{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "# `MaxwellVacuum`: Solving Maxwell Equations in Vacuum with NRPy+ and the Einstein Toolkit\n", "\n", "## Authors: Terrence Pierre Jacques, Patrick Nelson, & Zach Etienne\n", "\n", "## This notebook generates `MaxwellVacuum`, an [Einstein Toolkit](https://einsteintoolkit.org) thorn for solving Maxwell's equations in vacuum, in flat spacetime and Cartesian coordinates. This thorn is highly optimized for modern CPU architectures, featuring SIMD intrinsics and OpenMP support.\n", "\n", "### Formatting improvements courtesy Brandon Clark\n", "\n", "[comment]: <> (Abstract: TODO)\n", "\n", "**Notebook Status:** Validated \n", "\n", "**Validation Notes:** As demonstrated [in the plot below](#code_validation), the numerical errors converge to zero at the expected rate, and we observe similar qualitatitve behavior of of the error nodes for Systems I (ADM - like) and II (BSSN - like). Also validated against the NRPy+ [Tutorial-Start_to_Finish-Solving_Maxwells_Equations_in_Vacuum-Cartesian](Tutorial-Start_to_Finish-Solving_Maxwells_Equations_in_Vacuum-Cartesian.ipynb) notebook.\n", "\n", "### NRPy+ Source Code for this module: \n", "* [Maxwell/VacuumMaxwell_Flat_Evol_Cartesian.py](../edit/Maxwell/VacuumMaxwell_Flat_Evol_Cartesian.py) [\\[**tutorial**\\]](Tutorial-VacuumMaxwell_formulation_Cartesian.ipynb)\n", "\n", "## Introduction:\n", "This tutorial notebook constructs an Einstein Toolkit (ETK) thorn (module) that will set up expressions for the right-hand sides of Maxwell's equations as described in [Knapp, Walker, & Baumgarte (2002)](https://arxiv.org/abs/gr-qc/0201051) and the [NRPy+ `Tutorial-VacuumMaxwell_formulation_Cartesian` Jupyter notebook](Tutorial-VacuumMaxwell_formulation_Cartesian.ipynb), both for System I:\n", "\n", "\\begin{align}\n", "\\partial_t A^i &= -E^i - \\partial^i \\varphi, \\\\\n", "\\partial_t E^i &= \\partial^i \\partial_j A^j - \\partial_j \\partial^j A^i, \\\\\n", "\\partial_t \\varphi &= -\\partial_i A^i,\n", "\\end{align}\n", "\n", "with associated constraint\n", "\n", "$$\n", "\\partial_i E^i = 0,\n", "$$\n", "\n", "and for System II:\n", "\n", "\\begin{align}\n", "\\partial_t A^i &= -E^i - \\partial^i \\varphi, \\\\\n", "\\partial_t E^i &= \\partial^i \\Gamma - \\partial_j \\partial^j A^i, \\\\\n", "\\partial_t \\Gamma &= - \\partial_i \\partial^i \\varphi, \\\\\n", "\\partial_t \\varphi &= -\\Gamma,\n", "\\end{align}\n", "\n", "with associated constraints\n", "\n", "\\begin{align}\n", "\\Gamma - \\partial_i A^i &= 0, \\\\\n", "\\partial_i E^i &= 0.\n", "\\end{align}\n", "\n", "This thorn is largely based on and should function similarly to the [`BaikalETK`](Tutorial-BaikalETK.ipynb) thorns, which solve Einstein's equations of general relativity in Cartesian coordinates, in the BSSN formalism. Further, we generate the C kernels for a number of finite difference orders, so that users are free to choose the finite-differencing order at runtime.\n", "\n", "When interfaced properly with the ETK, this module will propagate the initial data for $E^i$, $A^i$, and $\\varphi$ defined in [`MaxwellVacuumID`](Tutorial-ETK_thorn-MaxwellVacuumID.ipynb) forward in time by integrating the equations for $\\partial_t E^i$, $\\partial_t A^i$ and $\\partial_t \\varphi$ subject to spatial boundary conditions. The time evolution itself is handled by the `MoL` (Method of Lines) thorn in the `CactusNumerical` arrangement, and the boundary conditions by the `Boundary` thorn in the `CactusBase` arrangement. Specifically, we implement ETK's `NewRad` boundary condition driver, i.e. a radiation (Sommerfeld) boundary condition. \n", "\n", "Similar to the [`MaxwellVacuumID`](Tutorial-ETK_thorn-MaxwellVacuumID.ipynb) module, we will construct the MaxwellVacuum module in two steps.\n", "\n", "1. Call on NRPy+ to convert the SymPy expressions for the evolution equations into C-code kernels.\n", "1. Write the C code drivers and linkages to the Einstein Toolkit infrastructure (i.e., the .ccl files) to complete this Einstein Toolkit module.\n", "\n", "The above steps will be performed in this notebook by first outputting *Python* code into a module, and then loading the Python module to generate the C code *in parallel* (using the [`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html) built-in Python module)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Table of Contents\n", "$$\\label{toc}$$\n", "\n", "This notebook is organized as follows\n", "\n", "1. [Step 1](#initializenrpy): Initialize needed Python/NRPy+ modules\n", "1. [Step 2](#mwev): NRPy+-generated C code kernels for Maxwell spacetime solve\n", " 1. [Step 2.a](#mwevrhs): Maxwell RHS expressions\n", " 1. [Step 2.b](#constraints): Divergence Constraint\n", " 1. [Step 2.c](#driver_call_codegen_funcs): Given `WhichPart` parameter choice, generate algorithm for calling corresponding function within `MaxwellVacuum_C_kernels_codegen_onepart()` to generate C code kernel \n", " 1. [Step 2.e](#kernel_codegen): Generate C code kernels for `MaxwellVacuum`\n", " 1. [Step 2.e.i](#feature_choice): Set compile-time and runtime parameters for `MaxwellVacuum`\n", " 1. [Step 2.e.ii](#parallel_codegen): Generate all C-code kernels for `MaxwellVacuum`, in parallel if possible\n", "1. [Step 3](#cclfiles): CCL files - Define how this module interacts and interfaces with the wider Einstein Toolkit infrastructure\n", " 1. [Step 3.a](#paramccl): `param.ccl`: specify free parameters within `MaxwellVacuum`\n", " 1. [Step 3.b](#interfaceccl): `interface.ccl`: define needed gridfunctions; provide keywords denoting what this thorn provides and what it should inherit from other thorns\n", " 1. [Step 3.c](#scheduleccl): `schedule.ccl`:schedule all functions used within `MaxwellVacuum`, specify data dependencies within said functions, and allocate memory for gridfunctions\n", "1. [Step 4](#cdrivers): C driver functions for ETK registration & NRPy+ generated kernels\n", " 1. [Step 4.a](#etkfunctions): Needed ETK functions: Banner, Symmetry registration, Parameter sanity check, Method of Lines (`MoL`) registration, Boundary condition\n", " 1. [Step 4.b](#mwevrhss) Evaluate Maxwell right-hand-sides (RHSs)\n", " 1. [Step 4.c](#diagnostics): Diagnostics: Computing the divergence constraint\n", "\n", " 1. [Step 4.d](#outcdrivers): Output all C driver functions needed for `MaxwellVacuum`\n", " 1. [Step 4.e](#makecodedefn): `make.code.defn`: List of all C driver functions needed to compile `MaxwellVacuum`\n", "1. [Step 5](#code_validation): Code Validation\n", " 1. [Step 5.a](#convergence): Error Convergence\n", " 1. [Step 5.b](#errornodes): Behavior of Error Nodes\n", "1. [Step 6](#latex_pdf_output): Output this notebook to $\\LaTeX$-formatted PDF file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 1: Initialize needed Python/NRPy+ modules \\[Back to [top](#toc)\\]\n", "\n", "$$\\label{initializenrpy}$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.507318Z", "iopub.status.busy": "2021-03-07T17:08:32.506577Z", "iopub.status.idle": "2021-03-07T17:08:32.512074Z", "shell.execute_reply": "2021-03-07T17:08:32.511489Z" } }, "outputs": [], "source": [ "MaxwellVacuumdir = \"MaxwellNRPy_py_dir\"\n", "import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface\n", "import os, sys # Standard Python modules for multiplatform OS-level functions\n", "\n", "cmd.mkdir(os.path.join(MaxwellVacuumdir))\n", "# Write an empty __init__.py file in this directory so that Python2 can load modules from it.\n", "with open(os.path.join(MaxwellVacuumdir, \"__init__.py\"), \"w\") as file:\n", " pass" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.518405Z", "iopub.status.busy": "2021-03-07T17:08:32.517372Z", "iopub.status.idle": "2021-03-07T17:08:32.521039Z", "shell.execute_reply": "2021-03-07T17:08:32.521607Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting MaxwellNRPy_py_dir/MaxwellVacuum_C_kernels_codegen.py\n" ] } ], "source": [ "%%writefile $MaxwellVacuumdir/MaxwellVacuum_C_kernels_codegen.py\n", "\n", "# Step 1: Import needed core NRPy+ modules\n", "from outputC import lhrh # NRPy+: Core C code output module\n", "import finite_difference as fin # NRPy+: Finite difference C code generation module\n", "import NRPy_param_funcs as par # NRPy+: Parameter interface\n", "import grid as gri # NRPy+: Functions having to do with numerical grids\n", "import loop as lp # NRPy+: Generate C code loops\n", "import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support\n", "import reference_metric as rfm # NRPy+: Reference metric support\n", "import os, sys # Standard Python modules for multiplatform OS-level functions\n", "import time # Standard Python module; useful for benchmarking\n", "import Maxwell.VacuumMaxwell_Flat_Evol_Cartesian as rhs\n", "\n", "def MaxwellVacuum_C_kernels_codegen_onepart(params=\n", " \"WhichPart=Maxwell_RHSs,ThornName=MaxwellVacuum,FD_order=4\"):\n", " # Set default parameters\n", " WhichPart = \"Maxwell_RHSs\"\n", " ThornName = \"MaxwellVacuum\"\n", " FD_order = 4\n", "\n", " import re\n", " if params != \"\":\n", " params2 = re.sub(\"^,\",\"\",params)\n", " params = params2.strip()\n", " splitstring = re.split(\"=|,\", params)\n", "\n", "# if len(splitstring) % 2 != 0:\n", "# print(\"outputC: Invalid params string: \"+params)\n", "# sys.exit(1)\n", "\n", " parnm = []\n", " value = []\n", " for i in range(int(len(splitstring)/2)):\n", " parnm.append(splitstring[2*i])\n", " value.append(splitstring[2*i+1])\n", "\n", " for i in range(len(parnm)):\n", " parnm.append(splitstring[2*i])\n", " value.append(splitstring[2*i+1])\n", "\n", " for i in range(len(parnm)):\n", " if parnm[i] == \"WhichPart\":\n", " WhichPart = value[i]\n", " elif parnm[i] == \"ThornName\":\n", " ThornName = value[i]\n", " elif parnm[i] == \"FD_order\":\n", " FD_order = int(value[i])\n", " else:\n", " print(\"MaxwellVacuum Error: Could not parse input param: \"+parnm[i])\n", " sys.exit(1)\n", "\n", " # Set output directory for C kernels\n", " outdir = os.path.join(ThornName, \"src\") # Main C code output directory\n", "\n", " # Set spatial dimension (must be 3 for Maxwell)\n", " par.set_parval_from_str(\"grid::DIM\",3)\n", "\n", " # Step 2: Set some core parameters, including CoordSystem MoL timestepping algorithm,\n", " # FD order, floating point precision, and CFL factor:\n", " # Choices are: Spherical, SinhSpherical, SinhSphericalv2, Cylindrical, SinhCylindrical,\n", " # SymTP, SinhSymTP\n", " # NOTE: Only CoordSystem == Cartesian makes sense here; new\n", " # boundary conditions are needed within the ETK for\n", " # Spherical, etc. coordinates.\n", " CoordSystem = \"Cartesian\"\n", "\n", " par.set_parval_from_str(\"reference_metric::CoordSystem\",CoordSystem)\n", " rfm.reference_metric() # Create ReU, ReDD needed for rescaling B-L initial data, generating Maxwell RHSs, etc.\n", "\n", " # Set the gridfunction memory access type to ETK-like, so that finite_difference\n", " # knows how to read and write gridfunctions from/to memory.\n", " par.set_parval_from_str(\"grid::GridFuncMemAccess\",\"ETK\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 2: NRPy+-generated C code kernels for solving \\[Back to [top](#toc)\\]\n", "$$\\label{mwev}$$\n", "\n", "\n", "\n", "## Step 2.a: Maxwell RHS expressions \\[Back to [top](#toc)\\]\n", "$$\\label{mwevrhs}$$\n", "\n", "`MaxwellVacuum` implements Maxwell's equations in flat spacetime and in Cartesian coordinates, which is fully documented within NRPy+ ([start here](Tutorial-VacuumMaxwell_formulation_Cartesian.ipynb)). \n", "\n", "Here, we simply call the [Maxwell/VacuumMaxwell_Flat_Evol_Cartesian.py](../edit/Maxwell/VacuumMaxwell_Flat_Evol_Cartesian.py); [\\[**tutorial**\\]](Tutorial-VacuumMaxwell_formulation_Cartesian.ipynb) NRPy+ Python module to generate the symbolic expressions and then output the finite-difference C code form of the equations using NRPy+'s [finite_difference](finite_difference.py) ([**tutorial**](Tutorial-Finite_Difference_Derivatives.ipynb)) C code generation module.\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.528207Z", "iopub.status.busy": "2021-03-07T17:08:32.527274Z", "iopub.status.idle": "2021-03-07T17:08:32.530747Z", "shell.execute_reply": "2021-03-07T17:08:32.530240Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_kernels_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_kernels_codegen.py\n", "\n", " def Maxwell_RHSs__generate_symbolic_expressions_SystemI():\n", " ######################################\n", " # START: GENERATE SYMBOLIC EXPRESSIONS\n", " print(\"Generating symbolic expressions for Maxwell RHSs SystemI...\\n\")\n", " start = time.time()\n", "\n", " # Set which system to use, which are defined in Maxwell/InitialData.py\n", " par.initialize_param(par.glb_param(\"char\", \"Maxwell.InitialData\",\"System_to_use\",\"System_I\"))\n", "\n", " par.set_parval_from_str(\"reference_metric::enable_rfm_precompute\",\"True\")\n", " par.set_parval_from_str(\"reference_metric::rfm_precompute_Ccode_outdir\",os.path.join(outdir,\"rfm_files/\"))\n", "\n", " rhs.VacuumMaxwellRHSs()\n", "\n", " end = time.time()\n", " print(\"(BENCH) Finished Maxwell RHS symbolic expressions in \"+str(end-start)+\" seconds.\\n\")\n", " # END: GENERATE SYMBOLIC EXPRESSIONS\n", " ######################################\n", "\n", " # Step 2: Register new gridfunctions so they can be written to by NRPy.\n", " # System I:\n", " AIU = ixp.register_gridfunctions_for_single_rank1(\"EVOL\",\"AIU\")\n", " EIU = ixp.register_gridfunctions_for_single_rank1(\"EVOL\",\"EIU\")\n", " psiI = gri.register_gridfunctions(\"EVOL\",\"psiI\")\n", " C_SystemI = rhs.C\n", "\n", " Maxwell_RHSs_SymbExpressions_SystemI = [\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"AIU0\"),rhs=rhs.ArhsU[0]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"AIU1\"),rhs=rhs.ArhsU[1]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"AIU2\"),rhs=rhs.ArhsU[2]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"EIU0\"),rhs=rhs.ErhsU[0]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"EIU1\"),rhs=rhs.ErhsU[1]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"EIU2\"),rhs=rhs.ErhsU[2]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"psi\"),rhs=rhs.psi_rhs)]\n", "\n", " # Avoid re-registering GFs later\n", " del gri.glb_gridfcs_list[:8]\n", "\n", " return [Maxwell_RHSs_SymbExpressions_SystemI, C_SystemI]\n", "\n", " def Maxwell_RHSs__generate_symbolic_expressions_SystemII():\n", " ######################################\n", " # START: GENERATE SYMBOLIC EXPRESSIONS\n", " print(\"Generating symbolic expressions for Maxwell RHSs SystemII...\\n\")\n", " start = time.time()\n", "\n", " # Set which system to use, which are defined in Maxwell/VacuumMaxwell_Flat_Cartesian_ID.py\n", " par.set_parval_from_str(\"Maxwell.InitialData::System_to_use\",\"System_II\")\n", "\n", " # Avoid re-registering GFs\n", " gri.glb_gridfcs_list = []\n", "\n", " rhs.VacuumMaxwellRHSs()\n", "\n", " end = time.time()\n", " print(\"(BENCH) Finished Maxwell RHS symbolic expressions in \"+str(end-start)+\" seconds.\\n\")\n", " # END: GENERATE SYMBOLIC EXPRESSIONS\n", " ######################################\n", "\n", " # Register gridfunctions so they can be written to by NRPy.\n", " # System II:\n", " AIIU = ixp.register_gridfunctions_for_single_rank1(\"EVOL\",\"AIIU\")\n", " EIIU = ixp.register_gridfunctions_for_single_rank1(\"EVOL\",\"EIIU\")\n", " psiII = gri.register_gridfunctions(\"EVOL\",\"psiII\")\n", " GammaII = gri.register_gridfunctions(\"EVOL\",\"GammaII\")\n", " C_SystemII = rhs.C\n", " G_SystemII = rhs.G\n", "\n", " Maxwell_RHSs_SymbExpressions_SystemII = [\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"AIIU0\"),rhs=rhs.ArhsU[0]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"AIIU1\"),rhs=rhs.ArhsU[1]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"AIIU2\"),rhs=rhs.ArhsU[2]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"EIIU0\"),rhs=rhs.ErhsU[0]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"EIIU1\"),rhs=rhs.ErhsU[1]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"EIIU2\"),rhs=rhs.ErhsU[2]),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"psi\"),rhs=rhs.psi_rhs),\\\n", " lhrh(lhs=gri.gfaccess(\"rhs_gfs\",\"Gamma\"),rhs=rhs.Gamma_rhs)]\n", "\n", " return [Maxwell_RHSs_SymbExpressions_SystemII, C_SystemII, G_SystemII]\n", "\n", " def Maxwell_RHSs__generate_Ccode(all_RHSs_exprs_list_SystemI, all_RHSs_exprs_list_SystemII):\n", " Maxwell_RHSs_SymbExpressions_SystemI = all_RHSs_exprs_list_SystemI\n", " Maxwell_RHSs_SymbExpressions_SystemII = all_RHSs_exprs_list_SystemII\n", "\n", " print(\"Generating C code for Maxwell RHSs (FD_order=\"+str(FD_order)+\") in \"+par.parval_from_str(\"reference_metric::CoordSystem\")+\" coordinates.\")\n", " start = time.time()\n", "\n", " # Store original finite-differencing order:\n", " FD_order_orig = par.parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\")\n", " # Set new finite-differencing order:\n", " par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order)\n", "\n", " Maxwell_RHSs_string_SystemI = fin.FD_outputC(\"returnstring\",Maxwell_RHSs_SymbExpressions_SystemI,\n", " params=\"outCverbose=False,SIMD_enable=True\").replace(\"AU\",\"AIU\")\\\n", " .replace(\"EU\",\"EIU\")\\\n", " .replace(\"psi\",\"psiI\")\n", "\n", " Maxwell_RHSs_string_SystemII = fin.FD_outputC(\"returnstring\",Maxwell_RHSs_SymbExpressions_SystemII,\n", " params=\"outCverbose=False,SIMD_enable=True\").replace(\"AU\",\"AIIU\")\\\n", " .replace(\"EU\",\"EIIU\")\\\n", " .replace(\"psi\",\"psiII\")\\\n", " .replace(\"Gamma\",\"GammaII\")\n", "\n", "\n", " filename = \"Maxwell_RHSs\"+\"_FD_order_\"+str(FD_order)+\".h\"\n", " with open(os.path.join(outdir,filename), \"w\") as file:\n", " file.write(lp.loop([\"i2\",\"i1\",\"i0\"],[\"cctk_nghostzones[2]\",\"cctk_nghostzones[1]\",\"cctk_nghostzones[0]\"],\n", " [\"cctk_lsh[2]-cctk_nghostzones[2]\",\"cctk_lsh[1]-cctk_nghostzones[1]\",\"cctk_lsh[0]-cctk_nghostzones[0]\"],\n", " [\"1\",\"1\",\"SIMD_width\"],\n", " [\"#pragma omp parallel for\",\n", " \"#include \\\"rfm_files/rfm_struct__SIMD_outer_read2.h\\\"\",\n", " r\"\"\" #include \"rfm_files/rfm_struct__SIMD_outer_read1.h\"\n", " #if (defined __INTEL_COMPILER && __INTEL_COMPILER_BUILD_DATE >= 20180804)\n", " #pragma ivdep // Forces Intel compiler (if Intel compiler used) to ignore certain SIMD vector dependencies\n", " #pragma vector always // Forces Intel compiler (if Intel compiler used) to vectorize\n", " #endif\"\"\"],\"\",\n", " \"#include \\\"rfm_files/rfm_struct__SIMD_inner_read0.h\\\"\\n\"+Maxwell_RHSs_string_SystemI+Maxwell_RHSs_string_SystemII))\n", "\n", " # Restore original finite-differencing order:\n", " par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order_orig)\n", "\n", " end = time.time()\n", " print(\"(BENCH) Finished Maxwell_RHS C codegen (FD_order=\"+str(FD_order)+\") in \" + str(end - start) + \" seconds.\\n\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 2.b: Constraints \\[Back to [top](#toc)\\]\n", "$$\\label{constraints}$$\n", "\n", "Finally output the C code for evaluating the constraints, described in [this tutorial](Tutorial-VacuumMaxwell_Cartesian_RHSs.ipynb). In the absence of numerical error, the constraints should evaluate to zero, but due to numerical (typically truncation and roundoff) error they do not. We will therefore measure constraint violations to gauge the accuracy of our simulation, and ultimately determine whether errors are dominated by numerical finite differencing (truncation) error as expected." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.535657Z", "iopub.status.busy": "2021-03-07T17:08:32.534948Z", "iopub.status.idle": "2021-03-07T17:08:32.538636Z", "shell.execute_reply": "2021-03-07T17:08:32.539483Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_kernels_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_kernels_codegen.py\n", "\n", " def Maxwell_constraints__generate_symbolic_expressions_and_C_code(C_SystemI,\n", " C_SystemII,\n", " G_SystemII):\n", "\n", " DivEI = gri.register_gridfunctions(\"AUX\", \"DivEI\")\n", " DivEII = gri.register_gridfunctions(\"AUX\", \"DivEII\")\n", " GII = gri.register_gridfunctions(\"AUX\", \"GII\")\n", "\n", " # Store original finite-differencing order:\n", " FD_order_orig = par.parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\")\n", " # Set new finite-differencing order:\n", " par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order)\n", "\n", " start = time.time()\n", " print(\"Generating optimized C code for constraints.\")\n", " Constraints_string_SystemI = fin.FD_outputC(\"returnstring\",\n", " [lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"DivEI\"), rhs=C_SystemI)],\n", " params=\"outCverbose=False\").replace(\"AU\",\"AIU\")\\\n", " .replace(\"EU\",\"EIU\")\\\n", " .replace(\"psi\",\"psiI\")\n", "\n", " Constraints_string_SystemII = fin.FD_outputC(\"returnstring\",\n", " [lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"DivEII\"), rhs=C_SystemII),\n", " lhrh(lhs=gri.gfaccess(\"aux_gfs\", \"GII\"), rhs=G_SystemII)],\n", " params=\"outCverbose=False\").replace(\"AU\",\"AIIU\")\\\n", " .replace(\"EU\",\"EIIU\")\\\n", " .replace(\"psi\",\"psiII\")\\\n", " .replace(\"Gamma\",\"GammaII\")\n", "\n", " with open(os.path.join(outdir,\"Maxwell_constraints\"+\"_FD_order_\"+str(FD_order)+\".h\"), \"w\") as file:\n", " file.write(lp.loop([\"i2\",\"i1\",\"i0\"],[\"cctk_nghostzones[2]\",\"cctk_nghostzones[1]\",\"cctk_nghostzones[0]\"],\n", " [\"cctk_lsh[2]-cctk_nghostzones[2]\",\"cctk_lsh[1]-cctk_nghostzones[1]\",\"cctk_lsh[0]-cctk_nghostzones[0]\"],\n", " [\"1\",\"1\",\"1\"],[\"#pragma omp parallel for\",\"\",\"\"], \"\",Constraints_string_SystemI+Constraints_string_SystemII))\n", "\n", " # Restore original finite-differencing order:\n", " par.set_parval_from_str(\"finite_difference::FD_CENTDERIVS_ORDER\", FD_order_orig)\n", "\n", " end = time.time()\n", " print(\"(BENCH) Finished constraint C codegen (FD_order=\"+str(FD_order)+\") in \" + str(end - start) + \" seconds.\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 2.d: Given `WhichPart` parameter choice, generate algorithm for calling corresponding function within `MaxwellVacuum_C_kernels_codegen_onepart()` to generate C code kernel \\[Back to [top](#toc)\\]\n", "$$\\label{driver_call_codegen_funcs}$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.546014Z", "iopub.status.busy": "2021-03-07T17:08:32.545114Z", "iopub.status.idle": "2021-03-07T17:08:32.549303Z", "shell.execute_reply": "2021-03-07T17:08:32.548658Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_kernels_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_kernels_codegen.py\n", "\n", "\n", " SystemI_exprs = Maxwell_RHSs__generate_symbolic_expressions_SystemI()\n", " SystemII_exprs = Maxwell_RHSs__generate_symbolic_expressions_SystemII()\n", "\n", " if WhichPart==\"Maxwell_RHSs\":\n", " Maxwell_RHSs__generate_Ccode(SystemI_exprs[0], SystemII_exprs[0])\n", " elif WhichPart==\"Maxwell_constraints\":\n", " Maxwell_constraints__generate_symbolic_expressions_and_C_code(SystemI_exprs[1],\n", " SystemII_exprs[1],\n", " SystemII_exprs[2])\n", " else:\n", " print(\"Error: WhichPart = \"+WhichPart+\" is not recognized.\")\n", " sys.exit(1)\n", "\n", " # Avoid re-registering GFs\n", " del gri.glb_gridfcs_list[7:17]\n", "\n", " # Store all NRPy+ environment variables to an output string so NRPy+ environment from within this subprocess can be easily restored\n", " import pickle\n", " # https://www.pythonforthelab.com/blog/storing-binary-data-and-serializing/\n", " outstr = []\n", " outstr.append(pickle.dumps(len(gri.glb_gridfcs_list)))\n", " for lst in gri.glb_gridfcs_list:\n", " outstr.append(pickle.dumps(lst.gftype))\n", " outstr.append(pickle.dumps(lst.name))\n", " outstr.append(pickle.dumps(lst.rank))\n", " outstr.append(pickle.dumps(lst.DIM))\n", "\n", " outstr.append(pickle.dumps(len(par.glb_params_list)))\n", " for lst in par.glb_params_list:\n", " outstr.append(pickle.dumps(lst.type))\n", " outstr.append(pickle.dumps(lst.module))\n", " outstr.append(pickle.dumps(lst.parname))\n", " outstr.append(pickle.dumps(lst.defaultval))\n", "\n", " outstr.append(pickle.dumps(len(par.glb_Cparams_list)))\n", " for lst in par.glb_Cparams_list:\n", " outstr.append(pickle.dumps(lst.type))\n", " outstr.append(pickle.dumps(lst.module))\n", " outstr.append(pickle.dumps(lst.parname))\n", " outstr.append(pickle.dumps(lst.defaultval))\n", " return outstr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 2.c: Generate C code kernels for `MaxwellVacuum` \\[Back to [top](#toc)\\]\n", "$$\\label{kernel_codegen}$$\n", "\n", "Here we generate the C code kernels (i.e., the C-code representation of the equations needed) for `MaxwellVacuum`.\n", "\n", "\n", "\n", "### Step 2.c.i: Set compile-time and runtime parameters for `MaxwellVacuum` \\[Back to [top](#toc)\\]\n", "$$\\label{feature_choice}$$\n", "\n", "NRPy+ is a code generation package that is designed to offer maximum flexibility *at the time of C code generation*. As a result, although NRPy+ can in principle output an infinite variety of C code kernels for solving systems of partial differential equations, generally free parameters in each kernel steerable at *runtime* are restricted to simple scalars. This leads to more optimized kernels, but at the expense of flexibility in generating multiple kernels (e.g. one per finite-differencing order). Reducing the number of kernels and adding more flexibility at runtime will be a focus of future work.\n", "\n", "For now, `MaxwellVacuum` supports the following runtime options:\n", "\n", "* `MaxwellVacuum`: Evolution of the Maxwell's equations.