsAyLvya7Ht0IQBBDfl2ZDKW8k++R2Ug+sQVZdilP7fgSOeh4b7/a6Nu0mVAoZ1/d8TPrhr7ByDyL8mU+wbEZBIwgC13a9S+axTXj3m0jII/9rlv9fQRC4uuMtihOO0X78mxoVDJVZV7i26x28+03Eo3vz98bRa1pBydXk5GSWLFnCxYsXAXjwwQfp3r07kZF3t95pydzzoqGyspKdO3fi7e3N0qVLiYmJIS4ujj59+rBixQr8/PyaPAne3je7l3181O3mL168eM+JBoDghxdSkniG1P2rsJqy/K4btfkOeJy64iwyjmzE3NELn/6TNWTpX5FIpbR/7C0EBFL3rwIkBI58rtULB4lUSsCwmTi178O1Xe+SsO1/ZB7bQvDYhXrXuVQiNcChbTcc2nYjZNzrFF89pQ5hurCH7FPbMbV1xa3zcNy6jMTaq32r/9vdLoq6ShAdyFpHKWsg98IeUvevoqE8H/vAbrSdtlIv+5DUlWQRs34elZnxePedQPCYhRgYmzbb/CqlkoRtr5F79jt8Bz1F8JiFzfb/mnbgC7JPbafN4GkabRgnr68m+svnMLayJ3DEHPH60wrZtGnTTeHngwYNYu3ataJouJe5cOECCoWC4OBg3n//fczNzcnIyGDAgAGMGjWKmJgYSkrUtcKtrG5eFFtaqmNACwsL/zKuIAicfv/3nQev3o/eVVM0fcTQ1JLwZz7h7LLHSNjyKuFTlt/1hbPd2EXUl+Vydde7mNp74NJhoIas/SsSqZTQx94GQSB1/0okEnUX6XsBG+9Qus3fRkH0AZK+X8rFFU/hFDqA4DEvYenqr2vz/oLU0BiXDgNx6TAQRWMdRXFHyI/aS8axzaQf/goLZz98Bz2Fd5/W9T92J8jrq8UFjBapzLpCzpld5F3ci5VHECY2zoRNXoJDUA+9fN/zo/aRsO1/IJESMfUzXCP+vniHphFUSspSLlEQvZ+sk9tBpcRv8DSCHlzQbO9T7oUfuf7jx7h1GUXbB+ZrbFxBEIjfvIiGsny6zdsiVksCsk5tJ/vUTgAsERBQ6diiuyc+Pp4+ffo0Pfby8uKbb77RoUW6p1WKhpycHBQKxS3P8/DwaBIE06ZNw9xc3azL19eXl156iVmzZvHLL780VVD684VOesO1amz810RgiURCr4W77+r3aAnY+nYk6IH5JH2/lMxjm/Ed8PhdjSeRGtDxqWVc+GQSsV/No9v8rdh4a8+LI5FKCZ3wDgjqah5IpASOmKO1+fQJiUSCW6ehOIcNIPPYZlL3r+LUO6Pw7vsYAcNn612+w28Ympjj3mUk7l1GIqutoDD6AHkX9yKrKdO1aXqBoqEW9HDx2pKR1VaQf/Enss98S3XONbWIDR+MV+9x2Ad21UuxoJTVc3XXO+Sc3oWtXwQdn16GuYNns8yde/4Hru16F3ldZdNzBibmzSoYSpPOEr95EfaB3QibtESjoVAZR7+mMOZXgh9eqJeeJV3g3Xt808Zo7II+tzi7eSgsLOTjjz/Gzc2N559//i/Hb1URs7a2tmlzGNQbx8XFxc1iu77SKkVD7969yczMvOV5165dw85OvTD6c7Wkzp07A5CUlISrq7pEXk1NzU3n/PbYw6P11BS/E/wGPU15ShSJuz/A1rcjtn4d72o8QxNzOs/8grMfPsqlVdPp8dIujZXG+zskUimhE99BQCBl32cgkRA4/N5JdjIwMqHN/VPw6P4QyXtXkHl8K7nn9xAw/Fl8+k1sltrpd4qxhS1evcfh1XscYk0HNYqGGkD/FrEtDUGlovT6WXLOfEthzEFUChnWXiGEjHsd9y6j9LokcHXedWLWP09NQSpthkwncORzzVpAQFZTdpNgAJo1b6w6N4nLXzyLhbMvnaav1GjDuPL0GJJ2L8W5wyB8Bz6lsXFbHTrOadi/fz/btm1jy5YtvPHGG385fjsVMe3t7amtrW16TW1tLQ4O+tFfRVe0ymy5pKQk6uvrb3kLCgoiKCgI+GuIkY2NTdPPTp3UOwnZ2dk3nZOVlQVAeHi4tn8lvUYildLhiQ8wtXUhev1cZLUVdz2miY0TnWetRSlvIGrlVOT11Rqw9J+RSKWETXwXj+5jSNm7Qu11uMcwsXIg9LE36f3qT9j6dSTxuyWcfGckhbGHWsSCXB93e3WBiZWDmCB+F9SX5pK87zOOvz6IiyueouTqKbx6PUrPRT/Qa9EP+PSbpLeCQRAEsk5u58wHDyOrraDL7K8IenBBs1cc8+ozEWOrP/bckeDWeXizzF1fXkDUyqkYmJgT+ew6jMytNTa2rKacmC/nYmrnSofH3xevOXrMsGHDWLx48d8eq66u5oUXXmDy5MlYWKgbnnp6ejJs2DDmzPk90iA8PJzc3Nymxzk5OXTseHeboi2dVvnNYmJigqmp6S1vEokEPz8/evTowZEjR24a47cPSteuXbnvvvtwcHDg/PnzN51z7tw5unTp0iQ87mWMzG0If+ZTGiuLifv6ZQTV3cczWrkHEjH1M2oL04leNwfVnzrKahqJVErYpHfx6Daa5L3LSdm/Sqvz6StW7oF0mb2eyGfXIZEacPmLWVxY/gRV2Vd1bZrIbVBTkComQv9HlPJG8qP2cWHFUxx7fSAp+z7D3NmHjk99zIAlpwgZ9zo2XiG6NvNfqc5P4dLq6STv/RT7gC70fuVHHNs1f3EDeV0lV3e8iay6BOsb75ldQKTW+xeolHKyTu3k+OuDaKwpJ3LWWo16qAWVirivX6KxuoSIKcv1VjjqB+qcBk3f/it/jiD5jX+riBkTE0NCQgIAjzzyCCdPnmw6fujQISZP1l6BlpZAqwxP+q8sXryYMWPGkJaWRps2bQB11vzEiRPp0EHdAOZ///sfn3zyCYsWLcLMzIz8/Hx+/PHHez4p5o/Y+nag3cMLubrzbdIPrafN4Kl3PaZjcE9CJ75N/OZFXNn2BqGT3tXq7o5EakDY5CVNDYhAQsCwmVqbT59xat8Ph+BeZJ/aQfLe5Zx+/yE8uz9M4APPY2rjrGvzRP4BQakQcxpuk6qca+Sc+Za8iz8hr63A1N6dgGHP4tFjTLPF/98tspoykveuIPvUDgxMzGk39lU8uo3WibepOu86l7+YRV1pHhFTVuASPpiMo19jH9hVa3PWl+WTc2YnWSe3I6suBcCz97gmwaIp0g6uo/jKcULGvdGsnbPvdS6WxXGyOIoGZSMW9ndXofE3brciZkBAAM899xxLly7F3NycXr163ZQYfS8iigZoauo2a9YsIiIiqKysxNnZmbfffrvpnLlz52JiYsK0adMICwsjKiqKTZs2MXLkSB1arn9495tEWUoU13/8GFu/cOwDu9z1mJ49HqauJJvU/aswd/LCf6h2F/ESqQEdHn8fEEj+6RMkEgn+Q2dodU59RWpgiE+/ibh3GUXqL6vJOLqJ/Ms/02bwNHwHPoWhidipWd/QtkeupdNQUURB9AFyz39PVVYCEkMjXDvej0ePsTgG99CbniW3QqWQkXlsMyn7V6FsrMOrz3gCR8zRWSWf/Es/E795EYamlnSft6UpQdhvkPbi/huryzjx5mBU8sabng96cIFG5ym9foHrP36CW+fhePedoNGxRf6dLvYd6GKv3rz93viERsb8LxUxJ02apJE5WwuiaLjB6NGjGT169L+eM2PGDGbMuDcXj7eLRCIhdOK7VOVcI+arefR6ZQ8mVnefOBQ4ci51JdkUxh7CyNJO6+Vr1cLhAxDg+o8fg0SC/5DpWp1TnzEytyZ4zMt49XmMpO+XkrJ/JXkX9uDS8T68eo/H3NFL1yaK3MDasx1lqZd0bYbeIK+roiz5AqVJZylNOktNfgrWXiEIKiXtHnkN9y6j9LZS2N8hCAJFsYdI/P4D6oqzcGrfl6AxC3XWeVqlVHD9x09IP7gO2zadiJi6otk8kcYWtjiHDaTg8v6m56w822FsYauxORqrSojdMB8LJx9CJ74j5jHcDgI6T4T+N+6kIqaIGlE0iGgcIzNLIqYs5+yHjxK7YQFdZq+/6907iURC2KT3iP7yOa5sex0JErx6j9OQxf8wp9SADk98gIDA9T3L1MJh8DStzqnvWDh502na51RkxpP2yxrSDq5XdwUP6Yt33wk4te/bYnZqWyvlqZfu6ZwGpayB8rRLlCadozTpLJWZCSCokBqZYh8QiUe3h3AK7Y+lW0CLWwBWZV/l2rdLKEs+j6VbAJGz1+MUortwCVlNOTFfzaM08QzefSbQ7pFXmrXamkQqxSGo+02iwbFd7395xX9DUCmJ3TAfeV0lXWavx9DU8tYvEmk2Tp48ybBhw2567urVq38JO/ozYkXMO0cUDSJawdqzHSGPvk7C1ldJ2b9KI70PDIxMiJiygui1z6qbFUHzCIfHPwBBRfaJb1DU19B21PP3fHUaW58wOk1fSX15ATmnd5B9aieXVk/HzMETrz7j8ewxFhMrseGRLlApZEj0uEyuplEpFVRmJVCaqPYkVKRdVr8HUkNs/ToQMGwmDkE9sfHtqNHSm81JQ2URyT9+Ss657zCysCVk/GK8ej2K1EB3X+FV2Ve5vHY2DZWFhE58F69ejzS7DXlRe8mL2otjSB8cQ/pwfc/HuIYP1tj4KT9/TmnSOcImL8HKQyx4cvsI0AzN3SIjI4mJibnpOXf3Wye//7Ei5m95qyBWxLwdRNEgojU8e46lPOUiKT9/jl2bThqp5GFgZELEtJVEr51Nwrb/ISBoPVRJamBIhyc+5PoPy0g7sIaa/GQ6PPEhRmbirpOZnSuBI+fiP2wWhbGHyDq+jes/fETy3uW4RQzDu+8EbNtEtLgd3ZaKoFIiqJStsyzeDQRBoCbvepMnoSz5wo3eFGDlGYx3v4k4BPXAPiCyxe8MK2UNpB/eQNqvX6BSyPEb9DT+Q2dqtIzonZB74UcStr6GkYUN3edvw9a3+ctQ5p77nrjNi7Dz70zE1M8xNDHDt/9kjXk6i6+eImX/Kjy6j8Gzx8MaGVNEs5iZmREQ8N/D8v5YEXPEiBFNz4sVMW+NKBpEtIZEIiFk/GIqs64Qu2EBvV75AVNb17seVy0cPid67WyubHsdoFmEQ9CYlzBzcOfat+9x7qNxdJqxGgunf3eD3itIDYxw6zQMt07DqM5PIfvkN+Se+568iz+qF3J9J+AeOQpDUwtdm9qqUcgasXRrS11Zjq5N0QhKeSN1RRnUFKZTW5hOY2UhBdEHmqrkmDt54xY5Qi0S2nZvNd4tQRDIj9pH0p6PaCjLw6Xj/QQ99BIWzj46tUullJP0/YdkHNmIXUAXIqYsx8Ta8dYv1DDZp3eRsO01HNp2p9OM1U0FGTQlGBoqCojduABLt0Daj/9rYzCRWyPoQU5DY6M6QV71pxLwhoaGYkXMO0QitISuTS0QqYEhQz+/pmsz9IKa/BTOLB2LtWc7uj6/WWMudaW8kei1sym+cpz2E97SunD4jdKks0R/OReA8Gc+xTG4Z7PM29JQNNSSF/UTWSe2UZ2TiKGpJR7dRuPVd4LOkjZbO41VJRxZ2BMDEwsGfxKta3NuC0EQaCgvoLYojdrCdGoLM6gtUouE+rLcm/IzXMIHY2Bkin1QdxyDemDm0Ppij8vTY0j89j0q0mOw9goh+OFFOLTtpmuzaKwuJebL5ylLPo/PgCcIHvNSszeNA8g8sY2r2xfjGNKHTtNWYmBsqtHxVUo5Fz59nKqca/R8+TssXf01Ov69QOz8Xsz0H6vxcX8wvUBUVNRtnXv48GFWrVrF7t27CQ4O5vXXX2fEiBFYW//upVuzZg2nT59uqoj52GOP8dBDD2nc7taEKBq0hCgabibv4k/EblhAm8FTCRr9osbGVcplRK99ttmFQ21xFpfXzKS2MI12Y1/Bu98kMQTnHxAEgYr0GLJObCX/8n4EhRz7wG5495uAS8f7dLLwaK3UFKZx8s2hGJhaMvjjy7c8XymXUZ2biJm9O8ZWDlr9DCsaaqgtTKemMJ26G6JAfT8Dpay+6TwDEwssnH2xcPH7/ebsh4Wzb6v2VNUUppOy7zPyo/ZiYu1E2wfn3+i3oPvCApWZ8VxeOxtZTRmhE97Bo9uDOrEj48jXXPv2XZzDBhI+ZYXGc1QEQeDazncovnKcwFFzce8ySqPj3yvEzu/JTH/Nh3T9YBp126JBRDuI4UkizYJ7l1GUJV8k7dd12PqG4xJ+v0bGNTAyvpHj8Kw6VEkA7z7aFw4WTt70eHEHsRtf5OrOt6nKTaT9uDeatXJIS0EikWDXJgK7NhEEP/wKOWe/JfvkdmK+nIuJtROefcbhFNwLW79wvVggtWQUddXAX0sJ/hN1xZmcXareEZQaGmNq746ZnRuW7m2R11aAICAISnWHd0FQ50wI/3RfAEGFIKhunK++LzEwpr4ki8bKot8nlkgxc/DA0sUPh7bd1KLghkAwsXG+ZwS4IAiUp14i48hGCmMOYuPbAf9hs2hz/1S9EUg5Z3dz5ZvXMbF2ovuC7dh4t9eJHWkH15P0/Qe4hA8m/OmPNX6tFQSBxN3vk3l8MwEj5oiCQUTkbxBFg0iz0e6RV1EpZMRsWECXOeuxD7j7xm/wJ+HwzY0ch2YQDoamlnSatpLkvctJ/WU1tQVpREz7XCN9KVorJlb2+A+eRpv7nqH46knyzv9A2oF1pO77HCMLW5za98U5dACOIX10nuzZElHKG7BwaUNDVcltnW9q50qnGatpKMujviyf+rJcGsrzaSgvoCr7irpKmESKRCL9/b5U/fif7kukhkgNJGoBKJFgaGqJhbPPH7wHbTB39G6xlYw0gUopp+DyATKObKAyMx4jcxvaDJmGd79JmNm66No8QF2F69p3S8g6vhWHoB6EP/OJzhrHpexfTfJP6uZqHZ78UCveyeS9K8g4vAGf/o8TMHy2xscXEWkNiOFJWkIiNcCr51icwgbi0mGgrs3RGxqryzj/8QQaKgvpNncTNj5hGhtbKZcRvW42xQnHaP/YW80iHH4jP2ofcZsXYWxlT+fpq7D2Cmm2uVs68vpqSq6doijuCMVXjiOvrUAiNcDOPxLnsP44hQ3A0qWNrs1sEZRcO83Fz57C0Mya+5eJbnx9Q15XRfbpnWQe3URDRQEWzn74DHwCj26jMTQx17V5TdQWZZL0/VIKYw/id98ztH1wgU7KuwqCQMq+FaT8vBL3rg8SNnmJVuxIPfAF1/csw7PXI4Q+9vY9X1L7TimMO0Jx/BGUl44wo82/N8u9E/aYRYvhSTpGFA1aQsxp+Gfqyws4//EEFA01dJu3FSv3QI2NfZNwGP8m3n0f09jYt6Iy6wqX18xEVltBhyc+wK3TsFu/SOQmBJWSivRYihKOUhx/jOq8JADMnX1xDu2Pc9gA7Pw7i2Fg/0BZykXOfzwRQzMr7l8mdoXWF2qLs8g8+jU5Z79D2ViHfdvu+A16Cqf2/fRqgapSyEg/tJ6U/auwcPalzZAZuEeOuPULtYAgCFz/8WPSDnyBZ4+xhE58WyvhixlHN3Ft1zu4dRlFxyeWiiGSGiB2fk9RNLRSRNGgJUTR8O/UFmdx/uMJAHSbv02jpUt1KRwaq0q4vHY2FWmX8R/2LIEj5ujVoqClUV+aS1HCMYoTjlKadA6VQoahqSWOIX1wDu2PY/t+rabMpiaoyIjj7NKxomjQA9T5ClFkHN5AYdxhJFJD3CNH4DvwSb30RJanRZOw7X/U5F3HJWIIIY+8hqmOQqV+yy/IOLwBrz6P0X7cG1q5jmaf3kXC1ldx6Xg/4VOW67RZXmsidn4PZvhpPll+j3msKBp0jPgfIqITLJy86TJnA+c/mcjF5U/QbcE3mNndfQ8HuJHjMPVzotfN4cp2dY3t5hIOJtaOdJ27iavbF5O6fyU1eUl0eGKpXjWZqsxKwNqzXYvYUTNz8MCn30R8+k1E0VhHadJZiuOPUpRwjILL+0EiwdY3XB3GFNofS/cgpPewSJMam2Dm4IGstlLXptyzqPMVfiH98AaqshIwsrDFf8gMvPtNxNTGWdfm/QV5fTXXf1hG1qlvMLV1pdOMNToNqVVXMHqbzONb8On/OO0eeVUrifF5F38iYdtrOIb0oePTn4iCQUTkNhA9DVpC9DTcHpVZCZz/dDKmNs50m79No0nEao/DHIoTjhIy7g18+k3U2Ni3QhAEMo9tJvG7JVi4+tN5xmrMHb2abf5/oqGigGP/G4S5oydtBk/HveuoFlnyVFCpqMq5diOM6SiVmfEYW9qjUiqw8Q7B2jMEK68QbLxCsHDxaxECSRPUFmVwYvHg2y65KqI55HWVZJ3aQeaxzTRWFGLh4ofvwKfw6PYgBsZmujbvLwiCQEH0Aa7teofGqhJ8+k+m7ai5Ot3gEFQqrmxfTPap7fgNepqgMS9rRTAUxPxKzJdzsQvoQuSstRrv9XCvEzu/B9P9Rmp83B/NE0RPg44RRYOWEEXD7VOWcpGLnz2DhYsv3Z7fjJG5jcbGVsplRH85B0VtJQ7tehEwfHazlnMsSTxNzJfPg0RChyc+xDm0X7PN/XcIKiUFMb+S+stqqnMSMbV3p839U/DsMbZFf3E2VBZRlhJFWdJZqrKvUp2bhEohA8DA2AwrjyCsvUKabpZubVtl9Z76snyOvdavRTV3a8molHLKrl+gKP4IOWe+RSmrxyGoB74Dn9S7fIU/Ul+Wx5Xtb1KccBQrz3aETXxHo0Up7gRBpSR+62vknv0O/yEzCHxgnlau1cVXjnNpzSxsvEPpMucrvSlt25oQRUPrRRQNWkIUDf+N4qsnubR6hlYu5EqFnCvbXiP33Pe4d3mA0EnvNeuCsbYok+S9K8iP+gnPXo8QPGYhRmZWzTb/3yEIAsUJx0j9ZQ0V6dEYWzviN+hpvPuM16tQqjtFpZRTW5BGVfZVKrOvqoVEzjUUDTUASKSGWLoHYO35u5Cw9gxu8b97Y3UZR17uLooGLaJSyChNOkdB9C8Uxh5CXluB1Ngc7z7j8Og2GmvPdro28R8RVEoyjm0m+adPQRAIHPkcPgOe0HlojkohI+GbN8g9+x0BI+ZobXOnNOkcUaumYukaQNe5X4tlnbVE7PzuTPfVfAL9jxZXRdGgY0TRoCVE0fDfKYg+QPSXc3Fo243Os9ZiYGSisbEFQSDtwBqu//gJdv6d6TR9ZbPWHFfKG0neu4L0Q+sxtXEmdNK7OIX0abb5/wlBECi7fp7UA2soTTyDkbkNPgMex6f/ZIwtbHVtnkYRVCrqSnOoyr5C1Q0hUZV9FVl1qfoEiQRzJx+sPdth7d0e7z4TMDJrWSJCXlfF4Zd7IDEwZMinsbo2p9WgUsgoSTxDweVfKIo7jLyuEgNTC1zCBuEaMQTHkD5676mrzLpCwrb/UZWVgFP7voSMX4y5g6euzaKhopDodc8hb6jGs9tDtBk8VSvzlKde5uLnT2Nm70G3eZt11nPiXkAUDa0XUTRoCVE03Bm5574nbtPLOHcYRMTUFRqPt8+/9DNxX7+Eia0LkbPWYunqr9Hxb0VFeixxmxdSW5CKZ8+xBD+8SOdeh9+oSI8l9cAaiuIOY2BigXefx/Ab9BQmNk66Nk1rCIJAY2WRWkDkXKMq6wpVOddoKM/n/o+j9X4h+GcaK4s5sqiX6Gn4Dyhl9aiUir/8HyrlMkqunaIw+hcK4w6jqK/G0NQS5w6DcO00DMd2vTS6saEtFA21JO9bQcaRrzG2sidk7Ku4dh6uF123y1KiiPlyLorGWsImvYdb5+Famacy6woXPp2MsZUD3eZv1cuE9NaEKBpaL6Jo0BKiaLhzMo9v4eqOt7RWN7s8PYbLa2aiUsjpNO0zHIJ6aHT8W6GUN5KybwVpB294HSa+g1P7vs1qw79RnZtE6oEvyL/0M1IDQzx7jqXN/VMxc/DQtWnNhry+psV5GUC9ODrz/kMYmlpyv5gIfVuceGsYtQWpGFnYYu7ojVLegKBU0FhZhKKhBkMza1w6DsI1YigOwb1aVC5MUfxRrux4k4ayPLx6jyNo9AsazRm7UwRBIOv4Fq59uwQzBw86TV+JlXtbrcxVnXed859MwtDEgm7zt2Jm766VeUR+J2Z+d6b7DtX4uD9ZJImiQceINcZE9A6ffpNQNNRyfc8yDE3Maf/YWxrdFbPzC6fHS7u4tGoaFz97hvYT3sKr51iNjX8rDIxMCBr9Ii7hg4nbtJColepE5OCx+uF1sPIIIvzpjwkc+Rxpv64l+/Qusk/txL3rA7QZMu2e6M7cEgUDQGNVsfqOnibg6iNmdm7UFqQir62gsrai6XnPHmNx7TQEh6AeLa6ZYH15AYnfLaHg8n4sXP3pNn8b9gGRujYLUHt2Era9Tt6FPTiFDqDjkx9qLbegtjCdCyueRGpoTJe5X4uCQUTkLhFFg4he4j9kOoqGGtIOfIGhiYXGS++ZO3jS/YUdxHw5l4Qtr1BXlE7bBxY0a7UTW9+O9Fr0Ayn7PiPt4JeUXDtF6MS3cWqv2wpLv2Hh7EvYpPcIGD6H9EPryT69k9zz3+MaMRT/oTP0OuHzXkVeW3GjvKfuQ0/0FUGlpDovmYq0aKpyrlF6/dxNxw1MLOj+4nas3YN0ZOGd01hZTNqhL8nMOjlIAAAgAElEQVQ5txtDY3MCR87F7/6peuMdqSvJ5vLa2VTnJhIw8jkChs7S2jW3Ou86id9/CCoVXedt0mgDUZFbIAggqHRthYgWEEWDiN7S9oH5KBpqSD/8FQamFgSOmKPR8Y3MrOg8ay1Xd75N2q/rqC3OouMTS5u1prra6/ACLuGDid+8kKiVU9Veh4cX6k1lDzN7N0IefQ3/YTPJOLyBzBNbqc5NwsjCFo9uo3HrPEwvQh5E1PHrSlk9RkYtKxdDm8jrKqlIj6E8LYaKtMtUZMahbKgFwLnDINw6jyDv4k8gqDC50S+mpS0wG6tKSDu4jqwT36BSyPDo9iABw2frRW+Y3yi+epLYr+YjCCo6z1iDc9gArc1Vknia6LVzMDCxoOu8LVi6BWhtLhGRewlRNIjoLRKJhJBH/oegkFMYexBFfTXBYxZqdGdKamBI+/GLsXD2JXH3+5wvy6PzjDXNnvxr69uBngt/IOXnz0j7dR3F104SNvEdvfE6AJhYORA0+gXaDJ5K/qX9ZBz9mivfvM7VXW/jHDoA964P4NS+v97sat6TSCTY+HaktjhL15boBEGloqYwjYq0aCrSoylPi6a2IFV9UCLFyiMIj64PYusXgW2bCMwdvZBIJKgUMirSouk2fwvmji1HMDRWl5L+6zoyT2xTi4WuD+A/bBYWzr66Nq2Jpsp1P32KlVtbIqZ9joWzj9bmyzm7m4Str2Hh2obIWesws3fT2lwi/4wgKHVtgogWEBOhtYSYCK05BJWSa98uIfPYJlzCB9PxyY+0UtWmMO4wsV/Nx8jSjsiZX2DloZvwhIqMOOI3L6ImPxmPHg/T7uFFeuN1+COCIFCVfZW8C3vIi9qLrKoEI3MbXDsPx6PrA9i26aQXFVruJVL2ryL5p08xsrTnvqXnbv2CFo68vobKzFgq0mIoT7tMRXosivoqAIwsbLH1C8fWLwK7NhHY+IT9Y/8XlUIGEkmL6Y7eWF1K+sEvyTqxDaW8EfcuowgY/qxeiQVQ/33iN71MYexB3CJHEDrxXQxNzLUylyAIpOxbQcrPK3EI7knE1M/0IkfsXiRmXjem+dyn8XH3WqWJidA6RhQNWkIUDZon/chGEr9bgq1vOJ1mrMbESvN1tiuzrnBp9XQUjbVEPLNcZ1WNlHIZqT9/TtrBdRhbOxI64W2cQ/vrxJbbQaVUUJp4htwLeyiMOYhK3oCZoxceXR/AveuDereYaa1c/2k5qftXtjrRIAgC8rpK6oqzqClIbfIkVOddV8dPSyRYugVi5xeObZtO2LYJx8LZr9WJVrVYWE/Wia03xMJIAoY9i4WLn65N+ws1+SlcXvssdcVZBD30Er4Dn9Ta30OlkBG/5VXyLuzBo8fDhE54q8UIwNZIzLyuTPMZpPFx91pliKJBx4iiQUtIpAZ49RyLU9hAXDoM1LU5rYaC6APEbnwBU1tXIp9dp5XFaENFAZdWz6AqJ5GQR1/Dp98kjc9xu1RmxhO3eRE1edfxGfA4fgOf0vvSp4qGGgpjDpJ7YQ+lSWdBELDx7YhHtwdx6zxcbKqkRdIPracw/gi1BWkM+uCsrs35T6iUChrK86kryaauJIv6kmzqirObHivqqwGw9QunpiD1hhdB7Umw9evYqneVG6vLSD+0nqzjW5rEgv+wWXpbyawg5lfivn4ZAyMTwqcsx6FtN63NJa+r5PIXsylLPk/gqOfxHzqz1YnFlkJh3BGK44+giPpVFA2tFFE0aAnR06A9ytOiubR6OgCdZ6zBzr+TxudQNNQSu2EBRfFH8Lt/KoEj5+osVl8pl5F5bBPXf1oOqPDpOxH/oTNaxOK7oaKAvIv7yLvwA9W5SUikhji174t71wdwDhvY4pqn6TvJe1eQ8vPneutpUDTUqEVAcRZ1JTm/i4OSLOpL8xBUiqZzJQZGmDt4Yu7khZmjF+aO3pg7emHh6o+Fk0+zVjrTFbIatVjIPL4Vpawe98hR+A+b2exNKW8XQaXk+k+fknbgC2x8OxAx9XPM7Fy1Nl9daQ5RK6dSV5xF2KT38Oj2oNbmErl9RE9D60UUDVpCFA3apbYog6iVU2koz6fjU8twjRii8TnUX4DLyT3/A8aWtnR8chlW7oEan+d2qS/LI3nfZ+Se+x5DE3P87p+K78AntBYjrGmqchLJu/AjeRd/pLGyCENTS1w7DcW10zDsA7qIAkIDpB74gut7lt22aKjOTyF+80KMLR0wsXbA2Ep9M7FyQKWQI6gUqJQKBKUCQaVAUCrVj1W/P6c+rrz58Y3jKpUSbnTdrivOQlZTdtP8RuY2mDt6Ye7kfZMwMHfyxtTWReONHVsKarHwFZnHt6CU1eMWOZKAYbP0ViwAKGUNXFozk9LE03j1epR2j76u1Y2Wysx4olZPRyWX0Wn6Sq16M0T+GzHzujLNu7/Gx91rnS2KBh0jigYtIYoG7dNYXcblNTOoyIgleMxCrcXMFsYdJmHLqygaagga/SI+/SfrdJezOi+Z6z9+TFHcYUysnQgYPhvPXmNbTAyvoFJSev08eRf2UBR/VL04Vcqx8++EQ1APHIJ7YuMdes8uGO+GzONbKLh8gKq869z/4flbnl+dm0Ti7g9orC5FVl2KrLoMQaXALqAL5SkX//3FEilSA0MkBoZIpIZIDAyQGhghkRogMTBEKlUfMzCxwNDEDDNHT7UocLohDBy9xFK9f6K+LJ+cM9+Sfni9Wix0HqEWCy2gZKggCCRsfRVb34549R6n1bl+K1phbOVA5Ky1LeL9uZcQRUPrRRQNWkIUDc2DUtZA7MYXKIz5FZ/+k2k39hWtLDYbq0qI3/IqxQlHcQjuSYfH38fUVntu99uhPPUSST98RHnqJcydfWk76nlcOw1rUfG8Clk95ckXKU08Q0nSGapzEgEwNLPGoW1XHIJ74hDcs1UmtWqDu62eJKhUyOurUNRVg0QdIiS9IQIkBoZqkSA1UIuEeyA8qDlQKRUUXzlO9qkdFF85gW2bCMzsXAkY9qy4GP4bMo9t5uqud7HxCqHzzC+avTy2yK2JmdeFqd6aLxe+zzpXFA06RhQNWkIUDc2HoFKSuPsDMo5sxLnDIMKf/lgrDdoEQSD79A4Sv12C1NCY9o+9iVvn4Rqf57/aVBR/lOt7llGTn4yNdyhtR7+AY3BPndp1pzRWl1KWdI6SpDOUJp6lvjQHABNbFxyDe+IQ1BOH4B4YmVlTlXONysx4KjPiaKwuEUssAkk/fEj6kY0YmVm3uEToe4360lyyz3xLzpldNFYWYWLjjGePh/Hs9QjmDp66Nk/v+PN1vuNTy1pMaOa9higaWi+iaNASomhofjKObuLat+9i4xOm3oGyctDKPLVFGcRufJHKjFjcuz5AyLg3dL5YFVRKci/sIXnvChrK8rD2CsG2TWfsAyORGpogNTTG1M4Vqxa2c1lXkkVJolpAlCadRV5b8Y/nDlhyClMb52a0Tv9I2b+K4isnqCvOFEWDHqJSyimOP0b26R0UXz0JgFNIX7x6PYpTWP8WE2LY3Chl9cRueIHC2IP49H+cdmMXieGLekzMvC5M9eqj8XH32eSLokHHiKJBS4iiQTcUxhwkZsMCTGyciHx2ndZKEqqUClJ/WU3q/lWY2LrQ4fGlOLTtqpW5/gtKeSNZJ7aS+N0HwF//tQctPY+xpV3zG6YBBJWKqtxEkveuoDj+yM0HpQb4D5mOrV8EVh5B6iTaezCcKXH3UjKPb8bQ1FIUDXpEXWkOOad3kXP2O7VXwdYFr55j8ewxVu9LKOuaxupSLq2eQWVmHMEPL8Jv4JO6NknkFoiiofUiigYtIYoG3VGRHsul1dMRBBWdpq/CPiBSq3PFbnyBupIs/AY9Q+Co53VWmvWPFCUc59KqqTc9Z+PbgR4v7moVi+ny1EtErZyKoqEWEDA0swJBuOmxlXsglm7qm5V7WyzdA7XmfdIXUvZ9Ts6571HK6kTRoGNUSjlFcUfIPr2TkmunAAlO7fvh1XscTu37IjUw1LWJek956mWSfviI6vzrhE16D9fwwbo2SeQ2iJkXyRTPXhof92fbIlE06BhRNGgJUTToltriLKJWTqGhLI+wJ5birsXcA0VjHYnfvU/2qe0gkTZVAbJw9sPCtQ3Wnu10slBP+3UdST982PRYYmBM4IjZePYci4m1Y7Pbo2lqCtO4uOIpGsrz1eKwbTeqsq9Rk59MTV4y1fnXqclLRl5X2fQaY0t7LN3bYuUWgKV7WyzdArByD2w1VXyu/7Sc1F9WYWxpL4oGHVFXkkX2Da+CrKoEU1tXPHs9ovYq2Lvp2rwWgaBSknpgLSn7VmBq707E1M+x8Wqna7NEbhNRNLRexK0OkVaJhZM3PV7YwZVvXidhy6tUpEUT/NCLSA017wUwNDEndMJbGJhYkHF4PeUpUZSn/H5hC3/mU50kTPvd9zRFCUcpT4nCIbgXRubW5F/6meR9n+EaMRjvvhOx8+/cYj0Pli5t6PnydxTE/IpTaH+kBobqikt/CBMTBIHGquIbIiKZmrzr1OSnqHfjG2ubzjOxdcHKLRBL9xteCbcAzJ18MTK3brHvj0jzoZQ1UJRwlOxTOylNPA0SKc5hA9S5Cu37ivH3/4GGikJiN75I2fVzuEWOpP1jb2FkZqlrs0RERBBFg0grxtjSjo5PLVPHeR/9mor0GCKmLMfM3l0r87V7+GUqM2IpT/1dMBiaWmIf0EUr890KidSAjk8uI2nPR7QdNRdzR29qC9PJOvkNOWd3kx+1DyuPILz7TsS9yygMTS10YufdYGLtiE/fCf94XCKRYGrjjKmNM47tft/5EgSBhrK8G0IimZr8ZKrzksk6sQ2VvBEAa69QqnMTMbKwwcjCFmMLG4zMbTGysMXIwgZjC1v1Y0tbjMxvPLZQ3zcwMdeJ2DAwMsHGO4z6stxmn/teo768gOKEYxTFH6U06SzWnu1oqCwkcORcPHs+rPOSzC2RovijxG1eiErWQNjkJXh0HyOK9paIAAgqXVshogXE8CQtIYYn6Rf5l/cTv+UVpAZGdHzyQ5zaa74cHKibZZ169wF+S0KWGhoTOvl93CNH6NWXn1JWT97FvWSd2EpV9lUMTS3x6P4Q3n0eu6drwwsqJXUlOdTkXaehqoSG8jzktRXIayuR11Ug++1+bQVKWf0/jiMxNML4DwLD2iOYkHGva93+xO/eJ/PENgxNLcTwJA0jqFRUZsZTlHCUovijVOeor+9mDp44hw3ANXwIdgGdRa/CHaCUy7i+5yMyjmzEyiOI8Gc+1evu1yL/TszzkUzx7KHxcX+2KxHDk3SM6GkQuSdw6zQMa49gor98jqiVU/EfMoOAkc9pPBnRyiMIj+4PkXtuNy4RQ2msKCT94Dpyz+yk3dhXsfII0uh8d4qBsRlevR7Bs+dYKjJiyTq+haxT28k8thmHoO54952Ic4dB91yypkRqgIWzDxbOPrc8VylvVAuKukpkNRXI634XFH8WGPL6qmawHgxMLbAPiKQqR9yw0ATy+hpKE09TFH+U4ivHkVWXNuUtBY1+EaewAVi6+uvVhkBLo7Yog5j186jKvoJPv0kEjXkZAyMTXZslclcICCh1bYSIFri3VgQi9zQWLn70eHEXV3e+ReqBNZSnRdPx6WUar+0f9NBLmDt54zvgcaRGZmSf2k7y3uWceu9BvPuMJ3DkXL0peyqRSLDzC8fOL5zghxeRc+Zbsk9uJ3rdHHVZyN7j8Or16D3f/+DvMDAywcDWBVNbF12b0kRjRSHVuUm6NqNFU1ucRXH8UYoSjlKWfBFBKcfQzBqn9n1xDhuAY0gfjC1sdW1mqyD3/A9c2f4mUkMjOk1fhUvH+3RtkoiIyL8ghidpCTE8Sb/JObebK98sxtDMkvCnP8GhbTetzierrSBl32dkndiGoaklASPm4N33Mb1s5iSolBQlHCfrxFZKrp5EIjXEJfx+fPpNwi4gUtxV1WMStr1OeVo0suoSMTzpNlEp5ZSnRlN8I+yotjANAEu3AJxCB+Ac2h/bNhH3nNdNmygaariy/U3yLuzBLiCSjk8uEytLtSJinu/MMx6az+Xbb18phifpGFE0aAmJ1ACvnmNxChuIS4eBujZH5G+ozk0i+svnqC3KpO2oubQZPB2JVKrdOfOSufbtu5QmnsHSLYB2Y1+9KUFX36gtyiTr5DZyz+5GXleJpXtbvPtOwKPrAxiaihVN9I3L6+ZQk5+CvLZCFA3/gLyukqrsq1RmXaW+LJe8Cz+iqK9CYmCEQ9tuOIX2xzmsP+aO3ro2tVVSmXWFmK/m8f/27js+ijJ/4PhnN72XTU9IIaTQQ5OgFAUEpSmop9jAxul5dg/bidjRExXRn56dEwsgnA1BqjSpIRACIUASUkjvdTdb5vfHhpwRpGaz2c33/Xrta7OzM/M8T2Z3dr7ztMayPHpMuJ/Yq+6TgMxOlKRtoOzABgy7V0nQYKckaLAQqWmwDQZtPelfPUvRnpUE9h5Jvxn/snjTIUVRKE1bT8byV2kqzyeo3xgSpz15Tu3orcXY3EThnpXkbfqS2vyD+Mcn4+zpR3D/KwnsMwonNy9rZ1EAuxfeib6plqaKExI0ALq6SmrzD1Kbd7AlUEinqaKg9f2AXiNw9QkmsO/lBCReKoGwBSmKwvGNi8j8779w9vIn6Y438Y+zzshywrJSHx7IXWGD2n2/qzX1EjRYmQQNFiJBg+1QFIW8LV+T8e3LuHgFkHT3AvxikiyerlHfzPGNn5O16n1MhmaiR88k9qr7OvWY5IqiUHM8jZL9azmx47/oasvMd2gTkgnufyVB/UZL/wcrOvDF05gMzZQf3talggZFUdDVlJoDg5NBQt5BtNXFreu4B0bi3a033t16tT67ePlbMdddh66ukgNfPElZ+q8E9RtD31tf6TT9ukT7k6DBfknQYCESNNiemtwDpH78ENqqYhKmzSb6ihkd0n5fW1PKke/f5MSOFTh7B5BwzWOED51q8aZSF0sxmajO2UfJ/rWU7F9LY1keqFT4RicRnHQlwf2v7NS1J/bIXNNQR1NFgd0GDSfn2GgNDvIPUpN/iObacvMKKhUewd3x6dYb78iWACGiJ07u3tbNeBdVlrGNA/95guaGKnpOe5LIUbdKvyg7J0GD/ZKgwUIkaLBN+sYa0v7zJKVp6wkeMJ6+t77SYU1vqo+nkbHsJapz9uEd2YdeN/wTv9iBHZL2xVIUhfqio5TsW0vJ/nXU5h8EwDM0rjWA8O7WSy4WLCz9qzkYdA1UHP7NZoMGRVHQN1TTVFlIU+UJtJVFNFWeoKmyEG1lIQ7OblQe2w2Y+455hvZorTnwieyNV3iiTU5UaG90dZUcXv4qhbu+J2TAVcRedS/e3XpZO1uiA5iDhgHtvt/VmkYJGqxMggYLkaDBdimKQs66Tzjy/Xzcg2Loe8tLHXbxrphMFO75icz/vo6uppTQIZNJuPYfuPnZ1uyyTRUnKNm/jpL9a6k8tgcUE67+YQT3G0tw0pX4xQ6Szo8WYAs1DSajAV1NqTkQqChEW1XYEiCYH9qqIoy6xjbbqJ1ccfMPw80/DN/uA3HxDsC7Wy+8whNkTP9ORlEUCnd+R8byVzFoG+g+bhaxV90rx6kLkaDBfknQYCESNNi+ymN7yFn3KaUH1hM5fDrx1zzaYU0cDNoGstd8SM66T1CpHYid+ACRl/3FJptYNNdXUnpgIyX711GesRWTXoeThy9BfUcTnHQlmoRhOLq4WzubdiHlg/twcveh7OCmcwoamioLqcrei6OLB45unji6eODg6onayQVMRkwGPSajHsXQ3PJ3y7OhGaXlvZOvzev97+/W5YZm9PVVNLUEB7rqEhRT24mfnDz9cPMPbw0MXP3DcPMLxU0TjqtfGM6eflJLZQMaSnM5+PUcKjK349t9IH1ufhGvsDhrZ0t0sNSHB3BnaP923+8vAToJGqxMbvUJ8Sf8ewzGOyKRoz+9w/GN/6Fk/1p6Xv80IYMmWPwCxtHVg/gpjxBx6fVkfjefvE2LyVr1HpEjbiZ69Ayb6mzs7OlPxLDriBh2HQZtA+UZW1v6QazjxI4VuPiF4uYbgl/sIPx6DMav+wDpJHmBdDWl5zUCUHV2Kvs/ffSU5X6xg6jKSrm4zKhUqB2dUTu74eTmhatvCP49huCmCcfNLxRX/3Dc/ENx8w/Dwdnt4tISVmUy6slZ+wnHVr2H2sGJ3jc9T7fhN3b6fllCiPMjNQ0WIjUN1tNYntfuY6zX5KWT/tUcavPSCeg1gt43Pdeh47jX5h8ie+1HFKWsQu3gSHjyNGLG3mXTHY3Nk2qlUHZwC1VZKdTkHkAx6gHzxFp+sYNbAolBuPmHy53mc7D20YGEJ0+jKGXlOdU0GLQNaKuKMGgbMOjqMWgbMGobQK3GpG9G7ehkvvB3cEJ18u82y5xb/1b/7n2Vg5M0P+siqnL2cfDLZ6krzCR4wHh63fDPTjVLuuh4qQ8P4M6Qvu2+318C9VLTYGUSNFiIBA3WUZWzj53zpxM9eibxUx5B7ejcbvtWTEZyN33JkR/eQjEZ6DHh78SMvbNDZ3VuKMsjZ90nnNi+HJPRQOjAq4gZNwsfO+hgaGzWUpN7gKqsPVRlpVCVtReDth4AV98Q/HoMMgcRsYPxCotDpXawco47F5PJxM43pxPY5wpyNy7qtH0axOkZ9c2UHlhPbd5BEq593NrZOSt9Uz1Hf3iT3M1f4uoTTK+bniO43xhrZ0t0AhI02C+5FSTsind4It0uu5GcdZ9QeXQXSXe91W41Aiq1A9FX3E5w0jgylr3Eke/nU7jrB/rc/AJ+se0/vNzpeARG0mf68/SYcD+5GxeRu/krilJ+JqDXCLqPm4V/3CU2e0fewdkV/7ghrRM+KSYjdYVHWgKIFCqP7aFoz0oAHN288Os+wFwb0WMQPlH9unxHS5VKRc3xNDTxydbOijgPtQUZFPy2nMLdP6BvqMbVL5TYq+7t1BPNlexby8GlL6CrKSVq1G3ET3m4U+dXdDBFQVFM1s6FsACpacB8h27+/PkUFxfj6+tLRkYGI0eO5N57722z3tKlS1m3bh1xcXHs2rWLu+++m/Hjx592n1LTYF3Fqb9wYPHToCj0uflFQgdPbPc0StI2cGjpC2grC+l22V9ImPoPnNx92j2dM9E31pK3+SuOb1xEc10FvjFJdB83i6C+o+2uPbGiKDRVnqDqWEpLILGH+qJjAKgcnfCN6otf3FDc/EPxCOqOZ0h3nL00NhtEXYjfXr+BoH5jpKahk2tuqKZo948UbF9Obf4hVI5OBPcbS8Sl1xOQeGmnrUXTVhdzaMmLlOxfi1d4An1ufgnfmPbv8CpsW+pDSdwR0qfd97smyCg1DVYmNQ3ACy+8QGpqKt9//z0Aer2enj17EhgYyHXXXQfAJ598wty5czl8+DAeHh4UFBTQu3dvVq5cyfDhw62ZfXEaIQPG4x3Zm/2fPsa+Tx+hInM7PW94pl07XAb3G40mfijHVi7k+MZFlKStp+f1TxM6eFKHXag6uXsTe9W9RI+eScGOFeSs/Zi9//4bHiGxdB93D2FDJndo8ylLUqlUuGsicNdEED70GgCa66uoyk5trY04sX0FupqS1m0c3bzwCI7BIygGz5DueAR3xyM4BvfAKLurmVAUhdq8dDQJUtPQGSkmI+WHf+PE9uUU71+LYtDj3a0Xvf7yLKFDJuPs4WvtLP4pxWQib8vXZH7/BorRQPy1jxMz5g67ObcIIc6N1DQASUlJjB49mjfffLN12Y033khgYCDvvvsudXV1REZGct999/HKK6+0rnPTTTeRmZlJamrqKfuUmobOwWTUc/Snd8he8yGeIbEk3fU2XmHx7Z5Obf4h0r+eY24ekngpvW963iqdlE1GA8V7V5H9y4fUFWbi6hdKzNi7iLj0+i4xrKliMqGtKqK+JIeGkuyWRw4NJTloq4v/t6JKjZsmHM/gGHMgERSDR4g5oHDxDrTJ2gmjXsfuhXcQ1G8MOWs/lpqGTqKhLI8TO1ZwYvsKtNXFOHn4EjZkChHDptnEZGd1JzJJ/+pZqnP2mc9t01/AI7DjBoEQtif1oSTuCO7Z7vtdE4zUNFiZ1DQAgYGBLFu2jGeffRY/Pz8URWH//v088cQTAPzyyy9UV1eTnNz2Dl5ycjJLliwhPT2dPn3avypOXDy1gxMJ1zyGJj6Z/Yv+wW+vXUfPG56h22U3tuuFoXe3Xgx7fAl5W77hyPfz2frSRGKv/hsxY+/Gwan9OmOfjdrBkbAhkwkdPImyg5vI/uXfZCx7iWM/v0vUFbcTOeJmXLz8Oyw/HU2lNgcDbppwAnu1rQE0aBtoKD3eEkRk0VCSQ31JDpVHd2Nsbmpdz9HVE4+gaHxjB4Ki4OjmhZObF45u3ji5e+Po5omTmzeObt7m99y9OsUdV7WjM1VZe/HvMcTaWenyDLpGSlJ/oWD7CiqP7gSVmsBew0m8/imC+o7p0HPChdJWl3Bs1f9RX3SMhtLj9JvxOmGXXGOTAbUQon1I0AC8+OKLXHHFFYwcOZJPPvmEzz//nBtvvJGZM2cCtNYkREa2vbsSFWW+k7x7924JGjq5gJ6XMfzpH0hbNJuDX82h4vB2+tz8YrtOlqZSOxA16haC+48l49tXOPrj2xTu+oHeNz2PJmFou6VzTnlRqQjqczlBfS6n8tgestd8xPH1n5Gz7jMCew0nYtg0AnoO77Rtpy3B0dUDn8je+ET2brNcMZnQVhe3BBPZ1LfUTmirS6g4vB2Dtg7OUiHr4OyGo5tXS4DhjZO7V8trb3PA4e5t8eYcKpUK//ih5onZRIdTFIXq4/s58du3FKasxKhtwD0wivgpjxA2dKrNzOre3FBN9pqPyP31CxSTkajLb2XgrIU4e9rvzQbR3hQULNER2r766dkiCRow1xisWbOGqVOnMnToUG677TbeebJ+3FcAACAASURBVOed1jsq5eXlAHh5ebXZztPTPFpESUkJf6QoCtvmTWt93W34X4gcfpOliiDOgYt3AIPv/5icdZ9w5Ie3qMk9QNKdb7V7Rz5X32AG3L2A0vSpHFryPBnfvoyrXwhxkx465YK1I/j3GIx/j8HUF2eTt/krCnf/QPHeVbj4BBE+9FrCh03DM7h7h+ers1Cp1a0zEQf0vOyU9xWTCYOuAUNTHfrGWvNzUx2Gplrza209hsZa9E21GJrq0TfV0lxXSUNZXsvyOhSTge5j77Z4WWrzDuIVEmvxdISZvrGWyqO7qMjcTlNlEaVp63BwcSdkwFVEXHodfrGDbebOvEHXSO7GRWSv/RiDtp6wIZOJm/Rgh85HI2xX3tZvyN+6FAD7bwjbddll0FBQUIDBYDjreuHh4Tg5me/8ZWdnM3XqVI4cOcIXX3xBQUEBP/74Ix4eHri4mO/c/fHkr24ZncbZ+dSqZpVKxWVPrrjYooh2plKr6T7uHvzihrD/00fZMX868VMeIWbsXe0+2lBQn8vRxA/l+K9fkLPmI36bN5XgpHHETXrQIv0qzsYzpDu9/vJPEqfNpvTARgq2ryBn3Sdkr/kQ35gBRAybRsigiTi5ydCJv6dSq3FqaaLk5h923tsrioJJr+ug0awUHLpA3xVrMTZrqcpOoeLwdioyt1OTdxAUE2onV0IHXk2fW18hdOBVNjX8qMnQTP62pRxb9X8015YT1Hc08VMewSs8wdpZEzYkcvhNrTdGUx/qD4rRAqlITYO12WXQMHz4cHJzc8+6XkZGBomJibz//vssXryYLVu2oFKpeP7553n++ed58sknWbhwISEh5mrl+vr6NtuffB0eHt7+hRAW5ReTxGVPfUf6l/8k87t/UXFkB/1ufw0X74B2TcfB2Y3YcbOIHDGd4xs+5/j6zyjZv5bQwZOIm/B3PIJj2jW9c6F2dCZkwHhCBoxHW1NK4a4fKNi+nPSvnuXQspcJGTCOiGHX4R831O6GbbUGlUqFg7OrxdMxGvR4hSfi7B1o8bS6CpPRQE3uASoyt1ORuYPq7L2YDM2o1I74xvQj9qr70CQMwzcmySb6KfyeYjJSuPsnjv60gKaKAvx6DGHgPe/iFzvQ2lkTQnRSdhk0ZGZmci6DQp2sQXjppZeYPXt2a83B3LlzycjIYMmSJSxcuJCBA80n0fz8fPr169e6fV5eHmAefUnYHid3b5LuXkD+1iVkfPsy2165hn4zXycg8dQmKhedlpsXcRMfIGrUreSs+4TcX7+gOOVnwoZeS48J9+OuiWj3NM+Fq08Q3a+8m5ixd1GTm0bB9hUU7fmJwl0/4KaJMDdfSp6Ke0A3q+RPnDtDYw1Vx3YTOmiCtbNisxSTibrCI1Qc2UHF4e1UHtuFUdsAgFdEIpGjbkGTMAz/HoNtqjbh9xRFofTARo788Cb1hUfwiujJ4Ps/JqDXCJtpSiU6O5nczV7ZZdBwMhg4VzqdDqOxbVXaqFGj2LRpEwBjx45Fo9Gwc+dOJk783yRhO3bsYMiQISQkSDWurVKpVESOuAm/7gNI/eRh9n/+D0IHTyZ+8kMWGaLU2dOPhGsfJ3r0TLLXfGjuY7DrB7pdegOxV9+Lq691OkuqVCp8o/vjG92fntc9Rcn+dRRsX86xVe9x7Od38Y8bSsSwaQQPGN8lhm61RQZdIyq1Q7vORdIVNJbntTY3qsjcQXN9JQDugVGEDZ6MJnEYmvihdtERuPLobjK/e4PqnFTcg6JJuvMtQgZeLTWKolM7pq3lmLaOBneNtbPS5ck8DcDDDz/Mzp072bp1Kw4O5tFkZsyYQUBAAPPnzwdgwYIFvPXWW2RkZODm5kZRURHx8fF8/fXXTJo06ZR9yjwNtsfY3ET2mg859vN7uGki6D39hVOG7Wxv2upislZ/QP62ZeYAZuTNdB83q92bSV2opspCTuz8jhM7VtBYloeDqwehA68mfOhUfLsPQO1gl/cdbFJDaS5pXzxB1KjbyFj2kszT8AeKyUhDWS51JzLNj4LDmIwGyg9tBsDFJwhNQjKahGFoEoZdUP+Vzqom/xBHvp9P+aEtuPgE0WPi34kYdl2nGCpY2J/Uh/oxI7BHu+93XaiLzNNgZRI0YJ4B+tVXXyUtLY2ePXtSX1+PRqPhiSeeaO0oDfDBBx+wbds2+vbty549e5g+fTpTp0497T4laLBdlcf2kP7lMzSU5BA+9FoSr3sKZ08/i6bZWFFA1s//x4md/0Xt6EzU5bcRc+XdnWaWWEVRqMraQ8H2FRTvXYVHUAxNFQUE9BpOYO/LCeg1wq7nf7AF2uoSNj49gt7TX+DoTwvsOmgoP/wbeVu+RhOfTFCfy3HTtO1X1lxfRV3hkdbgoO7EYeqKjmLS6wDz8MgewTH49bgEr9BYNAnD8AiJtbvmOQ2lxzn649sUpfyMk7sP3cf/lahRt3ZIHxvRdaU+1I8ZAe3fX29dmLsEDVYmQYOFSNBg24x6HVmr3yf7lw9xcvem5w3/JHTwRItfVDSU5HD053cp2vMTji4eRI+5g+jRM3Fy8zr7xh3EoG2g/NBWStM3UnZwE811FaBS4RPVj8A+owjqPQrvbr2lyUMHa6wsIvXD++l+5d0cWvqiXQcNR1cu5NjKha2vHd3NE++5B0ZRX3QMXfX/hsF29vTHKyIRr/CElkciniGxONjxfBY1eenkrPuM6py9NNdVEj16JjFj72rXeWmE+DMSNNgvaVsgxGk4OLkQP/lhQgdezYEv/8n+zx6lcNf39J7+vEWbLXgEx5B0x3xix/+Voz+9w7GVC8nd+B9ixt5Ft5G34Oxu/eDB0dWDkIHjCRk4HsVkojb/IKUHN1GWvoljKxdy7Kd3cPbSENh7JIF9Licg8TK5WOkA2soTNNdX4eThY+2stCuTUY+upgxtdQna6hJ0tWWUHdzcZh1DYy2GxlpUaic08cl4hZuDBO/wBFx8usZoUorJSMn+dRzf8DlVWSk4uHjQffxf6Xbp9Z2muaPoGhRFOkLbKwkahDgDr/AEhj3+Dbm/LubIj2+x5cWJxE95hKhRt1h0NmWvsHgGznqXmryDHP1pAaXpG8la828ikqcRNepWqwzVejoqtRqfqL74RPUlbsLf0dVVUn5oC2UHN1GatoETO/6LSu2Ab/eBrbUQnmHxdtcMpDPQ1ZajrTyBiw0NuapvqkdXXYy2xhwQaKtK0FWX/O91dbG5Jut3FeL+cUNpLGs7pLazdwAD//oefjEDOroIVqdvqqPgt2/J/fULmioKcNNEkHjdU0Rcen2nqqEUQtg+CRqEOAuV2oHo0TMI6j+Gg18/R8aylyja8xN9bnkZr7A4i6btE9mbwX/7kOrcA+RuXETelm/I/fULAnuPJOry2wjoOaJTNQNy8fInfOg1hA+9xjzG/fH9lKVvovTgJo589wZHvnsDV98QAvuMIrDP5Wjik3F09bB2tu2C2sEJV98QFOPZJ7a8GCajAZOh+dSH/jTLDM0Ym3UYmmrRVhebA4LfPYy6hlP27+Thi6tvMK6+wXh364mrTzCufiG4+gbj4hOMm38ojq5erHmkPya9jtAhk+kz/XmbHQL1QjWU5ZH76xcUbP8Wo7YBv9hBJE57gqB+Y2SAAmF9FpncTVib9GmwEOnTYJ8URaFw949kLHsJfWMtrj5BhA29FvfASFx9g/EIisI9INJi6etqysjbtoT8zV+jqy3DPSiaqFG3Ep48rdPP5KytLqbsoLkWovzwNozaBlSOTgT1HoV7YFRrjYWbJkJqIi5A2cHNZP3yb3pe/xR73rvnnPo0ZK/9mKM/vo3ayQW1ozNqJxccHF3wDIunrjDztAEB59vsQOUAmFCp1Lj4BJkv/n2DcfUNaQ0OXE4GBj5B59xJN3/bUtSOLoRdMqXLfF4URaHq2B6Ob/iMkrT1qFQOhA6aQPToGfhE9bV29oQAYO+DfZmhaf+5fdZH+EifBiuToMFCJGiwb7q6Sra+PInm2vK2b6jUjHzuFzyCoiyavsnQTHHqGnJ//Q/VOftwcPEgYljnarp0JiZDM1VZeyk//Bvlh3+j7kQGikEPgJOnHz6RffCJ6odPVF98o/p2mXbpF6M0fROZ371B31tfJuX9v55T0FBxZBflhzZh1Ddj0uswGXSY9DpcvIPQ1ZaaA4k/PFROpy477eN36zl7+OLs6WfRJn32zGRopijlZ45v+Jza/EM4efgSOfwmIkfdbLW5XYT4M3sf7MPt/u0/YemGbn4SNFiZ1GEKcQFcvPwZOWcVG54agUmvbV3uGRJ7yvCPlqB2dCZsyCTChkyi+ngaub9+0dp0KaDXCKKvuL3TNV36PbWjc8uY+MkkXPMoJkMzdYVHqDl+gJpc8yMr4/3Wu9ouvsH4ttREmIOJPji521eH34ulmIzUF2ae1113TfwlaOIvsWCuxMVorq8kb8s35G36El1tGR4hsfS++QXCL7lGJvETQnQ4CRqEuEBO7j70unEO6Yufbl2mrSmjLH0TQf1Gd1iTCd/ofvjO/BeJU2ebmy5t+YY9791jU02X1I7O5tqFyD7AdMA8w3Ft/qHWIKIm9wAl+9e1bvP7Jk0+UX3w7ta7S89WrRgNqBycUKnltG7r6gqPcnzj5xTu+gGTXkdArxH0veJVAnoO77Q3AoQQ9k9+XYS4CBHJ08jf8jU1uQcIGzKF+qKjHF4xj5z1n5JwzWP4xQ7ssLy4+AQSN+HvxI6b1dJ06Qsylr3EkR/eIiJ5KpGX34pncPcOy8/FcnRxx7/HYPx7DG5dpm+soSY3nZrcNGpyD1B1bA9Fe34yv6lSE9TncgDcAyNxD+jW8hyJmyYctaOzFUrRcVRqNV7hiaikE6xNaqoqpnjvKopSVmIy6GkoySZ86LVEXTEDr9D2n11XCMuSjtD2SH5dLERRFNK//CeBfUcT3G+0tbMjLESlVtNvxuvkb11C/JRHQO1IwbalHFv1Hjvm30RQ39HET3kEr/CEDsvTaZsubVtC7qbFBCeNI3TQBIL6XmGTzRuc3H0I6HkZAT0va12mrSltCSQOoK0uoSY3jfLDv7VpNoZKjZtfKO6B3cxBREswcTKo6Ow1MefKwckFukinYHugqymjOHU1RSk/U5WVAoB3t15EjphOyIBxOHvKLOvCdpSkbaDswAZrZ0NYkHSEthDpCN21GXSN5G5cRPaajzDoGggbMoW4SQ/iHtD+I0qcC11tOflbl1CTf5DS/etwcHEnqO9oQgdPJKDnCByc7OsuvKIo6GrLaCzLo7E8j8ay/JZn82t9fVWb9Z08/cxBxO9qJ9wDuuHs6YejmyeOrp44OLt36qYhxalrOPjNcwx58DN2v3OHXc8Ibcua6ysp3reG4pSfqTiyCxQTnqFxhA6aQOigCTYxkIEQZ7L3wd7c5tf+HfQ3RgZJR2grk5oGISzA0cWd2Kvuo9vwm8he8xG5m76gKOVnIkfcROxV93X4DK0u3gH0mHA/islI5bE9FO1Zab7DuecnHN28WmogJqJJSLaLMd5VKhWuPkG4+gS1ad50kr6pvk0Q0dTyXJ2zj6KUn0Ex4RuTRHXOvt/vFEdXz7YPtz88t3nPq8177gHdzDUBliqzWk1zXQWKSWZi7Wz0jbWU7F9HUcpKKg7/hmIy4h4UTexV9xE6aILF53sRQoj2YPtXB0J0Ys6efiROm03UFbeT9fO75G3+ioLty4kefQcxY+/s8BlbVWoHNPFD0cQPpdeNz1JxeDtFKSspSf2FE9uX4+zpT8iAqwgdMhG/7oM69Z31i+Hk5olPt174dOt1ynsmQzNNlYVoq0tpri3DoK1H31SPQfuHR1M9+sYamipOtCxrOO1kZSdd+uSKlo7elmHUa/EMjUNtwcBEnDuDtoHSAxsoSvmZskObUQx63DQRxIy9k5BBE/GO6Nll5pcQXY8ik7vZJQkahOgAbn4h9LnlJWLG3sWRH98ma9V75G3+ktjxfyVy1K0WvQP9Z9QOTgT2Hklg75EYp79A+cHNFKWspGDHCvK2fIWLb3BLk4mJ+ET17TIXOGpHZzyCovEIij7vbRWTEYO2oU1gcfJvS076B2AyGHBy98bJ3dei6Yg/Z2xuojR9E8UpP1OavtE854VvMFEjbyF08ER8ovp1me+REML+SNAgRAfyCI5hwN0LqMm7hyPfv8nhFa9xfMMiekx8gPDkqVZrGuTg5EJw0pUEJ13Zcod0I0UpK8n9dTHH13+GW0A3wgZNJHTwxA7t1G1rVGqHlgt37w5P29BUB6jsrn9KZ2YyGqgryKAqay/1xcco3P0jRl0jzt4BdLv0BkIGTcCv+0C7rbET4rQU5fxnjhc2QYIGIazAJ7IPQx74lIrM7WR+P5/0L58hZ93HxE95lOCkcVa9G+no6tE6+pK+scbcFnvPSrLXfkTWLx/gGdqD0EETCR4wHs+QWLlz2kkYdPWt/S6EZeib6qnO2Ud1dgpVWSlUH0/DqGsEwCOkB2GXXEPowKvwj7tEZr8WQtgdCRqEsCJNwjCG/WMZJfvXcuSHt0j96AF8ovoSN/kRAnpeZvULcid3HyKGXUfEsOvQ1VVQnPoLRXtWcvSnBZzY+R0mvQ5N4qVoEoahSRyGq0+QVfPblRma6nF09bT6Z8aeNFUWUdUSIFRl7aXuRKb5DqpKjXdEIhHJ0/CLHYRv7CDcLDBajBBCdCYSNAhhZSqVipCkcQT1HU3hru85+tM7ZK3+P458P5/IUbcQNngSDs6u1s4mLl4aokbeTNTIm2mqKqY0fSOVmTsoPbCBEztWAOAZGtcSQFyKf9wldjP/gS0w6XWoHeTu9oVSTEbqTmRSlb2Xqqy9VGWloK0qAsDBxR3f6P70uPpv5iAhpr/U6AhxBtIR2j5J0CBEJ6F2cCRi2HWEDp5E0Z6fyFn/GemLnybzv68TMex6IkdOt9o8D3/k5hdC1IjpRI2YjmIyUVuQQUXmdioO/0b+tqXk/vofVGoHfKL7o0kYRkDipfjG9Lf7WZmtyWQyoKutsHY2bIKiKOhqSqgrzKLm+D5zTUJOKkatefQrF58g/GIH4Tf2LvxiB+EVnmAXQxELIcTFkLOgEJ2Mg5MLEcOuIzx5GpVHd5O3aTHHN3xGzvpPCOpzBZGjbiUg8dJO07lSpVbjE9kbn8jedL/yboz6ZqpzUqk4/Bvlh38ja/X7ZK16DwdnN/x6DCYg8VI0iZfiFZbQacpgD5w9/Kg+vh+T0WDtrHQaiqKgrSqmvugo9cXHqC86Rn1RFvXFxzA01eER3J2G0hy8QuMJGzLFHCjEDsTNP1yaeQlxwRSQmga7JEGDEJ2USqVCE38JmvhLaKoqJn/rN+RvXULpgQ14BMUQOeoWwpOndvhcD2fj4OTcOhdE/JRH0DfWUnl0V2sQcXjFa4B5FuaAlqZMvrGD8AyKkSDiIrgHRqJSq9FWF1s7Kx1OMZloqir8X1DQGiRktZk7w9nTH8/QOMKGTMYztAeeoXF4R/S0ymhXQghhayRoEMIGuPmFED/5YWKv+hvFqavJ27SYjGUvceSHNwm/5FoiR93SaWeVdXL3Jrj/WIL7jwVAW11MxeHtlB/+jYrM7RSl/IyzlwZjsxbviAS8I3rhFdET72698AyNkyFEz5GThy++0f3RN9ZYOysWY9Tr0FYVUV+cTX3xMRqKWmoPSrJbRzECcPEOxDM0lohh0/AM6YFnaA88QmJx8fK3Yu6F6BoUkJnp7ZQEDXYqb+s3RA6/ydrZsBp7Lb+DkzPhl0wh/JIp1OQeIHfTlxRs/5a8LV/hH59M1KhbCOo3hoLt33ba8rv6hhCePJXw5KkoikJDcTZVOanU5R+itiCDgh3/xahbDIBK7YhnaCze3Xr9LpjoedbaFXs9/n9G31RH0e4fQO2Ai3cQxmattbN0XkxGPbqacnQ1pWhrStDVlKKrLkVbU9qyzPysb6jGL3YwVVl7AHDxDcYzpAcRl96AZ2gsXqFxeITEUpy6uksd/z/qap//P5Lyd+3yC8uRoKGTKknbQHC/0Re8ff7WpRd10rjY9K29fVcov09UX/rdPo/EabPJ/+1b8jZ/RepHD+DqG4KimAjufyUuXhqLpd8e26tUKjxDY/EMjW1dpphM5G1dgrOnL7X5h6jNP0T5oa2c2PHf1nXcArrh3VIb4d0SSLj4BLe2Q2/v41+a/iva6mIcXTxwcHbDwcXd/LeLOw4ubji6uOPg4oHa0RmVSmWR/59R30xzXTm62jJ0NWU011dSdyKT2oLD1OSl4xUWT7fLbsDVJxCT/uKChovJv2IyUpy6Fk1iMiZDM0ZdEwZtHdpq84W/rqbEHAT8Lihorq80Twj1Oyq1Ay7egbj4BuERGIl/j8G4+ATjERRFwtR/4Bna40+Dx67w/T8TKb+U35rlN0/uJn0a7JEEDZ1U2YGL/NJaOX1rb3+xrJ3/89ne2dOf2HGz6D72LkoPbCR302IqDv/GxmdG0v3KWcRPfsii6bf39iq1mrr8g/S55SVCB17dulxXU0ZtwSFq8zOoLTA/SvataX3f2dMf72696D5u1gXn+8/yn791CaVp688h7w44OLuhmIy4+oa0BBXuOLq44dASZDi5eaEYDeYLar0Ok6EZk17X8rf5dWNpLpme/hgNOkx6Ha5+odTmpbdJyzM0jqbKE3iFJRA7/q8E9h6FT2Tv8ypnwfbllGdsQ+3giMrBEZWDE2oHR6qyUig/uLk1/ZN5NBl0GPXNbV6b9M0t65mXKaY/dMRWO8Lvl6lUuHgF4OIThKtvCL7R/cx/+wTh4hOEi28wx9d9Sr8Zr1ltgjRb+v5bgrXzL+W37fIL+6VSlD/c3hHtIiAggOjo6AvevqamBh8fnwvevqysjMDAQKulb+3tpfxSfim/lN9a6Vt7eym/lN+a5d+3bx9JSUkXvP2fCQgIYPXq1e2+X3HuJGgQQgghhBBCnJGMbyiEEEIIIYQ4IwkahBBCCCGEEGckHaHtjMlkYv78+RQXF+Pr60tGRgYjR47k3nvvbbPe0qVLWbduHXFxcezatYu7776b8ePHWynXltPc3ExKSgoFBQVcf/31XW6W1+eee47c3Fw+//zzNsvt9fjr9Xpee+01Fi1aRFFREYmJicydO5dJkya1Wc9ey2+v5Tqdrn6s/6irfdf/qKud6+W3XliFIuzKc889p0yZMqX1dXNzsxIbG6t8++23rcs+/vhjJSIiQqmvr1cURVHy8/MVb29vZcuWLR2eX0spLCxUZs2apYwZM0ZZtWqVotVq27y/ZMkS5Z577lFef/115frrr1dWr15tpZxazs6dOxVnZ2dlxowZbZbb8/F/+OGHlUcffVRZvny5Mn/+fCU4OFhRq9XKpk2bWtex1/Lba7n+TFc+1n/UFb/rJ3XVc7381gtrkKDBzvTv31955JFH2iz7y1/+otx///2KoihKbW2t4uvrqzz11FNt1rnxxhuVpKSkDsunJe3evVsJCQlRnn76acVgMJzyflc4kTY0NCi33XabMnz48DYXEvZ8/E+cOHFKuXbt2qUArf8Dey2/vZbrz3TlY/1HXfG7flJXPtfLb72wBunTYGcCAwNZtmwZVVVVACiKwv79+xk0aBAAv/zyC9XV1SQnJ7fZLjk5mX379pGenn7KPm1Jfn4+EyZMYMyYMbz88ss4OLQd572uro7HH3+c2267DQ8PDwAiIiK4+uqreeCBB6yRZYuYM2cOzz777Cnlt+fjX1JSwuzZs9ssGzJkCP7+/q3fB3stv72W68905WP9R13xuw5yru/qv/XCOiRosDMvvvgi5eXljBw5kl27dnH//fdz4403MnPmTABSU1MBiIyMbLNdVFQUALt37+7Q/La3J598ksrKSubNm3fa97vCiXTVqlXExsYSFxd3ynv2fPwHDBiAr6/vKcu1Wi2XXXYZYL/lt9dy/ZmufKx/r6t+10HO9V39t15YhwQNdiY5OZk1a9ZQVFTE0KFDqa+v59lnn23tFFZeXg6Al5dXm+08PT0B8x08W1VTU8PSpUuJjIzk9ddfZ+TIkfj6+jJ58mRycnIA+z+RVlRUsHTpUu67777Tvm/Px/90duzYgYeHB3fffTdgv+W313Kdj65yrE/qyt91Odd37d96YT0yelInV1BQgMFgOOt64eHhODk5AZCdnc3UqVM5cuQIX3zxBQUFBfz44494eHjg4uICcMrIEmq1OX50dnZu5xJ0nF27dmEwGEhMTGTevHm4u7tz/PhxrrjiCiZPnsy+ffts7kR6vsf/qaee4uWXX/7T9Wzt+F/I5//3XnnlFT766CP8/f0B2yv/ubLXcp2PrnKsT7K37/r5sMdz/YXoqr/1wnokaOjkhg8fTm5u7lnXy8jIIDExkffff5/FixezZcsWVCoVzz//PM8//zxPPvkkCxcuJCQkBID6+vo22598HR4e3v6FuAjnc9F48kdi1qxZuLu7AxAdHc3s2bP529/+xurVq23uRHo+x3/nzp2MHj2asLCwP13P1o7/+X7+f++9995jzJgxXHPNNa3LbK3858pey3WuutKxBli0aJHdfdfPhz2e68+Xvf3WC9sgQUMnl5mZiaIoZ13v5AnypZdeYvbs2a0nxrlz55KRkcGSJUtYuHAhAwcOBMydyPr169e6fV5eHgBJSUntXYSLcj4XjX5+fgCndIg72TEsMzPT5k6k53P877vvPnbv3t3aPAOgqamJbdu28e233/L000/b3PE/38//Sd999x21tbU89dRTbZbbWvnPlb2W61x0tWMN8Pnnn9vdd/182OO5/nzZ22+9sA0SNHRyf7wYOhudTofRaGyzbNSoUWzatAmAsWPHotFo2LlzJxMnTmxdZ8eOHQwZMoSEhISLz3Q7Op+LxpP/qz9WO/v4+LQ+R0REALZzIj2f47948WKampraLLvllluIiIjgtddew9/fH29vb5s6/uf7+QdYs2YNaWlpcL3s5gAACAZJREFUzJkzp83ytLQ0m/v8nyt7LdfZdMVjDfb5XT8fJ/NuT+f682Vvv/XCRlhzvFfR/h566CElOTm5zZjVt99+u/Loo4+2vn777beVqKgopbGxUVEU8+Q4np6eyo8//tjh+W1vw4YNU6ZPn95m2fr16xVA2b9/v6LX6xWNRqM8++yzbda5+eablSFDhnRkVjvEqFGjTpnwyZ6P/+bNm5XLLrtMWbZsWetj6dKlysMPP6ysWrVKURT7Lb+9luvPdOVjfTpd7bve1c/1Xf23XliHSlHO4TausBl6vZ5XX32VtLQ0evbsSX19PRqNhieeeKJNR9EPPviAbdu20bdvX/bs2cP06dOZOnWqFXPePtasWcO0adNIS0uje/fuAMycORODwcDixYsBWLBgAW+99RYZGRm4ublRVFREfHw8X3/9NZMmTbJm9tvd5ZdfTnR0NJ9//nmb5fZ4/Pft28fIkSOpq6s75b2oqCiys7Nbq/Ltsfxgv+X6IznWp+pK33WQc31X/60X1iFBg7A73333HR988AEDBgygpqYGT09PXnzxxTZNXeREKoQQtk3O9UJ0LAkahBBCCCGEEGckk7sJIYQQQgghzkiCBiGEEEIIIcQZSdAghBBCCCGEOCMJGoQQQgghhBBnJEGDEEIIIYQQ4owkaBBCCCGEEEKckQQNQgghhBBCiDOSoEEIIYQQQghxRhI0CCGEEEIIIc5IggYhhLBhdXV1vPzyy9bOhhBCCDsnQYMQQtgog8HArFmzuPPOOwGoqKhg7969rFu3jqamJouk+dhjj1FSUmKRfQshhOi8JGgQQggb9a9//Yvx48cTGhoKQGpqKs888wxXXnmlxS7sn3rqKWbNmoVWq7XI/oUQQnROKkVRFGtnQgghxPmprKxk2LBhpKen4+Tk1Lo8KyuLHj16kJOTQ3R0tEXSXr58Odu3b+eNN96wyP6FEEJ0PlLTIIQQNujLL7/khhtuaBMwADg4OFg87WnTprFhwwYKCgosnpYQQojOQYIGIYSwQStXrmT48OFWSVulUnHNNdfw6aefWiV9IYQQHU+CBiGEsLAPP/yQ2NhYVCoVycnJFBUVATBz5kxUKhXDhg07r7v2BoOBTZs2MXTo0LOu+/LLL6NSqQgKCuLpp59m/fr1XHLJJXh7e7Ny5UreeOMN5s2bx/XXX88777xDbW0tr7zyCvPmzeO6665jwYIFp93vyJEj+e677845z0IIIWyb9GkQQogOUFZWRmRkJP/5z3+44YYbAGhoaOC2225j+fLlqFSqc97XiRMniI2NPW1n5OPHjxMTE9Pap2HFihWsXr2at99+G3d3d8DcH0Kj0TB+/Hi+/vpr/Pz8OH78OLGxsdx33328/vrruLu7k5OTQ2xsLPn5+YSHh7dJp7a2Fo1GQ319PS4uLhfxnxFCCGELpKZBCCE6QGBgINOnT+fdd99tXfbNN98wd+7c8woYAIqLi/Hx8TnresuXLycrK4sPP/ywNWAA8Pf3ByA5ORk/Pz8AIiMjMZlMDBo0qHXdqKgoFEUhOzv7lH17e3ujUqnIyck5r7wLIYSwTRI0CCFEB3nwwQfZvHkzaWlpABw4cIB+/fqd936MRuNZA42PPvqIWbNmcabK5N+PrqRWm38OYmJiTllmNBpPu71Go6Gqqupcsy2EEMKGSdAghBAdJCkpieHDh7Nw4UK2bNnCiBEjLmg/gYGBZ71Y7927N4sWLWLOnDkcPHjwgtI5G4PBIE2ThBCii3C0dgaEEKIreeCBB5g5cyYAH3zwwQXtIyQkBKPRSF1dHV5eXqddZ9y4cQQEBHDzzTdz++23s3PnThwd2++UrygKVVVVBAUFtds+hRBCdF5S0yCEEB1o2rRp+Pv7Exsbe8FzKri5uZGcnExKSsop751sjnTy+e233yY/P585c+b86Trns+ykyspKHBwcCA4OvqAyCCGEsC0Oc+fOnWvtTAghRFehVqvJycnhsccea9M5+XyVlJSQlZXF5Zdf3rrsm2++4dFHH6WwsJAtW7YQEBBARUUFixYtYuPGjWzbtg2DwcCjjz5Kbm4u+/fvp7a2FrVazYwZM8jNzWX37t3o9Xrq6uq46667WpeZTKY2Q7xu3LiRrKws7rnnnov5dwghhLARMuSqEEJYmE6no7y8nPDwcLRaLXPnzmXevHkXtc/8/HwmTZpEampqa4fljjR79myCgoJ4/PHHOzxtIYQQHU+aJwkhhIX985//ZOTIkQC8//777XJ3vlu3bowdO5bly5df9L7Ol06n4/vvv+eOO+7o8LSFEEJYh9Q0CCGEhe3atYv33nuPHj16MGjQICZMmNAu+21sbGT69Ol8/PHHBAYGtss+z8W///1vysvLeeaZZzosTSGEENYlQYMQQtiwoqIi5s2bx4IFCzokvWPHjvHEE0/wzTff4OTk1CFpCiGEsD4JGoQQQpyzOXPm8I9//ONPh3oVQghhnyRoEEIIIYQQQpyRdIQWQgghhBBCnJEEDUIIIYQQQogzkqBBCCGEEEIIcUYSNAghhBBCCCHOSIIGIYQQQgghxBlJ0CCEEEIIIYQ4IwkahBBCCCGEEGckQYMQQgghhBDijCRoEEIIIYQQQpyRBA1CCCGEEEKIM5KgQQghhBBCCHFGEjQIIYQQQgghzkiCBiGEEEIIIcQZSdAghBBCCCGEOCMJGoQQQgghhBBnJEGDEEIIIYQQ4owkaBBCCCGEEEKckQQNQgghhBBCiDP6f1QaT0+/x/70AAAAAElFTkSuQmCC\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "sl = yt.SlicePlot(ds, 0, 'Ez', aspect=1) # Create a sliceplot object\n",
+ "sl.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
+ "sl.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
+ "sl.annotate_streamlines(\"Ey\", \"Ez\", plot_args={\"color\": \"black\"})\n",
+ "sl.annotate_grids() # Show grids\n",
+ "sl.annotate_sphere([90000.0, 90000.0, 90000.0], radius=12000.0,\n",
+ " circle_args={'color':'white', 'linewidth':2})\n",
+ "sl.show() # Show the plot\n",
+ "#sl.save('./particle_in_ring_plt'+str(filenum)+'.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['positron_plt10.png']"
+ ]
+ },
+ "execution_count": 147,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_x'),('plasma_p', 'particle_position_y'),('plasma_p', 'particle_momentum_x'))\n",
+ "p.show()\n",
+ "p.save(\"positron_plt\"+str(filenum)+\".png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ 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\")
