diff --git a/.gitignore b/.gitignore index 54f9541f..8a364cf3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,13 @@ *.pyc *.swp -*.py~ +*.py~ *.ipynb +*.npy +*.npz +*.png +*.mp4 +tests/random_tests/ +tests/test_qrt/ pytest/htmlcov pytest/.cache .pytest_cache/ @@ -9,7 +15,20 @@ pytest/.cache .cache/ __pycache__/ .ipynb_checkpoints/ -.eggs/ -*.egg-info/ .DS_Store -Base_case_analysis.txt +*.png +*.mp4 +/tests/test_qrt/ +/tests/random_tests/ +/tests/random_tests_res/ +.eggs/ +RootFinding.egg-info/ +Time_plots/* +*.csv +.gitignore +*.pdf +.vs/ +tests/Constant_term_testing/ +tests/Progress_testing/ +yroots/.vs/ +Tester.py \ No newline at end of file diff --git a/Base_case_psuedo_code.txt b/Base_case_psuedo_code.txt deleted file mode 100644 index 5dcb5700..00000000 --- a/Base_case_psuedo_code.txt +++ /dev/null @@ -1,95 +0,0 @@ -subdivision.subdivision_solve_nd: - Checks to see if there's a bad approximation by - checking if coeff is none (full_cheb_approximate - return). - return subdivision - - Checks to see if it can throw out any inteval - If so, - return zeros - - Trim coefficients - - Check if everything on the interval is linear - by checking that the shape of the coefficients is 2. - If so, solve in zeros. - except singular matrix, - determine whether there are no roots or - infinitely many roots. - - If polish - return polished zeros - else - return zeros - - If not everything is linear, check to see if something - is linear by looking at the shape of each coefficient? - If so subdivide with interval checks. - If intervals are length 0, return zeros - else pass in new good_degs and return subdivision_solve_nd - - Division variable cases: What do they do for - multiplication? Additional subdivision? - - Get the polynomials as Multicheb polynomials - Solve for zeros using (division) - If not integers, run interval track data? - Else increment divisor_var until you find a good - direciton. - - ### Why check if zeros are integers? - - If the length of the intervals are zero, return 0 - else recursion with new good_degs - - ================================================================= - - Base Case executes when - 1. Coef =/= None - - Approximation is good - 2. Everything is linear - 3. Not a singular matrix - 4. Return polish or not polished - - Division executes (2 cases) when - 1. Coeff =/= None - - Approximation is good - 2. *Nothing* is linear - 3.1.1 Not instance int (nothing went wrong) - 3.1.2 List of intervals is not len 0 - - 3.2.1 Is instance int (something went wrong) - 3.2.2 Find a good division direction (while) - 3.2.3 There exists a good division direction - - Either throws out interval or recurses - 3.2.4 Solve in div direction - - ============================================================= - erik_develop update - - "Base Case" executes when - This is basically using linear algebra to solve it. - 1. Coef =/= None - - Approximation is good - 2. Everything is linear - 3. Not a singular matrix - 4. Return polish or not polished - - Multiplication Matrix executes when - 1. Coeff =/= None - - Approximation is good - 2. *Nothing* is linear - 3. Not instance int (nothing went wrong) - 4. List of intervals is not len 0 - - Subdivides when: - 1. Bad approximation - 2. Some (but not all) are linear - 3. Nothing is linear and something goes - wrong with multiplication/Macaulay - -If it has the possibility of being linear (partially linear) -then subdivides until it's linear. - -If nothing is linear but the approximation is good, then it -uses the Macaulay method. \ No newline at end of file diff --git a/CHEBYSHEV/DEMO.ipynb b/CHEBYSHEV/DEMO.ipynb deleted file mode 100644 index 128ae15d..00000000 --- a/CHEBYSHEV/DEMO.ipynb +++ /dev/null @@ -1,207 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using Telen VanBarel construction to solve for roots." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "#Local imports\n", - "import TVB_Method.root_finder as rf\n", - "import TVB_Method.cheb_class as Cheb\n", - "\n", - "#python imports\n", - "from matplotlib import pyplot as plt\n", - "import numpy as np\n", - "from scipy.io import loadmat" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.44 s, sys: 96 ms, total: 1.54 s\n", - "Wall time: 961 ms\n", - "287 zeros are correct to 1e-08, out of 343 total zeros.\n", - "56 are bad, but 56 of these were not real or out of range (expected to be bad).\n", - "0 might be lost\n", - "0 seem to be lost after newton polishing\n", - "Differences between the 'bad' inrange zeros and the polished ones are []\n" - ] - } - ], - "source": [ - "# Enter the desired dim and degree.\n", - "deg = 7\n", - "dim = 3 # number of polys should equal degree so that the zero locus is \n", - " # discrete. (with probability 1)\n", - "\n", - "# Create random Chebyshev polys of the desired the degree and dim.\n", - "polys = Cheb.polyList(deg,dim, 'random')\n", - "\n", - "#find the roots\n", - "%time zeros = rf.roots(polys)\n", - "\n", - "rf.check_zeros(zeros,polys,tol=1e-8)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 19.7 s, sys: 936 ms, total: 20.6 s\n", - "Wall time: 12 s\n", - "648 zeros are correct to 1e-08, out of 1000 total zeros.\n", - "352 are bad, but 352 of these were not real or out of range (expected to be bad).\n", - "0 might be lost\n", - "0 seem to be lost after newton polishing\n", - "Differences between the 'bad' inrange zeros and the polished ones are []\n" - ] - } - ], - "source": [ - "# Enter the desired dim and degree.\n", - "deg = 10\n", - "dim = 3 # number of polys should equal degree so that the zero locus is \n", - " # discrete. (with probability 1)\n", - "\n", - "# Create random Chebyshev polys of the desired the degree and dim.\n", - "polys = Cheb.polyList(deg,dim, 'random')\n", - "\n", - "#find the roots\n", - "%time zeros = rf.roots(polys)\n", - "\n", - "rf.check_zeros(zeros,polys,tol=1e-8)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2.48 s, sys: 64.9 ms, total: 2.54 s\n", - "Wall time: 1.48 s\n", - "698 zeros are correct to 1e-08, out of 900 total zeros.\n", - "202 are bad, but 200 of these were not real or out of range (expected to be bad).\n", - "2 might be lost\n", - "0 seem to be lost after newton polishing\n", - "Differences between the 'bad' inrange zeros and the polished ones are [array([-7.74857956e-12+0.j, 9.50345358e-12+0.j]), array([ 1.66428982e-10+0.j, -2.04399941e-10+0.j])]\n" - ] - } - ], - "source": [ - "# Use this cell to test the root finder.\n", - "# Enter the desired dim and degree.\n", - "deg = 30\n", - "dim = 2 # number of polys should equal degree so that the zero locus is \n", - " # discrete. (with probability 1)\n", - "\n", - "# Create random Chebyshev polys of the desired the degree and dim.\n", - "polys = Cheb.polyList(deg,dim, 'random')\n", - "\n", - "#find the roots\n", - "%time zeros = rf.roots(polys)\n", - "\n", - "rf.check_zeros(zeros,polys,tol=1e-8)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compare TVB to Bezout in dim 2\n", - "### Run with TVB in Python and Bezout in Matlab\n", - "### Run with 8 gb of RAM and an i7 processor" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "domain = np.array([n for n in range(2,51)])\n", - "mat = loadmat(\"bezout-outer-times.mat\")\n", - "Bezout_times = mat[\"times\"][0]\n", - "TVB_times = np.load(\"tvb_times.npy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(domain, TVB_times, 'b-', label=\"TVB\")\n", - "plt.plot(domain, np.array(Bezout_times), 'g-', label=\"Bezout\")\n", - "plt.legend(loc=\"upper left\")\n", - "plt.xlabel(\"degree\")\n", - "plt.ylabel(\"run time\")\n", - "\n", - "plt.show()\n", - "#plt.savefig('TvB-vs-Bezout2d.pdf')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/CHEBYSHEV/TVB_Method/TVB.py b/CHEBYSHEV/TVB_Method/TVB.py deleted file mode 100644 index 5b5c4e14..00000000 --- a/CHEBYSHEV/TVB_Method/TVB.py +++ /dev/null @@ -1,400 +0,0 @@ -import numpy as np -from scipy.linalg import qr, solve_triangular, qr_multiply -from CHEBYSHEV.TVB_Method.cheb_class import Polynomial, MultiCheb, TVBError, slice_top, get_var_list, mon_combos, mon_combos_highest, sort_polys_by_degree - - -""" - This module contains methods for constructing the TvB Matrix associated - to a collection of Chebyshev polynomials - - Methods in this module: - - telen_van_barel(initial_poly_list): Use the TvB matrix reduction method - to find a vector basis for C[x_1, ..., x_n]/I, where I is the ideal defined by - 'initial_poly_list'. - - make_basis_dict(matrix, matrix_terms, vector_basis, remainder_shape): Calculate and - returns the basis_dict, which is a mapping of the terms on the diagonal of the - reduced TVB matrix to the terms in the vector basis. It is used to create the - multiplication matrix in root_finder. - - clean_zeros_from_matrix(array, accuracy): - - find_degree(poly_list): Find the degree needed for a Macaulay/TvB Matrix. - - add_polys(degree, poly, poly_coeff_list): Adds polynomials to the Macaulay Matrix. - - sorted_matrix_terms(degree, dim): Find the matrix_terms sorted in the term order - needed for telen_van_barel reduction. The highest terms come first, the x,y,z etc - monomials last, the rest in the middle. - - create_matrix(poly_coeffs, degree, dim): - Build a Telen Van Barel matrix with specified degree, in specified dimension. - - rrqr_reduce_telen_van_barel(matrix, matrix_terms, matrix_shape_stuff): Reduce a - Macaulay matrix in the TvB way--not pivoting the highest and lowest-degree columns. - - clean_zeros_from_matrix(array, accuracy): Set all values in the array less than - 'accuracy' to 0. - - row_swap_matrix(matrix): Rearrange the rows of a matrix so it is closer to upper traingular. - -""" - -def telen_van_barel(initial_poly_list, accuracy = 1.e-10): - """Use the Telen-VanBarel matrix reduction method to find a vector basis - for C[x_1, ..., x_n]/I, where I is the ideal defined by initial_poly_list. - - Parameters - -------- - initial_poly_list: list of Chebyshev polynomials - The polynomials in the system we are solving. These should all be the - same dimension (same number of variables). - - accuracy: float - How small a number should be before assuming it is zero. - - Returns - ----------- - basis_dict : dict - Maps terms on the diagonal of the reduced TVB matrix to the - terms in the vector basis. - - vector_basis : numpy array - The terms in the vector basis, each row being a term. - - degree : int - The degree of the Macaualy/TvB matrix that was constructed. - - """ - - dim = initial_poly_list[0].dim #assumes all polys are the same dimension - poly_coeff_list = [] - degree = find_degree(initial_poly_list) #find the required degree of the Macaulay matrix - - # This sorting is required for fast matrix construction. Ascending should be False. - initial_poly_list = sort_polys_by_degree(initial_poly_list, ascending = False) - - # Construct the Macaulay matrix - for i in initial_poly_list: - poly_coeff_list = add_polys(degree, i, poly_coeff_list) - matrix, matrix_terms, matrix_shape_stuff = create_matrix(poly_coeff_list, degree, dim) - - # Reduce the matrix to RREF, but leaving the top-degree and lowest-degree terms unpivoted - matrix, matrix_terms = rrqr_reduce_telen_van_barel(matrix, matrix_terms, matrix_shape_stuff, accuracy = accuracy) - height = matrix.shape[0] # Number of rows - matrix[:,height:] = solve_triangular(matrix[:,:height],matrix[:,height:]) - matrix[:,:height] = np.eye(height) - - vector_basis = matrix_terms[height:] - - basis_dict = make_basis_dict(matrix, matrix_terms, vector_basis, [degree]*dim) - return basis_dict, vector_basis, degree - -def make_basis_dict(matrix, matrix_terms, vector_basis, remainder_shape): - '''Calculates and returns the basis_dict. - - This is a dictionary of the terms on the diagonal of the reduced TVB matrix to the terms in the Vector Basis. - It is used to create the multiplication matrix in root_finder. - - Parameters - -------- - matrix: numpy array - The reduced TVB matrix. - matrix_terms : numpy array - The terms in the matrix. The i'th row is the term represented by the i'th column of the matrix. - vector_basis : numpy array - Each row is a term in the vector basis. - remainder_shape: list - The shape of the numpy arrays that will be mapped to in the basis_dict. - - Returns - ----------- - basis_dict : dict - Maps terms on the diagonal of the reduced TVB matrix (tuples) to numpy arrays of the shape remainder_shape - that represent the terms reduction into the Vector Basis. - ''' - basis_dict = {} - - VBSet = set() - for i in vector_basis: - VBSet.add(tuple(i)) - - spots = list() - for dim in range(vector_basis.shape[1]): - spots.append(vector_basis.T[dim]) - - for i in range(matrix.shape[0]): - term = tuple(matrix_terms[i]) - remainder = np.zeros(remainder_shape) - row = matrix[i] - remainder[spots] = row[matrix.shape[0]:] - basis_dict[term] = remainder - - return basis_dict - -def find_degree(poly_list): - '''Find the degree needed for a Macaulay Matrix. - - Parameters - -------- - poly_list: list - The polynomials used to construct the matrix. - - Returns - ----------- - find_degree : int - The degree of the Macaulay Matrix. - - Example: - For polynomials [P1,P2,P3] with degree [d1,d2,d3] the function returns d1+d2+d3-(number of Polynomaials)+1 - ''' - - #print('len(poly_list) = {}'.format(len(poly_list))) - degree_needed = 0 - #print('initializing degree at {}'.format(degree_needed)) - for poly in poly_list: - degree_needed += poly.degree - #print('poly.degree = {}'.format(poly.degree)) - #print('degree adjusted to {}'.format(degree_needed)) - - return ((degree_needed - len(poly_list)) + 1) - - -def add_polys(degree, poly, poly_coeff_list): - """Adds polynomials to a Macaulay Matrix. - - This function is called on one polynomial and adds all monomial multiples of it to the matrix. - - Parameters - ---------- - degree : int - The degree of the Macaulay Matrix - - poly : Polynomial - One of the polynomials used to make the matrix. - - poly_coeff_list : list - A list of all the current polynomials in the matrix. - - Returns - ------- - poly_coeff_list : list - The original list of polynomials in the matrix with the new - monomial multiplications of poly appended. - """ - poly_coeff_list.append(poly.coeff) - deg = degree - poly.degree - dim = poly.dim - - mons = mon_combos([0]*dim,deg) - for i in mons[1:]: #skips the first, all-zero (constant) monomial - poly_coeff_list.append(poly.mon_mult(i, return_type = 'Matrix')) - - return poly_coeff_list - -def sorted_matrix_terms(degree, dim): - '''Find the matrix_terms sorted in the term order needed for telen_van_barel reduction. - The highest terms come first, the x,y,z etc monomials last, the rest in the middle. - Parameters - ---------- - degree : int - The degree of the TVB Matrix (degree of matrix is highest degreefound in find_degree) - dim : int - The dimension of the polynomials going into the matrix. (dimension = how many variables a polynomial has) - Returns - ------- - matrix_terms : numpy array - The sorted matrix_terms. - matrix_term_stuff : tuple - The first entry is the number of 'highest' monomial terms. The second entry - is the number of 'other' terms, those not in the first or third catagory. - The third entry is the number of monomials of degree one of a single variable, - as well as the monomial 1. - ''' - - highest_mons = mon_combos_highest([0]*dim,degree)[::-1] - - other_mons = list() - d = degree - 1 - while d > 1: - other_mons += mon_combos_highest([0]*dim,d)[::-1] - d -= 1 - - xs_mons = mon_combos([0]*dim,1)[::-1] - - sorted_matrix_terms = np.reshape(highest_mons+other_mons+xs_mons, (len(highest_mons+other_mons+xs_mons),dim)) - - return sorted_matrix_terms, tuple([len(highest_mons),len(other_mons),len(xs_mons)]) - -def create_matrix(poly_coeffs, degree, dim): - ''' Build a Telen Van Barel matrix with specified degree, in specified dimension. - - Parameters - ---------- - poly_coeffs : list of ndarrays - The coefficients of the Chebyshev polynomials from which to build the TvB matrix. - - degree : int - The top degree of the polynomials appearing in the TVB Matrix - - dim : int - The dimension (number of variables) of all the polynomials appearing in the matrix. - - Returns - ------- - matrix : 2D numpy array - The Telen Van Barel matrix. - ''' - - bigShape = [degree+1]*dim - #print('degree = {}, dim = {}, bigShape = {}'.format(degree,dim,bigShape)) - - matrix_terms, matrix_shape_stuff = sorted_matrix_terms(degree, dim) - - #Get the slices needed to pull the matrix_terms from the coeff matrix. - matrix_term_indexes = [row for row in matrix_terms.T] - - #Adds the poly_coeffs to flat_polys, using added_zeros to make sure every term is in there. - added_zeros = np.zeros(bigShape) - #print('added_zeros.shape = {}'.format(added_zeros.shape)) - flat_polys = list() - for coeff in poly_coeffs: - #print('coeff of poly_coeffs = {}'.format(coeff)) - slices = slice_top(coeff) - #print('slices = {}'.format(slices)) - added_zeros[slices] = coeff - flat_polys.append(added_zeros[matrix_term_indexes]) - added_zeros[slices] = np.zeros_like(coeff) - coeff = 0 - poly_coeffs = 0 - - #Make the matrix. Reshape is faster than stacking. - matrix = np.reshape(flat_polys, (len(flat_polys),len(matrix_terms))) - - if matrix_shape_stuff[0] > matrix.shape[0]: #The matrix isn't tall enough, these can't all be pivot columns. - raise TVBError("HIGHEST NOT FULL RANK. TRY HIGHER DEGREE") - - #Sort the rows of the matrix so it is close to upper triangular. - matrix = row_swap_matrix(matrix) - - return matrix, matrix_terms, matrix_shape_stuff - - - -def rrqr_reduce_telen_van_barel(matrix, matrix_terms, matrix_shape_stuff, accuracy = 1.e-10): - ''' Reduces a Macaulay matrix in the TvB way--not pivoting the highest - and lowest-degree columns. - - This function does the same thing as rrqr_reduce_telen_van_barel but - uses qr_multiply instead of qr and a multiplication - to make the function faster and more memory efficient. - - Parameters - ---------- - matrix : numpy array. - The Macaulay matrix, sorted in TVB style. - - matrix_terms: numpy array - Each row of the array contains a term in the matrix. The i'th row corresponds to - the i'th column in the matrix. - - matrix_shape_stuff : tuple - Terrible name I know. It has 3 values, the first is how many columnns are in the - 'highest' part of the matrix. The second is how many are in the 'others' part of - the matrix, and the third is how many are in the 'xs' part. - - accuracy : float - What is determined to be 0. - - Returns - ------- - matrix : numpy array - The reduced matrix. - matrix_terms: numpy array - The resorted matrix_terms. - ''' - highest_num = matrix_shape_stuff[0] - others_num = matrix_shape_stuff[1] - xs_num = matrix_shape_stuff[2] - - C1,matrix[:highest_num,:highest_num],P1 = qr_multiply(matrix[:,:highest_num], matrix[:,highest_num:].T, mode = 'right', pivoting = True) - matrix[:highest_num,highest_num:] = C1.T - C1 = 0 - - if abs(matrix[:,:highest_num].diagonal()[-1]) < accuracy: - raise TVBError("HIGHEST NOT FULL RANK") - - matrix[:highest_num,highest_num:] = solve_triangular(matrix[:highest_num,:highest_num],matrix[:highest_num,highest_num:]) - matrix[:highest_num,:highest_num] = np.eye(highest_num) - matrix[highest_num:,highest_num:] -= (matrix[highest_num:,:highest_num][:,P1])@matrix[:highest_num,highest_num:] - matrix_terms[:highest_num] = matrix_terms[:highest_num][P1] - P1 = 0 - - C,R,P = qr_multiply(matrix[highest_num:,highest_num:highest_num+others_num], matrix[highest_num:,highest_num+others_num:].T, mode = 'right', pivoting = True) - matrix = matrix[:R.shape[0]+highest_num] - matrix[highest_num:,:highest_num] = np.zeros_like(matrix[highest_num:,:highest_num]) - matrix[highest_num:,highest_num:highest_num+R.shape[1]] = R - matrix[highest_num:,highest_num+R.shape[1]:] = C.T - C,R = 0,0 - - #Shifts the columns of B. - matrix[:highest_num,highest_num:highest_num+others_num] = matrix[:highest_num,highest_num:highest_num+others_num][:,P] - matrix_terms[highest_num:highest_num+others_num] = matrix_terms[highest_num:highest_num+others_num][P] - P = 0 - - #Get rid of 0 rows at the bottom. - rank = np.sum(np.abs(matrix.diagonal())>accuracy) - matrix = matrix[:rank] - return matrix, matrix_terms - - -########## Utils ################## - - -def row_swap_matrix(matrix): - '''Rearrange the rows of a matrix so it is close to upper traingular. - - Parameters - ---------- - matrix : 2D numpy array - The matrix whose rows need to be switched - - Returns - ------- - 2D numpy array - The same matrix but with the rows changed so it is closer to upper - triangular - - Examples - -------- - >>> row_swap_matrix(np.array([[0,2,0,2],[0,1,3,0],[1,2,3,4]])) - array([[1, 2, 3, 4], - [0, 2, 0, 2], - [0, 1, 3, 0]]) - ''' - leading_mon_columns = list() - for row in matrix: - leading_mon_columns.append(np.where(row!=0)[0][0]) - - return matrix[np.argsort(leading_mon_columns)] - - -def clean_zeros_from_matrix(array, accuracy=1.e-10): - '''Sets all values in the array less than the given accuracy to 0. - - Parameters - ---------- - array : numpy array - accuracy : float, optional - Values in the matrix less than this will be set to 0. - - Returns - ------- - array : numpy array - Same array, but with values less than the given accuracy set to 0. - ''' - array[(array < accuracy) & (array > -accuracy)] = 0 - return array - - diff --git a/CHEBYSHEV/TVB_Method/__init__.py b/CHEBYSHEV/TVB_Method/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/CHEBYSHEV/TVB_Method/cheb_class.py b/CHEBYSHEV/TVB_Method/cheb_class.py deleted file mode 100644 index 1db2abb5..00000000 --- a/CHEBYSHEV/TVB_Method/cheb_class.py +++ /dev/null @@ -1,908 +0,0 @@ -import numpy as np -import itertools -from numpy.polynomial import chebyshev as cheb - -""" -Module for defining the class of Chebyshev polynomials, as well as various related -classes and methods, including: - - Classes: - ------- - - Polynomial: Superclass for MultiPower and MultiCheb. Contains methods and - attributes applicable to both subclasses - - MultiCheb: Chebyshev polynomials in arbitrary dimension. - - Term: Terms are just tuples of exponents with the degrevlex ordering - - Methods: - -------- - - match_poly_dimensions(polys): Matches the dimensions of a list of polynomials. - - mon_combos_highest(mon, numLeft): Find all the monomials of a given degree and - returns them. Works recursively. - - mon_combos(mon, numLeft): Finds all the monomials _up to_ a given degree and - returns them. Works recursively. - - sort_polys_by_degree(polys): Sorts the polynomials by their degree. - - get_var_list(dim): Return a list of tuples corresponding to the variables - [x_1, x_2, ..., x_n]. The tuple for x_1 is (1,0,0,...,0), and for x_i - the 1 is in the ith slot. - - slice_bottom(arr):Gets the nd slices needed to slice an array into the bottom - corner of another. There is probably a better (vectorized) way to do this. - - slice_top(arr): Construct a list of slices needed to put an array into the upper - left corner of another. There is probably a better way to do this. - - match_size(a,b): Reshape two coefficient ndarrays to have the same shape. - - makePolyCoeffMatrix(inputString): Take a string input of a polynomaial and - return the coefficient matrix for it. Usefull for making things of high - degree of dimension so you don't have to make it by hand. - - -""" - -class Polynomial(object): - ''' - Superclass for MultiPower and MultiCheb. Contains methods and attributes - that are applicable to both subclasses. - - Attributes - ---------- - coeff - The coefficient matrix represented in the object. - dim - The number of dimensions of the coefficient matrix - order - Ordering type given as a string - shape - The shape of the coefficient matrix - lead_term - The polynomial term with the largest total degree - degree - The total degree of the lead_term - lead_coeff - The coeff of the lead_term - - Parameters - ---------- - coeff : ndarray - Coefficients of the polynomial - order : string - lead_term : Tuple - Default is None. Accepts tuple or tuple-like inputs - clean_zeros : bool - Default is True. If True, all extra rows, columns, etc of all zeroes are - removed from matrix of coefficients. - - Methods - ------- - clean_coeff - Removes extra rows, columns, etc of zeroes from end of matrix of coefficients - match_size - Matches the shape of two matrices. - monomialList - Creates a list of monomials that make up the polynomial in degrevlex order. - monSort - Calls monomial list. - update_lead_term - Finds the lead_term of a polynomial - __call__ - Evaluates a polynomial at a certain point. - __eq__ - Checks if two polynomials are equal. - __ne__ - Checks if two polynomials are not equal. - - ''' - def __init__(self, coeff, order='degrevlex', lead_term=None, clean_zeros=True): - ''' - order : string - Term order to use for the polynomial. degrevlex is default. - Currently no other order is implemented. - ''' - if isinstance(coeff,np.ndarray): - self.coeff = coeff - elif isinstance(coeff,str): - self.coeff = makePolyCoeffMatrix(coeff) - else: - raise ValueError('coeff must be an np.array or a string!') - if clean_zeros: - self.clean_coeff() - self.dim = self.coeff.ndim - self.order = order - self.jac = None - self.shape = self.coeff.shape - if lead_term is None: - self.update_lead_term() - else: - self.lead_term = tuple(lead_term) - self.degree = sum(self.lead_term) - self.lead_coeff = self.coeff[self.lead_term] - - def clean_coeff(self): - """ - Remove 0s on the outside of the coeff matrix. Acts in place. - """ - - for axis in range(self.coeff.ndim): - change = True - while change: - change = False - if self.coeff.shape[axis] == 1: - continue - axisCount = 0 - slices = list() - for i in self.coeff.shape: - if axisCount == axis: - s = slice(i-1,i) - else: - s = slice(0,i) - slices.append(s) - axisCount += 1 - if np.sum(abs(self.coeff[slices])) == 0: - self.coeff = np.delete(self.coeff,-1,axis=axis) - change = True - - def update_lead_term(self): - """ - Update the lead term of the polynomial. - """ - - non_zeros = list() - for i in zip(*np.where(self.coeff != 0)): - non_zeros.append(Term(i)) - if len(non_zeros) != 0: - self.lead_term = max(non_zeros).val - self.degree = sum(self.lead_term) - self.lead_coeff = self.coeff[self.lead_term] - else: - self.lead_term = None - self.lead_coeff = 0 - self.degree = -1 - - def __call__(self, point): - ''' - Evaluate the polynomial at 'point'. This method is overridden - by the MultiPower and MultiCheb classes, so this definition only - checks if the polynomial can be evaluated at the given point. - - Parameters - ---------- - point : array-like - the point at which to evaluate the polynomial - - Returns - ------- - __call__ : complex - value of the polynomial at the given point - ''' - if len(point) != len(self.coeff.shape): - raise ValueError('Cannot evaluate polynomial in {} variables at point {}'\ - .format(self.dim, point)) - - def grad(self, point): - ''' - Evaluates the gradient of the polynomial at 'point'. This method is - overridden by the MultiPower and MultiCheb classes, so this definition only - checks if the polynomial can be evaluated at the given point. - - Parameters - ---------- - point : array-like - the point at which to evaluate the polynomial - - Returns - ------- - grad : ndarray - Gradient of the polynomial at the given point. - ''' - if len(point) != len(self.coeff.shape): - raise ValueError('Cannot evaluate polynomial in {} variables at point {}'\ - .format(self.dim, point)) - - def __eq__(self,other): - ''' - Check if coeff matrices of 'self' and 'other' are the same. - ''' - - if self.shape != other.shape: - return False - return np.allclose(self.coeff, other.coeff) - - def __ne__(self,other): - ''' - Check if coeff matrices of 'self' and 'other' are not the same. - ''' - return not (self == other) - - -############################################################################### - -#### MULTI_CHEB ############################################################### - -class MultiCheb(Polynomial): - """ - A Chebyshev polynomial. - - Attributes - ---------- - coeff: ndarray - A tensor of coefficients whose i_1,...,i_{dim} entry - corresponds to the coefficient of the term - T_{i_1}(x_1)...T_{i_{dim}}(x_{dim}) - dim: - The number of variables, dimension of polynomial. - order: string - Term order - shape: tuple of ints - The shape of the coefficient array. - lead_term: - The term with the largest total degree. - degree: int - The total degree of lead_term. - lead_coeff - The coefficient of the lead_term. - terms : int - Highest term of single-variable polynomials. The polynomial has - degree at most terms+1 in each variable. - - - - Parameters - ---------- - coeff : list(terms**dim) or np.array ([terms,] * dim) - coefficents in given ordering. - order : string - Term order for Groebner calculations. Default = 'degrevlex' - lead_term : list - The index of the current leading coefficent. If None, this is computed at initialization. - clean_zeros: boolean - If True, strip off any rows or columns of zeros on the outside of the coefficient array. - - Methods - ------- - - __add__ - Add two MultiCheb polynomials. - __sub__ - Subtract two MultiCheb polynomials. - mon_mult - Multiply a MultiCheb monomial by a MultiCheb polynomial. - __call__ - Evaluate a MultiCheb polynomial at a point. - - """ - def __init__(self, coeff, order='degrevlex', lead_term=None, clean_zeros = True): - super(MultiCheb, self).__init__(coeff, order, lead_term, clean_zeros) - - def __add__(self,other): - ''' - Addition of two MultiCheb polynomials. - - Parameters - ---------- - other : MultiCheb - - Returns - ------- - MultiCheb - The sum of the coeff of self and coeff of other. - - ''' - if self.shape != other.shape: - new_self, new_other = match_size(self.coeff,other.coeff) - else: - new_self, new_other = self.coeff, other.coeff - - return MultiCheb(new_self + new_other,clean_zeros = False) - - def __sub__(self,other): - ''' - Subtraction of two MultiCheb polynomials. - - Parameters - ---------- - other : MultiCheb - - Returns - ------- - MultiCheb - The coeff values are the result of self.coeff - other.coeff. - ''' - if self.shape != other.shape: - new_self, new_other = match_size(self.coeff,other.coeff) - else: - new_self, new_other = self.coeff, other.coeff - return MultiCheb((new_self - (new_other)), clean_zeros = False) - - - - def _fold_in_i_dir(coeff_array, dim, fdim, size_in_fdim, fold_idx): - """Find coeffs corresponding to T_|m-n| (referred to as 'folding' in - some of this documentation) when multiplying a monomial times - a Chebyshev polynomial. - - Multiplying the monomial T_m(x_i) times T_n(x_i) gives - (T_{m+n}(x_i) + T_{|n-m|}(x_i))/2 - So multipying T_m(x_i) times polynomial P with coefficients - in coeff_array results in a new coefficient array sol that has - coeff_array in the bottom right corner plus a 'folded' copy of - coeff_array in locations corresponding to |n-m|. This method - returns the folded part (not dividing by 2) - - Parameters - ---------- - coeff_array : ndarray - coefficients of the polynomial. - dim : int - The number of dimensions in coeff_array. - fdim : int - The dimension being folded ('i' in the explanation above) - size_in_fdim : int - The size of the solution matrix in the dimension being folded. - fold_idx : int - The index to fold around ('m' in the explanation above) - - Returns - ------- - sol : ndarray - - """ - if fold_idx == 0: - return coeff_array - - target = np.zeros_like(coeff_array) # Array of zeroes in which to insert - # the new values. - - ## Compute the n-m part for n >= m - - # slice source and target in the dimension of interest (i = fdim) - target_slice = slice(0,size_in_fdim-fold_idx,None) # n-m for n>=m - source_slice = slice(fold_idx,size_in_fdim,None) # n for n>=m - - # indexers have a slice index for every dimension. - source_indexer = [slice(None)]*dim - source_indexer[fdim] = source_slice - target_indexer = [slice(None)]*dim - target_indexer[fdim] = target_slice - - # Put the appropriately indexed source into the target - target[target_indexer] = coeff_array[source_indexer] - - ## Compute the m-n part for n < m - - # slice source and target in the dimension of interest (i = fdim) - target_slice = slice(fold_idx, 0 , -1) # m-n for n < m - source_slice = slice(None, fold_idx, None) # n for n < m - - # indexers have a slice index for every dimension. - source_indexer = [slice(None)]*dim - source_indexer[fdim] = source_slice - target_indexer = [slice(None)]*dim - target_indexer[fdim] = target_slice - - # Add the appropriately indexed source to the target - target[target_indexer] += coeff_array[source_indexer] - - return target - - - def _mon_mult1(coeff_array, monom, mult_idx): - """ - Monomial multiply in one dimension, that is, T_m(x_i) * P(x_1,...,x_n), - where P is a Chebyshev polynomial and T_m(x_i) is a Chebyshev monomial - in the lone variable x_i. - - Parameters - ---------- - coeff_array : array_like - Coefficients of a Chebyshev polynomial (denoted P above). - - monom : tuple of ints - Index of the form (0,0,...,0,m,0...,0) of a - monomial of one variable - - mult_idx : int - The location (denoted i above) of the non-zero value in monom. - - Returns - ------- - ndarray - Coeffs of the new polynomial T_m(x_i)*P. - - """ - - p1 = np.zeros(coeff_array.shape + monom) - p1[slice_bottom(coeff_array)] = coeff_array # terms corresp to T_{m+n} - - largest_idx = [i-1 for i in coeff_array.shape] - new_shape = [max(i,j) for i,j in - itertools.zip_longest(largest_idx, monom, fillvalue = 0)] - if coeff_array.shape[mult_idx] <= monom[mult_idx]: - add_a = [i-j for i,j in itertools.zip_longest(new_shape, largest_idx, fillvalue = 0)] - add_a_list = np.zeros((len(new_shape),2)) - #change the second column to the values of add_a and add_b. - add_a_list[:,1] = add_a - #use add_a_list and add_b_list to pad each polynomial appropriately. - coeff_array = np.pad(coeff_array,add_a_list.astype(int),'constant') - - number_of_dim = coeff_array.ndim - shape_of_self = coeff_array.shape - - if monom[mult_idx] != 0: - coeff_array = MultiCheb._fold_in_i_dir(coeff_array,number_of_dim, - mult_idx, shape_of_self[mult_idx], - monom[mult_idx]) - if p1.shape != coeff_array.shape: - monom = [i-j for i,j in zip(p1.shape,coeff_array.shape)] - - result = np.zeros(np.array(coeff_array.shape) + monom) - result[slice_top(coeff_array)] = coeff_array - coeff_array = result - Pf = p1 + coeff_array - return .5*Pf - - def mon_mult(self, monom, return_type = 'Poly'): - """ - Multiply a Chebyshev polynomial by a monomial - - Parameters - ---------- - monom : tuple of ints - The index of the monomial to multiply self by. - return_type : str - If 'Poly' then returns a polynomial object. - - Returns - ------- - MultiCheb object if return_type is 'Poly'. - ndarray if return_type is "Matrix". - - """ - coeff_array = self.coeff - monom_zeros = np.zeros(len(monom),dtype = int) - for i in range(len(monom)): - monom_zeros[i] = monom[i] - coeff_array = MultiCheb._mon_mult1(coeff_array, monom_zeros, i) - monom_zeros[i] = 0 - if return_type == 'Poly': - return MultiCheb(coeff_array, lead_term = self.lead_term + np.array(monom), clean_zeros = False) - elif return_type == 'Matrix': - return coeff_array - - def __call__(self, point): - ''' - Evaluate the polynomial at 'point'. - - Parameters - ---------- - point : array-like - point at which to evaluate the polynomial - - Returns - ------- - c : complex - value of the polynomial at the given point - ''' - super(MultiCheb, self).__call__(point) - - c = self.coeff - n = len(c.shape) - c = cheb.chebval(point[0],c) - for i in range(1,n): - c = cheb.chebval(point[i],c,tensor=False) - return c - - def grad(self, point): - ''' - Evaluates the gradient of the polynomial at the given point. - - Parameters - ---------- - point : array-like - the point at which to evaluate the polynomial - - Returns - ------- - out : ndarray - Gradient of the polynomial at the given point. - ''' - super(MultiCheb, self).__call__(point) - - out = np.empty(self.dim,dtype="complex_") - if self.jac is None: - jac = list() - for i in range(self.dim): - jac.append(cheb.chebder(self.coeff,axis=i)) - self.jac = jac - spot = 0 - for i in self.jac: - out[spot] = chebvalnd(point,i) - spot+=1 - return out - -############################################################################### - -def chebvalnd(x,c): - """ - Evaluate a MultiCheb object at a point x - - Parameters - ---------- - x : ndarray - Point to evaluate at - c : ndarray - Tensor of Chebyshev coefficients - - Returns - ------- - c : float - Value of the MultiCheb polynomial at x - """ - x = np.array(x) - n = len(c.shape) - c = cheb.chebval(x[0],c) - for i in range(1,n): - c = cheb.chebval(x[i],c,tensor=False) - return c - -def polyList(deg,dim,Type = 'random'): - """ - Creates random polynomials for root finding. - - Parameters - ---------- - deg : int - Desired degree of the polynomials. - dim : int - Desired number of dimensions for the polynomials - Type : str - Either 'random' or 'int. - - Returns - ---------- - polys : list - polynomial objects that are used to test the root finding. - - """ - deg += 1 - polys = [] - if Type == 'random': - for i in range(dim): - polys.append(np.random.random_sample(deg*np.ones(dim, dtype = int))) - elif Type == 'int': - Range = 10 - for i in range(dim): - polys.append(np.random.randint(-Range,Range,deg*np.ones(dim, dtype = int))) - for i,j in np.ndenumerate(polys[0]): - if np.sum(i) >= deg: - for h in range(len(polys)): - polys[h][i] = 0 - for i in range(len(polys)): - polys[i] = MultiCheb(polys[i]) - return polys - - - - -############# Cheb Utils ####################3 - - -class TVBError(RuntimeError): - pass - -class Term(object): - ''' - Terms are just tuples of exponents with the degrevlex ordering - ''' - def __init__(self,val): - self.val = tuple(val) - - def __repr__(self): - return str(self.val) + ' with degrevlex order' - - def __lt__(self, other, order = 'degrevlex'): - ''' - Redfine less-than according to term order - ''' - if order == 'degrevlex': #Graded Reverse Lexographical Order - if sum(self.val) < sum(other.val): - return True - elif sum(self.val) > sum(other.val): - return False - else: - for i,j in zip(reversed(self.val),reversed(other.val)): - if i < j: - return False - if i > j: - return True - return False - elif order == 'lexographic': #Lexographical Order - for i,j in zip(self.val,other.val): - if i < j: - return True - if i > j: - return False - return False - elif order == 'grlex': #Graded Lexographical Order - if sum(self.val) < sum(other.val): - return True - elif sum(self.val) > sum(other.val): - return False - else: - for i,j in zip(self.val,other.val): - if i < j: - return True - if i > j: - return False - return False - - -def match_poly_dimensions(polys): - '''Matches the dimensions of a list of polynomials. - - Parameters - ---------- - polys : list - Polynomials of possibly different dimensions. - - Returns - ------- - new_polys : list - The same polynomials but of the same dimensions. - ''' - dim = max(poly.dim for poly in polys) - new_polys = list() - for poly in polys: - if poly.dim != dim: - coeff_shape = list(poly.shape) - for i in range(dim - poly.dim): - coeff_shape.insert(0,1) - poly.__init__(poly.coeff.reshape(coeff_shape)) - new_polys.append(poly) - return new_polys - - - - -def mon_combos_highest(mon, numLeft, spot = 0): - '''Find all the monomials of a given degree and returns them. Works recursively. - - Very similar to mon_combos, but only returns the monomials of the desired degree. - - Parameters - -------- - mon: list - A list of zeros, the length of which is the dimension of the desired monomials. Will change - as the function searches recursively. - numLeft : int - The degree of the monomials desired. Will decrease as the function searches recursively. - spot : int - The current position in the list the function is iterating through. Defaults to 0, but increases - in each step of the recursion. - - Returns - ----------- - answers : list - A list of all the monomials. - ''' - answers = list() - if len(mon) == spot+1: #We are at the end of mon, no more recursion. - mon[spot] = numLeft - answers.append(mon.copy()) - return answers - if numLeft == 0: #Nothing else can be added. - answers.append(mon.copy()) - return answers - temp = mon.copy() #Quicker than copying every time inside the loop. - for i in range(numLeft+1): #Recursively add to mon further down. - temp[spot] = i - answers += mon_combos_highest(temp, numLeft-i, spot+1) - return answers - - - -def mon_combos(mon, numLeft, spot = 0): - '''Finds all the monomials up to a given degree and returns them. Works recursively. - - Parameters - -------- - mon: list - A list of zeros, the length of which is the dimension of the desired monomials. Will change - as the function searches recursively. - numLeft : int - The degree of the monomials desired. Will decrease as the function searches recursively. - spot : int - The current position in the list the function is iterating through. Defaults to 0, but increases - in each step of the recursion. - - Returns - ----------- - answers : list - A list of all the monomials. - ''' - answers = list() - if len(mon) == spot+1: #We are at the end of mon, no more recursion. - for i in range(numLeft+1): - mon[spot] = i - answers.append(mon.copy()) - return answers - if numLeft == 0: #Nothing else can be added. - answers.append(mon.copy()) - return answers - temp = mon.copy() #Quicker than copying every time inside the loop. - for i in range(numLeft+1): #Recursively add to mon further down. - temp[spot] = i - answers += mon_combos(temp, numLeft-i, spot+1) - return answers - -def sort_polys_by_degree(polys, ascending = True): - '''Sorts the polynomials by their degree. - - Parameters - ---------- - polys : list. - A list of polynomials. - ascending : bool - Defaults to True. If True the polynomials are sorted in order of ascending degree. If False they - are sorted in order of descending degree. - Returns - ------- - sorted_polys : list - A list of the same polynomials, now sorted. - ''' - degs = [poly.degree for poly in polys] - argsort_list = np.argsort(degs) - sorted_polys = list() - for i in argsort_list: - sorted_polys.append(polys[i]) - if ascending: - return sorted_polys - else: - return sorted_polys[::-1] - - -def makePolyCoeffMatrix(inputString): - ''' - Takes a string input of a polynomaial and returns the coefficient matrix for it. Usefull for making things of high - degree of dimension so you don't have to make it by hand. - - All strings must be of the following syntax. Ex. '3x0^2+2.1x1^2*x2+-14.73x0*x2^3' - - 1. There can be no spaces. - 2. All monomials must be seperated by a '+'. If the coefficient of the monomial is negative then the '-' sign - should come after the '+'. This is not needed for the first monomial. - 3. All variables inside a monomial are seperated by a '*'. - 4. The power of a variable in a monomial is given folowing a '^' sign. - ''' - matrixSpots = list() - coefficients = list() - for monomial in inputString.split('+'): - coefficientString = monomial[:first_x(monomial)] - if coefficientString == '-': - coefficient = -1 - elif coefficientString == '': - coefficient = 1 - else: - coefficient = float(coefficientString) - mons = monomial[first_x(monomial):].split('*') - matrixSpot = [0] - for mon in mons: - stuff = mon.split('^') - if len(stuff) == 1: - power = 1 - else: - power = int(stuff[1]) - if stuff[0] == '': - varDegree = -1 - else: - varDegree = int(stuff[0][1:]) - if varDegree != -1: - if len(matrixSpot) <= varDegree: - matrixSpot = np.append(matrixSpot, [0]*(varDegree - len(matrixSpot)+1)) - matrixSpot[varDegree] = power - matrixSpots.append(matrixSpot) - coefficients.append(coefficient) - #Pad the matrix spots so they are all the same length. - length = max(len(matrixSpot) for matrixSpot in matrixSpots) - for i in range(len(matrixSpots)): - matrixSpot = matrixSpots[i] - if len(matrixSpot) < length: - matrixSpot = np.append(matrixSpot, [0]*(length - len(matrixSpot))) - matrixSpots[i] = matrixSpot - matrixSize = np.maximum.reduce([matrixSpot for matrixSpot in matrixSpots]) - matrixSize = matrixSize + np.ones_like(matrixSize) - matrixSize = matrixSize[::-1] #So the variables are in the right order. - matrix = np.zeros(matrixSize) - for i in range(len(matrixSpots)): - matrixSpot = matrixSpots[i][::-1] #So the variables are in the right order. - coefficient = coefficients[i] - matrix[tuple(matrixSpot)] = coefficient - return matrix - - -def match_size(a,b): - ''' - Matches the shape of two ndarrays. - - Parameters - ---------- - a, b : ndarray - Arrays whose size is to be matched. - - Returns - ------- - a, b : ndarray - Arrays of equal size. - ''' - new_shape = np.maximum(a.shape, b.shape) - - a_new = np.zeros(new_shape) - a_new[slice_top(a)] = a - b_new = np.zeros(new_shape) - b_new[slice_top(b)] = b - return a_new, b_new - - -def slice_top(arr): - '''Construct a list of slices needed to put an array into the upper left - corner of another. - - Parameters - ---------- - arr : ndarray - The array of interest. - Returns - ------- - slices : list - Each value of the list is a slice of the array in some dimension. - It is exactly the size of the array. - ''' - slices = list() - for i in arr.shape: - slices.append(slice(0,i)) - return slices - -def slice_bottom(arr): - ''' Gets the n-d slices needed to slice an array into the bottom - corner of another. - - Parameters - ---------- - arr : ndarray - The array of interest. - - Returns - ------- - slices : list - Each value of the list is a slice of the array in some dimension. - It is exactly the size of the array. - ''' - slices = list() - for i in arr.shape: - slices.append(slice(-i,None)) - return slices - - -def get_var_list(dim): - '''Return a list of tuples corresponding to the - variables [x_1, x_2, ..., x_n]. The tuple for x_1 - is (1,0,0,...,0), and for x_i the 1 is in the ith slot. - ''' - - _vars = [] - var = [0]*dim - for i in range(dim): - var[i] = 1 - _vars.append(tuple(var)) - var[i] = 0 - return _vars - - diff --git a/CHEBYSHEV/TVB_Method/cheb_utils.py b/CHEBYSHEV/TVB_Method/cheb_utils.py deleted file mode 100644 index 55e1ebe6..00000000 --- a/CHEBYSHEV/TVB_Method/cheb_utils.py +++ /dev/null @@ -1,423 +0,0 @@ -# A collection of functions used in the F4 Macaulay and TVB solvers -import numpy as np -import itertools -from scipy.linalg import qr, solve_triangular -from scipy.misc import comb -#from TVB_Method.root_finder import newton_polish - -class TVBError(RuntimeError): - pass - -class Term(object): - ''' - Terms are just tuples of exponents with the grevlex ordering - ''' - def __init__(self,val): - self.val = tuple(val) - - def __repr__(self): - return str(self.val) + ' with grevlex order' - - def __lt__(self, other, order = 'grevlex'): - ''' - Redfine less-than according to grevlex - ''' - if order == 'grevlex': #Graded Reverse Lexographical Order - if sum(self.val) < sum(other.val): - return True - elif sum(self.val) > sum(other.val): - return False - else: - for i,j in zip(reversed(self.val),reversed(other.val)): - if i < j: - return False - if i > j: - return True - return False - elif order == 'lexographic': #Lexographical Order - for i,j in zip(self.val,other.val): - if i < j: - return True - if i > j: - return False - return False - elif order == 'grlex': #Graded Lexographical Order - if sum(self.val) < sum(other.val): - return True - elif sum(self.val) > sum(other.val): - return False - else: - for i,j in zip(self.val,other.val): - if i < j: - return True - if i > j: - return False - return False - -def row_swap_matrix(matrix): - '''Rearrange the rows of matrix so it is close to upper traingular. - - Parameters - ---------- - matrix : 2D numpy array - The matrix whose rows need to be switched - - Returns - ------- - 2D numpy array - The same matrix but with the rows changed so it is close to upper - triangular - - Examples - -------- - >>> utils.row_swap_matrix(np.array([[0,2,0,2],[0,1,3,0],[1,2,3,4]])) - array([[1, 2, 3, 4], - [0, 2, 0, 2], - [0, 1, 3, 0]]) - ''' - leading_mon_columns = list() - for row in matrix: - leading_mon_columns.append(np.where(row!=0)[0][0]) - - return matrix[np.argsort(leading_mon_columns)] - -def clean_zeros_from_matrix(array, accuracy=1.e-10): - '''Sets all values in the array less than the given accuracy to 0. - - Parameters - ---------- - array : numpy array - accuracy : float, optional - Values in the matrix less than this will be set to 0. - - Returns - ------- - array : numpy array - Same array, but with values less than the given accuracy set to 0. - ''' - array[(array < accuracy) & (array > -accuracy)] = 0 - return array - -def slice_top(matrix): - '''Construct a list of slices needed to slice a matrix into the top - corner of another. - - Parameters - ---------- - coeff : numpy matrix. - The matrix of interest. - Returns - ------- - slices : list - Each value of the list is a slice of the matrix in some dimension. - It is exactly the size of the matrix. - ''' - slices = list() - for i in matrix.shape: - slices.append(slice(0,i)) - return slices - -def slice_bottom(matrix): - ''' Gets the n-d slices needed to slice a matrix into the bottom corner of another. - - Parameters - ---------- - coeff : numpy matrix. - The matrix of interest. - Returns - ------- - slices : list - Each value of the list is a slice of the matrix in some dimension. It is exactly the size of the matrix. - ''' - slices = list() - for i in matrix.shape: - slices.append(slice(-i,None)) - return slices - -def match_poly_dimensions(polys): - '''Matches the dimensions of a list of polynomials. - - Parameters - ---------- - polys : list - Polynomials of possibly different dimensions. - - Returns - ------- - new_polys : list - The same polynomials but of the same dimensions. - ''' - dim = max(poly.dim for poly in polys) - new_polys = list() - for poly in polys: - if poly.dim != dim: - coeff_shape = list(poly.shape) - for i in range(dim - poly.dim): - coeff_shape.insert(0,1) - poly.__init__(poly.coeff.reshape(coeff_shape)) - new_polys.append(poly) - return new_polys - -def match_size(a,b): - ''' - Matches the shape of two matrixes. - - Parameters - ---------- - a, b : ndarray - Matrixes whose size is to be matched. - - Returns - ------- - a, b : ndarray - Matrixes of equal size. - ''' - new_shape = np.maximum(a.shape, b.shape) - - a_new = np.zeros(new_shape) - a_new[slice_top(a)] = a - b_new = np.zeros(new_shape) - b_new[slice_top(b)] = b - return a_new, b_new - - -def get_var_list(dim): - '''Return a list of tuples corresponding to the variables [x_1, x_2, ..., x_n]. - The tuple for x_1 is (1,0,0,...,0), and for x_i the 1 is in the ith slot. - ''' - _vars = [] - var = [0]*dim - for i in range(dim): - var[i] = 1 - _vars.append(tuple(var)) - var[i] = 0 - return _vars - - -def mon_combosHighest(mon, numLeft, spot = 0): - '''Find all the monomials of a given degree and returns them. Works recursively. - - Very similar to mon_combos, but only returns the monomials of the desired degree. - - Parameters - -------- - mon: list - A list of zeros, the length of which is the dimension of the desired monomials. Will change - as the function searches recursively. - numLeft : int - The degree of the monomials desired. Will decrease as the function searches recursively. - spot : int - The current position in the list the function is iterating through. Defaults to 0, but increases - in each step of the recursion. - - Returns - ----------- - answers : list - A list of all the monomials. - ''' - answers = list() - if len(mon) == spot+1: #We are at the end of mon, no more recursion. - mon[spot] = numLeft - answers.append(mon.copy()) - return answers - if numLeft == 0: #Nothing else can be added. - answers.append(mon.copy()) - return answers - temp = mon.copy() #Quicker than copying every time inside the loop. - for i in range(numLeft+1): #Recursively add to mon further down. - temp[spot] = i - answers += mon_combosHighest(temp, numLeft-i, spot+1) - return answers - -def mon_combos(mon, numLeft, spot = 0): - '''Finds all the monomials up to a given degree and returns them. Works recursively. - - Parameters - -------- - mon: list - A list of zeros, the length of which is the dimension of the desired monomials. Will change - as the function searches recursively. - numLeft : int - The degree of the monomials desired. Will decrease as the function searches recursively. - spot : int - The current position in the list the function is iterating through. Defaults to 0, but increases - in each step of the recursion. - - Returns - ----------- - answers : list - A list of all the monomials. - ''' - answers = list() - if len(mon) == spot+1: #We are at the end of mon, no more recursion. - for i in range(numLeft+1): - mon[spot] = i - answers.append(mon.copy()) - return answers - if numLeft == 0: #Nothing else can be added. - answers.append(mon.copy()) - return answers - temp = mon.copy() #Quicker than copying every time inside the loop. - for i in range(numLeft+1): #Recursively add to mon further down. - temp[spot] = i - answers += mon_combos(temp, numLeft-i, spot+1) - return answers - -def sort_polys_by_degree(polys, ascending = True): - '''Sorts the polynomials by their degree. - - Parameters - ---------- - polys : list. - A list of polynomials. - ascending : bool - Defaults to True. If True the polynomials are sorted in order of ascending degree. If False they - are sorted in order of descending degree. - Returns - ------- - sorted_polys : list - A list of the same polynomials, now sorted. - ''' - degs = [poly.degree for poly in polys] - argsort_list = np.argsort(degs) - sorted_polys = list() - for i in argsort_list: - sorted_polys.append(polys[i]) - if ascending: - return sorted_polys - else: - return sorted_polys[::-1] - -def makePolyCoeffMatrix(inputString): - ''' - Takes a string input of a polynomaial and returns the coefficient matrix for it. Usefull for making things of high - degree of dimension so you don't have to make it by hand. - - All strings must be of the following syntax. Ex. '3x0^2+2.1x1^2*x2+-14.73x0*x2^3' - - 1. There can be no spaces. - 2. All monomials must be seperated by a '+'. If the coefficient of the monomial is negative then the '-' sign - should come after the '+'. This is not needed for the first monomial. - 3. All variables inside a monomial are seperated by a '*'. - 4. The power of a variable in a monomial is given folowing a '^' sign. - ''' - matrixSpots = list() - coefficients = list() - for monomial in inputString.split('+'): - coefficientString = monomial[:first_x(monomial)] - if coefficientString == '-': - coefficient = -1 - elif coefficientString == '': - coefficient = 1 - else: - coefficient = float(coefficientString) - mons = monomial[first_x(monomial):].split('*') - matrixSpot = [0] - for mon in mons: - stuff = mon.split('^') - if len(stuff) == 1: - power = 1 - else: - power = int(stuff[1]) - if stuff[0] == '': - varDegree = -1 - else: - varDegree = int(stuff[0][1:]) - if varDegree != -1: - if len(matrixSpot) <= varDegree: - matrixSpot = np.append(matrixSpot, [0]*(varDegree - len(matrixSpot)+1)) - matrixSpot[varDegree] = power - matrixSpots.append(matrixSpot) - coefficients.append(coefficient) - #Pad the matrix spots so they are all the same length. - length = max(len(matrixSpot) for matrixSpot in matrixSpots) - for i in range(len(matrixSpots)): - matrixSpot = matrixSpots[i] - if len(matrixSpot) < length: - matrixSpot = np.append(matrixSpot, [0]*(length - len(matrixSpot))) - matrixSpots[i] = matrixSpot - matrixSize = np.maximum.reduce([matrixSpot for matrixSpot in matrixSpots]) - matrixSize = matrixSize + np.ones_like(matrixSize) - matrixSize = matrixSize[::-1] #So the variables are in the right order. - matrix = np.zeros(matrixSize) - for i in range(len(matrixSpots)): - matrixSpot = matrixSpots[i][::-1] #So the variables are in the right order. - coefficient = coefficients[i] - matrix[tuple(matrixSpot)] = coefficient - return matrix - -def check_zeros(zeros, polys, real=True, tol=1e-5): - """ - Check whether 'zeros' are, indeed, all the zeros of 'polys', and how many are - outside the sup-norm unit ball |z|_\infty < 1. - - Parameters - ---------- - zeros : list - Supposed roots (usually found using the root finder). - polys : list - Polynomials that 'zeros' should be roots of. - real : bool - Whether to check that the bad zeros are real (real=True) or - not to check (real=False) - - Prints - ---------- - The number of correct zeroes found with the total number. - The number of incorrect zeros that were out of range or nonreal. - - Returns - ---------- - A list of bad zeros. - - - """ - correct = 0 - outOfRange = 0 - bad = set() - bad_inrange = [] - if zeros != -1: - for zero in zeros: - good = True - for poly in polys: - v = poly.evaluate_at(zero) - if np.abs(v) > tol: - good = False - bad.add(tuple(zero)) - if good: - correct += 1 - - bad_list = [np.array(zero) for zero in bad] - for zero in bad_list: - if (np.abs(zero) > 1).any(): - outOfRange += 1 - elif real and np.any(np.abs(np.imag(zero))>tol): - outOfRange += 1 - real_status = 'not real or' - else: - bad_inrange.append(zero) - - print("{} zeros are correct to {}, out of {} total zeros.".format(correct, tol,len(zeros))) - print("{} are bad, but {} of these were {} out of range (expected to be bad).".format(len(bad), outOfRange, real_status)) - print("{} might be lost".format(len(bad_inrange))) - - better = [] - diff = [] - newt_bad = set() - for zero in bad_inrange: - newt_zero = newton_polish(polys,zero,tol=1e-10) - better.append(newt_zero) - diff.append(zero - newt_zero) - for poly in polys: - v = poly.evaluate_at(newt_zero) - if np.abs(v) > tol: - newt_bad.add(tuple(newt_zero)) - - print('{} seem to be lost after newton polishing'.format(len(newt_bad))) - #print("Differences between the bad zeros and the polished ones are {}".format(diff)) - - return bad_inrange, newt_bad - - - - diff --git a/CHEBYSHEV/TVB_Method/root_finder.py b/CHEBYSHEV/TVB_Method/root_finder.py deleted file mode 100644 index 3109c196..00000000 --- a/CHEBYSHEV/TVB_Method/root_finder.py +++ /dev/null @@ -1,345 +0,0 @@ -import numpy as np -import itertools -import warnings -from CHEBYSHEV.TVB_Method.cheb_class import MultiCheb, Term, TVBError, match_size, match_poly_dimensions, get_var_list -from CHEBYSHEV.TVB_Method.TVB import telen_van_barel - -'''This module contains methods for finding the zero locus of a - 0-dimensional ideal defined by a list of Chebyshev polynomials. - Roots are found using a variant of the Telen-vanBarel method. - Speed and accuracy are good for real roots whose coordinates have - norm 1 or less, but ill-conditioning makes them less accurate for - roots with norm > 1. - - -Methods: - - roots(polys) : Find common roots of 'polys'. - - sortVB(VB) : Sort a vector-space basis to improve speed of eigensolving. - - TVBMultMatrix(polys,f) : Find the matrix rep of the multiply-by-f - operator in the TvB basis - - _random_poly(dim) : Construct a random linear Chebyshev polynomial for TVBMultMatrix - - newton_polish(polys,roots) : Use Newton's method to polish a collection of - approximate roots. - - check_zeros(zeros, polys): Check whether 'zeros' are, indeed, all the zeros of - 'polys', and how many are outside the sup-norm unit - ball |z|_\infty < 1 or are not real. - - -''' - - -def roots(polys): - '''Find the common roots of the given list of polynomials. - - Uses a variant of the Telen-vanBarel method for reducing the - Macaulay matrix to find a basis of the quotient ring - C[x_1,...,x_n]/I, where I is the ideal generated by 'polys'. - Given the basis, finds the roots by finding the left - eigenvector of the multiply-by-f matrix, where f is a random - (or pre-chosen) linear polynomial. - - Parameters - ---------- - polys : list of polynomial objects - Polynomials to find the common roots of. - - returns - ------- - list of numpy arrays - the common roots of the polynomials - - ''' - - polys = match_poly_dimensions(polys) #polynomials might not all be the same dimension, initially. - - m_f, var_dict = TVBMultMatrix(polys) # m_f is the operator whose eigenvectors give the roots - # var_dict gives the location of each variable in the - # basis (this is needed for identifying the root in the - # eigenvector) - - # Get list of indices of single variables and store vars that were not - # in the vector space basis (if x_i is not in the basis, this is indicated - # by var_indices[i] = -1). - - dim = max(f.dim for f in polys) # Number of variables in C[x_1,...,x_n] - var_list = get_var_list(dim) # Variables x_1,...,x_n, given as tuples: x_1 is (1,0,0,...,0) etc - var_indices = [-1]*dim # Make a list of the indices - for i in range(dim): - var = var_list[i] # x_i - var_indices[i] = var_dict[var] #location of x_i in the TvB basis - - - # Get left eigenvectors of m_f - - eig = np.linalg.eig(m_f.T)[1] # This is one of the two most expensive steps in this algorithm. - num_vectors = eig.shape[1] - - eig_vectors = [eig[:,i] for i in range(num_vectors)] # columns of eig - roots = [] - for v in eig_vectors: - if v[var_dict[tuple(0 for i in range(dim))]] == 0: - continue - root = np.zeros(dim, dtype=complex) - # This will always work because var_indices and root have the - # same length - dim - and var_indices has the variables in the - # order they should be in the root - for i in range(dim): - x_i_pos = var_indices[i] - if x_i_pos != -1: - root[i] = v[x_i_pos]/v[var_dict[tuple(0 for i in range(dim))]] - roots.append(root) - #roots.append(newton_polish(polys,root,niter=1000,tol=1e-10)) - return roots - -def sortVB(VB): - ''' - Sorts the Vector Basis into degrevlex order so the eigensolve is faster (in theory). - - Parameters - ---------- - VB : numpy array - Each row in VB is a term in the vector basis. - - Returns - ------- - VB : numpy array - The vector basis sorted so the lowest terms are at the top. - ''' - - VBList = list() - for i in VB: - VBList.append(Term(i)) - - return VB[np.argsort(VBList)] - -def TVBMultMatrix(polys,f=None): - - '''Find the multiplication-by-f matrix on C[x_1,...,x_n]/I, where I is - the idea generated by 'polys', expressed in the TvB basis. - - Parameters - ---------- - polys : array-like - The polynomials to find the common zeros of - - f : MultiCheb polynomial (linear) - The linear polynomial defining the multiplication-by-f operator. - If None, f is chosen randomly. - - Returns - ------- - multiplicationMatrix : 2D numpy array - The matrix representation of the multiplication-by-f operator on the TvB basis. - - var_dict : dictionary - Maps each variable to its position in the TvB vector space basis. - - ''' - - basisDict, VB, degree = telen_van_barel(polys) - - VB = sortVB(VB) - - dim = max(g.dim for g in polys) - - if not f: - # Get random linear polynomial f - f = _random_poly(dim)[0] - - slices = list() - for i in range(len(VB[0])): - slices.append(VB.T[i]) - - VBset = {tuple(mon) for mon in VB} - - # Build multiplication-by-f matrix 'mMatrix' - mMatrix = np.zeros((len(VB), len(VB))) - remainder = np.zeros([degree]*dim) - - for i in range(VB.shape[0]): - f_coeff = f.mon_mult(VB[i], return_type = 'Matrix') - for term in zip(*np.where(f_coeff != 0)): - if term in VBset: - remainder[term] += f_coeff[term] - else: - remainder[slices] -= f_coeff[term]*basisDict[term][slices] - mMatrix[:,i] = remainder[slices] - remainder[slices] = 0 - - # Construct var_dict - var_dict = {} - for i,mon in enumerate(VB): - if np.sum(mon) == 1 or np.sum(mon) == 0: - var_dict[tuple(mon)] = i - - return mMatrix, var_dict - - -def _random_poly(dim): - '''Generate a random linear (Chebyshev) polynomial of the form - c_1 x_1 + c_2 x_2 + ... + c_n x_n, where n = dim and each c_i is a - randomly chosen integer between 0 and 1000. - - Parameters - ---------- - dim : int - Number of variables - - Returns - ------- - polynomial - Randomly generated Chebyshev polynomial of degree 1 and dimension 'dim'. - - ''' - - _vars = get_var_list(dim) #list of tuples corresponding to the variables - - random_poly_coeff = np.zeros([2]*dim, dtype=int) - for var in _vars: - random_poly_coeff[var] = np.random.randint(1000) - - return MultiCheb(random_poly_coeff), _vars - - - -########## Newton ########################################## - -def newton_polish(polys,root,niter=100,tol=1e-8): - """ - Perform Newton's method on a system of N polynomials in M variables. - - Parameters - ---------- - polys : list - A list of polynomial objects of the same type (MultiPower or MultiCheb). - root : ndarray - An initial guess for Newton's method, intended to be a candidate root from root_finder. - niter : int - A maximum number of iterations of Newton's method. - tol : float - Tolerance for convergence of Newton's method. - - Returns - ------- - x1 : ndarray - The terminal point of Newton's method, an estimation for a root of the system - """ - - m = len(polys) - dim = max(poly.dim for poly in polys) - f_x = np.empty(m,dtype="complex_") - jac = np.empty((m,dim),dtype="complex_") - - def f(x): - #f_x = np.empty(m,dtype="complex_") - for i, poly in enumerate(polys): - f_x[i] = poly(x) - return f_x - - def Df(x): - #jac = np.empty((m,dim),dtype="complex_") - for i, poly in enumerate(polys): - jac[i] = poly.grad(x) - return jac - - i = 0 - x0, x1 = root, root - while True: - if i == niter: - break - delta = np.linalg.solve(Df(x0),-f(x0)) - x1 = delta + x0 - if np.linalg.norm(delta) < tol: - break - x0 = x1 - i+=1 - return x1 - - - - -######### Testing Funtions ########## - -def check_zeros(zeros, polys, real=True, tol=1e-5): - """ - Check whether 'zeros' are, indeed, all the zeros of 'polys', and how many are - outside the unit ball |z|_\infty < 1. - - Parameters - ---------- - zeros : list - Supposed roots (usually found using the root finder). - polys : list - Polynomials that 'zeros' should be roots of. - real : bool - Whether to check that the bad zeros are real (real=True) or - not to check (real=False) - - Prints - ---------- - The number of correct zeroes found with the total number. - The number of incorrect zeros that were out of range or nonreal. - - Returns - ---------- - A list of bad zeros. - - - """ - correct = 0 - outOfRange = 0 - bad = set() - bad_inrange = [] - if zeros != -1: - for zero in zeros: - good = True - for poly in polys: - v = poly(zero) - if np.abs(v) > tol: - good = False - bad.add(tuple(zero)) - if good: - correct += 1 - - - real_status = '' - bad_list = [np.array(zero) for zero in bad] - for zero in bad_list: - if (np.abs(zero) > 1).any(): - outOfRange += 1 - elif real and np.any(np.abs(np.imag(zero))>tol): - outOfRange += 1 - real_status = 'not real or' - else: - bad_inrange.append(zero) - - print("{} zeros are correct to {}, out of {} total zeros.".format(correct, tol,len(zeros))) - print("{} are bad, but {} of these were {} out of range (expected to be bad).".format(len(bad), outOfRange, real_status)) - print("{} might be lost".format(len(bad_inrange))) - - better = [] - diff = [] - newt_bad = set() - for zero in bad_inrange: - newt_zero = newton_polish(polys,zero,tol=1e-10) - better.append(newt_zero) - diff.append(zero - newt_zero) - for poly in polys: - v = poly(newt_zero) - if np.abs(v) > tol: - newt_bad.add(tuple(newt_zero)) - - print('{} seem to be lost after newton polishing'.format(len(newt_bad))) - print("Differences between the 'bad' inrange zeros and the polished ones are {}".format(diff)) - - #return bad_inrange, newt_bad - - - - diff --git a/CHEBYSHEV/tvb_times.npy b/CHEBYSHEV/tvb_times.npy deleted file mode 100644 index e5f3039b..00000000 Binary files a/CHEBYSHEV/tvb_times.npy and /dev/null differ diff --git a/Chebfun_results/actualroots_1.4.csv b/Chebfun_results/actualroots_1.4.csv new file mode 100644 index 00000000..56975c50 --- /dev/null +++ b/Chebfun_results/actualroots_1.4.csv @@ -0,0 +1 @@ +-0.25,0.25 diff --git a/Chebfun_results/actualroots_1.5.csv b/Chebfun_results/actualroots_1.5.csv new file mode 100644 index 00000000..819c74c3 --- /dev/null +++ b/Chebfun_results/actualroots_1.5.csv @@ -0,0 +1 @@ +0.730769230769231,-0.465384615384615 diff --git a/Chebfun_results/actualroots_6.2.csv b/Chebfun_results/actualroots_6.2.csv new file mode 100644 index 00000000..0d89bef3 --- /dev/null +++ b/Chebfun_results/actualroots_6.2.csv @@ -0,0 +1,6 @@ +0.0001,-5e-05 +0.0001,0.0002 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b/Chebfun_results/test_roots_1.5.csv @@ -0,0 +1 @@ +0.730769230769231,-0.465384615384615 diff --git a/Chebfun_results/test_roots_10.1.csv b/Chebfun_results/test_roots_10.1.csv new file mode 100644 index 00000000..27f28d53 --- /dev/null +++ b/Chebfun_results/test_roots_10.1.csv @@ -0,0 +1,17 @@ +1,-1 +1,-0.875 +1,-0.75 +1,-0.625 +1,-0.5 +1,-0.375 +1,-0.25 +1,-0.125 +1,-6.44710952757908e-18 +1,0.125 +1,0.25 +1,0.375 +1,0.5 +1,0.625 +1,0.75 +1,0.875 +1,1 diff --git a/Chebfun_results/test_roots_2.1.csv b/Chebfun_results/test_roots_2.1.csv new file mode 100644 index 00000000..bbea9d5e --- /dev/null +++ b/Chebfun_results/test_roots_2.1.csv @@ -0,0 +1,6 @@ +-0.851255480369189,-0.922635074322014 +-0.851255480369189,0.922635074322014 +-0.605564983707128,-0.778180559836294 +-0.605564983707128,0.778180559836294 +-0.291125279181606,-0.539560264642983 +-0.291125279181606,0.539560264642983 diff --git a/Chebfun_results/test_roots_2.2.csv b/Chebfun_results/test_roots_2.2.csv new file mode 100644 index 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/dev/null +++ b/DemoNotebook.ipynb @@ -0,0 +1,1627 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

YRoots

\n", + "\n", + "YRoots is a numerical rootfinding package to find all the real roots of a system of equations in a compact interval in $\\mathbb{R}^n$ under mild assumptions on the roots. For example, we assume that there are only finitely many solutions in the interval." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Installation\n", + "\n", + "In Linux or a Mac terminal, download YRoots from github with the following command, while in the directory where you want YRoots installed:\n", + "\n", + " ```git\n", + " git clone https://github.com/tylerjarvis/RootFinding/tree/master \n", + " ```\n", + " \n", + "Now make the yroots module availabe to Python with: \n", + "\n", + " ```\n", + " pip install -e ./RootFinding\n", + " ```\n", + "That's it. You can use yroots in this notebook or in your own python code." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Some examples of how to use yroots" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "#imports\n", + "import numpy as np\n", + "import yroots as yr\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "#plotting tools\n", + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multivariate Functions\n", + "\n", + "To find the commmon zeros of a set of multivariate functions, input a list of functions and a search interval. The syntax for this is:\n", + "\n", + "```python\n", + "yr.solve(funcs, a, b)\n", + "```\n", + "\n", + "where `funcs` is a list of $n$ **vectorized** functions in $n$ variables and `a` and `b` are array-like objects of upper and lower bounds (respectively) of the search domain in each dimension. For bivariate systems, the optional parameter `plot` allows the user to graph the zero-loci and roots of the functions.\n", + "\n", + "YRoots returns a numpy array where each row is a root." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Two variables, basic example\n", + "Here is an example of YRoots solving the bivariate system of equations\n", + "\n", + "$$0 = \\sin(xy) + x\\log(y+3) - x^2 + \\frac{1}{y-4}$$\n", + "$$6 = \\cos(3xy) + e^{\\frac{3y}{x-2}} - x.$$\n", + "\n", + "Solutions of the system subject to the constrains $-1\\leq x\\leq0,-2\\leq y\\leq1$ are common roots of the functions\n", + "\n", + "$$f(x,y) = \\sin(xy) + x\\log(y+3) - x^2 + \\frac{1}{y-4} $$\n", + "$$g(x,y) = \\cos(3xy) + e^{\\frac{3y}{x-2}} - x - 6$$ on the search domain $[-1,0]\\times[-2,1]$." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Percent Finished: 100% \n", + "Total intervals checked was 30\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [30. 6.6667 0. 0. 63.3333]\n", + "CPU times: user 35.2 ms, sys: 1.37 ms, total: 36.6 ms\n", + "Wall time: 36.1 ms\n", + "[[-0.73720226 -1.65461673]\n", + " [-0.410034 -1.40471685]]\n" + ] + } + ], + "source": [ + "#define the functions\n", + "f = lambda x,y : np.sin(x*y) + x*np.log(y+3) - x**2 + 1/(y-4)\n", + "g = lambda x,y : np.cos(3*x*y) + np.exp(3*y/(x-2)) - x - 6\n", + "\n", + "#search domain bounds\n", + "a = [-1,-2] #lower bounds on x and y\n", + "b = [0,1] #upper bounds on x and y\n", + "\n", + "\n", + "# compute the roots and time the process\n", + "%time roots = yr.solve([f,g], a, b)\n", + "\n", + "print(roots)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Percent Finished: 100% \n", + "Total intervals checked was 30\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [30. 6.6667 0. 0. 63.3333]\n", + "[[array([-0.73720226, -1.65461673]), array([-0.73720226, -1.65461672])], [array([-0.410034 , -1.40471685]), array([-0.410034 , -1.40471685])]]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the zero loci of each function and the common roots\n", + "\n", + "roots = yr.solve([f,g], a, b, plot=True, plot_intervals=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How good are these estimated roots? Let's compute residuals, or the function values at the computed roots. We expect them to be very close to zero." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "residuals for f are [6.24500451e-15 1.24900090e-14]\n", + "residuals for g are [7.46069873e-14 6.83897383e-14]\n" + ] + } + ], + "source": [ + "#Compute the residuals for each function and root\n", + "print('residuals for f are {}'.format(np.abs(f(roots[:,0],roots[:,1]))))\n", + "print('residuals for g are {}'.format(np.abs(g(roots[:,0],roots[:,1]))))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Two variables, second example\n", + "\n", + "Here is a slightly more complex system on the search domain $[-1,1]\\times[-1,1]$ from [this paper](https://link.springer.com/article/10.1007/s00211-014-0635-z).\n", + "\n", + "$$f(x,y) =\\sin(30x−y/30)+y$$\n", + "$$g(x,y) =\\cos(x/30−30y)−x$$" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Percent Finished: 100% \n", + "Total intervals checked was 5\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [ 0. 20. 0. 0. 80.]\n", + "[[array([-0.00256965, -0.00256965]), array([0.00256965, 0.00256965])]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 713 ms, sys: 26.8 ms, total: 740 ms\n", + "Wall time: 750 ms\n" + ] + } + ], + "source": [ + "#functions\n", + "f = lambda x,y: x + y\n", + "g = lambda x,y: x - y\n", + "#search domain\n", + "a = [-1,-1] #lower\n", + "b = [1,1] #upper\n", + "#time\n", + "%time roots = yr.solve([f,g], a, b,plot=True, plot_intervals=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 2840\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [ 8.1338 15.8803 0. 0. 75.9859]\n", + "[[array([-0.98523348, -0.94930797]), array([-0.98523348, -0.94930796])], 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i4J2kxhcNFrNsloAxpsJaOzaPdZwqFuK4AMAY00zq+HgdUOx4pdlq8mynhNyQfveJCAONMS8i1eK1JuuufwfeVcj4j0tk0Z9HjTE1wJqsyY/OcbsAu1BIKSIiInLKUUgpIiIiIiInk4E5Lpcd3i3lZ6F6IJw1rXbqayHVAd0Fztsz341Za9uMMfcCl6RNvs4Y825rbTTPon+edduhiBaQxpgqUi1/X1vwzs6uGlBIuQDHBYAx5i+BfwGqFmJ95L+ooAHwZ017YoG2W4yrgA8AobRpDvAea+1nT8D+5LNQz6P5Lg6od5l2aI7bhdzqcRERERE5BWhMShEREREROZkUWim4nLidrF8MpUXMO7JA2/x61u1acsfhm2GMKSd3fM7fWGuPFLKxqYDy1yxsQAnzq8Q8lcz7uDDGvBf4EgsXUEL+v092VTHA4AJuu1AXkRlQArx7GQaUsDTPo24XYWRXYhZjPsuKiIiIyDKlkFJERERERGRxLXTF5EJILNB6fghkt9XMrpRM9xqgImtadtCZz2fIbZ0LqfaV/0YqvLwIWE2q+q7EWmvSv2bZv5VuXseFMeb5wKdc7hoHvge8ndT4kVtI/V+UAT6Xv1Ex3MLQE1EV6/bYvXdqjMqVqMRlWmwe68tXnS0iIiIiJym1exUREREREVlcky7TfmCtfd2S78kCs9aOGmNuBt6QNvkKY0yrtbbdZZHsgHAI+Ekh2zLGnAW8OWvyGPBW4HtFjGlZTMWpFOffXKZ9k9R4jEOFrMAYU+zfx636MzsIXwqfBc4FXpI2bTXwO2PMH1lrd52AfTqR3CofK+exvoWszBURERGRZUKVlCIiIiIiIourz2WaW4vKk1V2JaQPuCF7JmPMJuAFWZO/b62NFLidPwWyq+zeaK39bhEBJZxaj/2yYYzZAlyQNfnn1to/LzSgnFLs36ffZdqJqF4eB/4YuCVreiPwG2PMc5d+l04ot5a782l9vVRts0VERERkCSmkFBERERERWVy9QHaItv5E7MgiuRs4kDXtTS7zvYnckLGYVq8vzbq9x1r74yKWn7ZpDsvI7LL/PgAfmcN6iv379JHbavXsOWx33qy1UeBVwPez7qoBbjfGXL7kO3Xi9JDbovWseazvhPxNRURERGRxKaQUERERERFZRFOVgo9lTd5qjGk6Efuz0KaqGL+ZNXlbeuWYMcaQ2RIWUiHjQ0Vsam3W7XuKWDbdSqtoWyrZf5+ItfbhOaynqL+PtTYGPJo1+VxjTPkctj1v1toE8Hrgq1l3VQC3GmNetvR7tfSstXFgd9bki4wxcz0P5TYWrYiIiIic5BRSioiIiIiIuMuuzgLwz3Fdd7hMe+Uc17UcfRNwsqaljz95ObnVo98ochsNWbcHilx+elzL04tdTgoy77/PlNfOYZm7sm4HgBM25qu11rHW/gWpcSrThYGfGGNes/R7dULcl3W7GXhRsSsxxmwlt5WwiIiIiJwCFFKKiIiIiIi4G3WZVjHHdf3MZdrfGmMCc1zfsmKtPQrcmTX5tcaY0qmf35R1XwL4dpGbGc+6nR2KFeLdc1hGCpP996kttmrOGHMpcP4ctv0Tl2nvOtH/X9badwEfy5ocBG4yxrxp6fdoyf3AZdrfzmE9/3e+OyIiIiIiy5NCShEREREREXej5FZTzmk8Q2vtH8it9toE/Otc1rdMZVdGVgOvNMZUkVs1equ1tqfI9Xdm3X5JMSGYMeYlwBuL3KYULvvvUwq8oNCFjTFlwJfnsmFr7f2kxkZNtwP4p7msbyFZa/8eeG/WZD/wdWPM/zkBu7Rkpv4uu7ImX2mM+bNC1zE1jueNC7pjIiIiIrJsKKQUERERERFxYa11gKeyJv/RPMZU+xBgs6b9jTHmn6bGbCyaMeZMY8y3jDG1c9ynhfQTYChr2p8DfwqUZU0vttUr5I5BuQl4ayELGmPOA24C5vQ4S0Hcxgj9uDEmONuCUxW3PwK2zmP7H3GZ9gFjTNHVs8aYEmNM8zz2JYO19l+At5P5/2+ALxhj3rdQ21mmPuoy7WvGmFnbXRtjXgj8FP3fioiIiJyyFFKKiIiIiIh4uzfr9jbgq8aY7PEVZzVVTelW2fVh4DfGmIKqzowx9caYtxhj7gAeB25g7mNlLhhrbYRUEJjuRcB7sqZ1A7fOYRM/dJn2OWPM271CXmOMf6pa7bccbw87Modty+weBA5nTXse8GNjjGdrXmPMhaSqIK+emjSnv4+19k7gM9mrB/7VGHPz1LiGeRljNhpjPggcAq6cy37k2b//JNX2OJl11/8zxmS3hD1lWGt/Qu7/bgj4H2PMD4wxL8i+8MMYc6Ex5kuk/m8rpyZnj28pIiIiIqeAU2L8ExERERERkUXydXKr9f4c+HNjTC/QC8Sz7n/YWvsWj/V9BNgOvC5r+mXA3caYZ0i1hd0DDABRoAaoBc4gNV7f6SyDUNLDN4C3pd32kQp2033HWpvdRndW1to7jTF3Ay9MmxwA/gN4hzHmJ6QqXyeBRuBM4OXA6rT5u0m12P3nYrcv+Vlrk8aYfyL1P5Puj4FDxpj/Ae4H+kiN7boOuAq4iMxKub8CvjXH3XgfsJNUOJ7uVcArjDEPkxo79TCp/68wUEfqWLkQOGeO2y2ItfZbxpgxUmF+KO2uDxpjKoB3WWuzq61PBW8j9bx1Vtb01059TRhjukgFuC3kjv37KKnnztuypmcHviIiIiJyklFIKSIiIiIi4sFa+5Ax5pukKqCyNU59ZRvKsz5rjHk9sB/4O3LbGG5lfi0vT6ipx+tJUqGPl7m0ep12A6mKvaas6VtJBVT5jAAvIzcokQVirf2GMealwHVZd5UDb5j6yufj1tpvG2PmFFJaaxPGmKtJBaXZ++AjFYheNJd1LxRr7Y+NMS8Hfkxq3M5p7wDKjTH/31Sr6VOGtXZgamzJO4BzXWYpw3u83ydJBd07XO5TVbSIiIjISU7tXkVERERERPJ7K/A5YEGCA2utY639EKn2lo/Nc3XDwFeBsXnv2MLJF0I+aK3dM9cVW2uPAJcDbUUu2gZcYq19ZK7bloL9OfC1IpeJAH899X8xL9baiLX2euCvgZ45riYB9M93X7xYa39Fqp3saNZdbwG+Y4w55S4ot9b2Ac8h1fI6WsAiSeBLwPOstZ2kqsmzDS/cHoqIiIjIiaCQUkREREREJA9rbdRa+05gA/BeUhVQbaRCjNg81vsra+25wDVT6xwocNEDwFeAVwMt1tq/mBoPcrn4DrktcKfNp4oSAGvtU8AFwAeBrllmfxr4G+Ds+YSjUrip/5e3kKpa/cMssw+TCqJ2WGu/sMD78QVS1XnvJVV9O9tFBg7wAPD3wAZr7c8Xcn9c9u9u4MXk/t9fR2q8xpLF3P6JMHVs/COp59K/Am4H9gHjpJ5Lu0i1u/4H4DRr7VuttdPVks0uqyz0OVNERERElilzag53ICIiIiIicnIxxhhSrUg3A/VTXz5S1VZDpFrEPm2tHTpBu7jsTD1mZ5NqIdlAqn3mKKkxB3dbaw+dsJ0TAIwxTcDzSI0NWkOqiq6bVIC8ay7jk85xP2pJjTvZRKpNcympcGwAeAZ4Ki0Qk2XGGPNd4Pq0SUettetO1P6IiIiIyMJQSCkiIiIiIiIiIsvSVFXpEWBV2uT/sda++gTtkoiIiIgsELV7FRERERERERGR5eqNZAaUAHefiB0RERERkYWlSkoREREREREREVl2jDFbgIeB6rTJk8Aaa63GpBQRERE5yamSUkREREREREREFo0x5mPGmKLGkDTGPBe4i8yAEuC7CihFRERETg2qpBQRERERERERkUVjjIkAAeA3wM+Ae4CnrbXxrPnqgOcBbwauJffi+k7gbGtt36LvtIiIiIgsOoWUIiIiIiIiIiKyaKZCypKsyXGgBxgmFWDWAg2A8VhNDLjaWnvnYu2niIiIiCwthZQiIiIiIiIiIrJoPELKYnQDr7TW3rtAuyQiIiIiy4DGpBQRERERERERkcX0E2B8DsuNAZ8BzlFAKSIiInLqUSWlnFSMMQY4DzgXWDU1uRt4DHjULuABbYypJzUWxmagnNQHqv3AH6y1/Qu1HREREREREZFTnTGmFHgBcAlwDrARWE3q83aYVCA5QKoF7MPA3cDt1trBE7LDIiIiIrLoFFLKDGNMK3ARcPHU9wuAyrRZDltrN5yAXcMYEwTeAbwTaPWY7RjwWeDfrbXxeWzrHOAjwB/jXm2cBG4F/t5a+/hctyMiIiIiIiIiIiIiIrJSKaRc4YwxzwPeQyqYXD3L7CckpDTGrAV+BuwscJFHgJdba9vnsK13AJ8GAgXMngDeba39fLHbERERERERERERERERWck0JqVcCLyC2QPKE8IYswr4LbkB5SSwB3gaiGTddz7wW2NMQ5HbejepSszsgLKTVPDZmTU9APy7MeZvitmOiIiIiIiIiIiIiIjISqeQUvIZO9E7AHyT1JiQ0yKkWr42WGvPtNaeATQA7yYzrDwN+HqhGzHGXAL8c9bku4DzrbWrrbUXWGtXkwp1f5c1378aYy4qdFsiIiIiIiIiIiIiIiIrnUJKmTZKKpT7F+A1wAbgmhO4PxhjrgCuSpsUB/7IWvs5a+3E9ERr7bi19t+AK6fmmXaNMeZFBW7uXwB/2u2fT23r0fSZrLUPA1eQGpNyWmBqeRERERERERERERERESmAxqRc4Ywxm4ESYK+11sm67zJSrVanLemYlMaYB4D0CsWPWms/PMsyHwU+lDbpXmvt82ZZ5irgl2mT+oHTrbW9eZZZBTwF1KdNvsJae0e+bYmIiIiIiIiIiIiIiIgqKVc8a+1+a+1T2QHliWaMOYvMgHKcwqoV/3lq3mmXGGNOn2WZt2Td/o98ASWAtbYH+OIs6xEREREREREREREREREXCilluXp51u0fWmtHZ1toap4fZU3+E6/5jTElwB9lTS50LMvs+a4yxoQKXFZERERERERERERERGTFUkgpy9XLsm7fXsSy2S1X/zjPvJcB5Wm326y1hwvZiLX2EPBs2qRK4NJClhUREREREREREREREVnJFFLKsmOMMcDZWZPvLWIVf8i6fc7UOt2cO4/tuG0re30iIiIiIiIiIiIiIiKSRSGlLEfrgbK02+PW2iOFLjxVCTmRNqkcWOsxe/Z4lU8Vuh2P+Wcb/1JERERERERERERERGTFU0gpy9G2rNtH57CO7GWy17lQ2yp0OyIiIiIiIiIiIiIiIjJFIaUsR6uybh+bwzraZ1nnQm2r0O2IiIiIiIiIiIiIiIjIlMCJ3gERFxVZt8fnsI7sZbLXuVDbKnQ7RTHGrAIai1ysArgAGAGGSVV5xhZif0RERERERERERERExFOIzGHnfmetHT5RO3OyUEgpy1F20BeZwzomZ1nnQm2r0O0U6+3APyzQukREREREREREREREZOm8HLjlRO/EcqeQUpajcNbtuVQDRrNuly7StgrdjpwMKi6Acx4Ak6cT9tgu2H0+YJdst+QU56+EC9shUJl/voe3QGT/0uyTrAxbvwOrXp9/nn1vha4vLc3+yMqw9oOw/mP55+n8T9j/9qXZHznltABvJvVBt57UgPEhU0ao/Gxa4l0YawnYOKXOJJO+UhImiMXQGawnNv4oMSxtQD+QAL4OdM5zn3xBP75QAH9J6ss39d0fmvrZ5b70+XPmCwfxhfyZ803PUxLEH/LjDwfnudcyV/GxCLe+5LNgQnDeHijd4j2zE4NHz9B7PFlYO26D2ivzz/PsX0L3V5Zmf2RlWHUDbP1W/nkGboOnrl6a/ZGVofwcOPdhMHkijsl98Mh2ILlkuyWnOBOAC49AqMV7nt+bpdufU4BCSlmOsqsZQ3NYR8ks60yfXjaPbRW6HTkZrP9Y/oASoGInNLwG+n64NPskp76GV88eUAI0vQUOf2Dx90dWhvBp0Pi62edb836FlLKA/LD6HbPP1vTncOQfId6z2Dskp6CrOR5QngkYIBpeRwjoC9RR6YyyKj6MD4cyO05vSQPjwTICfodoSTMVyUHO9/l4pqqU4bIgb6wq5SdntBwPFqeCwFRIePy7P+Q/HhKWZN5nfDpJsZL4S6YC4ua/yB9QAvhCsP6j0Hb94u+YrAwl62YPKAFa3q6QUhZW6/+dfZ66q1KfQyLPLv7+yMqw5v35A0pIvRavuh56vr00+ySnvtqr8weUUjSFlLIcjWXdzq52LER2RWP2OtOnp4eUxW6r0O0U64vAj4pcZjtw8/SNb3zjG2zcuHGBdufksnfvXgC2b99e8DJD4wFe8f8uwRZQIHnuVV/iszfOvcLjtttuY+PGjUXtnyyer3/961xyySUL8veYy7H3jq+ew2OHZp+v9vR386OvX0LAP78q3r1793Lw4EGuuuqqea1nMeh/w91cjiuv5aYf418+cw23POiffSXhDbzx/3yO555VkrP9ue7XQi1/qlrO/wfz3bddB2p419fdh9xujnVy3tijNMe6CNkYF1zxHi56vsPY1q3E6uvzrnehjqXl/NifaiyWBEkSOHz3B99j54Xns37zhplpCZP6nsz4efo+hwTJ1H0kp26nfi7vHeS5t9xPPB5jy1PHCDgOg7XlDNVWsHp/OwZLTc8oiaSDcSzWZ6j29xFfVUnIGLq2thLsraJmcIJzA37azlpNkzF0v2Ingw0LNaqDnOp8QT+/ueu3vPWL5/NMx+zzB5v/lE//9QQlgYReUxfRSnmO/97da/ny7QXMWHEuX/jWo5y5bmRR92elPO4n2ol+nB98tpb3/vdZBc37Fx/4Fa+/9Ogi79HSONGP+0qQ7zGOxg0v/+TziBTQE2/zC7/I1/7qxkXYw5OfjuPi/d13dnDv3oJnPzWe8BaZQkpZjrKDvvI5rCN7mXwh5ap5bKvQ7RTFWtsDFFU+YEzmFdor+QVmcjI1VGgxv//Nd4extrCr3J88Us2a9adTUTq3sGj37t2sX79+xf59lpuGhoYF+3sUe+wd6fHx2KGaguYdHAtxZOxsrr44u8t0cSYnJ4lEIsvy+NP/hru5PKd5Lbd7927WrVvPQ79sKng9nbGLWb9+MGf7c92vhVr+VLWc/w/ms2++7m52/XYfb+56mJCNETMhukLNtIdWc8HYI6yJHsuYf+KQw8btE9DXR7K1ldjll+M0N7uue6GOpeX82C8Wa6fCQpsk5iSI2wRxmyRuE8Ts9O30r2RqupM5ze3nmE2k1jt9n3N8WiK93dYbm7mHdu6hfd6/z/n79zFmIlSPjWOcBJGgn6Pra2g+NogxUDIZx+dYkn4f/c0V1PeO4XcsJZE40dIQNYPjtK+vp3wsSjCepGpwguG6cja1dfFIwywVcbKsBfATNH6CvgBBM/3lJzYRpTRQQn1NHSW+ECFfgJAvmPpuUt+D07d9QUIm9T3oC3Dw2QPsfeIprnrplTPrC02tf3Q4zDMdBXTKAOJJHwPJc3nO5l69pi6ilfIc//uv1BU8793PbOfVVyxuSLlSHvcT7UQ/zv95Z1XB8z6wfy0f/f/mcppv+TnRj/tKkO8x/t9HQ0RiBVx4C+zvqqC68Qxa6p2F3sWTno7j4nQN+HjgmfwX0WaZyzB2K45CSlmOssO5NXNYR+ss60yfvmke2yp0O7LM3fFwdudeb4mk4d49Qa64QK8zMj8/uae44u3bHiyZd0gpcqy/nM6Bwj7MATzRsRl4ePF2SE5pvq4uSu68E9PeQeKBMtZGj7dVP3dsNzsm9jDqr2B/eDNhG6Ex3kfAJkhMBIi2l1Oyuh5/ezulN91E5NprSW7efAJ/m8XlWJsRCMaywsH0YDCWFiQmMuZNCwydzHmzg8TpZU+lUbZr+scBqBpKBTZD9eVYn6F0Mg5ASSQBQKQsBD4fkbIQ5WNRSiIJoqUhwhNxrM8wVF9OY9cIVUOTDNeVz6xX5s+HLxXmmQABEyA0Fe4FTSAtQEzddp3H7eepQNF9uQAB48dn3C9G3LVrF/X19Vy47cLifxkzRufYPtaVrMq565bHCv9sAfC/jwR5ztbid0Ek3d6jfp46XPh4tL97LIS14PHvIVIQa+H3TxQ+ctLjB4Ic6fGxbpXCIpmfXz1Y3GvtPU+EeO1lGqVL5ue2B0tIOnrhXGgKKWU5asu6vXYO68hexqsIuw14TtrtdUVuJ3v+wou9ZdmIJ+C3u4sbjvSux0oUUsq8/WFPccfdg3sLP+kg4mXXwaKu+qNzuIGuoezu5iKz8+/fT/iWWyCRoLPfUDbSy/qpEDJg46yK9zDpK6U51sWOiafoD9Yx6TvehT/yeCcVHQdJbtmCU1tL+JZbmLzuOs+KyoWUtMnjVYMZVYD5AsLjYaJraFhMdaHMSSCeegz9ydSJz0g49brpc5yp76lINhHwZXyfnj493/Ry0+uZXu+pxEBGkJce/IVyAsBAzrz5wsNQRrVi5jz+2cZ/P0X8dldxJ0739W/hjDNqF2lvZKX430eKO+66B/0c7vazofnUe46TpXO42097X+EXQALc9kCY/++aiUXaI1kJHAduL6LYAODuxxVSyvw98HT+83ibmsY4cP8/LNHenDoUUspydBiY5Ph4j+XGmPXW2sOFLGyMWU/mOJPjePd/zg4VzyhmR4HTZ1mfnAQe3BtkZML9hEljTZLeodw33HcVGWqKZEsm4dFn3UPHnVvi7NqXe9/h7gDdgz6aanXVqczd7kPuIWV9RYT+Mffq3of2NXLVixZzr+RU4+vqmgkofQMDcP8hzpg4/tzVEuukxIniI0nAOMRskLr4ALsr1tMXaKDMGacu0kFjfAL/3qdJbNtKvLaa+B230vunr8gIBg8H+kkah+jY05nhYVr7UrdA8Xjb0lSQOHr+OH8I9JI8dDfOKVVfuHIkgqn3bEl/6n1dOBJnGHB8qduOL3XVcyDhEJ36nj59er5wJJ6xnun1LhbvKsFAWmWgPyc89Jo/mDFvgDtuu53TNm7mnB1nzwSIfnw5w0XIwkgmUxVqbrw+W3QPl3G4d4KtaxQWydw9+oz7Z4vT18XZezTgOrzJfU8FFVLKvPzhSffnO2MsrQ0Ox3pzn/P+sCekkFLm5fEDAXpcXk8BmuuSdLl0Drr7cVWPy/w91Ob+WvvHz4nwjleOE4g+yYv+5zNLvFcnP4WUsuxYa60x5nHg4rTJl5AKLwvxvKzbj1trvc407c66fUmB2/DaVvb65CRwr0c1W1nY4QPXjfHu/6zOue9AZ4DD3T7WNykskrnZezTA2KR7OP7Xrxjnzf9S43rfg3uDXPNctXyVuUk6hscOu49V9NJz2nmqcx2PH8h90/1MZ+7zoKxMFkvUiee0I405meFfy60/IzTRTWBgiMpnDjKRtARXldBfVQU+h9UdEQLRSaonxzBYBisrGKqrorXiKSJbW/D5HJ4yCUaPDVI9OE7iyU7azlqNHTbc/ngngw0Vx3dqeti3vqfm98upWP2kN1RfTkP3CCM1pVQPTVDTP05PczWTpUHKxqNEwwHCkTjhiRjj5SHCE6muGNFw6mPxZDiEjfmp748QNH4SdTVU+8sZa17LtvBal7AwwEBvPwHrY23LmpwxD93mT58+HSgudlhYHglQ6YSpDpwaY4Atd4/tDzA45v4e7yNvGuPtn63Eknv/3Y+H2LpmcrF3T05R1sLDHiHlVRdH8flgz6Hc+x94OsR1l6uySObu90+6H3c71ie48qIon/5hRc59u/cFFBbJvDziccF3SdDyD28Y422fzf382jPkp+2Yn+1rdWGGzM2xXp9n5fhrLo1w1qYEe1W+NCcKKWW5+gWZIeVLgZsKXPalWbd/nmfeu0hVWk5/Yt9aaNWmMWYDcFrapNGp9clJ5omD7m9uXnBmjJee793S9ZFnQqxv0gc6mZuHPa6+CgUsl++MsqE5waGu3JdphZQyV77ubiofPMbrDn+LkI0RMyG6Qs08WnEeXaEWzt3QT3VdM48fCNIc6+S8sUdpjnURsjEqxx1Cd1STOPtsnKamE/2ryBRrLUmcjJAwZnO/T1cV5lQRprcj9bz/eMVh5KIYd/m64MjdefertneUKw7sxljLtmc6iCWSjK4vp31dGOuL03JkgFjS4pvwUdmVarUZ9MUIlMSoSsSpcsqZDKcuIDq2vo6ysQjBeJKqwQmG68rZ1NbFIw1bluIhlhPEh8+9legsAWDVBaex/cAdjFeMEoqPkhwup+zucp4u28xZPXvBQmC8ixKTYEMkQv94JUkToGNyDdb6eKTrPNbsaqexOUldvZ/W9ReAMUxe+gbP575dR3YBsLNm51I+RLKM7drv/h6vNGS58sIIzRWDdI5tyLk/dZGQQkqZm/Y+n2dV0QVb4wyN+VxDyvuf1hU6MnfWwu89Kimfd2aM87fGXe/rGfLTOeBjdb0u+pa5cbuwFlJdqV5yXhS/z7qOG/iHJ0NsX6vXWpmbh9q8u+pduF1Dgs2HQkpZrm4BPpp2+zXGmL+x1o7lW8gYUwm8Jmvyz7zmt9ZGjDG3A69Im/xmoJDm0W/Ouv0ra62ekU5CTxxwfyrceVqCxhqHbWsTtB3NnefJQwFe+YLF3js5VXm1iDhrU5xwCC7cFvcIKdVqWIrj6+qi5M478XV0UH2gl7XR4x3R10aPcuHoQ7SHmzm3tIpmXxWJrt+zJnosYx2m38LDByndvZtkayuxyy9f6l/jpGCtJUFypoVoZtAXPz7NyQ0Tp39u2zhMR9leHurpmLVaMWYT2KVsS1rgUHKbnukGoGpwgkAiSTzop31dHXaqpWbpZOqEVSiaIBoO4E84+CyUROJES0PU9o8xWZaq+LU+w1B9OY1dI1QNTTJcV05N//jC/27iyoBrK9HjQWFaiOjZfjQzSJwJF6fm/+ZXv8Gll7yAnWedO1NdOOdxC+ugdNMQ9uhRBjf46PnDEJXJGBtNFybpJ2jjRJJlrKKbUpMkkXToDKyiLDFJzAQ5c2IPJTbGyKSP6i1bwBiSra26OEOK8qTHBZDnbI5TWgKrKw65hpRPHtTpGZk7r6oiSJ20H5s0fP22spz7DnVpOAmZu8PdftcW1pAKKc/Z7B5SAuzeF2R1vS6+lbl5bL/7a+bZm+JUlll2bonz8DO5505Sr9EKKWVuHvC4sOe0NQnqKjVcyHzoXbAsS9bax40xDwEXTk2qAN4LfHiWRd/L8apIgPuttbP1/voamSHl/zHGfMFa2+u1gDFmFfB2l/XISaZ3yEenS696gLM2xme+u4WUTx3SU6jMnVc7pgumrja9eHucH/2uNOf+Jw8GGI9AufvQgSIZyo4do/SuuyCRAGsJDE2yY2IPAZsgYQL0BhvoDTayNfEkW3/azpmlv+NgtAWDQ2O8l8Z438y8wwcradhSi7+9ndKbbqJsyxYm1qw50b/irBxrZ0JCr4Bw5rsTzwgXU8FiZpWi17zxhQoNm6CTSZjoXpgH4ASYDhGrhlInAIbqy2cCSgCf40x9T/X5miwLEYonKYkkiJaGCE9kntCKhFPPl/5karlAfGW2aPLjywz+fLktRHNDwtTPIV+AgOc83mMeBlj8VqShMaiwYSr8ua95cxF98YspvekmStZuY1c4wraJvZRMXUfYkOjHWAsVYXAiJMNhSmMRwk6UzlAzJTZGzAR5LHwarbVlEAjoogwpmlfYeObGBACNZe2u9z9zLEA0DiUqbJM5eMTjAsjNqxPUVlqec7p3WPTIM0GuvlhhkRTvqcPuz3fGWJ57RiosWt+U4HB37nyP7Q/ouJM5mYimXjPdnLM59Vp73mnuIaXXMStSiAf3ur/WXrRNNUvzpf9MWRLGmOwzdi+y1t41y2IfBm5Lu/1+Y8z/Wmtde4wZYy4F3pc1+UOz7Zu19lZjzP3Ac6Ym1QNfM8a8ylqb807eGBMiFUjWp02+x1r769m2JcvPE3muWD57U+rNzRnrE673P6mQUuaob9i4VkkCXLg9PvXd/U1O0jG0HQlw3lb341JkWkl/P8333AONjfgGBvDv38/qvhiRRMnMPI3xXmJmHyPlPqoOdFFR0cvmRJSmiXZCWS+BZm8nwSE/yS1bcGprab7nHtqvuGJO+5a0DjESJIxDX3zYIyw8HgrGpqZlB4LToaNb4DgdLiZYmYHWiTQdIk6HitMh4zTH55v6Ph1+pd4m+pzp75nVHOFI6lhM+lPLJYLuFxctpcxqQvcqw1SQGMxTXRg8HiJmVRe6BY++uVYXrjBOczORa6+l579upT9Qzn1Vz5256GLcX0FLrJOm1TUkysNEu6OMdcfoDK1mKFAzc+GGf8LwR74o8WuvxWluPtG/kpxEYnFcL26EVLcMgFVlHa73J5KGvUcCMydYRYrhVUk53W5zVa3jGRbtPaKwSObm6SPuz3cbmpJUlqXe15272f24271PV2TI3Ow5GMRxaeUKzFTvnu5xHq/taIBkEvwn/uOEnGSicdjr8R7vojwXAklhdIZdMMY8D3C7dPmcrNthY8xLPFbTUUDFYlGstb+aasU6fQY0CPzaGPN+4CvW2gkAY0w58BfAJ6fmmfZLa+2dBW7u/wK/43gjs2uA240x77HWPjo9kzHmfOBfgUvTlk2SquCUk5BXH/vmuiSNNamTpNNXPWfrG/bTM+hjlVrjSJG83tjA8UrK01qTlIUdJiK5J6XbjimklNk1PvQQvmQS38AAgbY2rGMZtRV0lLQw4SunzBmnOdZFiY1xwfBe/BVVYAwvnPwDh/1NxPwBeksbmQyFKLfj1JhuKiJjOE8+wuDW9fT4HMZ3/5qfnB/OqjQ8Xn0Ys5nTp+dL4kDt1I6233dCHydZeNMh4nSoGI7EGU67f7I0SNl4lGg4QDgSp3QiTjLgmwktp0NMAOPYmcrMkZrU29Wh+vSmGRCwPvzWRzhYkhUYprUj9U0Hge6hYsgEeOj+B1nXsoatm08jZAKETDBnzMPpMHGxqwtlfpKbN3P7mW/k6H33sDZ6lJ5gEz3BVMvWltAwW1Y/BaOjBFqb+HVsB6P+yozlDwbWsufSi9m8ufFE7L6cxJ45FiCWcH9+OGtD6r1bXWkPoUCSWCL3DOmeQwoppXjJpHeb4fNPO37i9Iz17mGRV7AuMhuvqrTT1x1/Hjtnc5yf3ZvbBuixA0FsqqmGSFEe8xiyqaLUYVNL6mJJr2KDSMxwoMvPaa26kFWKs7894BmO79yikHK+9E5EAL4LrC9gvibgDo/7/ht400LtUJo3APcBG6duh4HPAp80xhwgNVTOpqnp6fYXsz/W2t8bYz4AfCpt8mXAI8aYDqATWA20uCz+Xmvt/YVuS5YXr/Eoz0oLJs9Y7/1is+dwgFW1KuuX4jzjcSKgrsqhuW6qDaIvFVQ+tj83pHy2XS/fK4mDJYnDaHIiI/ybbl86XWGYHgQGu3toGXoG63cIPH0ME48zWFPJ0RfUYYL9lPl7Mf4kfcTZfLCTsoOTREYjRMagNGwJb7F0ntaE8XVSRqrOrc3xMXY4Qs3gBIm2YdrPWo0dHOTB/Xcx2FBxoh8mWSYC+JlsqKGkd5J4XTWh4TgVHQkOlVThEMAm/RxxqigfPUzSTlJPnLJYkkTc0EU9E4lyuiZW0RHdiE3AlsFDrG+M4C8PUb/uXPy+ABde9nqub26ZCQx3794NwM6dO+e170f6n2BDQwM7SjfM/4GQE+6hkbX8qPlGmmOdnDf2KM2xLkI2Rvn6ZiZvOB+npQVfeydtn4jgT8SJmRBdoWYerTiPrlALF8eG2Iwqi6Q4Xl1aQgHLaWtSny98xmFD4yjPdNbkzPfkoSAQWcQ9lFPRkR4/0bjHidO0kHLb2gS3PZg7T9sxlRTJ3Oz1qKTcnhYQnetx8n5ozMeRHj/rmxQWSXEe2+9+UcZZGxNMX+u4bW0Cn8+6hkpPHQ4opJSiPePxWhkMWDY263iaL53llGXNWtttjHkR8DMyKztLgR0ei+0Grs03pqTHtv7ZGJMkFVSmP/OsnvrKlgT+1lr72WK2I8vL4x5XnJ696fgb6YZqS3V4jOFI7kn4Jw8GeNG5CimlOF4h49Y1iZzbj+0P5pxg3fgrQ6i5gsTZZ+M0NS3FLosLay0JkhmhYdwmiM6EhvGsQDGeM28hgWOiduoN79HfF7xv5z+8D19ghOqBccaiI8SDfp5ZV0KZbyhn3liFj6TfEIqmjr9YSYBIOJgxhiCA9Rna19dTPhYlGE9SNTjBcF05m9q6eKRhy5wfR1k8PkxO29D0dqLprUQzqgt9WctMfb/3nj+woXUdZ2zdnrveqSpFnzH4Srspbf8WVFpsx8N07DGUHaugrXQ7zlTL0uqxAOt9j2Ew+JMWg0PZUALHn2Qouor6geOVvk5NKeGztlEZKCfZ2opt3TjLby5yvDKoK9TCL+teNjP9bVeOE3vpWOrGmWdy1231POsyrlFqmkJKKY5XNdv2dQmCaYfZ5maPkDLPUBQiXp5t9w4Zt7Qe/3yxba37SdT9HQHiCTKOUZHZTEThYJf7sZdexTY9jI6bZ9sVUkrx9ngMvTTd6hUgHILNq5Ou7/GeOhTg5ZfoPZ4Ux6vrwObVCb1+LgA9hLLsWWsPG2MuAt4JvAP3wBCgg1SV5eestXNKjay1/2qMuRP4GHAVx9u/pnOAXwIfstY+NpftyPIwPmk42uP+pjr7jfTqmj6Gu3JDyj2HNI6CFMfX3U39H+7jzV39hGwso3Jja2t1xrwXVB6lqutu1kSPZUz3tzsEd08Q3L2bZGsrscsv15hZWTIDxMwAMDp1O54WCGYEixlhoUt4mHafQ/aQy8vDdHvMqqFJINUeMzt0nBaOJIiUhSgbT710jtSGCEfcTyZYn2GovpzGrhGqhiYZriuf2ZbMbjo0dA8Mg4RMgM5jHdRWVNPcsCrjvvS2pOnLZrcinb4vZAL4zcJWRrQNPczqpho2lrg1ljjOaWrCWb0aX0cHPau2YvfsoyneQ01iiK5QMxO+ciImRHN8BL/fkAiHwecjOJog7ETYED0ys66YCXKkYRtbayshECB2+eUL+jvJqSmZxPWkFKSurE932uqE67z71LVA5sDrxOlZGzMriTY1jXgur/aHUqxnPJ7v1jQmKTs+FDnb1ri/v4snDAc6/Z4hpoibtqPerQ/T271WlFpW1yfp6M99X7q/PcBLztNF31I4x4EDne7PedlDNe1Y7/4e76nDOo8nxfMKKbeu0WvnQtAnL8Fau2EJtjGvj1lToeM/G2M+DZxPqqpy1dTdPaSqJx+11s57cEBr7W7gj40xDcDzSbWTLQfGSbWR/YO1tm++25ET70Cn98nb7esy39y0VvfydNeGnPn2HlVrHCmMr6uLkjvvxNfRwap9ZZRHj18DsTZ6lAtHH+KCgw34ul6A09yMf/9+rnjyViaiQQwOjfFeGuN9BGyCxESAZFc5/qZ6/O3tlN50E5FrryW5efMJ/A2LY60lbpMZFYaj5XE6A0OEJg7NBIkxJz71czynQjF3nswg0i7TAHEpBOKpN8r+ZOplMRL2/iDmcxwSAR/Gph6vRMCHz/F+OZ1e1/S6p7d1MjKQEfDNhIG+9Ntp9/tc5vUIG2cqFNOWKSQ0/P5vv8+2bdvYWT+/1qUnWvTFL6b0ppvoMg08WR5m28ReSmyM9VMBpN/n4FRV4E8mCTQ1YcNhRvclOJhsImGCJEyA3mADvcFGznWSbA0kiVx7rS7IkIIc6fEzGXP/+LE9O6RckwCX9of78lQmiXjx6paxY0Pmcbe5adR1vvGIj+5B30z7f5FCeB13p7VmHXetCfw+S9IlWHrmWEAhpRTlaY/xKEtLLBuyqiM3r064h5R5zsmIuOno9xHxeI+3eXXmc94Z6+P89A+546F6jaUqkk9bgRdAytzov1JOKlMh5ENTX4u9rT7gp4u9HTlxDni0JgkFLGsaMt9Ut1T3u857qCuA4zDT917EjX//fsK33AKJBJGIpXy0lw3TgWPaifiNiaOU3nQTsec+l9B999FY4VAfH2bbZBshm3kFfnJPB+GjB0lu2YJTW0v4lluYvO66BT2Bn7TOTDgYtfGMMDAVGKbdN/PzdIiYVrWYFi5G0yobc0LEs+AR+qFHRerzlQimnt+S/tSTUzgSZ9hjXsfnI5BwsFNlG4GEg5PnSS0ciWese3pbC8WHL6ciMCP88+VWD3pVF2auJ5izXMD4MSpXWRROczORa6+l759+RX+gnvuqnptxsUVVlY/e1UkijY20TE7iGx+nc9M5PNKZ+xy2J9HKJdc9RwGlFMzrSmdjLFuzTiRs8RiTaH+H3uNJcUbGDf0j7gdMdkv/NfXeXQgOdPoVUkpRnvUYJyv7+a4kCJtakq6h5t4jAa55rtofSuGe9hqPcm0Cf9YhuXl1knueyJ13f4dOS0tx8h0zW1Znvqc7fb17eNTe52d80lBeunIvapbiRGLe7a0VUi4MvRqIyIp1wOPNzfrmZM6b6saKIdd5IzFD96CPlnqdSBB3vq6umYDSNzCA8/gBzpjInKcx3kvM7GOVsx4SNZT913+RXL+emmiMsyb3gzVEp9rCTvjKKXPG2Zg8RkU8gn/v0yS2bSVWU0Xkjp/T9dqXp8LA9MrDqSAxauMcLT1G74YRvtn764wQ8fj804FkggS6mvpkNVRfTkP3CCM1pVQPTVDTP05Pc7Vry9dIOEB4IkYiMBVoTsSIhLOeH63BcXz4HWgciBIyAeLVVVQ4JUSbN3B26aapSsOga3A4XZEYSgseDzyzn6D1c/YZZy1qi1I5cZKbN/O9NX9OxYHfsDZ6lJ5gEz3B1Di6Z66LU146TllFBdFLLyVx1lkM3DrI0V+M5LTCdqpWcWOzmlhI4fZ6hJTrVmW2PoTcSqNpkzHD0V4f65v0Hk8Kk69Ly4bmzPdUlaVxaiochsZyQ81DXX4u2RHPmX6iWWux9uQ9oev3py5McvJ0izgZWevdnnpLSzzn9926Ju4aUrYd9S/KY3OqPu7LzYl4nL3C8e3rco+7TS3uz2n72xfnuFsqOr4XX/Zj/Gy7+8VAq2qSlIeTpP8ptqz2fi092GU4Y73Od8DKOY6NMXO+QPlAh3d76+wL0WRuFFKKyIq1v8P9TfXmltwXmAaPkBLgULdfIaV4KrnzTkgkMAP9mL17mRyHREmY3tIGIiUhyuwYTZFeqhnF2ddD52Qj5f1dRCI9+CJRqpsS9FdV09FaAcE+yvzd+PxJHggmWXNsiKqBMRJPdtJ21mrsiOH2J7oZbMgdP3VGGGiGjvGnl+wxkMXjwxwPAH0BSqZ+tmdVUP3kEL6qUspLYpiYQ/XDPg7UrSVJEJv04Th+SBguDj5NYOwowbIyQsEgFY6lsmMN+w+eh2MDOEk/1jH4rOWS4FNsbY5jK4L4Slsh5qP60rfiNDUVve9jyV4AGoLVs8wpJ7MHR9ZyqPlGmmOdnDf2KM2xLkI2xjnrHLpLe5m87DIqX/xiAGwyxNcfrs1dyRBMRMkJl0S8POPRjt+tlaFXJSWkrtZf36SxsqQwB7vcT6+UBC2rXT4rbGxOsmtf7slWr7G2lpq1lkQiQSQSIRqNkkye3CdzzzzzTMLhML29vSd6VxZU73CAkQn3saIbyvvo7c28OnJNnQ8ozZn3qUNmUR6bU/VxX25OxON8oLPOdXpz1XDOfjSUVQC57/m7Bv0cPtpPWfjkPJ+i43vxZT/Ge/avdp1vbcNkzt8hZCHgayThEi49/sw4jWXu40OvNCvpOPb7/YTDYUpKSggEAgWHll6tXoMBy8bmk/v90XKxPN79ioicAF5XO29anfsCUxaKUlkaY3QylHPfwU4/zz1j+V3tLHOXtEkiadWFERsj6sSIzLQ0jaXud2JT86Tu6ynvI2GS3N7RRtTGCXf38bynHyDpJDjtyWMEEkmGWsvpf24dxtdJKWCBbsfQenic3sEJyp7sYqA8RElXnFjADzUh+ncEKPUNZeyjAxxdV8vW0UmC8SRVgxMM15Wzqa2LRxq2nIBHTdIZmGkvOl1dWDJzO7P16PQ80+Mcprc0DZkgh/cfJGD9nLV9x/F5ptqaBryqDldD7629hPv6aDpjHWMPtxE+7Gfzkb6pitwwZc44zbEumrc7DFofwapafGVl+EZHqUtOckHfrozq3eZYF6X+KLQYklu2QDTKZGMjlXMIKGVliMZTYwMCdIVa+GXdy2bue85rhxg6kGBb3fETXNnjF6U73O3n9HX6ACiFOdTt0frQ5UrnilJLS12SzoHcZZ49FuDynQoppTBeny02NCdd2wZvbE6wa1/umNEHl8EYbbFYjJGRkZM+mFwJDnR5X8GzqTm3fevG5ojrvB39IawFdcGXQiQd6Oh3H/N+TUPucbe+ybuV8OGeEk5fN7lg+yantkM97s95G1yOsYAfWupiHO3LXeZYX+65PTn1JZNJxsfHGR8fx+/3U1VVRSg0+7HwrMdY9ZtXJwgqXVsQehhFZMU66HGV8iaPq2BW107Q5hZSelw1LUvDWpsVJqZ9zwoRj4eKx+dpvzzAj6oe5uZju1LzO/G5tzmdPjymzmee//R+YjZO9eA4gUSSeNBP+7q6nJab1mdoX19P+ViUkmicpN9QEklgfUk619a6tuicXm6ovpzGrhGqhiYZriunpt97jCNJmQ4QS0wwIwycDgtL0lqTlsyEh9NBosfttCrGVGXjwo11mEgMAdAaaihqud4LL6T19ttxams5umoHscMHKLEx1kePzMwTDFh8JUGeaWjgrHAYysuJb99OSVtnzrwA48kQE5u3EKitxentpe+CC1g1799QTlUHu/yebXE2tyZ45EDmtDWNSXw+67rM4a6AQkop2OFu9/dmXlc6n7Ym4R5SepyQEHHjFS66dWkB2Njifjx6jXm0VGKxGENDQzOtXY0x+KZS1pN5HOe6ujp8Pt/M73KqONxT5jq9rjJBQ40h+7SfVwvrSNzH0EQJjdUL+1p7qj7uy81SP87dfUESSfdtbWhOEghkHndrV1lCAYdYIneZI32lnLXp5LzoW8f34st+jA97hJSbWuI5xx3AulVx95CyP+w6/0q0Uo7j6fc1juNgrSWZTDI0NERNTc2sQeURjwsgN7sUucjc6L9RRFakgVHDoMsYMACbVrufSGipnaCtoyZn+qETfCLhZJS0DhEnRsTGpoLBVIgYcWJp01Oh4uR0FeNUwJh+//Q0yzzGx2nwEWUcFqGN/HRgWDWUujJ0qL581sCxqWOYkkgCn2MxSYdI2P0K1WnT9/uTqRMOgfjJ/ybJj28qHAymBYWZ4WBJWqiYChzdf84MGlMViAEWLkBczqL19XS94AWs3rePTup5pKqJxngvjfE+AjZBwgTwNdYRv6CC/oEBRjZtItzYiK2qwqlu5anO8Yx5e4MN9AYb2RyM0BTw0fWCFxCtrz/Rv6YsY/s9xsjy+yzrVyV5JGt6MACtDQ5He3JfVw97fDAUyTY2aegfcX+Pt26V+2vkltYkdz+eO/2Iy7Eo4sWrktIrjPQOKQMnrKItPaA0xuD3639guTva635i1a2KEmBNg3d1eHtfiMZqVbTJ7I54HHcAaxtzA0e/D9avivFsRzjnvsPdqmiTwkRihs4B9/Mj61e5P7etbXSf7vXcKaeu6XNA0+9tkskk1tqCgkq3z6fg/dlCiqeQUkRWpAMd3k9/XlfCrK5NjeeRPa5W4z1+QmeXkTj77DmNy3aysNYSswkiToxJJ8rkVMDodjuScXu6PerxADJuV8YL+XRgOB0gFhI4Wh/4HIvjMwQSDuFInOE8y4QjqQ+BSX/qhGwiuPgnk6bHQSyZCgszQsTpaSZAMCtMdPu5ZCZYDPLzn/yM07ds5/yd5y3677BSTKxZw+SFF/LsI/dh6aIn2ERP8Pjz1I7GOJgo/eXlHHr1q6k480xCv/kNlcfaGQivyph32tGStVRd90ImOjuX8leRk5DX2M/rm5KEPJ4ONzQlXD8EerXvFMnmdaUzwLom9yuSvE4wKKSUYniNJbnJI4zc1Ox+PE5GDd2DPprrlnaMNmstIyMjOQFlIBCgsrKS8vLyosZvWm6GhoYoKSmhtDR3PMaT2aBLpx+A7RtL2LhxY870DRbKw5bxSO7fMWpa2bhxYT+nnaqP+3Kz1I/znXvcXx9X1VjOPH29631nbArybEfu9N7xOjZurFzI3VsyOr4XX/pjvOegwVr316Dnn7+KjWsbc6afuy3A93+XO3/nYJnrc+RKtBKO4+lxtsfHxxkdHSWRSOD3+2eCypGREerr6z3f4xz2+EywPs9wJVIchZQisiJ1PtXL1QP3zQSNMROiK9TM3vpzWVXj/tS4zX+IG7vuYk30WMb0ULcluHuc4O7dJFtbiV1+OU5z81L8GgWx1hK3yVSQ6ESZtFEmnbSA0U6FilMVjZMz044HkBGbmj6visUVaDownA4QCwkc4wE/PscSDQfwjzvU9I/T01ztWoFpHDtTrTlSk3pDOVRfDqSCxBITPB4aToWB0bFJIqMTbF67iZKpsRHT50sPDsNpAeLM/YtYiRhI+vBxcp74Ws6c5mb+q+EtjIz0ZlxgETMhNpxZzeQbtvK7zk7W1dfjNDcTuf56fN3dHHvkIPH2voznyEcrzmPijEpObx4HhZQyi6O9HmM/e5ywh9QHvXueyJ2uSkoplNdJBL/PobXBPfRZ2+h+TLb3+XEcXMcTFEk3MGoY8ujSsrHIdq+Qqspc6pAykUjMjEE5HVCWl5fT0tJy0gaT6Xw+H36//5Rr73ekx6s7kPH8XTe0wJ6DbuvyEwgs7N/6VH3cl5ulfpwPdbsfd5tbrec+bF3rvq4j3b6T9vjQ8b340h/jg13uz09+n+W0tX7c/gxb1rovc7THYAloPEFWznEcDAYpLS2lvr6ezs7OmXEpp9//JBIJgsHcK2kjMehyGRYCVEm5kE7to09EJIuvq4uSO+9k7e19XDiaedXp2uhR/jhwP6U31eUEjTU9PVyw5ynuiLZgcHJaJkbbyylZXY+/vZ3Sm24icu21JDdvXpB9nm6NOjEdMs6Ejalp0wFj5v2Z9815jEWZl6H6chq6RxipKaV6aMI1cLTW4CT8lAegaTCGU16GP2FTfQ8TfsrHLTWPwP66dSSdIE7Sj036sQkffxQ+wGpThykP0bBuJwFfgOe+6Ab+vKWVgHF/E7Vr1y7anmnjdef90VI9DHKCOU7qpFMk1MIv616Wcd9FLx7CacptB+Y0NXHk/K3clsxtydTepzZgUhivtjhr83yY87oaVSGlFMqrkrKxKkLA4zDyOibjiVRFW0v90oZFcvLxGusevC/MqK201FQ4ruHmoS4/l+xY2jHaIpEIcLwdWiAQOGUCylPZ4S736euavC8u3dhi2XMw9+96yCMAEMm2v939WNm02nuZ9R5Np4726LiTwhz2eI7a0Ixn2LhptftzYdIxHO6GLa0LtXdysjDG0NLSwqFDh0gkEhhjsNYSjUZdQ0qvC29BIeVCUkgpIiuGf/9+wrfcAokEI2MhVsW7c8ZbqymrzQkafV1dnLZ7N1XlDdTH+9g22UbIZp40sE93EOw4SHLLFpzaWsK33MLkddfhNDenXuxsnAknyoQTYagyxsFgH5Oje2amjU99n3SiM2FjKmRMjbkoy0NJWovTsC9EiQml3Q4y0jdMwPpY17KWEl+QmufsZMuh2/CXGep69jA6ZAn8uoG9ZdtwpgNHx0fIn+RdZz2G38SwtUFsSQkTXV1MbNxC6YEDVAzA5sN9dIWamfCVUOaM0xzronTVJGWNYRJbt+MPlJFsbSW8et2Jfphkmeka9BGJeX2g835T7VVxdKxXJUVSGK8PdGs8qtbA+5g82uMnmQQNjyaz8aqkbK6Z8FzGq5ISUsexQkqZjdcY9WVhh6Za7+NnY3OSXfvcQsqlP1UTjaYuWvJNlQ5XVlYqoFzmhsdgaMz9b7QxT2OfTS3uJ+0PqUmGFOhAh1dI6R2OewXnHf0Qi+M5FIDItCPd7tPzXZSxqcV7fQc6DFta1S1sJTLGUFlZyeDgID6fj2QySSQSoaKiImderwsgjbF5L76V4iikFJEVwdfVNRNQ+gYGWH/oMI0TmS8mjfFemvp8+AY3ZgSNJXfeiS+ZpHyyn52J/dhgkkTQT19lHdGSAGV2nD7/MJMTEZKPd9O5pZnhqjA9N+/jf689m3EnikPayYkdAAPQv2dJH4OVJoA/FSRmhIrHv5f4goRNiAf/cD/bN29j89qNhH1BSkxo5vv0PNPtT32znKjZdXQXADtrdqYmVEHphmP4OjrwbTuTwXueoX5smAvGd08FjuWUOeNs9nfi7xsHY0hu2YINBomNjRFZtYqxyUkYgZJojPXRIxnbG3dCJLanjlcCAWKXX74oj6Wc3A52eqc6+VrNtTa433csz5WEItOsTbXKdJMvEPKqpIwlDJ0DPtY0KiyS/LyqbltqJwH3sdtqKiwVpQ5jk7lh0bFePxdt1wVjkl9Hv/sFPOtWJcn39nHdqiS79uWemfda32Kx1s60ep1WXl6+pPsgxTvoUUUJsMEjiEzd57G+ToXSMjtr4YDL2JKQP6Rcu8r9PmsNx3rzV2GKABzudn+OWudRpQtQFoaWektnf+6yqbBdIeVKVV5ezuDg4Mzt6fEpsy/Q8hqjvqXOoUQXVywYhZQisiKU3HnnTEDpb9uLE/eRKAnQV1FHpCRIBWM0RXogMEbXo0dp39TESHWY6Df24J8YJ9oSJbyng5q1SYbqyulZV4f1DRAAYsBjThmthyepGZygdP9hjpy1mlDnGIGeFpyG3CtxJJfBEJ4KEcO+0FQ4mAoMwyaUNj1IiS9EaVol4/TP4anpIRPEbwo7ufPs0/dy9po17Kw8c1F+r+iLX0zpTTfh1Nayr2YH9OynxGYGjpWVDjYYnKnEJRDg0CtfSd0TTxApL6eteQORXpNT+VtRV8eG2igEAkSuvXZZjYUqy4fXm+raCofqcu8PZV7Vbh39fqw+y8ks+oa9K3jzVlJ6hJSQCp8UUspsvK52TlVSuoeUxqSOy71H3EJKVY/L7Lwqx1fPUoW72uOCIK+LPBaLTXthT2/3KsubV+vDYMCyut57uQ3N7m/kjvZCPOHdNlEEoGcQxiMelZR5qtbW5/moerTH5A04RQCOeIaU+Y+dzavdQ0pVj69s0+9z0kNJt5DS6wJItXpdWHrrISInvbiTYNSZYDQ5yVhykmdC3UyaGAcHxhhzJqGzk7PafkPcibNhz0FMPEHirHL619WBr4cwkAA6nBDmsKVmMErl0XY6albT/FQHQw0VVEbiBBJJ4kE/7evqMsYUBLA+Q/v6esrHogTjSaoGJxiuK2dTWxePNGw5IY/LUjBAiQlR6itJhYm+EKWmZCYszB82Tv+cmh40gVOypZTT3Ezk2msJ33IL7baBI1WrcsY0nagrobzVgWgUp7eXrhe8gF3j45x9xRUkf/EL6ib87B5qpCeYeYlgcsIh2dqaM4aqSLqO/uLHBQTvSspIzNA3cur9r8rCOpon2FmbJ2isKrfUVjgMuozRlgoBVNEm3qbH4HXTXDMJ1Hguu7bRYe+R3Ole6xNJ5xUqrvF4LZ3m9Vrb3nfiw/FT8X35qcYrpFzbmL89uleVpeMYjvaook3y86pmA9icp3VmRSnUVVkGXD5HHPZo4ymS7miP+/T1s4SU65qAJ1zW16vXuZWs0Pc5XhdAqtXrwlJIKSLLjmMt484kw8lxhpPjjCYnGU1OMJacZNTJ/TlnzMbpzkQjqW/nP76PoeQY1QPjmHiiqKCxdDKO6R/DH0+dUB2qL89ZLn35ofpyGrtGqBqaZLiunJr+8YV8aBZMerg4HSaWzgSHJRkB43QAOT1varnU9EJaoAokN29m8rrrePqOByijg55gU0bgeH5dL/iGmWxspO+CC4jW17MN2LJjBz/u62N88Awe+lGM5lgXIRsjZkJ0hZp5umYnr7nOn7eNmIjXidPV9fnfVK/xGJMSoF0tX2UWXm2BS0OWhur8lUVrGpOuIWXHElcWycmne9BHNO7+othS6z0mJXifaDiqkFIK4BUq5qscB+/xnzsH/DgO+E58VinL2CGPdq/5Wr1C/vEqD3aqok3yO9brPj0csjTW5F923SoYGMmdnqqQ03En3sYnod/jQtl87V4B1ni0Gj7mEXqKpPO6YNFrmBKZG4WUIrJkEjbJSHJiJnwcSUx9T44zPDV9+ueMMRznaToorBqaBIoLGn2OQ3gijjM1fyScv+H49P3+ZGr/A/HFedEqMcdDw/TvZb4SSk369PSv4/OUmKCujl5i8cZm/q3yL2gIdHHe2KMZgePzXng61a8+ncqmJla5LFu+toJf1j3Xdb29Q72sqlX7Q/HW4XHidHWeEBKgodohFLDEErnPFe19flaXLMjuySnKq/Vha0P+8dkAWuodnjiYO71rQGfrJb98Y+amKim9eVW8eR3LIum8LqKY7bXW64KheMLQO+yjSe/xJI9DHpWU+dpqApSXwqpaS89g7vJHVNEmszjW49XOn1nf461tsuze53bc6dyE5Jev2na2dq9rGt2nt6uSUgpw2COkVLvXhaWQUkQWRNImGUyMMZgcYyAxymBihIHk6NTPYwwlxxh3Iidk36aDwungsJig0fH58DkO8WBqWjgSZzjPsuFIqqoz6U+dSE0E3V/MAtZHZaBsKjQMUz4VJJb5w6mg0VdCqQkd/zkjaAzhNzphdrLpHPCRSBq6Qi38su5lGfddf3U/TlPCc9mmGu/qj2N9PoWUkpdXu9fZKil9vtRYWYe6ct8utvf5Wd26ILsnpyivsGi2qiKAFo9j0+tYFpnW6RFkh/wxKsL5WwV7VVK296miTfIbGTeMTLgfIF7tXKd5jUkJqepMhZSSj2dIOcsJe4A1je4hZXufKtokv3aPSsrWxtmPG69j06uNp8g0ryDbGOsZQk5rbXA/7jr60Xs8yWt0wjDq8R5PIeXCUkgpIgWJ2wR98RF6EoP0J0YYTIwxkBiZCSWHk2PL9qPMdFA4HRwWEzRGS3yUTsYZqSmlemiCmv5xepqrsT6DkwiQTARw4kGS8QCbGizrh4YJ+SsINK2nurSB8Y07OH3VZZT5wpT5U4HkL27+Gadv3c7OnTsX+1eXZcSr5SbM/uamuixOSdC6trDrGvCTGlVVxF1Hv0cLullOnE7P4xZSHuv1caFCSsnDK6QsZOyO1XVeIaXOIEh+nR5Bdk3Z2KzVHV4BejRu6BvWBUHirT3Pc9NsF2Y0VFnP93gd/X7OO03v8cSdtXB4ju1eAVY3wKPP5E5XZZHMxmscv9mCIoC1bm2DyD/OpQh4h5Sr6yGUvw7Bs91rImnoHoSW+vnunZyq8n3+zHehmRRPIaWIzEhah/7ECD3xQXoSQ/TEh2Z+7k+MYpdtDJnfUH05Dd0jrkEjgHV8JONB/DZAY5lh3dAgIX85k5UNBEJhWkocxh2oJYkdtez/5SqeCu3ASXsK9VmHa7c/RhWV2LIg1avPAGOYvOhqnLLMBvk+qzfgK1Gnx5ubilKHqvL8/1vGQFNtkiM9uS/bXlUjIgDjkQBjk3Nr9wrQ2ug+T77QXQTgaO/cw/GWeo8x2lRJKbPo8HhNrA7PPkb42jxhkroWSD5er4k+n6V5luPGmFRng4NuXQvUaljyGBlPVXi42TBLu1dIVVK66eibz17JSuA1jp9XEJTOqy3n0e5U8K4RacSLV7vXtQUcd/kC9GM9hpb6k/Ncpyy+VFGAO3W7WFgKKUVWqIlkhCOxXo7Eejg69dUTH17QsSBPBIMh7AQotSEay2up8JXSdP46th34HaHVflb1tNHZbgh11PF0+HQSiRBO0g8Yzt0Y5XnVe/BTjS0LEi1vBZ+Pmh1bKL3/fhJrNlB28DANk4NcHHmQrlAzE75yypxxmmNd2O5xqDQkt2wBY0i2tuI0zTKCt6wYnR5vblrqCvufa6lzOOLygTDfmyaR3pGw532ztXsF7zZ17QqLJA9r87V7nf05z+vYHBzzMRmFUo2HKh68KylHZ122Pk9FW/eguhaIN68wsanGIVjAGZfVDQ4HXSriVD0u+bTnCRNXe7Q2zJzHa71KiSS/Yx6VlGsLqKRc51FJGY0begahqW4eOyanNK9KynUFnHJrqMbzPd6xXrjw9PnunZyqvN6L1Vc5lMxSwSvFUUgpsgIkrcOBaCf7I+0zwWRfIl/D0+XDABW+Uir9ZVT6S6nwlVIx9XNlxvTU9zJfmMd27wZg5+apdqqroHTTJL6ODnzbwkw+u4/ayCjnj+7OCBq3HD3A5OAIFhjbsIG+gQHKTz+dyCtegfPQQ8SrqgifvxOn4ygliRjro0cy9nXChijfvgmnthYCAWKXX76kj5Usb10e1R3NHm0Nc+fzqizSCSzx1uMRUvp8tqAr/7yq3o55VMmJQKqyw6uCt7AxKb2Pzc4BP5ta1FpH3Hm9JtaUjs26rDGwqtbhaE9u4KTXWsnHq5KykOc78L4wQ10LJJ+OPGHi6gJaF3oFmaqklHySSejsd79vPpWUAEd7DE11qmgTd0c8KinzHVPTjElVU+7vyL0vFbrruBN3XkUBs405LsVTSClyioqR4JHxZ3l84gBPTh5k3Imc6F3KEMBPlb+Man85VYFyqqd/9pdTnfZV6S/Db+Z/Yij64hdTetNNOLW17C45i43RNkpsZtBYWhHBCQQYX7OGeFUVdX4/5ddfj9PczLPnnkvDsWOYhgaeaV2D09tPY7yPgE2QMAF6gw0k1lRTX5uAQIDItdfiNBfQZ0dWDK9KSq/wMWc+jxNYXuGnCEDfSKnr9FUFVnd4Vb31DfuJJXyEAid39b0sjq5B7+elQi7MaMlT5dvZ71NIKZ46PCopqwsIKQGaa5OuIWV3nmNapL3Po616ngsu0nmd6FIlpeTjFSY21thZx2cDaPWopOwbNkRiEA7Nfd/k1NU5AEnHPSBv9WghnG5VLQQDlngidx1e4acIeI+XW0glJaRC9P0dueto753PXsmpzmt4pULP40nhFFKKnGLK19Rw+lsv5Usld5LsPTFXA5WaELWBSuoCldQFqqjzV1ATqJwJHqv8ZZT7wpglHHDAaW4mcu21mP+5hU4a6KqqozHemxE0rj9zE+HTagkbMxM0JtetA2Bo1SrazziDpp4eKsr62RdsoieY+W6oJRIn2dpE7PLLFVBKDq8wsZh2r25SLehE3Hm1ey2k1SvkD5QGx0M0VS+vC2Bkeeie59gdZSVQU+EwNJb7vJm64CM+n92TU1Qy6R0mpiopZz/j7nXCoUuvtZKHV8Vja4GVlK0eY0R7he4iAB397p+lWwqoooT8LWE7+2Fjy1z2ShbDaaedxuHDhwFYv349zz777Anbl2M93udwCmn3agy01OExjElxFW0f+chH+NjHPjZz+4477uDSSy8tePmV6mR83KxNBeRuCmlvDd4XZni1Lwa48cYb+fa3vz1z+5lnnmHDhg0FbU9ODV5DSRTaEU0Kp5BS5BTzom+/mUBpiOQitSswGGr9FdQHqqgLVFEbqEiFkf7KmWCy1HdiB4tKJBLs37/f9b6BjRdxtGSQtdGj9GQFjVuCh0h2dzPZ2Ejfzp1ER0Zg1y4ADhw4AED09NO5/ax6+kd7aI51EbIxYiZEV6iZ+xvXU3r6MejsTH15OHLkCNu2bVvA31iWi3zH3uHOF+J2gjQ5cYhdu456rnP62Iskm4Fzc+4/1gu7po7TQrS1tZFM6g3VSpBIJNh/LOp6nzN5iO9//7aZ2/v27eO2226jra0tY76JWAh4m+s6du3p5iUXlS/Y/sryl0wmc44RNw88sRqozZleVRrj6T3Hn6+mn9/c1JQ9j6GxypzpDz/ezebKzOXa2trYvHnzrPsly1MikSjouJrNwGgJiaT75fRjA3tpa5v9/akvsR3YkDP92cPjnq+1Ov5ObolEgj179sxp2T179nDffffx7NCbcHuP1334Qb7//cddl7vssstmbntVUnYP+ognKKjzgaw8XpWUawqoZgPvE/apdRs2tqj9oeQ66lF1Vha21Oa+bXPVXG854hJ2dqiSUjwMjBjX6lvI/1yWzqsd8TFVUkoeXkM+FNotQwqnt7sip5hA6cL0ZanxV7AqWENToIbGYO3Uz7U0BKsImuX91GGt5ciRI673PdvTytebb6Q51sl5Y49mBI0HarsYWLeaiepq6OtLfU3p7u4mEEj93oOBC/hl3cty1t0y3kdb252z7l9XV9ccfzNZ7ryOPWuhb/QlrstERg/Q1uZ9on762EuUPYtbSDkZC/DYkwcIBwurLDpy5AirV68uaF45uVlr6Rpw7/d1xuYqLrnkkpnb/f39bNq0iR07dmStA4K3Jognc5/3DxydxF5YtrA7LctavtfXdHsPVLhOLw+NZIRR6a+t2Up924Hcs137jk7mBFpHjhxh06ZNs+6XLE/GmIKOq9kcGfDu9zU5tI8jR2Y/e+pEKnALKTv68AxSdfyd3Ky1HDhwYE4dXg4cOMCmTZsZu8/92Lro3DVccnbu82F1dTUHDhzgoosuAry7ZVhr6B32nVQnwmKxGI8/nhvMLgfDw8OUlJQQDrt3mViuzj77bEKh3M/47R5jUhZaVVRVDpVlltGJ3PXopL148Wq52dqQqpIshFe1b2eecVZlZesc8D42mgscx3SNR6VvvupgkQ7PYZt04f9CW95Jg4gsugpfKetCjawtWcXa0Cqag7WsCtQQ8hUwkMUyFQwGeelLX8rOnTtz7vvxPWG4G7pCLRlBY1WZwyc+/sd517tt2zZ27txJ4O4wv3gy9/6oreN1r3vdvPdfTl5ex17vsCH5P+4vua+59hLO3XJR3vVu27aNutUX8O93ud//vMtfy2mthb1J2rVr14JUrMjyFwwGcYLugfSFZzdz4YXH21I/9dRTnHHGGVx44YU58zZWR+gYyD1+126+iGBw/sGCnDwCgYDn62u6J75WCS6vk9s3Vua8Tk6/tmZ7eKiSp12u6amo28brXpfZUr2YanJZfvx+f0HH1Wx++UAJ/CZ3eihguex5O9i+3f1YSxe4O8zPn8idHnW83+Pp+Du5BYNBrrnmGtfXv9k89NBDPLRrH9F73K+yf+FzTuPCHa53ce+99878vKrW+z1cz+DJFVI+/vjjtLW15Vz0tBzU1xfYB3UZma7yveCCC3Lu86qkXF1gVdH0vG0ub+U6+opruykrxzGXNq0Aaz2q1Ny01LvP2+XRzlPEa+gcn8/SVFfYOlo9LuDo6AfHAZ+GgZYskRgMjMxv2CYpnEJKkRUkZAKcFl7D+lAT60KNrCtZRa2/cknHhjzRvMYqqi6byDjJtGPHDtcrVgFW1bi/GA2M+ojFIXTy5ruySLryjCnUUuCJp6Y8J7C6+v0Fh5SysgyMubc3bC1gzJhpDVVxOgZyqw681i3S5fFaW8h4lNO8nhs1Rpt46fBox9RSnyy4uqPZ47V2cMxHJAbhhWlYIqeQoQnvjgKFjg1YW2EJ+C2JZO6B2jN08p013bFjx5xCX3GXSCRcp3d4VVJ6BEBuWhssbUdc2m56BKAiXuP3eVWpuWn2CJU6PcZZFfE6NlbVQKDAjwZerbATSUPvEAWHnbJydOcZk76lXuffFtrJ945XROasxISo8JVSF6ikJVS/4gJK8A4p6ysiMz+3tbXlHZumNm3ebH0eV9nIytbpceWf32dpqCrspH04BHWV7vN6rV9WtsmYn8mY+/Voq4soJmiodm8lrJBSvHR7PCe1FNEWx2teryupRTo9AuxCLwYCaMpzVXS+ExWycg1NlHre53UiPpvP530RZI+OO3GRSEL3oPt9xVZSuvFqJSviVe1YaJvhfPN2Fjkm5Yc//GFisdjM16WXXlrcCuSk4XUer9CLgQCa88zr9XwqK5vXeJSgMSkXgyopRVaQUWeCB8af5oHxpwEo85XQEqxndbCOllA9q4P1tATrqfKXnbLhpdebmy3rygtuMzbQ+RTQ4nrfydaSSZZGl0cf+6ZaB38R556a65IMjOYew17Htaxs+ULEYq4UbahUSCnF6fI4qb6qiEpKr3n7R3xqySSuvE4kFBeO5wspfaxv0lXTkskrpKyvhpIiKm8baxzXSvGTsZJSFl/XADjO/MakzDevKinFS7fH2ICFXpQB3sFSzxAkkxT1+VhWhi6P486rdbCbxhowxmKtS9eCQbW4llydHufxKsscKkp1vCw0hZQiK9iEE2V/tIP90Y6M6eW+cCq8DNXREKihIVBFQ6Ca+kAV5f7cln8nE6+r4ItpQVdZGifgc0g4uScNdCJB3HhVOhY72HZzncNTh93Wr09ykmswT4hYzFWnDVUKKaVw1qYu2HFTzNgdXlVFScfQP2porNYHQ8nUPTT/SsqKUkt52GE8knsMq2uBuBn2CCmLeZ0F788i3fpsIS68Wr1CcZWUrR7z5lu/rGxelZRNdYW/L2v2CJYcx9A9WNwxLCuD14VoxVyUEfBDQzX0DrmsX+Ohiguv465Z41EuCoWUIpJj3ImwL9rOvmh7zn2lJkRDMBVY1qeFlw2BauoClYR9y3uwHqejm6sHHqc51kXIxoiZEF2hZrb4TgOqC1qHMVBTHqVvNPekhFoyiRuvSspiB9v2ejPUlacNhaxcXiFieUmS8tLCn6saqmKu0/OFoLJyDYwaYgmvq+wLvzDDK6QE6B3001jtPkaXrFxeF4o11TgwXvh6musc9ne4dC3QBUHiYmjSfUzKYtqqA6yqcX9+7FW3DHHhVelYErTUVxW+nmaPYKl7MHXR0SnaXEnmaHQCxiNeFW2Fryff82PXgEJKyeU1JmXRFwTVuYeUqQphXQApmTzbDBdZbCCFUUgpcor5/V/dxJbXXUjz87csyvonbYyjsV6Oxnpd7w+bENX+cmoCFdT4K6gJlKe++ytmplf7y/CbpT3R4+vqouTOO7nmyeGck6dro0d5wQP3EDZNxC6/vKD11VbECPQPcd7YoxmBZ93vq/GdvRWnqWkxfg05SXmNoVbsFVhNtR4nsIZ1AktyDY67h4h1VXGg8Ofgeo9KyqHxEEmPVmOycuULcorpWlCfZ7xePeeJm16PkLKxxiFWREjZVOuwvyN3ulqrixuvdq/Fnjj1anGtLi3ixqvSsbWhuGDRq/3/ZNQwNgmV7hl8DsdxuO+++3j22Wfp6uoiHA6zceNGnv/85590w8jE43Ha2tp4+umn6e7uZnR0lPLycmpra9m+fTs7d+4kEFiYU6lPPfUUu3fvpqMj9aKzevVqLr74YjZv3rwg63cTjUa59957OXbsGF1dXfj9fi644AJe+MIXei4z/Zj89r5jwDWu8zTVFh7w1FdBwG9JJHOPjVQYtbRhUXd3Nw8++CC9vb309fXh8/moqanhtNNO45xzzqGmpmZJ92c2bW1tPPHEE/T39zMwMEA4HKahoYHTTz+ds88+m1BofoUD8Xic++67jz179jA0NERVVRVr1qzhhS98IbW1tQvyOxw5coRHHnmEnp4eBgcHqa6upqmpiUsuuYTm5uac+b3Op+Rr92qt5fHHH6etrY2+vj6Gh4eJjr4B2JAzb7cqKcWF12eLfBfTytwppBQ5xfQ9fJi+hw/z49/9gvbGcZ6ePEJHvMgRyOchYmNEEjG6E94jTxug0lc2FViWU+Mvp9JfdvzLV0qVv5xKfyllvjC+eX6w8e/fT/iWW4hHksQTpayK99AY7yNgEyRMgN5gA+UlFfjb2ym96SbKtmxhYs0az/WV9Pfzpo6vEescybmv9pkDlH7rfpKtrcQuvxzH5Q1WIpGgra3Ndd07duyY95tKWX76PE6oe4WOXhqq3d8M6YS9uPGqpGyojAGFt+72avdqMQyN6/lKMnm1xPT5LI1FfKALBqCu0nEdh1cn7SVbLA5DY14nEpIcy20O4qnZ47XZ6wSZrGwL1u7V4/nRq42xrGwdHh/vi2l9CLAqT7DUPTh7SJlMJvnc5z7Hv//7v88Eben8fj9XX301H/vYxzj99NM5dOgQW7dunbn/hhtu4Gtf+1rOcoXO5+UlL3kJd99998ztWMy9K8i0vr4+br75Zn75y1/y+9//nrGxMc95y8vLee1rX8v//b//ly1b5nZh+K233sqHP/xhnnjiCdf7L774Yj7+8Y/nDQ693HjjjXz729+euf3MM8+wYcMGjh07xic+8QluvvlmhoaGMpa55pprcrbl+phUvQDOdg8pP/6Pf83fve8dsz4mM/t34REoWZtz/yte+3bo/krOdLdj4CMf+Qgf+9jHZm7fcccdXHrppXm3Py0Wi/H1r3+dr3zlKzz55JNY6/6/4Pf7ufjii3nDG97A6173OsrKCkzuF1hfXx//+q//yg9/+EOOHj3qOV95eTkvfvGLufHGG7nyyiuLukggGo3y6U9/ms9//vMMDOSmdn6/n1e+8pV84hOfYP369UX/DrFYjC996Ut89atf5emnn3adxxjDeeedx9/93d9xzTWpY81xoHuw8ErKI0eO8KlPfYqf/exn9PT0ZN65dQOs2pCzjNeYl3P105/+lDe84Q1EIpGZaR/60If48Ic/vKDbkcXl9ZmzmM+0UjiFlCKnqDpbwSV1FwAwlpxkX6SdZ6LtPBs5xrFYH/YEtjKwwIgzwUhsYtZ5ffio9JdSNRVe5oaZ07dLqfCVEvIFM5fv6iJ8yy2QSBDpHOK5I7sJ2cwT7o3xXlqeAV9wM05tLc333EP7FVe47o9//35ab7+ddbE69lNJY7w3I/AM9teCrZwJPCPXXksy60rIjo4O1q1bl7Pu6eBy586dsz4ucnLxenPjFTp68bpiy+sKL1nZBsfcA0Sv0NFLvvn7R9XyVTJ5VZs1VjsEijzX3ljjHlLqOU+y5btYp7HG4VgR62ry6HKgSkpxMzThfsK6pch2hZ6VlIM+td2UHD0eJ+y9KiO9NOUpiuoZNGxp9T5nMDAwwDXXXMNDDz3kOU8ymeTnP/85v/vd77j55pvZsGFDcTu4BAYHB1m3bh2JRGFt5MfHx/nGN77B9773Pf7jP/6DN7zhDQVvy3Ec/uqv/oqvfvWreed74IEHeOlLX8onPvEJ3vOe9xS8fi933nknr3vd6xgeHi5ofs/HJJR7ATYAiRG+899f4kff/2bhj0mswzWkpGR1Qfs4H/fffz833HADhw8fnnXeZDLJvffey7333ksgECjq771QvvKVr/C+970vb3g+bXx8nFtuuYVbbrllJqAuRHt7O3/yJ3/CY4895jlPMpnkRz/6Eb/97W+59dZbizpv9eCDD3LDDTdw8ODBvPNZa3nkkUd41atexcte9jK+853v0D/i8+zek35hhrWWj33sY3zqU5/yvjAh1uU6eSErKf/rv/6Ld77znThO6nXd7/fzH//xH7z5zW9euI3Ikugddv/wqkrKxaGQUmQFqPCXcm75Fs4tT13VNulEORbrpSPWT0d8gM54P52xAUad2UPDpebgMJwcZzhZWJ+uoPETqvYTdoLc1XWA5//kQepHR6kamqTimXYqywNECNMVambMVhGOxlg12UuIMfx795LYvh1fMknDww/DS16Sse6S/n7Cd92FL5mkMdnHqpEncwLPyu5ugg8bklu24NTWEr7lFiavuy6nonLbtm0KI1cIx/GupCz2CqxGj1BzPOJjMgqlyoskjVclZX1lcSFldVkCv0mStLlv0r22ISuX1xi8xbR6nbaqJknb0dyPK6oel2z5qmsX6rW2f0THnWSyduHavTZ5jEkZjRtGJgzV5RorS47r8WhaVEzLTYCKUigLWyZcxhnMd9J+dHSUq6++mkcffTRjujGG9evXU19fT39/P4cOHQJgZGSEV73qVXz3u98tav+WQjKZzAnj/H4/69ato6qqipKSEoaHhzl06BDRaHRmnmg0ylve8haMMdxwww0FbcsroGxqaqK1tZXR0VEOHTpEPB7HWssHPvAB19aXxdi9ezdvetObmJg4fq5n/fr1NDQ0MDQ0xJEjR3KW8XpMqpvPxPWwiKeCn6Iek1in+/RgS/7l5ukHP/gBb3nLWzL+ltNaWlpoamrCGEN/f7/rY7PU3vOe9/D5z38+Z/r0MVpfX08kEqG7u5veXvchmWYzNDTElVdemdHtq7W1lebmZiKRCPv27ct4vPr6+njVq17F7t27qaqafRDcX/ziF7z+9a9ncnIyY3ooFGLDhg1UV1czMjLC/v37M467W2+9lSuuuIJ/+Jefeq57+rU2Ho9zww038OMf/zhnnlAoxNq1a6mrq+NIErpd1uNVqVmsD33oQ/zzP//zzO2ysjK++93v8rKXvWxB1i9LK99QErLwFFKKrEClvhJOC6/htHBmS9PR5ASd8YGp8LKf3vgQfYkRBhIjJDk5noTjNkncl2TcF8M52sXYsX2MW8u2ZzsYNUmSp5fTsy6M8XVTOfX2ZDBheLx9hNqBSdjTz8Hta/EP9fFI2y3Q1EyZr4Tu+kn8j9zFkPVjRwbYMNhDpy0nakroCjUz4SunzBlng9OJiY8TmAo8ndpaQr/5DZHrrz/Bj4ycKAOjxvPKv8Hup9m163jb4Nna/earvOwd9rFu1cnxfypLwzOkLLKS0ueDyvAEQ5OVBW9DVi6vsGhuIaWqx6UwXsdEMGCprSjupL1aq0uhxqM+oomg633FhpSNeZ4juwd9VJcXN0SAnNq6vULKIispjUlVUx50yYtSJ+3dnz/f//73ZwSUxhje9ra38e53vzujY9DBgwf5+Mc/zre+9S1GR0f5m7/5m+J2cAmdf/75/Mmf/AlXXHEFZ5xxBiUlme9xE4kEd999N5/5zGe4/fbbZ6a/4x3v4LLLLmPtWpeqwDQ/+MEPcgLKyy+/nE984hOcd955M9OGhob45je/yT/+4z8yMTHBO9/5znmN6/nXf/3XTExMEA6Hede73sVf/uVf0tramrE9r7az2Y/JR75Vyr/clDtfbXmE9EMy32Pynve8h+uvv54v3HYGtz6aczcXv/AV/OMXcluJtrTMP7x8+OGHufHGGzOq7Kqqqnj3u9/N9ddfn1N1ODg4yD333MPNN9/sGn4tts9//vM5AeWmTZtmWqFmjw959OhR7rjjDn7wgx/w29/+tuDtvO997+PAgQMEAgH+8i//kr/+67/OGBd1fHycL3/5y3zoQx8iHk99jjx27Bif/OQn+eQnP5l33Xv27MkJKJ///Ofzt3/7t1x++eWEw8eHIBkdHeWHP/wh//iP/0h3d+pc3UMPPcRnvvB94N056/b7LI3VqZ8/8IEP5PyNzjnnHP7u7/6OK664gvLycgBuutPwxo/n7qfXc2qh4vE4f/mXf5lxIUZDQwM//elPueiii+a3cjkh4glcO/qAKikXi0JKEZkx3UZ1a1Z46VjLcHKMvsQI/Ynhqe8j9MWH6U+MMJgcO6HtY71seib1xqZqcIJAIkk86Kd9XR3Wl/UmP2A5tLaa0Mg4wdgEzkgfg3XlHHv4dh55Xqr6tLZmlI6Rp+m0lm1dHQRqkgQ2ldO5ehW+ZCel8QDJRJB9sXKa4iNUDoxh9j7K4M4dBA/103t4G8HmNYR9IZIlnDShr8xfvpOaNeXHr0gspN1vdVnE877eIb9CSsmwUCElQFV43DWkHFRIKVm8KsdXeVQJ5dPg8QFQY1JKtl6Pcfsaq52i22R6XR09MOojmQS/hgiUKf0j3heWrS623WueE149Qz62rlFIKcd1e4yfVmwlJaTGpTzY6VJJ6XHS/oEHHsgI24wxfP3rX+f1r399zrwbN27kn//5nzn//PN5xzveMVNZuZyUl5dzzz33cPHFF+edLxAIcPnll3P55Zfz8Y9/nH/6p38CYGxsjC9+8Yt5w5rR0dGctq033ngjX/ziF3MCyJqaGt75zndy6aWX8pKXvKTgFq1euru7qaio4Oc//znPe97zcu6vqanhBS94QcY0r8fEq83wSy49kzNe9A8FPSZnnHEGZ5xxBve1+1xDSsffwItf/OJCf72CRaNRrr/++oyA8vTTT+cXv/iFZ8BcW1vLtddey7XXXssnP/nJgtqtLpQnn3yS97///RnTXvnKV/Lf//3fOQH6tLVr1/LmN7+ZN7/5zTz66KM5IaaXAwcOUFJSwg9/+EOuuuqqnPvLy8t517veRVNTE29605tmpn/729/mIx/5CMGg+8U6iUSCP/uzP8sIKD/84Q/zwQ9+0DV4r6ysnBlL86UvfSn79u0D4Df37IHTctffVJt6T3b77bfz7//+7xn3/dVf/RWf/vSn8fl8Ocu4GRgxxOIQcv9V8hodHeVP//RP+d///d+ZaZs2beLnP/85p53msuNyUvD6TAvQOIfPtTI7hZQiMiufMdQGKqkNVHIarTn3J22SgcQYQ8kxhpNjDCXGGUqmbg8lxlPTkmPE7dI+kdf0p1rEVg2l3hQN1ZfnBpRTrM8wVF9OY9cIVUOTDNeVzywP3oGn8SUJMkkwrdPTY06ArUMTBCdHOdqxh+G6cvb9/hszgScvD/FV7uG/D99HqS9EqSmhzFdCvCJKyAbY09dPqa+EUl9qeqkvNHO71BeamlZCiQnh0wA1y16fx4lTgMsuOaOoN8JHDz6J39dM0sl9+c73JkpWnngCRiYWZkxKSIWUboY8tiErl9dzUX3VHE6cqpJSCrRQYz8DNFS5L+M4hsExQ0P18rswT06MvhHvN3HNRVa0lQShpsJhaMxtHF4/UPxrt5yarIWeIff7GmuKX98qj5P2Xi1lP//5z2Pt8efBt73tba4BZbo3vvGNPPbYY3z9618vfgcXWWlp6awBZbYPfvCD3Hnnnfz+978HUmFNvpDy+9//Pj09PTO3zz77bL7whS/krZDcuXMnn//85zNCobn61Kc+5RpQevF6TLo8WgA31Vo++FfFPSbTFXDZ+oYX5/zGt7/97YyQvL6+nl/96lcFV2imV58uhU996lMzVYsAz3ve8/je976XE7p5Sa/OLcTHP/5x14Ay3fXXX88Xv/hFHnzwQQB6enp49NFHPf9//ud//oc9e/bM3P6Lv/gLPvShD826L62trfzgBz/gwgsvTI3rGGxynW/6uetjH/tYxvTXve51fOYzn3FdJt+FHD1DsKZx1t3L0NXVxbXXXsvu3btnpp133nn87Gc/o6nJfb/l5JB3KIk5fL6Q2SmkFJF58xs/jcFqGoMe7zRJDWI94USngsxxhhJjDCXHGUqOMpKcYDQ5OfV9goj1GOS6SIF4KhT1J1MvIJFw/jRo+v7p+aeXh4UNPKfFbYJ4MsEIU+NDTO3egbHCxhIwQNikB5ipEDPsC6W+mxDh9GlTt8O+4NTtEsK+ECETmFcLGcnPq5KytsIp+ko9Y6C2PE7fqMsYbTppL2n6hn1Y3P+vG4ockxKgIjzpOn1wXJWUksl7DN7iL1Ty+gDYk+fiD1mZvE4kzKUd02yt1RuqdfW0pPSPur+Rq66AsrDrXXk11riHlN2Deo8nxw2NQSzuUUlZZDgO3ift3ao1h4aG+NnPfjZzOxwO8+EPf7ig7Xz0ox/lO9/5TkYl28nsuuuumwnkenp62LdvH1u2bHGd95vf/GbG7Y9+9KP4CyjLv/766/nsZz+bEYAUa926ddx4441zXj5dl0cF7/RFGcU8Jqs8jjuvcHy+sivtPvnJTy5IC9nF0NnZyc033zxz2+fz8eUvf7nggLJYra2tvO1tbyto3te85jUzISXArl27PEPK9Fa1ZWVlOWFiPmeddRbXXHNN6vkmuMp1nqY6y0MPPcT9998/M62ysjLnb50u3wVEXf2GNY2FX4jW1tbGNddckxF+X3HFFXz/+9+noqKi4PXI8uR1Hs/vs9RV6oLFxaCQUkSWhDGGcn+Ycn+YVvL3P4o7CUadiZnwcjQ5waiTGWSOJicYcSYYS07ieLSaTQRTb/yT/tSLSzgSJ1+zlHAknjH/9PKwsIHnQrHApI0xmYxBcnTO6/FhCE+FmqW+VHAZ9gUzbqeHnsdvBymZCkBLTJBSXwi/0YnjbJOHerh64Pc0x7oI2RgxE6Ir1MxQ/dlA8VVoteUxAv1DnDf2aMY66/5Qje/srTi6Yk/If0JzLpWUlSUTrtOHJ0KoukPS9Y14hJRzuOLUK2AaGDUkkhDQS45M8R4Ltfj3X/UelZQA/cM+QCGlpAx4hJTFVlFOa6pJ8uyx3FM0CiklXb6x07yCn3yKqaS8//77iUaPD1dx1VVXUVdX2AHf2NjIlVdeyS233FL0Pi5H2eMX7t692zWQGxsb45FHHpm53dTUxBVXXFHwdm644YZ5hZSvfvWrFyzc6up3n95clzruCn1MwPu4G48YJiJzu9DDy7Fjx9i7d+/M7fr6eq677rqF28AC+81vfkMyefy9xpVXXrmobUP/5E/+xLNla7Zzzjkn4/bRo0dd5+vv7+ehhx6auX311VcX3H522ktf+tKpkNK9vLGxBu64446MaX/2Z39GTU2N5zprK1PjlccTuYG7V6Wwm/vvv59XvOIV9Pcf/6e44YYb+NKXvkQgoKjlVOBVBNBY47BI1wusePrPEZFlJ+gLUOeroi5QNeu8jrVMOBHGkpOMORHGnUmeOthGxMTZuBZqB9sINgQoHWmnqjtGZ10AU5LEmMwPcMaxM9WOIzWp3q1D9eUz9y9k4LncOKSqXCeIzivsBAjgT4WWUyHndIA5HXyWTE3L+Hnq/pK0sHN6GZ85eV/9fV1dlNx5J9tv72NkNDOMXBs9ytpj9xP+Xh2xyy/HaW4uaJ0l/f28sf1rxDtHcu6rf+YApd+6n2Rra1HrlFOT1wl7n0lSVZYoen3lJe6VlAopJV0sjmsVEMyt7abX2IDWGgZGfKyqVasdSenLcyKhWKEgVJc7DI/nrlOt1SXdwJj76RSvk++z8Wol3O9x8YesTF7jAoL3eGv5eC3jtp300AHg0ksvLWpbl1566bIOKePxOL/+9a/53//9Xx5//HEOHjzIyMgIY2NjGS1u3aSHFekeffTRVMvKKZdccklBVZTTXvjCFxY8r5sLL7xwXstPPya333En3YOfA3L3/S1v+GPeMvTrnOlejwl4P98B9A7B+gX8KDtd3Tntsssu8xzXcTn4wx/+kHH7yiuvXNTtFdMatrExMzAcGck9LwGp3yH9f+b8888ver9mxgoNebR7rbH84efFPVY+X+o575hL87JCq3h/9rOf8YY3vCFjrM33vve9RVWKyvLnGVKq1euiUUgpIic1nzFU+Eup8B8fFNKJpd4onX/Jcyjd9y1Yu5Fgt6V7D/gPr6KtdBsm6OAPxfEH41y0fYyt488SsBXEwz5GmzZSTpKDZ5/P+lCYCSfCaE0FDd0jjNSUUj00QU3/OD3N1a4tX2cLPE9lCZKMOZOMOe6BRrGCJjAVdk4FmSZEiS8wFXAGp24HKTHBqe/Hb7vfH1yS4NO/fz/hW26BRIKJyRCr4t00xvsI2AQJE6A32EBFqBZ/ezulN91E5NprC1pn6+23sy5ezwEqaIz3ZqwzOFALtjJjncnNmxf9d5XlqdejHWZleGJOV/5VeoWU4yHAfbxKWXm8qigBGuYQFuVr1dk9qJBSjlvIMSkhdQLCLaT0av0kK9OgRyXlXMYFBO8qXoWUks6rkrKyzFI6h9zFq/rSbTvpbQ0BduzYUdS2zjzzzKLmXyrWWr75zW/y93//9xljRxZjaGjIdfp8H7MzzjgDv9+fUVlXjI0bN85puZzHJNgMF3/BfeZ4l+tkr8cE8l/M0TtkWN+8cO0UDxw4kHF7LoHZUlrq/V21yr2dqpvy8szzWulBXbr0ylWAD3zgA3zgAx8ofufAs91rY83cHqumOsux3tzzeJ0DBjy6tE378pe/zGc+85mZCw98Ph+f/exneetb3zrrduXk4jW8yFw/W8jsFFKKyCnLaWrCWb0aX0cHic1biD22nybbQ01iiK5QMxO+csqccbYFjtJQGoNgFYnt26kpryXZ2sq2s66fWdctkzHOjwRp3NBAoO9hnFiUgfureKxsHb6Qgy8Qxx9MEPBHOWfyMNXJALFQkER9HUGSHNymqra5iNsEcZtgdAHfB6SCz+BM5Wbf5QFurXicB3qOeYacYRMklBZ2hkzq59DUfcG0cT1L+vsJ33UXJBL4BgbYcOgwDROZHyob472s6vXhG9yIU1tL+JZbKDnrLKL19a77PL1OXzJJY7KPppEnCNnM6rWqrm6CDxuSW7bMrHPyuutUUblC9Q27X2Xv1bZ1NhUey41OBtV2U2bkGxt3Lled1lU6+HwWx8k9nhUWSTrvdq9zewPRUO2wryN3uiopJd3guPvplLlUs4FCSilMj8e4gHM97ryWG48Yxieh/Pi1wDmhU6GtXuc6/1JwHIc3v/nNfO9735vXetLb4KbLfszqPT7veQkGg1RWVuYN/PKprKwsehnXxySU5zNlrNN1stdjAlBTAdg4mNyLPXqGCtzRAg0OZibuxYRyJ8LAQGbf0ezqxYUWDs+9t65XhXH27zAvQY9KyjqbsR1jDA0N+YeWAu/nvN4CKik//elPZ9z+1re+xWtf+9rZF5STjtfnWl0ku3gUUorIKS364henqsoq63iirIxtE3spsTHWR4/MzFNi49hgcCbcIRAgdvnlGeuZqK4m2tBIwBfAd9p2Qnv30jgyzo6OgxmBZ3NsmJraEtasXkVi+3bWVKcCz0vOv46IE2XSxvji177EeZdcwJrN65h0Ykw6USadKIe6jhIzCcpqK2emTToxJm3q57jVGEgL4XjwOXXVX4OPYwxybKLA/h4uDBAyQXzV8JK7djM5GKFyeJLV+7sJVvooqQjRV1HHhL+UcCRO/egwgVCM8ScHGN+2Gae2lsCjd3PsypfQGRuYCUpDvgAWS+NDD0EgQHBkhI0DXXTaMqJTY1tOH3ubbScmPk5g714S27fj1NYS+s1viFx//az7L6cerxOaFR4VkbPxWs5iGBz1zamlopx6vAIcv89SW1H8FfF+f+qkvVtlsFcoJSvPeATGIwvbkqneYzmFlJJucMy9knLu7V4VUsrsvCop5zIe5WzLdQ/CprSQcnw8s3tGWVlZUdvKrsJaDj7+8Y/nBJRVVVW88IUv5LzzzmPt2rVUV1cTDoczxu17/PHHed/73jfr+sfGxjJuF/uYQepxm2tIWehYg+ncHpOy6o24XbJosPz8p/+N31f4YwJgDPidAZL+3ACqb6joXc5rdDRzSJvleBymyz5mKioqTtCezN1cj9dcPgi6B4+rajL/tmVlZQWNv9pQ7T691+Mi33QlJSUZ4fv3vvc9Xv7yly/r9sEyN14XxKrd6+JRSCkipzSnuZnItdcy8d2f0x+o576q5+a0yUxsrSC+ti71TjkQIHLtta7VZ70XXsiGJ57Aqa0lsX07pvNgTuAJMOmESGzfkBF4+oyhzB+mjDAlw5bViRrOKctsxbnrYGrcwp3bdrr+LnGbyAg1J50YE040FX6m/RyxcSadKBEnRmQm5IwRdeJEbWyWBhYyFxaI2ji1/aOU9Q0wai2rD3QwmUwyurqc9nWVWF8CGGUSaHdCcHiU2OAEib3DtJ21GmsMv42F+HHHoxnrrts4Suvjj7PP+NnSeQRftcVpqaV9VQtJO4o/OcFk0s/TTh0t/gnK+4exe3cxuHMH/oN9dBzaDE3NM1Wgw74JosEkE8kIJb4gfqMSuFPRUoWUkDppr5BSwLvda12VM6c2w5D6IOgWUuqkvUzzam8Nc7/a2esERO+wXjPluMFR99MphbR7PXLkCLt27cqYNtLfBOR+DugdtDnzZmtra2Pbtm2zb1hOel5jUs41HM9XgdkzaNi0+vinx+yAbWKiuA4d2SHnidbd3Z1TGfXe976X97///bMGQ4W2X80OxIp9zGBpHzevx2TTxR/mrf+WO399NVzx0hcDhT8m03zJfteQsntw9rabxciuJl1ux2G27GNvbGysoArB5ST7ueJv/uZvuOqqq4pez9B4kOs+6/7ea1WNpbKycqZSdmJiAsdxZg0qG2rcj61CwvFvfetbvP3tb58Zb/WXv/wlr3zlK7n55pspLS2dZWk5mXhWUuq8x6JRSCkip7zk5s08/aI/4+jt97M2epSeYBM9aS0jXrJ6DAwkW1uJXX65Z3vMaH09kWuvJXzLLTi1tbRvWcWByaGcsQbD9XWcVhvNG3jORdAECPoDVPmLv/pymmMtUTsVXjqxnFBz0okRybl/+uepr6n7HcWdOTY90w1A1eAEgUSSeNBP+7q6nLFLrc/Qvr6e8rEowXiSqsEJhuvK2dTWxSMNWzLm3bivm6RxqBgYhWScaNhP7+lhynxDOdt/2IGtA+MEJ0c42vEkw3Xl7PvDt3nkeWnrrAbOh/uO/hcAPnyUmEBGO9ugCaQqOdOmT/+canXr9nOA0Mx80z8HCRr/TCtcWTq2s5urB/bQHOsiZGPEpipvw60BoPhWW+V5Qsr+Ef19JcXrw9x8rjitq3RfdkAhpUzJ12Z4ricSvCravFppy8rkVUk517ab1WUx1+mReIBo3EdJ0Pt43rZtW9Fj3cnJyauSsmmOlZRV5VAStETjuc9v2duqqanJuD19or5QhbaAnO9nh0KDwJ///OcZY+q95S1v4WMf+1hByxb6u2Q/Zn19fQUtNy0ej+dUAi4mr8fkcz/yeI9Xc/znYlt8+p0+4i7TF7qSsrY280l5ruOOLpXstsi9vb1s2PD/s3fm4W1U5/7/zGiXbNmWvMWOk5DN2QiEsiTsJSWELb3cphRCoRTo7XpbWtofLbcX2j5QWrrR0tuVUgpt2QtNIYS1LG1YkpCwZN/jeN8k2do1M78/ZCuWpVFkW7IT6XyeJ481Z86cGUknZ0bne77vO21iLmaUDA9rXFtby9KlS0fczgf79PdVV8Q/q0GRUtM0urq6jhjOt0rHSZnNM96iRYt4/vnnufDCCxP96IUXXmDFihU89dRTR71LV5A9elF7xOLs/CFESoFAUBS0mOu5r/Z6aiOtnNT/TmLiHrMJTrYSXLgQtSZ9rPuhKDNmELzySswvv4x1Z0eK4AngjqhHFDwnClmSsEkWbLKFUc6fAPEHwKgWGxAso4QT4mXcrTnUuRlSowlxM6xGEyJn0mst3c+TY4/y7viqTKcn/sPO43akCJSDaLKEx+2gqs2H0xPE63Ikjs93m0NRUQlqEYJK+omxXJBewDRiloyYhoig+6b68Fj30uqJxOsMq6svhhqRJSFYAMhtbVheeomlG3vp6EteddoQbmL27oNU/r0O6uuhri7rdo2ySnmJirWnPWkMjUhm7P8sQa6em9UYKihs9EJh6gk+2eBy6uS66RP/5wVx9Fy1FpOGwzq6SXsRdlNwJEJh8IfTuzuycVJOmTKFRYuSXZMl1QZ4MH39ycctYnKVmBgTQIduuNfRtSdJUOuCA+2p+zo9ydvHHXdc0vaWLVs499xzsz7X+++/n1W94Tnyhopm2ZCtEPjWW28lbX/2s5/N+hxbt27Nql66z2wkbNmyZcQOxbGg95l0etPXrxriSsv2MxnEoKQXubMJuzkSZs5MXgS8cePGnLafa2bOnMnLL7+c2N64cSOnnHLKBF7RyBkuqu7Zs2dU7XTqOMchfq+dOXNmUtsbN248omOzskzHSanTx4dz/PHH88ILL7B8+XJaW+P5WF999VUuvvhi/vGPf4wqD6zg6CIcBa9fb/GtSMOVL4RIKRAIioLBidM28yTWuC5OlE+uUvjO+SNbzajW1hJatYq9dg/r9+xOcSrtrzmRy1cV9vAqSVJcHMKEMweRz1RNI6JFCatRglo4LmAOiJehoQLoQFlcFI0SUaOJv4N1wwPtxBj/hwdjNH5OgxKfRApZM+cAGdw/WH/w+Hy3Od5EtBgRLRbfyHQ5k6CZAJs9TSM+hxFDkmhpSoiYxgFHpzG9SDogfpqGbicJosYhgqnxqA6Pa9izB+vq1RCLEQrbqI62pzi9LYYI5vZ2uO8+uPxymD07q7YdXi+f7/o95vbW1H0fhLGF1x+1izME44eeSDkWJ6XbKcQiQWZ6+tJPYLmdKqM14+iJlHr5aQTFx3DxZiijFYv0xjuIj3lCpBSAfrjX0Tp4IR7+8EB7artxZ9HhCf2TTz45af9rr73GF7/4xazP89prr2VVz+l0Jm2PxPnW1dXF/v37s6o7vN2RhEx+5ZVXsqp30kknIcsyqhr//7tu3ToURcFgyO43xeuvv571NeUCvc+ky5O+3w3N75ftZzKIQUuvuOsJ8aPlzDPPTNp+5ZVXCIfDR20ewTPOOIPf/e53ie21a9fyuc99bgKvaOQMX7ww0r4xiJ5zvLxEw2yKf1bPPfdconzt2rVHFinL05d3eUHTyOrZce7cubz44otccMEFHDp0CIj/377wwgt5+umnUxzUgmOLTBF7KoWTMm8U9iy6QCAQDJCPpMe2qdWscaX+kJFjGorSQZa/OwTEHZ5WyYxVNlNGbkJkKJpCSI0S0aIJh2d4QMwcdHGG1SgvvfYyU2YdR3lVRaI8LnrGEqLnoAh6JMdnzBT/0hVDvL9ZQ1EyLcizhqJJ9QePz3ebhUgMhZiqECB85MpjQEY6LHqmEUFNQ8rMQ4RRk0694eLo0HZHIojKbW0JgVLq6WFhRxNGNbm/VkU7qWr3YPTOhlgMHn0UrrvuiI5Ky4EDzHvrLTTlJFqQUvL6GrrKYGYZhuZmbA89RGjFCpQZMzK2KShMdJ2UY/gxpx/uVYTdFMTRE6wzCT5HolLn2EBIxh8ChzXtbkERoTdxCqMXKcsdGgZZQ1FTxzexMEMA8Ql03XCvrtGn4nA705d3D/vRsWTJEsxmM5FIPALLs88+S09PT0p4ynR0dHSwdu3arK7HZrNRVVVFZ2cnEHdgZpNrDuCxxx7L6hwQjw40lEgkkuLiTMfmzZt58803szpHSUkJJ510Ehs2bADin8NguMhseOCBB7Kqlyv0PpMOT/r6g07KkXwmg1gNPnxpyjt1BNHRUldXx4IFC/jggw+AeJjihx56iGuvvTan58kV5513HkajkVgsvsh37dq17Nq1i1mzZk3wlWVPfX098+fPTziH9+zZw9q1a1m+fPmI2tHrC4P32QsuuIBbb701Uf6Xv/yF7373uxlFQr1oB9GYhM8PZZnT0SaYNWsWL730EhdccEFiYcTbb7/NBRdcwLPPPpvVuCg4OskUsWcsvy8EmREipUAgKAryEYJO7+akqhIev4RbJ0SdYHwwSAYcBgMOMv/QfGfbiyyePJ1F7kUZ68HQMLdDhEs1QliLxcVMe4jK6F7cNWWU9h/A2GFkR6QeyaQhGRRkg4JsVDAYFSqdYap6whiQ6a+IC7Med6pA63E7qGz34Su3UeYJUN7tp6O2LG3IV0nVEuFdfeU23TYFo0dFizt7tQjk+flURsYsGZHKNIyagb83v5/kAB3qDj3x769T1teLw+OnfGcTpaVGwrKZDmsVfoMdWyRMlb8bkyGIYc8O+t1OZHcl8tN/R7rhBjS9HLMtLbhefJGAolCrdjHVtxvzMLHe1dyGaYOMMnMmakUF1tWrCV55pXBUFiF6C4L0BJ9scOkcK8K9CgbpyoNImSnfTJdXxmEVExTFjp6T0mCAilFGepNlqChV6fKmLlISIqUAoD8IoYjOpH356Nt1Z5mjrby8nI9+9KMJITAUCvG9732Pu++++4jnuPXWWxPiZjYM5n4D6O3t5cUXX2TZsmUZj/F6vfz4xz/O+hw1w1IV/Pvf/z6ieKgoCl/+8pezPgfAtddemxApAf73f/+XZcuWHdFN+de//pV33313ROcaK3qfiV6eyMry0X0mAGW2MOk8spmc6qPlv//7v5PC+X7rW9/iggsuYNKkSbk/2Ripqanh8ssv569//SsAqqry2c9+lhdffDErof5o4Wtf+xrXX399Yvumm25iyZIllJXpDDhp0BPHqwfE8UWLFnHGGWfw73//GwCfz8eXv/zljOJ+pnm6Tm/2IiXEwzkPCpW7d+8GYNOmTZx//vmsXbuWqqqq7BsTHDVkeuaqKBHzvPlCiJQCgaAoGE+REuKTp27nxIfZFOSWoWFuSfObcutxp1C1rZPauhpMLf1E2xUq9mvssM1BHZIzscIR4/JZ72GQq9BKTTRMOxlNAs95V3B5lTvJzblOfoIZkRil9dMpb/8AolF63jGyw1WHZATZoCAZFAxSlAV9zZTGDETNJmJuFxZUDs2ZTHKgJsGxgooaF0NlgCh90VDaehWdfTQc3EqPptG4q4VeRSE620HrFDua7MGCBxXoUDVMByQivb3ENr7EjuPr0Nr+xfP1W+mdU8Iz6i6sG589HN5WNnHWk+txxDoxm3s5TtlPuEomYjDRWeIiaLBhCUdwRvrwBCNIH2ygv3E6SkU5kWcfoeuKy5LEVJOc7BgVFB5SWzsX9bybEgZ9qjYTKB9Vm3r3WiFSCgbp1nnG03PhZkOm58Nun8zUGiFSFjt64QiryuJi42hxO7W0ebGESCmAzDnTKstH/7RfpZOjbbiTEuJiz+OPP55w3P3617/m1FNPZdWqVbrtP/DAA9x3330juqbly5cnREqAW265hTPPPBO73Z62vt/v56qrrqKpKfu0EUuWLOFPf/pTYvs73/kO5557LjabLW19RVH4zGc+M2LH4JVXXsl3v/vdhDP0vffe47//+7/51a9+pXvMpk2bRiX8jRW9z6TLm371hbtUHdVnAjC9oZRdab6uDk/2YTez5aqrruKuu+5K5C/s7u5m+fLlPPPMM0yePPmIxzc3N9Pf3z+ikMBj4f/9v//HE088QTgcjxL0r3/9i6uuuor7778/qzC1GzduZObMmSMSBHPNqlWr+MlPfpLIVbpr1y4uvfRSHn74YeqOEMlnkPbu9GPT0IgF3/72t7nooosSY9LDDz9MVVUVP/7xj5HSdKJMeaO7vRIz60c2ljY0NCRCv+7YsQOIu7+XLl3K888/T61YuHvMoffMVV6iYiyOQGUTgpghEggERcF4i5TdXplZ9UKkLDbCLhehykqQJJSZM4m27KIm2kF5zEObuZaA7MCu+pmttGLo8ifqIUmo9fWYJ03BDEkhbzdIVdhcU5lsNCLPWYRx+3bKmzRm7O8eaLMcu+qnNtKGu9LOlBorsTlzaCirQKmv5/QPrRpwgCpEtCjvvL+ZXfv38JELzyeiRYmo8XyRg2FtU19HjlgnokVRhQw6YUzf2Q6AszeAMaYQNRlonuJKcdtqskTzVDeO/jCmqIKzN4DX5WD6jjY2Vs4khkp/LAgEgbj4GWs6gE/TaGzrApNCsMZB85QSNDkKRAkBu1QrgQN+ynsDxLZvjoufu3fx/JY+eiv1l6LK5RIGZKwH12OSDImwuKY0blHTsBC5yXUMiW1jhnZMkgFZEpO8+UBua8P0wkt8fKcXVUvudw3hJmb/4++Y+mehLFs2Yoetu1SjNtLKSf3vpIifaksDcl3NkRsRFDS64V7H8IzntGsYDRoxJXVySwjkAsggUpaPrV2Rh1eQiW6vvmpTOQYtwqUT7rUzjUi5ePFiPv3pTydER03T+PSnP8369ev52te+RkNDQ6Lu/v37uf3223nwwQcBmDZtWtb5Iq+88kq+/e1vEwgEgLiwt3z5cn7+85+zaNHhCDihUIhnn32W2267je3bt2M2m6murk7kicvERz/6Ub7xjW/Q19cHHHZA/fSnP+XUU09N1IvFYrz88svceuutvPPOOwDMmTOH7du3Z/VeSktL+dGPfpQUXvTee+9NfD4nnXRSotzj8XD//ffz3e9+F7/fT1lZGZIk4fF4sjrXWNH7TNqc/ybdFPIvfvq/7F//Z2BknwnA2Uvm8FwakTISlXjyHy9T5jj8+3LSpEnMmzdvZG9mCGazmb/85S+cc845CeFv27ZtLFq0iK997WusWrWKqVOnJh3j8Xh47bXXeOyxx3jyySf51a9+NW4i5bx58/jhD3/IjTfemCh74okn2Lx5M9/61re49NJLqahIji1+6NAhnn/+eR566CFeffVVdu7cOaEipcFg4JFHHuHMM8/E640PJm+++SaLFi3iC1/4AldeeSWzZ89OOa69vZ0NGzbwzDPP8Nf1/wmlqe7m6orDfWPp0qXceOON/OxnP0uU3XPPPbz++ut861vfYtmyZTgch+dXSu1gMqpEY6n31dG6eOvq6njxxRdZvnx5IsTt9u3bWbp0Kc8991xWQrjg6EHvXitCveYXIVIKBIKiQC8U2JhyUlrAZtEIhkXeGMFhOk85hWnvv49aUcH+ynkonXuwaBGmhg8m6tjNKprJlAiPidFI5LzzsmozNmcO0qH9WALJbQKEMBGbc1xKm3EHqBEzRko1K46QkamW3E7sxzQl4f6MaFGiWmyIiBlLK4hGtRhhLUZUi9fb13QAa6kNS4kt0VZ0yLExhPCfjsEQv05PXFz0uB1pwwFDXKj0uB1UtflweoJ4XY7E8cPJlfiphyppqChE1fjk03h8vQbkYc5Og46gmU78NA0rGzzGkCKmDi2TkdOu4i0UDHv2YF29mlBAQdNsVEc7knKWdpoqCfd30fNugNru7qxzlkYiEXa/9hrul97ns62pYbsbwk0Y/vAKlhl1WKqrCbvd+Xh7gmMAPdFwLGH3JSkedrPTk7pcukc84wnQFylrxpiCSm8CTG/BpaC40JtANxo0ykcQonA4egKn3kTtXXfdxTvvvMPmzZuBuFD5f//3f/zqV7/iuOOOw+Vy0d3dzb59+xLHlJaW8otf/IIVK1ZkdU1ut5vbbruNm2++OVH25ptvctppp1FfX8+kSZPw+/3s37+fYDCYqPOzn/2MRx55JCuR0u12c8stt/Ctb30rUfb2229z5plnUlNTQ0NDA6FQiIMHD+LzHc6e2NjYyB133MHHPvaxrN4LxF1lr7zyCvfff3+i7MUXX+TFF1+ktraW+vp6+vr62L9/f1JY3LvvvpvvfOc74yZSpv1M1m+CM9JPH+/f+RYwus/kPy4+nf95NP2+Kz75JQjtTmxfffXV/OEPf8i67XScdNJJ3HfffVx33XUJodLr9XLbbbdx2223UV9fT3V1NZIk0dXVRVNTU0qOzvHkC1/4Avv27ePnP/95omzPnj3ccMMNGAwGpk6disvlIhwO09bWlnDqHk00Njby2GOP8YlPfILe3viNs7e3lzvuuIM77riDyspKampqcDgc+Hw+uru7k9/HwuvStjs89/Mdd9xBU1MTjz/+eKJs8+bNfOITn8BsNjNlyhRcLhd+v5/m5maisz4AS31Ku5nc6keipqaGF154gYsuuigxNu7atYuPfOQjPPfccykiuODopVv3t4UQKfOJECkFAkFRoBcKbKw3GbdT5VCnyBsjOEzY7Sa0YgXW1avpNFSyw1lNVbQzadI+UlPOjJOd8VlQo5HQihUZ3UVD21QrKtg/rYa2nb0pQoCruoJpFZGs2sw1RsmA0WAgfQCm7Hj4pYdpbGxk0az0+UFVTY2LnAMCZ2RAxIznBo0mCZpDxdG4YBpLHBMZEEWj6rDtgXrHmhhqjMav16DEx7OQ1ZSx/uD+wfqDxw8nX+LnRKKgooxTTtFBZCRMg0JnGhE08XrAKbp/mpce2x4O9AaS9qcTRocfO3T/eIijclsb1tWrIRYj2u5hiW9zSs7Sqmgns3v7iZbXQSyWdc7Sfc89h+XJJymxVyIxNWUc7TRVEgg5sDc3U795M21nnQWL0o8dgsJG10k5xmc8V6mWVhDoFU5KAfpikXBSknCRCMbOli1bkpxbXb709dzOsYXG1AsV261zPqfTyZo1a7j00kvZuHFjolzTNPbu3cvevXtT6j/xxBMjnqT/yle+ws6dO1PEqebmZpqbm5PKZFnmRz/6EZ/5zGd45JFHsj7HTTfdxN69e/n973+fVN7e3k57e3tK/QULFrB69epE2NCR8Jvf/AYgSagEaGtro62tLalMkiR+8IMfcNVVV/Gd73xnxOcaCymfialSv3K0c9SfyaTKDHETTVVJImWu+PjHP05dXR1XX311ipCdrl9NND/60Y+YMWMG3/zmNxOuYoiHHk73f+1o5Nxzz2XdunVcffXVSblZAbq6uujq6tI/2FSdtrhq2MIKo9HIX/7yF2bNmsWPf/xjotHDv0cikUgiX2SCaKeOSDm2RDmVlZU899xzXHzxxYn3unfv3oSjckYWCzUFE4/eAsixpJIQHBkhUgoEgoInpujfZMYS7hWESClIjzJjBsErr+TAW2+g0UaHqYYO02Hn4imuCEgRlPp6Iuedl5WYONim+eWXsb7XmdImgDUSG1GbxxqyJGOVzFhlc17PMyiGDhUuE2LngEP0sOB5uCxRf1AcVWMJN+hQAXUw9G5Ui+UkTG7MFB+DFEN83LGGomRaBGoNRZPqDx4/nHyJn8WGihYX0rUoWcm2tdDCId7zHtkBkAkJ6bAL9AgCp65wOux1d3mIZmMvzlALJslIzXOr0SJ+TL1eDDt3Y8ZIWLImhbceDNFq2b8fuaoKtaIC88svE8qQu0pua6P29deRKyqoMRpY4nsjrfhp2wTyiTOQFSVe/5RTCnLsE2RG75lrrBMJFTrH9/QVrjNakD0dnvTl+RMpj41+t3DhQiAeHvNow+v1YrFYsFpT3flHK42NjYnPFDKEoBtjREc9J2VfQCIcAUuaR+/Kykpef/117r77bu655x5aW1tT6hgMBi688ELuuOMO5s6dm3Wo10FkWebXv/41S5Ys4Xvf+x4HDx5MW+/MM8/kBz/4QVKI1pHwf//3fyxZsoTbb79dV/Sprq7mC1/4AjfddBMWi2VUIqUsy/zud7/j0ksv5bbbbuODDz5IW+/UU0/l+9//PmefffaIz5Erkj6T9vT5KAG+/pVrue2Wz43qM3FYwWLSCEdT+7VsqUbtG/FlZ8UZZ5zB1q1b+c1vfsO9997Lzp07deuazWbOOussPvnJT7Jy5cr8XNAR+NznPsd//Md/cNddd/HYY4+lFc8HKS8v54ILLuC6665j2rRp43eRR2DGjBmsW7eOp59+mnvuuYd///vfSY7h4RgMBk455RQ2WeoJp/lJmW5hhSRJfPe73+Waa67hzjvvZPXq1foO5Gh612mXTvWRUFFRwdq1a7n00kt54403ADh48CAf+chHWLt27biFDBaMnnyZXASZESKlQCAoeDKteM+FSJkOIVIK1NpaHpx8Pd2BrpRcajManQSvaUStGVnIVbW2ltCqVXwQ87G+eWdKfrZ/Tj2ei1dZ8vSOioeEGEp+xVAARVMGRM5Ykjt0aIjcXft2o0gqtQ11if3RIW7R6voQNT17MFYasPlaqGiP0eSwgwlkg4Ikq4m/kqolHI6+chsQd0imI1/ip2B80EYqjmbDHHgfD0+3vUdFZx/Ldm5G0jQat7RglBSMJzton+LCzH6MioymGuiMwd52lbJeP2z5F4dOmIG8cwebtqjsmNFPm30HO7o9SYLonKdfRMWDvb+f8rYenA4jQSy0W6oJGOxYI2Gqg51EwyHYtgW5shLVWYb5pZcIXXVVrt6t4BggfLCdD7duSLkfvlNyEm7n2O6HrhI9kVI84wmgvSd9eU1F+vJsOdZ/W5jNZk4++eSJvoy09PT0YLVasdvHEvdjYtELRVil44TMlkzhsbu8UF+Vfp/RaOTrX/86X/va1/j3v//Nrl276OjowGKxcNxxx3HmmWciy/KYheFrrrmGq6++ms2bN7N582a6urrQNI3JkyezZMkSjjvuuKT6L7744ojP8clPfpKrrrqKzZs3s2nTpsQ5qqurmT9/Ph/60IcwGA4/255zzjkZRZZMXHrppVx66aVs2bKFTZs2JQTeuro6TjvtNGbOTE6XsGvXrqzb/sMf/jDmsKiDDH4mv31kL1/+Xfo637v1KxgHPpaRfiaSFHcBt3Sn7vvdfY9zzfLM/frWW2/l1ltvzfp8Q7Fardx4443ceOONHDhwgA0bNtDZ2Ulvby8WiwWXy8Xs2bM54YQTkvIZThS1tbX89Kc/5Sc/+QmbN29m+/btdHV10dfXh8PhoKamhrlz57JgwYKkfpqOsXxu06ZNG3W/B7jkkku45JJLCAQCvPXWWzQ1NdHT00MwGKSkpAS3201jYyNz5szBZHZQckH6RbKZcvDOmDGDe++9F0VRWL9+PXv27KGrq4tAIEBJSQl1dXX86c1TWLsx9djOYQtBRvv/yel08uqrr474OMHRgX6UlokL/1wMCJFSIBAUPN0ZVrzrrZLPFr2JhJ5jZLWzIL/0+GTazJNY47o4qXzB6V7UmtCo2zVPrmaNa1ZK+aSYAmQIlyI46jBIBmySAZucYTI9Gl9GvMh5Ytrd8pnHY9v7ANg1TO0b2LVPxXKgnB22OajS4Qfs+VPCLPQ/S41Ujr2inClzPoSCxocuvQLr5vXUT53M1BnHJVyhZXNdlAa3ECk3Y2nfx9RemZ1KLZIRJIOKLCtIBhVJVqgsjVDdE8YgGQhWlGKQZF3xU1AYZMpZKqFikFUgBhbYN62M2X4/pkiQSGcrXpeDwOY36ThjJh20sb3vcIizis4+rPs2Ixk1GltaaIspxOY66JhiBrkjEVK6W9XYf8CDpzdArK2dHVV1sPUg/9zYRH9VeRpHqAGjjkN00G16oK6fsOUQfT4DxiHHDc81akzThqHAc48ebchtbVheeglldyun9CWPNQ3hJk7pW8+8f5Yjuz48anety6lSG2lNWWjUsNmF3D5jxAuNBIWFnlg0PE/WSHHrLKA8VkRKQX7Rc1K6nGNrN9OEf7dPX6QcRJZlzjrrLM4666yUfT09Oor+CJEkiUWLFrEoj6Hdx+McQ5k/fz7z588fl3ONFkmSKKtKn2Pe5dQSAuVocZWlFynj7vHxEQSmTp16zOQLHO8+mi/sdjsf/vCHM9ZpTdMvBnFlIRYZDAYWL17M4sWLU/b9q0WGNCJl9xhyUgoKB91wr8JJmVeESCkQCAqeTE7KsYYCO9ZXOwvyh6bp94N8Ong1bWw5aQTHHmpNDWpdHXJLC8rMmUR376Ym2kF5zJMUdnN+RzMOtR+5rAzzvAWYzSXQ0EDFrFOoWreVmdIkTnEff7jhpQ2w+7c0u4zEYs3UUEL5RjlF/JQ1lWUz3qNKrkIrMTF52skgSZyz9Goi1ZWH3Z9DnKJRLca2XTuISSoN0xqIaErK/sG8oWnLh+YQHTg2qinHXD7RY5l85SzNJH4Ob7N5qhtHfxhTVMHZG8DrcjB5+yE2ukfp2JgC++jnjZ6Rh3CTIBFSd1DENCcJmsnCpnGYgDq4b3h9veOH7ysmgdSwZ08iF2owKFEdbU/JV9ppqqLc04ztoYcIrVgx4nPIbW1ctOVxZrSmhgNrOLgf2wP/ToRXFxQneiJlJrEnG/Se8foCMuEoWDJHXhcUOLq5UMvGJuZkEjnHmqNNcOzT5Un/jDHW8NYw6ExKbV8vH6qgeMjUB8Z6r60sT1+uN8YKigu9EPsi3Gt+ESKlQCAoeHp0hCK7VcU6xmiOQqQU6OEPSURieg83Y5xIKE1/fCQm4Q9JlNjEREKxEV66FNtDD6FWVLC1ZD5V/l1YtAhTw4dz99iIoRgMBGfMwOl2g9EIF16o3+ikSTB5MjQ301tdzSRVZZLSTnmfJyXnoKErAGUSysyZIEko9fVotbWYAJPO42Z/LD75v6hkTs4+B1VTiaYRPAdFzHg+USWpLJP4GdFixLQYkYE8orG0bRenMJqvnKX5Ej/zjQaJPjERGDEkO0Nl40DZEDFTTnWWmtOKpoPHHFlENUkGDNL4hXaW29oSAqXc04P93b3MCyTXqYp2EpV3Y++fgmauwLp6NY4RhHccFEEnBSV2YqIq2pkkgsa8LtBKMDTHRVD7zJkEJk/O8TsVHM0EwxDQCYihN/GZLZkmwHr7ZGpdYoKsmNGbtB9rvzMZobxEw9Ofer/VE+QFxUO+xHHQF8h7hEhZ9Og5xyEeJngsVOoc35XhnILiQFX1jS5CpMwvQqQUCAQFj94NpqJk7A/VQqQU6KG3+grGHmY4U5iJnj4hUhYjam0toRUrsK5eTSuV7HZWpkysV0xxEjI4mFxWFhcoL78c6uoyN3zRRWgbNhByOIjV18OuA1jCyeInQFgyEZszHbWiAozGCXMXyZKMRZKxMH5WE03ThgmfMR2hVBkQPAf2q8ME1AFBdM/BfdidDmyljrRiakxT4iLqBLtG85WzNF/iZ6ETQyGmKQS1gTxB4/gxyEhZuUD1XKG9c2XetTTh9clpXaZDt6uf/wdSJICp14txxy6UkERYcqQsnHBIIQzbtqLOm4daUcG0bduInXrqkd/LEBG0IuJjiW8fZi2aVMfc04FpAygzZ6JWVFD7+us0L1uWr49XcBSSKRzcWCdO9RaiAfT2S9S6xta+4NhGbwJ9rP0O4s4kT39qec84ht0UHJ3oOsfLx952pY7Qmem3tKA40Ot3pXYN8xh/6lXq5PEVDl6BNyChqDqh1cc4jyfIjBApBQJBwdObZkUo5GYVjJ4jrrdfiJTFTiaheqx9L9PDUY9PZkq1eHgqRpQZMwhccSW7n1pPLYfoMNXQYTqcM212dQitp5lITQ1cd92RBUqAujp6PvIR1D/+EbWigi01k9C6ulNCK2pTynBXxMBoJLRixahzwB2LSJKEWTJiztFj9cMvPUxjYyOLZmXONaNqWpKzM5YkiqZ3i2YSRyPaYffooBg6vI1QLIw6MLR53A4q2334ym2UeQKUd/vpqC1L63qUVC3hcPSV2xLHpyNf4qcgf6hohLUo4WFiXtYcb+RN9vJmz96M1So6+1i2YzOSptG4pQVjTMFT56D3VBkTB3GqMqoq0xGDKYe8HPD7kLb00nTCdAJygHeCb/JuZ0eK8GmUDEgqdLV1sPDpf1HW6cHm68dxsA2H1U5YstBurqFfdmCPhagJdaBGAsjbtqHNmYOsKFRu2AAf+cjo3r/gmMO7u5WLepJzlbaZa3mn5CTcZZPG1HZ5SWYn5biuQBAcdXR50pfrTbiPBHeZxu7m1Hu4CH8o6OjVC/cqnJSC/NGjI1SPNdRrpjZ8folwBCxjjLgmOHbJOI83xrRNgswIkVIgEBQ8ejeZXKyC0ZtICEUkgmGwWcZ8CsExil6ybaNBw2kf2w86p11DljXUNCu89M4rKA76K2r5bdUN1EZaOak/eQI1vNDB+xE7hgsv5LhsBMoBwlOnsvW005hfWorV2s2BYeInwLRoJJGfrZgEyolEHiKOppf7cs/DDz/M7MbZHH/iQlRzC45Df0YpiWHt3MTu/SrmtnJ2lx+HZARJUpEMCrPrwszs/QB7yIShpIxw7UzcEvg+9GFqO7pwlJdQUu5MiKeWmm6cXfsJl5Vi6A1R2R2is0ZGkUCSk++5IxE/Bcc+evlKkTVk4mF2ByXqttklOLf4MEUCRDpbCbgcGPdt4c26sG77Ff4+6r276DFpNLa0ELIpaPUxOqe4kORWSgfqdasaGw94KO8NEPuglZ0L6pH6DvK7d35CoKr8cMjdUbhL9bYHw/emC+VbTPlIJ5yWFlizBtvbhzilL3lXQ7iJU/rW436sAS65MLuFQGmwmOIpKZy+9pT7eMkrJciVc1Frao7ckKDgUBTo6Uu/ryoHk/Z6bsxMzmFBcaAnGOZCLNLtd0exk7K1tZWtW7cC0NfXh9lsxmIZ+8TPSSedREVFxZjbKRT0nJRjTZ0D+g5eiLsp6yrHfArBMYpeujAQ4V7zjRApBQJBwaMn2ow15CZkXu3s9cvYLOImVqzoPdxUlKqMdT5RluPhitP9eNMLbywoDga//zbzJNa4Lk7ad92yLgKvt4+qXX9ZGaErruDV3QE6X9ma4h7pPH4uH1olxKFiQBoI7UndFEyTp2JpaUGePZfIjj1UhP0s9O4cEnozyNz+Q1QYImhYsc35EGX2CpT6eqbO+RjBhwdco7MPu0blc8/GdvAB2vpaKJcUnAYr5m1uHmk+HlWWkGUVSVaQ5RgnRHcwszZCH2Fi9unMLXEx6YwVLKoqT+sETXaIDvxVD79u6+7AZDVjsppTjlUQ9/OJJt/5SvVE0OHn0GSJ5qluHP1hTFGFUo8fr8tBzbZ9bHTNHOvbHDFGBoTLDIJn++xemhxbea+r84ghdYfnHDUm/qbuNyAXj0i6cyc8+ijEYgSDKtXDQqp3mirpc1RhaGmC++6Lh1SfPXvEp5Hb2vhC199xdLek7Cv5IIwtsF4sCipSevtB0/KT7x70BaejWSwSjA96fSAXYYZdOn235ygWx1944QVuuOGGvLR7zjnn5LzdYxW9BRLuHIjjeg5egN4+IVIWM3omF5tZwy5MKHlFiJQCgaDg0c1JmSHnS7ZkymvZ2ydT6xKTmsWK3sNNrlZfuZxq2nP09ImJhGImk0idi4UZTKphjeu4lOILCUHGoJyCQiS8dCm2hx5CrajgPdsCpkV2YNGSc5ZKoX76jGFobMSSRc5StaYGta4O2trwT56Ms7ubsv5OTut7MyXvoI0wlnozPZMnUYKNiqnzsE07ct5BPR5+dUA0nZkaajceXvdwTtGhIXLThdfVE0n1wu8O5pRMVx7VRHhHyH++0nyLoPkihkJMPUIfcUEXnezp78zpuSXISvA0Dtufjct0+LGD7tShdQ3SOC3MamlJCJR0deF8bwfzAsnhjauinRi13dA9B9zueP1sQ6sPYNizB+vq1RynmOhESsktLXeVwXFlGJqbsT30EKEVK1BmzMj1uxUcpei5iiA3k/Z6zqJM5xUUB3pOSj2BcSToCZ1dPtA0xry4V3DskldxvFR/n8jDW9zomQ1cwkWZd4RIKRAICp5eHdEmn+FeQYhFxY6egzcXK50BXDp9L1MMfUHho5cPV5I0yh05yBuj0++Eg7c4UWtrCa1Ygfmp1bTiptW5JGVSvbTOTVmDhaqamqxzloaXLkV9911aIxFwu9F62rBoxiTxEyAkmQnNmkk0FkY1GDKKn2MlHl7XhJnMwlg+0DQtIYxGhztBh7lD4+LpcKE0WRgdLqDGjlBHPUomavKdrzTfImghokGin4B+KN18IQ84u9O5PNO5P9MJqh2WNso0D94OCbNsxCTHw+uaJFNiu+KpRzCH+jB292Lcth0lDGHZSpspeeGEU4rABx/AggVxofLZZ+H667N7L21tWFevhliMGtXLTN9uzMPyvLqa2zBJMsrMmagVFVhXryZ45ZXCUVkkdHv1f1vmJOymThtdGc47UqZNm0YkEslZe4L8o2nx8JfpyIVYpNdGJCrhD0GJbeznEByb6DkpM4VqzRaLGRxWDX8odXzT6++C4kB/Hk+IlPlGiJQCQYGyfft2gsHgRF/GhLBjx46k7daus4DUzNd+z0E2bTqYUp6Ogwf169nMHyEYSR1ON72/H3u0I6W8ubk55Roh9boF44Pe9zEahrazc+98SJMpTla62bTp3azb1Ot7sroISM1JtHNfD5s2bc14jQcPHmTTpk1ZX8N4sXfv3om+hKOSkfTPd7bWAqm5TByWKO+9t4mDBw9SVpY6C7Vnzx40Lf0Pvi1btiT6jN83FZibUqelMzziPiXGvPQczf8P9Maj0KxFHDT30xBuomNYztKpriaQwxwEuo4/nrDPBwN9JdO91bxkCdZ//IPWSIT+mjp2eialhFbsNFUxyX+AvkAvu048kZrWVmhtHfX7O5o/+2wxMvQHngSYBv6NHhWNGAqKpKKgoUgKMdT49kBZTFIG9qlp66qSlnRM4jUqLR2tmO0WjBYTqgyqrB3+Jw28DeLOxsp2H75yG2WeAOXdfjpqy9K6HUeTrzTfIqgg96hohLUo4WFi3oiwAxo8ty/9s1lFZx/L3tmMpGk0vt+CMabgmezAc7oLM/sxqTKqItMZA3tzDzu8/ahvHeLA8dOQDsmsr20hUOnChIwBA0YMeLp66CrpILj17xiQMWgyJzzzOqWdHmy+fmb0d+O3mAjLFtrMNfglB/ZYiFLlEH29PUjr19M/bRpRp5Pg/ffTfMEFo3//A0iSlHg+KCkpASAYDGIwFE6/DofHX0jPJd3bu7ioZ3tKyP1trkWgOggExtZ+qc1E/D9EMl0ejcAYGj/WP/djhXx9zr4AxJT0CrbDEiIQGNsCHbtZBtLb2g61B5lSfXQslBrKypUrWblyJQBerxeLxYLVas1J22P5v3aso2laUj/u6HUDqYJRqS1KIDD2/l5RWppWpGzvjhAIjOG54hikWMdpRVFQ1bj4OPh/b8feHiB19YQJT9bzHQcOHMjZNRYTQqQUCAqUffv2EQqFJvoyJoRYLEZLS0ti8rPbe1baegf2bOQFz+as2lQUhb1796adUDVyKuBKKX9r424CTW+llHd1dfH222+nbauurk5M3I8ziqLofh+jaauzs5O3336bLZ1fBRpS6rQ1fcCDDz44ojabm5t5++23k8o7ui2kEynf39bKg12Z21+4cOFR2c/a29sxGsWjyXCGj2mZ2Nx+OnBimkZ6ePDBBxP96YUXXkjavWPHDioqKqiurk45VFHikw8PPvgg+/xnkk6k7PJqKW1mgxjzUjma/x9UV1fz9ttvp4xH3ugknqn9MbWRVk7qfydpAvW5no20GUx4OjpgzZqk4/TGt0HKzWZOaG2lrF+hw3Rikvg5yMv7OtjZYMXk92MYRR8cyrZt247az/5YwjDwTx+ZoZNO21e/z9VXX838+fNTamqahopGFAV5TjuTtv0dtUKh7NAWpGiMlledbCudDkaQZQVJVpGlGGdJO6iKWlGMBpwVDZQALY2ncjwlxFDp7fPQ3NaCo6wEzQCqDF6LlaqwB1+JlbIeP+VduRVBBccmmXKVSqjxPmcELNAys5SSLX2YomG03i48LgfseI9tlcNylVYClUbeYh8QF0LrvLvoMmk0trRgdCloLgedU1zIcmti+n63qhE8EKW8N0Cs7T12uieD9wAvB1W8rlJkVUJWwaBK8ddafFtWJQI+P5IKZQ5nomxoHSMGjrNNxYBMRbQck2Qg2KdiNpgGwvPG3admyYh8jOYijUajR8V1DzoJzebURbTpkNvasLz0EpP+1ckpfckJsRrCTSxV3oL7KlLylKY7j9/vx2QypT13qVWjNuJNuY/39tUQOTAFtSb1HpwNR8vnXujk63Nu69KPluIwhwiFxipSSuiJlG1dUaqdsTG1n29E/44z0nFNrw2bzYbNZiMYDNLlSS9QO22REc116o17ZQ47hzpT+3dHr5LUfi7e29FOsfZjVVUTIqXPF7fQHmrXCSsQ7c44bzE4VzNlyhRax7BotpgRv8IFggLlwgsvZM6cORN9GROOosDPrkhdEQpw7VWXcPbCZWM+xwvfKOX9fanlp55xIf99WWri84cfHsh5tSg155VgYsjH9/HRb1dwaHtq+SXLl3DzFQvH3P7tfy7hl0+llk+ZsYiffuenY25/ohD/N8bGTx9z8HKahXuzj3Px0zv1+0W249KL75h54/up5RG1lG984/+JvDE54lj7f/DWNhPP/C+0mSexxnVx0r59f7kEm0XnwCyJHmrngWuaUtwj75ScxK/u/DJXzM/Namdxf54YDh48yPz58znllFOOXLmlGw4dAns52vsfULI3yPHdu4bkKw1RG2lj6nQT7rJaWLCAqW43NDRw1sWHw26uX7+edVvWccX5VyTK5Lp2bA88ANUaJu8GpGiUrlcm8YbSiGQAyaAmRNBlJduYbgyj2g3YJ89HlcC95BLmu8tTcpMeam9BQaWi0pUmN2lyuN3k0L0KMYovhOzRxnjkKs0khA5vv3mqG0d/GFNUodTrx+tyMHlfC+11M9M1fZiq+NIBH/rXs4Xe+IvBaJxt6etJDITGlQbD48aFTNNAmTnx15Rcb6Asqd7Qukll8WPNshGzNHCMHA/tOxasVit2e/rfhuPFoFsjq+vYuRP5qacgFiOmGKmOtqdEFTBaKynp6YGnnkJduRJmz854nuGfgaZphPbu5aR/v8hnW7tSLqEh3ETZw28hT2tAu+CCEeVZ1TunID/k43Pen9olEkyfUkZFhtx+2VBWFk9LoWmp42qMMlyuo89JORzRv0c4rmVow2g04nDEF331+tOP91Pq7LhcI4sDnO47qq4wsGV/at1g1I7LddgZm4v3dixQjP04FovR2xt/9nE64+7JMvdxsD+17okLpnDFFVek7hhg0GW5aNEitm/fzg9/+MOcX2+hI0RKgUBQ0PT2S2kfeAEqMuSTHAkVOrktPTq54QTFgV5uwIqS3PzQ0ut33n6hEhUzvTrff0VpfnOhxhQJf0iixHb0TyQIco9e7g6bWRuzQAlgmlzDK3ULCIRSz+P1e8Z+AsGxw0UXwX33gdtNdM4Cwpu3Y9EiKflKDRYTLJgTzwtoNMKFFx6xabWmBrWuDrmlBWXmTIzbt1MT6eC0ft8QETSee9BREcRWZyHWOIdqcwVKfT2l089I2+6m/QOTFpUjF79VTUvJRTo8p2j63KTJ+Ui37txGaYWTMleFbv3IsPKYpqAg8u+MR67S8RBCc4mGRliNEibKeOvoMnJC8DycPzRZEDXLpiQRdFAcjYWj2E1W7P32AfHzsMg6WD/p2CFlRsmALI3zb7uWFuTHH4dYDLq7qdm+EzWQ7CqrinZS2mOC7lngdiM//jjqtdeOSEgMv/8+8mOPUeGRkDCl5JbuNFUSDLspaWpCuv/+JCFUUPjo5UKVZY2yHAQQMBigohR60uQBFLkBi5dIFPoCerkBc3MOPYHd05+b9gXHJnrzuGU5mj8W6CNESoFAUNDoCUWgL/KMlHId0SnTuQWFj75YlKN+5xD9TpBKr45YlLtFGfoiZE+fECmLlR5f+n7ncubux1y5QyOQJrJTb59YmFFU1NXB5ZfDo48ScLh5w7kk7YT68adVQbkcFygvvzzrCfvw0qXYHnoItaKC2Jw5SK3704qgIc1MbM401IoKMBqJnHdePt4tsiRhlkyYx5hXNLq/mUZLI4saRyaUqpqqK2IOvo4MdX5qMSI6Amgsjag6WHew/vBjNSb+njIeuUrHQwgtFFRUwmqEMJFxF0hNw9yi6Z2hhx2hQx2mh8VPE8RUTJKR0ogjIbQOukWHOkrta55BikWRunuQtmxBjUBYsqQsmqiWwkhbtqDNnw9uN9Jzz6F9+tPZvamWFkxPPglAWdjLEt8uzMNyvFZFO5HfNMIJs0cthAqOXfSEwooSkHP0s9PlTC9S9vgkOAruA4LxJ/7dp8ddlqPFt8707XRnOLeg8NGbT9NbrC3IHUKkFAgEBY3ehD3kbvJUT3QSE6fFi6bpr8DKnTiu5+AV/a6Y0XO0ufLc7yA+3k6pFg/vxUiPzv3OnUORsqJUpaU7dbJfLMwoQmbPhuuuw/vHZ9FoosNUk5Kv1G4DGhriDsoRTKSrtbWEVqzAuno1akUFTTOq2R/0pIig9koXMyvCYDQSWrEiKQ9cISFLMhZJxoKJici4qWhqVq7RVBE01XHa1tWOIqmUljtTQvKmiKdqLBFq1+N2UNnuw1duo8wToLw797lKx0MIFYydqBYjqoxPfryKzj6WbdyMpGnMeb8Vk6LSM62UzsXVQDs2RUZVDTRHzTh7PBwK9qG920P7okYMvl3s2mQj5KrAJBtx9NsSImgkEKYkZqck5sAsG6la/QSGsA9Tjxfrrj1YJJmwbKHVWEtALkkIoUpoDEKo4JhGTyxy5cjNBuB2auwm9TzCSVm8dGX47it1UgaOFL0+nE4wFxQPevNpuYpMJdBHiJQCgaCg0XN3WM0a9hyEoAN9h5II91q8BEIS0Vj6hxs9B+RI0ROL/CGZaAxM4g5flOg6KXP0UF3m0JBlDVVN7d+ZFoUICptunXttrhy8oB+1wCvutcVJXR07l13Pb1e3clL/O0n5StsttZi/eBJMnjSqppUZMwheeSXml1/Gur0jrQhaE1VQ6uuJnHdewQqURwMGScYgmbFiHnNbmw4OhN2dnZ2bVNM0YpqCam7B/85PUW0KDQ4bWiTC/hcr2F0xDQwgywO5SuUYF5XswyWXoZQYYMpcJkka7Y2LmBM2U+YqS4inu/btpsxdjsVhI6rF0KprMLcFCJQ7qPAEqciDECo4thiap9QQixE2GWg+zolRDmMknFT3oNuEdUsIU1Chu2UPXpeD3f9azcYzMucprejsY9m7cSG08f0WjDEFw4cc9E5xYZH2Y1JlNFWmMyqxo70XlzeAtqmdphNnYOh5l3ffCBGtrcQkG7HIpiFu0LgzdDAEbyQQwhE9LIwO3Tc8VK8kkpsfdYQPtnJRz7specGlaScC1Tk5h174TiEWFS+Z3Iy5Cvfq0gn3KswGxUsms4EI95p/xBSmQCAoaPTcHblyFUGmcK/i4aZY6cnw3ec7zDCAxy9RlaMwKIJji3yHGZbluNCebmwVjrbiRd85nrtxSG9hhrjXFi/dXmgzT2KN6+Kk8opSMEweW9tqbS2hVavYbvSyft+ulMnZjvqF/OeqsQtngqMXSZIwSUaom4KvsgZrVxfG2XNRP9hOhbefhb6dQ8JuBqiNtFHXqFBir4AFC6gpd0NDA9HqhXR3dyeF2r3rT3dx/vmLWDQrXiZ/uB3v+rvAqtJQWkKoP8bOf9Sx0zErLoQaBoRQKcY1U7YghWwEYlFmT16EImmUL7mEOe4yXcdpRI3R2duFgoqjrISIGkvrIo2oUWKoIhfpUcB45CkdKoQaYwpRk4HmKa6B82jIBgUMCphg/9RSzFv6MIUjRNpb8Loc9G14/YhC6EiQkBIhdS0DYXTNQ0RM84AQak7af3jbPPgv5Rjj4X1DjjHLpvHPM3os0dKCtHYtC15oJjBsIWJDuIlZe95G+mM92gUXjDn0r8sJtZHURUcNG6qh9QSYNLpFR4JjF71cqKV2DfPYIuAn0Pud0tuXm/YFxx6BsIGYojOHLETKvCNESoFAUNDkO/QhZAr3Kn70FCueDN99vsO9QlwwqCornvxEgsPoOylzO+alG1v1FoUICh/9sDg57Hd6IqW41xYtPTqTSO4chQEDME2uZo1rdkq5U1GBztydSHBU03nKKdQ//zxqRQXeqXOJ7DiQNlep0WaCBXPA7Y7nQ73wQmhuPmL7ak0NocpKrF1dKDNnYvxgO9WRTsqi3pT8gzabH1Uz0u5yM9PsQqmvp2z6mUc8x6ZDmZ2kqqrS2Rnv07JBJoZC3ZTJaDLxvKPqYSEzMuR1VI3nEo2oMaJqNCGKRrQYMfXw63i9eP3B14fbiSXaUTTx/Arjk6d0PITQkaChxfuFEsWvBHPath4GyTDgAs1e2DxcNnT78DGWgX/pxNVjRhTduRP58cchFiMYgupoe2rYc0slUlMT0v33o65cGQ/FPhpaWrh463PMaG1J2TVp70Hk37+N1tCQEzFUcOyg52bMVahX0A/3KsIMFy++gL5MJsK95h8hUgoEgoJGT6Qcj4lTr/8Y+REiyDmZQv067fkN9wqD4Q/FJE+xEVP0xx1XBuftSBFikWA4emNeuWMcwr36hTherOiFgctVGDDQ73e+gIyigEGkAiwKwm43bWedRd3u3fhsbt5w1lEV7UyZtD/zjCowyXGB8vLL4xPqWYiUkCyEMm8OkR0H0wqhEclEaNo0QpEIGI1Ezjsv5+9XlmTMyJQa7RiN4ztlpGpqPCfooNipRolqSlwcHRA5k4TPocKoGj0sjiYdH68biASJSXGnaGSoyDoglmocPROQ45GndDyE0KMdRVMIKAqBcTqfcVAUHSJsWlLEUFNC7ExyiQ4RSoceY5HNmCUjgaifUqOCpBgwy0YM0ihvUC0tCYGS7m6m7d+FOxBNqlIV7aSm2UCsfQaaywUPPUTkk59EG6HjUd69G9OTT1LdDzuQU8ZVyeMGLUdiqOCYQk+kzKVQpBfuNRCSCEfAIgJmFB3eDCJlpvk3QW4QIqVAIChoxicEXfq2QhGJQJic5b4UHDvohXt12lWMOZrQtFvAZNTS5r4U4Q+LEz03G+TaSakXGkeIlMWKR0cozBSWeqTohnsV/a5o6daZsddbGT8ayjII7b6AJFZVFxGByZMJnnIKXb9/DY3ulFylJoOK0SRDQ0PcQTlCx89QIRR3BRsrJlEW6k4RQp0zSnGaW1B7ewmtWFFwOVFlScZqMEMO8pAOp6enB6vVit1uT9mnaRqKpgxxhR52fkaGOEgPi6eDbtDoEJdpLMllGn8dTRJLI2qMsBKNl2sKUTWGmia8rsftoLLdh6/cRpknQHke8pSOhxAqSCamKcQUBb8Syvu5DMhJgudwd6dFSt1nkU3MfvR5SnzdWHv7cO7YhyRJaA4L7dZq+qUS7JEwVcFOLFIIaetWmDcPzeXC+MILRK+5Juvrk1pbMT35JMRilAV7WOLbh1lLFkNt3R1Ib5rQZs8Gtxv58cdRr71WOCqLAE9/+nI9YXE0VDj1n+F6+mCSO3fnEhwbeP3pZTJJ0ihziGf+fCNESoFAUNB4dSbtc+vu0G+rt0/GbhErbooNvXCvuRSKJCne9zo9qRMEmZycgsIlU07IXPY9vUl74WgrXvTGnLIcrjit0M3/LMa7YkUvHFdunZQZohb4ZSpKj30nkSB71Npa1i/5FL9d70/JnRZ2ueGzp40pd9qgEGp++WVMpi46lGQhFCAYDWOqr2Lr7NnMmDFjrG9JMIAkSRglI0aM2POsvQUCcd/eoFiqaEqKkKmUH6Kk488oFTHsPe8QDUQ59Lqdve4pYADJoCLLCjIxzjMfpBQ7MZuBsknTcEgahz50GrNtNqKaQgwl4TwNK5FEHtLxEEIFE4eCSlANE1TDWR9T0dkHOzYjaRqNH7TgiykEZjromGIGuYMSOgDoUTVsB7oJ9AaIbW5m98IGDD1G1r3VRrDalSR+mtIIoRbZxOx/PE9JsAdbbx/uzr2EbTJhyUK7pYZ+2YEtEqY+2kk0GMC45QOYvwDcbqTnnkP79Kfz9bEJjhL0nJTlORQpMz0vdvuESFmM+ILpZbJyh4YsfnLmHSFSCgSCgsajE/ow1/nZ9PD2y9RXCpGy2NDPz5bb1VflJRqdntRyPXFeUNhkcpTlcszTD7spntyLFT2RMpf5n/XEokwOYkFhoxvuNYf5ijK5gUXfK056+yXazJNY47o4qbyx3g+Txi7aqLW1hFat4smXPZTv3Z0khLaZa7GeNpsTT9yDf8eOMZ9LcHRgkAzYDAZshiHhd6ZXIh33DtKhQzBvEf4NWyjtiDKns2lYntIujpsvY7JWos2fz2SXG62hgSWnfzpFDIXDblKbzUas5hByz+8JV4QwdW9AUmKo71XwUng6khEkWUEyqBikKCdF99FgKSPmMKA1zKEWjejpH6HM7RxwjUaTHKRD3adxYTRedjSF1BWkMn1nOwDO3gDGmELUZKB5iitFuNZkieapbhz9YUxRBUdPH16Xg7IPdrL7jJlHPE9FZx/SEDHUaFBQ5w+Koe2UDNRrVzXWH+ihvDeAsqGJ3QunYGg3sO64JsLV7rjgaYiHwrXI5sR2skvUnFpmMGORhrweEFCNow2RK8g5epGh3BncjyOlIoPg6emTQIxXRYdeTspczqUI9BEipUAgKGj0JpByadUvz9CWXthPQWGj5+zJpYM3U3vCWVSc6ImUVrOW07DTwkkpGEowHA9vno6c3mt1REp/SCYSBXPmVFqCAkTPSZnLUGClNg1Z1lDV1D4uohYUJ3rfe6k9t67asMvFGs/FKeUNsp8T2ZPTcwmOTrTly5Huvx/cbjxTFxDZuTNtnlKj1YzWGA+HidGIdsEFR2xbkiRM9Q1IDdNg3z6kxrmYduzA3dfHCe3bhwmhbThsMVyzy9Dmz6fKGRdCF3zo6qzex1BhVEE9HAo3ETI3lhA6E47PJNFzSJ2hQuhAntJwhv1RLTaKT754GXTLOj1BIO6WTeeshbhQ6XE7qGrz4fQE8bocieOPxGjFUHuPD6/LgfP97WzMQgwdKYMhcq0DwmVC+Bx4bR22bTGYUEIxSkJ2SkOOIfVMmAf+WgyHjxl1ntAiRG5r46Ked1MW6jSoJwA1Rzw+G6xmsFs1AqHUPq73jCkobPTCvYpQr+ODECkFAkFBozeRkMukxyYjlNhU+oOp5xK5sooT/X6XeydlOoSjrTjp0QmLU5HjJO/6jjbR74qRTN97Lu+1rgxOdE+/THWFWOFabIyHk1KWocyupV3Rr5eLVVDY6C3IcdpyK4aU2qJpy73iXls81NWhrlyJ/Pjj+Gxu3nAuoSramZSn1Gur5Kwl7ngeCKMRdeXKEeXr05Yvh9//Hs3lQps/H7k3vRAa1Mxo82eNSAgdjiRJGDFgNBjyHlJ3EFVTiWmKjtA54PQcEDuHCpthXXE0OmR7mIg60Max7BY1RuOLLQxK/JkqZM28Amxw/2D9weOPxHiJoSNlNCFyR4JRMiQJnwnRc9DZOSBoHhZD48LoUNHUKpuwGizJdQzmgnGBSq2t8MornL/pIB3D5tIawk2c89ZbSH+cHB+DcpCb1FUKTl9rSgh3x8tVMOPEMYVwFxx76IV7FU7K8UGIlAKBoKAZr7CbFaUa/cF05xcTCcWIfr/L7cONnqNNhKArTsZjUQboryQUTsriJJNQo5dHcjRkym/p8UtUV+TsVIJjhPHISQnxvpcuQoF4xitOdHPw2nMrUjos6dsT99oiY/Zs1GuvpfOu59BoocOUnKe0vEQjGguj1tcTO/98tEmTYCDMaygUwmq1Zm6/ro7oZZdhevLJuGPz+MVs7exOEkI7TZX0l1SxxK2NSgidSGRJxizF3XHjgaZpSWFvE2KnNmw7IWpGk8qGC6ZhNaIrrobUSM4F0ZgpLnQphvg4Zw1F8Waobw1Fk+oPHn8kxksMPdqIaQoxJYhfSTNxNEYMkiEuaA4RLg8LnvHtQWHzsPCpX2YdcIqaJROSND73HXn3box//zvIMuGwRnW0PWUsspvcSE1NSPffHx+LZs8e/QlbWri+7XlobU7ZVbrlAPLv16M1NCCdfXZ8bBUUPHrhXnOZwkSgjxApBQJBwRKJxsPApUNP3Bkt5Q6VJlIfysVEQnGi56DNdbhXPdFTTJwWJ3rjTe4dvHrhXkW/K0YyjTeZhMWRkklsj1/DsTlhJRgdqgq9fen3ZcoxNBp0F2aIBUFFid5CsFyHey2x6jgpxb22+KirY92p1/PbDR0pbp/uuioin25Eq61NOWwwxOqRUGfOpO/jH8fx2muYDramCKEAUkRDrZ8MF+bGvVSoSJKEeSD34XgQG5L/s6OnC8lsQDYbhoidhwXPkBolMiB6hgfFUS1KWDlc11HvpbxnL4pbxeQN4+oO0VYlI5lSn8EkVUs4Gn3l8X7mcWeXl3e8xNBiQtEU/HkQQCWkhIBpHeLsjL+2YDWYsclmDKqMRTbh9Jck1bPJluRjDOb0wmdLS3yxhKqCz8fCjh0YlOT7YFW0k7rdRnDHw1vLjz+Oeu21oxuTdu5EfvxxpsVUDqCluNQNXW7QKpGamjD/+c9EL7sMFi4c/QcpOCbw6oiUItzr+CBESoFAULBk+hGfKY/kaNDP0SYmEooRvZyQuXbw6olPwklZnEy0kzIUkQhF4vk9BMWDXr+zWTQsOZyjs5nBYtIIR1PHNxFavfjw+uPzWOnIZbhX0A+Z7RHPeEWJ3rN9aY7DveqLlOIZrxjp8UGbeRJrXMl5Sj+yQMU2fWwCuc1mg2nTiE6bRu+sTtZv2JGSB+6dkpP4/OWVOV8EIhgbRtmIESN2gxXVFMVqsWK320ffYMkFyK2/B7eG1Psm3V1Rtj9Tww5bIxhBkhVkg4rJGOXzjTuQjDFUm4Gy6QtR0Kg5bwUnVJcRViIDImiq+zOiRimp91LRs29ADI3g7gnRXiVDjsVQwdjR0HIeAne48GkzWDjzb29T0eOl1BukZncrznKJiGym3VZJwODAEopQ5e8FLUDo/U0o8+YiuysxPPs08nU3IEsjeCZraUF+/HGIxahWe6jz7cSsJd9zXYc6kN40oc2eDU5nXECtrBSLNAocPSelCPc6PgiRUiAQFCyZQtCV5zzspgh/KDjMxId7FROnxYjeeJPrlX+Z3HFev4zVLB7ii4neccqFKknxMbStJ3X1fLp8gYLCpjuD5SLn4V51xlBxry1O9J7xnDl3UuqFexX9rhjRu89V5GC8kyQpIWy5505ljWtm2no9vqgQKQudSZPQJk9GOnQIbfZsYh1bqYl2UB7z0GauJSA7sKt+pmptOCpDYLSizZuPw+ZGa2igfv6y7M5TsixJDFVDIfa/WMNmeU6SGCrLUT7q3sZkOYLmNOGcfjwxNKrPu5QFVU7CStwRGh5wgoaUCBEtRliNEB58rUQIawP1BlyjgolluPBZ0dlHuGkf7ZpG+a4WPDGF8HQHzVOcaLIfI34UoF0F4wEf5b0BYpva2HF8HVrHOp5v2E6w2oVtIFenzWDBJlsS23JMo8Rkp9TiwGawMOOxtZT2d2Pt7WOaby/dJpkwFlpNk+jXSrErQSqkNoiEkLZsQWpsRHO5kJ57Du3Tn57YD0+QV4RIObEIkVIgEBQsngzOilyHey3TcbR5xQRW0aFpGfIV5Trcq56TUojjRYluv8uxWJTJie7pl6gRuQGLivEa7+JtarT1pJb7xKR90dGjk48SwJWHnJTpEOFeixO9MS/nTkqbcFIKDqM35uUy9zNkHj/jAr0IeVfoaMuXI91/P7jdtE2aT+TgTixahKnhg4k6dqsGZnPcZeZ2g9GIdsEF2Z9kmBjK++9TG23ntIg3SQytjbTh0ILYq61I846nZEAMbZi/fPTvbyBv6KCrM5z4F0kImYe3IwNhcqOEBverEUKDTlE1ij8aJEqMiKYk2sh1rtBCZ/rOdgCcvQGMMYWoyUDzFBeanHy/02SJ5qluHP1hTFEFZ28Ar8vB9B1tbKwsIaRGjniuis4+lm3fjKRpNL7fgrFEQZrioHuKC5N8kApAU2W2xWRibV7Kevxo73fQeuIszO/vZtdGI0yahE22YDdYsRks2A1xQdQuWxNlNoMZgyTCEh9r6IqUOb7XCtIjREqBQFCw6K04NRk17Jbcnstp1wv3KiYSio1ASCISS/+9u3Ie7lXfSalpceeRoHjQDfeaYyelM4P4FHd4iNyAxYRev8t1eGvI4B4X99qio0cnH6XBAM4cR4ET4V4FgygK+ALpv3enPbcipcOSXqTsC8goYkF/0aEnUuZ6UYbTDrKsoaqp99VMi0MEBURdHerKlciPP06v2c0bziWp+frcbj60uDL+Y9NoRF25csRhMIeKocrcubB3D5ZwshgKEJbMKHNnYByNGJqGXOcN7enpwWo9HGZ3UAQ9LGgOEUAH/g4KpKEBwfNwztBBMTRZEA0NKY9phfc7azCcr9MTz6fpcTtSBMpBNFnC43ZQ1ebD6QnidTkSx2dDNoKoJKtgVtk9uYTZXh+mcAR/exMtLgcH1j3LxjPSu82HY5FNcdFywNUZFzOt2Ae2HYa4qOkwWg+/NtiwD+yzGSxC6BxHVFU4KScaIVIKBIKCRS8cUnmJmnPxRi83oAjJVHxkCjuY64cbvX4XUyT8IYkSm1jxVUzoh3vNbb+zmcFs1NKK8SIfavGhJxDmOtwr6IfdFE7K4qNXR6SsKMn9Ah39cK9ivCs2vAH977wsx+FeS3XCvQIEwjlM+Cs4JujRCa2e6/DWsgzlJekFSb3FIYICZPZs1GuvpXXr82g002GqocNUk9hdqfWwafNmtIYGKq68kqmzZ4/8HEPEUM3lYtdkN96DniQxtNNUia/OxRSXMmoxdLwZKoKWMob8oDrENCWR8zOkRhJC51ABNKxGCSnhw6+HuD9D6kC5clj4DA0IohOFMRq/fxoGVuCErJnvcYP7B+sPHp8N4ymIDrp0exn94GmVzUMEzPjfTMLm4P4Sgw2H0SpEzhHgDxtQtfR9Qc8cIMgtQqQUCAQFi14Yrly7ikDfSenLMJkhKEwy5ajKtViU6WHJ0y9EymJDz9WjJ2aPFkmKhz/s9KT+6BELM4oP/TDD4+ekFFELig89kbI8D/nS9O61YrwrPjKlcSjNsZNSL9wrQF9ITOMUG7164V7zELXAVZpepIznoI6fb9asWRw4cOCIbUmShMPhoKysjFmzZrFo0SIuu+wyFi9enOOrFuSate+9x//0ziY86RJO6n+H2kgbZi1CRDKz3tfHHc88QxvAb35DZWUlH/nIR7j66qtZunQpspzl/XFADFVXr8ZsaU0RQwEqowpqfS3qihVHvUA5HhglA0ajDQe2nLaraioRNTYgWh4WPocKoUNfB9VwoiyohAnEQvEQuNph8TSoRFA58vxHzBT/TakY4v3GGoqSIfU41lA0qf7g8dkwnoJoLhj8LLujo7Oy22QLDmNctCwx2nAYbAMCpi1RVmKwoQZjVOCk0lCBwxh3f0pFFparL6j/bJWPOWRBKuLpViAQFCy9eqEP87AKRk8EyCRYCQqTTM6KXItFmURPT7/M5Cqx4qtY0DTw6Qg1eRnzHBqdntRykaOt+NAb8/LR75w6PxCFWFR8ePSclHkRKYWTUhAnU7QMpy23E5d64V4B+oNHr5MyEonw3nvvTfRlpMXr9WKxWLBarRN6HaFQCCDr61i4cCHdvvRTd7kO9wqDY166aBkjb0vTNPr7++nv76e5uZlXXnmFn/3sZ5xwwgncc889Qqw8Clm3bh1f+cpXePfdd2HeanBNYo3r4uRKLb9I2uzq6uLhhx/m4YcfZvr06dx+++2sXLkyuxPW1RG95hrebulga/fOJDG0zVzL1sbjueCaMkz23LsSBYeRJRmrwYzVYKZsFMcHAgGARNhbSM7/mSxyhoeInREqZjsp9W9BrnFQ3t+E1CGz1+BCMmvIhhiyUUE2KBhMMWRiCTejrzwu1Hrc2cf5H09B9GggqIYJRsJ0ZXyXqcjICQHzsMhpp9Rgo9Rop9TowGm0D7y24zTaKTHaMR7Dzs2+oP616+WnF+QWIVIKBIKCRT/c6/i6O0RuwOJCr9+V2lWMOX5my9SXRY624sIfkogpeu7xfITdFDnaBHF0c1LmJdyrTtQCMd4VHZnCveYavX7nD8lEY2ASv6iLBr1nPFlScVhzLFJaY0hoaGnEIn/YlIcggrnhvffeY+nSpRgMx+5E5dGEoiisWftPwtHT0u7Pj0iZvnw0IqUe7777Lh/+8If5/e9/zyc/+cncNSwYE7fffju33347qjpw3zNUpK1XW2XBEK6no6ODaDR5QcXevXtZtWoVGzZs4Ac/+EH2J59UzRrXrJTiD2kxIPsQm4Kjh6Ghb53GDELihxuQd/+eqKUeU3uUaLNKyX4rO2xzUKXD992p1QrXTNuGZpFQHEbqZy0iJmnMu+A/+XB1OSElTFCNEFRCBIe4Pb2hPiLEiEgxjLVd2LsiBF2llHkClHf76agtSxvyVVK1MQmixzIqKr6YH1/MD+Hsj7MbrHHR0nBYwBwUMUuNjiGv7ZQZHTgMtqPGsam09nJRzzMpCyXeKTmJMruY4xgPxE8qgUBQsOitcM91yM14m/q5AQNhcEzsgl3BOKIXdtBpz704bjSAw6riD6U+NPUFxINUMZHJ3ZGXsJu6eXiPjh8ZgvFDP2pBPhYEiX4niDO+4V4zLwiqKhMhoIoFvUUZNlM45wsSZSkuVPaHUl2T/SEjR/N8mcFgwGgUU025whfIEIIuL894OteRQSf64Q9/yMKFC1PKFUWhpaWFAwcO8PLLL/PGG28k7bvhhhuYM2cOJ5988lgvWzBGvvSlL/G73/0uudCYXqT8yheu4fMfvQJVVXn77bd59tlneeKJJzh06FCiTmtra8JddyRCoRDlJea0+0TUgiJg0iS0yZNh3z7U2bOJHtxBTbSD8piHNnMtAdmBXfVzfFcbkj2EJBmQ5szDaS5Ba2igfPaSjM339PRgtVrjLk9nK3LH76FKQ+p9gz5viN1rathVNh3ZqCEbY8jGGAZDhMvce7BLTqI2icCkGZTL0H7ymcwvsRJQwgTUMEEl/i+q5Tbk+7FKQAkRUEK005NVfYNkoNzooMxUQrmxhDJTCWVGB+WmEsqMJZSZHJQbSyg3leRN0DR2dFDy2mvMXt/BKX2VSfsawk2cEXobxyPlRM47D7W2NufnFxxGPDkKBIKCRdfdUZqPEHT6bXr9Mg6rCA9QLOjmZ8uDOA5x8dMfSncd4gddMZEpT1Y++p6eO1OEuD42iUQibNmyZVTHdnuXAqmOmd7OPWza1D7GK0vG01UPHJ9S3tkbY9OmTWNu/+DBgzQ2No65HUH+0XP05CPca6Yx1NsvU1U2vvmJBBOH3rOVzTwCm8EIcFiiOiKlieqj1UopyDmZQtDpuR7HQpmOUSiekzI9J510Euecc07afYMCwW233cbatWtZtWoV/f3xQVxVVW6++WZeeumlMV+3YPT88pe/TBEo582bR3vtTLrTLAoaXKwoyzKLFy9m8eLF3HLLLfzud7/jJz/5CR6PZ0Tnt1qtuJzpBfdMCzEFhYO2fDn8/vdoLhf7XAuQurZj0SJMDR9M1LE5NDCb0WbPBrcbjEa0Cy4Y2YkGBFHp0CG0xkZM77xPVbAHZ6g/SRCtjbThnh3DYSsn2tiIu9SFcfp0Tl7y6bTNRtVYPDenGiaghBLiZUAJJcTMwfLAYLkSwj/wN6CECar5eZY4mlE0he6oL6ucmwbJMETAdFBhcuIyleI2l+E2OXGZnbhNTuwGa9ZiprRrF+V/+xuSohCOylRH26mKdmHUYsQkI52mSsL2SgzNzdgeeojQihUoM2aM9W0LdBAipUAgKFj082TlfsVppkTKXr9EnTvnpxQcpfgCeg7e/DgtykpUWntSJy98IuxmUZEpvG8++p6ek1KE3Tw22bJlCzt27BixQKeoEoFw+txopTb9fGqjpcSafpWyX+caBIXLeIZ7zeik7JcBIVIWC3oLceymNKvFckCJNUp7mjRS/SETR228V0HO6Q/pT9uNZx5ebw7CvS5fvpxf/OIXXHfddYmyf/3rX7S2tjJp0qSxn0AwYjZt2sQ3v/nNpLLTTjuN1atXM21VendjjcuM3Z787GW327n55pu5+uqrufzyyzEajUn5CY9EmSN9B/P2S1ittqzbERyj1NURvewyLH//O51GN3ucS6iKdiYJRqZ6N9riynguJaMRdeVKqKsb8am05cuR7r8f3G60+XOJbN+XIogCRGUz2vzZaE7nEQVRk2zEJBtxMvpwsIqmJATMoeKlXwnijx0WNQ/vS34dUMJoFG50D0VT6In66DmCoGmRTbhMTtxmJy5TWYqQWWOuwGG0QUsLhieeQFIUDL291O9pwhBInlerinZixojcOxW1ogLr6tUEr7xSOCrzhBApBQJBwTKejrZMiZTjYpGYwCoW9HOh5sdJWaYTRlZPLBUUJnpOSodVzUu+tDK7Th5eEWb4mKWxsZFFixaN6Jhun/44c/IJMzh++tSxXlYSfpMJnkgtD0aMHL9w0Zjz/u7YsWNsDQjGjfEM9+qwahgNWtq8v+JeW1zoLQiym/MkUtp0FmZkEK0EhYeek9Ji0rBZcn8+vXHUm6O0gFdccQVf/epX8XrjCrymaWzZskWIlBPEZz/7WSKRSGJ70qRJrF69GrujgmA4/ZiXSRyvq6vjpZde4uWXXx7RdeiJ4/EFcRIlQqcseNSZM4ldcw37X3kVjSY6TDV0mGoS+6vcKkgaWkNDXDAchUAJQF0d6sqVyI8/jqHKxfryWirCnSkOuto5bsrcEmga0csuwzDa82WJQTIkcjiOBlVTCakR/LFBMTNIfyxIvxLErwTpiwXwKyH6Y8Gkff2xYEG5OMNqlNZwN63hbt06JQYblz69jbruECVd/dTuaUPTTMTM5TTL9QSkkoSj1kkI4/btxObMQa2owPzyy4RWrRrHd1Q8iKdbgUBQsOhNJFTkwUlpMYHVrBGKpJ5T5MoqLrw6Dl5nnpyUpUKkFKA/zuTDOQ4ZclKKkExFRSbHdqbFO6NFb1EGxMc8V2nhrh4WJNM7juFeJSkeWr0nTahD4R4vLvQWBNnzFO61xJLekd6XJgSsoHDpC6aftstHqNdM7eYqlYTRaGTmzJls3LgxUdbZ2TmiNkKhENu2bWPbtm10dXXh9/spLS3F5XKxYMECFixYgCyPbeFcMBjkvffeY9u2bfT29hIMBrHZbDidTqZOnUpjYyMNDQ2jbl/TNN577z22b99OZ2cnfr+fyspK6uvrOfPMMykpydMXPISXXnqJzZs3J5X93//9HxUVFbRlSClXfoTnLYvFwoUXXpjVNaiqyq5du9i8YTuwMm2d3j7GJFKqqsrWrVt5//336erqoq+vD7PZTElJCZMnT2bGjBk0NjaOqc8cPHiQjRs30tHRQW9vL2VlZdTU1HD66adTK1xXWaNNmsQD9dfTF27lpP53qI20YdYiRCQzffOqUD9zIuRiQcPs2ajXXou6ejUWSxcdarIgChCKqmgNk4mcfTbaMbCIQpZk7AYrdoOVqhEe29ndhWaWiJm0uKg5RMAcKmj2xQL4YgH6B/4eq+Kmqa0D9VATzZpG474WulQFzzQH3VNsmDmAFLQSCdrZ463E1ttBWPFi2r0L9eRTMDQ3I7e3o9bUHPlEghEhREqBQFCQaFoGJ2WeHG1Ou0ookrrSVeRoKy48ek7KPOWk1HMGi35XXIx3LlS9dvWcxILCJNMinExh0EdLxvzP/TKuUhG1oFjw6IV7zYNICfG+19OXOr6JMa+40BNp8uWkdOiEuE6Xp1JQuOiKlHka7/QWounlAh4Nw/OF2WxHVqAOHTrEo48+yrPPPsubb75JOKw/OV5RUcGnPvUpbrzxRupG6H7avXs3d9xxB0899RR+f2b7aF1dHeeffz433HADp512Wlbtd3Z2ctddd/Hoo4/S2tqato7ZbGbZsmV85zvfYeHChSO6/pFw9913J20vWrSISy65BICeDBEVxxpava+vj6eeeorVq1fz2muv0dvbC6YqOC29SPmZz9/Mnd++YsQRP3w+H3fddRcPPvig7mc9iNPp5Mwzz2TVqlVcfvnlWbUfiUT4/e9/z1/+8he2b9+eto4kSZx00knccsstXHrppSO6/mKlpw+6zZNY47o4qfzcc2IwKYe/L+rqiF5zDU++6KN893tJgmibuZa6s05g/qdr0AKB3J3zKMUgyVgNVuzWkbk4Y2osLloqQXwxP32xwICQ6R8QM4eUKwF80QARLfcpQUbK9J3tADh7AxhjClGTgeYpLjRZQkLF7AhgdgSgElpVjdItXZj7FHoOxJCqa4i++TdYdhHHWWqxyXkIaVCkCJFSIBAUJP6QlDYsF+Rn4hTiP+g6PKnlwtFWXOi5KvLlpNRrt0/0u6JCTyzKVy5UPYdmptyYgsLDpxPeV5I0Smx5yP+cwRks7rXFg6rqhx3Mm0gpohYI0F+IZjOFgdz3hRJr+ok8Ee61uOjXCfear2gZ5Top1bz++EJgaYxdXVEUdu/enVQ2Y8aMjMe89957nHLKKWhadu+5t7eXu+++mz/+8Y88+OCDLF++PKvj/vznP/P5z38+owA6lJaWFv70pz8RiUSyEin/+Mc/8vWvf52+Pp2VNgNEIhGefvpp1qxZw7e//W2+/e1vZ3U9I6Gvr48XX3wxqez6669PvM7knB3rvXb27Nl0dw8LxRjr1a3/8mvvsHjx3Xz3u99NyZ+px7vvvstHP/pRWlpasqrv8/lYs2YN7777blYi5dtvv83VV1/Nvn37MtbTNI2NGzfysY99jIsvvpg///nPOByjz1tY6MTNBun35eMZz2azEat2sKYn1RV9ilkB8rPgt1AwykZcZicunFkfE1Yj+KIBvLF+PNF+vDE/nmjfwN9+vLF+vFE/nlg/ASU/i8DKu+M/JJyeIAAetwNNTj/mabKEx+2gqs2H3NNDU7lM14G3eak9jIzENEst82xTMRhCVCvZfw6CVMTTrUAgKEgyujvylhtQOIsE+t+3cLQJ8onexGlFvsY7nX4XCMlEomAWJo+iQG8Cq9SmMcYoa7rtSpKGpqULuynGvGJhcKI8HfkKf+jUecbTE+oFhYleSPO4kzL3CdP0RErhpCwufEdJuNdIVCIYBrt1bO0//vjjeDyexHZdXR3z58/PeEw0Gk0RKM1mMw0NDZSWlmIymejt7WX//v3EYocdyF6vl//4j/9g7dq1nHvuuRnP8eKLL3L99dennMdutzN16lScTifhcBiPx8PBgwdR1ZE9Y992223ceeedKeVOp5PJkyfjcDjo7Oxk//79iX2qqvK9732Pnp4efvrTn47ofEfizTffRFGSI1AMdfrp5X425yAXaiiULDxIkkRdXQ0tWgBNSuPkMpShaRq33norsVjsiKJte3s7y5cvTxFCjUYjU6dOpaKiAoj3j6amppTrORJPP/00V111FcFgMKncbDYzbdo0ysrK8Pl87NmzJ6k/PvPMMyxbtoyXXnoJq3WM/5EKFH8IFJ3AKPkY8yRJwuVM/yynF7VDMDYsspkqi5kqS/kR60bUKL4B8XJQ0PRG++mN9dET6aMn6qU74sMb86OR/cIdYzTeyQxKfBwPWTM/Vw3uH6w/eLyKxt5wK3vDreAEs2pgY0crMyRX1tciOIwQKQUCQUEy3nmy4u2KHG0CfSdZvlY7C3eHAMCTJgwh6I9LYyWTI90bkKgqE7kBiwG9e22++p0sx4XKdOObcPEWD3oTp5A/J6WeK13kHS8u9EKr50+kFOFeBdA/ziJlpnu4p39sIuXLL7/Ml770paSyr371q1nnAjz77LNZsWIFS5cupbGxEaMx+bMJhUI8//zz/PCHP2T9+vVAXOi79tpr+eCDDzLmebzpppuSBMpzzz2Xb3/725xxxhkYDMlu1mAwyLvvvsuzzz7LI488csTr/tOf/pQkUEqSxFVXXcUXv/hFFi1alPT+W1pa+NWvfsXPfvYzotH4QoVf/vKXLFmyhI9//ONHPFe2/Otf/0rarqurY9KQvHt699qKkrG7aQEaGxv52Mc+xvLly1m4cCGhUIhF/2WlNV0uTGN54uUdd9zB8uXLOfnkk3XbvuOOO5IEyunTp/Pd736XSy65JMXFqCgKO3bs4IUXXuBvf/sbTU1NGa97y5YtKQLlkiVLuPnmmznvvPOSxMe+vj4effRRvvOd79DeHg8vuX79er7+9a/zy1/+MuN5ipVMc2f5mk8p0xkWetPkIheML2bZRKW5nEpzecZ6MU3BE+2jO+KjJ+qjO+KlO+o7LGRGffRG+lAGnLExU3xMVwzxsdcaiuLN0L41FE2qP3j8cCKywjuBXbxjgdN++J+8dfPfRvBuBUKkFAgEBUmmSSM9UWes6DopxSr7okJv0l7PhTFW9CZOhbujuNAP95qnHLwZ2u0LyFSVidyAxYBXZzGE3v0wFzgdGr40qWFEiOviYSJESr3Q6sLBW1zojXnxcK+5R99JKaZxion+0DiHe80gfnr9UFeZWv7OO+8kOcYGUVWVlpYWDh48yCuvvMLrr7+etP9jH/tYimiZjoaGBjZt2nREx6XVamXFihVccsklfOELX+C+++4D4sLfX/7yFz772c+mPW7r1q1s27YtsX3uueeydu1aXfHUZrOxePFiFi9ezG233ZYSvnYoe/fu5Stf+UrSsY888ohuCNq6ujpuv/12LrjgAi655JKEGPbVr36VSy+9NGcOvB07diRtn3jiiUnbetEycpEL9amnnuKcc85JKguFQjgdWlqR8sqrP89DP3kAiIuKP/3pT/nrX/+q2/7f/nZYGKiqquK1116juro6bV2DwcC8efOYN28eX/nKV5L6wXBisRif/OQnkwTKb3zjG9xyyy1pQ7iWlpZy/fXXs3z5cs4///xEP/nd737H9ddfP+Icm8WAJ0MaWD0xcazojaV6qQUERx9GyXBEMVPVVHqifXSEe4jNeRqjfyNqjQWLZz/u7iAdtWVpQ75KqpYID+srjy9I87iPHLJ50jmzR/dmihjxdCsQCAoSPReZ1axhydPiY+GkFATDEI6Ob25APbFI9LviQi/c63g7eEE4i4qJ8c7BC1DuUDnUmTphq+dwEhQemUTKsjyledK914rxrmiIKRKBUKaclLknU7jXLFPzCQqAvoCOkzJPizIyiZSePgnShNS7+eabR3SOxsZGbrzxRq677jqkLGx51dXVuiJTOmRZ5he/+AWvvvoqe/bsAeJuRj2RcteuXUnbN9xwQ9buTlmWmT1bfzL6Jz/5CYHA4dVVv/vd77LKkXnWWWfxwx/+kC9/+csAdHR08Ne//pXrrrsuq+s6Er29yTkgq6qqkvfr3GtdpWMffIYLlIPo/V6eNfcUVq1alRAmn3rqKbxeL2VlZSl1PR4PHR0die3LLrtsRH1n7ty5uvueeOIJtmzZktj+zGc+w0033XTEPlxfX88jjzzCKaeckggTfPfdd/OnP/0p6+sqFjI7KfNzTr2xVC83puDYRJZkKs1lVJrL4JyVyDt7Ue1TCR8IIkWjNP2jjKDNRKnsxWwKIduixJwapVIfpqhCzGjAVxEPR723sXaC301hIn7NCwSCgkQvH1++3Gyg71jSEw8EhUcm92K+wgzriUV9QYkRpkoRHMPo/aDLVw7ekoHcgOnoEy7eokFXHM+TgxegVIS4Lnr0cgSVlYAhveFozOjea0W/Kxr8Yf313TZzvkTK9OFeFVUmooiQr8VCn45ztiJPE/Y2CxgN6ce8XEzaz549mxtuuIGVK1dmJVCOFrPZzH/+538mtjdv3pySQ3CQ4eUmU27+f/X09PDnP/85sb148WI+8YlPZH389ddfnySwPfnkkzm5rsFrG0p5eXnStt69Nl8RCyBT1AJYtWpVYjsWi7Fhw4a0dfP1XQLcc889idd2u53bb78962OPP/74pJyf//jHP1Jyggr0HbxWs4bVnJ9zlusscNO7FkEBMGkS2uTJIElEa2qQO7uY79vKiR0fUNPZS0lrjLI9MaZu8LJgV4gZXgMVU+Yw3VqPefI0YtVVRz6HYMSIWSSBQFCQ6Ic+zN+yYzGBJcj0IJsph99Y0HN3aJpEf1D0vWJBb2FGvsSiwdyA6RBiUfEwEU5KvQVBev8HBIWHbp6sPE6c6oUwFqHViwd/hjyQ4+2kBAhG8zRbKzjq6AuOb7hXSdJ3LOUi/OHOnTv5xje+wYwZM7j//vvH3mAGjjvuuMTrWCyW5IIbSl1dXdL2Qw89lJPzv/rqq0mi2VVXXTWi400mU5Lr8I033kg48cZKf3+y4my325O2e3UXQObk9GnRm0/x+iWmTZuWVLZ58+a0dSsrKzGbD4+Pa9aswevNlG0uO7q7uxN5TgEuuugiKioqRtTG+eefn3jd39+v+x6KGZ/OGFNqU+np6RnVv/7+foLBIIFAIO0/u85Co94+jUAgQCgUSspXKygMtOXLwevF3NJC1F6KIsnY1CB1kRamhfZTF2nBpgaRDDIGZzmT2vqYFy3nrMu+wV0N/8WXay6jwSzEylwiwr0KBIKCRDcvYB7dHXqOJRGCrnjIlJsqX30vk/jp8Ut5FQsERweapr8wI5/ff6k9fW5APeFKUHjoCYP5yoUKIrS6IINImc+JU52xVIR7LR4y5YHMl0jpsKR3UgKEopa8nFNw9NEfTN/38pWfDeLhD7vS6DrxBZmp4+ELL7ygG8KzqamJvr4+tmzZwl/+8hfWrFkDgM/n47/+6784cOAAt912W9bXFggE+Mc//sGrr77Ke++9l2jf7/cfUUjo6upKW37qqafidDrx+XxAPJzolVdeyS233MLxxx+f9bUN59///nfS9oc+9KERt9HQ0JB47fP5aG5uTiobLSUlyR1oaEhayLQgKHe/LVRV5dVXX+XZZ59l48aNvOP7DJRenVLvjw/8jT/e8vGkMr3v0mQycfbZZ/Piiy8CsG/fPs4//3xuv/12PvKRj2Qdxnc4//73v5P611i/S4Bt27aNqp1CxqNnNijRUvpsthgMBnw+n65z227RAHtKuacvnis1EolgsYh7biGjKKS7tQEgS6BoKiElSGu4nRe7X2abEqUt2pv+AMGoESKlQCAoSLw6Tp5MedTGim54EuEqKhr0HqrNRg1bnha8l2YIYRwPuylivhY6gZCEok6Ee1ylmdTV/cJZVDxMhDiu52jzin5XNOhNnOYrPxvoPz+K8a548IfTOyklScNqiuTlnPZMTsqImDAtBjRkAmE9J2X+zhtfBJl6jx+Nk9LhcOB2u5k3bx4f//jHeeSRR7j22msToS7vuOMOTj75ZC6++OKM7USjUe6++25+8IMf0NeXITlxBjweT9pyq9XK17/+dW699dZE2RNPPMETTzzBnDlzOP/88znrrLNYvHgxtbXZ5yLbvn170vbpp58+quseSk9PT05ESpfLlbQ93G3o1QntmytxfPXq1XzjG99g3759hwunXQzp7uWG1NyTmdyRt9xyCy+//HLCdbp582YuueQSampqWLZsGeeccw6nnXYajY2NWV/v8O/yW9/6Ft/61reyPj4dw/OCCgbz3qZSUSrhcIw+8biiKCl9fpDJtenP2ReUKC93EQqlWZUrOOaR1q5FKysjMmkS8qH3MKAQMlrotZShmMEihyhR+vEpAcK9fWyfXE+XqRv5pRdoW3HCRF9+QSJ+VQkEgoJEz9E2EeFe+0VuwKJB11VUopKvlCuZhHchkBcHmb7nfLrH9SftRb8rFvTvtXnsd7r5ikS/KxZ6dSZO85snK32fDoYlovpmN0EBoZeTstSmIedp+LEYVd3cgCLca5FgcKJpepP2+ftdqyeA5iIn5Sc+8QluuummpLKbb745owsyGAxyySWX8D//8z+jFigBIhH9BQU333wzN9xwQ0r59u3bueeee7j88suZMmUKCxYs4Gtf+xpvv/32Ec/X3d096mvVY9DtOVaGhyrt6OhI2tZbfJuLqAXf/va3WblyZbJACRDzpD/AmCpShsP6DvYzzzyT3/72t0lhXwHa29t58MEHueGGGzj++OOZMmUKn/rUp3j66aeJRvUXhUBqDs9ckIswtIWEpml0e9Pn6cxXeOt423rXI+UkxLXg6EHRFNrCPWzdsY69O9/ifd9uulq301IFbSdbaD7DSmBBjOjMCL6ZRvaeUMneGZV43CVUdvQjaRqV7T4qunJwMxSkIJyUAoGgINHPk5W/iVM9R9tgbkARdrPw0c2FmkcHr9kENotGMJxmtbMINVwU6DnHYYLc4yI3YNEwEfmf9QRQj+h3RYNnIsK9HmFBkNspnvEKHb9OuNfSPN5nJSne93rSOEtCMeGkLAa0NA6yQcpGbyo6InpuOT2X00j50pe+xF133ZXY3rlzJ2+++SZLlixJW/+///u/+ec//5lUVlVVxdlnn83ChQuZPHkyTqcTm82GwXDYefrCCy/w05/+NKtrkiSJX/3qV/zHf/wHd955Z0qo1qHXunPnTn75y19y+umn85Of/EQ3ZGc+RKhc5aQc7iJ89913k7b1nZRjG/MeeOCBpO8ewGazcdppp2GcfAov7k89pn7qfO746v1ce+21WZ/nU5/6FKeddhq33347Tz31VFqBuq2tjYceeoiHHnqIadOm8b3vfY8rrrgibXt6LtyxkKvvspDo8aUXKfM73un3aa8fqp35O7cgd6iaii/mpzviozvqHfjrozvipSfqoyvipSfiQ0HlQ//ezUxvK2U9fszRAFGLgQONVWhpVp1JqsbsLS2YogrO3gBel4PpO9rYWDkz4/V0vXMwX2+1YBEipUAgKEj0nDwT4aSE+PUIkbLw0RMF8ymOQ3zSPpgmFFQm8UpQOPRlEGcyhQMeK06dtoWTsnjQC3WZ15yUwklZcMRiMbZs2ZJ1/f3Ns4E0rgp/K+vXH8qqjS1btnDw4EE2bdqUVf12jw1In2/tzfXbqXPphwLbsWMHM2bMyOo8gqOTWCzGvqb0YfnMsp+9e/dSVpZeTNqyZQuVlZUjPmc0GuWVV15BUhcD5Sn7AzrOTkFhoRnKdfdZjUECgZH9vszkPhtKidUGpLp1u70xAoFgirgSCoVSchoOEolEsFqtSWW1tbVMnz6dvXv3JsrWrVuXVqTcvHkzDz74YGLbZDJx55138rnPfS7FKTecoe1ny7Jly1i2bBn79u3jxRdf5NVXX+Vf//oXLS0tKXXXrVvHOeecw/3338/KlStT9ttstqTte++9l/r6+hFf01AWLlyYtlzTNEKhUNbtnHLKKUnbzc3N7Nu3j5qaGgC8/emVGYsxTCAwuhACkUiEW265Jansk5/8JN/73vcwGAw883YZL/489biwYmP+/PlJZbFYTLfPDTJlyhR+97vfcdddd/Hyyy/zr3/9i3Xr1rFjx44U5+7+/fu55ppreOONN7jzzjtT2jKZkkN+f/7zn+eCCy7A7/fjcDhGlbPwuOOOG/ExhYwkSQR0QpmPVRzPRCYBVIiUE4+maQTVMJ5of/xfLP63J+KlO+o7LEBG+1C09CL3cMq74xZZpycIgMftSCtQAmiyhMftoKrNh9MTxOtyJI4fjqxJzLY10Oir4tNf+MEo3m1xI55sBQJBQaIbdnMCQh+CyA1YLEyEqwjifa8tTQQaMWlfHOiJ0fnMhQoi7GaxE45CKHL0LAjqC4p+d6wiSRLvv/9+1o6T1o4G0omUPZ37WLdufVZtvPHGG9TW1rJjx46s6gciZvREyi07mulzdaTdB3Dw4EGmT5+e1XkERyeaptHWlV7ckVUf7e3t7N27FylNbP+9e/fidrtHfE5Jkjj++OOpbDaRbi4sGBVOymIgk0hploOMQJMC4uJ3un46HIfVSDqRstcXF8KGCzyRSERXINMLo1lVT7HUmQABAABJREFUVZUkIh46lH6RyeOPP550vltvvZUvf/nLR3oLwNhCdB533HF85jOf4TOf+QwQ/7/8z3/+k6eeeooXXnghIdRGIhGuu+46Tj31VKZMmZLUxvAFCnPnzk0RB3NJpnC2w1m4cCEGgyGRGxTieSKvvvpqFAV8gfQLLxzmMKFQ5tCoerzyyitJYWXPPfdcfvzjHwPQ39+PzZz++n1+iba29qQyRVGyFmWtVisXXXQRF110ERAPw/vGG2/w3HPP8fTTTxMMBhN1f/3rX3PiiSfy0Y9+NKmN4QtR3G43ixcvxu/343a7sdvtWV2LIDPefr0wwxMjUvp0rkcwdiJqNCE6eocJkPHXfYnXUS23uRWM0fi4Z1Di43jImj7v+CCD+wfrDx4PMMnkYq51CuaWGHWxck497mS2e7anbUeQGSFSCgSCgkRPLMpn6MNMjiUxeVoc6Ivj+RUp9cR3PZeToLDQC69aatfylgsV9J2UfUHR74qBTGF9JyK0eiAkE1PAmGoqFxzlGAwGzj//fBYtWpRV/V+uc0MaU9uZixdwxYXZOxYbGxuzPqeiwC2r0+9bcuZyzlqoPzGcrVtTcPRiMpmorpsNqUYqZk6rZMm0JZx++ulpxYf169ePKi+d0Whk/vz5THrHwY7m1P0RxZpaKCg8dMK9Oh0aVVWuUTVptVqPKKjUuNPf4wMREy6XKymkKoDT6cTlGtn1DBcv9VyeQ3M/yrLMf/3Xf2V9jq1bt47omjIxffp0pk+fzvXXX88HH3zAypUrEyJrKBTi17/+dYoDb9q0aUnbu3fvzptIKUkSTqcza7HM5XKxdOlSnn/++UTZww8/zFe+8hV6M6T9nDyplBF+1QmGfx9f+tKXkvpNbaVt+CEARGIS+w+2JpVZLJYR97lBXC4Xs2bN4pprrqGrq4vrr7+eZ599NrH/3nvv5dOf/nTSMXPnzk3abm1tHfX5BfrozuPlMdyr2QRWs5Z28aUvs1lXMICmaYTUCL6Yf+BfgL5YIPHaF/Pji/rjZUoAX9RPUM3O2Z8PYqb4PUwxxO911lCUTEslrQMLM2SjiWpTObXOKUyuXM5Maz0VxlIANh0Uz/tjRYiUAoGgINGbPM3nxKnVHHcuRWJpcgMKZ1FRoOukLMmvi1bP0aa3ElFQWOiFV9UTEXOFXh4u4aQsDjLd1yYqtHpfQKKiVIRWL3R0x7w89juDIS6Q96VZ/CNCqxcHejkp853OoVwnN6BwUhYHek7K8jxO2ANUlKYv783hb4umpqak7aqqqrT12tvbk+pUVFRk1b6qqrz++uujv8AMLFiwgF/96lcsX748UbZu3bqUeueccw5//OMfE9uvvPIKV155ZV6uaTTceOONSSLlxo0befbZZ5m76ELdY8YiFg11UQLMnj07aTvT8+ML/8wuUsJIqays5IEHHmDatGn4/XHb+saNGwmHw0khXM8999yk41555ZW8XE+x400fRXNcIlOV97dxUv871EbaMGsRIpKZkn9WIdU0otXW5vX8RxOD4VX7Y0H6YgH6lSD9sSD9Snx7UHwcKkL2xQI5dzvmE4/bQWW7D1+5jTJPgPJuPx21ZYmQr2rMSCRgxxSzcPwkK5P7Y9gsJWgNc1AdlUSnnUikZM4Ev4vCQ4iUAoGg4NC0iclJCfFJ+25f6rnTTWoJCg89J2V5nnNS6ucGFP2uGNATBfM9caqbG1BM2BcFeuMdQFkeoxZk6tc+IVIWBXr3tnwvzHDaNfrSrKgXCzOKg/5Q+lBg+Y+Wkb48FM1jPHfBUYOuSKkjIuYKvfxvPh0BYaRs2rSJzs7OpLJZs2alrTs01OtIwpn+4x//0A0hmwtOP/30pO2urq6UOkuXLsVoNBKLxSfvH330Ub7//e+PKgR0PvjIRz7CCSecwLvvvpso+8IXvsCDT74PpBeD9RZODEVRFF5++WXOP//8pPJ0YYKHkinv4LMvpIrAuaKsrIz58+cnXLuqqtLT08OkSZMSderr65k/f34ih/aePXtYu3Ytp556at6uqxjx6CyEKM/n831LC9e3PYe5PTVcQumWA5j/uA61vh5WrIC6uvxdR44ZKjbGhcZAktgY/xsv94T7CGoR/GqI/lgQtcBTVe1trGXB9m5M1U5szWHUXgP21x1ss8wlHHSgRE2AROOkHmbF9mOIyWhmE9GBsTumkxtYMDaESCkQCAoOf0hCUcc/3Gu8fZVuX+rkmZi0Lw76JsDdkal9vesRFBYTNWGvF3ZTLMooDvSclAZZw26doNDqIv9zwZMpF+p4POM1kxpPWIx5xYE/rOekVCGPXa9MOCmLGk0n3Gt5HvOzgb447u0fe9uapvHd7343qUyW5SRX4lBqa2vZvj2e36u3t5etW7cyb968jOfo7+/n5ptvHvvFZmC4KJnO4VlTU8NVV13Fn/70JwD8fj9f/vKX+ctf/pLXaxsJv/71rznnnHMS4Xebm5v57xu/DfL/pa2fKX8fxN2S1157LZMmTUoRKWtqapK2161bx8Ihk/2Z7uNRJX0o2FwxPCx3uu/za1/7Gtdff31i+6abbuKZZ57BahXht3OBqoK9t43T+zYluRnbzLVUB+cBx+X+pDt3Ij/+OFNjCm1AVbSTqmgXRi1GTDJi6HLDcWXIzc3I99