\n", " * Finite differencing of orders 2, 4, 6, and 8 via runtime parameter `FD_order`\n", "\n", "Next we set up the default parameter lists for `MaxwellVacuum_C_kernels_codegen_onepart()` for the `MaxwellVacuum` thorn. We set these parameter lists as strings to make parallelizing the code generation far easier (easier to pass a list of strings than a list of function arguments to Python's `multiprocessing.Pool()`)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.555634Z", "iopub.status.busy": "2021-03-07T17:08:32.554690Z", "iopub.status.idle": "2021-03-07T17:08:32.556927Z", "shell.execute_reply": "2021-03-07T17:08:32.557394Z" } }, "outputs": [], "source": [ "# Step 2.e.i: Set compile-time and runtime parameters for Maxwell and MaxwellVacuum\n", "# Runtime parameters for\n", "# MaxwellVacuum: FD_orders = [2,4,6,8];\n", "\n", "paramslist = []\n", "FD_orders = [2,4,6,8]\n", "WhichParamSet = 0\n", "ThornName = \"MaxwellVacuum\"\n", "for WhichPart in [\"Maxwell_RHSs\",\"Maxwell_constraints\"]:\n", " for FD_order in FD_orders:\n", " paramstr = \"WhichPart=\"+WhichPart+\",\"\n", " paramstr+= \"ThornName=\"+ThornName+\",\"\n", " paramstr+= \"FD_order=\"+str(FD_order)+\",\"\n", " paramslist.append(paramstr)\n", " WhichParamSet = WhichParamSet + 1\n", "\n", "paramslist.sort() # Sort the list alphabetically." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "### Step 2.e.ii: Generate all C-code kernels for `MaxwellVacuum`, in parallel if possible \\[Back to [top](#toc)\\]\n", "$$\\label{parallel_codegen}$$" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:32.568160Z", "iopub.status.busy": "2021-03-07T17:08:32.567333Z", "iopub.status.idle": "2021-03-07T17:08:39.635538Z", "shell.execute_reply": "2021-03-07T17:08:39.636003Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "***************************************\n", "Starting serial C kernel codegen...\n", "***************************************\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_I RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.02069687843322754 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.019797563552856445 seconds.\n", "\n", "Generating C code for Maxwell RHSs (FD_order=2) in Cartesian coordinates.\n", "(BENCH) Finished Maxwell_RHS C codegen (FD_order=2) in 0.6086132526397705 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.02014780044555664 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.017313480377197266 seconds.\n", "\n", "Generating C code for Maxwell RHSs (FD_order=4) in Cartesian coordinates.\n", "(BENCH) Finished Maxwell_RHS C codegen (FD_order=4) in 0.9328193664550781 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.019754886627197266 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.017818450927734375 seconds.\n", "\n", "Generating C code for Maxwell RHSs (FD_order=6) in Cartesian coordinates.\n", "(BENCH) Finished Maxwell_RHS C codegen (FD_order=6) in 1.4266307353973389 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.01975274085998535 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.017722606658935547 seconds.\n", "\n", "Generating C code for Maxwell RHSs (FD_order=8) in Cartesian coordinates.\n", "(BENCH) Finished Maxwell_RHS C codegen (FD_order=8) in 2.298555374145508 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.057259559631347656 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.020059585571289062 seconds.\n", "\n", "Generating optimized C code for constraints.\n", "(BENCH) Finished constraint C codegen (FD_order=2) in 0.1122744083404541 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.0180513858795166 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.021723031997680664 seconds.\n", "\n", "Generating optimized C code for constraints.\n", "(BENCH) Finished constraint C codegen (FD_order=4) in 0.15365290641784668 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.017536640167236328 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.01952958106994629 seconds.\n", "\n", "Generating optimized C code for constraints.\n", "(BENCH) Finished constraint C codegen (FD_order=6) in 0.23769807815551758 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemI...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.017617225646972656 seconds.\n", "\n", "Generating symbolic expressions for Maxwell RHSs SystemII...\n", "\n", "Currently using System_II RHSs \n", "\n", "(BENCH) Finished Maxwell RHS symbolic expressions in 0.018214941024780273 seconds.\n", "\n", "Generating optimized C code for constraints.\n", "(BENCH) Finished constraint C codegen (FD_order=8) in 0.41301679611206055 seconds.\n", "\n", "(BENCH) Finished C kernel codegen for MaxwellVacuum in 7.064037561416626 seconds.\n", "\n" ] } ], "source": [ "nrpy_dir_path = os.path.join(\".\")\n", "if nrpy_dir_path not in sys.path:\n", " sys.path.append(nrpy_dir_path)\n", "\n", "# Create all output directories if they do not yet exist\n", "import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface\n", "for ThornName in [\"MaxwellVacuum\"]:\n", " outrootdir = ThornName\n", " cmd.mkdir(os.path.join(outrootdir))\n", " outdir = os.path.join(outrootdir,\"src\") # Main C code output directory\n", "\n", " # Copy SIMD/SIMD_intrinsics.h to $outdir/SIMD/SIMD_intrinsics.h, replacing\n", " # the line \"#define REAL_SIMD_ARRAY REAL\" with \"#define REAL_SIMD_ARRAY CCTK_REAL\"\n", " # (since REAL is undefined in the ETK, but CCTK_REAL takes its place)\n", " cmd.mkdir(os.path.join(outdir,\"SIMD\"))\n", " import fileinput\n", " f = fileinput.input(os.path.join(nrpy_dir_path,\"SIMD\",\"SIMD_intrinsics.h\"))\n", " with open(os.path.join(outdir,\"SIMD\",\"SIMD_intrinsics.h\"),\"w\") as outfile:\n", " for line in f:\n", " outfile.write(line.replace(\"#define REAL_SIMD_ARRAY REAL\", \"#define REAL_SIMD_ARRAY CCTK_REAL\"))\n", "\n", " # Create directory for rfm_files output\n", " cmd.mkdir(os.path.join(outdir,\"rfm_files\"))\n", "\n", "# Start parallel C code generation (codegen)\n", "# NRPyEnvVars stores the NRPy+ environment from all the subprocesses in the following\n", "# parallel codegen\n", "NRPyEnvVars = []\n", "\n", "import time # Standard Python module for benchmarking\n", "import logging\n", "start = time.time()\n", "if __name__ == \"__main__\":\n", "# try:\n", "# if os.name == 'nt':\n", "# # Windows & Jupyter multiprocessing do not mix, so we run in serial on Windows.\n", "# # Here's why: https://stackoverflow.com/questions/45719956/python-multiprocessing-in-jupyter-on-windows-attributeerror-cant-get-attribut\n", "# raise Exception(\"Parallel codegen currently not available in Windows\")\n", "# # Step 3.d.ii: Import the multiprocessing module.\n", "# import multiprocessing\n", "# print(\"***************************************\")\n", "# print(\"Starting parallel C kernel codegen...\")\n", "# print(\"***************************************\")\n", "\n", "# # Step 3.d.iii: Define master function for parallelization.\n", "# # Note that lambdifying this doesn't work in Python 3\n", "# def master_func(i):\n", "# import MaxwellNRPy_py_dir.MaxwellVacuum_C_kernels_codegen as MCk\n", "# return MCk.MaxwellVacuum_C_kernels_codegen_onepart(params=paramslist[i])\n", "\n", "# # Step 3.d.iv: Evaluate list of functions in parallel if possible;\n", "# # otherwise fallback to serial evaluation:\n", "# pool = multiprocessing.Pool() #processes=len(paramslist))\n", "# NRPyEnvVars.append(pool.map(master_func,range(len(paramslist))))\n", "# pool.terminate()\n", "# pool.join()\n", "# except:\n", "# logging.exception(\"Ignore this warning/backtrace if on a system in which serial codegen is necessary:\")\n", " print(\"***************************************\")\n", " print(\"Starting serial C kernel codegen...\")\n", " print(\"***************************************\")\n", " # Steps 3.d.ii-iv, alternate: As fallback, evaluate functions in serial.\n", " # This will happen on Android and Windows systems\n", " import MaxwellNRPy_py_dir.MaxwellVacuum_C_kernels_codegen as MCk\n", " import grid as gri\n", " for param in paramslist:\n", " gri.glb_gridfcs_list = []\n", " MCk.MaxwellVacuum_C_kernels_codegen_onepart(params=param)\n", " NRPyEnvVars = [] # Reset NRPyEnvVars in case multiprocessing wrote to it and failed.\n", "# # Steps 3.d.ii-iv, alternate: As fallback, evaluate functions in serial.\n", "# # This will happen on Android and Windows systems\n", "\n", "print(\"(BENCH) Finished C kernel codegen for MaxwellVacuum in \"+str(time.time()-start)+\" seconds.\\n\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.641090Z", "iopub.status.busy": "2021-03-07T17:08:39.640298Z", "iopub.status.idle": "2021-03-07T17:08:39.643653Z", "shell.execute_reply": "2021-03-07T17:08:39.643127Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No traceback available to show.\n" ] } ], "source": [ "%tb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 3: ETK `ccl` file generation \\[Back to [top](#toc)\\]\n", "$$\\label{cclfiles}$$\n", "\n", "The Einstein Toolkit (ETK) ccl files contain runtime parameters (`param.ccl`), registered gridfunctions (`interface.ccl`), and function scheduling (`schedule.ccl`). As parameters and gridfunctions are registered with NRPy+ when the C-code kernels are generated, and this generation occurs on separate processes in parallel, we store the entire NRPy+ environment for *each* process. This results in a tremendous amount of duplication, which is sorted out next. Once all duplicated environment variables (e.g., registered gridfunctions) are removed, we replace the current NRPy+ environment with the new one, by setting `gri.glb_gridfcs_list[],par.glb_params_list[],par.glb_Cparams_list[]`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.659905Z", "iopub.status.busy": "2021-03-07T17:08:39.659287Z", "iopub.status.idle": "2021-03-07T17:08:39.661516Z", "shell.execute_reply": "2021-03-07T17:08:39.661951Z" }, "scrolled": true }, "outputs": [], "source": [ "# Store all NRPy+ environment variables to file so NRPy+ environment from within this subprocess can be easily restored\n", "import pickle # Standard Python module for converting arbitrary data structures to a uniform format.\n", "import grid as gri # NRPy+: Functions having to do with numerical grids\n", "import NRPy_param_funcs as par # NRPy+: Parameter interface\n", "\n", "if len(NRPyEnvVars) > 0:\n", " # https://www.pythonforthelab.com/blog/storing-binary-data-and-serializing/\n", " grfcs_list = []\n", " param_list = []\n", " Cparm_list = []\n", "\n", " for WhichParamSet in NRPyEnvVars[0]:\n", " # gridfunctions\n", " i=0\n", " # print(\"Length of WhichParamSet:\",str(len(WhichParamSet)))\n", " num_elements = pickle.loads(WhichParamSet[i]); i+=1\n", " for lst in range(num_elements):\n", " grfcs_list.append(gri.glb_gridfc(gftype=pickle.loads(WhichParamSet[i+0]),\n", " name =pickle.loads(WhichParamSet[i+1]),\n", " rank =pickle.loads(WhichParamSet[i+2]),\n", " DIM =pickle.loads(WhichParamSet[i+3]))) ; i+=4\n", " # parameters\n", " num_elements = pickle.loads(WhichParamSet[i]); i+=1\n", " for lst in range(num_elements):\n", " param_list.append(par.glb_param(type =pickle.loads(WhichParamSet[i+0]),\n", " module =pickle.loads(WhichParamSet[i+1]),\n", " parname =pickle.loads(WhichParamSet[i+2]),\n", " defaultval=pickle.loads(WhichParamSet[i+3]))) ; i+=4\n", " # Cparameters\n", " num_elements = pickle.loads(WhichParamSet[i]); i+=1\n", " for lst in range(num_elements):\n", " Cparm_list.append(par.glb_Cparam(type =pickle.loads(WhichParamSet[i+0]),\n", " module =pickle.loads(WhichParamSet[i+1]),\n", " parname =pickle.loads(WhichParamSet[i+2]),\n", " defaultval=pickle.loads(WhichParamSet[i+3]))) ; i+=4\n", " grfcs_list_uniq = []\n", " for gf_ntuple_stored in grfcs_list:\n", " found_gf = False\n", " for gf_ntuple_new in grfcs_list_uniq:\n", " if gf_ntuple_new == gf_ntuple_stored:\n", " found_gf = True\n", " if found_gf == False:\n", " grfcs_list_uniq.append(gf_ntuple_stored)\n", "\n", " param_list_uniq = []\n", " for pr_ntuple_stored in param_list:\n", " found_pr = False\n", " for pr_ntuple_new in param_list_uniq:\n", " if pr_ntuple_new == pr_ntuple_stored:\n", " found_pr = True\n", " if found_pr == False:\n", " param_list_uniq.append(pr_ntuple_stored)\n", "\n", " # Set glb_paramsvals_list:\n", " # Step 1: Reset all paramsvals to their defaults\n", " par.glb_paramsvals_list = []\n", " for parm in param_list_uniq:\n", " par.glb_paramsvals_list.append(parm.defaultval)\n", "\n", " Cparm_list_uniq = []\n", " for Cp_ntuple_stored in Cparm_list:\n", " found_Cp = False\n", " for Cp_ntuple_new in Cparm_list_uniq:\n", " if Cp_ntuple_new == Cp_ntuple_stored:\n", " found_Cp = True\n", " if found_Cp == False:\n", " Cparm_list_uniq.append(Cp_ntuple_stored)\n", "\n", " gri.glb_gridfcs_list = []\n", " par.glb_params_list = []\n", " par.glb_Cparams_list = []\n", "\n", " gri.glb_gridfcs_list = grfcs_list_uniq\n", " par.glb_params_list = param_list_uniq\n", " par.glb_Cparams_list = Cparm_list_uniq" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 3.a: `param.ccl`: specify free parameters within `MaxwellVacuum` \\[Back to [top](#toc)\\]\n", "$$\\label{paramccl}$$\n", "\n", "All parameters necessary to evolve the right-hand side (RHS) expressions of Maxwell's equations are registered within NRPy+; we use this information to automatically generate `param.ccl`. NRPy+ also specifies default values for each parameter. \n", "\n", "More information on `param.ccl` syntax can be found in the [official Einstein Toolkit documentation](http://einsteintoolkit.org/usersguide/UsersGuide.html#x1-184000D2.3)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.667072Z", "iopub.status.busy": "2021-03-07T17:08:39.665943Z", "iopub.status.idle": "2021-03-07T17:08:39.670371Z", "shell.execute_reply": "2021-03-07T17:08:39.669820Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting MaxwellNRPy_py_dir/MaxwellVacuum_ETK_ccl_files_codegen.py\n" ] } ], "source": [ "%%writefile $MaxwellVacuumdir/MaxwellVacuum_ETK_ccl_files_codegen.py\n", "\n", "# Step 1: Import needed core NRPy+ modules\n", "import NRPy_param_funcs as par # NRPy+: Parameter interface\n", "import grid as gri # NRPy+: Functions having to do with numerical grids\n", "import os, sys # Standard Python modules for multiplatform OS-level functions\n", "\n", "def keep_param__return_type(paramtuple):\n", " keep_param = True # We'll not set some parameters in param.ccl;\n", " # e.g., those that should be #define'd like M_PI.\n", " typestring = \"\"\n", " # Separate thorns within the ETK take care of grid/coordinate parameters;\n", " # thus we ignore NRPy+ grid/coordinate parameters:\n", " if paramtuple.module == \"grid\" or paramtuple.module == \"reference_metric\":\n", " keep_param = False\n", "\n", " partype = paramtuple.type\n", " if partype == \"bool\":\n", " typestring += \"BOOLEAN \"\n", " elif partype == \"REAL\":\n", " if paramtuple.defaultval != 1e300: # 1e300 is a magic value indicating that the C parameter should be mutable\n", " typestring += \"CCTK_REAL \"\n", " else:\n", " keep_param = False\n", " elif partype == \"int\":\n", " typestring += \"CCTK_INT \"\n", " elif partype == \"#define\":\n", " keep_param = False\n", " elif partype == \"char\":\n", " # FIXME: char/string parameter types should in principle be supported\n", " print(\"Error: parameter \"+paramtuple.module+\"::\"+paramtuple.parname+\n", " \" has unsupported type: \\\"\"+ paramtuple.type + \"\\\"\")\n", " sys.exit(1)\n", " else:\n", " print(\"Error: parameter \"+paramtuple.module+\"::\"+paramtuple.parname+\n", " \" has unsupported type: \\\"\"+ paramtuple.type + \"\\\"\")\n", " sys.exit(1)\n", " return keep_param, typestring\n", "\n", "def output_param_ccl(ThornName=\"MaxwellVacuum\"):\n", " with open(os.path.join(ThornName,\"param.ccl\"), \"w\") as file:\n", " file.write(\"\"\"\n", "# This param.ccl file was automatically generated by NRPy+.\n", "# You are advised against modifying it directly; instead\n", "# modify the Python code that generates it.\n", "\n", "shares: MethodOfLines\n", "\n", "#EXTENDS CCTK_KEYWORD evolution_method \"evolution_method\"\n", "#{\n", "# \"MaxwellVacuum\" :: \"\"\n", "#}\n", "\n", "restricted:\n", "\n", "CCTK_INT FD_order \"Finite-differencing order\"\n", "{\\n\"\"\")\n", " FDorders = []\n", " for _root, _dirs, files in os.walk(os.path.join(ThornName,\"src\")): # _root,_dirs unused.\n", " for Cfilename in files:\n", " if (\".h\" in Cfilename) and (\"RHSs\" in Cfilename) and (\"intrinsics\" not in Cfilename):\n", " array = Cfilename.replace(\".\",\"_\").split(\"_\")\n", " FDorders.append(int(array[-2]))\n", " FDorders.sort()\n", " for order in FDorders:\n", " file.write(\" \"+str(order)+\":\"+str(order)+\" :: \\\"finite-differencing order = \"+str(order)+\"\\\"\\n\")\n", " FDorder_default = 4\n", " if FDorder_default not in FDorders:\n", " print(\"WARNING: 4th-order FD kernel was not output!?! Changing default FD order to \"+str(FDorders[0]))\n", " FDorder_default = FDorders[0]\n", " file.write(\"} \"+str(FDorder_default)+ \"\\n\\n\") # choose 4th order by default, consistent with ML_Maxwell\n", " paramccl_str = \"\"\n", " for i in range(len(par.glb_Cparams_list)):\n", " # keep_param is a boolean indicating whether we should accept or reject\n", " # the parameter. singleparstring will contain the string indicating\n", " # the variable type.\n", " keep_param, singleparstring = keep_param__return_type(par.glb_Cparams_list[i])\n", "\n", " if keep_param:\n", " parname = par.glb_Cparams_list[i].parname\n", " partype = par.glb_Cparams_list[i].type\n", " singleparstring += parname + \" \\\"\"+ parname +\" (see NRPy+ for parameter definition)\\\"\\n\"\n", " singleparstring += \"{\\n\"\n", " if partype != \"bool\":\n", " singleparstring += \" *:* :: \\\"All values accepted. NRPy+ does not restrict the allowed ranges of parameters yet.\\\"\\n\"\n", " singleparstring += \"} \"+str(par.glb_Cparams_list[i].defaultval)+\"\\n\\n\"\n", "\n", " paramccl_str += singleparstring\n", " file.write(paramccl_str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 3.b: `interface.ccl`: define needed gridfunctions; provide keywords denoting what this thorn provides and what it should inherit from other thorns \\[Back to [top](#toc)\\]\n", "$$\\label{interfaceccl}$$\n", "\n", "`interface.ccl` declares all gridfunctions and determines how `MaxwellVacuum` interacts with other Einstein Toolkit thorns.\n", "\n", "The [official Einstein Toolkit (Cactus) documentation](http://einsteintoolkit.org/usersguide/UsersGuide.html#x1-179000D2.2) defines what must/should be included in an `interface.ccl` file." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.677801Z", "iopub.status.busy": "2021-03-07T17:08:39.676506Z", "iopub.status.idle": "2021-03-07T17:08:39.680578Z", "shell.execute_reply": "2021-03-07T17:08:39.681246Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_ETK_ccl_files_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_ETK_ccl_files_codegen.py\n", "\n", "# First construct lists of the basic gridfunctions used in NRPy+.\n", "# Each type will be its own group in MaxwellVacuum.\n", "evol_gfs_list = []\n", "auxevol_gfs_list = []\n", "aux_gfs_list = []\n", "for i in range(len(gri.glb_gridfcs_list)):\n", " if gri.glb_gridfcs_list[i].gftype == \"EVOL\":\n", " evol_gfs_list.append( gri.glb_gridfcs_list[i].name+\"GF\")\n", "\n", " if gri.glb_gridfcs_list[i].gftype == \"AUX\":\n", " aux_gfs_list.append( gri.glb_gridfcs_list[i].name+\"GF\")\n", "\n", " if gri.glb_gridfcs_list[i].gftype == \"AUXEVOL\":\n", " auxevol_gfs_list.append(gri.glb_gridfcs_list[i].name+\"GF\")\n", "\n", "# NRPy+'s finite-difference code generator assumes gridfunctions\n", "# are alphabetized; not sorting may result in unnecessary\n", "# cache misses.\n", "evol_gfs_list.sort()\n", "aux_gfs_list.sort()\n", "auxevol_gfs_list.sort()\n", "rhs_list = []\n", "for gf in evol_gfs_list:\n", " rhs_list.append(gf.replace(\"GF\",\"\")+\"_rhsGF\")\n", "\n", "def output_interface_ccl(ThornName=\"MaxwellVacuum\"):\n", " outstr = \"\"\"\n", "# This interface.ccl file was automatically generated by NRPy+.\n", "# You are advised against modifying it directly; instead\n", "# modify the Python code that generates it.\n", "\n", "# With \"implements\", we give our thorn its unique name.\n", "implements: MaxwellVacuum\n", "\n", "# By \"inheriting\" other thorns, we tell the Toolkit that we\n", "# will rely on variables/function that exist within those\n", "# functions.\n", "inherits: Boundary grid MethodofLines\\n\"\"\"\n", "\n", " outstr += \"\"\"\n", "# Needed functions and #include's:\n", "USES INCLUDE: Symmetry.h\n", "USES INCLUDE: Boundary.h\n", "\n", "# Needed Method of Lines function\n", "CCTK_INT FUNCTION MoLRegisterEvolvedGroup(CCTK_INT IN EvolvedIndex, \\\n", " CCTK_INT IN RHSIndex)\n", "REQUIRES FUNCTION MoLRegisterEvolvedGroup\n", "\n", "# Needed Boundary Conditions function\n", "CCTK_INT FUNCTION GetBoundarySpecification(CCTK_INT IN size, CCTK_INT OUT ARRAY nboundaryzones, CCTK_INT OUT ARRAY is_internal, CCTK_INT OUT ARRAY is_staggered, CCTK_INT OUT ARRAY shiftout)\n", "USES FUNCTION GetBoundarySpecification\n", "\n", "CCTK_INT FUNCTION SymmetryTableHandleForGrid(CCTK_POINTER_TO_CONST IN cctkGH)\n", "USES FUNCTION SymmetryTableHandleForGrid\n", "\n", "CCTK_INT FUNCTION Boundary_SelectVarForBC(CCTK_POINTER_TO_CONST IN GH, CCTK_INT IN faces, CCTK_INT IN boundary_width, CCTK_INT IN table_handle, CCTK_STRING IN var_name, CCTK_STRING IN bc_name)\n", "USES FUNCTION Boundary_SelectVarForBC\n", "\n", "# Needed for EinsteinEvolve/NewRad outer boundary condition driver:\n", "CCTK_INT FUNCTION \\\\\n", " NewRad_Apply \\\\\n", " (CCTK_POINTER_TO_CONST IN cctkGH, \\\\\n", " CCTK_REAL ARRAY IN var, \\\\\n", " CCTK_REAL ARRAY INOUT rhs, \\\\\n", " CCTK_REAL IN var0, \\\\\n", " CCTK_REAL IN v0, \\\\\n", " CCTK_INT IN radpower)\n", "REQUIRES FUNCTION NewRad_Apply\n", "\n", "# Tell the Toolkit that we want all gridfunctions\n", "# to be visible to other thorns by using\n", "# the keyword \"public\". Note that declaring these\n", "# gridfunctions *does not* allocate memory for them;\n", "# that is done by the schedule.ccl file.\n", "\n", "public:\n", "\"\"\"\n", "\n", " # Next we declare gridfunctions based on their corresponding gridfunction groups as registered within NRPy+\n", "\n", " def output_list_of_gfs(gfs_list,description=\"User did not provide description\"):\n", " gfsstr = \" \"\n", " for i in range(len(gfs_list)):\n", " gfsstr += gfs_list[i]\n", " if i != len(gfs_list)-1:\n", " gfsstr += \",\" # This is a comma-separated list of gridfunctions\n", " else:\n", " gfsstr += \"\\n} \\\"\"+description+\"\\\"\\n\\n\"\n", " return gfsstr\n", " # First EVOL type:\n", " outstr += \"CCTK_REAL evol_variables type = GF Timelevels=3\\n{\\n\"\n", " outstr += output_list_of_gfs(evol_gfs_list,\"Maxwell evolved gridfunctions\")\n", " # Second EVOL right-hand-sides\n", " outstr += \"CCTK_REAL evol_variables_rhs type = GF Timelevels=1 TAGS=\\'InterpNumTimelevels=1 prolongation=\\\"none\\\"\\'\\n{\\n\"\n", " outstr += output_list_of_gfs(rhs_list,\"right-hand-side storage for Maxwell evolved gridfunctions\")\n", " # Then AUX type:\n", " outstr += \"CCTK_REAL aux_variables type = GF Timelevels=3\\n{\\n\"\n", " outstr += output_list_of_gfs(aux_gfs_list,\"Auxiliary gridfunctions for Maxwell diagnostics\")\n", " # Finally, AUXEVOL type:\n", "# outstr += \"CCTK_REAL auxevol_variables type = GF Timelevels=1 TAGS=\\'InterpNumTimelevels=1 prolongation=\\\"none\\\"\\'\\n{\\n\"\n", "# outstr += output_list_of_gfs(auxevol_gfs_list,\"Auxiliary gridfunctions needed for evaluating the Maxwell RHSs\")\n", "\n", " with open(os.path.join(ThornName,\"interface.ccl\"), \"w\") as file:\n", " file.write(outstr.replace(\"MaxwellVacuum\",ThornName))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 3.c: `schedule.ccl`: schedule all functions used within `MaxwellVacuum`, specify data dependencies within said functions, and allocate memory for gridfunctions \\[Back to [top](#toc)\\]\n", "$$\\label{scheduleccl}$$\n", "\n", "Official documentation on constructing ETK `schedule.ccl` files is found [here](http://einsteintoolkit.org/usersguide/UsersGuide.html#x1-187000D2.4)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.688178Z", "iopub.status.busy": "2021-03-07T17:08:39.687048Z", "iopub.status.idle": "2021-03-07T17:08:39.691036Z", "shell.execute_reply": "2021-03-07T17:08:39.691690Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_ETK_ccl_files_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_ETK_ccl_files_codegen.py\n", "\n", "def output_schedule_ccl(ThornName=\"MaxwellVacuum\"):\n", " outstr = \"\"\"\n", "# This schedule.ccl file was automatically generated by NRPy+.\n", "# You are advised against modifying it directly; instead\n", "# modify the Python code that generates it.\n", "\n", "# Next allocate storage for all 3 gridfunction groups used in MaxwellVacuum\n", "STORAGE: evol_variables[3] # Evolution variables\n", "STORAGE: evol_variables_rhs[1] # Variables storing right-hand-sides\n", "STORAGE: aux_variables[3] # Diagnostics variables\n", "\n", "# The following scheduler is based on Lean/LeanMaxwellMoL/schedule.ccl\n", "\n", "schedule MaxwellVacuum_Banner at STARTUP\n", "{\n", " LANG: C\n", " OPTIONS: meta\n", "} \"Output ASCII art banner\"\n", "\n", "schedule MaxwellVacuum_Symmetry_registration at BASEGRID\n", "{\n", " LANG: C\n", " OPTIONS: Global\n", "} \"Register symmetries, the CartGrid3D way.\"\n", "\n", "schedule MaxwellVacuum_zero_rhss at BASEGRID after MaxwellVacuum_Symmetry_registration\n", "{\n", " LANG: C\n", "} \"Idea from Lean: set all rhs functions to zero to prevent spurious nans\"\n", "\n", "# MoL: registration\n", "\n", "schedule MaxwellVacuum_MoL_registration in MoL_Register\n", "{\n", " LANG: C\n", " OPTIONS: META\n", "} \"Register variables for MoL\"\n", "\n", "# MoL: compute RHSs, etc\n", "\n", "schedule MaxwellVacuum_RHSs in MoL_CalcRHS as MaxwellVacuum_RHS\n", "{\n", " LANG: C\n", "} \"MoL: Evaluate Maxwell RHSs\"\n", "\n", "schedule MaxwellVacuum_NewRad in MoL_CalcRHS after MaxwellVacuum_RHS\n", "{\n", " LANG: C\n", "} \"NewRad boundary conditions, scheduled right after RHS eval.