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['electrons_plt10.png']"
+ ]
+ },
+ "execution_count": 148,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p = yt.ParticlePlot(ds,('plasma_e', 'particle_position_y'),('plasma_e', 'particle_position_z'),('plasma_e', 'particle_momentum_z'))\n",
+ "#p = yt.ParticlePlot(ds,('plasma_p', 'particle_position_y'),('plasma_p', 'particle_position_z'),('plasma_p', 'particle_momentum_x'))\n",
+ "p.show()\n",
+ "p.save(\"electrons_plt\"+str(filenum)+\".png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def r_cl(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ "\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc))**0.5\n",
+ "\n",
+ " return r"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.add_field((\"index\",\"r_cl\"),function=r_cl,units='m',sampling_type=\"cell\")\n",
+ "#yt.SlicePlot(ds,'z','r_cl')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def rad(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ "\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ "\n",
+ " return r"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds.add_field((\"index\",\"rad\"),function=rad,units='m',sampling_type=\"cell\")\n",
+ "#yt.SlicePlot(ds,'z','rad')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Store quantities in numpy arrays, and plot with matplotlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ex_external(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
+ " + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
+ " E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
+ " Ex_ext = Er*c_phi*s_theta + E_theta*c_phi*c_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] > 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ex_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ex_internal(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r*r3*B*s_theta*s_theta\n",
+ " E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
+ " Ex_ext = Er*c_phi*s_theta + E_theta*c_phi*c_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] <= 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ex_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ey_external(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ "\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
+ " + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
+ " E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
+ " Ey_ext = Er*s_phi*s_theta + E_theta*s_phi*c_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] > 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ey_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ey_internal(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r*r3*B*s_theta*s_theta\n",
+ " E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
+ " Ey_ext = Er*s_phi*s_theta + E_theta*s_phi*c_theta\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] <= 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ey_ext.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ez_external(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r_star*B*r3*r_ratio*(1.0-3.0*c_theta*c_theta) \n",
+ " + (2.0/3.0)*omega*B*r_star*r_ratio*r_ratio\n",
+ " E_theta = (-1)*omega*B*r_star*r3*r_ratio*(c_theta*s_theta*2.0)\n",
+ " Ez_ext = Er*c_theta - E_theta*s_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] > 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " return outside.d*Ez_ext.d \n",
+ " #return outside.d*Er.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ez_internal(field,data):\n",
+ " xc = YTQuantity(90000, 'm')\n",
+ " yc = YTQuantity(90000, 'm')\n",
+ " zc = YTQuantity(90000, 'm')\n",
+ " r_star = YTQuantity(12000, 'm')\n",
+ " omega = 6245.8\n",
+ " B = 8.03e-6\n",
+ " r = ((data[\"y\"]-yc)*(data[\"y\"]-yc) + (data[\"x\"]-xc)*(data[\"x\"]-xc) + (data[\"z\"]-zc)*(data[\"z\"]-zc))**0.5\n",
+ " c_theta = (data[\"z\"]-zc)/r\n",
+ " s_theta = data[\"r_cl\"]/r\n",
+ " c_phi = (data[\"x\"]-xc)/data[\"r_cl\"]\n",
+ " s_phi = (data[\"y\"]-yc)/data[\"r_cl\"]\n",
+ " r_ratio = r_star/r\n",
+ " r3 = r_ratio*r_ratio*r_ratio\n",
+ " Er = omega*r*r3*B*s_theta*s_theta\n",
+ " E_theta = (-1)*omega*B*r*r3*(c_theta*s_theta*2.0)\n",
+ " Ez_ext = Er*c_theta - E_theta*s_theta\n",
+ "\n",
+ " outside = np.zeros_like(data[\"r_cl\"])\n",
+ " outside[data[\"rad\"] <= 12000] = 1.0\n",
+ "\n",
+ " \n",
+ " #return outside.d*Ez_ext.d\n",
+ " return outside.d*Er.d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ex_total_ext(field,data):\n",
+ "\n",
+ " return data[\"Ex_internal\"] + data[\"Ex_external\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ey_total_ext(field,data):\n",
+ "\n",
+ " return data[\"Ey_internal\"] + data[\"Ey_external\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yt.units.yt_array import YTQuantity\n",
+ "from yt.units import kboltz\n",
+ "from numpy.random import random\n",
+ "import numpy as np\n",
+ "from yt.utilities.exceptions import YTUnitOperationError\n",
+ "def Ez_total_ext(field,data):\n",
+ "\n",
+ " return data[\"Ez_internal\"] + data[\"Ez_external\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "ds.add_field((\"Ex_external\"),function=Ex_external,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ey_external\"),function=Ey_external,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ez_external\"),function=Ez_external,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ex_internal\"),function=Ex_internal,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ey_internal\"),function=Ey_internal,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ez_internal\"),function=Ez_internal,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ex_total_ext\"),function=Ex_total_ext,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ey_total_ext\"),function=Ey_total_ext,units='',sampling_type=\"cell\")\n",
+ "ds.add_field((\"Ez_total_ext\"),function=Ez_total_ext,units='',sampling_type=\"cell\")\n",
+ "\n",
+ "p = yt.SlicePlot(ds,'x','Ez_external')\n",
+ "p.annotate_streamlines(\"Ex_external\", \"Ez_external\", \n",
+ " plot_args={\"color\": \"black\"})\n",
+ "#p.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_p\",col='b',p_size=5.0)\n",
+ "#p.annotate_particles(width=(2.e3, 'm'),ptype=\"plasma_e\",col='r',p_size=5.0)\n",
+ "p.show()\n",
+ "p = yt.SlicePlot(ds,'x','Ez_internal')\n",
+ "p.annotate_streamlines(\"Ex_internal\", \"Ez_internal\", \n",
+ " plot_args={\"color\": \"black\"})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read data back-transformed to the lab frame when the simulation runs in the boosted frame (example: 2D run)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# read_raw_data.py is located in warpx/Tools.\n",
+ "import os, glob\n",
+ "#import read_raw_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#species = 'beam'\n",
+ "#iteration = 1\n",
+ "#field = 'Ex'\n",
+ "\n",
+ "#snapshot = './lab_frame_data/' + 'snapshot' + str(iteration).zfill(5)\n",
+ "#header = './lab_frame_data/Header'\n",
+ "#allrd, info = read_raw_data.read_lab_snapshot(snapshot, header) # Read field data\n",
+ "#F = allrd[field]\n",
+ "#print( \"Available info: \", *list(info.keys()) )\n",
+ "#print(\"Available fields: \", info['field_names'])\n",
+ "#nx = info['nx']\n",
+ "#nz = info['nz']\n",
+ "#x = info['x']\n",
+ "#z = info['z']\n",
+ "#xbo = read_raw_data.get_particle_field(snapshot, species, 'x') # Read particle data\n",
+ "#ybo = read_raw_data.get_particle_field(snapshot, species, 'y')\n",
+ "#zbo = read_raw_data.get_particle_field(snapshot, species, 'z')\n",
+ "#uzbo = read_raw_data.get_particle_field(snapshot, species, 'uz')\n",
+ "\n",
+ "#plt.figure(figsize=(6, 3))\n",
+ "#extent = np.array([info['zmin'], info['zmax'], info['xmin'], info['xmax']])\n",
+ "#plt.imshow(F, aspect='auto', extent=extent, cmap='seismic')\n",
+ "#plt.colorbar()\n",
+ "#plt.plot(zbo, xbo, 'g.', markersize=1.)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Read back-transformed data with hdf5 format (example: 3D run)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "5e10*1400*1400"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "5e6*1400*1400"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "685708*1400*1400"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "anaconda-cloud": {},
+ "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": "3.5.2"
+ },
+ "widgets": {
+ "state": {
+ "11d243e9f5074fe1b115949d174d59de": {
+ "views": [
+ {
+ "cell_index": 6
+ }
+ ]
+ }
+ },
+ "version": "1.2.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Source/Evolve/WarpXEvolve.cpp b/Source/Evolve/WarpXEvolve.cpp
index cbf2ffaa8e4..164f3a6e7bf 100644
--- a/Source/Evolve/WarpXEvolve.cpp
+++ b/Source/Evolve/WarpXEvolve.cpp
@@ -156,20 +156,15 @@ WarpX::Evolve (int numsteps)
Bx = Bfield_fp[lev][0].get();
By = Bfield_fp[lev][1].get();
Bz = Bfield_fp[lev][2].get();
- Gpu::ManagedVector Ex_stag, Ey_stag, Ez_stag, Bx_stag, By_stag, Bz_stag;
- Ex_stag.resize(3);
- Ey_stag.resize(3);
- Ez_stag.resize(3);
- Bx_stag.resize(3);
- By_stag.resize(3);
- Bz_stag.resize(3);
amrex::IntVect ex_type = Ex->ixType().toIntVect();
amrex::IntVect ey_type = Ey->ixType().toIntVect();
amrex::IntVect ez_type = Ez->ixType().toIntVect();
amrex::IntVect bx_type = Bx->ixType().toIntVect();
amrex::IntVect by_type = By->ixType().toIntVect();
amrex::IntVect bz_type = Bz->ixType().toIntVect();
- for (int idim = 0; idim < AMREX_SPACEDIM-1; ++idim) {
+ amrex::GpuArray Ex_stag, Ey_stag, Ez_stag, Bx_stag, By_stag, Bz_stag;
+ for (int idim = 0; idim < 3; ++idim)
+ {
Ex_stag[idim] = ex_type[idim];
Ey_stag[idim] = ey_type[idim];
Ez_stag[idim] = ez_type[idim];
@@ -177,12 +172,6 @@ WarpX::Evolve (int numsteps)
By_stag[idim] = by_type[idim];
Bz_stag[idim] = bz_type[idim];
}
- int const* const AMREX_RESTRICT Ex_stag_ptr = Ex_stag.data();
- int const* const AMREX_RESTRICT Ey_stag_ptr = Ey_stag.data();
- int const* const AMREX_RESTRICT Ez_stag_ptr = Ez_stag.data();
- int const* const AMREX_RESTRICT Bx_stag_ptr = Bx_stag.data();
- int const* const AMREX_RESTRICT By_stag_ptr = By_stag.data();
- int const* const AMREX_RESTRICT Bz_stag_ptr = Bz_stag.data();
auto geom = Geom(lev).data();
#ifdef _OPENMP
#pragma omp parallel if (Gpu::notInLaunchRegion())
@@ -200,15 +189,15 @@ WarpX::Evolve (int numsteps)
amrex::ParallelFor(tex, tey, tez,
[=] AMREX_GPU_DEVICE (int i, int j, int k)
{
- PulsarParm::DampEField(i, j, k, geom, Exfab, Ex_stag_ptr);
+ PulsarParm::DampField(i, j, k, geom, Exfab, Ex_stag);
},
[=] AMREX_GPU_DEVICE (int i, int j, int k)
{
- PulsarParm::DampEField(i, j, k, geom, Eyfab, Ey_stag_ptr);
+ PulsarParm::DampField(i, j, k, geom, Eyfab, Ey_stag);
},
[=] AMREX_GPU_DEVICE (int i, int j, int k)
{
- PulsarParm::DampEField(i, j, k, geom, Ezfab, Ez_stag_ptr);
+ PulsarParm::DampField(i, j, k, geom, Ezfab, Ez_stag);
});
}
for ( MFIter mfi(*Bx, TilingIfNotGPU()); mfi.isValid(); ++mfi )
@@ -224,19 +213,21 @@ WarpX::Evolve (int numsteps)
amrex::ParallelFor(tex, tey, tez,
[=] AMREX_GPU_DEVICE (int i, int j, int k)
{
- PulsarParm::DampEField(i, j, k, geom, Bxfab, Bx_stag_ptr);
+ PulsarParm::DampField(i, j, k, geom, Bxfab, Bx_stag);
},
[=] AMREX_GPU_DEVICE (int i, int j, int k)
{
- PulsarParm::DampEField(i, j, k, geom, Byfab, By_stag_ptr);
+ PulsarParm::DampField(i, j, k, geom, Byfab, By_stag);
},
[=] AMREX_GPU_DEVICE (int i, int j, int k)
{
- PulsarParm::DampEField(i, j, k, geom, Bzfab, Bz_stag_ptr);
+ PulsarParm::DampField(i, j, k, geom, Bzfab, Bz_stag);
});
}
}
}
+ FillBoundaryE(guard_cells.ng_FieldSolver, IntVect::TheZeroVector());
+ FillBoundaryB(guard_cells.ng_FieldSolver, IntVect::TheZeroVector());
#endif
if (warpx_py_beforeEsolve) warpx_py_beforeEsolve();
diff --git a/Source/Initialization/InjectorPosition.H b/Source/Initialization/InjectorPosition.H
index 6d83c397884..7cd2430dddf 100644
--- a/Source/Initialization/InjectorPosition.H
+++ b/Source/Initialization/InjectorPosition.H
@@ -12,6 +12,10 @@
#include
#include
+#ifdef PULSAR
+ #include "Particles/PulsarParameters.H"
+#endif
+
// struct whose getPositionUnitBox returns x, y and z for a particle with
// random distribution inside a unit cell.
