{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 1 - Introduction to Acoustic Problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.1 - Introduction\n", "\n", "This notebook brings the description of the acoustic wave equation, which we also refer as the *acoustic problem*, \n", "including the discretization by finite differences and the setup for the tests we employ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.2 - The acoustic problem\n", "\n", "Let $D=\\Omega \\times I\\subset\\mathbb{R}^{3}$ be the domain of the problem to be studied, where $\\Omega=\\left[x_{I},x_{F}\\right] \\times\\left[z_{I},z_{F}\\right]$ is the spatial domain $D$ and $I=\\left[0,t_{F}\\right]$ is the time interval of the integration. As usual, $\\partial\\Omega$ denotes the boundary of $\\Omega$, \n", "represented bellow.\n", "\n", "\n", "\n", "The spatial variables are $x$ and $z$ (which represents depth) and $t$ denotes time. The function $u(x,z,t):D\\rightarrow \\mathbb{R}$ represents the displacament of the acoustic wave, obeying the following equation:\n", "\n", "\\begin{equation}\n", "u_{tt}(x,z,t)-c^2(x,z)\\Delta(u(x,z,t))=c^2(x,z)f(x,z,t).\n", "\\end{equation}\n", "\n", "where $f(x,z,t):D\\rightarrow \\mathbb{R}$ denotes external forcing and $c(x,z):\\Omega\\rightarrow \\mathbb{R}$ is the speed of propagation of the wave. The initial conditions are given by:\n", "\n", "\\begin{equation}\n", "u(x,z,0) = 0.0 \\hspace{.5cm} \\mbox{ and } \\hspace{.5cm} u_t(x,z,0)= 0.0.\n", "\\end{equation}\n", "\n", "The usual boundary conditions for the wave equation, leading to a well posed problem, are either of Dirichlet or Neumann type. At the surface ($z=0$) a Neumann boundary condition will be employed, while at the bottom and lateral boundaries we initially employ Dirichlet null boundary conditions:\n", "\n", "\\begin{equation}\n", "\\left. u(x,z,t) \\right|_{\\partial\\Omega_{i}} = 0.0 \\hspace{.3cm} \\mbox{ for $i=1,2$ and $3$ } \\hspace{.5cm} \\mbox{ and } \\hspace{.5cm}\n", "\\left. \\displaystyle\\frac{\\partial u(x,z,t)}{\\partial z} \\right|_{\\partial\\Omega_{4}}= 0.0.\n", "\\end{equation}\n", "\n", "These boundary conditions on $\\partial\\Omega_{1}$, $\\partial\\Omega_{2}$ and $\\partial\\Omega_{3}$ are not natural for the seismic applications, being imposed by the necessity of employing a limited domain for computational purposes. They are the source of artificial wave reflections, which need to be mitigated by special treatments, with the use of absorbing boundary conditions or absorbing layers, which are the main subject of our notebooks.\n", "\n", "As a source term we employ a Ricker wave, given by: \n", "\n", "\\begin{equation}\n", "R(t)= \\left(1-2\\pi^{2}f_{0}^{2}\\left(t-\\displaystyle\\frac{1}{f_{0}} \\right)^{2}\\right)e^{\\pi^{2}f_{0}^{2}\\left(t-\\displaystyle\\frac{1}{f_{0}} \\right)^{2}},\n", "\\end{equation}\n", "\n", "where $f_{0}\\in\\mathbb{R}_{+}$ is the peak frequency. This will be used as a point source at a fixed position $(\\bar{x},\\bar{z})$, such that $f(\\bar{x},\\bar{z},t)=R(t)$ and $f$ is zero elsewhere." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.3 - Finite Difference Discretization\n", "\n", "\n", "## 1.3.1 - The computational grid\n", "\n", "The time interval $I=\\left[0,t_{F}\\right]$ is discretized as:\n", "\n", "- $t_k=k\\Delta t, k=0,1,\\cdots,m, \\mbox{ where } \\Delta t=t_F/m$, \n", " while the spatial domain grid is given by \n", "- $\\Omega_\\Delta = \\{(x_i,z_j),i=0,\\cdots,nx, \\ j=0,\\cdots,nz\\}$, \n", " with $x_i=x_I+i\\Delta x$ and $z_j=j\\Delta z$, $\\Delta_x=(x_F-x_I)/nx$ and $\\Delta_z=(z_F-z_I)/nz$.\n", "\n", " The choices of the values of $\\Delta t$, $\\Delta x$ and $\\Delta z$ will have to obey a CFL restriction,\n", " due to the fact that we will employ an explicit temporal discretization.\n", "\n", " Normally, the variables will be computed on the grid. For example, the velocity field $c(x,z)$ will be evaluated on $\\Omega_\\Delta$. The source term, which is time dependent, is evaluated at points $(x_i,z_j,t_k)$.\n", " \n", " In some cases, the discretization will require to have fields on grids which are staggered (in one or more directions). For instance, a spatial grid staggered only in the x-direction will be given by:\n", "- $\\Omega^x_\\Delta=\\{(x_{i+1/2},z_j), i=0,\\cdots,nx-1,\\ j=0,\\cdots,nz\\}, \\mbox{ with } x_{i+1/2}=(i+\\frac{1}{2})\\Delta x$.\n", "\n", " Grid staggering in other directions, including the temporal one, are defined analogously. \n", "\n", "\n", "\n", "## 1.3.2 - Discretization of the wave equation\n", "\n", " We employ a stantard second order explicit discretization of the wave equation, based on centered finite differences. Using the notation $u_{i,j,k}=u(x_i,z_j,t_k)$ we have the following discrete set of equations:\n", "\n", "\\begin{equation}\n", " \\frac{u_{i,j,k+1}-2u_{i,j,k}+u_{i,j,k-1}}{\\Delta^2 t}= c^2_{i,j,k}(\\frac{u_{i-1,j,k}-2u_{i,j,k}+u_{i+1,j,k}}{\\Delta^2 x} + \\frac{u_{i,j-1,k}-2u_{i,j,k}+u_{i,j+1,k}}{\\Delta^2 z}) + c^2_{i,j,k} f_{i,j,k}\n", "\\end{equation}\n", " for $i=1,\\cdots,nx-1, \\ j=1,\\cdots,nz-1, k=1,\\cdots,m-1$. \n", " \n", " The boundary conditions complete the set of equations. We have that $u_{0,j,k}=u_{nx,j,k}=u_{i,nz,k}=0$, according to the Dirichlet null conditions at the bottom and lateral boundaries. Moreover, $u_{i,0,k}=u_{i,1,k}$ due to the zero Neumman boundary condition at the surface and $u_{i,j,0}=u_{i,j,1}=0$ due to the initial conditions. \n", "\n", " This is the basic discretization of the wave equation, to be employed in all notebooks. It will be complemented\n", " by some other terms and / or equations, when considering how to discretize the absorbing boundary conditions\n", " and absorbing boundary layers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.4 - The standard problem\n", "\n", "In order to simulate the acoustic problem with the several absorbing boundary treatments, we define a standard problem to be used not only in this notebook, but also in the following ones. The spatial domain $\\Omega$ will be defined by the following parameters:\n", "\n", "- $x_{I}$ = 0.0 Km;\n", "- $x_{F}$ = 1.0 Km = 1000 m;\n", "- $z_{I}$ = 0.0 Km;\n", "- $z_{F}$ = 1.0 Km = 1000 m;\n", "\n", "with\n", "\n", "- $\\Delta x$ = 0.01 km = 10m;\n", "- $\\Delta z$ = 0.01 km = 10m;\n", "\n", " The integration will be carried out from \n", "\n", "- $t_{I}$ = 0 s = 0 ms;\n", "\n", "to\n", "\n", "- $t_{F}$ = 1 s = 1000 ms;\n", "\n", "The choice of $\\Delta t$ has to respect the CFL condition:\n", "\n", "\\begin{equation}\n", "{\\Delta t}\\leq\\displaystyle\\frac{h\\nu}{v_{max}},\n", "\\end{equation}\n", "\n", "where $\\nu\\leq\\frac{1}{\\sqrt{2}}$ and\n", "\n", "\\begin{equation}\n", "h = \\min\\{ \\Delta x, \\Delta z \\}.\n", "\\end{equation}\n", "\n", "The term $v_{max}$ is the maximum velocity propagation in $\\Omega$ and is given by:\n", "\n", "\\begin{equation}\n", "v_{max}=\\max_{(x,z)\\in \\Omega}\\left\\vert c(x,z) \\right\\vert.\n", "\\end{equation}\n", "\n", "The choice of $\\Delta t$ respecting the CFL condition defines the number of timesteps $NT$:\n", "\n", "\\begin{equation}\n", "\\Delta t =\\frac{T}{NT}.\n", "\\end{equation}\n", "\n", "In all the simulations we employ $\\nu=0.4$. In this way, the temporal parameters are given by:\n", "\n", "- $\\Delta t$ $\\approx$ 0.0016 s = 1.6 ms;\n", "- $NT$ = 626.\n", "\n", " The source $f(x, z, t)$ will be a Ricker source with the following properties:\n", "\n", "- Postion at $x:$ $\\bar{x} = 500 m = 0.5 Km$;\n", "- Position at $z:$ $\\bar{z} = 10 m = 0.01 Km$;\n", "- Peak frequence: $f_{0} = 10 Hz = 0.01 Khz$;\n", "\n", "The graph of $f(\\bar{x}, \\bar{z}, t)$ will be generated when building the code. We employ a synthetic velocity model $c(x,z)$ with the following properties:\n", "\n", "- Minimum propagation speed: $v_{min} = 1500 m/s = 1,5 Km/s$;\n", "- Maximum propagation speed: $v_{max} = 2500 m/s = 2,5 Km/s$;\n", "\n", "The figure of the velocity profile will be generated when building the code. We will consider 'receivers' along the $x$ direction (at all discrete points between $0.0$ Km and $1.0$ Km) at the depth $z=0.01$ Km, where we measure the displacement $u(x,z,t)$ throughout the time integration. Based on these values at the receivers we bild a $seismogram$ at the end of the integration." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.5 - Numerical Simulations\n", "\n", "In the numerical simulations, we initially import the packages of interest, in particular, those that are part of the Devito library. First we import the basic Python packages." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plot\n", "import math as mt\n", "import matplotlib.ticker as mticker \n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from matplotlib import cm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From Devito's library of examples, we import some specific functions, such as:\n", "\n", "- *TimeAxis* which generates a temporal structure;\n", "- *RickerSource* for the setup of the source term;\n", "- *Receiver* which generates a structure of receivers;\n", "\n", "So, we import these sample libraries and other importants libraries with the following commands:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "%matplotlib inline\n", "from examples.seismic import TimeAxis\n", "from examples.seismic import RickerSource\n", "from examples.seismic import Receiver\n", "from devito import SubDomain, Grid, NODE, TimeFunction, Function, Eq, solve, Operator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Previously we used the expression *configuration ['log-level']='PERF'*, whose objective is that during execution we can verify the execution parameters of the operators used. We define the spatial parameters to be used in the simulations. In Devito, the mesh data must be defined taking into account *meter (m)* as the unit of measurement. Thus, we have the following variables:\n", "\n", "- *nptx* and *nptz* correspond to the number of points (nx+1 and nz+1) in the $x$ and $z$ directions, respectively;\n", "- *x0* and *x1* determine the starting and ending point in the direction $x$;\n", "- *z0* and *z1* determine the starting and ending points in the $z$ direction;\n", "- *compx* and *compz* the lengths of the domain in the directions $x$ and $z$, respectively. These values are obtained from *x0*, *x1*, *z0* and *z1*;\n", "- *hx* and *hz* correspond to $\\Delta x$ and $\\Delta z$, respectively. These values are calculated in terms of the start/end points and the number of points in each direction, respectively;\n", "\n", "These variables are defined in the code as follows:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "nptx = 101\n", "nptz = 101\n", "x0 = 0.\n", "x1 = 1000. \n", "compx = x1-x0\n", "z0 = 0.