\"\n", "\n", "schedule MaxwellVacuum_BoundaryConditions_evolved_gfs in MoL_PostStep\n", "{\n", " LANG: C\n", " OPTIONS: LEVEL\n", " SYNC: evol_variables\n", "} \"Apply boundary conditions and perform AMR+interprocessor synchronization\"\n", "\n", "schedule GROUP ApplyBCs as MaxwellVacuum_ApplyBCs in MoL_PostStep after MaxwellVacuum_BoundaryConditions_evolved_gfs\n", "{\n", "} \"Group for applying boundary conditions\"\n", "\n", "# Compute divergence and Gamma constraints\n", "\n", "schedule MaxwellVacuum_constraints in MoL_PseudoEvolution\n", "{\n", " LANG: C\n", " OPTIONS: Local\n", "} \"Compute Maxwell (divergence and Gamma) constraints\"\n", "\"\"\"\n", " with open(os.path.join(ThornName,\"schedule.ccl\"), \"w\") as file:\n", " file.write(outstr.replace(\"MaxwellVacuum\",ThornName))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.697315Z", "iopub.status.busy": "2021-03-07T17:08:39.696569Z", "iopub.status.idle": "2021-03-07T17:08:39.702382Z", "shell.execute_reply": "2021-03-07T17:08:39.702910Z" }, "scrolled": true }, "outputs": [], "source": [ "import MaxwellNRPy_py_dir.MaxwellVacuum_ETK_ccl_files_codegen as cclgen\n", "\n", "ThornName=\"MaxwellVacuum\"\n", "cclgen.output_param_ccl(ThornName)\n", "cclgen.output_interface_ccl(ThornName)\n", "cclgen.output_schedule_ccl(ThornName)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 4: C driver functions for ETK registration & NRPy+ generated kernels \\[Back to [top](#toc)\\]\n", "$$\\label{cdrivers}$$\n", "\n", "Now that we have constructed the basic C code kernels and the needed Einstein Toolkit `ccl` files, we next write the driver functions for registering `MaxwellVacuum` within the Toolkit and the C code kernels. Each of these driver functions will be called directly from the thorn's [`schedule.ccl`](#scheduleccl) in the ETK.\n", "\n", "\n", "## Step 4.a: Needed ETK functions: Banner, Symmetry registration, Parameter sanity check, Method of Lines (`MoL`) registration, Boundary condition \\[Back to [top](#toc)\\]\n", "$$\\label{etkfunctions}$$\n", "\n", "### To-do: Parameter sanity check function. E.g., error should be thrown if `cctk_nghostzones[]` is set too small for the chosen finite-differencing order within NRPy+." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.708967Z", "iopub.status.busy": "2021-03-07T17:08:39.707913Z", "iopub.status.idle": "2021-03-07T17:08:39.713787Z", "shell.execute_reply": "2021-03-07T17:08:39.713226Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", "# Step 1: Import needed core NRPy+ and Python modules\n", "from outputC import lhrh # NRPy+: Core C code output module\n", "import NRPy_param_funcs as par # NRPy+: Parameter interface\n", "import finite_difference as fin # NRPy+: Finite difference C code generation module\n", "import grid as gri # NRPy+: Functions having to do with numerical grids\n", "import loop as lp # NRPy+: Generate C code loops\n", "import indexedexp as ixp # NRPy+: Symbolic indexed expression (e.g., tensors, vectors, etc.) support\n", "import os, sys # Standard Python modules for multiplatform OS-level functions\n", "# We need the function keep_param__return_type() from this module:\n", "import MaxwellNRPy_py_dir.MaxwellVacuum_ETK_ccl_files_codegen as ccl\n", "\n", "make_code_defn_list = []\n", "def append_to_make_code_defn_list(filename):\n", " if filename not in make_code_defn_list:\n", " make_code_defn_list.append(filename)\n", " return filename" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.718821Z", "iopub.status.busy": "2021-03-07T17:08:39.718080Z", "iopub.status.idle": "2021-03-07T17:08:39.721222Z", "shell.execute_reply": "2021-03-07T17:08:39.721775Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", "def driver_C_codes(Csrcdict, ThornName,\n", " rhs_list,evol_gfs_list,aux_gfs_list,auxevol_gfs_list):\n", " # First the ETK banner code, proudly showing the NRPy+ banner\n", " import NRPy_logo as logo\n", " outstr = \"\"\"\n", "#include \n", "\n", "void MaxwellVacuum_Banner()\n", "{\n", " \"\"\"\n", " logostr = logo.print_logo(print_to_stdout=False)\n", " outstr += \"printf(\\\"MaxwellVacuum: another Einstein Toolkit thorn generated by\\\\n\\\");\\n\"\n", " for line in logostr.splitlines():\n", " outstr += \" printf(\\\"\"+line+\"\\\\n\\\");\\n\"\n", " outstr += \"}\\n\"\n", "\n", " # Finally add C code string to dictionaries (Python dictionaries are immutable)\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"Banner.c\")] = outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.728367Z", "iopub.status.busy": "2021-03-07T17:08:39.727374Z", "iopub.status.idle": "2021-03-07T17:08:39.731626Z", "shell.execute_reply": "2021-03-07T17:08:39.731008Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " # Next MaxwellVacuum_Symmetry_registration(): Register symmetries\n", "\n", " full_gfs_list = []\n", " full_gfs_list.extend(evol_gfs_list)\n", " full_gfs_list.extend(auxevol_gfs_list)\n", " full_gfs_list.extend(aux_gfs_list)\n", "\n", " outstr = \"\"\"\n", "#include \"cctk.h\"\n", "#include \"cctk_Arguments.h\"\n", "#include \"cctk_Parameters.h\"\n", "#include \"Symmetry.h\"\n", "\n", "void MaxwellVacuum_Symmetry_registration(CCTK_ARGUMENTS)\n", "{\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\n", " // Stores gridfunction parity across x=0, y=0, and z=0 planes, respectively\n", " int sym[3];\n", "\n", " // Next register parities for each gridfunction based on its name\n", " // (to ensure this algorithm is robust, gridfunctions with integers\n", " // in their base names are forbidden in NRPy+).\n", "\"\"\"\n", " outstr += \"\"\n", " for gfname in full_gfs_list:\n", " gfname_without_GFsuffix = gfname[:-2]\n", " outstr += \"\"\"\n", " // Default to scalar symmetry:\n", " sym[0] = 1; sym[1] = 1; sym[2] = 1;\n", " // Now modify sym[0], sym[1], and/or sym[2] as needed\n", " // to account for gridfunction parity across\n", " // x=0, y=0, and/or z=0 planes, respectively\n", "\"\"\"\n", " # If gridfunction name does not end in a digit, by NRPy+ syntax, it must be a scalar\n", " if gfname_without_GFsuffix[len(gfname_without_GFsuffix) - 1].isdigit() == False:\n", " outstr += \" // (this gridfunction is a scalar -- no need to change default sym[]'s!)\\n\"\n", " elif len(gfname_without_GFsuffix) > 2:\n", " # Rank-1 indexed expression (e.g., vector)\n", " if gfname_without_GFsuffix[len(gfname_without_GFsuffix) - 2].isdigit() == False:\n", " if int(gfname_without_GFsuffix[-1]) > 2:\n", " print(\"Error: Found invalid gridfunction name: \"+gfname)\n", " sys.exit(1)\n", " symidx = gfname_without_GFsuffix[-1]\n", " if int(symidx) < 3: outstr += \" sym[\" + symidx + \"] = -1;\\n\"\n", " # Rank-2 indexed expression\n", " elif gfname_without_GFsuffix[len(gfname_without_GFsuffix) - 2].isdigit() == True:\n", " if len(gfname_without_GFsuffix) > 3 and gfname_without_GFsuffix[len(gfname_without_GFsuffix) - 3].isdigit() == True:\n", " print(\"Error: Found a Rank-3 or above gridfunction: \"+gfname+\", which is at the moment unsupported.\")\n", " print(\"It should be easy to support this if desired.\")\n", " sys.exit(1)\n", " symidx0 = gfname_without_GFsuffix[-2]\n", " if int(symidx0) >= 0: outstr += \" sym[\" + symidx0 + \"] *= -1;\\n\"\n", " symidx1 = gfname_without_GFsuffix[-1]\n", " if int(symidx1) >= 0: outstr += \" sym[\" + symidx1 + \"] *= -1;\\n\"\n", " else:\n", " print(\"Don't know how you got this far with a gridfunction named \"+gfname+\", but I'll take no more of this nonsense.\")\n", " print(\" Please follow best-practices and rename your gridfunction to be more descriptive\")\n", " sys.exit(1)\n", " outstr += \" SetCartSymVN(cctkGH, sym, \\\"MaxwellVacuum::\" + gfname + \"\\\");\\n\"\n", " outstr += \"}\\n\"\n", "\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"Symmetry_registration_oldCartGrid3D.c\")] = \\\n", " outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.737795Z", "iopub.status.busy": "2021-03-07T17:08:39.736683Z", "iopub.status.idle": "2021-03-07T17:08:39.740720Z", "shell.execute_reply": "2021-03-07T17:08:39.741348Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " # Next set RHSs to zero\n", " outstr = \"\"\"\n", "#include \"cctk.h\"\n", "#include \"cctk_Arguments.h\"\n", "#include \"cctk_Parameters.h\"\n", "#include \"Symmetry.h\"\n", "\n", "void MaxwellVacuum_zero_rhss(CCTK_ARGUMENTS)\n", "{\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\"\"\"\n", " set_rhss_to_zero = \"\"\n", " for gf in rhs_list:\n", " set_rhss_to_zero += gf+\"[CCTK_GFINDEX3D(cctkGH,i0,i1,i2)] = 0.0;\\n\"\n", "\n", " outstr += lp.loop([\"i2\",\"i1\",\"i0\"],[\"0\", \"0\", \"0\"],\n", " [\"cctk_lsh[2]\",\"cctk_lsh[1]\",\"cctk_lsh[0]\"],\n", " [\"1\",\"1\",\"1\"],\n", " [\"#pragma omp parallel for\",\"\",\"\",],\"\",set_rhss_to_zero)\n", " outstr += \"}\\n\"\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"zero_rhss.c\")] = outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.747142Z", "iopub.status.busy": "2021-03-07T17:08:39.746396Z", "iopub.status.idle": "2021-03-07T17:08:39.751784Z", "shell.execute_reply": "2021-03-07T17:08:39.752426Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " # Next registration with the Method of Lines thorn\n", " outstr = \"\"\"\n", "//--------------------------------------------------------------------------\n", "// Register with the Method of Lines time stepper\n", "// (MoL thorn, found in arrangements/CactusBase/MoL)\n", "// MoL documentation located in arrangements/CactusBase/MoL/doc\n", "//--------------------------------------------------------------------------\n", "#include \n", "\n", "#include \"cctk.h\"\n", "#include \"cctk_Parameters.h\"\n", "#include \"cctk_Arguments.h\"\n", "\n", "#include \"Symmetry.h\"\n", "\n", "void MaxwellVacuum_MoL_registration(CCTK_ARGUMENTS)\n", "{\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\n", " CCTK_INT ierr = 0, group, rhs;\n", "\n", " // Register evolution & RHS gridfunction groups with MoL, so it knows\n", "\n", " group = CCTK_GroupIndex(\"MaxwellVacuum::evol_variables\");\n", " rhs = CCTK_GroupIndex(\"MaxwellVacuum::evol_variables_rhs\");\n", " ierr += MoLRegisterEvolvedGroup(group, rhs);\n", "\n", " if (ierr) CCTK_ERROR(\"Problems registering with MoL\");\n", "}\n", "\"\"\"\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"MoL_registration.c\")] = outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.760231Z", "iopub.status.busy": "2021-03-07T17:08:39.759008Z", "iopub.status.idle": "2021-03-07T17:08:39.762787Z", "shell.execute_reply": "2021-03-07T17:08:39.763444Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " # Next register with the boundary conditions thorns.\n", " # PART 1: Set BC type to \"none\" for all variables\n", " # Since we choose NewRad boundary conditions, we must register all\n", " # gridfunctions to have boundary type \"none\". This is because\n", " # NewRad is seen by the rest of the Toolkit as a modification to the\n", " # RHSs.\n", "\n", " # This code is based on Kranc's McLachlan/ML_Maxwell/src/Boundaries.cc code.\n", " outstr = \"\"\"\n", "#include \"cctk.h\"\n", "#include \"cctk_Arguments.h\"\n", "#include \"cctk_Parameters.h\"\n", "#include \"cctk_Faces.h\"\n", "#include \"util_Table.h\"\n", "#include \"Symmetry.h\"\n", "\n", "// Set `none` boundary conditions on Maxwell RHSs, as these are set via NewRad.\n", "void MaxwellVacuum_BoundaryConditions_evolved_gfs(CCTK_ARGUMENTS)\n", "{\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\n", " CCTK_INT ierr CCTK_ATTRIBUTE_UNUSED = 0;\n", "\"\"\"\n", " for gf in evol_gfs_list:\n", " outstr += \"\"\"\n", " ierr = Boundary_SelectVarForBC(cctkGH, CCTK_ALL_FACES, 1, -1, \"MaxwellVacuum::\"\"\"+gf+\"\"\"\", \"none\");\n", " if (ierr < 0) CCTK_ERROR(\"Failed to register BC for MaxwellVacuum::\"\"\"+gf+\"\"\"!\");\n", "\"\"\"\n", " outstr += \"\"\"\n", "}\n", "\n", "// Set `none` boundary conditions on Maxwell constraints\n", "void MaxwellVacuum_BoundaryConditions_aux_gfs(CCTK_ARGUMENTS) {\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\n", " CCTK_INT ierr CCTK_ATTRIBUTE_UNUSED = 0;\n", "\n", "\"\"\"\n", " for gf in aux_gfs_list:\n", " outstr += \"\"\"\n", " ierr = Boundary_SelectVarForBC(cctkGH, CCTK_ALL_FACES, cctk_nghostzones[0], -1, \"MaxwellVacuum::\"\"\"+gf+\"\"\"\", \"none\");\n", " if (ierr < 0) CCTK_ERROR(\"Failed to register BC for MaxwellVacuum::\"\"\"+gf+\"\"\"!\");\n", "\"\"\"\n", " outstr += \"}\\n\"\n", "\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"BoundaryConditions.c\")] = outstr.replace(\"MaxwellVacuum\",ThornName)\n", "\n", " # PART 2: Set C code for calling NewRad BCs\n", " # As explained in lean_public/LeanMaxwellMoL/src/calc_mwev_rhs.F90,\n", " # the function NewRad_Apply takes the following arguments:\n", " # NewRad_Apply(cctkGH, var, rhs, var0, v0, radpower),\n", " # which implement the boundary condition:\n", " # var = var_at_infinite_r + u(r-var_char_speed*t)/r^var_radpower\n", " # Obviously for var_radpower>0, var_at_infinite_r is the value of\n", " # the variable at r->infinity. var_char_speed is the propagation\n", " # speed at the outer boundary, and var_radpower is the radial\n", " # falloff rate.\n", "\n", " outstr = \"\"\"\n", "#include \n", "\n", "#include \"cctk.h\"\n", "#include \"cctk_Arguments.h\"\n", "#include \"cctk_Parameters.h\"\n", "\n", "void MaxwellVacuum_NewRad(CCTK_ARGUMENTS) {\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\n", "\"\"\"\n", " for gf in evol_gfs_list:\n", " var_at_infinite_r = \"0.0\"\n", " var_char_speed = \"1.0\"\n", " var_radpower = \"3.0\"\n", "\n", " outstr += \" NewRad_Apply(cctkGH, \"+gf+\", \"+gf.replace(\"GF\",\"\")+\"_rhsGF, \"+var_at_infinite_r+\", \"+var_char_speed+\", \"+var_radpower+\");\\n\"\n", " outstr += \"}\\n\"\n", "\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"BoundaryCondition_NewRad.c\")] = outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.b: Evaluate Maxwell right-hand-sides (RHSs) \\[Back to [top](#toc)\\]\n", "$$\\label{mwevrhss}$$" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.770364Z", "iopub.status.busy": "2021-03-07T17:08:39.769475Z", "iopub.status.idle": "2021-03-07T17:08:39.773297Z", "shell.execute_reply": "2021-03-07T17:08:39.773853Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " ###########################\n", " ###########################\n", " # Maxwell_RHSs\n", " ###########################\n", " common_includes = \"\"\"\n", "#include \n", "#include \"cctk.h\"\n", "#include \"cctk_Arguments.h\"\n", "#include \"cctk_Parameters.h\"\n", "#include \"SIMD/SIMD_intrinsics.h\"\n", "\"\"\"\n", " common_preloop = \"\"\"\n", " DECLARE_CCTK_ARGUMENTS;\n", " const CCTK_REAL NOSIMDinvdx0 = 1.0/CCTK_DELTA_SPACE(0);\n", " const REAL_SIMD_ARRAY invdx0 = ConstSIMD(NOSIMDinvdx0);\n", " const CCTK_REAL NOSIMDinvdx1 = 1.0/CCTK_DELTA_SPACE(1);\n", " const REAL_SIMD_ARRAY invdx1 = ConstSIMD(NOSIMDinvdx1);\n", " const CCTK_REAL NOSIMDinvdx2 = 1.0/CCTK_DELTA_SPACE(2);\n", " const REAL_SIMD_ARRAY invdx2 = ConstSIMD(NOSIMDinvdx2);\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.779395Z", "iopub.status.busy": "2021-03-07T17:08:39.778615Z", "iopub.status.idle": "2021-03-07T17:08:39.781634Z", "shell.execute_reply": "2021-03-07T17:08:39.782157Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " path = os.path.join(ThornName,\"src\")\n", " MaxwellVacuum_src_filelist = []\n", " for _root, _dirs, files in os.walk(path): # _root, _dirs unused.\n", " for filename in files:\n", " MaxwellVacuum_src_filelist.append(filename)\n", " MaxwellVacuum_src_filelist.sort() # Sort the list in place.\n", "\n", " Maxwell_FD_orders_output = []\n", " for filename in MaxwellVacuum_src_filelist:\n", " if \"Maxwell_RHSs_\" in filename:\n", " array = filename.replace(\".\",\"_\").split(\"_\")\n", " FDorder = int(array[-2])\n", " if FDorder not in Maxwell_FD_orders_output:\n", " Maxwell_FD_orders_output.append(FDorder)\n", " Maxwell_FD_orders_output.sort()\n", "\n", " ###########################\n", " # Output Maxwell RHSs driver function\n", " outstr = common_includes\n", " for filename in MaxwellVacuum_src_filelist:\n", " if (\"Maxwell_RHSs_\" in filename) and (\".h\" in filename):\n", " outstr += \"\"\"extern void \"\"\" + ThornName+\"_\"+filename.replace(\".h\", \"(CCTK_ARGUMENTS);\") + \"\\n\"\n", "\n", " outstr += \"\"\"\n", "void MaxwellVacuum_RHSs(CCTK_ARGUMENTS) {\n", " DECLARE_CCTK_ARGUMENTS;\n", "\n", " const CCTK_INT *FD_order = CCTK_ParameterGet(\"FD_order\",\"MaxwellVacuum\",NULL);\n", "\n", "\"\"\"\n", " for filename in MaxwellVacuum_src_filelist:\n", " if (\"Maxwell_RHSs_\" in filename) and (\".h\" in filename):\n", " array = filename.replace(\".\", \"_\").split(\"_\")\n", " outstr += \" if(*FD_order == \" + str(array[-2]) + \") {\\n\"\n", " outstr += \" \" + ThornName+\"_\"+filename.replace(\".h\", \"(CCTK_PASS_CTOC);\") + \"\\n\"\n", " outstr += \" }\\n\"\n", " outstr += \"} // END FUNCTION\\n\"\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"Maxwell_RHSs.c\")] = outstr.replace(\"MaxwellVacuum\", ThornName)\n", "\n", " def SIMD_declare_C_params():\n", " SIMD_declare_C_params_str = \"\"\n", " for i in range(len(par.glb_Cparams_list)):\n", " # keep_param is a boolean indicating whether we should accept or reject\n", " # the parameter. singleparstring will contain the string indicating\n", " # the variable type.\n", " keep_param, singleparstring = ccl.keep_param__return_type(par.glb_Cparams_list[i])\n", "\n", " if (keep_param) and (\"CCTK_REAL\" in singleparstring):\n", " parname = par.glb_Cparams_list[i].parname\n", " SIMD_declare_C_params_str += \" const \"+singleparstring + \"*NOSIMD\"+parname+\\\n", " \" = CCTK_ParameterGet(\\\"\"+parname+\"\\\",\\\"MaxwellVacuum\\\",NULL);\\n\"\n", " SIMD_declare_C_params_str += \" const REAL_SIMD_ARRAY \"+parname+\" = ConstSIMD(*NOSIMD\"+parname+\");\\n\"\n", " return SIMD_declare_C_params_str\n", "\n", " # Create functions for the largest C kernels (Maxwell RHSs and Ricci) and output\n", " # the .h files to .c files with function wrappers; delete original .h files\n", " path = os.path.join(ThornName, \"src\")\n", " for filename in MaxwellVacuum_src_filelist:\n", " if (\"Maxwell_RHSs_\" in filename) and (\".h\" in filename):\n", " outstr = common_includes + \"void MaxwellVacuum_\"+filename.replace(\".h\",\"\")+\"(CCTK_ARGUMENTS) {\\n\"\n", " outstr += common_preloop+SIMD_declare_C_params()\n", " with open(os.path.join(path,filename), \"r\") as currfile:\n", " outstr += currfile.read()\n", " # Now that we've inserted the contents of the kernel into this file,\n", " # we delete the file containing the kernel\n", " os.remove(os.path.join(path,filename))\n", " outstr += \"} // END FUNCTION\\n\"\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(filename.replace(\".h\",\".c\"))] = outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.c: Diagnostics: Computing the divergence constraint \\[Back to [top](#toc)\\]\n", "$$\\label{diagnostics}$$\n", "\n", "The divergence constraint is a useful diagnostics of a calculation's health. Here we construct the driver function." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.787163Z", "iopub.status.busy": "2021-03-07T17:08:39.786524Z", "iopub.status.idle": "2021-03-07T17:08:39.789476Z", "shell.execute_reply": "2021-03-07T17:08:39.789944Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Appending to MaxwellNRPy_py_dir/MaxwellVacuum_C_drivers_codegen.py\n" ] } ], "source": [ "%%writefile -a $MaxwellVacuumdir/MaxwellVacuum_C_drivers_codegen.py\n", "\n", " # Next, the driver for computing the Maxwell Hamiltonian & momentum constraints\n", " outstr = \"\"\"\n", "#include \n", "\n", "#include \"cctk.h\"\n", "#include \"cctk_Arguments.h\"\n", "#include \"cctk_Parameters.h\"\n", "\n", "void MaxwellVacuum_constraints(CCTK_ARGUMENTS) {\n", " DECLARE_CCTK_ARGUMENTS;\n", " DECLARE_CCTK_PARAMETERS;\n", "\n", " const CCTK_REAL invdx0 = 1.0/CCTK_DELTA_SPACE(0);\n", " const CCTK_REAL invdx1 = 1.0/CCTK_DELTA_SPACE(1);\n", " const CCTK_REAL invdx2 = 1.0/CCTK_DELTA_SPACE(2);\n", "\"\"\"\n", " for filename in MaxwellVacuum_src_filelist:\n", " if \"Maxwell_constraints_\" in filename:\n", " array = filename.replace(\".\",\"_\").split(\"_\")\n", " outstr += \" if(FD_order == \"+str(array[-2])+\") {\\n\"\n", " outstr += \" #include \\\"\"+filename+\"\\\"\\n\"\n", " outstr += \" }\\n\"\n", " outstr += \"}\\n\"\n", "\n", " # Add C code string to dictionary (Python dictionaries are immutable)\n", " Csrcdict[append_to_make_code_defn_list(\"driver_constraints.c\")] = outstr.replace(\"MaxwellVacuum\",ThornName)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.d: Output all C driver functions needed for `MaxwellVacuum` \\[Back to [top](#toc)\\]\n", "$$\\label{outcdrivers}$$\n", "\n", "First we call the above functions (output above to the `MaxwellVacuum_validate.MaxwellVacuum_C_drivers_codegen` Python module) to store all needed driver C files to a Python dictionary, then we simply outputs the dictionary to the appropriate files." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.797103Z", "iopub.status.busy": "2021-03-07T17:08:39.796027Z", "iopub.status.idle": "2021-03-07T17:08:39.803122Z", "shell.execute_reply": "2021-03-07T17:08:39.803649Z" } }, "outputs": [], "source": [ "import MaxwellNRPy_py_dir.MaxwellVacuum_C_drivers_codegen as driver\n", "\n", "# The following Python dictionaries consist of a key, which is the filename\n", "# in the thorn's src/ directory (e.g., \"driver_Maxwell_constraints.c\"),\n", "# and a value, which is the corresponding source code, stored as a\n", "# Python string.\n", "Vac_Csrcdict = {}\n", "Reg_Csrcdict = {}\n", "\n", "# We'll need lists of gridfunctions for these driver functions\n", "evol_gfs_list = cclgen.evol_gfs_list\n", "aux_gfs_list = cclgen.aux_gfs_list\n", "auxevol_gfs_list = cclgen.auxevol_gfs_list\n", "\n", "# Generate driver codes for MaxwellVacuum thorn (i.e., populate the Vac_Csrcdict dictionary)\n", "driver.driver_C_codes(Vac_Csrcdict, \"MaxwellVacuum\",\n", " cclgen.rhs_list,cclgen.evol_gfs_list,cclgen.aux_gfs_list,\n", " cclgen.auxevol_gfs_list)\n", "\n", "# Next we output the contents of the Reg_Csrcdict and\n", "# Vac_Csrcdict dictionaries to files in the respective\n", "# thorns' directories.\n", "for key,val in Vac_Csrcdict.items():\n", " with open(os.path.join(\"MaxwellVacuum\",\"src\",key),\"w\") as file:\n", " file.write(val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 4.e: `make.code.defn`: List of all C driver functions needed to compile `MaxwellVacuum` \\[Back to [top](#toc)\\]\n", "$$\\label{makecodedefn}$$\n", "\n", "When constructing each C code driver function above, we called the `append_to_make_code_defn_list()` function, which built a list of each C code driver file. We'll now add each of those files to the `make.code.defn` file, used by the Einstein Toolkit's build system." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.810628Z", "iopub.status.busy": "2021-03-07T17:08:39.809947Z", "iopub.status.idle": "2021-03-07T17:08:39.812457Z", "shell.execute_reply": "2021-03-07T17:08:39.812946Z" } }, "outputs": [], "source": [ "# Finally output the thorns' make.code.defn files, consisting of\n", "# a list of all C codes in the above dictionaries. This is\n", "# part of the ETK build system so that these files are output.\n", "\n", "def output_make_code_defn(dictionary, ThornName):\n", " with open(os.path.join(ThornName,\"src\",\"make.code.defn\"), \"w\") as file:\n", " file.write(\"\"\"\n", "# Main make.code.defn file for thorn \"\"\"+ThornName+\"\"\"\n", "\n", "# Source files in this directory\n", "SRCS =\"\"\")\n", " filestring = \"\"\n", " list_of_C_driver_files = list(dictionary.keys())\n", " for i in range(len(list_of_C_driver_files)):\n", " filestring += \" \"+list_of_C_driver_files[i]\n", " if i != len(list_of_C_driver_files)-1:\n", " filestring += \" \\\\\\n\"\n", " else:\n", " filestring += \"\\n\"\n", " file.write(filestring)\n", "\n", "output_make_code_defn(Vac_Csrcdict,\"MaxwellVacuum\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 5: Code validation \\[Back to [top](#toc)\\]\n", "$$\\label{code_validation}$$\n", "\n", "Here we will show plots demonstrating good error convergence and proper behavior of error nodes in the systems." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 5.a: Error Convergence \\[Back to [top](#toc)\\]\n", "$$\\label{convergence}$$\n", "\n", "**Code tests adopting fourth-order finite differencing, coupled to 2nd order Iterative Crank-Nicholson method-of-lines for time integration**\n", "\n", "Inside the directory *`MaxwellVacuum/example_parfiles/`* are the files used for this convergence test:\n", "**maxwell_toroidaldipole-0.125_OB4.par & maxwell_toroidaldipole-0.0625_OB4.par** : ETK parameter files needed for performing the tests. These parameter files set up a toroidal dipole field propagating in a 3D numerical grid that extends from -4. to +4. along the x-, y-, and z-axes (in units of $c=1$). The parameter files are identical, except the latter has grid resolution that is twice as high as the former (so the errors should drop in the higher resolution case by a factor of $2^2$, since we adopt fourth-order finite differencing coupled to 2nd order Iterative Crank-Nicholson time integration.)\n", "\n", "**Second-order code validation test results:**\n", "\n", "The plot below shows the discrepancy between numerical and exact solutions to x-components of system I $\\vec{E}$ and $\\vec{A}$ at two different resolutions, at t = 2.0 (to not have errors at the boundary propagate too far inward): dashed is low resolution ($\\Delta x_{\\rm low}=0.125$) and solid is high resolution ($\\Delta x_{\\rm high}=0.0625$). Since this test adopts **fourth**-order finite differencing for spatial derivatives and **second**-order Iterative Crank-Nicholson timestepping, we would expect this error to drop by a factor of approximately $(\\Delta x_{\\rm low}/\\Delta x_{\\rm high})^2 = (0.125/0.0625)^2 = 2^2=4$ when going from low to high resolution, and after rescaling the error in the low-resolution case, we see that indeed it overlaps the high-resolution result quite nicely, confirming second-order convergence. We note that we also observe convergence for all other evolved variables (in both systems) with a nonzero exact solutions." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.817111Z", "iopub.status.busy": "2021-03-07T17:08:39.816486Z", "iopub.status.idle": "2021-03-07T17:08:39.860340Z", "shell.execute_reply": "2021-03-07T17:08:39.861377Z" } }, "outputs": [ { "data": { "image/png": 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1XhoDDSNGjIiioqI6YY0PmjJlSgwZMiTPVQEApJMnNAAAtFDz5s2r8xjWf5TGQEMu+9tOnz49Z2sBAAD5s27duli+fPk+z6+pqck6lstrjFzp2rVrDBgwIOt4fa0DAQBaE4EGAIAWKukmWJs2beLMM8/MYzXJdu3aFdOmTYvbbrstZ2vOnTs3Z2sBAAD5M2/evJyt9fzzz8fdd98d27Zty9mauZAUMJ87d25UVVXlsRoAgPQSaAAAaKGmTp2adez000+PsrKyPFazp507d8bTTz8dl156afTs2TNGjRoVjzzySM7W/8tf/hJbt27N2XoAAEB+zJkzJ2drrV+/Pq666qqora3N2Zq5kBRoqK6uFtAGAPj/BBoAAFqgysrKeO6557KOF7LdxHPPPRcXXnhhHHTQQfHRj3407rvvvtiwYUPO98lkMjF//vycrwsAADStXP8y/+yzz47OnTvndM39dcYZZ0S7du2yjms7AQDwN20KXQAAALk3c+bM2LVrV9bxQgYali9fHj/72c+aZO1jjz02zjjjjBg6dGgMHTo0jj322CbZBwAAaBrvv/9+LFy4MKdrnnvuuTldLxc6duwYQ4YMiRkzZux1XKABAOBvBBoAAFqgpJtfHTp0iMGDB+exmro+8YlPRNu2bWPnzp2NXuO4446Lc889N84999x4/vnno1evXjF48ODo1q1bDisFAADybeHChYnXCk8++WRUVlbG448/Hn/84x/j/fffT1yvuLg4PvnJTzaohl/84hdx4oknximnnBJFRUUNOrchRo8enTXQsHDhwtiyZUuUl5c32f4AAM2BlhMAAC1QUqBh2LBh0b59+zxWU1eXLl0a/YSIc889N5YuXRrLli2L//qv/4r+/fvHpZdeGh//+MeFGQAAoAVIajfRpk2bOOuss+KCCy6IJ554IjZs2BCPPvpolJaWZj3n6KOPjq5du+7z/pWVlTFx4sTo379/HHXUUTFp0qSYP39+ZDKZBv137Iuk66La2tqYPn16zvcEAGhuBBoAAFqY9evXx5IlS7KO57LdxDvvvBP33HNPTJs2rUHnNfaRr2eddVaccMIJjToXAABIv6RAQ//+/aNjx467vy4tLY3zzjsvOnXqlPWcQYMGNWj/p556Knbs2BEREStXroxbb701Bg8eHH369Imvf/3rMWvWrKipqWnQmkm1JYUxtJ0AABBoAABoceoLF+xvoOGvf/1r3HHHHTFixIjo2bNnXHbZZXHXXXc1aI1zzjkniov3/lb0gAMOyHre9u3bG7QPAADQfGQymZgzZ07W8aFDh+71eNJ1QrZzsnn88cf3enzt2rVx++23x5lnnhm9evWKL3/5y/HMM8/Erl27GrT+B7Vt2zb+H3t3Ht9Enf8P/JU0vYEe9MDS0oKUAnK0iEBbQKAorKiIgOJ3u4KcuqIVxHNXORZFxXUVF0RAXOUQBBZFRZQWVGgBKRSQuxQqLQVael+hTTK/P/zRJTSZTNLMpElfz8cjD9v5fGbmXeTITF7zed99991mxxloICIiImKggYiIiMjliN30CggIQGxsrNXHzM3NxT//+U/Ex8cjPDwczz77LH7++WcYDAYAwPfff29V2CA4OBhDhgwB8EdP26FDh+Lf//43Ll26hJiYGLP7MdBAREREROS6cnJyUFRUZHbcVDhBEATR64SwsDDJ56+trcX27dstzrt69SqWL1+Oe+65B+3atcOUKVOMVnawhljg/NSpUygoKLD6mERERESuROPoAoiIiIjIvsQCDUOHDoWbm5uk45w5cwZbtmzBli1bcPjwYdG5NTU12LFjh1WtJGbPno0JEybgoYceQnBwcMN2sSVXGWggIiIiInJdt99+O86ePYuMjIyG14kTJyAIAgDTgQatVtswborY9cWtfvjhB6uvOYqLi7F69WqsXr0abdq0wQMPPICxY8di5MiR8Pb2tri/pRX0du3aheTkZKtqIiIiInIlDDQQERERuZDc3FycP3/e7LjYzTJBEHD8+PGGEMPx48etOveWLVusCjSMGjXK5Hax/rdVVVVW1URERERERM5DpVIhOjoa0dHRmDhxIgCgrKwMBw4cwJEjRxAeHt5oH0vXCGLXF7cy125CqoqKCqxbtw7r1q2Dr68v7rvvPowdOxb33XcfWrdubXKfHj16IDg42OzKFGlpaQw0EBERUYvGQAMRERGRC7HUY/XWQIMgCDh8+DC2bNmCzZs3Izs72+Zzf/vtt7h+/To8PT1tPgYgfsORKzQQEREREbUs/v7+GDFiBEaMGGFy3NI1gjWBhl69eqF79+44efKkVTWaUl1djU2bNmHTpk3w9PTEiBEjMHbsWDz44IPw9/dvmKdWqzFs2DBs3LjR5HHS0tIgCAJUKlWTayIiIiJyRmpHF0BERERE9iMWaAgLC0OXLl0A/PEU04IFC9CpUyf07dsXixYtalKYAQAqKyuxc+fOJh0DYMsJIiIiIiKSztI1gjUtJ1544QWcOHECp06dwsKFCxEbG9vU8gAA169fx7Zt2zBx4kSEhIRg1KhRSE9PbxgfNmyY2X3z8vJw7tw5u9RBRERE5IwYaCAiIiJyEYIgYNeuXWbHk5KSoFKpUFVVhaSkJMydOxe5ubl2OXdgYCCeeOIJtG/fvsnHYssJIiIiIiKSyp4tJ27o2rUr/va3vyErKwvnzp3DO++8g/79+9taopH6+nps374dgwcPbliVQaw1IGB5JT4iIiIiV8ZAAxEREZGLOHHiBK5evWp2PCkpCYIgYPr06fj111+bfL6QkBDMmDEDO3fuxJUrV7B69WrExcU1+bj2bDmRn5+PL7/8Es899xy0Wm1TSyMiIiIiombGni0nTLn99tvxwgsvYP/+/bh48SLef/99DBo0qMktIAwGAyZPnozCwkJ06tQJkZGRZucy0EBEREQtmcbRBRARERGRfVi6yZWUlIRly5bhiy++sPkc7du3x8MPP4xx48YhMTERbm5uNh/LHFtbTuj1ehw5cgQZGRlIT09HRkYG8vLyGsbHjx+PxMREu9ZKRERERESOJXaNoFKp4O3tbbdzRUREICUlBSkpKbhy5Qq2bt2KLVu24KeffoJer7fqWAEBAVizZg1CQkIA/HG9tnr1apNzd+/eDYPBALWazycSERFRy8NAAxEREZGLEAs0dOnSBfn5+Zg1a5bVx+3YsSPGjh2LsWPHol+/frLfRLO15YRWq0X//v3N3kjMyMhgoIGIiIiIyMWIXSP4+PjIdv3Srl07PPXUU3jqqadw7do1bNu2DVu2bMHOnTtRX18vum+fPn2wefNmdOzYsWGbWKChuLgYR48etcuKeERERETOhpFOIiIiIheg0+nw888/mx1PSEjA+PHjLd5YuyEmJgavvvoqDh06hJycHCxevBgDBgxQ5IkgW1tO+Pr6it7gy8jIaFJdRERERETU/Fi6RlBCUFAQJk+ejO+++w6FhYVYs2YNHnroIXh5eTWaO23aNKSnpxuFGQBg2LBhoudg2wkiIiJqqRhoICIiInIBmZmZqKioMDt+6NAh5Ofnix6jR48emDdvHo4fP45Tp07hjTfeQJ8+fZrcG9ZatgYagD+CG+ZkZGRAEASb6yIiIiIiouZH7BpBrJ2dXPz9/ZGcnIytW7eiqKgIGzduxIMPPggAWL16NVasWGEy6NCuXTvccccdZo/LQAMRERG1VGw5QUREROQCxG5uqVQq/Pbbb6L733///fj666+bRU9WsZuOUgINS5YsMTlWWFiInJwcdO7cuUn1ERERERFR89EcVmgwp1WrVnjkkUfwyCOPoKKiAu7u7qLzk5KScOLECZNjv/zyC+rq6uDh4SFHqURERETNluPvWBMRERFRk4kFGuLi4vDJJ5+YfAoIADp27IjPP/+8WYQZAPGbjnV1daJtM8RWaADYdoKIiIiIqDl67YSxr/IAACAASURBVLXX8MUXX+D333+3elW1qqoqs2OODjTcrE2bNvD29hadk5SUZHaspqYGBw4csHdZRERERM0eV2ggh6mursaJEydw9uxZlJSUoLKyEq1bt0ZAQAAiIyPRt29f+Pj4OLpMUfn5+cjMzERubi6qqqrg5eWFsLAwxMbGonv37o4uj4iIWoja2lrRD+qTkpIwefJkxMXFYdy4cTh//nzDmKenJ7Zs2YKAgAAlSpXE0k3H6upq+Pv7mxyLiIhAREQE8vLyTI5nZGTg8ccfb3KNRERERERkH5cvX8bChQsbvm/fvj0SEhIaXnFxcaIrGzTnFRqsdffdd0OtVsNgMJgc37VrFwYNGqRwVURERESOxUADKeb69etITU3Fzp07kZqaipMnT4omrjUaDfr164cZM2bg0Ucfhaenp4LVmicIAtavX4/3338fmZmZZudFRkZixowZePbZZ53u4omIiJxLRkYGrl+/bnb8xlM+cXFxyMzMxMSJE/HNN98AAJYtW4a4uDhF6pTKUp9bsUAD8McqDRs3bjQ5xhUaiIiIiIial1vfo1+6dAmbNm3Cpk2bAACnTp1C165dze4vFmiwdG3R3Pj5+eGuu+4yuxJDWloa5s6dq3BVRERERI7VPNYVJpd25MgRTJo0CaGhobj//vvxwQcf4MSJExaXj9PpdMjIyMDEiRPRuXNn7Ny5U6GKzcvPz8egQYOQnJwsGmYAgN9//x2vvvoqunXrhvT0dIUqJCKilkis3YS7uzsGDhzY8H1AQAC++uorLFq0CNOnT8fkyZOVKNEqloKAYkvKAuJtJ44fP47y8nKb6iIiIiIiIvsTCx0HBgYiJiZGdH9naTkhlVjbif3794sGOIiIiIhcEQMNJLvNmzfjs88+a9KHB/n5+RgxYgTmz59vx8qsc+7cOfTr18/qcEJeXh6GDRuGb7/9VqbKiIiopRMLNMTHxze6iadWq/Hyyy9j+fLlcpdmEyktJ8SIBRoEQcD+/fttqouIiIiIiOxPLNCQkJAAlUolur8rtZwAxAMN9fX12LNnj4LVEBERETkeW06Qw7Ru3RqJiYno168f2rVrh6CgIFRWVuLcuXPYsWMHsrKyjOYLgoB58+bBx8cHL7zwgqK1lpeXY+TIkbh8+XKjsaFDh+Lee+9FZGQkiouLcfToUaxfvx41NTUNc+rq6jB+/Hjs27cPsbGxSpZOREQurqysTHTVILGbYZZuDDqKlJYTYnr37g0fHx+jf4tvlpGRgREjRthcHxERERER2YdWq8WhQ4fMjouFlW9wpZYTwB+tAt3c3KDX602Op6amYuTIkQpXRUREROQ4DDSQojw9PTFmzBhMnToVQ4YMgZubm8l5b775Jnbu3IkpU6YgLy/PaOyVV17BiBEj0KtXLyVKBgCkpKQgJyfHaFtwcDC2bNmCQYMGNZr/1ltvITk5GTt27GjYptVq8X//9384evQo3N3dZa+ZiIhahjfeeAMGg8HsuFigoblqassJd3d39OvXDz/99JPJcbEnwIiIiIiISDmZmZmor683Oy4l0OBKLSfOnz+PcePGmQ0zAMCqVaswe/ZshIWFKVgZERERkeOw5QQpwtfXFy+//DIuXbqEL774AklJSWbDDDfcc889OHToEKKjo4226/V6zJkzR85yjRw8eBCff/650bZWrVphz549JsMMANC2bVt88803jdLSp06dwtKlS2WrlYiIWpaDBw/ivffeMzveqlUr9OvXT8GK7EOj0cDDw8PsuJSesWI3Pvfv3y96g5CIiIiIiJQhFjbWaDS46667LB7DVVpOfPPNN7jzzjsbrVp7q/Lycjz11FMQBEGhyoiIiIgci4EGkt3999+P8+fPY9GiRWjbtq1V+wYHB2PTpk1Qq41/q6alpeHq1av2LNOsRYsWNbpAWLRoEWJiYkT302g0WLlyJdq0aWO0/d133xVNnhMREUlRV1eHKVOmiK7OMHLkSKddFUhsadimBhqqqqrw22+/2VQXERERERHZj1igIS4uDj4+PhaP4QotJy5cuIAxY8agrKxM0vxt27Zh06ZNMldFRERE1Dww0ECyGzBgAEJCQmzev3fv3njwwQeNthkMBqN2DnK5evUqtm3bZrStffv2eOqppyTtHx4e3mjupUuX8N1339mtRiIiapnefvtt0Q/lVSoVXnrpJQUrsi+xJ6mkBBri4+NFx9l2goiIiIjIsQRBEH1fLqXdBOAaKzR07NgR8+fPt2qfmTNnori4WKaKiIiIiJoPBhrIKdzaugH4I7kst82bNzdaknry5MkW22XcbMqUKY22bdiwocm1ERFRy3Xy5En84x//EJ2TkpKCvn37KlSR/YndeBTrkXtDYGAgunXrZnacgQYiIiIiIsc6d+4cioqKzI5LDTSIXR84S6ABAF555RWMGjVK8vyioiLMmjVLxoqIiIiImgcGGsgpdOjQodE2JVpO/PDDD422Pfzww1YdIzo6Gj179jTalpqaKrpEOBERkTl6vR5TpkwRbV/k5eWFlJQUBauyv6au0ACI3wBloIGIiIiIyLEsvSeXEmjQ6/XQarVmx50p0KBWq7FmzRpERUVJ3mfNmjX4/vvv5SuKiIiIqBlgoIGcgqkPLry9vWU/760XVq1bt24UTpAiMTHR6Pvi4mKcOnWqSbUREVHL9OGHH2L//v2ic7RaLQYNGoR9+/YpVJX9ifW6tUeg4cKFC7h8+bLVdRERERERkX2IBRo6dOiA8PBwi8eoqakRHRe7rmiOAgICsGXLFnh6ekreZ8aMGaioqJCxKiIiIiLHYqCBnEJOTk6jbe3atZP1nBcvXmzUh+6uu+6yqt3EDab6eGdlZdlcGxERtUznz5/H3/72N0lz8/Pz8eSTTzrtikBNbTkBWH6ii6s0EBERERE5jtj7cXu0mwCca4WGG/r06YN///vfkufn5eXhlVdekbEiIiIiIsdioIGcwtatWxttk7sv+JkzZxpt69Spk03HMrXf2bNnbToWERG1TIIgYPr06RafQLrB19cXGzZsgFrtnG/37NFyIiYmBoGBgWbHGWggIiIiInKMsrIynDhxwuy41ECDpWsDZww0AMCUKVPwxBNPSJ6/bNky7NmzR8aKiIiIiBzHOe9wU4ty/PhxHDhwwGibv78/Bg8eLOt5c3NzG23r0KGDTccytd+FCxdsOhYREbVMn376KdLS0iTP/+STT9CtWzcZK5KXPVpOqFQq0RuhDDQQERERETnG/v37IQiC2XF7BRqcreXEDSqVCkuXLkXv3r0l7zNlyhTU1tbKWBURERGRYzDQQM3eCy+80GhbcnIyNBqNrOe9evVqo20RERE2Hat9+/ZQqVQWj09ERGRKQUEBZs+eLXl+SkoKHn30URkrkp89Wk4A4jdCDx06BK1Wa1VdRERERETUdGLhYh8fH8kf5Ltiy4kbvL29sWXLFvj5+Uman52djQULFshcFREREZHyGGigZu2zzz7Djh07jLb5+voq0heutLS00TZbU91ubm7w8vIy2lZSUmLTsYiIqGURBAFPP/00ysvLJc2Pj4/HO++8I3NV8rNHywlAPNBQX1+PzMxMq+oiIiIiIqKmEws09O/fX/KDTK7acuKG22+/HZ9//rnk+YsXL8bhw4dlrIiIiIhIeQw0ULN16tQpPP300422/+Mf/0BYWJjs5zd1QXRrKMEa3t7eRt9L7YFuizZt2tj8qqyslK0uIiKy3ubNm/HVV19JmhscHIwvv/wSHh4eMlclP3u0nACAu+66S/RmKNtOEBEREREpS6fTNWovezOp7SYA8WsDjUbjEtdGDz74oOSHu/R6PSZPnoz6+nqZqyIiIiJSDgMN1CyVlpZi9OjRjS5KhgwZgpSUFEVqMPXGvymBhlv3raurs/lYllRWVtr8IiKi5qO4uBgzZ86UNFetVuOLL75AeHi4zFUpw14rNPj4+CAuLs7sOAMNRERERETK+u2330RbRdgr0ODr69uoBayzWrBgAYYNGyZp7tGjR7F48WKZKyIiIiJSjrS1u8gpVFRUwGAwKH5eT0/PRqsPNMX169cxZswYZGdnG22/7bbb8MUXX0CtdlwOpykXQbfuKwhCU8shIiIXN3v2bBQWFkqau3DhQiQlJclckXLEAg2W+uTeKiEhAQcPHmy03cfHxyWe2CIiIiIiciaWQsXx8fGSjyV2beDs7SZuptFo8MUXXyAuLg4FBQUW58+fPx8PP/wwunbtqkB1RERERPJioMGF9OrVC7///rvi501JScH7779vl2Pp9Xr83//9H37++Wej7W3atMF3332Hdu3a2eU8Uri7uzfaVltba/Pxbt1Xzg9QWrdubfO+XKWBiKh52LFjh+ReqQ888ABeeuklmStSlr1WaAD+CDR88MEH6NChAxISEpCYmIiEhAT06tVLcm9eIiIiIiKyD7FAQ/fu3REQECD5WJZWaHAlISEh2LRpE+6++27odDrRuXV1dZgyZQr27Nnj0IfDiIiIiOyBd3Cp2RAEAdOnT8d///tfo+3e3t7Ytm2b6HLRcvDx8Wm0TavV2ny8WwMNpo5vLxUVFTbv26ZNG4YaiIgcrLKyEjNmzJA0t1OnTvjss89c7iZVq1atzI5VV1dDEATJKyfdd999yMvLc5l2HEREREREzkws0GBNuwlAPNAgdk3hrBISEvDPf/5TUkvejIwMLFu2THIbQyIiIqLmyrXufJNTmzVrFlavXm20zd3dvSF5rLTAwMBG26xd4voGvV7fKAzRtm1bm45FRESu75VXXsHFixctzvPy8sKWLVuseoLJWYg9TWUwGKwKGbZq1YphBiIiIiKiZqCgoAC5ublmx60NNLSUlhM3e+aZZ/Doo49Kmvvyyy+L/noTEREROQMGGqhZ+Pvf/44PPvjAaJtarcbatWsxatQoh9QUGhraaFt+fr5NxyooKIAgCEbbQkJCbDoWERG5tj179mDp0qWS5i5duhSxsbEyV+QYlm4+Wtt2goiIiIiIHE9sdQYASExMtOp4LanlxA0qlQqrVq1C165dLc6trq7GjBkzGt2XJCIiInImDDS4kNzcXAiCoPjr/fffb1LdixYtwhtvvGG0TaVS4ZNPPsEjjzzSpGM3RWRkZKNtUp6WNcXUfh07drTpWERE5Lq0Wi2mTp0qae6UKVMwefJkmStyHEvLwzLQQERERETkfMQCDW3btkV0dLRVx2tpLSduaNWqFbZs2SIptPHjjz/i888/V6AqIiIiInkw0EAO9cEHH+DVV19ttP3f//43Jk2apHxBN4mJiWm07fz58zYdy9R+po5PREQt24IFC3D27FmL8+Li4vDhhx8qUJHjWLoxZ2sbKCIiIiIichy9Xm82aJCQkACVSmXV8Vpiy4kbunfvjlWrVkmaO2vWLFy5ckXmioiIiIjkwUADOczKlSvx3HPPNdq+ePFi/PWvf3VARcYiIyMRGBhotO3gwYMwGAxWH2v//v2NtsXFxdlcGxERuZ6srCy88847Fuf5+/tj8+bN8Pb2VqAqx2HLCSIiIiIi1/PBBx+grKwMR44cwbJly5CcnNywimlCQoLVx2uJLSduNmHCBDz77LMW55WWluKZZ55RoCIiIiIi+2OggRxi3bp1ePLJJxttnzdvHubMmeOAiky7tW9fRUUFfvvtN6uPk56ebvR9YGAgunXr1qTaiIjIddTX12PixInQ6/UW565ZswadOnVSoCrHYssJIiIiIiLX5Obmht69e+Opp57CmjVrcP78eRQUFGDKlClWH6ultpy42eLFixEfH29x3ubNm/Hf//5XgYqIiIiI7IuBBlLc1q1bMWnSpEYrHbz00kuYO3eug6oy7d577220bevWrVYdIycnB8eOHTPaNnz4cKjV/ONHRER/WLx4saTA3N/+9jfcf//9ClTkeJZWoGCggYiIiIjIddx2220IDg62er+WvkIDAHh4eODLL7/Ehx9+iHvuuUd07l//+leUlpYqVBkRERGRffATVVLUjh07MGHCBOh0OqPtzz77LN566y0HVWXeuHHj4ObmZrRt9erVVrWd+OSTTyAIgtG2CRMm2KU+IiJyfqdPn8aCBQsszktKSsL8+fMVqKh5UKvV8PHxMTsu1iuXiIiIiIhaBrHrgpYSaACA8PBwzJw5EytWrBD9ua9evYrnn39ewcqIiIiImo6BBlLML7/8gocffhh1dXVG26dNm4b333/fQVWJa9euXaMnYfPy8vDxxx9L2r+goADLli0z2hYWFoZRo0bZrUYiInJeBoMBU6dOxfXr10XnhYWF4YsvvmgUsnN1YjfiuEIDERERERFxhQZjUVFRePPNN0XnfPrpp9i5c6dCFRERERE1HQMNpIiDBw/i/vvvR21trdH2v/zlL1i+fDlUKpVs587NzYVKpTJ6RUVFSd7/lVdeabTtpZdewtmzZ0X30+l0mDZtGsrLy422P//88/Dw8JB8fiIicl0fffQR0tPTReeo1Wps3rzZpuVXnZ1Yz1t7BBqKi4vx7bff4tVXX8WQIUMwbdq0Jh+TiIiIiIiUI3ZdIHY94cqefvppJCQkiM6ZPn06V70jIiIip8FAA8nu+PHjGDlyJCorK422jx8/Hp9++inU6ub927B///5ITk422lZZWYmBAwdi7969JvcpKSnB6NGjsX37dqPtMTExmDlzpmy1EhGR8/j999/x8ssvW5z3/vvvIz4+XoGKmh+xJ6psvfm2d+9eTJkyBd26dUNQUBAeeOABLFq0CD///DNSU1NtLZWIiIiIiByALScac3Nzw6pVq0QfqMrNzcXf//53BasiIiIisp3G0QWQ65s1axZKSkoabT9x4gTuvPNOm4/bt29frFq1qimlSfbhhx8iPT0dFy5caNhWVFSEQYMGISkpCffeey8iIiJQUlKCo0ePYv369Y0S4p6enli/fj1XZyAiIgiCgBkzZojefAsJCcGCBQswffp0BStrXuRoOZGdnY3Vq1ebHMvNzUVBQQHCwsJsOjYRERERESmnrq4OOp3O7HhLDTQAQLdu3fD666+LhhaWLFmCgQMHYty4cQpWRkRERGQ9BhpIdvX19Sa3nzx5sknH9ff3b9L+1p7r+++/x5AhQ3DlyhWjsbS0NKSlpYnu7+7ujg0bNqBPnz5ylklERE5izZo1+OGHH0TnLF26tMXfWJKj5YSlpVf37duHsWPH2nRsIiIiIiJSjqVrgpbacuKGF198EV9++SWOHTtmclwQBCQnJyMpKQkBAQEKV0dEREQkXfNe65+oGYmJicGBAwcwYMAAq/YLDw9HamoqHnroIZkqIyIiZ3L16lU899xzonPGjBnDD9UhT8uJLl26IDAw0Ox4enq6TcclIiIiIiJlWbomaMkrNAB/PGC1evVquLm5mZ1z/fp1DBs2TMGqiIiIiKzHQAORFTp06ICMjAysWbPGYruMDh06YOHChTh16hQGDx6sUIVERNTcPfPMMygtLTU77u/vj6VLl0KlUilYVfMkR8sJlUolukpDRkaGTcclIiIiIiJlWbomaOmBBgC48847MWfOHNE5R44cwfz58xWqiIiIiMh6bDlBsvvpp58cev6oqCgIgmC346lUKiQnJyM5ORl5eXk4ePAgfv/9d1RVVcHLywthYWHo3bs3evToYbdzEhGRa9i6dSs2bdokOue9997DbbfdplBFzZscLScAIDExEd9++63JscOHD6O2thbe3t42H5+IiIiIiP7njTfewI4dO5CQkIDExETEx8cjODi4ycdlywlp5s6di3Xr1iE/P9/snPnz5+O+++7DXXfdpWBlRERERNIw0EDUBBEREYiIiHB0GURE5AQ+++wzvPjii6Jzhg8fjkmTJilTkBOQY4UGAKIrNNTX1+PQoUMYOHCgzccnIiIiIqL/2bVrF/bu3Yu9e/c2bIuOjkZCQgLGjx+PUaNG2XRcrtAgjbe3N55++mm88sorZucIgoCRI0fi3LlzCAgIULA6IiIiIsvYcoKIiIhIZunp6XjiiSdQWFhodo6Pjw9WrFjBVhM3EbsBaalfrpi+fftCozGf62XbCSIiIiIi+9DpdDhw4ECj7dnZ2fjss8+wf/9+m49t6ZqAgYb/efnll9GnTx/ROSUlJRg7diwMBoNCVRERERFJw0ADERERkYwKCwsxevRoi+2P3nzzTXTs2FGhqpyDXCs0+Pj4IC4uzux4enq6zccmIiIiIqL/+e2330Tfu4utnmaJ2HE9PT3h5uZm87FdUWpqKjw9PUXn7N69G4sWLVKoIiIiIiJpGGggIiIikolOp8P48eNRXFwsOi8+Ph4zZ85UqCrnIdbztimBBkD8xmlGRobFAAoREREREVlmKSw8YMAAm48tdk0gdi3RUgUEBODTTz+1OO+1115DWlqaAhURERERScNAAxEREZFMXn/9dfzyyy+ic9zd3bFq1So+PWSCXC0nACAxMdHs2LVr13Du3LkmHZ+IiIiIiMTbuXXv3h0BAQE2H1vsmoDtJkx77LHHMHLkSNE5giDgkUceQX5+vkJVEREREYljoIGIiIhIBtu2bZO0VOeLL76I7t27K1CR85Gr5QTwx6oYYsRuvBIRERERkTRi76vFQsZSiF0TMNBg3po1ayyuYFFSUoKxY8eirq5OoaqIiIiIzGOggYiIiMjOcnJy8Nhjj1mcFx0djddff12BipyT2E222tpa6PV6m48dHh6ODh06mB1noIGIiIiIqGkuXbqE33//3ey4WBs4KdhywjZBQUFYuXKlxXm//vorUlJSFKiIiIiISBwDDURERER2VFtbi6SkJNTU1IjOU6lUWL9+PTw8PBSqzPlYeqrK0q+xJWI3UBloICIiIiJqGkvvqZsaaGDLCds9+uijuO+++yzOW758Of7zn//IXxARERGRCAYaiIiIiOxEEATcd999ok8h3TBnzhz07dtXgaqcl6WbkE1tOyF2A/XEiRMoKytr0vGJiIiIiFoysUBDUFAQoqOjm3R8tpywnUqlwooVKyStZDFlyhQcPHhQgaqIiIiITGOggYiIiMhOnnzySfz0008W58XExGD+/PnyF+TkLN1ca2qgQaxnryAI2L9/f5OOT0RERETUkokFGhISEqBSqZp0fLacaJr27dvjww8/tDjPYDDg7rvvRkFBgQJVERERETXGQAMRERGRHbz11ltYsWKFxXm33XYbtm/fDm9vbwWqcm5yr9DQq1cv+Pj4mB1n2wkiIiIiItvU1tbi8OHDZseb2m4C4AoN9jBp0iS8+uqrFufV1taiV69eKC8vV6AqIiIiImMMNBARERE10apVq/DKK69YnBccHIy9e/eiU6dOClTl/CzdhBTrmSuFRqNB//79zY4z0EBEREREZJvMzEzodDqz4/YINIhdDzDQIN3ChQslXc8WFxcjNjYWFRUVClRFRERE9D8MNBARERE1wdq1azFt2jSL8/z8/HDgwAGGGawg9woNgPiN1AMHDojehCUiIiIiItPS09PNjrm7u6Nv375NPgdXaLAPlUqFN954AykpKRbn5ubmIiEhgSs1EBERkaIYaCAiIiKy0bp16/D4449bnOfl5YXMzEx07NhRgapch6enJ9zc3MyOyx1oqKqqwm+//dbkcxARERERtTRiq5316dPHLi34xK4HWrVq1eTjtyQqlQr/+te/JF3fnjhxAsOGDWOogYiIiBTDQAMRERGRDdauXYvHH38cgiCIznNzc0N6ejo6d+6sUGWuQ6VSiT5Z1dSWEwAQHx8vOs62E0RERERE1hEEQfR9tD3aTQBsOWFvKpUKn332GZKSkizOPXz4MO69916GGoiIiEgRDDQQERERWWnNmjWYOHEiDAaDxbmbNm1Cnz59FKjKNYndiLTHCg0BAQHo3r272XEGGoiIiIiIrJOdnY3i4mKz4/YKNLDlhDx+/PFHxMTEWJz366+/4t5770VZWZkCVREREVFLxkADERERkRU+//xzyWGGBQsWYMyYMQpU5brEloq1R6ABEL+hykADEREREZF10tPTRceVCDSw5YTt1Go19u/fj4CAAItzGWogIiIiJTDQQERERCTRZ599hkmTJllsMwEAo0ePxt///ncFqnJtcrecAMRvqObm5qKgoMAu5yEiIiIiagnEQsGRkZEICwtr8jkEQeAKDTLy9/fHrl27oNFoLM49ePAg7rnnHpSWlipQGREREbVEDDQQERERSfCf//wHTzzxhKQwQ5cuXbBu3TqoVCoFKnNtcrecACw/IcZVGoiIiIiIpBN7/5yYmGiXc9TW1opemzHQ0HSxsbFYuXKlpLmZmZkMNRAREZFsGGggIiIismD16tWYPHmypDCDj48Ptm3bxhtodqJEy4kuXbqgbdu2Rtvc3d3Rv39/zJo1C507d7bLeYiIiIiIXF1paSlOnjxpdlyJdhMAW07Yy6RJkzBt2jRJcw8dOoThw4ejpKRE5qqIiIiopbG8ZhQRERFRC7Z69WpMnTpVUpgB+KMtRUxMjMxVtRxKrNCgUqnwwAMPoKSkBAkJCUhMTMSdd94Jb29vuxyfiIiIiKil2Ldvn+i4UoEGBsztZ8mSJTh06BDKy8uRk5MjOvfw4cMYPnw4UlNTERgYqFCFRERE5OoYaCAiIiIyY9WqVZKfRgGA2bNnY9y4cTJW1PKI3Yisqqqy23k+/fRTux2LiIiIiKilEms34evri549e9rlPJauBRhosB8vLy/88MMPCAgIwLvvvouXX35ZdH5WVhaSkpKQmpraaCU8IiIiIluw5QQRERGRCStXrrQqzDBw4EC89dZbMlbUMimxQgMREREREdmHWKBhwIAB0Gjs83wdV2hQVlBQENzc3PDSSy/h7bfftjj/yJEjGD58OIqLixWojoiIiFwdAw1EREREt1ixYgWmT58ueX5oaCg2btwId3d3GatqmcR63zLQQERERETUfOh0Ohw4cMDsuL3aTQCWrwXEriOolz5TYAAAIABJREFUaV588UW88847FucdOXIESUlJuHbtmgJVERERkStjoIGIiIjoJh9//DFmzJgheb6bmxs2bNiAsLAwGatquZRqOUFERERERE1z7Ngx1NTUmB23Z6BB7FpApVLB29vbbueixl544QW8++67FucdPXoUgwcPZqiBiIiImoSBBiIiIqL/76OPPsKTTz5pcV6XLl3Qq1cv+Pj4YNOmTRgyZIj8xbVQbDlBREREROQc/Pz8MGfOHMTHx8PDw8NoTKVSYcCAAXY7l9i1gI+PD1Qqld3ORaY9//zz+Oc//2lx3qlTp3DXXXehqKhIgaqIiIjIFdmnaRkRERGRk1u2bBmefvppi/O6du2KXbt2QavVoqamBnfccYcC1bVcbDlBROQcDAYD1Grpz0xUVVWhsLBQxor+x9fXF6GhoZLnX7lyxegJY09PT/j4+MDHxwceHh78kIyIyIzbb78dixcvBgBotVocPnwYGRkZyMjIQHl5Ofz9/e12LrFrAbabUM7s2bOhUqkwe/Zs0Xm5ubno0aMHjh49inbt2ilUHREREbkKBhqIiIioxfvXv/5l8QYM8EeYYffu3bwBoyC2nCAiajq9Xo/a2lrU1NSguroaNTU1DS9rvzc3Jzg4GHl5eZJr+uGHHzBu3DgZf+r/efTRR7FhwwbJ8//6179i69atJsfc3Nwawg0+Pj7w9fU1+t7UNrE5nTt3RkhIiL1+VCKiZsPLywsJCQl2bTNxM7FrAbFrCLK/WbNmQaVSYdasWaLzCgsLER0djQMHDqB79+4KVUdERESugIEGIiIiarG0Wi3uuece7N271+Lcbt26Yffu3VY94UlNx5YTRNQSGAwGlJaWoqKiwmxw4MbXY8eORVRUlKTjFhcXo3379rh+/bq8PwAg2jPdlej1elRWVqKystIux/v4448xffp0yfMnTJgAvV4vKTxhaqxt27bw8vKyS+1ERI4kdi3AQIPynnvuOahUKjz33HOi86qqqtC7d2989dVXGDVqlELVERERkbNjoIGIiIhapAsXLmDw4MHIz8+3OLd79+7YtWsXwwwOILZcbH19Perr6+Hu7q5gRURE0mi1WhQWFqKwsBBXr14V/bqoqAh6vV7Scbt16yY50ODt7a1ImAFoOYEGe7P2Q7evv/4aWq22Seds3bo1QkJCEBoaipCQELNfd+7cmf/GElGzxZYTzc/MmTPx3nvv4eLFi6LzdDod7r//fsyfPx+vvfYaWzkRERGRRQw0EBERUYvz1VdfYcKECZI+5LnjjjuQlpbGMIODWPqgp7q62q69eM0xGAw4ffo0IiMj+cQXETWycOFCHD582CisUFFRIcu5rAkOKPkkvlarhcFggFqtVuycrsDHx0fyXL1e3+QwA4CGFSZycnJE5124cEFyeEan00Gv18PT07PJ9RERScEVGpofNzc3vPvuu0hOTkZdXZ3F+XPnzsWePXuwefNm+Pn5KVAhEREROSsGGoiIiKjF0Ol0mDNnDj744ANJ83v06IG0tDT2tnYgSzcjq6qqZAk0VFVV4eDBg0hPT0dGRgb27duHsrIybN++HX/605/sfj4icoy6ujqTKyfExMTgwQcflHyc3bt3Y9euXTJW+j/WBBrUajV8fHwUWz2htraWHyJZyZpAQ21trYyVNBYcHCx57p49ezBs2DD4+flZXPnhxtd+fn58KpeIbFZVVWV2jP8WOc748ePRq1cvDB06FJcvX7Y4PzU1FV27dsX333+P2NhYBSokIiIiZ8RAAxEREbUIly9fxgMPPIBDhw5Jmt+jRw/s2rXLqpv5ZH9SVmiwJ0EQMGTIEKSnp5tc/j0jI4OBBqJmTBAElJeXS271UFZWZvI4f/7zn60KNCi5io+1f+8pGWiorq7mh0hWsibQoGRbD19fX6v+XxYWFgIAysvLUV5ejrNnz1rcx93dvVHQwVwIIjg4GB4eHjb/PETkerhCQ/MVExODc+fOYdiwYThw4IDF+VeuXEHfvn2xfPlyTJ06VYEKiYiIyNkw0EBEREQub/fu3RgzZgzKy8slzW/Xrh3DDM2Epf639g40qFQqaDQas73s09PT7Xo+IrJOfX09zp8/j9OnT+PMmTM4e/YsCgoKjMIKUpY4tuTq1atWzVdyJR9rP9S25gPzplLyA3dX0VwDDdaGdG4EGqxRX1+PS5cu4dKlS5LmBwQEICQkBBMmTMC8efOsPh8RuRax6wBL1xAkPx8fH+zbtw9/+ctfsG7dOovz9Xo9pk2bht27d2PlypWKvn8hIiKi5o+BBiIiInJZBoMBixYtwt///nfJ+wQHByMrK4thhmZCSssJe0tISDC7dPyBAweg0+mg0fBtNJHSli9fjmeeeQY6nU72c1n74WxzDjRIeUrV09MTPj4+DS9fX1/R783NsebfznvvvRenTp2y6mexVevWra2av2TJErz55psA/lj1Q6vVoqamBjU1Naiurm742tw2Kd8LggDAukCDvUN8Yqz9PW1tCMgWpaWlKC0tRUlJiVX7CYLA1hZELogtJ5o/lUqFtWvXIioqCm+88YakfdavX4/MzEx8++23iI6OlrlCIiIicha8E0tEREQuqaSkBI899hh+/PFHyft07twZ+/btQ1BQkIyVkTWUbjkBAImJiWbHampqcOzYMfTp08fu5yVqaerr6+Hu7i55fkhIiCJhBsD6D2flbDnh4eFhFB5o06aNVfsvWbIEdXV1ZsMI3t7eNoe0DAYD6uvrG17V1dUoLy832nbjpdPpRLcp9f9WrVZDo9HA3d3d6GVq243trVq1Mrldo9HY9EG5IAi4fv06ampqrPr/2aZNGzz55JOSghM3hyZsYW2gwZYVGmxlbW3Tpk3DL7/8gpiYGHTt2hUxMTENr+DgYIYdiJwUW044j4ULFyI0NBQpKSmS/m06e/YsevXqhTVr1mDcuHEKVEhERETNHQMNRERE5HIOHjyI0aNH4/Lly5L3efjhh7F27Vp4e3vLWBlZS6PRwNPTE9evXzc5LkegYcCAAaLjGRkZDDQQSSQIAgoLC3HmzBmcOXOmoVXEmTNnkJubi/LycslPqHft2lXmav+nqKgIBoMBarVa0nwpH7C2adMGISEhCA0NRUhIiMmvAwMDbQobCIKA+vr6hg+za2trG752c3ODIAi4du1ao5UFbp4ntu369esmwwgGg0HSr48ruzkIceNrDw8PeHt7G4VIbv3elm2BgYFYunSppN+XN4cmbrwqKipQVFRk1KLF1Nd6vV6RlhO2sra248ePIzs7G9nZ2fj222+NxgICAowCDjcCD7fffjs8PT3tWTaRyxIEAcePH0f37t3h5uam2HnZcsK5PPPMM+jRowceeOABSddwWq0W48ePx7PPPovFixfDw8NDgSqJiIiouWKggYiIiFyGIAj46KOPkJKSIvlJT5VKhcWLF2P27Nl8Qq+Z8vX1NRtokKPlhL+/P+644w6cOHHC5HhGRgZmzpxp9/MSObPr16/j3LlzRoGFGwGG8vJys/udPXsWsbGxks5x++23Q61WK/IhusFgQElJieQVe6Kjo/HnP//ZbGAhODjYbGCuvr4e5eXlDa/CwkKj78vLy1FWVmby+4qKioYPq/V6vT1/CUginU4HnU6H2tpaxc7p5eUFb29vtG7dGn5+fg0vf39/o+9NbevZsycGDRoEX19fk+97DAYDysrKrP5z1lxXaBAEAWfOnDE7Xlpaiv3792P//v1G29VqNTp27GhyVYfQ0FC+ZyS6yY2n6du0aYP4+HgkJCQgISEB/fv3t7rljzXYcsL5DB06FCdPnsQ999yDs2fPStpnyZIlyMjIwH//+19ERETIXCERERE1Vww0EBERkUuoqqrC1KlTsXHjRsn7tG7dGtu2bcOQIUPkK4yazNfX12y/bLn6iSckJJgNNKSnp8tyTqLmThAEXL161Si0cOPr3Nxcm4IGZ86ckRxo8PT0RMeOHZGTk2P1eaTy9fVtCCFY8wF1p06d8Pbbb6OoqKjhdf78eWRlZZkNJdzYpuQH4eQatFottFotSktLbT6Gm5sb2rRpYzb4cON7f39/BAUFITg4uCGc4+fn1+gD/eXLlyMvL69hlQdTKz/cWPmkqaxZoaGoqAhlZWVWn8NgMCAnJwc5OTnYvn270Zifn59RwOFG6KFz587w8vKy+lxEzi4jIwMAUFFRgR9++AE//PADgD+CQT179kR6erosAQO2nHBOHTp0wJEjRzBx4kRs2rRJ0j6ZmZno2bMnNm7ciBEjRshcIRERETVHDDQQERGR0ztx4gTGjBmD7Oxsyft0794d33//PTp06CBjZWQPYkvGyhloWLlypcmxixcvIj8/H+Hh4bKcm8jRtFotsrOzTbaJqKiosOu5xJ6cNiUmJsaqQINarUZQUJDFVg83Pqi98QFIXV0dioqKkJWVZRRSuPGh7K1fi61CQdQc6fV6lJaW2hSKcHd3R3BwsNHrxp+h4OBgdO3aFYMGDWr43t/fHyqVCnq9HiUlJWYDD7d+be7feGtWaLD27xgpysvL8euvv+LXX3812q5Wq1FWVibrE+lEzZG5sK/BYEB5ebls4QK2nHBe3t7e2LhxI+Lj4/H8889DEASL+5SXl2PkyJGYO3cuXnvtNUXbmxAREZHjMdBARERETm3dunWYOnUqtFqt5H0ee+wxfPLJJ2aX/6bmRewmqFyBhsTERNHxffv2Yfz48bKcm8jRnnjiCWzYsEGRc9kSaLjxtHRQUBBiYmIQHR2NsLCwRkGFkJAQtG3btuGGd0VFBfLz85Gfn98QRsjJyTEZUrB3cIPIldTX16OgoAAFBQWS5ru7uzes8nBr+CE4OBh9+/bFbbfdhvDwcLRr1w4azR+3qqqrqxsFHa5evYrbbrtNcq1yBBrMCQkJsSrMUFVVBTc3N74fJad3Y4UGUxISEmQ5p16vF73+4woNzZ9KpcKsWbMQGxuLhx56SPJ7r/nz50On02HhwoUyV0hERETNCQMNRERE5JS0Wi1mzZqF5cuXS95HrVbjvffew7PPPsvex05E7IakWO/cpujcuTOCgoJw7do1k+MZGRkMNJDLiomJUexcp0+ftmr+jBkzMG7cOMTExKBt27YN28vLy5Gfn4+8vDycP38ev/zyC/Ly8hoCDHl5eaisrLR3+UQkQX19PS5fvozLly9bnKtWqxEWFobw8HCEh4cjIiKi4es77rgDI0aMgLu7u+RzW/t3TFNY+3fnxx9/jBdeeAFRUVG46667kJSUhOHDh6NTp04yVUhkfyUlJTh16pTZcUshYVtZCjUz0OA8hg4dimPHjuH+++/H8ePHJe1z8OBBlJeXw8/PT+bqiIiIqLlgoIGIiIiczoULFzB27FhkZWVJ3sff3x9bt27FkCFD5CuMZOGIFRpUKhUSEhKwbds2k+NiT6IRNQc6nQ6HDh1CWloawsLCMGnSJMn7du3aVb7CbnH27FkIgmA2ZCYIQkNY4UYwIT8/H5988olRYIFhBeekVqvh7u4Od3d3aDQaRcKGer0eOp0O9fX1qK+vl/18ZB2DwdDw59octVrdsKLDzaGHm8MPYWFh0Gg0mDJlCnr06GHUPufcuXOy/L+3NtBw+vRpCIKACxcu4MKFC/jyyy8BAFFRURg+fDiSkpIwbNgwq1psEClt//79ouNyrdDAQINriYyMxK+//oqpU6di/fr1Fuf/+OOP6N+/P7766itF37cSERGR4zDQQERERE5l27ZtePzxx63qV967d29s27YNHTp0kLEykotYD1y5Ag0ARAMNhw8fRk1NDXx8fGQ7P5E1BEHA6dOnkZaWhtTUVPz0008Nf0/279/fqkCDXCs0eHh4IDo6GjExMUavy5cvIycnB9nZ2cjNzTUKLuTn58u2EktL4+XlBW9vb/j4+DS8bv3e1DZvb294eXk1BA9ufmk0miZtU6vVDv01EQQBer2+Idxwc9ChKdvq6upQW1uLmpoao9et20zNqampgcFgcOivS3NnMBhw6dIlXLp0CQcOHDA5R61Wo127dkZBh4EDB2LixIno2LEjNBoNLly4gDNnzjS8Tp8+jcLCQpvrsvZDNXPtMHJzc7Fq1SqsWrUKANCrV6+GgMPgwYNF3xcRKS09Pd3sWKtWrdCjRw9ZzmvpGoB/TpyPt7c31q5di379+mH27NkW/y08c+YM+vXrhzVr1mD06NEKVUlERESOwkADEREROQ2tVounnnrKqjDDn//8Z6xcuZL9iZ2YI1pOAOJPlOl0OmRmZmLw4MGynZ/IkkuXLjUEGNLS0sz2s7d2Wd4uXbo0qa527do1Ci0EBwdDq9Xi/PnzyM7ORnZ2Nr7//nucO3eOgQUT1Go1/Pz8jF7+/v6Ntvn5+aF169YWgwleXl5wc3Nz9I/V7KhUKmg0Gmg0mmbzPkEQBNTX11sMQlRXV6OiogLl5eUoLy9HWVlZw9e3fl9bW+voH0txBoMBBQUFKCgowK+//tpoXKVSISIiAtHR0YiOjsbo0aMxZ84chIaGoq6uDjk5OUZhh+zsbNTV1Yme09owmLlAw62OHTuGY8eO4b333oNGo8GAAQMaAg79+/e3qgUHkb2JrVrWv39/aDTy3Hq29N6BKzQ4J5VKhZSUFPTq1Qtjx45FaWmp6PzKyko89NBDeP311zF37lyHByWJiIhIPgw0EBERkdPYsWMHysrKJM1Vq9V477338OyzzyqyhDXJxxEtJwCgb9++cHd3N7ssdUZGBgMNpKiysjL89NNPDQEGqb3hDQYDfvrpJ8lPr/n6+iI8PFx0yXdPT8+G1Ra6du2KmJgYtGvXDiqVCpcvX24ILaxfvx7Z2dmoqKiQdG5X4eXlhaCgIPj7+5sMIty67dbvW7VqxX+7WiiVSgUPDw94eHjA39/fLsesq6tDRUWFaOjB1LaysjKUlJSgpKTELnU0J4Ig4OLFi7h48SLS0tKMxtRqNSIjIxEdHY3OnTtj0KBB6NSpE3x8fFBTU4OcnJyG9hVnzpzBlStXAFgXaCgrK7NpNQidToe9e/di7969mDdvHlq1aoXBgwc3BBx69uzJvztIMfX19SYDQzfI1W4CYMsJVzd06FBkZWVh9OjROHr0qMX5CxYsQFZWFtasWSM5wEtERETOhYEGIiIiavYMBgPmzZuHf/zjH5LmBwYGYvPmzRg6dKjMlZESHNVywtvbG3369DG7nLXYE2lE9qDVapGRkdEQYMjMzLR5Kfq0tDSrluPt2rUr8vPzcdtttzUEFmJiYhAWFgYPDw9UVlY2tIn48ccfsWzZMotP0TkzLy8vhISEIDg4GMHBwUZf3/p9SEgIfH19+aEiNRseHh4ICgpCUFCQTfvrdDoUFxejqKgIhYWFKCoqavT1zd+XlJRAEAQ7/xTKMRgMuHDhAi5cuIAff/zRaMzNzQ1RUVGIjo5ueII4LCwMKpUK4eHhks8hdXUGS6qqqrB9+3Zs374dABASEoJhw4Y1BByioqLsch4iU44dO4aamhqz44mJibKdmy0nXF9kZCT27duH6dOnY+3atRbnf/PNN+jfvz+++uorq1sAERERUfPHQAMRERE1a+Xl5UhOTsa3334raX5cXBy2bt2KyMhImSsjpTiq5QTwx5NlYoEGQRD4oSXZjV6vR1ZWVkOAYe/evdBqtXY5dmpqqlXzP/zwQ+Tn5yM7OxtHjx7F0aNHsWnTJhQXF9ulHkfz9vY2GUQwF1JgQIFaMo1Gg9DQUISGhkqar9PpUFJSIin8UFRUhOLiYqcJQOj1euTk5CAnJwc7duwwGtNoNOjUqRO6deuG2NhY9O7dG7GxsYiKimr094e9Ag23KiwsxIYNG7BhwwYAwO23346kpCS8++67aN26tSznpJZLLNyrUqnQv39/2c4tdg2g0Wjg4eEh27lJOd7e3vj8889x5513Ys6cOdDr9aLzz5w5g379+mHt2rUYNGgQrl27hujoaIWqJSIiIjkx0EBERETN1unTp/HQQw9JvumbnJyMFStWNJs+2GQfjmo5AfwRaPjXv/5lcqy4uBjZ2dno0qWLrDWQ6xIEAdnZ2Q0Bhl27dkluq2OtU6dOoaCgAGFhYY1qKCgowNGjR3HkyBEcOXIER48eRXZ2ttN8wHirVq1aISIiAuHh4Q3/vfVrPz8/BhSIZKLRaBASEoKQkBBJ8+vr63H58mXk5+cjLy8P+fn5jb6+fPmyzSvUKEWn0+Hs2bM4e/Ysvv7664btfn5+6N27d0PAITY2FqNGjUJmZiZOnDiBn3/+Gampqbh48aLda8rJyUF5eTk++ugjux+bKD093ezYHXfcYbe2OaaIXQOw3YRrUalUeO6559C7d2+MHz/eYri2srISo0ePRnR0NC5fvoxPP/0U48aNU6haIiIikgsDDURERNQsff311/jLX/6CyspKi3Pd3Nzw7rvvIiUlhR9QuSBHtZwALPf+TU9PZ6CBbLZgwQLMmzdP1nOoVCrExcUhKSkJdXV1OH78eENo4UaA4dq1a7LWYE+tW7c2GVK4NaxARM7D3d0dHTp0QIcOHczOqa+vx5UrV8yGHvLy8ppt6KG8vBy//PILfvnll4ZtGo0GXbt2bQg4TJgwAX5+fjh8+DBSU1Oxe/dulJSU2OX8SUlJUKvVdjkW0c3EVmiw9B66qcSuAdhuwjUNHToUhw4dwpgxY5CVlWVxfnZ2NgBg/PjxeO655/D2229z5Q4iIiInxkADERERNSv19fWYN28e3nzzTUnzg4KC8OWXX2Lo0KEyV0aO4sgVGsLCwhAVFYXc3FyT4xkZGXjiiSdkrYFcl1xLMXfu3BkDBw5Ep06d4O7ujuzsbOzatQtLlizB9evXZTmnPbi7u6NTp06Iiooyu7pCmzZtHF0mETmAu7s7IiIiEBERgfj4eJNzdDodrly5YjbwkJOTg6KiIoUrN02n0+H48eM4fvy4UW/49u3bIzY2FtOnT4e/vz+Ki4uRlZXVpBZESUlJVs3fu3cvAgIC0L17dwaFyay8vDzk5eWZHXdkoIErNLiuyMhI7N27F9OnT8e6desk7/f+++/jwIED2LhxIyIiImSskIiIiOTCQAMRERE5XFVVFT766COMHTsWycnJ2Ldvn6T94uLisHXrVkRGRspcITmS2E3JqqoqCIIg6w33hIQE0UADka0GDRoEd3d31NfXN+k4bdu2Rffu3REYGAitVouzZ8/iP//5j32KtDONRoOOHTsiOjra6NW5c2d06NABGg0vUYnINhqNpiEEZU5ZWRnOnTuH7OzsRi97rYjQFJcuXcKlS5fw3XffNWzz9fVF7969ERoaivr6ely8eBEnT56U3BZo+PDhVtXwzDPP4MiRI2jXrh2SkpIaXmIraFDLY+l6Te5AQ1VVldkxBhpcm4+PD9asWYM777wTL7zwAvR6vaT99u3bh9jYWKxfvx4jRoyQuUoiIiKyN5XgrI1RiUgWbdq0aVjevXXr1qioqHBwRUTk6k6ePImxY8fi9OnT8PLykvz0WXJyMlasWAFvb2+ZKyRHS01NxT333GN2vKamRtbfB0uXLsXMmTPNjpeUlCAgIEC281PzZjAYcPToUaSmpiItLQ2LFy9Gz549Je9/9913Gy1DLoW3tzc6duwIjUaDvLw8lJaWWlu2rNRqNaKiohqFFqKjoxEZGQl3d3dHl0hE1EhJSUlDuOHW0ENZWZmjyzOiVqvRsWNHtGrVCsXFxcjPzzc5r2PHjjh//rzk4167dg3BwcEmx6KjozF8+HAkJSVh6NChCAwMtKl2cg0pKSlYsmSJybHg4GBcvXpV1sDxiy++iMWLF5scGzhwIPbs2SPbuan52LVrFx555BEUFxdbtd/cuXPx2muvwc3NTabKiIj+wM9aiOyHj78QERGRw6xbtw7Tpk1DbW0tAEgKM7i5ueHdd99FSkoKl8FtISz1wa2urpY10GDpCbP9+/fjT3/6k2znp+ZFEAScP38eaWlpSE1Nxa5du4xuot57771WBRqGDx9uMdDg5uaGiIgIuLu7o6CgANXV1Th58qTNP4M9qFQqREZGmgwtREVFsUcxETmdwMBA9O/fv1E7IEEQUFxcbHJVh+zs7Iab1EoyGAzIyckx2ta+fXv4+vqiqKioIehm7eoMu3btMjt24+f96KOPoFKpcOeddyIpKQnDhw9HYmIiQ8YtjNgqZQkJCbJfp4m1nLB07UCuY9iwYcjMzMSYMWNw5MgRyfvNnz8fGRkZWLdundkQFxERETUvDDQQERGR4rRaLWbNmoXly5dbtV/btm3x5ZdfYtiwYTJVRs2RpWVjq6qqEBQUJNv5e/bsCV9fX7M3TtPT0xlocHFarRbffPMNfvzxR6SmppptQQL8saLI7NmzJR87KSkJr7/+eqPtoaGh8PDwQGFhIa5fvy56Tjm5ubmhW7duiI2NRa9evRATE4Po6Gh06tQJnp6eDqmJiEhJKpUKQUFBCAoKQnx8vNGYIAgoLCxsWNHhxIkTOHLkCI4cOYJr164pWuelS5eMvg8KCkJ+fj4+/fRTDB48GJ06dbL4IXNaWpqkcwmCgMzMTGRmZuLtt9+Gp6cnEhMTkZSUhIcffhhdu3a1+eeg5q+6uhpZWVlmx+VuNwGw5QT9T1RUFNLT0zFt2jSsX79e8n47d+5EbGwsNm3apMjvWSIiImoaBhqIiIhIURcuXMC4ceNw+PBhq/aLi4vD1q1bERkZKVNl1FxZuikp9oSWPWg0GgwYMMDsTX6xJ9TIuWm1WqxYsQKLFi3ClStXJO3z888/o66uTvIKBV26dIG3tzc8PDzg6emJ4uJi6PV6XL16tSml26RNmzaIjY1FbGwsevfujdjYWHTv3h1eXl6K10JE5AxUKhVCQ0MRGhqKxMTE/8fefUdFcb1vAH+WrhF7xYISY0FEBUWlaUzUYK9YYgVji9FYYxdb7MYWG2pEjWhsWKIGNBoREEWKCCIqFkQjE7ElAAAgAElEQVRjww64lP394ZGffsPOzsLOFng+53CScO+deUiMzsy+c9/c7ysUCjx8+DC3uCEmJgbR0dG4ceMGtNX59enTpzhx4gROnDgBALCysoK7u3vuV/369WFkZPTJmlOnTuXrXO/evcPff/+Nv//+GzNmzEC3bt0wd+5c2NvbF/jnIP0TERGB7OxspePa+HBY6PqfBQ1FT/HixbFr1y44Ojpi8uTJyMnJEbXuwYMHcHd3x7Jly/Djjz9yB0giIiI9xoIGIiIi0pqjR49iwIABaveMGzFiBFauXInixYtLlIz0mZiWE1JzdnZWWtAQHh6OrKwsmJjw0rqwkMvl2Lp1KxYuXPifN15VSUtLQ3h4ONzc3PIcf/z4MYKDgxEcHIxz584hOjoaCoUit/WOtlhbW+cWL3woYKhZsyYf5BIRaYBMJoOVlRWsrKzQoUOH3O+/efMGsbGxuQUO0dHRuHLlilb+DHjw4AH27NmDPXv2AHi/85mbm1tugUPJkiWRlJSkkXMFBAQgICAAvXv3ho+PD2xtbTVyXNIPISEhSsdMTU3h6OgoeQa2nKD/JZPJMGHCBDRt2hT9+/cXfQ2fnZ2NCRMmICQkBFu3bkWpUqUkTkpERET5waeuREREJLmsrCzMnDkTS5YsUWtd6dKl4evri169ekmUjAyBmJYTUvv4TTOZTAY7Ozs4OzvnfhkbG0uegaSXmZkJPz8/zJ8/H/fu3cv3cU6dOpVb0JCcnIxz587lfiUkJGgqriimpqaws7P7ZNcFe3t7lClTRqs5iIjo/QetLVu2/KR1RXZ2Nm7evJlb4PBhR4eHDx9KmuXZs2e5hQfA+zecixcvjrS0NI2dY9++fdi/fz/69++P2bNno06dOho7NumO0O5kjo6OKFasmOQZ2HKClHF3d0dMTAy8vLxw5MgR0esOHDiAqKgoHDx4EI0aNZIwIREREeUHCxqIiIhIUg8ePEC/fv1w7tw5tdY5Oztj9+7dbDFBKh+KamOHhhYtWmD27NlwdnZG8+bNUbp0acnPSdqTlZWF33//HfPmzdPI26kHDhzA06dPcfz4cdy5c6fgAUUqW7bsf3ZdqFevnuj2F0REpH3GxsaoW7cu6tatiz59+uR+/9GjR7k7OXz4a0JCguit1NX1v4UMxsbGMDY2hlwuL9BxFQoFfv/9d/j7+2PQoEGYNWsWbGxsCnRM0p2cnByEhYUpHddGuwmALSdIWLly5RAQEIBff/0VEydOFP37WFJSEpycnLB+/Xp4eXlx5zIiIiI9IlNoq3kfERmEkiVL4vXr1wAAS0tLtbeFJyL62F9//YX+/fsjNTVV9BqZTIbp06fDx8eHW/hTrhIlSih9cOnv74++fftqOREVBtnZ2di7dy/mzp2LxMTEAh+vdOnSyMjIQEZGhgbSqWZnZ5e7VXjLli1RvXp1PnglIirE0tPTER0dndu2KDg4WGv37GXKlEFaWhrevXtXoOOYmJhg6NCh8PHxgZWVlYbSkbYkJCSgfv36Ssf379+Pnj17Sp6jbt26Sq/dli9fjokTJ0qegQxDTEwM+vTpg+vXr6u1rn///ti4cSMsLS0lSkZERQE/ayHSHH5KQERERBqXmZmJWbNmqd1iokqVKti1axfatGkjUTIyVJ999pnSggZt7NBAhUtOTg4OHDgAHx8fxMfH5/s45cuXh6WlJR4+fIiMjAy8ePFCgyk/ZWRkBAcHh9wCBldXV5QrV06y8xERkf4pVqxYbsuKKVOmIDs7G7GxsZ+0Nnry5Ikk537+/Hnu31euXBkWFha4f/8+srKy1DpOVlYWtm3bhqlTp2o6ImmBULsJAJ+0U5ESd2ggsRo1aoTLly9j7Nix2LZtm+h1u3fvxoULF3DgwAE0btxYwoREREQkBgsaiIiISKPu3bsHT09PhIeHq7WuY8eO+O2331ChQgWJkpEhE3owKdRDl+hjCoUChw8fxpw5c3DlypV8HcPW1hYmJiZISEjA06dP8fTpUw2nfM/MzAxOTk65BQzOzs58Q4yIiD5hbGyc22Zo7NixUCgUuH79em5xwz///IP79+9r/Lz//vtv7t9XrVoVVlZWuH79uui3DocOHcq2EwYqJCRE6VjNmjW1tuuG0PU/Cxrof3322WfYunUr2rZti+HDh+e+La1KUlISmjVrhlWrVmH06NHcCY2IiEiHWNBAREREGnP48GEMGjRIrS3UzMzMsHTpUowdO5YPCEgpoQeT3KGBxIiPj8fAgQMRGRmZr/Vly5bFy5cvC7Sjg5DPPvsMzs7OuQUMTk5OsLCwkORcRERUOMlkMtSrVw/16tXD8OHDoVAocPfu3U92cLhx44ZGz5mSkoKUlBQA77dVzsjIEOxXb2pqipkzZ2o0A2mP0A4NLi4uWsvBHRooP/r27QsnJyf069cPFy9eFLUmKysLY8aMwenTp7Ft2zaULl1a4pRERESUFxY0EBERUYG9e/cOU6ZMwZo1a9RaV7t2bezduxcODg4SJaPCokSJEkrHWNBAYlSuXLlAH+KkpqZqMA1QunRpuLm55RYwNGnSBKampho9BxERFW0ymQw1a9ZEzZo1MWjQIADvd1cIDg7OLXDI745Fefm4qFkmk0GhUPxnzrBhw2Btba2xc5J2/fnnnwgLC0NISAhCQ0MRGxuLnJwcAICzs7NWMsjlcsFWJ0L3DUQ2NjY4f/48Zs6ciaVLl4ped+jQIVy8eBEnTpxAw4YNJUxIREREeWFBAxERERXIzZs30atXL8TExKi1bvDgwVi7di23UCdR2HKCCqps2bIYP3485s2bp5PzV6pUKbd4wd3dHXZ2djAyMtJJFiIiKroqV66M3r17o3fv3gDeF+yFhITkFjhcvnwZ2dnZBT5PXsUMJiYmGD9+fIGPTbpjY2MDGxsbfPvttwDeF7FcvHgRoaGhaNu2rVYyqLr25w4NpIqpqSmWLFmCr776CgMGDMCTJ09ErXv48CHi4uJY0EBERKQDMkVedxhEVGSVLFkyt5ecpaWlWtvGE1HRs3fvXnh5eSEtLU30muLFi2Pz5s25D8GIxOjatSuOHDmS55i3tze2bNmi5URkiF68eIFatWrhxYsXkp9LJpPB1dUVPXr0gIeHB+rUqcO2OkREpPfevHmD4OBgBAQEICAgAI8fP9bo8YsVKwYPDw/06NEDHTt2VLl9++HDhxEbG4tx48axEJoAAMnJyahRo4bS8aioKDRu3FiLiciQPXr0CIMGDUJgYKDoNRMmTMCiRYtgZmYmYTIiKgz4WQuR5vCVICIiIlJbeno6hg8fjr59+6pVzODg4ICYmBgWM5Da2HKCNKF06dIYMmSIZMc3MTFB+/btsWnTJjx8+BDnzp3Djz/+iLp167KYgYiIDEKJEiXg4eGBTZs24cGDBzh37hzGjRuH6tWra+T46enpOHjwIAYMGICKFSvCw8MD/v7+yMzM/M/c7OxsTJ06FbNmzUKtWrWwZMkSXveRyl8DbDlB6qhUqRJOnDiBZcuWwdjYWNSalStXwtnZGTdv3pQ4HREREX3AggYiIiJSW7du3eDr66vWmokTJyIsLAy1a9eWKBUVZmw5QcqI2XBOoVDgn3/+Qbdu3bB69WqNnt/CwgLdunXDjh078PjxY5w8eRLDhw9HpUqVNHoeIiIibTM2NoabmxtWrVqFu3fv4tKlS5g2bRrq1KmjkeNnZmbi5MmT6N+/f27BwvPnz3PH/f39kZCQAAB49uwZpk6dChsbG6xcuRLp6ekayUCGhy0nSNOMjIwwadIkhIWFoWbNmqLWXL58GU2aNMGuXbukDUdEREQAWNBAREREagoMDERERITo+eXKlcPx48exfPlybslI+Sb0YFLXb+opFArcuXMHu3fvxoYNG3SapSi5cuUKunfvLligIJfLsXPnTjg6OqJ169Y4fPiwqAIIVUqWLIn+/ftj//79ePr0KQ4dOoSBAweiTJkyBT42ERGRPpLJZGjatCl+/vlnJCQkIC4uDvPnz0eTJk00cvyUlBRMnToV1apVw/fff4/4+HjMnTv3P/MeP36MiRMnwsbGBmvXrkVGRoZGzk+GQ9W1PwsaKL+aNWuGmJgY9O/fX9T8N2/eYODAgRg8eDCL7ImIiCQmU2jiiR4RFRrs60REysjlcsyaNQtLly4VvaZNmzbYtWsXqlSpImEyKgpmzZqFBQsW5Dnm5OSE8PBwrWWRy+WIiopCaGgoQkJCEBoaiocPHwIAypYtiydPnsDIiHXDUomPj4ePjw/27dsHAKhYsSKSkpI+eXj97NkzbNq0CevWrcv9b1NQ5cuXR9euXdGzZ0+0adMG5ubmGjkuERGRoUtKSsKhQ4dw8OBBhIaGauSYMplMVBFitWrVMGPGDHh5ebF4uog4fvw4OnbsqHQ8KytLdOsAorwoFAr4+flh1KhRooumvvjiC+zZswcODg4SpyMiQ8LPWog0h09aiYiISKWkpCS4ubmJLmYwNjbG4sWLERQUxGIG0gh92qHh6NGjaNGiBSZMmIADBw588oF5amoqEhMTtZqnqEhMTMS3334LOzu73GIG4P2bmr/++isAICEhASNHjkT16tUxY8aMAhczVK1aFT/88APOnDmDhw8fYsuWLfDw8GAxAxER0UdsbGwwceJEhISEICUlBevXr8dXX31VoA+Vxb5/df/+fYwaNQp16tTB1q1bkZmZme9zkmEQuva3sLBgMQMVmEwmw5AhQxAdHY3GjRuLWnPjxg20aNECq1atwqtXr5CVlSVxSiIioqKFBQ1EREQkyN/fH40bN8bFixdFza9evTrOnz+Pn376iW+pk8YIFTRoe3vPli1bCo5r6s1Eei8pKQlDhgxB/fr1sXv37jw/4FiwYAHatWuH+vXrY9OmTQXqq127dm1MmTIF4eHhuHfvHtasWYPWrVvDxMSkID8GERFRkWBlZYVRo0bh1KlTePToEX777Td07txZ8mLAu3fvYtiwYahfvz527NiB7OxsSc9HuiN07c92E6RJdevWxYULFzB27FhR8zMzMzF+/HjUqVMHrq6uSE5OljghERFR0cFPGYiIiChPb9++hZeXF/r375+7PZoqvXv3xpUrV9CiRQuJ01FRo087NFhZWaFmzZpKx1nQoBl3797Fd999h7p168LPzw85OTlK575+/RpBQUH5Ppe9vT18fHxw5coVJCYmYsmSJXBycmJRFhERUQGUK1cOQ4YMwZEjR/DkyRPs3bsXnp6ekn7ofOvWLQwePBgNGjTAnj17BK8fyDAJXfuzoIE0zdzcHKtXr8aRI0dQrlw5UWsePXqE8PBwNGjQAEePHpU4IRERUdHAJ3RERET0H9HR0XB0dMRvv/0mar6FhQW2bNmCvXv3onTp0hKno6KoRIkSSse0XdAAAM7OzkrHWNBQMCkpKRg9ejS++OILbNmyRbLtWsuWLYvp06cjMTERMTExmDNnDho2bAiZTCbJ+YiIiIoyS0tLeHp6Yu/evXj69CkOHjyINm3aSHa+69evo1+/frC3t8eBAwdY2FCICF37C90zEBVE586dERMTg9atW4te8/r1a3Tp0gXjxo2DXC6XLhwREVERwIIGIiIiAvC+T+0ff/yBNWvWoHnz5rh+/bqodfb29oiMjIS3tzc/CCTJCL1tlZ6ervVthYUKGq5du4bU1FQtpikc/v33X4wbNw6ff/45NmzYIFkP7Hr16mHjxo1ITk7GwoUL8cUXX0hyHiIiIsqbhYUFunfvjtOnTyMqKgqDBw+GqampJOeKi4tDr1694ODggCNHjuTZuoqE/fHHH9i5cydu3bqlF//+2HKCdKVq1ao4deoU5s+fr9ZObmvWrEGzZs2QlJQkYToiIqLCjQUNREREhGfPnuGbb75Bnz591Hp74Pvvv0d4eDjq168vcUIq6lQ9nExLS9NSkvdcXFwEx8PCwrSUxPA9efIEkydPho2NDdasWYN3795Jcp62bdvi+PHjiIuLw4gRI1C8eHFJzkNERETiNW7cGNu3b8e9e/cwa9YslC9fXpLzxMTEoGvXrnBycsKJEyf04oN5Q7Fs2TIMGjQItWvXRpUqVdCjRw8sX74coaGhkl23CWHLCdIlY2NjzJw5E+fOnUONGjVEr7ty5Qrs7Oywb98+CdMREREVXixoICIiKuLOnz+P+vXrIzAwUPSaMmXK4NChQ1i3bh0sLCwkTEf0nqrtY7XddsLOzk4wE9tOqPbs2TNMnz4dtWrVwvLly5Genq7xc5ibm8PLywtXrlxBYGAgPDw81HqbioiIiLSjcuXKmDdvHu7du4fNmzfD1tZWkvNERESgQ4cOcHZ2RlBQEAsbVHj79i2ioqJy//nRo0c4dOgQJk+eDBcXF6xZs0YnmZRhywnSFhcXF0RHR6NHjx6i16Snp8PT0xPfffcdMjIyJExHRERU+PBpHhERURGVk5OD+fPnw93dHU+ePBG9zs3NDTExMejWrZuE6Yg+peptK6GtZ6VgYmKC5s2bKx1nQYNyL168wJw5c1CrVi0sWrRIkmKUihUrwsfHB/fu3cPWrVvRsGFDjZ+DiIiINK9YsWL47rvvcPXqVZw8eRLt27eX5DwXLlxAu3btMGLECEmOX1hcunRJsLWbql3LpMCWE6QvypQpg/3792PDhg1qtc3ZsmULGjVqJLrNJxEREbGggYiIqEh69OgRWrVqhdmzZ4t+K8nIyAg+Pj74+++/Ub16dYkTEn1K1cNJbe/QAADOzs5Kxy5evIjMzEwtptF/r169wvz581GzZk3MmzcPr1+/1vg57OzssHXrVty9exdz5sxBxYoVNX4OIiIikp5MJkP79u1x8uRJXL16FcOGDYO5ubnGz+Ph4aHxYxYmQkW6ZmZmcHBw0GKa99hygvSJTCbDyJEjERkZidq1a4tel5iYCHt7e+zYsUPCdERERIUHCxqIiIiKmFOnTqFu3bo4f/686DVVq1bFmTNnMGfOHJiYmEiYjihv+tZyAhB+Iy0tLQ0xMTFaTKO/3rx5g8WLF6NWrVqYPXs2Xr58qfFzdOjQAUFBQbhy5Qq8vLzYCoeIiKgQadCgAXx9fZGcnIx58+ahUqVKGjlukyZNuOucCkIFDY6Ojjq55mLLCdJHdnZ2iImJgbe3t+g1crkcgwcPRv/+/XVyP0tERGRIWNBARERURGRlZWHq1Klo27atWh8odu3aFTExMXB3d5cwHZEwfdyhoXnz5pDJZErH2XbiPW9vb0ybNg2pqakaPW6xYsUwYsQIXLt2DX/++Se+/vprwf8eREREZNgqVKiAWbNm4e7du9i+fTvs7e0LdDxvb29eOwjIyclBWFiY0nGh3cqkxB0aSF8VL14cW7ZswR9//KHWr0V/f3/Y2toiNjZWwnRERESGjQUNRERERcD9+/fRvHlzLFmyRPQaExMTrF27FocOHUK5cuUkTEekmpmZGYyNjZWOC/XSlUrp0qXRoEEDpeMsaHhv/PjxGj1elSpVsHDhQiQnJ2Pjxo2oV6+eRo9PRERE+s3c3ByDBw9GdHQ0Tp8+jU6dOuXrOBMmTMCCBQvYJkyJ69evCxak6qqgQei6nwUNpA969+6NuLg4ODo6il5z7949ODg4YNOmTaLbghIRERUlLGggIiIq5I4dO4Z69eohMjJS9JpatWohIiICY8aM4VtLpBdkMpngA0pdbdEp9CCXBQ3vtWjRAh06dCjwcZo0aYKdO3fizp07mD59OgutiIiIijiZTIY2bdrg6NGjuH79OkaPHo3ixYuLXi+XyzFr1iw0a9ZMrXulokLVtSx3aCBSztraGhcuXMDUqVNFr8nKysLIkSPRrVs3vHr1SsJ0REREhocFDURERIWUXC7HDz/8gM6dO6v1Ye+gQYMQGxuLRo0aSZiOSH1CPXF1VdDg4uKidCw5ORnJyclaTKO/5s2bl691MpkMXbt2xdmzZ3H58mUMGDAAZmZmGk5HREREhq5OnTr49ddfkZycjEWLFsHKykr02piYGDg5OWHatGnIyMiQMKVhESposLGxQeXKlbWY5v8JXfcL3S8QaZuJiQkWLVqE06dPo2zZsqLXHTlyBHXr1sXly5clTEdERGRYWNBARERUCN2+fRuNGzfGunXrRK8pVqwY/P394efnxzdbSC8J/brURcsJQPWbadylAUhLS8Pu3bvVXtejRw8kJiYiICAArVq14m4xREREpFLZsmUxdepU3LlzB9u2bRO9o1N2djYWL16Mxo0bIyQkROKUhkHoOlZXuzMAbDlBhqdNmzZISEjA119/LXrNv//+CycnJ6xYsYItKIiIiMCCBiIiokLnjz/+QP369XHt2jXRa+zt7REXF4e+fftKmIyoYPSx5cTnn3+OChUqKB0v6gUNZ8+ehb29PVauXCl6TcWKFbFv3z4cOHAAtWvXljAdERERFVampqYYOnQo4uPj0adPH9Hrrl+/Djc3N4wdO/aTD85fv36N27dvSxFVLz179gwJCQlKx3VV0KBQKJCWlqZ0nAUNpK8qVKiAwMBArFixAsbGxqLW5OTkYNKkSfj666+RmpoqcUIiIiL9xoIGIiKiQiIjIwNDhw5Fnz598O7dO9HrJkyYgIiICNSqVUvCdEQFp48tJ2QymeAD3aJa0PDq1SuMHDkSX375JW7duiV63cCBAxEfH49evXpJmI6IiIiKiooVK2LPnj0ICAhAlSpVRK1RKBRYu3YtGjZsiFOnTuHYsWNo0KABevfujaysLIkT64ewsDDBcV0VNKSnpwu+rc6WE6TPZDIZJkyYgIsXL6JatWqi1/3999/44osv2IKCiIiKNBY0EBERFQKJiYlo0KABtm/fLnpNqVKlct8QMDU1lS4ckYboY8sJQPiBblRUlM6KLXTl+PHjaNCgATZt2iR6TbVq1fDnn39ix44doreGJiIiIhKra9euiIuLg5eXl+g1d+7cQdu2bdG5c2ckJyfj8uXLWL16tYQp9YdQUa6lpSXs7Oy0mOb/qbrm5w4NZAgcHBwQHx8PT09P0WtSU1OxYMECnd73EhER6RILGoiIiAzczp07YWdnh6SkJNFr3NzccP36dbRt21bCZESapY8tJwDAxcVF6Vh2djYiIiK0mEZ3nj17hkGDBqFjx464f/++6HUjR45EXFwcOnToIGE6IiIiKurKlCmDrVu34q+//oK1tXW+jjF79uwi0XpCqKChRYsWorfM1zRV1/wsaCBDYWlpib1798LPzw9mZmai1gQEBMDJyQnx8fESpyMiItI/LGggIiIyUG/fvkX//v0xaNAgZGZmilpjZGSExYsX4+zZs6hUqZLECYk0Sx9bTgCAo6Oj4C4nRaHtxP79+2Fra4udO3eKXvP555/jzJkz2LBhA0qWLClhOiIiIqL/165dO1y9ehVjxoxRe21aWhq8vLwE2x4YuszMTFy8eFHpuK7aTQCqr/nZcoIMzaBBgxAXF4d69eqJmn/t2jU0a9YMu3fvljgZERGRfmFBAxERkYFq1aoV/P39Rc+vXLkywsPD8dNPP8HIiJcAZHj0dYcGCwsLODo6Kh0PCQnRYhrtunfvHrp3747evXvj8ePHotYYGRlhwoQJuHLlClq3bi1tQCIiIqI8lChRAmvXrkVwcDDq1Kmj1tqzZ89izJgxhbaoISYmBunp6UrH9bmggTs0kCGqXbs2YmJi8P3334uan5aWhm+//RajR4/Gu3fvJE5HRESkH0x0HUBK6enpiIqKwo0bN3D37l3cv38fr1+/xtu3b5GVlYXixYvjs88+Q4UKFWBtbQ1ra2s0atQo39vOERERaYNCocCqVasQHR0tek23bt3g5+fHt6DJoAk9oNR1L1FnZ2dcuHAhz7GwsDDk5OQUqkIihUKBnTt3YsSIEcjIyBC9ztbWFtu2bUPz5s0lTEdEREQkjqurK6KjozF37lwsW7YMOTk5otatX78eSUlJ2Lp1K6ysrCROqV1CxbgymUyn13GqrvlZ0ECGyszMDOvWrUOHDh3Qp08fUfe3GzZswKVLl7Bv3z7UrFlT+pBEREQ6VKgKGt6+fYvTp0/jr7/+wrlz55CQkCD6RuRjZcuWRbNmzdC2bVt88803qF+/vgRpiYiI1Pfy5Ut4e3vjwIEDouabmppiw4YN8PLygkwmkzgdkbT0dYcGAHBxccHKlSvzHEtNTUViYqLobUT13b179zBixAicPHlS9BoTExNMnToVM2fOhLm5uYTpiIiIiNRTrFgxLF68GL169YKXlxdiY2NFrTt58iRsbW2xYsWKQnW/JdQuzc7ODqVKldJimk8JXfPLZDIUK1ZMi2mINK9Dhw5ITExE9+7dER4ernJ+REQEHBwcsGPHDnTq1EkLCYmIiHTD4F8Tk8vlOHDgALp3745y5cqhe/fu2LhxI+Li4pCdnQ2FQqH217Nnz/DXX39h0qRJsLOzQ+3ateHj44PExERd/7hERFSExcTEoGnTpqKLGWrWrInY2Fh4e3sXmodrVLQJ9cTVdUFDy5YtBceFHgwbipycHGzYsAENGjRQq5jBwcEBERERmD9/PosZiIiISG81bdoUERERmDt3LkxNTUWtefnyJYYNG4Z27drh9u3bEifUDqHrVhcXFy0m+S+ha/7PPvuM971UKFSpUgWhoaFYsGCBqF3+nj9/js6dO2P69Ol49eoVMjMztZCSiIhIuwy2oOH+/fuYOnUqrKys4OnpiSNHjkAul+cWJchksgJ9Acg9VlJSEubPn4/69evjyy+/REBAQKHtk0dERPpp+/btaNGiBW7evClqfu/evREXF4e6detKnIxIe/S55USVKlVQq1atPMcsLS3x/PlzLSfSrDt37qBNmzYYPXq06H/X5ubmWLx4McLDw9GoUSOJExIREREVnJmZGWbPno3IyEg4OTmJXnfq1Ck0bNgQv/76q0E/M0xOTsb9+/eVjjs7O2sxzX8JXYey3QQVJkZGRpgxYwaCgoJQsWJFUVK7G24AACAASURBVGsWLVqEOnXqoGXLlrh7967ECYmIiLTL4AoaEhMT8e2338LGxgbLli1Dampq7o3CxwUJee28YGpqilKlSqFy5cqoVq0aypUrh+LFiwNAnvPzOt65c+fQs2dP2NjYwNfXF9nZ2br810FERIVceno6vL29MXToUFF96o2NjbF582bs3bs39884osJCn1tOAP//gNfGxgYDBgzAhg0bEBMTg+fPn2PixIk6Tpd/CQkJaN68Of755x/RaypVqoSYmBj89NNPMDEpVF3uiIiIqAiws7NDaGgopk6dKnrN27dvMWbMGIwfP95gixpU7Sqm64IGVTs0EBU2bdq0QVRUFFxdXUXNf/ToES5fvgw7OzsEBARInI6IiEh7DObp4p07dzBnzhzs3r0bOTk5nxQxAPjkRqFevXpwdHSEvb097OzsUKNGDVhZWaFMmTJ5HjszMxMPHjxASkoKrl+/jtjYWFy5cgXh4eGfXCh/fK67d+9i5MiRWLRoEebMmYPBgwdL9aMTEVERdfPmTfTq1QsxMTGi5teoUQMBAQFo0qSJxMmIdEOfW04AwPz587F8+XJUrlxZ11E05v79+2jXrh0eP34seo2lpSXi4+NRtmxZCZMRERERScvY2BiLFi1CVlYWli9fLnrd6tWrUb58ecycOVPCdNIICQlROlaxYkXY2NhoMc1/CV3zC90rEBkyKysr/P3335gxYwaWLVsmas2bN2/QvXt3TJw4EUuXLhXVuoKIiEif6X1BQ1paGn7++WesXLkS7969+2TnhA+7JpQpUwadO3dGx44d0apVK9HbMH1gamoKa2trWFtbf1JpnJ2djYiICPz9998ICAhARERE7vmB94UNd+7cgZeXF9auXYs1a9bovFKZiIgM35s3bxAUFIQhQ4bg1atXotZ069YNv/32G0qXLi1xOiLdEXrrKjMzE3K5HGZmZlpM9CllLScMVWpqKtq3b4/k5GS11m3bto3FDERERFRo/PzzzwgMDMSVK1dEr5k1axYqVKiAESNGSJhM84R2aHB2ds59JqorbDlBRZWpqSmWLl0KZ2dnDBkyBC9fvhS1bsWKFbh16xb27NkDc3NziVMSERFJR+9L8+rUqYNFixblbrP9oZDB3Nwc3377LYKCgvD48WNs374dvXv3VruYQYixsTGaN2+OadOmITw8HPfu3cPy5ctRv379T3aIUCgUiIyMhJubG7Zs2aKx8xMRUdGza9cuVKhQAT169BBVzGBsbIzly5fj4MGDLGagQk/VQ0p92KWhsEhLS0OnTp0QHx+v1rouXbqgZ8+eEqUiIiIi0j5TU1Ns3bpV7TecR48ejYMHD0qUSvPevn2L6OhopeMuLi5aTJM3tpygoq5bt264fPmyWjtzBgQEwNnZGS9evJAwGRERkbT0vqDhwYMHAP6/cKBmzZpYvXo1Hj58iJ07d+Krr76CsbGxVrJUrVoVEyZMwNWrVxEaGoo+ffrk3sx8qFD+kJeIiEhdc+bMwcCBA3OL+FSpUqUKzp49i4kTJ+r8TRkibVC1jSwLGjQjMzMTvXv3RlhYmFrrLC0t8euvv/L3IyIiIip0mjZtinHjxqm1JicnB/369cPZs2elCaVhly5dQnZ2ttJxfdiVli0niIDPP/8coaGh+O6770SviYyMhK2trdq77xEREekLvS9oAN63drC3t4e/vz9u3LiBH374AaVKldJpphYtWsDf3x83b97EmDFjdLq9MRERGbacnBz07NkT8+bNE72mTZs2iIqKgqurq4TJiPQLd2iQXk5ODry9vXH8+HG11y5evBjVqlWTIBURERGR7s2bNw/W1tZqrZHL5ejSpQuioqIkSqU5Dx8+VPq81czMDA4ODlpO9F/coYHoPQsLC2zevBl+fn6iP5d4+PAh6tWrh4sXL0qcjoiISPP0vqChdu3a8Pf3R1RU1Cc7IugLa2trrFmzBomJiRg6dChMTEx0HYmIiAxIWloaGjVqpNZWpDNnzkRgYCAqVaokYTIi/aPqIaVQT10SZ8qUKdi5c6fa65ydnTFy5EgJEhERERHphxIlSmDjxo1qr3v9+jU8PDxw69YtCVJpTr9+/ZCamorY2Fhs2rQJgwcPxhdffAEAcHR0hIWFhY4TCl/vs6CBiqJBgwYhIiICtWrVEjU/LS0NLVu2zNc9HxERkS7p/afv165d01pLiYKoXr06tmzZIrg1GxER0cdu3bqFZs2a4fnz56Lmly1bFrt27YKHh4fEyYj0E3dokNayZcuwYsUKtdeZmprC19dX7wqPiYiIiDTtm2++Qf/+/bF792611j169Ajt2rVDSEgIKleuLFG6gjMyMoKdnR3s7OwwfPhwAMDjx4/x5MkTHSd7jy0niP6rYcOGiI6OhpeXFw4cOKByfk5ODgYNGoTIyEj88ssvWkhIRERUcHr/1NEQihk+Zmh5iYhIN44cOYL69euLLmZo3LgxIiMjWcxARRoLGqTj5+eHKVOm5GvttGnTYGtrq+FERERERPpp1apVKFeunNrrkpKS4OHhgZcvX0qQSjoVK1ZEgwYNdB0DAFtOEClTsmRJ7Nu3D6tWrRJdaL5q1Sq4u7tDLpdLnI6IiKjg9L6ggYiIqDBRKBSYPn06unbtiszMTFFrhg4digsXLqjdr5WosDExMYG5ubnScbacyJ9jx47B29s7X2vr1auH6dOnazgRERERkf6qUKECVq5cma+10dHR6NatGzIyMjScqmhgywki5WQyGcaNG4eQkBCUKVNG1Jrg4GBYW1vjzp070oYjIiIqIBY0EBERacnr16/x1VdfYdGiRaLmGxsbY9u2bdi2bZvgh7hERYnQg0ru0KC+kJAQ9O7dO99t03x9ffn7ExERERU5AwcORNu2bfO19uzZs/j222/ZtjYfuEMDkWotWrRAYmIiGjduLGr+v//+i7p16+Lo0aMSJyMiIso/FjQQERFpQVxcHOrVq4czZ86Iml+qVClERkZi6NChEicjMixCvXH1taDh0aNHCAgIwJQpU+Dq6oqDBw/qOhIA4OrVq+jUqVO+3xAcOXIkXF1dNZyKiIiISP/JZDJs3LgRxYoVy9f6gwcPYvTo0VAoFBpOVrgJXe8L3ScQFTXly5fH5cuXRe/EJ5fL0aVLF0yZMoXFVkREpJeKXEFDbGwscnJydB2DiIiKkCNHjsDBwQEPHjwQNb9BgwZITk6Gvb29xMmIDI/Qm1f61HJi9+7dGDx4MGrXro3KlSuje/fuWLZsGUJCQvDPP//oOh7u3r2L9u3b48WLF/lab2VlhcWLF2s4FREREZHhsLGxwdy5c/O9fvPmzZgzZ44GExV+bDlBJJ6RkRG2bNkCX19fGBmJ+xho2bJlaN++PV6/fi1xOiIiIvUUuYKGRo0aoUSJEmjevDlGjhyJzZs349KlS3j37p2uoxERUSGjUCiwdOlSdO3aFXK5XNSavn37IjY2FpaWlhKnIzJMhtJyYt++fdixYwdu3br1n7HQ0FAdJPp/T548Qbt27UQXWeVl3bp1KFWqlAZTERERERme8ePHo0mTJvleP3/+fKxbt06DiQqv7Oxswee3LGggytuwYcNw4cIFFC9eXNT806dPw9HREXfv3pU4GRERkXhFrqABAN69e4eIiAj4+vpi1KhRaNGiBSwtLWFvb48hQ4Zg9erVCA4OZiUiERHlm1wux9ChQ/HTTz+JXrNkyRL4+/tDJpNJmIzIsBlKywlnZ2elY1FRUTrL+ubNG3Ts2BGJiYn5Pkb37t3RvXt3DaYiIiIiMkwmJiZK3352dnYW1UJw7Nix2Lt3rxTxChVV189sOUGkXLNmzXD37l1Ur15d1PwbN26gSZMmCAsLkzgZERGROEWuoKFWrVoA3r81+/FXVlYW4uLisHPnTkyYMAGtW7dGmTJlUKdOHfTp0wdLlixBUFAQnj59quOfgIiI9N2zZ8/Qpk0b+Pn5iZpvamqK48ePY8qUKRInIzJ8hrJDg1BBQ3Z2Ni5duqTFNO/J5XL06NGjQOcuWbIk3yIkIiIi+oijoyPGjx+f+8+WlpZYv349goODsXXrVgwePFhwvUKhwMCBAxEUFCR1VIOm6lqfOzQQCStfvjxu3rwJNzc3UfOfP3+OVq1a4ffff5c4GRERkWomug6gbbdu3cKrV68QFRWFyMjI3L9ev34d2dnZn8xVKBS4desWbt26hf379+d+v2rVqmjSpAkcHBxy/1qtWjVt/yhERKSHEhIS8M0334jemq906dI4f/48GjRoIHEyosJB6EGlUE9dbXN0dISZmZnSdjOhoaFo3bq11vLk5ORgyJAhBX5QvnTpUlhZWWkoFREREVHhMHfuXBw8eBD29vZYt27dJ88JfX198ezZMxw7dkzp+szMTHTv3h1nz55F06ZNtRHZ4Ki61mdBA5FqZmZmOHv2LEaMGIEtW7aonJ+ZmYkBAwYgISEBc+fOzXM3GiIiIm0ocgUNwPs3y1q1aoVWrVrlfi8jIwMxMTGfFDpcvXo1z95sKSkpSElJ+eRGpFy5cmjSpElugYOnp6dWfhYiItIfQUFB6Nmzp+iWRTY2NggODuaHg0RqMJSWExYWFnB0dFS6RWdoaKjWsigUCvz444/w9/cv0HHc3Nzw3XffaSgVERERUeHx2WefITw8HOXLl/9PC0FTU1Ps3bsX7dq1Q0hIiNJjvH37Fh4eHggJCUGdOnWkjgwAuHz5MsaMGQNnZ2e4uLigZcuWqFKlilbOrS62nCDSDCMjI/j6+qJu3bqYPHmyqDULFixAQkIC/Pz8ULx4cYkTEhER/ZdMoVAodB1CX2VlZSE+Ph6RkZG5RQ4xMTFKK4I/3LDIZDJkZWVpMyqRxpQsWTL3w1hLS0u8evVKx4mIDMP69evxww8/ICcnR9R8Z2dnHD9+HKVKlZI4GVHhMnbsWKxduzbPsbZt2yIwMFDLiZSbNGkSVqxYkedY2bJl8eTJE6284bJw4ULMnDmzQMcwMzNDTEwM6tWrp6FUREREREXL8+fP4ebmhri4OMF51tbWCA0N1Urh+6pVqz5plwG8b9fr7OwMNzc3jBgxQvIMYoWEhMDV1VXpeGpqKsqUKaPFRESGb//+/ejbt+9/dq5WpmnTpjh8+DBfzCESiZ+1EGkO9wgSYGJiAnt7ewwZMgRr1qxBcHAwXr58iYSEBOzevRuTJ0/GV199hbJlywJ4//bbhy8iIioasrKyMHbsWHz//feiixk8PT1x5swZFjMQ5YOhtJwA3hcuKZOamorr169LnsHX11dUMcOCBQsQHBystGBhxowZLGYgIiIiKoAyZcrgr7/+Qo0aNQTn3b17F+3bt8fz588lz5TXrmG3b9/G77//jvXr10t+fnWw5QSR5vXq1QtnzpwR/f9PREQEnJycEBUVJXEyIiKiT7GgQU0ymQx16tRB3759sWTJEgQFBeHp06e4c+cODh06hFmzZqFjx466jklERFrw8uVLdOrUSenb4nmZNGkS/P39YWZmJmEyosJL6EGLPrWcAIQLGgDp204cOnQII0eOVDlv7NixmD59OlxdXREdHY05c+bA1NQ0d9zW1hZTp06VMioRERFRkVC1alUEBgaifPnygvOuXr2KLl26ID09XbIsCoVCsAWGqmtZbRO61jcxMeE9NlE+ubm54eLFi6hUqZKo+SkpKXB1dcWhQ4ckTkZERPT/WNCgITVq1EDXrl0xd+5cHDlyRNdxiIhIYklJSWjZsiX++usv0WvWrFmDZcuWaWWLeaLCSqg3rr4VNFSuXBk2NjZKx6UsaPjnn3/Qr18/lTvH9OvXD7/88ktu6zRzc3P4+PggOjoazs7OkMlk8PX15QNiIiIiIg2pW7cujh8/rvKN6PPnz6NPnz6StbVNTk7GgwcPlI4bUkGD0D0CEalma2uLy5cvo3Hjxqhfv77K+WlpaejRowcWL17M3aqJiEgr+IkKERGRmoKDg+Hk5IRr166Jmm9qaor9+/fjhx9+kDgZUeFnSC0nAOEHwVIVNERHR6NLly549+6d4Lx27dph+/bteRZZ2draIjg4GGfPntW7h9lEREREhq5Zs2Y4ePDgJ7ti5eXo0aMYPny4JB8YCu3OAOhfQYPQtT7bTRAVXNWqVREZGYnLly+jb9++otZMmzYNQ4YMUXnvSUREVFAsaBDw+PFjHDt2DNu3b8cff/yBc+fO4fHjx7qORUREOuTn54evvvoKz549EzW/ZMmS+Pvvv9GzZ0+JkxEVDYbUcgIQfhCckJAg+vcSsZKSkvDNN9/g1atXgvOaNWuGAwcOCO68YGRkBHd3d43mIyIiIiJALpcjPDwcs2bNUjn3t99+w7Rp0zSeQai4tlKlSoI7jemC0LU+CxqINEMmk6FYsWLYvXs3fHx8RK3ZsWMHvv76azx58kTacEREVKSxoCEPN27cQKdOnWBlZYWuXbvC29sb/fr1w5dffgkrKytdxyMiIh3IycnJrTzPzMwUtaZq1aoICwuDq6urxOmIig5VLSf0bbtLVW+2hYWFaexcjx49Qrt27fDo0SPBeXXq1MGff/7JrXmJiIiIdCA0NBRNmjTB7NmzsW/fPqxYsULlmiVLluCXX37ReA5lPrQe0ydsOUGkPTKZDHPmzMGePXtgYWGhcv758+fRvHlzxMfHayEdEREVRSxo+B8XLlyAk5MTTpw4gZycHCgUiv98/a/MzEy9e3hORESa8/btW/Tq1QuLFy8WNd/V1RUNGzZEeHg4bG1tJU5HVLQIvX2lUCiQkZGhxTSq2dnZwdLSUum4ptpOvHr1Ch4eHrh165bgPCsrKwQGBqJChQoaOS8RERERifPq1St8//33cHV1zf3QLzY2Funp6ZgxY4bK9RMmTMCuXbs0kuXNmzeIiYlROq5v7SYA7tBApAt9+vTB2bNnUalSJZVzb9++jZYtW+LkyZNaSEZEREUNCxo+8vr1a/Ts2RMvX76EQqFAiRIl0Lt3b4wbNw7169dXuu7NmzeoUqUKXFxcRFVVExGR4bh//z5cXV1x6NAhlXONjIywatUqnDt3DpcuXULVqlW1kJCoaFH1sFKot64uGBsbo0WLFkrHNVHQkJGRgW7duiEqKkpwXunSpfHXX3/B2tq6wOckIiIiIvEOHz4MW1tbrF+//j8vRc2fPx8DBw7Ed999p/I4Q4cOxYkTJwqc59KlS8jOzlY6ro8FDULX+SxoIJJO8+bNcenSJTRq1Ejl3FevXqFjx45Yu3YtXwAlIiKNYkHDRzZt2oSHDx9CJpOhSZMmSExMxN69e/HLL7+gTZs2SteVKVMGffr0QVhYGGbNmqWyZzERERmGS5cuwcnJCdHR0SrnWlpa4tixYxg3bhxkMhnMzc21kJCo6FG1nazQm1u6IvRA+OLFi6Lb2OQlOzsbAwYMwJkzZwTnWVhY4NixY7Czs8v3uYiIiIhIfXK5HBMmTEBKSkqe4+/evcOIESOwbt06dOvWTfBYWVlZ6NWrFy5cuFCgTCEhIUrHzMzM4OjoWKDjS4EtJ4h0p3r16jh//jy6dOmicm5OTg7Gjh2L77//vkD3ukRERB9jQcNHjh49CuD9G7Z79+5F5cqVRa/t06cPgPc3IdxWiYjI8O3btw/u7u54+PChyrk1a9ZEaGgoPDw8tJCMqGhT9faVoRU0pKeniyqaUmb+/Pk4cOCA4BxjY2Ps27cPDg4O2LFjB3JycvJ9PiIiIiJSj5mZGTZt2iQ4559//sGOHTvg7+8Pd3d3wblpaWno3Lkznjx5ku9MQruENW3aVC8L9Nlygki3SpQogYMHD2Ly5Mmi5m/YsAEdOnTAixcvJE5GRERFAQsaPhIfHw+ZTAYXFxfUrl1brbUtW7bMrQY+d+6cFPGIiEgLFAoFFixYAE9PT2RkZKic7+zsjPDwcL71TKQlhtZyAgBatGgBmUymdDy/bSdSUlLw888/q5y3detWdOrUCfPmzcPgwYPRqlUrJCQk5OucRERERKS+r7/+GoMHDxacM3nyZLx48QJHjhxRubX706dPMXfu3HxlycnJQVhYmNJxfWw3AbDlBJE+MDY2Rtu2bQXvbz926tQptGzZEjdv3pQ4GRERFXYsaPjIh1YRderUUXutTCaDnZ0dFAoFrly5ouloRESkBRkZGRgwYABmzZolav6AAQNw+vRpVKxYUeJkRPSBIe7QULJkSTRs2FDpeH4LGrZs2aJyC8+lS5di8ODBiImJwbJlywAA58+fR6NGjTBv3jzI5fJ8nZuIiIiI1LNixQpUqFBB6fiLFy8wduxYlCpVCidOnECtWrUEj+fn54fXr1+rnSMhIUHwjWl9LWjgDg1E+sHJyQmtW7cWPT8hIQHNmzfHP//8I10oIiIq9FjQ8JEyZcoAQL634bW2tgYA3L59W2OZiIhIOx49eoQvv/wSu3fvFjV/4cKF2LFjBywsLCRORkQfK1asmODbIPpY0AAIPxgOCQmBQqFQ63hZWVnw9fUVnDNx4kRMnjwZ2dnZGDZsGLKzs3PH5HI55syZgyZNmuS7oIKIiIiIxCtXrhxWrVolOGffvn04evQoqlSpgsDAQMHi+Tdv3oi+f/2Yqms/Qyxo+LBrLhFJ70PRVb9+/USvSU1NRdu2bbFt2zYJkxERUWHGgoaPVK9eHQDyvQVSyZIlAQDPnz/XWCYiIpJebGwsnJyccOHCBZVzzc3NsX//fkyfPl30FntEpDlGRkYoXry40nF9bDkBCD8YTklJQXJyslrHO3bsGFJSUpSO9+7dG0uXLgUArFmzBhEREXnOi4+Ph7u7O+7evavW+YmIiIhIff369cM333wjOGf06NF49eoVateujRMnTsDExETp3A0bNqhdGBsSEqJ07PPPP0elSpXUOp62sOUEkf4wNzfHrl27MHnyZNFrMjMz4e3tnVt0T0REpA4WNHykVatWUCgUCA8Px7Nnz9Re/+EPYm7dS0RkOAIDA+Hs7Ix79+6Jmm9mZoYGDRpInIqIhAg9sNTXHRpcXFwEx9XdJWHDhg2C4z4+PjAyMsKdO3cwc+ZMwbmjR4/O3WmMiIiIiKQjk8mwceNGwevZ+/fvY8aMGQAABwcHdO/eXencmJgYhIeHq5VB6LpTX3dnANhygkjfGBkZYenSpSp3nvlfy5cvR8+ePZGRkSFRMiIiKoxY0PCRHj16AHhfkPDhjTZ1fHizztLSUqO5iIhIGocPH0bnzp3VeqO7adOmqFKlioSpiEgVoS1l9bWgoVatWoJvu6lT0HDr1i0EBgYqHW/VqhVsbW2hUCgwatQopKWlKZ1bvXp1LFy4UPS5iYiIiKhgrK2tsWDBAsE5v/76K8LCwgAAo0aNEpy7ceNG0ed++vQpEhMTlY4bakEDW04Q6c64ceOwb98+mJubi15z+PBhdOrUSW/v34mISP+woOEjzs7OaNmyJRQKBX755RccP35c9No3b94gLCwMMpkMVatWlTAlERFpwp49e9CzZ0+1dtXp168fTpw4gVKlSkmYjIhUMcQdGmQymeADYnUKGjZt2iQ4PnLkSACAv78/Tp48KTh3/fr1LMYlIiIi0rIffvgBzZo1UzquUCjw3XffQS6Xo3Xr1qhbt67SuXv37kVqaqqo834oklBGXwsaFAoFd2gg0mO9evVCYGAgSpcuLXrN6dOn0b59e7x8+VLCZEREVFiwoOF/bNy4ERYWFsjOzkaPHj1UPjD+YOnSpblv+DZv3lzKiEREVEC//fYb+vfvr1bPvsmTJ2PXrl1qVZwTkTSEHliqs+OKtgk9II6OjhaV/d27d9i2bZvS8QoVKqBHjx54+vQpxo0bJ3gsT09PdOrUSeU5iYiIiEizjI2N4evrC2NjY6Vz4uLisGTJEshkstyC1bxkZGTAz89P1HmFimhLliypt+0V5XI5srKylI6zoIFI99zd3XH+/HlUq1ZN9JqQkBB8/fXXoouyiIio6GJBw/9o2LAhtm/fDlNTU8jlcowePRrNmjVDVFRUnvMzMzOxcOFC/Pzzz7nf8/T01FZcIiJS06+//govLy8oFApR82UyGVatWoWlS5fCyIh/bBLpA0NsOQHkXdBgbm4OFxcXTJgwQVQP0f379+PZs2dKx729vWFmZoaJEyfi6dOnSueVLl0aq1evFheciIiIiDSuUaNGmDx5suCcBQsWICEhAYMGDYKFhYXSeRs3bhR1jxsSEqJ0rEWLFoIFFrqk6hqfLSeI9EODBg0QFhaGhg0bil4TERGB1q1b49GjRxImIyIiQ2ei6wD6yNPTE5UqVUKPHj3w/PlzREZGfjLevn17lC9fHi9evMCFCxfw4sULKBQKyGQytGjRAm3bttVRciIiErJ06VL89NNPouebm5tj165d6NWrl4SpiEhdhthyAgAcHR1hbW0NR0dHODs7w9nZGQ4ODmrt/CLUI1kmk2H48OEICgrCjh07BI+zfPlyVK5cWfR5iYiIiEjzZs+ejf379+PmzZt5jsvlcgwfPhxnz55F3759sX379jznJSYm4syZM2jTpo3Sc8nlcly6dEnpuL62mwBUX+NzhwYi/VGtWjUEBweje/fuOHPmjKg1sbGxcHd3x+nTp9Xa4YGIiIoOvmqqRKtWrRAVFQVPT08oFIrcggUAOHXqFPbs2YOTJ0/i+fPnuWuqVq2K33//XVeRiYhICYVCAR8fH7WKGUqXLo3AwEAWMxDpIUNtOWFubo47d+7gwIEDmDhxIlq2bKlWMUNsbCzOnz+vdPybb75BpUqVBLckBoAvv/wSXl5eos9LRERERNIoVqyYyna3wcHB2LJli8prvA0bNgiOR0dHC+4Ips8FDaqu8VnQQKRfSpUqhRMnTqBv376i1yQmJsLd3R23b9+WMBkRERkqFjQIqFGjBvbs2YMLFy6gb9++KFGiRG5xw4cv4P3bcH379sWFCxdQs2ZN3YY2MG/fvsXFixexa9curFmzBgsXLsSaNWuwc+dOnDt37yZBFwAAIABJREFUDmlpabqOSEQGTqFQYMqUKZg7d67oNdWrV8f58+fh7u4uYTIiyi9D3aGhoFQ97B41ahR8fHyQlJSkdI65uTk2bdqUW6hLRERERLrVpk0blcWmU6ZMQfXq1dGkSROlcwICAvDw4UOl46GhoUrHjIyM0Lx5c9VhdYQ7NBAZHnNzc/z++++YNGmS6DW3b9+Gm5sbrl+/LmEyIiIyRGw58ZFBgwahcePGaNy48SdbtDk5OWH37t3Izs5GbGwsEhMT8ezZMxgbG6NKlSpwcXFB2bJldZjccLx79w6nTp1CUFAQTp06hfj4eMEefyYmJnBycsKIESPQp08ftd5i1KQhQ4bAz89PI8e6du0a6tWrp5FjEZGwnJwc/PDDD1i/fr3oNQ0bNsSJEydQtWpVCZMRUUEI9cgtrAUNb968EWwjUb16dVSpUgUrV64UPM6cOXPwxRdfaDoeERERERXAsmXLcOzYMTx+/DjP8ZcvX2Ls2LEYNWoUhg8fnuecrKwsbN26FTNnzsxzXKigoWHDhihZsqT6wbVE1TW+0P0BEemOkZERli1bhmrVqmH8+PGCnwN8kJKSAnd3d5w6dQoNGzbUQkoiIjIE3KHhI7t27cLkyZOVbuFmbGyMxo0bw9PTM/cGonPnzixmECE6OhpDhgxBpUqV0KlTJ6xevRpxcXEqL2KysrIQGhqKwYMHo3bt2ggKCtJSYiIydNnZ2fD29larmOHLL79EcHAwixmI9JyhtpwoCH9/f7x+/Vrp+LBhwzBixAhkZ2crnWNvb6/W2zFEREREpB1ly5bFmjVrBOccOHAAJUqUgKWlpdI5mzdvzvN6UKFQICQkROk6fW43Aai+xi9evLiWkhBRfowbNw579uyBmZmZqPmPHz9G69atERERIXEyIiIyFCxo+EixYsUAAE2bNtVxksJn//798PPzw8uXL/N9jPv376N9+/ZqbRtPREVTZmYmvv32W2zfvl30mr59++LEiRMoVaqUdMGISCOKWssJhUIh2BPZxMQEWVlZiIyMVDpHJpPB19cXpqamUkQkIiIiogLy9PREx44dBedMmjRJsCd9cnIyjh8//p/vZ2ZmYtCgQXB3d4eFhcV/xvW9oEHoGt/CwgLGxsZaTENE+eHp6YnAwEDRz91SU1Px1VdfCRZjERFR0cGWEx+pVq0abt68yb5rWmRpaQkXFxc4OTmhcuXKKF++PF6/fo2bN2/i5MmTiIqK+mS+QqGAj48PihcvjsmTJ+so9fsPUmrXrp2vtXndOBKR5rx79w59+vTB4cOHRa+ZNGkSlixZAiMj1vkRGYKi1nLi0qVL/7km+ljbtm2xfPlywWOMHTsWTk5Omo5GRERERBoik8mwfv16NGjQQOmOBA8ePFD5stDGjRvRuXPnT75nZmaGRYsWAQDkcjmio6MRGhqKkJAQhISEwMXFRTM/hESErvHZboLIcLRq1Qrnz5+Hh4cH7t+/r3L+q1ev0K5dOxw9evSTFuFERFT0sKDhIy1btsSNGzdw69YtXUcp1MzNzdG9e3cMGzYMrVu3VlpF/fPPPyMoKAje3t5ITk7+ZGzatGlo37497O3ttRH5P5o2bYqzZ8/q5NxEpFxaWhq6deumVnuaVatWYdy4cRKmIiJNK2o7NAjtzgC8344zPT1d6XiNGjWwYMECTcciIiIiIg2rUaMGFi5cKHiP+scff8De3h5XrlzJc/zEiRO4ffs2atWqlee4mZkZnJyc4OTkhB9//FFUT3tdE7rG54tpRIbFzs4OYWFh8PDwwNWrV1XOT0tLQ4cOHXDw4EF06NBBCwmJiEgf8VXUjwwYMAAAEBISgocPH+o4TeHz2WefYerUqUhJSYH//7F339FR1fn/x1+ThBR6gBAEDCBWmkpZkCIsiPSuLoIIKh1ZAVEJZCV0XHYFFKQEaVJclGajioArigqi9CJSVkACBBJMIWV+f/gjX2Iyd5Iw985M5vk4J+fA/Xw+M6/sesInd9738165Ui1atHB6JFzLli21Z88e3XPPPVmup6en0wMaQBYJCQlq06ZNrosZChUqpFWrVlHMAHgho5uWzvrrepu4uDi9//77DsfLlSunPXv2GL7G3LlzeXINAADASwwZMkT169fPccxms2nw4MEaOnSow/V2u10xMTG5fj+bzSabzZbnnFYy2uNT0AB4n4oVK+rLL79Us2bNJMnpiakpKSnq3LmzVq9ebUE6AIAnoqDhFo899pgef/xxpaamasSIEe6OU6C0b99eJ0+e1JQpU1S6dOk8rQ0LC9MHH3yQbWPz+eef67fffnNlTABeKi4uTi1bttTOnTtzNb9EiRLasmWLnnzySZOTATCD0YfzycnJSk9PtzCNuZYsWaLk5GSH4/Hx8Ybrn376abVp08bVsQAAAGASf39/xcTEKCAg68G61apV03//+1/Nnj1bvXr1UpkyZRy+xrvvvqsbN26YHdUytJwACp6SJUtq48aNGjdunD777DOnxUmpqan629/+puXLl1uUEADgSSho+JOYmBhVrFhRq1atUmRkpLvjFBgNGjRQ2bJl873+wQcfVMeOHbNcy8jI0MaNG283GgAvFxsbq+bNm2v37t25ml+hQgV99dVXatq0qcnJAJjF2Y2OxMREi5K4Xnp6emb7M7vdrrlz5zqc6+/vb/i9lipVSjNmzHB5RgAAAJirZs2aeu211yT90SJi3Lhx2rt3rxo2bCjpj3auzz33nMP1Fy9e1Nq1ay3JagVaTgAFU1BQkF5//XW1atVKW7duVYkSJQznp6enq1evXnk6hQYAUDBQ0HCLv/zlLxo/fry6dOkiPz8//fOf/1THjh31yy+/uDsaJLVu3TrbNf6/AXzb+fPn1axZM+3bty9X82vUqKFvvvlG1atXNzkZADM5u2npTW0n4uPjtXnzZkVHR+vxxx9XaGioatSoodTUVG3fvl1Hjx51uNbZSRT//ve/b6ugFAAAAO4TFRWlXr16ad++fXr99dcVFBSUZXzAgAGG6+fMmWNmPEvRcgIo+Bo0aKBt27Y5Pd3Zbrerf//+mjlzpkXJAACeIMD5FN/x/fffZ+lBbLfb9emnn2rTpk3q0KGDunTpoqZNm6pixYpuTOm7IiIisl2j5QTgu06fPq0WLVpkPsnsTLNmzbRu3Tqn1d4APJ+zm5ZGT3B5iiNHjuhvf/ub9u/fL7vdnm183759hqczONOiRQv17t37diICAADAjYKDg7V06VKH41WrVtXjjz+uzZs35zi+Y8cOHT58WA888IBZES3DCQ2Ab6hdu7Z27Nihxx57TBcuXDCcO2zYMCUmJnLKNgD4CE