struct InjectorPositionRandom
@@ -41,14 +45,31 @@ struct InjectorPositionRegular
amrex::RandomEngine const&) const noexcept
{
using namespace amrex;
-
+#ifdef PULSAR
+#if (defined WARPX_DIM_3D) || (defined WARPX_DIM_RZ)
+ int nx, ny, nz;
+ if (PulsarParm::ModifyParticleWtAtInjection == 1) {
+ nx = ref_fac*ppc.x;
+ ny = ref_fac*ppc.y;
+ nz = ref_fac*ppc.z;
+ } else if (PulsarParm::ModifyParticleWtAtInjection == 0) {
+ amrex::Real ninj_per_dim = std::cbrt(PulsarParm::Ninj_fraction);
+ nx = ref_fac*ppc.x*ninj_per_dim;
+ ny = ref_fac*ppc.y*ninj_per_dim;
+ nz = ref_fac*ppc.z*ninj_per_dim;
+ }
+#else
+ amrex::Abort("pulsar sim does not support 2d yet");
+#endif // endif for 3D
+#else // else ifndef PULSAR
int const nx = ref_fac*ppc.x;
int const ny = ref_fac*ppc.y;
#if (defined WARPX_DIM_3D) || (defined WARPX_DIM_RZ)
int const nz = ref_fac*ppc.z;
#else
int const nz = 1;
-#endif
+#endif // 3D ifdef
+#endif // PULSAR ifdef
int const ix_part = i_part / (ny*nz); // written this way backward compatibility
int const iz_part = (i_part-ix_part*(ny*nz)) / ny;
int const iy_part = (i_part-ix_part*(ny*nz)) - ny*iz_part;
@@ -58,6 +79,17 @@ struct InjectorPositionRegular
(0.5_rt + iz_part) / nz
};
}
+
+#ifdef PULSAR
+ AMREX_GPU_HOST_DEVICE
+ amrex::XDim3
+ getppcInEachDim () const noexcept
+ {
+ using namespace amrex;
+ return XDim3{ppc.x, ppc.y, ppc.z};
+ }
+#endif
+
private:
amrex::Dim3 ppc;
};
@@ -123,6 +155,25 @@ struct InjectorPosition
};
}
+#ifdef PULSAR
+ AMREX_GPU_HOST_DEVICE
+ amrex::XDim3
+ getppcInEachDim () const noexcept
+ {
+ switch (type)
+ {
+ case Type::regular:
+ {
+ return object.regular.getppcInEachDim();
+ }
+ default:
+ {
+ return object.regular.getppcInEachDim();
+ }
+ };
+ }
+#endif
+
// bool: whether position specified is within bounds.
AMREX_GPU_HOST_DEVICE
bool
@@ -132,15 +183,26 @@ struct InjectorPosition
y < ymax and y >= ymin and
z < zmax and z >= zmin);
}
-
- // bool: whether position specified is withing pulsar injection region
+
#ifdef PULSAR
+ // bool: whether cell-center position specified is within pulsar injection region
+ // So that these cells can inject particles
+ // a buffer factor of 2*dR (2*dx) is used to ensure cells partially cut by sphere
+ // inject particles
+ AMREX_GPU_HOST_DEVICE
+ bool
+ insidePulsarBoundsCC (amrex::Real r, amrex::Real r_min, amrex::Real r_max, amrex::Real dR_factor) const noexcept
+ {
+ return (r<=(r_max + dR_factor) and r>=(r_min-dR_factor));
+ }
+
+ // bool: whether particle position specified is within pulsar injection region
+ // So that these cells can inject particles
AMREX_GPU_HOST_DEVICE
bool
- insidePulsarBounds (amrex::Real r, amrex::Real R_star, amrex::Real dR_star) const noexcept
+ insidePulsarBounds (amrex::Real r, amrex::Real r_min, amrex::Real r_max) const noexcept
{
- //return (r<=R_star and r>=(R_star-dR_star));
- return (r<=(R_star+dR_star) and r>=(R_star-dR_star));
+ return (r<=(r_max) and r>=(r_min));
}
#endif
diff --git a/Source/Particles/Deposition/ChargeDeposition.H b/Source/Particles/Deposition/ChargeDeposition.H
index 02b3badcb7b..48b0c676f79 100644
--- a/Source/Particles/Deposition/ChargeDeposition.H
+++ b/Source/Particles/Deposition/ChargeDeposition.H
@@ -79,6 +79,25 @@ void doChargeDepositionShapeN(const GetParticlePosition& GetPosition,
amrex::ParticleReal xp, yp, zp;
GetPosition(ip, xp, yp, zp);
+#ifdef PULSAR
+ // temporarily adding this to check if turning off j-deposition
+ // within a region that is corotating makes a difference
+ if (PulsarParm::turnoffdeposition == 1) {
+ const amrex::Real xc = PulsarParm::center_star[0];
+ const amrex::Real yc = PulsarParm::center_star[1];
+ const amrex::Real zc = PulsarParm::center_star[2];
+ // find radius of the particle
+ amrex::Real rp = std::sqrt( (xp-xc)*(xp-xc)
+ + (yp-yc)*(yp-yc)
+ + (zp-zc)*(zp-zc) );
+ // change the locally defined particle weight, wq, to 0
+ // within the region defined by r<=max_radius_nodepos
+ if (rp <= PulsarParm::max_nodepos_radius) {
+ wq = 0.;
+ }
+ }
+#endif
+
// --- Compute shape factors
// x direction
// Get particle position in grid coordinates
diff --git a/Source/Particles/Deposition/CurrentDeposition.H b/Source/Particles/Deposition/CurrentDeposition.H
index 196e275d714..585ae72d49d 100644
--- a/Source/Particles/Deposition/CurrentDeposition.H
+++ b/Source/Particles/Deposition/CurrentDeposition.H
@@ -110,6 +110,25 @@ void doDepositionShapeN(const GetParticlePosition& GetPosition,
amrex::ParticleReal xp, yp, zp;
GetPosition(ip, xp, yp, zp);
+#ifdef PULSAR
+ // temporarily adding this to check if turning off j-deposition
+ // within a region that is corotating makes a difference
+ if (PulsarParm::turnoffdeposition == 1) {
+ const amrex::Real xc = PulsarParm::center_star[0];
+ const amrex::Real yc = PulsarParm::center_star[1];
+ const amrex::Real zc = PulsarParm::center_star[2];
+ // find radius of the particle
+ amrex::Real rp = std::sqrt( (xp-xc)*(xp-xc)
+ + (yp-yc)*(yp-yc)
+ + (zp-zc)*(zp-zc) );
+ // change the locally defined particle weight, wq, to 0
+ // within the region defined by r<=max_radius_nodepos
+ if (rp <= PulsarParm::max_nodepos_radius) {
+ wq = 0.;
+ }
+ }
+#endif
+
const amrex::Real vx = uxp[ip]*gaminv;
const amrex::Real vy = uyp[ip]*gaminv;
const amrex::Real vz = uzp[ip]*gaminv;
@@ -377,6 +396,25 @@ void doEsirkepovDepositionShapeN (const GetParticlePosition& GetPosition,
ParticleReal xp, yp, zp;
GetPosition(ip, xp, yp, zp);
+#ifdef PULSAR
+ // temporarily adding this to check if turning off j-deposition
+ // within a region that is corotating makes a difference
+ if (PulsarParm::turnoffdeposition == 1) {
+ const amrex::Real xc = PulsarParm::center_star[0];
+ const amrex::Real yc = PulsarParm::center_star[1];
+ const amrex::Real zc = PulsarParm::center_star[2];
+ // find radius of the particle
+ amrex::Real rp = std::sqrt( (xp-xc)*(xp-xc)
+ + (yp-yc)*(yp-yc)
+ + (zp-zc)*(zp-zc) );
+ // change the locally defined particle weight, wq, to 0
+ // within the region defined by r<=max_radius_nodepos
+ if (rp <= PulsarParm::max_nodepos_radius) {
+ wq = 0.;
+ }
+ }
+#endif
+
Real const wqx = wq*invdtdx;
#if (defined WARPX_DIM_3D)
Real const wqy = wq*invdtdy;
@@ -696,6 +734,25 @@ void doVayDepositionShapeN (const GetParticlePosition& GetPosition,
amrex::ParticleReal xp, yp, zp;
GetPosition(ip, xp, yp, zp);
+#ifdef PULSAR
+ // temporarily adding this to check if turning off j-deposition
+ // within a region that is corotating makes a difference
+ if (PulsarParm::turnoffdeposition == 1) {
+ const amrex::Real xc = PulsarParm::center_star[0];
+ const amrex::Real yc = PulsarParm::center_star[1];
+ const amrex::Real zc = PulsarParm::center_star[2];
+ // find radius of the particle
+ amrex::Real rp = std::sqrt( (xp-xc)*(xp-xc)
+ + (yp-yc)*(yp-yc)
+ + (zp-zc)*(zp-zc) );
+ // change the locally defined particle weight, wq, to 0
+ // within the region defined by r<=max_radius_nodepos
+ if (rp <= PulsarParm::max_nodepos_radius) {
+ wq = 0.;
+ }
+ }
+#endif
+
// Particle velocities
const amrex::Real vx = uxp[ip] * invgam;
const amrex::Real vy = uyp[ip] * invgam;
diff --git a/Source/Particles/Gather/FieldGather.H b/Source/Particles/Gather/FieldGather.H
index 7f3eeda297f..b8b636474ce 100644
--- a/Source/Particles/Gather/FieldGather.H
+++ b/Source/Particles/Gather/FieldGather.H
@@ -439,13 +439,41 @@ void doGatherShapeN(const GetParticlePosition& getPosition,
getExternalE(ip, Exp[ip], Eyp[ip], Ezp[ip]);
getExternalB(ip, Bxp[ip], Byp[ip], Bzp[ip]);
+#ifdef PULSAR
+ // temporarily adding this to check if turning off EB-gather
+ // of self-consistent fields within a region
+ // makes a difference
+ int dogather = 1;
+ if (PulsarParm::turnoff_plasmaEB_gather == 1) {
+ const amrex::Real xc = PulsarParm::center_star[0];
+ const amrex::Real yc = PulsarParm::center_star[1];
+ const amrex::Real zc = PulsarParm::center_star[2];
+ // find radius of the particle
+ amrex::Real rp = std::sqrt( (xp-xc)*(xp-xc)
+ + (yp-yc)*(yp-yc)
+ + (zp-zc)*(zp-zc) );
+ // set dogather to 0 if particle within user-defined nogather radius
+ if (rp <= PulsarParm::max_nogather_radius) {
+ dogather = 0;
+ }
+ }
+ if (dogather == 1) {
+ doGatherShapeN(
+ xp, yp, zp, Exp[ip], Eyp[ip], Ezp[ip], Bxp[ip], Byp[ip], Bzp[ip],
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+ }
+#else // if not a pulsar simulation
doGatherShapeN(
xp, yp, zp, Exp[ip], Eyp[ip], Ezp[ip], Bxp[ip], Byp[ip], Bzp[ip],
ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes);
+#endif // end if pulsar
}
);
+
}
/**
diff --git a/Source/Particles/Gather/GatherExternalPulsarFieldOnGrid.H b/Source/Particles/Gather/GatherExternalPulsarFieldOnGrid.H
new file mode 100644
index 00000000000..47315c71bb5
--- /dev/null
+++ b/Source/Particles/Gather/GatherExternalPulsarFieldOnGrid.H
@@ -0,0 +1,562 @@
+/* Copyright 2019 Axel Huebl, David Grote, Maxence Thevenet
+ * Revathi Jambunathan, Weiqun Zhang
+ *
+ * This file is part of WarpX.
+ *
+ * License: BSD-3-Clause-LBNL
+ */
+#ifndef GATHEREXTERNALPULSARFIELDONGRID_H_
+#define GATHEREXTERNALPULSARFIELDONGRID_H_
+
+#ifdef PULSAR
+#include "Particles/Pusher/GetAndSetPosition.H"
+#include "Particles/Gather/GetExternalFields.H"
+#include "Particles/ShapeFactors.H"
+#include "Utils/WarpX_Complex.H"
+#include "Particles/PulsarParameters.H"
+#include
+
+
+/**
+ * Returns the x/y/z component of the E/B applied field for pulsar set-up.
+ *
+ * \param ii, jj, kk : indices in x, y, z
+ * \param mf_type : staggered location of the field
+ * \param domain_lo : physical coordinates of the lower corner of the domain
+ * \param domain_hi : physical coordinates of the upper corner of the domain
+ * \param dx : cell size in x, y, z
+ * \param cur_time : the current physical time of the simulation
+ * \param Field : 0 for Bfield, 1 for Efield
+ * \param comp : x(ncomp=0), y(ncomp=1), or z(ncomp=2) component
+*/
+AMREX_GPU_HOST_DEVICE AMREX_FORCE_INLINE
+amrex::Real GetExternalFieldAtCellLocation(int ii, int jj, int kk,
+ amrex::GpuArray const f_type,
+ amrex::GpuArray const domain_lo,
+ amrex::GpuArray const domain_hi,
+ amrex::GpuArray const dx,
+ amrex::Real cur_time, int Field, int comp)
+{
+ amrex::Real xcell, ycell, zcell;
+ amrex::Real r, theta, phi;
+ amrex::Real Fr, Ftheta, Fphi;
+ amrex::Real Fext_comp;
+ // compute cell coordinates, (x,y,z) corresponding to (ii,jj,kk)
+ // based on the staggered Bx location on the yee-grid
+ PulsarParm::ComputeCellCoordinates( ii, jj, kk, f_type, domain_lo,
+ dx, xcell, ycell, zcell );
+ // Convert (x,y,z) to (r,theta,phi)
+ PulsarParm::ConvertCartesianToSphericalCoord( xcell, ycell, zcell,
+ domain_lo, domain_hi,
+ r, theta, phi);
+ if (Field == 0) {
+ // Compute External vacuum fields of the pulsar (Br, Btheta, Bphi)
+ PulsarParm::ExternalBFieldSpherical( r, theta, phi, cur_time,
+ Fr, Ftheta, Fphi);
+ } else if (Field == 1) {
+ // Compute External vacuum fields of the pulsar (Br, Btheta, Bphi)
+ PulsarParm::ExternalEFieldSpherical( r, theta, phi, cur_time,
+ Fr, Ftheta, Fphi);
+ }
+
+ if (comp == 0) {
+ // Convert (Fr, Ftheta, Fphi) to Xcomponent
+ PulsarParm::ConvertSphericalToCartesianXComponent( Fr, Ftheta, Fphi,
+ r, theta, phi, Fext_comp);
+ } else if (comp == 1) {
+ // Convert (Fr, Ftheta, Fphi) to YComponent
+ PulsarParm::ConvertSphericalToCartesianYComponent( Fr, Ftheta, Fphi,
+ r, theta, phi, Fext_comp);
+ } else if (comp == 2) {
+ // Convert (Fr, Ftheta, Fphi) to ZComponent
+ PulsarParm::ConvertSphericalToCartesianZComponent( Fr, Ftheta, Fphi,
+ r, theta, phi, Fext_comp);
+ }
+
+ return Fext_comp;
+}
+
+
+/**
+ * \brief Field gather for a single particle
+ *
+ * \tparam depos_order Particle shape order
+ * \tparam galerkin_interpolation Lower the order of the particle shape by
+ * this value (0/1) for the parallel field component
+ * \param xp, yp, zp Particle position coordinates
+ * \param Exp, Eyp, Ezp Electric field on particles.
+ * \param Bxp, Byp, Bzp Magnetic field on particles.
+ * \param ex_arr ey_arr ez_arr Array4 of the electric field, either full array or tile.
+ * \param bx_arr by_arr bz_arr Array4 of the magnetic field, either full array or tile.
+ * \param ex_type, ey_type, ez_type IndexType of the electric field
+ * \param bx_type, by_type, bz_type IndexType of the magnetic field
+ * \param dx 3D cell spacing
+ * \param xyzmin Physical lower bounds of domain in x, y, z.
+ * \param lo Index lower bounds of domain.