\n", "z1 = 1000.\n", "compz = z1-z0;\n", "hx = (x1-x0)/(nptx-1)\n", "hz = (z1-z0)/(nptz-1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the spatial parameters are defined, we start the construction of the *spatial grid* using the Devito DSL. The required parameters are:\n", "\n", "- *origin* is the point of origin of the grid;\n", "- *extent* is the vector with the lengths of the domain in each of the directions;\n", "- *shape* is the vector with the number of points in each direction;\n", "- *spacing* is the vector with the spacing in each of the directions;\n", "\n", "These parameters are set as follows:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "origin = (x0,z0)\n", "extent = (compx,compz)\n", "shape = (nptx,nptz)\n", "spacing = (hx,hz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the mesh parameters are built, we can define the structures that represent $\\Omega$ subdomains, that is, particular regions of $\\Omega$, named *subdomains*. In the present notebook, there is no need to split the domain in particular subregions, so we define a single *subdomain* that correponds to the full domain $\\Omega$. This *subdomain* is built with the following command:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class d0domain(SubDomain):\n", " name = 'd0'\n", " def define(self, dimensions):\n", " x, z = dimensions\n", " return {x: z, z: z}\n", "d0_domain = d0domain()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After defining the spatial parameters and *subdomains*, we generate the *spatial grid*:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "grid = Grid(origin=origin, extent=extent, shape=shape, subdomains=(d0_domain))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The velocity field is set bellow, representing two different layers:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "v0 = np.zeros((nptx,nptz)) \n", "p0 = 0 \n", "p1 = int((1/2)*nptz)\n", "p2 = nptz \n", "v0[0:nptx,p0:p1] = 1.5\n", "v0[0:nptx,p1:p2] = 2.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include a routine to plot the velocity field." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def graph2dvel(vel):\n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscale = 1/10**(3)\n", " scale = np.amax(vel)\n", " extent = [fscale*x0,fscale*x1, fscale*z1, fscale*z0]\n", " fig = plot.imshow(np.transpose(vel), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.title('Velocity Profile')\n", " plot.grid()\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " cbar.set_label('Velocity [km/s]')\n", " plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we include the plot of velocity field." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2dvel(v0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build the *temporal grid* we use the command *TimeAxis*. Before using *TimeAxis*, we need to set the following parameters:\n", "\n", "- *t0* is the initial time simulation in milliseconds (ms);\n", "- *tn* is the final time simulation in milliseconds (ms);\n", "- *CFL* is the numerical value that imposes a restriction on the size of the time step, in order to obtain stability of the method;\n", "- *vmax* denotes the maximum velocity propagation. This value is obtained by analyzing the maximum value of the *vel* array;\n", "- *dtmax* is the upper limit for $\\Delta t$. This value is constructed from the *hx*, *hy* and *vmax* values.\n", "- *ntmax* is the number of time steps required to reach time $t_ {F}$;\n", "- dt0 is the value of$\\Delta t$ that can be used in the numerical method and that guarantees its stability;\n", "\n", "The parameters mentioned above are given by:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "t0 = 0.\n", "tn = 1000. \n", "CFL = 0.4\n", "vmax = np.amax(v0) \n", "dtmax = np.float64((min(hx,hz)*CFL)/(vmax))\n", "ntmax = int((tn-t0)/dtmax)+1\n", "dt0 = np.float64((tn-t0)/ntmax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate the *temporal grid* we use the variables given above and allocate them to some local variables of the *TimeAxis* function:\n", "\n", "- The start time *t0* is assigned to the local variable *start*;\n", "- The final time *tn* is assigned to the local variable *stop*;\n", "- The local variable *num* corresponds to the total number of steps in time,*ntmax*, plus 1, where this increase represents the initial time step.\n", "\n", "We can then generate the *time_range* as follows:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "time_range = TimeAxis(start=t0,stop=tn,num=ntmax+1)\n", "nt = time_range.num - 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have defined the spatial and temporal structures, we need to know the symbolic values that are associated with these grids:\n", "\n", "- *hxs* and *hzs* represent the symbolic values of the *hx* and *hz* parameters, respectively;\n", "- *x* and *z* represent the symbolic values of the variables $x$ and $z$. We emphasize that in Devito we do not have the explicit values of $x$ and $z$, but their position in the spatial grid, that is, *x* and *z* are integer values and mark positions in the $x*hx$ grid and $z*hz$;\n", "- *t* is the symbolic representation of the temporal dimension, similar to that represented by the variables *x* and *z* in the spatial dimension. *t* is also an integer position and represents $t*dt$;\n", "- *dt* is the symbolic representation of $\\Delta t$ time step;\n", "\n", "These symbolic values are defined with the following commands:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "(hxs,hzs) = grid.spacing_map \n", "(x, z) = grid.dimensions \n", "t = grid.stepping_dim\n", "dt = grid.stepping_dim.spacing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To generate the Ricker source, we set the parameters:\n", "\n", "- *f0*, the source frequency in $Khz$;\n", "- *nsource* is the number of sources we want to include in the simulation;\n", "- *xposf* is the position in meters of the source in the direction $x$. This position can be defined by a point value (when choosing 1 source) or a vector of positions (when choosing more than 1 source);\n", "- *zposf* is the position in meters of the source in the $z$ direction. This position can be defined by a point value (when choosing 1 source) or a vector of positions (when choosing more than 1 source);\n", "\n", "**Observation 1:** *xposf* and *zposf* are associated as follows: these generate the ordered pairs where the sources are positioned, that is, when we define these variables we are defining the positioning of the source(s).\n", "\n", "**Observation 2:** The Ricker source will be used to compose the external force term $f(x, z, t)$ that appears in the acoustic equation.\n", "\n", "We choose a single source, with frequency of $0.005Khz$, positioned at $\\bar{x}$ = 500m and $\\bar{z}$ = 10m." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "f0 = 0.01\n", "nsource = 1\n", "xposf = 0.5*compx\n", "zposf = hz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ricker's source is generated by the class *RickerSource* whose local variables are:\n", "\n", "- *name* is the symbolic name of the source;\n", "- *grid* is the grid where the font will be placed;\n", "- *f0* is the frequency of the source;\n", "- *npoint* is the number of fonts that will be placed;\n", "- *time_range* is the structure that contains the time informations that we are using in our simulations;\n", "- *staggered* is the type of positioning of the points;\n", "- *src.coordinates.data[:, 0]* is the positioning of the source in the $x$ direction;\n", "- *src.coordinates.data[:, 1]* is the positioning of the source in the $z$ direction;\n", "\n", "We choose the following values for the local variables of the *RickerSource* command:\n", "\n", "- *name=src;*\n", "- *grid=grid;*\n", "- *f0=f0;*\n", "- *npoint=nsource*;\n", "- *time_range=time_range;*\n", "- *staggered=NODE;* We use the NODE option which is for grid points of type *non-staggered *;\n", "- *src.coordinates.data [:, 0]=xposf;*\n", "- *src.coordinates.data [:, 1]= zposf;*\n", "\n", "With the above definitions we generate the Ricker font as follows:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "src = RickerSource(name='src',grid=grid,f0=f0,npoint=nsource,time_range=time_range,staggered=NODE,dtype=np.float64)\n", "src.coordinates.data[:, 0] = xposf\n", "src.coordinates.data[:, 1] = zposf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to set the receivers we need to define:\n", "\n", "- *nrec*, the number of receivers that we want to insert in the grid;\n", "- *nxpos*,the position in meters of receivers in the direction $x$. This position can be defined by a point value (when choosing 1 receiver) or a vector of positions (when choosing more than 1 receivers);\n", "- *nzpos*, the position in meters of receivers in the $z$ direction. This position can be defined by a point value (when choosing 1 receiver) or a vector of positions (when choosing more than 1 receivers);\n", "\n", "**Note:** *nxpos* and *nzpos* are associated as follows: these generate the ordered pairs where the receivers are positioned, that is, when we define these variables we are defining the positioning of the receiver(s).\n", "\n", "Below we include the plot of Ricker source." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "src.