+PunIlDHMA55OYGsOvhAb+BeN/Y8O3g8nbQ8q0fD6gHMUYJQNukxOX2YnbVIZ7yN8ai4tqcwW2Q39GO3iQvpkVeF5uojQQ4QRpG23mWgKyA7vqZ177XgwGFSQJZebM+N/6etRhY5kgNwiRUiAQFByZQ9BNTPhDIRYVBxORCxX0XUueDE4nQeEwUU5KvX7tC0hoGnnNhymYePT6XZkjv7lQS22ZnZSCwibTd5z3e63OmOoRTsqiwK/npLRrkCN3Wdr2dQQBIVIWCXpOyizcbGPBqZPWMByVCGdvZkzB7/fz1a9+lTVr1iSVf/KTn6SysjLtMYsXL04KrXnLLbfwt7/9DVnWyZsZCHD55Zcn8kVmwz333IPRaORTn/pU1jkdf/rTnyZtn3TSSWnrfetb3+Kxxx4jEIhb8R977DHKysq4++67MZuzc0T39PTwm9/8hoULF3LJJZdkdUy2nHzyyXz/+9/nG9/4RqJsy/ZDkEYHtls1TDqzyIqi8OCDD/I///M/dHZ2cvXVV6fUWbJkSdL2j370I1auXJn47ktsIMsaaroF58byrN7PP//5T1544QW+9KUvUZel6+2pp55iz549ie25c+emFR5XrVrFT37yk0RuzV27drFq1SoefPBBZszILid2NBrloYceor29PekzL3paWtAeW81nWlJzODeEm5jz3DronQ4XXpg7N2NLC/Ljj0MsRrXSzXG+XZi15Fy5FU0dGDQZddYsqKlBfvxx1GuvzeoaNE0jokUJKGECSojgwF+/EkqUBQZeD4qMASXuZBzcDqvRI56n2LDJFspNJZSbSlIESLe5DJfJidNoR5Yyz4Vpy5fDffehlJezvdRJVXA3Fi3C1PDBRB2LLYJmsqHMnIlaUQFGI5Hzzsv3WyxahEgpEAgKDr18lDAOuQFF2M2iRs9Nkcn5kwv0HW1i4rQYmLgww+kn7GOKRCAMDrGouKDR63f5XgxkMoLNrBFM46YTjrbCJ9N3nM+845BpIZrod8VAv25OSjW/IqWOGBUP9yqc44WOZnCmLc+7SJlhoVu6nHHvvPNOIqTpcDo6OvB4PLz77rs8+eST9Pb2Ju2vr6/n9ttv1z3fqlWruOuuu1DVeH9fs2YNl112GXfeeWeSozIUCvHMM8/wv//7v+zevRuAOXPmJFyYmdi/fz/33HMPt956K5deeikrVqzgtNNOSwr3OcjmzZv50Y9+xGOPPZYok2WZa6+9Nm3b06dP57e//W2SaHfvvffy6quv8rWvfY1LLrkkxWGoaRp79+7ljTfe4O9//zvPP/88wWCQe++994jvZTR85StfYdu2bdx3333xAh1BcHguVFVVWb9+PU8//TSPPfbYEYXhs88+m6lTp3LgwAEgnpf0nHPO4ec//zmLFi1CkuILM3r7Uo+tmzKPFs8LR3wvPp+PH//4x/z85z9n6dKlXHbZZZxxxhnMmjULadgquqamJv7whz/wox/9KKl8qFtyKAaDgUceeYQzzzwTr9cLwIYNG1iyZAlf/OIXufLKK1PybEI8r+qGDRt45pln+Pvf/64r4hYtA27GWFcICVOKm7HTVInVWA5NTXDffXD55TlxM0pr10IsBt3dzPRsoVuTCA84NwOynRL6qZJbiIb9KFs241VnEawope/x37LzE8sSAuOgAOkfFB3VcGKfoilj/3yKAJNkpMzkoNxYQrmpNC5CJl4fLi8zObDIOQp3X1eH8rGPod17Lx0GF7udVSl9L+wMYawvhXAYtbOTtrPOItDaCq2tKc3t2LGDxsbG3FxbkSJESoFAUHBkclJO1Cp74e4ofDLlQs1/CDrh4C1m9Pqd3oR6rsgkCPQFZBxWMXlayOjda/Pd7+LnUAlG0oTdDIoxr9DJlP8x/894egvRRL8rBvzh9E5Kp10jzXx6zsjspAzm8cyCowG9cK8TlQsVwJcmN+9oQ6tOnjyZNWvWUFtbq1tnzpw5/Nd//Re/+c1vEmXPPvsszz77LA0NDdTW1tLf38+BAwcSbkWAs846i6uuuorPfe5zWV+P1+vlz3/+M3/+858BqKqqoqqqitLSUkKhEAcOHMDj8aQcd9NNN+k6KQE+8YlP0Nrayje/+c2E2Lpr1y4+//nP8/nPf56GhgbcbjdGoxGPx0N7ezt9ffkcWVL59a9/TW1tLXfeeaduv2tv2cVJJ30Mi8VCR0cHbW1tiTCxw0n3nZpMJu68805WrVqVKNu1axcXXXQR5eXlTJs2Db/9aZDrU4698urP8ZNv/Dzr9xONRlm7di1r164FoLS0lNraWsrLy1FVldbWVlpaUnMQnnHGGXzxi1/UbbexsZHHHnuMT3ziEwnB3ePxcMcdd3DHHXdQWVlJTU0NDocDn89Hd3d3Sv5VwRCGuBmVzh6W+PamuBmrop0435fhhPngdsOjj8J116V1MyqaSkiJEBwiIPb6vfQGvBhUM0E1LijS0sKMLS8RU6LUbN6JVB5Bayihc0oFRnMT5cYYkqTxgarRe8BLeW+A2Hud7Di+Dq1X4vl3++itzPNqkWMYg2TAabQP/HNQanTgNNopHdgeLC8zllBhKsFusKYsIhgPtFmz8Pznf7Lvnztx0UKHqYYO0+FFI6fXtoAcIFhVRdfJJxPOkE+4sbGR+fPnj8dlFyxCpBQIBAWH3mSR1axhST+/kDN0V9mLUGAFTyDMhOVC1et3wsFbHOiJRfmewMrUr71+iVpXXk8vmGB0Hbx5jlgA8b7X4UktzyRgCQoDvfuaJGmUZAgFnAv0nEWi3xU+igrBSPqpk7ISNb8iZUYnpRApCx09J2XpEfICjhVnJpEyB85hi8XCNddcw/e//33KytILYkP58Y9/zMGDB1PCxDY1NdHU1JRS/9xzz+XRRx9l9erVY7rOzs7OjAKTwWDgm9/8JrfddtsR27rxxhuZN28en/nMZ2gd5sLRex9DsVgsVFVVZXfho0CSJL7zne+wdOlSrvx/2+hIU0cJd/HBBx9kbGfu3Ll8//vf5+KLL067f+XKlezevZvbbrsNTTt8X/V4PGzevBlO7ICSVJHSaNEXB7Khr6/viMLvJZdcwgMPPIDBkLoQbijnnnsu69at4+qrr2bDhg1J+7q6ulLylQ5HkiQmT56c3YUXONLatWixKLHODgw7P8BmgqjRSKfDRchswaH1Ux3qxBcO4dvwGt2zJ9NfZqfnj7fy1n8uwT8kjGpQCRNSs4tH/aF1u5ECrZT1+HGG+ojaDHQ02jHKycdrskTzVDeO/jCmqIKzN4DX5WD6jjY2Vs7Mx0dy1CEh4TBYKTXaKTHYKDHaBoTHQRGyJEmQdJoc2GTLhIiOoyFaVc2vXedQaW/jpP53kvKhms+cS9llcymtqaF6oi+0CBAipUAgKDgmKgQd6IfdFGJR4TOxIejSt98vXEVFgV7fm6h+B4PXJMLbFDIT5RwHkf+5mNF1jts0dNKT5Qy9kP56z52CwiGg46KEeE7KQ3k8t56jLRQ1H7X5nxVF3P9zhSaXpi3P5HTMBXo5KWHkCzPMZjNOp5Pq6mpOOOEETj/9dFauXIk7gyMlXRt/+9vf+OUvf8mPf/xj2tra0tabNm0aX/3qV/nsZz+rm7MyHbfddhunnXYaa9as4fXXXz+iYFhSUsKKFSu46aabOP7447M+z7Jly9ixYwd/+MMfuO+++/jggw+ShLp05zn77LO56KKL+PjHP05FRUXW5xotZ511FldefQ4/fzzNzpgn7THV1dVccMEFfOpTn+Kss846ojjxzW9+kxNPPJFvf/vbvPfee8k7FW/aY3yBzMLhIBdddBHPPvsszzzzDP/85z/ZunVrxs/YYDDw4Q9/mC9+8Yu6wmo6ZsyYwbp163j44Ye59957efPNN4lE9AUyg8HAKaecwgUXXMCVV17J9OnTsz7X0YaqqYTUCCElQkiNEFTCeAI+QmoENQRBJUJIDRNUwgTVCCElnLbM0t7FmRveRFUVGt9vwYiCdIKDzikuNLkLCxADWlUT8gFf3M24zcf+4+vQ9rVycK9t1G7G8u74agunJ77Yx+N2oMnp+60mS3jcDqrafDg9QbwuR+L4Y4lBsdFhtFFisFFqtFFisFNitGGMSpSZS6iwlVFqtA0RJO3YDZYj5nc8lglFJWKqRJt5EmtcyWPAZef2oNaIvKDjhRApBQJBwaHnKhITp4J8MpFhhkt13CPhqEQ4St4dxIKJZaLGPLsFjAaNmJJ6fhH+sPDRy/+cKY9VrhALgooXvcnx8QgzrDemigVBhY9ePkrI/5inJ0ZpyPhDUt4dxCNl4cKFvPTSSxN9GWnxer1YLBas1olNmh0KhQCyuo7Lvt9Iuye1PN/RMgwGcFg1/KHU8c3rj4fozJaenh6sVit2ewblMwtkWebLX/4yX/jCF9iwYQPvv/8+3d3dGAwGampqOOGEEzjhhBOSjrnmmmu45pprjti20+nk8ssv5/LLLwegubmZHTt2sH//fnp7ewmHw9jtdlwuF/Pnz2fBggVYLJZRvQ+r1coXv/hFvvjFL9LZ2cnbb79Ne3s73d3dqKqK0+mkpqaGOXPmMGvWLEym8f8hp/fb4sTjp3PZJ76L0WjE5XJRWVnJCSecwLRp00Z8juXLl7N8+XK2bdvG+vXr2b9/P5Ik8dTOqXTua01xNC3YWUXkwAFIkyd0KCaTiaVLl7J06dL4e/F62bp1K3v27KGrq4tAIIDFYqG8vJyZM2dywgknUF5ePuLrH2TZsmWsWLECgLfeeoumpiZ6enoIBoOUlJTgdrtpbGxkzpw5OBx5Xl2QhuGC4qComFwWHiI4Hq4XSqkXfx3RciPafGjrPhRNpaw3gDGmEDUZaJ7iShEL8+FmNEbji2kMSvw3Rcia+f/Z4P7B+oPHjzcSEiUGG3ajNS44Gqw4DLaE+JhaZk0IkQ6DVVdszNU4fSzSH9RfAFE6DkYXwWGESCkQCAoO/YnT8QlBlw4xYV/4ZHJSZnKc5YJMYlRfQMJSdnRNYAlyi54wk2/3uCTF+15PXxqRUoQ/LHj0c6GOR9QCsSCoWNFzLeq5HHOJfmh10e8KHX9If/Iy331PL9wrxPve0SZSms1mTj755Im+jLQcLZOwg3kTs7mOUFQvzHBOLyn9ORzgD6WWeyfYQGQ0Glm8eDGLFy/O2znq6+upr08NOZprqqqqRuTeGy+ktlYu6tmcJBK2mWtpPGM+X/nKV1LqD80FOhJsNhtz585l7ty59PT0YOvtZcrNL3CoNTVXZO3eA8i/X4/W0IB2wQVp8xGmo6ysjCVLlrBkyZJRXWO22O12PvzhD4/6eFVTiaixhBgYHhAGw0PEwZAaJaSGCatRQsrgvuhA/XAakTF3gmI+mEg3Y8wUF6YUQ/y50hqKkt7DS2L/0PqDx48UCQm7wYLdYB34Z8EmW1IExhKjDbvBSonBit1go8QYL7fK5mMmjOqxQqZFrmXjsAhScBghUgoEgoLDO4Eh6PQmKoS7o/DRm6Q0yBr20S20zZqSI4TdrCwTYbcKlUgUQpGJdI+r9PSljm9izCt89ATB8bnXijy8xYrevTbfriKAUlv6e21fUPS7Qqc/nMFJmecxL1NYT69fom5sadoERzGaBj4d7Sff4V5hIO9ld2p5n18CxMRtQdLSgrR2LWe/2cz+Yc/3DeEmlmx+A9MDNcTOPx/tCI7GIzHoKB4U6w179mBes4a6kEwzUBXtpCrahVGLEZOMmHtcoFUhNTUh3X8/6sqVMHv2mK5hNMQ0hbASiQuFaoSOcBeoEsTkhOMwLiKGE3UGBcPwgMgYFxWjSSJkWD16xcR8MZFuRo/bQWW7D1+5jTJPgPJuPx21ZWlFUknVEoJo0FWKw2DFN6meuSVTscnJguPga8ew7cHXQmQ8+sjkpByP37WCwwiRUiAQFBx6TspxmcAS7o6iRc855rRrec8XpBfuFaBPhKEraPQWZcD4PFTrjavCWVT46AmC4+Fo018QJPpdoaMXtWA8wr2W6IV7Ff2u4NFzUpbYVAyjM1NkTXn6lISAyP9c6PQFQNP0Fmbk//zxZ7x0If3zf27BBLBzJ/Ljj0MsRjisUT1MJOw0VWK3VGLp6MDyyCO5FQlbWrCuXg1ARaSPJb6dmIc5AJ2dHUhv7kGbPRvcbuTHH0e99toUR6WmaUS12BC3YZSweth1GFYPC4xDxcbBeok6OvsUTYy5uWK83IwyElbZjMMYdyLaDRbsJ1fRsPdfmOpkKjo0Yn6Vkjcs7CqbjqKYUBUDasyIFpX44uxdWIwaxhIbc+csAUlCXfGZI4YeFhwb6C02NMgadqsQKccTIVIKBIKCQ9/dMXHhXvuCEppG3sUqwcShO2E/DmGGM03Oikn7wkZvUQZMbN/LdF2CwkAvD994iEViQVDxou+knLhnvEhMIhQBqznvlyCYIPRyUo7HYiCrGUxGiMZS94lnvMImU1jV8cj/rCeETnS4V0EeaGlJCJR0dzO/bSexUPKgUxXtZNp+I9RnFgmPxKCI2K8ECasReoJ+bKsfJhLqxej1UN25FxwqUZORrtJyQhYLDiWAI9xNS18YdWML7TPr6C+30f2nXfzrP06OC4tDXIyacPoeE2R0M2pSXChUDBgx4LJLNHh7MRntKLUNzHS4qZo5j0mTz8ZmMGMbcCzaDJZ4CNWBfw6DlWgwgt/vx+VyJZ1f2uZAOnQIFlbTv34rjhaVua0HaTPXEpAd2FUftZE2TOUBZKMBrbERJAmtoUEIlAWEnpNyPMwGgmSESCkQCAoOvUnK8Zk4TT9JpmkS/tDRlzdGkDv0HIvjMYFlNIDdqhIIpQpD/SL8YUGTaYJyPNzjwtFWvOh9x+MSZljnXiqc44WPbtSCcQn3mjlqgdUsnvEKlYBOuNfxWAwkSXGxqCuNvSRTPnTBsU8mMXA8clKW6qTM9AmRsuCQ1q5NCJRs2YKkqERNJjps1QRNFkq0fmrC7Wj46Xv3bXyNxxGsKKXv0XvYcfn5A07DKJEB52FkiGMxPKQsNFBnqIhY0dnHsnc3I2kajVtaMJoVlLkOWqe40GQ/Mn6CwF7VTvhAkPLeAKZde+g4vg7tYC++pgp6K8fhP4TgiJgl04A4aMYmW7Am/lqwyWZsBgvWgb82gwWnzcfUtr9jrJAo7X2PfftjGA5Ws80yD0U1MujkXjgtxIcrd2IyVYHdxNSZS0CWYXl2bsaYlD6UrrZ8OdL994PbTWzuPCK7dmHRIkwNH0w+XjYhzZ2F0e0GozGeE1VQMOiZDfTmdgX5Q4iUAoGg4NC/yUxcniyIT+gKkbJw0Zs4HY9+B/HJ00AotVxM2hc2eo5FSdJwjCA8SSwWY8eOHSM+fzR0PFCfUt7U0sumTe+PuD2A+fPnYzYLS9LRjKJAv05onPGIWqD3o1FM2Bc+ExlmOFP+5/6ATJXI/1yw+MPpw72OV66ishI9kVI84xUyer8tYJzCvZZAbaSVk/rfoTbShlmLEJHMzNhcDa0nCCfROKNpWjwf4oDQF078i4uB3YEetIgEAZlwQiiMJomDg/UjWpSwEt+2tndx+jtvoCgxZrx3EENMQTvRQc8UFwb5EIPyX4eqYTrQR3lvgNgH3ew4vg7NK/HSB+ExiYTTd7YD4OwNYIwpRE0Gmqe4UnIDarJE81Q3jv/P3nmHuVGd+/8zI43KanvxNu+6e10BYwwYCAYDpiUOCSVgApfAJaQQknDDTW4gcEO4hBTCLzf3QkggtATTQi4OMaEXB0ywwcbGxt3eXW/vTX1mfn9oJUsrjbaqrHw+z7PPas6cmTmSjqac73m/b78HxaeS2+Wkp9DBzN3NfFg8e8zHP1pRJHNIRLTKg+KibMEqW7ANiok2WcFmsmAbFBxtYUJj8HVwW83tR5bkUI7REVEM0qxDgWjG+Yvx791BsbOLE12bwqIZB5jXdBhZ94HZDPPmBQTKiYhmrKhAu+QS5OeewzyliI25xVG5UNuUYmYtyqOkADCbAzbHo4weFqQ38SIpBclFiJQCgSDjMI6kTJ0VGEDPgERFUcKbIEgRqRw4DRxHp6UrulxEtGU2PXFyocqj0GsaGxuprq4e9fGzLDH854ABA2u84QgKpUuWLBnT9oLk0O82Pq8kZUKQQdScGLDPfFLqlhFnopm41mY2AwaRlMmaiGYkSIl+l9m4DzVxQee2CIGw2VLG9rwl2CwlCTmmruu4XC6kpibO+uhNqptaoupMPVSH/PsP0KuqAhFFR/mAfVA8DBcFPZoPjx54HS4oDn3t1f1Dyrwx6wVfJ8LGdOkn+3CpHvI6BzClQCTM7wiE5uZ2u4CABejQY4e3obvIQUlzL7ndLnoKHaHtMxVpMJ+izWTBKivYQkKi5cjrsOWh4qE9rH5QkLSZLJikiU2o7JTGlqw2PJpxT+4i8vv3REUzWnU/KBZYuBAGoxk5//yJafjcuWjXXIPlxZfRaaRVKaVVKY2o4vF70SrL0VavPurPd5mIUU7KZN3jCY4gREqBQJBxpNJ2M54QGojwELPsMxXDXKhJsKADEVl0tGIUSTmW/Gw1NTWjFgdn7nLA5uhySSkQQmMGE29gPBnX2nj5nzWNUQn0gsmF0cSMvBSLlEIgz2wM7V6TNBHNyNpT9LsMpbER6e9/p2xDA8v6Ii9oVZ56Vvg+wP9gMf5zzkEPiySy2+1I40yg5XK58O3YQfb69ZQ6YR9yVGSRqacI9GKk+nqkRx8NRBbNnTuu4yYCTdfwan68IbFwqCA4VCT0DlnvPyIy6rGFQ28CxcNkkWqR0OwLjI+Y1MD51G2LHbkeJLg+WD+4faoxIUcIh9ZQFOJQUVEJiIUm5YjAOER0DN9Wkczj/l2nNWHRjK2mIj7NXR51zsmemkPVSaWYrNaAQHnZZRMrFlZUYLr+K/zhT20s7tkSNTFk6ap5lJxXjDKaKFHBpKHPIJIyGfnuBZEIkVIgEGQcxhFtiX94sFlAMev4/NE3kmK2c2aTyn4HcXK0iX6X0RidV5I188/IwnogTqSdYPLTZyCOQ3JcC4bL/yxmvmYuhpb+SRhIMMXL/2wwC1uQGaRvJKXodxnHnj3Izz0Hfj9eD0zxtURZD/qzi5EbGrD88Y/4vvAFtNmzcbsDOR9GZbUYA6mpiez161EkiSJfJ8t792DRI/O5OTpakd5X0OfOhaIi5OeeQ7vmmhGJBpqu4dP9eDX/EaFwiGgYq9wTJihG1A8THgNi5JHtfXpstw9BJKkWCf1KQCBQTYHzmc3tI4a7dQib2xdRP7j9SFAkM1Y5KA4qWIdEJ1oGXw9dd6TcEiEuWmQFV88AufZs8rJzx/YBCELRjAf/8go5NERFM04tdAYSNFdVBSIoExDNKEngKixnvSl63xfbnUDsvJaCyY/RPbx4nkw+QqQUCAQZhV8Flyd1dq+SFLiYdfZGt0FEtGU2xjkpkzMDK9soskiIlBmNUeR4MnOhxkJMyshs4n2/6WC7KR4qMxNdj+NakOL8z+Kcl9kYRVLGOxdNJEYipbjHyzAaG0MCJR0dFH26hwXOSKGtxNeGTVZQeudAURHKiy8GBML8/DEfNmhV6tV86C+9gMfTi7mnh8LmXThtOj7FTJujEI/FQpbmwuppp63fg7allY45VTjzHXT/6W4+vPg0fOHi4aBo6PJ7AsKk7hfCYRqSTJEwFt1FDopbeunNt5Pf7aSww0VjQQmqrqCrJjTVhKbK6KqJ2WUwo7cPRcnFXDGNvJxCeucuZFb1ikjxMWSDekRstMrKhFucAnSaVBRZDK2Pm4oKHiz5V3KszVF5cHvn2/FfexKWWbMS2oQ8R+z8z+IeL7MxiqQUOSmTjziTCgSCjCLVFnSB42h09kYLkkYiliAzMOp7yet3xvaHgszFaPJD0gbsDY7TL/pdRmN0vrNZdKzxJ+BPCPH6d69TojLxTRCkAKdbQtWM7F6TMyEoxx47/7O41mY2RnmWk5Z33AFl3qaogdvKHYXILbPQSkuH34kg7ZH+/nfw+9Hb22HnDjSvht+i0GovwaXYcOj9lHpasVsH6Pv4A/pqZuIqyKH/2f9l5xfOxKv70U2ERMIj0YVHIg5dfjc+XcWPGrI01Qj044K2Plbt2Iqk69Rsb8RsVtEXOWipLkSXuzEDXqBOs6DW9pHf5cS/q4+DiyvQDzSyd5+FrmIDb2JB2hIuEuZ1O8nvGKC1LC+m5atZlyjqdKNIZrSiQgqUHDyVMzg2d3ZIFLSEIhMDwqAlrNwaEhCPCIq24g5yOv6IL9eNrWsruldl18sl7LfPQ5OOPOfIusYXFuykQM6DHAul05eAJKGduQaKy6PaKphc+NWAE8+ApZz1hRdGrFv1mVb0MoPZOhNIrpFrQWanPT3qMRxPEXavSUeIlAKBIKOIF62YrIg2IRYdnaTa7tVooExE8GY2xjl4kxTBa499HGF9mNkYnVeSdZ2Nd5x+kf85Y4k3Ec3ITWCiMZyYIWbZZzTZve1c0PluVJ6qMu8cIC+xB29s5MyP1lPYdDhqVfXhQ9gffxe1shLvypVoZWWJbctRiK7rqLoakbvQq/kHLUuP2I76NH+UVWl4NOER4dCL2+/Bq/tR0UIWp1ktHXxm8wdomp852xsw+1W6qx10nFaILDcSHDtv1XSU2j4Odznxf9LB7sUV6D0Sb+xyjVsgnLmnBYDcLidmv4pPMdFQXRglVumyRMO0Ihz9HhSfSm6Xk55CBzN3N/Nh8exxtUEwNiQkFMkUyn0YEAojhUFLSDRUsEqD5SaFnDN7mNH4V8x5Mnmd2+nrUjn0WiG7HXNQNQVNNaGrMnYT3HzcLiSLBNkWZtecHBAJP3s9lI9DJKwuQqqejn/vXrQ5czDv3k2pr5V8fzfNljKcsoMsbYAybzNymwdypIDVsCShV1WN79iCtCGeEJis54s8hw6ItE1HG31uEUmZLgiRUiAQZBTxbI+McqdNNEbHEZFFmY1R30ua3avISXlU0mcQoe1I0vnOaMDe7ZXw+sCShKg6QfIxzIWapH4n8j8fnfTE+W4DA0uJx2gCiMgNmJnIzc1YX3+dy/Z14vJGDp1Ueeo5+d0N2PRSHH19CTm+tbYWXn6ZKf1+JDRKfG0R+Qm1vkLQszE1NGBfuxb36tWoCbbDSzWarhkIgMGchX7D9b3OPjQTaDKhnIVGYmJw2RcWaZhIlu48iE/3k9c1kDKBML8joBLkdruAQIRdrGi6YDu6ixyUNPeS2+2ip9AR2l4QiUVSsMjmSJFw8C+QIzGQ59AiKWHRh+aICMRI0dES2k+wTJHMdHV1YbPZRp+XdApIc5qQDh+GBcfR/85OCnr6WNL7SYRIOENqRmp1B8TBCRYJ9fPOg4MH0QsLYeFC1O17sapepnnqIup5ZQV9YSAXKmYz+rnnjvvYgvSg12m8LlnPFzmGkZTi2SKTMZpcLUTK5CNESoFAkFGkg92rsVgkBrAyFU0zjmhL1sCpkVgkBuwnF16vlx07doy4fmPrCYAtqtzZ28yWLXtGvJ/m5uYR1w0n3kNjn0uiSBE395mI8aSM5HzfkhToe519sfI/i3NeptI7YHwflbToceGWcdRg2r8f27p14Pfj99uY4muJEAjblGKsphxMDQ0s+PRTrAsWwLJlE3Z8a0cHhRs2QFkZue52lvfuxqL7IurYOltRNoM6ezZaQQG2detwXXFF0iIqA4JhQNDzaWECYZjAF1v8C74OKw9GJEYIh0PW635UPTMj5dNBIDT7Ap+tSQ2cT922+DPNguuD9YPbTwYkpJBwGBQHLbI5IurQEiYYWqVAbkPLoKhoGSy3yZYwAdIyZB9mLJKCJKX/9UE/7zykRx+FoiKaKxbiPbwHqx4pEtptOlgsAYFyokXCigrcq1eTvX49FBXxcUkxtt62qHNu/pwiioskMJvRLrkEKiom5viClNOTFpGUscvjTZITTH76nAaRlMLuNekIkVIgEGQURkKg3aKjJOmMl2Nkf+gWNzeZyoBbQjPIk5U0+0MjcVzYbk4qduzYwe7du6mpqRlR/QFP7AEkh9U/kc0yJJ4o1e+SKMoVImUmYmxvnbyHuZwsjc6+GPmfxYSgjMVo0o1i1rFZktMGo2utsHvNLOTm5pBASUcny7rrowTCEl8bZXtBdsxCVlUKX3sNTjllwgbNSzZtQlJVaG+npGEHHbqOZ9BmNhjZVK03IfkGMO/aha+mBl9BHtprL9F5+SV4dR9+XQ1Zk9aZO/BLGt7+Xfh0f8RfMAKx19uHHxXVp+PT/ZgPWPAHoxOHCIY+3Y8/QwXDVJAOAqFfCQzUqqbAddTm9tETp77N7YuoH9x+rISEQykoGFoiIhAVKToaMSgWWiQlYHM6WCdUPmhtOrS+IpknhXiYNCoq0C65BPm55+i2FrExd3lU5HZXcRHHn1wcmCmWAJFQnTUL75e/jPmdd1AsDbQqpbQqkfl2PX4NvWpqQBwVAmVG0dtv/HtMddom4YiWuWga9LtFJGW6IERKgUCQURha0CVx4NRolr0YwMpc0iGC16iPi6iiyUdNTQ1LliwZUV1Nij3ls2Z2GUuWjDxP1quvvjriuuHEO7cGJo2IGYiZiJHtUTIf5kT+56MPo/uoHLtOssaahd3r0YH19dfB70fu7ETfuRuLrkQJhGXeZixaQCC0yXJAUHzpJfRrrx0UBwOCXrc+QJc8QJ2nFa/ux6/7GSiX2K+04unfiVf349NVaq11+CWNg539WFramNazmzZZwrelDp/sR52XTVNlCShtOOQWJFmlSfLzYXM3uZ39+D5pDOUnfGVrQ3R+wpzB/+07R/5B9E/YRyoYhlQLhBCI3ixu6aU3305et5P8jgFaSvPRdDOaKqNrJjRNRldlpuRAVU8XijkLX+lUptsLyJkxl89OOTkkBFoHoxR9Lg9Zlixy7dlRwmGo3qAIKYTDFDJ3Lto119Cy8xV0okXCOXk6SBp6VVXCREK9vBz9K19h/dutyFu3RuUBzjv1WI7/SunwOxJMOowiKW0WHUuygg0MIinFeErm0u8CXU/9GLIggBApBQJBRmEkFiVz4NQ4ok3c3GQq8UTK5OXJMu53uk7SBnAFySXVtpvD2b0KMhMjQSZZ/Q6M866KCUGZi5EzQLaBg0UiyM7SKfM2cXz/RxGDp/m7i5BbZqKVisHTdELTNXy6ik/30y+58Usah71tg5aigUjAoHDoHaxnbm5h5oFNaJrKlF27UP1+vDPzqSsvQDL3YTH1oMoqzcjs7+knp6sPWdJ5X5PQ39/DS2Uf01k8ZKQzD2jafGT5MwotfArtnx4pC6aR661l6Uf7cJj6yOscoN/Vi89ion1hETY5OvdlXXUBc/tcE56fUJBcYgmErWV50ZavuoRJlyjqdGOWTKhFBeQr2TgrprEwe3pYFKEyaE0aGVXod/nIstjItjki7EwtkkJWQSf57WsxlUhYejYj+/zsfbGMPbZ5aNKR86+sa6ya8yllpiKwW5g662SQJLRV18fMT9jZ2Tm2XImC5FNRwSvHXMfLh1qirnNZ06egXX/shOSgHA5vcTlvFFZGlS9QVMQEyMzESKQ0smBNBDn22OV9cfJlCiY38fKNikjK5CNESoFAkFEYzrJPqkhpYPcqBuwzlnj5RpNm92rQxzVNwukBR3TaQkEGYNT3kmW7qZgDM1zd3ujzmzjnZS5GAnRS7V6NREphrZ6x6E0tXNC5ISqywjllMWBN+PHl5mZWbN5ATlN71LopjYewP/4P1MpKvCtXJi0n4GRB1/UwQVAdjB48YjUaaT+q4tPC16uR62LVD9bVIpfV8MHs/MH/jZvitnXppn343E3kdQ5gdXfjU0x0LSwgR26Lqrs/L5u5fb0oPpWsrj56Ch3M2N1E5zgFwnTITygYGRJSRCSgNUwQPGJdGl6mIKugSGYcVnsoJ2K2vZuqpv/DVAC5nduQ/Sr1bxTyiWUemm5G12Q0VUZS4ZqZu5hqAbItzKkZFAg/F1sgHEpcwXBGJdK0TUiHD0PNPKQdO6hQW8jr646KIja1e6BACuQnlCT0qqqkiFeCxNPrhGZLOesLL4wov2GJCuXJeq6NXd7vSsrhBSnAaOJtbhJFSqNjiWfazKU3jgAtRMrkI0RKgUCQURhHdyRv4NQouiOekCWY3KRDnqx4kSR9ThmHTcw6zTR8fnDFEAcBsuNEOE402XYNtzfaZkyc8zKXVEfwgvGEINHvMg+5uRnr669zzOvtDPRFXlSrPPVMPfQ+ticLEyoOmvbvx7ZuHcX9EhJKVK4ut7kQ9GxMDQ3Y167FvXo16qxZCWnLeNB1HRUtTNCLJfyFRxfGqKMNrTdM/cHlyUI6CITpkJ9wshLIW2gORRKGC4SSX8dqtmJXbIMCojki6jBcSBy6bWTZkXKzNHqbVaczMCoaIRQWgzT7UEAgXHAs0o4d5Hb1slT6OEogzO7zQE5iBEL9vPOQHn0UiorQFy5E37UXq9vLNE9dRD2vpKAvnAtFRWA2B+w/BRlBr8EpzEg4TARGYlFPnKgnweTGOJIyic8WRs5UwqUlY4n33WYLu9ekI0RKgUCQURhFdyR14FQk3D7qSIcI3ngzvXqdEmWFSWuKIEnEs1NNtsV1e4zESeKBbvLg8/k4cODAiOu3dpwKRM/A6OuqZ8uW2glsmTFu5wKgOqr8cFMPW7ZsHdM+6+rqqKmpGV/DBBNKUBzE78frVZjia4kQB9uUYmzmwoSKg/66OnoefBBZVZFa/Szv7cCi+yLqmLtbUTaDOns2WkEBtnXrcF1xRVzRVNXVaEFQGyrwDRUADSIJh4k87F86wD/Mrai1byPmhccnHQTCdMhPOBGYJdOgaKjEFA6VGKJfhDgYEYUYXDYWEs2SCVkynqiS7raj4QKhZ+5CvB/vxapHC4SyNYECYUUF2iWXID/3HBQVsaOsGK21Leq8q8wspqIIMJvRLrkkIfkJBamhxyAXbTIj2oyEKSMBVTD5Mep3edmxyxNBrsGlQTzTZi5GkZQmWceepGADwRGESCkQCDIKQ5sIkZNSkECM8mQZRfokgnh9XORoy0zSwWY4cCxxzpvsjEagBBjwxH6EyLL6J6I5IyLLEvtYTq94vMkU5ObmkEApd3Yy7VAtOc5IAajE10Z2g0bHnnx8ubloDz6I48YbMVdHC9hGhOct9A4V/3Q/2trfofY2UKxDcWcTUo4Fn2KmPacAj9WC3e+iZKCDFqcHfVsLzbMrGMjPouP5n7PxohPpyelDlTSeqf8oQlzUkikXKoCQJ0dEOgiEI85PCEiaHore7M23h7YfiqxLmHUZm2LFIgUEwvA/i2RC9aqYkQPWpZgozi/CZrbGEAgDy4psRpHNWAfFRGWImBhPMBTEIEwgdOcUsTG3OCpqu00pZsEJRZAvJU4gnDsX7ZprkF5+GYulgTqllFYlMt+ux6+hV00NCKRCoMwojByCkpob0FAsSl4bBMnFSIBOpjhu1O+cHgn/0WtQkNEYnVNys3QkMZSRdMRTvEAgyCiM7F7TIU/WgFtCE44BGYmROJ5My80sm44s62hadFuMfheCyU28WZ3JjOI16ucDQqScVNTU1LBkyZIR1XX7Y+f/WzS/iiVLpkxkswyZtd9B2TtNHN//UUSOwhx7EUsrZqKVlg6/kyHs3r07AS0VjBRd1yMiBbNf+St+Ty9SZye2PfuQkCDHQlt2IW6LhSzVxRRnO1bFiV7XTMfMcjosMq7nfsWeq86LEARDtqSDEYd9xw/wrtKGdmhDZN7CIRS09bGq91OkQp3C7Y14bCr+CgeN1YXoshNw4gLqNQdarZv8LifW/Qc5tLgCvaGb7sZCuooHwwDEANekIBECYcQ2AH4di6xgV2yDAqEZn8uDSZcpyMnHdmwhhdtfw1KQR36rhsmv0bLJxL7CaYH8hKqMppnAD+dn7aeUAnAo5Fcdh0k2ceyKK7ikrGJQjDRhlsx8vHUrgOF5XtM02toCeTfN5sAw0YzyGaHXgiQxKBD2/fFldBppjSEQWq0qelVVYgXCigr0r3yF1z9qpXvD1qhcwNqyY/nMV0Z/nRWkP0aD9sl1CIpdLiLaMhcjK99kiuO5caxl+12Qm5O8tgiSQ69BvzNK4SVILOKOUyAQZBTpkCfLyLtc1yUG3OLGOhNJh34nSZBt02POfu0X/S4jMZrpDMaTJRKBUcSwEMczE02D7J5mPtO3JWrQcoprJpB4b2m5uZnPbHoHW1NH1Lqixlrsj/8DtbIyoTkKM5nwvIXB3IWReQdjRR3Gth4N2pXGzlMYKOu7SOEhZQNq7duhNhS09bFq91YkXafm00bMfpWemQ5aqgvR5S4UwAc0agpSrR+py431cB19iyvQO+rZW7v5iDgYixFaOM3c0wJAbpcTs1/Fp5hoqC6MEqx0WaJhWhGOfg+KTyW3y0lPoYOZu5v5sHj2KL8BQbIwIYdEQrNkwiKbcS9aSMGefkwludia3JgGdAq3ahwonoqGGU2T0VUTOYrGGaY6ssil2+ukomAOxxSVsPjCK7m4oiJkSbpr+076unpZsvhYFNmMGRO/+MUvOOecMyIEwy1btgCwZM4SKIO2vAYKBgaoXDITPvmE/XtU5tS3DOYntIXyE+ZWenDk2/HPnYei5KBWViJPnZuqj1QwEVRUUHvhtTy4ri1qIk6zpQzphsXo0ycmB+Vw+KeUs76wMqq8VNEQMy8yE+PcgMlrg1H0nLB7zVyMvtvk5qQ0XicE8szESKRMZpCL4AhCpBQIBBmF0aB9Mu1e40XPCfvDzMTI7jWZ/Q4CfS+Wr764qc5MjPLc2q06ShLv8IzE+HgiqmByIjc3w/rX+Wpjb9S6Kk898195G1tTeULFwWCOwqI+kLBEWeE5TUWgOxKaozCZaLoeLQKGIgPD8hRq/iHCYWxBMLSdFqMsrJ6eTGtQiwRDohnTRRwMRsnldruAQJRcrIi6YFu6ixyUNPeS2+2ip9AR2l4QHwlpiAWpKUo8NA8uh6+Pfh2wLQ0ty0fq7Pt0DybdFBALB8ti2pFWgv2TXOTGRuQF5Rx+ay9Ku8z0Az2DAqGDLG2A+dZmyqv8YM6moSCXEjmPyrnHU1lzSsTumqVaVN1Nlsk2qs+kbdky8jdsCOQdXLQI9ZOdWNXo/IReScE/byZaQQGYzXhXrhz15y9IP3qd0GwpZ33hhRHlZpOObVryrNWNBu2FWJSZ+FVwGkxuTWZuQKNnCyMBVTD5Mfpug4K1y+Ua875dLhderxenM75fsFmSGPTHj0KMp2QmRjkpkz2OJwggREqBQJBRGOVoy05ibsB4EUxGooJgcmMkPmfbkjsDy9B2U0RSZiRGkYrJPN8FjifsXo8GguJgX6+KhD1mniyLKbHiYHiOwlx3D8t7D2HRfZHt7G5F2Qzq7NloBQXY1q3DdcUVEyKaqrrGUAvRgDhonM8wuq5xdGHE9oP79R+lkSrpIg6afYHP36QGzqtuW+zBqyDB9cH6we0nE9Gi33DCoCmQi1CKJyTGFh6D60zISAlO/tOmHQYg22Qftq7nrLOwr12LVlBAbfEC9I79WPVIgdBu1dAVBXX2bNwNDegmE5x//oS111NUROfZZzN11y4oKmJ7yYnIXd1R512m51Fc4AezGffq1SJ6PEMwEgFzskhqniyjgdr+sesFgjQmnvicTJcWo0hKkZMyczGye83NAkVRUFUVv39sEzTMZjM5OcN7tcbr4z5tdBONBJMDI/FZ2L2mBiFSCgSCjCIdbDfjipRiBlZGYiQ+Zyc9kjK2ONVvEOkpmNwYne+SPfPP6PwqIsczh3BxUGvvZnnv1ihxsMTXRsEOHXn+xIuDQayvvw5+P3JnJ0WH99AmyXjNCq32EpxmG9naAGXeFlzufrQdW+idOwN3fg796x9nzyXnxIwgDBcSD8/vZKfjI9Y37hxSz4dPV9Hi5C4UTCzpIg76FRMAqilwHbW5ffTEqW9z+yLqB7cfC2ZMIxQADV7L5lBOwg/e+4DqiqnUzJobIRQeESEDr82SKeFi4WRAKyvDvXo1tnXr6FSK2JU7JWpihm9KPjNOyAVJQmtuDgiKE5wf0DNtGpxyCrz0EmZlH80x8hPOUr3C3joDMRJjjHL1JYpsw9yAyW2HIDkYRRUB5CTT7tWg33l8Eh4vWEdo2S6YPBgJ5LkOHUmSsNlsZGUl9gRot4NJ1lG16PugwHOtEK4yDaNzXjInZQiOIERKgUCQMWia8aB4Mgfts2w6kqSj67FuboRYlImki1hkFNEmIngzk3SYlAHGOSnF+S5zCBcHrXv3YNHNeAZzYzllB1l6P+W+JiS1D/3TT3DXzMabn4vr5ec5fNnnoiIK/eECoRYZQRi1flBYdLR2ctqn/0TTVGZ/Uo/JpGJa6qCzuhCTfJjg/OhWTeeTWif5XU78u7rYvbgCff8+XtvjjZ+jECAPwAvevgR/ooLhSKU4GE53kYOSlj7687Io6HJR1O7icG4Zqq6gqzK6JqNpJnRVZmqByozuHsymbJgyFZutkOLquTid1Zh1mZnTZgwTXXhEMDRLZuQJFAsPdW5jWkkR8+3VE7bPTEedNQvXFVdQv3kjOs20DhEIlxZ4QQoIhDtzczl22rTENKSiAq67jpfe2oX/o/1R+QkHlszjuDVJ9GEUJAWjqKJkCkVgLBYJ68PMJJ6dajJzUubEyUPY64QSIVJmHMYiZfLaIEmB43XFeAwQFteZiVFOyhyRkzIlCJFSIBBkDANuKaYwCMm9yEhSQCyK9fAmxKLMxMhm2Ei8SRTZNiFSHk0Y2b0mvd/Zdcq8TRzf/1HE4KlMCXLLdLTS0uF3IpgwdF1HRcOr+aKiBb16LOtRP7VT+jjU/yEffHoQv6SiShp+NPyShqO9i5M/2Yiua0z/pA5JV+HYXNqrClCUOvJlDUnW6NJ0ttb2BMTBHa0BcbBP4pUdncOLgyNg6af78eg+8roGMKUwR6EgOXQXOShu6aU3305et5P8jgFaS/NRMaGrJjRNDomEuRYfpe0eFN2Mz55NoepAqZzPipxjDAXBTe9/QFX5VGpmzTHIaxj4b7W1Y6t/jDZnC/nSLmS3n/1v5bNdWYAWls9Q1jXOmLqdCikHPUshv3IxSBKu5Zfgb2wEYEn2glR9nIIxopWV8dyMa6nt64i6xk2dlYfr6rlopaUMPPVUwtviLijhlcJ5UeX5FhcQnSdYMLkxjqRMD7cMYfeamfQZDNhDcqN44wmifU4oyU9aUwRJQNehz+Cckuzo8Rx7bJFSnPMyE6NrrdHkf0FiESKlQCDIGOJZCyb9gc6ux7zg9TolhFyUeRiJgMn2sjeylxWznTMTubWZCzp3RUVVFKgLgOQ80cnNzZy08R1uaOqIWudoqcX++DtHvQ2druv4UcPsQwMi4dD8g8Hlw6UDuKz1NHR7IsqHWpN6Y0QgBu1J9dHaEc0M/DtEf9SqpQf30Su7yescQFe9eK0mWmZnY5Ij88IkWhxMlxyFggDhop4lQgiMzj0YtB5VJFOMumb+9sJfWXrMEubNrglFEWad3sWUw88i50BW+1Y0j589f61gn31elDh4edm7FHvsaGYzUt5cSvJL8Z72L2hFxhMk6jq2M6O4mIX26fHfaFk5UlUVZSYTst2OedcuKprayHK/fySSWBugzNuMtcsJRRLq7NkgSaiVlYFJGoMipWBy0uuUaLaUs77wwojy6mV9aKXJ87x02GJbGBtFAQgmN+li92oUuRkv4k4weTGyPrQqelItVuP1c9H30he32z2m7QbcoGl5MddZTG68Xi82W3JyQhqe8/qF3Wsm0mvkTCVEypQgREqBQJAxxBNi0sV2c8AlIQyZMo90sBkG45spEUmZWcjNzVhff51T3+ugoC8yT1uVp55Fu97D9mRxwoVB0/792Nato7AXJCxR+bq6tSLQHZgaGrCvXYt79WrUWbMS1p7RoA5GGAaFwyNCoG/wtRpTIPTqvlAEojeUq3BodOLgvsPWjepMMAP20QfdBxL19kdFuoiD6ZKjMN2QkSNyCiqSGYscK1dhrGhBU8hi1CLHy3kYLTxOZN7CtxvWUb2giBp71ZHCqilYp85AbmxEnzMXddsuSn2t5Pu7o8TBslI3tpwc/PPmYSsoOCIOThCes87CvnYtWkEB/nnz0A7WYvV6meapi6jnlSz4581AKygAsxnvypUT1gZB6jByLchNshWYkUgp7vEyEyPxOZnWhxCIKoqFyEmZmRgJgHlJHsDIiSNSBsZ8ki8gWCxHVNrTTz+d1157zbDuddddxxNPPBFa3rNnD9OnT09k81KO3W5wshgB8cbxrGY3Pp9vzPseLYHxm1hpm5LWBMEQHn/8cf71X/81tPzQQw9x9dVXT8i+jScECbvXVCBESoFAkDH0x8l/5kiy/aGRvWyfSxYiZQaSLrkBsw36eb9b5AbMFILCIH4/Xq+VKb6WCGGwTSnGas5PuDAoNzeH2pHt6mZ5by0WPfIBssTXhnmTjjZnNlpBAbZ163BdcUVM4TQYbeiRfPjRaPN1h6INA7alQfHPd0QwjCMihpdH1g/sSxMzYUdMuoiD6ZKjMB5mTGECoXG+wdD6GNGFlljCoDw0+vDIPkxS5p7fw8XB3unz8e6txapHi4Oy1Yx/3uyEiYNaWRnu1auxrVuHVlDArvIyXI2dUeded1UeZQV+MJtxr1591EaPZxpGImCyrcAcViFSHk0YiUXxxJtEYHQ8n1/C4yWp0XWCxGNk95rsCF7FDHarjssT3R6RGzA9kSSJrKyxdRRfu/G6qeV5ZJmSN9HQ6JzXLyZmZCRG+Z+NHMoEiUWIlAKBIGMwEopkWSfLmty2GEW09bkkyuOP7womGaoKAwYiYCpyA8ZCDGBlBuHCoNzZydyGWpzOyIe2El8b5Ydl5OkzhhUGR4KqayFhLygKenUfhX9/jn53O3JnF7a6A+TmSHjNZjpyCnDbFLL8LkoGOmhxutG3tdAyu4K+PDsdz9fy7kXLYkYw6gD5gwdueH88H5VgAkkXcTBmjsKyvJhRnZKmU9AxgKxLuPKzsepm1IIiSvw5mHQZMzJmXcaEHFjWTZiRqTtQy8zqGcyomhYpGMoxBMYhoqJZMiFPYHShIFIcHHAUsTG3Iipiu00p5rhlWZjNUkLFQXXWLFxXXIHljTdQlA5qlVJalchozamqD7Wy9Ki2t840dN34HirpE9GM7F4NIj0Fk5t0z0kJAWvQEiFScuedd3LXXXeNqK7VaiUvL4+ysjKOO+44zjjjDL7whS/gcCQ5RNaAdBHHg8d0eaLLRURb+nPo0CHmzp0bUeb1eg3rx4ukzLGDarzphGPU142skAWTG6NrrbB7TQ1CpBQIBBlDv9tgFoxNJ9njhka5CPudEsS22xdMUgYM+h2kIpJSiJSZjPX118HvR+rsRN69C0mV8SkW2uwlOC1WsvV+St2tmEwunDs+omfudNx52fS++Ac+veSsQaHRFxldqPvwan4azjLzbO5m/nx4a6jMp/tRiRbaC9r6WLV3K5KuU7OrETMq/hoHTdWF6PIAEuAC6jUHWq2b/C4n1v0HObS4Ar2hh/7GYrqKRUz5ZGG04mDQ3rU33x7afmiEYSxLUUsosjAyAjG4Pufk45l9aD1yFhS27sDkV2l8K5/t1hpU3YyumtA1Gd0Pl1fsZLpJR89VqJ5xAkgSrlVXD2sB+tTWp6jxzWBJ3pKJ/yAFYyIoDnY98jY6nbQOEQdNso7ZPJCU3LdaWRnuNWt46+AATa9/GpUPuGnxfE5akx4DzYKJweUFVUuPfEVZNgO3jDS7x/N6vWzbti3VzYhJT08PVqs1abnNjAjmbYvXjqbWhUBuVHluGtlu9g5ASX7SmpIQhoopV111FQ8//HDCjufxeGhtbaW1tZVt27bx+OOP8+1vf5vbbruNb33rW5jNqR2iNRJi8hzJH7DPsUNrV3R5f4rsXgWJI559dE4WdE+gSDmcFa+RpbbI/5x5aJqxQJ7sCUGCAEKkFMREkqRZwInAVMACdAG7gPd0XR9bNmRBUtm1axcu19E1zWzHrkqOhOEcwWLysGXLlqS2xetcRODnE0ldYzfFPXXRGwhSxt69e8e1fVuvDZgSc13dwR1oSZx219FaQZm3hOP7P4oYPB1wFrLzdReewsKktWUs1NVN/t+GRsC21C9p+CUVP4P/JS2sXDOs0+nvQpV11u/dNlgeWJfd1s2KHZvRdI05nxzG7FfxLHTQWV2ILDeEbKRbNR2ldgB3lxP/rm52L65AP3iQf+zT4guDRTIeBsA//HucuacFgNwuJ2a/ik8x0VBdGCVa6bJEw7QiHP0eFJ9KbpeTnkIHM3c382Hx7DF+woJxo4NJk5A1CVkDWZNw97uwSGbsig1ZkzANlsuahDN/Bnl9HciyguLvw99txfEPO3vzZ6JpZjRNRldNFNp6OEXdgqNXRpXN5DqLyXeZ6K1exGnv5xs0RgO8g39HUAf/wm84mwFLq4vs7m76JStF7U3kd9tYon0SlaPQ52+hxT5AR3k57h076C8oYMebbw770WzcuBG/fwQ/AsGE0tDQwKuvvsru3bsN63xknssr5cdFXd+6swpYm/sJTlmGt94a8TE/+OAD6urq4h7TiF09Z/Ju4YVR5cc27+Wpp56KKg9e28ZyrIli48aNHDhwIKVtmCj8fj+SJGEyjS5KeyzfQ687C7g+5roNb61n35YOINCf8vJiz0LcsWMH27dvjzjujh07qKioiKg3tF27d++O2Gdnaz8wI2r/Pf3aiJ5zhnvfkiSFjpedHbhfcLlco/6cP/roI7Zt20ZNTc2otksWHo8HjydGeFYKiNcOo4FTq9mL0zn2Eft40UyxUCQJiG0D1NblprxgeNeY0R4zmQwdL/H7/Tido3t2G2++vL6+Pr7//e/zxhtv8Pjjj2O1js0CaiL6dWePDYj+zWfZ1FF/LqNlaD/JsmXHbEtnz/h+AxOBqh75PGL176H3ki6XK+GfXzoRaxwy3vtv6zITS57Ituu43c4JPWcP993YFRsQ/Rvs6U/8byCVpPN5euj37/F4JuS7CAzTxb53a6jbxRY1nndQfGpra8e87dGMECkFEUiSdBHwI+B4gyr9kiQ9CvxY1/U4zuHjbschYNoE7e4xXdeviXOsa4BHxrH/t3VdP2Mc2yeEgwcPhmZpZhLBAYbq6uqodYfqY89GNUsDoxqQ+OCDDygrK4t5jJHicZYSS6Rs63Sz/cD2lA+Cxvscjzb27Nkz5odBgKaeIsN1zYd3M9A+9huo0fRFR08PC99r44am6PaYOg5hf3wTakEBh+bPZ8BgIG2kJKr/NDQ0JPS3oaOjyjqaKfA//LUm66gyqCYNTeZIWcT6IdsOrtfClvXxuq6VBF9E3ozP3F+HT1LJ6xpIuTAYjJTL7Q48gHYXOWJG1QXb013koKS5l9xuFz2FjtD2ggBSmCBo0iQ8Ay5ys3LIsWejYMKMKfQ/+NqEHIg2DCs/Uk/GjBkFOcY6EyZZjh7ziRf8VQ7F+xUsra2Y55XgereR7A4/C1oPhYmDvRyTVcfUQhPk5LNTlnHYHOQtWoT5/Asm7LNS5syheN06JFXFXF4O77RjdcXIUZiVi2VJDSV5eegmE+2rV3NKSYnBXo+Ql5fHq6++mvJIhqMNTdNYvHgxCxcuNKzT/F4JzZZy1g8RByuL3Pzg/KpRH/OUU04Z9TZBPuwo4d0D0eU2x5SY+x3PsSaKjo4OZs6cGfczniz89a9/HdN7Gcv3UNdmhRdjrzv15MWUFQQG9ZYtW8bevXvZuXNnVD1Jkrj44otRlCNCT15eHk888QSNjY2hMlVVaWtr44MPPggtd3d3h9a3eo8Fzozav9Nj4pVXXh2Ra0xFRYXhM5HJZGLRokUAobxiHo8HWR7djY3H46GmpoYlS0RE+njw6bFDGC2Si95xJOXTdR2PxzPiAWnND7EiOgFa2lz0Fg8vHrhcLnQ9PaNShg5+q6o66jGUoc8uZ5xxBt/4xjdi1nU6nTQ1NbF582ZeeumliIH2l19+mf/4j//g7rvvHtXxg/h8PqRx2kd19cb273VY/QkfW1IUBV3XQ8dxWO3EEim7+kb/HU00mqaF2uD1eqP693333cd9990XUZbqNieTWKJivPcfEMejybYHPueJ6NtBVDXSOt3j8US0zWYxEVuk1DP6O4zVj9OFoRNBfD7fhHwX7V3G9zctTXugv3vM+25qahrztkcz4glcAIAkSVbgYeDKYapmAzcCX5Ik6RJd199JeOPGT/pOCUkg559/PvPmzUt1Myac4EzhWA++Tc854OPobaaW5XL55ZeP6jjjfbiuxcE7+6LLc/KnsrhqMeecc05KH97jfY5HG3V1deP6PjbtUnjsb01R0R3NljL+ZeVJWKrj2wsOx0j6omn/fmzr1lE3JYvanVrMnF0L5y9EMkmc7HTiPvts1FmzxtymRPQfXdfRJJ0ZNbOoWTQfr+bDo/tC1qNe3R9YDtmVDv7XfHhCtqVHrEyD24Xvw0/sfE6TgXQSBs2+wOdoUgOz5922+Il2g+uD9YPbpysyckT+wUibUiW0ziIrUZakQ7ezSErEunBL0+CyLEU+ID311FOccsopLFu2LEWfQAwqK+EPfwC/n3/uLcU2sAurHikO5uRBblERzJuHrb6e4rIypn7jG8wYEi00bmbPhmeeAb+flw9p9NdHn+/UuSXMXyCD2QyXXcbUIXlx4tHT0zPqewbB+Fm4cGHcPv+WgenBlEJb0n8rc3cCMQJzJSUvvX63YezcuZMFCxakbftGQzLfixxnjuNnlh9Lfs6R5dGKoKtXrx7VueajPWYu+GF0uY7MjTd93zDVxEjRNI22tjaA0ESN/Pz8UU/ayMvLo6dn7NEHggBGkZRTK3IpK8uJuS5RWBUdjy+6PYq9kLKy4ftdZ2cnNpstJH6nE729vRHLVquVwlE6z9jt9ojlqqoqLrroomG3a2xs5Morr+Tdd98NlT366KN873vfY/bssU0sHO/n7PbHjpyeUmgZ9ecyXgpyY7dF1e0UFo59gvFEoChKxOeRrv07VQz9XQFx+48mxRaL8nPk0HYT9RkPnZyen58f0bbSothtGfAoSf8NJJt07cdBd4fw5Yn4Lpqju2mIKy65gCkjcAowYteuXfzsZz8b8/ZHK0KkFCBJkgw8DXx+yCoVqAN6CHjLhIfelAAvSZJ0tq7rG5PS0LFjMP9VkGn0GeRkMcrTl0iM8tT0uyRI7nOlIIHIzc2U/OUtbmiKTpgxzVtHwdNvJjxPltzcjG3dOvD7cQx0s7y3FoseOdusxNeGaZMONbPRCgqwrVuH64orRt0mVVfxaD4GJA9+SaXe0xohEAZfe8KExMDrQcFROyIuxirXTtJ5hxY4/N5EfkQZQToJg34lMGigmgIPcTa3j3jDkTa3L6J+cPvRIOsSVlmJEgYtBiJiUAy0DBUIJXPcfVgkBZPBg/JRTUUFXHYZPPMM7eYi6nOXR02GcEwthuUlIMvojY10nn02UydaoASYOxeuvRZeegmLpT4qRyHAdD9QVQXnnx9ou2DSY+ScHi9fWqIwylcUL6eSYHISL2At2268LhFkx8mP1OeSxi1SCtIHXYd+g5z3OUnudxA457V1R5eLc974qKio4Pnnn2fBggV0dASsozVN4y9/+Qu33HJLStpk9J1mp+Baa3RMIwFfMHkxvMdLwXXNKBdh/9GVSeuoIN65xGEfu0ApGDtCpBQA3EK0QPlb4Ce6rjdCSMj8PPD/gKC/XxbwjCRJi3Rdn+jpklcCY7kFvwS4IWy5FVg/yn38AnhlFPVjpPMWpIJ+g4tMKkRKo2MaCamCyUcwetHcBBKWqAH73qwi0B2YGhqwr12Le/XqcUUvxsLr9dLzyCPY2ttRentR9tZj0XPxmhVabSW4FCvZ+gCl3hYG3P3IO7bQM3carrxsel/8AzsvWYknGLE4+D9cPBwqNqoM3qzlDzagadOEvh+BMakQBo3oLnJQ3NJLX34WBd0uSjrdNOZb8WsKmiqjayZ0VUZTTUzJ9lLa1Izik8kvnUaWrZDSaTWUFJ4yKA4qUZGIQ6MNd3z8CTKSiPxONYPiYN1LLwHR4uDUIkAGqqrYm5/PjGkT5dofg4oKuO463t3TxIGu6Cj2ziXHc8F15Yk7viDpGIlFRoJhIjESCcbhwChIU4wG7LNsgUDtZBLveaZfPF9kFE4PaFrs7zRV5zwhUiaGgoICLr30Un7729+GyrZv356y9vQOGPS7FIiURtfaPiEWZRzpdI9nJI6Le7zMw+g7Nck6WakN1j5qESLlUY4kSUXArUOK/0PX9XvCC3Rd14C/SJL0AfAPYPrgqqnAzcAdE9kuXdffHb5WNJIk3Tmk6E+6ro82wdlOXddfG8vxBaklrSIpDWdgiQidTCA8elHu7GV578Go6MWpzlaUzaDOjh29qOoq7jBh0KN7Q4KhR/Pi1n3Ulw0wYKultssZLSbqPrRD9Zzc9RGyIjGruR7Z7se8zE1LdSFmuT4UtNum6eyudZLf5cS/q4vdiyvQDx7k3X0aXcXZCNKfoDDYm28nr9tJfscArWV5MS1fTZpEUYcLs2TCk5dDlmbBVlbFPFvVEbtSOSgIBqIK39/wLvNm1zCjanpENGK4ZeneT3dj1k0sW/lF8hufQsoDpWMzks9H3T8K2azPj7DrkXWNZUUfktsPqslKecUikCRcJ38RLXfkNsgyYvA1baio4LHK63D7oy2uexeUwQ3HQ3k5rsceS0pz9LLoHIUAX5g4TV6QJvT0xy7PS8WAvWF0R3LbIUg8hgOnqYgqsgmR8mihz2n8vJjrSMHk27gRbYH2+P1+3nvvPfbv309LSws2m42ZM2dy2mmnTXh7uru72bhxI83NzbS1tWGz2SguLua4445jwYIFE368RDN//vyI5fb29jHtp6enh7fffpvOzk7a29vJzs6mpKSE4447jrkjtL03uo6l4pxn1Nc/+bSWF174kNNPP52CgoJxH+fDDz9kx44dNDc3Yzabqa6uZvny5VRWVo573xPFxx9/zEcffURraytWq5XS0lKWL1/O9OnTx73vvXv3snnzZpqamvD7/ZSVlbF06dKk5rA2jOANE6r37t3Ltm3baGpqwmQyUVJSwrJly6J+P+PFqK8PJ47rus62bdvYtWsXbW1tDAwMUFxcTGVlJaeddlqUZel46e/v57333qOpqYnm5mZsNhunn356yif1tra2smPHDg4cOEB3dzd+v5/CwkJKS0s58cQTKUuQu9hY6DG4x8ux6yPK8y2YeIRIKfh3Is0n3wEMjZN1XW+QJOlfgXAR77uSJP23rusdCWrjiJAkaS6wfEjxoyloiiBFGAmAuVnJD9V32GIfc8DAukeQvui6PpgP8YiImPf3P9Pvbkfu7CK78SCeHB2fYqYjNw+PTSFLdeFwdlDf40L7qJ7DM0vpy7fR9tROXlt9LD5JRZNGMMgwHfbTBz2HYq5eun8/PkUjr3MA2e/Hp5hoqC6MEq50WaJhWhGOfg+KTyW3y0lPoYOZu5v5sHhsOU8Eo8eMKcJW1CqZ8bo8mHUTxbmFoTyHljDrUutg/dyTlzDn0HrkLAnf9jfIURxYXi1kl2MuWlgEI374zuJt2Ewquk3BbK8Er8zyFd9EKzUWBnft2sDiqqksyVlsWKdFCzwpWsqr0SsqkRobUWfPxrxrF1O8LZzk7qHZUoZTdpClDVDmbSbL3Idugq4pUyiSJNTKyrjtEKQ/vQPQaYkWB887E0hy8KLhLHshFmUcRlZgeSmYZ2MoUroCNo1iYCNzMOp3qYjucMQTKcXzRUYRz3knJRFtWTrEmDDW5wSPx8M999zDgw8+GFNcM5lMfPazn+Wuu+6ipqaGQ4cORQhmV111FQ8//PCI2vHiiy9y3333sXHjRvz+2PPQq6ur+da3vsXXvva1qNxz4cyZM4fa2tqo8ieeeIInnnjCcLuHHnqIq6++ekTtHSnSkIuGzWYb1fYbNmzgrrvuYsOGDYafy+zZs7nhhhv4+te/jsViMdyX0f3TU0/+jhs+/68jbtPZZ5/NO++8E1r2er2Gde+8807uuuuu0PKrr77KihUrsFtUIHrW2ae767n00ksxmUx88Ytf5O6772baGNw7HnvsMe655x72798ftU6SJM4++2x++tOfcswxx4x639ddd11EP9qzZ4+hoPj2229zzjnnhJZvu+02br/9diCQp/6uu+5iz549Mbc98cQT+dnPfsapp5466ja+8sor3HbbbWzdujXm+oULF/LjH/+Y1atXA6P7TkdLU9sAkBtVnucI9Icf/vCHfPzxxzG3nTdvHnfffTef/exnDff/+OOP86//Grv/Rgn4uafBMRui6nl9Eh4vWIf8fNra2vj5z3/OM888Q1NTU8xjWCwWVq1axX/+53+OuD8Zfd47d+7kpz/9KX/9619xOiN/sN/61rdYuHAh1dXVdHZ2ApCVlUV9fT05OaPLOXXo0CFqamrQ9cC9x4IFC2L2FV3Xeffdd3nuued47bXXDPtqkEWLFvGd73yHNWvWjDrn9URjdL7LScH4sSCAECmPYgYtXL8ypPg/9eBZyABd11+XJGkD8JnBohzgMuCBiW/lqLhmyPIWXde3paIhgtRg9ECXihwtRtGbbq+EpotoykSi6hoezYtH9+HWfLh1byAyMVR2ZJ1H99J2gonXHDt5t6U2VOYOi2z06j7Cv82Ctj5W7d2KpOvU7GrEbFXx1zhorC5El92AmwHggJZHZa2f/C4nhfXNtOVXkN3WSVZn94RFL+Z3BKZ/5XYHpvZ1FzliRtZBQKjsLnJQ0txLbreLnkJHaHsBAYEwFEGoYJHNWCUlEEU4+NoqmVHksDqSGWtYfkPrYN3w9eGRiLFyHW7ZsgWAJXOGmfWYC/bph5EbG9mSXcg0p0xBbx/H922PEgatXf0gS6izZ4PHg6ukhJwJFgY9Z52Ffe1atIIC/PPmodcewqp7meapi6jnlRSc5eW4HQ4wm/GuXDmh7RAkF103jixKp9yAwpIp80iniDajfqeq4PaCXVhEZQzpdL4zmcBu1XF5ou/zBoRTS0YRL09WSq61BsdsaOnjhBNOZvfu3YbbqqrKCy+8wJtvvsnzzz9PdXW1YV0jWltbufLKK3n77beHrVtXV8ctt9zC7373O9atW8esCU61kQh27doVsTx79sgmkHq9Xm644Qb+9Kc/DVt337593HLLLTz44IM8//zzzJs3L2Y9o2gxRXKPqE0TRUNDA48+/Gew/Vv0SlNAzFJVlWeffZY333yTv/3tbyOOIHO73Vx++eWsX2+cFUrXdV599VXefvttHnroIS6//PIxvY+x4vV6uf7661m7dm3ceh988AFnn302Dz744IjFc13X+c53vsMDD8Qfvt2xYweXXHIJN910E7/85S9H3PaxYHQN27b1XR7/UbRjSji7du3ii1/8InfccQe33jrUJHAMqL2Gq3qdUBImUj7yyCN873vfo6+vL+4uvV4vL774IuvXr+e2227jtttuG1PTnnzySb761a/GFYgtFguXXXZZyELa6XTy/PPP8y//8i+jOtYf//hHwqWBq666Kma9H/zgB9x3330j3u8nn3zCv/7rv/LII4/w9NNPM2XKlFG1ayIxsrdOhROfIIAQKY9uTgFKwpYPAG+NcNuHOSJSAlxECkXKQcF16Fnz0RQ0RZBCBgxEylQk3M6265R5o+3wmi1l2HonbtZZJqDpOt5B8dCteXGFiYruQcEw+NoTJiJGio6BdW7di09XR9eAmSb6aANX28iq72kBILfLidmvpjR60ewLvFeTGpjt5bYpcesH1wfrB7dPd0zIYWJgQCy0yJGCYqA8Uhg8kvPwiH2pNWw5fJ/yJAh9CQqDTpuNppxqvC2dUcKgLOlgUfAP2gxrbW20n3ACE337r5WV4V69Gtu6dWgFBeydWkp3bVdEXtY2pZiS8iyqHDvQTCbcq1eH7I4FkxO3F/wGp43URHfELjeKfhJMXtIpoi2eSNA7IETKTMLQ+jAF/Q4g267h8kRHFgmnlszCyB1IkvQI+8NkYXTOe+75l+kbIlBKksSMGTMoLCyks7OTgwcPous6vb29fOELX+DJJ58c1bH37t3LZz/7WQ4ePBh1nGnTplFUVITH4+HQoUP09/dHbHf66afz5ptvjtjqNBX09vby3HPPRZSdddZZw27n8Xi46KKLeP3116PWlZeXU1FRQW9vL4cOHcLnO5KSZN++fZx55pmsX78+StRTVXAanEsUOXkiZXd3N+eddx5N3afDnBgVzJERd+3t7Vx88cVs3bqV3NzoaLxw/H4/X/rSl3jppZei1k2dOpXS0lK6u7s5ePAgmqbh9Xq59tprKS9PrlXItddeyzPPPBNaLigoYOrUqZjNZg4ePEh3d3donaqq3HDDDSxYsIATTjhh2H1/+9vfjsiBGqS4uJiqqio8Hg+1tbUMDARm6fz3f/83xcXF439Tceh3xz7nbd10ZGJCTk4OVVVV2O126urqaGuLHLv58Y9/zIIFC/jCF74wvsb4jUXKPieU5Ade33HHHfz0pz+NqpObm8vUqVNxOBy0tbVx6NCh0DpN07jzzjvp7OzkV7/61aia9dJLL3HttdeiaYFxHFmWmTFjBgUFBbS1tXH48OFQ3auuuiriO/7jH/84apEyfPKDyWRizZo1Meu53dHnhoKCAkpLS8nNzcXj8dDa2hoVZfruu++yatUqNm7ciN2eggsbxs8WQqRMHUKkPLoZOiXl1eGiKMPrDlk+Q5Ikh67rqZq7fhaB/JhBfMDo7oAFkx6jSEqj/JCJQm5uZuZLb3BDU0/UuipPPUu3baMyR0YuL5+0A/a6rodyKbp0z6DA6MOteXCHCY4BkTHW6yN1PLqXyXQbkE7Ri34lMFClmgI39ja3j+hedwSb2xdRP7j9eJGQQgJhSAQMFw7DohKD661hAmLw9YbX32Zm9QyOmb8oELk4KESaJJFgDo4Ig9qGDfTZCtiYW0OJry1CGOx3FHH8CY6A36DZTPNnPoOnqCgh7VFnzcJ1xRVY3ngDs9JOq1JKqxIZsZmvOekvKODQ/PmTYja7ID7xIhRTMWhvdExh95p5GEZSpplI2eeE0sLktUWQWNIpghcCeSljTakTImVmYRRJmZMFcgqCZkeSh9dsNvPd736Xb37zm1RUVITKGxoa+OUvf8mDDz5IX18fN91004iP63Q6ufjiiyMEyhkzZnDLLbdw8cUXR+Qj9Pv9vP7669x+++0hl5K2tjauvPJK/vGPf0RZvz722GO4XC5aWlq45pprQuXnnHMON998s2GbJjLnZWtrK1dddRWtra2hsmOPPTbC+tOIH/3oR1EC5fnnn8+tt97KiSeeGCrr7OzkkUce4c4778TlCjy7dnR0sGbNGjZt2hSRJy9ezj1FHiYh3wTy/e9/nwMHDiCXnkAs08WSsul872c/47bbbgsJsIcPH+anP/1pTNEonF/96ldRAuVll13GbbfdFhFd2tzczP/+7/9y77334vf7uf7668f9vkbKk08+yYEDBwA499xzue222zjxxBNDtsCqqvLiiy/y7W9/m8bGxlDZt7/9bd599924+/6///u/KIHytNNO4+677+akk04KHcPj8bBu3Tp+8IMfUF9fz09+8hNKE5iuw2hiBmovJ554Iv/2b//GWWedFRKhdV3nnXfe4cYbb4yI5L755pv53Oc+F2Ujes4554S+91/96le8+uqR4exHH3004r31OBUuNwgM7B/8GTz22GMRfU2SJK688kq++c1vsmTJEuSwE3VjYyP3338/9913X6i//s///A/Lly/n0ksvHeaTOcJXv/pVNE0jLy+PW2+9lS9/+csR4nFLSwt1dYGJy8uWLaOmpib02bzzzjvU1taO2BY5mF84yNlnnx1XqM/JyeGLX/wi559/PsuXL49Zt6GhgSeffJJf/OIXIZF9586d3HrrraMWbCcK43s8YfeaKoRIeXRz3JDl90a6oa7rjZIkHQKmDxZZgAXApolo2BgYOi3kRV3Xx5ZxXDBp6XXGvrlJ5kwY0/79gciifhUJe5SA0KYUo2om7G1t2Neuxb16NWqSB+79uopL8uKVVGo9Lbg0L27Ng2tQRHRpnogylxZZ7tI8k05YnEjSKXqxu8hBcUsvvfl28rqd5HcM0FqWF1M0lTQo6nBhlkz4CvPJMzkYKKtmgW3aEeFQNmOVLINiowVrKNpQCb22SAr7d+3DrMscv/g4rLKCGVNUPpWxsK3fQrGaTalSMHzloxR11iw+WLSIOW0V6GhRwmC+QwPJiVpZiXflSpwGuTEmCq2sDPeaNbzb3M++zl1RkeOfzJ3BZ44fOq9JMFlJN5HSMJJS2L1mHMZ5Y5LbjuGOKQTyzMJoln0q+h0Y56Xsj5PDUDD5MJp4mypxPNdowu+g7aaiKDz77LNccMEFUVUqKyv50Y9+xGmnncZVV10VFREZj3//93+PsEK98MILeeKJJyKEtSBms5lzzz2XM844gy9/+cu88MILAHz88cf89re/5dvf/nZE/VNOOQUgIsoJoKysbESRjPFobm6OGeEIAeG1qamJ999/nxdeeCHCJrKsrIw//elPEQJHLDZv3syvf/3riLJbb72Vb33rW1H5LAsLC/m3f/s3zjzzTFatWkVvbyBKbP/+/dxxxx3ce++9obrxrl/JjKQ8cOAAVquVW77/Le56Pnp9n1Piu9/9LqWlpREC8xNPPMGdd96JosR+Nq+trY3IfQmB6Lv/+I//iKpbVlbGT37yE0499VQuvvjiqH6SSIIC5Q9/+EP+8z//M2q9yWTi85//PPPnz+fEE08M5SXctGkTH3/8Mccee2zM/Xo8nqjfwZo1a/jDH/4Q1eesViuXXnopK1euZOXKlXz66acRkXoTjZFIedKyhby19k56enoihEdJklixYgVvvPEGy5YtC4m1DQ0NrF+/PpRHM0h5eXlIOBsazX3KKadE5At1ewFDkVLiwIH9EZ+j3W7n6aef5rzzzou5TUVFBXfddRfnnnsun/3sZ0OTBb773e/yuc99bsQ5aFtaWigrK+OVV16JaddcWloaIbZeddVVIVtZXdf505/+xA9/+MMRHWtoXt4vf/nLhnX/5V/+hZ/85CfDRjFXVlZyyy23cNlll7Fq1arQteDhhx/mtttuo7Aw+TP8eg0mBIlIytQhRMqjm/lDlneOcvudHBEpg/tLukgpSVIuMDSm/9Fx7tMEzCRgh6sDHUCTruvxzcYFKSWnu5mTu7dEDZKXuGYCib/oyc3N2NatA78fW28Xy3vrsOi+iDolvjaKmptQKrIC9datw3XFFSOOqFR1FZfmZUBz49I8OMOEQ6fmCQiLQUFR94YtHxEa/aiQP7jDpg8m9kM4Ckh09KIimbFJChZZwd3rxGGxU5idH4pCtA6us0oK7upCqj/eQEHFDIpbd2H2qXRszOVD01w0TUHzm9FUE7pf4qKiHcyzaeg2hanTTgBJwrXiarQxzIrs1AIPAtmm1FhzHO30ZmfzRvll/K0hL8pSuq2sGNfVM458rwkWKYOoJWWsL4yecHGSOd6vQzDZiGejmk52r/0u0LTURJwIEoMQKQWpIP3sXoVIeTTQHyeSMhVkGx3XlAMEcpLFEijDOf/887nllluGjXQLUl9fzx/+8IfQ8uLFi3nqqaeiIiKHYrVaeeyxxzjuuONCwtL//M//cOONN2IyJceZ5dVXX42I1BqO3NxcrrjiCm6//XZKSkqGrf/rX/86IlfcBRdcwB133EFnZ6fhNscffzz3339/hNjwhz/8gdtvv528vDwg/gQvRUpeJCXAf/3Xf3HcZ5bGFCndXgmfPyCw3X///XzwQWA8o7W1lY8++oiTTjop5j5/97vfRVhTXnDBBTEFynDOO+88brvtNu64446xv5kx8LnPfS6mQBnO3Llz+cY3vhGRL/Lll182FCn//Oc/R1huzp07l9/97ndxRfGioiKefvppli5dGmEbPNEMGNi9fvW6yzGZjNtXUlLCD3/4Q2688cZQ2csvvxwlUo4GqwJmk45fjT4P9znhTw/cGxKGIdCvjATKcD7zmc/ws5/9LBRN3traypNPPsm111474rY99NBDhvlkh7JmzRpuv/32kD3sSEVKt9vNn//859ByXl4en//85w3rH3/88SNqT5Bp06bxwAMPhD4zl8vFM888w9e+9rVR7WciMLrHEyJl6hAi5VGKJEl2YGjW8vpR7mZo/Zqxt2hcXAaE3zq3AMZZsIfnh8B/AzlDyjVJkrYTsLr9H13Xa8dxDMEEIjc3Y3rlda6pj/aPr/LUs/DVd7C1leFduTKh9qrW118Hvx+5sxNl/24suhmPZKXZUoZTdpClDVDub0LSNWy1B+gvdODKz6bvpSfZc8k5YaJj9P8jEYyJuzkUjIxg9GJffhYF3S7yWj0czrGh6hY01RT485soyZWoyNOo6OlHMediLZtOYVYR/TMXYumbioLMorkLQ8Jj8L8sHbkRf+qpp6ipqWHJ7CUx27Il205uQQNVZjNyjQXzrl0U9QxwnPPTiH5X5m3GbnaBXUKdPRskCbWyckwCpSA9cHrMNFvKWV8Y6dy+fI4XrbQr6e0xupkfiJE7SzB5iTeAlRKxyGCehK6DywMOMY8iI9C0OGJRCvqdYgabZXC2/RCESJlZpJvda5ZRJKWwe80o+gyiinIdqRk4Nezv5hzy8/O55ZZbRrSf73//+9x///309Aw/ge2BBx7A7/eHlu+5555hBcogWVlZ3HTTTSHb1traWj788MMIG9R0wW63c+WVV3LttdeOSKDs7u7mL3/5S2hZkiR+/vOfj+hYl112Gb/5zW/45z//CcDAwABPPfUUN9xwA2BsMwzJtXutrKzk61//Op8cNO7v/S4oyIFLL700JFICbNmyJaZIqet6VHTY3XffPaL23HzzzTzwwAM0NzeP8B2Mn5/85CcjqnfppZdGiJRBq+NYhOcYBLj99tuxWCzDHmPevHlcffXVPPzwwyNq01gwyklZmGuCYfy7LrnkkgiRMt5nMBIkKfBM0xUjPKW5rZ8//vGPoeWTTz6ZL33pSyPe93XXXcddd90Vsnj+y1/+MmKR8tRTT2XVqlUjPtbUqVNZuXIlr732GhDI0/vPf/7TUMQPsm7duoicp5dccsmIoz1HysqVKykvLw+J5hs3bkwzkVLYvaYKIVIevRQD4XchPqDVoK4RDUOWp4yrRWNnqNXrn3Rd98esOTJipecGkIFjB/++I0nSQ8DNuq4nd1qZIIKgvarLaWyvajVnY2pomDB7VU3XcQ8KiAOamwHNjdp0mPKDW/BpPop27UT3+fDPzaVxajaypZUcxYes+OmS/GTVmqCrF//O99m9uAK9X+KV3U66iqOtawQTR1AItEkWbLIFq6Rgky0c2L2PqtJKKkvKsUmWUJ0j4qEFWyh3ooLjzG4KGp7ClAtKx4d8/ClYDhWy2z4PLUxgnHesi2P6P8WkZ6HbFbLLawLRi8vOo2PQkqTaOv7T5vuFhVj270dWVZSiInyHerDqXqZ56iLqeSUF/7wZaAUFYDbjXbly3McWpA6XN/YtXKpm/hndzDs9IpQtkzAasHfYIUkBChEYRncQePAUImVmMBDHZS5VkUU5WbFFynjRxoLJRzpF8EKcCUEikjKjSDe715w4dq9f+MIXRjyAnZWVxUUXXcRjjz02bN2///3voddjsWA9++yzI5b/8Y9/pKVI6XK5eOCBB3jggQe4/PLL+c1vfhOKbIzF+++/j9d75OJz6qmnMnfu3BEf75prrgmJlAAbNmwIEykNNtJVTFKMC16CuOiii1AUZdh7vIIcoqIG6+tjx13s2rUrQmQ8/vjjR5xf1Gq1ctlll/Hf//3fI6o/XhYtWjTiti1atAiz2RwS9I0sWTVN4/333w8tOxyOUUUbrlmzJmEipc8Pbm/s58WRXGsLCwuprq4O5WOcCFvabHtskfLDrXtCdq0AV1555aj2qygKK1as4NlnnwUC4pymacNaPAOjEkODfPnLXw6JlBCwcR1OpBwqZl911VWjPu5ImDZtWkik3Lp1a0KOMRwikjL9ECLl0ctQNcSph3tGjIyhQ1VJV1gkSZoFnDak+NEkHNoMfA04VZKkc3Vdn1A/PUmSphCwmh0NyU1smAaE26tqbd0s790a0161cCfIC2ahFRRE2Kvquo5H99GvuhjQ3PSrLvo1F/VlA/TaDrKroysgRKoBIdKpuXEOipP6kBldSzfuY7aribzOAczubnyKiY6FhdjlyJmiOhIN04pw9HtQfCq5XU56Ch3M3N3Mh8WzE/6ZTRYkwBomGNoGhcL4ZUFB8YgAGRQbLZKCbJAz8eeP/ZwV59SwpDB2tGIUFXlIlVOhsRF19mz8O/ZT6msl398dEb1YXXsYU7Y7ELU4NHpxUKQcLwsXLgSguaKCsg0b8OXmsrt8Fn0N7iixvqS8gGkFXjCbca9endCoYkHicXqMRMrUzPwzypPlFJGUGYWRSGkU0ZhohrPdLCtKXlsEiSNedGIqRcq27uhyEUmZWRiJzuln9yomBGUSk8nudfny5aPa1/Lly4cVKbu6utixY0doecmSJSMaxA+nujrSNCw8t2WiueqqqwwFHZ/PR3d3Nzt37mT9+vU8/PDDoTyRTz31FDt37uTVV1+loKAg5vbhUYMAZ5xxxqjaduaZZxruz3CSjdqHwWN0QgjaR47EWn1o9GnwsxzKpk2RWalWrFgxqjadfvrpSRMply5dOuK6iqKQn59Pe3s7gGGU8u7duyPyny5ZsmRU0XHLli2LEEMnkrj3eCMUi0pKSkIi5UgitYcj2+C5ZufuSBF8NN9VkKqqqtDr3t5eGhoaIsqMWLZs2aiP9YUvfIGbbrop9Lt49tln+dWvfmUYQdva2sorr7wSWp49e3Yof+9IOHToEM899xybN29mx44ddHR00NvbGzGxIhYdHR0jPsZEYhQ9LkTK1CFEyqOXoYLiWDJhD40gTEUY2DVDlj/SdX37GPe1FXgReI9Avs12wEsgmeFi4FzgeiB8atti4K+SJK3QdT2OCdqo+QaQXOP7SUi4vaplzx6syPgsJtocRXhsCg76KPW00udzom9vpWlWOb15Vlr+fIBXVx/DgOoO5GccynSAPuiri15nQH5H4OvP7Q78LLqLHOhy7IueLkt0Fzkoae4lt9tFT6EjtP1kJ1wctA0KhjZJwSZbsQ2Ki3bZEhITw+vZJUsoatEqWQxFxXTAc9ZZ2NeuRSsoYHvWImb07o6KXrRqfnRFQZ09O2HRixaLhSVLlsCSJcjLlmF54w1yGtvZ35pHqxJp5Zrn96NWVibc9liQHFze2OJf6iIpDURKtxApM4k+A++IVA3Yi9yARwfxvstU9T2j44p+l1kY2r2mSqS0xZ6IJHJSZhaGkZSputYaTUQy5Y44P1qQkdTfs2dPRM7Fl156aUS2lPGIl68xmSiKQklJCStWrGDFihXceOONXHjhhSERddu2bXzta1/j6aefjrl9UIgJsnjx4lEdf+bMmeTk5IQEq/r6enRdR5Ik+o2uX/7Ywl+imDIl4DQUL3I4cM7TcTgifxThUW7hBPOTBglO9B0pixYtGlX98TAS299wHA5HSKQMz7kZTuOQCdKj/d3abDamT5/Ovn37RrXdSIib736E57zwfmDUB0ZDIHo8+jzc2BIZXjkaAc+Izs7OEYmU06dPH/W+7XY7F198MY888ggQmADy4osv8sUvfjFm/WeeeQZVPTI+OtJI0draWm6++WZefPFFRh/7RIS9bDLpN+gqwu41dQiR8uhl6LSZsfg3eIYsJ3UevSRJEjA09vzRMezqI2CprusfGaxvGfx7TZKku4E/ABeFrV8K3An82xiOLYiDpusMaC76VBd9qpNe1cmn1sO4JB8Hd+5j3t438apepu7YD7oP0wlZNFcXIsnN2AAVaNIU5Fo/+V1OLPsP0LG4AlNjL3JLKf4JtFc1+wIXc5MauKC5bUrc+sH1wfrB7VNFUFy0yxbskhW7bMEmB/7bZWtIRAy+Dta1SuGvI/MpZjJaWRnu1auxvrCOFqmI5tzlUVbDOQU6A5UW8HjQ2tpo/sxncDY1QVMTu3fvpqZmYtP4amVluNesYXNPP1tadlHmbcaie/FKFpotZXw8dyFnrxHeh5mCUSSlUURjojHOSSmj6yR1BrYgcRhGUqYqmi3OKc3owVMw+Ui3XKjxjitEyswi3c55DqNISpGTMqMwiowdaVTRRGPY32UL9ixja9JYxLMyDZKIqBqjCLtUU1VVxdNPP82SJUvQtMC4wF/+8hfef/99Tj755Kj6XV2ReeeLi4tHfcyioqKQSKmqKn19feTm5hqK46gxfC8TSDDCz24FWdbRtOh2GV1rjQSSodF1hYWFo2pTUVHyrDnGk//P6P0PFYFyc3NHve+R/HbHwtF+jzfSc9NYvjMIRHYHRUoIWL4aiZRr164NvZYkaUQi5aZNm7jgggvGFcE6XKRlojDqeyKSMnUIkfLoZegUm7FMTRuauXws0Zjj4UxgWtiyF3hytDvRdX3bKOp2SZJ0MfAsEH5m/4YkSb/SdX1onk5BDHyan261n251gG5/Pz3qAN1qf0iI7FWd9KlO+jUX2tBE2YM3DEvf3UeLr4u8zgF0nxefYqKhujAqelGXk2Ov6lcC0UKqKfBQaXP7iHeZtrl9EfWD248WCQLCoWQhS7ZiH/yzDYqL9jDhMbzs0J4DWHQzSxctwSZbMB0l4uJEos6aRfclV1D7/GaqPPW0KqUR0YtTCw6D7MZVUkL7CSfgCXu4qampGfUMzpGiTZnC+sJo9+fjZB+QHrOIBePHSKQ0zBuUYIwGzlRNxqeZsJhSOxFDMDEYPczlpSi6w2wGqwU8MZ5thViUOcQbwDKy5Eo0RgJ5vLYKJheaZjzZIVW5AUVOyqMDo8jYeDn6Ekl2nHtLnzY6QcVqHTqEFE0iomqCAmA6Mn/+fM455xxefvnlUNljjz0WU6QcGIi8yAyNJBwJWVmRHSkoUhpev5IsUgaRpMC1tidGu0ab/7m/vz9ieehnMBxj+ZzTCY8nMr5kLJHJI/ntjgUjy01IpbV67HK3d+IllJGemxQlfhCEEaeeeiozZ87kwIEDALzyyiu0tbVFRezu2LEjwmb79NNPHzZ6s6Ojg9WrV0cJlIsXL+a0005j1qxZlJeXY7fbsdlsSGGzlv/93/+d7dvHaoI4MRg5BKVq0rdAiJRHM/1DlscyXWfoqXvoPhPNvwxZflHX9YSbWeu6rkmS9K/ASiB/sNgGXAr8vwk6zP0EhNDRMAt4YYKOPyY0XadXHQiJjuECZLc/8L9HHWBAG7+enW72qt1FDopbeunNt5PX7SS/Y4DWsryYbZI0PXT8vvwsFMmMNqWEKksJdtkaITZmydZQBGOWyYZdsuAw2UJ1xmqL2qe2AuAwjX2mngB6civ4Q9l1lHmbOL7/o4joRceZc8k7axE5paVMSWKbjGbZD4hZ9hmFy+AhKVWz7OPNOPT4LFhMIqwtE0i3/GwQGMASImVmY/RdOuwwyjRlE4aIpMx84kVjp87u1SgnpbjHyySM7tlTJ44br2vrHN39XXhePCOGCkhnnHEG3//+90d1nKEY5XhMF04++eQIkfK9996LWW+oWDZUtBwJTmfkhSonJwcwHrBHTV0Uak5WbJHS0JrWgKGf29DPYDjG8jmnE0OjIMfyfhIVjWx032RRdGzjc3keM0b3eJIS+Tk+9NBDVFZWjutYxxxzzLi2H45gRORPfvITIJAX9+mnn+bGG2+MqPfMM89ELF911VDTwmjuueeeiMj32bNn89hjj40of+ZoJwpMNF4feH0G+Z+F3WvKECLl0ctQQTFLkiRJH52B9NDHs6SJlJIkZQMXDyl+NFnHH4yo/ANwc1jxKiZIpNR1vRVoHc02UhK89DRdp0910uHvocPfS4e/l/bB/x3+Xjr9fbFzPCaAdLJXlYCm+dUcs6sLqdiBtdGN3e2ldJuXndlz8KtWVL+C5jOjeU0s6t1CZa6DnPwCpk1fhiTLuM68Gq20dNhjCdKL4KBQs6Wc9YUXRqz72tltaFOSf4NjnBtQDGBlEkaRlKnKoZBlkCcLwONXyIlKYy2YjKSb9SEEIkvaY1gXCLvXzMFoACue3W+iESJl5pOOFnRiItrRgWEkZZpFjgPUNYzOpWVobrxYDLUwtdlsnHXWWaM6zmQjmIcxyOHDh2PWGyq2jsUaN3wbk8l0RKScwEjK0YqARjgM+l4wJ+VIGSrSjfZzS4QFcTIZam87kt/hUJqbmyeqOREYTYBM5T2e0XiK2ZofsTx//vwRCXKp5stf/jJ33XVXyA74iSeeiBAp/X4/zz//fGjZ4XAYWsKG8+yzR+JqbDYbL774IjNnzhxRm1KdJzje/brRvZYg8QiR8uilncBVPXgHrABTCOReHClDp4yMSlQbJ5cSKZK2AC8l8fgArxMpUk5sgrkUoes63Wo/Tb5Omgf/2n1HhMhkiZDDkQh7VRkJh2xD7feRqzgozS3CYbKTJVvJkm04gv9NNhyyLVRul63I0yXsn/4JubEReeFU5J07GajTmTXQTrOlDKdsI0vrp8zbTK7kxJQro8+ZiyTLqJWVQqCcpMSbuZ6qiDYjewoxyz6zcHpjW0SnKodC3EhKf4qmwQomHKMHulRHUsZCiEWZg1F0R0r7nRApM55432XaRVIKkTKjMLZ7TdU9nvG6jz85AJw24n19/PHHw9YZajG4b9++Ee9/suLz+SKWh9pzBqmuro5Y3rZtGxdddNGIj3PgwIGIaNbq6urQZHfjnJS9uFyjm/nV3t4+qvpGBJ6no9s1WrvXGTNmRCyH21qOhFTbUo6XefPmIUlSSKQaye8wnMOHD0/YdzoUI3E8lfd4Ruc8iy1S7N23b9+kEClnzJjBaaedxoYNGwDYsmULO3bsCKUfClrABrnooovIzs6Ou8+6uroIsXvVqlUjFihdLheHDh0a5buYWAwjxzG+1xIkHiFSHqXouu6SJKmOyJyO1YxOpKwesrxr3A0bOUOtXv+k67o/iccHqB+yXBKzVpqi6zrt/h6afJ0hQbLJ20Gzrwu3nprExaNhWHtVXUL1KaCayMZDeZsXh8mBVFpNtr2YgekLqZlyJtmyDYfJTrZsD4iNksRTTz1FTU0NS+YsGVWbPGedhX3tWrSCAliwAKX9INZ+L9M8dRH1/GYb/dOLsRYUgNmMd+XKifpYBEkm3qBQqsSieCKlrgfyewgmN7qefjkphUh5dCAi2gSpwCiiLS1FShHBmzHEi6RMne1mbNcCp1tGVcE0thT3gjTD6PkiVf0uywaSpKPr0e36x8bRiR0vvvjisHWmTp3K7NmzQ+Lkvn37qK+vp6qqalTHGgnyEM/w0RmLTRx1dZHjBUMjK4OcdNJJEctvvfUWt99++4iP89Zbb0Usn3jiiaHXxjkpe2ltHXk8Qnt7+4QJEEbX+dHe451wwgkRy++8886otg+KO5OVgoICZs2aFfGb2r17NzU1I4uzGMnvdqwYieOJciwYyW/eSKS050QO+b711ltcccUVE9a2RHLVVVdF9OM//vGP/PSnPw29Hlp3OIaeE+bOnTvitvzjH/+ImpiRbOK7ZQiRMlWkKIuHIE0YKiouGOX284fZX0KQJGkGcPqQ4keScewhDD2rji2TcZLwaj72uA/z9+4P+N+WF/he/YP8qOFR7m9dx1+6/sHG/p0c8raktUCZJVvJU+2U+fLIOu5kplvLKCufS7G9GHt3IZbX8zm44ST2vrmCXa+tZO9bp+P65BhOrIfppkKmZE1hWsUiZlnLmXPSZzkmayYzbRWUKgU4TLYx5XYMRysrw716NZjNaAUFHJ51Ijuz5tOmlNBlLqBNKWFn1nw25Z+ALzcXzGbcq1ejlZVN0CckSDZGM52tio6SomlARmKRqkl4kz2VQ5AQ/LoZTY99C5eqmX9WBcxy7MFTty+tL4+CUWAoUqbS7tXQCiy57RAkjnTsd0Icz3yMBGdJCog2qSCeBZnTI2ahZQKqGhCdY5Gqc54kGV9rd+2t56OPPhrRfjZt2sTmzZtHVHfVqlURy/fff/+Ithst481VOFG8+uqrEctz5syJWe+kk07CYjky+e/dd98dVaTpo48+GrF8+ulHhtUM75v8fWzfvh1NG1lKiXALyPFieI83yq9p/vz5lIY5V3300Ufs3LlzRNt6PB6efvrp0R0wDfnc5z4Xsfzb3/52RNupqsrvfve7RDQJiJdKIjHPtCP5zRuda02WAszmI4M8zzzzzKSxAr744osj8kCuXbsWTdPo7u6OEKGrqqo444wzht3fUHF3NKLjSPteIonnMuaIk8ZGkFhEJOXRzVbg3LDlU4DHRrKhJEnlwPSwIh8wsqv8+PkXIj0fPtR1/ZMkHTucocpSW8xaKeSwt41/9u9ij/sw9d42NNLvZGuTLOSbHeSZssk3OcgxZQX+ZDu5g6+D/82SiU2bNrF//35qamrIVuqxtbfjKZqK51A7Bf0DHC9tH7RXdZClDbCgoxar3s0A0D9lCr6WFlwlJTQ0NoKBF/+BAwfG9Z6sixdTvHkzbrebVqWUViXSytWmduMqKcF1xRVCoJzkDBjmjEnd7Kt4IlW/S8KqiJlhkx2vajw6msq+Z7f66XNFR02KSMrMQYhFglQg+p0gFcTLwSunaKp3vGt8v0sSs/8zgHj5RVN6zrMbnN9MOdx444288cYb2GzG96cul4tvfvObIz7eTTfdxO9+9zv8/sAMy/vvv5/LLruMJUtG53Q0HLm5uZhMJlQ1kNImFRaEzz33XJSd6Pnnnx+zbn5+Pl/84hd56qmngIBQ8P3vf58///nPwx7nz3/+M++//35oOTs7my996Uuh5d4BI7vXPrq6unjttdeixOOh9PT08Mtf/nLYtowUY9eC0U3KkCSJq666KqJtP/zhD/m///u/Ybf91a9+RUvLaAzn0pNrr72W++67L7T84IMPcvnll0dF5w7l3nvv5ZNPEjfcaiSOJ8qlZWhe10OHDoVsT0PHNriWur1mrrzySh57LDBsPjAwwE033cSf/vSnxDR2AsnJyeGiiy7iySefBAJ5SV9//XVqa2txu92hemvWrImKNo1F6ZB0Ve++++6I2vHSSy/x17/+dRQtTwxG9+tmk4ZNDF2kDCFSHt28CHw/bPlsSZIkfWQeF0PvTt7Udb1/4poWGylgmH/1kOJHE31cA4YmXxhq/5oSzA4rU1fNZ63lPVoae1PWDhMyeSYH+eZs8k3ZYa8HBUmzg3xTNjZ5dFcAXddDdiiHCwtZsHcvsqpyMGsmRQMtWPVIe1WTNECfx0PXlCm4vV60ri52zp3LwO7dhsdoaWmJmCE1JqZP59O+MurbsinzNmPRvXglC82WMj7NqeS4c5uZIgTKSU+/yyCazcCOKxnEm2Xf75IoyhUDWJOdeCJlTlbq+p7dosaMPBEiZeZgFFkkxCJBIjHMhZqG/U5E8GYORiJlXirzZMWbiCbyUmYE6ZhKAiA7C4gVMCRns3nzZi6++GIeeeSRmDalbW1t3HjjjWzdujUiL148Zs6cyVe+8hV+//vfAwGR86KLLuLpp5/m5JNPHnG733zzTZ555hkeeOCBmOsVRWHOnDns2hUwBfv444/Zv38/s2bNGvExxsNzzz3H9ddfH1FWWFjI1VcPHfI6wre//W2eeeaZUGTjX//6V+6++26+9rWvGW6zdevWqPXXXnstubm5oWXD+ya1BwiIeqeddlpENFY4AwMDXHnlldTXT9ywmJFYNJZ7vK9+9av85je/CeX7XL9+Pffccw8/+MEPDLd5+eWXueuuu0Z/sDSkpqaGNWvWhEQqv9/P5z//edauXcuZZ54ZVV/TNH7961/zox/9CGDEv93R0uc0ysE74YcCYMGCSAPB559/ngsvvDDy2Eb57l3wH3f/B88++2woAvPZZ58lLy+P//f//l9ElHM8Ojs7+e1vf8sxxxzDZz/72dG/iTFy1VVXhb5/CNi8Dp2YMRKrVwjks62srKShoQGAzZs388wzz3DZZZcZbrNp0yauueaaUbc7ERidQ0QUZWoRIuXRzXtAO1A8uDwTOAN4cwTbXjdk+YWJa1ZcTgfCs157gScN6iYMSZLMROfFfD3Z7YjFueu+geKw0kJiBUqrpFBkzo34KzTnUmzOpdCcg0O2j9s+NRaKonDOOeeEZlGazj4b27p17O40s9EzkxJfGyW+dsy6H79kRp45m7mn5FEkSSF71ZE8dNTU1Ix7pub/TbXy4O78qHK7uR9oHte+BemB0SzOVA4ixLuxCthHiRuvyY5XtRquS2Xfy7LE9hN2+4Xda6YwmSLahFiUOfSmYb8TNsOZT3r2u/gT0QSTn3jfY+r7Xoy2mXKAgF3pokWLuOyyyzjllFMoLCyko6OD9957j6effpre3sDYxPXXXz9i68hf/vKXbNq0ia1btwLQ1NTEypUrWbNmDddffz1Lly6Nmljc39/Pxx9/zN///nf+8pe/sGfPHqZNmxb3OOecc05IpFRVlbPOOovrrruOxYsXk52djRQ2prFgwQLKy8vj7q+5uZnXX489NOT3++nq6mLnzp387W9/i4qgDL7vwsJCw/0vXbqUb3/72xFRcf/5n//Jpk2buPXWWyPyL3Z1dfHII49w5513Rthazpo1ix//+McR+zUWKfsA2LZtG+eddx6//vWvI8ZJ3G43L730EnfccQe7du3CYrEwZcoUDh8+bPgeRspEXmunT5/OD3/4Q+64445Q2e23386OHTu49dZbmTdvXqi8paWF//3f/+WXv/wlfr+f6dOnpyTKdqK59957ef3110ORoZ2dnZx77rmsWrWKCy+8kKqqKrxeL7t37+aZZ55hx44dACxatIicnBw2btw44W1K9rPFihUrUBQlZE/6xBNP0Nvby4UXXkhZWRmKorB/fzFwQtS2/a7ABIoHH3wwQsx76KGHePvtt7n55pv57Gc/GxVlqOs6Bw4cYOPGjbzwwgu88soruFwuHnroocS8SQPOPPNMpk6dGvptPv/88yHRHmDZsmWjyi155ZVX8vOf/zy0fO2113Lw4EG+/vWvR0yAOHz4ML/73e+477778Hg82Gw2ysrKUvqbMhQprWKsLJUIkfIoRtd1TZKkR4HvhRXfIUnSW/GiKSVJOgv4TFhRH/BMYloZxVBh8K+6rncm6djh/ACYPaQsWUJtXBSH8eD1aMk1ZVGuFFKqFFI8KEQWm/MoNOeQLdsjbthThTprFq4rrqD+vffRaYqyV80v8ILkRa2sxLtyZVLtVY0i2uIJDILJhdFAQqryAgI4hrF7FUx+0tXuNctqIFL6RCRlJqDrcSyZ0lAsEpGUmUM8281UYdTvBlygaamzAxVMHEbnEKPvPhnEu8czSkEgmFz0G0QVQXpOCAqKlADd3d387ne/MxQhV69ezc033xyxPp57kd1u5/nnn2f16tUhu0m/38/jjz/O448/jsPhYOrUqeTl5eF0Ounu7qahoWHU0V433HADv//970OWh42NjfzkJz+JWfehhx6KG+UIAcF2aI7JkWAymfj5z3/Ol7/85WHr3nnnnWzbti1CDP3b3/7G3/72NyoqKigvL6evr4+DBw9G5YorKiriySefjMrNZ+SWERQpAd5//31OOukkKisrKS8vZ2BggEOHDuFyHdn4vvvu4+mnn54QkXKi3TK+973v8e677/LKK6+Eyp5++mmefvppqqqqKC0tpbu7m4MHD4YsgM1mM7///e8555xzxnbQNKKoqIgXX3yRc889l87OI8Oor7zySsRnEk5xcTFPPfUU3/jGN0JlJpNpwtpkbPeamGfaKVOmcOWVV0bkZ33hhRd44YWw4dzcU+GYf0Rt6/NLeLzwpS99iaamJn7wgx+EIpr37t3L17/+db7+9a9TVVVFUVERZrOZ7u5uWlpa6Ovri9pfspFlmTVr1oSExXCBEuCKK64Y1f5uvvlmnnzyydBv3ev18qMf/Ygf//jHzJ07F4fDQXt7O4cOHYo4L993332sXbs2pSKlkb11lk1NcksE4YjHJ8HPgHCb1hVEWsBGIElSJTB0usevdV1vj3cQSZL0IX9njLahkiRlAZcMKX50tPsZss+vS5K0cpTb3AzcOaR4na7rH46nLamk0JTDQvt0zs49ni8Xnc0tZZdxb9XX+HnVV/lu2SWsKVrJqrwTWOqYyzRrKTmmrLQQKINoZWWsrb6WB8tvYFPOMuqtVbRYSqm3VtE+ewmuq6/GvWZN0vM/GokFqq7gV9Pn8xOMHSNLpniWq4nGZgFZjn38eHluBJMHo4kOVrOKeeKeGUeN3SCSUti9ZgYuD6gGz23pOHBqONgmmHQY2r2m0nYzjlA14DZeJ5g8pGO/k2XIMnDMMEpBIJhc9Ltjf4+SpOMwnqOWcIyOffa5F0XleBuKJElcf/31rF27NiKaDyAvLy/utlOnTmXDhg2sWbMmavxhYGCA3bt388EHH/DJJ59w+PDhmAJlVVVV3GPMnTuXRx55hOzs7Lj1EskJJ5zAO++8w7e+9a0R1bdarbzwwgusWbMmal1jYyMffvghe/bsiRIoZ8+ezZtvvhnlGKWq4DR4Tjz/3M9ElTU0NLB582Y+/fTTkEApyzL33ntvlH3teDAWKcf2TKsoCs8++yznnXde1Lr6+no2b97Mvn37QgKlxWLhkUceYcWKFWM6Xjpy7LHH8tZbb7F8+fJh6x5zzDG8+eabzJ07l/7+I8PGw/1uR4PhhKAEPlvce++9nH322cYVVGNBMfh88Z3vfId169bFjKyur69n69atof4US6C0Wq2UlJSMuu3jxcjO1Wq18sUvfnFU+yosLOTPf/5zVOSo3+9n586dbNq0iYMHD4bOy7Is84tf/ILrrhtqzJh8jJ4Ts4Xda0oRkZRHObqut0uSdDdwd1jxTyVJqgbu0nW9EUCSJBlYDfwaqA6r2wjcm6TmXgLkhC03A38f5z5PAu6XJOljAtGgfwd26LoeMaVkUCA9i0DU6elD9tEB3DzOdiSNLNnKDGs5M63lzLJWMN1aOuq8kOnIgFui2VLO+sJIP/kFJ/ailaZmpDLebGeXN4VKgmDCMJq1nspoNkkKRHL2xnh4EyJlZmAUSWk3iGRMFjZLbAXLI+xeM4J4s9bTMaKtX0RSZgzpGNEWr8/3O1P7mxBMDEbRHansdxB4vnDGEMLT6R5v9+7dqW7CpGX77ilAtOjnsKU2QtvonFY1YwGP79jBk08+yQsvvMD+/ftpa2vDarUyY8YMVqxYwcUXX8ySJUtQFIWurq6I7cMtAY1wOBw8+uij3HzzzfziF7/g5Zdfpru7O+42NTU1rFq1iksvvXREOSwvvvhiTjnlFP74xz/y9ttv8+mnn9LV1cXAwMCE5uGTZZnc3Fzy8/Opqalh6dKlfP7znx9TmhmLxcKjjz7Ktddey3/913+xYcMG/P7YzwKzZs3ihhtu4Bvf+EbMvHnxJnbd/sPvcvFZhdx5553U1dXFrHPaaadxzz33cOKJJ476fcTDKJou1jlwpNjtdtatW8cjjzzCz372Mw4cOBCz3tlnn80999zDMcccM/aDpSnz5s3jrbfe4sUXX+TZZ59l8+bNNDU14fP5KCsr4/jjj+eSSy7h4osvDkVNhv92J1KkNHSmSuC1Nicnh7/97W+8+uqrPPfcc2zZsoX6+nr6+voCwr7ab7htvwuKB9/+qlWr2L17Nw8//DB/+MMf+OSTT+KeL7Kzszn99NO54IILuPTSS4ed4JEIampqOPHEE/nggw8iys855xzy8/NHvb8lS5bw/vvvc+utt/L000+HBP5wJEnirLPO4s4774ywo04lRs+JdmH3mlKESCmAQDTlKUB4xt6vA1+VJKkW6CGQBzJ/yHYu4DJd17uT0EaItnr9k67rEzUie+zg338BfkmS6gm8by+Bp4TpQKxR1j7gc7qu75+gdiSUKksJJ2cvYIallKmWKVjkzDkFGM0ebms5gNdbNOIk1hNJPKHK5c2cz/5oxqjfpVKkhMAAVqxcSkaRn4LJhVeLHUlplBMyWRgdX0RSZgbpKlKKSMrMxzCiLQ3FcQi0N37GMsFkIB1z8EJgIlpbjPJ0sfRP5wH9np4erFYrNlsKQxKHw5QTszjl/S6OtXpxcTE33XQTN910U8w64baSQ3MwVldXD61uyDHHHMMTTzyBpmls2bKFPXv20NHRQW9vL1lZWeTn5zNz5kzmz58/pgil8vJybrnlFm655ZZRbXf77bdz++23j/p4E8Xpp5/O6aefzsGDB9myZQudnZ10dHTgcDiYMmUKxx57LDU1NXH3Ef8eT+fqq6/mqquuYuvWrWzdupX29nZ0XWfq1KksX76cGTNmRGzz2muvjbj98T4/h1G/G7zHmz59Ol6vd8THCucrX/kKX/nKV9i8eTM7duygubkZs9lMVVUVp556KpWVlRH1R3Ochx9+mIcffnhEdVesWDHm9wABm9GxIEkSn/vc5/jc5z43bN3e3l5qa2tDy8P9bkfzvRjdr+eMQqQcTX8LIkkSq1atYtWqVVHrWrtg6sWxtxv6W7HZbHzzm9/km9/8Jm1tbXzwwQe0tLTQ0dGBpmnk5uZSWlrKvHnzmDNnDooyssm7Y3lPI+Uf/4i2sg0/T4+WyspKHn30UX71q1+xYcMG6urq6Ovrw+FwMH36dJYvX86UKVMithnN+7v66quHtdkeLcaRlMLuNZWIUXJBMDflpcAjwOVhq0zATIPNOoBLdF1/N9HtAxiM7DxzSPGjCTqcmYAoOxz/BK6cLAIlQL23jfrOtwGQkam0FDHNUso0aylVlhJKlQLs8uTMl2g0e7izrZYdO5rHNDtxvMTLSyhEyvTB7/ePecZ3Q/PxQPRAh6uvhS1bdo2zZRjO7BwOs3QaEG1ZtGtPA1vyYs+AHQ27d+9m1qxZ496PYGwYRlKmWKQ0jKT0iUjKTGDSiZQikjJjiDXpBlJs9xqnzwuBPDNIW5HSYCJcukxEs1gsaRMpMZTOzk5sNhtZWekb6rzpcOwJkKnudzlZBv1ulNfaN954I2J56dKlo26LLMssXbp0TNtmMnl5eZx33nlj6t9GuZ/hyLVWkiSWLFmS1HGVZOQdP+GEE9L2nJUuvPXWW6HcizC2360RRueQbINzTjKI75YhAbHbVlJSwoUXXhhz3dFAYWEhn//851PdjBFhZBmdJSIpU4oYJRcAoOu6G7hCkqTngNuA4wyqDgCPAT/Wdb01Sc0DuBoIP4t8qOv6JxOw3weALgIWrouJHS0Zjht4C/hf4G/6RHp/JBkNLSBaetv4R/+RjzJXzmKKUkCpkk+pUhB4bS6gRMnDLKWnRalfhfz+Zo7v/4gybzMW3YtXstBsKeO4otSd5oTd6+RAkiRD65rh6OhZHLPc2dcyIVZXLS0tmM1j6MPaEmKJlPWNXRPSrrq6OmbONJrDIkg0XjV2ZKLdmtqZf3YDkdKrCpEyE0hXkdLQ7tUFuh6wwBZMXnQ9PcWieDP8B4RImRGkY78D47zn6RJJKRgfxv0uxS4tca61I+XQoUP87W9/Cy0XFhYyf/78cbZMMBHEy/E4moi2icboHs/nl/D6wCIeMZLC/fffH7E8knyWIyUdr7U2C5hkHVWL/l2M5pwnSF+M+p1D5KRMKUKkFESg6/qfgT9LkjSbQL7GSsACdAOfAu8OCpqj3e+4npp0Xb8LuGs8+zDY7z8JREQiSZIFmE8girKCQP5LBeglIGTuAbbouj52L4ZJQK/mpNfjZJ+nIaJcQqLYnEuROY9Cc07gz5RDoTmXQnMOBaZslBTYx8rNzZj+9jo3NPVGravy1HPCu3XYG/OQy8vRysqS2rYsm44k6cTq/iKSMn0wmUycc845Y5oV+tDmQmiPLj/phAVc/vnp428cgbwBo23bs7sKqO+KLp8191guv3z2uNu0ZcuWce9DMHZ8anravdqU2Md3+4TdayZg9DBntYCSwkua0SCGqoLbC/bJaRAhGMTlAc1gvCCVA1hmM5hNKn41etKZiOLNDBI9cFpXVzem+ynNezwwJar8YF37mFw8Fi5cmJK0GILYTIT1YSLINnDI7RuhOO7z+bjuuusi8pVdddVVyKlMtCkIYXS+k2WdrBS6IxtNyoCAWFQoRMpRo+s60ihm8P3+97+PiIAuLS3lggsumJC2aJqxI1oqz3mSFBDIe2JEGAu3jMzAWKQUdq+pRIySC2Ki6/o+YF+q25FMBsXHjwf/Ji07H3ibOV8+CSVnYu8mdXTa/D20+XsM6+TKWRQEBcxBEbPAnEOeyUGuyUGeyTGheTBN+/djW7eO3l4VCTslvjZKfO2YdT9+yUybUowiq9jb2rCvXYt79WrUJFpU+nxebIoaU5B0ekQkZSZg9GCe6pyU2QYzwAbcYiAgE/BqRnavKpC67zjLIJJTRFJmBoYD9ikeOI0nGPQ5hUg52YlnQZfqiDa74qMvhkgpZtlnBukY3QHGrgWuMTxbBN01UpEWQxAbY+vD5LZjKEb9vrmtH69Xjit019bW8t3vfpf33nsvVGa1WrnhhhsmupmCMRJPHE+lI0Vc200XFOYmry2Zwve+9z1ycnL46le/SkVFhWE9t9vNvffey09+8pOI8q997Wsjzqs4HPHul1J9rc3Jii1SjtbiWpCeGEWPi0jK1CJESoEgw9jz2Eb2PfkBv399LS0FTj527sej+5Jy7F7NSa/XSa23xbCOXbKQZ3ZECJfB/3mmrNDrLNkad4aX3NyMbd068PvR2rtZ3rsVy5D3WeJro2xfH97pleD3Y1u3DtcVVyQtonLHjh1Y5ALyvH1RVrTTP3YhrzwdrbQ0KW0RJIZ+l1HemBRbMhlYDRvNVBRMLrwGkZSBnJSpi4YwiqT0+IVImQmk64D9cANYUwqS1xbBxBMvKjGVOSkBrGYffTHyUguRMjNI9MSM6urqMYmDVf/MgZ3R5TZHiRAbMwCjgdPcVOdCNXi2aWnrZ+rUBZxzzjmccMIJVFdXk52dTV9fH/X19bz99tu8/PLLERGUAD/+8Y+ZPXv87i6CicFIeEn1PZ6R3SuIa+1Y6e7u5je/+Q333HMPp556KsuXL2f+/PkUFhbi9/tpb29n06ZN/PWvf6WlJXJc77jjjuPf//3fJ6wt8e7xUpmTMnD82OV9cXJSCiYPRhMzApGUIqAkVQiRUiDIQDSfyixtCheWzMOr+fnUXcs+dyOHPM3UeVuTJlrGwqV7cfm8NPtieFGGYcZErimLHFMWOSY72bKdHFMWvdZObLqF6pfeJtfTjb2rH+veg1h0Mx7JSrOlDKfsIEsboMzbjKL5sBw6hFxSglZQgOWNN3CvWZOU92rt6OD69kdxdET7gZbVtmF//BBqZSXelSuTbkUrmBiMRL9Uz8AS+YoyG48/thCZZU2tSGkU3SFEyswgXUXKeANYwnZz8tObprlQwXhihuh3mUG6nvOMJqL1i4loGYHROS/VEyANr7WmHLq7u3n22Wd59tlnR7SvG2+8ke985zsT1jbB+DF6RjTKRZos4ouUQiwaD5qmsWHDBjZs2DCi+gsWLOC5556bsChKGOYeL9VOLXYdiP5dCLvXzKDPwKnFYdUQImXqECKlQJDhWGQzx2bN4tisgM2ppms0+7qo9bZwyNNCraeZBl8HPj21ucyG4kelU+2jU+2LXJEFBW19lO3biqTr1OxsxCypmJZm01ZWhu5vw+Ltwe21cNBTSJXaSU5XH/rubQwsPQ5LnZOBhgPYKqpRpMSdAk3791P5yitUe8rpxBJlRWvvMYNegamhISVWtILx4/OD25uedq+GA1hCpMwIfFrsSEqbktocCjaDnJhCpMwMDK3A0jiSUohFk59432GqB7CsSuxJf6LfTX503ThKJ9XnvCzhlpHRGEW0pVosMjzfmrIJDOQP/+xTVVXFHXfcwdVXXz2RTRNMAMZ2r6l9ps2KY9kvIinHRnl5OZIkoesj+24VReGaa67h7rvvJi8vb0Lb0m8QOQ6pd8swutaLe7zMwDiSUti9phIhUgoERxmyJFNhKaLCUsTy7AUAaLpOt9pPi68r4q/V30WHvzft5qfN3BOwncjtcmL2q/gUE43TCzDLXsx4I+pu16zM3dGB4myhvn4zPYUO9r31Kz48dTYWyYxDtuOQrThMdhyyDYdso7aqD7e1HlefhSyTLVTukG04TDbMUvyZNUErWllVKVbbqemtjbKindLmQtnciDp7NlpBQdKtaAXjJ95gUKpFSqPjuzxiACsT8BuIlHZraiebZBlEUqqaGVUT+VAnO0YP5akeRLBaQDEHJo4MRQxgTX6MBuxtFjCn+EnWKJJywJ3khggmHI8X/AbzflItUhpNRHMKkTIjMBo4TbXda7x+/8Mf/RfbtrzHgQMHaGpqor+/H1mWKSgooKSkhOOOO46VK1dy6aWXxs1dKUgd6TopQ5YDz7WxJtoOiHu8MXHXXXdxww038Morr7Bx40Z27dpFXV0dPT09eL1ecnNzKSoqYt68eaxYsYIvfOELVFdXJ6Qt8e7THdFu+knFKIrXKe7xJj1+1fieKcuW2knfRztCpBQIBMiSRKE5h0JzDvPtkTcgPs1Pm7+HFl9AsOzw99Kp9tHl76PD38uAlvyrdH5HIDY/tztwV9Nd5ECXY19kdFmiu8hBSXMvud0uegodoe29uh+v2keX2gfhGmIl1HGA9zsOxNynVVLIkm1kydbQn122kmUKlC168U3ynF34+luocXcxoFjwYKPJVE4/OWRpTvKkQ0g+H+Zdu/DPm5d0K1rB+IkXlZhqkVJEUmY2hjkp0zSSEkQ0ZSZgJFLGs+JKFtl26OqLLheznSc/6RrBC2ATkZQZS9w8WSk+5wmRMrMxykmZ6nOeUSoJgBu+8T3Ki4y37ezsxGazCYEyjUnXCF4InHNjiVnCdnPsVFVVcd1113HdddeltB3G0Ww6corntxpd63vjRH8KJgdxxXGriKRMJUKkFAgEcVFkcyjyMhZezRewZfUH/oLiZZfaR6e/nx61H+8EW8mafYGBeJMauIC4bfEHv4Prg/WD248Vj+7Do/oC4uYQCtr6cBwctKJtbMRSpOKc46CjuhBFriVfl9B8CrtVM7T2ktc1ADu7aTluLuZ9dRzcU4heVo5dtmKXLZH/JQs22YIsiYikdKAvjuCXamscka8os/GmaSSlUU5KMM6jKZg8pGt+tmAbhEiZmaRzvxMiZeYSb/A71X1P3ONlNkZiUdrmpCRwzosnUgrSn3QVx8G47wWsQtPN80swGtL5Hs9o0rlwaZn8xLtPd4hIypQiREqBQDAuLLJCmVxImVIYc72u67h1Lz3qAD3+AXpVZ+C1OkBv6H+gbKRRmX4lYLeqmgJinc3toydOfZvbF1E/uH0iiGVF21BdGIr0lCQdkyVgSbunysHc3h4Uj4fulkMBK9pNL/LhqbPjHsM2KFZGiJhS4L9tiLhpGxQ3h4qegvEz4JIp8zZxfP9HlHmbsehevJKFZksZub1VkF+asrYZDWCJfEWZgVEkZVacSMZkEC+S0+MXt5yTnfQeSIhdLsSiyY/xgH1y2xELq1nYvWYqcXOhpqlIKSIpM4N0jR6Pd3xhuzn5MbR7TfHEWzCO5hTX2smPUU7K7DS4xzN6thDnu8lPfJthEUmZSsSIkUAgSCiSJA0KaFZDITOIT/fTqzrpVQfo9g/QpznpV930qU76NRd9qou2/g5cBXnILX305tvJ63aS3zFAa1leTMtXSdND9q69+YE7je6ixCXQmigr2ni4dS9u1Uv3OCb5mPJlLLqJ5w9vxSZbQsJnUPy0SoP/ZUsognNoPZukHLWRnXJzM1Oef4sbmrqi1lV76/D8v7X0TCmh/YQT8BRFTi1euHBhwu2OjGf+HX3fVaahqnFyUsaJZEwG8e1exeSIyU46i5RGbRCznSc/RgP2qbbcBBFJmclMRpGy3y2h6yAJrXLSouvpe62NG0npEhFtkx2j+6V0uNYGnmujT2ziWjv5MZyUkQb9zkgcF88Wkx8jcRwgS9i9phQhUgoEgrRBkcwUmXMpMudC7DF4tmzZgrViJsuaP8Kf68PUvonDTX5sb+dwqKQS2apisngxW7zkOtzM6TqM3Q9es5negsDT3YGasoS9h1Rb0Y4UVdJwSRouf7wY1JFhkcwBcVOyYpWVkMgZEj2lgNhplRVsUvB/QOC0Bv8PrlMkM1Kaj66Y9u/Htm4dpmaQsFDia6PE145Z9+OXzHTbCpF0DXtbG5WvvELzZz6Dc+pUAHbv3g3AkiVLEtpGh10zjPLUG6uQKlIX5SkYH/GiYe0pjqS0xbV7FTkpR4qqquzYsSPVzYiipX0BED3Jp7ujnk2bmiPK9u/fj64nb8BS880F8qLK9+5vYtOmwyPeTzp+7kcD8fr83gNVQPR9m+brZtOmvQluWXycfb0xy5vb+tm06dMktyY+e/bsYf/+/ezcuTPVTRk3GzZswOeLLRBPFB9+mgfMjSo3mzS2ffzhuPc/nnONkUipaRJuL9gNnqEEyUPXdVyu0Y9kuzzgV6OvZQCK7MbpTPVktFzc3uj70PYuD06n8T2o1+vFZrMlsmmCcWKYdzwdJqKJiLaMxXhSRuonPRhPgEzvsSrB8MSz9LdbhEiZSoRIKRAIJh2ewkK0igrMjY3Ic+aj1e4lr9vNvI56mi1lOOU8rNoA8xzdLCjPApsD/7x5TC/Iw1k2hZpjVjOguRhQ3QxonrDXgb+DTXWYHAqaVaJfc+MbRU7NdLaiTRRe3Y9X9dPL+KczSkhYJSUkWtpiiJvWGOJmzP+yBYukYJrASE+5uRnbunXg9yN39bK89yAWPXKgrJJWqhpAnT0braCAin37cC1bhlaWOHHc6/WGBrysHR3kvbqdG5qiBwOqPPVYHn0L0/QKvCtXJrRNgsQQT6TMsqZ28EqWwGLy4lWjoya9QqQcMbqus337dnp6xj+JZCLp6J5NLJGypWEv7723PaJsz5499PT0JO09uAYKiSVSHqht4b333hvxfj744AMuv/zyCWyZYCTE6/MH61YSS6R09beO6rtNBN2d+THL2zvdKW/bUPbv3895553HggULUt2UcaPrOn//+98ZGBjefWSsbD08m1gipcXkmZDv9qWXXuKb3/zmmLY1EikhcI9gt6Z+cPdox+Vy4Xa7Ry3MxRv8NnJJSSbZdj2mSCkG7Sc/fQbfYVpHtAm710mPkaV/OkTwOgxO30Icn/wYRcPaLSqyMB5LKUKkFAgEkxLPWWdhX7sWraCAA4ULsfbsw6p7meapC9WxOVR0RQmJRbLZjOmc86mwFMXZMzz1xlPU1NSwZHYg2s2r+RnQ3Dg1N/2qC6fmGfxzR7x2a17sZV3ktO/HU5iLlGZWtJMBHT1kZ9vDxAw+KZIJq2QJRXwGRVCrpGCRFdqWmnjPvo/6LneoPPK/BZusYJEUSl5Zj9/nQenqoaB2L926Cc9ghKJTdpClDTBLbkLyDWDetQv/vHloBQVY3ngD95o1E/J+YrFjxw52797NEoeDsg0b6OuTkaiKivJsU4rx+hxkNzRgX7sW9+rVqLNmJaxdgomnP45IaVNSG0kJYDX7YoqUwu515JjNZs4555yER1yPlv96LTdm+RmfWcrFpy+MKq+pqUnae9jQkssnjdHlldU1XH55+aj2pShCUE828fr8O825cCB6m3lzqlIuKG88uJ2tMYLqzLaClLctFgsXLmTZsmWpbsaE0NPTk9DPWHvdBu9HlxfkWibkuHV1dWM+1zjiiFVOj7DdTBdsNhtZWaMLQ/NHZ5EIUVJoY5S7m3BysiTaY8w98qkWsrKM+7PbLdSkdMdIeEkPcTx2uRDHJz+G4ngaRPAaXWtFLtTJj5FIKaxeU48QKQUCwaREKyvDvXo1tnXr6DAXUZtbEiXImMrymHpCXiA5i9mMe/XqMUWOWWQzFjmbArKHrSuvOBl77eOQrWNu+YDDDS5M66ewN38msqIhKz5MZj92m4vzHPuxaFn4rBKOKVUoqOxctIgpZhsuzYtL8+AntZFRmYBPV/Hpg3cisT7OWSa208D2noa4+ylo62PV7q1Iuk7NJ42Y7RryklzayqeA1opNNeFXTRwiiyJnD9mdffDpJtqOm4fpQCOH9pVxyOLBrJswOw8eEUolBatsDvyXFMySaUyWt8cUF3P89u1QUoLL18Xy3o1RUZ4lvjZsH4G8cBZaQQG2detwXXGFiKicRMR7ILdb00Ok7PNEl7tFJOWkxyinbToMYBnmaBMDWJMeo+jxeNFkycJq9sYsHxD9btJj1O/S4XyXZTMeRBtwyYAYZJusxIsMS4fIIqM2iJyUk590tns1Ou+KnJSTn3S2ezU834l+N+kxyknpiHN/JUgOQqQUCASTFnXWLFxXXEH9u++j00SrUkqrciTXXkGBFyQvamVl0qwttdJStIoK5MZGtLlzyereQ3FHFznu7WHRdk4q1SYqazSw5OOfN4/i7ALUykoWHxsZbefT/SHB0q15Q69dmge3HnztDaszuKwH/rs1Lx49sXl7jhZm7mkBILfLidmv4rOYaJqZi1n2YCZSldlWoDC3243iHqCleS89hQ72/fN5Pjx1NgCvtRrnhApa3lpkM97jPGwz9/Fq055BEdOMJSwK1DpY1mpt4biP3qXRB7buPhz7DpJlUfBINpotZfSTTZbPxRRPG5onuVGegoklnt2rTUn9pAarEnvQXti9Tm5UFVwegwc6e+of6LIMBCunQZsFkwdjsSj1/c5qjn1/Fe88LZgcGE/KSH2/yx7G7nUiSWZuYUH8CQ7pYLsZEIui2ygiiyY/RpFF6RDRZiSUCtvNyY9hv0uL813s8gG3hKYhbEEnMYaRlEKkTDlCpBQIBJMarayMJ6uvpdvbzvH9H1Hmbcaie/FKFtrm5OK6ugattHT4HU0g4Va0/TPm4a0/FGVFK6FHWNFiNuNduTJqX4pkRjGZyTWN/QlB1bVBgTMgbrp0Lzv37cKLn7LqioC96qCgGRA+vXg0L65QuQ+35sGt+9CP4lmyQWve3O7AXU13kSOmjS+ALkt0Fzkoae4lt9tFT6EjtP1whFveYgM3A3R64m9bMNBHVc9eOnWdml2N9PpVOMZBe7UJk3w4lKmtW9P5tLaLwi4n2ictHDpmOnLfdjZtc+KZUoRFNg9GdwbE0KAwqsiBsuC6w+ZOzLqJek/rYL1AJKhFNmNmbJGggpGhNbRyQefGiHNds6WMT3IXY0qDhyWrKfagvbB7ndzEE/vSIaLNUKQUYtGkp98d+8SWDv3OZhBJ6fFJ+FUwT74044JB0jmC12YBWdbRtFhi0djPeeH3brquI0kSmiYG7JKJUYSO2aRjTYPbKMNBeyEWTWr8KjFzjUJ6R/AaCQ2CyUN6R/Aar3N5jHOlCtKfPiOR0hKY8C3GslKHECkFAsGkY/fu3RHLnT0raLWUs77wwojy3PLtFDQ2QGOMRFVxqKurG77SMGTNnk3Zhg206wobc5fHzA147JIszObxWdGOBJMk4zDZcJiOZP8e8LUBsCT3mBHvR9f1UGRnuLDp0gdFTc2LRw+ImsHIT4/uC4meHt0XKvNovklnZWv2BdprUgMDNm5b/Miw4Ppg/eD2iSAqylMx0VBdGCWi6rLE4WkFZPW7UHxe5I4OegodyNu3snMwynNE5AT+vdC0NWqVjBQSLC1SmLgZZmlrkc0o0pH1wXVNJU5MSiuSc39I+AxGjwbqB8qUMVriTmbk5masr7/O9I2tLOuzRayr8tRzmutdKl92IZeXp9S+12L2UeZtipo0MsXWj9ySlfRJI4KJIZ5tajrYHxoJByKibfJjFFkULy9fsrAYRFJC4DeTn536NgrGhtE5L14UY7KQpMA5ry+GXdl4LK4lSUKW5Qhh0uVyYbVax7xPwegwEl3SQSgC40F5IRZNbuLZV+akwbU22xa73CifoWDyEOs6BukRwRtvUlKfS4iUkxmjiTVZVg1Zlo+6caZ0QoiUAoFgUrFw4cKoMpc39lR1u2VsotAZZ5wx7guTc+pUGlatQn/9E3TkKCtaAJ86gDStImlWtONFkoLik0IejnHvz6f78Wi+I+Kl5sU9KGAGxc7Q/8F1R0TOoOgZXOfFqyc2H59fCfQzdTBczeb20ROnvs3ti6gf3D4RJCvKcyRo4ZGgo2UW7KaX11s/jVtNgpgip2VQyFTkI6/D6yhhdSIiQCVTlBBqkcxjzg860Zj278e2bh34/fh8Jqb4WqImPQxYcrC3tWFfuxb36tWos2YlvZ3Wjg4ur/0Tekv0wH1Vawv2x+uTar8tmDj644h96TBoL0TKzCWdcwMa2b1CoN1CpJy8GIl96SCOA2RZ9ZjCwnjPeRaLBbfbHYqkHBgYID8/f1z7FIycfsP8bMlthxHGEW3iWjuZiScyp0dEm8E9nhDHJz3Gdq+pv9bGmxwiJmZMbozE8SybhsWSBrYFRzFCpBQIBJMKi8XCkiVLIsrcvtgRbYvmT2PJkvJkNMuQgwvP5cEdvqioomZLGY1fnEbZgpKUti+VBK1ss5mYaWiarh0RLnUvHi0ganp1/+B/H27dh3dQGA2KoR/v3E5ReQnWbHtYuS9QX/OhEZhR3l3koLill958O3ndTvI7Bmgty4spBkqaHhL+evPtoe0TRTpHeSYCHfDq/iPCdIKaHxRDhwqiQTFTCRM8jwicYevCyo/UDUaDBpY9kg+zbgoNCA5Fbm4OCZRyZyeluw+xwBlpv1bia0P26yi9eeD3Y1u3DtcVVyRVCDTt30/lK69Q4cmmkaKo6HHJagI9C1NDQ0qFVMHYiJcnKx3EIiORUuSknPwYikVpII7HEylF35vcpLM4Dok751mt1pBICYFISp/Ph6KIvNLJwFgcT3JDDDCeEJTkhggmlHiRlOkQxWvU/0W/m/yks91rvPOu6HuTGyOR2WHThHtEihEipUAgmNR4fODzp+9AQrZdpzmGFS3A6sP/YJrncApaFbDMrampScmxE4UsydglK3Z5dDcWP39/K+ecs5glc5bEXO/7/+z9aXBkV3qmCT539Q1wwLEEPIDYAyRABrdIkplJUSwxSSVTypRCpVa1Ksmq1nS1dU/azI+2sS6bsimz/jNjMzZWPTY2P9qmu9Vt1VXKmkqyFqkrQyUqRWYwU0kpmZlcwC3IQOyBCACOffX1Lmd+OODhDvfrAUQA8Hsc5zGDuePcxY+7Hz/33POe9/uES8l3cV+8Q+ftH+AlXOKzH3Prjkd8IcH1/qNopkAzPHTDY6i3wOjKBFHPxo3omH1pujSftTMjpIw4pT0Idxtml6fMVIuhWfbobqS7/PDPb/1NjZhpbQiav/6/vU/P8iqdKzkGrk5TMjXsdIS5jh7yRpxIsUTf6jJxUUCfmGG+28JLdeP+6N+x8N0/YN5YxxQ6i+5qleBqomu7l8ByU0jVPY8eZ4Hjq5exRe3kfWI5j/XBdCUPbyuEVMX908ydE4+0/lobNHGq3B1yI0QTsSgUImVwxADV9uQmMNxrCO4toImzaBeclFBO8bApVE5OTnLkyBFMU00d7TXrAUPNMLiKoEm41yYilyL8NLtehcHFG1SHZuKqQg4CnZRhaHfNRMq8RnmmQCEjQX1HPOIpJ2WLUSNNhUIhNWHPkxWP+oHbgsLU7gcjIyMNQ+cq6tl0fDI0TOzoafSpKfSRx3CuXKWzUGJ0/jYZO01OTxD3szzWt8ixbhMifbijo6STKbyhIb7y1Gs15/WET0k4lHy34tysuDh9h6Jw+dtf/ZzedD99g4dq961yexZ9B/3QYWJzRQqpTrqX86FyeSq2h0CUnbxV4l5qbg1/6jYLQtB3bYp1z2N5IMHksQRCLwElisCUbzF0a5WVpRzupY8Yf3wQcWWctz5fYamvA4B/c+f9mtcz0CtCqK2ZrHzL5E8TH3Jh+iq2ZpTbvX5XMLU0k8XoPCYGiyuiapvFib98k3hhEWdtjtPZDGtmgqIWZdpKk9USxNwCp8UtNMfBvHQJd3QUP5XCfucdCq/V/i4U4cSfmuHbi+/VRQT4sucshtH6RQ7NXEVClHO4KeSjUALPD69YZJvBYeabuY8V4Wc933ghT0eTcf1+Et+jENe6rhOJRCgWi3ieh2EYOI7DrVu36OjooLOzE8uyQhEK/37wfR/P83DdvU0R4XnlhYg7fZ2VdROov6Z2xMSe13k7xCON67eeb16//frcDzr3+zkvr+k0mhrWNEHEdGn11xazG9fPcTVyeRe7xUZv1b7vD9eDQqnxlxe3PVz37vW2FZ+xroFpmLhe/fVuZb22fu3AQWjHQggcx2FxJQrUGxuSiXJubkXrUCKlQqGQmmY342EIBRaPlAf4QtTX88ixUc4+cR95+xQto/jyy8Refx0/leLLzsc4XLxMRJQ4Xpyo7GP7LsKyKo4xTJPSSy/VncvYhvPzxuzHjKSOcLa7sctzE/0bLxG7/X3oFFjzH4DjsPTuId7zHwYT9A2Xp647fMO+zDG9Cy9mwOAog5rAf/ab9CQ7y0LoRq5QR7gVEXTTTViqElEbNGnFLnPq8gwAyaUcpuvhWAaTx3rqxGeha0we7yWxXsRyPJJLOVZ6Epwaz/Bh33DDc3v4eNW5Q7t05llnvrgeXKGNFaW/WrpRKUrNrfHK1Y/RhGBkegqzz0PrKbFwrAdLn9g0inLHA+P2Mt2LOfzPZ7j5xAn0tc/45afrFA/11rg8rS0i6WxkFhOd7JpVt89m+N2tYXVNzUSXdBI1TOiZDJELFzjxi1meXYvWbDtavM1vlH5F9AfdLc8zmogK0qXphqHVndtHsY8N3PskitDRLBdqGMZ4uiaIWC5Fp/6WOltQkxwyE9T2wpKTci/z8CaTSZaWlnBdF9/30XUd3/dZXV1ldXX1gc/fSlzXRdf1PZ+E9P3y5PVOX2d6ZgDobXDCHDdu3N6Fmj0YxVwPUH+tX1otcePGjfoDNtivz/2gc7+f8/VbncDRuvKY7XPzZvD3ul8sL0aAxikiLo7fojvRWrFIte/7YzWnA6MNty0vTnLjxl1reas+43hkhNVc/cKM67dmudG7tq912WsOUjtey55sWJ5K2oBKONpKlEipUCikJuwipaZBLCLINahns8k3RTjx02kK584RPX+eWb2Xm8nn6nLv9R1L0vN4d/nLN00K587t+eS9PzCAPziIPjWFNzyMeekSffkFnlkfq3F5pksZOlJ5OgcTuKOjxKNll2f64W/v6PU+GvsIH8EjT5wpC5kbLs/ShrBZ2hA2i1ufCxdnwyVaEmUhtOjffb60toJm6whDoygcxAEPo7LpeE0ulwfLy72Jhu5YKAuVy70J+jOrJJfzrPQkKsfvJdsVUjFg4liK6FoeyymhLyyw0pPA/OxTPnm+sZBaYSPkz98uXN1R3UyMspi5xRFq1YiZxt1t+haRtObPqDquarteJZhqFsYuhtFtNca1a5V8qI5jcMiZqenr5qw+SonelucZ1TMZTv/oJ3xverlu29HibSLf/ymRU4MtF1IVOyfXROhrFqliP4nZXkORUuWklJsgJ2wYHLzQRKTcBQevrut0d3eztLSE53l4noemaTV/ir0hW2gcmSAeCUd/F1SPoHor9p9NgXwnBOd+9u/rfLtNzApOkbKe00jG9raOB0G0aQW5YpMxXkj6vJjtNxQp1UI0eRFCkA1oe2HIwXvQUSKlQqGQmmYTWIk9HrBul0RUkGuQY6SRcKkIP97p0+RffZXr53/FAJPMWgPMWnddOg/3FUBz8YaG9nVSvNrl6Y6OIqZu1rk8AUqajTt6oqnL815oaBhoJIwoCaL3PmCbvPHGG4yMjHD27FmEELh4OL5bI2qWqp2dW8RRZ8MFWhJu+fnGdkds3afqfCK8IU1Mp3xTbnjlvqwQbR7PaHP75v6bx+8lYRZSXTxc4ZH39s+xrqMFCqGmZmDrZZen3UD83Nx/ciAHdgZ3fbyhcFojrG5s280co3A3zyiui764yKHxmzyaq72m9jtz2JjoS8dblmd0U0jtXPHRiNYtGpmz+nBKCeItFlIV90czwSUsYlE0YPJUhXuVm7DnpAwMcb1L9xaGYdDd3c3a2hqlUqkmR6XMLC4uEo1Gicf3NuFZLldOeLXT18kG5KSM2W4oQvBFAkJc5wpa0/rt1+d+0MlkMti2TTS6s3uzpZXGaT9itsv6epMIK/uEcIPvf+aXCiQje+d8KhQK92y7qn3fH2vZ4MUNEbNU06e06jOORTygvv1l8zsP5x12DlI7DlpIGJYx3kFGiZQKhUJqgpyUui6IhSTncUdUMNegXK2ylxf3UJr/qfe/It1ZH15w/UyC/O8+ij+wv+EFq12efirF9eOHmC4s103Y96RTnEqV9s3leb9omoZFOR/oXg6VhRBVIuaGeLkhfDpbxNDq/TYF03KZVxE9N4VSR3g1wmjJd3HZmWjoWuWbN88oC1DRgsNKk/2jBadm/83j9xIZhNT9xG+QW3THnIQrrPLT+fFtH7KZY7RW9DQ2nJ6NBNGtbtHabSff/Evi+XkiS6t0XLmO7xm4kRgzkTTrdBB38wwUZumk2LI8o9VCanRtiedWJ7C3fO79zhyxj0F/7HTLhFTF/RP2cK8AUbtxH6bGePIiRPD9RVjaXbM8vLuFaZqkUik8z6NQKFAoFPA8ry3EyrAS5CwKi6soqB75knIVhYX7ERkc0VjUTERFOAQLI7hfE3qceFxdb2VEBiel6vPaD03TyAW4/8MyxjvIKJFSoVBITdBK53hEEJZoREEXu6C6K8LP5uRVxj7Mmz3fqdn2n764gD/QmpV1my5P+513sD6Zq3N5AnR4++/yDDOapmFrFnaDVZK7jS/8isC5KVx+dulzXHyOP3SyUrYphvYfj5BauYQ9kKBr7TYdyxp3rEMUfNAMH133Ko9Rs0DvfB4DnfVUeUX0cm/jldG7iQxC6kGgkmN0F+6tUnNrvHJtI8/opSmWXI/lowkWnjcx9Dt0bey35Avitxa5spTD/zzD9cePoa9+zHsfL3PnMcGlyMe8m7lRcY6aVblG74qiBubG42Yu0drQulXHabXHxS5cqDg949fGsYVFcSMPZXWIa1HMtkxIVTwY7uQM3158ry7P6KfJs0RC0nfErMbX+93IDahoDUUHPD/sImXjidO9uLcwDINEIkEikZDeUfmzn/2Mhx56iOPHj+/p63z66acAO36dktdYLOrvidLf3//A9XpQDg80XgGcL+n09fUH3nvv1+d+0PnBD37AN77xjZ1/znpHw+LuTiMU7U4I0DSBEPUNzIr20N+/dxFTtvNbVu37/rBmgh0Fx470YlTpgK36jLs6Asaaegch+GnsKgenHWvkAkRm5aRsPUqkVCgUUhM0CRSmC0xciZRtR7PJx1a3PT+dpvDaa7y/tsYHU+N1k7sfPPQY33pt90K0KraPrulENZsod2/Kpr1OAB6N1d8Q6F9PE7v8fRgcwpoqoDkOPR8ZfMgj+FXhPXXh83L0ZxyJJUikUhw98QxoGqsvvcZjM5N4+IycGa1xgW51i/7l2z9iePQh+g4f2hBRvVpBVbgsrS7haj5WLFLZlu/rxphdZ7U7Rtdyju6FLLPproYhXzVfVMK7rnaXkz7sh5Cq2BnbzTMqdI3J4z0k1gtYjoO9uMxKT4L4xS9Ye36YNVbIFJpJ1vdPam6Nb33xCboPI59PYjk+PN7FzOEBfBaI+Eu4ns5tN0ZqZZ7etVWML1aYPTuKfu02Vy534wwcqnOUzncXSJhLdBYma4TRWqF198PrKmrRMxkiFy5w/FezPLtWe706WrzNC8VfEXu9OxSLbaK2R7pUH1Xh+Efd6C88tO9RFRQPTtMxXmhEyoB7iz3OkyV7TspNJ+he55jbFHJ3+jpBTtjOeDjy4nXEAsL5C42Co5MIuL3Yr8/9oOM4zn19zs3ys4XlO4tFRMNw1rmisad13M5vWbXv+yPIjRiLCCyzdlurPuOgPi1X1Nvu+z4o7ThXBD9gIVqr5/EUSqRUKBSSEzSREI+E5wKzHyGZFPtLswmssLQ9f2CAN3uG68qfMBxgcf8rpNgx/sAA/uAg+tQU3vAw5qVLdOdm+Zq3UucW6xGLiBh4w8OgaXhDQxiHh4hmZgHot7qbvtbPr77JUyePcTZ1NnCfsakxAM4O391Hf3mG2OT3EZ0+hZs/YWU2jzFxiCvJ02CAZnjouo+uO3x3YJwYXbgxA21wFFcD8ew36e9K1rhLt4qkK9lVXM3HiJgVgdUR3o7D5yq2R5jzjG5y6vIMAkHHchbddSnYBjMPd2Drubp9bxyKYV1cwiquspC5xkpPgmvvv8mHz9f3j4zC5yzzF5lPm75+dXjdrQLm1vC55tYyvdZB2ijc7lZHafV+OrrUIsG92MwziutSKhkccmbqwpYX430YIcgzmlhZ4T+9/n2K06t12wZvXCf2/fdU5AIJaTrGC4lIGVQPle9ebsJ+XxsPcPBC+b42LE5jxc7IBixuCNP3mYgKcg1ytqo+T16C2l1Y+jtQ83jtSDbfJMxwk2ucYn9QIqVCoZAaGZyUQYMbFQpMXpq5YMPS9lS7aw+KL79M7PXX8VMp3NFRCl9OEBEljhcnavbzTZ31E8eIpFJgmpReemlf6lctpOaOHEVfukJfbonO/Kd1QmqXmSdiJ3BHR0lEU3hDQ6Qf/u17vsbY2IY4erZWQPWFjyO8GrdnM7Fzcx93c9+G+9w9n7Ol7H7yisqIDHlGWy2k7mZ43Z2ioW0Jg9tM4AwWQ7ceb26E3N08ZnN79WuZmoG+hwJpdZ5RfXGRQ+M3eDRX+yH3O3NYwkRfOt7SPKPGtWs8+stfcqUwyk0S9DtzNWJqbKUbRDIUYqpiZzQbJyVi4ZjAUmO89iTo/iLs9xZQnvjt72r/MVI7EtzuwtHfQbBwpfo8eclK0O4SAX3vehOhSxFuwhwRTaFESoVCITmBOSnDtPIv4GKnVv7JSzORMiyrTgNX/ql2JxV+Ok3h3Dmi58/jdaf4m46husnwOauPE0fmeTgZAdOkcO7cvk7YbwqpTjLJYvowpSWjoZDq6jbG6Gn8XRJSdU0noulE9iGf6Ca+EHgNhM8aMXNTCPVd3KYC6BaBdUM0XVhdwoiYaKa+cayHz/7dsMuQZ1QGIXWvEAiKwqEonJa8vonRQCA1AoXP7bhI51MF4uYip3/0txSLy9hLK8QvX0O4Bo4VJ2OlWdc6iLs50qUZOii0NM/oppiqex49pXkOr36KveX76J2dwfpAwxsebqmYqtg5zVbZqzGeYi8JanthmbRv1v5V25OXoPvaoDmMVqAcbe3HekCfEZbrLATnf1btTl5kmMc7yCiRUqFQSE3QDVFYcsaAWvnXjgR9dzFbYO79/Py2CAwFpgbV0uGdPk3+1VfhR+8gWGHWGmDWqs1zdtqYI9/f35JJ8E0h1f/jP6bUGeO95NMNhdQjj3aQTomWCKm7ha5p6JqJtYdD6DfefYORkZEa56gn/IbiZuW57+LgVblDq/erFk/rnaK15R6lvh7M2Szr3YnQ5hmVQUhtV1w8XOFREKXdO+kITM7dpvPKx2hCMPLFFKbrsXw0weLzOpZ+ixSA0Fh0NeITC1xdXsf/NMO1x46iLX7E3/xilmxvNwY6htAxNx5rnm/dVnnUMKv+Hzn9MDE7Wuc+NTZykUYuXADXJZrNcnJpnHkRpbiR93nTPZ7QptCcXEvFVMX94U3O8O3F9+pyen/ceZaYHY6+Qzkp2w/Pg3wp3JP2zcIwqvsLeQl0tIWk3YFamNGOqIhoilbQNCJaiPq8g4oSKRUKhdQE5u4I0QVGDW7Cz/j4+I72v3jpMJSnTGuwTKcSmnI3mJiYuPdOAWTu9AHP1JWv5eCjj8Z4kIh94+PjjIyM3P8JFDvGT6eZ/b1/yB//mctX1j+qmzy1vtrL0y8/xKEWCX/e6dNMvvIKzp//RwR6QyHV8fJ4Q2mVn+0+MDQdQ7OJYu/5a23mGSUpsBY+QJRKZN45xEfmw2AKdN1H030M3eGVxDhH6caLm5hDj+BpAvurv8XixE16+nvpHeirEUAbP9Y+33SfCoKv48u9CfpmVlntjoVWSFXsjFOXZwBILuUwXQ/HMpg81lP7nWoCzRJMneym42IOy3GwV1ZY6UmQvHaVK+kGeUbvh7nG13EdjUPzeb755RiG0DiZvwMpDf9oF9Ppw3hiGctfpeTrXDY66cyv0bG4hvjyfWafGkW/Nsn1K/24A4c2HKWNw/GaQflINRVebC/RMxkiFy5w9P1Znl2L1mw7WrzN84VfEXu9OxTXsERUkC5NNxwPuHeOYh4ZuPdJFKGimcgXlvvaqA26LvD9+ro2m/hVhJugOYmwiOOgFn23I2HPwQvBvwHV38lLULuzTIG9f8GZFAEokVKhUEiNWoGleFAGBwd3fEy+1Hglfdx2H7Q6Nbz44oto96kmRgPq4gsdx9OxzfsPHTUyMsKZM2fu+3jF/ZEtaGTsw7zZ8526bc+k/tcW1KiWYm8v4899nX8x8Qc8tvJJ3cTp46+couebPa2upuIeVOcZ9YaHMS9dont9kWfcsbo8oz2Hc3T0xHFHRolFynlG+x76TW5/+AYjnSc423P23i8YgCe8QCGTF2YYuPXv8GIeXbOfgeOwOhbh08QxMEAzvLKQisuT+Ql6/QhuxCDaP4SlCcYff5JjdmJLOF6XglPE1wF1ed53Wp1ndDv4CIbG71ASLl2LWXTfw4kZzI3Gieqrdft/4ps8vJzHKnjMZq6w0pPg6q9+yIfP35+YqqM3zTfa7P8bR9YQ4hJT04VyrlG9HH7X1q2N/627ZZqJVfe/2dYiqXHtWiUfquMYHHJm6qIB5KN9ocgxqmcyPPTWT/je9HLdtqPF20T+5KdYJwdDIaYqto8MebI0rXxfu5arr6u6r5WXIIE8TCKlmk9pPwLF8ZD0d9AkMpVqd9ISOH8cov7uIKNESoVCITXZQkDujoD48a0gKI+IGtyEA9M060Ir3otf3Yk3LO/psnZ0nr3ETgVf4h8afYqeTjUQk41mN+IRaxdDLz4gyx2HeNOoF1J/L7EMFPe9Poqds5ln1E+lcEdHKYxPNMwzqtkW7uipXcszWo2hGRia0dg9euwwseMX0aem0EeexLx0iZ4pl4dvTG0IqR0bQuoc3d0m6aFe3NFRDnWWhdTHn2gccvONN97g4ZGHefKpJyv5QN0tIXE3c41Wh9fdFDvdLY7Qcjjd8jm24yDd7/yjYUKWPKOtFFN9fIrCv798pEfglviCv5744r5f36zKLXpXwLQqYqetmwEC6NZ9a8XQzec1QumWYy3NvO9FW/diM8corou+uEj/pRs8mqsdI/U7c5i+ib50vKU5RjfF1M5lH41ow9DqpVKCaAjEVMXOkCUEXVmkrC9X4V7lRYZF3yqNSfsRGGY4RO1O5UJtP2TIwXuQUSKlQqGQmqCLTFjC4kCTMBEBAqsi/MiR6D24LtmCpkRKCWkqUhq76+J9EKK2x2q+vjzoZlQRPjbzjEbPn8dPpfjbjiF6ivWT4b1nOulO+S3JM7pVSBXTNxsKqSXNxh09sW0hVUPD0AximkFsL99AAJ7wt+QObSZq1oqfboN9asTWDYHVxaO0KbBWxNZyeauQJc+oLGLqXuCKcj7SvN+axSabAqnmC9yzDl/e+ZO7zlF9++5Se8u2Y3/5F8QLi9hLqyQuX8d3dVw7zrSVJqsniDsFBoozJCi0LMdoqVTi6s9+xtBbb6F7Ht78Os+tZrG3CNb9zhyxMdAfO91SMVWxc5qJlDLc1ypHm7zIMJ8SVBfV7vafUqnExYsXH/g8k5mz0GCknVudZWzsy5qyiYmJlqSaUf1d+yFDeOuDjBIpFQqF1MhwkQnMoaAm7KVF5nYHamAtK0Hfm6H7mEZ4Jr6jVmPBVK06lQvv9Gnyr76K8fY7uP5KwzyjlpXHGxpqSVjBrULqzROHuJNfrhNSuwZSnE6VWiKk3g/l/KM6EfY/MYoQoon4ufl80zV6d58g12gjh+nmOaqF1FypwEpKjjyjsoip7YgjXBxv4/oSgRl36YHPmZpb45WrH6MJwciXU5iux/KRBAu/ZmDpE3Rv7LfsQfzWIteWc3ifz3DjiePoa5/yi09WyR/qaZBPtHG+0c3na8d0rltzmLkbAflI7+5vagYXL17E/PM/RzdNrNVVkpO3mBYpihvh1KvDcItStmViquL+yRa0wDyjnStHIRGOPKMqN2B7IYQc4V6D2p2KTLX/XLx4kfHx8QcWDQtO4/FQ1A7PPW1Qu1M5KeUlKBJfIkSR+A4ySqRUKBRSEzQwDdWgWoWJaDtkCBPR7DegbujkJFAcj4RrUB0LuLnMqz5POvx0msXf/wf88b/zGk6cvvT3TpD6al/L6rcppNrvvIP12VxDITXmuS0TUmVD07SyMLLPt4hvvPEGTww+xjOZD/GTHtb8B+A4zP6slzFruJxnVPfRDR9dc3k5cpkjdOHFDBgcZVADnn2FQ13JAJdprTh6V1R1K+7AknAR3Psavtwrh5iq2B6nLs8AkFzKYboejmUweayn7vsUBkydSNFxMY/llDAXFlnpSRD5/HM+v58co183eZsveHt2e+F3e511vtUxxTo6D125Q9H28R/TmR5I47OE7a9Q8nUm3AQDhRV611YRX77P3FMj6NfucPVKH97AoToR9I65hCF0eoozDQVSSzPaOhdpWNAzGQ7/8Kd8b7peeD/lTtD5r38SmutYYPhDdW8hJbkiCBH++ZSguqh7i9aw01Q5jdD/XbJh+ekThzh7tnasND4+/kCvdb8EtTvX0yg5YO//ekLFAxI0jxemMMMHGSVSKhQKqZE54XahpOF5YKhF9dIRdCMepsFNrJmTMq8mnGQkqL+LRcKz4hQgajWuj1p1KifreY2MfZg3e+rzjP63R+ehhSFCYcNR+dprjBXWeP/OeJ2Q+t7px/j2a9GW1lFxb4o9PYjBIYypKXhoBPPSJZLLK5x1P69zivUeLpDsSeCOjpKIlvOMph/+rQeug7eRF7SR43OzXH9+hsFbf4YX89Cv/Jxo0aLjFzY3+4fALIupmu5j4PI8EyQ8GzeiY/WnSWlQeOwMaSvRMH9peEYQB4dW5hjdCSeuZPDw6VhcQzglspbB3EicqL5Wt++4b/LwxQJWIcts5iorPQmu/uo8HzYSUzvLD+enPw58bR29JkSuXXF8NnZ9VoffLe9bL3xufW5W7Vv9Wvoe5SENE5t5Rq0MaNh1eUbX4r0gEhghyTMaj4qGjs8TY93oLz6EPxAOx6diezRbNB0PkbMoaD5F3VvIS3Bkqp21u90KP9uI25lO4PmG237x/ud8/ZmHsW17T15bsTfIEBHtIKNESoVCITUyXGSaOtqKGp3x8NRVsT0CnZQhane6XhYqG60wVS5eOQkUKe3wTCJAcJge1e7kpFkItzD1ef7AId7sqZ+EHzUcYHH/K6TYMVvzjBbHJxrmGcW2cEdPbTvP6HYxNANDM4jSZMLpxHFix6+gT01xtfsw/fNZElOC4anZDTG1c0NMXeTEiIkV6cMdHSXdWRZTn3qycdhNIQQefkBYXK/O/dkoN2l1KN7afKQek5kpEl0dROJRir5TPsZ3y8838pMeRGTJMdpKMdXHpyBKFFrQ3ZsYDRyejfOQNhZEa/+f6ykQMxeI5W/XiaW2fvcYAx1tHwRSPZMhev48uC7G0irPrd6oyzN6JDeL9QF4w8MtzzOqZzL83ct/xgvTM3Xbhm5eJ/b990Lj+FRsj1xA6EMI1xgv0MGr7i2kJTjs5s7a3W6Fn21EvEno2YuXbtEZcx7YUarYX2SYPz7IKJFSoVBIixBN7Pohusg0yw2oREo5CRrchKndQXklYr5Yb9VVeWPkJMjBG5fESRl0M6oIN83yJ4epz4tHGperdicPW/OM/rxziO7CXF2e0Z5HO0ml/JblGd0UUwuJBCuxfkrTaw3FVFe30EaHtyWmappWEWRiBDTmB+CNn7zBr/3ar/Hs48823C6EqAiXjnAp+eXnJd8pPxfl5+Xt3t3yjceScHB8D0eU/3f98j5F38EVLsWNfTePdarOWfLLomorkCXHqCxi6m7jUhbZC6K0Oyd8GC6yzF/OfN50Nw1qXKP2FtGzflutm3Q+MouBzvpaY8fo5r6H3vqPaKUc1tIK3bevsSIMilqkxj0+bEyjOa3PM7rp+DycN1jEqHN8xle6QSRD4/hUbI9mTsQwRQhSuVDbj6D7i/uJiLYb4WcbMbgUfA9x+MhDwP5EU1DsHjJE4jvIKJFSoVBIS9EBzw+YtA/RxKnKDdh+rAeESw1bwu1EVDDfYLZNtbtwsd08G9duPkQlNlsVvrvGxMRE/QE7OHcmk9nWftshajV25Kh2JyfNRL5mYaX3m6D+V01gycVmnlHrx+9Q8lYa5hk1rXxL3TqbYqr/2Wd4yU7eSz5WN2E/Z/Vx4kyCvm5aJqbuBE3TsDULW29NgiVf+OWQutXiqGgslI5fvcylq+OcffbpuvyjjUL0BoXudYRLtreL/pm10OcYlUVMbRcEVNrIfREvP/x84VrgLqm5NV4Z/xhNCEYuTmHGPcxnE2SO9GGKCTp9A+HpTACxuRxdS+v4Fxe58+Rp9MtX+OwLg9Kh3rviqV4riNYKqyZ2lbi66ULdrnO02vGZKi7z3Oq1Osdnz+wM1gdaKByfiu3TbIzUbKH1fqOclO1HkEAepnbXrC5FR11XZSR40Xd42t1BRomUCoVCWmQJQddMMFWTp3ISdEMUpnYHatWpDJw5c2bb++ZLjW+G1pan8AY9XNdtKEqeOnXqvut3PwSFe1XtTk6Cvrd41EcPkUkx6FqrxHH58NNpFv7gH/DH/9ary3uWsdP8xh+coPfrfS2to3f6NF987WucnV1GoDcUUx0vhzd0WIU+3Aa6phPRdCLbEElNLc/K3G1eTD754K/7mzPEpr6PSPqY8++D4zD/sz4+MB4Co5xjVDc8dM3lpchljtGFFzfRB0fxNTC/+i2Gurs2QusGh9+tDtPrCo+ltRWMiIUw2JYQttyboG9mNfRiqmL7nLpcDpmaXMphuh6OZTB5rAd0gYEL3G0XV44meHh1BatUojA3yUpPguWxdxvnGb0PNAEGOqbQMTAwRPm5iY4hdF748w9Iza7SuZJjYG2efK9JSbeZT/SQNeJES0XihQXmltfhw1mWThzBSSbJ/cn/wI3f/k2uLt/msH6MFTdbFkd1ExNjX0LqKpqTK2gNc4zORQaILR5DpMORY7TZGE8IUE1JLhwXSm7451OaCVcFR8kpMqLCvYYb9atSKBTS0mzCW4bwJKBW/8mKLGEiggZbSiwKD7Ztbzs8TeIXyYblX3v6Uf7p/+mfPlA93n777Qc6vpqgcK+qv5MTecJbN65PoaTheWCoBc9SkS1oZOzDvNnznbpt//ToPND68JXZri4yz3yVP37/yYZi6rO/dZLki72trqaiCf7AAP7gIPrUFOKhhzEvXSK5usxXSp/WhNxMlzL0pvN09iZwR0aJR8s5RvsfeuW+Xve/++/+O775zRc5e/YsQghc4eHiVRyj1QKnIzxuDf2CQ+/+Bzo6+ujUSugFl6UPDb7oOo5mlsVUTffRcXkyP0HKs3EjOnb/YXo0QfGxxxi0EnVOUsd3EerS3BJamWe07vwauPi4mk+1OAplx6e2kGFFF6TvTEHEo3g4weSxGEIvoFOgBNzwI5RurdO9lMWbusqV3kHE6gw/Kdks/VYHr/MrXr/zq8p5NdiSO9SsCZ1bnSO0kSt00z1qbznerNnfwNatyv+GFqKVVSFAz2QY+vO/5nvT9Xm7HxITxP/VT0OTYzQRFQ3F1Iydxp08inUkHGKqYnvIMo9nGBC1BYVSfX0LAYuHFeEm8L42RO3uIKNESoVCIS3ZgJCbUHZ4hIWIBYYuGoamVQ4POQkMExGySfvAVadKLJKSwLA4u9Tu7hUadjvbJyYmWM/OAPV5iOYXc4yNjd1XvUZGRnZ8nGJ3CGp3YVtx2ux3kC9p6uZTMmQJQWfosNKR5k27Xkz9bscSsEu59BR7xmaOUT+Vwh0dpXD5VsMco1gW7uipbeUY3QmappUFFUxieuN8pCd/bYCV964SnZ+HodPEb96ka9bloVszG2JqckNMXSDVYzN4uA93dJR0Z1lMffLJxvkLx8bG8PA58+RjFadnaYsjdFPMdPHuhuSt2tZw/4qbtHp79bbyX3h+yfuPLHlGgxyfWwVVoWtMHu8lsV7EcjySSzlWehKcGs/wYV+941MApY32sV/o6FsEzE1x06oRSW3dqhNHawVUq27b3TC6phSi6GaOUSsDGnZdyPKc3gsiEYoco3omw/Bf/YTvTS/XbTtavE30T36KdXIwFGKqYns0y4UauvuLSIBIqcK9SokK9xpulEipUCikRZZwr5pWnjxdyzUQKZVYJCWB4Q9DNrhRTsr2Yi/b3eDg4D33OXXqFNevXw8UK13XxfM8MlNXgV+r2x4UrvZejIyM7CgsrmJ3kSUsTtOoBQUlUspGtskEVtiutfGoIN9gAqtZPldFeNjMMRo9fx4/leIXyUE6c/N1OUaTj3TSm/JbkmPUtm0G/tE/Ivb66+C66P39GBduEsnXi6klLNzRk9sWUw30QHF0LxFC4OHX5wr1NwRO7l8QrQ+/W94nW8whDPC01vchsuQZDZPj80Hx8SmIEgWxsXhkj3XeTVF0q4BZJ3A2Ekv1u/uWy2vF063lBtu/3lTnGDWWVnlu9UZdjtFYdhbrA1qeY3RTTO1c9tGINsz/XColiIZATFVsH1mclACxiIC1+vL7va9VtJZ1Se5rDypKpFQoFNISNLjRdUHM3ufK3INEVLCWqy9XYpF8uB4UHTnCRCiRsr0IWvm3G2GGTdNkZGTknqFnv/rVr97zXD+4EOXD/7G+3Nfi2w5tqwgPsoTFUfmf2wtZFqIBJKI+C6v1E8TNhFZFuPBOnyb/6qvYF96h6C2Tb5Bj1LbzLQ19uFVMvX58gExxqW7CvvdwipOpUkvE1J2gaRomBqZmEGN/RNI33niDkZERnnzqqZocoaUtDs9N5+fW8tr9q0XUatHUIzM/gzDAiJh4mo+LX3lko1uQJc+oLI7PMFIjiu5xkCcNDX7f4k+Mn/Pvbn+ErZdFTLvaCbpRdvaHP6NrbZn48jqdM5O4SZ2SZjMT6yenJ4iWihz35ikWC2hffEZp9GHo6UX/8Vu4/+Afou+TQ7RaTI2uLfHc6kSdmNrvzBEbA/2x0y0VUxU7o6lIGboxXkA6CeWklJKg+ZSw3dceVJRIqVAopKWZuyNsydODVv2rcK/yIUsIOmgS7lW1OymRxtGmwgy3FYEO3rC1O5X/ua0IaneWKbCbz5HvO0F9sGp3cuGn06z84Wv8j//Gb5j37Nf+k5P0P9/aHKMVMfWddzDH5pltIKZ2em5o8siFFV3Tyk60PZoOe+Ov3wCoC1UvEPiUHaRmeoTjYz9CxDx68ZicjBCZ6eJa6gSYoOkeuu5ztHeFR9evEy9auGaEaGca24OrJx/jVKkTr0oAdTUPT/PxNvJLboqjnnb/Cpksjs+DjkCApVHAoeA5gS7R1NwaRye+YEEIRq5MIWwP9+EE08fiCH2ZCMsI4JYvcG+t072Uw704x/jjg4iVD3nrwwnW+rpq3J+bbtCtouhmWNxG+1Rvr5xHr3aImsQuXCg7xxcXSVwfxxYWxY3+uDpfsChlMS9dwh0dxU+lsN95h8JrjUNcK8JBs7mImCTzKUUlUkqH7wePzcM2j3dQUSKlQqGQFlkm7CH4oqfcHfLRbFAdtrannJTtRaCTUpJ2p8RxOZHlWtusPqrtyUfQJELY2h2oa207kS1oZOzDvNlTn2P0Hw8tAPuXPy8IP52m8NprfLC6xoeZ8Tox9eOHz/DKa7FWV/NAsxkefysaGgZaOTRnTxrRmyY6P0/pyAmc20t0r+V4bPlqlQiTZ3R2koFECUGU9cETHPWS5Hv6eSb2NdhmJFWxIYxudXXWPGpeRdysPGo+vR0rDEzexOyE+GKGQxmPa1ofWAJd99F0H93w0XSPzmiRnvk8GrC2z45PxfbYnRyjHbi+BxT3rJ6puTW+dfFjdA9GPp/EdH38x3qYHjiMJ5ax/DWKns4tJ0nXyiy9yyv4789w6+FjaDfHeN+aI9vVheFrGJ6G7msYPuVHT8Pwy2Uaza/TExMTPP/883v2Pg8q/tQs3158r+76dan3KXQ9XDJF0DxevhSueiruTb4EQgTdX+yx3V2xLdSvSqFQSEtQwu2whYiA4BVYjXIYKcJNUBx7CN/kqXLwthdBbS9s4UmC2l2hpOF5YKiFp1IRFLIybO0uYpXDvfu+yv/cDgSN8cJ2nQXlHm8ngtodhK/tuf0DvNkzXFf+FbMELO1/hRQVLMvaVgh9/fDhSp7Rn1zMM7hwmYiozTOa7NVIpFJ4w8NENvKM5l99lUP75JLVe75ObOH70Cuwlj9gfdWl85c247FR/Kqwn7rw+W+e+ATbdBAxi+MnnsbXNVZe+i6Rn7/LieFTPPTISCW36NYwuqVKPlKXUlUO0urtrnApVeUprZRVbQ/XrzR8yJJj9NTlGYQGHatZdN+jGDGYG4kT1Vfr9r01lCBycQXLKeH5S6z0JPBLV/ji4fr+cSsmBlblz6x7Xnqkj/8w/wumFl0iFUeoVeMIXewqkjFWuF2cxdYtIppFZGMfY59C48qCnskQuXCBE+/N8uxatGbb0eJtXnR+SfQHqVBFAgi8r1VOSunINckVP3HzS7Ts+j3PcebMGWw7ZLnF2gglUioUCmmRJQQdKGdRO9HsOwtb21Mh6NqLwD4vZOFJmk3k5kta6MQtRXNkaXeaVq5TI5FB9XnyocZ4ilbQFiH9VX8nDdV5RmeNXm4ln6PfmavJM9p/LEnqse7yRa4FeUb9gQH8wUH0qSm84WGMj8cZcGbpdpfrwm5qc+tgaXjDw6DriKEh7MPHsB2DTj/KYbtnT+sqhMBlM1eotyFeOht5RJ2qXKLOFiHUqRNJS8JpIKDePefmtnD1CvdGlhyj+yWmuni4eOSDdugCugwurI4Fn+QR+JQlfjj9cd0mEwNbNzcEzrJ4GamEtq0VNG3NrDyPbimLaHbdMaYml0hmXLtWyTHqOAaHnJm6nMpORy/G5CSx11+ncO4c3unTra52cE7Kklyfv6L5GC9q3btvGx8fB7jnAiTF/aNESoVCIS0yTWCpcK/tQ7PvLGyr7NUEVvsghDxhN5v1wdmCEillQ5Z2B5siZX25utbKhyzhrUGN8dqJZqvsw9b24pHG5dkm70ERPjbzjN44/z4D3KnLM/pwbx40r6V5Rosvv0zs9dfxUymKD49SunarzvEJ4Bo22uhp/A3HZ+mll/a1npqmlR1wxv5Mc1aLoqUqQbPa4bl1W0X4bFBeEi4l39lyLqcikLpBiSZ3gCw5RmURU++Fi4fre+T2IDSujk5kw9lZLXbeLbMromi0SuiMVIROu3Lc3UcbSzPQtN0dv+iZTEWg1BcXGbh8k0dzteE1+505bEz0peP4qRTR8+fJv/pqyx2V8aggXZquy1V96KpFZFFJKjLRbFz+7NOP0JsM1zjvIKJ+UQqFQlqCJrDCGO41KAG4msCSj6DvLGoLzJAtqFMTp+1DoUTDMJYQvrCbzSZyVduTj+DcgOHL3aEcbe2DTOK4yknZPgR9Z5omQrcIMqgPVu1OPtxDaf6n3v+SdGf9RPj6mQT5330Uf2Dg3ifaI6odn1pfiveSg3WOzzmrj+OPdtCfEi1xfLaCalE0vg+v5wu/IoLeFTqdiqD5//r//L956tmzHBocqOQWdTSvknvU1TxiyXm6p1z8ZDf2UonktMvtjjiaJTbyi3pohoemCTRfVByJq/ucY1QWMbWV+PjkRYm8V9rV82poFbdnRLM3BM67IuZ6fBVLGNxczN4VRrfsE9EtopVHi9SP364IlOb4OJR0ilqszo3dSQHz0iXc0VH8VAr7nXcovPbarr6/naBnMnzr0z/l4enZum1HMuscfTNDdGYmVOFpFcE0Gx+FbT7loKJESoVCIS0yTWApR1v7EPSdhS0MGDRpd2oCSzqkCkHXpD6q7clHkCsnbBP2oK617USwOB6+dhdT7a5taBbeepeNJQ9MYL57dZ2Vjs2+ImMf5s2e79Rs+09fXMAfcFtRrRo2HZ/aX72DYKXO8Qng+Hm8obSasN8jdE0nqtlEaZwPLTmjURybQ1uIYQEWEN2yj188Svf0bTTRRSS3xMJ8D5Hb3XU5RkcO3eAp/xPiazqeadFR6iM+qzH56CiPX+rE1wS+LvAMgadvPNcF/tb/N/bxdSpl1eUEdFfLvQn6ZlZZ7Y7RtZyjeyHLbLqrYcjXVoqp7YhAUBAlCl4JyNXvsOHiv7g6ta3zpebWeOXLjzGExujFKWzXZ/5IF9NPdOL5K5jeOgXP5EYpRXR5ikVnBS6NsXT2DObNRWZufcBKR4kFY505Z4XohgBq7oHjs5rN8LSHcjo3MesWZohoB/h+6MLTKoIJmocwdIGt1LFQoL4GhUIhLev5xhOniRCuglHujvYh2yDfGYR0wj5ALFITp/LRNMxwyPq8e4V7VciFVAuClHu8bQgUx0O2KAOUk7KdkKq/CxIpSxq+D7qK+ioNsixE89NpxB+9xv/8Zx5n18ZqHJ8ZO83T3zpF1zf2Nu+kIph0Os2xY8cYGRlpul/H4iLR+XmIRBALyw1zjD65fI1DcR8RS7B+4gSHk0ny/f2cefT5nVfM2/hruOmuy9PV/HJ4VM3HGjrK8Mc/RiR8kloJreCy9KHBF93H0UxRdnzqPobm8OjqLTpKGq5p43R1ExWQeegotm/iaR6eFp7f0EHk1OUZADqW1tEch5xlcOdUgqi+WrdvxhckL2ax8qvcnvqclZ4EV//mXzL2/DBjLPLvJz+s7KujEdVttC6whMGPpi4R022iul0W8xs9r/xvEdVtYlqk4gKtFjyrw9N2FdZ4bvU6tnBq6hpfnqb7Ug49FgtVeFpFMM3GeGFbiHZQUSKlQqGQFplC0AWKRWoCSzraIU+W62mUHLCbp/dQhIjmebLC1efZZnlFotcgPK2atJePQPe4RH2eutbKR9CCoI5YuPo7UAvR2ongdidPfwflfjuMdVY0RqZ895oGq51p3rS+U7ftDxPLsAe59xTbwzAMRkZGOHv2bNP99MOHib3+OrguFz7Pc2Txcl2O0aStkUil8IaHiWzkGM2/+iqH9lGAiU0W0Kem0O1ezEuXWLoFD92a2RBTO4n5WdKlJfpScY4PRnFHRzmaSuENDfHcV++GCN0Mk1v0HUrCqeT6LPkuxc3/fafm+WZY3en5DK7mk+jqoCgciv5meF2Hor/56AQ6QhVUXK7J5XLS+OXeRENHLIDQNZZ7E/RnVkku51npSVSO34qPIOcXYeMWebnUwPW5TTQ0oppVETJ/4z98SN/yGp0reXqWMli9OiUtwkysj5yRIFIoMVRYwPEKuF98ijM6gt7Th3XhxxT/wT+873oo9haZFqIdVJRIqVC0KZcuXSKfz7e6GnvKzNyzVOJNVLG6lGFs7Mr+V6gJywsngNG68vmlPGNjY/teH8Vdrl+/vqP9r9w4BSTrN3jrofsub08ngV9ruO0X739GZ6z14aOq2el3cZD48k430Ntw25Xxj7ltPdjE/W5/9lHrZbLFehX84qWbpJjZ1ddqN8L2O1jPfbNheWbyGmNjcw23TUxMNCzfa0qFs0B93q6JO/OMjX15z+PD9tkfFBp97tOzX6E+UB1kV2cZG7u0D7W6N5vtfGHtGPBo3fbFlWJoxgXXr1+nq6ur1dXYFS5evMjExMSefLaXr5+g0RhP87O7+nqTk5OMj48/0Dkm5jqAX2+47ZcffE5Px73zlD1oHWRjv/r4nX6uVzOdQF/DbdevfMrcHafhtlZhG98g2+Ae/IvxWwxY03Xl6tq6P+ykX4kPD5N+910yHON28rm6UJaxbigMWVAs4s/NkXnhBXLT0zBd//3uFZGBAYY++QTd87B6eyndWq4TUwHWXY3p3l6cjbpOnj1LcQf9tQHENv7K/93NZ6mNzwAGIyPHAo9/8y//khOnT3Jq5DQuHs6GK9TRvIoz1NE2yjfyhLobz92N8q37upqHs7Hdl9wJajplG63hle9VC9Hmq6Q3t2/uv3n8XiIQlfyeqcwaYuoO80LQe20K1/QonkwweSyK0LOYZPGA276Of8une2kG9+I8448PIlY+4ML7E+R6u7GFiS2Mjcfq59VljcsN9j8UwkHop8evHAXqx8KGtr052Z1c22/durWTqik2UCKlQtGm3Lhxg0Kh0Opq7ClLq081LF9fzYTupntlKUIjkXI164eurgeNmZkZTHP7l8PJ6cZCke+she67nFnrJkik/OLSLbrj6/tan3ux0+/iIHFlpvHNsYbPzWtfPnCIkt3+7E3t1ylnw6nlxsQs/Vq4fidhI0y/A19olNzfarhtLnODcX+y4bbBwUE8z9v3PrFUOEEjkXJmPrutuoTpsz9INPrcF5Yfa7hvdm0mNNfazXa+tHCHRiLlek6Epq4zMzN89tlnrKystLoqD4zneQwODu7JZ3tnqrthufDWd/X1PM/jV7/61QMt6FgppggSKX/8k1+Qii5s6zx79VmGkf3q413XZWpqatvf7+TaSaBxGM2//dlfYeh7P0m/E3z3GRotFP7ok8u407+oK//yyy/VtXUf2Gm/0hmJMmkeY8idrMsx6uc/ZPnGHZY7O7l08iSrX34JX957sddu02cYPHX1Krrvc6PjEXwvUiOmzll9+HGPh+Y+w19Y4OOREeY/+mhX63CvPnJ2ZgbLNDFE85uxzTyhsZpSjfK0fPDvo5L/syoHaDnPZ22Zb2zdx298jHG3bD8coK5VFn09oyy8RQsOzUYi0YJTs//m8fvFZnja5FIO0/VwLIPJYz117k+ha0we7yWxXsRyPJJLOVZ6Ehy/PMWHz8fJce+FQkFoPpiujulpmJ6O6TZ4dHUsr/y4dbt+j7bYiINwDzQx1dGw3Mkv8fbbb9/z+EwmQ39//7Zea3ofF3S0E+3dAhWKA8xv//ZvMzpaL4q1E//DL3phqb78uWcf57vfGd7/CjVB/2mUf99gcY5pJ/nud7+7/xVS1LCd0DibfJbthAb3KaeO94fuu5xa0Pl//lXjbb/5rd9jeChckx6ws+/iIPHmLyP8j+/Wlydi8Oqru9PudvOz/+9/HmVlqr78iaee47vfempXXqOdCcvvYC2n8d/8aeNtv/Ptb3B2OFxu7I9WOvmwwdzcwOCpbffPYfnsDxpbP/f/9aMemK/f79mvPMp3f//E/lVsG3T+PMLrHzTYYCRCNS5QbfveXPqTDmgwD3/8SN+uf5cP+n0srGr88/+i8bZ/+Eff48yJcPXPYSGMv4N3xmz+zf+jvtw0BP/0//KP979C9+Av/5suVhpca1/4jd/ie7/7d+rK33jjjVB+7u3ITj7nogPHvxwgXZrmK+sf1eQY/a0/HOX0dx7FHxjgxb2t8j3RMxnsd97B+Ys5PsjZNWIqwKlul6//4R9SeuklvtKifIAytm8hRDkMrnAq4W6LfomicCn4pUp5QTjcmprA0Ty6+1MUK/tWP5Yq/5dE7bVnuTdB38wqq90xupZzdC9kmU13NQz5qvmiEt51tTtWOX4/2avwtDtB6ODYPhty7Y6PtzWTmB4hoUeJ6xFieoT4xvO7/0eIG1ESevmvaHmcOTXKM2effuD6h5VbbyTgs/rykycG+Cf/5J/c8/hNt+V2fuuXLl3in/2zf7bjOh50lEipUCikJSimeBjzZAXmKwrI9aUILzLFsm9WJ9X25CKo3YU155TK0dYeNMuTFca2F1PX2rYhqK8IY7tT/V37EJzvXp52B6rtyYZM9xYQfK+trrWtxXXdHbmiV3MWMEDGPsybPbU5Rp8e/FuWp6ZgqsGKwz3AcRw0TQt2cj3yCG9e+xq3ppdqxNSMnebdIyc49MjtcijafXQvnTlzBtu29+31dhtN04hoFhGs6gi3DRm7XnY1nu29tzjjC1HJ1VkQJbwXJumZ+De4cZfk7KfgOMy/b3K55yiaJdAND910MTSHR1Zu0ekaOLZJqSeFpfncGDm8G29328gQnvZelIRLyXNZ8XYgmD4N7zHHv7r1i7JwaURJ6DESeoSEEauImYkqYTOhRytCp6Htf4janWLOzvDtxb+u60M6vDNs9TgrWoMSKRUKhbQE3YCHUaQMqlOzCWBFOJFJHI9H1ARWuxDY7pp8x60kqF6qz5OLZt9XGNueanftg1TX2oA6lVwN1wNzfyOVKR4Ame4tIhbousD36+usxCK5aJcxnmp3rWVqaorHH3+c3t7GqUm2UtKCBbZ0fye9vfsnwP35n/85p06d4syZM4H76EfSvNlTn8bkkUiW3t7cXlavjosXLwLbc1QdNHRNI6rZRHWbLhJwLEXs2CfoU1PoI09iXrpEYspn+PYsGTtNTk8S8bOkSxn6UxbHBgdwR0c52p3CGxri+adf5Qf/9g1Ojpzm4TMjFDZcm5VHv8S12zdwNI+edB95v0hhY3vBr/rb+L8omuf4lS087W5TFA5Fz2HRW9vRcVHNpsOI0aFH6TTiG89jdBpxOivP75ZF9Obi726iZzJELlzg63+7QHyt9nWPFm8z8uV7RH/QS+mll/Bb5MZWlFEipUKhkBaZVjsH3cy5nkbRKU80KOQgsN2F0N1hmWCZAsetr7OatJcL2ZyUamFGe9BsMUMYr7XK0dY+yOQsuteCoGQifHVWNCaorwhju9O0cr3WcmqMJzsyOcdBXWvDzJkzZ3j22We3te/FG8Hbfu1rT5Denta5K3zxxRc8+uijTev+y4BUm0JPbPs97yYLC9vL+6uA4ssvE3v9dfxUCnd0lOKlm0REiePF2i9VWAbu6Ch+KgWmSemll9A0DUNoxIRFn9XV8PyxYjn/49nUdpyePkXhkN8iYBZ9h7xfpOd4DH3yV9idGl1r89gLcN3oRbN9dMNFN73Ko6G5LQ9PGxYKokTBLTHfVNK9i6WZdG4Ilh1GjE49Vn404nTqMbqMBF1mgqSRoEOPoWv3d30xrl0jev48uC6uY3PImanLa2sbKYzJSWKvv07h3Dm806fv67UUD44SKRUKhZS4HhQdeSYSmk1g5YsaESt8dVY0RqaJUyi3vZUGImW+pCYSZEImVxGoVfbtQrOJ7jD2eUHtLq/anVQI0WRhRhjb3T1CqyuRUh5kHOOtNTAQqWutXKgxnqIVrDcxH3bG968e2yURbVyeK+xvPRQ7x0+nKZw7R/T8efxUiveSh+kuLNSJRZx0GUx1gWlSOHduT1xtuqYT08p5GRtu/3ofKz+7DIkkh+I6xaxDx3sRxmOj+FUhTXXh83985GNEfoV1p8QTR7+Gq/kMPP87PN7XSd4vbfwVKfjF8nNRrJTlqwTSg4gjXBa9tW05NnU0Oo14Wbjc+EsaCbo2y8xyWacRx9Luylx6JlMRKPXFRYZv3+JQrjYcb78zx6FpHX3pJH4qRfT8efKvvqoclS1CiZQKhUJKmq3UDGNonGYuu2xBo7sjfHVWNCYwFFgI2x1siJQN0hGo1c5yIVu7C+rzVLuTi6CJU8sUWCG8i1B5stqDogNegxCWEM5J+5it8j+3C0HfV1ivtYFikbrWSoVsY7zAa61qd1Kxng/eFmus37SUeIBImVUipRR4p0+Tf/VVrB+/g+MtM2sNMGsN1OzzuDmJNzTU0rCb/sAAhb4+ovPzeMPD6J9fYsCZpdtd3ghPmyC+EZ7WnM/iobPSN8Cw1YE3NMSjJ762s9cTflmwFLXiZd4vktsQOLMbz8tlharn5UefcF4rdgsfwYqX3VaOzYQepcfsJGV08sIP36c/u07XSoHUlQk036aod5Cxar9H289jXrpUcfHa77xD4bXX9uGdKbYSwukFhUKhuDdN3R0hDI3TNBSYmsCSCulW2atJ+7ZAphB0EDxprxxtciFdf6cm7NsC6Ry8zZyUqu1JhXR9nhrjtQXStTvlpGwLgkTKRAx0vfG2VhLkpMw2EVsV4cJPp1n8e6/xP/1bn6+sf1QWiESJkmaTsdMMvvQ0D7/21VZXk7lnn2XorbfwUym80VFK4xMNw9O6hkX2yAkKpVIlPO1O0TWdhBElQUADvwdCCIrCqYiXOb9IzqsVMhs9z/oFsn4BR7j39bphJesXyJYKrM9dZ37iCxaEYOTSFOuuR3Y0wdIRA92ZJlKIUshHuZbrp684Rd5bxbwyDs98FWNyEn1mBn9g4N4vqNhVlEipUCikpNlNUChDgd0jX5FCHmTLG6Mm7dsDmXLwgpo4bRdUu1O0gmbfVxjbnlqI1j7I1uep3IDtgWztTl1r24MgB2JHbH/rsV0SAfUqlMDzwDD2tz6K+yNX1MjYh3mz5zt12363/29aUKN6ir29ZF54gcGrV9H7UvwiOUSfM1cXnvbQSAc20/hLS3sWnvZeaJpGVLOJ6jY993G847us+wX+9C/+Nw6fOsLgqaOs+3lyfoF1r0BuQ8zMeoWKsJn1Crh49z55Czl1eQaA5FIO0/VwLIPJYz2gCywzjxXLQ6q871VfoF9cxFr3mL5VwOvrY+1v/gVrL3+DAaubATPFISvVwndzcFAipUKhkJJmq+zDGAqsWZ2avRdF+AjMGxPWkEwB9VLtTi7aJl+RandS0S79XdHRcD0w1QSWFMjmpIxYoOsCv0GIWtXnyUW7XGvVGE8uAp2UagGkYg8JdFLen6Frz2lWr1wxnHk0FfXkCsE23ZgdHuErd+QI+WefxX7nHQxzmVnqw9M6fgFvoJ8vHn6Y06dPt6imD4alm6T0DjryFoNuN2cTw9s6ruQ7FdFy3dt8zLHu51nz8qx7edb8jceN8v0MS9u9UA4Nm1wud3TLvQmE3vgaJXSN5d4E/ZlV4kvr3E5FmZ8c58JybaeTTEbp8zqZW/E5ERngmH2IqG7v7Rs5YCiRUqFQSElTkTKEk6emARFLUHQaTGCpVafSIISEE1hqtXNb0C5ikWp3chGYJyuk/V0zAStX0EgmwllvRS25fLOFaP4+1mR7aFq57a3l1BhPdtrlWqtESrnIBkzaq3an2EvWc43LQytSNnF4ZvNKpJSFbJMxXsQKV+hRP52m8NprvP4XDsenP6kLT3vyG8MMHL1Bdny81VXdd2zdwtYtUnRua39fCPJ+kTU/tyFc5qsEzVyNoLnq51jz8ogHEDVNpyx4G175vqEQtZruv7l9c//N46tZNQqsGgWuL80BoAFpq4fj9gCno4M8ETtFl5m47zorlEipUCgkJWji1DYFVkh7tniksUipbujkoeiA18ApAeEMMwxKLGoXZMtJqcTx9qBd2h2Ur7VKpJSDoAl7TRPEI/tcmW0SjwjWGkz4qj5PHoQIzpssW5+n7i32h1KpxMWLFx/4PDNzzwL1ndv6yjRjY1ce+Py7zfzsEPB4XfnymsPY2Fhd+cTEBCMjI/tQM8VOCAz3GlKxr5l4GvReFOGj2bgoTE7Kata60rxZPFJX/lJslQFutKBG8qFrWjn3phGF5nohAJ7wWffyrHjZyt9q1fPy/zlW3GzD0LOuVQ6f4xnle4powWGlyetFC07N/pvHN0MA084i084iv8h+yQ94h4ejQzwdf5iOfXSNthMhncpXKBSK5sjmZoNy3ZbW68tVaBx5kC3MMDQRi1S7k4p2cXeodicX7dLuAPIl1fZkIajdxSICLaRfo+rz5CdXBCHkur8IqleQ2KrYXS5evMj4+PgDC3D5UuPJ0KgVzgn7aICQUHBUTHWZCBL2QuukbBbuVYmU0hA0xtM1gW2GL1oGqMW3rcDQdLrMxD2diUIIcn6xImIueessumt0Dzl0LXyJ2+OiLefpXsgym+5qGPJV80UlPOxqd9myvdy7c0ekQDBeuMN44Q5aBM7+t99m7P/+5o7Pc5BRIqVCoZCSoAFBWFc6g3K0tQOy5cmC8qRuI1S7kwvZ+jzV7tqDwDxZIW13zUTKZuGlFOFCtnYHqs9rB5p9V2EMMwzq3iIMjIyMcPbs2Qc7idHRsHj4VJqzZ7se7Nx7wJzXOAeX41oNP4vxAxgKUQayATkpO5qEVW0lceWkbAuCrk8x25dvIZq61rYcrcqhOUhvpVz/9RPErn8fEgJr7gNKhSI33+7hxqEhjFgJK1rEihWw7SwPLd/Gcjxc02A1VbaSXx9JP1C9hAbHf+cJJVLuECVSKhQKKZHVSdkItcpeHpqKlDHJJrBUu5MK2fo8dTPXHgSF3UzEQtrumuWkVG1PGmRblAHqWtsO5AL6Owhv21PieHsg28KMQAdvScP3QQ/+KSlCxLpkImUsUs4BLRo0vyDBVRE+gqNlhNM5DsF9sRrjhRd/YAB/cBB9agpveBg+u0RqdY342nUydpqcnsT2DdKlNc6c7sKKdrL80HEiiSiZQ3GOHXuSqLPEnLPSMJysYm9QIqVCoZCSoAFBWEPQgZq0bwfyzcK9hrTtBQ2qVSgwuQjqJ8La7oImsBxXw3EJbe5gRS2ytTvbBNMQuF59vdW1Vh6kHOOpUGDS08xtHda2F1QvNcaTi8A+L6wiZbPQ6kUttAuZFLUEhnsNqUip62U3ZSNBUjkp5SHoWhuzw7ngG9Q8nqwUX36Z2Ouv46dSrJ18BG38ModL0xwv3kLHx0enpNkYWgc8coZkKkXSNOn73Vd59FDZSekLnwV3jcnSHL+c+JQ5c43FSI6cX2zxu2tP1BSRQqGQEtnyZIFa7dwOBLmKAGKNIx+1HDVxKj++H+zwkHECK1fU6DLDWW9FLcHujnBOJGhaue2t5hqIlGq1szTIOMZTE1jy01Z5x1W7k4p2cVJCue0pkVIOgpyUYfgaMMcAAQAASURBVM1JCeW6KZFSbmR0Ugb1ec3GDYrW46fTFM6dI/aDH2BO3kEIjaIewfA9hNDwNZ2iHkHXQb9zBxGLkX/tNfz03VCvuqbTb3XRb3UhCmsAPDX8FPPuCteLGd5bv8ilwu1WvcW2Q4mUCoVCSoImGztCfFMUuNpZDW6kITjkph/a0EZB7U4NquUhXwre1hFSsajpBFZBoysR3r5acRcpr7VRwWquvlz1efIgY7hXFQpMfoLana6L8C5EU2GGpcf1oOQGXGtD2uc1zf9c0Ojfx7oo7p+gEKmd8f2tx04IElBVuFd5CI7SEs57WlDXWukRAtfXIODrMnQQG/s1o4jLnLnG5MovuV6Y4noxQ0E0mahR7BglUioUCimRcpW9Wu0sPbKtdAaVC7UdaJYnKxbSttc0FFhJtT1ZCF7tHM52B8rR1g7IloMX1BivHWh2b6GF9GtslhtQIQdSOnibRctQ9xfSIKWTMiAUbU5FXpSGwHsLO7xOysD0OepaG2r0TIbo+fOIWIy13iMgxon4RQzhoSPQhCAmCoCJf+QIIhYj8sMfMvf3f5/JXotMaZFpZ5FpZ4FpZ5GVVLZ84uVWvqv2RomUCoVCSgJXYIX0Zg5U3ph2IOi7CrU4ribspafZdxXWtqcmsNoDGRcEKUeb/MiWCxXUKvt2oJ3anYrSIg9NRcqQtr17hXtVyIFsOSlBOSnbgaA+YmlhEsdx9rk22yN40fc+V0SxIyIXLoDroi8u0nnjCtOawbR9mExkgGLEImkuc8SfZtFfw/ns50yc6me2U2fqTz/jwrknW139A4kSKRUKhZQE5QbsiIU3TITKSSk/UjoplTguPdl88HcV1rbXzGmn+jx5CBJYwpxvSi3MkJ/AdhfS/g7UGK8dCLrW7kW7c12X8fHxBz7P5EQP8NW68nxJ48OPxtDvo/mdOXMG2w5pfNs2pJlIGdY+L2aDpgmEaJD/WfV50iCjkzIeJFIqsUgagq61vd0RtJCGLYjZaownE57wyE7eoDQxTt4rkBj/hKxewh1NMHvSxI7fIqKX540nfR1uuXQv5ei4XmDy8UH6ZlZJza+z1NfxQPVwC+EU3cOMEikVCoWUBK1aCuuKU1BiUTsg5Sr7gAmOkqvhuGCpkUDoaeqkDOkEVsQCQxd4foMJLOXwkAYZ+7wgsUhda+UhqN2FNbw1KHG8HdjPKC1TU1McO3bsgc8TsYLD4xUdY8fh8zaF07Nnzz5QvRTbp9mYKKxjPE0rX2sb1V2N8eQhyH0YZpFSOSnlJ6iPOHyoE9MM58RELNK4XI3x9hchBOt+nhUvy5K7zoqXZdlbZ8VdZ7nyPMuan+Mrf3uV4fVpuhazHM0v4sQMFs6ksPTaiWSha0we7yWxXsRyPJJLOVZ6Epwaz/Bh3/B91fOI1cdwrp//8+/+73fjbR8owtkDKBQKxT0IclKGdcUpqHxF7YCUebLu4WjrMsNbd0WZoHanaYJYSM0OmxNY6w1Wy6o+Tw5cD4qOfI42JRbJTztFLVAT9vtDqVTi4sWLD3SOazdOA5115cJdZ2xs7IHOvZVMJsM3v/nNBxYDo71G4LaHRp6ivzu8EWYUZZpdm8Le5zVaNKyutfIQ5KTsiO9vPXZCoEipnJTSEJyTMrzXKzXG2zuEEBREiVUvx5qXZ83LseblWPU3Hr0cK162/Odmcdne4qvuhXL+yORyuaNb7k0gAsJLCF1juTdBf2aV5HKelZ5E5fjtYKBzzD7EcHSIZxIPczwywKVLl3DWVLLcnaJESoVCISXtlJNSDW7kIajdhXoSoVnemIJGVyK8dVeUadbuQhoVByj3eY0mQNQElhw0uzZ1hDncq1oQJD1BbS/MDl7V7lrLxYsXGR8fZ2Rk5L7PUXAaC34x273vc+4191qIpgg/Qf1dxBKYwRp0y0lEBfMr9eXqvlYOSk55MVojOsKckzKgbkGCqyJ8BEdp2Znzfz9RY7yd4QiXrFdg3c+z7uU3BMhN4TFfER83H7crPO4E0ymf0/DK4nchajXdf3P75v6bxzeiQ49xOnKYU9FBTkcOc9wewNKVvLYbqE9RoVBISaCjTU1gKfYQKSdO1QSW9MjY7qBJn6cmsKSgWZ6sMLe9wNyAqt1Jg4x9nnLwtp6RkZEHciYmPqh3UQKkD3XuevjTt99+e1fOc6+FaIrwI+M9Lag+L4y4rrttR/lK1gC+0nDbzWsXiRRzu1ize3Pt2jWEuHebX185BgzUlU9OL/H++1f3oGaNuXjxIn19ffv2eu1EUJ8XDbmTMl2a5ivrH5EuZbBFiZJms7x+iMjiWqurt6fUCo4FshvC47pfILvxWP6/XJ71CxRF63MxulZ5lY9nlCPwRQsODdbVVIhu5I/c3N+1DBJ6lMNWD+mNv9zEEt1enBee/Hpo86fKjhIpFQqFlAROYIXY0aYSbsuPjA7eZi5PNYElBzKGGQY1gSU7TUXKELc91e7kR8ZrbVDd8uo6Kw3ZBuHJIdxikVqIJj8y9ncQXL9mYwfF3qJpGp999hkrK82m4sss5ToIEim/vPgBCxPLu1u5e3D58mVWVlbuWfeFOWgkUmbm1vj5z3++N5VrwMTEBC+++OK+vV47EbwwI5wipZ7JcOYnP+V700t1246VJjjyF9fwfA/9xRfx0+kW1HB7lHyXrF8gt/GX9YvkvMJGWZHLJ1a4nfiCdzM3yPrFiugYBsHxfljuTdA3s8pqd4yu5RzdC1lm0131IV99gy7D4uiyT9ToIJo+xZGOQcyRr/H3jn6nRowcc8uh/5VAuXcokVKhUEhJO4XdzKtJBGmQUSxSE1jyI2N/B8FtT/V5ciBznqxGqHYnDzL2eYFjvJKG74PeOJW6IkQE9RFhbnfN8lLnVSokKZDROQ7qWhtGDMPYdq7bK5MG/9c3G2/7wz/4Nume/ReMtuOGnzYTXBivL092p/nud7+7RzWrZ7fzFB8kOlYyfHvxkxpHYsZO05/vaXXV6jCuXSN6/jzJRYFGhH5njn5nHlO4uJrJnNWH7wk6lpeIvf46hXPn8E6f3rP6OL5Lzi+S94tVjxuCo39XcMx65ce7ZQUccY+QqmmAPLRJftfrI2mGv5gmm+rEmMrCukb3hxqXk6cp5RM4hShOPsajQz4vdl/EoBsRt3AOj4CmkX/qWXwlRu47SqRUKBTS4XlQKMl3QxfkpCw6Gp4HRojzjijKBE4kBHy3YcAywdAFnl9fdyVSyoGsocACw26qdicFsoZ7VQuC5EfGPq/ZOCBfgkR0HyujuC9kXIim6+X7i3yD+yJ1rZWDoHYXNIYKC4EiZWmfK6K4L4Kc4xDua626t5AXPZMhcuECr15bwXFrv6+jxdt87ecLdGZM9MOHQ+FIjCwsEP3pT8F1ia0v89zqLewtrsJ+Z46uL9fIJ+PgukTPnyf/6qsN6y+EwBFelcBY2HgsVcpyfqFGgMz7pRpRci9yN8qKhkbSiNNlJOg2Oug2Nx6NDrrMBN2DHRz98j8Sy8yhn1niyltXiaxqPKxNk7HT5HRB3J9nZHoKw82BpuEND5cfh4bwB+od24q9R4mUCoVCOqR1dzQLu1nU6IyHt+6KMjKGZNK08u9iNddgAkuFZJKCoO8pzP0dNAm7qdqdFAR9T5YpsEJ8B6FCq8uN41I3ebVJmPu8WNPQ6jqJaDjDmCnuIqODF8ptr6FIqa61UiDjvQVALNK4XLU7OWg2Jgpz21Mh/eVk05GI6+K5cQ45s3WOREvXiM3tjyNxO/S//z6YJvriIsmb40QwKFkmc4leCnaEhFhnoDhLwc/RsbjErekvWO+Os/TD/55f/f5zFfGxUCU0KpHx3tiaSdKI06nH6TTKfxUx0uyg20jQZXSQNOIY2j3ClHzzt+D11/FTKa52n6Fz7QoRUeJ4caKyS8T3EJaFNzyMn0qBaVJ66aU9fpeKIEI8xaBQKBSNabc8WaBESlmQMRcqlFedrubqyxtNainCh4yuIgj+XaiJBDkInDhV7U6xy7iuy/h4OX7bWt6kUb4pgNs3LxEpru5jzbZPZjkG/EbDbe9/9AWHU/n7Ou+ZM2ew7SYxPRW7hrTX2ohgsUG56vPkQF1rFa0gqL+LWAIzxNGdAtudEsdDi57J3BUo5xb5+uonDR2Jg9dKuCf67ulI3AlCCErCpeCXyIsiRd/ZeF6i4Jf/8hsCYkGUKsKiU7jJc6ufcl2HkxdvovkuxjMJFo/1oOvTxAEBzPga5i1B95KLe+VLbj4+iJjIcHMixlJfxwPVvZ2I65Ea4TFpbHnUY3QaCTqNGFF998a8fjpN4dw5oufPs2D28nmyry5kr9dtog1pUCziz82ReeEFctPTMD1dd77x8XFGRkZ2rX6KepRIqVAopKOZSBnm1c7NhCwVhk4OZF1lrxxtciPrKnvV7uRG1v5OuTvkY2pqimPHjgFQcIJnR6O2u19V2jFRK3h1fLP31IxN4XY7+cUUD07bRS1Q9xZSkJe03QVFLVD3tHIQOMaLhbvdqf5OPiIXLoDroi8uwqVxbGFR3MhDmdMTxP0s6VIGy88SvXkTra8PN9WN/+O/ZObv/10KfomlZJGb1jyl9UsUNgTFwobgWBAlMolZHM3jrenxivhY3uYg2Hmbfvr6VfJ6ia7FLJrj4lgGk8d6EHptOxO6xuTxXhLrRSzHI7mUY6UnwanxDB/2De/WRxgqoppNhxGjU4+RMKJ06DE6jBgJPUqHEaOjpjxKQo9iaK1b+eCdPk3+1VeZ+OkvMZli1hpg1rq7GPLp5BzoK+T7+5l/5hmKvb2B5xoZGeHMmTP7Ue0DixIpFQqFdEgrUjZzUqrJUymQdZW9yt8hNzLmyQK1yl52gvIVhb2/UxNYcjIyMsLZs2e5Mhk8kfHM2Uc43BvOsKnZQvC24ycf5ezDTvAOilDQbtdaJRbJQbu1O3WtlYN2HOMJUU5zothfyrkWXQqiVHEqFkT5kcwUQzc/xvVcesa/wHMdCidTTKR70ew1bGMZ3/SY0QTXF/IkF9dwPv8Jlx4fRKxovPXJZNmR+Ch8whLMX2xciU3zXXF33lP3QhaA5HI5CsZyb6JOoKy8f11juTdBf2aV5HKelZ5E5fgwY2smCT1KXI8S1yMsTc/Tm0hxtH+QhB6tFyA3RMdWCo73i59O86+G/gtKYo6vrH9EupTBFiVKms0TT5+h649G6BwY4FCrK6pQIqVCoZCPZjfdYR5Y3yvcqyL8yBruVTna5Ea5OxStoN0cvIUSagJLAppdl8Lc9mI2aJpACJUbUFZkdY+rMZ7cyBruNTD/s2p3UiBru4tHBenSdJ3QkLHTuHeOYh1tHC5ecRdXeBR9h+KGG7EoHEobj8UqgbG4+eg7TMdncDSPn2au1TgYixvHBbkVn37vKsO56bIjMb+IYxmsPNlNUp+p2/dWRxcPr2dD4Ug0nXJ0DMMrL4wrRK2m+29u39x/8/i9Rkcjpkc2xMYICWNTdIzSsVEW33AzxvUIcX3juRHB0mrloDf++o3ygsGe9ozekStqzNuHebPnOzXlz3x9BX+gyUpDxb6iREqFQiEdQYNq0xDYzccPLSXaZAJLrXYOP64HJVfSCayA+hVUTkopaDeRMr9Lq1wVe0vQdSnMQhEE108IjUIpOBysIhw0W8QQ5j5P18tRCxr11/lSCyqk2BFCtKFIqcZ4UiDtGC/IwavanRQYMzN8e/HdOqGvNPg4EM6Bkp7JMPxXP+F708t1244WbxP9/k+xTg5SeumlB85lGAZ8ISgJpyIEFjeEw8r/lecbDsYt/9fsW1XucR8RKTabxA61HFkdia5Vdgt6hg5AtOCw0mT/aMGp2X/z+HuhoxPT7YqAGNMjxPUIMT2ypdze2BatlMf0CBHNQlOrL7eFrGO8g4YSKRUKhXQEutlCvvJP04InsJSzKPw0WxkctJo4LASudlbtTgpkXe0cmBtQtTspCPqeZO3voPyegsJfK8JB0LXWNgVmyCNMxSOCXIMJPOUsCj+FEvi+pH1e0IIg1e72HMdxuH79+gOdY27xazQShVYWpxgbu/ZA595L5meOAvW5uZZWSoyNjdWUTUxMMDIysk81UzRDz2SIXLjAUz+dp7Bm12w7WrzN0Wu/IPqDntAJfca1a0TPn6djyUcjSr8zR78zjylcXM1kzurDcRJEJyeJvf46hXPn8E6f3vN6CSHwNUFBc1h01+46EqsEwlKVUFhquM2lKEqUfLd2fyF/mHhZHIlbWe5N0Dezymp3jK7lHN0LWWbTXbUCq9DwXANbQO9sjpgeIdc3wJCdInrkYb7V9QxRzd4iPN59Htcj2JqpRMZ9wPebLL5V94WhQomUCoVCOmTN3QHlcGANJ7DUpH3oaeruiIW77SmxSG6C+rywr/xTIejkRtZrbbP65YoavQEhqRThQNZ2B+paKzOyOnhB5R1vJQ8qUALkS41XX0Tt1kzOb5eg+hWckK8mOcBsCn24Lo5jcciZqRP6IkYPxj4LffdCz2Qq9Y6uLfHc6gT2FgGv35kjOgb6mdP4qRTR8+fJv/oqfjqNEKIc4lQ4lIRbEQFLFeHQrXIsulXlVWLiFlfielcOFw/31rv4X/P5GTNw5+ct+oTCy345EndKRLOI6BYxLUJ0w6VYdivaRPUI3vFujn7yN/QMnqRv9hKm6zH910k+sR7F82x818T3dHQh+J2uX9JT0OkfGmTg6DOgaeSf/4f4KRV6OCzkSzSMZgdy3F8cJJRIqVAopENmq348KlhYrS9Xk/bhR+YJrMCQTCrsphQE9nkhF8eD2p2aOJUDWUPQNXNKqmtt+JF5jKeiFshLs+8o7BNYakFQaxkZGeHs2fvP4SX0eMPyh08PcvZs6r7Pu9dMFRuvynB9u+7zGB8f348qKZpQLfTpi4ucuHGTzlxt2M9+Z47uGQN96USd0LfX+AiKmsuyu05RODjCrYiC/W/+GVYug7awiH3xMl3dEVzLZCGZpBCziLt5+teWmC8V0T65w+TpNCtdUeb+/SV+/LtPUhIO/m4vUNN393TtyrYciRtovqiEd13tjlWO38QUOnEjSlS37/5p5cf1xRUsYXI8fZSoblXKoxvCY0Srfm6ha82/wLFEkq7UNEdNE33Exrx0iZ7VNb7ifEbGTpPTE8T9LOlShoSxjDDBGx4GTcMbGsIfUAJlmGg+xruP8MeKPUOJlAqFQjpkDfcKTSYSlFgUerL5JoObkLc9NYElN4HOIlnbnZqwlwJZw+I0q59qe+FH1v4OmoTdVO0u9AS1Owh/21MLguRG1vta5eCVi8iFC3jFIsvXrtFx8yZeIUFR66wTXHRnlfz777N+4gROMkn+X/5Lbn/rFTx8XM3D1XwcvMpzF4/0sSF8Q1CqciVWQpdW/V+qcTKW/98UI72vb7oR/7am3qm5NV659jGaEIxcmaJkeDjDGpPHOhC6AzisA1m/C+fWAt1LObpuTJJ5fJDEdIHY3CKFvo6WfOYHHRODzCPHeeLSIqI3hjWVw8wLej51udF3BE9YG45EA1sIvmldIVKIEOtM0nX0KUzd5OkX/wGvpY/ww3/3v/HIyGjggpCxO2OMj49zeGQznGwJKFEE7meabXx8nDs9PdjXrqF7HlZvLw7LRESJ48WJmn2zLuSPHiaVSoFpUnrppft4RcVe0mzOK+zX2oOGEikVCoV0yJqfDZRYJDNNV2CFvO2piQR5EULeCaxgB6+GEOU8vYqdUyqVuHjx4p6/TmbuaSBaV76+kmFs7PKev/79Uk5j81sNt336+VVYWwo8VuXNaj1B/Z0MuUTVGE9emk5gyeqkVGM8KQi8r5Wg3aVL03xl/SPSpQy2KFHSbDJ2Gm/yKMaQchLtB77wK0Kgk4BFPcvNYqYiBpaEi57JcOTmGMvLy/Td/BKhaywPdnCzpwvNymEaa7iGzxQmiYU89toqTuZzxvuG8Ndu8SPPZamZ0Lf0+Z69v1OXZwBILuUwXQ/HMpg81lPnxBO6xuTxXhLrRSzHI7mUY6UnwanxDB/2De9Z/doJWzOJaja2bhHRrLLrULeIaDbZpTUsYXBkYLDKpVjeFtEtoppFZMO5GNEsorqNqRlwAmJf/mv0qSn0M8eY/dvLWDcNjl9dqwjkHX6Wk9oUfUNFHGETHXkc20riDQ2hD5W/O53m17MzZ+rz4z4Ip06d4vr167x35AgPffwxuufxSd9T6MtOXYjkjj6H3zkbB9OkcO5cqHK5KsrIHC3joKFESoVCIR2y3sxBcB0LJTWREHaCVtmbhsBungO+5QRNYKl2F35KLni+nH1eULsTQqNQCs7fpmjOxYsXGR8f33MxLSivVNQKd54sQwfL8HC8+voXVa6s0CNzuNfAhRnqWht6mrldg8L4hoWga61y8IYfISC5muHvrI3VCX0966eAnlZXsSF6JsNDb/2E700v1207WrxN5E9+inlikNJLLx3YCXtfCFzhbnES1roJncr2xvsUK/tUb7vrQCz5Li5VY7Lv2NzmA5j+oKYuT//iKku5abrWs3SZJXKWwcwZjS59uq7ek6kOEhdXsRyXxPJ6y4W+zfCfyeU8UA7/2ShUKJSFyuXeBP2ZVZLLeVZ6EpXj2wkdfUMgLAuDm/kVI5p19/lm+YZgaGtmWUTULOwNUdHeCIsa0S1szUJvsnp0bLLsVDw+shme2ocNn+K93IqRgQGGPvkE3fO40XkckZmpcyTaVom1YpG5nh46ikX8uTkmz56lODYG3HsRoW3Xh5l+UL761a8C5f7Ofucdrv3bBa5kTWat2gUYQ70Oxsk+8ge4vws7uUJweF8Z7i8OEkqkVCgU0iGrqwjUameZaUsHr2p3oUfm8CT3CrspgzMqrDxoDqztoP+gs2H5qRNpzp5N7ulrPyiJmMbyen354SPDnD17NPA4lTer9QSO8SSYRFA5KeUl6DuK2gIj5GsbVLhXOdEzGbQfXeC/mlqt23a0eJvRv/oZ0Uw6dEKfce0a0fPn6Vjy0YjS78zVOYscJ0FkcpLY669TOHeu1VWuwRPehkhYFvycOuHQu/u/cHF8h1JVmVPlUNz6f7Xo6Ai31W+1gsxCn+mURVijHCaDQrT5yuDN7Zv7bx7fCkyMigC4KSKWxUKrxrFY2UczK2V3BUSz4lbc3NfS9n8a/0GcisXeXjIvvED63XdZsbu4mDxV12+UOqNoR2bp7unBNwwyL7xAsbd3F9/B/eOn0xRee42f3Mgyvfpl3YKSmSce4euvJe59IkXLaBbSP2bvY0UU90SJlAqFQjqCbrplmPBWYTflJWjiVIbVV4HtToWgCz0yhydp1ifnihq9hLv+B53A3IAhb3dQFouWG5Sra234kXpBUJBYpK61oacdc6GqdhdeNoW+fNZDI9ZQ6LOMDowqoc87fbrV1UbPZIiePw+uS3R1iedWJ7CFU7NPvzNH9CPQz5zGT6WInj9PIh4POGMZT/g17sBNobBG9NsQC6tFRadGaKx1GDb+38XH38uPKJTILPS5VnmViGeUnVDRgsNKk/2jBadm/83jgzDQq0RCC1s3K88bC4w2Ec0kc3sKUxiMnnqYd3/yM04fPckTjzxWc5yhBbu3ZOOBnYpnz6I/+yzZ/9vfIm7PMmsN1DgSTw+4nHl8AG9oiNJLL9G1ZWFGGBYRFnvTvNlzqq78nF2Apq1S0WqCF0D6Kv1MyFAipUKhkA6ZV9mrfEXyIvWEvRLHpaXZyr+wT542+22oPi/8tGPYTdXuwk82L2+7C7rWqnCv4acdc6GqdhdOqoU+fz5Y6Ou5WCv05V99dd8clUIIPPwqZ2FZCOz60Z+RLyyiLy5hX7tGZ0LDMU3mO3soRCziXo7+7CJLpQLGZzPMDg+x3hXHKMIPO2z+cupLnGpR0T+4wuF+stdC316y0ttBejZLIZWkb6VEeqnEVEcHRdfGdw18X8f3DIRncDTl8/BKFtPsIpY+QV+8l/VTZzgz8Bt1TsWykGhiaPf33sZK5TZ7Jn6Cz9Z+QZ/XwYCV2s233nb46TRvP/6fc+HOSl0uW/NoD/k/Oo0/EN5ctmoeT15kvqc9aCiRUqFQSIfMq+yVWCQvMrc7la9IXto13Ktqe+GnHUOrq3YXfmTOOx4U7lW1u/Ajc7uLRwTp0nTdpO9CbAB95lioJ31lx3XdHTt8jvzoR0Tn57FWVzGv3MYWXRQ3wgbm9ARxP0u6lKGwNoP7/vusnziBk0yS+5f/Kze/9U1czcfVPDz8ynMXH0/zcTfKvI0yV/Nx8crbNrbXbNs4T/Wxm2Viy08iNbfGK1c+RhOCkUtTmK6H90iC6WM9CH0NHSgAd/wY4laO7qUc5tVrzDw+SMzRKK5MkrE6du2zV2yf5d4EfTOrrHbH6FrO0b2QZTbd1TDkq+aLSnjX1e5Y5fggNkOaWppZcSLefV4WAjcFQkvfKN9wJW4KhpZm8Dc/eZdTR0/wxOhj2BshTm3NxI7OE5/6V5AUWPMfoDkOdz7o4D3nUfwqp6IufH6t/3P6ozFEzCJx+GHQNPLPfgs/pvrAsJAramTsw7zZ852a8tfO5PEH6kNfhwmVd1xeZL6nPWgokVKhUEiHclIqWoHMgxuVr0he2jncqyK8CCG3e1zl4ZUXqRcEqWuttAQJyWFfZa9nMjxy4ad8b3qpbtvR4m0if/JTxJGh0OU1lB0hBC4etzK30WyDfMTF1wWeDr4uKn9e5Xm5PLG8wtenPwXhc2LiNugC56EIdw4nwVwmaiwidI9ZTXBt1qFraR13+jMu9Q0i1m7yluez1Ncaoe/U5RkAkks5TNfDsQwmj/XUCV1C15g83ktivYjleCSXcqz0JDg1nuHDvuFWVP3AYmJg6yZzj57iyUtLiN4Y1lSOuOvT97nD9b4hfGFV3Ig4Gn9Hm2BAdELcIjL0OIZucuLX/x6/mT68ISialbyKtmai71JI08/Xfkm/10na7qkpF+k0/uAg+tQU3vAw5qVL9Bdm+VputU7Yj9t5iGl4w8OgaXhDQ2qRRsjIFhq3FynGeGoeT1pkDul/0FAipUKhkA6Z7fpqAkteAttdLPztLsjd4XoaJQfs5qlJFC0kqN3ZpsBsXfSlbWEa5XqW3Pr3oJxF4abkguera61i/wma7OmQ4FqrJrDkxZiZ4duLP6txImbsNIb7GBBtdfVqKJVKXLx4kfidO6TffZfSsonGUMO8hk4pTiRkeQ33Al/4OJV8he7dkKK+W1OW6cvj29MsrVKzvTqkac05fLdxufAQCPh7ZafZBPPbqufTd66z0lmiazGLpzs4HQaLZwwS+mLdvrcTKWK5fFnoW2690Lfprksu54Gyu66REw/KQuVyb4L+zCrJ5TwrPYnK8QrQ0MpCn2Zh6WbFbbjpLrQ18+6fvikM3nUjbuZMtDSjyo1o8i/+l3/ON57/Db7yxNlaAfEYxL741+hTU+hnjmF8eYmJixbHLmcrQl/HhtCXPu7R1dGJOzJKPJLCGxoicfwBchHuAsWXXyb2+uv4qRTu6Cj+nZtERInjxYma/UqahTt6Ej+VAtOk9NJLLaqxIohcoXF5Ihr+kM9qAaS8yDx/fNBQIqVCoZAOqR1tKgSdtMi8AquZ8ylf0rCt8L+Hg0pQ37Dbbrb7CVm2HWzzZUpuvQr+xfhNerSZBz7/mTNnsG37gc+jqEXmMMOgwm7KTNC1Nug7DRMqJ6V86JkMkQsXePbdebS12mvJ0eJtTl1+j+gPekPlRLx48SJ3fvUr/s61a+ieRyK3FpjX0P5QoD8yvK95DX0hKqJekGi4KQq6W7dvbHO25ELc3OZulJV8p/LcES7ednMaDsMlVmDx8p5+BkHILPSZjgeA4ZU/60K0+QrHze2b+28eH2YM9C3CYPnPqglNamJrBlaVmFjtLrS0u+FON0VEe4sD0UBH03b/umBlISZsonr9uLha6HMeGqX0xURDoU+zLdzR06ES+vx0msK5c0TPn8dPpbh6ZID50lLdooz0UIrjqRKYJoVz50LTZyvuInO0jFikcXm+uL/1UOwcmaMDHTSUSKlQKKQjaHDTLLRgWFArsOQlaNJehnbXNOxmQaMrEf73cFDZr5u5qakpjh07tqvnBIhYHuuF+omsQunBbaCbourZs61d4d2ONLsmydDnKSelvMieG7ARykkZToxr14iePw+ui+NaHHJm6ia9fSfG3Ic38T/+mMwLL5A7cqRyfCsXyXx9cZHB/n70xUW85RvMC6thXkO/uI715ReURh7CSXVTePvPmf37f7dOMNwUAq9HJvHwmVou3RUNfbdKdGwkHFbv5+ESfjGqVcgs9LlWedzmGWV3XrTgsNJk/2jBqdl/8/idoqNVnIRl0c+sEf0qYqJuYmlVLkTdrBx3V2is/T+yRYzcrdClYaRa6Csme3gv2dh9feKJDqIpETqhzzt9mvyrr2K/8w6mNc+sNcCsVRvKtcdz8YZUeOswEzyPt88VuQ/UvYW8BC76luCe9qChREqFQiEdcq/AUu4OWZE1XxE0d6Aoh0e42U/n+MjIyK4Lfl0dJgtr9eWH0ic4e1bliQkrzW64pejzlFgkLW05xlPX2dChZzIVgVJfXOTUrVukcrUCUL8zR+ecj7XajZNMkn73XSZfeYVib++OF8kIIfDx7wp8cVjSs0wUZ2uEwmoBsNp9WO0azOeuoK18wZxtc+SLq+C7+Gc6mT3ah25O0Wn46LrPEh6f3V6keymHezHD+OODiFWNtz6dCc5rGN94XL5+vx+togmtEvp2g+XeBH0zq6x2x+haypGazzLd04cnLHxPR/gGvmcgPJ0OI8eh6WksR8e3k+hTRfpOn+T3U79eIyxaDQTF6jJbMzG0kOc2CBn3iooSefxx7J99gkBvKPQtLd9kLZli/uxZiqurMDa2l9WtMDExce+dAB55hD//sofZubm68NzvHzlG9yOTMD1d/ttDxsfHGRkZ2dPXaEeCxuFS3FsEzKcokTL8BDkpZWh3Bw0lUioUCukInLSX4CKjVmDJi9TujmbhXlXb2xc2c0jtlKvXTwDJ+g1+lrFdnDjIZDK7dq5q1KS9nMge7lWFVpcXqcd4EUG6NM1X1j+qmTxdWhtAnzmGP6AWZoQBXwist9+i5BTQFhewxi+jCQ0/bjMb66NgR0j4WQ4V5ojHCkRnVigkj5Hr7MC48XOufeVFZmM609o4H85Nb4QnrXUbbn1eEm45h+Emv2Nzmw9g+oMd1//pm1dZMnJ0zc1RLOVwLIO50TiWnq/bd/J4L4n1Yjmv4VLr8xoedGqEvuUc3QtZZtNdDUO+ar6ohHdd7Y5Vjm/EXbehWQk3elf4qyqrKr/rICw7FK3AbSaWZhD7xhI9k29gJDXshQ/RHIfZv+njF96j+FUORF34/N3+z3goNoBIWgyeeQbr4kXyx5/n0a5n9uBTVWwyODh4z32Kvb2MP3+OP/74dN21KmOnefK3zhA72rkPta3lxRdf3HYI3Fyqjzd7vlpXftaeByZ3uWaNGRkZ4cyZM/vyWu1EO86n5IsaQsAeRHBW7BIyt7uDhhIpFQqFVLgelFx5V8KocK/yIvMKrGaignIW7Q8XL168r1W3+VLjoVrUkiOcmgp/KCdB/R3IcUOnrrVyIgR0rWX4jbWxuonTnvVTQE+rqxiInsnw6IWf8r3ppbptR4u3if7JT/GPqDB01QghcPEqYUK35h2szVfoNXUXzsRn8TSfn89MbHEkbhxXFdq0c26ZVy59jCYEIxenMF2P1eEEc8d6QJ8lCnhAxtcxbuXxlnK4l1e5/Pgg4uZtPryu33UiZvdmgU8zZM5reFDQ0cuhSbeIhu7jcfou5zD6u4lPlyit+3R/BNd7j+Bjlp2Ivk7SFvymfZMYXYi4ReLIExi6ycgLf8h30oe3hC/dJ7fhYBf60FG0qSm84WHMS5foL83ytdxqXZjheCQPMQ1veBg0jfVUCrcnvP13u2Ca5raioojLJhm7lzd7vlO37b//5iyJWLjHeT+9loCf15cbVlKlgAg52ULjkMoyL4AUQqNQkiNk7UFlPyNTKR4MJVIqFAqpaFd3h+NqOC5YqlcOLTIPbiIW6LrA9+vfg5q03z/uJ5zqDz9tHJKtvzexqzfib7/99q6dqxrlpJSToH4hYglMCSKvBYZ7Vf1daIksLGD+yb/mv5pardt2tHib0b/6GdFMOpQi32Zuw+SSQCPSMM+X68axJieJvf46hXPn8E6fbnW1K9SFIm3gDAwSC6u3uw3zHG4RF7eIkLs2gtqcHMzP33PXU5dnAEgu5TBdD8cymDzWUyf0CV0LpRNR5ryGrcKknMvQLThEDZuOaKLKMVh2EVp6veuw8qjXl1uayU/eusDp4yd54tHHa0KWGkG5DQch9lkCfWoK/dEBJv/6CpE5nVM3FzeEvjhxP8sjkSkOHS2BmcAdGSVqp/CGhogce2x/P7gtFF9+mdjrr+OnUrijo/i3bxIRJY4Xa0N1ljQLd/QkfioFpsnNRx7hSMA5FfuP9HnH1RhPSoRolj7H3+fa7Jymi76LmhS/nYOKzKkkDhpqOlyhUEiF7O6OZoOXfFHDMsP/Hg4qMg9uNK2cR6HR70eFPww3socnCcrfodpduJF5UQaocK+y0be0xNBbbyGSh9CINRT5LKMDI4QiX3Vuw9j6Ms+t3sIWTs0+/c4c1gcCfXQYP5Uiev48+VdfbSi2esKvCHrrWgFX87lTmmvoHqwXDKvdiJtuwrLgOPnoAl/EP+I/Tl5sKBiK3ZMLQ4/sTkSZ8xpCWTC0NKMiCpoNQpJuhh9tJB6WHYoWtmbcPVav3VYJX7pxfn0jDt8bb7xRXjB2encWeX2Qs0j5Cfqsrm0fUy303e5/FHf+Wp3QF7F9hGXhDQ9XhL7SSy/tSp0fBD+dpnDuHNHz5/FTKa4cGWDRWarrr5eGujmWcsA0KZw7R/b991tddUUVQfcWsYhAD9DXw0TgAkg1xgs1uWLwNhnuL5rN4+WKGr0HaBwlG7LPpxwklEipUCikopn7ph0GN8lE+N/DQUX2wU08KsgW6svVqtNwE3TDLUN/ByoPr6y0Q3/XCNXuwoeeyfDEpUssJpP4mVWeW11vKPLZv1pn4eQgTjKJ/8d/zOQrr1Ds7d2zegkELj6e5lc9euVHzcfbKHv4zXeIzy8SWVmj6/oUXV02rmUyn+ymELWIu3n61paYWs+hfXSHyVOHWOuKs/DGOD/93bN4bJxv45y+VtV2uzcep3Zhgj8J4ICz9uDnkhzZnYi7mddwq2DoFl1ModOVSFZEv3s5DhuJi9W5DWvdhwZ6kMtQUsbHx3d8THx4mPS77zLl9XAt+VzdwozlZJzEkAPFIv7cHJkXXiA3PQ3T03vwDnZO5PHH6fvgAxynwKw1wKxVm283sb7GDc9n/uxZiqurTExM7DjdgWLvkH0hmoqWISdNHbwS3F80uwdSaUzCTdB8inK/hg8lUioUCqloNgCQITfgvZyUivAi/Q2dcrRJSdANnQz9HQTn58g3WU2raD3S93cRSJem+cr6RzW5DfOFfvSZE/gDA/c+iWJfiFy4QGFtjahhkJyfwRZdFDfyUJZznK1z2M1g+CWsWzconThKMdlB9MO/4dZvv4ir+biah4fA07yKoFgt/rls7nNXYKwWBu+WeZX9a8TCAFJza3SvXEYzBSNTU+RMD+chmDzWgdCLQJEskPM78W6V6F7K0X07w0z3ING5IvrSHCt9jUN6K/aOsDoR60XBWvfgZlnyyTT9n/4IvbOTmF6gs+jR877Jl52n8ISF8HSEryM8eCV6jRNaLyQs+o+dxdBNvv6Nf8gfpQcbCoZjY2MAnB1Wed22w6lTp7h+/fqOj8sdOcLkK68wfSuDYK1O6BvuWAF9jnx/P/PPPLOnCzLuh2JvL5Pf+hY/Xu9l/uezdTmEP3r4CAPfCoegqqhH+oVoykkpJe2atglU2ws7MkdEO2gokVKhUEhFs3CvMqyEaXYhLKgcbS3Bdd1trYLOFl5pWD515ypjY/fOgdRqdPE80FlXfuX6JGNjt/alDmfOnMG27X15rXYh6IZOhv4O1ESCrARda2Vod3omwxM//Wu+N71Yt81amCD2/Z/hDQ2FMrdh2NkMRVqqDjHqbw0z6m0JN+pWHVOb6zA6s8DZyz/FG41jXp9kJSLgCZ/5Yz2Y5m26dB9d91kSPlc3RD53ep3xvkHE6hQ/K5kstVDkkz234UHlXk5E4ZdFPt/XieLRP1/AEgZOVxedfpTlrjQnS330dqWIGJGNkKJGjVtw03EYJDz+i//ln/Mbz/8dvvL4U1gbx2vaNq+LhyD2hFfOa/jkIOalS8xdE4zcnqwR99OlDJ2HckT7I7gPj5Iw43hDQ8QGj+/hp3uwsCzrvvKNb/LPPujik/xS3YKankeeoOsfD9M5MMChXa7zbvIfP+/g+5eerSt/safI2bN3r6/34zZV7B2B9xYBC1rDRpCYqvLdh5tmTsp4wKLWMBFt8vtQ83jhRvbFtwcJJVIqFAqpCBrcGLrAlqBHu1e4V8X+MzU1xbFjx5ru43gant84PFXUam3Yr+0SCahnobQ/uYk2JyjudyLnoCL7oFqFZJITY3aGby/+rM4doZ14DIi2unqBGNeuET1/nu5lgUakYW5D/HgocxvuBCFEQP7B+jyFJb9RuVcrNG6W+c6WfWvP5e9yvp2nP73KnLtCl1cC4VKIGMw81IGh14Z7FVo4RT7ZcxuGHQ22hBQ1Av43WFtaxUQn3Z/G1hrlOry7b+LXlxma+A8YHdA5/ym643LtLw/xZeQRfGEiRHm8pwuf//zkp6RNBxGzGDr1DGga+d/6owd2Y1tZSIgIceP++tPqvIbu6Cj+rZt1eQ0BSpqNO3oyVHkNFXfJFTUy9mHe7PlOTfl//WQWf2C9RbXaPmqMJyeyOymDxFTH1XBcsCSYEzqI5Js5KaP+Ptbk/rBMsEyB49a/D9XnhRvZ+7yDhOq+FQqFVARO2EcF212A3Eqapb1R4Q9bx71WQS+vBzeuJx8f5olT7l5Ua1fp+2Gc8an68u7eQc6e7dr/Cim2heyDahVmWC70TIbIhQs8++482lqt6/lo8TYnL79H9Ae9oXQh6pkM0fPnwXWJZ5d5bvVWw9yGxvsC8fAwfipF9Px58q+++kDvxRf+FsHPqxEHq92Fzpa/Ru7CxgJivcjYDsgu8sme23AnBAuE9eKhXf1/Azfh5v9BQqKtl52FJtt3F45NboQp7dnGQqjjEDv2RdmJ+NAj6F9coi+3xFfzH1Q5EbOkSxniq1lIaHjDw6BpeENDoQgX7afTFM6dI3r+PH4qxaXBNOu3F+sWZWSPdjOUcsA0KZw7F7p++6Aj+xhPRcuQE9lDH94rfY5lyvE+DhrZQnA+4pgkgZZidmORUvV54UWI4O9Hlj7vIKFESoVCIRWy52fTdbAMB8ern8hSK7DCS/PwJHK0PSUWyYnsEwmBIZlUuwsdmy5EXBfXsTjkzNRNeEeMVGhdiJELFxCug1hYoOPWOFFDp2RYzMX6yds2cT9HujDLWiGHfvFDlh86Qb67g7X/+M/54g9ewhUeV46vkolf5uO52S1C4lZn4l3B0CP8q7/DiuwiXytyG5oYW8KJNhMPzUoo0stfXGKg5xDHh45i6XedhtW5DoPOYWoGugwrAXdAtROxMDxK6cuJhk5E3bZwR0+H0ononT5N/tVXsd95B9NcqMtrCDDoOyq89R7zIKFMF5afB+pn5xfnb+9bKoYHYWHuKHCmrnxxpVTJbwowMTHByMjIPtZM0Yx2jdIC5ZCvyYQc7+OgEbwow0cP1i9DRTwqWM3Vl6tQw+El18QIIkufd5BQIqVCoZCKoDxZMl1gbMNVIqVkNE30LolAHlRP1e7CTTP3uAwEieOq3d0/juNw/fr1XT1nZGGBobfeQvc8rNVVjlxdpjtXO2PQ78zRedtn4XI3TjKJ/8d/zOQrr1Ds7W14Th+Bh4+r+Xh45UfNx8XH1by72zbKyo9b97vHsRuPyfkVXvriQxCCkYtTmMJDP5tg4VgPmj5NfKNOMz6Yt/Ll3IbjK4w/Poi4cZOfXxPl3IaHAXKQnd7Vz1fRmFaIfLuFJmCtp5N0Jku+u4PepQKH5gvcjnfg+tZGTkMD4ZXzGw505Dk6t4DpRyh2DnDU6cJOnOQ3siOYQsdExxA6BjqmKD+fuH4LXWj8vd/7Typio67d30zeG3cmGUkc42yXCrkOtU7EQmcP7yWHGoaHHn4qgZ0ktE5EP52m8Npr/ORmlqmVL+tCdE89/gjPvZZodTXbljNn6gW6nRCUckH6VBJO6/pmxb1pVwcvNL9nV7QW2RfeQpP7WtXuQkuzOYeYJH3eQUKJlAqFQirawapvmw7ZUqyuXDmLwkuz70aWtheYN0YNqkNN0MpMmdpdujTNV9Y/qpk8LTr96DMnQhE2TzYuX77M1FSD2M1NEAh8DXxdVP48XeDr5bKn3v0lYnWZ2HqWQ5MzGFYcozfGQmc3hahNzM3Tn13EIkf+5gSTxw+x1h1j4cM3+Ol3nto4JxvnLP+JfVwVfebKFALoWsphuh6OZTB5rKcudKjQw5nb8KCy3Jugb2aV1e4YXcs5uheyzKa7GoZ81XxRCe+62h2rHI8Ac1PYQ8cQxl3Br0r82/pYfm5UjtsUBo2qc5nC2PL/3X0MdCKHH+fo2JsQ9enWfPSCS+rnccaMx/CrxERd+Dzb9Ut6dAvfjOEmhujydEonvka81BP4+cwXTU6dOkWHUT9mVDw4m07E9X//EwTLDZ2Ilp3FGxoMvROx1JvmzZ5TdeW/YxegqfSveBBs236gXOs+kYblow8d5ezZ/vs+735xp9C4/p6o/VwexG2q2H1kn09p5qRUiyDDi+wOXghue2oeL7w0NRtI1PYOCkqkVCgUUiF7uFcA23AalhdUmIjQ0g7hXoPqqdpduEmuZPi1lbE6d0Rv7hQQPLkdBvRMhqfe/Wu+N71Yty06P0Hs+z87sGHohBC1OQmFS0k4OMJlKVlkwlyA7NWaUKObzz/rnaHjdDeZHu46EKsch0FlBPzUU3NrjBgZCj2CkckM+ahHdtBj6piN0PNAnjxw208wdKtA95JP78wc84cGSawuYRWWyy7EFiJ7bsN2wcSo5BPcGm7U3hKC1NIMup4+zvD1d5h0bpPQEjjrNom/jXG16ySeMBG+ge/p9MR9fr/nChGtDzpsDh//CoZukn/5j/gvBw5vO2/hXhDLZMq5DWMxzEuXSM/M87XcL+pyG/bEc3T0deKOjhJNpfCGhuh8+eWW1VtRxk+nufFb/xl//FapbjFNxk7zv/vPjsGJQ62u5j0JcgOoMV64kd1ZpHJSyons8ynNfh+qzwsvQWE3ZXHwQpM+T7W70NJ0Hk+itndQUCKlQqGQiqCVMM1W1IUN23QblquVf+El6LsxdIElyZU00Emp2l0o0TMZjLcu8I/urNZtO1q8zSN/9TOiM+nQCnyb+Q27lwQakboweotuH4h4aPIbClEOTVryHRzhVQTBTeGwJFwc360qd+vKtwqK5XO5lfNtnmuzLJBH4ROWYO7zxtsfMVghBzRIinIfnLo8A0BSYhei7LkNdxsdrWFeQruSx7Bx7sLyY1V5g32rz2VWHXdfeQv7IHYqx8KHt9GHHkL/cpmOWZdHZm5ViXyrjNrTdJIFw8Z9aJSIEcEbGkJPD+7NB7gDqnMbuqOj+DdvEinU5zYsaTbu6MlQ5jY86OSKGhn7MG/2fKduW+ToTAtqtHPiKrS6dAgR/P1EA77PsBF0b1F0NDwPDBX1NZTIPp8SrU/jWkFFCAov7eykVO0uvOSbOSmb9CWK1iDJ1KpCoVCUaYeclKbe2El5/eYMY2NX97k2e8uZM2ewbfmv/s3yArbQwLEjgn4jalAdPjYFvmLeQyPWME+WpXeERuDbip7JED1/HlyXeHaZ51ZvYYvafq/fmcN8X+A/NIyfShE9f578q6/WCK6+ELiiVgDcFP5KwuWWtYCLR2H9iyqh0KkVC/168XCzfKvYKJDnOrKbtIMLMcy5DavdhdVuwrt/Ro27sFYcNKuO2Soq3t13q2Bo3GfewlZQfPll/D/9U5xkkjuHBinNXSMiakW+iOUjLAtveDh0Il91bkM/leLS4TTZycW6Pjt/tJvBlBPa3IYHmSChKGIJaUQWFYJOPooO+L7cuQGbiVr5kkZHTI73cdCQfT5F08ptr1H/phZmhBfZneMAscYRrsmX9rceiu3TNCelRG3voKBESoVCIRWyJ3oH8N31huVFR55Jxe2wmX/kQXK1hIX2GFSr8CQyUC3w+XNLPLc60VDg6/kS9DOnAwW+vaJaOHS2iIcl4VESDgNv/gfsXAZjcZnIxE26usE1Tea7uihGLeJOnr71JaayRcSn00ydHmCtK8b8v7/KT37vbM15m7IZYXT+y71+221NO7gQt5vbUPg6poDehTwmBk53F51EWe49zEkOkV/L4RVdTh8/ia2b2JoVEKq0XlDcWr4pHO7YXdjGlEolLl68WFf+454eThsGy5FePkkeqluYke1PMfxMZ3lmMoQi32ZuQ/uddzCshYa5DY/4zoENbx12WuXu2M08fYvzx4FH6sqXVgqMjY3tqE4jIyO7Vi9FMO2QSqKpSFlUImVYaYf5lHiASJkPCCmqaD3B7W6fK/IAxAJc7mpBUHgJancxW6C31/RrW6BESoVCIRXNHG2yYOmNl1p1dKU5ezaxz7VRbId2CE+inJRyELlwAVwXfXER+/JlbGFS3MiNVZ3fzPTWMS9dKocXTKWwLlxg9dU/vOsk3OIsLJfdfV4STsVlWBIuc88Y/DjxBe/N3K4JTVpq4FBsRmpujVeufYwmBCOXpyji4QwnmDzWgdBLQIl1IOsncW8t0L2Uo/P6HaYeH8SezqHNZMi2OL/hQaNVLkRDaBjCwETHFDoGOobQMTfKDKHffdxSZgq9cqwhdJJHhnjo459gxKEHDyPvcfvHPXxmP4ovTHxPR/g6uhD8Xs8vOCIS+KaJGT0NBZ14+mVOLPXw9ttv8+KLL/LV/q/e13tSNOfixYsNRZBMMsnkK68w/foqgpk6ke9M0gGtGGqRz0+nKbz2Gj+5niWz+mVdbsPZJx/ha6+pMV4YCXQV7eG9xeDg7oYqjpiNF4sUnJ31zyMjI5w5c2Y3qqS4B83G37Lc1wZN2INytIWZdl58q9pdeAkMMyxJeGtQ4V5lJLDdSXKdPWgokVKhUISGoBX21WTmngbql1utr8wwNrZ7K5L3ErfYeAJeOdrCS9B3I8skApQH1enSNF9Z/6hm8tRx+9FnTuAPDNz7JIodI4TAER4FzcHFI+Msbgh/9eKhnslw4voH+L5L/6VLuL5L6XQ3tw+nwFzF1pfxDY8ZTePS0jqdS2s4n09z6bEhvGXBWx/dYOl+Bb5TBmvMQX7ugd5vO+Q3PGgs9ybon1kjl+qgZ7lA/2KRyWQM1zcRvlER+IRvcCTlcXJ5FdPoQBs4Sjya4tDxUfp6nqtzG5qa0dCNuFcOw9idEvrUFHqkG/PSJQ5Nr/LM6lidwN/TkaOjpxN3dJRoKoU3NETnyy8DZReRZTV3kioejJGRkboIC2+//TbF3l7+4pE/4MO5pbrrVNfxFPk/GpbiOlXsTfNmz6m68t+zCtBU/le0ilZM2Jum2fC3cL/cWG9sRXFFpC0imrQj7eCkbHYflFOOttDSDou+lUgpH23h4A2oq5rHCy/tsCjjIKFESoVCERqCVthXUwxYERy1Wh9ubruYeuO7toIaVIeWoBAesgxu9EyGsz/7a743vVi3LTY/Qez7Pwu1Q2Wv8IW4Kxb6TlUIU4ei7+BshC8t1rgOnRphsdG26ryJJeGUMx12b7zo5C8D6/P0r65SKEzTtZjFLizjWAZLZ1J06PXC4fXOTh5eW8NyXDqW1kMj8LVDfsMwoAuNiG5V8g1WC30LM/Mk4530p3oDwpBuPN8ShrQ2H6KBrVvYmkkkukDH1P8PugTW/AdojsOXF1Jcj47iV+U21IXPKyc/5TAdiLhF9+BjoGnkv/6f4CdbLx4VX36Z2Ouv46dSuKOjeNduESnV5jYEcHQLd/RU6HIbKsoTpxn7MG/2fKem/P/wRBZ/oHGo/LARNIFVUPmK9pz7DZ967cZJIFm/wc/uKFTqTpiYmNjVsKpB49GCmjgNLc3CA8pyfxG3g7epthdeAhffStLuQPV5MiL7fAoEuz5VuwsvSqSUCyVSKhSKUHGvVcX6G50Ny08eH+Ds2cbbwkYycQkazLWplX/hxZyb4duLf10Xvs12zwCxVlevKca1a0TPn6d7RaARqcv1teT1gYhjTE4Se/11CufO4Z0+3epqA+AJj2KVGOgIt/J/OVxptVC4VTTc+N93tjzffujS/aYdBL52yG/YCB19QyysFf+2CoO2vuX/ze0bjkJLMzYeTewNobAiGm6U/9m//feMjowGXgvf+Os3ABgZOdykxt7GX7HyrNBk7yOuS3R+Hquri8TNW/SXZkk6y3UuRC0zz1rUZf3ECZyZGfL9/UxOTcHU1P19sLtMfHiY9LvvonseH/d8BW1praa/m7P6WOnR8Ysr+HNzZF54gdz0NExPA7svHCh2RtAEVrO8Z2EjMLS6GuPtKQ8SojRfarwAMiLRAshmriIhyulcFeGiWZ8gS58XbRbuVYU/DCVCtHkaE3WtDS2BuQElancq3Kt8tMO9xUFCiZQKhUIq2mFwY+pOw3I1qA4feiZD5MIFvvruAuZardhytHib4fH3iP6gN7QORD2TIXr+PLgu8fVlnlu9hS1q21+/M4f1vsB7aBg/lSJ6/jz5V1/d1vvxhV8RBYvCqTgQi1WOwuJG2a3oBA4elxdWGouGvsPCk0v8KrLI92+9R1G4+Ph79dGEknYQ+PYzv2E5L6FO3IpWBMBNMbDOPahvV1SsEg31u/8bVY7CvUaj+bXg1KlTXL9+fVdfc+7ZZxl66y2cZJLlIycpTWSJiHoXIpbB+okjOMkkvmEw/8wzu1qPByV35AiTr7xC3wcfoJswsyW3IcAhsUi+v5/5Z56h2NvbopoqGtEOq51jkcblaoy3t9i2fd9hTX/4aeMw6Yd6E3sWKvV+XZ9BBN0H+b5G0YFoE8ebojUETWpbpsCSZJbOMCBiCYpO/XtRfV44Kbng+fKH3VRikXy0hZNSiePS0Q6LMg4Skgx/FAqFokw7DG4so3HMr7zK3REqNh2IuC6OY3PImalz5Nh6KpQOxE0iFy4gXAd/YZ7kxGVipoZjWszGeylEbBJ+jkOFOZbzWcTn77Pw0FFyXQmWz/9/+ej3f73GpVjc4kws+uUci9tm03C6dvse+3ggz895V9lPgW+vWOtNMjibo9iTxFopkV4qMRHrxBNWJbeh7xkIz+D0gMvwyjqmmcQeOE53rIfVk49yqv/XG7oNt4qIH3/8McCBy7dlWdau5jLbxDh5kuj586x3ebz3eazOdT1n9XHy1xL0dgGmSeHcObpC1udV+M3f5CdrebLvXaxzwOd+Y5QX/usODjU4bLeFA8XOaIeJhKBQYGriNLy0hTjexNGWL2pNHW+K1tAO7Q7Kk/aNRMpm4WwVraPZtUimtqfEIvloh5yUQdda1d+Fl3ZodwcJJVIqFAqpaIeLTJCTUsWybw2lUom33367ZnI6sbLCo7/8JbrnEc1mGbri05uvdbX1O3PErxe5qpsUEgn8zz7ji699jWxX147rIBD4GviGwNMFniHwdH/jUVQeq7c7modvgG+yUebX7JNcWuXli2OAYOTiFKbmoT2VYO5YD+izRCmHgMz4OsatEt1LOdzLa1x/fBBxa4rxWxGW+hq7CxR7w3Jvgr6ZVVa7Y3Qt5+heyDKb7moY8lXzRSW862p3rHJ8IzQ0Ipuin16do9CqhC+99uVljqSPcLhvAFs37+6vmVtEwtoySzMZ//xLTKHzzFNPY8RmiU19H5ICa66c3/DSzzv5zKzPb/jN9c8YEHFEzKJzcLSc3/Cr38FPtD6/4UHEO32a/Kuvkv2znyBYZraBC9GysnhDg6F1j1dT6EnzZs+JuvK+aA5Y2/f6KO5NO4zxgnNSqjFeWAmatJcpSkuzupbbnjzv5aDQNiKlLVhuUK76vHDSTMST6VqrcgPKR+C1VqJFNEHX2qA8r4rW0y7X2oOCEikVCoVU5Ntglb2lN3ZSqpV/reHKlSv89m//dk0esiM/+hHRVAprdZWOqSlu0sHKpgvHiNPBGmlnmrjt0Le6ymJvnHwywsnVW3z267+Oo3nlP7zKc7dS5t99rnm4G/uIXf76j13LIDToWsxhuh6OZTB5rKdO8BK6xuTxXhLrRSzHI7mUY6UnwanxDB/2De9upRQ1aGgVd2BEN8meeYTu8TWMvk6i0wXMrCD1sc+NviN4wkJ4Or7//2fvz8MjOe/8TvDzRkReSFyJo5AF1MUqkChWiSKLhyRSzVY3KVFSy112H26LdLt9ja0d7+7sTo/ds94ZP96Z9TPj7m17PZ6zd+yetnqmKffd1ZbUOkhRoiRKFEkUSRVZKNaJKgCJG0ggr7je/SMBVAKZkQVUITMjIt/P8+STkW9ERr6R+eb7vvH7vr/fT6fTkDwbvUEH3ciOKKWOQwyZGvc//ff5mfTwlqAY2xASdTTEHRJS/cbv/Aaf+NQDnOnfu3deXJYFfCEE7tAQ7vAw2vQ0zugoxsWLpK1ZOgrV+Q1jS3noEzijoyAEzsgI7pASKFuJm05z/bN/g9/6C5NH19+q8kL86798BP1ELR9E/+FlbFMebf4lDIYE5d0RPLyMi0Ey2Nerq2p7/sQzOlCA2h3UGWtVu/MluZB4Uqo5XvAI80I05UnpXzyjtASo3bUTSqRUKBSBwXXDYUjwEinV5Gb/kVJu5UQsSnMjX6JJsSJXov6xQXIPx7mezlF0LSKZWRKlqzhJh0OXr2BGLPJjgoXDUSKRG6T0cojTJVeSuLEOy3ns6Utc7h9GrszxTqHDFx6Im1523SsFoOxlV8sjD8pC5Up/ksFMlu6VAqt9ya33tys62pbH4G0hcYdHoRbZ8kasPrbsZRjb2H/l0hUMqfHI6Ye3ciEa6NvFw0OQuNCLNj2NdmqYme98QHRO49jV1S2Br9PNMRaZIX3UAqMTe+wkPaUShf5BDhz5cOu+sApKzz5L4sUXcVMp7JMnca7cIGZV5ze0tAj2yeO4qRQYBuYzz7SoxopK8iVBJnqQr/R9rmrf/3R4tgU1ujs8QzKp1c6+JQyGBBUKLHiEIcxwvbqqtudPwtDuQPV5QSMs4V696qrEcf8SioVoqr8LHGFod+2EEikVCkVgKNTW9gDoCFCYCEMzSZszVV4q2dwBtNmjbe1JJKXElg7FLSHRpOhuPKRFqWK76JpbguPO7UpB8k4tI/rZQ/yI67B6HYDHzl9mxlqiZymHaeaxIjrTJzrRNXt7XX3ugWhYZTFVd1wAivFIvcO39m8ev/l+PyJgQxwsi4GbeQpjOwTFaEVI06gW2RIMt28bW+eqfK1XhCbdD1adsriTMuoL2JUC362hU5jzV4jJ7QJf3HCRkQjO6ChuKoU7P8/C44/XzLHXLHbm8usYHSX96qtojsN432Poy9mq/Ibyvh4GU/ZWfkO/hw9tF7xutDVNEqvfjfgKz5BMypDgSxxXYNrBNyR4tTsVgs6/eC1cCEu4V9Xn+RPPhbcBaneg+rygUW+hVhj6PNXf+ZcweI979c8FU+C6oO2v+UCxD3i2uwD1d+2EEikVCkVgqLcyLhFrYkXuAS2T4TMffItHZ25U77MmSXzx2zgjI4HI91WJLR2KrklhQywsuCVuRBYxsVnLvkOhQmwsuCVKGyLjpqBYrPBwdHFbei1h8UC0IzoAjl6eLceLFqt1jo8XrW3Hb77/boliEEEnStlj0M6b5LM5xu67f0NAvC0KxjZEwrffPM+hoWHuP3aiXLYhNFZulwVJ/Y7hS4OKm05TPHuW+LlzrMT6Od99gEFrfpvAt9SdpHPEhFIJd36e1w4dYqi/v2V1Pn36dFVZ/tAhpp57joE33kDXqZnfcNQxA9nfhR2vm7lEVBKkv50KuxksfvzeVc99k9cvkjCzTazN3XNrMgV8tKrctAVvvHkeXdu9UWRiYmJbKHpFYwhDnqyoUV5I4rrV16L6PH8ShlyooMbaoOHV7iKGJBIg67DypAwWpgW2E/yxtm7UAhOS8SZWRrErvPqEoI217UKAhiGFQtHu1FsZF4QVWPqVK8TPnWOwsMA8x6qEh/nIAI7dgT41ReLFFymePYtz4kTD6iOlxJIOBbdEUZrk3RKlDaGxKM0t0bG4sb/gmjuExdvH2NTwutt0GFu62LBraARh8UBc6U8yMJsl25ugZyVP72KOuXTPluAqXQ3X0ZCOTofuMrRYIkIEevsYoAur/xhnuG9LZIwKnQjGlvhYfhi392+VGxg1ciBeuHGBd999l3/wsbOedV6Yu8QDqSHOJO9v6Hfjd5wTJyg8/zyZC99HMlsl8B3rXANtlsLgIAuPP85Qf39NobBZRKNRzpzxyGX5yU/y8mqBwg8vVOU3LD1xkodfaH1oZMV2wpAzBlRIJr+y0+sawHEcSrb3bWki6o9xdTfEDO+6Fi2NZGz31zI2NtbSvr1dCIN3hxDlPq9WvjlltPcnYQlBp8baYKHanaIV1PPgDdJYW88xolASJAN0Le1CWEKrtwtKpFQoFIGh3k223wcZLZMhfu4c2DY9pSxPZl8jKq1txwxa8xhvSMRYOYRj/Nw5Cs8/7+lhZLk2BVmisOGdePt5x7bcub8sPHqKi21Oqz0Qd2JIDUPqRGRZGNx8bbiiXCYiRKRW3pY6BuXn7kPD3H/+W+gdgj4cjILD9a8NcCF6CseNIGW5vpp0+bn+10i7CVzDQMSPQVFDT/8Eg8t9u6ihC7jYWNhAweOo1dVVhoeH9+dLaQPcdJqvnvrb/HBuuSo0dOdYip5fG6VraKilIV53SyGV5it9x6rKh2J5YK3p9VHUJyw3cyoEnf/wEtx0XefQEW+PwcceOcmhwdZGWdgtiQHvOcDYyUcY7A3GdbQToTHaxyS5YnW56vP8SWgWBHkY7Yt10rQoWkdY+juv/4lqd/4kLLlQ63nflcfa4FxLuxCWsbZdUCKlQqEIDPmiqJnLMRNN07V6BHr9a66PvfQS2Dba0hKHl6eZlUcw9QhziQEK0RhdIstQaZa1Yg7x4zdYvP8wuZ4OFv/sv+f1n3uSglsirwTGpnAnD8RKhCu3wruu9yaJCgNncIDhSD8xESGmRYlpEeJbORIjt8s3cijGRHQj5GmU+LZjytvaPcRXTNyy0Kan0WK9GBcvMjS1QkfuTTLRNHktSYebI21m6Osq0Jnqwj55kngqhTMyQtezz97153pRy4NG4U2+JMhED/KVvs9tK/97D+Vwh9ZbVKu943UToLw7/ElYwuKoUGD+w8vremJigqLlLe4FyZBQz9im2p4/CYMnJdTp8+oYhxWtIywLgjzneKrd+ZKw9Hde4rgaZ/1JXWeDALU9NccLHmFZmNEuKJFSoVAEAi2T4cCfvMIXZpar9h0u3aTv338L91Dzc5uZrkXOLZYfTpH8lphYIu+WX2uZDB/64FtYjsXh967gDNjoY3MsH0thaDfp2jjXnAuRG0V6l/PYl7JcfWgYOTnN9ckEywMqJOJ+YqBvCIQRlmcXGeztp6+zl5gWpftMmqNXvoORFqRmJYblMPW9BB/0HMdxIriOjuvoSEvj+aEJhjQH0RXl2H1PgBAUnvkV3KGhO1eiCZSefZbEiy/iplLYJ0/iXL5BzDY5WprcdpylRbBPHsdNpcAwMJ95pkU1VlQSlkm1Z74iZcDyJWFpd171LZSaXBHFrqgrUgao7dUT8/Oq7fmSsPR5XkZ71ef5E88FQQEy2EOdsJvKg9eXhEUcV+Feg0U7eFKqtudPPPu8gI217YISKRUKhe/ZzOVozEgEsapcjsuxAQT3lsvRcm3W3SJ5t8i6UyDvlirExwI5t0TOLWwJkeV9BSx5Z2/Gx85fZtZapmcph2OWsOI608dSVZ55UhNMHe0nuV4iYjl0L+dZ7UtyfCLDmwOje7qesKGjEdeixESEuBYtPzY8Esvbmx6LG683PBR3bpffH0EXt42hv/r//VV+7m98ijOjG94dByBxwil7IJ7sQbx3kc6MzanZ6xUeiCukzQxd8Rx6UscevR+EwBkZ8Y1ACeWQocWzZ4mfO4ebSvHu4EFYXKzKhaod72Yg5YBhUDx7tqlCv8IbL8NikG7mwNuQoELQ+ZPQGLA8Dfaq3fmRollbpNQ0Sax+amhf0RH13qfanv9wHO+xKGje4171VWKRPwnPWKvEoiARlmgZ9UL6S1nO06vwD/W8DIPU9uovRFONzo+EZSFau6BESoVC4Rssy+Lq1avbymKLi4x8/etojkN+0ubJ7GJVLse0M0vxW+vkDh3C6u7G+a3/hSuf/mmWB7ooahYFYVHULIpi+3ZRWJQ0i5KwsUXjcgRthgPtXiln61vpT9YMHQploXKlP8lgJkv3SoHVvuTW+4OGvpEnsSvaQUKLERMRElqMhBYti4siQlyLEd8QF2PbhMfbgmNMixARzR2uKj0QzdGTmO9PEpPVHogiGsE+ecLXHojOiRMUnn+e6Msvo0eWmIkMMRfZLqRaTglnJN10T2RFfbwMPF7ii1/x9KRUN3O+pOCRz6cj3tx63Cv1DPbKgOU/vDwpE1EZqN8q7rEoA5TR3o/UWywTNAOWCvcaLLzE46C1O68+T83x/IlnuNeAtTuvOZ7jCkybQC1uage82p2uSaIBUiUMHSKGxLKrr0eNtf7DssF2wtHntQsB6g4UCkXY2SlQAgz+6EcIx4bsCv1z07iJCHZUZ7G7G7PDICnXGcovsiTzOLeWuXF8gJXeGAsXFnjp7MMtuIpqDKvsbak7ZSG0GK8/a97cv3n85vubgYBtHohxESWhxba8FxNb+zbKN4TGRIVHY2JDeHzn/DsANfNP+Y1auRI7RkdJv/oq+YLgte4nqzx45yMD9B7K01kq4c7Pk3n6afIzMzAz04Ir2AUPPsgfHD9MzLxeldN19r4+rAdXy3VvUP1VPsq9E5aVfyoEXbAIjXeHh+HUdQUlC+J1PN4UzafkIVIGLRyTrkMsIilZ1f8j5dHmP8KSJwvqLAhS7c6XhCUEnfKkDBZhEce95nhQbnuxSLCuJ+x43tPGg7UQDcptr5ZIqeZ4/iNMc7x2QYmUCoWiJUgpybslVp0ca06erJPjxoE17DhMd1wkr5lEFxd4am0cO2ozlpnG6HSQR5JkjvQhtbJ3YQ645vYycsOhdzlPenKB1Z5hBmazpBbWfZHL0Y6UDW+OrgEQL1qs1jk+XrS2Hb/5/jshYJuo2KHFtrY3PRgTIrZRHt0QFzfKNz0cRRQtaDPFe2RwcLBmef7QIaaeew7xrXeQaMzV8EDUjWsUBgdZePxxSv39zajuPbHWPchLfaeryn8+fg3qtsr94fjx4w3/jDARmpBMKtxroPD24A1Wu6t381k0RV2PN0Xz8fKkDJrhFMr/lVoiperz/Ee9PKH1jOB+xDsPr2p3fsRzjheSdqf6O3/iJY4HbY5Xr77lthes6wk7nvcWAevvoHx/kc1Xl6s+z3/U824NWp/XLiiRUqFQ7DuOdFh1cizZ66w4ayzb6yzbayw75edVJ8+ak8dmh4fgln5RFiAfu3INSzj0LOcxbAcrojN1pC9wuRxX+pMMzGbJ9iboWcnTu5hjLt1TfR1SYDgagwtF4iJKbuAAw5EUsZEH+HT34zsExe3iYjmcavsJjPuBruuMjY15enyOH/sMv/WuyaPrb1V5IP6tf/gLJI4e4ECT63y3DL3cDZeryzu7hzhzpqP5FVLUxTMkU8BW/qlV9sEiPB689fPG9HYG63rCjpcnZRCNCImoZKVGuQoF5j/CtMre05NStTtfEvaxVoV79SehWQCpcgMGinqelEHDS1hVY63/qNcXJAPY9toBJVIqFIo9k3OKLNirzNurLNlrrFQIkMvOOlknj9yH1WtBzOUYkTqdRoIOLb7lydh75iD3XfkOkbRG/6xgaWqG7uUkP46PYdsxXNvAsQywBWdTrzNMB8lUivThx0EICj/xN3D7hu784YqGkC8KMtGDfKXvc1X7oodmW1Cju0cZEoKDlOE3YKmwOP4kLIaEO4UCU/iLsHlS1kK1O/9RqGNU7AhYSGg11gYLr7YXuLFW9XeBIjQh/evUV7U9/xGWdgeqzwsSdT0pA+jF2w4okVKhUFQhpWTVyTFvrzBvlcXIze0Fe5WcW2xKPVqZyzEmIiS1OEk9TlKL06HF6dTLz8nNh765nSCpx7j0zkV0tGqPvCFInLDRpqfRTnaxfHmKnmyOD7sTZKJp8lqSDjdH2swQcWeR/eCMjoIQOCMjuENKoGwlXgb7eFSi7y4Sr29QBqzgULLKufNqEbQbOnUzt/80Msfr/OJHgepEoqtLU4yPV+eO9itrBQP4ZM19429PkJ1dqyqfnJxkbGyswTVT1KJotoFIqcZa31FvkVbgPItUaPXA4Lrhzw2o5nj+xLPdBU0cryMwqD7Pf4Rl4S2oOV6QqPebBK3PaxeUSKlQtDmrdo4pa4Fb5jy3zAWmzAXm7BUsabe6avuWyzHqGsRlhLg0SLhR4jJCQkY4duAw3ZEkHXqcTq1CgNTjGGLv6pOO5rmv9OyzJF58ETeVYiGdhmmDmGNytDS5/cBYJ9FH78dNpcAwMJ95Zs/1UOwvYckZAyo8SZCoZ9wJi+FUGbDujtOnq/PK7ideHm2xiNvQz91vYhHvhUpe16hoHSXbK9xrkyuyD6ixNjjUm+Np3tN6X6LCvQaHevOfoBntPXOhmgLXJXD/o7DjmZMyYPe18Tqe7qrP8x9hCTMMao4XJLx+E02TRJUa5kvUz6JQtBHL9hqXire4WSFIrrk1sj77hJq5HId6sewojhXFMSM4VpTumM79B+BgtkA00kff8Anu6xpCjj3Gzx/9LPpdCI77jZtOUzx7lvi5cxQ6Org69BHk4jqD1gKGtLGFwXxkgNEHejg6YINhUDx7FjedbnXV256w5AUEtfIvSIRp5Z9XuzNtgeMQOI/kVhONRj1z6O4LWu38tPcfH+bMmVTjPnefkbJ8E1rLI/nw0THOPGxWlTfSQ1VRH6+clEEz2IMaa4OE12+SCNg4C/XFIoW/yFcPP1sEre3VW0hSMCEZb15dFHem4NH2gnZvIURZLKrVv3ldo6J1eNpTAjnHq12u2p3/qBexQKipkS9RIqVCEWKKrsml4i3eL0zyfvEGGWu51VXypEOLQc4maur0J3rocKP0HOnj2NvfI9pxgH4cMrcScHWYq/GTuOL2sszO9DIH58aJZS1cwyDvxmA+y00jQen8O027homJibph4pwTJyg8/zwrb72FnofpyBBzke2hXI+6Js7ICOYzzyiB0ieEaVLtVWcVFsd/1FuNGbS2V2+VbNEUJBPBup6wE5aclEKU/yvrhRoGLOXF6zs8c1IGrN1BvbG2yRVR3JFQ5cnyDPfa5Ioo7kiY5nj16ls0BckA9uFhxvu+tskV2QcScS+RUs3x/EZY7i1A2VOCRJjseO2CEikVihDS/8hh/iTyBjcnv45La8OzRYVBSu8iZXSSMrpI6V30Gkl69CTdegc9epIuvYOIMHj99de5evXqbaGvEzpTU8QXFjAPHcOZXmTInKPXWtmWy/HBuWvEuleRQO7QIdA0CoODlPr6mnqtY2NjdwzD56bTvP7QQ3ww8Ms4b98ibWaIShNTRMlE06w+epLHX+hsUo0VuyFU4UlUKLDAUC9PVjxgIZnq/VfyJSVS+o1QhbiOSdYL1eX1/l+K1uCVkzJo/R3UWWWv2p3vCNccr3a5muP5j3pjUNDEojvN8foJ3n8pzISqz1NhNwODZ5jhgPV3oOwpQcLrNwnivUW7oERKhSJkfOxf/lXST53gBgsN/yxdanS5MZIbj86dzzJGVBoIdg4ODi5ZVsiyslFiWRavvPJK1Wfc6uvj1AcfoDkOlzoeIF28RUxuz+Uo9BxrpRLLBw5QNE3c5WXee+ABcrsI22bbNkII9H2KN7ibUHFTU1NM6zHe7Ptc1T5r7Rbj4+P7UpdWE5SweZlMpm5dr14fBbqqyh1rLXC/1cLsCPBQVfnqmtnwa9lNe5icnLzjMUFlr/+HH0+mgP6a+y5dHCei7+/kempqqmH/2cxKAvhEzX1vjL9HureGirQLgtLH7JVW/g+khFzhuZr7Zm5dZny88XOL/UTnJ4Hq8LUTH9xkvPNWVXmY+yA/Mzk5ydJqsea+tdUM4+OXmlyjeyO39iHgUFX5VGaF8fHmRfio5OrVqy35XL9z+ep9QHf1DjvX0HlRI/qazMxB4OGq8rWcE7j5aqPwSx8/MdUDDNTc98HEeW5FvXMq+42ltRjw0zX3vXX+IgsDOd9872FnN/38ev6TNctnpq4wPj6331VqKEL+BFC9uPuDK1OMp5rX5lT7vjOZuTNAdeznXHaW8fGLd3y/n77jXPZB4GhV+czcKuPj55ten/3CT9/xfnHp6lFqzfE0WWj4vOjGjRsNPX9YUSKlQhEy0k+d2Nfz6bYgUdRJlIzyc1EnXtJJFA1iprZDgDQBkwJrFGBPMunk5CQPPfRQTU/E2KlT9H3zm7xxq49r9mEGrfltuRz1o4cZ+FCMQSGQus7SJz/Jw0erJw61+PM//3OOHz9+Rw/I/eLChQt88MEHZDqvACer9l+6PMnv/u7vNqUuzWBwcLBqwjMyMrJvovB+YJomr732mufEbGIyBoxWla9n5/jGN77R4NrtL5cWz1BLpFzL2025luHh4brC0tTUFLZtN7wercC2baanp3d9A3B9ZQz4aFW5Jhxeefnr+1w7WFhY4PXXX2/IDUrO6sRLpHzlOz+kPzF71+e+U5sKIq38H9iOjis/U3PfXOYaE/Z0k2t0j7iPU0uknJxaYKKjut2EuQ/yM1NTU2TXaxvl11dnA/cfL+QOUkukXFzKt+xaZmdnMQx167+TqUztPLuuvdbQ36oRfc3inE0tkbJQCu+inr3ilz7+g7nq/mGT61ffQwuQQ07BiuIlUl66fIvC4pxvvvewc6d+XkoomrUXoi3M3mCCgIkUzqPUEilvTS82tc9T7fvOLK2eqlmey87vepG/X77j3NoAtUTKpZVioMdaP33H+8XUdI1FaIB01hv+W83MzDT0/GFF3akoFAoAevQkh6IDjEQGGIkOciDSy6DRQ1KLI5qQVXh8fJz+/n6eeOKJ6p1PPAFPPUXm57+Kkb3J3I5cjkePwMgh4PBh+OxnOTQ8vOvPfe+99zh16lTtz20QJ0+epDM6zPTl6n3pkVH+xi/9jabVpdls5u08c+ZMq6uyjXp1Wvpfu3gjU11+euwYv/Zrv9bgmu0vf/F6jK/8RnW50Dt9cy1+bB+t4Ms/iPHHv1ldnkyIhvxWX/rSlxr23a/lBb/1K7X3ff6v/13OjIbrhmg/aNX/YGVd8A//pPa+n/2ZZ3joeLB+q98530MmW13+4OnH+PzPP1jzPaoPag1vvNoPueryJx77EJ//y8ebX6F74JLVyXevVJf3DR7m85//fPMrtIFq29Vc/Hed8H51+dHDgw3/rfb79xg6H+W3X6sut2W0pe3Ob/jhf/CNN6P8j9+pLo9FJC88H6zfyrLhH/9Z7X0/8YlP8+QpC/DH994O1Pueiyb8p3+k1dz32U9/go+ctBpZtX3n937cy62V6vIHHjzD53/pgabWRbXv+vzuOymo4aj72JkH+fwvHtnVOfzyHd8USV6uEeCjt2848GOtX77j/eL6i0m4UF1+aLi/4b/VxYsX+fVf//WGfkYYUSKlQtGGHIz0cTQ6xKHoYFmYjA7QpVd7GviK4WF+99DfxZQzPLr+1rZcjqun0vCFR+HgwVbXctccPNANNUTKaLwnVBODMFDwSIIeyETvHnUumAIpoQnrERS7xCtnTBATvdfLc6NytPmLunmyAtjneeUrUu3OfxSt2relQezzvOqs2p3/CNVY69Hf2Y7AsiGiLD++wasvCGK7ixgQMSSWXX1Nqs/zF/VzoQav7amxNjh45QYMYrvzzEmp2p3vCFO7axfUVFWhaCN69CQ/n/oJHu24n4gWvL9/vggL0YN8ZUcux0//JBAcfRKAqOHWLC96CGKK1hEmQ4JXknDXLRuwopEmV0jhSZjanaFD1JCYNQxYqs/zF/UMO0Fse16GBK/FJ4rWUbJqe3cEUhxX7S4weI61IWp3UB5rI0bwrimshGkBJJQF8loipZrj+Yu6c7wAtj2vPk+1O/8Rpj5Ptbvg4NnuAnhP2y4ET6VQNAUhxAngI5QTqkSBZeAi8H0pZbGVdWsWQoh+4OPACSBJOQjVFeB7UsrFVtbtbll1cvxvC1/jd/kmB6N9HIoOMhIZCIw3Zc6j5XXEmluP/SAWqZ17Sa388x9eK7C8BD8/47XKHsqr/6KR4F1TWPFajVnPCOlnErHaIqVadeovvPo7CGbbU2JRcChatXNV1xu3/Iry4A0OXmNQIOd4dfrofEnQ1RG8aworXmNtEPs7KLe9bL66XPV5/qLenDuIbc9zrFVzPN/h2ecF8d5CtbvA4GnHC2C7axeUSKnYhhDirwD/BHjU45B1IcTvAP+VlHKhwXV5BfjEPZzib0spf+cuPvdh4L8G/hJQa1m3I4T4MvBPpJTv3EP9WoaNw01znpvm/LbyHj25kZNygMFID4NGOS9lyuhCF7VXuDcL14WetRl+ekeo10w0Te/6owTNldJTpFSTm6Zj23bdxNnzi08A1Up4diXD+PgHDazZ/nNrMQk8XXPf629eYLC71LS6nD59mmg02rTPCxph8qSEssF3tUa+OWXA8hd1DVgBbHtKLAoOJS+RMojtToUCCwxhCgWmQqsHhzAuRKuF6vP8Rdg8KeMeC9XrLbhTtIZQhVb3aHeF5plRFLskbPaUdkCJlAoAhBAx4N8Cf/0Oh3YC/xfgrwkhflFKWSPle3ARQvzfgN+k/n9DB84CPyOE+FUp5X/flMo1gVUnx6qT473ijW3lGhr9RjeDRjcDkR4GNsTLwUgPA0YPca3BQsP0NOaffoUvzNyq2nW4dJNjf/YjuHUYPvtZGB5ubF32CeVJ6R+mp6c5csQ7YbuXd0fc4zf0M17tDrwNxI1gUxRW+Ve98byZC6ARAep4tKk+z1d4/R66JokG8K5B5SsKBq4UWE7tMSiIhgTlwRscQhWCro4nlOrz/EXYDKdqQVAwqCfeBbHtebe7JldEURcpw9Xnqf4uOIRJHG8XAmhuUOw3QggN+PfAX96xywEmgVXgPqCnYt8g8FUhxCellK81paINRgjxq8C/qLFrBpgGhtnurmcA/1oIIaSU/7oJVdwVl3/vde77xUfR99Ga6OIyb68wb69AjZCrcRGl1+gkpXeSMrro1TtJGZ3bnpNaHCHuYuC+dAl+//ex12wELoPWPIPWAoa0sYXBfGSAqD4IN2/Cb/82/NIvwQMP3PM1NxovgUvFsm8NY2NjnoKZ+N1kzfIT96U5c6an5j6/srTm3b6Oj57m9DG7ibVR1MPrRieI4ZjAu96qz/MXXgb7RExyN0N4q/EK2ajanb8wbe85azDFotrlRbO59VDcmVB5UtZZM1pQbc9XhG0hWlz1eYHAa45n6JJIAC3DKjdgMDBtcNzw9Hn12p2UBPJ+Kax42lMCOMdrFwI4FCkawD+iWqD8X4D/t5RyGraEzL8M/Ctg0+WoA/h9IcSHpJSrTajnp/Z4/IXdHiiEeAr4jR3FrwD/mZTyrYrjHqfsaVkZhvZfCCF+IKV8fY/1awg//tcvc/UP3uTX/uCfMxVfYd5u/E9TlCYZa4mMteR5TEQY20TLbr1j45GkW+9gTq5y/eItXCnRNkb2yPw8A+fOIRyH0uwaT2YXiUpr23kHrXn43jizo0ewe3qQ//JfsnD2LNbg4K7qfunSJa5cucJ7771391/AHrhy5QqO4xAz3Jr7VVgc/+FtwGpyRfaBekY3FRrHX4TNgOVVb9Xn+Yuw3cx5tTu1yt5fmE7Ec18Q2149D15lwPIXofLuuENOSoV/CNtCNDXHCwZhWpQBdcZaJVL6inoehkHs87zGWscVmDbEvKe0iiajPCmDhxIp2xwhRD/wX+wo/sdSyn9eWSCldIE/EUK8DnwXOLax6xDwq8A/bXBVkVJ+s4Gn//9QDuO6yZ8Dvyil3Lb+T0r5hhDiOeCPgc9tFBsb77+X/Jn7Sn5mlZ+yH+TkoZPMW6u8X7jB+8VJLhYmKcjWLGm0pH3bG7MWfeXH19wrRCyNqKXx7NfPM5hdo2ulwNC1RbqSPRRJMGMcZF12Ey+ZDJVmKaxMo721wOLBgxSTSdZ/93e58LGP7apeV65c4TOf+QynTp3at2uth5SSS5cuEYs4pM0ZHq2RY5OZw3BwqCn1UdyZMIUCi0VACImU1dekbuj8RdgMCWqVfTBol3an+jt/UdeTMoBtz8uAJaWgaHrnM1I0nzAtCNI0iEUkJav6mpRnkb/wFMcD2O5AhT8MCmHq78A7WoZqd/4ibLlQ6wmrRVMQiwTvmsJK2MbadkCJlIpfA7oqXn8H+HWvg6WUU0KI/wioFAz/UyHEv5ZSLjaojg1FCPFZ4KmKokXg7+4UKDeRUppCiL8DvAf0bxT/pBDiU1LKbzS2tntnMNLDYOTD/GT3h3Gky5S5wJS1wC1znilzkSlznjW30Opq3kaAFXXpXF0lsb5MLiE5dHkOO+HgjOSZOxJH02boZgaAJVNnbiZOailPp71A7lg/3brNwUd70Q6O0Kkl6NQTG89xklocTWjbPvL06dM88cQTTbvEH/7Jn3D0y/+GL8xU5888XLrJ8q9/CSs9wMLjjzP6kz9JNNrgnJ+KungZ7b1ujPyMEGXjaa1rUmKRv/Dy9AqqcVsZsIKBV0jAIHqzgQozHBTC5klZr84FUwTymsJK2LzHE7HaIqUaa/2Fl1gU5HZXC7UgyF+EzYNX5bsPBvU8qoPY59Wd45UEPcngXVNYCdtY2w4okbKN2Qjh+rd3FP+/pJR1/7FSypeEEK8CT28UdQG/BPzP+1/LpvAf7Xj9P0op5+u9QUo5J4T4n4B/suM8vhMpK9GFxpHYAY7EDmyVSSnJOvltwuW8vcK8tcqam29ZXY9fmgWgezmPYTtYEZ2pI31IbftAI6IOVw938UB2jUjJJDt3g9W+JJd/9B948+OjVecVQIcWp1NLkNTjrD6wzJL7Jj++MUenkdh4dJSFzc1tI0GHHkffIW7eDbEbN/jYe+/RP6IjSNfMsenYkJifJ/Z7v8e1QoGxn/3Ze/5cxd0hZZ0cbUG9oYtCvkZuWRXu1V+EbVKtDAnBwKsfqJfrzM94LSZR/Z2/qOtJGcC2dycDFl3B7MfDiOdYG9g5nmSlRrkaa/1F2MQi5dEWDNrl3kKFGfYXYQv3Wm+huurz/EXYxtp2QImU7c1TQGXivquU8zDuhn/LbZES4K8QQJFSCBEDPr2j+Ld3+fbfZrtI+VkhRNTLA9OvCCHoMZL0GElOJY5u21d0TRbsVeatVebtVRbsVRascp7LRXsNl9p5FfeD3sUcAN0rZS/Plf5klUC5idQEK/1JBjNZulcKrPYlt95fdSyQc4vk3CLYQB8scoN3Mjfq1kcg6NBjdBoddFUImckNYbNrQ8wsi5vl7aQeJ2kkiGtRhBAwPU3fN7+JLiUjUXgy+1rNHJuHb0mMU6OQSuG++iraE0/gptN7+wIV+0LJAjdEid5B3dAFhTDlyQLV7oJCmMJbg/LuCAqh86RUBqxA4LreXtWh6/NUu/MVYQu7qdpdMGiXewvV7vxF6MK93mkhmsI3hG2sbQeUSNnefG7H62/cyYuy8tgdr39KCJGUUtZWhvzLTwHJitcTUsr6atUGUsrrQogPgPs3iroo56X0tTflXohrUQ5FBzkUHaza50iXZXuNZWedFXudZWeNZXudla3X66w6OSR3NwAYlgOA7pSF0GK8fgbqzf2bx2++f7+QSHJOkZxTZNYjDKMXGoIOPc5z594hZa/QeyKBMfsOfYMxTBFlrmOAnJYkVrAYWF+mYJZIvPcO8sAgblcPkZe+Semv//K+Xo9id9SdVAf1hk6FP2wqpmly4cKFPb9vceUpoNqFaHnhJuPjuxqmds3p06f39Xy18LqhU+3OX4Rtxalqd8HAsmvP8RJRiXbvQSyaTj0Dlmp7/sErvDUEeI6n+rxA4BnSX421igYSOk9KdU8bCLzanaZJogFUJOrP8ZpYEUVdHIea4e8huGNtOxDALkGxjzyy4/X3d/tGKeW0EOI6cGyjKAqcAn60HxVrIo/seL3r72CD73FbpNw8X2hEynroQmMg0sNApMfzGEe6ZJ08K84aK3aOJWeNlQ0hc83Jk9145NxClZRpR/TyOfSydSpetFitU5940dp2/Ob7/YCLJJKZIzKdISclY8vrmJpD8WiEqSMdSK2ATgEbmHUFxo0cvct57JkFJgaGOf/eTb795hSlAwN0aDESWoyEFiWuRcvbory9+TquRUmIiu2NfRGhuvy9UjAFaXOGR9ffIm1miEoTU0TJRNN0Z4+y3Rk9GCjPouZy4cIFJiYmGBsb29P7ilbtPiwW2V8P9omJCQBs297abgRrK2PAfVXlmfk1xsfH9+UzTp8+rXL43iNhW3GqVtkHg5JTe34SWMNpnXor73H/ELY8WeBteFPtzl9450JtckX2iQ7V7gJB2DwpvepdMAVSglDNzxfUWwAZxN+onu+E6vP8Qz3bVlDneO2Asli3Nw/ueP3eHt//HrdFys3zNVSkFEL0AEeBXmAdWARuSSnv1m1uP76Deudra3ShkTI6SRmdUOemy5Eur739OgXNYvj+w6w5eTqPxuhauYA+GKEje4v+eYuZPgMt5oDYPqgIV26Fd832JoByeFg/sdscm1ITTB3tJ7leImI5dC/nWe1LMnLxJm/2x1h21u66Dgb6lmC5JWiK6DbBMy4qxM/NY7dE0AhxURY7RRBnlHtEy2To+sOX+cJMtTx+uHSTw3/2CvELw5jPPBOocLxeBixltG8cY2NjnDlzZk/vcWS89rnuP8SZMwP7Ua1tTE9Pc+TIkX0/7yaxSO1huuQhxu6VTYF1r9+zYjteuRqDasDy6u9KlsBxQPfPeqa2xvTypAxou4saZQ+BWuHi1VjrH+rlpg1sn+fp0dbkiijq4rkgKKDtzitHm2p3/iJ8C9G89xVM6Aio6B82wtbuNK18f1FLBFOLvv1D3TleQNteO6BEyjZFCJEAdlojb+7xNDuP35ubyB4RQowDHwZ2Bn5aF0J8D/gj4ItSyr0E49xZZ19/B2FFFxpJGSPpxDidOAaA9rF+EpfW4dARIhmX6RkH/S8OMNHxAFrMQY9Y6FGT/p4Cn+q4REQmseIaXQeOEMXmwoc+RJ8eYd0tYEq7tRdI43Js7gUbhzW3wJpbuKfzaAjiWpSYuC1cxrTq7cTWdnSjPMKsniUqdZbsNeIiQlyLogn/xXLTr1whfu4c2XkXQZxBa55BawFD2tjCYD4ygKF1oE9NkXjxRYpnz+KcONHqau+KREzW9A49cb4X7RP34w4NtbqKCurkBmygAetuxNTd8t3rHfDd6nIj2qWERR/hJaB4GSD9Tt2QTBYklUjpCywPT8qgGhGEKBuwcjUMJEqk9A91V9kH1Cnfy2iv2p2/CF3YTdXuAoGX0T6oHrx3yv8cVNE/bIQtlQSU++qaIqXq83xD/TlecNte2FEiZfsyAFT+ay1gbo/nmNrx+sA91ejOPOJR3gl8euPxXwsh/hMp5R/s8pw763xrj3Vq9nfQNrhDQ7jDw2jT0zijo7jTHzBkzdGbXSETTZPXknS4OcbmZ0gftSDSg33yJH2dKZyRET708Atb5zJdm5xbYM0psO4WyDlF1t0C606B8Yl36U33EetJsG4XWLcLrDl58nYR9y7zadbCbzk27wUXSd4tkacEe61Wd/np92+9sVUUFUYNoTNCbEPEjIkIsY3nuHZ7X/m5/J7YxnviIrJnT0/HcbaFuYwtLjLy9a+jOQ7ZqSJPZleJSmvbewateeR3sywePoTV3Y37W7/F1HPPUerv3+MX0lxii4s8d36WT8xkq/b1XHiX1d/4EwqDgyw8/nhDr2ViYoLjx4837PxhwDsUWDAn1SoEXTDwFMcDKhbVExryJUEyoNcVNkzHOydlUEnEJLlidblaZe8f6hkTgzrWei0oUWOtv/AMuxnQMUmlkggGXrlQgyrm1euny/+xYF5X2PDMwRtQcRy8x1rV5/mHfB3XpaCOte2AEinbl84dr/NSyr3+U3e6du08ZytIA78vhPhNKeU/2sXxO+u8V3e1hnwHQogD7D3ZXTDcuOqwMx9abGiIkbffRnMcPug6hj4/Q0yaHC1Nbh1jCItssUju0CGsUgl3fp6pM2co1clxZlCOF9xLnPe/s8L9Dx3m9OnT246RQlLCoohFEZMCJoWK7aKs2Mbaei5iYtVQ7sKUY3O/MaVd9nZ18/tyPoHYEjErBc+drzcFz+mDBRIDBSKReSJS59Sb36HoFkhk10hNZ1gTXRRFnEzktjh+0MqgOzad16+zfuwYVnc3A2+8wdSnP70v19AIOm7dIv3qqxwsDnCDZJV3aGI9Dq5LYn6eka9/nczTT5M/dKhh9bl69Sof+chHGnb+IGNaYDvhWnXqvcq+ufVQ1Cds+YqUASsYeIV7DbIRQfV5/ieMIqXKw+t/pGyfsVa1O3/RiigtjaTuHE+JRb4hbOGtQfV5QaAQwpD+7YASKduXnWJajbW+d2RnzMhGiJRF4BvAV4HzwGVghXKGwwPAk8DzwM+w3TP0HwohFqWU//wO57/X76FR38E/AP7pPp0rEOwUCQFK/f1knn6a9KuvshJNcan7WJWwspRK0nvSBE3D1XUyTz+9Jw+wn/qpn0IIweLioucxESCCQTcGkLjjOR1cSsLGFPbW88GOEv3WFWKdOvrUFYami1zXBnB00CM2mlF+6BELXdi+z7HpZySSojQpOiaru1l38FN9XGSVi6ySml+jK3sBEZWMZaYxkg76Y0VWjvQR5TqGo+PaOrO24MaSRs9yDjl/mZnhUfRSnqnEdZyhIWLCILrD4zMqjK3tTaE0qhkY6A3P8allMiReeQUGBxnW1hnOvlPlHTqQdTg8pZU9l1Mphi9fpvDEEw3Lt7lzUYLiNvVurINqtPfOk6Vu5rwwTZMLFy409TMXlp8Eqt0PlxduMT5+val12Q+W1mLAT9fcN/72RRYHto8Rk5OTjI2pyP3NxvQI9xrURRmgVtkHAS9jYtSQGAFdG+j1n1FjrX8wbXBq5KuF4Irj3jkpVbvzE97hXoPZ7uqG9FdikW8IW3QgUGNtEMiHMKR/O6BEyvYlvuP13aQ137kW+M7qzd74l8D3pJS11CMLWAeuAv+HEOIngC8BIxXH/DdCiK9KKd+u8xn3+j00+jtoG6LRaO28ZGfOoD3xBGv/5PvI6VnmIkPMRW7nzHtwwCI9XMIZGcF85hl6GiSo3CvagQ+R+OIXYUAy9fZ1BmMdDL0T4Q35IG5FTkZNuvxC+h2OGw5Op07q8EPYwmXoqc9xqr+Lglsi75YouiWK0qTglh9Ft1R+liZF16S0Q4BS7I7jl2YB6F7OY9gOVkRn6kgfUhMIXHTNRY+Uv9tLySQPZFeJlEqsz91ktS/JzJsv8ebHR/f0mUJCROoY6OVnqRORGgbbt8v7tHIZt7eFLYmgE9OiG+fZOGZjWyA49Bd/QXxhgUg2y8HpeRZljJKIbgudnHAmyS0vIX/0oy3v0MLv/E5DvEMnJiaw7dbnivUr7eTdoULQeXPhwgUmJiaaKpqVrNqW+VjEbVod9pNYxDsmude1KppPKD0pvURK1ef5Bi/BOKjjLKixNgjUneMFdGGG6u+CQehyodb5v6g+zz94iUVBbXdQZ6yt472naC5e4088KtG0mrsUPkCJlO3LTo/Bu1lLsDOQ0d14Y3oipTy3h2O/K4T4KeA1yvk2oexZ+c+An63z1iLQUfF6r99DQ78DRRk3neYrD/5tfji7zKPrb5E2M0SliSmidB5JUfiVUdyhoTufqIVU5tic6+ujY22N/sIMH7VWtwlFaTODrq2Q7ymWhaLFdQqDg5RWYnSsmHQg6CdOtb6+4/OQWMLGFA6msDFxtr/eeLa2XpfLrB3ltgimYfpu2fRg7V4pO0mv9CeRWu0JjtQEK/1JBjNZulcKrPYlt96/F6Sg/HvsOcHn7hicz/Pp9fPoMcH9mVuIPolzKMWtAwdx5TqaUyDv6FwyBukp5ulcXkPLXGNtcAyxPk3BzWAPHCCCvvEwKrZ1NLH3WV5PTw/vvvtuA642HORLgrQ5U9XfZaJpulaPwsheo4G3HmXAujvGxsZqL+BpEK6ovdbq/hMjnDnj75y7tbDqrIU4et9Jzjy4fUGP8vBuDWH0pPQKJaX6PP/gZUwMchgwlRvQ/9QTT4JqtPeqt2kLnMbc3ijugrCFGY4YoGuypmeyGmv9g1fYzSDP8VSEIP8TxjDD7YASKduX9R2v6ysetdlpydp5zqYipbwshPhHwP9WUfwzQog+KeWSx9vW2S5S7vV7aNR38D8Bf7DH95wA/myfPt93FExBJnqQr/R9blv53/twDneopU1v15SefZbEiy+iDw6y2NmJs64TM7fn2ASwtAjrx9JY3d24us7C44/v+bM0BDEZISZreyfsFgcXWzhlAZPbAmblwxT2xr7NMrtiu/za2hThfD5vM6zynbTulMXZYrz+97e5f/P4zff7iSOXprFw6FjOIW0TK6GzcCpGUluuOvZtN8ID60UilkNm+QqrfUkuX/xyXe9QA70cvnYjx2dM33jeLNMr9m08r8kIq3mN769dIKZFiIoIUWGQTZosaTnmrVWiwiCqGURFBP0uhNCgomUy9P7+t/jCzErVvsOlmwz/yStEzw9jPvNMw0LxNoJETHoKr87UYfQRfy80aRc8DVgB9WiLGGDosmaOV2XA8g+h9KRUBizf42nACnK7UyHofE+9sSeoba9u2E3V9nxD2Po8Icptb71QfV2q3fmHsLU7qLP49m7iEyoaQtjCW7cLSqRsX3aqOh1CCCGl3Ms/dmeSPD8oRV8EfgPYdDPRgE8Cv+9x/Drl3Jab7DXxX0O+AynlHDC3l/c0OqddqynsDKy7QZDiibvpNMWzZ+Hdd+k8cIB3ikdZv7W0LcfmfGSAoyd6+cQDFhgGxbNn6TlxotVV3xeklJSkRcm1tsLSboamLVaEqi26FiVpYroWxY3jS/J2ecm1tt7nsr+TDDtSDv/n6GVRLF60WK1zfLxobTt+8/1+otHeobZ0sB2HnLNHR/LHDL64+I3tZQ/BWyzxB1NvbCs20IlqBhFhbAmaZQGz/LpcXs4DGt3cFhvHb5Xdfl/1ecrnaLUYql+5QvzcOfRZiSBWlYN3PjKAITrQp6ZIvPgixbNncQLQP2iZDCf+wlt4jf27VzCOBU94DSOeeWMCvNo5Hq1twFKeRf7BdGqLlEE2JKiclP6nXiiwoOLVV6sQdP6h3m8R1LG23n8mr4z2viGUfV5Msl6oLlfhXv1DGHNSxj1skGoBpH8Im+d4u6BEyvZlAZDc9muKUBbrZvdwjpEdr/ckqjUCKaUrhHgF+KsVxfWSOc0BxyteH9rjR/ruOwgrYfHucE6c4L2PfpSu5WX0K6IqxybAsGtt5dgMk8FeCEFcRIlrUXr2vB6gGiklNs420bLkmlXCpiktihvCqLn1bG+JotenbhBJxiGqsdKXZGA2S7Y3Qc9Knt7FHHPpnpqinnDlloCX7S07Va/03/t17Tdh8A61cbBdh+o0wPuLjrYhbFaImBVCZ6W4GakQSW8fd/uY8rPOgr6OITWW7LVt4unOhSVaJkP83DmwbbSlVZ7MXie6I7fsoDVPx3mJMzqKm0oRP3eOwvPP+7qf2BReO5dcBPGawqtlJYkFTHgNI67rLaAEbaytxMuApQwJ/sG0a9+SBtmQoEJc+58wGrASOxORbKDEcf9Q77cI6ljb4dHuQPV5fsJzjhfkPk+Ntb4njGE3PUOrq3bnG8KYd7wdUCJlmyKlLAghJoGjFcVH2JtIeWTH64v3XLH94eaO1/WSd00AH6t4vfOa7oRfv4PQEabJTa6nh6mPfIRvOQ8wk32/KvTh7MMP8uQL/hO7/IYQopwfUTfo1GvnUdsNf/Mf/02e+exnGRsbI3rwNIfGv4KbcEjxPvOzBtGZFJdT9yEMiaY7CN2hv3Odx+1LdJQMbCNGtOsAfQ6Y9z3IsJXE3gx1i4MtXCzh4LQov2cYvUMbhYOLI02Kzj4u++4uP/37Wz/aVhwROpEK4fMTf/omA8tZOlcLDExm6D+gYeoRFjr7yOsJ4iWLgbUVlgslxI/fYP2B+3BSKcyvfon5z//cdq/SLbG0/LibvKH7QaXwmlhb5snsZE3hNfEWaKdPBEZ4DSv1QhQFcazdxDv8YZMrovDE8shJGeR2F/cw2qt25x/CaLD3ErlUu/MPXkZsXZNEA2qdq+cBqsJu+gPLBssOX5/n6dGm+jzfEMaxVoX09z+eeccDuhioXQjoNEixT1xku0h5CviRx7G1eLDG+fyAteN1PbehnXU+tcfP8ut3EDrCGJ6k1J/mK33Hq8r/crQIdWUkxX7S29u7tW329WMODBJfWKB46CjO7Dyp9XU+nJ0gE02T15J0uEVORqdI95lIEqwPH+Oo002hb5CnEk95Bn12KXt+WsLZEDHdDRGzomwjB+jmaxt3Y3ujnOrtOwmgK/3h8w4NA5Z0sGTZOzQ1v4Y+M82KlAxdncYSDoUjSaaOdCE1G1ijANxyY8gb6/Qu57Enskw8NIy8/AFfv7DO8kCn52dteYhWCJibr4udBXSpsfAxnW91XOT9xeUtb89KodMQ+rbQuJEKr9CI0LcJo7ooC9uxl17a8AxdInl1gqiMUNpYjFH+L+VImxmkmcO4eBH75EncVIroyy9TfOGFpvwOitsUSsIzb2hP9hgw0Ooq3hVenkUq/KF/CGO4V+Xd4X/CeG+h2p3/8Vp4G49Kgpq9pV5frcZaf1BPPAl0n6c82nxPKMdaj3anwgz7B6/fIqhh1dsFJVK2N+eBT1e8fgr4d7t5oxDiIHCsosgC3tuvit0jO90v5usce37H66f2+Fkfv8P5FPuE5yATZAOW16RarcBqKrq+3VNw/oknGPn617G6u7mS6qczd4uYNDlamtw6JmaYuIZB7tAhrO5uXF1n4fHH636OhiCKQVQa7HMqTSQSu1LgrNiOjoxw/PxLuMl+OkUJc03S/brB9QPDiKiL0Fw03cHQLB5au0GHCZYRodDbjSEl1x84uL+VVVRx/FI5iEH3ch7DdrAiOlNH+qqEZKkJpo72k1wvEbEcupfzrPYlOT6R4c2BUc/zb3mIShN26tmb2sARnUvMcmltLwEVaqMhOLBQ4Ln3z6NLwf3v3cSwXdxTvUwdOIhLFsPNUXQ0rts9DDvr9K+tIi++yeIjD6JdnWXyyiGcoQO3xVDttiBaKZCWBVQ1nb1XtEyGjj95mS/MVC+QOVy6yZFz3yY+cTCQYchVbsBqTNPkwoULra7GFvniR2qWh3GVvTKc+gfve4smV2Qf8ezvSgIpCawIFibC6N1RT2xQnkX+oJ54Emh7ime0DNXu/EIo7XhqQZDvCWNI/3ZAWXXam/8A/OcVrz8phBBSyt38a5/b8fpbUkoP/6Gm8xM7Xu8M/1rJK0AOthLkPSCEOCqlvHGnDxFCHAPuryha2zifogGEcZBJeIQnUSGZmsvg4CCTk5PbyiYPHeL+8+eZlSd4t/vJqjx6K11QHMwiTRN3eZkPHnmElYUFWFho0VXUJ2vG6FxZYaWjn8TCMokVwfFbi1sebXE3x0EzQ3ogj0Rn8eBBhqcTrKdSPHTlBPKKxNUkjiZxdImrgaNtlOny9rYmcXe+rlFecEokupJoMQNTlvOGlr0K25NN79XulXLivJX+ZE1PVygLlSv9SQYzWbpXCqz2Jbfe7xdcJCMTtyhJi56lHI5ZohjRmX8wToe2UnX8e67ggQt5IgWHqZmLrPYlufyD3+fNj3sLrzvRegWG1Hjx5o+2hMudHqGG0G+XaZv79RqeoRViqFZZtl0kbVUY3f1mM29ovk7e0IiWRA9o3tBETNb0ED023oP21AO4Q0N3PknIuHDhAhMTE4yN1Uvb3jwst7Yn5ezMNcbHM02uzf6wsnSC7bcJZRaWc4yPj9/z+U+fPk006jGRVOyKgkea6yCLRV5GX8cVWDZE66clVzSBMN7TRgwwdIntVF+bEov8QT3xJIx9nmp3/iGMfZ5yNvA/YRTH2wElUrY33wcWuB2/6zjwU8C3dvHev7vj9Z/tX7XuHiHEJ4CdlrOXvI6XUhaFEF8Hfq6i+O8A/3QXH/d3drz+CymlkpcaRBgTH3utOlVhcZrL4cOHMQxju7F2bIzSqVPM/Q/zyLUV5iJDzEVuG7JPDSzTPxijMDjIwuOPM9Tfj5/N3PrAAH1f/zqa47AWSWO+tVLlHQoQ7eqmcPgQnd3ddOg6heeeY6y/f/8qIoENLfL0ke0GVldKfu8PXuT4AycY+9CDlNxN8dLe2LY3Hhama2NtvK7cb23ulzaWa1OSFeUbxzhVroStx7DKX4rulOtWjNe3Im7u3zx+8/1+otnCqyskpnAwnfy9VXwPbIbR3RnyNrJD/KwlcFbuNza3N4TTlS6TOT2LLjUMdJbt9fL5N8RXbR9dYSrzhrK4wpPZGzXzhna9I2FsNHB5Q7VMhl/44I9Zn6n2ED549QqJL/4AZ2QkkB6i98rY2BhnzpxpdTUAsN3aYlvM8F9/vVtiRu1+uWTde57niYkJAN/8fkHFy3Aa5FBg9e6LCqYgGgnutYUFr1x5Qb6nhXL91/LV/ykV/tAf1BMpgxx209Oeotqdb/D6LYIsUtaLWqDwB2EUx9sBJVK2MVJKVwjxO8A/rCj+p0KIV+p5UwohngWerihaA36/MbXcPUKIJPCvdxS/K6W8eoe3/lu2i5T/ZyHE/yCl9AwTK4Q4APyDGudRNICwJnr3WrWoVmA1l02Bspax7//xSopJd7HKA+fIQx+i5x88QNfQEAdaUOe7Qb/vPuLnzqHFXM5dru0t9aGf6KA/JsAwKJ49S08TvaU0ITBcjYSM0m90N+xzXOluEzs3t8tCpr1dDN0QO80KsXO7ILrxXteuLtuDZ6gdKRutHb3smRcvWnWz0saL1rbjN9/vJ8IovO5kWxjdcsH+cBrOs7T18ku3Xt+220C/LXJq1YJolddolWh6+73Hv/JVOgqLxJezGFeu0xHVKYk4s9Eh1rUkCavIUHEezVlHC1je0E0P0eGiwQdoVX1eYrUXZPc2D1FF85HS25PyQ6fu48yHR5pco/1hPJOoWS70pBIXfYKn4TSEXkVQNtj1JIN7bWHBM9xrgO9poSzur9VYJ6aM9v7Ay3Mcgt32vOqu2p1/8BSLAjzWerY7ZcfzDZ4L0QLc37UDSqRU/DrwfwI6N15/gnII2H9e62AhxAjwb3YU/3dSyroxDoUQO3uCn5ZSvlLn+P8O+HUp5XS981YcPwD8e+DDO3bd0SNSSvllIcQPgI9tFPUD/1YI8QtS7nAnKH9WlLIgWele9KqU8mu7qati74Q2PInKoeB7CiVBJnqQr/R9blv54cfWcIea57G1HzgnTlB4/nmcP/oWkmrvUADbzeGMDIfaq0gTGnERJU4UGqjtuVJiS4c333kLG5cHTo9tCaG3xczydt+xJL3Z94kMddG9Pok7ZTBhDSMiIDQHTXcRmkMi7tDXaTK0NIcmdIqpLgyhs9rfeecKNZkwCq9+wcbBlg6FWjlG90Bqfo3nrpxHSMnYxWkMzYEPJ1k4oqNpU2wuFVh2JW/cWKZ3OY/z7jSXP3QYsazx7R9Os97fg4G24fVZftY3X0u95j6j4pjKfduP0beO1RAI9jYuxhYXGdnwHk9kIzyZvVXlIZq6dYvit0q3cwv/1m+xEo+DT0KgtgslC9LmbNVioEw0TWr9PrZPt4ODyoXqf8K4yr6eF6i6v/AHXn1AkNsdePd5RVM0crqt2CX5Ov9/rxQ0QaBeu1O0HilDOtZ65K5WaZv8Q1jH2rCjRMo2R0q5IIT4b4D/pqL4vxVCHAH+2aZIKITQgLPAfwccqTh2GvgXDajafwJ8QQjxVeAPge9JKa/vPEgIcRj4PPCrwE6L+p9KKf9kl5/3j4BvA5sJpn4W+LoQ4j+TUr5V8XmPUb7eT1S81wF+bZefo7gL6oXrCHJ4Es9Y9mrln2/wantBbXduOk32l/46v/U1u6ZB+Gf/ylEOfXiw1dUMBZoQRIVBXEaYmJigU1bfzWhADLCTx0ksXwSRImrPIhaipKZgInEStyLv4dH+LA93vUEsH8c1OjFi93O0oNGX/iyPLKewhVsWsISLg4stnI0yF6dyX8X2/NIC3f293JiaJH3oIJ2pbqxKr1DXxpbOlqC6GVL3Tv+Alf4kA7NZsr0Jelby9C7mmEv31Az5Kly5Fd4125vYer+isRy/VA6B2r2cx7AdrIjO1JG+qt9IaoKpo/0k10tELIeOlTVW+5IMXr7O5NDu84beC5oLmisqHqBJUV3mCjQp+InX3qJYXKUzm+e+4hql7iglLcpcxyB5vYNEqUTCmmY9twKXVlkeTlPsSJJeX2PVeY68U9wKxSv2MbyuYjtaJkP0yy/xhZls1b7DpZsc//K3iV8/GMiFM8q7w/+EbY4H9euu0kn4A6/fIR5goQi8jfaF0u3V8IrW4d3uJFqAU6zXa3eK1uMV3hqC3ed5LQhS46x/qNfnKfyLEikVUPamfAr4SxVl/zHw94UQN4BV4D6gd8f7CsAvSSlXGlSvGPBXNh4IIbLAzEZ9IsAQMOzx3leBXcchk1J+Vwjxjyl/F5v8FPCmEGJ643OHgYM13v5rUsof7PazFHunnkEnyO76Kpa9/wljmIhETNb0DgX4h92LgN38SoWY06dP3/GYUl8fxYEB4gsL5A4dwpldYMiao9deIRNNk9eSdLg5Ti3cIOasIIHcoUOgaRQGBzH7+tEBXWrEMLijglhJxzFOHz/Nvzr3r3im70HOjN05DKGUEhsHy7WxqgTM8jMfzzBy/U9w4jap2QtgWSy9ZTCRGgEDNN1BaC66sHlo/SY9joEVjcLAIJ1Csnx6lG6tY+t8fswlGnSanTf0XnA1cDXJbhp3an4NvbhAtlNy8NocRrfDel+SmSNJpLZKlFUc4Lobw7oRpXc5T2R9mqv3DdOT1vkar/Clm29snW9nqFzDK3zujv2bOURv7/d+r1GRr9SoKNvP/KN+YzMc71rWQZCoGYI8oiW3heN1mhiC/F5JxCBtzlQtCFpeP4A2exR3yM+ZrNuDsM7xvFAeHv7Ay7sjyO0O6iy+NYUSKX2AZ7sLuMFeeVL6m3az4xVNgZQQ4ul7YPDO/9zceij2hhIpFZu5Kf8q8L9R9krcRAeOe7xtEfhFKeX3Gl2/Cro3HvVwgd8E/staoVrrIaX8DSGEQ1morIxKMkxtMdQB/qGU8l/t5XMUe6duuNcAT27q3cwp/EE4w5PUyVek2t6+E41Gd5V/TDt4kMSLL4Jt8+6tEaK5y8SkydHS5NYxnR0uyVQKZ3SUWCoFhkHh+ec50GQPIyEEEQwiep1p5LGjJI5dRpueRjsZxbh4kZ5JyYnrCxvCaw8JN0fazDA4GOfQgRj2yZOMdKdwRkZ44pHt64w2c4lWeXVuhM+9ePkStnA4dOwIdoVYuiWcbgiq1o59m2XbyjeOtfctyaQ/CWve0Lv1EO1ezrPal+T4RIY3B257iJbbiAM0f1m+gb4lWkZ2PG8r17xE1NrC6E1jCR2N3lJmmzBaeZwuGudaoWUyxM+dA9uGxRWezJ6vCsc7aM3T82OJ9uAobipF/Nw5Cs8/HwiPSi2T4dTLr/CFmeWqfUfMSRJf/DbOyEggPUTDRCjneHU8U+qFe1Q0jzDmZ4M6nkVq8a0vCG27U/YUX9NudjzHFZg2xOrfUimaQBjneO2AEikVAEgpi8DzQog/BP5L4BGPQ3PAvwP+KynlXAOr9PeBZ4CPA4d3cXyGck7K/0FKefluP1RK+S+EEC8B/wz4LLfDv1biAl+hLIS+fbefpdg99SaZQV6B5VX3oilwXQIdeiUshHG1cz0dol5IFkVjcdNpimfPEj93jqVIP9e6B6s8i0S6h8OP95SXZxoGxbNn99XAPTExsW/nAogNDTHy9ttojkOkvx/rarZKeAXIWZKZ/n6sUgl3fp6pM2cojY/v6bMKE/OMjY1xpvPkvtXflW5tEVM6mK6FvW2fUyF8bhc/b7/XKQuo7s5jnG3vc5vkNRrWvKFB8hC9E5v5R4tynzvnrvLTn894T2MFYpsgGt0QL3cKmoanZ6i3B+rhr/45ieIyseUs+gdXiesGJREjE02TE50k7ALp0iyGs45x8SL2yZO4qRTRl1+m+MKuA6W0hE0P0a4liSBW00PUsTsC6yHqB0zT5MKFC/d8nrX8M9RKTp2Zvsr4+Ow9n78ejtOYRR6aBrGIpGRV93nKs8gfeIWgC7rhtK5HW4DDOoYFT89x5UmpaCD1FikEWSCvH7VAEIsE99rCQhijZbQDSqRUbENK+UfAHwkhRoGPAiOUp5UrwPuUc0MW7+K8e5olSCn/V+B/BRBC9AMPAkeBQSBJ2YtxGVgAxqWUV/dapzqffR74S0KIAeAnKHuTJikLtFcofwcL+/V5ijvjdTMnhAx0oveOOjcFBROS8SZWRlGF63rf4AR5cqNp5Ru6WtemQg23FufECQrPP8/UD36AZIa5yBBzkdshAbt6TRBmQzxwhoe9oqffPaX+fjJPP0361Vexuru5cOB+zLl8lcF+IB3h0e4lXF0n8/TTlPr79/xZY2Njuwqtuxc0oRETGjGauxz29770IifGRlkv5vjhm6+TPjSMq0kcTeJqEleUQ59WPhxtR5mQWyFSnW3HsrFPkuvuYmhmjbWeDnqWw5M3NKweos1GIst5aeX+hgBPza/x3OXzCCkZe38aAwftTJKlI31EtMmt3BLLtuDtWyv0LuVxfzzLtQ8fRVt7hx+8naV4oH8XXqO78yqtFFrvNdRupYdox/oKT2Zv1PQQNd6QiLHgeYj6hQsXLjAxMcHY2Ng9nSe1PsdPZt+uys89kG/sAoyJiQmmpqY4depUQ86fiNUWKdUczx+EcQEk3CEPb4Dv18NCaD0pVfocX1M33GuABfK6kalKgp5kcK8tLIR1rA07SqRU1GTDG/GuPRL3EynlIvDdjUczP3cB+NNmfqaiNt7xxGWg473XSxZeNAXJgN80BJ16XoVBX+2cUCKlb3HTaf7w+N9murBYlcvs+PFuCr8y1pBcZoZhlD0RdxGadk+cOYP2xBNEX36ZjquL3Ip0bRNeAQ4mLAYfuw/zmWfoUYZ6NARRDPriPXz245/a/99k83P6ZknMfxFSksjSG0zechD/Ic0HXaMIHYTmIDTJyECRz3ZNYIgUTodOdOQUjoCOj36G+1K9G+F1qz1J7R3hde0dXql2hSfpfhJWD9GwsNtwvBiSySMp4msFIpaJvrjEal+S6I9/zLsfH61x5v3FQK/hLVr/9cN/9m161pZJrKzTfe0WPd06JS3KXGKAvJ4kbpYYzC1SKBaIvPc2pbEHcPtSON/4D+RfeH7rXAY6IsgT3CZwL+OVlskQ+cZL/J2pH1TtO1y6yWcvFTjYkW5oON7JyUksy2J8j1EDdoPOTwHVKx0nPpjkUHz6rs55+vRpolGlNO0HYQ1Bp8Ju+huv30G1O0UjCWtOynoCq7Kn+IOwjrVhR4mUCoXC93h5UtYT+YLAncJEgBpAW0m98CRB9uCFcttbXq8uV6Fx/EG+JMhED/KVvs9tK7/v8SzuUKFFtbp73HSa4gsv8Mr1HFOr71d5rcw8/CBPveBPr7ww4w4N4Q4Po01P44yO4tz8gAOlBXrM7Ebe0CQdbo7TuWl6RvKgJ7DHThKPlfOG9o0+sy/1kFJu5RqtFDbtzfC4G2WV+Uh3bleKo4MjRYaWrmAM6HRkpxmYt5nsSCIit4VXTXcQmouGExgP0bAQlHC8m6F2C7sMtZuaX2N48gILUjL2wTSr0sF6IMnMkQ6ktkqUVVxgzpVcvJGjdzmP/d4iEw8NI7Nv8/XxDMsDnVvn06WGLjUMys86t1+XtCLJeAc/mLtV5Ska9fAinesrEIssECvcuKOXqY4WWpF0MxyvWXQQJGqG4zW0zqaE4716tRwI6F49QncSizikzZmqhU4n3i0RG+ml1Ne3p/NthoJv1IKZdsPr/sIrbGVQ8Exjogz2vsDbnhLOdqeEIn/g1d9FDEkkwGrEnTwpFa3FtMB2wtnnhZ0AdwsKhaJd8JrcBDlEBNQfINXkpvWEdeUfeLc91e78gXfemCZXZJ8p9af5St/xqvKfjRahrq+bolGUnn2WxIsv4qZS3Bg4jVi6XJU3NBZ3kJEIzmg5PCWGgfnM/giUAEKIslCyT7cl2k98mMTVL0KHJDL7BosLDvFXephInMQVt5M9d0Qc/q+n30ZoLm6nwcGjj/D+BxPEnv00nz740Zr5RW+Lo5sC6qZI6u1JWuscjfAgDQphDce7Ww9RqQmmjvaTXC8RsRy6l/Os9iU5PpHhzYHbHqKOcHGES02JtAdWMJnKr+y+gg/Ae6zytdk753IUsCV41vYavXPo3OqcpNv3e5/bQBeNScpeGY7XnV/myexkzXC8fe+B9qETTQnHu98RDLRMhr8z+7+jz8xU7XtwpcRjb11tSMh4xe4Jawg6z3sLtQDSF4S13XndG9WLiKRoHu2WCxWgqNpey6k37gS9zws7SqRUKBS+xzM8ScDDoaoVWP6m3m8Q9DARKjSOvwlt3hi12tl3uOk0xbNniZ87x3K0n4nuwSrPIvtAL8ce7wYhwDAonj3ra+P2Tg9RbXmCIWuOXntlm4fo8HoGfWEdNA33/geI6TGKXf0kUoc4EjvQ8HpKKbFxsNwNUXNr28bGwXSrRc3K553ep9Xl29+zTVR1bVzRmv4krOF4g+IhuhskbInqrUBD1BU4i515dKnxo7lpT6/QaA1B9ehXv0JHcZHYcpbIxDUSEYMSMTLRNDnRScIqMGTOYTjrGBcvYp88iZtKEX35ZYovvNCS72IvbHqJHjIjZBBVfXlsuQdkT1O8RBXehDUEnadYVGpuPRS1CW278/LgNQVSEujUQGHAy9kg8Pe0dRYO55U9peXUteMFvO2FHSVSKhQK39OeK7DU5KbVhNmT0uu/o9qdP/D0Hg94u/Pq89SK09binDhB4fnnmXnzNSQZ5iJD2/KGPtpjgSgFyvum0kN0/fiDmJPXqzxEAaQRwbn/tofo9Qcf5FCT6iiEIIJBRG/+7dj4+DgOLh96+KGqELt78QStFkqrQ/buPN9afxcDs1myvQl6VvL0LuaYS/fUFPSEKwMTjjesHqKtwEVSkhalHV6OW2x8tZP5pV2fMzW/xnOXzyOkZOz9aQzXQTycZPFIH4Z2k56N41Zcybs3Vkgt53B/nOHqQ0fRsm/z/fMrFA6kvD1HNe/wuTtD8c7pWUo9gnzcZk0UyTr5rX3aXYbarfQS7bdWuS97ucpLdGAmQ+QNbcsrvtFeooradK9m+Jml81Wh7/tzJ4BUq6t319QTixStxyvca1jvLVxXYNoQqz8UKxpMWO14mgaxiKRkVV+f6vNaT107XsDbXthRIqVCofA9YfUqikVACImU1denPNpaT5jDRHjV3+sGVtE8pAzvDV0iVrtc9XfebOYCawa/e/AXmclaVXnM4inoejRRzmM2M1N+BICO0VHSr77KomvwWveTNXPPpQ+uYZRKuPPzZJ5+mvfff79pImWr0dGIa82PIa19cpbE9Bdxux0iC28wM+ug/8UgH3SfAB00zUXoLr2JPM9E3yFRjGEbOr3Jw3RbMHfkIzxW6CqHQ8XF3nh2hIu98Vz52q0s3yrb/740rB6iYWEv4XhvHU3RsV4gYllElpZZ7UvSceE93u8drXXqvdMNfDrCFAu8zgK/d/OHW7sE4o6hczf3lb1Fy2UfPvdtuteXSCyvc785RaFLUNJizCUGyGtJ4laRgcg8xVIe8d47lMYeQPb14X7jqxReeH7rnGHNRbpXTNPkwoU7h0beC7HFRQZ/9COev9qN7WwPaXy4dJPDf/RnzF1IsvD445T6+/f1s5vB0sIx4GRV+eJykcnJyX3PvarYG+EN91o/MlUsEuzrCzqe97QBb3dQvoZaIqWKENR6wuxsEHaUSKlQKHyPZ5iIgA8wQkA8WjsMjvIsaj1ek5uoITECbkuMe9ilVbtrPaYNjhvOhRnenpTqZq4Wp0+fburnlSydTHSAr/R9blv5r5y8xCN9V5tal/0gf+gQU889By+9i0Sr8hAFsMlhDfbfNgq//36Lats+VIbjlfc/gDv9AQO5Fbry724Lx3syNsORwzp09mOfPEl/KoUzMsLo0/cedtOVEsczXK6NWRE6t9p7tHq/JR0GhvMMLV1FH9BIZGc4uGxzq6sDqYPQ3LL4uvHQcALjIRoWghKOVyIxNzyRd0tqfo2hG+8yJyVjH0xjdDrYR5JkjnQgtRVirCCBC65g5UaO3uU89nsLTDw0jMyO8/XxKZYHOgFqiqB2l4mOxiuZq54iae08pNtzj0a3PEyr3+M3gfTChQtMTEzsm7DWcesW6VdfRdgOrtPNAWu2atGMIRwS8/OMfP3rZJ5+mvyhYC2ZiRoOaXOmaqGTZfXSNbDe6uq1Pfli7fKg21Pidepfvr8I9vUFHc+0TQFvd6Dua/1MvQXQYWh7YUaJlAqFwvd4hj4MuFcRlFfy1BLDlGdR6/Fqd/XC9AYFFZLJv9Rb+Rf0tqfCDO+NaDTKmTNnmvZ54v/orFl+39GDnDnTU3NfELh2+tP81oVqD9FMNM3P/8e/yNCDg2xmoGym52o7UxmO9+aBU1gLV6rC8cYiLjIS2QpNiWFgPvPMvny+JgSaMIjs462w9vQjJK59ETokkdk3EJbFpe/2cF57EFfc9prSpMvnD73LEc3F7dRJHz2DK+D0T/4izx1IeYTT3Z5f9NbMLWzh0jfYXyNn6c5wveXt9eI60hC4AlzcfbvuoBDmcLx78RKdOtpPcr1ExHLoXs6z2pfk+ESGNwfKXqLldrTjWjf+JrPFbEOvo5bHaKUg6i2Oegul5TC8t8s3Q+yuOfmtcq8Qu2NjY/syBmuZDIlXXoHBQdz5JT6Wfa0qFO+gNc/YHCQPnsBNpRi+fJnCE08EJhSvlskQmXyZgZnFqn3dKzd46sYNkl/7GtrBg4G5prDhZVvwWrgaFOrZg9T9ResJa5hhUPe1fqZedLCg93lhR4mUCoXC94Q6TITH5EaFiWg9YW53XmKXlzCraB5hDk+iwgz7m7D2efGoJBM9WOUhCvD/7F5gCP+KD2HFTacpnj1L/Nw5VmL9vNN9oCocb24wxejjXeWwE4ZB8exZXxu3Kz1EndFRjIsXOWDO8VFrdZuHaNrMEF/Oo/VruPePEddjOCMjHDhcHSbRi/FrZQHtTP/uBZQvfelLW6KLI92a+UV35iC9s2Bqb8tLutv8prIFni1hDscbFC/RO3FbIK0RYma/eISaIXajOzxAnW4LXWq8PHO5WjDVjG05Rm+LotUCa1ToHPyLP8MprRFZXiVy8TIxEaWkxZgx0uS1zq1+wXBzGBcvYp88iZtKEX35ZYov3LvneKPRr1whfu4cvSsgiFb15Vn6EVKSmJ8n8eKLFM+exTlxotXVbjvCPMfzQt1ftJ6CR5QmrxQgQcKr7Sk7XuvxbHdRiY+CNihqoERKhULhezxzUgZ8Ug1KpPQzoW53Hteg2l3rCXN4Es92p1ac+gKvlb9hbXeg+rxW4pw4QeH558n8+PtIZqvC8Z7utkCUcEZGMJ95xtcC5SaVHqL2yZO4V28QM7d7iAJYWhT75H377iG6W3ShoYvWLCWXUuKwO5HUq/zWzBSOcEkN9lUdUymaVoqphYFetNl1sr0Jelby9C7mmEv31BTzhCsDFY43zF6izUAiKUmLUqV344YmPV9au6dzp+bXeO6D8wgpGXtvGsN20B9Lsnykj5i4TsTVkK7GggUXZ1ZJreRw35nl2umjiKW3+eH3l8kN9KJLDR0NQ2roUsNAQ5f6xvPtfcbG9s5jNt+nU37sF7HFRUa+/nU0x8GZMXkyu1zlJXogO0fvxetEYjGwbeLnzlF4/vlA9OlhIqz3tXXneOr+ouV4iuMBjw4E9ewpTa6IogrPSHwBT53TDiiRUqFQ+J6wrvwDFcvez4S53XmK46rdtZwwe1Kq/s7fhLXPq7fKvmS1b9vzS2jb3zv4c1yel1XheLuPpCj8yiju0NCdT+ITKj1E3VSKC0NprNmlqtxzzrEehlJ2IDxE9xshBAY6hrh7z8Txa+PA3jxJtWdnSUx9EbpdjMU3mJ210b96gEtdo1CRN7Q3afJX+i8SoRenQ0cffhBXQOQjn+Fwb3fNfKQ7w/FWCqY7y9wGeJGG2Us06NQPxSvRdAd0ByIweV8P8Qs5IpaJll1itS9J5Nr7XBke3d9KSdBdgeaC5oqth761DZoUaM5Gmawo33H8R17+EXJ1leRanp6pJcxoipKIkYmmyYkkCavAUGkep1Agee0a2uBgoLxEw0RXNsPPLL1dFfa+Pz8K9La6endNos56Gy9vKkXz8BTHQyAWeYUNVfe1rSfM4njYUSKlQqHwPV7CSdANp6A82vyManeKVlBXpAz4xLpeLlTXBW3/Fvcr7gLvfEXBbnexCAghkbJG/uc27fNOnz7d6ipsUbI0MtEDVeF4v/DhHO7QeotqdfdseohGX34Z3VhkaoeHKMBxxwyUh2gYqAzH647ej5u5xEB+ma7C29vC8d5vzJDSCmB0YI+dJBFP4YyMMHD/J/elHo50efP8W7z07Zc5eGSEYyfu4/4HH6gRKreW2Lkz9G65PDWc48DSVbQBQTw7Q9+cza2uBBgSoZWFMKG55UfAvESDji9D8QpwdImjA/cgmqfm1yiJWeb7JX3TM9g9DtwnWDjSh67donvjuGVX0nGjD3dpFfnjV7j60BG07Hm+f36ZwoG+26F2NaMi7O7t7ehm+FyP/Vuvte3lulCTSijnC4299BLPX1mtEk8Ol27yyLdeJZ4fCux4pGnleWotYahd53h+wus3CPq9BdRJY6JEypYTVs/xdkCJlAqFwvd4JtwOweRGeRb5lzC3O5Xo3b94hScRQgY+0Xu91c5FCzpCkJ8kqLiu9/8/6AszhCivdq4VfqldV9lHo1HOnNm9F1ojMf6kt2Z5R4DHWjedpvjCC3ztQoH11y5Uea7kHz/JmRc6W13NtqMyHO/U0CnM2avE5PZwvDHdRUYiOKOjDQnHqwuNCDq6CXFTp8dNMBIduKdzak8/SuLaF6FDEpl9g5mMQ/SlFBOJk7gVQs1Qj8mvHHsXTXNwOw0OHHkYV8DJp3+Bnz6Q8gi5a3Pj1iSOcBlIH6i53ztc70b4XdfGbtPcv2EOxVvfS/Q2UhNMHe0nuV4iYllEl1ZY7UuSvPA+F3v32Uu0Ag1tW67RTRFzp8C5K5FUi1S83vG88T79HrzDG8VmvlBsG9vu4IA1V+XZH9G60KemAp0v9Kg7zYml81VjrcicAFKtrl5bE+ZF38qO5188w72GoN2FHSVSKhQK31P0MCKGIUyEV9Jwr2tWNA/vdtfcejQCb4+2JldEUUW9kJtBT/ReT+AvmkKtbmwh9Vabh+F3ScRkzWtUhoTW42XACsMcr9CX5it9x6rKB2J54N5y3Sn2TmU43my8nx91DzFozW8z2q/2pxh7vKu8uiEg4XgrvUSd0VG0uUsMWXP02ivbvESPmzNEunKg6cj7x+gwEjgjIxw8Ut+zerxU/i+eSd39wgYpJTYOlntbxDRreInuZtveeO/WtlsrtO7tsLytFEjDHIrXl16iFbi4FKVJUW7c3DS4GWiIKvHytuC5KXLqRL0ET63ydWSbF2lUi1SJq9odbgq0TGZLoGRxiY+svF2VL3TQmmdoArTECdxUKnD5Qje9RP/e9Aprhe2es4dLN7n/q68SXw2ul2gYCLNHm1r07V/C3O7CjhIpFQqF7/EOQdfkijQArxVYKjdg6wlr6EPw/u+odtd6anl7QX0vxKAQr3NjUCgJ6Ar+fyuo5OssUAhD21N5eP2LlyFhYe4W4+M3mlyb/aWQewgYqSqfyiwzPv7uXZ93YmKCsbGxe6hZ+7IZjnf20veRzDK3Ixzv/V02iGLgwvFWeokuH3kQc+ZalZeoHpEN9RKthxCCCAYRvfnmJ1e6W6LlH/7ZH2+F2K0WQTeeXZvX3vghGIK+wX5s4eLgbjw7OKIsuDrCxREuNuXnzWO2XguXlf4kA7NZsr0Jelby9C7mmEv31BTzghaKN8xeoneDi6QkLUqbQmCDLy+yGQa3QgRdPbXMD4rT/MH4D/jEV9+ibz5L52qBgzfm6U11YmpR5joGyGsdxE2TgfUlVrIL5L53k/nhNMVkkvXfvMqPn/zYVt5RIUHgv7lS79wc958/j+Y4uM6HOWCtVXmJ3rixjrt2Hveb3+SDRx5h5cCBhtTl9ddfV2OyB14ebWGwp6j0Of4lzHa8sKNESoVC4XvqeRYFHTW58S9hXoGl2p1/8bqZC4NXUT1PStX2WktdT8oQtz3V7lqP18KMeCT4BuyYUfsaita9eUeNjY35Kq9o0HDTab7x0N/iW1MrPLr+1rYQgZFDKQq/Moo7NHTnE/mISi9Ru7eP17rTVV6i2Y5+Pvx4MlBeovuBJjRiQiNGZNchdqfn3gNgrOvYXX+uRGKkT3H4ra/gJlz6uMj6kiD6zT4+6LkPYYDQyvlC4xGTT0bfIVGM4hgayc5hEjbcOPYIJ0tdOGwKos42QdRLHG0GYfYSDQLWRu5aqBhEu4FujcT8AtHcErmE5NDlOayog5m2mDrShdTWMVjHBmZdgXFDo3e5SHR9mmv3DSNFnotHr7E8UA5JLiQY6OhSw5AaBnr5edu2jsHtZ31b2Y79shzyenN7s1xD7FoMjS0uMvLWW2ipFJFslo9lf4RT2j7PG7TmObBokkwPYHV3M3DrFlOnTlHq79+fH6CCycnJOx/UpoQ53Ku6t/AvYbbjhR0lUioUCt8T5kFGeVL6F892FwKDvcqh4F88RcoQ93eg2l6rCXu4V9Xn+Rev+c7JBw5z5sxgk2uzvxx6pxPeqi6Pd6R8kxO0XSmUBJnoQb7S97lt5b/8oTzuUDBD8W56iZq//W0kS1VeohFdgsgFzku0FRw/fpyrV6/e0zkEAqdvEHtgiPjCAqVDR5E/niG1usbD2YvbQvEeldMMHiwhibE+coxht5tC/yAPdzwG+b19rkTiIHGEg73lAepiC2eb52flPkds7N92/G1hdNtxG8/FVC9GZp313o7QeYkGnbvLF+rQvZxntS/J8YkMbw6U84VKARYOlmjswiEhqRI7t4uat8s/8tIP0cQKyVyeA5PTJJMJ1uIJ5uKD5LQkcbPEgfwiQhZJ3LiKffQYsrubgTd+xNSnP9PQ61Bsp2s1w88svV2VL3QgPwr0trp694RXZCqVPqf1hNmOF3aUSKlQKHxPmN311Qos/xLq8CSq3fkWr98gFO3uTuFeFS2j3vcfhrbnuSBItbuWE+Y+r0O1O9/iZUQMeioJN51m+md/md/6QanKSzQTTfP3/sZhZDpYXqKtIBKJMDY2ti+LCbSDB0m8+CLYNu/nDmIuXa0KxdsRkyRTKZzRUWIboXgLzz/PAR8LyVrnLInMF6FXEll8A0yTD758kMtdJ0AHoblouoMmbH5a/wFDMk6kqxNj5EFcAdGPfobDvd3b8pOa0trIN1p+mO7tkLzbyjeeFbXxe77QmvUQYAkHCwewPI9Lza9RWL5BUZeMTU2T1RzyJzQyR5JIbYk4SwDMu5LojTzOch47c4GJwWHk2iQv2ZL1/p4tT9DIlkenTmRTCK0QSSNbHqOV25v7ysc5sbKQ60p5x1yh7cJmvtC/9sFqlU3lcOkmj77yKvFSsPOFqshU/iXM9xZhR4mUCoXC11g2WHb7hYlQ3h2txzPMcAgmN14TNNMWOA7oKgJTy/ASx8PQ38UiIIREyuprVH1ea6nnvR+GtuftSdnkiii2IWWdPi/EY60yYLWeMI+18Sg1vUQB8qlZEi2oUztTGYp3LdHHa91DVaF4ZV8fpx7vDFQoXndoCHd4GG16Gmd0FOPiRdKleXpK2W1eomkzQ3dXnlgqQmLswyRiKZyREfpHn72nz5dSYm/kETV3iJdb2+7mtlMhhpaft+/f/r4twdTdFEgtLGnjEoz+Icz5Qu/VS/TopWne/HjH/lbqL0f5bb7Lb9/47kaOUIOYiBDVyvlCK7ejIkJMi1Qcs7FfGBvl5f2xrfdtbGsRDBGMG3T9yhXi586BbWPbHRyw5qryhUa0LvSpKRIvvkjx7FmcEydaXe09o6K0+Jcwz/HCjhIpFQqFrwl7CDqvgVIZTltPqHMD1vnvFC1IBuMeKJSEOby1EOW2ly9WX2NB9XktxavdRQ2JEYL+IBGrXa4MCa3FtMF1wzzW1i5Xc7x7Z2Ji4p7ev7D0UaD6B1pZnGZ8/Mo9nftO3Gvd70TdOZ4plJGuBWyG4p3/599FMl8VivdwpwOiELhQvKVnnyXx4ou4qRT2yZM4E5PErO1eogCWFmH92OEtL1HzmWfu+bOFEESEQQSDfZacPHE2PDpND3FzU/jcKZqa0tomiJqV73W3v94URMv5Ju+OMOcL9buX6GZ7yFGEfdZ6NTRiwiCqRYiLKFFtQ+isEDY3Rc34puC5Q+gsl1WfQxPa/tQxk9kSKMXSEo8vv01UbveMHbTmSV8CLXkCN5Uifu4cheefD0y/t0kiJkmbM1VRCzQG0WaPBS63dZjwyncfBntK2FEipUKh8DXh9+6oXa5W2beecIcZ9t5XKAmSITAOBxWvdhcGryIo93n5YnW516IARXMIe1gc5dHmT8IeZtgzFJgSx++J06dP3/M5ilZtI3ws0ngPorGxMRyncZ9T779TMAWpgHiDhQ03neaVM3+LP72arTJqmwf7KfzK8cAZtSu9RN1UivH+YeLZhSqvqXQ6y4e7g+Ml6oUudBJCJ6F5rEDZR6SU27w4t0TMHWLnpihWci1e/IMvcfqRhzjQfz9Ds1eJ9Gp0rMwwOOdwPdoLhkTTHYReDsUrNJeobgUqX2iYvUTvhItLQZoUHJNV9ldsjQidmIje9uLUIluv4ztel0XQ6JYAWrl/6Ov/Ad0qEl1eRXv/ElEZobQRbrzSu9pwcxgXL2KfPImbShF9+WWKL7ywr9fUSLRMhse++x2+MLNYta97/gaJL34ncItOwoS3Ha/JFVHsGSVSKhQKX1PPyyEMIqUyYPmXMId7rZsb0BSgDFgtw6vPC0N/Byofql/xFMfD0u48rkOJ462lXaNlqP7u3ohGo/ecK1DotX2vRo8Pc+ZM6p7OvRsa6U2p8j/7l4Ipaobi/dzJIu5QPV83/7LpJRp9+WUMY7nKSxRggDyFwb5Aeku1CiEEUWEQ3YPJ9s/f/XcMd0YYTH+UkbcWId5FN+uwrNFxvYOJxEncCo+5uGHxNw+9SrTQiWsY6Ikx3BLEDn6KY9luLFxs4eAIF3tj267YdoSLRa39G8/bth1sXLjHLijMXqKtxJIOliyw7hbu+hyp+TWemziPkJKxH09j2A7amW7mhw8g5CwJR8exdW5ZcfpX5+nLruG+vcj0qeNo8xP8+JDE7O8nupHvs/wwiEqd69OTPDD2wD5e8d2zGc42tQqCaFX47jX6QSYDH842yIQ5MlXYUSKlQqHwNXkPV30Ih1ikYtn7F8/JTQi8DOutsldtr7WEfVKt8vD6k7C3OzXW+pN6C7LCMNbGIrWvoWSpdtdq/NLnNUKszOYjQO18f+ffmSA7u7an801MTDA2NrYPNVP4pd3tN246TfGFF/jj75ZIXnx3m5doJppm8sGH6f50ggNKoGwouq4zNjbGqTNnSGQyaNPTaIkEhdcuMWTN0WuvbPNoO8YM/bYNXd3YJ0+SSJXzhQ4885caUr9K79By2FyrIvythemWvUZLW68r9m287h/Oc2DpCtqAIL46w9CyycJAlJIUG16iDkKU/0/ClYHyEg06tfKFzhzvwdBMYHuc+yuDHegXVohYJrn1GVb7kizcfI03j4zWPvnPR/n/8R2iN76/4cUZJb4RsjauRbc8Oyuf41p0w8uzXJbQYhueoeV9UWEgxN7mY5XhbJO5VZ7MXq8KZzu0NkfkDXBGRwMdzjbIhNmOF3aUSKlQKHxNoUbusk3qhawMCmqVvX/xTLgd4nYHqu21mrCH3VR9nj+plScU2qDdKZGypXi1OwhH21P9nX/xw1h7/PhxIpH6oQrvhnoha0seYW7rMTY2ti8hdhXefV4YFt4CrPem+Xbfkaryn4l/H8g2v0JtyObCh9jQECNvv43mOEx1H8acnicmt+cLjUYcssUiuUOHsEol3Pl5ps6coTQ+3qrqY2w8kttexbf2x1If5fDaIhjd9MocWs5m9fU438g9vuUlKoSLrll8RLzDSOcKjqFjdxxnyATz0CfoXe+u8O50sDa3N7w9rY1tq8o7tHysI9ymfy9BoBn5Qk1pYzo2kL/n+goJEakTlWWP5fL2be/NqDRu798of+jL36F7fpnEao7YBzPERDdFEScTuS3+HzQzrC9l4Ec/Yv3YMXpPnAhcONug42XHC8O9RdhRIqVCofA1+ZCHe1XeHf5ESiiatfeFud2BanutxlMcD0G7A9Xn+ZXQh3tV7c6X1BPrwmC09+rvSpbAdUHTau5WNAE/9HmRSOSew9bWQkoQQiJl9TUeOfYAZx6yarxL0Qz80O4aiVe+L8tRZsdmMDg4uLVd6u8n8/TTpF99ldV4L+Pdo1VhKc3ubgZPFkDTcHWdzNNPU+rvb+EV3JlSXx/FgQHiCwvkDh2i8/p1egsLfHTtB1V5D3ujefROncKhY3SSoNA/yIHuUQ7cYxcokdvEzFdf+x5PPPURjt1/nFKF12f1dtl7tLTDk7QkTUpu+dl0bWyCmTczaPlCpQBTOJg4QJ3wbRuk5tfoz77PTFQylpnGSjroj5VYOdJHxL1Bl2Pg2gbTpk7/EvQs57AmL3A1ZdFtzXD9gwOI9EESWpSEFtt67tBiJLQYhlChiPcLz7RNIRlrw4yaLSgUCl9Tb6VzGIw7XkY4ZcBqLUWTmsYdCH5IJoCoAZomcd3qa1QeHq3FSxwPQ7sD5dHmVwoe9+ahaXeeuVCbXBHFNrz+90JIT2N3kOioI7QWTEjGPXcrGojrhjv/sxBlsahW/6by8LaWsIZ73cRrrDWVSNkUNsO9bi1+OHMG7YknKP3Gd5FX56vyhY70OqSHCzgjI5jPPENPQMJRagcPknjxRbBttMFBoq9dI7ay3UsUwNbjJJ4YI5ZKgWFQeP75hoQcnnl/kgNON2OJw/tyPkc6lFxrK+xtaVPY3BI4LYqutbWvUui8XVY+dvO9m2Uujetrwp4vtFY426kjfUhNoGkOmuFArARJuNzTxQP5NSKWQ3Etw2xkjcuv/ylvftwjnC0QEfpt0VKURcz4hoC5KWQmtGjF9nahMy6iew5fG1a87i/CMtaGGTVbUCgUvsZzFUwIVtjDHcJuKgNWy6gnmITHgCVrhp1SnkWtxXthRpMr0iC8rsNLnFU0B+8Vp02uSIPwbneqv2sl9VY6h8HOUq/fLpqCpMqN0xIKdcabesJykEhEZc3/l+rzWkvYvTu8vMeVJ2XrcNNpvvPo3+IPL2d5dP2tbflCiwf7KfzKcdyhoTufyEe46TTFs2eJnzuHm0ox+8AA7y1nt3mJzkcGsFP9fDhVBMOgePZsYHIC6kKnQ9fp2OfzSimxcW57dm4ImEXX5P3LE1jC4eDRkQ1R09wSOIuuuU0cLbkmJWlvPJePgXJ414HZLNneBD0reXoXc8yle2qGfA1ivtBGh7O1pIPl5Mk6dxfKViDo2BAsO7Q4yY3nDn3jubJs8zg9TlKLExP7H3q+VVg2WHa4x9owo2YLCoXC14R5pTPcOTegMmC1hroh6ELS9uJRyBery+sZ7xSNJ+wGLG+PNmU4bSVhD0GncgP6Ey9P1jDkfobd5H8Ox/8raNSf4zWxIg0kEZOwVl2uPClbi9ccO+xjrRIpW0vRFGSiB/lK3+e2lX9mrIg7VM/Xzb84J05QeP55oi+/jH59tspLFKDLcbe8RIMiUDYSIQQRDCK6QZLtK+Hz9gIAZ7o+tOfzulJiSgs7MkXXrf8dO2nTMX+e7KpF4judXO0/jDAkmuGg6Q4R3eRp7RodThQrpmEMpOkRLqunH6Bbi28TPv2E38PZSiQ5t0jOLUJdH9ZqNDT0xyTj2ipfm75IUo9vCZudWpykHqdTS9x+1uJ06gliIuI7782iKUibM1WLMjLRND3ZY8BAq6uoqIOaLSgUCl8TfoO9976yQBuO6wwa9QxYYQkToXK0+RPP8CQhWbAQ9/j/qHbXWsIegk7lQvUn7ZqDF1TbayX1omWEZqxVfZ4v8R5rm1yRBuF1b6FEytbiuSAo4GOtm05TfOEFPuhe5kfvX6kSJK4eeIRffkG1vUajCUFcRGHkPuKHjqNNT6M9cBr9h5foni/x4MLktnyhR+QSJ+43IDaAffIk6e4UzsgIjz7ywtY5XelueHGWPTfPfe3LDB8b4fCJo2XPTteiKE2KG88l16TomhQ3vEC3bUsTS967QBjmcLYuLm4EVimwahZ2/T4DfZuAmdS2i5mdepzkxnOnlqBL72iosKllMsTPvcwXZqp/mcOlmxz/8reJXz2oFi74GNVjKxQKX+NlwKpn+AkS9W4OlCGhddRbaR6atqcMWL6kXh7eMKA8Kf1J6NudEsd9Sdg9eJVI6U/qjTeh6fPUHO+emZiY2PdzZtd/mlomuMzMNcbHZ/b985rN2upJ4FhVeXbdf15R7YS3PaXJFWkUB4f4St/JquKEJoG55tenjSk9+yyJF1/ETaWYG34Q89Y1YnJ7vtBYRCIjEZzRUdyNfKHmM89sO48mtI28jOUVHN25CCN2ioc7TtxVvd4cfwtL2DzwoZNlYXNDvCy6JgXXpOiWKEqLglva2lfYFDtdk4JbojDQi5hdC20427vBxmHVybHq5GCX3XxE6HRpHXTpHXTrHXTpZfGyS9t8fbs8qSXQhbar8+pXrhA/d478koUgyaA1XxUCemUxj/XmNO7582Sefpr8oUNV5zl9+jTRaFg6x+ChREqFQuFrwp6Tsp4xRIVkah3tEe5ViUV+JOx9nmfYTWU4bSnt6tGm2l1rCXt/p0RKf1J3jhfytqfmeLvj9OnTDTlv0artSRMzGhsGsFlEPa7DcpXBt5WEPzKV9xxPSkKR4zooVOYLXUv081p3ukossnr7eOjxzvIP06R8oRqCmIzQZ3Tf/TmenSUx9UWcbhtj4Q3WV5a5/s0Ul1PH0CIummGjGQ66ZvKT+lVSbpyC5iC6++kzIuRPnyKlxyi4JUrSbNt4aZZ0WHLWWHJqxKTfgQCSWmKbmNmrd9Krd9KjJ+k1ytt982skz50D2yZ79TpPZrNEd4QMHrTmGbq8RunoCFZ3N+lXX2Xqueco9fdvHbO5OOnMmTP7es2K3aNESoVC4WvCHvqwnjFEGRJah9d3r2uSaEhGTpWjzX84Dpgeid7D0ucpw6k/CbtHWz1PStcFbXeLdBX7TPjDDHvvU31e62iHhWhqjndvRKPRfTdSSgmmXVuk/NCD93HmkZF9/bxW8O2rSfh+dblmdDa/MootvBbFhGWsrZdLuGCGJ5xyUNjMF7r4m99FMl+VLzTd6YAoBC5fqDs0hDs8jD49jbh/jJ733qNvco0PZy9VhLMtkjZnGToMqe4+Zvr7GbW7GRx7jIe3hbOVlGTZQzPvlsqemq65bbuwtV2qOG6jXJb2JYSt35HAultg3S3U9dT85J+9zcHFPH1Zi6H5WfoGYpSIk4kNseZ2ESs4DKwt0xOPoS8uYg8O4qZSDM3NUfzkJ5t2PYo7ExJTq0KhCCthX2UfMUDTJK5bfZ1qlX3rqGewD8tqTJWvyH/U8+pSIegUjSTsY23d/M+WMmC1irCL40KU/0O1rrNgtqBCCsC73Rm6JBIS64iXQF5U7a5lWDY4Ne73IDx9ntdcVeWkbC1hD+l/p6gFYRFjg4SbTvPdJ/4mv3dpjUfX39qWL3TtYD+FXzmOOzR05xP5jMpwtpw6hfvBJDFrezhbAEuLYp88jlUq4ep6jXC2Yiucbd9d1sWWzpaomXdL5N0ieaf8nNt8XfnsbL4uUZThmQyk5tfon8tiSUnvtWkc3aF4LMLUkThSyxInC8CSKzk/ucLAcgntvWUWH3mQxNUZFq6foHv4OANGT4uvRAFKpFQoFD7HO9F7c+vRKDYNWLliDQOWWu3cMsIeFge8/0PKcNo62tq7Q7W7lhL2Pu9OUQuUAas1eC1OCIs4DuX/UG2RUs3xWkXY+ztQodX9SL3vPixtz6vvViJlawn7gqA7h1YPx3UGjUJJkIke5Ct9n9tW/tOjJdyhldZU6h6pDGfrplKM9w8Tzy5U5T7sGO2mN+Xgzs+TefppehrgLWoIne6N/I17xZEuBbdEzi1uiZvf/N636B8ZpG94kPzGvpxTLD+7BdacAnm3hPTZ/+n4pVkAupfzGLaDFdGZOtJXlStUaoLJIynia9NESusszl5ltS/J5e/9Lm9+fBSAeI9BpxvnzbkZDkb7SEf6GY70cyDSu+vcmIp7Q80WFAqFr/EMBRaS0IdQXu2cK1aXq9XOrSPsXkWgPCn9SN5jUQaEJySTt3eHanetxDO0eljaXZ3rUAas1tEOYpEaa/1H2MMMgwqt7kfqffdhaXteY60SKVtL2Ps8lf/Zn4TVjrcZzjb68svo+nJVOFsAyy3hjKSZOnNmW85Dv6ALjU49Qaee2Cp7dzHB2MAIZ3q9Q527UlJwS6y7BXJOgXW3yLpT2BAyb29XPq+7RVzchl1L72IOgO6VAgAr/ckqgXITqQlW+pMMZrJ0rxRY7UtuvR+gqNkUtXUW8pcgf/t9OhpDkRTDkX4ORsvC5eHoIP1GNyIsYdZ8gpotKBQKX+NlOA1LeBJQq539SNg9eEHlK/IjdVfZ1wlXGSSUwd6f5Gt480N4+rx6C0xU22sdXu2uHeZ4RTXWtoywhz6E+nl4Fa2h3vw6LG1PeVL6k3zI+7x6C5vUWNs6PD14Q9Du3HSa4gsv8MffLdF58d1t4Wwz0TQ9P/EAoy90Uxofb3VV9xVNCJJ6nKQeh0hqV++RUlKUJutOgTUnT9bJs+be3s46edacPGtunrUNcXMvGFY5N6fulIXQYjxS9/jN/ZvHb76/Hg4u09Yi09biNvEypXcyGh/h/vgI98cOkY6klGh5j6jZgkKh8DXt4NHmdS1KLGodYQ+LA3VW2SsDVstoh3CvSqT0J17hdsMy1tYzxKmxtnV4j7VNrkgDUWOt//Ds70IyzoJqd36kned4phIpW0rY72vrzvFUn9cy2iFaxnpPmu/0HakqfzixxjY1q40RFfk3ByO9dzzekQ7rTpE1t0LAdAqsOjmyTo4VJ8eKvc6qk6MoTeyIXn6fXg7HGi9arNY5f7xobTt+8/13w7Kzzo9yE/woNwFAl9bBWPwQT3WdRqgoPXeFmi0oFApf0w6TG+VJ6T+8VpyGxWAP3teixKLW0Rar7JUHr++Q0vt/H/SQTJvUmzOottc6vMLah2qOp8Za3+G9ALLJFWkgHWoBpO9oh5yUXuFDLSeCDMclBpLQh3ut4zjlFR1J0XjC3u7Ae1GdsuPdPbrQ6TGS9JC847FF18R54MtoubeQB2dJrF/FnZJcEQPoCQsjXsKImgit7DUpXLkV3jXbWw51u9J/58/ZLWtunjfyl3gjf4mBaCdHPvcQk19+d9/O3w4okVKhUPiasOfJAuVZ5EfaQhxX7c53eLW7WERi3P0iP1/h1d+VLIHrgqZy0jcd0wbXDffCjFgEhJBIWX2dqs9rHe1gwPKe4zW5IootwponqxLvsbbJFVFsUU8sCYtA7pV3HMBy1ASvGUxMTGx7LSUUSp+peezUzQ8Y15eaUa2GEzU+hWlX3yxduHiVpL3Q0M+enJxkbGysoZ8RRLzT54R/rFUiZXOIa1G0M0+RePcypLuI3FphebZA11SUicSHcYUGQmJETYaH1vh09wU0maQUg8jgQTpkiWtjBxtStwVtnUf/yeeUSLlHlEipUCh8TVvkjVGrnX2Hl9E6LAZ7qDOpVitOW4ZXuwtTf+fl3QFQtKAjRGEeg0I7hKATomw8rdW/eYV+VDSedpjjeRnt1RyvdeTbYKxV7c5/1IvSEpYUVvX+QyUrJKvtfMzw8HBVmWl7i8Mxw21kdZpK1HBqipSq3bUOL6Gu3mKGoKHseK3HHRrCHR5Gm57GGR3FvXaBIWuOXnuFTDRNXkvSUcjxYHGawcMliPRgnzzJQFcKZ2SExx/7PMv2Got2lreuvktWK+L2R8iYS8zZK0gVtrWpKJFSoVD4Gs8bupAYTgFiHhM1tdq5dYQ9dwd43yAor6LW0Q79Xb0b06IpQuVBFRS82h2Eq+0lorKm0UCtdm4d7THWqlX2fqMd8t2rKC3+o537O4CSpTwpG41hGIyNjXHmzJmtsqU17//8ww/dz+ljdjOq1nA6O3TWi9XlB0eOc+ZMtXi7n+z0XlWUyRfVWKtoDqVnnyXx4ou4qRTX+8eIZKeJSZOjpcmtY2JRFxmJlIXMVAoMA/OZZ9CFxkCkh4FID3mz7HV95kC5D7Vcm1l7mWlzkWlrkRlzkWlriQV7RUmXDUKJlAqFwtd4hnsNUUgmFXbTf7RDCDovo4hqd62jLQyndf5D5bYXnmsNCu3gSQkb17JWXa76vNahxlpFK2iHcK+q3fmPtkglUedaanm5KRpP28zxlD3Fd7SDHc9rvqraXXNx02mKZ88SP3eOIjFWIwMcNGfodrIIKcnpSRZ7j2I9ni6H1zEMimfP4qbTdc8b0QwORQc5FB3cVp5zilwpTXOpeIv3C5NMWY0NKd1OKJFSoVD4mnY22qswEa2jPULQqXbnN9q53YFqe62irgGrDdqeanetox36PBUKzH+0Q7tT/Z3/aOd2B8qTslXU+8+3Q9tTfV7raOc+T7W75iOTSWQsRnr5OpZZzuWxpndv7U+vTWL8eAHryScp/tzP3VGgrPkZUrJgZ5k057hpzjFtLjBvr+zXJShQIqVCofAx5UTvXiJlkyvTQJQBy3+0Q6J3r2tRIehaRzusOK0neqk+rzW0+yp71e5aRzuEP/Qca1W7axnt0O5UCDr/0Q757uv9h4rKk7Il1Btr2iFqgdc9vaKa/Q5bu55/Fqj+389OX2V8fHZP55qcnGRsbGyfarZ/qHbnD/QrV4ifOwe2zaXUhxD5BU4Ur5J0c0gEWb2bbE8a575+RLGIyOXueM6CW2LWWmbOWuGmOc9Nc45Jc468q37cRqJESoVC4VuKpve+MBntPQ0JljIktApPsShEN3PKgOU/PMXxEBmw6q2eVW2vNXj1d0JIYpEmV6aBqD7PX7huHaN9G4y1Ku9462iLMMMeizkLpkDKcrQzRXNphzDD8TpzBtNSImUrqLf4NFxjbe1yZU/ZHadPn973c5Y8/vPxiLPvn9UqvNpdSbW7pqFlMlsCpba0xIOZKxRLAkcYZPUeAATQU1xEWN1I2yZ+7hyF55+ncKCfeXtlS4x8v+MKq3qB35t8nTW30NoLa1OUSKlQKHxLvUl1mMJEKO8O/+FlOPWaiAaRevmKXBc0FZWp6bRDu6t3LfUWpigah9f3Ho+Gy5CtPNr8RbGOSBemaBlefZ5qd62jHVJJ1PVoMyERa2JlFID3fW2Y7mk1rXw9teazXoKForF43VsIIYmGyBqswm7eG9FolDNnzuzb+WwHbLe2MeH0qeOcOXV4T+fbDy/P/fYUBVicPwacrC5fKTI+Ps7ExIQvPUDDROyll7YESmNiAt2BkkiSiQ5RjMXo1lc46EyjGTmW3p1l+kSa5e4IU384wZ//pR2/3ebcyN2furlWeAT5ZhGiYUmhUISNfLFdVv55iUVNrohiC6+2F6Z2V88IXLSgQxmwmk7ey3AaonYnhLcBS4Uabg3t0N9BnVX2aqxtCXXneCEy2isPXv9R8PjPh6nPu1PUgjBda1BoB3Ec6oiUtlr92ArqzfFCtRBNjbW+wm/57hvhKQoQM2qLUOZGfzc2Ntawz25nHOmSdfIUpq/RfeMCJadE98V3ca0SudEkc0cSRDtuENPKauOsKzBulLCW80Sv5Fl5aJiOmXVSC4dYHujc9/rpaBx1Bvg3f/u/3fdzhx0lUioUCt9Sb1IZqpBMHmKQmlS3jnYIQVfvBqFoilD9x4JCO4QCg3Lbq/UfU31ea2iH8NagQqv7jbpzvBD1efWiFihag3e41yZXpIHcObR6eP5jQcE7331z69FoElHJSo1yU+WkbAntMsdTY62/qPe9t8Kest+eoptMrMTha9XlUiQa8nlhx3QtVp0cq06eVSdH1sltvM6xauc2yvKsu3kk8Nj3LjOam6FnKcfh4jJWRGf2ZD8RrbjtvFITTB3tJ7leImI5dC/nWe1Lcnwiw5sDo/tS9w4txkOJ+/hwx3FOJY5y49I1/uXl+X05dzuhREqFQuFb6q3AClNoHBWexH+0Q0imeJ0bhEJJQFd4rjUotNMq+1qoPq81qHan2l0raJeQ/p7tThlOW4bXf74d2h0oo32raId2B/Xy8CqRshV4ieNhSiUBao7nN/Ie7Q7C1eepOV59LGmz7hRYcwqsuXnWnEL5tZu/Xe7kWXPL5UW5t/A2vYs5ALpXyvkjV/qTSK32dy81wUp/ksFMlu6VAqt9ya333w1DRorDsUGORA9wX+wgx2MH0YWKGHCvKJFSoQgpFy9epFAIdrLfCzd7gf6a+ybeHydqBHOCc+3aNSYnJ7fi4p+/cRL4dNVxS6sFvvSlLzW5dnvDtm2EEOh6cG88X3vtNWzb3laWK3yq5rHTty4zPr7QjGo1nPlsDPjpmvveOv8+c/355lYIuHr1atM/00/MLjwGxKvK11ZnGR/f/zwalTTzu9d4GkhWlV+6fIvxnptNq4dfafb/4Mq1Y0B3Vbl0coyPjze1Lo0kt3YKOFJVPjO7yvj4eaB6fA4ix48fJxKJtLoad+TyTDcwUHPfBxNvMxO3a+4LGrPTaeCRqvK1nNPU/1cY2nYl99LO1wvPAtXz1pmpK4yPz91jzXZHo/v51XwEeLbmvvG3L7KSWb+n81uWFcg5W605f7PIzD8CJKrKc9k5xsffb3p9Gob7FLXmFDenFhkfV3O8RvLBBx9Ul105AvRUlQtZCNUcb231JHCsqjwzn234dQaxL2w0N+Y7gcGa+65cepel6b2JUX79jmduDZI2h3l0/S3SZoaoNDFFlKXVQd57aZ1SX1+rq7hr7vQdu0hKwqIo7PKzZlESNkVhlR+aRUFYFDSTorAoaBaWaGxORmMj56PulMO6FuP154Wb+zePN3aRM1KXGj1ugn47yYDTxYDTyYDdSbRCTltnjnfYPn+8cePG7i9EsYUSKRWKkHLt2jWKxeKdD/QxV2arjYkAmnC5evliYPMoZDIZPvOZz2zFp893pOBH1cdJkeCpp55qcu32xp//+Z9z/PjxQMfa7+np4Rvf+AaGUR4SXSmwnM/UPHY+c50JZ6qZ1WsYuVIcL5Hy0uWb5BaaL8bOzs5u/Q7tyPLqh2qWr6/ONdyw3MzvXjqPUUukvDWzEBoD+r3Q7P/BVKa3ZrlrrYfq98hlh6glUi6v5reuc+f4HDQuXLjAl770JT7ykY+0uip35OrCsOe+yWvvo2/kkfEzr7/+Oul0miNHas9XAeZnTWqJlEVTNPX/FfS2Xcm9tvOiWXsh2sLsDSbE5L1Ubdc0up8v2QZeIuWlK7cwV2bv6fyvv/46n/rUpwLXnnbO+ZvJ0srJmuW5tfk99QW76XdaiWs/TC2RcmFpjYmJy02pw+Rk+X/s1++oUVy6dIlYbHv84FszHvnWnFyo5njr2QFqiZQrq4VQ3UPtJ438n0wuDXnuu3njInOGtafz7dd3vJ/XnFxdZfQHGb4wc6Bq35HSJIkv/hAnleL6gw/y/urqvn3uvSKROJrEirjYevnZMlym03nWOm/xQTGLZbjYERfLkOVtw8X2oYOIHSkvOHP0sgdjvGixWuf4eNHadvzm+3EkYtVGW7EYivWRLEXpKOokigYxU0OwaXjOsUaONe48h5qZmbmra2p3gteTKhSKXfHZz36Wkydr3wwFhb94Pcb//Gp1eSIGzz//+eZXaB85ffo0TzzxBACzHgvJLMfYOsavvPfee5w6dcr39bwTq6urfP7z5TaVK8Kv/lHt437mMz/No/eHw7ujUIL/4s9r7/vET3+Wx8f2dvOwX4yNjbVtDoffOd8HNbThxx89xed/7ljDP79Z3/0X3+5husYdxIOnHuXzvxDscWu/aOb/4PL/3gnvVZcfHhnY6hfDwDU3ybdr2EdT/SPbrrNyfA4ileOZn3nlfJR//Up1ua5J/voLv9T0+twtd/qvvvpOlH/z/epyyzX4a3/t801dcBf0tl3J3bZzy4b/+x/Wjv7xmec+wcdONW/u08h+3nXhP//T2vue/sRzfPxD936dQW1Preojv3QhRS275pmHT/L5v3Z4T+fy81z5999PcWOpuvzw0TE+//nHm1KHTc85v35HjWJycpJPfepT26775r9PwrvVx46kU4GYK+yWKT3JSzW0yJ6+g025Tj//J71o5P/ktfci/MuXa+/75ed/gbsJwrUf3/F+XbN+5Qrxc+eYOtDFFVwGrXkGrQUMaWMLg/nIAA+ePI1uCD6Wz/PWgw+SP3Ro375rKSVFaZJ3S+ScInm3RN4tUnBL5Da2N8vyTuXr8sOlxkLA+wdYBpZpfjStu2WlP8nAbJZsb4KelTy9iznm0j1VIV+lqxGXBiPLDnG9AzF0jN6OQezjZ/ipkb9En9HFO+ffhtT+/R8uXrzIr//6r+/LudoJJVIqFArf4hXLvRXJthtJIla7vFACKQmsx2hQydfJXRGmtlcvF0lxbxFYFPtEvtguuQFrl6s8Wa3BMydliPo7UO3Ob7TLHM8rX5GUAtOGmP8j84aKevnJwtT2NA2ihsS0q69X9Xmtwev+IkztDurkpLSDmxokyLR7u1P9XWvIFwVpc6YqDOpiYojIwhHcIW9PS7+jZTLEz50D2yaxtsKT2RtE5faFP4PWPMYbEjE2iptKkX71Vaaee25rv5SSkrQouCUKbllsLFQ9zNtC45bAeHtbEq7/8N1wdSzN/e9lMPt6MabzxIs2PW9ofNB9HLPUgVWMYxfjuCWDv3viXQZFEdkRoW/4NAhB4YlP4kaqw2ErWocSKRUKhW/xSvTuJeoFlY7qFHRbFM3wXa/fqWfA6gjRbyFEWfyqZSiuJ9QqGoeXONwRD9dNiJdhJK8MCS3BSyzqUAYsRQNpH3Hc+3oKJUEsEq7r9Tte/R2Ec6ytJVLW+w4UjaNdFmZ4XU/JUiJlK/Ca44Rtjue1oLPefb2iMWiZDEe+/G2+MFPtUn2/nCTxxVdwRkYwn3kGN51uQQ3vjeg3v4lrmbhLC8SvXaQjqmFFDBY6U5RiETpkjgOFBRYLBeQ7c2RGD7KQ0Jj/8R/yf5yc3BIglchYn5iI0K130KN3bjwnyw8jubXdfbiDgYt/jDEzg3b6OOs/fIP4TZ0HxAyZaJq8Bh3uImkzQ2IlBx0CZ3QUhMAZGQm0WB5WlEipUCh8i9fNXD2DTxCpJ0IWSkqkbDb1bmbC1vbiHiKlMtq3Bu8+r8kVaTBKLPIXXguC4iEbe1S78xeeImXYxtk6huBy2wvX9fqddpvjreaqy1Wf1xrapc/zuh7T1ppcEwV4t7sw9ne1UIsymstmGNToLAiiVWFQc1o/yCT61BSJF1+kePYszokTTamblBIbB1M4zFrLFF2TgmtSdEsUZXm74JqUXJOCLG3s235MbHaBn3z/daR0GbswjYEDH04yd6QPqS1jACYw5cbhRo7e5TyxK9dYf2iY+HwRNzNNfsAjT2zIEQg6tTideoIuvYNOLUHXtu0OuvTElgAZ13ZnALE++UmMF1/ETaVYPnQc89Y6MWlytLQ9x7iIRbBPnsBNpcAwMJ95phGXqbhHlEipUCh8S9vczN1BpFQ0l3rGm/C1PcnyenW5uqFrDe3iWeT1P1KG09bg9b2Hsb+rhervWkPBw3M8bAuz6v2PlIdH8/Fqd9A+RvuianctwStaRtj6PBXu1V+0zb2Fx/9IpTBpHpVhUI2VLE9mr1WFQR1Zy2C8Du4DD+CmUsTPnaPw/POeHpVSSmzpYBouWa3ALXOe4oZ4WJQWpa1tk6JrVWzfFhcLFa/d1EYuxqnX7uoaH7s4iYukZzmPYTtYEZ2pI33VORA1wdTRfpLrJSKWQ/dyntW+JMcnMrw5MHpXn+03DHSSepxOLbH13OUpQCZIanE0sf+LVdx0muLZs8TPnSOf7OG17odq5gh94JEkkU7AMCiePRtIL952QImUCoXCt3hNqkMXnkSJlL6iXfIVAZ5h5krKaN90pGyfPk8ZTv1FuxiwlCelv2j3dgeq7bWC+iH9w9b2apcro31raJ85Xu1y01KelK2gnUL618qBuLp+AG326P+fvT8Pcuy68zvRz92wJHJDrsjM2rcsVrFIJsmiRLbU3aIotrrlZi/ubou0Ldvt8eiF7Yg344l5E+/NTMx7MxMTz/Ybx4zH82b0pt12yws5cm+qlqiVbFGUSIksskgWq1hZe2ZVZiL3HdtdzvsDQCaQwEVtwAXy3vOJqEjg3gvgXNTBXc73fL8/GevoAeHXXgPLQllaJH7rChsq5HSd+bY+MuEQMbHJiDnN/NoaxserLB7Zw0Z3G8vf/F9497eeKRMYM06OrMiLjjYOPAlvMQfT7zR1H7sX8/EEnStpAFZ6YxUCZRGhKqz0xuhPrtG5kma1J7b1+lZCAdrUCM6mSYfRxmBHHzE1QpsWoV2NEFMjxLRoyeMIMTVKSNFRlNa4jrUPHyb94ous/POXEajMGYPMGeW/ecPYxB4Z3rUxw0FBipQSiaRlCUrca606h1Kk9B63fqdrAsNnZ023wWA5cOo9OQtsJxjHPPd+53FDJEBwosCicsC+pXCrfey7fldjILiWq0/SGGrGvRoeNsQDpHu8dRAiOPe1tZ2UtreNCRi2bTM+Pl62bG7hSaBysGF1Ocm5c1c8atndYZomiqKg6/d2wx1eXKT/1Yt8dabyIL4/N8nqP/k/Sff3s/Dkk2R7e+vV3C0mJyc5dOhQ3d/XCwSCrGOSFbnC37wwmHVMMjuXCXNr26JrsbguMrfIL3zyNrZjceTj2+htNurjMRb39aCoSaKAA9x2QogJhe7lFawr69w8NYyYTHJjMsryLohB1c38MUyz847MzB0uHIrri9sXX98IIkqINjWc/6dFth+rka2/scLfqBrecj9G1TCqovDKK68wOjrK2JGxhrWxkTiJBB888xt87eMDFZMVkqEEqa/sxdknJyu0Oj4bbpVIJH4iKLPsazkpUxnv2iHJE5R+BzXqd0hx3HNqxgz7rO+5zbKXA6fNwe1791+/cx+wFwJaZDJwYAhMpH+Nkjoy7tV73I53YUOg+SyNUtZoax1yFjguE9GCcq7N16SUImUjmZ+f57HHHqO3RIiziVTdtrsrXLZdK/AXf/EXHDp0iJMnT971a8ITE/S8+SazWQ2F3qoxj+FQhOjGBvE332TpuefI7t9f13afOnWK69ev89RTT9X1fXdiC3tLNNwWDMv/5ooRqGXPK4XGjMiR6s5gYSMmf/zAbXvi4lWywqRredPXMaiWkb9QsLW8MzySMVmtsX0kY5ZtX3x9NXQ0omqISEFcjKohomUiY7jgcNwWGovLomoYrQFRqruNrKmRDA3xas+XKtaF9sw2oUWSe0WKlBKJpGVxG7T3WzxJTZFSikWeE5SBU6gVBSYHsLwmSBF0br8lOWDfHIJyzHMbCHYchZwFYZ+5qFod12u8iL/6narmBbCsWbm/8lzrPa41eH12ngVZ/7mVqFlKIiDn2qypAWbVdZL6cfLkSU6fPr31fCA3w68tvVHhKhqLP162XStw8eJFTpw4cfftmp6G730PEglC6ws8vfZ2RQ3EfnOexE0D4+Rx6O1lz6VL8MwzMDxc17a/9dZbW49tYZMpCoTCJOdYBXEw5yIu5raEx/y2eadi/vn2tla9Rf46ngr8GINajZXeGH2za6x1R+laSdG9uMlcoguhqDi2hm3qOJaObekMtsOBlRUMrZ1MRx97zC72HPg0h/p/kWiJCBlVwkTVMCFVyjMPSs6sLgJHQwJVari7AvkrkEgkLUtQIuhUFcIhyFaJ/ZIxdN4jnZRyln0zqBlB57NjnqwN2FoEJXaz1v5kc4prjV5JY3B18Pqs30G+70mRsjUIyr0FyHNtKxGkeve1nZQSz5iehldf5Tcu3GZxvXzV3uwtnnjjXQjthV/91boLdp7x6qtgWbCwQOzaBUJCkC1GO6ox2pxNErkkIpuDjz+Ghx+G3l7Eq69i/f7fzjsK7ex2vKldqIVY/OvkyNgmGSdL1jZLlhX+OiZZO8eKs8bG4yl+PvH/JSssHJxmfzOe08oxqHcirBhE1FDexajkhcOt52qYiBLaet719FMcnvg2ekyle/48mmUz+b1BPtRO4rAtkKnC4Rf3nmeETkSbQSY2ArZK16d+HScmI0cbRdblPOPHazy/IkVKiUTSsrgJdG7ur91M1EWklHGv3uPW7/w2iAByln0rUes799uFtRw4bS2CklpQ63eUzil0xvy1v61O0CYErVYxCcgJQd4TFOc4yNSCViJIkf5u/S7r4nCRNIDLl+Eb3wDLwrIdBqpEoBpqP9y6BX/4h/B7vwfHjjW71WXYwiZjF1yGBbEwWyImMn2bvvGfY9sWXR+dx7JzZA50MTHYhWKk0LUNLM1mRtH5eH2e9uU1zHdvMf7wCOZth+8NfVjfGoghQAR3dnkjY1CroaMRVo0tgTFSEBjzjw0iapjolrgYJqIaRJUwk1dvEhI6YycfIaqGCSsG6r3EpHZAdP8N1Olp1GMn0C9dYiC7yKesn1eI4+HlFPQo2EeOQDZLur+fjkEpUDaSjJuT0mfnWT8jRUqJRNKyuN1E+y0KDPKRrysblctlbUDvcXd3eNwQD5BiUetQ67fut2OedPC2FmmXMZWg9DuQx7xm4HqN58OBBNdzrRSLPCdQ4rhLOQmZ0uI9wXJSVl+es6RI2Whs2+b6T37C0Pg4im2jr67y6NwSqmWVbddvztP5wUfMWiNYXV2If/bPWHjhBcz+/rv6HCEENg4mNiYWJjYW9tbzXOF5rrDOxMIU29uWvq50+7UDG3zbvIz98z/Dofbv4omfXuXI8gxdS5vs3VjENDRWn+igS52p2PZKdxvH1lYwcjbRpTVyu6gG4m7BNQa1JPLVsTU0NGIaJJZNQmqYVN8gI6E44ZGjfKHzibzAWBAXw2qISEGE/NH3X+fo/sM88fAYYdXAUO5PysjZywD06J33va/Zz3+e6Msv48TjWMePY1+bIGzm2J+dLNvOVA2s44dw4nGc+XkWnnySgfv+VMnd4DYZxm/nWT8jRUqJRNKyuA1Y+81VBO51KaVI6T1BiT4E9ws2OWDvPalaTkqf1cpzEyHkwKn3CFEr/tDjxjSYWhNN3IRaSeNwu77x47nWvf6zt+2Q1Epp8WO/k9d4rUKtSVh+63vRsCCRm+HxjffL6iAux/pQZw/iSCdRY/nOd7ilqYRTG/TOJAmp/eSiIebaesmEwsScTQYy82TMRewLM8zsHWCzs43FV/9/vPX849iqwFYFjiqwNYGjsv1cFTha/m896xluEQHuMio1KDUQm4WGSkQNEVIMwqpBRDHKRMPQloPRIKQYdH36UY7d/C56m0rP3AUmb4N+u49LbaPYdgjH1kAonD6S4dn2i2hKLyJmMLj3SVAU0p/5mzg97seGsymDTidKuxb18FuojpNIkHnhBSJnzuDE43w8MIQzv1jhVhYHuxiIW6DrJD/7WbK9vc1uuu9xEyn9dp71M1KklEgkLUugIpncREo5gOU5gZpl7zJwKsVx76lVJ8tvhd7d+50cOPWarAlCBONcW9NJKfue57imFrhcD+1mpHu8dZD9ToqUzaDWdfVuT2rJ5XJcuHABgPDiIv2vfsJXZyqHGffnJol+/U3skRFyzz6Lk0h43dSmUnQf5hyTnLDICQtTWOSESc6xCssK63ZuU3huipLtSl6TdUxMYdH213oQ377NpBCMnp9mvd2GfSvM7+sBdY4IYANJR0WbUOletuhYmmV6ZBgts0Bam69vBGoD2c01EOtNafRpaCsC1dgSGMNKqCA0bouKETXE9M3bGELl5NETZdGp9+VU7ILogel8DOrow9jXr9K7ucqTmx+UxaAenp5C60mDUohBVRTskZFdN3nBPnyY9IsvEnr9dTR9iaQxyJxRvg9HndzW8S41U+nwldQfVyelz+5p/YwUKSUSScsSJLGoLVJ9uaxJ6T3uA1j+63dyAKt1cPvO/Tjzz+23lM4pCAGK7H6eEaQIulr7I8Ui7wnWRDQpUrYKgep3siZly5DKKtXdhW2DaHP7dt0AfSkXLlxgfHycsViMxJtvMr9moDBMf5U6iI7dhjY1RfTll8m88AL24cPNbj5CCCxhb4mCOWEWhMFyUbC4zE1MdN9me92dIkwflCduzAPQuZxCt2xMQ2NqX0+Fw1CoClP7e4ltZDFMm87lFKu7LALV6xqI9UBHI6TqhEvEwqI4GC4+VkOF5/r2Y3V7fajMyZh/rt1LTcUSzpn5gaZDkaG67F9pDOrlrpP0pK4QFuUxqCHbQhgG9pEjOPE46Dq5Z5+ty+d7jZNIkHnpJb7zYZrsuxfKju3JUALz9HEeeakg+kuR0hNyVvXfgt9KmPgZKVJKJJKWJUhikdssWulo8x5ZJ0uKlM1A9jtwHAXLBkNenXpGLZHEb+daXQNdE1h25T5n5THPc4I0Ec1NLJL9znuC1O+kg7c1UJNJ9n37Db46s1Sx7hiTRL/+o13vLnykr4/Hz5+H/n7CGys8vfY2IWGWbdNvzqO/K2A0L05Ezpwh/eKLVfe51HVoCntb8HN1FlqYjkmusO10NIml2Jydmy57/U4R0Sws88uvP0gRqHdTA7GI4oitfVvrjm69vhoa6rYrsUQgDKl6/nmJWFj8e/79D9kzMMTRA0eqrg8pOhHVQFP8XZe1NAZ1Ue9lvLOvYrJC+0gXA0905Wek6jqZF17Ytce9Iql4gu/1HKhYPhLdBDY8b0+QyUgn5a5HDgNJJJKWRdaklCJlMwjSLHs5gNU6BMlJWWuf0lkFQ/ffPrcqtSYk+LXvbaQr91ke87xHHvNkv2sGgbq3kBPRmo527RqRM2eIzIFCqGLAPqX1gGhvOXdhNRwhsMpiSvN/Z7U1Dr33BnO2g7q8jHHzOp0dCjldZ7EjTiZi0Gal6d9cZC6dgY9mmT0yzEZXlMU/neSnv3l6y5VY+r4P5DosJhSlZuuy77uFQESgCjDQmD66h5MX5jG7ulG1dWI5h/6Pc1zr24MjDBxHzddBNBU+lbtGXyaE0HX6YvsZyGkw/HkOrPWiCw1DaOhCQ0dF495difa7Cxz7whhjsaMN2OHdRTEG9dYPf06EaeZ2xKAOx7OgmLt+YkYpsoxJ6+Bek9LjhkjuGylSSiSSliVIziIpUrYOcpa9HMBqBrLf5cnkFDpj/tvnViVIca9QFCkrl8tjnvcEKS1Dnmtbh2Cda6kaMeo4/aizB3Z1xOhuQE0miZw5A5aFvrLG02s3KtyF4fUkxll1K/qwlrtwJ0IILGxMx8IUdpnAV+o4zD+3t+ofbq13qmxfZfm247C6gBXPrhNeu8i0EIxemkYXNtZojJl9PQh1EwVIA7ecGM5Ehu7lFOFrN7h5ahgxtcr6dO+uqYPY6rRCBKoi8rGmeeFP3RL/9MJzA63qY11oKLbAEPn6ipXvoWGIvIiooIABezoEkYUFjKEO2m/eZGEywr7Lm1s1ENudTRK5JN09Jm3tYTZGDhAX7aT7+unuPpAv0CmpO04iwR+N/F3Q5irOP0ujHaRfOu6r80/E5fpBXuN5T9ZycVL68BrPr0iRUiKRtCRCBEukdKtJKUVK7wnSwKnbbymT87ghEvd+50d3R83agB42RFJTpPTjuVZOCGodgnSN5zaDOyP7nee4nWv91u/UZJLHf/JjvjqzWLGufWGC6Nd/7CsnSytQFA2LtQzbv/8XWNk1lKUluqavk4mAaejMx3rIhkK0OWlGsnMspbM45xdZODpCqivG8jf/Oe/91i+Ui4QFITInLDae2OAn+hz2xI8RLRBUeuhy3q0YhDqIrc6dIlCFUBC2hmNrdIYcBpdzhJQQ6339DBhdiOHDfDr2EKFCxKmh6IQLMachJf/XUHVCil6IQ9UJlUSahlQdHQ3Fo+Ly6tAQ0ZdfBstC7e/HmZsgbJXXQATQoh1ETx8iXKiBmH7xRQbqeNwbHx+v23v5hXRWYTU0xKs9Xypb/gtPr+AM+uvix7X+sxQpPSdrVndCb67Pce7cJzVfOz4+zujoaCOaJbkHpEgpkUhakloiiR/t+m41KVP+uobbFbgNVvux37nGk8iLas9x73fNH4CqN27HO5B9D8CyLM8GPD660QM8VXXdxY/Pcb/jTKZpoigKut5itxrOZ4BKx8aVa1Oc65lkcnKSZ555xvt2BQzHgawZnNhN6aRsHdqWkvza0sdlzo5kKMFAehToaHbz6kIxYrR7pXrE6KroBRHbFRGjD4It8jUN8//yrsJZscJqe45L6VtlDsJSF2HxsVUQBXPC3Hps7nAa7nyP4i89Pr/O8+MfoAjB6CfT6BEb8XCM2X09CHUFHcgBN50I5sQm3cspxJVNJk8NIybnuDHZ5u4uNIAWECeLBKkOYiMxFL0gBBbEQEUrFwALIqCh6GWiYEjZ3ubHq/+eYzmH/r29dM2fJ73ucO07A1yKHsd2DISjAAqqcPjPHvsAQ+lDtBsk9j4JikL6s1/B6d89DrfSGohOPM7HA0M484tlx7t5ow9xsIv+uOWbGoi7AbeJaEG6xpNxr95jhLuqLg/rd7ZNj46OcvLkyXo3SXKPtNjIgUQikeQJWp0s6aRsHdz6nh8dbXLgtHVw7Xcuzq/dTM24V3lDx/T0NPv27fPks3IusThhw7pvgRLgRz/6Efv27Wu5GaluN6k5695rEEnun1qO6SCda+WkDO9Qk0nCr73Glz5eZma9/Li3N3uLz77zEyKdA7vWWZjL5bhw4QLhxUVGvv99VNvGmsnx9NpyRcTogDWHcZb7ihi9XxzhYDoW2aJgKMrFw/LHhb87tslWbFe5rhhxmnPyImNVHoZzs3/SsH2FYLkL/V4H0VC0gmioF9yFRfFQ316mbouL5eKhvuU2NMoEyPw2RbeioeiodXAf/uVGjFBPhLiuox47gXJ2nL7UMqfT721FoLY5mwyZSYzFDVAU7CNH8n9HRnZlBGexBmLo9dfR9CWSO2ogAhx1ctI57iG2DTkrOMlUsv5z6+AW97p/7wBjYzGPWyO5H6RIKZFIWpJaAzdtER9e3MgIupbBNYLOh/1OipStQ7DqZNWuSSnJz+YcGxtr+OdMpquffGJR9YE+vxiZ48U+3As93VFIVlnev4exsbiM7PKI2rVQPWyIR8hzbXMpOguxLGwzwoA5W+G0MbTOXe0svHDhAuPj43z+xg1U28ZYW2MwOcWm6CBbcIsWBYqEmUTk1lE+uUhu9AhmvIv0979J8q+9sCXuVToF7bLn4wdXWXTO8saVKyWiYql4WC46msJq9lfkKUFyFzajDqKOViEW5qNJ86JfanUDXWgkevsLgqBR5lQ0dgiJxXXGDnFRr5N46CXzp09z4Px5nHicxb0nyE3cICzKI1A1VSAMY2uiArpO7tlnm9jqB8NJJMi89BKvfpgm9+6FCqe8/dRxHnlJ1j31ilrXNkG6r5Xlc7wnSOMpfkWKlBKJpCWpNYDlRyel26CcjHv1HjeB3I/9zm2fTEvBskG/93EDyX2SClAsjqHnB0hsp3Kf5aC9t7jezPmw34EUi1qFoKVlyFn2zUNNJrcESrGwwMnZSUTOKdum35xn4GKWRfoxOztxvvY1pp5/nmxvLydPniQUakzev0BgbwmC9laU6LYYaJdEj5ZHkJbGjlrCItk2T2zfMvOXruHEbPZevY6ImYhTNvN7e9H023RpDorqsILN2YllupdTWBeSjJ8aRqwpfP/8vHvE6E4GYYZJWGrIV7Pr8bu7sJRiHcSN7hjxlQz9SxmmusLYwsCx1XwNRCf/d2/c5uDKKrrWjjK4l7ZInIH9x+nreXpbQFSNHbGnRUFxW0xUldrpB+emzwEwNtpaE6W8INvbuxWButnWw9udiYrI5/W2XsaejIGi+CoCNRUf4vs9ByqWj0Q2gQ3P2xNUUjXLNvnvGs+17ri8xvOcIMUM+xUpUkokkpak5ix7H55kpJOydQjSDKxaF2zZnIIe9d8+typu4rgf+x3k+95mRoqUzSZ4ImX15XK2s7fUSsvw4zEvEoJEbobHN94vc3gYWh/q7MFdGbG3Wwi/9hpYFurSEun330e1ukgp0XJnYS6J4WwQu3mT1QP7sDrbib33Fm8fP8i8ts7BY4e3hMN8nGjxsV0QD+3y9SWPTWGXiIn2lqiYOp3hDTWJmPhxnXYUnrhxlVlzma6lTXK5FKahMXcshq6W30wIdn/EaKvTDHfhTlSUrfqFedfhtui35RwsuBENpVwELHUaVnMglr5H5NlFOqdfRukSGItnUUyTKz/q5bx+HKdETFSFw7P7zjNMB6LNoHv44XwdxE//Nk6nPAbWk2IE6tL/+mMEi8ztiEDtjDmgpHwXgSonorUGtcbx2mRahqSBuN1ftPnw3sKvSJFSIpG0JEGLiZAiZesQpJqUtX5L6RzEoh42JuAEqd9BftB+M1O5XIpF3hKkeGuQjrZWofYAlr/6nppM8tTbP8aeWaxYF5+bIPr1N303UPwgCCHyYp5jYYp8VGjWMfNCXyE69KqYYa4nw883PqkQ/3IlbsTQ7AKnrryB49jsuXgVoWWxTmjMjkRQjRk6VAdFtVlWbGZubZBeTmEl1xnvH0as3ebqiMq7nYuQfL/+O9qApIogRYy2OkV34Vp3lK6VFN2Lm8wOduMIPe8udPLuwqiuEI8KRpYXMdQI2b4h9oS6iew5wuc7x7ZExqKgaCg6Z3/2DnuH9jB6+FhZXGlxfdF1qCkexaEM7UEMD6NMT2MfOYJ+6RIJc5a29ErFZIDwcgriu78O4m7ASSQ4+8zf4msfblZMkNEG+kl/5YDvvnvX+s+y3r2nBM9sIO8tWgW3cQQ/lpLwK1KklEgkLYnbxY2iCMK1E3N2JVEXd4cUKb3FcSBrBicmwq3fQXEmmv/2uVUJnqPNZSBB3tB5SpDirQEiLgMJcgDLW9JZpaqzMBlK0La8F2L+GDgt1kKMr4JCqCJyb1PtBRFr2VqItnC26guaRbFQWJX1Bh2TbImouHN9Tmy/R66wben7ltYzNB0L526uPY7BxYXv1dzkiQ+uMmMu0rW0STaXwgxpzI22EVLTFdv6wVkYpIjRelAU9Urdg2WP1YLTcMt9qFVup1Z/fdsvLjNw+z+gdii0LZxj/Boo14a4Gi13Fz51LMNn2i6iKXFEzKB/7+N5d+Ev/E2cnurHwcnF8xzo6+NEdL9XX9UdyX7+80RffhknHsc6fhz76gRhs7wOIoCpGljHD/miDuJuIJ1VSIaGeLXnS2XLHzlg4gz6L6tZOtpag6CVbZLieOsg4153P1KklEgkLUnaZRZMJJQvn+A32iLVl6eqOI0kjcOt34E/Hby1LtjkDZ23BClmGNzdUvKGzluC5+CV4nizUZNJhr75I746s1yxbl9ukq6X/9IXzsLSWoixzVWeXrtJSJhl2wytzWGcBfvIEZx4nMiZM6RffLFivx0htl2CO6JFi5GiW07CkijS0vWXD6yx7LzHT65eqyIgWiUCYkE4FCa2KK/duNsImrOwFSJG7xe9GEeqGlvxo+HC47BqYGyt07eWlT4PKdvPy7cvbpt/fuGDjzn7s3d58fe+jNrIG8q9A4T3HESdnkYcPYZ99SqD5hzdVrm78PDtKbSedN5VuIvdhU4isVUH0YnHOT8whFhYLJuUMW/0oRzsoj9u+aoOYisTtGs8mZbRGrR6Itr4+Hhd32/mdj/wRMXyVEZw7ty5rc8cHR2t6+dKKnHre1Kk3D1IkVIikbQkqYAN2LvGvcroQ0+pGU/iw75XU6SUYpGnuP3W/djvAMJG9f1yczJLGkPQxHE5y765FJ2FRrK6s3Al0trOwq0o0oL4lw5bLKmbTGRny4TBnLAY/vY3CaVm0ZdWiU7fpLsbTENnsbOLTCREm5Wmf2OZ6c0szkczTB9OsNEVZeFPrvLGbzyxFVlqChuLOjjcEjDNBCxOPPh77RKC5iysFjE6l+iqKswqjtgSYde6o1uvB9BQK5yCW1GiBbdg8vYMid4BhgYSW7ULy0XCEgFR1QmroUItw+1ttgRJVUcrcRc2kqgSQhNKYwXKAqXuwsudJ+lNXSEsyt2FIcdCGMbWRIXd7C4s1kEMvf46mr7E9I46iADHnJwvJqHsFuQ1Xh5ZSsJb3Cb+GbrAaLICcfLkybq/p6FXv1bIWhpC5A0Wo6OjDflsyTamBZYta1LudqRIKZFIWpLAzfyTNSlbglpumlrRqLsV6aRsHdxn/nnckAK5XI4LFy7U5b1M0+T69etlyzbXfxcYrtj2yvUpzp27WZfP3cnJkycJhXz4Q34A3M4xzep3jaYtJKrGjMYv96LOHmp283xNqbNQX6nuLBx27s5ZKITAwi6pXVjNYbijTuHObZyd29kl27m/Zxlj8HMWYOZs2eL4/DrPX/8ARQhGL09jqja5IzGm9nUgVBMw2QA2nU7MiUW6l1N0XL/F9KlhjOlNnOQ0a33tDfhfCBa72VlYpDSStJpYWHysKxrxJ/Zw6NqP0BIQnxVops30WxGudh/AFgbCVnEcFWyF34pfZlDpg3aDwf2Po6k6n3n2b/J3E8OodyEYvvKXr/BM/5OcPnjag29hd1LqLlzQ+7jc2VcxMaNjpJP+x7vzo9g+cBc6iQSZl17i2+cyWO99XBHnLZ4a5dRLHc1uZmAImqvI7dpV3tN6SytHboZCIcbGxur6nnab+wSoh06OyXqIHhG0mGG/IkVKiUTSkqQyUqQEyJlg26A1f5wkEATNSRk28nVehajcb3lD5y2tNtv5woULdYumKQqUpe/VdTYEi5Xbzs2vP/DnVaMY7VPvG9PdTqv1u0aiJpN85t036ZxZqFg3lLxJ9Os/4eTUFMbRo01o3e7FFo6rsJdztsW/oW//OaHUHPrSMpGpCbq6wTJ0Fjq7yIbzzsKB1DLTmxnERzNMHx4qOAuv8+PfeLxCMGzlHnro8iwAncspdMvGNDSm9vVUONqEqviiFmKrUi9nIYCCUlUY3HYbVoqJpbUMywRGVednP3mb/cN7eejo8aoipFF4/3ty/PVD9HAWdXoa9Xgn+qVLdCQtjidvlUSMrpHIJWl3Ngl3hrCOHieqRbBHRjCG9jzYFy6poOgunPzBO8SYYm6Hu3BPTwYUy3fuws14gtd6Kmtm7o1uAhveNyiguE2+9eM1Hsi0jFbB9d7Cp+N4d5r07dffW6tR02wg/w92DVKklEgkLYnbxWRbxJ8nmLYaM6zSWWhv864tQaZmDQUfXlgrSn7WaTU3lYwa9ha3G7pmxpOMjo7WVdQrfa/+73bAzcpt2rv6GRvbV7fPlNQmspTk15YuVrgdEtnjgH+cXMWY0d51BQWjws1ih3tAtNO+vEzfmTNw5AgcO9bsZt8TQoi8kKc5rFgbFaJehVvQsUqchpWOwaqv3eFazAkLhzvXLdzpLNR1G/NIjKl97Qg1B+QKzsIOchM5updTtF+fZOrUMPr0BlZykJVd5CwMWi1Er9HRwHKIhdsq3IX6lstQo3tsiP1X30BLKHQnHZyMTfvPQlyLH8BxdBxHRTgaWPDlgSu0Kz0QC9G3bwxN1Xjqc3+Dv5kYxlA0NKW+swWvLp9jz0APRyIjdX3f0ohR6/hx7PFJwmZ5xCiApYawjh/a9RGjuwEnkeCPhn8fTZ+rcPEvj3aQ/vLxXVeD8k643TPJuuPe4paW4dfoQzchQtYdd6fe9RkBxq/uAboqlqtkt2o03iuTk5MtW9PxzslU/vy9tRqtXgtVcndIkVIikbQkbheTfrXq14qBkCKldwQxJiIaElX3W97QeUvQjnlu+5UzvalLFXTUZJLwa6/xhXNL3Fwvvx3Ym73F6fd/SuTf9/vC2VEaM9qRWuPptRsVMaORlXzMaMRxUGwbvvEN+P3fh+HKSOK7wRZOXgR0THKORU6YhceF59X+isr1xffIFl8rLEzHJFtYvv0Z+XUAnIaf3v6DB/3a6krQnIVBqYWoK1pZncFQWR1Co6RWoV5Ro3Dn8nChNmHxtWHVIKToWzULi+sNRefs2bO8dfYtvvzlL9du4ABEj1h5Z+FDXWTOjRObEoxOT5U4CzdI5JL0hDbR9AjWseNoehv2yAix4QOefI/1pDRi1InHOdc7TGRtoWxSxrzRR9uRTrrjti8iRncD6azCRmiIV3u+VLb8s0+v4Az6r7aHdLS1Bu6xmx43xCNkv7s3GlUjMWtWn9QTNnbHtc29IsvntAapGqdSv46n+BEpUkokkpYkaDERNUVK6WjzDLcLSVVtfqH3RiFv6B6MetRutB0F0/qVquve+slrtJthDKP2IPedaLV6jG79LmvJbOtGU3QVYlnYVpgBc7ZiANtQu9Cmpoi+/DKZF17APny42c2+b4wf/gDLzMLSIn0zV9jUFUzdYK6tl7QRIeZsMmLOs55J0bG+xuJqG2vLJuv//n/k0u99IS8Gih1iYYlImC1ZX1xn7axdGHCC5iz0uhaiilLhJCxGkRaFxNmpJIM9AyT6B7fFQsVFJFS2xcKiQFgqRoZVA13V0e6idmGzKXUWrux/iNz1m4RFpbNQCRtYR4/4wllYjBgNvf46mrZcETEKYDpZ7JGELyaitDpCBPG+Vt5btALBq0kpnZT3QiPqMwL85Gb12fXxrsh9f14jHJ/1opZLT7rHvaN2IpqHDZE8ED4dcpVIJLudINXJgtozGjP+m2DbstSK3LyXskC7CVeRUl5U3xX1qN2YreEeDGkm169PPdD7t2I9RreBuZzV+oPeu5lSV6G6tMSJmUmyKatsm36aZGDeAAEAAElEQVRznj0TKurIQZx4nMiZM6RffLEuA9mOcLbjRstiRE3M0ijSHfGkOccsjyqtFl9aqIFYGkcam1vmuU/ez8eMXphGD9soj8WY39cD6hxRwAFuOwpiIkO3aeHcvMxHHRuI1av86HyO5V0UM9qqBMVZqKBgKBqZvjiR+QxmTzfhVZPEksXNcBeO0BGOmo8ZtTUcR+XEiMWh1Q10vRNjcD9d0R7WDhxnf99nKuoYutUuvBux8JW/fIVnnjnN6UOnPfgmWodSZ2Eq1sPbnUMVcc9LoT6ePN2Wz8D3ibPQSSTIvPQSf/Jmlo7x8xWR3l2fOcaRlzqb3cxAYFr5yWjV8O99rRQpWwHX+1qfls9xu7TIygnfnpJqwRImjaS2k9LDhgScWoKwXycE+REpUkokkpbE7YTu13iStoj7Oumk9I6gRW6CHEioBw9au3F+xX2A+diRvezvj7eUwFgPIiFBIjdTUZ+pKxxBnT3lu/pMrUL4tde2BEptfBzNUTH1MLORftJGmJjYJJGbRVU2yV78gLWjB8h2d7D+6r/h6u88X1LDsKSeYYkwWBQMZ08t8EFkjT++fW6r9qEpLOy7qF9YT05engGCEzPaqnjtLATKRDxjh7uwmtBnqNu1DLfrGupV3md7/Xe/9R2OHjrC4488RkjRUVFRFAU1Mkt06uvQITDmz+JkTT56M8Z49DhOiaCoCodj5of0ijZE1KBj+DgoCulP/TqH2+UxsF4UnYUrf/BjBIsVzsKwIUDZxB4Z8Z2zcKNriDd7Kus8P9a2DqS8b1AAqXU97df7C7f7dTlg7y1Bu6+t5eAVAt9OOG413GOG/dnvas27y5iy03mFW78L6QJNBjXtGqRIKZFIWpKgOSlrxb2mMt61I+ikXVyrtf5/djvuIqXHDQkwtWoo+LF+h5pM8svv/4ShmfmKdYm5FNGvn/PlYPGdcITAEhZ2CDaULLPmcplDcKeLMF+fcPv5znU7BcTo3BK/cOltHMfm8Me30CyL3KkYS/t60NVbdBTaMe8IQhPrpJZTWJeWGD81jLh2le9fzt69qzAGYIF1pw0bS9BiRlsRHY1UbxfhuTS5ni5CqznisxYTkQ4cDBxbzbsLbY2uNjjYbzO0uo6hd2IM7qMz2sPqgYfY2/fMDidhNeFRw1B1dDQUD0YjIzmNNhEiqpZfJDiDgzjDw6jT09hHjqB9colBc45ua6WkFuImiVwSfXET2hXsI0dAUbBHRuQkjQbgJBJ89Itf4WvvpComx2Ti/aS/csCX37uMP2w+tb5rv97XygmQrUHQYobd+l2+rAaEHqxyhuQucTvm+bXfaVpeCMtZlfst4169w7Xf+fQ861ekSCmRSFoSt5OMf2Mi3Ne5CWeS+hO0i2pwF2DlAJZ3ZHJKVVdhMpSgaz0F3R13fpNdQrEeYt+GymX0itg9O9QBIt4S9RCFEFuxoXnhzyyLFN0pDN7rutJtikIiAL8ZYoKfwdTP6ro/T1y8SsbJ0bW0iWZZgXAVBiVm9G4oRpHurF1YGidazUFY3Lb0+Za7UNV3OBN3ug81VEVFjc4Snf46dOadhVduOEQnOiqchQMHszySu4AmYgVn4UNbzkJnlzkLS2sh8tBxrE8mCTuVtRAt1cA6ftgXtRBbnXRWIRka4tWeL5Ut3z9o4QwuNqlVjcVtcE4OnHpHzTpZPr2vdbtvkv3OW4I2aF87dlMhZPhzv1sNt2OeX/sd5PteNZFSTszwjiD2Oz8iRUqJRNKSBC0mQlHyQmU195p0tDWeyclJzp07x7UbB4HKGj3C2uTcuXPeN+weMU0TRVHQ9bs/vefSjwMDFcsnb89z7py3ReonJycfqPbibkRNJun547/kqzMrFev2Zm8x+to17KE+1KGhXe8qLK2H2Jle5+m164SEWbZNdHUG46yGfeRI1XqItnDuIPyZZc7D4vJbkdvYisPFhUVyBeGxuH2pS3HnXz8RRFdhM2JG7wZd0QipOiHVKAiEBmHVIKQa28tVfWvZ9jbb60KqQaiwvPjv2vgVLnz4Mb/+xS9hqOXCYjGKtBnsdBba169WdRYem55GM1N5R+EudxaW1kJ04nHO9gzTmVoom5Qxb/TRdayDjrjjm1qIrYxb+QS/lpIAd7EoKyPoPKNWWoZf72tdnZSy33mKdFJuk8kpdMb8ud+tRtAS0SDf99aqJKjLc613BG382K9IkVIikbQkbjNh/HySiYarC5LSSekdObN6bcDQLonc/NGPfsS+ffvuSegLGdVrxOUs9zqJkvpQdBXqMwKFcIWrcN7oQ1ccQvPzTXcV1sIRYlvwK0SQFgXCnDC5aSxg4TDw7R8Q2pxFX14hvjCJEs+LQIvdnWTDIaJWmtjmErc3TZwPp5g6PMhaV4T5P77Ea7/+KLkHqWkYLfzdmKrbfu82gugqXOmN0Te7xlp3lK6VFN2Lm8wlusrE2XzUqEqbDj3zKQw0lP5++sNdOHsPc7r7IcIlwmCpmJh3Em4/L92uVHDMb7MtMGpKY46vWWWJyZRBItTTkPd/EEqdhdfiJ2lfv0JYlDsLQ46NMIytSQq73VlYrIUYev11NG25ohYigOVksEeGAhdv3QyCOMtexm42n5pOSp8K5K4xw9JJ2XDGx7cnmKYyzwGV1xvTt69z7tych63yhuRKFPilquve++Aiie50Qz43iJNsaxHEcTxZPqf5uH3Xfu53fkSKlBKJpCVJBXIGVvXlUqRsPPv27WNsbIxvfVy93lp/T4yxsTGPW3XvjI+PMzo6ek9tHXqzEy5VLm/vHGBsLFLH1t2Z0htrv1PqKlSXV3l67WaFq3DAmqNnfJ3NPXvAsipchbUQQmDjkHNMNpUsluJwOzdf4jA0t2NMHZNbiU02IxNMLKfICYuZtiQWDu/OTVcRH80yJ6PFHcSrdojPr9N3/QMUIRi9PI1u2OQOx5ja14FQbSDNOrDhxMlOLNK9nKL7xjSzp4aJzWSJzC+Rvtt6iJKqtKqrsBSdvMOwsuagQUjRtuNI1cpI0vJahfnlsc+ssG/ym6gxhc75j7DSNte+M8Cl6HFsR0cIFYSCKhz+wUMfkl5W6ejtZXT0c6Cq8BtfhaGhhu93ECh1Fi7pvZzv7KuYmBEa6mLoya58vIRPnIVOIkHmpZf4xg9yDEx8VBHpPfRLR9n3u13NbmYgcLu38GspCYCwy72FHDj1jlrCnF8dbW6lJGS/ayzDw8Nbj4WArFn9ui0S2n2Tzu6GkO6+X1mXiciS+uM2duXXSRngfq6V5XO8w+1c6+drPD8iRUqJRNKSBHEGVpuLHiRFSu8IYr+LhETVeogHP+lBnT28K2P2Wh1b2Gg/+C7ZXBqWFonevEJbSMHUDeZjPaRDYWJOikRmjpS9AZPj3Oyw2OyOsfrNf8H7v/2ZbbFwh3CYf54XIB0K/ba78MHT77g36gBcYx1Wb+afFweYUrN12edDl/Pv07mcQrfsQNRDbDXuxlVYRHEE8cVNVBQy8U6iahhlYJC9oX53UVA1MBSNkKJz4cMLDPUPcmjvgfL6h6pe8friY13RUOsdRxqD6L5x1Olp1GMnUD++RF9qmdPp98piRhO5JNrCJgLI7N9Pl6rC3r1SoKwzRWfh7R//HJiucBb2dmdBMbFHRnznLFzvHuLs+t6K5c/E1oDGuDsk5QQxCkw6KZuPW78L6QKt8XN/moLsd81B1/WtyapZExxR/ft++MRhxo7v87h1jWdt071/HTx8gkcPN6aMQ5Am2d4NQRxPcZtwIo953uF+jedxQyQPhBQpJRJJS+I268jPM2HcZp3WqmUiqS9u/c6vDl41meS5D/+EwzOVkT8Hbt0g+vWf+nKw2A0hBBZ2hdsw65gloqBZFmU6EbmFpTh8srhUIRhmnRK3Yomg2DW/yvOXCq7Ci9Poug2PxJjb14NQFwkDFjDlGDCh0r2cwrryCTdPDSMmZ7g8EWF5l7kKg1gPsd5UcxmWCX5qdfGv6Crs+PSjHL35HbSoQs/cRUTW5uZr3VyOHcMWeiH2VAMbvnrkY7o1gYgY7DvwJCgK6c995a4nLazP3GS0c4Sxjocb/K3cmdKYUfv4cXLjkxUxowCWZrA4NER/VxfoOvzqrzapxf7GSST49/t+n3V7vmJyzPzRTtJfGfXl5BgZf9h8XK/xfDxwKsWi5hO0ewuQ/a4VCKKD9041KSXeEMSalG6/KXmN5x2pAJ5r/YgUKSUSSUvininubTsagW3bXLhwoWK5Yz4EVAoPV67d4t13kx607N65fPky165d4+LFi81uyn1z7do1urryUWuu8SQuAvJuplgPsX9D5SZ6ReyesREH0YE2NdUy9RBt4ZSIfSZZl8elomB2y2G4HVGaLRMby92IW+7Du6VY63D99l2/JIiuQj/WQ7wf0XDnukpHos6/+6N/y2c+/QyPnXykZJ2G+qB1DLsgemA67yocPYX10TjxlU0eW71Q4SqMLG9AWME+cgQUBXtkZNcKR6Uxo0pfnLc7R6rWf0081E569iNmFxdZ/JVfITs1BVO7q4bphQsXePvtt5vdjDuysPx3WA4N8WrPl8qWp8QPSP7lXzapVffO22+/zfXr1+/KRbG++jvASMXys+9f4JXszxvQunLefvvtrWud3c799vNPLn0BOFGxfHbmBq+88v06tOzuuZe+8yBcu/wp4NMVyyduL/DKK3983++7W/vThx9+6Pkx8uzEKPDFiuXCTvHKK6/c8/t51XcehGvzw8DvViy3bAXTAkOOQjacWqJcxKeD9oYOmiqwncp9l2KRdwTRSSknZjSfIPY7PyIvDyQSSUvi50xxIQTnz59ndTVfDWxyMu/myKb/U6qJlFevT/HK/H8A8rUTW4lr167xxS9+kRMnKgd9dgtCCJaWlgCILCb5taWLFTWjEpnjVPu/2a2U1kPsyqzz9Nr1inqInYuzGGfzIoUTj99VPURHOFsOwm3hsOAq3CEiVtvm2rFlrrZ/xGszV6oKkHesfbhLCKKr0It6iAoUaheWiH4FETG7kUYXGn3dPYXlRoVIGCrZvlRUDKlGxTJd0esfTVogvCrodtqI6x11f+9SV+HawePkrkxUdRWqYQPreP63j66Te/bZurfFS4oxo6HXX0dRVypiRgEsJ81aVxcfjYwwsLkJu3DyzU9+8hOee+45Hn300WY3pTbfjVZd/PCJQzwz1u1tWx6AxcVFDh06xMmTJ++47cvn27m+ULl8cGg/zzzT+MzHubk5rl+/jtKg45aX3G8//9bVBExULt873MszzzxTp9bdHffSdx6EK+kE3/ukcnkk2vVA+7xb+9PPfvYzvvjFLzb8ey9lVumHdyuXd8b0+/o/8KrvPAjdt9rgjerrMjkFQ9/99/OtTi1Rzg/jKdVQlLwgsZmp3PesubuOVbsZ6R7fJmtWXSxpAK4OXilS7iqkSCmRSFoSP8+E0XWdL3zhC4yNjQFw7ty5/N9v93OtygDW4WOn+MLDKwBbr2klTp48yenTp5vdjAfiw+98h+i/+3c8/8ESN9fLT417s7d46v2fEvn3/b6IPbWFg/aD75LJpWBpkcH5qyTDCqaus9DeQyYcos1O0WbNsZTO4JxfYP7oHja62lj+8/+Jn//Wp8gWxcMyobEOImIPQBayy/XY1ZbFj65CNxQUdKGSincRnc9i9XQTXjUZXLS5YXRjCwNha1tRo8JROX3EZGRtE03vJprYT19bLxuHTnJi4BfdnYuqjo7mOmBaPM6OHWu9Y6iXlLoKM+09vN05XNVV+MgTbYRCCug6mRde2PXHPSjs+0sv8fU/txhd+KBiMspDXzhMcuV7/PIzz+zqc9qJEydavv1uh7CHHzrM6dPNdezfCxcvXrzr7zvxp0AV41NXfIjTpxtf+/Re2robuJ99if5J9eX79g5w+vRAHVp193j1//HWzerLVaP9gT57N/cnr9vt9n/Q3RW5r3bshu++rc993dn3P6Y75hKZVCfGx8cZHR1t6Ge0OrVESj+Mp7gRDsFmpnJ5urFdTlKCm0jp537nlvYmHbze4TZ+7Gdx3I9IkVIikbQcppWPg6mGn08yMiaiOYQnJjjx85+jPvQQthlmwJytGLA31C5PY0+FEFuxpFnHLPzNkSkVBgvLSx9fOrzCZOwCP52d2LF+W1TsmF8pr4cYteFUsR7iEgZgAjecMLmJDbqXU3DlMlOnhhG35pm+1b7r6iG2Gl64Cu+EoWhlrsPN1Q3aw210t3cRKjzXhUqib4CQYpSLgqpetixUsmz7PTVCqoGGygcffEB4+ASnk+9Dh8CYP0t63SL20zbGo8dxSmJMVeFwzPiQqBNFRA1iQ8fy9RBP/wpO2+6MG201iq7CtX/zBoKlqq5Cw9jwbT3a1Y4Erzpfqlj+6+2rsOJ9e4KGEO61ttsi3rbFS9xi491i5iX1J1Vl4BqgzYeR/kVkv2s+Qex3tcqzZK0HjK6/C0ZHR1vaaeoFbkIR+Hs8xc01JcdTvMPPiWhuuEUoy37nHdJJ6Q+kSCmRSFqOmoXe/XxxIy+qvWd6mp4f/pCUbaMuLXEiOUk2ZZVt0m/Os2dCRR05WDX21BJ2QRDMkdkSFIviYF5YzJUszxSW7xQYM2XiY+5eKyMWGguQgXQVS26BINZDbDVWemP0za6x1h2layVF9+Imc4kuhKqUOQo1VNo1k4GFLFE1TKpvkGGjG33kCL/U8WiZQJgXDA3CW0LitmB49dIVDKEyduox16jSV37yCqOjo4wdKTi8pwvOw976OA+zPT04w8Oo09PYR46gfjTOoDlHt7VSUQ9RXdiAkD/qIbYqTiLB+HN/k6+9meHxjffLXIWL0UEyf2ufb79z99nO0PihU0nOBMepvi4mRUpJA3H7rv0sjrsJYbLfeUcQ+53b8Q7g8NGHOTqyexJBdiu1nIN3CHDZ1UiRsrkIARmXvudrkdKl39WaLCCpL+7nWv/2Oz8iRUqJRNJy1DqZBzEmIiMHEuqGLWwydo60nSPjZAn96b9j1V4Ha43UxVlCEbCiOoud3WTDBm1Oir6NJaxQlpmPbjN1eJC1zgjzfzzOD379EbLCwsFltLVFCWI9xHqwXe/QKLgG86JgbjODjkZfVw/hgrswvCUQahgltQ+Lr419doU9t/4crV2hfeFDbt0WKN8aYjzyEA7bLsnRkRxPGj8hnIkSi8cZ2Ptk3lX4ma/g9N69gLToTAHQrlWvA+cVpfUQc8fc6yFaagjt+GHf1ENsVdJZhWRoiFd7yl2FnW0OzuB8k1rVeGoNYLV53JYg4uaihNoD27sdV5FSRtB5hlvfC2K/c3P3SeqP7HflZGT8oSe4TfqOhASqj2dkuYpFst95QiYHQrj1PY8b4yHSbNB83GOGPW6I5IGQIqVEImk5ap3M/S1SChK5mQpXy95LPYRPpcn29DS7iZ5jC5u0nSNtZ0v+ZUg7OVJ2piA45pdnnFz+uZMlY2e3hMh08bGdJSe2XZLx+XWe/6AQe7o2Q9KySR+OMb2vB6GmgTSbQMrpwJ7I0b2cpvP6bWZODROdSROZXyK9C2NP/VgPsVRALMaMhgsuwu0Y0oLTUC0IiQW3Yf6xUXAelouJeWeiUXAfVq93uFXr8Og9OA73QXTvBdTpadSjx3EmrzCQXaAr9/MyV+FDyWnCHSsI8IWrsLQeotIXd62HuPdkO4Nx4at6iPeCbdv84Ac/YHy8SgG7OnJ2YhT4YuUKJ8Urr7zyQO/99ttvc/369Ybvw/2Q3nwRqKw/97N3PqR99R2eeeYZ7xsVIGo5uPw8aO/maJNikXe49T0/9zspjjefIMa91to36SzyhqBGH0qxqLkEdRxPOnibj9t37ed+50ekSCmRSFqO2rPs/XmSCS8u8uy5f8W+mUr3ysHpG+x99Sbp/n7UoaGWH7A3HWtbUHSyZQJjqlRs3LGuYjsnS84xG9bOoMaeNqseYqhEDNwWEMvFw4mrN+nv7mFvYs9WdGkxtjRcZftwiTBZTUBsZUpdhTd7TmKsXK1wFYbbbBxdZ3PPHsI+cRUW6yGqP3gdwWrVeoiWk8YeSfiyHuLdIITg1KlTDa+nNKv0w7uVyztj+gMLdYuLixw6dKgla0L1nI1ye6Vy+dDIQQb6GltvWFJblAti/KFbLJqk/gRRLHLrd9lcPpZvl1067Upc+52Pj3chI9+3RJXb9qwctPcENzHYr2MpRdxFSo8bElCCWgvVza2Xlf3OM4I6McNvSJFSIpG0HLVmHEV9aNdvu32bxJtvktsY4Rp6hbNIW+8BxyE6P0/05ZfJvPAC9uH6D6Rawibj5Eg7BQeiyBWeZwvLcmRE+fOJE4t84rzGv/zgRwVxMYMlWs9pV42gxp661UN0FBVhazgl/0Y6HfYsL2GoUXL9w+wPx+nYe4wvdX1qK9I07CIcFiNRw4pRtQZiNV754SuMjh5mrLs+dRBbmVJX4XKol2ud/RW/fRJdGIdSoKq+chU6iQT233iJ/+OPbR5bP1fmHE+GEjzxxUN0fy54zvEiuq5z8uRJTp8+3dDPeetm9eXdXZEH/uyLFy9y4sSJhu/D/dDfC9ysXN7Tt4fDESlSNpqaTkofXuMVqVULVeIN0klZTjrrb6GsVXBzrfq53ylK/phX7TcnXbze4Bar63dXkWv5HCmOe0JQnZSyJmXzcXVS+lgc9yNSpJRIJA0jl8tx4cKFiuWmaXL9+nXX112dHwF+p+q6M3/+CqpyfyeaQ4cOYRiNqxR/8uRJQqF7G2FTk0kSb76Jatt0ZhZ5eu06IVHuHmxfmqX70gabe/aAZRE5c4b0iy9uiRVCCExhF8TDLCknS1pkt8TEVGF5piAs5sXHEuHRyZEWWcz7ERc7YZVV2IWDbLsx9lRBKbgRdcJKaMtdGC4IhDMTU/R0xhnpH9ohGupbj9s/u1pWDzG95nDlL4a5EjmOo2y7JFXh8DuPfEBI6UbEDPr2jOXrIT7zIk58d8aNthpFV+HUz99GkKxwFX6qKwfqJOn+/rLfvB9QFFhuH+JVY7hi3Zc7lgE5itVogugqghrxh1lADtg3HOmkLEeKlN7hltQSxH4HUqT0iiCfa6sd36RY5A3SSVmOFIu8oVbtTz/3PRkz3Hykk9IfSJFSIpE0jAsXLjA+Ps7o6GjZ8qJAuXN5kXW9r2ptxvlwP48N9N1Xbcbx8XGuX7/u+pkPSrHu1tjYvTnAwq+9hmJbqGurDM5exzIElqEzH+shGzGIiU3arDnWnXWcWxe5GNtkrSvC/J/e5EcvjJEuOB0tdod7sZVodOypoehElBDhoqBYFAxVY2t5SDGIqKH83y1XYv5vpOQ1xb+GoteMNX3l9VcYHT3GWE+NfrijHqL+wTgDuXm6zNWyeoiJXBJlfgMMxRf1EFsVJ5HgT4/8PpOpxYpj3v5DXUQ++zDZnh4GfCRQFomGRNUbCjmQ4A1u4ojfB61ljbbmUjvS37t2eI3b70qKlN4gRI1jno/7XS13sux73iDPteXIQXtvcPue/T5gL8Wi5lI7Ec2/fU/2u+bjNn7QFvFvv/MjUqSUSCQNZXR01FW4q7ZcTSZp/9YbWDNLFesO2zc5/X4ae2TkvmuV3auIeLcIBFnFYsFcJeVkSTkZNgt/U06GlL297PpDS3wce48fnnuDpz95GytkMpqcRo/aKI/GmNvXg1AXMch7iW44EXITm3Qvpwhdu87iqWGUqVXMZD/rfe0N2Z8gUBZ7upyidzHD7c4wjghhW/m4U2HrOJbG6JDN4dUNDL2LSGI/PW29bBw6yYnBX8qLiIrBtUtXMYTGE4+MEVJ0VEVt9i66UloPMXP0OLlrExX1EAFszUA5fgTHJ/UQW5V0ViEZGuLVni+VLd/7+DpDPT9tUqsaj7yhay5uYpGfhSKQjrZm4+YqCusOqtq6580Hxa3f1RJtJfWjWIOxGn4+5t3JSSlpPPJcW468xvMGt9+3nyM3wd2tJ/udN9Q6r4QbF2jWdKSDt7k4To24V58f8/yGFCklEknLoF27RuTMGcKzoBCqqM+2Fo2D6ECbmmpYbUZHiIKYmGHDTrPhpNm0M2wUxMZNuyA6FmJVi9umu7MIBZi6C0GhCyDH/otXMYVF10oK3bIxDY2pfT0VdRGFqjC1v5fYRhbDtOlcTrHaE+PQeJL3+o7Udf93AyoKES1MmxYmqoWJqKH837LHIaJquORxiEhh++Jr2hLLtK98nVkxw+Z7P6Q7FiP0w17Go8dxSgRGVTg8t/QmsbSDoxtAJ8aSzUZ0D7lLy+SAdSA5Psno6CgRtfWLapXWQ1R647zdOVzxe5s3+jj0cIyeLnxVD7EVcYsn8ftFtdtAQq2oIEn9CGJ9NpAiZbNx+57DhgMET6SU/c4bajml/XzMkyJl8wmigxfcXbxSLPIG9wF7jxviMW5uPXlv4Q1uolwkJPDxPLQaxztv2xFUMqb7Oj/Xu/cjUqSUSCQtgZpMEjlzBiwLfWWNp9duVNRmHBFJjLMq9pG8s2tnbcadmMJi086LiNP6ChnFZGP9PBt2ekuE3CyIjHkhMk3KyXBfssB9XPd2L24C0LmSBvLOvp0CZRGhKqz0xuhPrtG5kma1J7b1+t2Apqh5gVAN06ZFCmJhiOjOxwVhsa1EUNwSFbUIbVqYkGrUjDy9a/b3w74DKDOzzPX00JmFQXOObmulIvY0tr6M0MnXBVVV0v39FbHDo6OjnDx58sHb5RHFeojOq68jWK2ohwhgixT2yNB9O5cld0dQ68aEjer7l5UDWJ4Q1DpZbgN0ciDBG9xcRZGQ421DPEb2u+Yia6FWIkVKb3A91/q430EtJ6W37QgqrvXZ/H5v4XKuzdYQMST1I6h1AeU9bXOpWQvV533Pb0iRUiKRtATh114Dy0JdWiJ+6wqrQiOrhMrEooPcxs5tYl88z9qxA2x2x1j59r/mw9/+RdbtFGtOmg07zbqdYt1OkRYld0Edhb+LF5uyf9XQzXwdSc3OD8xlIrUzMIrri9sXX99IDEUjqhQcimo470hUQ0SU/PPrn1zh8N6DjB44WiJChrecjcXHd6ql2DR+7dcQZ89iDA5id+4hN1099rQ93oV6/AjhQuxp+sUXfVEj0EkkyP71l/janzsV9RCToQSf/dJB2p7pbXYzfU+tWad+xj3u1eOGBJSgOindRNhaIoakftR2UvqXWk5KIaAVL5H8RC1Bzs/HvJoipTzXeoKMey1Hxh96g7y3KEc6eL0hqJGbbuK/7SjkTAj5OOq2Faj1+474fGKG35AipUQiaSqOEGSmb2LfukrWzhIb/5CsZpI50MmtkThaZJm20Bx6KMeMnuX9iVW6l1NYn8wzfmoYsanw9jWL5V1Ym9EyNABsLZ99EcmYrNbYPlLIMShuX3x9NVSULVExqoZpU8OElWpCY4hIyXYRxShsEyaiGmiK+2cAvHI7yaf3HeP0wOl72PMWYniYpeeew/lX/4p0rMc19vTRJ9swDMWXsacRg6r1EAF+v3MJkFNPG43b7L82n19UuzuL5ECCF7gNnAbV3SFdRd7gWpPS7yKly/FOCMiZ7u4PSX2o6aT0sVikqvnB0VyVSzl5zPMG6aQsR17jeUNQnZRSpGwust9VkskphFyclpL6IJ2U/kGKlBKJpCEIIUgrOTbVHB+nbrBqp1i1N1ixN5mMTZFSc3zj1vus2Zs89tPLHFmfoWtpk73pRcx2jdWTHXSo8+Xvib9qM670xuibXWOtO0rXSoruxU3mEl0IVcGxNRxLx7F0bEsn0ZZheN6kTYvBwF5ikV7W9p9gX98v0lYQFYt/o2qIsFKnSNQAkN2/n4uf+hRja90IlqrGnur6BvbIiC9jT1U1H1GSNSv7i7yhazxCuDsHoyGg8YbppuF2w5qS/c4T3AZOYwEdOJUD9t7g9j37Pe61liCRykqRstG4TcoAiEW9a0cziIalSNlMAluTUoqUTcXte/a7WCRrUjYXNwev/yfe1hYpO2P+3v9mU1Ok9Hnf8xtSpJRIJPeFLWyWrQ0WrTWW7XUWrXWWrDWWrPX8P3sNs7swuj73XvmLiwMxhdV+rc0YVQp1FNUwbWr+b0yNMHH5BkPdAzz86UcZnfgeG5lVerAxclGuvjrIJ+ETOGL78KwKh8/ve4P2bJhYPE585BQoCulP/wZO+2CNFkjuls2uLi6d/pt87c1MRezpYnSQzN/ahzPo3+86EpIiZbPImiBEjWictMcN8hA527m5yAi6cmT0oTe49TvfOynvUBsw3uG+XvLg1HJShn0ewxYNUTWpRYqUjUeI4EarS5GyubjWu/e5q8jt3kLGDHuDm1jk97jXWvsn7y8aT824V5/3Pb8hRUqJROJK2smSNJeZLfybt1a2RMhVe4N6He5buTajjkZMi9CuRolpEWJquejYpoaJaRGmr98mLHTGHnp0a7mqqFXf85UfvsJodB9jJ8aI7p9mbvF9zD0HaJteoj+9RGfmnbJanIlckvDyCkIH+8gRUBTskRFfi2bNIJNTqsaedsUcnMF5l1f5g0hIsFpF65cDCY3nThfVlo9FSreBEtnvvEEOnJYjB+y9wdVJGXCRUtJY3B28+UQJPyOPec2jVo1t359rXSP9vW1HUHH7nt1KLfgFd3Hc23YEFfd+52+hyO14B/K+1gvcJiEoivD9RDS/IUVKiSTgOMJh3lpl1lwuEyTnzGXWnJQnbWhkbcZSDKHRZcSIqVFiaqRMfGyvsiymRu46NvWclf+uBozuu2pLkeznP4/z4YeYnZ1k2wfIXZ0gLHLsz06WbWdpBpkDw4TjcdB1cs8+e0+fI7kzQa0LCDUcbTIap+HcKZ5k3cO2eE3YpT5HVt7MeYJrnSyfD5y610L1th1BxbUmpc/jXmsNDMu+13iCWoMXpEjZTGo5eP0erR5x6XfyGs8bXGsD+lwscr23qJIYJKk/7jUpPW6Ix4Rr/K5k32s8tWqhygpYuwspUkokAUIIwaK1xs3cLDezSSays0zm5siKKoVKPKRWbcadKI7YindNx9tpV6NsJvbxWNthOrU22tU2OrQoHVobHWqUdi1KuxrlyvlLaKiMjY15vXs1cRIJkp/9LIk330SJx3m7c5h+c55+cwFdWFiKzrzRR/vhNN3tFug6mRde8F1dxFYgqPEkIAftm8mdnJR+Fillv2suQXVSuomwcsDeG9y+56DHvUoaS1CPdyBFymZSS6T0e99zPddKkdIT3O4v/H5fK0tJNJeg9rta4r/se43HLVLX785xPyJFSonE58ybq7yfusLlzG0msrNsOK2XHXh9NMGRizOsx9tgag09pdD7scn1+D5MK4KdM7ByIUbaMzwXTRJVB1A7whw4cDpfm/GXvoIzUDv6VKN1s5xSe/Yw9fzzdN+eQ7DKnDHInFG+P5a4Rbo/TvrFF6VA2QAmJyfZWJkCOivWCSfNuXPnvG/UfXD9+vX7ep1jPQ10VSy/dnOGc+duPGCr7p7JyUlGR0c9+7xWIMiF3t32r9Z3IqkfQXUWuQ0M1xpMltQPt34n414ljSSoznGQdXibidvxDoJ7rpUipTfUchb5GTcxzLQUbBu0uwvgktwnqYD2O0MHVRU4TuX+y/vaxuMmBPvdOe5HpEgpkfiQtuEu3tWu86fT7zOZm2t2c1BQ6NCidGvtdGkxzJUMbU6I4yOH6dJjdA+1s/fit2ifXUR7eIXx715F31DYr6yRDLWRUiN0OZscb7tOx7ADqo515KivajNme3sxP/ccX/tTh8c33ieRSxISOXJKiGQowcBnTnPolMaAFCgbQjKZZHVzueo6YW0wPj7ucYvuj9nZWXT93k/ttnmKaiLldHLZ031PJpOefVarULtekX8urKv1o5XFw8DRiuVyxqk3uAkjfh+0dxs4tWywqwwuSOqLe21Af4uUhp4fHLWrlFGXImXjcT3e+VwoAumkbCa1vmO/9z332oDyPOsFroP2Prq3qEYtx14mpxCL+nv/m01Q+52i5PteKlO5//KY13jcvuMglG3yG1KklEh8xuP/9ZfY96VT/JTL4NEM2TY1TK/eSVzroFfvpEfvoEfvYOHaDDEnzNOPPoWmbDsZz03nXWljnY9sLVOf/xLKyy/jxONc6TpJ9+aVitqMoUgOYUSxjxzB8WFtRl2DhWiCV0Nfqlj3TNtZYMH7RgWEp556ignxCN++ULluz3AvX/7yl71v1H0yOjp6z7HGf36lm+tVutfR0VN8+csH69QySTVSteJefVLo/eTJk1WXh1ycUxlZu6PhCOHuLPJ7BF2t/TNtOcW+0bjWpPS5kxIgGoKNKoEmMuK68bg52vx+vAMpUjaTWg59v8fQufW7rDzeeYKbY9X/sZvu6zImxKLetSWIBLXfQf6YXu2Yn2luZa1AEOSyTX5DipQSic/Y96VTdX9PQ9EZ1LsZNOL0GV30aB306J1bgmRErX41eM7O144sFSjdcBIJMi+8QOTMGRaNXj7p7KuozSgGdPY+OZyfquTT2oyRkGAjXXmSzVqtG1frF4J9US3rdzQLt+84bAhUn/zsQ6FQVeH8vZnqIwWy3zWeWoOEfh+0r7V/OUuKlI3GLWIyECJluLpIKcWixhPompQug/ay3zUet+8477rxti1eI52UzcNxght/WOu+PS9k+Hv/m01Q+x2476M85jWeoNZC9SNSpJRIJAAoQI/eWRAjexg04oV/3XRrHahK40+u9uHDpF98kcnXfk6Y6YrajE91zm1FvOaefdZ3AiUURcrK5XLgtPEEWaQMuzj2pLuj8QS1Zgy4O0Vlv2s8tepk+X2Wea2BYdOWt0aNxs1Z5PealODe96RY1HhkTcpKZL9rPLUcvB7cWjcVt+OdHLBvPLWcW36fmHGnuFdJY3EvJeH/+9qw4SJSypqUDUc6Kf2DvBOXSALKoB7nQHiQA+EE+0OD7An1E1Kbf0hwEgn+zZ6/i63OVdRmHNnfT/orn/VFDUo33G7osqZPLFUtTJBn/kVcbhzkzVzjCfLMP9nvmketCLpaUVl+oFYdMDkhqPG4DWAFxUlZDSkWNZ5AOyllv2sast9VIq/xGk+t79jv9xdSpGwu7mKRxw1pAm4TjGW/azxuE5yD0O/8RvMVCYlE4hkjRh9f6HqcR9sOE1Vb9+4onVNYCg3xak95bcaBUx/4WqAEd0FMDpw2nkA72lz6nZu7VFI/XPudzwcRQMYMN5NaImUtEc8P1I57lbdGjcbNWRQJSZFS0jjc+p3fj3cg+10zCbSDVzopm4bbvQX4/75WipTNxW3swO/9DuR4SjMJcr/zG/JOXCIJEFPmAv964fvEtQ4Oh4c4FBniQCjBUKinpURLt5kwYd32tiFNwO3iRtakbDxBdrS5CWK1bnIl9SHI/U6KlM3DrS4g+N/hUcspmrPlhKBGE2QnpZsgVit+WVIfpKOtklrnAUl9CHK/czvepXMKQvg/7raZBNtJ6b5OlpNoPMG+r62+XPa7xiPjXv2DFCklkgCybK9zNrXO2dTlrWVdWowho4dE4V/xcafWhuLhXYQQ0LWe5JfXz5VFvSZDCXpSKrDHs7Y0A7cTac6UA6eNxnUgIQAxEW4iZdaUIwiNxn0Ay/8X1a6TMkw5gNVoguykDNesSSnPtY0myDUp3Z1F3rYjiATa0eYau+ltO4KIa302n59nobYQmzVlDF8jqemk9PmgvaLk7y+qiWUZeV/bcGQyVSVy8m3jkU5K/yBFSolEAsCqvcmqvcmlzK2y5W1qmEE9Tq/RSa/WSY/eQa/eSa/eRa/eQUg16tYGNZlE/f5r/MfTaxXr9mZv8eRPbxMJXyL37LM4iUTdPreVcBUppZOy4QR55p/boL0cwGo87v3O44Y0gUiN00cmFwynQbOoFfPn976XH8CqfnyT0eqNRYhgOyndflsydrPxBNnR5tbvak1WkdQHN7dqkPsd5K99g3B/1SyC7KQECBsuIqUUixpOkMdTIoYUKZtFkPud35AipUTiM9ZuLNB5sK9u75dystzIJbmRS1Zd36FG6dE7C8Jl/l9cb6dba2dTyRIVdzfaqV27RuTMGTIpG4Uo/eY8/eYCurCwFJ15ow9dtdGmpoi+/DKZF17APny4bvvZKrjd0GXlwGnDkTP/KpEX1Y0nFeCZf3eqGxOE76BZuA1OR8PBcLBGw9VFStOWt0aNpNbEF1mTUtJIZE3KSmS/azyu51qfTwaC2kJs/v5CXuM1ilpOSr9PRIP8/cXqZuVyeV/bWBzHPYXJ7w5ekOMpzcTtmNcWgH7nN+SduETiM15/8Q/ofWwvf+9//c+5ps9iisbWcVx30qzn0kzkZitXdoMi4Bu33qNLi9GttdOlxUhF1og5YfRUN11ajJ75DQa++U2wbcTCMk+vTRISZtlb9ZvzDF/bQO17FCceJ3LmDOkXX/Sdo9I97lU6KRuN2wVkEIQSeVHdPNy+42xqmXPnPmB8fJzR0VGPW+UNdxIp5QBW43BzdwRhwB7y+7m8XrlcOikbS00HbwCclFIsah6usZsBcLS5CWKy3zUeGfdaHXl/0VjcHbwiEBPRXO9ra4i3kgenZr37AIhFbmNG8njXeNwmQQZhHM9vSJFSIvEhix/c4ovmI+w9cIAPU9e4kp3iemaGWWvZ87YIZTtKdpK5/MJo/s+P5/I1MZ/75of0La7Tu2ay99oCfQmNrBJhLtrHhtJOOGXRt7YCZBCffAzHT0BPL6HXXyfz0kue71MjiYQEidwMj2+8X1aTM3HNILwkD9mNxC3LPggxEVKkbB5uM//CRn6CyejoKCdPnvSySZ5xZ5FS0ihqOSmDgNt+5ix5nm0kteIlgxD3KkXK5hHkY57sd83DzcEb5H4HtcUMyYPjWp8tAPe04O4WlWVMGot08FZfLs+1jcet7wWh3/kNeScukfiYmBbhmY6TPNORH+DesNNcy05zPTvDtcwMt3JzZHc4Fr0mPr9O79waCEHfzbzzM7UnxtS+doSaRSOLBcw6oE9YrCzPYH08x7VTewmthTj70ToMDtGhtdGhRenQ2mhXo7SrEWJahJgapV2LIBAotPagt5pM8sXzf8KJmbmKdfuT6+x9dZbI7Kyva3I2EylSVuL2nUjqh5sYN5yIMzY25nFrvEWKlM3DtT5bQG7m3PYzZ0snZSOp7aT0/7nWVSySA6cNJ8hxr277KPtd4wlyLVTppGweQa/PJu9rm0Ot33UQHG1y0nfzCPoxz09IkVIiCRDtWpRH2w7zaFu+lqMjBCv2OjPmEsncEklziRlzmaS5xIaT9qRNhy7nY2I7l1Polo1paEzt60Go5ScaoSpM7e8ltpHFMG3altdZ7YlhnD/Pe513bqvSDRFh8M2p83kBU40Q06LE1AjtBTFz5+OYFkFXvBmwbLt9m+iPfkT/psot9IqanOpmDBzH9zU5m4lrln0ALqrdBuyzcgCr4bjWQg3ARXUtQSzT3PkzvifIA6fgvp+mrQH+d/Q1CzehCCAcamx5glbArd9l5Cz7huPmpAxE3Ktbv5PXeA0n0P2uxjVeVg7aNxTXe4sA3NOC+z2UW71ESX2oJQIHoe+5CWKy3zUeV/d4APqd35AipUQSYFRFoUfvpEfv5GT0QNm6DTudFy/NJRatNRatNZasdRatNVbtjbpVCutezFc171zJC40rvbEKgbKIUBVWemP0J9foXEmz2hPbev2dEAqkFZO0uXRP7YsoIdrUcP6fFik8jmwvKzye0RcJC4OkubS17G4FzvDiIok334T+froy6zy9dr2iJmf78jTdl9Ko0aiva3I2CyHcB2uCEBNRa8apEASifkmzCHK/C0snZdMIukjp9vsyLSlSNpKgOyldo8CkWNRwgnzMc+t3tg2mBYYcEWoYQe53ug66BlaV+SfS0dZYgu4qCrtcT8h7i8ZSq+ZnEPqeW7+Tx7vGE/Rjnp+Ql6QSiaQq7VqUo9oIRyMjFetMYbFibbBgrbFkrbForbNorbJorbNkrbFqb2Lf5SCfbubvXDQ7v30mYtTcvri+uH3x9Y0iI3Jk7BxL9jrUcvZ05P/8+dS5rUVhxSC6Q8xsU8Pc3r/GeuQmy2sQVcN0v/+XbIgNNudzDM3fZFrRySoRZowhUmqMNmeTI8otVGsd/dIlrOPHceJxX9bkbBaWoyFEcB1tbhdwjqNgWhCq/bOUPABBnu2sa6BrAsuu/A6CPpBw4cKFhr7/1RsjwHDFciu3xrvvjj/w+1+7dg0hWrcP5zLHgK6K5XMLG0AARo+bhKxJWX25rFfUWBzHfUJQEOJea9YGzEqRspEEOWYY8n1vPVW5POjXeI0myCktIGM3m0XQnZRuvy/Z7xpPkMdT/Ia8JJVIJPeMoej0G930G91V1ztCsOmk+fmF99hUsvQdSLBqb7Jib7BibTKzPk9KzZJWTSwj7za0NRWASMZktcZnRwoZgMXti69vRbLCJGubrNgb5SuG4DYTvLc0QXx+nedXLzFrCNTz0+gxG+2JGMv7ejDEBB2Wjm3p3LBCRBehZ20BLv6c+bHj6NemmLg6iJIYIqqG8/+U0NbjiBpCU9Tm7Pwuw7R1ErkZHt94n0QuSUjkyCkhkqEEXesHgL5mN7Gh3Kk2YCgADpdm4XbjEpSL6khIsJGWImUpw8PDrK6usri42LDPWF2vfkzTyNTlc0+fPo2iKA3dhwdBERmqiZRSLGosbt9vSLMC4diXImVzqBVrGgRH251Eys6Yd20JGm6/7SDEvYIUKZuF2zEvOPcW1ZfLiOvG4va7VlVBKADKg+x3zcFx3CN1gzIxw08E4FAhkUi8RlUUOrQ2eu12emlnrOPhsvXnpvNuw0cfexRn9FVIncUZmiWyeQ1jVudSdg9axEIL5dBDObSQiR7KoQhnK951rTsK5ONhdzO1anKq2KiajR7O3+Fe6ejg2IV1jGyaheQ1VntiXP35n/HeLxxxff+wYhBRQ0SUEBE1RFQNEVZDRAvPi8vy68P5xyXbRwrb6oqG4tMRRGN+npM/e5uvziQr1u3N3mL/X7xB5MoQuWef9W287p1Eys6YvMBrFG6zToMST5IXKSuXu82IDAK6rjM6OsrY2FjDPqP93Y6qywf6Ohv6ua1C4o1OuFy5PNLWDXhTkzuIuLmKDN3ytiFNwq1GmxQpG0stB68UKb1rRxBx63tB6HcgJ2Y0C7dr6CCUkgCIuIixUhxvLO4lTEQgJqLJftccapVMkCLl7kOKlBKJpGmoior+2GmiH30CiYMYtxfJzNn0TDiMR0/glLgAOyMZ/lriJ3QpfZgxlfDIQ2SxsZ/8HO0dUdbsFBt2mnUnxaadYdPJ4NStcmbjaHRNzqKbc5W7q93phoa6Q9DM/5s7ssKK8z4XJuaJamEiWoioFs7/U8Pbj7WCCKqFCatG6wiely/Td+YMS8urKDj0m/P0mwvowsJSdOaNPnQlhjY1RfTll8m88AL24cPNbnXdqSWIyVpZjSXoNRTcBrDkrNPGEvR+51qT0pa3Ro3E3UnZ2Oj+VsEt4lEO2DeWWt9vEBxtbuI4yL7XaIJckxJqXeO1yH2gT5HXeLI2YDNwnXgbkLIxMma4OdT6foMyMcNPyDtxiUTSVJzBQZzhYdTpaewjR7BnLzNoztFtrZAMJbZqMh7M3CYaWSPU0YF67DhD4Tj2yAj9o1+q+r5CCNJOlk0nw4aT4aPLH5NRLfr2DLDpZNl00mza+XWbTpoNO0PKyZAVtQpP1p9Wr8lZxMZh08mLv2X0wRw3+SB5867fS0XJi5lqmEhRwNwSNQuPS0TOLeFzx/OIGqJNi9y/6Dk9Dd/4BoptY8/N8/Ta24R2/P/3m/Oob6+zuH8Es7MT52tfY+r558n29gJw8uRJQqHdf/UTCeEad+vM7IfB/mY30bcEvYZCxCVKWN7QNRa3ftcWkH4Xdul3piNvjRqJm6soFBAnpdtgiRSKGktNkTIAtQEjNQQxOSGosQS9JqXbMc8tmk9SH1JBv7eQYlFTCPo9rdu9hTzeNZZa6UtB6Xt+Qt6JSySSppP9/OeJvvwyTjzOTOIEueR1wiLH/uzk1jaRkIWj61jHj+PE46Dr5J591vU9FUWhTYvQpkXoB1asGQDGumrH2JnC2nJibthpNp0MKSdLautv4bFdvmzTTiPu4/rDzzU53XAQ+e/PzkIdNGEFhbBqbAmXkYLQGVENImq48Lzo5Cy4QLUw+7/xLWLrC6RWkgyvJ1nSB8kqEWaMBBuigzY7TSKXRLdNjJs32ThwALOzk76zZ5n6lV9hfHwcYNdHI6rJJAOvvsZXZ9Yq1u3N3qL/G28QuejvuNtm4jbrNCjxJHIgoTnIWfYuIqV0UjYUVyelHgwnpWv0oRSKGkrNKLAAONqkk7J5SCdl9eXyGq+xBP0az+0eSva7xiL7nXu/E4JARN42g9pOymD0PT8h78QlEknTcRIJMi+8QOTMGdYivfysc7AidtPpbqfneJZIQaDMvPBCQwQTQ9Hp1tvppv2eXvf+ufexcDhyanRbvLRLhc1tUfPq7RsYHWHUqI7Z34c6u85ad5SulRTdi5vMJbqqRr4qjvBdTc56IBBknBwZ5+5H+eLz6zz/8QcoQjA6MY3ea6McXWZxXw+GOkkcEI7KoqlwPSnoXtrEmRvn9tAhtMwq4+H9JMdUFsVVbi2nCSkGYdXY+htWDEKKTqjweHuZgdFC9T21a9eInDmDnbNQaKsad2tZ7b6Pu20WQri7GGoNKnpFUYhvJLb5KSBesfzGRJJz567V7XPGx8cZHR2t2/vtdtwHToNxM+c2kGDau2/iz27CzVUUFCel24B9NgeOA6pafb3kwQh6TcpwKD84Kqoc9qRI2Vjc+l4QYobB/VpWOngbi+sEyIBc47k5eGW/ayyuTsqACEW1okUzuWBcbzQD6aT0F1KklEgkLYF9+DDpF19k7upPEcwxZwwyZwxurR+JbYI6gz0y0pKOLgUFA40evYMeOmpu+8prrzA6OsrYkTHUZ2dZPftPEBGHkfZbrK3muPmDHq72HEAxHDTdQtUtdC3LL6kX6XIimGGVcP8QcRzWTo7SpUVIO1lyIhiDfPXg0OVZADqXU+iWjWloTO3rKROHFdVBCcONvZ0cW9vAyKUx55Ms9MRYP/dTLv3CEQDOr07d02crKHnBUtW3hMstEbMoapYInNXWhxSdiBoqrNMJFbYT91CHVU0miZw5A5aFvrLEM+sfYjiVcbdd50EdO4wTjxM5c4b0iy+23O9vt5I1QbhYsNsizb2oPnnypCef4+agyln1Ha0fHR31bJ92A26zToNyAy2dlM1BOind12VywYmA9JpaQlwQ6hUpSn4/q30PUqRsHLUmogXlt+7qHq8xqCx5cNyu8doCIxZJJ2UzcL+3CHa/g/x3E5TvwWtq/a7ld777kHfiEomkZXASCV577G/zvVurFbXxNgfDHPm1MTo+//lmN7OuOIODZPr6iCwswNFjGO+PE19b55H18ZKanCaJ3ByJPRadXT1Yx48z0JGvyfnYYy9tvZctbNJOjrSTJe3kSDlZMiJHpvA86+RIi1zBdWiS3lqfK1tvBkDsLDpSO1fSQN6RWs29CiBUhZXeGP3JNTpX0qz2xLZefz8IRP57txswnfNT8FPmiU6+WyJe6mXiZqjg+Hz0zBt0bSzTtrJB/MotOtt10oSZi/SzqbYTyWUYSC3imBn45ALi+EPQ00vo9dfJvPTSndsiuSPprOJaC7Rj9QDQ17S2hUIhT6KM+7/bATcrl3fFE4yN3ZujXXL3uM2yD0osjpswIUXKxuJak1Lz/3UH1BYp09ngCBde49bvouHgxK9Fwy4ipXQWNYxaAnBQJgS5HdMyskZbQ3Hre8G5xpMiZTNwm3wQ9H4Hxb4XjO/Ba9zuaRVFEJK3dbsO+V8mkUhaikxOIRka4tWeL5Utf/bQFJ/vOd+kVjWW+dOnGfn+93HicdJHjpO7MVFRkxPA0oyaNTk1RaNdi9KuRR+oPbZwCsJlUcQsCJpObkvUzDg5ssLk46uf0D3QQ7SrjbSdI21n86Jo4XGrCp66mXdtaLYDQCZi1Ny+uL64ffH1LYcCFg7rTop1x32z+Pw6iYnzzAnB6OVp5i0bczTG7L4IQl0kyiIAC47g+sQK3csprI9nGT81jLqq8cb706T744RKXJzhqo91QoqOoRYfF4XS0vVG4bmOrTr35AZtFe4nFjW8uEj0x+f46kylELc3e4vYH/4xc691s/Dkk2R7ex+4fa0adSoHEppD0OvGSCdlc3AbODWC4qSUtQGbgpsQFxShCGo52rxtR5CQImWt2E15jddI3K/xPG5Ik5D3Fs3BzTke9H4Hsu81klr9LigT0fyEvBOXSCQthdsMrLBRQ3HZ5WR7e0l+9rMMX72K6Onh7c7hqrUBe45u0t7gmpwAmqIS0yLEtDtP6beuT/NM4lOcPn66+nrHzjs77SyZgnCZdkoeF57n1+cdn1uP7YIL1K6/6GkZ+bpjtpaPlIxkTFZrbB/JmGXbF1+/W7mbuFvIu0in9vcS28himDadyylWe2IkPrnJez061Htc+Sl4kzmMibcqRE2j8Dy85QbduX5bFK22vvjYKDzXqU9t0PuJEG27fZvEm2+ysaG61gLVVZvo/Dwj3/8+yc9+ltSePffdxlaOOg0boqqb9OjFOOrsEZzBwTu/ieSeCXokU1iKlE3BrSZlOCA1KSN3iHuVNAbXuoABcq7KGm3eU6sWalBqUkqRsjm41gYMyDVexJAiZTMIei1Ut3sLgIzpukrygKQCfrzzG/JOXCLxKZcuXSKdTje1DW7uolquo9n5x4HKUYPN9YX7civdzWfWg3t5/8nJcodk8bX2qVNE3vwAgVpRkxNgYekDlL29LIyNkV1bg3PnHrzhD8jk5CRdXV33/DoNaAfa0cmfimJ3fpECNg45rIp/JhYmNllMTOz8clFcZ5PDJIeNWdy+txdjNsVGd4yulRTdi5vMJbqqRr4qjtiKd13rzrtUV3rvor0tTDPjbu8GU1iYwmKTGiM8D4giQEdDEyq6UNHRCn9VdJF/rJU81sse73idXrleF2p+GzRUtr/b8OIiiTffRLVtjNUNnl57m5CorAU6cGWd7P4RzM5OEm++ydTzzz+Qo/LChQs111+/fv2+3/t+CS8u8pmfTXFyJlWxbvDKRVb/yZ+S7u+/ZzepaZpN2Z968c4772BZjRVt1lPPkj8Sl5OcusG5c8mGfnYrkJxOAI9VLM+Yyh1/K63MtWvXEKJ1b8qnZg4DPRXL05vLu/J7v9fve3lDB6rHaL/73scszzTuuv3y5cst3TfuhXv93j+53A8cqFiuiAzvvtuclBbPf6v2SaCtYvH4lUnefXf2nt+u1Y81bnjZ7om5MPBI1XXjlz5gJXl/o9a76btfW9kHVE42m5tf5VwL3Mfeid12Pfn2229jWRZrm79MtaHe2ZmbnDs37Xm7vGb69gDweMXyVMZpSL+7ceMGk5OTDR9vagSHDh3CMGonSt0t08lTVDvPpDYWOXfuwc61u+F3mMpqwBeqrvvw/GUyi2veNuge2TlGuVu4fGUE6K5YrpFt6nlmYmKiaZ+9m5EipUTiU27cuEEm07gB/rvBsiymp6crTni2bTM/P88777xT8ZqJuRFgoGL5rYlx3k6/XfU1d0t/f3/dT77JZH4wN5FIMDw8fFcXp+fPny8bgC79ntKhGH829NWqNep4NE78ALCwkP/XAgwPD7O0tMRbb73V7KaUoQHRwr9tVCAEhGjbOMrI7XkUESZz8zo2/WiTA1zpPIxiOKiajaLZtOnLjKXeJpTKYOkaG2kNNZnl5okOtMubhGIRhKEgNHB0ELqS/6vlH7cqvo27vQeEAiY2ptL4fVEcUB0FzVH4/GsfsMw6HRtphucW6EuEyKkhFjripPQokUyO3rU11s0llGtLJPckyLS3sfqXf8q5X3xq631UR0F1KHmcX64IUFC2jnX79u27qzbOzs6i695dFnbPzXH0gw/oXNzDCoMVblJ7JcTi/AJiYYHw5ctMPvYYKwOV54ZqvPPOO3zhC19oWffonejq6uK73/1uQ/8/Mtnq9Z3nZycZ12488Pu/8847JBKJu+5/XrMwm6OaSGnZOufPn2d1tZa3vnW5fPkyq6urLdv+mdk+qomUG6tzXLlypS7udi+xbZvvfve7d/19Zy0DV5Hy/Y+Zu3nvYtHdcu3aNTStPgkCzeZev/dLl8eoJlLaufWmXb/ezW/1Xs/jtd5nY/3/ClS+z2t/+RZMv3vPn9HV1VXz+r9eba83Xh4jp1b6cBMpP/rgZ8RC95e12+rH+VIW5hSqiZTLq5ldIejstuvJrq4ufvCDH5B2GQJanL/F+PhVbxt1D9Tr2nEumaaaSJk11Yb0u2QyyRe/+MVd00+KXLhwgVdeeYWnnnqqLu+3sHS46vL0xuIDf+9e36feD7aj4CZSXrs+BeutPUFgamqq4ZNkG8HkVPVoAkWkm3qemZmZadpn72Za+1cukUjum1/91V/l+PHjzW7GPfPCfxUnealy+e/+9l/hH/3u57xv0B0ozs4ZG6s+6FSNyclJvvCFL1R9zWYG/re/MVhRkxPgH/72Io8f230XDq1KtL0ddXqac3Nz9CyG6FtdpiP9EclQgpQao83Z5KHwHI/tTUCXgnX8OIfjceyREX7ppZfu+P6OcMgJi6xjkhUmOWFuP3ZMssLaWpYTJpPJ28yvLNIe78RUbCzFxsTGUpz8cwrLFAdbebD446DH3XqNUMFWBZ3za3Qur2Lqgv7pBYRukxpQmdrXiVAdYJMUkHYiiIkw3csp2peTTO0ZRjDLdO9tlvsqa1iWoqAQUnSUp2LoqGyGsltxuUaxRmjp38K6oZENBnoHGNqzv2x5SDHKtjcUrWSdjqbce19Qk0miL78MDz2EtbrOyM1KN2lX2uFERsE+cgQnHucR2yb9y7981zHXJ0+e5PTp6jHUu4HV1VW+/OUvN+S9hYB//PI8Y+vnKibD/MbYUU5/7lN1+ZzR0dF7Ojd6yRsfhviDKmPrtgjx3HNf4PHHW7Pdd0Mrf+9/fKkbqhh19+/t5zd/8zd33W/23Xff5a233rrr36ptw3/x59XXffaXnueZk43NA3vmmWd23XdcjXv93pN/EuObH1UuHx7sathx9m6402/1fu4x3N7n+8l2ZquEYCSGD/KFL3TX/ZhRr7Y3Aq+Oke9dNvinP6y+7q9/+bceqE5bKx/nS7mtxnityhhxd89QU39798Juu55cXV3lT89UH7R/7tlneO7xJz1u0b1Rj779s4sG//tPKpc7QuWv/s6XMRowCr7b+kmRet5v/MX1bpiqXP7wycN8+csPXr5jNxz3/otvCiy7cjLYM599js891vr56rvhO97J8pk2/riKYXKwr6Op55lLly7xj//xP27a5+9WpEgpkUhaCrcaCrUKUfuJaI0b1oy5+2e/txLZz3+e6Msvk4pEWO89ir42Q1jk2J/ddtuGDAdhGFtCCbpO7tln7+r9VUUlooSIqHc3CpFrG8vH3VUmX1bgILAKLsCicGkVBE1LsbFwttZZilMQO7cfh7uG6JwxsTq7MJaz9C9kSPYYOBp5F6m6LYL6Me62WTxoLdBD40ne6ztS8zMEgqww88ZhYMO6y5n6e+EmG/xs6d7idFSUHSKmXlUU1YTC+so6ulA5/a236Z5fJbaySe/8AuvtBlk1zFykn02tjUguR9hKks1kUT85T250FHp60X74A6y//jdQfeAEahZqMon6vdf4j6crI4f2Zm9x7Ls/JjKTIPfssw2re9wKuF1TCNTCTGhJI3CrV2RoFlCfuLFWRtPA0AWmVfk9yFpZjcOt7mL4AUSi3UZYr54aYdpy0lmjSNe4/Ar7/3AHuJ9r5T1t43CE+/kkKOMptfYzayoYejC+B69xrXcfkH4H+b63ka78HrLyGq9hBH382G9IkVIikbQUQb+oVlUI6YKcHMBqOE4iQeaFF3DefJOVcA9XOg9URE5u9sU58mQHKAroen77Bg3ch0Ihz2auqd2zROe+DnGBsXwWxTSZ/3kvb2ZO4CgqKAJVs9EUk1/pvMiRcA47ptGx52EsRdD3zF/hZG9XwSFqFZyh249zhZqSRdeoKewtN6npWGSFhcODuUF3I61eC/R+cAqiaFbchQMoDPH5dU4uT5DWBKPT04iYjbU3RnJfFKEuEWEJgElHwZ7YpHs5hXVhnvFTw4jVs3z/vQnW+7rKhFBD0coE0bljy8w67/CzaxP57VSdkGoQKv5Vis+N8uWFv4aiEy5Zb6g6mqI2+JtsPNq1a0TOnCGTslGIVhzv5o0+dLUdbWqK6Msvk3nhBezD1aObdjuRkCCRm6kara7MrTe7eb7F7TomKCIl5PueFCm9xU0cD8q9BUBIr37NZTpyOKhR1Op3QZlr5SpSyuNdw7Bs9990rcnQfiISdj+2p3PQHnVdLXkA3CYEPYhrfLeRFykrl7t9N5IHJ+jjx35DXpVKJJKWIuUyE2Z+doJz56rkRzSZ8fFxRkdH6/qe4ZAUKb3CPnyYdx5+mI7LexBsMGcMMmdsx5Gc6DRByWKPjPjKWeQMDuIMD6NOT2MfOYJ+6RK96Tk+tbFWFnebyCVpd9KEh0NYx47TG+rGHhmh/eAzD9wGS9h5IbNE1Pz2919leP8e9h8+WCJw5kVNc0sEzcfkFgXR4vqcKDwuWW+K1opHlrVA6+UmbcdybMDFptADi9zmk4XbdWu3vhVzW0XUVEtETWWHIFpYH1KN/DaKjlFYHy5ZV3ydoeqkRQ5bEQgh6lY/Tk0miZw5A5aFWFjm6bXJiojdfnOenougnjyME48TOXOG9Isv+ua4V0RNJtn/6ut8daYy5Hpv9hb7vnODyMqMr475rYLbbOeQ1lrH6kYSCQnWqyQmyGu8xuHW74Lk7nAVKWsIGpIHQ/Y7cLvMlQP2jSNXU6QMRt+rdXuVP9cG43vwGnnMc3fJu01akTw4aZfzSbR66rWkxZFXpRKJpKVwG6QJucQUNZvR0dG6F0mXA1jestbezuvH/jqXU1S4atr3xUl/5QjO4IPXUWg1inG3TjyOdfw4zvTNirhbAFMJYR0/cM9xt3dCVzR0RSOqbl9Bdm6EGLHinGo7WJfPcITALIiVOWEWBM7iv7yoWVy2tY2wMEu2KzpCTae4TX67jUwKS7ERmkJOmDh3ccMra4HuXjepJWwsu4YwWm8+BW9O/M9bLtFqrlG95Hl+mYa+tU4rc5se/vZ3iaUXiSyvE758k7awTk4JkwwPsKm0EzUzDGbmUa1NtEuX4PhxnHic0Ouvk7mLGry7haKbtH3VQSFS1U3q2ATCTdoMajspg4Gbo0AO2jcOOcve/T5KipSNQ/Y7d0ebvKdtHLV+07Uchn6i1m9M9r3GIY957oKs7HeNQ/Y7fyGvSiUSSUvhNhPmxOgBxsaGvG1Mk5ADWN6TNTWSoQFe7flS2fK/d2oTZ3CjSa1qLMW428iZMzjxONf2DjKbWa4YsO8binMwnmt43G0jUBWFsGIQxgDqm+1z7ly+QnsxotcW9rao6ZSIoFuOT5vug510rn+Cnuiic2MSdUbnUmYvGAJVdVA0G1V1CIdtEvEsieV5NEUn19NFWDFI9Xahou7qqFzpJr03TGFjigcTR+Pz6zx//QMUIRi9NI3u2HAqxvy+HlR1ho7CdkuO4KOJZbqXU9jnp7l6ai/qmsZPP5hjs79nW/wsCJ/6lkBaKaTeSmxihaZJr4fK16vb2+tlr8u/r4paN/foTkrdpOE1dzdp7/gmavcjvnaTNovO1SSPLX1QEbHbl14G4s1unie4DZrIWfaNw83d0RaQAXuAsCFFSq9xdRUFqN/JuFfvqSlSBmTQvqZI6fK7lDw48pgnj3nNQPY7fyGvSiUSScsgZKF3QA5gNYOcVb3enN9rKNiHD5N+8UVCr7+Ori9UxN0CdDmW7+JuG4GmaEQVjShhcDE8qk91EL20CEP9GFMbpJZtem7ZjEeP52uBFhiOm3xm/3k0pRfRbjC8/0lQFD7z3Ff4W4OD2MKpEEBNUe4IvXz9KrbikNg7XOImtbacouXbW8zMJzGiYfSIsbW8uE7UMRZJukm9534jdtuW1/Pu1QtXufYLR+7tQw/ANdb5yeKVe3qZgrJD9KzuJNW3HKXl7tGdQmjpa/Z89y/IZlcILa8RvnqNiGaQVcIk9QQbSjttdppELolirqJfupR3mPvQTdoM1GSS8Guv8TdurmLa5f1ub/YWT5wfp++bl2BkBIaHm9RKb5Cz7L1H3lvIuNdmIPudHLBvBlKklE7KZiGPefKY1wxk3XF/Ia9KJRJJy5CzwHHkTBi3AaysvLhpGFmzuvARhBoKTiJB5qWXeHtpg/OzlyocLhdOnOS5l+rrQgwqO2uBOlNXGDTn6LZWymqBHl+aQYttgqJgHzmS/zsyshU7rCkqmhIiQshVEHXMNQDGOh+9q7a98qNXGB0dZezIWNlyIQQWdkHYtCsEzLxj1K4QPouPLWFvRe1awqZ7aIP+xesovRBenaFvIcdUVxh0UDUbRXVQ1PxgquKIrXjXte58H1zpjd3z9x50dlPErkBsien1JD6/zvNXCm7Si9Polo06FmNpXw+GOkEcEEJhyVS4eTvL+uoKzsdz3HzkAOr6x7zzUYrMQM+WQBoqiJ87I3dLRdSQujOSt3y70nVaAx2kzaYYsYtlYdltDJhzFY59TbEIzc7CH/4h/N7vwbFjzW52w4iEBIncTEW8/MH3u1A/fcyX8fLNJu1iRA/SvUXYJe7VsuXEn0bhNnAapH7nNkicNRWEAJ+e9pqKrEnpXhcQIGu6r5M8GPKYV+uY53FDAoSsheovpEgpqYqiKIeBp4A9QAhYBi4BbwkhMk1ojwGMAieBQaAD2AAWgY+Aj4UQuzf/TgLUnmEUpJkwYUPOwPKarOnmpAxOv8v1DvJqT2XttWeMHPlTgKQelNYCvTVwAnPhWkUt0LDhIAwjL2TWuRbovaIoCgY6hlafS0b1F58gevPrEBMYc2dZW7G4+IPecjepItAUi//k1AeoqoXTrtGz7xFs4MBnfpNP93cXxM+8SGqViqeOxcfjF+kfGqCrt3vLbZpzCrVGnWJtUrPweFtA9SsyYvfu3KSKIiAkuH0gTtuFNIaZRVlcYKUnhnr+Az65VzfpPVDqIK0VqbszInen4Dk1uIkIzWBuRO/w+u330RUN1YOIXWdhiafXPqwasZuYXkIfPg6WBd/4Bvz+7/vSUakmk/z2lT8lPTNbsS5x4xrRr/9MJhc0AHd3h8cNaSKGi5OylqAheTDcynQEqd/VutzI5CAadl8vuT9qOyk9bEgTUZS8QFFNNEvJuNeG4Dju59ogiUVhl9+YTERrHLLf+Qt5VSopQ1GU3wT+a+Bxl002FEX518D/Swix0OC2HAR+B/gC8BlqFxRbVRTl3wL/sxDinrLFFEX528C/ut92Am8IIX75AV4vKSBFyjwyJsJ7spaLk1LO/JO1UOtMaS3Q1UgvH3YO0G/OlzmLNvvjHHmyI3+XvQtrgdZip5tUfX+8qps0kUuiz2+gaTrW0eN06u3YIyOE9o/d8TOyE7d5ZuRxTh85fdftsoVTIWKajkXWyeWfbwmdZpnwmXNMsgWxM//6wrod2+RKtsmKbdG0nlG6bsiI3dZ3k9bNQXoQrrDOGwuX7+llOpqLoFndJbr1XK3uDC0+3/edb9GWWSK8vEb4k+tEdZ2sEmHGSLCpxmgzMwxmZzEck+i1azA4CL298J3vwN/9uw/2XbQYRUfpcEbnOmrFcT+62gWiC21qiujLL5N54QXsw5UThyT3jnR35Cc/VUPGvTYO6e64c+xmkH6DXmFa1ZXhaEgEyrkacREp5XhKY8jUcAoGaTKCHMfzHvcJQfL8shuRV6USABRFCQP/Evjrd9i0HfiHwF9TFOV3hBA/blBb3gA+dQ8v6wL+AfD3FEX5L4H/UQghj0q7jFon7yDdxLjNcpRiUePIucW9yn4nL6obQLEW6OyFtxDMVtQCPdFhgpL1raOm1E2aOnKc3I2JCjcpgK0ZiOPeuEk1RUXTQkQ076aZCyHyLtBSIVNYfHD+A86d/5Bf+vzntqJzS+Nzqz0vukG343a3txMDA0TmM6TjHcRX0nQvbjKX6Koq0vk1Yle6SWtjYWMJm4yo34VGfH6d568WInY/yUfsKo/FWNzXg6FO0l3YbsWGmQlBdmUB8e53uPHIAdSkxjsHZ8gM9mKo+ZqjIdUgpOoYqpGvR6rm64+GVB1DMQirOnphW2Nr2+3Xlm5b+lpNabwIX+oojWdXeXrtaoWjdDCZxDirbjnoI2fOkH7xRd8d/5tBx2qSX1v6qCLOvjd1lPwtpP8JucS9SpGycUhx/G5qAwbnu/AK05H3tCDFIq+pOY4XILFI1h33HlcnZYDEcT8hr0olKIqiAv8n8Bs7VtnAJLAKHKT8Lq4f+I6iKM8JId6uc5MM3AXKDDADLAAx4Aj5ONoiIeCfFtr7D+rcLkmDSbnUjIFgzYSRF9Xek3FxUsp+J/tdo3ASCb5z4u/w87nlitpk7fvipL9yxLe1yUrdpPT08HbncIWraN7oY/SRGB0xfOcmLaIoSt51purEiGwtTypxujZCHI/urcvnqJ+bJXrr69AhMBbOMjtro/zFEJfbj6JoAkXL1wHtjuX4vcGL6EoPdptGZOQktiLo/PSvcaSnq0z4LBVEi4+tojjqWMwtL2BEDNSQXvY6m+Yk80s3qffcTcQugNBg6kAvsQvTGLkc2sIiqz0x9A8+ZLyBEbtFVNQyQdNQS0TOMjHUqBBAF5w5pkY2+P7qe1Vdp0VH6fB3v4mVXSe0vMKBlZssKipZNe8oTSnttDmbxNVpFDOFfukS1vHjOPE4oddfJ/PSSw3/DvyKmkwSfu01fvfyCmup8lj/vdlbPPnjN4nYg76cDLSTsEvcqxQpG4ebk1LeW+SR9xeNIefipAxSvwN5X+s1bsc7gEiABHLXfidjhhuGPNf6C3lVKgH4z6kUKP934L8TQkzDlpD5G8D/BOwrbNMGfENRlIeFELXGeh6UG8AfAT8A3hVie+qxoihR4K8C/z2wv+Q1f19RlE+EEP/iPj7vnwLfv4ftZbG2OlF7BpaHDWkybidUmWXfOHIuNSkD1e9cbiDkzVzjyOQgGRri1Z4vlS3/jx5O4QyuN6lV3lB0kzpn/hLBSoWbFMByUtgjQ4EYQG4kOyN2ndnLDOTm6VpeLYvYPbIyQ2doE7QI1uhxwuE49sgI3Yd/6Z4/85Ufv8Lo6ChjY+XxvI4QWAUX6F2Lnju3ccqXbz8uX1b6+tXeDvpm11jrjtK1kgqkm9RrWj1it4iDQ8bJkXHu00W6F24uv+m6Oj6/zvNXCo7Si9PocRvtcIzlfT2E1RuEhIKwVS4JyE6v0bWUwrkwz8QjB1E3LvLe+SyZgd471he90/PNiMWK2GQpt1YmyKpK9euf3U4xXhfLwrKjDJizFRNhDKUjMPG6IcMmkZupmBS10hYnvJRpdvN8iayTJUXKZuA28SBoA/Zu+5utEUsquX9k2aY8MhHNe9zrjgen3/kJKVIGHEVReoH/csfi/7sQ4v9dukAI4QB/pijKO8BPgAOFVXuAfwT8Nw1o3k+B/xb4gVt0qxAiDfxbRVG+DXwPKC1A9d8pivLvhRBL9/i5F4UQP7yvFkseiFozsIJ0Q+e2r1lT3sw1AiEgK+Ne3Wf+yX7XMIIeBeYkEqRefImvfcuuGDhNhhJ84YUDHHmyr9nN9AWlEbsziRPkktcrInbDUQdhGFtxk42I2FUVhZBiEKJ23Gq9USOzRKb/CLvTJrRwFmGazP24l3PGEdAFqpp3k6qKxWeVj9mrd2JFNcTwcYYVcE5/gb7OTkzsgkhaKaYW43etksfFWF6nSQ7SZiIjdvPcyVGqKAJFtxHAjb1dHFvbxMhlEAtzLPXEsD96j4/q4Sh9DN4V34Nz3ytbXBQzQ6qxI0LX3VFa9lwpidItc5yWb6urO2N78++rNKBIWmm8rrq0xBOLt9Ds8pHpfnOeoaugdh72fbxueHGRp9/9KeGZymvdg+ZN9r56g8jsrJwQVGdca1IG5BoPak/2rFXDTnL/SJEyT1iWMfEUKVLmcRfHZb9rFO5xr8Hpd35CipSS/xvQUfL8x8A/dttYCDGlKMp/BJSKeP+poij/XAixWKc25YC/IoT49t2+QAixrCjKbwKXycfAAnSTd1n+H3Vql6TBuJ1gDF2gBShpLSzjSTzFFhoCOQPLbfxYzvxrHHLmH4QNUdVNCvAPOxcBy/tG+ZDSiN21SC8/6xysiNhd6unh+JPtoCi+i9h1BgcRwyPo09NwdBTj0iV6N1YYy3xc5iZN5JJ0dmXp3NOOdfw47ZG8mzRx7Nce7POFU9XxudNBWlxXKnBaO7av7hrNP19cXUILGSghtWzbZiAjdvO0uqO0KKynnRo1FxqIoZQLo2Vi6U4BtPB8yVlkdu8631r5WUXMrq7oHPjOt2nLLBJeXiV2+QZhTSejh5kNJdhQ2mmz0gxm5tBt/8frateuMfL975NcC6EwVHHcXzB6wXEC4yj1Erfr50Bd40knpedIkTKPjHv1FilS5gkbst95jRxP8RdSpAwwhQjXv7Nj8f/TzbVYRAjxmqIobwKfLSzqAH4P+N/q0S4hRA64a4Gy5HXTiqL8EfD3Sxb/ClKk3DW4zjgN2AlGxkR4i+W4uzuCdHFT62ZOiLxuIakv8phXe5a9jLiuL8WI3bmrP0UwVxGxe7DDAiWDPTLiS0dNqZvUOn4cZ+JmhZsUwFSNLdGiXm5SVVEJKyrhBjtIq8XsCiHyQhQ2ORcX6E73584Y3dLo3Mpt7R1u0vzjzd4uBmbXAx+xKx2ltTGFhWlbpO51N0dgcuVnFYvj8+s8f7UQr/vJNLplw6MxFvf1oKm36Spst+wIrkws0b2cwvk4ybVT+1DXPuCtD5ZID/SUROZWiqA7a46Gaqxzq1UqaPx5vugoVW2b9tQKT6/dJCQqHaVdn6yhRqO+d5R6TWwlya8tfVyREjGQGaV8jrh/0TXQNYFlVx735aB9Y8i5iJTRsMcNaTIRKRZ5SrrGPKdAlc+RZZs8RYgayVQBGk/xE1KkDDbPAP0lz68DP7rL1/5LtkVKgN+kTiLlA/Im5SLlPrcNJa2HtOrnkTP/vKWWSBmkvufW7xxHwbQg5G06YyCQxzxQVQjpgpxV+V3IaJz64yQSvP7Y3+a7t1YrInYZ6SH9lcM4g4N3fqNdSKmb1InHuTSUYMNeqqhT15/QORgP+8ZNqihKXhhBJ6p6N0qpPjdLdPrriE4HfeEsS0sm174zwJXOw6CBUojYjYRMnlXfpN+KEepsJ3rwYRxF0P3sX2G0vysvrDoWWcfMC1qF5zlh5v86FjnHzIukjrkVx1t87HggBtVCOkq95U7xukWEqnB7fw9tGxkM0yS8tMJqT4z2C5cY765DvO6d+DS8KeYITbxdpZZo/nG6PYUuVN6bm9muMarevUg69N0zWNk1xNoifckZ1tUOskqYpD7EptK+5R7HtHzvKPUSNZkk/NprvHBhmbn18t/v3uwtnnn7J0QiA76cDFSNSEiwkZYipVe4OSmDNmAvx1O8pZZQFKSJzm6/M9nvGkMtE0fQJmb4BSlSBpud2WqutR+r8IMdz39ZUZSYEKKxmUR3ZnnH866qW0laEjdXkR/dbOPj467rluYPAMcrl6+kOXfuXOMaFVBmZt2HC/3Y99yota+ZnELIZUaq5P5JBeiYV4twqLpIKW/oGkM6p1SN2P2dh9I4g2tNapU3FN2koddfR9MXK9ykAHGx4ls3qZc4g4M4w8Oo09OIo0cRP7tMX2qZjvRHZRG7+8UM8a4UbV1ddJ46TU9HL+zdy9HHfuuB2yCEwBYOOcfMuzydfISuWRA28wLotsCZ2yFw5h9vPy++R7bwusXVZZbXV+jq7a6I8S3+XemN0Te7FnhHqVe0erxuKY4iyIgcGeEyylaYHHYzde8VVeLz6zx/peAoTU6Tjdpoj2dZ3tdDSL2BIRSEo7JowdUpm57VGZwL80w8chB14wJnz2fJDvRu1yyt5QxVKwXTOW0NDZVZc3mHi1RHU9QH+NZaG+3ata16qLYVYcCcrZgIY6idgYrX3e9Ms3/pwwpHqTp7hHx1Hkk9cXNSBu3eQiZTeUuQxvFqIfudt9RyqAat7/kFKVIGm8d2PH/rbl9YiFa9CRwoLAoBJ4B369GwB2Bkx/N61cmUeIB77Q5v29FohoeHa64P6U7V5VlLzqZvBLZw72BBuri5k0jZGQvOd+EV7jUUPG5Ik4mEBOupyuVSpGwMmYAPJDiJBJmXXuL165sk1z6pGDi9/NAgz760t9nN9AWlEbtzwyfITV2viNiNhAS2ppE+fJjO3l7QdfjVX63L5yuKgq5o6KpGW13esZx3332Xty68xZe//OWq64UQiMgM0amvY3fYhBfeY3HBwvhBD1e6DqGUOEo7ojn+SucVDDqwoxr20CiDCuSefI6uzs7KKN5idC/Fx3bJ48LyJtUjbSYyXjfPnRyliiJQNBs0mDzQTeRCCiOXQSzMsdQTw/noPT76hQdwlHbm//zx1HsVq1SUGhG65W7QnQKnrmiEVN31NaGS7YrrSp2mjaQYr4tloS4t8ejCJE62vK52vznPnmsKau8h38frFh2lvz+1wtpmuTC9N3uLo995k8j6oJwQVGdkTco80knpLbIuYJ6ISxqS7HeNQdZC9R9SpAw2D+14fvEeX3+RbZGy+H7NFik/u+P55ft5E0VRNOAQ+ThcQV7snBFCrD9Y8yS1cJsJ47cTjK7rFTWjSrm4GK26XFEjrq+R3D/dPZcgWX1dsGoouK/LmO7rJPeP28SMoEUyhV3GkOWs08bgGskUoJhhgFR8iFd7DlUs/1xkugmt8SelEbsbbT283TlIvzlf5iyyuntQDkTY09WVFyh/7/fgDpO5dguKoqAkhlFH9qJPT6MePY62eJmetXUeW/+kwlHafzQHegfW8eN0ROPYIyOMjP76fX9+aT3Sb/zZHzP25OOcOHWyzDWarRKRu9M1mitE7W47TLedp1YhercYy5t/XxPTyYuqXiPjdfO0sqPUQZAVJlnh/cWl8hS8pczz8uS7JUKmhqHmxcyiG1TfIYAW1xll4me5wDry3TOY2TVCy6tEL19FFwYbeqQQrxujzUmRyCUxnJTv43VLHaWGEmFgx3F/3ujDtoLlKPUK17jXgF3jhd1ESllKoiG4OSl3S7/L5XJcuHDhgd/n1kQf8GTF8rWNHO+88w6G4T5x6uTJk4RCARp8qgO1RMrd0vck5UiRMqAoihKlsl7jrXt8m53bj95/ix4cRVE6gd/ZsfjV+3ir/wfwz6msaO8oinKefNTtvxBCTNzHe0tq4FqfLWAD9nLmn7fUqknpN4G8FndyUkrqixBy1mkRWb/DW4KSWnAn3H5nOdO/UYTNoBixu/j/+QmC+YqI3UTMRigT5AYH4fd/3zcCZSmljtLVAw+Rm7pR4SjVNYEwDOwjR3DicdB1cs8++0CfW1qPNGxqdCsxRqL9D7o7d40jnC3XZ9U43RKBcytGtyBwFiN6S19rFUTV+aUFFleW6B3sy4upIu8atYRFtq8HfW6T9e62QMfrSkdpdYQKOWxyTpX4hgegLF734jS6ZeM8GtuK1w0JBacQrzsxvcr6yibOhXkmHzmIunGR987nyAz2lgmiIWWnszT/b7Y3TchYIJS6ueUsLV3f7GjdnY7SJ5YnyaUqHaVDV1TUvoO+d5R6jXRS5pHjKd6y2+9pL1y4wPj4OKOjDzakvbQwRTWRMmfpXL9+3fX9i6WgpCHh3nBLB4Ld0/ck5UiRMrj0AaW/aBOYu8f3mNrxfOCBWvTg/FdAe8nzBeBb9/E+R12Wq8CjhX//iaIofwD8IyFE+j4+Q1IF6e7IIy+qvcV0ESkNXaD7Y0L9XSFFSm8xLbAdecwDeczzmt0+kFAv3PY3H61ePXZdcn84iQRvPvm3ePnyOo9vvF8Wsbsx1Iv2tIH2q7/KQR8KlFDuKLW747zdmahwlC6F+3jsyTZQFNB1Mi+8sOsH7FVFJayohFUDqJ4Scj+8++67vHXpLb782O9VfubnZ4lOfR06Bdr8O8zNbqB+e5DLHUdQ9O14XU01+a34u7RlIti6xmZsH90mGE++wP54b1l90bwQapXUHbXL4netkuWly5p5RJWOUm+5U7wuikAtxuvu7ySysYGRy+AszLHcE8P66Cwf3m287lH4hFW+P3dn14+KuhV3WyZiqpWi5k5xNFRSc7RSON12nG5tX3jP9h/+cEug1MfHCQuNdSVS5hxP5JIoZtr3jtJm0L2xyK8tVUbZD2aOUz5U5m/kBEhv8cM4Xq2ks7vlg5sT8NPK5bYwOHbswd9fUo6sSek/pEgZXHZeoaSEEPf6K96ZA9O0qx5FUZ4B/tGOxf+9EKK+0yS30YH/C/ALiqL8ihBipp5vrijKAPmo2Xth12ekSCdlHjlg7y1uTkrZ77aRfa/+yHiSbeQxz1tcI5kCdsxz29+cpSJFyvqTySkkQ0O82vOlsuXPHs2yt8v/4SRFR6n5b95AsFThKAUQbOCMjMgabQ+AMziIMzyMOj2NOHaMyOo4/bNLdGY/KBNJhq0k+9pVaO/BOn6ceDwfr3to9Pm6tEMIgY3jWkf0u699n+G9e9h/aH+Z8JkrETlvz05hI4j3xckVBNCK2qRbr7XKXKUrvTH6ZtdY644G2lHqFa0ar+vgkBE5Mh6d3uPz6/zKJx+iCRi9MI1hOWyOdjI7EMFhkbCzjGWr3LLa6Fxdxcis4XzyDnOPjaJeu821yz1YgwNboufdCqMaKooS8GvG6Wn6/vzP+d3rGZZT5YFge7O3+Mw7PyXS1R+Y84tbOogsJdEYZAmTPGHd/f4hf38hqSfpGr/nOwRISFoUKVIGl52CYuY+3mOng7ApImVB0HsFKJ3y+S7wL+7xrT4g77x8i3y9zQUgB/QAp4BfAf4e0FXymlPAXyiK8ktCiHreXfx94L+p4/vtCtwGToM2C8Y1gs5ScBxQ5fVNXbGc6ncxQet3IR0URSBE5e8wK8WiuiNn/m3j6miTtVAbgnRS5nE/12qA97X0/I4Ux/OOyuQLf4OvvZmtcJQmQwlWvryX0L7BO7+RpCal8boLe0+Qm/QmXrcURVHQ0dAVjSjhivXxtTD7rB4ei7m7587dPAfAWN+9Oy9EZIbI1B/hdNiYN99ASVtM/rCHS52HQFVQNRtFdVAVi1/WP2aEDuyojj10jEEFck8+R1dHR5k7tNRBmtshmG4LrDZOACd5yHjdPIcuzyIQxJY3UUyTtKExc7idkFo5b3xyQCdyIYORsVlIXmO1J8bVd7/Fe3frKC1BQSG0ww1aGoNbLnRqGKpRIX6WukdDqoGh6KzFTObEKsnMEiFVJ6wahFQDXdFaSxS9fBm+8Q1Cc3M4TicD5mxFDVBd7QpUDdBISJDIzVSca8N6H+rsQZxBea6tJ+7jeB43pMmEdPdjuRQp60+t8WM5Zro7kSJlcInseH4/c4qyO57XL8fnLlEUJQz8GbC3ZPE68JIQ4m6v9t8HnhBCvO+yfrbw74eKovwPwB8Cv1my/gngvwX+s3touqQKrnWyKu/tfU2tAbuMCW0B+z4ajZuTMmgD9oqSn3FWbUZarVlqkvujlkswaH3PfbZzCw0A+Qg3gVz2uzyyJmVjkOJ4nkhIVHWUAmzG5wg1NSTUH5TG62629VSN191s7+WxJ2O+itctRUkMoY7sRZ+eZn3Pftpv3qR3Y43H1j6piN3s7s3QnejAOn6cjmjeUToy+uv3/dm2cHjvg/exFIeHHn5oS9Dc6RTd+dja4TjdGZ+bF0d3iqbbgmlOWIgm/X5kvG6eZjlKBYKsMMmKOs9uOwXvi9f4gw9fK1usouSFTDUvaBbFy1DpcyX/PKyFqq9XDcJqqCB+hsrWlT4PqUbt2qLT0/CNb4Bloa+uMrZ0JV/IqYR+c579NxTUwUOBqAGqJpOcfuvHiJnFinU9cxNEv/4mtkwtqCsZs/rvPBywa7ywUctJ6Y/jfCsh7y38hxQpg8tO5+T9zHHZKZXcjxvzvlEURQX+LfBMyWIb+OtCiKt3+z5CiI/uYdtlRVH+KvAfgN8uWfX3FUX5Z0KInXU6JfeAnGWfJ1zj15jOKrQFLAqy0biLlB43pAUIh0RVASPrcuMhuX9qCb/RgPW9sFH9mFbLbSq5f9zOtUE7t7gNnGTlIEJDkAMJeWoN2OXPtcH6PhpFMV536X95E8FCRbxud5sDSsrXA9VFR6nZ2cnGgQPYMynCdrmjFMBSQ1t1AevhKNUUFQMNQ2jE9Y47v6CO2FuCZ3md0JyTj9n9wV++xtDeYfYe2FsWoVsUSXNlomipa7TcPZpzyoVWGa+bJyiOUgdBxsmRcbyZxakrWonQWSpyGnzqT94mPrdK+8omXddu0tGtk1EizMd62NTaiGZy9G4sI5QsmQvnSI3m3ePW9/7s/8/en0c3duX5neDnrQAIbuASZJCxKSIUEVKklpAUmSllZS1SLlWVzrDLdlVJKnfavbir+8w5M92e8bTPmWn3mZ7p9rjn+Ex75rTdOd7tdkl2ZVW5orKUWcqUUpVKLamQFNpiYSj2CJLgvmN72/wBgAQIvEcSfABJ4Pc5hwfAW4CLy4v37r3f+/v+WHzht1cjTouRqGqQILoH0G7cIHr+PIkFUDArFqiktV7w4i0VVdoIZB4vjxEQSZmVSMrQkbFF8yEiZeuyvO71+sjKzbA+cnL9e9abfwT81ZLXHvA3Pc/7k3p+qOd5rqIo/xnwLNBd2BwFfhP4n0P6mH9EXgjdCseAPw7p83cEucnkCfq+MoEVPrYnkZRFJDdg48j4DOag9dqev92rtLt64Ota0GLiuL/dq0wi1ANpd3kk/3PjcAcHef+Z7/DdT1cq7XX39ZH+TnNb/hUjSt3vfhers5OP+h/FmJupsIFMH/QYSrQ3RUSppmjEFP+FJh8uRHhgsI8z7afC/eBoctVe15z+gPSyxa1Xe/m86yhorNnrqjZ/uWeENjpwYhrO/pMMKJB96jk6OzorxNJyu928OLqUXkHRFVwVbHaXqCcRpfXB9hxsx2HFKY8JSEwtsXLnc1Kex8mRMVY8h/SROKOHonhqGo00OSDpamh30qTmUthX5hh5ZAjv2iVe/WSGub7yjE06Wj6KUzFWhUtj/euCJW7+tbFqr1t8blR7XXZOfcRQNZkkev482DbtqUWeXryFuS661licxHifVavvZo8qbRR+/ZdYqy2ADMpJacn1LWxk/rj5EJGydVkvKLYpiqJ4nreVX/P6JX8NEykVRfl7wO+u2/x/9DzvXzTi8wsRlf8c+Fslm79BSCKl53mTwORWztlVeRFqRG4yeWQCq7FYTnWRstWiikBEykYSFCXYagM633YXIOQKtSP32jy+4rhEUtYF31X2cr1bRazVwyedVara6555wMIdmN2hUjUO59gxRr/xDfrefx9NUyoiSgEOeVNNHVHaEAYHUYYPYIyNoTx4EuXDEXqWFnl8+XKFve7AqRyavt5e99ymP+rll1/m5MmTnDlzBtdzy/KBlgqa1qrYWWqhW37c+vNWRdISy93cuve0PNt3uaxElDaWo9cmAOicS6HbDpahMXqop6K+PVVh9HAv8eUshuXQOZdioSfO0ZEkH/SV5wC1cbBdh1RFZqdwKRVDiyLm0ukFrkU/5u2Ju+tETaPidaRMOM1b6Q6++ieouTTG3Dx9928w4+lkCzmfi7/BYSeJYi2jX726GkFuvv46mRdfrOv3bXZis0l+ffZSRa7tgcwpoH3D85uFoJyUEkkZPhLB23yISNm6TJMPxyr+qg1gH/nci5tleN3rLYlqtaIoyt8B/s66zf+953n/70Z8fgmvUS5Snmzw5zcdKZnAAjYQKWXSPnT8Iilbrd1BkEjZ4IK0AH5CkaF76C2mkfjnpGxsOVoBywbHlXst+NsqS07K+hC4yn53BQPVlSA7b1kQFD5+C4Ja6XqX7e1l9Jvf5I/HHsb87NOKSeSFxzs5++KJnS7mnqdor+smEswffojcrdtEvEp7XcU0sB88Hoq9rqqomIqKSbCtaph4noeDuxrZaZVEfvLVJPvu/j5Om0Pn5KcoVg7rksnPjcOggaI5qKqLis2pxdv0eBHsiEqkf4hexePKF77AsNFe9p4bCaOtzE7lAA2DqmJoBywyz1h6fsvvl5ha4hsjH6F4HicvjaEbDtqT7cwe6EfzRok7Gp6jMmprXF3K0DG7jHd5nuTjD6Jev83ISDvOwD6MkohRUzWIrBNII4pRJpaqTRAosB3UZJLIa6/xzY9mubNULi8czN7jSx+8RfT3+ltmEYzkpGwsfnMFrdTHazZEpGxRPM9LK4pyFzhcsvkQWxMpD617fXXbBdsARVH+d8DfW7f5H3qe99/V+7OrcG/d6/4dKENTIdEdeQJFSst3l1Aj/jkpW6vdQZBY1NoDsHrgt/KvNdud2L02Cr92B63X9sTutbH4iUVR04N0gwuzgwRa+su9NnRkbLHGctcg7/asH75DT+zzHShN81G0142eP08q3sM7nfsr8uHNmn08dbYNFGXP2usqioKOhq5oxNRI+c5DA8QOfYY6NoZ68lH0q1fpnbA5MT1WiGZrL0SUTtEZi7D/aC/2qVMMdOQjSh99rHo0m+d5eVHLcyqE0eLz0ijQ9ftK7XJLt1XuK+YhtXD3gCzaKjlAN0O1qNKxIwkU1UFftxLqSleMEwtzGNkMCxN3WOiJc+/9V/ngK8ervXUghqLDkw4X3R/S+cnPiKgmUdUkohllz6OqSWT1eYSIahDVzPyjahLRzNVjitsNdXdP1xfzf2LbOHaEfdZEhZ24oXa1VP5PTfXQVBfHrRxLyPgifPznUxpcECE0dvdVT6g3VykXKR8GLmzh/IeqvF/dUBTlO8D/d93mfw781/X83ADWy0WNW8LYpPiuhGmxm0zQ+ELEovDxEynTK7NcvPhJg0uzs9i5LwI9Fdtv353g4sXrdf/8u3fvcvJkawSly8TpGmIz3DiC6rTV2p7f93U9FcsGQ0ZJoSIiZR5dA13zsJ3K+pBrXviIFdgaYnFdf5xjx0i/8ALz/+SneMxU2OtGDA+Ulaa21y2NKLVPncKbvVU1ojSrmNinDm8qolRRFAzyuQ0rhNE64XgOL/3+v+PJLz3Fw499gaxrkSv8ZV0Ly7VXn+dcm6ybywukrkXWzRX2r99uVbwuPno1iKKSA3SNnYoqtTwbDFggxUI6VVvhfdAUlYhaKnYWhcy8wFncFlMjBWHTJFoQQYv7S4+LqiYxLYKpGmjbzAlamv9TnZ3ldPIu2ZRddky/NcXB2yrq0AMtlf/T1F3SuSoipeSkDJ3AsYWwJ5Hhd2vzEfDNktfPAP9qMycqirIfOFKyyQIuh1WwKp/3V8gLkqVXoX8P/M0t5tEMk/V316kdKUUTIZZMeTSZwGooRqSr6nYzIPF5s+L3nSWHQvjIxOkaEaP6dw7K2ynURlDOu1Yb0Pm1O8jfaw29teqj3vjavbZYu4N825M+XmOQBUFr+F3zJLojXNzBQT79pe/w3Qspnlj+sMxeN5PoJ/2dI7gDAxu/0R6lNKLUTSQYe7CPy/OLFRFWXk8vjyQyuzaiVFM0dFclrkTZF0nU9bM8zytEiZYLmaUiZqlAWnzdeyJO98oVjP3tdK3cw5h2uKn2guGhai6K6qw+xiMWPTN5Aa8Zc4A2Y1Sp47mknCwpJ/z8oKaio3kq3uMO10b/DRHFIKIahXyfxqqtbbR0u5IXTCOKzqEf/im57AKRuUXart1AdQ2yaoykUZ6D1/BSLZf/84AzyoOzn1VYq5uzJpUZ04TtIGOL5kNEytbm+8B/U/L6a4qiKJsU/b6x7vVPPM9bDq9oayiK8mvA7wGlS0/+FPhrnuftpIrwC+ter7d/FbaITCSsETU9ltOV9SH2h+Hj+ERSDu/v4cyZMw0uzc6y79V2uFm5vat7kDNn6j+QHRkZqftn7Bb8xKJWtCfxG0jIhH34BNVpy7kWBCyAyljQ0cCytAJ+bhlRE1ot/WzUhJVM5XbJwxs+MrZYw69/kZXojtDJ5BSS5n5e6flW2fZD+2zcgZkdKlXjKEaUmq+/jn5zsiKiFKDbdZs6onQrKIqCoegYqk6c2OZP/MZJuPVd6DwGkw5TyTna340wEjuFWxIpp3ou/9GBN+lKx3F1jeXUPh5o6+XsL//v+Uv7esh51qr1bdZde54rPM8VLHBznp2PEPU73rNW7XZzno1L46bqJKp0a+S8QtRjFMasrV2TElNLfOPzQv7Py2N5e91H4swf7MXw7tDh6LiORjKnkJhJ4SwV83+eQLt+m2sjnbgDA2tWt8raY0Q1iCrm6qOuaCh7JO+nmkxy4Ic/5G+OvslKtnx+6WD2Hk+9NUG0+zO55oWI9PGaDxEpW5u3gWmgr/D6KPDLwE82ce5/uu71H4dXrDUURfkl4A+A0mHVT4C/6nnejmXnUxRFB/76us2v7URZmgm5yayRFykrt8ukffjknOq3wlaL4IWgnJSNLUcrINe7NaTdNY6gnJStds3b2Fq9teqj3vhGj0e8FhQpJXq8UaR9AlBijXGM3FX4LcyQSMrwkXZXiKh88UWuts1zYeR6RVTR/QOP8cKLki1nW+zfDwcOwP37cOoUbvJdBqxJuu35Qg7QtYi2+OIsngYrBw7S19lJz2OPYQ0/QHsdi+d4Tl6wLMkTmvXyUaD55zZWQfDMuTbvf/Ihib4eEgO9WOsE0myJOLomnlqrYtt8b5y+iUUWu2N0zafonllhcrCrquWr4nqr9q7NGFVab6rl/xw91IOngoqDqheiUtvgVmcM41Ix/+dtFnri3Hn/h5vO/6miEFEMnDM2H+mL/HDs6qqAGSvm8FQKFrerAqdJVDWIFZ7HVJOIkre73a7NrR/F/JzR6Wl0NVY1P6fj0FL5ORtB0NhC2JuISNnCeJ7nKoryL4H/U8nm/05RlDeCoikVRXkO+GrJpiXy1quhoijKU8CfQNlysneBc57nVVl/3FD+DrD+zloXobZVcBywbJm0L+I3ae836BVqx/IRKVuz3UlEW6MQe5I1/CZOpd2Fj+SkXCPo+0rbC5+ghRlB0Q7NiESPNw5ZELSG5KRsHJInaw13YIBXeirzzXerLpItJwR+/dfhn/9z6O1l9sAJcvcmq+YAbU90w8njRAo5QNPfWG+MFj6aohFTtE3nEZ0evcbJ9gc407N5JyPP87A8G8ccI37/3+C0O8SmLrI8v8jdNzq5kTiMonsoWt76VlUtntVv0unGsCMq7fsOEVM8bj/6JMci8bLo0LwIamF5u88KdidpZP5PF4+0l4MI5Egxn9te3k9D0YkqeREzqpqF5ybp+AqGp3FzdpmoaqyKmnmxM1J4ns//WRQ91UKEZ2l+TmNxkbNz72Jny6/1/dYU++7mUI8+1FL5OetNkEuLsDcRkVL4+8B/AauLqH6JvAXs/7PawYqiDAP/dN3mf+h53nTQhyiKsr5H/iue570RcPxp4IeUu219BPxamLayiqL8l8CI53mvb+GcvwX89+s2n/c874OwytWKBK0eb8WVMH4TWGL3Gj5+ImVLikV+E6fS7kJHVv6t4dfuctLuQsdvwl7XPIwWGxUETRZnRSwKFcumag5GkHttKdLHCx8RKdfw+86WrSKR4+HiuxBN+niryH02JIaG4Ld+C/79v2elLcE7ncfot6YqoriePNuGoim7NgdorSiKgqkYMHSYyIEHUMfGUE88TOKzK3TdTXN69ua6qNIJBo5ARzyBfeoUfe0JnOFhHnrEP0+i63mrUZt5AdMqi+78yVt/ztETx9h/aJiMmyPr5PKPrkXGKT5mV/ON5o/JP8+6FvYeE0H3cv5PqxDRu7Teibggal1ZHN/0exWjOZ/944v0z6/QOZ+m/9592hMmK26UqbZeVtQ4kYxF3/IcipLCvvwJuVMnUBJ9aD/+Ec7v/LU9Y2e70+RyOS5dulS2bXLmKSBacezifJKLF6/5vtfp06cxTVEydyMtNh0hrMfzvGlFUf5H4H8s2fz3FEU5BPw/PM8bA1AURQXOAf8QOFRy7BjwD8Isk6Io+4FXgd6SzSvA/wQ8tdWLuOd5Pw7Y/SXgHymK8jH5aNAfApc8zyuLV1MUpQ14jnzU6S+ue48Z4G9tqVBCBX752aA1JxIihqyybxQSSblGRKI7GoZMnK4h17vGIe1uDb92B9L2wkYieMuRe23jkIi2NaI+17x8JKXd2MI0OeKWsYaffpHOKXgeyPx8CJw4Af/Jf8L8f/sv8LAqcoBqqoeirTR9DtDsc88Re+kl3EQC5fRD5EbuVo0qtTUD+9Qx3EJUae7ZZwPfV1WUfMQdJlQJPL888x6PKUc4O3i2pnJbbt7CNuPm1kTNUrGzdJubI+NYZNwsWad4Tnb13NLzMm4Oxws/L6jk/8yT8XLEkjPo40nmPY+Bm2NkFIflgwajh2J4ahqNNDYw4SrodzI4c7PYl6YZeWQIb/EDfvzBPTL7etA6wfR03kjeIKZGaFMjm3qMKmbLiJyXLl1iZGSEkyfXIvP9cmqbur8QPjIyAsCZM5uP2BYah4iUAuSjKZ8B/kLJtv8S+M8VRbkDLAAPAN3rzksDv+V53nzI5TkJDK3bFgd+r8b328xV+7HC3/8A2Iqi3CP/vXNAAjgCVOtiLwHf9jzvRo1lEwoETczEWnCRi9huNg7fnJQt2O7Egq5x+NmTtOQqe5nAahgyYb9GkBWQ5EMNl0CRsgWveRGfa560u/CRiLY1/NpdzpKclGEj99o1Al0LLLHlC42hIT585rf57mdxnlj+sCwH6GLHPtLfOYw7MLDx++xh3MFBMufOET1/Hq8nwXvdw/RkK6NKe092EE+4uyaq1FB1DFUnXpblKhws114TMNeJmZc+v8KV6yM8dvYMOdci61n5aE+vEOVZeJ3zbLJejqybf1zq7ZD8nwX883OW14WnKowe7iW+nMWwHDrnUiz0xDkyMsYHfW2r4vd0ZmumgQoKMdUsES+j614Xt+Wfx9UocTVKmxYlrkbQlL0lGJ88ebJMXBz8Z0scmX27IufxI90nRITco4hIKRRzU/4m8C+A50t2acBRn9NmgL/qed5b9S7fDqCTF2U34ufA74hAGQ6BImUrTiT4DNiyVmPL0Qr42r22YLvzmyiQidPw8bN7bcXJGpnAahx+eY1jm0sX1FQoSr7tVet/iMV1uAS5ZbRkZJEsCGoYvguCpN2tkrNFpAwbiaRcY6P8z60o3NaLrKWQNPfzSs+3yrYPJBzcgcDsTE2Dc+wY6RdewHz9dTRtviKqFMB2MzjD+5s6qrRIUQDt0NsqdyrLLEzd47nOrYk56tcmiI79K+wOG2P6fay0xe0fJ7ieOIJieKiajao7aKrFc+Z1ukryf0YVlxuPnuGw2VYQPgvRoJ6FtwdtxxuZn7Pqe+KRcrOkXJ8B3gZEFCMvXGpRlh5a4G7sMpenZwoiZkHQLOxffa5GMdWdlZLUZJLIa6/xl67MM7NU3oc5mL3H02/9jKi+ryV+482GiJQCAJ7nZYAXFEX5HvB/BR73OXQF+FfA/83zvMkGFa+e/GNgjryF6yNUj5YsJQO8AfwvwJ96nrf37qS7FL8Je5BVp6UE5e4UaiPnVP/ZS7tbQyZOw0dsN9eQCazGIdEd5UQMH5FSrnmhEtTHE9eCNaTdhY//Na/BBdkF+IuUeyuSYi/gtyAo2oILgoKi5fPXvNbsf9SDjE9UdKv18dzBQTIvvsjvvWJxaOzjiiirI88eZ+gvdu90Mfcs7sAA3tAwxtgY6oOn0D4ZIbGwzKOL10ryf6YZzCU5cNwjGsnn/+wt5P98uEr+T8/zsDxnTbgsPGa9vIj503d+Ru9QP/1DA6v70m52dX9e6MyRcfNWuI0SPfdyfk4gHy3rWMw6S9AF80xxc3lqw/MMRaNNjdKuxmjXYrSr0cJjbPWxQ4uVbdNDitrUbtwgev482Da2HWOfNVERLW2onWijo8ReeonMuXM4x46F8tlC/RGRUijD87w/AP5AUZTj5PM1DpNPIzwPXAHeKgiaW33fTY+6Pc97g81ZtG4bz/N+Tj4iEkVRTOAh8lGUQ0AHedFykbyQeQ246HmexBTVAclXVI6IRY3Dcqp3mKTdrSHtLnxELFpDJrAah0R3lBM1PRaqLGKWa164SB+vHLnXNg5ZELSG33fOikgZOtLu1pD8z40jm6suUvpZPTc7i52DvJI5ULH9ufgi+cxVQq2U5v9ceuAUuc/vVM3/qUYM7FPHN8z/qSgKpqJjotNR5ZZ0bfoDTvYOc6Z7c1GfedHTJuPlSLsbiJpejtGpcSzFIdrZtiqC5vfnz3epntuzVfNzWp7DgrPCgrMCm3R6iyom7VqMjqKQWSJgdqgxOrQ2urQ4nVobHVobmlJ5PYvMzBB94w2wbdTZWc5M3wWrPKd2vzXF8A0FNXEUN5Egev486RdekIjKPYKIlEJVPM+7Dlzf6XI0koL4+HHhT2gwfoMURfEwW/BK5TdhnJXBXKg4Djhu9QYmEwlryCRCdYqJ12thavaLQOWS+oXZcS5e3P7td31i+d1MsN2rtL0wkYnTcnwn7eWaFyqBkZQtaa3u0+7E0j905Jq3hp+ttyV2r6Ej7W6NoGh5ueaFi18kZSveZyEgjYm0u21Tmv8z3d7DO51D9FuV+T8ffbIN01Qanv8zL3oamBh0ahvnwLx45yIAZ05UiqBFwTNdEDbTbjb/3M3ReaSDjsXL6APtdC7dxZyymYh1s+JoqLqNqtto+pr9bTPn59yIjJcjY+eYDpRx8yhAXI3RqbXRqcXp0tpIx5b58sWfM5pTic+n6Pr8NroXYUWNkTSKEbwrDOaSGE4K/epV7FOncBMJzNdfJ/NiZQSvsPtowal/QRB2I345Y6JmPm9UqyFiUWMIGqS0YmSRWNBtntOnT2/r/JzPRELECMf25eTJk1sqo23b/OhHP9qW8ForS5k24G9W3feH/+EH7O+a3dL7vfPOO3R1dYVQsp3h448/5p133qnLe3/46TPA2Yrt87NJXn75j0L7nHfeeYebN2/uSHvaCtn0XwN6K7a/9e6HaNN7b83abq33q8lDwG9UbFcVlz/43st79jd76dIlbt68ycWLF7d03uLCQ8Dhiu3JycUtv9dmuXnz5p6s42pstt4dV8Gyv1l1370717jIXD2Ktynu3r3b8IVEfmOLbAuJlI7jNOT66LcQbX5m+wvRbt68ua3zG81KRge+VnXfx59eIzW92NgCbYG9dt28P2YAByu2O7mlut1bwiTstu3aTwOV/7+bt8a4ePF2aJ+z19pJkVr7MKVEHnmE7J9dxkOtmv9zevo62YF+ps+cIbu4CDV+1k7cM4usCp6qQRfloqL6pQ5iI7MwNIAxliY2N8fBzzxeSz2GWxIJqHouT2sf89B+B7tdo/fQo1i4DH/l2zze18m1O9fJKQ6J/b2k3CzpQp7JdMnzlJvF8uz1xWtKPGDZTbPsphmzZgBILC3x0PxNPvI8Tl4dY9524EycuYO9qNYY0UyEXDbKzVQPB9wsRmoR7dol7KfOEhm9jzoxgTswEPzBwo4jIqUgCLuClM8qe1n5V46fmCvURnB0RwMLskuISLvbNKZpcubM5ixnqqEa1VdOHj2ynzNnumt+31pRFIVHHnlk2+JrLSxnVPh+9X0PfeEJHj6Y2tL7TU5OcvPmTZQ9usLl3Xff5Vd/9Vfr8r94Z+IQVJmfHezv4Jlnngntc2ZmZjh69OiOtKet0P1ulGSV+dGhA8d45pm9t7p5t9a79Vk3/Kxye9T0eOaZZ/bsb/bmzZtMTExsWfRYWeyjmkg5t5Cum4AyMTGxJ+u4Gput94zl73E4MXaLkcxE2EXbNMlksuGf6ZuT0tqbdnO14Hked+/e3fjAbbKwVL1/uDif3PZvfGJiAl3fO9N4tqviJ1JevzmKuzjW2AJtgYmJCT799FMWFjaO/NkNjHz+APB4xXYrt7TrFi9VI+y27ViPUk2kHE3OhVofe/X+WmsfZj1Xhp/ij/d/mSeWPyzL/zkZ6Wf4lEqqqwump/N/NbIT98zN4A4M4A4NoY6N4Rw/jnfhAon0NF9aerckP2c+uq/bXEFVNdQHT9Ghx3GGhzlxJL9wVM/lx7pnEsFzC7bnVIiYGz9m8iKnk8VmZ3Ngboej1/J9ts65FLrtYBkao4d6QAVDy2BEM1CI0vzM9chdmsdYcbh352cs9rQz/voU9375KZS4TdyNML+o0Kd3sc/oplfvDC1nprA99k7vRhCEpkbyZJUj+YoaQ1B9tqJA7m9BJ+0ubPyveQ0uSAFN0zh9+jRnz1ZG2dUbK2BR6NFjpzn72Nbe7/Llyzz88MM78l3Col7l7/xJ9e1Dg4lQP2+v/A/6eoD7VbbvO8jZs5XRCLud3VrvN3zmd9vbNM6ePbtry70RFy5c4O233+b555/f0nl3lTivX6vc3t07tOX32grPPPPMnqvjamy23qcWFP7OH1ffd+7bX+PUwb07WVcLfn0821VxHNBaYH5O13W+/vWvb2uR2Wb4xz/vpVqg7pe/+CjP//qD237/kydP1v07hIXnwd/+QxePyojdr3z1a/zSY7t7JeRequs/+Gicq1UCdY8eHqjrvSVMwqzvP7zWza2Zyu0PnnyU558/GspnFNmL99da+zDr+cF7Eb77s25e6flW2faONpd/+rtf2tZ77wVK83MuHzmC+9lM1fycOcXEPnV0w/ycQeiKVrBAbdvyuZ7nkfPsVdFy2UmzUni+4qZ5/9JHxHs7iSXiJcdkSLkZst7OeyQXrXI75/P5ZOd743hq9fkUT1WY743Tn1ykcz7NQk8cfWqam9lxKMy1fDK7NgBUUOjVO+jXu+k3utmnd9NvdOVf610YqkhnjUJqWhCEXYFfRFuripQRESkbQjqgPlsxb4zvBJajYDugt8AEVqPwu+ZFW1Ac1zVQVXDdyn3pbOPL08ykMtW3t2LkOPh/b2l34ZLyqc+WbXfSx2sIgW4ZO7QgaCeJ+ti9Qj79QVz6eKHhdw9pxXanKGBoNjmn8svLNS9cLLv69G4rjmkBIj7B9OIQFC6+Y9oWaXel+Tmtzk5uHzzA3bRXkZ/T7u7ksYTT8PycRRRFIaIYRFSDBB0V++fu3uBk7ARnTlYuErA9hxUnUyZqrjgZlt0My06q8Ji3Zy0+ptxwB1O6lV9Ypjn5CYNM1N8to3R/8fji+dXw8Ji2F5m2F7mSKReXFaBP7+KA2c9Bcx8HzX4Omfvo1Nr2XPT0XkBESkEQdgV+gxQ/sa7Z8Z3Akoi2UMkETGC1Sse6lKDvnMkptMdar07qhe81L2ASsVlRlLxYsZKu3CcTCeGSDsj/3IqItXpj8J2wb1GR0t8to8EFaXKCBJBWvNcGXeczOYV4tPXqpF74tb1WHFsAGJpDrsr8sF+fRKgNy/ETKRtckF2Cr0OQiOOh4td38ROJmxHn2DHSL7xA+l/+S9SxbNX8nJ1uDmd4kNyzzzZcoNwuuqLRpccrcnIG4XgOy06mTLgsPi6VvnbSLLlplpwULv73SNvIr6RytHxUfjRjEWTEHc1YZccXz98qHjBlLzBlL3AxtRaq3qG2cdDs52Ckn+ORYU5GD2JKxOW2kRoUBGFXIHav5fh16rIymAuVoAmsVpxICFqQlrWgPda4sjQ7vte8FoykhPwEioiU9cdPLGqLNrYcuwURKRuDb7trUZHSv48nE6dhIpb+5QQt/Mzb+rdendQLESnL0bXqvv45WXwbKv4iZWu2O7/FKLLoO1z8nKla7T7rDg4y+s1v8l6byYWJdFl+zqQ5yJ3Bh/idF/devvta0bYobLqeVxAwUyw4K6uPi06KRSeF1pUklkyTTnTQNZ+ie2aFycGuqpaviuut2sMuducnsOZ7w637JTfF5cwdLmfu8Ge8j6HonIoe5JHYA3yh7YFQP6uVEJFSEIRdgd+EYKt2qiUnZWPwq09F8Vpq9V+RoAmsfF215u8xbFzXP89nq17zxHazMUhEWznS7hqDtLtypI/XGGQhWjkbuWUI4SEiZTmGVt1mT9pduOREpCzDTySTdhcuEmxQTra3h1d6Hq/Y3qnnqJqsWABAVZTVfJvD9FXsv3zA4OAnrzB4ZAB9+gJLi1lu/7iHm30H0GIWRiSLHs1gmim+sHIP03KxdI3FRD5/582T9Y1etTybT9O3+DR9C2Zh2EyQOD3E3KWxun5usyEipSAIuwL/qKIGF2SXIBNYjSHI+rAVLeZlAqsxBNlbtbXogM4vT5OIReHil5OyVSPaRKRsDJILtRxfCzqJ7ggVvzxZquphtuAsSGAfLyD9gbA1HAdytkQWlWL4RFLK2CJc/CIpW7XdyXxKY/C717aqSBnRqy/KyFlqg0vSXGR7esj09YGi4B5/ED4YIbGwxBcWb5A0B0mp7URdhcHcAo8+1A6xDjInjrOvs425gQTKiV9m3lnmxuQdltUsmajLnLNUt/KOqnP84j/5j/jjZ/5+3T6jGWnB7rkgCLsRP5uIVl35J53qxiD2JOWISNkYgqM7GliQXYSIRY1BItrKEXG8MfgtzGhdm+Hq91q/PolQG0HRbLIQrRyxuA4PsRmuxFD9RMoGF6TJ8RUpW3Y+pfp2aXfhIpHj5Zi6W3V7ztbwvNZcCB8WU2fPcuTTT3ETCeYOPUTu9m0iXo7D2burxyiKB6aBc/w4WiJBp65j/Npv88ud+UjKi7fzkZVnHjxDzrWZtheYsueZtOaZsueZshaYtOeZtZfwtukiplSxohWCEZFSEIRdgd8KrFbt3PiKlLLKPlRk5V85Qb83yYcaHoEiZYtOYPmJZDKREC5+YlHLipTS7hqCbyRliy7K8Js4zVrIBFaIBLlltCJBecdlfBEeqYD7R6uOa30jKaXdhYqIlOX4uhbIgqBQ8RvXZtPz5HI5TLO1brp+IqXrKdgOGKLC1Ey2t5fMuXNEz58nFe/lnc799FtT9FvT6J6NrejMR3t58ql4vjOt62TOncMdrG71aqo6Q2YvQ2ZvxT7bc5ixFxnLzXAvN8X93BT3clN1jb4URKQUBGGXIF725QSt/JMJrPDwa3cRo0XbXcAElkR4hIfkyapEIikbg0RSluN3r5V2Fy7+eccbW47dgt913nUVLBvMFsyJXQ9kbFGOpoGuuthupeWcuGWER5AAEtTPbmbE7rUx+ImUEbnXliFj2nDx6+Nl0/NcunSLM2fONLZAO4zpY/cK+WueobdmHyQsnGPHSL/wAvPf/SkeM0waA0waA6v726IuKCmc4WFyzz7rK1BuhK5oDBgJBowEZ+LHV7cvO2nu5aa4l5vkTnaCq5l7rLg+qzGFLSMipSAIuwL/nJSteRP3E8k8TyFnQ6RFB7lhI+2uHFUFU/eq5tKRiYTw8IvghdadPBWxqDFITspyxAqsMfi2uxa1ew1aCJXJKZgtulAqbMQtoxJDd7FzlSKl5EMNjyABpFXHF7pWfdJeItrCJSc5Kcvwu9fKmDZc/O61fT3xBpdkd+AXSQmQsaCjgWVpVtzBQa6d/gbXf/weZ5ffp8eeAWBW7+Vm5xOkv3MGd2Bgg3epjbgaZdDoYclJManOE1MjIlKGiIiUgiDsCvxX2bdmpzrYdlNp2Ui/sJEcCpVEzOoipUxghYdEUlYiYlFjkEjKciSCtzFIuysnsI9nKbDNHDhCHunjVRIxHNK5yikgmbQPj0zAQrRWbXv+kZQNLkiT4xdJ2artzs9SPms1thzNjr8zlX9EYTNjBnzvfF215u8xLNRkkshrr/HQT6cYy0SY0xPM6YnV/cczNzBfm91WFGWRtJtlLDfDmDXDWG6GUWuasdwMy256u19D8EFESkEQdgX+OSkbXJBdQtBgIpNT6IxL5yYMfCdOW3QwB/m2t5Sq3C4TWOERNCnTqtc8EYvqj+f5t71WFYuk3TUGyYVaTtB13q+uhK0jImUlfhEecs0LD4mkrETsXhuDiJTlRHy+t7S7cPG75gVFFDYzkaBISml726Lt/n1ib7wBto1t6+yzJsryUU4ZfahqL9roKLGXXiJz7hzOsWOB75lzbWbsRabtBabtBabsBSatOcZyM8xK/smGIyKlIAi7ArHdLCdoMCETWOHhP4HV4ILsIiSirf74DeZiptey+WZ9xSJpd6ER9BtuVdtNESkbg199tq7NcPBCNCEc/BdltObYAiDikytL2l14+NWloniYLTr7ZopIWXdcF2zXx+61RUVKv3uttLtwkUjKcgJzUgZE2gvBRGZmGHzzTejvR52dZfj6bZRUuSDcb00Rm9FQ547gJhJEz59n+fnfZr6/gxl7iWl7gU+jt1lSM7w2/jlT9gILzsoOfSOhGi3aTRIEYbfh37lpzU61nz0JiO1mmPiJRa264hRkQNcIfK93Ldzu/MQiEcfDwy8vILRuRJu0u8bg1/Zatd1tZOkvhIMsRKvEb/JUxhbh4Z/ChJZdiGaoPiKltLvQyARYmLaqSKkr1StF+njh4ueIFhRR2MxEDP/vLffa2um/cAHVcVBnZ9FHRlAslZwWZTreQzam064usN+aIGakmPjkPveO7WO202D09z/lx+ceXXujWOGxzgtSh90E3/vb/7/6fkgTIiKlIAi7AhGLypFV9o1BrMAq8RvISrsLD2l3lfgtzJCItvAIqsughTHNjJ9YIRG84SI5KcuRPl5jkLFFJX6Tp9LuwiPILaNV0TWJ4K03wfnuG1iQXURy7BbQX7E9a6m4Lqhq48vUjPiJvkG5GZsZQwuye21gQfYonueRcrMsOikWnRUWnRTW+D0S859jazZcuYuXyzFxOMbcL3WhqmligAOMuibcWUKdS9N58z7jjwzRO5EjMb3MXF973cs+aCQ4ET3Il+OnyN1e5H9567+p+2c2GyJSCoKwK/Ad0LWoJVNQRJUM6MJDbIYr8YtelnYXHn4rTlt5AivqI1YERf8JWyNIpGxVu1e/ibtsDpnAChHJSVlOkOWj3GvDwz/ffeveaw2fyBaZOA0PGVtU4peTMivtLjT8rncA0RZte0E5EbNW6/ZBwsbXIahFIykVJT+fUi1qslWjxx3PZcVNs+xkWHJSLLmpggiZ/1tafZ4XJR3K286T713nuL5A1+wKmewKlqExeqQLTy2vT09VGD3cS3w5i2E5dM6lWOiJc3QkyQd9x0P/XgN6ghPRA5yMHeDByAG69Pjqvqsshv55rYCIlIIg7Ap888a06Mo/U8/nLvG8yo6MWIGFh0S0VeInUmYDbISErSHtrhK/a71MnIZHYCRli07UBH1vmcAKD4mkLEdR8tf7aveCIMs+YWv4ikUtfK81/SLaWnTitB745Rxr5T6en0gpizLCIziSsjXbXlBuwKyltPTCgTDxa3tB9d/sHPbGOD77EYO5JKaXI6eYJM1BlOQxILHTxdsWnueR8XIsO2mWC8LjsptmyUmz4qRZctOr+1acDEtuipS7PWuk7pl83sjO+TQA873xCoFytXyqwnxvnP7kIp3zaRZ64qvn10pUMRkyexk2ehky+xgyehk2+2jXYhufLGwJESkFQdhxPE9Wna5HUSBqVI8+EBu68PCdOG1RcRz8I4tkIiE8/CfsW/N6BwG2m2L3GhopESkrCPre6Wzr1kvY+EVEt7Vw/fqKlHKvDQ1f14IWvteaYvdad8RmuBJD7F7rTlAkZasuzAiKpMy3vdasl7Dxu+ZFWtDuNTIzQ/+FC/zNsbdZTpfbsRzM3uP4K28SnR8g9+yzuIODO1TKNXKuzYqbIeVmmOvMctOYIrX0GStuJr/dybBc2F8UI1ecDDaN/d/qVv7zNCf/m85EjcDji/uLxxfPD0IBurUO+vRO+owuBvREXpg0++jROlBaNal0gxGRUhCEHSdng+vKgG49UdOr2umTAV14SERbJX7f3W8AImwd/3bX4ILsIvzEIImkDI8gwbdV295GIqWwffIL0arva2UR2O9eK3288PBrd63cx4v4RLZIuwsPGVtU4hdJKWOL8Aj6DY9c+YiI4XL69GlMs3U6fEE5EeWaFx5i95pHu3GD4VdfRXUcDLWdfdYU/dY0umdjKzpTRh+O3Yk2OkrspZfInDuHc+zYtj/X9VxSbpa0m119zAuPWVaczKrgWBQdV5+7GSyv5DfyMMAczFzedpnCxjY0ABwtL/xGMxYLAcdHC7YkxeOL50cVkzbLoNONcbzvEH16F/16F71GJ716J4YiEtlOI/8BQRB2HLEnqY7fd6/mby/UhkwkVCLtrv7IKvtKRKSsP36iW9Rs3dyLQSKZtL1wyObyQmU1RKSsRCz9w0MWBFXiF1kk7S48/FOYtG4fz0+kzFXfLNRAUJ/F1F1GRkYAOHPmTINKtPMEiWRirR4Ongfdy0meWP6wwtq0J6UArTHAUJNJoufP5wXKxUWemnsXK11+geu3phi4pqD2HMVNJIieP0/6hRdwBgbIYZNVbO7npkiViI0pJ1MmQFZ7nvGaf8Ay3xunb2KRxe4YXfMpumdWmBwsz0npWDp2NkqHqvHA/AIRvZPY4APsjw/gPPgEv37wW7SpET766CMAzpxonWvhXkJESkEQdhwRKasTkVX2dUdEykp8ozsCbISErSF5sioRu9f6EyRStipB313aXjgEWdS3skgZ8XGqEnE8PKSPV4mfSCntLjx8rQ9b+F6rq5KTst4ELYB84okztKJToRGQE1HGtdtHTSZRfvgavzu+WLHvYPYeT751n/ZkB+r+/bvC2rQeeJ5H1rMwX/1TlrNLWEtTtN25Rywaw4nqTHd0k4sZtDkr9KdmyWoZxj8ZZfTYPuY7o0z8/hV+8O3TeIlCv2Ts5zv7hXYhMcVk8fRJOj6bQ+tsozPqYNouc+/H+EA5hZWNYWejuI6G6rn8pf7PGIp24MUMOvafAEUh/cQv4GrRnf4qwiYQkVIQhB0nMIdCC+eN8bcCa3BBmhi/AV0rtzuZOK0/koO3Ej+xQoSi8PDNC9jCYzaxe60/QfXY2iKlWKvXG+njVeJnfyhiUXj45kJtYXFcclLWH2l3lQRGUkrb2xbajRv5SMCUg0KM/irWprriEJuaCtXaNExszyHj5ki7Oaa1ZSzFRk/dKmzLkvaypAv708WoxdV9udXXXVOLfGPkIxTP4+T4GLOaQ+q4x/ihHjx1BYAUcNdtx7mTpXsuTefNUcYfGaIjmaF7eom5vvadrYwGE1NMOrU4HVobnSV/HVobXVp87bXahqHqcBimfrBMdHqavocOol+9SvudHI9mr5E0B0mpcdrcFQZzSTrbUhBTcI4fB0XBGR7GHRjY6a8sbBIRKQVB2HGCVtm38mpnvwgP6VSHh1gyVSJ2r/VHojsqEbvX+uN3r21loSgoklLaXjgEiZQtLZDLvbbuyL22Er9JexlbhIe0u0r87F4tW8FxQNMaXKAmRNpdJZrqoSoerldZN3KvrZ2itSm2DdNzPL14F9Mr98/tt6YYurmM/cAQ2Paqtel2IypdzyPr5VbFxYybI1N4Xfo8aF9xv03J4onOwuPkx1su09FrE/m3mEuh2w6WoTF6qKfMihTAUxVGD/cSX85iWA6dcykWeuIcHUnyQd/xWqtkx9FQaddixNUoHVqMdjVGe9ljlHatjQ4tRlyN0a5Fa8r9OHX2LMOvvoqbSGCfOkXu+h0iXo7D2bvlBxoG9qm8pS66Tu7ZZ0P6pkIjEJFSEIQdJ2hgHGtha5yozyp7mUgID79Vp61tfyjtrt6I7WYlft/ddvJjYF16rNvGr921skhp6PnJUadKkIdEUoaDXwQvtHgfT+61dUfyP1di+tgfZmTCPjTELaMSP5ES8vUVj7Vu3YSFiJSVKAoccu7z8PwnFfkSleQxeCyx00Xck0Reew1sG3V2FvPaNUxPJ1uo19KINs2xiN6+jdfXSy7RSfZH32f8t84VREaLjJsj6xUf17ZlvBxZ1yrbNvfEAm8ZU1h3/nynv34F3TP5aMnO+TSQz5+4XqAs4qkK871x+pOLdM6nWeiJr56/GzA8jQ69jbgWpU2N0KZGaVejtGlR4mq0TIDs0PLPo4qJ0gA/6WxvL8mvfpWh69dxEwne6x4iujhdEcEbO9lJX8IBXSdz7lzTWg03KzLlIwjCjiM5KasjE1j1RwZ0lfh9d7GgCw9pd5UE2m7moEN6rNtGRMrqxExYTlduF5EyHMTutTrSx6s//m4ZjS3HbkJyUtYf6eNVonr+N4L3PviUr3zxBKbZwj/MEJB2V46aTHLghz/kP0++RdYqD9U9mL3HA3/6JtGZAXLPPisiRgmu5xaEw7xImBcLrVXRUBkf5+CtD7Bdm76rV7Bdm9zxLu7v70Ix54lp0yi6w7RicXPConNuGfuzNxh5ZAhvQeHVT5O1WZvu4suDbuUX/2hO/v6aifrkzilQ3F88vnh+mMQUsyA0RiuExriaFyDjWpS4Gss/V6P86R+c56ETpzhz5kzo5QmL1IEDpM+exXz9dSx3nkVjgEmj3MpV1zM4w8Py296jyJSPIAg7jl80m6F76C1s/yITWPXFssFxZUC3Hr/vnrOqbhZqQCYSKgkSKzJZ6GhrXFmaFV+RchcP/BtBLFJdpJRJ+3AQkbI6EZ/fndxrw8HzAnK0tXBEm18kZVbGFqHhd+9oZbeM2Zkx330j127THbd29cT4XkDa3RrFfInR6Wl0pY0uqzLayrU70UZHd22+xM1gu/mcihknnzsx42TJOIVciU7B2tTJkXGzpJ3Cfje3+nzanWX+0UU+vf/PybkWGc/C8vyjngGe/Pl1FtPjdM2uYGTmsQyNudMJ4upMxbH3Dic4sZxuKmvTathGfsLS0VQAohmLhYDjoxmr7Pji+aVEFIOoaq5GM+Yf889jqll4jKxuL30eVU1URd3y91Cr2CLvRtzBQTIvvsh3/9DhodmLFVHSX/gLRznwjZ6dLqZQIyJSCoKw48iEfXX8BhVZmcAKBYngrY7kQq0/YkFXSdAkSlDeYmHzSCRldfzankRShoPf77dotduqiGtBfcnZ4PlMuLXyvdY/klLaXVjIuLYSXfUfvB44/CCQalxhmhR9aoJfn/1pxYS9an8BaJ0E0KX5Eo3FRb608C5urvy6129NsW9EQe3O560LK19iNTzPI+fZZAuiYdYp5Et0cmRdn21lx2bJOhYZN0um+Fg4xvZCiMBrgxV7cdOHN5O1aVjM98bpm1hksTtG13yKxMwKk/t6yVkmjq3jrv5pdJoKxxeWMfQuooNH6I/3kT72CJ2LB4h4Ok984QxtagRdaeEO8ibwPLit7OdWz1DFvr8xOAfI5MFeRURKQRB2HBnMVScikZR1xW+FPbT2KvuI5EKtO36T0K3c7jaKpBS2T8qnHttaZ+6qKn5tTyIpw8EvJ2WrtzvJO15fZCFadSKGTySl5KQMDd8I3hZud7riH52Vs7Ye8SOsoSaTRF57jbNvTqMsla+6Opi9xwPX3iH6e71E9u0j29u7Q6VsHKX5Ettv38b0OllQ2iryJWKl0K9exT51CjeRQHvtRyw9/5vkvLy9ac61C4+F1569+nzN/jS///qDc9x1f8YfXf5wNZox464Jji7N89vfjdamYaKjEVNNoqpJTI0UnkeIKWuvY4XIxfwxJp1fXeTAvf+A3qHi3f4ZatrG/rCXV+a+gFsS0ah6Lidjlxnoa8OLGbTvPwGKQvqprzE+lo8279TEOmgzZC3/hWitfK9tBkSkFARhx/HPGdPaNxixe60vQdFZrTyBJe2u/vjVZStf8ySSsv74RQa2SSRlVSSSMhzEZrg6cq+tL0EL0Vq5j2do1SMpJYI3PGTxbSVBkZRZWyKGaqVoa4pt49gG+6yJCltTU02gjY4y/NFHJL/6Vdijtrqu55HzrLyI6NoVz7OuhZoc5+CtD3Fdm96rV7DUNKkjce72t4OxhKnN42ouE4pCx/QimcUlcp+Oc/WRIdx5ePXindryJfbCNJOwFPrX3nXUw9p0uygoRBUjLyaqJhHVIKaYRAoiYlSNEFXMVVGx+DyqmqvnXb80guFpPHXmya0X4BDEDn6KOjbGzIGDtN++TdfKFF9aerdCHO90MtCv4Bw/DoqCMzyMOzAAY/6W2EIlshCteRGRUhCEHSflM5HQijkUSpFV9vUlqB5befJUJk7rj9huVhL03UUsCgdpd9Xx+/7S7sJB2l11/K3VG1uOZiWwj9fCrgURw8/uNW+fpkhXb9uIpX8lQSJlTkTKmii1NVVnZ3ngzh26UuVRav3WFImkhjp3BNVxGHzzTdSzZ+tia2p7Djk3H3GYLeQ2zJa+LnmeWxUZi8fkn985NctI/CN+MHalLHIx59kb5kqE8nyJWmYOK6IxdVqlQ52sOPZeRwexS0sYlk3H3ErT5ksMm/XWpt0zK0wOdlW1fFVcb9XedbE7tno+5CMWowVBMaqUP8ZUk0jJtqhq8tF7H3Jo8AAPHztZIjDmHyOKgbLNm9d9LzgidCOyzz1H7KWXsDo7WT5yBO/aMhEvx+Hs3bLjcoqBfepB3EQCdJ3cs89u63NblUCRsoX7eM2AiJSCIOw4fhZDrTyYgyCxqMEFaVJkBVZ1/ATajCUTWGHhd83zs9ptBQIjKUUsCgW/emz1BUG+IqXca0NBRMrqiKV/fckE5G/fwJ2uqTH16lZ7nqeQsyHSwnUTFr4OQS18zVMUjwP2KI8uflSRM9GY1uDwTpdw71Fqa6qPjKDaGlklWhG5ZXpptKtXUHt7yHW2k/vR95n47b+0LiIxLwhanp1/vk4gLBUfc1VFRhuX6gsgtkQ3QK7mdHKSL7F+GIqGqRjMPnyMjpEFtL52ImMZjLRH4iOPmz0HcDwjn3vR0TBx+bp2iR6vGzUeoffQGXRV5+lf+Q7/8f7hLedcTE5f4YHePk7FDtXpG24Pd3CQzLlzuN/9LlZnJ6PHH+bKwkJFZLPa28vpRAZ0PX98HRYMtAIyj9e8iEgpCMKOI7k7quM3USB5Y8JBOjfV8Zs49TwFywZTJrC2je81r4VX/qlqvm3lqkwsZwMmm4XNI7kBq+Mb0SbieChILtTq+C1KkT5eOEje8eqYur+QkLOUll4sFRYyri1HTSb50qef0jPxv2Kti5o8mL3H42+O0zfdhrp/f8tN2Hueh+U5ZUJhXiRc/7oYjZh/rk9McOrGW7iOw/4rn6NYFnOHOhgfaEMxJmlXHRTNZVaxiY9O48ytYCfHGekfwrt6n1c/SdZma7rLafZ8iZvFVI18zkTNJKYV7E61gt2pFsnboGoRZpKTjN66z9NPfomIYuQjFwuRifnXJhFFJ6KaaMXciocgdvnfoo6NoZ4eZvKta5jjGg/cnCsI5DHa3BWOaeMcekAHw8A+cQpdj+etTYd2p8gYBs6xY4x+4xv0vf8+2pLCpDHApDFQdkyv6+IMD5N79tmWu96FSWAfr0Xvtc2CiJSCIOw4krujOmK7WV/86lFVPYwWvjsGTVBlcgqmTGBtC9sB25FrXjWiZnWRUiIpw8EvMrCVozvAXyyTSMpwkEjK6kgfr77IQrTqBImUmZxCR1vr1k1YdC8l+aWlixURg91LR4GenS5eQynmTOxaWkJXXBLWdEVkkeNAbGqK2EsvkTl3DufYsZ0uNq7n5sVB3WFJzTCem10TDkvEwlVBsfC6GIlolWzLldiVlkYgbtbCtBpPfnCdWHaCrtkVurJLWIbG2IkoUXW54tjRwz3ElzMYlkPnXKqpbU13Y77EIMyCKBjT8vkTo6pJtPhcW3udf4wQVQ0iZdsjBdHRIKbln5uqsSYobsCFiQu8nZzjFzq+sKVyF61N3USC8cGHySVvVlibRmMunmHgHD/eUtam2d5eRr/5TW5fP8qF659X3AcmDz3Kb73Y4hY2IRA0RmvltE3NQAtPwwqCsFsQkbI6vhNYsso+FIJWOreypWnQ7y6TU+iMt/bvcrvIxKk/sQgsVnFaEpEyHEQsqo5EUtYX33bX4pMIvtbqIo6Hgt+9Vtc89BZOgedn9wqyMGO7qMkk2quv8Z+NLVbsO5i9x8k/+ynRicGWiaApzZkYT6f50sK7YFXmTNx/O4sR6wfbJnr+POkXXqhaP/mow/ViYDHaMB+NuLp9nYhYFAjLz/cXEm0K5XwK3mYKxn7eiCrbNGJrWp2w8iWWoqISUXRM1ViNNjSV8uf3btzm0OABjgwfIqKZxNTImpi4KixGyoTHiGqgblJM3G0UrU2j58+zGOvl3c4B+q2psgUIsz09nHqqPZ8npgWtTZ2BAV7pOVGxfUB3gOnGF6jJkPmU5kVESkEQdhzJ3VEdWWVfX/zbXWt3bII6djKBtX2C6rDVO9W+uQFFLAoFv3psa/l7bfXtcr0LB9921+J2r9LHqy9+9djqfbzIBpGUQm0UIwZzaQeFWMWE/ZTRh6G2o42O7qqIwe3iFoRDq1QQLIiBPT/4Hnp6Cm1ujr6VGZbbIa0YTHcmyEYMYk6a/pVZHC2FdW+Sz+JLLHfFmPnDO7z5F59aFQ2tbUYdNiNia1qOqehESvIlqn1xzLE0kUyOoStZrnQeyedLdDQ8R8WzFR5P3eOg0QVtBvEDj6KpOqe++tuc2z9UECHzwuRmcie+/NrLPDP0CGcPnG3At90dOMeOkX7hBaZuvoXHZIW16QPtNiiZlrU2lT5effGrR1P30Fp4IVozICKlIAg7jkRSVsdv4jQrE6ehkPZtdw0uyC4jKLpFcmVtn6AcCq0uFklkUX2RSMrq+H1/aXfh4JcLtdXbnV8fV+6z4SBji+qYhsNgbpwnlj+ssKHzxo7Agb6dLuKeozRi0Jue4+nFu5heuXd9vzVFz2VQv3AMN5EIjBjcDp7nYXtOZbRhSdShVbbfL8rQDtxeEXG4jsTUEt+4/hGK53Hy6hh6h0KmJ8vYoTiemgIgDdxz47h3MnTPZYjeuM2dR4bwRhdJjfc1Zc7EsNhLtqYKYCr5iENTzYuJZkEANBW9sC//+tbIdfYl+jk0dJCIYmAoOhFVXz0/oupEFHP1+OIxatECqSxf4gHSFy6wcsvjcGaxkC8xTpu7wmBunM64TeeROPbJU0TNBM7wMJFDpxtWL82AOzjITx7/G7xyZ6HinsJwD+nvHMMdGNj4jZoQ6ePVF7/5lFbv4zUDIlIKgrDj+IlFkRa/yfjlBpQVWOHgO4HV4jkXN7J7FbaH2JP44xvRJpGUoeBXj7Iwo/p2aXfh4Cf2tnq78+vjyn02HPx+v5HggKOmRk0mOf7an/K74z+r2Hcwe4/BP/gp0eutY0caFpHXXgPbRp2dxbg2gukZZAvCb0ptI+4ts99OgrOEe+UzUieOkUt0kvqz73H/N/9C1YjBUtvS/PM1O9P7D81yOf4hfzp6qSA4WmXn7Yae5NFrEwB0zqXQbQfL0Bg91FNhu+mpCqOHe4kvZ1siZ2JYhGlraih6QSzUV4XEVeFwVUxciyws7ouUioyFcyLrzo8o+UhEZZN5VF6+O8nJ2BHOdJ2puW5K8yWaTzyB+sZ1IunyfIkAWcXAPvVgS+VLrAeZnELS3M8rPd8q2/4bp9K4A5XW162C33xSJqfgebR0aqEw8BN7W33+uBkQkVIQhB0nKDdgKyMTWPXFt921uhWYiJR1RURKf6IS0VZXUmK7WRW/diciZThIu6uO3wRW1lJwXVD3ZqqqXYPfAshW7eO13b9P7I03iM9OodBR1Y7UtpvPjnQ9Hh4WDktOqiDqOYXowrwQGCwUVoqJ0Ylpnrj2UxzX4fDlWyiOjfJ4nJlDPZjGbSJq3l5z1vW4dGeB7rkU9uVJRh4ZwltSePXy/NYjBrsAcmAthV4/YSE5E7ePipKPJFwn/BmKjv5Ygv2fv4W2r4+OpI1uOUx/oDHSdRjHNfBcDddRwVb4ZtsNDig9rGBB7DhPdQ7y5V/5j/jO4FB5FGKTUJovUenrY/KhQa59tFBpv9zXw8OJbEvmSwwTcWmpTtCCqKwlC/W2i8wfNy8iUgqCsOP45o1p8ZuMn2CRs2UCKwzECqw6pg6K4uF5lfWTFZFy24hI6Y9EtNUXmUiojt9EgYjj4eDb7lp8giZogiprye9yu+hTE/z67JsVlqaZwUeA1qrcyMwMg2++Cf39mEuLfGXpbXS3PL9fvzVF4hKokfrakZbieh52VSvRvHBYaltaXSR0AgVEq+SY4jbnyy5/zgTcq4wmrYUnP7nOjL1I1+wKTi6DZWgkj3WiquU2qK0WMdjMORNV1EKE4Vr0oVEWdagXxMX8Y+k2oxCBaJRGLipaWYRi8X00VP8IxP0Q+1jP25o+1It+9SrR6woPehOFCN5IwdY0Sc+BNLGuKPMHhokqEcyDR3GHDje20hpMMV+i+frr6COV+RIB9rlOy+ZLDBOZT6nORs5UrV4/2yUtLi1Ni4iUgiDsOP5WYK198w6awMtYkr9uu0inujqKAlGjeufPr0MobB6/dqeqHmaL98r8JuVFpNw+rgs5q/q+VhdDRByvL3712PKRlBtMYLVqxN92UZNJIq+9xpmfTJNaKv9xH8zeY/jGu0R/r6elJqb7L1xAdRzU2Vnab98mQjcpJVqeo81K4lnLKFcukzt5HCvRRebVP2b8t/9iXuwrCH22T8ThqiBYOHYqPoOjuLw2/nmFgGh7TmA+w72ERAxWZydyJhqKVhZtaCo6Rg1CYvEYQ9F57Yc/4sEjx3j89GOroqSmNC6fYxCltqb2qVNkLt8l4lXamiqmjn3qGFY2i6tpLWNr6g4OknnxRT5VF7hw8/OKBStzRx7lr7woisZ2kfmU6kQD+nD5Omvt+tkuvkEu0nfe87T4dJggCLsB/5tMgwuyywjq3GVzCm1yE94WfuJ4q7c7yLe9alZpYve6fYIWZTSZ49KW8fvtSUTb9gkS3Fp9wYuI4/VFInirE9jHs2QCqxa0GzeInj8Pto1tG+yzJios/ky1Z9dbmjqei+XZpJUcNi5Ja3ZV2CsVBCtFwlJ70rxgaE5McWrhEp7u4l4exdOyOKdVpg4aaPp9ujQXRXWZx+Hmndm8HemlZN6OdFHh1U+ntm5HClCc+8/OhVo3u41mjhjcDsWcicuJOD3zGRKTK0wk9pGxI3iOumpH6rka7brFgalp2rR2lIFDxKMJ5g6fYqDnGUy1KByWiIiqXhaJmI9Q3Hzew63QnjbodGN06fGND24wpbamdneCtzuGq9o4m0M59mWzTM7NYf/Gb9DVIoszijj7Bnil50TF9gO6A0w3vkBNhq+1equLlBv28YTtIE58zYuIlIIg7Dh+N5mIT76eViHo+8sE1vaRduePX8daOtXbx28wt8G8Vksgtpv1I0hwa3VrHGl39cUvJ2Wri5QR02MwN84Tyx9WRHi4Y4ehp3+ni7inUJNJjD/6I5Lj4xiLi+wfmSWWKp/q6LemiI15zFzrwursxP3udxn9xjc4/ou/iGlWXgg8z1u1HF1vPbrennR1u1tdRCx9D9uzGT89w6ex9/kP9z8pe4+cZ+OSF7DoLhRk9Oc118uTH19nQUvTNbtCLpfCMjUmTsTR1fIfpkdr2ZGGxU5EDG4HHQ1D0VYjDI2yKEMNY501adF+dL04WLZfLX2dPz/y7AxdY78HXR7G9Pvcu3GD/fNt/Hn2CVxlLV+J6rl81bhIb7tJPJGga+g0KArpL/8GbudAwDcRYM3W1Pvh63gsVLU1VbV7pPv7yf7ar3H8F39xh0q6c/j18bI+7iLC1pBIyuoEjetlfLF9/HJStnq7awZEpBQEYcfx7dy0eKTgRlZgwvbwFYukc0PEpw6k3W0fGcz5IxFt9SOoDltdLJJ2V18kkrISNZlk//df43fHFyv2HczeI/Hv/pzoJ/tbypJ0I2w3n6cw59kseClWojb3spOrAl/fD/6QlYnr2BNJDkzNoZkGWm+UmY5uMlGTmJ2mf3mWmJLBujfK2NEBlrvaSH7wMi8duYMRM6vmM6wbHQAW2PW1+xQ70vpSjBhc7I7RNZ+ie2aFycGuqnWsuN5qfS52x/KPvR1EFbPMmtRYJwIailZmSXrt8ggDvf0cHjq0el6peFjN7rT4Wi0RCOvK/mHcoSHUsTGc48fh5k16MtN8afndcpvhXJKu6DJeO/njFAVneBh3QATKzeIODjL1G7/Dd//Qqbro5Rv/xV9l3xP97Nvpgu4QfuMrGdOGg6Rtqo7M49UXmU9pXkSkFARhx/FbCdPq4fp+QhFIbsAw8G13LS6OQ9CArsEFaUKk3fnjt9pZxKLt4xfNBpIbUCIp60vHwji/Pls5cdqz/ASwf6eL13CKlqRa2kEhVtWez7bad60lqeO5+fyErkXOtcl5FpZrk3Wtku15MTHnru3LuTZWyTm50vcoPbfKvpxbEl1Y5HG4MP57ACSmlvjG9Y9QDI+TC0kWVIeFw3HGDpl4ahpIkwbuuXGG72RQ53J03htjvHuItsU09vw4U3oNlqZ7ALEj3RpFQbBaxKBRRUjsfuooJ26+hrYfeieukVp0aXuzjc+7juJ6RsHSVCWqOnzn4OeYaj9Ku8mBI0+hqRqZr/31LQtyL98f42T8MGe6ztSpFsKhNGfieF8fnbN61ZyJOcVg+cgRIokE6HrL5EwMk0xOIWnu55Web1Xs+7tD09AE+V9rxc8hSYSicPCfx2twQXYZQc5c0va2j7843thyCOEjIqUgCDuOrISpTlDnLiudm20jXvb+RHzmsKRTvX3keueP5KSsHxJJ6Y+0uzoxNgavvMLz1+9XtL+D2Xs8/GcXIH0Qfu3XdqZ8O4CaTK7mTDQXZ3l68R6mV+45129N0fkJqI8fw00kiJ4/T/qFFyoiKt2CFam1LvKvWjSg5dmM7kvxnnedu6Mr/iKhZ60TG9cercI5dj2jC2vk6LUJADrnUui2g2VojB7qqYhm89TWszTda3akpWioZTajRbFQV7RVW9H88+oC4tpzbfX1mz/5KQ8cPMwXTp1e3betvIa9EDu6hDo2hnoqTu7ta3RMWpyeulUWMXiEUeJtaVAN7AdPoqt600cMluZMTEWjTB14nGTOqViUofS209c5DrpO5tw5iR6vgaDxWauPL/y+v2UrOA5oO3eJawrEEa06hg6q4uF6lfUj6XO2j8ynNC8iUgpCk3L16lXS6fROF2NDPA8yuW9W3Xf/7udc1GYbXKL6c/PmzU0dZzsKUL1uPr10HW9pLsRStR5Ts2eBytnphbkkFy9+3vgC7SKc3BeBnortt+9OcPHi9bp97mZ/G3uZm7ePUvB5K8OxVrh48WLjC1Tg5s2bdHV17djnA8xODwHDFdsnpxe5cGFk0+9z48YNPG/vDlLqUf5PbseBh6vuu/Tp+6EP6vbS/+D2zQ7gVMV2y4Z3f34BrUHueGGwW+o9cucOPT/+MYrjYFmD7LOmKyamF+dVRt8dxbtwgRng0i4o91a5dOkSN27e5P2LH2ArLg4utuIUHt21R8XFxuXUKz+hbXqGyMISvbdH6e6OYBkGM52dZKImbVaavuU5Zq008x/eZfToAEtdMaZfvsob3z6z+j6O4uAoW6yvo/C59wnc/6Q+lbGDiKWpP9u1I53vjQOgego6Kpq39udkLVRXWf3TXFafrywsoziQaO9CLdmuFY/3lNXtWsl7lL6Pgt9ErgfkCn9ruECm8OfHlXc+YHbgPnOXx7dUj0HEl5d5+MoVVMdhRu/HUJYqIgZ1Ncv4zCxz+/aRGR3FTSa53NnJyssvb/nz3nnnHW7evMnIyOb7RTtJvK2NxcVFuphh0jhZkTOxPbPAB5OTLJ04wcqFC3Dhwg6VtJK9Utf35vqBF6vue+X736PN3BurrupR37dnBoDnq+773176HhHd3vZnvPPOOzs+hqqFS5cucffu3W2NP1fSzwGVSu/46E0uXpzYRunKqfccQVhtrvR9dPU5ck7lyu9LV27S4UzV5fO3w26fhymto/HJx4FYxTEri5NcvHhl0+9TT+7cudOQz2k2RKQUhCbl1q1bZDJBw7TauHs3P+A6dOhQKO9nORoev1p138TYbUZy4Q0it0OY33tiYgJd39zlV1W+jutVzpDeuDWGkb5b5YzG8t577zE4OBhae2gkC4uPVt2+OD+xKzqKtRDW/8PKPkQ1kTI5OV/XutnKb6OehH2dK/Lee+9xw/4bwImKfU5uqaa6DausExMTfPrppywsBMVY1Jfk2FNUEyln5lK8/fbbm36fa9eu4TjO1iMidgmO4/DDH/4w1P/FtckD+ImUH1x4i7Cr6tq1aywsLOxoe9osd2YHGMx1Vc3l9Cf/9Ifs+8LeibDaDfUeX1jg4Z//nJTjYCUn+OJ8X9Vowdj7SW7ui2EMDDA8OsrdWIzL22yILh6O6uIqHo6Sf+4oXv514fn6/cXnmzlm/XP7hIt3KsY/Vd7csGyJqSV6F66h6B4nx8ZYUh1yx1VGD3XgqRZgsQysuJ3Ydyy651Ik7iWZ7B6ibWoafW6apb7mtCTdLi1naep4qA4oq38eikP5Nje/bSLSz+npUZazGs6Ki2lZdL/tcLX7NI5n4DkqrquCo3Bm5SLtto2jqihTEbonHaxIDw+M5ViviV+6dInnn3+e06dPVy/jLnVynpmZ4ejRo/7lrpHIww/T8+MfM2218c5Ce4WNs9vTxZGvQr+i4Gkas1/7Go8dPryrvkM9+RPb5vrkl7nzpldxn507eop/+Hd356LkvVLXH99qh9eq7/vFXziLqe+NRUD1qO/+sRj8pPq+M088Q3f79kXKeDzOj370oz3R5y3FcRyGhoa2NbbP5L5WdftU8i4jyu2a33c99Z4jsG2bsbGx1XF1rTiOw9TUFO+99x54zwCV/ZH3P7zE0p2PK7Zv93+xXXbLPIwfpf+jseRhoDLqfmz0Jj/60Y82fK9G1PX4+O6Yx95r7N4WKAjCtvi1X/s1Tp2qjAzYLsWVVmfOhJMHY35Z4W//UfV93/7Wc3zhge13HMMg7O998uTJTb3X/+X7sFJFa376K7/CN8/ujkRtm/0uu41/8n4PVBkTf/GpR3j+27sn/9NWCeP/8YPbXYxMVm4/evwhnn/+4LbeeyN2Q3sK+/deSubecRir3H744D6ef776St8gwizrTtf90p/G+P5nldvjHb1brptnnnmGs2fPhlSyxnLhwgXefvvtmtqDHz/6wOQf/bRye8z0eOGF8D6nlJ1uT5tBTSaZ/fc/oXP8uxX7Dmbv8VcXFzjouuSefXbPWNDtdL3H/u2/RXnoFMzMkBodZURLk9MMJmN9pM0IcSfFQHaSji6NXi9Dtj/KUvs++uIrzH7tUFkeQ8sryXHoa0sakLewIWxOWBVL0vqxU5amCkqFpWjFc7XUUnTNdvTKp5cZ7NvHkQOHqxy73t507T10RUPdopgfy/7bvB3p0Bz61au4dzVI3iuxI11kMJdkuFvh8PAh7FOnOJBI4AwPc+bF6tFZL7/8MqdPn95z99nLly/z8MMPh1/us2fhmWf4+d/5Ad7n95g0BsoiBo/2wPAB4GDe4vrA0FDNH1W371BHLl++jNX7MK9cOlKx78F2OHt2d4679kpdL/hcEhQFnvnyU6EvRKsX9ajv7nv++x46fYYD+8L5nJWVlVD77XsB14X/6nvVJYVvfv2rfOULXw7183a6f7tV3vjdOGMzldu/+Wt/kd/65epuaTvNXqnjd/5ugttVOnq/+Atn+T8/X31RbqO5evUqf//v//2dLsaeQ0RKQRB2lKAcChHxFCdiVhcp03vDtWVXI172/kR88qFKTsrtk7WqT4bK9U5yodaTTE5hMDdeES240N6Lfe8A+sH6Lj7YjWg3bhA9f562eReFaEXky5TRh+uANjpK7KWXyJw7h3Nsd06k+uF5HjYOlluer9D2nHV5C9flM3TLt+fP8T83V3j/9sk5fvnyBTzP5eSlMXTDQT1jM3OoB1UdJ14o16TrYdxx6J5LYY98zOwjQ3jJy7z60R8y16TRgmJJWj/me+Psm1gmlWgnMZembzrNWOcglmvguRquo+K5+b+eaJYDU9PoboR0Rz/Hzf30H36I3p6n1+UxLIqEWkEkLAiFJfkRNdSaI/ZTY3c52XGQM52PhVwblWSfe47YSy/hJhLYp07hjt2usCMFyCkm9qkjuIkE6Dq5Z5+te9maiqEhfvbUf8rLI5X3WuXAIPzuE7B/l4aYNgDTqL6IRMa028cv73gswp4RKOtF1GdMC8H52oWNyVj++4LqvVWIGNXH9jKu3T5+dRiT+ZQ9j4iUgiDsKOms/01abjL+dSCdm+0jIqU/fnUQ9HsVNkfWri5SyvXOv93J9W57qMkkD/zgp/zueOVy3gfd66T/5+/R/+STeypacLuoySTR8+fBtokuzvH04t2qlqQ9V5dRuwzcRILo+fOkX3ih5jpyPW9V6LNKHoviYD5CcN22kmPLxEHXLhEO146dfXyeC+Ys/9vdn69ua+SV5cTIPVw8uiRasIJWsSTVFBVTMTBVA1PVVx8NVS9s18v2GauvDUyl5HnpcUr5sSOfXeGj9y/yV//SX8kLidFp4mP/Bro8jOn3USyLu28l+ICHcJW1lAmq5/LV/Z9y2OjBazPoP/4UKArpL/8V3M6BgG+1t3EHB8mcO0f0/HncRIJbh/cxlpmvWJTRsz/BsUQOdJ3MuXMtcz8Ik3QOkuZ+Xun5Vtn233yYXWuB2ygifiKlCEXbJkikbHWC6kDa3vaQebxg/BYgy7h2+2R8FreIOL73EZFSEIQdJegmLWKR/wqsrHRutk3abwVWRNpd1K/dWdLutkvOqswxC9LuwH8wJ+2udorRgtFJBQWjIlow1d4FrrunowVrwfjxj7CtLMzOYF4fIaZr5HSDqaIlqbvCvswUlpdi+bMPmD9xiHR3O4vf/ydc+iu/UhJpWB5VWComFgXJnJvfZtMAkSkK4LAjrqdItGAQjbYkXW9FmllO0xFrpyveUSYEroqEVQTEfNSgQaSaoFiIKIyUbdfRlNqsU7fCpHKPiKUR16L5DYODuENDqGNjOMePo1+9SiI9yZfshRJL0xUGc0naO1KQUHCOHwdFwRkexh1oXoGyiHPsGOkXXsB8/XW0j6Yq7EgBOlwbZ3i4pRashI2f6NEmYhERn7yIfpPNwuaRdudPkGghbW97BM3jybjWv+1lAyJQhc3hG0kp7W7PIyKlIAg7SpDFS0xWwkhkUR3xW/23QXBDSyDtrn74RVLKooz8qttqlqRzSwOoE4daYiI5TEqjBc2FJZ5evFkRLRhdHqf7ago1FgslWrAWXM+taiOac30iDku3lUUfVrcjza2zLo1PzvG1Kx+ieF7ektR2UB6PM32oB9QJYuQ1vglXQb/j0j03iz2ywMgjQ3i37vLeDaVpLUm3S6tEC24FBdAVnUxfguhUBqunG3PBYv+czVi8k4yt4zkabsGO1HM0hrodji8so+udGAOH6Ir1sHjkIQ71/UJBeKyS/1AtsSRVKsXCl19+OZ+r9wu7O7daray3NM2O3K1qaYphYJ862pKWpu7gIJkXX+TnC8tcHL9adp9NmoN8fOo033gxttPF3NOkqqToAIlog+BISs8TW9LtIJGU/kgkZf2QYINgfJ2pZD5l2/jVobS7vY+IlIIg7CjSuQnGXyxqcEGaDM/zX8UmK7D8V/5Ju9s+Obt6JGWr25OoySQPvfYGvzs+V7HvUO4usX/9hkR4bJHIa6+BbaPOztJ7/3NWPI1sYTK6GFl0TL2Pai+iX72az1WWSKD9+EcsvfCbZdGA63MV5lajCK2q+QyvHZvnbvwyb0/c3dDa1Glw2N/pa+MAdIolaeg0OlpwqxiKXiVa0FiNCFyLFqyMJDRKzynYj965cYuRS1f5xrNfrxQNFQ1D1dHRUBQFNTpBbPRfQ4eHMZW3JB39oI23rIcrLEm/3PsZA21teDGDjqGH8pakX/o2x9ploYYf6y1N3+kYpjtTmWe25+EOuhNuS1uaZnsHeKWnMmL+i0YOqLwHC5tHxCJ//ERK1wXLBlMWidaMX7tr9bEFgKaBoefb2HokH+r2kHm8YHydqUSk3DaStql5EZFSEIQdxe8GY+geWv0do3Y9EZ8BW9aWzs12yNngutK58cPPdjMntpvbJmtJJGWRXC7HpUuXaLt/n8E33yQ3a6AwVGFJOmX0MTa6iJpM0j82hvUbv9ESlqSwFmVoVRUKnXUC4Np+Y2KSB2+8i+s6DFwZIRtxsE50Mrq/A4w5ouo0qC73NNAnbLrmRsl9Os7II/tx5xVevXhne9GC/QAZSE+FVRWhIZak9WO+N07fxCKL3TG65lN0z6wwOdhVKQC7KgYevdNpdEUj1RGnnSjp4SN8ofNo1dyERUvRiFJuL7o+b2Gl0LiW71AJOVTHuJlhcvEWx6JDGx7rDgxUWJL2Zif50spihSVpm5mGeOtZkm6XVUvT117HcuerWppqerrlF7z49TfEWn37iEjpj+kjUkJ+4aiIlLUj7S6YqFldpMyKSLktgnJStuK4dj1yr60fIlI2LyJSCoKwo8gNJhix3awPsvIvGLEnqR9dS5P8+uzbFTZrfekTQOdOF6+hXLp0ifvvvccv3riB6jjEU8s8vXinwpK035qi68oi4x1xAAZ3wJK0iOu5WK5N1rXIuTZZN0fOtcm5VmHb2t/aa5usZ5FzCtu8wn6nsN+zy85bdlNknsrx5u3/T81Rhk++fx01O07X7Arx7CJWm8bs6R7a1MoomTuHE5xYTmNYNh0tEC0olqRrFK1IzYoowJJtauV2vWApuv7c9mfmOXLnT1DboGvqM2Yn0+h/1sdI20kcV8dzVVxXw1Bc/pNDf04kEyeeSLCQiXJAOcTwb/y3sH//TldL3VhvSerev13VktRSDOxTD7SkJel2cQcHWfitF/lf/51bYR2eNAd55jceoO8rvTtdzB3FL7pDxhbbxy8yS8Qi/0hKyItsHW0NLEyTIe0umFgEllKV28XudXsEOSxJFK9/sIE4U20PzwvISSnzeHseESkFQdhR/FZgyQ0mj4iU9UFEymAiMoEVOmoyyel33uE3r0+wkCofuR3M3uPpt35GVN/XctEdX56dZai/H3V2ltz8beY8vcKSdDCXpN2McnBhgeXFxXyOxddfJ/PiiwA4hRyEuYKF6ErMYsyb5dLirQoRsfT1emEx69rk3FzhsVRwLHntVVmKXQ90gNqvRRIt6M9utSTVUNfEQXV93sG11+XioLZqYaorOh+/f5HhgSGOHzladm6psLh2vLZqRRoa7RA7fAN1bAz1xGkY/5je5QWeXPmo7Dd90E0SmZ/HA5zjx/FGR8kNDDS1QAmVlqSfHxhgJjdXETk+ONzN4YTV0pak2yGdVUia+3ml51sV+/7r/TNAg67juxSx9K8fEtHmT0T379OIWLQ9/HKhtkm7A/yvedLutkdQXkDJMQtRnxRCMp+yPUQcb25EpBQEYUeRSMpgRKSsDyJSBhOTCaxQ0W7cIHr+PO3z8zgO7LMmKialDbUTbXSU2EsvkTl3bk/bmXqeh41TlncwV/xz13IYji1epWvuFrc64vSOXMHBInO4h9sD3Sh6Bk1bwVJdRhWTkZUMcWsJa/wSH+9bxln4gFc/uMdkbxR3vZj3GFzw3oArb+zE198VSLSgP5uxJPVcFddRMfHYN5vFUHS83h569A6yg0d4NHa0XERUK6MRS8XEokhYFAiriYeqUj1f7VaYmhzhRGKAM/ET236vWimNFhwfeJDc9HRFtKCuuSxls3DyJJFEAjeZZOGZZ3aszI1k1ZL09dfR9OmqlqR9rtXylqTbIbCPJ3nHZeK0jviJHiIWBUdSyvhie4g4HoxfPUhOyu0h0WzByDxefQiqv5j08fY8IlIKgrCj+HUOZRVMHl+bCFn5ty2CBiV+Al0r4ZeTUjrVW0dNJomePw+2TXRlhSdnf45ilwtA/dYUwzcU1MRR3ESCaJ3sTF3Pw14VDK2y6MPZziy3jWns5ZESYdEqERadda/zORErt+X/vE1EAT55/zptxgxdY3dR0jNYhsbCY110q2MVx37eG+PE0hyGZWHMzpHqiTN89R7JrzSnJel22a3RgrWgo5UIe0UB0Ki0Jq04plwMLIqJ8V+Y49DdP0aNK3ROfYJqO4y91sNF7Qu4no7rquApqJ7LX+j6OQ929uO1GwwdegoUhfQvfUfyAwZQGi3o9R/inc4HK/LMrsS7GXzyAP0DA6DrfP744zzc37/TRW8Y7uAgmRdf5GfJZT6fvlphSXrj9MP80ovifVgrshAtGHHLqB9+EW0iFoGpB9u9CrUjImUwviKltLtt4eeIJvfZPGKtXh+C0g9J29v7iEgpCMKOIpGUwUjC7fqQkUTvgURNj8HceEU+p6XUPtSJwzJBvwUir70Gto06O0vv+DiLzjBpJVJhZ6o7yyhXLpM7eZxcdyfpP/sPjP32t9fyFZaKi+7a87FYEltxeH9yrOwYy7XLhUUvLzT68jB8whxMX2pY3Yglaf3YTLRgEcX1VutysTu2en41ipakpRGBpWJgUSAcvXOPns4E+/sHq4iF1cXFtajCvABZjERUw/aMOgyxQyMFS9KH0a9epTc1x1nrg4rfZVybhy4D5/hxUBSc4WG5/m2CYrTg/P/8Jh6V0YJ9nS6DQymc4WHSzz7L/Btv7Fxhd5BM7yCv9FRGzT8XyQLzDS9PsxC0EE36eDK2qCd+EYGy+HbjnJRC7YhIGYxYXNcHv3uG32LnVsM/krLBBWkysgEipd8iLGHvICKlIAg7ithEBOMb0SYTCdtCVtn7oyaTnH79DX53fK5inz57l9i//vOWsqErz3doVRUKc55NtiySML/fmJjiC9d/iuM6DF++jt1m4ZxeZPJAH6oxRqfqomgO84rNjTuzdM+lsC8lGXlkCG9J4dXPppnraw8uYLTwmJqoe12EjViSbp9ywW8tutB7pJ3+a2m0fQnaxnMsz3t0fwg3ew7ioOM5Gq6jsr8jxePpm/TSBW0m7QceRVM1Tn31t/n2/v3r8h/qaJu0JH359Zc5efIEZ3rO1LkGaqPUktQ+dQrnxk0iuXJLUgBLMbBPncJNJEDXyT377A6VeO/hDg7y7pf+Ov/s8nLFgpeZgT7S33mg5QVfv1X2WZnA2haBVmAiFuF3q83kFDwPySW2DVJ+dq/R6ttbCTMgJ6VM2m8Pv4UZIlLmEZGyPvhFUso8Xh4/RzRZELQ9ZB6vuRGRUhCEHUUiKYPx6+SJTcT28Ks/TfUwWvjOWMyd2DnnoRCpsOibMvrAbds1uROL9qVZzyLrWhWiYd62dN2+EnExu/q69LnNSlcKG4d/cvtNXPxXfm/EkxevM5qbpmt2hWxuBSumMXmyDUNNlx3noTB6uJf4chbDcuicS7HQE+foSJIP+prXzrSZLEmLmIqOqRqFP52IamKq+W2R1e1G4bWOqZqFx7Xt92/f5cbV6zz71V8uizJcb12qK5p/lOEQxD6N5aMFH97HlVeuE0kpHL09U4gWjNHmrnAqfp++jgztHR3YJ08RMbtxhocxD51ubMU1mFJLUjeR4LP+p3CmlyuudwNDOkcSEdB1MufOtcTCjDDJ5BSS5n5e6flW2fYvH83hDlQuhGk1/CdOpY+3HWQCK5igOshaEvVXK54nEW1BqCpEzOqLMCSScntIuwtG7F7rg0SOByM5KeuDpG1qblp4KlYQhN2A7wosSXoM+HfyZJX99kjnlKp2pgvtrWtnWpo7sW15nqcX72B6Vtkx/dYU2gUP78TxTeVOdD2vLGdh1rPygmBRGCyIh1kvR861uXVgiZnYdS5Pz1aIjbnCuTnXLrxPPm9ifSojnLcRO9Ng6mVJWoquaCUioVkiDhpVhMNyUbFsv1IpNEY0A1MpnmdgqDrqJiMNg7hwBxbn7nO67ci23qc0WvBa12l6058T8cqjBc1YDlfXWzJasGhJar7+Oqo2xvg6S1KAXm+upSLHw8Yvb4z08fKI7WZ98BtbGLqHvvvWtjScYJFSESG3RiwbXJ91bSIW5Yn5iJQS0bY9/HKhtkm7A0SkrBd+fTy5h+TxFSmlj7ct/Pp4IG2vGRCRUhCEHUVWYAXj56suK7BqR00mOfLKT/nd8ZmKfSeVOy1nZ2p7DlnXIvbqn7KcW0KZmSV6+zrxmIKl60y395CJmLQ5K+xLzzCdTsOnU0weH2a5q43ZP/oHvPOXvrgWnbgqPFpbFxEPAKzA8mg9vmrDETvT6uhoGIrG+IMHOX1pCq+3D30sRcx26b+U42b/MK6n4zoanqviWQpfce+RyBi4eowjQ19AUzUGvvKX+cpAZc7DV/74+zx99ss8ffZLaErrzkiXRgtO631c6+yriI5WBzT04yrRgkDZatGC7uAgmRdf5I0ry8y/dbVs0UrSHGT2Kyf45Rc7d7qYexZxywhG+nj1QdpdMEH5wjI5ha641FMtBAkeIlLm8Rvfi1i0PSSSMhixe60PvmmbZCEaIJGU9cKv/kzdQ2vdYX/TICKlIAg7ikwkBCOr7MOlaGcamQQFs2LCPq31ghffNXamRdYiEkujEHNkPXvt0ctxb3CF5egdbs0ur+4rCobFSMZsSURjxrVwcUlMLfGNkY9QPI+TV8bQcfBOx5k41IOnzqMDOWDUjcCdZbrnUmjXr5N8ZAjv/gzTo10b505sQfaUnakHBhpRzcyLfcXowRJ7UVM1VoXAiGKsWpFGlLXtprqWH3HtvJL3UNaiDS/OXuRAh8sRXUc9fQj96lUmruocHEkVLEnjtLkrDOaS9A15RPUoy8NHOBgdwBkepuvIl6p+FdPWiChGSwuURYrRgvd+9HPaGGNyXbRgR+cEqEsttTCjGtm+IV7pqbzWv9CWBhYbX6AmQfp4wcgq+/og7S6YoHqQSfvaCRQpZfEt4C+aSbvbHiJSBuP3+5N2tz1877U+C7BaDXFEqw/Sx2tuRKQUBGFHkZtMMH714GevIfhTamdqzC/y9OKtCjvT2MokxvvgHN+cnel6PM/D9hwyXm5VDMy6OTIFkTCzKi5aZfvXi415S9PcmiC5rpy+HAFYgsXbW6kajl6bAKBzLoVuO1iGxuihngrbTU9tzdyJtRKGnamKsir4FYXDNUHQILWwjO5pDPb2rxMXC8eqevnrKkKiqeh879//PqdOnuLMmTMNraOps2c58umnuIkE9qlTOJ/fIWKXW5IC2KrB8pEjWJ2dLWVJGgbu4CD/aug/RdUnKyyuDx3uJfJcLx3PPbfTxdxRIjKBVRcyPhOn4paRR6I76oO4tAQTJJhJhEftSCTlxkgkZX3wy9Em7S6P2L3WB5nHC0bcMuqDtLvmRkRKQRB2FPGyD0YmsMIj8tprYNuos7P03v+cZU8lp5lMxvpIGxHavRUeUMZJZVK4lz5k/sHDpLvbWfj+P+PSX/mVCnExU1VstHDZe21XcifWhqFoJUKfUSb+RRSdjjP7OHT9TbQBha6kw8rcCu3vmtzoPoLjGXk7U0fFsxW+M3SNNqUP2k0GDz+Bpmr84nPfQR0YQlH8BzMXxy4CcObk9sRFhZ0ZMGV7e1ctSd1Ego/796POzpRFOE8ZfWhHO9nfOYqraS1nSRoGmZzCormfV3q+Vbbd6/0+Qz0yHJCJhPogEwnBSLurDzK2CMav3YG0ve0gIuXGyLi2PkgkZTAijtcHv9yAYveax89aXe6z20PGFs2NzEoIgrCj+HrZy00GyE8kDObGK6JfFlf2oU4cxh0Y2PhNmgTP88h59qo4mH/MkXGtqttK95kTkzw58ia2a3Pk0m1os9GfijFxuAddvUdH4TPuuB72nRTdcynskXlGHhnCu3Wbd294TW1n2oy5E3W0gs2oUbAj1TDV/PNiFOKqTamqF7bnhcbxu2PonsqpoyfKIhMjJSKkoeioAeIhAAMQO+6hjo2hPtRN5sfvEp/3ODk2WmJnusRgLklX2zKqZmI/eIqoFsEZHkYbHG5MZe0wRUtS8/XX0fRZJtZZkgLYXpZ0fz/TTz1F1y6wYN5r+N1rHzg0yOnTrXMf8UPyxtQHEYuC8bX0l3a3LWRsEUxQTkpJJ1E7ftFsIGJREYloqw8iUgYTFZvhuiCuBcH49fFytoLrgqo2uEBNgt+9VtpdcyAipSAIO4qswPJHTSZ5+PU3+N3xuYp9mnWX2L/+812fR8z1vDKxMONlS57nyLg50m6OjJsl4+WfZ9cJjKXWqV6NUYpPfnqdeWeZrtkVsHJYpsboYbEzLbITuRNLLUgjal4gXJiaozPWTl937+q2oh1p2et1wqFZEBhLRURNqb3nfzGXF11Ptx2p+T2KZJ97jthLL+EmEoz2HSa3YBHx1tuZeiimgV2wGW5FO1N3cJDMiy/yJ+9nUD76rGxRRtIcRPvySc4+eX2ni7kncZz8gLgaX3j4KKa5STvpJkZEyvrgJ1JKHy+PTGDVB9+xhYiUAJg6qKqH61bWk1zzaieV8d/XFm1cOXYzIlLWB7+21yYiJeBvcS3tbntIRFswgdbqlvw+a0Xmj5sbESkFQdhRZAVWdbQbN4ieP0/nnIdChH5rqsL+0HPa0EZHib30Eplz53BCjC6yPYe0myXt5ki72TJRsfg87eb4/PAiY21X+fnk/ZJ9VtlxuwGxMw2mWu7EiYFuHFfHczRcVytYk2rsa3c5OD+HobXh9B/gaCRB98ETdHQ/vSokrhcOi9GMpeJitSjEl3/6MidPnuTMicbmRawn7uDgqp3pYrSLi50PVvye5yI9PPVUOygK6HpL25mudA/y057DFduPxVYAESlrIWjSWSYS8viJF1nRb7eFRLQFE1QPMoFVOzJxGoyiQMSoPkEvkUW14yd4aBoYMusG+IuU0u5qx7bB9jG0kUjKPNLu6oPca4MJqodsTqFNRLWakLFFcyPdJUEQdhTp3FSiJpNEz58H26ZteZ6nF+9geuUzpf3WFNr7HpzIR15Fz58n/cILOAMD5Dx7VVhMe+VCY/F52s2S9nKrQmTGzZWJkjabtO/cD5CC1ETo9RAmzWhnCqDAqgAYUQ1SCyu0R9roae/GVA2ihe2lx5iFx0jJtvavzjN4/w/ROhTi0x+h2jY3/nQ/1yIP4ZZEJKqey68c+YQhuvDaDHoPPAaKQvqZ38btFstIP4p2plMX/zkeKpPr7EwjhgNKetdHRjeCiM9PMysTCTUTaEHX4guCivi2O7E+3Bb+C9Fat49XSiTg9ycTWLUjY4uNiZle1WgEiaSsHV/LTbnPriI5KcNHbIY3RtpdfRBL/2CC8j/nxxdST7UgfbzmRkRKQRB2FLnJlON6HtqPfkgqt4I3M03k9ue0tylYhsZMZzfZqE7cXaE/NUsylcb7eIz7xwZY6Iow8ftX+OG3T+NKh6eCnbAzLUVBWRUEI4pBVDVXIwujq9vN1dyI+WNNIqu5E82yiMRIST5FpSQi8eWfFSIRj28xEvHQMLEDH6GOjeE9eALl6lUGrAk6cgsluRNX8hacsynoVnCOHwdFwRkebqncqLXiDg7y2sPf4ifzj1TkmE13J0h/53hL1uPIyEjZ60zqMQqrH8q4PzbDSGyEkydPNqhkzYNEUm6M2L3WB/8+XoMLsksJ+v3l605+n7UgLi0b4zd5Kte82pG8gBsjdq/hE1R30vbySLurDxLRFszGfTyhFmT+uLkRkVIQhB2lazHJmdmPKvKPJZaPAYmdLl5NWK5Nys2y4mZYcTOk3AxpN0uqEMF4/fAi420jvDt5fzWqsbgvOjHN169+hOJ5nLwyhu45uA/HSR7qwVOXUYAUcNdtx7mTpXsuRdetUZKPDNGRzNI1vcRcX/tOV8Guo5qd6eRgV1XLV9WF3pk0uqJh9yTo1tpJDx7h0dhRTFUnqpgl4qJZEB2LQmKpiGgWtleKibuR0tyJ9qlTOCO3iVjrcyeCrRnYp462bO7E7WA5OklzP6/0fKts+3DPCu7A8g6Vauc4ffp0xTZTd6sem3M0Tp48WfUcIRgRKTcmIiJlXZCJhGCCVtlL26sd33Ynkamr+F3zJHq8dkSk3Bi/hQIiFtWOiJQbI+2uPsiCoGD87rMQHAEtBCPtrrkRkVIQhIaRy+W4dOkSAJGZGfovXOB3bnViu2rZcQez9zjwvT9m8rM40089Rba3t+K9Tp8+jWnW707keR5ZzyLlZlh2MtzX58gqNktLn5ByMqy4WVIFEXLFyZSJkpZnB7/5qkVq5a6HruVtUzvnUui2g2VojB7qqRDTPFVh9HAv8eUshuXQOZdioSfO0ZEkH/QdD6cS9gg6Wj4yUTWIKuseVZOoYpJ46jinbr6GMaTQcecjZiZMopOdjMRP4LgGrqPh2hon9i3yVeMTerUBvKjBgcNP5e1Mf+k7TR/lVpo70U0k+LBnH9Gl+YpcqOaxTnoSTsvnTqwFy6ne7Yrou9NOuN6YpsmZM+VRv0PvdcCnlcfG23srjhU2h4iUGyORlPVBRMpggiawMpIPtWbEgm5j/OpCrNVrx2/SWYSiNcR2M3xEpNwYaXf1QRYEBRNk9Z2V8UXNyNiiuRGRUhCEhnHp0iVGRkY4E48z+OabKLaD43ayz5qoEEJ01SE2NcXwq6+S/OpXSR04sPo+RXvAzU5Ye55H2s2y5KZZcdIsuxmWnTTLbrrkMcOym84Lj05ecHQpiejpKDzOXA6rOqrSPbMCQOd8GshHAFaL9oO8UDnfG6c/uUjnfJqFnvjq+bsZBYgoJjHVJKKaRBWDqBohppqromJRaIyqZlmUYnFbqRCpK5uwYu2F2NEF1LExZoaXsO/P0pVJ89jclTI701POTSKdi9DR0ZJ2psXciebrr6NqkxW5EwFsN4szPNjyuRNrwVekNFpTpKyGX25AmUioHREpN8YvTbG0u+0hEwnBBE1giUBeO9VyLYJY0JXid6/1E3iFjZFIyo0R283wSWX897VJ2wOk3dULudcGI24Z9cG33Yk43hSISCkIQkN5tK+PJz79FPr7cadmeXrxHUyvfLl4vzXFyQmIDx7DTSQYun6d9Nmzq4KIi8eKkuVedpJFN5UXGquIjyslr/dKnkbdyosVmpMXSDN+M6cFivuLxxfPrwemoq+KiFE1LzJOj03RE+9iqG+wRFQ0y44rHlt8bSoG6g5YnxbtTK3OTkY6B9iXvU3EK7czNWM5XF3HPnWqZe1M3cFBMi++yPden6Pr5o0KK+boMyc4+WLnThdzT+InUppGdYvTVkQi2sInSGgTa5w80u7Cx3UlX9FGSL6i+iDtbmP86kLaXe2ISLkxvmKRLAiqGYmk3Bi/erAdsG3QZVa8JmQhWjCGDprq4biV9ST32toRt4zmRi7HgiA0lP4LF0DXUWdnUUdGMD2DrGIyERkgEzHoUBcZtMbJuSs4n73H5PFhFrqiTPzR/4ufnHucRSfFcnc6H443/u5Of53QsY18VKCj5S1woxmLhYDjowU/sOLxxfNLUVBWRcKYGin8FcXD/POYUtxWeF08pnBOVDXRFLXivV9+42VOnjzJmd7db8NYtDN1v/tdZow+bnTup9+aKoviZZ+O8aBCtCBQtrKd6ULnPt7qOVWx/eG2ZWD3R+zuRnJi97ohvhZ0tgzmasVvMBczPXZ5qtyG4dfuHFfBdkDfRMC+UE42wK5UJk7z6BromoftVP4Qc5IbsGb88xXJBFYRv7rIyb22ZvzEIolmW8Pv2i82w7XjJ/AqCpjBa51bhqA+R9YSkbIWXNf/fiH32jWipsdKpkofb4PsUII/frmzpd01B3I5FgShrriex5KTYt5ZZnLhczrnbjISj9Bz5QqOlcV7JM7MA12YkVsUAzomXTDuZOieS+F+vszYI0N496dJjydY7mvPC5RNynxvnL6JRRa7Y3TNp+ieWSHZ34PjGDi2gWPpuLaOYxkMd6Q5NDVDXO8iNniE/ngf6WOP8OTgrxTExrzQGFEMFJmJBvJ2pqPf+Ab3PpmnfXmqws70ix2ToC7iDA+3vJ2pqVeP7pOVf7XjG0mpuzT1hW0L+FnjSLurHb+6C8qH12oE5gbMKbTHpK62StBvNsgCq9WIGNVFSrnm1Y5/dEeDC7KL8b/XNrggTYSfSCntbg2/uhDbzdoJiuCV4X+eoN9gOgvxWOPK0iwE5c2Wa94aERNWqlgyi7V67fguRJOxRVMgIqUgCDXjeR6LTooZe4EZe5E5Z5l5e5l5Z4V5Z5k5e5kFZ2U1t+OTo9cxjVm6kitEsotYhsbkiTiqWh5F5KkKo4d7iS9nMSyHzrkUCz1xjo4k+aDv+E581W1hKDox1aRNjdCmRplPztAT72a4d5CYGlndHlNNun5hiSP3/gS9XaVj+mPS8wvc+MEAn0cewi2JZFQ9l6/0vkPCNYgnErTvPwGKQvqpr+FGWyN/Yq1ke3v510N/Gc2c54nlD8vsTA8c7if29QQdzz2308XccUytenSfTJzWTnBOSumSQZDtZoML0kRIzpiNCXJWT+egXSawtkzQBIzkjVnDfwKr8WVpFsSCbmMiPpPI0sernZRfJGW0seXYzfiKlHK9qxmxGd6YjURKYev4jS1Axhel+Alncq+tHf+clA0uiFAXZEZMEIRAMm6OaXuBaWuBaXuRq7EbLKppzo9+yoy9SM7bvFdB90zeIrJzPg3kowY9tfpNxlMV5nvj9CcX6ZxPs9ATXz1/pzBdjQ4zTlyN0q5GaVOjtGkR4mqMNjVCXI0S1/Lb42p0dZuhll9qX/7zgkXqiSoWqXGIHbyKOjYGD56ECxcYtCboyC2QNAdJqXHa3BUGc0nazDm8ODjHj4Oi4AwP4w6IQLkZsrbGormfV3q+VbZ93yMfcaAnuUOl2l345UkUsah2giMpBfCfSMjKYK5mZMJ+Y4LqIt/2pK62StAEjLS9Nfwm8/zsrISN8b3miTi+iuThDR8RizbGNyelCEU1IzbDGxP0G5RxbW0E9vHkXruK7702QOQVgpFxbXMjIqUgCKsRkWPWTP4vN8O4NcOkNc+ymy4/uLgaNMDiwQ/dykdmaU5+Qj4TFLpQsr94fPH87aKjEdeidKgx2rUY7WqMuBajvSAyrhcd42qUkU+uoKJw5kz9cy9mn3uO2Esv4SYSLB85gj22QsTKcTh7t+w4SzVYPjJEpJA/Mffss3UvW7OQsyrza4LkBixF7F7Dx1+klHZXROxew0cGcxsTVBfS9mojaAJG2t4avtc8mcCqGbnmbYxEd4SPiJQbI3av4SM2wxsjkZThIwvRNoeMa8NH+njNjYiUgtBiOJ7D3dwUt7NJxq0ZxnKzjFszrLhVvKZCxja0fBm0vEAUzVgsBBwfLZjdF48vnl+KglIQF2N0qDHiWpT2EvGxveJ1rKYcjWoD88W5g4Nkzp0jev48VmcnF3tPE1mcp9+aRvdsbEVnyugjdQBOdC6BrpM5d66l8yduBc+DnF3ZlkAi2koZ7O+qul061bXjb/cq7a6IRHeEj2/uDpnAWiUoJ6VEtNVGUHSCTCSsIde88PGzGhYLujWk3YWPr0gp99pV/ARbiWarHRHHNyaoLkSkrA0RKTeHr0NQDQEfQh4RKZsbESkFocmxXJvbuQk+z4xyPXOfG9lxst7O3BXne+P0TSyy2B2jaz5F98wKk4NdFZavnqcQU1QG5ywiaoRc/xDHIn3sO/wwB/t+mclbY7R5JmcffoIOLYaqVI+K28s4x46RfuEF0v/yX6KqMGkMMGmUW7ke9KZI9/eTfuEFESi3gO0quF71zo1pSERbkXiseh3JhH3tSCTlxvhOnEq7qxm/CXsZzK0RbPfawII0EYE5KWXSfpWIj6mITGDVhufJwozN4JeTUtpd7YhYtDFBIqXnwRbXEAtIu9sMQdd+uebVhoiUm8NvEaSMa2tHRMrmRkRKQWhGFLipTvKjiSuMpO9jszsmwG+d2M/Dl6bQ+gZoG8/hpqD9XZNr7Q9i5aI4ORM7Z2J4Kv+H05+gKRZe3GDfgTOgKKS//Jdx2we4aOejPrv0+A5/o/riDg4y+s1v8r0bR+i8cYPBXBLTy5FTTJLmIHOPd/Fr31xmnwiUWyJnVY+iBIhIJOUqfhOnstq5diynetsTm+E1/CYSZAKrdmQwtzFB7vNBYpvgj1+7M3UPtfnWltWM3+9Q2l1t5Oz8YsdqyDVvDYmkDB8RizbGr4/numDZYAZnghGqkPYZl0m7W0PTwNDzbWw9EklZG37tDoL71K2Gn4OD3Gtrw/OC3DIaXBihLohIKQhNxtHfepKjv/kk580PIb3x8WERUQx69A66tXYSejvdWuGv+FyPc31uhIMdKkd0HfXhYebevUZ81ONh5TZJc5CUGqfNneSQN442vQKKgnP8eP5xeBh3YGDjgjQhi539/KznVMX2F2LXgeuNL9AeJ2v7z46O3b/Bg/v7G1ia3YvfBJZEUtZOzqk+ahO71zX8cnd4niITWDXiNxAW68M1VDUvnuXsyrqSa15t+E4iRKTdlSL32nBJB+TylGveGpInK3xSPmJHW7Sx5djNBAlnmZz08Woh5ZOxp01EyjKipoiUYeJ3r42ashCtFL97bVbutTURtFhexhfNgYiUgtBkPPq3vl6X91VR6NE7iKRVOtwoJweO0qd30Wd00qt30aHGNszzqKAwdfYsRz79FDeRYHL4IXKjt4h4OQ5n764eFzE8PMPAOX4cN5EAXSf37LN1+V57AVOrLmD45VUUgjn8QKXgW+TUg4c4ffpoA0uze/G1J5FOdc2I3evGBEW6ZHIKps9gT/DHV6SUwVwZEbO6SCnXvNqQCN7NIRFt4RJoQSfXvFWk3YWPRFJuTJDtZjoLnc1tklQXpN1tjlgEllKV24MiAgV/pI+3OYIcgoStIzbDzY+IlIIglNGptrHf7GXI6GXI7GWf3k2v3klC70BTVC5evAjAme4zNb1/treXzLlzRM+fZynWyzudg/RbU/Rb0+ieja3oWN09PPJUe97XT9fJnDvX0jkX/fIk5ixZplYLtuc/Qn7yzMOYpkS1gVjQ1QM/kVIiKdfYSKTsjMsAZKsErXYW1oiaXtUJLJm0r42MtLtNIWJRuMgE1ubws0WTidPa8RWLxIJulSDhTCLaakNEys3hJxZJu6sNESk3h/TxwkX6eM2PiJSC0KIYis5hc4Bhs5f9BUFyyOilXYvV/bOdY8dIv/ACs//gZ3hMMWkMMGmsWbkOxB1Q0jjDw+SefbalBUoA0ydPYi7AtlTwJ8hew8+SoxXxyychnerasX1yUhqaRFIWCVpln7EaV45mIutTbxGZOC1D8vCGi1+9Bf3GWxG/dpeVdlcTQfcJyZO1hp9bhtgM147vNU/EolUC+3hyzasJESk3h199SLurDb/5FJlLKcfXmUrutTUhImXzIyKlILQQ/Xo3X2k/zYnoMIciA+jKztmFuoODvHX2r/O/jSzxxPKHDOaSmF6OnGKyONhL+jtHWzYH5XoMP7tXH8FDCCYwX5FYga3i16nOiVBUMzmfSMqoRFKuEjS4lcnT2vC75kl+tnKifnljpN3VhF/UvUwilCMTWOEifbzN4ZsnS9pdzUhuwI0RkTJ8/OxKRaQsx9d2UyIpa8K/3cl9thTfsYUs+q4JESmbHxEpBaGFmLLn+enSJ9zKJTls7uOguY9D5j669J1JAGFOT/DE8tUygTJpDjK3/zTuQP0jOvcK6ZUZ4EDF9pm5Kr50wob4dW401cOQu+IqYk8SLrYDrld9YYGfpXMrEmj3GjD5LPgjlkybQ6554eKbC1XaXRnS7sJFJrA2h7S78BGxaGPE7jV8JJJyc/jVh+SkrA3/sUWDC7LL8b/XNrggTULQ71Ws1ZsDmY4VhBZj1lliNrXEx6kbq9u6tDiHzH0cMPvZZ3SzT+9mn9FNuxpDUcIfrKrJJJHXXuNLP5vBWCr3XjqYvcfxq+8Q/b1esXoFTp8+zb4+G25U7otEOzl9+nTjC7XHkQn7zSETWOESVG+mLiJlkaDfoUR41IZEtG0Ov/qQdlcbcq/dHH6irdi91oZfu9M1D10MSFbxa3eZnILnQR2Gf02NZYPj05UTsWgNiaQMHxEpN4fYvYaLjC02h981T8YWtSEL0ZofESkFQWDBWeHT9C0+Td8q296mRugvCJZF4XJOW6TLjeF5Xk0CZtv9+8TeeANsG8c22GdN0G9No3s2tqIzZfRhqAm00VFiL71E5tw5nGPHQvqmew/TNDl0IAE/r7Iv1o1pzjW+UHscv8GI2JOUEzRhLxNYWydo5Z/Yva7hl58NZCKhVnwj2uSaV4Zfjk5pd7Uh7W5z+OdClZtsLUi72xxB99qsJdEwWyUoCrAt2rhy7HYUJX+vrbYIQyIpa8Ov3sRmuBy/a5q0u9oQt4zN4WetLn282vCrN0P30GQhWlMgIqUgNBnpyUVi+zpDea+Um+VOboI7uYm1jYW3funueyS0DhJ6Bz16Owmtgx49/zq/vZ2oWt4bjMzMMPjmm9Dfjzo7y9G7d0ikyped9ltT9CQ11LkjuIkE0fPnSb/wQktHVPquwJKJ05rwX/nX4ILscqIygRUqEkm5ORQlL5BXqy/J0VYbvgsz5DdchkSPh4tfu5OVzuVIBG+4SATv5tjItUDqa2sECR1yry0n5iNSyoKg2pBIys0hkZThIiLl5vAdW0gfryak3TU/IlIKQpPx+u/8M449/0W+9B9/nQW1fjkLc57NhD3HhO0fydemRlYFy4TWzomPfsAMS0Qml+n+/A6qa5LT4ozr+0mpcdrclUJ+yjT61avYp07hJhKYr79O5sUX6/ZddjuyAitcfCewfOq5VQnMDZiTCaytEixSSiRlKRHDR6SUa15N+NVbRH7DZfjdA6Td1YZvuwtYANOKSB8vXPwm7KXdlRN0/c/kFLricn/YCkEiZVTEojIkoi1c/OpNFpOWI+0uXGRssTlkAWS4SLtrfkSkFIQmw1rKcvWfvMk//p3/nq6j+/gkdYOPUze5lUs2vCwpN0vKzTJqTZOYWmJo7jYrukfs0zGWbIeFB+PMHVLQnbvEcxHsrMnddDudCyvMWouoIx+x8sTjRO6myIzdIrr/MJqiNvx7hI3jOIyMjGz6+KmJQ8DDFdsXlrJcvHgxxJJtnZs3b+7o59fCjVtHWA0JLsF1Ujten9slzP/Hvek48NWq+97/8BK9HeGP6u7evcvJkydDf9/dQCa7uyMpt3pdqicqvwxU+qONfH6H4cj4huffvHmTrq6u8AvWIC5dusTNmzdDux6tpJ8DKj1wxkdvcvHiROUJIbAX7w0ry48CQxXbx5KzXLz4WeMLVAO7qd6Tk48DsYrty4uTXLx4pWzbXv3NhvFbnUwOA49UbF9YtkLtk+zVOq5GUL1fv3kIqPyeipveVX28nf6tJudjwC9V3ffhR5cZ7E4Hnn/r1i1mZma4fPlyHUpXP9588008L/zJzJvJKNV+xwAjVy4yM2qH9lnXrl3jxo0be6ruS+td4VGgUrm9PHKLC4npBpcsmBs3btSlvYTJ4vJjQKUCNz56gwsXZhtfoG1Qz/peWjgC9FdsHx2f4cKF7V2PL126tK3z9yIZEcc3hd8CKXFEq420z3yKRFI2DyJSCkKToqAwZPYyZPbyq91fZNFJcTub5G5uknvZSe7mJplzlhtWnqPX8hOhnXMpdNvBMjRGD/XgqQqaaqMZNmZ8BYC7+yF6aRkj5XDv/ocs9MS5/pN/wAdfOU6bGsHoVIl6Bu9O3KNda6Ndi9KhttGuxWhXo3RobcQLjxF19y2d9jyPu3fvbvr42WmNwVyCJ5Y/LESa5sgpJssrndx77wqpHZx0mpiYQNf31q1kdLx6fXn28q4RaWolzP/HzEoHfiLl1Wu36WtfDOVzSkkmG7+YolEErZiM7IKclFu9LtUTlaepJlLevT/FiLHxb3RiYoKbN2/WlDd5N3Dz5k0mJiZCux5lcl+run0qeZcR5XYon7GevXhvyKwcoJpIOT27smfuDbup3mcXTlXdvrI4XVGfe/U3G8ZvdWYKqokb6Ywbarvbq3VcjaB6vz8Wr3qO56R21e94p3+ri5k2/ETKkc/vsNAZnPM+mUzy+OOP8/DDlYsodzOWZfHDH/6QhYWFUN/33tw+/ETKjy++S8wIb0b6xo0b/Oqv/uqeqvvSeneto1QTKUeu3eZt95PGFy6Aa9eusbCwEHp7CZOVzKNVt9+6eYW3szcaXJrtUc/6npsxqSZSjifnePvtt7f13u+99x7PP//8tt5jryHW6ptDLP3DJe2bSqKx5RDqx+4YxQqCUHc6tTYebTvKo21HV7ctOinu5Sa5m53kfm6KSXueSWuerGeF/vndM3kBsnM+vzJ3vjeOp1a/OXuqwnxvnP7kIp3zaRZ64qvnp9wsaLBAmon0xiKJoeh0qLGCgJl/jKtR2tQIcS1KXI0RLzxvU6Or+9Q6Rmzqus7Xv/51zpw5s+GxajLJY1d/ysnxtyr2tSkuLyymcDo6yD377I7l7Tx58uSmvstu4ea/becHVRYfHxjqbYoBRlj/j6l5lf/7D6rv+9o3v82pgzsf/beX8MuFquBhaDsvUm7lulRv/pd34xQu+WU88tgXef7Xq08CrueZZ57h7NmzIZesMVy4cIG33347lOuR68J/9b3q3f1vfv2rfOULX972Z/ix1+4NHy938O7tyu0D+w/vqXvDbqn3f3c5AVXWnTz+2Emef/5Axfa9+JsN47fa+fMI/+a9Kju0WOjtbi/WcTWC6n309+PwaeU5Q4Pdu+53vJO/1YUVhb/7/er7nv3at3j06MaRf6dPn96T7WllZSX0tvDzKwb/4LXq+37n+b+MGfKa2b1Y98V6/2cfdDKxVLn/4Uee4vm/uPuE191yT/Xjb/+H6rPzX3/2Kzx7Zm+1EahffV/NtvOzKppt776DoVwPDGP3LYyvJ37j2lhERMpS/GxIxe61NkQcb35EpBSEFqZTa+N07AinY0dWt3mex6KTWhUspwqPk9YcU/ZCzQKmbuUFDc3JT8ZnosEdueL+4vHF87eK5dnMOkvMOlVGQwG0qZFV0TKuRsjFM0RcnXtzmSqiZrRwfARDDe+yqt24QfT8eTpmXRSi9FtT9FvT6J6NrejMuz0kx5bodxxiL71E5tw5nGPHQvv8ZiUjK7A2RVBnLysd6y3j1+4MzaYJAltCxc+yRVadbh2/Facg1jjrkbwx4eJXb9LuyvHLSSn32dqQCazNsVHecWFr+PVPVNXDkBm3Mnwji6TdbRnHAcuWa95mkLFFuPiJlNLuygmKpHRdUPd+JquGImOL5ke6TIIglKEoCl16nC49zoPR4bJ9nufx1sc/Z0nN0H9siDl7Kf/nLDNrLzHnLLHopKq+r23k82E5Wv5OHM1YBBl5RDNW2fHF8xtFMZ/mdLGUBQHr0sJY4Hk6Gm1qhJgaoU2LrD0veRzbl0I3Jomk71Ts05X891STSaLnz4Nt4ybv8vTiHOY6gXifPYnxQZL5kyfpPXGC6PnzpF94YcciKvcKMoG1OfwmTkEGdLXg1+50Lbw8Rc2CTGCFR9BksyzMKEcsmcJFFgRtDr92l7NlAqsWpN1tDlMHRfHwvMrrm9xrt45fnqyo6clCtHX432sbXJAmILiPJ+PaUqTd2/Xc5AABAABJREFUhYuIRZsjFtD3yFoQq3S+FgKQebzmR0RKQRA2jaIoxL0IcSfCmfiJqsfYnsO8vcycs8Ssvcycvciss0xiaIX4+CXSXe10z6fpnllhcrCrquWr4nqr9q6L3TEgbw+7F7BxWHRTLLop8NMejsI1FvnxxJWKXaaiE1MjfO2PP6J/foWuhTTdc2MofQZZJcJkWx8pNU4kY9G3PIcR0VDv3CDb24nS04fx2o/J/s5fq++X3ONEZpL8+uyVsvyeSXOQwexDwN5oZ43A0PMrwF238jcqq+y3jl+dGSJSViARbeEROIEllkxlRH0WZviJHkIwMpGwOYIm9DIWtMkE1paQdrc5FAWiRvVo+6AIfKE6kidr80R8zJSkj7d1gt0yGleOvYDfb1HaXW34LwiSe20pQYu+MzlF7HG3iPTxmh8RKQVBCBVd0egzuugzusq2q189zcI7/xPEBjjYEcfNZbn/ei9Xuw6hRWz0SA49kkXXM5zJ3qbdVskaKkuJNgBunmyN6MCcZxNPzqGPJ5n3PAZujGHrDpkjGqOHYnhqCo0UNjDhgn7HpXsuhX3pLUYeGcJbeJ/XP7hLtr+PqGoSUyOFx/zzmFK6rWSfYhIpPI8Wnmt1zMu5E6jJJNqf/RlP/2yCO0sdZfsOZu/x2CdvE/29vh3N77mbUJT8REI6W7lPJu23jt8qe1NEygr8J7AaW45mQFbZb56ITGCFikwkbA6/dgf5OmyTCawtERTRJpQTMb2qln1yzds6ElW0efxytPnZRwr+SB9v80huwHDx7+M1uCC7HL92B0WnFvmdbgVxy2h+RKQUBKEhuAMDZPr6iE5P4xw/jn71Kl0LCzw6f42kOUhKjdPm2gzmljh4MEpP5yD2qVMcTnSzMtjHg49+m2UnzZKT5urdz0krFu37ulh20iy76dV9K24ad4/f7I9emwCgcy6FbjtYhsbooZ6KqFNPVRg93Et8OYthOXTOpVjoiXPo6hgf9Lblozm3gaHoRBVzVciMKAZRtfS1ye3hZbKR+6SW1vYVz1l7bqApjbXrXU8xv2fy/n0y6QT7rImy/J5TRh+G2o02Oir5PUuIGl7VCT8Z0G0diaTcPH6rTjNiu7llZAJr84jda7iISLk5Aq3VczKBtVWk3W0eueaFh7S7zSO5AcND+nibx3dsIWPampBr3uaQ/M/h4tvuZEFf0yAipSAIDWPq7FmGX30VN5HAOnmK7OW7RLwch7N3yw80DOxTx3ATCRRdR/v6r3PA7F/drWfz4tuZ3jMVn+F6Hmk3y5KbYtnJsOykWHLTrDjpwmOGlJtl2U2TcjOsOFlW3Awubl2/+1YoWt12zqeBvNVtNVtcyAuV871x+pOLdM6nWeiJr56/XSzPxvJsloLEzoNwm2XembkR+F6GopWJl5GCeFkUPlcfVYOIkn+Mrj4aREqPUQx0RUPZZJKX0vyexuIij8/dJ7vuK/VbUwzeU1GPPICbSEh+zwKH3DGenv24whpXnTgOdO908fYUfiv/RKSsROxewyOoziTCoxxpd+EiE1ibQyawwkUi2jaPXPPCwy+CV9pdJb7tzqcOBX9EpNw8fr9Fud7Vhu81T8SiMqI+7kAg1uq1IPfa5kdESkEQGka2t5fkV7/K0PXr2N0J3u4Ypt+aqoho2/9IO+0JD3SdzLlzWxKKVEUhrkWJa1EI6BSU4nkeGS9Hys2y4mRYcTMFATP/fMXNcHdqlKxiY3REWXHTq8faODXWhj+6lX9PzckLp5mg3k3J/uLxxfN3E5bnYHl5oTgMVNSCyLkmdq4XMvOCqMHD33+dztQcsbklzLufY+px0m1tJM0BVuggamUYyExheGn0q1exT53CTSQwX3+dzIsvhlLevYaaTBJ57TX+xr155pfLbX8PZu/x0Ks/I5reJ9a4W0AiKTePb3SHTCRsmaABsFjjlOOfk1La3VbxPH/rPsmTVU6QFVjGamBBmgTJk7V5/KzVszJxumX8ogCl3VXi2+7kerdlRKTcPH59Xml3tSEL0TZHUH3IuHbr+I0tpN01DyJSCoLQUFIHDpA+exbnB6/jscCkMcCkMVB2jKGncIaHGiaAKIpCTIkQUyP06p1Vj7l45yIAZ06sRW96nkfOs0m5WVJuhrSbJe3mSLn5aM20my3sW3uedrNMLs7gmQo51cGrYuNlG3lrVEfLi0PRjMVCQPmjhVms4vHF85sZFzdft2QJ0okTU0u03foIxfM4OTKGrjqkT8LUoQiqOk4H4wDMuR7jd2bJzqVwP5vg9qNH0JY+5cInKXIDfQUR1MBUipGd+eelkZ7F15HSxy1Gfe4Gita42DaGGmVflYUEjt0p1rhbxK9TLTkpK5GJhP8/e/8dJ9l13nfC33NDha7OYbqne3LqAQYgMCBAEKBEUaBIUaQESSuJBqBgv7TXWu/u67C7Tvs6rtf2yrtey3HNtaWV6FciTEsiCUoUCRIgCRAEEQdpQg8mz3TOscINZ/+o6uqqrrq3K3Tu5/v59Keqbjx1+9Q5957feX7P+hE0iBC1NTuoWdoUpN6tH2HXTGbZFxMm2soAVvUERlJGN7kgO4DAiDax3awaEccrR2yG14+wXO0yEa2YoAlBUu+qR2uZmFEpoSKl1L2qCbpmEkm5exCRUhCETcfv6WHs536Fz3/Z5YGFN0usJD/12cMk7u9a+0BbjFIqL0i10Vjxfk+/8DT9/f3cf//9+QjOZO5v0U/TfLSZxrnzWN2NNM3fIj4NV1UnKuJjWC6m5WFYbva9cvP2rnOtcSBrDytkqS6/ZzuJhRS2k8GcnGS2PYHx7lucbz5RVxkUqli4XBY4lUUktyxSJHxaufV2wXorv375fVTZ2MpaVwG00BrXmJrigalbpJeKRbQuZ5z97xsYXWKNWw3RyRE+PXWppL1LtbdtddG2HZI3Zv2Qmc6VEzSAJfWuesKuWVgOxr1I2PWQulc9YRMzhGIkenz9CJqIFhWhqISgvjboGgrBhEUVyUS0YoL6AMdVuB5Yu3+O97oRKo5X6GS2V7BMMAyN75f+IKWvrZ6guid97e5BREpBELaEZFoxEtnP19s/U7LuH/aNwzbKEblRFEZwFmJ8qJH4pUno7cYeSkLGoemVCAPx0/hqxXrT0D6fO/42HYaH12jSfuheXHz2f+Rn+EBnM0k/TSoX3ZnUGVJ+Jh/tmfLTRcv8MhGdu4HtkN9Tk7UTTnnr75+lICdcZsXN5AeWuBBZ5LsjV0uFTsMqEUQjyipa3vPsM5BZIjI9gzVwhai2mFMxRiI9LBkJGvxFejIjKEescStl2Tr3R344Set88ZPbwfRteq9N0PdNG2P/fhF6c0ierPVDcsZUTlC9cz0ZwKqWoHoHUvdWY1tgGhpPBrDWhUCbYal3JUhfu35InqzKkdyA64fUu8pZy3bTiss1q5SwCQXS1xajVLbuLaXkHm89kDZv9yMipSAIW0J4nqy93cn43d34vb0YQ0N4J05gXbpEjzNKqztTIhZFphYxm0z0ydM0W414fX2cPvKhqs6ntcbRLimdIelnSPtOVlTzc3/55SufU75Dys9wZ3wIs8FGRcz8cn8bCcy7Mb9nIRpIa4e0dpj3gQZYZJ6x1HzVx2obn+eTAzlr3PNDWK7H0pkEU31dmPoOjZ6J75mMOAZNE4sYiwvoCzMM33cS48p1Lg0kcPfty0eCRlTuNS+CZiNHV9ZlI0JNtXtH/gutcz03wj5ntMQ611Ka+PiMWOcWIBZ064dEUlZOWKSVDGBVh+TJqo5owACWWIFVj7R5lRMUeSAW19Uj9a5yJBfq+iH1rnLChgBSDiTim1eWnU6YFb24FpQSs2EpVbpc8o5Xj7R5ux8RKQVB2BLCBrDC8vPsFdIf/zjxL34Rv60N9/Rp3Iu3iHoZDqdvFW3nGhHc08fw29rAssg89ljV51JKZYUjbJrN6qxin/5u1rr27Nlsrk6tNa72SOp0XshcFjzT+YhOh7SfIa2z65dfU36GjHbzy9J+hpR2yubtrBTJ71k5Qda4yvCxKB45uN0SJ35+GjudYm7sJrPtCQZf/xZvfKR6a1wDIx/Rmbk/zTvWPM8ODRDJiZ12TtAs+ZwXPa2iqNBy+0SUhVEQhbwZrLbOPXn7Jt1LxaJ3lzNOYiKFPbcPXFesc3PEIpqezHCJHbhtdmKMHsXv7l77IAIgebKqIezeI5mRAaxqEJGyOmKR8gNYYRP6hPKE2R8KxUgk5foRnAtV6t1qpN6tHzJgXzmxkN9i9jruvGuWyWQ4f/78pp93cKoBKJ+a6dqV95gfK3NDUwNnzpwhEtn5A4PS5q0fgW2e9LW7BhEpBUHYEiRfUTh+Tw+pxx8n9swz+G1tvNHRS+PCREkUVtPJJprbfLAsUo8/vuXihlIKW1nYWDSvg763EuVZKGyuCJirxc7lKNB0TiCN90zTPHENp70VcyZF++QSYz0tZS1fla/3dH7PrbLG9fFX7HBjkGKRqUz9NrursTCJGFZOwCwUNUuFzZn4NJY2GJ1xsI2VyM/sdiYRY5VYmltuGxYWZjZf7XPP5QVKa2AAwzNJl7HObdPzNN64gdHVtWetcwsfsqOTkxz/2hV+fbi0H2gavsrsP/svJLu6aH/qKaxDhza7qDuO4AGsTS7IDiAoTxYsR7TJvUmlhM0Ol4lopQTlBpRIyuoJisaSeleK5H9eP4LqnYhFpQT1tdLeVU9Q1LPUu1JC3TJ2aN07f/48AwMD9Pf3b+p5M07wxN+ItT4uVAMDAwD5ifA7maC6FxaRKpQnyFVJ2rzdg4iUgiBsCUEPwJapsaVlAsA7fpzkk08Sef55LHOaMbubMbs4gsjVKby+/WQee2zLBcqNoDDKE7Oh6v2NH3uU+M0vQKPGHn8dMg6XvtbH+4kTKFtjmB7K9DANh/+q8yLttOI2GKje07hK4z34E7Q1N5H2nXx0Z1q7pHWGjO/mhdG0zkaB7mR2uzWui4fre0B67Y1j2Zc3Z26Fb1cGhaJ7IsknL76FqRUnL9zGcnzmjrYy2NGMzzymt0jKM7nuttIwPcW8myR96U0mz96FeX2M29cO4Xd3E1E5UTUnlBYKo8uC6G5g+SH7bCJBz4sv4iw0oOiiyxkvmpixQCv4PosXLxL9N/+G9l//dbHHXQOJKqqc0HxFO3QAa6sIy0kpda+UoGsiA1jVobW0edUQlMNJ2rvqCa53m1yQHYBY+q8fQX2t1LtS1spJuVMpdLTaLPTl4MG6hz54D4nYJhZmBxB4jyd2r1UjOSl3PyIFCIKwJYg9SWX4PT2knnqKLz2foePaO0XWhyORHro+epIjf6Zlq4u5bVmd35Pzl+jOjNHqlOb3bLeWaGxowO0/TUOsDa+vj57+z1R+Lq3J5ETLTC5PZGr5/SqRs2g73yGjs4JnZtX7tM5u4+iNFwDFGnd90Gj6Bu6Q1g4tU4t4mTQp22T4VIwGY6Zk+6GDURrPu9jJSe4MXWS2PcGVl5+uyDpXAWargaVNvnT7zZyQWRzpaefFTTMveNoFgufyuvG2FA3WFI2pwVX7ZaNLLWXmo0Q3gg90dvLAu+9CVxeZ8VkemXuZiC5+etu/MMbBQbBbWnA8T+xxKyAyMcqnp75X0nfEnTOA+JcWEjbLPhUiugmlBN3j2ZbGlK6iBIloWx8cF3xfni8qJSiiTepd9chzbeUEzYEMsqcXgpFJGZUTNvdWrNWrI6jeQfh13qsE5X+WvrY6tA5LY7K5ZRE2DhEpBUHYEuRhrjrmW3p4pf1gyfKHG+aBpc0v0A6iML9n+sRdZC7dJKpL83sSsXFPH685v6ehFDEVIWas/12Sr/2seJkTNrPvM6sETZcfvvEKbd0ddPbsKxJEl9evFlEzfnaZj2amI0Hn6BxzrXFaZpZonVwUa9wa2SzrXA24ysdVPimvzumY/XCeGb4+8m7gJgry1rirBcwVwdMuEkVtY1kMzVnlrhJAbcNizJzj1BvfZxGTyPQs7XeuMolFyogxYq9MJujzRlDOAo03brBw5Ai47p60x60EY2SE6HPP8eGXJonOF48YHEzf5uTFl4n9fseujcKvhbD7jzD7UqEUucerjqDBFZllXx2SC7U6AuudDNhXjbR5lSOR4+uH1LvKEbeM9SOo3kVkIlpZJCfl+pBxQWtp83Y7IlIKgrAlJAMcF2UWTHmiAbPSZABrbQrze6aa2ni5ubfERnLc7uTk/QkiTWyb/J6FGMqoSAC9M/Ie/S2HOdtWne2Lpz3cyCANd/4TXqNLbPwcOBm8dxr5nj6GMgutcV3uXbrOPhXHjZk07ztMg/K5cd9DnIw2kFkWRrWbjwzNaGdPZXPbrda5GrIRwXp9G5629DyRuXe5qTX954ewLA/zwQQzh9qJcAPLM9C+yYgL704s0Jicxx++wJX9LsblAd4+r0nv68znHL12cB5fX2RwOJm1izZsokbONtews8KpYRM17Gxe0lx0aTS3zlDBuVZ2AubVq8SeeQZcF8+NsM8ZLWnv3EwMbi0S/+IXST3+uNjmkp393ZMZ5oGFN0siT/XwUTjZsdVF3DHIwGl1BEW0hUUrCKWISFkdEsG7fkhEW+UE1TvPVzgukvalCoJ+qw1RqXerMc1sWiHXK71m0uZVR9D1iku9K0tQ3nGpd9URdr3E7nX3ILcAgiBsCYE3N9LBlCXoIVcGsCpjOb/n4h98B81M2fyelr2I19e7JyOLTGVi9h4icuAIxtAQxqm7sS5dom1yiXvGrxZY46bpyYzQGFd0HWvDPX2ajsasNe7pe4Kj2bTWONorES8LLW4z2uGlV1+mo6eL7t7u3DK3aHunYJ9yx/PwN/GqBSPWudVx7PIoAM3TS1iuh2ObDB5qRxsKhY9p+IALUbgSb+LU3Dy2kyEzPsxse4KFt14utsftg1v6Ii/culhTeexchGhklYhZ+LncuqhhYRdus2yva6wIoIXLC49hqfWx0jVGRvICpTE1xfFbN2lfKha9u5xx4kNpZq520XHqlNjmkr1u8eee4y+NzODrYpH6YPo2B7/6ArE7PXuyf6gFsWOqjqB7XxnAqo5QCzp5vihBni3WD3murZxQ14KMwrbkmlWKTAiqjlhEs5AUkbJeZFJGdUgk5foQOhFNBPJdg4iUgiBsCXJTXR1yc1M/fk8PNz/9q3z+2UzZSJlf/ZVDcHTfVhdzSym0xnVPn0ZPXS9rjZshgnv6cMXWuEqprDCzxm3H9bG36W87wNnW6iJBl/G0n7O3LRA0c+LmaqHTyW/j5vJ+uqRz9reTs1O4eEQSMRztZbfPH8fFJTzSUaxzq2Oz7HErxdEujuey6KXW9bhhKFROxLTA13j3OVwe/E8FuUPNvE2ubSxb5hYsy30++SfP0rg0SWxmnqbL1zGUid8QZzTSzYKRoMFNsS85RoflkbhxA9XZiW5v39O2uYWRp6aK0pkpjbSfGHc5dCsjkacVIgNY1REUWST2h9URltMuLgJ5CUHRHWJ9WD0yMaNygiLHIWut3rSJZdnpBNW7oBx4e52oDQvJ0uVicV0dQfcmYbnd9zLiiLY+hI17St3bPYhIKQhC1QwMDNS93/Wbxyn3GOI5C5w7d25Dzl8PAwMD9Pf3b/p5lwnqeDMykFAVqYxiJLKfr7d/pmTd/3VgdAtKtL0otMb129oYPtnJhZm5kgF73dHBvW2pbWeNayqDuIoSN6J1HefcYLYNOnuyvFi6nCN0WfR0fLfos/7IMH03vowXc2kbPQ+Ow8irFte7+lC2Rhk+hulh4HL3zDUSGYWOxtEdnTQozfjdx2gwohUJoruB3WqPWw0anRXS/dwTaxySzmRVx2gbn4drb6G0pv/SEPOux+yxBOOHIihjON/jTvma+M051PQS7nvf4f17D2DMmrxwboSlrrYS8TOisnlHC4XRkmXG8rLs61RLmmFrhvb0aD5nqVUguFrKwliHyNF6WR15+uHZG5h+cUR2lzNO56VZZqIHJfK0QqzxUT499WLJZKBUz71Afe3zbkQGsNYHsXutjiAhQ+pd9cjk28oJu8XLih9yzSpF6l11BE76lvGUqkjKpIyqCJqYIcEG1RFU70Amou0mRKQUBKEqzpw5U/O+x44d49q1awwMDDA8Wj63k5uZr0iEPHbsWM3lqIX+/v66vnu9BN30hc0aF0oJuhlUaCLSIwIr1riR55/HvDZW1hq31ffx+vr2rPVhPkcoAT/MI4eIH7mStc49HcG8eIn4bcXxOxN569yov0hPZoSuZo/21kbiZx6iryVrnfvw2ZWItmVBtDCSs1AQvXR1ABefvsMHctus2OAW7hO+zmMpk0Sb4KvNH9gQe9z1Icw2txBtKAYPd5BYSGM7Ho3TC8y2J+i6cK3YNrce7oJ3mOaZ4bcDN7EwS8TP/KuxImYWCpvFAmr2faEIaq96v3rb5e2XBdLoc8/lBUprYICIhiWVKLC4zv5OY6ZJ440bGF1d+G1tezryNAxjZIToc89x9jsTLM0Xt48H07fpu/pDYr/fvmf7jiCCbKpkAKs6RKSsDnFpWT9ELKqctexehcqRelcd0uatD1LvqkPq3fqQSss93l5AhmQFQaiKSCTC2bO1WTECfOhDHwLgfLIRymiRRw5188QTT9R8/N2K3NysD0GibsT22AZBPdsGv6eH1FNPMZCY4bWBKyXRMHcO3MeTT4VHu+11Cq1znZOnyVy4VdY617cMFo4cIRpgnbuWIJrKRdudbapvEsXTTz9Nf38/H7j/AwUWuVkx1NVesbBZYKmbyYmf5dY5BftlLXW9oshTJ5dHVOxx14ftZpu7Fi4ervZI6c2fbWNh0jWxxCcunsPUihPnb2O7Pu7dzQzta8VjFstfIO0Z3HSbaZmfxlxKkrz4OuNnT2NcG+L6lW687u5QAdXOC6wrn7dDBOlGsGyb66XTzMy47HPc0ih8NwG3bolt7ipiEU1PZrjEiv7QxXaM0eP43d1rH0QgGTCAZRqaiNyylBD0bCE2w9XheuC4ATkpJU9WCWGDyWI1XB1BbZ7kQi2PtHnrQ1C9a5D2rixBv0dp76ojLF+29LW7BxEpBUHYEmQGVnUE2kTIzU1VBN3cRG2/7PK9jrevm6+3l9octxo+ML75BdpBFFrnppvbebm5jy6nNNede2iEI82JbWOdayqTuDKJE4VNCFD0tY8XHaJh8D/hNbnEJt7ETTnc+HYbV9qOoExQho8yPUzl8qnEFVp0I17cJNVzgnalmX3gx/hwojkvfN4eGaSxpZFIQ4x0zkI1K5w6pH0HT+/O37vY5laOi0fvwG3S2qFlahHfSbNgm4ydbiBulMbx3vQjRM+72KlpRoYvM9ue4MorX64p8tTEKBAvC61zV0eVltrsWsokYqyOFF3ZfqYpw5g5x53MeBnB1MTAQG2ASFpomztz9SpHbozRtRQr2qbLGScy6jJztV1scwswRkb4+Lk/4MhwaZ96dPA68S+8tKedC6pBni2qIyiVhEyArA6J4K2O0JyUUveqQvI/V0dwm7fJBdnhSF9bHUGW/lLvqiOo3lmmxhJTpV2DiJSCIGwJcnNTHTLzb30IqndRa+8MzleD1Lv6WLbOTX71O2hmylrnnjaGSXZ17dnBekMZGPsPYPUdIjI0hHHyLqwLl2ibXeADc5cLbDeT9GRG6Dzi0pRoxj19mpaGrD3ukbt+qeiYT3/naR599FEeuvehsuf0tJeNEs2Jl8WvTj4aNC9u+g7p3OdshOnKesd380Lo8rp0bp9sJOryMVz0BudZEtvc6tiqyFMPH09nSG1EdTgDbzHFl4fK5/ZWqCLBMyjnaOHnIEG0cN3hP/06DalJotNzRG9eJWrHmY0qRuxuFlUjDW6KfekxWoy02OYWsBx92rVgcA2rZCKLvdAGuglzcFCiTysgFZBLUfJklScoh1PKAa0Rh5EKCRtolufaUsLmT8mgfXUE5Y+VSMryBKbPkUnfVREUASjtXXkCx1Ok3lVF0PiT1LvdhYiUgiBsCYEiZcAMt71O0HWRh7nqCPKyj1i7M7KqXoIGEmQAq3L8nh4Gf+ZX+PyfOCVWfiORHpoebuLMj51h3x4UKAsptMdVd50mc7G8Pa5r2rinj+MH2ONWgqlM4qZJ3IyuV/HXRGudtc0tEkML3heIogNXBxi4+j73PXgWx1+x2V2x1fUKlq1Y5xr7emgYT5Nqb6ZtJkXb5JLY5oawFyNPNTqf03a9aBuf55NX3kJpTf/FISzTY+6kz+ShKJZxh5bcdjO+JnFzhpHpJfR73+XqvYcx5t7i5bdmuHOP5qr/Al+/9G5WODWyeUkjhl38Wdn55SvLVrbNRppmt4mu2tdW1oZEkdZKYfRpU3KeR+auEdHFI85NU6PYryu8Eyfw29ok+nQNjNFRPj31Ukk/O9x4HyB+r6sJimjTWpFxg6M/hGJCIynlGpZgGBCxNJkyFrkSSVkdgZNvZWJGWSR6fH0IGn+SPqM8gY5oUu+qQurd3kBESkEQtoTAHAriJ16WYJsIubmphkC7V4mkLIsMYK0PybRiJLKfr7d/pmTdR9pvb0GJth+F9ri6vY1XW/toT5fa43b0N5Fo87eNPW6lKKWy0WeGRYJY6LaRaynmxwb5eHN1+Z+NHx8lfvsL0KSxx19HOQ7X/7SXdyOnUFbONtfwMQ2Hn+u8yH6VwmuwiB84g6+g9ZHPcLqjJZ+D1C3MK+q7uHj5fKKudsnkxNLC7QvzmbLNuyeJPF0fjl0eBaB5egnL9XBsk8FD7SXiuDYUg4fbSSyksB2HyNQ0s+0JGs5fYP4jJ5hngtuzExtaVjsvaFo5AdPOv88vN+wi4XNl2+y6QmH0tr7DRFuK88kbgZGoy5Gn5iqr3ehzz4HrYkxN0Tv6Pje1STonqmWjxxc5qYZRziLWpUu4p09L9GkAxsgI0eee464XxhmcL558cjB9m/ZbrxD7/TaxzF1F0IA9ZCMWwtYLK0ierOqJRsqLlBJZVB0ynlIdEtG2PuzFepfJZDh//nxN+46PHgLuLlk+M5fi3LnyzifLnDlzhkhEZh0ALO3BercXEZFSEIQtIWgmjITrl0duqteHIFE3IjkpyxL2e5QBrMoJm0xgm+sX0bTTWbbHjTz/PKZZ3h7X1Sm8vv0y2FwGv7sbv7cXY2gI78QJrEuX6EiO8XBytkj46MmM0GYtEm+O4fafpjuStc1tPvYj61aWLz79RU72n+LMfffgaC8vYC5Hk64WN538e68ocnT1NqVRpaXvXe1WZKw705Ggc3SOudY4LTNLtE4uSuRpDWyVbW4tONrF8VyW1nNeUj+8N/qVNTdTkLfH7ZpY4rFLb2BqxfELt9AxH+90K3e69+OzgOktkfINLhutdHkLJKZm0ZfeYOL+uzCvjXDjai9e974SK16rjCXv8utG201vFcuWubgurmuxzxktmdyijA6xzC1DLKLpyQyXdXnwBg/Dqa6tLuKOIMilBeS5NohYRDO/VLpcnmurQ9LnVEdwGpNNLsgOZy/Wu/PnzzMwMEB/f3/V+0YDHLsybvhkx4GBAQDOnq1u0upuJajeib317kJESkEQtoTgm5tNLsgOQWwi1odAmwiJpCxLmAiZyiiaE3JTWAlhtsy2IXWvEL+nh9RTT/F7X3c4PPR2ycDp0cdO0Pt461YXc9tSaJvr9J8mdb68bS6R+m1zw1AoTAziRpT4uh55bbTWuHh5wTNTEAVaKGoaj47Sd+PLeDGPttHzKMdh5k2fi63HYDny1PQwcbl77g6tno0biWJ2dtOiNAtnTtNpNZSIqXutVdyLtrm1oCEvpndevM6Sn6ZlahE3k8KJmUyciZIwpkv2e9M3ODW9hJ30GBq+xGx7gis//APe+MiJ6grwMPzA/yrRN/60JIp02SK3JGpU5Wxzc+usIuvdwv3sVda7FrbKHj+7zsRU6x957N66xeznP4/hedhzc3Sen+TupeL61+WMExmHycvNOM3NdH35y+hf+ZU9P8nFGBnh4B8/z68Pl8aPH0zfpun3v0vsVK9MCKqAULtXGTwtS5ATSzK9ueXY6exFsagexJlqfQiKHt/t9a6/v78mwfDqfHkHHa2iIkBWgbR3ewMRKQVB2BKkk6mOoBlCkui9OoJuqiMiUpYl7PeYlFmnFRNsM+xLXs8A5pr38/XUgZLlP5GYBVKbX6AdQqFtrtPSxsvNfXQ5pba5vfc0kmjTO842txKUUthkxZY4UQjSRo4eJX7kKsbQEMbpCNalS7TczHD8xngu8rSZeC7ytKU5St/BLtzTp9nfnI08feD+UstNrTUefkmEZ3nBtDRqtGT7XGRouXykbskyD5/NdwUQ29zq2ZLoUwUOHo6brLnc9WBglOQRtQ2b6CqL3ZX1BSKnMvOfbWUxqO8w2pHk8tP/HmaH6NaKxM1BFFG8mM1IpIdFlSDuJulOjdOol2i8cYPbLdkMqV173DJ3OfrUX/BQxMv2Ea6bkOjTCgl6plVKS1qEAAKfa0UsqhjfD448lcii8gQ910q9qw6JaKsOqXfrQ2C9E7vXXYWIlIIgbAkiUlZHUISp2JNUR7jdq9woriYeEtkslkyVE1TvomIzHIg80NXOsm2u+/Xn0cyWtc217CW8PomSKYw8dU+fxrt+vmzkqaMiuKePrBl5qpTCwsRS5qZHkAL8/tNf5ET/Se7+wJmyouaKpW6Y2Bksmpbu55HsbMUcWxDb3CrYi9GnPj4pP0PKX58b17bWeUZnB1AdmrZ3h5g0PWaO+0wcimMad2jObTftaxpuTjIyvYSRnuftiMK6OMxLb02y1NVW1h53dU7Ros+Gtaa9bnb74HykW4kxMpK3x43MTfPI3C0i2inapssZp/FtMO7NRtvHnnmG5JNP7um+IoxkhrK2udMN3Zhjh/C7u9c+yB5D0pjUT8oJXifOVOUJfLaQelcVkrapOqS9Wx+Cx1Ok3u0mRKQUBGFL2Ks2EbUS1PnKgH11BN7cWB7SJZYSZDMMUveqQUTK6olJm1cXfk8Poz/7K3z+K27ZfGOf/qXDJO6XfGOFkad+WxsX993LwqhVElXU2N3Oybb0to88NVDYmDSamyeRGh8fJT74BWjysSZfh0yGkef3cS5yAkwwDB9l+BjK5ScbL3GAVrwGE7P3LnwFkYd/iqlb1+k7dICu/d1kfCcXderg+LnoU+2S9l2c/Do3b9+b8Z3cNjtHuJPo0/o5dnkUgObpJSzXw7FNBg+1l4jj2lAMHu4gsZDGdjwaZuaZbU/QfH6A96u1zK2Rwnyky8Ll0gcWeS+ywLeGL4cKpOXeB+ceLT2OpUyMAoE0+txzeOk0M1evkrh+k4huI53rFwrzFi/NTpC4cAHuvhu/rY3IHo8+DcIYGeHwn3yPXx+eKll3St8i/oXv4vX17fkJQauR59r6EZvh6pEJkOuDpG2qjrBnWq0RV6UKCRbHN7ccwsYiI7KCIGwJYhNRHZKTcn1IpYPsXkUsKsdaOSmFyhCRsnqC6p7MOq2cVEYxEtnP19s/U7Luf+kbhy2wBt2OLEeeRp5/HmWUjzyN+p4MNAfgd3fj9/ZiDA3hnziJdekSrQtTfNB9u0T4aO9N0tjWgNt/mngsa5vbeeLj3H79aR49cjcPHXqo9nJoPxvp6a8Il1lR01v5XCB+Fgmcvkda55b7ToEg6q3aflkwze6XzKRIexm0qaqy2p3pSNA5OifRp3WwJZa5NVKYjxRySfcaYIkFJtILG35+i2xkZ9fEEo9dehMcj2ODt0hZGu+0zWD3fjzmMb0lUr7BdbeVlvkxvOQE9qU3mLz/LoxrI9y42ovf3Y2lLCZaU1zXYyTmbxTlJ42uyk9qKGPDv99WsWybGx0DRaTENnfJ6AAttrnliAY5BIVEBwrFiEhZPUH2y+JMVR3iiFYdYbbfaUdEtkqRerc3EJFSEIQtQWbCVEdQ55txFb4Pxu4dA1hXgupd1N45ERibSehNtYiUFRNc70QkCiLYGmeTC7KDkQGsyvF7ekg99RTffPMVpt80SyJPXz55Dz/9VGyri7ltWW2bmxq4VdY2F9vGPX1sTdvcWjCUQVQZRA0bNsls97XXXuMHP/wBTzzxxIpIGmCLW5hb1HhkjEM3voofc2kbvYCz5NL8qsWNfb1ggTI9DMPHwOVh5w4Jz8KNRFGdXTQpmD1zilazociK12dv/ab3omVurbhk7Zu7Lt0g6adpmV1EaZeliMnEmSgNxnTJPjf9BNHzs9jJKQaHLzHbnuDKD/+AN5ajT0/De/r7cOH7oee2lJnLJbqSY3QlN2lO0DRWRM4gwTO/Pp+btOA4BTlNl/exlbWhFruFtrn27ByPzF0vsc2NLY5hvw7eiRNim7uKWESXtcg98XYrxmMnxSK3AuQer3rEdnN9CBSLJDdgWcJ+j2lHye+1QsSJb28gIqUgCFuCzISpjrCxl5QDDdHNK8tOJjAnpURSlkWp7G+y3HVLyqzTipFIyuoJmmUf9IAilBL2Gw3LN7uXWWhu5evtHy1ZfsZygFI7PyHLatvcl5t7aU1OlNjmtt3VRGubv+1tc2shL5ISLpYBcOwk8SM3MIaGME7HSJ0bIH5bcezOZC76tJVoLvr0eH8EK7oP9/Rpepuz0acP3l9qu+kV5RpdySm6LJI++/y36D9zmiPHj2YjSLWL6+dsdLWD43sF1rnLEaceGe3k7XUzBXa7qyNM9SaLpGKZWz1bEX3qag/X88hHkG4iy8JlobDp+GmSZ5YYHvnDVTa5WTHUUmZ2v4J1y0KrldvGVha93/gqbnoOe3qW1jvXmVcmaSPGsN3DkmqkwV/khDmMchaxLl3CPX1abHNzGCMjPH7xj/iR4dGSdX03rxH/wsviXFAByQB3IIC4iEVlEZvh9SGo7okjWnnWSp/TkpDrVgmB9U7au12FiJSCIGw6jgueLyJlNYTOwMooGqRzrojwnJRCOYJESpl1WjlBwpqIlMFI3pj6kVn21WOb5fsCiRxfm7xt7nPP43gzZW1zLTspg885CqNPUydPk7l2s2z0qWfacPrEmtGnpjIxlUmM8jMQXp+LclLt56GOe9f9u2its2JUGWvdrJ2umxdGl8XQwvWrrXUzhba8ubykGe1kbXl9h4XUIgstTXSPLDAvlrkVs9eiTzPaJeO5LHqp4hVNMJe6XfNx28bn+eT7b6G0pv/CEFbcw/xggulD7USN60R8A+0b3PbAHpmndWoR//w4Nz9wFGPhAm+855DZ15EXRJdzjC6/LxZLCwTTXIToQoPDlF5gIj1bFIVq7gBr3WWL3J6kyTRmiUVuYrYVdLNY5FZAmEWp3OOVJ+i6yATI6kgFjAFIvStP2HWR59rKCRp7EnF8dyEipSAIm05YZywzYcqzlk0Ee8zmq1aCxaKdNfCymYg1Tv0ER46LSBlE0ANHRupdxQRHjmvMvRdEVBG26ZZdnnal3lWC39PD3Gef4t//Z7/Exm8k0sOjP3+Uzo90bHUxtwWF0ad0ZKNPVw/Yj9udnLw3QUsT2zr6VCmVFVGwiJsbb+3x2muv8VZK88nmXsaTo7RyEZV0GX2+gzfMu7KWuYaPMnxM5fKj1jt06xieZWLtvwdtGjQ8/CmOtrXmbXhd7WWjS3OfM7koVFe7ZAqiUpe3WX7vaK+qfKRbiUSfrg/HLmcjAJunl7BcD8c2GTzUnhfIl+seFlw/2MypuQXsTAp/Yozp9gTu269xbtk2txY+AK/rZ+GtZ4sWZyM+V2x1bZW1v12xys1a7K7Y7tpF20eKrHOzn6NFNrt29rNa2d5SZsW2uonZ2bxFbmt6hkfmrpZY5HaOj2C/bohFbgUkM6qsZe5IpIeGqYOwXyxzVxMP6J4ykkqiYrQOTmMiLi3lCbsuksakcoLThck46G5CREpBEDadMJEyzA5hL7OWTYRQGUHXyha710ACrXFCbIaEYsLEIqE8wZZMm1yQHUxg5Lj0s4FYRnmRUupd5aQyipHIfr7e/pmSdX+lZxIof433IsvRp/qPv4OmfPSpq5fw+vZL9OkqllpaUI2N9JgmRjyOdekSHaMzPJg+l7PMTdCQs8zt2efQ2dWOe/o0bfGsZW77ifXLh1qYj3TZZjdT8L6cCPrD11+hs7uLfb3dBdsW5zIt3L50+cr2lTLTkaBzdI45iT6ti62wza2EZWvdpU2cd2mgshGehkVUlRM+s3/z/iynx17n1QWHxOwS++cmWGqxyRgRxho6WTIbiKXTJLxRpmbmib+3QPquU+i2drxv/TFLTz2Zt+Q1lYjlxsgIfc98l18fLs0ne9i5ReL//x1xLSiD2L3WT8YFreX5ohqC6h1I3auG4PQ5m1wQYUMRkVIQhE1H8mRVT5gjk+QGrJzAhNsSSRlI0I2fDNpXjuSkrJ6gnJTyMFc5kvu5eoLsXqXeVU6YbZq4ZZTi9/Sw9ORTfP4Zr2xEzMd++gh3Pdy51cXclhRa5rqnT5O+Wt4yF9vGPX1sTcvcWqkqH2mOO6Pv0d96kLOtZ+s6d95qFy9vs1te4PQwHxnlwI2vMpOeJuLO0+QbdL6puNx+MB99apgeBi6np2+QyCh0NI7q7KJJaWbOnKTVTOBol6STwt+jWtFes80Nw0eT9p2sDTPJwO3axue5NzXFuKlpHxxCNXg4BxIMHUqgjTls5vCAa75N+qamdXoc98IUA/f2ouff5tlzI0x3NgI5YbQgP2g2stMqyC9qlq4vsMstWZ6LQC1ct/zqKb3p+XbXYtky1x4FRaQkAn8m1g66USxzyyAiZf2E5kKV54uySLDB+iA5KfcGIlIKgrDphNq9ys1NWcIGlcX+sHKChLWIRFIGEpgbUOpdxaTS5ZeL3WswkpOyfoLtmKSfDSLQ7lXqXcVILtTqiUV0YPTpf9MyBYgfWDkKLXP9tjZea+0lqiZKLHNj/c10tXnb2jK3Voqsdo01rHaPnSB+5Aa95m1mjkDjjRs0jWiO3ZjMR59Gc9GnHc0RDvV14999N33N2ejTB+9/Kn+op59+mkceeYSzDz5QlIM0n0O0TN7RTNHnwjylq5brVdv4Lk5uWWHe0q1CbHOrZy2L3GW0oRg83EFiIY3teDRPLzHbnuDYwAhvdGYtcn00ae2Q1pvQLj4ML+pRIjd/kBcvl/OGrhY+V4umxWKoTaQgt2j2c7FYuiy0mhiBFrrGyEjeMteameORuesllrn7/VHs15VY5pYhFtGBFrkMi0VuJYTe44lYVJaIBUrpshGo8nxROTL5dm8gIqUgCJtO2Aws6WTKIzYR60PQjaCIlMGIWFQ/wblQpd4FIbOd62cpoK+NiWNBIEF2rxlX4XlILs8KELeM6rFDBrCkzQtn2TI38vzzOP40s2Uscy0rJdaHOZajTztOncLo6kJ98wbRdGn0qYo14t99NDT6VKkVq8/NRmudjRD1XdIFwmWhUFoibPouV29d49qtG5y+564CK143n1/UyQmuToGl7nJUaia3XGxzq2e7WuRWhCJbt7TLZpQiKFI0omwe+fIrtM/O0jizRNPwMG0dBhkjwniiPWeZm6EnOcV8KoM6f46lU8fR7W243/wys09+tkgM3WvWucbICCeeLW+RezB9m8jvfgfjkPQTayET0apHqawrWrn7Y3FEK08mk+H8+fNFy+YWfoxyEtbYyE3OnRssWnbmzBkiEXno2ImISCkIwqaTDonAEi/78tgWGIbG92UAqx6CcwPuHuul9SbogSPsdywUI3av1RMU7SftXeUE/UZlECGYoEhKgJQDib0znlczYbPCpe6VR6ns5IFkmah7sVZfG7+nh9RTT/EfvupyavytkgiZuz5znIOfatvqYm4LVkefDvT1MHdrqiT6dOFgKwfanG0bfaqUykajGTYJ4hXv99ptC39wgp/70Y/UfG4dGyY2+Lv4TR6RiTe4dtPHHmrn/ZZjKBOU6WEYPsd60jzkXyWim/DiJu7+fvYpTfLBj9PY1JwXQx1dJu+oX7rcY+feM4pFbuUERYq2jc/jDN5gTGvarg7hmh7powkGD8XRRgqDFBngtm/j35yjdXoJ99J01jL38gWefWcyb5m7jIGRjwJdES9XojyXxdHCaNHV20UKIkOzn+2S9RZmYHToZrBskds06aOIlVjkjtuduE4DcbHIXZNUSACz3OMFE4vospOW5bm2POfPn2dgYID+/v78soxnlN129TjewMAAAGfP1menL2wNIlIKgrDpBHXGSmki0iqVRalsbsByA1hpcQGrCM/LRsOUIyo5KQMJykmZloHTignOhbpzB5w2mqB6l9k6h7cdR2DuDhlECCQoJyVkrdUTMbl2ayFuGbURs3XZaycTgirnBr1cae8rWf6rPdOA3LQsUxh9apiTjJWJPu3VjkSfBqB69mP0HcQaGsI4eRrv6hXaF+a5f/5i3ja3wV/k9Nwg+zozYDfhnj5NUzxrm3ug/2drOq+vfTLa5Ut/9Ac88NAD3PWBM3k73Oxr4ftlO10nH2262o43v0y7RcdwfJd0geXueuRkFIvc+llPy9xlfHxSOkNKZ9hIDVyhciKmnbfCXS2GRpXNraOzTMavcHN6KVQEjQSIpIYqFTEKLXJj8zM8MnezxCK3yxkn9qbGuEssctcimVaBlrmNM4egZd9WF3FbEhSIETQ2JUB/f3+R0Oh45QcH7jp1hLNn929WsYQNRuQAQRA2naBZ4bFIVowTyhOPlB/AkhlYlRF2naJi9xqI2L3Wj0RSVo/Uu/qR3B3VExpJmVGwDoO1u52gemcaGluePAORNq8+XA9cT9q8SlmOPv3OjUWGZi+WDDaP3HcXjzy192xJK2XZNtdva+NK6xlaFt8nqottcyO+h7btfG7AINvcSjGUQUxFiLgGzaqB3ljnenyVULTWucjOZVGzNL9o4aujXdJeJvtZO3kr3oaOOSKX34LmdhJzPox73Iw3oixQpo9heNlX08fAFYvcMuxky1xdGB0a9ujTDcMM8u7sYMhGwViYJaLmj37lddpn5miaXaLryjDtnWbOIreDxZxFbuf8NAvpNJHz51jqP45ua8f75leYe/KzeVE0qmwstbURoVuNMTJC95eDLXPb//N30Adkcks5Au/xQib2CcXIc+3eQB4VBUHYdILyZMUl2XYokqOtPsI8/yMSSRlIYL2T6I6KCZqYEZZrdq8TNONU2rvKkYe56gmLpJS6VxlS72pD2rz6kDxZtZFq7+Hr7cdKlv9sJAWhsW57m0Lb3Em7gwvNnSUWkvHeFrofbMnOwN2mtrlroVQ2P6JtWFXZ6q7mrbtsRv/Lm1jNzTT5KfSsQfx6MwPx0/gF0W9xK8PPN3+dDr+ReGsr+w/fj6fgzEd/kU/ua8vnBc3k8pE6hZ8L7HHzn/3CdW4uv6hHRjsFx/Bw2f7PgWKZuzYuHq7vsUTWeqptfB5/6DaTWtN5dYgl7ZE6kmDwUEORRe6wb2HcnM1a5F5ctsg9z7PvTBRZ5C5HhEbzEaFZAXPmrmmuxd/l9bEhooaNXSCSRg2bqLKJGnZ++6gRyR8jqlbWb+ccocuWufaIRhEtae8mI50YNIBY5pYlyCFILP0rw3HBK5P2CuQeb7chIqUgCJtO0ECCWNCFI7kB60MiKWsjaPJAWN4xoZjQSEpp9soiUUX1E+ZaIJRnrZyUwtpIvauNwHs8GcCqiLCJaDKAFUzQ71JSSazNsm3u7e+8gs1QiW1ud1salNjmAjidnaiDB+mIRFiMRlFzQ3Q7Y7S6M0UWuX3uMJ2NPg2JRvyTp4iaUby+PvYdPL2h5Vu20i0UMzO+y59+6xv0Hj7A4eNHStatFkgzBaJo6bGcAgHVwa/h5l8sc6tnvS1yAyNCW2CGKW4tTdVVXgODaM7SNqYieREzUiByRnPiaFTZTETHsTFILUTzYueyeLosji6/t+oQQAstc83pWR6Zu1FimdvjjWG/Tj5yXCxziwl8rpVxvIqQiWh7BxEpBUHYdGSWfW0Ez7Lf5ILsUMJublYn3BZWkJl/9RMqUsp1LEvQBHGpd5UT2NeKa0EglrGW3auwFoE5eOUeL5SgvjboegrFhP0+4yKQByIuLfXh9/Twewc/R5rxkhxtEyebSf5aP35399oH2gPcuuceHl1aAtdliG4yL9woschVSpNoa1s3i9xKWbbSjVHcWLQsRDjgtvGBhtJo43rwtLcibpaJ9iyMFF3+3H3Ion12gMi+OC3zd3DuaK4anShL56xyPZThE7Ed4lGX9skkir1tmbvTLHJ9fJI6Q9LLMEsF527Ivnx/4sqamxoYxAybqIoUCZ2xXFTnSsTn6s82R//0T0mkpolOz5G4dZ2YYZFSMYat/SypRhr8RQ7pYZSziHXpEu7p0/htbUSef57UU0/VeVV2BzFbl83leeRcC8Yjp6SfWINQkVKea3cVIlIKgrDpiEhZG0GznWUgoTJCIyklN2AgEsFbPyJSVo/Uu/oRsah6wiIpJXq8MsQtozaCro+0eZUhs+xrQ6I76ieVUYxG9vP19s8ULX/g4Vn87tQWlWr7sdjSQuonfoLYM8/gt7XzcvP+EsvIcbuTsw80YJg71yK3EkxlElcmcaJQYYCb8cgB4u9/AfoOYg9nmBzM0DQUKbHMPbFvhp/svYZpOuiYzaEjH8RTmqkf/zP8fFd7QeSnUzb6M6OdvHCaXeaUjR4t3i6bj3S7tbRikbuCj8+Sn85a4VbxtdrG5/nklbdQWtN/cQjL9jAeSDB9qJ2ocR3bM9GeybAL744v0jK9iD4/xeD9J7Dev8KFSxHcfd1FomdMRfLiaMyI5JdFcyKqscvyfhojI/zClT9icXi0ZF3v9avEv/BDibhfgzC3DJmItrsQkVIQdimXLl0imUxudTHKcu3GMaCpZLnnLHLu3LnNL9Amc+vWrbU3KoOb/hDQXrL85q0Rzp27WmepaqPW77IVXLzTCnSUXffiC88ydOu9TS3PRvDyyy9z7do1BgYG1u2Y7w88DHy4ZPntwXGefvoP1+08AK+++iquGywSbBbref0AllKfKLt8dOgGxmx9v6H1Kuv169e5devWun/3Wrk63gv8Usly11P83u//Z0wjeCjk5ZdfpqWlZQNLt7GcP3+el19+ue7jXL/xOHC0ZPnNa5d4+ukX6z5+GBvRFm0Gr77yMor/Dl1m1PCb3/oeN97Z3n3edrjuP7x8FvhoyfLk0hRPP/102X126m92vX6rANOTPwccLll+7u2LPO3/oO7j79RrXI7z589z69atomeGKyNNQGfZ7S9fepvh+NbfWxSyXfrcgUsPAh8pWT40PMXTT//nwP12an1az9/sMnMLv045pemNV7+PO7R2hFO17MRrf/78ea5du8br/f1E770Xb+Q8GqPEIhdgaOQqTk8nE2fPkp6bgy0cG9huz7kHXJfYxAR2SwuenihrmXvs1iUmZ6bQwOT+/aQuXGChrY3zL7xS9fkUEMn9FWMA0dxfFo3GV+CbGs/Q+EbBq7nqc8ly8A3N2PQ4kcY4scZ46fa542qjpDCBiEVu/axlmWuYHpgeRODKwUZOzc1hZzIkx+4w255g7I3v8sZHToSdogRLG6gEWJ7iD6+8ga1NZg5Nc96b4XsDA9jazP1ZRLSJhZl91dnX5XW2NrExMahf9Ky1r264c4eeF1+kbWYfS8RLJmYwEmWkw4eREfy33mLkR3+UpQMH6jpnrWyn9m71d781kQC6ym57ZeBdpobSgftuFTdv3tzqIuxIRKQUhF3K9evXSaW25+zN4dFSoQ3Ay8xvm05lIxkcHKxJiHHSd1FOpBwem92y69bb24vneTvi/3Zl7EDgurMfuIt77jmziaXZGCYnJzl27Bhnzqzfd3k/2cM3L5Yuj8ZbefTRR9ftPAAtLS184xvfwLK29vbEdV2GhobW5WZda8i4nyq77vsvfovjHYN4nlfXuXp7e+v+DY6MjPCpT31qXetOPbTcTMD3yq/74EMfoSEaHP08NjbGtWvXUDt0Ju73v/99fuInfoL77ruvruP8p7fKT8o4erhn3X+7q9mItmgzmJycJDLqk3ZLB6aOn7yHR+8J7ke2A9vhul9c3A/vlC5vb20IrHc79Te7Xr9VgD+82MzAWOnyrn0H1+X3mkgk+Na3vsXsbNgQ7c7A87ySfu/6xP7A7W9ev4Rtbq+ImO3S5950uqHMHL1IrDm03slvdgXvK+XDOD5wz0kevXvfup1nmZ147a9du8bo6Gj+Nzt09Axf2P9YifXhSKSHtuMN0BWFiYns3xay3Z5z77S3c/f772N4HlcSJ9mXHCqxzG1MaJo6OkgdPkxXSwvaNJl4/HEe7So/wL+d+NprX8vewxwMbhd9rXFwcfBw8cjg4uLh4ueXZ/9cejtcOseuEGu1aZoZIjbpc93oxDcVyvAwzOyfMn0aImnaJ5eAvW2Ru5qtsMx1lQ8RcIAkC9mFvQZJUowzUvXxDE9heQoz/2oUvV+9bvlz4Xt8n7deP8err75KT4XRjs0LC3zovfe47vvEZtI8MjdZksszMTiLNzPFWHs7S7EY/pUrvHrPPcw1Nma/dm8v3/rWtwA4dOhQ1d+9Gmodo1xvbt26hed5mKaZHx8ZXewDfrTs9i99/9vErJVx75GREbq2QXs3PDy81UXYkYhIKQi7lJ/6qZ/i9OmNTTJfKxd/txEulS4/dHAfTzzxxOYXaAvo7+/n7NmzVe3zpzdayg5gHT1+N088cXCdSrZ7+dYbEf7dC6XLbcPlnnvO8NBDD21+odaZCxcucPfdd6/rd/nBjfLLDbtxQ67Z7OzsrmoHkmn4F79cft0/+Lt/kwf7nfIrt4AzZ7bP7yBWfi4LAHef+SBdbcHrN+J3sNmsR/ntaPnlx4/28tBDvXUdey126v/gwoULxF+FdJln9AOHTrLdv852uO5fCQh66WoP7jO2Q7lrZb3Kvf+PgfOly5vbenjoofWx/1pcXNxV/WshL74T4V9+t/y6X3nql9iOes526HPfHApYYTaElk1+s1l8HzIBY7r33XuKhx5Yl9MUsROv/WuvvcYPfvCDfPtzbdjkn327s8QiF+AfPfkh+jolDUcQZs4y9/yEzcuZwyWRWbrvEB/4ySO0GAZYFnz2sxw4dWqri10R6163D34UPv952O/D/MvgOFw8F+GcUWyRa2ifXz1yiaNxBc02d515GE9pFn7h10juayfjO6R9h5SfIe1lVt77DhnP4dqdG9wavs3Rk8fJaId03gp35X1aO9nPvoPHzqnfu8Ey1zc1GbN+M2L1yCFsbTFjx4kZWcvaZevaeMHnuBElbkQ5+V++SVP8bmLT80yM3mLSMEmTy+WZi3y+v3GIoz1xjiqVz+X5aF9fUS7PZdeIascOa6GWMcr1ptz3ffWSze/9nfLb/82//peL8rpv5vUK49KlS/zGb/zGlpZhJyIipSAIm47kK6qNoJyU6e2jcWxrguqdbckFDCMeIHSkJJdiRYTlyYpLovdAguodSN2rlGS6/HLJ3RFOUI5iqXeVEVTvGmKbW46dRtDvUupdZQTlK4pH9bYUKLcLco9XH2HXqSHkPmavE5YnVvLwhuMdP07yyScZ/P4P0QyXWOY+1DwHhgEHD8JP/RT0buyktG3N/v1w4ADcuQOnT8N777HPGePhTLFFbk9mhOh0BroUnD6NaVqYBw/Sfri/otO8NvQaP7g5yxOPfLyi7V3tZYXPXD7PtL8sZjpFYmbh+uXX0elxXOURbWwoXrdBAqhY5q6gFWSUy5Q3v2Zez7bxeT55tSCXZ6uHeSybyzPCDSzXwnMtLmGiJlI0Ty3ChVcYO3sa6+odrr+/D9Wzn7gRYdCaJqotRp1p4kaUBiOKpXbPda2EoPEUw9BERNXaVci/UxCETSeokwl7YBEgGnB9wkQQYYVAkdLceluL7UyQOC4DWJUR9vuUNi+YoHoHUvcqRcSi2ojYQX3tJhdkhxIojsuAfSgxEYvqQiZA1kbgPV7A71goJqi9A2nzCnEcpyiP7OySDZQXdN565xKzIwubWLpgHMdBKbXlaSjK8TvdT5J0Fkssc/uOGPDrn8oKdAJ8+tPw278NHR1wzz34ly6VWOQCOIYN95zObmdZWYF3g7CUiWWaNNSw77k7uSixk+WjxDztBQqcqZwAmo8C1bll/orImdaZ/LZp32GxrZmu0XnmWuO0zCzROrnIWE9LWctX5eu8vetet8wNy+Wp8DEjGcxIBg1cPBDn1Ow0djrJ5MhVZtsTXHn1Kyu5PJuyL380+Gb++BFlETeiJIwYDUaUBiOWFzAbjCgNZmzlfW798mtEWTvKLhyy98I9meGS9m66cR/m2GH87u61DyLsCLZfbytsC5RSx4EPAQfI5sqeJmvQ+QOt9ZYlOlTZ1vQB4H5gOcnDKPA28KbWet2eRJVSHcBHgONAAlgErgIvaa0n1+s8e5FUWkTKWgi6PiJSVkagSGlsvQ3JdiZokCVsYEZYIez3GQ0QQ4TwwT2pe5URdJ3CBGABolb5WehS7ypD6l1tBEVSSr2rjKC+VvrZcALv8UQcr4iw36e0eSu8//773HvvvXR0ZHNlNzQagdvGG9rp6NgeCu/Xvva1Lc/zHISrI4xEmkssc7s+cEMEykJ6e+Gzn4UvfQk6Oji//xEyw8UWueN2J9bxLg53rFjk7tQIVFOZNNQogJbjQu9zHHzr6+w70oU18To377hY3+zkSutRlO3n8nr6dDXP8ZA7QItO4MZMFruP0ax8hu9/hDPxhhWL3LxgmiGjd+9E8Y3O5ZnRLhnPZdarPueniVEsXJpZe9qhI3PMxa4zNatJmHESRiz7Z8ZoNOLEjSjGFoibxsgIh/7ke/z68FTJulPcIv6F7+H19ZF57DH8CvOFCtsXESmFIpRSPwf8XbJCYDkWlFK/A/xDrfWmZTJXStnAXwH+KtAXsNkdpdRvAv9Ka12zf6NS6j7gfwF+Gih3B+0ppf4E+Lta63dqPc9eJmhWuDzMhRMLjO4QkbISguqdRFKGE/S7lIHTypBIytoI6w+k7lWGRLTVRpDdq9S7ygi23dzccuw0pK+tD3FpqQ2pd/UhkZSVU5iD1Q159Dp6/G4e2tpUYnm2c/5PHfB4EXQPs6c5dQo+9zn40z/FtG6XWOQCOB5ikVuGdHs7qc5ODMOEk6fwbr5Px8IsjYsXiixz74reYV9bhsamFtzTp2lLtOH19XHizFOBx/a0XxTZmdIZ3rt8AUd59B45SNrPkNIZUr7DO5fepbG9hcb2przgmdIr+y1b5m4XtnMuTw+feT/JvJ8sXtEDQ9zizelbZfdTKBqMKI1GnAYz+7osYiaMOI3599m/RjNOoxHHNmqXncyrV4k98wyxMVBESnLwLpkdoBOYg4PEv/hFUo8/XvO5hO2BiJQCAEqpKPBbwC+vsWkj8N8Df0Yp9Yta6xc2oWwHga8Ca92uHgD+D+BJpdTPaq0HazjXX8kdI+y3YQKPA59WSv0PWut/Xe159joykFAbUclJWRdi91obYXavWiO5ntYgLBpBcgMGI3av9SNiUW1EAiIppd5VhojjtSF2r/Uhzxa1EdTX+j44LtgyWhSKiJS1YVlgmeCWGYeXNq8yguqeiJQB9PbCn//zPPvKMHMvFVtGjkR6sB59gB//8xKBWo7xhx7iyLvv4re1cb39DNHZKyWWuRE7jW9ZuKdP47e1gWWReeyx0OOayiCuslF8y0y7wwCcbTxdtO3SzZv0x05xtj94SNjXOi965gVMP0NSZ0j7GZJ+Jr8u6WfyImh+eV4UzdQteO7GXJ4azaKfYtFPQRXDZzEVoclsoCknWi6/bzIbaDbiNC5/NhpoNFdykhgjI8SeeQZcF3tmjkfmrhNZ9X+JLYxhvw7eiRP4bW3EnnmG6L33ks5F7Qs7D7ntFFBKGcB/Bn521SoPuAXMAkeBloJ1XcCfKqV+Qmv98gaWbR/wHbKWq4UkgWtkIx2PAoUZlj4IfEcp9Wg10Z5Kqf8B+OdlVg0DQ0AvUHjnYgH/SimltNb/qtLzCCF5Y6IykBCG2L3Wx1KAzbCIlOEEDbIsD2BFwicG7nmSAfUOZPA0DNPMDo46ZX6eYkO3NlpLTspaCbKHlMiiylgKSArRIAP2oYjda30Ei+PSz4axlrW6iJThLIX8PqWvDScehfml0uXS5lVGUF8bE5EylGTH/hKLXIDj8nsNJN3RQerxx4k98wzTkQ6uN3eVRLSlOqM0nPaJ5QTK1OOPb7r1pqEUMRUhZtQ/C9jXPmntFAiYad57/yKO8ug53EfKT5NaFkRz75N+mqSfIemncbs6scYWmZdcnlnh180w7s5UtH20xSKmbeb/8CL7ZhdpmU2RGB0h3WiT0jGGrV7m/RZimTQnjBGUs4h16VJeIO98/XUGf/InN/ZLCRvGtrztVEq9Dvx/N1L8Eor465QKlP8e+Eda6yHIC5k/C/wmcCi3TQPwJaXUPVrrsIkh9fA7FAuUKeBvAf9Ba72UK1sC+IvAP2FFrDwJ/DbZiMc1UUo9CvyzVYu/C/yPWus3C7Z7kGyk5Y8VbPfPlVI/1Fq/WtlXEpKSN6YmRKSsD4mkrI2wAaxURkTKtQiqdxFLY26/SZLbilikvEiZkgGsNck42YkE5ZAI3nCCohAkuqMyJJKyNoKuj9S7ygiOpNzkguww1rrHa94946QbguSkrJ0gkVLavLXROvg6SSRlONLX1oZ3/DjJJ59k6Icvoxkpscy9p3EKjKldkxvQKBPlOeuOAnC2ae0ctcZjo8TvfAHd5GNNvMbYhIP9bAdX2o5gRDwMy8OwXBpjaT4WuUrUj+FEDWJdvZjKZ/HMXbSZMZJ+mpSfgT003Jc2XBrGp/GGbjOiNS1XhnBsD+90grFDMQxjmBaG0VpxK23TMLpE2/QS6uIc02fvJrWQYnz2CrfTfbRbzTQYUZRYf+0YtqVISTYf4veVUr8P/A2t9fBWF2i3opTqAP5/qxb/ba31/1a4QGvtA19WSr0KfB84klt1APgfgL+/AWX7JPBTBYsc4CdXW8xqrReBf6GUehP4FrA8ZP4zSqkf11p/p4LT/e9kbVyX+Rrwi1rrotsVrfXruXL9EbA8BcvK7V8oXAohiCVTbQSJuGlHOt1KCLpOIlKGs5btpgxghRNU76LS3q2JDGDVTtg1CrKVFLKISFkfkne8NsKs1YW1SQX1tTIBMhSxVq+PoGsUscEwNrcsOw1p82on42SFynJImxeO1Lva8Xt6+MPjn+NOcpIHFootc5sP9NLx6UaaPv7xrS7mtsDv7sbv7cUYGkKfPIU5eZn2+TnuW7hUkMszw2E9wqGTQKQd9/Rp9jVlc3ned/9KLs83z71JBo9T9/az5Kfzf0k/zaKXyr9f8lMF61feO3rnjXUdu5wVhJunl7BcD8c2GTzUXhSJqpSGWIbLBxOcmpvFTqUYHr7MbHuCm4Pf44+Gs9nfosqmw2qm3Wqi3Wqmw2rKfjab6bCaaTIbMETE3DZsV5FymaeAn1VK/RPg/1wtGAnrwt8Amgo+vwD8RtDGWutBpdRfAL5dsPivKaX+ldZ6cp3L9o9Wff7fwnJgaq2/p5T6DeDvFCz+X4GPhJ1EKfVTwKMFiyaBPx9U37TWGaXU54ALwLLZ9UeVUp/QWn8r7FxClqCbQInuCCfo+shNdWUER1JuXGLy3cBaVmBCODIpo3aC6p7Uu7UJzZMlfW0oQSKl1LvKkEjK2pD2rj4CU0lIXxuK3OPVh7R3tSNtXu2EXSOJpAxH6l19pDKKkUipZe5fuOsSd7ff2JpCbVPSH/848S9+Eb+tjdnDd5EZvF6Sy9MyNdq28zkVy+XyVCiiWLRbzbTXUA5Huyx5K0LmYl7ULBQzUwzcuordFEM1mCx42dyTPlvTnizb3zbPJIGs/W05q1wAbShmOhJ0jczRPJNktj2R3x8grR2GnEmGnPJyhYVJm9VEh9XEPruVbrud/XY7PXYbrWaTCJibzHYVKR1WytYI/GPgLyil/prW+mtbV6zdRc7C9f+zavE/0DpoXlYWrfVzSqkXgR/NLWoCPgv8X+tYtnuBDxUsWiQbrbgW/wz4a8BybM+jSqm7tNYXQ/b5C6s+/1ut9XjYSbTWY0qpfwf83VXHEZGyAoLsXiVvTDhBkVdi91oZQQ8fEbO+xOi7nbBZ9vJAtzZBOSll4HRtguqe5KRcG8mTVTsRS3JS1kNQ3ZN6F05geyf1riIC+1p5tghF7vHqQ3Lw1o60ebUTdo8nOSnDkXpXH0HjTlFLJn2vxu/pyefydFvbeLm5pySX53S0k/sfbAClNiyXp60sWiyLFsLtr57+9tP09/dz9uRZALTWpHSGxZxgueinWPSSLOTfLy9P5kXNRS9Jch1iyywnW59ML9uepWLh+YWW1y9vv7x/Jbh4jLszjLszXErdLloXURbddhs9dnv+tdfuoMduw1Bil7ARbFeR8h7gXwKfAjRZB+ZjwFeUUt8C/orWemALy7dbeBToKvh8jWwexkr4LVZESoCfYx1FSkpzZH5Jaz2/1k5a63ml1H8B/lzB4p8DyoqUSqkosDqr7m9XWMbfplik/CmlVEQiftdGIotqQ+xe60NyUtaGWIHVR5CgJtaHaxNoySQDCWsSGkkpg6ehRMTutS4ksqg2xIKuPuTZojbkHq8+pL2rHWnzakciKWtH6l19BAUbBN0773WWc3lmfvd7aKZKcnkqpUEtbstcnkqpfF7OTloq3s/TPot+igUvyYKfZN5LMu8tZf/87PsFL5l/v+iXzvZx7WwmNs/MCoGxlMNsyDljKado++X96yWjXW5nxrmdKY5hiqkIh6PdHIvu51h0P0ejPTSa8XU5515nW4qUWuv3gU8rpX4G+D+B46yIlZ8E3lFK/RvgH2qt57aupDuez6z6/K21oigLt131+WNKqUQuP+R6sLpsz1ax77coFil/GvinAdt+DIqmlAxorW9WchKt9Q2l1PvAydyiJrJ5KSWacg1kIKE2gq6PRFJWhoiUtSFWYPUh7V3tBFoyyUDCmohIWTti91ofgYP2MjEjlDALOq2zk+yFYCQXam1IJGV9BN2PSD+7NnKPVzsiUtaO2L3WR9BzbUQiKQPxe3oYfvxX+PxL6ZJcniORHmaePIh9oHvtA+0QTGXQbDbQbDZUtL2nPRa8VF7QfPfqBfY3LnJkdhSzK0Zi7hbOqMtQm40Zc0EVj6EoX+ftXedas0LhTEd41Gi9pHSGgdRtBgoiL7usVk7FDvChRD8nYwc29Py7mW0pUi6jtf6aUuqbwF8H/jbQQFastIG/CvyyUup/1lpXGvkmFHP/qs8/qHRHrfWQUuoGcCS3KALcDbxWb6GUUgr4QK1lA15a9fk+pZQKEGDvr+M8y+c6WfD5fkSkDMXzwHFl0L4WglwO0o4MYFVC0My/bE5KsWsIIhririGzTtdG8mTVjsx2rh0RKWsnakkkZT1IZFFthIlFGQeiIraFIhOCasMwIGJn69hqpM1bG2nvakfcMmpHRMrakWeL+gi6TkH3zkKWWESXzeUJkGwbw2bv3quYyqTFSuTtaJPOBNHDH6Z/8k04cAB7xGH+qsa62cVAwymMqIcVTWNF0xzvW+Q+531sP0YmqlCdXdhemuunNj8iddydYXxhhpcW3qPDauaE1UXD/haWhsNiQIXVbGuREiBnnfmPlVK/A/xzsrkPl3/B+4D/oJT6b4C/rLX+4daUcsdy16rPF6rc/wIrIuXy8eoWKYHDZAXpZRa11reCNl6N1vqmUmqp4BgJ4CBQ7hjrcQ3CjiesIizqTwYSwgm6PlorMm64mCSsFUkpo39BGEZ2cDRd5qFEHujWRgZOaydokE8GsNYmaADLNMHe9nf/W0uQtbq0d2vjuuAGTKaXQftwwq5PKiMi5VpIX1s78aiIlLUiImXtBN7jSb1bExEpa0fqXX0E5qS0JZIyjLB7kWzqJrlXKSTd3o7f24sxNIR34gTelSt0O2O0zs0wEulhyUjQ4C9yemGQAx0aIu24p0/T3dzGdc8jbn2SYwdOMunOM+XOMeXOZd97c/llGb1xbmqT7hyT1hwf/8//NV/76P+xYefZjeyYYQqt9SDwhFLq/wL+Ndm8lcsWsA8CLymlfg/4m1rr4a0r6c5AKRUHDq1afLvctiGs3r6/9hKFHqfaci3vU3icfsqLlPWea6Ouwa4lzMJFrMDCiYbc3KQyKnBgVcgS9PAhIuXaxAJESrHGWRsZOK2doNnOYgW2NmK5WTuRgNng0t6tTeg9ngzah7KW7WZL4+aVZScS5JYhfe3axCKUzfUkbd7aSF9bO4H3eFLv1iRwIprysdYnFduuRepd7TgueH6Q3auI42GE3uPJc21Z0h//OPEvfhG/rY33W8/Quvg+UZ3hcHplWD/ieWjbxjtxAr+tDSyLibNnMVC0WU20WU1Ab8mxtdYs+Emm3Hkm3Tmm3HnG3BlGnSlGnGlmvfXJYmdGdozktm3YcVdMa/09pdT9wH8H/EOglRWx8peBn1NK/WPg/9Ral5kTKOToJHvNlnGAsSqPMbjq8766ShR8nDs1HGOQYsEwqGz1nmujrsGuRSIpayfs+qQyipaEXL8wJCdl7cRlAKtmJE9W7UjemNqR6I7aCcxJKYMIayI2w7Uj+Z/rQ6zVa0f62tqRvrZ2JCdl7QTVO9uSZ9q1kPaudsLG8SQnZThhwQRh13Uv4/f0kHr8cWLPPMOk1cHF5k66nHG6nAks7eIqi4a+FrofbMnmvbIsUo8/Tnpubs1jK6VoMhtoMhs4HC3NB5r004w404w604w4U7m/acacGXxEkN9IdpxICaC19oF/rZT6feCfAp8rWN0I/BPgLyil/prW+o+3oow7gNXzcZcCcjaGsXp6wXrN8V19nFqmMVRatnrPtSHXQCm1D+iqcrfj63HujUZEytoJv7nZxILsUESkrJ2YWOPUTGAkZVTau7WQvDG1IwOntRMkUorN8NqISFk7YRNXpM1bm6BrJPVubaSvrZ2gNq8htrnl2IlIvaudICFXhKK1kXpXO2HjeGL3Gk6o3auIlIF4x4+TfPJJ7jz/CjZDjNndjNkromJPWxqUg9fXR+axx/B7euDcubrPGzeiHI32cDRanNvS8V1uZca4nh7hWnqYa+lhZryFus8nrLAjRcpltNaTwF9USn2erAXsh1mJqjwOfFUp9SzwV7XWA1tX0m3JajEtVcMxkmscs1Y2s2z1nmujrsF/C/z9dTrWtkJEytoJsw6SGVhrIyJl7QTVPZl1ujZiQVc7Mtu5doIGsGTAfm2CJgRJdMfaiEhZOxJJWR/JtPS1tSL3eLWzJBOCakZsN2tnKWDEKiLPtGsiqSRqJ6i9A8mFuhYSSVk7fk8P/+ngn8dRYzyw8CY9mREiOkNGRZg82UTyV0/jd5dGQ24EtmFxPNbLkWg3JzN9XE+PcG7pCgOpWjLUCeXY0SLlMlrrN4BHlVJ/lmxkZQ8rYuUngXeUUv8a+Ida6/mtK+m2YvX8vlq65dXdVLzGsqxmM8tW77k26hrsWkSkrJ2wnJTZhNtCGCJS1o7MOq0dyUlZO1LvakciKWsnKK+O1Lu1EZGydiSSsj6C7oPD7p2FLOKWUTti6V87QX2C1Lu1CbpGtinRbGsRVO/SGdA66xoplCdsvCliikgZRlifkJYkdWvSMjfMoYW3iwTKkUgPMydP4He3bui5076Tt329kRnlRnqEW+kxXKS93Qh2hUi5jNb6d5VSf0g2Au0vk/1+CrCBvwb8ilLqb2ut/58tLOZ2YfX8q1pupVd38bVEPJZjM8uWAhrqONdGXYNdS9iDhzzQhSM2EbWjtYiU9SADCbUjebJqR+pd7YhIWTuRgNngMoC1NqEipdzjhRLdRSJlJpPh/Pnzm3rOpdQnyi4fvH2Fc9GJmo555swZIpHdX3FlQlDtSF9bO1Lvaieo3ond69qEikWZ4Ekbwho5KcXuNRSlstGU5YTelAQbBGKMjBB97jmeujZbcu0Opm9z3/MvElvsXrF6rRGtNTPeIqO53JMruSinmfYkzm0z2VUipVIqATwITAAXgPvIRlRCVqzcB/xHpdRfAv57rfWrW1LQ7cFq4+RaMiesjhpcLzPmzSzbAsUiZbXn2qhr8O+A/1LlPseBr67T+TeMMOtDGfgLJ2KBUhqty9zciEgZStgDr4iUayOWTLUjs+xrR+pd7QQOnEq9W5Mwy6pURgafwwj7bYaJcEJ2ACsWKd9n7LQ27/z58wwMDNDf378p5/M1OJ5Zdl00IDJ6LQYGsplizp49W3O5dgpi91o7IlLWjlj6107QNZJIyrUJtVYXkTKUUJFSBPI1iUUCREoZxyuLefUqsWeeAdfF8xrY54zR5UxgaRdXWYzbnVhGE+bgIPEvfpHU44/jHT9e9lhaa+b9JSbdeabcOSbdeSbdOabcOabceSbcWdJaQlq3AztWpFRKGcC9wIeAh3N/pwGjYDNd5r0iK2T+QCn1H4C/pbWe3fgSbztWi2kNSimlta4mtCOxxjFrZfVxVp+nEiot2wJZ8brWc23INdBajwFj1eyjdojCJ9aHtaMUxOzyORNS0qeGEnbzJyLl2khEW+1Im1c7QQOnUu/WRgZOaydqheWNkWsYRli92yG3qVtKkEi5E9u8/v7+TRP4FkN8bD5w5jhnTx3elHLsVMTutXaCcgM2SD+xJhJJWTtB9S5qyTPtWoRaq6eBpk0ryo4jKPezYWhsU55r1yIoL6U4opUSnZwk9t3vguvC5BQfmnmbyCoRscsZp3sAVOwYmbZmMl9+muu/+JMMRMZZNNKcn5jMC5JT3jyO3tz20dYmF59+eVPPuRvYMSKlUuoQxYLkAxRHsa3+ZesyywrXGcBfBH5GKfVntdbPrW+Jtz0TFF8jm6xYN1rFMfpWfa5KVAth9XEO1HCMSss2Bhyr41wbdQ12LWJ9WB/RiC4bjSozsMIJnfknIuWaSERb7UibVztBA6dS79Ym6Bo11OJNsccIi6RMpqFNBrACKTeJCqTeVUo8CjNlpjtKmxdOeL77TSzIDkUiKWtHJgTVjkRS1k5QX2uZDl/72te4cOHC5hZonXjxxRdxnI2deT00FSFruFfKK6+/Q19H9RVws+3Nt4qwibcyEW1tgiYoyzheKV2vvQaWhT85jr5wkURM4UYMplqbySQMGlmga3GSSZ1i7O3r3DzWyXxLnIlnrvDdx3O/7/UKoaoCC5MzDUf4UKKfyG2HT/zLf7z5hdjhbEuRUinVDDxEVoxcFib3rd6s4L2mVJTUwHngBwV/GeAfAL8KLHvC9ALfVEr9Pa31P1nXL7KN0VonlVK3gMKppYeoTqQ8tOrzpboLlmVg1eeDNRxj9T5BZRsAPlzwefV3WouNuga7lqAZWBJVVBlyc1MbQTbDIJGUlSADCbUT2OZFpc1bCxk4rZ0lGTitmaCclCARHmsRFN0h9a4yZEJQbYSKlNLXronUu9oJEoukzVsbqXe1E2b3euzYMe6+++7NLdA6obXmG9/4BouLixt2jrlUA0Ei5Q9ffZv9LVNVH/PVV1/liSeeqLNk25+g9k4mA1VGUNqDoOu6G/G0x4KXYs5bZN5PMu8lmfeWmPeXsq9ekmT6Go/Mv8VFPE6ev4Pleuh7Eowcakcb2bZhHljw2+i7OUnr9BJ9N6cYuLeXztE52iYWmO5s3JTvYyuTQ5FujkZ7OBLt4e7YIRrM7MzMSyIN1MS2FCmB6VWfV4uPy6+qYP088AorguQPtdZzZY79OaXU/w78C+CTrERV/iOlVFxr/XfX5yvsCC5RLFLeDbxWxf53lTneenATSLISKZtQSh3WWt+sZGel1GGK80wuArcDNl9d5mrv6DbqGuxa0gGT46IiUlaE2ETURtj1kfwdayOWTLWTKpN7AoJ/y8IKYkFXO5ILtXbWiqQUgpF6Vx9irV4b5XI8LSN97dpIvasdafNqR+pd7QRdo6jtc+bMGR566KHNLdA6Mjs7u6GC39yi4u/9cfl1j/3Ep7nveG2Tl23brqNUO4OgvjYm/WxFxCOanswwDyy8SU9mhIjOkFERDr/ZjPHBfvzu7q0uYtVkfIcFP8mCl2LBT7LopVj0Uyz6SeaWBci8CJlk0Q/x58/xwatXySiXlqlFLNfDsU0GD7WjjeL6pw3F4OEOEgtpbMejeXqJ2fYExwZGeKPzxLp/VwuTfXYrh6PdHI1kRcm+SAemKp8TXaiN7SpSKlZEyNVRksuv1yiOkny30nyKWuuLwKeUUn8O+LdALHfc/1kp9VWt9evr9D22O28BP1nw+VHgdyvZUSm1HzhSsMgB1sVXQmutlVLvkI2gLSxbRSIl8JFVn98JqRtvrfr8aIXnCDrX6uMJqwi2idjkguxQogH3v5KTMpywGWoSSbk2IlLWTlAkZYNEd6yJ1LvakTxZtbNWTkohGKl39SFtXm0E9bMAcelr10TqXe0Etnlicb0mQfUuaEKzsEJQvYvIxNs1CZsYnwmZ8CIE97XSz66NMTLCZ699GWd4pGRdz9UM8S+8gtfXR+axx/B7eja9fFprXNNnzkhyIz3CQk5QzL/6KRa9JAsFyxb9JI5e/zandTIbLdk8kwRgpiNRIlDmy20oZjoSdI3M0TyTZLY9kd+/VpqMBnrsNrrtNnrs9txrGx1WM4Yy6jq2sDbbVaQsRAFp4A0KREmtdd25/7TWv6OUugl8m5UIzb8E/Pl6j71D+GPgbxZ8/gmllKpQ7P3kqs/f0Vqvp+vzH1MsUn4C+GKF+35i1eevhWz7XbKRlonc51OVRm0qpY4AJwsWzeeOJ4QQZLspdq+VIXavtRF2fUSkXBuxZKoNrWWWfT1IvasdyZNVO2F2r1L3wpF6Vx/S5tVGeE5Keb5YC8n/XDuBbZ7c461JoEiZAd8HQ8aCAwmqdxFLnmnXImKBUhqtS/sNGU8JJywnpRCMefUqsWeeYX/S4jYGXc44Xc4ElnZxlUVspgV0C+bgIPEvfpHU44/jHT9e9Xm01jjaZdFPs+SnWPJT2fdeiiU/zWJu2dLyei9dtNx/SANjMPzq+l+EKrCcrPBpetnnsVQsPEp5ef3y9sv7B2Erk3azmQ6riXarmXYr+77LaqHbbidhyiyjrWS7ipQjFEdJvqG13pA5VVrr7yilngaezC360Y04zzblB8AE0Jn7fAz4GPCdCvZdLeR+df2KBcAzwD8q+PxLSqm/vJYQqpRqAn6p0rJprVNKqWeBny9Y/Dng71dQxs+t+vwNrbXMOV2DwBlYcnNTEUHXKczqSgiudxFLYyipe2shlky1kXEp+xAM8kBXCUH1znHB88AUd5VARCyqHdvUKJWdZLAaafPCkfxs9SF9bW2EuWWsMbYlECyoSb0LJ2wimrR5pZw/f77o8/UbCYIy7bz08hvEIsEThjaLq1evUqFZ26YyOtEPNJcsX5ifoDjrkbAapbL9Qrl+Yy/lBqwFESmrxxgZIfbMM+C6tDuzHJi7QmSVtLFvZAT7dQPvxAm8tlbUV/6Q6c/+HPP7Wkn6aa7bE6SVy+TsmyT9dE5kTOcFx7z46KVx2fnR1K6dfcD3zOxMlVjKYTZk+1jOUm55e8OK0O4m6GvaR0eBCLn82mQ0oJSMnW5XtqVIqbXu3eRTPsuKSHlgk8+9ZWitfaXU7wD/U8Hiv6+U+m5YNKVS6uMUi7nzwJfWuWzvKKVeA5YN9RuBvwH8vTV2/RusREVCNjfpWja0v0WxSPnfKaX+jdZ6PGgHpdQ+4L8tcxxhDYJubkSkrIwgixKZ+ReO3FTXR9Bgi8yyD0eiO+ojLBIhlYFEPHj9XkdEytpRKnudytmpSZsXjtS7+pC+tjaC+tqorSUaqwKk3tVGOlN+MgtIm7eakydP8m//7b9ldnZlqHlwppMgkfLFH7xGIrL1FfDy5cvMzs4WlXs7MDHZRzmRcnToBkHXVFghFtFl3b1k0nc4kfERPj01UJRPcSTSg+Xcs9VF2xZkIxk9UjorIib9NC1/+iXM5CjG1BTHUjdJtfm4EYOp1mYyDSYNXpKlpWmuL2Tw3rrJjWMdzLfEmfjyRZ57/L7sgRtzJ5ge2LLvtpnMdCToHJ1jrjVOy8wSrZOLjPW04HoR3FQUNx3FzURxU1F+7C6fnvkMkUgM78Ap7m3dz/uxFg7NP8jZE2e3+qsINbAtRcotYLDg/V67pfwN4L9hpen7MbIWsP9buY2VUn3Af1y1+F9qrSfCTqJUSajSj2utv7tG2f4e8KcFn/+WUurbWusXAs6xXPZC/s4a50Br/SdKqR8CH84t6gB+Syn1C+UieJVSEbKCZEfB4he11t9c61yCiEX1EmaNIwQTmOhd6l1FSL6i2hCRsj6CLOhARMq1kIi2+ohFyouU0uaFIyJlfUhfWxvybFEfUu9qIyzqStq8Ymzb5kMf+hBPPPFEftmVQZP//dvlt//MT/8CPe1bH0kJ0N/fz9mz22uw+z++3g5Tpct7e9o2vzA7kKBJ3yJSlic6OUn8936Ph74/gZov7jAOpm9zbOBl+r65yMSDD25RCevH0x5JP0PSz5Dy06R0Jv856adJ+RluxG6RUR5vjY+uLNfF23istFtt4/N88spbKK3pHxjCSnhkDiYYPNSONnzAJ4PFrN9J381JWqeX6Ls5xcC9vXSOztE2scB0Z2NwoXc4CkgYcRrNOE1GnGazgSazAQ63cfjtl+jqO0nn+EWWZuH9r/VyNXYXfkFOSNvwON74NqZnoaM2Ttd+QDF34sSWfSehfkSkzLJnn2C01hNKqX8C/JOCxf9UKXUI+F+11kMASikDeBz4l8Chgm2HgH++QWX7Rs6KdTn/pQ18Uyn1t4D/oLVeypUtAfzXwD/NbbPM17XWz1V4ur8OfA9YbvV+BnhWKfU/aq3fXN5IKfVBst/3xwr29chGcAoVEJifTR7mKiJqSyRlLQTVu6jkjKkImWVfGyJS1kdYJKXUvXBELKoPafNqQ/Kz1YfUu9oQkbI+pN7VRtj1kb52bYKEIpDn2rUIuj6WsSEZsnYdQX1DKiA9zF6m4c4del58EaOrC9e12eeMFuVTHLc7sY024uPj9D37LObRozXlU6wFrTVp7ZD2HZI6Tdp3SPkZkjpDys/+pXWGVG55VlDMionJ3PqknyapMzi6gnyuy5NjF4cqKt+xy6MANE8vYbkejm3mBMrieqYNxeDhDhILaWzHo3l6idn2BMcGRnijc+cIbgqIG1EajTgJM0ZjgfC4LEI2mg00mdn3CSOOqUrtLs4lztHUNkSPZWGcOsPSK5fZlxmnxZllJNLDkpGgwV/koD+CObEASuGdOJF97esj3d6++V9eWDdEpMwyAPwL4Cxw/9YWZUv4DeBR4KcLlv0l4C8qpW4Cs8BRoHXVfkngs1rrmQ0s268BL+fODxADfpOskHqNbFt4LLe8kKvAn6v0JFrr7yul/jbZa7HMx4A3lFJDwDDQC+wvs/vf0Fr/sNJz7XVkIKE+AkVKmfkXypLkQq0LmWVfG6Gz7GXQfk3WiqQUghGRsj6kzasNqXf1IfWuNoIGluXZojKk3tWGiJT1Efb7lNyA4ZSzKgURKSslakNPZpgHFt4ssi498Fozxgf68bu7t7qI2wJjZCQrUHpe1q705k1al4pzHnY547SNmNhdCZzmZmLPPEPyySfxe3rKHtPXPmntkPId0jlBMZ0TDFPa4f3oII7yuDm9lBcakzrDzbumuJB4k68OvlskQG7nXr51chGA5pkkkLUxXS1QLqMNxUxHgq6ROZpnksy2J/L7bwUGikSB2Jh9XXmfjYDMvRoxGs04DUYUo4zoWAs/bG8ncvUqhucx0nKAzJ0JojrD4fSt/Da26TOXSrF44ABOOo0/Ps7g2bMMDAzQ39+/LuUQNh8RKQGt9R3gf9zqcmwVudyUvwT8P8ATBatMsgJgOSaBX9Rav7TBZRtVSv048FXgvoJVceBMwG5vAY+H5ZQMONc/U0p5ZIVKs2BVb+5vNR7wP2mtf7Oa8+x1RKSsj6DrlJYZp6FIvasPGcCqjbAZuVL31iao3oHUvbWQiLb6kDavNkSkrA+pd7UR6NIi7V1FSL2rDREp6yNmB6+T59pwJJKydoyREZ648RUYHi5Z13U1TfwLr+D19ZF57LFAoW2vEPn2t8FzUXMz6IkJDK3wGyKMJ9pJRaMk/AX2pcaxoin0rdtMHNvP6NwiU1/5TV75+YfzQmRelNQZMmtFLDbkXmevFy9vAcjADqrilpMVdE0vawGbCmv0CtYvb7+8fz1ElEWDEaPBiNJgxEgYURJmrGhZgxElYcR46fkXOHXwBB+65wHiRhSltqYdPnMmO8w/0ttLz4svMh9r5tXmk3Q540VRvEuNLXSfXgLDwDdNRn70R0l3dNDf0ZE/hrDzEJFSAEBrnQKeVEr9Adk8jvcHbLoI/C7wD7XWY5tUtptKqQ8BfxX4K5QXDCFrPfubZHNk1vRYpbX+50qp54D/FfgpVuxfC/GBrwN/R2v9di3n2csEzfyTAfvKkIGE2gh6mJNIysoQK7DaELvX+ggb5JO6F46IRfUhbV5tSL2rD6l3tSHPFvUh9a42RKSsj7Dfp9i9hhP03C8iZTjm1avEnnmGAymbQVSJ6BGbaQHdgjk4SPyLXyT1+OObZl1aL1prHO2S0g4Z38nZoGZIazcvFKZ9h5R2ij6ndaGQuCIoxscm+ejFVyHq0z8yRNL1mDmeYPxQO9qYIAq4wLBvY9ycQ0+niN25xUCbi749ztDtxl2dT7ESXDsb9+KZ2SHlWMphNmT7WMop2n55f1ubRLRFS7SRBiNKvEBcbFglOiaMAgHSjGKryiWfdxd+SIsfp8FcbVK4uUQikWwe4LNnMR56CO83X0S/P8GY3c2YvRLl3Nnk0dObzE8qaNnjkwp2CyJSCkVorf8Q+EOl1AngYaAPiAAzwEXgpZygWe1x67rTzImO/0wp9X8AHyQbVbkvt3qMbPTkm1rrujOsa63fAn5aKdUJ/AjZaNIEWYH2KtlrMFHvefYqIhbVR2AOBXmYCyWw3kWl3lWCiOO1ETjT2dTYcge2JpKTsjZcF9yAybcNW/vcuWOQQfvaCLLok3pXGUH1TvracOQerz6C6p3rgeeBaZZfv9cJFSklindNbAtMQ+P5pb/ftKQxCUTr4DbPFpEyEGNkhNgzz4Dr0u7McmjuChFdfL26hkewXzfwTpzAb2tb07q0FrTWuNojrR0y2iHtu6R1pkAwLBYOi5c5jDdO4ODx9aELq/ZZX+vTY5duodG07JF8ihvBTEeCrtF5km2NdM6maR9zuRFpwfWj+K6F51j4rkUiYvDhkx5d8wNYkSj7+o5zsrkbr/8B/qvDn+Kdt7JxMWfPnt3ib7T5+D09/PDhP8tvXVgosWee6u4k+WtHxZ55lyFDZEJZtNZXgCtbXY7V5ETI13J/G32uCeArG32evUawJZMMJFRCUE5KeZgLJ+hhLuh6CsWEDWC5LlhyN1EWqXf1YVnZwVGvjOAmg/bBhOVyEvvDypCJGbURNGgv9a4ypN7VRqClv/S1FbGWtXoivnll2UkEtXeWKffFlRKNaJZS5UTKLSjMDsH1oCs1UjJgPxLpoSMl8+iDiD73HLhuNrfi9BVmtEE6d92WjAQN/iJdxiDKSWJdukSm/xROWwvut77O2J/5uQJRcUUYzGiXtHa4fmCeqfhVLkxMkdFOUTRj9jUrRGb87Pa6Hjlx2TF0g+8LdnI+xfXCViZxlY1cjBkR3MU0tjbpadtH3IgSNyLEjEh2vVp5H8+9Nj02Q+vQ09CisSdeZ3jEo+H7CQbip/ELcjcaLQ5Hou9iehY6GsfadxCUInn/B4u226skM4qRyH6+3v6ZouUfPp7B757eolIJG4XcPgmCsKkE5wbc5ILsUILE3CCrKyGL5CuqjzDbqlQGGuVuoixS7+onHoGFZOlyiWgLRizo6kciKWtD7F7rQ+pdbUje8fpYy1pdRMrySHtXP7EILJXx6JLn2vIYIyNE/vQ5fn14rmTdwfRt7r34Mp1fTUJfH/QGZUjaHfhak9FZoTBTIBhmxcPcX04UNEdGOX7tVTzfZd+lAYwmB6enhcHeJrCniJvjKNPjvO0zNzRL09Q87ntDDNzbi55VPPvOULh16QGARVi4s1lff8PZDvkUayWqbKKGXSAcRogWvI8ZkZz4mH1fKDQWio+WKrYROHfuHAMDA/T371t1RhdwcVliHpgvWHPAdYlNTGC3tODcHqHbGaPVnSkSx48kB0mqcTSwcOQIzugoya4uBoeGYGgod87+jb1o2xhx4ttbyLCiIAibigwk1EfQdZIZp+EE1juxAquIMFEtmYbGhuD1exnJk1U/sQCRUiKLghGRsn4koq02AgftZWJGRQTVu7DoaEEmBNXLWvd4QnmCfpfSz1ZOULSzpDEpZTmf4tKChyJekk9x3O7ExCMyOgq//dvw2c/CqVNbVl5P+9koQt8h7WXtTFN+JrcsQ8pbfs1krU19hxv+LW4fmeV3xr+ZFx0LBch8dKJ2cHTlItgHX7uClxqmZWqReGoWr9lk6oxFg1EcheUDtw61cmp+ac9bl65XPsVKiakIUcNGZXxsbdHe2EpM2cSMKDHD5vqlq3S3d3Gs70hWWFTLAqNdJEBGlY2xQdGHZ86cqXqf8Yceou/ZZ3GamxnpSZCZmiCqMxxO38pvY5kevmWxeOAATnMzvmky8eCD+fX9/f01nXu3IOPHewsRKQVB2FSkk6mPaMAktrQ8zIUiYlF9rGUFJpRHZv7VT0xytFWNiJT1IyJlbUhkUX1IvasNebaoD7nHqw1p7+onamt6MsMl1qV9rzZj3NUvucZyFOZT1JPTPDJ3qzSfojNO19gdrNn2bC6OL30JPve5shGVvvazUYY5cTDtZXICorsiJHoZUjlBcVlsTOXExsL90n5WPEz5Bes8B0e7tX3ZHhhavFjbvgGIdWn1zHQk6BydY641TsvMEq2Ti4z1tKANhdYK7Zl4roXvmTTb0D0xSsS3yXT1cjjSRtPBU3ym5eF8pOLq6Mbs5xWR0VDZ/8e5c+cAOHuiOP/i07fH6G84zNnm+zf7UuSJRCI15YU0jx4l9swzzDjw/J2TJRMM5hs6uO/HE8SUAssi9fjjtBw/vgHfYGci93h7CxEpBUHYVJLpgEF7iWiriKDOWGachhNY7yIa13X52te+xoULFza5VOvPiy++iOOsf1jt6IwN3F923Suvv8PIvvWZan/+/Pl1Oc52YSe1d57nbcvrr/S9QKxk+cXLN3itc7zsPpcvX+bq1as79jf94osvonXtdeTSnQag/IzbSxfeZDC+8RZMO/V/sHzt52cPA6vtnGBweIrXXru6+QWrkKtXr9ZVd+plfvEs5R4vh+5c4bXXgvPG7OT6sp597uCdNqA0WmN+0eG1196q69jbsX1fL3ZSX7vMdupzZxYsoPzA62tvvsfMSKmdwU7+za5XG3lxYB9wuGS5oZO89tp763KOcly+fHlL2/laOH/+PN6qBOPGyAi/fOsrmMPDJdt3vp8m/oVX8Pr6yDz2GH5Pz2YVdUvRWuPh56MHs38Ord/4QxZTU6iJCfx3B2hqjOFYFhNNraSjERq8JF2Lk2iVYvHqW7wemWahtYHJ37rMD37uoWJhMScq7iV2snVpvShUXiCMKpuYESGibKKGlX3NLVstIDY/+jDHbv0xs6lpOvBoNmLc/tY+3jLuxtcWkO13De3z1MF3aU/F8S2LngNns/kUH30Sv00mGQB4x4+TfPJJMr/1AppJxuxuxuyVa2NbGtTinmvvKkUmBO0tRKQUBGHT0BrSjsyEqYeoiJQ1EVTv4hGN8hXHjh3j7rvv3uRSrT9aa77xjW+wuLi+Mz4X0jGCRMrXXn+XwdaJdTnPq6++yhNPPLEux9oO7KT2TmvNu+++y+xsmJnP5uNmjlJOpBx4/yY/4K2y+1y9epVPfepTO/Y37TgO3/jGN2r+X1yf2E+QSHnu9R9gmRs/2LJT/wfL134yfh/lRMqRsRl+8IMfbH7BKuTy5cvMzs5u2e84lSkvdFx9/z2iCzcD99up9WW9+9zrQ0cpJ1ImU7ruerfb+tdCUjuor11mO/W5adcmSKR8/c33GL8xWrJ8p/5m6+1fC7l85QHKiZROem5D+4mrV69imiZK7Zxnv2vXrtHS0pL/vGxd2pe2GUWVRBbFZlpAt2AODhL/4hdJPf443hZGFhWKh472VnIh5vIeOtolrbOvy8vy67WLs0p0XF5fbnuf4narbXyeT77/Fkpr+i8PYUU8vNM+w4fa0cYiikWSwG2/gb6bjTC9hHX5CqP39qJvTjJ9qyU8n+IeYLOtS2vFVhYxZRMpERTt7HJlMzsxja1NDvceIpaLRowqi8jyNnn70+wxbGXV1lY0QfzQNbyJN8kcOII9OUnbwhQP69eK8in2ZEZIzC2hDVg8cICYUnh9fRIFvQq/p4fbn/kVPv9KqiRyfDzaTfLXDsk1CyDIMW473+MJtSMipSAIm0aYZZDkK6qMIJvIdI2OKnuF4HxFGtMxOXPmDA899NDmFmqDmJ2dXfeByMUU/J2vlV/3sY9/igdOrl8FtO3w2a07ibB6t92wLItPfOITNdnYbCS/+1YLw2VGEs7c8wBP/PzpwP12+m96cXGx5t/xi+9E+JffLV2ulOaXn/olNmtcc6f+DxYXF7nqneKFK6XrOrv6tr3Q09/fvyW/Y9+Hv/oH5R8tf/ITH+Uj94RHbuzU+rKefe5334rwH8toG5621+Ucu6l/LWQnWoFtpz7X9eBvfqX8uo9+7JM8cnf53+5O/c3W078WMv8f53G/9ydFg80jkR70oXs2vJ949NFHd9S1f+211/LCbaF1aYczy7G5KyXWpZ1DI9ivG3gnTuC3tRF75hmSTz5ZEmHka79A7HNzYp9T8LngvS4QBnOiYeF2RZ9zy+YfWOQlexzv5gsl4uFmcexydpJA8/QSluvh2CaDh9pL7Eq1oRg83EFiIb3n8ymuJsy6dDXK13l717nWeH7/ZSxMIoZFNCcaRo2scNgcb6KrpYOIsvLCYdSw89GK+fe511jBuuXjVZJL8dztnB1qy8b3HemPfxz/7bdxmptxu7pIX7hVkk8RgIjFwoEjOM3NYFlkHntsw8u2E4lFNCOR/Xy9/TMl6zKdoyLOBCBpm/YW8jsQBGHTCIv2i9rSyVRCUE7KVEahNZs2+LzTCKp70QiwtxxvaiLMFUeieIMJrHe7c5x4QwiKHg96YBEgGSSO29JHVErQPYm0d8GkQvrSsJx3wgpBAy6er3BcsOXJvSyBE4Lk2aIiLBMsU+N6pe2btHmlGCMjRJ97jg++MIE7X9y4HUzf5vDll4n9fseete1zfS8n+jmkvay16LCeZqYpw4XkTTr++A9IDb1PZG6OE6kJMi0Kx7aYbGolFbNpcJPMZ6a4PZvCeHuY4RP7mW+JM/mH1/jezz5QIDS6uGywM8Q26Lskn2L12MrKioW5yMLMPffQcXkRs6uF+HAaa0HT+iZcaz+Ip218z0R7Bs0R+FTDdWK0QSJC28H7MU2TD37sV/jlnl6iho2pSqMqn37h6ewEsZObM+lkYGBgU84DMHrgACffegu/tY2Xmh4piXoetzux92cwM4P409MMfexjLA0PQxkb50oYGBigv79/nb/F9iDomRayfW1jXO5ZyhE8niLXazcijzqCIGwaYYPK2zlvzHYiaADLzw1gRUT8KEtYTkr23rNb1ZhmNl+C48oAVjXsxDxZ242g6HGpd8EE9bVS7yonqK8VcTyYoPYOgn/HQjFhs8JTGZXNWySUENzXbnJBdjCH9RD9U2+VRAWqkeNA21YXb9uwbFOK6+K6Nvuc0dIBe6N929iUQtaqdDkyMO05OXtRZ9VnN5+rcPm1+L1L2s9kX71M8PF8B0/75QtyBm6+9wU+ef0tlK3pHxrCbvFYbE8weCiBNlJAikVgwG+l7+YkrdNLJK7d4s69vZhDC7gj3czuMevS3ZhPMaIsIuZyLsRcPkQzQmo+ydzkDMcPHcvnTFydPzFi2Nn9Q5Ybq2fk9UH8vSaMoSGMu3sYe+ky0Vsmx25M5qxLYzT4ixw3h+k4mgIrjnvqNLbdiNfXh+o7tjUXqgxnzpRP57BRtH/4w1yIRul77zIaoySfIsDCwnuYvW34P/mTdU/M6O/v3/TvuFmE3eMlM9AY38TC7CCCnr0a5Ll2VyIipSAIm0bYoLKE61dG2IyhtKOIyIyisgRagUX1Fpn37DyidnmRMijvoiD2JOtBUJsXlJ9C2JnWh9uNoAgsae+CCb3Hk4GEigidZe9A0yaWZSchbV7tLEcF/sWhWZZWib0H07c5/icvEpvq3rNRgYUU2pQaU1McvXGDpqViQa7LGad11MSYPhJoU6q1xtFegbVo1pJ0OTow+94ryllYuO79I3NM+2/w/StXSftuXiBcLSbml+ntkxNErEurZ7PzKVrKzIqGRoSIaedFxOxfhGhuWcywiRQIjFEjKxTGjEj2tWBZ1IgQy+0XMWzMAFvT1157jR+8/wOe+OBPVlXmSkh//OPEv/hF/LY2pg7eTWbkWol1qRXRaNvO2wxvR+vSSCSy+TbhH/oQE7OKz/2qV5JPcSTSwyf/5i9x/IOdm1umHUjY/ILsc63cs5RjJ6XPEepHREpBEDaNVMgse+lkKiNsoC+VUTQ1yHUsR9gAVnKTy7JTiUU0C2UulkS0BRN0bSSqqHKCbCLDchzvdYL6WulnK0fqXfXIRLT6CcvPnpIBrEBEpKyNwqhA24yzz5koiQp03eZtFRW4EXjazwuGjnZx/IL3Bcv3/8lXiS6NYU5N0/z+DbDB3mcz2dJCOmYTd1J0LkxjqQzDb99i6EQP8y1xxv/gfb7zs2dxC4THuuiBQW7C5M11+f6biViXVk9hPsXWmSRtE0kGW/fhaxvfM9C+ie8ZmMqgRY1xcNKnIdKM3XeYluYO5vrPcOzwR/OiYV50zAuQESKGlY1qNMvbme4G/J4eUo8/TuyZZ3Bb23i5udS6dDHRwT0PJrK5ESyL1OOP7/nJGcukHRWYT/Hv9k7ARlsv7wLWcssQyhN8j7fJBRE2BREpBUHYNELzFYlNaUWE5gaU3IqBBF2bmI2IlBUSnA91c8uxkwiKugqLlhGKCbpWEtEWTGC9k0j7ipF6Vz1h0c1S9yoj1C1DBrBKyGQynD9/nsaZu/j01Hsl0R2tC2JVGsTqqMCHpm/hporFsy5nnO7LCqPtWGBU4Hrjax8nZz3qaLcgOjAbFejkIgTTueXLuQ8zBZ8LLUozq97P+vMsnF3kjVv/d16A9AmwJy2gbXyeT157C6U1/ZeHcFyPmQMJBg81ow0HcFgAFv1m+m5OwnSapmt3GLq3l+jwEsboKIt7zKa0HLvRuhTAwiSSsx7N50M0Vt5HlJV9b2StSctuk7crLTiOYdHw49O0DT6N0QyRyTcYHfWwn+1kIH4avyAisb3R5X73S+xrbKS5owOOfhAMAz7xa9CzfwuvzvbBO36c5JNPkvq/X0AzWWJdGrc0qEW8vj6JHl9FmIgmz7WVEXad5PkiGJmItrcQkVIQhE0jNF+RWIFVRFhnLANY5dE6PDfg9CaXZ6cSFP0nN9XBJNPll4dFywjFBLV5MuM0mMB6J/1sxQTVO+lng5G84/UTeo8nfW0JV154AetrX+PXbp7C9YvtAw+mb/OB514gNtcjg81liD73XF6gtAYGiGKyqGK5/GwJGvxFepwRlLOIuniBTP9JnLZmUs9+leE/87PMNGW4pkfxp87nhMHygmE5oTCjSy1Kl1/rjjKs6MtD2l+qahexKV0fNtO61EDlBECbiGEVCIY2EWVirxIFI8rCzm23LCS+/sNXObT/AP3HT+W3Wd4uLzwqCyPAvnRd6G1G9R1ADQ3hnTiBN3KZbmeMVnem6Pd6whmm0VyCpiY4fTorUB48CPtFoCzE7+nh5qd/hc+/niqxLp1OdPO5XzuE39299oH2GJJ3vH7Cx/E2sSA7DBEp9xYiUgqCsGkEdTARS2Ns4L39biJslr0M2pcn44LWcnNTL0Gz/3Z6vVuOxNgIJqcfBqIly6cnhzh37mpFxzhz5gyRyN5VNYNyA+70ereRiC1O/QT1tVLvgkmGDLCIW0ZlhOaklLpXhHn1Kn3PPosyLX7gwz5ntMSq1Daado1Vqa99MtotsgwtzmHo5vMcFlmXFuQ+dHLbRkfHue/97+L7HgcuXIV0huSpZsYP2JjWIM2Gh2H6zOBx5+YkC9NLuOdHGLi3Fz2nePbdcabPNPKWfgnef2mrL82mIDal68NsRyM9Y4uk2prpnE3TNpbhVkMCn5x1qWfi+wYNEcWJbo/e2Xksqxmr+yDN8XZmj9zNwc6PFAmExRGLK4KktQ62pbcn3+VIZyd3xw+vw7evncJ8isM9d5MZLc2nGIv7eKZJ8vjxbCSlZcFP/dQWlnr7EotQ1rpUKc2/2zeG9LaliKV//YTdC4dN9NvriEi5txCRUhCETUM6mPqRWfbVIzfV60NwZNEmF2SdOX/+PAMDA/T396/7sdNO+dkXEbsyu6qBgQEAzp49u25l2mkE1juxtw4k6EFX2rvKCYp2TjnZ6Hwl3W0JQX1t1JaJaJUSs6EnM1wS3TES6YHho3CqY6uLuC1Ytio1PA9jdo4Pz71MRBd3Cl3OOF2XwIgeX1erUq01Ll5O8PNKxMFigTC7rlAgXF42cHSWOw0XeXVssPwx/GKBsRJL0kr54NtXGHWmaZlaxMkkcSImwycbsYziMHyNRAUus1ttSoG8rWjEsLN2pLnXwvdRwyJiRFa9Fm9bvH3x63tvvcvrP3yVX/mJJ4kPfQGaNfbE69wZ8ol9t6XEuvRAh8N97ruYOoGO2zT33g1KkXz4Z/Ab916UW2E+xbl4B680d5fkU5zuaCfRlKGxpSUrUH72s9Dbu9VF35YE3Q9rrXBciMjEqhLC0rvIJMjKMIxscEbGLb1flnG88mgd/HwhEby7ExEpBUHYNESkrJ+gvIAQHsWwlxGRcn0Izkm582+q+/v7N0QINKxE2eXHj/Ry9qzkyaqE3VzvNgp5mKufoIg2rRUZN7wv3qsE1juxeq0IY2SE+HPP8d+OzuL5xdfyYPo2B7/6ArFBsS6FFatSa26W+I2bRFULjm0zFutiKRKl0V+kOz2G6y2RPH+OuVNHSLU2Mv8nv8vAL/4EbplIQ6dISPSYaZrDUz5/dOetoghGV7usS43uBkjC0th6HK0qJCqwejbTptTCxFZm3lJ0OT9hNlrQZGxolH3tXezv6ikWF3NRhFEzspLjMCcU2oZVIhxm/zbYqjRHTNkYWuF3d+P39mLkrEuNAOvSk6PDmPFFUArvxInsa1/fnrbhXM6nOHblJTRjJfkUjzW6NCjFDc9j/IEHcGZn4bXXtrDE1XP+/Hlu3brFuXPnNvQ8N4abgUfLrnv19XdJxKqznb5169aGTHbdTgRNgIxHtEzcq4JopLxIKc+15QkTx+OlZlXCLkBESkEQNg0RKevHMLJRCeVmW8nNTXlEpFwfgq6V2JMEIxFt9SM5KatH+tr6CbtWqYwKtV7fq6iRUT499VJJBOCdhvsAUXXDMK9eJfbMM+C62GacjvREiXWp625f61Jfa9yCqMFZI4mHz430SFE0oJuLECy2LC2NQiwUBFeiELPbJcam+ejFV/F9j1MjgyxEPMwPOowcascwBmnMlWnM19g3F2mdXsK9NJ21Kr12je+87zDd2Rj6fYCVUZJNSJG42ezmqMCNwFYWqc42YuMpnPZWorMO+6dd7jQ0k3FtfN9Eewa+b6B9g8PtPidnF7CsZiLdh2lr6GDu6F0c6/pRbGWu5ETM5za08/altrIw1hjxf/q7T/Poox/ioWMPbdIVWF8KrUsnD5a3LjXRaNvGO3ECv60NLIvMY49tYam3B35PD8/d9+d49vZsScS96mtn6V6TdHs7/YYBk5NbXdyq6ezs5GMf+9iGnydiBbdhadeg/BTTvU1QTkp5tqiOWEQzXyYlsjzXlidsnEnq3u5EREpBEDaNoEg/sYiojiCRMi03N2UJizANsvQTSgnKDSj2JMEEzf6TmX+VE5gLVepdICJS1k+YCJnOKEjItVzGGBkh+txznP7+OA/NFzduB9O3+cTtV4j9fptEAAawbF2K62JMTfHhuVvZZNoFdDnjdF5UGI3HQq1Lw6xIZxsz3LGmMZeulQiD+e38AjGxZJsAMdF3cVk14NuSex1+fd2v19FLN3C1R8v0Ipbr4dgmg4faSyIBtSFWpUFsZlTgemPlxL2omY0EtJVVEBloFUUOFkcNZj8P3x7i5pXr/MjDjxaIhFZBpGLxe0uZKKUwYqPEB78ATRp7/HWU4/D+ywnOGXcV2ZQa2ufjfe+yz2hAx20ae09nbUo/9BmOJvZuFGAhhdalTnM7L5exLk01tnPmwcast7plkXr8cek/cqQdVTaf4i/elaS1/daGucPsJtpHg9uwk/33cmhfdfbay+k5djNBz/thubSFUoIdgja3HDuFsPFNmTC6OxGRUhCETUMGTteHaERDmRlYIhaVJxUw8w+k7lVD0EOI1LtgpM2rn6AAj52eC3UjCXrQlQlBlRN2rVKSDzVPYQSg55rsc0ZLIgC10bFtIwA3Cq01Pn4uWtBbZSVakKdQu+z/k68SWRrFmpqh+f0bNDVDyrCZaG4hHY3Q4C7RsTDDkkrjvDPI0PH9LLTEmfjDa7zwsw9UbkV6D5xjCsbe2cxLse6IVWn9zHQk6BydY641TsvMEq2Ti4z1tJS9jsrX+Ws21xrP7w9goIgakRKBMJIXCUuFQlut5DdcXl9oRVpoU1pOdDTrtCZ97c5rZMZHeajxdFX7rbYptS5doj05xsP+bJFNaU9mhMb5JWgVm9Iwlq1Lk//+BTSTJdaljbYPagmvr08muKwi6LlWni0qJ3QimjzXlkWeadcHmfRdHeKItvcQkVIQhE1D8mStD0GDpzIDqzxyc7M+BNa79OaWYychD3T1I3av1SP1rn5iEU1PZrjETm0k0oM3dBj2dW11Ebec1RGA+9+/gbdUHH3Q5YwTVRbG9OHQCMCNxNM+Gd8h4zs4vkvad7KRgL5Dxl95dXyHdO5z6XqHTG6Zk1uW9l1m/Blm753nwp3fKYo2zGgXXUHmwrbxeT557S2U1vRfHiLjeiSPJhg81IQ2MkCGBWDRb8K7maF1OknjtVsM3tuLNbSAO9LNbCXWpbsIsSotT2Eew9KoQDO/zFYWbQ/0cfTqdzF7FG2jGtPxSL0X4VXjGJ620Dn7Uu3Bh/1rnEhkoNGm+/ADmIbJjzz2qyS+931+5NGP8NBDO9NytBYKbUrd06dJXbxVYlMKgG3jnj4mNqVr4Pf0cP1Tv8rn30yW9LXzjfv4s792WMTdMshEtPoJtfQPmdy8lwmud/JsUQ3yXFsdMo639xCRUhCETUMGTtcHubmpjiAve8vU2NILVkxQvZOZf+VxPXA9afPqRepd9QTljYlHpd5VQmJ2ln1f/k/8+vBcybqD6dvs+9L3iF3Yv+ejO6LPPZcXKK2BAXAM0ipeElXUrFNYly7h9PfjtrXCt7/JzBO/UGQZOtOU4ZoexZ+6QEavCIFriYTO8jZ6Zdu8EJlb7unqbNuqJgGL7kxNux67PApA8/SSWJdWyHa3KlWAlRMGI8ouEQmXcxEO3R6iramFns7uIovR4m2tnOholqzPHtvMWZKuncewiC6IH89gDA1hnG7GunSJuWGX01O3C36/c9nfbyJFtDmCe/I0cTOG19eHvf8gBnuvDy60KfXb2nipqY/OzHhJ9HjXmSaa23yxKV3FrVu3OHfuXNGywdudjEQeLLEujVgenxv6FgwNbWYRS7h169baG20yY5MPArGS5XMzI9yayNq9CuGEPYfJ80V5Ap8tRByvCnmurY5kyGR4ea7dncjwrCAIm0ZgJKV0MFURZBMhImV5RBxfH4KsccISmu9lwn6PEj1eOYE5KaXeBRL0m5Q2b22iN29y9yuvEDt1F4qGkjxZ43Ynrtu4o+1LtdZ4+LlowfL5Bp0C+9BM4TLfwdUe9ugYd135Pr7v03vxfZTjMNbbxPAHmsAap8EcxTB8JnBJDE3y/uwCzntDDNzbi55VPPvWbaYLIwDPwFv6JXj/pa27MFuAWJdWTzVWpaYPHZNJTGWQaW+h0Ywz093Lsej+opyD5UTC8eExLAyOHjyyShzMioKl+2ajGC2yOQzX4unvPJ3NHdexNbnjVkcF6onrZaMC08rGPX1SogJzLNuUmt96Hl/PltiUAlh2UmxKKyRilZ9EknFNtM6mpNxKPvaxj1X0e95M0k75iRYRywO37CphFUF5AUGcqYKQZ4v1ISqOaFURNs4kAvnuRERKQRA2jaDON+xGUShFZmBVR1DuOql31REkrEm9K08y5GEjSHgTSglr77bDANZ2pHluhE9PvVNiU9q5eBJo2eribV+Ghmj/9rdZ8jzs2SkemXubiC5OQNnljNPyHhj3H18X+1Jf6wIhcLVQWJC7MCcmFuYfXL2do12un5rmSuM7PD98pUR8LNyn3hbog29eIZEZo2VqkaX0PI5tcvtEjJhRGn06eLSVxPmlPR8BWI69Yl1qYJRECFr5KEAzl6tw9V958bDxkQ9x9OYfYzZAy+i7TE+ksb/VzkBDf9aq1DPQvomtNH/1nncwTR8dszl4+EFQiuTHfq0iC8lzN7IRX2eb7t3oy7MlrI4KvHO8i4G52ZJJGXZnO3e3pSUqsAC/p4fZX/hlPv9fvLKW4D/280do/XDnVhdz23Ho0CHOni0W5b2G4Dbv7nvOioVpGawvlrf4PnKoh87UoU0uzc5EqezzRbkJjyl5ri1L0PO+iJTVIY5o1RFU72xLY9SXolrYpohIKQjCphEY0SaRlFURFNEmM7DKIzP/1geZ+Vcd4ZGUm1iQHU7YmH0qA/Ho5pVlu2OMjBB97jl+YWCGmYXiJ7eD6ds89P3vE1P7JLojAP9P/hjfc4gkF+DSeRpsk7RpMx7vJBmJ0uAv0p0aJ+UmUe+9wcypQyRbG5n74//A+V/48ZyQ6AWKiYWRia72yPguLussMrUDpCE9vb7HXYVEAK4Pm25dqsE2gnIVlrEUNVaiBstFE5azMbWVxeWLA1ja4OwH7ieiLEy1jharTRA/fD1nVXovsVcu0z43zwPz7xRZDR/Sw5gTi6AU3okT2de+PslxV8ByVGDk+ecxB8bKRgXu8z2JCixDKqMYiewvsSkF+Bt9E7DebfsuJeiZFrLXWJ7VSgl1CEptcmF2MFE7QKQUsags4ky1PogjWnVIvdt7iEgpCMKmEexlv7c6Gdd1GRgYqHn/dPIBYF/J8tuD45w7V/txdyvvXz1EueghQyc5d+4ct27d4tFHH938gu0wggYS0nJTXZZQkVImZlRM1Nb0ZIbLRiu4g4fgWGlbuBcxr14l9swz4Lq4bpx9zmhpRIzRvGNsSj3tr+QiLJufMGB57nPad3J2pg7pguUr+QwLlmmHxOg0j73yBkpr+pNDXHc9uC/BxKF2MEaJAxoY9RXWzSSt00u4A7NZ+9Lrt3j1qiq2L93l7JUIwI3EVibJzlai40mc9hYisxl6ph1Gm5tYTNtoPxsR6HsG2jfoTPicmJ3Hspqxuw/RHG9n9shdHOz8SEVWpF/5gy9z+lQ/D5x9YMO/27CfzZcWNzZmFkmhVelY711k7pRalUZtjbZtvBMnxKo0BL+nh9RTT/GOmuW1a++X9LMzRz/ALzwlM6tWsxSWJ2uPPdfWQ5izSDaCRq7laoIm3zbIs0VVyHNtdcg43voQWO8kgrcsUu/2HiJSCoKwaUhEW5ahoSEOHardjiViB+XvEM+DcqQDrkvEkkHSahCb4epIBdxUw95r82rFGBnh8Nef59eHS2OLDqZv0/R73yV2vHfPR3gYIyN5gdKYmuKBydsYbqlNae8VhdFyrGqbUq31qgjB7GtRzsK8HWl2u1v7F/H1RQZuT5WIguXEx7SfyYuKGT8babiZ3DUwBEDz9BKW6+HYJoOH2kuiA7WhGDzcQWIhvaftSzc9AnADWbYbjRg2EcPORxAuf44YFhFlZyMQDZuoYWHnlo8NjXL7+i0e/uBDAdGF5aIUswKioRRGbJT44BegSWOPv45yHKbebuC5hTP4auXexdA+9zWdp7s1gY7bNPXelbUuffhn8Bsriww0tEKxO/rrQqvShYYOXm7uKckf67S2c++DjVlfP7EqXRNvXzdfbz9Vsvyg5QETm1+gbY64ZawPYffD4tRSnqDrEouAXLLKCX6u3eSC7BAC65042lRF0PUKSk+015FIyr2HiJSCIGwaQZ3MXpwJ09/fX5KXo1J6XmiGS6XLG5v3cfZsrM6S7T6+cyVRdnl7a5yzZ8/WFdW6lwia+ReW0HwvE3Zd5MZ6bZYjAzOzHop4yQD0uN2J6yR2TGTgeuJrv8hGtPHZZ3DSs6ipKeKXrxCPGqQbbCYa20lFIjT4S+xbmiRNkul3xxg/eYDFlgTTX/1NXv+5R4sEyHL5C51aBMPDcE1fhKGL638BNgCxL62OmY4EnaNzzLXGaZlZonVykbGelrLXTPk6f33mWuP5/Uu2A6JGZEUYXBYJVfZ9OZGwcH3hfsuWpnnh0bCI5pcXb2eq2id4vTb8Gj8YmeSjTR+oaX+/uxu/txdjaAjvxAmsS5doS47x8Px8kXVpT2aEJpWENrEuXWbZqnTyn38fzXiJVWl3wgOVFKvSCgnK/SdCUXnCREq5x6uccEt/eb4oR9igvfxcKycojYk815ZHxKL1IThtk9S7ciRDJmUIuxMRKQVB2DSCZ/7JzU01SMLt6pCb6vUhKPefDGCVJ6jeKaWJyN1XKIWRgbG5aR6Zu0VEl0YGxt8C457jVUcGrjfL1qTpEsvRTFHkYHpVJOHq7ZeXTfiTTN09zbWh3y8SDTO5aEWPlWj6tvF5PjnwVtam9OIQluvh3Z1g9FA72pjBIjuzftCPwM15WqeX4P3L3Lm3F31rjFu3G/eUTWkQYl9azOr8g/Yq+9Dofe30vv99zH2dNA476GSafW/9v+z9aXBk55nfC/7Omhu2xJoFVKGKVagCWMWtKJYWqtlqURK1ddNutxeSdkue9r3Td+7cCH/wxMTExERMxNyJCbsnbsy9MTfutWKu7bbabdJtu91dkqhuSaSopkRKLJKgWESxUKwVVQAS+57LWedDYslE5klsmYkln18EIpFvnjx5cPDme97z/N/n/8C1xlO4/qpVqaviu/AMdzilxCFq0HzicTRV49EvvMjfSRxb3/+f/8f/zG987vNcunRpv//0mpNvXeoMDOBPFFuXAliKiTNwSqxL8/ASCd769Lf5k+GlIkvwxUQb6W+drmshdyfIvcXOEJGyMpTPpJS+V4ogO9Kw6bNY42M5zAQtkpd+VxpJNqgMQWOeiOOlkThe/SFhMkEQakbwRabGB3LICTpfYk9SmnK2OML2EbvXnZGxKFlLcS7WhTbZK4HTMoRee23dujRyaxjTN8iu1sfKzyzCWkG/fh1nYAAvHsd8/XUyL72Ej4/juyw76VWhcKNO4WZxMFvi+WbR0Ap4fU2ErIo1aRMsWJNbbnb6xkRuc7Ep3RMH1b5URV0VBvMsRNUNwVDfZCl69+Yd2lvaON7VvSE0qnqJbUvZkmrrYqSibDGuJyDyaw11bAz1fBv69evM3NLoy0yufk/D69/T1o4Msc4ITv8AhtGE29ODdry/8O/06/c6km9d6sXj3Huok7sr80WZ4w2drZyLZ8W6dBMZSyFpHuPV1m8WtP9mXxava35/DuoQEjjHk8BpSYLuaVXVx5AI27YpF2iW+4tiXBcsR4L2lUDGvJ0RJKKVqysrFCPxlJ0h4nj9IVMoQRBqhqyEqQxBk0FZ+Vca6XeVQexJto+aTNL7g5/xh+OzRa+dY4TId9+oOws6388Jh7lahva6najl2et1DC3fRk0mOXFnEM9zaR2+Bq5N5lQr97qaQU+ja8vYqseYYnB7KUXz/CL2R5N88uhx3IV3+av37jH1mQg/8/8S3vvL/f6zq47YlFaG7diX+p6K76nonk/7dBrVVbCbm2jVG8kkTvFY5HRh7UE1J/wVCoMbouBGZqK2vv3mbXdqRfrKT16hv/80F+O7s5PfCZszAK1b90pmAGKYOAMPSQZgGdasS83XX0f7qNi6FCDkuXV33dgOshCtMgTN8SxHwfNAlbL3BZS7t9hqjYewgaHnHEb8EgtV5P6iGMngrRxyX7szRCyqDOHAflfjAzkkSByv/hCRUhCEmhF4kQnJRWYnyKR6ZwSt/JPJzc4I6ne2o+C6oFUnkejQsVZLMTwJCmZRLcWU1gb+wail6Ps+LqtWpYbLoppm1JpetRbNExJXBUTby3/ubBIa821JbexNgqTjO2znG/epX95kPj1O8+wKSnoW29BY+FQLzep40ba3WqOcW57HsG3M2XkWWmP0Xh9l6vP1kxlYrzal5pqot16LsLhGYSivjqGpbmpTjIL6htHmWRJT/4lZfxp+/XM6Y1Gmf9bCr/xH8XwDz1PBV1B9j6/E3qXHjOHpOj0nn0bTddJf+FbdZUdvzgB8t/UY+txMUQagea6JzrgrGYBb4CUSZF56ifczi1y5f6MgAz9pJnj77CP89ktSd3wzEsCqDOVE3YwN0QDL/3pF6mRVBkXJ1aUsdT4laF9MUL8DiEjf2xGhgOmyOFOVRso2VYagWqiSSVkamePVHyJSCoJQM+QiUxmCVqzJpLo0svKvMkRMv6R9adJM4IyeQOutrwB9KfJrKRrzi3xu8U5RLcXw8iTGu+D29QXWUlwTD7OKg4PLpD1fIABuCISrtqR5YqDlO9ir2YnrbQUiYuF7vDXp8FPwNlMw9k4tT1kRkhm4Mw6CTemaJel6dqCqszy/REusidameJGIGCoSFQsFw3xxMZS/3aoAaag66g4zDLekFTgzjD39K6a6e2nxPDpnl7i0/H6RzXCbkaahrTFnM6zruD09dSdQrpGfAWi7c8yWyADU9IxkAO4AuyPBq61ni9ovGDZQnJ1f76Szcm9RCcrabloKUVlQWoDcW1SOsOmXXFAqQftiJJOycgTW4ZV+VxIp21QZxGZ4Z4hbRv0hIqUgCAVYlsXQ0FBV9r2S+TJQHFgcu3+LwcGpqnzmVly4cAHTPFxXuaCVf5JJWRoRx/eOmkxy6oc/5Q/H54teO5G9T+RP3sB8qLsugtC+7xdkF64Jflnfpu2H/wkjPYk6O0dj8i5WEziGzkxjM5mwQdRNE0vPMb6SxftwnPEzx1hqjjD9n2/zs791sVg8bFn90NFf7uefXFPqNTNwt5SyKU12tOKh4bsqnrf2qNIRzXJ83sNQI2Q7jtFrxokeP8tzTZ9ar3VYypY097gzS9JX3nyFp59+mksPX9qHs7JLvvEN/HffJROL4fT04D24W9K+1FIMsS/NYy0D8H+77HB68oOiRSx9Xz/Dqd+O7/dhHhrE0n9nBLllRERU2xFBbhmw1vfkfOYTJI6LSLlzZMzbPkHjHciYt1MCRUrpdyUJHPOk3+2IoGttue92PSP9rv4QkVIQhAKGhoYYHh6mv7+/ovv1fbDs0hkbIcOr6Gdtl+HhYQAuXqx+7aZKIpPqnSErsPbGmn1pw5yHQrjIvnTKaMexY0QOiH2pvZ5BmBMQs3m/rwmA2bznI+H7OIrH0PTMJuFxIzNxI3sx91iK+NQSz938AMX36R8eQzddnHMxRntb8dUMkGEFuO01kr1n0TKXJnZ7hAePdqOPLeMmu1hsb6jp+TqIHITMwEqioBRZjYbyswe1td83sgVnJ6YZvz/Gk489UUIsXKt1mPs98oVZOh78R7RGhcj0ICuLLjd+kOCTyABennio+h5/99FBQrTgxwzajz8JikL68/8Qr7U+swCL6O5m9stfxvs3/wYvHufmiS6mrLmi8a6zO86puCX2pZu4p3ZzvbWnqP2FxBwgvn3bReZ4OyMo+0AWou2McudL+l4xQVl+0u92jox526dctpX0vZ0R2O8CRJF6R8a8yhC0kEXGu9IEZTZLvzu6iEgpCEIR/f39FRfusjb4lL7IPHr+DBcHeiv6eUeZoBqeYotTmth8km/MflSU3dGZ6Qca9/vwDjT59qWhpTk+tzhSZF/aYU8RHQT1wplA+9I1vPUsRJtsnvC39vtkawbMJAuLv14XBDdvZ69mLVoF7c66MLnjKWtk9XF5dFfnaI3TNyYAaJpLoTsutqGtCpSF30tfVRg92UZsOYthuzTNpVhojXF6OMl77fVTSzGIUpmBk4nmkpaviuev27sutkTW378VuqKtW46GCmoaGnntm61JN2+vE1LNAnFx8z5DqoGuaCjKzsbmK1NXeGt0keeeeWrrjY+3Yx4/hTo2hnf2LO57w3TZk7Q480U2paH5ZVAU3L6+3GMd25QGkT15kmuf+QwXGhvRjWkmS9iXtvqO2JduwvfLZLTJgqAdEWgFJpb+JQlaiCYZbTujrN1r6bVZdY24tFQOGfO2j9i9Vo6gxcqWjHclkTGvMki/2xnS7+oPESkFQagJ5SbVQTYvQmnEJmJ7qMkkodde4/mhOSaWCrOrTmTv8/TbPycc6SS2tLRPR3gwcX1vXRCM/egyVnYBdXYW/eZNGqIqtq4z3RgnEzaIuSk6VmaYtTJoVydInulmuSXCzJ//Eb/425cKxMSsnxMYy3IOrjEPs8M1+VsridRS3BuGomEqBtPnz/D49TnsZgOUBWIWdA5ludV+AtfX8VwN31PBVngyfZu2jEHG02iN9XIm1sqxz/8dnGsf8/iFx3j8wmNFoqKhlrYnPcxkv/QlIi+/jBePM3fiYay7xTalquLjG8Z6LVSxKQ1mpbmZzAsv8IvJZT6evF60wOXG+fN88aXofh/mgaJcMFkCCTsjyOFaVtmXJui8BJVGEEpTzl1EMouKCRLHQ7IoY8dIGZPtE9TvQMa8nRIUT5F+V4znlRGLyliFC8WIvfXOEJGy/hCRUhCEmhDkJw7iKb5TggNYtT2Og8yaRSmOg+uE6bQniiz7DLUJbXSU8x9/TOj8ebh0eGqnub6H5dmk3SxZzyLjWqQ9i9v+BFOtGX65fI2sl7M1zeaJhNZqW372Ye71jW1dcvbL8aklnhtetS/9eAzdcfHOx0j2tuKri2hABnjgRfDvpWiZS2Heus3Uo934o/MsjrUyV0f2pUexlqKOtl6n0FR0TMXIiYmqUWA/airGeq1CU1l7LbfdxjZ52xe057IN1bVsw16IXPtT/Pv3me9ppOHuXeZuhzkxvLKeGRhbzQxsaXaJNYfxHzrFQPtp6O2l+dSn+eCXtzmutHE61r2/J7BGeIkEmeefJ3z5MiuxNt5uOlZky7wYbePJp2KgKGJTuk0ybQlebS22rv4tMwvM1/x4DjIyx6scEsDaGVKTsjJsXZNSyEdqUlaOIJFD+l0xqTL9boemHXVP0LVWFn0XkymzEC0Sqt1xHAWCrrW2o+C6oB2sqiX7Tjpbul2utUcXESkFQagJYk9SOQJtcWRSDRRalKqzszw2NYK3yauqw57ixG0Ftf00quvS+pOfwNNPQ3dlRY0gMTHjZsmWaM+6FpnV55nV7Qqf5x4tr8zdwjkYmv7Rno9d7Et3Ri1rKerk7EoLBEA1J/iFSgqJOftSo0BE1HPZhUpuPz/54Y85+9AZnnjk8XXxUN2niMdaZmDbuXOoHR0of3WXkFWYGQighhuIXHqU0GpmYLqOMwPdM2dIv/gic//r3+AzU2RT2hDxQEmJTekOCKxXJNbqRcgcr3IEnS/HVXBc0A9JAGut7nu1mV98BiheFDQ1McLg4P1d73d4eJj+/v49HNnhwtBBU31cr/i7LOUkigkSMmS82znBdq/S7zYjWUWVI0jkkHhKMTLHqxxl6z/bEDskc7xaEXQdENeCo4uIlIIg1IR0mSw/mdzsjKCVf66nYDu5QEM9E3rttXWBUh8eRnN00kqoqD6b4aXQr18nrKoorov36g/I/OPfJ+NZpFfFwbSbXRcGc79nSXu5trRrkfE2v26tv76lmHjAEfvSnTHfFqNjYolUvIHWhQwtE1nuN0RxfQPfVfE8Dd/VaI4onGhz6VlYwtCbMLp6aYq0snTqYU62/0ZBpuGdG7fQfY3HLzxakMmoVsGuNJbRafTCNGn7b2OZnxnoxeN8fCzByuhsUTZ0qreF7rgtmYGreIkEg7/xbb4zuMKTy+8X2JT6HR2kv3VKalDugECRUgJYRUgAq3KUy2jLWgp65OCfzwsXLtTssyyn9PUwpHt72m9/f39N/46DQMj0SWWKv8sy5hUjYlHlCAo2i0NQMdLvKkewzXBtj+MwIHO8ylHOaCljKcTCcj7zEdeC+qPOQ9mCINSKcqvStnBFFDYRNn0S1nhREDppJnBGezFOdu73IdYEx3fJeBZZ385lGXoWXnKUrnsf4bg28eEhsC2WT7RypyOOYmbQtWUczWVM0WlZWGZpYRFF9XnLA/eXH/OjYx/WlUVpOY6afamGmpd9mMsmDKkGmeU0uq/S2dK+am1qEFrPNMxlJpqrWYdr7zMVY/35WuZi+EuzNI79KTT7GNPvcv2WR+heC8ORAbw8UTFxJssTmSE0P4YfMWjsfhgUhfRnfoczDYUC0rI7CUCb3lTTc3UQWMsMNF9/HU0vzgwEOO7Zkhm4iawFSfMYr7Z+s6C9/4SD1zWzT0d1OAkWKWt8IIeAclZgUnd8Z0TKrA5PWxCL1O5Ydotpmly8eLEmn+VR2mvuXN8JLl6sj/lwpQibkMoUt5dbaFqvBN3XSsB+58iCoO0TnFUk/W6nSL/bPuXieOUWVgnFhMtY0ef6npzPfGTMqz9EpBQEoSaU8/eXujHbR00meejV1/nD8WIjyRPZ+0T/5A1Cp7sPZNDe832sVUExX1jM+BYZz87Znq7+nvGt3PP8Ns8im/e7Q7EI9qlf3KRvZZzm2RXc9Ay2oTH/qWZa1NGibe/GY5wbWsCwXRrmluvSorQctbQvBTAUDd/yCGsmMTNKSDUKxEQzTyzMCYdG3u+bBUWjYBtTMdACsg8HBwcBuHhujwHVRDdedzfq2BhuXx/ezZt02ZO0OPMFWbx9o6No7WlQFNy+vtxjT49kuJXASyTIvPQSP7mZYmrpWtGijJknB/jMS7KoIB+pz1Y5glbZW2JBV0S5mpRRsWTaEeUCL5YjAazNBAloUidr5wTVBhTbzWLkWls5RCzaPpJVVDmCrrW566yQj9QdrxzlRF25vygmcMyTfndkEZFSEISaEHSjoWv+oamvUylc191VrZ7ogwck3nyT1LKKwgk67Kki+8PxsRVic+N4H3xA8plnSB0/vufj9fCxFQdLcbFwsBUXa/V5xrOwVRdH87AUF1txN7ZdfVx7buNCledeYlFaOebbYrRPLLLYEqF5PkV8ZoVkexuur+O5OetSb/Wns8Hj1Pw8uhbD7zzB2XArHSf7aW99OicW5mUdlvrdVHRUReWVV16hv7+/ZhkYlWatlqIXj/NJywVaVj4h5BfWUjQ9F98wckLmai1Fq45rKW6HdGuCV1sfKmr/u2YaWKz9AR1gpE5W5ZDA6fYRK7DKUbZekfS9Anxf7A8rSeCYVyZAXa8EZdSHZVHGjhFxfPtIv6scQeNduYX19Uo5BxG51u4MmePtjMAFQdLvjiwiUgqCUBMkiLDB1NQUDz1UHHAvR2hmhsSbb6K6LtGVBT63+ADTL/RX67CnaBtexj3djd3UROLNv+Huc19mqb0ZS3HIFoiHTp6IWEJQzNvWUfZW16eWHDWL0p2ioqA40BiKrYuAaxmFa9mIoTyRcKNtw8Y0vNoW+815Oh/8J/RGhcjMIKrtMPrTTt7j4QL7UtX3+Pyxq/ToTfhRg3jPozn70s/+Hl5TfWUH5tdSnNHb+LipvWgxQbSnma6nmkFRpJbiNpEA1vYJutbKzdzOCVplL0GEYoLOiaH7aHW2EG2vlFtlL2JRIbYDnif3F5UiqO/JtbYYua+tHHKt3T6yEK1yBIUIxNK/mHLCrZRt2hnlzpdYqxeSW4hW+jVZmHF0EZFSEISaIDdzhWwnW8z2HdKeRdrL0vCnb6PETZTZWRpm7tLcBLapMdvcRDaiE3XTdCzPsmJY+GOzjITbmWsKMXXtFV57/vEa/VX7T60tSneLpqiEVTP3o4UIqQZhLUREMwmpJmHNJKKu/R4irBqEVttyz83c89Xfw1puXx+8N8hb777FCy+8sPeDPNFF6Pgp1LExvL6zqNev02FN8JnsQoF9acJKEo6moF3sS2GjluKD13+FwVhRLcVEPAuK1FLcCZLRtn3kWls5goRdWWVfjIjjlaPsKvsytT/rkXLXALEC2zlBQT8J2hcj19rKEdTvstLvigi81sp4t2PK3Vv4fm4tqZAjqN+FDFmItlPKWfrLfW0hlgO+L/cX9YaIlIIg1IR0tnT7UV4F4/s+Wd8m5WVJeZnco5vB6o/wk4X3ufLxHbJrGY6qs/H76qO7msEYn1riuU8+QPF9+q+Pobsu9rkYo72t+KoN2CwBy16cnnsztMyl6Lw7xdyj3bRPLBKfXmauvT7qtm22KG2ZWWEy0VzS8lXx/HV718WWyPr7N2Mqek4YXBUCN4TBnKgYVnOP+e1r266LiavvC622G+rhuPzm25c6AwN4d+4V2ZcC2KqJM/CQ2Jeu4iUS/MmJf4KtTPLk8vsFtRRnzjaS/v2BuhVxd4METrePBE4rR1BNyoyFBLA2EVQzRvrdzim3yl4CWIWUze6QvrdjgoKnsjCjGMloqxxBGbwy3hUjNSkrR1C/8zwF2wFTMgTXkfGucuhartyV4xafUxnzCilXC1X63tHlcERJBUE49GiTE3xj9ucFwfqkmWC+9THgYCuVru+y4mVYdjOseKs/boYVL70hQLp5QmTej0cJq9SvdnCLJWBpW59/+sYEAE1zKXTHxTa0VYGy8MLtqwqjJ9uILWcxbJemuRQLrTFODyd5r71vr6fhUHCn/xgD1ybJxptRHiwRtXzah7Lc7ejB9Y1cDUVHx7cVnlVv0WxHWEynOR4/x5PtXXz6+W/x7Z7edVExrJloSv0uEcy3L/Xica4lEmTGZ4tqoWZ7mzkWd8S+NI+sBZPmMV5t/WZB+6c+vYDXldmnozqcBAVOxYKumEwdLgiqFkE3wL4vAazNZAMy/ELS73aMpuVscm2neHyTMa+QcgtVytnmCqUJEjqk3xUj2eOVIzCjTfpdEUHfRRnvdk45kSNrK5hyTtfJlsmkFHZO2PRZTsscbyvKnY9yGanC4UZESkEQqoqaTBJ67TUe/ekUs0uhgtdOZO/TdfeXhP99a01sDz