+ * \param n_rz_azimuthal_modes Number of azimuthal modes when using RZ geometry
+ */
+template
+AMREX_GPU_HOST_DEVICE AMREX_FORCE_INLINE
+void doPulsarFieldGatherShapeN (const amrex::ParticleReal xp,
+ const amrex::ParticleReal yp,
+ const amrex::ParticleReal zp,
+ amrex::ParticleReal& Exp,
+ amrex::ParticleReal& Eyp,
+ amrex::ParticleReal& Ezp,
+ amrex::ParticleReal& Bxp,
+ amrex::ParticleReal& Byp,
+ amrex::ParticleReal& Bzp,
+ amrex::Array4 const& ex_arr,
+ amrex::Array4 const& ey_arr,
+ amrex::Array4 const& ez_arr,
+ amrex::Array4 const& bx_arr,
+ amrex::Array4 const& by_arr,
+ amrex::Array4 const& bz_arr,
+ const amrex::IndexType ex_type,
+ const amrex::IndexType ey_type,
+ const amrex::IndexType ez_type,
+ const amrex::IndexType bx_type,
+ const amrex::IndexType by_type,
+ const amrex::IndexType bz_type,
+ const amrex::GpuArray& dx,
+ const amrex::GpuArray& xyzmin,
+ const amrex::Dim3& lo,
+ const long n_rz_azimuthal_modes,
+ amrex::GpuArray const& dom_lo = {},
+ amrex::GpuArray const& dom_hi = {},
+ amrex::Real cur_time = 0._rt )
+{
+ using namespace amrex;
+
+#if defined(WARPX_DIM_XZ)
+ amrex::Abort("pulsar does not work in 2D");
+#endif
+
+#ifndef WARPX_DIM_RZ
+ amrex::Abort("pulsar does not work in RZ");
+#endif
+
+ const amrex::Real dxi = 1.0_rt/dx[0];
+ const amrex::Real dyi = 1.0_rt/dx[1];
+ const amrex::Real dzi = 1.0_rt/dx[2];
+
+ const amrex::Real xmin = xyzmin[0];
+ const amrex::Real ymin = xyzmin[1];
+ const amrex::Real zmin = xyzmin[2];
+
+ constexpr int zdir = (AMREX_SPACEDIM - 1);
+ constexpr int NODE = amrex::IndexType::NODE;
+ constexpr int CELL = amrex::IndexType::CELL;
+
+ // store staggering of ex-bz in GpuArray
+ amrex::GpuArray Ex_stag, Ey_stag, Ez_stag, Bx_stag, By_stag, Bz_stag;
+ for (int idim = 0; idim < 3; ++idim)
+ {
+ Ex_stag[idim] = ex_type[idim];
+ Ey_stag[idim] = ey_type[idim];
+ Ez_stag[idim] = ez_type[idim];
+ Bx_stag[idim] = bx_type[idim];
+ By_stag[idim] = by_type[idim];
+ Bz_stag[idim] = bz_type[idim];
+ }
+ // --- Compute shape factors
+ // x direction
+ // Get particle position
+ const amrex::Real x = (xp-xmin)*dxi;
+
+ // j_[eb][xyz] leftmost grid point in x that the particle touches for the centering of each current
+ // sx_[eb][xyz] shape factor along x for the centering of each current
+ // There are only two possible centerings, node or cell centered, so at most only two shape factor
+ // arrays will be needed.
+ amrex::Real sx_node[depos_order + 1];
+ amrex::Real sx_cell[depos_order + 1];
+ amrex::Real sx_node_galerkin[depos_order + 1 - galerkin_interpolation] = {0._rt};
+ amrex::Real sx_cell_galerkin[depos_order + 1 - galerkin_interpolation] = {0._rt};
+
+ int j_node = 0;
+ int j_cell = 0;
+ int j_node_v = 0;
+ int j_cell_v = 0;
+ Compute_shape_factor< depos_order > const compute_shape_factor;
+ Compute_shape_factor const compute_shape_factor_galerkin;
+ if ((ey_type[0] == NODE) || (ez_type[0] == NODE) || (bx_type[0] == NODE)) {
+ j_node = compute_shape_factor(sx_node, x);
+ }
+ if ((ey_type[0] == CELL) || (ez_type[0] == CELL) || (bx_type[0] == CELL)) {
+ j_cell = compute_shape_factor(sx_cell, x - 0.5_rt);
+ }
+ if ((ex_type[0] == NODE) || (by_type[0] == NODE) || (bz_type[0] == NODE)) {
+ j_node_v = compute_shape_factor_galerkin(sx_node_galerkin, x);
+ }
+ if ((ex_type[0] == CELL) || (by_type[0] == CELL) || (bz_type[0] == CELL)) {
+ j_cell_v = compute_shape_factor_galerkin(sx_cell_galerkin, x - 0.5_rt);
+ }
+ const amrex::Real (&sx_ex)[depos_order + 1 - galerkin_interpolation] = ((ex_type[0] == NODE) ? sx_node_galerkin : sx_cell_galerkin);
+ const amrex::Real (&sx_ey)[depos_order + 1 ] = ((ey_type[0] == NODE) ? sx_node : sx_cell );
+ const amrex::Real (&sx_ez)[depos_order + 1 ] = ((ez_type[0] == NODE) ? sx_node : sx_cell );
+ const amrex::Real (&sx_bx)[depos_order + 1 ] = ((bx_type[0] == NODE) ? sx_node : sx_cell );
+ const amrex::Real (&sx_by)[depos_order + 1 - galerkin_interpolation] = ((by_type[0] == NODE) ? sx_node_galerkin : sx_cell_galerkin);
+ const amrex::Real (&sx_bz)[depos_order + 1 - galerkin_interpolation] = ((bz_type[0] == NODE) ? sx_node_galerkin : sx_cell_galerkin);
+ int const j_ex = ((ex_type[0] == NODE) ? j_node_v : j_cell_v);
+ int const j_ey = ((ey_type[0] == NODE) ? j_node : j_cell );
+ int const j_ez = ((ez_type[0] == NODE) ? j_node : j_cell );
+ int const j_bx = ((bx_type[0] == NODE) ? j_node : j_cell );
+ int const j_by = ((by_type[0] == NODE) ? j_node_v : j_cell_v);
+ int const j_bz = ((bz_type[0] == NODE) ? j_node_v : j_cell_v);
+
+ // y direction
+ const amrex::Real y = (yp-ymin)*dyi;
+ amrex::Real sy_node[depos_order + 1];
+ amrex::Real sy_cell[depos_order + 1];
+ amrex::Real sy_node_v[depos_order + 1 - galerkin_interpolation];
+ amrex::Real sy_cell_v[depos_order + 1 - galerkin_interpolation];
+ int k_node = 0;
+ int k_cell = 0;
+ int k_node_v = 0;
+ int k_cell_v = 0;
+ if ((ex_type[1] == NODE) || (ez_type[1] == NODE) || (by_type[1] == NODE)) {
+ k_node = compute_shape_factor(sy_node, y);
+ }
+ if ((ex_type[1] == CELL) || (ez_type[1] == CELL) || (by_type[1] == CELL)) {
+ k_cell = compute_shape_factor(sy_cell, y - 0.5_rt);
+ }
+ if ((ey_type[1] == NODE) || (bx_type[1] == NODE) || (bz_type[1] == NODE)) {
+ k_node_v = compute_shape_factor_galerkin(sy_node_v, y);
+ }
+ if ((ey_type[1] == CELL) || (bx_type[1] == CELL) || (bz_type[1] == CELL)) {
+ k_cell_v = compute_shape_factor_galerkin(sy_cell_v, y - 0.5_rt);
+ }
+ const amrex::Real (&sy_ex)[depos_order + 1 ] = ((ex_type[1] == NODE) ? sy_node : sy_cell );
+ const amrex::Real (&sy_ey)[depos_order + 1 - galerkin_interpolation] = ((ey_type[1] == NODE) ? sy_node_v : sy_cell_v);
+ const amrex::Real (&sy_ez)[depos_order + 1 ] = ((ez_type[1] == NODE) ? sy_node : sy_cell );
+ const amrex::Real (&sy_bx)[depos_order + 1 - galerkin_interpolation] = ((bx_type[1] == NODE) ? sy_node_v : sy_cell_v);
+ const amrex::Real (&sy_by)[depos_order + 1 ] = ((by_type[1] == NODE) ? sy_node : sy_cell );
+ const amrex::Real (&sy_bz)[depos_order + 1 - galerkin_interpolation] = ((bz_type[1] == NODE) ? sy_node_v : sy_cell_v);
+ int const k_ex = ((ex_type[1] == NODE) ? k_node : k_cell );
+ int const k_ey = ((ey_type[1] == NODE) ? k_node_v : k_cell_v);
+ int const k_ez = ((ez_type[1] == NODE) ? k_node : k_cell );
+ int const k_bx = ((bx_type[1] == NODE) ? k_node_v : k_cell_v);
+ int const k_by = ((by_type[1] == NODE) ? k_node : k_cell );
+ int const k_bz = ((bz_type[1] == NODE) ? k_node_v : k_cell_v);
+
+ // z direction
+ const amrex::Real z = (zp-zmin)*dzi;
+ amrex::Real sz_node[depos_order + 1];
+ amrex::Real sz_cell[depos_order + 1];
+ amrex::Real sz_node_v[depos_order + 1 - galerkin_interpolation];
+ amrex::Real sz_cell_v[depos_order + 1 - galerkin_interpolation];
+ int l_node = 0;
+ int l_cell = 0;
+ int l_node_v = 0;
+ int l_cell_v = 0;
+ if ((ex_type[zdir] == NODE) || (ey_type[zdir] == NODE) || (bz_type[zdir] == NODE)) {
+ l_node = compute_shape_factor(sz_node, z);
+ }
+ if ((ex_type[zdir] == CELL) || (ey_type[zdir] == CELL) || (bz_type[zdir] == CELL)) {
+ l_cell = compute_shape_factor(sz_cell, z - 0.5_rt);
+ }
+ if ((ez_type[zdir] == NODE) || (bx_type[zdir] == NODE) || (by_type[zdir] == NODE)) {
+ l_node_v = compute_shape_factor_galerkin(sz_node_v, z);
+ }
+ if ((ez_type[zdir] == CELL) || (bx_type[zdir] == CELL) || (by_type[zdir] == CELL)) {
+ l_cell_v = compute_shape_factor_galerkin(sz_cell_v, z - 0.5_rt);
+ }
+ const amrex::Real (&sz_ex)[depos_order + 1 ] = ((ex_type[zdir] == NODE) ? sz_node : sz_cell );
+ const amrex::Real (&sz_ey)[depos_order + 1 ] = ((ey_type[zdir] == NODE) ? sz_node : sz_cell );
+ const amrex::Real (&sz_ez)[depos_order + 1 - galerkin_interpolation] = ((ez_type[zdir] == NODE) ? sz_node_v : sz_cell_v);
+ const amrex::Real (&sz_bx)[depos_order + 1 - galerkin_interpolation] = ((bx_type[zdir] == NODE) ? sz_node_v : sz_cell_v);
+ const amrex::Real (&sz_by)[depos_order + 1 - galerkin_interpolation] = ((by_type[zdir] == NODE) ? sz_node_v : sz_cell_v);
+ const amrex::Real (&sz_bz)[depos_order + 1 ] = ((bz_type[zdir] == NODE) ? sz_node : sz_cell );
+ int const l_ex = ((ex_type[zdir] == NODE) ? l_node : l_cell );
+ int const l_ey = ((ey_type[zdir] == NODE) ? l_node : l_cell );
+ int const l_ez = ((ez_type[zdir] == NODE) ? l_node_v : l_cell_v);
+ int const l_bx = ((bx_type[zdir] == NODE) ? l_node_v : l_cell_v);
+ int const l_by = ((by_type[zdir] == NODE) ? l_node_v : l_cell_v);
+ int const l_bz = ((bz_type[zdir] == NODE) ? l_node : l_cell );
+
+
+ // Each field is gathered in a separate block of
+ // AMREX_SPACEDIM nested loops because the deposition
+ // order can differ for each component of each field
+ // when galerkin_interpolation is set to 1
+
+ // Gather Pulsar external field using grid resolution on particle Exp
+ for (int iz=0; iz<=depos_order; iz++){
+ for (int iy=0; iy<=depos_order; iy++){
+ for (int ix=0; ix<= depos_order - galerkin_interpolation; ix++){
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_ex + ix;
+ int jj = lo.y + k_ex + iy;
+ int kk = lo.z + l_ex + iz;
+ int Field = 1; // 1 for Efield, 0 for Bfield
+ int comp = 0; // 0 for xcomponent
+ amrex::Real ex_ext = GetExternalFieldAtCellLocation( ii, jj, kk,
+ Ex_stag, dom_lo, dom_hi, dx,
+ cur_time, Field, comp);
+ Exp += sx_ex[ix]*sy_ex[iy]*sz_ex[iz]*ex_ext;
+ }
+ }
+ }
+ // Gather Pulsar external field using grid resolution on particle Eyp
+ for (int iz=0; iz<=depos_order; iz++){
+ for (int iy=0; iy<= depos_order - galerkin_interpolation; iy++){
+ for (int ix=0; ix<=depos_order; ix++){
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_ey + ix;
+ int jj = lo.y + k_ey + iy;
+ int kk = lo.z + l_ey + iz;
+ int Field = 1; // 1 for Efield, 0 for Bfield
+ int comp = 1; // 1 for ycomponent
+ amrex::Real ey_ext = GetExternalFieldAtCellLocation( ii, jj, kk,
+ Ey_stag, dom_lo, dom_hi, dx,
+ cur_time, Field, comp);
+ Eyp += sx_ey[ix]*sy_ey[iy]*sz_ey[iz]*ey_ext;
+ }
+ }
+ }
+ // Gather Pulsar external field using grid resolution on particle Ezp
+ for (int iz=0; iz<= depos_order - galerkin_interpolation; iz++){
+ for (int iy=0; iy<=depos_order; iy++){
+ for (int ix=0; ix<=depos_order; ix++){
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_ez + ix;
+ int jj = lo.y + k_ez + iy;
+ int kk = lo.z + l_ez + iz;
+ int Field = 1; // 1 for Efield, 0 for Bfield
+ int comp = 2; // 2 for zcomponent
+ amrex::Real ez_ext = GetExternalFieldAtCellLocation( ii, jj, kk,
+ Ez_stag, dom_lo, dom_hi, dx,
+ cur_time, Field, comp);
+ Ezp += sx_ez[ix]*sy_ez[iy]*sz_ez[iz]*ez_ext;
+ }
+ }
+ }
+ // Gather Pulsar external field using grid resolution on particle Bzp
+ for (int iz=0; iz<=depos_order; iz++){
+ for (int iy=0; iy<= depos_order - galerkin_interpolation; iy++){
+ for (int ix=0; ix<= depos_order - galerkin_interpolation; ix++){
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_bz + ix;
+ int jj = lo.y + k_bz + iy;
+ int kk = lo.z + l_bz + iz;
+ int Field = 0; // 1 for Efield, 0 for Bfield
+ int comp = 2; // 2 for zcomponent
+ amrex::Real bz_ext = GetExternalFieldAtCellLocation( ii, jj, kk,
+ Bz_stag, dom_lo, dom_hi, dx,
+ cur_time, Field, comp);
+ Bzp += sx_bz[ix]*sy_bz[iy]*sz_bz[iz]*bz_ext;
+ }
+ }
+ }
+ // Gather Pulsar external field using grid resolution on particle Byp
+ for (int iz=0; iz<= depos_order - galerkin_interpolation; iz++){
+ for (int iy=0; iy<=depos_order; iy++){
+ for (int ix=0; ix<= depos_order - galerkin_interpolation; ix++){
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_by + ix;
+ int jj = lo.y + k_by + iy;
+ int kk = lo.z + l_by + iz;
+ int Field = 0; // 1 for Efield, 0 for Bfield
+ int comp = 1; // 1 for ycomponent
+ amrex::Real by_ext = GetExternalFieldAtCellLocation( ii, jj, kk,
+ By_stag, dom_lo, dom_hi, dx,
+ cur_time, Field, comp);
+ Byp += sx_by[ix]*sy_by[iy]*sz_by[iz]*by_ext;
+ }
+ }
+ }
+ // Gather Pulsar external field using grid resolution on particle Bxp
+ for (int iz=0; iz<= depos_order - galerkin_interpolation; iz++){
+ for (int iy=0; iy<= depos_order - galerkin_interpolation; iy++){
+ for (int ix=0; ix<=depos_order; ix++){
+ // Add the external field contribution for pulsar
+ int ii = lo.x + j_bx + ix;
+ int jj = lo.y + k_bx + iy;
+ int kk = lo.z + l_bx + iz;
+ int Field = 0; // 1 for Efield, 0 for Bfield
+ int comp = 0; // 0 for xcomponent
+ amrex::Real bx_ext = GetExternalFieldAtCellLocation( ii, jj, kk,
+ Bx_stag, dom_lo, dom_hi, dx,
+ cur_time, Field, comp);
+ Bxp += sx_bx[ix]*sy_bx[iy]*sz_bx[iz]*bx_ext;
+ }
+ }
+ }
+}
+
+
+
+
+/**
+ * \brief Field gather for particles
+ *
+ * /param getPosition : A functor for returning the particle position.
+ * /param getExternalEField : A functor for assigning the external E field.
+ * /param getExternalBField : A functor for assigning the external B field.
+ * \param Exp, Eyp, Ezp : Pointer to array of electric field on particles.
+ * \param Bxp, Byp, Bzp : Pointer to array of magnetic field on particles.
+ * \param exfab eyfab ezfab : Array4 of the electric field, either full array or tile.
+ * \param ezfab bxfab bzfab : Array4 of the magnetic field, either full array or tile.
+ * \param np_to_gather : Number of particles for which field is gathered.
+ * \param dx : 3D cell size
+ * \param xyzmin : Physical lower bounds of domain.
+ * \param lo : Index lower bounds of domain.
+ * \param n_rz_azimuthal_modes : Number of azimuthal modes when using RZ geometry
+ */
+template
+void doPulsarFieldGatherShapeN(const GetParticlePosition& getPosition,
+ const GetExternalEField& getExternalE, const GetExternalBField& getExternalB,
+ amrex::ParticleReal * const Exp, amrex::ParticleReal * const Eyp,
+ amrex::ParticleReal * const Ezp, amrex::ParticleReal * const Bxp,
+ amrex::ParticleReal * const Byp, amrex::ParticleReal * const Bzp,
+ amrex::FArrayBox const * const exfab,
+ amrex::FArrayBox const * const eyfab,
+ amrex::FArrayBox const * const ezfab,
+ amrex::FArrayBox const * const bxfab,
+ amrex::FArrayBox const * const byfab,
+ amrex::FArrayBox const * const bzfab,
+ const long np_to_gather,
+ const std::array& dx,
+ const std::array& xyzmin,
+ const amrex::Dim3 lo,
+ const long n_rz_azimuthal_modes,
+ amrex::GpuArray const& dom_lo = {},
+ amrex::GpuArray const& dom_hi = {},
+ amrex::Real cur_time = 0._rt )
+{
+
+ amrex::GpuArray dx_arr = {dx[0], dx[1], dx[2]};
+ amrex::GpuArray xyzmin_arr = {xyzmin[0], xyzmin[1], xyzmin[2]};
+
+ amrex::Array4 const& ex_arr = exfab->array();
+ amrex::Array4 const& ey_arr = eyfab->array();
+ amrex::Array4 const& ez_arr = ezfab->array();
+ amrex::Array4 const& bx_arr = bxfab->array();
+ amrex::Array4 const& by_arr = byfab->array();
+ amrex::Array4 const& bz_arr = bzfab->array();
+
+ amrex::IndexType const ex_type = exfab->box().ixType();
+ amrex::IndexType const ey_type = eyfab->box().ixType();
+ amrex::IndexType const ez_type = ezfab->box().ixType();
+ amrex::IndexType const bx_type = bxfab->box().ixType();
+ amrex::IndexType const by_type = byfab->box().ixType();
+ amrex::IndexType const bz_type = bzfab->box().ixType();
+
+ // Loop over particles and gather fields from
+ // {e,b}{x,y,z}_arr to {E,B}{xyz}p.
+ amrex::ParallelFor(
+ np_to_gather,
+ [=] AMREX_GPU_DEVICE (long ip) {
+
+ amrex::ParticleReal xp, yp, zp;
+ getPosition(ip, xp, yp, zp);
+ getExternalE(ip, Exp[ip], Eyp[ip], Ezp[ip]);
+ getExternalB(ip, Bxp[ip], Byp[ip], Bzp[ip]);
+
+ doPulsarFieldGatherShapeN(
+ xp, yp, zp, Exp[ip], Eyp[ip], Ezp[ip], Bxp[ip], Byp[ip], Bzp[ip],
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time);
+ });
+}
+
+/**
+ * \brief Field gather for a single particle
+ *
+ * /param xp, yp, zp : Particle position coordinates
+ * \param Exp, Eyp, Ezp : Electric field on particles.
+ * \param Bxp, Byp, Bzp : Magnetic field on particles.
+ * \param ex_arr ey_arr ez_arr : Array4 of the electric field, either full array or tile.
+ * \param bx_arr by_arr bz_arr : Array4 of the magnetic field, either full array or tile.
+ * \param ex_type, ey_type, ez_type : IndexType of the electric field
+ * \param bx_type, by_type, bz_type : IndexType of the magnetic field
+ * \param dx : 3D cell spacing
+ * \param xyzmin : Physical lower bounds of domain in x, y, z.
+ * \param lo : Index lower bounds of domain.
+ * \param n_rz_azimuthal_modes : Number of azimuthal modes when using RZ geometry
+ * \param nox : order of the particle shape function
+ * \param galerkin_interpolation : whether to use lower order in v
+ */
+AMREX_GPU_HOST_DEVICE AMREX_FORCE_INLINE
+void doPulsarFieldGatherShapeN (const amrex::ParticleReal xp,
+ const amrex::ParticleReal yp,
+ const amrex::ParticleReal zp,
+ amrex::ParticleReal& Exp,
+ amrex::ParticleReal& Eyp,
+ amrex::ParticleReal& Ezp,
+ amrex::ParticleReal& Bxp,
+ amrex::ParticleReal& Byp,
+ amrex::ParticleReal& Bzp,
+ amrex::Array4 const& ex_arr,
+ amrex::Array4 const& ey_arr,
+ amrex::Array4 const& ez_arr,
+ amrex::Array4 const& bx_arr,
+ amrex::Array4 const& by_arr,
+ amrex::Array4 const& bz_arr,
+ const amrex::IndexType ex_type,
+ const amrex::IndexType ey_type,
+ const amrex::IndexType ez_type,
+ const amrex::IndexType bx_type,
+ const amrex::IndexType by_type,
+ const amrex::IndexType bz_type,
+ const amrex::GpuArray& dx_arr,
+ const amrex::GpuArray& xyzmin_arr,
+ const amrex::Dim3& lo,
+ const int n_rz_azimuthal_modes,
+ const int nox,
+ const bool galerkin_interpolation,
+ amrex::GpuArray const& dom_lo = {},
+ amrex::GpuArray const& dom_hi = {},
+ amrex::Real cur_time = 0._rt )
+{
+ if (galerkin_interpolation) {
+ if (nox == 1) {
+ doPulsarFieldGatherShapeN<1,1>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time
+ );
+ } else if (nox == 2) {
+ doPulsarFieldGatherShapeN<2,1>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time
+ );
+ } else if (nox == 3) {
+ doPulsarFieldGatherShapeN<3,1>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time
+ );
+ }
+ } else {
+ if (nox == 1) {
+ doPulsarFieldGatherShapeN<1,0>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time
+ );
+ } else if (nox == 2) {
+ doPulsarFieldGatherShapeN<2,0>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time);
+ } else if (nox == 3) {
+ doPulsarFieldGatherShapeN<3,0>(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ dom_lo, dom_hi, cur_time);
+ }
+ }
+}
+
+#endif // ifdef pulsar
+#endif // GATHEREXTERNALPULSARFIELDONGRID_H_
diff --git a/Source/Particles/Gather/GetExternalFields.H b/Source/Particles/Gather/GetExternalFields.H
index 91f868b8942..15993d5d579 100644
--- a/Source/Particles/Gather/GetExternalFields.H
+++ b/Source/Particles/Gather/GetExternalFields.H
@@ -3,18 +3,12 @@
#include "Particles/WarpXParticleContainer.H"
#include "Particles/Pusher/GetAndSetPosition.H"
-#ifdef PULSAR
- #include "Particles/PulsarParameters.H"
-#endif
#include
#include
enum ExternalFieldInitType { Constant, Parser
-#ifdef PULSAR
- , Pulsar_Efield, Pulsar_Bfield
-#endif
};
/** \brief Base class for functors that assign external
@@ -52,20 +46,6 @@ struct GetExternalField
field_y += m_yfield_partparser(x, y, z, m_time);
field_z += m_zfield_partparser(x, y, z, m_time);
}
-#ifdef PULSAR
- else if (m_type == Pulsar_Efield)
- {
- amrex::ParticleReal x, y, z;
- m_get_position(i, x, y, z);
- PulsarParm::PulsarEField(x, y, z, field_x, field_y, field_z, m_time);
- }
- else if (m_type == Pulsar_Bfield)
- {
- amrex::ParticleReal x, y, z;
- m_get_position(i, x, y, z);
- PulsarParm::PulsarBField(x, y, z, field_x, field_y, field_z, m_time);
- }
-#endif
else
{
amrex::Abort("ExternalFieldInitType not known!!! \n");
diff --git a/Source/Particles/Gather/GetExternalFields.cpp b/Source/Particles/Gather/GetExternalFields.cpp
index e8fbdd6a82b..c1fbe49daca 100644
--- a/Source/Particles/Gather/GetExternalFields.cpp
+++ b/Source/Particles/Gather/GetExternalFields.cpp
@@ -21,14 +21,6 @@ GetExternalEField::GetExternalEField (const WarpXParIter& a_pti, int a_offset) n
m_yfield_partparser = getParser(mypc.m_Ey_particle_parser);
m_zfield_partparser = getParser(mypc.m_Ez_particle_parser);
}
-#ifdef PULSAR
- if (PulsarParm::EB_external == 1)
- {
- m_type = Pulsar_Efield;
- m_time = warpx.gett_new(a_pti.GetLevel());
- m_get_position = GetParticlePosition(a_pti, a_offset);
- }
-#endif
}
GetExternalBField::GetExternalBField (const WarpXParIter& a_pti, int a_offset) noexcept
@@ -51,12 +43,4 @@ GetExternalBField::GetExternalBField (const WarpXParIter& a_pti, int a_offset) n
m_yfield_partparser = getParser(mypc.m_By_particle_parser);
m_zfield_partparser = getParser(mypc.m_Bz_particle_parser);
}
-#ifdef PULSAR
- if (PulsarParm::EB_external == 1)
- {
- m_type = Pulsar_Bfield;
- m_time = warpx.gett_new(a_pti.GetLevel());
- m_get_position = GetParticlePosition(a_pti, a_offset);
- }
-#endif
}
diff --git a/Source/Particles/MultiParticleContainer.H b/Source/Particles/MultiParticleContainer.H
index cf80106e587..f3395cb0576 100644
--- a/Source/Particles/MultiParticleContainer.H
+++ b/Source/Particles/MultiParticleContainer.H
@@ -273,8 +273,8 @@ public:
#ifdef PULSAR
std::string m_E_ext_particle_coord = "cartesian";
std::string m_B_ext_particle_coord = "cartesian";
- void PulsarParticleInjection();
void PulsarParticleRemoval();
+ void PulsarParticleInjection();
#endif
protected:
diff --git a/Source/Particles/PhysicalParticleContainer.H b/Source/Particles/PhysicalParticleContainer.H
index 036edf6d217..335fbd9f514 100644
--- a/Source/Particles/PhysicalParticleContainer.H
+++ b/Source/Particles/PhysicalParticleContainer.H
@@ -329,8 +329,8 @@ public:
#endif
#ifdef PULSAR
- virtual void PulsarParticleInjection () override;
virtual void PulsarParticleRemoval () override;
+ virtual void PulsarParticleInjection () override;
#endif
protected:
diff --git a/Source/Particles/PhysicalParticleContainer.cpp b/Source/Particles/PhysicalParticleContainer.cpp
index 106ef66e967..0acd3ef4168 100644
--- a/Source/Particles/PhysicalParticleContainer.cpp
+++ b/Source/Particles/PhysicalParticleContainer.cpp
@@ -24,6 +24,7 @@
#include "Utils/WarpXAlgorithmSelection.H"
#ifdef PULSAR
#include "Particles/PulsarParameters.H"
+ #include "Particles/Gather/GatherExternalPulsarFieldOnGrid.H"
#endif
#include
@@ -210,8 +211,10 @@ void PhysicalParticleContainer::InitData ()
// Init ionization module here instead of in the PhysicalParticleContainer
// constructor because dt is required
if (do_field_ionization) {InitIonizationModule();}
+#ifndef PULSAR
AddParticles(0); // Note - add on level 0
Redistribute(); // We then redistribute
+#endif
}
void PhysicalParticleContainer::MapParticletoBoostedFrame (
@@ -546,6 +549,12 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
#elif AMREX_SPACEDIM==2
scale_fac = dx[0]*dx[1]/num_ppc;
#endif
+#ifdef PULSAR
+ // modifying particle weight to achieve fractional injection
+ if (PulsarParm::ModifyParticleWtAtInjection == 1) {
+ scale_fac = scale_fac*PulsarParm::Ninj_fraction;
+ }
+#endif
defineAllParticleTiles();
@@ -580,7 +589,6 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
const MultiFab& Ex_mf = WarpX::GetInstance().getEfield(lev,0);
const MultiFab& Ey_mf = WarpX::GetInstance().getEfield(lev,1);
const MultiFab& Ez_mf = WarpX::GetInstance().getEfield(lev,2);
- //const MultiFab& rho_mf = WarpX::GetInstance().getRho(lev);
const MultiFab& rho_mf = WarpX::GetInstance().getrho_fp(lev);
const Real dt = WarpX::GetInstance().getdt(0);
#endif
@@ -590,6 +598,8 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
bool radially_weighted = plasma_injector->radially_weighted;
#endif
+ int Nmax_particles = 0;
+ int valid_particles_beforeAdd = TotalNumberOfParticles();
MFItInfo info;
if (do_tiling && Gpu::notInLaunchRegion()) {
@@ -600,7 +610,6 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
#pragma omp parallel if (not WarpX::serialize_ics)
#endif
-
for (MFIter mfi = MakeMFIter(lev, info); mfi.isValid(); ++mfi)
{
if (cost && WarpX::load_balance_costs_update_algo == LoadBalanceCostsUpdateAlgo::Timers)
@@ -652,7 +661,20 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
{AMREX_D_DECL(overlap_realbox.lo(0),
overlap_realbox.lo(1),
overlap_realbox.lo(2))};
-
+#ifdef PULSAR
+ const FArrayBox& Ex_fab = Ex_mf[mfi];
+ const FArrayBox& Ey_fab = Ey_mf[mfi];
+ const FArrayBox& Ez_fab = Ez_mf[mfi];
+ const FArrayBox& rho_fab = rho_mf[mfi];
+ const Dim3 Ex_lo = lbound(mfi.tilebox());
+ const Dim3 Ey_lo = lbound(mfi.tilebox());
+ const Dim3 Ez_lo = lbound(mfi.tilebox());
+ amrex::Array4 const& ex_arr = Ex_fab.array();
+ amrex::Array4 const& ey_arr = Ey_fab.array();
+ amrex::Array4 const& ez_arr = Ez_fab.array();
+ amrex::Array4 const& rho_arr = rho_fab.array();
+ const Real q_pm = this->charge;
+#endif
// count the number of particles that each cell in overlap_box could add
Gpu::DeviceVector counts(overlap_box.numPts(), 0);
Gpu::DeviceVector offset(overlap_box.numPts());
@@ -668,9 +690,40 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
lo.z = applyBallisticCorrection(lo, inj_mom, gamma_boost, beta_boost, t);
hi.z = applyBallisticCorrection(hi, inj_mom, gamma_boost, beta_boost, t);
-
- if (inj_pos->overlapsWith(lo, hi))
+#ifdef PULSAR
+ amrex::Real xc = PulsarParm::center_star[0];
+ amrex::Real yc = PulsarParm::center_star[1];
+ amrex::Real zc = PulsarParm::center_star[2];
+ // Find cell-center
+ amrex::Real x, y, z;
+ x = overlap_corner[0] + i*dx[0] + 0.5*dx[0];
+ y = overlap_corner[1] + j*dx[1] + 0.5*dx[1];
+ z = overlap_corner[2] + k*dx[2] + 0.5*dx[2];
+ // radius of the cell-center
+ amrex::Real rad = std::sqrt( (x-xc)*(x-xc) + (y-yc)*(y-yc) + (z-zc)*(z-zc));
+ // Adding buffer-factor to ensure all cells that intersect the ring
+ // inject particles
+ amrex::Real buffer_factor = 2.0;
+ // is cell-center inside the pulsar ring
+ if (inj_pos->insidePulsarBoundsCC( rad, PulsarParm::particle_inject_rmin,
+ PulsarParm::particle_inject_rmax,
+ PulsarParm::dR_star*buffer_factor) );
{
+ auto index = overlap_box.index(iv);
+ const amrex::XDim3 ppc_per_dim = inj_pos->getppcInEachDim();
+ if (PulsarParm::ModifyParticleWtAtInjection == 1) {
+ // instead of modiying number of particles, the weight is changed
+ pcounts[index] = num_ppc;
+ } else if (PulsarParm::ModifyParticleWtAtInjection == 0) {
+ // Modiying number of particles injected
+ // (could lead to round-off errors)
+ pcounts[index] = int(ppc_per_dim.x*std::cbrt(PulsarParm::Ninj_fraction))
+ * int(ppc_per_dim.y*std::cbrt(PulsarParm::Ninj_fraction))
+ * int(ppc_per_dim.z*std::cbrt(PulsarParm::Ninj_fraction));
+ }
+ }
+#else
+ if (inj_pos->overlapsWith(lo, hi)) {
auto index = overlap_box.index(iv);
if (lrefine_injection) {
Box fine_overlap_box = overlap_box & amrex::shift(lfine_box, shifted);
@@ -685,13 +738,14 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
}
#if (AMREX_SPACEDIM != 3)
amrex::ignore_unused(k);
-#endif
+#endif // end if amrex space dim
+#endif // End pulsar ifdef
});
// Max number of new particles. All of them are created,
// and invalid ones are then discarded
int max_new_particles = Scan::ExclusiveSum(counts.size(), counts.data(), offset.data());
-
+ Nmax_particles += max_new_particles;
// Update NextID to include particles created in this function
Long pid;
#ifdef AMREX_USE_OMP
@@ -759,31 +813,6 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
bool loc_do_field_ionization = do_field_ionization;
int loc_ionization_initial_level = ionization_initial_level;
-#ifdef PULSAR
- // Fab
- const int Ex_nghost = Ex_mf.nGrow();
- const int Ey_nghost = Ey_mf.nGrow();
- const int Ez_nghost = Ez_mf.nGrow();
- const FArrayBox& Ex_fab = Ex_mf[mfi];
- const FArrayBox& Ey_fab = Ey_mf[mfi];
- const FArrayBox& Ez_fab = Ez_mf[mfi];
- const FArrayBox& rho_fab = rho_mf[mfi];
- const Dim3 Ex_lo = lbound(mfi.tilebox());
- const Dim3 Ey_lo = lbound(mfi.tilebox());
- const Dim3 Ez_lo = lbound(mfi.tilebox());
- amrex::Array4 const& ex_arr = Ex_fab.array();
- amrex::Array4 const& ey_arr = Ey_fab.array();
- amrex::Array4 const& ez_arr = Ez_fab.array();
- amrex::Array4 const& rho_arr = rho_fab.array();
- const Real q_pm = this->charge;
- //amrex::Print() << " box " << x_box << "\n";
- //FArrayBox Ey_fab = Ey_mf[mfi];
- //Box Ey_box = Ey_fab.validbox();
- //FArrayBox Ez_fab = Ez_mf[mfi];
- //Box Ez_box = Ez_fab.validbox();
- //amrex::Print() << " Ey box " << Ey_box << "\n";
- //amrex::Print() << " Ez box " << Ez_box << "\n";
-#endif
// Loop over all new particles and inject them (creates too many
// particles, in particular does not consider xmin, xmax etc.).
@@ -847,108 +876,26 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
// the next generated particle.
// include ballistic correction for plasma species with bulk motion
- const Real z0 = applyBallisticCorrection(pos, inj_mom, gamma_boost,
+ const amrex::Real z0 = applyBallisticCorrection(pos, inj_mom, gamma_boost,
beta_boost, t);
if (!inj_pos->insideBounds(xb, yb, z0)) {
p.id() = -1;
continue;
}
#ifdef PULSAR
+ //amrex::Print() << " old xyz : " << xb << " " << yb << " " << z0 << "\n";
amrex::Real xc = PulsarParm::center_star[0];
amrex::Real yc = PulsarParm::center_star[1];
amrex::Real zc = PulsarParm::center_star[2];
- amrex::Real rad = std::sqrt( (xb-xc)*(xb-xc)
- + (yb-yc)*(yb-yc) + (zb-zc)*(zb-zc) );
+ amrex::Real rad = std::sqrt( (xb-xc)*(xb-xc) + (yb-yc)*(yb-yc) + (z0-zc)*(z0-zc));
if (!inj_pos->insidePulsarBounds(rad,PulsarParm::R_star,PulsarParm::dR_star)) {
- p.id() = -1;
+ p.id() = -1;
continue;
}
- // get cell center
- amrex::Real cc_x = overlap_corner[0] + iv[0]*dx[0] + 0.5*dx[0] ;
- amrex::Real cc_y = overlap_corner[1] + iv[1]*dx[1] + 0.5*dx[1] ;
- amrex::Real cc_z = overlap_corner[2] + iv[2]*dx[2] + 0.5*dx[2] ;
- // get spherical r, theta, phi
- amrex::Real cc_rad = std::sqrt( (cc_x-xc)*(cc_x-xc)
- + (cc_y-yc)*(cc_y-yc)
- + (cc_z-zc)*(cc_z-zc));
- amrex::Real cc_theta = 0;
- if (cc_rad > 0 ) {
- cc_theta = std::acos((cc_z-xc)/cc_rad);
- }
- amrex::Real cc_phi = std::atan2((cc_y-yc),(cc_x-xc));
- const amrex::Real c_theta = std::cos(cc_theta);
- const amrex::Real s_theta = std::sin(cc_theta);
- amrex::Real c_phi = 0.;
- amrex::Real s_phi = 0.;
- if (r_cl > 0) {
- c_phi = (cc_x-xc)/r_cl;
- s_phi = (cc_y-yc)/r_cl;
- }
- amrex::Real omega = PulsarParm::Omega(t);
- amrex::Real ratio = PulsarParm::R_star/cc_rad;
- amrex::Real r3 = ratio*ratio*ratio;
- amrex::Real Er_cor = PulsarParm::B_star
- *omega
- *cc_rad*s_theta*s_theta;
- //// Er_external is known
- //Real Er_ext = omega*PulsarParm::B_star*cc_rad*(1.0-3.0*c_theta*c_theta);
- //Er_ext += (2.0/3.0)*omega*PulsarParm::B_star*cc_rad;
- //// rho_GJ is known
- amrex::Real rho_GJ = 2*PhysConst::ep0*PulsarParm::B_star*omega*
- (1.0-3.0*c_theta*c_theta)*PulsarParm::rhoGJ_scale;
- int ii = Ex_lo.x + iv[0];
- int jj = Ex_lo.y + iv[1];
- int kk = Ex_lo.z + iv[2];
- Real ex_avg = 0.25*(ex_arr(ii,jj,kk) + ex_arr(ii,jj+1,kk)+ex_arr(ii,jj,kk+1) + ex_arr(ii,jj+1,kk+1));
- Real ey_avg = 0.25*(ey_arr(ii,jj,kk) + ey_arr(ii+1,jj,kk)+ey_arr(ii,jj,kk+1) + ey_arr(ii+1,jj,kk+1));
- Real ez_avg = 0.25*(ez_arr(ii,jj,kk) + ez_arr(ii,jj+1,kk)+ez_arr(ii+1,jj,kk) + ez_arr(ii+1,jj+1,kk));
- Real Er_cell = ex_avg*s_theta*c_phi + ey_avg*s_theta*s_phi + ez_avg*c_theta;
- // analytical surface charge density
- //Real sigma_inj = (( Er_ext - Er_cor));
- Real sigma_inj = (( Er_cell - Er_cor));
- Real max_dens = PulsarParm::max_ndens;
- amrex::Real fraction = PulsarParm::Ninj_fraction;
- // number of particle pairs injected
- Real N_inj = fraction*amrex::Math::abs(sigma_inj) *PhysConst::ep0* dx[0]*dx[0]/(PhysConst::q_e*max_dens*scale_fac);
- if (t > 0) {
- if (N_inj >= 1) {
- if (N_inj < num_ppc) {
- int part_freq = floor(num_ppc / N_inj);
- if (i_part%part_freq!=0) {
- p.id() = -1;
- continue;
- }
- }
- }
- else
- {
- p.id() = -1;
- continue;
- }
- //if (sigma_inj < 0 and q_pm >0) {p.id()=-1; continue;}
- //if (sigma_inj > 0 and q_pm <0) {p.id()=-1; continue;}
- if (sigma_inj < 0 and q_pm >0) {p.id()=-1; continue;}
- if (sigma_inj > 0 and q_pm <0) {p.id()=-1; continue;}
- // if rho is too smal -- we dont inject particles
- if (std::abs(rho_GJ) < 1.0E-20) {
- p.id() = -1;
- continue;
- }
- else {
- Real rel_rho_err = ((rho_arr(ii,jj,kk) - rho_GJ)/rho_GJ);
- // If current rho is much higher than rho_GJ, particles are not introduced.
- if ( amrex::Math::abs(rel_rho_err) > 0.05) {
- p.id() = -1;
- continue;
- }
- }
- }
- }
#endif
u = inj_mom->getMomentum(pos.x, pos.y, z0, engine);
dens = inj_rho->getDensity(pos.x, pos.y, z0);
-
// Remove particle if density below threshold
if ( dens < density_min ){
p.id() = -1;
@@ -1037,26 +984,6 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
}
});
-#ifdef PULSAR
- if (PulsarParm::EB_external == 1) {
- const auto GetPosition = GetParticlePosition(pti);
- Real* const AMREX_RESTRICT Exp_data = Exp.dataPtr();
- Real* const AMREX_RESTRICT Eyp_data = Eyp.dataPtr();
- Real* const AMREX_RESTRICT Ezp_data = Ezp.dataPtr();
- Real* const AMREX_RESTRICT Bxp_data = Bxp.dataPtr();
- Real* const AMREX_RESTRICT Byp_data = Byp.dataPtr();
- Real* const AMREX_RESTRICT Bzp_data = Bzp.dataPtr();
- Real time = warpx.gett_new(lev);
- amrex::ParallelFor(pti.numParticles(),
- [=] AMREX_GPU_DEVICE (long i) {
- ParticleReal x, y, z;
- GetPosition(i, x, y, z);
- PulsarParm::PulsarEBField(x, y, z,
- Exp_data[i],Eyp_data[i],Ezp_data[i],
- Bxp_data[i],Byp_data[i],Bzp_data[i],time);
- });
- }
-#endif
amrex::Gpu::synchronize();
if (cost && WarpX::load_balance_costs_update_algo == LoadBalanceCostsUpdateAlgo::Timers)
@@ -1065,7 +992,7 @@ PhysicalParticleContainer::AddPlasma (int lev, RealBox part_realbox)
amrex::HostDevice::Atomic::Add( &(*cost)[mfi.index()], wt);
}
}
-
+ amrex::Print() << " newly added particles : " << TotalNumberOfParticles()-valid_particles_beforeAdd << " total max particles " << Nmax_particles<< "\n";
// The function that calls this is responsible for redistributing particles.
}
@@ -1562,7 +1489,13 @@ PhysicalParticleContainer::PushP (int lev, Real dt,
const std::array& dx = WarpX::CellSize(std::max(lev,0));
-#ifdef AMREX_USE_OMP
+#ifdef PULSAR
+ const auto problo = WarpX::GetInstance().Geom(lev).ProbLoArray();
+ const auto probhi = WarpX::GetInstance().Geom(lev).ProbHiArray();
+ amrex::Real cur_time = WarpX::GetInstance().gett_new(lev);
+#endif
+
+#ifdef _OPENMP
#pragma omp parallel
#endif
{
@@ -1644,6 +1577,13 @@ PhysicalParticleContainer::PushP (int lev, Real dt,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
nox, galerkin_interpolation);
+#ifdef PULSAR
+ doPulsarFieldGatherShapeN(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ nox, galerkin_interpolation, problo, probhi, cur_time);
+#endif
}
// Externally applied E-field in Cartesian co-ordinates
getExternalE(ip, Exp, Eyp, Ezp);
@@ -1951,6 +1891,10 @@ PhysicalParticleContainer::PushPX (WarpXParIter& pti,
const std::array& xyzmin = WarpX::LowerCorner(box, galilean_shift, gather_lev);
const Dim3 lo = lbound(box);
+#ifdef PULSAR
+ const auto problo = WarpX::GetInstance().Geom(lev).ProbLoArray();
+ const auto probhi = WarpX::GetInstance().Geom(lev).ProbHiArray();
+#endif
bool galerkin_interpolation = WarpX::galerkin_interpolation;
int nox = WarpX::nox;
@@ -2025,6 +1969,13 @@ PhysicalParticleContainer::PushPX (WarpXParIter& pti,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
nox, galerkin_interpolation);
+#ifdef PULSAR
+ doPulsarFieldGatherShapeN(xp, yp, zp, Exp, Eyp, Ezp, Bxp, Byp, Bzp,
+ ex_arr, ey_arr, ez_arr, bx_arr, by_arr, bz_arr,
+ ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
+ dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
+ nox, galerkin_interpolation, problo, probhi, cur_time);
+#endif
}
// Externally applied E-field in Cartesian co-ordinates
getExternalE(ip, Exp, Eyp, Ezp);
@@ -2249,8 +2200,7 @@ void PhysicalParticleContainer::PulsarParticleRemoval() {
Real r = std::sqrt((x-xc)*(x-xc)
+ (y-yc)*(y-yc)
+ (z-zc)*(z-zc));
- //if (r<=(PulsarParm::R_star-PulsarParm::dR_star)) {
- if (r<=(PulsarParm::R_star)) {
+ if (r <= (PulsarParm::max_particle_absorption_radius)) {
pp[i].id() = -1;
}
});
diff --git a/Source/Particles/PulsarParameters.H b/Source/Particles/PulsarParameters.H
index 185411e41e6..fc7999308be 100644
--- a/Source/Particles/PulsarParameters.H
+++ b/Source/Particles/PulsarParameters.H
@@ -6,6 +6,9 @@
#include
#include
#include
+#include
+
+using namespace amrex;
namespace PulsarParm
{
@@ -23,10 +26,19 @@ namespace PulsarParm
amrex::GpuArray center_star;
extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_ndens;
extern AMREX_GPU_DEVICE_MANAGED amrex::Real Ninj_fraction;
+ extern AMREX_GPU_DEVICE_MANAGED int ModifyParticleWtAtInjection;
extern AMREX_GPU_DEVICE_MANAGED amrex::Real rhoGJ_scale;
extern AMREX_GPU_DEVICE_MANAGED int damp_EB_internal;
-
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_EBcorotating_radius;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_EBdamping_radius;
+ extern AMREX_GPU_DEVICE_MANAGED int turnoffdeposition;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_nodepos_radius;
+ extern AMREX_GPU_DEVICE_MANAGED int turnoff_plasmaEB_gather;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_nogather_radius;
extern AMREX_GPU_DEVICE_MANAGED int verbose;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real max_particle_absorption_radius;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real particle_inject_rmin;
+ extern AMREX_GPU_DEVICE_MANAGED amrex::Real particle_inject_rmax;
void ReadParameters();
@@ -42,184 +54,61 @@ namespace PulsarParm
return omega;
}
+
AMREX_GPU_HOST_DEVICE AMREX_INLINE
- void PulsarEField(const amrex::ParticleReal xp, const amrex::ParticleReal yp,
- const amrex::ParticleReal zp,
- amrex::ParticleReal &Exp, amrex::ParticleReal &Eyp,
- amrex::ParticleReal &Ezp,
- amrex::Real time)
+ void ExternalEFieldSpherical (amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real const time,
+ amrex::Real &Er, amrex::Real &Etheta, amrex::Real &Ephi)
{
- // spherical r, theta, phi
- const amrex::Real xc = center_star[0];
- const amrex::Real yc = center_star[1];
- const amrex::Real zc = center_star[2];
- const amrex::Real r = std::sqrt( (xp-xc)*(xp-xc) + (yp-yc)*(yp-yc) + (zp-zc)*(zp-zc) );
- const amrex::Real phi = std::atan2((yp-yc),(xp-xc));
- amrex::Real theta = 0.0;
- if (r > 0) {
- theta = std::acos((zp-zc)/r);
- }
- const amrex::Real c_theta = std::cos(theta);
- const amrex::Real s_theta = std::sin(theta);
- const amrex::Real c_phi = std::cos(phi);
- const amrex::Real s_phi = std::sin(phi);
- amrex::Real omega = omega_star;
- // ramping up omega
- if (time < ramp_omega_time) {
- omega = omega_star*time/ramp_omega_time;
- }
-
- if (r= R_star ) {
- amrex::Real r_ratio = R_star/r;
- amrex::Real r3 = r_ratio*r_ratio*r_ratio;
+ // outside pulsar
+ if ( r > max_EBcorotating_radius ) {
+ amrex::Real r4 = r_ratio*r_ratio*r_ratio*r_ratio;
// Taking derivative of phi given in eq 30 of Michel and Li
- amrex::Real Er = B_star*omega*R_star*r_ratio*r3*(1.0-3.0*c_theta*c_theta);
+ Er = B_star * omega * R_star * r4 * (1.0-3.0*c_theta*c_theta);
if (E_external_monopole == 1) {
- Er += (2.0/3.0)*omega*B_star*R_star*r_ratio*r_ratio;
+ Er += (2.0/3.0) * omega * B_star * R_star * r_ratio * r_ratio;
}
- amrex::Real Etheta = (-1.0)*B_star*omega*R_star*r_ratio*r3*(2.0*s_theta*c_theta);
-
- Exp = Er*s_theta*c_phi + Etheta*c_theta*c_phi;
- Eyp = Er*s_theta*s_phi + Etheta*c_theta*s_phi;
- Ezp = Er*c_theta - Etheta*s_theta;
- }
+ Etheta = (-1.0) * B_star * omega * R_star * r4 * (2.0*s_theta*c_theta);
+ Ephi = 0.0;
+ }
}
AMREX_GPU_HOST_DEVICE AMREX_INLINE
- void PulsarBField(const amrex::ParticleReal xp, const amrex::ParticleReal yp,
- const amrex::ParticleReal zp,
- amrex::ParticleReal &Bxp, amrex::ParticleReal &Byp,
- amrex::ParticleReal &Bzp,
- amrex::Real time)
+ void ExternalBFieldSpherical (amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real const time,
+ amrex::Real &Br, amrex::Real &Btheta, amrex::Real &Bphi)
{
- // spherical r, theta, phi
- const amrex::Real xc = center_star[0];
- const amrex::Real yc = center_star[1];
- const amrex::Real zc = center_star[2];
- const amrex::Real r = std::sqrt( (xp-xc)*(xp-xc) + (yp-yc)*(yp-yc) + (zp-zc)*(zp-zc) );
- const amrex::Real phi = std::atan2((yp-yc),(xp-xc));
- amrex::Real theta = 0.0;
- if (r > 0) {
- theta = std::acos((zp-zc)/r);
- }
- const amrex::Real c_theta = std::cos(theta);
- const amrex::Real s_theta = std::sin(theta);
- const amrex::Real c_phi = std::cos(phi);
- const amrex::Real s_phi = std::sin(phi);
amrex::Real omega = Omega(time);
-
- if (r= R_star ) {
- amrex::Real r_ratio = R_star/r;
- amrex::Real r3 = r_ratio*r_ratio*r_ratio;
- // Taking derivative of phi given in eq 30 of Michel and Li
- amrex::Real Br = 2.0*B_star*r3*c_theta;
- amrex::Real Btheta = B_star*r3*s_theta;
-
- Bxp = Br*s_theta*c_phi + Btheta*c_theta*c_phi;
- Byp = Br*s_theta*s_phi + Btheta*c_theta*s_phi;
- Bzp = Br*c_theta - Btheta*s_theta;
- }
+ amrex::Real c_theta = std::cos(theta);
+ amrex::Real s_theta = std::sin(theta);
+ amrex::Real r_ratio = R_star/r;
+ amrex::Real r3 = r_ratio*r_ratio*r_ratio;
+ // Michel and Li -- eq 14 and 15 from michel and li
+ // dipole B field inside and outside the pulsar
+ Br = 2.0*B_star*r3*c_theta;
+ Btheta = B_star*r3*s_theta;
+ Bphi = 0.0;
}
-// AMREX_GPU_HOST_DEVICE AMREX_INLINE
-// void PulsarEBField(amrex::Real xp, amrex::Real yp, amrex::Real zp,
-// amrex::Real &Exp, amrex::Real &Eyp, amrex::Real &Ezp,
-// amrex::Real &Bxp, amrex::Real &Byp, amrex::Real &Bzp,
-// amrex::Real time)
-// {
-// // spherical r, theta, phi
-// const amrex::Real xc = center_star[0];
-// const amrex::Real yc = center_star[1];
-// const amrex::Real zc = center_star[2];
-// const amrex::Real r = std::sqrt( (xp-xc)*(xp-xc) + (yp-yc)*(yp-yc) + (zp-zc)*(zp-zc) );
-// const amrex::Real phi = std::atan2((yp-yc),(xp-xc));
-// amrex::Real theta = 0.0;
-// if (r > 0) {
-// theta = std::acos((zp-zc)/r);
-// }
-// const amrex::Real c_theta = std::cos(theta);
-// const amrex::Real s_theta = std::sin(theta);
-// const amrex::Real c_phi = std::cos(phi);
-// const amrex::Real s_phi = std::sin(phi);
-// amrex::Real omega = omega_star;
-// // ramping up omega
-// if (time < 2.0e-4) {
-// omega = omega_star*time/2.0e-4;
-// }
-//
-// if (r= R_star ) {
-// amrex::Real r_ratio = R_star/r;
-// amrex::Real r3 = r_ratio*r_ratio*r_ratio;
-// // Taking derivative of phi given in eq 30 of Michel and Li
-// amrex::Real Er = B_star*omega*R_star*r_ratio*r3*(1.0-3.0*c_theta*c_theta);
-// if (E_external_monopole == 1) {
-// Er += (2.0/3.0)*omega*B_star*R_star*r_ratio*r_ratio;
-// }
-// amrex::Real Etheta = (-1.0)*B_star*omega*R_star*r_ratio*r3*(2.0*s_theta*c_theta);
-//
-// Exp = Er*s_theta*c_phi + Etheta*c_theta*c_phi;
-// Eyp = Er*s_theta*s_phi + Etheta*c_theta*s_phi;
-// Ezp = Er*c_theta - Etheta*s_theta;
-//
-// amrex::Real Br = 2.0*B_star*r3*c_theta;
-// amrex::Real Btheta = B_star*r3*s_theta;
-//
-// Bxp = Br*s_theta*c_phi + Btheta*c_theta*c_phi;
-// Byp = Br*s_theta*s_phi + Btheta*c_theta*s_phi;
-// Bzp = Br*c_theta - Btheta*s_theta;
-// }
-// }
-
namespace Spherical
{
AMREX_GPU_HOST_DEVICE AMREX_INLINE
- amrex::Real r(int i, int j, int k, amrex::GeometryData const& geom, int const* const AMREX_RESTRICT mf_ixType)
+ amrex::Real r(int i, int j, int k, amrex::GeometryData const& geom, amrex::GpuArray const mf_ixType)
{
const auto domain_box = geom.Domain();
const auto domain_ilo = amrex::lbound(domain_box);
@@ -227,16 +116,13 @@ namespace PulsarParm
const auto domain_xhi = geom.ProbHi();
const auto domain_dx = geom.CellSize();
- //const amrex::Real x = domain_xlo[0] + (i - domain_ilo.x + 0.5) * domain_dx[0] + (1.0 - mf_ixType[0])*domain_dx[0]*0.5;
- //const amrex::Real y = domain_xlo[1] + (j - domain_ilo.y + 0.5) * domain_dx[1] + (1.0 - mf_ixType[1])*domain_dx[1]*0.5;
- //const amrex::Real z = domain_xlo[2] + (k - domain_ilo.z + 0.5) * domain_dx[2] + (1.0 - mf_ixType[2])*domain_dx[2]*0.5;
const amrex::Real x = domain_xlo[0] + (i ) * domain_dx[0] + (1.0 - mf_ixType[0])*domain_dx[0]*0.5;
const amrex::Real y = domain_xlo[1] + (j ) * domain_dx[1] + (1.0 - mf_ixType[1])*domain_dx[1]*0.5;
const amrex::Real z = domain_xlo[2] + (k ) * domain_dx[2] + (1.0 - mf_ixType[2])*domain_dx[2]*0.5;
- const amrex::Real xc = 0.5 * (domain_xlo[0] + domain_xhi[0]);
- const amrex::Real yc = 0.5 * (domain_xlo[1] + domain_xhi[1]);
- const amrex::Real zc = 0.5 * (domain_xlo[2] + domain_xhi[2]);
+ const amrex::Real xc = PulsarParm::center_star[0];
+ const amrex::Real yc = PulsarParm::center_star[1];
+ const amrex::Real zc = PulsarParm::center_star[2];
const amrex::Real r = std::sqrt((x-xc)*(x-xc) + (y-yc)*(y-yc) + (z-zc)*(z-zc));
@@ -244,18 +130,77 @@ namespace PulsarParm
}
}
+ /** Compute Cartesian components corresponding to i, j, k based on the staggering,
+ ixType, and the domain_lo and cell size, dx.
+ */
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ComputeCellCoordinates ( int i, int j, int k,
+ amrex::GpuArray const mf_type,
+ amrex::GpuArray const domain_lo,
+ amrex::GpuArray const dx,
+ amrex::Real &x, amrex::Real &y, amrex::Real &z)
+ {
+ x = domain_lo[0] + i*dx[0] + (1.0_rt - mf_type[0]) * dx[0]*0.5;
+ y = domain_lo[1] + j*dx[1] + (1.0_rt - mf_type[1]) * dx[1]*0.5;
+ z = domain_lo[2] + k*dx[2] + (1.0_rt - mf_type[2]) * dx[2]*0.5;
+ }
AMREX_GPU_HOST_DEVICE AMREX_INLINE
- void DampEField(int i, int j, int k, amrex::GeometryData const& geom, amrex::Array4 const& Efield, int const* const AMREX_RESTRICT mf_ixtype)
+ void ConvertCartesianToSphericalCoord ( amrex::Real const x, amrex::Real const y,
+ amrex::Real const z,
+ amrex::GpuArray const domain_lo,
+ amrex::GpuArray const domain_hi,
+ amrex::Real &r, amrex::Real &theta, amrex::Real &phi)
{
- const amrex::Real r = Spherical::r(i, j, k, geom, mf_ixtype);
+ amrex::Real xc = PulsarParm::center_star[0];
+ amrex::Real yc = PulsarParm::center_star[1];
+ amrex::Real zc = PulsarParm::center_star[2];
+
+ r = std::sqrt( (x-xc)*(x-xc) + (y-yc)*(y-yc) + (z-zc)*(z-zc) );
+ theta = 0.0;
+ if (r > 0) theta = std::acos( (z-zc) / r );
+ phi = std::atan2( (y-yc), (x-xc) );
+ }
- if (r < R_star) {
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertSphericalToCartesianXComponent (amrex::Real const F_r,
+ amrex::Real const F_theta, amrex::Real const F_phi,
+ amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real & F_x)
+ {
+ F_x = F_r * std::sin(theta) * std::cos(phi)
+ + F_theta * std::cos(theta) * std::cos(phi);
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertSphericalToCartesianYComponent (amrex::Real const F_r,
+ amrex::Real const F_theta, amrex::Real const F_phi,
+ amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real & F_y)
+ {
+ F_y = F_r * std::sin(theta) * std::sin(phi)
+ + F_theta * std::cos(theta) * std::sin(phi);
+ }
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void ConvertSphericalToCartesianZComponent (amrex::Real const F_r,
+ amrex::Real const F_theta, amrex::Real const F_phi,
+ amrex::Real const r, amrex::Real const theta,
+ amrex::Real const phi, amrex::Real & F_z)
+ {
+ F_z = F_r * std::cos(theta) - F_theta * std::sin(theta);
+ }
+
+
+ AMREX_GPU_HOST_DEVICE AMREX_INLINE
+ void DampField(int i, int j, int k, amrex::GeometryData const& geom, amrex::Array4 const& Efield, amrex::GpuArray const mf_ixtype)
+ {
+ amrex::Real r = Spherical::r(i, j, k, geom, mf_ixtype);
+ if (r <= max_EBdamping_radius) {
// Damping function: Fd = tanh(damping_scale * (r / R_star - 1)) + 1
// for damping_scale >= 10 or so:
// Fd(0) ~ 0
// Fd(R_star) ~ 1
-
const amrex::Real Fd = std::tanh(damping_scale * (r / R_star - 1.0)) + 1.0;
Efield(i, j, k) = Efield(i, j, k) * Fd;
}
diff --git a/Source/Particles/PulsarParameters.cpp b/Source/Particles/PulsarParameters.cpp
index 85d08be6662..06f2abd9ec0 100644
--- a/Source/Particles/PulsarParameters.cpp
+++ b/Source/Particles/PulsarParameters.cpp
@@ -23,7 +23,17 @@ namespace PulsarParm
AMREX_GPU_DEVICE_MANAGED int verbose = 0;
AMREX_GPU_DEVICE_MANAGED amrex::Real max_ndens;
AMREX_GPU_DEVICE_MANAGED amrex::Real Ninj_fraction;
+ AMREX_GPU_DEVICE_MANAGED int ModifyParticleWtAtInjection = 1;
AMREX_GPU_DEVICE_MANAGED amrex::Real rhoGJ_scale;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real max_EBcorotating_radius;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real max_EBdamping_radius;
+ AMREX_GPU_DEVICE_MANAGED int turnoffdeposition = 0;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real max_nodepos_radius;
+ AMREX_GPU_DEVICE_MANAGED int turnoff_plasmaEB_gather = 0;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real max_nogather_radius;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real max_particle_absorption_radius;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real particle_inject_rmin;
+ AMREX_GPU_DEVICE_MANAGED amrex::Real particle_inject_rmax;
void ReadParameters() {
amrex::ParmParse pp("pulsar");
@@ -46,11 +56,53 @@ namespace PulsarParm
amrex::Print() << " Pulsar B_star : " << B_star << "\n";
pp.get("max_ndens", max_ndens);
pp.get("Ninj_fraction",Ninj_fraction);
+
+ particle_inject_rmin = R_star - dR_star;
+ particle_inject_rmax = R_star;
+ pp.query("particle_inj_rmin", particle_inject_rmin);
+ pp.query("particle_inj_rmax", particle_inject_rmax);
+ amrex::Print() << " min radius of particle injection : " << particle_inject_rmin << "\n";
+ amrex::Print() << " max radius of particle injection : " << particle_inject_rmax << "\n";
+
+ pp.query("ModifyParticleWeight", ModifyParticleWtAtInjection);
+ // The maximum radius within which particles are absorbed/deleted every timestep.
+ max_particle_absorption_radius = R_star;
+ pp.query("max_particle_absorption_radius", max_particle_absorption_radius);
pp.get("rhoGJ_scale",rhoGJ_scale);
amrex::Print() << " pulsar max ndens " << max_ndens << "\n";
amrex::Print() << " pulsar ninj fraction " << Ninj_fraction << "\n";
+ amrex::Print() << " pulsar modify particle wt " << ModifyParticleWtAtInjection << "\n";
amrex::Print() << " pulsar rhoGJ scaling " << rhoGJ_scale << "\n";
amrex::Print() << " EB_external : " << EB_external << "\n";
+ if (EB_external == 1) {
+ // Max corotating radius defines the region where the EB field shifts from
+ // corotating (v X B) to quadrapole. default is R_star.
+ max_EBcorotating_radius = R_star;
+ pp.query("EB_corotating_maxradius", max_EBcorotating_radius);
+ amrex::Print() << " EB coratating maxradius : " << max_EBcorotating_radius << "\n";
+ }
+ if (damp_EB_internal == 1) {
+ // Radius of the region within which the EB fields are damped
+ max_EBdamping_radius = R_star;
+ pp.query("damp_EB_radius", max_EBdamping_radius);
+ amrex::Print() << " max EB damping radius : " << max_EBdamping_radius << "\n";
+ }
+ // query to see if particle j,rho deposition should be turned off
+ // within some region interior to the star
+ // default is 0
+ pp.query("turnoffdeposition", turnoffdeposition);
+ amrex::Print() << " is deposition off ? " << turnoffdeposition << "\n";
+ if (turnoffdeposition == 1) {
+ max_nodepos_radius = R_star;
+ pp.query("max_nodepos_radius", max_nodepos_radius);
+ amrex::Print() << " deposition turned off within radius : " << max_nodepos_radius << "\n";
+ }
+ pp.query("turnoff_plamsaEB_gather", turnoff_plasmaEB_gather);
+ amrex::Print() << " is plasma EB gather off ? " << turnoff_plasmaEB_gather << "\n";
+ if (turnoff_plasmaEB_gather == 1) {
+ max_nogather_radius = R_star;
+ pp.query("max_nogather_radius", max_nogather_radius);
+ amrex::Print() << " gather off within radius : " << max_nogather_radius << "\n";
+ }
}
-
}
diff --git a/Source/Particles/RigidInjectedParticleContainer.cpp b/Source/Particles/RigidInjectedParticleContainer.cpp
index d18631ff153..db15b9e7de0 100644
--- a/Source/Particles/RigidInjectedParticleContainer.cpp
+++ b/Source/Particles/RigidInjectedParticleContainer.cpp
@@ -482,6 +482,7 @@ RigidInjectedParticleContainer::PushP (int lev, Real dt,
ex_type, ey_type, ez_type, bx_type, by_type, bz_type,
dx_arr, xyzmin_arr, lo, n_rz_azimuthal_modes,
nox, galerkin_interpolation);
+
getExternalE(ip, Exp, Eyp, Ezp);
getExternalB(ip, Bxp, Byp, Bzp);
diff --git a/Source/Particles/WarpXParticleContainer.H b/Source/Particles/WarpXParticleContainer.H
index c0e76044257..4e77ece573d 100644
--- a/Source/Particles/WarpXParticleContainer.H
+++ b/Source/Particles/WarpXParticleContainer.H
@@ -330,6 +330,7 @@ public:
bool AmIA () const noexcept {return (physical_species == PhysSpec);}
amrex::Array get_v_galilean () {return m_v_galilean;}
+
#ifdef PULSAR
virtual void PulsarParticleInjection () {};
virtual void PulsarParticleRemoval () {};
diff --git a/Source/WarpX.cpp b/Source/WarpX.cpp
index 57bfd4423cd..632b5891794 100644
--- a/Source/WarpX.cpp
+++ b/Source/WarpX.cpp
@@ -1115,7 +1115,11 @@ WarpX::AllocLevelMFs (int lev, const BoxArray& ba, const DistributionMapping& dm
Efield_avg_fp[lev][1] = std::make_unique(amrex::convert(ba,Ey_nodal_flag),dm,ncomps,ngE);
Efield_avg_fp[lev][2] = std::make_unique(amrex::convert(ba,Ez_nodal_flag),dm,ncomps,ngE);
+#ifdef PULSAR
+ bool deposit_charge = do_dive_cleaning || plot_rho;
+#else
bool deposit_charge = do_dive_cleaning || (plot_rho && do_back_transformed_diagnostics);
+#endif // pulsar endif
if (WarpX::maxwell_solver_id == MaxwellSolverAlgo::PSATD) {
deposit_charge = do_dive_cleaning || (plot_rho && do_back_transformed_diagnostics)
|| update_with_rho || current_correction;