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our case, we choose the number of grid points in the $x$ direction for the number of receivers, which are positioned along the grid line in the $x$ direction at the height $\\bar{z}$ = 10m. In this way our variables are chosen as:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "nrec = nptx\n", "nxpos = np.linspace(x0,x1,nrec)\n", "nzpos = hz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Receivers are generated by the command *Receiver* whose local variables are:\n", "\n", "- *name* is the symbolic name of the Receiver;\n", "- *grid* is the grid where the receivers will be placed;\n", "- *npoint* is the number of receivers that will be placed;\n", "- *time_range* is the structure that contains the time informations that we are using in our simulations;\n", "- *staggered* is the type of positioning of the points;\n", "- *rec.coordinates.data [:, 0]* is the positioning of receivers in the direction $x$;\n", "- *rec.coordinates.data [:, 1]* is the positioning of receivers in the $z$ direction;\n", "\n", "We employ the following values for the local variables of the *Receiver* command:\n", "\n", "- *name=rec;*\n", "- *grid=grid;*\n", "- *npoint=nrec*;\n", "- *time_range=time_range;*\n", "- *staggered=NODE;* We use the NODE option which is for grid points of type *non-staggered*;\n", "- *rec.coordinates.data[:, 0]=nxpos;*\n", "- *rec.coordinates.data[:, 1]=nzpos;*\n", "\n", "We then generate the Receivers:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "rec = Receiver(name='rec',grid=grid,npoint=nrec,time_range=time_range,staggered=NODE,dtype=np.float64)\n", "rec.coordinates.data[:, 0] = nxpos\n", "rec.coordinates.data[:, 1] = nzpos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to create a variable in which we want to allocate the displacement field, which varies in space and time. It will be a field of type *TimeFunction*. The parameters needed to create a *TimeFunction* are:\n", "\n", "- *name* is the symbolic name that is given to this field;\n", "- *grid* is the spatial and temporal grid in which this field will be evaluated;\n", "- *time_order* is the order of approximation of the time derivatives;\n", "- *space_order* is the order of approximation of spatial derivatives;\n", "- *staggered* is the type of positioning of the points (NODE means that the variables are located at the grid points);\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "u = TimeFunction(name=\"u\",grid=grid,time_order=2,space_order=2,staggered=NODE,dtype=np.float64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also need to create a field for the wave propagation velocity, a field which will not change over time. Therefore it is defined as a *Function* type field. The parameters needed to create a *Function* are:\n", "\n", "- *name* is the symbolic name that is given to this field;\n", "- *grid* is the spatial grid on which this field will be evaluated;\n", "- *space_order* is the order of approximation of spatial derivatives;\n", "- *staggered* is the type of positioning of the points;\n", "\n", "In addition, we need to associate the velocity values previously defined to the field's *.data* property, that is, we just need to do the following operation:\n", "\n", "- *name.data[:,:]=m[:,:]*,\n", "\n", "where *m* denotes the array with predefined values. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "vel0 = Function(name=\"vel0\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n", "vel0.data[:,:] = v0[:,:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have created the Ricker source, we can then create the external force term in the acoustics equation. This insertion of the external force term in the acoustic equation takes into account the discretization of the differential equation. Therefore, it is multiplied by $\\Delta^2 t$ and $c^2(x,z)$.\n", "\n", "To insert the Ricker source we use the *src.inject*, whose local variables are:\n", "\n", "- *field* is the field in which the source term will be injected;\n", "- *expr* defines the expression of the source to be injected;\n", "\n", "We set\n", "\n", "- *field=u.forward*, where * forward * indicates that we are working with field * u * at time * t + 1 *;\n", "- *expr=$src*dt^{2}*vel0^{2}$ *, where src represents the Ricker source that was created in a previous structure and $vel0$ denotes the velocity field of the problem;\n", "\n", "The source term is called *src_term* and it is created as follows:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "src_term = src.inject(field=u.forward,expr=src*dt**2*vel0**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To define the Receivers we use the structure called *rec.interpolate*. We need to define the expression *expr* to\n", "be computed at the receivers location, which in this case is the value of the displacement.\n", "\n", "In this way, the Receivers term is named *rec_term* and is created as follows:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "rec_term = rec.interpolate(expr=u)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now create the acoustic equation without the external force term. As defined previusly, *u* represents the displacement field and *vel0* the field that carries the velocity information. \n", "\n", "- The structure that creates Equations in Devito is *Eq(eq)*, where *eq* is the equation we want to assign;\n", "- .dt2 calculates the second temporal derivative of the field it is applied to;\n", "- .laplace builds the spatial Laplacian operator of a given field. In the present case, this operator is .dx2 + .dz2;\n", "\n", "The acoustic equation without the source term has the following form:\n", "\n", "- $ u_ {tt} - c ^ {2} (u_ {xx} + u_ {zz}) $\n", "\n", "Using Devito's notation, we have\n", "\n", "- eq = u.dt2 - vel0 * vel0 * u.laplace\n", "\n", "Then, using the operator *Eq(eq)* we create the *pde* that represents the acoustic equation without the source term:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "pde = Eq(u.dt2 - u.laplace*vel0**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For Devito to solve a predefined *pde* we use the expression *solve(pde,field)* where the parameters are the equation we want to solve (*pde*) and the field being updated (*field1*). In our case, we want to solve the *pde* in the *u.forward* field, so we have\n", "\n", "- solve(pde,u.forward)\n", "\n", "Now we need Devito to assign this solution to a field, where we use the command *Eq()* again, with a few more options: more precisely we will have *Eq(field2,eq2,subdomain)*, where *field2* is the field who will receive the solution of *eq2* computed in the given *subdomain*. Since the only subdomain we have is *d0*,we define the expression of the subdomain as:\n", "\n", "- subdomain=grid.subdomains ['d0']\n", "\n", "Altogether we define the *stencil* for the acoustic problem:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "stencil = Eq(u.forward, solve(pde,u.forward),subdomain = grid.subdomains['d0'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also need to create the boundary conditions.\n", "\n", "The *u* field is such that *u=u[t, x, y]* where *(t, x, y)* are integer positions. We know that $x\\in \\{0,nptx \\}$ and $z\\in \\{0,nptz \\}$. For the term $t$ the field *u* a priori stores only 3 values of *t*, since *time_order=2*. These values are:\n", "\n", "- *u[t-1, x, z]* or *u.backward* is the solution at the instant *t-1*;\n", "- *u[t, x, z]* or *u* is the solution at instant *t*;\n", "- *u[t + 1, x, z]* or *u.forward* is the solution at the instant *t+1*;\n", "\n", "We know that the first and last points of the mesh in the direction $x$ are associated with positions *0* and *nptx-1*. Similarly, the first and last point of the mesh in the $z$ direction are associated with positions *0* and *nptz-1*. Using the structure of the command *Eq(field,value)* we have that the condition of null Dirichilet can be described as:\n", "\n", "- *Eq(u[t+1,0,y],0.);*\n", "- *Eq(u[t+1,nptx-1,y],0.);*\n", "- *Eq(u[t+1,x,npty-1],0.);*\n", "\n", "The free-surface condition (using a backward discretization in the *z* direction) is given by:\n", "\n", "- *Eq(u[t+1,x,0],u[t+1,x,1]);*\n", "\n", "Putting all these boundary conditions together in a term named *bc* we have to:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "bc = [Eq(u[t+1,0,z],0.),Eq(u[t+1,nptx-1,z],0.),Eq(u[t+1,x,nptz-1],0.),Eq(u[t+1,x,0],u[t+1,x,1])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then define the operator (*op*) that will combine the acoustic equation with the source term, boundary conditions and receivers.\n", "\n", "- 1. The acoustic wave equation: *[stencil];*\n", "- 2. Source term: *src_term;*\n", "- 3. Boundary conditions: *bc;*\n", "- 4. Receivers: *rec_term;*\n", "- 5. To replace any symbolic terms associated with *grid*: *subs=grid.spacing_map;*\n", "\n", "So we define the *op* generated by the following command:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "op = Operator([stencil] + src_term + bc + rec_term,subs=grid.spacing_map)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before starting we reset the field *u* in all its values." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "u.data[:] = 0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We assign to *op* the number of time steps it must execute, using the local variable *time*, and the size of the time step, using the local variable *dt*, so that:\n", "\n", "- *time=nt*;\n", "- *dt=dt0*;\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Data type float64 of runtime value `dt` does not match the Constant data type \n", "Operator `Kernel` run in 0.01 s\n" ] }, { "data": { "text/plain": [ "PerformanceSummary([(PerfKey(name='section0', rank=None),\n", " PerfEntry(time=0.00603600000000006, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section1', rank=None),\n", " PerfEntry(time=4.199999999999998e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section2', rank=None),\n", " PerfEntry(time=2.900000000000001e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section3', rank=None),\n", " PerfEntry(time=0.00024199999999999943, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n", " (PerfKey(name='section4', rank=None),\n", " PerfEntry(time=0.00080700000000001, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "op(time=nt,dt=dt0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To view the result of the displacement field at the end time, let's create a plot routine as follows:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def graph2d(U): \n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscale = 1/10**(3)\n", " scale = np.amax(U)/10.\n", " extent = [fscale*x0,fscale*x1,fscale*z1,fscale*z0]\n", " fig = plot.imshow(np.transpose(U),vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.axis('equal')\n", " plot.title('Map - Acoustic Problem with Devito')\n", " plot.grid()\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " cbar.set_label('Displacement [km]')\n", " plot.draw()\n", " plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access the solution in the *u* field, we will access the *0* position in *u.data*, that is,\n", "\n", "-*u.data [0,:,:]*\n", "\n", "We pass this value as an argument to the function *graph2d(U)*." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5Sd02j8TNqz8h4kcwCW8Bp3S9A2ca8V3Sxs/AzTS1yJVHci3qqw/V+PbL0VZHJWm+BtDcXHsLxn9grrLg/qQ9mfTL1Umcgcm1imzZpKv2Wg2/b/brW7idx6/A7XjuUUP61L9DVprtssr6TTX5ngrjRzAJ3xruT+vXWXn0zLq+RdL3MpvgZiR9c4O0vyNffDX2V8bhbo0kfr3+AGAfJDZjAC7SFP80E2/y1wE4Ce6B/T6ASzXi0yySNnPjX5krLikNJDk6TVV7NmxNCCGEEJKLnDaCic3Ga3D/rE4B8DM4g91x1fmeM9wH4Cy4ZYHBcDMRLyX+0gghhBBCSAORxkbwLLjp896aeKQXkQ/hjtQaAueGJIqIbA+33f90VX0gCXsdbnnxWvi7IQkhhBBCSD2SZtfwIQDeUf9YoulwBruHpki7EmuPZoK63WOPAdgv2aafGhFpIyLPicgXiZIJERkpIrNEpL+I/ENEvhORT0TkoOT6L0SkSkS+EZFnq3H/QQghhBBSdqRRBPvCHX9lmQygT4q00zU8jmkygFZw57OmQkQ6w9km9gawi6pm+w5rD2fwey/c7sOvAIwWkVvgfMadB+Ci5POfQYqGqvakfSAhhBDSOEizNNwZa/0xZbMANTjGTZE2cz0nyQHhL8F5399V3SHl2awH4JzMBhQRmQ3nZHYwgD6q+n0Svg3csU7NM2GEEEIIIeVKWj+Csa3F9lDxGFKHtBn6wLmK+BjAEaq6JBJnqdmFPCV5H2sUvilw33kjALOCSomcDeerCq1bt95x0003XXsxtrt69eqa5VWrYFlt8ikVbbRlm9Cf7rffLi9OWS2bY+XK6r/5OqZViuF0sJRYidZBSG3J1bZ1pWXJ9NiQlUU+VKNubdvSSMvqXqESxi41Lc/x2xSr37ZpE1ofrfy2vt1FxrEt0kwij8UW5hHdrFnNMgCYfD799NN5qtoVAHqJBMtzheAL58h//yJkTeqBNIrgQsRn7johPtuXzQK40x5iaTPXczEAzofYxdUogcDa81QBAKq6IjkIw9Yv4yXePnkz6e4GcDcA9O7dWz/5z3/WXqyqChO8/74vv2UOOBgVOsCfONefzHw+VpEGoOL7cBCW5rlMQGvH8OGHYtiwZ6u9/pJplbTHKjRWrvFOpgPWHoCSP7natq5cXTI9NuQaDC5q/nVr2+s96Wr0q3N9SpldjLxfjt+mWP1Wvw/zrGje4KfwAUDQIv27RszXTzrJl3fbzZf79QvT9OzpidK8+f8yn7+F2+FZaCqck3fSSEmjCE6Gs/Wz9IGbpcuV9nARaWPsBPvAKWXT4sk87gLQAcAoEVmlqqNzJSCEEEKIj4DnypKQNH1iDNwh8VtkAkSkJ4Bdk2u50rYEcHRW2hZwx3K9rO7s0Vyoqp4Pt8njseQoIEIIIYQQUkfSzAjeA3f4+bMiciWSY4/gDgK/KxNJRDaDO/bqWlW9FgBU9f3k3MtbRaQl3PmN58IdT3ZiPhVV1YtE5Hu4cyGbqerjORMRQgghZA2cESSWnIqgqi4VkT3hjph7CG52+VW4I+aybfYEzv7V9rPTAPwGzkimI9xu3v1V9b18K6uqF4vIKgAPJ8rgo/nmQQghhBBCHKl2DSdnCh+ZI04VIruBVfU7AL9IXnmhqrH8LgVwaZZ8ah5pRwIYmW89CCGEkMYObQRJjLTuYwghhBDSyKEiSCzsE4QQQgghZQpnBIljSXUuGuufpu5AeqsgZGwD1KJ2tDLyimis+sHWpbTxf2PbBz6tv4rUCyVzD5fQuFYqcPaHWNgnCCGEEELKFM4IEkIIIWUAN4uQGFQECSGEkDKBiiCxUBFMy6pVDV2D4jJvXkPXYA1pDqBuLOwbCfvoLvUDhhxcL3UpBL+66y5PrhhSjJNL02HrcsWQ5xqoJmmo9KQlpg/sOyTwdoWXi1mdIlMy93AJjWtFoak/l0i9QEWQEEIIKQO4NExisE8QQgghhJQpnBEkhBBCygTO/hALFcFCksJeo2RvwlmzGroGayhln2odjXzRTjt58gm9/uXJ1zx6XJhJI7IJtMiQ7p6sX34ZxKnccENfrkU5A60cKUc2nFCLnEuDIUEfODaIc/zxj3nyI9N+7Mm3TvC//6JCVKxAlMw9XELjmiXVs6AINoChNSopd0pWLyGEEEIIIcWFM4KEEEJIGSAAmjd0JUjJwRlBQgghhJAyhTOChBBCSJnA2R9ioSKYlhaRprJhVm7dOkhSsjfhnDkNXYMGp2L33T355SvfCOLst9/bnvzzCTf6ESYUYiPIE0GI7jTAkysmxDdKbIyBuBrPpyqlYr31PPmCU78J4owYYb/PnZ4kG96JkMGpyq+Ja2zAhmfkncfQob6D6T+ObB/EqVi8OHV+1bVthdkwJBPCfgMcnSP3pUHIo4/6bf8oNjQx3vKkl17aNchj3+tNv3nzzRz1aGKU8LgWPAsiz4ucz5jYc4mQPGEvIoQQQsoAOpQmMagIEkIIIWUCFUFiYZ8ghBBCCClTOCNYF5qbjfjWxsNeRwlr3iVsS1MsKu66y5NlyAI/wn4N4/hZjz45CKt4ovDOk619XOcRoavZzTdXT54+vXSdYW++uW8TaL9PRZHKtfaasd9PQrPPAuDbp+63XyzOgZ6kd53kyRVDhhS4TiVGCY9rwbMg8rxI80ypc7mk7GGfIIQQQggpUzgjSAghhJQB3CxCYlARJIQQQsoEKoLEQkWQOKZMaegaFJXDImEy5LlIaENwhydVPLFJA9Uj5LNzbvJkubSBKpICW9eKBqprxRMxg8CZRj63PqoCwPd7KcYk8N+RFM8UrS4NQBMf1wgpBFQECSGEkDJAkhch2XCWmBBCCCGkTOGMICGEEFIm1N0BDWlqUBFMy6pVYdjy5b68bFnNMoBILqXBqFGRwD3qvRrF4sctNQxcWRo+8fSAsz254sUGqkiEly+1hnZ1P0e4WIR1LR3sbywvlsbjOHZf/GplE1o8jI5rpUHwLIg8L4Iw+8yJPZdqgLuGSQz2CUIIIYSQMoUzgoQQQkiZwNkfYmGfIIQQQggpUzgjSAghhJQJnP0hFiqCaYkZ5X73nS8vWeLLEePf1QWsUiF5fvHiSOheRn61PqqCHkaeVYs8rjLyNQ22MeSAIET/5BvoV5x/fn1VJm8+bOgK5EEp17XiRX8HkP7pT54s58c2aBR/19DKyH1h751ra5GvvYfrD3/Men7x8w1Uj9wEz4LYZhH7TLHPHG4WIQWAfYIQQgghpEzhjCAhhBBSJnD2h1jYJwghhBBCyhTOCBaSFqY5W7cOotiQNkb+tqAVSs/ESNjxx1/kyY8+Wj82gp1H+zZ0s47M38Ht3oOMo9xx9WMjeNttz3nyIReGda8oXZPAgKOMfEmD1CIdtq4jG6ISKbF2odMjccbc5vfhCy+snz5s750B4/K//+w9jCPrp+52zJr46M/rpdw02LE+eDpEnhfBM6WO0EaQxGCfIIQQQggpUzgjSAghhJQJTegAQVIgOCNICCGElAnNi/BKi4gcKCJviMgSEflGRCaKyJ450rQWkZtF5AsR+U5ExovIgEi8ZiJyuYhUicgyEflARI7Mo3ppv0MnEblXROaJyFIRGSsi20bi/VZEXhaR+SKiInJqoetSKDgjmJZ27cKwrl19uXt3X+7ZM0jSec4cT25vrjeUjWCMRy7zPbM9+mj9lHuksSfS++7z5PvPOCNIc3q3bp4s4+4qfMUAAJ08SW/r78kVxiZwZJFqUV88dYux9bq4ofwx5iasa+OZ+xgZCzR9SW+7zZPlQmvZu7AgdRln7p3XzL11vxnDAOB0c49KPdkEWuyYVVFPY1Ya7Fjf2UaIPC+CZ4p95sSeSyWKiAwB8KfkdR3cRFQ/hOaTlvsAHARnovwZgPMAvCQiP1XV97PiXQdgGIArALwL4DgAT4jIYFX9W4G+gwAYA2BzAEPhbrrLAYwTkX6qmu32diiA9wE8D+DkQpRfLKgIEkIIIWVAQ20WEZGeAG4FcImq3pp16aUc6bYHcAKA01X1gSTsdQCT4XydH5KEbQCnBN6oqsOT5ONEpBeAGwEURBFMytsNwJ6qOi4pezzcfq9fArggK24HVV2d1KGkFUEuDRNCCCGkmJwOd5jKnXmmOwTASgCPZwJUdRWAxwDsJyLrJMH7AWgFYJRJPwrAtiKyeSZARFokS8hTRGS5iMwWkVtEJLJtO1qf2RklMKnP1wCeA3BodkRVLdWDxAKoCBJCCCFlQrMivFKwG4ApAI4Tkf+KyCoRmSYi5+VI1xfAdFW1VlOT4RS/XlnxlgOYFokHAH2ywkYBuBLAI3BLzjcAOAPAwym+R18AH0XCJwPYVEQaz1p9FlwaJoQQQsqABvQjuHHyuhnArwD8F8DRAP4kIi1U9bZq0nVG3Ph1Qdb1zPsiVdWa4onI7gCOBXCKqj6YXBsrIgsAjErs/N6v4Xt0BlBVQ306AVgSuV7SUBFMS8wot4c5Wn3uXF/efHNYWr/zjieX9NzxKDvL3jDIGV94st08AgBYZx1fPmkrE6E2h89b825Aj/YPhq+48MJa5FsaWKOVc/axYyjwSglvDrFcbOq6j/k+d74Sbh55MAgpXWxf06OP9mR5IuyvwDe1KGkHXxw+3BNPX748SGHv0QajRMasGHasD9YhI8+LYAOJfeaUzmaRLiKSvXvpblW9O0tuBmA9AKeq6lNJ2GuJ7eDlIvLHiBIHON21uvDaxNsfwAoAo0UkW/95OXkfAOB9EWlu0n6f1C9tOY0KKoKEEEJImVCkGcF5qtq/huvzAWwJ4BUT/jKccrYRgNmRdAsAbBoJ75R1PfPeSUTEKJQ23gZwS8rVzdqtn7z/F8BmWeGnwW3uX4DIhu+scgqzdb+eoSJICCGEkGIyGcDOkfDMTFp1i2OTARwuIm2MnWAfuJm9aVnx1gHwA/h2ghnbwI+T9/kAlgHYvZryMsrowUl+GTKnQE4GsG8kXR8AM1S10S0LA9wsQgghhJQNDbRZ5OnkfT8Tvh+AWaoaOqd0jAHQEs6eEIDb9Qtn5/eyqmbsFP4OpxieaNKfBOAjVZ2eFa81nGuXiZHXbABQ1UkmfH5WfbqLyB5Z9WkPpziOSdMQpQhnBNOS5vBvazsza1YQxfb2r2pfo+Jz0km+fPMVDVMP+HaVOO7xMIqxpdHP3/dk6R6r+2+M/Es/j+PvCFJUPPpEvIolSMXO/h9weecIT74Gb/gJXmk89oBpeMV8nx9gcCSWf0CB7vyUJ1cYm95SouIJvy/q8ccHceTRX5qQm4wc3hf6uXFi3O8gX66qCitzxrHxStY3wZh1c8PUI4Id6+2zoHPkeRE8UyxpnktZNOBmkb8BGAfgLhHpAucY+ii42bXTAEBENoNbkr1WVa8FAFV9X0QeB3CriLSEm5k7F86h8xqlT1W/EpE/wNkbLgbwHpyyuCey3LqoaqWIPArgSRH5PYB/wc1G9gRwIIBLVfXTGr7HGADj4TaWXIK1DqUF5uZKlMWuADIe2fuLyJKkHk+mbLd6gYogIYQQQoqGqqqIHAbnquUaOJu6KQBOVNVHkmgCd2Kd1VVPg/vXfj2AjgA+ALC/qr5n4l0BZ/t3IZzy9QmAY1T1ORPvJLhTP05P0iyH2wn8EoAvc3yP1SIyGMBwALfDzS6OBzBIVWea6NcA2CNLPi95Zb5ryUBFkBBCCCkTGkoDUdVv4CtD9noVItVT1e8A/CJ51ZT/93DK4vU54q0GcFvyyhtVXQCnRJ6eI97A2uTfENBGkBBCCCGkTOGMYFpidjFvvunLo0d74ix7HWudFTUGtjhsu4auQpxddw2CKqwPR3NY+2SE9DngAE/++EXfXqyUDqzPRcVL7sjOymXLcHzyWfYbYWIZm8ACsN56dtUF+MXiws85/H690HXX4sWFsGn020Te6eLJ+tLao1AzbVuxn7V3Lw0qHg077GT4YWGf/2mYT/cgyL8euf+AHpGw+seOWaV8wKt9FrSPPC96WBvA777z5d0jm1+32abaMjNrr4RkwxlBQgghhJAyhTOChBBCSJnA2R9ioSJICCGElAlUBImFfYIQQgghpEzhjGBaIoa8uM3fff7h1Kme/HwkmxUFrFIhiR1dPn16aToYrnj//bzT/DUW+OKLda1Kg1Gx7baenNkYMnz4oRg07Nl6q0cxNoakLeeaqIPowpK94SbTtmravmLSpKLXo7YE/b4AfT5+/5XGZhE7ZsXGtVI5A2yRkR+MxBk8bpwnbxdzOm3JsVmEsz/Ewj5BCCGEEFKmcEaQEEIIKRM4+0MsVAQJIYSQMoBLwyQGFcG0vP12EPRpDpvAUrUHjDHs+vBUnkuufKcBahLy618bp8XXldQxjfXC3kaWSYNMyGf1Uo+f/tT8FuMb7rewdRk/vn5sWm3bvwnfRnBsvdSitLD36HXXlYZ9cWxcq7jyygaoSW5izwv7TGltnjlbRZ5LOPfcgtWJlAdUBAkhhJAygTOCxMI+QQghhBBSpnBGkBBCCCkTys+whuSCimBa5s0LghYYuTHZBFp+v+4VkdDSsPM50NgE/r2B6tGQDBD1A7Rhfpt9G9Am0GLrMr4e/Ao6fHtM+9tcpaXTRvWFvUevq7ffombi41pp2gjGsM8U+8yJPZdqQgA0r0N9SNOES8OEEEIIIWUKZwQJIYSQMoGzP8TCPkEIIYQQUqZwRpAQQggpA+hQmsSgIpiWyEHe273yiifbw8zfi2TzbeFqVCf6GfnDUjmJPUK5bQ758QsahOlBDbM5RH/WyZMrHmqQaqTC1lUeWlg/5ZqNO7Hf718HNe0NJKV6jy6JjGv9jPx+PdQjDW0iYTsYeTsbIfJcIiRfqAgSQgghZQJnBImFiiAhhBBSJlARJJZUfUJENhGRJ0XkaxH5RkSeEpFNU6TrLyJ3i8gUEflWRGaIyMMisnnKclVEwsMiCSGEEEJInck5IygibQC8BmA5gFMAKIDrAYwTke1UdWkNyY8D0BfAHwFMBtAdwK8BTBSRfqo6s471rz/OPz8IanPYYZ6895w5vrxoUZjP3Lm+/PnnvjxxoicumTAhyOJfRv6HkVeFpQZYx6RXbf3XIM7VKfIpDn80sj16vWlRcd99niwNZQ/4yS1BWEXv3g1Qk9pR8ZBvwKiffOLJ0vvieqnHQZHfT81vXHHGGfVSl4bD3sMXNEgtYuPa/bXIxz4odzHyjyNp2u20kx/Qv78vd+/uy127hpl07OjL3br5co8ekZKrh5tFSIw0S8NnAdgCQG9VnQYAIvIhgKkAhgD4fQ1pf6eqnuYjIm8DmJ7ke1VtKk0IIYQQQupOmj8HhwB4J6MEAoCqTgfwNoBDa0polcAk7H8A5sLNDuaFiLQRkedE5AsR2T4JGykis5Jl6H+IyHci8omIHJRc/4WIVCVL2s+KSORvFyGEENL0aVaEF2ncpPkN+wL4KBI+GUCffAsUkR8C2ADAf/JM1xnAWAC9Aeyiqh9kXW4P4EEA9wI4HMBXAEaLyC0ABgE4D8BFyec/51tnQgghpCkgRXiRxk2apeHOAGIOuRYA6BQJrxYRaQHgTrgZwftyRM9OtymAl+Bc9e0amWlcD8A5qvpGEn82gA8ADAbQR1W/T8K3ATBURJpnwtJy99gtgrCRI/2w8eO/MjEmRXJax8ibGflMT9ppJ+tJChg71pf3/MkPPfnBKVOCNJ8ZeYaR7z722CCNfvmlJ8uG9WPXpKN8q8eKk+ql2HrDWpvKGU83SD1uueU5T67o3bSGdGvfeMstoX+/iy+uH3tM+xvbAexP9VKL+sPew1JP97B+6T9W7t5wwyDO7Bx5hCM9cPLWW3vyN//05zH23DtMM2GC8SQ74WMTwz6CYvZ+23rST3+6gSefemqY4uwzV0fyIaR6RDUcHL0IIisA3KKql5vw3wC4VFVTu6ARkTsBnAHgIFV9OUV8BfA0nC3uxwCOUNUlJs5IAEeparussFZwm1tuV9XzssLPBnAXgE1UdVakvLMBnA0AXbt23fGvf11raDxvXlg/G7Z0qd2m8V3kW62wpRq5tSe1bRu6Gd1qK19u9p/Jnjx/2bKcpVpaRsK6bL+9J7/7QVWOXNLRo0dHzJq1qNrrO27e2ZO/mD69IOWWCtY24QN0KFjeudrWj9vLk1fPerdg9ShFmvXYMQibNWtaJGacfNo2F9vja08ObGgaORtt7juGeHe63Z7mU6i23XH7np4874MPgjgrc+TRKhK2fmt/XF79w76e/OmnYZqlS+3xAXZcts/eWMnrelLbtv7jtkuXMIUNGzRo0Luq2h8AthfRYjj/3hhYUwZpfKRR4hbCzQpaOiE+UxhFRG6AU7JOSaMEZjEAwPoALrZKYBaLsgVVXSEiiNQvow+1RgRVvRvA3QDQu3dvHThgwJprd98brqI/+KAvp5sRtANicyP39KQ0M4LtzzvXr1eKGUHLxpGwo8yM4KB9bsuRSzqGDz8Uw4Y9W+11HXWcJ1cMG1aQckuFfQJ5cMHyztW22dgZwW+GDSpYPUqR9pEZwWHDwp3S1ZFP2+ZirtkJ39RmBI8fNcqTB+Vot0K1bTAjuI+922o3I3hkjhnByy4L0wQzgqifGcGjjuCMIMmPNIrgZDg7QUsfhD07iohcAeAyABeoar6HVN0FoAOAUSKySlVH55meEEIIIeDmDhKSRhEcA2C4iGyhqp8BgIj0BLArnHJXIyJyAZzfwStUdUQt6qiqer6IrALwmIicoKpP1CKfOvGnyF/2SZNuNyEvFrzcCRPsjCHQo8cznvzNHVf6EU7K3yAn9i+5wtjXfP65P6PSvXuR7KtqUf9SpVckrGvgYy2fCfK64NujfnNx07IJzEX8+55n5P/VR1WCPvBQxM9e+kXrEiS4hws3653N558bO9cNi9Snr/THWOu+b/HiwyKJ8jJDT8X48Qd48pIl/xfEOfvMIGgN9CNIYqTpE/cAqALwrIgcKiKHAHgWwEy42ToAgIhsJiKrROSqrLDjANwKdyb5ayKyc9Yrrx3HqnoRnJfSR0Qk3NlACCGEEELyIueMoKouFZE9AfwBwENwfypeBXCRsdkTOIO3bOVy/yR8/+SVzesABuZTWVW9OJkZfFhEmqnqo/mkJ4QQQsoZzggSS6odv6o6A8CROeJUwWyBVdVTAZxau6oBqhrM86vqpQAuNWWkTTsSwMja1ocQQgghpCmR2vULIYQQQhovtBEkMagIpiTikQXAhHooOTQ4btfOBLz6qifmchVTWz7ubidZ34jEuqnuBT1r3EgcWuNJhiWFNZHfEndGYj0fCSs+eoDfMyoKv7ep0WHbRF4MN2cVB3+D0M8i/WQqzvHkUUGMEia4h+8pQKa/DELCManuRMdPM8a2a3eiJy9eXPiNIXH8Z078uURIflARJIQQQsoEzggSCxVBQgghpAzg0jCJwT5BCCGEEFKmcEYwJT/4QRg2Zcr2JuTVMFKduSJSri+/1eGBIpQb8paR11tv9yDO4sV1txGUQ//iyVfXOcfCcZWR9x7kO9m+Zpx1st0w9oAxKl5sGKNAe2TXya3DEx4fNOdjF8vO1RK2SXEcH+cm7CdbmroMGuQ7Tx47zrePu7bwlao19h4uBLHx5q3FBS8mylsP+GPslK/v9+QOHcJxGvhNEWriP3Niz6VccPaHWNgnCCGEEELKFM4IEkIIIWUCZ3+IhX2CEEIIKQMym0UK/apVXUT+LiIqIteniNtaRG4WkS9E5DsRGS8iAyLxmonI5SJSJSLLROQDEanxMIxa1r2TiNwrIvNEZKmIjBWRbSPxfisiL4vI/OS7nlrouhQCzgim5Oijw7Cbb77Ik5ct28nEiB0bb/1N+X1n9939PN54/psgh686+LZBYyOlFAN7OPTixcWySlrhSY9s6dvhnTC1OAfLb2fkI/FIEOcaGxbYBJYSRxm58PaK20TCfrm5/3tNn+630Sm+OWCUzTf37eFumh7+5h/lzqYW2DZ7siil1IZxpq81D+wZTwjSjDZhHxa6Ugn2HsXUwt8XsfHGjkkfF7xUhx1jtzJjsH79dZBmwGC/D7/5pvU7O8nIMR+WvTypdetdPTn2XGoMiMjxsAaPNXMfgIMAXAJnQnwegJdE5Keq+n5WvOsADIMzrn8XwHEAnhCRwar6twLVXQCMAbA5gKEAFgK4HMA4EemnqrOyog8F8D7c4HtyIcovBlQECSGEkDKhoZcBRaQjgD8A+DkQ+bcdxt8e7l/O6ar6QBL2OoDJcHukDknCNoBTAm9U1eFJ8nEi0gvAjQAKoggm5e0GYE9VHZeUPR7AdDiv5xdkxe2gqquTOpSsItjQfYIQQggh5cNNACar6qMp4x8CYCWAxzMBqroKwGMA9hORdZLg/QC0QngIzygA24rI5pkAEWmRLCFPEZHlIjJbRG4RkdClQbw+szNKYFKfrwE8B8A7BktVV6f8jg0KZwQJIYSQMqChHUqLyG5wM2P5LAv3BTBdVb814ZPhFL9eyee+AJYjtMmanLz3gZu1A5xyeDCA3wH4B4Afwi0r9wSQy6awL+KWKZMBnCwi7VR1SY48SgoqgoQQQkiZUCRFsIuITMyS71bVu7MjiEhLAHcBGK6qn+SRd2c4OzzLgqzrmfdFqqo1xROR3QEcC+AUVX0wuTZWRBYAGJXY+b2foz5VNdSnEwAqgk2Rk04Kw3bbzZfnzfMNeZs392UA2HJLX96hx1d+wHD/YPVZHW4O8ri3+moWlWPuu8+Tjz3j6Xopd6oxPL8Gq4I4V9eiK1ecdZYnyz1fmBg5zVdKnPae1MZctX+vY/Q08oM5HWgDmF73jQJ2g8nREUfP1sHyycbBclWKcmyb2DZrXIT99UjTbnrWRp5ccc89eZcSu/+KsTkkxG62CMekijPOqId6ALcb+cwOHYI4b1xyiR/w5DBPfG+WvzFw6tSwnO/N3sIuXXy5Z88aKlm/zFPV/jniXApgXeTvaVsAWOUuE16bePvD7UgcLSLZD46Xk/cBAN4XkeYm7feJkpm2nEYDFUFCCCGkTHCbXgtMMAkXlLkp3E7eMwGsk2XXh0TuCGCxqlq3GoCbads0Et4p63rmvZOIiJkVtPE2gFtSrm7Wbv3k/b8ANssKPw3AyCSfzgjJlBObvSxpqAgSQgghpJhsAaA1wo0cgNvpOwzAj+BcrVgmAzhcRNoYO8E+cDN707LirQPgB/DtBDNehjLeheYDWAYgPLPQMTt5PzjJL0PGvnAygH0j6foAmNHY7AMBKoKEEEJIeSACtCjCY3/lylwx3gcwKBI+Dk45vA9xx7uA89l3DYCjAfwFcLt+4ez8XlbV5Um8v8Mphicm8TOcBOAjVZ2eFe9SONcur1ZXYVW1jh6z63OaiOyhqq8n9WkPpzg2SnsiKoIp2arLgjAM8/yAdkaePh0Bzxgb2bfe8sQl48Z5crEcpKZhFyPLGZtF49U/hwUh1xhbqLvu8u3HhgwJHXNfc8+JBa1V6eHbPP7yZz/zLz/xhCfe/+fvghxOO8PYfpWQA23rYHmc6QP33ef3gdPPWzfMxHjkvfQhayfatAjtYENHyHfd5dtJDhlif/PDClqnumDHpJfM9X/UUz1i43THm3377nYTJ3ryDsbIfIfevcNMNt/cl62RoJVdyfFKNiCqughApQ1Plqn/p6qVibwZ3JLstap6bZL2fRF5HMCtyYaT6QDOhXPovGYQV9WvROQPAC4XkcUA3oNTFvdEllsXVa0UkUcBPCkivwfwLwCr4UyiDwRwqap+WsPXGQNgPNzGkkuw1qG0wLnGyf5+ewDoCqBbEtRfRJYk9SgZb/VUBAkhhJByoWFmBNMicEes2M3Np8FtMrkeTtP9AMD+qvqeiXcFnO3fhXDK1ycAjlHV50y8k+BO/Tg9SbMcbn/ZSwC+rKmCiYPowQCGw+0bag2nGA5S1Zkm+jUA9siSz0teme9aElARJIQQQsqBYi0N1xJVFSNXIaIgqep3AH6RvGrK73s4ZbHG84sTR8+3Ja+8UdUFcErk6TniDaxN/vUNTxYhhBBCCClTSuevQWPE/rNqbU6n2cj32QUAaNnSl42NR7te/iHj+77ySpDFVlVVnmztYGoybqiOmAOobh8YlwDbl459WC5Cu6Zy5CBPeu3U//PkvR66zo9u7QEbOWeY73MGpgRxXj3V2L0+9D8T48UC16rUCO1khwxpgGrUmls9yY5Z/bcPV98mBiG52crI1n66Z8yh3z77+PI22/hyt26+HLP3s8+Uus7mldiMICkNOCNICCGEEFKm8K8BIYQQUg5wRpBEYI8ghBBCygEqgiQCl4YJIYQQQsoU/jVIyZcrw6MFv1zih82a41+fY+RY2BJzGE0LYz/c4/IwD+OHFCcsM66U7r03TPSiMXq3RsiPPx4kke2buqF8UyLjLWH2ms96l3Gvu9upJk1FcatUclQEIXvudpcn2zaTIdleKDJte2XBa0YKw/ZmzNIPPgjiDD72WD9g2TJfPuCAMOMzz/TEj1vv4Ml3++cCAABmzfLlVWbsb2fG/m6Rg8nsfpIe5om9YdswzYZYHQZmwxlBYuCMICGEEEJImcK/BoQQQkg5QBtBEoE9ghBCCCkHqAiSCOwRKYmYmmDsWF+urPTlCROM7QkAYKqRjSEJlhq5PUL8w8l/9CPfXuXSS28PUhx7/QI/YIrvXPdDbBcppyoSRkoB/cvRnlxxSj8AwMYYjqtxoAszjoH7GE/BG23kO9/94oum5VDastFGDwRhf13Hdzj8sbl+ddbnTNtW/OUvXhw55YkC1ZDUHf/M29i4tt199/kBW2/tiY+/EtqD/843EcS//20dj38Sqcs3RrYGfT2MvGWQw047+bbcAwf61/feOyx130gYITVBRZAQQggpBzgjSCJwswghhBBCSJnCvwaEEEJIOcAZQRKBPSIlq1aFYdb91OLFNsaKFDmva+R2RrZ2JMCWW27mycOG+deP7fRyWEw/38jl05kzPTly3Dn0+ec9WQbvbGK8E0lF6s5lQcjV8J1HVpzyfBAnF9b+rfIL3z6uN+ZFUp2adzmlw0hPst8XAB6pRa4Vp5ziyVeb69cg4lQON9aiJJIbf0zS55t78uztw9/8UyNvtckmnnxsxA/r98P29eSKCn8Mnjo15rvP2n9/H4mTTfi8WLzYtxG0z5zYc4mQfKEiSAghhJQLnBEkBvYIQgghpBzg0jCJwM0ihBBCCCFlCv8aEEIIIeUAZwRJBPaIlOy2Wxi2446+fFlg4x86g+7YcVtPbjNvhh/hzTd9+T5rig58Om6cJz9yon+9IkhRSwYP9kRbk9lnKSz33FMqTok7edLQoQ8GMUaMONKEpNncU3i+hL/x43bkvxGkNtiNEg9HtgydYAzp0dZ3ijvPOCYHgI+MPNvI30bq0sbIGxt5m0iaLsYRMJb6ztgfmel/n9psDKkNdmMPAPyfkTfE4CBO/dDKk4YOHR3EGDHiZBOysIj1Sc9ZZz0XhG18j78ZpKI2zWo2zmG//XImsXfoVoMGhZHOOMOXd9/dE7/tsqknL1qUs1g09/fCYF2715CQWkBFkBBCCCkHOCNIIrBHEEIIIeUAFUESgZtFCCGEEELKFP41SEn7h/4chv3pT548zdhL/SOSz2eFrFQDY+1zAEDXW8+TZfEe9VUdvx5f+7/N2A5hXf94112eLENCG6RicE1gE1gaWEe7AFBh7afqCVuXylikiH1iqWJ/Y9sHrq4nm0G9y7d/Gzskcl98/bUnS4cTgzj1ga73uidXRMabhiKwNzV229WG1cAWkbBdjNzL2sWef36Y6Nxzay6IM4LEwBlBQgghhJAyhX8NCCGEkHKANoIkAnsEIYQQUg5QESQR2CPScvnlQdDwxYs9eUl91aWEqTBtMsLYQg2tL/9phx3miW9FonQZMsSEPGbkUXWuht62TxBWcWH9+AkkpctqI8f6iVz4SgFKOsmT3h9ynCfH7ou9zb0DtI3EKjx2rKhYXE3EJkrMftyGtTN2scMiz6WcNoKEGKgIEkIIIeUAZwQbLSISnoqQjl+r6v9qisAeQQghhBBS2pwEYA6A5Xmk2RTArQCoCBJCCCFlD2cEGzuHqeq/0kQUkRZIeW4qe0RKpiwODVZoE5ibeUaeiufxXwzE1MQeaMti2Qw+84wn7tKhQxClTxCydRBSVyouvLDgeZKmR7yfFOLe8Pu07fNfxZKYewdF8iM4Ncsm8L8YiPFFKaVpYZ85sedS4UcxUiK8CuDrnLHW8n2S5ptcEakIEkIIIeUAZwQbLaoa7iirOb4CSJWGPYIQQggpF6gIEgN7BCGEEEJIIyKxAfwpgE0AtLbXVfX+tHlRESSEEELKAS4NNwlEZAcATwPoASB2CLcCoCJYaFY1dAWaCKMA9AbWGIZvu+1zQZxJkw6ucznS4WlP1i+/DCMtMabXP5hR53JffdX/Pm/sFbtHG4aK44/3A47znQtjVOhAu+qJJzz5H+b67Eg51llyKyPHBh17f9mtbrFD0Tc28i5G7nn00X7ASSch4DHfiXjFo49GSmoYbF/aa6/a3BcLPanVf//ryfu2axeksPdOIYjd56Mmrb03ehe8xPKAz6Wy5U64vUOHAZiClLuDq4OKICGEEFIOcEawqdAHwDGq+rdCZMYeQQghhJQDVASbCp+igGc/xlZcCCGEEEJIafIrAFeKyKaFyIx/DVJCJ53F4Z13wrC2Bfmf86QnyYZdghiXX36QJ2+55RaePHXqi5F8P/akffYpDZvAip12AgBUtm2L45PPMuEeL841j17pJ3rUvw4cHsn5Xk/60Y/ae3LHjmGKZct8edWqmmUgnKSwcutgTxywaJEv//vfxm/qE08a2X7fGO97ku501prPmbatmDAhRT51x/alffZRT37lFWszGLpI33LLgZ78K//nxA03vBAp+clIWN2I3ec3FWw+o3zJ+7nEGcEmgar+XUQGApgqIp/CGgM7N4J7pM2PPYIQQgghpJEgIpcB+CWAuXAnh3xfl/y4NEwIIYSUCy1aFP6VAxE5SkRGi8j/ROQ7EflERG4QkfVSpG0tIjeLyBdJ2vEiMiASr5mIXC4iVSKyTEQ+EJEja9lKNdWnk4jcKyLzRGSpiIwVkW0j8X4rIi+LyHwRURE5tYDVuAjAXQA2VtVdVXWQfeWTGRVBQgghpBzILA3XsyIIYBjcrNWvAOwP4A4A5wJ4RURy6SH3ATgLwFVwB3B/AeAlEeln4l0HoALAnwAcAOAdAE+IyIFpKpgGEREAY+C+w1AARwJoCWCciPQw0YcCWBfIOlS7cLQB8ISq1mkmMAOXhlPS4te/DsKuvO46T640198qXnWaDE8W3hypGu4MQpYs8W0EP93/Ak/+ZuqIIM0jRp7zSsPYBB5jZJmwNwBg+LHrYVDyGTA2gTmJ+Y/zw/797zyzbHT4bSZr2nJt206GbyP413qpF7CL6WtHmOsnRJ437ff/zpMvWPJHEyO8L4pB/d3nTYvdjDzQyLHnUolysKrOzZJfF5EFAP4C97VeiyUSke0BnADgdFV9IAl7HcBkANcCOCQJ2wBO2bxRVYcnyceJSC8ANwIoiJuVpLzdAOypquOSsscDmA63VJv9EOmgqquTOpxcoPIzvAh3qki03fKFM4KEEEJIOdBAM4JGCcyQ+UfVvYakhwBYCeDxrLxWAXgMwH4isk4SvB+c73rrFX8UgG1FZPO1TSAtkiXkKSKyXERmi8gtIhLZkhatz+yMEpjU52sAzwE4NDuiqlrf+oXkVgAni8gVIrKjiGxhX/lkxhlBQgghhNQ3mV2t/6khTl8A01X1WxM+GU7x65V87gtgOYBpkXiA21I/Pfk8CsDBAH4Hd1jSD+GWlXvCLfXWRF8AH0XCJ8MpZu1UdUnkeqF5O3m/Dm5mNEbztJlRESSEEELKgeK5j+kiIhOz5LtV9e7qqyHd4RSYsao6sbp4ADojdI0CAAuyrmfeF6mq1hRPRHYHcCyAU1T1weTa2GSZepSI9FPV93PUp6qG+nSCO/qt2JwOd55wQaAiSAghhJQDxVME56lq/3RVkHYAnoU7Kvm0XNERV3iscXbaePvDncs7WkSyG+Ll5H0AgPdFpLlJ+32iZKYtp6io6shC5kdFMC1Xhob3LYYN8+S9jSfdvZdE/hjYsDlzfPkjM+v8978HWcx55RVPtnbY88JSS5ZTTrmlwcru18+XK84IN4eUKn2+/toP6HBi8mELAJPquzpNmOy2dG0btn2H+qzQGszIgd/HIo3w+3S/++xmkfohdp9f3QD1qC3WHf1RRu62zz5hov339+VttjGJuvlyu3ZhHjbMelaPeVovYRI7vDFwN9MeqjorR5IFAGKnZ3TKup557yQiYmYFbbwN4JaUq5u1Wz95/y+AzbLCTwMwMsmnM0Iy5cRmLwuOiByvqo/WcH2Eqg5Nmx8VQUIIIaRcaKCTRUSkJYDRAH4MYG9VTfOPdTKAw0WkjbET7AM3szctK946AH4A304wc9xO5kio+QCWAdi9mvJmJ+8HJ/llyNgXTgawbyRdHwAz6sk+EABGishcVR1rL4jIbQDOhHNfkwruGiaEEEJI0Uh8BT4MYC8Ah6pq5NDBKGPg/PQdnZVXCzg7v5dVdXkS/Hc4xfBEk/4kAB+p6vSseK3hXLtMjLxmA4CqTjLh87Pq011E1hzfJiLt4RTHMSm/UyG4HsBTIrJjdqCI/AHAOQCOyyczzggSQggh5UDDnTX8Zzhl7jcAlorIzlnXZqnqLBHZDG5J9lpVvRYAVPV9EXkcwK3JjOJ0OEfUmyNL6VPVrxIl6HIRWQzgPThlcU9kuXVR1UoReRTAkyLyewD/ArAabsfwgQAuVdVPa/geYwCMh9tYcgncUvDlcDaCN2VHTJTFrgAyNgD9RWRJUo86edZU1etEZCMAfxORXVV1mojcAuA8AMep6rP55JeqR4jIJgD+AGAfuC88FsBFqjojn8JE5HIAvwXwtqpaX5mx+ArgN6qar2fcgvPaW62CsMpKP2zs2PaePH68LzvsbPh8I1s71IjtCc4w8gGedN99YbmnV/r+LG986CFP9q0bi0fF0KGo3GADHD/UzVpfM6KynkoOseaXfeLRShMeHN9wNOK2j5gc1xOVQUjF0LUrV5UbbIBP6qkm1qrusp/9zJPvH/ggLGec8Y0nD8WLfoRXIo/CV+xYbsf6TkbeKsjipz/1a7v33v71gQPDYvccWEz3dbUm85C6Inllcw3ciSAC5/LErlSeBqdAXg+gI4APAOyvqu+ZeFfA2f5dCKd8fQLgGFV9zsQ7CW7Z9PQkzXK4ncAvAfiypi+ROIgeDGA4gNvhutN4AINUdWbke+2RJZ+XvIDCbC45D8CGAF4WkRcBnA3geFV9Kt+Mco5oItIGznv1cgCnwGkq18N57d5OVZemKShxcHgFgK/yrSQhhBBC6kgDzQiqas8UcaoQUZBU9TsAv0heNaX/Hk43uT5HvNUAbkteeaOqC+CUyNNzxBtYm/zzqIeKyPFwO57PAnBSbWca0/SIs+B2+PRW1WkAICIfApgKYAiq2awW4Q44G4HeKcslhBBCSKFouKVhUkdE5I1qLq0HNxN6nohkZhxVVfeoJn5Ams0ihwB4J6MEJiVMh/NsfWi1qbIQkRMA7AC3ll5rRKSNiDwnIl8kZxBCREaKyCwR6S8i/xCR70TkExE5KLn+CxGpEpFvRORZEelalzoQQgghhNQzqwF8H3ktglsuzw7Lyz4gzV+DvnDOHy2TkbWTpzpEpBOcfeEvVXWBSO2WxkWkM4Dn4Vw67ZK1CwgA2gN4EG7dfjbcEvRoEfkznOFFZi39Vjij1WPyLX/RojCsqsqXJwb+0d+N5FRp5LSbp2rCP17xjDN2CGJc1tW3e/lq8mWe/I++fYM0Lwch+dPTyDJiVwwf3gqDRuyahExHQ/HEEwd7cmPya4bg9/pH8v4RgIxD/7Prrz5NluzDEZK2jdwrjQXb5xsSWTMGAMOHtwrGiqoClBHz87HL5MmevMFA3zp47kOxkcCaoxWCnY28IogxceKuntyrl3899lyqEc4INlqKudScpkfUdMSLtXaNcTOAT+GcMdYKEdkUzpBzCYBdIwdYrwfgHFV9I4k/G05DHgygT2I7ABHZBsBQEWmeCSOEEEIIKVfS/jWo1ZEqybl+JwPYIXIGYFr6wE13fAzgiGocNi7NKIEJU5L3sUbhmwL3nTcCEHg0F5GzkUyjdO3aFZVvrM2yWWQRfc89fdmeVBHfR/MDI28YiVNX2gQhLVpUenLlDH+f8NLhw4M0vQtQE7vXejhaoUcPwfDhmSuprAvqhY0xsKGrkJrKVn7LDl/hTqTp0WMZhg/PnE5TOm3beFl70k+mbStb+TbrvVeEMzmlynA0zCkocdb24R49BC3MGFSI8Sc2g1E5w9/he8UV/v7FVatiJW9SgNpY7Ekii4IYIpWe3NmcaRF7LlVWZ0mWgTOCjRIRGQDgvXycVidp3s21qTdNj1iI6o9UyXWcyl0A7gMwS0Q6ZpXZPJG/y3IIWR0D4I59ubiGBliULajqimQJ2tYvM2JHz+VJDsm+GwB69+6tAwcMWHPtqWfCO+6113z5scd8eeXKtyOlvG/kQiwNW8Kl4a5dT/Dkryo/9uR/HOC7oAGACQWoSU8jD8NjGD68FYYNy/wUebk7KipX4/mGrkJqju/Z05MHVbml4eHDP8KwYZmjrBrmOLGmxdql4Uzbak9/yb3C2oiUMNdgcENXIYuT1nwaPrwV1jdHdlYVoIQ0S8PHnGyWhuc21NLwwCBGy5b+0vBxxk3wYYeFuQ4cUIN5GJeGGzPjAPwUzvdhTpLzkscB2Ak5OnCaHjEZzk7Q0gdrj22pjh8mr3Mi1xYC+Dmc3V5N3AWgA5wDx1WqOjpHfEIIIYSQpoQAOFBEtk4ZP/XJcWkUwTEAhovIFqr6GQCISE8AuwK4rKaEAAZFwm6Fcxo5FP6ZgNWhqnq+iKwC8JiInKCqT6RIV1A6dgzD7JnhW5ufZ9KkLSM5LTKyNVW0unUaN412ATZcbl682Je/XN//F7xL5PDyl5fV3c30qX/5iyefdsoouCXL0pkJbIzcZGahdOgNAIDKDXaCDnX+Uz+7yPejeuaZfh7jxt1qcn21gDUsRfYKQgYNusiT773Xv77FrRes+Zxp25tGVBWhbuVI9ia3Q4OxouKUU+pcQmxcs2OfHRvj5jp2jE1jDtDWyNZlfT8jh88L+0yxz5zYc6lGOCPY2LmqGJmm6RH3ADgfwLMiciWcveB1AGbCzdYBAKo5HqbSZiYiiwC0iF2rCVW9SES+B/CIiDRT1cfzSU8IIYQQ0kjZvJbpZueKkFMRVNWlIrInnAuYh+CmJ1+FO2Iu22avuuNhCoaqXpzMDD6cKIOPFqssQgghpEnBGcFGi6r+r1h5p+oRyZnCR+aIU4UUO4nz8YWjqrHjZi4FcGmWfGoeaUeiDm5sCCGEkEYLFUESgT0iJRFTk5w2gosWbRCkmTlzFxPSw8h2J1nMRtDanmzhSdtua7yOArj1Vl/e8K5rPfn9AtgDxlgw+GQTUu/mnU2Sb41cMWIEAKD38OFrPm+avGe43aTZ+te/9uRfLLFnswN/+MOtJqSU7Qh9G8Cf//wiT/59u9C8Zsp1/v/FSuPdKdsNe3bbksITjhV1txGMjWv9zNj3wgt+v7joov8L0kyaZPcff2bkNOO0tT3c1JM22SR0y5vLRjD2XCIkX6gIEkIIIeUAZwRJhKLZ8xFCCCGEkNKGfw0IIYSQcoEzgsTAHpGSVavCsCXmnJN0Znbtjbypka3NoI0PbLllS08ebA4LuOiisNRNH7vJk2df7XvPfyZMUhA6j3+hSDkXnoqDD/bl50KbucbEjBwyrrvOE9vjOhsD9tf7sZG7WCMmABhk3If2Nf7ou3YN08w1x4eb0x8wblyQZN6UKZ78L3MyzL/+8HNPrghLbfLYPn3Nc7U96bP4FGOseCYStoEZ+/b8nW9o9/zzvwzS3HprLxPHl6dOXRkp6Rsj2wW4cGy32GeKfebEnkuk6ZO40vupqganjIjIjgD+parN0+ZHRZAQQggpB2gj2FSoyUNLczh/z6lhjyCEEELKASqCjRoRaYa1SmCzRM5mXQAHAJiXT77sEYQQQgghJYyIXI21R8wpgLdriG69hdUIFcGUzIvo1+a4V3z0kS/PnBnzLbXQyNa2xLcb6dSpJSz9+vny3nv78qZzArOB4BDVSIziYA0YYeXS4YKeYzy5c27/6AWhYuhQT762yx+DOFcN8z0HTmnr+yh7rPDVAhD2k6DfGDu9asOaEMcZeeul/n1+7fA2QZqr5l3gyfXlj9D2aeDgaLySIBgrioPtw4eZsXHTgQODNHvv7VvHzprlX583LxynF9qhPjhn3u83M2eGfgRbtPDv8y5dbLlBkprhjGBjpjJ5FziF8D4ApidiOYCPAWMwnQP2CEIIIYSQEkZVXwfwOgCIiAK4R1VzniOcBiqChBBCSDnAGcEmgapeU8j82CMIIYSQcoGKYJNARPYAcDycDzp72KCq6l5hqjjsEYQQQgghjQQRGQLgDgDzAUyFsw30ouSTHxXBlFhHnkBoqGsNiOM7uOfnVe6iResHYTkdV8cimLAVedWi9lQGIV3gul3G6jlfa+fiMWLEF558dTXx6srqX/sunuQ6a8AfGvQbH7h45x0/j612dvf9OgC2SsI+rUMdiWOrrM+Ztv3atL20zb0Bw/alX5s+0Oy64mxMsn26tMje+dAiMlYUh2Dss+NlipMBbJRFi2KxrEPpYPeI4fsgZNYsf7OIfebEnks1wqXhpsLFAB4BcLqq1vlxzrOGCSGEEEIaD90BPFAIJRDgjCAhhBBSHnBGsKnwLoAtALxaiMzYIwghhJBygIpgU+ECAA+LyCeq+kZdM2OPSMk664Rhrc0+nY4dfXnu3HaRnKz9ibUL8R2Txu7ZlcYH9eLFJsLWPcJEW2/tiT1mzvTkj8MURWIHAG2SdwB4ud5Kzk1u26B82TkSdkBgE1iLfHd+05O1Z08AQGWrVhiYfK6wHs9J3pyQtCWwtm3FtH1tuM70gRcjcd6pcylAMfp04dgh63PohLtYBKOjGRvRIxw/F0/wZTsGx8dp62TabuxsbuTweWGfKfaZE3sukbLgObjTJ8aJyLcIDVBVVTdLmxkVQUIIIaQc4IxgU+FVuGPmCgJ7BCGEEEJII0FVTy1kflQECSGEkHKBM4LEwB6Rkk7heeCBaYn1JTVlSugDcM4cP2zlSt9GsGVL324ky0RpDeut58vmzPt4op19a7U+r7ziyR+FKVCIQwwHdu3qB8x9H8AAAO8XIPdC077gOe5/111h4JDnCpCz8RqQaeeWLdd+po1g3cnuv5m2rSq8F85YP3lnyJAC5Fz4Pl043s/6PCAYKyrnzq1zCRtHwvrYADM2xsbPpZW+bMfg2JBbVeXbGuYa67t1C/Owzxgrx55LpDwQkR8B+DXcA7UjgB+r6nsi8lsAb6jq39PmRT+ChBBCSDmQsREs9IvUKyKyG4DxALaGcyydrcutBnBOPvnxFySEEELKAW4WaSrcCOAlAIfBbT8/P+vaewBOzicz9ghCCCGEkMbDDgCOUFUVEbt7eB6ArpE01UJFkBBCCCkHOCPYVFiG6p1vbgTg63wyY49IScTHKPr18+W2/vnggWEvAMyZ48uLFlmnoj7tIj6pY0bF2cyeE5p+brzbbp7ccdAgT9573LggjXVXXlVzsQCAA428YMpXfsD6BwNYCcCElwT5nuCegnvvjQRuWOdsly7dy5Pvb+s83nY49ljcP2FCLAmpBdltmWlb2/Zt295a94Ki/aQQFKFPF4zsMWBlMFYcuL548t9S5NjTyAMicezYBzM2xsZPix2Dt9kmUhdbGeNA2jqLjo3r3bv78pZb+nLsuUTKgrcAXCQiz2aFZWYGzwDwWj6ZUREkhBBCygHOCDYVfg3gbQAfAHgSTgk8RUR+D2BHADvlkxl7BCGEEFIuUBFs9KjqByIyAMDNAK4AIHAbRt4EsIeqfpJPfuwRhBBCCCGNCFV9D8BeItIaQGcAi1T129rkRUUwJTFbDHsAuLUJmTcvTGPDrGydUq9albtcy8yZYZi1EcQ5vpuhLbp0CdJsYZxOrzCVa7XttmFBf/d9WMr6b1df0ZLj44LnWBGx15s82d/k1bfvB5687bbbB2k+7H+6n29bCeIAQG8AM/KsI6me7LbMtO1Npu31tNM8ebuJ9wf5TJrk/8aTJ/u/cUXf+O9Zdwrfp4vF+mas0M8/9+Qf779/kGbFpEme3Moa3u2zT1jQUUf5shkbZ/pZRrH2fLZYIJx4s3HskBsZgoOwXHnkhEvDTQ5VXYY6nv9Ah9KEEEIIKSoisomIPCkiX4vINyLylIhsmjJtaxG5WUS+EJHvRGR8sjRq4zUTkctFpEpElonIByJyZOG/Tc769hWRl0VkiYjMF5EHRKRzJF5d2uSHIvJrEblbRB40r7/kU1/+NSCEEELKgQaaERSRNnA7WZcDOAVuc8P1AMaJyHaqag9KtdwH4CAAlwD4DMB5AF4SkZ+q6vtZ8a4DMAzObu5dAMcBeEJEBqtqmo3ndUZENgZQCWAKgKPgjn+7GcDzIrKbqq5O4tW6TUTkZAD3J2m+QnDmKKxvwRqhIkgIIYSUAw23NHwWgC0A9FbVaa4q8iGAqQCGAPh9dQlFZHsAJwA4XVUfSMJeBzAZwLUADknCNoBTAm9U1eFJ8nEi0gvuJI46K4IiUgHgVFXtWUO0SwC0BHCwqi5K0s0G8DrcSSBPJfFq3SZwu4afBXBGpoy6QEUwJe1bhwfNt+9mDPh6+M25Aq2CNAsX+vL8+b5s/QzGzl1fmuO/U1VVLI3ve7Jn/2M8udtgXwZCW8Sqab78pz+F5Yzo/oIJubOaWpYis+qllL8ae7CrbYSIjVJFCrsl0jBUPPCAJx+BB4I4Rxj5r32LWCGP+unTheFGT5Luvh3z0KEfBinOf9KXe/Xy5WXLwlLsGFv1ji/HxlyLtRG0PmQBoKs528GmWX99X+7UKcyjlZ3osUbjUaWuJB/rhwB4J6PwAICqTheRtwEcipqVnkPgnM8+npV2lYg8BuAyEVlHVZcD2A9AKwCjTPpRAO4Xkc1VdToAiEgLOIXtFACbA5gP4FEAVyQ2d3XhEAAvZCtoqvqGiMyA+65PZcWrbZt0A3BOIZRAgDaChBBCSHmQmREs9Cs3fQF8FAmfDKBPirTTIztiJ8Mpfr2y4i0HMC0SD6acUQCuBPAI3JLzDXCOmB/OUZcaEZF14RTLNN+1Lm3yNoAf1qaOMUryrwMhhBBCGg1dRGRilny3qt6dJXcGYNbDAAALAETmQj1qSpu5nnlfpKrWPs6LJyK7AzgWwCmq+mBybayILAAwSkT6ZewORaQ5nI++DM2ScE93UtXMVG2nJH519e2d8nvlapPzATwlIvMBvBzLJ2OLmAYqgoQQQkiZsLo4C4HzVLV/jjixDQxp/CZJyrRp4+0Pt7litFHoXk7eBwB4P/n8XwCbRfJc6RXglp2rsspK+11r2yazAPwb4TJ4dr6p9TsqgoQQQggpJguxduYum06Iz4plswBAzKVKp6zrmfdOIiJmVtDG2wBuSbm6g7izrTcPBrBOlnw2gMFINqhkkfHjtxBOCavuuy7IkuvSJvfAzWo+A7c7OdzEkAdUBNOSxjv0Er9fhVtFgA2NR9ANjQVxjx5+v5hmrR0QbgaZZezBY2lM1QLH1TED6cmTfXnmTJvxPWGiRuTANuTHDV2BkucXRm7/SXiS0bl/2MqT77zT7m6KbWDwPbafc45vfX/Hzz8NUnzTu7cn12RZXb7YPj2mQWpRO/yNZiNGvBHEGDHiLE/eZBN/t0jfyKYcu4nDOmlu1y5MYzd62AMG7GECQLhxpf2qBX6A3bXy30VhJhZbuZhHaVvZLFTjhxTUA5PhbOIsfZD7oTEZwOEi0sbYCfaBU4CmZcVbB8AP4NsJZuztMuXMB7AMwO7VlLfGObOqetv0RGQwgBWqOjFI5eJ/KyJVqP67vm6+V23b5FAAl6jqbTnipYKbRQghhJAyIKMIFvqVgjEAdhaRLTIBItITwK7I/e9kDJw7lqOz0raAmxF7OdkxDAB/h1MMTzTpTwLwUWbHcBKvNYAOqjox8qrTKR1JfQ8SkQ5Z9d0Nbol5jIlX2zZZigLOulARJIQQQkgxuQdAFYBnReRQETkEzg/eTAB3ZSKJyGYiskpErsqEJRs3Hgdwq4icKSJ7AXgMbnfu1VnxvgLwBwCXi8gvRGSgiNwBYE8Av8qKVwnnKubJ5GSO/URkHxE5S0SeFhF/SSN/bgbwPYAxIrK/iBwLtxv5XwCezrdNquEBON+KBYFLw4QQQkgZ0FBLw6q6VET2hFPUHoLbEPEqgItUNdtwSQA0RzhJdRqA38CdvNERwAcA9lfV90y8K+Bs/y6E87X3CYBjVPU5E+8kAEMBnJ6kWQ6nlL0E4Mvafk8AUNXPRWQQnLXKaLhZymcBXJy9kzePNonxPwDHi8grcDOcsV3D4aHn1UBFMC0xG8EpU3zZ2nzEvJlaz6ObbOKJ7bfZxpO7d98CFmsTaIudGLFesGELF35hYsQ8FhtPq5gQidN0GDp0ez9gRMPUo6GI/Q0e+Lm/qa1D92F+hN4XF6Uud95Zs+zwy/788+GeXNnd33wXWhk2fWyfHtGo+3RsJeznnjRz5k5G3jmSZltP6tRpI0/uH9n7asOsTWD37mGa9vM+8wM+Mi7jZs705dhJAdarv7X/23rrME0NNoINiarOAFDjub9m5212+HdwJsrWTNnG+x5OWbw+R7zVAG5LXqlR1QoAFSniTQKwT4p4OdukGu5I3jcDsFcsa7gj6FJBRZAQQggpAxpwswgpLJsXMjMqgoQQQkiZQEWw8aOq/ytkflQECSGEEEIaGSKyHZwD7PUB3KWqc0SkF4AvVXVx2nyoCKYlZiM4daovW59q1lkfEPqBss6mmjf3xA0P6BmpjG9Ha20G33wzTLFsmbXvqzRy6KOr3PhjT98TXUXDVKPBOME6jgQg3Q9ugJqkxb/fupu6qvk+FTGnck0c26cbtYlgKuw4F7NrHuBJCxcO9OQ33/TtDIHcZncbdo2c5jXxP0Y2htrW4at19gqETg6N78yoH8Ea4NJw00BE1oE7VeQIrD1R5TkAcwDcBGcSfVna/Og+hhBCCCGk8fAbAHsD+BmADeFvsHkRwH75ZMYZQUIIIaQM4Ixgk+F4AFeq6iMi0txcmw6gZz6ZUREkhBBCygAqgk2G9QH8p5przeCfj5wTLg0TQgghhDQepgP4aTXXfgxrQJ0DzgimJbZZxG4OmWAMk+fPD9NYh9J2Q4k1/o2Uu2rVBp5sbY6XLYs5Xn3ZyNZZdDnysCc9eHGHauI1TY4xsvSN9NdGjP0+4VYY4K/1U5UG48GLrcPvr41sj2UtB+zGuBWetGyZGaMBTJvWx5ODWbXY8+G///Vlu1nkP2ZCJ+ZQev31w7Bsttyy5usGzgg2GR4E8CsRqQLwVBKmyYkmP0eeex05I0gIIYQQ0ni4CcALcEfTLUjC3gIwFsDfVTUvBwGcESSEEELKBM4INn6So/SOE5E/w+0Q3gDAfDgl8PV886MiSAghhBDSyFDVNwFEPAfnBxXBtMydG4ZVVXni6ilTPPmbSDatTD5tbIRttvHlZcuCPKxZoSkWwHuRkmkTaNFrbvXkiqsbph4NRZ+vjb1Yh6ZmL3aTJwXfFwA6NG270M+MbPu8lFmfj2PHxs2CGFOm+DaCwVkBkXE68PRvbAK/Nc8P31LR0d48L5p17epHiD2XaoA2gk0DERkMoKeq/ily7TwA01X1b2nzoyJICCGElAFUBJsMv8baTSKWdZPrqRVBbhYhhBBCCGk8bI340h8AvA/gh/lkxhlBQgghpAzgjGCToRmAdtVcWw9Ay3wyoyKYlsAoBMDUqZ74lU0SyaaVkTc1diIt5szJWRVrerJwoQmgPWDAY489F4RVHCeRmE2XgUaWDrbfNG1i33eckSvrpSYNR8XVvlHgY49pEOe44w6ur+qUKOH4uXDhQE+eNatH7mzMWL7KjPV2pI/ZCFrLw27mmRN9LpFy4AM4J6BPR66dCODDfDKjIkgIIYSUAZwRbDLcAmC0iDwB4B4AswB0B3A2gMMBHJ1PZlQECSGEkDKBimDjR1WfFpELAfwGwBFJsABYAuACVa1uI0kUKoKEEEIIIY0IVR0hIiMB7AJgfQDzAPxDVZfkmxcVwbREfJCtNjYg9rTJBQixfgM7GrnzEvMbtgvtQY2pCQB7tvD/IiWXF1tu6dsEbhmxB/xPENK0Gdjk/Qbm4tIgxLZJZRP3K2iJ3RdbbunbDU6dWm42g7Hx0x9jq6qMjWBknIYZy61fWft8+DZSqp2828A8c5rFfGPWAJeGmxaquhjAS3XNh4ogIYQQQkgjQ0Q6AdgSQGt7TVXfSJsPFUFCCCGkDOCMYNNARFoDuB/AMXC2gTGap82PiiAhhBBSBlARbDL8Gs4j2CkAHgJwHpy3oVMBbATgwnwy48kihBBCCCGNhyMBXAvgsUT+p6o+oKp7wPkY3D+fzDgjmJaI485cxr+xzSLWaag1EO5s5NUdbQjw0Uc25INISeWGf/b2pys39+SKeqxJqVCx3nqeLB3YTyy2TdS0WcXixfVZnXpnTCTM3jsSuNk+v1jVKWH8fvLRR/t6cmyctrMsdqxfZOQ0Wz3tM6djng6lOSPYZNgUwGRV/V5EVgJom3XtfgAPII9ZQc4IEkIIIYQ0HuZj7RFzMwFsn3WtC4B188mMM4KEEEJImcAZwSbBOwB+BOBFAKMBXCci68F5HLoYwFv5ZEZFkBBCCCGk8fA7uOVhALgeQC84m8HmcEriuflkRkUwLSlsBG2MmI3gaiMHB4238H+SOfZkcgATJ9qQdyMlNWUuCkL0mr94csXVVfVTlRLhwOR9XtbnEwabHvpouTkGTsNNnmTb7KJH13pmyLTt3+qhVg1JhfFYb+8tufqiSKpbi1WdEsEfY+0YHBunNzZjuR3rcz0/gNB2izaCBABUdSKAicnnxQCOFJF1AKyjqrab5CSVjaCIbCIiT4rI1yLyjYg8JSKb5k65Jv0PReQJEZknIt+JyCfJOXm50qmIXJ+2HEIIIYTEySiChX6RhkdVl9dGCQRSzAiKSBsArwFYDuezRuGmIseJyHaqujRH+v5J+koAZwL4Gs4TduRMHkIIIYQQko2InJxPfFV9MG3cNEvDZwHYAkBvVZ2WVOhDAFMBDAHw++oSikgzAH8B8KqqHp51aVzaChJCCCGk7nBpuFEzMo+4CqCgiuAhAN7JKIEAoKrTReRtAIeiBkUQzvN1HwDnpK1QTSSzk48D6A9gf1X9QERGAtgbwGEA/gi3k2YGgF+o6gsi8gsAF8C56BsH4ExVnZt34UtCL082ZJGRY3O0rYwc3JPm8HJjrgMAyPopEmqclG0CHOJJ+m6nIEbFjlfXV2VKggFGnvawAgBat65c8/nRE2kTmC+PGjvKwUlbAmvbdsCJ/olOqQ/0bKRUXO3fW/puaJMsOx5iQmIeChsz/hhrx+Cqql5Bio3NWG7HeutXMPa8sA/o4CkUeS6RJsvmuaPUjjSKYF8Az0bCJwM4Okfa3ZL31iLyDoAdASyE84Z9qap+l7aiItIZwPNwPnJ2UdXpWZfbw2m/wwHMBnAFgNEi8mcAW8Edv7IhnEXzn+HO5yOEEELKBs4INl5U9X/FyjuNItgZTnmzLAAQTs34bJy8Pw539MNlcLN51wLYBMDh1aTzSDamvAT3h2jXyIzeegDOUdU3kviz4VzBDwbQR1W/T8K3ATBURJpnwgghhJBygIpg00JEegH4MYDuAD4H8C8NlwxzktZ9jEbCJBJmyexKHqWqVyWfK0WkOYAbRaSPqn6cI48+AP4B4GMAR6hqbC58aUYJTJiSvI81Ct8UuO+8EYBZNhMRORvA2QDQtWtXVL6RleURRwSFLttrL09ua663jlTUNvh0I8/u0sWTly6sDPIYPny5CTk0UlLp0qNHRwwfnk+dO3pSZcRXQ+/hw+tWqUaGNQZo3boSANCs2ZI1n/NrYxIj05bA2rZdavpa73quU0MTu/+GD7dzAvn1vfzHhIbmv560cGHwOEHlHnt48vK+fT25jYm/MUKaG3mGkb/q0CFM9EZTN1YgItIawO0Afga/m3wvIn8BcJ6qWkWhWtIoggsRHoELuNnA2ExhNvOT91dM+MsAbgTQD07Bq4kBANYHcHE1SiBgzPNUdYWIIFK/jCunmI4GVb0bwN0A0Lt3bx04IMsS66abgvgfv/iiJ1v3fjE/gl2MvI2RtzrtNE/+x5n3B3kMG2YV/j8FcUqZ4cMPxbBhMWuD6rA2glsEMSoOOqiOtWpcWBvBOVk2gsuWDQQADBt2S/1Wqgny8MPPrfmcaduOwwZ5ccrNi+fxERvBQQfZsPxsBPMfExqaP3jS22+HNoK7jD7dkz994AFP/peJPy9Sin3w/tDIfQ44IEz0/PORnNbCGcEmwXAAJwK4Gs7U7ks487fjAVwFZ4J6QdrM0iiCk+HsBC19kFuJm5y82xnFzGyi9a8c4y4AHQCMEpFVqjo6RZrCk2KziDX2jRn/Wg00uCdb+zFim0Xchu2mTB9PmjPnLE9+sFuayeimxWFG/uQx/5Y68bizAQDDh++KYcPOrp9KlQEnnri2LTNt+5hp+8OO8/vjM/VRsQbkwR13DMLmzPHbpFs3+2c116OiseGPwbHNIruYsdyO9bmeHwA3i5BqOQ7ANar626ywzwD8JpkE+znyUATTOJQeA2BnEVkzDSMiPQHsitx/+16E8z+4vwnfL3kPzsiIoKp6Ptwmj8dEJNcGFUIIIYQY6FC6ybAOwknlDP9E6KCkRtLMCN4D4HwAz4rIlXCze9cBmAk3WwcAEJHN4AwnrlXVawFAVeeLyA0Afi0i38A5lu4PN3X5l3yMGlX1IhH5HsAjItJMVR9Pm5YQQggpd7hZpMkwFsC+ybtlXzhdKzU5FUFVXSoie8IZRTwEt6z7KoCLjM2ewBkt2lnGawEsBvB/AIYB+ALAzXDKZF6o6sUisgrAw4ky+Gi+eRBCCCGENGJ+D+AhEWkL4AmstRE8Bu5I9JOyV3FV9bOaMku1a1hVZwA4MkecKkR2EquqJpWuyfF0dXnG8rsUwKVZ8ql5pB2J/LxzryVyuHcuh9IxA0jrRDSXjWDsMPNw71jT4rbbfufJ841NYI09uglwWSTsqkt8G6ybjzvRxMhYGK2E+69FCkN2W7q2Pc60/SXmt7n25tCG9cYi1KyhiN1/9h697Ta/TS68sKk5N/fH4Og4ncNG0D4LYs+LRUYOLAIjz6WaKKUZwcR7yM/hFJe+cBupp8KZgT2gqjn3EIjIJnCTVPvA6R9j4SapZph4neAmoA4DsC6A8QB+rqqTCvV9knL6JvXZBc4sbgzcRtcFWXF6wOkw/QFsn9Rn80SHSsvryfu58A/sEHM9g92A7pHWfQwhhBBCSKFYF8CVcIdB3Aan5x4IZ462NYBLakqcnDT2GpzCdQqc2dr1AMaJyHaqujSJJ3AK2eYAhsJ5E7k8iddPVUPfP7VARDYGUAnnpu4oOL9nNwN4XkR2y1Jse8HN3L0L4E24pdx8OS13lPRQESSEEELKgFKaEQTwHYAtsmfLALyazN4NFZGrcpw+dhaALQD0zuw3EJEP4WYVh2DtKuQhcKec7amq45J44+Hc+P4SeeyuzcElAFoCOFhVFyXlzIabnTsMwFNJvDdUdcPk+pmohSKoqn8pQH3XkGbXMCGEEEJIwVDV740SmGEC3K5Y63bXcgiAd7I3nSZHz74N36P5IQBmZ5TAJN7XAJ4z8SAiXUTkDhH5XESWi8iU5KCJNBwC4IWMEpiU8wacHcGhWWFp3ObljYj0EZEjk5nJvOCMYFoithjW71OaP1o2zTIboYX/k8yLeRnF7BQlNQ623PK5IOyCP23lyRX1VJeGouJo3yPSDyf9NYgz5eamZmPVmPHv4pvNb/Pc1uFBTP/Z1j/evOKJJwpfrQbE9tgKcw//actPgzRTpzbmPu2PwdFx2ozldqyP+Q202GdKkCZPG0GgpGYEq2MPOPPIXMbOfQHEvJBPBnC0ifdRNfFOFpF2qrpERNrDKZHrwj12psO5urtDRNZR1RHVVURE1oVber63mnL6RMJrjYj8CUALVT0nkY+AO8q3OYBvRGQfVZ2QNj8qgoQQQkgZUMSl4S4iku0X+O7kpK68EJH94Oznfq2quWraGfHTzRbAnXyWHa+qmnhI4i4BcCGAzQBsq6oZj+FjRaQjgKtF5I4a6tQJbqNGdfUp9EmUBwC4Jku+BsDzcK75boE7cWRw2syoCBJCCCGkLsxT1f7VXUw2bHg7V61SJSJ9ADwKt+HCdx1RPeH0e+i9RFLG2x/OGfN0EcnWjV4CcCbcrN6H5lrme2TySlNOIeiGRLlNdiH3BXCGqk4SkT8CuC+fzKgIEkIIIWVAA24W2QPAOBO2RkFKfN69Arcce1iK2UDAzb7Z45gBNzuXPTO3oIZ4yIq7AdyO3pXVlLd+cqra9OxAEdkcwFdwSmB15cRsIevCdwDaJZ/3gLMayMzILgGwXj6ZUREkhBBCSDF5F8BOsQvJjNarcMrM/qqaxnwScLZ3fSPhfeAfbj0Z8Z25fQDMyDoYYz6cQndhNeV9Aueqxn6P2aq6QkSqaqiP9etXV94DcJ6IzABwHoBXsjahbI48nclSEUzL4sVBUCGO+16R43p8s8hXBSi5ofglgK+Td+DT4eFx1RWHTg3CGiuxv4cXPP+8J8vg7U2MUjai38jIJwQxWrce6Mm77+5f79YtzNU65H3zTV9etqwyUpdHjFwajrSnTAl/P5lyhyfr86d48h8Hh+Y8hZ5CqE8qpvr38KfPhve5HPrLLOnrIteo0PhjcHSc7uiLucb6NATPnMhzqSYaakZQVRdj7YzVGkSkK9Yek7aPqs7NI9sxAIaLyBaZkzOSGbtd4fvlHwPgNBHZQ1VfT+K1hxtosweRv8P5GZyhqjU9ZIPvkVXOKSLSIdmVDBHZDc7uMLwB6sYVcPX9AG5jTbZT6cNQ/TnEUagIEkIIIWVAKfkRTHbavgSgJ4DTAfRIZgczfJyZHRSRkwHcD2CvjDIH53j6fADPisiVcEuz1wGYCeCurHzGwJ0kMkpELsFah9IC4KaseH8AcCyAN0XkD3AzgG3hnFvvrqqeq5kINwM4CcAYEbkBQIck/38BeNp896OSjzsm7weIyFwAc7O+X7Wo6gQR2TSp21Qzi3o3nC/F1FARJIQQQkh9syGAHyWfH45cHwS3cQRwPo+bI8uuUFWXisiecArcQ8m1V+GOmFuSFW+1iAwGMBzA7QBawymGg1R1Zla8r0VkF7idt5cC6A432/YJgNG5voyqfi4ig+AcWY+GmwR+Fu6IOes70PqPuj15fx3AwFxlJeUthVtyt+EvpEmfDRVBQgghpEwolRnB5GzdVDtqVXUkgJGR8BkAjkyRfgHcrOPpOeIthDv/+Odp6hVJPwnu3ONc8fLeSZzMir6gqvOTz7nKeDBt3lQE0xJx3FkUG0FzUHnjtxFs70mjR++OZs0qMXq0Mxz78NBi7KxvOAYYeeeloTcBaWvNRc4tWn3qzkWeNGjQXp58/fVhil3afegHWIO/L78ME/1kQ18e7hsW/mPJwCDJlVf6YePGvWpi3BqW02D4v7EMPsuTl0b6yTtt/XvjjcJXqt748NBwVW306LXfuVmzStixIp3L5YYihY1gN38sL4qNYC0cSpNGy0gAO8NtahmZI67CneGcCiqChBBCSBlQSjaCJG+ydwNvXsiMqQgSQgghZQAVwcaLqv4v9rkQUBEkhBBCCGkkiEhrAP3h/Hkp3Ezhu6pqj7ROBRXBlMT+RH1bgHyb2YAuXTxx2rRYqkJYm9QXIz3piOdPR+Uee2Dg685mt6L+K1RQTjLy7Nd9W6+2bWPnlL9ctPrkRxcj/zaIccstvt/AX+xt7P8qKoI0K572PCVghrkeu2/aGHlTI+9y+OFBmtdu9cv+/VjffvHii+0577+KlBw1wq0H7vGktm3DP/ivm7500h6+zeCowleqaDwVCat4fq3dfuUeeyA0ezqiiDWqK/4YHB2nB/r3VzDW1wJ778SeSzU91Dkj2LgRkXXgXNKcBWAd+EfbLROROwD8SlXzUhKoCBJCCCGElDDJec3PA9gTzi3N3+D+ZwuATQAMhtvt3AfAgfnkTUWQEEIIKQM4I9ioOQrOt+JRqvp05Pq9InIEgL+KyBGqGpuIj1KI2WpCCCGEEFI8jgfw12qUQABAovw9AeDEfDLmjCAhhBBSJnBGsNHyIwBXpoj3PICIh9fqoSKYkti9U4j7qaMN6NfPEydNKmWnqjE286R33mnpyW/t/ACW9O2Ltx54oD4rVTD+z8hvjfYN+o/c40YT4+2i1ic/ehj5d5702GPWoS9wrPzVD9jvQk/8aM6cIM3HRrYxYtvaWhu5m5H7PB3+Cd5m/HhP/sVtt3ly98eO8eTjjvOvOy418qxInPog3EC0xx5LPXm06Wv/d6S/eeR2NC6yx4AlffsGY8XOO29mUhTUY0ZBiY7TZizvWIBy7DOHm0XKiq4I997FmAFgg3wy5tIwIYQQQkhp0wbA8hTxViD8b10jnBEkhBBCygDOCDZ6uovIFjni2KWfnFARJIQQQggpfZ5MEUfg/AqmhopgSmJr6LVpPJtPTyN/2XuACUnzu5cSV3nST571nfhWAOgNYGK91af2nB0J++hVYxO4l3UYXUo2gRsZuWabwGNXPRxmcf75nvgvc8j9+5FSZ6ernId1lLvAyIsQ8q2xT/zxkCGefOyfVvoJHgs30h133O9MyDAjf4GGw+9LRx7p97VXTV88ey/fZhAA7i58pQrG2KzPvQEMftY6/L7KyGcUt0J1IrTx/LL3UZ7c01y3z4LVKUqxz5x8bbs4I9ioOa1YGVMRJIQQQsoAKoKNF1X9S7Hy5mYRQgghhJAyhTOChBBCSBnAGUESg4pgSlpFwtoZOY3Hvz5G7njwwZ58wW9siqLNBheFa67x3RfNufqGBqpJ/hxjAz4P7W336v6CCQltgxqGTLu3zPrs+xS97z5jE9jJ1H1I6Kv0Q2MTaH0E1sYesDbEyrH3ZGtT1+2u9L/PsXd1DfJYet++nnzGGdYP6+VZnzNt+1UNNS0m/u+1117+5sHPI/31mO6+3eBfgxilw5wb/LHimmt+68lXX12ftcmXcJz+zW98G8E/mrG+z3PPefJHKUqxz5zYc4mQfKEiSAghhJQJnBEkFiqChBBCSBnApWESg5tFCCGEEELKFM4IpqWbPf0U6Gb8mM