3fJ+1l14XGEX2GjOowuzhIys2w7OWEx5VNYmTG3990nbVsv6b5NJDL9iuVGQg5oXK+LUZHcpGm+TQLrbH19x9UQqpBRAsRUUOrwmCI6KpAGFFDRLVwLmNRy72+tm1UC60LiREtTEQLYSo6yvi/ggcPIDrD0jsfMX7d5/hwmqTZTEqNEVu1KD37SAi9uYuFVo1WtYmus49D31P7fToOHGv2pebrr6PpM0X2pQAnfUvsSzchGW2VQ1bZbx/pd5WjXPBFAliFyCr7yhIyAkRKGfMKKGv3KgL5jgmsSSn9rgipk1U5guqOyxyvmMB+FyrdLgRT1nbThsYaHstBR+4tKkvIgOV0cbtYXBdS7hogfe/oIiKlIAhVQ7t1i/Dly+A4uI5Opz1RlIFlqK1oo6NEXn6ZzPPP4545s+39e77HipdhyU2z5KZY9tIsuen1x7W2ZTf3fMVL45F3QVubfc5er+wfXmF02wVAc3NZmZktKpSvvb62/dr7K4nqK5i+hpOyiephmsONhNAx0Qlh5H6U/Oc6Jsbq81z72o+KCi65n23VW7LwsVhhiVLyq9HdTfu776K4LvPxbixlsqRF6bKVJtt9kuTEBI2aBl//+t5PzBHFSyTIvPQSf/1xioVfXCvKiF58coCnXqoPS+HtIjd0lUNqUm4fEYsqR7hMjaeMpdAYlXO6hmQVVZagAJZYXBciAazKItbq20fmeJUjKONe+l0x0u8qx9bW6nJO10jLooyKEvR9FWv1QoL6HchCtKOMiJSCcES5fv066XSJCMcWDA8PV+TzQzMz9PzoR6iui7G4SOfHs+ipwiGnw54inISZG03YTU143/kOI899mfn2JlJqlpRqkVIsUqpFWrFJqxYZxSat2mSU3A91cC13jJzdqKupAIQzNgtltg9n7ILt194PgOej2uR+LB/VWf3dXm13QLFLt6/9rtigrrrYWpYFrGCYS3hAZvVnv2m3LJ4YHmYpHefjps/RYU8VCOTTRhtZ9QO4f5+xyUnmvv51xkdHYXR0vw99TwwNDXH79m0GBwersv8x5XF+tsm+FMBbuV+Rz7x9+/ae97FTKjXm5eN6YDlfK/nag5EbDCpz29pPNY4tiP0499tlKnkceKSofXHZKtnvRkZGaG5ursGRVYehoSFGRkZ29Z2amv00ULykfmF2nMHBmxU4uu1xWP8H+ed+fC4CfKHkdu99MERX80G42hUyMjKy9UZV4M7dM5TKO3DtpW3149u3bx/6/lJJNL4ARIrah2/eZzC2u3nKQR7jd8tHI3GgreRrwx8PomsHN8h8EP8fK0uPAMeL2scn5hgcvFrQdhTG+L2QyjxXsn3s/k0Gzek97XsrDuO5L3feJ8a7gceK2pdXnKrdzxxWJqafAsJF7YvzSQYHb3Dnzh1GRkZqev9QSRzHQVEUNK36pVbm0zHgvyr52p//xat0Nc5ve19vv/02juNU5sAOILfuPAQ0FbX77krNvqMH8Zq5a7xngFhR883bDxiM36/98axy0M7x8FgT0F7ytRvXP+BBqPKJGJXk3r17+30IhxIRKQXhiHLnzh0yme0H0NYCW93d3YyNje050PWZq1exlpaIpdO0jo+TcTtwQu1MN7ZiRTQalCW6MpOElCWW79xm5KF2FtuiTF2b4rXnH9/TZx815ttitE8sstgSIT6fpn0mw2hjFMsx8Rwdz9FxHR3P1jnVmqVvZhGDRkLxXlqVNlbaHua88gwhDAxNQ9GVUrGvXfG9732P06dPc+HChcrssIIYU1Oo//ED/NesIotSQ/N4/tFHsbq6GFxc5E4qxdK1a/t4tJXh9u3bTExMVO3mNJ06ARwrap+aXanIZ05MTKDrtZ2aOI5TkTEvH8s1gdIi5Qfvv83Eje3fgHR3d9ck2LAf5367zExBKZEylfFKnpvu7m5mZ2d56623qn9wVeDtt98mkUjs6v++uPRE6fb56o0LpTis/4P8c7+QjhEkUg7fGGG+aXuLDWpJd3c3ruvWPEA5Plk6iOBkl7Z1LBMTE1y9epWFhXJLsA4erutWZ4z2P02pidr90WmGw7v7rIM8xu+WW8neku2q4nHr5sF2STmI/4/UcjelRMqZuVRRHz8KY/xucT0F1ys9x5sYv8OwM7brfW+Hw3juy5336UmXUiJlOlvbxXqHgfnFR0u2L81PMjw8TDKZ5Gtf+9qBvC/fDrWMKyysaPCD0q9deORTnOvZ/mL/5uZmfvzjHx+4Mb1SjCVbSra79nLNvqMH8Zq5W3z3U5QSKR+MzTDcsH9j3kE7x7emugNfu3v7YzT14C5EAxgfH9/vQziUHJweKAhCRfn617/OwMDAtrdfWwV18eLFHX+W7TnMukvMOcvMuUtkRu9yIn2HtGvQ9utP8HrCWK0uM702qJOYgAU88CL03EthzMGx5ByLHRHaJxaJTy8z1340rSNDioGzYtEabaajsZWYFiGmh2nQIzRoEWJ5jzEtnHveNU/j/L/D6FZQln4Jts2DX7ZwxRvAU9T1fau+x1d7r3Mi0gpNBokLl0BV4cU/gGPFwlIluHbtGufPn+fSpUtV2f9e+bPwN/jOtXGeXH6/wKI01ZKg5//xJBw7xrF/+28P9N+wE65cucJbb73FCy+8UJX9f7DUyDt3i9sT3acq9pn9/f27GocOEjOLCv/zH5R+7b/+g9/n/KmDudr2oJ77hl+G+HdXittVPVq1vr7f7PZ/8Z13WqGEdvbppx7jhd/uq8CRHX3Wzv38ssL/PSCA9aWvfJNHHjqY3+P94KNUI5SIq5zq7dr2d/Sgjj/7wf/v3UYml4rbH3nsKV74nfO73u9RO8d/fSXEv/x5cXs4xKG4Nhy0/8fH2QZ+USKRoqOr91Ccz+2y1/O+nFb4Z39e+rVvfP1ZLvbJtaEUQee97T2TP/5l8fa+EjpS/a4S/JvBViiRqPvUkxd44XcfAuDChQuH9p62lnGFVJm1/GfOPsKlHeqkCwsLR7a/3vx3Dfz1x8XtJ7rba/o3H7Rr5m75kw+bGSuxJu/8hYu88Hv9tT+gPA7SOf7pByb/358Vt+uazz986R/U/oB2yPXr1/kX/+Jf7PdhHDpEpBQEYUtc32PWWWTSnmfSmWfSnmfWWWTOXWbOWWLJK1xp9ql3b4I1SfPsCq6VwTY0Rntb8dVCb1ZfVRg92UZsOYthuzTNpVhojXF6OMl77Qc3mGooOg1qmKgapkHLPUbVEBE1tP57bPX3iBoiqm2064rGH/3RH/HCC1/m0vltTsBPJaD3FDx4AAMD8NFHdGQn+UxmnqSZIKXGiHorJKwkoTkLupTcdqoKJ05UTaA8DKSzkDSP8eomi9LjnZRKCBS2ICS1AbdF2TpZZercCaUJGdLvtovUK6ocQf0OpO9tJqh2mNQr2h1Sh3d7SJ2syhIOvNbW+EAOOOXqZMm1ducE1QbM2OD7oMiwt47Uf64c5a4T6WztjuMwIPcWlSUUNObJHK8A6Xf1iYiUgiAA4OGzpGYYSt1dFyKnVh+nnUU8vG3vq2VmBYCm+Zx4Od8WKxIo1/BVhfm2GB3JRZrm0yy0xtbfX21UX8llMmrhddExpoWJqbnnMS1CTM09z7XnnpvqPgyd3/gG/Ot/DW1t8MgjeJ9cJ+RbnMwWWlTaqgGPDOS203X4+tdrf6wHiKBVktHikm3CNggKYGUlgFVAOht8kyGBhJ0TGMCyFAlgbSIdFMAScXzHBAURIBc8FTYIDCRIv9sVQX1PrrWFBF1r5Tq7OwL7nS0X2XzKBZIjIpDvmKAFkL6vYDnlr8X1hiwIqhyqCqYBVon5nCzMKCRItI1IPGVXBC6+lWttASJS1iciUgpCHWL7DuPWLPetSUasqdxjywSO4sHk3vev27kixpqbEzYzQRHmVdZeX9t+7f07JayYNGgRGtUIjVp0/fcGLUKjFqFhtT2qhrn10Q1MNJ68+OSuPqvmdHfD3//78Gd/Bm1tXO/+HMv3p+iwp9F9B0fRmTLa8R/q4HSbmhMo//7fz72vjpFJdWUJugmWAFYhZTMpZWK9Y8qdMwlgFSI3dJVDVXOBhFLjW1ZWOxcQJI5Lv9sdEsDaHpJVVFmCvq8yxytE5niVpdw5y1pKWVeDekMWBFWWsCki5XaQe4vKEjRHkWttIUFxPFmUcbQRkVIQ6oAVN8NQ+i7XMyOMZCcZt2dxN2dGVvCa6BgaAK6Wq5cYztiUsF1fJ7yakrC2/dr7dTQatSjNWpQmLUajFs0TG1fFR3WjzdhBhuODwzj8nTsHf/AH8MMfoun3mTS6mDS6Cjbpc8lZvH7963UvUEKwJZOIlLtDbDe3hwSwKkvQKnvInWsJYG0ggYTKEiRSyphXiPS7yhIUwJJ+V4j0u8oSdK0NWoRQr2TKuGVI39s5W1mrN8XknK4hY15liYRgsYSBl9i9FiL9rrJI+ZztIQvR6pNDGKUXBGE7TClL3J2/wkfpO9zKjuNTu8F8vi1G+8Qiiy0RmudTtMysMJloXrd89RwdJxvCyZo0qh5nFpYw9WZix07T09CJe/ZJvn7i68TUMIr4+BXS3Q3/5J/w+ofjJBfeJ2ElMX0LSzFJmgmyn36S5/6JFFtcI+gmQ+xed4fUydoe5Vbgyuq/nVPuJjhjKTRLAAvI1W4KvKGTVfa7Imz6LKaK22XMKyTofEQlkLAr5Fq7PWS8qywijm+Psnav0vd2TLk5Xrn6n/WIBO0rS9CiZREpC5FSEpUlKBaQkX5XgMzx6hMRKQXhiNHycILH/tlX+NPQL2C+9p8fU8NkHrlA1ycZIl2dtE6At5xm4s0G3lcew7bC+F4uU1L1Pb7edIVEcww/YtCQ6ANFIf3k5/G0SO0P/hCRbTvGq63fLGpPhPfhYA4wYhNRWQIDp2JPUkA560NZd7FzyouUNTyQA05ZcVwscXdFsP1hjQ/kgBPU90Jyrd0V4YDAqdSkLCSw30l2/a4ItPSXfldAUOBUUXxMia7tmHJ1PMX+cAPPCz4f5RxHhGCC+p7cWxQSmEkp19pdIdbq2yOo38kc72gj0yhBOGJ84V99G0Wt3gWuRWugQ2+mw2ihVW8krjXmHvUGWrVGTNWAXoh89KeoY2OoA3HSV67QPJfiU86vSZoJUmqMqLdCwkrSos9Bi4nblxMo3Z4evK6urQ+kzgkKJMjKv0KkJmVlCVz5JzdzBYgtTmUpJ7BJhscGYjNceYJENrE/LETGvMoi1urbI7jf1fhAjgjS77ZHUHZf2EAWou2CrSz9hRyZMoujygm9QjAST9keQff5MsfbHUHXWrm3KETuLeoTESkF4YhRCYGyWYvRqbfQaaz+6HE6jGY69BZC6vbSMbJf+hKRl1/Gi8dZPnWK7NgKId/iZHakYDtP13EGBvDicdB1rGef3fPx1wNBq+xlUl1IKlO6PSoZp7tCAljbQybVlaVcAEtWnW4gFnSVJ2iVeFbGvALSATXaxIJudwRea2W8K6Cca4Gwc4KutXKdLUSsDytLuawYudZuEHSdBbnW7pageIosvi0kcI4ni753hWRSbo+gBUHS7442IlIKQh1jKBrdRju9ZgdMZGlzG/jiI09vW4gsh5dIkHn+ecKXL2M3NfGr5kdpTM3SYU+j+w6OojNltOOccumJN4Guk3n+ebxEogJ/2dFH7Em2h0xuKkvgpFqCCAWISFlZyp036XsbSCZl5ZHagNsjcMyToP2uCAo4SwCrELnWVpZygVPflyzBNaTfVZaQuGVsi7JzPLnW7gqJp2wPGfMqi1irbw/pd/WJiJSCUEc0qlEuxvo4EzrGcbODhNGKpqgADN4fBKiIQLmGe+YM6RdfJP3Hf4ztaUwaXUwahVau5/Ux3J4erGefFYFyB0gm5fYQu9fKEjQptBwFzwNVrfEB7QHLshgaGqrKvj+5fRJoKn7BSzM4OLjl+y9cuIBpinfTGqaeq/Xk+8U3KxLA2qDcKnu5odsdkj2+PSSQUFmCbIYlcFpIUL+TjLbdUd5aXebOa2QCrrUy3u0ORcmdu1Lf56DFpvWILESrPGL3uj1kjldZ5N5iewTd10q/O9oconCiIAh7JeVlGLOmmbDnWHRTpL3qz8C8RIIHz32V/xz/2wCczN6lPz3MyexdAD554gtkXnpJBModIiv/tkegSCn6z64oV+epXK2Ug8jQ0BDDw8NV2bdlayXbQ7q75XuHh4erJp4eVhQlOHgqAawNyo3/ckO3OwJXOx+y8a7aBNofSr/bFZLBuz3EZriylLtOSN/bQAL2lUfsD7dGRMrKE7TwQkTKQmTMqywyx9se0u/qE8mkFIQ6wsXjZnaMm9kxWHgHgCY1SrfZjhFxaXVjNGcSdJtthNXKqDhqMkn3D/+KvzXdAsC90KmC1y989FPC/35SMil3SNCkWkTKQiSTsrJsZbsZPWTZC/39/Vy8eLHi+/3RcKxke2tLtCqfVw+ETb+kEGI5ckO3RpBQBLIwY7cEBhIkcLqO7wfPPcotbBGCEWv17SH9rrKUm+PlrrWHa45XLYL7nZyf3SIi5daISFl5guIBshCtEBGLKovcW2wPccuoT0SkFIQ6Z9FLsZgZgXDu+c+SNwBo1RpJGHHa9GbajWba9Sba9Wba9WaiaghlG4VJtFu3CF++jDE5i0ITHfZUUU1KXXHRRkeJvPwymeefxz1zppp/7pFB7Em2h4iUlSXIngTWJpIyaQS5masGIVl1uiVB58LU/UNlxXyQkNXOW2M5lLRiBhnzdkvQtbbcQoR6RK61lSXoOgsy5uUT3O9qfCBHiED7wzI29vVGOeeQclbNQjAST9ka3w92S5IFkLsjqN9JskEhgQuCysSjhMOPiJSCcMTwHA9V33s0ctZdYtZdKvlaWDHpMJppyxMuW/VGWrQGWvQGGtQI+sQE4cuXwXHQ5hf53OLbmH7hDKfDnqL79gpqx2N48Tjhy5dJv/jikc+odBxnz3aOybE24HRR+8JSlitXPtzTvnfCrVu38P2DO1GYW3gEiBS1TybvceXKJJD7G86fP1/jIzuciBXY9ghc+SeB010jAaytkYB95QkUKaXfrSPZHZUn6Foh19lCZMyrLOWEDrFW3yBosYD0u90jQfutCRrvQoYsRNstIlJuTdaWhWiVRmpSbg9ZEFSfiEgpCEeMH//df8nZf/gZzv7dS7iKV5XPyPgW960p7ltTJV/XUPnt713j2HyG1gWL+Ph9mpujZPwI4/oxlrwmQmmHruwkujePfv06zsAAXjyO+frrZF56qSrHfVBQFIWrV6+ysLCw633cvX+OUiLlStrlrbfe2sPR7YwbN26wsLCwp7+lmiwsnaOUSPlg5AZvvXUNyImUwvYou8perHHWkcBpaSzL2v0CDe/zQGNR8807owwO3tvbge2RCxcuYJr7f8cktjiVJxQQtBcrsA3KBVWk7+2OkNRC3RYy5lWWsgvRZGHGOtLvKk9g0F7sD9eRfld5pCbl1sgcr/KIvfX2kDGvPhGRUhCOGOnkIh/+Dz/mn//O/4mFbp/hzH0+yYyy4mVqdgxNUwuEk1PM+z5dt8bIaC72WY1kbwzUSRrJZbDNZDXujTqkFifh2jzTTwxg3hpl7FYPxrETNGlRmrQojWoUQz06w5WmaXzlK1/ZU226lndCfPdXxe2+EuaFF17Yw9HtnGrV9asE/88fN5Rs/8Izn+Z3f+OxGh/N4aecrYus/tsg0J6kzm2Gh4aGGB4epr+/f8fvNfXSi26y9v4uHx8eHgY4EGNgOiCIXO/i+F4Qu9etCep3IH1vt0i/2x4pGfMqytaW/gIEZ1KKW8bukezxrZE5XuURkXJrZI5XeYKutbaj4LqgaTU+oAOKuBbUJ0cn6i8IQgFRQjzZNMAXm57A833G7GmG0/e5kXnAzexYVUXL0zcmAGiaS6E7LrahMdrbiq8WXmjUkMvdU82YQ2MY2SwzE7dZaI1x85f/kfc+31ewbVgxaVwVLZu0KI1alEY1QpMWpUGL0KBGaNAixNQwMS2MoRzt4S1k+iSscZ5cfp+ElcT0LSzFZCrchTrRi9fVtd+HeCAIWpFWLhAjBFPuvGUlkLCOZFIGs9tFDa3NERgvbm/r6OHixZa9H9gRICibOSgbUNiawFqostp5nXJZ9FIna3cEXWsdVwJY+QRllooV2O4wdFBVH88rHt8kw2ODoPmu3FvsnqBrrfS7DaTfVR6xGd6act/Bcg5LQjDlFrRkbIjJHA8ob3EtHF2OdhRfEAQAVEXhuNnBcbODLzU/ie/7LLopxuwZRq1pxuwZbs6PMKut4FTAIrZlZgWApvk0APNtsSKBcg1fVZhvi9GRXKRpPs1Ca2z9/flkfIuMYzHlzG/rGEKKsS5extTwuoC59jhtTBL2DTqsKWJqhAY1fGiyNdVkkv4fv8Efjs8VvXYie5/wv30D73gP1rPPHvn6nlshYlFlMXTQVB9XAlhlkX5XecQaZ2uCVjuLLc7uCQokyKKMDcrZQMqYtzvK2m5KAGudwDFP+t2uUJRc30tlSs3x9uGADihBWVZBWVnC1gSJRVkRi9YJdGmRRRm7Jug7KyLlBuUyKeVauzvKfWeztkIsLOcVyrgWyH3tkeZwROQFQagoiqLQrMdo1mM8HOkFYPD+ID4+vY+cYcyeIWnPMe0sMOMsMG0vMuMs4uBua/+6ndtOc3OCZ2aLpfRrr69tv/b+vZD1bbKOzQyLpTdYdQH9/tiH600hxSCqhomqIWLa6qMaXm/baF97vtGuKrWxHNRu3SJ8+TINsx4KYTrsKTrsaXTfwVF0pox2HDuKMTpK5OWXyTz/PO6ZMzU5toOG4+ZsM0ohgdPdEwoIYIkl0wZiT1J5gmq0HcVAguM46zayO+HuSB+l6na61hKDg4MVOLLDQSVrhAZloR7Ffrdbyo39MubtjnLT5owlAaw1ZEFQ5QkbkCphthM0r6lHpN9VnsCalNLv1pF7i8oTJBaJ3esG6TLz3XJlYIRgymWg5sY8+U5D8L2W9LujjYiUgiCso6DQbjTTbjSzuVqf5/ssuCsbwqWzyLSde5xxFllwl/FWL6iOkVvi7Wo54S6csVko87nhVa+wte3X3l9rsr5N1rWZc5dghyuG18TKfAEztvZ8VfCMqCYRNUSmVWFOTbHgrBBRQxiKhqJsfROmJpOEL18GxyGyNM/nFu9h+oUH2mFPYbznow704cXjhC9fJv3ii3WZUSmF3qtD2AwKYNX+WA4qkt1ReQIzKY9gAGtsbIze3t4dvy/rlF4sYxp7d0g4LFS6Rqhk8G5NUOBU13wMudPcFeEycxQJYOWwHUq6OoAE7fdCPV1rd4uIlJUnsA6vXGvXCep3cm+xeySTcmtkIVrlKXfe5Fqbw/flWluvyK2jIAjbQlUU4noDcb2Bs/QUve75Hktumnl3Ge10G6HFX6N0xWhcuoc34XC/MYIesVF1p+B9iuev27sutkSAnD3sYSPlZUl5WQjK3MznywZ/xhX+7MEVADRUomqI8KrQGVkVNDd+zz0f+MFPaEzPEZ1fInTnLmHNIEuEMf0YKaWBqLdCwkriW8vo16/jDAzgxeOYr79O5qWXqnsCDiBpi5J1O5NmgqbFk0DHfh/ioSQcsNpZgvYbSCCh8gStsj+q2R27qd3Z/H5xFiVAZ1tDxUS7eiOwJuUR7Xe7QYIIlads/We51gISOK0WktG2NZLRVnkCRUrpd+uI9WHlkUzKrQn6Dpq6L/Wxd0m5OZ6MeTlsh5L1sUGutUcdESkFQagIqqKuW8iql54j8nESujsxxjJY91cIvRZnODIAuoceyqKHszTElvmi8SFNXhQrpBDq6KYZh+nzZ1BR8aiP7A8XjyUvzZKXDtwmPrWEcvsDFN+n/+MxdM9FvRhjtreVkHoHw9HxHI0JW+XjyWXi88uoQ3NMXOxHu3mPm8PN+IkEYdUkopqElVxWZ/7zsGrWzLa22qjJJA1/8Tp/OF6cw3sie5/ev/wZ4evHsJ59dh+O7nATGEgoU7Oi3pC6MZUnSOCVm7kNpN9VHgmcbo2IlJWnbE1KudYCW4iUErTfNTLmbY2MeZVH+t3WSL+rPEGZlOIOtEGQO5D0u91Tzq5U+l4OWYhWv4hIKQhCxfG6uvC6u1HHxnD7+vBGrtJlT9LizJM0E6TSMaLeCqcYpSth0dDYgjMwQEdjHLenhycvvoTv+6S8LItuikV3JSfiuSmW3BSLa49e7nHZzZDxj/YV/fSNCQCa5lLojottaIz2tuKruQu4qju5LNUw3Ik1YgwtYVjLLEzcZaE1xp13f8h7n+/b8nNCikE4T7Rcy+IMrwubJiHVJKLkHqfjGRr0OeLZJGHVJKSYhFWDkGKibsPCthqs1e1MzwXX7dTVGNpq3c4WWQa4I8T+cGsCAwkSON010u+2RgJYlSeoNmBmh5bwR5mgjAMRx3dPWZFS+h4gdbKqRVD956z0u3WkTlblCar/nD3at/c7QuZ4lUfsXrcm6D6rXF1FoTzlzp3c1+YQkbJ+EZFSEIR11mo5VYJQVxc9v/41quvyScNxjLklQr7FyezI+jambrGUzbJ06hR2Nos3NcXoxYtkBweL9qcBLUALESACtBW87uKRVRwyik1GtXOPeb9nFaegfcXP4uqH5wK3ZonbNJ/Ltpxvi60LlJvxVYX5thgdyUWa5tMstMbW378Va3U5F9je9vTDR8zz/fEPi15aEzzzHyNqiJBqEFaMnNi5Kmzm2lbF0Lz3hFWDsLK7up3KTHDdzsYPfZT+XN3Os1evYnz+89v7e4XgQIIEsNaRQELlCQycSiBhHel3e8dxnIK50P2RNuBS0XaZLAyWmKvUIzdv9wLNRe2Kl972ORoZGaG/v7/CR3Z4CbrOggSw1pAAVnUIsvSXjLYN5FpbeYLsD2W82yAjC4IqTtC5c11wHNAlWi4uLVVA10BT/ZJ1taUmZY5yC9HkWnu0kWFXEAQALly4UNH9ZdvaSD7zDIk33yTb3M27C61FGW3ZhgitF3vRdB1P00g+8wzZtratd14CDZWobxL1TbbrEjtwfgBb91l206x4aZbdDCkvw4qXWa0xmSHlZlfbVp+v1p70qe3FUbddADQ398dlgtI7Vll7fW37tffXkjXBsxIoKIQUY13MDKlGyeePXf4ZzcvzxOaXCd+6T0NUI6uEmAh1saI0ELaydKanUJw02vXrMDCA6ro0v/UWfOMbFTnWo07I9EvW+jw12Iz69Dm8rq79PsR9RwJYlUfqZG2N1MnaO2NjY/T29q4/D+mlJxSer+K4Crom5zbrlLaJN/XazzuOCroGuubjuMXfaRnzcohIWR3EdnNrZI5XeYLO3VGtO74bpN9VnnJCW9qCRomWy71FlQibPisZmeMFUe48iGvB0UaGXUEQADBNk4sXL1Z2pxcvol66RPb/9Qv8kUkmjS4mjQ0B42SHQ8/xDG5PD9azz9KcSFT287dBBGjSojt6j+f7ZH2LFXeToLn6uOJuiJm51zOkPYu0lyXtZfF2IXA6Rs6S1NVywcBwxqa42uIG4VU/sLXt195/WPHxyfgWGdcKzPKMTy3Rce9Dkr5P/40xsoqLdz7GZG8I1EliTAIw6/m8f2+OlrkU7tUx7g90M5F8iz9+/b/HTnTmMjg1k4gWWre3DWsbj+G8x/y20Hqbsa2sz8OImkzye5/8OcvjE0WvHbtzi8h3f7n+ffb24ft8EPC84JXfQXUVha2RANbWBNWNiYjN8I7o7+9fnw8pZSJUA+cv0hiVc/vTm7GS7a0tkW3PKyvp5HFUCJs+y2kJYAURNN6BXGv3goiUWxM075Br7e6RuuNbI/2u8gTZvULOyr5xZyGiI0ngvYVcZ/dE2ISVTHG7WPrnKDvHkzHvSCMipSAIVcVLJHjj4re5fHexKPPK6W4j/a3Thy7zSlUUIkqIiBqivYTFWTl83+df/H/+33z+2d/kzPmzpFfFzHTeT+75qqjp535XOicIT2VYicdonk/RMrPCZKK5pOWr4vnr9q6LLREgZw971NmqbucavqowerKN2HIWw3ZpWE6x0KpgfPAhH26jbudWKCi5zM5Soqa2amGrrdrerrZF1l8LEdaM1UezYJuwaqJv0/a2GqzV+jyW1rmJWpQZHV1oAb9pvdZn5vnncc+c2Zdj3U/SFiUzTZNmgpbFU0D7fh/ioURqUm5NUDBPAgm7p1zdmIwNjTU8loNKYL+TIMKeyImUxe1icZ0jqN+pqo8hEY5dE5RZJJb+OVwXbEcyiyqN9LutkYy2ylNOpJS6lDnk3qI6yIKg8ohbRv0iU3hBEKpOxlJImsd4tfWbBe2/05/B6yqXD3j0UBQF1YEGP0S3uX1rW/WLv0Xk/neh0UefvoJvWTx4rZ1rDQ+hGB6a7qDqDppm8wX9Ju1eFCek0tDZS0hxufHo4xw3ImR8a1UAtfC264t7SKhV3c6t8PHJeBYZz2LBqcgu11FRCWs5ATSkGoQ1E3NNyFRNVrwlZk8v8B9m3iCkGpiKjqkYmKpOSDGKfs/fJqQaaKglRdD8Wp8t2QU+t3izqNZn10QS410Vty9X6zN8+TLpF1+sq4xKNZkk/P3X+MPxxaLXTmTv89APfkb47rG6zjTdLUEO1xLA2kDqxlSecpZCuboxcqMc1O/K1VUUtibo/GVkYQZQ3vrwiJpZ1ISghRkSOM0hgdPqENTvpD7bBiIWVZ5y8+OgGqD1htgMV4fAxbcy5gHBi5BlIdrRR/69giBUHVn5t3e8ri687m7UsTG8vrPo16/TvjLPEwvXSJoJUmqMqJchYSVpO+bT0dqCMzBAW0Mct6eHC4+9VLA/3/exfZeMnyXj2aS9LBnPIu1bZLzitrSXJbvalvXtnAjnWyxbabwDUpPrMNbt3CkeXq5OqlvmzqkTkksf7Gr/KkpJIfPp//IOrQsLNM6nOJlNkm5TsNQQU9FWVrQo4axNiz/DciaLMjRIur8Pv7UV90d/ydKLLxBSV8VSRUdVStcvO+ysZZo6iy4KkaJM0ymjHV2N1X2m6W4JB2RlSeB0AwkkVJ6gWqggfW8N6XfVQewPy5OWRRlVQbI7yhPU70DqZO0F6XdbE7wQTa61u6Ws3atkUgIyx6sWMuaVR/pd/SIipSAIVUdW/lWG7Je+ROTll/HicZyBAdyb9wjZFiezIwXb2aqBM3AaLx4HXcd69tmifSmKksugQ6dpD+UqX3nlFc72n+P84xfIeBZZz87VjlzNJNwQNG2yqxmc622rQmfWs8n6Fhkv1+6wO7Gw3ut2VgIvr/bnGvGpJezRu0z6PvFbYxByyR6LMdobxlfTaKSxgWFPZ/neAi1zKZyP5xh+tBt/6SN+9Osp5tob1veno2GqOsZaBqeiY65mdE6dm+N+7BpXp6dXMzw3XlsTOYuf5/2++npQRmi1yM80ZWaezy1+UJRp2mFP0fyRj/pw/Waa7oUgsUhu5jaQG7rKU+7cSd/LkZJaqFVBMtrKI/cW1SEs19qySCZldZA53tYEz/FqfCBHiHLnLi2ZlIC4tFSL4DGvxgdyQAn6/slioKOPiJSCIFQdCZwWMzw8vKv3Rfv6SLz5Jqrr8n7rp9DnF4uytZbaFdzsIt7UFMlnniE1Pg7j4xX+C3KMjOQEUpXS/2Nz9acJDYis/pTHxcNW3NwPLpbi4CguluLiKC42HrbibGyjuDiKh9HURXhkhUwsRnwmTctkmvG2NhTTQ1EK+1q91u3cDbut9dk0l2KhNcbp4STvtW/U+nRwcTwXKDH7bIUZpri1PLWnY1ZRVkXQVTG0QNjUMFSDlegSOio3ZhZy26n6+jbGuuBZ+vmaIKorOqqiEHrtNXAc1NlZQjeGMX2D7GodylyW8woJK4nmLqNfv44zMIAXj2O+/jqZl17a+g8SCJt+yVqfc9Eu1IneQ1fbuBrItbbylKtJKfVQcxz2fmdZFkNDQ/t9GEW49meBlqL2O/fGGRy8veP9jYyM0N/fv/cDOyAc9n53UAmyGRZr9RwiUlaHIMEjY4PvIxbOyJhXDcravYpYBJTpd7IQbU+IpX95xImvfhGRUhCEqhO4EqZOJzfd3d27fm/q+HFGn3uO9nffRdUUJo0uJo3C4Pwxf4Z0RwfTTz1Ftm37dS93w2/91m9VPGNNQ0XzVcL+zgpahU50ceLqq9Do0aL5TE/paHe7GI4M4Gugag6q7tLWuMzXYu8TyoRxdI07K2H62hI0P/sSZ9sbSbur9rZuLvMz4+Zsb7OuvZotmss0zHo2fh3UIzsotT53godP1rfJrmUzlkrOXbX4ub6U3NNndU6n+OrHv0bzFc4OPUDxHbzzrYx2HsNjCc1dIeNp3LFbeEhdomFuAf/6e8w+cR719gQjt0/gd3WtC6FJbQEDjQl7bkMczRNE6xE1mWTgJ2/wh+NzRa+dyN4n8t03cHt66r7WpwSwKk8513CxAstx2DPahoaGGB4ePnACnqmXdpWw7KNpmb5T0kEZvIek3x1UxIKuPCJSVoegc+f7CpYjNY6hzJhXp/GUSqCqYBpglViEIZmUOeRaWx3kWluew35vIeweESkFQag6Yk9SiK7r9Pf3c/Hixd3v5Mtf5sczGZz3PirIKkqaCfwv9fP0f9NIZ+UO+dAQSSZRx8ZQIxH0n31Clz1JizNfkNE2kBqn+4QCDa04AwN0jI7S1jXAU0/87o4+y/d9LM9eFTI3xMtM/nPPIrupbfP2uUd7tRZoTgy1fadKZ2jn1EOtz71wYngMy3donl3Bs7PYIY2ph8NE1fmibQc9lXPzKYy0y4Pxj1lojXHz7f/Ae5/fyDSlKffwn0bfK3p/oVVu7jEo6zO/vWC7tfdtyhxNhxxWlCwpN4Oh6uhoNbXMDWKt1mfTrI9CqGStT8eJotd5rU/fh+blJF/clGmaNBO0Lj8EVHfBylFFVXOWTKWyJiWQkOMo1Mna85ysCrS/2gD3itubW49x8WLjjve3WwePg4osyqgOEjgtT7nzIGLR7tnKWr1cfeh6Qca86hAJiUhZDsloqw5Sd7w8Mt7VLyJSCoJQdYIs0eSGY2+sxBO81nqyqL03sgIs1/6ADgD5dTvvd57Hnr5FyC+s2xkyPHzDwO3L1Qb0kkkWnn56x5+lKAohzSSkmVDhFb6u75JxV+t4utlc3c51wXP1d89ez+hcEzxHJ8YYm05y7ET3eiah5TlYm373dpABKrU+y1PLTNOyVrl75SL8imn+3f1fAqDAet3QnMipbQigql6U5WmubmOqm7c31l8b1efQfY0H1lSRmFoqUzS/1mdocZbPLd4vWesz+9MlFk72YDc14X3nO8T+u/8Ovbe38ufogKImk6h//Rp/OL5Y9NqJ7H3O/vBvCI8l6j7TdLeEzdIiZVYCCYAsRKsWQQEssRnOcRTE8YNIoEgp/Q4obwEp2X67p1xMIGspEKvv77XnBY/9MubtjYhJyftasXvNIRlt1SHoeytzvBwiUtYvIlIKglB15CJTHcKBBbfrd3LjJRJknn+e8OXLLITb+HVTZ1Hm1Up7nL6nGnMFTnSdT554gvMdHft96AVoikZM14gR3tH7rkxd4a2bb/HCU8+X3c7xXbKeXSRelvq9s9cgvjCM2RmheekBTMBttQ0MH1XzUDQXVXPRdZeI4RCfSQH1U+vzqGaa+oDlO1iVzOpdTf75i7HBki/nZ4oais5v/cV7tM0t0biQou32KO1dBlnNZKYhzooeIZy1aF9YIOUvo44Ms/RQDzOqgvIf/iXOf/sH69miulKYeWoo2pGx0F3LNM2kXBQiJTNNdbUBrc4zTfdCUPC0nq+1+cgcrzpIvyuPiOPVQTIpy1Muq+iQTyf2lXLfW7FWz9XmDCIiY96eCIdKt0smZQ5ZEFQdQkF1eKXfAdLv6hkRKQVBqDopqaFQFSSAVRr3zBnSL77IxNBb+EwU1e0832SDkl2vYTf/xhv7d7D7hK5o6Nr2RFD1cz1EPvku9BzHGLdg0qXhlyGGIwN4ykZtrJaowx+e+xBNs/HDOsdPPYmr+Fz8wt/jm52tWL6dE708m6zvYK8/d9Zfu3p9iMbWZppam8n6NraXa18TTO21333nwFjiSqZp5cjPFI1PLaGNj7Pg+yRuj+GoLqkTJqO9jfiqCyyTBh54Yfx7K7TMpVBG7zDzaDf+/DA/+vgV5tobtvxMHS2X9aluFjLzbXW1AoEzf9uJUBLNV7GWI6vCp7aRIarmnm8WRzWlMv/z/ExTf3qOzy2OlMw0bb0G6oUzePE44cuXSb/4omRU7oCQCQlrnCc3Wel2v9OI2j+A19W19U6OMEFBe5nj7Y2gAFb2AAbsLctiaGiopp95f+w8ULwIKpOaY3Dwg23v58KFC5imRPnXCMoGPIj9bj+QRRnVodz5k8wiqYVaTYIEcsmkzCELgqpDUBxPxrscwTV4a3wgQs0RkVIQhKojNhHVIWjlX7bMast6wUsk+OH5/x2/mpwrCiw39MZJf6uv7gPL28Xr6sLr7kYdG8Pt60OdvlGy1mdvehxtegUUBbfvLLqqo/T00HL8LC3b/KzMvfv0h89xsX/r2mCe7+OsZvpZ6xmgG4Kn5TvrIuf6c9/B9nJC5/j0BK7iEWtuWH8t6+fqga4Jomv7KzdSzbfFaJ9YZLElQvN8ipaZFSYTzSUtXxXPX7d3rZdM091y+sYEAE1zKXTHxTY0Rntbi86rryqMnmwjtpzFsF2a5lIstMY4PZzkvfa+UrsuwMHF8V3S7i6jEdHcw99M39j2W1SUQtFz0+9jz2j8Z/+X/OTGNXRfQ/NVdNSCRw2Vx7//c5qm5ogurtB0c5xYOEZWCZE0u1hRY0TtDJ2ZKdKL07hXrrB86hR2UxPpP/5jRr/61d39vdRXUF9NJvn9+3+BkRwveq3tkyyR776zvuClXoVfCdpXh8OU0TY0NMTw8DD9/f01+0zLKb3YI2Rs351grU7nQatHup8cpn63HwQGTmW82xNlRUrpeyJSVpGgTFTJpMwhc7zqEHT+ghb+1RvS7+oXESkFQag6wen6tT2Oo4YEEsqTsSBpHuPV1m8WtP9Xj6Twupb26agOJ/m1PhdOPYw1eqeo1qem+gW1PtF1rGefrdoxqYqSq3eIAUR2/P7BkZzl6MVz5YOTvu/j+G5JIdPyHfj8ON33/hw34tIy8RFzM1kaf2Vwt7MbdFA1F0X1iJg2F9P3ibk6tmnit7UTU3xmzp8hpoYPVHboQaCWtT5rjYe/XjO2JMc0MmSZZipwH/GpJY4vfMKs4dM/NsaS6eI/4jLV24qqjq856zLr+dy6l6VlLoWTvMon7cdRFu/zhq2y3NZcJHzqq48FwqivrbfNTEzx8SfjnDl5Ok9g3RBa8zNJ9XVbXQ3lEPrgrVnpHs8aTKIUWemG5prBb657K92mxSSPz35QsBgoaSZoXTkDxPf78A4tQavsD2oAq7+/v6ZiX/SnzSXbuxNxER33gNxblEcCp9WhXE1K6XvB4jiIa8FeEbvX8gSOedLv9oRca8sjLi31i4iUgiBUHbmhqw5i91oe6XeVI7/Wp9sS5+2mRFHAftZs5+JT0fVan5nnnz/wmUVrWRR7wyCrNBOemybbdhweJInOw5nR6dVM02Yi3gqn1Rl6Om18Iix3n+Jcuol0Rwdf5bdgJrcnH5+Pbwxzuv8MA4+cxyqZ2bmaEVqQLequPi8UUG2/xGPevjy8Cvz91eGo1vqsFLvNNG2YX2ahNUbikxHe69w607SIh+AmSzvKHAXWhUw9rx5oqSzS4t/1kmJo/v7yrXWX1Ay6r5L2shiKjoa6K4E030q3zV7gzOLNIivdtrEkxrvq+sKMerPSVZNJQq+9xj+8s4DtFJ7jE9n7PPbam4QXu+o6y3QvBGVmiRVYDpnjVYdQwPmzHAXPA1Ut+XLdIP2uOoQMUBQf3y8+v3JfK5mU1SQok1LsXnPImFcdgpI1xBEth/S7+kVESkEQqortgOvJSphqEHSRlgBWDpncVJa1Wp/Wn/wMn9miWp8APst4h8T68MKFCxXb19SlS/T86EfYTU2MtDejLCSLMk1DIRtP11k5fhy7qQlP05h+6qmC/SgonD83wIXzFzD16qeau3kZov/58n/h5OlT9A2c2ySE2ti+i+2VEEg32ehuPHc3CaO5fThsXziUWp/lOWyZprk+UQPheDW56rsjbwO571SxQFpa7DQVnamnNH4e+YTQ5b+kaXmO6NwS5+xRMo0qWdVkMtJOSo0RtjN0mlNksmmUax+S7T8Hra14P/kh6ZdeytX95XBmkG6HtSxTHAfXjdJpTxYsWpky2tGVxrrPMt0LwXO8Gh/IASUogBwUcBa2R7nzl7WlHpS4A1UHRYGwAekS5zcjY56IlFUkaEwTkTJHYEab9Ls9ETjHk0UZgFir1zMiUgqCUFXKTarLWbsIWxMOzKSs8YEcUIIm1XIzt3u8RIKJv/WP+M6b2aJan0kzwdwLJwj1Ho5an6ZpVtQSTnvoIcKXLzM8qfLu1ENFmaYLnXEe/mIj4bxM0+Z9DtprikZE0YgQIpLViXsxekOdVfu89wffx8Hj/GMXCgTPzRmetu/QdqqBpsWPMbuaaFoeoWlB4xO7B0/bsNBVVA9Vc2lvskjMTaEpOlZrMyHFINXWjIp6oLNF94Jkmm4PH389A3lbnNYYmxpm4t5VJn2f/k/G0BtdnJMxkr0RfHWeEPP4wFVPYfbecs5K99o0w4924y8O8qP3R5lrbwCfAivdzda6Bc/LvVZi23xr3nyL3vznCtUJdIRmZuj50Y9QXRdtfpHPLqSLskw77CligylmlhK5RRnf+Q6jzz1Htq2t5D5HRkZqWs/wMBAyIWGNF11rQ3o76sRDdV9XO+j+IigTsN6xLIuhoaEtt7uTbAQ+X/K1K+99RGOkNorRQa19LPcW1SNk+iXPr2RSlhZv19hi+idsQdACA7F7Bd+XhRnVQhzRyhPc7+Rae9QRkVIQhKoiNRSqR9DkUCY3OcTLvjqETb9krU+AlfgkIerz/K5lmk4Ov4XPRFGm6dlGB5QM7iHJNK0GCgoGGg3a1jVE1c+0Ehmeg+4ujLEVFNum46rPVWMAT9nwm1N9jy/0XuWE0obfYNB98ilQFH7jy9/i211duL6XJ4a6WL6dqzHqOZvaC0XT9fbVLNA1AdXZtO1SehkHD8VU1/e7k4zR3SKZptVjt1a6TXMpFlpjnB5O8l57Hyjg4OEo+yOUq76yIXAGiJ8lBc4A0XPtveff+xkr/grhpWWi9x4Q0ZvIKmHGjQQraoyonaErO4HhLhC+e5flU6ewm5pof/ddRr/61X05F4cNNZnk02/9Df74TNFrrZP3iHz3zbq+loCsst8pQ0NDDA8Pb7kYIGQEj1dZW6Vx5yXAd8yaFf9BrC0a2O/k3mLPSI22YAIXZRh+3Vsw75WgTEoRKXPZ86UsmEGutXslaLw7qHXHa02wI1qND0SoOSJSCoJQVcqu/JPJzZ4Qu9fyBE1uNk+qXdfd1uryw8DQ0BBvv/12VT9jYqkF+HbJ1/7sP12mJbJ7G8l33nmH06dP7/r9+42XSPDjx/4xPx2bL8p+MY7HSX+rr+6zX7aL19WF192NOjaG29eHfv06XfYE0cz8aq3PGFFvhYSVJDyXgriC29cHioLb07N+njVFRVNMwlTnrmZwcBAoDKZ6vo+zyfp27WdN5NwsdtrehjD65i9/TmO8mcZ4Ew4erpL7Wf8dDzveSiiZIt3SQMtcmvj0CpOJ5pKWr4rnr9u7LrbkIszzbbGqnI+jwGGz0g3CU3w8XGylcqJ5fGqJlsWPUQyf/vExZg0X5QmXmd5WDHWEltXt5l24O5IhPpvCTX7EzbYTKIuj/DwbYrm9uSgzdOopjZ+Fhrk1u5Rnx1tYd7RcTdLNzzXl8EZt16x04wugYBZl5ae1NvBjdW+l27iQ5BuzHxY5OrSlzrLu+ywU0N/fv6Xw1z4V/N052/8opxL1kYUfxHbvLYSdE5RZJPaHZfrdJnHccRy+973vce3atVocVsV58803se3a+vsuLZwCOorax5IzXLlye1v7OCpxhM2IzXD1kJqU5dnumCccPUSkFAShqpSb3EjdmL0RZGklK05zbNeexPd9bt++fSRqh/385z/ny1/+Mo8//njVPiM5Z8Jfl37t0cc+zYmO3S89bW5u5vbt23z605/e9T72m4xFyUzTly6k8boW9+moDifZL32JyMsv48XjOAMDuDfvEXIKa30C2KqBM3AaLx4HXcd69tl9OuIcqqJgKgYmu/Pg+vjqm3zlK5e42B8cTFYbJ4gkvwstPsbMu0xOuvC9bj6JnQXNX7XC9WiKWrzYfQ1dacWNakSOX8BVoOmz36C/raVAHF0TT/MzSUv/XvzaUUKsdIPZdpapBg9OthJdzmDYDuGFxZyAe+sWtxJ9JXascZ0k1xeTFTlOFWWTiJkvcmroec+XogtoqAzPzJd8vfj5ZlG0eHt1lyKpmkyu1/psSC3wucW7RVa6xsIkxrvg9vXhxeOEL18m/eKLdZNRqSaThF57jd+7Mc/iSuF5PpG9z6U33yTsd9V1luleKBd4LrfwtF6QevfVI9ghqLbHcRDZbr9TFIXTp09z/vz5WhxWxfF9n7/6q79iZaV2i71mZwxKipQT87z11lvb2sc777zDCy+8UOEj23/KipQiFu0JyRwvj1ir1y8iUgqCUFVkBVb1EJuI8mz3hk7XdX7nd36HS5cu1eKwqs758+er+rdMzQW/drb/MR7dY0LHdm8IDyoSwCrPmo3bdon29ZF4801U1+X9+FMYCwsFWUVTRjvL7QoD2UW8qSmSzzxDanwcxser9BcUsh37vGqwOdPUnbhBlzVJi12YaXpmfpzG8ApoYZz+AbrMOG5PD81nfrNix+L7fp64ufmx8Hcnzzp3c3tOJM0XTPO28Urv261CzVGx0g3msGSZevhkfZusv40l6atWb9eXKiOQAmioJQXOrbJEH738Bk3Lc0TnloiOPaCpUSWrhpiMtJNSY4TtDIn0DJlsGuXah2T7z+G3tuL9+IekXnpxfZ/qEVh0VYq1LFMcB9eJ0GlPFF0PDLWp7rNM90K5BaRHIXjqOM6O5yH5jE88DhR73q4sTTM4eLizqfa7Dqjc1waTCbAZ3px9qmkaFy5cONT3tAsLCzUV/G7YDbx5s7i9veP4jo7DMI5ecVCJ41UPESnLU87iWjjaiEgpCEJVkclN9QhK7sjKilNAxKJqEQ6o3QFiUQLS78px4cKFHb8ndfw4o889R/u776JqFNX6BOjxp0l3dDD91FNk29oqdbjbor+/f1d/VyXIzzRNHjuPlbxNyC/MNA2FPXzDWM+6qkamqaLkstaMfbit8HwvzzrX5cOhD3Hw6Bs4W1YoLSWIOr7L37z9CzyzhWbPxm1qwZjL0jJh8aAxAoaPouQyVBV19afOrHQly3R7uHi4vkVmB8N+fGqJzntXmfR9+j8Zw9VcnP4Yyd4ovjpPiHl8IOn5XL23QstcCufaNMOPduMvDvKjwVHm2hsA0NGKBNDlR5e4Flrh9fGbgdmi5jaE1FLPU4qFhortOeiKVhVnivwsU3V2lk/N3kd1CicdHfYUxz5RUJtO12WWaSUIGT4Ja7zIsj5pJlCSD0Ffba+xlWZsbIze3t5dv99ySmdJm/r+1B6uFAehDmg4IPhsORK03647kLBzpHxOMFK2qXoEiW2Oq+C6oB3d9Y7bImhxiti9Hn1EpBQEoaoEBexV1ceQEWhPyAqsYBwX7ICbWplU741yq+zTu3d6PTKIPUkwpmnuPgD25S/z11MZ/MGPigKn6lf6+ex/3UhnZQ/3wOMlEmSef57w5csshtt4u6mrqH7dTGsrDz/VAIoCuk7m+eePVMBeVVRMRV231m3yckLhydDuar+O/PodZq1G4mOzKH4z4dQc2cUY5r04w5EBvDwbz9bIPF+NvkF0WcXVDBqz7TRMKCT7H+biR414qo+n+rgq+IqPu/rcU1l/zVM2tW1+vt6W91z18RRgHy71kmVaPbZtpasqjJ5sI7acxbBdmuZSLLTGOD2c5L32nJWug4vju2T8vAhjDFZYYjK7VPmDb8k9/PHIL4CcSLougKqrIigboqap6oEiaZCAeuqHPyCamSE0t0Dsxh1MTSejhZgwEywrDUSdNF2ZSXQvhX79Os7AAF48jvn662Reeqnyf/MRRE0maXrtNf4PyQU8v7Dfncje5/hf/A3hkcSht9LdTm3OIELfbynZfqKnfV8FvqOA3NcGI/cW1SNIHJd+J2Wbqkm5727GhlidT5eDFmZIvzv6iEQgCEJVKZdVdETdqGpGUE1K11NwXNDreHJTdlItK7D2hK7nVve5JRJiRKSEdIAlU0QCCXtmuTnBz1pPFrWfjqwAy7U/oAOAe+YM6RdfZOr2L/CZLMo0PdXogJLB7ek59IHlWmCaJp/95jc5f+0a6tgY6vHjuG/eoGtikhan0Er3rDPOQ21RiMZwBgZoj+esdM/VQBDxfR8Hd90G12HDFtfBxfKK64uWziB1i56Xsutda19pa6ZzYonFlgjN8ylaZlaYTDSXtHyttyzTvXJYrHS3w5pImvYtKuHIHJ9a4rmbH6D4Pv0fj6E7LjwWY6a3FU19QPPqdnOez817s7TMpfA+SnLr0V7UxQ9464NZ0p2tRVmgS9FFdFSuz8yXySoNrklaLK7uvh7pfpNvpatrEVrS00VWuo7dWPdWutG5JN+YLV4s1ZnpBxr3+/AONSGpSRmIZBVVD6mFGkzQPS3Ife1eKWdEkrEUYuH6Pb++L85U9YyIlIIgVJUgmwixJ9k7ZVdgWQoNkfq9iIs9SXWJmLCcLm4XkTJ4Ui03c3tHVtmXxksk+OkT/5gf3FsosujzultJf+sMXtfuMgvrlXwr3ZkT57Emi610dapvpRuEoigY6BhabW/l1C9PEBn7Ln6Thz79LnNzNrd+2MmNxj4U3V+3wQ0ZNi8krmPQghvVULsfxlN89E9/le6W5gIx1PFdPrlzk1hzAw3NjavWveUFU5+jN56KlW4wO8kyfXCylehyBsO2Cc3Os9Aao2HoOsMtfcU7rmE90s3C5njfPGPR61ybni3IOs3PQg3KOs0XS9deMxUdFXVXVrubrXQ/t3gfxS620m37WEGN1aeVrppMEnrtNX77ozkmlgpXgZ7I3uc3fvlzwrFOWQy0B2SOF4zcW1QPsXsNJqjfGbpf93akeyVcZoFB7rzX73fbdsDzRKSsV0SkFAShqkhWUfUod5FOW9AQqeHBHDDEnqS6REIiUgYhdWOqRz2tsl+rD7VdRscvkDRP8GrrNwvav9A1ztNjv4axsUoe3oFleHiY/v7+Pe8n30rXbm4taaWbbWzl/BG20i2F19WF192NOjaGf/Ys/q9u0J6aozH964Is015/nNZQGvQoTv8A0XAuy7Tj7HMl9/vKT6ZzFozntmeX6PpuSfEyP1N0o7105uja9snpCTzFJ9bSuF6bdCOrtLCW6do+q4FY6QZzmLJMt12PtB2myHBjeaJin62grGd/bs76tBozaL7KWxMjRVmgj15+g6blOaJzS8Rv3qexQWPFDzEZaSelxgjbGTpXZnGcDM61D8kOnIN4K95Pfkj6pZfQV+18q1GL9CCQn2XqOmE67YmiLFNDbar7LNNyOI6z5bwmtfwo0FPUPpacY3DwapWO7HBwf/RhoNiJIJOaZ3BwcP35yMgITz/9dA2P7PAT5Ewl4rhks1WTrZIN6pmgzHGQvlcPiEgpCEJVkZV/1aPcIvtsna/AKje5k8nN3hFrnGACb+jEkmnP1Msq++7u7h2/J+uUFihM/ehmV5Wiv7+fCxcuVGRfa1a66f/lb/CZKbLSbdQ9UFJ1Z6Wbn2U62f0w1oM7RVmmIcOvapappmhoikaYva/+GBzJBXi3K5CuWe2WEjEtb/X3Ula8qwLpuhXv+ja57ePHlumYuYPaphBeGKdrxuZBYwTyMlRVLfdYb1a6kmW6PXz89f4Im1aNrUZ9RtPzBc3xqSU6711l0vfp/2SMaccl0xcj2RvFV+cJMY8PTHo+xr0lludSOEPTDD/ajb84yI/eH2WuvQEFytji6oXZoUpwdqiuaIybSXRfRVm5WZBRujmDdM1md7cZpNthc5bp41MjuFmnYJsOe4re2wpqR31mmW6HsbExent7y25jai4Ja7zIEaItZhKa1ci2ttboaA8ell3aRjpk1MfYVk0C7y0kk1JEyioSCqiFCmtxvPql3N8vfe/oIyKlIAhVRSY31SNo5R+IRUmmTA0F6Xt7R0TKYGTMqx7hgBu6ozbe6bqeyyq7uD3RBCD6k+aS7T3HWne0H6EQL5Hg1td+n+98kC4KnK40dvKtb52sOyvd/CzT5WgbbzclirJM7ZZWHj2iWabrVruKTiUNK9Tf/BSRu9+FmI8x+S5O2ubqa3GGIwN4eXUOVd/lf3/u1zSrLl6DTmfv43gKDDzze3yps7Vk5qfju/zq/Su0dbXTkegsqjVqB2SflrLcdStRZHKHSJZp9diJle7oyTZiy1kM26VpLsVCa4zTw0nea+/Dh2CBdKes6u2vTX28rc0VlHWhs1gA1THzbHEnP63xN9EbfDKzsG6xGyys6hz/q+9hZRcw5xaI3LiFjk5Gj5A0EiwrDUTdFAkrie6n0K9fxxkYwIvHMV9/nUwN6hMfJsrNa9Rkkn8w+j0ujU8VvXZm3uHS+/VdWzv2s6aS7ce64gXndKcuHELwom+5pxV3oGpStialHfxaPVAuk1Lq8B59RKQUBKGqSMC+eohNRDCSSVldIqHS7WL3KmNeNamXTMrdEHRDJ/1u70RMn6R5rMhKNxLy+Z+6JvfpqPaXtSzT2f/h5/hMFWWZdsVcUNJ1HVTeKflWum5fH+q163TZk7Q48wVWugkriTGzghbT8M/2E9UjuD09HOstn0E8lrxGf3MvF+N7W7Tg+X6BuOn4Lh9eu4qjePSd68Nafc0pY7kbJKQG2fVm2uPokysstURpnk/RMrPCZKK5pOVrvWWZ7pXDZKUbhI+P5TtYvsOWR3NK42PG+XhpfMv9xqeWeO6TD1B8n/5rY+iOi/tYjNneVgz1LnHA91RmHYWR0XmW51fwhia5+9hDqMtDvHs1Q7azLTCrtKBNLRZJzQDhVFc01CNkrbtmp9uxrHIHvWjRi7EcB7+xru105d6iegRltMm9hdxbVBOJ4wWTLrNAQMo2HX1EpBQEoaoE1qSUVTB7RiY3wQRNqhXFJ1TeJUzYBiJSlsbzgrP6xOJ67wSt3M3W+YpTkGttNSmXOe77uWTBesRLJPjFp7/NnwwvFWWZLibaSH/rdN1lme6VfCtdBgawPh4pstIFcDQDZ+BMVax0t0JVFExFx8wLI8S9nAh4Orxzq+ptfeaXJoiMfheafPSZKywtONz8QRc3mvpQNDbscBWHb524hkkLblTH6HkYV4HwZ77GyXhLkTianJ7ExaMx3rQukhbWOS0WTI/aiCpWusFsJ8tUUT0wYeRkM+HlFQwrC9NTzLXG8D98n6uf76vKsenkLHDz7XODRM3CLNPN1rm5n/v6DDoqTZmxDZE0Tzg1Fb0qtrr5drpN6SU+t3gb0y+c1DXOTGC8q6zbh9ejnW5kLsk3ZocKrrNJM0FXdgBo2O/DO9QEzZM9T8F2wKjjiHngvYXc0+4ZTQND97Gd4nNc73E8cUSrb+p4yBUEoRaITUT1KGsTUecWJeX6Xb0GlCtJkEhZ7/2u/Mo/mVTvlSCL63q/mQOp/1xJNtuljdxvAT5btJ3vK1x57wMMrX7P8b3750iap4uyTC92TfPc2LswNrat/YyMjNDf31+NQzxU5Fvp0hbnnZYeWrOFWUVTRjvt/Y3E4t6Rs9INIj/L1Os7i//uMB2ZWZqyHxRkmR6zk7Q2pkCP4vQPEAnFcXt6aOv7Usn9rtcj7d97PVJ7rd5oXj3SQsGzcPuPbw7T2NJEU7x5XRx1SmSb5meZelWw2hUr3WAOcpapg4vjVVAgbsw9fD/5YeAmKkqBsLkueuZlgha05W03N6DyYegBS0uFr/X+8PtEMnOE5hZJTN1hVNPIKqFVO91Gok6Ks0oSxV6pSztdNZkk9NprfO3XczxYKvyuncje53NXfkH433eIY8EeKFcbMGMpGHr9zvHk3qK6hM3SIuVRK2OyU8QRrb4RkVIQhKoi9iTVQ1XB1H0smdwUEZRJKZPqyiA1KUtTflJdwwM5oojdazCyIKgydHcXZ4KZerAwYDkqhnZ0M4m2ImurJdvLnTOhPGtWuubrr6Np80VWugCOnyUVBcUAAQAASURBVMHtOVZXgen8LNOZE+ex7t0pyjJVVR/fMNYzriqdZVrJeqSv/DiZq9G3TYEUwPO9oszODVGzVH3RDeHz/th9HDzauto3RFXfobV7hc7Z26htCuHFcRJzFhPxMDagaB7qapaqonp1Z6UrWaaFePhkfZusvwv7isd03uYWb8/cWm+KTy3x3M1VO92Px9AjLurFNTvdEeIAvsKIo6A8mKdlNoX7YZLbj/SizA7y1tvTpNpb0HwV3VfR0NB9FR11vU1H2/S7gr66nZb/Omv7UFE4GHPK6IMHJN58E9V1yaS76bSXihasZFZUpt4bwfvgA5LPPMPIyAhPP/30fh/6oSJcxnEkY6/r93WJxPGqS8jwWSrRnpV4Ssl2TfXrOrO5XpB/sSAIVUW87KtL2CwtUtZ70F4m1dVF7F5LU1akFNvNPSN1Y4KRMa8y6LqeEw4ubggH0fbgTKH+gSfoaKlfQa7pndLhu66OpoJzuBWbs1frHS+RIPPSS/zpqzYnx35dZPF3+kt9HPudlv0+zJqSn2W6Emvl7aZEUe26pWgbTz4Vy1lmHMEsU1VRCSkqIXZet2Dwdm4cu9ha+L1Un/kUkTvfhaiPMfkuim0z+14zr61cwFM2FiGouHyl6SMGGl28Bp3O3sdxFeh/5u/wxc54nl1usHi6ue5oQQ1Sb0NgXcmmcBUPX1OwfQe3Chmk20GyTKvLdux0UXwwfEZOxgkvpzFsG2NhnoXWGLHbN7h+rPJ2uhuiZ7HQWaotJ3Bqea9rRUJpviia35aTRIvnb6GZmXWB0lhc5PHpMdxs4fegw56ic9TCaGnHbmoi8eabNIXDFT8fR52ICQlrvMi2PmkmcEdPQnPHfh/iviH3FtUlaCFpUPy0XpB+V9+ISCkIQlURm4jqEjJ9SBW313vQXuqzVZfASbWIlIGvycR670QkgzeQwOxxGfP2TLls1HIWz/VA0LVWxrvKsNB4jFdbjxe1fzm6AGRqf0D7zFqW6fz/8jf4zBRlmTZFPVBSuD09dZVluhfyrXTdvj7069eJpyf5zNJSgZVuwkrSRBqtWcM/209Uj+D29NDd+0jFj2lwcNWGd3Whg+d7BTVD821x8+uI5meIFtQU9TZE08GPPqCzO0FDc2OhSOo7LGdWyDgWiqHiKh7zrTHaJxZZbInQPJ+iZWaFyURzScvXessyrQQH1U7XVTxcpUbCuM+GyLmWzelrfOGN90hrizQspei+P0lDi0FaCTHV0EpaixLKWLQvzaMaK6gjs6ROncBqauTMTJIH/gztK2OYqkFINXKPmomp6BWvKXrYUZNJjl1+nT8cL15+cCJ7n/grPyM8UF+uBfkE3dcHLVYWdkZYFt+WRO5p6xsRKQVBqCqyEqa6iO1maaTfVZfATErpd4GvSd/bO0E1Kevd3hpkzKsm5c5hvfc96XfVJWhBXz33Oy+R4L3Pf5vvfLBSlPmidHSQ/tYpvK6urXckrJNvpesMDOBNFFvpAliKiTNwqipWuuVQFRVTUTF3kUG6mdF33uMrXznPxXPFmd6WZTE0NLT+3Dx2nuODr+KHXeJcx1ryuP3X7XzSfBpFA0VzUVQPTXH4WvQDmjImjq7R1NBDgwNjJz/Fo5kGXHwcxaWhuRFX8dYzRjcE1sKapZbv4HP0x1Cx0wUUcPBw8kTR+NQSxvQkS4pP94NxMorLSq/BaG8MX7UAiyww5hko91ycuRTe2E1utXWjtyh8b+oHfPejn5X8uALhcv3HLGgztc3bmJiqvr5dwWuaWbRPUzXQlNJW8AcJ7dYtwpcvo6RcFCJFmflTRjuO3YA2Okrk5ZfJPP887pkz+33YNUXmeNVF7mtLEySOSwmT+kBESkEQqopcZKqLrMAqjUyqq4tkUpYmKKsIpO9VAqlJGYyMedUjyGYYIFPmO18PHJXVzgfVbtZ1ngaaitpv3RljcPDujvY1MjJCf39/ZQ5sn0lnFZLmMV5t/WZB+4VTNl7X7D4d1eEl30rXi8e5e6qTkZX5ooB9Y1crZ+PZI2mlC2CaZpFNdSQ5gTo2hhqJkRkcpm10gYaVoaIs01P9KnpDO87AAO3xOG5PD+d+46VdH4ubZ59r+06eHW5+zdENy9yCbfN+LK/QZtfatM2aUGq5doFQVgvETrc027LBJZddOnqyjdhyFsN2aZpLsdAa4/RwkvfaS9vgWp6N5e2ipugO0RWtrACaL3iGVHO9fcIfZ6wjxZXl6xiqganomIpBSNEx856bio6uaLvODFWTScKXL4PjYC7M8rnF+5ibaq122FM0fgjq42fw4nHCly+TfvHFqo97mxdL7Cf23WN8Y/ZBkQ2uMd7J4ODMjvd34cIFTFOCgGvIfW1pxImvvhGRUhCEqhJ4kTlkAayDikxuShOUSSrieGUIyqSUDN7g14KsSoXtI+NdaXxfbuiqSTmht9773lHodxcuXNjvQwgkpJfOFLKcg5+lUk2OQr87aKxZ6Zqvv47+0VSRlS5A2HPrzko3P8t04dTDWLfvlswyVUwD51xfxbJMNUUjomhE9rSX7TM4OIiPz6OPP1ZgoWsXWOg6RXVErSLBszAjdE1cHb51g8Z4M1rYyNmp4rLU2ih2uiU4qDa4O8HxXRzXZcXdhS35Gbgx/VdbbqbAqmBpYKp6gYC5WdA0VZ3Q6u+GanDuBz+iMTVLeH6Jpht3iYYMLCVE0uxiRWkgbGfoykyBnUK/fj2XYR6PY77+OpmXdr/4YDsMDQ0xPDy8r4uKQjMzdFy5wjevGYwtFX7PTmTv8/DQHD0NKtNPPUW2rW1b+1xbDLaTeuVHHXFEK40svK1vRKQUBKGqyEWmugSdx7q3iZB+V1VEpCxNuX4nZWD2Trnxzvep23Nc7nsnCzP2TqiMA122+gkJB5qjsCCoVPbUQaHtL6MwWtze2t7NxYvNO9rXQc0W3Q3B/U7meHvBSyTIvPQS72eWuHJ/uCh75pdnH+GbL4X3+zBrSn6WaTrWyttNx4psIaeNdp66FAVVOdRZpgoKhqpjVCFE+E//+T/l0ufOFAgvoWMPc3zwB/hhjxauk1nw0f6qg5vNp0EDRfVQNRdVcfidxveIZkJFdrqPZRpX7VJdXMXDwVt9dHEUr7BNcXHw8JSDPU6IDe728IGsb5P1bdhBEnB8agn19gcovk//9TGWHBcejTHV24qqjtG4ut2c5zN8b5bWuRTeR0nuPHYKbelDfvXhElZney7Dc1UMvXtykfnwbcbnrbz23Gtrv29kg278riuls4H7+/v3bV6i3bpF+I03QNd5RzPptCeKsupbGpt5SHN46OrVurTBrRSy+LY0Ej+ub0SkFAShqshFproE2dDVu1gk/a66iN1raaTfVZdycZqMFSyeH3WkFmp1UZTceSx1noMWJtQLMuZVl1DQtfYA9rtaiqAjDwaAhqJ2K7PA4ODgtvez39kqBxWrvYtXW4vtIh/RbaD+7HTXskwX/vXP8JktyjI1NB/UlbrLMt0JmqaVFF4iyeSqnW4UfXCY9vF5GlMfFdnpHu/X0RvaKmKn6/leQT3QnC2uvZr96RZb4eZlhG7OHC31Wq7dXm13cdiZaCg2uNVlZ3a6rcSWMxi2jT4zy0JrDOPqVT78/Kbx8RiMkuKD+fs7OhYVdV2wXMv0tBstDF/jl5MPCCm59tDa63nbhVST0PqjUSCGhlQDLUAALXs8eTa46uws58fvkUkV9t8Oe4qeeyrq8YdqaoN7FJGyTaWRe4v6RkRKQRCqylGpV3RQCQpg1fvkJqg2oFiBVYYg61IRKaXfVZOtbDfr9bpSTrCo13NSaYJEynq/1kogobocllX2tbbMzdql7W5Dxs7EgP7+/gNt97tfBC2ArGeXFi+R4OpvfYvv/DLNk8vvF2SZrjR3kv7WSbyurq13JBSQb6ebOTuAdfteSTtdVzNgoDJ2uqqiElZMwtQm5T9fFLVW64QWC572envHyRAtC8OYXTGal+4TnVP5xOtCMfz17NLco0dLzKJjNoOGynI8Z8t5VG1wK8VBstP18Ej7Fmk3b3X5aoT+QWpuT/vWUFfFTZOQulbbM0/MXBU8dV9lYWoO3de4+P1f0DQ1R3RxhbY7o6hKA244StJMsOw3ELGzdGUn8DJzpK9cwXzySZT29prY4B5F5FpbmnRAskW9LkauN0SkFAShqojtZnUJDGDV+eRGaqFWl7DYvZZEAvbVJVTmPOZu6OrzPEsmZfWRQEJpZMyrLkGr7LMHTKSstWVu7OdNJdsTnS0H1rr3MBH0/T2IGby1JGMpJM1jvNr6zYL27jYXr2t6n47q8BCUbR3t6yPx5pukXIW3mz5XZKc7ZbTTkkjRkM3iTU2RfOYZUuPjMD5e47+gemhAZPWHhnM0zt8EpY2wO4W+4tL8icZwZABP2Vigofoez/S8SUsqiqfrGKF+bg3d4Wt/59v80089S9a1sXybrGuT9Swsz1l9tMl6G22WZ5NxrXWhNONaZD17fTvLs8iuvjfrbrR7O/FYPUDUi52ui0fKy5Iiy5bJvJGcDW7vwifMGj79Y2PMai7Wwz7TvQaa+oA1g/k5R6FhZAV3dhlv6E0eXOxDX77G4EcOdlcHYcUoyO4MKQZh1WTUmMbwNZoz4wXtIdVAR0Opw5od4RAkrPGihS+nr8VRJ/rqduGL3FvUNyJSCoJQVYJEi6BMLGFnBGVoHbRV9rVGJjfVRTIpSxOUwSv9rjJslUlZr2QC+h1I36sUgQuCypz7ekDGvOpyWDIpa424FlQXqTtemqDxXsa7reno6Ah8LXX8OKPPPUf054P4qEV2ugCOP0K6o5Xpp54i29ZW7cPdV7KtrWTa2wlPT7Ny/DjGzft02ZO0OPNFNrjRxVl8HVaOHwdVZb6xkdaOLhr1KI1VjvQ6npsTOPPFzjwR1PJssq6F5Ttk84TPjJcngq4KqGui6fzKIsuZFEY0lBNNfQe/wgsQxU63NNu1wUX3eXAqTnQljWHb+NOTTLfGyP76Hd7bbIObz6pD+w+SHxa9pKISVvOtbc3V5zkRM6Ka3OldYjl8j9kFf1XcNAmvCp1h1cxlR6+KnoayfzKHZVkMDQ1tuV1oZobP/uIBZ8fTRa8lbn7Mwh/9OemOjm2PeRcuXMA0j0aAVe4t6hsRKQVBqBq+L2JRtQnM7qj7AFbp9qBaisLOCApg1btIKf2uupS7bgRZw9QD5QLHcq2tDEHf4XoP2gdlVkXFtaAiSL8rjdxbVJfgevf1fm8h/W63BNWkzGfp6a/wnffdoqyipJngC//473His+101vCY9xP12DEiL78MjsNcqBNrrLQNbkO8BXWgj9CqDe7SuXM1O0Zd1dBVjSjhiu3zypUrvPXBW7zwwgsA+L6Pg5sTQVdFS8uzya7V/Vx9buXVAs2u2uZmfTvvtY33qp3HiE5lybQ2EZ9PE59ZYTLRXNLyVfH8dXvXxZYIcHTtdPfTBtfbTtZnN9xnhXfn7m65Pw01J1gWiJgGYTVUIH6uCZv5r0fyRM+IGsJQdpblOTQ0tGW96+iDByTefJOplRZmaS7KHlciMfA8IlNT9PzoR7ns8ePHA/e3lqV+VJwk5Fpb34hIKQhC1bAd8Dy5yFSToABW1q7tcRw0ZHJTXWSVfWnE3rq6lMvAr+eFGWVrUopAXhGCvsNZp377ne8HzzVkYUZlCOx3dW4zHLQgSiz9K0NQv7PqvN8FL0STflcJwqZf0k4X4L9pmQXq5+bWSyTIPP884cuXyTS08XZTd0kb3MefimIYCug6meefZ+XKlf0+9IqiKAoGOoamE6uQGKp+8Vki978LjT7G1Lsotk3ytXbe1c6D5qNqLqrmoao2z2hXORlqwYlqqN0DOIoPl75CR3NTLht0Vfy8ff8u4cYIoYbIent21T4369sVzwatBkfJBtfFY8XLsEJma6vbLVBRiahmTrxUzBJCZmHbuDFNw4VOWs91E15/34bYqSaTRN54Azo6mLi9RMviNUy/cGxrWprgxKiC25erw9t98ybpS5fwEom9/TGHBHHLqG9EpBQEoWpInazqE1SjTVY7i1hUTYIC0JJJKf2umgRld0B9j3lBf7up+6hqyZeEHRJUG7Ce+50lC9GqTtAcT2oDBvW7Gh/IISOoJuBm7t9rBT5d1G45Cu++N4hW5evKVlko+4X0u+qia6CqfsnrSj1ea90zZ0i/+CKLf/IzfGZL2uDq+jJuTw/Ws8/mBIwjJlJWA6+rC6+7G3VsDLevD/36ddoz01zKvldkp9vUmKKhN4rTP0A0HMft6aHr3NeL9vnKa6/kMoXPFmeybc4GzRcvrfzfvdxr98bu4yguzR2tq5mhdsHr+SJo1rNxK1QbVGxwS+OtCZ5eZntvWLW1/f54oa3tmtj5lcu/pmNumeaFDG2Lk6Q6VCwlxGS0jRU1RijjELVmSWezqNc+xBroR21tx3jtJ2T/4T+q8F93MJEFQfWNiJSCIFSNstkdstq5IgRdrOs9gBXkZS8rsCpDUCal44LjgF6nswvpd9VFAlilCRrv5TpbOWRBUDFB4x1IIKFSSN3x0siCoJ1z4cKFbW9r6sGpJ7ajoZnVzZjp7+/f0fHWipRca6uKokDIKL3gsV6dWrxEgo+f/X2+82amyAZ3JtJF5tu9eF1dW+9IKCD7pS8RefllvHgcZ2AA987Nkna6tmrgDPTjrdrpWs8+u+PP2mk26ODtnOh3sW171p2u764LmQUiZoGo6ZD1LbJrQqlnkVkVPqcXZ3EUD23NBjees8FtERvciuLhYU5MERqfZMn36b49hqe5ZE7GGO2N4qsZNDI4wB1PxbqXomUuhTM0zfCj3fgL7/LT9+6T7WwnqoaIrP+YpKJLmJ7O5IKTa1NCRLUQMTVMVA0TVUNE1RCqcjBWrm5Vt1MZO8U3Zm8V2X57d5sZHFwGjlYNTqGQOg0jCoJQCySTsvpIAKs0YrtZXcqtGs9Y0FCns4tAexIJYFUERcl9h1OZ4vNcz/aHErCvPlIbsJhy8wwZ8ypDKMBtLVvH/Q5kzNsNpmluu15VKB48iTv38OO0NdXneZZ+V30ipl9yAUw9L77NWEpJG9yWBg+va2qfjupwk2+n68XjfJz4FKnxdJGdbkuXSV/cWLfTPYh2m5qiEdV2Xxt0cGwQgE998ZsbNrjT7+KkbT75/jFuNp5BMXI2uIrmomkWf7t1mHAmhKOrdB/rp1PxWf7UF3m6oWFdAF0TSzOeRda3STkZbFyo368yp29MANA0l0J3XGxDY7S3tUgI9lWF0ZNtxJazGLZL01yKhdYYJ66P8l5bhAV3U/3P1cXjH8zdL/v5EcUkpq0JlznxMqaGiK62jXWkMIwpoun7uddW20OKsaO6nFsRVLczNDNDx5Ur/N6NCHMrhSviT2Tv86lfT9OjGvwqHgeOTg1OoZA6DSMKglALUmWsH6VOVmUIujGu58ApBP/9QRmAws4odx7TWWiI1u5YDhJBmUUSwKocYRNSJRx30nU85km/qz7B19r6jbbIQrTqI/2uNLIQrbqEyywyyPW9+jzPUier+siYV4zM8arDmp2u+frrqNr9kna6Df5ioZ3uEWazDa539Tqd2WmarcUiG9wONYPjmyz3nOJM5DhuTw/HB3637P4HBwfx8bnw+KOrGZ458TLj2atZnhuCZtazyay2ZX2LtGdxd2wEsyGMFjHWt8t4VsXsbmvBWvZp03wayGWflspUhZxQOd8WoyO5SNN8moXW2Pr7d0vat0g7FrBYeoMzcINr/HjiWkGzikpsVbSMqREa1DANWoQGNUJMCxc8rrVH1BBqGWGzv7+/QGTUbt0i/MYboOuoikanPVG0aKC1pYmHNJfQrVsku7tBRMojiYiUgiBUjawEsKpOcHZH/d7Mgax2rjZlRco6Fouydul2qVdUOSR7vJigrCrpd5UjqB5qPfe7cmO9XGsrgwTsSyNiUXUp9/09Cn1vu7U5NzM+8TgQKWpfXpxmcDDYuu4wcFDqgAZda8vFFI46QU4hcp3dO14iQeall/jRRyPM/XKyyF7y1/09fPWlY/t9mFVnbUwMdXXR8+tfo7ouVksXlrJU0gZ3xc4y2dxMQ1PTjmxwFRRCqkEIA7SdrWh+5fXVup99hcKU7TtkPZu0Z5H1rZzo6VmkfSuX1bma2ZlZ/71Q/FwTRNeEz2qi2zmrdM3NCauZcIBdxiprr69tv/b+WuPhseSlWfLSwNy23qOgEFPDNKyLmJFVETPMQmiWsG9gpu7SqEWJTy3R+Zd/Aa6HOjvL49MjeFmnYH8d9hTHbymobadRXZfEm2+iXrp05BcP1CMiUgqCUDWkJmX1CbqZs+rY+hBEpKw25cSPerahC6xJKeNdxZAAVjFSk7L6BF076tlmuFxNyqgI5BUhqBZq1qnffgfBArm4ZVSGoOssHP77i+7u7l2/17JL1/MKGfsTOK4kB6UOaPC1tsYHcoCQevfVZ7m5g1dbLxW1f9ac2IejqS353/tsWxvJZ54h8eabrERbeLvpETrsqaKMto7+ZRpCPh3Hju27Da6h5Op9NmjFC0h2iud760JmxrNJe1nSXpaMn/t9TehM+xYZL7u+zVpb2svmbG2V0tcEx9AAcLXctSScsVkoczzhjF2w/dr7DwM+PstemuVSwuaqPv3GZE4c//Jf/pq2mUVaFy1O3p6hqV0jTZipSBvLNBJKO7QtLqBg4X/8EXp7J05TM+brr5N56aXa/mFC1RGRUhCEqhEkFOmaj354rrEHGlllX4zjguOKSFlNtrJ7rVdEHK8+MuYVI9aH1Uf6XTFi91p9gjPHa3wgBwjfl2tttSl3Hg+7W4au60U2c9sl9L2Wku0nejq4eHHvAXIBQuIQVETQeC9uGZUjZJS2DM06GnD4FyGUo6he8cWLqJcukf33b+C/P1fSBre7J4Z68ujZ4KqKmqvPuMv6npCztfXwefix82RWszXTXk7UbDodJ7b0EVpXM43LdzGnNG76nSimh6o7aLqDauQeNdVet3ddbMldX+bbYhX5Ow8S8akl2iYXUXyfzrtT2J7LcrfJaG8jvmqjMosNTHig31ticS6Fk5zkZvtxwtcn+PBqFi3RTYveQFxrIK430qI10KLHaFCjZS1nhYOJiJSCIFQNCSJUn6BzWS6L9ahTLrtD+l5lKHdjXM8ipYhF1ScU4IxTz6vsxfqw+gRbq9f2OA4SQf3O0H00WYhWEYLGu4yl4PtQj7EX2wHPk2ttNSnnQFfPYpG4FlQfua8tRu4tqo+plRYiLUflqIuUpfASCT557vf5zhtZnlx+v8gGN/vtEyjdXVvvqE5RUUqKneqlKJGPJ6G7A2NsGXPeoemKznBkAE/ZyNRXfY9/9sQgquriNmi09j6KjUfX53+bR9sbWfGyZLwsac8i5WUZn53AUhy0mElqNfsz5WXxD0H96NM3ctnKTXMpdMfFNjRGe1uLanX6qsLoyTZiy1kM2yU2v8xCq4/34XtcaSydj6qh0qzFVoXL2KqQ2Uib3kSH0Uy73kxYldUeBw0RKQVBqBoiUlafcoHTeg1glQugSCChMkhNytLImFd9JIBVjPS76iOZlMVIv6s+QefS8xRsB8zy5YyOJJLBW300LbfYwC5hKyxjXjHS7yqH1B0vRvpd9TEDMiktWwPqcxVkxlJImsd4tfWbBe2m7qN0T+7TUR1uvK4uvO5u1LEx3L4+1MFhuuxJWpx5kmaClBoj6q2QsJKok8touoZ/doAmvQG3p4fzpz5Tcr+D9wcBuHh2IyPW832yvsWKmyHlZVnxMqS81d/d3O8rXna9LeVlmFyawTcVrAC72mqwli3aNJ8GctmimwXKNXxVYb4tRkdykab5NAutsfX3l8LFY9ZdYtZdCtymQY3QrjfRvipadui5x3ajmbjWgKqUtnkXqoeIlIIgVI2gjCqxJ6kcQTcovq9gOcGr8I8yqTKZfBHpexVBVXPBUavEfZtkUhYj/a5yiFhUTFD2uASwKof0u2Kk31WfcucyYymYZWoHHlXKLUiRvlc5wqaIlJuRjLbqE1QPta77ndSkrDonT3SWfkGLApmaHstBQca76pD90peIvPwyXjxOum8A6/Y9Qr7FyexIwXaubuAP9OHF46DrWM8+u6PPURWFiBIiom6/WPcrr7xCf38/jz3x2KpwWShornhplt0MK16aJTfNipdhxU2z5KVZcTM4u8g61u3cezQ3t1AgU87KIe/1te3X3r9blr00y1aau1Zx/VkNlS4jTrfZRo/RTo/ZTrfRRpvehFKPmSA1QkRKQRCqhljQVZ9yE8WspQTe7B1lsrYEsGpB2CwtUta3/WHpdsngrRxB149y3/ujTlbqFVUdCZwWI3O86lN2jmcrcAisvCqNuGXUhrDps5Qqbq/na62MeXtjeHh4y23SK48Dx4rax8ZnGBwcqsJRHXxGxx8BokXt6dQsg4MfFrSNjIzw9NNP1+jIjg4NkdIe9VaJhRr1QlbsrauCl0iQef55wpcv47e28nZTNx32FB32NLrv4Cg6U0Y75x6N0dQA6DqZ55+vae1PTdFo1KI0alHYZsKD7/tkfZvlVfFy2Uuz7KZZ9jKsuBmWvdSqwJlhanmWtGqRUR0cI/fdc7VcxmI4Y1PavJX11/O3X3t/NXDxGLNnGLNneJcbG8egmKvCZRsnQp2cCx+nS4+LcFkhRKQUBKFqSO2O6lNOhKzXAFa5mpQSSKgcYRMWSzhs1LdIKatOq01IMtqKkGtt9Qla2FvPtVCDrL1FHK8c5c5lpk77XjlLeXEtqBzB9VBrexwHiSCnkHIlEIQc3d3d29rO1MvVBqxPLKd0ED4UcK6EnRN0b1HPizLELaN6uGfOkH7xRbzLP8Vnnkmji0mjsMan46dwe45hPftsTQXK3aIoCmHFJKyatNNcdtvBwZw97eNPPI7X/ypq6j287glCK7donNX4OHMCLeygmRb66o8WslB9b93edbElAuTsYWtNxre4nR3ndnYclnNtLVoDA+ETDERO0B/uJa431Py4jgoiUgqCUDUkYF99ygWw6rU2oNQrqg1BQZl6tnuVMa/6BAZOpd8VIf2ucoQDBN96Fsel31WfcgvR6rXvZcosRJO+VznCkj1ehIx5u0fXdfr7+7l48WLZ7brfbYQPi9sjDW1bvveoEvlR6YB/z7Hic7KdbFWhGHHLKCZoQYosRKsMXiJB6sWX+M73XZ5cfp+ElcT0LSzFJGkm+NLvnOLcpfb9Psyqoioq+hOXiHz4MXSdwrg/jb7k0HbTZTjyMF5ePUjVd/k/PvI+pmKxqGRIxU7SHYmx8tRv8mTUYN5dZt5ZZt5dwaN0jdlqMu8u88uVj/nlyscA9JqdPKJ2Q33mjOwJESkFQagasgKr+mxVr6geCep3qupjyFWvYohIWYjjUrJ+E8iYV0mCzmVdr3aWwGnVkZqUxUidrOpTdo5XRqw7ykhNytogY14xcl9bfaTfFSP9rvpIvytG7i2qT9j0SZrHeLX1m0Wv/bdNs8DRt8zwurrwurtRx8Zw+/pw3x+my56kxZknaSZIqTGi3goJK0l0bhlNM3E6j9HqN9Bx+lMk+v924f58nyU3xby7zNy6cJl7nHOXmXWWmHEWqy5kjliTjJiTfPFP/oCf/qN/XdXPOmpIuFYQhKohtTuqj4iUxZTrd2IVXzmCbNXqVaSUOlm1IWjMKxe4PurItbb6SACrmMB+J+NdxZA5XjFBf7euyUK0SiJjXiG2A64n19pqE1h3vE7dgUCutbUgqN9lLAXfpy7jB1JKovqUtfSvozEv+6UvEXn5Zbx4nPmTD2PduUvItziZHSnYTjENnLN92NksnqZhPfts0b5URaFZj9GsxzhJV9HrAK7vMbcqVk45C0yv/kzZuccVL1Oxv625r7Ni+6oXZCovCELVEJuI6lMugBVU8PyoI/2uNoQDMinraVKdTzl7ZVl1WjkCA1j1nEkpq+yrTrkM3noNYIn1YfUJsreG+q2HKv2uNgSPeTU+kAOClJKoDUFjXl3P8WTMqzrlYgRZuz5jCLIAsvooSu57XOpcZ+pozPMSCTLPP0/48mVSDa283XSMDnuKDnsa3XdwFJ1Zs52nLkVBUfCmpkg+8wzNu6zVqSkq7UYz7UYz/Zwoej3tZZm2F0jas4zaM4xa04xa08y6S3v9U4VtICKlIAhVIzCQICuwKoapg6L4+H6JyU2dipRyM1cbgjIp61WkLJtJWYc3t9UiOLujxgdygAgKGku/qxzlzmXGCra/PsrIgqDqUy6AVa/Z4yJS1oag77H0u2LkvrZySAZvMTLmVZ+tXAvq8VxLv6sNgSJlnY157pkzpF98kYX/7W/wmWHS6GLS2MiEDBk+KCu4PT2MXrxItq2tascSUUOcCHVyItTJpbz2tJdlzJph1J5mzJrhTjbJiDWJL0UnK4qIlIIgVA0Ri6qPokDYKJ3FVS6z6ygTlFUUlSBCRQkMYNWp3WtQvwOxxqkkoUBxvL5u5vIJzKSUflcxQkb5AFY9fscDa1LW4bmoJiGjdACrXjOLguYY9bhQoJqERCwqIFVmbiuZRZUj6Fpbr/0OguMpcl9bOYLGO1jre/V3rkWkrA0y5m3gJRJ8+Jvf4jvvpHhy+X0SVhLTt7AUk0y8g/S3TuF1dZEdHNyX44uoIc6EuzkT7gZytrHXMyP8xdwvuG9N7csxHUVEpBQEoWrI5KY2hE2/5A1MvQawpN/VhqCAYL2KlGIFVhuCbubq1d4aZMyrBeUCWLlrbf2da+l3tSFs+iysFLfXYwALpN/VirBcawsQt4zaIHXHixHXgupTbiFa3cZTAu7npd9VFrmvLUSbmODJ5U8KBMqkmWCy/TG8rv3rfBnPYtSa5r41xQNrivvWFGP2NLbv7tsxHVVEpBQEoWoErrKXQEJFkdXOhUgAqzaISFmIiJS1QQJYxUhNyuqzlRVYPSJuGbUh0P6wTPb+USao38m9RWUR281CZI5XG4LuLerZ0l/ua6tPuYUG++FMNTw8XPsP3cT45EUgUtS+vDjJ4ODHO97f8PAw/f39FTiyo0WwtXptj2O/UZNJQq+9xsM/m2J8qfBCcCJ7n7aRXxH+93GsZ5+t2jH4vs+im2LKmWfSnmfKydWlfGBNM+XMV+1zhUJEpBQEoWoE3VBIIKGySCChELmZqw0iUhYS1O8UxceU2VbFCLp+1Ot457pgORK0rzblrh/1FkhYQ661tSEogFWvQXvpd7VB+l0hIlLWBrmnLUbGvOpTdiFajRcEXbhwoaafF4TlqCXbQ/ruMsf6+/sPzN92kJAxD7RbtwhfvgyOg+vodNoTdNjT6L6Do+hMGe1oWhva6CiRl18m2tdH6vjxXX2W67vMOcvMOItMOvNMrYqRk/Y8084CWd+u8F8n7BQJmwnCEeX69euk0+l9PYa5haeB4rvcmakHDA7eq/0BHRBGRkYqu0P380BjUfOtO6N1eZ7vPRgAYkXt2fQCgyU87G/fvk1zc3MNjqz63Lp1C9+vzU3r4nwv0FXUPj45x5UrN3e1z6GhIUZGRkr+nw46Qzc6gE8VtZu6ywcfHPy/5/bt2/t9CNti7EEn8GRReyrjHcp+U4qdXCMylgZ8pfR+7g4TtRcqdFT1QdD3IJUNPs8fXr1BZmaxikd1MJmZ+yyl5nhz02MMDu5sPLlz5w4jIyMHIntgp5w+fRrDMKq2f8/+HFA8R7l9b5zBwTvb3s9hGeO34t79c0BDUbudXTxU14CK3wtUmIX5c8DpovapmaVDdZ43s9vvwdXbbUBrUbuqeHx0dRClhvFk27YP3ff5nXfewXGcLbcbHWkHnipqz2Q51P1ut7iegu18teRr9+/dYJC5grbDfk87NDTE7du3a/6/dlwFKH2er177BH9pvuz7D/p4vhuyjlay3dS9Xe9zaGho1+89LGPeTuexrv1pSl1b7o4kGRy8VZXPDKJW/Tj/eEMzM/T86EeorouxuEj7tRnOpwrn1B32FOYEzNxowm5qQr96lQef+UzRfj18UorFspphRc2yvPqzkveYUiyo4fV65AdXa/dhRwQRKQXhiHLnzh0ymcy+HsNyqvjiATA3M34oA1GVYnR0dFs3atvFdS5SSqR8MDZTl+d5crqnZLuVmS95PiYmJrh69SoLC4c/mN/c3Mzs7CxvvfVW1T9rakKhlEg5MbW4689/++23SSQSh7Lf3r1f+qZNV+xD8fdMTEyg6wd/WjiRTFNKpMzayqE4z9thJ9eI5Ww48LXxB7dRlqcrdVh1QdD3wPVUgkTKm7dH8ZfGqnxkB4+lleJAMsDC3M7neMlkkq997WuHbpX90NAQr7zyCp/+9Ker9hmu/SilRMqx5NyOzvNhGeO3IjlZPO8AsDKLh+oaUOl7gUqzNB+nlEg5v5g9VOd5M7v9HtwZKz4XAIbmcONGbc/HO++8w1e+8pVDNV42NzfzV3/1V1ue++TUMqVESsdTufbxDTS1vrIHM3bwApiJsTsMZyYK2yYmuH37NkotVfMKcvv2bSYmJmo+xvg+KHwFn+Lswdt3xjFS5cWbvY7na+JQb2/vrvdRaRaXnijdPr+z/0+l/rbDModxHIf333+fd955h0QiseX2SwunKSVSDn8ywo8zP97257quy8jIyJ7Ocy3mJSMjI7iui6ZpjIyM8JmrV7GWloil0yRmZrAyrWSVLpJmgpQaI+qtcMweJ+LOkr16i9utjSw1R4iNvs/3MwqW6ZI1PbKrj7UUIIMIZVXa5kMYQyn+4r//wX4fzqHj4H/LBUHYFV//+tcZGBjY12P4o582wXJx+zOf/xR//7cOz41VNejv7+fixYsV2dcrQ3HuzxW39z/8BC/8g7MV+YzDxM8nm6BEcsG5vhO88MILJd9Tyf9HvXBfifH6jeL2eOuxwPO8HQ7t/+KnYf7tr4qbW5rMPZ2PWnIYzv3b1wy+8/Pids/X+Lt/7wX00gt/Dx3b/V88mFL5v32v9Gu/+7e+ykPHdmfLVM8Enfv/81/4uF7x3e/nn/kyv/VE/Xkg/o8/b4ESCaRPf/YiL3xl5/PPCxcucOnSpb0fWI1ZWFio6hj/5zdauD1T3H62/1FeeOGhHe3rMIzxW/HufCOUMGvoe6j70Fxr1zjI/4+p/xLlr0uUHWts6Tp053kzuznv4Z+H+Fcl1t81RPV9OR+Hcbzczlj5/ic6//PPSr/2t3/3BWKR+hIppxYU/i9/Wfq13/ntL/Nwb/Ec7+mnnz50fWONK1eu8NZbb+3Ld+r/+n2lZMmSzz79Rb56aetaJnsZz9cyRw/S9eB//VUblIgvffbSo7zwzb5t76eSf9tBvmbms7aAZDvH+tE/b8F6a5wnl98nYSUxfQtLMXniTAsvfPvbeF2lF2ZtplLnudrneO04H3/icTJj94j8SYasZxF9bxDPbGFSa+RecyNaeIFoaAotlGVOs4iOrGDNKSS0NAsPxVHcaVLGJHNtxc4atUZH41z4OOcjJ3kkcoouI46iKFxvvs7/xL/Y78M7dIhIKQhC1UgHePhLDYXKIl72hUjtjtoQ2O9s6Xf5SL+rLCEj+HxmLQW9zgJYUierdoRNn5VM8fmWa20h0u8qi9QGLCSw34Wk31WSoO9xtk77XVrGu5oQLuOcnbEhFqndsRwEys0vIgHXBmF3REy/ZOwqU6cl6oLmGEFzEmHnqMkkv/Pxn/O58Ymi147fu03ku2/j9vRgPfss3jayMvcT13dZdjMseSmW3DTLbpplL537Pa9tqmmOjGKTvfcznvzFTfqWxmmeXeFEagbb0Lj/cBNNauH58IHRk23ElrMYtkvTXIqF1hinh5O81759wbwShBWT42Y7x80OTpidHDfb6TbaMFSR1iqFnElBEKqGBLBqQ1DQvl4Dp0GBhIj0u4oSLI7X+EAOCLIoozaUC8qkrfoLYAX1O5C+V2lCJqyUcNGXMa8Q6XeVRRaiFSJiUW0IBYhFddvvAsY7ubeoLOUWG+T6Xn2db5nj1Q651hYSOMeTBUEVQbt1i/Dly3SlNGbQ6LCn6LCn0X0HR9GJLraA34Q2Okrk5ZfJPP887pkzVT8uH5+MZ5HyMiy7mdyjlyHlrj56GZbdNCkvuyFCuinS/jZvhvIcj1pmVgBomk8DMN8Ww1dL9ztfVZhvi9GRXKRpPs1Ca2z9/dUgoph0GnE69Ga6jDjHzQ6Omx206U2oh9RO+7AgIqUgCFXB94MDCVGZ3FSUoBVt2bpd+RcQSJB+V1GCszvqc+IW2O8kiFBRygVl6jGAVXaVvYx5FSXouyzZ44VIv6ssQWNeueD1UUbE8b1jWRZDQ0Nlt5kY7wYeK2pf/P+z9+dhcl75fR/6Oe9WVV29VS/oQjf2rUGAIAfcpKE00gyXGXlGQ9lah5Q0VuzESm6ee2+uH9/k5t44snOTx7GTOL5JHHu8xRpbIjO2NkiaGc0IFGc4Q0gESZAEQaBBoIFuAN3V+17bu5z7R1V1V3XVW90Aaumu93yep5+qOu/7Vp16+9R5z3u+5/f9rTnrdm2N5vTp01hWc8J4VH/XGLYe4wULNcZrHL4iZUCvteq+tn5oiQThc+fAcejOLPLp5ZtYsnTirnd6CvMdgXvsGF4sRvjcOVIvv7ztiEpPSjIyy5qbJullSHkZ1rx07s9NbzzPv056aWafWOD75hTe+Pfr8bXLMOycXbXuegCkq4XSF20v7F84/kGJamH2mN3sMbrpM7rYY3bTb3Szx+wmqoV3bW7f3Y4SKRUKRV2oFlmgbCJqi9+KtiDezIGyJ2kU/lZgqt0VoyZOa0u185kJoFjk188LIX0jYRQPhurzNvA8/9+b6vNqi2+7C2B/B2ritBZcuXKFkZERhoeHffcJmZUn/2xHq1e1qjIyMgI0L2ebcgdqDFXHeAG81ipL/8bhdz6zToMrskNQfV79CJ0/D46DNj/PgZkbTEqNjLBIWHGSWpQ2b412bQLsNbh6hfTwUdLdHax8+xvc/PkvkPIyJL2c+Fj4m2qfISMcfufu+yS9NCkvi7zfhbsNnitzzFxYpavnxhXhtM1Slf3Dee/lwv6F4ythCYOY3kHM6KDHaKdb7yBmtNOjd9BttNNjdBDRQrX5IoqaokRKhUJRF9SgunEou9dS1KC6Mah2V4p/u2twRVqckFplX0LKTxw3QS0ArS2qz9ugWo4mlSertoRVuyvBb0GQEinvj+Hh4aqC34xb+Ydse2bThMJmou4tGkO1vONBtFavthDNUjO5NUVZXG/gutCTTPDE6nvEswksmSWbF9G6Vw4Dvc2u4o6nYJuayouIqfxzOXmPwVuXcDybnmtX8docsrFO7g52Iqx52oxpNNPhppElfWeB7oUkzpUpRs4MIlcE37m2zEJfe/kHFtrvLhLVF3uj9E0ts9wdoWsxSffcGtPxroqWr8KT6/auqe52uvQo9t4jPNt+ik49Sk9ekIwZ7cT0Dtq0kIqE3KWoS5tCoagLflavoG7oao2vBV0AB9WgrMAahcrdUYrKk9UYlBVYKardNQ6Vh3cDlSercfhbqze2HjsF/zFegyvS4lRrd1IGbxGM74Ig1e5qSvW84wFrdFTv74L2G6w3fosgg9butEQC7Vvn+fXJ5bJt+zN3OP6t7xG+t5fsc89t23p0t+FJj7SXJS2zpLxs7rmXJSVzQmMqLzomvQzpIgGyIEiudqWwhYOsYJv65J/f4Fhqkq75NfT0AnZUZ+F0N1FtvmQ/Cdw72Et0NYNpu3QuJFnqiXJkJMG7fccadCbqy+hwnGMfT7Ica8O7u0TUgfhImutdB7DtCG7WwslYuGmTF6xbnNAd1sgSDp8g3jFI6ie+itc30OyvoagxSqRUKBR1QeVQaBxq4rQUlTemMfhNJKSyIpATWGqVfWOolq4iiH2e6u8ah8rDu4Ea4zUOtSCoFNXnNQa/dielwHbACpiduGp3jUHTwDIkWaf8fAexz/MTx1W7qz1BWvTtl5e47e5d4m++STYlERyi356h357FkA6OMJgx+1haSCHenaB/YgL7r/wV3KNHm/ANKuNINx+9mCXtZSqIjBnSnr0e4ZiW2aL9C9uzZOVDhiNWcUUvRAN2LqaAXDRhpchBAKkJFnuj9CeW6VxMsdQTXT9+p9OmhejQ2mjXI7TrETq03OPS5DxhafLokZN07G1j38d/SDQxi3FmCePaNRY/0TmQXSNhdZDUInR6a8Szt4kNJDH6DJL7BkDTcIeG8AaUQNmKKJFSoVDUhWpJxtWkfW1RE6elqEjKxlA9N2DwVpf7tTs1kVBb1ARWKaq/axx+5zRoq+xBRVI2Er/ojiD2d6AWBDWK6rabAqvK9lZEXWsbR9hSY7wCKgdv4whbknh2sszi9MSHMbSpYy0liFTKSxyamyP+5ptorou+tManly9gyVJv/357hr03V5npjgAQP3eO1MsvP3BEpZSSjLTJePa6YJj2smSkvR7JmPZsMl6WGweXSbSN8O70ZMV9U14Wh8q5lHcShp2ro+56AKSrrb4t2l7Yv3B8I2nTQrRpYaJamHY9vP489zpChx7ZECS1CO16GF1Uzhd56fYlAE5HDgGgff5LaK++iheL4Zw8SeaTMUIyy8HMeMlx0jRxTh7BzmTwdJ3sc8/V9TsrmocSKRVlCCFOA08CewEdmAM+Av5CyoddVvLAdQoDjwCngH4gCizl6/aelHKkGfVS+FMtokXd0NUWv4mEjB28mzmAtM/3Vjd0tWUr282g/c7VRELjUBNYG2TUhH3DCNIq+62oNr5Qba+2+EZSBnWMp/q8hrDVGK8zGqzzrdpd4whbkuVkeXkQr7Wq3TUGLZHgp6/+Ds9MTpdtG7p9k8jX38IdGmopi9PNeYkjv/VbaP39aPPz2HO3mZIGmXweyqQWpc1bI24n6AibWMuLLKx0sJTpIPknv8PYL3yxRGzMFKIXZZaJtikc4fJGYnRdWMztY5ORWbbdkvcCJCGZqMPZaByOmRPvXD0XbhlO2yxV2T+cTwJf2L9w/IMQEua6sJgTGkNE9ci64PjRxQ84FN/PmWOniOq5sjYtjC6qhIY+JF48TvqllwifO4cXi/F2bJDI8mxZBG/byU76Yi7ezAyJz3yGrhb5HSrKUSKlAgCRyyr7HwD/BXDCZ7c5IcQ/Af57KWXd48yFEI8CPwu8APwoG+mAK+07DfxL4H+TUk7c5+f8HeA3Hrym/KaU8tce4viWJFnNCixgEVb1xneVfQAnsKSsEtGm2l1N8Wt3UJjADtYNdCpTuVxFUtaekCWhwgRWEBdm+EXxqXZXe/wWBFUSzFudapGUamFGbfFbZJ9pgr21n0VcI1lLv1ixfPLuTS5dmtny+NOnT2NZakC4FdXcMNK2/7ZWRV1rG0fIp+1lgtjufO9pVburFfrNm4TPnWPPms4MepnFqTkXAdmPfu8ekVdfJf3SSzvK4tQPKSVZ6ZDJC4KFqMOMZzNqzpAVLgvLl8h4NvrUFCduvoXrOcSvfoLr2biPdJDY34cwJ+nQXTTDZVE4fDi2QPdCEmdylUt9S8gVwXc+XmChr71yRUL5x/Rcw777TmaxN0rf1DLL3RG6FpN0z60xHe+qaPkqPLlu77rcHUEgSPfF6De6aNPCtGkhIlqIqBZieXaJkGdwbP/h9cjHje1hIlpoS7FxdvY6B3t7ORoerMt398M9epTUyy9jvf46trvIijnAtFkauWwYadyhIe6dPUumt7eh9VM0FiVSKhBCdAPfACrf9W3QC/xXwCtCiJeklHW5SxVCDAHfBh69j8P2AP8l8J8KIf6vUsqv16Nuiu3jt/IvZEq0+i3GCSQqumODajewatVpbdlqlX3Q8F/t3OCKBAB/i+vG1mMn4PedVburPeFQ5fK0zwKFVqa6W0bj6hEEdlJOykoWcY3Ek5B1KkcRWObWFmgjIznjneLoEUVl1BivFOWW0Th2Up/XbFQkZX3REgnC586B49CdXuTTyzfLLE675rOY74zhHjuGF4sRfkiL081IKbFxsYXLjL1EVubsTdPSJuvZOWExLy4WP2Y3v5Z2zho1L0pmpe2/XLigJ87nrolPvnMDM5Oga36NzswKtqkzeypMSCuNS5EI7h3sJbqawbRdOheSLPVEOTKS4N2+YzU5H62MjsbUI4d4/NoCoq+N0EQKKy2JvS8Zje3D8Sw8x8B1TAxP4yt9NwmLPkS7xb5DT2FoJqnnv1rRevjSnZyN6tmOxxr9tWqCF4+TfuUV/um/dzm9eKnEcjlhxTnz5SMMvdBD5tKlZldVUWeUSBlwhBAR4E+AZzZtygK3gQxwhJy9aoEjwJ8JIZ6VUt6oQ7Vi+AuUa8AkMA90AcfIWdIW6AR+UwgxIKX8H+pQN8U2Ubk7GoeasN9A5clqHNVSKASx7amJhMahcgNuoK61jcMvkjKIE6d+v7WwJRHBOx11ZadN2G+2iGskySoLAs6cOsbZ4QCGWtUJJVKW4ueWoRZlbJ/x8XEubWOC2bM/TW6ap5Sbtye4dOl27Su2g1n8uIsvzs+XTdhnFg9x6dKdsv3Hx8d59tlnm1DT3Uno/HlwHLT5efbP3mBSamUWpyfEOMLOol+7in3iOHasi/R3/5DEL/7Mulg405NGWAmWlz8gIx0yXrY0inGTqJjdJC7KWL5C9y405TwUovU6F1NALtqvUmQfgNQEi71R+hPLdC6mWOqJrh/fymhoRDQr/5eLUIyIoudaaH1bYmwCS+qcOXE6VyZy202hIw4JIld/C21iAu30fpYvjmDdMjgsFvLtLkyXt8agk6BLXwPdwjl+EkMzcYeGWio36mY8D8a0QcZ6yiM5/1p8HlBjvCCgRErFP6RUoPSA/w74n6WUCwBCCAt4Jb9v4RLaD3xDCPG0lLLe2Xs/Ar4OnAfel1J6hQ1CiE7gV4G/Sy7Ss8A/EEJclVL+0QN83t8CPriP/e/LXjYo+K44VbY4NWenTWA1k2rfWbW92qImsEpRImXjUNHjG/ha0Kl2V3OUOL6B6u8ahxrjbaDGeI1DjfFKUZGUjSNkehXLs/aD52LbbYTm5ui/eJHPvQdXV7pLtu3P3GHo5hsM/YnD7FNPKevDPFJKHOmWRB7a0iG9LhhuCIVZaaMnpjl+8wKu5xK/OoLb5uKc7GByqA3MGaJ6Ak13GdMk3t0FuhbWcK5MMnJmELks+M7lqQ2L0xNwhcX1yMTdhmHnpnN1N/fbS1dbiVy0vbB/4fidiCl0IiJEaF1gtAiJ3GNYCxHWTCKi8NwirFn51/nnWoiwsHIC4zZX4V2yc2La4VDlaNvM888TefVVvFiM1SOPkB2/TUhmOZgZL9lPGibu8VwEL4ZB9rnnHu5k7HBSVRa5t6lrbWBQImWAEUKcBP6jTcW/IqV8tbhASpkF/rUQ4iLwA6A7v+ks8FXg/6hTFf8Y+G+llH/ut4OUchn4x0KIc8D3gMNFm/+REOJPpJT3u+TiXSnlG/ddW0UJagKrcagJrA2qfWfV9mqLmsAqpVpkkaK2qD5vAxVJ2ThUu9tAtbvG4XdOs47A8whUCgU1xmsclgFCSKQsP+dB7PPUfe3Dc+DAgW1FYfeea4O75eWx3kHOnu2sQ812FvrNm4TfeAMMgxshkz32VEmOxBmzj/ZIjMO65PDlyyU5EguW1jsRT3pkvbxo6Nmk3SwZLxdpmPGyZFybq3KMewNJvrP0LvZ6tKGz/jwrnfUoxMLzrJcXIKWD9Dc4LePJd26gZybpml+jI7OC3aYzd7qXsLZctu/dgz20raZb1uLUMXMLAFw9N6AIp22WquwfzicmLuxfOL5WmMIgLCzCmkk4LyrOJ2aJRTuJ9wzkyvNCYmiTyBjRrPVtYc3CEDtvcYMXj5N+6SXC584hYz1c6Nxblgt1xuzj8bNtmKYAwyD90ks1sxjeqVQf4zWwIoqmokTKYPN3KbVK/TebBcpipJRXhBB/C/gXRcW/IYT4tw8gBFZjBvgxKeVb2z1ASnlHCPGzwLtA4Xb9KPBZ4Ls1rJtim/jnyVI3c7XG75zajsB1Qd95Y7O6oSawGodpgKZJPK/8nGfs4E1g+Ua0qeiOmuP3W84EcOLU77em2l3t8YuYqZYLuVVRUUWNo9o5TdvQ5pMrtRVRY7zGIUTO1r9SZIOy9N8grK61NSdsQTw7yROr75XYnB6/3I02dbyl7Q6LcyRq8/McHhujO1kapdZvzxBL6GgLh2qWI1FKiS2dnIiYFw+zXl4kzAuK2XVB0S4RFtNedtPzjW0FEbIgTG6Lw/DJwpsP/F22i7I43WCxN0rf1DLL3RG6FpN0z60xHe+qeD6EJ9e/+3J3BICV3g6iWpiQMNeFw4JQuLawgiUN9g0MEhbmevRiqCAy5o8JaxZhYRLSLHRRvvrqte+9lrObP9EaOaXdo0dJvfwyzjfeQLLAtDnAtFnat7lyDW1okOxzz7W8QAlbjPHUtTYwKJEyoAghYsDPFhVJ4O9s49D/A/jbwMH864PAC8C3alU3KeUUMPUAx70vhPg28MWi4i+gRMqm4LfKPqJWwdScsE+eLICMA20BEin9csaAmrSvNUJAyKx8zoM4geXX57WpdldzQj4uRKrdbaDEotrj3+6CJ46rqKLGEaoybk5nRaCuMdXyjqsxXu0JWbLiAqxALkRT19qGoCUSfPnq7/LsZPlU0NDtUSJfv4A7NNSyk/bFORKNkRE0RycjwkyH95A0wrSzykA2gaWvkv34A1ZOHCbT3cHqN3+LG7/wee7E1/ihvMb1OwvrAmHWs9cFw7Rnl4qN+fKsZ+PdRxRiK9DKFqfV0NHWRURLMwkJE/FYJwOfvIWxp5eOSYfMqkfn2wY3YodwXRPP1fEcg3YLfqH3E0KiD9othg4+haEbpF/4q76LBy7dy+WhPRtrDXGxlnjxOPM/98t87U/tskUZCSvO1M8fpG+4v9nVbBhVx3jqWhsYlEgZXL5E6f//DSnl6FYHSSk9IcT/Qamg+ZepoUj5kLxJqUh5oFkVCTrK+rBxqAmsDarlBtvi3kPxAEQsWXFAGbQcbVJWix5vbF2CgLLd3EC1u8ah2t0Gqt01jq2t1YMzxlORlI1F9XkbqIUZ9Ue/eZPwuXMMJHXm0cvsD6PL3SA70e/dI/LqqyU2p83AK0Qf5q1Gs55T+lo62N7G84I1aeG5XSjPl4WnZnlm5Ae4nsvhK7fRbIel420sHopharfpyn/unCcJjy3hLiRxrs7lciTevM53RlIsHGrnpvwYJj5u2nnZLew0i9PNCCAkLCzNyEcdmljCJCQMQpqVe54XGcse89GJ1nqUorn+vKL96SBEPgyhTUygnepj6gfXiSQEJ+5OkrDiJLUobd4Kx/RJOsQa6BbO8ZOYuok7NNTS0c31JmRJEtZevtnzpbJt/3XXLH3sTjH8QVBjPAUokTLIbO4Fv3Mfx36XUpHypx+6NrVjYdPrrop7KeqOylfUONQE1gZ+gxvLkIGyvW0UagIrR9ahYt4mUH1ePfA7p0ETx0FdaxuJn9WQiiraQLW72qPyP29QzS1DLUSrPWqMl0NK6FpN8LkKkS6xlSNAT7OruOOxbZvx8XEuXbpUcXtobo6h73wHzXUJLxl8evke1qZsQl0Td0n/WYa1ffuwOzvxvvY17n3+82R6e9f3kUg8JI7wcHDXH13hlZb5lePhiI3jCuXr+wk3v0+urJY8+dENltw1uubXkHaWtKlz71CszHJTaoJ7B3uJrmZaNkdiI3hYi9PF3igAGhqaIwlpJu1WdF1UDGlGXlQsFhGtkteFSMaxT25hSI1PnXpsfZspDIRoXF+bef55Iq++iheLMRk/RXZqlJDMcjAzvr5POOIhTRP32DG8WAwMg+xzzzWsjq1ItTFeJbv1VkaJlApQImWQ+dSm19vO/0gu72MGKGRBGRRC9EspZ2pRsYdkaNPruabUQuF7kQlSVF+jUBNYG6iVzo3FdwKril1HK1Ltd6bsSWqPb07KAIpFvrkB1bX2gXAch5GRkYrbJu/FKR8+w0rS9Z14bVXG7g4D7WXldmbpgc7F+Pg4zz77bA1q1npUHeMF7FpbzaVFK09hpXhI/EXKBlekiWiJBOLb5/n1yeWybfszdzjxJ98nPBlvWfvRWiClZOST65x8/BShnih2XvyzcdafH3zzLdasFObiMkcyE6x1adimzlxnN+mwSdRJ0ZFeYYk08t7H3AsNsNIVYfbj3+GNl85i59/Pwd21S3NVjsT6YaAXCYM58VCeaWfPJymMPT20T9o4SY/OdzRGYwdxZc7iVDo6uoRf6r9BWPRA1KL/wFl0zeDZz/0qf23vELrQee21fL7Esw9maZp2c1OWfWbz4iu8eJz0Sy8RPneO5XAvf9E5UBbNvNAbY/ipjlzOF8Mg/dJLqt97SKqlwgrePJ7/NuXUEhyUSBlAhBAmsHmZ1bY9IaSUGSHETeBUUfEjwE4QKT+z6fX1B3kTIUQIOAL0AjY5sXNCSpl8uOoFByUWNQ41gbWBshluLEosylEth4JK9F57fNtdgCZOC6hrbW2ZmJjgwIHKmQJCRmXLJdsJnjqSsStbE/idI8WDU3WMZ/tuaklUf9dYwhbEs5NlubIOvNuJ9tRwy1v8FexH00kXQaRswn7G7MMQ7TvGfvR+8KRH1nPy+QgdsoXchSWP/tuzFbbn8hyWvi7s653M/0bljbK6xGZW+PzUBwgpGR6bwIi62Puj3DvQg9QyQIZV4IbXxdCYQ/dCkq6xCRJnBglPpdFmp0n2lS+a2W0ENUcigCkMQpqJcMFJZenr6skJisJYjza0hIFZ9HzzNqvI8rQQwVgQJHVRYZw2CJHLkbzFaT8r74wQGdM5Nj6dtzgN0+atsddJEDNSoIdxTpxEN9pwh4YIDbZeVin36FFSL7/M9I0fIplm2hxg2tzo54+0OyDSLZ0XttFUG79kAiZSJqu4tDQwqFjRZJRIGUyOUPq/T0kpZ+/zPe5QKlIOA99/2Io9DEKIo8BPbir+5gO81T8md47Cm8odIcS75PJv/u87JHJ0x6ImEhpH9UjKBlZkB+AnyqqoovoQ8rl/Dly7U/YkDcVvNWXQVpyCWphRD/xWwy9rlRte1tEfePX8bqX9rc6K5XsHuh7oXPhFryqq25gGrc9T9xaNQ0sk+IXR3yM7mSjbFh/NEvn6X7T0RLWWSBA+dw4cBzm7wKeXx8vsR/vtGXo+Bu30UbxYjPC5c6RefvmBzocnJc567sJcbkO7JM/hRtnEniRvyxvcmUhWEA6LhUaHrJfNP5Zuc+TOEbSOXJ8CoHMhieG42KaeFyiDZXO6U3MkaggsLWdNGtatXESiZmLlcx2GNIuQnnsMlzw3CelWbnthv/X32HhuaRsi4sWLF3nrg7f4yle+Upfvsplii9O1I4+QHbtdZnEK4Bkm3vFgWJx68TjnH/81vnNnqWyBihjqIfXVoy2/QKWRGDoYusRxy8c3rTrG8xvzX7s+AMTKyk3dXndpGRkZYXh4uJ7VUzQZJVIGkz2bXt97gPfYfMzm92wGf49cjukCHwMXH+B9TvmUG8CP5P/+CyHE/wj8XSl30Ch/B+GXNyYSqlyueHCqTmAFLKLNTxxTFhH1IaRyAwLVIymV3WvtCZkqT1YBv9+aWphRe/zanesJbAfMAN1V+fV5qt3VHj2AE1h++LY7dZ2tKYUIwsGUwRhaWQRhZLELZNeujCDcLuaffhfHzsD8HPr160QMDdswmW7rJWWGiXpr7EnPkHKSZD96h4XjB0h1t7P8h/+cyz/3kxsio3Sw14VGt7S8SIh0uI/phCNwXX4Idz6s3wloIMrmNMeD5khc645iSp1sLIa56NHf3UtPZywnAmo5ETC0LhiaRaLhRnlhn439rbyAaDQ8N2IjKbY49WIxLnTurRgx/din2rCs4FicpjKChLWXb/Z8qaT8F06l8AbKra8VD0fIDM4Y7/Tp077b/FxaLGMj/+/w8HDV91DsfgJ0O60oYrMfxoOM7DYf01SPDSHELwK/sKn4/y2lrNddawT428BnhBBfllKu1vLNhRB7gP77PGxH3R2q1c6NQ9fBNCS2E4zBTTVUVFFj8ZsYDJzda5XI0Wq5JhQPht/vOWjiOPgvzFDtrvZslf/ZNIJznVFjvMYStiSrqfJzHrRrrTE9xRfn3yyJ7EhYcVb6zwBqFWQtKEQQupkM7avTfHr5TlkEYfedO6T/LMvavn3YnZ30/97vIX/lV+o2ce9JiY2LKzwWnJWKkYa2dMlKZz0a0Zal5bZnY0uXm8cXGY1e5nuJ0QrvkxMRo9MLvHD1vZz96JUJDM9FfCrKzIEe0KaJAB4w5QmMsTTdC0mc68uMnBlE3r7Du6OChRawH20ULWlzKkF3BZon0L3CIyWvNbd4m8DuOErP0jxCWISyq7QlXaLfj3I1egLPM/E8Dc/VwRG8YPyQPkfH0y261vroTArmDw5jfn2EX/vPfp6nH3m62Wdg11CwOHX+3Z8hWSyzOAVw5Br60GDLRo5vxjffvRrj1YWwBWvp8vJWdKayLMvXceXyTKRieWfUCJxjTZBRImUw2TxqrtAlbklqi/dsGEKIYeCfbyr+XSnlH9zH20jgAvDHwNvAVWCe3D1IL/AE8NPAX6XUBvazwGtCiJ+pcUTl/wX4jRq+X8NRYlFjCVtKpIQqUUWq3dUFv99z4NpdtZyUqu3VHNXuckipxKJGUi2/bDor6GgLzjlXrgWNJSdSlpcHpc/TEglC58/z2OszLK2UipH7M3fYO/rnhH+7JzATyPUkdP48OA6LN29ycG6KNRkhkxeDk1qUNm+VDv02OKtExm4xE+siJbK0/cnvMflLX/aNHCwIhiVRhN6miMISYTFnSboeZVhwgLv71sN9wV6ANKTnfXc5fX0SUPajjaKRNqeaFBhSx0DDkNr6c11qGOj5stxzPf/cXC/X1/fJ7Z8vKzk2/35skataUD4T2waDQ2nCs7OYR9ppv32bzkSGx2avF/3+1ohnEwz0pWhra2P10CF6OztJ9fez/5lniC9VO3MKP7x4nKVf+BW+9l27zOI0YcWZ/NmDxE/db/zA7sXvvlbdW9QHtfg2h2p3ClAiZVDZnGvxQdZobDbzrLzsoc4IIXqAPwSKk+PcBf7GfbzNd4DfllJe99k+kf/7IyHEfwu8BvxY0fYvkRMV/9f7+MyWx3cFlrICqwthS7KSLC8PygRWAdXuGosSi3L4fV/LkOj1SRETaPyiBFtxxWk1bAc8T/V5jaJadGq1aOpWRInjjcVP/PVLrdBKFKxHcRwcx2CPPVVmxWfqPS1tPeqHJ2WJCLg5gtD2irdtCIR3wndw8PhkfjkXeZjfLzw1x6euv4H0XAYnbxAe8Fjr7mB2fw+GMU637iE0jxueJDXm0L2QxEiu8rElkdcn+M5Hcy0RQajsRxvLcm8He6fXSPd00reUpXcmy3hbO640ka6G5+lIV0MXGo/udxhcWsEwOjEHDtAV6WX50CMc6vtxTM3AEoU/E0sYmHm70kK5JrYQD5uMtncvkVdfBcdB9PWTuTNOyCvPkRjp7CBy9iihfI7E1MsvsyceV/mdH4KwJStanAL8l12zxO/HknmX4+vSou4tts19/Ra9zwDRsuIbN+9yKXZn25+323M1qnanACVSNgUhxD8C/u8N+Ki/K6X8OxXKN0dOPsi6582eOg8SjflQCCEiwDngeFHxGvCzUsq57b6PlHLbSzKllHeFEC8ArwOfLtr0Xwkh/qWUsoJMFExUJGVj8ZvACtqkvVqB1Vj82l0mYO1OTdg3Fr/zGjTrw2orbFXbqz1+OSkhgG1PXWsbSsSSxLOTZREe+y52oj06jDcwsPWb7EIK1qM4Dtr8PAdGbxNKeiX79NsztM/oaAuH8GIxwufOkXr55YZGVEopcfFKcwwWbEZLBMSNyMK78TVSoXHuLWbKIgrtirkLXbKeXfK+Lt7WlatEYWnxcqnw8eSHN5h1luiaX0O6WZyIztSxdnSt1O611SMIW9J+9D6xNBNLMwhpVv7RzJflchdaIr89n+dw8/4l+2omoyM3+PjDK/z0F76EKfTcPsLEFAZmZIbIxNehU2LOvMP8nEv4jU5GIifxikTFiOnymPYBuowiIyYdg4+AEKR+5Kfx2lujDyzOkWh3xXirY6hijsTBM+1EYzIwORIbQbXxS0YtvgWUW8Z2ud+ciSGj8jUj625/pXMr5Gr0nz9ucEUUTUWJlMFkc/7EzZGV22Fz5GRNczJuhRDCAL5BaURjlpxAebGeny2lTAshvkrOErbwG9oDfB74/Rp9zP8O/Lv7POYocD8Wt3VFedk3lrDP5KmKaMuh2l198Ju0V+0uh5qwrw+hFo/gdRxnWytw51dC5IYf5dy6+THuUmtEc5w+fRrLav4datWclFUsn1sRda1tHFoiwcu3fx8xOVm2rf9GhsjX/wJ3aKglrU4L1qPa/DzGyAjC0ciISJn1Ya9MY1y7hnPyJG4shvan32X55Z8vsxidMBZx8RDJm0WWpOXC4OZ8hsXRhqX75WxJbekiuc+2fwhusAKLt+py7h4EFUGYo5H2o9vFICfsmcIgs5amIxylK9qBpVmEikTBYmGwIBaWCYzrj5W3m8JAiNpe02yxyJ01k71WT9k2b2AAb3AQbWIC99gxxMIIA/Y03c5iyW99cDWBPrsKQuAeO5Z7HBpquUUahRyJ7jdfR7JUMUeiYSRxA5QjsRFUW4jWKvcX20Xd1z4c1fIuViLWHYbp8vLe/n2cPRsr39Ci+KZtUpGUgUKJlMFks6BYHlu+NZuPaZhIKXKj5t8klyOygAv8spTyO42og5TyhhDiHPCzRcU1EymllNNUvFT5U+ubiYfFz4IqsjkGV1ETlO1mDmUT0VhUDoUcflFFqt3Vh1bv7yYmJjhw4MCW+6Udf9uykLn7Izlgwy7pfm7264WKpNwgqfq8hlCwOh3KmEwiyiJqQotdILt2pNWplBJnXdBzfSMFNwuCBfFQn0xw8MM/RUqX/VduoTku03u7mXi8C4w5IvoMQvOYwaEtMc+NpRXsyxNcOzOIXBR859JYufVoR/5x+nLDz8duQEUQ5ljsjdI3tcxyd4SuxSTdc2tMx7tKBFvP1cHTaDcle+YzWMKE3l56jU6ye49wtu1YLkowbzNqCj33vDiCsFCW36+wvbjMEgaGMNCK7vNfe+01nn32WZ5+9OlmnJ66kHn+eSKvvooXi7F65BGy47cJyXKbU2mYuMeP4eVtTrPPPdekGtcXLx4n8TO/wtd+36mYI/GLv3iQ6KeCkyOxEVSL1gqcM5USixqKckTL4TufosTxQKFEyubwx8BsAz7n+z7lm8WvoQd4783H3Jeg9pD878ArRa8l8DeklP++gXUAOE+pSLm7TcBrjFqB1VhafdJ+uyh7ksbiN2gMXrurXK7aXX2oZvfqeaDt7JRD22J4eHhLYS487h+t8eTZU/R3qettLVETWBt0LSd4fP79sonT3rWjQHBWfdeTYqvTXnuJQ8s3sGSp7Wb/RALzHQ332DFfq9OCFamjeyRFlll7aZMVaalgmIscLIogXBcOXabapnGEx4WpOyXbN1ubFt73YXjy0g0ixjJd82tkvTR2SGfyZJiItli278ThLtqvrLWk9Wgj2YkRhNtBR9sQ9jaJfAUxsPB6bPQ2fV09DA0MbhIRDay8aBj98UUOjP8BWlTQOfMhC3MS/Vt7uBY5iesZSE8DBLGow185/iG6lkW2m+w98FTOfvQnvoq3p7Wi++pNsc2p193Dhc69FW1OP/VEG4YhAmFzms4K3xyJ/83QDDyo3bOiIkLk7i8q3cMGbfGtmsdrLGrRdw7V7hSgRMqmIKX8LvDdJlZhFHDY+P9HhBD9UsqZ+3iPzUv8r9WkZlsghPgHwH+8qfhvSin/VSM+fxObsxir5WxFqHxFjUWJlDlUu2ss/u2uwRVpMioHb2OJVBOLbGgLSMR+tf692jlSPBhqAisnnIXOn+eVW0vYTul33p+5w5nzbxJeGVAWdD640qssCHrlUYZDf3wOa20aY2GRY/Y4mZjENnXmOrvJhE0iToq1zDz31rLIDya4dyzOaleEmX9/gzd+5mzJ+0kkPA0/YBruXXjwL1DoW1P3c8v4YCjr0caznQjCAsKT6+d4uTuyfryGViQQbliUFguGOUHQXH+e269KdKEwuPXJKAYaZx55NC86buyvie2vTHrt/GsMDx/jbKzKIqAoRA6MoE1MoJ04hfzhdfqSCzyderfEfvSoPYk+u9by9qONomBz6vyfbyBZqGhz6nhriIDYnPrd04K6v6gXfmM8NZ+SQ7W7+qDm8XIoZyoFKJEykEgpbSHETUoj/04B39vO8UKIEHBkU3HdRUohxN8G/p+bin9DSvmP6v3ZPtibXlf3xAkQtgOup8L1G4myicihVmA1lpBvuwvYoFrZ4jSUqrkBs4K2gJz3ar8z1ec9GK7rVs0HOmQ/wvD8R2URhHcvmlzSWjuqoe3uXeJvvolwXFznCHvs6bIol5Ulm5l37+K9/z6Jz3yG5L5923rv8fFxnn322Tp/gw1c6WF7DlnPJus5ubyCnkPGs8l69sY2Wdgnv5/nYOefZzybSW+SyWOL/NPpP9wQGD2faETp4G0zb2FsZoXPj76PkJLh6xMYUZfs/ij3DnQgNRuwWQWued0Mjc3RvZCkc/Quk2cGCU0mEVMJ1jZbne4ylPXow1OIMNywEc0JgNlkBl1q9HbGioRAna4nD3Js9HX0vYKeqesklzyiP4zwSddhPGkiXQ3P0+gMSX6x/zqW1g/tJnsPPomu6aSf/6uI+N66fJekmzOhGjAbE6ldbD86GT9FNjFaZj8ajnhI01yPZG5l+9FG4cXjzP/8L/O183ZFm9Opnz9I33Aw1oVXu4dX9xf1QYlFOfzua4Nyf9VolDNVDpXvXgFKpAwy71MqUj7LNkVK4Ek21tICTOZzKNYNIcR/Bvw3m4r/Jynl5rJGsnn5Xv2XFe8Sql1QQ+oiUxd8B9UqTxagJuzrhd95zQas3alB9cOTzWa5cuXKtva9O9cG/ETFbe++d4W+Tp+kyE3i9OnTWFbtQxv9VpwausRojuvermdmZobDhw+XlYfm5ui/eJG/MfEDktnS26f9mTs88YMp9i6GmX3qKTK9vY2qbsMIzc0Rf/NNNNdFX1rmR5cvlFuP2jPsv51EGHHszk7ib77Jvc9/vur5kEhcJK4JqzJFIj2fEwllTgTcEBILImGpcFgiNBYJjtlKx3kOdn6b85BWpCX0wXTyZu3eDzhyfQqAzoUkhuNimzr3DvSURbRJTXDvYC/R1UzLWZ3uVutRPzZHCa4trxG1InRFO0vyFZZHHOZyEhZblm6UV4tUNHwjDC9dugTA2eObogn7IHIkmYsgPNmOe+E67QmHU1NjRRGEyxxiknZ9DTQT5/hJQrqFOzRUN4GyGRTbjy6He/nzzoEy+9H5nh5OPtWeC7MPgP1oowhb0tfm9De6Z+kjGAsQksoto+H4ppNogFhUbYFcI5ES0pkvVNx2Z+wGl/S5+3q/kZERhodVVqxqNLPd7SRU+hwFKJEyyPwR8EtFr18E/t42j31x0+s/rEmNfBBC/IfAP9xU/M+klH+rnp+7DX580+vN9q+BpaoFnVqBVRdCplqBBVXEItXu6oJfuwuK9WEBFcH78Fy5cmXbN7KW4R+xlnV21sR0YdJhq/ySD4Lq7+rD5nyg+s2bhN94AwyDkAnta1NlEYTt7Z0c1l0OX75M+qWXcI8ebd4XeEAKVqTruQWLLEh7Xv0BRreJvrBKePoOne2CrGkw2x4jE7Zoc1L0rc3hhdLokyvMRwdZ62hj/pNv8eenf8Q3/+F63sK/YvG/yG/BB99q7knYISir09pYj1ZCkyJvLaqXCnva5jyGpWJgcc5Cs2LuQ91XLDSEjhCl9X7ttddyfc2x2l8bHobiCMKlg4+QnbhVFkFo6DIQEYQF+9GZmz9EMl1mP3q43QGRxh0aCoT9aKOoFjSdCpBDkN8YL2TKlsi9vhMJ+bS9ejtTnT59ur4fcB/YrkDi1/buf4HA8PDwjvp+O5FmtbudhrqvVYASKYPMNynNS/lZIcQRKeVotYNE7g7r1zYV/0Htq7f+eV8BvgYlV8rfAv6Ten3mdhBCdAM/t6n4fBOqsiNJVgliUSv/6oPfxTtoIqWv7aZqd3VB2ZPkULk7asNmcciP2SX/9nXk2GkeOejUslo7Fm1qii/O/7DMDm0s/DjKgb42aIkE4XPnwHHQ5ud5amEcO1XavvrtGeLXBVrPEbxYjPC5c6RefvmBJ6w9KXE2iXnF9qHrQmKFPIaF59kSITB/jFdsQVq+j58VaWxmhc/fyFuPXpvAcFyck1EmD/QgtTUEa6SAu14bcixF90IS6+Yot84MIu8uMHcvxsIutx5tNEGwOhVQJgiWCHuPdRH/5Ifoe3pon7TRHZf59wUjnQdxMZCehnQ1pCv4rH6LA3RDm0nHvsfQdYNHfuKXeCm+t0QovPLBR2iIuiwaaRWKIwid7h4udMbLIggXw708/lQ0EBGEXjzO+cd/jW+PL5XZjzLUQ+qrR1UOyhpT1dK/Sp7GVkMtgGw8fue23otvLcvaMdelxVX/7/rYo8d57Egw7rEaibIZzqHmUxSgRMrAIqWcF0L8PvDz+SIB/B3gq1sc+teAQ0Wvx4A/rXH1chUS4qeBrwPFa8V+H/g1KWWzE//8j0B30essoJZ/56kaSakuMnVBDW5y+K04Uyuw6oPKhZrDv901th5BodqigyCsstcSCULnz3Pih9M8vRIu2bY/c4cX7v4F4d+OqciOB2BzTsrBb3+L0OwMxvISHWNjWCJKOhJmJtJHygrT5q0ykJ4hY68w9+4kM4cHSXZGWfy3/xPvfulHcYWHKzwcPBzh4VJ47eZe58uc4kfR7CFuKcp6tPE02uq0ehRgIVpQZ2VhGR2Nvf3x8kjEsv3LowmLLU11tLLowhL2QuRDK2c9eqof49o17o7oHHVm8tajYdq8NeLZBL17M7T3tOEMn8SyunGHhjD3nyp7S80nOkRRSiGCMPuvv4dkviyCUBMSxFpgIgjTWVHRfvTnHknhDSw3qVaty1Z5x4OCEikbj1p8qxzRmoGax8uh+jwFKJEy6PwG8LNsiIC/KoT4lpTy1Uo7CyFOkRPnivn/SimrTgkKIQ4BtzYVH5ZS3q5yzGeBf0dpKMB3gF+SUtZs+Y4Q4v8FfFdK+e429zeAvw/89U2b/qmUcrJW9drt+K2CAXWRqRdKLMqhVmA1FjWozqHaXf0pzlnpehDPPl4W1ZCw4oy+BWJ16wn6euWJrAWbhbJi2u7eXc8NOD/XwR57qcx6NCu7mHn3Xbz33yfxmc+Q3Ldv/fid/L23wpPeesRgdlOEoF8UYUm0oFcoK49SLDxP/dogP4xO85Y5Q8fMEi+sXkKEJMOJCVK6S+a0y+wBA7QpIoAEpjyBMWbjLiTR7t3iXs8gcmGBsbWuloggVNajjcNAxxQ62b4e2mYyOD0xQks2PdMut8xuvHwEoefqSE8jGhKcHHTYu7SKYXRhxQ/Q3dbDyuFTHO7/8RKx8M+++zpHDxzmzKkz66Kj6WNF6sele/lchj2Nifgoth51Tp4kMzJeZj0KgGXinDzS0tajjcaLx5l86Vf42luZitfa5Vf2ow8FI4JQjfEai2mArklcr/y8B+n+QrW7xqPua6t/V9X26kOzInh3Gn7fV7W7YKFEygAjpfxYCPEvgL9RVPxvhRCPAP+zlHIBQAhhAr9MLi9kd9G+HwK/Wet6CSEeBc4BxeEBCeAfAz+x3RvpPCkp5Q+rbP8p4O8JId4CvkHOsvXaZiFUCNEFfBH4z4FPbXqPm8B/cz+VanUythrcNBo1qM6hvOwbi1+7cz2B7eQmGoKAb7tT/V3NKOSsfKyvj/6LF/lPEhfwZOl535+5w5k3JohNRpl96ikyvb0V36ueeSJrwczMDIcPHy4rD83NrQuU5vIyh8ZvEU2W2kD22zMYnsBc7sDu7CT+5pvc+/znyfT21vR75yxJ/UXBrGeXWJCW7+MWiYaVBMPcscmuFK7w+Be338Sl/hGGWiyEnf+cQ58kABVBGATr0c3oQsMSJpZmYmlGLoehZqw/t0TucWVhmbnpWU4ePVEWVWgWRQ76RxfmXheea/l7HC08ReTe16FDYs68w8SkS/R2GyORk3hiw2Bmb8zmUXkZXbYhIybte0+CEKSe+RKHo6UC0jtrJjEvSr/Z1dBz+TAUW496sRgXOgfpSs2WLcyIPdJBLOa1vPVoowlbsmIEIUAyNk2Hj0V0q6HcMhpP2JKspYMtUip3oMYTMtV8SjVHGjWPVx9UBG8O37RNqs8LFAGZPlRU4f8BPAE8lX+tAX8b+C+EELeADHAE2LwUfBb4hVpGNRbxFNCxqSzOg+W+HKPUntaPZ/N/ABkhxF1gCXCB3vx7VEpRngD+kpRy7gHq1rL4rfzTNRkY0aLRqEE12A44rhKLGkmoynnN2ALTCMZ59+vz1KC6tpyNRnni8mUwDEzdqzhZ3dnZzWHd4/Dly6Rfegn36NFmV/uBqJSfM/Jbv4XW3482P48xN8cdTSOTj2pJ6hHaWSVuT9JppumdWWOtJ0qmO8reu1e58+yXMMwY10WClZUPsb1SQbBMKPScspyFpa8bJDxVGnk1CBVBmKPR1qPFaIi8OLghDG4IhiYhzcAsCInCT1DMlYc0c2PfksfN762ji+3V+eLFi7w1+hZfeeZzD/wdK+ENDOANDqJNTOAeO4aYus6APU23s5i3Oo3S5q1xdHoSPboGQuAeO5Z7HBpqqRx5BetR6/XXsd3FMutRAN1IBcZ6tJFUG+Ol7fKb9VZFTZw2npAFa+ny8vzlJRD43lvsTjOMXUHI59xmA5SGsZojWptqe3XBr91lAtTfgX/ba1PX2kCh5IKAI6VMCiG+QM5atdgbxwKGfQ67Dbwkpbxe5+o1ixCwnVnNbwL/gZRyus712XVU8xO/v0BYxXZRkZTKnqQZVDuvqSy0RxpYmU0U24PWGtu2GR0dXX89MfUVoHxSeGZqnEuXxsvKYXfbbjaDQhQheZHu08vjuZUJRfTbM/ReFWjRnO1f+Nw5Ui+/3JRJa1d6ZKVNUmRxhcdkdp6s3BxdaBdZlbq519Ih+5lu3my7zgcz0+vbQlOzPHn9+3iey6GPbyFsh9kDUeafFVjmLUIi91uc9ySRsXluLyRxPp5h5MwgclXwnY+XNqxH5ypbySpKCWIEYSUWe6P0TS2z2t1GbDFFz2yae10hXJmzHpWejudq4Gkc6k2zf24OgzCZ7jj76SLUe5TPiUcx0DHQMNHXnxv55299/4c89xOf48nHz+ZzFpr5CMTtW5G2GsVWp/P7T5FNjJZZnRqWRJom7rFjLW116sXjrPziK/yT17yK1qM/+pcP0/+ZypHzigenmhiSG3MHY2yt8mQ1HhVZpKwPm4Gv7WYV4a7VUPMpjUfN44GU/tHjfmmtFK2JEikVSCnnhRAvksuz+J8Dft5U88A/Bf6elHK1UfWrM/8dcBX4DHAS2Grp9CrwLeB/k1J+v85127X4D6obXJEA4TeREKSclFUH1WoFVl3YyRNYBXvQ4WG/9TYPTkGgLLz3wLeXOTP/TtnEqT6RBqJlx+90u9GdSP/Fi2ium4siHBkhhMGyCJVEFcWzCcgmMa5dwzl5Ei8Ww3r9ddKvvLJuT5qVDqsijSM87mZnyHp5YdAnirDsMW9jWtnidOO1V7Am7c5/gYm/2P6XPdvJx0zC2kaq6ycv32DBWaFrfg03m8lZjx7qQmilFqitbj3aSJoZQVgNTQp0NHSpYRQepYaOhuYJDKljCB2jeHv+UUfLbc8/1yvsY0g9v1+urC1+mkPvfQsRlnRzjZV5gTnay+gm61FLt/ni/jcJpUw8I4ITHqQrrZGNP0Vkvqfqd/Imk/SJDvaEYnU5Z7uRYqtTpzvGhc5P02/PlESPr0V7efSpKAjR8lanqYzwtR79v+2dAwIU6tIgqk1IB2nyVOUGbDxq0l61u2ag2p1/uzMNiV6fYW3gUe0uFzUqperzFEqkVOSRUnrAPwf+uRDiDDkL2L3kRLs54CPgL6SU9x10LqW8DWy7h5VS/mvgX9/v5zwIUsrvAt8FEEK0AafIWbvuJWdxqwGLwALwMXBZykZ5nO1eUpnK5coWp36owU31HArK7rU+7PQJrEqWmbXkyb17CZ0/zyu332JprdSXcn/mDj86Ns2nrx5XFnQ+eNJbFwhXtDQOLmOZqTLhT08k6FoYZUnziIzcRdg2qYMxbvd2IcwkhraKrbtMYNK1tEZ6eRn7o2k+ObMPZ+kdvv3ObWb72jY+uDv/OHGxGV/7gVDWo42nEEG43B2hazFJ99wa0/GukvOeiyTU0D3YM5/BFAZebw8xo4N0/BBnIofX8w+aQt/ITagZRXkJ9aJ8hYXyfF5DrTx34XatSGtJJDGNNjGBFomQ+fPK1qMH5CS9jgMdHTgnTxKOxXCHhuh4/vkt37+wcENRSsHqNP3Pv49krszqNGxIEGuBsDqtNqZQ9xf1oeoYT0UWqXuLOqLua/2/q7I+rB8qglf1d81A9XdbjPFU2wsUSqRUlCGlvAxcbnY9Go2UMgm8k/9TPAS+uTvUBaZuqMFNdSuWZubvqKft6MPiOA5SSkyzuo2hHyspA3ih4rb3PxxhdXrlvt5vfHy8LpGP9aDt7l0ib7wBjoPrRthjT5XlR9Q1D/3ePSKvvrpr8iNKKdctSdftRz17XTjMbooqrPR6Y9+cnenmskKkoktRFGBX/nGy/BL85F/c4Jg5T9f8GgupeWxTZ+bxbrq0ybJ9x3raCF1ZxLRtrPlFUj1RDo5MMLvLowiV9ejWGOiYQl8X9kpFv8LznPhnaaXbC/v87jf+PZ/50R/n2OGjtP/YIvvH/wA9Cp0zl9EcF/e9Hr69fBpPmnieBlKgSY/PhD/meH8W2W4yeOApEILUT361ZXIDFluPTg+dInuv3Ho0ZAbDerTRePE441/8Fb52MV1mdTrfNsBf/+qBlmln1fC7twC1yr5e7PSFaI1C2W42HnVfq2yGm4F/u2twRZqI6u8aTzVxXEoCkS6r2jyeckQLFkqkVCgUNcd3BZa6wNQNv4Fjxg7AqCbPTs2hcOXKFRKJBKdPn25aHfy4cuUKly9f5sUXX3yg40OmRzw7WTFHlDUrId6aw4zN+RGfmLuD5pQaDfTbM+wbT6MdOF2T/IjFdqXF4l+xkJj1FQftEhvT9df5SMWstFl5co0fGNM4Y9+rxSmqKSqKcOdaj/phoG9ECGqbhcKNyMJ1kVArLjPXowbvjd3FQGf4yPH8++RFxvw+G9GIBloN7uLPffybHHtyD2ejxyAKkQMjuQjCE6cwrl2je3WRp+ffK7MZ7nbTsEfgHjsGQuAODbWUcFRsPboS6eFC50CZ9ajd3cOZp9ofyHrUdd0du5inGleuXOHChQt1/5zr0/tIWD9X0er0J1///21rAuvtt9/myJEjdahdY6jmltEWalw9gkSoylqYzH17K+0MHMfhu9/97n1Fby8u/0dAW1n5pXffQpu5XsPabY8LFy7Q1dW19Y47iCtXruC6218s5ZciJkhi0YOIlLv1WlqgUddUPz4ZeRp4tqz83uQcr73273yP2+3X12L8ouTVPF79qJYSK2MHI2VWtTlLJZAHi9acPVQoFE3FbyWMiqSsH6EqIqXngaZV3NxSVI2kbPLA+vTp0zz99NNNrYMfS0tLD2SJqiUSWH96nl+ffKts2/7MHX78wxRDevy+LOh2i+VfcX5EfWQE3dPJGhZT4T5SZpioXCOenUKINdJXLrF84hCp7g5W/vg3Gfn55xkL38ERHtfnlkpExkwh0rBIPFyPPqy307gJzcwhWg0VRbg969ECwpPrwuxydwQBrPR2EvYMdHTarAjWJtvRnCholkYb5iMOjYoCY/F2Pf9+5vpzTdTmonPJzs2En247VJP3u1+KIwidkyeRc7fKIggBMsLEOXm8pSMIC9ajc//jD5DMlFmPxttdEKkHsh6VUjI6OorYZcvFf/CDH/DCCy/w+OOP1/Vz2m9H4fuVtz31zI8RMrfuu7u6uhgdHeWZZ56pce0aQzV7UTWBVR+EyJ3bSmJJtcjWnYwQgjNnztzXwkHvDyur4I89eoxnH+2rVdW2zfT09K7rL0dHR1leXubSpUvb2j+TehLoLysfvzvDpUu7417hYUlMnwXCZeWry9NcunSt4jHj4+MsLi7uqrZRTKOuqX6MZuJQQeO1wl08+2y5eFlgt19fi/FbENTsuZRWptoYLp0VgRjjVHdEa/3vr9hAiZQKhaLmKHuSxlPNzjRtB2OVuV+70zWJqa52NUW/eZPwuXPgOFh6G93pmTKrU8fu2BFWp450K0QZ5l5nyiISbTJepejE/P6eTdv0HE+vvI9neSQ/uovmOOhPRpk/0IOh3aUj/7kznsQas0ktLOFcW2DkzCBy9CZ/9onNQl97bqeVO005J7uN3RZFuBUCigQ/cyOCUMu9/uTqdQ4N7WdPrH9dNOx+6hjDo3+Kvhf6pkbQHZfFd9p4zzyG5xlIT8NzdXDh823XGbYcRLvFgUNPoWk6qRe+yrsTEwB1zdPaahRHEHqxGBPH+/l4aamsvxM9vTwaS993BOFuw4vH+eHTf5Xfur5SFkG/Eu8l9dUjDxRBahgGX/7yl3fsYp5qnDp1qu71NqoETZ0+8xSxDv/txbz1Vvmiot2CyknZHPxEyt1qu6nr+n0tHJQSMk7lbY+dPk4zuqyPP/64If1OLbl48SKvvfbatvcPmZUXl2XsnTWeqycZp/J3tQyvYjnk2vduvZYWaGbbftvntkwY0S3rtJuvr8X4z+M1uCIBopqdae7/0fpjnGpuGc1M26RoPGraVqFQ1By/lTBKpKwfW+WNaQvABI7KodAYtERiXaDU5uf59MoHCLvc6rTnY4HWdqSq1amUEke6ObFQ2qyFHWb0FT5J3yVTFFlYHGWYWc97uPG8kqCY6k7j4OHV2L70ySs3yAiHroU1NMfBNnXuHegpi2iTmuDewV6iqxlM26VzIclST5QjIwne3eX5ERvNw0QRFo7fivXIwE0Rg8W5C0u2a8Z65GFxvkNLGCU5EW9eu4GBxqdOP74edWigV13l/jf/wd/kxV/9Kc6eKBITeyFyZClnPXqyDePaNdqnMjyauVlmPRrbk8ToN3COn0DT9A3r0bxIqahOpYju0Jkz9L3zDmtrybIIQoDObJZbrsvs2bNklpdhm9Eiu5GxOydIWEfKrEefGJjlxYl3HqidjY+PV41SCDqRKgvNUhm2LVLuZvzGeIYuMYKjWzScoOcGtB3wcymt9rtUlHPgwIFtL5La+2YnVAgWbO/qD8xCK/N3OiuWH9y/h7NnK49rd4sjzU7F7zedyjS2Hs0kqebxGo7K/7xFTkrV9gKFEikVCkXN8csXoW7m6sfWg5vWv7ins6JifsRUdz/a1KGWyg/WSBzp5sVAm4y06fz275FKL6DNzxO5fpP2NkFKM5jt6CYTsmhzU/SvzZEkjfPhJIlje1ntamPud8d482eeykcv2nlh0kEWt81PwUVmIfHew1e8TmN6lR+xMQjEuig4f+oo4SuzOF3dWFaKNsdl30iWj7v24WHguTrS1cGBx5N3GaQdGTExhh5F1wwGf+xn+YmB+LrgeOPqdQypc/bMp/L5DKuLhg/DgjcJQJextVC6FZutR7M3xypaj0rTxDl5pKWtR+tFf3+5vRxApreXe1/4Am+3WVycSpXl4B2LP8KpLzQ+N1kz8IvuCBm731Z5p7KVSBkEVL775hB0kbLa76ut3IlTUSN8212ViexWQ/V5jUeJlFXanRKK6ka1ebxqEYathF+7swyJrhaiBQolUioUipqj7F6r4zhOzVc6ziyHgc9W3Hbp/atM9SRr+nkFbNtGCIFhNPdyEpqbo/t3P+HXJ8u3RZdGWfoH3yDV38/sU0+R6e0t2T4+Ps7w8HCDalofXOmRcbOkvdzf+nO3+LVN2stsPHez3PMmmDy+wP869XtkPIeMzJL1nPXIxoxn47JhKxSbWeHzn7yPkJLhaxMYjkvmRJTJAz1IbQ3BGingjteGN5aieyFJ+OZtxs4MIu8tkZzs27A63aUEPT/iRkShWRJpWFxWyHMYKoo09NuvsM8nV0fQpcYTZz6FpZnoaBvC4QGY+fbfIzw7y+DpIxjXrpG5p3Hgytp6FGE0H0XY1SmJ7e/EOXmS9lAMd2iIzkOlFk0z3l0AovrummHcbD16MTaIJWbLrEdDJzrZE3Nb3nq0Hui6zvDwsG+kxlgyxDe/311W3hv2fCMbWo32C5WjO+J7Oh84wkVFf1SnmkjptzCw1VAuLc3B7/xmAtLukmn/bcqCrn74WUtm7MrlrYhyCGo8fr/poFxnwf9aq0TK+lHtWpIJyoIg1d8p8iiRUqFQ1By18q86ExMTHDhwoKbvaVWJYMg4Wk0/q5g33niDAwcONFXka7t7l/ibb5Kcb0fQS79dmh8xpXeB5xGZmWHoO98h8ZnPkNy3ryl1LRYTU26mSEjMrAuKaTf3lyoqy+RfZ1w793qTAJmVPglztkMvzKbGtrXrketTAHQuJDEcN5BWpzsxP6KGWM9vGNpkQ2ppxXkPC2VmmX3pOxfe5sDgfk4ePVFhH3PdzrReEYdTXs6atc1HOJx5+mmGvvOd9ShCb+pWxSjCrDBxTh5v2ShC9+hRUi+/jPX669juAosVrEcNM407NET2ueeUQFlj1ASWcstoBtVyQQUlwsOv3ak8WfXF7/z6TSi2GtWiWFQkZf0ImcGO4AUV0dYMVCSlmsdrBsruVbU7xQZKpFQoFDXHz8teXWQ2qBap8SCspSpbnSasOMOdhzh99njNPquYkZGRmn+X+0FLJIi88Qb09zM/tsKnly9gydJlttHkFPvvCdxjx/BiMQZv3CD19NPrk/eVIjgKuRLTMpvPjWjnhMF8dGHhdUY6+cfN5RuvlztXcT2J+/a5hxMTdwDK6vT+8yP2zCXR0cj0dNGuRVgaGCLudGJInd6unnURMFSU57BYUAyti4U5AXL9uTBzORA1E0M8vPA5Pn+Zg/29nIzUdgFFrcj09pL4zGcYvHEDLxZj7PAebq8tlkURRvf0cCKWaekoQi8eJ/3KK3zt9x2GZ98v6/Mf+dJRDvxUrNnVbEnUxKmaOG0Gyu61yr2Fand1JRzwPq9aJKUSKetH0G2Gwb9vVwuC6offooxCbtog2E76RlKqeby6YeigaxLXKz/3QenzVLtTFFAipUKhqDnK7rWxaIkEPd89z69PLpdt25+5w97f+z7hW/GWjKoJnT8PjoOYn6dv4jpZDbKGyUxbL2krRFSucpgpltMp3I8uMn98P2tdURbO/WPe+ys/RtrLMvbIPFei7/EH9y6vC4xpL4tXqzyehRuaFmj+rWZ1GioSBEOaWWRham6Ih0XCoSVMOn/kDHs/+D/Ror3E2wyyqx63z3dzPXoC1zOQno7nakhH8NcPvEm/HkeGTfYffAqEIPXZr3JwYgKAs8ebI+7vVpL79pF6+mms11/HuDLDdIUown2eG5gowjExyM2eobLyX40vAAEK7WsgfuMY1xPYDpgBuLNSY7zGYxq5SSynwiU0KCKlanfNIegLM6r9vpRYVD/8ftdBieAF1ec1g60WBLW3Na4uzUK5FjSHsCVZSwdXpPRvd6q/CxoBuJVWKBSNxu8GQq12rj36zZuEz50Dx8HQ2ujJzJRFFrl2B/q9e0RefZX0Sy/hHj3a7GoD5CIV8zkU0zK76bldXl4kIGakTWhqlmevXcBxHY59dAej3UU7EGXuQA9CSxABPGDU08mMZeheSOJcX2H0zCBybILrY6FcfsQugCwEKM/Jg9JIq1MDfd2qNCRMrHz+wg0RsYKgWPT63u27GFLjkeMni44rCI7mg1uXdsNM7G3Cs7O0DT+G+84IscU1PrV0ZT0/Ypu3xl47QXhxETo7cI8dAyFwh4bwBgYgL1Iq7p9CFOF72WUujl8viyL8wdEzfPmV3T9zuFWOPscVOO4XKm67M3adS3Kh4nvu9vy7zWYrSybTaP1xjsoN2BwiIVipkF68WqRXK6HyZDWHoEe0qZyUzcHvdx2UdgfqWtsMlEipcgM2i7AFaxWuN0FJJ+Hf3zW4Ioqmo0RKhUJRc9RKmMagJRLrAqU2P8+zKx+gOaVKW789Q+xjgRY5gheLET53jtTLLz9whJEnPVJePl+izLLYkWXcmMNdG/EVFjdEx0yJAOnwcFF1T358g7SXpWthLbD5ERvNZqvT3rkU0739ZFwT6eYiCD1Xx3N1YmHJ4cUlDL0dMbCfaLiHxYOPEO99Ni865oTDkDDXoxi/de6POXnkBE996kl08XC5VC/ZuZH+8XB5pNnDUpwfcX7/KbJj5fkRNSGRppHLn9ii+RGbids/wDd7ym2shw0HmGt8hWpIf3//lvtUyzUc8slRPDw8zOnTpx+4XgoIV7EdSmcFHW2tP85RE1jNwU+krJYzr5VQ+Ypqy1YLYQqkko8De8vK703OcenSlRrXyp9mLbLx+32ZBhhqJq1u+E1MB2XC3naoaP0IamFGPakqUgak7alrbXMIevS4CnJRFFBDK4VCUXPUSpjGULA61ebnMUZGCGGwKkIl0VzxbAJhJ9GvXcMdPkGmu4P0d/6Qe7/003mxMUvay5QIj6m8uJjyMrlHmVkvy2zK98hpeJ95mPmo4d9f5UfcGg1BWLcIaRZhzdp4rudery6sMD81w+njp9bzHxaLhoXXheeRz87Td+8b6J2C0Nx7CNtm5WIP59dO4xWJipr0ONN5hcGuDmSbSffgozmr0x/9y3gdA771Ddk6FsZDC5T1pjg/4lpbDxc64/TbpVHMy5EYgydXCecFylbNj9gsWnkCS9f1LXP9ziz6/0Yef+wEJ/fvLGvlVqFa5Ew6INH4KqKtOfhNnqYDYveqxPHacT+LVfwWvdhuY8dpzVpko/ICNge/BUEZOyAT9j7XWVB9Xj2p9rsOzLVWRfA2Bb8xdFD6PGVvrSigREqFQlFz1Aqs2mNLZ11ITHoZ3Mm79I9dxnFtukeugJ0ldSjG+J52RGiZsDEPusOM7nBraoH5hRWcjyYYOTOIXBZ85/JUzup0F9NK+RENoRPWLSJaaF1AjOihdUEx99wkrFmE8ttDmkVkk+i4sc0krFuYwqhqaXrx4kXeuvUWP/cjn9leRYdi6EMH0CYmcI8dw7h2jVhqmh9ZWSkTxztECrpFudVpi1DIjzj/j7+PZK4sP2I0bIOWDEx+xEYT+AmsKmKsEovqh19+NgiODZ3fQgA1aV9f/BZmqOgO1d/dL5ZlVV0EU8zeix3wYXl5JNq77ffYzSiRsjkEPRdqte+p+rz6Ue13nQyISOl7rVX3FnUlFHCLa9XuFAWUSKlQtDDZbJYrV2prxWPbNqOjo1X3WVr9j4Hy3HN3x69z6VL9bfhOnz6NZe2ssE1Xuqx5GZJemnSPYNyYx1kdWRcdc9GK+edlj1ls6ZS835M/vMGxtUm65tfwUnPYps7c4910aNNln333YDdtq8mWszptZH7EAgKxHmlY/mhtvC6UaRZTdyaJtXfx6PFTFQXIsGZhaPdfl2aRef55Iq++iheL5axMp8qtTgGywsI5eailrU69eJx3nv2rfO2DNZ5Yfa8kP6Lb28VTXzxNx/PPN7uaLUOxPd3E3QGgfIJ0Nely6dKlLd9nt+dmrLbKXuXJqh9Vc1JW+Z+0Er6WTGritK74TZ76iSithhKLmkPQcwP65aRsU+2urgQ9F2o1i0d1ra0fW+WkDAJJP7cM1e7qSuD7PNXuFHmUSKlQtDBXrlyp+WRsQaCs9p6OVzmibX7mHhCuWV0qUZjErsfqXk9K0nnhMOllWPPSJL107tEtvM4JkUkvzZqbXt+vxCb1BZMJLsPs5Qeui7I6Lc+P2D23xnS8q+Q8SE9DSI0wkv7ZNLor8Dq72Wv2oA8d47MdjzN67Qbxnj0cHjqYFxctwppJeF10tIhoFpYwMYVeNTKxEpey0Ct6eSp2stanoCl48Tjpl14ifO4cXizG2KE9jK0tllidzph9tA/0cDyWaXmr03RWkLD28s2eL5WUH4sv8ZWeC02qVeux2eYtZFaOhM46Wwv+rZCbsdpNq7LGqR9VRcqATCQoS6bmEHSRUrW75uA/cdrgijQJJY43h2riuJRwn7diu47qbhmNq0fQqJaaKOjXWhXRVl9aOY3JdlBjPEUBJVIqFC3OVnmtHhS/93Rd6EtNlUUVJaw4j/Vs316o3tjSYc1Ns+ql1h9X3RSrXpo1N8WaVygrFh4zSHbGhbKVrE6L0dFykYaaRUjk8ygWCYWmNFieXcCUBrH9/Rz+4Pvo0X66hcPUvTDmvV6uRU7ieiaeoyOlxmP7Z/kR/UNC6TY8w2Dw0I+iGwapH/8qXu8Ar92ZYrjtAGc7P9Xsr79rcI8eJfXyy1ivv47x0UyZ1SlAyHMDYXXqt/LPMrwG16S12WxPt2ZW7vOyjs7jj59F29lpTR8ala+oOYRMEEIiZfn5D4pIqfIVNQe/iemgTJyqdtcc/CdOg9Hf+Vk8KpGyvlQTi9LZ1j//aozXHITItb1KwlBQrrV+oli136Ti4fH7XVeLqm4l/H5fqt0FDyVSKhSKmqElEmjfOs+vTy6XbdufucOTP7hD2BypuWjhSY9VL82Km+SusUBa2Cwtf8BaXmTcLESWRTbuQpphdeqHJgURPZQTFIuExdzzUFFUYk5o3NjHIizMjedaLofilvRvPI3cAW1iAs3qYfKPb9CTWubJ1fdL8iOeWbhLb7cDHR05e1LDaLn8iM3Ai8dJv/IK76WXuXjnetmihAvHHuWnX6lv5PROwG+izi/ST1Ebqq0kT9utb8Xm1+50TWJVX7OieAiEgLBZOcohCLkBHRccV1kyNYOdEklZbLvdSGbnnwHKT8LS/CSXLt3Y1nu0gtV3o1EWdJXL21p/eNtUtnItaPXrjcpJ2TwioeCKlFKq/M/NIujXWtXuFAWUSKlQKGqCfvMm4XPnSK65CCL02zNl9o+G5qLfu0fk1VdJv/QS7tGjvu+X9WyW3SQrXpIVN8WKm8y9dpMsF5WtuClWvdRGhGNH/g3mr9b/SzeR7VidFhCeXLd3Xe6OrB8vEOuiYUQL5Z5rRc/zImOkIC5qISL5fQvRjn/47/+AR06cbFqEbHF+xJHOR9mTvl6WH9FyHaRp4h471tL5EZuF3R/nmz3Hy8pPmzYw3/gKNRi/1c4hQ4mU9aTaBFYmK2hr8Zsav5W1aoV9/QlbsuL5z9itP5GgbIabh59I2UgrsGbaZGfsyovrrPu41raC1XejCfrEqV9OSmW5WV+qjvFsATvE2ahe+N1bGLrEqN06Y0UFIiFYWCkvD4Ltpu2A6/ktvm3t31yz8bPTzQTkWqvuaxUFlEipUCgeGi2RIHzuHDgOcnaBTy+PY22KVOy3Z9h7cxWv51GSXVFSv/N1rv/cCyR6wyy5ayy6qyy5ayy7a6y4qV0f6Vhvbg3v5ZGrM9i9MYyJNSKOZHDE5mr7ARzPwnN0PMdA2hpPpG4yQBh0k4VMHwfTvfz4c3+L/zC+777zK25Gq2B510iK8yPOGr3c7Px0mUDeva+T3k9150JgWjw/YjMIBXwCy++mNWQqu9d6Uu1mOdf2WvumRq04bR5B7vOq5slSba+u+FleNTK6Y7PtdiMRRrRi+dHDezl7truxlQkQyoKucrmKpKwvftdZCMa1Vo3xmsdOuNY2CxXB2zz87muD0N+B6vMUGyiRUqFQPDSh8+eRjoMzNwXXr9ERlTiWYD7WiR3ViLJK/+oci2SY/+B1xo/0s9IVYfaPxzn/0uPNrn5TEBKieiQfpRiiTQvlIxg3nm+UWbRpIdq08Pr+4YMWbSO/nbM6PX0Y49o10nc0Di4vr1uddnhrxLP36GzLEOozWd13iL2dnfQ8/jj23v3NPgU1o5Afcexbb9PFvbL8iAd70iCcQORHbAZBX2Xvn5NSRVLWk62swFodlbujeQS5z6uWJ0tFFtWXnWL32iz8RDHV7uqLX3RHEPo7UDkpm0W1MV4QrNVVDt7m4XutDUK7U24ZTcM//3Nj69EsVCSlooASKRUKxbbwpMeSu8akvsSqnmZy8W3mnGWyE+OcuXaetJvh+JW7GNLFfSTKxIEepJa7s1tCZ9nrZ2hsju6FJENj84ycGaRvapnY7CoLfe1N/nYPhoC8mBheFxGjhdd6iKgWzpfltkf13Ot/9r/8Ez7/uRd44uwTD/X5xVanzsmTyMStMqtTAGm1EXn6FKG81Wnq859/qM/diXjxOP8q/tdpC0/xxOp7JfkRF0+2k/qFR1QOyjrht8ItKKvs/SbqwionZV0JvEipVpw2DX+RssEVaQLVRUrV9uqJX+RWECZOwf/3pfq8+hL4iVO/SEolUtaVqnnHAzDG840qUtfZuhPkBUEqkrJ5BHkBJPjfX6g+L3gokVKhUJSw6qaYtOdJ2PNM2QtM2vNM2wvMOyu4eNCZ33Ex9/Dk5RuseWm6FtYwHBfb1Ll3oKcsN6LUBPcO9hJdzWDaLp0LSZZ6ohwZSfBu37GGfsdKRIRFVI/QroVp1yNEtQjtepioFikRHguiYzQf1agJ7b4/S3NA8PADjmKrUy8W49ahPdxJLpblAu0c6OFYLNPyVqfprGDZ2ss3e75UUv78swt4AwGZTWkCfoPHapPZrYSfWGQpu9e6Um0CKwiT9upmrnn4T9q3fp9XrV9Xq53ri1+f55czr9VQkUXNIegTp745KZVIWVcCvxBNuWU0jSCLlH6R46BcC+pN0K+1vve16lobOJRIqVAElJSXYSwzxd3sLIm8KJmwF1j1Uvf1Pt1zawB0LuaOW+yNlgmUBaQmWOyN0p9YpnMxxVJPdP34WmJ5Bl1We5HgmHts18J5ITJCVA/nHrUw7XoYXezOLPQFq1Pr9dcxLs+UWZ0CRLzWtzp1Xcg6agKrGVRbZS9lLhVoKzIyMgLAzPwzQPkIenVpusE1ChZBn8BSq+ybR5BztKlV9s0jyBOnoPIVNQu//s71BLYDZovPJqmclM3B0MHQJY5b/rsPwhhPuWU0Dz8xLgjXWjXGax5BvreAKm4Z6r42cLT4sFKhUAB4UjJlLzCameRWZpLRzCST9hy16PINO2dpqLu5qKF02Ky6f2F7Yf/C8dXQ0ejU2+jQ2+jQI3Rohedt+fJcWafexo3LI+honD179mG+1q7Ci8dJv/IKF9dWuHh3pMTqNGHFuXjiUb74SmvfTe/kQbXjOFy5cqWpdfDjypUrXLhw4aHeY3R2EPiFsnIpBb/16jcwtO1HFL799tscOXLkoerTCE6fPr3+PGNXXuAwGO/m9OnBRlUpcOg6mIbErrA4IRATWL4rTtXNXL0Jm5XPcSYI7c7nOxq6bHmxotkE2XbTdqgoVoCawKo3oSrnN2MLTKO1z7+fM4MSKetPyFQi5WbUGK/+BHlBkHLLaB5+5zdjN7giTUBK1ecpNlC3kwpFi+LiMWbOcd2a4t/c+XOSXn1GVo6Zm6B39ZztaThts1Rl/3DaXt/fEgYd4S5ORw7SpbfTrUfp1KPrgmSn3kaHFiGihRDbDMfSuX/71VbB7h/gmz3l1rmPGTYw3/gKNZBq9o7NtieRUjI6OrrtNtxIfvCDH/DCCy/w+OOPP/B7xO60wRuVtz3x5I/RHtl+bsauri5GR0d55plnHrg+jcCyrPWFEJoRrbjP0UN7saz7i0xX3B9hK7gipW8uVDWJUHeCbMmk2l3zCPLEabXflmp79aXaBGEqI2iPtPb59/t9NfveIghEQpK1Cna7Qb7WqkUZ9Udda8sxDYmxO03Hdg3+Ebyt39/ZDnieur9Q5FAipULRYkT3xzj8s2f5l6E3SIbzyk0dU6Mt9kbpm1pmuTtC12KS7rk1Ev092NkITiaEnQ7hZC10x+TJfQscX3UxQ23I/Sehaw/2iU/xwsCL9atggAjyxGk1K4xmr8AyTZMvf/nLPP30002thx+nTp16qLpF+/y3PXL6CeK99/d+b7311gPXpRmovDHNI2xJVpLl5UHu81S7qz9BjmhTeQGbR5AnTqtGd6hV9nUl6PmfVU7K5uF3rQ1yn6eutfVHXWvLUe2u/vi5FmTs4N7Tgmp7QUSJlApFi/Hcv/3r6CGDJPW7czSlzh4rRq/RycCTBzh16wdEhkz2zIywMCPQ/jjO7chJPLER1dgdSbPPG6XNEUgrgt3XD4Dz2GN1q+dOxXGc9Xx2tWR2agg4U1a+vJrl0qVLNf88gNHR0bq87/0yPhsF+ituu3n9MguT/ncW4+PjDA8P16lmrU+1iZog3NCpPFnNI8hiUdovukO1u7oT5AVBvjlj1IR93Qn0xGlVtwzV59WToOd/9o2kVH1e3fH7bQeh3alIyubhe60Nwr2FandNI9j3Fjs32EDReJRIqVC0GHqoNj/riLCImz3ErR7iZg97jG56jU4mrt7GkgZPnH0it+MARA6n0CYm0E7oyOnrDNjTdDuLJKw4SS1Km7fGkcxdQqFF6OjAPXYMhMAdGsIbGKhJfXcTQgjGx8dr/r5zM1BJpFxLybqIogBTU1MYRvMvJXcW9vhuG7t9jRnL/84ikUjUo0qBIegipX9EmxpU1xu/c1xtRWaroNpd8wjyREJSrbJvGkGeOK3qlqGix+tK0EXKpM84VuWkrD9Bvtb6RrQpcbzuBHpBkBrjNQ3V31VGtb3g0fyZZYVC0VTatQj7rH72mj3EzVhOmDR76NTbKubQm5P3ysoyzz9P5NVX8WIxJuKnyE6NEpJZDmY2hLhQyMYzDJyTJ/FiMTAMss89V9fvtlPRdZ0XX3xxPaddrej8ixD/5u3ycqFH+MpXvlLTzypmeHi45t/lfvmLqyb/0/nK237l5Z/FMhtbnyBRTaQMRESbiqRsGmqVfTmq3T08Wy3qWV4cBg6XlU/NrtTNtWCnMHr7MNBZvsFZe6jvPj4+zrPPPvvgFQsAftfaQFxnq0xgqT6vvlSbIMwE4FrrZ/fapsSiuuPX9oJgf+jrWqAm7OtOoK+1yi2jaVS7p/U80LSKm1uCqm4ZaowXOJRIqVAECA3BPqufw6G9HA7FORLaS7/RVVGMvB+8eJz0Sy8RPneOpVAvFzsH6Ldn6LdnMaSDIwzobqfnZIZwXqBMv/QSXjxeo2+mAAiZwZ2w91uBpWsSU13p6kq1HHitvurUccF2lFjULFSfV46awHo4BgcHt9wnZLgVy7NOC88g5MnYesXykFn5nChqh9+1NpMlABNYapV9s7AMEEIiZfn/IAjXWhVJ2Tz8x3gNrkgT8B3jqXuLuhPoXKhqAWTTqDafkrFbWyiuFknZptwyAoeaulUoAkTc7OFIaC+HQnEOh+I1ESgLuEePknr5ZVY//CbHUpdpd1fRcXHRyWoRhBUGzcYdGiL73HNKoKwDfhM1WUfguqBXnltsCfwmSsKWpEZNXOGDZYIQICs0v1a3oas2QacmTuuP301zECZOld1rfTAMY0t3gO+PtsEPKxxrdTTdVaDe/NFH7RXL+3qiD/Xd62VJ30ps5VrQyqKJX59u6GohWr0RIjd5WmmCvtXHeLYDrs/6i1aeMN4p+Ocdb/0xXrX7WkV98YuS9ouqbiXUvUXz2MpavZWFYjWfoihGDesVigAxYc8xYc/BygcAtGkhDllxDoYG8javMQbMGGHt/pesaIkEofPnMZKrjFt72ZudXBcq291V2tIp7LZ+JVDWkWoRNOmsIBpp3Yu8/6C6wRUJIELkJmsq3by1+qpTlUOhufhPYDW2Hs3A1+5Vtbu6E+SJUzWB1Ty2yv/cyiKlypPVXCKWrPg/aPX8z9VECSVS1h+Vd7wc1efVH5WTshx1b1F/ts7/3Lr/A792ZxqypYMsFJVRIqVCEWCSXoaP02N8nB4rKe/So+uC5YCRy1M5YMboMToqvo9+8ybhc+fAcXAdizYviSVtPKGTFSFmzD46u0McTs4SefVV0i+9hHv0aCO+YqCoaruZhWikcXVpNH43Dq286mwnEViRsooYpvIV1R+/G7ogiEUqb0zzCHa7U1ZgzaLaNSWZht6uxtWl0ah211yC2udVG8OqMV79CWq7A3+Rsk31eXXHV6RUCyAVdWRrkbJ18ft+qr8LJkqkVCgUZSy5ayy5a4yk75aUawjaOi06vDDvz0zTY3QwOJfh1O++gSN1ogtrHL1zh/5kqTdOvz1D17SLORgDxyF87hypl19WEZU1ptpkTcZu8RVYalDdVPxu6Fo9ok1FUjaXVp7A2sr+cnn1s1Qaxicmb3Hp0mR9KtUERkZGGB4ebnY1SggH2WbYZ9JeuRbUn63sXlsZ/0jKBlckoPhdazMt3uf55aMEtSCoEfjd17Z6uwMVPd5MfO9pW3zhLah210yCLFKqyHFFMUqkVChajHf+63Mc++Vn6B6uvQDoIVnVM6zqGSbXlgB44Y8/YHVpmY6lFAdGZzBjGtHuCNNtvazKDqykR8/SMqaeIjy2gNsbQ+/tx3r9ddKvvFLzOgaZUIAHN2qVfXPxmyhs9fwd1X5XSiCvP2GzNcWiwcHBLffJOJX9b0KGTwKtXcrw8DCnT59udjVKaGVxfCvUaufmUdUto8UnT/0iWNR1tjGEWvRauxVVIylb2F55pxBku1e/hSfqWlt//K61gY6kVO2u7kS2cERrZdJ+NsOq3QUSJVIqFC3G3e98zN3vfMzvfO+P+Mgb54Y1zYpeH6UgNrNC7/QyQkqGxuYREhb7Itw70IPUAFbIAAlPQx9Lw0IS58r3+eTMPqxli/c+XEGLD9KlR9f/Oouet+thdKGMyLdLoFdgqRwKTcVvYN3yE6dVIinVwLr+tKpYZBgGw8PDnD171nefrFN5CH/6kcOc/dRQvaqmwP+6EoSJU99rrerv6s5WOSlbGdXumktQ8z9XW2inRMr649fuMi3e7qBan9fgigSQajkppQTRwkM9vz5duRbUn0DP46mFaIoilEipULQovbKdZ9KHeTp9iIHTB7iVSXArk+B2JsGEPYesgfXnketTAHQuJDEcF9vU8wJl6YVUaoJ7B3uJrmYwbZf2hVWWeqKYly/zbmeq6me0aSE6tDY69AjteqTq83a9hZMuboNqF/JWH9z4fT9lE9EY/CZrWn3i1K/d6ZrEVCOsuhPUiVPbAcdVfV6zqNbuWn0CS1mBNY+qImWL93kqT1ZzCWpEW7UxbLWoF0VtCGoEr5TK/rCZ+F1rPS83/rbMxtankagFQc1D18E0JLZT/j9o9T5PWforilFTaApFiyMQDFl9DFl9/HjHowCkvSxjmSluZ6cYy0wxZS8w7Sxgy/uzieueWwOgczEnNC72RssEygJSEyz2RulPLNO5mGKpJ7p+fDWSXoakl2HKWdhWnUJdBhFpcn7yEzr0NqJamKgezj1qEaJaiKgeIaqFac+Xt0q0ZtiCeHaSJ1bfI55NYMksWWGRsOLIicNworfZVawbalDdXHxXnbb4xKmaRKgtW+Vh3MzC3FHgeFn53EKSS5cu1ahWD0c9cipWu1lVba/++J1jKQVZB0KtPIGl+rymoWm5ydGsXb6t1RcEqXbXXFrVtWAr/HJSmgYYahat7vjdw7W6OJ51wPNUn9cstnItaGmRUl1rm0rYUiJlMardBRM1vFIoAkhYsxiO7Gc4sn+9zJOSeWeZKXuBhLPAlL3xt+iuVnwfw86JmrrrAZAOVx+1FbYX9i8cX0symkMGh8VM9QjNYsLCKhIyw0XCZqRieUQLEdVCO0rc1BIJQufP859OL5VF2OzP3GH/ue8TnoiTfe45vHjt85U2G79BtRIpG0M1a5xWRuXuqB0PknfQMitfQ9L2zumb65FTsWouVNX26k61m+ZURvhGf7QC6lrbXCKhYIqU6lrbXHwtrqtY3rcCfnavyuq1MQQ1gleN8ZpL1dyAGehqb1xdGo261jaXsCVZSZaXt/q1Vt1bKIpRIqVCoQBAE4I+s4s+s4vTHCrZlvayzNiLzLsrfHj7KitaGqMnQnfkFiEti6trAITTNktVPiOczs2sFPZ3zJ0xkZyWWdJOljmW7+s4Sxi0aWHatFD+Lydgrr/Wi7dtbPcMamK3W0C/eZPwuXPgOJh6hJ70LP32LIZ0cITBjNnH7KxL27v38N5/n8RnPsPhL3wBy2odDwW/CTplE9EY/ETKVrfdVO2udliWVTUHYyXen6ps8a0bbff9XrsJv+gOUPaHjaDaTXPGFlDD6/tOQ+V/bi4Ri4rj7Na/1qpV9s3Er8/L9Xeti59IqaxeG4PfdaXVc1JWEyRUn1d/VP7nclS7awy+fV6FxWmthK9IqdpdIFEipUKh2JKwZrE/tIf97MHL5IS8s3vOYh23MNfeh/3TkLrG4Krg1avDSMvGDKcxQpn8XxbDyKzbuy535yaWF3ujzfpKNSErHbLuqm+kqS8/a/Ev5Ju0jV8sETUj638WEc0iXCgXuee58lxZWLMwhI6WSKwLlNr8PD+6NI7MOiUf12/PMDiaxgztwe7sxPi93+NGJMKpF16o4dloLpH5BF+c/7jM5jaeOQm08JLHHYLfhI3fBE+roFb+NRe/iLUgr7JvqzK5oqgN1SIlW92SyU8Mqzapp6gd4cC6FlQuV+2uMYQCmv/Z73elIikbg9+CvyCP8dT9Rf0JtEipxKKm4netDWqfp/q7YKJESoVC8cA4jz2G+f770NePeXsMy7bZ88kKV8Mn8YS2vp8mPX6u/y32OFHMrnbC+x4hIx3cpz5HpD3Ekru2/rfiJvFaOAqhgCckq16KVW/7trSbMYXOT/3BR+yZX6NrKc3g6BRdvRopQkxHe0lqUUJpm97lRQwrS+fMPTI9UdI97WTeuUD6uZ8gJEyE2L0Dn4LN7YuX5rm9UnpJ25+5wzPv/ZDwb/e3rM3tTiGodq8qqqi5BDeCV01gNZOqVmABbXuqz2sMfm1PXWsV9UTlpCxFiZSNIajtruoYT/V5daeqSBnQMV5bWLW7RuD3+w5qn6fuaYOJEikVCsUD4w0M4A0Ook1M4B47hn71Gv3ZaTrtRRJWnKQWpc1bI55N0BGZRwtrWCdOE7diuEND9A1/qfw9pceKm2LFS7Lqptafr7gpVt0kK16qpDzptfjMTBXapxdpm5plTUr23Zog5bqs7oty70A7UssiyJIFEp6OPpYkuZDE+XiekTODyJV7/OP3/gGLfR2EhUlYs9b/IsIilI/mDIlCVGf+T+T3yW9LhRxSIovtORhCb6jgWWxz6zoh9thTZTa3htaFfu8ekVdfJf3SS7hHjzasfkEiqCKl78o/NYnQEAI7gVXl+ylLpvpT7RynWzhvjJT+vy3V7hqDnzgS1GutaneNIbDXWp/flYrgbQyBbXdVhDBlNVx/gh1JWblctbvGENQ+T43xFMUokVKhUDwUmeefJ/Lqq3ixGPbxk2Q/HicksxzMjJfsJw2D1UP7CMViYBhkn3uu4vtpQqPLiNLF9qxgXemy6qbXhcyPbl4lpWXp2tvLqptkzcuw5qVYc9OseWlWvTS2dLZ+413AketTAHQuJDEcF9vUuXegB6mVXuilJrh3sJfoagbTdulcSLLUE+XISIJ3+9pJySwpNwvuA1TiLPwFs3x9/AI6WpmYGdZMQqL40SK0LormykKaSViYhEq25exs/dhsc3tqcpxMstzmdt+YhjZ0GC8WI3zuHKmXX1YRlXVgp4qUIyMjdX3/23dOUMlOOJtZ5tKlS77HjYyMMDw8XMeaBYOg3sz5rTg1dImxM1I9tzRVRcoWbnu2A66nJhKaid+1tlqe2lZA5clqLn7nudUt6FROyubi1+5sR+C6oLfoeEflpGwupgGaBp5Xvq3Z97X1RC1Eaz7Bva+tXO5n+a1obZRIqVAoHgovHif90kuEz50j09HDhc4h+u2Zsoi2jhMputodMAzSL71UM6FIF3qJqJm25wA4Gzvre0zWc1jz0iXi5ZqXLn2ef73qpUi6OXHTo8JotYkUcnx2LuYsYxd7o2UCZQGpCRZ7o/QnlulcTLHUE10/vla4eOvnrhYY6IQ0c124zImZucdnfv8tYitLRBeT9N24Syiik7XCTEf6WRNRwpkse5Jz6KzB1St4J08ienqxXn+d9Cuv1KR+ig0iIYhnJ3li9b2SvKC91+Mw+QTs3dvwOp0+fbrun5G1K8+QhM3qiv/w8HBD6tfq+N3MOa7AcWlZwU7l7mguppEThB23/P/QyhMJKk9W89mpC4LqjZ8YpizoGkNwLegqlyu718aw1YKgaKQ1f/9+vyvLkC0rzO4khMhda9cqZORp5Wtt1gHPZyGaGuM1Bv9rbYMr0mCUM5WiGCVSKhSKh8Y9epTUyy+z+nt/hmSRaXOAaXOgZB9Nv02qv39HRLJZmoGltROrEAHlh5SStMyS8jIkC39ueuO5lynall5/vealSXkZsnWI3jTsnBCiuznxNB02q+5f2F7Yv3D8TsXBxfFc1kiXRHnGZlYYvnOdZSkZ/mSCOccleSRK4kAUqS0QZgGAGU9ijS2xtpDE+WiKkTODaEs6339vgvSeHixhEtJMFk/Oc7vtIy7NTBHKl4XWIzuN9QjPjfKNCNCQMLGEsavzej40ExP8yFvfhMm7ZZsGJ+7A1y7C/v3wl/4SDA42rFqWZXH2rP9ihVrQ/uedFcvjezrr/tmKrSew2lt0AktFFTWfsCVZTQVLpFQ2w80nsHl4VXRHUwnqxKlfhLKye20MW+V/jkYaV5dGovq75hOxKouUrdznqQje5uMXOdjK9xag7msVpSiRUqFQ1AQvHufuT/8KX/uWXRZNlbDiPPZTp2nb38GeXWq1KYQgIkJEtBA9D3C8I911AfOf/ea/5PEfeYLBw0OsFQmcaS9DysuSkvlHb+MxI+3y9zRzyyldXQMgnLZZqlKHcNou2b9w/G7jYW1uB67e5t2eostfNywwx+21uQeqj4B1wTMkcsKmVRAwNZO1thU6vCiXx6ZyEaF6Pqennre/1SzCej5StLhct3a+AHr9OnzjG/QsOwi8sihqd7UPvH64cwf+1b+CX/xFOHGi2bWuGeG5Sb44f7Wsv9ubfQS2aVmteHCq3bykstDeohNYasVp8wmZsBqwCSwVSdl8/Caw/GwpWwW/CSzV5zWGUGAnTiuXtymRsiFsba3emr9/5ZbRfHyt1Vv4WqvGeM0n5Get3sL57kH1eYpSlEipULQo165dI52u/UiqWo636xOdJKxn+WbPl8q2fWbpnxBJTtS8Ppupdw66WjD9/hip9v0YTg9dQBcmYFIpt10BD4ktHLLCJSscMsIh3mbTa98g1G7QMT9BeMZl0uhj1dPRDAfdcNEMB81wsPTsur3rcndu5n6xd3eKKDvN5lYCGWmTcW0gWb5D/kbnUuLWfb+3AEwMTHQsDIz8o4mBhb6+bX0fsel1/hhz/Ti9ZPvNmzeR8sEGgObMDH3nziFcF28yw6eXJ7E2iemh+SmW/iRD+uBBnK4u5D/8h8y+9BJ2f3/F97xy5Qrj4+NV8znuBEJzc/RfvMizPzC4s1L6u92fucPwD7/L9H8vmH3qKTK9vU2q5f0xOjra7CrcNxMLEeAnK25799LHDHTtztmErf4Xn4weBMqjeIVM7Zjfjm3bu7JNXbhwAcfZ2vlA5yeBchV85MYdLkXv1aFmzef2dDvw4xW33Ri5zNy9B1doR0dH6erqeuDjm8XDXEMfhOTKIaD8+nl3YpaLF6uPMbZ7fW3kb/fIkSOYZnUXEICVtc8B5QvrpiZvc+nSZB1qVn92U/84NTkIPFZWvrCY5u23397W/3CncD99zd2JI0D5GG5tZZqLF8dqXLPt0+h+pxZcuXKF0dHR+xqjzC6HgM9V3Hbpg2vM9Nb2fm6nMPLJPqC8jQqZ2fL87dZraYGd0raFfJRKY7yr129zsX+mrHy33L9WY2K+jUrjC4DRTz5idbq591S76Zr5oKwunwIOlJUnppe4dOn9un9+s87x0upPUkmamkmMcenS7r2nGhtr3jhhN6NESoWiRXnrrbeIxWLous74+HjN3td1XWZmZnj77bfLtk2nh4FnKx73wXsXGNjTU9O6FJNIJACIx+MMDg7uaLFyYWGBt99++6HPxfjqKp++PoWQkshCBt116ZlY5Yb2LJ7Q1vfTpMcT2R8Qkmu4hk4Sl445h7W+XuLfs5EGeKbAM8ErPDdAmhvPc9tErswAaTRvRVer29wWI4EsDlkc1thGIoz7va/7KclH7jX+aPUamgvCkQgXNAdE/nWunLLtn/nBh/TMr9C+kiJ2N4lr9ZAmxJQVZ5V2InaaQXuCmXujMDHBZF8fyXCYxdFR3j5zpmJ1PvzwQ06fPv3Av9/Cb+rAgfIBfi1wXZfMRx/xqZER7nkeq6tPsMdeK8vBu7q8xurHU4SuX2f8U59icc+eutftYZmamsIwdsawcHPf6HfOllJR/ETKkevjLHYuPPBn1/v/VO1ztvpf3J3oqFgunbUdc+17++23efHFF3dd7tWuri6++93vbv1bkM9QaQLrzr1ZRsLN+x+8/fbbxOPxurTf8fkB/223rzFtljs+bJepqSlGR0d3tnNABVzX5dvf/jZLS9V8LGrHwlyISpOIE4kF3nrrrarHXrhwgXg8vmUfMT4+zpkzZ+r+271y5QqvvfYazzzzzJb7prPPVSyfnR5nxKg8qaauufdHtevu7LRLJZEyY2u88cYbFY/Z6nNq/X/Z7vtOTU1x+fLlbf1mJ6Z6qSRSzs/e2/L3Vk+uX7+O67q7qr8cHR1lamrqvsYoa1l/kXLkxjhrs7M1qp0/9bym+nF3orINiPCSW56/3XotLdDoa6ofTvYQFReifTLOW6JciNzu9XUnM7HY57vtztgIC6HmipQ77ZpZD9ZW9lBJpJxfTDWkbTXrHCfTle/l52buMDLySYNrUzsmJ3fnIrpm09q/coUiwDz77LMcPHiwoXnR/ux9iz/9b8vLdU3yj/7n/6Gun11YubYb8sCNj4/z4osv1qSukd/6LbSJCbSFBYxr13AmMoiFPydhxUlqUdq8NeLZBHvbHY4dPI5z8iSHYzHcoSGeeuWVB/5cV7q8/cG7XLt1nf1HD2KTi/C0hYudj/bM5p/bwsWm8NzBFl7R89w2eR/3UkG1ua0LukDq4FqFtJ/b+0fEZlawLq2x1ibYd3kRvc+FnkXmDvSga3fX1//OuRC900n3fJIec4WVR/vo0jS6f6qHzEAvVj6npyUMTM3EuRpjT08/fYMHsDQjn/PTzG838vvmj9Fyz01hoOdF+Xr3Ax//6Z8SOn+ePYcPYy4v03HrCtlNlo/99gydWpLeY3F6T5zgMdcl9dnP8m5+oLqT+6jh4eEdUb/NK5H96rS4KviNP678Hs+98CXOHLn/XMCNupZs9TnV/hdjr0Xho/Lywb09fOUrX6lZHR+W06dP8/TTTze7GvfN0tLSlufxX77bwfRKefmpR5/iKz9zqk412x71+h2/dcXkH75eeduvvPJzGA95aX322Wd3XXu5ePEib731VsN+d1cz7fywgibXN3BgW3XYTtu4dOkSvb29DflfbOe3JiX8zd+tvBjtxed+nM9+qrLIuRvuC3bKNReqX3d737X4139efoyLxYsvvljxmK0+p9bf+37ed7vn/XdGuqHCHOPjZ07wla80Lsd6JXZbf/kgfWU6C/+fc5W3/eRn/xJPn3zwhTH3Q6N/p9O/28bvfVBePrCna1vnb7e1jWIafU3149982MVEhcmFR04/wVd+brjiMTupP38Q3r1u8g/+tPK2l3/pLxMNN7Y+ldjt53grbsso36ugycX6hhr2m2jGOf4vz1VuXM9/7tN8/qknG1qXWnLt2jX+/t//+82uxq5DiZQKhaJm+OaMUX7idSPz/PNEXn0VLxbDOXkSOX2LkMxyMFO6IjorTJyTx/FiMTAMss9VXpW+XXSh8+SpTxGWZkHdemAkEgdvQ9AsCJi4m8pcssKls2OB3onb0OkRWsiyZybDnfY2hOmhGS6a7iK0XNSk8GTL2NzuJLadF1SH8QMxwispTDuLmJtlsSeKuPw+VzqPlb/xEIyzxsWF2/dVHwMdSzOgS2JInT+6d6VEyLTyeUL9RE5LGLnHIiG0UG5p+W3CpP/iRcKxGIOWhTE3x7TUWRHhskUBbUaS9tu30fr78WIxrNdfh0ceedDTrfAhbEni2cmKeZDl5GE4sjusdu8XlZOy+fjlymrlHG1+38005EMLlIrt4fcbb+V2l3XA89T9RTPxa3e2a+BJ8Mm4sOtR19rmEqpilNPKfV5Ktbum45f/uZXzjqeqfLeIz/lQ1JYgtjspIe2z3kS1u2CiREqFQlEz/ETKNjWJUDe8eJz0Sy8RPncOLxZj/Eg/N1eXyuwnw3t6OBnLgGGQfuklvHj8oT/bsqymrGbTYj9OZPbr0CMxF97BTtl89Ho3I5GT6za3QvPQNZu/dvRDOkUWN6rTse9RHCHp/fRPc6q3k7SXJSNt3r/yIf1DA3T1xUh7WdJuJv+YJeVlSLtZMvnX3n37qbYmOy0vqIOL47mQdzletlPVD3gAYjMrfGH1A3QL0lcmMB2P5HAXiT1tuHIBy10i6+mM2+10Ls0TSqZIXn2H2bMn0UYnuLXfI9PTg7PaViR8GmWRooVyrciyWVGOlkjQ/afn+fXJ5bJt+zN3GPr97xMei5N97rma9Hc7CXWtbT4hs/K5zjQmsKMp+C5EUxOnDSOIE1h+7Q5Un9co/NodgO1ohEyvcZVpIGrxbXMRIrcgqJIg6Tep3Qr4CbB+i6MUtcfvXPsJyK2AX38XtiSauiVtCL73Fi3c7tJZkD6WaupaG0yUSKlQKGqGGlQ3B/foUVIvv4z1+usYV6eZNgeYNktzR+31XNyhoZaYsPcGBvAGB9EmJnCPHcP7cIQBe5puZ7Esoq29ZxUrZOGcOEmv1Y07NET7kdK8qct3bvHs/k/x9NHq1jhSSrLSIeNmSeWFzIyXJeVm14XMVJHAmfGy+X3sEqEz92hvbHezZOX921I2kyDlBS1w5PoUEkl0cQ1sm6SpkzgWJaStlu07vjdE+IqDmV4gMXmdpZ4oc+M27+4/xvdmr2/r8wrRocWRnmaRiLlZ1NwcEVosgppCL4oUNUvfRxiYYneFQek3bxI+dw4cB0tvozs9U7Yww7E70O/dI/Lqq6Rfegn36NFmV7tmhOcSfHH+aln0aDz7CKAixRuB36R9NUFlt+MbVaQmERqGiuAtJazaXkOodh+XdfSWFSnVfW3z8RMpW/laqxaiNZ+wj1gUxGut6u8ahxrjlaLaXjBRIqVCoagZqUzl8morcBW1wYvHSb/yCh+4y1y8fb1s8nr1yBn+8iuhZlezZhTb3C4fPkn2k7GKNrdayMQ5eawmNrdCCELCJKSZdJq1FQJc6ZFx84JmkZhZEDjTXqay2OnZZNzs+jHzKwssJleIdLaR9Wwy0q6LABrEvKCNjh5djw7Fp2OtMdozcIFZ/s877xYJmDqWZlYWQjcJpaXCp14illYSWjXxYDdcWiKxLlBq8/N8euUDhF26pL7fnqHnY4HWdgQvFiN87hypl1/e9Qs0tESC0Pnz/OTb81xbKR3C78/c4bEP3yL8230tsRhlp+MnzLXyREK1VfaKxhDECaxqYoRqe42h2nnO2BodkQZWpoH4RU2pdtc4IpZksUJ5S/d5akFQ01FjvA1Uf9c4AhnBq0RKxSaUSKlQKGqGGlQ3H2fPAN/sOV5WfkR3gLnGV6hOFNvcptt7uNA5SL9dHk312JNtWJaoqc1tPdCFRpsRpo2Hy0p/8eJF3vrwrZLk6lJKbOmsC5YZz8aWDllpk/FyZVnplIiahefrx3n5ffLHyD0DhGfSJGPtdC8m6Z5bYzreVVG0a5W8oK0ePeppkMEh45ZHhtYDAz0nZuaFTmM98tMg1Z7EkDo6GobUuDo3vy5unv7D1+lcWyCysEL3J+O0t+msyRBToT0ktShhO82e5Byek4KrV3CHh6GnF+v8edK//MsN+W71oDh61HUs9thTZf2dqXW3bPToTiOYEwmVy9UYr3EEUaSs9t2U1XBj2CqSslVR97XNJ4h9nrIZbj6+7e4+I3iz2SxXrlypRZXuC8dxkFJimtXvVYv55OZ+oKusXCfNpUuXtjz+9OnTWJaKTHgYgph3vOoYT/V5gUSJlAqFomaoRO/Nx+9i3oq2OAWb2+V/8z0k8xVtbg1ztWVsbh8UIUQu7yHbv1HZCu1zU0TufB06JObsO2STNjf+OM71jmMIQyJ0F0130TWHn+u/SiddOBEdBocZFOA89QKxzo6cCJoXSm3pcCdxDysawoqEyORFUVs6ZD0Hh+aKfEGMHq0nDi6OdEm5FZSPTU31+soUkMsL2nX7fYSUDF+fIOO4ZE5EmToQRmqzRJgFYNaThMYWWV5I4lyZYuTMIHLpbV5/d4y1/lhJdKch9JIoz7XoCnpeGDVEqSWuX8RoadRo6X56DXKLbo4ePTExxlqy9PfQb88Qv6uhHT7cUtGjO5WIJYlnJ3li9b0S54KDV3vQpo7iDQxs/Sa7DP9V9g2uSIAJ0hivgJ9LC0BEtb2GUDWS0mndZGX+eXgbXJEAo0TKDVRUUeOoVf7nK1eukEgkOH369MNX6j4/9/Lly7z44ovbPiZjV+7LLWPr+++RkREAzp49u+3PU5QT8TE981sk2AootwzFZpRIqVAoaoZa+dd8/C7mGbs1b+a8eJyRF36Vr72ZLpssno0MkPmrB1pysrjZbM4Lqn10jf70HJ2ZlbK8oN1ammgkinPyJNFwDHdoiPjwlyq+72t/9hrDw8OcPVZ+k+NKr0zULIiYhbJPbt/EwWVg396S6FG7KBK0ECla/B4bkaIOHpVzKy32RumbWma5O0JXQKJHdxpHrufEys6FJIbjYps69w70lP0PpCa4d7CX6GoG03bpXEiy1BPlwLUJ3u1to6qFbn5ioiCMPiwaWs42tyha1BQ62Y4MhtT5wdRYmVh688Ayy+FbJBZtTGEw/EffJZqcI7K4QvvILTShIaNtJEJ7WBPttNlp+tMzmDKNdvUq8uRJZE8P1uuvk37llZp8D8UGWiLB8x/8ew5PzpRtO3zvFpGv/7AlF8eohWjNx+9ct3IEb1Ktsm861US5rN2aC7Ck9BckVLtrHLUSi3YTvu1OieMNo5bi+OnTp3n66acftkr3zdLS0n2Jht8brXyf2tMdVuJjg/Cdx2vhMZ6KpFRsRomUCoWiZqhE780nkBZ0GUHC2ss3e0qFr842D2+gfBJZURuK84K6j5wkOzJeMS+oo5s4J48+dF5QXWjowiKs+d+li2zOqvRs14PfTLnSzQuZBREzJ2TymQTG+/+MJW+RTpGFlEv7WyFu7dkHOmi6i9BddBweW71OR0ZDhsPofQN0CcnsscO0uyGEqa0LohLVN94Pjc4LWgs8PDLSIyNL82YWRuATqcXygwbhDuO8tzhObGYF/VY+evTaBKuOS+pElOkDYYQ2RTs5MXXek0TG5kktJHE+muT6mSG0JZ3vvTdJsr97I8rTJ+Kz9PXmqFF9XWA1ivKNGhXEVw0N8YD5RncDBdvd/lWNWxhlNuPmSgxkR0va7lqzU3xx/ntlOa8j9mmgRZPS7TCqTdhLCa3406sWVaS1bhDfjiKIkZQZG6RU97XNJqj3tZVoC6t21yiCGcFbuVwtRGscIbPyuXY9ge2A2YLqTTW3jC0y6yhalBZs5gqFoln4DdxUqH7jCOIElmp3zaE4L6jWG+NC51DFvKB7TrYTickdnxe0gC50IkInom3yXDkQx3z0R5j/8EPkUJTQzXGikx7HE4l89GhnPnp0ngN7NXq69+CdOsXezlz0qLAegeUNKxwpJS4b0aHFgmixQFr8WLC+LXld2O5zTNazsaXbdLvcWtDqeUEr8aDRox0Layz1RBm4eot3e441rL4CgSUM3Ccd3vW+Rfv738vlHdXMdUvc9ecl5blHSzPX85SamkGo6PX6tk3HFMpqYa1bjWLb3c7UCp9eHsXaJD53zE9hviNwjx1rGdtdLZEgdP48P/rDOcIrpb+5/Zk7nLh2gfBv97Zc5OhOxG9cI6Ug60CoBSd01Biv+eg6mIbEdsr/F62ak1JZ0O0MgigWqT6v+QRRHPdvdw2uSICp9htPZQSm0Xp9gN9vSi1ECy5KpFQoFDVD5VBoPkGcwFLtrnkU8oJar7+Ori8yTXleUMdL4Q7FW2IC2/3CF4jPz4PjsNq2h+y9sYrRo9HuLrxTx0qjRycnS/YRQmCgY1QSROuAJz1s6W7k+VwXNnORo+e/d574vkGGDu0rEz3LRFGv9HXJ8/x71kMUDWJe0N0WPSqRuahRE1ZIsZJJNeyzdaGViKCVhVFzXQAt3Z57PSknmNiT5M9Xr65HlBaiR4e+fQ4ns4y5sER8+hZ3hU5GC5Ew9+Zsd701jotJhL2Gce0azsmTeLHYrrbdLUSO4jg4jsUee6psIYqpxVoycnQnUm1ck84K31X4uxk1xtsZhK3KIqVfHrPdTjUxQrW9xuEXxdXKImW1SXtFY/DN/9zK7U5da5tONXvTdFbQGW29/4Vqd4rNKJFSoVDUDP/cHY2tR5DZagVWK05g+bU7ZYvTGLx4nPQrr/Bvzjkcm36/zArw+PNHGfhSrNnVrAnF0aPpjh4udA5WjB499UQbekSURo9uEikbjSY0QkIjROWVCu8vhjky0M/Z9lM1+byCKFopEtRef50TSJ1N+9yZvIcjPFzh4eDREevElg7R+BKxuVt4vR7WUpb++QzTfTquJhC6ixAbv/lWyQsaxOjRB8WVHq7MkvYeMlnVEbg++yclRbGZFT7/Sd529+MJjDYX/ckoCwd6sLRbmFIgXY0xF7SJJbrn13A/nGL00QOI+fe5cGGOtd5uYp3dhHULM2+hW8lm1xB63k63eHvRNlG6Ta+TxW5x5Kg2P8+x8TH6kqXtqd+eoW9SQ1s43DKRozuZsCWJZyfLcnAnrDjO3YMw3N/sKtYcPzFCWW42lrAlWUmWl7dqJKXKk7UzCGIkpd+kvWp3jUOJ4xuodtc4qs7jtWgeXr/flLIZDi5KpFQoFDVDrfxrPtUGkhlbQAvmwPNvdw2uSMBZ7ozzTedLZeVf7FgC0o2vUJ0oRI+u/PYbSBaYNsujR01zDXdosCWiRx+UrUTRaly6dank9dnhnEWu9pM/SmTs6xCVmNPvIGybpYt9nF89jSc0hPAQuocmHJ5vu8LxaBY3atC1/wyukAz+2M/wRH93mShqSxc7b6ObmJ0qEUbXt+ePcRoQMVpMEKNHdyJb2e4KIRGGCwaMHewmtJrEtLMYSwss9USJjI7w8d5jjKXnal43gVgXLO0nsrynL3Lu3uWSKNDNYuhmAbQ4r2hh28FvfZO29ByhhWWi10cRUscJRUiYA6yJDtqcFHsy05gy1TKRozsZLZFg3x++zq9PlvcA+zN36Hr1e4RP7G25645aZb8z8BtTZ1s0J6Wye90ZhH0WOvstUG0FVJ/XfKqlz2lVfMVx1e4aRqTK3FVuHq/1UIsyFJtRIqVCoagZarVz86kWKdmqq/+SalC9I/Bre+kqEz27FS8e55PP/ypfeyNTMaIl+2v7Ye/A1m+kuC+8gQG8wUG0iQncY8cwrl2jJzXNj6ys5POCRvN5QRN02GmsTgvnxEliZifu0BCRQ09v+RmXxnMCaUEY3bJOUhaJnm6RsFkaLWpviiAdmxjHxaNnoK/suPGJO4SiEcLRCPqeadpnbDI9XeiLaXrmUkwNdEMF7bFVokd3IjvZdlci122ZsSBLmmX74RaGxGZW+PyNfOTo1QkMx2XleJS5AzqGdpeu/H6LnmRmbB53IYn3UYKbZw6iL3/AW+8vktwTKxM+K4ukBnfia7wnR1mZ0XLl+fyjIc1ct+MtWPQWvzaFUZco0p1GwXZXrroIIhUj+G072pK2u8bMFF+c/3555Oi+R4Fws6sXGPwEkozdmgthVCTlziCIkZRqPqX5BLHd+UZSKmeqhhHawtK/FVERvIrNKJFSoWhhRkZGGvp5cwufBsqXAM3P3uXSpdt1/eyRkRGGh4fr+hm7gWorsIJmE6FWnDaWoK06TWUFCWsv3+wpjR4NWxL2TjepVq1P5vnnibz6Kl4slovcmrpVMS9oVpg4J4+X5gWtA5oQWMLEus+I0UujuX7rbE+5GPra668xPDzM2eNn0T73OSJ3vg4dEnPmHWTG5tofDjLSdgJ0EJqHpntowublwY/pERncqE5036O4QtL76S9zurerJCJ08/NchOiG9W7Zft7GPvWOHN2pBM12d6vI0QJSE9w72EN0NY1p21jzucjRtisfc7X72PY/8BDclO/z7dH377uuxTlGc1a5OYHTyucizW3L7yOKXhflIjXy+28WQkuOL36//L5S1n+cUWy7ay0v8OnlcSxpl+zTb8/Q/gFojx1tGdtdLZEgdP48T7wxS3aldICxP3OHA59cIPzbvS0XObpT8Vv417qRlP7btuj+FTUkaGKR56lJ+51A0Nod+Ee0tan5lIaxVd7xVkRF8Co2o0RKhaJFOXr0KI7jNPQzMz55SUJG/SfnhoeHOX36dN0/Z6dT7QammnXRbsZ3UK1u5hqK38Da72Z7t6PsmJpDcV5QLxZj7PAebq8tlkUVRff0cCKWKc0LugvZHD3KlWsM2NN0Ly+WRY92RdcIR0I4J07Sb8Vwh4boOPJsbeuTjxxdjxCtEDlqS4fv/eD7HB0+xtDB/Ru5SD2brOeQlXb+tUM2X5bbZ1O5LN3Hw6vpd7kfgma7u5MjRzeTlQ5Z12HNbY6tuPY0vD3+T7a00t1svZt7XT03qSkMBr/9BziZZcyFJcKf3CCkWWQIkTD3siaitHlJ4tkEZNdaxna3EDmK4+A4JnvsqbI+3tR6WjJydKfiG0nZojkpq6Uw0VpTl92RBO3eIm37b6u2EFlRW/zane0IHBeMFuz2/BYVq/vaxmEZufQRUpb3b60qUqp2p9iMEikVihbFNE3OnDnT0M+UIlKx/NjRfZw929fQugSVQK7AUpGUOwI/gbxVJxKUSNk8CnlBrddfx7gyUzEv6D7XxR0aaokom+Lo0dTRR8heG6sYPSosE+fk0bpGjxZHjlYzkr06f5Ez4iBPD2xtsbtdXOnmRE7PxvYcMp69LoAWygoiaOG5XSaC2uv7bxZBHemwuLbCanqNSDRSJMK6LPZG6ZtaZrk7Qtdiku65NabjXRWFu1aw3Q1a5OjD4OmQ9DJAldCrByQ2s8LnP8nb7n6cs93Vn4iycKAHS7uFJQWepzHnwCcTS/QsJvGuTDP22GG01Su8czlNek8vhtC5d3SRRNsI1+YWq1jv6tw1Z5iXWaylGyWWu2ZR9GkhulQXtZ+pLY4c1ebnOXT7Nu3J0gUK/fYMXVM62sKhlokc3elELEk8O1lmcX/sGoROR8j09DS7ijVFjfF2Bn7RNC17T6tyoe4Iqi36TmcF7ZHW+1/493kNrkiAESLX9pLp8v9F0IINIj75iBWtjxIpFQpFzVD2JM3H0MHQJY4bnBVYvpGUKodCQwnaRILKGdNcvHic9CuvcMle5uL49bJ8ZW8ePcOXX2mNO5zi6NF0Rw8XOgcr5qQbPhvFiLLro0f90IVORNeJ6PX7v168eJG33n+Lr3zlKyXlMjxB+N7X8TpcrNl3Sa7Y3PpuDze6jqzb7grdQxcOP9c7QpgO3IiBu/cEAwKyT71AV0fHJqtdN5/PNPfaKSl31614mxFBGrTI0Z3Klra7QqLpLugwfrCL8OoaZjaDnJ1hvieK9+F7XP6xvO1uP0yT4NpKovqHtgMS/t21C1vWTxdaRRvcnJBpbrLQzeUaLYics940E4OrfHfp3ZIo0sPf/BZtqVlCC8u0fzKKkDpuOELCirMmokScFAPpGSzSLRM5utPREgn+8vXfZXFyqmzboXuL7P/mLKn+frS9e1vmuuM3xlMWdI3FN5KyRSfsq90zqfuLxlFNEE5lob3yuvxdjW+fp9pdQwlbkKxgDNKqaZvU/LFiM0qkVCgUNUPZbu4MwpZkNaVESrXitLH4WoGpdqeoI07/AN/sOV5WfsJwgLnGV6hOFKJHV7/xBpKFitGjhrGGOzTYEtGjOw0RH0Qb2o8xMYF2/CTm+yP0LK/w+MrVMtvdXpHFNDtwTp6kI5Kz3R0a/vIDf7YnvRJhs2CtWyxk5p67vHnhB+zZO8De/YPr+2RLBNANEbQ8B+mGOJrq60afXg1M5OhOZafb7rrSw5VZ0t4Dzp4dgFsLb66/jM2s8Pmb+cjRaxMsOC6LR6LMHjDQtbt05vdb8CRtY3N8spDCvTzJzccOoC1f4ofvz5Hsj2EKnUx7Gh2NC1N3qtrprm/TKkeWVtxXGOhoCNGa45tiCta78ZTBElrZApnIahg8j8jMTEtZ72pTU3xx/gdlC6Dmuh8DVGhRo/CzOM1UsUXdzahIyp1BNWvd3HxK6/0vlFi0Mwjaom81n6LYjBIpFQpFTZDS31Nc5VBoLDmRsry8dVedVi5X7a6xhHzcAFvW7lXdzO0I/H7nfv3CbsaLx7n1U7/K1/40U2a5l7DipL66H+/AwNZvpHggim13MydOkr1Z2XbX0U3EyWM1s93VhEZIaISobrkK8Mnsuwz37uVs56ce7jOfnyJy7+vQ4WHMvQPZLLe+vYdr7cdAB03zEJqHJhy+HLvGHrpx23T0wUfwNLCe+Sn2d3eVCaOlrzfKpxdmCUXD6JaxYd+bF1eDTNBsd7eMHM0jNcG9g71EVycwbYfw/BJLPVE6r4zwSSFyNH+q7qYW6lJXAVvkH62eb/T20Crp0B0Wl0XZdj+xtHib1gCBtNh6tzuzyKeXb2LJUnWoez5D97UMa/v2gfP/Z+/Poxy77vxO8HPfgiUQG2JFRuTGzGRGJJNbcpNIrUVKrCrJRbsWVYlUWdW2e0Y9Pu7j4/HY3dPT3Xb3TE+Pfabbdo+n29Vju1yqhazNVZVVolSiSFEiRYpMksktkxG5bxGB2Hdsb7nzBxARQAAPmZEZAbz34n7OiQPgvQfgArjxlvu93+/PDnz0rpZOE335ZY6/Os30cqVrf1/+On1Xf0rs97vUZKAGEd1lTsp6n0tdXzSO3Vg+Rzkp/YFX3wtrv/MaT1Eml92LEikVCsW2kLeoWeQZ1MlNo/HKcA/joD2ouFe/4PV/ntv+Ml2+IOvxuVQUWGPZdRdzeUhH9vBi11er1pl7q6P4FNtHeewuXUnP2N1D9ybo6iDQsbtufz/uwADa+DjukbvRPxmha3WehzMf1HCOZmhtbcEeGiYeKzpHe+7+0pbe74Ufv8ATTzzBoycqa5i60sVyi2JmYVN9Ucst1RSVpZqirk3etbHkWt3RYk3S8ufam2uYytI2rlOzTmkzonbL2W2xu353jla8P6yL7rdVj3QfXGGFn85duq33N9CrHJ5r4qina1SrLZoWa5FOoaOhSw0Djc58mj1/9We4hRUi84scmL3MFBo5LUba3NgHtIhraPYqrVeuoPX2Bjp6d801im1jWwZ91mTV/j2id6GPjYXKNepnPK8twnqOV+dzqfGUxlFXpNxlArm6rm0su++6Vo0fKypRIqVCodgWVDyJf/Cs3xHWkxuPz6X6XWPZbSfVXp8rphy8DWW39bt6+ztNa3BjdiFrsbt89xUkizVjd203gzO4J/BOm3LnqHV0mMLZazWdo0RM7OHD2+YcLUcTGlE9QnHuVeOLQDnSKYmd9rpAapfEzbxr8fEnZ/jok495/HOfqXCK2lXxvEXnaGX8buX2hRrPXehO0DO5tGtid3ebc/ROsCm6kHNym2YgtlY+fO3D13n6XCl698w4RqeDfijBwv4uIuIypqMhXZ2LroAJl/a5VTjzOtfvP4S28gnvfWyT7+uuFEQ1g5nYNIbUmF/Cw4FaeX+zmKqJnTnQlbtGtbk59l68gpGpnKTQa02TmNbR5g/iJpOBd40GgVhEkipMVKVHzMb70Sb34/aHKz3C69xV0yQRNXLbMHabk9JxIG+p8RQ/sOuua1Xcq2IT6lCnUCi2hXoCmDrINBaVZV9E9bvGsuvEcTXzzxd4fd+7rt+p/V3DcFMp8t94jt/8U7dm7O5nvnoXic90N7uZd0y5c7TQ1sWb7YM1naMH7m8lnpSBdo56oQuduK4T12tHVKyKaSYWYjycqK6Lux3I2ASxsd/GbXOIzryLtCzmfpLkHeMIwpCIstjdzxsX2Es7TlzH2TNESoD1yJdItrdjSZvRi+dp7WijLdletx5pzsrjahIbF9ngulu7zTnqZ+pF7wokwnCAoih8+UAHR1dWMQtZ7Jk0i10J8h+8zbtr0bvllOYavDV3+bbapaPVdIpabXl0qfH65JWqmN010TMiDK7tWcWKjpFZNivE0X3f/UtacvNE5pdInLsIro5lxkmbe1gRrbTYGVKFSZLkMEZGsIeHA+0aDQJaOs3wD17lWxPVkc378teJ/faruHsHAz8hqJx617S7oASub4iaIISsmRIWxvGUep9JXV80Fq/v2yvBKeh4ls9R/W7XokRKhUKxLdQ9uVGD9g3FUywKaTyJV99TWfaNZbdFMmWUSNkQRkdH666/fjUJfKpquWUL3nn3NPoWTRejo6MMDQ1t7UkNRNWM8QdREyajKV6MVMfu/idt80A48tXXnKOrf/5DJAs1naOmmcEZHAjVQLFfEKk9aIP7MMbHEXcPY46M0LW8xIOZs1Wxux3dBTpSrdjDw7TGi7G7A0N/bf21XnhpiqGhIU4Mnaj7nqdPn6a7u5tHHnkER7rrMbjFOF2rFLdbFrlb9tguReuuOU/XaouuP5Yb0bqzi3MsLC/S3tVRFElxKPR2YU5lWEkmdoVz1M/4NXrXwcWRhWoHaWlUayJbT9YGDsAllvnJ7IX1RcnpZZ6+UHKNfjKOYTssHEwwt1/D1K6SBJCCOVuQuDbHpaVV3I+nuHL/QcTyR5z6cJVsXzcRjxql3jG7tR+Xi6qG0BG7UJ1ai95tn5MIojUnyNh2C2bIone9S0k0th27HSEgZkK2xqlcrWVBp95n8iojpNgZvBKZwjqe4lm2SV3X7lqUSKlQKLYFFffqH7y+b68Yj6CjnJT+YNc5eFXM8I5z/Pjxm24TNbxrxhVsnXhka/F/Q0NDt/S+zcJLHFf9rrEIURxIqDWgGLb6z24qxY2/9uv85nesms7RX3n2AOZwb7ObGVrKY3ft4WHc61dqxu5amok9fNe2xe4KITCEjqHptNzRK9Xm1KlTvHHmDb7+9a+vL9OenCR+49vQLjFn3oFCgRsv9fNh9G7QKbpGS87Rr3aMMEASp0UnMngPjoCWT/0ch5KdWNLm2sQYrnBJ9navu0bXonZtaVMoOUftTbdr7lKnyfVIm81uit6t5xpdR0iEKRm7q4PEmVVMK4+YnWGxK4H20QeM1HKNbhObRczM/auMuK/wp2ffIyIMIppJRFu73bhvltZFNQOzYhuDiDAxNYOoVrxdf27pObrQmiaOlkfvxlcWeHzpKhFpVWzTa01jviPRho+EKnpXpCf5ytxPqo6zE60PAPX/BxXbSywia17rhfG6ViWiNYZCocCZM2fqbpPLPAhU78c+/Pg8L7zwIwAOHTqEaW59f3D8+HEiEX/NePCamKHK5+xelEipUCi2BeWk9A9eB/WwOik9YyJUv2soUY9z5bAN2K+hHG07TyQS4cSJ+q6fWJf3qezR4Qfo6QjX76H6nX+IR2TN42oYo4ZzeUE6socXu6qdo//z3ilocCzobqI8dtdNJjm/t585a77KVTQ/2Mn+pBXo2F23vx93YABtfBznyBGMkRGSq7M8srxS5RztIkOiI449NEwsWnSOdh3ZEGZPXz4NwInu+scQz7ZIdz0Gtzoed3Nd0Xr1SCvjdNceT0ynicSjmPFIxTZr79Xs/6jdFL3rV9foGlapr0FpNLcFMiyQXl7YsffUEFWiZ7m4ubYuWiGObmwT2bT8qpxgvj3Phdx4yXFadJBGtA33qC6KfSb68svrtUHbLp8jIg3yJbGufB8gCyuhid7V0mmiL7/MsR9PM75caV3bl79O9/W3iP1+UiUWNJDdVMZEjeM1hjNnztw0sSdq1p7gY8sIQ0NDjI6OcunSpS2n/qwlE93surrRqOtaxWaUSKlQKLaFejnpKqKksXg72hrckAbgunVObtTMv4aiCr0XUf2usdSb4Vvse+H6PVS/8w+7aZ9Xb1BODSTsPGuxu5FXXkE3ZmrG7vY4Fs5g8OuzbXaO5s5eq+kcxTSwhw9vm3N0M5rQiAqNKCY7ESL7wqsvFON3j1QPGEopcXArRM21aF0bh4JbEkMpW75JTK3nGt0soBZrkeZwhMTGQYqiUNczucRSZzz00bu7yTV6q7hIcm6BnLuNF4/3wAfpP/RcrSHom8ny9Cfvo0nB3WeuY7gu7j2djPencFjCcFbIuxpXrA72ymU6FxaRn7zD7IljaJfSXL04gNPfvy6Ert9qlXG6xShdA80HUbpr0bbYNrZt0GdNVk1C0bRu9JBF2/odzzImIZz0XW8iuzrH216GhobqCoWDb7fBR9XLYy1JTpzYqHfvN7HxdlFmA8VmlEipUCi2Bc/6bKrQe8OJ7aLagPVqKKgs+8aymwbsQdVQ8As3FynDhadIqfpdw/Hc5+2iASxNk0TU1WRDcFMpcs89x2sTK1ycHamKA7x07z184bmdCGZtLOXOUaczyU/aBmvWo+u/t5W2pAy0c9QLIQQGOobQidOYgmCnT59ev+/g8sCTv0zL2O/gtDnEZt5jYc7myg+SXOi8qxi9q7sIzSVqFPhy5EPiuQhGawuF1N30CFh5+Ek+29pWJp4WxdD55QUcISscpOWCqWzCxKLd5Br1My6SwdEb5KVFx9wqrpVn1dSZPhYjrlX/Ih+7GkeXMpg5h/GJURa7Elz46R/z7haidw30KgGz/Hbq6Dw3Ep/w8cxMVd1QU6vefquiaHm0rTY3x+CFK4hMZdR0rzVNfFZHmz8YqmhbvxOLSFKFiaqY+33vtKM9OITb33/zFwkIdUVKNQmyoXiXbdIJ28RbUNe1imrUZaVCodgWVH02/+B1MhnGeJL6tVAb2BCF58lkGPsdeAvkqt81lnoXMWGMuFb9zj/spn1evdrPaiJaYyl0p3ixq9pF86VIHlhoeHt2gjXnKN97BcliTeeoYWRwBgcC7xz1Izoa5p59GIMHiIyPo919DPPUKMnFFe5fGq2I3RywJ+jdm0cSJT78EO0txejd/cO/WPO1T0+UYnhrOEgBnJKoaa27QDdF67p2TQfptfHr2Lh09/fUdI2uvc7k7BRGPIIRNYrv49osd7ftGteo32l09K6Ng+2WRelupgtmmOLCytSWXteLclE0Igy+8Gfv0j2/TNtilj0X09imIDoQYaa1i4weJ5a36F5eICYslj+eYeXoXTjJJNZ3X2D667+0IYKWBNOC4VKQNo500YW2LW3ejWjpNF+/8qc4E+mqdX0X88S//VYokgvWqDcRzVSKQUPxup4r2BoQLte+ZYPtqDFkRSVql6NQKLYFlSfuHzxrKIRwwF7VUPAPu81JqfZ5/kA5KYuoftd4dtM+zysuXsX5Nx7vftfghuwwbirFzC9+g9/8j06VkyUdSfH01w7S/nBPs5sZasqjd1cOHaNw7UrN6F1XN1jdt5foNkTv6kInLnTiW3ze6UvF/e6JrvoReC/86IWquD0tNkls/Nu47Q7mzDtgFZh4JcmH8aNF16hWdI3qmsVTsXPspQMnrsPAEAMC3Ee/TE9He2Us77rYWjtet/xPHb03CHv0brkompxeRp+YYFFKUpfGyTgOC/0Jxva3ITUbWCYLjLlRBq+uoM9nsEeXGL1vAHnhPN8/s8J8T2vlGzwCb8iT8PbJdUdnVItU3UZ1c712aFSvXr++3Xrt0bXb4nOja/c1E0PoiBDNVlqL3x3ImdxAVDn5YwsdIDsq4neDjmfkppqI1nC8zvEKtk7YRMq643hKpNy1KJFSoVBsC2rg1D9416QM31mmqpPlH+r1OykJ3UWO2uf5g2id8atQ7vNUzLBv8JrtHDaxCOpE+qt+13C8+10I93cFQTqyhxe7vlq17p/snSFsA3Z+ozx610128Wb7nprRu6mhJQxDBDZ61+3vRw4MoI+Pw91HMUZGSK4sc2Lh4wrXaKqQpntPnvauBPbwMIlY0TWaOvqV237vzfVH1+qNbhY41+qNFqTNW+++zb67DtA/0E/etbGkRcG1Kbgbt5Zrk3et4n1Zua5QElL9yG6K3j10bhKA9vkMhu1gmTpj+7uqnKNSE4wd6Caxkse0HNrnMyx2JTg0mubdHu9oW0vaWI7NqpPb0c+hIaoEzDVhs3g/sn6/uD5SKXqWbX9FTrPUWmCsMFOKyTXXXaK62Pnftjx+t6uwyL6lC0SkVbFN70Qa8x0N58iR9fjdREuwo9ZFepKvzP2kajLQ1YEHgPoTBRTbi3fca/ic0fUMFC0xdX2xW1EipUKh2BY8B+zVLJiGs1tm2YMq9O4n6sVN5goQb0xZpYbhNSCs4kkaixDF40ytCQv1atYGFdXv/MNuSi3wnGWvjrMNx7PfhVGkrBvpr/peI1iL3rX/4FUk8zWjdx25itXbHeg6eeWuUXt4mPy5qzVdo5gm9vAh3G1wjUKN+qO3oMOMpc/w+KGjPLr30dt+X1e6WNJZFzQ3BMyNv7xrlwmcZSJnrWVVjzeW56VFzi5g38KkgoXuxK6J3m10tO1O4SLJuQVybgG2Q/u+F94b/92qxRpaWbStuV7rM6qZG481c0Pc9FgXXRc+zYrtTGEQf/nl9fqgh+cvMicF+ZJgtzZZoUcbR1gZjJER7OFh3GSSg598gv3YY9vw4RuLlk4Tfflljv5kikeXYxXr9uWv86UbbxH7/WRoYm2DgKdIaQdnAsatUu+8VZ3j7V6USKlQKLaFevWKFI1lV82yVwNYvuFmsZthG8xWEzP8Q8xDpAzjPk852vyDqv+s9nfNwOt/PYz7O3WO5w/cVIr5X/kGv/myVTN6974n7yVxoJW+AA9iV7pGk7zZNkBHdqbKNdp5rI1k0g2sa3QNTWhEhUZUa4xL6tSpU/zkjZ/wK7/6tXWHaKEs8nbtPp9JM3jlT3FiNsnJMwjLYuqUwcXuvWBKhOaiaS6asLhv+SqtBYFtRLE7kpiWzdSRA3ToiQ1Xqo/d1mGPtt1uXFxyskDO2ZkZiMnpZX7u7IcYCI6eGUPrkrj725no34MjFzCdZfKOztlYB5HVVVrnFpEj7zL34D3IwhQ3lkdhtW1dGI0Kg0UtQ0S2sGrniOlmQ9ygt8parC22jW3p9FmTVfs7V+uuiLV1DlfXw1ZsL17neAXLP31nu8h4lAIGVU5iN6NESoVCsS14Dcopq35tRkdHd+y1pyf3Acerli8uFzh9+vSOvW8z+PhSN9BVtVwTLmc+On1LEaPXrl3jiSee2P7G7TJuXhswPPsCx4GCrcQiv7CbagMqR5t/2E39zuszqZjhxqP6XRElkDeWWER6Ru/+ctuPSZBpQqu2lzXXaOTlV7CchZquUcPM4gwOKmfRbSAQmJqBWW8I8uAB4gcvoI2Pow1HMEZGaL8Eh69Pr7vZYu4qqcIC3UmdrrZWVvYd5JjVTra3l8dO/H0ikY3RbVdK7PXIXGtdDF2LvC1sEknLY3cL0ubsuU9oS7bT1tWxEbnr2jUF1q2Korsp2jYIHDo3iYtLfG4VrAJWTGd6qIWYtlSxnQuc7tA4Op/BzDrcmPiExa4E6euv85396coX7QAk/H/e/S4AhtCJaRGieqR4q5nE9AhRrfS4FIcb0yLryyu2qbPeFMYt1wYtj7XV5ubYc/4KTsat2KbXmiYqDLT5A+uxtkF2ywcFr3ObvB2MuNdCocCZM2duadvRsQ6gdm3x86Pv03FiuGJ/rtgdKJFSoVBsC95OygY3JAAMDAzs6OtHzdoXSLkQzsDKe3ymqOmGrgai36knkoTNWaTiSfyFV98LZeymcrT5Bi+Bbjf1O7W/azwq7hV0TRJRZbIaSr1zvDDVynJTKZZ+9Tn+tz9wa7pGP/037qL3c93Nbmao2Ry96168StSqjt7VW1qJP3o/0VL0bvbZZ3E3DWhrQhSjPjFIUBlneStYV8YYig5xYujELW1fFEWrxc/NoqglbZxEhsTCx8Rb+2mdGyc27XBN9JGTBkJz0HR3/TYRydE/V0ATOtlkG4bQWQxQtG0QaET8ri0dVpwsK052+xpeQkNbFzGFKyncl+PGxB8Wa4GKSCnWNkJMmNz7Fz+kfXWeloUVOs9fw5UaMhFnMtrPimglbuXpy0zTTh59ZARKsbaRV14h99xz2952xQZe5XG8xrz8xpkzZxgdHWVoaOim29aLsL1y6RPiEYcTJ25t36sID0qkVCgU24JXvUM1cFqNYRgMDQ3t2EH3erb22Y0rI6E70F9ZrX3BmYiLW/6sO+lq3U3UdVKGbNC+Xq1D5WhrPLvJWaRESv+wq/qdcvD6Bq8IrFDWHVf9zjfUO8cLW62sXEF4ukb//sAs21N4T+HF5ujdj/v34EzNVkdRHuigL2n7Knq3KIqaRLj5LAr76VayZ/8lRFxatWW0vE3vB5J3xDCu2BD+NenyN3o/4kisB9lqsvfgIyAEn3/qb/Ib/T0VAujJF/+Cex+8j8PDd5N3CxRcm7xToCBt8q5FwbE2lpdui/VHay/PuYXi81xrB781fxD0+F0Xl4yTJ+OUMjQTcCE/XrVdcnqZzivvI6RkaHScnO2wsDfB1P4YaJO0MgnAnCuJX53l/HwG96MJLt1/AH3pfd76YJF8X09J/DSJaiZL8QVMdCYWCkSFue4SjYoIsdJtVDOJicj6827V9bnbiEUkqcJE1SSZqWg/Ir2/2c27JW51nHNO1j6hjRiSY8NHt7tZioCgREqFQrEteA2cqrjXxrOrBk49suxVBF3jqR/32sCGNIB6TinV9xqP9z6vwQ1pACpa3T94139ubDsagRLH/UO9czwpCVWKhOe1hTrONpx64/WFkImU9VzJ6ljbGNajd195BcOYY6JG9O5htxDo6F1j3z76HnqoGG0bj2OMjNCdnuNThZ+uR9u2uKukCmk6EhmIC5wjR0AInMFBZCqFCZj6xpBua9ZkQHRxb/uhbW2rlHJdzFwTLyuETMeiIC3yTqFim7X7eceqEEMrXsexyMviNpZs3gSA3RK/e+hcUYRsn89g2A6WqTO2v6vKNSo1wdiBbhIreUzLJjq3wGJXgtjHZzjzmSOVL1qaM/7+wvVbaoOAmsJlxWMtsn4/JkzSPVki5gyx7LV14bP81k/1Pm8XLZ3m6Euv8q2J+ap1+/LXif72qwxKh5lHHmlC67YfdU2rqIUSKRUKxbagosD8g9cs+2wYB7A8Tm5UzHDjqVfgPBMygbx+nawGNkQB1BOLwtXvwFsAU/u8xuMZuxky5zjU6XcesVSKncOr37muoGBDNEQxqOrawj/oOhi6i+1UR7uGKe4V6p87qL7XONxUitxzz/Hdj7Jk3zpTFb2be3SYB59rbXYz74jN0bb581eJyupoW0wTe/gQbinatvDkkw1tpxCCqF6spbiTvP3227z+1k/4hV/+G2W1QYviZV5a667R4v1ijdH8Wq3R0rb5te3dtTqkVtl921MIXehO0DO5xFJnnI6FDJ2zq0ylOmpGvgpXrse7LnXG158fBBoRa3szJJCTBXJOgUVu8fWOwAiLfH+ydr1DU+glUTNCXIsQFaVbzSQuNup+xtaXF6NvY1q0tH3RARrXIk0RPPWLF4mdPEnbnIsgRq81XeUet60W4vPTDH7/++h33YVz+HDD27mdeB1r1XF2d6NESoVCsS2oSCb/UO87zxW8s+6DiBrA8g+aBlFTkreqf5OwiUX1RAjV9xqPqtGmjrXNwOs7D9v+DpST0k/Ujd0sCKJmeH4Tr7SMMJ3HBomo4dQUKcPmpMx49DtQ53jNIJtM8WLXwarl/bEMsNzw9mwnm6Nt3+oYILEyUyVOtA630510fBVtuxMIIdBdjXa9Zcfew5WyFI9bcoaWhEv3cxP0Xf0jnLhNx9RHLC04tL4Z4UrfIBig6Q6a7hA18jxSGKPFMbAiEWR3D4aVZ+LufcREhLy0kPh3PxH0WFsvLOlgySzL7p3X+9wseMZESeAsux/X1m6jJeGzKHbGtej6elPcmtyipdPETp4E2ya+vMDjS1eJyMqI5V5rmsi7ErOnA6u9ndjJk8U6vAHeF6hxPEUtlEipUCi2BTWA5R/qx26KUA1mqygwfxGL1BYpw+Ysqvd5wvT/FRS8jjNhEymlVPs8P7G7otWVOO4X6p3jZQuC9kR4fhM1AdJfRAyH1Xz1gHbYnJQq0t9fhD21oDza1nIWmKoRbWuauUBH2/oJTYhitCgmbeXzK/b3Ez9wphi/O/Qg+TfPkRiXHJmYKsXvthFzVznIPEePRCDahz08zGBHkp+OjfEL5tOcOHACKSW2dChIm/c+Ok0i2c7R40PknEIpGrd06xbIlWqEVix3rLL1xWjc8se2vDORcLfE2t4J2yV4Gujr4mVMi2C3FohIgw9npteFzZgW4dh3fkB7dp6W+RWil64QN01yMkbaGGCFVlrcDKlCGllYpfXKFVYOHgTbJvLKK+See257PnQT0Ccn+crca1Uu+eXe+5rdNEUTUSKlQqHYFjJqJoxvuJlIiY9n920V77jX8HzGIBGPShZrpLaEbdDeq9/pmsRUZ1YNx2vAOh+yfmfZ4Lhqn+cXdpWDVx1rfUP9tIxw9T11beEvoqZbc3k+ZE7KuhPRVLR6w9kNqQVuKkX22ef4//6Bw4PLp6sG7R/6hUOknuxqdjNDT3n87sKBYxTGL1fF7xqGRJomzpEj6/G7V44dY29pvRACUxiYGLTKGN2ijcOJwW1ro+066/U8N0TNfEnktMg5eUYun+f8lYscu/+e9UjcvFsgJy3aUoskZy/jdrmYiwV6ZnNM9mpQY/8e5FhbP2DjsOxmWHYzxQWlOT5XVmbWt0lOL2Neeh8hJUMj4xiug3ggwdz+LkztCp1S4FomUwWNc9OrdMyt4E6M8NGgjnHhGpfPdUFqDy1ajBYtSlyL0qJFadGL92/VzdlItHSa6Msvc/8r0ywuV0Zj7MtfZ8+lnzL4Vyuhqb2p2Br+67EKhSKQqMLH/uHmImV4UK4if7FbnEWe/U7t75qCV7xhWGbZr1FP/FLOosbj5eAN2/4OvD+T6neNp2795zoxlUFEneP5i6hZ28ETtrhXr2Nt1JRo4TKNBgLvuuONbcdOU7Bh3BxgvGugat1/NjAHWNVPUmwr5fG7dmcXb7anqmoDLsa7eeCRBAixHr+7eupUw9poaDqGptNCzHObxBWH/ESaX/jC41XrtC88RvzqtyEhMaffQVgWN37YxSnuA12i6Q5Cd9H1Ak+3fsJBLYnTomMMHsNBYjz2NKmONgrSLoqk0mJqfhpLOERaYxvOz1Kcro0/42H9wqFzkwC0z2cwbAfL1Bnb37VeJ1QIiR4poEfgYkuCoyuLmFaOhckrLHYluHDqL3n3M0c8X98UBi1aFNEuiUqDn0xeLQmZMRJajIQWJaHHSWgxFlsLLGgZVpwsLVoUTWz/AW+t9ia2jW0b9FmTVfHWEb2L+HR4am8qtoYSKRUKxbag4l79gxIp1cBps/AWKRvckB1GDZz6C6/6ZLul34Hqe81gt/Q7UPs8P7GbnJQq7tVfRAwPJ+UuiXtV+7vmsNsnQIIaT2kka/G7hf/wIyRzVfG7uiZBrFbG7zZQpLxT3P5+3IEBtPFxnCNHMEZGSGZm+JT9VinWNkGLu0qqkKarP0OiJ449NMzeaBJncJCeu5+ues3TN04DcOLuE1XrHOmQcy3ysrB+uyZk5koOzzVBM1djm7ws3i5klpCmwBZOiDLBWHeqti8Uo2UXuhPrAuVmpCZY6E7Qm16ifSHLYldi/fleWNJm0bGhNJdoMrvkvfG9cJo5/uB6sT+3VIiZMVr0KAmtKGgm9I3la49btTgSiaB2+8trb2pzc+y7dIVopvK8oteapm1axxxMhKb2pmJrKJFSoVBsC16Dcl6DeIqdo94s+2zIBk+9LlBVFFhz8J7tvDsGElS/aw67ZQCr3udRfa/x1Ot3UhYn2YcFFbvpH3bTRDRPB6/qd00hauwOJ6W6tvAX6hxPTcxoNG4qxfgv/Dq/+Uaeh1beq66Z9419aAP9N38hn1Iea2sPD2NfuErUqoy1BcA0sYcPrcfaFp58csvvpQudhK6TqOP8vBVeeOEFhoaGeODBBymUiZr5MgE05+bJSYvsWg1PWYzEzboF8rJ0W3pe1s2Tcy1cak++aRSGVTyu6k6xHblYdd3nctbWr22/9vydIOPmybh5ZupWLa1EdEJMmvzF2Me06nFatThtpdsH/vJHdGQXSMyv0n7+CsIxKGgJJsw9FeJ4t8yFqvamYmsokVKhUGwLyknpH+pdRIcu/tAj2kyJ483B6//da4A7qHiJ/V4irWJn2S0DWPX232rwtPF4feeOK7BsiNQfZwgUno421e8ajqGDaUgsu/o3Cd85nnJS+gmvuNfd4qRUx9nm4PX/HrZri3qfR/W9xhOPStKRPbzY9dWqdZnkFK0B9vOVx9q6ySRvdw4QW5qpit2MD7XTk3TWY2394GbThCAmIsS0CB3cWX1MKSW2dMjKkqhZEjbXRM5sSeTMrQuiG6JnVuaLt25+3fF5O9hmcZKPoxePo7GcVVcSjOWsiu3Xnu8XpICssMhasxUJ1cnpZVovb9TenLcdFg4lmN8vMJzrJAomjhXhei5O6+wK7dkcjF0kfbCb2LUVsmMXiA8c9GWNTcX2on5hhUJxx0ipalL6CV2HiCEp1BrACtug/TZEgTmOw5kzZ7arSU3lzJkzvPnmm017//nZvw4crFr+/gejvMDrN33+22+/zaFDh7a/YduMigLzF16DN0Hd39m2zUsvvcTo6GjF8iuz/cDXaz7nxb/4IyKG3YDW3RpvvvkmHR0dzW7Glvnggw9ueR96fb4PeLbmut99/j/SEml8dMGbb76JbW9/P8hkvwRUCxETYxc5fXr6jl//0qVLgewvZ86c4dKlS5w+fbqh72vqT2HZ1Sr42ZHLJJmq+Zxr164xNDR009e2bbsh50S38t2lpx6CGu6LlcUpTp8eqfv6o6OjHFZ1jLYVz7jXADkpHcepOrZu5sKlA0B79Qo30/D/9VoEcX95J/vKman9wD1Vy+eX8jv+e1y6dGlHX7/ivSbbgJ6a6y6e+5DpGzcXQYLYN8pp1jG1FlemWoHP1lz39jsfk2zdOMe71eOrn1iLtY288goFZ4GlTbG2AKaZq4y1DRlCCExhYGKA3nJHr+VKl+y6yFkpYK49vjJxjYJwaO1uX19u9E3RMp0nm2ylYyFD5+wqU6mOmpGvwpXr8a5LnXGgGA8bBOrV3tQ1C920gAwAN/oELWcsTCvH9RvvF2tvvvovefczR0hoMdr1Fjr0BO16onTbsn6/Q0+QNFqJaWrmeFBRIqVCobhjCja4rpp16ifi0doiZT6gg/ZebMdsZyklly5dQoQgm+/111/nS1/6Eg888EBT3v/PR9sZmaxe3t23lyeeeOKmz+/o6ODSpUs89thjO9C67UPNsvcXXpMSguqkFEJw3333cfz48YrlkfNt8MPaz/n8Zx9D85GZZWpqKpD71Z/+9Kf83M/9XNV3X4vLkzF4ufa6Bx/6ND3tjReNOzo6eOmllzCM7bvEkxLy1s/WXDeVvsqovH7H7zE5ORnI/nLp0iUmJydvKnpsNzqfA6pFyqvXp+gRtduSTqdv6bUbdU50K9/d/GLt/8OVpambfufXrl0LxKSnIOHlpCwEyEkppeTatWt1t7kxUUOgBFx7teH/67UI4v7yTvaVC7MGtUTKTPbmgvOdMjk5ua3H03pcnvUWga5eHrmliWhB7BvlNOuYWouZlXa8RMqRc1foSiyvP77V46vfcFMpcs89x7/5Y4fjC6erYm3v+2uHGPxyV7ObGQg0oRXrNNaJtT19ubgvOdG7UbtT+5nPE7/+bWiTmDPvIAsFxl/p5cPYYbSIg2bY6IaNYeT5nH6BZN7AMiIk+vZh4nD23nvpNaJk3QIZN4frU4fvdtXeXHVzrLo5Jqy5uu/XokXp0ttIGsW/Lr2NLqP4lzTa6NQT6CI4E6x2E0qkVCgUd4wq9O4/oma4nEVeeIkQW3G0GYbBL/zCL/Doo49uV7Oayj333NO0zzLwXeCj6uVtHf08+uit1e544403trdRO0BGOcd9hVf5jqDu73Rd5/jx41X/x1Mek+hjEfjUp/y1/zp79mxT90V3wq22u2fce93QsRMcGtjGRm2BxcVFvv712o7b2yFXgH/wJ7X/l77ys1/k0eHbi7jazBNPPBG4/nLq1CneeOONbf2+b4X/6ccxlmsYJh986Am+/qUT1Su2gGmaDTknupXv7nc+TEINo+7DJ+7h6798oO7r+8GJEzbC4KQ0DIMvf/nLnDjh/X9y+fcTUMNMvG+gq+H/614EbX95J/vKyGsxXni3erlmtjbk9xgaGqrbX7aL1z8y+VceE9H+5jd+5ZbrXAetb5TTrGNqLSbnNf4f36u97ks/+wxH9+5cLcBG4rpwVRvgalf1Sevf3jNHRWanYttx+/txBwbQxsdxjhzBGBmhJzPPQwsfkY6kSrUaM6QKabpSktZInJWBgzzUehRncJD7Htio1SilJC+tUi3J3LqbM+PmOHftInlh09GXJOPmybp5Vt0cK26OjFMU/pwdrM/Z6Nqba/U0b1gzNdcLBB16gi6jjW6jnV6jk36zkz4zSZ/RSUK/sxqqittHiZQKheKOqSdSqkH75uDpLApZ/Q5Vr8hfeNUC9aodGlRUDV5/4e2kbHBDdpiMx/9RIt7YdiiKtNS5fg3TPq+eI1kda5uD17HGq15yUFHR6v4iang4Ke3gOClvBe9riwY3RAHsnrrjXjUp41F5ywKlYvuol44Tpr5X77xBHWsbQ/6pp4g//zxuMok9PIx76SpRWeBAvtL1b4kIKwcPYrW3g2FQePLJivWirF5nF20V66L54oXJie7aEy6klPzeHz3P/qN3cfDYYTIlx+JqScBcdXNk3BwrTul2i+Km32pvSiQLzgoLzgqX8hNV61u1OH1mJ31GUbjsNztJmV2kzKRyYO4wSqRUKBR3TD23ioo/bA4xjxj2sA3ae/U91e+aw24RKb0uTlW/aw67ZQDL6/9IDZw2h3rfe5j2eXXTMtQAVlPYPfs8daz1ExGz9kBkwQrXgJ06x/MXu2Z/5/F51ATI5rAdImWjajxvZiu1PRdWTaB22tGVS58gVlZu+hqqBvSd4aZS5J55htjJk7jJJGdSKQrpOXqtGQxpYwuDabMH62AHd7XfwNV1cs88s611QoUQGK5Gm4yxP9p3y89bc2+uujlWnCzvn/uInGbRvbefZTfDipNl2ckSS82RmL5IJiC1N1fcLCv5bJWAaQqdvZFe9kf6ORDp40C0n5TZhS7CNVmrmSiRUqFQ3DH1BrDUBV1z8Pregxp/6IUawPIXXs4iLwdYUMnklLvDT3gJJfWOTUFkNVt7edxjUopiZ2mpI1Jmco1rx07j5e4AdaxtFl7HmrDt89RENH/h5aTMBSju9Vao52hTNB6v792yBbYDRki6n9e1hep3zSFigKZJXLf6d7nVY22jajxvZiu1PedW2zzXjV2/QGG+nt+tiKoBfec4hw+TffZZIq+8gmHMcsPsZ8qsFI/vcgtke3uZeeQROnwiCpe7N7uNdubsYi2MEx2Vjk3tC58ifrVYe9OYOYVbyJP+URcftx5Aj9roEQs9UsA0cpzIX6GlAAXDYCXZCkguDW2fIHsnWNLhcj7N5fxGHVpTGOyL9DIU28eJliPsi/QGti6wH1AipUKhuGPqxr2qE+um4B0FFq4DpooC8xdezqIwDdhDndnOKt66KXgNWNuOwLLBDMnZrpc7T8W9NgfDKA6O2jXG7XeLk1Ida5vDbp+IpkpJNIeo6RH3aoXLQeB5jqf2d02h3qSEbF7Q1hKO30Vd0/oLIYp9r5Z4fKsiZaNqPG9mK7U9z93Q+e+/W3vdr/7yV+lP3jzKU9WA3h7cVIrcc8/x/U8yLP7kLKlCmogsUBAR0pEUKw8N86UvnG92M2+L8tqb7pG7MUZG6FhY4v7Zc2W1Ny1ShUUGB6LEjDZW9h3k55NHye7pY+j+v8aSs8qikyndFv+WnEzpdpWM25yLL0vaXMpPcCk/wXcX36bbaOdEyxG6RP26m4rahGTYRqFQNJP6TsoGNkSxjneNtvAMYElZLxqnwY1RAN7OotDFDKualL6i3veeKwhMIxy/i5cjWcW9No94FJYz1ct3i0ipBu2bg9egfT5E53igjrV+I2J4xL2GzEmp+p2/qBu7aYG3DyxYKOe4/4hHak+0DdN1rUpE8x+Z5B5e7LqranlXLAMEU6SE6tqbuZFrNWtvEjFZ2b9Re5Mv/SwDkW4G6K77+pa0WbBXmbOXmHdWmLOXmbeXmHNWmLeXmbOXycmd/+edtZf4wdJ7EIXP/Otn+cnfe37H3zNMKJFSoVDcMV4Ft+MRVei9WeyG+h31LhDUwGlz2C1OSq++pwawmkOszv97mGbZe/0f1YsdVewsXiJlmCKuvQZOhZBE1SThpuA1ATBMTkrHgYKtHG1+IuIR95q3Q+akVHGvvqLexNPibxWO38XrGl31u+bhNZ5SLwY/aNQbG1LXtc3B67o26ON4m2tvvtE2SFd+uqr2Zs89bcS08S3X3jSFQa/ZQa/Z4blN1s0zVxIs553i7ZS1wJQ1z5S9QEHa2/VxAeh95MC2vt5uQImUCoXijlGxOP5jN4iU9QbjVN9rDl4ipddEhp3gVupv3Clzi58BqkdNZmducPr01ZrPGR0dZWhoaIdbtjupdxHdyL6303gJX161YBU7j9d3vxuclPGomojWLDzTMkLU7+qdryp3R3OIecS95q2QOSk9U1pUv2sG9a7pQnVdqxy8vsMranc39Dtdk0TURLSmEOZI/7Xam8YPXsF2F5mqUXtTN7Nku3am9mZcizIYiTIY6alaJ6VkwVlhylpg0p4viZcLTFrzzNiLONw8+lhx5yiRUqFQ3DFqxqn/8JplH6YBLFUny394ipQN6nfHjx9vyPvkCrUH5LwG8ACGhoYa1r7dRr1Z9mEaSFBOSv/h1ffCJVLWXq5i1ZvHbpiIVs+pompSNoeIuTviXr3SMtS1RXOoG/e6C/Z5an/XPHbDsdZL+FL9rnmEvd+5qRSzv/QNfvMPHR5aea+q9uYXfukg+ej1hrdLCEHSaCNptDHEvop1tnQYL8xytTDJtfwkVwtTjBVmlHC5AyiRUqFQ3DFq5p//8BKIwzADaw1VQ8F/eIkljYp7jUQinDhxYsffx6X2Bz16ZB8nTvTu+PsrKqk3IabefiJoeP0fqZqUzaPZEzMagdfAqTrONo/dcI5XLzJZCeTNIeoR95oLm5PSc5/X4IYogJukZYToHM+736ljbbPYDcda1e/8R4uXkzJk+7t0ZA8vdn21at0/HpyBmcaLlPUwhM7+aB/7o33Qdh9QrIE5kr3OD5beZTR3o8ktDA9KpFQoFHeMclL6j7DPwIL6J2qq7zWH3TBgD3XqxqgLuqawe2bZ116u4l6bh2cd3hDt8zxn2avjbNPwjAIL0QCWqpPlP6IeTkrb0XBCZCZQ17X+wjRA0ySuW/27hOkcz7PevZqI1jR283iKOs42D+9+1+CG7CB1yzZFJNkGtuVWybsWaWuOG4UZLubHuZAbY8peaHazQocSKRUKxR2jalL6D68TyzANYNU7uVGDp83B60LadsC2wQjJWYfa5/kL0yjWTnFCPoDl5aRMKJGyaez2mpSK5uB5jhei/Z2aiOY/vJyUEK7IV3WO5y+EKA7aZ3LhPsfz2uepa9rm4eWevnZjmkJBJxIJvr3aM9JfieNNw7NsU4j2d5k8pAoTNeNe25f2M9fEtuXcAhPWHBOF2eKtVbydtZea2KrdQ0iGCxUKRTNRM7D8h9cMrHo1foKGV7/TNImpjm5Nod4FTSYP7SH4XWwHLFsNYPmNWESyWmMAK1yD9rWXq4GE5rEb3ONq4NR/eJ1fh2kAq+4se9X3mkKkTt3tfIgiX9XEDP8Ri9SeqLUbjrUqdrN5eH33UzPLnDkz1pASIzvNSra2DV7t75pH2NMytHSa7j/+Id+aWKhaty9/nf4/fhVL2Mw88siOvL8rXRacVWbtRWbtJWbsJWasjfvzzvKOvK/i1gjBcKFCoWg26qTaf3idWIZqAKuOOC7C8zEDRb3YyWwe2hONa8tOUdfdofZ5TSMelazWGMAK0z5Pxb36D6/aeKEaOFWuIt/hPcu+se3YSbyOtaYhMcKjhwWKaD2R0tYa2JKdw7LBdtTkW7+xK9zj6ljrO7y++3iiCxhrbGN2iGs3ZoDOquVqHK95hHkcT794kdjJkxgTEkGUXmuaXmsGQ9rYwmDa7MHQWohPTTP4/e+j33UXzuHDW3qPvGsx7yyzaK+y4Kwwby8XhUh7iVl7kTl7GYfGZNSv3phvyPuECSVSKhSKO8azXlFMndw0C6+B0zANYHm5QlW/ax71HF1hGbRXEXT+xGvQPiz9DrzjXluUk7Jp7GYnpRqwbx6xEA9graH6nf+IGt6DeoWQOCnrJc6o64vmsSvc4yq1wHd4CXX5EMVb5zz23arfNY+w1kLV0mliJ0+CbaPNL/L40hUi0qrYpt+eIvIumB0dWO3txE6eJPvss7ipFK50WXIyLDgrLDirLNgrxft26bGzwqK9QlY2d8BTILgvfheHFpP8J1/7WlPbEkSUSKlQKO4Yr8E4r0Fjxc4T5hlYa3g7eBvcEMU69cQSL4ElaNSbta1mnTYPr31emGbZezkpVdxr8/BysXr9VkFEpWX4j91cd1z1u+ZR10kZEpGy3v+Q6nvNw/McL0z7PHWs9R1ex9qw7O/A+7OoCUHNIx6VNes1zrX0E51bIt/V1ewm3hbRl18uCpRzc7RdPk9E6hQ0k6mWHvIxk1axxKCdZj67irs0weRd/VxdmCf9J5d46Zn7WXYyuPizX/YY7RyODnAkNsi98YMkjTZG5kfwaXN9jRIpFQrFHeMlfClXUfPwzLIP0YC9Z302dVLdNOo6KUPi4q0nPihHW/PYDYP2XkJ/It7Ydig28NrnhWVSBqj6bH5kd5zjqX7nNyKGt0iZC0nca73JnKrvNY+ouQsm36rxFN+xG0TKgse+W/W75qCl0xz7wat8a6I6JnRf/jp7v3OBXF8v2p49uKlUE1pYHxdJXlhMFOZYcbOsOFlW3CzO+A0OXnyDglMgdfYctmGhnYgzf6gDQ7uOQVHPG3N1uGrROZ8jceMGN5ID6OOLaJP9uD2tzf54AJhCZ4/ZzaHoHo7EBjgcHSRp+KNtYUCJlAqF4o5R8ST+Y1c4KVXMsO/Y7XGvap/XPHZDFJiKe/UfXt99WPZ3oI61fsTrHM+yBY4DegjGT9W1hf8wdYnARVI9qB2WuFd1judPdsV1rZqY4Tu8vnsvYS+IeAmuan/XeNbqNbbPe9drdBxJfHqa+PPPk3vmmS3Xa9wKrpRk3BwrbpZVJ1cSHXOsuJnSbfnyohiZSZYugMbfrHith9+5gMyn6ZhbJV/IYEV1Jg51ILXK/Z7UBGMHukms5DEth/b5DItdCQ6Npnm358iOfdZaRIRByuxij9nNHrOLPZEuBsxuuo12NBGefYDfUCKlQqG4Y1Q8if/w+u4tW2A7YIRgLMFzxqnqd03DNEDTwK1RtigsziIVBeZPPJ1FIXFSSunt4vWKHFXsPJ5OyjCJlJ6pBY1th2KDeseabEHQGg/+scirhrqK9G8eQoCp2xSc6h8hLM6iehNMlFjUPDzrjgcspaVQKHDmzJma61azX665XF3XNg+vfheW/R1416RU17SNpbxeY3xlgceXrlbVa+y1pun8ZJn8gUGw7Yp6jfWwpM2qyJMXNudzY2TcHBk3z6qTI+PmWC39XRye40zre/zHG++z6ubIuvltSyvtnF0FoH0hC8BCd6JKoFxDaoKF7gS96SXaF7IsdiXWn7/dtGhRuo12uo12eoyO9ds9ZhddRjuaCMc4QpBQIqVCobhjVDyJ/6h3QZMLyQBWJqfEcb8hRNFZtJKtXhcWZ5HX/i5iyFC4V4JK2GfZF6za4j8oJ2Uz8RKIw7K/A+Xu8CP1vvtsAVpDEAGt+p0/iRgeImVInEWZehPRzAY2RFFBWM7xzpw5w+joKENDQxXLXQl5Wzna/IZXvwvL/g68+52aENRYKus1niMiDfIiQjqSIqO10MoKKXscxy4QuXqR+c4omc4Ei9/5bc7+8s+QcfMbgqOTq3hckDZ0lt4o/Y53IzoBCmBv/+czrGJcvO4UL2ZzNzmgrq1f237t+VulRYvSqbfSZbTRbXTQUxIhe4x2uox2Erqa6es3lEipUCjuGM+BBCUWNY1YvQGsfDhEShVB50/iHiJlWJxFnhF0qt81Fe9Z9sEawPKi3v+PqknZPHZFTUo1Ec131HOxFgftg//bZLzO8VS/ayqmXnv0MizOIi+RMh6RaOHRJQJHmNIyhoaGOHHiRMWyeud49UppKHYWr34Xlv0d1Il7Vde124aUct3NWBA2F3PjZNw8WTdPxs3DxDiHLv4U27Xo/+QcrrRwj7cytb8LI3KDTsNGaC5zruTyVYvO+Qz2yDuM3jeAvCT48QWLeZ/Ua/TCNov9zNGLB9JYzmKxzvaxnFWx/drz1zDQ6TASdOgJOvVWOo1WOvVWknpi/X6H3kpEU5JX0FC/mEKhuGO8LuiUo6151BOIgxaN44USx/1J2J1FKt7an3jOsg9Jv6sneqkBrOaxK2pSqn2e76j33QfNWeSF6nf+xEuktELiLPKalKH6XXPxOsdLTy1y+vT7NdcdP36cSCQYdjBVSsKf7IaalO1LU3xl7iekCmkiskCh5N7ryRwF2pvdvKbjSpesWyAnC2TdPDm3QNYt3i/+Va7LrG+TLy0v/rm4nm7GYr3GCTrmVmnLL2OZOtPDcUyt8uLPT/Uat8pCd4KeySWWOuN0LGTonF1lKrWpJqUU4Bi0mzqpmTwR10T27Wc41kPm4L0M9z1JZ0mEbNXiCBXFGkqUSKlQKO4Y5WjzH/Vm2Qdx1mktPB1tapZ9U/ESTMIyaO81AKz6XXPxnGUfkgH7eiJlQiXVNI3dUZNS7fP8Rt2alCE/x1MO3uYS8RApcx6RgUFDpWX4k5jp5WirLRaNjo4CVDkW/Uq9/bY61jaPMDsptXSa6Msv86sXZ1jOVg4c7ctf59M/eZ2Y3kfhySdvWvPQjzjSIeda62JhzrXIuXly0iLnFop/srAuOuZKgmNWloTHktCY31QXcifwa73G7SAqTBJajPy999J3Pku0r4eOtMPSnKDltRZGE0exrBhOIYJTMBketPmZno/J5uKDUQGYAAEAAElEQVS4hkH/4H0gBNlP/TXclv5mfxxFA1AipUKhuGPUAJb/qDeIE/ZZ9nE1kNBUvATysIiUauDUn4SlXpEXKu7Vn4TdOQ5qIpof2c3neKrfNRcvJ2UhBIP2oK5p/YrXPi8S7wyMEFmPehPq1D6vecQiklRhgodW3qtyGpqz9cIq/Y1+8SKxkyfBtnGdCH3WJL3WDIa0sYXBtNlDRG9HHxsj/vzz5J55Bufw4R1vlyNdcm6B/LqoWFh3I070ZihEbzCxUFgXEddExsr7FjmZx5K3V8OwGTSrXuNWMYVOqxYnocVo1ePFP23jNqHFSF8eIy5NHr7nQVq1OOZa3Oo+iH/chjY+jnasm8UfnKdtweI+cb5UdzNBi7vKsclxdDJIYHXvXmJC4AwO4vYrgXK3oERKhUJxR0hZL3azwY1RrLMrosC86mSpWJymEnYnpYoC8ye72UmpjrXNw2t/V7DAcUAPwbi9it30HxEDhJBIWf3bhP0cT/W75uIpUoYk/lDt7/yJV93xXEhKmORU3Kvv0NJp7v7+q3xrYr5q3b78dQb/8iKxqUkKTz7ZhNbdPlo6vS5QanNzPDx3Dc2p3K/3WtMMXBBoHYdwk0liJ0+SffbZKkelLR3yrsWylsPC4VJunJy0yLvWumiYdwvry9bEx3IRsnxZXWHxMMASLFzc/i+lyWx3vcabYQqDFi1KQouR0GLF+3qM6+eukEr2cWTwLhJ6jJbS+jY9TkKLE9Xqi6cAp+0MAEmjrWpd/qmniD//PG4yyaWu48QWLxCVBQ7kr61vE0k4SNNk5eBBrPZ2MIzA/Y8p7gwlUioUijuiXn1D5SxqHqYBhi6xneqLnvBEgdVerkTK5uLlLKonsgQJ1e/8idf3H5YB+1WP/x8hIKpEyqbhVZMSivuK1pbGtWUncBzIW2pCkN8QoniOncmF+RzPw9Gm+l1T8RIpc2FxUnpNgFTXtE3F63gTlv1dRomUvmLNadg26yKI0WtNVzkNHZt1p2GnrsPQULObXRenJCjGvv8dVvLLiLk5oufOEYtpWIbBTGuSXCxCi5OhLzNLjizTH6aZPLKH5Y44M//xKj/+6w+XhMWi6GhTEhU7Sm+yqdai4ta4pXqNJYQr6ZxdRSDIJdtIaDGW+wc5FttfEhvjlQKkHiOhRbk2eoWoNHjs/keIaLVloBd+8AJD8QOcaH9gRz6nm0qRe+YZYidPMm92c6W9t+p/S0t1sPeRDqzJSVxdJ/fMM4GMG1bcPkqkVCgUd4SqoeBfYhHJSrbGAFZIBu1VFJg/CXuNNu9+1+CGhJy1ekK3yszUAeBY1fL5xRynT5/eplZtH6OjowxtYUDDS+RviRUFC0Vz8NrfQXGfF3SRsp7Irwbtm0ssUnu/EJrUAnWO50u8RMow1GgDFffqV7yON2ERKb0+hyZcImrEtqGUOw1jKws8vnSVyKa6hL3WNMnRFbT2CG4yyd0ffUT+nnu25f1d6VJw7WL9RMci7xbIOvlilKlTIFe6n3VKLkWnWDsx52xsN+vOsXD/Mh9e/3cVgmJyepmnR99HSMnQJ+MYtoN7T4L0/i6ktoQO5IEbbgx5dZXO+QyRi5eZuW8AObZIdqKH+Z7Wbfmcux0djbgWpUWLUrj3XnrPZTD7umidsDBtydTbOqPth7GdKI5l4NoG0tJ4Ovoh+91WWpNdHDj4SLFe4xe/edM41BV3GsBToGwUzuHDZJ99lrE3fopkgimznylzo+0dnQUQBbK9vcw88ggdDYgZVvgLdchTKBR3RL2Lg5i6oGsq8ahkJVu9PCzOIu8osAY3RFGBl7MoNAOnKoJuxzl+/PiWnxM1a0cE+XXgdGhoaEuf00ukVFGvzaXe5IQw7PMy9dIyVN9rKp7ucQ/na9DwSmpRx9rmElFxr4omEPa649mCqFn7cL6lF33qoKrH1kCiL7+8HoXaemmUiDTJl+pQrtXNSxXSYNmIT85SGDqCIyxi777Ouc8Ol2JNLQquxcXoGBE5w/nr8+siY36tbmJJUMw71oYA6ebJu9bNG3krtEDGWa5YdOjcJADt8xkM28Eydcb2d1U59qQmGDvQTWIlj2k5tM9nWOxKcGg0zbs9R7anfQHGQCeuRYlpEeJaZOO+KN6PaxFipdu4Fi0tj3Bl9BIRafDY/Q9jCgOxNst0b1m9xnt6MUZGaL3oMHRjrKzfLZEqpGlLLkKbwDlyBAJar9FNpfiDu/42U/mZqn3e5OF2st8cYmx8vNnNVDQJJVIqFIo7om6hdyVSNhUvsS4MA6dQpxaq6ndNJfxxr6rf7TSRSIQTJ05s6TkXl2t3PJfoll/Lj3g5kRPxxrZDUUldJ2UI9nl10zKUo62peNbhDbmzqF7EsmLnCb2T0uO6Vl3TNhev/V29mNSgoKXTHHzxx3xrYrZq3VF5mfi3f4wzOEjhySdV7OEWkFJiSYeCtNbdhAVpUZD2+v3irb1+35icYujiT3Adh9Qn58CxcY61k97bjTDTtOkOQneY12wuXcvROT+HfSbN+H0DyKWP+P7H/6HSadgCSGD8TLO+hgo6Z1cBaF8ozmJf6E7UjBSFolC50J2gN71E+0KWxa7E+vODioZWEhAjxETpVjOJiTKhsUxcjFWIjhv3TXF7MsqiWxSJIzVqO5bXa7SHh7FHLxB1Kus1AhSEycrBPUSTyUDXa8wWBOnIHl7s+mrF8sOPLOH2Z0GJlLsWJVIqFIo7QsW9+hfP+h1hmXWqIpl8iZdIWa9+bZBQ/c6fhL0mpZfglVAxw03lZjUpg446x/MvYd/nqbhXfxLRa7t88rYOuI1tzDZTKBQYT88D1bN/1ES05hJWJ+Va7cPopEBgVtVnWxWdINvXax/mnnkGJyTxh1JKbOmsuwnzboG0XGCxtcDZ7NUNIVFaFFy74v6aUzFfEh037lvkS9sWpI1ka/+3D797gVh+ko65VTryK1imzsw9UaJatTgXRKehYRVTZ3SnuK/OxarFsnLW1q9tv/b8RiIQxISJm7NpMWJ0trQTF0Wn4prAuH6/5GaMlhyO0ZKDcU2UNIS+4V70GeX1Gt1kkve7H8JcylXVQh0Y1Li3fREMI9D1GtV4isILJVIqFIo7om7cq4rGaSpe33/QL+jWULGb/sQrAjAMA/bgPWtb9bvmEvb9nWfcq3IVNZV6338Y9nnqHM+/hH6fp461vsTLSVmMew22SHnmzBlm5u+uuU6VkmgusYisGYeajqRgYh/sCVbcIVTWPjQWl3h86XJV7cO9qxOY7+g4R47gJpPETp4k++yzDREmHOmUxMNiHGnBtUpxpUVBcf12fZlVIThurLfWI04La8tLy9xaIuK9cHryT3f889Ui7E5D2yw63h29GM8dy1ks1tk+lrMqtl97fj0Egqgw112K0ZJjMSpMouuiYmT9/s2WRUqxqC+88AJDQ0OcOBz8dBwv1uo1Rl55BU27VlWvEaDPnSPb29uw/cBO4ZlMpc7xdj1KpFQoFHeE1wFG1ySR+pOzFDuM10ykMESBWTZYtord9CNeTsrVGvVRg4iXOK76XXPx+v4LtsBxQA94Et2qh0hZryaiYueJmKBp4NYYm/eK6A0S9ZIX1D6vuXh9/2E4x5NSDWD5FdOoF/dae12QMKMdNZer/V3z0NJpjn7/h3xrYqFq3b78dSL/4YdoB4IXh1pe+7D7+nlWpV5V+/CwdgNh5TBGRrCHh3GTSSIvv8zyc79WchcWXYNWyU04357nvJwgP/thmUBYJg6u/xXXFdaERdcqExuLt7ZsvGuu2QTRabgVFroT9EwusdQZp2MhQ+fsKpN9SRzXxHX04p9dvD3c63JgYQFDT0DfPlpi3fQfGGZP92crxMeYMLk4cgFT6jx6/0OVtRYVW8ZNpcg99xx/8splEpdmqyZlXLu3l9afnaMvQPu6WnhNRFOTbxVKpFQoFHeEZ+0OFcfUdMJcr0hF0PkXr5PLMAzYA2RyKp7Ej9Rz12QLgtZ4sH8fFffqT4Qo7vNqTcIIc01K05AYARf+g45n3fEQRKtbNjiuur7wI2GvSen1OdQ5XnNYi0NtnXURxKriUKfNHiyrhRafxKE60inFj24SEEsi4iVzGlu4WBd+yIHL7+K6Dj0jn5CP2OQPdXIt1QXGChF9EUd3uWoYxOdWaJ1bxvo4zci9gzgLkr9693Jl7cM17oEP5Jtw4c3Gf/gQ0Ain4e2iIYhqEWJ6hLherJ+4dn99uVaMOp1NTzN2+TqPP/zpkiux6GJs/dwie278R/Q2QWLmfZYXXc795R4uxodxhbbxXtLluQffR6cd2WKSHLwPhCD76V/Ebat2Lc+4Y0DtWouK22OxtYsfdX26avnPR68Dc41v0DZSbyKaOtYqlEipUCjuCK8DjIpjaj5hrd8Byt3hZzxrUoZEpFQxw/6k3v99tgCt1SWmAoWXyK+clM2nxUOkDMM+T7nZ/MtuPcdTx9rmEvESKW2t5vKg0bY0zVfm3qhyr/RmjwLtzW7erqI8DjW+ssDjS1er4lB7rWli70m0eyrjUMuRUmJJp0owLEh74/F63UO7tLz4+PyBJdIt5zg9Pbm+TcVzpI1VJkjWjC8tp6QrLv3kAqvZCTrmVjFy81gJnYXjnbRpU1VPuZBo4+jSMqZlkZhfDkTtw6BSy2k4leqoGfkqXLke77rUGV9/voFOVDOJChO34BDXI3S1JYnppUjT0u26uKiZxPRiTcW1ddEKAbIYnRrRzFt2KJ6aPMUb6Tk+03a8csX+vcT3vo82Po68+yjuqVH6rSk67YUKB++AnUafXQEhcI4cKd4ODuL2By9WOajUj1YPNrk6k+nUOJ5CiZQKheKOULNg/IunkzIEA1j1BuHUyU1z8apJGQZXEah9nl/x6new9psF+/fx+v9pUbE4TcdLKA6De1zFW/uXMMe9qrQM/xJWJ6WWTrP3e9/jVy+0sJStPKHYl7/Op994nZjRF7hIUb/iJRwW7ztY0qbvxT8lkkmjzy3QcuEyHZ0C2zCY6eggH43QYmfoWVlgKp9DfDjB+OEUKx1xpv/4Ai995V5sHP791Z9gyTuIId4DkIHVie366ED4ax/6nTUhMSKM0m3xz3igkz3nf4LR10Nb2sawHGbe1RhpP4TrGuuRqNIWfEGc5XCkG9EaYTHXyl25Hr741D9CpAbW3+f06dN0d3fz6D2PNvHTVpJ/6inizz+Pm0wyu/ceCtcuE5UFDuSvrW9j6BJpmuu1UDEMCk8+2cRW7z68jrW5gB9rQY3jKeqjREqFQnFHeDkF1AGm+XjFbuZCMHBab/BXzbJvLqF3UiqR0pfUO+aEwVmknJT+xUsoDsM+z9NJqfZ3TcfLzRqGiWj1RErV95pLRLdJFSZ4aOW9CrfhYmsP2uRdgXTarEWKxmZmcJz99FmTVZGiptaO7pNI0Z3CkQ4F1y7VMCz+bdy3q5bd0q1jUZAWS+4KqycynLr2b0rCZP3afcnpZZ6++D5CSobOjZN3HawjCcb2tyK1AlBgBVh127CvFuicz9B26Trj9w0QmVjFnNvDSk+rb+enhb324Z2iIYrCoWYQLQmIa2JiVBhENt2PCnP91nN96fUiwkQXHm60PRD/wEQbH0c71o0xMkLHNcHRqxMlp2GEFneVVCFNT59FrDeKffcw+tgYVm9/hUDpV9xUitwzzxA7eZKVli7ebE9VxShn2rp58JFEsaaBYZB75hk1OaPBeKUWFKzgOylVWoaiHkqkVCgUd4RX0WM1YN98vA7yYRiwz3rUBQTV95qNlzgehjpZUGefp+pkNZV6FzVh2OfVihMFVZPSD3ju80IsUqrjbPMJc91xr+MsqL7XTKKzs3zx4zfZP3Gqat1B6yrxb7+GMzgYKLdheaSoubTEw/NvodmVAlCvNc3gBYHWeagiUnSnP6MrZclZuOE2tFybxdYCl+UUYn5kk5BolUTGgqfYWCE6ysrn2TcRDu+YKOTdW4tVOXRuEoD2+QyG7WCZOmP7u6rchlITjB3oJrGSx7Qc2uczgYhD9XPtw1shUoogjWpFcTCmF+9HtbXb8ujS0nLdLHte8XF5lGlUi3D2g49596enePZXv37L0abbTbnT0B4exr1yhWiu0mkIYGtR7OG7cJNJ3HSamUceoa8pLd46zuHDZJ99ltl/9RqSGabMfqbMjQkmXS0uiEzg9udhwjvuNfhOyowax1PUQYmUCoXijvCuSdnghiiqCPMs+0y9mAjV95qKZ/RhCOJepfSuo6D2ec2lXo28cAza117uJZApGofXPm81BPs8L6FVzXRuPrs17lX1veagX7zI4Pe/T3Yly2W6qpw302YP0mkJnNsw+vLLYNtoc3O0XrmC7iTJiWhFfbZUIY3prqKPfII9dBQr2UHhpb9k+td+aUM8LKtvaJXVKaxaXiY2lkebltc5LK5zsPEQDe+F0/J1OPd6Y7+sBhL2ONTtqH1YEwmm0IkbsXVxMKIZJcGw5DbUTCIVYuKauBhZr4tYLkJG9Y1tY1oEUzPQvJyId0hcRNCkaJpACZVOQzeZ5JNUiuz4XNX+Lre/gz1JGwyD8w8+SH93d9PafDu4qRRvffo3+N/PrFQ546f6esh+M5jO+LDg5aQMQ9yrOsdT1EOJlAqF4o7wEotUHFPzCfMse6/PEDUlWvBTMAKNV/ShZYNdvJYLLLkCSOmxz1Mn1U3FNEDXJI5b/fuEwUnpJRapuNfm4yUUh2FihndNygY3RFFFmCeieZ3jCSHVhKAmsOY21ByHttwijy+9SURaFdv0WtPopyQMHWmI29CVErtM/FvUMti4XM6nK0RCuyQErkWMXhlcYSF2ifOzi0Qmpzl+4TVc12Hgk/O4eh7nmEl6bwxhTNKqO2iayxw2F8bm6Jhfxf54gtH7BpCLgu9/mGa+p3VHPt9uJ4xxqAY6misw0Fg8fpT20UX0nlai41ncJUnXBw6Xe/fiSAPp6LiuRn9Ljk85k8RphxaT+N770DSDQ5/7Gj/XvwezFF8aEcXbP/nDP+IzT3yGRx/2Tw3EILLmNIy88gq6MVvlNATY5xbWnYYLr75KEOW8bEGQjuzhxa6vVix/9HABt3++Sa1SAJiGVXN5PgROynrnqWoMWRHgoUKFQuEHVBSYf/GcZR/iASzV75pPPdEkW4C2AJ951PvfUXGvzUWI4sSM1RoRMmEQKb0ELxX32nx2ZU1KNSmj6Xid43m5/YNEtkDNuofzrX3oUweUu6PBrLkNzaUlBmaucV32kxeRMrfhCnvsNE5hGf2TM+SHjlDobCf7V3/K+K/9Qplz0OFCJI0jHGYW3WqHobt23/F0INqluolVLsOO0u3Eu/U/zD6AFVi+zsOnL3CjME3H3Cq5/CpWRGf6WIyoVu3Cu3Ggi5aVXKAiRYNMo+JQBWCKDaHPFMZ63UJTGEyPT9LZ2k5/Vy/muhhYuU3586KlZaYwiGob6yOi6D48ffo0ACdOnCB+5veKtQ+PD3LmOxcws4IDF5fW/6/a3FWOtU6QSrlgtGIPDROPJHEGB2nZ/4DH5wn++a5fcFMpcs89x8vnM8z86GzFsSgdSTF3YpjHngv2JAU1nuJfQl2T0qPfaZokWn8+imIXEOChQoVC4Qc8B7DUyU3TCbWTUjl4fUs9h81qFtpaGteW7abe/47qe80nFqkdsRkGscgr7lU5KZtPqJ2U6hzPt3hF2wf9HE9Lp9n/nR/xrYm5qnVDXCP+7R+pOll1WKtjuPHnVMWQboh+G+uux65j42ILBwfJhzPTWK5NdHKGE+dexXVdBifOQ7sFe+eZ2d+FYVynU3cQmsu8K/ng6gKd8xnsM5NFt+Gy4Psfz1a6DddSKucvNOX7KSfskaJBZrm7jT1TGfJd7ZiLBVLzBW60tGK5Jq6rIx0N19GRrsaBLocjiysYRjuR/gN0xru4kOjhSyvHOHro7nWRsCgkmhWCpCn0utGiL7z6AkNDQ5w4emLbP2N57cPR9uP05c4TlZW1DyMteaTZgnOk6FDGMCg8+eS2t0XhTa47xYtdd1Ut/5VIFlhqfIO2EXWO51+8alLmQxD36jV5OB6RNDHpWeETlEipUCjuCHVy41+8ZsGFYpa96ne+xctVBMEXi+qKlMpZ1HQ8J2aE2EmpavA2Hy+hOBuCY23Ga5a9co43nXhU1nQb5gu9aJMHA+k21C9eJHbyJNFJEESq6h5m9W6QicDUPXSkgyWdCjfgmqNwvj3PVWMWuXq+SkxcjzGtUbfQy2V40zqGNyO+6fHKOAAPf3iBaXuRjrlVXKeAFdOZvLsVXauMopOaYOxAN4mVfGDchmGMFN0pBKLoDNSMUk3DjfqGmx/Xup24PsaV85f53OOf3RAMS+7DCjeiMDGFjh6fIj7+bWiXmNPvICyLC2+2cFq7F7esHqImXT6/5yP6jRZk3KRtYBiEYOrgQxy2uri/5VATv7X6lNc+nDF6uNjeU7XPM/r7GXhkbzEuxDDIPfOMmpzRYEKdTKUmffsWLydlKOJePevdN7YdCn+iREqFQnFHeA5gqZObpuN1oA9D9KGKoPMv9ZyUQRcpvfZ3oAbt/YDXcSfoziKo7RAFSGweWFY0HK99XtD3d6CiwPyKlk5z/49quw2jM9eIf/vHgXMbrtU9xLYxFpZ4fOlyVd3D+OoU5jusu4pute7hWv3CNcHQrhD4nArnoXfkaOV269uUbV/YtN6lzv/JPfAB8zD98XZ8fTtGmN2GjYoU3SkiwiCim0TFhkgY0QyiWqQYR7pJKKwUD42y55jr23sJkMZNHIc349SNU+Rm0jycuPuWtnf7+3EHBtDGx3GOHMEYGaGvMMWnrJ+WxQyvkiqkiccz0CtwjhwBIXAGB8l3dd12W3ea0dHRisfR++7j0l8u00W6qvZhZ0d6/TMFaX8eJrwmQIZjPKX2cnWO13y8nZTBj3v1Gk9R4rgClEipUCjuEK8BLK8TOkXjCPXMP9XvfEu9+EmvyMqgoJyU/ibM+zxPJ2WdSQGKxuDlHg9D3KvXQII61jaPNbdhx4JEEK1y3sxFekC2+NJtKKVcrym4InNkozYThTksaZP83p+wmptFm5unY+IyVgIs02CmNUkuGqHFyZLIzzCdySM/mmbyyCCrHS3M/+n/xFu/+HiVgJjpyGHj8u+uvH777kJFqN2GC90JeiaXWOqM07GQoXN2lalUR00RVrhyXXBd6oyvP399PazXItyIEt1wCxoVbsGNqFGzzE1YWRtRr6qPuPbcP/vjP+Wzn36Cxx57rCHfU7Moj0O1h4dxLl4kWqiMQwWwtAj28F2VcagTE01qdX2OHz9etSzf3c2/7f81OhLTVc5499GjZH/t3kA648NCS5jL53ie4zW4IYoqTN2qudxydEqH18CiTC6KeiiRUqFQ3BFeg7/qINN8dmNNSuVmaz71RJOgD9p7/e/omiSiCr03nbDu82wbrNoTauvGKysag2dNyoBPygBomU/zlbmPKwZN05EUfbkhoK3Zzdt1lLsNW1YWeHzpapXbsNeaxjglce+udBtuxpFuzdqFk/oSK9JFWxjFcm0KrlX6sylIC8u1yZeWFdcXt8m7VsmJWLZ9aZu8LD62XBtZ7i48AW+Nf5vk9DJPn38fISVDI+MYMQdnIMHE/i6ktoIG5IBLbgv5q1k65zMYFy6Qvm8AeWOWyRsdlXUPAYJvNvAFQXUb6mjrop4h9HWxb2F2nrZYgu72Llof7GXvhdcw+vtoTzvkl/K0vWVysesArmus1z6UjuAb/eeJiy5IROje/yC6ZvDoF7/BN1J7MDUDgztzGm7ps0nRsPdqJuVxqG4yycd9j2BPr1ZMypg2e8ju62QgaVXGofpUpIxEIpw4UVnfUkoo2DrpyB5e7PpqxbqnPz+P2x+C7PgAE9ZrC1Dlc/yMl5MSivuLIKP6naIeSqRUKBR3hDrI+BcvZ5ftCGwHjACf36i4V/9iGsW+ZdeYPB/0+EMljvsb7zq8wR5IqCd2qbjX5uNZkzLA+zstnSb68st89aN5JpcrTxb25a/z+bdeJ9bap+LntgFXulXRovbmWNJSvcGB7/w50dUpjPkFYteu0pkEyzCY7eggHzOJWzl6VuYZX80jP5xg/HA/yx1xZv74Am9+eYifmrP8h6tvYEkHF4+p+O2ABEZfa9h3cOjcZPGt5zMYtoNl6ozt76pytAWx7mHQ2Q63oYa27h6UloOBRlu8tabzcN1BKAxMrbYz0RD6hiuxtM25MyPo6Dx0/4NEhIEmaqvUL/z4BYaGhjhx9wnoh/j7oI2Pox1LMvXa+7RMwtGxiVKkaIQWd5UBO01XNAN6DPvoMG1GAmdwEDl4145974oizuHDZJ99lsgrr6DpY1VxqABdKwtcdgQzJ06QX1qC06cZHR1laGioSa3eGpYNjquuL/yK12+QC4F2rMwG/iVieIuUQY98VU5KRT2USKlQKO4IlWXvX+rFsWXzgraW4P5GXv1ORR/6g5YYLNUoQxR0Z5ESx/1NWGc713MgKydl84lHIVWYqIppixgpmHgI9uxpdhO3xFqcKLaNY8fosyarnCum3u7LONE7QUpJQZa5/9adgiVn4KZ1BdfmorzE1YEV/mL+zQpxsZZDsbzmYfl2jpdYuInk9DJPXyq5Dc+NY+FQOJxgbH8bUrMAixVg1W3HvjpL53yGtks3GL9vgMhEhpblPUW3oQ8PV2Gue+g31hyGa4LgmshXyOQxpIYuNXQ0+pI9mMKg4+EDHLn4CloKEldzuMs52t+MMdJ2CFcaSFfHdTRw4JmO8+wVBWiNsOfAw+iaTu6p30CkNvaBp0+fBuDE4RNeTbwtxmVxtkhM21pOYXmkaLr/CIWpOaKyMlJU1yTSNNdroa5HiioagptKkXvuOV47t8LMj0eqnP3njvXT+bMzFc8ZGhqqGa3qR+rVu1fXF83HK/q03u8WFJTZwL88/aXP8c9fqr3uwF3HGtuYbUb1O0U9lEipUCjuCO/Cxw1uiKKKegf6XCHYIqXnDCw149QXtEQ9RMqAx71mcuqk2s+EtSblaj2Rsk4NWEUDGB/nxI9e5FsTN6pWJaeuw2+egn374Od/HgYGmtDArVEeJ6rNzXH/9DXcfOVs7l5rmv2XBFrPoYo40e1yVG7ULdyIDPUSCa2y5RvblrarWl5vXVEwvC32w+XFt7bls9cjzG7DMNc9rIcuBVE9UtMluFHDUN8QFbWSsCgMTGGW3feucbguRpa293IYromHa5w4WhIReyB+OIM2Ps7sYA7j7Fna5wrcM3G15DZM0OIukSqkabUzRLoi2HcPE9UjOIODFQKlHymPFF2OJXmzfaiqzutqopsTjyRAiMpIUUVDKfQO8GJX9YSYX05lOXFiqQkt2h7qipTq+qLpeP0GQU9pAVV33M/0JFs81zlEIMC1tr3Hj1W/UyiRUqEILSMjI2Sz2R1/n9Xsl2ouT49d4PTp6R1//yBy7dq1m2+0DaQX4sAXaq575/RZUp073z92ivTUCaA653B1aZLTp0e29FqXLl2io6Njm1rWXC5evIiUzT/B07gPqFZPzo5c5lRypvoJJc6cOcO1a9eqBsvWsCyLS5cubVcz1zl06BCmefOikucuHaCYhVeJkFnPNgeFnfheG83q8r3A3qrl4+l5Tp/+8LZfd6f6nRdvv/12xT7pwngcuLfmtudGTjN94zbFlR3EL/uirbKVdkevXqXrBz/AvRFB0Fk1sL1MF2M3HBgbQ546xdyXvkT+wIEdbf/N9qE3Y+/3vkdsZgZzaYnWK1fQ7A5yRpTJaB85M0ZCrtBfmMTKz7PwziRzd+0l255g6Xf+FR995XPYwsXBLbt1cJDF26p1Lo5wsXFYvT/LTwp/hnzrz7EDPPCyk4TZbdjsuodCgoGOLjUMSm7C0n1D6uilZUb5eoqP9bVt1rfXNrYvW1e5vcYrL73Cgf377zCSUgJW6W/jXuY2Xml0dNRzXbS/n8EPPmCuUMBqa8NZ0Ik6lW5DgGXLxenuwcrncaenGTtxgvymfVG997kTbvV1L1++zLVr16q2T7S0sLiyiESrihRtMXN8fOZ9VpJJrhw7xuqpU3Dq1La2fyu8+eabgbtuOXPmDG+++eYdvcaNa18EHqhafv7iGC+88J07em0v3nzzTS5durRj/RZgarkT+I2a637wV3/OOy0rt/xaQewb5dzpOcxOMDHWD1Q7v1cy7no7vcZ2RkdHm/J73Or3uLj8BWpJAtOTVzh9evyW328n/z/WaNT42U6yle9pOWsAtcdZ3//gHCtTW5uYcSvvvR3f8a28z9jEfUC1CJtbneX06Y9u+XX8ztWrV5vdhECiREqFIqRcvnyZXG5nbUtSQq7wdM11U+lrjMrtPZlYO3Du379/W1+30YyNjWHbOz+ovJxrwUukHDl3jcWOuTt+j7fffptUKtXw32R24Z6ay1eWZrZ8UjM5OcmlS5cQIvgzIh3H4Xvf+x6Li/WG93Ye176LWiLlJ+eu0ic/8Hzem2++SSqV8vwNr127xn333betEU5nzpzhhRde4LHHHrvptmPj1QIlgGutrrc5qPupyclJDMPfp4U3+26zKylqiZSz85k7uth5++23+fKXv9yw6LBEIsHv/d7vcf/99wOwYA+RKnRXxYmmIynOv/ohdpf3bNtGs/YbDQwMMDc3xxtvvNHkFm2Nc+fOsbi4eNN9aGJxkXveeouJuTmshRYeX9KISKtim/6lSezXJpnv6yOXSOD+1m9x9lOfYrVswOpW9xcSiSvA1eSmP3A0iSuKj0cunaNjfyevzJ+u2s7VZHHbsu3Ll7fPLfHF+XdBlwxNjLOsO/CQxfz+LkztGmvTOGZcSfRqns75DPb4eS51DyAXpvkwmyjGid4OLeKWY093K2F2G1bUPZzP0DGVZTzZi1OKE5WuhutoRESBFrFC99gKuiXJaTG0dJ5lU5D82EY4VPx1dyQxhYHmguYKNFegl27Xl8ni39ZwS3/V2KW/mzE+NobTgGuBW8W2bcbHx3Ech+npad5+++2K9amlJR4bG6OQyXA9/gSdplsVA31OS9N9+QKupvHT/ftJv/hizffq7e3dkQHngYGBmx7r0+k0P/dzP1fzeP58Sz/P/5FZdazVUkme/vtfQO/pqSGRNZ6pqanAXbe8/vrrfOlLX+KBB27/G3xnto/XL1Yvb2nr5YknnriD1nkzOzvLoUOHdvT879xYHP6q9ronHj9BZ+LW992JRIKXXnqp6deBt4vjOLf0f9xIptM5aomUOUust9NrbMe2bT766CM++uijnW5m1fsKIXjpJY+80BIrmc/VXD569n2Y9L5er8VO/26NGj/bSdaOs7dy/LNdHS+R8vU33+Vy25Utv//NfqPt+I5v5TNeu7EHGKxaPj15taLP+m1fsFUmJiaa3YRA4u/RKIVCcdv8/M//PMPDwzv6HnkL/sGf1I4N+vmf/QKPDVs1190u63VMTmxvHZNmMDQ0tOOfYzkj+G/+sva6Lz71FU4c2Z4TvUZ8ls383kdJmKpe/vCDw3z9a/u2/HpPPPEEjz766Da0rLmcOnWKN954g69//etNbcdvne4gXWOC3z33PszXf7F+HYV6/en06dN0d3dv+2+1uLh4S9/ZlecTcKZ6+b7BrvXnB3k/1Yz/5a1ws+/2vNXKazUGsLp7993x/8Tx48cbuo/45JNP+PKXv0x0dhbx6iX2Tfxm1Tb78tf55soKbmeMwpNP+iJ+Lsj9f42b/R+4UhL53d9BDN/N3PnzaHNjjJktWIbBVLyHXDRCwsnQl5umpTNGrLDE/IFOsp2tHNHSfPz0MFYpzpR5BweXyfZIde3CTXUNb4nhPmaBWbYefTf8/jiuCR1zGXTHwYqEJ040DDTabWgInYhmEtGM4q0o3jc1g6hmYmpG2TKT6Np263/FxzeuXOfi6AW++NnP14w1NYVB7Gdm6Rx/Hq0dzNl3OXteYlzp5WJ8GLcsnvTx4Ryfj51FT4A0TfY98AjpyUl6P/cV/vZTT23p8/kBvx9zN6Ol00ReeYU//J05xmb0CrchwBOHk3zq6c9TePJJPu+D45EXXsfzlz6BdARe7PpqxfJPH4cHf75Rrbs5Z8+e5Z577gncdcudtvmQRyCGGevYse+iEd+1VSe2/3OPP0RsiyV0VldXm34dGCbeOGPyv/+kernjGvzK176OUTq0Bm1/DvC/PtdSMzX067/6C3z54Z9tfINuQhC/49tFSvjXvyZx3erJKL/0K7/OFx4o7Mj7NuI7/uD/2cn5+erlT3zqAf7xbwS/xv0aIyMj/LN/9s+a3YzAoURKhUJx23gVPQZoUZniTedmNSmDjFffUzUp/UFYawN69ju1v/MF8agkVZiockF0tHSjTR7C7e+/+Yv4iJYbN0i99hqzi+0IklVxovPRHgQt6GNjxJ9/ntwzz+AcDs/F3a3gSrdCzCtIu1TPsFLgqxT8ykRA114XBwvS5urQPOdbP+AHE+drbFfctm16gadH3kdIyVB6HCPhIPblmd7fBdoUMYpjPmlXQ19zHJ5b4tx9A8gr13nvkr7hOIyUPkhutknf4AZhjhMNMhoaEWGQ7+kiPp3H7uokumixZ97mSqQDh5Lb0NFwXQ3patzVlWPPWJq4iBHpP0xnvIu3CxpPrRzj7kNHiAiDKxcu0dWe5MF7H1gXFE2xIUB61S7cKqeuCpbnbnB/yyHvjQYSMLAXxsdxjhzBOXeBfmuKTnuhrO7hKoeu30BP5kAInCNHQAiyvb3ku7q2pa2K+ripFLnnnuO7H2TJnzpT5ey3Hhvmgedu003tA7zqPydU7Wdf4FWDO5tvbDu2m4xH+zUNopHa6xSN42bjKa3xYF4Duq73eJC6rm0+QhRrg2Zy1b9R0Mfxan0mUON4iiJKpFQoFLdNPZFSFdxuPoYOhi6xnfCd3HiJXarf+YO4x+8Q+H7nsc9T/a75aOk0n3n7NRIT1TVPu6+fZ/Gf/zFdDzyA8/TTvnAc3oz2lRVSr72G5jiYiws8vjRSFSe6x53CfAecI0dwk0liJ0+SffbZpn0+V0osirUH5+3lCvHPKgl9hTLhcLNbsFwMrN7O2nAalr3OtkeEJgHykF/w3OTQuUkA2uczGLaDZYbDcRjmONHtQCDWnX+m0NddgWv3I5qBUXq8PLeELjUG+lI1tjeIlD0uOhaNivXl2+slsVCLTRIf+za0SczpdxCWxdk3E3wcqXQbatLlZ1KnaF2FWHsr3QPDIARvtLVzxOrjwZbiRIa8PUe36OJAiz/2h/mnniL+/PO4ySQjbcfpy54nKivrHpqujTTN9X0ehsHMLnFV+IlsV4rvdR2sWj4QywDLDW/PdpHxECnjW3SyKXYGr98h8CJlnX4XoETf0FLvGi9bgNZ4AxuzjWTrGPG8ruMVjSUWqb1/qPfbBQGvcTzV7xSgREqFQnEHZOqIlPVmnSkaRzwqWc5U/071BOYg4DkDS/U7X+B1khn4fufRfrW/ay76xYvETp6ke1kgMKsch1m9g9npafjgA1Kzs4FwHB67fBmtrw9zaYnusSlWZIx8ya2y5io6wATCWsUYGcEeHsZNJom88gq5554DwKlwEG64C6uEwSoBsXh/8zpb2hTKIkkrtndtbJySyAfcCFYtyq0QVsdho+NEbxcBGKXIUC8B0KghHkZqbFsrenR9G23jcUQY6OLWP9/pG6XY467tE9Dc/n7cgQG0ktvQGBmh354kka90G6YKaWLxRSSwuncvMSFwBgfJaNvjjNwp3FSK3DPPEDt5khmjh4vtPVX78s697fQ82FkcuTcMcs88Q35p69HGijvD65yn3nVhEPByUno5+BSNpcVDpPQS+YKCZ79T4rgvqPc7FK9rg3kNWH8cr4ENUXgS2vEUj3E8JVIqQImUihoIIY4DDwN7AB2YBT4G3pJSBrta8RYQQgwCjwMHgDiwBJwDXpdSrjSzbX5Bxb36n3hEspypXh70gQSv9quYCH/gGfca8H6n4l79h5ZOEzt5EmybtswSjy9drnIcRpenODy9zGo0CrbdFMehlLIUQ2qRdy0Krk3BtSr+8qXb61Pv0WquMKlF2DN+kYIpyB00udrfAUYWQ1/F0hyuGxHOLs+QmF3CPjPF+Xv34Sy+y/ffvc5Udxx3u12GCiC8jsOF7gQ9k0ssdcbpWMjQObvKVKqjpgArXEnn7CoCQSbZSlQzcfp6SZld66Kf6Skklt3Xitu8+9Y77E0NcrQURVpbhNQxNQMDHbFL7SXlbkN7eBj3/FWidqXbEMASJgt79tDa3g6GQeHJJ+HVV5vT6C3gHD5M9tlnufrdt+lgjCmzv6Lu4cGuHAgbZ3Bwow5vqQ6uonHEPCIow+poU3Gv/sBLOPGKSw0Knv0uoA69sFFPOAnyeErdcTw1nuILwipSejkpVb9TgBIpFSVE8Wr/bwH/BXDUY7NZIcT/Bvy/pJQ7Pg1cCHGFokB4u/yMlPLV23jfLwD/FPiixyYFIcQfAP+tlPLK7TYuDKi4V//jJRblgn5yo2I3fY1nvwt43GtsLs1X5qrrMPXlhoHg1mEKMtGXXwbbRpubo3f8PAtSr3Ic7nfTaPY8rVeuoPX2rjsOV5/9ek03YaWj0GKiN8O78hLjE7kKMbHg2uTdwrrgmK8QHSvXFVwL9xZnWz88eoEjezTMuTE63FWsVp3F4y4d2kTVtqMdLRxdXMAsWJhz82S6EgyOXCf9Gf/FiYYFPzoOBQJT6LgFh5geoSUS94wijVS5CIu3bZ+6n74P/gAt0UOqxcBadbn6SiejrUdwXRPpariOBo7gPx/+mJguIWZy4OAjxdqAP/PN2677OjZzhsPdvfXrFioq3IZuMsmHfXsQs7MVbsNps4fC/gjt+igtuk7umWcCEXG9hptK8e9Sf4dEbLKqvvD8cCvZrx0LXH3hsBHWczzlpPQ3oa1J6RX3qupR+oK6NSkDPJ6ixvH8T1iPtV77bOXgVYASKRWAEKIT+EPgyzfZtBv4r4HnhBDPSCnP7HTbGklJqP1nwD+6yaYR4G8CvySE+A0p5Z/seON8itcsGCGk5yxbRWPxdLQFOMvecaBgK0ebn/H6/w/qjFMtnSb68sv83Afz3FiuFBj25a/z+Ls/Ifb7vUW3iuK2kVLi4K7Hh27EjlpY0ua6MYctXAorI1jSRk9Pctfld3Acm96REfK6TeFQJ9dSXQhzmYi+iKM5XNcEiTmXtrkVnI9fZfTeQZwFyffevcR8zy2Iy4dhVL4P197f6a8ACG+caFi4meNQutq6oJcwJf3zBUxhQncPPUYH9p5DPNRyN6YwWJpbQJcag317SvGiteNIKwTHGnUMdTSEELzwwgsMDQ1x4nbq9HXAdPItVkZGWO3ag5geo3N+lQcXPqmOE51fQWgC+8gRKMWJKuGoMay5DSOvvIJuzJHe5DYEOCgXWUkmyT79NB0+j7auRTYvWI7s4cWur1Ys//Jn53H7A3wCGxLC6u5QTkp/E9a4Vy8nqHJS+oP6NSmDu89TiWj+J6zR6iqZSlEPJVLucoQQceCvgMc2rSoAV4A8cAhIlK07BPxQCPGElPJCI9rZIP4X4O9tWiaBG8A0sB/oKVuXAP5ACPE1KeWfNqaJ/qLeAWaXJnH5Dq9ZmEEeSKh3QaBObvyB98y/BjdkG1ird4ht49gx+qzJKtdKROtAHxsj/vzztBw5Qmbv3mY3e1txpLsuFBbKahcWpLX+eLP7sOZy1yp7bunPtbBKdQ4L8iZuw7bS7UxxjtTDb18gl52gY26VSG4BK6YzfzxJmzZV9dTLbR0cXVnFtCxa5pdZ7EpwaDTNuz3+cxyGNU60GRjoRfegZt5SPcLL5y/S09nNvj2DNUTColCY+OwCe6//GVoraFfeRMs6XPhOPyPxYRxpgiweozTp8ncOf0ivyCNbTfbsLzkOP/9N3L6iqLQTtQvvhOQ3vkHkX/9rLMdhPhWjMDlPVFbGiQohIWJiHzmCm0xuxIkqGoabSpF77jn+8r0c8r2Pq9z9Kw/tYd/gawx1dze7qVvGssF21ACWnwlrpL+nWKRESl/g5aS0HbBtMAI6sqnEcX9jGmDosuZxKciOtnrjKfXco4rG4TUhKMj9Tso6ca+q3ylQIqUC/mcqBUoX+B+AfyGlnAcQQkSA50rbJkvb9QJ/KIR4VErZiNGwSeDXt/icD251QyHEr1ItUP4J8H+VUp4v2+4pit/D/aVFOvDbQojTuzH61cuqr1yU/sFr9l+QZ/55FdsGdVLtF2IRSaowURXV1tHSjTZ5KDCOm/J6h9rcHMcnr2FnK0sz91rT7L8i0FKHcJNJUq+9xtjTT+9429Zch9XCX7VAWLmstP2mx2PH5jibeI+/HDuzvmzttR2f1jYMq+PQj3Gi24GOtiH6adXuwCpRUFu7r2MKs+x+5Ta1hMS1GFNtizOmXvjBNEOxQ5zorCMaHoD4vrNo4+PM7j1A65Ur9OVnac+/U+U4NOdWoU3gBMRxaOzfT9e3vkXs5EmWbXjzcoRea7piYsZivJuHH0mAEGAYgYsTDROZzhQ/7KqujNEVm2BfE9qzHagIOv/jndIS3GsLgNVs7eUq7tUf1IsCzBagLaAjmypm2P/Eo5LlTLhESq/xlIghMfx5GbHrCGPca94C11VlmxTeBPRQrtgOhBDDwP9h0+Jfl1I+X75ASlkA/oMQ4hTwOtBZWnUC+CbwWzvcVICclPIHO/HCJRH2n21a/G+AvyulrNhTSilfFkJ8HvgB8EhpcRvw3wG/sRPt8zNeUQNKKPIPYZztrGb++RstneYzb79Gy8RM1br+9BXi334dZ3CQwpNP+n5gu7zeoTE6im4brIpotRDhZjBGRrCHh9Ech+53TpF78vMVQt+aWJgvPS6UuQvzFeLhxvq8W3Qv5tdcjJvExVutcXhLdAAUwFrevtfcYcLqOLxZnGg5wpXrYutSZ3z9+TdDABFhVrgIK8XD4uPN4t9afcNaLsOIMLh8/hI6Gvcfu7dMUCy6GDWhbev31EzyTz1F/Pnnsdrbud7Rga2ZRJ1KxyHAUt4he7APK5/HnZ5m7MQJ8qdPr68fHR1laGio0c2vy1qc6Nz/9mMks0xtihNtjbsgMoHZj4cZr1nn+UJw/9fqxZipczx/4Bn3GuCBU/B2UiqxyB94xb1CUWBua2lcW7YTLydlvc+raCzxiGQ5U7283sRpv+O1v1bHWf8QxrhXdY6nuBlKpNzd/HcUnYBr/M5mgbIcKeUZIcT/Bfi3ZYv/iRDid6WU1k41sgH8HeBg2ePzwD/YLFCuIaVcFEL8BnCaYn1KgG8IIf5HKeXIjrbUZ3gdZJRV3z+E8uSmzgWB6nvNZS0atWtJIDCrHDgFswtk63o0au6ZZ3AaXC/LkS55p0Detci5BabkIkutBUay10tCYdF1qKUnOXj5XVzXpmfkE4Rls3Kgk2v9XQhjmYi+gKu7TApB++wSq4vLWB+nGTm+B2f5Ct9/75/fWr1DxW0TJsehhkZUM4loJtNDdzF0egK7oxNNX8ZYFXR/ZHGlbxBHGqV6hzp9rS4P5W7QIlshbqIPHEfXDPqf+Bt8pn9PVT3DNbHQ1AwMdMQO5LJnnOLkhH4zeZMtg42bSpF75hl6/7SY9v/98f20ZuaroqCtfS53ta/i6jrpz32O/Kb4zaGhIY4fP96Mj1AXN5Xivc/8Br95erXKEU9vL9lvHvS1I3S34J2WEVyRUtXJ8j9e/S7IA/bg7WhTsZv+oJ6T0ktgDgKe/U7VpPQNnsfaEI6nqOOsf/BKp/NKswsCdc/xYqrvKZRIuWsRQiSBXypbJIF/egtP/S3gvwHWsoUOAF8Cvrud7Wsw/+mmx/+jlLJuCXYp5VkhxB8Af7O0SAf+FvBf7ED7fIvXQUbNgvEPYcyyr3di5lWDU7HzlEejtmWWeHzpMpFN81eii1OY74BTqmUWO3mS7LPPrjtxJMUI01UnR75MMFxzGV42Z5iQq8xPSfJOgZxrkXcL5N0COcei4BbFx7xrkXMKFEpCZLkoaddKKL8X3pv8k4pFD799gWyp3qGZW8AydebuS9KmTVc9/VpbG7Ezy5iWRevCqq/rHYaJnXQcClh3AVrZPIloC+0tbetCYkQziGoRIppBRDPXl5evLz6u3mbjfvH1o7qJLjYE01OnTnF58n2O9fVh7mlj9p05jFWDfedX1128CXeVYx1j7O11wWzHHh6mNZbEGRyk465Pb+8XraiJc/gw8td/nd5XXiHy3jxTVqXjEODB9jy9Dw9TePJJOgLmOMzmBenIHl7s+mrF8mP7Ldz+uSa1SlGO1/l23gqnSKmiwPyB1yB2kK8twNvRVk8cUzSOeo7WIA/aq37nf7x+i1yhse3YTtQ4nv8JY9yrOsdT3AwlUu5evkrl7/+qlPLSzZ4kpXSFEL9FpaD5NwioSCmE2As8VLZoBfjDW3z6v2NDpAT46+wykVI5Kf2P128R6Jl/BVGz3mE6kiKxsA/iyt2xkzjSXRcP14TEvGvR9d0/xshOoc3N056+TL5dYJk6s+2d5GImCSdDfHWe8dUc7ocTjB/uZ6kjzswfn+eVZx6kIG1ynyrwIzEJ11+r/eatFKfUXH5nxz9nWOsdBhlNCgypETOjRITB6vFjdI4uo/e0ER3P0WK5dL3vcqlnLy4GrqshHR1pwxfkCF25CNG2NmKD96FrOgc/9zW+3J+qcByuRZ6WOw1feOEFnnjiCR6979GGfdZP7rqLB3M5rPZ2znfsIZm5RFRWxolGXQdpmuuiP4ZB4cknG9ZGRclR+dxz/MfX8rSNflR1TOr87N0cfq6j2c28LbyiwNRMZ//gNcs+F2SRss7gm+p7/sBrwD4b4AF78BaLlJPSH+w2kVL1O/8Qxjq8ahzP/3iZDYKcWqDSMhQ3Q4mUu5evbnr8/S089yUqRcq/dsetaR6bv4efSClvdWT5J0AGWKuAMCSEuFtKeX7bWudzVE1K/xO2mpRaOs3gyVf51sR81bpD9jUSv/tDVSeLDSEx526IiHlZch1W1EbcqJGYl1apPuLa8k3blYRJm2onYnJ6macvvI+QkqHRcQzDwTqaYGx/F1LLATlWgFW3Detqgc75DG2XbjB+3wDRiQz61BS5ntaifc0nhLXe4XajoREtCXuR9fqDlYLf2vLIWuyoZpbVPDSJrNc6NKsEwzW3oSkMPnz/AwBOnDhRfPO9ED/TiTY+jnZ8AHF2hGtnTQ5eWqyqG5rst4hHo8SHHiAeLToOW/Y/2Lwv7iYstbaSfvRRUq+9xqzZzUh7f1V8cmygg9QjHSAEGAa5Z57Z1fu9ZrLSsYfXuvZXLX8wvkzxVDF4eA36eg2aKBpPOJ2U3utuchhWNIh61xZSFg9JQcN1vV1RKnbTH9RLy/ES+oKAqoXqfzzFooCOp4ByUgaBMDop6/3PKJFSAUqk3M08uOnxG1t47rtAHlibRzkghOiVUlZn4PmfBzc9vuXvQUppCyHeBr646fV2jUjpdXKjDjD+wSs2IYjxJGv1Ds00CCJVA/bLLd0gE02td7gVpJTY0iHr5NcjSXOluNLNy3KbbvNOgaybJ+9YZcvzrLpZco8VePXq/9LQz3Lo3CQA7fMZDNvBMvWSQFm5j5CaYOxAN4mVPKbl0D6f8W00atDrHRroRDRjPbJ0Q0g0N4mAxcfnz47S39XHgb37K0TFNaHQLBMgI8IgWhIUddHcwfD8U08Rf/553GQScWyYwifXqhyHALZmsnJwH9EAOQ4ze/cy9vTTXD+7RJRJpszKONG+ZB6EpSZm+ICwTQiCOrPs1cCpb/AaOA2ySFlvAqQW3I8VWAqFAmfOnKlYdu16J1AdKy6l4O133idibO060LIshBAYxs4MTR0/fpxIpH49iHoil3K0+QPDANMAy65eF8qalKrf+Qbv8ZQQnuOpcTzfEEYHr9d1kaFLImoimgIlUu5KhBAmsHk0+OytPl9KmRdCXATuKVt8DNhxkVII0QPsBdqBJWAWuCGlvN2j6bFNj2/5eyjb/ot1Xi/UeEUNqBlY/iEepWY0amusG23yEG5/MKJRy+sd6gu16x3uzdSvd3gnSClLNQ3zZJ0NUXBNNMw6xfvZ9WWlxyWxMesU6yZmnXxJYCy+hot7x22rogmDeGGMRt2peocG+rqbcF0I1AyiNQTA8vVe4uL68pKQuPY62hatDC/cGGcosZ8T7Se2+E01FzeVIvfMM8ROnkR2JTmVHCSZq5zAMG32IA7YDLZnA+c4zHd387uDv4ytzVftx2ePtpP95lBg9uNhxuu8JxPCgQTlpPQP3gNYwVXzVEqLvzhz5gzpdJrjx4+vL+vPtnhu39LaS0dia0kSf/EXf8GhQ4cq3mO7WBNY1xMYPPASikDVBvQTLTFYXKleHmgnpapJ6XvCWD5HOSn9j9f5dpD7ndc5nqpHqVhDiZS7k0NU/vZZKeXMFl/jOpUi5RDw4zttWB36hBBnqS0CzgkhXgN+H/gTKeVWroyGNj2+vsV2bd5+8+uFGq9ZPOrkxh9o6TRPvPUa0Ynqf+/e9BXi3349MA6c6Msvg22jzc2RvHqeBamTL9X8Wot0PKRNIKxVjJERCkNHKSQ7KLz0l0z+2t8oug1lgZy7FnlavM1Ji/MHlph33+VH58+vi4qbxcecW0Ci+rUXYYlGXRcJhcnS8SE6RpcwelqJjueIFzbVO3R0pKMhbcFXWq7QQTuyJUK+ZS+pgs7Rz32Lr6T2VAmNzXYehhHn8GGyzz5L5JVXMPSFKschwJBMk+3t3baJC40kW9CYjezhxa7KhPqHPrWI2x/gkbkQ4XVxHeiBBDURzfd4uzuCe5zx7HdqAKtpHD9+nEcf3ajJ3F5nXszwPQ+xt29rr3/27FnuueeeivfYTmZnZ2+6TT2RS7nH/UNLdPeIlC1KpPQNnud4AZ6IpiYE+R+vfqdihhVhRomUu5PNlw5jt/Eam5+zxcuRLRPH26XYBfz10t9FIcTfkVL+6BZft3fT4xtbbFejvwdf4TWQoGIims9aNGrXkkBgVkWj5s0ukK2+iUZ1pFuMMS0JibmSkJhzC4j0OIOXT+O4Ft0jn5AzbQqHO7i+pxMRWSSmz4Fuc8NwMCcXaZtbwfp4nNH7BpCLgu9/mGa+p9X7zffAGFdhrnGfN2w0IhpVQ2BIDVMYtEUTRLUIUc0kqhdvY1pk/X5UixDTzY1ttAgRzaxYtrb92Q/O8N5bp3j2a89Wug/3QfzM75XqHQ6SOz2KedGoUe9wgj1DFobRij00TEc+T7a7l779993el6m4LdxUitxzz/HCX1kMXP+gwnGYjqTg0Rif/qxFX8AESoCcR3SjOtb6By8BJchRYF6Dby0x1e/8QihrUqp+53vqiSf1aor6mXpxoSp20z94CcZB7Xfg3fdULVT/4FkbMIRikbq28A9ev0Wgry1Uv1PcBCVS7k42j9bfTs7e5ufUUQAaymHgZSHEP5RS/qt6Gwoh4sDmkfGtfhc78j0IIfqoFlBvRsMVJjWQ4E/Ko1HbsrWjUSMLdx6NKqXE1lxWRZ50YY6sLNVNXHctlmJN3Tw5uXa/cv3afauOAfrhn15gPjtBx9wqem4eq0Vn/ngnrVq1Q/Tq/k6OLmd8X+8wbCx0J+idXGI1maBrMUfvbJ7riRYcaZYchzpu6e+eQZu7FlcwjXYiqQMkW7pZvuse7u77PG/86HXuGjzA8aF7iJbFoUY1E0PonD59mu7ubh59cPtm218VUXRXqxmPWl7vcOHAMQqXrtSsd6hFTey7i/9L7vQ0M488srtmrPiIpfYU73ftrVresvRKjQpawcDLFaVmnfoHr/Mer8lcQUDFvfofTydlgEVKNQHS/9RzFga1NqByUgYDL4G8Xlyv31FOSv/jGekfaJGy9nIVM+wfPK8tgtzvCrWXq2taxRpKpNydbBbSbue0LnuT19wuloDvAX8FfABcLi1rAQaAJ4C/BXy27Dk68C+EEJNSyhfqvHatNm/1u9ip7+HvAv9km15rx1ADWP6kPBq1Z+w8c1XRqCvskxM4hWWcsx+xcvQQuWQry9/9fc79ypfJuqX6ieVCopsve2yVhEcL+ZjkdaZg/Kc79nnCWO+wWRjo6/UPI8JcFwKjpSjSqDCJaKXbkjgY2XQ/ur5+43nxJ+doH/996JCYM+/g5CzO/LCT0fgwblm8qSZdftH6gKRsQcZNWvcMgxBkH/sKbks/I4unSKU62B/1h8RXXu8wk+jizfY9Va7k2UgPjzzSApoAwyD9uc+R7+5udtN3LV4XOcnuAY4fr10n1M9YjsBxlZPS73jOsg/hbGc1kOAfvPYBQXZSqgg6/1NPtAuqo62eyKXEIv/g1feCGvcqpbewr8Rx/xCP1F6e8xBcgoDXsVZdW/iHev1OSqgxv9r3KCel4mYokXJ3svmU53YOr5tPp3YikOIfAd+VUtaoPMBS6W8E+PdCiF8E/j3QWVovgH8nhHhVSpn2eP1ap35b/S4a8T34Fs+TG+Wk3DGklOSlRdYtkHXz639rTkUmxjl46S1sx6b/k1Es3cI51sbkvm60yATtho1m2ExLl/euLtI5n8H+ZLoYjboi+P5opn40ahMIS73DrRARBjE9SkyPFKNLS7GmcW1jWVyPrEeWxrQIMT3CjcvXufjJeb78xaeIlkTHNXExppno4uaxqrfFnkHcgQG08XGcI0cQZ0fot6botBc2RaOmMWZXISFwjhwBIXAGB3H76xQ4ajJr9Q4X/u2PkcxW1TuMmhK01fX6rpmJiSa2VuE1SaarZy+RSPAmLOQK3v+z6oLOP3j1uzDWK1L9zj94DWDlLQ0Z0J9JDWD5n5hHv4PgikWrm6cdl4hFQN+hU2fF1vGMew2oWFSwwPG4TFUxw/7Bu/5zCM/x1Dieb/Dqd1IK8lb9Y7FfUdcWipuhRMomIIT4l8Dfb8Bb/XdSyn9aY/nmy4fb2b1tnlO47ZckUso/2sK2fyqEmABeYUMobAH+b8B/7vG0Wm2OeCz3Yse/Bz+jnJS3hyMkGVEgXZgj4+bIuPmKv6ybr1ie3XQr8f5+Hz51ATtXjEZtzS9hRXVm7okS0SoH56UQjB3oJrGS9300aiPqHd4uGhoxPUJcj5YERLMoLpZEwzVRMVa+fm1ZSWCMlwmNcT1KRDPRxe25IE5dkSwsXONorDrucqcpj0YVx4axRq4RdaujUS3NxB4+jJtMgmFQePLJhrd1q7ipFB9+/pv85tsZHlp5r6LeYS7ZS/abBzeEViVSNhUvt43X8crv5C3v/VdMXdD5Bk+RMqCuIlBOyiDg9VtIKbDcYCorXsK+6nf+QYhiJGCt/VtQRUovN5uKPvQXnnGvHiKz36kXj6yclP4hnHGvahzP79Q778nmhaeI6We8+l0QBVfFzqBEyt3JZmfi7ZwCbXYM1nI7NhQp5U+FEP+cypjU54QQf19K6dZ4Sq02x9ia0LhT38P/CtyySFviMPDn2/T+t8RunoFlS4dVJ8eqmyPj5lhxc2RKj1fdXJWwmCkJj1k3j/Uph9eYhPE3t71dYYxGXehO0DO5xFJnnI6FDJ2zq0ylOmp+LuHK9c+w1Blffz6AhigKgyKyHlka0yLMjE/R39XLYO8e4vqagBgt3tfMogCpR4uOxpIguSZCmsJABDFrYwcoj0aVXUlOdQ7SmauMRp02e0gebaM16YJhkHvmmS3VQW0mmbwgHdnDi11frVh+MGXj9s82qVWKzXhd5AR1ln3eVk7KIOA1kB1UcRyUWBQE6v0Wll0/dcKvqJqUwaAl5iFSBnRihpe4mthVGUn+x+tYG9R+V09cVU5K/xC2CZCgJqIFgboiZUGQrGNc8Csq0l9xM5RIuTvZLKTdTpGmzc9pukhZ4l8B/zXFupQAXcAjwNubN5RSZoUQTtm2UPxcC1t4vx35HqSUU8DUVp7TDKGkYynNp+dOV7iK0pEU3auHKH71/seSNhknvy4urrq5CvGx4n7Z47y0mt30moQpGlUAURFh5p7DPDiygOhJEB3PIpeh632XS937cKSJ6+i4tkF/IstD2Yv0kIREhK59D2LoBo/9zN/kN1J7MYVe8//khVdf4IknHuPRQ482/kOGjLVo1Mgrr6AbC1XRqACWzOEM7qHw5JOBEShBDZwGBa/fI6iRTCruNRjsppqUqt/5h3qz6C0nmJf5nv1uF0yADBItUag1PSuo7nFPkVIJRb7CM+41ZP0OlEDuJ8J4jrebzQZBoZ5wF9S+p87xFDcjmFcvwec7wEwD3ufHHss3i1+Dt/Ham5+zJUFtp5BSzgsh3gPK1YYhaoiUJaaB8lHyvcDYFt7Sl9/DTqOl05gvvcw3ry5VrduXv849L/2Y2Eyq4SKEKyVZN8+Km2XFybLsZll1siw72fVlK27x8Wrp1q9i4+3ih2jUmCjVSNQixEQx3jS+/rgUgaqZ64/Xtr1y7hIRqXPi+IPEtAiRNZfiAYif/T208XG043sZ+e4FzFXBwUsb9Q7b3FWGW67T3Z4j0daGfXQY02zFGRxEDhy848+kuHXcVIrcc8/xB9+32HPtg6pJDANfOMK+X+5sdjO3jHIVBQOvCNSgznau56RUfc8/hDEKTM129j/1BON8QEVKz34XwFizMOPlaFsNaNyrV7u94kUVzcFLNA5q3Gu9/xcV9+ofvCYEBfXaAlTcaxCo91t4TZ72O6rfKW5GMK9eAo6U8iXgpSY24RJgs/H7x4UQvVLK6S28xv5Nj0e2pWXbw3UqRcreOtuOUilS7gfe2sJ7+fl72BH0ixeJnTyJlbMRtNBrVcc5mlor+tgY8eefJ/fMMziHD9/2+xVci0Unw7KzymVzhqwocGM+x3KZ6LjibNx3Axh7sJ3cbjTqcmccQ2q4fb3sNXtKQmKpTmIpIrVcaFxb99rLP+LufYdKwqJJVETQbtPVu+oUd0EdRrW5u7ze4bmO43RlzhOVlfUOo/ECrmFgDw8Hqt5hWFnpSPFiV3VtzM+2LAHBG01QTspg4F0bMJgXc15OSoEkGsw0x1AStn7nON6ztJVI6R/qxr2GTaRU/c5XhM3R5ilSKqHIV+yWfgdKIPcTXud4QXWzgTrWBoEwOilVv1PcjGBevSjuCCmlJYS4SNFhuMY9wI9u5flCiChwaNNiP4lzm61x9YbxRoAvlD2+Z4vvdazG64UWLZ0mdvIk2DbuzDyPL31AZJMTsdeapvssaPcdxk0miZ08SfbZZysclbZ0WHIyLDmrpVvv+zlZVkistXRbzxq4C9HQiGtFh2Lu3uN0n1vF7O0kMWFhWC7ptw0uJA/iOBFc28CxDKSl8Wt95+jVbERrlGhsLyKv8YWf+Ye4/f03f9MSH65ESLoJkkbrzTe+A8rrHc4Y3Yy291QJ5KJPx7hbI1YSKINU7zCMhM1Z5BmLo06qfYXXbOeg9ru8VVukjEUcVDlc/+A5yz6ggwj1Bj/UPs8/1It7LQS0JqWKGQ4GXgJKvfhKP6PiXoNB6By8HnM2dR0iwdyFhxKv40+2IHDdBjdmG7BssB11rPU7UROEkEhZ/VsFdRKkOsdT3AwlUu5e3qdSpHyCWxQpgYeB8lPEiVINRb+wWZWo5xB9f9PjJ271TYQQBvDYTV4vVERffhlsG21uDnN0lIg0yZciHDNaghZ3lT32BDhLFM5+wNzd+1lqjzFz8n/l9b/+CPPOCgv2CktuptkfxVdoaCS0KPHS39r9Fi1Gix6lpfxxKTo1Lta2j2CuxaIC7IX4x+3FaNR7+tE+GSF+XnD0xkTpdzJocRdJFdIk9FWMVgP77rsR+TzZ3l7atiBQNpq1eofXXnqLFsar6h22tk+BtoQzOBi4eodhJGyzTr1maXsNmCiaQ9jqxuQ8RMqo6Z/6wQrvfmfZAtsB486T1BtKpuC9Lh5pXDsU9ak3gKWclIqdxMvRFjaRUjkp/YWXOB5UJ2XGo92JGGoimo+od62XC2DloHoTN9Wx1j8IAbFI7f1bts55up9R53iKmxHMqxfFdvCXwK+VPf4y8D/e4nO/vOnxX2xLi7aBksvz0U2Lr9d5ync2PX5CCJGQUq7ewtt9Bmgpe3xOSnnuFp7nWwqFAmfOnKm5Ljo7y7733sNxbdpHzpLP26wO9nNjsBO9ZYl4bBojlmNeL3D26iKd8xnskVlG7xtAXhVcux5hvmdn3XbNxBQ6CS1OoiQktuixMrFx47ZFi/HGD1/n7r2HeOj4g7Ro0Y3ai9tEeTQqx4axPrlG1K2MRgWwdBN7uOh4daenmXnkEfq2rRU7g5tK8dsDfwfNmOKhlfcq6h3uP9hN7Klu2p56qtnNVBA+J6VnDQV1Uu0rvGZiBrXfeTkpo4YSKf1Evf1ANi9oawnWfqLeDO2WWLA+S5gRotj3asWRF5xg2nDULPtg4ClShlAsUvgHr34XVCelpziuJkD6inr18oLoaFPneMEhHpU1f6+w1aRU53iKNZRIuXt5kcq6lF8UQhySUl6q9yRRVFL+k02L/3z7m3fbfJ1K4TAP/MRrYynldSHEaeBEaVEr8KvAb93Ce/2dTY/99D3cFmfOnGFkdIR9w3exqGdZ0nIsaVmW9Rz7PzrNbPQ6ifkl9rGA1aqzcrxAhzZe8RoSwdiBbhIreUzLoX0+w2JXgkOjad7tOdKkT3brmMKgVYvRosVI6DESWtmfXlquxWgt3V97HNFufXd6dvltunYwIrU8GtVNJnm3a4D2zExV7dDOu9toS7pgGKQ/9zny3d070p7tJlsQLEf28GLXVyuWD97/HgNdfjJ1727C5mhTca/BIGwO3rztHfeq8A/19gO5QvBEynqivtrn+YuWqKw50F2wg3mZ7zmApQZOfUXY4l69YjcT8ca2Q1GfsDkpvcRV1e/8xc0mogWNuiKlOsfzFZ417wN6Xes5nqLO8RQlgnn1orhjpJRzQog/A36ltEgA/xT45k2e+reBg2WPrwI/2Obm3RZCiBTwP2xa/H0p5c2yRf8d8K/LHv+XQojnpZSel1lCiGNUOlFd4D9sobm+IOvmGS/MMmbNMFaYYbT1CnOPZfiRNlm17YGFGWzh0L5QvIpb6E4gtdoHGakJFroT9KaXaF/IstiVoHP2Vsyp24uBTpsep1WP06pV3ratPdaL7sdWLU6LFsXcgtjoZ9aiUSOvvIKuz1dFowJYMoczuIfCk0+SmZhoUku3jteJddQIYFGIEBM2sUjFkwQDr0imXEBjcfKWVnO5clL6i3q1AYMYyVRvhrba5/kLr31eMe41ePsJNSEoGHj1u6CKRV5OShXp7y+8nJRB7Xde4rhyUvqLeuc9QbyuVXGvwcHr3CeI/Q68xdV611GK3UU4RuMVt8s/AX4JWBsB+5tCiO9KKZ+vtbEQ4h7g/71p8f9dSll3+EUIcRC4vGnxXVLKKx7b7wH+M+BfSinn636Cyvf4M2CwbLGkKLzejP8f8I+B/aXHR4F/IYT4u1LKqr2lEKId+DZQXpXn96WUZ2+lrc3CkQ5X8pOM5q5zJT/JmDXDrL1UuVGdZCjDKg506E5RCMrF6sdIra1f237t+XeCLjXirklvS5J2rYVWvaUoQmpxWvVYlRAZFea2xqgGDTeVIvfcc/zhDwr0XfmwIho1HUmR+vzd7P9aR3HjgIiUBcu70Hs0Yje4NYp6eMa9BjSexKvdauDUX4QtZjhXUE7KIFBvQDGIfa/uAJYaSPAVXr9HEEVK160Tra5qofqKsMW9KrEoGIQt7lU5KYNBPeEukOd49SaiqXM8XxG269qsGk9R3AQlUu5ipJRnhRD/Fvg/li3+3ZJL8F+sCYRCCBP4BvA/A51l234I/PYONC0K/LfA/1kIcRL4E+CnUsrxzRsKIY5QjJ/9e0DHptX/Skr53s3eTEpZEEL8l8Dvly3+z4AeIcR/JaU8X/Z+TwL/Ari/bNuVUnt9hURyozDNSPY6o7nrnMvdIC9vv7K3bRYHSh29qGnHchaLdbaPlaqIr22/9vzN6Gi06y206S206y106AnaSrftpWVrjz/54AwCwYnDJ2q+lqI2yx17eKdrX9Xyx1uWAI+rcp9S74QsZgZrMC7seM0+D+xJtcfMPxVP4i+8ZmIGMY4JIGfVPlVX+zt/EbYoMK8BrFhEotc+nVM0Ca++l7dNilUvgkOuzmWKOtb6Cy/xzkvs8zueNSmVWOQrPPtdQEVKr3hkVQvVX9QT7oI4+dbrmlYISUxNCPIVYbqudV01nqK4OUqkVPwD4CHgkdJjDfhvgP9CCHGZ4tXtIYq1GsuZAb4mpdxJ61Ir8FzpDyHELDAFLAFxYA/Q6/HcPwL+4a2+kZTyeSHE54D/U9niXwF+WQhxHZgGDgA9m57qAn9LSrnZKdo8NMHeLx3jdyI/YW58ZdtedqE7Qc/kEkudcToWMnTOrjKV6qgZ+SpcSefsKhoablcXfWYnkcFhfqHz03TqrXQarST1Ntr1FhJa7JbdjoLgHYz9QJhqA9Y7IYuZKu7VT3j3uwY3ZJvwughVM079RZj2d+Ad9xoxXFDHRN9Qbz8QxL7nOYigZjr7Dq99XtFJGSyUuyM4eMZuBvYcr/Zy5aT0F179znHAssEM2G7PS1z1+pyK5mAaoGsSx60+RgXxHM/zmjYq2cUhZL4kTOVz6tZCVed4ihIBO4wrthspZUYI8bMURb0ny1ZFgCGPp10BnpFSntvh5m2mu/RXjzzwX1F0gm51T/f3gBxF4XYNQTEGdn+N7TMUBco/3uL77ChP/f5/StvBbubYPoES4NJQiiNnJ1hKtmDfWMC0HPrPr3ApuZ/C/5+9Pw+O68rvfMHPXXPDllgIEOAmLgJESiVRJdaictkuqVSLbNNt97Nbkrvd8doz43jTL2Im5k34xftj5s3MHzPR83o6ZiZmoluvY3qmq19b6up222bZ5dok1+KSqoqiqBIFiqAILiABJABiR253O/NHZoKZyLxJgMRyT/J8IhCZee7NzAPcg3PPOd/z/f2KKdxCDK8Yww50Xuu/SULvR2uPcejwc6Bp5H/lDwm6+u//RYptJ2xwE8Wdf2NjY02P35lPErY3YXLiKgd6hhoeU+w+oYneJdz5B2rnnyyEtTvP13BcsJtHKo8cBbdZuFc1jI8KhgG2KXC8+n4iivfa+9FsAUsRLcL6PMeXrLMj/D4L6l4bNcLEuzCxL+ooJ6UcNBPvcgXo3LitPuIoJ6UcaFpp/LOWr79HNbtvRRW1EU0eWinca9NUEmqMpyijVjcUCCEWNE17CfgjSrkZj4ecugD8K+D/IoTYXgWslhngfwV8Cfg8sBll6xbw74B/2Sgs7GYQQgSUQsx+C/g/Al8MOdWhJOr+7yLloCzTfuR+Ou7mSeoxes1O+sxOeo91cuKoTe/sKu2njrL8zk3ujhsMaUUydhqhJ+kIshzV7pAyC6AbeMePg6bhDw0R9CuBcq8IG9xEbVB96tSp+55TDFmwBzg5cphTp8L2Vih2m9B2J+GgGsLrrSZ00aLZQnbe0bAtua5XWJ8XtzzUMD5aJGKNRcqo3Ws3Q2h/pxYRIkfYPUhGJ2XTXfbqXhspQp2UckUYXkflpJSDpvmfZRQpw8RxJVJGjmRMsNagn5BxXhvWT6v7bPRopQhBzXJWq7anqCDf7EWxI5QFun8N/GtN056iFAJ2P2AA88BHwM+F2HpSQyHETbYQk0wIkQf+n+UfNE3bT8nVeYhSuNUkJaFwkVL41/MPKkyGfP/fAn+radoB4Pny98aBVeAT4O+EECvb9X1RIK7ZDNo9xFag20/xuaOnGbR6SBm1I2T9N46TeOMNMD2mBpI4M9eJCYfDxYn1c2IxF2FZ+MePE6TTYJo4L7yw8SsVu4gsgxvbtjl9unm+UfdKuDPgs2eeks4l1crI0u42S9juP+UsihbNrkfB0ehMyXW9wpyUMRXeOnLEbcFytr5cxj4vtL9T4ZgiR1if50goUjYN96rutZEiLO+4tCJlmKNNOSkjRTMnpYyhhkPFcSVSRo6w3IAFp5QvSybCo2XsckUU9yXsmhQkvNc2c1KqTZCKCvLNXhQ7jhDiEnBpr+tRQQgxDUzvwffeAb6529+709iayYn4EMdiQxywexmye+k22tE0jYuZiwCciDcOmRkMDFA4e5b4uXMUiHHX6mW/M02Hv4ImBFkjxUzbAdznDpTiYpgmhbNnCQYGdvNXVGwgbFHxzvQiFy+W/tXHxsYYHo6+CzFsUK3rAlvd0SJFK4UncT1wGzikQC2cRo1mIoqMYTfDnJQx09/lmijuRyu5x8P6abXTOXqEipSefLu2moYCU20vUoSJKGFiX5QRokluQLVoHymaXY8wwS/KqJyU8hAmFuUKGpIZeEMjfKj7bPQITdsk4dyieU7KXayIItKoJV2F4hGi00jxm12fYyR+iF6zA+0BMmOLVAoRi9E9ewnDKVkGVo2O9eNDuduYHy3hfv7zFH7nd5RAGQHCBpxF996+v+Hh4U2FW91rmi2cqkTv0aKVclI23fmnJnSRotkEW8YJXWi4V1uJlFFDpvzP9yOsn1YLWNEjrN21UrhX2xSY4dH+FXtAmFgko0jpeuCH3FKVkzJaNHN6yZgPNdTBq0TKyNFs861sImXYuFTNaaPHo7ABEtT8QnEP+WYvCoXigVn2s/z7+beAUojXkpOyj4PlHw8fk/BVAGN8nPi5c+B5XO97mtX5OxwrXCcVZBForBgdLLTtw39sAK1QQMs2iHum2HXCFrDiye77hleNGsrdIQ+tFO612URADaqjxf3CvcpGaLhX5aSMHOF93i5XZBtQ91p5CGt3MoqUKqy6PDTLSSkEUm0cbCasKrEoWrRauNcwYVW1u+gRuvlWwrlFTjkppaGV1lPCxPG4LdBli5ms2DHkm70oFIptoSAcrhWnuFa8l85T64LOIMnPZu+wz+yiz+ykzyo99t7NrguU+sICxyZusVT08TWTFaOz9H6guziP5vYgPI/4uXPkX31VuSn3mFYKuxkmFqk49tEjdOefoxEESDUYVU5KeYg3iXAoo6NNOSnlITRHWwstJKgFrOgR1u6KEoZ7DR3jqXYXOcKclEEARQfiEoVJbRYmVIlF0ULXIW433vzTSuFelYM3eoStNeSKGkjU3wHklZNSGlop3KvaAKnYDEqkVCgU6wgNlowcS7nxumMv/eWHHFwo0rfqMTQ+i27Z0J5i2hhkJegkXiwy4MxgixXMK1fwRkYI0mnst9+m8Npre/DbKCqELpzKmHBbDaqlIdkkN2DekWvhp2kOBSWQRwpdLwkpja6ZjLvsQ52UlhIpo8ajEOJa9XfRo5XCvap2Jw/NRJRcUTKRspmTUolFkSMZbyxS5iSc14YJqyoXavRoOsaT7HqpqAXy8Cg4KVW7U1Qj3+xFoVA05Zf//Hs88b/4VeyO7VMA0nOrdM8ukxOC+PgUy57PyuEUM4dSaPo0nUwjAp25nEXbZIHpQgH94/dYfvZJ4jcXWJ34iLahx2jTk+gyxf9pEcIEvJZaOFWDm8jRLG9MvqiRkmjRsZkDT7W96BEmUsq46zRMpEwokTJytFTeGLUhSBrC2p3jKyelYudoJqLkCtDdsXt1eViUk1IuWmrzbUidlTgePVppjKfutfIQfxSclBKtCSl2HiVSKhQtxo3/9D53vjvK//Dt/5G5jjxXC7dxxcMtZh69OgNAx2IO0/NxLYPJQ90I/d6NRtMDzLYid06kSI4uYxVy3J66zHJ3ims/+Vdc+MJxDHS6zDY6jCQdRopOI0m7nqTTSNFuJEuvy+W2Lt/iymbxfZ+xsbFd+765mQPAk3XliysuFy9efKjPvn79+kO9f6vcnDgBDdLTe87KA/8u169fp7Oz8yFrFg1GR0e5fv36Q1/X7WBqMQH8WsNj7128TH9n/db1iYkJhoeHQz/T8zxGR0e3q4rA5v5ml270AN115RqCyx9drMm9NDY2xrFjx7a1jooSnucxPl7v9N+Iya8B9Ss8V67e5mJycsvfu9t9xOjoKJ7nEQhwvMYipa1Eyh3hYe7P+exTwFBd+WRmkYsXLz1kzTbHdt2T5xY+QyNrwMriNBcvXtuW76jmfn2/IpxQkdJrPs3fiftpI7YyLhm/eQxoryv33bXQ96t77t7QLDdgM2fiXuP7fl27/+B6G/BEw/Mvf/QelhmtBdSrV68yPj7O5cuX97oqm+YnP/kJQmzP31HnKaC+AY5eucn5nrlt+Y4K4+Pj21bvRqzlPg3U57+4MzHG+fMrW/683ejTH1VaKX2OclLKQ7P0ObKhxHHFZlAipULRgrirRZ7xDzPSP4ITeHxSvMPl/C0u528x7S5s+fO65rMAdCyVtpou9aRqBMpqhK6x1JOiL7NCx1Ke5e7U+vt9Aua9Fea9+w+6Y5q1LmaWHpNk48skAhuy7bQbCVJ6gnYjQVKPoWvyJLgTQjAxMbFr37d4FxqJlLmCeGixdGZmBtPcvVvJ9Gx/w3K3uPLAv8vMzAzXr19HawGX7/Xr15mZmdlVETyM5XySMJHyytUJljoW68ozmUzTzxRCbPu12szfbHzqaMNyy3C5erX2fRMTExw92vh8xcOx6b5TfIZGIuXtybuMJbb+v7HbfUTlu8LyUQLElUi5IzzM/Tm3NkgjkfLuQm7X+uTtuicvr55uWL6ylNmR3+V+fb8inAcVKXfiftqIrYxLpmd6G5b7TvgYT91z94ZmImUuwiKlEIJLly6xvLy8XvZx5hCNREpdCzj/i5/uYu02x/j4OF/72tc4efLkXldl07iuy3e+852av/uD4jtHaCRSjn0ywTva9m7SvHr1KsvLy9tS7434gY4fnGl47NrYh4iFqS1/5i9+8QteeeWVh62aogGPgpNSiZTRo5UiooUJq6rdKapRIqVC0eLYusmpxBFOJY4AUAgcJp273HbmmHTmuOPc5Y57F1d4oZ9huqXFUMMPSp8Rb+5yrByvnF95/1YoCpc5b5k5r2pSUF5z/sncJzXnakBKT9BmxGnTE6SMBG3l1+16sqa8XU/QZiSIadaeiVKmafLSSy9x+nTjRcDtpv1nMX7w02meXXufASeDLRwczWalfR+vfelLBP2Nhb/NMjw8vGu/yy8WO6CBiePE0cGHmpQ9//zznDnTeKIoE+fPn+edd96JxAR1Javx3/9142MvfPk3+NTR8D4nDMuy+K3f+q1tvVab+ZvF/i7O/+ed+vKONqPufVFwsbYqlmVtqu/8N++3M7taX37yqed45bcfbFFvN/uI8+fP8+abb1JwlEi52zzM/fnjYhvvNDAy9vYf2tU+eTvuyf/vn/VA/T4SPv/ZT/HK10881GcrtpfQnJRB82n+TtxPG7GVccmH2XZooEUePdIf+n51z90bmoV7jXLYzUZ9/F//LMbrf1d/bipBJMbTjTh16pR085ZsNrstf8//6cNOphpohiMnn+WV/2L7Hfk7Nc9dzmr8N/+58bHf+o0XHmieBKW+XbH9JEPutVLmBlSONmlI2I3L80UNIUCmPfYqlYRiMyiRUqF4xIjrNsfigxyLD66XBSJg1lviJ2PnWTZymL1JZr0l7rrLrAQ5PKu0UOobJbdivODSbD9hvODWnF95/04hgLUgz1qQp+GqWgNMDFJGnJRe+knqMVJGnKRe+klVvU5VlcX1mFR5NfVMhif/9of88XT930V3J0h840f4Q0M4L7xAMDCwBzXcGqGDGxXLPnI02xXXLMdjFFGDarlolV2nYfkoAeK2EimjRli7k62/g/A6hwliir0j7F7rByblvYLSEOruUO0ucsRs0HUIGrSxKId7bYRasJeLVgm72WxMqpxF0SNsrUG2uQWoea1MNFvjyjvNNwxFDRVmWLEZlEipUCjQNZ0Bq5vj7j5w4XTfvd2ChcDBOfEttLX38Ps7SK7ewF70meqKoSccNK32pqIFYj2860pXyfq41JPavV9mk3j4LPtZlv3slt6noZUEzYbCZknITOgxkhseSz/2roalNcbHiZ87R8eiQCNGnztHn3sXU3h4msmc1cvU5Ap6JkPwwQdkvvhFHvvqV7HtkC1bEUAtJMiDZYJpCDy//prJlkchF+IKSEg0MXiUaJWQTE1FSuWkjByh4rhk/R00yRujNgRFjmaLO81CRkcRtRFNHjSttDi6lq8/FuVwr41QC/ZyETb2Lji7W4+HpZmoqtpe9AjbLJOXrN1Bk/UUda+NHM02aeWLmlR9hbrXKjaDEikVCkVT4rpN8tlfIfHRdRhMY01l0VyXD77fw5XEMHrMw4wXsGJFrFiO5xmlO4hRtMHrSaMLj+vD0XfobRaBIBsUyAYPNgOPazbitMeHxipvT19rKGg2EjiTWxQ59UyG+Llz4Hkk15b4/MotbOHWnNPnztF5eZXC4SHcjg7MP/9zriUSnPzylx/od9sNkgvTvLwwWhO2NmMPMFAcAdr2unqKDSRigtVc/YBUttA4alAtF+ELCXK1OyVSykVou5NMHIcmIqXq8yJHS4mUqt1JRTIeIlJGONxrI8LGBmrBPpq0yr22WZQF1edFj3iTsJuyoe618tBsjFdqe/JcM7UBUrEZlEipUCjuS9DfTzA4iD41hX/8OPrlK+xzZul0l8jYA+RWUySDLANOhv59Pv19PXgjIwx2pvEGBzl9+u+x6K2x6K2y5GdZ9XOs+FlW/FzpJyi9dkXrL7oWhAMxKJJlobg1FydATLOI63ZJvNRsErpNvCxgVsrjms0Tf/0D2vOLJBZXid28ScK0KBAnY+5njXaSQY4BJ0PKjpGen8fr64N0mvx778Eui5SO4zA6Otr0nNj8PH3nz/PlCzHmVhM1xw4Wb/OFn/+EeGe/NGFrHxUStmA1V18uW/hDtYAlF6GhwCRrd81yUtpKpIwcYe4O2ULQBUGTPk8tYEWOZrvsm210iCJhuQxVKLBokow3Ls82EC6jjNqUIRetMsZT4V7lolXCDIOKWiATza6JbG1PieOKzaBESoVCsSmKL75I4o03CNJpCsdHcD6eICYcDhcnas4TloE3MkKQToNp4r74Ih1Gig4jxeFYf+jnCyEoCOeecFktYpZfZ1bvktccHNPHEQ+WTF52isKl6LtNw9Sm51Yxrn+AJgTDV6YwhY/2TIqFQ91Y+i3SQsP3DOZcnY9ns3QvZRGXF7g+fBBjaZ4LY+cQ/f3EdJu4ZhPXS8Jo6blNTLNKYqhubcvvNDo6ytjYGMPDww2PJ+/cYeAnP0H3fXx/iH3uTF3Y2mJOw5j0SLzxBoWzZ/GPHduWuikejniL7HZWebLkolXaXZgLyjJcdLl+lUeC8DDDu1yRh6RZ6DK1kBA9muUjahUnpQqtHk3C2p5sTkqVJ0suQu+1kkXLCGt3ui6Ibc8UW7GNtEoqCd8Hx1PzWlm4X7hXmQjro9W9VlGNEikVCsWmCAYGKJw9S/zcOYrt3bzbMdQwx2HH4zm6ywJl4ezZTbvaNE0joZVCm/Zb6YbnXJy6CMDp06dxAo+1IM+an2ctyLPq58kGedb8AmtBrvxYPu7nWQsKBATb9veIMkevzgDQsZjD9Hxcy2DyUDeisqqtCQzLAwuuH2rDXF3BKjr4K3MsdKe48973uPCF45v6Lu0z8DPm+bM7H5DQbWIVUbMsaFZEzcrze+UWsbITNK85HBs+zjPPPIOm1Q5e9EyGxA9/CH196AsLnF64gF+oFaj73DkGJovox08SpNPEz50j/+qrylEZAVo9b4zacRpNWqXdhbmgbMNtWK7YW8IEPOnCWzdzd6g+L3I0Dffq7V4e9O1A7bKPHp7nhUY7Ef5JIFVXfvXabc6fz2z6O65evcr4+DiXL19+0GqGMj4+zpkzZ5qeo0L6y0VYnyfbvbaZg1eT61d5JGiVdtc0F6oa40WO+4d7lQd1r1VsBiVSKhSKTeMfO0b+1VdZ+7O/RbDErNXPrFXrjjTNG/hDQzsedtPWTbr1drrN9k2dL4QgHxRZDfJkqwTMXFAkV84xmQuKZP082XJZ6VgRIVGsd4Cu+ZLLsmOpFG9pqSd1T6DcgNA1lnpS9GVW6FjKs9ydWn//ZhA6FHApeMsPUeHSw7+59dMqQbMkYv7KX5ynZ3mF9qUc+8anSbbr5NtjzCZ6yRkp4gWH3uwChp7FvfxLisOPo3X3IH7wHdw/+IdYmrrN7SXJmGDAmebZtfdr8ogefK8D/blhgv5wd3WUCHVSqkF1JAmb7MgWFifMBWWbj2YkgajTLBSYEEiz6Nhs0aOZa0+xNzTbZV905RoDqQWs6CGE4NKlSywv14/znfx+GomUn1yf4p3Ezzb9HePj43zta1/j5MmTD1PVhggh6jZAbkQ5KeUirM+TLdyrandyEXYf8nwNL5BnQ1BTkVK1vchhmWCZAreB+1W2ea1aT1FsBrlmLgqFYs8JBga48bV/xOvfdeqEh4w9wHNff4L+3/j1va5mHZqmkTTiJI04bCGESiAEhaBINkTIvFdWqBE280GRotgbt4vplnKVGX7JOVqIN/+FK8cr51fev9sISiF/C74DfilsrTd5i1khSI9PseL55I+kmDwUR+hZTLJ4wEygYd5yKC4u4V2eZ+ypQcTKRb534Q4rvR3EyoJnTLeIaxaxKndnTLO4cWgFL7jMnak8ccMirseIG2UXqFHO+2nYpfC35TJDk2cyslfomQy/f/3PKUzX76bvu1Yg8Y2f78qGhu1ALZzKRauEZCqECAwxUzkpo0hYfxAEGo6HNOHb1AKWXBgGmLqHF9T3F0VXrrGKiloQPSzL4qWXXuL06dN1x86NdzF+t/49R48/ySuvHNnS95w6deq+jscHZX5+vulxNcaTi7D+QLpwryHtToXcjCbNhBTHk2dZvZmYr+610SRhNxYpZZvXqmgZis0gT2+qUCgiQ66okbH38+3u36g79l/1f3cParRz6FXiZt8W3+uLgHyVaFl5/qOf/YT0/l669/eSD5x1cbPyPF9+XhAPFpvQs0ruG98oLU7FCy7NfI7xgltzfuX9e819w9aWEbrG5OEeUmtFLNenYzHHcneKo2MZLvS2lYRjihCmvQ7CHa7wd7evbLputmYSN2Il4dOIlULdloXNiqCZqBI244Z9T/jU7z2/91j6LEtvjduyMT5O/Nw59hdMbqLXhYbWZmwQAxiTk1LkEY0vZHh5YbRuU8ZAcQRo2+vqKTbQzNEmEwVHhXuViWYLWLmCRsySYxLedAFLLSREEstoLFKGhYyOKmoBSy5a5V6rxHG5CBPxZFuwD8v/rNpdNGkqUvqS7EJDbUSTkURMsJKrL5fuXhu2IUj1eYoqWmM1VKFQ7CphNxhTDzANdZOpYGg6bUaCNiNRU3517gLD3Qc5na7flVxNReTMB866wFkIiuRF6XUhcEpiZlCkINz1c419syTmiuTSbXQu5eiazzI70Nkw5KsWiPXwritdpXou9dSHbtoLdjNs7VZxhIfjbX/IRUPTS87PKtdmSdi0yuKntS5yVs6xjXshcifEFAsdRa4XprB1i5hmYWsWtm4S0yz0XXCA6pkM8XPnwPPodpbZv3INe4OruPtuAeu9O/jHj9fkEY0aeiZD7K23+PovF7mzWrvYe7B4m+fP/5T4n/ZJ4QZ9lAhbwJItb0xoTkrlpIwkTfPGOBppSULHN81JqRawIolteOQbdAuOJ49IKUSTHG1qASuStMq9VonjctEy4niok3KXK6LYFM0crq4vz7J685D+qs+LIq2SDzXM7a7anaIaeXpThUIRGcImAXF7b8KEtiphIuf90L/0ayRufwPaBdbd9xBOkcm3+riUOIZuBeimh256mIbDr1nX6Bcp3LhOZ/8RkgTc/NSneSyWoBi4FMqOzkLgEOzyAqusYWsfBl8E5PwiOb/44B9yEj7MfLPhIRODmF4SLmO6WRIwq57HyoJmpdzWzPL5ZulHry23NHP9ta2bpc9/6y3wPPSFBR5buMZdoVMsOw9zeopkkKVNv4XmuphXruCNjBCk09hvvw16dELTVdygeB6uG2efO1PjBp2zerGNTmncoI8SrbKAFZqT0lA5KaPI/ZyUshC2gBW3RZS6aEUVVkie2jA3dhTJOyCEWsCSidD8zxL1dwD5kCG3anfRpFVC+ocu2KtNGZGkebhX+Z2UmiaIK4E8krRCHt4gCBdV1QZIRTVKpFQoFFsmdAHLUgunUSDo7ycYHESfmsI/fhzzyhX68os8u3ypRigacDKkB3x6ejrxRkboTqXxh4YYeeq1us8UQuAKf12wLAQOReHwvR/+gH0H9jNweLCq3K1xeBbL5QXhrp/jhcZevUerhK2NEh4+XuCTpRAe/vYh6Jlb42sff4gpNE6MTkJXQDDUydS+/fhiBTPIUvANxoxuutwZUgvLiCvvs3D6JMaNOXKPD3HT6iGRn6gTRS3NxCdAiJ0fyFa7QfWFBZ6ancAr1PZvfe4ch29q6P1HI+0GfRQJzVck2QJWWD455aSMJs0WtGVqe8pVJB9hIaCLEjkplbtDPhItkhtQOXjlIqw/kOk+CyoXqmw0uy5OCzgpkzGBJte/0CNDK+ThVWM8xWaRpzdVKBRb4sqVK+Tz+S29Z2xsbHPnXTsIdNaVa6Kw6c94EMbGxhgeHt6xz28lii++SOKNNwjS6ZJT7fotYsLhcHGi5jxPs/FGHiNIp8E0cV54oeHnaZpWEo0w6TCS6+UXluIc79/H6fantlQ/X/hlIbMkalYcm07gURAOxcCl97BN1/IV7H0JOlfvYM8LrpvdaJZANzx000c3Sj+G7kY2bO2jxJGrGTzhk1rIItwibtxgbiRBQq+Xly8EGo8vZrHyPrenLrPcncJ3cny3TfDdmdHGX5AGTUDs/F+XwtmWw9/GjMpzC7scHtdef10+ZlSdX1V+R8yzlnSZcRfLjlGLru9/b12gNMfG0D2ToharE/mtIFfvBn3iiR3+KyvuR8IWDDjTPLv2fk0e0flEP/rMIYL+/r2u4qYIDfeqnJSRpNlOYJlCMoUtJKidztElVKQM2egQRVSYYflItni4VxV2M5qEipSOhhBII7SoDUFy0cxlKFO419D+TrW7yBIeWn2XK/IQqDGeYrPI05sqFIod5+jRo1y/fv2+QuPtycbiT8x0OXr06E5UDYDh4WFOnTq1Y5/fSgQDAxTOniV+7hxBOs3lgQGKmYW6cJXu4U760x6YJoWzZ3ctr56hGfcNZat/boDE1W/A0AGsaYfsikfbO3HGEiMEVbkVdRHwv37qA2w9wG8z6Dv0NL4mOPrF3+b53q51N2ex4uoULsXApSgcCoHLzalbpLrasZKxUohbv5Tbsxg4+CLYjT9Hy7AbeUQFrDtyt41PwXuT/xaA9NwqX7nyAboQjHw0jeUFuKc6mN3fjxBzxPwFvMDgjpugN7tEsLaK+PgXzD4zjDF+h08OehR7usmuVsLhln4s3Vx3hN5zhxrYuoWBjibLqkrE0TMZRn7wQ/54erHu2MHibWL/9oeIA0NS5BFVOSnlwjZB1wVBUP+/LFOoYeXukA87JNxrWMjoKNIsbJlytEWT0NDqEoWggyZikWp3kSQRCz+WdyDZ5HiUCM1Jqe61kUTTStem0UYuV6Zwr2qMJx1hfZ5M99pm8yDV9hTVKJFSoWhRRkZGGBkZ2fL7PvOZz9z3nDvfTMGl+vKhga5NvV+xO/jHjpF/9VXst9/GtOa5bfUza9U6iA4HDv5QNBfsN4at9S+M0e/O0uUt1Tna7MU1dF0nODFMykzgDw1x+NAzm/qeN99+k+eff54zT52pO+YGHnm/eC8/Z/n5epnvkA+KFP1ySNvy48bXBd9ZD3ub94t4Qr58mZuhFfKIHr06A0D7Yg7d8yhYBpnj7Vh6no2/zbXAQh8tYBV87mbGWe5OsTzhc+HgcX46f23T36mjrQuaVjm/p7VB0KwInfeETwtbM8rnW1Xnm+s5QqvLKo+mZqK3qCBaySPasRigEafPnavbmHHnzipDgS9FHtG25bu8vHChxg2asQdIHpBHeHiU0LTSbudsg0UDqURKtcteOqwQd3XYRoco0ux/JKXEokjSKvmf1aK9XDS7LrmCJs11C/s/Uf1ddAkTKYtSOSkbl6tNGdGlFe61aiOaYrPI05sqFIrIoHacykMwMEDhtdf4/pUci393uW6xe+XZEZ57rW2vqxlKddjaxUNP4Ny8WRe2VtMEmm3hHT9+37C1W8XSS8LQduMLn4LvlvJ2+s66CFqsEjnXywKHgn8vt2ex7vyyU9R3yXl5XOHDHo1ZWyGP6G64QTcSIEpOX1F2yO2wVmuVxc1qAXP1yRXGYh/wk8zNDa7P2sfqY7XHawXTe+cY6NrOhxysziOaWFvi8yu3sEWt47DPnSP+/ixL+eP0PP74eh7RqG3QsObm+OylSxyd+nfMrda6zQ8Wb3P86m2GvtuBvn9/5Or+qJOMC7KF+nKZwh+qEHTyER7ude/vqZul2QJWWKgzxd4SFg5VphB0QqiNGbLR7LqUcrTJcd3UesrWcRyH0dGQlCC7gMmvAfVRoGZmV4Cu3a7OA6E2ZchHWJ8n09wiHyKOg2p7ilqUSKlQKLaMGtzIR6FngG93P1ZX3mHngZXdr9AmqQ5bm0328G7H/jp31HKih08/lyrZWHY5bO2DYmgGKdMgRXxbP/f8+fP89J2f8vd///colnN7OsLDKYe4dYRHUbg4W3juiPJjUHnuhk7/l3pS9M6ssNKVoHMpR9d8ltmBzoYinxaISOYRbQU36P1whV8Ss6maMbTBKstMF5rJyg+GiVESRvWSA7QikjYSQDcKnNVi6KQ1i4GOnb9ZI5Ramknv9/4a1y1gLizTfuMqtrAoljdjVLuuE6aNffMmel/feh7Rwmuvbfvv/MBcvUrvuXPMrK6S8WGfO1PnBrU0j8TcnBRu0EeNMDFFppBMKielfFgh4V5bwUkZtwWGPL/GI0UruDsKDgih5rUy0exeJNO9Vq2nbJ3R0VHGxsYYHh7ek++PWY3neG4gTwJbtRFNPsKujVT9XbOclPL8+yh2ASVSKhSKLZMP2bWjFrCiS9iuTBkWEiphaxf/1Y8RzDO7IWxtWyIALRfZsLW7jYaGrZvYmLTvwMKeEAJP+CUhsyJilgVMfiVD/63/hJ/w6Zr5iOyqR/vPbG7uGwRLoBs+mh5gmy7PFW+T9E1c2yLo6SGpCSaODmIKHV8L9mwfdCu4QaOGh48nfPL+Q9oryqbv783U7qJOz63ylbEP0IRgeHQK0/cxPt3G/FAfBpO0+TpBYJDxdNJ3A9LLa+iX32Xq6ePon4wzeiWGs693g0B6zyE6ly4wLjIkV26U/rd0qxxO16p5/dCO0akp+OY30XyfVD7PM3fPEzi1eXH73DnSs6tYg0PgeZF1gz6qJOOCAWeaZ9fer4lcMPiLDvSnhgn6++//IXuMcnfIR6wVnJRq4VQ6wvqEsI0OUSRsTguqz4sqzfoEmdqe6vMejOHhYU6fPr0n353ujHF7vr68raMP2Hoknb0g7H8kub37phXbSFif0Oz+FTWaRSzQdz7gkkIilEipUCi2jNr5Jx9h7g5ZwkQEAwNceP4f8/r72brFX/r6yP/hESkWf1sBTSvlT7QaDSEOD5E4fAV9agp95BnEL66SnBQcm5orO9o6SQRZhvxlhodjENuHNzLCgc40/tAQN27pDMeHeeaZZ3CFX+XiLD2Ojl0m2Zni8PGjJXeoX3J8FoOyazQoOUYrzwu+s15W/VgISuVO4CE2yKGt4AZ91KjkEe1YzGF6Pq5lMHUkja776Bti597oaMcaXcUqrrE2e5vl7hSzF/6WC184Hv4FwzAq3uE/fPxO03qYmrEuWlZC38Z0q+QgLQuZdlnYtHSLWPmxUn78jb8htTSPs3SHvrV5lmImOTvOTGwfWT1Fwi2wL38XiyLJWzcQvT1o3T3Rc4M+ouiZDK/e/AuC6em6Y/vGiyS+8XMpNtOEjfFUyM3oEpaTsugZ7Hj88G1COXjlQ/a5BdwnT5Zqe5Hk/uFe5UCFGZaPsD7PkSonpRrjyUZYn9AKmzJUf6fYiDy9qUKhiAyhNxm14zSyhIZkapA7K6rkihoZez/f7v6NmvKTh12C/oU9qpViI9V5RNeOjuDcrs8jCgJhWfgb84j+8IdASQi1tZIbtJp5f5IerYcz3Se3pa5CCN49/3PeOf8uv/n3fouicBFfnKZn4j/gJ33aZz+kkHW583dJbvQeAJOSG9TwMfD4Vf0mqSCGF9OJ9Q3SowkuHDtG2k9ixEwc4eEGHo7w8AnuXyHFA7EXeUQb4Qkfz/fJ+U0Sb4SQnlvlK1fKbtBbU5gdGu6RLLOHYqDPUpG+5wNB/JZHsJjD++iHjD01iLZk8KP377DW11XlAjUaOEPvhc81NaN8bEOZbmKuv89Yf37v/N3NNSoLxvg48XPnGCpa3EGrC0seW+wE0YkxORn5ML2x+QwvL9TnsN7vjrBuZ1ZECtts7KQsOPKIlGoDpHyEOQ09X8NxwW4eLT8SNA1Bp9peJDENsE2B49VfO6nCH4b1eWo9JbKE9QmOJ0FnV0a1O/lohdDqagOkYrMokVKhUGwZtZAgH4lY4/JWGNyodhctqvOIBt3dDfOIzlm9PPNMEtPa2zyiJVeogeXppM32UuGhbhKHhktu0OGnEBfGaJ9xGZ6d3JDfcI6jTxjodg/eyAj97SU3qB97AlaoC0XkiwBXlARLt5zjs/K64hitCJr3zquE0i2V3Tu/8hnuhvMrP3IsSm8XrZBHtJEbdPJQd53YKnSNycM9pNaKWK5Px2KO5e4U+z+e4EL37ib10NHrxEy33cFE5+3pa+uCp1UWOKsFz3rh1KgJs2s1OcfUDDQtOvcuPZMhfu4ceB7dzhIHV8axRa1o1HnrNoW8Q/bAAdyODoLXX2fyK1+h2NNT93mnTp3Ctnc/QYueyRB76y2+fGGBG6u1U8SDxducufhT4n/aF3kn6KOIHeKkbIWclEooii7NFhdzRQ3biv61a+ZEUfOL6JKIhYiUMs1rVbhX6QgVKX2JRErV7qSjJfLdh7jclTiu2IgSKRUKxZZRgxv5aImQTCpPljRU8oj63/whgsW6PKIArsiiDQ1GcsG72g26cOgJnJv1blBNE2i2hbfRDdog1COAoekYmk2cnRcfAiFqRE23ShhtJGo6wuO9Dy6Q7uuhp7+3XhCtElS9qjC8lc/09tip0wp5RKPiBt0KAQEF4VCo7oLLM4vZ4uqOfreJURIt9cai5v0E0Y1u0cqxxY4iGWOZieLspgXS2FtvgeehLyxweG6MFWFRLLsPK5sa2vWb6F6Otps3WTtyBLejg9733mPyq1+t+ayxsTGgfpPDTuI4Dje++10GfvITdN8nl9vPPne+bmOJZcjhBH0Usc3GIqXjyeN2VmM8+WgmIJfmF9G/ds1ELdX2oksiJlhuMOyRZV4rhFpPkZHQcK+uvj5+izozd58D6hNQri5nuHjxak3ZXm2aU9QSGu5Vkv4OlNlAsXmUSKlQKLaMWkiQj1YIExGa6F0NbiJJMDDA0u/9Aa//wK3LI5qxB5j6ncMMnOzb62o2pNoNmk32NHSDLid6+PRzKdA2uEFDRMrdRNc0YppFjM3v7J2bGmO4/Qinu7cujgRC4Am/LGS6da5OJ3DLOUZLx73yc3eDe7RWPPVrxFVXeOTcAr4W4GuCoCp8bivkEW0FN+hu4uHjCZ+872zvB5+EX7LIX05/EHqKSSUsrkHv3RxfGnsPQ2gcvXwLqyfAO9jF7X1dBCKHHhTIBTrjyQE6Cz6p+WWs+dusHjqJ4c+jdWbx+/vXRdBOY4A5Vply5usE1J0KsXvtxz8m9ud/jp5OY62s8NTcJF6h9r7a585x4JaOPvQYQTpN/Nw58q++GrkNJo8qtuEy4EzX3Wvz+TT6zHEpcnZn1QKWdDQTKWWZX4QtnNqmwNz7/UuKEGR3FhVdCILGdU2p9ZTIErbWtbzWeKNQFCmFga8nbtX+DnuxaU7RmJbISRnSN6v+TrERJVIqFIoto3bCyEfYoFqqwY0SKaUjGRMN84gC/HeddxmIcK6siht04V/+GMF8nRu0LRGAlsMfGoqkG3Q30atyiKYa7M7dLi5evAiUJsy+8NeFT359ivY7f0qQ8knOfQCuy+w77Yy2HQFDoBsBmu6jax5f4Ab9QRIvbtDVf4R2TTD9zOd5KtFW5zqtiKU5J48wIdhBV0gruEEfFTx8vKDUd524fI01P0/nQhbXyeOlDOZP2bTpd2veEwAXAnh8IYuV97k9dZnl7hTX3n2TC184fu/EjtLDf556v+F3NwqxW3KLloTMuyML3Ex+xIXZ6SoX6T0XqKWZZffpvWOp9/+WWE+cpOaQmptm1jApxGLMxAbIkiLhFegvzmKJPOaVK3gjIwTpNPbbb1N47bWd+BMrtoCeyfDly3/Fiel36o7ZCzdJfOMd/KEhUqs7625+WPJqbiEdza6NLPMLFWZYTkLntZI4i5SDV07C+jw3sBkeHpZC0NP+p8a5xY8d2c/p0527XBvFZghrd7LcZ0Gt4yk2jxIpFQrFlgl1tKlBdWQJ23Eq0+AmdJe9aneRRfYFrGBggAvP/2Nev5itc6iwr4/8Hx6RwqHSihiagaEZpfC5Q8dJHDhWyiP6+CnMK1foXsxxKnO9KuRmkQEnQ3e/oLe3C29khO5UKY/oiVPNhZY333yT559/nmefe7YkXgalfKDFwL2XGzRo/Fg5Xgzc8vvKbtP116Vzk0NLdC3cwO/1MZYKpOeyZPq60UwBWu3/UVTdoI8iux2mt2GI3ZoKwSLz3MzNb+rz0nOrfGX5CjOWwLg0hen5FE+mmD/UjaHfqWimLAaC27fmWVvME3w0zfhTh9FXLvLTDxbI70vXhdQNFUirXlfylFqawUJHkWljme7iTOh7jR1wkcqOMT5O/Nw5erOzTNBV5/hfsHtApDAmJzn58cd4g4MQ0UVUez7Dywsf10VdGHSfAFT/FkWaCXkyjPFALZzKiuyL9s0cn6rtRZewPs8N5AmJqiKiyUdof+doBAHoEgyPVbtTbBYlUioUii0TNgEIE8IUe08rhHtVDl75aBoKTJKQTLmi1tANeuqIS9C/sEe1UmykOo+oNzJCcPMmsUJtHlEAR7PxRh6rzSO6SQzNIGEYJIzYdlcf0tMw+zr0BSzf+i5Tt++i3xhgLDGC0EEzAnTdR9c9fiv1Fvv8JLHOdtoPPImvwb7nf5NP9aarwuzec4LWhNddzzHqNzzHqwq5u5PO0VZB9jC9R6/OANCxmMP0fFzLYPJQd53QKnSNycM9pNamsFyP2MISy90p2kevcLXreKOP3hon4UMWOdckzK6OXpdXtFHO0LDj9XlKq98Tfrx0bGdC7T4MeiZD/Nw58Dw6Ckt8fuVdbOHWnNPnzmGeFwQnjqP7finv6JkzkXL+65kMsbfe4kvnF7i6Wrs0cbB4m2c+fIf4n/Y+8hELokjMKuXnFqJ+PCfL/EJtvJUT2ee1zeqpXLzRJdRJ6csjUoau46l2F1kSTaadeQdSOxfEaNtQ63iKzaJESoVCsSWCIDyUiprQRZewgafna7geWBLcDdQOLPmwTDANgefLu4CldtnLQXUe0SCd5sr+AbKTCzWuojmrl/zBLgbTbm0e0Siwfz8cOAB37lA4fJjg9gL97ixd3lKVGzTLgJOhq2cVM5XAfvwUPXZXKZTjY5/f9ir5IqgRMmtFzbKYuUHwvHlnAk/z6Rvob3j+vdylXo146m34fFmQPUzvbjtBH4aAgKIIKG4Q4nYLA32DeFkSNBuJnqvJFUx0rs4vNxVWG79u5Dg10LTa6xJ76y3wPPSFBYbuTnBH7KNYdh9W9xfCWcO8coW4rqP7fqTC9FacoHgenhtjnztT12dbehfG5CSJN96gcPYs/rFje11tRRlNK80vGi0+yu5oU2O8aCN7TkrlpJSTMLHIC5pvUIsSal4rH/eLTCVDXke1jqfYLBIsSysUiijRbNKpBjfRpZnLteBoWGb0r50aVMtJIiZYzdVfu4IseWPUApY0VPKI2m+/jWHV5xEFOBC40c0j+vLL8G/+DV5nJ7e6DmDmTGJioxtUEJg6a0eOEHsAN+hWMDQdQ7NLIXU3SUex5BA8nX7wkJJCiHXh85546d9fMC07Qhud17i8XjzdqkC61JOid2aFla4EnUs5uuazzA50NhT6ohimV3Yn6G7iE+A3C7VbTXkh8+PV6W37fpN7uUd772b50pUL6AKOXZ7Aby/i7ctyZ18nPln0IE8+0LnhpjliZOlYWiZRKDCrd1G88T63xg9w3cpzVxQxlsawdQtLM7H1Us5SW7dKz7XSYyOR9GGpdoLqCwsMT90in6ttT33uHIO3dfTDJfd7/Nw58q++Gr2++xEmYUOuUF+ed3a/Lg9CaCoJNcaLNGFiUUGSdqdyUsqJ7OFehVDzWhlp5nItradE/9qpe61isyiRUqFQbAk1qJaTZtcmV9RoT0b/2qlBtZwkY4LVXH259E5K1d9FkmBggMJrr/G317NkVurzm80+/QSffS2i+c0GB+H3fx/xL/4Fy3YHVzuerssxtxjrJnEkTk9HR/TcoNuEpmnYmom9B9OUN958g+PDj/Pk00+W8o82cHxWi5rG52Y4dONbBPs90jMfk1vx6HxP52b/fjA0dMNH0wMs3eWp1VukHHBNG6ezk5iA6RMHiQUmvhbgEcAud4uyO0EfJTxKInxeOJz4eJy1IE/nQhbXyeMmDeZP2aT0+lykHwTw+HIWy/RZXrvDhL3ItZ99kwtfOA4C/mzsZ5v6flszMfWSkFknampWWdy897ryfD6YY2poje8un193jtqayWPf/g6p/F1iiyu0fXIDHQ0/kWTG6iertxF3C/QXZ7FEAePKFRgZIUinI+UEVZTGQtbd6bq83f3vtqMfH4l83m41xpOTsOsj+9wiZglMdVuNLGFrDp4kIqXjgR+oPk82mq11SdPnqXU8xSZRIqVCodgSTUVKdZOJLM12YEkTkkktJEhJWL8gTUgm5eCVEqd3gG93H60r/22rAE1lmD3m8ce5e/YsmQ//HLGg17lBE7aH0G6R7+tTjqIdQEPDRCehx0jom8g92jZC4rE76FNT6CNJ+PlV4hMGj03Uhuk9FGR46vE2SLbjjYxwOJ3GHxri+c/cE1uEEPgEoS5QJ/Dw8ENzirrC48OPP6Kzu4t0b7rGMdpIYHWFR6G3G3M2y2pXUkon6KPKXoTpdYSH43vkHsQ8exBuLv50/WV6bpWvjH+AJgTDV6ZY8nyKT6S4e8hG16doL5+3GAgmb82TXczhX5pm/FMH0Vfe5+8+mCPXl8bSTIrtBQyh89OZW+theKtD6ZpV4XkbhdothdNtHoY3avlIo4KeyfCPbv8FVqbeMdx9tUjiG7+IbuSCMtbdDC8vXKnb0NTtngSSe109RQhhEYKkmdOqBXspCVtzkCUnpQozLCdqHU/xKKFESoVCsSXU4EZOmoV7VYMbxU4SNrCWpt2F9Hky5H94lAltdxKEGXb7+vj2sa/wkf8rde4Up7uL7udtDn7mM+yL6KLvo0bxxRdJvPEGQTrNypEncO7cqAvTq+sCYVn4x48ThITp1TQNk5KoknjAumQnbjGcOM7p3s2F29VfnCEx+Q3oEFjz58mvuVz7636uth+vcYLqmsergx+ToosgaWINPYGvQfyzX+NwumsTYXYbh+L1yuXFwN11F6nMyB6m9+jVGQA6FnOYno9rGUwe6q4TWoWuMXm4h9RaEcv1SCyslETW0U8Y/8Lx0knl1Yyp/NKO1VdHx24gYFYEUHuT+UbtqvfMpQskzAVShTt1n1f92mT7Q+1uB5V8ogcdixm0Otd/bLETRGdk84nqmQyxt97iV96dJ71a+/9zsHibU5ffIf6nvZEWWB9lWtVJqea00SZsPcUNbIQEl05FRJOTeJN1POn7PLV+rNiAEikVCsWWUIMbOblfuFcZULtO5SRMLJKm3YXUM6H6u0gT7uDd5Yo8IG5gk7H38+3u36gpf2zfCs913tmjWikaEQwMUDh7lvi5cwTpNO92DNQt2N+1enn200nQtUiF6Q36+wkGB9GnpvCPnyC4OEZfYYGO4gc1TtABJ0NPMotpJvGGR4jHSk7QnuMvbks93njzDU4MP86TTz9Vdns2zh1aETs90dhNWnnvvRC9VeJoxYkaIprKhOxhevfCCfowBAQUNpuPdLMMwyhL/E3m0n1P3egCrThDN4qajQXS5mLpPYG1Xny1tMbtpDqfaK+7zNGVa9jCrTmnZyqD9Z6+vjEjSvlEk3fukPjhD8Hz8Fybfe5MTX89Z/ViGV2RFVgV4WKRLFFaVH42OQlbTxHouH70HfcqIpqcWCZYpsD16q+fLH1e6DqeWk9RbECJlAqFYkvki+HHEnJEungkabbJvtk1jQpBUEkMXo8a3ESbsEmP7E7KZJNdjYq9JxESqVMGJyWEh46KW3KJKY8K/rFj5F99Ff9Pf4RgoS5ML4An1tAiGPqw2gm6dPgJnOs365ygAFrMwjsR7gR9GDQ0DHTiuk2c3R9MCiHWRUsPvxRaV/j3nlMrdFaOu+vhd2tD8c7Mz+JpAW2d7fc+t0n4XY+t/V8v9aTonVlhpSshZZhe2Z2gu01FTIfdH7AbXRqG0Hnj9vl14fJX/+I9updWaV/K8bg2g9uv4egW8+1psmaCeNGh21tmIe+ifbTIyuNH8NJdON/+90z/g7N14mk24bEksiw6q+u5TC3N3BEHaWp5mYH334e+PvSFBU7cucXahhjGfe4c/Xd09KOPRU5gVZSQOVoGhM+BmoV1VOw9zSJTFd3oJxNtNvdWbS/aJOzGIqUsfZ5yUio2ixIpFQrFlgh1FcUEEYxGpCij66XBTaOBjAyONrXzT17CxaLdrceDokIyyYnsuVDdoPE/Ttx+tBbnZSIYGODu7/4Br//QqQvTm7EHmPu9Q3Qc27fX1ayj2gmaS3Xzbsf+OifovN3Lc2eSoEXLCbpdaJpWEk0qU+OHXGu8ePsiAKcf31zY3aBaJK0TM+udovrnZjl84xzBfp/umcvklpZJX9C53jcIJmh6gKYHGHh8KnuHDt8kr2m4nV206SbzJ48RDyyELvC10nfvJrI7QR8lfE3gaz6OnwNK+US1qSmWhKD/+hS67ZMfSDF5qB2h+8AaeeDjIMnKrXm6FnN4Y5cYe2oQMX6d732cZ7G3rfZLnobz4rtw8bs1xbZmYusWlm6WxEvNwtZry2yt6rlu3TumlcTOaTFFwc7jrV3B0kx6b3xIVsDaXJHU1XF0YeLZCaatAbJ6iqRboL84gy3ymFeu4I2MEKTT2G+/TeG111BEg2RMMOBM191rNfrQZ44Q9Pff/0P2ELVgLyfN5n4FVwInpUrbJC2JmGAlV18etok/SggR3uep9DmKjSiRUqFQbAkVclNeEnGJRcpmg2o1uIk0sotFzTZmKKKL7GGGlZNSTpIx0TBML8D/qWOODoI9qNX9qThBF//1jxHM1zlBY5YALYsfQSdoK6BrWkmQ2ezUvG2ExGO30aem0EcS+D/6EalpeOzWYl2Y3v37E+zrjnNZ1znpDtH3qU9z5vRrXLx4kZ6eHs6cOYMvArzAoxi4Zceojxu4uMLHCVycslu09NzFDUrlFXepE7glATXwKAZezXvvLi2wuLJIV2/3PfG1rxd7Nkc2nZLSCfoo82D5RH06FnMsd6c4OpbhQu/xTX2XIzwc32OLRuN6UvCju2Ok51b5ipHhqi7QLk1hej65kRTzh3QsfYKu8umLPmQmFnAWcwQfzXD9U4cxVj/kZ79cobCv554LVDcbhNTd+PxeeF1bqw/Xa1d9hoEeyfyjUUPPZPj0T3/MH0/P1x1rn71F4hs/jvy9KmwOpOYW0abZ9Sm60V9aD5sDWabAin71H2lkXk8puhAEag1ZsTlUV6RQKLZEttB4l5gSiqJPMiZYaFAuw+CmmbCgdmBFm9CQTJKIRWpjhpzIHmbYDUJESuWkjDTNFrCiHpIpGBjgl7/6h7x+PlfnTin29JH/w+i7Ux4lqsP0zu/fj79sEfPrw/S6uoU3cpTC5CRJw2gYptfQdAzDJmZsf6jd8+fP887ld3jllVfWy/QXZkjc+QZ0CKy77+HkXD75q/180nZ83Qmq6wG65vHq4GU6tDR+0iQ2dBJfg7bPfZ3j3V24wmNi6g4+AT39vfVhdNdzk1bnLq3Pd6ru5ptHtnyi1WxWYMWAO4fTJNfyWK6DOb/AcncK+6OPuPSFzQmsD4KGFipwVouf0yeWmA/e48KNyXvO0rLrNLbRUapbxKqO27pJbMNxQ4u+A6yCMT5O/Nw50sugYde5/ldED4hU5POJWnczvLxwpS7iQo93EkjudfUUITSb+8ngpFS5UOVF5s23TSOiqXU8xQaUSKlQKLZEWP5CNbiJPlIPblR4EmmRud1BuKiVSqh2F2Vkb3ehIqVyUkaaZiKlFBuCClpDJ+jR/R5Bf71rRbF3VIfpLaRSfNj3NPriSs2C/ZzVizjSSV/aI8hkyHzxi3RGwFkU9PcTDA6iT03hHz9O8Msx9hXv0ums1DlB08k1YnYCb3iE/bE0/tAQ6WO/vv5ZF6+XQr+e7t5caN2NCCHwCWoEzXshd72q8pKg2Twf6UYB1K8RSKvLc04eYUCgyTWWkDmfaNQFVoEouUeF1/zEHphjgkuzE83P2ySGpmNr1rqIWftobQir21gItbXa82PVx3SLJZGlaPnkg+IDu0b1TIb4uXPgeaSyy3x+5Sa2cGvO2efNYr1HqV+JYD5RPZMh9tZb/Mq783Sv1v7vHCze5tToO8T/tDfSLtBHmeYiZfTDkKuQm/Ii8+ZbtY6n2ApKpFQoFFtC7cCSF5kHN2HtDlTbizoytztQeWNkpVm7E4LI51BW4V7lJGE3cVJK0OepMZ5cVML0rt26hT5JXZhegOOBgz80xOWODg4cOLBHNa2n2gm68tgIzrVbxES9E1S3LbyRkuCAaTZ0gj4MmqZhYmBqBnG230kaxptvvsnw8DBPP/PMBndnfQ5St+p5nWi6QVRt9N6NQqkn/JLY+gBxVGXOJyqzwLqT+CIgL4rkg5CdyNvFp+HdiX8JlFyj9obwuJU8oiX3qFl1vPT8U+d+SMfaIsmlNRJ3btPRZlDUY8wm+sjqSeJOkX25eYrFPPrHl3CGh6G7B/0H38f7g3+IvscDv4oLFM/D92z2uTN1m0psoyvyLtBHGcsEQxf4DUJXFmUQKdUYT1pk3nzbdB1PCeSKDSiRUqFQbInQBXt1g4k8obHsJRjchNVR1wXx3VtTUjwAMg+qgyA8Ib2a0EWbsHuSH2g4HsSar03uOeFOyvs4LBR7imWCaQg8v77fiHq4V1BjPBkJBgYY/dzn+Lb3IublW3XhAwvPjfD0a21k33xzr6taQ7UTNJ/q4d2OwbrQjXNWL6fPJDFMDUyTwtmzLecuKuUjtbDZ/ZuSEKLKBepvEDI9HOEzdm0MTws4cOQQnvDpPZyka+UKdn87HWsTaNMmY+4gmKDrAVr5Jx7z2dflMLA4h6GZuN2dxHWbfG8Xlmbgit0X/GQWWFsNgaAoXIobnJBhpOdW2XfrEjNCMHx1Ck/z8UZSZA4lEPoC8XJCk7lA8OGtVboWc3ijc4w9NYhYfo/vXbjFSm9HjRBaLYDWPNfvPb9+YJVs/Bbzy6LuWM379EqZtV5WLYpWu0D1hQVO3LnFWq72f6DPnaN/Ukc/+lgkXaCK0gbHZFywmqsfKxWc6PcPaownLzLnpGwa7lWtpyg2oERKhUKxJVSYCHkJG4DK4O4Iq2MyJiLviHrUCXMWydzuQE3oos79HG0xK9rXT+WklBNNK92XVhosYMnc56kxXvTJdvby0+4n68r3xXLA6u5XaBNUnKDL//ZHCBbqnKCaJjDMLP7QkAp/uANomoaFiWWELwnlvbsAnG4bAUD/XC+Jq8swOIA1lcNd8UlfhLHECEFVbsPedo+/f+xDDK0H0WYxePg50DS+8OIf8of9/SWBtOz4/E9/8Wecfu5ZRp46iRt4OIGLE3i4ovToBO56eTEoCajF9TIPR1Q9D9zS+eXQvCu5VRzfRbMMcj2daJkVVroSdC7l6JrPMjvQ2TDkqxaI9fCuK10JoBQeVrE3bDafqNA1Jg/3kForYrk+HYs5lrtTHB3LcKG3jbxwyPvO5r/4AEyQ5fzizS3X2cRADzRMdF78y4v0zK3RvpRj6PosVrtJvCPG3bY0OStOPO/Ss7KMZrmsfDTHyuOP4aW7cL7978n8g99uKJCWBFGjlFf0AcLnKh6MhC1ILU3X5e5+7FIO/YVfi3Tu7tBoGWqMF3lC1/Fk2ACpIqIptoASKRUKxZZQYSLkpRWdlM3yfymiQdigOsyhGCWa/W+othdtmk2480WNrrZoXz8V7lVeknHBSq6+XOp7bRPRXxENwjYwRH0BKxgY4KNf/0Ne/2m+btF3uW0f+T88HOlF30eNjflE9bmr9LuzdHlLNflEjxSmMTqzoGn4x4+XHoeG1q+lpmklxxkmMdegS0txMLFv2+t7/vx55hfnOX36NPqXZxj/3p/Q032Ig213cAsO43+zj6sdx9AM0PQA3fDRNY/f6blCF534SQNt8AkCDfQzX2Ggs2NDjtHakLsbw+86DxleV1Ei6vlEG+Hhgw6puVWSdxcoIDh8ewZX81kdjDF5qAOhB0COHJAPEohbObzFHN7YpZILdPw63/s4z2JvW9Pv0in9PwXP+rxnLPKXk5eqxEwTq5I3tCxu1oueFrZmVJ1Xdb5e6zTVqzYjPGromQz/5fRfEJ+drju2/+YsiW/civSmmrB1PDXGiz7haUx2uSIPQNjcImYJjOgbkBW7jBIpFQrFlsgWGg9M1Q6s6BMadlOCMBEqT5a8yBzuVeVClZdmIrIMbc8NYg3LlZMy+oQt9kh9r1VjvMgTMxv3DTK0u1xRI2Pv59vdv1FT3p/2Cfrv7lGtFGFU5xNdPvIEztSNunyihiEQloV/fOfyiW6VoL+fta4uenWd4MQJuHSF3twi7fkPawTWASdDr1EgEU/hDY+QjKfxh4bY9/hXH+77y+7RjTlC65/fEzhdUQnHey8f6ZVrY/T299He3bnuMi2W3aNF3113lhbLrlJvD0Lrbjcy5xPdHhfo8abfEZTD52KDS5E1d+eUCxNjXbjcKGZWwt1Wv64WSmM1528QRKvONzeEy40ClZyiB4sWd9HqwpMnVgwQiUjnFLXuZnh54UpdWPge7ySQ3OvqKZqQaDwtlGaM1wi1lqJohBIpFQrFlggP9xrsck0UWyVs0V6GEHRhAzAVgi76SO3gVSKltDS7PjJM6MKclAlb5aSMOjJvzFDRMuQlZjUeh8vQ7rJ5NcaTiep8okE6zbsdA3UL9ouxXp55LlmKgR2hfKI3T57k0Pg4QTrN6mNP4Fy9VSewAmgxE29kewXWUv5RE/shl+De/H6G5/c/x5kTZzZ1fiCCWiEzqBUx74XKdShuCLNbHULXEVWheMuhd6tD8TpBKcdk6biHYPv+f2XOJyqjC7QZHj5e4AM7a+GyNPOeqFkWMJ22AiYGP5u901DgrD7fChFEY1Vi6GZdodU5RXv9ZU6sXMPekE+1fzaH9d70+saMKOUU1TMZYm+9xa+8O0/3aq3Af7B4mycvv0P8T3sj6wBVtOZ6itoAqWiEEikVCsWWyBUal6sFrOgj9eBG7cCSFqnFcZWTUlrkd1I2Filjlo+KGBdtQu+1EojjoeFeVX8XeeJW4w0MMt9r1RgvulTyibr/rnE+UQDBGkHEQh9mOzvJfPGLDF67Rr6tm3c7BusE1jmrl6c+nUSPRUtgfVB0TSdm2MSMxuOKnWA952hZ1Hzvgwuc/+ACX/7aS+uuUUfcyx1acZM6G5ykXvkxvX+NvvnraD0a8eVp+hcdJjtiYIGm++hGgKaXNmpELZ+ozC7QvaTSDoB7497yn+5ObnFbvsPSjLJgWRItY7q1QcgsPX/63I/pXFsitbTGsDNJvk3H0W1mYvvI6ikSTpG0kcdz8mgfjxKMjKB192C//TaF117blro+KBUHKJ6H59rsc2fq+jtL74q0A/RRwnEcRkdH68pj1ucani/DnDZ0jKfmFooGKJFSoWhRrly5Qj6f3/bPnVv4HFAfb2BpYYqLF69v+/e1Itev783faWXxOFAfLmZ2PsvFixcf6DMnJibuf9I2cP3W40B9Pg7fXXnguq9/9vXrdHZ2PtRnRIXR0VEmJiYe+m+ynUxOdAOfqSsvuhrvXbiIUbWJ9X7/G2NjY9t+rZr9zT680bjuAFdGL9IoEtHY2Ni21m+32Kt+aSts5W8rBOjaVwhE/S7pS6PXsQubDyE4MTGxq33Eh5cu44vGDom5zE2W87vT7z4Isrb/Cjdu3GBiYuKhfo+Vpd8BDtWVX/jgCm/6P32I2t2fd999l+vXrz9w/W/d/vvAgbryG9c+4s03f/aQtQvn3XffxfNaxyW82/8HExMTrORmaDTGu7uQ4+LFi3Vte2JigqeeemrH6zY6OsovfvGLpuf8/PJngfoFuOzqHG+++R/ryj3PQ9M0DMNYH4fK1vfI0ubv93cde+IMr+8/UpdPNGMP8JMnV9H3d8H0dOknhJ28x46OjrK8fM9zd/36dTh6FP+ppyh+9zICvaHAOj9/jWJ/H3dPn6a4sgIRGlfv9phkO7j14TVGf/Q+qfzmlx91wC7/5Jb7SN38BE0ksdYM4r5P7Fqaj+MnCSpuOE1gaB5fTf4tPa6Ob1q0Ob0kZ+GDfT0M/Nilp7+PQBf4uiBY/2HDa1E+BxZXl7CSNlYiVlVePmeT5kyZXaCtTkkw98kSsgsfSM+tsv/WR8wJwfDVKcwOH/9IisyhOEKfJ8E8AFcCk9Vb83Qt5vA+mmHsqUFY1PjBz26wmm5HDzSMQEP3S49GoGH4Wqncv/faCCplek1Z9bnHDx8lboXE/6wiNj/P0Pe+h+77WCsrHL21QmFDzvQ+d470DY/59m7cjg6C119n8itfYexutEKtyzBP3Q7Gxsa4evUqBw8eXC+7ffs2UySBX607/+5iYdvWfXbqb3zt+mNAR/0B/8HXIGXg1q1be10FKVEipULRoty4cYNCIXzA9aAsrz3XsHxlcUq6BYK9YmZmBtPc/e53dSlFowWs5VX3ga/d4OAgvu/v+LXPzDbewewVlx76u2dmZrh06VLNIoas+L7P4OBgpP4XZxb6CRP6Prp8nbh1L1zO/f43PM/j0qVLXLp0advqV1ns/P73v1937NriKRrV3dKL/OAH9edXiNo12Ax71S9tBc/zmJqa2vTmCFP/NRw/UVf+8/cusXD9o01/r+/7XLx4cVvbXTPeu/gJ8I8bH/v5Dznccxff93dtk8hWkbH9V8hkMnzta1/j1KlTD/wZ//lKO1dn68u7+w7y/PPPP0Tt7s/8/DxHjx594Pr/q5+lG5Y/fmxwR+ve2dnJ97///cj3QZtlq33Vw+I4DuNXPwS+UHdsfinP97//fUZHR/mDP/iD9bZx5swZPvnkEy5fvryjddM0jX/6T/8plhXuHnpv/gA0qEZ/b6phu/vWt7613s53+n9qp5Clzd+vLd/N9ZOxP1+XTxTg4MX/ntRHa/f9jp2+x1bfk6rHOpcHz3Bu/2frBNa5WB9DIzq5zk64e7f0EyEGBwdZWFjgnXfe2euqbJq/+qu/qul/HoTeYhF7dhazs5PE+Dj7F2bpWF2pyyfab/t0dXSRP3aMo52dOP39/P3f/u0H+s5vvfOt0HuqEAKfABcfD3/90cPHqXo+0KPRPfsJdjpO19IkqXm4UjgIlkDXSw5Q3fDRjIC+9hz98zMYGBS7OrAxyfZ0oKMRbGP4XMXmedicoofHp7nwhe118v6UWfAFug+aB7oHmlf/+kt/+yHLy6u0r+TovzWDYXWjdbUzG+8jqyeJFx325eYJios4v7xFpqeXXCzOrbExfnHsGM8++2xkxvMyzFO3gxs3brC0tEQ8Hl8vi8fjmE7jdd2lFadm8xnAoUP1GyWbUXnfZv/GW/ke3/e5PGYAw3XH8mtzkWlfO8F0k81ZinBa/79coXhE+frXv87IyMi2f+4//1HjBawvPn+aV158Ytu/r1UZHh7m9OnTu/qd+e8kONdg/p9I9fDKK6/sal22ys/mO2C8vvzE0aFtqfteXI9HhSu3Df7F242Pvfybv8e+dG0erShdi//8kzjn/h/15Z3tJn/yJ3+y+xXaYaL0t98O/v3/3GamQUSor738u/z9X3159yu0SV7//36LH/9142P/wz/7P3Bi6NEM/bVbnDp1ijNnNpfrqxEH/gZooIG3d/Zz5kx//YFt5PLly5w8efLB6x9i2Bg+fpAzZw42PrhNLC8vR34sEmWOvhXnf/Mv68tT7X38yZ/8CW+++WZd246KwNfxk8blgwOdDdvyQ7fziNAKbX5iVucb/8vGx/7J/+y/5nB/41ype0llrPOtd2P8jz/tqhNYO1MB//qPP7tHtWtNJiYmHvreytAQ/Jt/A54H/f2IO1eIFerziZqpDjqeH6GjpwdME/7JP+GxwcEH+spt6WsOfhZefx32B7DyLl7epftDn7HEyD0XKKCLgN998grphAsdFsNPfh50HV79Y9i/H18EpbyflfyfGx99l4+vXeHKtTGeOfPseujcSj7RSghdp5yb1Kn+Cdz1MLxF4W5rPlHZiWxOUUMruXntSjTc2jql51ZJskaxB45MLUGnSbHbJ3NIR+gLxFkAYC4Q2Ld8rEWNdnOJqZODdGk6nb+9j7FBl5gGMd0mplnEdKv8aBOvem3rVvl19Xk28fXzLQzt4R3BrTZPbUTFWbjx9/yzH8f5Tx/Un6+bbevjiLD3bvY7x8bGNvU33sr3XLx4kcRHPQ2P7d/XIf0YqBlXrlzhn/2zf7bX1ZAOJVIqFIotofLGyEtYjjYVy16xkzTrG/JOtNteWA65lGp3UiBrn+d44Y4jda+NPsmQCFzZ7Y/Av+2E1TEVb1yuiA6JkHYX9f4OVLuTGZnzP6s5rWQMDsLv/z5885vQ08OlfZ+Hu/X5RJPH+hjq0UsC5e//ful9e8n+/XDgANy5AyMj+O9/RL87S5e3VOcCjS06kNBgZKQkUB48WHo/YGg6CSNGwggP86lrWZbnbvOljmceuLrrDlHhlQTQdYGzlF+0WvCsnFPJH1ksn7P+fMM5TpVo6ghXCneorDlFH9YBOvjJHS7s276bsIlRJXLWip1x3V4XOOMNyhK6zWrSZVnPs+LniGs2lmagNcq70qIkYoIBZ7rO+b+Y7EOfOULQv7ObIB+GZFtvw/K47VMK7K1Q3EOJlAqFYkvkQxbtlVgUfUIX7EOuaZRQYpG8NFvsiXrby6p2JzUJW84+r6hESqlJ1UcYBiC7/RH4t52wOrYld7ceiq0T2t9FXCgCWAsRKdtC/pcU0SFphx8LmzNGBTW3kJDHH4d/8k/gb/4G07rNVIN8ok5ASdz7+tf3XqCs8PLLJRdoTw+5E0/ijF4hJupdoHrMgidHoOIC/frXd72qmqZhYmBqBgn9/nkPHwZP+OuiZVGUhcygLIg2EDUd4eEGpXOn787gaT6pzvYaIbUiiFYEU4+HEwllzSkaNQeoh48XlPN/Psgl+RRcYJ43b5dyXOtoJSFTs4npFonyY7XwGa8pKzk7S4+ln8R6uY2uRVcs0zMZnnz7h/zxdH14oEPOBIlv/Bh/aIjYvn0Uexq7FveSvNtYdopbSqRU1KNESoVCsWl8P9z5pETK6BO2uC3DApba7SwvzfqGqLc91e7kJqztRd3B6/hNREp1r408Ye6vnMQipXK0RZ/Q/q6oISLebah2Jy9NnZQRv9eqKC2SMjgIf/RHfOen0xR/XusqytgDJJ5/ll/5o/17XctaqlyghbYe3u34PH1uvQv0s5/vg3iEXKA7jKkZmIbBg+yDujhRDjn5ePOQk4EI7omcwT2HZ7U42uh5RfDsGcyxb2EcvVcjvjxN95zDZGcMzRJoho+uB6CV+gwtEOvi3kpXaZfNUs/25qPcLLI6QDdLgCAXFMlRfDDRcwOxKmGzImLGdZtEQ1EzVnpefp2oOt/StldiMcbHiZ87R8eSQCPWsN/wvSTG5CRDH3xA5otfhIiFxC04jYX6kkjZvF0qHj2USKlQKDZNs4Vdtes0+oQJK0VXw/fB2JuNfptCLSTIS8IGTRMIUX8No+5oU05KuZHXSdl4eG4agpiay0WeMGEl6k5Kz4Oi0/iYEouiT1h/J4RGIeS6RoVQJ6Vy8EYeXS+1vUZzxKjfa8PGeGojmhwUe/bX5RMFGN5Z89+DU3aBrr7+NwhuM9vABWrbRM8FKjm6ppeEJezQvNtN3//FZ0jc+AYkBdbMe0xP+1jf76nKKSrQjYCe9jx/9Pg42AI/ZbDvxDN4muDoV3+X5/d1UgwcioFL0XcoBC4F38EJXAqBQ8EvUghcioFDwXcoBE75PAdPPJgCJ6sDdK8oilJe12UezkFqYtwTOTWLhB5b/0mui5ulx+qyhFY6xyPALLsL9UyG+Llz4Hkk15b4/MotbOHWfF+fO4d5XqCNHEf3fQZ+8hP0M2cIBgYe6vfYTtqW53h54b26DSUDTg/w2F5XTxExlEipUCg2TbPJpprQRZ9mgl7e0WhLRPcahrW9Zju4FdFA00rXqdE1lNZJqURKKUiGOdoi3u7CnJTqPisHsoZ7bVa/sN9JER3uN8aLMionpdwk4iEiZcTvtWpuITdS3msHBxn/+h/x+nfqc8vdTQxg/FfPruegVESDoL+fYHAQfWoK//hx9NmrDXOKHslOEe/2wIzBqSdJJXrg4EEGT73wUN/vC39duCz4Du9f+oD3P/qAL37pV8uu0JLjs1h+rDhC+4eK7Fu4htmrk1iZou9ugTsdcTRLoBs+muGjRdABKjsePmtBnrXgARPRp0EXGqmJX/Dlc7+kb3GNzuUC/Tdm6O41KOo2c8leslqKWMGld22RXLFI/PKHiL4+REcX9ltvUfiDP9jeX+wB0DMZDnznO/z2lRhzq7Ud9sHibZ6+NE/8T4/ivPBCpERVxd6iREqFQrFpwnacglq0l4FmC9z5YrRFSrXbWW6SMdEw1GHUF7CUk1JuwvqHfHGXK7JFwnJSqvusHIQ6KR9wvWK3aBaOVolF0adp2M2IO9rCnJSq3clBMiZYaFCej+AYz/M8xsbGAJiYPAnUL8S7hUUuXvxgV+t16tQpbLtJgk9FHbLea9fykLHrXaDpdkDpk5Gk+OKLJN54gyCdZunwEzjTN+pyippmAFYMRrY3p6ihGaTMBClKIs+U1kXXqs2p5JGm79N/5RSJ6/ccoMt3l7B/0F3lAAVNDzB0h//ysUt0aA5+yqDtwJPcXVoge/BXeK3nJE7gURBOjRBaFGUXaI1A6lAo5xVVPByBJjBnZrGnZ1gRgv3Xp8gLn8KRFJOHkgg9j0EeD5gJNMxbWboWc3iZu4z1DcLl2/zk/Wncfb0k9RhJPU7SKD+WX6f0e6/vGmvEhIlnBAi2Z65ZCVMbv3sX3x9inztTF6bW1DWMyUkSb7xB4exZ/GPHtuW7FXKjREqFQrFpmgkKSiyKPjLnBgxb6FCL9nIQmg814gunYfVT/Z0chPUPUe/vnDCRUrU7KQhz8Eba3UG4UATKSSkDzTbPRL3PC81JqdqdFIQJ5FFsd1NTUxw6dAiAohuSJ8ve3VxsFdH0dMTyiEUdKZ2UNHGOq/4usgQDAxTOniV+7hxeVzfvdgzU5QZcjnfzhc/3l2JgRyCn6EYHaCz7cUMH6ICTob07Syxm4z0+Qq/dxWpPO0Ptw5xu/9TWv1cEVWLmPVGzWCVqOqIU5rZYfqw8LwYO+cq5Vcd9gh34C0Wbo1dnAOhYzGF6Pq5lMHmoG6HX3leFrjF5uIfUWhHL9elYzLHcnWLg45tc6N6k3NNRfjwDPxWzJCd+QUqPl36MOG16ouZxzpojJkz6nDlSeoI2PY6l3/uu6jC11soKT9+dQji117DPnWNgsoh+/CRBOk383Dnyr76qHJUKJVIqFIrN00xQUM6i6BOWrwgkEIvCREq1aC8FiZD8MFF3tIU5KROqv5OCsD4viu6OapRIKTfJkP6umVMxCjQTKdvU4mnkaTbGi3qfFyYqqHYnB7JtRBseHub06dPEf9DZ8PiB/WklGEpAq91rVX8Xbfxjx8i/+iru/+9HCBbqcorqugBdi1RO0WoH6NrRkzjXbtU5QAE028QbOU6QToNpcvch+j9d09fzK24XrvB488++yeETj3HsiRMlYVM463k8i8IlHzj3xNDy8ZIwWltWCFwCCUTPSvjdjqVSh7HUk6oTKCsIXWOpJ0VfZoWOpTzL3an1928VoUE2KJANmnSkbaWHv5r6cL0oplnrouav/cX79C2v0rFcoGviBslUklwiwbQ1wJroIF5w2Je/ixWsYV65gjcyQpBOY7/9NoXXXnugeitaByVSKhSKTRO2YK/rgljjNVVFhGgaCizCC1i+D4WQfEpKHJeD0AWsCLc7CF9gU+1ODmRyd1QTlpNStTs5aObuEKKUpzeKKJFSbmSNliGEWrSXDcdxGB0dXX/tu58BuuvOu3FrhosXr235813XRdM0THP7l6oymQyu6wJNUkmoe60UyOqkVP2dvAQDA0yf/Ye8/k6xLqdoxh7A+6NnMQ9GJ2ZvtQM019bNux2DdQ7QOauXTz2XRLM1ME0KZ89SXFnZ66rXYGkmlqfTHsQZsnsf6rOEEHjCJy+KJWEzcMkHxbKD0yEfOBSCIgVRKq84PfPCWX9eeb2ToW1Nt+ToN/ySoFqIN19srRyvnF95/25RFC5F30VkpnAnbzItBB3XpljTfYrHDTKHUqDP0cYcAIs+ZG7l8JcXYHSezDOPY1+7yc2racz9B2jXE7QbyfJPgrhmo0V1AqXYVpRIqVAoNk0zN5u6Z0SfsB2nEN3dzgD5EIES1EKCLIRdp2Z5bqOAcvDKjWzujgqO13h4rvo7OQjLkyUEFJxwZ/leE7awaxpgq41okcc0wDYFjlffv0X5Xuu4pc1ojVA5KaPJ6OgomUyGU6dOAdCR1Buep5lt9PT0bPnzv/Wtb3H06NH1z99OXnrpJa5fv85nPvMZNcaTnNCclBEXKUPDW6v+TgoStmiYUxQgm4bG/uy9o+IAXfl3jR2gAJa1hj80hPPCC6Vwmxcv7lFtdx5N00qiJyYdRn1O4q3gi6AkWAbFspuzuO7ozJfLC4FDrlxeOl5cF0Ur5zfKA+lZpXDkvlG6v8YLLstN6hIvuDXnV96/22w6TK0Bd46kSY5OYTk5srO3mepOce38X3PhC8frPtfSDNr1JF1mG51Gii6j/Fj1ustMKTGzBVAipUKh2DShO07VZE4KNK3kLGoU9ivv7EGFNkkzQUG1PTmQ1Ump+jy5CRP1mm18iAJFz25YrtqdHDTLK5XNR1ekXMs1Lld5suQhGRc4a/X9W5Q3ZqhcqHJy6tQpzpw5A8DgOeBK/TntnQOcObP1/FKXL1/m5MmT65+/3bzzzjuAyjsuO6EiZZM+JQqE5qRUIqUUNNswmC1AZ9suVmaTBAMDXP7SP+L1nxTqHKALyX4K//gQQX///T9IUYOh6aSMUojTB0UIQVG4ZSGzyIdXPsLRPJ55rJPUyihGfwcda7doW9a5ofcSmGCYHrrpYZguuuVhaN56eNeVrtLAaann4QTYB2WnwtS6wmfBX2XBX236/THNosNI0bVBwOwxO9Z/knpMCZkRRomUCoVi06jQh/KTDBEpo7yA1cwBoJxFciCroy20z0uodicDYTnaot7uHF85KWWm2UJjtgAPF6hq51B5AeUnGRMsrdWXR3lDUDPXk1q0l4OkpI62sP8LNa+VA1nDvYbea5O7Ww/Fg9FsE0OU86HmilpDB2h3R0DQP7dHtVJomkZcs4nrNtDOrF/y4j72madIXLkLg/uwprJorkvf+zoXjScJtHvRC3QR8Hv7P+SI4bFMAT95jJFUF92ff5kT3W3kgiK5oLD+mPUL5INi+XWRgthep8Jeh6ktCpc5b4k5bwmKjc+Ja3aNaLnx52FEZ8XDo0RKhUKxaULD4qjJnDQk44L5BmkGor2ApZyUshPWR0S53YEK9yo7sra7UCelutdKQVORMsIOjzBHmxKK5KHZhqCorn+HOXhBOSllQdawm2peKzeytjt1r5WbZv1Ds8gAe41yjstF0N9PMDiIPjWFf/w45pUr9LmzfDb3MzL2ADk9RTLIMuBkSCRyGH0mxQOHSRGn+8gpnj76hU19jy98fv7L9yhqHp/cHmfg8BD7HxsiGxTJBnnW/ALZoFD1PM+Km8XRGouJMoSpLQiHSfcuk+7dhseTeowBq5v9Vvf64367h7TRjq4cmDuOEikVCsWmUQv28hPqLIrwon2zuqndznIgrZNS7bKXmkRIu2vkJo8SoTkp1b1WCpoJK7mQXb1RIExAVU5KeWi2MSOqImWz/wnV9uQgTFyJsqsI1KK97ITda6Pe7tS9Vm6a9Q9R3oim+jv5KL74Iok33iBIp/FGRgjGx4k5DoeLEzXnObqNN/IYbrFIYBg4L7yw6e8wNIOEsEkIm441m4NeN6fbRpq+5+LFi/gEPP6pkXURc83Pkw0KdB226Vj6GLMvRmrlNsw53GlPoMc9DMut+RwtEJEJU1tNLihyvTjN9eJ0TXlMs2pEy6OxAQ7bA9i6ktW2E/XXVCgUmybM0aYW7OVBxkX7sHZnGoJY8wgSiogQ2u4inBvQccH1VJ8nM7LmQnW8xh2bWkiQg2STnJNRXjwNcwCohVN5COsjojzGa5qTUjmLpCAs3GuU+zsh1LxWdsLuta5X+rEiutKp7rVyE7NA1wVB0CB9ToQ3oqmNt/IRDAxQOHuW+LlzBOk0H+17Dm8uS597F1N4eJrJnNVL/mAXg2mXYG6OzBe/SOfA1nNBbxUDnQ4jRYdRKyrqn+sjcfUbcOAgVsZjLrOC/VaascQIQgfDdjFtB9Mq8Fud5+kSbThxnZX+I8TxuPzkk3QbFqtBDlc8XOjX7aYoXG45M9xyZqCcOtNA53Csn+OxQY7Hhzga20+boTrzhyGit26FQhFF1A4s+ZHR0das3amIC3IgZbtrsqir+jw5CJt4e76G44Id0U0Ojh8iUqqFBCkwzVLbctz6Y1EOQxdWNxVyUx7CNgTliho0jiK954Q5T+I2GDsf9UuxDcgYdrPogt9AZAB1r5WFZvembB662nevLltB3WvlRtNK88C1fH3/Eelwryq8tZT4x46Rf/VV7LffxjAmmbL6mbX6a84ZClz8oSEmT5+m2NOzRzUtsTFMrT/9Af3uLF3eUilMbfFemNregwXaOzrwRkZIp9L4Q0OcfPo1AIQQFIXLqp9j1c+x4udY9rMs+1mW/CzL/hpLXun1WrB3/3g+wbrr8nsrFwB4zB7gi+1P0bFntZIbJVIqFIpNowY38iNjjja101l+WqndAaQSqu3JQDMxOVfUsK1oXsdiiJNS9XnykIqHiJQRXsBS7g75aXqvjahIGZqfTbU7aQi7VlEWKZuO8dS9Vgqa5n8uRFekVPda+SmJlPXlUR7jqfUUeQkGBii89hrffn+KwvtTDDgZbOHgaDYZe4CZTz3B515LUbx4ca+rCtSGqc30H8eZXSAmasPUalqAsMxSGNt0GkyzJkytpmnENZu4btNndTX9Pld4LHv3BMwlf41lb2399aK3yry3gk+wU79yDTecDDfmM8RjFqf+619n9P/1w1353lZBiZQKhWLThLme1IK9PMgY/lCFJ5GfVmp3oNqeLDTbQJMtaHS1RfM6On7j4blqd/KQSsDian15pBftQxbX2qOazFBRR9i9NpvXIKIL9mH/E2rBXh7Cwm5GecFejfHkp6lIGeG2p3JSyk/Y/CLKYzwVEU1+cl29fKf7dF35WbsALO9+hUKoDlO7kkjzbscwfe5cTZja5XgnQyM54mWBsnD2LMEDhqm1NJNeq5NeqzO8TkKw7GeZ91aY95bLj6vrzxe81W0XMQuay4l/+DklUm4RJVIqFIpNE7YDSw1u5CFsUB3lfEWh7U4tIkhDqLsjwuFesw3C+FRQfZ4c3M9JGVWKXmPLU1goR0X0kDH8YaijTeUFlAYZx3iq3cmPclIq9oKm4V4j3PZUnyc/4RuCdrkiWyBb0BuWq/UUeYjbjXM0RnE9pRKmdu7qXyIImN0QpjZpF0C/gz80hPPCCw8sUG4WXdNIm22kzTaOM1h3PBABS36WeW+ZjLvItDPPtLtAxl1g0V/b0bopalEipUKh2DRhixxqwV4epMwNqMRx6Wk1J6USi+Sg2cQ7qn2eEOB4ykkpO6EiZaQXsBqXq4VTeZDxXhv2P6HCvcqDjJsymo0B1PxCDu4X7jWqqJyU8hM2Ho9yuwtN26T6O2mIWyEiZUTHeMHAAD96+vc4N9nFs2vv14SpdbsSnHn5U7S/+OJeVxMAXdPpNtvpNts5ET9QcywfFJl2SoLltLvApHOXG8Vp8sLZo9q2NkqkVCgUm0blpJSfMHEl70RzcAOwppyU0hM2AcoXNYQALYLNL2yXfcwSmMYuV0bxQMjopHQ8CETjBqZESnlIhiye5oq7W4+tEJonS4V73TEcx2F0dHTbPm9l8ThwvK58dj7LhD3B888/v23ftV2ocK/yEyauFB3wfTAiOGZqJlKqjWhyYFultuU3WLfPRVQsCoLwuqk+Tx5CnZQRbXcQ3u5S8d3J06d4eMJEyihHy8g7Jhl7P9/u/o2a8sd6pnit+8M9qtXWSOgxjsb3czS+f70sEAFT7jzXClNcyF7lk+LkHtawtVAipUKh2DTK0SY/ykmp2AvCBGU/0HA8iFm7XKFNoHacyo9lgmUKXK/+WkZ1QtcsBJ1qe/IgpZNShaDbdUZHRxkbG2N4eHhbPi9ueQ3Li24EVaIyqt3JT7NrlStGM69tWOjDREygNz6kiBiaVsqHupqrPxZVsaiZeKr6PHkI28gQ6TFeaJ+3yxVRPDBh4V7zETb0FULGn7bh7nJNtoesX+C2M8stZ4ZbxVkmnBnueit7Xa2WQomUCoVi04Qtnip3hzyE7/yL5oI9hIuUqYRqd7LQ1NFW0IhZ0buWYTkplYNXLpIxwXIDkTKqfZ7Kk9UahDkpo7pwCk2clMrdsaMMDw9z+vTpbfmsD2YS8HaDA3qKQ4cObct3bDcqP5v8NLtWa7moipRhc1rlKpKJVDxEpIyoWBTW34G618qElE7KkM2Zqs+Th5gpT07KCgWnsUgZM6MtUhYCh1l3iRl3kRmvlKNywpllzlve66q1PEqkVCgUm0aFe5WfsGsV1dCHEL6Q0KbanTQ0zQ1Y1Ei3R+9ahk/moldXRTipuGA5W18e1T6vmUjZpjZmSIOMTkolUsqPnGO8xuWq3clDs5DQUV20D51bqPusVISFGo5qu2smUqqclPIQtlH69tQC58+P7+h3j46OMjExwcWLF7f0vsWVX6PR8v/87C0uXqwNVTk2NsaxY8ceppqKHSDMSRnlMV5iaZ6XFz6qyUeZsQfw+9r3umo4gcu8t8qst8iMu8Ssu8iMu8ist8Sy32DxQLErKJFSoVBsCiHCJ3QqBJ08hIZ7jfDgRonj8iNjbsCwcKCq3cmFbCGum9VL3WvlQbaFUwivm1o4lQcZx3iq3clPMydlVPs8lUqiNZBtQ1Cz/wflHpeHsH5iKrPMO++8s6Pf/e677zIwMMDY2NiW3pfN/1rD8oW7txkb+6SmbGJigqNHjz5wHRU7Q6iTMoJjPD2TIfbWW3z1wykmVttqjh0s3ubg+AxD342j799PMDCw7d8fCMGqn2PBX2XBW2HBWy39+KsseqvMeytkg50foLjZ4o5/R6uhREqFQrEpHA88XzmLZCfsWkV1wR6UON4K3C/caxRR7a41kM1Z1Oz/ISwHjiJ6hC6cRnTBPgjCc2WphVN5kDGk/1qDUI2g2p1M3C/caxRRY7zWQLYNQc3+H9TGDHkI6yc6u4d45ZVXdvz7HyRM/H/7l4076pde+DwvffrTNWVbdWkqdoe43TjveL6oISJ06zLGx4mfOweeh+/BPneGPvcupvDwNJM5qxdL80jMzZF44w0KZ8/ib8G5Wwxclv1s+WeNZa/yPMeSv8aCVxIiPRqLurtBt9HOkWIP/+dX/rd7VgdZUSKlQqHYFCpPVmsQtmCfLZQGN1oE17FCc1KqdicN9wv3GkVUu2sNZFu0zxb0huXJeIDe+JAigoQt2ocJgXtNs3qpsJvyEHavdT0NP4hmB6LCvcpPzAbDAL/BemBUxSIV0r81kG1DUFi9dB3i9u7WRfHgyLYB0vOh6Ko+T3YSts+AM82za+/XhU8t3j6419UDSg7KikCpLyxwavY6zgZne587R8dcDmtlADyP+LlzZF/5B6zs62LNz7Pi5+4JkBUx0suulxeEsze/3H3Yb3VzOnmcZ5LHOWj3MTY2xv9+bm2vqyUdSqRUKBSbQomUrUHYgr0faLge2NYuV2gTqN3O8mMaYJsCx6u/lmFhVfca1e5agzD3oWp3ip1EOneHypPVEjTrJxw/mtP+sLan2p08aFpJLFppkMIpqn1e6L1WzWmlQrYNQaH9XTyaG4UVjZFtbtEsSotax5MDPZPhmZ/+BX88XT84Oli8TeIbP2TI9Lj73HN7ULt7xN56CzwPbWEB7crHmFpAMWVztz1NMWHSxir78nPYxhrF23d5LzHPYofN1H/8kB+cfXpP674V2vQEh2P9HLb3cSjWz2G7n7TZdv83Ku5LNGcrCoUicqjBTWvQ7FplCxq2Fb1rGeYsCktar4gmiVhjkTKqu05Dd9mrdicVzdzjUUQ5eFuDMBdYMzFwL2kmJChHmzw0E1gcL4K70Aj/n1DtTi5CRcrI9nnqXtsKJGVzUqr+riWQL0qLWseTmUr41K6VGTQO0efO1YVPdZ0UicU5hr73PYzHHttS+NT7EQhBPiiSDQpkgzy3zHmKusvC8vtkgwJrQYGsn0fLZDg99kMc3+Gxj25heD7OqRSzh7oR+gImUABuBymGbhXoWszRdWOKmacG6ZlxSd9dY7E3WkKficE+q4t9VhcDVjeH7X4Ox/aRNtrR1M6SHUGJlAqFYlOowU1rcL+wm+n26F3L8EX7YJdrongYkjHBcoMFrOjmpAwJu6kcbVIRdr2i2+7UwmkrEBqCLqIL9s3EU7V4Kg9NnZSSiZTtyd2th+LhkM09HnqvVRvRdgTP8xgdHd32z82tHgH66spvT85z/vz1h/788fFxxDYme/voSh9wpK7c1AucP3/poT9/J/7GinrC1lOi6qRU63jyUh0+NZld4vMrk9jCrTmnz50j8QF4/R24HR3Ez50j/+qrBAMDNecJISgIh1xQLImOfoFsUOBje4qi7jF1aIWp5BXenbnNWpBfP54NCgiq2kl7+XFxrObzP33pGit+js6FLIbn41oGk4e6EXpt+xO6xuThHlJrRSzXp2Mxx3J3iqNjGS70Ht+uP92m0YAes4N9Zpp+K80+q6v0aHbRbbaja9FMl9CqKJFSoVBsimYLumEhLxTRo9kCVhQdbb4PeUeFP2wFZMvfERYqSrU7uQibfMu2kKDanVxIt2DfLNxriOCqiB7N+omibOFeVbuTCtk2ZmTzjf9X1L12Z9A0jUuXLrG8vLytn7s4b9JIpJyaWeadd9556M+/evUqy8vL21bvK1efpZFIGbgr21LfX/ziF7zyyisP/TmK5oRugJRsbgEqxHXUqYRP1RcWSN+5wZTowjEt5pK9FGybFKsMODNknRxMTLPw2H7mVvPM/8X/nXf/3mfIBcWqnwIBDa53qvyYAMjBA963u+ZLu9E7lkofsNSTqhMoKwhdY6knRV9mhY6lPMvdqfX3bzeWZtBtdNBttpM22+k22+k22uk2K2VtWFo0x8iPIupKKBSKTRHmKkrEBLraXCINzSbf2Xz0BtZhAiWoQbVsyBYaJzTcq3LwSoVs4rhyUrYGYQv2UQ33GlYvw4CYvbt1UTw4soV79TwoOo2PKQevXMjW580t5CmvyNag7rU7g2EYvPTSS5w+fXpbP/dGkOLH1+rLu3oObJtYNzw8vG31vvPNFDQwTB4Y6Nq2+lpW9Pr6ViPsXltwNHy/NHaKEspJGR0CISiURcOCcMiXn+eDIvnA4Xr8FkXN46O78xiZDJ+6+jZu4HFo9DqYDsZzDouHuzH0O+va4kygYd4q0LWYx568xVi3i7g9w/Sdtl0Nn2q6PgCGX1orKcSb90WV45XzK+/fCnHNptNI0Wmm6DCSdBopuo2SGNlTFiHb9IQKzSoRSqRUKBSbInzhVC3Yy8T9wr1GDZULtXWQL+xmSC5U1e6kQrZ2Fy6Oq3YnE2ECi+OWhBkzYjOwMIdnKg5qXi8PtgmGLvCD+otWjKBI2TQXqgr3KhVhfV5U3eMFp7GKoJyUchE2NpJtjKc23spFszF53tFoi1jY6GB6hpcX3mXAyWALB0ezydgDjHadxoyaohphAiEoCod84FAIHApBkXz5dUVkrIiOhQ3iY6W8KJxGXsZ7VO6la3f49C+vMect07mQJXCLuLbB1OHohk/1rFJb8o3SGkq84NLMgx4vuDXnV94PkNLjdBopOowUXUaq9NwsPVb/xPTojW0VD0fEpsgKhSKqhImUURuEKZpjGhCzBEW3/npGUaRUO/9ah5ZxtKk+TypaJSSTWsCSi7Bwr1BatO/cvY3Nm2It17hcudnkQtNKfcVqrr4fcfzoLeQ0c9m1q7YnFbKFuC64jZfB1NxCLtQYT7EXNE2fU4iOSKlnMsTeeosjP5/lzGqt3f1g8Ta/6vyC+J924bzwQl3+wlaiIi4WgnsCY14Uy0LjPZGx0ECArH5PQYSEftghoho+NYylnhS9MyusdCXoXMrRNZ9ldqAToWsEvoHv2HiOhe/Y9ForHJzzaTc7Se0/ymBbH+7x03z5wFdpMxIq/OojjLryCoViU6g8Wa1DMtZYpIxi2M1mk0w1oZML6RYSQsIfq3YnF60ijqt2JxfN8umt5aMnUjZzUirkIhkTrDYQnaMY7rVRPSs0E/oV0SM03GuTa7yX5MOclOpeKxVh1yuKc1poMrdQ6ylSkYwLBpxpnl17v86d6N45DOn6PKm7jTE+TvzcOfxikbt38+xzNfrcu5jCw9NM5qxeislejMlJEm+8QeHsWfxjx/a62utsFBZX2hzumIuQvbbuZiwGDgXhrouNBeFQCNxy+b333te9GFH2InzqZtCFRkyYdMXaSekJUkacNj1O33NHeeLG3xIbMumbvYIoBIz/zT4u2ycJhFX1/oDPdn6Hbt0ilU6TGjgOmkb+018kMNt3pM4KeVAipUKh2BQqT1brkIwLFtfqy6MYGkc5KVuHULEogu0OVJ/XKkgX7lW1u5bgfiJl1Airk3JSykdYn+f40Zv2N/tfUG1PLsJE5Vxxd+uxWfKOclK2AmqMp9ht9EyG/X/1Fn88vVJ37GDxNuk3f0T80v49dSfqmQzxc+fA81gaH6f/6hSxXO3uuD53DkuY6IuHCdJp4ufOkX/11QeusxACR3hlYdGtERCLwqUYuBSCYlVZ6bxClaBYDFzyokgxcCkKt/YLngRYgLkPH+yPIiHbGT419D2ajeXpxISJv+bQk+xiqGc/bXqcNiNBSo+XRch7zz/+5SgaWn2u3l5IPLaKPjWFPmzhfDBGb26Rz+bPk7EHyOkpkkGWASdDh7WEaIvjHy8JlP7QEEF//9b/SIqWI3qzFYVCEUlUuNfWQSZnUVi7Mw2Bre5gUiFTu3M9cDy1kNAKyNTuILzPW12a4uLF8R3//lOnTmHb9o5/T6vTLJ9eViaRUuUFlI6wPi+KTspmIqVy8cpF2PWKYn8nRHhOSjXGk4tWGeOp9RQ5qLgTraKHRpI+d67Onei6bXvuToy99RZ4HvrCAm03b5LX4xTLTs+KULTfm6ZdzyM+/ojc48codnWQ/c5/5ObvfZ2r9gyu5jG77K+LjcWyiFgQzrqIWBEgC4GDI1wp3YpRpln41GoCz8QSGvvuFrCEid07wEG7m7aDw5ztep6kHiOlx0jqcZJG+VGPkdRjGJrBxYsXARgbG2N4eJjTj59uVJ11NML71+KLL5J44w2CdJqlIyM447eICYfDxYma84Sps3bkCLF0GkwT54UXHvCvpGg11BKvQqHYFGE7/1R4EvmQaddpszDDWvSqq2iCTO2u2eKGWsCKNo7jMDo6uv56+k4v8FzdeWt51idlUeLu4vNAvUgYt3YmZE81Y2NjAPU7YxVbppnAEsUcbWFikRKK5CPUSRlBkTJMwIrbYKpVCqkIDfcaQZHSDQxEyCKrGuPJRZhIWXA0fB+M+xuJdhUVpUVeqt2J1vICz6/+Eiuodfv1uXN0XAL99LFtcScC+CLAES6FwMURLqtJlylzCSt3Y92hWP1oZjKMXPs7/MBj/8efEBhF7h7Wmdkv0K0pOgwf3fBZxCd5a54bizm8y7OMPTWIWNP43sfLLPaWXZeL1x7mT6bYJJZmktRjJHSbIO9hC5OBzj56nx3i2PiPsfbr9M5cxV/LMf9ukgvGSXwvhu+ZBK6JFsBv9nzE/mCewDQZPPjpUvjU518l6NoZd2JlztiI5PHjDPzkJ8wWbd7t+HxDMb/YXeRYRweYJoWzZ1s6J6pia6jhv0Kh2BQqT1brIJNYpNpd6xA2AZep3YFaSIg6o6Oj6ztBAeK21/A8P9BxfQ3LiNb1DHN3nDg6yOnT6V2ujeJBsS2wzJIreyNKpFTsJOHhXi2ImM8htN2pUK+RxvO8ms1AAIvzA8DBunNn57OcP395S59/9epVxsfHuXx5a+/bDOPj4xjx8MVQtflWLpqNyXNFjfZktK5ns823imhT7U40x8aICZO8Fiu7E5OkRMmd6LtrBJcvkR0+RrGrnbW/+Q/c/P2XKQbOejjUYlB6dAK37FKsCI2VcyplDq7YsEnxU3CBeZj9ZcN6fvr9aySdWToXshSKWVzbYPJ4CluvveEKNCYP95BaK2K5Ph2LOZa7Uxwdy3Ch9/hO/RlbDh2NuG6XRcbyj2aTKDsVEzU/G87TS+eZ2r25X2UD7ekTp2EfJI45pfCpIyny58/TvZjlmcLHdeFTU2Ye0Q7ZAweI73D41FOnTjU9njtwgMmvfIWVv7qCQGfW6mfWulcXQ/d58bEjdD/9NPmvfEUJlIoalEipUCg2hcqh0DrIFBonrN21qXYnHaHieATb3Vo+vE4qJFP0GR4eXncDmp3hQ93HR07T1Rat6+mJxqqQanfykYrDUoP8z1EMfxhWJ5UXUD7Cw72agNvw2F4RJlK2qzDDkUYIwaVLl1hevpcda/rOp2gkUi4sF3nnnXe29Pnj4+N87Wtf4+TJkw9b1TqEEFy73XjzEkBK3Wulopm4F0WRMrWY4eWFDxlwMtjCwSmH4OwvnAA697p6LY8n/HVB0KkIg2VB0BFuSSDccNwRLlZmlic/+SFB4HHg8nU01yV4qo27h7qxrAm6DB9NEywEghu3luhazOFdnqtyJ67ecyfuMF3zWQA6lko32KWeVF140ApC11jqSdGXWaFjKc9yd2r9/a1ORVyMlwXFhG6XXus2CS1WeiyLiDHNanhOQrexNBNtB8N7VYdPXTtyhGBhqWH4VEezWTtyBLfsTtzJ8Km2bW8q6s6fx3+T16/neHbt/Zo+L9/VR/d/93u4KgelogFKpFQoFJtCOdpah1BHWwTFItXuWoewdtfMtbhXNKuT2u0sF836ilxRi5xIqfq81qEt2VikjGL4Q5WTsnUIu0cVPRtZRErl4I02lmXx0ksv1SxSaj+M82cf1J9r2p288sorW/6OU6dOcebMmYeoZThXJpqEqVNjPKlIxQUDznTdInjGHsC9fRjSfXtdRaAUKjT21lv83ifLdfPtg8XbfPrHPyHu9uO88MIj7Sryhb8e1rQiGNY+93DKouKt+AQuPp/ML1Msl98TFmvPL5V7BAQPVK9Pf3CNjLtA50IWx8nhWgYzJ9ow9Np7qtD33p1ouiXnpeGXftdCvHmo98rxyvmV90cSAXHdJqZbJHSbmFYWDTVrXTyMazYx3SauW8TLxxN6lehYFiDtHRYXH4aNoVQr4VMXHIfL3Scp+Fpd+NRkV4IR5zLB4iJTv/7r5KanYXp6099XiUC0nWTzGhl7P9/u/o2a8kP7PIL++W3/PkVroERKhUKxKUITvauFU+mQKdxrmHCqFuzlI+yaRVGkbPa/oBaw5KLpLvuItT0hwvs8FbVAPmTK0RbqaFNOSukIu9cWI5iTUjkpWweZomU0+19Ixh9MxFDsPnomw+Bfv8UfT6/UHTtYvE36P/yI+Ef791z4M8bH13MZel6Sfe5sncBgG+0Yk5Mk3niDwtmz+MeO7Vl9wwiEwK0SCR3hrb92Am9dHKy83ig0FhuW137elkTEyvhk9faO/L7VyORO9KxS6FDf0AGIF1yWm5wfL7g151fev12YGMR0i1iNkGiVhUR7XUiM6xYxzb7nViyXx8pi41/953OcPDHCs6ef3db6RY1GoVQr4VPTP/sZ+ozDrDVQEz4VoK+wyFo6TfDVr265vxseHubUqVNN80w+CCoHr+JBUCKlQqHYFOom0zrIFO5VtbvWoRUcvImYwNjeuZtih7mfkzJK5B0QImxDkFo4lY0wkTKK4V6VWNQ6hIZ79SMoUuYal6sww/Ih0wbIMJFS0wTJ2C5XRvFAVIQ/3/HQSNLnztUJf67btifCn+M46zlbY/PzDH3ve+i+j7m8wnOLeWxR677rc+cYuAp68hhBOk383Dnyr766JaFhXUA0fVb1AlPOPI5wcWvEQA+n2l1Yfl2seu1WnVvjRBRufW7ERwiZ3IlLPSl6Z1ZY6UrQuZSjaz7L7EBnQ1FVC8S6gLrSlUBDI9fTSTKwsYRBZ6J9XVyMlUXEimhYeiwfWxcd68+pzrf4MJiBjkb07ifbTdNQql/+Mn/+r1Z575tjdc5x49kn+S/+ebTCUITNs1UKE0UzlEipUCg2hQpB1zrIFHYzH9bulJtNOsKuWbagIQREKeJKtqA3LFftTj6aXbOo9XnN6pNQ91rpCBNasoXdrcdmWA0Ri1JKLJKOsDGeTE5KFe5VPsJzoWq4HlgRWnUKE+yTMRGpsaiiMXoms+5MNJcWeH71l1hBvfBn/TzP/OP9uB0dBK+/zuRXvkKxpwcouZVs296R+o2Ojq6HT+w9/wuE76KtLBO/OUHCbMc1LWaTPRTsGMkgR39hFifIs/rReyycOEi+q43lb/0rfvm7v1oWDr0aYdEVXo0jsUZAfA7eYQ6mfr4jv9ujStTciQAG+roYaK87FS3sp7sZ/OTvMPv76Mj4mJ7P4vsmH6WO4gcWgWcQ+AbC1flK4hrHDRetzebgkecwdJP8l/+QC1NTAJw+dv+8g4rdxe/v59vd9aGDnzZcYGH3K9SEtbxax1NsnQgNFxUKRZRRjrbWQabdzqGDG9XupCPsmgWBRtGF+M6sFTwQ2ZB2l1JuNumwTLBMgevVX9Oo9Xm5EHEc1L12t/B9f90B8bB4xRNAV135+M0M58/vTHiy8fFxhNh6W1lY+hRQbyGay9zg/Pm721Cz5mzX31wRPsZzPDNyf+ebt48A9Tnjirl5zp+/Hvq+q1evMj4+zuXLl3eucjvM+Pg4nZ2de12NbaNpaPWiRqcZnXtYmGCv7rNyEHvrLfA89IUFzLEx4pjktBgZe4CcniIZZBlwMmjeKm03b7J25AhuRwe9773H5Fe/ytjYGAGCU08/WRWatBK6tCz6lcum+3K4sUlml/11F2L1eU6VgFgRD7OdObzP+FxeuMGX1i6gxQTDmSnylo/2jMfcoW7QZ4kDAZAJdIxbBboWc3hXV/jkqUHEzTt8eMNksbdtr//cCh7Onaijk+/tIm20E9NNbO1e6FNbM9cdirZulnMsWmVnYrlcM9fdirZu8dd//i2eOD7Mc6c/3biyA5D4pYE+NYX+RDfmlSukbsCwO1n1P7LCgJOheyCH3WPhnRjG0E38oSGC/n4oi5SK6KEioilaHSVSKhSKTaHyZLUOzRxtUUPlQm0dmvUV2YJG3I7ONQ1tdyo8iZSk4oKltQYiZcQmdGHiOKh77W4hhODSpUssLzfbI785cmudNBIpb0zM8c477zz05zfi6tWrLC8vb7n+K9knG5bfufUx7wSfbEfVmvKLX/yCV155Zce/51GgmZNyu9r2dnF7qotGIuXK0mTT/5Hx8XG+9rWvcfLkyZ2r3A4jhGBhIVquh4eh2T0qV9DoTEXnHpZcXuHlhb+uC5d3t/NTQIR2zD1iVEKWbhQBq1/rmQxDN39JEHikxy6juS6Fw93c6E2DWcQwcjh6wKQWI70q8FdyuLNXuNZ/EC87yfcDjZnPxvmhloGJH9+/UsdgjBVYvLb5X6S83+zU2CQAHYs5TM/HtQwmD3XXCVtC15g83ENqrYjl+nQs5ljuTnF0LMOF3nrHlGL7MTHKIqFVEhHLQqGtW9iaSdfp/RwZ/xHGgEZ6RmC6PivvG1y0h0vuRL/kTsTT+KJ5jeGUh9Zmc/jIGTRdJ//iH5bEv23A8nUMwjc1AhRffJHEG28QpNN4IyMUrt4iJhwOFydqT7QtvJGjBOk0mCbOCy9sSx0VO4dMZoNQkVKtpyiaoERKhUJxX4Ig3OGh7PryoXZgKfaC++2y7yE61zQ0vLXq76QkGRMsrdWXR63Pa7ZRRLW93cE0TV566aXwfDBb4N35Dj64U18+eOA4r7yy76E/P4zh4eEt1/+//cvG8TVfevHzfPnZkN3624xlRS8cqYyEjY8c39q2tr1d/OW1LpisL//UqWO88krzBd1Tp05x5syZnanYLrFTmxX2gmRcMOBM8+za+3Xin3vnMPTUi9G7ztQUvX/xF3zpl1NcWT1cc+hg8TZ9Ez8j/qfdOC+8sKV8gK2KEAKfYN0hWBEL3Zqcht4GYbH8Oqh9nXne5NttH/L29LV751R/XuDhcf+8fZ9+9xrHc9N0LmQR+Xlcy2Dh2U669PqO5EZvAmt0EctxMOcXyHanGLxym+kv7I7wV3HTdSyV4lov9aQaOu+gJFQu9aToy6zQsZRnuTu1/n4FaGhl0fCekOjmi1jCoKeju6Z8M89jevmx7GQ07pc3cR8kjnkld+JIJ+aVK6QnHZ7I3qpyJy4z4GRoby9gtpt4Jx5H0/V77sRdJBgYoHD2LPFz5wjSad7tGKQjd7cub2vnE+10pwMwTQpnz6p+TwJC1/EiKFKG1Umt4ymaoURKhUJxX/JNFnJV+EP5CBsYRG3BHsJzA7YlVLuTjfs5KatxHGdPw9LdmHgcqA+x5LsrXLx4cUdz2Si2H1l2nYb1wbouIhUOWbE5ZIla4HpQdNVCQqsQ7qSMXicSNr9Q7U4u9EyG/X/9Fn88vVJ37GDxNt3/4UfER/fvrfh39Sp885vYs7P4vsY+d6Zuwd42ujEmJ0m88QaFs2fxjx3bm7o2wRdBjbhXLQZ69xEKw95XER0biZHBdm0gPKCTYxGKiw/1MTIJf6ZbEl0NvzRnLcSbb8SpHK+cX3m/DJgYWJpRdh6W3IdbFQ6bPTcx0DYkjL148SIAp0/szsabje7E4M5YQ3eio1l4Iyf23J3oHztG/tVXsd96G8dfYtbqZ9aqFUstK48/NKQ2ZkiEXOt4aoyn2DpKpFQoFPel2YKasuvLhywL9gBrKkxEy9Asj+jGtjc6OsrY2BjDw8M7Xa2G5J3GO2qTtsfY2BhApNwoiuaEtb2oiUXNJnNatKqq2ASy3Gub/R+o0OryEdbfOV70pv1hecfVApY8GOPjxM+dw3c8NJL0uXN14p/rtu2t+Dc1Bd/8Jnge5vIywzO3WMslak7pc+dou2ugLx4hSKeJnztH/tVX77tw7wu/1g1Yfl0rDLpVjkO/TjTMJGfwtIB3Z26HugwrrwMe7U2aMgl/nlWaS/hGacNtvODSLNh2vODWnF95/8NioK87By3NJKaZ62FMKyJg5XlMs7DW8yaapfNrhMfK+6rfa2FozcOPtgIb3YnjB55hxvHr+rt4bzfD6WIk3InBwABLv/ca//I/BA1d7l/43cfo+0LPntVPsXXC5hZFV8PzwdyebmNbCBvjqfQ5imZEb7aiUCgiR9MQdGohQTqahXsVgkgthoct5qrQh/Jxv3CvG3mQkIXbReLvOhqW7+/v3DPhVPHgyBLiWoUZbi3ChJa8I0e7AzXGk5GwdhcIA9eXo+2pdicHeiZD/Nw58DyspQWeX/0lVuDWnNPnztH1Eeinj21J/HsQAhHgBh7FoCQKlsRBl/h//nfo2Tm4excxfhnTBr0L7rZ3k4/ZpPwcfdkFYmaR+UvTzBwfJNuZZPHP/2/87Hc+hyPckvgY1AuI2+I0jJUf83MP/1ktTlSEv82w1JOid2aFla4EnUs5uuazzA503nN+Co3ANxCBToct6F90sDWb1Z4+9lmdBINH+UxqZF0srHYpWppBTC+JjtXOxbe+8wNOHDnGM6eefqQExN1i3Z349tuY5nhDd+JA4EfKnZgtaGTs/Xy7+zfqjv03Q/OAt/uVUjwwzeaE+aJGezI64yc1r1U8CEqkVCgU96Wpk1ItJEhH2MBACI28A8lYw8N7ggoT8fDsdejUamLmSxS9+gWCS6M3SLlzTExMREIEDM2hoHb+SUlo2M2QHZ57hervWoswoSVsZ/FeocZ4rUXzBaxoTf3bljO8vPDLOndHX/440LXX1VPch9hbb4HnoS8sYI6NEcckp8XKOdqSpMQa+70MvrNG8PFH5B4/hpPuIP/d/8Tt3/9N3KDkLHQ3OA+vH1glF3zI+zcmS2Jj4JaFx0pIUveeEFkurwiIG0nPrfKVDz5AE4LhS1OYwmftWIrpQyZCX8UACsCdIM7QrSzaYg7z2jiZpwYRdxaYvdPJYm99+H/F3nBf4a8KLRDr4V1XuhLr778fOvq6W9DJFUlYMdoTbTVi4D2x0KwSC831Mlu3aP/CMocmzmGkoHPuEovzAcbf7ONKfARfWIhAAzQ6kx6/+/iHGFovos1i4NBzoGnkv/iHBH1by2XYlrfoCBJ0mvf/PRUPRjAwQOG11/hZ5gof3l2ru39lBoY58sQ1mJ4u/WwzExMT9z+pipnlONA4D/rN6x/hLObryvcympGiOc02cWULcoiUam6haEa0ZioKhSKSqAWs1qLZNcsVtEjtbgpreypMxObZ69Cp1cRtr6FIWXCjNRxRg+rWIqy/UE5KxU4iS94YNcZrLVJxwYAz3TC0mzZb2OvqASUHXuytt3hlfBlnQz7Ug8XbPPvDnxDP9UfGiSI7gRB4VaFGvbpwpGWxcEM+woqAuDGX4VxqntT8EvrV9wkCn8OXb6C7HuLpFPMHu7Gtm8T0UnjNhUBw49YSXYs5vMuzjD01iFjV+N7oUrj4dwAmuAaz1x76dz96dQaAjsUcpufjWgaTh7rrRC2ha0we7iG1VsRyfToWcyx3pzg6luFC7/GHrofi4dAAW7OYeeIIT11ZIOiJY05lSXgBBz4pcqVzCB8T4RsEgQ6uxqfW7rCfNkTCQh88haEb9D//OzzfP3BPTKwSGythTw3t3jzlzTffLEV2OfYAkV1SkDh0FX1qCv3xk4ifXqU3t8iZ/IWykJ8iGWQ55k5j3M2CpuEfP156HBoi6N+aQPkoUknBsRdMOHpDd2Kvngcevu8K49d//dfr8nM2o+CEz7ETVuPwx8PDw5w6dWrLdVPsPE3X8SI3v2js4m5LPNphyxXNidaqoEKhiCRhNzzTEMSap4JQRJCmuQGLGmxH6KJtwHHB9ZRYtB3sZejUajraTJZz9eX7Bh7j9OmBPZ1sVhPW56l2Jyey5wZUmzLkRPZ2B0oglw09k+HAt97mj6frAyAeLN7m6PduEc9P7Kn4V8lhiOfhuUn2ubN1Ob0so31vcxjuIH45bGhWFCnYPhl3oc5R6G1STGyU49DdIDA6gYfHNufhs+HT16+x4K3QuZDFdwoULIPpox3oeu137bX4V3HSdSyV3EJLPamGrrtKXZd6UvRlVuhYyrPcnVp/v6KeasehpZt1bkKrynX40cUPOXLwMAf7h0rH9Abnls+viIeV/ImWbmJilISZw5D4+N+XhL9ThzGvXMG9o3PgUr5G+BtwMnS0a3Qf6sAbGaE9nsYfGqLrsc/t6t+o+OKLJN54gyCdZqr/JE7mOjHhcLh4zw0XTwQIy8I/fpwgnQbTxHnhhV2tp4zstYh2cKgb3qsv90QsEnPuCuJq+JL/Z597UkUJkoym6XMiNr8ITduk1lMUTVAipUKhuC9hocnU4pWcNLtuzRYrd5s15e5oOVLxAKh3Ukap3UF4n6fanZyEXTdZwm6qyZychF23qPV3YYsICVtg7F76LsVDUhH/jIKPRoI+d65O/PM86sS/3QwJH5ufZ+h730P3ffTFFT63kscW9TkM297PMX98ALejg+D115n8ylco9vSsnzMxMcHzzz//UHURQpTEvfWQoVWhQ8shRd2qUKPr54kG55efu8K7F5Y08BqGKvVFlYPgWfjZ5Dce6vfYK2QR/0y3JJoafunvXog3311bOV45v/J+GdAAM1QoNOuO2eti4b1wpbXlzUXEreQ6/L9evMjne49xOv3w4k218OeNjCDmbtQJfwBFLLyRE3sq/AUDAxTOniV+7hzL8R5+0dFf1zcv9aQZea4dNA1Mk8LZs8pBvgls295TMXARG/6yvjx6c4vw/9OEWsuTjmZrEVGaX3g+5B21nqLYOkqkVCgU9yXMVaTcHXKSjIeHWMhHaHDTbKCl2p6chDqLIheeJKTPU4NqKZFFLArLkan6Ozlp1t8JUVqPjAJhC1gpFY5JGvRMZt2daC8v8PmV2w3Fv32f5ND7niJIp4mfO0f+1VcZnZ7etZDwfefPo/s+1soK8VsT2KKLomYzE+snZ8VJBVn2u9PoooB1a5zckQPkO9vQ3/8h11/+VTwtwCdg+bjOu+Iq1++s1IiDdYKj8HADl2JwT3QsBk7pvAb5CxWbRxbxz7NKOy18o9TPxQsu9T7je8QLbs35lfc/CBrUuAJLgp+xLgBWxL+1xVVMdPb39tcdqxEKGx6z1j9z3W3Y4lQLf0E6zZ1jfXyyuly3KcPu7WYkXdxz4c8/doz8q6+SufoOghlmrX5mrXuhXE90eKAV8IeGVIhriQgTWjxfo+gSmWhj4RsgA/TN7zNQRATbKkWz8/z66xqleW0zV6ea1yqaoURKhUJxX8IWTpWTUk4SdvixKIlFKgRd6xGaoy1Cg2pQjrZWQ/bcgGrHqZyEXTchNAoOJGK7XKEwpmd4eeHv6vIXznZ8CmgyYFBEhthbb4HnoS8sYF4dI45JXovVhT/U3FXMK1fwRkYI0mnst9+GJ55YDwkfiKAm3Ghd+NCgOvSoXxdadOP56yFLA4/E7DyfzV8mSPg89sktsraH/mmXxcNpbOPGekubDwS3bjmlHIbTV/mkdxCxPMN7hfi9HIbPmvyt+AgmP9qzv/mjzl6Kf1thqSdF78wKK10JupbydM/nme7uxRcWga8jAp3ANxCBTl9bwKGlRSwjib/vAEdjaToPPk6q63NlJ6LRUDDcKBxuVTS8OHkRgNM90QkR+f9n78+DJLvu+17wc+69udeWWVt2Ve9d6CqgARCNhRBJQQQBipRIu2VZtkxANiXL9vDFi4mZmIgX8psIO2zPWzx+4/CbF5qxn8aLKNoS+GRbS5MCKYCAQEEiSGyNrYGu3ru6qyprX3O9y5k/srIqszJv1pbrrfOJqMjMc7dTlafOPfd8z+/7a3UKwp//1VfxXZ0tE/4ABmy7ZYQ/Jx7nTx/8NX44uVyWLzhwPEr66yMqB2WbUTU3YEYQ8LXG2F09W3iPcECymir/XtMt9Fyr8t0r9osSKRUKxY6oCXtvoWl5e49KA5lqliCNJlXFLkVFeLQnboPSVlr5ByonpdfocIkeb6X+Dtzr06H6u7ZkJ0umZttsaYkEgVde4f7X5kislSqmx7J36b/7Y4K/F2uJCd7DhiMllmu+weLchRbadILjty7hODa94x+DaZE5GeVWXxR8GXQtRU5zuCcCdC3bWKkZrMtzXH/oOPbqJf50ZIL5/gjO7dexqV9f89hH11m1U3QvJpFmjpxPZ/pkD2zr9pqdw1CxO4rFv+7lFD0LSWbj3RUtX4UjN+1dV3tCAKz0dhIU/g1xT9+0JfUJg8XZBXq7owxE+/NWpZpv8yegGfg0H4GNzz7N2Hifjyrc2jdfHjiyQOjffRNtCLS1H7O6sMC1l3r5OHAOp8iuVJMOTx//kGN0I8M+YsOfAiFIf/Zv4fQo8agVceJxMs8/zye+Fd66ca1soc3c8Yf5pedbZ6FNMi1I+I/wYuyrJeVffzCFM7jWpFop9stOY7xoZ2s8M7rZzyp3oPYlEpSspsrLW2k+RYmUiv2iREqFQrEjrtaHKlR/X1iWxfj4eFPrEDC+QDpbHsZx+codBozpJtSonPdu9QK9ZeUCyfjHl2pmlTcxMdEQmzNFe9i9Og6kqtkfmhU3KVoYtwU1rdTuQE0keI1wUBLPTZdFTST8ccx7J6C7v2l1K+QvxLLImQYD5ky5VZ4eK8tfeNiQUm5GFZZHF7pHDxZHHJouEYc5abHauYYlHP7LvUv5cie/j8XurTAf+8l1VtLTdC8mEelFTJ/O0qPd9GiTZftO9EUIXl7Bl8uhLyywHoswdO0e0wP1F//aJYfhYURDK4oGLBUMC5+LIwaX55cInezi7M330Y9A78xVDMsm/X6QH4sRHMeH4+SjFLEEP61f42yHiejwc/Tk4+iaTuaLv+oaOfbtH36bz372szxx9omD/3LHe+D4Cbh3D8bG4Ec/oj87z5O5H5dFGnesJaFTYI+MgBDYw8Mquq0NkIODvBg7W1Y+IGxgvvEVckFFtHmLaov1W+n5Yl3N43mOdniurZabVbU9RTWUSKlQKHZEDapry9TUFMePH29qHYK+yhNgGbMx9ku7IZ2rXJeg326ZXF6KvRFxGZS2kt1rtQF+OCCVSNmGtEtOSrcHOuVa0H5oiQTD33mVb0yXGyAey96l59s/JHj/kaZEKRbnL9QWFzl64za+VGkEXb85R8e8jrZ0siR/YbMiKqWUODibImHOqSz+bdqLFomKhc9mhWMq7b9ddKwrhSfxA6RHbBfxr11yGDYbAx2/ts1atORHL7UVrZCrcLu4WCkfolEkOupib64Cl+5egjCMnA6gTU2hjUUwrlyhdzbHuaVbZeJfZ0cGX5cP675RDM1ovPj3la/Af/gP0NtL8tQZsjeWCMgcJ7ITJbsJnw9r7AxONAqGQe6ZZxpXR8W+aYcJe3B/1lEpTNqTnSIpWwU1j+c92sGZSqVtUuwXJVIqFIodWU8rC7paU8j/0yxi3T4SyxXK+09w/nzzojuKubEWrFjeGRY1/ds1O6r1MOE2KG2XQXUkKEmtN7AyiprgGsHbQu0O3OujJhLai0KUopGxEYToN+fKohTNXEfTohRL8heOjyNMjawIlQkLvWTK8hdmnn8eRzplEYElAp9TLBZujzYs/ZzbKJsYW2Q88h5/OnXFVXyUqP+DSrSL+NcuOQwLaIhN69CCsFewEfVt2I4W25AWog8Dmg9fwX60sI8w8Gv+IutSg6sfj/PBu+/zi3/1r+HbECQNYaC10Sq87LPPEnrhBZxoFGtsDDl3q6L4l8WHNXZf88S/oSH45V+G3/990h1R3ugaq9gvH/9UB+GoBMMgc+GCsrluE6o9W0hJyyxsVWKRtwj520SkVAsgPUc7z6cE/RK9dWIiFC2IEikVCsWOKLtX79EWK7DcrA9Vu2tb2iGibUeRsoF1UdQGt/4ukxPYNi3zsKTute1PcZSif2WRz6zexS9Lw6/7zTm6PgTtkTMHilKUUmJhl0QIJkMWc/oa1zOTFaMLjcQsJ2+8ydraGsMf30CzbGYHYkyM9YOxjk9fxdZsptHpWFjl9voq5kczXH3oKPbKW3z/7dss9IVr+SfL0wOQg1w12UpRiXYR/w6Sw1BDI93XQ5cexi98pFaTdIU76Ono3hAFt8Q/f0l+QsNFaPSV778tr6EuNEQd1Y1lkSCcNYgaHXW7Rr1x4nEyFy4QvHgRJxplcqSf8bWVMvHP6ItxfzTbXPHv7Fn49V9n9V/+/5DArG+QWV9pNKffn8IeHlJ5eNsMtzGelIJ0DsLl2U2aghrjeQtdzwuV6Vz599pKiyBVu/Me7TCfohbeKvaLEikVCsWOqJV/3sPtu1ODakU9aYeclG7iOOTb3lwD66KoDdXuVamsoDPcGn1Kx0qCryy+V5a/sD99Bog2u3qKKjhSYkmLwMvfI5NLweICgfFrdAQ1spqP2XAvGX+AsJNkID3PanqVzDvTzJ0aItkdZvl37/LmV38KWzhYwsbGwRLO5qsR9GHhbItetMpjCz8FbzEPiXcr1vOxN6+TzU7TvZ4k7aQwAzrT5ww6tdmyfe/1RAhfXsFn5ggurrASi3ByfIqFvvrnL1TsnoOIf4Xjd4uOtmkdahTlLtyNNWnXE2OM3XoJfUjQP/sJa0uS4GudXOs6gyMNHFtHOhrdQcnfHv4En2YjOvycOPk4QtNIP/v1TYvQb397I2fhuRrkLFQcCPvMGdLPPYf/1VfxXZutKP712Tb28HDzxb+hIca/8Ev81l/2VcwV/LVfOYHvbGu4ySh2TzXrwFRGtIy1oHqu9R7hoBIpFY3H7bk23VLtTjnxKfaHEikVCsWOKJsI79EOkZRuid5b5WFTsXfaWRwH1fbalUhIEs9NV5yUzN49QedocycltUQC/w9e4VdurSBlafs7lr3Lgz/4c4JL8eZP8LYJjpTb7ESL7EWdctvRXJG16M1jayyErvPx/GLF7W7nsrCJzq3xpSvvIaRk9OMpDMvGeTDC7PEYUpsnANhAwtHR7zj0LKUxJm+TiA0hF1eYXo+x1OcSVXWAnIXFtEv+QkV1NLRNi9CFB87wqStLyN4QvqkUIcth+GqWKz3DONKHdDQcRwNTcG71DnEtAiEfYugBNM0gdfQxetdjjJ65zyXv4VaZtsfchSVEIXRqIZ/DcDTA+utX6V7I8NDitRKr4fuMaYLBJGg61n33ITSt8TkMFXvCicfJPP88V/zLvHX9etl9NnHsU/zN56vbEDeKVFYn4T/Ci7Gvlm37zWOzoGyl60a90mtML4WAz1fc9ta7nxDvSe/73BMTEzvvtEtWk1+sWJ6YvMGlS+ULhfbDxMQEo6OjNTmXYmciQYeF1fL7optA0wzc0jZFgkosalfc5lPWqyy0bjRudVFzKYqdUCKlQqHYEbebTIcSKdsWN4G5lQY3bmKREsfbl3awJ3Gri8+Q+Ftjjk2xB7REgmPfeZVvTJcbIB7L3qXzd39IcOxI0wTAQv5CK2uBDDNgzpZZ5fn15uUvPCiFCMPtQl9OmiUiX7El6fZ8habjLhaWiJDOlmC4b4YBkrA+uedDT1+dAaBrKYVh2Zg+ncnjsTIRUGqCyRO9RNaz+EybrqUUK7EIp8cTvFPnKMV2yV/YLgjYFPEKOQULAl++bOu9TysXAGenZtDROH3sZJlAWC1KUS8WC49D6OPfzYt/545jXLlC7p7GsY/SJPzdJXlGO0OS3tPdWGNjdAaj2MPDDHWdZfyjcXJmH7k6/70Cg4MMv/8+mm1zvfMU2ux0WQ5DnzBZzWRIHj2Kmc3izM0xef482UuXNveZmJjgs5/9bJ1rq9gr8sggL8bKBZIYDrSID0Uq625vHFKTp3VjaGiobucO+tzvS5ncweysn3766ZrYPjsSMmblqdegv0arkBQNp53nU5QjWvvSDsEGqt0p9osSKRUKxY4omwjv4WoT0UK2m27RdardtS+ukZQt1O7UoNo7FARAkbIRhOg358oEQNNsngBYnL/QnlviM6vvV8xf2PcJaMbB8hdCPoehvWEZmisT/izuGYt5i9Hk+A4RhxvvN8/hso9jHUwwbDPaIUqxXfIX7odicW+7GGgUWZT6i7drpdsL5ds/F5/TrxWJhRwsb+GlW3nh7Xzngwf63bPPPkvohRdwolGssTHk7K0y8Q8gJ/xYYyM40SgYBrlnnuFcLHaga++pnr29JJ56ivjrr7PsjzLedbKsX16IRoiO5UDTcHSdxFNPke3tbVgdFfunHcZ4qWzlqKKQ30JrneAnz2EYBqOjo5w/f77m505m3LedOHU/5882XwSslkrikQfPcP7siZpcp17RqorKtEOf5yaYqufa9qUd2p2aP1bsFyVSKhSKHXGz3VSDm/alHWwi3B7oVLtrX9wsPlpp5Z96mPMGxQJgcG2Rz6zerSgAdr4P2iMHFwD3g/8HP8CxcrCwgDZ+laChYeob+Qt9QcJOksHsLDk7hf3ROyydPU66p4PV7/5bLv/SF8pEwpw0twmIdpmwKKtZ2XVuvM5dbsjv7zXaIUqxEfkLC3kLK4l65UJiXgy8Pn6N/mgfx48c3dpHqxBZ6HIeQ+hoNYh2aVeceJzMhQsEL17EiUa5e7qfG+srZYsyRKyDc1EJhkHmwgWceBw/1EU4cOX8ebQnnmD1H/8IOTlTlsPw/j6T+FB2M4dhd4X+WE3EtyZu46RMTmDZYLTAGoe0SyRlZ/jw9h/tTsjvvq1VbDerppJQk/ZtSztEtKlF397DzarXzdq3GahF34r9okRKhUKxI243PDW4aV/aYQXWusuDpRrctC9u351pCczmL3QG1Mq/dsc0TW7evMnR73+f4Pw8vtVVOm7fJkCUjAiU5D6L5xKkVhfQ33qL9ZMnMbu6SH3zP3Dryz+LLRwsYWPhlLy3hIPNxmfhbGzf2pbfnt9mb2y3hL1xzNb2zvkVnv3kXZCS0ctTGLaN9kiE+eMxhDZDiHxmrBlHYNzJ0LOUwhpfYfyhIeStCd68IdzzFyqaQqtFKVayIk2eu5/IR4s4XQEC/gwdlk3fBzY3+o5iSwPpaEhbR9rwM3KCuOyEkA//8INoms7Rz/0Sn4/Hy0TCQpRimRXpLvn2RILR0EnO9zRQLPMY9pkzpJ97Dv+rr+K7Mlsm/gH0Ohns4cGm57d14nG+O/Z3eWtmqSxXcOfJKOmvj6gclG1ItfF5KiPoijR/HJV0iaRUY7z2RdMgHHRIVXhubBWxqFo91HNt+9Ie8ylKLPIa7SCOq7RNiv2iREqFwqNcuXKFdHr/ieIL2A6ksz9Xcdv0vatcMhYPfI1z587h91dZBqmoOe2QG9A1F2pIJXpvV6oNTFvlgU4kZvnK4uslk6YJfxzr6INAsNnVO3RIKUvzGBZZiRaiA4ujBX+c+pCO4AoLy9eQusPw1C2WNQtzJMS9eA/4lgnqi0jNZkZIOmez9CyvYSY+ZLxvGGftNi/ZTt0FwHPXppFAdwvnL1TsjWpRitLRNn8cRyMgIL5k4tP8rPX1M+DrRg6d4dORsaKIwbzoNz89i47GyWMnSnITVrIp/ZOL32X09Fkef+TRylakR2Hue6sE5+cZOncS48oVJi4bnLi+uingRzYE/MFjFj1dnVhjY4QD+fyFnScfb84fV7ErnHiczPPP85FY4a1b18ruY4ljo/zy8507n6gBrKcFCf8RXox9taT8v3k4iTO43qRazhtI7AABAABJREFUKQ5CtTFeskVESrdISiVStjeRoCRVwfa1VZ5rqzkVdahJ+7bFzSHILXqxGajFt97D7V7bKnMp4O6IptqdYieUSKlQKKqSMd1X9ldLVL9bCpZNDbWaUrT3CiyXBwJF6xMJSuK56bLIiYQ/TvZubfKx7BctkSDwyiucf20ec6100cSx7F1OXn+D4O/1EhgYOPT5saSUWNiYTqlImJMmOZeyYsvR4v2Ky8yysvz7PXESHvvLm0wbK3QvJsk6KcyAzsI5g4i2ULb73Ug3octJfKZN53KyYQJgO+Qv9CoG+mY+wu02oguz83SHOuntiW2Jf1plm9HtZeGnlhi6+4foHRCZfx/dtEn/ZIAfrJ3DKXrk0qTDsx2XORNNISM+4sceByFIP/V1nP7y6LFLtwt5Cx/e8XcLmDoB8lGNbsw98QSB3/s9iEYxYr1kSFfMX4jfhzV2piR/oaJNODLIi7GzZcW9Wgaqxvc2DrdJXBXd0b5U++5a5fkinascSemYa1y6dKnBtckzPj7O6OhoU67tFSJByVyF8lZpd27RbKD6vHam1dPnSKlsN71IxEXoq5b7ttGoeTzFflEipULhUcbGxhgbGzvweRKL7rZdjz5yH2eP1j+HkqL2tINI6TqBpVZgtSVaIsGx777KN6bLJ0iPZe/S9bs/5NzaXfS+voaLgPqNG5v5C03Tx4A5U5bLy69H0ScnGX7vPRJPPQUtuLDCkRKrSOArFQ03ohAds+L2qydXmQ6P887cdEmU4nahsXBs1dyGTaYdBMB2yF/YCKoJhsU2otUEw+L9N8tK8iCWHqNVsSP99g+/zejoKOfP7uP/+/gxQsc+RJuaQrtvDP3KFWKpeZ5cfbPMZrhTZiAmsEdGQAjs4eGG2VuO/MzPcCudxnn9dVKRHt7oeoR+c66szzv2cAeRbfkLFe2B2yRQJtcCSQE3UNEd3qMdRMqUSyRl0N+8e+ro6Cjnzp1r2vW9QKs/17qJVgGfxF99+KdoYVrdmSqdA8dRIqXXaPX+DtQYT7F/lEipUCiqUu1mp24y7YubZWpL2USowY1nKIiAesZGEKo4IW6aHXQsLRF76SUSTz1F6ujRhtRNSyQ2BUptcZGTt+/QnSqdrOo354jO6GhLJ9Fsm/jrr6M98cSuJ+3zlqXl4mGlyMKCmJgrEhPNzbKiqMPNz+b+ow6LiQOkIJnY/zlahHYQAFstfyGAkKCjEdQDZZajfm27tWj5dr/wlQqKRfuXioY6fuHbUTBsR7LPPkvohRdwolGssTGYu1UxSjFla0z39mBmszhzc0yeP0/WJYqn1lE2fr+f0b/6V9GeeILUxT9D/mi5Yv5Cvz+FPTzU9PyFir3jNoFVzR2l0bhFFqkxXvtSLUKiVZ4v0i45KYcGO5WrTxvjZpnaKu3ONZpN9Xdtjdu9Nt3i7Q4gotLntC1u7S5nCUwLfC2g8rjP46l2p6hOCzRfhULRylSzq1ArsNoXVy/7jIbjgNYC88ZubU/ZRLQXxSJgYGWRz6zexS/Nkn36zTk6P4D1sL0pAk5+6UsNqZ//Bz/ANnOwOI8YH0dIgRMKMBvuJeMPEHGSDGTm8PkzrH00x/JQL8nuMHe/8294/xef2hQIs5ui4naRMf+qaBytKABup1r+wgJSCnB0QppkYDGLTxjI3hgxo5NM/CQPh06XiYRuAqJblGLx9g/eex+BUBO1B8CJx8lcuEDw4kWcaJR7Z/q5urZStiiDWCdf6prC0XUSTz1VNXq8XlE2TjzO3a/+bX7ru2ZFC+6/8fwJ/GP9Nb+uov64jc9tRyNrQqAFInfcxnjq2aI9KKTr2I5P/1lMu/we+sFHtwhmKxlyljMxMUF3d/eB6ufG1GwvMFBWrtpde+P2XNsqtpvJdOUHa9Xu2ptWj2hza3eg2l47s5NrQU9H87/b9UzlthcONLgiirZDiZQKhaIqVVdgqcFN27LTaudmr2RXORS8Q+CVVzajFI2r4wSFQZpAmf2hzKXoXUngRKOYXV30vf02fPGL2NLeFAGzGxGEBUEwK01yG+XFkYdZaW7ammarRClGZhd5+pO3EVIyenkKw7JZHYkwdzwG2ixBwAYSjoZ+J42zlMKYXCMRG0LeXuGj2z6W+jqa/SdWbGM3AmAB4chNe9fVntDm8ZXQEEVCoE5A87HdUtS/YUlaSRAs3tbxuWVOTFxEiwi65z5kftZB/34/V0JjONJA2hpSahyJmvy1Ex+iayayw8fQ8Y38hZ//es3tQQWtManS7thnzpB+7jn8r76K7+psxSjFPs2m/7HHyD3zDN1NjFJcTwsS/iO8GPtq2bb/7fgstLCts8KdauOkVEYQ8DX/e026TGCpMV7rU23RRMhvY6bLRcr0HqJ4n376aYQQLCyU55E+KHOL4Yrlqt21N65iUYuIlG5iaUdQRRW1M66LvtshklL1eW1LO4iU7vN4qs9TVEeJlAqFoipug+qgX2K0jmuUYo/sNLhptkiZNcF2yaHQ7LopSrGlXUE0zAuCYnqaI7c/wHYsouOXEaZJ5niMm30xhC+LrqfI6TaTwk90dZ7ORRN7+jJX+4axVyf4397+n1noqzyhVAtGxycB6FpKYVg2pk9n8nisTMySmmDyRC+R9Sw+06ZrKcVKLMLp8QTv9I3UrX6KnSlECxaEwNRKkpUzZ+i9mkTv7yY0naXLchj+xOGj8HFsfEhHw7F1sOHx7ATH9W5k2Efk6MPomsHoU7/MV+NH8sJikeCoixre9CIQOn4tn7/w7AM409foSy7zROrdEgH//sUp9Egqn7ewCfkLFfvDicfJPP88V3wrvHXjWlmU4syxT/E3n29+OFu1CVw1gdW+uE2cQn6MF+1s7ndr2+52eMqCrvXx+/2uEfddHTqr6fLygfgpzp8/Uuea7Yzuy1UsVxZ07Y2bbWrLRLQpe2tP0uqRlNUiiVXba1/aIv+zsrhW7BMlUioUiqq43mDU5FVbs9Mq+2aTzAjiuemKFnSx9ZNAX7Or2FY4SNJOloxjkpPmhqhobkYnFqILM9Jtu0l2I//h5raNCEUL9/x9j71xnZHUNN2LSZz0AqZPZ+mxbqLavbJ9b/WGOJvS8JkmoeU1VmIRTo5PsVBHEbAQQde1nJ9RW+6NVIy2g7xQudwboT+xStdympVYZPN4RSkGeom45xe+zSjCwufNbdv28wkdv+bbsiItHL8tOtEv8jkSNVH6fX37L77N6OgoT54dyAuAD8QxrlwhOalxanlpUwDs3Ijg7eoy6TwWwRodI+iPYg8PEzj+YEP+TsX5Cyf6H8Cev1GWvzDod5A+H/bICE40CoZB7plnGlI/RQ04MsCLsbNlxb3CAXZnfVhP1l2swMJBpyVs3xX7o+oYrwUiPKrmu1fPF22Nm1NLKzxbAGQsf8Vy9Vzb3rh9f63Q34F7Dl7V7tqbVu/v3O61PkO2hO27Yn+0+jyeaUEmpxZmKPaHEikVCkVV3CawVKh+e1NtcNPs/B1aIkHoD1/lG9PlWeSOZe9y8rs/JHj1CLlnnsFpok1erZFSYmFvCoP51xxZx9p4LS43yTq5zc/5KMZcRSEx05PDERIm/rzhv1Ori4CGmRdYdTvfn2WC1Z/YCtsL+xeObwcEoqLgVxAQZ6cS9ES6Gewd2BQPtwTBLVGxWGj0lwmN+f22C4fNoFgAtMbGkLO3ygRAgCw+rLH7miIAFucvXPL3crlrgH5zriR/YXagh9OPd4EQYBhkLlzwVL/nddwi2lplpbPbxKkSitqbat9fs8d4sIMFnZrAamvcIhJbpc/LWpXHedWijxWtj1uf1wr9HVTJwav6u7bGbT5lPSOQMj90byYq97M3qZa2yW1c30iqRvCqtqfYASVSKhSKqrhOYKlBdVsTCUrXSEVr8hSc7m1KvfQbNwhevMj6vIMgWDZhP+frw6eF0ScnCb3wApkLF7DPnGl4PW3pkCkSCd1FxLx4mNsQGjNF+xTExMzGsTlp4tQj/1cTx6qtLgJavrx9p63nF2MEMybl0vgWwYxZsn/h+IMgYEPwK40i3BIAfZsCYGBTJPQRqBh16CNQJBhulmsGBjqiytPyt1/LRyCeH61s4dZuFAuATjTK3dP93FhfKetPgv0xRqPZpgmAhfyFU++9gSRRlr/wU90miCz28LDnFmYcBtwmgjI5gW2D3mTbfNc8WWqM19aEqkxgJV0WHzaSapNoagKrvXGbPHXLQdpo3ERK1ee1N26Lp1tFHHezVlftrr1xG+M5jiBrQrBy4HbDUCKlN9F1CPkl6QrRiq0QSVl1jKf6PMUOKJFSoVBUxT3psbrBtCtaIkH3K6/w386slOV9PJa9y/Af/TnBu/GGT4hriQTBixfBsmBhmc+s3sEvzZJ9+s05Oj+QyLN568PgxYukn3uuaj2llJvCYKZITCyIjNP9KbKBe0wv57YJj1v7ZJzSbdUsThVbtIIIWI3l3gh9M6us9oToXk7Rs5BkNt69Ge0ppUDaOo6tEzEk8aUcmtTJhjsY9EVh6DSf7XigRBQsiIaBjYjFggDp3xAUt1uY7iQeKvbO+Pj45vvAQw/R9/bbZDKZMgEQoDeT4ZZtM3/+PNnVVbh0qdHVBeA/Dv4S95assoUj/Sd7SH/9PpWDsk3ZyXazM9zcsZQSKb2JpuUte1MVhKFWmLSvtspePV+0N62eoy2r7F49iXu7aw1x3PVeq9pdW7PTGC/ob+73q9I2eZdwsIVFSpULVXEAlEipUCiqolZgeYtCpCKWhd8I0ZmcL4ssMnOdTYlUDLzyClgWYnGR0I1xgpogZ/iYC/eS8QeIyHUGs7OsZVM4H73FwshR1nsiLP3xb/L2L352Q0w0iyIWtyIUq7bWMwCrsHyjIb/nYWInEbAY4chNe9fVntDm8W4U7EuLxcDi96mVdXSpcaRvsERADGxYkgY0g47PrXBi4iJ6RNA99yHCtLnxvQE+Cd6PIw2kk5/c0KTD3xv5gH7Rx6rIsGz2ci4ySvqpr+P0KfGolTh9+jQ3b97c/Jzt7WXyy1/mDV+ID+fXyyLHZ46Nct+XrzexxnnSpk7C38+Lsa+WlEfPr+MMqtyn7Uo1K7f1dPNFSrfoDjXGa386gpJUpry8FazAqtq9qrbX1rSySCkl5FwiKVW7a2/c7Hpbxe5VLfr2JtVsotfTglhnk8d4qt15lkjQYWG1fBGGW6quRlLNsSPiYgmvUBRQIqVCoaiK6+BGrYLZN7lcjpdffrkk2qcRRFZWeOAnP0GzbYLJJJ9e8iGs0oFCvzmH78erXJ+OkIlEcD78kI+ffJJkd3fFc1qWhRACTdewNYmtSyxdYuvOxmvhp/SzpTsl+3curfKFD99BIrnv8iSGYaM9GmHheAyhJQgBDpBwNPQ7WXqWUljX1rj90BByYpqbEyGW+jrq/0dU7BqB4N7YUc59Mo/VG0WfShI0HY5ey3ClZxhb+jajFKUp+FTyHl3rYIQ6iUWO05fTOPq5X+PpeLxMgAwIH4aoHoF4aSofEXe+t4p9aQRCx6+hTU2hnX0A+dEV+lJLfDr9Ngl/nJQWIewkiecShFeSEBIkjx5F5nLYw8Mquq0F8fl8edva86Xf+/hykH/xRnk/NhyyOX++s1HVc0X8buU6qBWn7U21iaBWmLR3q4Nqd+2Pq1jUApP2biv9QwHZdAtkxcFwez5shf4unQNHVp487VR9Xlvj1u5aIaoI3CM63XK4KtqD1h/jqXbnVVp5QZDb4hBNk4SabIGsaH2USKlQKKqiVtnXnmvXrvHzP//zjI6ONvS6R7//fYLRKL7VVTqmpkiLblZFiNngAGl/gA7WiJsJ/AGbjrV55noDJLv9xNeu89bnnyQnbExhkRM2OWFhCpvVzDoENGxdIg8wJjr7wSSWLuleTGJYNqZPZ/J4rCziTmqCyRO9RNaz+EybrqUUK7EIp8cTvNM3csC/0OFDQ2yIf3nhL7DxWrAiDQg/gZIoxa39CjkSA5q/YjSjgY44KQhd+d28CHjuFMaVK1j3NI5+mCHh7ykRATs7wB/UcUbO0Cs7Sff1c/zk43X/G2SffZbQCy/gRKMkT99PbvwOAZnjRHai9G/l92GNncHMZnGWlsg980zd66aoHa38MAfu0U3qXtve7GQF1mzWXSawVLtrf9yE5lbo89xW+rvllVO0D26RRa3Q36kIXu/iZpvaCu0O3Md41SLxFK1PtaiwvN16c9PDqGcL7+L2HbZCn1ctgldluFHshBIpFQpFVdQq+/pQKdJnr0gpyUmLtJMl5WTJbLymnSxpJ5d/L/Of9cQM/ux1rLDFses3SftMrIcclk5KfPptCuZHc47EfyeLuZSCqRvc6x1CLi1yd727cqRiREB1M9VdUbD57FpOA3mbz0qWoJAXKpd7I/QnVulaTrMSi2we71UKwmFgm5iYf60sJPo3BMR7N+9goPPg2QfKhEZD1D9koVgEtMbGYO5WRREwK3wsHDlCR1cXjq4z//jjDNS9duDE42QuXMjnN+2I8UbXEP3mXJkN8gOPhdFDAmdujmuPPMKZBuZrVRyclhcpVW5AT1JtIqgVbOjc250Si9odt7bXCnavKpWEd2nlCF5lQedd3Npd1hTkTPBXdvltGG7tX43x2puQPx8d5jjl328rj/HUvbb9cR3jtUK7c5s/Vu1OsQuUSKlQKKriKlKqm8yBcaQkK3Mk7QypEoExuyk8pmWusvi48d5hdw/Vj713nVlzme7FJHYug+nTmTrVDdue15sVqWiY+ZWGup3/fTLB6k+The2F/QvHNxufMAhWiEzMv/eXRB4WohGD2pYAGRT+EgHSvyFOagdYdqZZ6wCcCDTHmrRYBHSiUSZH+hlfWykTAY3eGA9H3iGs6ySeeopsb++Br70XS+XAQw9hvnQZicasb5BZX+nfa3HxBjPxPn5y5oyr/bGidXF7mDMtQdaEQLMnsJQlkycxdAj4JFmzvA9vBYHcdQJLTZy2PW4ROq3Q7tQCSO/SyguC1lLudVB2r+3NTrabfl9zv1+1EM2bCJFve5X6llYQi1TaJu/Syvda1d8pDoISKRUKRVWUJVN1HCk3RcT8T4bkxmvhc8re+px0MgT+L/fz7zv+HPv2Dw9kkboXWj1S0fLlI/psPd/eghmTlSr7BzNmyf6F4/eCcEC3BXbKpCfSRU+ki6Dw498QC4MbYmFQ828Ji5ufywXFgPChieYnK29F7DNnSD/3HP5XX8V3bbaiCNhn26xHo6S/9KWaCJTnzp3b0/7Z3l7eefIX+a0rR3l0/V3iuQR+mSMn/MwGBviZv3KWXG+M5Pg4p0+fPnD9FI1lpwmsQBMnsGwb0i72PMoKrP2JBCuLlC1hyeQ2kaDaXcOoV35yM/Mp4EhZebqFJ7BUdEf708oWdNWiiNXkaXtTTXRJZQXRzuZ9v1KqyCIvkxcpy8tbQSxyy8mq7rXtT0tbq6sFkIoDoERKhUJRlcOyAktKSUbmWLczJJ00STtD0sn/rNtp1p3S8nSRMCn3aHeq9QaxamCRuhdaPVJxuTdC38wqqz0hupdT9CwkmY13lwip0tHA0YloksGFLD58iJ5e+unC6T3NpxnBj4FfGPnXjR8fpZ8LP7qhgQGXr19mZWWFr33ta3X9HQ87TjxO5vnnueJf5q3r10tEwIQ/TuLYp/j6T/1XRmsgUAL4/f49WypPZgMk/D28GPtqSXlX2OHffXGuJvVSNIdqE5DraUGsiRNY1SYy1ARW+xMJSRbXystbwf7QdeLUY2O8VmWvi2n2QtBXedzW0tEdqr9re9oxuiPgk/jUrFhb08rW6ukcFe1AQd1rvUAr224q1wLv4jYX2xLPFi5BLh0qyEWxC9RwTKFQVCWynOAri++XCQoDmRGgp9nVq4iUkpSTZc1J5QVGO50XG50MyYLgaKdJOtlN4XHdyezaOrUdaUakIkBICxDSCz9+gnqAsB4kpPk3yoKE9AA9z/4UZ6b/BF9Uo3vpQ+7dtfG91MuV8Ci27cexdKTUeOS0w4WBKxACunzc9+BnQNPguW/AkfKIgd3yox/9aN/HKvbI0CAvxkbLimMt8P+n7Em8S7V8U82ePK0W3dEZVm2v3ck7T5TfQ5vd7kD1ec1mP4tpdsuJS53wQXn5uou1dCNRE6fepZVFShXd4V12cstoJioXqrdp5T7PzeJaLYBsf9rRtUCN8RS7QYmUCoWiIloiQeCVV/jlaytkcqU3mmPZuzz2w9cJZgbJPfMMTjxe17pIKclKkzU7lf9x8sLjmp1i1cm/Fj4XttktIHi0EruJVCwgHElsIYUuNbLdXXQ4AZa7Bzmd68cvdfzSwLfxeu3yOKeOHGfk2Cn8+AhgEMC3Ga0oEGCT/6lKB8LfjzY7S7p/mOz4FLHkKo+tv0/CHyelRQg7SU7dmWB1dRWEIH3mDNb0NLnBQebv3YN79/b1t7l8+fK+jlPsj1Z+mFP52bxL1QmstMYuOqm6US1Plnqga3/c+7zmi0Wuq51Vu2t73PLZtvS9Vk2ctj3hgPsYT8p8DrdmoaI7vItbuwN3y8tGUa3PVX1e++M2Xmp2uwP3RUlqjNf+tON8imp3it2gREqFQlGGfuMGwYsXwbIwzTAD5iz95jyGtLCEwZyvD7/WiT45SeiFF8hcuIB95syer2NLm3WRIaXleC91g1UryYpd+rNmp1l3UpiyeRPI7YQmBX5pbIqJfmkQkAbiVIjBD5LoHd10iCxGxiZ9yeBH8kFsx4djGdimgbQ0Pm1f5ljPGo5h4AuNQE4jcvRnOZmMlV3v/hODCCEwFg1AAiY5THL7qPv6Aw8wfO8emt/Plc4xelN3CMgcJ7ITm/sYgTQ5IDk8jOn34+RyTN5/P9mFhf3+yfjwww95+umn9328Ym+4DaqzpsB2mjtpr3J3eJeQHzRNVrTcarYlUzVrHrXauf2pNmnfbNxtN9WkfbvT2hZ0auLUq7h9h44jSOcgHGhwhYpQ0R3eRdMgFJAV83s3e0FQtT5Xtb32x/Ve2wJjvHWXRZAqgrf9aekxnlr0rTgASqRUKBQlaInEpkDpzC3y5Mr7+KVZsk+/OcfgVdBCZ3CiUYIXL5J+7rnNiEpHStadFIvWGovWWqnwWCRErjvpLcfY2UuN/UVblIDwEdYCeZtULUBYCxDU/JvvQ8XvReFz3j41rAXwCQNRaZnyWQjd6kCbmkLzxTGuXMGcs3lo9lpJpGI8lyAWyNDR2Yk1NkYwGsUeHqbz2Wcb8vvrp04RvHiR1Y98XO0aot+cKxHIgye7OfHpboJCgGGQuXCB7n0I5MWMj4/j81XP0amoHdUEv6zV3O/B1YJOTdi3PULk216lqMVmi0VuK52FkISVSNn2tOpqZ8umzCmjgJo4bX/c+o5WiO5wt3tV99p2p6q1elojHGjed6yiO7xNJOiQzraetbrKO+5t3O5bbpHbjcRNKO1UfV7b06rPFlDlXqv6O8UuUCKlQnFIyeVyFW0uj37/+wTn5/GtrhK8OYFf9pDdyENZELKOWNPgrJD9+D0WR46x0h1k9o9/k9d+4VEWrTWWrDWsJlrnNZugyAuGET1IWAsS3hAVw1qQiBbgT/7gu/zMpz/HuTNjm+UFYVIX9RvQZp99ltALL+BEo1hjY7B8syxSESCLH2vsBE40CoZB7pln6lan7dhnzpB+7jnuvPoT/Ewx6xtk1je4uX0wlgVhYg8PN8RqWFF7qk0G5ZosUro9UKpISm/QEZSspcrLm/1AV836sJnWeIra0KpWYNWur/q89se13bVCviJl9+pZqo3xkhlBfwPrsh3V7rxNR0gyv1Je3ux7rVu78xkSv1oj2/a06hgva4JpqT7Pq7RyTkp3lxbV7hQ7o0RKheKQcvnyZcbHxxkdHd0sCywsEJyfB8chfO8uGWmx0h/g7vAAvkiaYGQBPZRmycgyfmeVnqUU1vgi1x4aQk4IZu+FWOrraOJvVTsEgogWpGNDaOzQQkT0YL5M2xAf9YLwGCyKdNxZaHzpo29jh5fImotkgaXG/EoAhEdGiL/+Opptc/PIWa4vamVWvslIlHh2AmdujsRTT5Ganobp6QbWEv5t39/BEMs8uv4u8VwCv8yRE36uxjLYj0bJxmL5OtWgXhMTEyX/B4r6Um2Ammm2SKmswDxNq1rjqOgOb+MW0db0dqdESk/Tqv0dVLECU+2u7an2HTa77blH8Kp25wVa1XbT9dlC9XeewHWM1+x2V6W/7QyrttfuuI/xWjeCV91rFbtBiZQKxSFmdHSU+x4e405uhuncIuGb72F3LKHNzzHAAmaHTubJdfq0ZMlxEsHkiV4i61l8pk3XUoqVWITT4wne6Rtp0m/jjl8YRLQQHRsiY0QLEtFDdBS9LwiSES20KTpqdQph6e9v3jri1NGjTH7pS/S9/TbGfKosUhHAZzuk+/uZf/xxsr29TalnOmeQ9B/hxdhXS8qPfOoSR2MzTamTojZUt3v1N7Am5biusleDak/g9j22aiSlmsDyBq1qyaTyZHmbaiKllDQ1SnvNpe2pidP2p9p9q9kipVoQ5G1adWGG26IMZefvDVzHeC3a34Hq87yAW+74dFZg26CXO183DDehVFn6K3aDEikVikNE0s4wkZvlTnaG9yPjzOlrrN/94eb2Zycv02etcmxxFYDl3ghSqzzAkZpguTdCf2KVruU0K7EIPQvJivvWGgOdTj1Epx6mUw/RoYW3PmshOored+phAlpreanous7o6Cjnz59vXiW++EVu/9kib/0PN0siFRP+OJc6z/P3/qHOQJPG1lJC2qx8e3ro/pOcf2SoptcbHx+v6fkU1Qn5W9fu1c2aRz3MeYNWFYtcozvUhL0ncLVkUiKloo64tTvbEWRNCDZxTZASi7yL35e3saxkM9jse62aOPU2rWp/qCJ4vU3LPltUiahTfV77U20BdSormrroy9UtQ/V5il2gREqFwsPY0uF6ZpL3Ujf4KH2bOWt5a2OFyQnDzOeR1O38wCUTrC4YFLYX9i8cvx90NLr1CN1GJP+qR+jWOzbFx64NIbJDDxEUfoRK1HVg9OFBXozdX3FbKjtDJNjgCm2QzoHjqAc6r6LrEApI0hUmDbLNtntVFnSexq3/aLY1zlrKZeLUZZWsor1oVQu6ahNY4YDq89qdnWw3g1UWDNUTx4Fkxq3PU+3OC3QEJUvr5f1bs/s85VrgbVrWWl2J457Grf9oerur0t+q+ZT2p9p3uJ5urkipLK4VB0GJlAqFB+n/9En+c/IvWLj1AzKatevjLF/eF8DW84PpYMakQv75TYIZs2T/wvHF6FIj4vgJO37CMpB/dfyEZf519uYUAVPn3JkxBJVuaBaSVVZYrVqXdmJycpKXX3656RF80ysx4O9U3Pa7375IVzC14zksy0IIgV5DT4m1TBj4BxW3vf7ai1y/tFCzawG88cYbWNbu/0/ajWa3s0oEjS+QzgbKyicTq4yP1/b73QtzS5+j0gqOlYW7XLp0B2jNv2ctuHnzZrOrUBOqfT+59MNAeST2xOQily59VMdaVefW3fuBSFm5nVvm0qVLDauHV9v2XqjH/8Hi3FHgwbLyhWWz5t/vxMTErvf9YHwAiJaVh/wW77/fuHYHe6u3YndML4WAz1fc9ta7nxDvSTe2QhskszrwsxW3TU5c4ZK982h/YmKC7u7uGtessVy+fJmJiYmG9vGNwqd/HgiVlX8yfpfjocnGV2iDucXPUHGMtzTJpUu3Gl+hQ0Qj+vhc6kHgaFn5vaklLl36sO7Xd+PGnbNAR1m5Y67W/P/fK2P5dmJ+9gjwqbLy5TWrqf37e9f7gFhZuaE5fPxR+913VNsuZX41AHyh4ra3L13haO/eHe5q0U/nLIFpfbnitql741wSywe+Rrtw586dZlehLVEipaIMIcQ54DHgCKADC8BHwE+klN6dyfcIn/vNr9H/xEkmWd/zscu9EfpmVlntCdG9nKJnIclsvLui5atwZN7e1YFkKIRvVZLrDhF7z8KXlOgpMFISPQuCzOZxqY0fgEQiAcCjjz7K1fGr+/l125K5uTl+7ud+jnPnzjW1HoklP7xcedsDDz3J8f7sjuf4zne+w+nTp2v6u9ydC8B3K2/77JMPcSSWq9m1ALq7u3n55ZcxDG/eEi3LYmpqanPgWfi/i8fjTauTYz0OlIuUN+/M8pfpHzM0NMTc3BxvvvlmQ+s1t/Ao0FlW/t67f0Fo6fKmGD80NOQ5QWdmZsYT/wPb23sxC7PdVBIpb9+d5+WXXTrDBnDjdi9woqx8eeFuw+vlxba9F+rxf7C8IKkkUq6nay8MDw0NYdv2rs57807llc4+LdPwNjA5OenpxULNYD3rLlJ+cvUuK93NWRC0nC5fkFFgZuoarC/teI6hoSEWFxf50Y9+VMuqNZQ33niDeDzuyf5Wl49TSaS8c2+R8VDzft+V9Scrlq8uTXrye2glGtHHZ5JxKomUc4vppn6/0zOV05TYueWa18srY/l2YnHOpJJImcxoTW13N+5WjtT1G9m27O9U2y4lbfpxEyk/uXqPZGx2z+esRT+9nnW3YpudusF4av5A528npqenm12FtkT9lysAEHnvzL8L/EPgrMtuC0KIfwP8P6WUdU0+KISoZSz4P5NS/tMq1/qnwD85wPl/R0r5awc4vqb0P3Fy38feHI0z8vE0q9Ew1r1lfKbN0O0lbvUPk0t3kEuFySXDdOgGvxS9RUiLI7r9nBx7HIQg/fWv4wwO7vp6hdVlTc3N2AQmJiY4d+4cTzzxRFPrsbTmvu3UmYd5dHTnc3z88cc88MADNf1djCp69U9/5lPEump2qU1WVlb42te+VvsTtyCt8H/3p/9dNyu3y8sHh0b43JP5nK3N4Dv/qgcqBJeM3TfMz33xmOf7qqbnyq0zqd/p4MPvlJcPH7+f3/iN32h8hTYY/39183GFZ7ZPP/4Av/EPmlevw0qt/w9ib/v5jxXWW1gy1NT7Tvr7IXirvLw/FmxKvbze/zSadBb+UYX+DuCpz3+FJ8bMxlZog6v3dP7pn1Te9su/9PMc6T08FohebfP/8YMuplfLy8/e/xhf+xtjja/QBv/zDyo/QDz91GP8jZ9p7sLRw0C92/sNu4PXb5SX9/Qea+q99i9mu6BCENjoyHBd6uXVfqVVGf7Az7+vsF4mZ/v55V/+GlqTMko4rwThJ+XlsW5/2855qLa9hW3D//2PK2/77M98mZ9+cH9jvIP+je/MaK5jz7/xi1/mxOD+04O1G1euXOFf/It/0exqtB1KpFQghOgBfh83750teoF/BDwvhLggpbxc77rViNqGXXmUbj1C/Ohxeo9nic+l6BkbwvzLm8jxLI/KKdb0DjIihC00omGLyCkLNA1rZASEwB4e3pNAqWg+neWLnDdZ29nptW5Uu3ZHlTor2ge3XFmmE2jaA4iUkDEr58QcOTkAHJ6Vf17Frd2lVJ4sRR1xyxuTyQksG4zauaXvCZWD19sE/aBpsmKO72QT+7xqebpUnixv0G452tS91hu43bua2d9BlTGe6u88QaRKbtFUVjTte1a5UL2NrkMoIElnK4zxquScrzfVrh0Jqran2BklUh5yhBAh4E+BT2/blANuA1ngNKXJkk4DfyaE+KyU8noj6nlAXIwjDy+dWphoNkif1cFPnXiE4/4Beox8rgTtFz5L+N/+W4yZa0zlIGyn0KVDh70VPBt0JGI+gPXYYzjRKBgGuWeeadavo9gnhgEBP2QryPjJTHlZo1h3SZMU8IO/soakaDNcRUrbT/6203iyJphW5YmEcEDZEHoBt4mCpk9guU2cqgksT1Dte0xmBN2R5nzPbuK8anfeQIj8vXYtVf49pypMajWK6hNYqu15gVa819o2pDJq0t7LuE2AJ12+90bhJlJ2qnutJ6h230plmilSqnbndTpCDuls+UrHZt5rq11bPV8odoMSKRX/ilKB0gH+J+B/lVIuAQgh/MDzG/tGN/brB35fCPGElLIeMds7RXW68X8GfqHo8yUp5Qd7PMd/B7y/h/2n9nj+phH3xfj57k9zPjzC5ffzCeQfDp8u2Uckk5DLQTJJRguxbkDUWibk5FWrtBZgLdxDrDeAfvcuTmcn6V/7NZwm5rdT7J/OUGWRshUjKVUUpXdwe6DLOQGaJVKuVVnhH/YrkdILuLU7N5GwUajVzt6m2gTWerp5IqVbu1dCkXfoCMqKY6pmRrS5tzunabZ4itoSbsFIymrCfERNnHoC1zFekyN419QYz9NUE17WM4KBBtZl+7UroYQi7xAJSuYqlLfiGE/XJAEVbKDYBUqkPMQIIcaAf7Ct+G9LKV8oLpBS5oBvCiHeAv4C6NnYdB74OvDbta6blPIHez1GCKFVqMs393H5d6SUr+3juJYnYS7y2/Pf51to9HZGiFvd5NaDHPH1EvdFCc4uELx4EdnVhXP0KJFP3idk5sPaTJG/qxjSIZRbQmR6sAcGwOdDRiLVLqtoYTrCML9SXt6KImVnuLH1UNQPtwmhnB1ocE22qBbdEQ4cnvwJXsbtwbzZE1jKCszbVJ3AauZEgpo49TytOGlfKbIToDOs+juv0IqRlMpm2Pu0YrsDSKoxnqfZaSFas1h3udeqhWjeoRWt1as904rmdsWKNkGJlIebfwYUx4f/x+0CZTFSystCiP8O+HdFxf9ECPGfpJT7y8xbW74IHC36bAK/16S6tDQ2DrPGGrPGGh/M3wNAAL/w3XGOreToX7WI30qw1NnFktOH5fjQcQg5aTrsdXwBkIaBc/QoMhTC/+qrZJ5/vrm/lGJfuOWlbEmRUkVSega3QbXZRJHSbeIUlN2rV3DPV9SaVmBqAssbVMt31oqT9qrdeQe3BUEt2e7UxKlnaMXcgNUcE1Tb8wbVFqJJSdMmyN2cWtTCDG/QsiKlGuN5Htc+rxXHeKrdKXaJEikPKUKIKPDXi4ok8E93cehvA/8YOLHx+QR5cfB7tazfPvm1bZ+/K6Wcb0ZF2pGeuTUC07PMSUns2hQzls3aSISZ4zEsW2Kmw5jpGFYywOPpBGFnBf3qJ8gnPo0+OYk2M4MzONjsX0OxR9yiE1tSpFSRlJ7BLVKnmZGU1Qb0IWX36gnc8hWlswLbBr08rUfdkVI90HmdUEAihETK8u+5WgR3vVHtzvu4TZ665SNtBG5ClbLc9A6t6FqgIim9j9v3aNmCrAlBf4MrtIG613obnwGGZmE55VPrzcz/vO6ag1e1O6/guhCtifdat2urCF7FblEi5eHlq5R+/69JKW/udJCU0hFC/DalguZfo8kipRCia6MexXyz8TVpPlOvXeXIUyMIfW8TX6evzgDQtZTCsGxMn87k8RhSE+iaie4zCXatAnDTkRiXl/Albe7eTpPu7WH+tRVmvvAZYkYnPXoHPUaEbr2DHj3/6tdUd9OKtKJIuapESs/j9oCUz0nZHNwmEQI+iaGrgXW7ksvluHz5MgD3prqAz1bc7423PiDSBFvfrKlhO1+quE1Fd3gDTYNwQFYUZ5q52tktelxNYHmHVlxlv5ZSE6dep8NlQZCbxXQjqDZpGw6otucFqo2Z1tOCoL/x37PjuLt1qDGedwgYOaxc+VxXK1qrK0t/79CSdq8u/Z0SKRW7RakGh5evbvv80h6OfZlSkfKvHLg2B+dvAcVmkLPAi02qS1N587//A0KDXfyj//ovueJPsObsTm3qWUgC0LWcz0G53BtBapVvcFITLPdG6E+s0rWcZiUWITczybupa67nD2sBevQOuvUI3XqEHqODtcASYcdPT3aabj1CpxbGp8TMhuIqUqYbW4+Sa7s02S6V+tQzuA1Uc3awwTXZwm0CqzOsHubamcuXLzM+Ps7o6Chhv7sImc4aTREp0xUmNQp0qLbnGTpCkmSmvLy5EwkufZ4SizyDW/R4My2u3dpdl+rvPENr2gy7TZw6TXFRUNSeamOm9bRGX3fjx3jV2rwa43mHgGGSzJWXN9Mtw63tqQVB3sFNcG7mgiC35xo1n6LYLUoNOLw8su3zj/Zw7DtAFiiEvAwJIfqllHO1qNg++dVtn39XSnlo/fnSM6t8zjrLr535KjezCa5nJrmRneJGZoq0rDCCAgwzP3DX7fwNJBP0Vb1GYXth/8LxbqScLCkny5S5sFW4IZC9PP3xZlFQ+OnUw3TqITr1MF1auORzp7ZRrocJa0E0lYH5QLiJlOstaPfaoXJSegbXSMpm5qR0GdCrh7n2Z3R0lPPnzzO44P7QdmrkIUaGGz+BdWvafXZUtT3v0BGSzCyVlzd3lb1bn6cmEryC24KgVozuUKvsvUMr5qR0ywuo7rPeodp32aw+z63dgWp7XiLgMyuWt2T+Z9XuPEM73WvVAkjFblEi5SFECOEDRrYVf1xp30pIKbNCiBvAA0XF9wNNESmFECPA57YVf/OA5wwAp4FewAQWgCkpZROlm72jC537gsPcFxwGwJGSKXOe65kprqfvcWttmmU9iS0kli8/UWpv2MQGMyYrVc4dzJgl+xeOPygZmSNj5ZizlnfcV0PQoYfoLBIyO7QQET2Yf9WCW+83Xv3CQChhcxM34U9FUirqidsDktlMkVJNnHqenazAmkG169669gHz9ypPfOyHc+fO4fc3KSnTIacVxSI1geV93L5Lt/tdI3BzLVDtzjtUy0kpJTTjMcytv1NjPO/QimO8an2tmrT3DgG9chBAUxcEqXut53FzLWimpf+6m81wWLU7xe5QIuXh5DSl331aSjm/x3PcpVSkHAX+/KAV2ye/tu3zu1LKDw5wvv8v+b/Rdt9BSwjxDvn8m/+6yZGj+0ITgqP+fo76+3m661MwCI50mLNWsE5/F23lXZx+P8GVCWILKWbj3RUtX4UjN+1hV3vyKtdyb+MVJAfJqp1i1U7lpeRdYKCXCJcRLUiHFiRSJHAWl4W1AGEtiC6aZ5tQTzrDEM9N8+j6u8RzCfwyR074Gbgch+lH4ciRhtdpNVm5vEvlpPQM7jkp/ThNGsMq60PvE642gdWkB7pq1w35a2cIMT4+DsD58+drdk7F7nHr85q12lnKapZMqs/zCq24yt6tz1MTp97B7bu0bEHWhGAT1soo60Pv4/eB35DkrNbJ/1wtF6p6vvAObpGUTRWL3ByC1MIMz+D6bNFUcbxyu1P9nWK3KJHycDKw7fPkPs6x/Zjt52wIIh8S93e2FX/zgKd9wKXcAJ7c+PmHQoh/CfwzKWXj/eFqiCY0Bn1RtCe+ROiTBBw9gi9hY6VNrn2/n1v9RzHCOXyhNL5gBn8gyf2rd/GZNpahsxrNK0c3R+NN/k12h4XNip1kxU7uWtgECAgfYS24IVoGCOtBIhsCZrjkdeO9nn+NaAF00aLJTqam+NxPXsQ3fa9s0/DkXfitt+DYMfj5n4ehoYZVa9UlktLNmlbRfnS45MkCjazZnP8Xt5V/bqsUFe2HoUPIL0nnyr/rVLNESpcHSZ9u88TjjzS2Moq60WpiUToHtuMyaa8msDyDm+DcmjbDqt15hWrRicmMIOhv/HetIikPB5GgJLde/l03617rNmEvhKy6cE7RXgSM1rJ7tW1IZ90WZihLf6/Qas8W4B49rtqdYrcokfJw0rHts0vcUlW2H7P9nI3iGeB40ecc8HsNuG4I+MfAU0KIvyqlXK/lyYUQA0D/Hg87c5BrOoODOENDaFNT2CMj2B9coS+5TGcqRcIfJ6V1EHQE8dwKD4x0EQh1sn72ND2dIRYGu1k78zkWrFWW7STL1jordpKMS/7LdiQrTbK2yZK9tudj8wLnlpCZ+JzBd5y3ef92grCRt6QN6xuvxtb7yMZ7v1Y9P+i+uHoVfv/36VuzEDj0m3P0m/MY0sISBtZqHzj9cPcu/If/AL/8y3D2bO3rUYHgwjRfWSyN7Ez448SzjwKNj+xU1J5qwl8625yhyXpGrfw7DISDlUVKtxXH9cZttWso0NbrnxTbcHs4b167c79uR1hNJHgFN8G5qTbDrhFtqt15hZ1yA/Z2NUOkdBnjqchxTxEJSZYqzMo0K7Komq26yj7jHQJG5TmvZrW7aiKVWhDkHapZqzcL5VqgOChKpDycbBcUM/s4x/aMdc0SKX9t2+fvSikX9nEeCbwB/AnwJvAJsAg45PNSPgr8FeBXKbWBfRr4thDiF2ocUfnfAv+khufbFdlnnyX0wgs40Sirp+4nd+0OAZnjRHaiZD8tYGCPjRCIRgkYBpGvPMfRaHkkZcbJsWyvs2LlIxeX7XWW7SQrVv51LrVIUstiC2/ftLYEzo2npmGND5ngw5mJ6gdu4BMGYT1I2AjkxUs9tPE5H7GZFzc3rGo3BM+wESSk5yM+w3oAX3Euzqkp+P3fB8uiMz3PZ1bH8cvSFYCBpTl44zqMjUFvb37/X//1+kZUTk3Biy/y1z+5R3Jbr3Qse5fzf/YWOI2P7FTUnmoD1VSuOZGUbiv/OtWEvafoCDksrJZPVjZr1anbxGm4hlaviubTahMJq1XyZHWpSXvP4CqOuyzKaQQqJ6X3Cbu6ZUAqo5F/vG4sbn1elxrjeYp8n1f+HNGsBUFueQHVAkhvEXQTKVswlYS613oH14VoLRhJqZ4tFLtFiZSHk+25FvcT7pbd9jm0z7rsGyFEJ/DXtxV/cx+negn4PSnlVZftUxs/3xVC/I/At4HPFW3/KnlR8Tf3ce2WwonHyVy4QPDiRVLhXt7oGiqLsJvz9fHI42EMnwDDIHPhAk68stVrUPMT12LEfbGK2y9duoREMvrwA6zYSdbsFGtOOv9a+HHSrNop1uw063aKtIeiM3eLKS1WrHVWrP0H7GoIAvgIYPCFP36Pvtk1ulbS9E3MEuszyIoAc+FeklqEQNZkYH2R2YUFxI9mSZ45heyOYv3r32Tpwi9uiZ3AjRs3kPLgg47AnTvEfvADhG2TzcUZMOfL2t36isbkjyeRb73F4he/SPbEiQNfF+Dy5cs1OY9i91R7QErnmhRJqazADgWtJha5PUjWMh+lYvdYlrWZv7OWJFdHgVNl5dMza1y6dKnm19uJa9NdQF/FbTevvc/s3ca2v4mJCUZHRxt6zcNAh8ukUDPzFVWLLFJ4g2qW0c2617q1eWXp7y1aLXrc9dlCtTtP4Wb32mrtDtS91ku4LURLZTQcB7QmrM1wW5ih2p1ityiRsgkIIf7fwP+1AZf6Z1LKf1qhfHvk5H7S1wd2OGcj+JtAcZa6GeB7ez2JlPJHe9j3nhDii8CrwGeKNv0jIcS/l1K6ZNNrH+wzZ0g/9xzL3/whkkVmfYPM+gY3t2tCYviS2MPD5J55xlWg3C0CsRkBmA9arY4pLdbsLSFz1U6xviFkrttpkk6GpJPZfJ9yMqhbIjhI0uQIzi3gn51hTUqG7kxh6TaZkxEmj4eQWhqdNBYw7Whod0x6llJYt1cZf2gImbjGS7PrrEY70W2BYQvSjya5xsf85fxtdFug2xqGLTbeC3Sncplha+i2QJOCyMoKD/zkJ6Rsm8B6ik8v3y6L7Ow35+j5YJG5o91kIhGc3/5tPn7ySZLd3Qf+27z55pt87WtfO/B5FLun2gTWbkTKXC5Xc3F5Zv5Jym9tsL48xfj4OKdPn67p9RTNoV0msEJ+ZffaDKampjh+/PjOO+4Rt8jYVJMWZaSq2Gorgdw7uPV3WVOQM8Ffh2wC1ZDSfWGGiizyDkE/6JqsmPe2WREeKqLtcOC6EK3F3DJU7mdvEfBVXkS/6pKDud6sV3HLUH2ed6i22CGZEU2xM3ft85Slv2KXKJHycLI9HGt7ZOVu2B45WdOcjLvkV7d9/l0pZd1nVqSUGSHE18lbwhb+hwaALwF/VKPL/GvgP+/xmDPAH9fi4k48zgef/zq/9aM0j66X5gZc7xog/fUTOIODO5+oDviEQczoJGZ07mp/RzqknCzrTpqknSl6zZDceF8saha2202wImoEp6/OANC1lMKwbEyfzuTxGFIrHcxKTTB5opfIehafadO1lGIlFuH01QTvfK4D25D5EOxwkAyQKQuu3h0GOl+++AFzwxm6VzL0zs0i4xo5zc98R5SkHiGYMeldXSHSDb3mGsmBAWQsxkDQZuWvP0tA+AloPoLCV2pruwd8vgbP1B1ydB1CAUk6W/5dpbI7271evnyZ8fHxmkbeuIkF4WD+tnLz5k0+/elP1+x6iubg9kDXahOnkUDlVdmK+jM6Osr58+dres6f3AvD6xU26B01v9ZumDHLF2RA3qbx8ccaX596RK8qdsgNmBHEfI2dwEplwakgXAF0qgkszyBEvu2tJMu/62bZH665iAXK7tVbuIqUVUSbeuJufajanZdwtXttmktL5f5OCElYCeSeYaf8z40WKU0LMjm39Dmq3Sl2hxIpDyfbBcXIPs6x/ZiGipRCiNPAU9uKv9mo60sprwshLlJqN1szkVJKOQvM7uWY/Qgz1VhLCRL+I7wY+2pJ+XCfjTM4X9Nr1RNNaHToITr0EOxSh5JSkpVmPhLTzpByshs/GZIbr+mNsqSdj9Ys3sdp4djNnoUkAF3L+bSyy72RMoGygNQEy70R+hOrdC2nWYlFNo+vFZ1zy3TMLJKRkhO3pshKm9TRCJPHu5CaA6yRBiYdP9xZy0d2XlnJR3Ze/YSXPlhkqW8rJa6ATdEyIHwbr1ufg9s+BzQf0/0pdN8sRupWSXmwaD9NNC9/k1fpCDl0r82WLYQ4/n4K7enP77gQotZCgl0SmL/F2MgQo7FRNYnuEdwe6Jo1keA2cRoO2IDqd7xCy0XwuggFKrrDW+w0gRXrbOz3naySF07ZH3qLSFCyUuGRodVcC1S78xbtYumvrA+9hVskZfMieCtfNxyQ1HjKUNFEqlqrN6HtVbUZVs8Xil2iRMrm8CdAI1SeP3cp3y5+De/j3NuP2ZOgVgN+lbweUeBdKeWHDa7DK5SKlJ5KpuO20vUwDKqFEASFn6Dmp9fo2tOxBYGzIFwm7SJBc6PsL99+g8HjRwj1dGxuSztZMjJH2snWVeI0zLyFoG7nV3BmgtWV28L2wv6F42vFgSM7xxO80zeytR+QkTky9h5yl56BcVZ5ZfYT1118wigSPfM//iJB0y98BISBfyOis7DNX3TMVplBUPPjFz58Qq/5AoN2QEsk+PXpPyI4O122LX5rltC37tTMUnq3RBYTfGXxvRLBNOGP05c+05DrKxqDW7SOm1hYb9xW2ectN/fjxq9oRdxsjtxWu9cbt3Z3GMZ4h4lq9lrrKQ0a7BriFjkOqu15DVfXgmYtCFJ2r4cCd7vX1rLdVOK4t3CLpHQba9Ubt/6uS0WzeYqdFqI1GjXGU9QCJVI2ASnly8DLTazCTcBi6/sPCSH6pZRzezjH9oQ9V2pSs10g8jP6X99W/M1GXb+Iu9s+9zehDnVD+Ynvj2KBM+ayz/ilv+CZvvs5P1oeBeZISXZDrEw7W6+bQmahXJaWF++fle42gZYvb6Vp6/nvN5gxWany+wQzZsn+heNrRatFdrphSgtTWqw76ZqeVyDwC2NLyKwgagaEQWBD1AxoxoYguiWOFo4vvC8c5xMGWgsKoPqNGwQvXuSE6WMWQb85R785jyEtLGEQWjVAhtAnJwm98AKZCxewz9RPKNQSCfw/eIXnb62U5U86lr3LI6/+OQzkuBeN1q0Oisbh9pBU7cGqnrhGdwSVSOkl3CYkm2YF5nJdNYngLSLttMpetT1P4RY10TzXAmVBdxhwXRDUYvdaJY57i4BRee4lmdFwHNAarJG7LkRT/Z2nCAeq56RsNNXcMjqVxbVilyiR8hAipTSFEDcojfx7APjhbo4XQgSA09uKGyZSAp8HThZ9zgG/18DrF9g+GvFUUju3CVsVql9fNCEIiQAhrXK+qN1gS4eMk8tHFDo50k6OjJMlI026T3bQtfoxxmAHXWsTRJYFN0Q/wu+g6RaaYaMZFppu4dOtTRFwtSefhna5dz/u0O60WmRno5HkI2+zdn3yzxUiQIvFy1xHBgOdN2cnN8TQLeGz8L5YEN3+fksUNfZsg6slEgQvXgTLos9a4czqdfzbRPWB2TS+t6ewR0ZwolGCFy+Sfu65ukRUFgRTK2vhOGEGzNkSwXTO14df70Cfm+OBq1fRv/jFugqmivrTenav1SIpFV7BTSzKmgLTAl+Dn8hcJ07VBJanCPrB0CWWXf59N2PSXlmBHR5aKXrccfJiQSVULlRv4XavbVouVLXo+1AQdLF7BUhlRcMX4bgGG6j7rKfQdQj5JekKeSBTTejzVCSlohYokfLw8h6lIuVn2aVICTwGFCso0xs5FBvFr237/B0p5UIDr19g+4z5XiJRWx614rR90YVGRA8SIVi2TXuym9D4EgzF8U2lEaZJ95s+PgmO4RQJTpp0+PrJD4hrFnaHQd/xh7GEw/Gf/gUe7+vajNj8yftv09Mfpbs/SsYxycgcWcfcFEWzjkl2QyytlKuz1SI7vUYhAhSAgp67oQPfTS0e+PwG+qZg6S8ROY1NYXQrwtPgwe/8GV3JJUJLa5zNTpIMC3JagERggJQWIZjL0m1MYucyiE8+hrH7IRbD/+qrZJ5//sD1LaZYMLXnlvjM6vtlgmm/OUf/FViLd6HZdl0FU0VjcLuHNStvjNsEViSgREovUe3hPJkR9HS0xgSWmrD3FkLk297yequIlJXbXSgg0dVwzlO4uhY0wf5QRfAeHlxFyhaLpFTtzlu42b1Cvs9r9Pe96tLPdqloNs8RCTmkc+UDKLfxVj1xu7/7DElQmQMpdokSKQ8v3wX+VtHnnwX++S6P/dltn79TkxrtAiFEBPilbcXfbNT1t/HT2z5vt39tKleuXCGd3r815cTkp4BwWXkuPc+lSx8doGZbjI+PMzrqqVSeLY8zOIgzNIQ2lY9UM65c4Yg9Q9faMgl/nJQWIewk83n5FlNoPTrOfaN0GGHs4WHOnHi05Hwzk1cY7TjN+d5y69pipJSY0t4QMXNkpUnayRE53Udk/SOMeJTI+m30aR9XMsfAJ9ENC0230Qwbn98i1pmlfzGDhmCtJ982ax3ZqdgbFjaWY7Mb093o3Bo9t99DSMno1SmMLhvnZISZ4wHQ5ghvrPO46giSd1boWUphfZTg2kPDaCs6f34pQbo/il/4MDuzGFLnRzMTRWKoUSKIFt77SgTTwmcfAy99F81M41tcwbhyDb/0kd3IQ1n8f2A463Tcvk2wuxssqy6CqaJxuK0iblbeGLcJLBVJ6S12yhvTaJHS1S1DTZx6jkhQsrxeXt5K+YpUVJH3cM0N2ELtDlSf5zXcc1K21hhPLfr2FoEqkZRNsVZXdq+Hho6QZL7CCv9WuteqCF7FXlAi5eHlRUrzUj4thDgtpbxZ7aCNfJC/tq34j2tfPVf+BtBR9DkBfL+B1wdACNFDuVj6SqPrUU9S2cpLmmsZ3TE6Osq5c+dqdj7F7sg++yyhF17AiUaxxsaQ1+4QyOY4kZ0o2S8nfFhjp3GiUTAMcs88s+9rCpHPvejHAH1L/Nae+FlCn0zDkRi+yVVSyzaxuzbjodLIzniPyd84+SG6NoDs9HH85GPYApa+8DV+sb8nb5laFMmZkyaZDTE0H81pboij1rZXk4X1JfBp2JrEor3tY1uZ01dnAOhaSmFYNqZPZ/J4rCwHqdQEkyd6iaxn8Zk2HUtJVmIR+j++yTufG8nvtHHnmkov76su0bk1vjS+IZhensJwbPTHOlgY7kdnkoitIW2daUvj5mqGyPw6/vV13lu/jn7tFp+MhzEH+suEUZ/YJo5uK/MLA0PoiBbMFXpY6Ag5xHPTPLr+bn4xhsyRE36SmQG0mRM4g4MNrY+bOJrPSanwCtUe0FtpAktNnLYWuVyOy5cvH+gcPvE5oLOsvBn2h279XZdqd57D1bWghSZOQbU9r9FK7a7adVVOSm9RLZKylfo81d95D7e+pCluGSkXlxYVwavYA0qkPKRIKReFEH9EXvQDEMA/Bb6+w6G/Tmk+yDvAD2pcvWr86rbPvyulbMZM3r8Eeoo+54DvNaEeroyNjTE2Nrbv47X/2lOx/NSJPs6frx41p2htnHiczIULBC9exIlG+WjwCHJ+oSwXHye76YtaYBhkLlyoi8Xl9shOe/Iag+YsPVZpZOfY4jR6RxKEwB4ZyWegHx4mNHSC0AHr8O1vf5vR0VHOnz+PLW0yGyLnpvDp5MhJq0j0zIucuSIBNFd4lebW8Y5JTlpkpblluXqIKeQ37VrOR3gv90bKBMoCUhMs90boT6zStZxmJRbZPL4WVBJMp05G0TQbbZtQPd4d4uzqEj7TZm32DiuxCFNv/2BLMN0jAvALH76SKE+j5HPxq08ztuxzhQ+f0PFrvor7+ov2L3zea95Qr2GaJjdv5tdfBRYWGPruFb4xXb4IR5ubYOV/+T9I9/cz//jjZHt76143KaFj5WF+eu1SiWCa8MfpT2vAUN3roGgMkSqRYsm0Bg1eIKMiKduDy5cvH9h1xC0q++qNGS5durHv8+6HqzdOAV1l5ZqT5NKlS5w7dw6/X3mCeQFX14ImWNAlq1yzWt+saD/c2t16WsNx8o+PjcRtEZK613oLv2G6bnMTbuqJa05K1d95joibtXozRErV3ylqgBIpDzf/BPjrQOEu9neEEN+TUr5QaWchxAPkxbli/gcppfvSofxxJ4Fb24pPSSlv76WyQogTwNPbir+5l3NUOOd/D7wspXxnl/sbwL8A/t62Tf+7lHL6IHVpNdQKLG9jnzlD+rnn8L/6Kj7/Ivd8g8z6SiOIztg57OFhcs88U9ccfMWRnRP9D2DN3yAgSyM7g34H6fNhj4zUJLLTDV3oRHS9Yj7Pg+BIJy9YFomZpQKnRVbmyDlWmei5FQm6JXoWbzdle0R/Gma+nrqdf0DKBH1V9y9sL+xfOL4WNFMwlZD/PqX7A20tMdDxCb1I7CwWSHV8Gza4d0+vMBe6xs3F9YoCqVEihupF2/PCaauKogWBMnzvHvHXX2d+3YdgKJ9vdNvCDNuWhObmGH7pJRJPPUXq6NG61SuwsED3G+/wD6Z+XLbtWPYuT75xl2DwSt37X0VjCAdA0ySO0xq5Ad2Ego6gmsBqNQqLqPbL4HfDfDJZXl4ph1G9SWYr3/fDQYvx8XEAtRDSI7hFTjSjv3PLz6ZpknCgwZVR1JVqonMq29jcgLYNqYxL3nElFnkKTeQXBKVz5dPrzXDLcHMtUG4Z3sMtkrIZeXhd250SKRV7QImUhxgp5cdCiH8H/J+Kiv+TEOJ+4H+VUi4BCCF8wK8A/4rS6MEPgN9pUHUhH0VZ3PO9I6U8aHLEnwP+uRDiR8Dvk7dsvbI9OlMI0Q18BfgN4JFt57gB/D8OWI+Ww20CSyXc9g5OPE7m+ed56XKa5BuXyyJ5Uo+O8cjzHTufqBb12IjsXPT3crlroExAyPT3cObxLhCirpGd9UITGkHhJ6jVPkKgIIDmpLklckqrSMi0SkTNO1N3sYRDT3+UrLNxnOv+Vs2iQC1ffkLU1vN9SzBjUiGFwibBjFmyf+H4WtBKgmm9sbCxpE3arrqeCAYgQZrLq1MHup6OVhLVWRIVuvleL7HD9W3bvl0k9RUfs23f3djnPtzXx6Mffgj9/YSSy3xm9Q3820TifnOO4QmJNpZfCDF0/TrpJ56oSz+j37hB8LXXWJc6AqeiYOrXbfTJSUIvvEDmwgXsM2dqXg9F4xAinxuw0gN8M1Y7u01eqHxF3sNtlX24K875843N6/1/vF1uOwswPBBROeo9RsQloq0ZE6duwmhXWKLc973FTvmfGylSVrPUVpP23sNVpGwhu1fV7ryHW3RsM1wL3Nq6erZQ7AUlUir+b8CjwOMbnzXgHwP/UAhxC8gCpynNAwkwD/zNBlutbrei/WYNz/3ZjR+ArBDiHrBC3n+rl7zFbaWePgH8vJRyoYZ1aQnc8hW5TXYo2hezL86LsZNl5T2+NLDakDoUIjsn33sDSYLZbZGdn+oxQWQbEtnZbmwKoPhhFzrepZv5W//53t1FKzhSYhbZ2+aqvi+IpfkIz81oT8eiI75CdOEWTq+DfyVHbD7LdK+O8EmEVjrAFo7cjFZc7ckb+i731m4ytZUEU69h42DLHBmZgwasaRFQEuVZEDMLIub82CKDr/4F767l6FhOEbs1STSmk9P8zEZ6SRkhQpkcvetLrGUyBC5fInn2DE4sivWnf8jKc3+z7Lw+YaDtc2ZTSyQIXrwIloUzv8xnVt+rKJgeubGGFnsEJxolePEi6eeeU/1em9MZkqylystbaQJLWTJ5D9d8RS7j/Hri1tbVs4X3cIvYacaiDPfcz6rdeY2dRMpGUq2tq3ut9wgHLBbXy8vd+p964nZN1e68h9sYryntztWlRbU7xe5RIuUhR0qZEkJ8GfjPQLF3oh9wW9J6G7ggpbxa5+ptIoR4CigOI8gBv1enywW2XcuNF4G/K6WcrVM9moaUagXWYcJ1BVaDBzdOPM4fjfxdriwt8uj6uyWRnQOne0h//T6cwcGdT6SoKZoQBISPAD46D6DNaZ9/ktCdb0FE4pt9m4V5G/17g4yHxnA0gaY5CN0m5M/w905/jC56sSM6kaMPYgtJ9DNfZay3m6xjcmdqgpmFObr7erCEk48WFM629zYWG68b5XKjSS/3RuibWWW1J0T3coqehSSz8e6Klq/1FkwVB0MCZpWI36i5BgvTTEvJ6PUp1qVN9nSEyeNdSC2FToockHB09DsZepZSWFeWGH9oCHn1Y176YIGlvvKI8mIb3e2RnW6Roz5hcO47f0ZXcpHQ0jqhaxN0RnSyWoDZYD9JLULIzDCQWgCZxfnkI8zRUYjF0H/wMvav/O0do0YVrUtHSBLPTZfd3+JvdKE9MNrQ+5vbZK0a43kPt0nJdRcbwnriNq5UqSS8h+vEaVMiKSu3dTdLWkX7Um0yXImUinoSdsn/3IyFaK59nrIZ9hxuUYrNaXduNsOq3Sl2jxIpFUgpF4UQP0s+z+JvACMuuy4C/zvwz6WUFdYJ1ZVf3fb5O1LKxRqc938CPgGeAsbYOQ5pHfge8P+RUv55Da7fkqRzYNlqIuGw4DqB1aS8MQn/EV6MfbWkfOCxdZzB/ecBVDQfZ3AQZ2gIbWoKe2QEbWmcQXOWHms5by+sRQg7SYasaSI9SdADWGfH6PdHsYeH6Tz905vnyoUe5HLmMqT3VgcbBwsHI36WE5e+jww5xLBZmtMIvtbFjdgJhC4RuoOm23QF0zxhjeNflxAM0TV4gjCSiU99mtFgZDNK1JT2lm1uDS1yFQfn9NUZALqWUhiWjenTmTweKxOkpSaYPNFLZD2Lz7TpWkqxEotwejzBO33lw6Jd2+gWEZ1bo+f2e0xJyejVKXLY2PdHmDkeRGoLhMibMsw5Ev8di56laazLs3nBdOVtXnrnDuv93WX2uAVh1CiLJN16Px+cw5AaS6sUHa+7vN9+3p0tdRXV0RIJ/vbdP8SeTpRt672WI/StnzTMKcBxoGNlhp/ZJpYm/HH6U6eBWF2vr2gsbgvRmjPGU6kkDgsdLt9pKqNh26A30JBCLbw9PISriJTV7FfrwVrSfSGImrT3HqFA5VQgboJhvZDSPQ+vyknpPVppQZCK4FXUAiVSKgCQUjrAvwX+rRDiIfIWsEfIi3YLwEfAT6Tc5km2u3PfpjSX5H7q9/eBv3+Qc7ic92XgZQAhRBh4gLy16xHyFrcasAwsAR8DH0op2ycZ2T5Zd5lEAPeHTkX74jZgbY4FnZtNhGp3XiD77LOEXngBJxoleeZ+chO3CcgcJ7ITJfs5hg/nvnxuQAyD3DPPlGz3+/2cP787u1o3QokltKkptGAXq392je6FNOcWbxYJplkeDM1x33CQNb2L9eGTfCoygj08zNiDz1c9tyMl1qb9rbUpZhaLmIUcomZJ2carS3lhW64oX6gpLdTQ351CFGzXcl7RXu6NVIyYhbxQudwboT+xStdympVYZPP4WlAbwbQDU9rk3fj3QD4ImJ8s3tpX3Q10/JqbIFoqcPqEXtGCdyvKdGu7UXwOzSg7t47W9gKpfuMGwYsXOZYzuINWloPUv9QNsrshOUi1RALxvVf4xnS5lfux7F1GX/ohwekjylbdQ7hZWrbSKnuVr8h7VBMAkxlBV6Rx37maOD08GDqEApJ0tvw7b7RY5CYSGLok5G9oVRQNwDWSssHieKZKsIESKb2HW3Rso/u7/DXVvVZxcJRIqShDSvkh8GGz69FopJQp4O2Nn0NNtZU3atVpbRgfH292FTZZWTwB3F9WPreY5dKlS1WPnZiYqLp9ryytfJ5Kt6bFuTtcujRZ02sVmJiYYHTUzd1aUUuceJzMhQsEL15ExKK80XWkbMJ+ztfHgw+HCQYFGAaZCxfqMlleLJjeip1DX7xeJpgGgzbS52P95EnMrq6KgmklNCHwCx9+fDWv93aklFjSLhUySwRPcyPSc0scLRZCC1Gg127fINzVQbgr4iqQFvZvJwwzX1/dzj/EZYLVv5PC9sL+heNrQSsJpnvFwsZyGv/dC0SViM+t97sRQIsF1MoCq44dgBwWtrTRaiCQFucg7TVXGFq9Xp6DdDqB720Ne2SkrjlIC2Lp+pqNIFSx7w3okYaIpYrG0UpuGSqi7fBQbVJyLd1YkVJNnB4uOkMO6Wx5qG6jI4tco9lCkjZfe6WoQMhFpGx0+pxqoqi613oPt5zezchJqVJJKGqBEikVCkUZ1W5qyu714AwNDTW7CiW4rfxL5Xa+RTz99NM1jXJxu2Y4oOwzvYJ95gzp555D/sGfIVlm1jfIrK80F1vWTuIbHqprNE+xYLro6+V6V3/ZpL080sOJx7swZ2ZwdL1ugulBEELkBZYDDum+/YMFRkdHOX+2eoSqsyGKbomhZqlI6mwXNu0tYXRje/69XSJ+bo8iLT7OOUCsqOXLT1TZen5FaTBjslJl/2DGLNm/cHwtaCXBtF2QyE3RvSH8gp/f5i/57Tt/iZCgo2FIDR0Nvei1uMxAQ5d60b5io1zn/Hf/kq65ZcIr6xxfXSAZ9pEVfmaCg6S0MKFclgiTrCwtId5eZO3ECayubrLf/G0mv/xzNfu1AgsLDL/0Epptk5lJ8ZnVtXKx1JzD95M1Fk4OY3Z14fzWbzH5pS+R7e2tWT3cUIuF6kdLuWUou9dDQ7VJyYbnBlTt7lDRGZbMLpeXN1wscml3yurVm7jNVSQb3N9Vc0RTbc97tNQYT+V/VtQAJVIqFIoy3B7mwN02SrF7DMPIiwEHtKusFXfTAfiT8vKcHWhoHR0H0i4i5cPnTnL+4fqIu60U1XpYcOJx0r/yPL/1fZtHK+RF+9JfPcmZx/rqXo+CYDrx4x8jmS4TTH+qJwciR7q/n/nHH6dbRRVtRIka+DGINOiatnRKozq3CaG5DVGz5POG2BmMLNG1Po7e2Uto0eTIXI67gR4sIRCak88/qjkIzSZgmMQWUgCs9uT9UZd7a/dbtpJgqtgZKcDCwRL7e7iOzq1xavkGK4ZkdGoKvdvGORVh9ngMtFnCG/vddATZOxY9SymsxGXG+4eQa3d4xZKs9XaVCKTl4ugO2zZeH373dVbkKqH1dSKTCcKBCDkRIBEYICkihMwMg5l5DMfEf/v2ZvR439tvM/nlL9fuj6poOB1BSTw3XXavXVofQJs5gTM4uPNJaoSr7aZaAOkZcrkcly9fJpPTgZ+tuM87718jNV/t7ldbpmYeBYJl5SqS0pu0SvS4ygt4uAj5XXJSNtju1a3dgWp7XsRtQdB6RiAlDY3adhPk1b1WsReUSKlQKMpws0OJBB20xtubK+qM24B1LdXYwU0qK5DSZXCjclJ6js6QJOE/wouxr5Zt+0Z4kTPsOQXyvnDicb594teZTc+XTeKeHuki/fVRJqemGlIXRWV0oaELP0H2nsTn45Mpjn28TnxkEN/y2wjT5O77Qd4wH8ARWzc0TTpc6PuI+0MmMujjxMnHsIVk6Qtf45cGekujPouiPXcjlBb27YyvEFu4heyV+FdMonM5pqI+MEBoDpq+NcEhHLlp71oPwVRRfw6ag/TE1Sne+Vy40qn3RHRujSMrV5n35cXSZNCGhyzmjsfQtGk6N/ZbdCTjd3L0LKWwEx9yte8oYvUur5ka673dFcXQncTR3exjoGEFIWmlMaWFgd72eUhbBS2R4ME/e41vTC+VbTuWvUvwd36Ic3S4ITlIsybEUomKC5P6UqeQqnvzBJcvX2Z8fJyzZ0fRhMSpMK5PZRs7BeXm0qImTr2JW462aouw64GrvbUSijyJWyRlq7Q7UMEGXsStv3McQSojXO1ga42U7gK5utcq9oISKRUKRRlugxtl9epNOlwGrLYjSOcgHGhMPaqtcFWr7L1HwAc+Q2Ja5d97o/PGrKVERcF09MlVnME0KJGybcnGYmT6+kAI7JERjCtX6M/O8mRylYQ/TkqLEHaSxHMJwr40hPP7CU1DDA8THjrJwWWiPNrnnyR051sQkfhm3+bmHQfjVj/jobENwVQiNElfeJILfVeIaf3YEYPosYdxhOTET/81fqo/WiKEFlvtbr7fJo6a0mJpbRlbSHwhf5nAakrrAIa6CjdaJQfpfsXSjuV1VmIR4tcmeGdgpCZ1ceWCn//Em/ynO28CYKBv5QvVtnKHFucZNSrkFN2+3e+Sh3T7sfnj8p/1GuQibQUKOUi7lx0EwYo5SHO5MIEG5CDVEgn0777CN6ZXy7Ydy95l9Ps/ZK3DZP7xx+tyfUVjKbjFdIRkxUnLwaH7OH/+eMPqI/9TZ8Xy1aV7XLp0u6bXOnfuHH7/3hdUKWqH6+LblrEZViMuL+KWPqfRkZRudq/hoIOhDFk8RzURci3dOJEyncvPHVZC9XmKvaBESoVCUYarHZNaBeNJOqr4xCfTGuFAY6IYqz08qrbnPYTIR1MurpV/7w3PV6RWO3uauSee4OSHH+JEo1hjYzB5m4DMcSI7UbJfVviwxk7hRKNgGOSeeaam9XAGB3GGhtCmprBHRrBuXWPQnKXHWi4RTEdS40SMNL7OTsTZMbp9HdjDw5w+8ei+r31p+hIA50fKLbyllNg42yJF7TKhsyCCWps5Q7fE0fL8onZJuSXtsghUS9pYeDfPZqvkIG0VsXQvWOTbT1rmoMFGCgIqCqDbRdL9fN6+rdJ2XRw86kJLJAhevAiWRSS5zGdW71TOQfqORLt/BCcaJXjxIunnnqt5RGVBLM1WEUt9eoTQ3BzDL72EfupU3cRSRWPpDEtWU+XljR/juUzau4gK+6WQPqJV0nkcVlrF7tX12cIl8knR3oRaJCelW7tzW5SuaG+qzVU0ss+rFjGsclIq9oISKRUKRRluNxl1g/Embl72kB/o9vc0ph5rSfeBlFqBVVtaJQ+nX/8ZqBCn5rYKtF6oPs/bZHt7yVy4QPDiRZxolJsnBkhkl8smynsHo5yK5sAwyFy4UBf7w+yzzxJ64QWcaJTr0QeJrF4rE0wD/iyOYWCNjdVNMC1GCIFBXjAJEYAGrrR2pCwRPa2yKM8d3jsWJnbF6NEtUdWuGHHq1Fn9apUcpK0ilrYLEjbbEmQbfn0NsSli0u2gS43vTH5UVQDdHik69t1X6EwuEFpeJ3LzDh1hg5zmJxEYIKWFCZlZBtLz5DJp9E8uY4+OQqwX3ys/IPsrf7t2v0uRWOrMu4ulPR9JrFg3ZldX3cRSReNpFbFo3WXx7QNjRzl/vr+hdVHUn2ppTBqJ23OtWgDpTcIuOSmb4Q5UCTWX4k12msdrFNVyoaq2p9gLSqRUKBRlKLvXw0W1h6VGPtCtughFuiZVDoUacu7cuWZXYRO3/B3VBrq1JmdC1lR9ntexz5wh/dxz+F99Ff97c8z6Bpn1DZbsE7Et7OH65mdz4vFNwXRB7+WDrr6yyKLFrg56xroIbgiU9RJMWwFNCPzChx8fjU5JZ0tnWxSozf/wz/9HBo4M0j80iKNJHCFxNPLvi38EZZ/tbftkw934zXVSnRGiiymic2kSfb1gSIRWKpDWMwdpq4ilit3hIMlKk6w0YWNYtGpmdn18dG6N0K33EFIyOj5F2rZxHogwczwG2tzmsqB5R3Llzio9SymsyzOMPzSEXBG8/NYd1vq6SvOJbuYQFRVzixpSR0eU5SB9+Lt/QdfcEuHVJOGrCSLBMFkRYCYwQFKLbIils6RWZtFmJtFGRyEex//qq2Sef772f1xFQ+kIOcRz02V5SIff7EZ7/CzO4ODOJzkgUlaLaFNjPC/iFqno9pxZL9R8yuEi5Gb3mhZImXcQagRui0CUOO5Nqs2RNXLRtwo2UNQKJVIqFIoy3FacNsrTXNFYqtl/NHK1c7Vk2x5ID9Uy+P3+lrGiGugNcnOmvPz6rRkuXbrekDosJ31A5Ymye3c+wZ9ZY3x8nNHR0YbUR1E/nHiczPPP8876Gm9Nj5dMmib8cd4aeZCfez5Y93oUBNM7r/0EnakywfREeBw0re6C6WFHFxq60AiwFVk45OvlVOcxRocO8P8uARsCp8Y49smLEHHo0STpFdBeHMznINUEmuYgNAdNmPzKkb8klA5jGzpW+DSDOTCHP09svRtbOFg42MLBxsHaeN1ebovK+2SjUXyJJMmeCD3LKXoWkszGuytavtZTLFU0hv3mIO1aSrESi3Dq6jTv9EcwxcEiaKNzawyvXGPRJxmdmiIdtJEPWswdjyG0BB0b+y06kvE7Fj1LOazZj3lncBWxovHDd6dJ9feU5Qx1y0daZq+rbeQbLcpvWtju1ypb8xronshH2gpoiQR/69YfkJwuH+QN3LxB6Fs/bsg9Lp2DvnSiTChN+OP0p08BvXW7tqI5tEoEr5so2qHsXj2J28JbKQWpTONyA7rZW6t25000Lf/drlf43huZD9Wtv9M0SVgFGyj2gBIpFQpFGWrF6eFC1yEUkKSzlXIDNnAFlqvlpmp3XsVNIE9mGzc8SeXcr1V44BwdHW2pCFTFwZDxQV6MjZSVn9NMYLEhdXDicb41/Ovk5FzZ5KnZl+HkVx6m89lnG1IXxRa6rjM6OlqzhRyhRAJtagotFEJ/b5zBmfIcpPFcgv6YxNfZgzU2Rlc0ij08zPDTf7MmddA6ZgglvgU9Et/C21y/7SBuxrnacR9ClwhNInSbkXiGZyPj6CKGE9YJHj2HA3T91FcY7e3ZsNQtzTO63YK31K7XLstjakoLu9FJJg8ZrZKDdL9iacfSOiuxCIOf3OKdCv10Pdmej7Q8Z+huBNLCfjp+YZRZ8Rb2SQcskiLLup3e3KbVIB9pKxC+d4/Qa68xnDG4hlbmFhBc6gHZhT45SeiFF8hcuFCXPKRaIoHv4qt8Y7o8dvxY9i73fe+HBG8fUYuBGoBlWQ1LNbG8cAwoH7MvLOe4dOnSns5lmmbeEt/Y+3PJ/NJTQLm9+srCBJcu3d3z+XbDxMSEWlTZJNwiKSEvFjVMpHRZ9K3m8bxLR0iyni4vb6QzVbX5Y7X2S7EXlEipUCjKcFsJ06Xys3mWzpBDOltu6dYKXvaq3XkXt+821DHI+fOhhtRBv+k+FPqpx+8n2qke6ryG24N6o1fZr6U0lvxHeDH21ZLyr574Fk/FYg2ti6I+FOcgzZ0dI3fjTlkOUgBL8yPGztQlB6kzOIgzNIQ2NYU9MoJ98zoDuTm6l1ZKxNIHzCk6B1KgB7FGx4j782Jp95mfqVldoJCHtFTs/OMXv8OxU8cZOTtCriynaLnwWSqUugui23OTmtKuey7SZtMqOUhbRSzdCw3NR3oefsI8/+nujzeLdLSKYmhB8Nx1FGnxZ62ySFo4V+G8GlpNokgDCwvEX38d+vvpNZfpW71Rloe0byaB720Ne2QEJxqtSx5S/caN/HmXHATBMqF0ztdHQI/UXShV5JmamuL48eMNuZZbRFtqHwsgX3vtNY4fP74v4c/teuGgu5ilaF/c2h3khcPBaGPq4SpSRtTzrFfpDEkSFcob+Vzr2u5UsIFijyiRUqFQlOFm99qhbjKepSMkmV0uL28Fu1e18s+7uPUpjRTHq+VdVQNrb+JmedTIyPFqebL8+u7zzylam+IcpFp/lDe6hipOlh9/oIOBqKxbDtJisfRazzk6166ViaUBaSN9vk3hoNZiaYF8HlIDf9FjaCRt0G93cjo4VPPrbceWTkWBszgi1E3gtIrKCp8L27fKq59bUt/7SqvkIG0VsbSdsHGwZY5ME4YeAlFR/NwpgnR7lGnvO3/KxPokmZVJhhbnWQ36yWn5PKTrWoRwLkOIKVaXFhBvLbJ+8iRmVxeZb36TyS9/uSa/S2BhgeGXXkKzbdanMnxmdaVMKO035+j6UMJo/YRSRSm1dCmoxpzthz8uL8+Ye093UUj3sJ96p01/xfKH7j/O+fP1aWeNilZVlFNVpGzkc62b3auy3PQsbhbXyYbO47k4oimbYcUeUSKlQqEow82/XIlF3sVtcFNNwKk1KtH74cOtT2lsu6s8qA4FJEZt5moVLYarON7AdpfOgWVXvl5Ar3MEj6KhFHKQ6i+/imSlLAcpQNZOYw/H62Y7WCyWzum9fNTVVyaWhoa7OfJ4NwhRN7G0FcjnIq08edwI7E3Bs1TUrBQ1euP2TWwcjhwb2mar6x5lGowv0j1/Gytm41vJ0r+YYaZPx9FB0xwQW/1fPXOQtopYqtgdEkluQ3TfL9G5Nb60ehPRI+n7cAqj20aeijB73AfazGYe0tuOxLxj0bOUwkp8xNW+IVib4M9MyVpfN7rU0NEwpI4uxdZ7NIyNbbrUMAqv28oefPfPWZVrhNbW6ZxOsO6PkBUBEv5BkiJCyMoQz8yi2ynElStYY2M40Sj+V18l8/zztfmDKppGR0gSz01XzENqTx5DH66ch76WmBZkci6LvtV8iicJ+d0X1riltKkH7hFtSizyKm7frZtwWA/cHdFUf6fYG0qkVCgUZbgNbtRNxru4CYGNjaRUNsOHjdZod8pm+LDhJo7nLEHWhED1gJ+aUG3Cwq9XSCyiaGuceBzn7zzP7/yRzQNLl8omTh/84mliX6qvxW9BLL39yk8IMlUmlh6JZUGY2MPDKkdbHdGFji52J8DpubyAeL7rkV2fX/v8TxO68y3okPjm3kaYJnNv9PN65gEcIfI5SDUHXZh8PvAew0YY29Cxw2eI58Aafpq+9W4sHBzhYOFgCwcbB2vj1Rb5n+3bnM19JGY0RiCRItUToWcpTc98itl4d0XL13qKpYrGsd88pJ3LKVZiEYavTfLOwMGs/qNzawysXGXOJxmdnkKGbHjYZP54DF2bpGtjv0VH8tadRaJLaewPp7nx8HG01Uv85XsLZAZiZTlGK78v/Tzpm8FAx5e6tVnm1yofp3sk/2iroSUSjL78Z3xjerls27HsXfzffA3fqaG63+PW0sJVKO1LnQT66nZtRXPQRN6ppdLiV7XoW1FPXBd9t4AzlWp3ir2iREqFQlGG2+Rph5q09yxuVgyNtD9cS6ocCocNN9tNN6uaeqBshg8f1b7b1ZSgv7v+371buwMIGMru1aus9wzyovhqWflfCa0A9f/enXicfz/49zC02bLJ05X7O0k/N4YzWP8oE0X92J6D1Lhyhf7MLE+urZbkII3nEnRFVtF7ddJHTxIhSLq3n77uM/SZO19nJwJDpzj23osQdOgRknv3AnBzmKuRswhdInQHoTmc6F/mp/UPYFlgRLqIHH0UW0DvZ/4K53q7q0SNWiX5S7dv3267a0oL2+P5SJtNK+Qh3btQOoXPtAgurrASi9B1eZxrPSP7u/hGqOhLs5d33FVDw7eRa7Q4N2h5HtGdhVJ/cX7RCsf6i/Kaah4WRwt5SLuW3fOQZnMRgnXOQ6olEgT/8FW+MV0eO34se5fTf/JDgteOqMVAHqQrLFmvsM6w2pi/1qjn2sNHpIXtXtWib8VeUSKlQqEooVqeLDW48S5u1jOtMKhWEbzexe27beiKU5WD99DRGXF/YFpLafR31z8X2mrSfaIwoHJSepbOkGS+gudloy2uM/4jvBgrFUuf/uklnMFcw+qhqB/FOUitsTFI3CrLQQpgGxFCT5wjsJGDNP3ccwzUcNI8lEigTU2hhUKIP73OoDlLz8pyiVh63pliqF+wFuhg/ehJ+v1R7OFhOk9/tmb1KOBIx9Uud3se0eLthfel+UhLc5eaRflJt+c2LYiqXh9VtEIe0lYQSneDg0NWOmRlDVYE7AENbUMQ3RIz/dqWiLld3Ny+n2+nfTaFUmNThN1t5PiBfq9EguDFi2BZhNeX+MzqRMU8pKF3QXvoTN3ykBaE0rV5d6HUr4fR6yyUKppDZ1jCQnl5I+1e3Z4vuqo8+yjam1ZIn+MWbKDmUxR7RYmUCoWihGp5spRY5F3cREpl96qoJ24WIA0dVLtEbap2512qLbhpVNtzW5ShaRKfpnJSehXX/M8NutdWy5OlLJm8Q3EOUica5c6pAe6klssmyyMDMe6LZuuWg7RYLL3afY7u9WtlYmlA2kifj/WTJzG7usAwyD3zTE3rUUATGgGhEcBHo81kpZTYOJui5R/88R9y4sxJ7rv/bJk4ajoWFjamsyGCYpNzLFcxtTSv6XZxNF/uNCCKtBXykLaCUNrKODhkZI6MbNyCFA2B/EUfv6P/iN+/++5W1KhmbIskLY/8LESWlgqo+W3F2wde+i45M42xuELoxlX80k92w161OHrcySYx6pSHtFgolfPLfGb1TkWhtPMDiXPfSN2EUkXzcHOmatQYr1qwgZrH8y5uOSkb6Uyl2p2iViiRUqFQlFAtukMl3PYurZAbUOVQOHy4PcwlMxqOA1oDxtYqctz7jI+Pl3zOz0X+XMV9L31wA2d1se51+vCTQSBaVh72m8zMJOp+fUVzcBMpG2XJVM0dQU0keItCDlL/q6/i/3iuLAcpwJBl1zUHabFYOqf38nFXX1lkUXi4m/jj3ZgzMzi6XhextBUQQmCgYwidEBDM6fQ4YY76+xty/UIU6fYoz8pC5y4iSB0Ls1hIlTb6wCy+yXXS3R3EljPEFtLMDETBKO9b6pWHtBWEUkUpDhJ8ggwmGbv2kaPRuTW+NP4eQkpGL09hWDb6Yx0sHu1Hl/focHQcWyNhadxaTNO7uob8eImpT42gXbvB5SsBzIH+EpG0EBE6G0sT8M0TTE+URI76K0SLBl55BSwLbXGRyM2r+KVRJpQOWwk0ax2tTkKpork0ez4llVXBBoeRVgg2UIu+FbVCiZQKhaIENYF1OOkINn8FlrJ7PXy4DaohP7DuakA+UtdcqKrdeYJz586VlekaBH0WGbN8GJzMNmZonMxUju6IBCygXFg9KOPj44yOjtb0nIq94xo93qB7bTXLMbUQzXs48TiZ55/nY7nCW3euleQgTfjjrJx4mF943l/XOhTE0tsv/4QwU2Vi6XBvFoRJur+f+ccfp1tZH9aF4ijSul3jC1/gxvd+g77YUY51RkitWVz57hHGw6Ogg9AchGajaxa/fuojQqIXO2LQcfRBbCHp/+xf4aG+aEkkaMl7p1xMzW0TXK3+PvyzKZLRCN3LKXoWkszGuytavtZLKFU0lkp5SKdORhGajUFpZOzNrhDG5SV82Szrs3dZiUWYfefPeOdzLnlIz8LHrPCnM9XzjPbOJfm5T95Hl4Kzl++h6RLnXBfT8SPYchmfvUbW0ZiQndy2V4ksrCE/eZuF8/ej3Zzmzo0hnMHBUqFUK7z3bYmjG1GmQjROfFDsns6wJJ6bLsv5ffq9KNqXR+qe81uN8Q4nri4tjXSmcsuFquZTFHtEiZQKhaKEaoMbJRZ5Fze/eLd8ffXAre2pQbV3qTZwXU01SKR0EQdUu/MGfr+f8+fPl5X3dAoSFQIm++JnOH9+uO71emMiXLG8L+rj0Ucerfn1RkdHKwq2isbiFj3eqPzP1a6jose9ixge5MXY2bLyI9IG5ut+fSce598N/H0CxkzZ5O3K/R2k/9b9TE5N1b0eivriDA6y3tNDn6Zhj4ygf3Aln4d0dbnMdjPUmSQYCGCdHaPP34M9PEzHqYPnIdWemSF071vQJfHNv834TQdxe5BrHSMIHYRuIzSHk33LPNsxgUEUO6zjH34AW0DoyZ/jZLS7JGp0SxAtt9M1pbVpxZsxs1jYOBpYeNs2tpVohTykJ69OY0mbyGISaebI+HXmxsIEtdWyfS85OmeXUvgyNlPT46zEIlz/8X9xF0q3IWBTvNwuaC7ev8jN0Ie8PTu1GfFZvN9WmYFvQ/z0F+3nL4iiG/s0Ip9oK5DL5bh8uboQXY1bt24RWVnhC299wPnpVNn23isfs/K//MHmYpxsb++uzmuaZj4K39jdtP3EfASoHJ0/cfNDUvNZzp07h99f38VJisbimpOyoWmbVLCBojYokVKhUJSw4hJVpGuScFDdZLyK2wCiUROnWROyphrcHDaqTYqvZxrT9tTKv8NJZ1hWFCkbteq0WrszDIPR0dGK4qqivXFbENQou9dq7Vv1ed7F7V7bqPuslPnxpO0/wouxr5Zs+9mfWcIZzIESKT3B7Qce4PiNGzjRKNbYGLlrE2V5SAFMzYcxdgYnGq1pHlJncBBnaAhtagp7ZATr2nUGMvN0Z1dLhNIHkjfpHnLACGGNjhEMRLGHh4mN7L8ely5dAuD8+fM4Um4Km6URoVsCZyH/6HbbXbOCOFrRptcpL7cbkHu01WiFPKSNFEolkNtoF2V0wzKLTKRqk7ZAQ2yJnJqxJWJWiPTMl5VGfZZ+9lUQRPP76GhNjQ69fPnygRxH9Bs3eODaNe5mRlihu8zWXKxHwHEIzc0x/NJLJJ56itTRozue97XXXuP48eO7rlcy46sYyZnwx+lNrTE+nY86Vs8X3sJtYXWykTkpVbCBokYokVKhUJRQbRWMchbxLm5CYLXI2lqiojsOJx1VBq5rSQ0asBJd5aQ8nLhFtDWuz3PL3aHanZdp9mpnt/YdCkiMwxEwcShxFSnToiH5n1MZge2ohWiHgWR3N28cPcpn7t0Dx+aNrs+UTdjP+frw9ecYzGZx5ubyE/bT0zA9XZM6BAYHGX7/fTTb5nJ4lMHUrTKhNBDKIX0h7JGRmgulAJrIizv+OtrrVsKWTgXRc5uY6ZQKoeXWutbWNqfwfvt2m5w0N983k1bIQ9oKQmk9cJBkZI6MzFFP/VsgyqI4t0TN/Gtgc9uWwOnTfPnyElHUR6DouML+O9nk7ndxoJZIIBcXGTh1CrmWIbr6MX5Zmnu1c2WGY5Nis78Zun6d9BNP7Jh/uSCc7qZeWiJB9Puv8Y3p75dtO5Gb4KmPkhy1beb7+vb2CypaHje713ROYFrgq7PqkzMhk1OLvhW1QYmUCoWiBLcJrK6IWgXjZdxWOaVzgpwJ/jo/Y1fNoaDanmcJ+fNR2pUmLxs1ab+SVGLRYcR1YUaD2p37giDV33mZ5ovjqt0dRtwWBEkpSGZE3SeRVqrlu1djPE9x+vRpbgKTDzxA39tvo+swS2keUoCcPU26v2tP1oe7JdvbS+Kpp4i//jqzeh83u+JlQmnn6U7MR6MgBBgGmQsXdhQM2gFdaOiisVaOUsoyUTS3TSj9P/7r7/PgIw8zfOJoSSRpaaRosTBaLrJuF1QLLPdG6JtZZbUn1LQ8pK0glLYzEklWmmSlWTcxdLsQGiiywc12pDGkxvtzs2XiZqDoc6Aggm6KpT76X34RgYNvdYXh2TvcljrZjejFQuT2GTGNMJMYV65gjY3hRKP4X32VzPPP1+R302/cIHjxIvo0CPxl/d1quBdkZDOSUz91Clvlf/YM1eYs1tKCWGd9x3jVgg3UfIpiryiRUqFQlKD8xA8n1b7f9Ywg5qvz4MbFZhhU2/MyQuS/36X18u9/vVFikUvbUxOn3sZtUr7Zdq+NyMOqaB6u+Z8bFUnpch11n/U2O01g1VukrDaB1a3anqfw+XyMjo7ywPnz8MUv8p8/Njl67/0y68Fjf22EB36xh4F6VeT8ebQnnmDi77xJND3JrK9UKD3VnwFhYQ8Pk3vmGU8IlM1CCIFPGPiqTC1GpiQj5wY431mb3NhSyk2x0n56iq57v4vTYROauwSmSerdDn6inwIDNM1G6A6aMHk0e5tBwlhBnZ7Bk3QKydQjP8W5UMeWYOpsRYquZZJgCGzhUK2nagWhVFGdqkLoxmLsm8m95WiOzq3xpSvvIcY0lj78CCPkoJ3vZG5oAOQMIVvHtnVuOEGiqWU6F9eQn7zJ7COj6DfucfNaH/bg4Jb4uSmK5n8yfpuMMDEdyzUSVEskCF68CJaFtrjKZ1ZvlUVyHkvP4nsbfN3dmF1dBC9eJP3cc6rf8wjVLFXXUhqxzvpGaq9XsZVVdq+KvaJESoVCUYLbhL0K1fc2zR7cVIsi6VC5UD1NR1iytF5e3ojIIsdRk/aHFbd72qpLZG2tcbN7dYu0U3gDt37FLR94rVE5Yw4n1ezLV1OCodoGspWxVqVfrWb7rmh/0rE4L6bKc699NrAKpOt6bSce53/v/fvEQomyHG1rD3SQ/uv34wwO7nwiRcshxEZUHAYMnyZ09DTa1BTa2XMYV67Qu5Li3NLNkmi2eC5BVyf0He/BGhsjFsnnIb3vnHs027e//W1GR0d55JFHsLBLBMxiy1zx0wkG7/wXnJBN98xHzMzadPzYz+3+4bxQqtsIzaG/K8voyj0itg/TH0DrG6BLSFbO3Ue3Hs6fz7GwGpDuQnFwTl/N53jsWkphWDamX2f6dDeGlsMgV7LvR1E/Z5ez+DIp5hM3WIlFuP7mRd753Ij7BR6FHzPH70z8CIHYtLwNiLzNbUDz87k//AmxlVU6llNEZqaJxjSyWoDZUC8pPUIwk+OYuUAqm8V/Z57MyeMkcz3Il79H9ld+Bb/woamcTm1NtTmLaoEAtUJFUipqiRIpFQpFCe55stQkgpepJkI3c3ATCTroh9sBx/PkRZnyL7kRtpvraYGUKpLyMOImyjTM7rXagqBMQ6qgaAKtmv9Z5eD1Ns3O/+xm9+ozJKHGOlMqGkwzLa4zOchZgoT/CC/Gvlqy7ReeXsQZNF2OVLQb2WefJfTCCzjRKNbYGMyV5yEFyODDGrtvz3lIhRD4MPDpLtOnJ4YJnbiSF0rHHuHezetEUpKRqdkNobSTkJPkwdg8p2J+CPRjjY0x1JUXSh97pFQodaSzKYYWojtz22xvc9Ii55gltrjvXf6A7t4eevpiRfuZRftbmza7WWkqQfSAFCJiu5bzCy6WeyMVo2cBpCZY7o3Qn1ilaznNSiyyefxukIX8oPaW+BmdWyM7eZuElIzemMIybLKnI0we70JqSQySWMBtR8O8k6Inl8KavsJ7favI1fd46d1Jlvo68AmDYLGdbYm17dbngPBvfg6WffZv7hcU/h1zgCpqR9V5vAY4BFW7hnq+UOwVJVIqFIoSlAXd4WSnVfb1RkWzHV7c2l4jbDerWtCpPs/TNLPdgXvb644okdLLVMv/bFrgq/OTmVv7Vm4Z3ibkB4GNbNKCINd892GJmsP0Nm732oY8W1QRQtXzhbdw4nEyFy4QvHgRJxplcqSf8bWVkrx8c74+jFgv90czdclDWiyUftJ5joH0tTKhNCAtpM+HPTJSVSjVhEZAaAQKPqS7ZPXuLUbDI5zvO7/rYxwpN4VMU1pkne3CZv59tljwLBFNzc3Pm6Lq5j7mZrkpvSeGGmb+d9Lt/NguE6z+fRW2F/YvHL9fyiI5fTqTx2NlQqnUBJMneomsZ/GZNl1LKVZiEU6PJ3inb2Tzu19zahfdriFKhMvtgmZwIxK0WNjcEkT9W8LnxjZHqD7bDUOHUEDSvVbuGtD1ww60/vq6Brjda8Mq2ECxD5RIqVAoSlhxsWRSD3PexmfkBzfpbPmkQSNWO6uJ08NLM3O0VbP2VPaH3sY1oq1RuQGr2G5mFhpSBUUTqGrJlBL0dtX3nufe7tS91ssIAQE9Q8Yuz3vWiD7Pzc5YubR4H7e+pSFjvGrRHarteQ77zBnSzz2H/9VX8V+fLctDChCznLrlIS0WSme0Pm509dFvzpUIpdFjXfR9qiffKddBKN0PmhAEhZ8g9Q1rd6TE2owONYsiOwtCpoXpmOSK9skWb3e2BM9sUQRpISK08F5WzR5aWyxfXoGx9fzYKpgxWamyfzBjluxfOH6/NDKSc684SFJOlhTZ2pg1PAl/IWcJTfxkQ+T05183BNCQ5iewWe4r2r4leBYf5xeGZyI9tUSC/2buj+lamirb1vlxhtC33qpr/mXXeTwVRanYB0qkVCgUJbiLRephzut0hR3S2fLBcmNsIpTN8GHFfZV9/cVxNws6gG41ae9pOppoQSdl9ehxFUjZXMbHx+t27sRyCPh8xW0/fvsThqL1zdF2L/EYECwrz6zPcOnSlbpeuxoTExOMjo427fqHAb+bSNmAPLxqIdrhxc2JpxH5n1WerMOHE4+Tef55roWXeeva9ZKIooQ/zsTQp/hbz+8tOnEvFITSGxffZIDJMqF0pD8DwqqrYNCqaMV5RAnV5RpSSizsUvHTMTejQLdb5ObFTpN7M1NYwqYz1lMkfJaKopvl0tq83nJvhL6ZVVZ7QnQvp+hZSDIb764oFApHboqCqz2hzeMPQrMjORuNIyRJJ0PSOfiTUiHH55a4WS5kFt6HND8hLUBw4zVUtE9I86OLxoYL5nI5Ll++DED43j3ir7/OsewwqxhlCyMWPs5weS2H/OgjnB/8gGuPPELsp34Kn6+8rex3LO5MzfCVxb8o62+XYw9DnRc/KLyHEikVCkUJbqudlfWh9+kKS2aWysub6WWvJrC8j9sCiMZYgVW+Rsgv8ddvDkPRArhOnDag3aWyYNnuIuVs3WugcGNoaKiu548ELNdtqYwPqK9ImcxUfvTrCKrcbF7Hr2crljcmkrKyINWtcj97nuaO8Sq3O4GkQ0V4eBpjeJAXY+WT7UFHQp1HWWZ/nH/T+w+Id06XWS+mHoyQ/qsP1NV68TBTnDs0UmFBlhuXbl8C4Pzozja5jpSbgqXz+Um67r7ApRvvEhXdBKWPu3/ewc2+YwhDouk2QrfRsXjCvErM9mMFdAL9Q8SEQ/bBBxk0Qhvi595zhDY7krOdKcnxeUCt1ieMvJApApvCZUHQzIucW+XF+22VBQgIP9ouIzsvX77M+Pg4D/f1EX/9dTTbpt+a49zqbfyydDzfNb/A8aCP5NGjmF1d8N57fBwIcPTTnz7YL00+gjPwyiuce2WepbVSMfJY9i5HJn5M8Pdih25BhuJgKJFSoVCUoCLaDi9uEwnr6fqvdl53y4WqRErP4yZENyI3oNvEqYoc9z5uEbzraYHjgFbHbk/ZDLcuhmEwOjrK+fO7z+e0F6wqEyFHjo9x/sH6ioXW73RWLB8dGeT8+a66Xrsa9YxeVeQJ6JUjDxohUroJUmqM533cvuNG2L26LkQLWHW9xyuaj9tYKpMT5EzquhCx0N8l/Ed4MfbVkm1fe2YBZ9B9sZKi9dFEPgIvgA+O3kfo2Bn0H7+LNXyKjoUFuucz3D8/QcIfJ6VFCDtJ4rkFjh0PEu0MYo2NMdgZxR4e5lOfer7s/LZ0NqM3/+A7f8TxMyc4dfbMZnRnVm78OCYDxw2iy+P4B4J0rt4jPQG3jCj48gKpZthouo3PbyOkVfNITkUeU1qYtsUqqX2fQyAIaX7CWmAjWjP/E67wmvDN03lukFPvXCHc20VwaZVz6XFWpEZ2I4qx0PYe8GXoCmp0LSxg9fdDNErn0hIDFZ5zCmPx4khNN/y3bxP8znfQbJvJ6aMMmL6yHMABPYo+OUnohRfIXLiAfebMvv8+isODEikVCkUJypLp8OI2kdBUu1e1yt7zdLuusm+eFZiaOPU+bhNYUgqSGVHXe141UUC5FngbQ4dw0CGVKe/fGmJ/6JobULU7r+N3Eymb6Fqgni28j9si12aO8apFtCu8QbWx1GpK0Nddv75H2QwfLrLPPovzX/8rZlcXdn8/zvgEATPHiexEyX6m8GONncaJRsEwyD3zTMXz6UIjJDYEqaxBr93BmWBllw/tM0cJXfsWDB/DN20ydRsiiRDjoTEcsdXHfvnRNI84n5BKr2AbOoMnPoeF5L6nfpGn+ns2RdGCALophm6+5shJi4yTKykvfG5kHlCvIgs5PJ3KrhcldEB0bo3wtfcQUjJ6eQrfoI12tpO5+CA4swQsH6ZpcJVeunOLdC6uIq5cYvH0MfSFFPcmPsB35BgRLUhIC6AXtZdCpKab9WtgYYHAhkB5xO9Hm71MKhUo2affnCMy7aAt3YcTjRK8eJH0c8+piErFjiiRUqFQbOI47pOnauLU+zQzok1NnB5e3PMVNaLduVjQdShx3OtU61vW0nUWKatMzqpISu/THZakKuhFDYloc+nzelSf53ncRcoG5H9W99pDSzOfLdzathIpG49t27z88ssNi5pfTHYCv15x2wv/+UX6O6qZYm7xxhtvcPPmzT3V++5SP1AeIQfwykv/hYh/FyLEPnnzzTc5ffp03c6vKMeJx3lvdJQzto0TjfJ+/xH8ywtlUWX+0130RG0wDDIXLtRErHEGB3GGhtCmprBHRvj/s/fvwXFd+Z0n+L3PfL/xSAAk+AAoUKQoiZRYVZItV5lyya6qNt3tV5fkR/eOt2c2NjpiN2Jj/5yInY3ZifVORG/E9s5Gz0y3e+yettTudrvMqZJtyZSrSlWSSy/qRYkgCZAECCCRQCLfr/s6+0ciE0jkPRcJIBMgcH+fCEQC95kJHJxz7u97ft+f8dkMhs00okauLZvu+P2HkPw1AAKqx8aRkLwwx8ZwbPzCnt8DYww6M1Fj2rp4qaG2/qpZRsNS1dKgMb2xfV3Y3Cx41myOpQiQM6dvLwMAwtkKZMOErkhYOhWFLGqQobUde8MS8Vi2DKVaQKbMkI8HcPft/wkf/txk6xifoII9beJzuQQPk+G5pMBKrMEvehFY//KLHgQkL47/h5+hHAvBXyhjJJPBGoSODM6klkIcBci3bsE4exZWLAb1rbdQe8W+bySIJiRSEgTRolQVwBhvtTMFEo463NXO+5DdkSebYddykBm8vBq8JI4ffZxEyGJFABL9uzevbUsig99ju4s4QoT8DEtrnduLfV6YUdeBqkYZbW7FIz96mZQ01h59eNbqB1nv3k8i5b7DGMOFCxdw/vz5fblfsSoBf2W/77HHL+Px493ZMmYyGZw+fXpH7/v9OyHguv2+X3zhGch9LAMYiUQwOzuLr/Sg3hzRPauxGBYuXcJwOg1VWUNaGUZaaa87qhl1mGPJntfnq7/4InyvvtoQSL1P4LHKNDysPZNTtXQwRUHp5MlGXUKHTM6dIggCVEGGChnoUdu2GGvL7qxZGl7/27/GyPgoxk4fR9XSWuJmzdJQtzRU10XSmqW1ttfWj9HZ0evzm9a94Vyjln0uEQAT7cc8JgrIJQIYTBUQzlWRjwda5zepMg3wAjWUWttmiysd14qtFPHS7Y8heBimUouoGBaKj0WQGghDNwsQtSrKuoI79SSCpSpKRgni7ZuoPXMJ6sMHQGoJSI706tdAHEFIpCQIogXZk7gbXpByXwIJPLGIMniPPDxL3/3I7iALOvfitPCm0fYcigfuESebYaH/3S1xwByU/aHTWB6hPu+RpVeZR0ZNs93OW6zTS6jevXvh16QUYZqA1EfBhvdsEfD0t/Yv0Yksyzh//jwuX768L/czHaZwY+PncfnZ7q7zxRdf4Ny5czt63w+r9tuDPuC5r/X/87/zzjt9vwfRST2RQO2Xfgmvf1QD++hzJLUUVKZBW88wY5en8Pgr9nXB94KVTKJ29SqEP7+GVXkAmXAcg/pKWyZn9XQQ/ok49OVlWJLUs0zOfiEKAryCCq+otrZFiyrGjQQuBuwtSJ0wmYV6S7zcKmRuZHZuFTdrVh3V9WOqloYaq0Nn/Xs+3Amy3ngfktmYR9W8zoV2m/ubxzfP3ylbMzjrioSliRA8YgmeTQInADy0GPw3c1DKJubn6sjHA5i5vohbLzyBoORFUPQjKHkREn0ISwGEJD8iUgBhyb/+FYBH7GMBYeKRhERKgiBaOIkCVBvw6MMLJOzHKvsc14KOAqdHHV67q+sC6jrg6ePclGtBR/3dkcenArLEYJid/Vu/F2bwstPJscAdHNSCICdXBJrjPZr0MutIEewzh/alNiAtRHMtTuNaqSb0tZwI79ki6DPQs5Qf4pFEkoBwACiUO/flbbb1klzRfnu09/oU8QiiDyTxRvxEx/ZRpQKA0zj2iDkxgfQ/+B3MX3sPx+vzHZmcXx2uAIKF6uAgVp99FpGJib68j0cVSRDhl7zww7vnaxnMXBct641XprV+bgiadVRZQ9SsWnXULb1x7KZtVUuDhb3Nuw2lMYaZUmOc89Z0OJlYe2t62/HN83fKXjM4I5kSSlYVJasKILvt/TyCgtC6aLlZwAyti5hh0Y+oHEBECkASaFw/CpBISRBEC8qkdDcHld3BGD+ARUH7o49T31KoCBiM9LM2IGVSuhVBaLS9tWJnG+h3bUCnTEri6HNQC4KcMuao7vijiaqquHjxYk+uNRB7G3YRrNWshhs3bvTkHjxypV+y3b6amsGNG2kAjYzRqamdZ0gQjzZOQnSx0l+R0jmTkoKZR50IT6QsdW7rJTwRNBLo732JR4OQj+MQ1Odni0xwBH+U/AMktSVcKn3UlslpXfSh+vwTWFhc7Ot7cAOyICEo+RCUfLu+RrOO54Z4WUfFqrdem983f65ZGspWrSVwlowKcokABpYLKER9iOQqiGbKSCcjtoKhYLGWuFiINt53LrG7Dmm/MzjrTEfdyGPVcK4jLAAISwFEpSBichAxKbT+GkRMDiEqBRGVg5BJyHzkIZGSIIgWvACWR2F9zWYiHg1CnEBBvwOnlTpss5kAIEqB0yOPUwZPviRiMNI/WxVeLdQIieOuIOizsFbsbAPFPtfhJXHc3fD6PF5md6/gZVKKIkOQUzeOODokB7zA/c7tpVp/J/imJaCq2YccAptqA05NTe1bvTpi/9jeWr1/8y1enxr0Hr36ZEQnkSAwn+7cnuuzSMm7fjTY3/sSjwZct4w+W6s3HdFS6ghej3+nbd9//e1lWB4AJFI+ErTqeIoyIti5WHjjxg3IycdxaeED6EEd3pWPUMjrmP9xEPcGjkFQLUiyAVE2oEg1fNWag8+UoCkyKtEQBFiYndqd3e9BZXBuBwOQN8vIm2U80Ja5xzUyLxvC5aAcaXwpUQzKEcTlMCSh/+4ihDMkUhIE0YJn90o2YO6Al91xkBZ0kSC1vaPOdpmU/YQs6NzNQfV5uRLP3pr6OzfAC2D1PZOSaqG6ms2C4GYqmoKnn77YtzaQtclWb3LpqQk8ebrTFo84OhzoHI9z/aCXalK6AV7mYt/tXkmkdDW8kiF9X4jG6e9UmcHn6eutiQPAiA9AHhuHurgI8bFz8H44jfBKHWdX55FSk6iIQfitMpJaBo897oHgGYZx9izUeh3VwQF87dL/BWWrhrJZQ8Wqo2xV8aOf/QSxZAI1UUddMOCLBVEyq+vZnTWUrdqBZXD2ioJVQUGrYE7rXMEiQkSiKV4qUQzKUQwoEQzLMQwpURIw9wkSKQmCaMGb3IRohb0r4K12LtdEmGajvkc/4AVOAbI/dANBH4MgMDBmY7vZZ6thst10NwdXG5AyKd0MXxw/mExKanfuwKfWbbcbpoCqBvj7FMSkUhLuxqM0HHnq+v7Xf+YtCOIJ9sTRgitS9tvulXP9CImUroC30LXfC9F4c7wgjbNHlvqLL8L36quwYjFUJ89Cu/8AHqbhRH2u7ThDVCCcnYQVi8FaWcHqs5cxJKrwiioScrh13J30R5iKjbd+vjjVWW7A9CzA8/CPsVhdREwwIJsyFt7x4sHwCETVhKTqkFQdslTHY2vzUHULuiyhEPMDwK4zOPcDCxZWjDxWjDxQa/8dKoKEUWUAx9RBHFcHcUwdwJg6AJ9IKwB6DYmUBEG0oKwid+MkRherAqLB/rQDp0xKXp1M4ujQrA1oZzftVEOtF/DaHmWPu4ODWu3MsxmOUrtzBbwFQQdVk5LanTvwKRp3X6Eswu/pTztwnONR23MFQZ+9SHlQC9GCPsqkdAPRkP12yqQk+glv8U2/n2lpjuc+rGQStatX4b12DeJAHO+GRzGor2BQX4XMDBiCjBVlAGeeCCASAiDLSL3wAuqJxK7vKSXHoBw7AV86A2PsFPwLawgumZhMpdczOKPwWGUktRROj8mIRY+hPvUYxiJBlJIDGD7/LRTNKkpWFWWziqJZRcGqoGhWUDDLyJsV6OzRW0ikMxMPtOUOK9kBOYJxdQiP+8Zx3ncScZkz8BBdQyIlQRAteKv4SShyB9tZMvVLpHSqhUr2JO4g5Ge2QYN+BrBqGqAZlEnpZngLcPpdNyZfsr9+pE99LPFocVA2wzwRlBaiuQOfYp9JCTTmYcl4f+5LmZRE2G8hU+icz/WzzzNN/hyS7F7dAS+Tst81KXkiKImURxfTNPHmm29ienoan8+fAfDtjmNWswZee+21HV333XffxezsLKanp7c99u0vLwN4vmO7UVvFa6/9BwDA3NwcvvGNb+zoPRCPNubEBKovvwzxB2+BIY+0Moy0Mtx2TN2owBwbgXblCipLS3u+Z/3FF2F98gn0cBg5eRjazH3bDE5BlWCcPQshFoNPloFf/oc453POpGSMoc50FMwKCmYFebO8LmA2RMz21woMmHv+PHth1chj1cjjo8odAMCIEsd530mc950EA81xdwOJlARBtMjxAqcUwHIFvOwOoPmg3x+xmpe15PR+iKNFJGDh4Uqnn3A/A1jOGbzU57kBXiZlv203eX1ehPo8V8DrX/qdVcRrd7QQzR04iZSNttefQA+vP/V7Lch9KiNAPFocRB1ep/kj2b26g4Oye80V7bfz3g9x+GGM4cKFCzh//jzE6TD++Gedx9QMD772tech7mCql8lkcPr0aZw/f37bY99fPW67fWTIh+efb4iXkUgEAhUhP3JYySSsf/IK/se/tHCp9BGSWgoq06AJKlJqEl/55VN45uvr2ZM9ECmtZBKpF15A8u23UfbG8W54pCODM6PGcezxIvyxGCDLqF29Ciu5vdWrIAjwCg0r2iEl6ngsYwwVq46cWULWKCJnlpE1isiaJeSMErJmEVmjhBrjO4n0miV9DUv6Gv628BEiqg8jv3AGSz++s2/3PwqQSEkQRAteACsapACWG9guk7Jf8O1JSChyC9zagH3MaHPM7iBrHFfAa3cHZclEGW3ugLcAp6oJ0HRAVfpzX7L0dzeKZEASLZhW51y/n30et7+jxUCugbcQop/W6k6LjQKUSekKeDUg+51JybV7JRfAI4ssyzh//jwuX74MxhGjLSbg8ScuI7wDsfqLL77AuXPncPny5W2P9V63335yLNJ2fiaT6f4NEIcGRQbywSReV7/Tse+3fTkA/IVqu6Fy7BgWXnoJ7CdrYMh0ZHCqsg6IZZhjY9CuXOlKoNwpgiAgIHkRkLwYUwe4x1WtOrLromXOKCNrFrGqNzIg00YOBbPS8/cGAHmxiq/+v34D3/va/7Mv1z+qkEhJEEQLXiCBMindgaoAXpWhptnVjelfAIuXPRIhcdw18DLIeLX7eoGTSEl9njvgLYTod01KXvCUFgS5A6f+pVARMBDpk7U61UJ1NYIABLwGChW1Y18/s8fzJerv3A6vz+vnQjQn4T3kpUxKN8CzVz2ompQxEildgZOtb66EHYmUOyFL7c71hAMWqtr+OVPVEwnc+Oqv4n/8qNyRwVmLBDD/7fMIvfhiX+69E3yiBz7Vg1HY1+KsW3pDsNRzWDHyWDVySOuN14xRJNvWfYZESoIgWnAt6CiA5RrCfgs128lNHwNYtMre9RxIJiWnvxNFhoCX2p4b4GW09XNRRk2D7UIQgPo8t+BkZV6qihiI9Ml2k9Of8vpf4ugR9Oj2ImUfx9ocbwEktbsjy9b6aUbtPIBOK8IHC3ncuPFJX97Dx/fjACcg6fdQJqUb4IqUfcykZIwvgpLdqztwypjNlwAM8/fvBZ7NMNVCdQ9hP8NytnN7sdpPZyoRKXUEr8fbMzjHYyn8Xvzjvt23l3hEBWPqgG02pm4ZWNQzeKit4qG20vqq7qOFrNsgkZIgiBaUSUlEAgzpXOd2Xr3SXsCvSUntzi3w7Ab7KRblOdcO+xmoXIc74I1t/bQ+dKqFStnj7uDgrNV5NSlprHULvFp8B1H/ORKkdncUsaudFuBkLpZr/QtFlWv2vtmyUMXdO9OQ+rD2cnp6GlNTU72/MLEreHav+XJDTOzHXL9UBSzOVI7EInfg9HfOcoTEXsC7NtkMuwfeIkinZ8+9wosR+tTe2sseFIoo44RnGCc8G6sLGGPIGAU81FYwW1/CJ5VZLBs26jCxK0ikJAiiBT+QQIFTt8CrxdfPyQ2/JiW1O7dwEHavPAs6Xu0k4ujB6+/KNRGGCcidSeV7hieOA1SH1y2EfA4i5QHU4aU5nnvg1eLrp8U1fwEktbujiKqquHjxYtu2MzMB4Gedx1pSpOPYXvHlmtd2e7CP9SinpqZsRVriYOBlLuoGUK0Dfvsmsid42WwAXzQljhaqAvg8jTa2lX7WQyWbYYK36LCvC9E4sRq/UuvbPQ+ailVH1ixi2cjiobaCNbOPqw9cCImUBEEAaKz64wVPyZLJPXAzi/q6yp4XOKV25xZ4WbN9zaTkiePU7lyD09hWqAiIh3rfFnjiOEBikVuQJCDgtVCudbaF/lqrcxai0RzPNRxEJmWO0+eRS4t74C0I4tWk7wXcwKlax9TUVN/EUeLRwUkUzJf6JFI6iFBk9+oeIgF7kbKfVsO8TEoSKd3Do1T/2acefjtUg5lY1rN4qK1iUVvFgr6KBW0VWbOP/8gEiZQEQTQoVQVYFsf+kFY7uwbeynanwPpe4VvQUbtzC3zbzX7ak1ANXrfjNLYVyiLiod7XBuTVZ5MlBl9nqTjiiBIJMJRtFhn3y2q4kTnCq0lJfZ5b4NluHoRbBo217oE3x+ONh70gz7Gg8yqHP3BKdIeT7WauBIx0lh/bM052niQWuYdoCEitdW7vVyalZfFroZLNsHvgLXbN9XGOxxNAvfLhsHvVLQOrRgErRg6rRh6reh4rRh6rRh7Leg4WaK6635BISRAEAOcHRcoscg+82oB9rdHGy+ClVfaugb/Kfv9X/lG7cw9Of+t+1eHliQHhANVCdRPhgIXFTKefcL8WZjiN4TTHcw88q8uDqIUapcxx18BbdNjXBZCcTEqfcjgCp8Teccpc5Ak6e4UnUvo8gIcWorkGnjDYr5qUhfU6qzt5L8TRg7vou0/PtACgrCzj22ufIamloDINmqAipSaR0A0Anr7dt1s0y0DWLCJnlJAzS8gYBawaeaysi5F5s4R+PgUxi56xdgqJlARBAHBeRU2rnd0Dz/atnxZ0/KA9tTu3wKuhUKwKYAx9EW94mZSxELU7txD2MwgCA2OdDaxfNnRUg5cA+PVH+7UgyGkMp7HWPfg5dq+0IIjoJ7y/tWYIqGmAtw/iDS+7w68e3TpZRDsBX8Ne3bQxxeiX7SZPhIqH+3M/4tGEJwz2Sxx3ytCkDF73wFsQ1I84nieTweD77+NXb77TEVM5Xp/H+el7GPubOMSREVjJZM/vDwA1S0POKGFtkwiZbb0WkTVLKFsHM+YLEPCYmcT/8Dv/9wO5/2GGREqCIAA4Z1JSIME98Gwi+rUCizF+AIsnXBFHD57doGUJKFUFbs3KvUCBU0IUgZCP2WYR9asOL9femtqdq+Baqx9AJiWNte4hyLF77acVGGVSEk71lvNlEV61922B1+4ok9I9CEIjm3Kt0LmvX7abVBeQAPgi5X63O4DanpuIBhmS2hIulT5qy2y0zEGIyydhDQ/35D7SzAzG3ngDomlC00MY0pcxqK9CZgYMQcaKMgBZ0OFbWYHv1VdRu3oV5sREV9c2mYmCWdn0VW59nzfLKG76vsYeLft2r6DiMe8xTPmO4ynfaazOLOEP72cO+m0dOkikJAgCAN9yJ+C1IHc6khFHFF4mZb8CWOWaAJNTC5XEIvfgFCQvVPolUlJNSgII+RkKlc7tvCyMvUKZlATgYK3epwVBvDmeKDKEfDTWugW/x97utV+ZlDUNqGm0EM3t8J4tgMaYOBzr/T15Y62PalK6imjQXqTsl+3mGomUBB4tkdLJ9pg4OoipFC6/8yP8V0udxVC96Tn4/uTHMMfG4BkaQj2R2NN9vNeuQTRNKPkCLq7+PRSrfW45qK9gKJWFMnQCMAyI3/vPSP/2VeQGQyhZVZTMKkpW1VaIPKjMx93gFz0YV4cw5T2Os77jGFeHIQkbz1urWDrAd3d4IZGSIAgADoFTqlXkKni2b/sdsAdILHITToJ0viRibKD3bYFXc5D6PHcRCVhYWO1cicOzA94rfHGc2p2b4GWR9SuTktvfBRjE/iXREY8YQY7da/8yeB1KSVAmpWtwspRuLKCw8ePcI9xMSrJ7dRU8cZAyKYl+whUp+ySO89pzyA/IFPE/8kgzM/Beu4ZEgUGAB4P6Sltm46o+AGb5IS0sYOzjj5F64QXg4sVd3Uv+2zdR0yowi6vwPphH0O+DropYi4RR98sIsjIGSxkwoYLSw09x07OIQsSL1b/4AtevPtXjT74/SBCRVOIYVRM4pg5iTElgTB1AVApC6EdNIpdDXRZBEAAoq4howBNoilUBloWeBzOda6FS0N4tOAaw9tl2k/o8d8HrZ/qVWcS3GaZ25yZ47c7OergX8NwQqN25i4DXPpOyWhegG4DS48iA00I0Xl1W4ujh9wCKzKAbne2hX30eb3El2b26C55Y1K9MSq5IyXkfxNEkyhGl+1WTksRx99LMbIRhIFTN4bnCA6isM7NRfI9BmJqEaJpIvv02zGcvojQYQ9mqtb6WBiuoeeZRE3TURQPvpxdRae43a1CXV/D1L9+HwBimlhahyybMxy0sjcfBxMYCoCKAkhXD2AML0WwFow8yKF4YxcByAbHVErIDj2ZnKACISiEMKhEMyBEMyhEMKhGMKAkMKzHIAlkL7hckUhIEAcChLiAFEVwFr+A2YwKKVaHnwqFTLVQn4Yo4WngUwKcyVG1s4Xg2hXvBsviBMcqkdBe8Pi9f6VNmESejjcZad8ETB/uVwcu7boT6O1cR4GRSAo0xMRHubXtwGr8pk9I9CELD3jdTsJnj9SuLlzPHa4iUSl/uSThz8+bNfb8nMyYAxDu2355N4/33H2x7/szMDBjrvl+8P38GQLRju1ZN4f3357u+zm45iN8x0QnPYnW/M3h5Ij1xNDCZCeHNv0JZK4FlVuF9cAfBgABDkZAJR1H3yQhYZQyWM1iq1GB98hD3TyaQi3qQ/o//dWdm4wQAbPLH3lIO5ZnphwCAcLYC2TChKxIWxuNgYvt4y0QBCycSCJTqUHQT4WwF+XgAp6dT+HBgsve/iC7xix7EpBAScniLGBlFXA5BEUgeexSgvwJBEAD4ASyqk+UunETIXKkPIiWn3fm9FjwUQ3AVkaCF6pqN7WYfrIYLFQGMUUYbsf+1AQsc8ZPanbvgjaVOmWd7gWf3GiOhyFUEvQ4iZVlEItxb203e+K3KDD61p7ciHnEiAQuZQuf4x+ub9oJuAJUa5/lCJZHyIBgdHUU+n0cmk9nX+6pCEnYi5WrO6uq9XL58GYIgdP2+14pnbLfLrLgvn/2zzz7DN77xjb7fh3BmvzN4eeInZVI+2liMoc40VK06Ks0vs9b6vrrltfFVQz5SQl3QEfrg+3jp1seNzMYvFyFbJqzHA+uZjY203TKAihWG8UBHNFvH0NwKspHdZTZGM41rhnNVAEAuEegQKJswUUAuEcBgqoBwrop8PNA6vx+ERB+ichAxKbT+GkRs089RKQiPSGP/YYBESoIgAPBtcWiVvbtwyl4sVkQAvQ1oOtXJItxFNGghZSNS9iOT0mnlPmVSugu+7Wa/arTxxHFqd26C2+72uTYgtTt3wbN7BfojkHPbXdAClfFxF7wFQf2wVnda3OZX6gAovWi/kWUZU1NTuLjLOmi7ZeLzIPBx53ZBifXlvRj/1r5tnT0zjIsXwz2/31amp6ehKBSIP2ic7F4ZQ8/HP55ISZmU/cNiDDWrjirTULMaQmPV0lCx6o3tloYqW3+16m3HVNeFxxrTsKtZ+PrU6vTtZQD7l9ko642FbJLZiAfWvM59TXN/8/jm+d0iQkRI8iEiBRCS/IhIfoSlAMKSH2HJj4gUQFQKISoHKAvyCEF/SYIgADjUK+JY4RFHE8dMyj4EsLgWdJRV5Dp4wnQ/2l22yL8mtT13wVuYwVu4s1d4QXuyt3YXvH6mqgmo6+i5kwBvQRBZbroLv8eAKDJYVmd76IfVMLfd+Ukcdxs8Zx7e8+decGrLPrXW8/sRjy5RzhjXL5vhLNeZivo8N8ETB00TKFeBoL+392OLS/j22kdIaimoTIMmqEipSZxklwCM9PZmhxzGGOpMR81aFxfXRcamiFhnOqqWhtq6iNgUFSstobF53C4Fxh6y35mNhtJYTG5KjX7OW9ORdzjeW9PbjjcUCTIkBCUfQpIPQXFDgAzbCJB+0QuRVrS5DhIpCYIA4JDdQVlFrkKWgKDPQqna+ZDVjwwPrgVdiNqd2+AF7fsRSOBdUxAYwhQ8dRW8YDmvntVecKyFSgEsV+EkDuZLIoZivRUPeYFTsnt1F6LQ6POyNnOvfiwIcsqkJNwFL5OyHwuCckX+vDFAIqWr4C2+7YfNMGP8eApPLCWOJtEgkNSWcKnUKRwWZi4heKFHwuHiIvD663j+pw8xuMVK9nh9Hs998j7wb44D3/oWMDram3seAIwxmAJDVdCxqudb4mFtXUzcEBr1NnGxZmmotYmQ2iMhLvaK/c5szCUCGFguoBD1IZKrIJopYymRgGF6YGoqTE2BoSuQLQEngmmcTgNhZQADY5M4Ex4Gzj6D3zzxKxBIeCQcIJGSIAgAfAGKsorcRyTAUKp2bu/HAx1/xSm1O7fBWxDRj9qAvGBs2M8g9mdxNfGIws+k7H1DKFYF2wwmgIL2bsPJtSBfETAU6+39eP0o2b26j0jQsp179cNanVtKgtqd69jPhWh2IjwAeBQGReLXZSWOHry5VT8s/Ss1AYbJEympz3vUmZ6e3vW5qVRq44fFRYz8xev4r5Yedhx3vD4P+V+/DzzZA+Hw9m3gz/4MMAzU6xaG9BUM6quQmQFDkLGiDMCrDALz88Af/RHw27+9+3vtApOZDdFwXSSsWxpq6xmMzUzGzds2b68xDXWr/XvzqxbexjKw8M6+fo5HmV5kNjZRBBlizUJA8sHLZHiYgtF4EgHRg4DkQ0D0Iv7813B67gdQgxLE++9idckDaWYYd31nYQkbfeqAP4sT3mmELBH+WAjK0HFAEFB96hIJlMS2kEhJEAQAftCeJtXuIxKwsLBqUxtwHy2ZKIPXfeynFRgvGEuLMtwHb4zbT3trAIhRn+cqnPqaRv+0s9XN28GvSUl9ntvYz8wi3vhNC9HcB6/d9aMWqlMpCYqPugueS0W+LMCy0NOFiU7zRhprH23Onz/fmwutC4eBugEBFgZthMNqdYtw+NhjO7/P4mJLoMTqKqYWp6FX2mtOD+orOHX/LnDiLJBIAH/2Z1AuXbJt9BZj0JiOuqVvCIhMR731qrfEwnrruEbmYvN1q7ho9HgeS3SyNbMxlikjNZCAYaowdRmWocDUZZi6gpEQMFUowzJ9QGAMx4MT0M48jW8e+2UERC8UUcZrr72Gqamp1vUvPralbm8I8I3PQFxcRObYOIz0Cob1NKJGDik1iYoYgN8q44w2C3+wBOb3w5ycBAQB5tgYrOHhff4NEYcREikJ4ohy69YtVKs26XA2MAZkiy/Z7suk7uLGjZVevjXXMzs7e9BvwRGRfQVAvGP77Zll3Lhxt6f3ml96FoC3Y7teWcKNG7d7ei8ej/rfwy1UihMAznRsX1yu4MaNGz2915d3TgEId2xXxXLP73VYcOv/wfJCHMBXOrZXaiLe/+BjyFLvxMPbi2EAA7b75mY/RnapkeFx586dnt2T2Bn79X/AGCCLL8GwOoNFH346C6my2tP7rRV+yXZ7bmUGN26ke3qv3eDW/mc/afYrEnsWdv3Q7XuruHFj95kkdswtXATg69iuV9O4cePLnt7rsOG2Nl/OnwIw1bE9tVLr+bzr8+kTACId231KhcbXA+Kg2ntqMQzg+Y7tjAn46c8+Q9Dbu8zae8tBAIO2++bvfYpiWuvZvXi4rV95VNA0DbM/+QlGpqchmCbkfB4/X1qDZLa3r0F9Beynn2L5/BiMSATsX/wLrF69Cn3Qvt3MzMyAsfbnEItZiP3lX0BJL0PMZRGYvQdJ8EAPKlgJxVFTVQSsMoaqq6hZFdx/Zw7Lp0dQjPixmPsQ13/5Av7T3Q+gCyZ0mKjDgNXDZx1ib8hMhGc9k1G1JKjr33uYDJVJ8DAFa0srkA0RE2NXcO7G30HyNXoeqWpg/m9U3FCfa8tsFJmF874P4fWaKNaBsibDu1rBvBpF/bONMbGb8dEzPIyxTz7Bmqbhnm8CidIyPEzDifpc6xhFKaOs61iNRBCs12GtrGDh4kXUXRZjefDgwUG/hUMJiZQEcUS5d+8earXu6m5ohgzDtF9KmEnPYNpa6uVbcz3Ly8uQ5Ue4+9XPwE6kfJgq7ckKxY7V3EXb7dXSUs/vxeOR/3scIHNzjQnn+Ph43++1cL8KO5EyU2A7agvvvfceksmk43t+sGAvFGmVZbz55pv78nkfNdz0f7C5XWdyA7ATKQHgk5sPEPR0t9inm3Z3K2W/T4CFhw9uQlxfhH/79m14PJ5t77mf/59uYT//D7zKL6BU93dsv3tvFRFzd+OfXTs0TAk1/Vdsj8+uzGAai7u6Vy9xU//TC3bzv9/sV5iWgZ1IuZAqdz3WdtPfAUB67Qnb7bVyY6wF3Nt/uaXNN9tqSf8O7ETKbLH7OV637e7+Q3u/bMkqdD2+OkFj7845qPa+WorATqQEgE+/mEciUHA8v9s2BwB30mPcfQtzN7Es9j+b0i39yn7R7f96NptF4bXXcFMUEahWkcxkoLJx1GQv0r5BVFUPAqyEZD2NQmkF9Y/msDA2gFLIh8x/uI8ff+NJWDLAZKy/CrBkwPoa8KX8JX5QvNX4WQZiayW8tPwxBMYw9WARRWbCOBfA8ngcTMxCAaABWLA8wIMSotkqPA8e4P6FUag5AZKWRSYc7P8vz4UIFiAbImRTgGQKkE0RstF4bfwsbNovtv3cPE5kvIxsc/1Lw6ChYnFxEd9PfYrS3QcYF0WsMWAkm8OwIeOr9b9vy2xMaikopRWk1GXMRyJg+TzuM4b3Pvyw7Q7vv/8+PB4PRkdHsbi42Gr/WxmQJDxZKGDBPI3b4dMdGcMPVQXeoIZRnw+VbBZ3nn4audVVYLW3CzAfdZaWKIa+G2gEI4gjyre+9S2cPXu2q2MXMyLwPft9v/kPr+CxY2TX0GumpqZw8aK9QHfQ/HQljM9txtSh0Sl897s9KvS+zr/4ccJ2+9efv4DvvjTZ03s58Sj/PQ6S5ur2/fjdLGgp/J3NXJhJEXz3u9/d0bW2+3t+kAsBNom646NhfPOb33RtW3DL/8Hmdr2wKuK//1v7477xS7+OybHux7/tfn/f+4kH+Enn9mgQeOXljTY+NzfXVTvcz/9PN7Ff/wf/8h0VJRt98PELz+O739r9/be+/3RWBP7C/thfv3oFj594NGq0uaX/6QW7+d9v9iur7x/Hjc5SWYgPTeK737XP5rCjm7/X//R+HLCJST3/lcfxtePfBODu/ssNbb7ZVh+UL+E/2iRRmEJ4R3O8bn5nn/zrEGCTqDt5agBPiBf2PM+jsXd3HER7zxYF/Ld/bb/vhV+8iidPbz/+dfu+/+6vsrj3vR8gqaWgMg2aoCKlJnEr8TR+95X9qwfohn6lFzDGYMCEZhmoMw11y4DG9E22pwZwPwJDMBEfS6Ju6dCYgTrToa3bnjaONTCZeAzxv/0cZREY+TyF2qAAYWoNa+NxSOJDNCXBtMWgPBARzQqIV3NYmfTDJ5QgDFVRGOhOODx9exkAEM5WIBsmdEXCwngcTGwXt5goYOFEAoFSHYpuIpytIB8P4PR0Ch8O7F+M5TAgAPAIKrxi48snqvAKze898DVfBRVe0QP/pm3eTfsUcX/llRs3bsCTyeDyZ58BhgExm4X243uopNszGwFACcdx8puXMB6LAbKM6ssv4xvJZMc1ux0fxVQK7/0ffgZhYRFpZRhpZcPK9eKpVZw/PozBZ56BduUKJmzu4wZu3bqFP/zDPzzot3HoIJGSIAjkig51skJUQ8FtRIP2f/N+1KTkXZPanfvgWS7lSyIYQ0/rCPHqFYV8uu124ujC6++AZjvp3SKdLKfdUX/nTng1qno91jrVyXJq/8TRJMb5m/ej/nOW83xB9dncB6/WfKEi9HyOl+fUV42FLKDcu/sQjz5hf6PdJbUlXCp91CYg+q4HIQYe33OdNDGVguf6dZz4aRqXi+0lTI7X5/Gi+TN4/zQG7coVWC4N1u8Wi1nromBTQDTWxcL27zeEQwN1S9siJhot4XHj2MY2C9vYnAbWX7PO5W6eSa9Aly1E1soQ+ywcRjONTiycazi95BKBjvtsvl8uEcBgqoBwrop8PNA6/yggQ9oQFdeFRZ/ogUdU4BNVeAS1JSZ6BKUlOHpEdV1wbHypggLxkBYsricSqF29Cu+1a7BiMSxMDOJOKd9RC1VNxDEVqwOyjNrVq3vui6xkEv929A+gW+mOvnX05BDCvxxF6MUXe/QpCTdBIiVBEFjjPMwB/ILzxNGl+UC3lUKlt5M30+QHY6nduY8gRyDUDAFVDfDvzZ2rjTwnaB/0kkjpNvweQJEZdKOzTTiJO7uBF7AnocidRDjjHK9/2i15jjgOABFqe65jv9odwF8QFA/RHM9t8IRp0xJQqgoIcZ49dgNvQVA0QCKl21BWUvjna3+JRLHTtsD/eQ2+3Pswx8Z2LSBKMzPwXrsGGAbqdRlD+nKHOAAlAWlhAb5XX0Xt6lWYExO9+GgHDmMMBjOhNUXATeKfvbC4/vPmjERmtLIS9Y7jDBg9XCjYT/ZTOJT1xu9EMht9as2rOB7f3N88vnn+QSBCgFfywCetZyCuf++T1jMTN/3slTwNUVHywCupeHD7Hr785HP82reurguNChSB5AwAMCcmUH35ZahvvQXPnXRHZiMAJHRrT32dHbmSgKI6gtfj32nbPnLxBsbjyz25B+E+6L+aIAhuECHos6BQL+E6eAEsXjvZLXkH0ZMCp+7DSSDMlUT4Pb1rEzxxnERK9yEIDZFwJSd17Ot1n5fjLAiKcjJMiKMNXyzqbbvLctqdKjP41J7eijgE8BZF9Lq/q2lAVeP1eRZQ7+ntiEecmMM4t1bsrUjJa8vRIIOe7tltiEecpoB4ylJRBDrqpokrYWAsumsBUUylWgKluLaGkTv3gUp7/zqor8CflSBmT8KKxeC9dg3Vl1/ue0YlYww6MxvC37otaUM03BAO9U1Zhs19+qYMxObPG1mIG8c2BUa2XSaiS9hP4dBQGs8rptTo57w1HXmH4701ve345vndIAuNTEWvpLaExQ2RUWl97xMbQqJf8jaOFTeJj+vn+kQPVFGBsMuMRUsoYL6iYFCJ7Or8o46VTKL2yiu448vh/bt3O2yn50efwm+/4twud4JhAsUKxVOI3kPyA0EQXLvXGK10diX8ANb+ZXdQZpH7cJrQ5ksiRhO9axO8tkyTancSDTCs5Dq354q97fN4doo8+0XiaMNbjMOzKtwtXMeCoNVTi0XicMBbFNHrOR4vcxxo2G5WSKR0FU6LDxt9VC8XovHF8ZWe3YV4lNksICatAp4o3IHK2uf4kfklKIYEc3JyRwKipmm4efMmjv31X8O7ugqlUEDw/n1oZT/qQhgpNYmKGIDfKiOppRBHDfKtWzDOnoUVi0G5/rfIvfxbbcKgtimrsCEGbhYPN/Y1RETDVnTULB3FZ0r4iZyG+eBHJB/uI/spHOYSAQwsF1CI+hDJVRDNlJFORmBBgmVKsAwJlimDmSKGAzWMrGhQLAXl0ACGjTAQPIWvVU5DYRJuffYlvv7cz+PC1BPwSkpLXPRIDRvU/a6xSOwd9fgQXo9PdWz3mgxA71bpOLlvhCieQuwB6nUIguCusqfAqTtxWmXfy7oxvHYHkN2rG3HMpOy17SbVpCQ2wevzeO1kt/DtXqm/cyMRTuZQr2sD8sQnXo044mjDs90s10ToBnrmoOIoUgYZKqu9uQ9xOIj4GQSBgTEba/V9GmtjJFK6Bs/1660Mx7PFGRSYgPp6RlFTQIxp9xDJZsHefx/FkyeghcMo/y//GvL/6f8IyCI0pqMQ1LAgZyFX7rXEwNkH91C6dxuB3F1YooWxxVnkRQPZMS/mB/yAkodHyoKJJpYFIJjOYaZYgP7ZEm5dGIWVA9746D6yA8Hef3AFAMmT+w5POLS1fDUExFerkJiIeiSMgOXBWmQYJ7QEFEiQmQSFieuv0qZtje2hsXE8fuMtSD5gEAz5FQnC/zaKO97HYQkbfV/YW8MvJ9+Bp+6FJctggXEkDBE48XOI1uMAgIW5m3js+VE8GTkaFsQEv2xTTRNQ0wBvjxxUnMZtXgkfgugGEikJguDX7iCR0pXwagVphoBKTUDA15uHH97kRhJZT22fiMOBKjNIqMNEZ/FJp6zbnaIbfHuSME2qXQk/s2i/7F5prHUj3EzKHi/K4FofcsQq4mjjtCgiVxYwGOnN/MtxIVrQwkJP7kIcFiSpYXFtNw6u9dC1wLIAXzaF54o3OuzukvXTuN2zOxH9hjEGE1Yj03BT1qDezD5sbdvIRtSZATmVxpmZv4dpmUh+OQ0lasIYC2NhNAjIa/BJq4Bk4pYko7JkIJwtQ099junBUbDiA7xx8/+3ISA+AXyENSD96cYbCwDPVGewIOcQWSujZlWgeyQsnVcQFDtXXzwMhuC/WYSiGwhly8jHAzg9ncKHA5P79JskdooMCaooQzAAhYkI+0JQBRmqoMAjKuuv6z8LCh4GGMZ1DfGx0xhIfwnJMCF/GcLr+cdhWkojw9GUAEPAJesWHh83wXwKjp9+FhAEVH/l92END2//xtbxLVQhLi5C9IZRevsOhrQVRPR8mwh/xlhCwjCAUAjG2bPwxmIwx8YQevHF1nWmp6f78esjDpBoiD+3z5VEJOO9mfs7ub6QMxWxF0ikJAjCYcUpCUVuxClgni31TqTkWdBFgows6FyKIpZgWp0iZS8zKZ2Ep7BP69l9iMMDL7Oo1/aHvLYXc3igJI4uPMeAntd/5tq90hzPjfD6O6CxIGgw0n1tLCec6t2rvSuLRBwiokEL3rVlXCp91CYgxn8Shjg5taMgvR1iKgX2+nX8s8VCx77j9Xmc/9sfofbwM3guXdrTfdwMYwwGMzcJg1teLR162/6GcHjvWBFZ3wxuZXIb263285sWps1aihrTYe0iK/CZD+5CrC8hslZGsF6AEZKQOR+HX8y1fxYAcydieKxUhaKbCGcrXQuI0UwZABDOVQE0Mulss+YAMFFALhHAYKqAcK6KfDzQOp/YPaogwyMoUMWGUKgKcuv7tu3i+nEtgXGL2Lj5vPVt0npG4o0bNwAAFycuOr6X12oPEI7VcEyWIU4pkG/dQiJXwtOLNzvsfwNiHRAEmJOTjdexsR33ffUXX4Tv1VdhxWKYGzgHPT0DD9Nwoj7XOsavWGCK0rIzhixDu3Jlh79l4rDhFL/NlgQk4725D8/1RRIZ/Gpv5pGEOyGRkiAIZDkrWJ1W4hBHFyeRMlcScWywN+2C2+4ou8O1qEIZNSQ6tvcyk9JpxT7Zk7gTnkjYa9tNXmYRiUXuZL/qP3PtXmmsdSVOAayGoN2b4BLZWxObEVMp/O8WvwdraaljX3y6Dt+f/Azm2Bi0K1e2rQlohzQzA++1a8jnLQjwYlBfwaC+CpkZMAQZK8oAvEoA0WIRY2+8AenUKZgTR8Pi0NwsCm4S/jbEPwO6teXnNlHRgLZJGNQ3nbNVhNSZsTsz0WPAA5SB4sNef/wO9kNAlPVGPymZjXG05nVeedHc3zy+ef5RRYTQIQqqmzIPG9+3/6ysC4YeUWl9r24+f32fKihQBBniI7aieeXyZZz87DNYsRiMs2ch3ugUDgGgylToU5NgexAOrWQStatX4b12DatyAl+Ehzr6vNpQFJPPhht1emQZtatXd9W3EocLp3JdjcVj/V2IRskGxF4hkZIgCO4gEycLOlfiaAXWw+Ap14KO2p1rUcSy7dy5l5mUTnWyyO7VnfCC9r3s7xijPo9ohyeOl2siNB09yzbjW/qTWORG/F4GWWIwzM7+zcmidafwrhWnBZCuw//wIXw//CFO6DLuQ+wIpsuZCMAikBYW4Hv1VdSuXt2RgCimUvBeuwYYBox0Ds8VHkBl7fO5QX0FsZsM/loNomnCe+0aqi+/3POgPWMMBsxGtqHVKfBtFQ3tshGbYmPje3NdODS3ZCxunGOB/qc2sx8CoqFIAABTaoyv3pqOvMPx3prednzz/INAgADPutDXzDJUBLmVibiRaSivb2/PUlQ3Hbs5g3FzVqIEEYLLlIp6ItESDq1YDKWnLuOLpVLHYokVZRAXg1V4ZWlPwqE5MYHqyy9j/sN3wZBCWhlGWtnIyLwY1wGhvqfFH8ThQ1WAgNdCudY593eKgewU3hzPSSQliG4gkZIgCG4AK8apTUgcbWQJCPstFGzq9vHaym7g1d2iwKl7UYWS7XaeXeFu4LVhn8rgUWhi7UZ4tQF7abtZrgm2ogBAC4LcipPNb7YkYjjWX9cCEovciSA0agNmCp3topeuBdxnC+rvXIUnk0Hy7beBwUEMmjmMFGY6BcSlFJQPxJYt4U4FRM/164BhQFxbg+/ubahMRn29DmVF8iPISkgaS4BRQjK7CrOYQa4eROWv/xMe/vY/sBcRm19bsgkNZiITzMIQTPxg4WabtWnzGLa7XEOiR+yHgJhLBDCwXEAh6kMkV0E0U0Y6GbHN2BQs1srOLER9rfPtaNZCVLZkDzZFRI+gQGllJK4fty4WKoKMD/7+PYyPHMPZiakt564fL8qQIblOQNwvmsKh+tZb8JaWOoTDJoX4GNg/+sU9C4dWMon/cPK/wFwh02GjfXwygurvP7ZnG23i8BENMpRrndt7KVLy5ou08JbYKyRSEgTBDWDxArfE0ScatBcpexm052Z3kAWda1FFjkjZy+wOTn9HdQHdCz+Tcn9shqO0IMiVbFc3ZjjWm/us8eqOU5/nWmJBC5mCzRxvH1wLIrQQzVUMvv8+RNOEuLaGU2t3sQxAl1UsewdRVTwIsDL84kOs5fLwfF5Eceo06tEQSq//O9z5zZfaxL+7JwpI++7g09WVVu1D7/Iqnrn9Y1iWiRM378ESDAhPB5EZj0NV7sMjNvq5NYvhxoM8onEBxtItfDxQACsKeOOLHLIDwZ19qGZinl7s6e+K6A39EBAVQVoXDRvCYfX8OcRulyANhuFbqiOgmYh9zDCbGIPFFFiWCGZKYAbwLe99xBAB/AoCx56EJMqYeuG38Z3kyHoW4oYgKQp7m3fOZz7DyYEBPO4b39N1iN1jJZOovfIKarNpvP/2XJtwmFKT+Ch4Cc99U8XTSaMn98uWRKTUEbwe/07b9lNfLcAarvbkHsThIha0sLDaudhiP9wyIgGa4xF7g0RKgnA5ThZ0tNrZvUSDDHPpzu08gWc3cK0PKWDvWhTRvg5MLzN4+Znj1N+5FafagIyhJ7U1nARPWnXqTpz+7g2Bp7+1AeNhanduJRK0kNSWOjIvkn8fgXipN5kXQiqFb6990RGgPYmzAHYoChE9hzHWZiFq2NiS6sxsq2XYtB/dalPazDjcaknqT6/ha8UbYKqF8ucPIcVMyBMBLI/HIYvzCK2/l3uWCO2BhWh2FcatLKYvjILNCHjjdr1dQBwBgApQWmxteuazu1gzioislWHqdegeCamJMESxvf9kooCFEwkESnUouolwtoJ8PIDT0yl8ODDZ99830XtEiJtEvo3sQ3YhiMHbVchDMfiXNLCqheiHAmYSxxsCoimCWRKYIeC34jPwIQr4FYSPPw1JlHDhF17Gb4yMQhVk/OWffw+Pn5nCpYuX2m9+DPDdjEBcXIR4bgTax9NQZ2Scms01MnjFAPxWGUkthZEzGlQ1AGPqLLxqDObYGDzjTxzI74zYPwInh/BXiSfAWOdDRK6U7dl9eKUpyJnKvfCeL3qZSblms9ANIJcWYu+QSEkQLqdSBzSD5ylOk5vDSKlUwve///09XaNe+ocATnRsf/eD2xjSftKx3TAMCIIASeq+xsbt2d8CMNqxfeH+p3jttZ85nnv69GkoSm8Kds3NzWFqaqon1yL2hirYr0rvaQ0FXlYRCUWuhZe9bVoCSlUBIf/ex0InG8VID65PHD68KuDzMFTrdgGs3vR5hsm3y47THM+ViKkUXr7/n1FcWu7YN3h3Br4/+fs91bASUyl4rl/HL36QxcNi+5zweH0ez33yU3j/dBCeoSHUE4ldf46jhMWsDoHPrjaho0i4SVTUtm7jHNtvnvniLnTBRCRbhmSY0BUJC+Pxjoy2vQiIzSy4cK6RMZRLBGwz5pr3ySUCGEwVEM5VkY8HWucTvUGGBEWQ1i1JG6KhIsjIra4h5AtiIBJvr3O4bkG6WWRUBallY9rcrmyxPVUEGRIv63AU8H3mWxcQh1C7MQ3PjIyJB5l1AdHXEhCjj9WhKH4YU2ehKmGYY2OQj288E0qWAAH27an+4ovwvfoqrFgMmfHHoc3ch4dpOFGfaztO8ikwzjSsjCHL0K5c6dnvm3h0EcVGVpmdiNirmvdU756wg1eyq1ftDuC7tJBISewVEikJwuU4Bf8ps+hw0hQo9yK8DX+hYtomk1LxDdte980338T4+PiO7qm/FbHdfvp42PE609PTmJ2dJWHxCKKKHJFyX+xeKWDvVpyyt7Ol3oiUa1xbHAs7WNtBHDHiIQsLdRtLph65Fjj1nZRJ6T78Dx/C98Mf4rgm4UtIGNRXMKivQmYGDEGGJxsFWBjSwgJ8r76K2tWrMCcmur6+NDMD77VrgGGgVvNiSF9uu/6KMgCfEoG0sICxjz9G6oUXgIsX+/eBdwhjDAZMGMxsiYN2GYSbhT6tlUXYvq2ZmWhsrVm4fmzxUhk/VVZg3X8bJo7m/+J+CIiy3hBbJbPxO6x5nRcwNvc3j2+ef1QRIGyIgetZh0pLENzyKm6Iis3tTRGx7efN+7dcj2dX+tqPX8PU1BQuntmf//fNAqJx9iy02Qe2AqIuqhDOTuxKQLSSSdSuXoX32jUUvAm8Gx7p6FNz3gSevRxoWHLIMmpXr+65DiFxeIgGLVsRMcdZPLZTyjUBOifZIE7Pta6Ft/C6t2VM7K+VoGcLYo+QSEkQLocs6I4mU1NTuLiHwM+pD0L48Red2yXPgO11p6end3zPiuaz3f7kuTFcvLj96vq9fL7NTE9P9+Q6xN7x8ETK/cikpEUZrsVprMuVRIwP7b1t5DjtjsZZdxPl1o3pTZ9HC9GIJuFSCcm33wYGB5HQc3iuMAOV6W3HxJaXoXwgwJxsZP14r11D9eWXuwqqi6lUS6AU19bwRHoO0Nprbg3qKzj5QIA4dBqiaSL59tsQL1+2vb7JrM4sQW524SZ7UofswoZwuPWY9p/3Layr7teNDo79EBANpdF/mlKjr/PWdOQdjvfW9Lbjm+fvF1tFQ55w2Ph+I6OwsU2yzTbsPG/jmhJECL3wrD9kbBYQlaEo3g2PdgiIK8oARqeCGI2xXQuI5sQEqi+/jJX/79tgWEVaGUZa2bDLDvstQKjsKTudOLzwLFfzPVp867QQjZ4v3Auv3fW0fA4t+ib6BImUBOFynNL+ycvevcT77GVvmkCuzFv5R5Nqt6IKJdvt+bIIwwTkHsSS1qgGL7GFiJ9BEBinbkxv+jyqGUPYwet3ejXW8mrGON2bOJo8fu8exKEhiGtrOJG5i4dMRH29TmSzfpqHLUHQy5Bv3YJx9iysWAzK9esovPzbbaJeyacjLRVwp/awJQ4mf/A9qJVlyGtZhO7chz8gQI/IWA1HUfMqCBhVJIpZ1MQ6Up8u4uF4HMWIH4U//3/g7V97tkNstI5odqGb2A8BMZcIYGC5gELUh0iugmimjHQyYpuxKVislZ1ZiDYWShYSIfgEtSHwOWQMKi0hUcLacgYyRJwYHd8QDtuExoZNqSxIbYKhKsgQXSoaHgRNAVG9/hYkKYc02gVEAKjqVZhjyT0JiFYyiR8/80/wbz4tddT59Y4OoPr7p3pS55c4fPDKSfRKLOItgARIpHQzvEWIvXJpAfiZlLQAktgrJFIShMvhDTBhv9UTQYA4nPR7BVauLNgKAgDZRLgZnt0r0BB5BiJ7F3T4mZQkFrkVSQLCfoa8zcKJXtXv4PWdJBS5G95Y26t2V5tL49tr77YFTVNqEl/GL8LnoUneYYIxBmM9668s1GEKFha1zLZWpAYzIKfS8PvLmJcykL+ch+kzoZ8J4+FIGJBz8EoZMNHCrMggrOQRyZahf7aE6QtjMHMMb3x0D9mB4MabeQr4ABkgdQMAEFsp4qXZjyEwhqnbi6gbJrTJABbGg2BiHUAdJQBlKwTzgYZotoLofArL0VFIi4vQUvPt1yeOBDsVEGOZMkQIqMVC8IkeYGgYx9XBlvi3spRGNBDCYGygJSJGnp3AY7N/C2lEwMDyNJaWAP/bftyJTMCyZFiWCGZKePqkjq9iBiupB4gkBnD85NchihJqv/RPdiwg3XjQaPcXI4+OVTFhj5VMovY7r+DP/lrH8YVPOsbCsRcmkfz16J7vky2JSKkjeD3+nbbtv/B4HdZwbs/XJw4n3EzKHtm9OsVlogF6rnUrPIG6VwtvNR0oVR3sXrWe3IZwKSRSEoTLoWLbhB1RziqoXgVOeeI4QCuw3IyTSJktiRiI7L12EN+ehNqdm4kELNugQa8CCRlORhtljrubeMhCUlvqyL44/kkc4vLErrMvxFQKnuvXMf7TNC4XvW37jtfn8aL5M3j/NEb2c7uAMQYTG1akmrVRd9DJTnRzncKmBamtlalNHcPmMS2i66+L73X1np/54C4mBwR41lIo1QvQfRLWzscRENc6jn3gj+KxUgWKbiCYLSEfD+D0dAofDkxyr3/69jIAIJytQDZM6IqEhfF4hxjFRAELJxIIlOpQdBPhbKWr6xP7jwhxXQiUbGxJN7a125RuZBXKgozw157G4CevQgwMYMSvgNVN3L8exe3AYzCZDGaJsEwJMIF/dvom4hID8yoYP/ksIAio/uLvt/WBr/1wva7hY5vEwQTgO12AuLgI8awf927PIKTpOL8yuylLOIfH2EP4wzUIloDqsXH4RRnm2BhluLkEfXAYr1e/07H9slwEUNnz9bkLIMktw9Xw4mn9rjtO9e7dDa/f6ZVI6SSOx0IWapme3IZwKSRSEoTL4U2SyILO3ThZ0DEG7NWpyNGCjoL2rsVJpFwriMDY3kRKxvgTdMpoczfRIMNcunN7vxdmxMM01roVMZXCt27+R5xaWu3Yd/zBffj+5Ke7qmPlf/gQvh/+EDAMVGsyhvTljjpcgpKAtLAA36uvonb1KsyJiR5+sv1ja93ClsDHEQD1NjHRZr+19Th7sZDtX+XCntC0uAznqgAaGW522WxAQ0jMJQIYTBUQzlWRjwda5x/U9d2ODKllHSoLEreGYbOOoWxjUaoKSoe42KxrqIhbryNBEnoQ4Q4DK7F34V1dhW/qAqQvbyGWK+Pp/M02m+GkloK0WgaCjXqoEIQdCYj1F1+E79VXYcVi+MT7BE7r0/AwDSfqc61jPMwAUxQsDQxgJBwGZBnalSt7/4zEoYAXtO+VtTotgCTs4Ntu9tfSn9qdu+H9/auagGod8Hn2dn2nOF48xLBIIiWxB0ikJAiXw1sJQ9kd7ob3MKcZAip1IOC13d01vMm5z8Pg3+PEiTi8yIIGVTahGZ3BsV480FVqAjSDVwuV9WIxNXFI4YnUvAzIncJ7oCN7a3cizczAe+0akhUB01AwqK+0CYlGIQ6w4I6FxEA+j+RHHwGDgxDX1jBy5z7ESnsbG9RXEMhJELMnYcVi8F67hurLL+8po9JirD1LkJNJuDV7sCkY3h0vYMV3B5+trnaea7VnF24WGK1DJhYeFLLeWOAjmY22UPMqjsc39zePb55/UNd/VBCALRmEW0W/pjAotW3bEBg3xMAP/v49HB85hqmJM7bHbr6HKPRmHDoIVi5fxtgbb8CKxYDHz8KanoPHaBcQAUATVBhnTzeO26GAaCWTqF29CuUvriElDGA5HO/oU8dPh6A/FkPlnXdgSRJqV69SFrmL4MU1eJloO4VfSoLmeG4mwfn7OzlK7QTedejZwt04OeLlSiJ8nr21jzWHTOBY0MLinq5OuB0SKQnC5ZAFHWEHz+4VaNgfBrz9mdxQuyNCPh2ZYqdI6TQh7pY1h2BELGShQCKla4lzHuh7VYc3U6A+j2ggplLwXrsGGAai9QKeK9yDyvS2Y9S1NJQPAHNyckdC4okvvgBkCVZmBeL0NJghwPB4sOIbRFVV4bcqSNbT8HmKKN/8EPkzJ1GNBlH8/r/Bl7/54iZBsNO2tCPzcFPWoYE9ikyjwENUgBKFNvqBoTTGVFNq9Gfemo68w/Hemt52fPP8g7q+HbvJLtxqR7rVzpQnFjaOlyBDgrBXK5F15jOf4dTAAM77Tvbkeo8q9UQCqRdewOjdu7BiMXw6NAo1t9qR3e05FUYkZgKyvCsB0ZyYQPpXfwfz33sPx+vzSCvDSCsbmZiXkxVAsJALhbDw0kuIHNLscWJ38MTCnolFtOibsCEWsl9I1at2x4/j0QIuN+NkM50riRhJ7DWOZ9/ugj4LqvMaNYLYFhIpCcLlcFdgRWhS7WacrC+zRRGje5zc8CbVZLlJhH0aMsXOVN1eiEVO2ZjRoIXC8p5vQRxSeA/0mXxvAgnK6jK+vfZxW93BlJrEmDEBINaTexCPFoyx9izC9a/oX/9nVGtZiGtriC3PohoQoMkyVoNx1DwK/GYVw5UMVitVWJ+tYGVyDKWoH9nv/Qu89w+fs89QZAaC6Sy+EXyINcYw9dkiZMNE7nQAmfE4BHEJ/vX3tWwB8oMykK3AmM5j+sIo2L37+OldhuxA8EB/Z0R/yCUCGFguoBD1IZKrIJopI52M2FqyChZr2a8Wor7W+TxECCgnIhhNV1GPR6DkNcTSGh74QrAgg5mNuoPMEqFIAh4fMzGaL8KyPKiHhnHOdwz5E2cxlvi5LdajneKhsikjUeyRWEj0n8qxY6hevgz1rbfgUdewvEVABICqXoc5ltxTndy0dxR/lPwD2zq/7Bkfqs8/gff++I/xzUSiFx+LOETwxKL+272SWORm+AsgBVgWIO6x+fFcWnj3JdyBUwZ3LxZ98zPHqb8j9g6JlAThcnhiUYLqZLkap5qkvbDG4QlOZE9ChH267fZeBBJ41xAEhmiAYc52L+EG4mHLNrAZ/CIBcfl017WxtiKmUhDfuI7fvVfo2He8Po9LP/oxvPm9BWaJ7bEYs8kIXP95U0bgcqIKpi4hXxDbjmtlF3bULDRsaxdqlmGbXRhbKeKlOx9DYAxTtxYhSybMxwNIjcfBxAIkAHUA85YX1oMyotkKxLt3sXRhFGw+g6WHYa6QeHZ6AQAQzlYgGyZ0RcLCeLxDiGKigIUTCQRKdSi6iXC2gnw8gNPTKXw4MNnrXz2xTzhZkVoXglDeW4QYHoDfo0E2TMQ+AmYSxzaEREsCTODnzWkcY2EIfhX+YxcgiTImX/gtfCs50sou/P73vo+piTN49ulLkAQJom8ZvsU/AcIMysoHeLhgwXc/hGnfWVibrErH4jqewmeQWAAFUYIRGEPSM4Lq134NVmh3fSxxOLCSSdReeQVvfFKF9v7NjgU7tYtncf6VvS2SaAZfU+oIXo9/p23ff/Ory7Ao8uVaeItge1F3nOrdEzx4mbSWJSBXFvac8cgTnCie4m4UuZHVWKp29ku9WfRN7Y7oHzRVIwiXQ3avhB2y5DC56YFYxC/0TuK42wlxRcperPyzv0YkwCDt3G2OOCKIqRS+8dGP4V/KdOyLLTyA709+AnNsbMdCYrPuYKloQoCvo0bWijIAnxLYcd3Bw057/cKm4Ke32YhutRbdvJ9X29BWhLR2aEd6BvgSeWDtdl8+++nbjXTtfgiJzey3cK4KoJH9Zpcp17x+LhHAYKqAcK6KfDzQOp/YO51ZgNImG9GtmYJSK4PQ+fiN7Xdv3YEMEU8/8WTrHAki34p0FPjR2o8wMTSE5LlxyLdu4eGsjNP315BSk6iIAYSsMpJaCkMjIiLxIIyps/CqMZhjY/COP9l2OdUQoUKGJDQGTmt4GNboKMTFRZiTk9Dm7mBYTyNq5FrX91tlnM8uQvJXAEFA+dgxQBRhjo3tehEIcQhJJvF6/GTH5pBQBdC5mGcn8J5pQ34LCkW9XA0vs6gXz7TFigDD5GVSUjzFzTj9/bNFEfHQ3qzyKY5H8IgGGUrVzu25XsTxeJmUtCiD6AE0XSMIl8Otk0UrYVxPPGQvUvIExp3AndzQpNr1hH2a7fZeBBIynGvQw5x7aQqJw2VAgNohJOZZAmA7FxI31x000jk8V/i4o+7goL6CgS8ZxLPtdQf3E8YYTFhtGYU8wa89i1BvyyDULL0zm5C1C4lOGYZuoZ9Coqw3fq+S2ejPal7nwjDN/c3jm+cfJWRIW0TADfFQ5WQdtn0vtluMbhUL26/V2CYLvatbyCNjrWfNSnwb1q18eeoUnq7VYMViMM6eBWYfwFPRcKLe7iFQhwrj7ClYsRggy9CuXOnq+vUXX4Tv1VdhxWKYiZ2Hmr0LD2u/vt9ngikKzMlJ6PU6LEnq+vrE0YCXadELa3XeswXN8QhexlpPsoocrkG1Ad2NU2bZWkHExOje5l1cu1fq81xPPGTh4UrnCuxeOKKROE70ExIpCcLFVOtApUa2m4Q98TDDXLpzO29ishPIJoLgEfbbZ1Ku9SCQwLe3pnbnRjYLiZFaDs8VHtgKifL7DNaZdiFxu4xKz/XrgGFAXFuDevs2VCajvm5r18wqSmopKFYJwq1bMM6ehRWLQb1+HZYE1AQdWaPIFQl1ZuCuugRTsJDO22Ug2nxv2WcdMlAQbb/op5BoKI1ghCk1+jlvTUfe4dremt52fPP8fiBCsBf11usPriylEQmEMRgbgCJIHEFwPbtQ7E4wpLqF7RSCQaQuX8bo3buwYjFMjyZRXVzryO6uHotiOKYDsoza1atdZ49bySRqV6/Ce+0a0uIA5sODHYs+pLEIxp+NAIIAa2UFqRdeQIRsrl0FbxFsP+tk0RyP4C2CrWkCKnXA79n9tZ3aLi2+dTd+D+BVGWpaZxvhLaroFsb41xiIULtzO1FOVuNe2x3AX5hBSS5ELyCRkiBcjNMgRQ90BK8N9GJyQ5mUBI++ZlKSSElsYrOQmHh4Gw85QqJRK0L68gvoU2egx8KovvGXSP3jX2sJhxozsJyowlKXkCsIkFLLmJh9D5ZlYujWNOowoE1GMJ+MAkoeHjELS7KQFoDPsiUE14rQP0/h1hOjMHMM+X90EX8cfQd4+I7zB2gmUWXv9vtXRfSIfgqJuUQAA8sFFKI+RHIVRDNlpJORTitZS4RkMiRWq1AEGWY8hqgURDk5jse945tsRiWobRaknVmDW0XCjeOlNlvTpi0oj9d++BqmpqZw8bGL2/0KiT1QOXYM1cuXob71FrzeDB4ow0gr7VarSV3flb01AJgTE6i+/DJmf/gzyFhEesv1BxJ1QGhcf+HiRdQTiZ58LuLwwMu06M2zBc9ykxbiuJ3tbDf9nt0/B6xysoBliSHsp7bnZgShYYG5tNY5B9rrwoxyTUBd5ziiUZ/nenhtoCciJW+sDVK7I/YOiZQE4WKcbDspXZ/gWjJxLIJ3At+ehCY3bqefNSnNhWV8e+0LJLUUVKZBWxekTglnAQT3fH1if7AY25QJqLeyBLVN2YOtV0tv26YxA0v+FPyrWfjufATLMnHsi7sADLAnQkgfT0CUFxEWLQiSiRxMfDyXRTRbgXEzhekLo2AFAW98toLswKY2s6mW4TPv34VZW0JkrQxvLQfdKyF7PoaguNrxWWaCITxWKELRdQSzpW3rDhKHm61CYjxTRSoxAIPJYKYEZomwTBHMEhFRNYyspKFYCkqhBIaMEMzQCVyqnoAMERITITMRK0tpHEuO4Ymfex7Wj/4/iIWGccyvorRWwcxfD2LaPwXLkmFZjeuKjOH3jr2NEXEQgs+DsRPPAoKA6td/n2oDugArmUTtlVfw7lIJMz+41TEe3pk6h597xb+n6//Pg38AkaVxqfRR2/WL50Ko/s5ZWMPDqN+40cNPRRwW+M8Wfax3T3WyXA8vqwhoiJRjA7tvI07Wh5TMT8TD9iLlXvs8p/Mpo43YrbX69PT0ttdeWn0BgNqxvZx7gBs35jE9PY2pqamu3idBbIVESoJwMbzJjSAwWglD8Fc773FSbZpArkx2r4Q9YZ+OpLbUEdxczQ9DSI2DJXceRBdTKXiuX8fX38viYbH9QfF4fR5f+/yn8P7pIDxDQ5TZsUuawmGnaLhJJLRsRESmb7Ix3SI0bhEYe1bT0AM8c/8ulvQMImtl1LUKdEVCesoPRay1HcoALJxIIFCqQ9FNhLOVbYXEftYdJPpDs3ahUTfglVQEvL71rEHFxk50c41DuT3zcNPxbRmH67amnl9cQ3zhNUhhQM18BEHXsfz2AP7ePAdL2BhbRWbhXPB9DCleWLIMFjgBGCKE8ReQqMXb33xiFOcfOw9VVfGZL4EgZKhTT8D70TQGyjmEKjc6soOTVh2iLMGYnAQEAebYGAmULkMeHcbr8c76ul+1NADZXV+3aUFnqCN4Pf6dtn1XvpGFNWzvlkC4A17wvFgRoemA6uyA7QgvQyRB1oeuxymusVenFl48ZTBK7Y7gx1P23u74CjjFU4jdLAgaHR3t6trFaqdACQCRQGN+NzU1hfPnz3d1LYLYComUBOFieINULMQg9a80EXFI6Ndq51xZAGM8mwiaVLuZWLWK5z74T7CWOovDHK/PQ/63P4J0YnRHNnTSzEyr7mCt5sWQvtxRg8uvRiAtLGDs44+ReuEF4OLRsRw012sQaltEQ83S20TBxaEK6p6HWM7prW1NAbFNUNxUG3HzMXsWDveZfgqJ/aw76AYkiBsin9gQ+hri3xZr0VZdQ6XDglQV5DY7UlVUHPeL6wLha6+t245O9KkPGA1DHDsOYXER5uQk5Fu3MKyn8dVyoUNIjMpVBOMhGGfPwhuLwRwbQ+jFFx0vf//cOYzPzMCKxbAydg7avXvwMA0n6nOtY0SBQfQqMCYbdVYhy9CuXOnP5yUeWXgWmHut/5wvCzBMWohG2JNwcEzJlkQMx/qQ0UbPFq5HloCw30Kh0tlG1kp7S3fk2b1Sf0cA/bPd5PV3ikw2wwR/QZCTuC3LcuMZyCEOYphAqWb/XHvpwglcvNCd0EkQPEikJAgXw/PCT5DVK4H+iZROk3KqSele/A8f4sr9+0h4hyDgOAb1lQ4xsVoNILKwAN+rr6J29SrMic4skM2IqVRLoBTX1nB+eR6C3m4nO6iv4NQDAeLQaYimieTbb0O8fHnHtbh2ismstkzCrQLiVpGwtY8Z0C17IdFORLTQ5f/UaQAoALmZfn7sR4Z+Con9rDu43wgQoLYyBTcLg40sw40Mwi01C8WtYmCnQKhuyUTcKhgeVeovvgjfq6/CisVgnD0LYek+PKV2IREAakyFcfbkjoTEciSC1AsvYPTuXeTUBN4NJzv60koogUvPBhrFkmQZtatX+97fEY8evADWXq3VHS3oaI7nepyEm0x+byIl7/kiHqaAPdHo8+xEyu3sD7eDRErCCb5Y1J94SixINsNugmfPWsgMA+gUG1dywA2O3f7c3Ny2Nq1OjmpU/5noBSRSEoSL4U2OaFJNAECC81C/VhTBGHY9AXZ6GKQaCu5ETKWQfPttpBhDqJrFc4WHUFmnmOj5CBCfnIAVi8F77RqqL7/sGFz3XL/eEiilW9MQdBl1wdORseRBGfKtW1ASCWjhEKTrb6L43d9qiX71zaLhFpFw875WdqGTiLi+z+xWPCT6Qj+FxK11B6OZMtLJiG2mpmCxVlZmIeprnc+jaUuqCDKYbkGGiJAv2IVI2G4/ai8QdgqIEkQIFO3oKVYyidrVq/BeuwYrFsPDiSHcr+Y6FmX4EnFMxuo7FhIrx46hevkyFv/7n4BhBWllGGllw8p1OGwCQhXm2NiOstKJowXPuSJbFGFZgLjL+KlT4HWAxCLX47QYkbd4tlt4Ajs91xIAMBC2cD/Vub1ftQEHyGaYgJPd6x4XBJE47nqcLFUjfntr/aomQzNEqPLu2smKQxxvkPo8ogeQSEkQLsap0DtB8NpBXRdQrgkI+nYXbFrltDu/14K/0+WTcAGe69chmiZC9TriD2exiBhqNmKiWStDuvUljKnHoEcj0N78Ppb/8T+ExnTUrYYImE5UwdQU6nfewsl7H8A0DQzeugVLM6BNRDGXjENQCvBIWViSiWUBuJcpIpwtQEstYnpgFNYXD/DGjQfIDgQP+ldD9IleCYlNa1JzvZZh0OtH/YknEL9dghn2QmB5CHkVsY8ZZhNjsJgCZomwTBETQyaeKM/DjzCYX4Xv2AVIooxP0iJ+o/AMnnz8Cccsw+ZK2L5ZkxJ9w5yYQPXll6G+9RZ80+kOIREABjVz10KilUzijSf+Ka7dKXTU962PJ1D9/dNUg9Ll8MQi0xJQrAqIBHY3x+OtsveqDH4viZRuR5H5tpuZPdgf1jSgXOOVMaHnWqJ/DkH8Rd/U3xH8/mevNSkpc5xQVZVrzeofkID/1f6846cvYjTR2S55WZmb4WWOA5RsQPQGEikJwsXwAgkJWgVDwLkdZAoigr7d1U1byXFWnNLE5khgMbaecbghHDayDTdnJeqor2cVSstpnJl5FyWpiGG1hqwImGc9WBoNAMoKAlIKomgiIxi4u5hFJFuC8fkSpi+MghUEvPFpql1MPAMAeZR+ehfV6hIia2WotRx0RUL2yRhC4krHe74fDOGxUhGKbiKUqyAfD+D0dAofDkzu2++N6A0C0JYVqApKKyuwKfaV8yXglBcnZixISQHRlAXJMFH5VMEH6mlYlgzLksBMETAFPKtNY0T2oaIbGPedQTIygp+/8nv4L5JjkOxqGY4Bvs9DYPPzyB33Y/4neaimgFOzuZbwHrTKOJddRDKoA3IQxtRZ+NRG3UHt4UMMmEEMK7GD/WUSfcVKJlF75RXc9ebw/uzdNiExpSYxl3wKv/mKsw2xE6t5ESl1BK/Hv9O2/beeqsIaLuz17ROHHKcA+lpBRCSwuzmek0sLJWUTQKMt9Np20zFwSiIlASARse/znNpON5DdK+EEb6ztl90r9XcEsL21up1I2Q28/i4WtCA/OtVKiEMMiZQE4WLI7pVwwnFyUxBwYpdJGLzJzWCU2t1+YTITdashFNbX7UjrTEOtKSCuW5Le88zDEEzMrpVaomJ9U93E+hYxsmF5urPA5jMf3IVcX0KkWIbqk1BULKycU+EVO4PoD0/E4C9VoegmwllnMbGZ+RbOVQE0Mt/ssuQAgIkCcokABlMFhHNV5OOB1vnE3tlc13Cr1ahHUGwtShv1DpvWpMqm4+3qGSqb6iZK29qT3li8AfiA8xMhiIuLEB+PQL51C8UlE2fXHm7K4C0iqaUQ8pShDMuQzkzi2NAYcGwcyshxx3s06w4mHnsMP/iohsnSNDysve6gl+lgigJzcrK97uCf/ElPfu/E4cB3YgivxztrwMg6A2PpXQs7PEsmGmsJwDmQmSmIODWyW5GSLDcJZ+JhC/dsbDedatZvh5PQNER9HgH+Ytg9i0Vk90o4wBtrc2UBpglIuxR2Mnkaawk+sSCDIDAw1tlO9tLn8cbaARpniR5BIiVBuBierU6cih4TAMJ+BlliMMzOyc1eAgn6wjK+vfajjqyRiHQOgH8P7/howRhrZB4yDfV1EbEhDBoNcXGToNj4WUOdtWcu1tuERL11Xtf1EJt/jsKDvn3OfomJst4IsEpm47PWvM4ZSc39zeOb5x9lZEgtsa9ersEvexH2B6GKSks83CwgbhYUVbEpOm4IhI1tyoYYuX6ejO2Fw4OgKSRasRiMs2chZO51CIkAoEl++C4/Ds+6kFi9cmXbazfrDgp/fg1pcQAr4TgG9ZW2uoOnToegT8YaBX53WHeQODrwAkqGKSBfFhAN7tJanbI7CAeCPgaPwlDXexvAolISxHb0I7OItyhDltiu+1DiaMETDffS7so1oKqRWETw4Y19jAnIlYVd2wLz7V6p3REN8TsWZLa1nvckUvIWZVC7I3oEiZQE4WK4gQQaZAg04ubxkAUxne6oaRX6YQji2Nkd1bQSUyl4rl/HV99eQ6zYPvwcr8/jydvvwPunA7uqv3XQGK3MxEY2Yr0lCmpt4mA3+1sWqUw/6I+1L/RLTDSUxtJUU2r0c96ajrzDdb01ve345vkHwWbxsCUMbhEJPaKyrUDYzEBsZiYqggRV3Dhnc33Dll3ppHvqGzaFRO+1a7BiMaw8PoAv8oU2IXFFGUA9PIALseqOhURzYgLzv/y7mL/2Mxyvz3fUHXxupAII1q7rDhJHA2fXAhHRYG9tNym7gwAac7yBiIWF1c6xjmfL3w3k0kJsRz9qA3JLSUTIZphowGt3e7F7dTqX+jwCcI6rZQoiEuHdzfHI7pXYjkTYsm0n/cikJJcWoleQSEkQLsU0gVzJ/qmNVsIQQENU/C/T34O0tNSxL/RFHb4/ea8VXN8OaWYG3mvXAMNAterBkL7cIQQEPBFICwvwvfoqalevwpyY6MfHagmKNaahZmnIBzXMy2tA+S5qLdFwI3vRXmTUdpeZSHTQLzExlwhgYLmAQtSHSK6CaKaMdDJim6UpWKyVkVmI+lrndxzXsi1VWpmELatRsd12dEMgVBoWpWLz+w2xsD0z0V48JPqLOTGB6ssvQ33rLfgeLncIiQCgaGzXQuKiOoo/Sv4BktpSx2IP6aseVJ95ckeLPYijx4DDKvpMQcTE6M4DWJblIFLSHI9YJxG2Fyl5K+W7gURKYjt4QfQ1jlVwN/Da3SAtyiDW4S3QyZYEGCZ2VU/NKdhPC4IIYHtrdWB3IiV3YQaNtcQ6ibCFOwud2+2yK7uFa/dK/R3RI0ikJIgjyl/91V/h448/5u4v1X2wrP/Sdt/K4pe4wTrrwe2E8+fPQ1XVPV2DODiaouIpU8Y8xA6bQjkTAdiGqBiVJGCqs6YW0BA7mwKluLaGqcU5GDWj7ZhBfQXjDwWIx07DisXgvXYN1ZdfhpVMQmdGQ1S0NNSZjpSUhy6YYOU7rW22r5aGWsc2HcbWh4EngBtYA1Y+69evk3Cgl2KiIshgmgmvqKJ4/iwi0wXIA0HI8yVYBSD+sYXZgWMwmQJmSrBMCQm/hReEB/AhgqpgQPOfwvngIM6+8I/xqyMj8AhKS5R8VG1Lib1hJZOovfIKlo9n8P5n9zqsqD8KXsLv/IYIn2fn125a0KXUEbwe/05ru99r4b/79gotbyDg9zJ4VYaajWXcblc758oCTIss6AhneEGlvWQW8V1ayHKTaMDrg/ZSSoIXsKf+jmjCawuMCcgWxV1lAvH6SkVmCPupzyMAnwcIeC2Ua51tZbeuBTUNKFQoo41wph+uBVRKgug3JFISxBFmiiMaAcC9dJC7Lxqo7+m+09PTAICLF91jG3iU2CwqDlt5HCvchbrFejSxkILygQhzchJWLIYzn32G+rlzrf0GM1GzNFQtDcG/+R6qtSyEtQx8t2egegSwgIRMOIK6V4HfqmCwlIWu1LD4yUM8nBhCPuJF+j9+ib/51Sc6sxTD668kKh461HX70mYWoSrIwJMhDN+pQRqKg93JImpJOHMXeFc4CZMpsEwJzJTADAFn8/cQEwWogSgS41+DJMr46i/+Ln4/OQZFkCEKwoZt6cWL8N389xAXF8HOJ3HnjdtQFlWcnM0jpSZREQMIWmU8EVrE8LAOyAGUhxMIw4/IibPwjJ8/6F8Xsc8ETw/h9fg5230r+RWMD+38AYwXgKDsDqKJIPAz2jK7FIucRCZa7Uw02U+RkgJYRBOe/WE/AqfU3xFNnNrCakHAYHTn13Tq72hNI9FkMGqhnOpsK7sda3k1eJv3IgjAQaTci8U1uRYQfYZESoI4opw6dcpRJCx8ws9y/MbPnYNCvYPrYIxBZwaUN15HSSsCmQxOlWaQ8zMYioTVcKwhKpoVhPUsUpUazE9TWJxIIj3AsPb5n+GPHptBzdJa2YqxlSJeuv0xBMYwdWsRsmFCeyyAxfE4mFgDUEMZQMUKwXigIZqtIHpvEcsXRhFK1RFeLSA7wBfUid4iQYSnJSAqMGs6ZCYhHorCIza2eVq2po3vPYLaZlfqEZTWsc3aieq6ralo98Q+Cvg+9UJcXMQNeR6jpgnfmoDJh+l1MdELv1VGUkvBizpC40GoT16CIAdgjo2BjZ7kfp76iy/C9+qrEAYGMBv3I1y5Aw/TcKI+1zrGFzLBFAXm5CT0eh2WJHVlYUwcPZwesFZz4u5ESl7tDnqYIzbBqw2Y2aX9IYmURDfw+iGnAKgTjDnUK6J2R6zDtXstimAMuxJ3uGMtBeyJdRxtN/O7s92krCKiGwYjFu6nOrfvdqx1ysAcoj6PWGc/Mynp2YLoFSRDEIRL4U2K4iGLBMpDiMUs1CwNNaah5NOxJOUhV+6hatUbGY2sjup6ZmPNan5fR401Xpv7Iit5vDS9Lip+uQg5aMIaD2BpPA4mliAAqAK4YwVReVBHNFtBcHYOCxdGEVzJQEmvoLRJVDx9exkAEM5WIBsmdEXCwni8w8qTiQIWTiQQKNWh6CbC2Qry8QBOT6fw4cDkPv4mH30EoFXbcEM4VNoyFD022xqCoQrPunDoEdSN89e3SUJ7kPzGjRsAgItn+psV3RQTK14vSoOD8C8WOsREAKgLKqSnL0MYiAGyvK2YaCWTqF29Cu+1a0gJA7gZHuiwLg4cj2DkmQggCLBWVpB64QVEdlh3kDgaRAIMqsygGZ0R0pW8BMDoPGkbVnmZlBREIDbR60ACL4gQ8FrwkhM/sU6vMykLFQF13V5hGoruru4WcfTg1UzTDQGFioBIYOc2mbznWgqcEk1UBYgELOTLNhltuxxrKXOc6IZej7VONsO76T+Jo0mvny3KVQHVuv0cj8ZaoleQFEEQLoVrQUeB0wPBYlZLOKxY9dZr8/utP299rTFt42JPAUAGSH+84/fRa1GxWT8wnKsCaNQPtKs12LxmLhHAYKqAcK6KfDzQOv+w4hE2RETvumDoFZR1sbC5Xd0QFzcd1xAR5TZB0rOekXjU6iI2xUTr7behh8PQRh7DFwvlNjFxRRnAijKIc/4aorLYOL4LMdGcmED15Zdx+/X3EMcC0sow0spwa/+JwRogGDDHxrBw8SLqiUQ/PyrxCCMIjYesxUxnRluvLZkoq4jYzH6JlBREIDbDm/Pvtr9LO2R3DNDzBbGOU1tYyYmIBHqX0UZjLbGZgYi9SLnbsZZ3Hk+IJ9wJrx/a9Vib7XxOad7niIUIiD2QiNgL1rt+tnA4j54viF5BIiVBuBReIGEwQiudd4vBTFSsGipmHWWrhrJVQ8WqoWzW2kTFO49lMR38GN9fuNna1iYyHiC9FhVlvdGeJLMxcal5Fcf7N/c3j2+e33cY4BVVeDeLh83vW6KivC4yqi3BsLm9ee7WDEdbe1PCFnNiAu898QSO+f2IM6FDTGySCR+D5+WvdyVQNqkPJPEvI/8MSd8SLpU+QlJLQWUaNEFF/ckgqr/2OKzhYdTXM0cJ98ITKXttyUQBe2IzvRYpuYFTCiIQm+AF0teKAkwTkOzjoFzSWbKgI7bHSThczoqYHNvZ3N80G1axdlCfR2wmEbYws9i5ned6sR20IIjoBt6c38m21QnKHCe6gfdskS2JMExA3uEcL5Pnx7VoYQbRK0ikJAiXwpsUuT2IwBhDnelt4uJWwbFs1Rti5PprY1sNdaZ3d5M4ANSBLg/fT3otKhpKY/ZjSo325q3pyDtcz1vT245vnr8VYbOoKKqt7ESvsC4urmciejfv42z73/78Gh4/M4VLFy85flai/xSCQSx885sYHhnFJ383h3hluSUmptQkPgpewskXfEgm6zu6bvNhLqWO4PX4d9r2/d6vrMIapsUZRANeZlGvAwmU3UFspueZlJzzqN0Rm+EFNBkTsFYUd+yuwusnIwGyGSY28HmAkN9CsdLZXnazIGitJMCy7IOn1OcRm+EF0ndr98pd9O3yeArRDq/d7XoBJOc8t8fxiHachMPVvIhkfIdzvLx9XE6VGUJ+shkmegOJlAThUtxg98oYg8YMlKwqymYNJauKklldf934ubzlZxNH53ewU3otKuYSAQwsF1CI+hDNVRFbrWEh7IHJVJiGBMuUYRkSVFHC+TELo/kSZDkCf/IUBgMJVE4/gYvJK5sERgW3Pv0CEsSeiYqSJUAAZTw+SrDkMN4/fR4Lq52T4Uy+sOPrUXYH0S28gGavAwm02pnYDE+kdLLPdIKX3UF1sojNOPVDqwUBg9GdXY8WZRDdMhixFyl3k9HmZJlIYy2xmQSnPWR2Ocdb5jxfDMWo3REb9Npa3Q1xPGLvDMb4i7BXcjsXKZ0yx8k8jOgVJFIShEs5rIHTmqWhaFZRNMsomBUUrAqKZgXFdfFxqxipM8qQ2gmbRcVIroJYpozUQAKGpcAy5PWvhrg4Gqzg+EoGATmMghzACT2B06eex9mhr2N+5j5UJuOZr38HQwv/CXJYgCfzER7MW1BmE5j1nYUlbLTBk0MansDnkJgfzKcgMHIGEARUL38Tlrfd8lPGDr0piEPJQMSyFSl3s9qZF+iXJYZYkFb+ERv0MpOyXAMqNQraE9vDC2iu5sVd2W5ybYap3RGbcBKtV3MSML6zOXQ6Z99Qyd6a2MpQ1MLsUud2XhtyYtXhHFqYQWyGl1m0G9cCw+SfRwsgic3w5vzlmohKHfB7dnY9WhBEdEM8yCCJDKaN08ByTsSFHV4vU7BXIunZguglJFIShEsRUsv49tonbbXZUmoSpzAJILpv74MxhqpVR8GsoGhVGsKj2RQeK21CZMGsQGPGvr23w44MCV5RhU9U4RM98IkqvKIHPqHxs3fTdp+owid4EHohj2PzfwklJCCw+glE3cTdvx7CHc/jbaKiyCx8LfEu4paCQCyGOcGLhBlC5CvfguUfhmHkAACxY2cgj41DXFyEOTkJ7f4dDOtpRI0cUmoSFTEAv1XGU5lFSGoFEASYk5ON17ExWMOdNQmJo4thGJiengYAKHgGwGDHMV/eXcWNG7e415idne3Y9sGnxwDEOrZH/TV88slGHcrp6WlMTEzs/I0ThwrDMDAzM2O7r146AeDxju0PlzXccKhZatfulrI+APZ92Mri57hRr9juS6VS3PsQ/cU0zVYftJ9kU2EAz3e+H0vAj965iViwu7rVzXY4l/oFAJ3+mnp5DjduzO3lrfaFubk5TE1NHfTbONJsHl83E/JdQbHa2Vbe/2QOQdNGRYJ9fwcAt2aeABDo2K4igxs3PmnbRuOtO+CNtwqeAjDSsf3LmSxu3Pjc9lq8dvfBzRHYzfGCXg03P28ft2l8PRgMw8Cbb755IOPrZu7ffQrANzq2zz4s47XXXuvY/u6772J2dtb2feerATD2v7e9z42/fx3Lt1b3+nZ3zHvvvYfTp0/v+33dznbtO12MAvgntvv+7f/6OhKBnbkEzcz9PuzmePOzH+C11z7u+jrvvvsuIpHIju59UNy8eROmSckHO0GSGgLicrZzIY+TyxQP3iIiWgxE9BISKQnCZYipFOQ3r+O37uTBWPtqmOP1eXz1Jz+Gt5qEduUKrGRyT/cymIm8WUbOKCFnltZfyx3f6yQ82uIVNkREv+iBX/Suf++FX/Rs2udtCZFeUcXf/OXreHziLC5ffGbnNx0HfMc/h7i4CHbmMQi3bmHUTCFUzLeJikktBa+cAwsB5uQk2MICqoODCG0SFZsTdc/wMMY++QSiaeJz32OI5x/AwzScqG8ESn1BE0xRYE5OworFAFmGduXKnn+HxOFCEATMzTXahWCMw06knEvVHYMcy8vLkOX26c2d+/YPYD650Hatubk5erh3AYyxVjvbSjXPYCdSZgryjtvdvQx/DF1duonyqr3wlMlkuOcR/cWpbfSTfDUAO5ESAD6+uYTRaHfBzuXlZUiSjEzRvp50tTCL6Wl7gf4gIeGg/2weXzfjk7+CYqNYeht37ucwqtj3eXb9HQDML5+1v7mW6ug/abx1B9w+VU/CTqRcSOvcsZbX7m7f89ke71dKHdei8fVgEAQBFy5cwPnz5w/0fZR8cfzFJ53bK0YIzz/fOQZnMhmcPn3a9n1/Oe8HfmB/nysvnEM8tP/xjUgkgtnZWXzlK1/Z93u7me3ad6kq4b/7G/tzT575Ki6cLO/oftXvh2y3P/PkMTx/0d/1ddLpNGZnZyEcAq/O2dnZQyOoPkoMRe1Fyt1YDafW7M8ZJntrooeQSEkQLkKamYH32jWUSybAfBjS0xjUVyEzA4YgY0UZgF8NQlpYgO/VV1G7ehUmZ5UzYwxFq4JVvYCMUUDGyCNjFJEzS1gKraAs1lF98KN9/oSPFjIk+CUPAqK3JSz6RQ8ezsxhODKA06MnW+LihgDZ+N4rqpCE3dUpUA0JMnZ3LgDUX3wRvldfhRWLwTh7FpiZgcdoFxUBoKAJUE+ehCcWg5VKYfXZZzG0vm/zJL2eSCD1wgtIvv02UsIgpsOjGNRX2tqe71gE+rMRQBAAWUbt6tU9i+TE4UOSJHzzm9/ExYsXcVsP4v0Hncf4wqfw3e9+1/E6U1NTuHjxYuvnG/9zCPii87izp6Nt13LKlCOODoqitNrZVo59ruDfvdd5TkX34h/9xnfhsdd+AHS2u796zwP8XedxXpXhn/7ur3PrdxyESEY0kGWZ2zb6iWEC/7fXWcfiMQC4+LVv4xef7i6TEgCOnTiP//f79g31N/7B83h26vKu3ydxeNk8vm7mtS+CSNuMj8dPP4vvfpcjOqKzvwOAf/1Bp9gJAD//lUl899dH27bReOsOeONtSgng7budx8v+Y45zPLt2N/vvg4CN8DRxPNhxLRpfDwZJknD+/Hlcvnyw409FBvDvOreXazLOPXEZgS169xdffIFz587Zvu+0bn8PWQK++Y2LEHf/KL4n3nnnnYO5sYvZrn0zBnhUoG4zlUskz2En/xa1OlCq2e/7ucsTuHyxe4cCp/b9qPH+++9T294FvDImdsLldvDK5ww71L4kiJ1CIiVBuAQxlYL32jXAMKClcniu8DFU1j67HtRXMHiLQXy8kc0m/eV/xsPf/BZSCS9WjTxWjQLWjAJWjTwyRoFvvXrEehav0BAPg5KvJSIGRC/8UuP74CYRsnlMQPRCEWTblWmvXX8NU1OncDGyv0HQbrGSSdSuXoX32jVYsRjun3wOuQe5DkE7MurBP5hoiIp3nn4aw4lE6xqqqrYHES5ehHj5MmZ/7z3EsYC0Moy0spF1eWK4BggGzLGxnmTxEoefYc6kejf2JKs0qSa6xKmey2pexNhA96tFneoCHoJFy8Q+IksNu6TVfGfQYHmHfV6mxC9uNER9HrEFXp/Hq3nlBC+AxQuSEe6F1+54bciJFKePpDpZxFZGEvx9Sxlg8lj311riJOUOx3FgAiXxaCIIwHAMmFvu3Lec3dm1nI5P2q8TIlwMrz7ursZayqQk9oEjJiUQBMHDc/06YBgQ19ag3rkNlcmor9ehrKkeRNQMRq0F5PUitE+XMHMqjkxYxur3vsD1q08d9NvvCQIEBNYFxqDka3yJXgTFxvet7aIXfsnbyoDcbUbjYcacmED15ZehvvUW/P5V3N4iKgJASDKA8QSqV64g98MfciqvbVBNJPEvI/8MSd8SLpU+aquHqj0VRPXq41SDkmjBExCXdzGp5gX5KXBKbMWpTazkdihScoL8TkIo4V6GY/YiJU/s5rHmJFJSn0dsgSfk7LTdmSawWqCxluiOwaj9HG81L8KydibyLHMCpyNxWpRBtDMywN/XK5GShCLCjiGeSLm2s+s4HT9MbY/YAi+estM5HmP8mpQkUhK9hERKgnABYioFcXERYAzs7m1UzApqJwNYPKlCDS8g4qkDAJYsGeIDC9FsDYP3l7F2YRQDywXEVkvIDgQP+FN0oggSQqIfYcmPkORHSPIhIPoQlBrCY7sY6YNf9EKk9JWusZJJ1F55BZ9Xinh/ebpNVEypSfz0xAW89Ao/GLqVplCUUkfwevw7bfv+6bdWYQ1TMIHYYDhuP+EtVkSUa0DA2/21eKsFKWBPbCUaYJAlBsPsHCt2mllEWUXEThiKWrhps32nlkw8kTIatOBVd/HGiCMNbxzcaQbvWlGEZdnPsWmsJbbCaxOGKSBXFhAPsa6vleL0kbx5JOFeQn4g6ANK1c59PNGRR4ojFjllaxLuZThmv32nmZS8dqfIQPTRC9cRBwzf7nVnc7x8WUBNs5/jJWmsJXoIiZQEcYRhjCFt5FB67wdglRno6SUMlBahByTkz/vgF3Ptx4sCFk4kECjVoegmwtkK8vEATk+n8OHA5L68Z6+gIiT5EJL8iEiBxvfrQmS4+bPU+NkrqIei0Pdhx3tiCK/HO//+Y5oJYLXr6yyv8QOtSVqBRWzBaVVeOivh1Eh3ojZjwAqt/CO6RBQbmUUpm/5qp6tOUxn745NkuUnYMMTpj3ZqycQTKam/I+zguhbsMIDl1D+SSElshdffAY0+Lx7qfpzkWdCNUOCUsGEkAdx52Ll9pyIl73gSKQk7eFmO6Z3avXJEyuE4qJQE0UGv7F55tuoAlc8heguJlARxBJH9KuYSBfy3i/8eC/oqXrz/CQa0Ao6v5QAAuUQATLSfxTBRQC4RwGCqgHCuinw8gGimvOf3pAoyolIQUTnYeJUCbd+HpQDCkh+qqOz5XkRv4U1uVnIiGOt+Qsyz6fSpDCF/9yumCXfgNOFdzopdi5TFioAqZ+Uf2W4SdgxyRMrVHWZSLnEWZtCKU8KOXgUS1or2aeYURCDs4PVHqwURhtmol9oNvHYqCAyJMPV5RDsDDm1iNScCx7vrr6p1IF/muGVQn0fY0CuRkpfRRnavhB28TMqdtrsFzvpwEscJO3jPFuXazpyp0g6uLuQQRPQSEikJ4ojx5P/1JRz/lfP4IrAG6I1tst54SJPMxgBS8zoLgc39zeOb5/NQBRkDcgQJOYyYHEI1XUDA8uDJ0+daIqRP9FDW4z5hmiamp6d7dr3scgTAcx3bNUPA2+9+jpBPx+zs7LbX+eDTcQDRju3RQAUff3yj6/czPT2NiYmJro8nDicBLxD0WShVO4NPTqv5tuIU4KcAFmEHr0bbTm03edkdSaqTRdgwzKnRlt6p7SYnk5Ky2Qg7eE4WliVgJSdiJNFdu+GNtbEQg0IRB2ILqgLEghaypc520xhr9a6u4zQuUyYlYQdPzKFMSqKfjHLqoS6s7Ow6vOPHHOqtEu7FybVgJSchkOzumZTnrhEPWfBQjgnRQ+iRgSCOGKd/41LHNkNpPMCZUmNwCeeqCJTr8FZ0iJYFSxRR8yvIJoKo+lV4a3rb8ZYiY1COIiGHMSiHkZDDSMgRDCiN15DoaxMgb8w1BKdzvhN9/ayEPYwxzM3N9ex62UoIdiIlAHz0+SKS4TUsLy9Dlp2HlDsP7J/a/FJ2R6Lq3NwcTp8+3fXxxOElGbNw10akdFrNtxUnyzoK2hN28DKLeKKjHbrBr2HZbdCfcBf9tnulDF7CjmGHRRNLa92LlNxFGbQYiOAwGLUXKXdS/9lpXKZSEoQdvRApGQNSJFISO2Bs0H77YgY7cqZ6yBEpj3GuT7gbp1hHOifiZJciJS28JfYLEikJwgXkEgEMLBdQ98qIrZaQWC4gMxRqmw35y3XE02UgFES4YsIjRxAdOYPHwmOQHv8KfuPYSwf4CYidIMsyvvnNb+LixYs9uV5dB/6b1+33Pfv8d/DzTzRE7ampKcd7/nQlDNhokU88NoDvfve7Xb+fGze6z7okDjdDMQt3Fzu376RWFq9OVthvwWcfyydcDu+Ba2kHImU6J4Ix+4gDBU4JO3iBhFJ1Z5ZM/JqUFEggOomHGFSZQTM6+6vGgiCjq+ssZsjemtgZg1ELt21sN3eyMIMXOA36LAR8VEqC6KQXImW+BNQ0+31JEikJG3iZjnUNyOSBgWh31+GJlDwRlHA3AS+Dz8NQrdvM8XYw1vKOpXr3RK/Z2dJcgiAOJbNTSYTyVQymCpBMC5JpIZaqwL9gwvdAgDKroj6TQLAQx9SygbF0BTHmRTR5Cj7RA/PJpw76IxAHiEcBokFOhkcPMtoocErw4LWNnYiUvLqANKkmePAs4pY4QXg77GpaNqFVp4QdTn3SSq77tse1e6U+j7BBEIBhXp+3g4UZSxn7Y0cT1N8R9vCybHltyY4U5zmEFgMRPHohUjodSzUpCTucRERenUnbY8nuldgBgtCbeArvuZaeLYheQ5mUBOEGGEMiXYTCJAhKCEq6CNUy4ROq0AQFJnREUEQkVYUY8oMpCmBZEMplGFNTsIaHD/oTEAfMUNRCzsaSaXEHgQRe3RhaZU/w4AXtd2K7yQt2jVDglODAExHTORGmCUhd6EW84L5XZYgGKbuD6MTJkmk5250lU91QUNXsH++Gyd6a4JCMmZhPd3ZsvVgQNEr21gQHnpXwThYE8dooLQYieIxwxJxMvpHV5lG3v8aig6hEIuXRopuSNHNzc3j++ecdjxmOAaIIWDbd3uIq8NTk9u+lWgeyRft9x4a2P59wJ0NRC/dTndt5blN2pHljLSUbED2GMikJwgU8884sUqMRCHodqBdghBjgNWB6ACYzSDChMg0SMyHUamChEODxQJqbg3blykG/feIRgLcSvheBBMpoI3jw2sbyDrKKFlbtj6W6gAQPXlDdtISua2XxhPThmNl13RnCXQR8DAEvp8/rUiwqVAPcfTTWEjy4Y+0O3DJ4i9ZILCJ48BaL7SSDlz/WUn9H2ONUMzK11t015pbttw9EAC+VkjgynD9/HlNTUz25liw3hEo7eNmROzmOMikJHrxFkDybfjtSnOcQyqQkeg1lUhLEESe2UkQiXUDdr8ISRUAAqlEF6XAYal2Hp2ZAsBhqpgrLEuGBCLGUgxHxA8nB7qt4E0ea0QHe5Ka7QEK1DttMTAAYpgAWwYGXZbu8gwAWbzarAnwAAG9eSURBVAJOFnQED6eg+tKa2FX2N88Wh2clSxBAI7A+u9TZvznZB28mX+OLlEO02pngwOvTunUt0HRgNU+ZlMTOcLJWZ6y7R1Be38izMCaIUQcxZykDnEhuf435tP3245TNdqRQVRUXL17c9rhusi2BRrajnVVwt3avjiIl1aQkOPBiHour3c3xGOOXeKIFQUSvIZGSII44p283lvqFsxWYsoj0cBjVgAf+ioa6T0Xdt+FpUmIqEukiLMHEdLSGtDiDh9f/EIu/+BUMyzEMKVEMKTEk5DDiUgghyQ+RRExXMBzVAPg6tt+dr+PGjRswTRO6ruPGjRu25y9lfQDsbYOpbgzBYyhqP6kuVES8+94n8Crtbef8+fMdx/KE9DEKnBIcEmEGVWbQjM7xbSkj4eKkse01eMF9shkmnBhJWJhd6ty+0OWCoGwlZLs9GrQQ8O7lnRFHGV69om5FSt4KewAYHaA+j7CHNx5qhoDVgoDByPbW6Lws8xFaAElwiAYblq51rXNft3UpeSLlOFXIIRwY5WTxdp1JyREzo0Eg0BmmIQgA/GQDnk3/VgoVAVXNPuZLcTyi15BISRBHjMK9VYRPbSwRjGbKAIBwrgoAWB2JYHk0Cm9FQ2y1BF9Vh2hZsEQRVZ+CfMyPSK4KT70RhPWuZnGvnsK9eqeRuQQRMTmImBRCXA4hJjdes3IGAcuDqlWHTyTPk6MAq80BuNCxfbXgxfT0NBYWFiCtF2qzs0XJFPnRUapJSfBwahvZkgcjsWrr582rWJuCuWEKSGdfsj2fxCKChyA0sinn0p3T5G5t6MiCjtgNYxxBZ5FjW70VnkhJmeOEE1zXgi7tXp2s/yl7nODhlGW7lJEwGHFeEMQYXyCnsZbgIQgNseiezYKghxzxcSuUSUnsBl62Y7ciJa99UhYl4QQ/k7I714KHKw5zPHq+IHoMiZQEccT4u9/9Ixx76Rx+5f/8W1iL1CHrjYFDMhsPazWv0nj1q1ga76zsHlkrI5Krto5vnm+HCQurRgGrRgGob9qxHiP7T3MfwiuoiMlBRKQAIlIA4darf9PPfvhEDwTKynxkGQjVbbfnKipOT57F3NwcgIZAaWeLMssRKUN+C0Hf9iulCXfiFGSKJS/g4jnddt/s7CwAIJp8Cgz2/QpldxBOjMQtzNkEA7q13SS7V2I3jPECCXvMpBzjrKImCICfdbZWFFHXAY/ifP4Sp30GfRZCfprjEfYMRizIEoNhds7TUmsinjztfH6xIqBS49VCpT6P4HN8yF6k5ImP3R5HIiXhBK9uZNd2r5zjqB4l4QTvGaCqCciWBMRDzvO0BY4trCQyyqQkeg6JlARxxGCmhfm/+hzP/No/xy9f/RZyMR354i1AblSC99Z05B3O99YaQX9TagxGhtJ9QWU7akzDkr6GJd25Er0iSAivi5dN4bIpaIZEH4KSHyHJh6Dog58EzX1nIFyz3c6Y4Jgl2eThiv3k5hgJRYQDQR9D2G+hUOlsP41VffYiJdAQzDXvk9z9ZPdKOMELcPKC8ZthDFjkZFImacUp4QDPkmlhj5mUxwap3RF8nOr3pdZEnBh2Hi8XuLWfaZwl+IhiY6y1y9Lops+j7A5it/BsWeeWtz+XMf5xx8nulXBgr5mUvOMok5JwwslNZXFVQjzk7FowzxlrRxIWpL2FigmiAxIpCeKIcu/ePdz65AsM6kGMFcNQvKeA0i3kv/AjWz8GT7QEb6gIUd4YtASLtexhC9GGsX0uEdiX96szExmjgIxR2PZYESKCohchyY+g5EVwi4i58drYHxB9kITushAIewZC9iIl0LB83Q7e5ObYIAWwCGfGBkwU5jr/f7sJYC1yAqdBn4VwgLI7CD48MbGbGm1O2R2USUk4wQskLGdFaDqgbpPRlquS3Suxc5xWwqfWpG1FSp7dK9UFJLZjNGHaio1OFsJNeM8WkshorCUc4YmUD7oQKfMloFS130eZlIQTo5yMx9V8o0aqR3U+n5tJSSIl4cBQ1IIkMphWZ5LHYkbEE6ecz+fFXCjZgOgHJFISxBHl1KnGaFOYnETk9m3okQjC/hCMtBfxTyxM+y7BEgWo/gq84QImjy3hdHERHkNEVRZQiPkBALNTyYP8GLZYsFCwKihYFadEqhYCAL/oRUD0IiCtv4o+BEQPApIXftGLoOiFv7Wv8eUVPRApYxMAEPAa8KkGIqUVXCp9hKSWgso0aIKKxM8qKMklx/O5kxvK7iC24fiQhS/nOrfzrEc2w7NIHKHsDmIbeAHObuxeeYFTgLI7CGd4NtSMCUhlRYwP8fsuxpwyKanPI/gEfQwhv4Uix7Xgq487T7Z5tXpprCW2g+ta0MWCoHmOS8tIwoJM2R2EAzyRshu7V6djSKQknHCyZV3KACdHnM9/yMukJLtXwgFJaoy1dvE43oLuzfBcC8ZIpCT6AImUBHFEOXv2LM6ePQsA8KVSEBcXocs+WPdmMaynETVySKlJVOoB+DNVPGcVEVFUwJuEPjWF0UgQa8MRyFNfR1rPYVnPIm3kkDHy0NnhGpAYgLJVQ9mqAc5uBm0IEOAXPVvEzXVRc13c9Ise+EQP/KIHftELn+hBQPRAEeQjZUnryWTwz9f+FfyZziV8iburGNU+hVQoQBronCWLy8uY/ORnOJVaaQmbKTWJj4KXcGwwuB9vnzjE8CbATjZfTRY54jiv7htBNElyMoAW10QwBjh17/Npyu4gdscxh9qRi6uSo0iZLwuoG/bL8Gm1M+GEIDTayJc2rgVznP5sMzwbbFqUQWwHr43sJXB6nBZAEtvAEykXV7GtawFPpBRFfqYcQQDAMQcRez7tLFJqeqN92kHiOLEdownTXqTsylqdU7aJxlqiD5BISRAuoP7ii/C9+iqySgKfB/yYqtyCh2k4Ud9IT/JCA1MUmJOTYLEYvLKM2K/8Jl4ItWdSMsZQsqrIGiWsGQWsmcXW91mzhKxRRM4ogR0BfY6B7UrcBAAZUku89Ilqm5h5/3gRFc8cikVp0/Z2wVMWHp0lwNLMDMbeeAMT1jBS8GJQX8GgvgqZGTAEGUrWA8HPEMxmEX/jDUinTsGcmICYSsFz/TrExUWcmA9ANzcaxfH6PC4X38c3Pk5A/OovwEo+ehm7xKMBbwLclUhJmZTELuHVUqvUROTLAqJBvl0wL7tjbMCk7A7CkZCfn9HWCNrzM9qcLLB5GZoE0YTnWuCUGd6EJyjRogxiO0b3UP+ZNw+kwCmxHTyRkrFG3b9To/xzedlsIwlAoegq4UA40PgqlDv33V8CXniKf+7cMmBxhtRT22RgEgTvuZYXK9kM3xGN5nhE76FhlCBcgJVMonb1KrL/w+vIyH68G36uTWiyRAni40Hog4nGcmpZRu3qVVvhSBAEhCQ/QpIf4x77ZVsf3PgQFVHD6NQ41owicmYZRbOCvFlGwSwjv/5Vser9/ugHhgETRauColXp3DkGzOMe3svc456vCDJ8ogqf0BA5vetip2/91Ss0v/fAuy6CekUVPlFFXTGhwwRjbM/ZnGIqBe+1axBNE0krjVOFBaisPUgaXtEQUJdQVRSIpgnvtWvQnn8e6jvvAIaBSpUhVku3CZsrygBWlEGMVR/C9+qrqF29CnNiYk/vlTiaOGVSbpfRxptUU302YjucLGzm0hKiQf7KFV7m0XGHLLitTE9Pd3XM1NRU19ckDgejCQvTNiLldhbXvP2SyBxrDhIEwM8+462gb1LT+DbYZAVGbAdvAcVSZvs5Hr/ePbU7wpkTHJESaIhBTiIlL5OSstmIbjg1Anxyt3P7vSXn85z2b2cTSxC8sdZpgSMA1HVgOUtzPGL/IJGSIFyCOTGBv3vq9zH/3ts4Xp9HWhlGWmnM0GMhC2yoIaaZY2PQrlzZU2abBBEhy4tJ75jjcTozUDAr68JlBQWjvC5kVlpCZsGsoGRVDp3F7F7RmQHdNFCAjci5Hc8A72IF/8uDd9aFzYaguVXYbO7zCiq84vrXlu8Tb74BGAaUQgFnCstIMx/q63atFTEAv1XGBHuIsGkisbQEKxaDkMvB/6/+FYxz5yCurUH8bBbntnyMQX0FmnAXMe0EYEThvXYN1ZdfpoxKooPjnFV6VU1ApiBgIMLPaOOtDqRJNbEdyZgFRWbQjc4I6VxawpOn+SIlz+51fKi7djc66hAd28TU1BTOnz/f1bHE4WE0YWJ6vvMRbbtAAi+raCRhQaIMXmIbeMIOrz9r7XfItDyZpLGWcIaXbVvVBKwVBSTCTq4FZPdK7I6gH4iFgGyxc9+DZedz5zj7SaQkuqHXIuVwHPB79/6+iKMNr9TNdpmUvEVoAC0IIvoDiZQE4SI+147hj5N/gKS2hEulj5DUUlCZhsCwAP3pIIwnn4Q17LC0sMcogoyEHEZCDm97bM3SUDKrKFlVFM0qyuuvRbPS2layqiiZje9rTNuHT/BoY8HasKvdBbGVIl669TFEBpxNLUKKWWDHo1gaGoHJcpCNEmqmjC+NAajZRcTLdcgLMyiWVqGWq9AeyPA/XEKtJkJXAkjJoyghCL9VQVJLwYs6Qve+hOk9CysWg/rWW6i98kqPfwvEYcdpArywKmEgYi8WVeoSVvNkT0LsDklqBDpnlzqnyu/eSGHMc79t29zcXCurkZtJ2eXDnCzLmJqawsWLF3f2pokjAa/P265GG0/EpBq8RDcc5yyiWFiVYFmNemt2PFi2b3eCwCiARWwLr90BwIOUhESYN8cD1gq8Olk0xyO2Z3zYXqTkiZDb7T82uPf3RBx9eNas91PO591b3Nn1CGIzvFI3SxnnOR6vhAkAjFH5HKIPkEhJEC5ibj2QkFJH8Hr8O63tv/+LFWjftJmlP0I0s/sGEOnqeJ0ZKJu1dRGzhpJZQdmqo2xVG69mDZV1Aa9sNl4rVg0W+Ct23cbp242nsFC2DNEwoHkkpB8LwCt2tpV7xxJQbi7CV8lBX8qg7lXg/2QRq0EPcsMBrF2WoYj3EWMCLFPCqibg2HwO85Uy2M0MFp6ahHTnLm5+qcAcHoZHUOARVXhFBR6h/TUrlqEwGRWzBo+oQhK299InDi+DEQuqzKDZZLQtrEp4asI+gLWU9XOveTK5wyKzhCsZH7IXKZdzvo5tpmlienoajAH3Uy/aXs8pGEsQTbh1Y7axe6XMcWIv8BZRaIaAlbyIYY5lME+kHIlb8Cg9e3vEEWUoasGnMlS1zjneg7SMS4/Zz9ec6pJTJiXRDePD9hltPDvXJncX7LeT5SbRDbx2sttMShIpiW7gPQtohoB0TkSS42rAWwAZC1oI+ChuSvQeEikJwkXwsju6taA7TCiCjKgcRFQOdn2OxRhqVh1lq46KVUPJqqJi1jbETbOGilVHubWvjopVR9Wqw8DR+x1GM42q7uFcFQCQSwTARPviMEwUkEsEEFstQbQaExZPTUc+5sfCeHzjPIFBlA1ABlJnggjfLEDRNFRXFpCPB7D60Y/w4c9NOr+xdZ3638//PQBAEaQ2IdMjKvC2RE51XfDc2OcRlE3bFBQCOrJiGWtGoXWMLJA33qOCKDbqKNxPdU5ZnIJUSzl7kVKRGa38I7qCNzaWzSQuXmz3VnrzzTcBAKWajErdPjJ/FMdaovfwAglzaecabbw53hhlFRFd4LSIYm5Z4oqUcxyR8sQw9XfE9ggCMD5sb3F9P8Wf4/Hmf4LAaGEG0RXjHPOoBw4ZbdU68JAjYp45tvf3RBx9eKLifBrQDUDhROh5mZYkUhLdMO4wJ7uXkvgiJdV+JvYZEikJwiVYFv+BjgKnDURBgF/ywi95gS4zNptoloGq1RAtK1atJV5WrPqW72u4tzQHOaACXqm1nT2CGZyy3mgXktmYtNS8zkvia14FimGCQYCnZkBgrCthczBVQDhXRT4eaAmjO0FnJnTWsPvdFReAj5DBnz38oLVJhAivqEDdImi2v26Inuq6MKraHiPDI6hQRRkyJAi8CDPB5diAidrcSptNtSaoiL4dhXj5jK1NdWWuhG+v/aDt+JSaROb0k5Ak9QA+BXHY4D3Q8Wq0TU1NQYrwLVopk5LoBp64U6iIjnV4ZxftH+tOULsjuiAWZAh4LZRrnRm5D1clXIZuex5PSHIKiBHEZk5wREpeli7AH4eHYxZUyuAlumCcU0PSqSblLMdyEwAmuisnTrgcnqhoWQ2h8jSnHfEyKU8me/O+iKNNLMgQCVjIlzvnePdTEp47x5njccZhWgxE9AsSKQnCJSxnRdR1e3GERMq9o4oyVFFGBIFtj33trdca9cYmG8FsxhjqTO8QOKuWhpqlrX+//jPT2vZVrDpqVh01pvVc5jSUxqTElBqTGW9NR97heG9Nh2ABpixAtBiYIHQlbAIbQmhTGD1oLFiNvwfqPb2uCKElZDYEUBnqJqHTs3Xb5mNFGeoWIVRtO1Y+kgKomErh9x7+ORaXOpcux6fvwfcn78IcG4NnaAj1RAKBfB4nv/gCA/fuI1xsX2xwvD6PU8vvwvunCWhXrsBK0pMdwYcnFs2v8Ot38AKnisyQ5GQiEcRmTiX54+D9lIyBSGcgIVsUkC3Z272eGiF7a2J7BKFRy2963kakdKhJ9IDT55E4TnTLyWEDgKdju5NI+ZBjQXeMAqdEl5zgPALcWwJMs1GbfCt3H9qfI4pk90p0h5OoeH/JXqQsV4F01v6cUySOE10gCMDJpIlPZjrnc05j7ewiZ47n8KxCEHuBREqCcAk8GzCALJkOGkEQ4BUa1qRxhHZ1DYsxaOtC51/84HsYPXUcxydPrIuaDWGzIXiui5xsY1vri2nQ2EYwM5cIYGC5gELUh0iugmimjHQyYpsZKVgM0UwZTATqXhmemgEmil0Jm8CGENoURo8qFhhqTEPN1PpyfVWQ2wRPVZA3CaLt29WW6LlxTFP0VIXGvqJYg8xE1C0diiBD3GcRVJqZgffaNZw0RCxBxqC+gkF9FTIzYAgyjNU4wIKQFhYw9vHHyJ85g/DPfgbRNDFTOYYhfbnt+BVlANFAFNLCAnyvvora1aswJyb29TMRhwfeAp66LmA5K2LExjZ4nuNYMDZg2oqaBLGVwajFzWibXZLw7FSnSHlviT92TozSHI/ojmOD9hltvGcIxvj76NmC6BZesHM3gVNyLCC6hWfPWteAuWV78WeGV48yCcrgJboi6AcGIsCqTYCEly3Js3oFyO6V6J5TSROfzHR2VLNLfFmIt2+Sni2IPkEiJUG4BN6DXthvIRZ69KxGiZ0hbhI6A1UFSTOCJ/yndnwdk1moWxpqTIfxCwuIzv0pDL+BYPpTQNdQ+1zFh75jEBULomRClP//7d15mFxHfej9b3XPqpEljXZZ1i5Z3ncDtlmMDSEscQiBEJOEQMjNQnJzL4TkktwskOXmDTchudlu8uYlcUISs4QAJoZgMDEYA8bgBW+SbdlarF2y9tl6qfeP0yP1nOme6Z6lu0fz/TzPeaRTXedUzXRN9enzO1WVJ5vJsfHQTuYMQaGri0J3B0MMkm/L1BTYBDi+oBtIAqOauKGYTwLNE536Nq00EPEfd34TSNZ6PRMIPTOyszzo2Xk60Jn8v70s6Fn+2vAxnWXnKO+JMvv20XXHHZDPs4LjXHf8OTriyJvzmYMHaHsAips20n7iBOs+9Sl2LlhAJp/ngr0PEnMjg0hLcgdZtT9D5sg6ir29dN1xB/233uqISlU01iwDO/ZnKwYpq33WOmOBahVCciPhse2jg5TVgpHbqtxE6O6MVdcSlNJWVVljqFq/duh4oK9CMB0MUqp21drK3hcyDAxBV4UZ+p+pMr31+hW2O9Vm06rqrz21q3KQ8pkqQcoNK6emTpod1p1bX5DyuSrTDGcy1ddWldLWLq88s0q1afuPnAi8cMJZWtRYBimlWaLak87eOFW5bMgk63LSBasuoHv1ZjJ79pDZfBltW7aw4lCOTU/tZ1/Hcvoy8+gsnmL50D6yoYfFqwp0X/US2nbsoLh4LgwOcPRIjm1fWMozC9YRhgOb2QLZbI439j5FV5xLvivDqWXrmRci+664jou7exgs5hiMQwwUcwwWhxiMOQZj5bny1Ti5mCcX85xiAKaj63hLOx/hXrp3fpubPvsQi4+cZN7Rfpbt38/i5Rly2XYOzl1IX3sXXf05Fh0/wYGTg3Q8doTs0BADRZi//zihCKfaFjHY3sW+jmWcCj105wZYNnCIOdl+slu2wAUXUOztpeMrX2HgbW+bhh9GM92CuZFz5hQ50Tf6C9rOA1leUmH9jqefr/xZu9Yb9qrDuhUFHts++mnnamvDPFflBsP6FXnOwlnANU3WVhnRtq1KQGjn/uq3Evx+oVpV+3yMMfD8wSwbV458vVCo/sBGOq9UzTlzYMUi2Ht49GtPPw+vefHo9KpBSqfcVB3WrYAHnhydXi1Iua1KkHLlYmj3jr5qVO0ab/u+LLHCmJUxZ2nxgSBNE7s0aZaoNi2ONxE0lsGbb6b79tsp9vaSv+ACOh7cRmccYs3gzhH5+unm+JqFdK5cSfHoUQpr1hByOQbue4rFfUc4p/9UKbDZw5ziKdbEvZy7KQft88hfcAELenoprFzJxourB4uKMTIYh/ju9x4iFwqsv2AjgzHHQHGoFNQs/X84uBmT9IGyIOdwvqFijoGYIxd9CqylhECeIm37D9C17yCnYuS85/aQo0DfeT3sXj2fmCkCffQB/cUu4o5TLD5wggUvnOLwknNYdOwERxf2EC85wcHV7WQye09PovxCMbJtx2EOH+mj+Ohetl22muzxh/jGwy/Qv3QhQ3MHaSPD/QeepyO0nx45mowSbaN9eARoaKM9tNFeSh8eCdo+Im/ybyY4x+dMFUKyrtpj2zMsH9rLVScfZPnQPjriEMs+U6QjN5/8ZZdRXHbmMeand1e+tN50nn2Nare+yhPK1aZdqnYjYaz1LaW0jSsrt7vdh7Kc6g/0dI+8i1VthGV3Z2TJAkfwqjarlhYIIRLj6Ccqtu8fHaTcdTDLUL7y0xcbz/WzVrU7f1XlIOXWnaPToPqalBurTB0rVVJtitanq7SvJ7ZXTq82ZbFUSbXvBMf7Mhw+PvoztdosLXO6iixf6DWepodBSmmW2Pp85T/3DT5xqjEUly9n4JZb6LrjDoq9veSuvoYn9p0atc7fwfYlLGrfxZK2Nvp+7ufo+OY3ifk823ovprBvdGBzXk+R2N5OYeNGir290NbG0E03jVmXTAh0h056YidEWNM5+flNirHIv3zyY6w9fz2bLt7MUHFkMHPE/0uBz+EAZ5I3P2Kk53CeXPTvajLWP7UfgHlH+mjLF8i1Z9m9euGoaYNjJrB7zSJWPH+ETDEy91g/mWKkfSg/Zv6ek4O05/J0vXCMYwt7mPv4FrYu2AilgUvP9x2Zsp+ljSwdmVJQczjoOSKgeSbAWZ7WHkYGO4fPMRwUTfJnR+TPGhCdclf27OLF+77CeYMj7xwUd+Zpf/g52h9+mMLKlcw7eZLCnmNcve0/Tgcyh0IH+zqW8+Dcq9i4sqtJP4FmompPOz+3N3naOT06stpIN6c+VD02VQlSAjyzJ8vlG0a+Xm3k+JpljuBV7Trb4dxFRXYfGt2eKk1D90yVB28B1hukVB3OXwVffXh0+lO7Rqfl8rDzQOXzGCxSPTZXmWr4ie1QLDJqDftqQcqL1k5hpXTWG+vBxe37Rn+PqDbIZf3ygtd4mjYGKaVZoFCAZ6qM7ti8yi9zGlthwwb6b72Vjq98hfmF3RzsWMqB9tEBwt3d57Hq1jdTXL6c4pIldN1xB7vyi9kxbylLcgdHBDYLKxeQu2Zecqe1rY2BW25pyrqAmZChrZihJ3ayrL13ys5bjMVRAc5kvciy0Zwxx1Axf3pk53DacN4zx41Mmw2jP4fXKp13NFlb8+iinorrmkISeMx1JP1bz6lBitkMuY62MfMfXdTDkn3HmXe0n2MLe06XNx3yFMgXC8DgtJUxLEPmTBC0LKg5KkCaGR0w3bX8FIWOPQyc7BwVIG2vcs6zPSia3baNt+78Dx4YzBAojujHundlyGw6h+KiRbRt3cqb7r6b/NOHufbEJSPOsWpwF9eeeICX3L+AzPJXuv6pxjU0NET+xHZg9Fxzx/syfPUbjzF/zpmphmOEZ3bfXPFcrhmjepy3uEhXR2RgaPTn59O720YFKbfsqvzd4vzzDI6rPmuWFSoGKZ+rMJJjW5XvtCsXF+jxeSDV4fwqwaJKI9q2703uqVTimpSqR7Xg4ql+2Lkf1paNtIwRntxROf+FVc4jVbJkQZE5XcWKa4lXWk6i2kjKdT4AqWlkkFKaBbbvzzKYq3zDfrNT0KkGxeXLGXjb28js38+2b26n/fDBUSOFfuD8Y7xoeRLoGw5sPnbXt5nLbg60LxsR2LxxxSCEHIWVKxm66aaz7sZ9JmToDp10Zzqn/NzFWCwLeObLgphnprIdivnToz2Hg5tDxZGvD/8/Fwtlx+bJT8tik/VpyyV1yBaSqUQGukavzVaukE0utjPFSDELhezYj/cNn2/4/MPlzXRFigzEIQbiENQ7C8ta2MYJ7j30dM2HVAuKdoQ22ioERdtDtmLg9PRrmWqvnckTGvToZmbfPrruuINlc2FR7iSb+7fSUbYubjgeyW7dS1s+B8Ui3QMDxO07WDcwhznF/hEjzY90LmLx8efh9tsZuOUWChs2NORn0Mz0+OOP039kF5WClAB7Xuhh/pyjp/eP9XXQN1i5j8yffJKHHjqT9+KLL6ajo2MKa6uzSTabTDX8xI7R7Wnb7tE3sLbsrHwr4QIfgFSd1q/I843HR/dNlQLh26qM7tjgKErVqVqQcvs+GEg9W1htNBvAetekVB3GCi4+uWNkkPLgUTh8rHJeR1KqHiEka0A/sWN0kPLZvVlWp9KqLSXhZ62mk0FKaRZ4qspUryHEquvPSJUUly1jx9Wb+dr3RgffnjswMipyqnc5H5770yxr3zdiLbeh0EG8ai79b7pwxFpuqk0mZOgKHXTRAdVnvJqwQiyeHuX58OOPkKPAhgs2nhn1GfMjAp25mD8dJB0q/T8X8yNGgJYfMxRzFKmwOnuZfHvygw0HH7sGclT5fgacCTYWS6Mns4Wxz981kBtx/uHyVJ9JBUUnaDjQWSmI2TH8WoVRpGf2s+zp2EdbzBD6to0aMTocDF34pf+gkBtieTzOJX3bCEQGSw9lDK+te9ngM5xzaD8hRuYMDJA5uZ8XDx5nX8eZuwtLcgdpKwYyR9ZT7O2l64476L/11rPuwQxNrWsuW0XPl4ucqvC0c/acC7nyyoHT+996ovpDHCt7+07/f+vWrQBceeWVU1hTnW02rSxUDFI+lRq9NpjLVHzyHpylRfWrFth+ckfbqCmuq639nF67UhpPtSBljLBtz8i0R56pnHfdCuie+mdSdRab1wPnLYXnK0wf/Phz8NqXnNkfKzh+8dqprpnOduuWV77G27qrjdVl01bHmAQuK9lwrp+1mj4GKTWjhBDaSB4tvwRYBBSAvcB3Y4yPT3FZK4HrgDVAN3AceAr4eozx5FSWNd22VpmOac2yghfVqtuFa/KVg5T75wJnbpw+t7eNGAP7Olbw+YWvH5H3p994iOIyL3BaUbZsFOi8YjcA6zpXjHNU7WKM5CkwVMyfDloOlQKbH/3YP3PZNVdw9fpe5p54nPblC5h7cgcLjmU5NbeXXUfnkskWCNkCmUwx+Zc8PUOQiYH+ni46+4doHywQirHilK+hGE9P73p8QfLzHV3UM2U/n6ZXLhZKa75OYvrc0tt994EnK77ce/AE37flYUKMbN6xh8XrixyZP5dDK5eSYS9zChliMcuO54+zoj1H94l+5vUPcKobetqGGFyxmCNzFtA1OMSi40eZlxng5GNHOLl5HYXeXnJf+DgHf/RNdITs6RGnbeHM2qXFEInjBPJ1dgshWU/y0ecyLB/aO+JBnwWfjHTk5pG/7DKKy5bx2PbK13jz5hR55Usvdt0Y1aXaw4tPpx543HVoLjFWblwXrDZIqfpcsKZym3nhRIb9RzIsX3jmSShHUmqqrD83Wf+vWOFBu/S6lI9sq3yOyzdOfb109rtoTeUgZTooWS1IuWg+LJm6lWo0S2xenefO+0enP7G9jVeXBSmfP5Sp+KAkuN69ppdBSgEQQlgPvIgkAPgi4EqSwNywr8YYb2xC1QAIIcwF3g/8PLCwSp6twB8Ct8UYJ3x3L4TwCuADwI1VsgyFED4O/FaMcftEy2mkrbuqPOnsVK+agIuq3EjYeWgu+cIAbaXmVu3GaVs2snqpFzezVQiBdtpoz7aRDg12H4iszS1m7bWX0f3kAVixmPbdJwi5HBftPMmDu6+kWLYOYiYW2dy/hbb5vbTlBuhZvAyO7Wf/3nVwx0qenrsJspFMNglovvSC57lo6BDZ0EthTpaOlRdRCJGuF38/q3vns2PPLvKhQO+SReRKI0NzxTyDMU+utA2PMs0NjxCNeQNKZ5n1T+0HYN6RPtryBXIdWfauX0A2kyNLMgq369QgbQPHOXROG6sODRJCpNCV59iSbrqXHuHIqsAAsKfYQdhxgsyRY+S3HGPrpecSn3mKux4/wZHFcytX4MVwb9xP+45vnB7lOSKYmZoit3wEaPr/w8eOHIE6Mjhafq42GjelrsZ2w6KdvOib/8l5gyMXx8ptL9D+8LO0P/wwxXPOofehlfzUvjBqCvb1Fy8yQKm6baoSpHxuX5Z8gdPXeNsPVu6/ujoia30ITXW6cIzA9padbSxfOATAif42Dhyt/L12o6M7VKeO9mQk5Lbdo197dBusKbtH/z2DlJpCF62Fux4YnZ5ef7JakNKpXjURl6yt/Fm7fX+WgdyZEZaPPlt9lpZq14nSVDBIOYuFEG4BfpYkKLm4ydWpKoRwKfBZYN04WTcDfwe8NYTw1hjjWLMDVionkAQ5f2WcrB3ATwBvCiH8ZIzxU/WU0wzVRlJuXuWXOdXv4ipBylwhy7Y92dPt6pFtlS9uNq/Kn77JJVVSXLaM4rnnktmzh8LGjbRt2cIK+rnu1DfZ07bi9HSby4f20RmHOBm76TjvPHq6u3l2bobBY10syx1gwdGjp6fn7Cme5ILtTzGvuxOy3eQ3X0BXZy+FlStZuPFmAB56NmmYVy6qfTrEGCMFimXre+ZPT32bK5vmtjyomSuNIj2Tljuddnr/9PEGRBtteKTtvKP9QDLSNj0qt7eUp2MwRyGbIcRIvi0LIdDVd2btypgJ7F6ziJ6Tg7TnCsw70sexhT2s37qP7y6ufmcrBk6/56em+gccQ4AKQc0zwdF0cLNiwDOTHF8+hW7bqPVIs6lysmRC5Sd2Z6M5zz/PW7ffxz2DWQJFluQOsiR3iLaYJ+zKkt2RIRw7SubkSa5+toNC5lIOty0CYNXgLq498QDrz1tKZt8NTi2sumw6r/J3g1w+sGN/9vQ0XzsOVQ5SblqZJ+s1nuq0aF5kWW+B/UdGN54ndrRx4xVJkPKZffOrnsMlTDQRl66vHKT8zlZYc2Hy/5N9lfOAQUpNTLUg4xPbGTHFtUFKTaWLqwQpYwzsPbaYy0v733u28v3jtcvzzOvxXoSmj0HK2e0m4HXNrsRYQgibga8wOoh6EniWZLTnWqA8GvIa4AshhJtijAPU7s+AX0ylReB54CCwOlWPHuDjIYS3xBg/XUc5DZUvwLY9lf/Uz3fNGE3ApvPyZDORQnH0EI0ndrSfDlJWu7i5fIPtTuMbvPlmum+/nWJvL/kLLiD7zDP0dgzS3r9zRL6h0M72JVdz7pUQnniCJ+evY09+MZv7ttAZh1gzmOTvbCswr7Od2N5OYeNGir290NbG0E03TaqeIQTayNLWgLuyw1Pl5or5EQHRXFnAMx0Arem1Yp4DRw7R1tVOpiM74rXZ+DWkLZf0YcNrnQ50jX7gors/CUR2DuQpZgL5tiyZ0i8rk5o3LGYCRxf1sGTfceYd7efYwp7TgdBWE+H0ez+pKXUnIEtm1IjQM8HRbIXRo6NHjraPyFstz5nA6vD/My005LDz8GGW33svxXnLWZQ7xeb+rXTEM4Hv7sE+wjf20dYWKCzoJXtqiFcWvsKuzlWczJ7DwfbFHGxfwuawk+7bdzJwyy0UNmxo4k+kmWT9ijwhxIpTuX7v2fbTQcqdVUZSuh6lJurC1fmKQcond575PrF1T+Ug5eL5Bc5d1KDFsXVWuXozfObe0enf2QI/XApSPvZcEjiq5DI/XjUBF1cZfnH8FOw+mKxZGWMyoreSC9dMX9109lq1pMA5c4qc6Bv9YOjuY0sY/u73vSojKS9d5zWeppdBSlVzCkbNxNdQpfUnP8nIwOALwHuA22NM7tiEEBYC7wV+DRjuba8DPgT8Uo1l/QijA5SfAn4txvh0Wb6bgQ8Dl5WSssA/hBAeatWpX7fsbGMwV/nmm9O9aiI622HjykLFEbpP7Gjjh16aBMcfe67yxc1l63MV06VyxeXLGbjlFrruuINiby/Fa67heP9xDj97lLaYJx/aTt+MXzoQuW5BjlPvfjd7f/9+DrfN55vzrhsx+mjJokDxgnkUFy1KHk9ta2Pglltm1Cij8qlyp9rHvvYxNm/ezJVXnhlFGmMkXwqG5qoEPodHepaP9syXvZYfkV4YFRxN/l84fXyR5t9kzLcnN0kL2eSSomsgR3pqhuFAZKYYyRQjxUygWBptWcyM/uI3HOgcDnwOB0J1RoEihTjEQBMi421kaQtZClfl+U72CJ95/ntngpqZSiM/Kwc9RwRTR4warR44TY8gXfLAA2QKBZZmDnFJ37MEIoOlaVyJcFnf9xgqZGkv9JN5djvLB3vIh3bWDTzHvo4VLMkdZCg8w8rMGsgvoOuOO+i/9dbG/1I1I3V3Jtd46TUoAR58OrnGA3h2/zkVjzdIqYm6YHWeex4Zveb9lrIg5VNVgpRXbMg7vbUm5JoLKqfvPQxH+5LVjx55pnKeud3JdLFSvcYaCfndrUmQcttuOFRlbrhLxptjTqogk4GLVue5f0vHqNd2H10M7CbG6oMNvI+n6WaQUpAE/h4o274NfD/w982sFPBTwKVl+0eAl8UYnyjPFGN8AfiNEMITwD+XvfTzIYQ/Lw8yVhJC6CCZ5rXcXwPvTq9tGWO8O4TwcuDLwDWl5HOADwI/WduP1Vj3b6kcKOpoi2wySKkJunhNrmKQ8vHSOpRP787SP1T5bsEVG7y4UW0KGzbQf+utdHzlK2R372bO6kV8bd/KUfkeGjiPN/zwtXSsXs5f//UVvHjgPlYN7uJA+zIOtC8D4IaNQxQXJ9OFFVauZOimm2ZUgLIZQghJIKWBl4uFWBw1+vPM/plgZj4dFE0FPKuNJM3FAqeG+shTJGaTUYPF1HjRo4t6WLz/OMcXdDP/aB8LDp/iwPL5I6Z8HQ5ExpAEHPPtWQa7kt/TwJzRn7tdA0m/Nxz4HA6EqjXkKZCPBeiAHIOczDduFGmGzOnA5ZJDfbz8xCNk2uH8J/ew8LzIwbnzeX7pfArxFJt37KDQn+MQWdpe6INigZ7sCfads4RcyHByXhvnDPZxTv4Uc3a+wPHujRQX9pK/67McvfwSsmQ4Xjh1OkiaJeMapBrl6k25ikHK72xNbmq9cOocDp3ornisQUpNVLU17596vo1c6aWn9lYJUm70u4Um5urzq7+2/dAirqd6kPLSDclNf6leC86BNcthx77Rr933KPzgy+Abj1U+NgS49sLprZ/OXhetrRakXALsZt8LGQ4dq/w99bL1XuNpehmknN3+FvjzGOOoSQSafcOiFDj8jVTy+9IBynIxxn8JIbwW+PFSUhvwAeDHxinuXSRTxg57GnhPOkBZVs6xEMJPAg+RrE8J8GMhhD+IMW4Zp6yG+/aToz+AAC7fkKOr8kvSuC5ck4evj05/ZFs7xWL19Sjb22JyrFSj4vLlDLztbWT276fnK4+x65F+OuIQQ6WRRQ/OvYp9HSt448ALrDhc4PHCBTy+/AKWD+3lqpMPsnxoHx1xiO4NeXJX9JK/7DKKy5Y1+8dSFdmQIRs66GL6PqAeeughgNOjRgsjRngWiC/fy4Jdt1PsKdBz4BEGjh9n9709PN27hpCFkC0y52gXy08cYOngEB1kKAwEjg/1QgF2LVnDwOF2Mtkic7qG6G4fZOkLg2RDlv7euWRDhqOLmjpZhVpIkSKDschgzLH5yW0MhBzzj50iP9RPcW6Wwxd30JM5TNepQRa1H4C2yJwDJzjZkax1kOuAtt5TFLpzzFuyh70re1m54wR7jvSRf/IFtl56LvHEY9x10UGOLJ7Lv+y6/3TZo9cgTY8KrT6CtPqo0ZHnGyuv65C2pqvPz/Gx/0yCkOWfpd37B2n/i4Nc/qWnec+BI/Rn5oz6LL76fINFmpgLV1f+fjCYCzyyrZ1j/XM4fKKrYh6DlJqoJb2wehns3D/6tecOLgJy3F/lDtTlTvWqSbj+kupBSqgepLxkHczza4Qm6JIq61LuPb6IQjHw6HPVw0SXOpJS08wg5SwWY3y82XUYw2uAVWX726ltZOcHSIKSw1HWt4QQ3h1jrDJRAgA/ndr/g/HWsowxPhFC+DjwE6WkLPBO4H/UUMeGemBr5WDRiy/0A0YTV20++sPHMzy5s61qkPLC1Xk6K78kjam4bBmLfmQZH79zCacGRt/YfuiZNp7de+apv30dK/j8wtef3n/3LxxkqLf5U4mq9WRDlu6QpZvSNHOr5tG9ahOZPXvIbL6E+MADrDh+kvaDO9jXsZy+TA8DuQUsPPUcxfYOsqGDk/keOvdmeLZzHU8PXHP63K+88HnO73+KRZklxLntnLf2GgiBV9z8EwwtXVxxGtwvfPmLrFy9kjUb1o0YNTo8EjQXK48azY8YaVo4fWz6GLWu4bVK5x3tB5JRvcMjeHtLr3UO5MgUI4VshlMLO+k5NUTnQJ7B7g66+nLETGD3mkX0nBykPVdg3pE+ji3sYf3WfXx38cYR5TVzDVI4M4q0eHWeB7JH+LfnHxkVMK1lWt1694fTWmkt0lZy9aYcKwb38Lojn+e8wecBOKdwgk39T3PqE7tYdRhuyT3Psew8nunexKrBXVx74gFyy89lSd+LKc5zlgLVb/OqPN2dkf7B0X+X9z3WzsEj1R8wu9xZWjQJV2+uHKTcfmgRR08d5KEq83JdNcYoTGk8L70Mbv/y6PTvbIWBQfhmlSDlDZdWTpdqcfHayp+XuUI7W/fM5/lTlW/WrVxcYOE5TViTQ7OKQUq1qh9M7f99tZGN5WKM20IIXwVuLCW1A68Dbq+UP4RwHnBVWdJJ4BM11vEjnAlSDte5pYKUzx/MsOdw5aH6L7pgqMG10dnk2s052rKRfGH0jYR7H+3gG49XH8ErTVQ2C5euz/OtJ0a3r4eebqetylXNpvPyLDVAqToM3nwz3bffTrG3l5Nr19J58jCdA0OsGdx5Ok8htNGfjxxbupLcvlP0FPqYVzzBuoFn6cv0MKd4ivP3PUFnHIJzzqGwcSOEQGHlSuLy5bRDxal0F5zo4Lz8Qi6bs37Kf67ydUbLg6PlQc3yUaX5ClPsJumVptgdGQxNnyviF9vxDK9VOrx26fBapgDd/cnnZ+dA8pDQwJwO8m3JAxuZYiz9mxwXM4Gji3pYsu848472c2xhz+kAaCsZHkVKezLN7qkGTrMLZ9YirTYCtJZA6fDxlfOPHTht9sw11Vw0tJWfPXw3MVcgUGRj/9NcfupRsjFP9oUcPX1Z5jDAsqF9bBjYxvd6LuPp7k1c07OD7tufZeCWWyhscIiR6tPRDi+5cIj/fHj0upT3PtpBV67y4n/nLSmwZL6fL5q4qzfDp782Ov2ZA0v49lNDVLsLddPV01svnd2qBRuHcnD3d+HRZyu/fv0l01cnnf0uXFP9gaCHnl3E43tHfwYDXO4oSjWAQUq1qten9u+q49gvcSZICfAGqgQpK5RzX4yx1rs49wF9wJzS/uYQwqbx1sBslGN9bfz79yqvFwNwzWY/ZDRxPd2Ra87P8a0K0wn/413dPLu38sfLFRuc6lWTc8WGXMUg5Vce6qSzo/JdhJde4kMZqk9x+XIGbrmFrjvuIDdvHscvWswT3+1kSe4QbTFPPrSxvXM1qwd3MX+owFDHORRCFginA5ldHZHOOESxrY38BRdQ7O2FtjaGbrqpaT9XM9YZHVaIBXJjjPDMVVhX9EyQtHD6mPJRo5XyD6WOzcX8jAmPDq9VOrx2addAjuGpQIYDkMMByXxbhrZ8klYsjbYsli2ONRzgHA54DgdAdcbwWqQDTWogbWRrH/2ZORMEfaHrEFky7D+ar/h6pel2c3OgLwzRXxykLWRpo3KQNLNvH9133sF5ve307T7CZae+x+qhXYQYaYs55h7vY04+UgyBvswc8qGd645/i5WDu1nYdQHk59F1xx3033qr6z6rbjdcUjlI+Z2nOuhp31TxGB+A1GRde0Hl9FODXXzkrnMrvrZ2BWxYOY2V0llveNrW4xXuPv7Rx6gaHL/ekZSahM52uO6iIb7y0OjP2i8/upJ9RyuPpHzJRX7WavoZpFTLCSEsA8q/1Q4CD9ZxivtS+1eMkTf92jdqLSTGmA8hfJuRAdErSNa0bJ6Fb4Cl7+CH//A68oXKa/1sOi/vUH1N2ksvHaoYpKwWoAS4+arGTyens8v1Fw/x158bvRDHkZPV1zYzSKmJKGzYQP+tt9J/220sKh7kha5VHGgfOd3ciex8zht6nmfnredktocluYOnA5nLFgVOrprL0Pz5dJUClAO33DJrb9xnQ5ZsyE7reqOVxBgpUKwpIPrVb9zL0nOXce6qlRVHiOarjhodHTQdDpjWM4L06KIeFu8/zvEF3cw/2seCw6c4sHw+MRNOByCHA5JtuQJdpdGVg13J5+7AnDM3FroGkteGA57DAVC1juEgaX+s8zOq9Azid4/uqP2YN3TwUb7JR3d+83RSpYDoyz/zHRYePcHKc/tZcnIfi+Ixsh1F8pksHQMDDBUj2WyRYiZLOwMMdGRpLxY4L+yg5+AJXjiwgcLCXvJf/BQv/OgPjxhtOjwKtT1kiUQCrTmSVM1zQ5XrtYGhwMDQgoqvvexSr/E0OS+7HOZ0QV+FBX+e21/5oe9XOYpSk5TNwnUXwxe/Pfq1ex6qfMySBbC+ctxcqtkrrxisGKTcd3ROhdyJV1/tfTxNP4OUakUXpvafibGub+/ppc03hhDaYoyVhnCly6qyLPqYZd04xvka76LPAZAf44H5FzvVq6bAyy8b4o9qnRwZuGJjjuULnXJTk/OyS4eY01Wkr8K6lJWEELnuYvs8TUxx+XJ2v+Y17Pr2t9mz53wGd5+iIw4xFDrY17GcB+deRYiR1x75AqsGd3GgfdnpQOb1q/azp/AcizMZCitXMnTTTbM2QNlMIYTT03qeXne0ii2Hv8PmxSu4ct7lU1Z+9RGkhVFT6GauO8CCRz5KnNvLwm4IuRyFpwLf6VzJob7A0hOHCcU22oeOMa9vkKG2NnJtGfoyPZCDA3MXQT5Le7Z4eg3L4wuSG6xHF41+uEOzW67UNof1HjxB2LOHozGyed8eutqHyMzJcXxeN4sOnoBspK+7nePzuph3bICugRxzM8c5tGQu846f4uhgnoEtR9l66bnEp57krkePcGTx3IplhwWQJUPnzvurjiIdnla30pqiY03FW/5aW8iWzjMybybUdg2hxrpsXZ55c4oc76v9Gu+1L/LGqSanuxO+71r4zL21H3OzQUpNgZdeVjlIWc0rroAWnSleM8grLq/v3siGc/OsW+GMLJp+BinVijan9nfVc3CM8WAIYQDoKiV1AOuoPMJxUmVVyJ8+X0vyy5ymwpUbc8ztLnKyv7YbCd9/re1Ok9fdCa+6aog7vtE1fmbg4jWOHNfkXHzxxQDcO7iBj36t8nRzf7f8XSwf2stVJx9k+dA+5oQBFm0+Dkuvp/t1r2Ng1apGVlktpK4RpOs3c7D363QdOsSCC5bStmUL3f2Rw48d4Vh2GUsG+8mXgpTZmCGTybKzcy39Az0cy87jsWNXcdPFu7i2/SDZbJ7Y1caqtVdRCHDO8svZdGwB51+4ueoo0DPT5VYeHVo+mjRfCnCdDdPsKrH+qf0AzDvSR1u+QPtQnuPzu+kcyJEpRgqZwPH53RACxxd0034gT7YY6RxM8nX3DZFvzzLvSB/HFvawfus+vrt4Y8WyYoA8RfLFCkOXGiBDhvYKAczqAdPKQdFa1x9N76uybDaZUu6u74z9QMmwa87Pscw1xzUF3viy+oKUrkepqXDLDfCb/1/t+d/26umri2aPTSsLrFxcYPeh2q5HXuUoSjWIQUq1oqWp/ecncI49wPrUOSsFKZdMsqzdqf103VvOysUFbqzzyRmpkvY2eMVlQ9x5f23Botdc25wbUTr7vP4lAzUHKd9wnRfVmpyOjg6uvPJKlq7K8M9fjxSLlR9h3texgs8vTJa6/sHrBzjvvccq5pPGcvDaa1l5110Ue3vJX3AB3c88w7y2QTpzh5hXOEFncZCBTBdzin3kQgeL8i9wIubY0bmaDYPPcMG+J8l2d0IIFDZuIptpg5UryS5cwfwinNuxqCE/R4yRIsWqQc98Wfo9X/8qS1cu59zVK0+/NjJgmmeoWAqAMvLY3PBrnJnGN8/IEYKqzYLS6Nt5R/sByHW0QQh0DiST0QzM6TgzhCMEBuZ00HNykM6BPIPdHUn+0vHHFvacPl8rKlJkMBYZjM1ZY2nxps6Z8WRrE3z/tQM1Bylf/xKv8TQ13nA9ZDJQrCHm/aILYWnv9NdJZ7/LNsK1F8IDT46fd+E8eP11018nnf1CgBsvH+Sf764+vWs5p3pVoxikVCtKzws0kW+46WNGzTUUQugG0o+O1FvWuOVMRAhhKaMDqOOpsuT7SDdf8jxPP7297jrVY8eOZI2c7u7KazjMdnv37qWrq2vafj/Tff5yr75kPnfef8W4+VYu6oNTj7Nly+TLnOr21cjf10zT6L/lQ4cOsWPHjnHLW9WTob3tBnL5sUfxdrUXuG7dI2zZUmm279Fmc981m/4OJvM+X7/5Yr7+5OJx871o3TNs2XK47vPD7HovWk0r/O6fOnmS/Rs20L5jB6FQgLVrOXWiyJGdA/Rnulg1+Dz9mS6KBBbnD5ONRYYyHawZ3El39ij5U32czAb6V68mVygQjxxh33XXtWT/liGZ7qTw+FG6+npZmp269UojyVqkBYrJiL1QOP3/0+mhcHp/xP8pUAjFEfnHfr1APrVfCDNvHGlbLgnsZgvJXfrh9UwzxeRnybeN/Mwd3h9+vZANI44fPp9GG+wfYMeh8a93ZrqJ9DsXLw0snPtiXjg5fqBy86In2LKl9ocga73OHEsr9qWtbu/evWzbto05c2q7Id4s12xaw7e3jn875y037OLxx483oEb12bZtG3v37mXLVHzhVs0m275fd1UvDzw5/kKT33flCzz91N4JlVHJ7t276ezsbPm/S7BtT4frN57DP9991bj5Fs4dZEF4jC1bZt51bTM999xz6aSp+5JzFgsx2tA0WgjhHcDflyV9NcZ4Y4PK/kvg3WVJfxhjfH+d57gfeFFZ0ptjjJ9K5VkCHEgd2h1jrPmbTgjhtcDny5IeizFeWk9dq5z3A8BvT+jgl47zN/3AWhjcMaFTSxVd8hVY8Mqx8zz9Ltj/d42pj2aHDX8NK3527Dx7/gye/W+NqY9mh/k3w6VfHjvP0N7ks7au5bSlkVYArwVWAYQuOPeXINPNotxhLu57nAxFjmfP4UhbL3OK/bQVB8kf/TIHYz8HgQjkgU9QeSoRTbMA2Y42Mh1ZMh1tp/+f7Wwn29lGpj2b/Hv6/+1kO7Nk2tuS9I62ZH/42Er5O7JkOsvyt5ftt9U/pejNdzzC4v3HWfXsIeYf7aMtVzg9fWvXQI5Tczs5dc6ZWQx6TgzQc3KQga52jvfOOZ3/2II57Fq/mEPL5nH3LVO3vuvZZOedj/Lg797Z7Gq0rpW/Aus+NHaew3fAkz/YmPpoduh9LVz8+bHz9G2BBy8GnGZYUyQ7F160N/l3LA9fCye/05g6aXa48DOwaJzP0Wd+Hvb9dUOqc5b7wRjjHc2uRKszSKmKmhyk/AjwU2VJvxtj/K06z/E14GVlST8RY/ynVJ5VwM7UodkYY81XnCGEm4C7y5K2xRgrL75Sh0kFKSVJknRWWA5cVfq3AxgqbUuAeWMctwv4AjB1z9zrbPc64FqStSsuAs4BTgJzSNpbnmSdiwgEYCXJtEwHgT6S6WROAE+QPAX6ACOf5JQkSZJmobfHGD/a7Eq0Oqd7bYIQwp8CjRja8cEY4wcaUM5US49knMiw6PT8MJVGR1ZK66iSPplyJEmSpLrtw0CPGuPz2NYkSZKkKTbWs6UqMUipVnQytd9VMdfY0otEpM9ZLa2L+gKNtZQzEX8FfLLOYy4Fbi/bfzPgpO06m20APlu2/4PAtibVRWoE27xmG9u8ZhvbvGYb27xmG9u8ZhvbvGabC4B/Ldt3ruYaGKRUK0oH+nomcI70MaOChzHG/hBCAShftKUHODqV5UxEjPEAo9fLHFMIIZ20Jcb4+FTUR2pFFdr8Ntu8zma2ec02tnnNNrZ5zTa2ec02tnnNNrZ5zTYV2vxUDWg6qxmkbI47gUMNKOdrDShjOqSDc+dN4BznjnPOYQdJlvkpL2t3HeWsrLEcSZIkSZIkSZIklRikbIIY45eALzW7Hi1sa2p/dT0HhxCWMnKK2CHg2THKKg9Srgbur6O4dN2cXlWSJEmSJEmSJGkcmWZXQKogHejbEELoqOP4C1P722KM+RrLuqiOciqVZZBSkiRJkiRJkiRpHAYp1XJijPuAfWVJncDVdZzihtT+w2PkTb92fa2FhBDagBfVUZYkSZIkSZIkSZIwSKnWdWdq/9V1HJvO+7k6yrk+hNBTYzk3AHPK9p+KMT5V47GSJEmSJEmSJEmzlkFKtao7UvvvDCGE8Q4KIWwAXlGWlAM+Xy1/jHEX8FBZ0lzgR2qs47tS+5+t8ThJkiRJkiRJkqRZzSClWtUXgefL9tcC76zhuA8A5cHMT8UYj41zzEdS++8PIXSNdUAI4ULgrWVJReC2GuonSZIkSZIkSZI06xmk1LQLIawNIcTUtnasY2KMg8Dvp5L/KIRw0RjlvA348bKkAvDbNVTxb4GdZfvnA39SbeRmCGEe8I9AR1nyv8QYn6ihLEmSJEmSJEmSpFmvrdkVUHOFEF5V5aV0MLB3jLzPxhifncJqDfsI8IvAxcN1AO4NIbyHJCiYBwghLATeA/x66vi/qWWNyBjjUAjh/cC/lCX/HLA4hPDrMcanhxNDCDcBfwJcVpb3JPBbdf1kkiRJkiRJkiRJs5hBSn2pxnyXjZH3gyTTrE6pGGMuhPAW4OvAwlLyQuAfgL8MIWwDuoF1QHvq8G8D76ujrNtDCC8Dfr4s+c3AD4cQdgEHgTXA4tShReCdMcbnai1LkiRJkiRJkiRptnO6V7W0GOOTwE3AjtRLc4HLSaZmTQcovwy8JsbYX2dxv0gySrJcAFYDVzM6QNkH3Bpj/Nc6y5EkSZIkSZIkSZrVHEmplhdjfCSEcCnwayTTsPZWyfo08CHgIzHGOIFyisB7QwifIxkd+rIqWYeATwK/2WIjKA+S1Lt8Xzqb2eY129jmNdvY5jXb2OY129jmNdvY5jXb2OY129jmJyBMIJYjNU0IoR14MXAJsAgoAHuBB2OMj05xWecB15OMpOwCTpAEQr8eYzw+lWVJkiRJkiRJkiTNJgYpJUmSJEmSJEmSJDWUa1JKkiRJkiRJkiRJaiiDlJIkSZIkSZIkSZIayiClJEmSJEmSJEmSpIYySClJkiRJkiRJkiSpoQxSSpIkSZIkSZIkSWoog5SSJEmSJEmSJEmSGsogpSRJkiRJkiRJkqSGMkgpSZIkSZIkSZIkqaEMUkqSJEmSJEmSJElqKIOUkiRJkiRJkiRJkhrKIKUkSZIkSZIkSZKkhjJIKUmSJEmSJEmSJKmh2ppdAUmtLYTQBrwYuARYBBSAvcB3Y4yPT3FZK4HrgDVAN3AceAr4eozx5FSWpeYKIVwMXA2sALLAYeAx4P4YY76ZdZOmg20+YT8/e7Rimw8hdAEXAhcBS4Ae4Fipbg/GGLc2o15qvhDCBuBFwHlAB3AE2AJ8I8Y40MR6BeAq4ApgaSl5P/AISZuNU1jWIuAGYAPJ38YpYBtwX4zx8FSVo9bQam0+hNAObAYuBpYB5wAnSfrn7wGPxRiLja6Xzh6t1uabwX5+drHNS63B6/kaxRjd3NyasAHrgR8F/gS4D+gDYtl2T5PrNxf4PZIvhrHKtgV4JxAmWdYrgP8co5xB4B+Btc1+39wm9T4H4KeArWO814eA3wV6GlCfanWYyPaBccr6wCTPf1uz3z+3CbWxlmrzpTptn2RbvHGC5drPz4KtRdv8JcBvAV8DhsZp3/uB/wWcO4Fy7Odn4Aa8EfjuGO/LCeDPgcUNrlc78D7g+THqtgv4ZaB9kmVdDnyW5EHESuXkS69f1uz3y21K2lbLtHlgHfArwF2M/i6c3o4CfwFsmkA575hk/3xPs983t0m1s1Zq8/dMsi2+Y4Ll2s/Poq1V2jyT/95Z83Wy/fzs3YCVwA8B/w/wFZKHoMvf2+1NrJvX83VsTvcqNVAI4ZYQwp0hhIMkTzLcDvx34HqSESUtIYRwKckTq/8TWDhG1s3A3wFfCCHMn0A5IYTwIZKL9RvHyNoB/ATwWAjhh+stR80XQlgAfBH4CHD+GFkXAb8BfK80CmemGGp2BdRaZkGbr4n9/OzRam0+hLAyhPAo8CjwQeBlJF8Ux7IU+DXgyRDC26erbmq+EEJnCOGfgE+TPNlczVzgF4EnQggvb1DdVgH3A/+b5MZLNecBfwR8szRKfSJl/TfgO8AtVF8KJlt6/bshhP86kXLUfK3U5kt1+RbwLPAh4NWM/114PvALJNcJ7yuNSpCqaqU230z287PHWd7mvd+i00IIN4QQ/i2EsJskAPhvwP8AXkkyE0PTeT1fP4OUUmPdBLwOWNzsilQTQthM8vTJutRLJ0kCl08DudRrryEJVHbVWdyfkTw9W274SZIHSUZblOsBPh5C+KE6y1EThRC6SW5cvzr10hDJNI+PkkxBUG498J8hhI3TX8Mp8e/NroBaxyxp87Wyn58FWrTN95KMoqzkFPAM8G2SUZ+F1OvzgH8IIaTbrs4CIYQM8HHgx1IvFYDngIdJpgEut4TkWve6aa7bUpJR51emXuoHHgeeBNJTtF1N8rdU1/eLEMJ7gT9l9BIwe0lGYOxNpbcBfxZC+KV6ylHztWCbbydZTqSSgVKdHgCeYPSN6Q6SG35/MQ310lmiBdt8U9jPzx6zoM17v0XlriUZPXlusytSidfzE+OalFLrOEVyc7ZpSutPfpKRQdQXgPcAt8cYc6V8C4H3kow2GH7Y4TqSJ2Fr6uhCCD9C8vRWuU8BvxZjfLos383Ah4HLSklZkhuHD8UYt9f8w6mZPkyyFsKwIvD7wJ/EGI8AhBA6gLeV8vaW8i0BPhFCuDbGmL6BPBXSN9Nr9YvAD5btPxRj/F6d53gfyfzztdpT5/nVXK3a5tP2Az9e5zE1t1v7+VllJrT5x0imFL4beDiWrW0WQphHMpL3gyQjPYd9KITwZIxxIjdG7Odb168w8nMc4K+B340x7oHTN/t+kORL/+pSnjkk7fWSGGP6Rt9UuY1kDZlhA8D7gb+NMfaV6tYD/AzJ1MTDDwhuIpnd5JZaCgkhXE9y3V7uHuCXY4wPluW7huTp7leU5fvjEMK3Yozfru1HUgto5TYPyQ30fwC+BDww/J2zVK9u4IdJliFZU3bMu0v980SClf+bZIrZWh2ZQBlqrlZv88Pq/T76eK0Z7ednnVZs8z/GxGaMezPws2X7B4DP13kO+/nZ6yTJaOFmug2v5+vX7Plm3dxm00ZyMRBJ1nn8D5I1mW4BljN6DvV7mlC/n0nV4QXgojHyvy2VP0cN64SQPAH7XOrY/0uVtS1Jpvd5IJX/H5r9frrV1KYuIJn7vPy9u3WM/BeTXCCW539ns3+OsvplSEaAldfvl2o47gOpY25s9s/iNm1tpKXbPCPXBtk+jeXYz8+SrVXbPMkoykjy5PVLajxmFcn0g+V1e4Ya1gixn58ZG0kQOr1WzfvHyL+yQl/2wWmq2/elyhkCXj5G/lcwep3VV9ZY1n2p4+4AOqrk7Sj9HZXn/2qz30u3mttVy7V5kpuHEfh6qd1XvDZIHdNLMvq9vF5HgIU1HPuO1HHvaPb74jZ9Wyu2+bKy7ikvZ5p/D/bzs2Rr5TY/wZ/nG6m6fbiGY+znZ9FGsmxaLLX7/yQJ1L2Z5GGmG1NtYXuD6+b1/AQ3p3uVGutvgY0xxkUxxu+PMf5mjPGOGOO+ZlesNMLhN1LJ74sxPlHtmBjjvwD/VJbURnKTbjzvAtaW7T8NvCeWes4K5RwDfpKR0/38WAjhghrKUnN9kGRU1LCPxhhvr5Y5xvg4yeiTcr8dQhhvHbFGeRXJnPHDcsC/NKkuak1nW5ufKPv52aNV2/xB4IYY4xtijN+q5YAY4y7gTSQjQYdtYOz1VDWz/Coj16r5GvCH1TLHGHcDP51Kfk8IYVGl/JP0u6n9/yfG+LVqmWOMX2V03X9vvEJCCK8Fri9LOgy8K8ZYcb2nUvpPlfINe3kIYaIzUqixWrHNDwFviDG+NMZ4V7Vrg1S9jgBvZOTU4QtIRllK5VqxzTeU/fysc9a0+RDC+SSztJW7rQlVUWv7HMmDrwtijK+MMf5qjPFfY4w7ml0xvJ6fMIOUUgPFGB+PMW5rdj2qeA3JCIJh24G/r+G4D5A8gTHsLSGE+eMck74g+oMYY3o+7hFKwdKPlyVlgXfWUD81SQihl+Rm77BIbUHsvwfKLy7WkAQHW8E7Uvv/HmNMr6mnWeosbfMTZT8/C7Rym48x7o8xfmMCxz1MMttFuddMSaXUVKVpztJ9ygfGC5DEGO8G7i1LOgf4kSmu26WMnDL5FMlUZeP5ECODNteHEC4c55h0//yXMcaDYx0QYzwA/NU451GLadU2H2McijHeOYHj9pBMC1vO/lmntWqbbwL7+VniLGzz70jtPxTrX1pHZ7kY47YY4xOxbPmOVuD1/OQYpJQ07AdT+39f41Ot24CvliW1A6+rlj+EcB5wVVnSSeATNdbxI6n9dJ3VWl7PyLWP74kxPjveQaULjXSA/I1TWK8JKa1Zlq7HbY2viVrYWdXmJ8p+flY5W9v8van91RVzaaa5nmQd1GHPkky9V4t03/TGKahPuXRf94kY44nxDirl+WQq+Y3V8ocQOhkd1Pm7WipYId9rSzOxqHW1cpufKPtnjeVsbPN1sZ+fdc6aNl8KuP5EKvm2JlRFmiiv5yfBIKWkYa9P7dezyPSXUvtvqKOc+2KMpyrmHO0+oK9sf3MIYVONx6rxGtWmGuWtjFz4fSILuOvsdra1+Ymyn589ztY2fyS1P94MEZoZ0u31S7U8kDecN7V/YwihZwrqNKxRf0s3AuX13lrr1Fgxxu0kU3cPO4dkHR21rlZu8xNl/6yxnI1tvl43Yj8/m5xNbf5mXFpHM5vX85NgkFISIYRlwPKypEHgwTpOcV9q/4ox8qZfq3kqthhjHvh2HWWpua5I7dcz7d53SdrhsHNDCEuqZW6Qn0zt/3OpTUrDrkjtz/Q2P1FXpPbt589eV6T2z5Y2vzK1f7hiLs00V6T26+mb9pAshTCsA7ho8lWCEEIALksl1/O3lL4Ov7x0zkqumEQ5lcpKn0+t5YrUfku0+Umyf9ZYrkjtnw1tvl5XpPbt589uV6T2Z3KbT99vcWkdzRhez0+eQUpJAOm5rp+ptthuFU+k9jeGENoq5hxdVvrYessab55uNUEIoR3YmEqu+b2OMQ4C6fVbm/ZehxA2Ajekkm+b5Dk7QwgXhhBeGkJ4cQhhYwhhzmTOqeaZyW0+hLA4hHBFCOHlpX9XjXFBXAv7+VlgJrf5Grwstf/URE5iP99yWrVvWgOUt4tTMcadtR5cenK6fAR6DyPXmS/Xqr8DTY+z8f2eqv45G0LYFEK4PoRwXQjh/BDCOVNQPzXXjGvzIYT5IYTLStfhV4UQ1oQQspM45Yz7HWhSzor3u7S0zg+lkm+b5Dnt59VIXs9PUrUggqTZZXNqf1c9B8cYD4YQBoCuUlIHsI6RQ8inpKwK+dPnU2tYz8jPmP4JPAW3i5FP8m0GvjbZik3QO1L7D05yAfe/JPkddaXS8yGE7wJfAP5qvIWv1VJmYptfGkJ4gsoXpS+EEO4lmWLnUzHGQh3ntZ+fHWZimx9XCGEDo6e8mcjU3vbzLSSE0M3otetapW+abJ85fEz5eTYDlW6M2D/PEi3e5iekdBP7zankifTPvw78GckUZ+WKIYRHSaZc+4tap05Ta5iJbT6E8BDJyJv0AJKTIYT7gE8B/1h6sKtW9vOzxExs82P4EUYGePYzuaV17OfVaF7PT5IjKSUBLE3tPz+Bc+wZ55zD0lO51VvW7hrLUXOl35f0+1aLlnivSyPKpnoB94sYfeMakhv+LwY+AOwIIfzOJJ+kVePMxDbfTfWn5haSLPz+cWBrCKGedQrs52eHmdjma/EHQPlI4ieAByZwHvv51rKYke9rjmRt6XpMV3udiuvwWus22bJa8W9WlbVym5+o3wDmlu0fAv59AufZxOgb15DcH7sceB/wTAjh/5aCAJoZZmKbv4LK92XnAq8B/l9gewjhLXWc035+9piJbb6aqV5ax35ejeb1/CQZpJQEI7/sAZyawDnSx6TPOfykV/pGXL1ljVuOWkJD2lSD3MTIJxSHaMwC7t3AbwJfDiHYzlvf2dTm0zYAd4cQ/tt4Ge3nZ5Wzrs2HEH4ESN8I/PUYY5ymIu3nGyf9++2bwPs6Xe21kX9Lky2rpf5mNaZWbvN1CyFcD7w3lfx7Mca+SvmnQBvwc8D9IYQV01SGptZZ1ebLLAc+EUL43zXmt5+fPc6KNl+axeSlqeTbGlC0/bymktfzk2SQUhKM7pAGJnCO/nHOWS2t3rJqKUfN16g21QjvSO3/e4zx8ATOE0kWtP6fwKuB80imNOkCVgI/APwNo39XNwIfc6RNy5tJbf448AngXcA1wCKgHZhPMrLyXcDXU8dkgT8JIfzoOOe2n589ZlKbH1cIYTPwt6nkf4sxfraO09jPt65Wbq+NrNtky2qZv1mNq5XbfF1CCEuBjzHyIagHgL+o81QPA78HvA5YS/LzdJAEgV4N/BFwLHXMpcDnQgg99dZbDTdT2vwA8Dng3cD1JCNYOkhGfW0Afhy4k+Saotz7Qgjvr+H89vOzx0xp8+N5R2r/wRjjoxM818PYz6s5vJ6fJNeklASjpyMbmsA50uskVJoyodK0Z/WWVUs5ar5GtalpVVpY/U2p5NsmcKq7gH+JMT5V5fU9pe3fQwi/R3Ij5oay119P8kX2zydQthpjprT5XwG+EGM8WeG146VtC/B3IYQfAv4OWFB6PQAfCSHcE2PcV+X89vOzx0xp8+MKISwkuWE4ryz5eeBn6jiN/Xxra+X22si6TbaslvibVU1auc3XLITQCXwaWFWWfAJ4Wx3rZT8IXB1jfLDK6/tL25dDCP+L5NrnjWWvXw38DvDLdVRdjTcT2vyHgfuqPPCaA04CzwL/HEJ4Kcm1wsqyPP8rhPCFGOMjY5RhPz97zIQ2P6YpXFrHfl7N5vX8JDmSUrNCCOFPQwixAdsHmv2zTlD6qYuOCZyjc5xzVkurt6xaypn1WqDNN6pNTbe3MHoB9y/Ue5IY4zfGuHGdzvs88Crgm6mXfiOEMKfCIcI2X6sY4yerBCgr5f008FpGPmk3h2SUWDX28w1im58aIZmi+A6StWuGnQLeVM+oefv5ltfK7bWRdZtsWU3/m1XNWrnN1ySEkAH+iWS02bAC8GMxxmdqPU+M8Xtj3LhO5z0C/DDwb6mX3h1CWFnhELWOlm/zMcY7ar22iDF+nWSmhUNlyYFklNhY7Odnj5Zv8zV4JbCmbH9CS+vYz6sFeD0/SQYpJUHyxF65SiNhxpN+8qLSTfBKafWWVUs5ar5GtanpNtULuNckxjgAvB0oL2sp8H3TXbYm7Gxp8yPEGL8FfCiV/LbSjcNK7Odnjxnf5kMIbSRTH5ePaBwiCVA+MJ1l2883XCu310bWbbJl2T/PHK3c5mv1V8Cby/Yj8F9ijJ+bzkJjjEXgp4GjZcldjF6zWK3lbGjzI5SC8b+SSn5daQaIauznZ4+zoc2n77dMdGmdutjPaxp4PT9JBiklwegOaSJzsaePGdXJxRj7SZ5+nUxZ45ajltCQNjWdQgjrgZelkm9rVPmlL6V3pJK9ed26ZnybH8P/YWTfvZBkLctR7OdnlRnd5kvTS/0D8Iay5OEROnc1og728w2VbltzSm2gHtPVXhv5tzTZsuyfZ45WbvPjCiH8AfCzqeRfjjH+fSPKL420+btUsv1za5vRbX4M/wgcLNvPkMzGUI39/Owxo9t8CGEuyYjGcrc1qnz7eU0xr+cnyTUpNVvcychpMqbL1xpQxnQ4kNo/bwLnOHeccw47SLJodXlZu+soJz39QrVyZrtmt/n0+zKRaTOa/V7/JMmUOsMms4D7RN3NyDUxNze4/JnENj9NYoxHQggPAteWJW8Gvl3lEPv5xrDNT85fAW8r24/Az8QY/7WBdQD7+UY5RPIeD3+ut5OMXN1fxzmmq71OxXV4rXU7AKyfRFn2zzNHK7f5MYUQ3g+8P5X8OzHGP2lE+WXuBt5btm//3NpmbJsfS4yxGEK4h5EjvMZqi/bzs8dMb/NvYWSwZEJL60yS/bymitfzk2SQUrNCjPFLwJeaXY8WtjW1v7qeg0MISxk5vHyIZMH3amWV37xeDdxfR3Hpum2p49hZowXa/LMkU9gNf850hxCWxBgPjnFMWtPe69ITiG9PJd/WqPLL7ErtL2lCHWYE2/y028XIIOVYbdF+vgFs8xMXQvgQ8HOp5PfGGNNPUzeC/XwDxBj7Qwg7Gbnu0Wrqu5E3Xe01fR2+agLnSB9TrW5bgZeU7dd1zV8hv/1zi2rxNl9VCOEXgD9IJf+fGONvT3fZFdg/zyAztc3XqJ62aD8/S5wFbb4pS+uk2M9rqng9P0lO9yoJRndIG0II9Sy8e2Fqf9sYFxfpsi6qo5xKZbVEZ6qRYow5YFsqueb3OoTQycgng6Cx7/UrgLVl+xNawH0K5FL77U2og2pwFrT58dTTFu3nZ4GZ2uZDCL/J6PWdfjvG+KfTXXYV9vON06p90w6gv2y/J4SwplrmtFLeOWVJpxh9021Yq/4OND1m1PsdQng78Oep5L8D3jOd5Y7B/nnmmVFtvg5eh6uaGfl+hxDWAS9PJTdkOu8U+3lNFa/nJ8kgpSRijPuAfWVJncDVdZzihtT+w2PkTb92fa2FhBDagBfVUZaa6+HUfs3vNUn76yzb3xtjbOQUBO9I7X+uEQu4V7A8tV/PCCU13sOp/ZnU5sdTT1t8OLVvP3/2eji139JtPoTw34HfSSX/cYwxndZI9vON83Bqv56+aQUjH17KAU9MvkoQY4zA91LJ9fwtpa/Dv1c6ZyUPT6KcSmWlz6fW8nBqvyXafJXyfpgkIFm+1MIngP8yRnuebvbPM8/Dqf2WbfN1ash1eIn9/MzycGp/prT59NI6340xPtagssvZz2tKeD0/eQYpJQ27M7X/6jqOTef9XB3lXB9CqHWR3xsY+WTJUzHGp2o8Vo3376n96WpTU6rUHpu2gHvKS1P71Z6kUmuYkW1+PKURb9emksdqi/bzs8eMafMhhJ8GPpxK/n9jjO+bznJrYD/fOOn2+qrS9O61+L7U/n/GGE9OQZ2GNepv6R6SJ7OHnV/rU94hhLXAprKkE6XzqXW1cps/LYTwWpIZS7JlyXcCPx5jLE5HmTWyf555ZkSbn4B62uI92M/PJjOuzbfQ0jpgP6+p5fX8JBiklDTsjtT+O2u5uAkhbCCZGnNYDvh8tfwxxl3AQ2VJc4EfqbGO70rtf7bG49QcnydZr2zYjSGE9NR+o5Ta3TtSyY18r99M0i6H7QP+o4HlAxBCWMDoYOndja6H6jJT2/x4fpSRgcNB4L5qme3nZ5UZ0eZDCD8K/A0jn9j+Z+Dnp6vMWtjPN9w3gENl++uBG2s8drr7pvR1+FtCCHMr5iwTQjgHeEsquWrdYowDwF2p5J+qqYaj8/1HjHGoxmPVHK3c5gEIIbwC+BRQvtTIfwJvLk0r3hSlmR3S66XZP7e+lm/z9Sr9jWxIJVdti/bzs85MbPMvB9aV7TdlaR37eU0Dr+cnwSClpGFfBJ4v218LvLOG4z7AyJt+n4oxHhvnmI+k9t8fQuga64AQwoXAW8uSijTvaSvVIMb4AvCZsqRA0l7G81OMnHZkB/DlqapXDVphAXeAPwIWlO0PAV9oQj1Uoxnc5qsKISwHfj+VfFeMsW+cQ+3nZ4GZ0OZDCG8A/pGR33s+A7yjySN0wH6+oUrv922p5N8e76G8EMLNwMvKkk6QTEM5lXX7HvBAWdJc4FdrOPRXgfKR6t+KMY43VVu6f/6FEMKSsQ4IISwF3j3OedRiWrnNl8q5hmSkQHdZ8reAW0o34Jrp/cDGVFpLBK1UXau3+XqVZiL5s1TyozHGZ8c51H5+lpihbT59v+Vzpe8UjWY/rynl9fwkxRjd3NxaYCMZURDLtnsmca61qXNFYG0Nx/1c6pgXgIvGyP+2VP48cH4N5XSQ3JAsP/b/AqFK/nkkHX15/o82+z1zq6ktXgQUUu/drePkP5LK/64ayplQm69wnjUkgZHy81wyyd/B+4Gr68jfBvxxhZ/n/zT7/XSr6f1ryTYPrAA+CPTW8bOsJVmfoLyMInBVDcfaz8+SrVXbfOmYG4H+1DFfBDqm+HdgPz9DNmAxyY248t/7+8fIvxJ4LpX/d2soJ/3e3ljDMd+fOmYIePkY+V9RylN+zM01/h6+mTruDqC9St4OkkBSef6vNfu9dKtta9U2D1xMMvqn/JiHgAVT/PP/PHBTnce8l9HfBz7b7PfSreb3r1Xb/P8Bzq3z57i7Qjk/VOPx9vOzZGvVNl/lHHOA46nzvGGSP7/9vFv5e3tj6n3dPolzeT3fyPeu2RVwc5ttG/CqKtuHUp3FI2PkXT9OGWsrdKZra6hbO/BY6rjDJPPFt5XlWwj8LqNvSv5lHb+HWyvU8ZPAplS+m0q/i/J8J4B1zX4v3Wp+r/8m9f4VgN+hLFhSanvvIAmMp/8O2mooY0JtvsJ5fit1ju9Mwc9/T+lc9wH/Dbik0s8EzC/9XTxU4Wd5BljU7PfSreb3vOXafFn+EyTTXL6JKjdKSJ4o/T3gaIUy/qSO34P9/CzZWrTNX8LomyB7gVuofn1VbbthnLrZz8+gDfi1Cr//vyrvE0lG3r6R0Q9b7KaGIEqF899YY92+mDquv9Sm5pTl6QH+O6MD8HfW8Tt4KaOv4/+T1EMowNVl7Xt4ywMvafb76Fb71mptnuTBqd2p/CdL/WO9/fOrxqnXbaXzPwz8OnAV0Fkh3xzgB4CvVvhZDgEbmv0+us3cNl+WfwD4NPBjVLmGAVYBv0JyzZIu49N1/A7s52fR1optvso53p46x15q+B4wzjnt52fhBtxA5euCX069t/uq5HsVYwzKKZXh9XwDt1CqrKQGCSFMxR/dB2OMHxijjLUkT0aVWxdj3D7eiUvT7X2dJBBZ7iSwjWQ6nnUkNxvLfZukw+4fr4yysv6K0etBRZLFqg+SjGpbnHq9CLw1xvivtZaj5gohzCG5ELwm9dIQSTsdJFk7IT1X+yGSG8NP1VDGWibY5lPneYaRa3781xjjX9RzjgrnvIeR67ZC8jM/DxwjuahYRHIDvtI07PtInrx6ejL1UOO0Ypuvkh+SB1EOkARzukluHFabJuSTwI/GOqbItJ+fHVq0zb8D+PvxzlujHTHGtWPU7R7s52eMEEKGZDqvN6ReKpDcuDtGcq27IPV6P/DqGGPVNXnLykhf778yxnhPDcctI3kqel3qpX7gWZIpldcD6emztwHXxRgPjldGWVm/CvxhhZf2kNw0PJfkMyHtl2OMH661HDVfq7X5EMKNJDfRpkSMseq0hiGE2xg9tWCe5DrkGMnnVC9J/5z+fgvJQ1OviTF+cyrqqsZotTZfJT8k1997S/VpB5aR9L2V3EvSFuu532I/P0u0Ypuvco67SR5QHfbHMcb31XOOCue8Dfv5WSeEsJ3kXsJk/EOM8R1jlOH1fAO1NbsCklpLjPHJEMJNJBc45R3+XODyKod9GXhLPRfMJb9I8jThe8rSArC6tKX1Ae/0xvXMEmPsCyG8hiTAUX5B2gFsrnLYdpL1aMa9cT1VQggvY2SAcjoXcO9MlVXN50na/IFpqoemwUxp8yWLSttYBkmeSv2TWP/Tbfbzs8AMa/ONYj/fomKMxRDCW0iC2D9a9lKW5IZBJYeBN9dyE2+SddsfQnglyXV4+XV3N8n0mJU8TPK3VPMNjVJZHwohFEhubGTLXjqXyjfJC8D7Yox/Wk85ar5WbvNN0sboG4eV3A/8WIxx2zTXR1NsBrX5eaVtLEWSNax/I8aYq+fk9vOzx0xo8yGE1cArU8m3TVNx9vNqGq/nJ6bSk7ySZrkY4yPApcAfkKwbVc3TwH8Bvi/GeHQC5RRjjO8luaF57xhZh0imJ7wkxtj0BexVv5gshP5q4GdIprSr5gXgfwGXxhgfbUTdyqSfvpuqBdx/H/hr4HGSC4LxnCS50f+KGOPrvXE9M7Vgm99PMsXIZ0r/r8UOkmlf18cYPzyBAKX9/CzSgm2+keznZ5gY40CM8VbgzSQ3Bao5RTJd2kX1jhaYqBjjDuBFwP8geQq6mj3ArwIvjjHummBZf0wyAvpOkhvhlRSBfydZd/VPJ1KOmq+V2/w0+7/AnwIPArUEeAaA/yCZEvA6b1zPXC3Y5n8G+BjJ6K5a7CNZx3JzjPF/1BugHGY/P3u0YJtPezvJw6rDvhtjfGwKzms/r5bj9Xz9nO5V0phCCO3Ai0nWV1pEcvNtL/DgVN9cDCGcB1xPMrqmi2TahaeBr8cYj09lWWquEMKlJGsFrCB52ucwyXqo90/0C9hMUJoS8SKSqUZWkIxQzpCs/XcEeAJ4NMZYy01uzSCt1uZDCCtIRritJpludQ5JoPAIyfSvD8QYx7qYnmi59vOzRKu1+Uaxn5+ZQggbSa53V5KMAD4KPAncF2McaGK9MiTryFwOLC0lHyC5+fhgPdNv11DWYpK1bdaTrJNzimTaqftijIemqhy1hlZt89MphNABXEgyuuZc4BySqf+Ok/TPTwEPxRiHmlZJTZtWavMhhEUkbXENyTILPST3WY6QTIX/UIzx2Wko135+FmmlNt8o9vNqRV7P18YgpSRJkiRJkiRJkqSGcrpXSZIkSZIkSZIkSQ1lkFKSJEmSJEmSJElSQxmklCRJkiRJkiRJktRQBiklSZIkSZIkSZIkNZRBSkmSJEmSJEmSJEkNZZBSkiRJkiRJkiRJUkMZpJQkSZIkSZIkSZLUUAYpJUmSJEmSJEmSJDWUQUpJkiRJkiRJkiRJDWWQUpIkSZIkSZIkSVJDGaSUJEmSJEmSJEmS1FAGKSVJkiRJkiRJkiQ1lEFKSZIkSZIkSZIkSQ1lkFKSJEmSJEmSJElSQxmklCRJkiRJkiRJktRQBiklSZIkSZIkSZIkNZRBSkmSJEmSJEmSJEkNZZBSkiRJkiRJkiRJUkMZpJQkSZIkSZIkSZLUUAYpJUmSJEmSJEmSJDWUQUpJkiRJkiRJkiRJDWWQUpIkSZIkSZIkSVJDGaSUJEmSJEmSJEmS1FAGKSVJkiRJkiRJkiQ1lEFKSZIkSZIkSZIkSQ1lkFKSJEmSJEmSJElSQxmklCRJkiRJkiRJktRQBiklSZIkSZIkSZIkNZRBSkmSJEmSJEmSJEkNZZBSkiRJkiRJkiRJUkMZpJQkSZIkSZIkSZLUUAYpJUmSJEmSJEmSJDWUQUpJkiRJkiRJkiRJDWWQUpIkSZIkSZIkSVJDGaSUJEmSJKlBQggfDiHEsu0LdRy7KoSwK3X8709nfSVJkiRpuoQYY7PrIEmSJEnSrBBCWARsA+aXJb88xnjvOMctBu4FLihL/usY489PfS0lSZIkafo5klKSJEmSpAaJMR4G/iCVnN4fIYQwF/g8IwOUHwd+YWprJ0mSJEmN40hKSZIkSZIaKITQBTwFrCpLfkOM8c4KeTuAO4FXlSV/EfiBGGNuWisqSZIkSdPIkZSSJEmSJDVQjHEA+M1U8u+FEEJ5QgghA/wTIwOU3wDeZIBSkiRJ0kxnkFKSJEmSpMb7KPBI2f4VwFtTef4SeEvZ/qPA62OMfdNbNUmSJEmafk73KkmSJElSE4QQXgP8R1nS08BFMcZ8COF3GDnachvw0hjjvkbWUZIkSZKmi0FKSZIkSZKaJITwJUZO5/qzQCfwZ2Vpe0kClM82sm6SJEmSNJ0MUkqSJEmS1CQhhCuAB4Hh9SiPAvPL9o8Ar4gxPtrwykmSJEnSNHJNSkmSJEmSmiTG+DDwz2VJCzgToDxFsgalAUpJkiRJZx1HUkqSJEmS1EQhhNXAM0B7WfIQ8AMxxruaUytJkiRJml6OpJQkSZIkqbnexMgAJcCXDVBKkiRJOps5klKSJEmSpCYJIbwduI0zU7wOi8C1McbvNrxSkiRJktQAjqSUJEmSJKkJQgi3AB/hTICyWP4y8KGGV0qSJEmSGsQgpSRJkiRJDRZCeAXwcaCtlJQD3gA8UZbtphDC6xpdN0mSJElqBIOUkiRJkiQ1UAjhKuAOoKuUVATeHmP8AvAbqex/GELwu7skSZKks45fdCRJkiRJapAQwvnAfwDzypJ/Kcb4MYAY46eBb5e9dgnwjoZVUJIkSZIaJMQYm10HSZIkSZLOeiGE84D7gNVlyR+IMX4wle9m4MtlSbuBTTHG/umvpSRJkiQ1hiMpJUmSJEmaZiGERcBdjAxQ/mU6QAkQY7wbuLssaSXwnumtoSRJkiQ1liMpJUmSJEmaRiGEucBXgGvLkj8G/FiMsVjlmBcB95clHQc2xBgPTVtFJUmSJKmBHEkpSZIkSdI0CSF0Ap9hZIDyi8DbqwUoAWKM3wY+XZY0D/it6aijJEmSJDWDIyklSZIkSZoGIYQs8Engh8qS7wdujjGequH4i4BHOfOAcQ64MMa4barrKkmSJEmN5khKSZIkSZKmx98wMkD5JPC6WgKUADHGJ4CPliW1A38wddWTJEmSpOZxJKUkSZIkSZIkSZKkhnIkpSRJkiRJkiRJkqSGMkgpSZIkSZIkSZIkqaEMUkqSJEmSJEmSJElqKIOUkiRJkiRJkiRJkhrKIKUkSZIkSZIkSZKkhjJIKUmSJEmSJEmSJKmhDFJKkiRJkiRJkiRJaiiDlJIkSZIkSZIkSZIayiClJEmSJEmSJEmSpIYySClJkiRJkiRJkiSpoQxSSpIkSZIkSZIkSWoog5SSJEmSJEmSJEmSGsogpSRJkiRJkiRJkqSGMkgpSZIkSZIkSZIkqaEMUkqSJEmSJEmSJElqKIOUkiRJkiRJkiRJkhrKIKUkSZIkSZIkSZKkhjJIKUmSJEmSJEmSJKmhDFJKkiRJkiRJkiRJaiiDlJIkSZIkSZIkSZIayiClJEmSJEmSJEmSpIYySClJkiRJkiRJkiSpoQxSSpIkSZIkSZIkSWoog5SSJEmSJEmSJEmSGsogpSRJkiRJkiRJkqSGMkgpSZIkSZIkSZIkqaEMUkqSJEmSJEmSJElqKIOUkiRJkiRJkiRJkhrKIKUkSZIkSZIkSZKkhjJIKUmSJEmSJEmSJKmhDFJKkiRJkiRJkiRJaiiDlJIkSZIkSZIkSZIayiClJEmSJEmSJEmSpIYySClJkiRJkiRJkiSpoQxSSpIkSZIkSZIkSWoog5SSJEmSJEmSJEmSGsogpSRJkiRJkiRJkqSGMkgpSZIkSZIkSZIkqaH+fyrPWISnqshHAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 13.3 s, sys: 195 ms, total: 13.5 s\n", + "Wall time: 13.7 s\n" + ] + } + ], + "source": [ + "#functions\n", + "f = lambda x,y: np.sin(30*x-y/30)+y\n", + "g = lambda x,y: np.cos(x/30-30*y)-x\n", + "#search domain\n", + "a = [-1,-1] #lower\n", + "b = [1,1] #upper\n", + "#time\n", + "%time roots = yr.solve([f,g], a, b,plot=True, plot_intervals=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of roots found is 363\n", + " Maximal residual for f is 7.549516567451064e-15 \n", + " Maximal residual for g is 8.659739592076221e-15\n" + ] + } + ], + "source": [ + "# show the number of roots (should be 363?)