5o+BO73Z55U9lms0n6oz/T2rVr9eyzz6pSpUoqV66c2rZtq6ioKK1Zs0anTp1yY2LfkdMvLyEhIW5IAsDdTpw4oSZNmuSqmGHIkCF68cUXtXHjRooZgALCWZ9cbyhoKF++vA4cOJBjMYMkbdiwQWvWrHG4vmTJkg7HgoODNXfu3My9LAAAAAqmQYMGGY7fToGsJzHa3zv73QCAd6levbp27typO++80+nc0aNHKyoqyuHv1QCAgoMTGm6xZcsW/fDDD5lfx44dU0ZGhiRl+Ufx4sWL2rRpkzZt2pR5LTQ0VA8//LBq166t2rVr629/+5vl+Qu6nD64LFeunBuSAHCnQ4cO6bHHHtP58+edzp04caLGjBljQSoAVioILSeKFy+umjVr6scff8xx/IMPPlBaWprD9VOnTlW5cuU0ZMgQ/frrr1nGoqOjdffdd7s0LwAAADxP+/btVaFChWz7wZuWLFmiKVOmqHDhwhYncy1aTgC+5Z577tGXX36p5s2b6+TJk4ZzJ02apN9//11vvvkmRf0AUIBxQsMtWrRooZEjR2r58uU6dOiQ4uPjtWvXLs2ePVv9+vVT3bp1FRwcLOn/TnK4+XXlyhVt27ZN//rXv9SzZ083fycF09q1a7Ndq1u3rhuS/FHUMnbsWDVv3lyVKlVS4cKFVbRoUVWqVEl16tTRgAEDtGLFCl2+fNkt+YCC6ocfflDTpk1zVcwwffp0ihmAAqogtJyQpIYNGzocO3z4sMOxYsWKqUePHurUqZMOHTqkwYMHZ964eeihhzRixAiXZwUAAIDnCQgIUL9+/RyOX7t2Te+//76FicxBywnA91SqVEk7d+7U/fff73TujBkzNGjQoMyHUwEABY/Nznk8eZKenq7Dhw9nnuKwb98+7du3T1evXs2cY7PZlJ6e7saUBc+BAwdUs2bNLNdKliyp2NhYBQSYf9BInz59tGTJkjyvK1y4sPr27atXX31VFSpUMCGZ6xUvXlwJCQmS/vjAJD4+3s2JgD/s3r1brVu3zvLzNic2m01z5szRgAEDLEoGwGp2u12BgYEOTzBYs2aNunTpYnGqvFu2bJl69eqV53WDBg3SO++8k+Xarl27NHDgQC1atEh16tRxVUQAAAB4uF9//VWVKlVyeC+ybt26+u677yxO5Vq1atXS/v37cxybNGmSRo8ebXEiAFa5ePGi6tWrpzNnzjid26tXLy1cuNCSzwuA3OCzFsB1+MmeR/7+/qpRo4Zq1KiR5Qb0qVOnsrSrgGu98sor2a4988wzHr85SUxM1FtvvaWlS5dq6dKl6tChg7sjAV5px44dat++vdNj5P38/LR48eJ8fUAIwHvYbDYVKVJE165dy3G8IJzQYGTgwIE5vta+ffvk58cBbAAAAL6kQoUK6tixY44nm0rS999/r3r16qlNmzZq2LChGjRooJIlS1qc8vZwQgPgu1atWpWrYgZJeu+995SUlKTly5crMDDQ5GQAACt5zR3Pf/zjH1q/fr3OnTvn7ig5qly5srp06aLx48fr448/dnecAmXJkiXauHFjlmtFihRRZGSkmxL9wWazqWzZsrr33nt1//33KywszGGfrqtXr6pTp06aOXOmxSkB77dp0ya1adPGaTFDQECA/vOf/1DMAPgIoxuXzn5eeIoqVaooPDw8T2saNmyoWrVq5ThGMQMAAIBv6t+/v+H4999/rwkTJqhNmzYqVaqUatSooZ07d1qU7vYZ7e8paAAKtk6dOqls2bK5nv/hhx+qa9euSk5ONjEVAMBqnv14+y0mTZqU+WFxs2bN9Pnnn7s5Eaxw+PBhDRkyJNv1CRMmqHz58pbnqV27tjp06KCWLVuqVq1aKlasWJbxK1euaMeOHZo3b542bdqUZcxut2vEiBGqWrWq2rdvb2rO4sWL53vtzSOQAE+wfv16PfXUU7px44bhvKCgIK1evVrt2rWzKBkAdytatKjDMW85ocFms6lRo0Zas2ZNrtcMGjTIxEQAAADwNocOHdKECRNyPd9ut+vgwYMKDQ01MZVrGe3vjX4vAOD97rzzTn3wwQdq0aKFw7aTf/bpp5+qQ4cOWrduHUVPAFBAeN1jXHa7XXFxce6OAQvExcWpU6dO2X5padasmV566SVLs7Rv31579+7Vnj17FB0drUaNGmUrZpCkUqVKqUuXLtq4caO2bt2arXo0IyNDvXr1Mv2/4YSEhHx/AZ5iy5YteuKJJ5wWM4SEhOjTTz+lmAHwMUY3JbyloEHKW9uJ0qVL64knnjAxDQAAALxFSkqKoqOj9dBDD2nXrl15Wlu8eHFVq1bNpGSulZGRocTERIfjfFgJFHyPPvqoZsyYkac1W7duVZcuXZSammpSKgCAlbzmhIabHB3pbyQ9PV3+/v4mpPE88fHxysjIsPx9g4KCFBIS4rLXS0lJUZcuXXT8+PEs1++44w6tXLnS8iOV8/PhQYsWLfT111+rQYMGio2Nzbx+9epVTZs2TZMnT3ZlRKBA+fHHH9WtW7dcVV4HBHjdP2UAXKAgtJyQ8lbQ8Nxzzyk4ONjENAAAAPAGJ0+eVPv27XX48OF8rX/kkUe85l5pUlKS7Ha7w3EKGgDfMHjwYO3Zs0eLFi3K9ZotW7aof//+WrhwYb4+VwIAeA6v+xTIaAPrSIMGDZSamqq6deuqbt26GjhwoAnJPEOtWrV0+vRpy9/3pZdeynOVpCPp6enq0aOHduzYkeV68eLF9emnn6pcuXIueR8r3HXXXVq2bJlatWqV5frs2bM1fvx40z6Izen0iNzilAa429mzZ9W2bdtc/7eYkJCg1q1b6+jRo7rrrrtMTgfAUxSUExpq166twMBAp6fRSM57IwMAAMA3VKhQIV/3SG/KS1Gtuznb21PQAPgGm82md955RwcPHtS3336b63WLFy9WRESExo0bZ2I6AIDZvK7lRH6kpaXpp59+0sKFCzVkyBB3x4EBu92u/v37Z+slHRISoo8++kgPP/ywm5Ll3+OPP66//vWvWa7Fx8frm2++Me094+Pj8/11O8UQwO26evWq2rRpo3PnzuVp3ejRoylmAHyMUa9cbypoCAoKUt26dZ3Oa9mype655x4LEgEAAMDTBQUFKSYmJt/rC1JBg9HvBQAKluDgYK1Zs0bh4eF5Wjd+/Hi9++67JqUCAFjBJwoapPy1qrhVx44d9cQTT2jq1KnaunWri1Lhz4YPH66FCxdmuVaoUCF98MEHatq0qZtS3b7u3btnu/bFF1+4IQnguW62mjl48GCe1nXo0EFjx441KRUAT1VQWk5IUqNGjZzOefLJJy1IAgAAAG/RuHFjDRgwIM/rbDab6tevb0Iiczjb23NCA+BbKlSooA8//DDPJx8PGDBAGzZsMCkVAMBsPlPQcLvOnj2rtWvXasyYMWrdurW74xRIUVFRmjlzZpZrfn5+WrZsmdq1a+emVK7x6KOPZrt29uxZNyQBPFNGRoaef/55bd++PU/r7rvvPr333nvy8+OfM8DXFJSWE5JUp04dp3NefvllzZkzRxkZGRYkAgAAgDd44403dMcdd+RpTbly5bzqdE5aTgD4s8aNG+vtt9/O05r09HQ9+eST2rt3r0mpAABm4hOgPLDb7ZlfnurUqVNZclr1NWPGjNvKPWXKFE2aNCnLNZvNpnfffVdPPfXUbb22JyhXrly2a7GxsW5IAnimMWPGaMWKFXlaU6xYMa1bt04lSpQwKRUAT1ZQWk5I0oULF5zOSUhI0ODBg/Xoo4/q6NGjFqQCAACApytRooRmzZqVpzW///67VxXJ0nICQE4GDBigvn375mnN77//rnbt2unUqVPmhAIAmIaChjy43bYVyNnMmTM1evTobNdnzZqlPn36WB/IBCEhIdmuJSYmuiEJ4HnmzJmjqVOn5nndsmXLdP/995uQCIA3KEgnNOSloOubb77xuu8PAAAA5unatas6d+6c6/nx8fHavHmziYlcy2jva7PZFBwcbGEaAJ7CZrNp1qxZatCgQZ7WXbhwQW3atNGVK1dMSgYAMAMFDXCrmJgYDRs2LNv1adOmafDgwW5IZI5Lly5lu1amTBk3JAE8y0cffaQXX3wxz+vGjRunjh07mpAIgLcwKmhw1mfXk+zdu1fffvttruePGDFCtWvXNjERAAAAvM2sWbNUvHjxXM+fO3euiWlcy2hvX6RIER5AA3xYUFCQVq9enePpyEaOHDmizp07Kzk52aRkAABXo6ABbrN8+XINHDgw2/Xo6GiNHDnSDYnMc/jw4WzXwsLC3JAE8By7d+9W9+7d83zUZefOnRUVFWVSKgDeoqC0nMjLzeS77rpL0dHR5oUBAACAV6pQoYLeeOONXM//+OOPdfbsWRMTuY7R3p52EwDKly+v1atXq1ChQnla9+WXX6p3795e1YIHAHwZBQ1wi7Vr16pPnz7ZNgyvvfaaxo4d66ZU5tm4cWO2azVr1nRDEsAznDhxQh06dFBSUlKe1t1///1asmSJ/Pz45wvwdQWh5cS1a9fy1G5i7ty5Kly4sImJAAAA4K369++vxo0b52puRkaGFixYYHIi1zDa2xv9TgDAdzRs2FCzZ8/O87pVq1bp1VdfNSERAMDV+EQIltu4caO6d++utLS0LNf//ve/a+rUqW5KZZ64uDi9++672a63atXKDWkA94uNjVWbNm0UGxubp3XFixfX+vXr83SMJoCCqyC0nFi2bFmuiy+effZZtWzZ0uREAAAA8FZ+fn6aP3++AgMDczU/JiZGqampJqe6fc5aTgCAJPXr108DBgzI87p///vfeuutt0xIBABwJQoaYKmdO3eqa9euunHjRpbr/fr104wZM9yUylzDhg3T1atXs1yrV6+eKlas6KZEgPskJiaqY8eOOnHiRJ7W2Ww2rVixQvfee69JyQB4G6Obl2lpadn2Gp7Gbrfnut1EWFiY/v3vf5ucCAAAAN7ugQce0JgxY3I19/z58/r4449NTnT7OKEBQG699dZbatSoUZ7XDRs2TGvWrDEhEQDAVShogGW+++47tW/fPtsR87169dLcuXNls9lMe+9Tp07JZrNl+apcubLTdRcvXtSsWbOUnJyc5/fMyMjQq6++qqVLl2YbGz9+fJ5fD/B26enp6tGjh7755ps8r50wYYLatWtnQioA3spZv1xPbzvx1Vdf6cCBA07nlS5dWhs3blSZMmUsSAUAAABvN2bMGL3wwgu5mjtnzhyT09w+o329s98JAPiWwMBAffjhhypfvnye1tntdvXs2VO7du0yKRkA4HZR0ABLHDhwQK1bt1ZCQkKW608++aQWLVokPz/P/E8xMTFRQ4cOVdWqVTV27FgdPHgwV+t2796tFi1aaNq0adnG2rZtq9atW7s6KuDR7Ha7hg0bpvXr1+d5bdeuXTV69GgTUgHwZs6exvL0thPOTmfo3bu3YmJidOjQIdWuXduiVAAAAPB2/v7+mj9/vnbu3Kl//OMfuueeexzO3bp1q44fP25huryj5QSAvChXrpzWrFmT6/Y7NyUnJ6tjx446duyYSckAALcjwN0B4BuGDx+uK1euZLt+8OBB1alTJ9+vW7duXS1YsOB2ouXKuXPnNH78eI0fP15VqlRR7dq1VatWLZUtW1YlS5aUzWbTlStXdPToUW3fvl0//vhjjq9Tq1YtrVy50vS8gKf597//rVmzZuV5XbVq1bR48WJTT3AB4J2c3bz05BMaYmNj9cEHHzgcr169uhYtWsTPPgAAAOSLn5+fmjRpoiZNmqhmzZp66qmnHM6dN2+e/vWvf1mYLm9oOQEgr+rXr685c+bk+rSamy5fvqzWrVvr66+/Vnh4uEnpAAD54ZUFDcnJyYqLi1NoaKi7oyCXUlNTc7x+6NCh23rdkiVL3tb6/Pjll1/0yy+/aPXq1Xla17hxY61atUrFixc3KRngmd5//3298soreV5XsmRJrV+/XsWKFTMhFQBv580tJxYvXqwbN244HB84cCDFDAAAAHCJTp06KTw8XL/99luO44sWLdLEiRMVHBxscbLcoeUEgPx4/vnntWfPHr3zzjuZ1+rVq6eEhAQdOXLE4bpffvlF7du31/bt2ymaAgAP4pnn/Buw2Ww6evSoypQpo4oVK6pt27YaNWqUVqxYoYMHDyo9Pd3dEYEsihYtqvHjx2v79u2644473B0HsNSOHTvUu3fvPK+z2WxauXKl7r77bhNSASgIvPWEhoyMDM2bN8/heOHChdWrVy8LEwEAAKAgCwwMVN++fR2OX7lyxfD0MHfjhAYA+TV9+nQ1adJEktS/f399+eWX2rx5sypUqGC47vvvv1f37t2VlpZmRUwAQC543QkNdrs988/nzp3T+fPntWnTpsxrgYGBuv/++1WrVq3Mr+TkZHdERQEQERGhr776Stu2bdPOnTu1d+9eXb582em6QoUK6eGHH1b37t31wgsvcCoDfNKhQ4fUuXNnw6eQHZk0aZJat25tQioABYWzm5dGvXbdaevWrfr5558djvfo0UMlSpSwMBEAAAAKun79+mny5MlZ7qveau7cuR5bVGu0r6egAYCRwMBAffDBB9qwYYP69OkjSbrzzjv12WefqXHjxkpISHC49pNPPtGLL76oOXPmcIIiAHgAryto+PM/Hn/eiKekpOjHH3/UTz/95PA1tmzZogcffFBly5Y1JSOy2759u1vfv3Llyg5/aTPi5+enhg0bqmHDhpnX/ve//+nkyZM6e/asLl26pMTERNntdpUsWVKhoaG68847VadOHYWEhLjyWwC8yrlz59SmTRtdvXo1z2ufeOIJjRo1yoRUAAoSf39/BQcHOyxc9dQTGubMmWM4PnDgQIuSAAAAwFdUqlRJ7dq10yeffJLj+K5du/TTTz+pVq1aFidzjpYTAG5HeHh4ZjHDTbVq1dLatWvVunVrw1MY5s2bp0qVKikyMtLklAAAZ7ymoOHOO+/U2bNns1yz2Ww5VsfZ7XbDD69vPvUbHh6uhx56KMvXvffe69rgKHAqVqyoihUrujsG4LESEhLUrl07nTlzxnCen5+fMjIyslyrWbOmFi1aROUzgFwpUqSIVxU0/O9//9PHH3/scLxevXqqU6eOhYkAAADgKwYOHOiwoEH645SGW3vNewpaTgAwQ4sWLbRw4UI9++yzhvNGjx6tO++8U88884xFyQAAOfGagobTp08rLi5OP/zwQ5avo0ePZvtAzNEHYX8udLhw4YI2bdqUpWVF4cKFVbNmzSxFDp5YnQwAnig1NVVPPPGE9u3bZzgvIiJC7733nvr166djx45JkkJDQ7V27VqesACQa0WKFHHYCsoTW04sWLBA6enpDscHDRpkYRoAAAD4ktatW6tSpUo6ffp0juPvvfee3njjDRUrVsziZMZoOQHALL169dKZM2cUFRVlOO/5559X+fLl1bx5c4uSAQD+zGsKGqQ/Puxq3rx5ln84kpKS9NNPP+mHH37Q3r179cMPP+jAgQNKSUnJstboNIdb/f7779q9e7d2796dec3Pz4+nhQHACbvdrgEDBmjz5s2G80qWLKkNGzaoWrVq+vbbb/XMM8/os88+0/vvv6+qVatalBZAQWB0A9PTTmhIS0tTTEyMw/EbmXQ7AAAgAElEQVSSJUvqb3/7m4WJAAAA4Ev8/f3Vv39/jRkzJsfx69eva8WKFRowYIDFyYxxQgMAM40ePVpnzpzR/PnzHc5JTU1Vly5d9N///lc1a9a0MB0A4CavKmjISUhIiOrXr6/69etnXktPT9ehQ4eynOSwb98+xcfHZ1mb2yKHm0/SUdQAAI6NGzdOixYtMpwTGBio9evXq1q1apKkEiVKaP369dq1a5caN25sRUwABYjRiS6eVtDw8ccf69y5cw7He/furcKFC1uYCAAAAL7mhRde0NixYx32jJ8zZ4769+/vMfdA09LSsj20ditOeARwu2w2m2bPnq1ff/1Vn376qcN58fHxatOmjb755hvaUQOAG/i5O4AZ/P39VbNmTT377LOaPn26tm/frqtXr+rEiRNatWqVIiMj1bp1a5UtWzazDcWt7ShuFjr8+QsAkLOFCxdq3LhxTuctWbJEjz76aJZrfn5+FDMAyBejJ7I8reXE3LlzDcc97Uk4AAAAFDzh4eHq2rWrw/Eff/wxy6m17uasSJkTGgC4QkBAgN5//33VqVPHcN6vv/6qtm3b6tq1axYlAwDc5PUnNOTFXXfdpbvuuktPPPFE5rULFy5kOcnhhx9+0C+//JLtlAYKGgAgZxs3blT//v2dzps2bZq6d+9uQSIAvsJbWk6cOHHCsB1Ps2bN9MADD1iYCAAAAL5q0KBBWrVqlcPxOXPmqEGDBhYmcoyCBgBWKl++vPbs2WM4Z//+/erWrZs+++wzBQYGWpQMAOBTBQ05KVeunNq0aaM2bdpkXouPj89W5HDkyBGHx7EBgK/au3evnnzyyczWPI68+OKLevnlly1KBcBXeEvLCaNenJI0cOBAi5IAAADA1zVt2lT333+/jhw5kuP4f/7zH02fPl2lSpWyOFl2zvb0tJwA4AonT55U586dtX///lzN//zzz/XCCy9o6dKlPAgLABbx+YKGnBQvXlxNmzZV06ZNM6+lpKRo//79mQUOAODrTp06pXbt2jk91r1z586aMWMGG3wALucNJzQkJydr4cKFDsfLli2rLl26WJgIAAAAvsxms2ngwIEaNmxYjuMpKSlavHixRowYYXGy7DihAYDZvvnmG7Vt21ZxcXF5Wrds2TJFRERo0qRJJiUDANzKz90BvEVQUJDq1q2rfv366Z133nF3HABwqytXrqht27a6cOGC4bwGDRpoxYoV8vf3tygZAF9idAPTWbGVVVavXq3Lly87HH/hhRc4phIAAACWevbZZxUSEuJwfO7cudna8bqDsz09BQ0AblfVqlVVrFixfK2dPHmy5s2b5+JEAICcUNAAAMiTjIwMPf300zp8+LDhvPDwcH388ceGN0kA4HZ4Q8uJOXPmOByz2Wzq37+/hWkAAAAAKTQ0VN27d3c4fvz4cW3bts3CRDnjhAYAZgsLC9O6devyff9y8ODB2rlzp4tTAQD+jIIGAECezJw5U5s3b3Y677ffftOkSZOUlpZmQSoAvsjTW07s379fX331lcPxNm3aqHLlytYFAgAAAP6/gQMHGo7PnTvXoiSOGe3pCxUqxElnAFzi4Ycf1oIFC/K1NiMjQ7169dLVq1ddnAoAcCsKGgAAufbTTz9p1KhRuZ4/Y8YMPf7444qNjTUxFQBf5ektJ5zdBB40aJBFSQAAAICs6tWrp9q1azscX7dunc6fP29houyM9vSczgDAlXr06KGXX345X2vPnDmjIUOGuDgRAOBWFDQAAHIlKSlJPXr00I0bN/K07osvvtAjjzyi5ORkk5IB8FWefELD9evX9d577zkcj4iIUJs2bSxMBAAAAPwfm81meEpDWlqa3n33XQsTZWe0p6egAYCrTZ06VS1atMjX2hUrVmj58uUuTgQAuMnjCxpatWqlffv2uTuGUykpKfrnP/+pt99+291RAMAUo0aN0sGDB/O19sUXX1RwcLCLEwHwdUWLFnU45u6ChlWrVikhIcHheP/+/eXv729hIgAAACCrp59+WsWLF3c4HhMTI7vdbmGirIz29Ea/CwBAfgQEBOg///lPvltDDh48WKdOnXJpJgDAHzy+oGHLli2qW7eunn76aR04cMDdcbJJTk7WO++8o7vvvluRkZGKi4tzdyQAcLlNmzbprbfeytfaZ555Ri+99JKLEwGA8xMa3Hnzddu2bQ7HAgIC9MILL1iYBgAAAMiuaNGi6tWrl8PxM2fO6Oeff7YwUVa0nABgtdKlS2vdunUKCQnJ89r4+Hg9++yzSk9PNyEZAPg2jy9okCS73a5Vq1bpwQcfVLt27bR9+3Z3R9LFixc1btw4RUREaOjQofr111/dHQkATBEbG6s+ffrka23t2rU1f/582Ww214YCABnfxLTb7UpKSrIwTVZnzpxxONapUyeVK1fOwjQAAABAzgYMGGA4fvbsWYuSZEfLCQDu8OCDD2rhwoX5Wvvll1/qjTfecHEiAIDHFzTcekS53W7Xxo0b1aJFC913332aNm2aLly4YFmWjIwMbdiwQd26ddOdd96p8ePH69KlS5lP/9nt9nxV7gGAp7Lb7erbt2++ftaGhYVp7dq1/FwEYBpnx8y6s+3ExYsXHY7Vrl3bwiQAAACAYzVq1FBAQIDDcaN9rdloOQHAXbp3765XX301X2vHjh2r7777zsWJAMC3eXxBw5EjR9S5c2fZ7fbMJ3ztdruOHz+uUaNGqWLFimrSpInefPNNHTlyxOXvf+3aNa1bt07PPfecypYtq/bt22vdunVKTU3Nkik8PFzvvvuuXnnlFZdnAAB3iYmJ0UcffZTndf7+/lq1apUiIiJMSAUAf3D2VJanFjSULVvWwiQAAACAYzabzXB/6qkFDZzQAMBskydP1uOPP57ndWlpaerZs6dh2xwAQN44Lr/1EBEREVq9erW2bdum1157TXv27MlS2GC327Vr1y7t2rVLr7zyisLCwtSkSRPVrl1bNWrUUPXq1RUREWFYaXzTpUuXdOzYMR04cED79+/XV199pf379ysjIyPz/aQ/Nvo2my3zRIahQ4dq9OjRKlasmHn/QwCAxY4eParhw4fna+306dPVrFkz1wYCgD9xdhPTXTcPbty4obi4OIfjFDQAAADAk5QtW1bnzp3LccydBQ1G+3kKGgCYzd/fXytXrlS9evV08uTJPK09fvy4RowYofnz55uUDgB8i8cXNNzUvHlzfffdd1q/fr2io6P1448/SlKW4gbpj032mjVrtGbNmizry5Qpo/DwcBUuXFghISHy9/dXcnKykpKSFBcXp/Pnz+vGjRtZ1tx8zZtufa+QkBANHDhQo0aNUlhYmCnfMwC4y40bN9SzZ08lJibmeW3v3r314osvmpAKALLy1JYTly5dMhynoAEAAACexBtPaKDlBAArlCpVSuvWrVODBg3yfJ80JiZGbdq0UZcuXUxKBwC+w2sKGm7q1KmTOnXqpC1btmjmzJnasGFDltYPUvZCBEmKjY1VbGysJDmde9Of59ntdlWsWFGDBw9W//79VapUKVd8SwDgcaKjo7Vnz548r6tXr57mzp2b5ecnAJjFU1tOOLvpS0EDAAAAPIk3FjRwQgMAq9SsWVOLFy/WU089lee1/fr1U/369VW+fHkTkgGA7/Bzd4D8atmypT755BOdOHFCY8eO1d13351ZdCD9X1uInL6k7O0jcvq6+XrBwcF66qmn9NFHH+mXX37RqFGjKGYAUGDt3LlTU6dOzfO6smXLas2aNQoODjYhFQBkFxISYlhA5a6WEzeLaB2hoAEAAACexGh/6mxvayZaTgDwFE8++aRGjRqV53WXL1/Wc889l9nWHACQP15b0HBTlSpVNHbsWB09elQ//PCDpkyZombNmikoKCizIOHPX1LW9hE5fVWpUkUDBgzQmjVrdPHiRb3//vtq3769/P393fntAoCprl69qmeeecbw9JqcBAQE6MMPP1TFihVNSgYA2dlsNhUuXNjhuCee0BAcHMzNVwAAAHgUo3a6nNAAAH+YOHGiWrduned1mzdv1ltvvWVCIgDwHV7XcsLIgw8+qAcffFCvvfaa0tLSdPDgQe3du1fHjx/X6dOn9euvvyo+Pl6JiYlKS0tT4cKFVaRIEYWFhalSpUqqXLmyatasqbp163ICAwCfNHjwYJ09ezbP62bOnKkmTZqYkAgAjBUtWtThjU5PLGgoW7YsbXkAAADgUbyx5UTRokUtTAIAkr+/v1asWKF69erp559/ztPaUaNGqUWLFqpZs6ZJ6QCgYCtQBQ23CggIyCxwAAA4t3z5cq1cuTLP655//nkNGjTIhEQA4JzRk1nuajnhrKABAAAA8CRGe9Rr164pJSVFQUFBFib6Ay0nAHia0NBQrV+/XvXr18/TQxQpKSnq0aOHvvvuO9r1AkA+eH3LCQDA7Tt16pQGDx6c53X169fX7NmzedoYgNsY3cj01BMaAAAAAE/ibI8aGxtrUZL/Y7fbaTkBwCNVr15dS5cuzfO6AwcOKDIy0oREAFDwUdAAAD4uPT1dvXr1Unx8fJ7WlStXTqtXr6aqGIBbGR01S0EDAAAA4JyzPao72k7cuHFD6enpDsdpOQHAnbp27aoxY8ZkuVasWDE1atTIcN2MGTO0efNmM6MBQIFEQQMA+LipU6fqv//9b57WFCpUSB9++KEqVKhgUioAyB1PPKHB6Ak2ChoAAADgacLCwgzH3XFCg7O9PCc0AHC3cePGqV27dpKk+++/X999950++ugjp/dLe/furUuXLlkREQAKDAoaAMCHffvtt4qOjs7zurfffttpxTEAWMHoRqZRz10zGT3B5uxmMQAAAGC1IkWKqHDhwg7H3XFCg7O9PAUNANzN399fy5Yt09ChQ7V7927dd999KlWqlJYsWWK47sKFC+rbt6/sdrtFSQHA+1HQAAA+6vr16+rZs6fS0tLytK5fv34aMGCASakAIG9oOQEAAADcPqN9qjsKGpzt5Wk5AcATlCxZUm+99ZaKFy+eea1FixYaOXKk4br169drwYIFZscDgAKDggYA8FHDhw/XiRMn8rTmkUce0dtvv21SIgDIO09rOfH7778bvi8FDQAAAPBE3lbQwAkNADzZxIkT9dBDDxnOGTZsmI4dO2ZRIgDwbgHuDuCJEhMTdeTIEV25ckUJCQlKSkpSSEiIihUrplKlSum+++5j0wzAq61du9ZpFfAjjzyi77//XqmpqZKkO+64Q6tXr1ZQUJAVEQEgVzyt5YSz/sIUNAAAAMATeVpBAy0nAHizoKAgLV++XHXq1FFycnKOcxITE9WzZ0/t2rVLhQoVsjghAHgXChokZWRkaNOmTVq9erU+//xznTlzxumaiIgItWjRQl27dlXr1q3l58dhFwC8w7lz59S3b1/DOVWqVNHGjRt18OBBdevWTZcuXdLq1at1xx13WJQSAHLH005ocHazl4IGAAAAeCJPK2hwtpcvXLiwRUkAIH+qVaumadOmaejQoQ7nfP/994qOjtakSZMsTAYA3sfnP4Vft26dqlevrvbt22vRokU6ffq07Ha77HZ7jvNvjp0+fVqLFi1Shw4dVK1aNa1bt87i5ACQdxkZGerTp4+uXLnicI6/v7+WL1+u4sWLZ57S8MEHH+iRRx6xMCkA5I5R71xPLGgICwuzKAkAAACQe95U0BASEiJ/f38L0wBA/gwZMkRt27Y1nDNlyhR9+eWXFiUCAO/kswUNdrtdI0eOVLdu3XTs2LEcixhuXrv168/jknTs2DF169ZNI0eOtCw/AOTHW2+9pS1bthjOiYqKylK8UL58eXXq1MnsaACQL97UcqJEiRK07QEAAIBHMipocNZWzQxGe3naTQDwFjabTQsXLjR8uMFut+uZZ57R1atXLUwGAN7FZwsaxo8frzfffFPSH/9g+Pn5qUWLFpo4caI+++wzHTx4UJcvX1ZiYqLS09OVmJioy5cv6+DBg9qwYYMmTpyoxx57TDabLfM1pk+frnHjxrnz2wIAh3766Se99tprhnMaNGigqKgoixIBwO3zppYTnM4AAAAAT2W0V7148aLD02zNYrSXp6ABgDcJDw9XTEyM4ZwzZ85oyJAhFiUCAO8T4O4A7nD48GFNmjRJNptNdrtdXbt21b/+9S9VrlzZ4Zrg4GAFBwcrNDRUDzzwgFq1aqXRo0frzJkzGjlypD788EPZ7XZNnjxZTz31lB544AHrviEAcCI5OVk9e/bUjRs3HM4pWrSoli1bpoAAn/ynAYCX8qaWE0ZPvQEAAADuZLRXTUpK0u+//26493Y1o728lTkA4HbEx8fr3Xff1cyZM53OXbFihdq1a6cePXpYkAwAvItPntCwePFipaWlSZKGDRumDz/80LCYwUhERIRWrVqlYcOGSZLS0tK0aNEiV0UFAJcYNWqUDhw4YDjn7bffVtWqVS1KBACuYfR0VnJystLT0y1MQ0EDAAAAvJOzvarRPtcMnNAAwNtlZGTo4Ycf1ogRI3T69OlcrRk0aFCu5wKAL/HJgoZNmzZJ+mOj/sYbb7jkNadOnZq58d+8ebNLXhMAXGHTpk1Oq4CfeOIJ9e7d26JEAOA6zm5mWn1KAwUNAAAA8EaeVtBw/fp1h2MUNADwBn5+fnk+bSE+Pl69evWy/OEMAPB0PlnQ8L///U82m02PPvqoChUq5JLXDAwMVNOmTWW323X27FmXvCYA3K7Y2Fj16dPHcE6FChU0b9482Ww2a0IBgAs5O26WggYAAADAuTJlyhiOe9IJDbScAOAthgwZosDAwDyt+fLLL132IC4AFBQ+WdCQlJQkyfWb35vVwcnJyS59XQDID7vdrn79+unChQsO59hsNi1dulSlSpWyMBkAuA4nNAAAAAC3LzAwUKGhoQ7HPamggRMaAHiLcuXK5fmUBkkaO3asvv/+exMSAYB38smChjvuuEOS9NNPP7n0dffv35/l9QHAnRYsWKD169cbzrHb7Zo5c6Z27Nghu91uUTIAcB1nNzONjqp1NbvdrtjYWIfjFDQAAADAkxntV432uWag5QSAgmLEiBF5XpOWlqYePXpY/pAGAHgqnyxoePjhh2W327V3715t3brVJa+5ZcsW7dmzRzabTQ8//LBLXhMA8uvYsWMaNmxYruZ+9NFHatasmerWravly5dT2ADAq3jSCQ3Xrl1Tamqqw/GwsDDLsgAAAAB5ZbRf5YQGAMifmjVrqmXLlnled/z4cQ0fPtyERADgfXyyoOHpp5/O/PNTTz2lDRs23Nbrbdq0Sd27d8/8e36OEAIAV0lNTVXPnj2VmJiYp3V79+7V7NmzZbPZTEoGAK7nrIWYlQUNzm7yckIDAAAAPJnRftWTChpc3UYYAMyWn1MaJCkmJkbr1q1zcRoA8D4+WdDQpUsXNW7cWHa7XVevXlX79u3VsmVLrVy50rDX/K0uXLig999/X48//rjatm2ruLg42Ww2NW7cWF26dDH5OwAAx6Kjo/PdYy2/m2sAcJfAwEAFBAQ4HLey5QQFDQAAAPBmnlTQQMsJAAVJq1atVK1atXyt7du3r86fP+/iRADgXRzf/S3AbDabli1bpr/+9a/65ZdfZLfbtW3bNm3btk2SFB4ersqVKys8PFwhISEKDAzUjRs3lJSUpN9++02nTp3Sb7/9lvl6N49nr1Klit577z23fE8AIEnfffedpkyZkq+1lStXVufOnV2cCADMV6RIEV27di3HMU85ocFms6l06dKWZQEAAADyypMKGmg5AaAgsdlsGj58uPr165fntZcvX9bAgQO1fv16E5IBgHfwyYIGSYqIiNDu3bvVq1cvbdq0KUvP+AsXLmQpWPizm3NtNlvmn1u3bq0lS5bQGxmA29jtdr366qtZfp7lxUsvvWT4lDMAeKqiRYt6fEFDmTJl5O/vb1kWAAAAIK+8paCBlhMAvFHPnj01evRoxcbG5nntRx99pJ07d+rRRx81IRkAeD6fbDlxU5kyZbRhwwZt3bpVnTp1UkhISOaY3W53+HVTcHCwOnfurM8//1yfffYZxQwA3GrLli3avn17vtYWL15czz//vGsDAYBFjJ7QsrKgweimBO0mAAAA4OmM9qyXLl1SRkaGZVk4oQFAQRMSEqLBgwfne31kZGS+H2QDAG/Ho7iSmjdvrubNmyspKUlff/21Dh48qKNHj+ry5cu6fv26kpKSFBISoqJFi6p06dK67777VL16dT3yyCNZiiAAwF0yMjIUGRmZ7/X9+vVT8eLFXZgIAKxjdEPTqPeuqxk9tUbhKwAAADyd0Z41LS1NV69eValSpUzPkZGRQUEDgAJp8ODBmjp1qlJSUnIcv/VU8D/btWuXPvnkE3Xo0MHMiADgkShouEVISEhmcQMAeJMPP/xQe/fudTju7++v9PR0h2N///vfzYoGAKYzOnLWU1pOcEIDAAAAPJ2zPevFixctKWhISkoyHKflBABvVbZsWfXq1UsLFizIcdzZCQyjR49W27ZtaWkJwOf4dMsJACgIUlNTFRUVZTjHUTGDJD355JOKiIhwdSwAsIyntJygoAEAAADeLDcFDVZwtofnhAYA3mzYsGGG44UKFXI4duDAAa1YscLVkQDA41HQAABebtGiRTp+/LjD8eDgYMP1w4cPd3UkALCUN7ScoKABAAAAnq5UqVLy83N8u9iqggZne3gKGgB4s+rVq6t169YOx1NTUw3Xv/766w5bVgBAQUVBAwB4scTERI0bN85wTnJyssOxxo0b6y9/+YurYwGApTihAQAAALh9fn5+CgsLczjuKSc00HICgLcbMWKE4bjRz7lTp05p/vz5ro4EAB6NggYA8GKzZs3SuXPnHI6HhoYarne2eQYAb2D0i75VBQ3p6em6fPmyw3EKGgAAAOANjPatnlLQwAkNALzdY489pho1ajgcd3ZSzYQJE5SQkODqWADgsShocKH58+dr/PjxGj9+vLujAPABcXFxmjJliuGcq1evOhy766671LFjR1fHAgDLeULLicuXL8tutzscp6ABAAAA3sBo3xobG2tJBlpOACjobDab0wfNwsPDHY7FxsZqxowZro4FAB6LggYXmjNnjsaNG+f0+HcAcIVp06YZFixEREQYfrg2bNgw+fv7mxENACzlCS0nnD2tZnR0LwAAAOApPL3lhM1mU3BwsCU5AMBMPXr0MCxauHLliuH6adOm6dKlS66OBQAeiYIGFzP68BAAXOX8+fNOq3CNNrQlSpTQc8895+pYAOAWntBywtnNXU5oAAAAgDfw9JYTRYsWlc1msyQHAJgpKChIQ4YMcTiempqqe+65x+F4QkKC09N7AaCgoKABALzQhAkTlJSU5HC8Vq1aSkxMdDg+YMAAww8AAcCbePoJDYUKFVKJEiUsyQEAAADcDk8vaKDdBICCZODAgYanzly6dMmwiGv27Nk6c+aMGdEAwKNQ0AAAXubEiROKiYlxOO7v7294kyEgIEBDhw41IxoAuIXRTU1n/XddxejnbtmyZXmKDAAAAF7BEwoajPbwFDQAKEjCwsLUu3dvh+NxcXGqX7++w/GUlBRaoAPwCQHuDuAOZlWs3bhxw5TXBYBbvf7660pLS3M43rRpU23bts3h+FNPPaWKFSuaEQ0A3MLTW07QbgIAAADewmjveuXKFaWmpqpQoUKmZnDWcgIACpJhw4Zp3rx52a7fc889Gj58uJo0aaKHH37Y4f3gxYsXa+TIkXrggQfMjgoAbuOTBQ2VK1fmKTkAXmnfvn1auXKlw/GgoCBdvnzZ8DWGDx/u6lgA4FZGT2mlpaXpxo0bCgwMNDUDBQ0AAAAoCJztXS9duqQ77rjD1Ay0nADgS+6//361a9dOn376qSSpWbNmGjFihNq1ayc/vz8OWR8wYIBmz56d4/qMjAxFRUVp9erVlmUGAKv5bMsJu93u8i8AMNvo0aMNx7t27aoff/zR4fijjz6qunXrujoWALiVs5uaVrSdiI2NdTgWFhZm+vsDAAAAruCsoMFo3+sqtJwA4GtGjhypnj17as+ePfriiy/UoUOHzGIGSYqKilLhwoUdrl+zZo2+/fZbK6ICgFv4bEGDzWZz+SkNFDUAMNOOHTu0YcMGh+PFixdXfHy84WuMGDHC1bEAwO2c3dS0ou0EJzQAAACgIHBWjGu073UVWk4A8DXNmjXTsmXLVLt27RzHy5Ur5/TU3VGjRvEZFYACyydbThQrVkwJCQmy2Wx67733XNZLvm/fvvr5559d8loAcCu73a7IyEjDOX379tX06dMdjt99993q0KGDq6MBgNs5u6lJQQMAAACQO8WKFVNQUJBSUlJyHHd3QQMnNADwVa+88ormzJmjK1eu5Dj+xRdfaOvWrWrZsqXFyQDAfD5Z0FC7dm3t2LFDkhQcHKymTZu65HWpEAZglo8//lhff/21w/Hw8HDVrFlTgYGBDm86DB8+PMtRZQBQUHhCywkKGgAAAFAQ2Gw2lS1bVmfPns1x3IqCBlpOAEB2JUqUUGRkpF555RWHcyIjI9WiRQvuAQMocHzyp1qdOnUy//z999+7MQkAOJeenq7Ro0cbzomKilKfPn105swZRUdHZzsiMjQ0VL179zYzJgC4jbtbTiQnJxu2/KGgAQAAAN7EaP/KCQ0A4D5DhgxRhQoVHI7v2bNHq1evtjARAFjDJwsa6tatm/lnChoAeLoVK1bo4MGDDscrV66s/v37S/rjpsPYsWN15swZLViwQNWqVZMkDRw4kF/6ARRY7i5oiI2NNRynoAEAAADexJMLGjghF4AvCwkJUXR0tOGcqKgopaWlWRMIACzikwUNt57QsHfvXjcmAQBjKSkpev311w3nTJgwQYGBgVmuBQcH64UXXtCBAwe0ceNGDR061MyYAOBW/v7+Cg4OdjhOQQMAAACQe0b7V2d7X1fghAYAcKxPnz669957HY4fO3ZMixcvti4QAFjAJ+GGeZ0AACAASURBVAsa7rnnHhUvXlx2u11Xr17Vzz//7JLXrVu3rpo2bapHH33UJa8HAPPnz9epU6ccjtesWVNPP/20w3GbzaZWrVrpjjvuMCEdAHgOoxubRj14XcHZU2p/bgMEAAAAeDKj/asVJzQY7d8paADg6wICAjRp0iTDOdHR0UpKSrIoEQCYL8DdAdzl888/z9wclylTxiWvGRMT45LXAQBJSkhI0IQJEwznTJ48Wf7+/hYlAgDPVbRoUV2+fDnHMbNPaDC6qVukSBEVLlzY1PcHAAAAXImWEwDguWJjY1WjRg3VqVNHe/bsyXHOr7/+qtmzZ2vkyJEWpwMAc/jkCQ3SH20nmjZtqqZNm6pEiRLujgMA2cyYMcPwKMdGjRqpXbt2FiYCAM9l9KSWOwsaaDcBAAAAb+PJBQ2c0ADAVx0+fFj9+/dXRESEhgwZoilTphjOnzx5sq5evWpROgAwl88WNACAJ7t06ZKmTZtmOGfq1Kmy2WwWJQIAz+apLScoaAAAAIC3MdrDXr9+XYmJiaa9d1pamlJSUhyOU9AAwJfY7XZ9/vnnatu2rapVq6aYmBglJydr27ZtKlOmjJo3b+5wbVxcnP71r39ZmBYAzENBAwB4oClTpighIcHheLt27dS4cWMLEwGAZ+OEBgAAAMA1nO1hjU6TvF3O9u60nADgK3755Rc99NBDeuyxx7Rhw4Zs4zNmzHB6SsP06dN14cIFsyICgGUoaAAAD3PmzBnNnj3b4bjNZtPkyZMtTAQAns/oxiYFDQAAAEDuOdvDmtl2wtnenRMaAPiKChUq6PLlyw7HV65cqYoVK6pr164O5yQmJmrixIlmxAMAS1HQAAAeZty4cYbHK/bo0UO1atWyMBEAeD53tpwwekKNggYAAAB4m7CwMMNxM09ocLZ3p6ABgK8IDAzU0KFDHY6npqZq1qxZmjhxovz8HH/UN2/ePJ08edKMiABgGQoaAMCDHD58WIsXL3Y4HhAQoPHjx2vhwoWaO3euqX0rAcCbeGrLCWc3gwEAAABPExwcrGLFijkc54QGALBG//79DX/uzZ07VxEREerTp4/DOWlpaXr99ddNSAcA1qGgAQA8SFRUlDIyMhyODxgwQBUrVlRUVJQGDRqkO++8U1FRUTp//ryFKQHA87ir5YTdbqflBAAAAAoco32sOwsajPb9AFDQhIaG6vnnn3c4HhcXpyVLlmjs2LEKCgpyOG/FihX68ccfzYgIAJYIcHcAV1q6dKlpr+3v769ixYqpZMmSCg0N1X333afAwEDT3g+A7/n222+1Zs0ah+OFCxdWVFSU/vOf/2QWMFy5ckWTJk3SP//5T/Xo0UPDhw/Xgw8+aFVkAPAY7jqh4fr160pOTnY4TkEDAAAAvFHZsmX1888/5zjGCQ0AYJ2XXnpJs2bNkt1uz3F8+vTpGjhwoIYMGaI333wzxzl2u11jxozRJ598YmZUADBNgSpo6NOnj2w2myXvFRAQoOrVq6tx48Z6/vnn9dBDD1nyvgAKJrvdrlGjRhnOGT58uMLDw3PcmKampmrJkiVasmSJTpw4oapVq5oVFQA8ktGNTWd9eG+Hs5u5FDQAAADAG7nrhAajvXuhQoVUqFAh094bADxR1apV1blzZ61duzbH8RMnTuiTTz5RZGSkYmJilJCQkOO8Tz/9VF9++aWaNGliZlwAMEWBbDlht9tN/0pNTdW+ffs0e/Zs1alTR40aNeLIHgD5tnXrVn3xxRcOx0uVKqVXXnlF27dv1759+xzOe+yxxyhmAOCT3NVygoIGAAAAFESe2HKCdhMAfNWIESMMx998802VKVNGr7zyiuG8yMhIhyc9AIAnK5AFDTabzbKvmwUOX3/9terXr68ZM2a4+9sH4GUyMjIUGRlpOCcyMlIlSpRweGzYTc42twBQULmr5YSzm7llypQx7b0BAAAAs3hiQQPtJgD4qkaNGqlevXoOx3fs2KE9e/Zo+PDhCgsLczjvq6++0qeffmpGRAAwVYEraPjzSQpGY46+8rLm1uKGGzdu6OWXX9aCBQus/JYBeLnVq1drz549DscrVKigIUOG6OjRo4Z9zh544AG1atXKjIgA4PHc1XIiNjbW4VhoaChH4gIAAMArGX0gZrQHvl1Ge3cKGgD4KpvNppdfftlwzptvvqmiRYvqH//4h+G8yMhIpaenuzIeAJiuQBU0ZGRkZH5NnTpVwcHBkv4oSggICFDHjh01Z84c7dq1S+fOndP169eVmpqqy5cv6/jx41q7dq0iIyNVpUqVzMIGm82mBg0a6NChQ0pNTVVsbKz279+vd999V0888YT8/PyyzLXb7RoyZIjhkfAAcFNqaqrGjBljOCc6OlohISFOT4AZMWKE/PwK1I91AMg1o+NnExMTTTtS0ejpNNpNAAAAwFs5O6HBrP01LScAIGfdunVTRESEw/FVq1bp7Nmz6t+/vypXruxw3oEDB7Ry5UoTEgKAeQrcJ1/p6enq1q2bIiMjlZKSIkl6/vnn9b///U/r1q3TgAED1KBBA5UrV06FCxeWv7+/QkNDVbVqVXXq1EmTJk3SiRMntG7duszCht27d+uRRx7Rjh07VLp0aVWvXl3PPfecVq1apWPHjqlRo0ZZNvGpqakaN26cu/4nAOBFFi9erOPHjzscv/fee9WnTx9dunRJS5YscTgvLCxMPXv2NCMiAHgFo6e17Ha7kpKSTHlfChoAAABQEBntZW/cuKH4+HhT3peWEwCQs4CAAP397393OJ6WlqZZs2YpKChI48ePN3ytf/zjH7px44arIwKAaQpcQUO/fv20du3azFMZli5dqgULFhgek5aTjh07au/evWrYsKHsdruuXbumTp066eDBg1nmValSRTt27FCrVq0yW1BI0kcffaRjx4657PsCUPAkJSUpOjracM7EiRMVEBCguXPnGn4YN3jwYIWEhLg4IQB4D2c3N81qO0FBAwAAAAoiZ3tZo33w7aDlBAA41rdvX8PTaubNm6fr16+rR48eqlGjhsN5p06d0vz5882ICACmKFAFDRs2bNDixYsl/dH+ITo6+raeWC5evLg++eQTlStXTjabTb///ruee+65bPP8/Py0cuVKlSpVKsv1zZs35/u9ARR8s2bN0rlz5xyO16lTR926dVNKSopmzZrlcF5QUJAGDRpkRkQA8BrObm4aPel1OyhoAAAAQEHkroIGTmgAAMdKlCihvn37Ohy/du2aFi1aJH9/f02ePNnwtSZMmGDawx8A4GoFqqDh1h/Q5cuX16uvvnrbr1myZEm9/vrrmS0l9uzZk2OhQsmSJTV48OAspzTs2LHjtt8fQMF09epVTZkyxXDOlClTMgumfvvtN4fznnnmGYWHh7s6IgB4FWf9dCloAAAAAHKvdOnSmfc4c+KOggZne34A8AV///vf5efn+KO9GTNmKD09Xe3bt1fDhg0dzrt48aJmzJhhRkQAcLkCU9Dw66+/6quvvpLNZpPNZlPXrl3l7+/vktd+8skn5efnl7mJ/+CDD3Kc16VLl8w/2+12HTlyxCXvD6DgmTZtmuLi4hyON2/eXI899pjsdrvefPNNw9caPny4q+MBgNdx1wkNsbGxDscoaAAAAIC3CggIUOnSpR2Oc0IDALhHlSpV1K1bN4fjJ0+e1Pr162Wz2TR16lTD15o2bZouXbrk6ogA4HIFpqDhu+++k6TMkxSqV6/ustcuXbp05g1pu92ub7/9Nsd5Dz74YJYe9leuXHFZBgAFx/nz551Wv06ZMkU2m02ff/659u/f73Beq1atXPrzDgC8lbObm2Yco5iRkWFY0BAWFuby9wQAAACsYrSfNdoH3w6jfTsFDQDwhxEjRhiO33xArkmTJmrbtq3DefHx8U6LHgDAExSYgoZTp05l+XupUqVc+vqhoaGZfz59+nSOc/z8/FSmTJnMv1PQACAnEydOVGJiosPxrl276i9/+YskOT2dwdnmFQB8RUhIiOGRuGac0BAXF6f09HSH45zQAAAAAG9mtJ+l5QQAuE+DBg30yCOPOBz/6quvtHv3bklZW7XnZNasWTp79qxL8wGAqxWYgoakpKQsf3d1lfDly5cdvtetSpQokfnnm6dFAMBNJ0+e1Pz/x96dR0dZ3/3/f032BAgJYUBQZLUsCQIJ1oVvbY8irojiHqxLtd7iLrb2ttV662mt9q5LXWqL3uqpsghyo4Ib4FKt4jYBFcIaVpFlsgDZyDIzvz/4kZuQXJ8rmcw16/NxDueUec9c8+aPyocrr+v9njnTsp6UlKQ//OEPkqTS0lK98847lu/Nz8/XGWecEfIeASAWuVwu4xNbTgQa7G7iEmgAAABALIu2QAMTGgDg/9g96Pb4449LOjhZvLi42PJ9DQ0NevDBB0PaGwCEWtwEGg6NQDv0ZN6KFStCdu0ffvihVUDi8CkMRzr80J2VlRWyHgDEh9///vdqbm62rF9zzTUaOXKkJNmupZgxY4bxaWQASDSmG5xOrJwg0AAAAIB4FolAAysnAKBjLrjgAg0aNMiy/tprr7VMG3/wwQeVkpJi+d4XXnhBa9euDXWLABAycRNoOProo1v+dyAQ0Ouvv26cpNAZs2bNajVt4fDvOtLhkxxMwQcAieebb77R7NmzLevp6em6//77JR2cMvPPf/7T8r19+/bVtGnTQt4jAMQy0wjacE9oSE5ObrWyDAAAAIg10TahgZUTAPB/UlJSdPvtt1vWfT6fnnrqKUnS0KFDdcMNN1i+1+/367777gt5jwAQKnETaJgwYYLS0tJafl9RUaHf/e53Xb7ujh079Mc//lEul0uBQEAul0unnXZau+/dvn279u/fL+ngpAhTOg5A4rnvvvuMq2huvvlmHXvssZKkZ599Vg0NDcb3pqenh7xHAIhl0bRywu12Kykpbo7aAAAASEDhDjQEAgFWTgBAJ1x33XXKzs62rD/33HMtP7O69957jVPFX3vtNZWUlIS8RwAIhbi5y5qdna1Jkya1hA4CgYD++te/6pFHHgn6mjt27NAZZ5zR8h/8Qy677LJ23//ll1+2+v2oUaOC/m4A8WX79u1avHixZb1Hjx665557JEkHDhzQM888Y/nejIwM3XjjjSHvEQBiXbhXThy+kuxIrJsAAABArDOdaSsqKuTz+UL6fQ0NDcZrEmgAgNZ69OhhOXkhMzNTxcXFOnDggCSpX79+xokOkvT3v/895D0CQCjETaBBOribPjk5WZJaQg2//e1vddZZZ2nVqlUdvk5DQ4OeeeYZHX/88Vq3bl2r6Qznn3++xo0b1+7n3njjDUlqeQL7lFNO6eKfCEC8ePXVV43TGX7961+3rKmZNWuW8UmHq666Sm63O+Q9AkCsi7YJDQAAAEAsM51p/X6/KisrQ/p9dmd2Ag0A0Natt97a8nMxSTrqqKP0hz/8Qdu2bdOzzz7bKpx29913G9djvvbaa2psbHS0XwAIRkqkGwil8ePH64477tCjjz4ql8vVEkRYunSpxowZozFjxuiss87S2LFjNWjQIGVnZystLU3V1dWqrKzUqlWr9OWXX2rRokWqrq5u+eGjy+WSJOXk5Fg+NV1TU6M333yz5TuTkpIsV1MASDyzZ8+2rOXl5emOO+6QdDAQ9dhjjxmvdei9AIDWTDt1wx1oYEIDAAAAYp3dmXbPnj0hDfLandlN530ASFTHHnusLrnkEq1evVp33XWXLr/8cstVxTk5Obr77rtbJgUfqaqqSu+9954mT57sZMsA0GlxFWiQpD//+c/as2ePXn755VahBklauXKlvvnmG9trHBlkCAQCysnJ0Xvvvaf+/fu3+5nnn3++1WqKn/70py1PWwNIbGvWrNGKFSss68XFxerRo4ckacmSJSotLbV87znnnKORI0eGvEcAiAfRNKGBQAMAAABiXUcCDfn5+SH7PiY0AEBwZs6cqe7du7f8TMvk2muv1b333mu54mfWrFkEGgBEnbgLNLhcLr300kvq16+fHn30Ufn9/lb/ETeNfD/8Goe/f+TIkZo9e7bGjBlj+ZmpU6fqjDPOaPk9Y4YBHGKaziAdDDQcYjedYcaMGSHpCQDikekGZ01NTci/j0ADAAAA4llOTo5SUlLU3Nzcbt10Hg6G3ZmdQAMAtO/Qw3Id0bdvX02cOFHvvfdeu/U333xT1dXVnbomADgtKdINOMHlcunhhx/W559/rpNOOkmBQKDl16GpDaZfh96bnZ2t+++/XytWrDCGGaSDY33y8/NbfnETG4B0MBRlCjQMHjxYJ554oiRp/fr1WrJkieV7jz/+eFbZAIABKycAAACA0HG5XMZzbagDDaycAIDwOPwBuyPV19frjTfeCGM3AGAvLgMNhxQVFenTTz/Vt99+qzvuuENjxoxRcnJyq4DDkb/y8vJ07rnn6sUXX9QPP/yg+++/X2lpaZH+owCIUV9++aU2bdpkWS8uLm6ZCnPcccfp3//+ty688MJ2x4PNmDGjQ2PDACBRhXPlRFNTk6qqqizrBBoAAAAQD6Ip0JCVlRXS7wOARHXBBRcoIyPDsm43cRgAwi3uVk60p6CgoGWMe319vdauXavKykrt3btXDQ0Nys7OVk5OjgYMGKCBAwdGuFsA8aQz6yZcLpcmTJigCRMmqKysTH/961/1wgsvqLa2VkcddZQuv/xyp9sFgJgWzpUT5eXlxjrrxwAAABAPTOdar9cb0u8yndkzMzOVlBTXz+YBQNhkZ2dr8uTJmj9/frv1JUuWyOv1cm8DQNRIiEDD4TIzMzVu3LhItwEgATQ3N+vVV1+1rI8ZM0ajRo1qtzZ06FA9+eSTeuCBB/Tcc88pOztb6enpTrUKAHEhnCsn7J5GY0IDAAAA4kG0TGhg3QQAhFZxcbFloMHn82n+/Pm66aabwtwVALSPWCsAOOTDDz/U7t27LeumXWWH5Obm6u6779aNN94YytYAIC6Fc+UEgQYAAAAkgmgJNJjO+gCAzjv77LPVs2dPyzprJwBEEwINAOAQu0MfKyQAILTCuXLCdPM2IyODJ8gAAAAQF8IZaDCd2Qk0AEBo+Hw++Xw+paen6+KLL7Z836effqotW7aErzEAMCDQAAAOqK+v14IFCyzrp556qo499tgwdgQA8c90k7OhoUE+ny9k32W6edunTx+5XK6QfRcAAAAQKUxoAID4UFtbq6efflrDhw9vWTVhN0F47ty54WgNAGwRaAAAB7z99tuqrq62rHdk3QQAoHPspiKEcu2EXaABAAAAiAems+2+ffvU0NAQsu8yndeZgAYAwdmxY4fuueceDRgwQLfeeqvKysr06KOPKhAI6Kc//an69etn+VnWTgCIFgkTaNi+fbv++c9/avr06TrppJM0bNgw9e7dW+np6erdu7eGDRumk046STfddJNefvllff/995FuGUAMMx32UlJSjOO8AADBsXtqK5SBBq/Xa1kj0AAAAIB4YXe2NZ2LO4sJDQAQOqWlpfr5z3+uQYMG6eGHH1ZVVVVL7euvv9ann36q5ORk41rk7777Tt9991042gUAo7gPNHzwwQeaMmWKhgwZomuvvVYzZ87Ul19+qU2bNqmyslJNTU2qrKzUpk2b9OWXX+of//iHrrnmGg0ePFgXXnihPvroo0j/EQDEmL179+qtt96yrJ911lnKy8sLY0cAkBjsbnKadvJ2lmlCg9vtDtn3AAAAAJFkd7YNZaDBdF4n0AAAnbNx40a98soram5ubrf+2GOPSbKfJDxnzpyQ9wYAnRW3gYb9+/fr5z//uc444wwtXrxYPp9PgUBAgUBALpfL8teh9/h8Pr355ps6/fTTdfXVVxtHxwPA4RYuXGgcuci6CQBwBisnAAAAgNCyO9uazsWdxcoJAAid8847T8cdd5xl/fXXX1dZWZmKioqM75s9e7YCgYATLQJAh8VloOH777/XuHHjWv5De2SIQVLL64f/ktRuuOGVV17RuHHjtGPHjkj+sQDECNO6iaysLJ1//vnavXu3PvvsszB2BQDxL5wrJwg0AAAAIBF069ZNWVlZlvVwBRqY0AAAnZOUlKQ777zTsh4IBPTEE0/I5XIZH8DbunWrli9f7kSLANBhcRdoqKio0KRJk7R58+ZWQYbDgwv9+/fXxIkTdfHFF+vqq6/WxRdfrIkTJ+roo49u9b7DP7tp0yZNmjRJlZWVkf4jAohiO3fu1AcffGBZv+CCC9StWzc99dRTmjBhgk4++WTNnz/fcvQXAKDjomXlBIEGAAAAxBPT+TaUgQZWTgBAaF111VXq1auXZf2FF15QVVWVrrjiCuN1TA/wAUA4xF2gYfr06Vq7dm2baQxjx47V008/rV27dmn79u1asmSJ5s2bpxdffFHz5s3TkiVLtG3bNu3evVvPPPOMCgsLW01tkKS1a9dq+vTpEfuzAYh+8+bNk9/vt6wXFxertrZWzz77rCTp888/16WXXqphw4bp8ccf1/79+8PVKgDEnbS0NKWmplrWQzWhoba21ngtAg0AAACIJ+EKNLByAgBCq1u3brrxxhst63V1dZo5c6aGDx+uoqIiy/fNmzdPTU1NTrQIAB0SV4GGzz77TK+99lqrIENWVpZmzpwpj8ejm266yfYGs9vt1vTp0/X111/r+eefb0n/HprU8Nprr+nzzz93/M8CIDaZ0qp5eXmaNGmS/vnPf7aZ9rJ161bNmDFDxxxzjBYuXOh0mwAQt0xPboUq0OD1eo11Ag0AAACIJ9EQaGBCAwAE5+abbzY+/PHkk0+qsbHRuHbC6/Xq/fffd6I9AOiQuAo0PProoy3/OxAIKCcnR8uWLdP111/fEnLojF/84hdatmyZevbs2er1v/zlL13uFUD82bhxo7788kvL+iWXXKLk5GQ9/vjjlu+pqanR6NGjnWgPABKC6UZnqFZO2AUa3G53SL4HAAAAiAamQIPd2bgzWDkBAKHXv39/40qJH374QfPnz9dll11m/DkaaycARFLcBBqampq0dOnSlkkKLpdLTz75pE488cQuXffHP/6xnnzyyZZrBgIBLV26lH33ANqYM2eOsV5cXKy33npLGzZssHzPlClTNGzYsFC3BgAJIxwTGuyeQiPQAAAAgHhiOt8yoQEAot+dd95prD/22GPq37+/fvazn1m+Z+HChaqrqwtxZwDQMXETaPjiiy9apXhHjBihK6+8MiTXvvLKKzVy5MiW39fU1LB2AkArgUBAs2bNsqwPGDBAEyZM0GOPPWa8zowZM0LdGgAkFNNu3XAEGrKzs5WRkRGS7wEAAACiQThWTvj9fuMPykznfACA2dixY3XaaadZ1ktKSvTxxx8b107U1NRo8eLFTrQHALbiJtCwffv2lv/tcrk0derUkF7/oosuUiAQaPf7AGDlypVat26dZf2KK67QypUr9dFHH1m+Z/z48fp//+//OdAdACSOSE9oMN3sBQAAAGKRXaDh8HumwaqvrzfWmdAAAF1z1113GeuPPfaYLrroIqWmplq+h7UTACIlbgINh24sHzpADx06NKTXHzJkSLvfBwCS/WGuuLhYjz/+uPE9M2bMMO4pAwDYM93oNO3k7QwCDQAAAEgkpjNufX19SILDdmd1Ag0A0DVnnXWWRowYYVlftGiRvF6vzjnnHMv3vP3226qqqnKiPQAwiptAQ0NDQ6vfZ2ZmhvT6h0YHH/ph45HfByBx+f1+zZkzx7I+atQo5eXlae7cuZbvOeaYY3TxxRc70R4AJJRIr5wg0AAAAIB4Y3fGDcWDX3ZndVZOAEDXJCUl6c4777SsBwIBPfHEE8a1E01NTVqwYIET7QGAUdwEGtxud6vf//DDDyG9/s6dOyX93wSI3r17h/T6AGLXJ598oh07dljWi4uL9fTTT6u5udnyPbfffrtxnBcAoGNYOQEAAACEVjQEGpjQAABd9/Of/1x5eXmW9Zdeekknn3yyMUTG2gkAkRA3gYZDB+tDExTef//9kF7/gw8+aPX7vn37hvT6AGKX3SHu/PPP1z/+8Q/Levfu3XX99deHui0ASEjhWDnh9Xota0eGbAEAAIBYZ/dgl+l83FGsnAAA52VmZuqmm26yrNfX1+vll1/WhRdeaPmejz76yPhwHwA4IW4CDePHj28JMwQCAS1btkybNm0KybXLysq0ZMmSluu7XC6NHz8+JNcGENsaGxs1f/58y/pJJ52kf/3rX9q7d6/le6677jrl5OQ40R4AJBxWTgAAAAChlZaWZrxvwcoJAIgdN910k9LS0izrTz31lC655BLLeiAQ0KuvvupEawBgKW4CDX379lVhYaGkg4GDxsZG/eIXv1BTU1OXrtvU1NTqOi6XS+PGjWNCAwBJ0nvvvaeqqirL+uWXX64nnnjCsp6UlKTbbrvNidYAICE5vXIiEAgQaAAAAEDCMZ1znQ40JCUlKT09vcvfAQCQjjrqKE2bNs2yvmvXLnm9XuMEStZOAAi3uAk0SNK0adMUCARafv/JJ59o8uTJ2rdvX1DX27dvn8477zx98skncrlcLde+8sorQ9IvgNhnOrwlJSWpZ8+eKisrs3zPhRdeqCFDhjjRGgAkJKdXTuzbt88YmCXQAAAAgHjkdKDBdFbv1q1by+RcAEDX3Xnnncb6k08+aZzS4PF4tG7dulC3BQCW4irQcPPNN2vw4MGS1BJAWLp0qYYPH66ZM2d2+CZ2bW2t/vGPf2j48OFatmxZy+sul0uDBw827hgCkDiqq6v1xhtvWNYnTpyoF154wXiNGTNmhLotAEhoTk9osLtZS6ABAAAA8SiSExpMZ3wAQOeNHj1aZ5xxhmX9m2++0YgRI4zXmDNnTqjbAgBLKZFuIJRSU1P1t7/9TZMnT5bP52sJNezZs0fTp0/XjBkzdPrpp6uoqEjDhw9Xz5491a1bN9XW1mrfvn1at26dPB6P3n//fdXX17dMZDh0nZSUFP3tb39TampqEqL1CwAAIABJREFUhP+kAKLBG2+8ofr6esv6SSedpAcffNCyfuKJJ+rkk092ojUASFim3boEGgAAAIDgRDLQYDrjAwCCc9ddd2np0qWW9XfeeUeDBg3Sli1b2q3Pnj1b999/PxN0AIRFXAUaJOnMM8/UzJkzdd1110lSy39MA4GA6urqtHjxYi1evNh4jcODDId+73K59Nxzz2nSpEkOdg8glpjWTaSnp6u0tNT4+RkzZnDgA4AQs5vQcOhcFyyv12tZc7lcysvLC/raAAAAQLRiQgMAxJdJkyZp1KhRlvew33nnHf3yl7/Uc8891259w4YN8ng8Gj9+vJNtAoCkOFs5ccg111yjuXPnKicnp1U44dCkBbtfh94rHQwz9OrVS/PmzdPVV18dyT8WgCji9Xq1ZMkSy/rpp5+uhQsXWtYHDhyoqVOnOtEaACQ0083O5uZmNTY2dun6ppu1eXl5Sk5O7tL1AQAAgGjkdrsta6bQb0eZVgUTaACA0HO5XLbrkKuqqox10wN/ABBKcRlokKRLLrlE3333nS677DIlJye3CSuYfh16b0pKiq644gp99913uuiiiyL9RwIQRebPny+fz2dZT01NNdZvv/12paTE3ZAcAIg4u3G0XV07YQo0sG4CAAAA8cp01vV6vfL7/V26PisnACD8pk2bZgysVVVVafTo0Zb1uXPnGu+BA0CoxG2gQZL69++vOXPmaOvWrXrggQf0k5/8RBkZGcbpDJmZmTr11FP14IMPasuWLZo1a5b69esX6T8KgChjSp/27NlTH3zwgWW9R48eLWtxAAChZff0FoEGAAAAoPNMZ93m5mbt3bu3S9dn5QQAhF9GRoZuvvnmVq8lJSXp0ksv1fLly7Vs2TIVFxdbfn7nzp366KOPHO4SAKSEeDy4X79+uu+++3TffffJ5/Np3bp1Ki8vV1VVlWpqatS9e3fl5uaqd+/eGj58OKOCARht2bJFn376qWV9xIgR+uKLLyzrv/zlL5Wdne1EawCQ8OxudppG2XYEgQYAAAAkIruz7p49e9SrV6+gr8/KCQCIjOnTp+tPf/qT0tLS9Mtf/lK33nqrBg0a1FK//PLLdc8991h+fvbs2Tr99NPD0CmARJYQgYbDJScna9SoUZFuA0AMmzt3rrG+ZcsWy1pSUpJuu+22EHcEADiElRMAAABA6HUk0DBixIigr8/KCQCIjD59+ujNN9/USSed1O5DeIMGDdKECRMsH/BbsGCBnnnmGWVkZDjdKoAEFtcrJwDACaZ1E7m5udq9e7dl/eKLL9bAgQOdaAsAIFZOAAAAAE7o1auXkpKsbyWbzskdwcoJAIicSZMmGScKm9ZO7Nu3T++8844TbQFACwINANAJ3333nb777jvLul0SdcaMGaFuCQBwmKysLGO9qysnvF6vZc3tdnfp2gAAAEC0SkpKMp53TefkjmDlBABEr0svvVQpKdYD300PAAJAKCTcyglEjx9++EGbNm3Stm3bVF5errq6Ovn9fvXs2VO5ubkaOXKkCgoKlJqaGulWLZWXl+uLL75QWVmZqqurlZaWpj59+mj06NEaO3asMbmO2DRnzhxj/aqrrtL//u//asOGDW1qp5xyik488USnWgMA6OB6sYyMDB04cKDdelcmNPh8PpWXl1vWmdAAAACAeOZ2uy2nUjKhAQDiV+/evTVp0iS9/fbb7dYXLVqk/fv3G6c8AEBXEGhAWNTU1Ojf//63PvnkEy1fvlzffPONKisrbT+Xnp6uiRMn6rrrrtPkyZONKcBweuutt/Too4/qX//6l/x+f7vv6dOnj6699lr96le/Uu/evcPcIZwQCASMadPjjjtOf/rTn/TQQw/prbfe0mOPPaaPPvqopX7XXXeFoUsAQPfu3R0JNFRUVCgQCFjWCTQAAAAgnpnOu04GGrp3796lawMAuq64uNgy0NDQ0KCFCxfq6quvDnNXABIFj48jLJ5//nmdffbZeuihh/Thhx92KMwgHfyL8K233tLUqVN1wgkn6KuvvnK4U7O9e/fqggsu0HnnnacPP/zQMswgHfyH3COPPKLhw4fr9ddfD2OXcMry5cu1detWy3pxcbFcLpeSkpI0efJkffjhh/r66681bdo0DR8+XFOmTAljtwCQuExPcHUl0GB3k5ZAAwAAAOJZpAINTGgAgMibMmWKMjMzLeusnQDgpKgPNCQnJ0fdr2iZEpBoVq5cqVNOOUWvvvpqRL7f6/VqwoQJeuONNzr1ucrKSk2dOlV///vfHeoM4WJ3KLviiivavFZUVKRXXnlF3377rZKTk51qDQBwGNMNT9NuXjsEGgAAAJDInAw0mM7pBBoAIPK6d+9ufGBv2bJllmuJAKCrov4n86axvohd3bp10/jx4zVixAgNGzZMeXl56tGjhxobG1VZWanVq1dr6dKlKisra/W55uZmTZs2TT179tRZZ50Vtn6bmpo0ZcoUlZaWtqkVFRXpggsu0ODBg1VdXa3S0lK98sorqqqqanlPIBDQTTfdpIEDB+rss88OW98InaamJs2bN8+yXlRUpOHDh1vW09LSnGgLANAO00hapyY0pKamqmfPnkFfGwAAAIh2TgUampub1djYaFln5QQARIfi4mLNnTu33Zrf79e8efN06623hrkrAIkg6gMNkuRyuSLdQgsCFsFJSUnRT37yE5177rk644wzNGbMmA49rf7uu+/qlltuaRVs8Pl8+o//+A+tXr06bP+g+eMf/6jly5e3ei0rK0svv/yypk6d2ub9f/rTn3TLLbfopZdeanktEAjo6quv1rp165Sbm+t0ywix999/X16v17JeXFwcxm4AACaRWDnRp0+fqDqzAgAAAKHmVKDB7ozOhAYAiLxAIKCsrCylpaVZhtBmz55NoAGAI6J+5YR08D+U0fILwbnlllv08ccf6ze/+Y0KCws7PHr/rLPO0vLly9s8+b5t27awrZ7Yvn27HnnkkVavJScn65133mk3zCAd/IfWiy++qOuvv77V616vVw888IBjvcI5pnUTLpdLl112WRi7AQCYOLVywhRsc7vdQV8XAAAAiAWmM29lZaWam5uDuq7dGZ1AAwBETlNTk2bPnq0TTjhBEydONE7U+fzzz7Vp06YwdgcgUUT9hIYXX3wx0i0gwtxut5555hlNnDix1esLFizQdddd5/j3P/744zpw4ECr1+644w6deuqpHfrs0qVLtXXr1pbXnnvuOd17773q3bt3yHuFM+rq6rRw4ULL+s9+9jMdffTRYewIAGASiZUTpqfVAAAAgHhgd+YtLy/XUUcd1enr2p3RWTkBAOG3d+9ePffcc3ryySf1/fffd/hzc+bM0e9+9zsHOwOQiKI+0HD11VdHugVEgdNOO019+vRp9YOE0tJSx7+3sbGx1doIScrMzNS9997boc93795d//mf/6np06e3vFZXV6dXXnlFd9xxRyhbhYMWL15sfFqAdRMAEF0itXICAAAAiGd2Z949e/Y4EmhgQgMAhN/111+vBQsWdPpzs2bN0m9/+1vWcgIIqZhYOQG4XC4NGjSo1Wu7du1y/HuXLl2qqqqqVq9dfPHFysnJ6fA1iouLlZWV1eq1uXPnhqQ/hIdp3URqaqouuuiiMHYDALDj1MoJAg0AAABIZB0JNASDlRMAEH1uvvnmoD63Zs0affvttyHuBkCiI9CAmHHk2of09HTHv/O9995r89rUqVM7dY3s7GydfvrprV776quv2gQlEJ2qqqr09ttvW9bPOecc5ebmhrEjAIAdJjQAAAAAodejRw/j/bhgAw1MaACA6POzn/1MY8eODeqzpgcEASAYBBoQE2pqarR+/fpWrw0ZMsTx7/3000/bvHbKKad0+joTJkxo9Xu/36/PPvss6L4QPgsWLFBTU5Nlvbq6WmvWrAljRwAAO6YduwQaAAAAgOC4XC7judeJQENaWppSU1ODui4AIHgul0szZswI6rNz5syR3+8PcUcAEhmBBsSEv/3tb20mNEycONHR72xqatKqVatavTZkyJCgfmBx8sknt3ltxYoVQfeG8LFLk37wwQcaNWqUzj33XL3//vsKBAJh6gwAYMWJCQ0NDQ3av3+/ZZ1AAwAAABJBuAMNTGcAgMi57LLL1L9//05/bvv27e0+LAoAwSLQgKj32muv6b777mv1WmpqqqZPn+7o927ZskWNjY2tXgt2KkR7nzty4gSiz44dO/TRRx916L1vv/22Jk6cqLFjx2revHnONgYAMDLd9LTbz2vF6/Ua6263O6jrAgAAALHEdO61OzNbMZ3RCTQAQOSkpaXp1ltvDeqzrJ0AEEoEGhCV9u/fr8WLF+v888/XJZdc0iZY8MADDzi+cmLLli1tXjv22GODulb//v2VkpLS6rXNmzcHdS2Ez6uvvtrpiQvffvst0zcAIMKcWDlh97QZExoAAACQCMI9ocF0tgcAOO+GG25QVlZWpz83b968Nj/XAYBgpdi/BXDGm2++qd///vetXmtsbNTevXu1c+dOy8/NmDFD99xzj9Ptaffu3W1eGzBgQFDXSkpKUr9+/bR9+3bj9RFdgk2RXnLJJSHuBADQGaanuOrq6uT3+5WU1Llcr93NWSY0AAAAIBGwcgIAEkuvXr10+umna9GiRZ36XGVlpZYuXapzzz3Xoc4AJBICDYiYyspKffPNNx1+//Dhw/XII49oypQpDnb1f6qqqtq81pVU+JGfraysDPpadrKzs4P+bHV1dQg7iV3r1q2Tx+Pp9OdSU1NVUFDgQEcAgI4y3fQMBAKqr6/v9I1R083Zbt26caMVAAAACcGJQAMrJwAguhUVFXU60CAdfGCQQAOAUCDQgKjXq1cvPfzww7ruuus6/TRlV7SXDs/IyAj6epmZma1+X1dXF/S17BBK6Lo5c+YE9bnRo0crLS0txN0AADrDLoBYW1sb0kAD6yYAAACQKFg5AQCJp7CwMKjPvf7660HdgwGAI4Xvp8NAkCorK3XDDTdowoQJWrx4cdi+t6mpqc1rXQk0HPlZ9kdFr0AgoFmzZlnWU1NTLWtFRUVOtAQA6AS7fyibbphaIdAAAAAAmM++NTU1qq+v7/Q1WTkBANEt2HvedXV1evPNN0PcDYBExISGOLJ//375/f6wf296enqb6QMdcc011+iaa65p9VpNTY0qKiq0cuVKLVu2TC+//LL27dsnSfr88881efJkXXnllXruuee6FC4IlsvlCtlnA4FAV9ux1KNHj6A/y3QH6euvv9bGjRst6+2FXQ4h0AAAkWd309M00taK1+u1rLnd7k5fDwAAAIhFdmFer9erY489tlPXZOUEAES3fv36qW/fvtq9e3e79eTkZPl8vnZrs2fP1hVXXOFkewASAIGGOHL88cdr69atYf/e22+/XU888URIrtW9e3d1795dAwcO1JQpU/THP/5Rd9xxh1588cWW97zyyisqLy/X4sWLlZycHJLvbU97T+EHkzK3+qyTawn2798f9Gezs7MTPtQwe/bsoD8b7PgtAEDoMKEBAAAAcIZdmHfPnj2dDjQwoQEAopvL5VJRUZHefvvtduu9evWyfBDk3XffVUVFhfLy8pxsEUCcY+UEolp2drZeeOEF/eY3v2n1+rvvvquHHnrI0e/Oyspq89qBAweCvt6RgYb2ro/I8/l8mjt3rmW9f//+lrWUlBSNHj3aibYAAJ1gt2eXQAMAAAAQnI4EGjrLdD63O9sDAMLDNJnY9CBoc3OzXnvtNSdaApBACDQgJjz00ENtnnx/+OGHVVFR4dh39urVq81rwYyoPuTIf5yRSIxOH330kXbt2mVZP+qooyxr+fn5EVmFAgBoLSMjw7gmikADAAAAEJzMzEzjqtNQBxqY0AAA0cE0mbimpsb4d0NXJiIDgESgATEiKSmpzZSGuro6zZo1y7Hv7Nu3b5vXvv/++6Cu5ff7tXPnzlav8cOP6GR3uDL9I9uUUgUAhI/L5TLe+OxsQDEQCBBoAAAAAP5/pvNvMIEG0/mcQAMARAe7e9/jx4+3rH388cfavn17qFsCkEAINMSRLVu2KBAIhP3XE088EZY/36RJk9q89vHHHzv2fQMHDmzz2rZt24K61s6dO9XU1NTqtcGDBwd1LTjnwIEDWrBggWX95JNP1saNGy3rBBoAIHqYRtN2dkJDTU2Nce0UgQYAAAAkklAHGlg5AQDR75hjjjGuHTJNNpZkXPMMAHYINCBm5OTkqGfPnq1e27x5s2PfN3jwYKWlpbV6bdOmTUFdq73PDR8+PKhrwTnvvPOO9u3bZ1mfMGGCfD6fZd00dgsAEF6mJ7k6G2iwuylLoAEAAACJJJyBBiY0AEB0cLlcxvvf5eXl7U69PoS1EwC6gkADYkpmZmar31dXVzv2XampqSooKGj1WllZmbxeb6ev9fnnn7d5bdy4cUH3BmeYDlXJycnq3bu3sT5mzBgn2gIABCGUKyfs/u43PaEAAAAAxBvT+bez980CgQArJwAgRpgmFK9cuVKXXnqpsV5aWupEWwASAIEGxIxAIKDy8vJWr+Xl5Tn6nRMmTGjz2meffdbp63z66aetfp+UlKSTTz456L4Qevv379eiRYss65MmTdL69est6yNHjmwTuAEARE4oV07YPWVmCrwBAAAA8SaUExoaGhrk9/st66ycAIDoYZrQ4PV6NXHiROPnmdIAIFgEGhAzSkpK1Nzc3Oo1u71MXTVp0qQ2ry1cuLBT16iurtayZctavTZ+/Hj16tWrS70htBYuXKiGhgbL+rRp01RSUmJZN6VTAQDhF66VE7m5uW1WVAEAAADxLJSBBruzORMaACB62N0D9/v9GjJkiGV99uzZCgQCoW4LQAIg0ICYMXfu3Dav/fjHP3b0OydNmqTc3NxWr82fP1/79u3r8DXmzJnT5h9nl19+eUj6Q+iY0qGZmZk688wztWrVKsv3EGgAgOgSypUTppuyppu5AAAAQDyyCzR05odVdmdzAg0AED0GDhxofFCzpKRExcXFlvXNmzfriy++cKI1AHGOQANiwoYNG/T000+3ef2CCy5w9HvT0tJ01VVXtXqtrq5ODz30UIc+X1tbq4cffrjVa5mZmbryyitD1iO6bvfu3W2maBxuypQp2rJlS5sJIYczjdsCAIRfuCY0EGgAAABAojGdgRsbG7V///4OX4sJDQAQO1wul/E+uMfjMQYaJNZOAAgOgQY4btasWZ1e03C4TZs26YwzztCBAwdavX7aaadp5MiRHbqGy+Vq86ujZsyYofT09FavPfroo/r3v//doc9u3ry51WvXX3+93G53h78fzlu4cKFxX2NxcbE8Ho9l3eVyaezYsU60BgAIkmnXLoEGAAAAIHh2Z+DOrJ2wO5ubzvUAgPAzTSouLS3VyJEjjffK58+fz9oJAJ1GoAGO27Bhg6ZOnarCwkI99dRT2r17d4c+V1VVpf/+7//WmDFjtHXr1la11NTUdic2OOHYY4/Vr3/961av+Xw+nXXWWZZBjbq6Ol133XWaOXNmq9fz8vL0X//1X061iiCZxlzl5ubqzDPPNAYaRowYwRMDABBlmNAAAAAAOCOcgQbutwBAdDkUaMjKytIpp5yiW265RS+++KK++eYbrV+/XpKMUxp27dql77//Piy9AogfKZFuAIljxYoVWrFihe644w6NHDlSRUVFGjVqlHr16qWcnBz5fD7t27dPW7ZsUUlJif71r3+poaGhzXWSkpL0P//zPx2ezhAK9913n5YsWaIvv/yy5bXa2lpNnTpVJ5xwgqZMmaLBgwerpqZGpaWlevnll1VZWdnqGi6XSy+99JJxxxQiY/Xq1Za1yZMnKy0tTSUlJZbvMaVSAQCRYbrxaben90her9eyxtQlAAAAJJq8vDxj3XR+PpLd2TwrK6vD1wIAOG/SpElatWqVRowYoeTk5Hbfc9FFF+nuu++2vMaqVas0YMAAp1oEEIcINCDs/H6/Vq9ebfwhspWsrCzNnDlT06ZNc6Aza2lpaVq0aJF++tOfau3ata1qX331lb766ivj510ul5566imdd955TraJIPj9fpWWllrWx44dq8bGRn333XeW7yHQAADRh5UTAAAAgDNSUlKUl5enioqKduuhmtCQlZWlpCQGDANANOnZs6d69uxpfM/gwYOVnZ2t/fv3t1tfvXq1zj77bCfaAxCnOBHCcaEaDXf22Wdr9erVYQ8zHNKnTx999tlnnQ4l5Obmav78+br55psd6gxdsW3bNuM/ngsKCrR69Wo1NjZavqewsNCJ1gAAXRCqlRN+v9/4hBmBBgAAACQi0zk4VIEG1k0AQGxyuVwaNWqUZT2Yh10BJDYCDXDcr3/9a23cuFF//etfNWXKFB111FEd+pzL5dJxxx2nX/3qVyotLdXbb7+tQYMGOdusjdzcXC1atKhlWoPL5bJ8r9vt1t13361169bpoosuCmOX6Ay7w1N+fr48Ho/xPePGjQtlSwCAEAjVyomqqir5fD7LOoEGAAAAJKJQBRpMZ3MCDQAQuwoKCixrBBoAdBYrJxAWQ4cO1W233abbbrtNkvTDDz9o48aN2rJli6qqqlRbWyuXy9UyrmjAgAEaO3assrOzQ/L9gUAgJNc55LzzztN5550nr9erzz//XJs2bVJ1dbVSU1PVt29fFRQUqLCwkLF4MWDVqlWWtZycHPXr108rV660fM+PfvQj9ejRw4nWAABdEKqVE3Y3Ywk0AAAAIBGFY0KD6UwPAIhu+fn5lrXS0lL5/X5+fgKgwwg0ICL69++v/v3769RTT410K13idrs1efLkSLeBLjClQfPz8+VyufTEE0/oxhtvlMfjUUlJiTwej1asWKG6ujoVFRWFsVsAQEeZnuZqaGhQc3OzUlLsj8IEGgAAAIC2WDkBADAxBRpqa2u1detWDR48OIwdAYhlBBoAJDS7QIMkpaSkqKCgQAUFBbr66qslST6fT+vXrw9LjwCAzrO7+VlbW6uePXvaXsfr9VrWkpOTlZub2+neAAAAgFjndrsta6ycAACYAg3SwfvyBBoAdBTzXAAkLL/frzVr1ljWTXu+kpOTNXLkSI0cOdKJ1gAAXdSRQENHmG7G9u7dm/GIAAAASEimCQ2mUPCRWDkBAPGpX79+ysnJsaybHjQEgCNxBxZAwtq8ebPq6+st63YpUgBA9LK7+RmKQAPrJgAAAJCoTGfh8vJy+Xy+Dl2HlRMAEJ9cLpfxgUECDQA6g0ADgIS1atUqY51AAwDErnBMaCDQAAAAgERlOgv7/X5VVlZ26DoEGgAgfpnurxNoANAZKZFuAAAixXRoysvL4wdVABDD7G5+mnb1Ho5AAwAAANCW3Vl4z549crvdttcxncsJNABA7AgEAtqyZYs8Ho9KSko0depUY6ChtLRUPp9PycnJYewSQKwi0AAgYZkCDfn5+XK5XGHsBgAQSkxoAAAAAJzTkUBDRyZfms7ldmvkAACR9frrr2v58uUtIYaqqqqWWvfu3XXSSSdZfvbAgQPavHmzhg0bFo5WAcQ4Ag0AEpYp0GDa7wUAiH5paWlKTU1VU1NTu3UCDQAAAEDwcnJylJKSoubm5nbrpnP04Vg5AQCx6y9/+Ys+/fTTdmsej0fXX3+98fOrV68m0ACgQ5Ii3QAAREJzc7PWrFljWe/IUwQAgOhmugHa0ZUTXq/XstaREboAAABAPHK5XMbzsOkcfThWTgBA7CoqKrKslZSUqE+fPurdu7fle0wPHALA4Qg0AEhIZWVlamxstKwTaACA2GcaUduRCQ1NTU2qrKy0rDOhAQAAAInMdB4OxYQGVk4AQHQzBRq2bNmiiooK4332VatWOdEWgDhEoAFAQrJLfxJoAIDYZ3qiqyOBhvLycmOdQAMAAAASmdOBBiY0AEB0KywsNNZXrFhhvM/OhAYAHUWgAUBCMh2W+vbtq9zcXP3ud7/T/PnzVVZWpkAgEMbuAACh0NWVE3Y3YQk0AAAAIJF1NdDg9/tVV1dnWSfQAADRbcSIEcrMzLSsezweY6Bh7dq1am5udqI1AHEmJdINAEAkmAIN+fn52rBhgx566KGW13JyclRYWKjCwkIVFRXpvPPOY/QhAES5rk5oINAAAAAAWOtqoMEUZpBYOQEA0S4lJUVjx47V8uXL2617PB7dcsstlp9vbGxUWVmZhg8f7lSLAOIEgQYACcm0nys/P18ej6fVa3v37tUHH3ygDz74QNLBf5jzD2sAiG6m/053NdCQkZHB3wMAAABIaF0NNNidyZnQAADRr7Cw0DLQUFJSYrvaefXq1QQaANhi5QSAhNPU1KT169db1vPz81VSUmJZHzBggNxutxOtAQBCyMkJDW63Wy6XK6i+AAAAgHhAoAEAUFRUZFkrKytTcnKy+vbta/ke04OHAHAIgQYACWfDhg1qamqyrLc3oeFwpkMaACB6mG6A1tTU2H7e6/Va1lg3AQAAgERnethj3759amxsNH7e7kxOoAEAop/dvXK7KQ2m1dAAcAiBBgAJx+6QNHLkSOOEhsLCwlC3BABwgJMrJwg0AAAAINHZnYlNAWHJ/kzOijcAiH4jR45Uenq6ZZ1AA4BQINAAIOGYDkn9+/dXZWWlqqurLd/DhAYAiA1Orpwg0AAAAIBEZ3cmtls7wcoJAIh9qampGjNmjGXd4/GooKDAsr5+/XrjNGUAkAg0AEhApr1cdusmJAINABArurpygkADAAAAYK2rgQbTmTwpKcn4xC8AIHqY7pd7PB7jhIampiZt2LDBibYAxBECDQASjmlCQ35+vnHdRP/+/dW3b18n2gIAhBgrJwAAAADndOvWTVlZWZb1rkxo6N69u1wuV9C9AQDCx7SiecOGDRowYIDx86YHEAFAItAAIME0NDQYE592ExqYzgAAsYOVEwAAAICzTOfirgQaWDcBALHD7p755s2b1b9/f8u66QFEAJAINABIMOuORbaQAAAgAElEQVTXr5fP57Os201oMKVNAQDRpSsrJ+rq6ow3WN1ud9B9AQAAAPHCdC7uysoJAg0AEDvy8/OVlpZmWS8pKVFBQYFlnUADADsEGgAkFLvxVVlZWdq7d69lnQkNABA77CY0BAIBy7rX6zVemwkNAAAAgPlcbHemtls5AQCIDWlpaRo9erRl3ePxKD8/37JOoAGAHQINABKK6XA0YMAArV+/3vh5Ag0AEDtMN0F9Pp8aGxst63ZPkxFoAAAAAFg5AQA4yHTf3C7QsGHDBjU0NDjRFoA4QaABQEIxBRrs1k307dtX/fr1c6ItAIAD7G6Cmm6g2t18ZeUEAAAAQKABAHCQKdCwbt06DR482LLu8/m0bt06J9oCECcINABIKHaBBo/HY1kvKiqSy+Vyoi0AgAPsboKadvaabr5mZ2crIyMj6L4AAACAeNGVQIPpPE6gAQBiS2FhoWUtEAioqanJ+HnWTgAwIdAAIGHU19errKzMsm43ocF0KAMARB+7vbvBTmhg3QQAAABwkF2gIRAIWNZN53G7szwAILqMHj1aqamplvW1a9fq2GOPtawTaABgQqABQMJYu3at/H6/ZT0vL08VFRWWddPYLABA9HFq5QSBBgAAAOAg09m4vr7eeOZm5QQAxI/09HQVFBRY1ktKSpSfn29ZJ9AAwIRAA4CEYXcoqq6uNtYJNABAbOnKygmv12tZc7vdQfcEAAAAxBO7s7HpXM3KCQCIL6YJxx6Ph0ADgKARaACQMEyHokGDBmnNmjWW9d69e+uYY45xoi0AgEOysrKMdSY0AAAAAF1jdzY2natZOQEA8cX0QOCaNWs0bNgwy/rGjRtVX1/vRFsA4gCBBgAJwxRoKCgokMfjsawXFRXJ5XI50RYAwCHJycnKzMy0rBNoAAAAALrGbkJDsIEGJjQAQOwxBRr8fr9SUlIs64FAQGvXrnWiLQBxgEADgIRhCjSMGjXKGGgwjcsCAEQv041Q04hbAg0AAACAvbS0NOXk5FjWTedqVk4AQHwZPXq0kpOT262lpaVZ1g5h7QQAKwQaACSE2tpabdq0ybLev39/415HU7oUABC9TDdCrZ4ICwQCBBoAAACADjKdj1k5AQCJIzMzU/n5+crIyNCJJ56om266Sc8//7xWrFihmpoaXXPNNRo8eLDl5wk0ALBiPd8FAOLImjVrjHWfz2esM6EBAGKT6Uao1Q3Uffv2qampyfJzBBoAAACA/9OnTx+tX7++3RorJwAgsbzzzjvq06eP5XqJ/Px8bd68ud3aqlWrnGwNQAxjQgOAhGBKd7pcLpWXl1vWc3NzNWjQIAe6AgA4LZgJDaabrpL9nmAAAAAgkQQzoaGpqUmNjY2WnyPQAACxqX///pZhBulgoMEKExoAWCHQACAhmA5DQ4cO1bfffmtZLyoqksvlcqItAIDDTDdCrXb2mlYQSUxoAAAAAA5nCvxana1N0xkkAg0AEK9MgYbNmzfb/v0AIDERaACQEEzjqvLz81VSUmJZZ90EAMSuYFZOmCY0uFwu5eXldbkvAAAAIF4EM6HB7gdWpnM8ACB2FRQUGOt2q6MBJCYCDQASgmlCw5AhQ1RZWWlZLyoqcqIlAEAYhHrlRF5ennF0IgAAAJBonAg0MKEBAOLTiBEjlJRk/aNJ1k4AaA93YwHEverqam3bts2yPn78eFVXV2vNmjXyeDwqKSmRx+PRypUrVV9fz4QGAIhhwaycMAUaWDcBAAAAtGY6I3u9Xvn9/jY/vLI6ix9CoAEA4lNmZqaGDBmijRs3tls3TVoGkLgINACIe6WlpcZ6fn6+UlNTdfzxx+v444/XtddeK0lqbm7WunXrNGTIkHC0CQBwQKhXThBoAAAAAFoznZGbm5u1d+9e9erVq9XrrJwAgMSVn59vGWhgQgOA9rByAkDcMx2CkpOTNXz48HZrKSkpys/PN47AAgBEt1CvnCDQAAAAALRmd0Zu73zNygkASFwFBQWWNQINANrDT+kAxD3TmKphw4YpIyMjjN0AAMIpmJUTXq/X8jNut7vLPQEAAADxxO6M3F6gwbRyIi0tTSkpDBYGgHiVn59vWdu2bZuqq6vD2A2AWECgAUDcM6U6TYcnAEDsY0IDAAAA4KxevXoZp1u2Fxg2TWhg3QQAxDe7e/J2K6QBJB6irgDiHoEGAEhcppuhBBoAAACArktOTlbv3r0tz9GdXTnBugkAiC+BQEDbtm1TSUmJPB6PqqqqlJycLJ/P1+77V61apRNPPDHMXQKIZgQaAMS1vXv3aseOHZZ1074uAEDs6+yEBp/Pp/LycsvPEGgAAAAA2urTpw+BBgBAi/Xr1+vFF1+Ux+NRSUmJKioqWmppaWkaOnSo1q9f3+5nTQ8oAkhMBBoAxDW7ww8TGgAgvtkFGvx+f6vxuBUVFQoEApafIdAAAAAAtGU6J7cXaKipqbF8P4EGAIh9O3fu1MMPP9xurbGxUQMGDCDQAKDDrJebAUAcMB1+UlJSdNxxx4WxGwBAuNnt362vr2/1e9O6CYlAAwAAANCezgYaTBMa7M7wAIDoN27cOGM9KyvLskagAcCRCDQAiGumw8+PfvQjpaWlhbEbAEC42T3ddeSNVAINAAAAQOeFMtDAhAYAiH3Z2dnGhwkPHDhgWduxY4f27t3rRFsAYhSBBgBxzRRoYN0EAMQ/u5uhR4669Xq9lu9NTU1Vz549Q9IXAAAAEE/cbrdlrb0zNisnACD+FRUVWdZ27dpl/CxTGgAcjkADgLhmOvi4XC799re/1YIFC7R582bjznQAQGyyG1fbmQkNbrdbLpcrJH0BAAAA8YSVEwCAI5kCDevXr1dKSoplnUADgMNZ/9cCAGJcRUWFMem5bds2zZs3r+X3vXr1UmFhoQoLC3XmmWfqtNNOC0ebAAAHhXLlBOsmAAAAgPaZzsoVFRVqbm5u9YMrVk4AQPwrLCy0rDU0NGjo0KEqKytrt06gAcDhmNAAIG7ZHXq+//77Vr+vrKzUsmXL9Oc//1mvv/66k60BAMKksysnCDQAAAAAnWd3Vi4vL2/1e1ZOAED8MwUapIMPGFoh0ADgcAQaAMQt06EnLS2tTaDhcKZxWACA2JGRkaGkJOsjLxMaAAAAgK6zOysfec5m5QQAxL+cnBwNHTrUsm5a67lq1SonWgIQowg0AIhbpkDD0UcfbfysXXoUABAbXC6X8QkvAg0AAABA14Uy0MCEBgCIH6b77Hv37rWs7d69WxUVFU60BCAGEWgAELdMgYaePXta1jIyMjRy5EgnWgIARECoAg1utztkPQEAAADxpEePHkpPT7esE2gAgMRkmoS8detW42dZOwHgEAINAOKWaSyVz+ezrI0ZM0YpKSlOtAQAiADTDdEjd/d6vV7L9zKhAQAAAGify+UyBoCPPGcfeQ4/HIEGAIgfpkBDQ0ODUlNTLesEGgAcQqABQFzas2ePysvLLeummumQBQCIPaYdvIc/GdbQ0KB9+/ZZvpdAAwAAAGDNdF4+fEJDIBAwTmgwnd8BALFl3LhxxvpRRx1lWSPQAOAQAg0A4pLdYWfnzp2WNQINABBfOrpywjSdQSLQAAAAAJh0NNDQ0NAgv99v+V4mNABA/MjLy9OgQYMs65mZmZY10wRmAImFQAOAuGQKNKSlpRk/W1hYGOp2AAAR1NGVE0fu9T0SgQYAAADAWkcDDaZ1ExKBBgCIN3ZrJ6wwoQHAIQQaAMQlU3rTtNMxLS1N+fn5TrQEAIiQjq6csAs0mP7+AAAAABJdRwMNpnUTEisnACDemB4g3LVrl2WtvLzc9l4NgMRAoAFAXDKlN1NTUy1rxx9/vLEOAIg9HV05YfpHcrdu3XhSDAAAADAIVaCBczcAxJdgJzRITGkAcBCBBgBxJxAIGA86pn84mw5XAIDY1NGVE16v1/J9TGcAAAAAzExnZlZOAEDislvxbHrAMJYDDX6/P9ItAHGDQAOAuLNr1y5VVVVZ1k0/sCLQAADxJxQrJ0xPmwEAAAAwn5lrampUX18viZUTAJBo3G63BgwYYFnPzc21rJlWS0c7n88X6RaAuEGgAUDc6Upq0y4tCgCIPaFYOUGgAQAAADCzOzMfesDELtCQmZkZsp4AANEh2AcJmdAAQCLQAMAgVv/CNaU209LSLGupqakqKChwoiUAQAQRaAAAAACcZ3dmPnTeNq2cyMrKUlISt6wBIN6YAg179+61rK1evVqBQMCJlhwXqz9fAaIRp0MAlmL1L1xTarNHjx6WtYKCAqWnpzvREgAggkyBhsNvphJoAAAAAILndruN9UPnbdOEBtPZHQAQu0yTkRsbGy1rVVVV2rVrlxMtOS5Wf74CRCMCDQAsxepfuKZAg2lvVbBjrwAA0c20g5cJDQAAAEBoZGZmGh8k6UigwXR2BwDErq7ce4/FtRONjY0x+/MVIBoRaABgyfTD/2gVCASMB5x9+/ZZ1gg0AEB86sjKiUAgYAw02D1tBgAAAMAcBGZCAwAkrr59++roo4+2rKekpFjWTCumo9WGDRsi3QIQVwg0ALAUiwnCHTt2aP/+/ZZ1074t09grAEDsMt0UbWhoUHNzs2pra3XgwAHL9zGhAQAAALBnCgJ7vV5Jrde+HYlAAwDEL9P998zMTMtaLE5oWLNmTaRbAOIKgQYAlvx+f8xNaQg2rZmcnKzjjz8+xN0AAKKB3dja2tpa43QGiUADAAAA0BFdndDAygkAiF9WE5JTU1ONgbZYDDSUlpZGugUgrhBoAGC0devWSLfQKabDTWpqqmUtPz9fGRkZTrQEAIgwu6e8CDQAAAAAocHKCQCAlaKiIqWnp+uEE07QjTfeqJkzZ8rj8ai6ulp33XWX5edWr15tnLwcjQg0AKFlvZQGAHRwNNKQIUMi3UaH2QUampqa2q1ZpUMBALHP7qZoTU2NbaChd+/eoWwJAAAAiEsdCTSwcgIAEtOZZ56p6urqdh88zM/Pt/zc/v37tWPHDh1zzDFOthdSrJwAQosJDQCMYi1JaAo01NfXW9ZM+7sAALGtqysncnNzlZaWFuq2AAAAgLjDygkAgJXU1FTLKcqmQIMU/KrpSGhubta6desi3QYQVwg0ADCqq6uLdAsd5vf7jYEG01gqJjQAQPzq6soJ1k0AAAAAHWMXaAgEAqycAAC0MWDAAPXo0cOybrrvH202b96shoaGSLcBxBUCDQAs9ejRQ/fff3+k2+iwbdu2Gf9RbCUpKUljxoxxoCMAQDToaqDB7XaHuiUAAAAgLpkCDY2Njdq/fz8rJwAAbbhcLuOUhlgKNMTa1GsgFhBoAGDJNNEgGtkdaqzGWY0cOVJZWVlOtAQAiAKmkYbSwR2+Xq/Xss6EBgAAAKBj7MLAXq+XCQ0AgHbFS6BhzZo1kW4BiDspkW4AQPTy+/2RbqFTTIeanj17avfu3SotLZXH41FJSYk8Ho+++eYb1k0AQALo3r27qqqq2q2xcgIAAAAIDbuz8549e4yBhu7du4e6JQBAjLALNPj9fiUlRf9z2kxoAEKPQAMAS/EUaCgoKFB6errGjRuncePGtbze1NSk6urqcLQHAIigbt26EWgAAAAAHNa7d29j3S7QwIQGAEhcpkBDbW2ttm3bpkGDBoWvoSARaABCL/qjTAAiJtYCDatWrbKsWR2GUlNT1atXL6daAgBECdON0ZqaGgINAAAAQAikpKQoLy/Psr5nzx7V1NRY1gk0AEDiKigoMNZjYe2E3+/X2rVrI90GEHcINACwFEuBBr/fb9xNZUp3AgDin2l0bU1Njbxer2WdQAMAAADQcabz865du1RfX29ZZ+UEACSufv36KScnx7IeC4GGxsZG/eY3v9E555wT6VaAuEKgAYClWAo0bN682fgPYgINAJDYTE96bd++XT6fz7JOoAEAAADoONP5ecuWLcbPMqEBABKXy+Uy3sePhUBDRkaG7rvvPt1www2RbgWIKymRbgBA9PL7/QoEAnK5XJFuxZbdYYZAAwAkNtON0c2bNxs/63a7Q90OAAAAELdM52e7szeBBgBILIFAQDt27JDH45HH49H27dst32taOR1tysrKIt0CEFcINAAwqqioUO/evSPdhi1ToCEvL099+/YNYzcAgGhjGl1rd1OVCQ0AAABAx5nOz3Znb1ZOAEBi+POf/6wPP/xQHo/HuAb0cGvWrJHf71dSUvQPn9+4cWOkWwDiSvT/vx5ARMVKktCUzszPz4+JKRMAAOfYrZywkpSUpF69ejnREgAAABCXTIEG09lbYkIDACSKpUuX6t133+1wmEGS6uvrbYNx0YJAAxBaBBoAGMXKX7ymCQ2smwAAmG6M+v1+y5rb7Y6J5D8AAAAQLUyBBtPZWyLQAACJoqioKKjP2a2ejhax8qAoECu4OwvA6NVXX9WMGTP07LPPRroVSz6fT2vXrrWsE2gAAAR7Y5R1EwAAAEDndOUMTaABABJDPAcaGhsbtWXLlki3AcSVlEg3ACC6LVq0SJJ02mmnafr06RHupn1lZWVqaGiwrBNoAAAEu4uXQAMAAADQOcGeoZOTk5Wenh7ibgAA0aiwsDCoz5lWT0eLrVu32k4kAvD/sXfn8VGVZ//Hv7NlhSQEwo7sS8ISSMC6VK1UFBWL+/Ko1WqrtQu1rX2otYtYtbb9WbfKU5eKUmy1bq11wbqhqFQxYTEkhIQlLIGwBAjZZzm/PyhomCVzJnNmJpnP+/Xq63mYs8xlZnLuO/e5znWZQ4UGAGGpqKiIdwhBdTaJefnll/WPf/xDW7dulWEYMYoKAJBIIn3SKy8vL8qRAAAAAD1bpAkNmZmZstlsUY4GAJCIRo0apZycHNPHdYcKDbSbAKKPhAYAYdm5c6f2798f7zAC6mwSc9999+mCCy7Q8OHD1b9/f82ePVs/+9nPtGfPnhhFCACIN1pOAAAAALERaVIw7SYAIHnYbLaIqjSsX79eXq/Xgoiip7q6Ot4hAD0OCQ0AwpaoVRrMZGXu3btXb7zxhn7zm9/I6aTrDgAkC1pOAAAAALGRk5MT0ZpLpHN2AED3FElCQ1tbW8JXQCChAYg+EhoAhK0nJDQcMWrUKPXp08eCaAAAiYgKDQAAAEBs2O32iKo0UKEBAJJLcXFxRMclatuJ1tZWSbScAKxAQgOAsJWXl8c7BD9ut1uVlZWmj4t0sgQA6J5IaAAAAABiJ5J5NAkNAJBcIqnQIEllZWVRjqTr9u7dq8zMTI0dO1bLli2LdzhAj0NCA4CwJWKFhqqqKrndbtPHRTpZAgB0T7ScAAAAAGInknk0LScAILmMGTNGvXv3Nn1cIlZoqKiokM/nU3V1tRobG+MdDtDjkNAAIGyJWKEh0skLFRoAILlQoQEAAACIHSo0AAA6Y7fbI3rwMBETGhLx3gnQk5DQACBsNTU1CZddGOnkhQoNAJBcIl0cjaT3LwAAAJDsIplHk9AAAMknknX6ysrKiKo2WykRq1sDPQkJDQBMqaysjHcIHUSS0DB8+HD17dvXgmgAAIkqksXR1NTUiEofAgAAAMmOCg0AgHBEUknZ7XarqqrKgmgiR4UGwFokNAAwJdEG5kgSGmg3AQDJJ5J+vP3795fNZrMgGgAAAKBniyShIZI5OwCge4t0rT7R2k4k2n0ToKchoQGAKYk0MLe1tWnDhg2mj6PdBAAkn4yMDNPHRLIICwAAAIAKDQCA8IwdOzai638iJTQcPHhQO3bsiHcYQI9GQgMAUxKpF9SGDRvk9XpNH0eFBgBIPna7Xenp6aaOIaEBAAAAiAwJDQCAcDgcDk2bNs30cYmU0LB+/fp4hwD0eCQ0ADAlkSo0RDppoUIDACQnsyVsSWgAAAAAIkPLCQBAuCJZr0+khIZEumcC9FQkNAAwZePGjWptbY13GJIim7QMHTqUG1QAkKTMPvHFeAEAAABEhgoNAIBwRVJRecOGDWpra7MgGvNIaACsR0IDAFN8Pp+qqqriHYYkqayszPQxtJsAgORldoE0Ly/PokgAAACAni0zM9N0yzcSGgAgOUWyZu/1erVhwwYLojEvkdp0Az0VCQ0ATEuUjMNIKjTQbgIAkhctJwAAAIDYMTufpuUEACSn8ePHm06CkxKn7USi3C8BejISGgCYlggDdGtrqzZu3Gj6OCo0AEDyouUEAAAAEDtm59NUaACA5OR0OjV16lTTxyVCQkNzc7O2bNkS7zCAHo+EBgCmJUIJpfXr18vn85k+jgoNAJC8SGgAAAAAYoeEBgBAuCJ5EDEREhoqKytlGEa8wwB6PBIaAJiWCBUaIpmsDBo0SIMGDbIgGgBAd0BCAwAAABA7JDQAAMIVyYOIZWVlFkRiTiLcKwGSAQkNAEzbsGGDPB5PXGOIZLJCuwkASG5me/Lm5eVZFAkAAADQ85lNaDA7XwcA9ByRrN1v3LhRra2tFkQTPhIagNggoQGAaW63Wxs3boxrDJFUaKDdBAAkNzNPfPXu3VtpaWkWRgMAAAD0bFRoAACEKz8/X6mpqX6vOxyOoMf4fD6tX7/eyrA6lQjtuYFk4Ix3AAC6p/Lyco0fPz5u7x9JQgMVGgAguZl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"text/plain": [ "" ] }, "execution_count": 1, "metadata": { "image/png": { "height": 500, "width": 500 } }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(\"MaxwellVacuum/example_parfiles/Ex-convergence.png\", width=500, height=500)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.866880Z", "iopub.status.busy": "2021-03-07T17:08:39.865992Z", "iopub.status.idle": "2021-03-07T17:08:39.874140Z", "shell.execute_reply": "2021-03-07T17:08:39.874605Z" } }, "outputs": [ { "data": { "image/png": 