0z1ztHstnNBgwf7okjek/Lu2oNx15ByFXn+PZTD5awXY/12Njnv//1ZOkeO9v0zqLVJz/sUZLXJO//WfP5xhv9OKf3/9BPcpF/LvKsqqqgFBti+3hDYutSZeTO5vv0uNGeAw2cfqt/X8+9cTtPvuyya7KkTNvaTt1QNoN+X+zefZsghpo+PeAHP/DkNwpfqVpjrW3tWHL11eF4A7xatPrUlREj/LH8j5/4Y/1uxkYwdm9Zf5rBUyjyXKoJzgiSGJwRJIQQQggpUzgjSAghhJQBnBEkMagIEkIIIWUAFUESg0vDhBBCCCFlCmcE07LJJkHQpmaziNWqe0ay6Xaaf270r0ZuZWKcnHfVGo5TwqDLLvbEz+qpJrnYIhK25113efKmA22sg1E62M0hl3vSJZf0AAB06zZtzedLzzUuzi/zNxesGjfOk/3e7Pg273o2HLau9vt0M98XAFrc6bfJpWZDyfz5PdamT9r25pv9tges0/SG2jxyfhCy6UB/Q8IM0+dnDRkSpGmoezYo97LLTMAtkVSlu1kEuNaTfjXyQU/+rXkW7PbAA0EOVUbuYSNEnku54IwgsXBGkBBCCCGkTOGMICGEEFIG0EaQxOCMICGEEEJImcIZwbT06hUEbTFhgidb157dtt02SLP63vs9+Ybm1oH0wtrUrkG4/PJOQdi8G0I7l4bAHsZ+8tFHB3HO/ffZnjxzZql4vw7bFbjAk44/3u+PFyemmZMmrf2Mxx7zsxg/3hMXpahJGyNbJ+mxAcTa6tkJiNWRNPYfqc3X1gMA2qeIk82iSFgX0ya2zS6+eG0fybTtrFl+2z/6qP/bxG3ZGua+tn363H9f48l3HD02SPPbJ57w5BWFr1Yq5hmbucsvvz+Ic4M1zywp/N/8hhv8sf767/3vs83EiUEOXSZN8uSgj0eeSzXBGUESg4ogIYQQUgZQESQxuDRMCCGEEFKmcEaQEEIIKQM4I0hiUBFMS8QWo72xAWzfurUfIfCDBVx0kQ0ZUbd61Sv7etJvzww9jo0qEZudX623nif/7dS/BnHuPGiMCXmviDWqieZGDv0z7rTT9p586qn+9Q2//BAA8J+V3635jClT/EhLl3qi6a2B/R8QLhl0NHLMz6C1KbM2gWlsBK1sbT6B0F6qnZE7Gtl+XwBBm9g223DnD9d8zrTtqadu58WZNs3/bSZMiPjXDO7z72O1KQJ+n77zTr/PH/xCeF/86u++9WXF4sWFr1YK/m7k2Hhzww37mpCXi1afuuP3gYsuus2T/1hREaToZvxaYtkyX87TRpCQGFQECSGEkDKBM4LEQkWQEEIIKQO4NExicLMIIYQQQkiZwhlBQgghpAzgjCCJQUUwLT17hmG77ebLxnD3y12PCJKMOHKlCWmoI97zZ9CgoX7ArhsFcax77PriVBswZ44nHtR2fiTVPUWqTb4c7kkiewUxBg/25f79TYSJyfdduXLtd29hbu8e/pH17YzhebuZM4Nyl+WQY8+UXJtDarNZJLZ0YQcvuxkk2ByyySZhJqZNgjbL7kdJ2/bv728Wsb/NxInh76c6y4Q8GdalXvD7/EEH7RrE0KX+vXNq27aePLLgdYoTjCW7hnUdNOgLTx43rpQ3i/hj/YgR/rPgiivC58WGx1X5AdNMq8SeS4TkCRVBQgghpAzgjCCJQUWQEEIIKQOoCJIY3CxCCCGEkAZFRDqKyBwRURHZO2WaTUTkSRH5WkS+EZGnRGTTSLxOInKviMwTkaUiMlZEto3lWcfv0FdEXhaRJSIyX0QeEJHOJk4PERkhIuNF5Nvk+/YsdF3ygTOCadlwwzCsX78a5VdfjWU0KRZYolzsSa9d5Dujvf1Q35aovtguEtbzlls8ed/DrLvh64tWn/zZwci+7dPAgWEK6zfW+i5Hx47ufd68tZ+33tqP0864XLbXlywJym1t7Ahb2+mENNMLtZmCsLZ6aeJY2TaS/f4A0K2bL1ubwUxbAmva1mZrf5vY7zdunLVvs7bBDeXM/JYgZN/DfuvJL5t7a7uL/XEBAD4MQgrP7XPC8ea1u/wxScbZuoXfr3TwnwWvvmrHBeAEa4du+3DsuZSDEp4R/B0ATRtZRNoAeA3Acjgv/Ao30I8Tke1UdWkSTwCMAbA5gKEAFgK4PInXT0MD3lohIhsDqAQwBcBRcD7tbwbwvIjspqoZ8+heAI4B8C6AN2FPamgAqAgSQgghpMEQkV0BnASnqN2XMtlZALYA0FtVpyX5fAhgKoAhAH6fxDsEwG4A9lTVcUm88QCmA/glgAsK9DUuAdASwMGquigpZzaA1wEcBuCpJN4bqrphcv1MlIAiyKVhQgghpAzI2AgW+lUXRKQlgLsA3Ij83GgcAuCdjBLovp9OB/A2gENNvNkZJTCJ9zWA50w8iEgXEblDRD4XkeUiMkVEzs6jPi9klMCknDcAzMguJ2tmsGSgIkgIIYSQhuKXcMeJ35Rnur4APoqETwbQJ2W8TUWkHQCISHs4JfIgABXJ+3MA7hCRoZH0axCRdeGWntPUp+Tg0nBaOnUKw743B8cb+6J57xSxPgXnkCBEl/7Yk/9m/Il9VdT6VE/obQt4b+AvPPmViyeYGA1lm7lBJGxnT2rd2jcyi50jb+3SFi3y5TYZe7+JE9fa/mXbt8US1cberzaksfezFKMusXrYMNtm2TaESdvaZsxlMwgA48f7gcuW7WxixMyU6uMOC++LV17x7533bvTvrSPQMDaCsdb426HehA506VJPlrbhuObMxUqPefMigQONzaq1k4w9l3JQpAmpLiIyMUu+W1XvzpVIRHoBuBLAIaq63JnzpaYznL2fZQGATiZeVTXxkMRdAuBCAJsB2FZVpybXxopIRwBXi8gdqlrdwNQJgNRQn97Vf42Gh4ogIYQQUhYogO9zxqoF81TVurlfQ7Jho7lXE6dU3QHgWVV9pZblxjaXWG1SUsbbH8A/AUwXkWzd6CUAZ8LN6n1ormW+RyavNOWUHFQECSGEEFJM9gAwLjtARI6Fc5nQP5l1A4DMtui2ItIhseWrjoVws32WTvBn5hbUEA9ZcTeA29Frj//KsH7i5mV6dqCIbA43Ya01lLMgEl4yUBEkhBBCyoaizAjm4l0AO5mwwQDWhbOhszwD4Gs4FyzVMRnO/s/SB8DHJl5sZ24fADNUNeM3az6cQndhNeV9Aueqxn6P2aq6QkSqaqjP69XkWRJQESSEEEJI0VDVxQCybQghIvPg/O5l0w/AHwAMg1umrYkxAIaLyBaq+lmSZ0+4WcbLTLzTRGQPVX09idcewMEAHsmK93c49zUzVLUmA92J1YSPAXBK9kymiOwGZ3dYmoapCVQEC4lxyBs/D9w6M7fGvjFb02JwhSfp5PZBjL+azSEfBzHqh5424PLLgzi33mpD3i5KXXJjPT0PisTZ0ZO2NV2iS5fcpVib8VVd3O+3YlUzzFjkPq/Twf9NW5p8V5oFELv3Cci9nyS2p6MQe1By+YpOE8fKzZsjoGVLX7ZtsnzR2s+Zto0a9WcR+/3sbzxhwo4mxvxITs8aeVkkTjHw751bb/UnQB6M3H89b7jBk6sKXqc4/zLyEjNm6eRwskn62jH4N4WtVLXYsd6vR/R5EXHyXjeKZiOYN6paBdNVsjaLfKCqb2WFnwzgfgB7ZZQ5APcAOB/AsyJyJdyXuw7ATDh3NBnGABgPYJSIXIK1DqUF/k7lPwA4FsCbIvIHuBnAtgC2BrC7qvo7k0JuhvOFOEZEbgDQIcn/XwCeNt/zqORjZiA4QETmApib9f3qDSqChBBCSNlQcm7s0tAMbrPJGk1RVZeKyJ5wCtxDybVXAVyUtdwLVV0tIoMBDAdwO9y/9fEABqnqzKx4X4vILgCuAnApgO4AFsEphKNzVVBVPxeRQXCOrEcDWAH3b+7iiO/AJ4x8e/L+OoCBucoqNFQECSGEENLgqGolIrtsVXUkgJGR8BkAjkyR7wIApyevmuItBPDz5JU3qjoJwD4p4pXUTmIqgoQQQkhZUDpLw6R0oCKYlmUR+xxrqGXiHHJYzyDJ5Ze38uQbbrjOxPi9kfM5cSfDAUHI0KH/58l/nHagJ1f0fbEW5dQPgX/e/fcP4kwYYkOWBnGKg3UYbTenDQhSbLutn2ZHYy5m/JIDCO3drFPjjCnRihVAVVWsnmEXtvJ334Vpli/PLw8gtAm0dndpsLZ7MRtB68jZyuus48vrrpt/Htlk2tZ+P1u32O9nf+Nly/w+MGnSXpESTSPgZSMXy+G0f+9MsL7Zzwzvv14NZCNosXbMFX3DTZx6gD8+XtDrb548YsTtCKnN+LiFkX3H3Jdf7v++h+y/Iszimfd92d747dqBkLpCRZAQQggpCzgjSEKoCBJCCCFlAxVB4tOsoStACCGEEEIaBs4IpiXmPOyjj3zZ2m9MmRIk+e1xh3nyoYdu58mPP36bJ7//flhst26+fKHxg/6Thy8I0jw1wt+kVBFmW7IEHg47dgzirLeeDbH2OZMiOeeyI4w59LNO5Xc19djek/feO8zhJz/x5S23NKVGirX2bpaMD0CRtTZr1gVZvjIALF7sy7lsBqsLyybmVzBmA5hNzHYvl02g7RMxc6pcJlbZ1zNta8sxruti3TO4Z63PuH/+c6Mgzdixx3ry4sVbmxjWV6Y15gOAHE4P0TYS5t87wb0V+YKhF9LSpeJF395voNmk+sehQ4M0/zzxOU++zR+mA3NxAOjXz5eP9X9O/GTdD/2AG58JM5k2zZdt28c6W41waZiEcEaQEEIIIaRMSaUIisgmIvKkiHwtIt+IyFMismnKtJuKyF9EZIaIfCsin4rI9SIS+ytq06qIXJ+mHEIIIYTkYnURXqQxk3NpWETaAHgN7rDlU+Dmlq8HME5EtlPVatfXEmVvLJwfhF8DmAG3tnYNgC3hjnMhhBBCCCENQBobwbPgjEZ6q+o0ABCRDwFMBTAEoeO7bHaFU/j2U9WME6xxItIZwDARaaOq39a69oQQQghJCW0ESUgaRfAQAO9klEAAUNXpIvI2gENRsyKY8Z78jQlfBLcsndcxK8ns5OMA+gPYX1U/EJGRAPYGcBiAPwL4EdzM4y9U9QUR+QWACwB0BjAOwJmqOjefcgHErYGtp9V33vHEmM18q6uv9uSfdO3qy9byPFLuFGON/8yj/vXSdQ1dO4K9BRGPyXvv7W+6mTDBOr1dP5Kz7ZZ2l0afIMWWW/qOgK1v6139vSP40Y/CUu1GgVarzH+hFLspVrdu48mZjR4TJwLbbOM+271LueQ0m0WWmvn/NJtFCuFQOs1mEXvrpNksksv2PlvOtK3Np9my/H+/FYP93y/mBPzf//blt9/2NyL9/e++PHVqzMG0dbFsN4/Etnn4G6KCDU+Ryka+caPhQyuPGBHEaW3CrjLXt4510Olmh9Ao/+ZZPdd/DEXcSSPIdeedfblX4G4/B1QESUgaG8G+AD6KhE9G7EnpMxZu5vB3ItJHRNolh0RfCODOmpaVLcks4lgAvQHsoqofZF1uD+BBAPcCOBzO5f5oEbkFwCAA5wG4KPn857RlEkIIIYQ0ZdLMCHYGsDASvgBAp5oSquoyEdkNwGg4xTHDvQDOT1vJZGPKSwCWANg1MqO3HoBzVPWNJP5sAB8AGAygj6p+n4RvA2CoiDTPhBFCCCHlAx99xEdUteYIIisA3KKql5vw3wC4VFWrVSZFpDXcSuXGAK6DW7L9MdzM+sOqem6OshXA00majwEcoapLTJyRAI5S1XZZYa3gNrfcrqrnZYWfDeAuAJuo6qxIeWcDOBsAunbtuuNf//rXtRe/iiy7LFjgy2bdLNaywVq4daDWzEzSRtbVlpnfbJG5XvMv2vCs06MHls8Kmr9aOhi5TWQ55PMlfqw5c+xgF5t8tnHs2a7hcs866/i/VwdTObts2MZfAQQAtPKPm4ZoLXbdmX6yOsli6dIlaNvWVcKuUH7/fc3y6kg1csWJpbFhOYaYKGJuFHtbxMKs3Lx57jxsHCtn356Ztg3yiTVCDlT8TFZE1gW/NSvOdun+6699efny2AKtXbu344n5wgCsb8Fu3fw43duZggF8a/zdhTFqJt8xob6x43ZHI7e2HRYI7RtsPzE3aKrnhbV/6Nw5TLSBb74yaNCgd1W1PwCIbKvukVpotlxTBml8pJkRXAg3K2jphPhMYTZnABgIoJeq/jcJe0NEvgZwt4jcaZZ4YwyAM/C62CqBWSzKFlR1hbgb09YvM9xGj5ZX1bsB3A0AvXv31oEDBqy9eOWVYQJz0Pp75rJxBQoAsEOdtVIrB3oPH45Phg1LHX+AkQdGHL7OGPZHT77zTv+69f0NhDZmWxt/vdYcBwAGDvTlNtOMhZF1Ij458nCzxnmWmPdoa/C2vrF5TIzZKld/j4HfJUqvsT/NyyAu4dtVvtZq7f9idoVWAc0lA+H/oVwyECrd9vds08JoV7F2z2U4mWXHtaZtbZz5833ZGlYCoSduS8wxcI8evry930G/7eXbxVZWhlkYs+Wge8ZsPDM2phmOPtqXNx0eOqx/zdjQ/SvMtkbyHROaAtY6s0ckjv3Lu4ONcPnlNgS4viaPa7QRJCFpFMHJcHaClj4ILZEt2wJYmKUEZsiMEz+EW8KtibvgJoVGicgqVR2dIz4hhBBCotDvH/FJs1lkDICdRWTNuUMi0hPONcyYHGnnAOgkIvaPTeaQrc9TlK+qej7cJo/HROToXAkIIYQQQkhu0iiC9wCoAvCsiBwqIocAeBbATLjZOgCAiGwmIqtEJHtn/UgAiwH8TUROEZFBInIJgOEA3kV4WGa1qOpFcO5hHhEROqImhBBC8iKzNFzoF2nM5FwaVtWlicuXPwB4CM5+9VUAFxmbPYGzPG6WlbZKRHYGUAF3GkkXOAXybgC/Uc3PSl5VLxaRVQAeFpFmqvpozkSFIuLPz1p/WX9UVcWqS5kRbNN54okgzqbmhPffnjnQjxBzImd/0/ff9+XnjYEVAFxnnLtNnuyJK4x9WMw3mMXehFEDVmtD1s34KMvYA554IvDww/E0XYyfxBQ2gm1Mu1m5cxoHfzEDv1xYQ8I0DgutwaKV09gIzptX/fVM2xr/b0E/ipRja5/G714rKxs70TZ9fYudAyNOKw+0hq5H9fNl24+AsN3GVvpy5P6LeTAkNWPtw2N2Vva0hQ2M3CPm35aQPEk1QqvqDABH5ohThcgmJ1X9GMAxtamcqsbyuxTApVnyqXmkHQk3S0kIIYSUIZzBIz61+KtOCCGEkMYHdw2TkDQ2goQQQgghpAnCGcG0WNshhKd2VtVLRcoP47YbMyJ2MZtWVPgB3bv7csw+zDhVm20ux6xvrC2UtfOJnS9tyWUTGLMRbGfq38bKyXdZduih+PjNN1Plm6Zca6cWDBhpbAStY900WEfqKWwErd2dtc+M/TY2rCY507bWbsvKMWenucpJYzPY2tiftjdOAjewTgMBdLvjDk/e2EawzjOB0Fb0c9+5Q+z+s/coKQxVRrbPnB6R51LNcEaQhHBGkBBCCCGkTOGMICGEEFI20KE08eGMICGEEEJImcIZQUIIIaQsoI0gCaEimBbrZBXAovqvRVliDeurInGWzJzpyauNHDNmt5tDrGw3gpQS1W042QTA30yYjZMhcFgcKSfXZpEWkU0czUxYbZYd7OJVbDHLbrDItVkk5tw714aSbDnTtrXZ6NFQtDey3SyysdkwBQCdjWx/v9j2hDSbpEjdWWQDIs+l3FARJD5cGiaEEEIIKVM4I0gIIYSUBVwaJiGcESSEEEIIKVM4I5iWVaElEO1i6gdrpxaz91uUI461/wPiNmONBdsbM5ZCq7M+18Z6iFRPdts2Fqydq5U/i6SxdoTWZjBmrxmzLyWFJ3jmRJ5LueGMIPGhIkgIIYSUBQr6ESQWLg0TQgghhJQpnBEkhBBCygYuDRMfKoJpacGmqi/aGdnaLMWmsRcZ2doENmZ7QEKKRey+sPeOvd86RtLYe9TasjU228pGA59LpACwFxFCCCFlAd3HkBAqgoQQQkhZQEWQhHCzCCGEEEJImcIZQUIIIaRsoPsY4kNFMC2tWwdBbLy6s1UkbGsjtzGydYoLcDMIIcXC3n/dInHaG7mnkadE0nxa2wqVMcEzJ/JcIiRfqMsQQgghZQFtBEkIbQQJIYQQ0iCISHMRuUhEPhKRZSIyX0TGishGKdJuIiJPisjXIvKNiDwlIptG4nUSkXtFZJ6ILE3y37YI36WviLwsIkuS7/GAiHQ2cXqIyAgRGS8i34qIikjPQtclH6gIEkIIIWXD90V41YmHAPwawAMA9gNwGoAPANS47i0ibQC8BmdNdAqAnwHYEsA4EWmbFU8AjAGwP4ChAI4E0DKJ16Oulc8qZ2MAlQDWBXAUgPMA7A3geRHJ1rV6ATgGwEIAbxaq/LrApeG0tLNujkPHxyTE/tM4BkAVgOMSeeuOHcNEPXv68vz5nrho5syc5Vo7whk5UxBCgNAG0E6vxOx6O26yiR+w/vqeuENVVZBmyqJFaz5XIRwruKUhJHjmRJ5LNVNaS8MichzcY+Enqvpu1qUxKZKfBWALAL1VdVqS34cApgIYAuD3SbxDAOwGYE9VHZfEGw9gOoBfArigAF8FAC6BUzAPVtVFSTmzAbwO4DAATyXx3lDVDZPrZwLYt0Dl1xrOCBJCCCGkIfg/AK8bJTAthwB4J6MEAoCqTgfwNoBDTbzZGSUwifc1gOdMPIhIFxG5Q0Q+F5HlIjJFRM7Ooz4vZJTApJw34OYhDs0KK7n/OFQECSGEkLKhNJaGRaQlgJ8AmCwiNyX2eytF5J8ismeKLPoC+CgSPhlAn5TxNhWRdkl92sMpkQcBqEjenwNwh4gMzfFd1gWwecr6lBxcGiaEEEJIfbM+gFYATgXwGdxS73K4Jda/i8guqjqxhvSd4ezsLAsAdDLxqqqJhyTuEgAXAtgMwLaqOjW5NlZEOgK4WkTuUNVV1dSlEwCpoT69q/8aDQ8VwbREbNloIxiynZGP2Np4BbzySsxZd11sPWqUk3faKcxkmTmyfqI/FnR85pmw3Oeeq7FerSJhVUau7g4npKlgB/yekTjWBtDe060PPjhMdNhhvty/v0kU2v1vPWHCms9z1l0XV5mx4qkpvvfBDyN1LTeCZ07MxrpGFEWyvuwiItkD9d2qendGSDZsNDdpMiuSLQEcqKqzk7hvwCmGlwA4Nke5GgmTiJwm3v4A/glguohk3yovATgTblbvQ3MNiXKYyStNOSUHFUFCCCGkLCjaZpF5qtq/hut7ABhnwtokFfo4owQCgKouSTZz/ChHmQvhZvssneDPzC2oIR6y4m4At6N3ZTXlrZ+4eZmeHSgimwP4Cu67VFfOgkh4yUBFkBBCCCHF5F0A3vKPqn4nIp+h+lm0XFOXk+Hs/yx9AHxs4sV25vYBMENVlyTyfDiF7sJqyvsEbunaLmPNVtUVIlJVQ31erybPkoCKICGEEFI21L/7GFVdDCBm7/c0gKEi0kNVZwGAiKwH4KcA/p4j2zEAhovIFqr6WZK2J4BdAVxm4p0mInuo6utJvPYADgbwSFa8v8P5GZyhql/VUG51dotjAJwiIh2SXckQkd3g7A7TuMNpMLhrmBBCCCENwXAAiwD8TUSOEpFDALwAt2x8YyaSiJwsIqtEZI+stPfAmXo/KyKHJmmfBTATwF1Z8cYAGA9glIgcJyL7JWEC4KaseH+AmxF8U0TOEZFBIjJYRIaJyLMpvsvNcFr2GBHZX0SOBfAwgH/BKbxrSL7rUQB2TIIOSML2QAPAGcG0RIxyw5DyYnAkrP9tt3nyP3/i++ocPRrYfvtK/PKDgQCAxW+Fefzwh7582GG+ufqmvXoFaVp36eLJP370UU/uYjegALAu5e1fwEVh1QJH1d8aueQcRJEmg/3X3sbI7SNpOhp5AyMHZ3EB2MJu7Dj+eF8+9dQgzYyeAzzZ7uf6z3/CctZbb+22lO23r8Q/R/qRjvjnH/26Xhiu2D0fZtuk6RgEBCE5KC2H0qr6pYgMAHAL3MkizeCUtj1UdXJW1GZwm00kK+3SxM3MH+BOJxEArwK4KGu5F6q6WkQGwymdt8OdWDIewCBVnZkV72sR2QXAVQAuBdAd7jHwCYDRKb7L5yIyCM6R9WgAK+AU04sjvgOfMPLtyfvrAAbmKqvQUBEkhBBCyobSUQQBQFU/hVumrSnOSAAjI+Ez4I6My1XGAgCnJ6+a4i0E8PPklTeqOgnAPinildROYi4NE0IIIYSUKZwRJIQQQsqCovkRJI0YKoJp6dAhCOpoZOsyNbRKa9wcYuQd/vnPIM7pd/7Ykx+40Fre/Q/Dh3+Lm2/OOJMNLZvWW893wv6RObTnjDN8eyQA+Mkw30YQP/iBJ27x6qtBmi3Gj/fkRcaOcFGQwrmfz8baCFo51gdsmJVXpEhjnV9n0jTH2n5p4+QrNzVig50Nq0nOtK11Tm7ThK6TwzQ2TiyNDbM2gVaOObjvaGVr//fTn4aJ9trLlw8/3BP/uTg8Leu+6335scd8efHiTyK1W2txO3z4tzjpJH+sOO003774/n/uHOTQ7Cc/8eSS3ppZC2wf6GgjRJ5LhOQLFUFCCCGkbCgtG0HS8NBGkBBCCCGkTOGMICGEEFIWlJb7GFIaUBFMS8RfUztjb9PN2JhVFbE69cHWRt5htO9Kac/LfgzLuHFPm5BKI38H5/j9mUTeLMhj8WLfFujee32bpVURY7alJ/l2S3teYnwNxmyhjPFhx2nTfHnWrDCNDZs715fnz/fE1YsXB1nksiNMYyNo42TkzwHsmXzOZQNo84iZkNs0Nk4sTTFM0WNLFzbMynZwi+WRy94vW860rU2Ty/4vTRxr7wcAzdZbzw9Yf31f7trVl3tYz5iRMOuDc5ttwjS77eaJr73l137UqDDJyJG+rGptct8JE+F/WZ93BfAb7+oDDwz05Koq31YRAF4zY9KnR/qeRKZESm1MdDOyfeY0dj+CpDTg0jAhhBBCSJnCGUFCCCGkbOCMIPHhjCAhhBBCSJnCGUFCCCGkLKCNIAmhIpiWmFFuz56euPEU3zS5qmiVqR+O2313T772oyM8edy4mPvW+1PkvBLAF8nnLyLXfeN01U6e/PzzO+QsYdYsP4/+/fcM4vTazQ9rNW+2H2HOnDDjL0x9zeYQzJvnic0WLQqyaGfCrIxIGiwxrqwXLvTlZKPSgtatsd3WyTafpUujcVLLAFavXOnL9npY05LZLNKsZUs/wBrax8Ks3Lbtmo9r2tbG6eT3T7SLuHa240cuORbWxThNt5tHNtoozKObv91gRZeNPdnsjwIATDTOoCsrffn558M0qu+ZECvHNotksxLAZybMl8eNax6kunagPyZdZcasijffzFFuabOxDTDPnPw3iwA8WYRYuDRMCCGEEFKmcEaQEEIIKQu4NExCOCNICCGEEFKmcEYwLda5KwBssoknWhvBxsSBscBnnvHEq9e3NmQRY6GCYGz14Dtxnjs3dJz71lsbeLI1d6uqCkux5jbdum1cowwAXX7ky9ZEp411Fx2z97Nh1v4vjY2glTNfuHNn4Oc/j8f57jtfXr48nkcWzYz3bitHvXvHwupKi8hQZcNyyTEbwXXW8eV11/XlbHu/TNvafKxNYBobQRsnhY3gt8bttO0mxjwVADDnIyMbs9fYfWGHsYkTfXnu3K/CRLDO1+09XAjC8ebqq/f15KvmP+PJB1o7SgB/K2idikswAplnTvS5lBPOCBIfKoKEEEJIWcClYRLCpWFCCCGEkDKFM4KEEEJI2UD3McSHimBa0vgRtEki2SwqSGUKz4+33TYIe/D5zibE+uSK+QAsBK2MbJcyjH88ADNn+rI1wYqYvwUuAbt29eU0rt1CuU2Nsqub31PaGZPHdluH5drv02zVCj8gYw/4738Deyb+EfP1G5jG3q8x2wjG8sjlRzBbzrSt+TFWt/D7qzXNjIUFcsRl5SJjq5fL3WQac9S5c335yy/DNNZu0N5bsfsvvEftPVwIYuPNBE968Hnfj+DJkXHtb5MmFbJSBaNjJKw4fgQJ8aEiSAghhJQFtBEkIbQRJIQQQggpUzgjSAghhJQFnBEkIVQE0xLzDdbDN+7qbOw1tokY7bxVwCrVhYE24Pzzgzjjx9sQ44cO7SM5f5NnTTaIhG1mZHPGKtoiF9ZlXsx+ymLd6sVsvWw+bU1VrFuvWLexYbnc0gExd3e+DVbr1s6ec/mq5vh0Xudovi2s+VtHc70AZnj1Sb7mizHTxVxmksuyfPNl2naZcZmXyz0jUL3bx+qux8IWL/Zle5R0mv6axq7Qfp+Q2P1n71F7D0ecHCLmj7AmYuONX1k7Zp0cGdcGDhniyZV51qJYbBMJs88U+8yJDhY5oSJIfLg0TAghhBBSpnBGkBBCCCkLuDRMQjgjSAghhBBSpnBGkBBCCCkb6FCa+FARTEvMKNc699zdd2a63XPPBUnsUeyf1a1WqbGm3ANthIFBCJa/Y0OMoTJ2iJQ0PUdN1gfQJivtFpE4vY3sx1lvvXCDSffuvrzRRr4c87tqf1K7ISMN35tVFrsJIM1milx+noHcfo8z5axcCcyZEy+7ujT51LUxbRbJFT8WVtPmkUzb5koT+/1ybkpJkcb2tTTYPl2rvQWGzz8P7z+7kQVoaeTYBpPs0a8Nwg0mls0jYf6YFGzUiYxrNuQjI8e2tRQDO/JtF4tkninBMyfvH5RLwySES8OEEEIIIWUKZwQJIYSQsoEzgsSHM4KEEEIIIWUKZwTTksZG8Cc/8cQN5oQnye8/wT8k/T1z3dqrRHzE5mTrSNhAG3DWWb5svwtC36Vdu/bx5Llz142UlMvCpj2AKgAnJPJGQYxOnXzHsb16+de7dQtz7WKMIK1NoLWPi4WFTpvDNLns7qzcvHmYR0tjPpXLdi8WVp0ssvZzrOxsctnUxcpJY5dXDLvBNHXNlSZNHjW1WXbbZpPm97NxrP1fzD7VDjm5bBFj3y+Xs+s0tol2aJgXucXnzPHtBqdN8+WFC/2xw/FF1ucqAKdE4mRjLZ2Brl19u0E7ZsXGNTv2HXXPPZ5cGSl5So6axbBPDOsw2lpYb7DTTmEm5plCG0FSDKgIEkIIIWUBFUESwqVhQgghhJAyhTOChBBCSNnAGUHiQ0UwLWlsBC1duwZBG/Tv78n7T5zoy5MmefK3ESMe6w60jZGbbR2xEhw0yJcPOsjPs0WrIMluu/mytXOaNi3067VokR9m7aXatQO6dPkCp53mfAXG/PvlsveL/RQ2rBB2dzFsHGtTZu3/0uRRm3Kru75kSdyGMm05hUhTKhTCrjCbTNvWxl9hrjhp0qxc6cvWr2Chys3l43BJxHDZhi1a5Mvz5vl2vy7O2rAuXb7A0Uf7NnK2HrGxwtoPm+E1Oq41O/RQT+5mOvlx48YFaVZP8a0Ev7V5hlVDGzsIbbutL9vK9usXZmLH8jrbCBIS0oiHeUIIIYSkR8GTRYiFNoKEEEIIIWVKTkVQRHqIyAgRGS8i34qIikjPtAWISGsRuVlEvhCR75J8BqRMqyJyfdqyCCGEEFIT3xfhRRozaWYEewE4BsBCAG/Wooz7AJwF4CoAg+GcR70kIv1qkRchhBBCakXGfQwVQbKWNDaCb6jqhgAgImcC2Ddt5iKyPZzn4NNV9YEk7HUAkwFcC+CQvGvcUKTZoWC9mdrdFrXAbgQpFs0idiP77l2zXFsq3wDuv5d2KsVgxgxgi55s22LAti0elW8Af32sntr2gANqliPYGRNu0SBNiZwzgqpal7vzEAArATyeld8qAI8B2E9EIv70q0dE2ojIc8ky8/ZJ2EgRmSUi/UXkH8ny8yciclBy/RciUiUi34jIsyISbuUlhBBCmjycESQhxd4s0hfAdFW1u+0nA2gFt+ycChHpDGAsgN4AdlHVD7IutwfwIIB7ARwO4CsAo0XkFgCDAJwH4KLk859r9U0IIYQQQpoYxXYf0xnOttCyIOt6TkRkUwAvwR29u6uqzjVR1gNwjqq+kcSfDeADOJvEPqr6fRK+DYChItI8E0YIIYSUDzRvID7FVgQFbi46Fp6WPgD+AeBjAEeoasSdKZZmlMCEjPfPsUbhmwL3nTcCMCuolMjZAM5OxOXSvPlHedSTpKcLgMjR9aQAsG2LB9u2eLBti0fvtR+/fgl4rkv1UWsNf7tGTLEVwQUANo2Ed8q6nosBANYHcHE1SiAALMoWVHWFiADhbOSK5N24fF+T7m4AdwOAiExU1f6xeKRusG2LB9u2eLBtiwfbtniIyJrjq1R1/4asCylNiq0ITgZwuIi0MXaCfeCUsmkp8rgLQAcAo0RklaqOLkI9CSGEEELKjmJvFhkDoCWAozMBItICwLEAXlbV5SnyUFU9H26Tx2MicnSuBIQQQgghJDepZgRF5Kjk447J+wEiMhfAXFV9PYmzGYD/ArhWVa8FAFV9X0QeB3CriLQEMB3AuQA2B3BiPhVV1YtE5HsAj4hIM1V9PGeiunF3kfMvZ9i2xYNtWzzYtsWDbVs82LakRtIuDT9h5NuT99cBDEw+C4DmCGcZTwPwGwDXA+gIt5t3f1V9L8+6QlUvFpFVAB5OlMFH880jj7J48xQJtm3xYNsWD7Zt8WDbFg+2LcmFqMY29RJCCCGEkKZOsW0EC46IbCIiT4rI18lpIU8lfgbTpG0tIjcnJ5N8JyLjRWRAyrQqItfXrfalTW3bNjnV5W4RmSIi34rIDBF5WEQ2T1ku2zZ9Ppcn7fVWyvhs29zpfygiT4jIvKyTiS5MkY5tW3PaTUXkL8l48K2IfCoi14tI2xRpm3TbikgPERmRPIO+Tb5vzzzS81lGCkajUgRFpA2A1wBsDeAUAD8DsCWAcWkGFwD3ATgLwFVwzqa/APCSiPQrSoUbEXVs2+PgTpH5I4ADAFwGYAcAE0Vkk6JVupFQgH6byWcLAFfAnZxDUPe2FZH+AP4JYB0AZwI4EMAtcGYuZU1d2ja5PhbO/devARwEd/LTxQDuL2K1Gwu9ABwD5+LszVqk57OMFA5VbTQvABfCHWzYKytscwCrAPwiR9rt4Zxbn5YV1gLAJwDGpChbAVzf0G1Qom3bNRK2GZwL+2vZtrVvW5PPS3DulCoBvJUyDdu2+rTN4FxcPV3Lstm21afdN2mffU34jUn6NmXets2yPp+ZfN+eKdPyWcZXQV+NakYQwCEA3lHVNf4HVXU6gLcBHJoi7UoAa3Ybq+oqAI8B2E9E1smnIiLSRkSeS6bmt0/CRorIrGSp9B9Zy0wHJdd/ISJVyRLLsyLSNZ8yi0yt21bDI/+gqv8DMBdA93wrwrYNEZET4GZZL69LRdi2HgPhfJr+vhAVYdt6tErevzHhi+AU8HxOl2pybauqdTnnjc8yUlAamyLYF0Ds2LfJcAN6rrTT1XdsnUnbCm6q/v/bu7dQqao4juPfHypYdPFICAXRY3goCBISteL00il6SEgIiqCC0DKxrF7MKEMiLJSy0EqotIuUD/XQxSy0h4ioyIdKKim6EYkdMYUs49/DWlNz9mydPZdjzszvA8Oe2XvW7M2Pmdlr1l6zViWSppMue5wLzImIXXWbTwOeJ10GmU+6jLdV0qPACHAbsDTff6LqPo+DTrJtIGkmMAP4ssVyzrZA0hCwBrgnIqrMxnO013G2483Ly6mSPpT0l6RfJT0m6aRWDsLZNtgOfA08LGlY0imSLiO1Mq6PiENVD6JPs+2Ez2XWVRM9s0i3Tadx2jhIU9UNlayvWra2vSmljtJvAweBuSWtYacCCyPPfSzpZ9KQOVcBw5HnPpZ0HnC7pEkxfj7k/0sn2Y6jNGj4elKL4MYWyjnbcquBr4Bn2z0AZ1vqrLzcAqwj9W2dBawEziad/Jpyto0i4g9J84CtpApKzTPA4qoH0MfZdsLnMuuqXmsRhNS/oajKZQZ1ULZmGPgA+AEYKbskChyqfXCy3Xm5vfAh2U2qiJ/Zwv4nWqf51KwD5gDXR0TZF1YZZ1v2BOli4AZgUUS0O9aTsy1X+/7bHBH3RcSOiHgEeAC4WlKVlnBnW/YEaSqpgj2D9CeTS4G7SbNKVW096vds2+VzmXVVr1UExyj/tTNE+S+ker8do2xtezOXkPq8bYyIg0d5zv76BxHxZ75bPL7a+qkV9ns8dJLtvyQ9BNwC3BQR21rYv7Mtt4HUqvqjpGmSppG+dCflx1X6Aznbcvvy8p3C+tr79oIK+3e25W4m9cG8MiI2R8T7uZK9DFhY64vWRD9n2wmfy6yreu3S8Oek/hFFw8AXFcrOl3RyoW/FMOmN/E15sXE2AKcDmyUdiYitFcr0ik6yBUDSctLltSURsanF/TvbcjPzbWHJtjHgDmBtk9dwtkcvC42tK7WWlSod+p1tufOBsYjYU1j/UV7OJF1mPJZ+zrYTPpdZV/Vai+DrwGyl8dQAUBqEc27e1qzsFGBBXdnJpEsV2yLicIX9R0QsJl3aeFnSgmYFekgn2SJpCWkaweUR8Xgb+3e25UZKbrtInfhHgFcr7N/ZlnsTOAyMFtZfnpcfV9i/sy33CzAkqfjHhYvy8qcK++/nbDvhc5l1Va+1CD5N6mj8mqR7Sb/kHyT1c9hQe5Kkc4A9pDHsVgJExGeStgBrJU0BvgUWkcbFuq6Vg4iIpZL+Bl5UmvN4S9NCJ762s5V0LalV6i3gPUmz6173QERUalEEZ0vj+3ZH8cUk7Qcml207FmfbkO2+3JVhhaQDpMGTZ5EG6X2uftiUZpzt+GxJf2y6E3hD0irge1K2K4BPSEPQVNKn2SLpmnz3wry8QtJeYG9E7MzP8bnMJlxPVQQj4lAegmANsIl0CeddYGmhn4NIMwMUWzxvBFaRWq6mkVpWRiPi0zaOZZmkI8AL+QP0UquvcSLpMNvRvH6UxtaVnaS+Qq0ci7OduGNxtuOtBH4HbgXuIs3QsJpU4Wn1WJztf2W/yz8I7yd9355BqkA+BayKFsfR67dss1cKj5/My/rvTJ/LbMKp/T8impmZmVkv67U+gmZmZmbWJa4ImpmZmQ0oVwTNzMzMBpQrgmZmZmYDyhVBMzMzswHliqCZmZnZgHJF0MzMzGxAuSJoZmZmNqBcETQzMzMbUP8AFQnidyCV8EkAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2d(u.data[0,:,:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Realize that the solution has a large amount of noise, which is generated by the reflections at the boudaries. The main objective of this series of notebooks is to present several numerical schemes designed to reduce the wave reflections on the computational boundaries of the domain during simulation.\n", "\n", "We now create a routine to plot the shot records of the Receivers" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "def graph2drec(rec): \n", " plot.figure()\n", " plot.figure(figsize=(16,8))\n", " fscaled = 1/10**(3)\n", " fscalet = 1/10**(3)\n", " scale = np.amax(rec)/10.\n", " extent = [fscaled*x0,fscaled*x1, fscalet*tn, fscalet*t0]\n", " fig = plot.imshow(rec, vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n", " plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n", " plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f s'))\n", " plot.axis('equal')\n", " plot.title('Receivers Signal Profile - Devito')\n", " ax = plot.gca()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n", " plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access the result of the displacement in the receivers, we access the term *rec.data* and pass this term as an argument to the function *graph2drec*." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "graph2drec(rec.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this plot we can clearly see the reflections of the waves at the lateral boundaries." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "assert np.isclose(np.linalg.norm(rec.data), 990, rtol=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.6 - Conclusions\n", "\n", "We conclude our introduction to the Acoustic Problem, including simulations in the Devito environment, highlighting the following items:\n", "\n", "- The simulation time when using Devito is small, when compared to routines that use pure Python;\n", "- Devito has a simplified language for building operators and equations, once the user acquires familiarity with the package;\n", "- When no reflection reduction strategy is used, at the end of the simulation we observe a large amount of noise in our image;\n", "- The constructions employed here can be used as a basis for implementing the methods with absorbing boundary conditions or absorbing boundary layers;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1.7 - References\n", "\n", "- Berenger, J.-P. (1994). \"A perfectly matched layer for the absorption of electromagnetic waves\", Journal of Computational Physics, 114(2), 185-200. DOI: 10.1006/jcph.1994.1159. Reference Link.\n", "\n", "- Clayton, R., & Engquist, B. (1977). \"Absorbing boundary conditions for acoustic and elastic wave equations\", Bulletin of the seismological society of America, 67(6), 1529-1540. Reference Link.\n", "\n", "- Engquist, B., & Majda, A. (1979). \"Radiation boundary conditions for acoustic and elastic wave calculations,\" Communications on pure and applied mathematics, 32(3), 313-357. DOI: 10.1137/0727049. Reference Link.\n", "\n", "- Fichtner, A. (2010). ”Full seismic waveform modelling and inversion”, Springer Science and Business Media. Reference Link.\n", "\n", "- Grote, M. J. and Sim, I. (2010). \"Efficient PML for the wave equation\", arXiv preprint arXiv:1001.0319. Reference Link.\n", "\n", "- Higdon, R. L. (1987). \"Absorbing boundary conditions for difference approximations to the multidimensional wave equation,\" Mathematics of computation, 47(176), 437-459. DOI: 10.1090/S0025-5718-1986-0856696-4. Reference Link.\n", "\n", "- Higdon, Robert L. \"Numerical absorbing boundary conditions for the wave equation,\" Mathematics of computation, v. 49, n. 179, p. 65-90, 1987. DOI: 10.1090/S0025-5718-1987-0890254-1. Reference Link.\n", "\n", "- Liu, Y., & Sen, M. K. (2018). \"An improved hybrid absorbing boundary condition for wave equation modeling,\" Journal of Geophysics and Engineering, 15(6), 2602-2613. DOI: 10.1088/1742-2140/aadd31. Reference Link.\n", "\n", "- Sochaki, J., Kubichek, R., George, J., Fletcher, W.R. and Smithson, S. (1987). \"Absorbing boundary conditions and surface waves,\" Geophysics, 52(1), 60-71. DOI: 10.1190/1.1442241. Reference Link." ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "hide_input": false, "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": "3.8.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 1 }