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fCTXJbvny5XLjxg1deu/evS1+C6qKr6+vvPrqq3Lo0CFp3769zeUBgCNomiYjR47UpdetW1e++OILZR6mnQDcAwENAAyrVq1asnPnTnn22WeVn2/atElmzJiRx7UCAECtb9++MnjwYPH2tn1Wt7Jly8oHH3yg/GzFihU2lw8AuZk0aZLs27dPl+7h4SGLFi2SLl262HV5wcHBsnXrVnn99dftWi4AAI+6c+eOLs3T0/lD5MuXL1emT5gwwe7L8vX1tXuZAGAPkZGRyqmBJk+eLDVr1hQvLy/dZwQ0AO7B9tFWAHCgYsWKycqVK6Vhw4Zy4sQJ3edjx46Vvn37SokSJZxQOwCAPWmaJn/99ZccPnxYEhISJC0tTYoUKSLBwcFSp04dqVixorOrmKf69u0rH3/8sS49OjraCbUB4E6uXbtm8gbK6NGjpWvXrg5Zrre3t0yfPl153g/kB5zrAM6naZqzq6C0a9cuXVqdOnWkUqVKTqgNAOS9O3fuyEcffaRL79Spk7Rs2VJE/p7i8/FrBQIaAPdAQAMAwytevLhERkZKkyZNJDMzM9tnN27ckG+//VZ5w8dSBw8elHXr1skff/whJ06ckEuXLsmdO3fE09NTihUrJhUrVpS6detKq1atpFOnTlK4cGGbl2mO+/fvy8qVK2XNmjVy6NAhOXfunKSkpIifn5888cQTUr9+fYmIiJCIiAizX/l75coVWbp0qWzZskX++usvSUxMlPv370vp0qWlXLly0rp1a+nRo4f84x//cNjviouLkxUrVkh0dLQcO3ZM4uPjH67vokWLSsWKFaVWrVoSHh4uXbp0ydOgFU3TJCoqSlasWCF79+6VU6dOSXJysvj5+Unp0qWlQoUK0rZtW+nYsaPUqlXLrsu+efPmw+X++eefEh8fL8nJyZKSkiIFChSQQoUKib+/v1SoUEFCQkKkRo0a0qhRI6lfv36O22RMTIysXLny4f+3b9+u+865c+dk/PjxZtWzefPm0rx5c8t/4CPS09Nl/fr1sm7dOjl48KCcPn1abt26JRkZGRIUFCSNGjWSn3/+OddysrKy5M8//5QdO3ZIbGysHDt2TM6fPy/Jycly+/ZtKVSokJQsWVJKliwpNWrUkObNm0t4eLiEhoZaVW8jrktbOXObFxGJj4+XqVOnyoIFCyQhIcHk92rUqCGDBg2SIUOGZNveFy9eLIsXL8723bCwMLPbwKiCg4OlQoUKcv78+Wzply9fdsjynL0diPw9d/CSJUtk/fr1EhMTI5cvX852s6d27drSvn17iYiIkCJFijikDnnFyMdBe+2fHzByuxr1/M/ebWCpiRMnSkpKii69cuXKMnbsWIct9wFrj9EPGLl/GWFfazTu0A+Nfq5j1DYwUn85efKkrF27Vnbt2iXHjx+XixcvSkpKimRkZEiRIkWkePHiUqVKFalZs6Y0atRI2rZtKwEBAXZbvlHbyFaO3l+vWrVKd6MrJiZG972YmJhc+1O/fv0cFnSUmZmpnOLJGcEM169fl2nTpunSO3bsKPXr17fbcr755htJTEzMlla1alV5+eWXzcrvqLETEeNc8xu13+fXcUo435dffimXLl3Klubt7S0TJ058+P+wsDBdQINqvw4gH9IAwEpbtmzRRET3N2bMGIcsb8CAAcrlBQcHaxkZGVaVmZmZqc2bN08LCwtTlm3qr0iRItro0aO1mzdvWv17wsPDc1x3WVlZ2jfffKM98cQTZtUpJCREW7NmTY7LvHr1qjZ8+HCtQIECZpXZs2dP7dKlS1b/RpXo6GitTZs2Fq3vggULaq+88ooWFxdn07JzW+eapmmrVq3SatSoYXbdunXrpp06dcqmemmapp05c0br3bu35uPjY9G6efDn4+OjdenSRVuxYoWy/B9//NGqck395dTPc1vPGRkZ2tdff62VLVs2x2UUK1bM5DLu3r2rLVu2TOvZs6dWvHhxq35Dq1attKioKIvbKi/X5ZgxY3TfDw8PN7uuRt7mNe3v/dzUqVO1QoUKWbTOgoODtY0bN9ptPRnZM888o/ttvr6+FpVh9O1A0zQtNTVV++STT7TChQubtfySJUtqX375pZaenv6wDFfZDox8HLTH/vlRRm1XI5//2bsNrHHv3j2tVKlSyuVGRkY6bLn2YOT+pWmO39devnxZ8/Ly0pUzfvx4m37bo9LS0rTSpUvrljFgwACLynGXfmjkcx0jt4GmOf/cRNP+XkcLFizQ6tWrZ9E6EhHNw8NDa9KkiTZnzhztzp07Vi/fWW3kaHm1vzY1jmTN35YtWxy2Pq5cuaJcZq9evRy2zJw89dRTurp06NDBbuWfOnVK8/Dw0C1jxowZueZ19NiJpuXtNf/jjLxvzq/jlNbK63Hx/O7cuXOan5+fbn2+/vrr2b73r3/9S7nebT2XB2B8BDQAsFpen7gdPXrU5Ant9u3bLS4vJibGqoGJR//KlCmTbaDJEjldKCQnJ2vt27e3uD4eHh7axIkTlcvbsWNHroNypn7jyZMnrfqNj7p79642ZMgQ5UWruX+FChXSvv76a6vrkNM6T0tL0wYPHmxVvfz9/a3aBh+YPn268qTdmr969eopl2GUgIYrV65ojRs3NmsZpgaJz549q/n7+9vtt/Tp00dLTU01u73yS0CDM7f5B8vv06eP1evNy8tLmz17tl3Wk5E1aNBA99uKFy9uURlG3g40TdNOnjyp1axZ06o6NG7cWLt69aqmacbfDox+HLTH/vlRRm1XI5//2bsNrLVkyRLlMsuWLaulpaU5bLm2MHr/yst97QsvvKArIzQ01Orf9bjly5cr62nJjT536YdGPtcxchsY4dxE0zTtzz//tHkdPfgbO3asxct3dhs5Sl7vr10loOHatWvKZTZr1sxhy8zJ1KlTlfuk8+fP26X8Dz/8UNmut27dyjFfXoydaJrzAhqc3e/dbZzSVgQ02FevXr1067JYsWIPrwsfWLp0qXK9L1++3Ek1B5BXPAUAXMRTTz0lTZo0UX62evVqi8r63//+J40aNZL9+/fbVKfLly9L+/btZe7cuTaV86jU1FRp06aNrFu3zuK8mqbJ6NGjZd68ednSN23aJO3atdO9tsscly9fllatWuleBWiJmzdvSnh4uPzwww82zVeZmpoqb731lgwZMsSu816mp6dLRESEzJw506r8ycnJ0r59e9mzZ4/FeSdOnChvvPGG3L1716plu5LExERp2rSpcm5QS9y9e1eSk5PtVCuRRYsWSfPmzeX69et2K9PonLnNi/y9rxowYIAsWrTIqvwif7+WdfDgwbJmzRqry3AFZ86c0aU98cQTdinb2duBiMjRo0elWbNmEhsba1X+Xbt2SYsWLey6T3AEox8H7bV/fsCo7Wrk8z97t4EtVqxYoUzv1auXFChQII9rkzuj96+83tcOHDhQl3bixAm7bVuPX2eI/P1K9PDwcLPyu0s/NPK5jpHbwAjnJiIikZGR0rhxY5vX0QOW7lOM3Ea2MPr+2plKlCghnp76Yfo9e/ZIUlJSntdnwIAB4ufnly0tMzNT5syZY3PZ6enpym2yV69e4u/vbzJffh87MXK/z4/jlDCWnTt3ypIlS3TpH3/8sW76prCwMGUZj08vBCD/8XZ2BQDAEp06dZLff/9dl75t2zazy1i8eLH07dtXMjMzlZ/7+flJo0aNpGrVqlKqVCnRNE0SEhJk165dcuzYMd33MzMz5bXXXpOgoCB5/vnnzf8xJvTv3183ABMUFCQtWrSQsmXLSuHChSUxMVGio6PlyJEjyjJGjBghrVu3lvLly8vx48elW7duuou+unXrSt26dSUoKEgyMzPl3LlzsmHDBrl586auvAsXLsioUaNk/vz5Fv+e+/fv5zqoVL16dalfv76UK1dO0tPTJT4+XrZu3SpXrlxRfn/mzJni4+OjnNfRGkOGDJHffvstW1qhQoUkPDxcQkJCJCAgQFJSUuTMmTOyefNmuX37tq6M1NRUee211+TAgQNmD/bv3LlTPvzwQ5OflytXTurWrSuVKlUSf39/8fb2luTkZElKSpKTJ0/KX3/9Jbdu3bLsxzpJVlaWdOvWTU6fPp0t3dfXV5o3by6VKlWSwMBAycjIkLNnz8rOnTst/m1eXl5SrVo1CQsLk1KlSkmxYsWkQIECcuvWLYmPj5d9+/bJuXPnlHn37dsnr7zySrZ5MvMzZ23zD0yfPt3kAH+pUqWkb9++0rVrV6lSpYoEBgbK9evX5ezZs7Jy5UqJjIyU+Ph4Efl7uxowYIC8+OKLFi3fVRw+fFgZaFO5cmW7lO/s7eDatWvywgsvmJxL3MvLS5599lkJDQ19uB2cPn1atm7dKmlpaQ+/FxsbK/369ZO6detatPy8YvTjoL33z0ZtVyOf/+XFMdJcmqbJhg0blJ917tzZIcu0hdH7l0je72s7d+4sJUuWlBs3bmRLnzdvnjRu3Nim33L9+nXlzfX+/fuLh4dHrvndqR8a9VzHyG0g4vxzExGR77//XoYPH57jjfJKlSpJgwYNJDAwUEqWLCm3b9+WGzduSGxsrMTExMj9+/ctXu4DRm8ja7nC/tqZPD09pXLlynLq1Kls6WlpafL555/LlClT8rQ+JUqUkF69euluis+ZM0c+/fRTZfCFuVatWqVs0yFDhpjMk9/HToze7/PbOCWMRdM0GTlypC49JCRE3n77bV161apVxcfHR3esPXjwoMPqCMAg8vJ1EADyF2e8WmvXrl3KZfr5+WkZGRm55v/jjz9MzrMXFhamLViwQLt//77J/EeOHNG6du2qzB8UFKQlJiaa/VtUr3KrVq2ark7r1q3TMjMzlWVs375dCw0NVdZn4MCBWlpamlarVq2HaZ6entrAgQO1c+fOKctLS0vTvvjiC83b21tZ5p49e8z+fQ+8//77Jl8T17JlS23//v3KfBkZGdqaNWu0J5980mT+lStXWlQX1Tp/fF7W4OBgbe7cudq9e/eUZaSmpmqfffaZyfn9pkyZYnZ9TL1KsGPHjmav6yNHjmhffvml1rBhQ00k59cmPsqRr2ZXredy5cpl+39gYKA2c+bMHOeTNfVKz0ennwkODtbefPNNbevWrdrdu3dzrVtMTIw2ZMgQzdPTU7nuzZmz83GOXJeOmHLCmdu8pmnahQsXtKJFiyrL6tmzp+51go9LSUnRhg0bli2fqm5GmmrAWh988IFyPVn6SnQjbgeapml9+/ZVluXp6am999572pUrV5T5rl+/rn3++ee6uqimNzDCdmD046A998+aZsx2Nfr5n73bwBYnT55U/k4fHx+zjrN5zej9y1n72hEjRujyFi9e3ORyzTVt2jRduR4eHtrp06dzzetO/dCo5zpGbwMjnJusXbtW8/LyUpbn7e2tjRgxQouNjc2xjPv372sbN27UXn31Va1YsWKaiPljJEZqI3sz0v5aNQ3FgAED7PArbTNkyBCTv3H8+PFmjXnZ0x9//KGsy5o1a2wq97nnntOVWbt27RzzOHPsRNMce81vpH7vLuOU9sKUE/Yxd+5c5XpcvHixyTy1a9fWfb9y5cp5WGsAzkBAAwCrOePE7c6dOyZvRB47dizXvJUrV1bmHTVqlJaenm52PWbMmKGsR7du3cwuQ3Wh8Ojfyy+/bFadLl26pFWsWFGX38fHRxs1atTD//v6+morVqwwq26RkZHKOg0ZMsTs36dpf8+HZ6q9Pv/8cy0rKyvXMm7fvq1FRESYvDjLbUDwUbmt8+eee05LSUkxq6xffvlF+duqV69uVv7jx48r6/D++++b/Xset3v3bu2zzz4z67t5HdDw6F+jRo20GzduWF3+0aNHtWeeeUZbtmyZyYvo3ERHR2ulSpXS1a1s2bIW7Qs0zfUCGpy1zT/Qo0cPk4Mb5uwTHvj4449z/G1GuJFti+vXr2vFixdX/ra4uDiLyjLidrBp0yZlXQoVKqRt3rzZrDL279+vlSxZ0tDbgasdB23dPxuxXV3t/M/WNrCVqTlpn376aafVyRRX6195ua/ds2ePsg5Lliwx+/eo1K9fX1dm8+bNc83nbv3QiOc6rtYGzjg3uXr1qhYYGKisT9WqVbUjR45YVJ6madqtW7e08ePHa9OmTdNL9EEAACAASURBVMv1u0ZrI3sy2v7aqAENps6jHvyFhYVpM2fO1JKSkvKsTnXr1tXVIyIiwuryzp49q9wWcuojzh470TTHXfMbrd+7wzilPRHQYLvbt29rZcqUUZ6L5eSll17S5fHw8NCSk5PzqOYAnIGABgBWc9aJW0hIiHK5GzduzDHfJ598osw3YcIEq+oxdepU5cmTuQMdOV0odOnSxaLBrl9//TXHiw4R0ZYvX27R7+vYsaOujOLFi+cYGf64xo0bK+vy3nvvWVSXe/fumVxfo0aNMrucnNZ5eHi4lpaWZlG9hg8frixr9+7dueadPn26Ll9ISEiePXXhrICGWrVq2XyBYW0Qw+P27dunFSxYUFdHSwf7XTWgIa+3eU37+4lF1SBLvXr1LA4k0TT1fsrebeAsqqdrRURr3769xWUZbTvQNE2rU6eOLr+np6e2evVqi+qyc+dOzdfX17DbgSsdB+2xfzZiu7rS+Z892sBWEyZMUNatT58+Tq2Xiiv1L2fsa1VvN3n++ectqsOjYmNjlfWZM2dOrnndqR8a9VzHldrAWecmQ4cOVZZRu3ZtLSEhwaL6WMNobWRPRttfGzWgQdM0rWnTpib7xoO/AgUKaK1bt9bGjBmjrVu3zqGBkN9//71u+d7e3trly5etKk+1nfv5+Wk3b940mcfZYyea5rhrfqP1e3cYp7QnAhps989//lO5Dnfu3JljPlPXLNHR0XlUcwDOQEADAKs568TN1MXw3LlzTea5fv26VqRIEV2e7t2721SXNm3a6MocNmyYWXlNXSgEBARY9SpIU4EeIqK98sorFpe3efNmZVl79+41K/+BAweU+WvWrGnVYN65c+c0Pz8/XXklS5bUUlNTzSrD1Dr39/fXzp8/b3Gd4uPjlQOWX331Va55Va+SN3fbsQdnBDR4eno69XWAKo8+HfDgr1OnThaV4YoBDc7Y5jVN0z777DO7XnSePHlSGZRizzZwhs2bN2seHh7KPmTuPvhRRtsOTL2+dtCgQRbXRdNMDwQ6eztwpeOgPfbPRmxXVzr/M8ox0lQwlS1PQTqCK/UvZ+1rJ06cqMvr5eVl9U2o0aNH68orXLhwrk/Ru1s/NOK5jiu1gbP6y7lz55SvMy9UqJB28uRJi+tjKSO2kb0YcX9t5ICGw4cPm5yyJqe/J598UnvppZe0adOmaYcOHbLoxnNOUlJSlPX597//bXFZGRkZuul9RETr379/jvmcPXaiaY655jdiv8/v45T2RkCDbc6ePasMYu/Zs2eueVeuXKlc99ZMIwvAdXgKALiYwMBAZXpycrLJPJGRkXL79u1saQULFpRJkybZVJdPP/1Uuaz09HSryxwxYoSULl3a4nydO3dWpnt6esonn3xicXnNmzeX4sWL69IPHjxoVv4ZM2Yo06dOnSre3t4W16dChQoyatQoXfqNGzdk0aJFFpf3qKFDh0pwcLDF+cqWLSvPPvusLv3PP//MNe+NGzd0aSVKlLC4Dq6ka9eu0qBBA2dXI5vBgwfr0v744w8n1CRvOWObFxFZsGCBLq1Zs2bStGlTi+siIvLkk09Kt27drMprVImJifLyyy+Lpmm6zwYPHiz169e327KctR3MmjVLl+bn5yfjx4+3uC4iIh9++KEEBQVZldeRXOk4aI/9sxHb1ZXO/4xyjExMTFSmG62PuVL/cta+tm/fvuLl5ZUtLTMzU3kszo2pfN26dZMiRYrkmNfd+qERz3VcqQ2ceW6SkZGhSx83bpw8+eSTFtfHUq7URpZypf21EdSsWVOWLl0qhQoVsijfqVOn5KeffpI333xT6tSpIwEBAdK7d2/57bffJDMz0+r6FClSRPr166dLnz17tvJ6JSdr1qyR+Ph4XfqQIUNyzJdfx05cqd/nl3FKGMvo0aPl3r172dJ8fHzkyy+/zDVvWFiYMv3QoUN2qRsAYyKgAYDL8fX1VabfvXvXZJ6ffvpJl9alSxepVKmSTXVp3ry5hISEZEu7c+eO2QMnKqobrOaoU6eOMr1Vq1ZSuXJli8vz8vKSWrVq6dJjY2PNyr969WpdWpUqVaRt27YW1+WBoUOHiqen/tC1atUqq8sUERk+fLjVeZ955hldmjnrSDX4e+TIEavr4QpeffVVZ1dBJzQ0VMqWLZst7erVqxIXF+ekGuUNZ2zz165dk5MnT+rS+/TpY3VdREReeuklm/IbSXp6uvTs2VMuX76s+ywkJMTmwa3HOWM7EBH59ddfdWmdO3e2+oZp4cKF5eWXX7YqryO50nHQHvtnI7arK53/GeUYmZqaqkwvXLhwHtckZ67Uv5y1ry1Tpoy0a9dOlz5v3jyL67Fp0ya5dOmSLn3gwIG55nWnfmjUcx1XagNn9ZclS5bo0vz9/WXo0KFW18cSrtRGlnKl/bVRtG/fXnbv3i1PPfWU1WXcuHFDlixZIh06dJDg4GCZNm2aMmjHHMOGDdOlnTlzRqKioiwqZ+bMmbq0mjVr5hrwlV/HTlyp3+eXcUoYR3R0tCxdulSX/uabb5q1bYSEhCj3DQQ0APkbAQ0AXE7BggWV6ffv31emX7t2Tfbt26dL79Gjh13q07x5c13a77//blVZVapUkfLly1uVNzQ0VJkeHh5uVXkiIlWrVtWlXb9+Pdd8cXFxkpCQoEu39cZEuXLlpEWLFrr03bt3W11mSEiITReM1atX16WpniB4XMWKFXVpa9assem3GFnBggWlTZs2zq6GUpkyZXRp+fmC2Fnb/N69e5Xp7du3t7ouIiJt27bVPYHqqoYNGybbt2/XpRcoUEAWLVokRYsWtduynLUdnDlzRnkc6dWrl9V1ETFeYIsrHQftsX82Yru60vmfkY6Rjz8l9YCp829ncKX+5ax97QOqgIPDhw/LgQMHLKqHKgiiYsWK0rJlyxzzuVs/NOK5jiu1gbP6S1xcnJw4cUKX3rdv3zwJ5nKlNrKUK+2vjSYsLExiYmLk22+/lXLlytlU1uXLl+Wtt96SsLAwq9ZRrVq1pEmTJrr0H374wewyLl68KGvXrtWlm3OjPD+OnbhSv88v45QwDk3TZOTIkbr0UqVKyT//+U+zyvDw8JAaNWro0v/66y/JysqyuY4AjImABgAux1Tggo+PjzJ99+7dypMZ1QWZNVQn05YOEj5Qr149q+vh7++vTLfl9eTFihXTpeU0tccDpl7Zr7qospTqwufy5cty7tw5q8qz9bWyqlcd3rp1K9d8qgGYjIwMadeunXz33XeSlpZmU72MJiwsLM9uhpw7d07Wrl0rCxculG+//VYmTpwo48ePN/mnuvi9efNmntTVGZy1ze/fv1+XVrRoUZufQPH19TU5UOJKPvvsM5kzZ47ys6lTp0qjRo3sujxnbQembvZY+yruB+rWrWvxq3kdyZWOg/bYPxuxXV3p/C8vj5G5MXXT1FSggzO4Uv9y1r72gS5duijLsOQtDcnJybJ8+XJdev/+/cXDwyPHvO7WD414ruNKbeCs/rJnzx5lel4FmrlSG1nKlfbXRlSgQAEZNmyYnD17VlatWiW9evXKdZqfnBw/flxatGihfDNAblRvaVixYoVcvXrVrPxz5szRTX3h6+sr/fv3zzVvfhw7caV+n1/GKWEcc+fOVZ4zjRkzRjmliCmqaSdSU1Pl1KlTNtUPgHFZPlkZADiZqQFVPz8/ZXpMTIwurUiRIlZHGD+uVKlSujRzL+oeFxgYaHU9TF3YWjPPXU5lpqSk5Jrv8OHDyvSnn37a6rrkVsbhw4eVkfu5sWWdi6gv0MxZR3Xq1JFnnnlGN4CWnJwsw4cPlzFjxkivXr2kS5cu0qxZM5MBO65C9cSUvdy6dUuWLVsmixcvlj179lg00G9KUlKSHWpmTM7a5lXTKFSvXj3XmyHmqFmzphw9etTmcpzlm2++kbFjxyo/++ijj+T111+3+zKdtR2ojslPPPGEzfXx9PSUsLAwkzcl8porHQftsX82Yru60vmfI4+RljJ1Pm2PY6u9uFL/cta+9gEfHx/p3bu3fPvtt9nSf/rpJ/nqq6+kQIECuZaxdOlS5dR+AwYMyDWvu/VDI57ruFIbOKu/mHozm2oKC0dwpTaylCvtr43M29tbOnbsKB07dpSMjAzZv3+/bNu2TXbt2iX79++XCxcumF3WvXv3pF+/flKiRAl5/vnnzc7Xs2dPeeedd7I9DJCWlibz5s2TUaNG5Zg3KytLGbjdvXt3ZSDS4/Lj2Ikr9fv8Mk4JY7h9+7byLQyhoaHKwKmcqAIaRP6ediI/PPQCQI+ABgAuJzExUZluKopTFaF/+/ZtuwwsmWLt092WRKI+TjWHpCPKfDyqXkX1as9SpUopL6osZWoeSUtev/soW9aPiHodmft6sylTpkiLFi2Uc1kmJibKtGnTZNq0aeLr6yuNGjWSpk2bSrNmzeTZZ5813FzWubF1PavcvXtXJkyYIF999ZXdnxw10o0be3PWNq9apyVLlrSpLvYuxxkWLlwob775pvKzYcOGyYQJExyyXGdtB6o3oqieILJG1apVLb7xfevWLZk2bZrVy+zXr59y0NydjoMixmtXEfc5/7M3U0/SGem46E79y5bzzAcGDhyoC2i4du2arFmzRiIiInLNr3qbQ7NmzaRKlSq55nW3fmjEcx13agNr+4vqZnCxYsVsfs2/uVypjSzlSvtrV+Ht7S0NGzaUhg0bPky7du2aHDhwQHbs2CFbtmyRXbt25bjtZ2VlycsvvywxMTFm30D39fWVgQMHyuTJk7Olz5w5M9eAhvXr1yu38yFDhpi1bJH8N3biSv0+v4xT5leOup51lAkTJigDQCdOnGhWoO2jcgpo6Nmzp1X1A2BsBDQAcDmXLl1SppsacLhy5Yojq6Nk7YWCI+aBd8bc8qqn21WvhbOGqQsfa9e5t7fzDoVNmzaVWbNmyeDBgyU9Pd3k9+7duydbt26VrVu3isjfr55s2LCh9OjRQ/r06WPz00x5wV7t/8DRo0elQ4cOEhcXZ9dyH1ANlOQXztrmVfuFokWL2qVsUzfgjG758uUycOBA5YDjyy+/LN98843Dlm2k7cBe7WdNOTdv3pRPP/3U6mU2a9ZMOQDkSsdBe9TLaO0q4lrnf/Y+RtqibNmyynQjvbnIlfqXM88zH3jmmWekRo0acuTIkWzp8+bNyzWg4fTp0xIdHa1LHzhwoFnLdrd+aMRzHVdqA2f1l2vXrunS8jLQzJXayFKutL92ZQEBAdKuXTtp166diIjEx8fLvHnzZPLkySYDPG7evCn//ve/dQFvORk6dKhMmTJFNE17mHbixAnZtm2bcgqQB2bOnKlLe+qppyyaeiS/jZ24Ur/PL+OU+ZWjrmcd4ezZszJ16lRdeosWLaRLly4Wl5dTQAOA/EkdJgcABnX79m2Tr9MLDg5WpqempjqySkr379/P82UaiepCydE3Nlx14GLAgAGydetWqVWrltl50tPTJTo6WkaOHCnlypWT/v37y5kzZxxYS9tZGmmdk6NHj0rLli0dFswAx1DtF+31OlBXeK3o4zZs2CC9e/dWBs9ERETI3LlzHfqEjrM48sa3kW4Ku9Jx0B77ZyO2qyud/9nzGGmrChUqKNOPHTuWxzUxzZX6l1GopodYs2aN8kbuo+bPn69LK1SokLz44otmLdfd+qERz3VcqQ2cRTWlSl4GNOTnNmJ/7RzlypWTjz/+WM6cOSO9e/c2+b0ff/zRomkIqlatKq1bt9alqwIWHrhy5YqsWrVKlz548GCzl/tAfho7yc/9HjDl/fff171d1cPDQ/fmF3OVKVNG+cYfAhqA/Mv5jwsAgAViYmKyRYM/ULhwYZOvPXXnV48Zib1uzOXHG3xNmjSRgwcPys8//yyzZs2SzZs3m/064YyMDImMjJQlS5bIlClT5PXXX3dwbZ0rMzNTevfuLQkJCcrPy5YtK61atZKmTZtK5cqVJTg4WEqXLi0+Pj7i6+urHKxu0aKFbNu2zdFVd3u2zgOeE1ebM3P79u3StWtX5aBS27ZtZfHixYZ4qhf2xXEwb3H+Zx1TTzodOHBAsrKyTL462NnoXznr16+ffPzxx9n6RXp6uixatMjktEeapikDGrp27Wr2WwfcrR8a8VzH3drAXvJyX+BubcT+Ou8UK1ZMFi1aJH5+fvLjjz/qPr9//75s2rRJ+vTpY3aZw4YNk02bNmVLW7ZsmXz99dfKqXF+/PFHXQC3j4+PMtDOHPll7MTd+j2wY8cO+d///qdLDw4Olt9++01+++03q8r19fXVpV24cEFu3rwpJUqUsKpMAMbFSCkAl7JlyxZlep06dUwOsKpObvz9/eX999+3a90eZaS5kJ1B9fuTk5PtUrapOZxd/UTV09NTevXqJb169ZKEhATZvHmzbN68WbZt2yYnT57MNX9aWpq88cYbkpCQIJ9//nke1Ng5fvjhB4mJidGlly5dWr7++mvp0aOHxTeC09LS7FU95EC1X7DXIL+99i95Yffu3dKxY0flUznPPvusLF++3CXfOGEu1dP2jj4+OIO7HQeN2K6c/1mnbt264uHhoQsgTklJkePHj0v16tWdVLP/z936lz2UKVNG2rZtK+vWrcuWPm/ePJMBDdu3b5ezZ8/q0s2dbkLE/fqhEc913K0NrOHn56dLy8tpdvJzG7G/NoYZM2bI+vXrldO3btmyxaKAhi5dukiZMmXk8uXLD9Pu3bsnkZGR8vbbb2f7rqZpMmvWLF0Z3bp1Uz5Vba78MHaSn/s98LisrCwZOXKk8rPz58/bNGWGKQcPHpSWLVvavVwAzkVAAwCXsnLlSmV6TicpqgtaTdPkk08+sVu9kJ1qndtrUMgdBi6CgoKkT58+DwcWEhISZPv27bJt2zZZs2aNcnD5gXHjxkmzZs0ezp+Z3/zwww+6tICAANm3b5/J12TnxtS8orAvVR89f/68Xco2NRWR0fz555/Svn175c2NBg0ayOrVq6VQoUJOqFneceTAtjXlhISEKN/8ZCt3Ow4arV1FOP+zlr+/v9StW1cOHDig+2z79u2GCGhwt/5lLwMHDtQFNOzfv18OHz6sfDPHvHnzdGnBwcHSqlUrs5fpbv3QiOc67tYG1ggICNCl5eW0Bfm5jdhfG4Ofn5+89tprMm7cON1nlk6/4O3tLYMGDdKVNXPmTF1AQ1RUlLJ8a6abMMVVx07yc79H3nLU9aw9zZ07V3lt4UiHDh0ioAHIh4z5vkgAUDhy5Ijs2bNH+VnHjh1N5lPd5ExJSZH09HS71Q3ZqV41eP36dbsMDB0/flyZnp8HLoKCgqRnz54yffp0iYuLk3379smrr74qXl5eyu/n14vgCxcuyMGDB3XpkyZNsjqYQURynT8a9hEaGqpLO3PmjNy5c8fmsl1hjsTY2Fhp166dchC3du3asn79ervNKWxkqqexzHmSyhz2Ksce3O04aMR25fzPeh06dFCmz5kzJ49rouZu/cteIiIilMFHqsCF1NRU5WuB+/fvb9G0I+7WD414ruNubWAN1Tq6detWtifQ83r5+aWN2F8bR3h4uDLdmmvhwYMH68YiYmNj5ffff8+WpnoYoWrVqg69yegqYyf5ud8Dj0pJSZF//vOfeb5cS86b+vbtKx4eHg//Ro0aZXbeyZMnZ8tbqVIliY+Pt6bKAMxAQAMAl/HFF18o0ytXrizPPPOMyXw1atRQph87dswu9YJezZo1lemqm9GWMlWGqTmf86N69erJ7NmzZffu3cqbSHv37rXb02BGsn//fl2ar6+v9OzZ0+oyz58/L9evX7elWjBTgwYNdGlZWVmyb98+m8qNi4szfFDKiRMnpE2bNsp6PvXUU7Jx40a3GXytVauWLu3KlSty9epVm8rNysqSw4cP21SGPbnbcdCI7cr5n/V69+6tTN+zZ49y2qe85m79y158fHyUbbtw4ULdXN6//PKL8m1Cls557m790IjnOu7WBtYw1b93796dJ8vPz23E/to4ypYtq0y3JuAqODhYXnjhBV36zJkzH/776tWrsmLFCt137Pl2BnMYdewkP/d74FETJkyQK1eu5PlyLQlomDBhQrZpYGbMmGHWm7FmzZqVLfihbNmyEhUVJeXKlbOssgDMRkADAJewe/du+emnn5SfDR8+PMcnhRo1aqRM37p1qz2qBoXGjRsr07dv325z2aoynnjiCQkJCbG5bFdTr149+e9//6v8bOfOnTnmVfUZo7+mLiEhQZdWqVIlKVy4sNVlPv4UiTVccV06Q61atZRzFP/88882lbtkyRKb8jtaXFyctG7dWnkRX6VKFYmKipLAwEAn1Mw5TAUgRkdH21Tun3/+KampqTaVYU/udhw0Yrty/me9GjVqSJMmTZSfTZ06NY9ro+du/cueBg4cqEu7fPmybNiwIVua6q0NTZo0kapVq1q0PHfrh0Y813G3NrBGw4YNlembNm3Kk+Xn5zZif20cpp78t3a6u+HDh+vSli5d+nCqsLlz50paWlq2zwsWLKg8DuUFW8ZOROx/zZ+f+z3wQFxcnPLa4fnnnxdN0+z2p+pPR44ckYyMDLPqWaFCBXnnnXce/v/evXvy6aef5phn6dKlMnTo0If/DwgIkE2bNknlypXNWiYA6xDQAMDwkpKSpH///pKVlaX7LDAwUIYMGZJj/ieffFI5+PbLL7/YrY7IrlKlShIUFKRLNxWUYq7Lly/L5s2bdemmLgbdQY8ePaRgwYK6dNXN/0cVKVJEl3b37l271csRVG9SsPWp9sjISJvyi7jmunQGb29vadOmjS596dKlVt+wzMrKsksbOkp8fLy0bt1aLl68qPusQoUKEhUVZfJpqfyqSpUqytcP2xqYYuvxxd7c7ThoxHbl/M82o0ePVqbPnTs3z27ymeJu/cueGjZsKNWrV9elPxrAcPHiReV6sOYmlLv1QyOe67hbG1ijQoUKyn6xYMGCPAmWzM9txP7aOFTXIyIi5cuXt6q85557TipVqpQtLTU1VRYsWCAifz+5/LiIiAgpXbq0VcuzB2vHTkTsf82fn/s98MD7778v9+/fz5bm6ekpEydOtOtyVMfw+/fvW/TGk48++ijbgyaRkZEm3xS4du1a6du378P7FMWKFZP169cr6wHAvghoAGBoycnJ0rlzZzlx4oTy8/Hjx5s15/hLL72kS9u6davs2rXL5jpCrWPHjrq0EydOyJYtW6wu84cfftC9EldEpFOnTlaX6ep8fX2Vr058/KLhcUWLFtWl3bhxw271cgTVmxhsmS7ixIkTsnbtWluqJCKuuS6d5bXXXtOlXb16Vf7zn/9YVd7s2bPlyJEjtlbLIRITE6V169YSFxen+6xMmTISFRUlFStWdELNnC8iIkKXtmrVKrMGE1VSU1MNF9Ag4n7HQSO2K+d/1uvSpYvJG0ODBg1STkdgL6dOnZLp06fn+B1361/2pJo2YsWKFXLr1i0R+XsA9/FAcj8/P+nVq5dVy3O3fmjEcx13awNrqKZjuXXrlvzwww95svz83Ebsr41h3bp1ynRT04LkxtPTUzl9xMyZM2Xr1q3KMbzcHkZyNGvHTkQcc82fn/s9sG3bNlm2bJkufcCAAXafGshUIIEl004ULVpUPv/884f/z8rKkg8//FD3vR07dkj37t0fvvWmcOHCsmbNGvnHP/5hYa0BWIOABgCGdfjwYWnatKns2LFD+XmHDh1k0KBBZpU1dOhQ8fHx0aW/8cYbPEntIK+//roy/d1331W+bSM3Fy9eVEbxlixZUvr06WNxeflFRkaG8kI6t6e+n3jiCV3auXPn5N69e3arm72pnuY4efKkJCYmWlyWpmkyZMgQu0wN4Yrr0lk6dOggZcqU0aVPmjTJ4vmlT58+LR9//LG9qmZXN2/elLZt28rx48d1n5UuXVo2bdokTz75pBNqZgyqY3dqaqp88sknVpX3xRdfOGVezty423HQiO3K+Z9tvvvuOylQoIAu/dy5c9KjRw+zbgBYauPGjdK4cWM5depUjt9zt/5lT/369dO9OvvevXuyePFiEVFPNxEREWFWELmKu/VDI57ruFsbWOO1115TPrn9ySefyOnTpx2+/PzcRuyvzff49D/2cvXqVVm4cKHys1atWlldrqrfHDx4UEaOHKn7bpUqVWxalj1YO3Yi4phr/vzc7+HesrKylPsBPz8/GTdunN2XZ4+ABpG/r2cfDfJas2ZNtnsSBw4ckI4dOz7snz4+PrJ8+XJp2rSpFbUGYA0CGgAYTmJiorz33ntSr149k693qlatmsybN088PDzMKrNMmTLy5ptv6tIPHDggAwYMMHterdykp6fLwYMH7VKWq6tbt65yzsyDBw/mOhfZ49LS0mTAgAHKV36++uqryrlqjW7VqlUm57G0xIoVK5Q3FEJDQ3PMV6dOHV1aRkaGwwZR7EEV8ZyVlSVff/21xWWNHTtWtm3bZo9queS6dBZvb+9sUe8P3L17V55//nn566+/zCrn3Llz0q5dO7l27Zq9q2izlJQUee655yQmJkb3WYkSJWTjxo1So0YNJ9TMOBo3bqx8KmPOnDmyZs0ai8ratWuXfPXVV/aqml2523HQiO3K+Z9t6tSpI2PHjlV+tmHDBnnuuefsth9OTU2V999/3+wy3a1/2VPZsmWlbdu2uvR58+bJ7t27lcF4tsx57m790IjnOu7WBtYoV66c8unxO3fuSI8ePRx+zpmf24j9tfm6desmDRs2NPk2BWtkZGTIoEGDlDfyAwMDldPkmCswMFC6du2qS1fdRBw0aJDZ43ePc/bYiYhjrvnzc7+He5szZ45ymxs5cqSUK1fO7suzV0CDl5eX7hr0gw8+EBGRY8eOyXPPPSfJycki8vf53pIlS2zahwKwggYAVtqyZYsmIrq/MWPGWFzWpUuXtMjISK1bt25awYIFleU++AsNDdXi4+MtXkZycrJWuXJlZZlNmzbVCHmibQAAIABJREFULly4YHGZD9y4cUObNGmSVr58ea1Xr15m5QkPD7fLunsgLi5O+dvi4uKsLnPMmDG68sLDw83Ov2PHDs3T01NZr4kTJ5pVRmpqqta9e3dlGUFBQVpiYqLZ9bH3Otc00/0gNxUrVtTKly+vTZ48Wbtx44ZVyz516pRWpkwZ3bIrVKigZWVl5Zr/iSee0OV98skntePHj1tVnwccsZ4fCA4O1pVdoEABbd26dWblz8zM1D744IMc9zHW1NdR69LWPmikbf6BrKwsrXnz5soyfHx8tHHjxpnsEykpKdq0adO0okWLZssXFhZm03qyl9TUVOU6FxHN399f27NnT57XSdOMuR2sX79emb9QoULali1bzCrjwIEDWsmSJXPsy87YDh7lDsfBRxmxXfP7+Z+jZWVlaS+++KLJtggKCtJ++ukns847VNLS0rQ5c+ZoFSpUyFbu22+/nWted+hftu5rTVm0aJGy3LZt2+rSypcvr2VmZtq0PHfrh0Y813GHNrC1v9y4cUN5bSUi2lNPPaUdO3bM4jolJSVp48eP16ZNm5brd43WRvZktP31gAEDdGUMGDDAyl9nP4ULF35YnwYNGmjff/+9lpSUZHV5ly9f1p5//nmTx/D//Oc/NtfZVL979K9AgQLalStXrF6GEcZONM0x1/xG6/fuME5pT/YcF88vkpOTtaCgIN06CQgI0G7duuWQZWZmZmq+vr7KY4M12rVrl62cqVOnauXLl3/4f09PT23hwoV2/hUAzOEtAGBn27dvl/Hjxys/0zRN0tLS5O7du5KYmCgXL16UI0eOmD2/8gsvvCALFiyQEiVKWFyvokWLyuLFi6VFixa6iP6dO3dKtWrV5JVXXpERI0aY9fRsXFycREVFybJlyyQqKsouEeP5TbNmzeS9996TSZMm6T4bPXq0bNq0SSZNmiS1a9fWfZ6VlSUbNmyQt956S06ePKksf+bMmcppCFzFxYsX5b333pMPP/xQ2rVrJy+++KK0bNlSgoODc8yXlJQkP/74o4wbN05u3ryp+/yNN94w6+mHvn376qKPT506JdWrV5cmTZpI7dq1pVSpUsrXrzZv3lyaN2+e6zLsbcSIEfLRRx9lS0tPT5dOnTrJP//5Txk5cqQUK1ZMly8zM1OioqLkgw8+yBYp7u/vL0FBQSa3MXO54rp0Fg8PD5k1a5Y0aNDg4XzdD9y/f18+/fRTGTdunLRo0UKqVKkiAQEBcvPmTTl79qxERUXpXr/ZuXNnqVu3ru6NPtY+AWSL4cOHm3zzR/PmzWX9+vWyfv16uyyrX79+UrFiRbuU5Qzt2rWTl156SX766ads6ampqdK6dWt59913ZdSoURIUFKTLe/PmTZk+fbqMHz9e0tLSHqbXrFlTYmNjHV53S7jbcdCI7cr5n208PDxk/vz5cufOHeWbNhISEuSll16ScePGydChQ6V79+5Svnz5HMvMysqSgwcPys8//ywLFiyQixcvWlU3d+tf9hQRESHFixeXpKSkbOkbN27UfVc1RYWl3K0fGvFcx93awBolSpSQ+fPnS/v27SUzMzPbZ8eOHZOwsDAZNmyYvPHGG1KtWjWT5aSlpcn27dtl0aJF8r///U+Sk5NlzJgxuS4/P7cR+2vL7d27V/bu3Stvv/22RERESJcuXaRVq1YSGBiYa94jR47IwoULZdq0aZKSkqL8Tq1atZSvhLdUixYtpHr16nL06FGT3+ncubPy3M8Szh47EXHMNX9+7vdwT+PHj1eO8X/66adWT1+WG09PTwkNDdW9JTMhIUESEhIs3v9MnjxZnn766YfnAu+88062z7/77jt56aWXbKs0AKsQ0ADA7rZs2SJbtmyxa5kBAQHyxRdfyGuvvWbTTaoGDRrIzz//LN27d9fNdZeamiozZsyQGTNmSGBgoDRu3FjKlCkjJUuWFC8vL0lKSpKkpCQ5f/68HDp0SDcACLVx48bJtm3bZM+ePbrPNmzYIBs2bJCaNWtK/fr1pWzZspKeni7x8fGydetWuXz5ssly33jjDenUqZMjq55n0tPTZc2aNQ9vFAQGBkq9evWkTJkyUqJECSlatKjcu3dPEhMT5ejRo7J3716Trx+sX7++vPvuu2Yt9/XXX5fvvvtObt++nS09KytLoqOjJTo62mTeMWPGOOUm/JtvvinTpk2TS5cuZUtPT0+XsWPHyqRJk6Rp06ZSo0YNKVasmCQlJT3cnlSvip0xY4bMmjXL5oAGV1yXzlS1alVZvny5dOjQQfnK2LS0NLNe3xkWFiaRkZEyZcoU3WeqgSRHO3v2rMnPVq9eLatXr7bbspo1a+bSAQ0iIv/9738lOjpazp8/ny09KytLvvrqK5k6daqEh4dLaGiolC5dWq5fvy6nT5+WLVu2ZLvhLfL/b/YYLaBBxP2Og0ZsV87/bOPj4yO//vqrDBo0SObPn6/8ztGjR2XkyJEycuRICQkJkVq1aknFihWlWLFiUrBgQUlNTZVr167JqVOn5ODBg7qbvNZyt/5lL76+vtKrVy/5/vvvc/3ugAED7LJMd+uHRjzXcbc2sEabNm3k22+/laFDh4qmadk+y8jIkOnTp8v06dOlcuXK8swzz0hgYKCUKFFCbt++LTdu3JDY2Fg5dOiQ8rX25sjPbcT+2jr37t2TxYsXy+LFi0VEJDg4WMLCwqRChQpSsmRJ8fX1ldTUVElJSZG4uDiJiYmR+Pj4HMssXbq0/PLLL3a7Xho6dGiOwRGq6Vys5ayxExHHXfPn534P93LmzBn5v//7P116lSpVZPjw4Q5ddvXq1ZXTfh46dEjatWtnUVlhYWHy6quvysyZM3WfTZ48WQYPHmx1PQHYhoAGAIZWpkwZeeutt2T48OHKp66t8cILL8iGDRukR48ekpiYqPxOYmKirFixwi7Lc3c+Pj6ydu1aad++vez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fvmcAgD1ZcX+EuQXwjIIGAEWaM2eOKXbjjTf69T137Nhhil1zzTUl6stTu507d5aoLwBAcGJegRX8ed7l5OQoLS2tRH2VVHZ2tt544w3dddddql+/vqKiolS+fHklJCQoKSlJ/fr106RJk3T48OGA5gVrBOvnauG8y5Urp4SEhGL3ExYWptq1a7vFmAvgSbCOlaJMnjxZvXv3VqNGjRQXF6dy5cqpevXqatasme677z69/fbbLGOMq0pOTo7279/vFqtdu7bCwsKK3VdCQoKpWI65BQCsF+j7I8wtQNFCrU4AgD2lpqZq7dq1brGKFSuqXbt2fn3fwhO25HmvKm94ardv374S9QUEixMnTmjkyJFasWKF9uzZo/T0dDmdTsXHx6ty5cq68cYb1b59e915552Kj4+3Ol3A75hXYIVAnHclvSlWEunp6XrxxRdN8cOHD+vw4cP6+eef9emnnyosLEx9+vTR8OHD1ahRo4Dlh8AKxs/V3NxcHT161C2WkJAgh8NRov7q1Knj9rTW4cOHlZeXV6ILjSi7gnGsXM7o0aNNsePHj+v48ePaunWrvvrqK0lSx44dNWLECN1xxx0BzQ8ItLS0NNMqLCUd406nU7Vq1XIb13zPwNXgH//4h5YvX67U1FSlp6fr4sWLio+PV3x8vJo2baoOHTqoU6dOuvbaa61OFVehnJwcTZkyxRTv1q2b396TuQUoGis0APDoueeeM8UefvhhhYb6tw7q+PHjpljhJ6C8VatWLdNFSk/9A2XJtm3b9Oqrr2rJkiVKS0vThQsXlJWVpbS0NG3YsEH/+c9/1LdvX9WpU0dDhgzhaVqUecwrsELh88LpdKpmzZol6svTE+R2Pe/y8vL0ySefqEWLFvrggw+sTgd+4svP1UCd3ydOnDBdGCxpzpI5b5fLpfT09BL3h7IpGMeKLyxZskSdOnXSoEGDlJuba3U6gN/4coxL5nF+4sSJEvcFBItnn31WX331lfbs2aPMzMxLRaipqamaMWOGBg0apIYNG6pHjx5av3691eniKjNhwgQdOXLELXb99derVatWfntP5hagaBQ0ADD55JNP9O2337rFKlSooOHDh/v9vU+fPm2KRUVFlaivkJAQRUREuMUyMjJK1BdQ1mRnZ+uf//ynmjdvrrlz51qdDuA3zCuwQuHzLjIyUiEhISXqy9P5atV5V7lyZV177bVq0qSJqlWrVuT/6eLFi/rjH/+ov/71rwHOEIHgy8/VQJ3fvsy5qLbMBygsGMfKlURHR6tevXpq1qyZEhISFB4eXuSx7733nm677TZduHAhgBkCgePvuSU3N1fnz58vcX9AWWEYhr788ku1adNGY8aMMRWpAv6wfft2j6tTvfbaayVe5c0bzC1A0ShoAOBm27ZtGjx4sCk+ZsyYEj9ZWBxZWVmmWOGbR8URGRnp9nt2dnaJ+wKChcPhUNWqVdWwYUM1btxYVapUKfKP7TNnzqh79+565513ApwlEBjMK7BC4fPOl+ecFLjzrlGjRnr++ef13Xff6dSpU0pPT9euXbu0detWHTt2TKdPn9b8+fPVq1cvOZ3mr5Zvv/22/v3vfwckVwSOLz9XA3V++3sukJgPYBaMY6WwKlWq6PHHH9fs2bN14MABZWZmat++fUpNTdXBgwd1/vx5rV69WkOHDlV0dLSp/cqVK9W/f39uPqFMYm4BfKNixYq65ppr1LRpU9WoUaPILbxcLpdeeeUVPfDAA3K5XAHOEleT7Oxs9erVy1SU2a1bN79uNyExtwCXQ0EDgEtOnz6t7t27mybODh06aMiQIQHJIS8vzxQrzaRduC1LXqKsatmypUaOHKkffvhBZ8+e1fHjx7Vjxw5t27ZNJ06c0MmTJzV79mzdeeedpraGYeivf/2r/ud//seCzAH/Yl6BFQqfd7485yT/n3c333yzli5dqu3bt+vNN9/Ubbfdpri4ONNx0dHR6tKli6ZPn66ffvrJ4962Q4YM0a5du/yaLwLLl5+rgTq//T0XSMwHMAvGsfKrmjVraurUqTp06JA+/PBD9ejRw+P+zaGhoUpOTtb48eO1d++NDYrZAAAgAElEQVRedenSxXTMjBkzNHnyZL/lCliFuQUomTp16uipp57SvHnzdPToUZ0+fVp79uzRli1bdOTIEWVmZmrJkiUaMGCAx5WAvvjiCw0bNsyCzHE1MAxD/fr10+bNm93iVatW1fvvv+/392duAYoWanUCQLDLzMy0pCo0PDzcY4VdSeXk5KhHjx6mC841atTQf//3f3t86i5QSrOMU+G2PBmCsqZr164aMmSIbrjhhsseFxcXpx49eqhHjx767rvv1KdPH7d901wul/7whz9o7969qlSpkr/TxmWUlXnFzphXyo5gGS++POck/593norfrqRFixZau3atkpOT3f6ezM/P18iRIzV16lRfpgibKek5bsX5fbn3Lk1b5gN4I1jGSsOGDdWwYcNitalcubLmzZunBx98UDNnznR7bfTo0erbt6/KlSvnyzQB22FuAYrWpEkTzZ07V3ffffdlrzNHRESoQ4cO6tChg4YPH65evXpp/fr1bseMHz9ed911lzp27OjvtHGVGTFihGbNmuUWCwkJ0dSpU1W9enVLcmJuAX5BQQNQSomJiTpw4EDA33fIkCGaMGGCT/oqKChQnz59tGzZMrd4TEyMvvnmm4BO1p6WFSvNnpuF23IBxTrBctMp2Pz+978vdpvbb79dq1evVnJystLT0y/Fz5w5o/Hjx+uNN97wZYooprIwr9gJ80rZZtfxEhYWppycnEu/+/Kck+x73sXFxenLL79UYmKiCgoKLsWnT5+uf/zjH5ZdAIJv+fJzNVDnt7/nAsm+4xLWCcaxUloOh0OffPKJfvrpJ+3du/dS/MCBA/ryyy/Vq1cvC7MDfIu5BSieNm3aFLtN/fr19cMPP+j222/XypUr3V578cUXtWrVKl+lB2jChAkaN26cKf7+++/r9ttvD0gOzC1A0dhyArjKGYahP/7xj5o9e7ZbPDIyUl9//fUVn/r2tfLly5tiFy9eLHF/hSdtT/0jMBITE1WpUqWA/wwfPtzq/7otXXPNNfrss89M8X/961/Kz8+3ICPAP5hXYIXC54UvzzlP/dtJ06ZN9cgjj7jFXC6XFi9ebFFG8DVffq4G6vz291xQ1Hvg6haMY8UXIiMjNXLkSFN84cKFFmQD+A9zCxAY4eHhmj17tmJjY93iq1ev1o8//mhRVihrJk+erL/+9a+m+JtvvqnHH388YHkwtwBFo6ABuMo988wz+vjjj91iYWFhmjlzptq3bx/wfDztz3z+/PkS9VVQUGCa8OPj40vUF1AWde7c2bQ8X2ZmptasWWNRRoDvMa/ACoXPuwsXLpR4laKsrCxTzO7nXe/evU2xJUuWWJAJ/MGXn6uBOr99mbMUnOMSgReMY8VXevbsaXr6j3kAZY2/55awsDBFR0eXuD+gLKlatarHm80Uy8EXZs2apSeeeMK0FcOIESP0/PPPBzQX5hagaBQ0AFexl156Se+8845bzOl06rPPPtM999xjSU7VqlUzxQ4dOlSivo4cOWL6Q6Rq1aol6gsoq7jphLKOeQVWKHzeuVwuHTlypER9eTpf7X7e3XrrrabYwYMHLcgE/uDLz9VAnd9VqlQx7R9b0pw9tXU6napcuXKJ+0PZFIxjxVeioqJMqz2WZswBduTLMe6prZ3HOGAFrl/BH+bNm6e+ffu6bZkoSU899ZRef/31gOfD3AIULdTqBIBgt3//fqtTKJGxY8eaJmWHw6GPPvrI0n0t69ata4qlpaWVqC9P7erXr1+ivoCyql27dqYYN52sFazzil0xr5Rtdh0vdevWNe3nmpaWpoSEhGL3FYznXWRkpGJjY3X27NlLsfT0dAszgi8F4+dqeHi4qlevrqNHj16KHTp0SIZhmAodvFE475o1a7IXLUyCcaz4UvXq1d1+z83N1dmzZ01LhgPBqk6dOnI4HG4FzyUd44ZhmG462X2MA4HWsGFDVatWTcePH78U4/oVSuP777/X/fffr9zcXLf4o48+qn/+85+W5MTcAhSNFRqAq9A777yjESNGmOITJ05U//79A5/QbzRq1MgU27t3b4n68tTOU/8IjP3798swjID/TJgwwer/uq0VvtAocdMJZQvzCqzgz/MuPDzc400yu4mMjHT7PTs726JM4GvB+rlauN+cnBwdPny42P3k5+ebLp4zF8CTYB0rvlJ4HpCYC1C2ePqbLC0tTfn5+cXu69ChQ6YbanYf44AVCl/D4voVSmrVqlXq1q2baVvR3r1768MPPyxR0bMvMLcARaOgAbjKfPDBB/rLX/5iio8fP16DBg2yICN3devWNe0VtW7duhLtO71mzRpTrPCyl8DVjguNKOuYV2CFli1bmmKezp8rKSgo0Lp169xi119/vUJD7b/Q3qlTp9x+Zzn+sqNFixamC3wlOb89tXM4HEpKSipxbpfjq3GZkpKinJwctxhzATwJ1rHiKydPnjTF4uPjLcgE8J/Cc0tOTo42btxY7H74ngF4h6Jp+MJPP/2ku+++W1lZWW7x7t27a8qUKXI6rb1tytwCeEZBA3AV+fzzz/WnP/3JFB81apSGDh1qQUaetW3b1u33zMxMbd68udj9rFy50u33uLg4NWnSpFS5AWWNpwuN3HRCWcO8gkC7+eabTTexCp8/3ti8ebPOnTvnFrvllltKlVsg7N69W3l5eW6xKlWqWJQNfC02NlbNmzd3i23dulWnT58uVj+GYWj16tVusaSkJEVHR5c6R08KzwVSycalpzbBMC4ReME6Vnxl+/btbr/HxsayNQvKHOYWILAKX8Pi+hWKKzU1VXfeeafb9oiSdOedd2rGjBm2eHiAuQXwjIIG4CoxZ84c9e/f3/RE6gsvvKCRI0dalJVnnTt3NsXmzJlTrD727Nmjn3/+2S12xx13WF5hCdjNtm3bTDFuOqGsYV5BoFWuXNn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"text/plain": [ "" ] }, "execution_count": 1, "metadata": { "image/png": { "height": 500, "width": 500 } }, "output_type": "execute_result" } ], "source": [ "Image(\"MaxwellVacuum/example_parfiles/Ax-convergence.png\", width=500, height=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "## Step 5.b: Behavior of Error Nodes \\[Back to [top](#toc)\\]\n", "$$\\label{errornodes}$$\n", "\n", "Because System I is weakly hyperbolic (see [Tutorial-VacuumMaxwell_formulation_Cartesian](Tutorial-VacuumMaxwell_formulation_Cartesian.ipynb) for more discussion), zero speed error nodes of the constraint violation sit on our numerical grid, adding to the errors of our evolution variables. In contrast, System II is strongly hyperbolic, and the error nodes propagate away at the speed of light, leading to more stable evolution of the evolution variables. The plot below demostrates the qualitative behavior for both systems.\n", "\n", "Contrast these plots to Figure 1 in [Knapp, Walker, & Baumgarte (2002)](https://arxiv.org/abs/gr-qc/0201051); we observe excellent qualitative agreement." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.880354Z", "iopub.status.busy": "2021-03-07T17:08:39.879248Z", "iopub.status.idle": "2021-03-07T17:08:39.887630Z", "shell.execute_reply": "2021-03-07T17:08:39.888268Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 1, "metadata": { "image/png": { "height": 500, "width": 500 } }, "output_type": "execute_result" } ], "source": [ "Image(\"MaxwellVacuum/example_parfiles/constraintviolation.png\", width=500, height=500)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "# Step 6: Output this notebook to $\\LaTeX$-formatted PDF file \\[Back to [top](#toc)\\]\n", "$$\\label{latex_pdf_output}$$\n", "\n", "The following code cell converts this Jupyter notebook into a proper, clickable $\\LaTeX$-formatted PDF file. After the cell is successfully run, the generated PDF may be found in the root NRPy+ tutorial directory, with filename\n", "[Tutorial-ETK_thorn-MaxwellVacuum.pdf](Tutorial-ETK_thorn-MaxwellVacuum.pdf) (Note that clicking on this link may not work; you may need to open the PDF file through another means.)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2021-03-07T17:08:39.895084Z", "iopub.status.busy": "2021-03-07T17:08:39.892303Z", "iopub.status.idle": "2021-03-07T17:08:48.203465Z", "shell.execute_reply": "2021-03-07T17:08:48.202625Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created Tutorial-ETK_thorn-MaxwellVacuum.tex, and compiled LaTeX file to\n", " PDF file Tutorial-ETK_thorn-MaxwellVacuum.pdf\n" ] } ], "source": [ "import cmdline_helper as cmd # NRPy+: Multi-platform Python command-line interface\n", "cmd.output_Jupyter_notebook_to_LaTeXed_PDF(\"Tutorial-ETK_thorn-MaxwellVacuum\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "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": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 2 }