\n", + "print(f'number of roots found is {roots.shape[0]}')\n", + "\n", + "# print the maximal residual\n", + "print(' Maximal residual for f is {} \\n Maximal residual for g is {}'. format(np.max(np.abs(f(roots[:,0],roots[:,1]))),np.max(np.abs(g(roots[:,0],roots[:,1])))))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So 363 real roots were found in this domain. \n", + "\n", + "Let's plot them and the zero loci:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 2840\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [ 8.1338 15.8803 0. 0. 75.9859]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the zero loci and common roots\n", + "roots = yr.solve([f,g], a, b, plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Bivariate example #3\n", + "\n", + "Here is a more complicated bivariate system on the region $[-5,5]\\times[-5,5]$.\n", + "\n", + "$$f(x,y) = \\sin(20x+y)$$\n", + "$$g(x,y) = \\cos(x^2+xy)-\\frac{1}{4}$$\n", + "\n", + "Notice that YRoots correctly avoids points that are nearly roots but are not roots." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 6668\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [25.09 6.8236 0. 0. 68.0864]\n", + "CPU times: user 7.58 s, sys: 107 ms, total: 7.69 s\n", + "Wall time: 7.78 s\n", + "452\n", + " Maximal residual for f is 2.1562449393905654e-14 \n", + " Maximal residual for g is 5.934142066621462e-14\n" + ] + } + ], + "source": [ + "#define functions and search domain\n", + "f = lambda x,y : np.sin(20*x+y)\n", + "g = lambda x,y : np.cos(x**2+x*y)-.25\n", + "a = [-5,-5]\n", + "b = [5,5]\n", + "\n", + "#solve and time\n", + "%time roots = yr.solve([f,g], a, b)\n", + "\n", + "# print the number of roots (should be 452)\n", + "print(roots.shape[0])\n", + "\n", + "# print the maximal residuals\n", + "print(' Maximal residual for f is {} \\n Maximal residual for g is {}'.format(np.max(np.abs(f(roots[:,0],roots[:,1]))),np.max(np.abs(g(roots[:,0],roots[:,1])))))" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 6668\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [25.09 6.8236 0. 0. 68.0864]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Solve and plot\n", + "roots = yr.solve([f,g], a, b, plot=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is an ill-conditioned system from [this paper](https://link.springer.com/article/10.1007/s00211-014-0635-z) on the domain $[-1,1]\\times[-1,1]$.\n", + "\n", + "$$\\Pi_{i=0}^{10}(y^2(4y^2−\\frac{i}{10})−x^2(4x^2−1)) = 0$$\n", + "$$256(x^2+y^2)^2+288(x^2+y^2)−512(x^3−3xy^2)=27$$\n", + "\n", + "One of the challenging things about this function is that $f$ is that it takes on a huge range of values. It written in such a way that the even very small outputs are accurately computed. By default, we assume that the approximation can only accurate to within machine epsilon $2^{-52}$. Because $f$ is accurately evaluated to very small values like $10^{-40}$, we overwrite this assumption with `trust_small_evals=True`." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 1320\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [23.8636 4.0909 0. 0. 72.0455]\n", + "CPU times: user 1.78 s, sys: 24 ms, total: 1.81 s\n", + "Wall time: 1.82 s\n" + ] + } + ], + "source": [ + "#functions\n", + "f = lambda x,y: np.prod([y**2*(4*y**2-i/10)-x**2*(4*x**2-1) for i in range(11)],axis=0)\n", + "g = lambda x,y: 256*(x**2+y**2)**2+288*(x**2+y**2)-512*(x**3-3*x*y**2)-27\n", + "#search domain\n", + "a = [-1,-1] #lower\n", + "b = [1,1] #upper\n", + "#time\n", + "%time roots = yr.solve([f,g], a, b, trust_small_evals=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "YRoots takes 10.5 seconds to find the roots of this system. Let's plot it and caluclate the max residuals." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 1320\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [23.8636 4.0909 0. 0. 72.0455]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(54, 2.8545806960740226e-26, 2.842170943040401e-14)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "roots = yr.solve([f,g], a, b, trust_small_evals=True, plot=True)\n", + "#show the number of roots and maximal residuals\n", + "roots.shape[0],np.max(np.abs(f(roots[:,0],roots[:,1]))),np.max(np.abs(g(roots[:,0],roots[:,1])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using YRoots for optimization\n", + "\n", + "YRoots can also be used for optimization problems, since the common roots of the partial derivatives of a function are critical points. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Rosenbrock function is often used as a performace test for optimization algorithms. It is well suited to be optimized with YRoots. \n", + "\n", + "$$f(x,y) = (1-x)^2 + 100(y-x^2)^2$$" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "#define function\n", + "f = lambda x,y: (1-x)**2 + 100*(y-x**2)**2\n", + "\n", + "#partial derivatives\n", + "fx = lambda x,y: 2*(x-1) + 200*(y-x**2)*(-2*x)\n", + "fy = lambda x,y: 200*(y-x**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#3D plot of the function to optimize\n", + "x = np.linspace(-2,2, 1000)\n", + "y = np.linspace(-1,3, 1000)\n", + "X, Y = np.meshgrid(x, y)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "ax.plot_surface(X, Y, f(X,Y))" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 299\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [24.4147 0.3344 0. 0. 75.2508]\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 789 ms, sys: 25.6 ms, total: 815 ms\n", + "Wall time: 818 ms\n" + ] + } + ], + "source": [ + "#find common roots\n", + "low = [-2,-1]\n", + "upp = [2,3]\n", + "%time zeros = yr.solve([fx,fy],low,upp,plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, array([1., 1.]))" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#evaluate at critical points\n", + "values = f(zeros[:,0],zeros[:,1])\n", + "#report mimima and minimizers\n", + "mimimizer = np.argmin(values)\n", + "values[mimimizer], zeros[mimimizer]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nick Trefethen's Hundred-dollar, Hundred-digit Challenge problems include finding the minimum of the function \n", + "$$f(x,y) = e^{\\sin(50x)} + \\sin(60e^y) + \\sin(70 \\sin (x))+\\sin(\\sin(80y)) - \\sin(10(x+y)) + 1/4(x^2 + y^2).$$\n", + "\n", + "(Problem 4, [here](https://en.wikipedia.org/wiki/Hundred-dollar,_Hundred-digit_Challenge_problems))" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "#define function\n", + "f = lambda x,y : np.exp(np.sin(50*x)) + np.sin(60*np.exp(y)) + np.sin(70*np.sin(x)) + np.sin(np.sin(80*y)) \\\n", + " - np.sin(10*(x+y)) + .25 * (x**2 + y**2)\n", + "\n", + "#partial derivatives\n", + "fx = lambda x,y : 50*np.cos(50*x)*np.exp(np.sin(50*x)) + 70*np.cos(x)*np.cos(70*np.sin(x)) - 10*np.cos(10*(x+y)) + .5 * x\n", + "fy = lambda x,y : 60*np.exp(y)*np.cos(60*np.exp(y)) + 80*np.cos(80*y)*np.cos(np.sin(80*y))- 10*np.cos(10*(x+y)) + .5 * y" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#3D plot of the function to optimize\n", + "x = np.linspace(-1,1, 1000)\n", + "X, Y = np.meshgrid(x, x)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(1,1,1, projection='3d')\n", + "ax.plot_surface(X, Y, f(X,Y))" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 27365\n", + "Methods used were ['quadratic_check', 'Base Case', 'Macaulay', 'Too Deep', 'getBoundingInterval']\n", + "The percent solved by each was [ 1.5969 10.4623 0. 0. 87.9408]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 45.1 s, sys: 633 ms, total: 45.8 s\n", + "Wall time: 46.4 s\n" + ] + } + ], + "source": [ + "#find common roots\n", + "low = -np.ones(2)\n", + "upp = np.ones(2)\n", + "%time zeros = yr.solve([fx,fy],low,upp,plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2719, 2)" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#how many zeros it found\n", + "zeros.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.3868184378651449e-11, 1.5284995491526843e-11)" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#maximum residuals of common roots\n", + "np.max(np.abs(fx(zeros[:,0],zeros[:,1]))),np.max(np.abs(fy(zeros[:,0],zeros[:,1])))" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-3.3068686474752376, array([-0.02440308, 0.21061243]))" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#evaluate at critical points\n", + "values = f(zeros[:,0],zeros[:,1])\n", + "#report mimima and minimizers\n", + "mimimizer = np.argmin(values)\n", + "values[mimimizer], zeros[mimimizer]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "YRoots can solve systems in higher dimensions as well. Moving forward, our goal is to increase rootfinding feasibility for high dimensional systems.\n", + "\n", + "Here are examples of YRoots running on systems in three and four variables." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Three variables, Domain $[-1,1]\\times[-1,1]\\times[-1,1]$\n", + "\n", + "$$ f(x,y,z) = \\sin(5x+y+z)$$\n", + "$$ g(x,y,z) = \\sin(xyz)$$\n", + "$$ h(x,y,z) = x^2 + y^2 - z^2 - 1$$\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ideas\n", + "\n", + "* memoize slicers the dumb way -- seems to work well\n", + "* memoize slicers better-- fully incorporated, not something to iterate through later -- NOT GONNA WORK\n", + "* value_arr memoized function for dim and degree\n", + "* only every declare it once and do it with the largest array possible" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "#define the functions and the search domain\n", + "f = lambda x,y,z : np.sin(5*x+y+z)\n", + "g = lambda x,y,z : np.sin(x*y*z)\n", + "h = lambda x,y,z : x**2+y**2-z**2-1\n", + "a = -np.ones(3)\n", + "b = np.ones(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 6441\n", + "Methods used were ['constant_term_check', 'quadratic_check', 'Base Case', 'Macaulay', 'Too Deep']\n", + "The percent solved by each was [72.8614 26.9058 0.2329 0. 0. ]\n", + "CPU times: user 25.1 s, sys: 388 ms, total: 25.5 s\n", + "Wall time: 26.8 s\n" + ] + }, + { + "data": { + "text/plain": [ + "(6, 7.657137397853899e-16, 6.021648860932705e-16, 2.220446049250313e-16)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#solve and time\n", + "%time roots = yr.solve([f,g,h], a, b)\n", + "#show the number of roots and maximal residuals\n", + "roots.shape[0],np.max(np.abs(f(*[roots[:,i] for i in range(3)]))),np.max(np.abs(g(*[roots[:,i] for i in range(3)]))),np.max(np.abs(h(*[roots[:,i] for i in range(3)])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The code below graphs these level surfaces and their common roots (code is from [here](https://stackoverflow.com/questions/4680525/plotting-implicit-equations-in-3d))." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: No contour levels were found within the data range.\n", + " \n", + "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:20: UserWarning: No contour levels were found within the data range.\n", + "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:25: UserWarning: No contour levels were found within the data range.\n" + ] + }, + { + "data": { + "text/plain": [ + "(-1, 1)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Fzp07Z2kPdmP7ifRhCc9+sNlsRKNRotEooDywcrmcFRO6rj+wO7Il+ny6YOoZnzVIKdnc3MRms5FKpVhdXeXChQuEQqE9z9U0jXPnzrG8vGzFQ6VSwev1omkad+/exe12W6Xbo8Bms9HV1UVXVxeg/H7MmFheXsYwjD2dYK2Y2B/PHeExDIN6vU48HieZTOLxeHjllVdUC20+j1hchHAYPB4olaCrC5HL4YzHMc6dQ0smj+9gdB1tYQGpaeB0Ym827ytwq1arOzrBTIFbs9mkUCjQ1dXVEri1cGSYMbG2tsbm5iZ+v5/XXnttx8pxN46T5NwPdrudSCRCJBIBsBYpD+qONFfknZ2drZh4wqhWq9RqNUvM/izAzOosLy+Ty+UIh8Mq+79VYt0OcwG9uLjI8vIy9XqdmZkZy4/KMAwmJyex2+1cvXoVj8fziY7N4XDQ0dFBR0eHdawHdUcGg0ErJkxTxEgk8tzGxHNDeHa3ES4vL9Pe3s6lS5fUEyoVxMQEJBLIv/pXVaanXEY2m2jr6xh2O3J4GLG2hqzVwOf7xMfkWV2FQADj/Hm01VWoVBTR2sJhBW5er5d8Pk8oFKJeryOl3KPwb9V3W9iN7TGRSqWIxWJ0d3dz7ty5Q78eHh/5AfZ0R0oprU6w7d2RbrebSqVCMBikXq9bx9kSfT5+tLe3P7b58UmxPSaSySTJZJLBwUFGR0cPfE06neZP//RP0TQNr9dLOByms7OTwcFBPB4P5XLZOi8vLCxw5syZ+y4mjor7dUfOz89b3ZEOh4NGo4Hf77di4nnrGH4uCM/uNsJiscipU6fuPaHRQMzNKbLR3w8moUkmEfPzsHXSlCbJKZfhgFT/YSGSSVzJpHrPcBhiMUSlcl+P9oMEbuvr61SrVa5fv47D4SAcDhMKhfD7/TQaDeu1LffbFkxsNxGcnp6mXq8zODiIy+U61OuPQ8NzHBBC7OiONAzJ7GyVQiFBtVri2rVrO7ojTdGn+dqW6PPx4Fn4Xs1MZ71eZ2pqCsMw6O/v3yFM3g+pVIq+vj4r2z4wMMDm5iZtbW04HA42Nzfx+XyMjIwwOzvLwsICo6Ojh/pOHiau9uuOLJfLVrfwtWvXcLlcVkyYlinPgxD6RBOe+7URJhIJKpUKGIbK7KRSyDffRMzOqlJWKIRYXUX29SHHx9E+/hi2SkiiUvlkB5bPo8Xj1CMRRZzMzE65fKRhTIFbZ2cnpVKJCxcuUKvVyGazBwrcTPt/aIk+n0dsX8Hmcjnu3r3L4OAgfX19rKys7Ngy5X54FHNFSvmJxm00YHFRY3XVT6HQw5kzDc6dO7OjO3J2dhabzbYjJmw2m/XeLdHn84f9TASHh4fp7e1lfn7+vqSjXC5TKpUYHx8nHo8zNTWlritb0HWdXC5He3s7fr+fgYEBlpeXicVi9Pf3H+r4PukcNDuGI5EINpuN06dPW51giUSCQqFgZU53d0fCyRJCn1jCs8dEcGNjRxuhEAJpGLCwAJmMEijb7UifD1EqKRLSbCK7usDnQ+g6FIs4Uyn1/IdFrYbtxg20zU0qg4PqWKpVKBYRUj7ULnzbLxQul2uPwC0fi5FdX7cEbts7wZrNZkvg9pzgIBNBU1CqaRq6rh9qrKclw2OiUIClJQ1dh74+g2Ty3ufY3h0JKiay2SyZTIalpaUd28SYnWCtmHg+cJCJoNvtBh48t9fX19E0jUgkgtPpZHp62jLklFJabeVmuSkSiVCtVllfX8fj8VjatMeB7deJ7d2RAPV6nWw2Szqd3rc7cjvReZYXyieO8OxrItjevqeNUAiBLR5HeL3I06cR6bTK7Hg8cPcudHcjt7wThM+H1HW0u3fBZlME5WFgGGhbk8loa1NbujqdqpyVz4OuQ72uHjviZz5o0jkaDaLz80QbDfSf/El0eKDAbW5ujqGhIWtH+OdV4HZS8CATQRNHIS5PE+FJJAR372oUCvATP6FTLAqSSYmmHRATDgednZ2qK5N7q/DdMWEuClwuF9PT04yNjVkr3VZMPNvYntXZz0TQhKZpB2Y9dV0nm83S1tZmkYCuri7K5TLpdBopJW63m87Ozh1lsd7eXiqVCisrK7hcrgeWzI4L97tOOJ3OI3VHulwuJicnOXv2LJqm7dHGPa04UYRnu2HaDhPBfVoB7ek0zrt3YXwcrlxRWZtyGUolRD6PcfYs2O2IQgHh9eLc2EBEo1SHhwkJ8VDERMzPQzqNfuYMzM+jVatIlwvb4iL66Cii0VDHcFyExzDQlpaQXi/YbFCpYPP7Hyhwq9fruN1u2tra8Hq9LdHnM4zDmAiaOCpxedJZnWYTFhcFhYIgEJAEg+D1qmyPlBxIeHbDZrM90P6/Wq1aJMjj8bRi4hnG9kznQSaCJu4XEzabjZGREUv3ZurJPB4Pd+7cYW1tzSoPLS8v02w20XUdXdep1WpMTU2Rz+d5/fXXd2y0+6hwlJLxYbojq9UqiURiz96RT7MQ+sQQnnq9Tjqdxu12WyaCr7766v5q+FwOezxOKRiEQEA95vEg4nEwDGQopB4XAtJpWFxEGAayvR3d4UDmckcmJmJ9HW1+Hux26OiAhQVspRJavY7UdeTYGOLuXSVcPqJXw0ETWczOIlZW0F96CW11FVGtIncF9X4Ctw8//BBN01hZWaFUKu0RuO0n+mwJoZ8uSCktPZfT6byviaCJ+61md+NRZXgOO3+KRZiY0CgUBC+/bNBoQDyuXlsqgWEcnOF5EPbrjnz//fcRQuyx/w+Hw9aeYM+D6PNZhunsnc/nsdvtB2b/t+N+ZV5TD5nNZqlWq+RyOaanpxkcHGRjY4M/+7M/Y3x8HI/HsyMr6HQ68Xg8dHZ2kkgk9mw/8ajwSTRy+3VHvv/++9b2Gdv3jjRjolarPXUx8cwTHjM1Wa1WuX37NkKIeyaC+6FcRnz0EVoqRXVLQ0O5jAS0uTnkSy/dExBHo5DJoBUK1Do6wOHAcLmQoFrWD0tMCgXE6ipGNIqQEup1DIcDz8oKcnQU+vpUOcvtVtqhh/gO9kygdBqxvq5Im9ttZXgeBCEEmqbR399vjVmpVMjlcocSuD3L9d2TAnO7lFKpxN27d5FScvbsOSKR+3cWHrWktfs9HxfW1wVra4JaTRAMSiIRSTwukFKVt+bnNYpFwdDQ8cw9s8Oxv79/R3dkLpdjbW2NQqGwpzvypIo+n1WYmc58Pm/FxEHZ/+04KCYKhQIffPABLpcLn89n6b7MbVMuXrxIPB6nVCrxxhtv7PnNTYF0X18f5XKZ5eXlnZ3D2477uPBJmwK2w4yJgYEBa+9IUwi9urpq7R25X8cwPLmYeKYJz/Y2wsnJSWq1Gm+99dbB6cF6HTE3h4jF0Gw2tFxOkYFsVpEDTUP29qp29HIZ2WggNjYwBgepbpESTddV2/rW7ukPRL2O9uGHaKurND//eZVFKpfRSiUc2SxydBSRSKjMjsPxwNb0/bBnItdqaIuLyLY28HpVZsfjOdLY28czBW6m6PNBArfdQmhN06wLgCkGbOHRwNTqmCnzWq3GuXNvMTPjIJuV+P0qeenxKC68HZqmPbSG53Gg2YTpaUE2K+jpkbS3SzIZYf0tFhMIAaGQRNP0Yz2u3Von0/7fFH3WajVyudyB3ZG7RZ+aplm3w1oBtHB0bN8upVqtMjU1RaPR4NOf/vS+JoK7cRDhSSQSbG5uMjIywsWLF3E4HNRqNWq1GtFolHA4TLVaRdM0yyNtO3K5HAADAwMUCgXW1tbweDxWs8nuYzgOHCfhgYNjYr+9I/frjmw0GvvGhJkFexR4JgnPdsFZJpNhcnKSU6dOWaxyX+i68toxDIyrV5Fra9Q7O0FKxJ07EAoht3vwrK8jajWk34+IRGiUyyQSCdXGXi4jPB7k2Nj9D3RLpCwaDfB60dbWlMFhMokoFtG9XmRHByKVUq3qa2tQrSpd0RGMqXZMZCnR7txB+4u/QH/jDXA41HtubiphwzFgj8Ct0aAwP09mfX1fgdutW7eQUvJHf/RH/Jt/82+O5Rha2IndJoIzMzMMDw+j6zqJhANdl9Rqgq3zLDYb+P27CZA4UknL1AWZGqHj+AwHoVRSLefLywK/XzI8LInF1JwvFmF2VqNcFgwOGlSrMD19vCf3B8Hlcu2Iif3s/7d3gk1MTJDP5/nhD3/I1772tcd2nM8TzEynruskk0nm5+cZGhpiY2PjUGQH9i/z1ut1CoUCL774oiVQ7urqsnx4VlZWyGazVgloZWVlT9Yvl8vhcDgsklCpVFhbW8Ptdu+7dcVx4LgJz4NwUHekaZy7vTsyEAhw+/ZtlpeXWV5e5l/8i3/xSI7pmSM8+5kImm2ES0tL+79ISsTUFGJ2FuOVV0AIxOYmBkAmg1hYwPgbfwORSKhSls+3w4Mnu7ZGUdexVasQi7EQCOBcWcExOUkoGrXa9nZDrKxAqYR+9ixaKqU6vNbWECsrGIODNEolVU5zu9Hm55EeD/j9qvRkaosOge0TWcTj0GhgjIygFQpIw0AUi4hGQ11QajU45hWlI5ul4+5d2gcHkVeuYBiG1QmWTCYpl8t84xvfsLIO4+PjDxzz7/7dv8t3vvMdotEot27d2vN3oT7w14H/DSgDX5ZSXjvWD/aMwMx0NhoNy0Tw6tWr2O12lpeXcTrVdDp9WtJoKFHvwgJcv65x6pRE0xQBajbt1Gq23Ybf+yKZTFIqlbDZbFSrVSYnJ3E6nbS1tVlp7OPSJGxsCGIxgcMBp04ZGIaa61JCNqvIDqi/RSKKCD3uk/tu3M/+Px6PUygUePfdd/F4PMzNzXH69On7jteKh6NhPxPBV155BcMwSB5he6D9MjwbGxsAjIyMsLKyQjwep62tzfpdo9EoGxsblmYns2Vjcv78eSuLWiwWd2y1MTg4SK1WY2lpiTNnzjySTPhxxsTDlNoO6o5MpVIsLS2Ry+X45je/SVdXF0tLSwwNDd13vIeJiae3f2wXTLZupo4/+ugjvF4vV69effDk2CIe0u8HTVNlHk3DsbCgCIDdjojFVPamVFLlrWYTvbOT6fV1EqurBKSky24nGAzS+7nPcfr0aQJbm8pdu3aNa9euMT8/TzqdRtd1xMYG2sSEyth0d4PNhtHdrcpNsRh6T4/amLRSUYRkcxM5MqLE00fU8VgTOZtFLC4iOzthYAAjEkGUSmjXr2NEIsrR+ZOaJu5Graa22+joQGxlpTRNIxwOc+rUKV588UU8Hg9vvfUWAL/7u797qGG//OUv8yd/8if3e8oXgLGt2z8A3v0En+KZxPaYyGazfPzxx4TDYV566SVcLpe1OvV6JWaIOBzK67K/H0ZGJBcuSE6dkoTDklrNxs2bLj76SHDzpmBuDtbXd06ZZrPJrVu3SCQS+Hw++vr6CIfDnDt3jhdeeAGv18v6+jp/+Zd/ybVr11hYWCCTyTxUBkjXYWFB8OGHGvk8nD1rWKU4XYeVFUEyqREMSk6fNqzPqLSkT5bw7IZp/z88PMylS5cIBAK8/vrrNJvNQ2U9W/FwOGyPic3NTT7++GM6Ojq4fPkyTqfzSMJ82Et4DMNgc3OTUCiE0+mkv7+fZrPJe++9RzabJRgM0tnZicvl4pVXXuHKlSv09vYyMzPDD37wAz744APu3r1rbXy7ubnJxsYGqVQKr9dLOp1mYmJih0HscX43xxUThmF84rHMMm8ikSCTyRAKhbh8+TK1Wo1/9+/+3QNf/zAx8UxkeMysjq7rLC4u7jERvC+SSUQqheztRWQyKjfe0YGWyeBMJpGf+QxEImq5mEpBLgenT1Pu7OTujRu0X7jAkN3OrRs3kH19NAHN48HhdNLp89GxVa80dS2pVIrFW7fwr60RtNvxNZt4t5Tp2sIC9PZiBALIQADD4VBXlHweGQggw2FVejqi47KUEtFsos3NIRIJjLExaDaVWaLDoXyDpFSZrmr1gTqeo7B3cecOIpPBOHtW7TLfaKir6hYMw8Bms9HZ2cnbb7/Nr/7qrx5q3LfffpvFxcX7PeWngW9KdbDvCyHCQogeKWX80Af/DGO3iWAmk9lhIgj3auz7aCEt/Y7DobI57e0QDOosLzfo6JB4vVAsqhKYqTX0eAqsrt7lzJkeLlzo40c/+pH1PiaMblEAACAASURBVFJKnE7nDtNLMya21/DNDJCpazkI5bIqYdXrEIkoYbL59GoVpqY08nlBV5fByIhkdVXpd3I5mJ5Wr3uSq9n7Qdd1Kxv2+c9/nl/8xV984Gta8fBg3C/7b+KohGf3883SpNmuXalUiMViuN1uBgYGuHnzJsVikXA4TKFQoF6vW4LmXC6H3++3hO6lUgn71iLa3DKo2WwyMTGBz+c79J52h8VxZ3g+SQa32WyyurpKJpOhXC5bnV3hcJif+Zmf4Ytf/OIDx3iYmHiqCc9hTQQPRCaDiMVUN1VvL7JWU2LkYhEtn0e32WBsDG7eRHZ1IbJZxMwMCbeb1UaD8f5+fOPjiP/6X7GXSuqifuMGwvTg2SZctnQt4TBaJkNjeJhsMEhueprpDz4gMDtLe62G49VXVY22WgUhsN24gXHpkso+lctHFheb35MrHrd0SKJeV5mi27eR4TD6uXPqs9VqR255vx/ExgZaOo202ZBerzJSrFR2EB5d17HZbNYGdseIPmBl2/3VrcdO9An+IBPBV1999b4ns9VVRWwCATV1FcG5RyJA+dbYbCoRqHi8pF6HyUnJtWspHI44Q0MvkM97mZiAtTU3Q0OqW2o/UrBb6/UgAgSKpKRSgokJDY9H8sILhtVuDsoua3lZY3TUYGjIoNFQfzMMSKUEuq5ht8unbjW7HdtjwtQ3HAOey3iAnfq1QqHA7du39zURhKMTnt0ZHrvdzqlTp6jX61y/fp21tTWGh4eZnZ3lBz/4AaVSCbfbTTabxe/3W8aC58+ft7Q8Pp+P8+fP09bWZhn85XI5CoUCuq5bWVK/379jAXMc39NxxsTDEp5cLsfKygrNZpNwOIyUkkgkQqlUolKpHOdn3hMTTy3hOYqJ4L4olRBLS0qvk80iS6V7G4LOzCDa2mjabEoc7HLB5iZNt5u1ep2K282LV69iTySQS0sIXacRDCIjkXulo1gMdguXDQNtcREtkcA+Nkbk1Cm0apX+jg7I5Sjmcqy53az95V9STyTwzcxQLRTQrl7FDqq8VSopFaZhqPLbIaDF4ziXl5Fvv62yOeWyKtNlMhhjY4itbTOYnUVbW0MfGbnv936ooNgqZRmRiGq1Nzt2arUdZM3M8JRKpeMW4+13kM/GlswPie2ZzgeZCG7H/DxMTgrMa6vLpQTLgYDS7pj8VJUi62znpVLWiMWmCYUC/PRPv0CjoVEoSPJ5mJ31EQxq5PMeCgUHvb2CQAB8vnsltO14EAFqNiWplJdQqECxGODUKSVpU58dlpcFa2uKCI2PKyKUz6sS1vKyYHNTcPq0QTgMCwtPz2p2N8yLRblcPs5FwHMXD7Az07nfFkK7cVTPqO2di7VajZWVFWsPOnNvLIfDwfDwMOvr6zSbTYsUjY2NEdimxfR4PMRiMaSU9PT04NkSygUCAWtfrVu3bhHc2qz6z//8z+nq6iISiVjlsk8yD5804dF1nZWVFTY2NggEAoyMjJBKpbDZbAQCAarVKqVS6Tidp/d82KeO8Jgr2Ewmw8LCAs1m8/4mgvuhVlMdWQ4HcmQEcfeuysY4nZYY2Th7Fn70I6jXkZpG6eOPmbbbGXzrLQbGxiAYhI8+Uq7L/f3IYlF1ZkmJY2ZGlcGczh0iYLG6qkTKIyPKqNDtBsPANjODbGsjGI3iP3UKTQgKCwvEw2HSTifZ//W/sEmJJxolaLMR0DSVKTnMybBcxpZMIptNcLuRQqiur2YT6XIh29oUCTHNnh5Apg4bFGJyEm1xkeanP41YWUFUq4hEAhkMwjYPJF3XrZN7X1/f4X6/w2EVGNh2vx9YO843eFpgxsTGxgaxWMxy/X311VcP1W0iJYyMwNCQpFBQouVkUjAzA6GQmmZ+v7qot7fXMHlpKpViamqKvr6z6HoHmiZxudR0b2+H7u4aIyMGyWQDp9OgWBSoRkONaFRapMrvl/vq5LcToEoFPv44S6mUor09Sam0wuSkgZQe1tc7KRaDRKOCSEQSCimSJuW98laxKOjpMRgakiwtCY5Tw/NJVrP7wczwHDPheW7iAe7FRDweJ5VKUSgUDpX9P+qc2N65uLy8zLe//W3C4TBvvvkm/f39eDweixTduXOHmZkZTp06hdPp3EOsent7LU3blStX9ryX2T5/6tQpuru7mZubY3FxkUajQSKRYHp62vJ7amtrOzIBepKEx/Q/unHjBqOjo1y5cgUhBJlMxkpiPI6YeKoIz/Y2wnQ6zcbGBi+++OLBJoL7wWw/lxJ5+rTy2XE6lflfowG6rspXbW0YUmJks8SvX6cei3H2H/wDvNmsKi15vYg7d5CnTsFnPgN37kCthr1QQGs00N94Q7WRl8vgcikn5bt3MYaGFBlaXYVGQ2lyajWMy5fRlpbU2PU6ro0NGBigu6ODbk3DWFigOj3Nxvg4q+k09fffx3fqFG1tbQd2gaHraIuLGE4nen//zg6znh6lW9ravkKbnkaaJK1aVR78++AwE1mkUohyGSMYVCkCTVPfebOpCOCOQ3wkJ3eAbwP/lxDij4DXgNxJ1Ctsz3Rubm6yvr7OSy+9ZG2DcPhxVEnL41F+mm1tkslJQTQqaTYhnRaUSjaWlz24XJJUah4p87z22lW8XhcHJQucTmhr02lvb9LebrC4KFhYUFmeYlFs7bMrsNsV8QkG9xKgzU2l15mf9zA0ZOPTnx5mYkLD56tjGFkmJsrUaht4PAWkjJDPh9B1L+m0neVljfFxg+Fhg2bzXvfWc0h4not4gL0xkUgkuHLlyuGz/0eAmRGq1+vcvHkTv9/P1atXOXPmzB4fmv7+fvL5PPV6fd/f1Waz4ff72djYoFgs7vFfKhaLSCnx+/0IITh16hTJZJJMJsNLL72E0+m0GhQehgA9CcJjGAaxWMwipaOjo1arfiAQwDAMIpEIlUrlscTEU0N4TF2C2UbYaDQIhUJHIztSwtwcbGlVxPQ08uJFRV6mpyEaVRkfKRFeL3qzydQf/zGhcJjht99WGSGvF7G6iqjV1LLY5UKsraE1GojFRbRmE93vV1mMeFxpghwOxNqaMjYslzH6+xFg+f7ItjZoa4PlZUQ2q3QvTieNzk7rPW1OJ36bDfenPoUWi9EIBEj7/TuMzMyJbe1eu7CANjlJc3BQvXe1qtrQy2WMzk7V7VWpKN+gbBb9wgXVHl+pPDzhqdUQy8sqc1SpqBJatYpIp5EDA4hmc9+S1lEn8s/93M/xZ3/2Z6RSKfr7+/m1X/s1/v7f//tfUT+z/F3gu6h2w1lUy+H/cejBnwFsN0yr1WqWO2wkEjky2dE0ldTb/Zjbraax16veb3NTcvOm5Ic/nGRgIER392kmJwVeL9uyNYrjCgGdnTX8fkmpdK9M4HIpDdDwsAQk1aoSP3/0kYbDoTI0IHA61Zhqigq8XsnAQBOv19j6/LC56cRujzIyIhgYMDh9usaNG0XW19N897uLLC4GCAZ9RKN2isUwzaaNalV1djWbTy/heZiS1n7x0Gg0+OpXv/qV5yEeYKd+rVarcefOHTRNs4z+HgU0TaNWq/G9730Pj8fDO++8w/r6OplMZk8cut1u6/mGYezJ8EgpsdvtdHZ2sra2RjgctjJIZmnO5XJhs9mobS0ce3t7uXXrFvPz85w5cwaXy7WjMcAkQMlk8oEE6HHr2orFIsvLy9RqNUvk3dHRgdPpZG1tjfX1dUuwXSqVHktMPHHCs5+J4PDwMJFIhJs3bx5pLM/6OobLhTYyos7ImYy6uBcKiHQa4+JF1b2UzbKeStHY2OB0Tw/ez34WsbKidD5ut2pj7+3FuHwZkcuB04l3aQktFELv7ESz21Umw+uFXA4tkwGnE9nTo97X61VGgqmUMjNsNNRmo263yrS0t6P39KDVashAwHJFli+9pNye3W4ctRqdw8MW4TNNmzY3N5mbm8NZKNBZLBLSNAwp0TweJdJ2OpFbTnJSStWZVi6rx8JhteXEfTq1HnRyF3NzaLOz6G++qa6ihQIimwWbTQmmtzJbpjDELGmVSqUjEZ4//MM/3PPY3/t7f8/qad9S3v+fhx7wGcJ+JoKjo6MEAgGmpqaOPJ4QKvnXbN7zs9zmU2m+K4VCArs9yxtvnOfChQClEhQKcmu7BkFXl+XqgN8vcToNy7DQPLnbbDu3mHO7we2W9PUpZ+RIRBGkVEppblZWNAYHDep1weamDV23USopTY7dDhcuqPdwOFQJzO+PkEp10tsriUQaGEaBTGadyclVMhk3S0t+KpUQTqeBEMdDUh6VaPkoMbFfPAB85Stf+V042fEA+5sImgTgQP+1Y3jPeDxONpulq6uLUChkZXGSyeQewmOaDbpcLjY3NwkGgzSbTatzrFKpkEgkcLlc3L17l3g8vu+CfnuMZ7NZyuUyH374ITabjbFdZrdHIUDHOY/vd50wDIN4PM7KygrBYJCxsTHLJqC9vR2v18vS0hJLS0v8lb/yV4CdMXFYDc/DxMQTJTymOVSz2bTaCK9cuYLH47F2lT004nFcxSJGVxdae7siGACrqyrjMTYGXV00UymWpqaoxuN4NQ1fV5fyrdkq08hCAVGrYUSjSsdTKiF7etAqFWg0qJ07h2NlRV1B0mnlpOxy0fzUpxBtbao1u1JBy2QwAgGM4WG06Wn1/EwGkc8rB+Rbt7DVaqrslUxivPEGIpNRWZONDUSxqByXtyboDtOmahXjxg3yHg+b+Tyb8/M0QyFs8/O4z57F19urjtflUr48Q0NIl0tlrdzu+7a9328ii1RKfTfd3ao06HajmVqpUAhptyuVWLW6g/A8opLWicR2E8GZmRlqtRpXr17F5XJZK8ejwuORpNPKW0d5WypyouuK8DQaDWsfukDAj98fQAiVzfH7QQiJrgtGR+UWCRKsrwsWF334fBr5vIuuLhsuF3R0SKLRg0WhHg9UKsr12eGAN99sYhiCdFrJ7HI5NxsbdmIxweXLOj6fJJ9Xz81mYWFBQ9cFp0/rbG7aqVbbGRsLk05rVKsGbW0ZbLYNNjczLCwoEaS52j2su+5+v8mjKmkdo0DzxGJ39t80EXQ6nRSLxYeKiQehXq9z69Yt7HY7Pp8Pp9NpmUiaxnj5fB6/30+5XKZcLrO+vk6j0SCVSrGwsGA5zgPW5qEjIyPY7XbcbjeFQoFIJILH47EWDdvnqJTScmJeXV1laWmJYDC47/YTJu5HgNbX10mn0+Tz+YfSAG3HQTFRKpWYnZ3l448/JhKJcPbsWfx+v0X0fD6fVZr3eDyW+aKu69jt9kceE0+E8OzXRtjb27ujjfBI7YPpNCIeRw+HMWo1WFxU5iP1OmJmBs6cgdFRclvCqdN2OyNOJxNtbUpvU62qUtbsLKKtTe2nZS5ldR0xO0ulvx/j1ClFGJpNtKUltJUVyOcxTp9GgGopbzbRJieRXq8a2+sFIdRu5bUasr1diYtdLmyJBGIroyMjEVWCKhTUmb9WU7fd7S5Sos3OYl9epu3Tn6at2cQ9NQVuN167nXWbjcWlJWzz8wT9fiKZDI7XXsOxuamyXbmcCq4DvsoDVwH1OmJqChkMIrq7FanRdUQ6jf7SS6q7DFQGrVpVGSVahOew2B4TZhfIwMDAjk1c77dz80HQNI2ODoM33xQUCqq8tLkpKBZVFqVYzJPLTXL+/ABDQxHu3JnaswOJECoc/H62BM0Sw4D33ze178oAsF7XtsLmnljZ51OvHxw0sNtVuWljQ9DWpgwPFS9WQuS7dw3SaXA45FbFVbC0pLG4qFGtSsJhqFYlp08rLdDmpspazc6qz9TZaeP119uYn2/HbncwNKQ2ONzY2LDa4E27/wN1cQf8No+K8Bxn2/FJw/aYSKfTTE1NMTw8bO3VBEoXcxzbmmzH5uYmk5OTVpfV5OQkFy9etLaO0DTNMgyMRqNWdtNutxONRhkZGbHEuOPj45bh4faSUl9fH3fu3MEwDDo7OymXy0xNTTEyMrKjm9VcAJlE6ajbT2wnQOb+blJKkskkMzMzO6whjuKOvjsmzGxYMpmkWCwyOjpKW1sbi4uLDA4OUigU6OrqIhaLqS2agHPnzlmNSLqu43K5HnlMPHbCc1gTwUOn3opF1X4eCNDo7kZu6WhkrQabmwjDQB8eZnFujo3VVS5evEjgBz9AAmXTka1UUhf0ZBJjYABCITWGxwPxOLhclE6dApsNrVDAtrioSlOBAAwMqCtBqQTt7Yh4XL3niy+qctKWY6ZYXMQYGVEeOeUy0uXCvVU6s8TFHo8iS+Gw5cuzm/CIWExtHdHXp8b2eNCyWWx+P4HRUXynT4OmoU9PU6jVyGoaybt3cRQKtAEBIfB7vXsMAk0cdHIXy8toiQR6VxfS6VRi7FIJ6XQio1HEwoLy5VlaQg+HrU6tR+jDc2JwGBNBeLiTu7lw8PlsW01/Eikhn5fo+jLJZI7BwRcpFl1MTOjE407W19XPZ5KV/UJR08DjMejuhra2Gm63HY/HIBYTLCwIOjpUDGuaEjA7nconJ5FQWaZXXjGscZtNmJ8XpNMakUiT8+cN7t7VOHvWIBiUTE8LikVBraam7OKi0gCtraltJ8bGoKfHwOtV5oOmaNnhUKvy3W3wpi7usAToUWh47HY7lUqlleE5APtl/3ebCIKKiYfJ8OynZzEMg5mZGfL5vPVelUqFcrlMMpkkn89T2lrYCSGoVCpWucjhcOByuSgWi5w7d47V1VUqlcqO47116xadnZ10d3dbGftkMklXVxeVLTvz/XYNqNVq2Gw2xsfHWVhYeOjtJ0xz0HA4vMMcNJPJ7OuNdT8CtD0mzN3eK5WKlbECGB0dZWZmhomJCZxOJ7lcjmq1itPppLe3d0emyhzPjI1HhcdGeD6xieB+qFYR8/PgciGHh3F/73vKatXjUa3ooRDVri5u/vjHtBUKvOL3oyWTKusSjWLk80p0kEohMhnVRh6JKDKQy0EspkS4XV00txT1tloNrV63tDiGywWBgMrOpNOIUgkjGlVbLWQykM+rNnFdR46PI27dUmWrXA6tUsEYGrK6vUS5rLxzLlxQ5aPdBoSZDOLmTdgqw4lqFcPvx7axgQiHMfr7oVJBhsM4NzZoGx2lrauLnkiEZrlM7S/+gnRnJ6upFNUf/YjA1rYAoVDImmT7ndzF5iaiUEAfHETouspkxeOqjNXTo7Q8hoG2tISxa+VhTuBj9lc4Edi+gi2VSty+fZtoNHqgieBRPURAEZ54XJLLCS5eVK+tVitMTU3Q29vGW2+9AGgUi5JcDny+BpWKYHr6XlanWlXiYsOA/TiBEPcMC2s1VU2+cMFAWUoJ1tYEU1Madrsqr2maIip+vyJfi4sa5TJ0dhp0ddUtx4RyGXI5DbdbMDqqdj/PZlUGqVSCyUmNRgPa2yW6ruHxQEcHrK0JDGPvBe0gH6AHEaBHVdKq1+uPbFfoZxVHMRGEh8t6mnG0fbxSqcTExATRaJSrV69af4vFYvzgBz/A5XIRCATo7u4mGAzicrmsklc4HGZpaYlsNmvFZ3t7O6VSiXw+TzAYpFqt0mw2cWxbZHZ1dZFKpYjH49a+W7s7t0C5Obe1teF0OhkeHmZqaor5+XnGx8ePVKbdj+Q9rDu6WQlYW1tjfn6eUCjEyMgIPp+PiYkJuru7sdvtnD59mqmpKdbX13n55ZcZHBwkk8lgt9t3XA/MmHjU28E8FsIjpWRjYwOfz8fa2trRTQT3Q7OpylW6jhwdhZUVhMOBrmmIjz5CnjnDxvnzTMVijI+P0yEl4i/+Arq7Mc6cUSUYKcHhUOTl1CmVralUlHYnn1fkpbcX4XZj6DoIgXNqCkMIjHPnlAJ0fV2RokoFUalg9PRAV5cSK0iJNjMDdjuyq0tdMTweWFtDVCo0twxFpMdjbXshfT5kIKCOb7vWptFQpoZLSyqr4nSqklIyiQSMjg5F9DIZRdgMAyMaRZTLUKngyOVw+f1433oLbWGBejRKxukknU6zsLCAEIJwOLwvu5ZbRIzTpyGbRVaryp9odFSRtFpNibsrFeS5cztE0WaqslqtWsy/BXXCMA24Dmsi+DAnA3VBMCxJWyKRYG5ujrGxc+h6O7WampLBIAQCgosXc7z22j2/nmJR6XXW1wVuN4TDqlwVCGBlU/YjYna7IkClEmQyAiEkZ88qcXIyqTYDNQz1/54eSVubJJe7t1LPZmFpSaO/XzI0ZBAIQD6/0/rq7FlVJms0BLouWV/X+B//w04spk7kPT12NE0d636Lxv0IkLmZodkZ2dbWduwnYfPkbv4+LSiYMeH3+w9lIghHd07e/hrzu4/FYiwtLXHhwoUdpSIpJWtra9jtdgYGBvZoZ8LhMOl0mr6+PgqFgtVtZP6tUqmQSqUIBoPW49uz3DabjY6ODuLxOJqm4XK5qJuBClYJrVKp0NPTA2CRntnZWRYWFjh9+vSh5+dhurQO645eKpXY2NhgbW0NXdd55ZVX8Hq95HI5AOt7rFQqFItFhBD4fD78fj+rq6t7PNnM5hbzcz8qPBbC02w2uXPnDna7Ha/XezQTwf1gGCqzUywiL15Um4Hm82hCoIfDyGaT2NQUKaeTq5/6FC4lHlDi5Y4OlcUpFpWw1zAU0RgeRsRiqpQVCqkMxuCgusivrKBttX275+aoDw0p3U46rUpM+bwa+4UXkKdPq4yOEEp4nM9jXLp0rwQkBLalJYzhYZr5vMoMuVyImzeRo6Oqxb1SUSQol1PEQUrE4iJoGsbVq6o1fHMTsbEBg4PUu7pwmWRqdhbh82F0dSkBsduNNjuLdLkwzIB1OHA0m3T29e3oAsvlcsTjcXK5nLUfTDgcJuTz4WxvV4JsKdESCaTZf6zrkEwqTU8opB7b1qllntx3C/Ked5h75rjd7iOZCB4VSjtgsLpqsLw8h8dT4+WXX8XrdTA1JXC5pLUzuhACc7EcDqsbSEZHVbeWSYC2zmmsrnpYXhZ4PHbMjZ/Nc1W1CisrKnPjdKo1gJpqyoXZ9Onp6JDY7coHaHnZjsvlZmFBY2VFMDRkMD5uMD2tbYmrIRZTJbEzZwyCQUWmbDZJsai2wejrM2hvh9u3BTduuGg01EnUFGur28EEaPtuzmZnZCwWo1Qqkc1md6T7nxYR9EmBGRNOp/PQ2f/tTsiHhUl4ms2mJdbf75qUyWTweDxEo1FLg7IdHR0dZDIZkskk9XqdaDRq7cQuhMDj8bC2tmb9u76+TiAQoNlsWpYTjUaD+fl5UqkUp0+fttrRtx9DuVwmlUpRLpex2+1WadvU4AwPDx/qcz9MW/puAlSr1ZidneXWrVuUy2WklJZjcqPRwOl04nA48Hq9lEolrl+/jsvl4tKlS1SrVeLxOEKIPR1u2xcBjxKPhfCk02mKxSLnz58/HrfdpSVFds6evec97/PhnpigpuvcbGujo7OTSx0diLk5laGw25HRqDr7brFs7+oqDA8ju7tBCCVcTiZVN5PHo4z6tsZ3JxIq41KpoKu+XGSxqNybpURGo8jBQUVUpFS6omJRCZT7+xU5KhZVq7qU6L29Sh+k8vaIRkO1qm9sKO+cQkGpMut11b1VLCoClsmojFKphO2DD9Db22l2dOCp1RSZWl9HP3MG0dNjaYDE+roiXdWqyr5o2p7WdIfDQUdHh2V81d/fTy6XUxmgXA5HqURXKkU4FsP9wgtovb1K7G23o83MqO/K40HabDs6tVon9/2xsbFBtVrl7NmzR/OaOiKUJUCR5eVFLl5sJxQaZWVFUK8rDYzTyZYpoCphxWJeFhZUR5YJc4d1dY5SxKJQgL6+MjabJB53kEo5KBY1ajXFdysVDa8XhocNHA7N2g/Lbpfk84rA9PZKurrU+xQKsLqqk0g4kFJgGFCrCSYnNRIJgcMhicU0dB3GxnR6eiSzs2rMZFJpfMJhyeXLBvPzGslknb4+nbExg1JJCbY3N1V7PSgCpkTV97bY2A1TZ2GWWLq7u3eUwMwM0FEJ0PaT+9O0o/uTRiKRoFKp8MILL7CyskKxWHzgtikPA03TyGQyzM7O7hFBb8fGxgZut5uuri7y+TyNRmNHSSoQCOB0Oi2vmVKpxNraGl6vl4WFBRwOB+l0mnq9TrFYxG63YxgGTqcTm82G3dxUWtNwOp2MjY3tyTCZC6L+/n6LJDWbTfx+P7Ozs9y8eXPHZqb3wyf14alWqywvL1Ov1xkYGMDn81Gv1+np6SGdTnP79m1isRiXL18mFAqRSqUolUqcO3eO8fFxJiYmmJ2d5cyZM3vIpelb9KivFY+F8Ph8PiKRyEOVsPb8SCsryjNnbEwJeldXlWdOo4GxuMiCzcb4z/6sCpRqFTkzo9yIr1xBBIMqg7PlvOxKpZCf/ayVfcHrVeP19KiyV7OpdD2VCoG5OeTgIM2hIaTdrjIWiYR6XVsb+uXL0N2tlqHlMloyiQyF1DEahiIe8/NIhwN6epTw2O1WhoV2uyIMmqYIUzKpslj1utIXra8rIXMkoshYPI5wOJS5Yr2OVi6j+f1oiYS6am0RF5HJQKGgNvfs7ERs7cyurW05zp85s0eRatZmTQJktmI2Gg0KCwvk43FWZ2aod3YSLBYJCUGwWES+/DLa+rr6/Fvt9TIQaJ3cD0AgECAUCj2SE7oJKSWlUonNzQVeeukSr7/uwWaDUkmyuQnxuOqYUjuLK9KTzdoxq73mz1UswsYG9PebfjgqSXrmTImXXzaYm6tvlcYk09MaU1MaDge8/rpOoaD2u9J1VaYyDI329ntlKhOVCmSzNiKRJmfP6szMaFtCZFhd1cjlbDSbqu29WBSk05JyWVlm1WqCaNSgv19amRspwWYTW631kq4u9X3cvCkolQSaJtnYUKU6uOctZGaAtnOX7TGxXwbofuag+0HX9VYs7INgMEggEEDTNKanp1lYWOBTn/rUsS4IpJRUKhXm5uZ46aWXDuwIKpfLlEol+vr62NzcBFS2JRqN0mg0yOfzzpKlbgAAIABJREFUFAoF1tfXuX37Nh0dHfj9fmq1Gm632zII7ezsxOVyEY1GEULsS0xMA0K/37/n75VKhVKpRDQa3Xc+rayssLKygtvtfmBTyCchPGtra0xPT+NyuRgfH6dYLJJIJPD5fAwMDFgdpd///vfJZrN88MEH1Ot1DMMgGo1SLBat237fga7r1Ov1R961+FgIj8fjwbFV3jgKzNSdxQZTKUV2wmFFLvJ5xPo6dSFYev99DJuNwZdfVhcRw1A6n81NleHJ5ZBDQyq7k89DLodhs8HQkLpfKqnnl8sYaitpiMXUJqRrazhjMSYjEYTDgSedpqlp2EoltTmnzaYyM+Uyxvg4tmvX1MX+yhW0rTFkpaKyL2++iUgklN5HSrTVVfQrVxQhqlRUJ9jyMsaFC0iHQ+3D1dmpslDlsvK+SSaRkQjGq6+qLrXVVRxb3WTG4KAiHYEA4u5dZWjY3W2RN21mRnWfuVz7tr0flJFxOBy0nzmDiEToXl5GLxQolssU02nWMxkKMzN05vMENzcJbm6qJXs0atVmj5p2Punw+XwPFROHRa1W49atW+i6zsWLZzEMj9Vx5fdb++XS26tKWkqzI8hmnSwsqJOi6a6sOKzS2mxv7CsWDX70o2vY7RK320+l0s6pU05GRpqWKDmTEayuaszNCVZWBOPjqkxlntekVGWqtTVVXhsaquLzKVIVCllTmZ4eA02DXE5QKKjMz7VrKqM0NCRxuyW6rsI+kzG7DXd+J0KAxyPw+SSjoxLDkJaw+sc/1rDZBJ2dcuv3UWW3YFAeuPJ8WAJkNm8ctcvmpCMQCGC32/F4PLz11lu8//77/P/svVmMXNl55/m798a+R2REZmRG7iuX5E7WaqtslyBZeijZGExbcD9Y8OihAQ2kmTc1YFu2DNsYDPrNDwbsB3uM8WgwhtqSZUm2ZGtxSdVFsqpYZDKZ+x77vm93mYcT95LJrUiqurrs0gcIYpGZGTcj7jn3f77vv6yvr79vgKfT6XDr1i0AlpeXH/twdbvdTE9PEwwGLWL5+vo6xWLRUlSZrsmzs7PMzs6yuLhIo9GgVCpZBNyhoSGKxSLBYPBYd+jeMrs9pVLJGh2Z1Wg0yOVylmP9vdXr9Zibm6PdbrO7u8vS0tIjXwOeDfB0u10ODg7Y2dlhf3+f8+fPU61W0TSNdrt9bMzX6XSIRqMUi0V8Ph9Xrlwhm80yOjrKxsYG165ds6I1VFU9RoI2f96/C8AjVBzPLqkFBLg5PBRBQHNzYvfyeqmVSuz/9KeMnjxJMxZDVhQBXN55R4xVvF4REdHtIiWTIkcrl4NQiH4wKEjPHo/otLjdGNGoeBp4vWL3vHGD4tYWXU1j4uxZKrkcrKyQyWYxGg208XH8Q0MEez3sZjhpOIwBSKWSuJZSCblSueu3U6kgNRrYy2XRfYlGoVAQSq16HQA9Hke5dk0AswsXRNyFrqP7/YIwPDMjuEt2O2q9jm1jAyYnMRYXhXprQGDWp6aQdP1uxES5jH7ihLi2dvsBwPNebUVjaAh0HdvNm4T39wmePw82G72JCdpXr9I8OCAty6g3b+IYnKa63e5Tcba++93v8qUvfQlN0/j85z/Pl7/85WP/LknS54D/E0gO/upPDcP4iyd+gQ9JPcuaMK3oH/cZmaGfi4uLFAoF/H6D+9MozG+XJAEsTH+dGzeaJBIGoZBh8XVqNTE28ngMolHw+w2OjnbY2HAwM7NMqZRjf19CktKcOFFmdNSNLMcYH/fi9dqpVkGWRaxEuy3x4x/bmJ/XB0DLwGaTiEQMEgnN6tB0u7CzIxMKGQwP64yOCgNCr9dAUQx0Xfw8m81A12VSKZl0GnZ3IZWS6XZldP3Bzf3ezpVJaPb7DfJ58TuHw+L3bjQgl5M4OhKjsmDwvVvtjwJAxWKRnZ0dSxhgpkI/DYn/o7YmRkZGuHjxIqVSiXq9zvb2NrOzs8/cEc1ms2xtbXHixAlSqffOVJUkCY/HQyaTYWdnh1gsRqlUIhqNMjY2RiAQwOPxYBiGNXra3t6m2+1y4cIFdnd3LXfhzc1NWq0Wi4uLhMPhB/bCfD5PoVCw7o17gXC/3wd4AMiYHCSPx0M8Hrc6YvPz84/cG54W8BQKBZLJJJIkEQ6HmZmZIRAIkMlk2N/ff0BplUwmabVaLCwsoOs6Ozs7jI+P4/V6MQyDhYUFzp07h9vtfsAbq9fr0Ww2nwrwPMua+MBk6YqioA48aZ7me6wHQrEowMnMDLTb6L0e29ks0s2bLE1PY3/pJSqpFA6vV/SjB+Md48IFpK0tQUTudJD/8R8xhoYwXnwRdnZEZwfhM2NcvCg6I60Wxvg42tERqRs3RHdFlnH1+7icTuzdLqGxMYz5eaoTE9RzOUrpNEangy8QwBcO4/d4kJtN4cuTywn+0MBbx3C7UdbWxPXF4yLU1PTgCQTQ43FBfNZ1Mf4KBMQRPJlEbrUwvF6xUzudkM9jqCp6NCqAzYAwLfd6YnQXDAoFWaUieE9eryWZf1jExJNwboxYDH1+HrlWE2BR07B3OriA4Pg4o0tL6OUyxVCIXC7HF7/4RQ4PD/nyl7/M7/7u7z629appGl/4whf43ve+x/j4OFeuXOG1117j1KlT93/p/2sYxv/6VDfUh6ie9RBg+o487DPSdZ2NjQ0ajYblzlwqlQbqF3GrCzWWGE2dO/dgF8RmE/8+NQUmX+foSIy16nWJalU4QMuyj729GLLsRFE8uFwKw8MhAoEJ3nijgyw3SKd36PclolEvly55mJwMkEzaSKVE96del8hkhBKrVJJIJhWiURudjuj6RKMG8/M6BweC86PrkMlI2GwSp07phEKCSK0oOtWqkKO7XEK2nsvZ2NmxMzQkHxtV3Qt47i0xArsLgAB6PYMbN2QaDQiHn55b8CgAlE6n+eIXv8idO3f4nd/5Hb7yla889mT+UVkTcHzPHx8fJx6Ps7Ozw7e//W3Onj3Lyy+/bLnz3luPephrmsba2hq9Xs9yZ85kMo9VdpngyASkDoeDU6dOkU6nicfjx17f5O7cunWLF154AY/Hg9PpxO1243K5WF5eptPpkEwmGRoashyY/X4/fr8fn89HLpdDkiRKpRI7OzsWjUCSJPL5PO1225Jym90g8z0yO2JTU1Ps7u5ycHDAtOkv94Tv0f3V6/U4ODigXq/j9/uZmJhgbW0Nr9dLPB4nGAyyublpcXTMLtbu7i6Tk5OcPXuWw8NDNjc3GR4eZn9/H1VVGR8fZ2xsDLvd/oAKLJlM8qUvfYm9vT1+//d/n6985SuPvdZnXRMfGOC590N60jrmsWCzCbCjKHR3d9m4eZPAyAjTi4swNCRM+2RZyMf398UOFg4LcvHU1F2+T7stOj+m30GlIjo+NpuImGi3kXI5altbFH7yE4Z9Pnyvvsr6978P5TK2alUEgp45A5JEsFQiWK0iBQIYkkQ9HKbocpFMpej3+yTW1/HZ7Sgvv4x9d9cK9FQqFdS5OXG0brVER6hQQJ+eFh49u7ti9ORyQa8nxlGpFEYigTE7KyTgwSByLofcbKKeP4+hKNDpIG9toZ84IX7vAQFbunlTfK/pkux0ivfivnpS4ydjagrd4RDjuWpVgClFEcxWjwelVCIaCrHvdPInf/InfPGLX+RjH/vYe55qr169yvz8PLOzswB89rOf5Rvf+MbDbuR/8/UshwBzTdz/GZmb7sjICEtLS8fcmSsVg709iUAASiXx9aZqSaSX3+WsBAJ9otG7MNjk64yNwfBwkfX1deLxk2xvh0mnM4TDApOPjPQJhaDVkuj3fYCPbjdOMKghSXWuXq1y61YSTVNotyP0el6Ghny8+qoYW5VKEuvrEpubbjRNoViUUBRBOG63BUg5OBDjrIUFnbk5wReSJINKRaJclojFdF55RWd7W6bV6hGL6ce4OpIkeEvhsOACmURtEGDqYSMwux007f0h3psAaG9vjz/6oz/ij//4j3n55ZcfC3bgo7Mm7j8EmLyp6elphoeH2d3dJRqNkkgkHnAgf9jIp16vs7KyQiKRYGJi4old/Le2tlhfX+fTn/40Q0NDdDodxsfHj8UymLW3t8e1a9c4efIk4+PjZLNZut0ujUaDer1Or9ejWCySzWbpdDrY7XZkWSafz7O9vW2NrHw+H263m2azeUyplU6nabfb7O3tHbtG8+cCVKtVnE4nTqeTZDKJ0+m0ZOz31pMAnkKhwI0bN6jX65w7d47Z2Vna7Ta6rlt7t9vtJhKJoCiKNeJLp9M4HA6Wl5ctPyHTyHFxcRGfz/fQsZ6pAtvf3+cP/uAP+Ku/+iteeuml97zOZ10TH+qR1jEXzYkJQDD5D3d2OGWz4fP7RYdE00BVsTUaOFZXkaanMaankVRV2LiGwwLs1Ovozz2HZBjg96MrijAojEZFyGevh+HxkN3aopJMMjszgxPQ/X4RBXFwgGIY9INB9NFRpG4XZW9PkKMByWbDNz2Nd3YW+fZt+jYb7UqFYqtFcm0NfzJJoFAgNAj71L1eQVIe5HcZTqeIblhZEbL4U6eQb9wQnZpAAKnTQYtGkRQF6R4/HNXrFaOmbld8/xtvII2NCel8p4PR7YrvHR1FLhYF6CoWkVwuAZ7uqadRVRmjo8ILaGdHZIc995zowsmyeI8HC6XT6RAKhfj0pz/9nj8zmUwyMfisQZzy3nzzzYd96f8kSdLHgA3gfzcM4/CJLvpDVM9yCLh/HRmGQTKZ5ODg4AEfEbjrwxOJwOKi6OjUaoK3cueOma0lJrg+nxgR3c8plCSDbDZJvZ5ievoiR0cu7HYhB/d6dZxOg1TKxs6OjN8v0WpJzM0ZTE8LIvG77wbZ3AwzOTlFv6+STHYxjAKNxiaNhkI8HsTpDCNJNoJBlWhUx++XMQyJVAru3JGp1UDXJeJxjUBAADJdF12dVkuMnWZmDOsc43RqRKMGCwvHuTrptESpJLG9LQ+m4iL+otW6N0jVfG/N/3//lYatVotwOMynPvWp9/zaj9KaeNghQJIk4vE41WqVoaEh1tbWWFtb45d+6Zcsw757AY9hGBwcHJBKpVheXsZ/LzuexwMeU41ljs/MB7RJKjb9dHRdZ3t7m7W1NRYXF9F13VIhybJMNpvFPlCpms+/cDiMy+XCZrNZ/5NlmdXVVfr9vkUEBixLkFqthqqqnDhxwlJpmRywsbExer0e3W6Xer2OrusUCgXy+TyvvPLKA52wxwGefr/PwcGBNV4z32+TqAwcGzd1Oh3m5+cJBoNcu3aNdrstUgwG73VpcLIyx273JqY/qsxu0Sc+8YnHfh08+5r4QEdaPwvgUVWVtdu3UVWV8ydO4Lx2Dd3jgZMnMQYfotLrIedyGEtLgrezsWHlUklbWxgTE2JnT6VAVXEKqQrGb/6mAA7VKrcLBYa3t1kaGUFaWhJAqVxG6fWgWMQYG0N1ucQ4p9kUTw+bDclmQ1tcxEgkxLG438eRzWI7eZKAqjI6PY1eKtHa2CCbSNBot+mkUiR1ndjuLo75eRgbQ97fR/J4ROaW14sRj4vOjq4LVZbDIbozmQzywQFGIIDmcIgje62G1O+LeAzDEGovSRKSfL8fye0W3R4z10tVhXzmnpPR08rIjfFx9EYDaXdXBJS2WsIlen8faSD3fJocrYeRmx+ySP8e+H8Mw+hKkvSfgL8CfuWJL/pDUO8Hr80M/VQU5ZHeVrIso6p3N3ePhwFhWLgvm6OeRkPEPuztebh+XXBqfD5wuXpsbd1iasrH8PAlmk0ZRYHnnzfY32+zuGigKBo7OzZ03UBVDVRVwukUZodrawqbm6a/j4HLpbC05OHMmQkymQmKxT7FYpWDgwaHhyrLy2V8Pgmvd4iFBRuGIXHzphjFKYpOqyWzu2vQ6RisrMh0uwaTkwbj4waKIkBKoyFeS5bNE/3dUVWpZODxiNR2wdWRyGSEZ1C3a9Bs6lYO2N1l8f6mpQNPlZT+UVkT8PBDgMPh4Pnnn+ef//mf2dvbs9RRzz33nBV/oGkadrvdCv10uVyP9LZ6HODJ5/MEAgEMw6Berx97yHs8HsrlMul0muvXr1tgyOQpmhLyiYkJPB6PpWAyQc/Q0NBDTRSdTiexWAyPx8Ph4SGzs7OWxN308zGTxoPB4CPvxV6vZwGQ7e1tlpeXj/GBHgV4yuUyR0dH6LpONBrF6XRaCeeZTMYaw50+fRoQz+Jut4vb7abT6RCJREin05ZpYrfbZXt7m1gshs/nI5lMWk7Nj6sPYk18oIDnflOl9yrzRq7Vatx+5x3m2m1ii4vI5bLgsYRCdwfy7Tb2QkFEPYTD4PEI8KFpwrSvUoGTJ7HkIXt7KIAWDKKkUtTbbbZ2d5mYniam69DroZ86JeIitrexdToYwSC9hQVa9QrS9rZQPgGSYQgOTSAg8rVUFengQHj7LC0h3b6N1G5j7/cJNxr4l5Zo1GqkKxXs3S7Fw0MyLhehdptQu403kcCl6xgjI/TqZeTVVZSxMfQXXhDX3+0ip9MY0ahIY9/eFkAmm8UIhdDPnRMOy80m8u3b6Jcvi1FWuy28epJJtLk5qwvDPYvwkeGhjyljacny+6FcRna5BCgbEO6eJhBufHycw8O7B9Ojo6MHfDIMwyje859/DvwfT3XBH5J61kOApmmUy2VWV1eZnZ19aPvaLAFexeZ+/x6hKAL/C2ssoXK6fVsfRExIHB7W2N7exeudx+sNce2a2FQSCcGJCYf7jI9ruFw9dvd0nI4wkQgWaCiVxKipVpOoViUKBcG9cTgkikVxe9hsdlQ1RjQKut6g11O4c8dFOl0in68wPOzA4QizvOxCVd3U6wY9rUsqK1Ovu3C5xEir28Ua2e3uisgJE/DcWyZXJxBg0CkSPCWT36TrEuk0gDRImBHdo/e7w/M0m/vP1wTEYjEuXrzIT37yE4rFIqOjo9hsNm7dusXm5iYnT548Fvp5v9rp3noU4DGdtkdGRqhUKse6OWbe1DvvvMP169c5efIkiUSCo6Mji3Pj8XgsO49Wu4Uh3w0VNaNEHlbj4+NWN8i8vrGxMcbGxshkMtQHQpbt7W0rf80wDHq9HufOnUOWZTpqB2ShdJuZmcFms7G7u8vi4uKxzte9e7uZ3Wfei5OTk5bc3uVy4fP5CIfD1Ot1MpkMa2trJBIJbANbllqthizLnDp1il6vR6vVolKpcHBwQKvV4vLlyxa3Z25u7pGfhwlenuZg/Kxr4kPd4RE5QGkajQZnL1/Gd+cOrK+LJPT5+btZU4eHAlQ4HHSmpixzPcJhpPV1satPTTEI7BFS81KJ3sgI+vQ0uXye8mBM5iwURBimyyU6NYYhgjE7HbIRJ9/sXKVWS/PL+x2Wxy7hcbnEETISET+/VhMdFE0TEnGbzerIGNEo+vg4UqGA3G7jqtcZCYeRTp4kfu4c/R/8gN7ODoc2G7atLdaNDIXiOolkGakb53n/KRyKCO6U2m0hnw+FkLpd5HpdRGHMzgpekKoK1+ZmU+zmA1m7VCqJsdP0tPV7GfcAnmcyf5IkwRfSdSHJj8WEKWSnAzzd5n7lyhU2NzfZ3d0lkUjwta99jb/5m7+57+WkUcMw0oP/fA2483QX/OEoRVEsFcaTliRJHB4e0mw2H+sjYpbptNxuCx6/yLMyf9ZxECQAkEospuNy7aJpRebmzpFKObl1SxCCnU6DTkfixg3Y3vbiHs5wo/Bj2l2DC0sJXrlwhVLWR70Osixk7Z2O+NnT06JrcnQk0elIDA8LtdXMjI7LZaCqOo2GHV0fot2WUZQxPJ4m+XyLt946otdT2WmmKElZEhMKidMjLNhfRu/bqVYlNjZkwmHxermcg2xWplw+Hi3xMNKymRk2NmYQixmoquk9JA3SXd6/kda9m/uTZst9VNbEe3U9FxcXaTabfPe73yWTyVi+Lnt7e3z9619nbm7uoQGj99ejAI+u6wSDQWKxmKWwUhSF7e1t3G43h4eHtFotfvVXf5WFhQVkWaZerxMOh2m32xag2Snv8Pd7f49maCx3lzkzfIZyufzAqM7Ml/R4POi6TrfbpdPpUK/XLVn74eEhdrud2dlZxsfHMQyDRqPB+vq6UIX1uuzpe6S1NB63h7gtTsKWYG5uju3t7WPxE/cCnkqlwurqKuvr68zNzRGPx5FlmV6vZxGjzffK7/cTiUSQJImDgwNUVbWCQE1wNTw8jN1u586dO6TTaUZGRhgbG+ONN94Qlib3y0TvKdOr7WkAz7OuiQ+Uw/M0BE2zneb1ei2LccPvRyoUMObmrBwsC5BUKujPP4+aTArgoWlIGxtQrWJcviyCQFstjH5fAKChIZpuN9t7e0jDwyxdvIjtW9/CcLsxLl5EWluzDPSoVmnGQrwpbxLL+RipKzQ6ddazK5yf/0X0hQXxepkMSjoNNhva5ctItdrdrsreHtrysnjaKApSoYD7zh1YXMQYHUXe2sIVi+HSdfzLy6Q9GvW91wnZfHSNBjdWf0qmr/Oq9wShXg95IJ83BqRm5fBQjMEGYysaDSSbTfgEDeIfJFXF8HhExIQsi6fQfcTlZ3ZGliSMmRm0j31M8It0HWkAeJ4mKd1ms/Gnf/qnfPKTn0TTNH77t3+b06dP83u/93v84R/+4WuGYXwT+KIkSa8BKlACPvf0F/w/vhRFoTN4j56k2u02+XyecDjMlStXnuhzEk6ufRwOg0JBOA7b7WJkU60ea+4NymB19Q5zcw5crstsbCgoikE8DidPChJxMCiIwYWqxjdfPyTgHScQanJYO+L/u97lE9OfpNGQcbsN/H6DU6eg2zUYH9dJp2U8HmEQ2OlIeL2io7S/L7Oy4sTh6OP3CxPDM2c0ZmZcvPGGh3Q6RqqW5nZlk0TMh6we8d821ql4eiwEnqfT8TMzo3DpksbKikwup1OvK+zt3Y2W8PuFquv+zroJ+sy308wBs9nE2Kvff/8Aj7m+nsZz5KO2Jh53MD5//jw+n4+/+7u/4zvf+Q5nzpyhUCgAcObMmSc6VJvWDveXy+VidnaWcrlMKpXi4OAAWZbxer00Gg1Onz7N5OQkPp/Puh/OnDkDiA5Dp9OhptZ4++BtfDYfiq6wmdqk5W7haQn35UqlQr/fR1VV6xparZY1Htvf36fb7VpcvHfeeQe3220BZUVRsNvtdDod/H4/HU+H7cNtQrYQzUqTH1R/wLxjnunJaUZHR0mlUqRSKRKJBIZhoOs6R0dHlEolC7CYnSQzEd53X8e/1+sRiUSIx+Nks1muXr3K9vY2Fy9eZHR0lL29PZxOJ5OTk3znO9+hXq/z3HPPUSqV0DSNWCz22JBck3/VarWe2HPpWdfEh1Kllc/n2djYIBKJEIlE7m42oZDw4ZHlu0e2vT2hmhogVNXnExLw3V1hD2vOcL1eAWA2NkBRqESj1FMpJuNxxsJh0REJhwUAiEYF9+Xdd8WDe3KSQtRJt7rPzE6JpiLjcwepdqt0x+I4Bi1HqdkUHjlTUxgTE6Lr1GwiF4uCgzMxgbS3J8jJbrcFPuSdHeRsFm1yEqVSQYvFKB+qxMsdXONx+pN25pcWaRoNjDffZNvtphsMElxfx3P2LLZsFikWQ5+ZEZ2f4WGRd5VIYJw4gdHtIudySLduob/6qoi+MCXp9z1wf6YoCEkSnZ3tbeTDQzRFQXI6n9pf4dOf/vQDBOevfvWrfPWrX/0mgGEY/xn4z892kR+OetpDgBn6aW48T/oZybKMzaZz5YqQWddqd00GMxkBgLpdAQZ0vUKr1WZ0dAa/P0q/D889p7O9LVlePJmMRCIhEsrtrga+UJuTJ7pUSzKt/ZPc0FIEMhL7+zKJhMHysk4spmO3QzwuODLFosLp0zrRqAZI7O3J1OtiLAUSqiq6MNWqzNoaA1m6QV9qkxiTmZ2R8fqmSZWi3NpyUDMMHI4j5ufrlEp2ZHmEaLTL0pLGzIz4Xet10bE5OhLJ8N2uMTBWvOvOzH0mDSYQ0nXtfQc8T9P1hI/GmgAeO/oBsW4WFhZ48cUX+da3vsXu7i7Dw8MsLCxYJNvp6emHStfNeliHx0w2L5VKlm/YyMiIJbleXl5maGiIlZWVY9dnxkV0Oh1yuRw7mR2yShalpYABvoCPm82bzOvzuAdKYtN0VFEUFEXh9u3bhMNhIpEInU6HqakpgsGgRb52u90kEolj0RIgiMPpZBqlrYADnDhx99xkGhnu3LlDr9ejXq9zdHRk/XltbQ1N04jH4/j9fnRdZ35+nm63S6FQ4PDwkGq1SiAQYHR01BqHOZ1OizwOWB2pfr9Ps9nE5/NRLBaRJAmn04nL5WJ1ddXK0Xpcmea0T0N9gGdbEx8qwGP6iDSbTS5fvkw2mz3+PeYG0WiIY9qtW8JBeW4OKZ9H6XRQXS4BVmo1jNnZu7ERXq8gKAeDHPl85F9/neDwMOFIBOn2bYxTp+DUKaR0WvjV//jHMD8PCwsYHg9y0A1VcJYq9IJBGl4ZzRfA5g9a3jd0Opb3DQNEK1+/jhEIWHEShtst3KJdLvqxmMjX+slPkFIp5EAAw+FAqtcJdWUqtRqax0XT46NGh5PKCCMhjejly6huN63NTYq7u6i1GpvlMr5AgEing9flEuTlaFRkffX7GD4fMgKU4XZDsYicToPDgbawcOwz+Jk2d1nGmJ3FqNXg6Ajb8PBTb+4flXqSNaFpGnfu3KHf7/Pcc89xeHj4VKPhe12uHQ6B5QXlQLgN1+uCd3/zZopMpkIuN0y3GyGRMJieFr43588bpFJwdGTQagn+jd0uEQxr9GY3cYX8FIoeVLlBLOCmWhU5WrGYTqVijpp0NjdlUikJRYF4XGdoSHRRBBlZolRSCYW6DA0ZbGwIcNVqCSVWNApBt4eKKqN1vHTQKKT6RIwxpmYi+HxhvF6NVKrG5maLSqXL7dubeL1OwuEww8MBQISZmiO9XE6ktfd6Asy53cLt2esVy/nuM/H96/Dc277uvlrnAAAgAElEQVS/P4zyo15PSuRXVRWn02l50Jj3dyAQsMZQ7wV47n+N1dVVNjc3uXjxIrOzszgcDv7pn/6JnZ0dPB6PNZKx2Wyoqkqj0aBSqbC3t4eiKFSrVcrlMnpXR/JL+EN+bA4b7rAbuSITaAdYXFy0AMO9FYlECAQCeL1ea3xkqp38fj9er/cBTpLT6cTn86GMKby+/zojzhH63T51pc6Me4ZgMGj5/WxubvKNb3wDTdNIJBKcO3fOSjY3R1dOp9OK0ej3+7TbbTY2NrDZbFYoKAhg2Gq1WFpawul0WnuT1+ulUqkwMjJCNptlZ2eHSqXC5OTkA8rR++tZRlrPWh8aDk+j0WBlZYV4PG75iDzAcfB4hNLK4xGZUGtr6GNjQrJeraIMwjGlRkPwUqamBFm52YROB21oiNV2G9kwODc5ycogmgJNwxgbuxva2WiIUdmtW8JvB0hsHuG1jZHW12h3oT8e56XzvyaATLksXKA1TVyfYUCvJ0JBzbT0fF6MxyQJOZVCn5ujHw4Ls0GXS4ydCgUkVUW+cYNRe4C2c4g73QqZsI9EUeHcyBhGvA+yjOLzEbTbCUoSO+PjJBYWaPj9VK9eJbW5ic0wcKXTBBIJAtksBAJoZ84Ik8NqVeR0DfyL6PUsgPa+hH0qCvr583SXlpC2tmi1Wo8M6Pso13utiVqtxsrKipVTI0nSe3qI3F+yLNRHb78tMTlpDMCOKI8HbLYeR0e3GBoKEAicpFTaBnTeftvOjRuQy8H4uIifEDQwgxMnYGhIp912kLad4d2dt6gWujh7EV6JXsHtgrExjbExnXRawWYz2N+XUBSh1nI4JN59V8bhENS3UklI3CcmVGZm2ni9OsWiWEbRqAQYxOM6tXqMXGGIG+sp1GYEpz7NuflZhofF9QWDEiMjQTqdMKlUH1keZX+/xs5OGU3bJRxWqFbjxGJe5uc96LpwVC4UJNJpmVxOot2WkGUhVwdBrH4/Zen3bu7/vW30/y3We3U9Te7J9PQ0v/Vbv8VPf/pTfvjDHxIOh3nuuecwDMN6wB4eHhKNRh/w/pJl+dhzpdVqkUwmGR4e5sSJE3S7Xd5++20ATp8+zc7ODpqmWbLxer1OvV5HVVVyuZwlz45Go8yoM9Qjda7tX6NrdPG7/FyOXSa7l32kv5kJwExRjykDN//tYYokl8tFMBhkfGicZCPJUe0Iw25wduksH5/+OA7FQafTIZvNUq1WabVaZLNZxsfHyefzZLNZ6/1xOsWhwHRENvk32WyWjY0NstksiUQCn89njfnGxsaYm5vj3XffpVgsIssyTqeTYDBILpcjlUpZXaL3EsF8pADPvT4iy8vLx6RrD2zusixIx+22GA1FIkiDGAcjFEIqFrEdHkIwiOR0iga114uUydDsdlnL5Yi/+CJjo6NI776LN5lETyQE2NE0cfRdWREmhCdPClO9dBpDlnHt7rJw9ixj4XOUbRKjV34N/1EO3VNAajQEsJmetnhFlEqCM+P1YoyOCnO+eh1pECehDQ1hW19HcrvRL1wQ3KKBc7KSz8MLL7CQOEfk0vNorQbR6ysw5kKfHBXjKKcTKZOBkRHa8TgOTSM2NoZit8OJE3QiEeq5HOl0muK1a/RPnCAQDOIPBPAVCsh7e/TPnEEe8IzeV8AjPnA0WUax2Z6Kw/NRqkd1eAzDYH9/n3Q6zdmzZ4/N1J+W/C82TA1VFdg/HL475V1fr7GxsUY8PofNFiUSgV/8xRrLy33abYV0WnBr7tyRUVUR0mm3iwR0nw+6XYUL8SvMh0fY0jTc8hyzozaKRTEmW1mx4XYLmbphwOnTGi6X6PhkMjK9HgNwIcBXoaAgy3aGhkTA5+SkwalTGrWahMNhcHQkk8k8j6vfZHJBxWH4kbCRzYrbt9eTcToF6djr7TM6amNiIkKpNMT+voTL1SWZbFOv59nbKxGNOojFQgQCYSYnfUxNCZ8iU66ez4OqSrjd758s/d6R1pOSlj9K9bg1sbOzQ6FQ4Pz58xZYrNfr/OhHP+LatWt8/OMfxzAMPB4PuVyOSqVCKpVidnb2GDfk/udKPp/H6XRa46u1tTWrg1Gv18lms9y8eRMQB3Ov18vMzAw+n88i7PZ6PYuX81LiJaJGlE6/w6mZU+xu7pLSUhZPx4yFMEdaIHxrqtUqzWbTUmWZ2WKmkaFJJrbZbBbdA+ATM5+g3CkD4DJc1CuCSF0oFCiVSiQSCc6fP8+3v/1ti0AsQoWL6LpOsVikWCzS6XQsebo5OjeDUk3glMlkCIVClgQ/HA6zvr6Ox+Ph1KlTZDIZK7R4amrqsWRls+4daf27ATwPu5FNHxGbzfZQH5GHbu7dLtKbb0IoJCTfhYIAK/E4SjKJLkkCrORyoKroskz5+nUOQyGWfumX8Pb7ol/dbmMvFtFeeEGQnAep5ww6PiwtCSDk8yH9+Me4Mhk4exZvMEYnGMSrykgHB0iqKsZngYDYvVUVfW4OuVAQ0vlgUIy6PB7k7W2RszU6ipLL4UgmxU4/NYV+7pyQs29vI+3swOnT6BcuEBqeQNreRumrqNGocF6uVkVkha6jDQ2h9vtIuo50eIgEaENDOMJhou02UbsdaWGB5tmz1PJ50tvbqLkc4Xod5c4d/F4vjoFrMzybLP1R9ax8hY9KPez+7vV63Lp1C4/H81AfkftPp+9VQp2hIyyjBJfF4zGoVA5YWakTjZ4nk3HidIpbtVKxU6mIdPFKRWJqCux2Ha9XjJdsNpFevr4Ob70VIpdTGB8fJuJqc+qUjVOndG7dEtQwSRK+ObWaWHL9vohp0DRIJIQZoumKUKlAMqmwu+vD45EpFmViMfHeVKsiZkKWweeVuXTRywsv6Ny8qVAsGvR6Iv1c0wzKZeGmXCg4KJVk7HbhN+T3G4yO2qnXnXi9IQKBGcrlDplMDVVNkU4LXs/0dIBYLMzEhItIxPTnef84PB/kafbfYj1sTZihn8Fg8AGy/sWLF3n55Zf5/ve/z1tvvUUsFiOfz9PpdAgEArz++uscHBzw/PPPW27L9wIe0+IhGo1ycHBAr9fj4sWLtFot9vf3cbvdNBoN/H4/IyMjFkg1R2bC50ql3+9js9ksNZRX8uJSXLjtblqtFoeHh5ZbsvnaJiDQdZ29vT1UVcXr9XJwcADA7OwsMzMzlMtlNjc3j70nNpvNAl3dThe9oeN0OilWihwdHREMBun1esRiMcbGxpBlmcnJSRwOBw6Hg9nZWVqtFr1ez+ogmUAIhNt0p9Oh0Wjg8/mIx+Osra1ZeV0meHM6ndb9rGkaxWIRt9uNpmkEAoHHkpXNMknLH8Rz4n9Yh8f0ETElcQ+rh7bvGw3R3VlcNA1ExN/lckiqSjseF+TmXA61VGLn2jVskQjLn/kMSrOJlMkIIFKtgt2OPjwMg3GTYT5IFEXwf1ZXhc/O0BDK3h5ypUJvYYG+CXLsduRsFn2Qoi5nMiLiYpDWri8uIh8cCINASRLRESdPImWz2La20NxuJE3D8HqRMxmhbopGrXgJQiHY3kYqFtHtdgGcxsfF6+7voycSSLKM5nCILlO7Lf5OktBdLkGW9noxJidx22y4p6YYzWTQr1yhUC2wX02hbGaQs1mUc+cIh8Ooqvrzzf0DqvsPAfeGfj5KrfC0yi5Tlh4IQDBoYLP1efvtLQzDjc93mmRS5vx5kV7eakns7nrZ27MRjQqVFYhwz+Vlg0RCAJTVVeHd6fVqA/diB/2+Sqsl02wyGAsJx+bFRY25OeFsLPJqxRipVJJxOAwWFnQ8HoOdHQmH426au67r6LroBr37roTbrdDrCeAyMmLg8QjitMcj5O+6Lr7X74d0GhTFIJOBfF7GbheS+GpVotuVGBvTcbsNSiUPNpubUGiERkOiUM2x/+4OirZH1OPA6QzSbkdxOt+/Q8DP18Tj6/41cW/o56Ocel999VWy2SzXr18nkUjw4osvYrfbrQRxVVXJ5/P0+33m5uaOPVcajQbdbtdKJB8fH2d7e9siJsfjcUKhEFNTUzgcDlKp1LGDucMhRkfmz8/lcpafj9ndMF/L4/Hgdrux2+3YbDbr85+ZEZybdDp9jHBtt9stNV80GkXTNIu4XKlUWF9f58SJE7TbbY6OjqwuWLvd5vz588zPzx9zmfb5fIyOjpJOp3G73YRCITweD6Ojo1b8RT6fx+/3Mz8/j9fr5eqNq2wdbSEfCfdoUxE3MzMDiD0rEokQCoUsnyCPx0Or1XpPZ2Wz7l0T/727nh8Y4DE3DDNFtVQqcfHixcdmKz20w+P3C1l6ryd4MyCUWqqK3Okgqyp4PDSbTXb+638lPjND9KWX7jIV+30RJxEK0U8k0LtdEYC5soIUiWB87GNiJDU3Bzdvik7NqVMYt24hlcs4CgU8ug4f/7joo9dqMDaGkUwiORziZ+3uCl+aoSE4OBDePINUdsPvR1pfR+p26Y+Nocuy6FaBAGNnzkCxiH75MtLqKrZ/+ie0c+eQAgExDjMM5GRSJLvHYgJESRJKLgeJhFCCDYJHpVIJIxoVcRPttoii6HZJhW38+eG36NogpiY56/FyaexTlMtlWq0Wb7/9NqFQiHA4TCgUes+sn0fVzzf3x5d5f5uZM/V63Qr9fFQ9C4dHOAWDptXIZG5x8uQiTmeMjQ2JQMDA75e4t2mUy4msqUhEdGNUFW7dklhdFblUMzMwPg4TE10uXeoRj9e5c6dLuQyHhxJbWzLNpjAZvHJFp9EQXSWv18DhMJAkCIWEOiqVkkgmZdJpiWZTZm6uy9KSzuGhiKdIpyWyWSFlDwQE4NrYkOn1xLUIuyzh9xOLGYyNGeTzsLGh43RKxOPCVVlVxRmnVIJGQ0aSZPx+mJ8XcRo7uQzfa/4FjbZONRvi1ZPnmGicIpNpEQoZ/Ou/vkM87icSEWviSfLmHlYf5Ob+b7FMwKNpGuvr63Q6HSv081HlcDh48cUX+eY3v8lbb73F3NwcIyMjlEolLl++TLVaJR6P02w2yWazlrIK7roGj42NsbOzw+HhIeFwmImJCWRZJhqNks1mrS6IOYoqFArIsozL5aLdbrO/v28loUejUfx+v9WdtdvtvPLKKxbYur/8fj+5XA6v18vIyMgxcN1sNsnlckxPTx/73mAwaIWiRqNRbDYb29vbDA0NMTExwczMzENdpuPxOO12m2QyiaqquN1unE4nY2NjDA0NUS6X6ff7bG1tUdJL/O3K36LLOmF/mFMjpzgbOks6nWZ3d5cf/OAHbG1tMTIywujoKP/yL//C1NQUvV7Pys56kvp3yeExvQ+uX79OJBLh8uXL79lJeOjm7nAIgOByCcbjYJRjxONIdjvS1haHyST1lRUWwmGcFy4I5mE+L74+lRLk51deobuzgzHonEiFAvr4uOD/ZLPi57bblkNze3ER2yC13P+TnyDFYkg+nxhROZ1IiQTq2BjKtWviCTEgLhter3BFHhsTXJ5mEwkwKhUIBESGV6UiyNbVqiAX/8IvCFVVuy1GVydOYIyMINVqIrIhn0efnxfgyjBwJZNgt2NEIlZ6uWzK30MhMQbL58XrBgL830ffwmZ3MluTcQYn+EH1HeaNMtPT0+TzeS5cuEC9XqdcLnNwcIBhGMcA0MMW0sPKHGn9HPA8vMx2+NWrVxkZGeHSpUvv2Ul4Fg6Ppmn0eofkchlGRi7RarlptYQPDoCuG7hcxsB40ECSDCYmBB1tagr6fYM335Tp9USQpxlPJ0kiZ6vfVwAhQVdVmJjQSCYlgkFYWRFKqGJRIhwWyrCzZ4Vc3G4X7hHJpEQsZtBua7jdOvm8RCol0+noGIbE0pLOqVMa9broErnd0GxKgwR4AYbcbqhUhIT++nWZfl9mYUHnxRfFNdXrghy9tSXGbUNDBsGBwLLZhG/euIbbdxK37GUyXGel832WEicZ1uPUanlU9QQ7Ow2OjsqoagqvV2NkJEAkEiYYDD7Vmvj5IeDRZcrSr169SiKR4OTJk0+0JiYmJnj++ef5y7/8S15//XV+5Vd+BV3XCYVCVKtV6vU66XSara0t4vE4lUoFm81GLpcjGo2Sy+WQZZnp6elj0mtZlpEkiU6nQ6lUYnNzE7fbzfDwMMFgkOnpaXq9Ho1Gg/n5efb29hgbG6NSqdBut2kJ50oLQD2sGo0GR0dHTE1NPfC79vt9KpUKnU7nGMndBIDdbpd0Ok02m8XpdHLp0qX3jHCYmpqi2+2ysbFx7B6UZdkCez21x5//45/TyXcYGhoibAvzo/SP+I+J/8hLMy9x69YtCzg6nU42NjZYW1vD5XKhaRqXL19+4q7ovc+JfzcdnkwmQ6vV4vTp04+VDN5bjyQ6ezyCuOtwiIwsWUaq19FkmXaxiLy/z4lTp5BHRwXJeXdXKLeaTTFjjUREFIIkobfbSPm8AFCD65I2NjAGYZ5StwuyTOfECWo+H85sFnckgv/6dfQrV0Skw8DNTd7eFmAqFBIXOvDlkep1tKkp5FRK7LbdLnKng+r3Q6WCvLUlyBEuF7rPh+TxiDGYzydCQft9pG4XzWYTMnyvV5goDg9DpYK9VMK4dAnJMITsvVDAcLnQp6aQej30YFCArvFx9OFhCukcc4TxlLM0JuKovTa1egFC04A4ZYXDYetzMluopVKJ3d1da2GEw2ECgcAjF/LPOzyPr2QySbvd5uzZs098GnraDo/JUfD53IyMXEKWZWZmDEIhYT5YrwtC8NqaTDhs0OspuN1izCT49xK9njRwZRb5VG63wegovPGGjb//+w6JRB9NU2g0JE6eNHjhBZ2tLcGfaTYl/H4Ih4WrcjYr8Xd/Z2duTnReDMOwHI6HhjoEg13qddGl6XRk5uZ0VFWi0RD3WDRqMDsruk/drky5bNDvi0ar8NgRMnNVFb4+uZw08NsRTtPBIJw4oTE+LkBQoyHR1foUay1iRgh7oIskGXSKI6SUPtMRHUXpYrPJuN0BQqEAnc4UqqqSTNbZ3q4gSQcEAhCPi0OB3+9/7Jr4+SHg0XV0dESz2eTFF198IPTzUWWuiV//9V/n+vXrrK+vEwwGWVpaotvtIkkSe3t7jI6OWiGdm5ubrK2tMT4+jtfrZWxsDL/fb61DVVUxDMPi5RweHtJoNDAMg6mpKaanp63pRL1ex263Ww/r/f196yFu8olM09BYLEa1WqVQKFidrO3tbTY2NqwwzHw+b+VopdNpDg8PuXnzJpcuXcJut1Or1SzScbFYZGhoiEgkwvj4+BMBb1mWmZ2dJZVK8dZbb+FyuYjFYsemLb6wD1/Ch7PrpFVtUTosoekaLa2Fy+UikUhY13Hx4kUymYzlUG2Gmb7zzjtEIhFrTTwKAJk5aN1u97Hd7fejPjDA43Q68Xq9Twx24DGbu88nOhabm0I+3u/TrFTYWV3F1W5zymaD4WEM01/G6xVcmFYLfXISaYCUHbkcjnffFRlb09OiXz42JjouDJRfskzX46FYqxGpVmkvL6PeuIGezcLqKsFSCfnXfx273Y6Uy6GNjiLZbGLkls0Krk8oJEZabjdSuWyGCAm+z+EhJJPo585hBINIICIg3n4bY24O/fx5od5qt7Ht76MNDWHMzAhStmGIkZuiCGn+AFBJlQrGzIwwaSwUBOjqdtGiUeR+n5n8MP3DXTTZQSHihrRBTBKL9WESSLNNa2bG9Ho9KpWKJVt0OBwWALr3xv454Hl8mQGDTwp24Ok6PJVKhVu3VqjV/IRCJ3A4xAjH3FOWliCXM0gmJRYWDEZHDep1lUZDp9uVBgRgQSUzDAMzozaRAFXtAnVstgi6bpDL9UinD1FVnevXw3S7PhIJiaEhwZ957jkBbNbXJd58UxkYHop8rXgckkmDTMbBxIRtEJgIv/zLGhcu6Fy7pqDrBvm8yMhyOiU0TYzERkd1nE7RvbHZRFxFswnb2zKHhzKaZqAoIm9LlsVobXjYsPyIVNWgWrUT9YZxT9xmeNhBs9uh32igtf1sb8v0+y6Wlw2mpowB+RrqdZmdnTDF4hCjozqy3Gdvr8r2dgFd3yEcVhgeFgDI5/MdWxMOh+OpnJY/SuXz+Sw/mictc004nU4+97nP8ZWvfIUf/vCHnDlzhrGxMRqNBvF4nBMnTlCpVKxORLFYpNFoWECj0WhYooD9/X22trYsO4h4PI6qqjgcjmNgB2BnZ4dqtUqv16NcLhOJRDg4OKBQKJBMJhkaGuLo6IiRkREikQiGYVijsWaziSzLzM3NWdw8l8tFNBq18rLy+bwFlEGMuVZXV9nf32d5eZmZmRlC5iH7CcvhcHDhwgX6/T5ra2tUKhUcDgfVapXR0VHC9jBDviHSgTShQIhaq0az1ESJK5ZazO12W+nt2WzWek9eeOEFrly5QrfbpVQqcXR0RKPRwOVyWc8Jr9f7wHMCeM+pz89aHxjgCYfDFoB50l/qkZv70BBGuYzU7aJPTZF/4w0O+n1OnjlD+Wtfg+lpjAsXxDjKzMPK50X21MWLlp+qo1AQ4GBiQoCcVkskiHc6wltnaopGLMbq2hqhfp+RVAo9EKA0OkrszBl6ySQdwyCVyWCv1fApCv5uF4/PB/G4UGUlEsLBbCA7l3Z3IRxGD4Ww5fMYmobcaEAwiH7hghilFYtIgHryJEogIK6lUsHodkX/PZ8XX7e5CS4XfbOzo+vIW1uiMxQMCn+fdlt0w4JBJLsdQ1H4Teks/2ArcdtVRu2W+ezYJ4lLfh6EOg8vh8PB8PCwZYbV6XSsxF0zYTgcDluJuk/Tqvzud7/Ll770JTRN4/Of/zxf/vKXj/27JElO4P8CLgFF4DcMw9h7wkv/UJWZT/M09TDTtPvLlLUfHmZxu89Trx9QrcLc3F2wo2mCq5NOSwSDBlNTosk4Pt7H7+/hcnnY2xPKLBEAComEAD3tdol8fpeRkSCNhp9AwImiwPBwHI+nzk9+olEu5zk66pFIOJmYcNHve+j3hZmgpsHMjAAeQ0M61arE7q7M1paNnZ3AgGgtSNTlsoTLZQxGUCKGIhRioL6SyecFCdokJj//vMbWlkyr1WFiwkCWjQFoEWOsUkliZUWMxsy4jX4ffvMXnmfVfYuKeoDbY+dzS58lfzvIQU7C61WoVMTXmUnqtZowULx0SWNkxKDZtFGvD1GvR8lmJUqlLo1GBVXNADWiUSexWJhOp4PL5ULX9SfmAX2U1kQ0Gn2mNWEejJeXl/n4xz/O17/+da5fv86ZM2doNBokEgkODw/J5/P4fD4ajQZzc3NMTExwcHCAw+FAVVXS6TTlcplqtYrb7cbhcFgeM61Wy+LtmL48lUqF27dvk8/n6Xa7jI6OWnwh0b0UIaCtVov19XV+9KMfMT09zcjICC6Xi42NDYsgbAIbv99vAT5TpBCJRKw/m/ldExMTzM7OPvQ+6na7xzKxHlbhcJhLly6xv79vGTlmMhnGx8eJx+P81vJv8V8O/gtltYwv7uNVz6uUsiWK0aKlRotGoySTSQqFwkBooFtBoU6nk9HRUUZHRzEMg3a7TblcZm9vz7JlMJ8TTwt0nnVNfKAcHhPAPI0t/kM7PDYbLCzQbzRY3d/HM2iryb0e6eFhMZpqNpGOjgQQMH1m7mWN53Io/T5aJCJ+ntcruiFbW6IjUyhwpKqUnE7ODg2RVVVL+dSdnkbO5XCePImzWiXUbKJ3OjTdbhrlMoftNq7dXbyRCPbnnyeQSpnHQqR+H3ViAr1ex3lwIHhFDocYs9ntyCsr1ihL7vdFTESxKMxT/H6RcN7piIiMREKMylZXxWitUBDKs5kZ4a7scgkfoVhMkKjbbbDbCVc6/MbL/4lmrYBzYgbnzj5Go2F9Tk9bLpfrgRu7VCpRKBT4h3/4B8rlMn/zN3/Dr/3ar71niNwXvvAFvve97zE+Ps6VK1d47bXXOHXq1L1f9r8AZcMw5iVJ+iwiFfo3nvqiP0R1f4rx40pRlMeOtPr9PisrK/T7HnT9ObJZ8Pl6/MIv3I1QaLdhd1ekgg8PC36+WU4ng26IxMSEIDRns8bgNtX50Y8KbG/XuXTpDK+/XiUUMpibU8lmezgcEoGAn3BYZnYWIhGVQKDK9nadf/3XGg6HgiR58XrdnDmjMDVlWOOuYlHEPMRiXcJhjd1dhYMD4c2TyUiMjuooiujQTE8bTE4abGwYNJvCI8jvN3C5RAzFwYHg8BiGGMstL+tEo0Kl1ekIpdbmJmSzCk6nAF2qGuZ/nvvfcPjrBNxukodO1DAkEhqqWiMWE7laR0cM+EUSk5NC3WYYAkxFInB0JM4kTqeDQCBGszmMqhq0Wh1u3qzi9Zb4i7/4CxqNBn/913/NZz7zmcd2+D6qa+Jp6v6D8ac+9SneffddDg4OeP3114lGo+zs7HDixAlLOu31ejl9+jTT09Ps7e2xsbHBu+++yyc/+UlLdVSpVAgGg5YCqlar4ff7UVWVlZUVDMOgVCrRbDZxOp0sLCzQ7XbxeDyWnHxqaoqFwaTB7XajKAo+n490Os3q6iq6rnPx4kUcDge6rh9Td5lldsczmQyZTAZFUZiZmXnotETXdVKpFEdHR4BQUz3O0TsajdJutykUCoyOjjI7O2uNlaSmxH+Y/Q/Y3DYSIwmODo44ODiwrqHX6zE2NkaxWLR4RpOTkw+9LkmS8Hg8eDweK9er2WxSKpUol8v82Z/9GfV6na997Wu89tprj50I/Cxr4gMDPHD3xnxS1c/j2veNVIq1d95h9NIlEhcuIO3tQShE6fnnMa5cEWquUkkosiYmxHjLfFCsrCAdHqJGo2iGcTd6IpmEWAx1eZn0t79NzW7n9PQ0SiaDGgoJnxubjb7TKWTniYQgtW1sIMdiBKpVfMPDDI+PozUa5M6cIZ3Nom1uEuj38QeDhPx+bMEgeiCAvVzGWFrCKBSE23G3K9Ldl5YwPB4ol2FiQoCWyUkB5Gw29IkJ7K+/ju52C88hTRNKsHodfWJC8JaOjkSnqBIZw9kAACAASURBVNcTERMejxinqSqGzYY0PEKg18fIii4XhoGxtPQzf8b33ti9Xo8vfOELfP/737cA0OMAz9WrV5mfn2d2dhaAz372s3zjG9+4/0b+DPD7gz//LfCnkiRJxsNmcR/yuvcQ8KSn/ccBnmq1ysrKCtHoPM1mHJtNxDusrircvCnh9YoRTr8vuhMnTtzt+IBYBhsbHnw+OHNGePG026IzFP//2XuzGMmy877zd+6NfY/ILXLft9oya+tmk2ySojgmRNqyZGsgvQwEGAMYGM+DBoLHwhiQAAMy6EcDMubJfvBYliBY9miEsSAuLXY3m9VrbZlVuWfkFhEZ+77HvWceTsTtqmJVd1WT0xTV/QGJWjL2uOfe73z/Ldrhr/96j7ExLy+/PINh6AQCkuFhA01T0vSBAfW4TqeCmmZmNLrdMPX6AKmUALo4nRVarQw7OzlOTz04nQHK5TDVqsbQUJeXXqrj80EgYHLliommwXvvCYpFjVxOkabjcYnbLdnZ0QiF4Pp1s6cqk1YcRT7vYGtLZ2rK7E16YHhY0mioZPbzc42xMUkoZJLLCc7PweWyoeshCgUNnw+CQRO7XeJ0GoyPS9ptye6uRigkCYdNdF1wdiYA9b5LJTAMwcyMycyMOhyllNTrcHDgJpfz4PFU+Of//J/zxhtvkEwmKZfLH9nwfNbWRL9eZBPw5MZ4ZmaG9fV1/vzP/5zvf//7XLx4kStXrqBpGmdnZ8zPzxMKhZiZmUHXdebn56lWq8zNzTEzM4PT6WR/f9+a5PQnPsVikZdffhm73c7o6Ch2u53d3V2Wl5dZWlqiVCphGAZSSrLZLJ1Oh9HRUer1Oq1Wy8rG6sP8c3NzjIyMEIvFuH//Pvv7+0QiEWZnZxkfH2e5dz4uFApomkaz2SQYDDI5OfnUa2i1WuXk5IRWq8X4+DidTscKD11fX3/m5zcxMUGz2eT09NSKkpBSUiwWOU+e43Q6mR5VhOq+Xcbx8TFSShYXF0kkEqRSKQuae57vTQiBz+ezpm2///u/z7e//W1LVv9RDc9PsyY+9YbnRRLT+2OzR6vvzJz64AMuDg/jHhuDhw8R5+eY8/N063V6GlxIpxEPHmDOz8PKijIG7HSUYWCng7G4iNlPV+90EI0GNbebzXSa2YUFJqamEO++C6enOKJRjFAIzePB8HgwV1bUHF0IzIsX1ZTIbkeLxeD0FPHyy4ysrDAiBHg8dH70I3IuFwe5HM1bt7AFAoSFwNA0WFlRCq1sFi2ZpPPyywivF5HPI1otRKOBMTenFF6ZDFomg/GlL4Hdjjg7Q7RaFpRFIKAmO62WUq8NDqrX6XYjtrcV1BWNItptpMuFlkgoYrTDoeQ3P8PqN7e6rvO7v/u7H7sQ4vG4haeDWojvvPPOkzcbB057x0JXCFECBoDsz/TFf0r1og3P0yCt/ok0mUyyunqVUMhDOi3px+9UKmq8fu+e4rlcvqzgn358Q6ulLtbxuKDd1jBNpWACtZRSqRq3bu0xNDTZgxxUEOj161UGBmzY7eBwGFSras06HOrCX6lIQiEJKGXSwIDOwkIAhyNIJBLl/LzO3bttcrkMwWADTbNxeGgyMGDQbNpwuWBkRMFOfr9BKiVIJNRk6uhIp1BQKjGVu6WiJUIhJX/3eEyuXTNYXDQt5+RKBe7eVXL02VmT1VUDp1OZC9psKqH95ERNjHw+9XdNMxEigNstyOeVN9HVqyYKcZC02wrye/hQkMtpRCImhYKgXldkaZVKr2C8K1dMarU2TqcTu93Ov/gX/+Jjv+/P2pp41BjweZVvT9sYf+UrX+GP//iP2djYYHl5mUwmg8PhYHBwkHA4bGVu9WttbQ1QE5K+wZ7f77dCQd1ut+VdA2pa8/bbbzM0NMT6+joul4t4PE48Hsc0TXZ2dhgeHsbn87G3t0e1WqXb7VIqlTg4OMAwDNbW1hgbG0MIweTkJGtraxY/Mh6PUy6XqVarxGIxpqenmZqaeqa3Tb/pcDqdFkTWbrct/kyfvP2sz3x2dpadnR2SyaTiowrB0tIStVqNo6MjYrEYtVoN0zSZnp4mlUpxdHTE4uIinU6HVCrF3NwcS0tLz/WdPVp91Mfr9fIv/+W//Njb/zRr4lOFtF4kMb1/n0er2+3y8OFDhBCsfelL2E9PkZmM4reMjSm35b09pbIKBhWsMzamAkQPD5HT04pD43Agw2HFkwmFlMNxqUTe6eRwf5/lv/f3CMZiSE1Drq0h9vbQ6nXaCwvYY7F++iJaPK6ah1QKEQgoo8F4HNFuK8fknR0IhxHZLG6nk+j6OqOFAmatRnZzE7NQYKPZRGgao3fv4nM68YXD9NMLxfa2Un3Z7YhaDRkMKsjL71dkZocDbW8P/96eSktfWVHhoD0oi3BYKbUaDUy/H5HPK2NCUFv3nqLMnJpSMvaehPJnVY+S0Z6n63/ahvQp93vaA/3C7mRfVGb+5ITnQwjLTTD4MqenGicnCuLJZiEQUNEIDociCK+sKC+cWo1HpiHKEDAaNVlYaDMx0cblUo9/fJzhwYMMo6OrGIaLkxO4eVMyMQHvvQeJhE4kIolEDFZWTIaH+5wcQSoF8bhGPC56cnUFRynSpiAQ8BKJ+PnGN0zabYPbt/M0Gnnu3j0in3cjhGBuzkcu5+81JMo80eUSvPRSl0JBydMrFUE2q1EsSh4+FD2llmq8/H6l7DJNyd27AqVnUNEYJycKWm82FV8oEFCS+V56Dbu7Gj5fl3weDg50ul31vo+ONEv55XAoXlA4DFevdnE6sZqrfF5lhoXDSlk2NibZ3FTf9fPC+p/lNfG8Dc+TE54+eXh0dJRMJmNNYoLBIJFIhL29vWf6+vQfpx+1MDU1BWCFvdpsNk5PT4nFYta/a7Uax8fHVnr4/v6+lUkF6oIMivu4sLDA8fExPp+P8Z5x7ujo6GOvQUpJp9Ph8PCQRCLB0NAQHo+Hk5MTKpWKFS7aHwik02my2axFhD8+Prbc2IUQbG1tWZL369evP3VzZbPZmJubIx6P8/DhQ+bm5vB6vRZZ2+/3E4vFLBfl0dFRYrEYqVTKUmX187ZetPrJ689L4v9p1sTPBdL6JFWpVNjY2GBqakodQO22kptvbcHgoIJjbDYMl0upnLJZ5MyM8srxeBClEuJP/gSGhpAXLyKyWfRmE8Png0qFo0yGitPJ2uwseiikHj+btTKmpMulzA2rVUzDQCsUIJNB+nwIwPR41H0uXsTsdBDxONrOjjU9MaNRBT15PNgOD/HabDRHRlhbXaXbaNC5f5/cwADpbhfj7l3cKysMOhzYZ2bQBgeVHL2XF8aFC6DraKenmIODKt6iUFBQlZSIeFzFTkQiKlW+XFYu0DYbhMPIRkP5Do2OIkxTTYAA2VMM/KzqRXO5JiYmOO2bSaIkqk8JHT0DJoGz3oQyCOR/+lf76dfzpkM/eZ/+glcQ1gO83kVsNjXOGR2VNJtQqQhKJXrhmE5CIbh2TcE+hYJKPE+nIZeDkREVBup2CxoNW+8EZPJf/+sJrVaHL35xiUpFTXJCISXtTibh4MCFz6diH7zeLktLJm43OJ2Se/d0dF0pv/x+KJclgYBqDk5Plczdbhdcv27g9UK9rmOzufB6gwSDY7jdXWy2IicnaR48yCCEk04ngN3u4fJlnQsXTA4ONNJp1dzNzZnk84JmUzVye3t23n1X4/hYw+9XsRG6rsjSL7+sJjT9uI333tM4Phb4fKKXBKNMDmdmTLJZ5TN05YrJ1JRpfbbVqpoqnZ3pqPg6A9OkByNKwmE4OBAMDCjvoeFh9Z31T+7Pq1r8rK0J+GSbgP7t+w7+S0tLfOlLX+K73/0uAI1Gg1KpRLVaJRKJPJNWYbPZWF5exufzcX5+TjabtYJCdV3n+9//PtVq1YpumJiYwG63W83MBx98AMDIyIi1TvvqpT7k1Pfx6XQ6mKaJYRi43W40TSORSLCzs0M4HEYIYQWTTkxMWKqn09NTyuUyjUaDYDDI7u4uc3NzhEIhNE3D7/fjdDqt2/zKr/wKAwMD/OAHP7CuoU+bFLndbq5du8brr7/O66+/zsrKCg6HA7vdbvkNHRwcWGq1UChEuVzm5OQE0zQtCO5FyzAMms3mp7ImfiEanrMzRZa6fPnyh3JFh0MRget15NycOtN0u+oCXiio38/PI1U8MzIeR/vhDzEDAWUTm82i1WrIRIKHmQzOK1e4GI2inZ4iYzHY2UFrNJA3byInJrDlcgivF2N8HHc6rUjG1aqaLkWjSvpts2G63YoF6nAgUim0nR26v/EbavJSr6tpTS6HEQopXlAyiaNWwyUlnpdeglyOdi5HMZcjVSiQqlYZqFYZKBbxZrNIpxNzdVXBc5UKWj5PbXISGYmgpdMqtLRcVo2Q3a6I1vk80u1WarR2W0Fhx8do/U5fRUIjeyqEn1Upw7s2rv644GPq5s2b7O3tEYvFGB8f50//9E/5z//5Pz95s/8H+G3gFvAbwGu/yFwFm832QjAvfKjCOjlJ4vFcJR73MDUlWViweteezbyCWzodFfrp9aqJSD+OoVYDj0dw9arysjk9hVJJ5+23BcnkNu32MHNzgxwdaQwNSSYmlLvx4SF0OoJGQ0fT1EXd7W6RTAqcTgUFZbOChQXJ/Lziu3Q66jB7802d/X2NGzdMRkYMTFNQq6nfeTwSv9/ANCWaZiMQGCAYjHB6KshkuhSLLRyODLFYmU7HwcHBMHa7m1BIw27XmJmRrKwYHB5KHI4SN2+O0GjA5qZGrabh9SoISkrlRTQwIHsu0LC2ZvKFLxhWM5NIqPul05JIxE4kohqmSERFWpRKis8zOqpIz+224PhY9L5ThZI7nXDhgsmjimHDMGg0Gh/pMP9ofRbXxCc11zw8PCSTyVgO/teuXeMv//Iv2draYnZ2lkqlwu7uLr/+67/+kec5IQQTExM4nU5OT0+5d+8ebreb09NTotEoFy9e5Pz8HLfbzcDAAEIIgsEg9XqdbDaL3W7H4/FYcu12u43H47F8tzKZDHfv3mV6etoiCC8vL2O329nf3+f8/JyxsTFmZ2ct9RN8qHry+/2WIWy73cZmsxGLxbh8+TKhUIhQKEQymaRWqzE6OsrU1BTNZhMhBIlEgkKhwNWrV59KLh4cHOTll19me3vb4vTUajUajQbRaJS9vT3Ozs7odDr4/X46nQ4PHz7EZrOxsrLygt+0qn7D87wTnp9mTfytbni63a6l+HlauKgcG1MEXbdbkXSzWdyJhNqGRiJK2aTrKl09k0FevowYHVVQld+PsbFBJpNhYXycYCikrgiVivLOmZlBvPeeeqLJSewbG3SWllQiezqNubaGbW8PMxRCjo2hpdMYq6sKzkokkFNTiDffVFOXYlGZGNZqiFZLpaVHo5jJpILflpeRMzOKexOJ4L53D9fYGHJ8nPFgkOroKPzJn5C02WiMjKDv7hKYn2cgnUaz22lMTSFHR5F+P/qtWzA6ivHFL6ppkJSIXA65sKAmRL3MMNHtYoyNoSUSitx8dgaNBtrP0OnyRQ9km83GH/3RH/HNb34TwzD4J//kn3Dx4kV+//d/nxs3bvCrv/qrAP8e+L+EEPuoXexv/cxe8M+hXnRNdDodGo1G72T2EqmUjs+nGpDNTXUIu1zKULDVgqUlicvV7LkiK+Lu7q6CgfqKrBs3lL9MICB5+LDLxkad6ekZvvAFL3a7JJUy8fsFh4eCdFowNqa4PwsLLS5dEkxMaNy9K9ja0tB15evj86mpy+iouu7WanD7tk42K1haMvna1wzsdjAM1XTkcko5Va3q9KfTTqcklYLzcx2vV+PCBTsul4+xsRGKxQaFgkpyttkgEHASCNh4/30fhYLAZlP8Gbtd8OqrJkNDJsmk4N49nUJBkMlopNMCu10yOKg4S3Y7TEwoyO3gQD3G8HAXn085R8fj6jPrdBQvZ2AA1teNnkGjpNGATEbw4IEiNi8sKOjs0TIM44UmPJ+1NdGfer7IJqC/JjqdzmPhopcuXbLMBj0ej5VuHgwGrUTyj6qhoSGrkTk8POSVV15hbm6O8/NzfD4fMzMzGIZBvV5H13UrALTdbnPnzh1LldVqtQAIBAJcuHCBYrHI4eEhuq5bkRG1Wo1kMonX6+XVV1+1YLBHyzRNkskk6XQah8NhDQAajQbb29uUy2VM0+SDDz7g+PiYkZERut0uhULBSmhPpVKAkqQ3Go2nTUaYmJig3W6Ty+VwOBycnp6Sz+eZmJjAZrORy+Ww2+0Ui0X8fj+FgnLp/2l8pV7Eq+2nWRM/F1n681S1WuX+/fvous6lS5ee3pH3AswAFZ8Qj6NXKpg3bqCVywqINwzEj3+sYK+VFQV1dbtkNjaob24S+dKXCE1OQqOhHJtTKeTQEFy5AqenllmgGQrRDYfRDg+x9dLRzb7XTSajyMDr64gHD9Sk6fwcRkcxx8ehWkXLZhFnZ5jr68jJSTRQERDn53Reegnh8SCKRcxIBK3TUblbTid6sYhvYABHq0Xw+nW609NU83lSh4eUHzygOzxMfXSUerOJKxBA+HyYhqGIGYahSNQOh3J/9njg/ByhachwmL6kRsRi4PdjatpTgc9PWi96cgf41re+xbe+9a3H/u9f/at/Zf1dStkE/sef1Wv8edeLrIk+hCWlnQsXLtDpqAu006kailIJUinBnTsa8/NqumKzweamjaEhCajpRTCooJ16XfHUHQ418dnfT1CtFpiZGeellzwEAhIp1RTo8NCKrKN/6AghyGR0qlWdhw+9GIZKV19cVEnpwaASRt6/r+IeIhHBpUsmDseHkyhdp9dsSEZHuwSDLRoN1VBsbWmUSsoJennZwGZTkFU2qyGEj1BIuTiHQh0cjhr7+zUqlSxCOCgU3AjR4eJFB5cvq/BUl0sipcnoqHKC9nrV5Ov8XHByIqjVBF6vpNOReDyC8XFJINDB4WgyN2dSqaim7/hYx+2WvdDTPp9HfValkmBqSjI/b/K0IY5SbdVf6OLwWVsTL8L17ENYdrudpaWlx/gdoVCI5eVlstkshUKBYrHIr/zKr3DhwgXefvvtj1WC9cUApmny9a9/nXQ6za1bt2i1WrhcLk5PT2k2mxbnxul0Mjc3h8vlIplMks/nGRsb4/z8HLvdzsLCAg6Hg0AgYKWit9ttarUapVIJn8/H8vLyM92Gj46OODw8JBgMEgqFSKfTJJNJyz26nwMWjUYJBoNWs9PpdCzvpwsXLjA/P8/Z2ZlFcn4avNWHsNrtNtPT0+i6jt1ux+l0WuaEp6en1vRn/lFvi09QtVrtU1kTfysnPPF4nOPjYy5fvszm5ubzPXgvi8r0eJBTU8h2WzVBDx6otPELF1ROVTrNzhtv4KzVGJ6ZoVmrKU6LaaoLv8ulSMmhkJpJl0rIaJTO+Di2eBxzZAQjn0fYbMi5OaWOGhxURoD7+6q5KBQU1ycaVcThoSG0+/fRMhm0Bw8wvvhFSKdpj40h2220npJLpNPoxSLCZsNYWlIToa0t9Pv3MYeGMG/eRKtWCZ+eKinrtWu0gkEy9TqpkxNa9+7hDAQIOp34ymVcsRiy59Ujul1Mh0NFTMzNqUat2VR/hsMYQ0PIRAL9Z6jUMk3zYyWGn+V63k2AlJLT01OOjxMEAle5cydGNKpItoGAah58PvUzPKxypWZnlS9NLAaplOLa3LmjLuhXrvR9dWB0FFqtLt///j7FoptIZIxk0k6loi4EhYKSpzscanqkUFNFfo7FdMplDY/Hxv6+i1BI8Eu/ZLC0pKYdug4PHmi89ZZKUV9fNxgbU3EPzyolA1fvxe2WLC8rErTbraYnzaYKHpXSxOFQk5RQSKfZDDI1FcDhkNy/3yUeT1MsZtjfL5BKeQiH/QQCQc7PHdRqGuEw3Lhh9D4jZXzo95vcv69MBkOhfrSFYHDQjt2u9k8OB7z0ksHgoLTIyZmM4OBA0Gh82Ox8RNbl52viY+p518TR0RHpdJpr165x584disXiY87JQgjC4TDVatXi7Iz3rEROT085OTlhenr6qY/f6XS4f/8+fr+f8fHxxwjUm5ubXLt2DafTSSQSIZ/PY5omU1NTpNNpBgYGGBwc5LXXXuP1119namqKaDRKoVCg2+3i9XoZGBjg4OCA1157jdXVVcbHxxkaGvqJBqw/QapUKty/f5/BwUEcDge1Wg2bzYbNZsPn8+HxeMjlcnS7XUZGRohGo9aQwDAM7ty5QzKZtEjMpmkSi8XY29tjcXGRq1evPva8KoJmlt3dXcrlMoFAgFarRbPZ5OzszOIW9dVRfbn6J61Py43/U294+uO9p5VhGJYZUx/C+lizwoMDAES5rCTZ3S6mpiloJpFQEFI0ClJSF4Ld3V3GvF6GL14kn0wqeKdQgGoVefEicnER0WyCEEoVVSxiXruG7bXXoNnEvHIF+49/rHhCgNQ0zJER9I0NtO1tzOlp9FwONE1xadbWPoTYADk+jhaLIQwD3TBUc9ZTS+lvvQUDA2oqVK9jhkLo2SyEQpivvKJ4Oz3ej/D7MS5cwNHtYkulmOuZFVbm5qjt7bFbrTJw+zZaLoftl36JULWKmJxUjs6Dg2iNhiIa+P30yQlmPI7t0djsn0F9bqH/0fVxJ/e+yVm77cTvfwkpdUZGmni9Ki4hl1MODKYpefll1fysrn64y52dhYODOp2OilNwOJR5nq4r3kyxWOPOnX2mpsZ49dVBcrk4DodSUxkG7OwIpFS3NQy4ccOkJy7BNDU0ra+e6jIxoXgx7bZqdHZ2BKWShsMh+fKXDa5fV+nnH1XZrN1KaB8elqyvm2xtab3nV+TjVktgs2kEAlCvazQaSgHl95tks4J63YHPJ7h2bZyRkTHqdbWD/tGP4hwcuFlcFKyvO/H5fIRCGkIo4rPbLbh50yQaNWk2Be+/rxymGw035+c6pgnT0yr3y+NR0vVoFFIpRWQeGzNZXPzQ4PFZVavVPm94nlHPswlot9tsbGzg9Xofg7BcLtdP8AWz2Sybm5t84xvfABQftF6vUywWyeVyFoH40SqXy2xsbLCwsGD55EgpuXbtmkWuNU3T+g7r9TrBYBDTNK1k9X6wZi6XY2xsjEQiAXyoKCsWi0gpmZubY3l5+amcrlqtxsHBAZ1Oh7GxMSYmJhgYGGD2UWSjd7vDw0NrypLJZIhEIrhcLoto3IfB3G43drudarWKYRhsbGzQarXI5XJcvXr1sXBop9PJzMwMqVSKg4MDFhcXuXLlCnfv3mVjY8NSwem6/sLRFk/W37mGpy9Lr9VqT/19tVplY2ODyclJxsfHrU63T0h7FqteKNtYmJtDLixgu39fkbwSCcTDh8iFBUQgQOHsjIfHx6ytrOAXArm0BIUC3UAAOTmJuHNHbWFDIfV4+bwy6fN6IZVSUBmglctIIRDn5ypY1O9X055gEPq+NqaJ6M/t63XlseN0Kl5NKoUcHsbxzjuIqSklJa9WkU6nanJ6ajPt/BxjYAAaDcxr15CDg+q+9TqkUshLl2BiAgk4dnYUqSIcxhuN4q9UGJESvvAF6t0uxb09dms1Gq0WI50O/loNr9uN7cEDzIUF9UFKiTRNbJ9QRfes+jxH66NL13VLQvpklctlNjc38XgWKJdHGBhQZoHNZpvpaQNN01GRbsr8rl5X052HDwUej7QOZ69XMjZmMjurMTUlabVUA/PuuznefLPYO+G6uHMHOh07zaYy/CuVBC6Xip+w2WBw0CQSUU1AuQylkka1qrO4aDI2ppqwrS2NRELj+FgNSBcWDDodJRs/OzOJRBRk1G7z2BTENJXiqVZzMTAgME2Ym5MW5NTtKg5SraYMA9fXu/zwhzrJpJoE1WqC3V2NblcQChlI2cbrVdOibteHafrx+xV/aGWlQKVS4q234gihUyoNUqsFuX7dzqVLJgMDyt+nXheUSm3Oz5XEPRiUmKaSq+u6anhUFrCCv2ZmlMHhx9Xna+Kj66Manj6Etbi4+BONytOaBrfbbcUZvPLKK7hcLgqFArlcDsMwODs7w+VyWQnj8Xick5MT1tfXre/oUdm7y+VibW2N4+Njzs7OrFgGTdOoVquUSiU6nQ66rhOJRPjGN76By+Wy1E6VSoXT01O63S7T09OMjIz8xFTHNE3Oz8+t5mlqasqaNJ2fn1OpVCzxTqVSYWdnB1DTq263y9bWFufn50SjUSvuweVy0el0CIVCeL1eqtUqfr+ff/yP/zGpVIpkMskHH3xAMBjE4XAQiUSIRCL4fD5WVla4desWt27d4saNG0xPT5NIJKhUKjgcDvx+/3P7iD1ZfQjy71zDA88+kB+FsJ4Mjfs4K30ZCChOyuwsdDoYyaRKQN/aUhOWsTFi775L8/iYl155BUc/O8vrRfh8aKkUIhRCrq0pMoFhWLlaPTYoIpGgOzmJ/fAQpKTZy8ky5+eVS3E2i1xaQrt9W6mholHQdTXt+fGPwWaj++qrShru92P6/ei1GrZiUaWiO52IwUHklSvI5WVEsaigrIcPFTcoEgFNQ7t7V015gkEVUArIeh1HNoucn1cNlssFhQKaaWJevIi30cCraUzeukXD76cCZE5OiJXLDMRiiMFBBkwTR6WCSCSwvUBo3/PU57vZZ1d/N9sPDexXH8KKx+NcurTG7q6PZlMdmgcHgmzWQy5nYBgqdmFkRDUHbreajHi9knRaTSiCQUmh4MXrlb3nk7hcJicnu9jtTX71Vy8xMWEjn5ccHQlOT51I2ebNNzUrxqFcVuTfSKSfME5P1u7A5RKsrnbw+Sr4/SaNBuztKV+bl182+OpXTe7eFWxva6i+X5DPCwIBBfso4z7lbXNyYmdsrM3srCSdVk3QwYGgVFKGgq2WIjJPTZmcnWnU64KZGUk4LDk+Vu+13VYZup2OTr2uJlqFgnqe0VGTRkPD7w/jcIQZH4d8vsPpaYtqtcDRUYZMxkk4HGB8PMD5BfsJawAAIABJREFUuZtUSiMQ0Hj5ZYNgUH2+lYqSs+/taRiGUmJNTEg+gg7yWH3e8Hx0Pe068SSE9bTm5mmcHK/Xi67raJrG6OgoV65cod1u89Zbb5HP53E4HOzv77OyskIsFqPb7XLz5s3HLuBPGuD24Z7x8XFCoZBFTg6Hw5bbcjgctpoGXddJJBLEYjE8Hg9ut5u5ubmnTr5N0+Sdd96hXq9bYcy1Ws1SRqXTadLptKW82t3dxW63Mzk5yfn5OQB+v59MJkOlUmFpack61g4PD5mamqJer1t8H6fTSTAY5Pz8nLOzM8bHx5mZmbHk5tVq1YLg4vG4pUgrlUqcnJxYafKftPrWJZ+JhscwDLa2tjAM46kqLPiIPK1++f2KiCClgmsCAQVzuVy0nU42334bn8fD6vw8Ip1Ger3KgdjvR7pc2I+PYWpKpY3b7R9Ohnw+5De/iXjnHdA0zHAYrVaDSESpvAIBzJkZbHt7SK8X2cvKYmxMwUN9X/7+1tTpRI6MqJTz2Vka168ji0VELocWj2P80i+pWIhGQ5kiHhyoic/Vq2C3Iz0etFgMc3YW49IlNV0yDPTXX8dZKGDOzaHF44ogXSqpoNTRUUW89vsRnQ6ugwMc8/MMut3IQIAOkA4GOX3wgO7mJu52m44QdDqd547/+Lj6/OT+0fWkIqUPYXU6Dm7efAmbTWd9XU0WSiU1WalUHPz4x4KBAcHYmORRoYXNpvr0QkHxeCIRyZ07GkKYdLuQSLT43vcOuHjRw9LSMufnGoOD6jEuXJBsbTW5e9dgYkJBM52O4rNks5KDAzg5EYTDktFRmJvr4HQaeDyKo3N6qpFMqsDPkRFJz9MTwxCUy4onc36ukNRoVPRS1gWvv65zdqYyrgYHu5imanZiMQ27XSKECi0NhRTMpuTvMDamzgudjiJdDwyopiMc7hKLNZiYkKRSglpNIxBQt61W1WTG6VRcolzOycCAk7k5P8vLQ7RaDUqlMrdvn/LBBx48Hp3Ll1vkcgamqeP3q31RLqfys6JRaSnRPq5M00QI8fma+Jh6kvrQbrd7k07PYxDWk/d5mmN5vWemGo1GCQQCxGIxVldXmZ6exu1243Q6icVixONxLl++bBn0PVrPugb1vXcODg4oFAr84Ac/oNlsMjIyQqPRsGIkRkdH2d3dxTAMZmdnGR0dfWaTUKvV2N/fZ2pqCiEE+byyU+rTO8LhMOfn5zQaDQYHB1lbWyMcDltTpP60aXt7m3v37hGNRhkeHqZeryN65/YHDx5QLBYRQlAoFBBCWInwzWaT4+Nj5ufnrWzEWq2G3++nVCpx+/ZthBCkUilcLhfj4+O8//775HK5F/uSe9U3mKzX6wz2DH3//6xPteF5lH3fh7AmJiaYmJh45gHwsQS2/omjl4flPj1F+nyUl5aI/eAHTNy4weDSknItdjpVY7OzA7UaeqkEfY6PEOoqoevKTFARHBDNJmbPldn0+dDyeXU2HhiA6WnM1VU15UkmkfTsTe12lW11dqbCPCcmlBy8F/lAo4E5NkbHZkNmMuhbW8jBQTWlAoyeO7S5tqacorNZdYZttTAnJ9U0R0qlOOt06Pr99LzuEXt7Ch4LhZTCrNVCVKuYly8jhUDLZpVr3Pw8jqkpxiMRuHgRcXxMQgga2Sz379xBahrhcJhwOGwZWr1Ifdqjyl/Ustvt1vHdh7C83nlKpSgffKC8dQIBBf8MDamfcrlJINDG67UTjaqJxvm5gp0qFZWVNTqKpdIqFFp4PCavvVbj7CxOKDRNtepjZ0fJwYNByeQkPdgGQiGDy5cVJFYsqiZrZ0fw4IEgFoP1ddXQ+HxdXC4bjYbOzo6XZlNjctIgGlXOzfm8WtMLCypuIRAwcTr7zshKfZVKqanN6qoklerQammcnKiGJhg0eynlyi1ayb+VuWI4LHnvPZ1mUxGdp6Yks7OqOdrZkei6incIhWB9vYvbDUdHgkZDWp455bJgaEj2YDY1HDVND3a7B7t9jNlZmJg4wW5v8N57+xgGeL0BIMjkpIfpadX0PG/1T+61Wu0jAx0/y/WkI38/jbzPp3lWPa0pkVKyv79vxRbous4HH3xAtVrF7XYTCAQYGBggFosRCAQIBAJPvQ4JIT5y0z05OUmpVLIch+fn53G5XJY54cHBASMjI0xOTj7Vibher3NycsL4+Dh2u1JgRqNRRkdHSSaTAJYrM8Ddu3fpdDpMTk4+9fWapkmn08Fms1GtVsnlcmQyGWKxmCXh93g81nt+NLU9GAyyt7fH3bt3uXz5siXn7ztDP3z4kPfff99q4Or1utUkvajRLDze8HwaXM+fy4QnkUhwdHTEpUuXLOz0WfWxE55eTAR2O6TT+A4PyU1OchqPc/XKFVwul1JfgWoolAYXTk7QOh3qi4uqeanX1e00DRYXkd2umtBUq2ipFO3r16lsbuL40Y9oDA7SLZcVX2dwEH1zU7kWezyYveR1/Z131NWi/3z1OqJWQzs6Us7LLheaYSDHxz/M/kokwOHAlsshAwHk6Khyd75/X2V2jY8jHA6k260mUbUaxuCgavYcDuWo3GphzsyoxsrhUPL4YBBzbk41PoaB7b33MKamFMGjXldNYzSKzecj2OmwfOEC3R7Wnc1m2d/ft3DdcDiMz+f72DHmoyf3J2HKz+vD6k94lAorTiCwTrfrxe+XOJ2C42N1O49HEZNDIdB1jeHhfp6TaoIcDtlz/lXKpb5KCqDb1djYSLO/b/LlL88wMuLoR81RrSrfnFpNSdzzeZ1sVqdSoec6rJqXtTXVFIRCgpdeMhkYEKTTUCjk+A//wcnRkYfV1S5ra5J8XqNSUQ7DDoeaCjUaqglT6iY1aXnzTZ1OR/QgIZPvf9+gXtdwONTv83mNXE7BUScnqvEZGVFQWDYryGRUaKfbLS3Lrd5SI5t1IiUsLn4oDw+FVLPkdqtGZWZG4nRKtrc12m21jFotSbGoEQ4r76GRETtDQz4mJiZJpzu8/XaDarVIsbiFrtuo1xVs4fF4PnZN9POhPq2T+y9q9dfE0dER5+fnXL169WM/r6dtjJvNppVi7vf78fv9uFwuzs7OLDir2Wzy67/+6xweHnL79m1WVlZ+wgPnaZmOj5bD4eDatWsMDw/z/vvvc3Z2RiAQIJ/PEwwGGR0dZXR09CeaASml5avTn0y5XC5CoZAlaX8Spms2m9Trdc7Pz5mbm2NgYIBOp0O73abVatFqtazIB5fLxfb2tqVSczgcjI2NWaqu/uOenZ0hhMDr9ZLL5ajVaty9e5dut2slqIMaWCwsLFg5Y0NDQySTSQ4PD9nc3OT999/H5XJZUJ7b7X6u60Tfh+jvHKQlhLBGac+CsJ6s55Kyz84qldXhIQWHA72Pw8bjiN1dGBhQsE4shlxZUfydeBwiERrRqGqWegejHBlR3jRHR0pBtb2NWatxfHRE1GbDNzZGqtXi/O23qaXTeMfGGMrncQwOqinL1JSCrcplME3lf6Pr6v88HmQ4jIxEkIaBXi6D3U73q19FSIlWqSA2NjAvXaL7a7+GcLmUWWCphDk9rcwMy2XF0XnrLeTKCsbVq4iTE9Xs9GX54+OIw0NIpVTERChkRUzI1VXMeFxJ0vN5RKWiGkHTpNv7jkSzic3nY2hoyJIbNptN8vk8x8fH1Go1fD6fdWA/zTfiUWz2yayYz+vDklKSz+dptWy4XC9hGDqTkxL1sSuvnGIRtreVT4wy2dY5OVEKoUBA9cqDg+pncVG5Grdaqo/udru88YadSkXj8uVJvvQlpUjqC/T29tRkSEp6HB4HZ2cm29uCTEZBWar3V34zMzP9qVOTra1Dzs8n6HaDCFFgdzfFn/5pk4UFJ6YZYmLC2XuPqilxu9VrPT5W6i0hJNPTqlnJZlU4Z7ut02yqIM5ORwV8Nhoa5bJKSm+3VTM1Niat9xmJYBkrnp0JTk81IpEu4+OPe+H03Z5BfU494SRSKo8dKSGZ1LHZlGw/FhOcnWnY7XZ8PkinnczNOZmbC+D1jlmmqIeHh9Trdfx+v7UpeNqa6J/cP596fnyl02lGRkZ46aWXnmtq8LRQ3Uwmw8TEBCsrK5imSSaTYWBgAE3TemuuxUwvHT0UClkNVt9B+dHH/shNd6/6rst9cnLfODeVStHtdi0ScB/WvH//PpVKxZKxt1ot2u02uq6Ty+U4ODigVlPBv5qm0Wq12Nvbs6Y3b7/9NuPj4481Y/V6nVQqRTQaZXp6mpOTE/x+P9PT09y/fx/gsWOv2Wyyv7+PaZrW83g8Hq5evYrD4aBUKrG9vW25KHs8Hiv8Np/P4/f7sdvttFoti5eUz+fZ39+n2Ww+tiaell/26Cbg71zDs729ja7rrK2tPTfR6bkONilpbW2xs7NDd2GBaadTqY3UVs4iEROPQ8/UTwSDqok4OVHkiFbrQ0/4vqdAIkHD7SZ5/z4Tfj9mtUrF4cATDjP60kt0222qjQalSoXqw4cYq6sEt7cJmSbOnoJLjowo+KnVQkYimLOzqtlwOLDl8zAzg/nlLyMKBcTREbZaTU2XAgF6Jh8KdnO7kVNTkEignZ4iKhVMmw3T61UJw4eHCsoKh+mf5YXLhbGwoCIver47dDoqiqPRQCQSaLEY3Z7FuGi1sPUDmJ7AU10uF2NjY9auo1qtks/nefjwId1u1wrmC4VC1kj60zyQf1FrZ2enZ0p2gddeU/EIrZb66n0+1Yf3fWFWVxV3ZnNTo1CAZlM1JfU6XLwomZ5WsEzfx6ZWq3H//n2mp92MjYXx+wVHR+p5h4aUUV6jIfD7Tebm4Pxc+e7ousn5ubrg1+tqirKwoGCjWg0KhTIPH25ydjZFqST55jfjQJdqdZlIpIUQVe7dy/Ld79a4ccOgXh8im40wOalbgZ5KNaYakmIRNjY0TFMwNtbpHfImi4tmzw9IsLpq9mItlHKs3VbLVSnGBO228gc6PNRpNg0GBh6Xh9frKg0+kxG8+qrBo4Nl0/zwXHT9ukE0qqZQ6bSgWoXDQydHRzpzc8pjp698drvdjI+PWxedSqVCPp/nwYMHGIZBKBSy1sSjgZifr4lnl5SSnZ0d3G43q6urz32/p4lb4vE40WiUr371q5TLZYtrUywWmZmZYXx8nEQiYfnMDA8Po+s6JycnOJ1OC376uAkPQD6fZ2tri4WFBer1OlJKywyxn4De3yz6/X7q9Tq3b99maWkJIYQFXfWrn+HlcDgsSC2RSNBut5mbm6PT6ZDJZPD7/QSDQcsUsN/YTU5OommaFY9RLpfJ5XIWl6lcLlMqlSiXyzSbTaLRKBMTE4TDYQ4PD/F6vSwsLPDWW2+RTCap1+tcu3aNbreLaZpEIhGOj48ZHBzE6/WSzWa5f/8+X//61/F4PExMTGCaprUm+iny/TURDAZ/LmviU2141tbWuHXr1guxup9nwpM9OyN5+zZTX/kKW+Uk22c7zB2HGKqhGofRUXXGA9jdhZERZG/EqBUKiHxeNSc98pa02cAwKG1ucmi3c2F8HK1UIq5p6B98gHHzJnuBANG9PZxXruBeXCR4+TJNnw/5539O0uGgOjRESEp8zSb+Wg0mJ1Wzo7bNyGAQIxhEDg8jBwYQ+TxyZARzeRlhmmjvvIN2cIA2OakgLGUWgjg7Q4CKvpASabMh7XbVFE1PKxgO1La6XofhYcXZabfRjo+RfW6PEKqJ6nYRlQpIiZ5OY2s21VXkI0oIYY2Ip6enMQyDUqlEPp/n6OjIwsxbrdYnGlXm83l+8zd/k6OjI2ZmZvizP/uzp+a+CCEMYKP3zxMp5a++0BP9Laj19XVu376N3w9f/rK6wGcyCi4CdWH3eFTD0b9IOxw6c3MtslnLQolaTVCrSasZeP/9HPn8AV/5ykWa9g/oBt8lGr2EVwxSLtOb2gguXjTpdgXptOz1yiZbWwY2m4J/5udV4KfNphqidDrP1laBiYnr1Go6dnsShyNHs2mjXj9gft5JOBxCygh2u2R2tsKtWy329885OuoCfoaG3Fy86GB6WkNKKBRUVlcmo5oPTQOvV3BwoOF2SzRNYrNJzs40Oh2V8H52JnqJ1CYulyJE1+s6DodgYqJLn/Nqmqp5PDrSsNno+fV8+Pn3oT2AxUXJyIi6sIVCEI1K8vkOnY6LkRHJ0pLJs7j8QgiLE9GPHCgWi9YEyGaz4Xa7P18TH1NCCNbW1tje3n6h+z15nWi322xvb1OpVLhz5w5TU1O0Wi02Nja4ceMGBaNAOpVmcWCRZrNpSdUvX75MuVwmFouxvLxsNRwf1fCcnJyQSCRYX18H4P79+1bExNbWlsWD9Pl8VvZVNpslGAxamVShUMhKQAeIRCJkMhlCoRAul8uCh6anp4lEIpaHjtPp/Amy76NmipFIhFgsxu3bty2perl3fvd4PExOTnL58mVrImmaptWUHR4e4nA42N7ettLaXS4X2WyWkZERy+dneHiYbDbLm2++yde//nXruTVNIxgMEgwGmZ2dpdvtUiwWyWazHBwcYLPZcDqddLvdT21NfOocnhetp40q+6UCEg/J53Ksr6/z/fSP+F/u/iFXk5LwG5L/6X/43/napb+vnMGGh1WwpmGo6IhoVHFaep46MhxWCqt4HFmrkdzYoFkosPJP/yn8+MfEf/xjHOPjjFerjIVCtO12Wp0Op06n4sWUy4TbbcI+H/5IhM7FizRPTynm86TPz2npOr5AgOFmE5eUaPU6zclJRLerRonvvAPRKN1vf1tJ5U9P0TY2MDsdjF/+ZbRcDqNYRNvfx1xdVQ1POo0sldCkhMFBzNFRtP19xe9JJBRhempKwWVHR6pxcjrB7UYcHiK9XsyLF5VLdSqFrVDAmJhQDdULfq99eAvUySYej7O3t8d/+2//jY2NDWw222OL4aPqO9/5Dr/8y7/M7/3e7/Gd73yH73znO/ybf/NvnnbThpRy/QVe6t+66pOWbTaVWj4yohRZfa8bn09NRHI5NaEIBCS5nIN4XCccVgnoAwPqwq2oYJL//t9PefBAcuHCdf7Pv/l/+aN7/xqHp0lHa/J/XPp3fGvllwkGFY9F1yGZVNlbhQLs7DhJJDpcv66MDJNJxeOp1Uzu3k2Sz5tIOUu12iGZjONyBVldfQWXS7Cx0aLZzPHGGxkKhSSrqzrdro+ZmTCVio1GQxKPtzk+rvNnf5Zifr7LzIwPrzeEzebqaQG6zM+bxGKCiQkTXYc7d3S2tnSiUcnwsOLwuN2SoyMNXVeZYaEQzM+bBAKSQkGFeZZKanJULqtE98FB5dfT3291OnBwoDhDLteHzQ6o/YJhqPeukmZMXuT0pes6AwMDFjTSarU4PT3l7t27fO973yOVSvEHf/AHfPGLX3yux/ssrQmHw/HCIdNPIgG7u7vs7OywsLDA3bt3SaVSTE5O8pWvfIX/9PZ/4nvN72EftOOwOfj3f//fMxWcIhaLsb29bQWNtlotHA7HM1EG0zQto9yrV69aarK+SWA/RiSbzfLee++RTCaJRCJcv36dyclJi3vj8/nIZDIcHh7idrst6Cufz1tZhAMDA5bnDajjKxgMUiwWmZiYwDAMTk9PCYVCVoZhvV63YNeNjQ3C4TBDQ0NWY96frDwKNWUyGUuyHgwGGRwc5Gtf+xqhUIhcLsfu7i71ep2lpSVWVlYsGPDo6MhSaz0tqgIUB2hwcNBq0Ppk5zfeeINbt27xe7/3e/zBH/zBT7g+P6s+yZr41Dk8L1rP8uHpdDqW2+b1Gzcob7zHv/2rf41twI60SyrC4Hf2/i2vT79MqOlH+nzqChIMquan20Wr1bBns2CzKa+bgQE6Ph87P/gBQU1j6uZNyvU6R9ksswsLBMbHkc0molrFIQTOYJDVuTnMpSVqW1tUzs5InZ3RrdXQhocJ+nyMTE+jZzK0NI1KIsGBELjffpvG2hqarmNUq+g9AnT36lWYmVHuzYCt1VJwWKGA6XajHRwoXpDbjRwcRDs9RTs8RJgm5uAgfSMW/fBQGSGaJrhcaoLV8wUS7Tamx6NUYD2itfT5VJp7MokZDqsz/ZPOcC9QDoeDUCjEjRs3ePXVV/mt3/qtx1QGH1d/8Rd/wQ9/+EMAfvu3f5uvfe1rzzqQf+HrafyEnqH2YynbTqeCcNJpQSzmpl7X+PKXVbMDatJTqbT567/ew24P8pu/OU5JJvjf/vjfodVHkXUHZniTf73xvzJvv0PQ60bTVEL50hLYbAquajRMDKPGzZvqdbRaUCx2uHcvRq0WxuUaYGqqCuzj883jcPgJBk2SSWi1XEg5zvw81GqSUKjI9naRhw9PyWZdLC/b+bVfc5HLBXG5gqRSBplMiXj8lL09nVpN7TIHB03sdgUhVasqVkLXlZRcCGX0p2mKZNxvWNbWTMJh5f9zdmajVnNaRoR9mO/oSKNSEVy5YtJuq/ua5ofRHI9WIiFIJAQOR5fZ2e4LNTtPq77fyVe/+lX+6q/+it/5nd8hGo0+9/0/S2viRcND+/d5tEnqNyKXL1/mP/7H/0gwGOSVV17hQeoBr73+GraODb2uY/gN/tl3/xl3/uc7/MPhf0gqlaJQKCClJJPJWJLtJyc8rVaLu3fvMjw8zPj4OKVSiZ2dHVZXVy3X4f590+k0rVaLxcVFgsEg8Xicra0ty+04Go0yMzODEIJWq0WxWOTk5ITt7W3LsycYDKJpGuVymW63aymk+llfxWKRRCLB1NSUNQ1yu92Ew2HcbjexWIyFhYXHXJpLpRKHh4fMzc1hGAbZbJZ8Po/b7WZmZobR0VHy+TyVSoWJiQmrGQqFQoyMjNBqtbhw4QKFQoHj42NOT0/5m7/5G37jN37jub6zvuHjt7/9bf7iL/6C3/3d331ms/S0+iRr4lNteD5JPS2OolKpsLGxwdzcnHXSSMoy3jaMVcBuQHrIQ9NlkKJOqILytRkeRs7MqAc5OcH28CG028rsr9Oh2mpx7949Zi5cYEjXKezucnDnDhcuXMB3coIcH0dub6MdH2Neu6b8cYJBNNMkeHJC0ONB3rxJx+Gg4HZT2tykEothGxvDPzxM0DQZ9Plgepq4w0Ee2H3wAHutxkA4jMPpxKVp6IeH6JUKna9+Fa1eV5BTNouMRpVztGEgTRNZqajAUZdLvaa+RL7dxlxZQUulFFenWMQcG1ONXp887fcrdVqpBKaJubqKeXSkGsBA4MPgoE9YfWy20Whw6dIlFvqOzs9RqVTKIjqPjo6S7mMOP1kuIcT7QBf4jpTy//7EL/jnVM+7CQiHIRxWPjQ2W5tqVTUA/bpzp8zrr5+xvDzBK68ECYXgrdMjXCPH0NHp1P3YdRvYGtj8BVoNN/U67O8rM8JoVPnbRCICIeqPOAZX2d6OkcnM4fV60LQyUh7xj/7RAg8eeDk+Vs1WIiFIJgV+v5LFK+fkCIXCAE4nzM+3GRwskMmcs7mpMTFhIxDw4/UGcbkGGB01+Mu/rLOx0aFazeNwqIZketpDp+PG51NTF4cDZmZM8nnJ3p6dclmwsKAUa6urajJksxmUyzaSSY0vf9lgYUGi0mN0Wi1F2I7FlGdQNCopl1Vqev+adnysvIQmJyXdbhddfzGp7bPq0TVx9erVF8of+iytCU3TPpYz87T79DfGyWSSZDKJz+djd3eX6elphoaGqNVqHJePcUVdGBkDo2hgN+xUQ1VKrRIRd4SZmRnGxsZIp9PW1GVwcPAxEroK8d1kaWmJUChEIpEgkUhw9erVx6Itms0mDx8+ZHNzk+HhYVwuF61Wy/LTAcVr/e53v2uRhQOBAD6fD13Xcbvd3Lt3j729PaLRqBU8GgwGrTDQZrNJtVq1CNj9mAqn02mdW/qePY+ea6SUlptzv6FzOp3Mz89bxG5Qooe+8iuXy7G8vEy32yUej2MYBi6Xi9nZWW7cuEEmk+Gdd97hH/yDf/DMANQn69E18YUvfOGF/N8+yZr4W9/wPDlO7Evar1y58pinQXRkHm9bYs82KQddGO0WLptOdHgObm+qnK3r1y1Cb993v+V0IoeGKB4e8iCR4NL0NB5NI1ar0c1kWL92DXsioXx2LlxAhMOwuakaC78fYRiId99FptMwOYmcm8NeKDAkJZoQmIODlC9fplAsknj3XTo+H36Xi0YiQeTmTSZzOdqRCHW/n8zeHtlSiZm338YxNYX7+nUcqZQyOPwv/wWZy2FevoxWKiFOTxH1OmYgQKe/yM7P1WRGRUgrKK9UUtOtPndHSjXRGh9X0Rm6jra5ibG+TjMaxev1osViyPl5NSX6hPWoLP1p2Ow3vvENyxn00frDP/zDF3maKSllQggxB7wmhNiQUh584hf9C1C6DuGwIBBoMT3dd2U+Y38/z+LiKuGwk+NjyOclg645DGnS6RpojioGbQJOJ+vLYVo1Zfp3eCh44w0VermwIEmlNN5+20+zKXC5MtRqB1y7domZGSfdbozd3f+PvTcPsus8z/x+59x935e+vVz0gl7Q6G6gAdBchZFIkZI9FkeqssY1HmcSS7IcTTlVLmdseonjuLxoEscVl8suxxNLpdhxXFZGNi2ZlkWKpriIAAkSQKOBXtD7cvd9X8/JH1/fA4AAiUU0aUt8q1AAus8999x7vu877/e8z/s8EtHoNFarhNersrIikckIywtFEcNuZUUQqqtVUZbqdiW2tozYbEFkOUg4DJFIiXS6SLG4Tl9fnVIpiMOhcPq0jUrFw5kzEo1Ghe3tDOVyi3zehMnkQFVtmEwSy8t6nE64//4OXq/Q3clkJJaWdFy5YgAMDA0J1KfVEqiQcFqX+Na39LjdKoODCnq9SPqqVZlWS9VUGvx+lUxGQlXfwcfvLuP6OXErPZYP5oSIe/m+r68ErK6usrOzQ7PZ5OGHH0aSJNxuN4uLi9RqNVq5FrJNRtbJ1Et1XKoLqSmRqWZotVpaSSoUCvHMM89QqVS0jXXPemJubg6DwcDKygrdbpcezw7eAAAgAElEQVQTJ05olI3efTabzUSjUWRZxufzMTQ0RKfT0f60220kSSIQCOD3+zVica+TudeZ9cADD3Ds2DE6nQ6VSoVSqUSz2dSQlp5H1vDw8C09rSwWC6Ojo8TjcQqFgkZY3jyQa3G73fh8PhwOxw02N72kqGe/4XK5NIPUdDrNysoK999/PyaTifHxcVZWVtje3uall17SvMtuFz2xyFuJRsK7Pyfe85JWj3F+p4O6B1UqisLy8jKtVuuWLe2ewBBfeOQ/8Vuv/DYbjg6zWZkvPvrbOH19EEwJjo7FIso8jQZYrcgGA51Wi81ajYwkcdJqhb091nd2UOfmGJ+ZQbe5iRoMiuOXl4XVQyCgtbLL3/oWql6PnEqJ3/n9qI0G2Gwo/+pfQaWCE3D09yMHAtTHx1k94LQk4nHkvT2Mk5M4QyFc7Tbh/n5kj4ecz8dOsYhtawtruYyv28WYyyFnMkibmyJx8Xjout2okgTtNnIuJ5IUq1VshSUJyWBAOXoUKZsFvV5we0Ih8V0coD9SsykSQlm+5gFWKsFdlKHeGrdrwX3uuefe9rWhUIh4PK4Jb73VM6cXqqrGDv7ekCTpBeA48C9qcb+XkGWZdrutme0CfOpTRwEdpZJKsSjMLEP2Pn7l+O/yqy/9j8gNFwZLmz947A/wOMwoNigUVBwOiaEhhZkZgSL5/QrPPivxxhsJKpUmH/3oHJmMTLt9GYvFxfHjQ4yMiN23ySRc05NJYfEgKGKCFOxwCKPNmRmV7W1IJkXCYberFIsSqupAp3MyOTlALFbn6tUN3G4DirLDzk4Tr9fF3JwRjydMJgO1WoPz57u89lqedlsmGNQzM6PDbLZgNksa30aWVfr6uuh0TZxO2N2VkGVRxtrfFxYUbrdoSVdV4a1Vr6sUi+JzmEwSU1NdbDYOEp67F1N7u1AUBb1eT6vVumWL7g/CnOg1OPT4fu9W9LierVaLc+fOMTAwQDAYpNVqMT4+ztjYGBsbGwTkAJ85/Bn+y85/QefU4XQ4+c0Tv8nmxqbmURU5kC6/3rJhb2+P8+fPA2jdShcuXMDv92vqzL0EIZVKMTExgdlsJhAIUCgUaDQaWsv69ShGKBTCZDIRDodvKHGWSiWefvppDQnqEZh71g8gxHszmQxnzpwhEAhoZa8e+bndblMulymVSpoBaCAQwGQy4XA48Pl8N5wPYH19HaPRiM/nIxaL0Wg0GBsbIxgMkkwm8fl8FItFZmdnNS5OsVgEYHp6mm9+85t86UtfYmZm5o7ENXtt6W8X7/aceM8Rnl599lYT/lYhyzKtVovXX3+dUCjE1NTUrcsAej33P/zj/L4cJqGUOH7/MBbzQT3Q5dKK9NLCAqTTqCMjKKpKPZPBHA5zLBSinclwJZNhyO3GNzQkBP+KRdQHH0Ta3kba3kY9cgR1bAzlwQeR9veRX3oJ9dgxlPvuQ6pUYGUFKZtFnZpCPVAwplpF6nRoSBJXdncZeuQR/KUSyuHDNNxuCtksO9kslosXMW5s4LTZ8Hm9eB56CLlYpJHPkx4aopnPY9zYILi4iNztorv/fvQrK8ijo2CxoOp0qMPDQtywXBZdWtUqqteLpCjCZb1aRarVhOrz/j50u6g+n0B92m0MqZQQOJSkuyIuvzV6A/le3NI/8YlP8JWvfIWnnnqKr3zlKzz55JM3HSNJkgeoqaralCTJDzwE/K/fwyW/r3ErH6C3i57/1muvvXaTUrkofQGIEs1/CHyCUXmMxfUOSnmS+Gtm/nFXod0W3JZwWGV6uvcaoUkjSWXa7QDT0wPE43WWlnaZnByg2/UeaOGIoVUui9y4h+4YDCpnzsjU6wJRmZ8XXJlOR+Tgg4PCLb1YlCiVVMbGFPb26iwtbRONRimVHOh0KoFAk0ajjCTtUKs1UVU/w8N2ul0nqZQOp7NDt1sjkylRLu9jt+uRJDcOhx2Xy8ToaBtFaTE5KQjOjca1lnSBkInEy2wWn6PdhrU1HTYbDA4Kdefez+HdS3i63a4G9d/tOb9f5oROp6PVat3VeL/T81YqFc6ePcv09DR6vV7zeRobG8Pr9dJqtVhYWOCJkSf42PTHUMwKc8NzmHQmkskk58+fJ5VK4fV66evrw2QyMTY2hs/n49vf/jaJRILTp09TrVa1FvTeQ79er7O6usrS0hIDAwNkMhnsdjsmkwmj0UihULjldVssFs3+oheFQoGLFy8yNDREOBxmenoau91ONptlZWVFa+/2eDwMDw9rxOVms8mbb75JLpdDp9NhtVrR6/WkUilsNhtut5uhoSGCwaDG+3mrJ1mxWKTRaFAsFjGZTBonqdepZbVatbZ0j8fD8vIymUyGarXK7Owsm5ubnDlzhi9/+cs89dRTt71vvY3xvcS9zIn3JeG5GwZ+pVIhmUxy/Pjx2+8KnE5sAyNE02msqQLSpRUUsxn18GHtEDWfRy6VqDmdrLz2GkZFYdztpnLmDOv1OiMf+Qju/X3UWEw4k7vdQs25r08IhgwOorZaYrJ2OppQIU6nQFqqVeSFBbp2O0xOijJSoUDG52PL4WDivvuwOxywuYkUiWANBrGqKn3z88h7e1QMBvJuN/WlJZqKwsDmJobxcXzj4+gXF2m4XHRXVynk85TffBOT0YjicNAdHETXaonyFSCvrwuRQrNZlK8OuEBqz3fsIOFRR0aELlGjQddgQAbUaFSU7FSVO3ZEfEv0HO5VVb3r7rynnnqKT3/60/zpn/4pQ0NDfPWrXwVAkqSTwM+oqvpZYAr4PyVJUgAZwVe4ck8X+z5Gz0D07SDdW0W5XCYej3PixAlc71B27N26aJ+dgaCELBvZ3VVIJGRiMZiYEG7ftVrPVqHKc8+tIEkuHnzQy8hIiu9+N8bk5CTj42b0epVCQSWdlkkkVNJp0a4+Pa2yuipx9apIbMbH1QNURmJxUQgatttQqQjvrw99SCRCa2sF0uk9Pve5Eba2rHzjGyIhcTpNWCxmvF4/Pl+HK1eqvPJKjd3dFGNjLU6d0uH3O9ncDLC1FWRvr0si0aLbLWC1NlEUIz6fzPHjCgMDMrmcRKEgSnA9/ythbyEQoEuXZIaHVWZmunQ6wvoimRT6Pm73u5vw3Ou5vp/mxJ2Ste8mKSqVSsTjcUZHR3n55Zfx+XzMzs5qD30QjS6VSoVOp6PpzJj1Zu2axsfH2djY0OQEXC6X1o49MjKC3W5nc3OTQqHAzMyMhlwnEgni8bhmDup2u0mn06TTaUC0T2ezWfR6PWazGYPBgF6v1xzWs9mslqQkk0n29vaIRCKk02nMZjPJZFJ7bSQiBC97HVPxeJxcLke32yUQCGiCsL3PG4/HabVaDA0N0Wq1MBgMWrlsbGxM0+7pqepvb2/j9XoZHBzUVJx73l2Dg4PANVFZo9HI3NwciqKwvb2NLMvcd999WufVww8/zMMPP/yO961X/oO7b2q6lznxnic81/ukvFP03HHj8Tg+n+/OINB8HkMmQ6W/X1g25/PXvLYAUimkdpsisLC0xNRDD3H1yhUWv/lNdIUCYw8+iD0YhEQCqVgUpN5OR6AkPh+S04kaDCIVi0gvv4waCKAajaKM5HCIBGNggK7DAXY7UqmEqtORisXYLRaZ+ehHha5DPC70eFZXUWdmkFRVlKlcLmyTk9h8PqTXX6fVaNBst8lks6RSKfqzWUxmM/aPfhSv0Yg3laKaTFJaW2M5laJ96BDueJxQLoelp8tzoNAmpVLgdgstoHJZdGp1OnTDYeEPVq/TttmEaarLhSSU7a5xnu4yvpfFvbejemuoqnoO+OzBv78LzNzTG/wzC71eT6fTuW3Co6oq6+vrpNNpgsHgOyY714cggXaJRkFIdCik02Jo9IDA1dUCL7yQIRg8QrsdIxY7jyzXOHFilmLRjNEoqsEWi8TAgOgOs9tVlpYkXnhB5sIFQfwdHRXdZLKsYjZLmk6QolzzrLpyRWJzM40s53nyyQkKBSPFoki6mk1Ip4U+j8kEhYKec+e8tFoeolGYnq6h02VYW9vh1VftNBp2vF4Lp06ZmZszsrgocflylUSiy7PPruH3Q7frZWTEjtVqRpZlIhHhepfNwuXLMk6nyuHDCoL/eO136+sy3a7yriER18P3d3vOH6Q50Xsg367cAeI7XV1dpVAoYLfbWVhY0EqGBoOBra0tzGYzo6OjVKtVFEVhYGCAnZ0dnn/+eR566KEbrBPC4TCTk5Ok02mWl5dZXV3l8OHDVCoVGo0Gdrud2dnZG8r0PZHJ6elpVFVlcnKSbrdLs9mk0WhoejW9MlOtVtO60PL5PMlkEovFQiqVolqtMjo6Sr1ex2w2E4vFbjDm7HmNud1uAoEAExMTrK6u4vF4tO+sZ0/hdDrxer1kMhlsNhubm5ucO3eOUCiEz+fDZDJRKBTI5/MaATkcDjM8PKwhV7lcjvPnz9PX16eV13omuCDWlvn5ec1xPZvN8uijj/LMM8/wpS99iaNHj96SV3T9d9ejutxt3MuceM85PDqdjq7oCRWlpls4pHY6HS5duoTZbGZmZob19TssQR+oDhu2t4W44EHiQbOJ9N3vogIxRSFXrXL8oYeQ3W7se3vIlQr28XFi6+vk2m1C1SoerxfHkSPoL18WLdvtNjteHRfX/o7BgsLxrS0OagKiLXx4GCmVQlpZgeFhYSGRz7Nz9SrE4xz7N/8GWbSzCMXnXpnNaoVcDlQV5aCWqkajsLmJKR7HdOwYxU6OxXCc8sIaEyk9KaMJY7GI3WpFarUIlErY/+2/pWU2U8hmSa+u0my16AQChEol7Bsb6IGu13vNSrun+qzToRqNyDs7dGQZqdsVzNhORzwJ7zHh6e0C7nUw/yDFnaCerVaLS5cu4XA4mJqaYn9//47PL3aSHZaXRbJit18TE1dVlatXN1HVIqdPT7OzI7O9bWN11UwwCJcu7VAoQDhsw+n0YjY7CYUk+vsFZ+elxS3+8ewuAVOEEKIT7403JJJJ8V7Hj4PXK5CkI0dUrlxReemlFD5fm6NHD/PKKzIejzD09PtVjh4VKNLOjoTVqrK+LmM2q8zNKeTzMpJkxuns57vLTd6ormDvBgmZQnS7O6yugt3uZn6+SLfrJhAYQ6drkUxWOXMmw/a2jMNhIh7XEQg4aDYNB3Q2leuaawBRqjMYoNN5d0nLvTnxQbxzXK/C+3bRbDZZWFjA6/UyNjbGyy+/TLfb5dFHHyWfz7O+vk6z2dQUiZ1OJ5FIhMnJSWRZZnd3l7W1NU39t/d86pluOhwOjhw5Qrlc1nR5ZFnm2WefpVarceTIEaLRKOFwGJ1Ox/b2NuVymau5q6xkVxjxjHDEfwS9Xk+j0WBoaOgGsrqiKGSzWU2QcmhoiNHRUW189ARe6/U6MzMz6HS6m8bO5uamxrNJy2le238NW8PGbHGW7e1tUqmU8JOUJLxeL9FoFFVVuXz5MrFYDJ1Ox8DAAMPDwwwODtLtdm/okFNVlWKxyMTEhHYv3srB7ZUNa7UaJpMJnU7H6dOn+c53vsOf/Mmf8Au/8Atvew8VRaHVar1n3nLvS0lLqVSQFxZQfT5RbhkaoidhWqlUWFhY0NoD6/X6HfmYAIJYXK+jTyZRZ2cF6VaSoFZD3dpiL5mkcPo0kx4PzUqFC5ub9JnN9D/6qOC4mM10HQ4KOh3JVovlc+dwJZMErl7lu+YdPrPymxglPYeTbf670jj/7dQUHDmCtL+PlEgIjo/Xi2qxoOzusrewgG56mshP/ASS0wm1GvKrr4r28pkZyGRAlkX5KBxGVRRh66CqQkU5GOSsOcMv//0vs7AHp1Lgd/Xzxz/xVxj/9hnWOx30DgdyoUDu7Fls0Sj+TgdjNIricFCKRqm/9hq729sYWi2c8Tj2QABXoyG293a7KHVVKkixGGogIBbkTEYIFfr9gt9zD3G7xeqDEHF9Sevtouei3iMPlsvluyoLy7JMp6MccGuuac602x3OnFnCbtfzoQ9N02w2sVgWOX58ELM5gqLA2Fgfb74J6XSZlZUqJtMeQ0OCdPy11Rf5tcT/gMFioFtz8MPyr/Jg6D9QqUjYbApOZ49ILFQOXnxRYXt7E4fDw4/9WJCdHQmbTYgfmkwqBoMY/oWCsIqIxYTQ4tiYSl+fyptvquztSXxr85v80fk/gOIQUtPN+aaR/+nh/5nBgSaZzBVAYWnJSLu9y/y8iZkZF9Wqi3PndGxttTh3rkaplGRgoMHjj0s0m2FqNQuKolnqaS3q7yZpudvt3hV/8Qc5eqjn231X+XyeK1euMDExgd/vZ29vj62tLU6cOIHP59N4Kz0SbywWY3x8XBN67CU/NpuNZDKJx+NhaGiIdrt9oHzu4NixY5po4UMPPYTRaCSZTGrE5J5WT7fb1fRjvr7+df7o2T/CIBtoK21+8YFf5D/O/keAm7SFZFlGlmXW19c5efKk5ldVKpVIpVJagtRsNt8W/e2ts0+vPM0vv/HLSEjIksxDgw/x1U9+ldfOvsbi4iJOp1MrgfXUmvV6PbOzs8iyTD6f58KFCxoBubfGLC8vYzAYblBz7nluvTVmZ2eJRCLs7OywsrLCyMgIKysrvPzyy29b2up2u/fE87zXeH9IyyYTajSKqtMJjZnFRdShIRLtNhsbG8zMzNygKHnHi7skgd9Pp1C4luwADVlmXVUJ+XxMGgyUVZWrCwscPnIEz4E7o9rXB9UqulwO3+HDeCcmxGsXFqh+5wV+qvmbuItN7t9rsu2G/8e8zGxjg+N99wuEptUS2jwmE41AgJ0zZxgolbCGw6hOp+iYyucFE9LnE2J/mYywhJiaEiusogjV58VFyOdRvV5+dvGLGJQWfTkoSnDJFOP/3v0GP5rO4j10CP+P/iiqw0E1n6d25gzbZjNtiwVPt4u53SbY30/Q4aBtMFC/epXEygr5V1+lVSzidrtxVyqYGg1Up5P2Qdu6vL0tynR3qXZ6ffTu2bv1sPh+jnca43t7e+zu7jI3N6fB6HdqZtgLcQ8USiW4dEmivx/0+hpbWws0GsMMD3spFHKsrq5eJ5wmnvi5HAwNSdRqToJBJ91uhGy2yZ/9v1l+O/N/oEgR2r51kNo8E/szPq2cYGTkKBsbgt9jNApvsO3tNtnsLsPDIep1J9/6FgSDwp/LbhfTQpIkPB5YWhLlpIkJhcFB8XujEYaHVVot+E/P/2c6+jYc+nsoHGIXE1ea36S9HmV2Nkw+34/XKzE5mQcyrK4KNMxmCxGNenG7hdJsX1+LdrtAPJ5kYaFNOKwnHHYwOOjEYBAPWkm69eJ+L9FTwP3AKf2d4502AaqqsrOzQzweZ35+XiPdbm1t0el0GBkZ4bnnnkNRFE6ePEm9XmdiYoLz58+zsLDAsWPHsFgsWqIwMTGBy+VCkiSKxSIvvPACx44dIxKJkMuJOXHkyBEGBwdJJpMoisLU1BThcJhWq6WVkur1OpdWL/H7L/8+nb4OdUMd9PA7r/wOnzgk3D3eOmcrlQqLi4v09/ff0NFUr9cpl8vodLq3Fd7tRc9I9KnnnqJpbEIb6MCLqRf5Q90fYkqaCIVCjI6O4nA4CIfD2O12YrEYqVSKRCLBwMAAQ0NDWsIXj8d5+eWXyWQy1Go1hoeHb9AX6t2jW4Xf79eMRY8dO8b29jbPPfcc/f39N4ge9qJX+nuvvOXen5KWqmrlJ7WvD55+msS3vkXiQx/i1KlTN7Tt3e3iLrlcdHplmGKRypkzLFcqjJw4gatSIV2pkF5dZdbnwyhJguNTrYqE5EB0T+nvR1pfR7VasVQqdMJe2ttdZAUsbZAVSNslLu8sEU4kCMZi6I8fRz16lOp3v8vVc+c4PDWF3eNBaTSQLl0Cm00QnA8d4kDiVmy3azXRNpJMgtuNtLWFrlBAdTqROh32O1mmWuCuw6YH5GaTpaXX+fGZJ3ENDwv3d7cbW62Go1IhVKvRPHmSyuoq2StXyC8vo/b3Yz1yBK/JJIoOR49Si0QoNpskvvtdqi4XdpeLTrUqBnKziTI2JkxO7/Fev9cD+V9y3Gpx78nWd7tdTp06dcMO726J/4LDo2C1Cqf0TKbI8vI2w8NTFIt29vb2gX1NOG1jQ8JohIGBa3f/xAmBwCwvQ7ttwliVkddGUap2aFtB10RpGXn+tTeZNvczMGDn+HE9Vis8/XSNlZU8jz7ahyRZiMUkolFhHFouS0SjIilaWhIJVqUiMTam8vDDwrsqlxOqx2fPClXlStYF/gVwpKAaomvJEW9/h1nnL3PpUk/lFkolN5Lkxukcw2hssbFRJZ0uIctJPB4d7bYHh8NNNGrh6NEu0CAWq/DKK9uUy2AyOYhGW9/Tvb0+PpgTdx63GuOdTofLly+j1+tvcFHvlQrHxsZIJBKk02lGRkbI5/NYLBYURaFer7O8vIwkSczPz2ulmx7XJ5VK8eyzz2K320kmk+zs7AAwNTWFw+FgbW2NSqWC2+3WSj8rKyvo9Xqi0SiNRoO96h56WU+neg3JkWSJv3z6LxnQiy6qgYEBdDodpVKJnZ0dxsfH2dvbo1araaWnSqWi2U7U63VqtRqFQgFFUTSV5Xa7TbvdZm1tjY3NDZqJJvSGlQSqRWUrt8XPPv6zms1Fp9NhcHAQv9/P4OAgVqsVs9lMPB6nUqlo/ofFYpFQKMTg4CC5XA6r1cri4iKKouDxeDQD0bfbCLhcLo4cEaU8l8vFq6++yte+9jU++9nP3sQ7/MFAeDodVJsNKZ2mvbnJRrmMs7+fuePHkXq49gHufreLO8EgtXodJInY+jr1s2c58vjj6Pv62H3pJQqBAEejUeRmEzUSEeTkRAJpcxN1cBD1yBHBYWk0BNHXYMDeP0Iw5adUT7Dqg2U/2GWZ+aE5pL09ksvLFOt1Gu027O8zdegQlkCA7kc+AqUS0tIS0vIy6oMPwsCAKGV1Oki7u6KLKhpFAshmNVVkdWoKaWmJU44jyJ03yZkUMhY4mtfz8flHcE2cFNtit1vwbTIZVI8HqdHAmErhbbfxGQyoJ09Sl2WSRiOphQWasoxpeBhnuUzQ58Pg99M4dYrm/j7xYpErqoq53cZWreKr1zG22+Ke3GX0Fpm3tj1+EDfGrXaz9Xqdixcv0tfXx9DQ0E27qXtBeFRVwWhUabd3keUYn/zkDNWqjpWVPcrlCqdPz7O9rUdRRO7dq2T23rpQEIadOh2cOqXwwyEv//tvblGM+8FYgWoARe4yZH2YtVUd9fo29XqFVstCNqvy4Q+PEgoZKJWEqJ/PJ/YaqgqLixKdDrz4orB60OtFiavRkLDbRRmrXheEZ50ODvujrFULKLUA1L1I1n0+evhJAorr4JpVVFV4ZoXDKoWCxBtvmGi3TZw65eGHfiiidcesrKyytmZhbMzMyIidkREvnY6fzU2V3d0a3W6HN954A7PZrPnF3evi3JsTHyA8t4+3zolqtcrCwgKDg4MMDAzccKwsyxgMBmRZplqtcvjwYdxuNyaTiYGBAa2DaGZmBq/Xq2nhOJ1OTCYT6+vr5HI5JicnMRgM7O/vU61WGRwcZH9/X2sB9/v9GnFXr9czPDzM2toahUKB/v5+5g/PI3tl8CF0rjsgIfGhYx9ibWGNpaUlNjY2NPLyxMQEiUSCnZ0d2u22lgwkk0lKpRIGg4FsNks6ndbsInqh1+sxGAwYjUY8bg/DA8NsyVuoehV0oMoq//6j/57+UL943h7UaHsbp97rewiVzWaj0xFjfXV1lbm5OWKxGHa7ncnJSc3ks1Qqsb+/z7lz5zTfL6/Xe9M6H4lE8Pl8XL58mUuXLrG8vMwzzzzDj/3Yj91UnnsvUc/3r0vL5aLyyits5vNETp/G12iINvBGA+nb34aREaFsfIcS1b2QzWa6ej1XLl1Ct7bG2NgYLZ+PhfV1vIEAM4GA8JYymaBeR221hLJwICDQkuuVmLtd1JERpM1Nnn74j3nyHz+LoVDA25X4xdO/xrT1AaR8HvVDH4JajUqphDsYZG15ma7BgO+g9mnxepEWF5GWloRBabeLdPWqeA+9XqA99boorQ0PC+5MMIh09ixfNv87fqWvwiv6FVpGHZ87/BM8ETiJajIh6XSiDHbpkmgxj0ZRh4bAYkF+800AlI9/HEsmQ9TlQu7rozswQN7hoHz1KuvNJv5yGX29jsNsxphMMvXAA3T298mZzeyvrZFvtbCFw/h8Pjwezx23Tvfg+w92s7eP6xf3TCbDysoKR44cuaXz71uPv5MQC2WXWGwVn6/DyZPHabfb7O1doL9/gPHxCXQ6wcEvFIRfVy4nurI8HsGhOXtWJp8XqE+7LaGX9Xz5E3/Cf/MXv0TLH0f1pPjtE1/mY8MjXPRKXLrkZmMjy6FDGTweA6++Gqe/H8JhOxaLC7dbeGW125BOwz/8g45yWWJmRsFkEtXUnmFqJiMofh6PyuSkygP+p/jvv/U51rJZ5GaAnx37bQZ0D1JtSExOKnS74vhDh1RcLvGZdLoe6iORSMg4nQ6iUQf9/YfQ6RQcjhy5nLASMJvNqKofi8WBTqfj+PHjNBoNCoUCa2trNBoNXC4XPp8Pt9t9x3OiZwXwwZy4fVw/xpPJJGtra8zMzOB0Om86tucbVS6XGRwcZGxsjEqlQiKRIBaLUavV8Hq9KIrC+Pg4gJb0LC0tYTKZmJub4/XXX2d/f5+5uTlcLhcXL17E7/cTDodvaZXgcDgYHR3ViMgWg4XfffR3eWr5KdpdoaL8Zz/6Zzw48CBuo1vzo6pUKpo2T89CIhKJEAgENNQpm81y6NAhbDabpmTc0xa6nrzscrlYWVnhKw98hZ95+WdYzixj0pn4o4/+EfOD82xublKr1TQvQ4PBgKIoVCoV9vf3SafTBAIBwuEwfX19zM7O4na7GRkZ4Y033tCQ5p4vl9ls1r6vnu/X6uoqrVbrhjmh0+kwmUzMzs4Si8U4d+4cS0tLvPLKK5w+ffqG7/FenNLvNeJewV4AACAASURBVN4XhKfZbLKVzZJzODhy332YDx2ChQWBhhyYc6o7O9dKXnfhq9LpdChtbTGqqgQPHaJWqXD1wgX6H36YoN0uuqiGhiCdRqrVem6EIuEwmUQpy+lENRpFx5LJBDYb01k3K4/8f+RffxHn9AkMs8fh9ddRjEZWFAVzvc6JoSF0B1o31VCI3PY2288/TwfwWK04223stRpypSLav0dHRUnLYEBKJsHhoPvkk0IQ0GAAmw0P8L986H/jpzeuMnn8JKZcDimXQ52Y0AjFUj4vPMFGR5FWVlDNZsEbKpWEwnKrJTyzXC5kpxNvfz++dpvo+DhNh4NUo8HG5cs4Mxm2+vvxt1p4x8fRf/jD9FutVBsNcrmctlPqZfU9Rc9bRQ++/2A3e/vQ6XS0223W19fJZrOcPHnyHb1o7hbh6XQ6ZDJJjh4dxes9RKVSYWlpicOHD2Mw9MiIKpGI2svHqdUEb0aWhWpyT7um5ysLMGKb4Xemvs7MgwlmR72Y9CZAJRrtEIvtEgyaeeyxMZaWBDnTbq9jMuVIp9fZ3S0hyy6s1gCLi25MJpUPf1jl5EmFq1clGg1hGrq4KFEqSfT1CSHFWEzCVY3wa8NfwXx0nWZtHqvVgNer4nSqJBIShYJQSbZYYG9PfIaHHhLnrdclEglIJCTcbujvV9HrdXi9foJBv+ZwvbiYYn19nYkJ2NjY0Jymw+Ew3W6XSqVCPp9na2sLnU6nzQm73f6OXVi1Wu2DOXGb6KGenU6H1dVVyuUyp06duiWBuVKpUCwWyWaz7O3tafzPTqejKSTPzc3RaDRuKMHE43EuXbrE1NQU8/Pz5HI5zSrBYDCwtraG0+lkaGjoHediLwHrdruUy2Xm++bZfWyXVDWF3+rHqDPSaDS0clw0GtXEc1VVJZPJcPbsWXZ3dykUCvh8PsxmM8FgELfbTb1ef1srEoBgMEgikSDijPBfH/2vxFNx5ufmNVqI0WikWCxSr9e1/1++fJlOp0MwGNT+9MZk7+90Oo3H4+Hw4cPYbDaq1SqxWIz19XVMJhPb29t4PB5CoRB9fX3a589ms2xsbGAwGLQ58fGPf5yJiQmefvpp/vZv/5ahoaEb+Dxvp8b/TxHvi1t6PB7H4/Fw7GMfuzYIbTaktTXRLj0/j9RqoTocSBsbWDY34cEHbyuC1+tmcTYahFWVtNnM3u4uE/39WIJBUFXRFRYIiFbzcll4Rj3+OFIsJlCXXE7YK/R2EtUqqsmEvL2Nbnwc/8z94omws4Oyuspmu431U59iKJtFWlsT5z98GJssY2u1kFdW6IyOUgQy+/us7O8TWl3FNjCA7fBhTHq9ICgPDYnEzuHQtH+oVMhWq8S8XubqLYjHhQ2E1Xqt5pBOo3a7QuzEZEKdnBTE5/5+QdzudoXas8uFOj0tzEO7XaS9PdRwGMVkIre8zJH+fqyhENnhYWpvvsnW+fOoBxBuT6EzGo3SarUolUrs7u5SqVQ0oSufz3fTglSv1z/Yzd5BSJLEzs4Ofr+fkydP3pYkezdtzfl8nqWlJex2OzZbhFdfzZPJxDlx4hh6vZnh4RtNSEHk+GYzjI0JBObAlQRVFRYRyaRQNmg0wKCXmR7sw6gTmj7VqjBMHBuLMjnpJxpVURQVq7XL5KSRfD5Mq9VHPK6QyxWIx6uoaoLhYTCbBapis5mo18V7RiIqRqNITrpd4e6uKHu02zWonqJalTl8WGVgoKf2DFtbkM1KXL4sMT6uMj4uWujzeaEGHY2qlMsC9VEUMd2WliSyWXC5JEAhk8lx5MgR7rvPSLlcIJvNsr29rZUBXC6XZinQbrc1h+teO3NvsX/rnHgvF/d/yaGqKhsbG4TDYebn5992zKdSKfb29qjX6xSLRaxWK5Ikkc/n8Xq9BINBBgYGWFhY0Mxas9ksV65cOZgTNl566SVqtdqB2neJWq2Gy+VicHDwjs0s9/f3tevVy3oijoj2u2q1yvLyMvfff/8ND3pJkrDZbBqh12azkc1myWQylMtlLl++fJNx9luj58m1tbWFzWbj1PwpVFWlUqlQrVZJJBJsbGyQTCYJh8MYDAb6+/sxGAy3TM57PKL9/X1NyBAEOTqXy/FDP/RDmM1m8vk82WyWra0tUVY7eE70XN9brRaFQoGtrS1qtRp2u53Dhw+TyWT49re/zac+9SlNW+/7NuFRFIW1tTUsFgvT09NiG1koQCSC2u2KJCccFlvLnR0Ih2FtDefVqwKBsVrfVv23p3R59OhRrhaLXNzdpZJOM3P8ONZOB/VAYU2KxYSvls2GdOGC6JKanRViezs7yG++iWq3o8zNCXHAXndVp4MaCCD5fEjnz9OMxdhoNAjPzuIaGEDa3BQJVDQqtpSqKtCVbhc5FMJjNOI1m4mGwzRtNkrZLJtXriAVi+jvuw+f0ynaxctl5AsXUBwOdmWZjt/P9LFjSDs74hpcLqHhoyhI6+sCkQqHxfdSq4HDIRzhXS7RJu/3C1HDlRXhpq6qgjvUalFOp1nb3mbaYsHkdoNej9fjwW+xMGQwUD9yhGyhoNW07Xa7ttj32jDr9Tq5XO4GUpvX60VV1XuCKr/61a/y67/+6ywtLfHaa69x8uTJWx4nSdLHgN8HdMD/parqF+9uNP7ziG63y8bGBi6XS2tLfbei1+E1OzvL5cuXWVx8lW7XxMzMNLWahY0Ncdz0tEBQe1Pr+ltWr3PQPg4jI8Kna29PGHVaraLcBUJa6utfb6GqGxw5Mo7ZLLosGw0xxS0W6OtTtSnc6Qg3dY/Hy8SEisVS5Y03KsTj6+zsmDEanYCZo0dtFIvKQdKlYrWu4vermM3jxGKSZnWxuSlpSVKhIP6cOKEyPa3Syzl6n0+vv2alcbDxJRxWkWWJq1dLbG/v4PfPoKomDAYVn8+njfdGo6GhCbVaDYfDgcfj0RCg3rjP5/PEYjFNhr83J3qL/93ED9qcaLVa7O7u4vf7GRsbe9vjeh1FtVqNgYEBxsfHNXSnXC4TiUQ0w86eQebW1haJRIKhoSGy2Sz5fJ5qtYrL5aJcLmOxWLS27buJSCRCLBajWq1SrVbZ2dnBYrHQaDRYWloiFAppCdf10Svb6XQ6DAaD5qmlqirlcpkLFy4Qj8dRFEUbh06nE1VVabVaXLlyhb/7u7/jgQce4NSpU8iyzM7OjiZW2Ol0sFqtGI1GrdOq99mazSa5XI5gMHiTIGYul2NqagpAc40/duyYdg6/33+DrUZPpbler+N0OrXnQDAY1BIwk8mkJbJ//dd/zRNPPKHNiffqOfGeJjyyLDM1NUUymRQ/qNcFGuFyiY6giQnBcWk0RMJw5Qq43dQjEYF4NBpIyaR4oB9wbVRVZXV1lWq1yvz8PABGrxdbpYLX52Mtl4OdHfyA/ehRPIoiUJtmE6lYRB0fF8KEyaQoM/l8mtu4arEIAnWjgXpwc1WrlXKjwbqqcvhf/2ts1SpqIiFIYSMjQmF5Z0esuIAaDmuaOmowiARYpqawpFKE3G74m7+hdPUqseFhdtfXMW1v07e5SVGvR33gAQ4Fg7C7K55CBoNAaUIhkVTp9aihEOr4uCjV5fMiESqXRRIESHt74rXtNlIuh7S1hRoMUnC72VpZYWpmBvP58+L4clnwiFwuCIcxWa1ErFYikQiqqlIqlchkMiwtLQFoWX1/fz8DAwMaqS2ZTFKpVPi93/s9HA4HGxsbmprp7eLo0aN87Wtf4/Of//zbHiNJkg74Q+CjwB7wuiRJf/vPUUb/dqHX65mYmND0L96N6Bnttttt5ufnURQFk8mEy2XGarWSzS7RarUxmfxYLAEMBgcgsbMjUa2KfNrpVG/aW6TToqsqHIbTp0Wb+/a2RLEI3/lOiStXKjz66Bj5vBlVFe3khYLw7LJYROJkNqvk82C3SwwPqwwPq3S70Gxa6XQcNJthEgmoVJpYLEkqlVX29wO43TaazQyDgya63SiKAjMzKq+9JpHLSSiKekCDkw5eL9HpXEt2Wq1b75VUVSRAoRC0WkkymS0+9rFj7OyYUJSbjzebzfT399Pf34+iKNqc2N/fP2irF3NiYGCAwcFBOp0OxWKReDxOsVjkj//4j+nv72d3d1eT6r9d/KDNiZ6H1e3QjV5CPzk5ydTUFMvLy8iyzIULF0ilUpopZjabJZvNsra2Rl9fH0eOHGFxcRG73a6p+JdKJY2nqNPp7trrS6/XMzc3x8rKCmtra1itVra2ttjd3SUcDpPJZKjX67jdbsrlMru7u+h0Oq1UZLVaiUajmM1mqtUqb7zxBhaLBVUVCbfdbkev17O7u8vOzg7VahWn08nly5dptVqYzWZNt8jv92u2GLIss7CwcEvfrFarRSKRwGq14nK5aDabpFIptra2NOQ+Ho+zt7fH/Pz826JdFouFwcFBBgcHNY+ubDbL7u6uRoHodbf1iODPP/883/jGNxgcHOTP//zPmZqaIhaLacatt4t7nRPvOYfHbDZfI1z2djr1uvC7MpnE1s9sFv2pnQ7qfffRXV0ViYfJhPTKK3D1KurJk7T9fhYuXcLpdDIzM0Oj0WBxcZGB/n76jUbweBiw2VC7XfJ6PYlmk/3Ll9Enk/h1OtyTk+h7PJpMRhB/IxGBMBUKArWxWIQsrdWKVKuxWypRTyQ48vjjGKJRuHIFaWtLKDsfaPeoBgNStytI1wfmoVQq4u9QSHhX9dQ03W5c3S5OqxUOHaKSz1PIZmkGAhR0OszPP491dBTr5KTo5AqHxSrcbms+XZhMqENDSPG42GqbTKLkVa8j5XKixbxeF8nYiy9SLBTYCoWYHhlBZzQKrzCfD6lWEwRujwf1LYuxJEm4XC5cLhejo6MaxJlKpbQJ3lvsXS4XxWKRxx57jLNnz/IXf/EX/Oqv/uodjY/eruI2cR+wpqrqxsG1/SXwJPAvbnEHMSdyudy7cq5Wq6W5OI+NjVGv17l8+TKHDh3StD6i0SidTodcLkcms8eZM0Xsdjs6XQhZ9pFK6UmlJGRZDH9VFUPXZhPE4WhURa8X0+bKFYm1tQR6fZ5PfvIwoZCO1VWIxyXSaXjkEVFuWlmRNAWI/X2JY8cEOmQwiPPv7goRwuPHRQfXxoYZq3WIcnkQm62KoixRLts5e7ZKJLLL8eMOgkEnQ0Mqfr9ot19bk8jnYXZWpdNRNVd34MDrSyIQAJdLxem8VqYDSCTiVCr7nDx5HIPBQLstqr/vFLIsCy2rA+n8VqtFNpslkUhQLpex2WwaItqbEw8++CAbGxv81V/9FT//8z9/R/f0B3FOmEymm0w1r4/eRtdsNjM5Ocny8vINpRan06lpufUqC06nU2tBX15eZnZ2VrOveOihh+h2u+RyOXZ3dymXyzidTvx+Pz6f746I6RaLheHhYdbX11leXmZwcJCHHnqIRCLB1taWNk5kWcZisdDpdGg0GjSbzRsEdlutFuVymUKhgCRJmEwmZFkmGAwyNDTEoUOH2N7eZmtri76+PiqVCmazmUajgcFguIkjFolEaLfbNyU8PVRlb2+PdDpNuVxGURQN5YrFYmQyGebn5+9YRPZ6jicIFCmbzRKLxTQKhMfj4cMf/jCNRoNarcaxY8coFov8zd/8DV/4whfu6H3udU68v+ahJpNYdSoV8PsFh+YADcHlEro1Ph+qzUY3l0PfM/HU6ahvbLDywgtE7rsPfzRKoVBgZWXlmnBatSpKWNkscrGILxLBOzUFkkRzaYmsxcKFRgP13Dl8Xi999TpWvV5wYFIp0OlQbTZR4hobQ7VY2FlYoKWqjB09Klp9TSZBBAiFBGemh9U3m+LfrRaq241Uqwm39WQStV4XaMpBVxZ9faLEZjLR+c53SJZKBE+fxmG3056bo1qtsm82U97fx9lqYe3rI9BuY9za4sD8Rzx5VFUkQUND4vorFeHtBaIV/upVaLXIWiwUKhVmpqaQWy3Uvj7UHiGj1YJcTrTm32aCGwwGQqEQoVBIgyx7hnalUokXXniBv//7v+eHf/iH7zjZuYvoB3av+/8e8EPv9pu8V3HX0gtvE6VSiUuXLmnu0LlcjrW1Naanp7XFvxd6vV4jLPbg80wmQza7jaJImM0BjMYAOp1Izo1GMaz7+1Wt5NVuq2xsxJmbq/DYY+OsrOgIhVRaLZVgUAClExMqu7sSb7whEwyqgGhBHxhQe+LqbG0JorHfL/g27baE260SCqlks3WKxVUGBg6ztOSm0egSj5coFrNYLGs0Gh7ATrPpQaczcvy4itEoXNmvOcALLlAsJhCs7W2x2QiHVRwOiMXiuFxpHnjguLawq+o11eU7DaPRSF9fH319fdp3ms1mWV5eplKp8Nxzz/GNb3yDz3zmM3ec7NxFfF/Nidt5LvZ0dHoaOLVajVQqRSQSwWKx4Pf70el05PN5nn32Waanp+nr6+PSpUtcuXJFKzH2iMk91ePr17Qeere9vY1Op9PKONbrNqxvDbvdTiKRIBgMMjc3pxGw7Xa7lnDYbDaNy2Oz2bBYLNqxIJDzkydPEovF6HQ6hMPhG9DAHrL48MMPUygUNORle3ubSqWiJWq9Fvye1lAvEVJVlUajgcViwW63s7y8zPDwMOFwWLM+qdfrNBoNjh079j0Jb5pMJiKRyA1Vgp5ZaqVS4R/+4R945pln+JVf+ZV3RGvuMW6aE+9LW/r1EtuqzYZUrQrLPotF8GYsFpHs9G6yzQabmxCNok5Pk19bY7VY5KjdjrHdZn9/n0QioQmnAahjYyKBAtEN1Sv32GxYrFYi999PRKejnU6TsVjYOyCrGdxuMbCLRXSdDurgIN1ajc0338RdKhF95BGhfXOQUKnhsEjcTCaky5dF4qAoIgmp1QRWfsCZUcNhKJVgcBApkRAih6dOIe3uUnC7STUaRAMBzHY7dDroFQW334+73UY5eZJqvU662WTz+efRZ7PwxBOEymWsxSJyMineNxyGdFpYQ+h0oj5hMoGqsl0oUI9EGO/vR1ZVpNVVcU1mM1KjAR6PKOPdwt/snaLn+WI2m0mn0wwODrKwsEAoFOLKlZs3mI899hiJROKmn//Wb/0WTz755B295S1+dq8aie973EvCI0nSDeJfsViMra0tZmdnMZlMWh1/fn7+tlYGkiThdAr14ZGREVqtFplMhkxmjUSiTj4f5sgRGy6Xj3hcZm8PVLXD2tpVpqc9fPzjI6iqpBGAQehrRiIqkiSSB59PIDeSpNJsSqyuCrTF61UJBIS9RDbbMyQUU97vz1OprPDEE9OUy3ZAxe2W2NvzkE57qFahWq1z4UIRWMfna+Fy2Wk2A6iq87rPJzj9fX2iTd3nUymVwGZTuXp1g0JBxuudZXdXwuEQx7wNVfCu7o/T6dTmRI84e+jQIc6fP3/T8R/MiRvjdnNCr9drnVuZTIZEIoEkSfT19RGPxzGZTOzu7rK5ucno6Ch9fX2Uy2WNYNvX10c0GtW4WW+NtyLaPe7W2tqaVpoKBAKajg1c8/aanZ3VxP7q9Trtdvtty0HXO5hfH263m93dXYrFIkNDQ9rPM5kMa2trzM3NYbVateTKbDZz9OhRgBsSNUmSNK5Oz3KjVCohSZLW0RYMBjl8+DAmk4mVlRVisRijo6NMTk6+q75vve/UZDKRSqXo6+tjYWGBiYkJzpw5c1PC808xJ95/t3S7XSQP3a5Y5VIpIagXiQjiss+HrtVCsdtRBgfZSKVoJhLM3XcfumyWjb09GsHgzbCbLAsuygGML+XzIkEJBgWiYrdDOo0xkSDidsPRo6j5PPlwmNzuLumLF8HnwyNJpJpNhqJR/LkcSiQCFosgDF+6BP39ApESH04QhWdnxTG1miBLZzICqTKbRct5LicQpFAIHA4K+TxrySRHP/1pTNvbqJ0O0sWLSEajSERcLiS/H7skYa9UkKJRupEI6VCIWDZL/YUXcHY6OAIBHOUyJosFtdsVgobVKmq9znY8Tluv5/DDDyPHYmA0IuXzSMvLIoEDwQ+6FXHhDqJXSvF6vXzhC1/g85//PD/5kz95y2Ofe+65uz7/W2IPuL7mNgDEvteTvl9xu93sraLXmi5J0k0ctitXrmAwGDh+/Pg97c6MRqO2K+t0FJLJIqVSmmJxjc1ND9msD0XJMDQUwedzAYI/k05DtysxNaWSTAoujd8vCMWFgsT0tEIwKMpJHo84ptORGBkRfKGFBYlWSyBCw8Mp4vENTp2aw2w2Uy4LlGliAiYmFDodSCTg0iULgYAJWQ7R6XTY2yuQTKbI5XbR6xWCQVGSMBgMN5CyLRaFpaUlul0jIyNjuN1iL5LPi7GfzwuU6XuJRqPBxYsX8Xq9fO5zn+OXfumX+OQnP3nLYz+YEzfGOyU8rVZL869SFIX19XVGR0cxmUyabMCZM2fw+XxMTEywvb3N+vo6gUCARx55hOnpaa28dafxVu5WPp8nk8lw9epVzGYzDoeDZDLJxMSElkTF43FSqRSyLN9kzXD9Z7nVhsRkMmkt5b22+P39fWKx2A2bmHq9Tl9fn0aI7m0+dTodw8PDdDodvv71r3P27Fk2NzeJRqMMDQ1piJHb7SaRSNBoNOh2u1y8eJFDhw6968lOL2q1GgsLCzidTn76p3+aL37xizz++OO3PPafYk685wnPTRoiwaB48INIDPb2RLJjtQpRP70evarSHBhgMR7HYjYzPjxMd3mZpXod+4kTjB60wt0Qqir0fHQ60ZLRbiNduoR67JjA2kEgG7GYQJdCIaRmE4/FgrdUQrLbSRw7xs6LL2JRFGLVKuVgEIfVittsRp9Oo7pcqIGAQHL0enH97bYob1Uqwhh0Z0e0gB90hgFgMKA88QSYzezt7dFYWWHugQfQezxQKolrKRaRDo5XPR6xUlcqIkGx2dCZTIScTkIf+QhsblKp1ylvbnL59deR63V8FguOoSGcpRLrzz+PrVQiOjsrrqFWg0pFdKIJ7X7R3j41dfc4PmJnc+HCBXw+H5///Of5uZ/7OT796U/f9XnuIl4HDkuSNAzsAz8O/Lt/yjf8p4ye5sjdvqbZbLK0tITL5bqBwxaJRG5So73X0Otl+vs99Pd7qFRgdbVIpbLPAw/UqdfXyGaDJBJ2NjbcFIsSFotwOk+lJOx2MSWaTVE+EqiNaA3f3JS0zi1AMwtNp2X29gpks0U+9rETiOaKW12XWCbSaZW+PnC7VYpFHaWSn2bTT6cj0W6XqFaT7O7u0u1CLtePweCk0zFz+fLlAwPJQ2xtQTQqOshqNSFZVShI3AYYe8foqWV7PB5+6qd+it/4jd/gR37kR+79hLeP76s58U6bgEQiQS6XY3x8nI2NDcrl/5+9746Oq7y233d60UijaRr13rtlY+NgDAEbHMDBDskPVrIAU0JZJLACJBBayAuQhJAX3qK95IWQx3sOCS8g2SBKjMHYVOOi3kejOl3Te/l+f3y615ItCVVsQHstrQXSeO69M/d893zn7LO3F/X19XC73ejq6kJvby+0Wu2kTYoBLS0tKCoqQnp6OhiGmbWqM1/weDxuYooQwo2kSyQSDAwMwOl0QqvVQqPRwOFwYGBgAAUFBQiHwxCJRNOeVeFweEYxRQDctJNQKMTAwAB8Ph+3sY/H44hEIiCEQCaTwe/3w+FwIBQKITIplFVcXMyJGm7evBl6vR6FhYWw2+0YHBzE0NAQNBoN8vPzIZPJsG/fPigUijllAJYCVi07OTkZN9xwA/7whz/gvPPOW/bjTMEpMXFaEp5pYD9Ym40aiaak0IrPpN8UEwggmJmJFosF+enp0BYWItjfj+GREei3b4d6NlY3w9CpLj6fjmbb7UAiAcIwdBopFqOE31iMrsJqNSXuGo0AgAmRCMN9fahavx5SkwmJpCTYNRpYrFb0OJ1QSqVQqlRQx+MQ9/SATO4WSFoarVBNZtxELKZVk0lhQwSDYIJBJFJSMNDfj5DNhiq9nk6MTSY4zOgoSFYW5QdN0eBnDAYwZjMS69fT6atAgP4tKwtJQiEUsRgyCEFUrYZdKMSw2YzwZ58hJRyGuKQE8UgEfKGQTsZpNEBODkgodEKGdhEIhUJcsnP99dfj/vvvn2+5cUa8+uqr+NGPfgSbzYZLLrkEdXV1eOuttzA+Po7MzMxmQsi3CCExhmFuA/AW6BPxeUJIx6IPepqxmApPIpHAsWPHUFhYCI1GA7fbje7u7inmn8sDmijQhKSz0wGXy4mtW4uxZo0QVmschw978fHHToTDI9DrhUhJUYHPV2N8XAink4fNmxPIzyfw+WiLy2ajY+1SKVBYSHk8iQQVCiwsTCAaNQDgo6ioEBMTtMoikZygvEWjwEwbc4mE/qSlEUilDAYHCQoLk6BUJiE/vxBHj8ZhNvvg8ZhhMIxBo5FCr5dNJpoi7vaXyehPKES4kfWFgt3FpqSk4LrrrsPjjz+OCy+8cLFfwdcyJmbbBLDEYrFYjP7+fvT29iIlJQVyuRzxeByffPIJ6uvrUV5ejo8//hh79+5FfX098vPz5xQQXCxGRkZgtVqxYcMGiEQibphjdHQUHo+Ho3C0tbUhFouBYRjweDwIBAKo1WrEYjEIhUIYjUYwDDMt0YhGo1Aqlejp6YHNZkNOTg66uro4qwjWioL99+FwGBkZGZzVg1Qq5QjNlZWVcDqdEAqF3Kg/SygeHByEzWaDy+VCcXHxsn9GABWIbGtrQ1JSEm644QY899xzOPvssxf9fouNiS884ZkRw8NgWltphSE/nz6ACQFxu+F2u+EEUK5UIs3vh6WrC3aHAwXbt0P6eSNsSUmA10sf8FIpFfebrOrAbKZcoak74WgUxOvFiECAoNeL+jVrwFcqKc9IrYZWIIA2M5Mj6drtdrR0dEBuMCBFqYQyOxtJoRBN2FgLaLGYJlRmMxizGYzLhVg0is7WVsgtFlQlJYGXmgrCWkK73VQBOhgEYRiQb3+bfh4A5dgkEjSZslrp9jmRoFWq0lLa2HpwlQAAIABJREFUUuvvh1ChgFqvx+joKPJTU5EUj8Ok08HS1YXY+DjSLRbIMjIo+W5sDJDJ6GezwOoOu4tVKpXYtWsXHnnkEWzbtm1h3/1J2LFjx4xl/0nS27fY/yeENANoXtLBzhAsdDfFeu1UVVVBrVZjfHwc4+Pj0zhsy4VYjI6TDw6OQCz248ILiyEQ8AEQ6HR8FBWlIpFIRU5OAkePBhGN2hCNHoXNlgq5PBmZmTKIxXJ0dvLQ1sZgYIABwGDr1jiXuIyMMPB6E+DxOlFcLIFeXwCtllZ/2I/G46Gih+3tdHIsOZlycGbi2vB4NPzYvI8QICuLj+FhBcbGrFCrqxGLiTE+boXDMQaLRQaZTIKMDDUnxpZILKrYyS3sycnJuP766/Hkk0+eIqW/UHwdY2K2ltbExATnM6VQKLBp0yaMjY2hra0NdrsdeXl5yM7ORnt7O4xGIy655BKUlZVBx9IOlgmJRAK9vb2IxWJYs2YNt5E/eZjD4/GAx+Ph6NGj6O3tRWFhIVJSUiAUCrnrEwqFcDqdIIRwnlcAXV+HhoaQnZ2N9PR0iESiafYS7BSbRqOBVCpFf38/pzfEwuv1gmEYZGZmwuVyYWJigpvOEovFUKlUGB4eRiQSgVgshlAoxJEjR7iETKPRQD5p9L1YeDwedHR0QCKR4MYbb8Tzzz8/q27OfLHYmDgzEh6BgK5iWVlgZV/J8DAsg4NwOp3IXLsWtkAAvrEx+DMzUXHZZRDMYxdL2Ad6MEg1cnJzud/zLBaaJGi1tOIDgFgsGBgYQKy0FGXV1WBcLtoSY1tuk2AYBgqpFIrUVOTn5SGWkgK3yQRDPI6Q1YqkRAL67m6kxmKUGySV0rHw4WFEy8rQYrdDrVYj32oF3G4kqqspWTsYBCbFy+D1UruJ/n6qO+T1Ug4QIVQsMT+fau9MnjsCAWqEGg4jpNGg5/XXkZ+RAa1SCSIQIK+6GgyPh6hAAC8hMNlscHi9yOzogLSyEoqaGizEItTv909b2H/3u9/hggsuWMA7rILFfBcTQggGBgbgcrmQlpYGi8WC0dFR8Pl8NDQ0zHt0dCFIJOLo7x9ETg4f69eXwGDgIR4HlxAUFJDJxIBBUpIcGRkypKXlgs+PIRicwOioAV6vH2JxKsRiLdLSVBgb46Ovj0FKCq36qNVhGAwdKCnRITMzE8eP02NPTTh0Olp1SU8n8Hio2nMiceJzCwZp5YcdNZ/6kVLufhA+nwHnnZeB7OxUeDwMNBo5AoF8tLbGMTpKd+UM44VOJ4fPp4dCocRsLbWZ4PV60d7eDplMhuuvvx5//OMfsWHDhiV9/l9XUMPbUzlUvb29sNvtqKiogE6ng1AohM1mg9PpnNSYosTiQCCAs846C3l5eZ9L2l8ootEo2trakJqaitLS0lnjlyXpNjQ0IDMzEyKRiBPpm5iY4KalysrKOA6O2WyGyWRCRkYG3n33XU6bh1X2noq+vj5Oa0ehUEAsFsM++Wxh4fF4kJSUBKFQCKVSybW7AMDtduPjjz+GRqOBzWZDSUkJamtrAZwQ2BwYGOCUp9nJr/l6xwGAy+VCd3c3hEIhbrnlFrz44ovcMU4HTlvCM03YKSmJ8mzCYUAsRtxohPH99xFPT0dxWRnCOh1aOzuBRAKhcBgdQ0PQBgLQaDRz38xJSZQHpFLRSk4wyI2AkOxsSjaevDmiJhM629uRLhZDV1xMuTft7bQNptGcqLKw8Hpp+ys5GUK/H5rkZKjLy5GorISvvR0erxfjLheYUAhJZWXIGBoCdDoctViQm5+PtORk4Phxug3VamlLz2AAqaqiVZuRERrwoRB4L78MiMVInHMOPbbfTxMkPh8kLw9MRwcdeRcKEairQ0tXFyp0OigDAUrcFolob4LPhzA5GakbNkBlNqNQpYKfz4dVq0X/8eNgGIbTnZjLJ4vdxSoUClx//fV46qmnsGnTpiXeEauYC2xZnB1hZQmxfD4foVAIHR0dHG9gIWTMuRAKhXDsWBtSUwtQXKwGj0c1bIaGGBiNDHQ6ApuNQVYWq7bMIBoFdDqC1FQhdLo06PU6dHQQeDw+JCeb4PMZEY8rEI0mYWxMA4DA4TiGiooieDxaWCwEU9ZkDoTQlpVeT/lA8TjN9QcHqSv60BDDibH7fCemxQB6v7a2tiE/vwYajRxKJThCst8PCIUCqFQ6RCI6EAK4XF4YjV6IxZ0IBsNcTMzlk8XyR8RiMW666Sa88MILHIl8FQvHTGT7YDCI8fFx6PV6lJSUoLOzk2vjqlQqHDt2bJKcTiAUChGJRLjppOWKiWAwyE3bpZ20EZ4L+kkhWABIS0sDj8dDR0cHrFYrjh8/zo3SA7QlevjwYej1euTn5yMej8NqtU4zMWUVvTVTJmo1Gg3GxsY4v7ZIJIJwOMwlQKwVSjAYRHd3N9ra2lBQUAAej4fMzMxpCdXJJG23281xfwQCwbxG9CcmJtDb2wtCCG677Ta89NJLqKiomP+HvQL4whMetk95SsIzScoNxuMY370byQUFUG7eDH8wiPbWVpQIhVBXVSFRUQF/IACbzYaWlhYA9IvWarWnlt6kUmqyqVRSAnFPD1UrLi0F+Hww0SgIwyBoMMBw6BCy6+uhjsU413bCtnpm2jmz5F+nk+rhTLayeLEYUpxOpNTXIzseR9huh8PthtHhgFkgQEpyMmS9vUhUVoIpLKTkCJGIrtIsmZrHo1UhqRQkOxu8jz+mra7JVZwZHQVnBhSN0omw3l749Hq02O2orKpCisMB4vNRXR6DgY7QRKM06Zvs/TKxGJKKiiDPzUU+TginzabnAJwoT8pkMtxwww34r//6L5x11lkrcq+sgoIl+2VnZyMtLQ1er5cz/9RoNFyL1Waz4dixY+DxeNNiYjFgvXyKikoxNkY1S6JRYGKCAZ9P20mRCOXVaLW07eX1UodyhgHy8wnCYaCnh8H4OA/JyckoKVEgJQVobY1gYsIJn68PwaAV2dlaxON8RKMJjI/zYLUyiEYJCGGg1Z7q9QXQW5/VLWUYICeH2l54PJQw7fXS5EipdKGzsxMVFdUYHEwCw0yvGhBC36e0lEy6qQMejwJebzKKizOQnR3ieA6sBcHJO112F8vj8XDLLbdg9+7dqK6uXtTnvorZIZFIUFRUBIVCAYfDgc8++wxr1qxBbm4uPvjgA5jNZnzzm9/EunXrOB0km82G4eFh8Pl8bkOwWPNWl8uFrq4uVFRUcPyZhcJqtcJsNqO0tBRisRhlZWUoLS2F3++H3W6HyWSC3++HTqfjzJelUilsNhvGx8c5/Z5AIABCyLT4VqvV3GRYXl4e3G43R2I2Go1ITk6GSqWCw+FAT08PzjnnHOj1ejidTgCYte3H4/E4+xSAboTY8fhAIADlpJSLSqXiqsxspS0Wi+Huu+/GP//5zxXjBy0Ep6XCwxK5uOrMpKSre2wM/QYDSgsKIC0qgm1iAgMDA6hOTUVSKASSnQ2Gx0NSUhKSkpKQn5/PaYawpTfW04bVRyBTP2RCqDaPSkVbaIEAJkZGYG9qQmFtLSQVFUBrK1UsDoeB8vITJqInQyQColHwxscRX7eOrpxiMZjjx6m6cU0NEI1CzOdDKhLBnJOD6s2bEQmH4Th4EH0ffgixUgl9MAiFwwExw0yvw08GJTMygkRBAR1PnzT+RDhMK0ORCE26ZDJ4xGJ0ejyoLSiALBikbu+xGEd45hkMlBfFMCdsOZKTOXI1vaTpwmlT9Rx4PB7kcjlGR0eRmpqKm266CX/9619RX1+/rPfG1xFskj5VV4eF3W5HT08PKioqkJSUxEm/V1dXc4sdO4qqUCi4aRB2ZDYUCkGlUkGj0UCpVM5rTN1qtcJgMKCmpgYikQxsV9hiYRCJ0IkmtZrm+mxSwzC0PcVuOP1+TPJ1aBLEVlycTsDjkSAtTQSl0ofa2nUIBAKw201wu7shFiuQl6eFUKiB3S6Y9n4u14n9AXBCJRk4QTZOT6dE6PFxAHCgq6sXFRV16O+XYXSUgVBIdXZY0Vn2PRiGJlGpqXRkPhikv5sqnMbudNkESCAQQCqVYmxsDDKZDHfccQf+8Y9/zFcFdhULALtBVqvV6Ovrg9vtRkFBAZRKJQ4dOgSbzYZrrrmGm06cqi3F6ujYbDb09PRwVQ+tVouUlJR5tZRNJhNGRkaWzJNLSUmByWTCwMAAQqEQ18qSy+Xw+Xzg8XjYuHEjBgcHMTw8jNbWVi5u7XY70tLSIJPJ4PP5AFChQ9ZXixACrVaLcDiMnp4eLv4lEgkEAgFkMhkMBgM+/PBDXHzxxdxmqb+/n+MVzQcSiQRZWVnIysrirCTsdjvnki4Wi2EymcDj8XD//fejqamJs/k43TgtCc9MhLRRvx/W8XGUbdsGkcUCU2cnTJEIGnQ6iLu7QaqrucrEVEzVDGH1EWw2G3p7eyGXy6HVaqFOTYWIz6fjJokEHSXXaDA6Pg73hx+irLgYAq2W8nXEYup6XlBAbSbmgloN4nSecEknhNo0pKeDCQZBkpPhsNth5PNRuWULxJPEhzSBAHlZWfDl5MDd0gLz/v3gRaOQ1NdD7fHQdpJMRvlMHg/l8Ph81IvLZgNjsyGRl0dJ0IEAbIRgCEDN2WdDOjQEpq+PVrUEAvqkCgQAjweJmhqqR+R2U8HBOQzyThbeslqt6OnpwVtvvYWXXnoJ559/PjIzMxfx7a9iJrAxwSYkhBAYjUbYbDZuFJUdwW1oaJhzcRKLxVw5mp1qMZvN6OnpQVJSEo2JGcr8hBAMDQ3B4XBwxyAEKC8n6O2lSY1EQhMftZoShsfGGGRnE1RU0IoPaw760Uc88HjAN76RgNXKsLqfiMcZpKebIBCMoK6uHiKRCElJSTMoPg+DEAYmEyVO8ngKCATUAV0ioUuBREKzlZOfVwIBEIs5YLH0T47YiqDTAUNDmKweUb5Pfj6ZlvBMxUyk5ak73aKiIphMJvT39+PVV19FU1MTLr744nl7Aa1ibrBJCJvosF5kkUgEJpMJWq0WUqkUn376KUQiEXbs2HGKdcJUSCQSzsspHo/D4XBgbGwMXV1dUCgUXEyczE9hzS69Xi/WrFmzIP7KTBCLxSgoKEBnZyeGh4c5y5HR0VG4XC6sX78eAoEAPB4Pubm5KCkpgdvthsViQU9PD0KhELKzs9Hf349wODxpgxIFACgUCm4Ca3BwEGKxGLm5uSgtLYVIJMLY2BjHf3K73dBoNHC5XIjH49NaYwvByVYSIyMjGBoawosvvoh9+/bh8ssvX1Drb6VxWlpaUxOeRCKBzs5OJIRCVFx8MQgh6LLbkRSNoi4nB3yLhVo7zENX5GR9BJ/PB4fRiIH9+0EEAmh4PKSo1RAFg+gdHweMRlQUFoJRqaj4RiwGotFQtWHWs2oOEI2GtrLkcto2AkBqa6n4oN+PkZEReMJh1Fx4IQThMJiBAfo0kErBAEhSq5HU0IAsgwFRjQY2gYBrJ6WkpEArk0ElEoGfnQ10dtJ2XGEh1eMZGQETCGCisxNjSiVqtmyhD7BwGHA6QaqrKYk5EgHj89GngF4POBxg+vupOeqUvvJcYHe0fD4fhw4dwsGDB+F2u0+xK1jF4sGOprPTG+3t7RAIBKirq0M8HkdbWxvkcjnq6uoWNDHBlvJZJ++Ty/xs60sikaC7uxsApgkWsklOURH1z2LtGQCaEGRkEBQXE669xEKnI6wAOVjZLYYh4PH6IZP5UV1dfwrJejbFZ7adlJSUCpFIC4ZJhcXCB4/HQCiklRmfj56nQACMjY3DanVh584TD6j0dIL8fDq2LhLR1pdIhEnODmAwMEhOJlOFyecMf6vVipGREcRiMbS0tODw4cMYHx9fsBP6KmYHj8dDPB4Hn8+H1WrF8PAwSkpKUFpaCqPRCIFAgKKiIlRVVS04Jqbaqng8HthsNhiNRgiFQq71JRKJ0NnZCZFINM36YalQKBTIzc3F0NAQurq6EI1GEYvFUF5ezt2vBoOBm8gC6PrQ0NCAgoICbkPPMAwkEgnS0tKg0+mmtbdYQ9Dc3FyIRCIYjUY4nU40NDRwbTWWQC0UCmfVAloITCYTLBYL3G43jEYjWlpaMDg4uOyTo0vBaa3wsIJ1Op0OWVlZVDitpQUZWVnIFoko81CrpeTiBd5sXJm/vByMzYZYMAibXo+h3l7Y/vUvCDMyUJqSQp3atVqa8LjddBxkvtBoqCggnw+43eANDFAHc4EAI729iPF4qFi7FszkSsw4HCA8HjXzdLvpZNXICCCRQJCVhfRJaXTWK8VmscBoMoHv8UAfCkFlMECSn0+30VIpHIcPI2QwoOr228Fn+TlOJ92+qtXUqd1goFpAcjl9QjmdYOJxJKqq5vWZ2mw2DA4OIhwO45577kFjY+O8nc9XMX+wMcGO+mdmZkKv1yMQCKCjowO5ubnTiI+LwUxlfrvdjq6uLrjdbu73Uxd2QgCLhfJc2FaS3U4Tn6QkmuhIJPR1PT00iSgooMkOn09D2GIBYjGCjo4OCIVC1NbWzOvhcXL1li2dT0z0QSAQQy7XIjtbDYlEirY26tHldI7AYgmhuLgMAsH066DvSVUiNBoy+Xv6mnCYVqtY712zmUFx8cxKy2azGSMjI3A6nfjVr36F1157DZmZmdzuehXLA3YTYLFYcPz4cTQ0NHAbWZVKxU1qLQVTK9ms2a7dbkdHRwc8Hg+USuU0a4flgl6vR0NDAwwGA3Jzc5E3RTw3GAxCo9EgIyMDarWa8/kSCARgGAahUAjr16/n2kRsO0kikUCj0SA1NRXHjh0DwzBISkpCb28vIpEIamtrORNSNoEMBAJLXlcAakDKTo3+x3/8B5qbm6HValfks1sKTlvC43a70d7ejuLiYqSmpsLtdmP47bdRXloKRXY2iEpFH+xLzaoFAkAigZDPh7KyEmMeD/ILCiCUyeD88EMYzGYIAgFkjo9DabGAt24dN7n1uRAKAaGQVneiUZCsLMQTCQx9+imUFgtSL7qIm8CCXE6Jx+npIJmZtIrE5wMqFZ2kmnKdnAtzOAwmEECgsBCu7m4MDwzA5fdD7/EgkJQEMcOgsKwMxGYDTCYwoRCIUEgrN4EANWBNTaWE7clWFiQS2tKbR0ZvsVgwPDwMr9eLhx56CHv27DnjbuCvAtiqp9PphNFoRHl5OZKTkznzz4qKimXZgZ0MiUQClUqF0dFRlJeXg8/nw2Qyobu7e1qZ32QSckrJVA2Z8nKSk4HqaoJEgk5LHTvGoLT0RJspEqEu5TxeHJFIOxSKU0dr54uTS+eU92PndshSqQYTE2GEQmJkZhbD6wVMJlrZIYRS39jzmgpCKG+nspKSrFnicyAw89IzPj4Ok8kEs9mMJ554Aq+//vqyPDBWcSr4fD5sNhsOHTqEtLQ0hMNhmM1mRCIRnHPOOStSTZNKpUhNTcXY2BiqqqpACJnmns7GxFIlICKRCJxOJ8rLyznfLTbhYcUCp05lsfD5fBgcHAQhBLW1tZxeDgCO+NzV1YX+/n6IRCJ8+umnUCqVqKys5N6frfq2trZyWjtLAevb19/fjz//+c9obm7m4vRMw2lJeAKTU1YsAYwVTqurr4c4FAJxOKhYYHHxiW3lUpCeDq/ZjNZjx1Cp1SKFrYJUVSEvFIInKwuBtjb0+f3wp6ZC7fNBq9XOj81vNAJGI0hFBeIjI2i3WpGRmwu1REL9rCwWqqI8qZhMFAq6hZx0Op+TJzRZopT6/ZAJhcC6dYjm5qJ/zx4k+vvhl8vRWlSENL8fKqcTQqcTOPfcE+xOm42KElZW0sqOwQAkJYFMMv3ngslkwtjYGBwOBx599FG89tprq/yEFQIhBIFAAG63G/X19RAKhRgeHobdbp+X+edi4XQ60dPTM81NfaYyv8GgRCSigN2uhtsthVpNkJtLE5tolLaDAgHaxpq8rREKUUFBnS4Mv78VZWWZy5oYyGQy5OTkICcnB9FoFC0tLZDLo5DLAYkkCJFIC5lMBUAIvx/o7GRgsdCqlEIxnfjMJjZiMS3w6nQEfv+pCc/IyAhsNhuGhobw9NNPo7m5eckPi1XMjmAwiL6+Pq6tw4rmbdq0adnGzE8GO300dSiAFRF0u92w2WwwGAwQi8XT2sELAVvFLSwshFarhcvlwtjYGEpKSiAUCuHz+Tjy78lgNXWcTiei0eg0TpFcLodcLodarUYkEkF7ezvGx8dBCK2uqlQqiMVihEIhzq2cPeZiYTQa4XK50NbWhr/97W94/fXXFz3B9kXgC094AoEAgsEgJwjV09ODeDxOhdOcTqCvj3JMVKqZNeQXAWswCGt/P+q+9S3Ijh2jJOOzzqKeVR4PUrxepFRWQp+Xh5BUyk3GhMNhqFQqjs0/04QLQwggFCKgUKDfYEBuZiZS5XKaVMTjYCwWKmCYnU1Jw5PmqMzQEK3EzLVLkUrpZFl/P9XcicXQ7XAgWalEHoDE2WfDl5wMm9kM+/g4ZKOjEHZ1IamhAYp4nFpnJBInRlsIoQnW5zxAWfd5k8mE3//+92hubl4WpdLrrrsOr732GnQ6Hdrb20/5OyEEt99+O5qbmyGTyb42WiZerxfRaBSFhYXg8/no6uoCn8+fpuC63BgfH8fo6Cjq6+tPWVhPLvMnEhEMD3sQixkhlQahUqXA5UqFQJCCDz7gQ6kkk5UddtqMcmqk0gCCwVbU1hZzI63LDZbvpNFokJeXN21Ev7d3GACgUGihUGghEsnh8TDo6KDJTWHhzErN7DVM/eiHhobgdDrR29uLv/zlL2hubl6Wa1qNiZkxMTGBRCKBiooKjmybnZ2N4uLiFfF5Amilwmq1zrjJYBiGVt2VShQXF3MVxo6ODs7IVKvVzqlfBpyQ9Zg62i4WixGNRmEwGFBcXAyv1ztr0hCPxzkbjZGREZSUlJzyGrvdDqPRiMrKSkilUhQXF8NkMuHIkSNwuVzg8/mcUOFi72GWzB0IBPDZZ59hz549aG5uXrQMxlSsZEx84QnP+Pg4pFIpBgYG0NvbC7VajbKyMloiZIUClUo6mbTEG5uddHF7vai+4ALwJxWdGZkMxOsFE4nQGrbfD2RnA6mpkADcyB074TJTmZ/NrIlMBj+AtqNHUVVdDUU8DpKdTWvokQgYsZgmcS0tIPX1IMXFVEHZ6z1FwfkU8PnUD8ztRqSuDgMHDyK1vByZeXkgIyOAVguF0QiFzweydSviOh08Ph/s+/dj1OWCODubmoj6fOBnZND3+5zWCFueHBwcxHPPPYfm5uYlm+2xuPbaa3Hbbbfh6quvnvHvb7zxBvr6+tDX14dPPvkEt9xyCz755JNlOfaZDJbs2tXVhUQiAZ1Oh+Li4hVJdtgx1EAgMG91ZqlUjIoKLXJzNZBIYtyEi9ncC4slDVlZcigUqQAEIIQmCtnZExga6kVdXeWKEXnZyg7L8wFOHdFnic92+wCCQT+kUkp8FghSIRLxIBYThEIMBgdPkJbZTTO7/AwODsLr9aKlpQV///vf8frrry9bi3E1JmbG2NgYkpKS0N7eDkIIMjMzT+GXLRdms4mYCydXGB0OB9f+T0lJgVarnaZLA5yoHtXV1U2bKJNKpcjLy8Pg4CB6e3sRj8dnjRnWn6u4uBh2ux1WqxU6nQ6hUAhOpxM+nw979+6FTqeDRCIBj8dDOByGXq+HQqGAXC6HUCjE0aNHYbFY0NXVxclWpKamzms9YNeQSCSCgwcPYv/+/di7d++cU3ILwUrGxBee8FgsFtx3333o6urCrl27cP755+PYsWMQCAR0mmTTJkhFoiVXd9jpL4FAgJp168AjhDqNJycjUVlJ51pDITrCPcsU2MkTLlPL/Oz58oNBuAYHUbd1KySpqSAiEcAwVBzQbudcDZlwmM7FKhQ0ydJqTwgNzoX0dETjcRwzmVCg00Ejl4NotScc5hmGTn8lJUGgVEJVUAB1aysYiwUuvR7usTGM/utfSOj1UBYXQzOpxzATjEYjpxj73//932hubl5WI8pzzz0Xxklz1pnQ1NSEq6++GgzDYMOGDXC5XDCZTEhn+yRfUYyNjeGBBx7AwMAAbrrpJmzatAlHjhzhJkYWUzafCWw1RCaToaZmfsRhgD74U1JoMTIeF4CQNOj1acjOJhAKA4hGzfjsswEcPapFYaEc8TgdHV63rm5FDBsBcAMP+fn5c1Yf5yI+Hz9+oi3h80nhctHPQyqlChbp6XRhD4VC+Pjjj9Hc3IzXX399WXaxLFZjYmYMDQ3h4YcfxtjYGG699VZs3LgRLpcLYrGYi4nluLfmaxMxF4RCIfR6PfR6PafVZLPZMDAwwJ1vLBabs0WtVCqh1+vR0dEBl8sFlUqFSCSClJQUyGQyhEIhmEwmDA8Pw+l0gs/nw+FwcJzKUCgEo9GI7u5upKWloba2Funp6UhOTuZaVmySHolEIJPJcPbZZyMtLQ1Op5NLxljis0ajmXHNIYSgp6cHhBC89dZbOHz4MF599dVljfOVjIkvPOHZtGkTLr74Yjz55JMYHBzE7t27cezYMWzatAlbtmyBWq1GIpHgFqLPKxHOhEgkgtbWVuh0uhMkW5eL6s/U1FDz0HgcpLKSjnlLJJ9bTTq5zB8KhdDX1we71QotAEtHB9RiMeTV1WBSUqiPl91OOTPl5bRGrlSCOXKE2lrMU8MmmJKC9qEh5OXlQWO1gmlvpzwglg/hdlPCtF5Pt9bhMCUr6/VIDoWQwuMhRyRCUK+HlWGmtepYMTqGYWAwGOD3+3HkyBG88soreP3117/wsfOxsTFkZ2dz/5+VlYWxsbGv/OK+ZcsWHDp0CFdccQU6Ojrw17/+Fa2trbjgggvwzW9+E2azGYQQLibmsjiYDeFwGK2trcjMzFwwF6uyknDtnZYWBn19DBoaCPR6QKGgbuQuVxFKS0Pw+wcxMGCGRCLByMgItFotkpOTl3VnzrqRl5SULIgceTLxORgMTvowdU4Sn9WNwKppAAAgAElEQVQQibRIJJKhVjOwWHohFMbw7rvv4uDBg2hqalq2Xex88XWNie3bt+Pjjz/G1VdfjSNHjuCPf/wjuru7sXXrVpx33nkYHx/nrHBOHsmeLxZrEzEXTlYl9vv96O7uhtfrhVQqxejo6KwxnJ6eDp/PB7/fj4mJCQA0YZfJZEgkEggGg/B4PEhNTYVSqeTeB6Cb80QigQ0bNiAUCnFDCCxcLhcUCgWXKAHgJsBYKRf2fNlWXSwW40jNbIuNbbe/8sor6O3txcsvv7xiHMPZsJSYOC2k5Z///OcAqN7Hzp07EYvFcOjQITQ2NmL//v0oLS3FpZdeivz8fESj0VPUk+cCa2pZVFQ0jVDIBAK0cS+TUTKwwUCrLItgkxNCMDo6CkIIzj3vPDCdnfD19MAqFML17ruQq1RIzciAzmoFk5pKEx4ej24bs7MpGXsepUO/34+23l6U1dXRSktvL2WJqlRUDdrrpexQjYYmVElJ1N9LqaSJVkoKGJMJRCKBuKgI2WIxJ7zldDo5MatEIgGDwQCbzYaDBw/itddeW7T8+lIwk1ngSvXrzzQ8/PDDAIDq6mpceeWViEQiOHDgABobG/H++++jqqoKl156KbKzsxGNRjlu2XzUk1lTy7KyskX17FlDzuFhOqGVnEz5L4EA7dwODlJ3caFwBAUFcZSXn4tEIgGHw8FNuMxW5l8o2GuprKxccltJKpVybQlWjM5uH4Pb3YlYLA6jcZDbNf/zn/9csWrVXPi6xgTDMHjssccAAOXl5fjBD36AcDiM/fv3o7GxER988AEaGhqwbds2ZGRkIBaLQaVSQafTzUs9eTlsIj4PiUQCRqMRSUlJWLNmDVflYXWl2KRlaiupqKiIkzeYeg0ymQy5ubkIh8PIycnhEpR4PI6jR4/CarXinHPO4SwkpsZYOBzG4OAgtFotMjMzYbfbkZycPGOiwhKfc3NzEYvFpgk0xuNxDA4OorOzExMTE9i9e/eShRgXg6XExBnhli4QCHDeeefhvPPOQyKRQHt7OxobG/Hss89CKpVi586dqK6u5nqbrDDUyR+2w+FAX18fqqqqTumBEpkMDI8HBAIgIhF4ZjOgUNCx7QUsIGyrTCgUorq6mn7QRUVIYRgk5+QAZjN8Hg+cRiMMw8MIKhRI1uloW0KjoS2veSzULLmtqqoKingcGBkB+Hwk1qyh7b5gEEx3N0heHohEQsfQJRI67q5WU/PTUAikoADIyOBc6AFwgnNqtRrd3d2IRqP46KOP8NZbbyEvLw8JVinuC0ZWVhZGRka4/x8dHf3aToaJRCJs2bIFW7ZsQSKRwLFjx9DY2Ignn3wSSqUSO3bsQEVFBaLRKDcuq9FoTkkmWJuI2traRSexZjMdR2cYukdgGOo7xTBUdTk5OQ5COpGZKUFBQQUYhgGPx0NaWhrS0tJmLfPPVjafDexUWU1NzbK2lYATYnRarRYdHR0AgOef34/3338fpaWlK0Ye/zysxsQJiMVibNu2Ddu2bUMikeDaKY8//jj0ej0uv/xylJaWcq0gnU43Y4K9XDYRcyEWi3Fj36wUg1Ao5Kx72PaqzWZDf38/Zx6q1WohEokQCAQwNjaG/Px87jnn8XgAYFqC5nQ6MTAwgNzcXEQiEUSj0VMqHexEmc1mA5/P5yo3nweBQIC0tDRuhF0kEuHpp5/Gp59+irq6uiWP5i8WS4mJMyLhmQoej4eamhrU1NTggQcewOjoKJqamvDoo49iYmIC3/72t7F+/XoMDQ1N4ziwxmuzjvGyCZDPB8brpbydsrIFJTtsv3fqTQyAmpRWVlI+jdcLhd+PpIoKMKmpCMlksCQS6DtwAGGhEMqcHGgJmbPMz7rMsg8ppq0NzOgoTV4KCwGGoeaiQiHlHkUitGoVidAReI2G8oZ6e0Fyc0/wfaaAEMKVJ5ubm+H1ejE0NIRgMHja1GK3b9+Op556CldeeSU++eQTpKSkfOVL9/MBj8dDQ0MDGhoa8Mtf/hKDg4NoamrCww8/DJ/Ph507d2Lt2rWcnDybTJjN5mk2EYuF3c4gHqdWDNEoA7OZ/l4mA+rrw+jtbUNxcdqsViMnl/lZWYqFTLiw48B1dXUr9pBiN1symQx/+ctfuLac3+9fsTHoz8NqTMwMHo+H9evXY/369XjsscfQ19eHpqYm3H///YjFYti5cyfn8yeVSqHT6aBWqzE6OrpsNhGzIRwOo6WlBTk5ObNKMUxtr7LO53a7nTPElslkcLlcIIRwk2kulwtyuZw7b7PZjOHhYVx88cUYGBhAZ2cn5HL5jLzLjIwMuFwutLe3Q6/Xz7uqxaq8p6Sk4N///d+RnZ2NV199FV6v97RVGpcSE8xM5aEpmPOPXzRcLheam5vR1NSEjo4OXHjhhdi8eTNCoRBSUlKQlZWFtLS0WTkOTEcHrYQIBDRRmGKc+XkIhUJoaWmZud8bi9Exc7WajpEbjSAlJSeEE61WMCYTokVFcEyOzLJlfrbSwmbLrDlkbW0tV0JnOjrA9PcjsWUL7S0kEkAiQVWUi4oAhYISl/l8aiuRnk6TL7OZjsefVFFiq1RisRi7d+/G6OgoXnjhhRVf1K+66iq89957nAneww8/zPnA3HzzzSCE4LbbbsObb77JPXTWrl079S3OhFr+GRUTDocDr732Gvbs2YO+vj5s3boV5557LlcyZx3Wl1IR6ehgIJdTsUGTicHAAFBTQyAQUC7Nye3jhYCdcLHZbPD5fDM6L4+Pj2NsbAx1dXUrdo8mEgm0tbUhOTkZTz31FBKJBJ555pkV38WuxsTyghACm82GvXv3oqmpCcPDw/jWt76FDRs2cM7eOTk50Ol0K9K29/l8aG9vR2lp6aJHvqcaYhsMBuh0OtTW1sJkMnHPuJGREVitVk580Gq14sCBA8jPz+fuD5b3EwgEEAgEMDQ0hN7eXlx00UXTODCzIR6Po6WlBSqVCo899hj0ej1+85vfrHi1cyVj4kuV8ExFOBzGG2+8gTvuuANKpRIVFRW49NJLkZWVNSvHgenvB3PsGMiaNZSsPE/4fD60tbXNzoEgBExLC+XSBIOA2UyrR1otEA6D6eqi7bMpx5wqZOVwOCAWiyEUChEIBDjxOQ5mMxUQrKkBYzCAMZupps/gINXVEQppeys/H4zDQfk7s7jTsrtYuVyOP//5z3C73fjTn/50Wnqxi8Dq4j4HQqEQGhsbcddddyEtLQ01NTW4+OKLkZ6evmCOw1R0djKQSmmFZ3SUqicnJ3sRjbYtC5eGxdQyv9PphEQiAZ/PRzgcRn39qd5by4V4PI7W1lakpqbiiSeegFwuxx/+8IfT1sZaIFZjYg74/X68/PLLuOeee5CTk4O6ujps3boVOp0OsViMayMtB7GebbnORKlYLPr7+zEyMsLp/lRUVEAkEiGRSHD+XpFIBBMTE3jnnXeg1WqxdetWMAyDzs5OhMNhALRly2rgbdy48XP5aLFYDMePH4dWq8VDDz2E0tJSPPzww1/6mOD/4he/mOsfzvnH0wmBQICcnBxUVFTgd7/7HXJzc3Hw4EE8++yzOHbsGJKTkyGYNON0u90ghECiVoNJS6Oclnl+cRMTE+jq6kJNTc3sCzvDgPF4qGnQZPUFqamAVArewYN0iqqychpRmTV+U6vVyMrKgtfrhdPphEAggMlkQigUgkAggEgkAkMItYZITgYzqZhMsrKo6KFEAmZggJKTy8rARKNUUVkqPWXsnS1PsrvYWCyG//zP/1yWZOfNN9/EJZdcgieffBKBQADnnHPOtL+/8MIL2LJlC3bv3o3nnnsOAoFgMQJqDy/5RJeOX5zuE5gNAoEA2dnZaGhowGOPPYa0tDS8++67eOaZZ9DR0cEt6kNDQ/B4PNw9+HmLmM3GQCCgA4AWCwM+fwLRaBfq6+uWtf3JMAzHZcjMzITT6YTX6wWPx4PJZEI4HD4RE8tUTo/H4zh+/DjUajUeffRR6PV6/O53v1uWhX01Jk4/RCIRMjMzsXnzZjz88MNQKpXYt28fnnnmGfT19SE5ORmEEAwNDXFtGolEsuD7y2w2Y3BwEHV1dctaOUpNTUUsFoPL5YJSqUQgEIBAIAAhBEePHsXAwAAmJiZgNBoRDoeRl5fHtXcEAgGUSiUny+B2u6FWqz9XRDYajeL48eNIS0vDvffeizVr1uDBBx/8SsTEl7bCMxtYBcjGxkbs3bsXwWCQ4zgwDLMgHQeW3Da1vTQbmKEhMJ98AlJXR9tZAHUm7+ujlZ1Z2meEEPT19SESiaCiogI8Hu+UMn+KXI5Mux2K0lLwjUYwFgsS27ZREUWTCRgdBcJhOv3l94P32WcgGzdO0xZiy5NqtRq//e1voVQq8cQTTyzLTRyPx1FSUoJ//etfyMrKwrp16/C3v/0NFRUV3GteeOEFfPbZZ3jqqaeWcqjV3ewiQAhBb28vGhsb8dprr4EQgh07dqC+vh6EEI7jwDpEn4zOTgZOJwOZjCAUMkEgGEVdXe2KtZdYfhmPx+O0USKRCBcTfr9/QZObs4Hdxep0Otx///2oqqrCQw89tCzJ1GpMnNlg2/qNjY1obm6GUCjEzp07UVNTg0QiAZlMxsXEXPc5myxNTEygpqZmRSrlLOn/zTffhEql4io4FosFJpMJbrcbfX19yM7Oxtq1a6FUKuH3+6dt0N1uNwwGA/Ly8uZstUUiERw/fhx6vR4/+clPcMEFF+AnP/nJVyYmvhR9jIWAYRgUFhbizjvvxJ133gm73Y69e/fiqaeegsFgwEUXXYRzzz0XJpMJAGbUcWAVml0u17zJbSQ5GdDrqTEney6BAB17nyXZSSQS6OrqgkAgmGbudrKQlcvlgpXHw8DRo0h2OpGanAyFywWRWEyNSbVaSsYOBIBQCKSsbJpHVywWQ0tLCzQaDX75y18iLy8PjzzyyLKVJz/99FMUFRVxLupXXnklmpqapt3Iqzh9YBgGpaWl+NnPfoaf/vSnsFqt2LNnD37/+99jbGwMl1xyCb7xjW9gdHQUPB6Piwl2p1pWRjA8TDA2ZkRSkhtVVSvXXpraci0oKOBiQiQSTZtwcTqdk/YRvZDJZBxRe76aIFN3sT/96U9x9tln45577lm2ytFqTJzZ4PF4qKqqQlVVFe677z6Mj4+jqakJv/nNb2C1WnHZZZdh48aNGB4ePiGKq9VO02FiRfji8Tjq6upWrN3DPo/Wrl0LkUjEGY2yU5AsmVmlUsFqteKjjz5CIBDAmjVrkJubC6FQCIfDAT6fP6eQLCvmmZGRgdtuuw2XX345br311q9UTHzlEp6TodFosGvXLuzatQvBYBD/+te/8Morr+DTTz/FWWedhYsvvhgTExPTeD8mk4lzo533TSyXUzFAoRAIBCi/JicHmGVyhVW9TU5ORl5e3qw3Fcfml8nARKMIFBbCPTSEnmPHEJbLkeHxQLFmDRRSKRVUlMlAcnO5lt3Uhf2+++5DTU0NHnzwwWVl2M8kBDWT1Pc///lPvP/++ygpKeEY/6v4YsEulDfeeCNuvPFG+Hw+vP3229i9ezeOHj3KCYDa7XaO46BWq+H1jkKjEaGkZP4KzQsFO8qr0WhOCIbOgKliaeyEi81m4yZcWF6GXC6f8VzZXWx6ejruuOMObN26FXfcccdqTHxNwTAMMjMzceutt+LWW2+F2+3GG2+8wQmAnn/++bjwwgthtVoRj8e5mDAajVAoFItWaJ4PptqnKBQK9Pf3Ix6Pc0kDQLlDIpEI+fn5YBgGxcXFOHr0KPr7+2Gz2cAwDCYmJlBSUjLreYZCIS7Zufnmm/H9738f119//VcuJr7yCc9USKVSbN++Hdu3b0c8HseHH36IpqYmvPPOO8jOzsall16KaDSK/Px8btR96gTVnBCJTrSyXC4QpZIqOM+QMLEVl7S0NGTNYGkxI+JxgBBICwshzc+HnsdDNBaD58MPMdLfj6DbjazBQYjXr0eyUgkeppcn7777bmzatAl33333sgfnfISgLrvsMlx11VUQi8V47rnncM0112D//v3Leh6rWDiSkpKwc+fOGQVAS0pKcNFFFyEejyM3NxdarRYOhwMqlWrZd7ORSAQtLS3Iyspa0Ng1wzBISkpCUlIS8vPzp024BAKBaT5BrK8Qm+zceuutuOKKK3DzzTevxsQqOKSkpODKK6+cUQC0srISW7ZsQTweR05ODiQSCSYmJpbUWp0NM9mnZGZmYmxsjLNSiMfjHDeHvb/YjkFvby83tONyuTAxMYGPP/74FENs1r09PT0dN9xwA26++Wb84Ac/WNZrAc6MmPhaJTxTwefzsWnTJmzatAmEEHzyySe46qqrkJaWxvVz6+rqYDAYIJFI5uQ4TEM0CmZkhCZAM1R32CQkNzd3YXLmsRgdcZ/U3GE6OyFKSYEmLQ0apRJxrRbukRFYg0F0ffIJF4gpKSm4/fbbcckll+BHP/rRiuxE5iMENVXo6sYbb8TPfvazZT+PVSwNJwuAHjhwAFdffTWys7MhEonwne98B9XV1ejr65s3x2E+YCUfCgsLFz3ezuJk76yJiQmu9SWRSGC326HRaHDTTTfh2muvxbXXXrsaE6uYFScLgL7xxhu48cYbkZ+fD5lMhh07dqCqqgq9vb1ziuIuFLPZp7BGoWYztW9JJBIghJxisSKXy5Geng6TyYRAIIDCwkIUFxefYogtkUhgNpuRnp6OXbt24c4778R3v/vdJZ37bDgTYuJrm/BMBcMwWLt2Lf7xj39g7dq1MJvNaGpqwuOPPw6z2fy5HAcOdjt4hw9Tc88Zxt7ZTLq4uHjhDuQsxygQoB5d4+MgGRnUlNTnAy8nB6klJUidPM7Ro0cRDAZx++23g8/nIyMjY8XKruvWrUNfXx8GBweRmZmJl156Cbt37572mqnmbnv27EF5efmKnMsqlgc8Hg8bN25EU1MT6uvrTxEA3b59OzZs2DAnx2E+8Pv9aG1tRXl5+bIa1bLXwBoh+v1+HD9+HG63Gz/60Y+QlJSEtLS01ZhYxbzB4/GwefNmvPnmm6iuruYEQH/xi1/A5/Nhx44dWLdu3SmiuAsVyvw8+5Ts7Gwu6eHz+RCJRJDL5SCEIBwOIxwOIxQKIRwOY3x8HEKhkHveTDXE9nq9aGlpgd1ux49//GMuVlYKZ0JMfOWmtJYbXq8Xb775JpqamnD8+HGce+652LJlCzQaDaLR6HQdB68XvJYWkJwcyqOZAlbLZyneLUxbGyASARMTYJxOJM4+G0wwCObTT0EaGkCys7mkKiMjAz/84Q9x3XXX4dJLL4XT6URxcfFyfCQzorm5GXfccQfi8Tiuu+463HfffXjwwQexdu1abN++Hffeey/27NkDgUAAlUqFZ599FmVlZQs9zOpEyhmAkwVAL7jgAlxwwQVQKpUcx2E+xr/T7FNW0KiWTar0ej2uu+46rr0bCASQn5+/YsddjYmvD04WAN2yZQvOP/98KBSKBRn/slo+1dXVc4qFxmIxjpah0+lQWVmJaDSK9vZ27jWsUahIJMLGjRunHZeNPZ1Oh2uuuQaPPvooZ980F39uqTjdMbGa8CwA0WgUBw8exKuvvooDBw6grKyMMzk92b9laj+XNaqrrq5ekm4JMzBAx9z1emoimpkJEomA192NxJo18MvlaG1t5XqxP/7xj3HllVcux6WfKVhd3M8whMNhvPfee2hsbMShQ4dQXV3NmZxGIpFZx8en2qespAM5u1tOS0vDtddei4ceegiXXXbZih3vNGA1Js4whEIh7Nu3D01NTfjoo4+wdu1azuQ0EonMavzL2qfU1tbOuyo0Pj4Oq9WKmpoa8Hg8TsRWIpGAEIL29nbodLppti9utxtdXV1QqVTYtWsXnnjiCXzzm99c9s/hNGI14VluJBIJtLa2orGxEW+88QaSkpKwY8cOVFdXIxaLcRwHhmE4q4glL+wjI+C9/z4SGzaAiUYppyccBiQSeDMy0NbWBr1ej127duHee+/Fjh07ludizxysLu5nMBKJBI4cOYLGxka8/fbbUKlU2LFjByorKxGJRDiOQyKRwOjo6Lz0rZYCj8eDzs5OqNVq7Nq1C7/+9a+xdevWFTveacJqTJzBiMfj+OSTT9DY2Ih9+/YhIyODMzkNh8Oc8W8kEoHZbF6wfYrH48HAwABUKtV0f0dQMUSTyYTy8nIugWIrSEqlErt27cLTTz99ivjfVwCzxsSyCwe8/PLLqKysBI/Hw2effTbr6958802UlpaiqKgIv/71r5f7NFYcPB4PdXV1+MUvfoGPP/4Yzz//PBKJBP7t3/4N99xzD/bt24e3334bx48fB5/Ph81mQzAYXNpBs7NB6urAuN30Z2gIiMfhSUlBW1sb0tLScPXVV+Ohhx5atmTn876ncDiM//f//h+Kioqwfv16GI3GZTnuVwlfp5hYt24dHnnkEXz66ad45pln4Pf78cADD+DnP/853n33Xbz55ptoa2sDj8eD1WpFKBRakXNxuVzo7OyESqXCNddcg9///vfLluysxsTS8XWJCT6fj40bN+K3v/0tjhw5gieeeAIOhwM///nP8dBDD+H9999Hc3Mzuru7wefzYbFYODuI+SA5ORl6vR4TExOwWCzT/uZwOCCXy7lkx+FwoLe3F3K5HNdeey3+9Kc/LVuy86WJCULIXD8LRmdnJ+nu7iabN28mhw8fnvE1sViMFBQUkIGBARIOh0lNTQ3p6OhYzOHOSExMTJBdu3aR7OxsUltbS26//XayZ88ecuDAAbJ//37S2tpKTCYT8fl8xO/3L+zH6yWBnh4SaGoioSeeIOYPPyT79u0jLS0tpL6+nrz99tvLdh3z+Z6efvppctNNNxFCCPnb3/5Gvve97y3b8WfA592vX8TPgrEaE4TYbDbyve99j+Tl5ZG6ujpy1113kb1795IDBw6Qd999l7S1tRGz2by4mDjpZ2RkhLzzzjvk8OHDpKamhhw6dGjZrmM1JlZjYjmQSCSI2Wwml1xyCSksLCRr1qwh9957L2lubibvvfceee+990h7ezuxWq3zuufb29vJoUOHyPj4OPH7/cRsNpNDhw6R4eFh4vf7idFoJO+++y754IMPSHV1NTl69OiyXcuXKSaWfUprPqzqM0FxcSWRmpqKG264Ac8++ywAYP/+/WhqasKhQ4dQV1fHmZyGw+GFS+TzeNQyIjkZ9vJy9BiNUGk0uPrqq/Hkk09i8+bNy3Yd8/me2CkFALjiiitw2223cUqgq6BYjQkqBnjbbbfh7LPPRjQanSYAum7dOo7jEA6HZ+U4zAesBo9MJsN1112H559//mQn5SVhNSaWB1/3mGAFQKcS6KcKgJ5zzjnYunXrNFHcuYx/c3NzEQwGYTAYUFlZCYfDAR6Ph9TUVFgsFgwNDYHH4+Hmm2/G7t27UVVVtWzX8mWKidMylj5fxcUvMzZu3Mj997Zt27Bt2zYkEgkcPnwYjY2NePzxx6HT6bBjxw6Ul5cvWMfBEY2ib2QESSkpuOaaa/DHP/4RGzZsWNZrmM/3NPU1AoEAKSkpcDgcKzre+FXE1yEmNm3aBIDeJ7MJgGZlZXEch1AoxHEc1Gr158aE1WqF0WiESCTCD3/4Q7z44ouora1d1mtYjYkvDl+HmGA3qHMJgBYXF2P79u0oLCycxvuZKorL4/FQUFCAwcFBxGIxOJ1OaLVaWCwWjI2NIZFI4Mc//jFefvlllJaWLus1fJliYlEJz4UXXgiz2XzK7x955BF8+9vf/tx/T+ahuPhVBI/Hw/r167F+/Xo8+uij6O/vR2NjI+677z5Eo1Hs2LEDa9eu/VwdB5bNL5PJcMMNN+Avf/kLGhoalv185/M9fV2/y5OxGhOLw8kCoF1dXWhsbMTzzz8PhmGwc+dO1NfXc4nMbMa/ZrMZIyMjYBgGt9xyC1566aUVqQSsxsT8sRoTi8PJAqDt7e1obGzEs88+C4lEgp07d6K2tvYUUVyJRILy8nI4nU7EYjGEw2G4XC4EAgHcc889aGpqWhEZhi9TTCwq4dm3b9+SDjofxcWvOljPk7vvvht33XUXbDYb9u7diyeffBJDQ0O46KKLsHnzZlgsFiQSCU7Hwe/3Y3h4mNvF/s///A9qampW5Bzn8z2xr8nKykIsFoPb7T5F9fPrgNWYWDoYhkFFRQUqKipw7733niIA+q1vfQvnnHPOKca/brcb4+PjiEajuPPOO/F///d/KGFtXpYZqzExf6zGxNLB4/FQU1ODmpoaPPDAA5wA6COPPIKJiQlceumlp4jiqlQqqFQqeDweuN1uPPjgg9i7d++K+bR9qWJiLoLPUlhDc5HRotEoyc/PJwaDgSM5tbe3T3uNw+EgF154ISkqKiIXXnghmZiYmPG9eDweqa2tJbW1teSyyy5byimfMfD7/eTVV18l11xzDamsrCTXXXcdefnll8nf//538uKLL5LLL7+cFBQUkNbW1hU9j/l8T0899dQ0Mtp3v/vdlTyl003OXI2J0wSPx0P+8Y9/kO9///uksrKS3HzzzeSVV14hL730Evnzn/9Mdu7cSYqKikh/f/+KnsdqTKzGxJkCp9NJ/vd//5d873vfI1VVVeS2224jTU1N5KWXXiLPPPMM+c53vkPKysrIyMjIip7Hlykmlv1GfuWVV0hmZiYRiUREp9ORrVu3EkIIGRsbI9u2beNe9/rrr5Pi4mJSUFBAfvWrX53yPnfffTd57LHHCCGEPPbYY+SnP/3pjMeTy+WLOc0vDaLRKDlw4ADZunUrSU9PJ5s2bSJ1dXXkqquuInfdddeKH3+m7+mBBx4gTU1NhBBCgsEgueKKK0hhYSFZt24dGRgYWMnTOd0L+2pMnAGIRCLknXfeIef+f/bePDju7Krj/dxf76u61dp3W7Zly7Itz4xnyQwhzEAWJhMghGQSCAxJeBV4VKCKqsekUvAChCSQFHtIqDdAFiAkPCqgNjUAACAASURBVJLKzDATXpiQbfbNY1uStUutVmvrfV9/9/3R7o5ky1ps2ZY191PVJXX37fu73f07t7+/c885941vlJ2dnTWbePDBB+XHP/7xa358ZRPKJnYb+Xxefvvb35anTp2S+/btk/fcc488efKkfPe73y0/85nPXPPj3yw2cc2U+9Vy6NAhGQwGpZRSBoNBeejQoXXb7fUTuco//MM/yHQ6LZ966in5wx/+8EYP50Zxoyd2ZRO7BF3X5ec+9zmZzWblY489Jl9++eUbPaQbxY22B2UTu4RSqSQ/97nPyUKhIP/jP/5DjoyM3Ogh3Sgue67u2krLHo+HWCxWu+/1eolGo5e0MxqNDA4OYjQaefjhh/n5n//56zlMxfVlN0QsKptQ7CaUTSibUKzlsjZxQ3dL3yiKf6v4/X7a2tqYmpri3nvv5dixY/Sus1O5QnEzoGxCoViLsgnFTnFDBc9GUfzNzc21reIXFhZoampat101Gnz//v286U1v4tVXX1Un8jaIRCK85z3vYWZmhp6eHr7+9a/j9XovaWcwGDh27BgAXV1dPProo9d7qK8LlE3ceJRN7C6UTdx49opN7PheWjvFO97xDr70pS8B8KUvfWndug3RaLS270goFOLpp59eU3vjptnf4wby6U9/mvvuu4/x8XHuu+++y+5XY7PZOH36NKdPn951J/HrBWUT1wdlEzcPyiauD3vGJjYK8LkR0UZVQqGQvPfee+WBAwfkvffeK8PhsJRSyhdffFF+8IMflFJK+fTTT8uBgQF5/PhxOTAwIB955JHa63fh/h67kpss6O9GB2cqm3gdoGzi5reJ//mf/5EPPfSQlFLZxE6wV2xi157IV8szzzxTS3WUUspPfvKT8pOf/OSaNm9+85vlM888I6WspH/7fD6p6/p1HeeNpq6ubs19j8ezbjuDwSBvvfVWeccdd8hvfvOb12No63GjJ3ZlE68DlE3c/DYxOjoqy+Xypu2UTWyNvWITNzSG51pyM+3vca1RQX8KUDaxGmUTe5utVtpWNvFjXg82sWcFj5Q3z/4e1xoV9KcAZROrUTahAGUTq3k92MRmdXhuWoQQdwEfl1K+5cL9jwJIKT+1qs1/X2jzrBDCCCwCjXKvfijrIIT4DBCWUn5aCPEwUC+l/L8uauMFMlLKvBCiAXgW+Dkp5fANGLLiClE2sTWUTbx+UDaxNfaKTezaLK0d4EXgoBBinxDCDDwIXBw2/ijwaxf+fxfw3Y1OYiHEW4UQo0KIiQtf+sXPPySEWBFCnL5w+9AOvZdryaeBnxFCjAM/c+E+QojbhBCPXGhzBHhJCPEa8L/Ap3fTSazYMsomtoayidcPO2oTe9QeYI/YxJ718AAIIX4W+CvAAPyTlPJPhRB/DLwkpXxUCGEFvgKcBCLAg1LKqcv0ZQDGqHzZASqG8t7VX6gQ4iHgNinlb1/Dt6VQXDHKJhSKteyUTSh72P3s2RgeACnlE8ATFz32h6v+zwG/tMXubgcmqie6EOLfgZ8DdpWCVSg2QtmEQrGWHbQJZQ+7nL28pLXTtANzq+4HLjx2Mb8ohDgjhPh/hRCd6zyvUOwVlE0oFD9G2cMuRwmerbNeWP7F64GPAT1SyuPA/wBfuuajWgchxC8JIYaEELoQ4rYN2m243qxQbIKyCYXix9w09gCvT5tQgmfrBIDVarwDCK5uIKUMSynzF+7+P8Ct12lsF3MOeCfwg8s1uLDe/DngbUA/8F4hRP/l2isU66BsQqH4MTeTPcDr0CaU4Nk6m0bzCyFaV919BzByHcdXQ0o5IqUc3aRZbb1ZSlkAquvNCsVWUTahUPyYm8Ye4PVpE0rwbBEpZQn4beC/qZykX5dSDgkh/lgI8Y4LzT5ywUX4GvAR4KHL9SeE+CchxLIQ4txlnhdCiL+54EY8I4S4ZWff0ZbXmxWKdVE2oVD8mJ22B1A2sdPs6SytnWYL0fwfBT66xe6+CPwd8OXLPP824OCF2x3A5y/8BUAI8T9Ayzqv+5iU8ltbOP5W1psVig1RNqFQ/JgdtgdQNrGjKMFzg5BS/kAI0bNBk58DvnyhwNVzQgiPEKJVSrlw4fU/fZVD2HS9WaG4niibUCjWomxiZ1FLWruXa+1K3EqFUYViN6FsQqFYi7KJbaAEz+7lil2JQohfEEIEgLuA/xKVvWAQQrQJIZ6Ay68378jIFYprg7IJhWItyia2gVrS2r1csStRSvlN4JvrPB4EfnbV/UvWmxWKXYyyCYViLcomtoHy8OxeHgV+9UIU/p1AvLouq1C8TlE2oVCsRdnENlAenhuEEOKrwJuAhgtuxf8bMAFIKb9ARVH/LDABZIBfvzEjVSiuD8omFIq1KJvYWfb0bukKhUKhUCgUoJa0FAqFQqFQvA5QgkehUCgUCsWeRwkehUKhUCgUex4leBQKhUKhUOx5lOBRKBQKhUKx51GCR6FQKBQKxZ5HCR6FQqFQKBR7HiV4FAqFQqFQ7HmU4FEoFAqFQrHnUYJHoVAoFArFnkcJHoVCoVAoFHseJXgUCoVCoVDseXaN4BFCNAohviOEiAoh/vEa9D8nhLhlp/u9EQghviiE+MSNHodCoVAoFDcLu0bwAB8FxqWUXinlB3eyYyGEF2gDzu9wv/VCiG8KIdJCiFkhxPt2sv/dgBBiRgjx0zd6HIq9jRDiISHEj270OBQKxd7FuMnz8rqMAjh27Bh//dd/DfCbO93397//fT7wgQ8wMTGR3sl+H3zwQXRd5x//8R85ffp01/333/+vQ0ND/3r06NGdPMwl/Nqv/RodHR0AH9usbalUwmjc7Gu+PN3d3TzyyCPfueIOdhZxowfAdbSJm42rOdf++Z//mUceeQTU57tddoNNKBQ3BTfcw1MoFKirq+Ps2bM88MADHDt27LJtdV3nj//4j+ns7KStrY3HHnsMs9lMNBrd8Bhnzpyht7eX3/md36GxsZG2tja+852r+w1Pp9P853/+J3/yJ3+C0+nknnvu4R3veAdf+cpX1m3/6U9/mt7eXlwuF/39/Xzzm99c83xPTw+f/exnOX78OHV1dbznPe8hl8sB8Oqrr3LLLbfgcrnWPH45enp6+LM/+zOOHz+Ow+GgVCoxMjLCm970JjweD0ePHuXRRx+ttb/cc+9///vx+/088MADOJ1O/vzP/xyAP/uzP6O9vR2Xy0VfXx9PPfXUFX+Oipubi8+1s2fPXvY8i8fj/Oqv/iqNjY10d3fziU98Al3XGRkZ4cMf/jDPPvssTqcTj8cDwBNPPEF/fz8ul4v29nY++9nP3qi3qVAo9gJSyo1u14WhoSHZ1NS0abs/+IM/kD/xEz8hA4GAjEaj8s4775QdHR2bvu43fuM3ZH19vfz2t78ty+Wy/PjHPy7vu+++S9rdf//9sq6ubt3b/fffv6btK6+8Iq1W65rHPvOZz8i3v/3t647h61//upyfn5flcln++7//u7Tb7TIYDNae7+7ulqdOnZLz8/MyHA7Lw4cPy89//vMyn8/Lrq4u+Rd/8ReyUCjI//iP/5BGo1F+7GMfu+z77e7ulidOnJB+v19mMhlZKBRkb2+v/NM//VOZz+flU089JZ1Opzx//vyGz1X7+s53vlPr+/z587Kjo0POz89LKaWcnp6WExMTm3wDO8Zm5+v1uClWsfpcSyQSG55L73//++U73vEOmUgk5PT0tDx48KB85JFHpJRS/vM//7O8++671/Td0tIif/CDH0gppYxEIvLll1++vm/u5uBG24O6qdtNc7vhHh6A06dPc+LEiQ3brKys8Jd/+Zf80z/9E+3t7Xg8Ht785jdv6BGqcvbsWT72sY/xlre8BU3T6O/vX7fd448/TiwWW/f2+OOPr2mbSqWoq6tb81hdXR3JZHLdvn/pl36JtrY2NE3jPe95DwcPHuSFF15Y0+YjH/kIbW1t1NfX88ADD3D69Gmee+45isUiv/u7v4vJZOJd73oXp06d2vQ9f+QjH6GzsxObzcZzzz1HKpXi4Ycfxmw2c++99/L2t7+dr371qxs+tx4Gg4F8Ps/w8DDFYpGenh56e3s3HY9i71I9106fPn3Zc6lcLvO1r32NT33qU7hcLnp6evi93/u9y3pEAUwmE8PDwyQSCbxeL7fcsidyDhQKxQ3iphE8Tz31FAcOHODAgQO1xyKRyKaCR0rJuXPneOCBB2qPnTt37rKiZ6s4nU4SicSaxxKJBC6Xa932X/7ylxkcHMTj8eDxeDh37hyhUGhNm5aWltr/drudVCpFMBikvb0dIX68VN/d3b3p+Do7O2v/B4NBOjs70bQff93d3d3Mz89v+Nx6HDhwgL/6q7/i4x//OE1NTTz44IMEg8FNx6PYu1TPtY3OpVAoRKFQWHPubnSeAfznf/4nTzzxBN3d3fzkT/4kzz777LV7EwqFYs+zKwTPa6+9tqngCYVCtLW11e6Xy2WefPJJjh8/vuHrpqenAdYIpVdffZXBwcFL2r7tbW/D6XSue3vb2962pu2hQ4colUqMj4+veR/rBSzPzs7yG7/xG/zd3/0d4XCYWCzGwMAAUm4en9na2sr8/Pyatn6/f9PXrRZIbW1tzM3Noev6mj7a29s3fO7ifqq8733v40c/+hGzs7MIIfj93//9Tcej2LtUz5GNzqWGhgZMJhOzs7OXPLe6j9WcOnWKb33rWywvL/PzP//zvPvd777G70ShUOxldqXgeeihh3jooYfWtDly5AjPPPMMExMTJBIJPvKRjzA5OVnz8Kz3GqgELB87dmzNhPrqq6+uK7CefPJJUqnUurcnn3xyTVuHw8E73/lO/vAP/5B0Os3TTz/Nt771Ld7//vdf0m86nUYIQWNjI1DJSDl37tyWPpu77roLo9HI3/zN31AqlfjGN75xyVLYZtxxxx04HA7+/M//nGKxyPe+9z0ee+wxHnzwwQ2fA2hubmZqaqrW1+joKN/97nfJ5/NYrVZsNhsGg2Fb41HsTTY6lwwGA+9+97v52Mc+RjKZZHZ2lr/4i7/gV37lV4DKeRYIBCgUCkAlmeFf//VficfjmEwm3G63Os8UCsVVccMFz+LiItFolMOHD9cem5ub4+67717T7r777uO9730vJ0+e5LbbbmNwcBC73V573XqvgUr8zmpxEw6HWVxcZGBg4KrH/vd///dks1mampp473vfy+c///l1PTz9/f383u/9HnfddRfNzc2cPXt23bGuh9ls5hvf+AZf/OIX8Xq9fO1rX+Od73zntsZpNpt59NFHefLJJ2loaOC3fuu3+PKXv8zhw4c3fA7gox/9KJ/4xCfweDx89rOfJZ/P8/DDD9PQ0EBLSwvLy8t88pOf3NZ4FHuTzc6lv/3bv8XhcLB//37uuece3ve+9/GBD3wAgHvvvZejR4/S0tJCQ0MDAF/5ylfo6enB7XbzhS98gX/5l3+5Ye9NoVDc/IhNllWue02MQqHAiRMnOHPmDCaT6bLtvvCFL/Bf//VfPPbYY1t+jeKmZzfUHFF1YhS7id1gEwrFTcGuEzyX47nnnqO1tZXOzk6eeuopfvmXf5lHH32UO++880YPTXH92A2T+66xCYWC3WETCsVNwZWX4L3OvPrqq9x///0Ui0UOHTrEF7/4xesmdqSUlEolcrkcJpMJg8GAwWBYk42iULyeuNgmjEYjBoNh3eBjhUKh2A3cNB6eG4Wu68TjcQKBAD6fj/HxcQ4fPozFYkHTtEsmezXhX1N2w4erbELXiUQiLC8v4/F4GBkZ4eTJk2iahsFgwGg01mxCXRRcc3aDTSgUNwU3jYfneiOlpFwuUywWicVi+P1+4vE40WiUsbExvF4vdXV1OByO2qSuadolk70SQIq9QtWrUyqViEQizMzM4Ha7yWQyjIyM4PF4ajZRPe+rNlH1jKqLAoVCcaNQHp51kFJSKBQolUrMzMywvLyMlJJkMkkwGOQtb3kLpVKJWCxGMpnEZDJRV1eHx+PB6XTWhI4QoiaAjEajEkBXz2748F6XNqHrOsVikVKpxOTkJOFwGLvdzpEjR3j55Zfp6+sjmUwSi8VIpVKYzeY1NnGxAFI2sWOoD0+h2CLKw7OK1V6ddDrN8PAwvvp6jg8M8MOnn8blcmE2m1lYWGBgYIDW1lYA8vk88XicUCjE1NQUQgjq6uqoq6vD7Xav2UF69WSvYh4Uu53VNpFMJhkeHqalpYX+/n7m5uaAioix2Wy4XK5acdB8Pk8sFmN5eZmJiQkMBkPNJlwuV80mhBAYDIY1sXHKJhQKxbVACZ4LSClrV7Dz8/MEAgGO1dfjPnOGlNtNPBymt7eXUqlEJpPB7/fT09MDgMVioampiaamJgCKxSLxeJx4PI7f70fXddxuNx6PB7fbjclkolQqEQ6HaW9vV0Gfil1J1dNZLpfx+/21+lUul4tEIrGm+vfFnmKLxUJzczPNzc0AtaXhWCzG7OwsUkrcbndNBJVKpdqFQ2trq7IJhUKx4yjBA7Ur2Fwux9DQEA6HgzsOHsR44Qo2HgxiWlqi8cLmoG63m0gkgs1mq03oqzGZTDQ0NNQKqJXLZRKJBLFYjGAwSLFYxGazkcvlqKurw2q11iZ1FfSpuNFUvTpVcT80NERdXR133HFH7XwUQtREzlYEiclkorGxsVZtvFwuE4/HazZRKpWwWCwUi0Xcbjdms7l2rIs9QMomFArFlfC6FjyrgzCXlpaYmpqir6+PBosFMTQEbjfyxAlWnn8em9GIMxBAK5VobGxESkkwGMRqtV6ya/rFGAwGvF4vXq8XqMRDhMNhpqammJqaIpvN4nA4ale7drt93ZgHFQituNas9nQuLCwwOztLf39/7dytUj0HpZRrxM9WMRgM1NfXU19fD1RsYmlpiUAgwOTkJLlcbo1N2Gw2FQitUCiuitet4NF1nUKhQKFQ4Pz58wDcfvvtmHQdcfYsIhBAv+ce0HW6WlvJ2O2I0VFMbjf09dHV1UU+n2d6eprDhw9jtVq3fGxN03A6ndjtdo4NDMDwMFkpiWgac3NzpNNpLBZLLehzvawXFfSp2GmqNpHP5xkZGcFkMnHHHXesiUFbTVXs7MT5V7UJp9PJkSNHkFKSTqdrS2DpdBqbzVYTQA6Ho7bvlhBizRKYsgmFQrEeVyV4QqEQVqsVp9O5U+O55lS9OjMzM6TTaSKRCPv3768EIJdKiIkJMJuR3d2Qz1NyOpH5PLrfT9DjYWl2lmxzMyaTiUwmw7lz5ygUCtxyyy2X/WHYCBEIoC0t4TAasfX11XaPzmazxONxFhcXSSaTGAyGWtqvy+WqbaQohFj3ileh2CpVm5iYmKBYLBIKhTh48GAtJm09LvbqbNfDsxlCiJoA6ujoQEpJLpcjFouxsLBQy46s2oTT6VxjE9XlL2UTCoWiylUJHp/PRzqd3qmxXHOqqbXFYpHFxUXy+TynTp2qeGd0vbKM5fcj774bEQ5TSiR4aXqa1Pe/T9JgwNTdTW5lBZeuY7fbcTqdNDY2EgqFmJmZobe3d1sTqzEeRxSL6O3tiEwGMhm4sDxms9mw2Wy0tLQAlT3G4vE4kUiE6elpgNrVrtlsxu/31zYurU70KuhTsRlVmygUCiwtLSGl5NSpU5jN5g1fd3EMz04LnvWOV7WJrWRHaprG0tISfX19gLIJhUJxlYKnehW221mdWptIJBgeHq6JldpS1OwspNPgcoEQSLudlakpMpOT1BuN2Hp6OPGGNzCby9HY1oajuZlwOExbWxsNDQ2EQiFOnz7NyZMntzamdBrn2BiisRH9rW9FnDuHyOWQl4kHMpvNa4I+S6US8Xic5eU4iUSYfD7L+Pj4GhGkAqEVl2N1/FosFuP8+fPY7XZ8Pt+mYgcuDVS+1oJnPTbKjgyHw+TzeSYmJtZkgq22CRUIrVC8vtjzMTwXFxEMhUIcP36cRCJBPp+vNJqfRywtIfftQywuQjqNtNtJTkxw0Ocj7/Ox6PdTMpsrVb4yGQAikQhWq5XOzk5CoRAA4XAYn8+38aBKJYyzs5QtFmRLCxSLYLHU+t0KlfgdH5FII8lkBy0t47S0tBCLxRgfHyefz+NwOGqp8CroU1Hl4iKC8XicwcHB2jm8FbabpbWdfq+U1dmRDQ0NLCws0NTUVFsGKxaLOJ3Omk2szo5UNqFQ7H32rOBZ7dWpptb6fD5uv/12NE0jlUqh6zqEw5WlrGIRTp2CRAKRzRKPRiEex3PrrQifj7GxMfxzc5gzGUilKBQKpFKpmnt9cHCQiYkJ5ubmsFqtOByOyw0MMTKCWFkh29MDRiMim0VqWuXvlt4bBAKCUEhgMoHNpiOEhsfjwePx1N5/KpUiFosxMz1NJpvFbrfXAqHtdjuFQgEp5SV7gqmgz73J5YoInjp1qvYDvx1PjZTyirO0LtffTlE9ry/OjqzaxMXZkW63WwVCKxR7nD0peFYXTKsWETx69Oia9HEhBCKVQiQS0NgIug6ZDNJmQ8zOEg+H0Xw+XC0t6Bdc4sVz5ygvLaGFw2QyGTRNq6XVCiHYt28fY2NjTE9Pc+jQoXWXBsT8PNryMghBubocGIuhBYNQKkFfH1wIvlyPfB6GhzVWVgSHD+vYbDA2Bpq2djIWQuByuXA5HHQvLCCFIL1/P7ELG6FWy/+vDvqsuvUjkQg+nw+r1aqq3+4RNioiWEUIQblc3lJ/VZGzW8+L9camaRputxu3201XVxdSSjKZDLFYjEAgsCY78mKbCIVCtLS01ESQEkAKxc3HnhI8qwum5XI5hoeHsdls3HHHHbUMjioin8cyNASdncjbb694eTIZEAICAUw+H7bBQUQ2C21teAFnOs2I0UijruNxOunr66NYLJLNZimVSpTLZWw2G6OjoyQSCe688861sQGRCGJlBb29HRmNouVySI8HbXy8ErtjMEA2C5eJi4rFwO/XSCYFVquktVUSj1d/eNb/TEQgANkswmjErmnY29ouW/5f0ypeouo+SauvuFXQ583Jaq9ONptdt4hgFU3TtuxlWf39X4+g5e2yFTEmhMDhcOBwOC7JjlxaWqptieHxeFhaWsLr9VIqlWqvV9vEKBQ3F3tG8Fy2iOCFasdrKBYxzcxQzOXAbq/Ez5jNEI0ikkmE0UjriROUCwXSfj8Zs5no/Dzi8GHGpSTy0kvsM5sRl1m2CoVCrKys0Nrayr59+yoPZjIYXnsNCgX0e++Fl17CkM8jIhFEIkH59tvRZmcry1oXCR4pYXZWsLAgqK+H/ft1VlYEQlQcU5ed3CMRxMICsqOj4snKZsFmqz29Xvn/eDzOwsICIyMjAJeU/1dBnzcPWy0iWGU7wuV6Z2ltlyv1Pl0uO3J+fp4zZ84AP86OrO6Tp2xCobg52BOCR9d10uk0Z86cwWw2I6WsFBE0mdZrjJiYwJBKVWJohIB8HmmxIEZGKHd0EDAaiZw+jW6zIaamYHycJV3H6XTSYDYTPXuW+ZkZ7nrrW2tej9WFAG02G5FIhFgsxtLSEs0+H9r0NJhMNTEjjUYsgQCisRFZX18RXkbjJYHLhQLMzGjMzQmKRbj99jLhsKg9NzEhSCahoeGiyT2fRxsaQoTD6AMDFSGXy20YI1QN+rRYLNx6663our6m/H+xWMTlctUmfIvFooI+dynV7UxGR0cBNi0iCJXvUNf1LfV/rUTOTp0zO7XcVs2OnJmZ4bbbbqtlR1aXwcrl8iU2UUXZhEKxu7ipBc9qr040GiUajTIwMECp1EoqVVkZukTzTE8jfvADDBYLsqur8lg6XfGGpNOEHQ6enZig0evlyH334ZiawtLeTtntpr+tDYPNxvlAAEOhgMPhwLbKYwKVZaJsNsvBgwdJpVIE5+dxzs7iXlykfOoU2uJiRdToOpaFBfSjRxH5PGSzlfihVaIkHq8sYUkJHR06mYygetGYTMLYmEY+L9A0uTaGR0q02VmwWpFtbZDLgdVa8fBskWrxtovL/yeTSWKxGJOTk7Wgz2rWSzUQuvp6FfR5/VltE5FIhFAoxIkTJzYsIlhFeXg2x2g04vP5apmYuq7X9skbHx8nl8utyQSz2Ww1m1DbxCgUN5abVvCsLiI4MTFBKpXCbrcTDrcxMwMXwlSwWsHplLhc4E4EMMbjyGPHEMvL6FZrxcMzMYEoFpFNTazEYnQfPIgnk8ERCuFxONDb2igHgxRLJQrBIJ35PEVdx+/3c+jQoTWTVjweBypLQT6fj8z4OOFz57A5HBhjMSiXEYkEWjgMgDxwADE8jMhmEbEYFApIXbKwqDE8rFEowL33lonFBJlMZXkrGBQEgxpNTTq9vTrT02sFj/D70V56ifLgIKJQqIioUqnyHq/iM9c0rXYlC6wp/++fmSGdyWC7kAlWLf+vaRpTU1NomsaPfvQjfvu3f/sqRqDYiOrWEMVikbGxMXK5HG63e0tiB7YveKrHg53JsNrpLK3rISaqcW/rZkfOzJDJZGrZkVWbqG5Jk8vlGB0d5dd//dev+TgVCsVNKHhWX8Emk0mGhoZob2+nr6+P5557DpOpkujU1iZJJivOm0BAkJ5apCM9g2l/O5Y6K5pFI2tzQC6FNjOD/oY3kPL7ycXjHDh6lPx3v0todBTn4cPIcpm0lEyeP09kdpaSrtPtcrESDrO4uFhLTYeK4LHZbBXXdjRKr8OBv72d6XSa3mQSw/IyRCJIs5lCU1MlHd5mqwQX53KUskUmzxVIlW24XBKLpSLapKwkcU1OCsJhgccjOXRIZ3n5omyZRAIRDEI+j1hZqbw4EkFbWakcq79/wyyw6me8FWrl/zWN7uFhyh0dZPftu6T8fzqdZmRkpLZn2Vb4wAc+wOOPP05TUxPnzp1b79gC+GvgZ4EM8JCU8pUtH2APsV4Rwa6uLpqbm3nlla1/JNtZ0qqWezhz5gzZbJZisUhjYyNer3db+8pdK3Za8GzHJlwuFy6Xi87OTqSUZLNZYrEY8/PztezIVCrFW0SSTAAAIABJREFUyy+/TCKR2FK/yh4UiqvnpoqqW73h59TUFCMjIxw/fpzu7u5agGB1jnM4oKUFenvhUHOM5vQ09cYE1HkIZx3MBq2M/ijH+ItxFsaSxJcLLMTTGItF6s1mGs1m8hYLk5kMQ6+8gqZpHPb5sACuU6dwud0klpb47ne/yyuvvEIwGCSbzVJfX1+5os5mEa++ikXXcQ4OkjebmcjlKuJjZIRSVxfSZKoEKes6mt9Pyt7Icws9BGeKdHfrtLXJmjZJpSqxPJmMoL1dp7m5kpklZbXmiIBSCc3vh7o69OPHwWRCrKygnTmDtNsrsULbWNbaElJWjiklWqlUK/3f19dHY2Mj5XIZXdf53ve+x+OPP84v/MIvbKnbhx56iG9/+9sbNXkbcPDC7f8APn/V7+UmZLVNTExMMDExweDgIB0dHRgMhi0LGNi6hycSifDSSy9hMpm47bbbaGpqoqGhgUKhwOjoKC+88AIjIyMsLCyQy+Wu5u1dMbslZV4Igd1up62tjf7+fm6//fbadhc//OEP+bd/+zfe//73b9qPsgeF4uq5KTw8mxURXE1Pz0Up2tkspsA0pgYP9R0WmluSyKZmwskoke8tkzzQQ7rFS+lclsmpHG0NVkJaGJu9hbjMMD0ywv19fejz82C14mhrw9DZiSkc5mhrK0PLyywsLNTW8XO5HFaTiZZoFHc6TbG5Gd3hIJnJEHnhBTz19bTY7Ui3G2kwVDwyoTArcTMzxV4y+QWarFnq651UtylbXBRMT2sYjZK+Pp1oVJDNVsTO/LygWLywa/XYGNrYGKU3vAEtFkO63YhQCC0QoHjqFFo6XVne2mQ7kO38UIhgEOJx9P37EakU6DrpbBa/34/f70dKSV1dHe9617s4efIkH/3oR7fU7xvf+EZmZmY2avJzwJdl5Rf6OSGERwjRKqVc2PLgb2JW20QqlWJoaGhNEUHYfmzNZu11XWdycpJoNMott9zCq6++ClQ8Q9UCft3d3bUCf9FolNHRUfL5PC6XC4/Hc908QDspeHZaPBmNRhwOBw888ABvectb+K3f+q1NX6PsQaG4ena94NlKEcHVmM2VmGCDAbRyETE5idVuwH1rL1p2prJJp0HDnVjAbcnR9JNHaM9MkLK4aTO7iDw7ysspH1EhQNjJNfTxzHeHKKcX0QePsJBOY1xaohCPI9NpbG43qVSKxsZGfD4fNqsV4+wsuVyOqMOBHo1CMonLYqEcDjPf0IClpwcDoFssyKFRZtP1RLQe6l0FXL2Csl65Ki6VKstxpRJ4PJKGhsoKFVQytMbGNCIRQSYjMEUiCED3ehGAtFoRwSDS40E2NaGFQhWFtJMenkwGbXgYSiX0gwchmWRhZoalRAKj0UhdXR0+n49kMkkmk8HhcOzkj107MLfqfuDCY3t+gq/Gr21URBC2n/G00ZJWJpPh7Nmz+Hy+NaKqymqhtLrA33YF0G4UKTsteHRdx2AwkMlkqKur2ymbeN3ag0KxVXat4Fl9BZvP5zcsIriaaBTOnBG4nTr1K5M49CyWFg+tzTpaxA6Li4iJCTCbKdbZkC435DU0kcbsSpPPTTA8X6T/znaMaclyoUhsZpmFkoeFfQeoyxlplm10mk3UNbkwnLqFyclJUqkUxWIRbzZLdyaD3t9fCUgeHyfZ2Ejg7FlSus6U2Uz09GnajEb0qSVmpvMMHxykvS5FT0OKqSUdPVsgnYbJycoSVmenTj4PoVBl0q1mbx0+rNPdrbM4mcQcCCD7+xE2W0XUGI1ogQD6kSPoAwOVYOnlZbDZkJ2dO/EFofn9yAt1jLL5PP7ZWTKJBPX79uF0OvH7/Xi9XjKZTE3w7CDr/QLtrlShHWY7RQSvhMt5eBYWFpiamrpsDZ/NPENbFUAWi2VNYb+rYSdFiq7rOyp4yuUymqaRyWRqBQ93gNedPSgU22VXCh4pJclkkpWVFQwGw8ZFBC8iGgWrRdKanSKfzTHv7KE8EkHGcrjcbuqHz+JocyF6D9CeH6KjTScSLPDS179OwWwmV9/AkcOnuOWuJlz+WaxWK7PGDr76oobPacCWs6HNJojogoWlMppdYnd2Ewq9SmF5ikNtLUivF2GzIe12hBC4FxZwuFxYb7uNO9/+dsYffZTx56bQlsvUpVIQP4e0Wciv5DCHksTnS4y37gcMdHfrNDRI5udFLUPL79ewWCrLW8sLOo75Scz6ItxxBzKTqcQF5fMgJXprK9riYiXl3e+HC9lhl2PLdVj8fsT8PKVDhwgND/PyxASmYJBT/f24u7uZmJjAbDZjt9trk/umm6pujwCwWrl1AMGdPMBuQkpJLBYjFouh6zqzs7McOXKkVjJgJ7i40nKpVGJkZIRyuXz5ularxred46wngBYWFojFYrzwwgs4nU68Xi8ej+eS0g9bobqX1k6wk31BRfBUPTw7eBHwurIHheJK2HWCZ3WszsTEBB6Ph1OnTq27L9V6aBp4UnO01seR93TS4W1g+alFhgIZTL4SkUCWBds+sloTkUU/3pksC+cmiU9N0fCLv8hPHxggVzRS19iI4bFHkT09eN5wJ8UfPYHVEuVYewpHJoSh/1ampwRjZ4t0HrKSWHKSei7EzF09+Pp6cIWWsB8QGJJJyrEY8YYGmi0WRLaIcAxSGH2GYrOJN//KGxgaF8jleRb/v5d5ZaWLSLmVozE/LW1tQGWyLxQqy1tSgtcrMZkqy3fG4BwaOsXqruvVYoluN3pzM0JKpNVaqf/T1FQrtMiqAmmr2dLknskg5ucpJpNMLi4S9ftJRCJ0lstkg0EsPT0kk0laW1vXuO/tdvt2ToXNeBT4bSHEvwN3APG9GK+weruUZDLJ+Pg4Pp9v0yKCV0I11Rwq2YZDQ0N0dXXR3t6+oYfjar0fVQFU9aQcOHCglto9NjZGPp/ftgDaaQ/PTSB4Xhf2oFBcDbtG8FxcMG1kZASz2czg4OC2+jGElzBGQsguJ+TzGIwCd7OVriU/HXUmrIN24m1WInYro98x88N/f5nERIDmnvtJyV6CKQNtpgzaynJFFOg6Hk0D0ojICiZjAmGx4DjeS3NslBIZjvYbcEz7CYgYy4ksgWk31kgGXzaEdyaHbjIR299KUz7H2Jki2kqcNo+B0W4XK8kY9o6jGLJJtCkL1oE3sr+wQld9hDPzRaLREtmsxvR0K8Wii7vuMpDNCiIRASsr5M5Nkbd7kPUGRDaL7nRWqiu3t/94eUtKxPw85YGBWpHDywmeTSd3KRF+P5F8nlAmQzmTwexwcELXsdXXsxiNkvD7AaivryeXy2EwGEin0zg3CZZezXvf+16+973vEQqF6Ojo4I/+6I/40Ic+9OHKEOQXgCeopOBOUEnD3XPFTFbbRCgUYnR0FJvNxsDAwDU5XlXwTE9Ps7S0xIkTJ7b0g7zThQcv3uRz9S7nWxVAu1nwVPtLp9NbFjzr2UOxWOQ3f/M3P/x6sQeF4mrZFYKnmlpbKpWYmJggnU5z4sQJxsbGttWPKZ3GHAuQdnjB40QEAsimJoSQWOenkUdPorW4qc+nsHQ04hUBLCsh2t90hOa6boZjZU6PF4lF47iacpja78YpU2gLUfpsi+zLuEi43dRfWForahq5aJTM8AyZWIC6/gZy+gjhvAdTbhkxnmC+aGapXEfJWcfQOei1v4DHnGXZZiSfyzIUCGHJuTDmXDS076e9z441bqfNayHfcODClhIZdD2Nps0wORkmmfRC3sHM+BxLgRKxHjvSVoZUCi2TqWR/+XzIeByRTkM0itQ0ZGcnYmJiwy0mNpvcC1NTxH7wA1Y6O/F6vXibmxkbHcVntVJ/6hSpF19k+Nw5jh47htlsJp1O15a0tnM1+9WvfvWSxz74wQ9+ofr/hWyU/3PLHd5kXFxEsFAocOzYMWZnZ6/ZMYvFItFoFLvdvm4G5I3iSgTQbhY8V+LhWc8eAD784Q9/Afa+PSgUO8ENFTyXKyJ4+PBhyuUy5XJ5652l09gWFym3tzOe3IdxKY8rbMIVqBTd0y02Sq0doCWJz80xND+PPZvE6rbRes8x9qXT1OVLnDm/gFhaxHT4FlL2JsLzKyylIe63EZZpXtrn5kAhiNv+PPryMsalMMvLOqKzA8plcn4/waUAXakg7jYDxhNHSZ/LkiqX6fFEcUeXCNf3Mz2TIofkf5cX6Vg8T8c9x7j1ziYCIoNeLqAHlgkYDrKyojE4aKO11UY63cjRo/s581qGySeGmLSGSZvLGNN+4gUHvqUlTG1t0N5eWQOz2xHnziFdLmRrayXty2LZMFNro8k9HAgw9uSTFBYXOXLHHTTb7SyPjyPKZTzt7UiHg1g6TVbTavFWqyf3HV7S2pOstol4PM7IyEhtWSmfz2/PJlb1udmP/8rKCufPn8dms3H48OFt9b+THp6tiJSLBVA15m+1AAJqG3xeSQzQanaD4FEoFFfPDRM81dTaUqnE7OwsKysrHD9+vLbssZ2qrxQKiKkphMmE96CH45E4GYOH4LIR6/Q4ZXsdM123cShlYi48jub3c0tPD087XXgP7KO5sREpJfW5HI7kCrFyFLc7hmZOUipP49FtzFrrmLYcZDZhgpjkgNNJ+8o8dfEk1kPdWAYHMZrNlJubMZ+L0Wyw09zXz7hzkFzhWY63leltlJCqJ/vTt2D+4hBjKxKH1Elmlwng5LUZA2m9hGtunnMWQWxfmaYmwf79Er+/8kMQiwniZ6K0ZmIcf+cxYstFZs/PQi7H8vg44WIRu9OJu1zGVV+PIxJBHjyISCQqwcxSVv5u8L1cPLkXCgVmZ2bIvPIK6Xwey/79hJeW8HZ2Ehsfx9bdDW4341NTxJJJOnp7a2UDqpN7dd8txeVZbRNTU1PEYjEGBwdrQnFbNnGBqhi5nJDQdZ3R0VEymQwnT55kZGRk2+PeKcFzpX0IIS4RQKOjo5TL5SuOAVrNtRI8yiYUiuvLdRc8Wy0ieHHGyGUplxGTk6Dr5Lu6MCRj9JhiyCN1lGfmyMg8oeODmBbgBz84T5PPy+FIEyFNJ+A5RLvZRiYUYnFxkfIzzxATbs6XJY7gAr7BAXw2DYdZErxlgMHjBxgpBTG9OkFfFHJJHb92lPiiHfsLGVr7fXiW8hyyxQlIB1NDLuJNJVq9yzSGliteFpeLVCjHSqaOpuRL1O2TpJo7kE0ZUvkE+fksSyE3mAyU3XlMNiOLi4JUCpaXBXo0ibsQoq5N4HQbiCbNWEp5GsoGGvv6aLvtNjLJJKmpKYJzcxAIUOjuxlco4NZ1bJEIolyGw4dhnUn84hTccDhMIBDAFApRv7iIYf9+unt7mZ2aYuLll8nncng7OvBPT1Nwu/EVCnQ3Ntb6qE7u243heT2x2quTTqcZGhqiubn5kno3262cDD8WSev9YKdSKc6ePUtra2vNq3olgmq3IYTAbDbjdrtpaGhY1wNU3eCzWgdoo/ex02npuq5jNBqVTSgU15nrKni2W0RwCx0ipqchl0MeOIDxlVdIL8aJFyw02GYxijLu/T6SDUm8sy9z29E2DDY72cUCQdnI8xOzJBYkzZEYbeUIjbqD5tvvJm5cpN1Rpr21leQzzxAuFMg07UcTgg6XiVxqmsblIYreTqIuHybTMiJrJhFtJzccYz7dxGu2drz5LB5PGGc2it3spNx3mKUX5wnGStjI0sIYqfY7MUWa6WjRCS8lYO4Mrbe+ASkFSVOOUsnB3JzgzBmNZKTETzVMY3EYyLi7kOkMab0BW2QRrdSM3LcPkc/jaGjANT6OPHAAuW8fKZ+P5MoKy88+S9psxup2Y56awtPWhs1mWzOZV7OqisUiMzMzpFIpXEYj+9xu/HV1WF0u3I2N7A8EOB0KsZROI1Mp6g0G6pNJIgYDvlVX0NUf20wmoyb3ddhqEUGoiJftLmmt5xWSUhIIBJibm2NgYAC32w1cuadmt++Wvp4HqFoH6OIdztcTQNfCw2OxWMjlcle93KZQKLbOdRE8V1pEcFP8fkgkkF1dICXmZJJc2kxxfAJp7qTU389QPI6+vMLA0ZNYJ85jMOp4bmkhvhDA4TrNbSfupqEgwNpLxtpLLNpGpGhjdPQMLut5YsJI0m5H6HaklDRms6wYjcRNJixmK9bwLEbi9PR7MRsmGcHOtPUIdU0aYnaR8NkcLs2KxdBBbMyDORnD64liti+QMELR04CespMIgStrQprSGDtKOHM27DKFoa0eIaCuTtKeGCP53HkS7Yco1btY/H6Z5XCW9iVBnc2Hx+lCy2aQmoZIp9Hr6xGFAk5Nw+V0ovl8FH/iJ8jPzhLJZJicnKwJkepkXy6XSSQSfP/73ycYDHLrrbdy0OmkuLREvKGBDrMZaTDgTqVICcFKPk9XLkeXz8fI0BCO3l4smkb1J7ZcLmMymVS8wkVcXERweHgYt9u9YRHBK/EyXCx4isUiQ0NDGI1Gbr/99jWp7VcjXHaz4LmY1Rt8bkUAXaslLSnl1c1/CoViW1wXwVMul3nxxRdpbm5mZmZmy0UEN2RxETE3VxE79fWI114Du52ixUNp9jRBu2SsYODQwH6amtoY/e4C+2eXcTQ3UmxsJDs5SWezh05Hjk5jHv0nB8gGIkTyCTRXHfknQkyYnExq+/FmS0RzJrJ6CtfyBPbGRhbtdprxEAmH0BYWCP/gLKGGAUZFC5qcY9/BeiLzQyRmwiQP7ed8UmfheT8tlhDu16ZYzNpIlHxYAjnmJgvcZjlPzykL+bSbpfkZco59kLQS1zQOHdIZbFmglMtQtrTjEymmVzSwSOqLZZb0RsJjTfTqdbijMTyuLHajC7PdAQZDZdf0XK6yt5bbjd3hwFZfT1tHB1JK0ul0rert5OQk6XQaKSUtLS3k83mm0mkcxSKiUKDOYoHFReYWFkhYLPR0d2MqlYgvLlIC2g8dquztoetwwSNhMBhqwkdRoVgs8vzzz9PW1nZNighWWS14otEow8PD9Pb20tLScknbq/Hw7Da2k6W1mQBKJpOYTCasVuuWlsA2Y6cFlEKh2BrXRfAUi0Xi8TgAvb29NRf6FRONVvaJam+Hrq7KYzYbptlZNIOJWZOPtL9EQ0M94dOSnM1PdiZCVrPh8PlYuZCl5Ct4WByN0HbYh/B6sWWzdKRTtLdJ5lpjxJrz1FnrcEQF+dkQSzOzJPyLrHR2Mu/Q8OoRCM1T0E2kdTMlbwZzU5z0VIJCDDyFFHPJEuX6A7SX0tx5dBHzwiKlpRAj3tsJzRrJj3rJLcYo1gWYzB5kpeRkaW6asYROq30frd0l+jrSrMwsEHJ7SZsaCC+X0Mwp7ix9n8SBE7xo92HU83gaDehDiyx7G8la+hCvShxeJ76R17D3dWBptFXq8FittUwtIQROp5NisUgsFsPn8+H1eikUCrhcLpaXl5lOpdAXFjhkt2M2GFgsFpnJ56lvbubOI0eYeP55xvJ5nI2NuBsaKp63XA7sdjW5X4ZisUgikcBqtV6TIoJVqstg4+PjtU0/L7eMciU/4lVhcTN5eDbjYgG0sLBAKpWiVCptaQlsM6oXAdVjKRSK68N1ETyRSASLxUJvby///d//jcvl4qd+6qe2tGneJRNXKoWYmQGnsyJ2stnK/lAmE+bpafy2LIk33sO9P91FMZwiuZCk+NoY4VwHGe8AvtM684YiTXkHTZEQ8dYOYjYb3nQaHA5K58eJYSHZ2Mx5/wh6e4I6PcdAaYQWq4OlBjPFOh+ibCUTCJEOHcFoAed9Axz6uX6Cs7CQDXJYxintO0bGXqL11l5OmoJosoQweCnVWTkf6sWl6bR6jeTqBB22fZTabmNpOUp0phV9fo5S/RB1plYMs0Gk0cSioYvwXByXjNJz0EDdTI5UJoVm9FJnztLtimBojJM72UcyY2BkuEh4No1MCNLd3RgW4zjyRbyxCHanhvlgZfuA6elp4vE4LpeLAwcOsLy8jKZpDAwMYDQamZub478ef5zSygqG558n2dZGxOul1+vFrmnY0mnOaxr3eDyV76pUqgme6r5Bu+0H8UYTDodrKeDXSuxAxZtw9uxZmpqa1t30cyfYySyt3bjhp5QSm81GR0fHpktg1SywjY6tbEKhuDFcF8HT0tJCMBhE0zR6enqYmJjgpZde4u67795wYqi642vr3Pk84swZSKWQR49CLIaYnUUePkxsbIxUJILz3jdhrTuMySlx+az4VkaR7iVSzfuwtrkxGsroYSssCcJhJ9HOkxinRmi1zyNEitzzrzBj3Ye9x42encHpdHJQSlYKeRy3HMWp6/SZIZo3MzxdYl9fHQlTI3lTA7MvxpjJtZF72c/YTIm6O/tpNZ7DZfSjmcwYxmbIdB9iYlEjvpKkWJxkfz7AXGMfrft8mFtzzIy46Sq8gr7fTShvYvI7L+EZbORM/jDLaRMNFgsHCWDVbcjBQYqzYImGMRbNiOUC0ufDZDNSX2emfWwOzQft3TbiXYKUyUh+NsjEoqRgtiAMCYLLU8zNDXPwYAv79u2rxZT4fL7aEpTL5aL/6FGWFhcJ1tdzwOslu7JCZnGRkakppqanSR04gNNqheVltOlpZGMjsr5eXc1ehvb2dubn56+ors5WWVhYIBKJcOjQITp3YsPYi5BSMj8/vyvTq6/l5qEbLYFNTEyQzWYvSYNf/fpqNty1FLoKheJSrovFCSFqcRynTp3C5XIRDofJ5XJomobZbF53cqqm4RoMBiiVKunnTify1lsrG0k5HJR1ndknn6SQTOLs68PaYqPhQBERmINUCqQETx2OyAoeTdB4+z4OzsyQKmqcWyyQLizz4uwcrdE8TYYy5kILpZYTHDjqo10WcNjtuObnmQmWmN/fiFGPExpLkMvnqJMBWu9uw0krnXUrmOwmIikzuWSaqDATC0vmx0zsFyk8ziKWqGSm/SBCm6PFtUw5u4Kp3ELa18ZyIUJpvEyjMYHVN0Pi2M9wYHGJ4vwIo/FWouEcWjGD3mph+XwC40EPKVcf47ESOtO0Tw/DySPoXV2IXA7dbse0EqTU0YOxo5l6S5r61gKGxUle6xwgshKltDxOKgUuVz/Foo///d8QLS1hUqkU3d3dte8hHo+TTCbxNTRgbGoikUjQaDJx1GwmYDRSbmigq72dcDDI4rlz2KxWLAsLOBobKZVKCCHUstY6VG1iu2z2Y14qlTh//jylUomWlpZrkh2XTqdJp9MUCoXaRr8rKyuV6tteLy6X64YK3J0WPBvFn11OAMViMSYmJsjlcjgcjpoAKpVKFAqFXScSFYq9znW7xKhO7kIIDh06RDgcJhKJ8Pjjj7N//37uvPPOS1Jxq/EHJpMJlpYgk0EePlypFiwlqVKJwDPP4HO7aXjrW5kOhTB56sgVDXBuCpuhgDx+DOFwUN9ow1iOEfrGN8hJScThICsi7GsFb7ODvmQMc98tBMMWVuad+MNe8tN2DM++StBYZk47giXTQijdBIHzNDtK5AzwtaeX8Na5yTcuYrL7aCTJwQdcNFsTvJKcZikbxjRnYMbuIJg5RPuhAg6nAf1sEFEqUepoIhi2YsLJEWMQr6vAnMMKJjOuYpGgZR/pOjOHnXO4pJGy1NBsZYazbRhmLORiEfJpO/VpOysLZexecGjZSqByWVLwNSNtSUQmA7EYmXyeSUMZq4hxy6Eu4mYz5bKB5uZOfvjDBJOTs+Tzfo4ePQpUJvvh4WGEEJw8eZJ4PM4rL73EUY+HxMgIK11dNHZ0cOTYMXxTU0iPh3R7O6lIhKmpKaLRKJ/61KfQdZ3z58/T19e36Q/Rt7/9bX7nd36HcrnMhz70IR5++OE1zwshHgI+A8xfeOjvpJSP7MyZen1YfRGwHaqvuZx34OJNP0dHR7ddW2czgsEgMzMz2Gw2enp6ahcsPp+PaDRKIBAgmUxis9nwer3U19djt9uvqwC6kVtLrBZAnZ2dlwigRCLBpz71KbLZLBMTE/T29iqbUCiuA9dd8FT/b7rgKcjlcoyPj+N0Ounv719Tk2d1oTURjyNdrkrszvQ0i34//kyG401N2JqakC0tiGyWbA7mzsXxDCf5/9l7s+A48vvO85OVWfd9HyjcJwmCN9i3LkuWtsdureyxR7EbG57QKiY2Qhvr2HnyOMKKDflpHzxPetvYdawd9mhC45FlK7SSu91yS92tbjavBkDiPguFuu+qrKqsPPaBAkSy2STQTbKpaXyegMI/qxKV+c//7/87vj/R70af6MfV0/AMusiu51n+wQ8YPH2a0Ne+xuhSiZHxMfSNTaxqmeCpGPZSB0ktI3olFhdeRaz0yJ37t9xs1gmtQizSJOZJI42MkaskyS3UCEazXCs4cTc3sPq76D4Zh0skI5swCmmUmp25sXNEvNvk0w3apR756ysUHHaulVyU2ts4LzRQK5fZsw2xbei45xZZbzuw98U5NS2SvlHEk21jD8fI+IfwOVVmnpeQ395hYbVBpX+adMuEca2NrV3Bk3CStySRuqbbOU43b5KXZXYqFQgKDA/0E3O7yVarhMNhAgEYHPSRzQbY20uRzWZxOBzkcjnq9ToXLlwgEAjcNkINgw27HeHUKSyKgk3X8cny7dydQABHJIJT1wlPT3P1+nU+//nP87Of/Yw/+7M/4y//8i8f6HHQNI1vfetbvPrqqySTSWZnZ3nllVc4efLkvUP/s2EY/+ujvk+fJB/F4PkwtWXDMNja2iKXy310xfKHoGkaS0tL9Ho9Ll26xHvvvQf8OlRps9mIx+PE43EMw6DdblOpVNjc3DwQ2dv3AD1u/ZlHncPzcTyU9xpAly9f5qWXXuLKlSv86Z/+KX/zN3/zQA/Sp2lOHHPM4+SJGTySJKGq6l2vmUwmkskkmUwGQRBYX1/H6/UyMjJyEAbZXxCMXykDK4rC6soKzkKBi1NTiB7P7aojXcfU7aLvpHHmHPjH/Oh2FyXFYM88xNZSleybe+jRC7hHzhMcniIcu8KwsUbGbKZh53AkAAAgAElEQVTpcBAUxdthsvYu+Z/+C1GTjMnj5q2eRLatE8regm6WtXKXgsPJRjOO3Fil5+6StDbpizbZ9p3iZmuM4fI6lV4VodijFDZoBztM9Am4YwZWr8zP/LDlGiXh80FFxV5rsdNwshyII2Zb1PM5iLgIZq/T3TxJLlelO5/COPl74PEzFqzRP2Cn8FoGl8+F/2SIkwMt6jUN/bWb1Jzn2GYc0wZ0WwrS5XXmpB4Wq4rk6NCRurTLKoYgYLfb2V8TVbXHyMgIPp+PK1euIMsykUjkIMTVbDYBqDYaSBcu0L11i2C1irS2hh6LgcOBYbUiAHQ66LrOyMgIIyMjfP/733/ofXL58mXGxsYYGRkB4Otf/zo//OEP7/dw/41HFMUPzInDHHOvkdTtdpmfn8flct1XsfxRGDytVou5uTn6+vro7+//gDFxbwKuIAg4HA4cDgd9fX135bnsqx273e4DD5DFYvnY53jv+TytzUMFQWBoaIhTp07xV3/1Vw8d/2maE8cc8zh54jk8d+JyuTh//jy//OUvEQSB7e1tNjc3+aM/+iOCweDdUvomE+VymcXFRcb7+ohVKhiCgHH2LGgaCAJip4Pl1gKK/xyWMyfw1neJDLboGRK//Mv3MSQbveFzrFfdFG4K7G7YiO4u0rk0Q6ujodZqFBSN6uJP8BodMpEoxQ0TLvMmFqvOuHALl8vJYidKSZjAZ97DZpTo93qYGNQRwuOgTuE0x3B2VfqyLRqDpzhxwcEtQSC/VqO1eJOy7qYgTTA+5OCFCw7yb+0wetKDWRsnvR7FYtQQvE5MAT9a14KYt+Cuu1nPG6h9Ml847cOjtDCl02gmEdkZwG+1YhGqBKNWxLhBNFJFblXI5at0dtIYqhU9fg6fxcL2jpm0skx5s0nvwhRTU3b216t2u0Ui4cfr9aIoCvV6nXg8frAg7ezsHMj2Nzsd9GTytrJyJoN+4QKmavW2Dk+5fFD6fhTRwXQ6fVeCbTKZ5N13373f0N8XBOEzwArwvxuGkTrCLflUIEnSx/bwFAoFVlZWmJiYIBwOP3T8RyGTybC5uXmXKjPc3afrYRVH9+a56LpOo9GgXC6zsLBwoD4sCAK9Xu9jazY9zQYPcKRmup+mOXHMMY+TJxrS6vV6H3h9eHiYQqHA8vIy1WoVj8dz0O14v2O6ruusLy/TXlriwjPPYM/nQRAwIhHYX0h1HXO5jK71UANRdI8PeiVQFCzbqzhT1xEuXSCSlDDv5nG7SpQKBbZkkWZIYG1L4o25N7FYVE4X2uy0RdaGX2QomuJktENw2GBMCGJ5aYKVK15ONnJoYp6CM8OAp02rN8BifZItJUG9DolOC6+kYzs/gddbRdxSMOU7mCoazelnEV1lfEKN969tYF1cRoqPIpncROoLeMbaWJoC5UQYc12ivZihriTIhYL4JTNr6So+tULFbGPJmERT6gh2G0K5DHY7+uwsnWqV0sYynsouIy/2Y53sx9yTibm8uPbyEB5krV4m914bpVdgeDhOtWpCVRUEQWBvb4/JyUmWl5ep1+vIsky73WZ3d5ehoSH8fv/BQms+cwZ9dRVTqQS9HqZ0GqFavZ03xNEe7vdbOO+zcP0j8J8Mw+gKgvC/AP8v8IXD3YlPBx83h+fOpp8XL17EarXed/zHMXjuDWHdmzd0p6Fz1BJrk8mE1+vF6/UyPDyMpmmkUimKxSJzc3MYhnEQ/vJ6vUdWJH7aDZ5Wq3XoTcCnZU4cc8zj5okaPJ1O575/u3jxItlsls3NTVRVxW63U6/XWVhYoNlsIooioWCQ04ODv67UGhq6nTMC0GrB2hpir0cnmUTXdHqCBePECVhfp9vp0PB6Cbs8WAM21I0NPKnrKF6Fot+JQ75GY+MmAccAEecw5dIIJdVMTTiFzV5Bm19Hb4ZpDPpYFhR2PGnOqj3GEgpzco9uV6Du6idglSmYDYRCAb+pRnwkTio8xdzNJeRWEdnmIxzqMhmpYNfa1Bs6YtdDU+tnd3eKydovEPcWWArbGZP81JJxyvI8w/IqlqkvkhQHcDiiZHc3eXdhB8/5ZylLUXrlOkrXgGoJY2yUtKKwvLFBqR3gQrtJxOOhYDZjLZfpszXp2et4vvQsgdUrbGk2EDrcuLFDt+vAZFKpVqvEYjFisRj1eh1FUdjc3KRYLGIymZiZmWF9fZ1Go3FbHdhiQR8dxbSyclvxOh7H6O+HXxm4R3m4J5NJUqlfb0x3d3dJJBJ3jTEMo3THr/8X8H8e9j58mvioOTz7DUb3m34eRtrhKBiGgSzLDwxhwe1Ft6N2WCgvUO/U6bl6DPuGP5KhIYoibrcbTdMYHR1FVW/fh6VSifX1dURRvN3mwaaz1dlCR2cqOEW/5/7l9k+7wXMUr+enaU4cc8zj5BMNae1jMpn4yle+QqfTYXl5mV/84he8+OKLzM3NMT8/z2//9m8zdPEiwvw8FIu3NXhUFfZ3nLduYdrYwDhxAlWq0dlscfOmQFJI42nXsI+PEJ324jQZdAyD9PY23U6HlNdLtGvB8GqcHRshuLCDbIlRGj1JrJfFMSji3dCRd1rs6KNcFhpkWg28VGjqV+hooySTTirOKM6Agiin6StvYREKmBMOtusNdlbm0YtFbLvX2QonKYWiiJtF3n13h5iSITjxLJsmL6f1FZKhNvk9iVojxKouMff66zjNNbyOLd4XfopX/AznRwfYWt1DtIk4+02YkFjeFKm/v8uCyUFeUSgrJRqZGn2eOHsV0Dc26NlsiIDk8qC6fBguFwgdTgwGSZwbYnl5hzff3CSfX2dg4ByDg4O0Wi1EUWRycpLLly+TzWY5e/YsFouFVCqFx+PB7/ffvgY2G/r4OGImA5qG4fMh/MrAPYqHZ3Z2ltXVVTY3N+nr6+N73/sef/u3f3vv/RQ3DCPzq19fARaPeEs+FYiiiKIohx6/b4iUSiXOnj17KMXyozYcFQSBTCbD1tbWQxv7qobKj9d/TLqSRjIkXt16lef7nmcmMnPoz/swJEkiFAodtKBRFIWN7AY/mP8BakfFYrZwzXqNr059lcn4Byv/HqcOz6N4r+M5ccwxT54n6uF5UIKmxWLhD/7gD/jRj37EjRs32NzcxOl0HsT1l5aWGHW5sPf1gc8H+zuuahWhXsdwOjH8foR2mzNnoWrL0buxSqPaZsc3w8p6FlNjhcCOCV03Y5+ZIbDZxbxRIhmaYGvvFoq2Q6+rUx+e4nS1gKuVwiYoaAEbe10fK22D2UIKyShQbPr4xcYGHe8MKUViYq9EwrJHv1RE9QWQzo+w98Z7tLteTgU69EoNtPMBXI0uW0UXyG16bTMpNYxitLCWNsknLKz17KxrEoJqIdbYxj5gRhbsVLdgqX0V589k/CE71hELAfE6aiIA1k1sXSvFsEiz1sHwjxFRG5h6fdRH+hGUHUqpNbyFFJXf/SodXx9Ct0PPZMKn61gsFkZGRnn77RyVShld1w8qtKxWK2azmU6nQ6/XIx6Ps7GxQb1e5/z583fvfO12tJdewrS2himbRbfZ4FeL9GF3s5Ik8d3vfpcvf/nLaJrGN77xDaanp/n2t7/Nn//5n79iGMY/AP+bIAivACpQBv7tR7srPzmOGtLab/qpKAoTExOHbs9iMpnuG0q+H5qm0W63yWazzM7OPjSPpqyUKeklovYovV6PgCvA1exVToVPPfISdIvFQtPcJBFLEHFG6CpdMuUMb668SS1Vw+FwHITAHA7HU1WldSf73qKjeD0/LXPimGMeN0+0SuthD3dBEJidneX1119HVVUCgcCBcFq1WmVF0zjtdN5OhnU6b/fUWluDYBBUFbHToef1YhkYILqyAtMe0qk6c0s/wBaM4G118LVDbNjO8NqbTTSLF18gSrS+hjRuoSfHGbK0sZrWaC7Pk5YzqLYOZVlgL6Bg91znlLzCntpHWY9gsmhsyaew6xGCboMBSxdz1wHDk4jxs2hanXDX4LlzFpqhAO4vj9JYUrAvdujG+rD1ygTP9GP++c8YzOVIOV6g1A2iBiMMGttE1Q54A8guC9mqDy3jRFBbVA2RSs9G9d0FtvUKJ3f3aE2OMZr083zMzbYm094sUg5fwmLz4HSGKO/pVDMV3rkpk2+UEQIK9nwe+6/aexgGKEqHWCyG1WplY2ODVquF2+0+8OZ0Oh3K5TKZTIZQKHR/9V6HA31kBNPVqwipFFI8fuRO6S+//DIvv/zyXa995zvf4Tvf+c4/3D5X4z8A/+HQb/iUctgqrTubfrZarSN9xmFDWvshLFEUmZmZOVTSsCAIYAACGDz+NgkmwYTO7f/FarHi8/kIO8NcGryELMtUKhU2NjaQZRlFUcjlcoRCoUO1sHkQjzKkta88LsvyB8JSD+LTMieOOeZx8tQYPHfqiPzhH/4hKysrLCwsYLPZsFgsiKKIKAgIdjutZhObyYT02msYwSDGiy8i3LqF2OmgagaZX6zj9kg4z4+z99570Ovxpa8+j2exR06ycfm9FRLtJMsVB5vVNnZTl53+GNVchxP2IqrjFubtd/FpCRovfAFXt8PzLoOGxUGzIdBRTTSCIo7E85izFqZDKqdjVdp5WDNGaG14abVESmkzY70lduWz1Nt+BnIaOByE5DWCCQl/GE7YbpHvpLDX97B59khaVHy9PFG/FyMnstF1sShcQFBs/O6IixGbTCsZpSMZ9DZ2cK7vslzoYom7IGeivTGPaGrjsHZwxa2EEyai9T3MgyZkY5SuGGIlb2Lun5ax6zUmLRJ6UadcrSDLLUZGhunv7yeTyVCpVOjv70dRFCKRCI1Gg+3tbQzDYGxs7MMXEpcLfWaGXqWCUK3SarUeSyfw33QeNid0XWdjY4NyuXzQ9HNzc/NIOTmHMXiy2Szr6+ucOnWKlZWVQycgB61BimKRQrUAGnQbXZ7pe+ZjeVYedOyof5SF4gLZVhZREOnpPWbCMwiCgNPpxOl0kkwmMQyD995770BxWlGUg/Cr3+8/cgn84zB4nsZ2HMcc8986n4jw4L3s64i43e4DHRFZlnnjjTfodDp87WtfI5PJ4Pb7Ufv6WJ2bw3PtGgOCgBQMHrSZECsVLLk8rV0b0sk4PVmmIwgMeDyY94os17sUQl7Cg2cJ53O4xZv4Iyr53RabRSt7GSuC4cAaDuM3nUE02Rj0m/G7CpTSDVqWc2hSga7ipOT9EqF2ixoZQlYNU3oXsz+KrnlpVE244jLxaJFmxsrbpVF6q02yZivuTo1oVUb2BfE0d7C6a7QnT1FOWRkYEuj3NnAoVbJOFzXLJF15GL8pwJcaKayBGHn3KBZZoeNWmbLb8Q6M8I5ZJWARMGxh2ivr7JRltIgL8fI6vmErzzevoTjsBGejJMN2bO0aRnqbWz4XC3sdCu/mSRVbdLs2fD4nkUiEZrPJ2toakiQxMjJCo9EAbje9HB8fJxgMPviCBwKodjuiLCPL8mPp5fSbzoPmRLvdZn5+nkAgwMWLFw8W3KMmOj/I4NlXv+52u1y6dAmz2XykRqAW0cKXB7/MFeEKxUaRi4MXGQ+MH/rc7uVhn+u1evnq+FdZK6+hGRojvhFCjtAHxu1reA0MDDA0NISu69TrdSqVykH/sv1O5z6f76E9rR6lwfNRQlrHHHPMo+ET9/Ds64hMTk4eJCgCfOYzn2F+fp433niDt956i4GBAXq9HouLi4SaTeauXmUnEOBCNIrLMDCGhhA6HQRZpuEZpbLUpaFW0U0hAqlVfr4n0U5ewFWNsZu5iUXexlnZxjh3mrH+IC7VzC+FPiY3CrQtOjvxGbx6m1xGp53KUJddCKdN9HtPontcxE+E6d97CzVTQc31s9vtsKdKpOolnP4gM4FdOpUmC6kCamkBm7VCcX0Zu6tM1uSiUQ3j3jB4Z3GTNaYY9bux+Xu4N9fxNUvI5ydZiL5EcHSSf51cR3l1m18oEQTFjbC7ghGPIMhuhNAZaDexW2Qmo05Ua5AVj5e2yYTU1HBlemTqJm76bdgqZbIlDaPVIWpqcGIijs/RY7c3R7Wq4Pd7kSTxoGRYURQqlQoul4tsNnuwGNpstl8nKz+A/Yf7UUNanxY+bE7se1xOnjz5ge/5qEnIH2bw7IewYrEYJ06cOPCsHMXgEQQBu2TnXOQcDXuDkeDIoc/ro+K1erkQv/DQcXfm8JhMpoNO5vsl8NVqlUqlwtbWFoIgHBhA9yuBf1whreM5ccwxT5ZPzMNzp47I7OzsB9zMZrOZV155hVQqxa1bt6jVapw4cQJHu438z/+Mxe+nbLWyu7ODZ32dhNuNWKuhhIKoegJzZ5HqnoGYE7ix2KZ37hzySpeY9hNUu4GWrdGRA6xoX2FKzlPLl7BYRaySxlg4y8gLAdSGRmW7i7KmYi7ssjA/ypwnTjca5lK+RKem4KpsI/lcNOP9hDolXI4CFZ+P0naHndYUHWxYexCP+HnRsg4np6gW7VR+nkXrOOh0u8guJ12Tmcxala28yFi9Q+5WDYvXT9D+CyzNNHVTmY6xg8MTJhYKEjEkQl4/admGYLXSaync+MEGbvsAroSL4UETyrKMR8jQ9Eps5CDkG2ay1GZb9VMRK9RubHAp7kcLeX7VyBUgeLADdblcOJ1O0uk0xWIRp9OJLMv4fL5DLQDHD/cHc6/Bc2fTz32Py70ctbLLZDJ9wIC5M4R1bxXWUcrYP8nmoIfhw85PFEWCweCBl7LX61GtVikWi6yvryNJ0l1NUB9lAvTxnDjmmE+OJ2bw3PngbTabzM/Pk0gkHqgj4vP5eOGFF3jrrbfI5/OEw2GGo1F2ej36vF5iHg82oLu1heHzYVIUVFHE4nUS6YoIzWUazgbd85MUTDVUQSUYsGCRVQKJMXJ1ie1NCavHiaVwC2dXoRoeIxJvU+sbof6j11FKe5j6rEj1Kic9q8hjCZq1OuXSCYq5CL16h6Xa55mwdnhmdJeqLUaxUkb1+OmbOUPQI1JQu5wJgqXuY8s4A6kbiHId66iTAasXNTLOtCFjSTW4IVzgSsdNS0nS3ywyePl9Ml6VUlUmFLWRPDNGn1zEKzYJDIxQebfFYsdOON1FM/doeFWsSpFKYxB3toQ4MkTKOI/am8durdAubaFGXuRGp8JqZ4O1jJ9Ea5AuMdLpPdzuOv39Iba2tnA4HAwNDTE/P3/QIFIQhPsq+t6P44f7g7lzE7CvO7Xf9PNBi/VRcngEQTgYv7/JaLfbH2pQHcXD83GOeZowm804fU5+VvwZK8oKPsPHZ4zP0N5r02g0aLfbpFIpAoEATqfzYxk/x3PimGM+OZ6YwQO33cypVIpUKsXMzMwHuqPfi8lkYnJyklwux49+9CNisRiBz36WnteLf2CAkixjSSap3rrFmxsbTAwN0a1UED06W6tLeJU6ZXOSlWYTr0Ng8MQZRupvsSdnkEfOUK04EWpm3FEzxto2bk+DVjjKa6sacmeIwE6YkJyGM+cwtTc4MWknZM5TXN2g8VsJtpttvENxGj4/TXuXt7sh5OUiploVYSRIZErHHq5xzpwmOTLG1kYfxbwORRO6XKXsUgjEJpDQsCoNtGIVrf8MFmcRZTyGeX0P2+oKjcmTlF3n8Bgj1NdU7K0yvak4gm6hVMxjrVZIBLuMfX6ANVkm8/4yzg6YdIWl8glsARsJt50+Q6faDfNft3bIddIkTDEEM1zV3uFE4luYzScoFLZ54408OztrBAI2XC4/y8vLnDhxgnq9TjQaPbR+iKZpxyGtB7Afntra2iKTydzV9PNhxxzlM3RdvyuE9aBNxv08Qh/GUVpLPO18f/H73CrdIuaKUeqW+Pv03/PvL/17XBYX77zzDpIksb29TbPZxOl03tUE9SgG0J0Gz8Ou9THHHPNoeWIGj6qqtNtt6vU6zzzzzKGk4vd3wF//+te5fPkyV69epa+vj5DNhmE2w/Awu1eu4DCbsUYi7DWb7K2s0JT/E8lShT2c6OdifLa2gdUEDVFFqzXJVh3o1j6s3ja2bhp1d55qtUYzeZJEMImysIOlmkUXnVg1AZvXzy3XWRY7SU4Z6/jbe1jf/GesiWGGT3sZCjaQzCJ7GYWW0uBmJw7bAuo/rNPcWOe/H1KQJpIo7iLd9B5jsQxKeZubLhvFnkxjYYUFQaK/ZeCZAnV0CFdtC7t5D7tTIx6xA32kexZMm2ny7Q7rDY36gofOQgGrmsX9oo9A3M1MKIm+epnyzns07F8k2gcTMzrp/28Vh3UILTFBZn0e8iMYzRTayRB5PcaYUKO/P4ooRshmrRSLNs6f/wz1upm9PRVNy2AYLV544RJ7e3sEAoGHlvvqun68m30AiqLQbrfpdDo888wzhwoTHtXDs29wXr9+nenpaXw+3wPH3+kRehiCINDpdI6sFv2w93zSdNUut0q36PfcVpQOOULs1nfJtDKMW8YxmUwkEgkSiQSGYdBqtahUKqytrdHpdA66wAcCgQ9t8bHPnXlth904HHPMMY+GJ2bw7OzsYDabmZ6ePvQx+7tTv9/PN7/5Tf7iL/6CH/zgB3zjK1/BI4poxSKaYRCPRDA0jWKxiNbsUXt3ibphZfZ/+lfMzkTZeadKotdAcm5QrbuoZTKErDp6tUlodw9LWMLb308oPoi3rw+HaZ5SZok9w4Qo9QgPR+juVijtZPhpMshU20dwN0OnVmFtV0J2pPGe/Qy+xgaNmInpkQxRa5VraRWznGM9Gye3FGV3QcNj30Ru5VHkNuW4jdDeTTzlKImzYfq7XZa6W6xv7zBZlGkFAojj0wT7/Ui1MqFqhlr8PEXzMPKOQUOyk9BylBWdpd4Ywo08plNBXI0YS6UmGVeXF61z+DxxBGcV85iNasuPbdtBNLiMz5Fi02GnWqpSxMAzIGKzeYjFVJrNMmNjAvl8hUDAh6Z5EMUTWK3PsbxcR1U3kSSZaNRJMHj/ct9j9/2D2W/EOjU1dehjjuLh2S9rl2WZl1566dDaOofx1uwv/KurqxiGcXCtA4EAbrf7IyX5flJeIskkYTaZUTQFq2S9/f8YGlbxg8aLIAi4XC5cLhf9/f0YhkGj0TjQSur1eni93gMP0L3fuaZpSJJ07OE55phPgCdm8IyOjrK3t3ekBMA7cxyee+45Ll68yGuvvcZSLscXp6dp53I4R0dx22wspdP0GgLiwg5mRwj75y5g93WRmw10iwXT1hZL7QYbTi+9YRMTEy16rW1E1x6905eYu2xHXhUxuk4SmQpOXxfBrFAyqjTSm3gMlVirQEmOU7UHMPtbeMQOlS2Vn/tDTHe2GAtBLOFgItmmLomYGiq5qSkqFgthqUjyGRvWVYNub5SiFqCS76OeWyXUqxI7OYZY9xGqt0gmJfoMgca5S1TkAnq3QbncRUoXaLpbdIMGfeEOnz+zTV1u8mbZj8nrodhqUP5pCikXQYpNgKxRSLWwKYtU81UsbQmL6OblSB+p9n+lLnYxhor0LypoxTqLLUgk7IyNVRBFC91ui05nD7/fSrkMZ84MMzjoRFWdtFoJdF0nm22RStURhBVsNoVYzE0gcLvcd38R3E+APuZuJiYmyOVyRzrmsB6e/RBWKBTC7XYfuvv4YZKWVVVlYWEBVVWZmppC13VyuRwOh4O9vT0ajQY2m41AIEAgEDhy2OdJI5pEvjrxVb6/+P3bHi5D53zsPEl3Eniw10kQBDweDx6Ph8HBQXRdp1arUalU2N3dRdf1uyrA9rvC93q9h3qDjjnmmEfLEzN47pTSf5juxT53PnxNJhOvvPIKi4uLzK+t8e7Zs7icTsxeL/NXrtJynSRVyeDtNnlmdproF5+lefMyG6rKzXcXqDXN1E9GGD8XRtMcxH0+KkoVMWxmru3EHbMxKGms7M6hSwUc7QYFfYQbHRefrVQROwEkQeCcu8byRBCx5GWwz8Sy2cozvTJoLRo9F9piiv/n+ghOoUu/y8R2/AViznlGRmsMRcxIapZ16xSbGx0S1TSCrqCoKq9eExB955GyWU5PDSIqu0jxIGGrTqTTQi712GxbuXo1jSVQYCoiIAWiqN4gbk1heFJEu95AMjqYTsfxu92kls300rt0pSAL8gy2eYmJcYGpdpuxkd+hbNpj6MI5Cu06v1zUMFkKJBJQr6uYzXFu3uywtlYikzETDIp87nMJwuHbu3BdNygUoFbzYBgeIpG+XxlADXZ3q2jaHjZbi2x290ghmJ/85Cf88R//MZqm8c1vfpM/+ZM/ufc+sgJ/BVwASsC/MQxj69Af8BRylE3AYTw8uVyOtbU1pqencTqdlMvlQ5/Lwzw8zWaTubk5BgcHkSTpYI6aTCai0SjRaBTDMGi325TL5YOwj9vtPjCADmt8PUlm47NEHBFyrRwui4up4BQm4eheKpPJdODdgdvG4b4BtLm5SbfbJZVKIUkS3W73UCrQn8Y5ccwxj4MnmrR8VIPn3lL26elpXnrpJX784x/z4zfe4MTICEaxzpRpmMWfvYPVaqP+0h8iXpxCcMeIJCfYffVtzF074vAEE5PjOONOSst5bEvLFNpOKg2VqrHC+UtxjIUSLVuNka+OUH3rOoLLxW+dvIij2uZWN8CEkcYttckJQZ4VtqFvhlrdzWAuRc+Q6VMkyp44pboLodPgqhhmt1iiV1/mrX/aotkfo5MS2EmMoMR1zimv4Qh4aLzfQO9rYnb7cKRUCus9NmsR2kqLwulhTqbXWcwZLJncOBImPHEF1/ZV6qlzLItfpJRb4Z13RM5lswyccuO9EETLFena+xF/3mCnEUX1TmDSu+ilKsvrTdpmNzFTDEUaRl95myGnGW1YQxB2CATchEIB8nmFdLqDorQIh2Ps7rool8HlMtB1g2LRRCRiMDqqYzZDowHNpodi0cvm5hCh0Co2W5FyucznPvc5vvvd7/Lss89+6PXWNI1vfetbvPrqqySTSWZnZ3nllVc4efLkncP+Z6BiGMaYIAhf53ZX6H/zUdeN+KsAACAASURBVO7HT5p9gbz9XKfD8CAPz51VWPtSD5qmHbmq68MMnn1DamZmBo/HQ7VavW/SsiAIOBwOHA4HyWQSXddpNBqUy2V2d3cxDOPAKDisxMGTYNA7yKB38K7XPm6YTZKku0rgl5aWyOfzlEolnn/+ef76r//6gWH+T9ucOOaYx8knYvAclnvd64IgcOnSJf7u7/6OtddfZ6Ddpv/Lv0N+Nc9pRx73cy+xKkVoR88wvwj6+yam6woz/+PvYR+O0VIaZMplrLeWWayI/Iw4vlKewLBIyD9OqbpLcmSIIi4y8hIXnztJf2mPZnaTkT4HcbdKodei2RskZT/LfN5Ht7hE3WsgGTW22wbC9h79gog5OYZwZpQJo4y75kPYWOUnKxqpZh+Dcgm/z4J8c4uMaRx/JMFzU0763A0ql6+QO/2vSKcEqpUKqasK2++vM2+ZxOUPcsnnJDjip13PstGR6Wnv0zW8nNI3SCZ6yHY/lYILaScP2R0EZxxzUGTCWyAs1/G5nVQGhpClJm8vypzYWMdtFWkZIPXilEo5dna2GRw8Q6cjIoppRkdNfP7zE8TjBo0GrK4K5HIiVqvBxIROpSLgdht4vWA2G1QqAkNDBpJk8Pzzz+NyuXjvvfce6sW4fPkyY2NjjIzcFrD7+te/zg9/+MN7H+5fBf6PX/38X4DvCoIgGL+hZUL7c+KwBs+HeXja7TZzc3NEIpG7qrCOoqvzYeN1XWd1dZVms3lXOfth8332hSy9Xi/Dw8OoqkqlUjnQvTGbzQetR562y/goNXj2+cIXvsB//I//kWvXrj107KdxThxzzOPiqTZ47vegiUQiSJJE05Bo1g2uvj3HszaBaMRH1isSNIqEsjeZX44z3dnD5+rh0Essb4a4sVNh591/oa9QIpJMMvZcEHdeJuwx4202yUgW3pyrofhcBEeex6QHqViLeIQUeqHNdbEP926e2EkrfcEQo+0UebMbfbcfwZlEbjXxp5YZ07dohcZ48XkrhnUY42odTfLQkl5kJtxAae2xfn2DSFMi13eSnH+QgdQ2TWeLnsWBPeEn6oL4/DWWiy3KjhoTI246Zh/b6xuk0ts0/KfYuHUFn9og3jNx6jNnGTidRBd16gkrxVslKrsu9OgkA4EKKzUFx+oCid+apm/SictkQ203cNskyl0XxaxCn6Zhdg0wN1ehUOgQiUCjsYsoTnHy5Cg2m4FhCHg84PNpBIPQbgtks5DNCsjy7SakgQBMTOhsbamIonig3/Mw0un0XS0okskk77777r3D+oAUgGEYqiAINSAIFA99Yz1FHHVO3G98Pp9ndXX1vsrMR12s7zViut0uc3NzBAIBzp8/f9f7fdSydEmSCIfDB3pOdzallWWZdrt9EP76pPNcHqXKMvxaquGw1+XTOCeOOeZx8URzeCRJOlR36PthGAZra2vkcjVmZr7EO4V/xCbaqN66xVpynNzks3Q2NaJ+E4OSzFj3Fq5+P2ntGYRNkU53C3+6jaD6KLtsuK12vGYf7UQVv83G9laVd7MZHFY7Ve0CXlMXqavjGLSSSHlZqw9R1/xMWwoUo1amxC0ilhzKC6e5/jdNaqpC29mHa8qN2O4xWHkf01yPriuMpgq0RAeeaA3J1qSbLtEnqASi/SgnZ0hv1ilfnSfvtWE03NSuFGh4B8nObxKOuRiajdAWU5Q8AvbcElp0iJxJxOmboVbuEM3/kjf/ycaw//eJmfIIhky33MaTjDPzJQfuRh3rnp/d6y1+fD1Fo3GKoWiL8VEbF8Iiy7hYeitNItSia7ERjXpxuUoYRptKpYnNNsytW1YaDYFuF5JJnclJg9tOCQNNg+1tgXzeRDxuMD5+O8S1H6o57GJ4v3H3WRjut1L8xu5kP8omYP970nWdlZUVWq3WfdXKPwp3vn+1WuXmzZtMTEzcV2zyo7SjuB82m41EIoEkSbRaLUKhEOVymcXFxYOqp0AgcKi+V4+aR23w6LqOoiiH7uD+aZwTxxzzuHiqPTz77O8yIYggXGR6WuX6tX/C1NzhpNtKK2ljKXMdj2cWp9OHtZVh2A/dM2cwtRqoTi+t+SzDW6/hO93P4slT9G5ssjF/GUltcq0n8fqWh0QsilSQCQxl+ZwvRbNVxG/tQ7MOk8ub6eoCwskhErYyNrGHXFMpXM+wUrIimrsMBjeRAzLeUAJXNkP2Wptm2gqf/SIVpc7GzX/EPHmaE6Yiuf4wQg9s3R6xVpWwyYR+YZb3frHJ8tUV0nKOgKxTH+pnos+HZaeDsWfHagqx7u1j2O/ihWcddFtubvz9Nk1R5tYv3mBROInYTeEyfLQHA1QVB0G1zeygFfP5MyykJBRKRFu/pJJ1U2AES3SIen2Xa1eahEZdjI2JQIulpWX8fhMvvjiBokC1KuB0GnS7AgsLt392u6HTgVpNYHRUZ2TEYH990DTtSDlbyWSSVCp18Pvu7i6JROLeYbtAP7ArCIIEeIHDZ+U+RdyZyH+UY+DuENbk5OQjC7vsh8x2dnbY29vj3LlzD9SLedQhH0EQcLvduN1uBgcH0TSNWq1GuVxma2vrICn445S/H4XH4eHpdDrY7fZDjf+0zYljjnmcPPUGj6qqXLlyBa/3BLVakEJBYGDgFAhulqpNnp8ewt43Qv7qT5i52MegVUboaLQtfrqim4bgplPUcesK7cgAAxfHOXXyIlt7BvnlTRbfbxL2+njpC2fxRvws/fQm470qVZMXZXGdlidKyxJnUH0HUyhGTisivQ/brvP4NrO0BY0d2zn+O1sKR5/ORqWBWdthOzlL741lPOYO/rEC0dIeCdlLMunHVsuxZEkSbdaI5BfImZwooSC3Kh0WKhYCbYkvDQUwhDOUnQrXd1s4ruSAFEu6QccHL50K085cYTfVphxz4vG1KDYXONFcJTFymkr4BOl1nXeu2Slv1eh4wpTtv03CX8BEiYTQoOIFyZGnzztEOy+S13eRgm6aTQW73cbKSgqLxYrDMYVhwIsvavh8Bs0mNBoCjYbA9esCZjOMjekMDBjcufZpmka32z20wNrs7Cyrq6tsbm7S19fH9773Pf72b//23mH/APwR8EvgXwOv/ybnKoiieGSvZ6/X49q1a/cNYX1c9tXQnU4ns7OzD8wtutPQeVyXYF/bZz/HZ7+h7X75u91uPzCAHkf5++MweLrd7qF1qT6Nc+KYYx4Xn0hZ+mEwDIP19XW63S4vvfQSvZ6NSsXgxAmDRsPF9PSzZNZu8VrOjfd6lrOTk3x2wEqplkGPn0XsisTDPVoNEc/uIhZdJhU8x5jQIeA30IZslG9WaZ86j8VRQ2utoVtPMGhLISpO3ugm8dc1hEILS6WLxyQRPBXGtbhBq9CmPDOEVV5Hdjk5GW4zODaA//deQnzjKku/3MWEA+fgAJgqmMtrxEXw93kQ4io9U5BI1YJ7Q8Ga3ua69Vkye1X2ihUER5IL2gq63UxFtaKXFVJmg9HaFVq+cQrufvytFP/yz3XG95bQHAlStiCJpoeBydOEVl5HEq4zNh3D6/QQiHYppxRWlBgqToS6TDHbJBVI0PRHqLXNaGtV4iELPkuBgUkP6+sVOh07+XydQOASKyt2xsc1NA0UBbxecLsNtrYgGIRo1GBg4IPP1/3d7GEf7pIk8d3vfpcvf/nLaJrGN77xDaanp/n2t7/NxYsXeeWVVwD+b+CvBUFY4/Yu9uuHvAWfSj6sY/r92A9h9Xo9nnvuuUcSwroTWZZJpVIEg0FOnTr10PGfRGsJi8VyV/m7LMt3qR57PB78fv8jOx9d1x+pEaXrOu12+3hOHHPMJ8BT6eHZD2EJQoB8PsTenoCm7TE2lkAQbifFnj7tR9NCtNtuFGWb3/7938V9M80vtwVqySDtkgntbYMx2xYT4g0qU7NorRA2yybr7+8w/1+uE0TC4nYi2mSkXo13/vE1Jg0oN62UbC7arSjCVovT/RKmk9M4uwkEp8Dq2jbbiwqR4QSf7W9hCzQRjB6ZvMbbN0+yt9hg0v4OrokAiruP+KkEnnoV0/vvowUCGP39WMsyloQLfylPozuPonRxSnU8kkxdL5I2jaIKBgGhTa2yTUmV0RNneCYZJdHcwTYyiD0gcL1uZi9lYJc7XIh0MYXH2ZOLbP/8HaLmUXTNg2s0xlemenj7NTb+bpd5yU9B8bFy08pi18YZr05NcFBryFzwW5Djdm7e3KDXsxOPhzh9WkPXBdJpAAFBgHYbbDYYGjIOtHnu5agPd4CXX36Zl19++a7XvvOd7xz8bBhGB/iDQ7/hU8xRNgH7IaxwOIzdbn/kxs5+4nM8Hj+0R+6T7qElCAJOpxOn03lQ/l6v1ymVSrTbba5cuXLg/fF6vR/JU/OoPTxw+1oepa3Ep2lOHHPM4+SJGzy9Xu+BY0qlEouLS3g8J1HVAPX6IqlUh15PotW6Xf5ss92u7EinV/nMKQc9zUpjfY2wplK1KqSXS7RaU4SbPWxqBbvDRavlQrM5eP/9LJm9dWZePIFJL9NZLTMS9lNtimyoZhpugWDUS9I/jSO7jC8sE/jcF9n4z6/Rq+RxTUxhfm8HQ15B8JRQinnWmhrVzQr6v8jUAy8x7ckQceZwxifojTyD/XwY/cplTJoGqgqNOvVCnl21xnBwgGbewviIjee9FWTFjpg8hSV+Hr0joFz+EYOawFbiPLY+PzZ/gnhtEZupwk4syfpmFpPFxoX+Lkp7lzmzjUKmTq64w3B5k9GZs0z8/mcImGtYPH7C7g4nRoJIqkr1RpaeUGegb5jlrRPsLdzg569v0Tc6yfz8VQxDYGZGZHLSAAwUBUoluHXLhCgKTE1pPCiisi9Ad9xW4sM5jMFTKBRYWVk5CGFls9lHljuzXwxQq9WYnZ0ll8v9xjYPNZlM+Hw+3G431WqVM2fOUKlUDow5i8VyEB47bNdzwzAeucHTarWO58Qxx3wCPHGDp9Pp3Pdv+yGsbLaCwzGLqlqJRAwuXGgwMzNCt+ulVjNYWxNoNqHXM5HP52mvZxkJemjUm2zbrVh6acJDKmZhmXNSjx5uTKZhetUmP13qkVBDzA5FEPoTkJExYk5CwxaCSxk2LrzA2laR+k6WrY1rXBKKqIKf9M4Klp3rNPQw1oiZmNTAHjXjUsOstQMU1X7kagnBJuAqzWEVYGR0GrtdIOs0EPJ5BIsF7dQplG6X9fQyhZvbtOwJur5pXFYFf2yNRC7FjvM8pZ4LU6bMtlkk1t3j1ItncaonePNWltLe+0TLKxRFka2wm44PokoFvVuAQJjY6AkE7DhCPsQbN8jVV2j+rEfaH8YoBnBoMXYyPoajErPxXbaNND5fhz/42u/wdxUP2XSGRq+PblfFbG7SaNhJpQRcLgNJgnLZRDgMw8MaD2l2D3DcJPEhPMjguVP/5s4qrDsNjcNyv/GKojA3N4fX6+XChQsH8gGHDbE9qiqtB53jx3mfDyt/39raOmh38rDy98fh4TnuLXfMMZ8MTzyH534JmvshLLfbi8NxkdVVkeHh29U+imLGMDQcDsjlBPx+GB01aDZdgMB2RmZK8lKxfhbRqVCsbKKVKgQ2t6kHWkz8u39HJ5Vi8/UrjMdf4qRkoW318v52EvPqHpqpy5wzjE3QiSU89MwB5KUUX/CvITWdKKILdUWmoE7QrrUYd5VxPm/G69HpecO8P2+wnQdnJMxnw5eR2x7kRgyTpY15OImxvkEr08R9cYyc3Cadz1GqC9hSCiQsjI0UsJcFTOYe75lcbKr9WI0KxZ23qSAyZpGRnbCznKa/8T66WCbVbKEwi9URJ9k3hG3rPcYHJgmMB7neA7VpZ9zqonnxi2jGTdqdLI1UDvvAGG8vqOSbu4gzLc42y9R8ERaWd7F2F0k4w9SUHrq+RCzWpFo1Y7OJVCoCOzvCgaDg+LjOIYtMjh/uD0EURRRF+cDrd4aw7tW/2TeSDrsQ30/NuVarsbCwwPj4OJFI5K6xRzFeDMN4Krw7d/JhhtN++ft+1/Nms0m5XObWrVuoqnrQ88rv9x98V8cGzzHH/LfDE/Xw3C9Bcz+ENTl5W+ujUIBgUKfbFchmBXZ3HXS7Ana7gMMBw8MGfj+8/noHQYDgmANbYoq62YEeP0M3mUdI57CoPVJdheybi9i1EidHEsRqeQaHDMSgzqCnx4pgofLWKorViTI2iT/h5ZlQDtNmGcPi4Z/ow57bIBgIUfB6GWus0jd+kY1rIvWdMu3hSSqrb2HVVAZfOMW4ppP2RfkJZ5HdIuGGgWXlTaQxFc3r5dV/fJXdZYWWdxazXqBPSNHZSmGUBBq6m/m6mY5pj6hXwlLeYdrjxDw5S8M8QXzQzlT5OmnJz/WumxMOldgzSfSr18nZ6ogTp1je3OJmVWS0VQR7jbxlAB8Ge7U6vkKTrdd+gb3/a5zu6zLhL9IqaZQs/WRzNdauprB2O5h9TXo+hXg8QrVaxOls0t+vs7h4W2NnYkLnKOkjxw/3D+fDcnj2Q1gnTpw4qE66k4+qnry/iO/u7pJKpe5bcn4Ub83TFtLa5zCeovuVv1erVSqVykH5eyAQOLRo5mHPC47nxDHHfFJ8YknLhmGwsbFBNlvGbJ5Fli3oOvxa3+y2oF27raJptz08U1MGsgyLiwLLy1XabYmGHsDqdGPtvcvAAChZGVsGGt7zrC5cZmdtg5f/hy+Tz29Tr0HAFyfUTBOOtZBGu6Su5+n/4rMYQx5KJWjuWTAyXTILFW6YzjDe3aZUvYmkalhsGuV6g2KuQ0kOkWuE8fuS+OwGflnCsNjxDnlJCipmuwWjUGC9qCL2R4jl60i2GHbRjNk7Sm/Kit2TxlJOcypzGXXss5gCTjL5DhNjL3Bi802qwydQn3kBl9SlWBYwV3XUeB/EqrgHdPq8deTWClu02VGapAvrhC09+v9/9t40SI77vNN8Muu+76u7qu8DQKO70QAIAuAtUhwdljgOa6WxPZYi1l57Yz7Memb2A2N3J8K7CoVpb6ztcYTW3pnxrhXjlRmSbFmSvRJlUhRFCiRx9oW+r6qu+77vytwPzS4DJAA2QJCQRv0wGGQVsjMLXfnPfPM9fj9/lYJmAJ1mAF0xwUzvaeK1KFqVjLL0Q6zGYbQKLc2hGcaHxtE0HOTCQSrqFPFilGJDg15vJJ9vsbRUZGdHxOmEoSGJg+q+7V/cK5XKYUnrDtyY9dwvYZVKpTsKCd6rRUun02F5eRlJkjhz5swtR87vJsPzs+qAfi+lMYVCcZPnVbPZJJvNEo1GqVarVCqVm9zf74V9C5FKpYLpIPXgQw455L7yQDI8jUaDhYUFBMGKXn+aeFykXodsFkymvZHn/Qcgmw18viYWy16Jq9EApRKiUQlJGiHf6MVotpAsB7l64QL+xUXqah+vVtxMm6cYNzbYuXiJMhqyhmlaSTNDxQgOfYZWIkdRYaVldeFWFlEmQxRqIp3TA2zOqnBXVIzbTMhmCwsdIw31EsL1BtH0OHltgMkjadz9fRhXV7CaFGgVIuVsHpSD2JOLKItXaWo0lGQT8qaCUtaHX73Iw58SWbpqpJGy01FqsFz/CQ2pTS3bIRja5YRLQbWhpOoZxGeXMW4us35RQC122JVrWNxWktkwpkv/QL5QYLOi5FhH4tSgi2FbE4vHTaFjZaFto/lGnTY+vCfHaJQWWZ5fYrS5hnjkn6Ew2UlF28gaBwNTEhpvg/mLb+L39hBLlSiVNFy8uMsnP7mB12tAkmzAwdI7+6WAw6fZO7O/Jur1OvPz8zgcjm4/ze24lwxPtVpldXWVnp4eAoHAbfcvCMKB9y0IAtVqFeCuPs/77fODcj96gdRqNV6vF1mWabVaOBwOstks6+vr3fF3u92OzWY7sPv7fpatWq3i8/k+0Oc75JBD7p6PPMNTq9W4dOkyZvNR2m0HOh089dTeBFCxCMWiwPq6QDotMDwso1CIVCp7xpSwpwGj1dbx+/P09k7g8zlI+3qxq9tIm5uE4gq26mr8Z2185lGR8vYqFxauk9cE0KvzdLBRaOhovrFFXm3mbcs4RE20xQblhEBe6aButqAZLnPOreehXBWrQctWyUzkupHQToFN0cVRaQ71ch/twQHUxSx6lQbRbKbkmSA/m0dcfx2bsoyhM8FupI/CVgO3tY1WX0WqJ8k1DKSCChrtNkXlNJ3FCBp1idFMiMXLLeaMJ+hsNXBIEXoXF0kUdKw2BMR0lvGRGTShdRoKBXnnkzgaUY6M+JmO74BCR2dkBHkpST4aoZFo4xwrYx6r04zX2GWaxtIi69eXqejO0tE1GDyqxSipiSvMqFUqRKGB1erHZEpjMGQZHJSo16ssLkaQJAmr1dod9b2dMN2NAU9vb+9Hdo79vLFvp3DlypXblrDezd1meFqtFouLi0xOTmK1Wu+47UHLU7Isk8vlKBaLGI3Gbk/QfhbkoNYJ797n/eB+qj/vn8f74++BQKA7/p7NZrsqyDeuidv1/Oz3XR0+BBxyyIPhIw14wuEwlUqFiYlHefttHYGAjN+/p+ei1YLZDImETLks0NsrMzwsEw4rWF8XSKUE1GooFMBojDExcYRz587RaDTIZlPImzFy5TJx2zjOugaFMcXCroi/rqZ/pJ+ioGLn2mWcR3pw9uvINOykjb0UNyUiEQhvi1SWjZRUGgYnHfS7srTtHTotM5nYLAMmF1mTG8+UEVs5jrncg1Rs0tqZJRzLklCqkR7zkEin2Jh/lUA1is5rJyQqqeS26VEmkFVmFvICV79xkZpqgN7iW8QUHmY1VozKMJOtChPVDEv6LUonHqWRlaBs5lijQ9Lqpqkf5rzfQL9Sha7U5NWajvKgD7UwS27lDSRTh47FQrbZ4tpKhkvLEjM28A1LOEYGaUgtsqYeHL5+stElhOyPOe3QoVA+wW5VRaYpUqu4CaZkeocsGI0t6vUwoVCQp59+uut0nc/nu07XSqWye5MzGo3dG81++v7w4n5nQqEQlUqFRx555MBGmQfN8OxPPlYqFaanp9832DnoviVJYmlpiXq9jsfjwWq10mq1CAQC5PN5lpeXu03ADofjjoHxh8H9DHhuNZa+P/6+//vcd3/fH3/XaDRd/Z8bx98P18QhhzxYPtKAx2azkUgksNu19PUBCKytgUq1V8aqVKDREHA6Zfr7QaHYu7gEAk3qdZnVVZFWC+LxOMViicnJMiaTGRoyW29eRDXUz+lP9uAtFEmZs7RDGUI1kbjNg0lpRqtdorx7EfX5UUaOV9EZVWxdD2JYCiOqVPi8kBOV9A7Y0K21aGwt8kakhqvsRLFdg3KbgdJPcJw4R6Vq4ER7Gc1IgIXSMCWLibTRSOX/ewNpJ0Z9+CRNpQa7JcBD7jz+sUGEQC/aiyHW52NoTHa8BgvruklKYoZRtw2L2kBPukwt4GQ3lqHZ6KVnO4iMA4N9kKnAMdhdJ7MaIZ07Q0QhYhqNUyilUKcb7GjMZEIRIhkbrSo8fLqXx077sbVqSF4vwZdeQmHw0lAbaVl7KIXeJFHqcPSxMQIeE9mdbTwtHUVRjcPRwutVsrJS56233uLpp5/eO2GUSpxOJ06nE9gb9c3lcoRCIcrlMkajEZvNhlarveeLezab5Qtf+AI7OzsMDAzwjW9845YWCoIgdICFd16GZFn+7L2fnQ8Gh8NBqVS6K1fwg2R4Wq0W8/PzGI1GXC7Xgf3M3i/DU6/XmZubw+124/f7WVlZIRQKYbPZqNVquN1uenp66HQ6FItFUqkUGxsbaDSabmCs1+s/1P6f+53heb/f3bvH32u1Wrf5eb9fZ7/0dbgmDjnkwfGRBjz7N0mNBk6elGm19spYhcKe+aRaLeP37/XvpFJ75SuFQkE6LaNQCBw/LmM05vnqVy+g1baZnZ3FaBxi7ooOa3kQZVXi/577Y2zpNXpVfXzx2L9G0W8iHdugofTz+IyGxYVFrqrN2I5MU01UyBQ0nFFt0/dwD3VnL4mNMtVqh9xCiuhGmI3AZxgoLKIvh9hV9fPZ5gpOEeYaddLbm6y0HUTKRTzjBs4OuLHYjOwGxljp9FPdVSOqG2iTVUx+Nw7XCO4eI8VIiajKhPLoDGccDhQhM0dSV1EqJDZnvkAiGUTbWOPx3E9gdJBwYAxtvUJL3cFRXCbT0lLxm/DV1lgOJdDXMjQVHtblPqSMhNXixm2vUDPpMDoNEAkhh8MEI21CujIqvcQJZ4ze6WOoMhmCq6sEV9pkYmmsTifPTZlIGVoUi17C4TAXL16kVCrdstFSq9Xi8/nw+XzdUd9cLsfW1harq6tcunQJt9vNY489duBGzRdeeIGnn36a559/nhdeeIEXXniBP/iDP7jVpjVZlk98gFPygeNyudja2rqrn3m/LEyxWGRhYYHh4WFq2hq/+3e/S/KtJDO+GV742As49c7b/uydAp4b3dOtViuRPeltzp8/T6PRIJ1Od9+z2WxYrVYGBwcRBIFGo0E+n2dzc7PbA+NwOLDZbPfdAf3DKGndDTqdDp1O1x1/L5VK3fLX0tISS0tLXLt2jRMnThw48PlFWhOHHPJh8ZEGPO++CKlUe15MDsf+RWrv/VgMYjGBaBQ2NvTUagpOn5YZGYFLl1ao1cI89tizvPrqJYrFHJ/+9O/gGe/wP/3D/8acus2kEKCl3eB/3Prf+T+Uf46j2aFg1FBbnWPUo0Z72oeoltjeVqDrlAj0t7CPOwm17JS0OuTIEpFwiqx+Brvfjs3UorCsZNBtQjswiNiq0VeDguwg3RYo22x4qzVWfnQBORhHJXsI+Mqc8HrJNH20Gw4uL0oYwxcQHXqSwTx5cwz9tAqzssWMuc3J0EXaZ57kW4lz1DU2xOSbqHY3yCRVhMQanZqeVC5Pa2URxvuRXAZsSQWdZBaj2oZh/BmyBQdibQtHaJu1nR1kr4mjTzkpx+MsLJWYK5qx2juMHXdxJLVCyGaj9/hxNuI5ri6+jkMpcOKXptHKuKQlgAAAIABJREFUJYanHqJYLLK9vU0sFuOHP/whv/Irv/K+3+/+qK/ZbEaj0eBwOFhZWeG73/0uv/7rv36g8+Q73/kOP/7xjwH40pe+xJNPPnm7i/vPPXfbgAx3zvBEIhGCwSDT09PIKpnP/NVnSJQS6NQ6Xtl5hS9+54t87wvfQyHeusR0u88TiUQIhUJMT0+jUqlYWVlBkiROnjyJQqFAp9N1SzzNZpNMJkMikWB9fR2DwdDVt/F4PEiS1A2Mg8FgdwT8fqkaP+iA50YEQcBsNmM2mzEajej1er7zne8wOzvLD3/4Q375l3/5QPv5RVoThxzyYfGRBjx34sbrk88HTqdMoQDVqkw83kKSBCSpxdraGgqFje1tgUSiwdTUEE8/rSC8XESzuIKzZkWOqklZlYRNGeJyAZfCjVA2Ecv14OpVUsrt0tldYEZoMTiRxxFwgigiGHREmxmuvyZj1p1GadTRa5/l2ISWsqWPcNOJVq8kupTiUtFCMJmmprLg8o6hvfY24Y6GjYoHnVggGY+z2Vymqp/AblRg0RsRkaFYoZOPs7ZTZG7RhVW2ctSQQqGA8LbMbm0NW2mLXCLOqsGKpp3FUFOxlR+iXlNTNR5hrN+F/2PHKL+cZiWSpoKN5UtNBh4VOK6JU4rFmTeYqMTLXF9ZpvDTGBnJy9RjU9hFEZ3NTCfSpOHzkaiIXAvq0Pb58HWSSPkwdZWKUiTC8PAwhUKBdDrN22+/zWc/+9kDT6TsSQnoUSgUPP/88xw9evTA50IikehOsfh8PpLJ5O021QqCcBloAy/Isvx3Bz7Izwj3cjO9VVAiSRLLy8u0Wi3OnDmDUqnk7cjbFJtFDCoDCkGBRq1hLbtGvBKn13TrRvJ3Z3gkSWJ1dZV6vc7JkydptVrMzs7icrno6+u7ZWChVqtvyvqVSiUymQyrq6vdpner1UogEKC/v59ms0mhUCAajVKv1ymXy9jtdhwOxz15hv0sBTw30ul0ulnOL3/5y3c1qfWLtCYOOeTD4mcm4Hk3KhU4nTA8LOHz1ejt3dPticVi7OwIiKIGr3cKm83O/PwWjVIZVavOuDJD0dYh3BYQ8nU850yY55I0GnW2/M8SVzuwJktsXPov2CpFFI5jGLz92ENFIpqrWMw2Bk+MoNY22b2wCHUfo7oa0WaQdd0obbUWey2OVmvC22snZ53CXNdSSetpafVIzhHEVhaLIoIqnqWlKJE8aqWRimPIZvAHVAweN3HpQgKnx8Jwq04+ruayOEhnuYDSY2MivknMbqfkHECvK2IV+jgViSD5elAfPYqDOIZ8jGy6jUZ3gjBazutqjLrDSDtbbMRSbFWHsDTr7Fwx4lT6GR0NILt72JotoNGYKSoHmFvLopX0iJkwx2bO4qluUamkSa6t4bfZcPn9TE9Ps76+Tjgc5vLly5w7d+5A39/7NWg+88wzxOPx97z/la985W5Okz5ZlqOCIAwBPxIEYUGW5c272cHPI+/O8Oz31Xg8Hvr7+7s3e71KjyRLCPLea/mdf3TK2+vI3BjwNJtN5ubmsNvtDA8PUyqVWF5eZmxsrKtX837cmOHYb3rPZrNkMhm2trbQarXd8te+0J/NZiOXy7G4uIgkSXdtAHq/A577ta/DNXHIIQ+W+xrwNJvNAz2R3c0FSaFQIEkdtFpYXFwknU5z6pSX0VGZbNaM03mE69czWNQOjlRHmG+vEPRpGE82GNA9gVJsIhriKGp56uqzGEWZgQEjPfrjBN+6yqs7Ap11PeVUjNMzJvSFDpPGa+Q9IoGzFtzqHIIAKqGOMnORDZuPEes6U1ojRYPIbj1CX0VCVBRwTZnJnBrm9e97sQgSksrBsKGPgZOjhMtWRoPfp+428fpaHpMQ5cln/Lhma6zbjnHUncKorbOEF3FHxfW0j0iqRm9DTc9ogCcVs9ilLDVRjSYTp7gmkGwYKCnt5Gs5srlFrn67hr7VpGHWMRLoo7zuo622oBkyUqlF8bqUeLVV4iUFb63XSBR1PDOtQFvNUrDYsRoqGNxeNNEoqlYLpVKJRqNhdHSUixcv8td//ddMTU0dqO/g/UZwX3755dv+rMfjIRaL4fP5iMViN1kfvOs8ir7z3y1BEH4MzAD/1V/cRVHsmvBmMhlWVlZuOdI+4ZrgUf+jvLL9CnJHRq1U8xuTv4Fdd/vR9/3sUalUYn5+nuHhYRwOB4lEolvS+iBCkkqlErfbjdvtRpZlqtUq6XSa69ev02g0cLvdNJtNfD4ffr+fVqtFqVTqlse0Wm03+3M7AcAPe0rrXtnPFt3OLf1wTRxyyIfLfQt4ZFkmEonQ19d32xHUG6X0D9qoKIoinU6HYDBINpvl/Pnz6HQ66vUizzwzxcCAn/V1JcFgiU88+z/gXH8bZ9vBMauVhzyPk60qiIZL7BSrGI7meWooh7eRIWo1Yhnr5ycbOd5YXOHU8FGS23nSpQJmKUVH/Diq1C6W5jabfX0sRmOIpRDK/hNIw8NUracILoYQf/IqpclTuAe8+IxlWtUUTnUb0WImbnFTTzhQrVVxe+vU62oubHuIVpr4/H1IUoAtZZWSWKalyWNopJgwyDhHLIy0+ljbypBp1RmMh1jGga1cwby5inVrkVjfBIs1NVpthd4e6On34O8dQ9Q4KEavkW37UClFjgaKWJ1WYvPLlKINyjsJtoURlLp+Hj+qxKVcoaTVYekJQCiEp9XGe/48m0ol4XicUqnEiRMn2N3d5cqVK7z44ov85m/+5vt+bzeKrBmNxrs6lz772c/yta99jeeff56vfe1rPPfcc7c6l2xAVZblhiAITuAR4A/v6kA/Q9ztQ0CtVmNra+udB4BTt9S9EQWR//RL/4k//8mfE6vHOD98nk+NfOqO+xYEgVqtxsLCApOTk2i12u5o+6lTp+5rg7EgCOh0um4Ja2RkhGKxSDqdJhgMolKputmfoaEhYM93L5fLsba2RqPR6GaGbvS/+lkuaalUKmRZvusx/V/ENXHIIfeb+3b1EgSB/v7+97043G3AA5BMJrHZbF0Bu/7+fjweDyaTCUEAm03B1laZpsXEMzMf59m0RDuqJLu4Q7x0ivnk06Cep7e4QfzaOiZDE+9TT7G5HcelbtETUHHcPkcsryarGea4W0HHVKTUSXMtmCPzpkxxu8xDWj0lVZXZYomYKkcmlsKtqJAtVckGjlFPbZPd2cEUTNI5M4LeDEM+Jf3aIjX/ANvrMiOTVSwVFxf/vkk2CeNTAzSTaazjbrSv/RXZYJRLoolIaheXQ8mn7PPYXU522y5iYbia0jK+q6aSTVLqCRBQiQh6C/2DPkZ9evIOL9srXvRyBZcyh1gysyMbWF/dwbLSJlWcwjIU4MhRA/btq6xn60jKcdRxkd5WE0Wlju74ccYqFVKlEsFgkEAgwKOPPkowGOR73/sejz/+OKOjo3f8zjqdDmq1mna7fdd9GM8//zyf//zn+Yu/+Av6+vr45je/CYAgCKeB/16W5d8CjgL/lyAIEiCy16+wdFcH+hlAEIRbmnveCVmWicViOJ1OTp8+fcc1pxSVfGbwMygUCvx+//vud2dnh1qtxvnz55Flmfn5ecxmM9PT0/d9lHx/dN7lcnXVn2+0d6jVamQyGXZ3d6lWq5hMJux2Oy6XC5/P181E5XI5tre3u5pQ9zMou98Bz73u6xdpTRxyyIfFfS1pHWQx73sHHUR3pFKpsLKy8k5ZS2J3dxeXy4XH4yGXy5HNZhkeHu6mvT0KBfVUipzdzllniRPNBPHTRzAMWimXh1B33JS3l3l9S+LyziLWhgKF1sxxfRprahnH8cfp9/ixRzJIhW1y2gJijx6v3UXvqbOoog06/jOsXV+hsJzCamijsRWQXTlkttjOVolpTWgUNfTRJXYzdozTdWLrSXbm1xib8mLz6chfSiJmVtmeTRAPT6GpZLDntAzHYKU5Rl52oq4FcVZXUdbDyD0mKoYBbAkRl20UlcKEV5uDTI7Slo54rM7Fdpnak9NUCkr6nGbEUoSoXQHCIipjHx65RK0zhqJnmhODFQYHwoSuXcA6NoldaSUtNOhYzchuB7jd6HZ3efTMGXp6ekin0xgMBh599FFee+01/vRP/5Q/+qM/umMD836/wr3cJB0OB6+88sp73pdl+TLwW+/8/wVg8q53/jPI/kPAQQKeUqnUnXw6aCP4QSbB9oOP/UbzaDRKNBplaGgIj8dzoOPcDZVKpTs67/onA72b0Ol0+P1+/H4/kiRRKBS6AZAoit3sT39/PwMDAzQaDQqFAvF4nGq12rWEsNvtB262fzf3M+DZD2rvxZD0F21NHHLIh8FHPpauVCrptNtQq4FavacueAv2VUtHRkZYWFggGo1y7NgxtFotwWCQVqtFu93G4XDQaDRQqVRMTE2Refttrq+sMS/rOOP14puwcbpYJDMf45hbJmn0c+HaLrm4gq2aAn0NauEs7Ycc6LaCJCtOlEITndGCs+8IvbEtVD4IKtQ05QBvX+mhXZQYzP8Ek9tNRDyGmBugc+QEU7pL2PJN0iceZnC4TiOUInytSjtRpe+paTQaN/FYC1enSstroeExYh0doD3fZETbIO15klLShaHXgzMuQWOToamTYBnnQvUoYiWORtxEOaBG5bbiXi8y0S7SLMuIAydZq9tY+/E8juXXqFd1VMen+LjDwIjDSUM9gMLuwjuswq2pIttsRAVwu0wMOa001wt01CpEQUBWqxEAdafD6OgoCoWCpaUlHA4HZ86c4dq1a3z961/nS1/60m2/6/vZ7PlfO0ql8kCZsFgsxvb2dnd67qDc2PNzK8rlMvPz8wwMDOByuVCr1ezs7KBWqwmHw9RqNZxO502qwR+EbDbL6uoqx48fP7A2036AY7PZGBkZodlsEk/FicVilEoljEZjd/prvzfI5XKRy+UIh8PAnjaQw+HAZDIdOIj5WcnwHHLIIR+cj3xKS6FQIJXLiBcvIvv9yKOjcIPkvSzLrK+vUywWeeihh2i1WmxtbeH1epmeniYcDtNsNrsmh6FQiKNHj+J2u9GpVCjW1ghUa+yKDhTeQR7WaLCZzZh3dqimUgTdbp55ys+EUsNbmxWSCS1mcZqaIU9ic5a3r/yARYcVu3OQvnEFw7SxljPUSlv8r+LvsVGNMy3286/Fj6Ey9pKwHsfZlHApm2jKMl6NhMGqRFdTQiyFoqbgv/ntKXLmMdY2wLTxGi6bjvzQKWzaOL5RH8qteeodO3oanPGl6fvVcd768xbxjMjFtp/vLfwl35P+gSdyHZ51DuAb/DTla98lIopYO1HknIZoNMxQr46TA0myWwVK4jDZ+jAv/3QH+XINd13LzIkWolMFJZl0Ok1GqeSITken0ULc3qUzJiCKOqjVEGIxBLcbhcXCyMhIVzfF6XSys7PDK6+8wsmTJ5mcvPUD5Y0Zi8PA5/bc2Nd2O24cDT9z5gzVapVsNnvgY9wpw7P/YDExMYFer+/2yp07dw61Wt0VFNzc3KRarWK1WnE6ndjtdmKVGL/x3d9gIbWAz+jjL3/pLznlO3XHzxKJRIhGo5w8efKu1KVvJFFJ8Lm//RyziVk0Cg1//Mwf81z/c2QyGRYXF6nVat2Sl9/vJxAI0Gq1KBaLRKNRisUier2+m/25k+/X/Q544N6kCA455JAPzgMJeNqCgOzxQLOJsLWFbLNBIEBTkpifn8disXQdo6PRKOl0mrNnzxKLxdjc3GRycpJMJsP4+Di7u7tsbW0xOjqKKIqIgoBeENEd/xhVnY18WYtZJ5JWqQjOZun752fQWptEFxex6TX8+lMDdBx2rscFdgsG/Oki7ZpMsWVhaanCbimKJnSFF3TfouaOM9CpI8l5/l9Vhf/F2Y9JO4gm18ZKgbrSgGA3EZZ6qF68ghxcwXbmJMvyDNqGzIAvSP7KVRSDT2IzOimG4gS32+wmjuDpVTM5kMbdDiFlE7g1GbJmA/+59tdI7SQebYqSVcuflbJ8LnecqXQOpa1CrOdJYskow7UwPWtbaO12TMesDFnNlHpsVJarNNU6/J4RjO1VfnLlKsrZBZojGkw6Nx69BTlZAIWClkGLIAqIwSC0WlCvA3s35ZmZGSKRCAsLCzz55JOsrq7y/e9/H7/ff0uJ+8On2YNzp4Bn30Xd5XJx5MiRbs/P3ZiH3irgkeU9mYdsNsvJkycRBIHr16+jVquZmZnpfncajYbe3l56e3u7ZaV0Os3G5gZfuvwl4vU4HbnDVn6LT3/j08z/1jxuw3sniPYfZPb1fD6It9av/d2vMZ+cR5Ilau0a/+blf8Oxf3EMt9KNRqNhZmam2/y8ubmJTqfDZrNhsVhwOBxIkkS9Xiefz7OyskKr1eo2P1ut1ps+2/0eS280Goc+Wocc8oB4IAFPR6EAsxlZrwedDuHSJSpzc8xZLAxPTNw0crlvaWC1WvnhD3+I3W6n3W539To2Nze5fPky5XKZmZkZ2i4XFYsZhd6GVrQSfTvI3+/sUN9t4Fc5sERi5HejmCoVAiYToUyL2PoCmrFejtks2I4O0ZnSI7SKoPKx9gORlYyHlDxGX+gIk8IV5t0tyh0VP1y+hmCzY83lMIyN0fPZh1n9fpjZhTL9BguyeYBMuIlpK4mm30itVsHWZ2TguI14SI+60CRUqqPxOhgfTGB0GeDCDs1MimooQbFZZ7le5EinwGgrTFocJqqLsd38Bicsx3H1TlPvO4GsyOAbkwjUY8jqDrtuK8WNPLGdNaaNYY5OW9H0uUnNKjGWqyh1CsIVgXrDxOvfWkRvsVJXejBIVcRSDaHdRvL7EZpNbjQZ6O3tRRAE6vU6SqWS69ev8+1vf5svfvGL72kU7XQ6NJvNe3LN/kXjdgFPNptleXmZI0eO3KR7s9/TdlDeHfC0220WFxfRaDRMT0/TaDRYXFzsBjZ32s9+WcngNZC5kKEj/9PnliWZV1df5XPTn7spaOh0OiwuLmIwGJicnPzAAcTl2GXacvuf9i91+O7sd/mc/3PdYE2n0+HxeJBlmUql0tX9aTabWCwWrFYrXq+Xnp4e2u025XK5u41Kpepmf+53D0+9Xr/tOP0hhxzy4fKR9/DsX9xlgwGhXEbWakml0+TDYaZ/93fRm80gy3CDAJrX6yUajWK1WvH7/VQqFdxuN5lMhna7jcfjoVarUa/XaRmN1C0WrBYVg5Yk5b9/kT6Ng1x/gMRCic2tDZ6wNJCydXLDM1RSElJO4oTDQavXT6F3BOuoQPiVVxFaVZ55yMnZo03+zx/HKDUCJJt24rIJM2qacYHI8jJWMUZmJM/ipQ7xhUWcsgaf18xluYdmoYE5+o9owyVWesaoFC3kLm/x6pqT2qUQbecs7gEljfourbqa5OoSSf0AVwpeCuVtcPlwSQkaSjtZk5HTZDnutOP2/nNKbQOqnh40iQxF2UrZJkIwRKuqpFLt54hTxUygSq3U4u35OKkfv4bF+hR9D/XRZ6ri7T+C+s05lvUOMpEyimyWHZ2MQ6nE4HSizuW638U+PT09PPHEE1y/fp21tTXeeustvF4vn/rUzePOkiTdVm/kkJt5d8AjyzLBYJBEInHLkfODmIfeyI0BT7VaZW5uDr/fj9frJZ/Ps7q6ytGjRw/kpr6PSWO6KdgBkJAQGgKXL19GpVLhdDoxmUysr6/j9/vp6ek58P7vhEVrIVPLdF8rBAUOrYPjx4+/J5gSBAGj0YjRaKS/v59Op0MulyOTyRAMBlGr1d3en4GBgZt8v7a2tqhUKqytrXWNTz/IBFin06FWqx1meA455AHxYEpa7TYYjXR2dtiZn6dttTIyNYUI0GohzM8jBwIIbjd6vZ56vY5Wq+3qgGQyGQwGAzs7O6hUKvx+P+Pj4+j1ejo+H96TM1QqIutvXeCM2woDAbRKJap0kxPHRyE5RyKbR1e0UK+YcBUrVOejiD09OJxeolEIxgIEnALa8T50+Sr/be4pvhl+myqbmPRuLMpzfLz3V7nw4y3ajT7Wr5tIFVs4PQ5URZkV9QSyO4TosCPblPRkg1SrIZYFJetbVuIrAnrZSL9HpKPvQa5XaG5XUIkesE0SCFgZXFlh6vhR/ia2REzlpiK78MgaPuX9HcI1Ea9yHveUl9hCivFqmCNH7IQ1PnKv7CLuvo1cVlM5PcaA3YTJ7Sa8vEpEYWKjkkaTDGKx27FoK4yf7cG4WKVNFvvAAJVIhJ3tbbTRKGqVCrvfj9ls7t5MbDYbY2NjBINBXnvtNS5fvozf72dqaqr7PR+m7w/Gu3t49rMvarWahx566JbZhbv139rf/kaRQpPJ1J3EmpmZuetMnElt4t89/O/408t/Sr1dR6fU8Yj/ET539nOIwp64XiQS4dq1a6jVaorFImq1+ia9nHvlP37yP/Ivv/svEdh7KDpmO8a/euJfHShzpFAocDqdXSPj/dH3UChErVbDbDZ3m5vdbjdXrlzB7XaTy+UIhULdLNd+8/PdZKs6nQ71ev1wTRxyyAPiIw94lEoljUaDqkLB9uYm9kAA71NPIS4uIpfLkMkg/PSnSIEA9ZEROqJINptFlmW8Xi+w5yvz5ptv0tvbi8PhoFwudy8ixWKRpcV5fKzwyLEBtDUToVCIVk8P3qeepbn6BiqbjbHHHfROGlm9WECViZIPuxFHTzJgypKWjTh6hnBZ8lTMPnTVLX7J8gjOyjnMjRcJnDzCc0/9Nsq1TTrtJhfXRqlnSzw+M0YtO8ab/5hH6zTSrmbwqOsM9mlw23rQaLTktiSqgpFJXwXfKQcWp5dO3xADm2usJ53sDnwRh01Cr3WhNV7Am9fw/NT/zBvCT2jLH+OTkh9VKofGYGSDHopbLVrZMha7xOWKg+h6mI5HiSORQ1/Uk8nYkXItNNk0Cq0OwWzD6VQzYXCgm5igUqmwsbxCdLPJcX0EJkfwOJ24RkaQ3G7yej2RSISVlRUMBkNX5dbtdvPpT3+akZERXnrpJV566SV8Pl93xPgw4Dk4+wHP/rRUf3//HUtLd5vhEQSBQqFAqVTqlnxWV1fpdDqcOnXqngOQf//ov+fh3oeZjc8SMAf4/NHPIwp7Adq+f9bZs2fRarXdrMrGxgZqtbobdNxLBvATw5/gR1/4Ed+6+C1Ge0f51VO/ilK8t0vZrUbf0+k0u7u73QctQRAIBAIEAgHa7TaFQoHd3V3K5TJGo7Gb/TlIE3a9Xj/Meh5yyAPigZS09qclJj73Oawm095oul6PEI2CKCKfOEGhVGJ9fp5CqURsfR0efxy9Xk8sFiOVSmEwGBgcHCQSiXQvIJIksbS0hGZzkz69mQs1K1CjU2/Sf26AyO4Wah30nTmDt1RCkCuYTo6gVJXwe9tItXWE9Qq+rJpEVkMoL1BvRekUoih2ckxZnuKZ0S9gcRiR2jLSyipCdhfTRD9POx0MekKsNfRkPn6E/qMqwvMeWrNl5hfNrFsE8oUapaaDseYKQ0MC7XEbhmqLeKeF0qAmo/BRMvdjU0SQRAW1WpuOQUWmbuez2XOsKY6gUwcZP9FBdbSP/OI2ofUlCvkOl3cHULht+McrPOk1kvDboVTBZavR2Ekg9U3TGBwitZoAwc6U1EGLFuvICJHFOFZBxKfXkSiViK6tIcoy5kAAi8WCXa2+qdFzaWmJdrvdLQM88sgjvPTSS3z961/nt3/7t9HpdMiyfFjSOiAKhYJsNksoFGJycvJ9R7XvNquwtbVFq9Xi7NmztFot5ubmcDqdN/lu3SvPDj7Ls4PPdl/vl+P2m6H39W/eLSiYTqdZXV2l0Wh0MyYHzf6USiUqwQr/9sl/e1dluPdjP3tjsVioVqt7vUoGQ3f0fb+X0Gq13vR3yeVyXL9+vWuM6nA4buv7ValUDh8CDjnkAfGRBjyyLJNIJMjlcpw/f35Pd6TRAIUCWaFA3N5GnppC9nrJXbhA2mDAmUpxtl7H8s6Ns1QqMTQ0xMjICEqlEq1Wi8FgoNlsMjs7iyRJaHw+pI6CuUIOW0eBIlMj/EaMQbeRmUEvpt5ehHAYdnbQSCKlIydR+HI01qMsvZLBbmriHpqhkasSjYqkQi0sqRCDT2lI+nw0S2m2/5856okqO5jwzBxHJ22jSCeRfWdxt3WcOlWns1tFsBZp+GcotfSU4mV6jggYEiFWtjNIhQ0cprMUHD6itgmmZ4pUbE2Ub64xF9FR0laomZuMPzxB86+jtIUEHX+axd0OS7U4lcgmhlwdja4PR8DCsVNmvAMnoVolUkwhlCtYhrWI+R0orJCzO0hk1hk0mmikk6yr8+xEspgqEna7F+uYAefUFEKrRaXZJNFovMfher/Rs9PpUCwWyeVyGAyGbqP566+/zlNPPQVwWx+tQ/4JWZZJJpNUKhXOnj17zwJ5t6JerzM7O4vT6aRSqRAKhYjFYoyNjXVLOveTfcd2URQ5ceLEbZt9dTpdN2PS6XTI5/N7k18bG2g0mm7251bNvZlMhvX19QN7ut0t7Xabubk53G43gUAA2GvWl2WZYrHYLQvum5parVZ6enro7e2l3W5TLBbf4/tlt9u7gf/hmjjkkAfHRx7waDSarrgZiQRCJII8MYFQryOPjyMPDVFOp9mKRNDa7YyMj1NOJFDUasSCQaJLS4yePo1SqaRQKJDP5xFFkbW1NYaHhxFFEasgUNve5qEhK/lGC0PnKIZ6B5O1H6MYgWqVXFGBYjmKfdqGd0SChhIGB0AKgUpNu7ePvLKDyyrxZH8PwfkkZVWcaLXC2o9WeGPFgr1fg0msISbSCKocMUlJWC9jbK2x+3qWWjyBTavA1Q9lrYmRSRUVSigNflq7KmLxGvE3EoStO2Qf7nDev8nOxgbO+bdo12Ui6g3Muo/hipdx9RuwZCR2xHGsLgdWs5vGZoZQrEhW14fd0MBjrICkplKpsFooEmg2oCaDUkkzmaSQq6CoVDjhaeIxOdnNXMft64PZGm2nGdHCngZPu43BYmGRsFtGAAAgAElEQVRweJhBQbijw/X+SPrExASXL1/mypUrBINBxsbGePvtt+/6Bv7Nb36T3/u932N5eZmLFy9y+vTpW24nCMIngP8AKID/LMvyCx/g1HxgyLLcDdrvZ7CTy+VYWlpifHwci8WCLMuEQiHUajXBYJBKpdIVE7wf3Mom4iAoFIqbsj/VarUbVOxnf5xOJzabjXg8TiQS4eTJk3dtV3IQ9h+a+vr6uuXzfQRBwGKxYLFYGBoaotVqkc1mSaVSN42+74+3y7LcbX7e2NigXq9Tr9e5evXqgcUW9/lFWxOHHPJh8ZEGPKIo0tPTw87Ozt4b+xfbahXZbgeLBVQq1iIRSvk8Z1wuOg4H2kAAoyhy/dIlKlevYrXbwelEq9VSKpWYnZ3liSeeQP9Ov4lWqWTQ5UKp13Py3DTVy5fJqSpkxVVCu2lMeg313SZ1XJQ6Q6hnq9izWyg0CiSPl6YZipkOcjLNQL/EsM+JrDjGT9ezVGUXupaa/oeOYxnWkX7jJ7QXw1y0eXE+EUBu71BNJciG40RUHUJ5NZORJCMf96FRtXj1p1cohDMk8mXKeiN9ugKechqjNM7V6y2ykTXM2ShqvQVcRzBVRPoUCQJnbcibSmqnH8JkV5JKQVA1hto6y/ARJxZvCzGZgFCQckwmk9Ohr5WpLAXJKASynQ7ZZpPeQgpjpcyKSuZInwOHS0k8XiMScCIIRYRQCNlu3xOEfOemdSuH6xvHfM1mM3a7nXPnzu0J0sViwJ67/ZUrV6hWq/zZn/3Zgc6R48eP87d/+7f8zu/8zm23EQRBAXwV+DgQBi4JgvDdn0ffIIVCgdfrJZFI3Ld97u7uEolEOHHiBCqVis3NTcrlMo888ghKpZJ6vU46ne7q4twYVNzLCPZBbCIOil6vR6/Xd7M/uVyOVCrF9evXkWWZwcHBu+pfOii1Wo25uTlGR0dvkgC4HSqVCo/Hc9Po+36Wal/Xx2q14na78Xq9tNttLl68yPLyMouLi1QqFf7wDw/m6/mLtiYOOeTD4sHo8OxfsPT6vZtqpQJ+P8TjSLkc0XAYq06HZ3iYeVEkWauh3thAbjYZ1OsxxmLIS0tc2NggDZw5c4ZEIsGlS5eoVquMjY1RikTwajQYEgmMajXu0VE6fX2U33qLwvw8xWaTxtgQSlMBBCvl9QaKVh0CR6lmEljFHEfHo5jLMWTDETSOUZb+dgGj5Sr/3SeOkVF6UQwb2Io7kTVOlL19HD1hpVq18MZqGqs4R0NQYfPaGAtIOOphOhth/AYvCXRYLFEeP6LAONFHOq1EaStAJknObSbgGyMX91GtFGmVE4TiappOPyWtFodTwGmsUZpPMDWmpWoC1YBI78M9sLUCOh0dqU6rJLIwt4bKU6Vx6iH6dVosvWZ0Gx3im6sMPvMwZpMJua8P2VtCEEBQiAi7YeSpKbiNw7kgCBgMBgwGA319fTeVJPan5nw+H+l0Gr1ezzPPPMOf/MmfHPj8OKA/1BlgQ5blrXc+04vAc8DP5cVdqVTel5u4JEldIb2ZmRmkd4Q8TSYTJ06c6GZdtFptt1H3xqBibW0NvV7fLSkdpAn3XmwiDopCocBut5NMJruZo31tomazid1ux+l0YrVaP5BWTrlcZmFh4a5H8/e5cfR9YGCATqfTzYhub293f4/7Hlqf//zn+fKXv3zg/f8irolDDvkweCBTWt2LuyiCwYBQqewJ3NVqKMplJgcGUKhUZHt6yG9sUJBlRjIZtBoNhqkp2i4XS8Eg7WCQkw8/jMvv50c/+hGJRAKDwYDZbGZgYAB9uQzFItjtUCwiiiIWjwdrPI786KOULRYykQipSoy2cgeF2UVJb2DkyDjx3TY7FRfWcBJdPUw+U+GUvc5Tn5zAblWTj1RotTVg0HHslBPXjBVjJkQjImHOZZHrdfRWFW33BHFlCeX2DhatAq+sxux2IG1ew6bqodF7FIE8w487qG9/i2M2FfLwOcLX0gTUXjoFG8nyFOElPa1snPo/XseVDONo1+h9pp9gqoO63aG2ukKhmCHrdnOkV+ZYocaOQ6RnrJ/W2EN01l6j2SiTMHnx+q0o6grIhWBoCMloRmg2EKQOstmMfBd6Ke8uSayvr5NOp/nqV7/KpUuX+PznP3/PFgJ3oBfYveF1GHj4fh/ko+Jup672kWW5G8Q0Gg3m5uZwuVyMjIxQq9VYXFxkYGDgPeWZdx97P8C5MVOxsLCAJEk4HA6cTudNsgT73A+biDvR6XSYn5+/SSPHaDR2A+1sNksymWR1dRWdTtf9e9zNiH2hUGBpaYnJyUmMtwny7xaFQoHL5cLlciHLMisrKxSLRX7/93+f69evMzY2drgmDjnkAfDgdHjeQTYYEJJJkKS9rEIuR//EBBw/zubiIgpJwma1MmowkHA4iMoyibk53GfP0kilkGo15ufneeyxx4hGo9hsNrxeL4IsQzYLDgeyXo+wuwv1+p6NxfQ0ss+HMZnEWC7T199Px2IhE4wQ78RY2UzTk0tSKLuJVXwU0xWU+Qq9soDZbaVo0LD26iLGlAG1344Q8CNJQL1O49omkqIPV68Sk61JzSagSYaJCnryPi/9jjhjqjY/zWSQtUfJi3Z6etLE00v4PvsxXPk8+UIFc2Gbj5/3Y69UEdQhIsMfJ5oeptfYxpzNYZTTvHJ9l+VXXqXXlUE9ImMx6VD29NCWZcytBja/E9ugCeeRDpeuJ0nnKnhO/xL1cppgRYn9rct0alYKajeyso48Obln6noPvSSyLLO9vU2tVuPSpUtotVp2d3cJBoPv2faZZ54hHo+/5/2vfOUrPPfccwc53K0aRORbvPdzwb1kePa1dRQKBYVCgcXFRUZHR7HZbGSzWTY2NpiYmLirrMu7MxWtVqvrTl4qlTCbzV0frZ2dHWq12ge2ibgdjUaD+fl5/H4/Pp/vPX/+7qCiWq2STqdZWlqi1Wp15RPulP3Zb4A+ceLEh6J+vG+nIUkSL7/8Mn6/n5deeulwTRxyyAPiwWZ4YC8gMRhAEMhnMuQWFuj1+WjpdDQXFxmfnCSby5F1OCjY7WzOzXFco0FcXydVrbKTSvGxhx7qysXfiOx27zmyK5V7isG7uzAystcvBMh6PWIiAY0Gir4+PHYz7ol+WK1Ty0f4ruAhsbPF8EARV4+TZtNGZ2SUrWs5UptxouoGRz99jnxeoNmE+maJ0m6RQqAPozpBj0vA5w5RvhYn4z2G2maAKuj0BsKKPl5NTNFIqwikNzn/3DC+IR/y7i7Npon8G0sE2m0CLhsNmxLdsTSFK2DKrzHoL9EUR9E3ZIT+RyjmFJSUWtSqCqTTpJRKZJMd/5E+bMpdQlevEt5Y59S5j1PQWRhxxlFKVepHBqjU25R3YqhdFlD13XOws7GxQaPR4Ac/+AELCwv8zd/8DWq1muPHj79n+5dffvmuj/EuwkDghtd+IPpBd/qguJcMz/7PJBIJdnZ2mJqaQqPREAqFSKfT96WxV6VS4fV68Xq9yLJMoVAgmUyytLSEUqkkEAh8KEJ6+z1BB+2nubHMuq+mnM1mSSQSrK6udst0Doejm/1JJBIEg8EPrQF6P7MD8OKLL5JKpfirv/orlEolx44de8/2h2vikEM+fD7ygOc90xta7d6/tRrptTWEYpH4zg5X4nEMqRTDajXxQICrkQjVdJpPP/cc0Rdf5Ec/+AG5/n4eOX/+pozRTccKh8FuRw4EIJdDTCSQ3O695uhOZ88cs1jc+3OfD9npRMoXkeMZUnkTiVyTgWM9fGJEYCcvcSUHu/+whFKwMDDmJViJcXlxm563y+Q7ZmbMQdR6ib6RKgaXG4wGtldriDUBuzaN0a2ndr1Cp63EeuwRLD1G1ldmGSg3Cb2+SUrSYslEKXVclP3DqBxqdrIZmq0WtUgE9WoCp5TA9OwptI0GI6UqZucZNtdquB8bYoIdqmo1C2GB6/EWhk4CvbRBcH2dQZ8Pp1ZPQaNBEU6ht6vRnx3DLsvIV9JU6jVot/eCw7tAlmXW1tbodDp8+9vfJhgM8uKLL97XiaNbcAkYFQRhEIgA/wL4tQ/zgB8m7856HgRBEFhfX6fRaHDy5EmAbiBy8uTJ+27c+v+39+bRbZXn3va1JdmSJVmyLMnz7MSJ7ThOQpykhAINQ+GFQMJpoXztgTB1oEBpDxQ6QrsOQ3t6WtrSkpextF0MPaeNEyBQCi0vLYQE4tiJHc9TPMvyIGsen+8PRcIOTuIkduIk+1prL7C1o72l7fvZ936e+/79JElCo9EwPj7OokWLSE1NndXC5xjj4+M0NjaeUE3QobM/MS+thoYGQqEQCQkJBAKBOU12Ytfi6aefJhgM8txzz83JTNgkzqiYkJGZC059wgPgdOKvqcHh8SCyslA4HCxYsABlJILHZsMeChGw2fh0cTGS00nH6Ci+wkJWfeYzlJaWTi9uJ0nRLjCXC1wuJK83WstjNCK1t4PHE016jEYknQ6hUhEKCvb9fQyPy8TEQAvLKnVYrQZURsgtz8DRbyYUEiQnj9FlXE7iqBqFrY2hVjc6VSoJhWEioQhjvQOM+gTBYBLDw+MsNwdJGGpmuMGJ6v99RJu2kKyMFLJ1vQidRKLST1AfQKX04HW5sHc58XeO4skowFN+LmM+O0vy89GUmLGQiq6sjPH33kPldVGSU0KgtR29wQR+NUk9vVgDOejTK3G7bdTtexfPxAQ+Sw6ivQO73kCxcwzyF4DJBP39CIuVcKL5uJKdmO7KH//4RxwOB7///e9PyG9oy5Yt3HnnnQwPD3PFFVewbNky/vrXv9Lf3092dvZ2IcT/EUKEJEm6A/gr0RbcZ4UQDcd90FOMQqGI+8bNhGAwyMTEBFqtloqKCnw+H/X19WRlZZGTkzMn5zgxMUFDQwOLFy+OSxHMVuFzDJvNRmdn53FZXRyOyct0eXl5dHR0MDIyQnJyMrt370an08XrlGajriYSidDQ0IBGo+FXv/oVOp2OX//61yeUBJ6NMSEjMxec9IRnWoaHGW1poUenIys3l+zcXNI1Gtrfe4+amhrCa9agN5mQwmH+3z//SXJBAVeuXUtGVtaR9T50OujtReroQBQWIhYvjv5eo0Hatw9RUBBtv/Z4AJC8HlzBAPbkABcW5+JQKfBodYiyMhKBBVpBJAJGowmfL5XKyjxGRuppDLSiVaoJlqWR4fGQqEqko6kLg8uDS5GPlJdKorsFjW0YxbIVeB0SqkgPKSNefBETJWuqKErzwpIyFF4HHoeDlFQPWrcbn9+MfTzCrh0jZCWkIJRqkCQmzGaUQ0MkT7gwKiTGBwbIUyeimHBQcHEV9a834vfYcHkHIMHAisuuIaGthaH9NpocTlTqDlKEIL2tjbC6FCkv7fDf4zTEVa3VajZv3owQgqeffvqEn2I3btzIxo0bP/H7rKwshBBxh1IhxHZg+wkdbJ5wLDdDp9PJvn370Ol0pKSkxM0/Jycis00sEamsrJz24eJECp9jHDhwgOHh4SnqzLNJbCYyFAqxcuXKeJIZO9f6+nrC4XC888tgMBxzkhKJROLX5qc//SnZ2dk88sgjJzzbdjbGhIzMXDAvEp6IwUCX34/KaGThwoV4HA52vvEGCiAlLw+b08mgy0XyyAi5ixdjLCwkLSPjqOJmQqdDMTSEMJkQy5dHLSyI1u5gNkdrfFJSogXT4TADLS0M946y6kufIdXXjXt3N6F+F8PpCzBZov82OTk6eeQbHMfX2U35+VkYztGicDvJrCqnvcNB3+4aAsO7ydLkENIaSbIU4Kxz4lNO4K/UkqB2UZi7kpQ9/6TD50ZkZyGFOhAjI0TOPRdfsJHkcBI5xYKM5D50fd0caBsmsuQSUEJvi5eWWhtWSYvPlIUlZZSFFiMMD+MwGtm1eze2WhuL0lU4M5PIzC8gM0vLRG8S+uwMVLmZJPnHGR7U4e71sz9hlESpHaNZN6XO4bDXKxKhvr4erVbLY489hsFg4Oc///msL6PITGVoaIj29nbKy8sJBoO0tLTgcrlIT08nHA4TiURm9RoczibiSMy08NlsNqNSqeKFvX6/P+7zNdtMTs7Lysri48ah5xoT2Ozv76exsRGdThc/16PN/sQ6yoxGIz/60Y8oKyvjwQcfPGHrDhkZmdnjlC1pTW6plZKTUWs05KWmYjQYaPn1r/GHw6z+5jdpa2ri/ddeI0+rZdnll+PMzmbYbufDDz+MqzZbrdbpBySdDmE2RzuzkpKQmpsRRmN0aUuvjy7haLUIp5OeV1/FnaSjIC8fVSgMoRAm3yCOrAJGxlUkaASdnRIlJQKvF3qG1CQFgyiam8lKVOFShvjHGx8hpBX4uhQsXFKBJaUAZ8MIbfudWPq7mMhOxqLTke0JMTGgIEnkY3NP0NztJMcYRjMwSDg9HY8Ik2gyg1VCmZFO4c5/EZwYoNPjJF8Nwt6Lv7cPjymbA61hlDYN+Y2DqLK1vNnbi8ZgIG9xKbrEMbTmZAq00YRMqBLQJ/pZuCyZcPsYyYEg7uLVJCnLyMzyEArZ4nUOsSfdQz2BJmu7PProo+Tl5fHQQw/Jyc4cEisKdzgcrFixAkmS6O3tRavVsmLFCpxOJ8PDw7S2tqLVarFarVgslhOqT4nZREiSdESbiKMxXeGz3W6nu7sbhUJBMBjEaDRSXl4+J39D4XCYffv2xVvbj8ShApsul2vKTNXkmJicyITDYWprazGZTHz/+99n9erV3H///XKyIyMzzzglMzyxDpNYrYekVpOZl8eIx8OH1dV4XS7KLryQA0ND1LW1saqsjAuys0lIScGUmorpYJfVdFPnVquV5OTk6GCjVCLOOSfaqQUQCKDYs4dIVRWoVEgeD0G/n84tW9Dr9Sxaey5123oRDic2e4gRYzmaoiwKCgVud/QtBgYknE7QmJLIdY8hDTpQrbuI3vc6GB0LYBz5O5mOVnRZq0lUp6MISBTqkgisKCR3eSFOh4P++hryliWiXJJPOa30jA7Q0TNObokRPB4iEVDoLcAI+Hwoc3NJCYYIdu7HoUjDa9uPQjmIVhUh5NDhDkl4nR52tvZRtGgxJkMFBzwTeJw2kjMySBEiuqw36IWAkkRjKmoTGPPSiSTp8LeBRqMlPz+f/Pz8+JPuwMBA3CU9Zu7Y1NRESkoKDzzwAEuXLuWHP/yhPLDPEpMfAmKEQiH27t2LTqejsrKSYDBIfX09ZrOZRYsWIUlS3K9p8hJNXV0dABaLBavVik6nm/F1itlEWCwW8vLyZu36SpIUVyAOBoPU1tai1+sJBALs3LkznlCcaOFzjJhRamZm5hHd5w93rsnJySQnJ1NYWBi3kujr66OxsRG9Xh9ve9+/fz9ms5l7772XSy65hLvvvluOCRmZecgpSXhUKhWhUAiVSoXH42FiYoIEk4mOv/0NdUkJ1rIyOoaGaBwcZFlxMUs9HqSUFMQhBZmTW1GDwWD8ydHlcpGSkoLVao06MB/cXxobA6czOquTlYUvHKb59dcpjEQwFhUhdFrS0yK497QS0eoJFBaiUUkoldGC0v5+iZQUQW5udGlL1ZWCZLez/91RxpMrWGLqxOZo5UAwQG6iGptLS4oxwLjGwaJVK8jMz2egtxft/v0UpBmwqz0Y1DZUI21M6NNRLCxj0GbDZnMg/GaSBwdIDDejs2jJ/tSnKOjR4R5rZaS/jYSSEnLMZrRJZlzZeoYmelAa0tAPjSBaaxnqTsHldFGcs4DR8ChG3xARSzH0CSQRiRYsazQotBo0mriLRPz6TH7Sdbvd2Gw2du7cyfbt2/nggw9YtWoV3/3ud+WBfRaQJGmKrk4Mt9tNXV0d+fn5pKWl4XK52L9//2EtHA5dogkEAtjtdtrb2+Pu37GYOFxCMZs2EYfDe1A7q7CwMG46G4lEphQ+JyUlxWeqjqeYOCbEWFBQED/GiXColYTL5WJoaIimpiaqq6v56KOPuOSSS7jzzjvlmJCRmaec0hkeiNYl9PT0oAiHsZx/Pgd6e7Hk5jK+Zw+XXX016R4PJCUhFiyYelc+hJilQWZmJpFIhPHxcYaHh+nYv59klwtTejpWnw+V2QxKJQ6VipYPP6TMbEaflxfV6RGC7HwV484QwUVZeHRqHI7o+0sSaDSCjAxBXt7B32kyEG0uAr1+tK4OytcZcDnLGWzrpXFPDSrLKKPDtWQWVJJpNiPt38+BvXvxDQ4y1tWFd/FiREkJee3t5OVn0jg8yAcffEAwKJGXp2TApSNpNEyAYkK9fkRaDuaFJkJ7nWSkp2MxGPD02Plw1MElV52PJRBgeNcuyhilyVyCUopgSdLgaHCgM0WI5OQS7OzHWddBiiUBRX60ADUSObz8jiRJqNVqRkZGKCgooL29nRUrVhAKhbDZbMf85CwzPbHW9FjCE7vxl5WVodfrsdlsdHV1UVFRMWPdm8TERLKyssjKyvpEQhGrT5m89DU6OkpLS8sxCxYeC06nk/r6esrKyjAajfHfKxSKuGJ3TEhweHj4mAqfY8R8sUpKSkg9OBs8m8RiYnR0lIKCAjo6Ojj33HOx2+3xhy0ZGZn5xymp4YklPMFgMO79U1VVxYEDB8jMzMQrBOvWro0O/kIgiovhGJ7yFArFx9P8ubkEdu7E0dZGkyShCAQQu3YxrlCwIieHJEBkZSF1d4PHQzC/GH3hApTKqE5hIBCdFIJo6U/sYdHjgRAGjCUlJFvMKOrqULxTg8GwGnPaEiR/L5GueiqXlaFcsABUKoTdTmhoCH1RETqDgcSUFBRuN6qcHNLWrCElEsHv95OQkIBGo8HlcqFW5pKYlIqur590wzD7hnvpbBsnWV2Mva2VoboOCi/6AlarlWAwyPDEBINOFx6LisyFpZQtVIJHIGlSGFdr8Iz5GHEN0V9SQUKCAoMhWpN0uHKPmIO01Wrlrrvu4tprr+XLX/6y/BQ7y8QEOYUQdHV1xTuWlEolHR0dOJ1OzjnnnOPuYDo0oYjVp8SWvhITE/F6vSxbtmzWWsIPJaZsfLhurxiThQRjhc+jo6Pxwufk5GSsViupqamf+D5iCVV5eTkGg2FOPoff74/HxFe/+lW+9rWv8cUvfnFOjiUjIzN7nLIZnmAwSE1NDePj41RUVNDd3Y2toYGC/HxKL7gA5eAgIjk5Kgp4Auv5klaLBkjS6bBeeikH3n+foUAArclEW00NSfn5pJjNmMNhpMZG6kMVWHM1ZGcLPB7o7JTQaGDRIkFxsSApCRwOePttBRaLkU9/2oCqdxilMswYJhJsA1QagiSOjVFwwQUkLFsWFVYEvL296JKSyL/gAlLdboTJhOR0IoqL8UYi7N69m0AgQHl5OUajETE6iqe9nUGNk37VKNrGRvrcbpwBDyMaKwPvfYjZk8CwLYRjZzMpai8WnY6uMQce1wTmzDIk3wSkWQAQ/gApGj+5C3WMLU7H4YTBQYmuLonKyk/qwEwe2G+//XZuuukmNm3adNzXQmZ6Yg8BgUCA1tZWEhISWLZsWbzgVqfTTTH/nI3jxepTCgoKaG5uxuFwoNFo4sW3Vqv1hE05J9Pf309fX99xif0dupw0MTHB8PAw3d3dcS83q9VKIBCgubmZpUuXzrr6c4zY7JHVauXWW2/lnnvu4XOf+9ycHEtGRmZ2OSUJT0yJdGxsDK1Wy/j4eLT1tbiYLLMZEQhE15D0+iMuY82Y9HTCkQgNLS2oU1KoysyMig1qNEy43Qw4HAzv2EGSQsFobhqGtExGRxPo7JRQKmHBAoFKBQYDDA9Db2/097m5Ue2ehN4u+oMWvCoNTmMIfWSchZKBiBD0t7aSFYkgWa24QiFEcjL67GzEwSdTUVYGkkTSwc9pt9vjT9iSQoFepaI4PR3J58NnsdDR389oZye2xr+wcFURCaZ8CoJuFF1DhEKjKNMyaKvpISwi6KWE6MxSaSlSYyOipw9hNKJZmU6mUSITQTAIwWC8Yz+Oz+ejtraWtLQ0vvzlL3PnnXdy/fXXn/i1kJkWIQT19fXk5+eTkZGBx+OhoaGBvLy8ab2kZoNwOEx9fT06nY5Vq1YhSVJcRDBmy6DT6eK1NMczuxTzWJuYmJgV3y1JkjAajfHlMJ/Ph91up6GhAafTSUZGBj6fj6SkpFnv+vJ4PPFi7ptuuokHHniA9evXz+oxZGRk5o6T3kvs9XoZGhrCbDaj1+vRaDR4vV7Wrl1L5sKF4PUitbZGM4tjlNs/HAGdjua6OowaDSUeD8p33kHq7kZhMJCSkMDirCxKV6/GetFF+BNUvPdeK9u3N+J295Od7SYxURAOQ2NjdDbEYID8fBGte2lvx9k5ii25CHfIhkEzzKKqlSgqKnCFwwzt3cuBzk6EXo8zLQ1NYiIJKlXU2DQSic5eHUx2Yt/HwMBA9MQPTvtLnZ0wMYHX5UItSWSmp5OpVJJalMNEtpq9gVZqmz+kqbaW9oFRJjSpJOnMDDS5OPDRMIM2BaGIAkZGkfS6qLXGQRISIClpal7p9XrjMzs333wz99xzz6wkOzfffDNpaWnT+mtB9OZ41113sWDBApYuXUpNTc0JH/N0wOl0Yrfbyc7OJiMjg9HRUfbt20dpaemcJTs+n4/du3fH3dVjs0cxEcHS0lLWrFlDYWEhXq+XPXv2sHv3brq7u3HHWhaPQqy13e/3U1lZOSfWChqNBqVSiSRJrF27loyMDIaHh9m5cye1tbX09vbi8/lO+DixAnKTycSmTZt46KGHZiXZkWNCRubkcdJneNRqNbm5uTgcDtLS0nA6nR9LybtcSDYbQqtFLFt2XEaWh+JyuWhsa6OkqAhjZiZ0dyP19yMyM6MJRyCA1NICej3akhJygkqMxhys1gBarQ27vYUPPvCQlGSmtTWb0lItRUUS+/ZJCAGS2Yx5SZChxEbMKWryUlKQ9HoiBQUY/H4yAgGGPvoIdUoKE3o9lpQUGBtD6u5GhELxoqBgMIgQgoKCAsbGxmfghNMAACAASURBVDCZTNGn2ECAsM2GNyeHuh07aHO7+fSyZXhbWvCYzRQCGrMZ6eKLSdq9m9HhA2QkRShNbsEQNBORkrF1eTFnGInkZ+DzpeFwRLvMYg/AsbwLPn6KNZvNbNq0iR//+MdcccUVJ3wdADZt2sQdd9zBDTfcMO3rr7/+Oq2trbS2trJz506+9rWvsXPnzlk59nxGq9WSnZ3N6OgoLpcLt9vNihUrZsXqYDqms4mYjslLX0VFRfj9/njRs9/vJzU1FavV+gmtJoi200/Wv5mrmq+YWery5ctRqVRoNJpPFD43NDTEVZStVuuMCp8nE6sLSklJ4eabb+bnP/85n/nMZ2bl/OWYkJE5eZz0hOeFF17giSeeoLi4mM9+9rNkZWXR3NxMWloaltRUEsrLo4KAk2YhjpdYkeSS1avR6/VIXV1gMBBZuTK6XObxIO3diygqQpSWxu/62dmC/HwVkEV6ehaBQISRkTE6OuwMDNiIRNT09+egVBoIpCUwYm9kwcqVZGedA8EgIjU1eiynk6zFi/HX1dFRU0MgPZ2stWsJdnWhTExEYbUSiUTiwnGjo6MUFhbS1dXFyMgIRqMRjyQx2NdHfThMf0sLxStXkpCejjkvD0tREcrublL9fkRBAZx3Hu6+PtIGBsixHaBviYFA715Uahv2glLMeal4ehR0dERndJKTwWCILmtJUjQ53LdvH6mpqdx000385Cc/4ZJLLjnh6xDj/PPPp6ur67Cvb926lRtuuAFJklizZg3j4+MMDAzM2SzHfOHJJ5/k5ZdfJicnh6uvvhqLxUJLS0t8KelEvMkO5Wg2EUdCrVZP8c+arNWUnJwcVyWORCLU1dWRm5s7Z9dOCEFHRwdut3taYcQTKXyeTCw5TE5O5uabb+a3v/0ta9eunbXPIceEjMzJ46QnPP/+7//OX//6V/x+Py+//DK9vb1ceeWVrF27lgMaDSqVKqqe7PWSlJR03Mfp6elhaGjo4yLJiQkYG0Pk5CCNjkZb3dPTEWYzWK1R3y0gI0PEO5YGBmDPHgX5+RKFhWYKCy3k5+cRCLipq/PQ29uCLa+XArUaU0IC0shIdOaIqH2F1N8PPh8Fa9Yw1t7O/q4utGNj9KWkkPHpT5MpSYSCQTo6Oujr68Pn801RcwVIyMnBMTBAkk7H8tWrKdPp0Ofnf5wQer0IhwORkoIrGMSv05FvMmEIBtFrtYiCArxjY4w2N7N3cBCQSEpKIzHRiterZWJCwm6XsFgm2LdvH0ajkZtuuolf/vKXXHDBBcf9/R8PfX195Obmxn/Oycmhr6/vjB/cb7/9dt58802EEDz33HPYbDauuuoq1qxZQ3d3NwkJCXFF8ePtoDoem4gjcagjeSxp7+zsxOv1kpmZOaXtfDYRQtDU1ARARUXFjGZrZlr4rNVq4+83Pj5OU1MTWq2WW2+9lWeeeYaqqqo5+UyH42yNCRmZueCUtKU//fTT8WTG5XLx5ptv8uKLL1JTU8N5553HpZdeGn9SjCnFxtWTj0LMJDAYDLJ8+fKP6wa83uiWkRHtNR8Zgbw8WLBgyr+Paa253dGaHZcL8vJEvMbF4ZAYH09m8WIFHk8HZWXLYe9eBv75T3yShGbRIlJzcjDqdKgGBhBZWVBeTnlhIdluN8p9+5AWLSLp4ICVkJDAwoULCQaDWK3WKdP/DoeD1v5+khcvpjgxEbfdTrLXi5Ak8HgIu9201NVhDodJS0rCnpSE0ufDlJ1NJDs7KizY2YkuHCbpggvINpniYnR2extut5ukJBN5eVr6+nowGAzccsstbN68mU996lOzcLWPjekcw8+G9ndJknjppZfiMTExMcHrr7/O73//e/bu3cu6detYt24dg4ODCCHiMaHX62f0/cyWTcSRzt9gMBCJRLDZbCxduhSv10tzczOBQGDK0teJXs+Yj5tOp6OoqOi43u9whc+tra14vV5MJhMajYb+/n7UajVf+cpX+MMf/kBlZeUJnfvxcLbGhIzMXHBKurQmz9zo9XquueYarrnmGkKhEP/617+orq7mH//4BwsXLmT9+vVxafejKcXG6gaMRiMlJSVTBwalMlqd6/cjkpJQHDgACgWisnJKxW4gEG077+uLdmLl5Qn0evD7o3XGExMSGs0YoVAzVVVLo8sC5eWkabWEc3KY6OlhpL6eTp+P9I4O1BkZGPV6Ek0m0nw+pIQE7AeLkyE6eEUiERISEkhNTY2fcygU4u23345bCjTs3o1ZoUDKyEDo9Ujt7ST09KDR6RhwudDb7YyNjWE1GJDy88FmA5Mp2tafkRFVVuaTYnQ9PT10dnbwxBNP8NFHH7Fp0yaWL18+F5f9qOTk5NDT0xP/ube3l6ysrFNyLiebyTFhMBi47rrruO666wgEArz77rts2bKFd999lyVLlnDFFVeQl5dHMBiMJxOHayGfK5uIQxkaGqKrq+vjejwgNzeXcDjMyMhI3JIhtpQUMw89FsLhMHV1dfHPMltoNJr4Ul0kEqG7u5vu7m5+9rOf0dDQwNe+9jXKy8tn7XjHwtkcEzIys828cEuPoVKpuPDCC7nwwgvjT3LV1dVs3ryZpKQkNm7cSEVFBeFwGL1eP6XGISZXH2vr/QR6ffS/bjf4fIiUlOjsyyE3gPZ2iYEBiexsgdUqsNmixcmJiZCeDuPjQ0A3VVXLP9YTychAaLUo9HpMkQgmhwOhUuFTKBj2++nYswdVKITFbEadkkJ/by9ev5+cg1YZDocjXiAK0WSnrq6OsrIyvF4vXdu3g91OSmlp1AdMoYie0OgoOStXsn/PHhqamlBpNFjU6qgDvNOJ1NWFMBiiSc80xOoB9Ho93d3dPPXUU3R1dc1qzcixcNVVV/H444/zhS98gZ07d2I0Gs/6qfvExEQuvvhiLr74YiKRCLW1tVRXV/OrX/0Ko9HINddcQ1lZGaFQ6BPJxMmwiYBo4XBMKPHQpTKlUjnFpiS2lNTV1RVfqrNYLEddvo55b+Xk5Mzp38TIyAjDw8OoVCqGh4d57rnn6OzsnJMOs5kgx4SMzOwhTTdlOokjvniyEELQ19dHdXU127ZtY3R0NF7jkJCQQCQSIRgMUlpaesSBXaqri4oAejyQkoIoLPzEPo2NH2vvxDR3yssFarWgra0Dl8vJ0qUVUwfASCRaI6TRRBON3t5oEbTPF5cwDjQ2YjMYGHa56OnpQQhBRUUFhYWFU6boY8rGeXl5ZGRk0NfXx/4//xmj10vVTTchpaVFbTDGxpDa23Hl59M1PMyH//gHK5YupfKgWKPk8SA1NCCWLkVMY/8wPDxMR0cHQgjuvvtuXn75ZUpLS0/8Yh2B66+/nnfeeQe73U56ejo/+tGPCAaDAHz1q19FCMEdd9zBG2+8gVar5bnnnmPlypWT32I+zOXPm5jo6upi69atbNu2DZfLxcaNG6mqqkKhUCCEIBgMUl5ejtlsnrNzaG1txe/3H5fbudfrxW63Mzw8TDAYjNfRHNpF5fP5qKuro6ioaE4Tt6GhIQ4cOIDH4+E73/kOW7ZsoXCaMWI2OUNiQkbmtOC0SHgOZXx8nO3bt7N161Z27txJdnY29957LyaT6Yg1DlJLC1JLCyI/P5qQTDOT0dQkkZAAxcUCmw1qahRkZ4eRpP2oVMq4Q/UUwuFot1dGBkgSUl8foqgouowkBFJTE4RCUZHBg55Ju3fvpq+vD7VajcfjoaKiguzsbBoaGli4cGH8JhUKhXj9d7/D4HBw3l13oeruZrinhxGTCamxEX9mJo5IBPvu3Xx63TosKhWkpiLS06OzV9MUp8Z8mQKBAPfeey//+7//y8KFC2f3Is0N82Fwn5cxMTIywmuvvcbWrVupqamhqKiIu+++O26vcDyu6UciHA7T0NBAUlLSFB2f4yUUCsVnV5xOJwaDIV6kvX//fhYvXjynHlX9/f309/czPj7Ogw8+yNatW6cUC89j5kNMyMicFigffPDBI71+xBdPFRqNhoqKCi644AL27NnDV77yFd588002b95MX18fRqORUChEd3c3Ho8HhUKBWq1GikSiy1kLF0breaZhZERCkqJNW729EomJYSYm6klLS6a4uHj6gV2hQBobQ/J6kcbHkez2aLKTnAzNzUjj41E/sINtwAqFIj4t7fP5SExMJBKJ0NXVhVqtxufzMT4+ztjYGK2trQz29WFSKimorESamCBYU0PIaMSYmIg5IwPF6Chau52CdetQBALRYiOLZVr/sYGBAXp6euJPsVu3bqWoqGj2Ls7c8qNTfQLM05jQarVUVlayZs0ampqauOGGG3j99dfZvHkzNpsNo9FIIBCIz2AoFAo0Gs1xJSqx5SWLxUJhYeGsJFAKhQK9Xk9aWho5OTkkJiYyMDAQd05PTExErVbPyXJrb28vQ0ND2Gw2Hn74YV599dXTyRR3PsSEjMxpwWk5w3M4IpEINTU1bNmyhTfffJPU1FQ2btxIWVkZwWBwRgWTLS0SMWHWYDCIy1XHokXZ09cFTULq6kLaty86e5SfH60ZCgSQ/vlPyM2Nzigdgs/nY2BggD179uB0OtmwYQOJiYk0NDTQ2dkZP4dktZrzTSYMFRVIHR0oOjuJrF8PkkTY7ab+jTdIVSjIvewyJKcT6cMPEZWViJKSKcfr6+tjcHCQ0dFRfvzjH7Nt27ZZG9jfeOMNvvGNbxAOh7n11lu5//77p7z+u9/9jnvvvTd+vDvuuINbb731WA8zH55mT6uYCIfD7Nq1i+rqav72t7+RlZXFhg0bKCkpIRAIYDQa4zExkzqVWK3cXC8vxZzbly5dChBf+gqHw/Glr5l2bh6JAwcOMDIyQnd3N7/61a945ZVXZu1znUUxISNzWnBGJTyTiQmTxWocvF4v11xzDStXrkSSJNRqdVxHZLKi7fg4tLVJRCIefL59LFmygNTU1KMfcGgIxa5dRFatilY3A1J7O7jd0aWswyRYMcfq4uJi8vPzP1G78N5776FQKDC4XEhKJYGuLpYXFKBeuxaSk7G//z49Q0Mszs5Gm5MTrU8aGEBUVcVnlCCqSzQ8PMzAwAA//elPeeWVV46axM2UcDhMSUkJf/vb38jJyaGqqooXX3yRsrKy+D6/+93v+Oijj3j88cdP5FDzYXA/rWOipaWF6upqXn31VSKRCNdcc028K0+j0cRjYjqDz5gIX1lZ2Zxp7MDHS66VlZWfUJsOBoOMjIxgt9txOp3xhC01NfWYC4tjHl8tLS089dRTbNu2bWaxPgPOspiQkTktmFddWrOJJEkUFxfzrW99i29961vY7XZeffVVHn/8cTo6OvjsZz/L+eefH/etitU4GI06cnPHaG1tYcWKcvSx7q6jodcjcnKiRcrBINjt0ToaIQ6b7AwNDdHd3c3y5cvp7u4mGAxOqaVxOp2kpqayePFiNImJjL33Hh8KwVv19eQqlZiKixkbHCQpJwdtamp0KSschknLZwDd3d2MjY3R3d3N448/zuuvv47FYjn+L/cQdu3axYIFC+JLY1/4whfYunXrlMFd5tQjSRKLFi3ivvvu49vf/jY2m41t27bxi1/8gp6eHq644grOO+88+vr6UCgUWCwW0tLS0Gq1cdXy41FoPhb6+voYGBhg+fLl04ojJiQkkJGRQUZGBpFIBIfDwfDwMO3t7Yd9iDmU2MNQzEblhRde4LXXXpvVJE6OCRmZ+ccZm/AcisViYdOmTWzatAmv18tbb71FdXU1O3bsYPXq1Vx22WVkZmYyMTGBQqFg8eLF6A6qL88IrTY6k6NWg9uN5HQiDIaP2+EPIVY3sGLFClQqFf39/bhcrriPFsDY2BgajSbasuv1YtbpqFq/nu6mJpIzM/G4XLS3tmIAkkIh0g8cIKm8PK72DNDR0YHL5aK5uZlnn32W7du3H9E/6XiYTg12Or+fP//5z7z77ruUlJTwi1/84nQpCj0jkSSJ9PR0brvtNm677bZPCICuXbuWSy+9ND6TolQqKSsrOyH186PR1dXF2NjYVMHQI6BQKDCZTPG/Z7fbjd1uZ9++fVNESyc3L8Q6y4LBILt27WLr1q289tprM3+wmSFyTMjIzD/OmoRnMklJSaxfv57169cTDofZsWMHW7du5eWXX8ZkMvGNb3wDnU5He3v7zGscJCleJCwNDESFDqdJmIQQdHZ24nQ6WbZsWfw9y8rKaG1tpauri8TExLiOSlxzw+sFhYJUqxWnEIzu34/GZKKoqIjSc87BEQwy0NODva8PXUICFouF8fHxeJv7Sy+9xKuvvjonSxEzUYNdv349119/PWq1ms2bN3PjjTfy97//fdbPReb4mE4AdMuWLfz5z38mOzub22+/HZ1OR2tr6wktI02HEIK2tra4q/rxKkHHvLPy8/MJBoPY7XY6Oztxu92kpKRgsViw2+0AvPvuu7z11lu88sorc5LEyTEhIzP/OCsTnskolUrOO+88Fi9ejN/v55ZbbmH79u0888wzKBQKNm7cyPLly+no6DhqjQMuV7RuJxyeVuwvVkMRCoWoqKiYMrArFAqKioqor6+npaWFtIMu6vGagoPLCGGnk1Slkr7ubnptNpbl5aEOh7GWlGAtKSE/EmF8fJyWlhZaWlp48skn8fl8vPLKK3NWdzETNdjJWjC33XYb991335yci8yJExMALS4uJiEhgS9+8Yu88sorbN68GY1Gw4YNG6isrKStrQ2tVhs1/rVYjsufK2Z7oVKpKC8vnzUl6ISEBDIzM8nMzCQSiTA2NkZzczP79u3jj3/8I5FIhG3bts3ZjJUcEzIy848ztmj5RBFCMDg4yLZt29i2bRv9/f1xk9NYO28s+YkvfdntKN55B7FoEaKiYsr7RSIR9u/fj1qtPqJuicPhwOPxMDQ0hCRJVFZWRp+AW1tR7N2LT6slcWQER1sbo5WVnFdWhm5wELF8ORiNcWNFSZJ46623ePvtt7noootIT0/nxhtvnJPvKhQKUVJSwttvv012djZVVVW88MILU+T4Jzs8b9myhZ/85Cd88MEHx3qo+VCgeVbHxKECoOvXr+dTn/oUiYmJUwxFZ5JIhMNh6uvrMRgMUzzkZptIJBLXDPrTn/7E3r17WbNmDQsWLODaa6+dk2OeZTEhI3NaICc8M8TpdPLGG2+wbds29uzZw6c//WkuvfRSLBZLXCU2zWLBoNEgKZVxhWWIDux79+7FZDJRUFBw1GMJIWhubsbpdMbdmTs6Okg6cADNxASJycl09fSgKCyktKAAxY4diKoqIgUFNDQ0oFar2bZtGzU1Nbz00ktHLOCcLbZv387dd99NOBzm5ptv5nvf+x4//OEPWblyJVdddRXf+c532LZtGyqVitTUVJ544gkWL158rIeZD4O7HBMHmSwAun//fi688EIuvvhiTCZTvH08LS1t2vbxmH1Kenp63GJlLohEIuzbt4/k5GSef/55BgcHee65506KfcpZFBMyMqcFcsJzHASDQf75z39SXV3NO++8w6JFi1i/fj1FRUX4/f4pNQ6RSIS6ujoyMzOPSfPG5XLR1NREXl5efHlL6uxE2rED99KlNDU2klNYSJrBAD4f4cWLqW9qQqvV8vLLL9Pe3s4f/vCH41pmmMfMh8FdjolpCAQCvPPOO2zZsoV//etfVFRUcMUVV5Cfn4/f759i/BsKhaitrSU/P5/0gxIOc8HkB40nnngCj8fDk08+ecp8seaI+RATMjKnBbOe8PzP//wPDz74II2NjezatetQ35c4RxPlOl2IRCLs3buX6urquN/Nxo0bWbp0KV6vl1AoRHZ2NkVFRcecfHR0dOBwOCguLo5aBPh8SDt30uPzMeJ2U2G1otTrCWdlsXdgAKPRyLPPPsvw8DDPPvvsKTMBnUPmw+Aux8RRmE4AdMOGDSxZsgSXy0U4HCY3N5eCgoI5S8hjzupms5mf//znJCYm8utf//q4C6LnMfMhJmRkTgtmPfqXLFnCX/7yF84///zD7hMOh/n617/O66+/zv79+3nxxRfZv3//bJ/KSUGhULBs2TIefPBBduzYwXPPPYcQgu9+97vccMMNvP/++9jtdvbs2cPu3bs5cOAAXq93Ru9dUFCARqOhs7MTn88HGg3hykpGXS5MbjeqpiYiKhW1vb2YTCZ+85vf4HQ6Z3XK/o033mDRokUsWLCARx999BOv+/1+rrvuOhYsWMDq1avp6uqaleOeSZyNMbFy5Uoeeughdu3axW9/+1u8Xi/f+ta3uPHGG9m9ezd2u52amhpqamro6emJ/n3PEqFQiD179mC1WnnkkUcwGo2zmuzIMSEjc3oy61MAM3HcPlNFuSRJIj8/n7vuuouxsTEeeughent7efLJJ2lqamLdunVcdNFF2Gy2o9Y4wMedW83NzTQ2NlJRUcF4KEQ4Px+zSkUwHGbv2BiWggJ+9rOfodVqefzxx2dtYI/dhCerxV511VVTrtMzzzyDyWSira2Nl156ifvuu4+XX355Vo5/pnC2x0RMAHR4eJj169fT0tISFwC99NJLufDCCxkaGjqsds6xEPP5yszM5Ac/+AElJSX86Ec/kmNCRkbm1LSlz1SU63TmgQceiP//l770Jfx+P3//+9/ZunUr//rXv6isrOTKK69kYmLiEzUOkwdntVpNYWEhbW1tuN1uRkZGUGu1JC5cSE1tLZlFRfz4xz8mKyuLRx99dFan7GdyE966dSsxA9rPfe5z3HHHHQgh5qzj5kzlbIiJRx55BIBzzz13igDoli1b2LFjB6tWreLyyy8nKysLv99PamoqVquVlJSUGf1dxzSnsrKyuO+++zjnnHP43ve+N6t/i3JMyMicvhxXwnPxxRczODj4id8/9NBDXH311Uf99zMR5TrTUKvVXH755Vx++eVEIhE++ugjtmzZwn//939jtVrZsGEDZWVltLS0oNPpSEtLw2w2k5CQQHJyMkuXLiUQCOByucjMzGTPnj1kZmbyve99j7KyMh544IFZr0+YyU148j4qlQqj0cjIyMisWlecDsgxcewcTgD0rbfeIicnZ4rJ6dGMf/1+P7W1tWRnZ/PNb36Tz3zmM/zHf/zHrH+HckzIyJy+HFfC89Zbb53QQWciynUmo1AoWLVqFatWreLhhx+mra2N6upqvv/97xMIBNiwYQNVVVV0d3eTkJAQ1zZRKBSkpqbS3d1NXl4e99xzD2vWrOH++++fk5vjTG7CZ+ONejrkmDgxYgKg5513XlxLqrq6mmeffRZJkuImpzEl8smeWT6fj9raWnJzc7n99tu5+uqr+frXvy7HhIyMzBROyZJWVVUVra2tdHZ2kp2dzUsvvcQLL7wwZZ/R0VGuu+46urq6KCgo4E9/+tO0HlBKpZKKgyJ/eXl5bNu27aR8htlCkiQWLlzIvffeyz333IPdbueVV17hl7/8JV1dXVx22WVccMEFDA0NEQgE8Pl8+Hw+/uu//ovPfvaz3H333XM2mM7kJhzbJycnh1AohMPhmDXH6bMJOSY+RpIkSktLKS0t5f77748LgP7sZz+jv78/bnI6MDBAKBTC6XQiSRK33XYbX/rSl7jlllvkmJCRkfkkQogjbcfMX/7yF5GdnS0SExNFWlqauPTSS4UQQvT19YnLL788vt9rr70mFi5cKIqKisR//ud/fuJ97r33XvHII48IIYR45JFHxLe//e1pj6fT6Y7nNE8L3G63qK6uFjfeeKNYuHChKCkpEY899phYsmSJyM7OFo899ticHj8YDIrCwkLR0dEh/H6/WLp0qaivr5+yz+OPPy6+8pWvCCGEePHFF8XnP//5uTylo/29noztmJFjYvaYmJgQf/rTn8SXvvQlsWDBAlFaWioee+wxsXjxYpGXlyeeeuqpOT2+HBPyJm+n73a0HU4ZJSUlor+/XwghRH9/vygpKZl2vzN5cJ/MtddeK37729+Kf/u3fxN33nmn8Hq9oqOjY86PO91N+Ac/+IHYunWrEEIIr9crPve5z4ni4mJRVVUl2tvb5/J0TnnAzOWHOxpyTHxMJBIRV155pXjyySfF5ZdfLn7wgx8Ij8cjurq65vzYckzIm7ydntu8VVpOSUlhfHw8/rPJZGJsbOwT+6lUKpYtW4ZKpeL+++9nw4YNJ/M0TxpCyF0ezA+RNTkm5glyTADzIyZkZE4LTqkU75E6W2bKgQMHyMrKoqOjg3Xr1lFRUUFxcfFsnua8QB7Yzw7kmJg5ckzIyMgcC6c04TlSZ0t6enrcTXhgYCDuJ3UosYLBoqIiLrzwQvbs2XNGDu5zxdlSCHu6IMfEqUeOCRmZM5N5ayxz1VVX8fzzzwPw/PPPT6tlMjY2ht/vB8But/Pee+9NEQCTJeCPzqOPPspFF11Ea2srF1100bTfE0Q1U2pra6mtrZUH9lOEHBMnBzkmZGTOUI5S5HPKsNvtYt26dWLBggVi3bp1YmRkRAghxI4dO8TNN98shBDivffeE0uWLBFLly4VS5YsEU8//XT834dCIVFUVCTa29vj3RQNDQ1TjvGb3/xmSjfFtddee5I+3fzhNCuEPeVFb3P/EQ/P4WLiww8/FLfccosQQo6J2UCOCXmTtzNzO9oO847Ozk4xMTFx1P3ef//9ePuvEEI8/PDD4uGHH56yz6WXXiref/99IUS03dRsNotIJDK7JzzPMRqNU35OSUmZdj+lUinOOeccsXr1arFly5aTcWrTccoDZu4/4twhx8TMkGNC3uTtzNxOaQ3P8WCxWNDpdEfdT5aA/xi5EFYG5JiYjBwTMjJnH6ddwqPX62e0nxCyBHwMuRBWBuSYmIwcEzIyZx/ztmj5RDkWCXjgrJWAn41CWJnTAzkmZoYcEzIyZyZHEx48bZEkSQW0ABcBfcCHwP8nhGiYtM/XgQohxFclSfoCcI0Q4tpTcsKnCEmSzMCfgDzgAPB5IcSoJEkrga8KIW6VJOlc4P8CEaJJ8mNCiGdO2UnL8tuxCgAAAWNJREFUHBdyTMwMOSZkZM5MztiEB0CSpP8DPAYogWeFEA9JkvRj4CMhxDZJkjTAH4DlwCjwBSFExxHe7zLglwff72khxKOHvL4J+C+iNxOAx4UQT8/yx5KROW7kmJCRkTlbOaMTntlEkiQl0afjS4Beok/H1wsh9k/aZxOwUghxxyk5SRmZk4gcEzIyMqcTZ2wNzxywCmgTQnQIIQLAS8AnF/dlZM4e5JiQkZE5bZATnpmTDfRM+rn34O8O5d8kSdorSdL/SpKUO83rc44kSZ+XJKlBkqTIwbqDw+13mSRJzZIktUmSdP/JPEeZMwI5JmRkZE4b5IRn5kzXm3voeuArQIEQYinwFvD8nJ/V9NQD1wDvHm6Hg8sRvwEuB8qA6yVJkttMZI4FOSZkZGROG+SEZ+b0ApOfTnOA/sk7CCFGhBD+gz8+BZxzks5tCkKIRiFE81F2k5cjZE4UOSZkZGROG/5/t15ePVZtFOkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#function for plotting level surfaces in 3D\n", + "def plot_implicit(fn, color, bbox=(-1,1)):\n", + " ''' create a plot of an implicit function\n", + " fn ...implicit function (plot where fn==0)\n", + " bbox ..the x,y,and z limits of plotted interval'''\n", + " xmin, xmax, ymin, ymax, zmin, zmax = bbox*3\n", + " A = np.linspace(xmin, xmax, 100) # resolution of the contour\n", + " B = np.linspace(xmin, xmax, 15) # number of slices\n", + " A1,A2 = np.meshgrid(A,A) # grid on which the contour is plotted\n", + "\n", + " for z in B: # plot contours in the XY plane\n", + " X,Y = A1,A2\n", + " Z = fn(X,Y,z)\n", + " cset = ax.contour(X, Y, Z+z, [z], colors=color,alpha=.2,zdir='z')\n", + " # [z] defines the only level to plot for this contour for this value of z\n", + "\n", + " for y in B: # plot contours in the XZ plane\n", + " X,Z = A1,A2\n", + " Y = fn(X,y,Z)\n", + " cset = ax.contour(X, Y+y, Z, [y], colors=color,alpha=.2,zdir='y')\n", + "\n", + " for x in B: # plot contours in the YZ plane\n", + " Y,Z = A1,A2\n", + " X = fn(x,Y,Z)\n", + " cset = ax.contour(X+x, Y, Z, [x], colors=color,alpha=.2,zdir='x')\n", + "\n", + " #set plot limits\n", + " ax.set_zlim3d(zmin,zmax)\n", + " ax.set_xlim3d(xmin,xmax)\n", + " ax.set_ylim3d(ymin,ymax)\n", + " \n", + "#plot each level surface individually, then together\n", + "\n", + "#f\n", + "fig = plt.figure(figsize=(10,20))\n", + "ax = fig.add_subplot(631, projection='3d')\n", + "ax.scatter(*[roots[:,i] for i in range(3)],color='g')\n", + "plot_implicit(f,'r')\n", + "ax.set_title('$f(x,y,z)=0$')\n", + "\n", + "#g\n", + "ax = fig.add_subplot(632, projection='3d')\n", + "ax.scatter(*[roots[:,i] for i in range(3)],color='g')\n", + "plot_implicit(g,'b')\n", + "ax.set_title('$g(x,y,z)=0$')\n", + "\n", + "#h\n", + "ax = fig.add_subplot(633, projection='3d')\n", + "ax.scatter(*[roots[:,i] for i in range(3)],color='g')\n", + "plot_implicit(h,'k')\n", + "ax.set_title('$h(x,y,z)=0$')\n", + "\n", + "#together\n", + "ax = fig.add_subplot(634, projection='3d')\n", + "ax.scatter(*[roots[:,i] for i in range(3)],color='g')\n", + "plot_implicit(f,'r')\n", + "plot_implicit(g,'b')\n", + "plot_implicit(h,'k')\n", + "ax.set_title('$f,g,h = 0$ and roots')\n", + "\n", + "#just the roots\n", + "ax = fig.add_subplot(635, projection='3d')\n", + "ax.scatter(*[roots[:,i] for i in range(3)],color='g')\n", + "ax.set_title('roots')\n", + "ax.set_zlim3d(-1,1)\n", + "ax.set_xlim3d(-1,1)\n", + "ax.set_ylim3d(-1,1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Four Variable Optimization\n", + "\n", + "As a final multivariate example, we solve the following system.\n", + "\n", + "$$\\cos(x_1) + x_4 = 1$$\n", + "$$\\cos(x_2) + x_3 = 2$$\n", + "$$\\cos(x_3) + x_2 = 3$$\n", + "$$\\cos(x_4) + x_1 = 4$$" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 571\n", + "Methods used were ['constant_term_check', 'quadratic_check', 'Base Case', 'Macaulay', 'Too Deep']\n", + "The percent solved by each was [30.2977 69.0018 0. 0.7005 0. ]\n", + "CPU times: user 12.6 s, sys: 563 ms, total: 13.1 s\n", + "Wall time: 12.8 s\n" + ] + }, + { + "data": { + "text/plain": [ + "(1, 2.0339285811132868e-13)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#functions\n", + "f1 = lambda x1, x2, x3, x4: np.cos(x1) + x4 - 1\n", + "f2 = lambda x1, x2, x3, x4: np.cos(x2) + x3 - 2\n", + "f3 = lambda x1, x2, x3, x4: np.cos(x3) + x2 - 3\n", + "f4 = lambda x1, x2, x3, x4: np.cos(x4) + x1 - 4\n", + "\n", + "#domain\n", + "a = [4,3.5,2,1.5]\n", + "b = [4.5,4,3,2]\n", + "\n", + "#solve and time\n", + "%time roots = yr.solve([f1,f2,f3,f4],a,b)\n", + "\n", + "#number of roots and maximum residual\n", + "roots.shape[0],np.max([np.abs(f(*[roots[:,i] for i in range(4)])) for f in [f1,f2,f3,f4]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Univariate Functions\n", + "\n", + "The `yr.solve` method can also be used to quickly find the roots of a univariate function. In this case, `a` and `b` can simply be entered as floats, and the `funcs` does not need to be a list.\n", + "\n", + "As an example, we find the zeros of $f(x) = \\sin(e^{3x})$ on $[-1,2]$." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Percent Finished: 100% \n", + "Total intervals checked was 6\n", + "Methods used were ['constant_term_check', 'quadratic_check', 'Base Case', 'Macaulay', 'Too Deep']\n", + "The percent solved by each was [ 0. 0. 0. 100. 0.]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 847 ms, sys: 27.7 ms, total: 875 ms\n", + "Wall time: 582 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "(128, 6.925292651413953e-11)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#define the function and search interval\n", + "f = lambda x : np.sin(np.exp(3*x))\n", + "\n", + "a = -1\n", + "b = 2\n", + "\n", + "#solve and time\n", + "%time roots = yr.solve(f, a, b, plot=True, abs_approx_tol=1.e-10)\n", + "#show the number of roots and maximal residuals\n", + "roots.size,np.max(np.abs(f(roots)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polynomials\n", + "\n", + "When a function in a system is a polynomial, it may be useful to pass it in as a YRoots's `Polynomial` object. `Polynomial` objects may be more cumbersome to create, but they have a special `evaluate_grid` method which allows for faster Chebyshev-approximations.\n", + "\n", + "If the system only includes `Polynomial` objects, it may be preferable to use the alternative `yr.polysolve` method which does not rely on Chebyshev approximations. The gains in speed depend on the degree and dimension of the system. Heuristically, these methods are faster than `yr.solve` for lower degree polynomial systems, but **these methods are only stable for roots where each coordinate has absolute value $< 1$.**\n", + "\n", + "We demonstrate how to create `Polynomial` objects and how to use `yr.polysolve`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### YRoot Polynomials\n", + "\n", + "The two types of `Polynomial` objects are `MultiPower` and `MultiCheb`, corresponding to multivariate polynomials in the power basis and Chebyshev basis respectively. \n", + "Polynomials in $n$-dimensions are represented by an $n$-dimensional array of coefficients. For a system with three variables, the $(i,j,k)$ spot in the coefficient tensor corresponds to the coefficients of $x^iy^jz^k$ in the power basis or $T_i(x)T_j(y)T_k(z)$ in the Chebyshev basis. It is probably easiest to construct this coefficient tensor by initializing a tensor of zeros and then setting each nonzero coefficient to the correct value.\n", + "\n", + "For example, $f(x,y) = 5x^3 + 4 xy^2 + 3x^2 + 2y^2 + 1$ would be initialized as \n", + "```python\n", + "coeff = np.zeros((4,4)) #4x4 matrix because it's a degree 3 polynomial\n", + "coeff[3,0] = 5\n", + "coeff[1,2] = 4\n", + "coeff[2,0] = 3\n", + "coeff[0,2] = 2\n", + "coeff[0,0] = 1\n", + "f = yr.MultiPower(coeff)```\n", + " \n", + "and $g(x,y,z) = 3T_1(x)T_2(y) + 5 T_2(z) + 2$ would be initialized as\n", + "\n", + "```python\n", + "coeff = np.zeros((4,4,4))\n", + "coeff[1,2,0] = 3\n", + "coeff[0,0,5] = 5\n", + "coeff[0,0,0] = 2\n", + "g = yr.MultiCheb(coeff)```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Polysolve\n", + "\n", + "The function `yr.polysolve` has many options for polynomial rootfinding, but the default parameters are the most robust for most systems. The syntax is\n", + "\n", + "```python\n", + "yr.polysolve(polys)```\n", + "\n", + "where `polys` is a list of polynomial objects. All of the polynomials must be represented in the same basis. For systems that come from Chebyshev approximations, it may be better to add the optional parameter `MSmatrix=-1`.\n", + "\n", + "As mentioned above, Polysolve is **only stable for finding roots where each coordinate has absolute value $< 1$.** Other roots near this region may be accurate, but how far away you can go before loosing accuracy depends on the degrees of the polynomials. By default, the system returns all the roots. To return only the roots which are guaranteed to be computed stably, use the optional parameter `return_all_roots=False`.\n", + "\n", + "Additionally, while `yr.solve` only finds real roots, `yr.polysolve` finds complex roots as well. \n", + "\n", + "Below, we find the common roots of \n", + "\n", + "$$f(x,y) = y^2 + 3xy - 4x +1$$\n", + "$$g(x,y) = -6xy -2x^2 + 6y +3.$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 3.25 ms, sys: 991 µs, total: 4.24 ms\n", + "Wall time: 3.27 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "(4, 2.4868995751603507e-14, 5.861977570020827e-14)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#direct construction of polynomials with coefficient tensors\n", + "f = yr.MultiPower(np.array([[1, -4, 0],[0, 3, 0],[1, 0, 0]]))\n", + "g = yr.MultiPower(np.array([[3, 0, -2],[6, -6, 0],[0, 0, 0]]))\n", + "\n", + "#solve and time\n", + "%time roots = yr.polysolve([f,g], return_all_roots=True)\n", + "#show the number of roots and maximal residuals\n", + "roots.shape[0],np.max(np.abs(f(roots))),np.max(np.abs(g(roots)))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/lib/python3.7/site-packages/numpy/core/_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot f,g and roots\n", + "x = np.linspace(-5,5,1000)\n", + "y = np.linspace(-10,10,1000)\n", + "X,Y = np.meshgrid(x,y)\n", + "plt.contour(X,Y,f(np.array(list(zip(X,Y)))),levels=[0],colors='#003cff')\n", + "plt.contour(X,Y,g(np.array(list(zip(X,Y)))),levels=[0],colors='k')\n", + "plt.plot(roots[:,0],roots[:,1],'o',color='none',markeredgecolor='r',markersize=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, the polynomias are of low degree, so even the roots outside the interval $[-1,1]\\times[-1,1]$ are accurate.\n", + "\n", + "We now find the common roots of the randomly generated polynomials higher degree polynomials A and B. In the first case, we return all the complex roots Polyroots found, and in the second we only return roots in the unit box." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "#get two random 2D polynomials of a certain degree in the power basis\n", + "degree = 20\n", + "np.random.seed(23)\n", + "A = yr.MultiPower(np.random.rand(degree,degree))\n", + "B = yr.MultiPower(np.random.rand(degree,degree))\n", + "\n", + "#A and B will have roots at infinity (which yr.polysolve cannot yet handle) \n", + "# unless their coefficient matrices are upper left triangular\n", + "A = yr.MultiPower(np.fliplr(np.triu(np.fliplr(A.coeff))))\n", + "B = yr.MultiPower(np.fliplr(np.triu(np.fliplr(B.coeff))))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 22.5 s, sys: 514 ms, total: 23 s\n", + "Wall time: 13.3 s\n" + ] + }, + { + "data": { + "text/plain": [ + "(361,\n", + " (4.708766937255859e-06+5.155801773071289e-05j),\n", + " (2.473592758178711e-05+3.540515899658203e-05j))" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#All roots\n", + "#solve and time\n", + "%time roots = yr.polysolve([A,B], return_all_roots=True)\n", + "#show the number of roots and maximal residuals\n", + "roots.shape[0],np.max(A(roots)),np.max(B(roots))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 23.1 s, sys: 560 ms, total: 23.7 s\n", + "Wall time: 13.7 s\n" + ] + }, + { + "data": { + "text/plain": [ + "(110,\n", + " (2.380873276308648e-13+3.597122599785507e-14j),\n", + " (1.1368683772161603e-13+2.858824288409778e-14j))" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Roots in unit box\n", + "#solve and time\n", + "%time accurate_roots = yr.polysolve([A,B],return_all_roots=False)\n", + "#show the number of roots and maximal residuals\n", + "accurate_roots.shape[0],np.max(A(accurate_roots)),np.max(B(accurate_roots))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly, roots in the unit interval are more accurate for these higher degree systems. Still, real roots outside the region are visually perfect." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.58861753+0.j, 1.66013536+0.j],\n", + " [ 1.26050846+0.j, -1.25135341+0.j],\n", + " [ 0.98242469+0.j, -1.11103665+0.j],\n", + " [ 0.57896567+0.j, -1.31114742+0.j],\n", + " [-0.96512672+0.j, 0.76684634+0.j],\n", + " [-0.04542403+0.j, -1.12553814+0.j],\n", + " [-0.38087033+0.j, -0.43197629+0.j]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#plot A,B and real roots\n", + "x = np.linspace(-3,3,100)\n", + "y = np.linspace(-3,3,100)\n", + "X,Y = np.meshgrid(x,y)\n", + "plt.contour(X,Y,A(np.array(list(zip(X,Y)))),levels=[0],colors='#003cff')\n", + "plt.contour(X,Y,B(np.array(list(zip(X,Y)))),levels=[0],colors='k')\n", + "#plot only the real roots\n", + "real_roots = roots[np.all(np.abs(roots.imag) < 1.e-10,axis = 1)]\n", + "plt.plot(np.real(real_roots[:,0]),np.real(real_roots[:,1]),'o',color='none',markeredgecolor='r',markersize=10)\n", + "real_roots" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# YRoots Logo" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "#making logo\n", + "one = lambda x,y: (y-.8)*(10*x+2.3)-.06*(2.5*x+2.3)**3+2+.2*np.sin(50*x)+.2*np.cos(50*y)\n", + "two = lambda x,y: (y-.8)*(-10*x+2.3)-.06*(-2.5*x+2.3)**3+2+.2*np.sin(50*x)+.2*np.cos(50*y)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percent Finished: 100% \n", + "Total intervals checked was 556\n", + "Methods used were ['constant_term_check', 'quadratic_check', 'Base Case', 'Macaulay', 'Too Deep']\n", + "The percent solved by each was [60.6115 38.1295 0. 1.259 0. ]\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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3OIkOKXuidYdWrXjqscfo17cvRqORLp07887UqYydMIHXX3+duLg4pv/3v5pdbjc4ncVttNtZvWIFr7/xBmazmdDQUGZ+8gnY7Uy4+246JSfTtUsXPp02jQ/ff59OnTvTpmVLevXsCQ7HX1pn/zqdWj92OyNuvpnx993HO2+/zbzZs0sFngCMuf127r3nHi3wZOVK5s2ezUOPPEJ2djZOp5NJDz5Ih1attEl9Rfsrui92u7a+rHUlJm7HRUQw7b33uOnGG3G73cTHxbH8u++0NkWPjUevfcuWvDh5MoMGDsTtdmM2m3nvzTfp1a1bhTqX9ejB1Fde4bprr2X5t98SG1viB6pOk8HF2eG5txBCGIG9wEC0IqgbgJFSyp3ltH8Q6CKlHOv5nCelDK1On90uvlhu/Pnn2hleEWe/AIrcO69zfb0LYuplfx3Zfv/+D0gMiOXJpreU1tfDdr2LhNa1zV46R3YdPUq7tm1rp13BZHApYcdRA2YjtEmsZjaRiiaZ1walWyeau3bvpl3Tpn8tyM1FNG68SUrZrTam+cJIrgewX0p5EMBTGHUoUKaTA0ai1ZurOWoyuPf1a6mdEBTHzryjbHWfplNEyzrXLxcfPib1plmRftHJ4LWlDJ2sXG3idkhQ2etrqlsnKN3aaZ7Hk8EbA8eKfE5Bq/5dCiFEM6A58GORxRYhxEbACUyRUi4oZ9sJwASApk2a1IHZCm9yT/Mb+PfuT3lu9ye0D0uiT3QyrUObEu80E+h2UOjIJ9ORy8mCdI7ZTnPMlsqpgnSynflIKQk3h9AkKJ7k8BZ0i2xLuFlVTfBl8gvgRJo2Ny7EAolx3rao9jzwwAP88ssvxZY9/PDD3HXXXbr12bNnTwoLC7UhsSdIZNasWeeiMM9HfMHJlRWOU9491BHAPCmLpcVoKqU8IYS4CPhRCLFNSnmglKCU04BpAN26dvXuPVpFrYkPjOKN5If4/vR6lqdu4L9HFhdvcLz4x7iASBpZYmlkicUgDGQ6cvkjay+r0zZjwEDXyNZcHd+TrpGtMQgVj1WXSKlN2Ha5tYvdZNKiHStLQel2Q1YenMmCXKu2TWIcxEedH0VI33vvvXrv87ffftOvhI+P4gtOLgUoOrRKBMqLChlBiQrhUsoTnr8HhRCrgS5AKSenOP8INJgZmnA5QxpeRmphJgetJ0jLOY2jwEpAZDSR5jAaBkbTOCiOIGNgqe2llBy0nuCX9K2sTtvMS3s/o5Ellpsb9adfbOcyK6ArqobTpeWBzMqDfJuWkaQoRoOWmSQoEAIDtOkARoPm2AoLBXk2E7kF2ucAs+bcYiO1PJIKRXXwBSe3AWglhGiO9vt7BDCqZCMhRBsgClhXZFkUYJVSFgohYoE+wGv1YrXCZxBC0MASTQNLNJibaUERDSsPuBBC0CKkMS1CGjMqcRDrMrax4ORa3j04jwUn1/JA85toE9a0Up2z2N0OJJrzvZBJzYTjZ7SRW1Cg5pwsAZqDklKb62Z3eFJz5YOjVDFpA4FmSUw4RIVBWHDtCw8oLly87uSklE4hxERgGdoUgk+llDuEEM+jpXRZ5Gk6Epgji4eDtgM+EkK40eb8TSkvKlOhqAiTwcjlsZ25LOZi1mfuYPqRpTy9axpPtRlN54hWFW67IXMXs44t45hNy5bRLKgh9190E61DL7xnv7lWyD/tSXQcB8FVKCDhcmmZ/91u7Tak2eDCJC+c22kKffG6kwOQUn4LfFti2bMlPk8uY7tfgfP3iami3hFCcGl0RzqFt+DhrW+x5NQvlTq5Dw8tIMORQ5vQpkgke/OOMf/Eah5vfUf9GO1DWAsgLlrLMiKQkFv5XCcjUCxVb0XPjOqinpzigsInnJxC4WvsyD1EhiOX/sFdK217fUIfvjj2A3vyjgIQbLQwKL6H3ib6JGYT2OyQZ4MwVx6/r1yJKzi4TrSNVis9rrzSa6V7xowZw+DBgxk2bFiVtylZP+7ZZ5+lb9++XHXVVbW2JykpiY0bN5aeVO0lHV/lwnRyemc80TvbhD/r+4Hth+1p/Cf1K1oExHGLaKudKxXo3kBLrmqUyDFHBhK4yByHxWoGaxXPsepkPDl7t76y0YyXjnNkkAtpcrM/RdA23IErOLjOnBzwVzaPmlAi00eNti+ZHQVwORyUFw+zYP58Bl97Le1banM5n3/6aW1FVfahKvbW5HiUpVub41qeZnXRKePJhenk6mMyuJT69uHP+j5u++dHvyPIEMBTSSMINBVJplOBbihhtKOGk7fy8vj92DFcnlyLFWG0WjHYbDhiYipt16Ndu3o/zobMTC5qLNl1THAsXYevF6OxShOOX3r5ZWZ+/jlNEhOJi4vjkq5dWbJ0KVNffpluvXqRlpZGt549OXzgAIcPH+aO0aPJt1oB+L+336Z3795IKXnw4Yf5cdUqmiclacmbPZPdk1q0YOyYMfywfDkT77uP3Jwcpk2fjt1up2WLFsz67DO2bNnCoiVLWLN2LS9OmcLXX37JCy+9xODrrmPYzTezYcMGHv7738m3WgkMCGDl8uWElfH/cgGPPf44y5YvRwjB+Lvv5sGJEwF494MPWLx0KQ6Hg6/mzKFt27bk5+fz4MMPs237dpxOJ5OffZahQ4bgcrk0nR9+0HTGjTung9GIzW7nxptv5uabbmL8uHE1///UFCHO28ng9Y/KeOJ9fR+2PcOdT5wlitCIeDCUuGj1sDs3t1ojHglVa1sfVbnLyHgSEGgkJAhseTo8O6uCk9u0aRNzvvySzZs343Q66dq1K5d063bOvmIaRiPxCQksX7ECi8XCvn37GDlyJBs3buSb+fPZs3cv27Zt4/Tp07Rv356xd999bltLcDA///ILuFykp6Yy/v77AXj66af5ZMYMHnzwQYYMGVL8FqcQYDBgd7kYPmoUc+fOpXv37uTk5BAUHFzmvk375BMOHTnC5s2bMZlMWuJjT7vY+Hj++OMP3n//faa++SYff/wxL02ZwoArr+TT6dPJysqiR48eXDVoEDNnztR0Nm3CJAQZeXnndPJsNkbcdht33nknd955Z+3+PzXlPM54olD4FIMb9uHdg/N4bMf7jEi8ii4Rrcu9UJxuF6cLM0i3Z2N1FWI2mIgNiCAxKO6CnGfndMH+FG1qQOMoyD9T/zasXbuWG2+8kWDPD4EhQyrO2e5wOJg4cSJbtmzBaDSyd+9eAH766SdGjhyJ0WikUaNGDBgwoNh2RWu5bd+xg6dvvZWsrCzy8vK4+uqrK+xzz549JCQknKvJFh4eXm7bFStWcO+992IyaWdh0TptN910E6DVrJs/fz4AP/zwA4sWLWLq1KkAFBQUcPTo0eI6LlcxnaFDh/Loo49y2223VWi3P6KcnEJRggFxl2AxBDD96FJe3juTYGMgrSwJxBNCoCOCApeDTEcOpwoyOFWYjkuWThRsMQRwcURL+sZ0pntUu2oXifU3UlLh9dnwt4shPkCr4xZnhMNesqesumYmkwm3W/tfna37BvDmm2/SoEED/vzzT9xuNxaLpUKdsxSt5TZm3DgWLFzIxRdfzIwZM1i9enWF9lVUe606bc/Weytas05Kyddff02bNm2qrNOnTx++++47Ro0aVWW7/IXz+8pTKGpI75hkekS154/sPWzK2sOBnKMcsadiL3ARaAgg0hxKk6B4ekV3oLEljrjASIKNFhxuJ6cK09mVe4QNmbv4LXMn0eZwbki4nGsa9Crb2UmJOT0dg+eZUEUYbbYqt6MaFUbcbm0EdixVm6wdGQpJDaFBdPkxLlLC+u3w/jcw90ctq8ktPaFjc09B01zt2WBdUVWtvn37MmbMGB5//HGcTieLFy/mnnvuISkpiU1//EGPPn2YN2/eufbZ2dkkJiZiMBj47LPPcHkCKPr27ctHH33EnXfeSWpqKqtWrWLUqFJ5KgDIzcsjISEBh8PBF198QePGjYHy68e1bduWEydOsGHDBrp3705ubi5BQUHnRmtFGTRoEB9++CH9+/c/d7uy6CisJFdffTXvvvsu7777LkIINm/eTJcuXf7Sufxy7XZlEZ3nn3+eF154gfvvv58PPvigSsfZX1BOTqEoB5PBSI+o9vSIal+t0jVtwprSL7YL45OGsCV7LwtOruXTo0v5LnU9/2g5khYhjUtt4w4Kwl2F52wCQMpK21b1t/jpDHjxM5i9UkvDVZKYCOjWBrq1hQ7NITpMK1q6cVsgC38NYv8pCA2Ce4fCI8PBllWkYndoqBbyX10qmydXCV27dmX48OF07tyZZs2acfnllwPwz0ce4dYRI5g1e3axW4/3338/N998M1999RVXXHHFuRHajTfeyI8//khycjKtW7emX79+5fb5wnPP0bNnT5o1a0ZycvI5xzZixAjGjx/PO++8U8yxBgQEMHfuXB588EFsNhtBQUGsWLGC0DL2b9y4cezdu5dOnTphNpsZP348E88GjJTBM888w6RJk+jUqRNSSpKSkliyZMlfOl26YDaZGD9hQjGdt956i7Fjx/Loo4/y2mvnT+Ior9eT8wbdunaVGz2VfnWhPmp5+au+v9peS93NWXv54NA35DqtTO04kcZBRSIxT5xg3fbtVQomMVqtGKxWHJXMaTJarVzasWOF9p5Kh27jtTRcw/rDwG5wUSNtrltWHhw4Dn/uhw27Ycfh4hHiASZJv/aFDP+bhVsHaKm3AHbt2kW7du0q3Y8KqeMEwpMnTyY0NJR//v3v+iQm1ivhsT/p1oFmqXMnNxcRHn5e1JNTKM57ukS25pUO9zJh86usTtvMbU0Gedsk5v+k5Zhc+x5c1qnitgWFcPCENooLD4FWkXlYCnX8saJQ1BHKySkU9cS27AO4kTS0lP88pT7p2lr7+9r/oF0z7dZkWVgLYP0O2HpAi5psGA1BbQUtvZN4pNpMnjxZe1Pbyco6s2zZMh577LFiy5o3a8Y3Cxd6yaLzA59wckKIa4C30dLYfSylnFJi/Rjgdf6qEvZ/UsqPPetGA540Arwopfys0g71znhSnQwWF5q+3pk49LK9lrrbC1J478xCOgQ2pr+9YfHz7+TJqgeeeCaDV9rOZoOTJyts0ysK3hsbzMOfRdBquOSBq/O5qYeNZnEujmcY+WlXAEv/sPDjjkAKHSWf8oUypKOT9x84RePoItGlLlftMmdA3WTP8EPdq6+4gqt//720bm2PZ0n0OA4q40n5CCGMwHvAQLTachuEEIvKqCYwV0o5scS20cBzQDe0ObKbPNtmVthpfUwG12n2/nmhr3fGE71sr6GuzVXI1BPLaBgQxeNJt2I0BhVvkJtbvcATIaoWeBIaWqm99w+Dyy+x8vSsAF76JpQX5xdv3yLBzb3XOhjU1Un31m6iQyVHUgVfLHPz6sJwLpscztb38s89kyMzs3YTgotSVzpKt/50VcaTMukB7JdSHgQQQswBhgJVKZlzNbBcSpnh2XY5cA0wu8KtVMYT7+v7q+010N2ZdZxsl5VJLYcTGhlfpqY3M54kd4KFr8OJNFi5EfYc0wJQ+l4MLRobECIA+Cug4KJIeCYhl7aNsrj1rWjW7A9jcB/PyrMZReoCf/pyV7q11zyPM540Bo4V+ZwC9Cyj3c1CiL7AXuDvUspj5WxbOj4bEEJMACYANG1y4dX5UniPxpZ4DBhYdOpnWoU2JcRUhSJr9UhGDnz+A8xbrT17c2hziklKgEtaQ5fW2jO7hBit8OnpDPh5UwAffx9MRChc0qZCeYXCq/iCkytrSk/JeQ2LgdmeCuD3Ap8BA6q4rbZQymnANNCmENTcXIWiejS0RHNf8xv44NACHt72FrcnDuLy2M4YhcGrdh04rmUp+ew7KLBDpxYw6RZoEq9NIdh+CDbtga/XlN7WaAjgms6FvDrJSEJ5sxmkrNlzlVrWkxNCcPvttzNr1iwAnE4nCQkJ9OzRgyXffFN9e6rJ6tWrmTp1KkuWLNG9L0Xl+IKTSwGKDq0SgWJRIVLKounZ/wu8WmTb/iW2XV3nFioUteSq+O40DW7AR4cW8vbBr5hzfCVXx/fk8phOxFJ2No8CHOQYCrEJO1KAxW0mzgZmmx13JUEqRqu13Iwnp9LhmY9h+nfayOyOQTDxZri4ZdlaOflaNpTTmZrviXhTqcYAACAASURBVImADnF5hLsqmUKQl8dzk9+k0JN6qrYEFhby78l/r/SWVkhICNu3bz83yXr58uXnMpAoLjx8wcltAFoJIZqjRU+OAIrlzhFCJEgpz4aKDQF2ed4vA14WQkR5Pg8CntDfZIWi+rQObcrrHR/g98xdLDr1MzOPfcfMY9+RGBBD00ZBhARHUCgdnHHmcsKZRba7dBSlIUzQXsQyJKYp3YOal99ZOSOo/y2H+/4DtkJ44EZ4/DbKH4l5CA+BriVvSeZ6XpVQGBhIYWD9357929/+xtKlSxk2bBizZ89m5MiRrP3pJwB+//13Jk2adM4JTp8+nTZt2milaB57jGXLlmmlaMaP58EHH+T5559n8eLF2Gw2evfuzUcffYQQgv79+zN16lS6demile7p3ZvDhw8Xs6O8vi6//HLeffddOnfuDGi5Iz/44AM6dapkwqKi2njdyUkpnUKIiWgOywh8KqXcIYR4HtgopVwEPCSEGAI4gQxgjGfbDCHEC2iOEuD5s0EoCoUvYhAGekV3oFd0B47bzvB75k52ZO7nkDuNAns2AQYzMYHhdIvQcmI2CIwi3ByCQRjIceSzP/MgP2du5+W0JdzS6ApGlTep/GyGliJ8uhTunqJN/P7kMWjdtB522EuMGDGC559/nsGDB7N161bGjh17zsm1bduWn376CZPJxIoVK3jyySf5+uuvmTZtGocOHSpe0gaYOHEizz77LAB33HEHS5Ys4frrr6+SHeX1NW7cOGbMmMFbb73F3r17KSwsVA5OJ7zu5ACklN8C35ZY9myR909QzghNSvkp8KmuBioUOtA4KI4bg/pxY1jXKqcM62VuyghLZ96x/cLXJ1ZzU6P+WIxVS6X00kzokww/vq2l7jqf6dSpE4cPH2b27Nlce+21xdZlZ2czevRo9u3bhxACh8MBlF/SZtWqVbz22mtYrVYyMjLo0KFDlZ1ceX3dcsstvPDCC7z++ut8+umnjBkzpo72XFES7z75VigU1SbDlc9h60nCzSGYSxZ1rYCoMC1PZUEdzy32VYYMGcI///lPRo4cWWz5M888wxVXXMH27dtZvHjxubI7ZZWiKSgo4P7772fevHls27aN8ePHn2tfXumeqvQVHBzMwIEDWbhwIV9++WW51Q0Utec8/z1XDnpnPNE7q4c/6/ur7Trp5rsLWZPxJ1sLjpF52oVJGIk3htM8IJbWAQ1pbo4j0GBCSskJZxYrM7fwXeEuhDDwROxgjCdPly1cRoaW124NYNBLMdzwDztLH0vHUnIAeDZQpSr1xMo7HkUzdNR1po6zmlXRtdsZe/vtRISEkNymDavXrNH2z+EgOzOTxg0agN3OjE8+Odd+0IABfPj++/Tv3fvc7UqDQRsHxIaHk5eRwbyvvmLYTTeB3U5S06Zs+u03enTq9FeFAbsdHA4tA4jdXm5fAONGj+b6m27i8j59iA4NLb1fPp6hpc41z9eMJ16hPiaD653Vw5/1/dX2Otbdaz3Oyye+IttlJcEQRgNzLHbpZKs9hdXW3QAYEESYgrG7XeS7CzAg6G1O4o5mfyM+ILJ88bw8fj92DFf6X4HJQcCT1yfwwsJkbv2PmyeuL55v4WzKMEdMTKW2G61WerRrV/p4FM14YjQSWFhYpWNRFQILCzXtqkw4NhpJbNaMhydN0j57nBVGI4/+61+MHjuWN95+mwFXXHFu+bjx49m7fz+dunXTStrcfTcTH3iA8XffTfIll5DUrJlWyVsIMBr55z/+wa0jRzLrf/9jwNkyPEaj1penTXl9AVzSvTvh4eHcNWZMxft0oUwG1ynjiSq1owf+Wk6mPvT91XYddCdtfZsCdyH/aDSUVs6wYtoZ9hz25h3jkPUEGfZcTMJIs+CGdDMnElsgKrejgvI9732bxJy1iXzzxO/Ehv81eqhqGZ+zbcsq5VOsXIqX5snVSLc21FD3xIkT9O/fn927d58bMdaFbqWoUjsKhaI+MBmMFDgcHC5IJRKIkdrznTynjTR7NgWuQgSCQIOJAEMAFoMZAwbKyXdQZVxuzUm49f59W9Nf5np9ufsQM2fO5KmnnuKNN94o28Ep6gzl5BQKL/Fwi1t5a/9c3j/pCSwuo2iAQBBsDKTQ7cApXQggOTCRv1n60yOqHYZqZE3JtRn5v6UX8e2mBgzteZL4iAskAsUHufPOO7nzzju9bcYFgXJyCoWXaBIUz9SOEzl4Zj/7jmwhM1R7nhFqsBBvCqexKZIGpgjMwohLujnqSOf37N2sLNjLq/s+p4kpmtsietHD0lyLChTir+CRU6cwp6VhCA6mwGFg4bYkpq9vQ05BAGN67mF8710Y04rbY7TZqlTu52zb8jKqKBS+hHJyCoUXEULQIqghLSIuhoSEctsZgeZE0Nwcy8AdjVhitrPGvIEpzm+5flMeA35MwZbcGVujRhgKC3EHBXHKGcM325OZv6kJWdYALklKZ+JVe2mTkAsE4y7RhwCQsuolf8qhrFB8haIi9IwNUU5OofAFqlD77dAJuP8VM8v+bIREIAz9ubn5NG6b8xAmhwvHgu95ZeAH7GrYi51ZDdmRFocQkl6tMxnVL4XOzXMAcFG+E6tyGR8oMwDEYrGQnp5OTEyMcnSKKiGlJD09HYtFn/RvyskpFH7C6Jdh2/4AxvTaS+smVoLdVpKX/Imp0IURiaQQw77jLHOOIik6l3v67GBAbyuNousujL8yEhMTSUlJ4cyZMzUXcbu1W6F1HeKudPXTraWmxWIhMTGx7uwpgnJyCoWfEGACpwtMBjeRwQ6isGEJkhiQSMCIRESE0ywih9bRGXRJTCchqn4j98xmM82bV5A4uir40TQQpaujZh3hE05OCHEN8Dbao4ePpZRTSqx/BBiHlqD5DDBWSnnEs84FbPM0PSqlHFJph3pnPCkj20SdonfWED3t91fb9bS7ijZ/Ns7A+PdC+e+v7eBXbdmz6WvoIQRGKXFhIKHgMBZZwNLdzZm3vTWd1qbzzyu30jo+u1L9s5PBq4LRZoOTZYSD1gV6HWt/Ozf8yV49bD1fMp4IIYzAe8BAtPpwG4QQi6SURdMxbAa6SSmtQoj7gNeA4Z51Nill52p1Wh8ZT3SavX8OvbOG6Gm/v9qup91VsLlxGHz7TA4nU06zJS+B09Z8jpxIw/mQEeFwg9lMr/FN6Nh+J/bszXy7uSEf/9GJsV/0Y/KN2xjQvpwUYGdN8NhR5cCTKjxHrDF6HWt/Ozf8yV69v/NqiNedHNAD2C+lPAgghJgDDAXOOTkp5aoi7dcDt9eqx/pwcqB/H/6s76+2e/uYSEmC6RQJbYP4v/QV7IiL4cz7U+GHdWR37oy1WTMM+fmEOAu4qXsKffu5eGJWO15c1JHO7WxEhDgrlqcagSdhYd4/HkrXt3SVkyuTxsCxIp9TgJ4VtL8b+K7IZ4sQYiParcwpUsoFdW+iQuEj5OXx+9GjuNLS2BF0lIuComnc+2o2RDfDFRx87pajOygIpCQixMmgLmfYdiSCU1mBlTo5heJ8wxecXFlxxmVOmhBC3A50A/oVWdxUSnlCCHER8KMQYpuU8kAZ204AJgA0bdKk9lYrFN5ASgw2GxJo7wzjx4BTLF0znc4/7CMnuRMFiYkgBFLC3rx4Fqy7iAW/JdAuMZcWDfO9bb1CUe/4gpNLAYp6nUSgVFSIEOIq4Cmgn5TyXEy0lPKE5+9BIcRqoAtQyslJKacB00BL0FyH9isUtUNKOHWq0mZ/HDQzb6WBZdv6czQ3gjy7keGd3mTgR89gcriIXfgd9/aeze+RvUnJjyDXHojR4GZwhyM82G87QZnOv/qDUvPcVMYTxfmILzi5DUArIURz4DgwAihWQVAI0QX4CLhGSplaZHkUYJVSFgohYoE+aEEpCoX/UOQWZFnsPhnG2z+05c+jURgNbtrFZtCr+RkiTFb+9stxTHY3RimRhkL6BH/D/pCutE/Ipn18Oj27FBAdagcCcKMlPC6vpE5dZTxRKHwJrzs5KaVTCDERWIY2heBTKeUOIcTzwEYp5SLgdSAU+MqTReHsVIF2wEdCCDdalfMpJaIyFQq/wBUcXGbAx+rtMUye3YaIECcPDT7IdU12Ei1ycQUFYbTZCImLhW0mpMuFNBs4MEwypNMi/mHsxe9p2bhCQnCVcZmXF2BS24wnCoWv4XUnByCl/Bb4tsSyZ4u8v6qc7X4FkvW1TqHwDk6X4LX5LWnbOI/X79pBWJAL8xkHbhGEOzgYAeS3bs2ef/+b9ikpmAYNomeSg5m2jSxzHyDWFlimIzJarZiKFFM9t9xmw6VTaiWFwlv4hJNTKBSlsTsN5BWYSE7KISzIdW752cATKSUuiwVrs2Zw442IRo0Y4Mxn9r7NnDLbibe5y4zgOht9WfK25NnblQrF+cSF6eT0zniid1YPf9b3V9v1tPvkSczp6aWCPiKAq9umMHdtIs0C07jx4sOYMzK06QFQfJSWl0de9hleTF0IEq50JXI6KLvM52sCys5sIiXmzMwq3Yb0y4wnSlc/Xb0064AL08nVx2RwvbN6+LO+v9qul25ubpkjK4BHh+4h22Hh1RWd2XYmjid6/EJAcECxtgIoCAnk3+lLOOxM59EmN9FMNqAZp8ou31NOdCUnT/L7+Z7xROnqp6v3dV1DlJPTE3/N6lEf+v5qu05ZIsoLPDEDr9y1h+krbcxc1YTDp6/ivTF/YAou/uxsrnsXBwpO8Vir2+ke3V77VS1EtRPmutLTVcYTpetbmrXkwnRyCoUfYTTAuIFHiY8o5PVvWrH+DwtXJP813cBos7Ep4jCdLU3oaYuA48chPx9CQrxotULhGygnp1D4AZsPRjBrVRMsJietm1hL3a5s5gphY8EJ9h/6g6yj2rORbn37eslahcJ3UE5OofA2UpYZeAKQbTPz7pqOLNnRjMTIPD665gcSI92lqnuPdrVljzmXp8zrGZGQxGBTay2LiqEa9eTKCYApC5XxROEvKCenUPgAZQWebDgYzfMLk8nKN3N770OM7XuAELuLHk2alg4oCQ3lNVcn3j/xLTPZxzJjGjcGdKB/yEUEGsxVM6KCAJiSqGngCn9BOTmFwtsIUSrwZNW2GP49py1NYq28ftdOWjXKByy4rG7NwZURUBJJOE9GjeWPrD3MPrKMD23rmLlvM72jO9I5ojVNg+IJNAZQ6LJT4LZjFEbiAiMJM3n6rSAApjy7FQpfRzk5hcLHsBYamfJ1K9o1yeU/d20nONBdre27RrahiyGBnWl7WCEP8Uv6Nlac2Vhu+8uiOzGp5XCMtTVcofBBlJNTKHyM4+kWrIUmbux5stoO7ixCCDpYGtOhUXfymtlYcuoXfs/cSY4jn2CThVBTMDZXAYetp/g5Yyuj7X8jto73Q6HwBZSTUyh8jGbxVmLCCvliTSKXtc8gONBV+UZlcNKZzdcH17E2/U/sbgcABgTpjhwABIJGlliua3ApsYGRQNXK7CgU/oRPODkhxDXA22hVCD6WUk4psT4QmAlcAqQDw6WUhz3rnkCrFu4CHpJSLqu0Q73TeuXm1llKmvNO319t19PuElGNZuDZQRt5ZP6lPPFJS968aR2BJm1EV5V0WlJKvklfx2zbHxiEkf7BbegT3IqWAQ0INgTgkm6c0oVJGDEKA7jRrodTpzBWtZ6c1apfWi9/+x8qXX00z5e0XkIII/AeMBCtgOoGIcSiEiVz7gYypZQthRAjgFeB4UKI9mj15zoAjYAVQojWUsqKf/rWR8YTIfTtw5/1/dV2vXTLiGrs3j6fZ1zbmbygE//5qSuPD9Yuh6qk05qT+hNf2jZxqbkZ45KGEm0OLbbe6HmVIjSUHg0bVs3mvDx90zj52/9Q6ep/XdeQSp2cEGIF8A8p5Z862dAD2C+lPOjpbw4wFCjq5IYCkz3v5wH/J7TCckOBOZ5K4YeEEPs9eusq7FGl9fK+vr/aXo9pva7smcOO1ON8va4x915/7K9KBBWk03K4nXyTvp7LwtvzSPgARHQZuSsrIjy8au1yc7WXP54jSlc/XR90clWZKfoo8KYQYroQoppXTJVoDBwr8jnFs6zMNlJKJ5ANxFRxWwCEEBOEEBuFEBvPlFOBWaHwJaSEXJsJISSGKkbrG4SBQIOZNEcO+bJQXwMVCj+gUicnpfxDSjkAWAJ8L4R4TggRVIc2lHX5lkylUF6bqmyrLZRympSym5SyW1ysiiNT+DZOl+CNhS34fnMDbuuXQoilasEnRmHg3uY3sM92ggdOzuJ/Kcs5kH8cVyV38BWK85UqPZPz3BrcA3wAvAiMF0I8IaWcVQc2pABNinxOBEpGhZxtkyKEMKGV2sqo4rYKhW9TIq1Xen4gTy3uzpbjsdzWbR/3dtmBwXPzoSqBJ32IJSFsMF84/mTe8VV8dfxHTMJIlDmMEJPFE3BixGIIICogjKZBDbg4ohXNgxMQaoK34jyjKs/kfgYuAnYA64ExwG7gYSHE5VLKCbW0YQPQSgjRHDiOFkgyqkSbRcBotGdtw4AfpZRSCLEI+J8Q4g20wJNWwO+1tEehqHfOBp6cyrYwcU430vMCmXzDVgYlnwKCOTtbTgAb9+zBkZ5eoZ7RauWZ7teTGRfOtpwDHLaeItORg9VViNPtxCXd2NyFpOScYXXaZjj2PS1DErm3+Q20CCnzjr9C4ZdUZSR3L7BDylLZWB8UQuyqrQFSSqcQYiKwDC3o61Mp5Q4hxPPARinlIuATYJYnsCQDzRHiafclWpCKE3ig0shKhcLXKJLW67mZyeQUBPDuhG20a5JXKhEzaPfjq5R6SwiiAsLoG9uZiuoRZDnyWJ+xnXnHV/HUzo94I/khGlnULX3F+UGlTk5Kub2C1dfVhRFSym+Bb0sse7bI+wLglnK2fQl4qS7sUCi8SY7VxLYjEYwfdJh2TXScS1iCSHMo1zToRaeIljzw53/YkLmLoQmX11v/CoWeVKMOR2nOhv0rFIraE2JxEhteyNodMdid9ftsLMOewyeHFwPQJrRpvfatUOiJ1yeDewW9M57k5WlziJR+/Wrrqa+n3WczngRZeaTfnzy5uAevftGUydduKjV1wGi1YrDZKpWsLEDFJd1kuPI5YE9lg+0QP9v24ZZu7ou6gra5Zsit5Prwx3NE6eqnq5dmHXBhOrn6mAyuZzYIf9f3V9v10i2S8aR/5yzuzdvHh6ta0TrRyqhLjxRrKgCEqLTmW9HMKHa3k1XZW9mcd5AT9gyynVbyXDbcntk2wYZA+kV05MbYS0kIiKq63f54jihd/XT1vq5riHJyeuKvWT3qQ99fba+HjCejBp5m68kYZvzcgpv7p2EyFo/5qnLgiSczyku7PmZrzgEaBkbTLLgh7cyhhJtCiA2IoFlwQ1qENMZsqOFXgT+eI0pXP13l5BQKRWUIAa0a5fHr7mgKHQZMxtoFDB+2niLEaGFAXDeaBTcg2hxOpDmMqIBQjEJVkVOc3ygnp1D4GH8eCmfu2sb0ap1R5UwnFfFY69uZcWQp/0v5odhyozDQJCieDmHN6RXdkfZhSRhErWLRFAqfQzk5hcLbFMl48u2OJkxZ3oGEcCvPDPgNc1rx/JNGm+1cZpSKMNps2jMSoH1YEq91fIA8p40TBWlk2nPJcuSSWpjJQesJlp/ZyNLT62hsiWNE4lX0iU5WmU8U5w3KySkUPoDTEsTbv3RmzvokuiZl8NLNfxIRbMRdYjK4AJCyaoEnJQg1BdE6tEmp5YUuO79mbGfhybX8Z/9s1kRu5pGWIwgyBtZ8hxQKH0E5OYXC2wjBfzckM2d9EjddeoIHrzuIyWjGhbnM5tXJeFIVAo0BXBHXlb6xnfn29DpmHPmWtw58yROt76jGTigUvolycgqFl5ES/rc+iSuSzzDp+oNV9U11jlEYuL5hH/KdNuYeX0mWI4/IEgVXFQp/Qz1lVih8gFCLg9SsQJwu7z4Lk1Jy1HqaAGHCYgjwqi0KRV3g1ZGcECIamAskAYeBW6WUmSXadEYr8RMOuICXpJRzPetmAP3QiqgCjJFSbqm0Y70znuTm1tls/TLRO2uInvb7q+062i1OneThvsd5Zml3Xp5zEc/dsA1jOT8/z2Y8cVcSfGK0WistyVMSh3TxYeYq1uXv4o6I3lhOV1Bc2B/PEX87N/zJXj1sPU8ynjwOrJRSThFCPO75/FiJNlbgTinlPiFEI2CTEGKZlDLLs/5fUsp51eq1PiaDC+GfWT3Ooqf9/mq7jtknnh50moCIHB77XwJdLgrjtdtzyrcBKn/elpdXLXuznHm8cmwhe23HuTX2Mm6Mu7zyPvzxHPG3c8Of7NX7O6+GeNvJDQX6e95/BqymhJOTUu4t8v6EECIViAOyqCkq44n39f3Vdr10heDR+8I5kAdTF4cyfmQorUoHQlad3FztVQV7HW4nz23/hNOFGfyr5Sh6xyRXvR9/PEeUrn66PujkvP1MroGU8iSA5298RY2FED2AAOBAkcUvCSG2CiHeFEKomGeFX3PPEO1H9uZ99dfnEespjtpOMypxUPUcnELhB+ju5IQQK4QQ28t4Da2mTgIwC7hLSnm2UPITQFugOxBN6VudRbefIITYKITYeCatgmcNCoWXcLlgyhfajYZubeuv36bBDWhkiWV2ynLWplX+SFuh8Cd0d3JSyquklB3LeC0ETnuc11knllqWhhAiHFgKPC2lXF9E+6TUKASmAz0qsGOalLKblLJbXKyqeqzwLdJzBUMeh69WwSsT4KJG9dd3gMHMC+3GkxScwBsH5vLm/rlYnQX1Z4BCoSPevl25CBjteT8aWFiygRAiAPgGmCml/KrEurMOUgA3ABVVMVcofJIVm410+lc8yzfCB/+Af42qfxuiA8J5sf14RjS+ip/Tt/LYjg9IK6z5Y2+FwlfwtpObAgwUQuwDBno+I4ToJoT42NPmVqAvMEYIscXz6uxZ94UQYhuwDYgFXqxf8xWK2vH2VzDo6SAigt389hHce4P3bDEKI8MTr2Ry27FkOLJ5cc9nONxO7xmkUNQBXo2ulFKmA1eWsXwjMM7z/nPg83K2H6CrgQqFjuw6DJPegZt6O5l1bxrBzRO8bRIAyREtmHjRMF7b9wVbsvfRPaqdt01SKGqMt0dyCsUFS06+9rdTczfBgbLixvVMmEnLjalGcgp/x9vz5LyD3hlP9M7q4c/6/mq7Dro9ImFUn0gmfxGM9aSBV8afwFBXPztrYW+Oy8YHqfOIMoTQxRZW9rXij+eI0tVPVy/NOuDCdHL1MRlc76we/qzvr7bXsa4APnvcRfg7+by2vCFZgXY+nFhYdwmaa2CvzVXIC0fmccaVy+SmIwkKialT/SrjJ/9DpauzZh2gnJye+GtWj/rQ91fb61jXBLw/KZfI4FymLAzjih4BjLiqDjuopr3/PfA9BwtO83jr22lflWdx/niOKF39dH3QyalncgqFlxECXhqRS7OGMG+19+xwuJ2sSdvCNQ16qmATxXmDcnIKhQ+w7aiJk+nQpMLEdvpiFAYizCHszTtKocvuPUMUijpEOTmFwotICXPWmOj371jiIuGx27xni0EYmJA0lAP5J3hhzwzynTbvGaNQ1BHKySkUXmLtn3DFQzDy1SDaNHLyy/vQsII4j/qgV3QH/t7iVvbkHeWpndPIduhYF1GhqAeUk1Mo6pkffofLH4C+E2H3UXjv/gJ+eT6NZg29bZnG5bGdearNaE4WpvPK3lm4zuVDVyj8D+XkFIp65B//B1f/A46cgrcfhoNz4f7BDkxGb1tWnM4Rrbgv6Qb25B1lU9Zub5ujUNSYC3MKgULhJRasBYMBZjwJAy7xLHR41aRy6RjeAoAzKlGzwo9RIzmFoh755HFIiIErJ8GQx2HvUW9bVD5fnfgRgaCTx9kpFP6IV0dyQohoYC6QBBwGbpVSZpbRzoVWaQDgqJRyiGd5c2AOWsHUP4A7pJSVxz7rndYrN7fOUtKcd/p6p/XSy/Y60u3fAPb+R/D2tyFMWRhKpzGCN261cn/3k3VgZBFqeZxX5O3kh8zfGRrWhSZZLsgqcb3oeY74+P+wFHqd0/50HPTQPE/Sej0OrJRSThFCPO75XFZ1b5uUsnMZy18F3pRSzhFCfAjcDXxQaa/1kfFECH378Gd9vdP/6GV7HekGh8ETd8Jdg62Me9vCA180QAi4b1RwHRhZhBoe5+OF6UzLWs3FIc25I/FqEOXc8NHzHPHx/2Ep9Dqn/ek46P2dVEO87eSGAv097z8DVlO2kyuFp1DqAOBsicnPgMn4ipMD/01dVR/6/mp7Heo2DIOFr8PVk5z8e3Es992jQ/RJDexdkfkzEni49UiMAZVs74/niNLVT9cHnZy3n8k1kFKeBPD8LS/fg0UIsVEIsV4IcbasZAyQJaU8WwskBWhcXkdCiAkejY1n0tLqyn6FolZIT4Udtw9V2nFJFwZhwGTwsZBPhaIG6O7khBArhBDby3gNrYZMUyllN7RR21tCiBZoSdxLUu5XhZRympSym5SyW1xsbDX3QqGoezbsgv4PwcotJp6/VcfnlNVkQNwluKSLN/fPUfXkFH6P7rcrpZTl5lQXQpwWQiRIKU8KIRKA1HI0Tnj+HhRCrAa6AF8DkUIIk2c0lwjoGE2iUNQOKbVq4N//Bl+ugt92QkwEzPqnjdu7W4FIb5sIQFJwAvcm3cB7h+bz+r7/8Wir29SoTuG3ePt25SJgtOf9aGBhyQZCiCghRKDnfSzQB9gppZTAKmBYRdsrFN5ESli5Eca8BI1ugA53wj/egwI7vDFRmwx++wDfGy1dFd+d8c2GsCFrFx8fWextcxSKGuPtwJMpwJdCiLuBo8AtAEKIbsC9UspxQDvgIyGEG80pT5FS7vRs/xgwRwjxIrAZ+KS+d0ChqIixr8CM7yAyFK7pCVdeAgO7UzyFl+/cqSzGtQ0vJdWeycKTa7mmQU+SghO8bZJCdSzC7wAAEIpJREFUUW286uSklOnAlWUs3wiM87z/FUguZ/uDQA89bVQoakpWrubg+l4M3/8HggK9bVH1uTq+BwtPrmV/3nHl5BR+ibdvVyoU5y0RoXDLFfDTn9BpDDz7MWze+1dEpT+wPkO7adIsuIGXLVEoaoa3b1d6B70znuid1cOf9f3V9hroCmDOBLgp2cJHK0J4aWYAL3wmaNnQyT1X5XPfQCshFqmPzXWgecCeyuzTP9Dd0pxWOUbIKXLN+OM5onT109VLsw64MJ1cfUwG1zurhz/r+6vtNdA1ACOugRHX2DmT7WDRehMzV5r41+cR/N8PYSx+zkZyrE4210LT4Xbyn9PLCTcFM7HpUDCVkY3FH88Rpaufrt7XdQ1RTk5P/DWrR33o+6vttdCNC4O7h2mvn7bAyH8buPmVEPZ85NYmffpQBor1aVs4ac/g6TZjCI+s4FalP54jSlc/XR90cuqZnELhBS6/GAZ1hwMnwO57Mwg4Y88GoE1oUy9bolDUDuXkFIp6pNAOS36BqyZpkZf33wCBZm9bVZqLI1oCMPf4Ci9bolDUjgvzdqVCUY/sT4Elv8LKTbB6M+TZoEE0vDsJ7r8RyPe2haVpEdKY6xpcypJTv9IwMIbrGvb2tkkKRY1QTk6h0IkVG+GZj2H9Du1zy0S4fRAM7q1NCA/wwRFcUe5qdh1p9mw+PrKY2IBIeka397ZJCkW1UU5OodCBjxfD+NegeQJMfQBu7gdJfjaX2iiM/P3/27v76KjqO4/j728SHgKIJuUpRBCogHrsFt2AsnqqqNSHrURdCmHrKQVc1mrXuq0usXi0K22X9g+ftroWVMSHWgRhFyuWRoS6HsWF42oRFAiwuyKBtCIahISHfPeP+5v2msxM5iZz584dvq9z5uTOffjdz/zyy/xy79z53dNruHPLL3h413LGnHw6PYq7Rx3LmEDsMzljQjD/GRh/Nmx5Cr5fE78OLqFHUTemVF7Kp8c+o/6zD6OOY0xg1skZE4LRQ2DTDnjh9XiNcJLMnmbv/ot9u/WOOIkxwUXayYlIuYjUich297MsyToTRORt36M5ceNUEXlCRHb5lo3J/aswpr2Fc2D0UJhyF5wzE/5tBXycpwMxp7O3eT9Ldq/hy31PZ0hpqnsaG5O/ov5MrhZYo6rzRaTWPZ/jX0FV1wJjwOsUgXrgt75VblfVZYH2GvawXk1NWRuSJqmwh8YKM39cswfMPRhYfzc8+WopD6zqw033duPWB5Wp4w9z1+QmTh90PNzMWajnVlXua1xGkcJNvS/4/N9MHNtInrSNjMUpb1htOAui7uSqgYvd9GJgHW06uTYmAy+p6qEu7TUXI56IxHPoqoQw88c1e8DcJcDMSTDj6mb+e8cRFtV1Y1FdKcs3lLJu/iGqRrWGm7mL9byxaRvbjuzllsFXM+CUyvYrxLGN5EnbyFic8ob9ntdJUXdyA1W1AcDdHbyj8yE1wL1t5v1YRO4C1gC1qtqSbEMRmQ3MBhg6ZIgN6xV1+XHN3olyBTj3HO/x95PhyzPgqdd6U/WXXS+7Q10oc8cn+wG4sHIcFKV4q4hjG7Fywys3Dzu50D+TE5GXReTdJI/qgOVU4N1XbrVv9h3AGcBYoJw0R4GqukBVq1S1qn+/fp14JcZ03tb/gzsXwoRboEjg2q9Enahjp7rP4DYeeD/iJMZ0XuhHcqp6WaplIrJPRCrcUVwF0JimqCnAClU96iu7wU22iMgi4LashDYmCz47DIt/A4/9Gt7aBkVFcOV5cM8sOHd01Ok6Nr7sbIb1quDhncsZWjqQytL+UUcyJrCov0KwEpjupqcD/5Fm3WnAs/4ZrmNERAS4Bng3hIzGBPbLOhgxFW6+1/v4497vwAfPw69/Fo8ODqCkqJg5I6+nWIqZt3URnx7Nw/HHjOlA1J3cfGCiiGwHJrrniEiViDyaWElEhgFDgN+12f4ZEdkEbAL6AT/KQWZj0vr58/CNe+CLlfDaQ/DW4/CPU2FwDM+SD+pZzg9Gf5P9R5pY+L8ro45jTGCRXniiqh8BlyaZvxG4wff8f4B2l3ep6iVh5jMmKFW481G44jx4YT6URH1pVxaM6jOEqwaN54WG17hp+HWUFveIOpIxGYv6SM6YglMk0NrqXVFdKEqkmJgP3GJOUNbJGZNFIjDvBvjtBvjrf4Ld6S6liol9zft5ce/rjCs7047iTOwUwMmUTgh7xJOwR/WIc/lxzR6g3JvPh25/14tbF/dl9N9CbfVBbrv6IKWpBvAPI3OWyjyurdzfuJwihZk9x/757yaObcTKDa/csMrMghOzk8vFiCdhj+oR5/Ljmj1AubOvhYnjD3H7Yz2467m+/PKN3qyed5ihA1Kc9AsjcxbKfP2Tzbx/pCH5qCdxbCNWbnjlhv133UnWyYUprqN65KL8uGYPUO7wk2DZfKjbAJNqi/nnpX14rDY7ZWesi2W+3bibk0v6cNGp54Mk+XQjjm3Eyg2v3Dzs5OwzOWNC9OEfYPFL0HwEvjg46jTB9S7pyeHjzRw8djjqKMZ0inVyxoTg4CGYuwBGToOl6+DuGTDnG1GnCm5i/7Ecp5WHdy2nVVs73sCYPGOdnDFZ9uZmOHs6/OQpb4zK95+GH870zpLHzZBeA5k+5Ere/HgLT3+wuuMNjMkzJ+ZncsaE5K2tMOG7MKgc/vMhuPAvok7UdV8bdAF7mv/IioZXOfOkYYwtOzPqSMZkzI7kjMmiuQvhlD7wxiOF0cEBiAgzT/salT37s3xP25H1jMlv1skZk0UNH8EZp8HA8qiTZFe3ohKGlg7kwNEQv+NoTAgi7eRE5OsisllEWkWkKs16V4jIVhGpF5Fa3/zhIvKmiGwXkSUikurrtsbkxNRLYO1b8K/Lok6SXR8cbmTDgfcYc/LIqKMYE0jUR3LvAtcBr6ZaQUSKgYeAK4GzgGk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Defaults to 1e-10. - - Returns - ------- - final_polys : list - Reduced Macaulay matrix that can be passed into the root finder. - """ - Power = is_power(initial_poly_list) - - poly_coeff_list = list() - dim = initial_poly_list[0].dim - degree = max([i.degree for i in initial_poly_list]) - total_degree = find_degree(initial_poly_list) - - for i in initial_poly_list: - poly_coeff_list = add_polys(degree, i, poly_coeff_list) - initial_poly_list = list() - polys_old = create_reduce(poly_coeff_list, Power, False, 'matrix') - poly_coeff_list = list() - for i in polys_old: - poly_coeff_list.append(i) - polys_new = list() - mons = mon_combos(np.zeros(dim, dtype = int),1) - mons = mons[1:] - - while degree < total_degree: - poly_coeff_list, polys_old = add_polys_to_deg_x(degree, poly_coeff_list, polys_old, mons, Power) - print("Reducing large matrix") - poly_coeff_list = create_reduce(poly_coeff_list, Power, False, "matrix") - degree += 1 - print("Final reduction") - final_polys = create_reduce(poly_coeff_list, Power, True) - - return final_polys - -def add_polys_to_deg_x(degree, poly_coeff_list, polys_old, mons, Power): - """ - Adds polynomials to a given degree. - - Parameters - ---------- - degree : int - which degree to go to - poly_coeff_list : list of matrices - - polys_old : list of polynomials - polys_new : list of polynomials - mons : list of monomials - Power : bool - """ - polys_new = list() - for i in polys_old: - for j in mons: - polys_new.append(utils.mon_mult2(i,j, Power)) - print("Reducing new polys") - polys_old = create_reduce(polys_new, Power, False, "matrix") - for i in polys_old: - poly_coeff_list.append(i) - return poly_coeff_list, polys_old - -def create_reduce(poly_list, Power, last_iteration = False, r_type = 'poly'): - if last_iteration == False: - """ - matrix, matrix_terms = create_matrix(poly_list) - rows_good, rows_trash, Q = utils.row_linear_dependencies(matrix) - poly_list = get_polys_from_matrix(matrix, matrix_terms, rows_good, Power, r_type) - """ - matrix, matrix_terms = create_matrix(poly_list) - print("Before: ", matrix.shape) - matrix = utils.rrqr_reduce2(matrix) - print("After: ", matrix.shape) - matrix = clean_zeros_from_matrix(matrix) - non_zero_rows = np.sum(np.abs(matrix),axis=1) != 0 - matrix = matrix[non_zero_rows,:] #Only keeps the non_zero_polymonials - #rows = get_good_rows(matrix, matrix_terms) - poly_list = get_polys_from_matrix(matrix, matrix_terms, np.arange(matrix.shape[0]), Power, r_type) - return poly_list - - else: - matrix, matrix_terms = create_matrix(poly_list) - print(matrix.shape) - matrix = utils.rrqr_reduce2(matrix) - matrix = clean_zeros_from_matrix(matrix) - non_zero_rows = np.sum(np.abs(matrix),axis=1) != 0 - matrix = matrix[non_zero_rows,:] #Only keeps the non_zero_polymonials - matrix = triangular_solve(matrix) - matrix = clean_zeros_from_matrix(matrix) - rows = get_good_rows(matrix, matrix_terms) - poly_list = get_polys_from_matrix(matrix, matrix_terms, rows, Power, r_type) - return poly_list - -def Macaulay(initial_poly_list, global_accuracy = 1.e-10): - """ - Accepts a list of polynomials and use them to construct a Macaulay matrix. - - parameters - -------- - initial_poly_list: list - Polynomials for Macaulay construction. - global_accuracy : float - Round-off parameter: values within global_accuracy of zero are rounded to zero. Defaults to 1e-10. - - Returns - ------- - final_polys : list - Reduced Macaulay matrix that can be passed into the root finder. - """ - Power = is_power(initial_poly_list) - - poly_coeff_list = [] - degree = find_degree(initial_poly_list) - - for i in initial_poly_list: - poly_coeff_list = add_polys(degree, i, poly_coeff_list) - - matrix, matrix_terms = create_matrix(poly_coeff_list) - - print(matrix.shape) - - #rrqr_reduce2 and rrqr_reduce same pretty matched on stability, though I feel like 2 should be better. - matrix = utils.rrqr_reduce2(matrix, global_accuracy = global_accuracy) - matrix = clean_zeros_from_matrix(matrix) - non_zero_rows = np.sum(np.abs(matrix),axis=1) != 0 - matrix = matrix[non_zero_rows,:] #Only keeps the non_zero_polymonials - - matrix = triangular_solve(matrix) - matrix = clean_zeros_from_matrix(matrix) - - #The other reduction option. I thought it would be really stable but seems to be the worst of the three. - #matrix = matrixReduce(matrix, triangular_solve = True, global_accuracy = global_accuracy) - - rows = get_good_rows(matrix, matrix_terms) - final_polys = get_polys_from_matrix(matrix, matrix_terms, rows, Power) - - return final_polys - -def get_polys_from_matrix(matrix, matrix_terms ,rows, power, r_type = "poly"): - '''Creates polynomial objects from the specified rows of the given matrix. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix with rows corresponding to polynomials, columns corresponding - to monomials, and entries corresponding to coefficients. - matrix_terms : array-like - The column labels for matrix in order. Contains Term objects. - rows : iterable - The rows for which to create polynomial objects. Contains integers. - power : bool - If true, the polynomials returned will be MultiPower objects. - Otherwise, they will be MultiCheb. - - Returns - ------- - poly_list : list - Polynomial objects corresponding to the specified rows. - ''' - - shape = [] - p_list = [] - shape = np.maximum.reduce([term for term in matrix_terms]) - shape += np.ones_like(shape) - spots = list() - for dim in range(matrix_terms.shape[1]): - spots.append(matrix_terms.T[dim]) - - # Grabs each polynomial, makes coeff matrix and constructs object - for i in rows: - p = matrix[i] - coeff = np.zeros(shape) - coeff[spots] = p - if r_type == "poly": - if power: - poly = MultiPower(coeff) - else: - poly = MultiCheb(coeff) - if poly.lead_term != None: - p_list.append(poly) - else: - p_list.append(coeff) - return p_list - -def get_good_rows(matrix, matrix_terms): - ''' - Gets the rows in a matrix whose leading monomial is not divisible by the leading monomial of any other row. - - Parameters - ---------- - matrix : (M,N) ndarray - Input matrix. - matrix_terms : array-like - The column labels for matrix in order. Contains Term objects. - - Returns - ------- - keys : list - Rows indicies satisfying the divisibility condition. - - Notes - ----- - This function could probably be improved, but for now it is good enough. - ''' - rowLMs = dict() - already_looked_at = set() - #Finds the leading terms of each row. - for i, j in zip(*np.where(matrix!=0)): - if i in already_looked_at: - continue - else: - already_looked_at.add(i) - rowLMs[i] = matrix_terms[j] - keys= list(rowLMs.keys()) - keys = keys[::-1] - spot = 0 - #Uses a sieve to find which of the rows to keep. - while spot != len(keys): - term1 = rowLMs[keys[spot]] - toRemove = list() - for i in range(spot+1, len(keys)): - term2 = rowLMs[keys[i]] - if divides(term1,term2): - toRemove.append(keys[i]) - for i in toRemove: - keys.remove(i) - spot += 1 - return keys - -def find_degree(poly_list): - """ - Finds the degree needed for the Macaulay matrix - - Parameters - ---------- - poly_list : list - Polynomials that will be used to construct the matrix. - - Returns - ------- - int - Needed degree for Macaulay matrix. - - Notes - ------- - For polynomials [P1,P2,P3] with degree [d1,d2,d3] the function returns d1+d2+d3-3+1 - """ - degree_needed = 0 - for poly in poly_list: - degree_needed += poly.degree - return ((degree_needed - len(poly_list)) + 1) - -def add_polys(degree, poly, poly_coeff_list): - """Adds polynomials to a Macaulay Matrix. - - This function is called on one polynomial and adds all monomial multiples of it to the matrix. - - Parameters - ---------- - degree : int - The degree of the TVB Matrix - poly : Polynomial - One of the polynomials used to make the matrix. - poly_coeff_list : list - A list of all the current polynomials in the matrix. - Returns - ------- - poly_coeff_list : list - The original list of polynomials in the matrix with the new monomial multiplications of poly added. - """ - poly_coeff_list.append(poly.coeff) - deg = degree - poly.degree - dim = poly.dim - mons = mon_combos([0]*dim,deg) - mons = mons[1:] - for i in mons: - poly_coeff_list.append(poly.mon_mult(i, returnType = 'Matrix')) - return poly_coeff_list - - -def sort_matrix_terms(matrix_terms): - '''Sorts the matrix_terms by term order. - So the highest terms come first, the lowest ones last. - - Parameters - ---------- - matrix_terms : ndarray - Each row is one of the terms in the matrix. - - Returns - ------- - matrix_terms : ndarray - The sorted matrix_terms. - ''' - termList = list() - for term in matrix_terms: - termList.append(Term(term)) - argsort_list = np.argsort(termList)[::-1] - return matrix_terms[argsort_list] - -def create_matrix(poly_coeffs): - ''' Builds a Macaulay matrix. - - Parameters - ---------- - poly_coeffs : list - Contains numpy arrays that hold the coefficients of the polynomials to be put in the matrix. - Returns - ------- - matrix : (M,N) ndarray - The Macaulay matrix. - ''' - bigShape = np.maximum.reduce([p.shape for p in poly_coeffs]) - - #Finds the matrix terms. - non_zeroSet = set() - for coeff in poly_coeffs: - for term in zip(*np.where(coeff != 0)): - non_zeroSet.add(term) - matrix_terms = np.array(non_zeroSet.pop()) - for term in non_zeroSet: - matrix_terms = np.vstack((matrix_terms,term)) - - matrix_terms = sort_matrix_terms(matrix_terms) - - #Get the slices needed to pull the matrix_terms from the coeff matrix. - matrix_term_indexes = list() - for i in range(len(bigShape)): - matrix_term_indexes.append(matrix_terms.T[i]) - - #Adds the poly_coeffs to flat_polys, using added_zeros to make sure every term is in there. - added_zeros = np.zeros(bigShape) - flat_polys = list() - for coeff in poly_coeffs: - slices = slice_top(coeff) - added_zeros[slices] = coeff - flat_polys.append(added_zeros[matrix_term_indexes]) - added_zeros[slices] = np.zeros_like(coeff) - coeff = 0 - poly_coeffs = 0 - #Make the matrix - matrix = np.vstack(flat_polys[::-1]) - flat_polys = 0 - #Sorts the rows of the matrix so it is close to upper triangular. - matrix = utils.row_swap_matrix(matrix) - return matrix, matrix_terms - -def matrixReduce(matrix, triangular_solve = False, global_accuracy = 1.e-10): - ''' - Reduces the matrix into row echelon form, so each row has a unique leading term. If triangular_solve is - True it is reduces to reduced row echelon form, so everything above the leading terms is 0. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix of interest. - triangular_solve : bool - Defaults to False. If True then triangular solve is preformed. - global_accuracy : float - Defaults to 1.e-10. What is determined to be zero when searching for the pivot columns. - - Returns - ------- - matrix : (M,N) ndarray - The reduced matrix. It should look like this if triangluar_solve is False. - a - - - - - - - - 0 b - - - - - - - 0 0 0 c - - - - - 0 0 0 0 d - - - - 0 0 0 0 0 0 0 e - - If triangular solve is True it will look like this. - a 0 - 0 0 - - 0 - 0 b - 0 0 - - 0 - 0 0 0 c 0 - - 0 - 0 0 0 0 d - - 0 - 0 0 0 0 0 0 0 e - - ''' - independentRows,dependentRows,Q = utils.row_linear_dependencies(matrix, accuracy = global_accuracy) - matrix = matrix[independentRows] - - pivotColumnMatrix = findPivotColumns(matrix, global_accuracy = global_accuracy) - pivotColumns = list(np.where(pivotColumnMatrix == 1)[1]) - otherColumns = list() - for i in range(matrix.shape[1]): - if i not in pivotColumns: - otherColumns.append(i) - - matrix = matrix[:,pivotColumns + otherColumns] - - Q,R = qr(matrix) - - if triangular_solve: - R = clean_zeros_from_matrix(R) - X = solve_triangular(R[:,:R.shape[0]],R[:,R.shape[0]:]) - reduced = np.hstack((np.eye(X.shape[0]),X)) - else: - reduced = R - - matrix = reduced[:,utils.inverse_P(pivotColumns + otherColumns)] - - matrix = clean_zeros_from_matrix(matrix) - return matrix - -def findPivotColumns(matrix, global_accuracy = 1.e-10): - ''' Finds the pivot columns of a matrix. - Uses rank revealing QR to determine which columns in a matrix are the pivot columns. This is done using - this function recursively. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix of interest. - global_accuracy : float - Defaults to 1.e-10. What is determined to be zero when searching for the pivot columns. - - Returns - ------- - matrix : (M,N) ndarray - A matrix of ones and zeros. Each row will have exactly one 1 in it, which will be a pivot column - in the matrix. For example, a 5x8 matrix with pivot columns 1,2,4,5,8 will look like this - 1 0 0 0 0 0 0 0 - 0 1 0 0 0 0 0 0 - 0 0 0 1 0 0 0 0 - 0 0 0 0 1 0 0 0 - 0 0 0 0 0 0 0 1 - ''' - - if matrix.shape[0] == 0 or matrix.shape[1] == 0: - return matrix - elif matrix.shape[1] == 1: - column = np.zeros_like(matrix) - if np.sum(np.abs(matrix)) < global_accuracy: - return column - else: - column[0] = 1 - return column - - height = matrix.shape[0] - A = matrix[:height,:height] #Get the square submatrix - B = matrix[:,height:] #The rest of the matrix to the right - independentRows, dependentRows, Q = utils.row_linear_dependencies(A, accuracy = global_accuracy) - nullSpaceSize = len(dependentRows) - if nullSpaceSize == 0: #A is full rank - #The columns of A are all pivot columns - return np.hstack((np.eye(height),np.zeros_like(B))) - else: #A is not full rank - #sub1 is the independentRows of the matrix, we will recursively reduce this - #sub2 is the dependentRows of A, we will set this all to 0 - #sub3 is the dependentRows of Q.T@B, we will recursively reduce this. - #We then return sub1 stacked on top of sub2+sub3 - bottom = matrix[dependentRows] - BCopy = B.copy() - sub3 = bottom[:,height:] - sub3 = Q.T[-nullSpaceSize:]@BCopy #I think this line can be taked out for this code. - sub3 = findPivotColumns(sub3) - - sub1 = matrix[independentRows] - sub1 = findPivotColumns(sub1) - - sub2 = bottom[:,:height] - sub2[:] = np.zeros_like(sub2) - - pivot_columns = np.vstack((sub1,np.hstack((sub2,sub3)))) - return pivot_columns - pass diff --git a/OLD_CODE/TVBMethod.py b/OLD_CODE/TVBMethod.py deleted file mode 100644 index 6645998a..00000000 --- a/OLD_CODE/TVBMethod.py +++ /dev/null @@ -1,387 +0,0 @@ -"""Methods for solving a system of multivariate polynomials using the -Telen and Van Barel's simultaneous diagonalization method""" - -import numpy as np -from scipy.linalg import eig -from numalgsolve.utils import match_poly_dimensions, sort_polys_by_degree, row_swap_matrix, MacaulayError -from numalgsolve.polynomial import MultiCheb, MultiPower, is_power -from numalgsolve.MacaulayReduce import find_degree, add_polys -from scipy.linalg import qr, solve_triangular, qr_multiply -from numalgsolve.Multiplication import create_matrix, makeBasisDict -from numalgsolve.ProjectiveSpace import pad_with_zeros - -def solve(polys, verbose=False, sim_diag="Telen"): - ''' - Finds the roots of a list of multivariate polynomials by simultaneously - diagonalizing a multiplication matrices created with the TVB method. - - Parameters - ---------- - polys : list of polynomial objects - Polynomials to find the common roots of. - verbose : bool - Print information about how the roots are computed. - returns - ------- - roots : numpy array - The common roots of the polynomials. Each row is a root. - ''' - polys = match_poly_dimensions(polys) - poly_type = is_power(polys, return_string = True) - dim = polys[0].dim - - #Reduce the Macaulay Matrix TVB-style to generate a basis for C[]/I and - # a dictionary that expresses other monomials in terms of that basis - basisDict, VB = TVB_MacaulayReduction(polys, accuracy = 1.e-10, verbose=verbose) - if verbose: - print('Basis for C[]/I\n', VB) - print('Dictionary which represents non-basis monomials in terms of the basis\n', basisDict) - - #See what happened to dimension of C[]/I. Did we loose roots? - len_VB = len(VB) - degrees = [poly.degree for poly in polys] - max_number_of_roots = np.prod(degrees) - if len_VB < max_number_of_roots: - raise MacaulayError('Roots were lost during the Macaulay Reduction') - if len_VB > max_number_of_roots: - raise MacaulayError('Dimension of C[]/I is too large') - - #make Mx1, ..., Mxn - mult_matrices = np.zeros((dim, len_VB, len_VB)) - for i in range(1, dim+1): - mult_matrices[i-1] = Mxi_Matrix(i, basisDict, VB, dim, poly_type) - - if verbose: - print('The Multiplication Matrices:\n', mult_matrices) - - #simultaneously diagonalize and return roots - if sim_diag == "Telen": - #Determine if the system contains only homogenous polynomials - homogenous = all([is_homogenous(poly) for poly in polys]) - #Find the roots - roots = Telen_sim_diag(mult_matrices, verbose=verbose, homogenous=homogenous).T - else: - roots = sim_diag(mult_matrices, verbose=verbose).T - if verbose: - print("Roots:\n", roots) - return roots - -def Mxi_Matrix(i, basisDict, VB, dim, poly_type, verbose=False): - ''' - Uses the reduced Macaulay matrix to construct the Moller-Stetter matrix M_xi, which - represents the linear map of multiplying by xi in the space C[x1, ..., xn]/I. - - Parameters - ---------- - i : int - The index of the variable xi to make the Moller-Stetter matrix of, where variables - are indexed as x1, x2, ..., xn. - basisDict: dictionary - A dictionary which maps monomials not in the basis to linear combinations - of monomials in the basis. Generated using the TVB method. - VB: numpy array - Represents a vector basis for the space C[x1, ..., xn]/I created with the TVB method. - Each row represents a monomial in the basis as the degrees of each variable. - For example, x^2y^5 would be represented as [2,5]. - dim: int - The dimension of the system (n) - verbose : bool - Prints information about how the roots are computed. - - Returns - ------- - Mxi : 2D numpy array - The Moller-Stetter matrix which represents multiplying by xi - ''' - VB = VB.tolist() #convert to list bc numpy's __contains__() function is broken - - #Construct the polynomial to create the MS Matrix of (xi) - xi_ind = np.zeros(dim, dtype=int) - xi_ind[i-1] = 1 - coef = np.zeros((2,)*dim) - coef[tuple(xi_ind)] = 1 - if poly_type == "MultiPower": - xi = MultiPower(np.array(coef)) - elif poly_type == "MultiCheb": - xi = MultiCheb(np.array(coef)) - - if verbose: - print("\nCoefficients of polynomial whose Moller-Stetter matrix we construt\n", xi.coeff) - - # Build multiplication matrix M_xi - Mxi = np.zeros((len(VB), len(VB))) - for j in range(len(VB)): #multiply each monomial in the basis by xi - product_coef = xi.mon_mult(VB[j], returnType = 'Matrix') - for monomial in zip(*np.where(product_coef != 0)): - if list(monomial) in VB: #convert to list to test if list of lists - Mxi[VB.index(list(monomial))][j] += product_coef[monomial] - else: - Mxi[:,j] -= product_coef[monomial]*basisDict[monomial] - - # Construct var_dict - var_dict = {} - for i in range(len(VB)): - mon = VB[i] - if np.sum(mon) == 1 or np.sum(mon) == 0: - var_dict[tuple(mon)] = i - - return Mxi - -def TVB_MacaulayReduction(initial_poly_list, accuracy = 1.e-10, verbose=False): - ''' - Reduces the Macaulay matrix to find a vector basis for the system of polynomials. - - Parameters - -------- - polys: list - The polynomials in the system we are solving. - accuracy: float - How small we want a number to be before assuming it is zero. - - Returns - ----------- - basisDict : dict - A dictionary of terms not in the vector basis a matrixes of things in the vector basis that the term - can be reduced to. - VB : nparray - The terms in the vector basis, each list being a term. - ''' - power = is_power(initial_poly_list) - dim = initial_poly_list[0].dim - poly_coeff_list = [] - degree = find_degree(initial_poly_list) - initial_poly_list = sort_polys_by_degree(initial_poly_list, ascending = False) - - for poly in initial_poly_list: - poly_coeff_list = add_polys(degree, poly, poly_coeff_list) - - #Creates the matrix for either of the above two methods. Comment out if using the third method. - matrix, matrix_terms, cuts = create_matrix(poly_coeff_list, degree, dim) - if verbose: - np.set_printoptions(suppress=False, linewidth=200) - print('\nStarting Macaulay Matrix\n', matrix) - print('\nColumns in Macaulay Matrix\nFirst element in tuple is degree of x monomial, Second element is degree of y monomial\n', matrix_terms) - print('\nLocation of Cuts in the Macaulay Matrix into [ Mb | M1* | M2* ]\n', cuts) - - #First QR reduction - #If bottom left is zero only does the first QR reduction on top part of matrix (for speed). Otherwise does it on the whole thing - if np.allclose(matrix[cuts[0]:,:cuts[0]], 0): - #RRQR reduces A and D without pivoting, sticking the result in it's place and multiplying the rest of the matrix by Q.T - C1,matrix[:cuts[0],:cuts[0]] = qr_multiply(matrix[:,:cuts[0]], matrix[:,cuts[0]:].T, mode = 'right') - matrix[:cuts[0],cuts[0]:] = C1.T - C1 = 0 - - #check if there are zeros along the diagonal of R1 - if any(np.isclose(np.diag(matrix[:,:cuts[0]]),0, rtol=accuracy)): - raise MacaulayError("R1 IS NOT FULL RANK") - - #set small values to zero before backsolving - matrix[np.isclose(matrix, 0, rtol=accuracy)] = 0 - - matrix[:cuts[0],cuts[0]:] = solve_triangular(matrix[:cuts[0],:cuts[0]],matrix[:cuts[0],cuts[0]:]) - matrix[:cuts[0],:cuts[0]] = np.eye(cuts[0]) - matrix[cuts[0]:,cuts[0]:] -= (matrix[cuts[0]:,:cuts[0]])@matrix[:cuts[0],cuts[0]:] #? - else: - #RRQR reduces A and D without pivoting, sticking the result in it's place. - Q1,matrix[:,:cuts[0]] = qr(matrix[:,:cuts[0]]) - - #check if there are zeros along the diagonal of R1 - if any(np.isclose(np.diag(matrix[:,:cuts[0]]),0, rtol=accuracy)): - raise MacaulayError("R1 IS NOT FULL RANK") - - #Multiplying the rest of the matrix by Q.T - matrix[:,cuts[0]:] = Q1.T@matrix[:,cuts[0]:] - Q1 = 0 #Get rid of Q1 for memory purposes. - - #Second QR reduction on all the rest of the matrix - matrix[cuts[0]:,cuts[0]:],P = qr(matrix[cuts[0]:,cuts[0]:], mode = 'r', pivoting = True) - - #Shift around the top right columns - matrix[:cuts[0],cuts[0]:] = matrix[:cuts[0],cuts[0]:][:,P] - #Shift around the columns labels - matrix_terms[cuts[0]:] = matrix_terms[cuts[0]:][P] - P = 0 - - #set small values to zero - matrix[np.isclose(matrix, 0, rtol=accuracy)] = 0 - - #eliminate zero rows from the bottom of the matrix. Zero rows above - #nonzero elements are not eliminated. This saves time since Macaulay matrices - #we deal with are only zero at the very bottom - #matrix = row_swap_matrix(matrix) #not for TVB's method needed bc QRP is on the whole matrix - for row in matrix[::-1]: - if np.allclose(row, 0): - matrix = matrix[:-1] - else: - break - - height = matrix.shape[0] - matrix[:,height:] = solve_triangular(matrix[:,:height],matrix[:,height:]) - matrix[:,:height] = np.eye(height) - #return np.vstack((matrix[:,height:].T,np.eye(height))), matrix_terms - - if verbose: - np.set_printoptions(suppress=True, linewidth=200) - print("\nFinal Macaulay Matrix\n", matrix) - print("\nColumns in Macaulay Matrix\n", matrix_terms) - VB = matrix_terms[height:] - - basisDict = makeBasisDict(matrix, matrix_terms, VB, power) - - return basisDict, VB - -def sim_diag(Matrices, verbose=False): - ''' - Simultaneously diagonalizes several commuting matrices which have the same eigenvectors. - - Parameters - ---------- - matrices : ndarray - 3D Tensor. Each matrix in the array must commute with every other matrix and they must share all eigenvectors) - verbose: bool - Prints information about the diagonalization. - - ------- - sim_diag : numpy array - The i-th row is the diagonal corresponding to the i-th matrix in matrices - ''' - sim_diag = np.zeros((Matrices.shape[0], Matrices.shape[1]), dtype='complex64') - b = np.random.rand(Matrices.shape[0]) - lin_combo = sum([b[i] * Matrices[i] for i in range(Matrices.shape[0])]) #random linear combo of mult matrices to avoid issues with double eigenvalues - vals, P = eig(lin_combo) - vals = 0 #don't need the vals of the lin_combo matrix. Delete for memory - if verbose: - print("The linear combo of multiplication matrices:\n", lin_combo) - print("The basis which simultanously diagonalizes the matrices:\n", P) - - for k in range(Matrices.shape[0]): - A = Matrices[k] @ P - i = np.argmax(P, axis = 0) #get largest value in each column of P - for j in range(Matrices.shape[1]): - sim_diag[k,j] = A[i[j]][j]/P[i[j]][j] - - #sligihtly less efficient - # for k in range(Matrices.shape[0]): - # A = Matrices[k] @ P - # for j in range(Matrices.shape[1]): - # i = np.argmax(P[:,j]) #get largest value in each column of P - # print(A[i,j]/P[i,j]) - # sim_diag[k,j] = A[i,j]/P[i,j] - - #brute force method - # Pinv = np.linalg.inv(P) - # for k in range(Matrices.shape[0]): - # if verbose: - # print("M_{} diagonalized:\n".format(k), Pinv @ Matrices[k] @ P) - # sim_diag[k] = (Pinv @ Matrices[k] @ P).diagonal() - - return sim_diag - -def Mourrain_sim_diag(Matrices, verbose=False, homogenous=False): - ''' - Simultaneously diagonalizes several commuting matrices which have the same eigenvectors. - Uses the gitlab account method. Only works for solving homogenous systems. - - Parameters - ---------- - matrices : ndarray - 3D Tensor. Each matrix in the array must commute with every other matrix and they must share all eigenvectors) - verbose: bool - Prints information about the diagonalization. - - ------- - sim_diag : numpy array - The i-th row is the diagonal corresponding to the i-th matrix in matrices - ''' - randvals = np.random.rand(Matrices.shape[0]) - M0 = np.sum([ Matrices[i] * randvals[i] for i in range(Matrices.shape[0])], axis=0) - if verbose: - print('Linear Combo of Mult Matrices (M0):\n', M0) - I0 = np.linalg.inv(M0) - if verbose: - print('M0-inverse (I0):\n', I0) - Mg = I0 @ Matrices[0] - if verbose: - print('Mg (M0-inverse @ M1):\n', Mg) - eigvals, E = np.linalg.eig(Mg) - eigvals = 0 - if verbose: - print('Eigenvectors of Mg:\n', E) - Z = np.linalg.inv(E) @ I0 - if verbose: - print('Mg-inverse @ M0-inverse:\n', Z) - sim_diag = np.zeros((Matrices.shape[0], Matrices.shape[1]), dtype='complex64') - for j in range(Matrices.shape[0]): - Dj = Z @ Matrices[j] @ E - if verbose: - print('Diagonalization of M{}:\n'.format(j+1), Dj) - sim_diag[j] = np.diag(Dj) - if verbose: - print('Simultaneous Diagonalization:\n', sim_diag) - - #normalize. If all the polys are homogenous, does it differently - if homogenous: - for i in range(sim_diag.shape[0]): - sim_diag[i,:] /= sim_diag[i, 0] - sim_diag = sim_diag[1:,:] - else: - for i in range(sim_diag.shape[0]): - sim_diag[i,:] /= np.linalg.norm(sim_diag[i,:]) - - return sim_diag - -def Telen_sim_diag(Matrices, verbose=False, homogenous=False): - ''' - Simultaneously diagonalizes several commuting matrices which have the same eigenvectors. - Uses Telen's method. For solving homogenous systems. - - Parameters - ---------- - matrices : ndarray - 3D Tensor. Each matrix in the array must commute with every other matrix and they must share all eigenvectors) - verbose: bool - Prints information about the diagonalization. - - ------- - sim_diag : numpy array - The i-th row is the diagonal corresponding to the i-th matrix in matrices - ''' - N = Matrices.shape[0] - - #only 1 root, telen's code lines 33-34 - if N == 1: #one by one matrices are their own eigvals - #unravel 1x1xn tensor into a row vector - return Matrices.ravel() - - #set up the tesor T (telen's code lines 36-41) - T = np.zeros(N,N,n+1) - for i in range(n): - T[:,:,i] = Matrices[i] - T(:,:,n) = np.eye(N) - - #compute the cpd_gevd, using random slices - S = S(indx{:}) - Apt,Dpt = eig(S() - - if exist('iperm','var'): - U = U(iperm) - - #get the roots, telen's code lines 43-48 - sol = U[2].T - for i in range(n): - sol[:,i] = sol[:,i]/sol[:,n] #element wise division - - sol = sol[:,:n+1] - -def is_homogenous(poly): - ''' - Tests whether a polynomial is homogenous or not - - Args: - poly (a polynomail object): the polynomial to test - - Returns: - bool. True if the polynomial is homogenous, False otherwise - ''' - coeff = pad_with_zeros(poly.coeff) - return np.allclose(np.fliplr(coeff), np.diag(np.fliplr(coeff))) diff --git a/OLD_CODE/bezout.py b/OLD_CODE/bezout.py deleted file mode 100644 index 2b64f2d0..00000000 --- a/OLD_CODE/bezout.py +++ /dev/null @@ -1,112 +0,0 @@ -# bezout.py - -import numpy as np -from scipy.sparse import diags, kron, eye -import numalgsolve.utils as utils -import scipy.linalg as sLA -import time - -def bivariate_roots(f, g): - """Calculates the common roots of f and g using the bezout resultant method. - - Parameters - ---------- - f, g : MultiCheb objects - - Returns - ------- - roots - - """ - F, G = utils.match_size(f.coeff, g.coeff) - n = max(F.shape) - # F, G = np.zeros((n,n)), np.zeros((n,n)) - # F = F + utils.match_size(F, f_coeffs)[1] # Square matrix for f - # G = G + utils.match_size(G, g_coeffs)[1] # Square matrix for g - - A = np.zeros((n-1, n-1, 2*n-1)) - a, b, c = (np.vstack([np.ones((n-1,1)), 2])/2).T, np.zeros(n), np.ones(n)/2 - for i in range(1, n+1): - for j in range(1, n+1): - AA = np.array([[0] + list(F[:,i-1][::-1])]) - v = G[:,j-1][::-1].reshape((-1,1)) - X,ignored = DLP(AA,v,a,b,c) - if i==1 or j==1: - cc = np.zeros(max(i,j)) - cc[0] = 1 - else: - cc = np.zeros(i+j-1) - cc[-1] = .5 - cc[abs(i-j)] = .5 - cc = cc[::-1] - for k in range(len(cc)): - A[:,:,-1-k] = A[:,:,-1-k] + X[1:,1:]*cc[-1-k] - - nrmA = np.linalg.norm(A[:,:,-1], 'fro') - for ii in range(A.shape[2]): - if np.linalg.norm(A[:,:,ii], 'fro')/nrmA > 1e-20: - break - A = A[:,:,ii:] - - ns = A.shape - AA = A.reshape(ns[0], ns[1]*ns[2], order='F') - - n = ns[0] - v = np.random.rand(n, 1) - a, b, c = (np.vstack([np.ones((n-1,1)), 2])/2).T, np.zeros(n), np.ones(n)/2 - X,Y = DLP(AA,v,a,b,c) - yvals,V = sLA.eig(Y,-X) - - y = yvals - x = np.divide(V[-2,:],V[-1,:]) - - t = np.copy(x) - x = x[np.logical_and(np.logical_and(np.imag(y)==0,abs(y)<1),abs(x)<1)] - y = y[np.logical_and(np.logical_and(np.imag(y)==0,abs(y)<1),abs(t)<1)] - - return list(zip(x,y)) - -def DLP(AA, v, a, b, c): - '''DLP constructs the DL pencil with ansatz vector v. - - [X,Y] = DLP(AA,V,A,B,C) returns the DL pencil with the orthogonal - basis defined by the recurrence relations A,B,C. - - Parameters - ---------- - a, b, c : numpy array, row vectors - - ''' - n, m = AA.shape - k = m // n - 1 - s = n * k - M = diags(np.vstack([a,b,c]),[0,1,2],(k,k+1)) - M = kron(M, np.eye(n)).tocsr() - - S = np.kron(v, AA) - for j in range(k): - jj = np.array([num for num in range(n*j, n*j+n)]) - AA[:,jj] = AA[:,jj].conj().T - T = np.kron(v.conj().T, AA.conj().T) - R = (M.conj().T @ S) - (T @ M) - - X, Y = np.zeros((s,s)), np.zeros((s,s)) - ii = np.array([num for num in range(n, n+s)]) - nn = np.array([num for num in range(n)]) - Y[nn,:], X[nn,:] = R[nn][:,ii]/M[0,0], T[nn,:]/M[0,0] - index = np.array([num for num in range(n,s+n)]) - Y[nn+n,:] = (R[nn+n][:,ii] - (M[0,n] * Y[nn,:]) + (Y[nn,:] @ M[:,index])) / M[n,n] - X[nn+n,:] = (T[nn+n,:] - Y[nn,:] - (M[0,n] * X[nn,:])) / M[n,n] - - for i in range(3,k+1): - ni = n*i-1 - jj = np.array([num for num in range(ni-n+1, ni+1)]) - j0 = jj-2*n - j1 = jj-n - M0, M1, m = M[ni-2*n, ni], M[ni-n, ni], M[ni, ni] - Y0, Y1, X0, X1 = Y[j0,:], Y[j1,:], X[j0,:], X[j1,:] - index = np.array([num for num in range(n, s+n)]) - Y[jj,:] = (R[jj][:,ii] - (M1 * Y1) - (M0 * Y0) + (Y1 @ M[:,index])) / m - X[jj,:] = (T[jj,:] - Y1 - (M1 * X1) - (M0 * X0)) / m - - return X, Y diff --git a/OLD_CODE/example.py b/OLD_CODE/example.py deleted file mode 100644 index a07497f8..00000000 --- a/OLD_CODE/example.py +++ /dev/null @@ -1,61 +0,0 @@ -# from the groebner library -from groebner.multi_cheb import MultiCheb -from groebner.multi_power import MultiPower -from groebner import maxheap -from groebner.groebner_class import Groebner - -# other libraries -import numpy as np -import pandas as pd -import scipy.linalg as la - - -# Example 1: 3-dimensional system - -A = MultiPower(np.array([ - [[1,1,3],[0,0,2],[6,4,3]], - [[1,2,3],[1,3,2],[5,1,4]], - [[2,4,3],[4,1,2],[4,2,3]] - ])) - -B = MultiPower(np.array([ - [[1,3,3],[0,3,2],[6,4,3]], - [[3,2,3],[1,13,1],[5,4,5]], - [[2,1,3],[4,1,2],[2,1,2]] - ])) - -C = MultiPower(np.array([ - [[2,3,3],[0,3,-2],[-6,4,3]], - [[-3,2,3],[1,1,1],[5,4,5]], - [[2,1,-3],[-4,1,2],[-2,1,2]] - ])) - - -grob = Groebner([A,B,C]) -grob.solve() - - -# Example 2: 2-dimensional system - -A = MultiPower(np.array([[1,0,-2,1],[2,0,5,1],[1,0,4,1],[2,0,3,1]])) -B = MultiPower(np.array([[1,0,8,7],[1,0,1,2],[0,4,1,2],[0,1,5,4]])) - -grob = Groebner([A,B]) -grob.solve() - - -# Example 3: Step-by-step - -# Step 1: Define the system. -A = MultiPower(np.array([[1,1],[2,3]])) -B = MultiPower(np.array([[1,1],[3,4]])) -C = MultiPower(np.array([[5,2],[2,4]])) -D = MultiPower(np.array([[1,1,1],[2,2,2],[3,3,3]])) - -grob = Groebner([A,B,C,D]) -grob.initialize_np_matrix() -input("The system as a matrix:\n" + str(grob.np_matrix)) - -# Step 2: Add phis. -grob.add_phi_to_matrix() -input("The matrix with Phis (?) appended:\n" + str(grob.np_matrix)) diff --git a/OLD_CODE/groebner_from_roots.py b/OLD_CODE/groebner_from_roots.py deleted file mode 100644 index fe1ab998..00000000 --- a/OLD_CODE/groebner_from_roots.py +++ /dev/null @@ -1,340 +0,0 @@ -''' - -Catherine Kellar -Nov 24 17 -Groebner basis generator given roots - -''' -import numpy as np -import scipy as sp -from scipy.misc import comb - -nchoosek = lambda n,k: int(comb(n,k)) - -def getMonBase(d,n): - '''base = getMonBase(d,n) - --------------------- - Returns a set of monomials of total degree d in n variables. Each row of - mon refers to a n-tuple of exponents of a monomial whereby each column - corresponds to a variable. - - Inputs: - d (int): maximum total degree of the monomials in the base - - n (int): number of exponents - - returns: - base (matrix): matrix of lexicographic ordered monomials of degree d and n exponents - - example: d= 2, n = 3 - - base = - - 2 0 0 - 1 1 0 - 1 0 1 - 0 2 0 - 0 1 1 - 0 0 2 - ''' - #MATLAB - ';' = supress output - # x = [1 2] is just an array - # x = [7;9] => [[7],[9]] - # x' => x.T - # x = zeros(6,3) => 6row x 3row of zeros - if n == 1: - base = [[d]] - else: - base = np.zeros(n) - base[0] = d - - for i in range(d-1,-1,-1): - ones_col = np.reshape(i*np.ones(int(comb(d-i+n-2,n-2))),(-1,1)) - square = getMonBase(d-i, n-1) - bottom_rows = np.hstack((ones_col, square)) - base = np.vstack((base, bottom_rows)) - return np.array(base).astype(int) - -def getMon(d,n): - '''#fullBase = getMon(d,n) or getMon(d,n,d0) - ---------------------------------------------- - - Returns a full canonical base of monomials of total degree d and in n - variables. Each row of mon refers to a n-tuple of exponents of a monomial - whereby each column corresponds to a variable. - - example: d= 2, n = 3 - - fullBase = - - 0 0 0 - 1 0 0 - 0 1 0 - 0 0 1 - 2 0 0 - 1 1 0 - 1 0 1 - 0 2 0 - 0 1 1 - 0 0 2 - ''' - if ~isempty(varargin) - d0 = varargin{1}; - if d0 > d - fullBase = []; - return - else - d0 = 0 - - if d0 == 0 - fullBase = zeros(nchoosek(d+n,n),n); - rowCounter = 2; - for i = 1 : d, - tempbase = getMonBase(i,n); - fullBase(rowCounter:rowCounter+length(tempbase)-1,:) = tempbase; - rowCounter = rowCounter+length(tempbase); - else - rowCounter = 1; - for i = d0 : d, - tempbase = getMonBase(i,n); - fullBase(rowCounter:rowCounter+length(tempbase)-1,:) = tempbase; - rowCounter = rowCounter+length(tempbase); - - return fullBase -''' -def diffBase(d,root,x): - #y = diffBase(d,root,x) - #---------------------- - # - #Applies the (partial) differential operator on a polynomial base vector - #of degree 'd' and evaluates it with 'root'. Graded xelicographic ordering - #is implicitly assumed. Multiple differentiation is also supported, x - #should then be a vector which indicates in which order there needs to be - #differentiated. - # - #y = column vector, contains the evaluated polynomial base vector - # - # d^n1x1 ... d^nnxn | - # ------------------ | - # dx1^n1 ... dxn^nn |x = root - # - #d = scalar, degree of the multivariate polynomial base vector - # - #root = row vector, used to evaluate differentiated base with - # - #x = row vector, index of the variable to which the differentation needs - # to take place, - # - # d d d - # 1 = ---, 2 = ---, 3 = ---, etc... - # dx1 dx2 dx3 - # - # when a higher order differentiation is required then x is a row - # vector indicating the order in which the variables need to be - # differentiated, eg. x = [1 1 2] means first a 2nd order - # differentiation to x1, then 1st order differentiation to x2. - # - #EXAMPLE - #------- - # - #second order derivative to y (= Dyy) of degree 6 - #evaluated in the point (2,3): - # - #diffBase(6,[2 3],[2 2]) - # - #first 1st order derivative to y, then 1st order derivative to x (= Dyx), - #degree 4 and evaluated in (-5,9): - # - #diffBase(4,[-5 9],[2 1]) - # - #CALLS - #----- - # - #getMon.m - # - #Kim Batselier, 2010-01-28 - - n = size(root,2); - monBase = getMon(d,n); - l = length(monBase); - #coef = zeros(l,1); - Dn = size(x,2); - - if size(x,2) ~= Dn - error('The ''x'' argument provided should be a vector indicating for each differentiation step to which variable needs to be differentiated') - end - - #check function inputs - if x > n - error(['You cannot differentiate with respect to x' num2str(x) ', there are only ' num2str(n) ' variables.']) - end - - coef = ones(l,1); - - #derivative of the monomial base - DmonBase = monBase; - for i = 1 : Dn - if exist('indices','var') - clear indices - end - #first run of the coefficients - indices = find(DmonBase(:,x(i)) ~= 0); - coef(indices) = coef(indices).*DmonBase(indices,x(i)); - - DmonBase = DmonBase-[zeros(l,x(i)-1) ones(l,1) zeros(l,n-x(i))]; - - #negative exponents are manually put to zero - DmonBase = DmonBase.*(~(DmonBase(:,x(i)) < 0)*ones(1,n)); - end - - temp = zeros(l,n); - for i = 1:length(indices) - temp(indices(i),:) = root.^DmonBase(indices(i),:); - end - - y = zeros(l,1); - y = coef.*prod(temp,2)./getDenom(x); - - function denom = getDenom(x) - xmax = max(x); - exp = zeros(1,xmax); - for i = 1 : xmax - exp(i) = length(find(x== i)); - end - denom = prod(factorial(exp)); - end - end - -def makeRoot(d,root): - #sol = makeRoot(d,root) - #---------------------- - # - #Evaluates a multivariate polynomial base vector. - # - #sol = column vector, contains the evaluated polynomial base vector - # - #d = scalar, degree of the multivariate polynomial base vector - # - #root = row vector, used to evaluate differentiated base with - # - #CALLS - #----- - # - #getMon.m - # - #Kim Batselier, 2010-01 - - n = size(root,2); - - monBase = getMon(d,n); - - l = length(monBase); - - for j = 1 : size(root,1) - temp = zeros(l,n); - for i = 1 : n - temp(:,i) = (root(j,i)*ones(l,1)).^monBase(:,i); - end - - sol(:,j) = prod(temp,2); - end - - end - -def getBasis(d, depth, root): - """calculates the subspace basis for the kernel, used in calculating a groebner basis. - Uses partial derivatives to achieve this. - - input: d(int): degree of monomial base - depth(int): max dgree of differentiation - root (array): the root at which we evaluate the derivatives - return: D(matrix): matrix with each column being the partial evaluated at the root - I(matrix): indices of differentiation - """ - n = size(root,2) #2nd dimension of root - finding n for number of variables that polys should be in - D(:,1) = makeRoot(d,root) - - I = getMon(depth,n); - - for i = 2 : size(I,1) - temp = ones(1,I(i,1)) - for j = 2 : n - temp = [temp j*ones(1,I(i,j))] - D(:,i) = diffBase(d,root,temp) - - I = I' - - -def groebner_from_roots(rootList): - """generates a groebner basis from a list of roots in n dimensions - - input: list of roots in n dimensions - must all be unique - - return: list of polynomials that form a reduced groebner basis for said roots - """ - a=[]; - b=1; - polysys=[]; - - m,n = rootList.size; - - mult=np.ones((1,m)); #there are no multiplicities allowed - m multiplicities total - - stop=0; - d=0; - - while not stop - d+=1; - # construct kernel K - # for now, make whole K everytime - K=[]; - for i in range(1,m): # for each root - # determine order of differentiation - ddiff=0; - while sp.misc.comb(ddiff+n,n) < m: - ddiff+=1; - #################### - D=getKSB(d,ddiff,root(i,:)); - #################### - K=[K D]; - - indices = nchoosek(d-1+n,n)+1:nchoosek(d+n,n); #indices of all monomials of degree d - - # remove multiples of A from indices - for i=1:length(indices) #for each new monomial - for j=1:length(a) #check whether it is multiple of a(j) - if sum((fite(indices(i),n)-fite(a(j),n)) >= 0) == n - #we found a multiple - indices(i) = 0; - end - end - end - indices(indices==0)=[]; - if isempty(indices) - stop=1; - else - #canonical decomposition - for i=1:length(indices) - [U,S,Z]=svd(full(K([b indices(i)],:)')); - if size(S,2)==1 - S=S(1,1); - else - S=diag(S); - end - tol=m*S(1)*eps; - rs=sum(S > tol); - - if (S(end) < tol) || (rs < length([b indices(i)])) - a=[a indices(i)]; - temp=zeros(1,nchoosek(d+n,n)); - temp([b indices(i)])=Z(:,end); - temp(abs(temp) 0: - old_polys, new_polys, matrix_polys = sort_reducible_polys(old_polys, new_polys) - matrix_polys = add_phi_to_matrix(old_polys, new_polys, matrix_polys, phi = phi) - matrix_polys, matrix_terms = add_r_to_matrix(matrix_polys, old_polys + new_polys) - matrix, matrix_terms = create_matrix(matrix_polys, matrix_terms) - old_polys += new_polys - new_polys = get_new_polys(matrix, matrix_terms, accuracy = accuracy, power = power) - groebner_basis = old_polys - if reducedGroebner: - groebner_basis = reduce_groebner_basis(groebner_basis, power) - return groebner_basis - -def sort_reducible_polys(old_polys, new_polys): - '''Finds which polynomials are reducible. - The polynomials that are reducible aren't used in phi and r calculations, they are just added - to the matrix. They are also removed from the poly list they are in, as whatever they are reduced - down to will be pulled out of the matrix at the end. - - A polynomial is considered reducible if the leading term of another polynomial divides it's leading term. - In the case of multiple polynomials having the same leading term, one is considered non-reducible and the - rest are reducible. - - Parameters - ---------- - old_polys : list - The polynomails that have already gone through the reduction before. - new_polys : list - The polynomials that have not gone through the reduction before. - - Returns - ------- - old_polys : list - The old_polys that are not reducible. - new_polys : list - The new_polys that are not reducible. - matrix_polys : list - The polynomials that are being put in the matrix. Any polynomial that is reducible is put in the matrix, - and if it is reducible because some other polynomials lead term divides it's lead term than the other - polynomial is multiplied by th monomial needed to give it the same lead term, and that is put in the matrix. - ''' - matrix_polys = list() - - old = old_polys - new = new_polys - polys = old + new - - polys = utils.sorted_polys_monomial(polys) - - old_polys = list() - new_polys = list() - - lms = defaultdict(list) - for p in polys: - lms[p.lead_term].append(p) - - # This list will contain one polynomial for each leading term, - # so all the leading terms will be unique - polys_with_unique_lm = list() - - for i in lms: - # If there are multiple polynomials with the same leading term - # we add just one of them to polys_with_unique_lm and add the - # rest to the matrix for reduction. - if len(lms[i]) > 1: - polys_with_unique_lm.append(lms[i][0]) - lms[i].remove(lms[i][0]) - for p in lms[i]: - matrix_polys.append(p) - else: - polys_with_unique_lm.append(lms[i][0]) - - divides_out = list() - - # Checks if anything in old_polys or new_polys can divide each other - # Example: if f1 divides f2, then f2 and LT(f2)/LT(f1) * f1 are - # added to the matrix - for i,j in itertools.permutations(polys_with_unique_lm,2): - if i in divides_out: - continue - if utils.divides(j.lead_term,i.lead_term): # j divides into i - divides_out.append(i) - matrix_polys.append(i) - matrix_polys.append(j.mon_mult(tuple(a-b for a,b in zip(i.lead_term,j.lead_term)))) - - # Now add everything that couldn't be divided out to the matrix, - # and put them back in either self.old_polys or self.new_polys, - # whichever they belonged to before. - # - # This means that the ones that got divided out are essentially - # removed from the list they belonged to, so they won't be - # used for phi or r calculations. - for i in polys_with_unique_lm: - if i not in divides_out: - matrix_polys.append(i) - if i in old: - old_polys.append(i) - elif i in new: - new_polys.append(i) - else: - raise ValueError("Where did this poly come from?") - return old_polys, new_polys, matrix_polys - -def add_phi_to_matrix(old_polys, new_polys, matrix_polys, phi = True): - '''Adds phi polynomials to the matrix. - - Given two polynomials we define two phi polynomials as follows. If the two polynomials A and B have leading - terms A.lt and B.lt, then call the least common multiple of these is lcm. Then the two phis are A*lcm/A.lt and - B*lcm/B.lt. - - Parameters - ---------- - old_polys : list - The polynomials that have already gone through the reduction before. - new_polys : list - The polynomials that have not gone through the reduction before. - - Returns - ------- - matrix_polys : list - The polynomials that are being put in the matrix. Both the ones that were put in earlier and the new - phi polynomials that are bing added. - ''' - # Find the set of all pairs of index the function will run through - - # Index_new iterate the tuple of every combination of the new_polys. - index_new = itertools.combinations(range(len(new_polys)),2) - # Index_oldnew iterates the tuple of every combination of new and old polynomials - index_oldnew = itertools.product(range(len(new_polys)),range(len(new_polys), - len(old_polys)+len(new_polys))) - all_index_combinations = set(itertools.chain(index_new,index_oldnew)) - - # Iterating through both possible combinations. - all_polys = new_polys + old_polys - while all_index_combinations: - i,j = all_index_combinations.pop() - if phi_criterion(all_polys, i, j, all_index_combinations, phi): - #calculate the phi's. - phi_a , phi_b = calc_phi(all_polys[i],all_polys[j]) - # add the phi's on to the Groebner Matrix. - matrix_polys.append(phi_a) - matrix_polys.append(phi_b) - return matrix_polys - -def phi_criterion(all_polys,i,j,B,phi): - '''Evaluates the phi criterion, given by: - False if: - 1) The polynomials at index i and j are relative primes or - 2) there exists an l such that (i,l) or (j,l) will not be considered in - the add_phi_to_matrix() method and LT(l) divides lcm(LT(i),LT(j)). - Otherwise, true. - - See proposition 8 in "Section 10: Improvements on Buchburger's algorithm. - - Parameters - ---------- - all_polys : list - List of all the polynomials. - i : int - Index of the first polynomial - j : int - Index of the second polynomial - B : set - Index of the set of polynomials to be considered. - - Returns - ------- - bool - Truth value of the phi criterion - ''' - if phi == False: - return True - - # Relative Prime check: If the lead terms of i and j are relative primes, phi is not needed - if all([a*b == 0 for a,b in zip(all_polys[i].lead_term,all_polys[j].lead_term)]): - return False - - # Another criterion - else: - for l in range(len(all_polys)): - # Checks that l is not j or i. - if l == j or l == i: - #print("\t{} is i or j".format(l)) - continue - - # Sorts the tuple (i,l) or (l,i) in order of smaller to bigger. - i_tuple = tuple(sorted((i,l))) - j_tuple = tuple(sorted((j,l))) - - # i_tuple and j_tuple needs to not be in B. - if j_tuple in B or i_tuple in B: - continue - - lcm = utils.lcm(all_polys[i],all_polys[j]) - lead_l = all_polys[l].lead_term - - # See if LT(poly[l]) divides lcm(LT(i),LT(j)) - if all([i-j>=0 for i,j in zip(lcm,lead_l)]) : - return False - - # Function will return True and calculate phi if none of the checks passed for all l's. - return True - -def calc_phi(a,b): - ''' - Calculates the phi-polynomials of the polynomials a and b. - - Parameters - ---------- - a, b : Polynomial - Input polynomials. - - Returns - ------- - Polynomial - The calculated phi polynomial for a. - Polynomial - The calculated phi polynomial for b. - - Notes - ----- - Phi polynomials are defined to be - .. math:: - \frac{lcm(LT(a), LT(b))}_{LT(a)} * a\\ - \frac{lcm(LT(a), LT(b))}_{LT(b)} * b - - The reasoning behind this definition is that both phi polynomials will have the - same leading term so they can be linearly reduced to produce a new, - smaller polynomial in the ideal. - - ''' - - lcm = utils.lcm(a,b) - a_quo = utils.quotient(lcm, a.lead_term) - b_quo = utils.quotient(lcm, b.lead_term) - return a.mon_mult(a_quo), b.mon_mult(b_quo) - -def add_r_to_matrix(matrix_polys, all_polys): - ''' - Finds the r polynomials and adds them to the matrix. - First makes Heap out of all potential monomials, then finds polynomials - with leading terms that divide it and add them to the matrix. - - Parameters - ---------- - matrix_polys : list - all_polys : list - - Returns - ------- - matrix_polys : list - matrix_terms : ndarray - ''' - matrixTermSet = set() - leadTermSet = set() - - for poly in matrix_polys: - for mon in zip(*np.where(poly.coeff != 0)): - matrixTermSet.add(tuple(mon)) - leadTermSet.add(poly.lead_term) - - others = list() - for term in matrixTermSet: - if term not in leadTermSet: - others.append(term) - - sorted_polys = utils.sorted_polys_coeff(all_polys) - - for term in others: - r = calc_r(term, sorted_polys) - if r is not None: - for mon in zip(*np.where(r.coeff != 0)): - if mon not in matrixTermSet and mon is not r.lead_term: - others.append(mon) - matrixTermSet.add(mon) - matrix_polys.append(r) - - matrix_terms = np.array(matrixTermSet.pop()) - for term in matrixTermSet: - matrix_terms = np.vstack((matrix_terms,term)) - - return matrix_polys, matrix_terms - -def calc_r(m, polys): - '''Calculates an r polynomial that has a leading monomial m. - - Parameters - ---------- - m : array-like - The leading monomial that the r polynomial should have. - polys : array-like - Contains polynomial objects from which to create the r polynomial. - - Returns - ------- - Polynomial or None - If no polynomial divides m, returns None. Otherwise, returns - the r polynomial with leading monomial m. - - Notes - ----- - The r polynomial corresponding to m is defined as follows: - - Find a polynomial p such that the leading monomial of p divides m. - Then the r polynomial is - - .. math:: - r = \frac{m}_{LT(p)} * p - - The reason we use r polynomials is because now any polynomial with - m as a term will be linearly reduced by r. - - ''' - for poly in polys: - LT_p = list(poly.lead_term) - if len(LT_p) == len(m) and utils.divides(LT_p, m): - quotient = utils.quotient(m, LT_p) - if not LT_p == m: #Make sure c isn't all 0 - return poly.mon_mult(quotient) - return None - -def sort_matrix_terms(matrix_terms): - '''Sorts the matrix_terms by term order. - So the highest terms come first, the lowest ones last. - - Parameters - ---------- - matrix_terms : ndarray - Array where each row is one of the terms in the matrix. - - Returns - ------- - matrix_terms : ndarray - The sorted matrix_terms. - ''' - termList = list() - for term in matrix_terms: - termList.append(Term(term)) - argsort_list = np.argsort(termList)[::-1] - return matrix_terms[argsort_list] - -def create_matrix(matrix_polys, matrix_terms = None): - ''' Builds a Macaulay matrix. - - If there is only one term in the matrix it won't work. - - Parameters - ---------- - matrix_polys : list. - Contains numpy arrays that hold the polynomials to be put in the matrix. - matrix_terms : ndarray - The terms that will exist in the matrix. Not sorted yet. - Defaults to None, in which case it will be found in the function. - Returns - ------- - matrix : ndarray - The Macaulay matrix. - ''' - bigShape = np.maximum.reduce([p.coeff.shape for p in matrix_polys]) - if matrix_terms is None: - #Finds the matrix terms. - non_zeroSet = set() - for poly in matrix_polys: - for term in zip(*np.where(poly.coeff != 0)): - non_zeroSet.add(term) - matrix_terms = np.array(non_zeroSet.pop()) - for term in non_zeroSet: - matrix_terms = np.vstack((matrix_terms,term)) - - matrix_terms = sort_matrix_terms(matrix_terms) - - #Get the slices needed to pull the matrix_terms from the coeff matrix. - matrix_term_indexes = list() - for i in range(len(bigShape)): - matrix_term_indexes.append(matrix_terms.T[i]) - - #Adds the poly_coeffs to flat_polys, using added_zeros to make sure every term is in there. - added_zeros = np.zeros(bigShape) - flat_polys = list() - for poly in matrix_polys: - coeff = poly.coeff - slices = slice_top(coeff) - added_zeros[slices] = coeff - flat_polys.append(added_zeros[matrix_term_indexes]) - added_zeros[slices] = np.zeros_like(coeff) - - #Make the matrix - matrix = np.vstack(flat_polys[::-1]) - - #Sorts the rows of the matrix so it is close to upper triangular. - matrix = utils.row_swap_matrix(matrix) - return matrix, matrix_terms - - -def get_polys_from_matrix(matrix, matrix_terms, rows, power): - '''Creates polynomial objects from the specified rows of the given matrix. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix with rows corresponding to polynomials, columns corresponding - to monomials, and entries corresponding to coefficients. - matrix_terms : array-like - The column labels for matrix in order. Contains Term objects. - rows : iterable - The rows for which to create polynomial objects. Contains integers. - power : bool - If true, the polynomials returned will be MultiPower objects. - Otherwise, they will be MultiCheb. - Returns - ------- - poly_list : list - Polynomial objects corresponding to the specified rows. - ''' - - shape = [] - p_list = [] - shape = np.maximum.reduce([term for term in matrix_terms]) - shape += np.ones_like(shape) - spots = list() - for dim in range(matrix_terms.shape[1]): - spots.append(matrix_terms.T[dim]) - - # Grabs each polynomial, makes coeff matrix and constructs object - for i in rows: - p = matrix[i] - coeff = np.zeros(shape) - coeff[spots] = p - if power: - poly = MultiPower(coeff) - else: - poly = MultiCheb(coeff) - - if poly.lead_term != None: - p_list.append(poly) - return p_list - -def row_echelon(matrix, accuracy=1.e-10): - '''Reduces the matrix to row echelon form and removes all zero rows. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix of interest. - - Returns - ------- - reduced_matrix : (M,N) ndarray - The matrix in row echelon form with all zero rows removed. - - ''' - independent_rows, dependent_rows, Q = utils.row_linear_dependencies(matrix, accuracy=accuracy) - full_rank_matrix = matrix[independent_rows] - - reduced_matrix = utils.rrqr_reduce2(full_rank_matrix) - reduced_matrix = utils.clean_zeros_from_matrix(reduced_matrix) - - non_zero_rows = np.sum(abs(reduced_matrix),axis=1) != 0 - if np.sum(non_zero_rows) != reduced_matrix.shape[0]: - warnings.warn("Full rank matrix has zero rows.", InstabilityWarning) - - reduced_matrix = reduced_matrix[non_zero_rows,:] #Only keeps the non-zero polymonials - - return reduced_matrix - -def lead_term_columns(matrix): - '''Finds all columns that correspond to the leading term of some polynomial - in the matrix. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix of interest. - - Returns - ------- - LT_columns : set - The set of column indexes that correspond to leading terms - ''' - LT_columns = set() - - already_looked_at = set() - for i, j in zip(*np.where(matrix!=0)): - if i not in already_looked_at: - LT_columns.add(j) - already_looked_at.add(i) - - return LT_columns - -def get_new_polys(matrix, matrix_terms, accuracy=1.e-10, power=False): - '''Reduces the given matrix and finds all polynomials that have new - leading terms after reduction. - - Parameters - ---------- - matrix : (M,N) ndarray - The matrix where rows correspond to polynomials, columns to terms, - and entries to coefficients. - matrix_terms : (M,N) ndarray - Each row corresponds to a column in matrix, each column corresponds - to a variable, and entries correspond to the exponent of that variable - accuracy : float - Entries in matrix lower than accuracy will be counted as zero during - the reduction process - power : bool - True if the polynomials are in the power basis, false for chebyshev. - - Returns - ------- - new_polys : list - Contains polynomial objects whose leading terms weren't leading terms - in the matrix passed in, but the were after reduction. - ''' - lead_term_before = lead_term_columns(matrix) - reduced_matrix = row_echelon(matrix, accuracy=accuracy) - lead_term_after = lead_term_columns(reduced_matrix) - - new_lead_terms = lead_term_after - lead_term_before - - #Get the new polynomials - new_poly_spots = list() - already_looked_at = set() #rows whose leading monomial we've already checked - for i, j in zip(*np.where(reduced_matrix!=0)): - if i not in already_looked_at: - if j in new_lead_terms: - new_poly_spots.append(i) - already_looked_at.add(i) - - new_polys = get_polys_from_matrix(reduced_matrix, \ - matrix_terms, new_poly_spots, power=power) - - return new_polys - -def reduce_groebner_basis(groebner_basis, power): - ''' - Uses triangular solve to get a fully reduced Groebner basis. - - Parameters - ---------- - groebner_basis : list - List of polynomials forming a Groebner basis. - power : bool - If true, the polynomials returned will be MultiPower objects. - Otherwise, they will be MultiCheb. - - Returns - ------- - list - List of Polynomials forming a fully reduced Groebner basis. - ''' - if len(groebner_basis) == 1: - poly = groebner_basis[0] - poly.coeff = poly.coeff/poly.lead_coeff - groebner_basis[0] = poly - return groebner_basis - - #This should be done eventually, Just make sure to pull out only the original GB. - #matrix_polys, matrix_terms = add_r_to_matrix(groebner_basis, groebner_basis) - #matrix, matrix_terms = create_matrix(matrix_polys, matrix_terms) - - matrix, matrix_terms = create_matrix(groebner_basis) - matrix = utils.triangular_solve(matrix) - rows = np.arange(matrix.shape[0]) - return get_polys_from_matrix(matrix, matrix_terms, rows, power) diff --git a/OLD_CODE/macaulay_sparse.py b/OLD_CODE/macaulay_sparse.py deleted file mode 100644 index 81e59466..00000000 --- a/OLD_CODE/macaulay_sparse.py +++ /dev/null @@ -1,56 +0,0 @@ -import numpy as np -import math -from scipy.linalg import lu, qr, solve_triangular, inv, solve, svd -from numpy.linalg import cond -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower, is_power -from scipy.sparse import csr_matrix, vstack -from numalgsolve.utils import Term, row_swap_matrix, clean_zeros_from_matrix, inverse_P, triangular_solve, divides, slice_top, mon_combos -import matplotlib.pyplot as plt -from collections import defaultdict -import numalgsolve.utils as utils - -def Macaulay(initial_poly_list, global_accuracy = 1.e-10): - """ - Accepts a list of polynomials and use them to construct a Macaulay matrix. - - parameters - -------- - initial_poly_list: list - Polynomials for Macaulay construction. - global_accuracy : float - Round-off parameter: values within global_accuracy of zero are rounded to zero. Defaults to 1e-10. - - Returns - ------- - final_polys : list - Reduced Macaulay matrix that can be passed into the root finder. - """ - Power = is_power(initial_poly_list) - - poly_coeff_list = [] - degree = find_degree(initial_poly_list) - - for i in initial_poly_list: - poly_coeff_list = add_polys(degree, i, poly_coeff_list) - - matrix, matrix_terms = create_matrix(poly_coeff_list) - - plt.matshow(matrix) - plt.show() - - #rrqr_reduce2 and rrqr_reduce same pretty matched on stability, though I feel like 2 should be better. - matrix = utils.rrqr_reduce2(matrix, global_accuracy = global_accuracy) # here - matrix = clean_zeros_from_matrix(matrix) - non_zero_rows = np.sum(np.abs(matrix),axis=1) != 0 - matrix = matrix[non_zero_rows,:] #Only keeps the non_zero_polymonials - - matrix = triangular_solve(matrix) - matrix = clean_zeros_from_matrix(matrix) - - #The other reduction option. I thought it would be really stable but seems to be the worst of the three. - #matrix = matrixReduce(matrix, triangular_solve = True, global_accuracy = global_accuracy) - - rows = get_good_rows(matrix, matrix_terms) - final_polys = get_polys_from_matrix(matrix, matrix_terms, rows, Power) - - return final_polys diff --git a/OLD_CODE/old_root_finder_code.py b/OLD_CODE/old_root_finder_code.py deleted file mode 100644 index 4730b2a2..00000000 --- a/OLD_CODE/old_root_finder_code.py +++ /dev/null @@ -1,353 +0,0 @@ -""" -CODE THE WAS IN THE ROOT_FINDER.PY DOCUMENT, WE NO LONGER USE IT SO IT IS BEING REMOVED. -""" - -def roots(polys, method = 'Mult'): - polys = match_poly_dimensions(polys) - - # Determine polynomial type - poly_type = is_power(polys, return_string = True) - - if method == 'TVB' or method == 'new_TVB': - m_f, var_dict = TVBMultMatrix(polys, poly_type, method) - else: - GB, m_f, var_dict = groebnerMultMatrix(polys, poly_type, method) - - # both TVBMultMatrix and groebnerMultMatrix will return m_f as - # -1 if the ideal is not zero dimensional or if there are no roots - if type(m_f) == int: - return -1 - - # Get list of indexes of single variables and store vars that were not - # in the vector space basis. - dim = max(f.dim for f in polys) - var_list = get_var_list(dim) - var_indexes = [-1]*dim - vars_not_in_basis = {} - for i in range(len(var_list)): - var = var_list[i] # x_i - if var in var_dict: - var_indexes[i] = var_dict[var] - else: - # maps the position in the root to its variable - vars_not_in_basis[i] = var - - vnib = False - if len(vars_not_in_basis) != 0: - if method == 'TVB': - print("This isn't working yet...") - return -1 - vnib = True - - # Get left eigenvectors - - e = np.linalg.eig(m_f.T) - eig = e[1] - num_vectors = eig.shape[1] - - eig_vectors = [eig[:,i] for i in range(num_vectors)] # columns of eig - roots = [] - for v in eig_vectors: - if v[var_dict[tuple(0 for i in range(dim))]] == 0: - continue - root = np.zeros(dim, dtype=complex) - # This will always work because var_indexes and root have the - # same length - dim - and var_indexes has the variables in the - # order they should be in the root - for i in range(dim): - x_i_pos = var_indexes[i] - if x_i_pos != -1: - root[i] = v[x_i_pos]/v[var_dict[tuple(0 for i in range(dim))]] - if vnib: - # Go through the indexes of variables not in the basis in - # decreasing order. It must be done in decreasing order for the - # roots to be calculated correctly, since the vars with lower - # indexes depend on the ones with higher indexes - for pos in list(vars_not_in_basis.keys())[::-1]: - GB_poly = _get_poly_with_LT(vars_not_in_basis[pos], GB) - var_value = GB_poly(root) * -1 - root[pos] = var_value - roots.append(root) - #roots.append(newton_polish(polys,root)) - return roots - - - - -def groebnerMultMatrix(polys, poly_type, method): - ''' - Called by the main roots function to calculate the multiplication matrix - if we are using the f4 Groebner or Macaulay implementation. It returns - everything that the roots function needs to proceed with the root finding - calculations. - Parameters - ---------- - polys : list of Polynomials - Polynomials to find the common roots of. - poly_type : string - The type of the Polynomials in polys. "MultiCheb" or "MultiPower". - - Returns - ------- - GB : list of polynomial objects - The calculated groebner basis. - m_f : 2D numpy array - the multiplication matrix for a random polynomial f - var_dict : dictionary of tuples to ints - maps the variable to its location in the vector space basis, so if - VB is [1, x, y, xy] then var_dict is {(1,0):1, (0,1):2} - ''' - # Calculate groebner basis - if method == 'Groebner': - GB = F4(polys) - elif method == 'Macaulay': - GB = Macaulay(polys) - else: - GB = new_macaulay(polys) - - dim = max(g.dim for g in GB) # dimension of the polynomials - - # Get the random polynomial and check for finitely many solutions - f, var_list = _random_poly(poly_type, dim) - - if not _finitelyManySolutions(GB, var_list): - return (-1,-1,-1) - - # Get the vector space basis - VB, var_dict = vectorSpaceBasis(GB) - - # Make the multiplication matrix - m_f = multMatrix(f, GB, VB) - - return GB, m_f, var_dict - -def _finitelyManySolutions(GB, var_list): - '''Returns true if the number of solutions N satisfies 1 <= N < infinity - - Parameters - ---------- - GB : list of Polynomials - Groebner basis - - Returns - ------- - bool - True if the number of solutions is nonzero and finite. False otherwise. - ''' - - # Check for no solutions - if len(GB) == 1 and all([i==1 for i in GB[0].coeff.shape]): - print("No solutions") - return False - - # Check for infinitely many solutions - if not _test_zero_dimensional(var_list, GB): - print("Ideal is not zero-dimensional; cannot calculate roots.") - return False - - return True - -def sorted_polys_coeff(polys): - ''' - Sorts the polynomials by how much bigger the leading coefficient is than the rest of the coeff matrix. - - Parameters - ---------- - polys : list - Polynomials to sort. - - Returns - ------- - sorted_polys : list - Sorted list of Polynomials. - ''' - lead_coeffs = list() - for poly in polys: - lead_coeffs.append(np.abs(poly.lead_coeff)/np.sum(np.abs(poly.coeff))) #The lead_coeff to other stuff ratio. - argsort_list = sorted(range(len(lead_coeffs)), key=lead_coeffs.__getitem__)[::-1] - sorted_polys = list() - for i in argsort_list: - sorted_polys.append(polys[i]) - return sorted_polys - -def multMatrix(poly, GB, basisList): - ''' - Finds the matrix of the linear operator m_f on A = C[x_1,...,x_n]/I - where f is the polynomial argument. The linear operator m_f is defined - as m_f([g]) = [f]*[g] where [f] represents the coset of f in - A. Since m_f is a linear operator on A, it can be represented by its - matrix with respect to the vector space basis. - parameters - ---------- - poly : polynomial object - The polynomial f for which to find the matrix m_f. - GB: list of polynomial objects - Polynomials that make up a Groebner basis for the ideal - basisList : list of tuples - The monomials that make up a basis for the vector space A - returns - ------- - multMatrix : square numpy array - The matrix m_f - ''' - basisSet = set(basisList) - basisTerms = np.vstack(basisList) - - slices = list() - for i in range(len(basisTerms[0])): - slices.append(basisTerms.T[i]) - - GB = sorted_polys_coeff(GB) - - dim = len(basisList) # Dimension of the vector space basis - - multMatrix = np.zeros((dim, dim)) - for i in range(dim): - monomial = basisList[i] - poly_ = poly.mon_mult(monomial) - multMatrix[:,i] = coordinateVector(poly_, GB, basisSet, slices) - - return multMatrix - -def vectorSpaceBasis(GB): - ''' - parameters - ---------- - GB: list - Polynomials that make up a Groebner basis for the ideal. - - Returns - ------- - basis : list - tuples representing the monomials in the vector space basis - var_to_pos_dict : dictionary - maps each variable to its position in the vector space basis - ''' - LT_G = [f.lead_term for f in GB] - possibleVarDegrees = [range(max(tup)) for tup in zip(*LT_G)] - possibleMonomials = itertools.product(*possibleVarDegrees) - basis = [] - var_to_pos_dict = {} - for mon in possibleMonomials: - divisible = False - for LT in LT_G: - if divides(LT, mon): - divisible = True - break - if not divisible: - basis.append(mon) - if (sum(mon) == 1) or (sum(mon) == 0): - var_to_pos_dict[mon] = basis.index(mon) - - return basis, var_to_pos_dict - -def coordinateVector(poly, GB, basisSet, slices): - ''' - parameters - ---------- - reducedPoly : polynomial object - The polynomial for which to find the coordinate vector of its coset. - GB : list of polynomial objects - Polynomials that make up a Groebner basis for the ideal - basisSet : set of tuples - The monomials that make up a basis for the vector space - slices : A list of np.arrays - Contains the inexes of the vector basis so those spots can be pulled out of he coeff matrix quickly. - - Returns - ------- - coordinateVector : numpy array - The coordinate vector of the given polynomial's coset in - A = C[x_1,...x_n]/I as a vector space over C - ''' - - poly_coeff = reduce_poly(poly, GB, basisSet) - return poly_coeff[slices] - -def reduce_poly(poly, divisors, basisSet, permitted_round_error=1e-10): - ''' - Divides a polynomial by a set of divisor polynomials using the standard - multivariate division algorithm and returns the remainder - parameters - ---------- - poly : polynomial object - the polynomial to be divided by the Groebner basis - divisors : list of polynomial objects - polynomials to divide poly by - basisSet : set of tuples - The monomials that make up a basis for the vector space - returns - ------- - polynomial object - the remainder of poly / divisors - ''' - remainder_shape = np.maximum.reduce([p.shape for p in divisors]) - remainder = np.zeros(remainder_shape) - - for term in zip(*np.where(poly.coeff != 0)): - if term in basisSet: - remainder[term] += poly.coeff[term] - poly.coeff[term] = 0 - poly.__init__(poly.coeff, clean_zeros = False) - - # while poly is not the zero polynomial - while np.any(poly.coeff): - divisible = False - # Go through polynomials in set of divisors - for divisor in divisors: - # If the LT of the divisor divides the LT of poly - if divides(divisor.lead_term, poly.lead_term): - # Get the quotient LT(poly)/LT(divisor) - LT_quotient = np.subtract(poly.lead_term, divisor.lead_term) - - poly_to_subtract_coeff = divisor.mon_mult(LT_quotient, returnType = 'Matrix') - # Match sizes of poly_to_subtract and poly so - # poly_to_subtract.coeff can be subtracted from poly.coeff - poly_coeff, poly_to_subtract_coeff = match_size(poly.coeff, poly_to_subtract_coeff) - new_coeff = poly_coeff - \ - (poly.lead_coeff/poly_to_subtract_coeff[tuple(divisor.lead_term+LT_quotient)])*poly_to_subtract_coeff - - new_coeff[np.where(np.abs(new_coeff) < permitted_round_error)]=0 - - for term in zip(*np.where(new_coeff != 0)): - if term in basisSet: - remainder[term] += new_coeff[term] - new_coeff[term] = 0 - - poly.__init__(new_coeff, clean_zeros = False) - divisible = True - break - return remainder - -def _get_poly_with_LT(LT, GB): - """Gets a polynomial from a Groebner basis with a given leading term - - Parameters - ---------- - LT : ? - Leading term to look for. - GB : list of Polynomials - Groebner basis. - - Returns - ------- - poly : Polynomial - Polynomial with leading term LT. - """ - for poly in GB: - if poly.lead_term == LT: - return poly - -def _test_zero_dimensional(_vars, GB): - LT_list = [p.lead_term for p in GB] - - for var in _vars: - exists_multiple = False - for LT in LT_list: - if np.linalg.matrix_rank(np.array([list(var), list(LT)])) == 1: - exists_multiple = True - break - if not exists_multiple: - return False - - return True \ No newline at end of file diff --git a/OLD_CODE/old_tests.py b/OLD_CODE/old_tests.py deleted file mode 100644 index 5f28235a..00000000 --- a/OLD_CODE/old_tests.py +++ /dev/null @@ -1,5 +0,0 @@ -""" -OLD TESTS THE ARE ON CODE WE NO LONGER USE. -""" - - diff --git a/OLD_CODE/projective_space.py b/OLD_CODE/projective_space.py deleted file mode 100644 index 59b76d1e..00000000 --- a/OLD_CODE/projective_space.py +++ /dev/null @@ -1,294 +0,0 @@ -import numpy as np -import itertools -from math import isnan -from numpy.fft import fftn -from numpy.linalg import LinAlgError - -from numalgsolve.DivisionMatrixes.ChebyshevDivision import division_cheb -from numalgsolve.polynomial import MultiCheb, MultiPower -from numalgsolve.root_finder import roots, newton_polish -from numalgsolve.utils import clean_zeros_from_matrix -import time - -def projective_solve(poly_list, rmSize = 1.e-2): - '''Finds the roots of given polynomials using projective space. - - Parameters - ---------- - poly_list : list - A list of polynomials. - rmSize : float - The size of the pertubations in the rotation matrix. The rotation matrix is the identity matrix - with pertubations of about this size in each spot to make it random. - - Returns - ------- - zero_set : set - A set of the distinct zeros of the system. In order to be able to put them in a set and not - double count equivalent zeros found in sperate hyperplanes, the zeros are rounded to 5 decimal spots. - ''' - dim = poly_list[0].dim - inv_rotation = get_rotation_matrix(dim+1, size=rmSize) #The inverse of the matrix is how the projective space is rotated. - proejctive_poly_list = project_poly_list(poly_list) - all_zeros = list() - for hyperplane in range(dim+1): - values = list() - cheb_poly_list = list() - for poly in proejctive_poly_list: - cheb = triangular_cheb_approx(poly, hyperplane, inv_rotation, dim, poly.degree) - cheb_poly_list.append(cheb) - #zeros = division_cheb(cheb_poly_list, divisor_var = 0) - zeros = roots(cheb_poly_list, method='TVB') - #print(zeros) - for zero in zeros: - pZero = np.insert(zero, hyperplane, 1) - rZero = inv_rotation@pZero - fullZero = rZero/rZero[-1] - all_zeros.append(fullZero[:-1]) - return getZeroSet(all_zeros, poly_list) - -def get_rotation_matrix(dim, size = 1.e-5): - '''Gets the matrix the projective space will be rotated by to ensure the solver works. - - Parameters - ---------- - dim : int - The dimension of the space, so the size the matrixneeds to be. - size : float - The size of the pertubations in the rotation matrix. The rotation matrix is the identity matrix - with pertubations of about this size in each spot to make it random. - - Returns - ------- - A : numpy array - The rotation matrix. - ''' - A = np.eye(dim) - A += np.random.rand(dim,dim)*size - return A - -def cheb_interpND(poly, hyperplane, inv_rotation, dim, deg): - '''Gives an n-dimensional interpolation of polynomial on a given hyperplace after a given rotation. - - It finds the chebyshev nodes in the given hyperplane, projects them back into projective space, uses inv_rotation - to rotate them back, and then evaluates them on poly to get the values of the cheb nodes on the rotated projected - polynomial, but in a stable way. The chebyshev coefficients are then found using a fast fourier transform. - - Parameters - ---------- - poly : Polynomial - This is one of the given polynomials in projective space. So it must be projected into proejective space first. - hyperplace : int - Which hyperplance we want to approximate in. Between 0 and n inclusive when the original polynomials are n-1 - dimensional. So the projective space polynomials are n dimensional. n is the original space. - inv_rotation : numpy array - The inverse of the rotation of the projective space. - dim : int - The dimension of the chebysehv polynomial we want. - deg : int - The degree of the chebyshev polynomial we want. - - Returns - ------- - coeffs : numpy array - The coefficients of the chebyshev polynomial. - ''' - nodes = getChebNodes(dim,deg) - newLevel = np.ones_like(nodes[0]) - shape = [1]+list(newLevel.shape) - newLevel = newLevel.reshape(shape) - final = np.concatenate((nodes, newLevel), axis = 0) - final[-1], final[hyperplane] = final[hyperplane], final[-1].copy() - rotated = np.apply_along_axis(mult,0,final,inv_rotation) - #values = np.apply_along_axis(evaluate,0,rotated,poly) - values = poly.evaluate_at(rotated) - coeffs = np.real(fftn(values/deg**dim)) - - for i in range(dim): - idx0 = [slice(None)] * (dim) - idx0[i] = 0 - idx00 = [slice(None)] * (dim) - idx00[i] = deg - coeffs[idx0] = coeffs[idx0]/2 - coeffs[idx00] = coeffs[idx00]/2 - - slices = list() - for i in range(dim): - slices.append(slice(0,deg+1)) - - return coeffs[slices] - -def triangular_cheb_approx(poly, hyperplane, inv_rotation, dim, deg, accuracy = 1.e-10): - '''Gives an n-dimensional triangular interpolation of polynomial on a given hyperplace after a given rotation. - - It calls the normal nD-interpolation, but then cuts off the small non-triangular part to make it triangular. - - Parameters - ---------- - poly : Polynomial - This is one of the given polynomials in projective space. So it must be projected into proejective space first. - hyperplace : int - Which hyperplance we want to approximate in. Between 0 and n inclusive when the original polynomials are n-1 - dimensional. So the projective space polynomials are n dimensional. n is the original space. - inv_rotation : numpy array - The inverse of the rotation of the projective space. - dim : int - The dimension of the chebysehv polynomial we want. - deg : int - The degree of the chebyshev polynomial we want. - - Returns - ------- - triangular_cheb_approx : MultiCheb - The chebyshev polynomial we want. - ''' - cheb = cheb_interpND(poly, hyperplane, inv_rotation, dim, deg) - clean_zeros_from_matrix(cheb) - return MultiCheb(cheb) - -def getChebNodes(dim, cheb_deg): - '''Gets the chebyshev nodes to approximate a chebshev polynomial of the given dimension and degree. - - Parameters - ---------- - dim : int - The dimension of the chebysehv polynomial we want. - cheb_deg : int - The degree of the chebyshev polynomial we want. - - Returns - ------- - getChebNodes : numpy arrary - The array has dimension dim+1. Iterating through along the first dimension gives the tuples that are - the Chebyshev node coordinates. So each Chebyshev node has one point in each of the matrixes in the - final stack. - ''' - cheb_nodes = np.cos((np.pi*np.arange(2*cheb_deg))/cheb_deg) - nodes = np.array(list(itertools.product(cheb_nodes, repeat=dim))) - stacks = list() - for i in range(dim): - stacks.append(nodes[:,i].reshape([2*cheb_deg]*dim)) - return np.stack(stacks) - -def evaluate(x,poly): - '''Evaluates a polynomial at a point. Useful so we can use it in a np.apply_along_axis. - - Parameters - ---------- - x : tuple - The point at which we want to evaluate the polynomial. - poly : Polynomial - The polynomial we are evaluating on. - - Returns - ------- - evaluate : float - The evaluated value. - ''' - return poly.evaluate_at(x) - -def mult(x,A): - '''Multiplies a vector by a matrix A. Useful so we can use it in a np.apply_along_axis. - - Parameters - ---------- - x : numpy array - A one-dimensional vector. - A : numpy array - The matrix we multiply the vector by. - - Returns - ------- - mult : float - The evaluated value. - ''' - return A@x - -def project_poly_list(poly_list): - '''Projects the polynomials in a list into projective space. - - Parameters - ---------- - poly_list : list - A list of polynomials. - - Returns - ------- - projected_list : list - The same polynomails in projective space. - ''' - projected_list = list() - for poly in poly_list: - projected_list.append(project(poly)) - return projected_list - -def project(poly): - '''Projects a polynomial into projective space. - - Parameters - ---------- - poly : Polynomial - The polynomial to project. - - Returns - ------- - project : Polynomial - The same polynomail in projective space. - ''' - Pcoeff = np.zeros([poly.degree+1]*(poly.dim+1)) - for spot in zip(*np.where(poly.coeff != 0)): - new_spot = list(spot) - new_spot = new_spot + [poly.degree - np.sum(spot)] - Pcoeff[tuple(new_spot)] = poly.coeff[spot] - if isinstance(poly,MultiPower): - return MultiPower(Pcoeff) - else: - return MultiCheb(Pcoeff) - -def getZeroSet(zeros, polys): - '''Finds the number of distinct zeros given a list of possibly repeating zeros. - - Parameters - ---------- - zeros: list - A list of zeros of the polynomias system. - polys : list - A list of the polynomials we want the common zeros of. - - Returns - ------- - zeroSet : set - A set of the common zeros, rounded to 5 decimal places. - ''' - dim = polys[0].dim - zeroSet = set() - for zero in zeros: - - #good = True - #for poly in polys: - # if abs(poly.evaluate_at(zero)) > 1: - # good = False - # break - #if not good: - # continue - - inf = False - try: - polished = newton_polish(polys,zero) - except LinAlgError as e: - inf = True - if inf or isnan(polished[0].real): - continue - good = True - for poly in polys: - if abs(poly.evaluate_at(polished)) > 1.e-3: - good = False - break - if not good: - continue - rounded = list([0]*dim) - for i in range(dim): - rounded[i] = complex(round(polished[i].real,8),round(polished[i].imag,8)) - rounded = tuple(rounded) - zeroSet.add(rounded) - return zeroSet \ No newline at end of file diff --git a/OLD_CODE/test_ChebDivision.py b/OLD_CODE/test_ChebDivision.py deleted file mode 100644 index 69cac76a..00000000 --- a/OLD_CODE/test_ChebDivision.py +++ /dev/null @@ -1,90 +0,0 @@ -"""TESTS REMOVED FOR NOW BECAUSE WE DON'T USE THIS CODE""" - -""" - -import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.ChebyshevDivision import division_cheb - -def getPoly(deg,dim): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - return MultiCheb(ACoeff) - -def correctZeros(polys, divisor_var, checkNumber = False): - ''' - A helper function. Takes in polynomials, find their common zeros, and calculates how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = division_cheb(polys, divisor_var = divisor_var) - assert(zeros != -1) - if checkNumber: - expectedNum = np.product([poly.degree for poly in polys]) - assert(len(zeros) == expectedNum) - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 95) - -def test_Division_Cheb(): - ''' - The following tests will run division_cheb on relatively small random upper trianguler MultiCheb polynomials. - The assert statements will be inside of the correctZeros helper function. - ''' - #Case 1 - Two 2D degree 10 polynomials. - A = getPoly(10,2) - B = getPoly(10,2) - correctZeros([A,B], 0) - correctZeros([A,B], 1) - - #Case 2 - Two 2D, one degree 5, one degree 7. - A = getPoly(5,2) - B = getPoly(7,2) - correctZeros([A,B], 0) - correctZeros([A,B], 1) - - #Case 3 - Three 3D degree 4 polynomials. - A = getPoly(4,3) - B = getPoly(4,3) - C = getPoly(4,3) - correctZeros([A,B,C], 0) - correctZeros([A,B,C], 1) - correctZeros([A,B,C], 2) - - #Case 4 - Three 3D of degrees 3,4 and 5 - A = getPoly(3,3) - B = getPoly(4,3) - C = getPoly(5,3) - correctZeros([A,B,C], 0) - correctZeros([A,B,C], 1) - correctZeros([A,B,C], 2) - - #Case 5 - Four 4D degree 2 polynomials. - A = getPoly(2,4) - B = getPoly(2,4) - C = getPoly(2,4) - D = getPoly(2,4) - correctZeros([A,B,C,D], 0) - correctZeros([A,B,C,D], 1) - correctZeros([A,B,C,D], 2) - correctZeros([A,B,C,D], 3) - -""" \ No newline at end of file diff --git a/OLD_CODE/test_PowerDivision.py b/OLD_CODE/test_PowerDivision.py deleted file mode 100644 index 3415fb9e..00000000 --- a/OLD_CODE/test_PowerDivision.py +++ /dev/null @@ -1,90 +0,0 @@ -"""TESTS REMOVED FOR NOW BECAUSE WE DON'T USE THIS CODE""" - -""" - -import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.PowerDivision import division_power - -def getPoly(deg,dim): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - return MultiPower(ACoeff) - -def correctZeros(polys, divisor_var, checkNumber = False): - ''' - A helper function. Takes in polynomials, find their common zeros, and calculates how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = division_power(polys, divisor_var = divisor_var) - assert(zeros != -1) - if checkNumber: - expectedNum = np.product([poly.degree for poly in polys]) - assert(len(zeros) == expectedNum) - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 95) - -def test_Division_Power(): - ''' - The following tests will run division_power on relatively small random upper trianguler MultiPower polynomials. - The assert statements will be inside of the correctZeros helper function. - ''' - #Case 1 - Two 2D degree 10 polynomials. - A = getPoly(10,2) - B = getPoly(10,2) - correctZeros([A,B], 0) - correctZeros([A,B], 1) - - #Case 2 - Two 2D, one degree 5, one degree 7. - A = getPoly(5,2) - B = getPoly(7,2) - correctZeros([A,B], 0) - correctZeros([A,B], 1) - - #Case 3 - Three 3D degree 4 polynomials. - A = getPoly(4,3) - B = getPoly(4,3) - C = getPoly(4,3) - correctZeros([A,B,C], 0) - correctZeros([A,B,C], 1) - correctZeros([A,B,C], 2) - - #Case 4 - Three 3D of degrees 3,4 and 5 - A = getPoly(3,3) - B = getPoly(4,3) - C = getPoly(5,3) - correctZeros([A,B,C], 0) - correctZeros([A,B,C], 1) - correctZeros([A,B,C], 2) - - #Case 5 - Four 4D degree 2 polynomials. - A = getPoly(2,4) - B = getPoly(2,4) - C = getPoly(2,4) - D = getPoly(2,4) - correctZeros([A,B,C,D], 0) - correctZeros([A,B,C,D], 1) - correctZeros([A,B,C,D], 2) - correctZeros([A,B,C,D], 3) - -""" \ No newline at end of file diff --git a/OLD_CODE/test_gsolve.py b/OLD_CODE/test_gsolve.py deleted file mode 100644 index 738136d1..00000000 --- a/OLD_CODE/test_gsolve.py +++ /dev/null @@ -1,262 +0,0 @@ -"""TESTS REMOVED FOR NOW BECAUSE WE DON'T USE THIS CODE""" - -""" -import pytest -import numpy as np -from itertools import permutations -from numalgsolve.root_finder import roots - -# groebner module imports -from numalgsolve.gsolve import F4 -from numalgsolve.polynomial import MultiPower, MultiCheb - -def test_sorted_polys_monomial(): - #raise NotImplementedError - pass - -def test_sorted_polys_coeff(): - #raise NotImplementedError - pass - -def test_reduce_matrix(): - #raise NotImplementedError - pass - -def test_solve(): - #raise NotImplementedError - pass - -def testF4(): - #First Test - A = MultiPower(np.array([[-10,0],[0,1],[1,0]])) - B = MultiPower(np.array([[-26,0,0],[0,0,1],[0,0,0],[1,0,0]])) - C = MultiPower(np.array([[-70,0,0,0],[0,0,0,1],[0,0,0,0],[0,0,0,0],[1,0,0,0]])) - x1, y1 = F4([A,B,C]) - X = MultiPower(np.array([[-2.],[ 1.]])) - Y = MultiPower(np.array([[-3.,1.]])) - assert(np.any([X==i and Y==j for i,j in permutations((x1,y1),2)])) - - #Second Test - A = MultiPower(np.array([ - [[[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]], - [[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]] - ] - )) - B = MultiPower(np.array([ - [[[0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]], - [[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]] - ] - )) - C = MultiPower(np.array([ - [[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]], - [[[-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]] - ] - )) - w1, x1, y1, z1 = F4([A,B,C]) - - W = MultiPower(np.array([[[[ 0.],[ 0.]],[[ 0.],[ 0.]],[[ 0.],[ 0.]],[[ 1.],[ 0.]]], - [[[ 0.],[-1.]],[[ 0.],[ 0.]],[[ 0.],[ 0.]],[[ 0.],[ 0.]]]])) - X = MultiPower(np.array([[[[ 0.,0.,0.,0.,0.,1.],[-1.,0.,0.,0.,0.,0.]]]])) - Y = MultiPower(np.array([[[[ 0.],[ 0.],[ 1.]],[[-1.],[ 0.],[ 0.]]]])) - Z = MultiPower(np.array([[[[ 0.],[ 0.]],[[ 0.],[ 0.]],[[ 0.],[ 1.]]], - [[[-1.],[ 0.]],[[ 0.],[ 0.]],[[ 0.],[ 0.]]]])) - - assert(np.any([W==i and X==j and Y==k and Z==l for i,j,k,l in permutations((w1,x1,y1,z1),4)])) - - #Third Test - A = MultiPower(np.array([[-1,0,1],[0,0,0]])) - B = MultiPower(np.array([[-1,0,0],[0,1,0],[1,0,0]])) - x1, y1 = F4([A,B]) - assert(np.any([A==i and B==j for i,j in permutations((x1,y1),2)])) - - #Fourth Test - A = MultiPower(np.array([[-10,0],[0,1],[1,0]])) - B = MultiPower(np.array([[-25,0,0],[0,0,1],[0,0,0],[1,0,0]])) - C = MultiPower(np.array([[-70,0,0,0],[0,0,0,1],[0,0,0,0],[0,0,0,0],[1,0,0,0]])) - X = MultiPower(np.array([[1.]])) - x1 = F4([A,B,C]) - assert(X == x1[0]) - - #Fifth Test - A = MultiPower(np.array([[1,1],[0,0]])) - B = MultiPower(np.array([[1,0],[1,0]])) - C = MultiPower(np.array([[1,0],[1,0],[0,1]])) - X = MultiPower(np.array([[1.]])) - x1 = F4([A,B,C]) - assert(X == x1[0]) - -def test_phi_criterion(): - # Simple Test Case (Nothing gets added ) - A = MultiPower(np.array([[-1,0,1],[0,0,0]])) - B = MultiPower(np.array([[-1,0,0],[0,1,0],[1,0,0]])) - - x1,y1 = F4([A,B], phi = True) - x2,y2 = F4([A,B], phi = False) - - assert(np.any([x2==i and y2==j for i,j in permutations((x1,y1),2)])), "Not the same basis!" - - #Second Test - A = MultiPower(np.array([ - [[[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]], - [[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]] - ] - )) - B = MultiPower(np.array([ - [[[0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]], - [[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]] - ] - )) - C = MultiPower(np.array([ - [[[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]], - [[[-1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], - [[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]] - ] - )) - - w1, x1, y1, z1 = F4([A,B,C], phi = True) - w2, x2, y2, z2 = F4([A,B,C], phi = False) - - assert(np.any([w2==i and x2==j and y2==k and z2==l for i,j,k,l in permutations((w1,x1,y1,z1),4)])) - - #Third Test - A = MultiPower(np.array([[-1,0,1],[0,0,0]])) - B = MultiPower(np.array([[-1,0,0],[0,1,0],[1,0,0]])) - x1, y1 = F4([A,B], phi = True) - x2, y2 = F4([A,B], phi = False) - - assert(np.any([A==i and B==j for i,j in permutations((x1,y1),2)])) - - #Fourth Test - A = MultiPower(np.array([[-10,0],[0,1],[1,0]])) - B = MultiPower(np.array([[-25,0,0],[0,0,1],[0,0,0],[1,0,0]])) - C = MultiPower(np.array([[-70,0,0,0],[0,0,0,1],[0,0,0,0],[0,0,0,0],[1,0,0,0]])) - - x1 = F4([A,B,C], phi = True) - x2 = F4([A,B,C], phi = False) - - assert(x2[0] == x1[0]) - - #Fifth Test - A = MultiPower(np.array([[1,1],[0,0]])) - B = MultiPower(np.array([[1,0],[1,0]])) - C = MultiPower(np.array([[1,0],[1,0],[0,1]])) - x1 = F4([A,B,C], phi = True) - x2 = F4([A,B,C], phi = False) - assert(x2[0]== x1[0]) - -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - if power: - return MultiPower(ACoeff) - else: - return MultiCheb(ACoeff) - -def correctZeros(polys, checkNumber = True): - ''' - A helper function for test_TVB. Takes in polynomials, find their common zeros using TVB, and calculates - how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = roots(polys, method = 'Groebner') - assert(zeros != -1) - if checkNumber: - expectedNum = np.product([poly.degree for poly in polys]) - assert(len(zeros) == expectedNum) - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 80) - -def test_Groebner_roots(): - ''' - The following tests will run TVB on relatively small random upper trianguler MultiPower and MultiCheb polynomials. - The assert statements will be inside of the correctZeros helper function. - ''' - #Case 1 - Two MultiPower 2D degree 4 polynomials. - A = getPoly(4,2,True) - B = getPoly(4,2,True) - correctZeros([A,B]) - - #Case 2 - Two MultiCheb 2D degree 4 polynomials. - A = getPoly(4,2,False) - B = getPoly(4,2,False) - correctZeros([A,B]) - - #Case 3 - Three MultiPower 3D degree 2 polynomials. - A = getPoly(2,3,True) - B = getPoly(2,3,True) - C = getPoly(2,3,True) - correctZeros([A,B,C]) - - #Case 4 - Three MultiCheb 3D degree 2 polynomials. - A = getPoly(2,3,False) - B = getPoly(2,3,False) - C = getPoly(2,3,False) - correctZeros([A,B,C]) - - #Case 5 - Four MultiPower 4D degree, three degree 2 and a degree 1. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(1,4,True) - correctZeros([A,B,C,D]) - - #Case 6 - Four MultiCheb 4D degree three degree 2 and a degree 1. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(1,4,False) - correctZeros([A,B,C,D]) - - #Case 7 - Two MultiPower 2D, one degree 3 and one degree 5 - A = getPoly(3,2,True) - B = getPoly(5,2,True) - correctZeros([A,B]) - - #Case 8 - Two MultiCheb 2D, one degree 3 and one degree 5 - A = getPoly(3,2,False) - B = getPoly(5,2,False) - correctZeros([A,B]) - - #Case 9 - Three MultiPower 3D of degrees 2,3 and 4 - A = getPoly(2,3,True) - B = getPoly(3,3,True) - C = getPoly(4,3,True) - correctZeros([A,B,C]) - - #Case 10 - Three MultiCheb 3D of degrees 2,3 and 4 - A = getPoly(2,3,False) - B = getPoly(3,3,False) - C = getPoly(4,3,False) - correctZeros([A,B,C]) -""" \ No newline at end of file diff --git a/OLD_CODE/test_macaulay.py b/OLD_CODE/test_macaulay.py deleted file mode 100644 index ca5ff01d..00000000 --- a/OLD_CODE/test_macaulay.py +++ /dev/null @@ -1,202 +0,0 @@ -"""TESTS REMOVED FOR NOW BECAUSE WE DON'T USE THIS CODE""" - -""" -import numpy as np -from numalgsolve.utils import mon_combos -from numalgsolve.Macaulay import Macaulay, find_degree, add_polys, create_matrix -from numalgsolve.polynomial import MultiCheb, MultiPower -from numalgsolve.root_finder import roots -import pytest -import random -from itertools import product - -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - if power: - return MultiPower(ACoeff) - else: - return MultiCheb(ACoeff) - -def correctZeros(polys): - ''' - A helper function for test_TVB. Takes in polynomials, find their common zeros using TVB, and calculates - how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 80% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = roots(polys, method = 'Macaulay') - assert(zeros != -1) - expectedNum = np.product([poly.degree for poly in polys]) - assert(len(zeros) == expectedNum) - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 80) - -def test_Macaulay_roots(): - ''' - The following tests will run Macaulay on relatively small random upper trianguler MultiPower and MultiCheb polynomials. - The assert statements will be inside of the correctZeros helper function. - ''' - #Case 1 - Two MultiPower 2D degree 4 polynomials. - A = getPoly(4,2,True) - B = getPoly(4,2,True) - correctZeros([A,B]) - - #Case 2 - Two MultiCheb 2D degree 4 polynomials. - A = getPoly(4,2,False) - B = getPoly(4,2,False) - correctZeros([A,B]) - - #Case 3 - Three MultiPower 3D degree 3 polynomials. - A = getPoly(3,3,True) - B = getPoly(3,3,True) - C = getPoly(3,3,True) - correctZeros([A,B,C]) - - #Case 4 - Three MultiCheb 3D degree 3 polynomials. - A = getPoly(3,3,False) - B = getPoly(3,3,False) - C = getPoly(3,3,False) - correctZeros([A,B,C]) - - #Case 5 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(2,4,True) - correctZeros([A,B,C,D]) - - #Case 6 - Four MultiCheb 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - correctZeros([A,B,C,D]) - - #Case 7 - Two MultiPower 2D, one degree 5 and one degree 3 - A = getPoly(5,2,True) - B = getPoly(3,2,True) - correctZeros([A,B]) - - #Case 8 - Two MultiCheb 2D, one degree 5 and one degree 3 - A = getPoly(5,2,False) - B = getPoly(3,2,False) - correctZeros([A,B]) - - #Case 9 - Three MultiPower 3D of degrees 2,3 and 4 - A = getPoly(2,3,True) - B = getPoly(3,3,True) - C = getPoly(4,3,True) - correctZeros([A,B,C]) - - #Case 10 - Three MultiCheb 3D of degrees 2,3 and 4 - A = getPoly(2,3,False) - B = getPoly(3,3,False) - C = getPoly(4,3,False) - correctZeros([A,B,C]) - -def test_get_poly_from_matrix(): - #raise NotImplementedError - pass - -def test_get_good_rows(): - #raise NotImplementedError - pass - -def test_find_degree(): - '''Test Case #1 - 2,3,4, and 5 2D Polynomials of degree 3''' - degree3Coeff = np.array([ - [1,1,1,1], - [1,1,1,0], - [1,1,0,0], - [1,0,0,0]]) - A = MultiPower(degree3Coeff) - B = MultiPower(degree3Coeff) - C = MultiPower(degree3Coeff) - D = MultiPower(degree3Coeff) - E = MultiPower(degree3Coeff) - assert(find_degree([A,B]) == 5) - assert(find_degree([A,B,C]) == 7) - assert(find_degree([A,B,C,D]) == 9) - assert(find_degree([A,B,C,D,E]) == 11) - - '''Test Case #2 - A 2D polynomials of degree 3 and one of degree 5''' - degree5Coeff = np.array([ - [1,1,1,1,1,1], - [1,1,1,1,1,0], - [1,1,1,1,0,0], - [1,1,1,0,0,0], - [1,1,0,0,0,0], - [1,0,0,0,0,0]]) - F = MultiPower(degree5Coeff) - assert(find_degree([A,F]) == 7) - - ''' Test Case #3 - Two 3D polynomials of degree 15''' - G = MultiPower(np.random.rand(6,6,6)) - H = MultiPower(np.random.rand(6,6,6)) - assert(find_degree([G,H]) == 29) - - #Test 3 - Simple Example in 2D - poly1 = MultiPower(np.array([[3,0,1],[0,0,0],[0,0,1]])) - poly2 = MultiPower(np.array([[3,0],[1,1],[0,1]])) - found_degree = find_degree([poly1,poly2]) - correct_degree = 6 - assert found_degree == correct_degree - - #Test 4 - Simple Example in 3D - a = np.zeros((4,4,4)) - a[3,3,3] = 1 - poly1 = MultiCheb(a) - poly2 = MultiCheb(np.ones((3,5,4))) - poly3 = MultiCheb(np.ones((2,4,5))) - found_degree1 = find_degree([poly1,poly2,poly3]) - correct_degree1 = 24 - assert found_degree1 == correct_degree1 - -def test_add_polys(): - #raise NotImplementedError - pass - -def test_sort_matrix(): - #raise NotImplementedError - pass - -def test_clean_matrix(): - #raise NotImplementedError - pass - -def test_create_matrix(): - #raise NotImplementedError - pass - -def test_create_matrix2(): - #raise NotImplementedError - pass - -def test_rrqr_reduce(): - #raise NotImplementedError - pass - -def test_rrqr_reduce2(): - #raise NotImplementedError - pass - -""" \ No newline at end of file diff --git a/OLD_CODE/test_rootfinder.py b/OLD_CODE/test_rootfinder.py deleted file mode 100644 index 12858c0a..00000000 --- a/OLD_CODE/test_rootfinder.py +++ /dev/null @@ -1,256 +0,0 @@ -"""TESTS REMOVED FOR NOW BECAUSE WE DON'T USE THIS CODE""" - -""" -import numpy as np -from numalgsolve import root_finder as rf -from numalgsolve.polynomial import MultiPower, MultiCheb -from numalgsolve.gsolve import F4 -import pytest -import pdb - -def test_vectorSpaceBasis(): - f1 = MultiPower(np.array([[0,-1.5,.5],[-1.5,1.5,0],[1,0,0]])) - f2 = MultiPower(np.array([[0,0,0],[-1,0,1],[0,0,0]])) - f3 = MultiPower(np.array([[0,-1,0,1],[0,0,0,0],[0,0,0,0],[0,0,0,0]])) - G = [f1, f2, f3] - basis = rf.vectorSpaceBasis(G)[0] - trueBasis = [(0,0), (1,0), (0,1), (1,1), (0,2)] - - assert ((len(basis) == len(trueBasis)) and (m in basis for m in trueBasis)) - #Failed on MultiPower in 2 vars." - -def test_vectorSpaceBasis_2(): - f1 = MultiPower(np.array([[[0,0,1],[0,3/20,0],[0,0,0]], - [[0,0,0],[-3/40,1,0],[0,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - f2 = MultiPower(np.array([[[3/16,-5/2,0],[0,3/16,0],[0,0,0]], - [[0,0,1],[0,0,0],[0,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - f3 = MultiPower(np.array([[[0,1,1/2],[0,3/40,1],[0,0,0]], - [[-1/2,20/3,0],[-3/80,0,0],[0,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - f4 = MultiPower(np.array([[[3/32,-7/5,0,1],[-3/16,83/32,0,0],[0,0,0,0],[0,0,0,0]], - [[3/40,-1,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]], - [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]], - [[0,0,0,0],[0,0,0,0],[0,0,0,0],[0,0,0,0]]])) - - f5 = MultiPower(np.array([[[5,0,0],[0,0,0],[0,0,0]], - [[0,-2,0],[0,0,0],[0,0,0]], - [[1,0,0],[0,0,0],[0,0,0]]])) - - f6 = MultiPower(np.array([[[0,0,0],[0,0,0],[1,0,0]], - [[0,-8/3,0],[0,0,0],[0,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - G = [f1, f2, f3, f4, f5, f6] - basis = rf.vectorSpaceBasis(G)[0] - trueBasis = [(0,0,0),(1,0,0),(0,1,0),(1,1,0),(0,0,1),(0,0,2),(1,0,1),(0,1,1)] - - assert (len(basis) == len(trueBasis)) and (m in basis for m in trueBasis), \ - "Failed on MultiPower in 3 vars." - -def testReducePoly(): - poly = MultiPower(np.array([[-3],[2],[-4],[1]])) - g = MultiPower(np.array([[2],[1]])) - basisSet = set() - basisSet.add((0,0)) - - reduced = rf.reduce_poly(poly, [g], basisSet) - assert(MultiPower(reduced).coeff == np.array([[-31.]])) - -def testReducePoly_2(): - poly = MultiPower(np.array([[-7],[2],[-13],[4]])) - g = MultiPower(np.array([[-2],[3],[1]])) - basisSet = set() - basisSet.add((0,0)) - basisSet.add((1,0)) - - reduced = rf.reduce_poly(poly, [g], basisSet) - assert(np.all(MultiPower(reduced).coeff == np.array([[-57.],[85.]]))) - -def testReducePoly_3(): - poly = MultiPower(np.array([[0,-1,0,1], - [0,2,0,0], - [0,0,1,0], - [1,0,0,0]])) - - g1 = MultiPower(np.array([[0,0,0], - [-2,0,0], - [1,0,0]])) - - g2 = MultiPower(np.array([[0,-1,0,1], - [3,0,0,0], - [0,0,0,0], - [0,0,0,0]])) - basisSet = set() - basisSet.add((0,0)) - basisSet.add((0,1)) - basisSet.add((0,2)) - basisSet.add((1,0)) - basisSet.add((1,1)) - basisSet.add((1,2)) - - reduced = rf.reduce_poly(poly, [g1, g2], basisSet) - assert(np.all(MultiPower(reduced).coeff == np.array([[0,0,0],[1,2,2]]))) - -def testReducePoly_4(): - poly = MultiPower(np.array([[[-1,2,0],[0,0,0],[-3,0,0]], - [[0,0,0],[2,0,0],[0,0,0]], - [[0,0,0],[0,0,1],[0,0,0]]])) - d1 = MultiPower(np.array([[[0,-3,0], - [0,0,0], - [1,0,0]]])) - d2 = MultiPower(np.array([[[0,0,0,1], - [4,0,0,0]]])) - d3 = MultiPower(np.array([[[-1]],[[1]]])) - - basisSet = set() - for i in range(2): - for j in range(3): - for k in range(1): - basisSet.add((k,i,j)) - - reduced = rf.reduce_poly(poly, [d1, d2, d3], basisSet) - - assert(np.all(MultiPower(reduced).coeff == np.array([[[-1,-7,0],[2,0,1]]]))) - -def testCoordinateVector(): - poly = MultiCheb(np.array([[0,1,0],[0,0,1],[1,0,0]])) - VB = [(2,0),(1,2),(0,1),(1,0)] - GB = [MultiCheb(np.array([[0,0,0],[0,0,0],[0,0,1]]))] # LT is big so nothing gets reduced - - slices = ([2,1,0,1],[0,2,1,0]) - - cv = rf.coordinateVector(poly, GB, set(VB), slices) - print(cv) - assert((cv == np.array([1,1,1,0])).all()) - -def testMultMatrix(): - f1 = MultiPower(np.array([[[5,0,0],[0,0,0],[0,0,0]], - [[0,-2,0],[0,0,0],[0,0,0]], - [[1,0,0],[0,0,0],[0,0,0]]])) - - f2 = MultiPower(np.array([[[1,0,0],[0,1,0],[0,0,0]], - [[0,0,0],[0,0,0],[1,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - f3 = MultiPower(np.array([[[0,0,0],[0,0,0],[3,0,0]], - [[0,-8,0],[0,0,0],[0,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - F = [f1, f2, f3] - - GB = F4(F) - VB = rf.vectorSpaceBasis(GB)[0] - - x = MultiPower(np.array([[[0,1]]])) - y = MultiPower(np.array([[[0],[1]]])) - z = MultiPower(np.array([[[0]],[[1]]])) - - mx_RealEig = [eig.real for eig in \ - np.linalg.eigvals(rf.multMatrix(x, GB, VB)) if (eig.imag == 0)] - - my_RealEig = [eig.real for eig in \ - np.linalg.eigvals(rf.multMatrix(y, GB, VB)) if (eig.imag==0)] - - mz_RealEig = [eig.real for eig in \ - np.linalg.eigvals(rf.multMatrix(z, GB, VB)) if (eig.imag==0)] - - assert(len(mx_RealEig) == 2) - assert(len(my_RealEig) == 2) - assert(len(mz_RealEig) == 2) - assert(np.allclose(mx_RealEig, [3.071618528, -2.821182227], atol=1.e-8)) - assert(np.allclose(my_RealEig, [-2.878002536, -2.81249605], atol=1.e-8)) - assert(np.allclose(mz_RealEig, [-1.100987715, .9657124563], atol=1.e-8)) - -def testMultMatrix_2(): - f1 = MultiPower(np.array([[0,-1.5,.5],[-1.5,1.5,0],[1,0,0]])) - f2 = MultiPower(np.array([[0,0,0],[-1,0,1],[0,0,0]])) - f3 = MultiPower(np.array([[0,-1,0,1],[0,0,0,0],[0,0,0,0],[0,0,0,0]])) - - GB = [f1, f2, f3] - VB = rf.vectorSpaceBasis(GB)[0] - - x = MultiPower(np.array([[0],[1]])) - y = MultiPower(np.array([[0,1]])) - - mx_Eig = np.linalg.eigvals(rf.multMatrix(x, GB, VB)) - my_Eig = np.linalg.eigvals(rf.multMatrix(y, GB, VB)) - - assert(len(mx_Eig) == 5) - assert(len(my_Eig) == 5) - assert(np.allclose(mx_Eig, [-1., 2., 1., 1., 0.])) - assert(np.allclose(my_Eig, [1., -1., 1., -1., 0.])) - -def testRoots(): - f1 = MultiPower(np.array([[0,-1.5,.5],[-1.5,1.5,0],[1,0,0]]), clean_zeros=False) - f2 = MultiPower(np.array([[0,0,0],[-1,0,1],[0,0,0]]), clean_zeros=False) - f3 = MultiPower(np.array([[0,-1,0,1],[0,0,0,0],[0,0,0,0],[0,0,0,0]]), clean_zeros=False) - - roots = rf.roots([f1, f2, f3], method='Groebner') - values_at_roots = np.array([[f1(root) for root in roots], - [f2(root) for root in roots], - [f3(root) for root in roots]]) - - assert(np.all(np.isclose(values_at_roots,0))) - -def testRoots_2(): - f1 = MultiPower(np.array([[[5,0,0],[0,0,0],[0,0,0]], - [[0,-2,0],[0,0,0],[0,0,0]], - [[1,0,0],[0,0,0],[0,0,0]]])) - - f2 = MultiPower(np.array([[[1,0,0],[0,1,0],[0,0,0]], - [[0,0,0],[0,0,0],[1,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - f3 = MultiPower(np.array([[[0,0,0],[0,0,0],[3,0,0]], - [[0,-8,0],[0,0,0],[0,0,0]], - [[0,0,0],[0,0,0],[0,0,0]]])) - - roots = rf.roots([f1, f2, f3], method='Groebner') - - values_at_roots = np.array([[f1(root) for root in roots], - [f2(root) for root in roots], - [f3(root) for root in roots]]) - - assert(np.all(np.isclose(values_at_roots,0))) - -def testRoots_3(): - # roots of [x^2-y, x^3-y+1] - f1 = MultiPower(np.array([[0,-1],[0,0],[1,0]])) - f2 = MultiPower(np.array([[1,-1],[0,0],[0,0],[1,0]])) - - roots = rf.roots([f1, f2], method='Groebner') - - values_at_roots = np.array([[f1(root) for root in roots], - [f2(root) for root in roots]]) - - assert(np.all(np.isclose(values_at_roots,0))) - -def testRoots_4(): - f1 = MultiPower(np.array([[5,-1],[1,0]])) - f2 = MultiPower(np.array([[1,-1],[-1,0]])) - - root = rf.roots([f1, f2], method='Macaulay')[0] - - assert(all(np.isclose(root, [-2,3]))) - -def testRoots_5(): - f1 = MultiPower(np.array([[0,-1],[0,0],[1,0]])) - f2 = MultiPower(np.array([[1,-1],[1,0]])) - - roots = rf.roots([f1, f2], method='Macaulay') - - assert(all(np.isclose(roots[0], [-0.61803399, 0.38196601]))) - assert(all(np.isclose(roots[1], [1.61803399, 2.61803399]))) - -def testRoots_6(): # test when ideal is not zero-dimensional - f1 = MultiPower(np.array([[-12,-12],[1,1],[1,1]])) - f2 = MultiPower(np.array([[6,3,-3],[-2,-1,1]])) - - roots = rf.roots([f1, f2], method='Macaulay') - assert(roots == -1) -""" \ No newline at end of file diff --git a/OLD_CODE/test_tvb.py b/OLD_CODE/test_tvb.py deleted file mode 100644 index f4efc613..00000000 --- a/OLD_CODE/test_tvb.py +++ /dev/null @@ -1,246 +0,0 @@ -"""TESTS REMOVED FOR NOW BECAUSE WE DON'T USE THIS CODE""" - -""" - -import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.TelenVanBarel import find_degree, mon_combos, sorted_matrix_terms -from numalgsolve import polyroots as pr -from numalgsolve.utils import InstabilityWarning, arrays -from itertools import product -import warnings - -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - if power: - return MultiPower(ACoeff) - else: - return MultiCheb(ACoeff) - -def correctZeros(polys): - ''' - A helper function for test_TVB. Takes in polynomials, find their common zeros using TVB, and calculates - how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = pr.solve(polys, method = 'mult') - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 95) - -def test_TVB_roots(): - ''' - The following tests will run TVB on relatively small random upper trianguler MultiPower and MultiCheb polynomials. - The assert statements will be inside of the correctZeros helper function. - ''' - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,True) - B = getPoly(10,2,True) - correctZeros([A,B]) - - #Case 2 - Two MultiCheb 2D degree 10 polynomials. - A = getPoly(10,2,False) - B = getPoly(10,2,False) - correctZeros([A,B]) - - #Case 3 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,True) - B = getPoly(4,3,True) - C = getPoly(4,3,True) - correctZeros([A,B,C]) - - #Case 4 - Three MultiCheb 3D degree 4 polynomials. - A = getPoly(4,3,False) - B = getPoly(4,3,False) - C = getPoly(4,3,False) - correctZeros([A,B,C]) - - #Case 5 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(2,4,True) - correctZeros([A,B,C,D]) - - #Case 6 - Four MultiCheb 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - correctZeros([A,B,C,D]) - - #Case 7 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,True) - B = getPoly(7,2,True) - correctZeros([A,B]) - - #Case 8 - Two MultiCheb 2D, one degree 5 and one degree 7 - A = getPoly(5,2,False) - B = getPoly(7,2,False) - correctZeros([A,B]) - - #Case 9 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,True) - B = getPoly(4,3,True) - C = getPoly(5,3,True) - correctZeros([A,B,C]) - - #Case 10 - Three MultiCheb 3D of degrees 3,4 and 5 - A = getPoly(3,3,False) - B = getPoly(4,3,False) - C = getPoly(5,3,False) - correctZeros([A,B,C]) - -def test_makeBasisDict(): - - - - - pass - -def test_find_degree(): - #Test Case #1 - 2,3,4, and 5 2D Polynomials of degree 3 - - degree3Coeff = np.array([ - [1,1,1,1], - [1,1,1,0], - [1,1,0,0], - [1,0,0,0]]) - - A = MultiPower(degree3Coeff) - B = MultiPower(degree3Coeff) - C = MultiPower(degree3Coeff) - D = MultiPower(degree3Coeff) - E = MultiPower(degree3Coeff) - assert(find_degree([A,B]) == 5) - assert(find_degree([A,B,C]) == 7) - assert(find_degree([A,B,C,D]) == 9) - assert(find_degree([A,B,C,D,E]) == 11) - - #Test Case #2 - A 2D polynomials of degree 3 and one of degree 5 - degree5Coeff = np.array([ - [1,1,1,1,1,1], - [1,1,1,1,1,0], - [1,1,1,1,0,0], - [1,1,1,0,0,0], - [1,1,0,0,0,0], - [1,0,0,0,0,0]]) - F = MultiPower(degree5Coeff) - assert(find_degree([A,F]) == 7) - - #Test Case #3 - Two 3D polynomials of degree 15 - G = MultiPower(np.random.rand(6,6,6)) - H = MultiPower(np.random.rand(6,6,6)) - assert(find_degree([G,H]) == 29) - -def test_mon_combos(): - ''' - Tests the mon_combos function against the simpler itertools product. - ''' - #Test Case #1 - degree 5, dimension 2 - deg = 5 - dim = 2 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - - #Test Case #1 - degree 25, dimension 2 - deg = 25 - dim = 2 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - - #Test Case #1 - degree 5, dimension 3 - deg = 5 - dim = 3 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - - #Test Case #1 - degree 5, dimension 5 - deg = 5 - dim = 5 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - -def test_arrays(): - deg = 3 - dim = 4 - k = 0 - a = arrays(deg,dim,k) - assert (a == [False,False, False, True, False, False, True, False, True, True,\ - False, False, True, False, True, True, False, True, True, True]) - - deg = 3 - dim = 4 - k = 1 - a = arrays(deg,dim,k) - assert(a == [False,False, True, False, False, True, False, True, True, False, False,\ - True, False, True, True, False, True, True, True, False]) - - deg = 3 - dim = 4 - k = 2 - a = arrays(deg,dim,k) - assert(a == [False,True,False, False, True, True, True, False, False, False,\ - True, True, True, True, True, True, False, False, False, False]) - - deg = 3 - dim = 4 - k = 3 - a = arrays(deg,dim,k) - assert(a == [True]*10+[False]*10) - -def test_add_polys(): - - pass - -def test_sort_matrix(): - - - pass - -def test_rrqr_reduceTelenVanBarel(): - - - pass - -""" \ No newline at end of file diff --git a/OLD_CODE/testfor_TVBMethod.py b/OLD_CODE/testfor_TVBMethod.py deleted file mode 100644 index e72e5257..00000000 --- a/OLD_CODE/testfor_TVBMethod.py +++ /dev/null @@ -1,151 +0,0 @@ -import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.MacaulayReduce import find_degree, mon_combos -from numalgsolve import TVBMethod as tvb -from numalgsolve.utils import InstabilityWarning, arrays -from numalgsolve.Multiplication import create_matrix -from itertools import product -import unittest -import warnings - -def test_TVB_paper_example(): - - #Power form of the polys - p1 = MultiPower(np.array([[1, -4, 0],[0, 3, 0],[1, 0, 0]])) #y^2 + 3xy - 4x +1 - p2 = MultiPower(np.array([[3, 0, -2],[6, -6, 0],[0, 0, 0]])) #-6xy -2x^2 + 6y +3 - - #Cheb form of the polys - c1 = MultiCheb(np.array([[2, 0, -1],[6, -6, 0], [0, 0, 0]])) #p1 in Cheb form - c2 = MultiCheb(np.array([[1.5, -4, 0],[0, 3, 0], [.5, 0, 0]])) #p2 in Cheb form - - #Homogenous power form - p1 = MultiPower(np.array([[1, -4, 0],[0, 3, 0],[1, 0, 0]])) #y^2 + 3xy - 4x +1 - p2 = MultiPower(np.array([[3, 0, -2],[6, -6, 0],[0, 0, 0]])) #-6xy -2x^2 + 6y +3 - - right_number_of_roots = 4 - - power_roots = tvb.solve([p1, p2], verbose=True) - assert len(power_roots) == right_number_of_roots - for root in power_roots: - assert np.isclose(0, p1(root), atol = 1.e-8) - assert np.isclose(0, p2(root), atol = 1.e-8) - - cheb_roots = tvb.solve([c1, c2], verbose=True) - assert len(cheb_roots) == right_number_of_roots - for root in cheb_roots: - assert np.isclose(0, c1(root), atol = 1.e-8) - assert np.isclose(0, c1(root), atol = 1.e-8) - -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial - of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be - MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - if power: - return MultiPower(ACoeff) - else: - return MultiCheb(ACoeff) - -def correctZeros(polys): - ''' - A helper function for polyroots tests. Takes in polynomials, find their common zeros using polyroots, and calculates - how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = tvb.solve(polys, verbose=False) - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 95) - -def test_TVB_power_roots(): - ''' - The following tests will run polyroots on relatively small random upper trianguler MultiPower. - The assert statements will be inside of the correctZeros helper function. - ''' - - np.random.seed(423) - - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,True) - B = getPoly(10,2,True) - correctZeros([A,B]) - print('Case2') - #Case 2 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,True) - B = getPoly(4,3,True) - C = getPoly(4,3,True) - correctZeros([A,B,C]) - - #Case 3 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(2,4,True) - correctZeros([A,B,C,D]) - - #Case 4 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,True) - B = getPoly(7,2,True) - correctZeros([A,B]) - - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,True) - B = getPoly(4,3,True) - C = getPoly(5,3,True) - correctZeros([A,B,C]) - -def test_TVB_cheb_roots(): - ''' - The following tests will run polyroots on relatively small random upper trianguler MultiCheb. - The assert statements will be inside of the correctZeros helper function. - ''' - - np.random.seed(59) - - #Case 1 - Two MultiCheb 2D degree 10 polynomials. - A = getPoly(10,2,False) - B = getPoly(10,2,False) - correctZeros([A,B]) - - #Case 2 - Three MultiCheb 3D degree 4 polynomials. - A = getPoly(4,3,False) - B = getPoly(4,3,False) - C = getPoly(4,3,False) - correctZeros([A,B,C]) - - #Case 3 - Four MultiCheb 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - correctZeros([A,B,C,D]) - - #Case 4 - Two MultiCheb 2D, one degree 5 and one degree 7 - A = getPoly(5,2,False) - B = getPoly(7,2,False) - correctZeros([A,B]) - - #Case 5 - Three MultiCheb 3D of degrees 3,4 and 5 - A = getPoly(3,3,False) - B = getPoly(4,3,False) - C = getPoly(5,3,False) - correctZeros([A,B,C]) diff --git a/Polished_results/polished_1.1.npy b/Polished_results/polished_1.1.npy new file mode 100644 index 00000000..803e3783 Binary files /dev/null and b/Polished_results/polished_1.1.npy differ diff --git a/Polished_results/polished_1.2.npy b/Polished_results/polished_1.2.npy new file 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For our mathematical methods and their comparisons with other rootfinders, refer to [this paper](paper). +YRoots is a Python package for numerical root finding. See DemoNotebook.ipynb for a JupyterNotebook demonstration of the code's capabilities. This project was supported in part by the National Science Foundation, grant number DMS-1564502. + + + + + @@ -17,25 +22,28 @@ NumAlgSolve is a Python module for numerical and algebraic rootfinding. For our (We are currently working on getting a `pip` or `conda` for download) Rootfinding can now be installed locally by using `pip install -e .` while inside the RootFinding folder. -The package can then by imported using `import numalgsolve`. +The package can then by imported using `import yroots`. ## Usage ```python -#conda imports +#imports import numpy as np +import yroots as yr + +#define the functions -- must be smooth on the domain and vectorized +f = lambda x,y : np.sin(x*y) + x*np.log(y+3) - x**2 + 1/(y-4) +g = lambda x,y : np.cos(3*x*y) + np.exp(3*y/(x-2)) - x - 6 -#local imports -from numalgsolve.polynomial import MultiCheb, MultiPower -from numalgsolve.polyroots import solve +#define a search domain +a = np.array([-1,-2]) #lower bounds on x and y +b = np.array([0,1]) #upper bounds on x and y -A = MultiCheb(np.array([[1,2,3,1],[2,3,1,0],[2,3,0,0],[1,0,0,0]])) -B = MultiCheb(np.array([[1,0,0,1],[1,0,1,0],[0,0,0,0],[1,0,0,0]])) -solve([A,B]) -#insert user code here +#solve +yr.solve([f,g],a,b) ``` - +If the system includes polynomials, there are specialized `Polynomial` objects which may be allow for faster solving. See the DemoNotebook.ipynb for details. ## Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. diff --git a/TelenTestSet.py b/TelenTestSet.py deleted file mode 100644 index 6aeed73d..00000000 --- a/TelenTestSet.py +++ /dev/null @@ -1,251 +0,0 @@ -from numalgsolve.polyroots import solve -from numalgsolve.polynomial import MultiPower -from scipy.io import loadmat -from numalgsolve.ProjectiveSpace import common_root_at_inf, roots_at_inf, pad_with_zeros -from glob import glob -from numpy import set_printoptions, isclose -import numpy as np -import traceback - -#functions for adding things to the right pile -def solved(filename, solver, error): - difficulty = filename.split('/')[1] - if difficulty == 'Easy': - i = 0 - elif difficulty == 'Medium': - i = 1 - elif difficulty == 'Hard': - i = 2 - else: - i = 3 - if solver == 'mult': - mult_solved[i].append((filename, error)) - elif solver == 'multrand': - multrand_solved[i].append((filename, error)) - else: - div_solved[i].append(filename) - -def not_solved(filename, solver, error): - difficulty = filename.split('/')[1] - if difficulty == 'Easy': - i = 0 - elif difficulty == 'Medium': - i = 1 - elif difficulty == 'Hard': - i = 2 - else: - i = 3 - if solver == 'mult': - mult_not_solved[i].append((filename, error)) - elif solver == 'multrand': - multrand_not_solved[i].append((filename, error)) - else: - div_not_solved[i].append((filename, error)) - -def right_roots(filename, solver, roots, percent): - difficulty = filename.split('/')[1] - if difficulty == 'Easy': - i = 0 - elif difficulty == 'Medium': - i = 1 - elif difficulty == 'Hard': - i = 2 - else: - i = 3 - if solver == 'mult': - mult_right[i].append((filename, percent)) - elif solver == 'multrand': - multrand_right[i].append((filename,percent)) - else: - div_right[i].append((filename,percent)) - -def wrong_roots(filename, solver, roots, percent): - difficulty = filename.split('/')[1] - if difficulty == 'Easy': - i = 0 - elif difficulty == 'Medium': - i = 1 - elif difficulty == 'Hard': - i = 2 - else: - i = 3 - if solver == 'mult': - mult_wrong[i].append((filename,percent)) - elif solver == 'multrand': - multrand_wrong[i].append((filename,percent)) - else: - div_wrong[i].append((filename,percent)) - -def percent_good_roots(roots, polys, ignore_out_of_range, tolerance): - #function for calculating what percent of the roots were good - if len(roots) == 0: - return 0 - correct = 0 - outOfRange = 0 - for root in roots: - good = True - for poly in polys: - if not np.isclose(0, poly(root), rtol = tolerance): - good = False - if (np.abs(root) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - - if ignore_out_of_range: - return correct/(len(roots)-outOfRange) - else: - return correct/(len(roots)) - -if __name__ == "__main__": - np.set_printoptions(suppress=True, linewidth=1000) - - #A ton of lists to sort test cases into - total_count = 0 - #difficulty is indicated by which sublist it's in. Easy in 0, medium in 1, hard in 2, impossible in 3 - mult_solved = [list(),list(),list(),list()] - mult_not_solved = [list(),list(),list(),list()] - mult_right = [list(),list(),list(),list()] - mult_wrong = [list(),list(),list(),list()] - - multrand_solved = [list(),list(),list(),list()] - multrand_not_solved = [list(),list(),list(),list()] - multrand_right = [list(),list(),list(),list()] - multrand_wrong = [list(),list(),list(),list()] - - div_solved = [list(),list(),list(),list()] - div_not_solved = [list(),list(),list(),list()] - div_right = [list(),list(),list(),list()] - div_wrong = [list(),list(),list(),list()] - - for filename in glob('./*/*.mat'): - total_count += 1 - - dct = loadmat(filename) - p = MultiPower(dct['p']) - q = MultiPower(dct['q']) - - error_message = str() - - try: - assert np.allclose(np.fliplr(pad_with_zeros(p.coeff)), np.triu(np.fliplr(pad_with_zeros(p.coeff)))), "p's coefficients are not upper left triangular. \n p.coef = \n{}\nq.coeff = \n{}".format(p.coeff, q.coeff) - assert np.allclose(np.fliplr(pad_with_zeros(q.coeff)), np.triu(np.fliplr(pad_with_zeros(q.coeff)))), "q's coefficients are not upper left triangular. \n p.coef = \n{}\nq.coeff = \n{}".format(p.coeff, q.coeff) - assert not np.any(np.isclose(np.diag(np.fliplr(pad_with_zeros(p.coeff))), 0)), "p has highest term coefficients close to zero. \n p.coef = \n{}\nq.coeff = \n{}".format(p.coeff, q.coeff) - assert not np.any(np.isclose(np.diag(np.fliplr(pad_with_zeros(q.coeff))), 0)), "p has highest term coefficients close to zero. \n p.coef = \n{}\nq.coeff = \n{}".format(p.coeff, q.coeff) - except AssertionError as e: - error_message += '\nNot Upper Triangular:' + str(e) - finally: - try: - if common_root_at_inf([p,q]) != False: - error_message += '\nCommon root at infinity:' + str(common_root_at_inf([p,q])[1]) - except Exception as e: - error_message += '\nFailed finding roots at Infinity:' + str(e) - finally: - try: - mult_solutions = solve([p,q], MSmatrix=1) - except Exception as e: - mult_error = '\n' + str(e) - not_solved(filename, 'mult', error_message + mult_error) - else: - solved(filename, 'mult', error_message) - multpercent = percent_good_roots(mult_solutions, [p,q], ignore_out_of_range=False, tolerance=1e-9) - if multpercent == 1: #if 100% of the roots were right, report that - right_roots(filename, 'mult', mult_solutions, multpercent) - else: - wrong_roots(filename, 'mult', mult_solutions, multpercent) - finally: - try: - multrand_solutions = solve([p,q], MSmatrix=0) - except Exception as e: - multrand_error = '\n' + str(e) - not_solved(filename, 'multrand', error_message + multrand_error) - else: - solved(filename, 'multrand', error_message) - multrandpercent = percent_good_roots(multrand_solutions, [p,q], ignore_out_of_range=False, tolerance=1e-9) - if multrandpercent == 1: - right_roots(filename, 'multrand', multrand_solutions, multrandpercent) - else: - wrong_roots(filename, 'multrand', multrand_solutions, multrandpercent) - finally: - try: - div_solutions = solve([p,q], MSmatrix=0) - except Exception as e: - div_error = '\n' + str(e) - not_solved(filename, 'div', error_message + div_error) - else: - solved(filename, 'div', error_message) - divpercent = percent_good_roots(div_solutions, [p,q], ignore_out_of_range=False, tolerance=1e-9) - if divpercent == 1: - right_roots(filename, 'div', div_solutions, divpercent) - else: - wrong_roots(filename, 'div', div_solutions, divpercent) - - #Report Number Solved/Right for each method - print('\n{} Test Cases \t{} Easy \t\t{} Medium \t\t\t{} Hard \t\t\t\t{} Impossible'.format(total_count, 128, 48, 15, 24)) - print('mult: \t{} solved, {} right \t{} solved, {} right \t{} solved, {} right \t\t{} solved, {} right'.format(len(mult_solved[0]), len(mult_right[0]), len(mult_solved[1]), len(mult_right[1]), len(mult_solved[2]), len(mult_right[2]), len(mult_solved[3]), len(mult_right[3]))) - print('multrand: \t{} solved, {} right \t{} solved, {} right \t{} solved, {} right \t\t{} solved, {} right'.format(len(multrand_solved[0]), len(multrand_right[0]), len(multrand_solved[1]), len(multrand_right[1]), len(multrand_solved[2]), len(multrand_right[2]), len(multrand_solved[3]), len(multrand_right[3]))) - print('div: \t{} solved, {} right \t{} solved, {} right \t{} solved, {} right \t\t{} solved, {} right'.format(len(div_solved[0]), len(div_right[0]), len(div_solved[1]), len(div_right[1]), len(div_solved[2]), len(div_right[2]), len(div_solved[3]), len(div_right[3]))) - - print('\n\nMult Failed to Solve:\n') - print('--Easy--\n') - print(*mult_not_solved[0], sep='\n') - print('--Medium--\n') - print(*mult_not_solved[1], sep='\n') - print('--Hard--\n') - print(*mult_not_solved[2], sep='\n') - print('--Impossible--\n') - print(*mult_not_solved[3], sep='\n') - - print('\n\nmultrand Failed to Solve:\n') - print('--Easy--\n') - print(*multrand_not_solved[0], sep='\n') - print('--Medium--\n') - print(*multrand_not_solved[1], sep='\n') - print('--Hard--\n') - print(*multrand_not_solved[2], sep='\n') - print('--Impossible--\n') - print(*multrand_not_solved[3], sep='\n') - - print('\n\ndiv Failed to Solve:\n') - print('--Easy--\n') - print(*div_not_solved[0], sep='\n') - print('--Medium--\n') - print(*div_not_solved[1], sep='\n') - print('--Hard--\n') - print(*div_not_solved[2], sep='\n') - print('--Impossible--\n') - print(*div_not_solved[3], sep='\n') - - print('\n\nMult Found Incorrect Roots:\n') - print('--Easy--\n') - print(*mult_wrong[0], sep='\n') - print('--Medium--\n') - print(*mult_wrong[1], sep='\n') - print('--Hard--\n') - print(*mult_wrong[2], sep='\n') - print('--Impossible--\n') - print(*mult_wrong[3], sep='\n') - - print('\n\nmultrand Found Incorrect Roots:\n') - print('--Easy--\n') - print(*multrand_wrong[0], sep='\n') - print('--Medium--\n') - print(*multrand_wrong[1], sep='\n') - print('--Hard--\n') - print(*multrand_wrong[2], sep='\n') - print('--Impossible--\n') - print(*multrand_wrong[3], sep='\n') - - print('\n\ndiv Found Incorrect Roots:\n') - print('--Easy--\n') - print(*div_wrong[0], sep='\n') - print('--Medium--\n') - print(*div_wrong[1], sep='\n') - print('--Hard--\n') - print(*div_wrong[2], sep='\n') - print('--Impossible--\n') - print(*div_wrong[3], sep='\n') - - print(mult_not_solved == div_not_solved) - print(mult_not_solved == multrand_not_solved) diff --git a/doubleroottrials.py b/doubleroottrials.py deleted file mode 100644 index 3f61161b..00000000 --- a/doubleroottrials.py +++ /dev/null @@ -1,897 +0,0 @@ -from numalgsolve import polynomial -from numalgsolve import polyroots -import numpy as np -import matplotlib as mpl -mpl.use('TkAgg') -from matplotlib import pyplot as plt -from math import sqrt - -#bools for which tests to show - -hyperbolas11 = True -hyperbolas_transformed = True -hyperbolas_moved = True -hyperbolas_transformed_moved = True -circle_ellipse_origin = True -circle_ellipse_moved = True -circle_ellipse_transformed = True -circle_ellipse_transformed_moved = True -v = True -if hyperbolas11: - print("Double Root Trials: \n p1 = x^2 + 2xy + y^2 - 3x - 5y + 4 \n p2 = - x^2 - 2xy - y^2 + 5x + 3y - 4") - print("Two hyperbolas that intersect at (1,1). Double root there, probably roots at infinity??") - - p1_coef = np.array([[4, -5, 1],[-3,2,0],[1,0,0]]) #p1 = x^2 + 2xy + y^2 - 3x - 5y + 4 - p2_coef = np.array([[-4, 3, -1],[5,-2,0],[-1,0,0]]) #p2 = - x^2 - 2xy - y^2 + 5x + 3y - 4 - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - - c1_coef = np.array([[5,-5,.5],[-3,2,0],[.5,0,0]]) - c2_coef = np.array([[-5,3,-.5],[5,-2,0],[-.5,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) # 1/2 T2x + 2 xy + 1/2 T2y - 5 y - 3 x + 5 == 0 - c2 = polynomial.MultiCheb(c2_coef) #- 1/2 T2x -2 xy - 1/2 T2y + 3 y + 5 x - 5 == 0 - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - #print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - #pmf = polyroots.solve([p1, p2]) - #print("Roots:\n",pmf) - - #print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - #cmf = polyroots.solve([c1, c2]) - #print("Roots:\n",cmf) - - #print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - #pmx = polyroots.solve([p1, p2], MSmatrix = 1) - #print("Roots:\n",pmx) - - #print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - #cmx = polyroots.solve([c1, c2], MSmatrix = 1) - #print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - #print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - #pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1)[:,::-1] - #print("Roots:\n",pmy) - - #print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - #cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1)[:,::-1] - #print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - #print("Power M_f\n", pmf)#along right line, inconsistent - #print("Cheb M_f\n", cmf)#along right line, inconsistent - #print("Power M_x\n", pmx)#along right line, weird - #print("Cheb M_x\n", cmx)#along right line, weird - #print("Power M_y\n", pmy)#along right line, weird - #print("Cheb M_y\n", cmy)#along right line, weird - print("Power M_1/y\n", pdy)#Good - print("Cheb M_1/y\n", cdy)#Weird - print("Power M_1/x\n", pdx)#Good - print("Cheb M_1/x\n", cdx)#Weird - - #graph the polys - delta = 0.01 - xrange = np.arange(-1, 2, delta) - yrange = np.arange(-1, 2, delta) - X, Y = np.meshgrid(xrange,yrange) - - ax1 = plt.subplot(121) - ax1.set_title("No M_x or M_y Matrices") - #polys - ax1.contour(X, Y, X**2 + 2*X*Y + Y**2 - 3*X - 5*Y + 4, [0], colors="black", linestyles="dashed") - ax1.contour(X, Y, -X**2 - 2*X*Y - Y**2 + 5*X + 3*Y - 4, [0], colors="black", linestyles="dashed") - #roots - #ax1.plot(pmf[:,0],pmf[:,1],".r",markersize=4) - #ax1.plot(cmf[:,0],cmf[:,1], ".m",markersize=4) - ax1.plot(pdy[:,0],pdy[:,1], "xb",markersize=6) - ax1.plot(cdy[:,0],cdy[:,1], ".c",markersize=4) - ax1.plot(pdx[:,0],pdx[:,1], "+", color = "gold",markersize=8) - ax1.plot(cdx[:,0],cdx[:,1], ".",color ="orange",markersize=4) - ax1.axis("equal") - - ax2 = plt.subplot(122) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2.contour(X, Y, X**2 + 2*X*Y + Y**2 - 3*X - 5*Y + 4, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, -X**2 - 2*X*Y - Y**2 + 5*X + 3*Y - 4, [0], colors="black", linestyles="dashed") - #roots - #ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - ##ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - #ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - ##ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - #ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - #ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Two hyperbolas that intersect at (1,1)") - plt.figlegend(loc="lower left") - plt.show() - -if hyperbolas_transformed: - print("Double Root Trials: \n p1 = 4.0*x^2 + (8.0*x - 8.2)*y + 4.0*y^2 - 7.6*x + 4 \n p2 = -4.0*x^2 + (-8.0*x + 7.8)*y - 4.0*y^2 + 8.4*x - 4") - print("Same two hyperbolas. Transformed a little more, now intersect at (1/3,2/3). Double root there, probably roots at infinity??") - - p1_coef = np.array([[4, -8.2, 4],[-7.6, 8,0],[4,0,0]]) #p1 = x^2 + 2xy + y^2 - 3x - 5y + 4 - p2_coef = np.array([[-4, 7.8, -4],[8.4,-8,0],[-4,0,0]]) #p2 = - x^2 - 2xy - y^2 + 5x + 3y - 4 - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - - c1_coef = np.array([[8,-8.2,2],[-7.6,8,0],[2,0,0]]) - c2_coef = np.array([[-8,7.8,-2],[8.4,-8,0],[-2,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) # 1/2 T2x + 2 xy + 1/2 T2y - 5 y - 3 x + 5 == 0 - c2 = polynomial.MultiCheb(c2_coef) #- 1/2 T2x -2 xy - 1/2 T2y + 3 y + 5 x - 5 == 0 - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - #pmf = polyroots.solve([p1, p2]) - #print("Roots:\n",pmf) - - print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - #cmf = polyroots.solve([c1, c2]) - #print("Roots:\n",cmf) - - print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - #pmx = polyroots.solve([p1, p2], MSmatrix = 1) - #print("Roots:\n",pmx) - - print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - #cmx = polyroots.solve([c1, c2], MSmatrix = 1) - #print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - #pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1)[:,::-1] - #print("Roots:\n",pmy) - - print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - #cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1)[:,::-1] - #print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - #print("Power M_f\n", pmf)#along right line - #print("Cheb M_f\n", cmf)#along right line - #print("Power M_x\n", pmx)#along right line - #print("Cheb M_x\n", cmx)#along right line - #print("Power M_y\n", pmy)#along right line - #print("Cheb M_y\n", cmy)#along right line - print("Power M_1/y\n", pdy)#Good - print("Cheb M_1/y\n", cdy)#Weird - print("Power M_1/x\n", pdx)#Good - print("Cheb M_1/x\n", cdx)#Weird - - #graph the polys - delta = 0.01 - xrange = np.arange(-1, 2, delta) - yrange = np.arange(-1, 2, delta) - X, Y = np.meshgrid(xrange,yrange) - - plt.clf() - ax1 = plt.subplot(121) - ax1.set_title("No M_x or M_y Matrices") - #polys - ax1.contour(X, Y, 4.0*X**2 + (8.0*X - 8.2)*Y + 4.0*Y**2 - 7.6*X + 4, [0], colors="black", linestyles="dashed") - ax1.contour(X, Y, -4.0*X**2 + (-8.0*X + 7.8)*Y - 4.0*Y**2 + 8.4*X - 4, [0], colors="black", linestyles="dashed") - #roots - #ax1.plot(pmf[:,0],pmf[:,1],".r",markersize=4) - #ax1.plot(cmf[:,0],cmf[:,1], ".m",markersize=4) - ax1.plot(pdy[:,0],pdy[:,1], "xb",markersize=6) - ax1.plot(cdy[:,0],cdy[:,1], ".c",markersize=4) - ax1.plot(pdx[:,0],pdx[:,1], "+", color = "gold",markersize=8) - ax1.plot(cdx[:,0],cdx[:,1], ".",color ="orange",markersize=4) - ax1.axis("equal") - - ax2 = plt.subplot(122) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2.contour(X, Y, 4.0*X**2 + (8.0*X - 8.2)*Y + 4.0*Y**2 - 7.6*X + 4, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, -4.0*X**2 + (-8.0*X + 7.8)*Y - 4.0*Y**2 + 8.4*X - 4, [0], colors="black", linestyles="dashed") - #roots - #ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - #ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - #ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - #ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - #ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - #ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Transformed a little more, now intersect at (1/3,2/3)") - plt.figlegend(loc="lower left") - plt.show() - -if hyperbolas_moved: - print("Double Root Trials: \n p1 = x^2 + (2x - 2sqrt(2) - 2.8)*y + y^2 + x*(-2sqrt(2) - 0.8) + 2.8*sqrt(2) + 1.91 \n p2 = -x^2 + (-2x + 2sqrt(2) + 0.8)y - y^2 + x*(2sqrt(2) + 2.8) - 0.8sqrt(2) - 3.71") - print("Hyperbolas, moved a little bit, now intersect at (.9, sqrt(2)). Double root there, probably roots at infinity??") - - p1_coef = np.array([[1.91 + 2.8*sqrt(2), -(2*sqrt(2) + 2.8), 1],[-(2*sqrt(2)+.8), 2, 0],[1,0,0]]) - p2_coef = np.array([[-3.71 - .8*sqrt(2), 2*sqrt(2) + .8, -1],[2*sqrt(2)+2.8, -2, 0],[-1,0,0]]) - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - c1_coef = np.array([[2.91 + 2.8*sqrt(2), -(2*sqrt(2) + 2.8), .5],[-(2*sqrt(2)+.8), 2, 0],[.5,0,0]]) - c2_coef = np.array([[-4.71 - .8*sqrt(2), 2*sqrt(2) + .8, -.5],[2*sqrt(2)+2.8, -2, 0],[-.5,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) # 1/2 T2x + 2 xy + 1/2 T2y - 5 y - 3 x + 5 == 0 - c2 = polynomial.MultiCheb(c2_coef) #- 1/2 T2x -2 xy - 1/2 T2y + 3 y + 5 x - 5 == 0 - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - #print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - #pmf = polyroots.solve([p1, p2]) - #print("Roots:\n",pmf) - - #print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - #cmf = polyroots.solve([c1, c2]) - #print("Roots:\n",cmf) - - #print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - #pmx = polyroots.solve([p1, p2], MSmatrix = 1) - #print("Roots:\n",pmx) - - #print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - #cmx = polyroots.solve([c1, c2], MSmatrix = 1) - #print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - #print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - #pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1)[:,::-1] - #print("Roots:\n",pmy) - - #print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - #cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1)[:,::-1] - #print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - #print("Power M_f\n", pmf)#along right line - #print("Cheb M_f\n", cmf)#along right line - #print("Power M_x\n", pmx)#along right line - #print("Cheb M_x\n", cmx)#along right line - #print("Power M_y\n", pmy)#along right line - #print("Cheb M_y\n", cmy)#along right line - print("Power M_1/y\n", pdy)#Good - print("Cheb M_1/y\n", cdy)#Weird - print("Power M_1/x\n", pdx)#Good - print("Cheb M_1/x\n", cdx)#Weird - - #graph the polys - delta = 0.01 - xrange = np.arange(-1, 2, delta) - yrange = np.arange(-1, 2, delta) - X, Y = np.meshgrid(xrange,yrange) - - plt.clf() - ax1 = plt.subplot(121) - ax1.set_title("No M_x or M_y Matrices") - #polys - ax1.contour(X, Y, X**2 + (2*X - 2*sqrt(2) - 2.8)*Y + Y**2 + X*(-2*sqrt(2) - 0.8) + 2.8*sqrt(2) + 1.91, [0], colors="black", linestyles="dashed") - ax1.contour(X, Y, -X**2 + (-2*X + 2*sqrt(2) + 0.8)*Y - Y**2 + X*(2*sqrt(2) + 2.8) - 0.8*sqrt(2) - 3.71, [0], colors="black", linestyles="dashed") - #roots - #ax1.plot(pmf[:,0],pmf[:,1],".r",markersize=4) - #ax1.plot(cmf[:,0],cmf[:,1], ".m",markersize=4) - ax1.plot(pdy[:,0],pdy[:,1], "xb",markersize=6) - ax1.plot(cdy[:,0],cdy[:,1], ".c",markersize=4) - ax1.plot(pdx[:,0],pdx[:,1], "+", color = "gold",markersize=8) - ax1.plot(cdx[:,0],cdx[:,1], ".",color ="orange",markersize=4) - ax1.axis("equal") - - ax2 = plt.subplot(122) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2.contour(X, Y, 4.0*X**2 + (8.0*X - 8.2)*Y + 4.0*Y**2 - 7.6*X + 4, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, -4.0*X**2 + (-8.0*X + 7.8)*Y - 4.0*Y**2 + 8.4*X - 4, [0], colors="black", linestyles="dashed") - #roots - #ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - #ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - #ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - #ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - #ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - #ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Hyperbolas, moved a little bit, now intersect at (.9, sqrt(2))") - plt.figlegend(loc="lower left") - plt.show() - -if hyperbolas_transformed_moved: - print("Double Root Trials: \n p1 = 4x^2 + 8 xy - (4sqrt(2) + 3.8) y + 4 y^2 + (-4sqrt(2) - 3.2)x + 2.8*sqrt(2) + 1.91 \n p2 = -4 x^2 + -8 xy + (4sqrt(2) + 3.4) y - 4.0 y^2 + 4(sqrt(2) + 1) x - 0.8 sqrt(2) - 3.71") - print("Hyperbolas, Transformed and moved a little bit, now intersect at (-3/2*sqrt(2) + 33/20, 2*sqrt(2) - 6/5). Double root there, probably roots at infinity??") - - p1_coef = np.array([[2.8*sqrt(2) + 1.91, - (4*sqrt(2) + 3.8),4],[-4*sqrt(2) - 3.2,8,0],[4,0,0]]) - p2_coef = np.array([[-0.8*sqrt(2) - 3.71, 4*sqrt(2) + 3.4,-4],[4*sqrt(2) + 4,-8,0],[-4,0,0]]) - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - c1_coef = np.array([[2.8*sqrt(2) + 5.91,- 4.0*sqrt(2) - 3.8,2],[-4*sqrt(2) - 3.2,8,0],[2,0,0]]) - c2_coef = np.array([[- 0.8*sqrt(2) - 7.71,4.0*sqrt(2) + 3.4,-2],[4*sqrt(2) + 4,-8,0],[-2,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) - c2 = polynomial.MultiCheb(c2_coef) - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - pmf = polyroots.solve([p1, p2], verbose=v) - print("Roots:\n",pmf) - - print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - cmf = polyroots.solve([c1, c2], verbose=v) - print("Roots:\n",cmf) - - print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - pmx = polyroots.solve([p1, p2], MSmatrix = 1, verbose=v) - print("Roots:\n",pmx) - - print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - cmx = polyroots.solve([c1, c2], MSmatrix = 1, verbose=v) - print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",pmy) - - print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - print("Actual Roots: ", [-3/2*sqrt(2) + 33/20, 2*sqrt(2) - 6/5]) - #print("Power M_f\n", pmf)#along right line - #print("Cheb M_f\n", cmf)#along right line - #print("Power M_x\n", pmx)#along right line - #print("Cheb M_x\n", cmx)#along right line - #print("Power M_y\n", pmy)#along right line - #print("Cheb M_y\n", cmy)#along right line - print("Power M_1/y\n", pdy)#Good - print("Cheb M_1/y\n", cdy)#Weird - print("Power M_1/x\n", pdx)#Good - print("Cheb M_1/x\n", cdx)#Weird - - #graph the polys - delta = 0.01 - xrange = np.arange(-1, 2, delta) - yrange = np.arange(-1, 2, delta) - X, Y = np.meshgrid(xrange,yrange) - - plt.clf() - ax2 = plt.subplot(111) - #polys - ax2.contour(X, Y, 4*X**2 + 8*X*Y - (4*sqrt(2) + 3.8)*Y + 4*Y**2 + (-4*sqrt(2) - 3.2)*X + 2.8*sqrt(2) + 1.91, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, -4*X**2 + -8*X*Y + (4*sqrt(2) + 3.4)*Y - 4.0*Y**2 + 4*(sqrt(2) + 1)*X - 0.8*sqrt(2) - 3.71, [0], colors="black", linestyles="dashed") - #roots - #ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - #ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - #ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - #ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - #ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - #ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Hyperbolas, Transformed and moved a little bit, now intersect at (-3/2*sqrt(2) + 33/20, 2*sqrt(2) - 6/5)") - plt.figlegend(loc="lower left") - plt.show() - -if circle_ellipse_origin: - print("Double Root Trials: \n p1 = x^2 + y^2 -1 \n p2 = 4x^2 + y^2 - 1 ") - print("Circle and inscribed ellipse centered at the origin. Roots at (0,1)M2 and (0,-1)M2.") - - p1_coef = np.array([[-1,0,1],[0,0,0],[1,0,0]]) - p2_coef = np.array([[-1,0,1],[0,0,0],[4,0,0]]) - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - - c1_coef = np.array([[0,0,.5],[0,0,0],[.5,0,0]]) - c2_coef = np.array([[1.5, 0, .5],[0,0,0],[2,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) - c2 = polynomial.MultiCheb(c2_coef) - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - pmf = polyroots.solve([p1, p2], verbose=v) - print("Roots:\n",pmf) - - print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - cmf = polyroots.solve([c1, c2], verbose=v) - print("Roots:\n",cmf) - - print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - pmx = polyroots.solve([p1, p2], MSmatrix = 1, verbose=v) - print("Roots:\n",pmx) - - print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - cmx = polyroots.solve([c1, c2], MSmatrix = 1, verbose=v) - print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",pmy) - - print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - #pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1)[:,::-1] - #print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - #cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1)[:,::-1] - #print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - #pdx = polyroots.solve([p1, p2], MSmatrix = -1) - #print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - #cdx = polyroots.solve([c1, c2], MSmatrix = -1) - #print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - print("Power M_f\n", pmf)#perfect - print("Cheb M_f\n", cmf)#really good - print("Power M_x\n", pmx)#really good - print("Cheb M_x\n", cmx)#really good - print("Power M_y\n", pmy)#perfect - print("Cheb M_y\n", cmy)#perfect - #print("Power M_1/y\n", pdy) - #print("Cheb M_1/y\n", cdy) - #print("Power M_1/x\n", pdx) - #print("Cheb M_1/x\n", cdx) - - #graph the polys - delta = 0.01 - xrange = np.arange(-4, 3, delta) - yrange = np.arange(-3, 3, delta) - X, Y = np.meshgrid(xrange,yrange) - - plt.clf() - ax2 = plt.subplot(111) - ax2 = plt.subplot(111) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2 - ax2.contour(X, Y, X**2 + Y**2 - 1, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, 4*X**2 + Y**2 - 1, [0], colors="black", linestyles="dashed") - #roots - ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - #ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - #ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - #ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - #ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Circle and inscribed ellipse centered at the origin") - plt.figlegend(loc="lower left") - plt.show() - -if circle_ellipse_moved: - print("Double Root Trials: \n p1 = 1.0*x^2 + 1.0*y^2 + 2.4*x - 6.2*y + 10.05 \n p2 = 4.0*x^2 + 1.0*y^2 + 9.6*x - 6.2*y + 14.37") - print("Circle and inscribed ellipse moved away from the origin. Center now at (-1.2, 3.1), Double roots at (-1.2, 4.1) and (-1.2,2.1).") - - p1_coef = np.array([[10.05,-6.2,1],[2.4,0,0],[1,0,0]]) - p2_coef = np.array([[14.37,-6.2,1],[9.6,0,0],[4,0,0]]) - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - #In Cheb form - - c1_coef = np.array([[11.05,-6.2,.5],[2.4,0,0],[.5,0,0]]) - c2_coef = np.array([[16.87,-6.2,.5],[9.6,0,0],[2,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) - c2 = polynomial.MultiCheb(c2_coef) - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - pmf = polyroots.solve([p1, p2], verbose=v) - print("Roots:\n",pmf) - - print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - cmf = polyroots.solve([c1, c2], verbose=v) - print("Roots:\n",cmf) - - print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - pmx = polyroots.solve([p1, p2], MSmatrix = 1, verbose=v) - print("Roots:\n",pmx) - - print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - cmx = polyroots.solve([c1, c2], MSmatrix = 1, verbose=v) - print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",pmy) - - print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - print("Actual Roots: (-1.2, 4.1) and (-1.2,2.1)") - print("Power M_f\n", pmf)#Spot. On. - print("Cheb M_f\n", cmf)#Spot. On. - print("Power M_x\n", pmx)#x-coord right, y-coord wrong - print("Cheb M_x\n", cmx)#x-coord right, y-coord wrong, worse than Power M_x - print("Power M_y\n", pmy)#perfect y-coord, x-coord off by 1.11, other by 1.08 - print("Cheb M_y\n", cmy)#perfect y-coords, x-coord off by 1.11 - print("Power M_1/y\n", pdy)#y-coord perfect, x-coord wrong. One x-coord pretty close - print("Cheb M_1/y\n", cdy)#got x-coord almost right on one root - print("Power M_1/x\n", pdx)#x-coord perfect, y-coord wrong - print("Cheb M_1/x\n", cdx)#x-coord right, y-coord wrong - - #graph the polys - delta = 0.01 - xrange = np.arange(-2.5, 0, delta) - yrange = np.arange(-2, 4.5, delta) - X, Y = np.meshgrid(xrange,yrange) - - plt.clf() - ax2 = plt.subplot(111) - ax2 = plt.subplot(111) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2 - ax2.contour(X, Y, 1.0*X**2 + 1.0*Y**2 + 2.4*X - 6.2*Y + 10.05, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, 4.0*X**2 + 1.0*Y**2 + 9.6*X - 6.2*Y + 14.37, [0], colors="black", linestyles="dashed") - #roots - ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Circle and inscribed ellipse centered at the origin") - plt.figlegend(loc="lower left") - plt.show() - -if circle_ellipse_transformed: - print("Double Root Trials: \n p1 = 2.08*x^2 + 3.92*x*y + 2.02*y^2 - 1 \n p2 = 6.4*x^2 + 10.4*x*y + 4.45*y^2 - 1 ") - print("Circle and inscribed ellipse centered at the origin, then transformed. Roots at (1.5,-2)M2 and (-1.5,2)M2.") - - p1_coef = np.array([[-1,0,2.02],[0,3.92,0],[2.08,0,0]]) - p2_coef = np.array([[-1,0,4.45],[0,10.4,0],[6.4,0,0]]) - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - - c1_coef = np.array([[1.05,0,1.01],[0,3.92,0],[1.04,0,0]]) - c2_coef = np.array([[4.425,0,2.25],[0,10.4,0],[3.2,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) # 1/2 T2x + 2 xy + 1/2 T2y - 5 y - 3 x + 5 == 0 - c2 = polynomial.MultiCheb(c2_coef) #- 1/2 T2x -2 xy - 1/2 T2y + 3 y + 5 x - 5 == 0 - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - pmf = polyroots.solve([p1, p2], verbose=v) - print("Roots:\n",pmf) - - print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - cmf = polyroots.solve([c1, c2], verbose=v) - print("Roots:\n",cmf) - - print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - pmx = polyroots.solve([p1, p2], MSmatrix = 1, verbose=v) - print("Roots:\n",pmx) - - print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - cmx = polyroots.solve([c1, c2], MSmatrix = 1, verbose=v) - print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",pmy) - - print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - #Everything Power really good, everything cheb fine - print("Power M_f\n", pmf) - print("Cheb M_f\n", cmf) - print("Power M_x\n", pmx) - print("Cheb M_x\n", cmx) - print("Power M_y\n", pmy) - print("Cheb M_y\n", cmy) - print("Power M_1/y\n", pdy) - print("Cheb M_1/y\n", cdy) - print("Power M_1/x\n", pdx) - print("Cheb M_1/x\n", cdx) - - #graph the polys - delta = 0.01 - xrange = np.arange(-4, 3, delta) - yrange = np.arange(-3, 3, delta) - X, Y = np.meshgrid(xrange,yrange) - - plt.clf() - - ax2 = plt.subplot(111) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2.contour(X, Y, 2.08*X**2 + 3.92*X*Y + 2.02*Y**2 - 1, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, 6.4*X**2 + 10.4*X*Y + 4.45*Y**2 - 1, [0], colors="black", linestyles="dashed") - #roots - ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Circle and inscribed ellipse centered at the origin, then transformed") - plt.figlegend(loc="lower left") - plt.show() - -if circle_ellipse_transformed_moved: - print("Double Root Trials: \n p1 = 2.08*x^2 + (3.92*x - 7.82)*y + 2.02*y^2 - 7.16*x + 6.825 \n p2 = 6.4*x^2 + (10.4*x - 15.11)*y + 4.45*y^2 - 16.88*x + 13.2925 - 1") - print("Circle and inscribed ellipse transformed and moved away from the origin.") - - p1_coef = np.array([[6.825, -7.82, 2.02],[-7.16, 3.92, 0], [2.08,0,0]]) - p2_coef = np.array([[12.2925, -15.11, 4.45],[-16.88, 10.4, 9],[6.4,0,0]]) - - p1 = polynomial.MultiPower(p1_coef) - p2 = polynomial.MultiPower(p2_coef) - p1_switch_xy = polynomial.MultiPower(p1_coef.T) - p2_switch_xy = polynomial.MultiPower(p2_coef.T) - - #In Cheb form - - c1_coef = np.array([[8.875,-7.82,1.01],[-7.16,3.92,0],[1.04,0,0]]) - c2_coef = np.array([[17.7175,-15.11,2.225],[-16.88,10.4,0],[3.2,0,0]]) - - c1 = polynomial.MultiCheb(c1_coef) - c2 = polynomial.MultiCheb(c2_coef) - c1_switch_xy = polynomial.MultiCheb(c1_coef.T) - c2_switch_xy = polynomial.MultiCheb(c2_coef.T) - - print("~ ~ ~ Power Form, M_f Matrix ~ ~ ~") - pmf = polyroots.solve([p1, p2], verbose=v) - print("Roots:\n",pmf) - - print("~ ~ ~ Cheb Form, M_f Matrix ~ ~ ~") - cmf = polyroots.solve([c1, c2], verbose=v) - print("Roots:\n",cmf) - - print("~ ~ ~ Power Form, M_x Matrix ~ ~ ~") - pmx = polyroots.solve([p1, p2], MSmatrix = 1, verbose=v) - print("Roots:\n",pmx) - - print("~ ~ ~ Cheb Form, M_x Matrix ~ ~ ~") - cmx = polyroots.solve([c1, c2], MSmatrix = 1, verbose=v) - print("Roots:\n",cmx) - - #flip left/right because x and y are switched. Same for M_y and M_1/y matrices below - print("~ ~ ~ Power Form, M_y Matrix ~ ~ ~") - pmy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",pmy) - - print("~ ~ ~ Cheb Form, M_y Matrix ~ ~ ~") - cmy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = 1, verbose=v)[:,::-1] - print("Roots:\n",cmy) - - print("~ ~ ~ Power Form, Division Matrix 1/y ~ ~ ~") - pdy = polyroots.solve([p1_switch_xy, p2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",pdy) - - print("~ ~ ~ Cheb Form, Division Matrix 1/y ~ ~ ~") - cdy = polyroots.solve([c1_switch_xy, c2_switch_xy], MSmatrix = -1, verbose=v)[:,::-1] - print("Roots:\n",cdy) - - print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") - pdx = polyroots.solve([p1, p2], MSmatrix = -1, verbose=v) - print("Roots:\n",pdx) - - print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") - cdx = polyroots.solve([c1, c2], MSmatrix = -1, verbose=v) - print("Roots:\n",cdx) - - print("\n\nCompare Roots:\n") - print("Real Roots: (-2.7, 5.1) and (.3,1.1)") - #Everything power bad, gives the same 4 roots. Everything cheb basically perfect - print("Power M_f\n", pmf)#Good - print("Cheb M_f\n", cmf)#Good - print("Power M_x\n", pmx) - print("Cheb M_x\n", cmx)#Good - print("Power M_y\n", pmy) - print("Cheb M_y\n", cmy)#Good - print("Power M_1/y\n", pdy) - print("Cheb M_1/y\n", cdy)#Good - print("Power M_1/x\n", pdx) - print("Cheb M_1/x\n", cdx)#Good - - #graph the polys - delta = 0.01 - xrange = np.arange(-4, 2, delta) - yrange = np.arange(0, 6, delta) - X, Y = np.meshgrid(xrange,yrange) - - ax2 = plt.subplot(111) - ax2.set_title("With M_x and M_y Matrices") - #polys - ax2.contour(X, Y, 2.08*X**2 + (3.92*X - 7.82)*Y + 2.02*Y**2 - 7.16*X + 7.825 - 1, [0], colors="black", linestyles="dashed") - ax2.contour(X, Y, 6.4*X**2 + (10.4*X - 15.11)*Y + 4.45*Y**2 - 16.88*X + 13.2925 - 1, [0], colors="black", linestyles="dashed") - #roots - ax2.plot((-2.7, .3),(5.1,1.1), '.', color = 'black', markersize = 10) - ax2.plot(pmx[:,0],pmx[:,1],".", color="gray", label="Power M_x",markersize=4) - ax2.plot(cmx[:,0],cmx[:,1], ".", color="silver", label="Cheb M_x",markersize=4) - ax2.plot(pmy[:,0],pmy[:,1],".", color="tan", label="Power M_y",markersize=4) - ax2.plot(cmy[:,0],cmy[:,1], ".", color="peru", label="Cheb M_y",markersize=4) - ax2.plot(pmf[:,0],pmf[:,1],".r", label="Power M_f",markersize=4) - ax2.plot(cmf[:,0],cmf[:,1], ".m", label="Cheb M_f",markersize=4) - ax2.plot(pdy[:,0],pdy[:,1], "xb", label="Power M_1/y",markersize=6) - ax2.plot(cdy[:,0],cdy[:,1], ".c", label="Cheb M_1/y",markersize=4) - ax2.plot(pdx[:,0],pdx[:,1], "+", color = "gold", label="Power M_1/x",markersize=8) - ax2.plot(cdx[:,0],cdx[:,1], ".",color ="orange", label="Cheb M_1/x",markersize=4) - ax2.axis("equal") - - plt.suptitle("Circle and inscribed ellipse transformed and moved away from the origin") - plt.figlegend(loc="lower left") - plt.show() diff --git a/exampleforpaper.py b/exampleforpaper.py deleted file mode 100644 index 597ba7bf..00000000 --- a/exampleforpaper.py +++ /dev/null @@ -1,54 +0,0 @@ -from numalgsolve import polynomial -from numalgsolve import polyroots -import numpy as np - -p1 = polynomial.MultiPower(np.array([[1, -4, 0],[0, 3, 0],[1, 0, 0]]).T) #y^2 + 3xy - 4x +1 -p2 = polynomial.MultiPower(np.array([[3, 0, -2],[6, -6, 0],[0, 0, 0]]).T) #-6xy -2x^2 + 6y +3 -c1 = polynomial.MultiCheb(np.array([[2, 0, -1],[6, -6, 0], [0, 0, 0]]).T) #p2 in Cheb form -c2 = polynomial.MultiCheb(np.array([[1.5, -4, 0],[0, 3, 0], [.5, 0, 0]]).T) #p2 in Cheb form - -print("~ ~ ~ Power Form, Mx Matrix ~ ~ ~") -print("Roots\n", polyroots.solve([p1, p2], MSmatrix = 1, verbose=True)) - -print("~ ~ ~ Cheb Form, Mx Matrix ~ ~ ~") -print("Roots\n",polyroots.solve([c1, c2], MSmatrix = 1, verbose=True)) - -print("~ ~ ~ Power Form, My Matrix ~ ~ ~") -print("Roots\n", polyroots.solve([p1, p2], MSmatrix = 2, verbose=True)) - -print("~ ~ ~ Cheb Form, My Matrix ~ ~ ~") -print("Roots\n",polyroots.solve([c1, c2], MSmatrix = 2, verbose=True)) - -print("~ ~ ~ Power Form, Pseudorandom Multiplication Matrix ~ ~ ~") -print("Roots\n", polyroots.solve([p1, p2], MSmatrix = 0, verbose=True)) - -print("~ ~ ~ Cheb Form, Pseudorandom Multiplication Matrix ~ ~ ~") -print("Roots\n",polyroots.solve([c1, c2], MSmatrix = 0, verbose=True)) - -print("~ ~ ~ Power Form, Division Matrix 1/x ~ ~ ~") -print("Roots\n",polyroots.solve([p1, p2], MSmatrix = -1, verbose=True)) - -print("~ ~ ~ Cheb Form, Division Matrix 1/x ~ ~ ~") -print("Roots\n",polyroots.solve([c1, c2], MSmatrix = -1, verbose=True)) - -print("\n\n\nSimple One Dimensional Example") -p = polynomial.MultiPower(np.array([2, -4,1.])) #x^2 - 4x + 2 -c = polynomial.MultiCheb(np.array([2.5, -4, .5])) #p in Cheb form - -print("~ ~ ~ Companion Matrix ~ ~ ~") -print("Roots\n", polyroots.solve([p], MSmatrix = 1, verbose=True)) - -print("~ ~ ~ Rotated Companion Matrix ~ ~ ~") -print("Roots\n", polyroots.solve([p], MSmatrix = 0, verbose=True)) - -print("~ ~ ~ Power Division Matrix ~ ~ ~") -print("Roots\n",polyroots.solve([p], MSmatrix = -1, verbose=True)) - -print("~ ~ ~ Colleague Matrix ~ ~ ~") -print("Roots\n",polyroots.solve([c], MSmatrix = 1, verbose=True)) - -print("~ ~ ~ Rotated Colleague Matrix ~ ~ ~") -print("Roots\n", polyroots.solve([c], MSmatrix = 0, verbose=True)) - -print("~ ~ ~ Cheb Division Matrix ~ ~ ~") -print("Roots\n",polyroots.solve([c], MSmatrix = -1, verbose=True)) diff --git a/numalgsolve/MacaulayReduce.py b/numalgsolve/MacaulayReduce.py deleted file mode 100644 index 08b9a140..00000000 --- a/numalgsolve/MacaulayReduce.py +++ /dev/null @@ -1,449 +0,0 @@ -import numpy as np -import itertools -from scipy.linalg import qr, solve_triangular, qr_multiply -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.utils import row_swap_matrix, MacaulayError, slice_top, mon_combos, \ - num_mons_full, memoized_all_permutations, mons_ordered, \ - all_permutations_cheb - -def add_polys(degree, poly, poly_coeff_list): - """Adds polynomials to a Macaulay Matrix. - - This function is called on one polynomial and adds all monomial multiples of - it to the matrix. - - Parameters - ---------- - degree : int - The degree of the Macaulay Matrix - poly : Polynomial - One of the polynomials used to make the matrix. - poly_coeff_list : list - A list of all the current polynomials in the matrix. - Returns - ------- - poly_coeff_list : list - The original list of polynomials in the matrix with the new monomial - multiplications of poly added. - """ - - poly_coeff_list.append(poly.coeff) - deg = degree - poly.degree - dim = poly.dim - - mons = mon_combos([0]*dim,deg) - - for mon in mons[1:]: #skips the first all 0 mon - poly_coeff_list.append(poly.mon_mult(mon, returnType = 'Matrix')) - return poly_coeff_list - -def find_degree(poly_list, verbose=False): - '''Finds the appropriate degree for the Macaulay Matrix. - - Parameters - -------- - poly_list: list - The polynomials used to construct the matrix. - - Returns - ----------- - find_degree : int - The degree of the Macaulay Matrix. - - ''' - if verbose: - print('Degree of Macaulay Matrix:', sum(poly.degree for poly in poly_list) - len(poly_list) + 1) - return sum(poly.degree for poly in poly_list) - len(poly_list) + 1 - -def rrqr_reduceMacaulay(matrix, matrix_terms, cuts, number_of_roots, accuracy = 1.e-10): - ''' Reduces a Macaulay matrix, BYU style. - - The matrix is split into the shape - A B C - D E F - Where A is square and contains all the highest terms, and C contains all the x,y,z etc. terms. The lengths - are determined by the matrix_shape_stuff tuple. First A and D are reduced using rrqr without pivoting, and then the rest of - the matrix is multiplied by Q.T to change it accordingly. Then E is reduced by rrqr with pivoting, the rows of B are shifted - accordingly, and F is multipled by Q.T to change it accordingly. This is all done in place to save memory. - - Parameters - ---------- - matrix : numpy array. - The Macaulay matrix, sorted in BYU style. - matrix_terms: numpy array - Each row of the array contains a term in the matrix. The i'th row corresponds to - the i'th column in the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - Returns - ------- - matrix : numpy array - The reduced matrix. - matrix_terms: numpy array - The resorted matrix_terms. - ''' - #print("Starting matrix.shape:\n", matrix.shape) - #RRQR reduces A and D without pivoting sticking the result in it's place. - Q1,matrix[:,:cuts[0]] = qr(matrix[:,:cuts[0]]) - - #check if there are zeros along the diagonal of R1 - if any(np.isclose(np.diag(matrix[:,:cuts[0]]),0, atol=accuracy)): - raise MacaulayError("R1 IS NOT FULL RANK") - - #Looks like 0 but not, add to the rank. - #still_good = np.sum(np.abs(matrix[:,:cuts[0]].diagonal()) < accuracy) - #if abs(matrix[:,:cuts[0]].diagonal()[-1]) < accuracy: - # print(matrix[:,:cuts[0]].diagonal()) - # raise MacaulayError("HIGHEST NOT FULL RANK") - - #Multiplying the rest of the matrix by Q.T - matrix[:,cuts[0]:] = Q1.T@matrix[:,cuts[0]:] - Q1 = 0 #Get rid of Q1 for memory purposes. - - #RRQR reduces E sticking the result in it's place. - Q,matrix[cuts[0]:,cuts[0]:cuts[1]],P = qr(matrix[cuts[0]:,cuts[0]:cuts[1]], pivoting = True) - - #Multiplies F by Q.T. - matrix[cuts[0]:,cuts[1]:] = Q.T@matrix[cuts[0]:,cuts[1]:] - Q = 0 #Get rid of Q for memory purposes. - - #Shifts the columns of B - matrix[:cuts[0],cuts[0]:cuts[1]] = matrix[:cuts[0],cuts[0]:cuts[1]][:,P] - - #Checks for 0 rows and gets rid of them. - #rank = np.sum(np.abs(matrix.diagonal())>accuracy) + still_good - #matrix = matrix[:rank] - - #eliminates rows we don't care about-- those at the bottom of the matrix - #since the top corner is a square identity matrix, useful_rows + number_of_roots is the width of the Macaulay matrix - matrix = row_swap_matrix(matrix) - for row in matrix[::-1]: - if np.allclose(row, 0): - matrix = matrix[:-1] - else: - break - #print("Final matrix.shape:\n", matrix.shape) - #useful_rows = matrix.shape[1] - number_of_roots - #matrix = matrix[:useful_rows,:] - - #set very small values in the matrix to zero before backsolving - matrix[np.isclose(matrix, 0, atol=accuracy)] = 0 - - #Resorts the matrix_terms. - matrix_terms[cuts[0]:cuts[1]] = matrix_terms[cuts[0]:cuts[1]][P] - #print("Macaulay1Rank:", np.sum(np.abs(matrix.diagonal())>accuracy)) - - return matrix, matrix_terms - -def rrqr_reduceMacaulay2(matrix, matrix_terms, cuts, number_of_roots, accuracy = 1.e-10): - ''' Reduces a Macaulay matrix, BYU style - - This function does the same thing as rrqr_reduceMacaulay but uses - qr_multiply instead of qr and a multiplication - to make the function faster and more memory efficient. - - This function only works properly if the bottom left (D) part of the matrix is zero - - Parameters - ---------- - matrix : numpy array. - The Macaulay matrix, sorted in BYU style. - matrix_terms: numpy array - Each row of the array contains a term in the matrix. The i'th row corresponds to - the i'th column in the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - accuracy : float - What is determined to be 0. - Returns - ------- - matrix : numpy array - The reduced matrix. - matrix_terms: numpy array - The resorted matrix_terms. - ''' - #print("Starting matrix.shape:\n", matrix.shape) - #RRQR reduces A and D without pivoting sticking the result in it's place. - C1,matrix[:cuts[0],:cuts[0]] = qr_multiply(matrix[:,:cuts[0]], matrix[:,cuts[0]:].T, mode = 'right') - matrix[:cuts[0],cuts[0]:] = C1.T - C1 = 0 - - #check if there are zeros along the diagonal of R1 - if any(np.isclose(np.diag(matrix[:,:cuts[0]]),0, atol=accuracy)): - raise MacaulayError("R1 IS NOT FULL RANK") - - #if abs(matrix[:,:cuts[0]].diagonal()[-1]) < accuracy: - # raise MacaulayError("HIGHEST NOT FULL RANK") - - #set small values to zero before backsolving - matrix[np.isclose(matrix, 0, atol=accuracy)] = 0 - - matrix[:cuts[0],cuts[0]:] = solve_triangular(matrix[:cuts[0],:cuts[0]],matrix[:cuts[0],cuts[0]:]) - matrix[:cuts[0],:cuts[0]] = np.eye(cuts[0]) - matrix[cuts[0]:,cuts[0]:] -= (matrix[cuts[0]:,:cuts[0]])@matrix[:cuts[0],cuts[0]:] #? - - C,R,P = qr_multiply(matrix[cuts[0]:,cuts[0]:cuts[1]], matrix[cuts[0]:,cuts[1]:].T, mode = 'right', pivoting = True) - - matrix = matrix[:R.shape[0]+cuts[0]] - #matrix[cuts[0]:,:cuts[0]] = np.zeros_like(matrix[cuts[0]:,:cuts[0]]) - matrix[cuts[0]:,cuts[0]:cuts[0]+R.shape[1]] = R - matrix[cuts[0]:,cuts[0]+R.shape[1]:] = C.T - C,R = 0,0 - - #Shifts the columns of B. - matrix[:cuts[0],cuts[0]:cuts[1]] = matrix[:cuts[0],cuts[0]:cuts[1]][:,P] - matrix_terms[cuts[0]:cuts[1]] = matrix_terms[cuts[0]:cuts[1]][P] - P = 0 - - # Check if there are no solutions - #rank = np.sum(np.abs(matrix.diagonal())>accuracy) - - # extra_block = matrix[rank:, -matrix_shape_stuff[2]:] - # Q,R = qr(extra_block) - # if np.sum(np.abs(R.diagonal())>accuracy) == matrix_shape_stuff[2]: - # raise ValueError("The system given has no roots.") - - #Get rid of 0 rows at the bottom. - #matrix = matrix[:rank] - - #eliminates rows we don't care about-- those at the bottom of the matrix - #since the top corner is a square identity matrix, always_useful_rows + number_of_roots is the width of the Macaulay matrix - always_useful_rows = matrix.shape[1] - number_of_roots - #matrix = matrix[:useful_rows,:] - - #set small values in the matrix to zero now, after the QR reduction - matrix[np.isclose(matrix, 0, atol=accuracy)] = 0 - #eliminate zero rows from the bottom of the matrix. Zero rows above - #nonzero elements are not eliminated. This saves time since Macaulay matrices - #we deal with are only zero at the very bottom - matrix = row_swap_matrix(matrix) - for row in matrix[::-1]: - if np.allclose(row, 0): - matrix = matrix[:-1] - else: - break - - return matrix, matrix_terms - -def rrqr_reduceMacaulayFullRank(matrix, matrix_terms, cuts, accuracy = 1.e-10): - ''' Reduces a Macaulay matrix, BYU style. - - This function does the same thing as rrqr_reduceMacaulay2 but only works if the matrix is full rank AND if - the top left corner (the square of side length cut[0]) is invertible. - In this case it is faster. - - Parameters - ---------- - matrix : numpy array. - The Macaulay matrix, sorted in BYU style. - matrix_terms: numpy array - Each row of the array contains a term in the matrix. The i'th row corresponds to - the i'th column in the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - accuracy : float - What is determined to be 0. - Returns - ------- - matrix : numpy array - The reduced matrix. - matrix_terms: numpy array - The resorted matrix_terms. - ''' - C1,matrix[:cuts[0],:cuts[0]] = qr_multiply(matrix[:cuts[0],:cuts[0]],\ - matrix[:cuts[0],cuts[0]:].T, mode = 'right') - matrix[:cuts[0],cuts[0]:] = C1.T - C1 = 0 - - #check if there are zeros along the diagonal of R1 - if any(np.isclose(np.diag(matrix[:,:cuts[0]]),0, atol=accuracy)): - raise MacaulayError("R1 IS NOT FULL RANK") - - #if abs(matrix[:,:cuts[0]].diagonal()[-1]) < accuracy: - # raise MacaulayError("HIGHEST NOT FULL RANK") - - C,matrix[cuts[0]:,cuts[0]:cuts[1]],P = qr_multiply(matrix[cuts[0]:,cuts[0]:cuts[1]],\ - matrix[cuts[0]:,cuts[1]:].T, mode = 'right', pivoting = True) - - matrix[cuts[0]:,cuts[1]:] = C.T - C = 0 - - #Shifts the columns of B. - matrix[:cuts[0],cuts[0]:cuts[1]] = matrix[:cuts[0],cuts[0]:cuts[1]][:,P] - matrix_terms[cuts[0]:cuts[1]] = matrix_terms[cuts[0]:cuts[1]][P] - P = 0 - return matrix, matrix_terms - -def checkEqual(lst): - '''Helper function for createMatrixFast. Checks if each element in a list is the same. - - Parameters - ---------- - lst : list - The list of interest. - Returns - ------- - checkEqual : bool - True if each element in the list is the same. False otherwise. - ''' - return lst.count(lst[0]) == len(lst) - -def get_ranges(nums): - '''Helper function for createMatrixFast. Finds where to slice the different parts of the matrix into. - - This is in an effort to avoid row_swap_matrix which can be slow. Instead, as we are buiding the part of the - matrix corresponding to each polynomial seperately, this tells us where each part should go in the whole matrix. - - Parameters - ---------- - nums : list - The Macualay matrix degree minus the polynomial degrees for for each polynomial. - Returns - ------- - ranges : list - The rows in the Macaulay Matrix that the given polynomail will be sliced into. - ''' - ranges = [] - for i in nums: - ranges.append(np.array([],dtype=int)) - start = 0 - count = 0 - n = len(nums) - for num in nums: - spot = count - for r in ranges[count:]: - r = np.hstack((r,np.arange(start,start+(n-count)*(num-len(r)),n-count))) - ranges[spot] = r - start+=1 - spot += 1 - start = ranges[-1][-1]+1 - count+=1 - return ranges - -def createMatrixFast(polys, degree, dim): - ''' Builds a Macaulay matrix using fast construction. - - Parameters - ---------- - poly_coeffs : list. - Contains numpy arrays that hold the coefficients of the polynomials to be put in the matrix. - degree : int - The degree of the Macaulay Matrix - dim : int - The dimension of the polynomials going into the matrix. - Returns - ------- - matrix : 2D numpy array - The Macaulay matrix. - matrix_terms : numpy array - The ith row is the term represented by the ith column of the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - ''' - bigShape = [degree+1]*dim - - matrix_terms, cuts = sorted_matrix_terms(degree, dim) - columns = len(matrix_terms) - - range_split = [num_mons_full(degree-poly.degree,dim) for poly in polys] - rows = np.sum(range_split) - ranges = get_ranges(range_split) #How to slice the poly into the matrix rows. - matrix = np.zeros((rows,columns)) - curr = 0 - - #Get the slices needed to pull the matrix_terms from the coeff matrix. - matrix_term_indexes = list() - for row in matrix_terms.T: - matrix_term_indexes.append(row) - - permutations = None - currentDegree = 2 - #Adds the poly_coeffs to flat_polys, using added_zeros to make sure every term is in there. - added_zeros = np.zeros(bigShape) - - for poly,matrix_range in zip(polys,ranges): - slices = slice_top(poly.coeff) - added_zeros[slices] = poly.coeff - array = added_zeros[matrix_term_indexes] - added_zeros[slices] = np.zeros_like(poly.coeff) - - permutations = memoized_all_permutations(degree - poly.degree, dim, degree, permutations, currentDegree) - currentDegree = degree - poly.degree - permList = list(permutations.values()) - - temp = array[np.reshape(permList, (len(permList), columns))[::-1]] - matrix[matrix_range] = temp - - if matrix_shape_stuff[0] > matrix.shape[0]: #The matrix isn't tall enough, these can't all be pivot columns. - raise MacaulayError("HIGHEST NOT FULL RANK. TRY HIGHER DEGREE") - #Sorts the rows of the matrix so it is close to upper triangular. - if not checkEqual([poly.degree for poly in polys]): #Will need some switching possibly if some degrees are different. - matrix = row_swap_matrix(matrix) - return matrix, matrix_terms, cuts - -def construction(polys, degree, dim): - ''' Builds a Macaulay matrix using fast construction in the Chebyshev basis. - - Parameters - ---------- - polys : list. - Contains numpy arrays that hold the coefficients of the polynomials to be put in the matrix. - degree : int - The degree of the Macaulay Matrix - dim : int - The dimension of the polynomials going into the matrix. - Returns - ------- - matrix : 2D numpy array - The Macaulay matrix. - matrix_terms : numpy array - The ith row is the term represented by the ith column of the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - ''' - bigShape = [degree+1]*dim - matrix_terms, cuts = sorted_matrix_terms(degree, dim) - #print(matrix_shape_stuff) - matrix_term_indexes = list() - for row in matrix_terms.T: - matrix_term_indexes.append(row) - - permutations = all_permutations_cheb(degree - np.min([poly.degree for poly in polys]), dim, degree) - #print(permutations) - added_zeros = np.zeros(bigShape) - flat_polys = list() - i = 0; - for poly in polys: - slices = slice_top(poly.coeff) - added_zeros[slices] = poly.coeff - array = added_zeros[matrix_term_indexes] - added_zeros[slices] = np.zeros_like(poly.coeff) - #print(array) - - #flat_polys.append(array[np.vstack(permutations.values())]) - degreeNeeded = degree - poly.degree - mons = mons_ordered(dim,degreeNeeded) - mons = np.pad(mons, (0,1), 'constant', constant_values = i) - i += 1 - flat_polys.append(array) - for mon in mons[1:-1]: - result = np.copy(array) - for i in range(dim): - if mon[i] != 0: - - mult = [0]*dim - mult[i] = mon[i] - result = np.sum(result[permutations[tuple(mult)]], axis = 0) - flat_polys.append(result) - #print(flat_polys) - matrix = np.vstack(flat_polys) - if matrix_shape_stuff[0] > matrix.shape[0]: #The matrix isn't tall enough, these can't all be pivot columns. - raise MacaulayError("HIGHEST NOT FULL RANK. TRY HIGHER DEGREE") - matrix = row_swap_matrix(matrix) - #print(matrix) - return matrix, matrix_terms, cuts diff --git a/numalgsolve/Multiplication.py b/numalgsolve/Multiplication.py deleted file mode 100644 index 410f219b..00000000 --- a/numalgsolve/Multiplication.py +++ /dev/null @@ -1,428 +0,0 @@ -import numpy as np -import itertools -from scipy.linalg import solve_triangular, eig -from numalgsolve.polynomial import MultiCheb, MultiPower, is_power -from numalgsolve.MacaulayReduce import rrqr_reduceMacaulay2, rrqr_reduceMacaulay, find_degree, add_polys -from numalgsolve.utils import row_swap_matrix, MacaulayError, slice_top, get_var_list, \ - mon_combos, mon_combosHighest, sort_polys_by_degree, \ - deg_d_polys, all_permutations_cheb -import warnings - -def multiplication(polys, verbose=False, MSmatrix=0, rotate=False): - ''' - Finds the roots of the given list of multidimensional polynomials using a multiplication matrix. - - Parameters - ---------- - polys : list of polynomial objects - Polynomials to find the common roots of. - MSmatrix : int - Controls which Moller-Stetter matrix is constructed. The options are: - 0 (default) -- The Moller-Stetter matrix of a random polynomial - Some positive integer i < dimension -- The Moller-Stetter matrix of x_i - verbose : bool - Prints information about how the roots are computed. - returns - ------- - roots : numpy array - The common roots of the polynomials. Each row is a root. - ''' - poly_type = is_power(polys, return_string = True) - dim = polys[0].dim - - if MSmatrix not in list(range(dim+1)): - raise ValueError('MSmatrix must be 0 (random polynomial), or the index of a variable') - - #By Bezout's Theorem. Useful for making sure that the reduced Macaulay Matrix is as we expect - degrees = [poly.degree for poly in polys] - max_number_of_roots = np.prod(degrees) - - m_f, var_dict = MSMultMatrix(polys, poly_type, max_number_of_roots, verbose=verbose, MSmatrix=MSmatrix) - - if rotate: #rotate multiplication matrix 180 degrees - m_f = np.rot90(m_f,2) - - if verbose: - print("\nM_f:\n", m_f[::-1,::-1]) - - # both MSMultMatrix and will return m_f as - # -1 if the ideal is not zero dimensional or if there are no roots - if type(m_f) == int: - return -1 - - # Get list of indexes of single variables and store vars that were not - # in the vector space basis. - var_spots = list() - spot = np.zeros(dim) - for i in range(dim): - spot[i] = 1 - if not rotate: - var_spots.append(var_dict[tuple(spot)]) - else: #if m_f is rotated 180, the eigenvectors are backwards - var_spots.append(m_f.shape[0] - 1 - var_dict[tuple(spot)]) - spot[i] = 0 - - # Get left eigenvectors - - vals,vecs = np.linalg.eig(m_f.T) - if verbose: - print('\nLeft Eigenvectors (as rows)\n',vecs.T) - print('\nEigenvals\n', vals) - - zeros_spot = var_dict[tuple(0 for i in range(dim))] - if rotate: #if m_f is rotate 180, the eigenvectors are backwards - zeros_spot = m_f.shape[0] - 1 - zeros_spot - - #vecs = vecs[:,np.abs(vecs[zeros_spot]) > 1.e-10] - if verbose: - print('\nVariable Spots in the Vector\n',var_spots) - print('\nEigeinvecs at the Variable Spots:\n',vecs[var_spots]) - print('\nConstant Term Spot in the Vector\n',zeros_spot) - print('\nEigeinvecs at the Constant Term\n',vecs[zeros_spot]) - - roots = vecs[var_spots]/vecs[zeros_spot] - - #Checks that the algorithm finds the correct number of roots with Bezout's Theorem - assert roots.shape[1] <= max_number_of_roots,"Found too many roots" #Check if too many roots - #if roots.shape[1] < max_number_of_roots: - # warnings.warn('Expected ' + str(max_number_of_roots) - # + " roots, Found " + str(roots.shape[1]) , Warning) - # print("Number of Roots Lost:", max_number_of_roots - roots.shape[1]) - return roots.T - -def MSMultMatrix(polys, poly_type, number_of_roots, verbose=False, MSmatrix=0): - ''' - Finds the multiplication matrix using the reduced Macaulay matrix. - - Parameters - ---------- - polys : array-like - The polynomials to find the common zeros of - poly_type : string - The type of the polynomials in polys - MSmatrix : int - Controls which Moller-Stetter matrix is constructed. The options are: - 0 (default) -- The Moller-Stetter matrix of a random polynomial - Some positive integer i < dimension -- The Moller-Stetter matrix of x_i - verbose : bool - Prints information about how the roots are computed. - - Returns - ------- - multiplicationMatrix : 2D numpy array - The multiplication matrix for a random polynomial f - var_dict : dictionary - Maps each variable to its position in the vector space basis - ''' - basisDict, VB = MacaulayReduction(polys, number_of_roots, verbose=verbose) - - dim = max(f.dim for f in polys) - - # Get the polynomial to make the MS matrix of - if MSmatrix==0: #random poly - f = _random_poly(poly_type, dim)[0] - else: #multiply by x_i where i is determined by MSmatrix - xi_ind = np.zeros(dim, dtype=int) - xi_ind[MSmatrix-1] = 1 - coef = np.zeros((2,)*dim) - coef[tuple(xi_ind)] = 1 - if poly_type == "MultiPower": - f = MultiPower(np.array(coef)) - elif poly_type == "MultiCheb": - f = MultiCheb(np.array(coef)) - else: - raise ValueError() - if verbose: - print("\nCoefficients of polynomial whose Moller-Stetter matrix we construt\n", f.coeff) - - #Dictionary of terms in the vector basis their spots in the matrix. - VBdict = {} - spot = 0 - for row in VB: - VBdict[tuple(row)] = spot - spot+=1 - - # Build multiplication matrix m_f - mMatrix = np.zeros((len(VB), len(VB))) - for i in range(VB.shape[0]): - f_coeff = f.mon_mult(VB[i], returnType = 'Matrix') - for term in zip(*np.where(f_coeff != 0)): - if term in VBdict: - mMatrix[VBdict[term]][i] += f_coeff[term] - else: - mMatrix[:,i] -= f_coeff[term]*basisDict[term] - - # Construct var_dict - var_dict = {} - for i in range(len(VB)): - mon = VB[i] - if np.sum(mon) == 1 or np.sum(mon) == 0: - var_dict[tuple(mon)] = i - - return mMatrix, var_dict - -def MacaulayReduction(initial_poly_list, max_number_of_roots, accuracy = 1.e-10, verbose=False): - """Reduces the Macaulay matrix to find a vector basis for the system of polynomials. - - Parameters - -------- - initial_poly_list: list - The polynomials in the system we are solving. - accuracy: float - How small we want a number to be before assuming it is zero. - - Returns - ----------- - basisDict : dict - A dictionary of terms not in the vector basis a matrixes of things in the vector basis that the term - can be reduced to. - VB : numpy array - The terms in the vector basis, each row being a term. - """ - power = is_power(initial_poly_list) - dim = initial_poly_list[0].dim - poly_coeff_list = [] - degree = find_degree(initial_poly_list) - - #This sorting is required for fast matrix construction. Ascending should be False. - initial_poly_list = sort_polys_by_degree(initial_poly_list, ascending = False) - - """This is the first construction option, simple monomial multiplication.""" - for poly in initial_poly_list: - poly_coeff_list = add_polys(degree, poly, poly_coeff_list) - """This is the second construction option, it uses the fancy triangle method that is faster but less stable.""" - #for deg in reversed(range(min([poly.degree for poly in initial_poly_list]), degree+1)): - # poly_coeff_list += deg_d_polys(initial_poly_list, deg, dim) - - #Creates the matrix for either of the above two methods. Comment out if using the third method. - #try: - matrix, matrix_terms, cuts = create_matrix(poly_coeff_list, degree, dim) - if verbose: - np.set_printoptions(suppress=False, linewidth=200) - print('\nStarting Macaulay Matrix\n', matrix) - print('\nColumns in Macaulay Matrix\nFirst element in tuple is degree of x, Second element is degree of y\n', matrix_terms) - print('\nLocation of Cuts in the Macaulay Matrix into [ Mb | M1* | M2* ]\n', cuts) - - """This is the thrid matrix construction option, it uses the permutation arrays.""" - #if power: - # matrix, matrix_terms, cuts = createMatrixFast(initial_poly_list, degree, dim) - #else: - # matrix, matrix_terms, cuts = construction(initial_poly_list, degree, dim) - - #If bottom left is zero only does the first QR reduction on top part of matrix (for speed). Otherwise does it on the whole thing - if np.allclose(matrix[cuts[0]:,:cuts[0]], 0): - matrix, matrix_terms = rrqr_reduceMacaulay2(matrix, matrix_terms, cuts, max_number_of_roots, accuracy = accuracy) - else: - matrix, matrix_terms = rrqr_reduceMacaulay(matrix, matrix_terms, cuts, max_number_of_roots, accuracy = accuracy) - - #Make there are enough rows in the reduced Macaulay matrix, i.e. didn't loose a row - assert matrix.shape[0] >= matrix.shape[1] - max_number_of_roots - - #matrix, matrix_terms = rrqr_reduceMacaulayFullRank(matrix, matrix_terms, cuts, number_of_roots, accuracy = accuracy) - height = matrix.shape[0] - matrix[:,height:] = solve_triangular(matrix[:,:height],matrix[:,height:]) - # except Exception as e: - # if str(e)[:46] == 'singular matrix: resolution failed at diagonal': - # matrix, matrix_terms, cuts = create_matrix(poly_coeff_list, degree+1, dim) - # if verbose: - # np.set_printoptions(suppress=False, linewidth=200) - # print('\nNew, Bigger Macaulay Matrix\n', matrix) - # print('\nColumns in Macaulay Matrix\nFirst element in tuple is degree of x, Second element is degree of y\n', matrix_terms) - # print('\nLocation of Cuts in the Macaulay Matrix into [ Mb | M1* | M2* ]\n', cuts) - # - # """This is the thrid matrix construction option, it uses the permutation arrays.""" - # #if power: - # # matrix, matrix_terms, cuts = createMatrixFast(initial_poly_list, degree, dim) - # #else: - # # matrix, matrix_terms, cuts = construction(initial_poly_list, degree, dim) - # - # #If bottom left is zero only does the first QR reduction on top part of matrix (for speed). Otherwise does it on the whole thing - # if np.allclose(matrix[cuts[0]:,:cuts[0]], 0): - # matrix, matrix_terms = rrqr_reduceMacaulay2(matrix, matrix_terms, cuts, max_number_of_roots, accuracy = accuracy) - # else: - # matrix, matrix_terms = rrqr_reduceMacaulay(matrix, matrix_terms, cuts, max_number_of_roots, accuracy = accuracy) - # - # #Make there are enough rows in the reduced Macaulay matrix, i.e. didn't loose a row - # assert matrix.shape[0] >= matrix.shape[1] - max_number_of_roots - # - # #matrix, matrix_terms = rrqr_reduceMacaulayFullRank(matrix, matrix_terms, cuts, number_of_roots, accuracy = accuracy) - # height = matrix.shape[0] - # matrix[:,height:] = solve_triangular(matrix[:,:height],matrix[:,height:]) - # else: - # raise e - - #Make there are enough rows in the reduced Macaulay matrix, i.e. didn't loose a row - assert matrix.shape[0] >= matrix.shape[1] - max_number_of_roots - - matrix[:,:height] = np.eye(height) - #return np.vstack((matrix[:,height:].T,np.eye(height))), matrix_terms - - if verbose: - np.set_printoptions(suppress=True, linewidth=200) - print("\nFinal Macaulay Matrix\n", matrix) - print("\nColumns in Macaulay Matrix\n", matrix_terms) - VB = matrix_terms[height:] - - #plt.plot(matrix_terms[:,0],matrix_terms[:,1],'kx') - #plt.plot(VB[:,0],VB[:,1],'r.') - - basisDict = makeBasisDict(matrix, matrix_terms, VB, power) - - return basisDict, VB - -def makeBasisDict(matrix, matrix_terms, VB, power): - '''Calculates and returns the basisDict. - - This is a dictionary of the terms on the diagonal of the reduced Macaulay matrix to the terms in the Vector Basis. - It is used to create the multiplication matrix in root_finder. - - Parameters - -------- - matrix: numpy array - The reduced Macaulay matrix. - matrix_terms : numpy array - The terms in the matrix. The i'th row is the term represented by the i'th column of the matrix. - VB : numpy array - Each row is a term in the vector basis. - power : bool - If True, the initial polynomials were MultiPower. If False, they were MultiCheb. - - Returns - ----------- - basisDict : dict - Maps terms on the diagonal of the reduced Macaulay matrix (tuples) to numpy arrays that represent the - terms reduction into the Vector Basis. - ''' - basisDict = {} - - VBSet = set() - for i in VB: - VBSet.add(tuple(i)) - - if power: #We don't actually need most of the rows, so we only get the ones we need. - neededSpots = set() - for term, mon in itertools.product(VB,get_var_list(VB.shape[1])): - if tuple(term+mon) not in VBSet: - neededSpots.add(tuple(term+mon)) - - #spots = list() - #for dim in range(VB.shape[1]): - # spots.append(VB.T[dim]) - - for i in range(matrix.shape[0]): - term = tuple(matrix_terms[i]) - if power and term not in neededSpots: - continue - basisDict[term] = matrix[i][matrix.shape[0]:] - - return basisDict - -def create_matrix(poly_coeffs, degree, dim): - ''' Builds a Macaulay matrix. - - Parameters - ---------- - poly_coeffs : list. - Contains numpy arrays that hold the coefficients of the polynomials to be put in the matrix. - degree : int - The degree of the Macaulay Matrix - dim : int - The dimension of the polynomials going into the matrix. - Returns - ------- - matrix : 2D numpy array - The Macaulay matrix. - matrix_terms : numpy array - The ith row is the term represented by the ith column of the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - ''' - bigShape = [degree+1]*dim - matrix_terms, cuts = sorted_matrix_terms(degree, dim) - - #Get the slices needed to pull the matrix_terms from the coeff matrix. - matrix_term_indexes = list() - for row in matrix_terms.T: - matrix_term_indexes.append(row) - - #Adds the poly_coeffs to flat_polys, using added_zeros to make sure every term is in there. - added_zeros = np.zeros(bigShape) - flat_polys = list() - for coeff in poly_coeffs: - slices = slice_top(coeff) - added_zeros[slices] = coeff - flat_polys.append(added_zeros[matrix_term_indexes]) - added_zeros[slices] = np.zeros_like(coeff) - coeff = 0 - poly_coeffs = 0 - - #Make the matrix. Reshape is faster than stacking. - matrix = np.reshape(flat_polys, (len(flat_polys),len(matrix_terms))) - - #if cuts[0] > matrix.shape[0]: #The matrix isn't tall enough, these can't all be pivot columns. - # raise MacaulayError("HIGHEST NOT FULL RANK. TRY HIGHER DEGREE") - - #Sorts the rows of the matrix so it is close to upper triangular. - matrix = row_swap_matrix(matrix) - return matrix, matrix_terms, cuts - -def sorted_matrix_terms(degree, dim): - '''Finds the matrix_terms sorted in the term order needed for Macaulay reduction. - So the highest terms come first,the x,y,z etc monomials last. - Parameters - ---------- - degree : int - The degree of the Macaulay Matrix - dim : int - The dimension of the polynomials going into the matrix. - Returns - ------- - sorted_matrix_terms : numpy array - The sorted matrix_terms. The ith row is the term represented by the ith column of the matrix. - cuts : tuple - When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate - where those cuts happen. - ''' - highest_mons = mon_combosHighest([0]*dim,degree)[::-1] - - other_mons = list() - d = degree - 1 - while d > 1: - other_mons += mon_combosHighest([0]*dim,d)[::-1] - d -= 1 - - xs_mons = mon_combos([0]*dim,1)[::-1] - sorted_matrix_terms = np.reshape(highest_mons+other_mons+xs_mons, (len(highest_mons+other_mons+xs_mons),dim)) - return sorted_matrix_terms, tuple([len(highest_mons),len(highest_mons)+len(other_mons)]) - -def _random_poly(_type, dim): - ''' - Generates a random polynomial that has the form - c_1x_1 + c_2x_2 + ... + c_nx_n where n = dim and each c_i is a randomly - chosen integer between 0 and 1000. - - Parameters - ---------- - _type : string - Type of Polynomial to generate. "MultiCheb" or "MultiPower". - dim : int - Degree of polynomial to generate (?). - - Returns - ------- - Polynomial - Randomly generated Polynomial. - ''' - _vars = get_var_list(dim) - - random_poly_shape = [2 for i in range(dim)] - - random_poly_coeff = np.zeros(tuple(random_poly_shape), dtype=int) - for var in _vars: - random_poly_coeff[var] = np.random.randint(1000) - - if _type == 'MultiCheb': - return MultiCheb(random_poly_coeff), _vars - else: - return MultiPower(random_poly_coeff), _vars diff --git a/numalgsolve/__init__.py b/numalgsolve/__init__.py deleted file mode 100644 index 6dd670aa..00000000 --- a/numalgsolve/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# Do not delete this file. It tells python that groebner is a module you can import from. diff --git a/numalgsolve/subdivision.py b/numalgsolve/subdivision.py deleted file mode 100644 index 43bf997c..00000000 --- a/numalgsolve/subdivision.py +++ /dev/null @@ -1,1254 +0,0 @@ -""" -Subdivision provides a solve function that finds roots of a set of functions -by approximating the functions with Chebyshev polynomials. -When the approximation is performed on a sufficiently small interval, -the approximation degree is small enough to be solved efficiently. - -""" - -import numpy as np -from numpy.fft.fftpack import fftn -from numalgsolve.OneDimension import divCheb,divPower,multCheb,multPower,solve -from numalgsolve.Division import division -from numalgsolve.utils import clean_zeros_from_matrix, slice_top -from numalgsolve.polynomial import MultiCheb -from itertools import product -from matplotlib import pyplot as plt -from matplotlib import patches -import itertools - -def solve(funcs, a, b, interval_data = False): - ''' - Finds the real roots of the given list of functions on a given interval. - - Parameters - ---------- - funcs : list of callable functions - Functions to find the common roots of. - a : numpy array - The lower bound on the interval. - b : numpy array - The upper bound on the interval. - returns - ------- - roots : numpy array - The common roots of the polynomials. Each row is a root. - ''' - interval_checks = [constant_term_check,full_quad_check]#, curvature_check]#full_cubic_check, - subinterval_checks = [linear_check,quadratic_check1,quadratic_check2,quadratic_check3] - interval_results = [] - for i in range(len(interval_checks) + len(subinterval_checks) + 1): - interval_results.append([]) - - dim = len(a) - if dim == 1: - #one dimensional case - zeros = np.unique(subdivision_solve_1d(funcs[0],a,b)) - #Finds the roots of each succesive function and checks which roots are common. - for func in funcs[1:]: - if len(zeros) == 0: - break - zeros2 = np.unique(subdivision_solve_1d(func,a,b)) - common_zeros = [] - tol = 1.e-10 - for zero in zeros2: - spot = np.where(np.abs(zeros-zero) 0: - common_zeros.append(zero) - zeros = common_zeros - return zeros - else: - #multi-dimensional case - #choose an appropriate max degree for the given dimension - deg_dim = {2:5, 3:4, 4:3} - if dim > 4: - deg = 2 - else: - deg = deg_dim[dim] - - #Output the interval percentages - result = subdivision_solve_nd(funcs,a,b,deg,interval_results,interval_checks,subinterval_checks) - - #Plot what happened - if interval_data: - - results_numbers = np.array([len(i) for i in interval_results]) - total_intervals = sum(results_numbers) - checkers = [func.__name__ for func in interval_checks]+[func.__name__ for func in subinterval_checks]+["Division"] - - print("Total intervals checked was {}".format(total_intervals)) - print("Methods used were {}".format(checkers)) - print("The percent solved by each was {}".format((100*results_numbers / total_intervals).round(2))) - - if dim == 2: - colors = ['b', 'g', 'r', 'm', 'c', 'y', 'k','w','pink','fuchsia'] - fig,ax = plt.subplots(1) - fig.set_size_inches(10, 10) - for i in range(len(interval_checks)): - results = interval_results[i] - first = True - for data in results: - a0,b0 = data - if first: - first = False - rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1],linewidth=.001,\ - edgecolor=colors[i],facecolor=colors[i]\ - , label = interval_checks[i].__name__) - else: - rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1],linewidth=.001\ - ,edgecolor=colors[i],facecolor=colors[i]) - ax.add_patch(rect) - - for i in range(len(interval_checks), len(interval_checks) + len(subinterval_checks)): - results = interval_results[i] - first = True - for data in results: - a0,b0 = data - if first: - first = False - rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1],linewidth=.001\ - ,edgecolor=colors[i],facecolor=colors[i]\ - , label = subinterval_checks[i - len(interval_checks)].__name__) - else: - rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1],linewidth=.001\ - ,edgecolor=colors[i],facecolor=colors[i]) - ax.add_patch(rect) - - i = len(interval_checks) +len(subinterval_checks) - results = interval_results[i] - first = True - for data in results: - a0,b0 = data - if first: - first = False - rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1],linewidth=.001\ - ,edgecolor=colors[i],facecolor=colors[i], label = 'Division Solve') - else: - rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1],linewidth=.001\ - ,edgecolor=colors[i],facecolor=colors[i]) - ax.add_patch(rect) - - plt.title('What happened to the intervals') - plt.xlim(a[0],b[0]) - plt.ylim(a[1],b[1]) - plt.legend() - plt.show() - - return result - -def transform(x,a,b): - """Transforms points from the interval [-1,1] to the interval [a,b]. - - Parameters - ---------- - x : numpy array - The points to be tranformed. - a : float or numpy array - The lower bound on the interval. Float if one-dimensional, numpy array if multi-dimensional - b : float or numpy array - The upper bound on the interval. Float if one-dimensional, numpy array if multi-dimensional - - Returns - ------- - transform : numpy array - The transformed points. - """ - return ((b-a)*x+(b+a))/2 - -def inv_transform(x,a,b): - """Transforms points from the interval [a,b] to the interval [-1,1]. - - Parameters - ---------- - x : numpy array - The points to be tranformed. - a : float or numpy array - The lower bound on the interval. Float if one-dimensional, numpy array if multi-dimensional - b : float or numpy array - The upper bound on the interval. Float if one-dimensional, numpy array if multi-dimensional - - Returns - ------- - transform : numpy array - The transformed points. - """ - return (2*x-b-a)/(b-a) - -def chebyshev_block_copy(values_block): - """This functions helps avoid double evaluation of functions at - interpolation points. It takes in a tensor of function evaluation values - and copies these values to a new tensor appropriately to prepare for - chebyshev interpolation. - - Parameters - ---------- - block_values : numpy array - block of values from function evaluation - - Returns - ------- - cheb_values : numpy array - chebyshev interpolation values - """ - dim = values_block.ndim - deg = values_block.shape[0] - 1 - values_cheb = np.empty(tuple([2*deg])*dim, dtype=values_block.dtype) - - for block in product([False,True],repeat=dim): - cheb_idx = [slice(0,deg+1)]*dim - block_idx = [slice(None)]*dim - for i,flip_dim in enumerate(block): - if flip_dim: - cheb_idx[i] = slice(deg+1,None) - block_idx[i] = slice(deg-1,0,-1) - values_cheb[tuple(cheb_idx)] = values_block[tuple(block_idx)] - return values_cheb - -def interval_approximate_1d(f,a,b,deg): - """Finds the chebyshev approximation of a one-dimensional function on an interval. - - Parameters - ---------- - f : function from R -> R - The function to interpolate. - a : float - The lower bound on the interval. - b : float - The upper bound on the interval. - deg : int - The degree of the interpolation. - - Returns - ------- - coeffs : numpy array - The coefficient of the chebyshev interpolating polynomial. - """ - extrema = transform(np.cos((np.pi*np.arange(2*deg))/deg),a,b) - values = f(extrema) - coeffs = np.real(np.fft.fft(values/deg)) - coeffs[0]/=2 - coeffs[deg]/=2 - return coeffs[:deg+1] - -def interval_approximate_nd(f,a,b,degs): - """Finds the chebyshev approximation of an n-dimensional function on an interval. - - Parameters - ---------- - f : function from R^n -> R - The function to interpolate. - a : numpy array - The lower bound on the interval. - b : numpy array - The upper bound on the interval. - deg : numpy array - The degree of the interpolation in each dimension. - - Returns - ------- - coeffs : numpy array - The coefficient of the chebyshev interpolating polynomial. - """ - if len(a)!=len(b): - raise ValueError("Interval dimensions must be the same!") - - dim = len(a) - deg = degs[0] - - if hasattr(f,"evaluate_grid"): - #for polynomials, we can quickly evaluate all points in a grid - #xyz does not contain points, but the nth column of xyz has the values needed - #along the nth axis. The direct product of these values procuces the grid - cheb_values = np.cos(np.arange(deg+1)*np.pi/deg) - xyz = transform(np.column_stack([cheb_values]*dim), a, b) - values_block = f.evaluate_grid(xyz) - - else: - #if function f has no "evaluate_grid" method, - #we evaluate each point individually - cheb_values = np.cos(np.arange(deg+1)*np.pi/deg) - cheb_grids = np.meshgrid(*([cheb_values]*dim), indexing='ij') - - flatten = lambda x: x.flatten() - cheb_points = transform(np.column_stack(map(flatten, cheb_grids)), a, b) - values_block = f(cheb_points).reshape(*([deg+1]*dim)) - - values = chebyshev_block_copy(values_block) - coeffs = np.real(fftn(values/np.product(degs))) - - for i in range(dim): - #construct slices for the first and degs[i] entry in each dimension - idx0 = [slice(None)] * dim - idx0[i] = 0 - - idx_deg = [slice(None)] * dim - idx_deg[i] = degs[i] - - #halve the coefficients in each slice - coeffs[idx0] /= 2 - coeffs[idx_deg] /= 2 - - slices = [] - for i in range(dim): - slices.append(slice(0,degs[i]+1)) - - return coeffs[slices] - -def get_subintervals(a,b,dimensions,subinterval_checks,interval_results,polys,check_subintervals=False): - """Gets the subintervals to divide a matrix into. - - Parameters - ---------- - a : numpy array - The lower bound on the interval. - b : numpy array - The upper bound on the interval. - dimensions : numpy array - The dimensions we want to cut in half. - - Returns - ------- - subintervals : list - Each element of the list is a tuple containing an a and b, the lower and upper bounds of the interval. - """ - RAND = 0.5139303900908738 - subintervals = [] - diffs1 = ((b-a)*RAND)[dimensions] - diffs2 = ((b-a)-(b-a)*RAND)[dimensions] - - for subset in product([False,True], repeat=len(dimensions)): - subset = np.array(subset) - aTemp = a.copy() - bTemp = b.copy() - aTemp[dimensions] += (~subset)*diffs1 - bTemp[dimensions] -= subset*diffs2 - subintervals.append((aTemp,bTemp)) - - if check_subintervals: - scaled_subintervals = get_subintervals(-np.ones_like(a),np.ones_like(a),dimensions,None,None,None) - for check_num, check in enumerate(subinterval_checks): - for poly in polys: - mask = check(poly.coeff, scaled_subintervals) - new_scaled_subintervals = [] - new_subintervals = [] - for i, result in enumerate(mask): - if result: - new_scaled_subintervals.append(scaled_subintervals[i]) - new_subintervals.append(subintervals[i]) - else: - interval_results[check_num-(1+len(subinterval_checks))].append(subintervals[i]) - scaled_subintervals = new_scaled_subintervals - subintervals = new_subintervals - - return subintervals - -def full_cheb_approximate(f,a,b,deg,tol=1.e-8): - """Gives the full chebyshev approximation and checks if it's good enough. - - Called recursively. - - Parameters - ---------- - f : function - The function we approximate. - a : numpy array - The lower bound on the interval. - b : numpy array - The upper bound on the interval. - deg : int - The degree to approximate with. - tol : float - How small the high degree terms must be to consider the approximation accurate. - - Returns - ------- - coeff : numpy array - The coefficient array of the interpolation. If it can't get a good approximation and needs to subdivide, returns None. - """ - dim = len(a) - degs = np.array([deg]*dim) - coeff = interval_approximate_nd(f,a,b,degs) - coeff2 = interval_approximate_nd(f,a,b,degs*2) - coeff2[slice_top(coeff)] -= coeff - clean_zeros_from_matrix(coeff2,1.e-16) - if np.sum(np.abs(coeff2)) > tol: - return None - else: - return coeff - -def good_zeros_nd(zeros, imag_tol = 1.e-10): - """Get the real zeros in the -1 to 1 interval in each dimension. - - Parameters - ---------- - zeros : numpy array - The zeros to be checked. - imag_tol : float - How large the imaginary part can be to still have it be considered real. - - Returns - ------- - good_zeros : numpy array - The real zero in [-1,1] of the input zeros. - """ - good_zeros = zeros[np.all(np.abs(zeros.imag) < imag_tol,axis = 1)] - good_zeros = good_zeros[np.all(np.abs(good_zeros) <= 1,axis = 1)] - return good_zeros - - -""" -The check functions are all functions that take in a coefficent matrix and run a quick check -to determine if there can ever be zeros on the unit box there. They are then put into the list -all_bound_check_functions in the order we want to run them (probably fastest first). These are -then all run to throw out intervals as possible. -""" - -def ext_val3(test_coeff, maxx = True): - a,b,c = test_coeff - """Absolute value of max or min of a + bx + c(2x^2 - 1) on -1 to 1""" - if np.abs(c) < 1.e-10: - if maxx: - return abs(a) + abs(b) - else: - if abs(b) > abs(a): - return 0 - else: - return abs(a) - abs(b) - else: - vals = [a - b + c, a + b + c] #at +-1 - if np.abs(b/c) < 4: - vals.append(a - b**2/(8*c) - c) #at -b/(4c) - if maxx: - return max(np.abs(vals)) - else: - vals = np.array(vals) - if np.any(vals > 0) and np.any(vals < 0): - return 0 - else: - return min(np.abs(vals)) - -def ext_val4(test_coeff, maxx = True): - a,b,c,d = test_coeff - """Absolute value of max or min of a + bx + c(2x^2 - 1) + d*(4x^3 - 3x) on -1 to 1""" - if np.abs(d) < 1.e-10: - return ext_val3([a,b,c], maxx = maxx) - else: - vals = [a - b + c - d, a + b + c + d] #at +-1 - - #The quadratic roots - if 16*c**2 >= 48*d*(b-3*d): - x1 = (-4*c + np.sqrt(16*c**2 - 48*d*(b-3*d))) / (24*d) - x2 = (-4*c - np.sqrt(16*c**2 - 48*d*(b-3*d))) / (24*d) - if np.abs(x1) < 1: - vals.append(a + b*x1 + c*(2*x1**2 - 1) + d*(4*x1**3 - 3*x1)) - if np.abs(x2) < 1: - vals.append(a + b*x2 + c*(2*x2**2 - 1) + d*(4*x2**3 - 3*x2)) - if maxx: - return max(np.abs(vals)) - else: - vals = np.array(vals) - if np.any(vals > 0) and np.any(vals < 0): - return 0 - else: - return min(np.abs(vals)) - -def constant_term_check(test_coeff): - """Quick check of zeros in the unit box. - - Checks if the constant term is bigger than all the other terms combined, using the fact that - each Chebyshev monomial is bounded by 1. - - Parameters - ---------- - coeff : numpy array - The coefficient matrix of the polynomial to check - - Returns - ------- - check1 : bool - False if there are no zeros in the unit box, True otherwise - """ - test_sum = np.sum(np.abs(test_coeff)) - if np.abs(test_coeff.flatten()[0]) * 2 > test_sum: - return False - else: - return True - -def quad_check(test_coeff): - """Quick check of zeros in the unit box. - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - - Returns - ------- - quad_check : bool - False if there are no zeros in the unit box, True otherwise - """ - dim = test_coeff.ndim - slices = [] - slices.append(slice(0,3)) - slice_direc = 0 - for i in range(dim-1): - slices.append(0) - - start = ext_val3(test_coeff[slices], maxx = False) - rest = 0 - - shape = list(test_coeff.shape) - shape[slice_direc] = 1 - for spots in itertools.product(*[np.arange(i) for i in shape]): - if sum(spots) > 0: - for i in range(1, dim): - slices[i] = spots[i] - rest += ext_val3(test_coeff[slices]) - - while slice_direc < dim - 1: - slice_direc += 1 - slices[slice_direc] = slice(0,3) - - shape = np.array(test_coeff.shape) - shape[slice_direc] = 1 - shape_diff = np.zeros_like(shape) - for i in range(slice_direc): - shape_diff[i] = 3 - shape -= shape_diff - for spots in itertools.product(*[np.arange(i) for i in shape]): - spots += shape_diff - for i in range(dim): - if i != slice_direc: - slices[i] = spots[i] - rest += ext_val3(test_coeff[slices]) - - if start > rest: - return False - else: - return True - -def cubic_check(test_coeff): - """Quick check of zeros in the unit box. - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - - Returns - ------- - cubic_check : bool - False if there are no zeros in the unit box, True otherwise - """ - dim = test_coeff.ndim - slices = [] - slices.append(slice(0,4)) - slice_direc = 0 - for i in range(dim-1): - slices.append(0) - - start = ext_val4(test_coeff[slices], maxx = False) - rest = 0 - - shape = list(test_coeff.shape) - shape[slice_direc] = 1 - for spots in itertools.product(*[np.arange(i) for i in shape]): - if sum(spots) > 0: - for i in range(1, dim): - slices[i] = spots[i] - rest += ext_val4(test_coeff[slices]) - - while slice_direc < dim - 1: - slice_direc += 1 - slices[slice_direc] = slice(0,4) - - shape = np.array(test_coeff.shape) - shape[slice_direc] = 1 - shape_diff = np.zeros_like(shape) - for i in range(slice_direc): - shape_diff[i] = 4 - shape -= shape_diff - for spots in itertools.product(*[np.arange(i) for i in shape]): - spots += shape_diff - for i in range(dim): - if i != slice_direc: - slices[i] = spots[i] - rest += ext_val4(test_coeff[slices]) - - if start > rest: - return False - else: - return True - -def full_quad_check(test_coeff): - """Quick check of zeros in the unit box. - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - - Returns - ------- - full_quad_check : bool - False if there are no zeros in the unit box, True otherwise - """ - for perm in itertools.permutations(np.arange(test_coeff.ndim)): - if not quad_check(test_coeff.transpose(perm)): - return False - return True - -def full_cubic_check(test_coeff): - """Quick check of zeros in the unit box. - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - - Returns - ------- - full_quad_check : bool - False if there are no zeros in the unit box, True otherwise - """ - for perm in itertools.permutations(np.arange(test_coeff.ndim)): - if not cubic_check(test_coeff.transpose(perm)): - return False - return True - -def linear_check(test_coeff_in, intervals): - """Quick check of zeros in intervals. - - Parameters - ---------- - test_coeff_in : numpy array - The coefficient matrix of the polynomial to check - intervals : list - A list of the intervals we want to check before subdividing them - - Returns - ------- - mask : list - Masks out the intervals we don't want - """ - dim = test_coeff_in.ndim - coeff_abs_sum = np.sum(np.abs(test_coeff_in)) - mask = [] - for interval in intervals: - test_coeff = test_coeff_in.copy() - - a,b = interval - # abs_smallest_corner = test_coeff[tuple(spot)] - - idx = [0]*dim - const = test_coeff_in[idx] - lin_coeff = np.zeros(dim) - for cur_dim in range(dim): - if test_coeff_in.shape[cur_dim] < 2: - continue - idx[cur_dim] = 1 - lin_coeff[cur_dim] = test_coeff_in[tuple(idx)] - idx[cur_dim] = 0 - - corner_vals = [] - for corner_pt in product(*zip(a,b)): - corner_vals.append(const + np.sum(np.array(corner_pt)*lin_coeff)) - corner_vals = np.array(corner_vals) - - # check if corners have mixed signs - if not (corner_vals.min() < 0 < corner_vals.max()): - mask.append(True) - continue - - abs_smallest_corner = np.min(np.abs(corner_vals)) - if 2*abs_smallest_corner > coeff_abs_sum: - # case: corner is far enough from 0 - mask.append(False) - else: - mask.append(True) - - # test_coeff[tuple(spot)] = 0 - # for dim in range(len(a)): - # spot[dim] = 1 - # neg_most_corner += a[dim]*test_coeff[tuple(spot)] - # spot[dim] = 0 - # - # lin_min = neg_most_corner - # for dim in range(len(a)): - # spot[dim] = 1 - # if np.sign(test_coeff[tuple(spot)])*np.sign(neg_most_corner) < 0: - # lin_min += (b[dim] - a[dim]) * test_coeff[tuple(spot)] - # test_coeff[tuple(spot)] = 0 - # spot[dim] = 0 - # - # if np.sign(lin_min)*np.sign(neg_most_corner) < 0: - # mask.append(True) - # elif np.sum(np.abs(test_coeff)) >= np.abs(neg_most_corner): - # mask.append(True) - # else: - # mask.append(False) - return mask - -def quadratic_check1(test_coeff, intervals,tol=1e-12): - """Quick check of zeros in intervals using the x^2 terms. - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - intervals : list - A list of the intervals we want to check before subdividing them - - Returns - ------- - mask : list - Masks out the intervals we don't want - """ - if test_coeff.ndim > 2: - return [True]*len(intervals) - padding = [(0,max(0,3-i)) for i in test_coeff.shape] - test_coeff = np.pad(test_coeff.copy(), padding, mode='constant') - #check using |b0 + b1x + b2y +b3T_2(x)| = |(b0 - b3) + b1x + b2y + 2 b3x^2| = |c0 + c1x + c2y + c3x^2| - constant = test_coeff[0,0] - test_coeff[2,0] - c1 = test_coeff[1,0] - c2 = test_coeff[0,1] - c3 = 2*test_coeff[2,0] - - #if c3 != 0, same as a linear check - if np.isclose(c3, 0, atol=tol) or np.isclose(c2, 0, atol=tol): - return [True]*len(intervals) - mask = [] - for interval in intervals: - def quadratic_formula_check(y): - """given a fixed value of y, uses the quadratic formula - to see if constant + c1x + c2y +c3T_2(x) = 0 - for some x in [a0, b0]""" - discriminant = c1**2 - 4*(c2*y+constant)*c3 - if np.isclose(discriminant, 0,atol=tol) and interval[0][0] < -c1/2/c3 < interval[1][0]: - return True - elif discriminant > 0 and \ - (interval[0][0] < (-c1+np.sqrt(discriminant))/2/c3 < interval[1][0] or \ - interval[0][0] < (-c1-np.sqrt(discriminant))/2/c3 < interval[1][0]): - return True - else: - return False - #If constant + c1x + c2y +c3x^2 = 0 in the region, useless check. - if np.isclose(c2, 0,atol=tol) and quadratic_formula_check(0): - mask.append(True) - continue - else: - y = lambda x: (-c3 *x**2 - c1 * x - constant)/c2 - if interval[0][1] < y(interval[0][0]) < interval[1][1] or interval[0][1] < y(interval[1][0]) < interval[1][1]: - mask.append(True) - continue - elif quadratic_formula_check(interval[0][0]) or quadratic_formula_check(interval[1][0]): - mask.append(True) - continue - - #function for evaluating |constant + c1x + c2y +c3x^2| - eval = lambda xy: abs(constant + c1*xy[:,0] + c2*xy[:,1] + c3 * xy[:,0]**2) - #In this case, extrema only occur on the edges since there are no critical points - #edges 1&2: x = a0, b0 --> potential extrema at corners - #edges 3&4: y = a1, b1 --> potential extrema at x0 = -c1/2c3, if that's in [a0, b0] - if interval[0][0] < -c1/2/c3 < interval[1][0]: - potential_minimizers = np.array([[interval[0][0],interval[0][1]], - [interval[0][0],interval[1][1]], - [interval[1][0],interval[0][1]], - [interval[1][0],interval[1][1]], - [-c1/2/c3,interval[0][1]], - [-c1/2/c3,interval[1][1]]]) - else: - potential_minimizers = np.array([[interval[0][0],interval[0][1]], - [interval[0][0],interval[1][1]], - [interval[1][0],interval[0][1]], - [interval[1][0],interval[1][1]]]) - #if min{|constant + c1x + c2y +c3x^2|} > sum of other terms in test_coeff, no roots in the region - if min(eval(potential_minimizers)) > np.sum(np.abs(test_coeff)) - abs(constant) - abs(c1) - abs(c2) - abs(c3): - mask.append(False) - else: - mask.append(True) - return mask - -def quadratic_check2(test_coeff, intervals,tol=1e-12): - """Quick check of zeros in the unit box using the y^2 terms - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - intervals : list - A list of the intervals we want to check before subdividing them - - Returns - ------- - mask : list - Masks out the intervals we don't want - """ - if test_coeff.ndim > 2: - return [True]*len(intervals) - padding = [(0,max(0,3-i)) for i in test_coeff.shape] - test_coeff = np.pad(test_coeff.copy(), padding, mode='constant') - #very similar to quadratic_check_1, but switch x and y - #check using |b0 + b1x + b2y +b3T_2(y)| = |b0 - b3 + b1x + b2y + 2 b3y^2| = |c0 + c1x + c2y + c3y^2| - constant = test_coeff[0,0] - test_coeff[0,2] - c1 = test_coeff[1,0] - c2 = test_coeff[0,1] - c3 = 2*test_coeff[0,2] - - #if c3 != 0, same as a linear check - if np.isclose(c3, 0, atol=tol) or np.isclose(c1, 0, atol=tol): - return[True]*len(intervals) - mask = [] - for interval in intervals: - def quadratic_formula_check(x): - """given a fixed value of x, uses the quadratic formula - to see if constant + c1x + c2y +c3y^2 = 0 - for some y in [a1, b1]""" - discriminant = c2**2 - 4*(c1*x+constant)*c3 - if np.isclose(discriminant, 0,atol=tol) and interval[0][1] < -c2/2/c3 < interval[1][1]: - return True - elif discriminant > 0 and \ - (interval[0][1] < (-c2+np.sqrt(discriminant))/2/c3 < interval[1][1] or \ - interval[0][1] < (-c2-np.sqrt(discriminant))/2/c3 < interval[1][1]): - return True - else: - return False - #If constant + c1x + c2y +c3y^2 = 0 in the region, useless - if np.isclose(c1, 0) and quadratic_formula_check(0): - mask.append(True) - continue - else: - x = lambda y: (-c3 *y**2 - c2 * y - constant)/c1 - if interval[0][0] < x(interval[0][1]) < interval[1][0] or interval[0][0] < x(interval[1][1]) < interval[1][0]: - mask.append(True) - continue - elif quadratic_formula_check(interval[0][1]) or quadratic_formula_check(interval[1][1]): - mask.append(True) - continue - - #function to evaluate |constant + c1x + c2y +c3y^2| - eval = lambda xy: abs(constant + c1*xy[:,0] + c2*xy[:,1] + c3 * xy[:,1]**2) - #In this case, extrema only occur on the edges since there are no critical points - #edges 1&2: x = a0, b0 --> potential extrema at y0 = -c2/2c3, if that's in [a1, b1] - #edges 3&4: y = a1, b1 --> potential extrema at corners - if interval[0][1] < -c2/2/c3 < interval[1][1]: - potential_minimizers = np.array([[interval[0][0],interval[0][1]], - [interval[0][0],interval[1][1]], - [interval[1][0],interval[0][1]], - [interval[1][0],interval[1][1]], - [interval[0][0],-c2/2/c3], - [interval[1][0],-c2/2/c3]]) - else: - potential_minimizers = np.array([[interval[0][0],interval[0][1]], - [interval[0][0],interval[1][1]], - [interval[1][0],interval[0][1]], - [interval[1][0],interval[1][1]]]) - #if min{|constant + c1x + c2y +c3y^2|} > sum of other terms in test_coeff, no roots in the region - if min(eval(potential_minimizers)) > np.sum(np.abs(test_coeff)) - abs(constant) - abs(c1) - abs(c2) - abs(c3): - mask.append(False) - else: - mask.append(True) - return mask - -def quadratic_check3(test_coeff, intervals,tol=1e-12): - """Quick check of zeros in the unit box using the xy terms - - Parameters - ---------- - test_coeff : numpy array - The coefficient matrix of the polynomial to check - intervals : list - A list of the intervals we want to check before subdividing them - - Returns - ------- - mask : list - Masks out the intervals we don't want - """ - if test_coeff.ndim > 2: - return [True]*len(intervals) - padding = [(0,max(0,3-i)) for i in test_coeff.shape] - test_coeff = np.pad(test_coeff.copy(), padding, mode='constant') - #check using |constant + c1x + c2y +c3xy| - constant = test_coeff[0,0] - c1 = test_coeff[1,0] - c2 = test_coeff[0,1] - c3 = test_coeff[1,1] - - ##if c3 != 0, same as a linear check - if np.isclose(c3, 0,atol=tol): - return [True]*len(intervals) - - mask = [] - for interval in intervals: - ##If constant + c1x + c2y +c3xy = 0 in the region, useless - - #testing the vertical sides of the interval - vert_asymptote = -c2/c3 - x = lambda y: (-constant + c2*y)/(c1 + c3*y) - if np.isclose(interval[0][1], vert_asymptote): - if interval[0][0] < x(interval[1][1]) < interval[1][0]: - mask.append(True) - continue - elif np.isclose(interval[1][1], vert_asymptote): - if interval[0][0] < x(interval[0][1]) < interval[1][0]: - mask.append(True) - continue - elif interval[0][0] < x(interval[0][1]) < interval[1][0] or interval[0][0] < x(interval[1][1]) < interval[1][0]: - mask.append(True) - continue - - #testing the horizontal sides of the interval - horiz_asymptote = -c1/c3 - y = lambda x: (-constant + c1*x)/(c2 + c3*x) - if np.isclose(interval[0][0], horiz_asymptote): - if interval[0][1] < y(interval[1][0]) < interval[1][1]: - mask.append(True) - continue - elif np.isclose(interval[1][0], horiz_asymptote): - if interval[0][1] < y(interval[0][0]) < interval[1][1]: - mask.append(True) - continue - elif interval[0][1] < y(interval[0][0]) < interval[1][1] or interval[0][1] < y(interval[1][0]) < interval[1][1]: - mask.append(True) - continue - - ##Find the minimum - - #function for evaluating |constant + c1x + c2y +c3xy| - eval = lambda xy: abs(constant + c1*xy[:,0] + c2*xy[:,1] + c3*xy[:,0]*xy[:,1]) - - #In this case, only critical point is saddle point, so all minima occur on the edges - #On all the edges it becomes linear, so extrema always ocur at the corners - potential_minimizers = np.array([[interval[0][0],interval[0][1]], - [interval[0][0],interval[1][1]], - [interval[1][0],interval[0][1]], - [interval[1][0],interval[1][1]]]) - - ##if min{|constant + c1x + c2y +c3xy|} > sum of other terms in test_coeff, no roots in the region - if min(eval(potential_minimizers)) > np.sum(np.abs(test_coeff)) - np.sum(np.abs(test_coeff[:2,:2])): - mask.append(False) - else: - mask.append(True) - - return mask - -#This is all for Tyler's new function -from mpmath import iv -from itertools import product -from copy import copy -def lambda_s(a): - return sum(iv.mpf([0,1])*max(ai.a**2,ai.b**2) for ai in a) - -def beta(a,b): - return iv.mpf([-1,1])*iv.sqrt(lambda_s(a)*lambda_s(b)) - -def lambda_t(a,b): - return beta(a,b) + np.dot(a,b) - -class TabularCompute: - def __init__(self,a,b,dim=False,index=None): - """Class for estimating the maximum curvature. - Parameters - ---------- - a (int) - the starting value of the interval - b (int) - the ending value of the interval - dim (bool or int) - False if this is not an interval for a dimension - integer indicating the number of dimensions - index (int) - defines which dimension this interval corresponds to - - """ - self.iv = iv.mpf([a,b]) - self.iv_lambda = iv.mpf([0,0]) - if dim: - assert isinstance(dim, int) - assert isinstance(index, int) and 0<=index0) or all(corners<0)): - return False - - min_corner = abs(min(corners)) - - x = [] - n = len(a) - for i,(ai,bi) in enumerate(zip(a,b)): - x.append(TabularCompute(ai,bi,dim=n,index=i)) - x = np.array(x) - - max_curve = abs(chebvalnd(x, poly).iv_lambda) -# print(max_curve * n * h**2/8) - return min_corner > max_curve * n * h**2/8 - -def curvature_check(coeff): - poly = MultiCheb(coeff) - a = np.array([-1.]*poly.dim) - b = np.array([1.]*poly.dim) - return not can_eliminate(poly, a, b) - -def subdivision_solve_nd(funcs,a,b,deg,interval_results,interval_checks = [],subinterval_checks=[],tol=1.e-3): - """Finds the common zeros of the given functions. - - Parameters - ---------- - funcs : list - Each element of the list is a callable function. - a : numpy array - The lower bound on the interval. - b : numpy array - The upper bound on the interval. - deg : int - The degree to approximate with in the chebyshev approximation. - - Returns - ------- - good_zeros : numpy array - The real zero in [-1,1] of the input zeros. - """ - division_var = 0 - cheb_approx_list = [] - try: - if np.random.rand() > .999: - print("Interval - ",a,b) - dim = len(a) - for func in funcs: - coeff = full_cheb_approximate(func,a,b,deg,tol=tol) - - #Subdivides if needed. - if coeff is None: - intervals = get_subintervals(a,b,np.arange(dim),None,None,None) - - return np.vstack([subdivision_solve_nd(funcs,interval[0],interval[1],deg,interval_results\ - ,interval_checks,subinterval_checks,tol=tol) - for interval in intervals]) - else: - coeff = trim_coeff(coeff,tol=tol) - #Run checks to try and throw out the interval - for func_num, func in enumerate(interval_checks): - if not func(coeff): - interval_results[func_num].append([a,b]) - return np.zeros([0,dim]) - cheb_approx_list.append(MultiCheb(coeff)) - - zeros = np.array(division(cheb_approx_list, get_divvar_coord_from_eigval = True, divisor_var = 0, tol = 1.e-6)) - interval_results[-1].append([a,b]) - if len(zeros) == 0: - return np.zeros([0,dim]) - return transform(good_zeros_nd(zeros),a,b) - - except np.linalg.LinAlgError as e: - while division_var < len(a): - try: - zeros = np.array(division(cheb_approx_list, get_divvar_coord_from_eigval = True, divisor_var = 0, tol = 1.e-6)) - return zeros - except np.linalg.LinAlgError as e: - division_var += 1 - - #Subdivide but run some checks on the intervals first - intervals = get_subintervals(a,b,np.arange(dim),subinterval_checks,interval_results\ - ,cheb_approx_list,check_subintervals=True) - if len(intervals) == 0: - return np.zeros([0,dim]) - else: - return np.vstack([subdivision_solve_nd(funcs,interval[0],interval[1],deg,interval_results\ - ,interval_checks,subinterval_checks,tol=tol) - for interval in intervals]) - -def trim_coeff(coeff, tol=1.e-3): - """Reduce the number of coefficients and the degree. - - Parameters - ---------- - coeff : numpy array - The Chebyshev coefficients for approximating a function. - - Returns - ------- - coeff : numpy array - The reduced degree Chebyshev coefficients for approximating a function. - """ - dim = coeff.ndim - - #Cuts out the high diagonals as much as possible to minimize polynomial degree. - if abs(coeff[tuple([-1]*dim)]) < tol: - coeff[tuple([-1]*dim)] = 0 - deg = np.sum(coeff.shape)-dim-1 - while deg > 2: - mons = mon_combos_limited([0]*dim,deg,coeff.shape) - slices = [] #becomes the indices of the terms of degree deg - mons = np.array(mons).T - for i in range(dim): - slices.append(mons[i]) - if np.sum(np.abs(coeff[slices])) < tol: #L1 norm - # and abs(coeff[slices] - coeff[slices-1]) < drop_off_tol - coeff[slices] = 0 - else: - break - deg -= 1 - - return coeff - -def mon_combos_limited(mon, remaining_degrees, shape, cur_dim = 0): - '''Finds all the monomials of a given degree that fits in a given shape and returns them. Works recursively. - - Very similar to mon_combos, but only returns the monomials of the desired degree. - - Parameters - -------- - mon: list - A list of zeros, the length of which is the dimension of the desired monomials. Will change - as the function searches recursively. - remaining_degrees : int - Initially the degree of the monomials desired. Will decrease as the function searches recursively. - shape : tuple - The limiting shape. The i'th index of the mon can't be bigger than the i'th index of the shape. - cur_dim : int - The current position in the list the function is iterating through. Defaults to 0, but increases - in each step of the recursion. - - Returns - ----------- - answers : list - A list of all the monomials. - ''' - answers = [] - if len(mon) == cur_dim+1: #We are at the end of mon, no more recursion. - if remaining_degrees < shape[cur_dim]: - mon[cur_dim] = remaining_degrees - answers.append(mon.copy()) - return answers - if remaining_degrees == 0: #Nothing else can be added. - answers.append(mon.copy()) - return answers - temp = mon.copy() #Quicker than copying every time inside the loop. - for i in range(min(shape[cur_dim],remaining_degrees+1)): #Recursively add to mon further down. - temp[cur_dim] = i - answers.extend(mon_combos_limited(temp, remaining_degrees-i, shape, cur_dim+1)) - return answers - -def good_zeros(zeros, imag_tol = 1.e-10): - """Get the real zeros in the -1 to 1 interval - - Parameters - ---------- - zeros : numpy array - The zeros to be checked. - imag_tol : float - How large the imaginary part can be to still have it be considered real. - - Returns - ------- - good_zeros : numpy array - The real zero in [-1,1] of the input zeros. - """ - zeros = zeros[np.where(np.abs(zeros) <= 1)] - zeros = zeros[np.where(np.abs(zeros.imag) < imag_tol)] - return zeros - -def subdivision_solve_1d(f,a,b,cheb_approx_tol=1.e-3,max_degree=128): - """Finds the roots of a one-dimensional function using subdivision and chebyshev approximation. - - Parameters - ---------- - f : function from R^n -> R - The function to interpolate. - a : numpy array - The lower bound on the interval. - b : numpy array - The upper bound on the interval. - deg : int - The degree of the interpolation. - - Returns - ------- - coeffs : numpy array - The coefficient of the chebyshev interpolating polynomial. - """ - cur_deg = 2 - initial_approx = interval_approximate_1d(f,a,b,deg = cur_deg) - while cur_deg<=max_degree: - coeffsN = np.zeros(2*cur_deg+1) - coeffsN[:cur_deg+1] = initial_approx - coeffs2N = interval_approximate_1d(f,a,b,deg = 2*cur_deg) - #Check if the approximation is good enough - # if np.sum(np.abs(coeffs2N - coeffsN)) < cheb_approx_tol: - if np.sum(np.abs(coeffs2N[cur_deg+1:])) < cheb_approx_tol: - coeffs = coeffsN[:cur_deg+1] - #Division is faster after degree 75 - if cur_deg > 75: - return transform(good_zeros(divCheb(coeffs)),a,b) - else: - return transform(good_zeros(multCheb(np.trim_zeros(coeffs.copy(),trim='b'))),a,b) - initial_approx = coeffs2N - cur_deg*=2 - #Subdivide the interval and recursively call the function. - div_length = (b-a)/2 - return np.hstack([subdivision_solve_1d(f,a,b-div_length,max_degree=max_degree),\ - subdivision_solve_1d(f,a+div_length,b,max_degree=max_degree)]) diff --git a/requirements.txt b/requirements.txt index 58aebf92..a7d1d3d4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ numpy==1.14.2 scipy==1.0.1 numba==0.37.0 -matplotlib=2.2.2 +matplotlib==2.2.2 diff --git a/requirements_dev.txt b/requirements_dev.txt index ec13369d..d49a9dfd 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -1,4 +1,4 @@ -pip==9.0.1 +pip==19.2.3 bumpversion==0.5.3 wheel==0.30.0 watchdog==0.8.3 @@ -7,13 +7,14 @@ tox==2.9.1 coverage==4.5.1 Sphinx==1.7.1 twine==1.10.0 -numpy==1.14.2 -scipy==1.0.1 -numba==0.37.0 -sympy==1.1.1 +numpy==1.18.1 +scipy==1.4.1 +llvmlite==0.31.0 +numba==0.47.0 +sympy==1.5.1 matplotlib==2.2.2 -pytest==3.7.2 -pytest-cov==2.5.1 +pytest==5.4.1 +pytest-cov==2.7.1 pytest-runner==2.11.1 codecov==2.0.15 diff --git a/sage_example.py b/sage_example.py deleted file mode 100644 index cfa342fd..00000000 --- a/sage_example.py +++ /dev/null @@ -1,40 +0,0 @@ -# #### To caclulate a Gröbner Basis - -x,y,z = QQ["x,y,z"].gens() -f1, f2 = y-2*x, 1+2*x*y -I = ideal(f1,f2) -B = I.groebner_basis() -print(B) - -I2 = ideal(x - 2*y, 1+2*x*y) -B2 = I2.groebner_basis() -print(B2) - -I3 = ideal(1+x*z, 2+y**2*x**2) -B3 = I3.groebner_basis() -print(B3) - -[0,-1,0] -[0,0,3] -[0,0,0] - -[-1,0,0] -[0,0,0] -[0,5,0] - -# #### To use verbose Buchberger's - -from sage.rings.polynomial.toy_buchberger import * - -I3 = ideal(1+x, 1+y) -B3 = I3.groebner_basis() -print(B3) - -set_verbose(1) -buchberger(I3) - -f1 = 1 + x -f2 = 1+ y -f3 = 1+x**2*y+x -I = ideal(f1,f2,f3) -buchberger(I) diff --git a/setup.cfg b/setup.cfg index f6c980c9..af164d75 100644 --- a/setup.cfg +++ b/setup.cfg @@ -23,4 +23,3 @@ test = pytest [tool:pytest] collect_ignore = ['setup.py'] - diff --git a/setup.py b/setup.py index 561aac60..7dbaa349 100644 --- a/setup.py +++ b/setup.py @@ -8,11 +8,6 @@ with open('README.md') as readme_file: readme = readme_file.read() -#with open('HISTORY.rst') as history_file: -# history = history_file.read() - -#requirements = ['Click>=6.0', ] - setup_requirements = ['pytest-runner', ] test_requirements = ['pytest', ] @@ -28,14 +23,14 @@ 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ], - description="A package for faster root finding.", + description="A package for numerical root finding.", #install_requires=requirements, license="MIT license", long_description=readme + '\n\n',# + history, include_package_data=True, keywords='RootFinding', name='RootFinding', - packages=find_packages(include=['numalgsolve', 'CHEBYSHEV/TVB_Method']), + packages=find_packages(include=['yroots']), setup_requires=setup_requirements, test_suite='tests', tests_require=test_requirements, diff --git a/tests/chebfun2_suite.py b/tests/chebfun2_suite.py new file mode 100644 index 00000000..6faf95af --- /dev/null +++ b/tests/chebfun2_suite.py @@ -0,0 +1,757 @@ +import numpy as np +from yroots.subdivision import solve +from time import time +from matplotlib import pyplot as plt +# TODO Description of where these tests come from, links to relevant papers, +# acknowledgements, etc. + + +def norm_pass_or_fail(yroots, roots, tol=2.220446049250313e-13): + """ Determines whether the roots given pass or fail the test according + to whether or not their norms are within tol of the norms of the + "actual" roots, which are determined either by previously known + roots or Marching Squares roots. + Parameters + ---------- + yroots : numpy array + The roots that yroots found. + roots : numpy array + "Actual" roots either obtained analytically or through Marching + Squares. + tol : float, optional + Tolerance that determines how close the roots need to be in order + to be considered close. Defaults to 1000*eps where eps is machine + epsilon. + + Returns + ------- + bool + Whether or not all the roots were close enough. + """ + roots_sorted = np.sort(roots,axis=0) + yroots_sorted = np.sort(yroots,axis=0) + root_diff = roots_sorted - yroots_sorted + return np.linalg.norm(root_diff[:,0]) < tol and np.linalg.norm(root_diff[:,1]) < tol + + +def residuals(func, roots): + """ Finds the residuals of the given function at the roots. + Paramters + --------- + func : function + The function to find the residuals of. + roots : numpy array + The coordinates of the roots. + + Returns + ------- + numpy array + The residuals of the function. + """ + return np.abs(func(roots[:,0],roots[:,1])) + + +def residuals_pass_or_fail(funcs, roots, tol=2.220446049250313e-13): + """ Determines whether the roots given pass or fail the test according + to whether or not the maximal residuals are within a certain tolerance. + Parameters + ---------- + funcs : list of functions + The functions to find the residuals of. + roots : numpy array + The roots to plug into the functions to get the residuals. + tol : float, optional + How close to 0 the maximal residual must be in order to pass. + Defaults to 1000* eps where eps is machine epsilon. + Returns + ------- + bool + True if the roots pass the test (are close enough to 0), False + otherwise. + """ + for func in funcs: + if np.max(residuals(func, roots)) > tol: + return False + + return True + + +def pass_or_fail(funcs, yroots, roots, test_num, test_type="norm", tol=2.220446049250313e-13): + """Determines whether a test passes or fails bsed on the given criteria. + Parameters + ---------- + funcs : list of functions + The functions to find the roots of. + yroots : numpy array + Roots found by yroots. + roots : numpy array + The list of "actual" or Marching Squares roots. + test_num : float or string + The number of the test. For example, test 9.2 one could pass in + "9.2" or 9.2. + test_type : string, optional + What type of test to use to determine wheter it passes or fails. + - "norm" -- runs norm_pass_or_fail, default + - "residual" -- runs residual_pass_or_fail + tol : float, optional + The tolerance with which we want to run our tests. Defualts to + 1000*eps where eps is machine epsilon. + Raises + ------ + AssertionError + If len(yroots) != len(roots) or if it fails the residual + or norm tests. + ValueError + If test_type is not "norm" or "residual" + """ + if (test_type not in ['norm','residual']): + raise ValueError("test_type must be 'norm' or 'residual'.") + + if len(yroots) != len(roots): + if len(yroots) > len(roots): + raise AssertionError("Test " + str(test_num) + ": YRoots found" + " too many roots: " + str(len(yroots)) + + " where " + str(len(roots)) + " were expected.") + else: + raise AssertionError("Test " + str(test_num) + ": YRoots didn't" + " find enough roots: " + str(len(yroots)) + + " where " + str(len(roots)) + " were expected.") + + if test_type == 'norm': + assert norm_pass_or_fail(yroots, roots, tol=tol), "Test " + str(test_num) + " failed." + else: + assert residuals_pass_or_fail(funcs, yroots, tol=tol), "Test " + str(test_num) + " failed." + + +def norm_pass_or_fail(yroots, roots, tol=2.220446049250313e-13): + """ Determines whether the roots given pass or fail the test according + to whether or not their norms are within tol of the norms of the + "actual" roots, which are determined either by previously known + roots or Marching Squares roots. + Parameters + ---------- + yroots : numpy array + The roots that yroots found. + roots : numpy array + "Actual" roots either obtained analytically or through Marching + Squares. + tol : float, optional + Tolerance that determines how close the roots need to be in order + to be considered close. Defaults to 1000*eps where eps is machine + epsilon. + + Returns + ------- + bool + Whether or not all the roots were close enough. + """ + roots_sorted = np.sort(roots,axis=0) + yroots_sorted = np.sort(yroots,axis=0) + root_diff = roots_sorted - yroots_sorted + return np.linalg.norm(root_diff[:,0]) < tol and np.linalg.norm(root_diff[:,1]) < tol, np.linalg.norm(root_diff[:,0]), np.linalg.norm(root_diff[:,1]) + + +def residuals(func, roots): + """ Finds the residuals of the given function at the roots. + Paramters + --------- + func : function + The function to find the residuals of. + roots : numpy array + The coordinates of the roots. + + Returns + ------- + numpy array + The residuals of the function. + """ + return np.abs(func(roots[:,0],roots[:,1])) + + +def residuals_pass_or_fail(funcs, roots, tol=2.220446049250313e-13): + """ Determines whether the roots given pass or fail the test according + to whether or not the maximal residuals are within a certain tolerance. + Parameters + ---------- + funcs : list of functions + The functions to find the residuals of. + roots : numpy array + The roots to plug into the functions to get the residuals. + tol : float, optional + How close to 0 the maximal residual must be in order to pass. + Defaults to 1000* eps where eps is machine epsilon. + Returns + ------- + bool + True if the roots pass the test (are close enough to 0), False + otherwise. + """ + for func in funcs: + if np.max(residuals(func, roots)) > tol: + return False + + return True + +def verbose_pass_or_fail(funcs, yroots, polished_roots, test_num, cheb_roots=None, tol=2.220446049250313e-13): + """ Determines which tests pass and which fail. + Parameters + ---------- + funcs : list of functions + The functions to find the roots of. + yroots : numpy array + Roots found by yroots. + MSroots : numpy array + The list of "actual" or Marching Squares roots. + test_num : float or string + The number of the test. For example, test 9.2 one could pass in + "9.2" or 9.2. + cheb_roots : numpy array + Chebfun roots for extra comparison when MS are available. + tol : float, optional + The tolerance with which we want to run our tests. Defualts to + 1000*eps where eps is machine epsilon. + Raises + ------ + AssertionError + If len(yroots) != len(roots) or if it fails the residual + or norm tests. + """ + print ("=========================================================") + print("Test " + str(test_num)) + + residuals_pass = residuals_pass_or_fail(funcs, yroots, tol) + if residuals_pass: + print("\t Residual test: pass") + else: + print("\t Residual test: fail") + + if cheb_roots is not None: + if residuals_pass_or_fail(funcs, cheb_roots, tol): + print("\t Chebfun passes residual test") + else: + print("\t Chebfun fails residual test") + try: + norm_pass, x_norm, y_norm = norm_pass_or_fail(polished_roots, cheb_roots, tol) + if norm_pass: + print("\t Chebfun norm test: pass") + else: + print("\t Chebfun norm test: fail") + print("The norm of the difference in x values:", x_norm) + print("The norm of the difference in y values:", y_norm) + except ValueError as e: + print("A different number of roots were found.") + print ("Yroots: " + str(len(yroots))) + print("Chebfun Roots: " + str(len(cheb_roots))) + if polished_roots is not None: + try: + norm_pass, x_norm, y_norm = norm_pass_or_fail(yroots, polished_roots, tol) + if norm_pass: + print("\t YRoots norm test: pass") + else: + print("\t YRoots norm test: fail") + print("The norm of the difference in x values:", x_norm) + print("The norm of the difference in y values:", y_norm) + except ValueError as e: + print("A different number of roots were found.") + print ("Yroots: " + str(len(yroots))) + print("Polished: " + str(len(polished_roots))) + print("YRoots max residuals:") + YR_resid = list() + for i, func in enumerate(funcs): + YR_resid.append(residuals(func, yroots)) + print("\tf" + str(i) + ": " + str(np.max(residuals(func, yroots)))) + + cheb_resid = None + if cheb_roots is not None: + cheb_resid = list() + print("Chebfun max residuals:") + for i, func in enumerate(funcs): + cheb_resid.append(residuals(func, cheb_roots)) + print("\tf" + str(i) + ": " + str(np.max(residuals(func, cheb_roots)))) + if polished_roots is not None: + print("Polished max residuals:") + Other_resid = list() + for i, func in enumerate(funcs): + Other_resid.append(residuals(func, polished_roots)) + print("\tf" + str(i) + ": " + str(np.max(residuals(func, polished_roots)))) + + if len(yroots) > len(polished_roots): + print("YRoots found more roots.") + print("=========================================================") + return residuals_pass,norm_pass + + # print("Comparison of Residuals (YRoots <= Other)") + num_smaller = 0 + if polished_roots is not None: + for i in range(len(YR_resid)): + comparison_array = (YR_resid[i] <= Other_resid[i]) + # print(comparison_array) + num_smaller += np.sum(comparison_array) + print("Number of YRoots residual values <= Polished residual values are: " + str(num_smaller)) + + if cheb_resid is not None: + if len(yroots) > len(cheb_roots): + print("=========================================================") + return residuals_pass,norm_pass + + for i in range(len(YR_resid)): + comparison_array2 = (YR_resid[i] <= cheb_resid[i]) + num_smaller += np.sum(comparison_array2) + print("Number of YRoots residual values <= to Chebfun residual values are: " + str(num_smaller)) + + print("=========================================================") + return residuals_pass,norm_pass + +def test_roots_1_1(): + # Test 1.1 + f = lambda x,y: 144*(x**4+y**4)-225*(x**2+y**2) + 350*x**2*y**2+81 + g = lambda x,y: y-x**6 + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_1.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_1.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 1.1, cheb_roots=chebfun_roots) + + +def test_roots_1_2(): + # Test 1.2 + f = lambda x,y: (y**2-x**3)*((y-0.7)**2-(x-0.3)**3)*((y+0.2)**2-(x+0.8)**3)*((y+0.2)**2-(x-0.8)**3) + g = lambda x,y: ((y+.4)**3-(x-.4)**2)*((y+.3)**3-(x-.3)**2)*((y-.5)**3-(x+.6)**2)*((y+0.3)**3-(2*x-0.8)**3) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + + # Get Polished results (Newton polishing misses roots) + yroots2 = solve([f,g],[-1,-1],[1,1], abs_approx_tol=[1e-8, 1e-12], rel_approx_tol=[1e-15, 1e-18],\ + max_cond_num=[1e5, 1e2], good_zeros_factor=[100,100], min_good_zeros_tol=[1e-5, 1e-5],\ + check_eval_error=[True,True], check_eval_freq=[1,2], plot=False, target_tol=[1e-13, 1e-13]) + actual_roots = np.load('Polished_results/polished_1.2.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_1.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, yroots2, 1.2, cheb_roots=chebfun_roots, tol=2.220446049250313e-10) + + +def test_roots_1_3(): + # Test 1.3 + f = lambda x,y: y**2-x**3 + g = lambda x,y: (y+.1)**3-(x-.1)**2 + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_1.3.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_1.3.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 1.3, cheb_roots=chebfun_roots) + +def test_roots_1_4(): + # Test 1.4 + f = lambda x,y: x - y + .5 + g = lambda x,y: x + y + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + # Single root has to be in matrix form because yroots + # returns the roots in matrix form. + a_roots = np.array([[-.25, .25]]) + chebfun_roots = np.array([np.loadtxt('Chebfun_results/test_roots_1.4.csv', delimiter=',')]) + + return t, verbose_pass_or_fail([f,g], yroots, a_roots, 1.4, cheb_roots=chebfun_roots) + +def test_roots_1_5(): + # Test 1.5 + f = lambda x,y: y + x/2 + 1/10 + g = lambda x,y: y - 2.1*x + 2 + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + # Single root has to be in matrix form because yroots + # returns the roots in matrix form. + a_roots = np.array([[0.730769230769231, -0.465384615384615]]) + + chebfun_roots = np.array([np.loadtxt('Chebfun_results/test_roots_1.5.csv', delimiter=',')]) + + return t, verbose_pass_or_fail([f,g], yroots, a_roots, 1.5, cheb_roots=chebfun_roots) + + +def test_roots_2_1(): + # Test 2.1 + f = lambda x,y: np.cos(10*x*y) + g = lambda x,y: x + y**2 + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_2.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_2.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 2.1, cheb_roots=chebfun_roots) + + +def test_roots_2_2(): + # Test 2.2 + f = lambda x,y: x + g = lambda x,y: (x-.9999)**2 + y**2-1 + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_2.2.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_2.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 2.2, cheb_roots=chebfun_roots) + + +def test_roots_2_3(): + # Test 2.3 + f = lambda x,y: np.sin(4*(x + y/10 + np.pi/10)) + g = lambda x,y: np.cos(2*(x-2*y+ np.pi/7)) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_2.3.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_2.3.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 2.3, cheb_roots=chebfun_roots) + + +def test_roots_2_4(): + # Test 2.4 + f = lambda x,y: np.exp(x-2*x**2-y**2)*np.sin(10*(x+y+x*y**2)) + g = lambda x,y: np.exp(-x+2*y**2+x*y**2)*np.sin(10*(x-y-2*x*y**2)) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_2.4.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_2.4.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 2.4, cheb_roots=chebfun_roots) + + +def test_roots_2_5(): + # Test 2.5 + f = lambda x,y: 2*y*np.cos(y**2)*np.cos(2*x)-np.cos(y) + g = lambda x,y: 2*np.sin(y**2)*np.sin(2*x)-np.sin(x) + start = time() + yroots = solve([f,g],[-4,-4],[4,4], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_2.5.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_2.5.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 2.5, cheb_roots=chebfun_roots, tol=2.220446049250313e-12) + + + +def test_roots_3_1(): + # Test 3.1 + f = lambda x,y: ((x-.3)**2+2*(y+0.3)**2-1) + g = lambda x,y: ((x-.49)**2+(y+.5)**2-1)*((x+0.5)**2+(y+0.5)**2-1)*((x-1)**2+(y-0.5)**2-1) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_3.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_3.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 3.1, cheb_roots=chebfun_roots, tol=2.220446049250313e-11) + +def test_roots_3_2(): + # Test 3.2 + f = lambda x,y: ((x-0.1)**2+2*(y-0.1)**2-1)*((x+0.3)**2+2*(y-0.2)**2-1)*((x-0.3)**2+2*(y+0.15)**2-1)*((x-0.13)**2+2*(y+0.15)**2-1) + g = lambda x,y: (2*(x+0.1)**2+(y+0.1)**2-1)*(2*(x+0.1)**2+(y-0.1)**2-1)*(2*(x-0.3)**2+(y-0.15)**2-1)*((x-0.21)**2+2*(y-0.15)**2-1) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_3.2.npy') + + yroots2 = solve([f,g],[-1,-1],[1,1], abs_approx_tol=[1e-8, 1e-15], rel_approx_tol=[1e-12, 1e-29],\ + max_cond_num=[1e5, 1e2], good_zeros_factor=[100,100], min_good_zeros_tol=[1e-5, 1e-5],\ + check_eval_error=[True,True], check_eval_freq=[1,1], plot=False) + + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_3.2.csv', delimiter=',') + actual_roots = chebfun_roots + + return t, verbose_pass_or_fail([f,g], yroots, yroots2, 3.2, cheb_roots=chebfun_roots, tol=2.220446049250313e-11) + + +def test_roots_4_1(): + # Test 4.1 + # This system hs 4 true roots, but ms fails (finds 5). + f = lambda x,y: np.sin(3*(x+y)) + g = lambda x,y: np.sin(3*(x-y)) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_4.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_4.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 4.1, cheb_roots=chebfun_roots) + +def test_roots_4_2(): + # Test 4.2 + f = lambda x,y: ((90000*y**10 + (-1440000)*y**9 + (360000*x**4 + 720000*x**3 + 504400*x**2 + 144400*x + 9971200)*(y**8) + + ((-4680000)*x**4 + (-9360000)*x**3 + (-6412800)*x**2 + (-1732800)*x + (-39554400))*(y**7) + (540000*x**8 + + 2160000*x**7 + 3817600*x**6 + 3892800*x**5 + 27577600*x**4 + 51187200*x**3 + 34257600*x**2 + 8952800*x + 100084400)*(y**6) + + ((-5400000)*x**8 + (-21600000)*x**7 + (-37598400)*x**6 + (-37195200)*x**5 + (-95198400)*x**4 + + (-153604800)*x**3 + (-100484000)*x**2 + (-26280800)*x + (-169378400))*(y**5) + (360000*x**12 + 2160000*x**11 + + 6266400*x**10 + 11532000*x**9 + 34831200*x**8 + 93892800*x**7 + 148644800*x**6 + 141984000*x**5 + 206976800*x**4 + + 275671200*x**3 + 176534800*x**2 + 48374000*x + 194042000)*(y**4) + ((-2520000)*x**12 + (-15120000)*x**11 + (-42998400)*x**10 + + (-76392000)*x**9 + (-128887200)*x**8 + (-223516800)*x**7 + (-300675200)*x**6 + (-274243200)*x**5 + (-284547200)*x**4 + + (-303168000)*x**3 + (-190283200)*x**2 + (-57471200)*x + (-147677600))*(y**3) + (90000*x**16 + 720000*x**15 + 3097600*x**14 + + 9083200*x**13 + 23934400*x**12 + 58284800*x**11 + 117148800*x**10 + 182149600*x**9 + 241101600*x**8 + 295968000*x**7 + + 320782400*x**6 + 276224000*x**5 + 236601600*x**4 + 200510400*x**3 + 123359200*x**2 + 43175600*x + 70248800)*(y**2) + + ((-360000)*x**16 + (-2880000)*x**15 + (-11812800)*x**14 + (-32289600)*x**13 + (-66043200)*x**12 + (-107534400)*x**11 + + (-148807200)*x**10 + (-184672800)*x**9 + (-205771200)*x**8 + (-196425600)*x**7 + (-166587200)*x**6 + (-135043200)*x**5 + + (-107568800)*x**4 + (-73394400)*x**3 + (-44061600)*x**2 + (-18772000)*x + (-17896000))*y + (144400*x**18 + 1299600*x**17 + + 5269600*x**16 + 12699200*x**15 + 21632000*x**14 + 32289600*x**13 + 48149600*x**12 + 63997600*x**11 + 67834400*x**10 + + 61884000*x**9 + 55708800*x**8 + 45478400*x**7 + 32775200*x**6 + 26766400*x**5 + 21309200*x**4 + 11185200*x**3 + 6242400*x**2 + + 3465600*x + 1708800))) + g = lambda x,y: 1e-4*(y**7 + (-3)*y**6 + (2*x**2 + (-1)*x + 2)*y**5 + (x**3 + (-6)*x**2 + x + 2)*y**4 + (x**4 + (-2)*x**3 + 2*x**2 + + x + (-3))*y**3 + (2*x**5 + (-3)*x**4 + x**3 + 10*x**2 + (-1)*x + 1)*y**2 + ((-1)*x**5 + 3*x**4 + 4*x**3 + (-12)*x**2)*y + + (x**7 + (-3)*x**5 + (-1)*x**4 + (-4)*x**3 + 4*x**2)) + start = time() + yroots = solve([f,g],[-1, -1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_4.2.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_4.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 4.2, cheb_roots=chebfun_roots) + + + +def test_roots_5(): + # Test 5.1 + f = lambda x,y: 2*x*y*np.cos(y**2)*np.cos(2*x)-np.cos(x*y) + g = lambda x,y: 2*np.sin(x*y**2)*np.sin(3*x*y)-np.sin(x*y) + start = time() + yroots = solve([f,g],[-2,-2],[2,2], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_5.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_5.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 5.1, cheb_roots=chebfun_roots) + + +def test_roots_6_1(): + # Test 6.1 + f = lambda x,y: (y - 2*x)*(y+0.5*x) + g = lambda x,y: x*(x**2+y**2-1) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_6.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_6.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 6.1, cheb_roots=chebfun_roots) + + + + +def test_roots_6_2(): + # Test 6.2 + f = lambda x,y: (y - 2*x)*(y+.5*x) + g = lambda x,y: (x-.0001)*(x**2+y**2-1) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.array([[1/10000,-1/20000],[1/10000, 1/5000],[-2/np.sqrt(5),1/np.sqrt(5)],[-1/np.sqrt(5),-2/np.sqrt(5)],[1/np.sqrt(5),2/np.sqrt(5)],[2/np.sqrt(5),-1/np.sqrt(5)]]) + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_6.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 6.2, cheb_roots=chebfun_roots, tol=2.220446049250313e-11) + + +def test_roots_6_3(): + # Test 6.3 + f = lambda x,y: 25*x*y - 12 + g = lambda x,y: x**2+y**2-1 + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_6.3.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_6.3.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 6.3, cheb_roots=chebfun_roots) + + +def test_roots_7_1(): + # Test 7.1 + f = lambda x,y: (x**2+y**2-1)*(x-1.1) + g = lambda x,y: (25*x*y-12)*(x-1.1) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_7.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_7.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 7.1, cheb_roots=chebfun_roots) + + +def test_roots_7_2(): + # Test 7.2 + f = lambda x,y: y**4 + (-1)*y**3 + (2*x**2)*(y**2) + (3*x**2)*y + (x**4) + h = lambda x,y: y**10-2*(x**8)*(y**2)+4*(x**4)*y-2 + g = lambda x,y: h(2*x,2*(y+.5)) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_7.2.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_7.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 7.2, cheb_roots=chebfun_roots, tol=2.220446049250313e-10) + + +def test_roots_7_3(): + # Test 7.3 + c = 1.e-09 + f = lambda x,y: np.cos(x*y/(c**2))+np.sin(3*x*y/(c**2)) + g = lambda x,y: np.cos(y/c)-np.cos(2*x*y/(c**2)) + + start = time() + yroots = solve([f,g],[-1e-9, -1e-9],[1e-9, 1e-9], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_7.3.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_7.3.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 7.3, cheb_roots=chebfun_roots,tol=2.220446049250313e-10) + + + +def test_roots_7_4(): + # Test 7.4 + f = lambda x,y: np.sin(3*np.pi*x)*np.cos(x*y) + g = lambda x,y: np.sin(3*np.pi*y)*np.cos(np.sin(x*y)) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_7.4.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_7.4.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 7.4, cheb_roots=chebfun_roots) + +def test_roots_8_1(): + # Test 8.1 + f = lambda x,y: np.sin(10*x-y/10) + g = lambda x,y: np.cos(3*x*y) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_8.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_8.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 8.1, cheb_roots=chebfun_roots) + +def test_roots_8_2(): + # Test 8.2 + f = lambda x,y: np.sin(10*x-y/10) + y + g = lambda x,y: np.cos(10*y-x/10) - x + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_8.2.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_8.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 8.2, cheb_roots=chebfun_roots) + + + +def test_roots_9_1(): + # Test 9.1 + f = lambda x,y: x**2+y**2-.9**2 + g = lambda x,y: np.sin(x*y) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_9.1.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_9.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 9.1, cheb_roots=chebfun_roots) + + +def test_roots_9_2(): + # Test 9.2 + f = lambda x,y: x**2+y**2-.49**2 + g = lambda x,y: (x-.1)*(x*y - .2) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.load('Polished_results/polished_9.2.npy') + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_9.2.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 9.2, cheb_roots=chebfun_roots) + + +def test_roots_10(): + # Test 10.1 + f = lambda x,y: (x-1)*(np.cos(x*y**2)+2) + g = lambda x,y: np.sin(8*np.pi*y)*(np.cos(x*y)+2) + start = time() + yroots = solve([f,g],[-1,-1],[1,1], plot=False) + t = time() - start + actual_roots = np.array([[1, -1.0], [1, -0.875], [1, -0.75], [1, -0.625], [1, -0.5], [1, -0.375], + [1, -0.25], [1, -0.125], [1, 0.0], [1, 0.125], [1, 0.25], [1, 0.375], + [1, 0.5], [1, 0.625], [1, 0.75], [1, 0.875], [1, 1.0]]) + chebfun_roots = np.loadtxt('Chebfun_results/test_roots_10.1.csv', delimiter=',') + + return t, verbose_pass_or_fail([f,g], yroots, actual_roots, 10.1, cheb_roots=chebfun_roots) + +def plot_timings(tests,timings): + labels = [test.__name__[11:].replace('_','.') for test in tests] + plt.figure(figsize=(8,5)) + plt.subplot(211) + plt.bar(labels,timings) + plt.xticks(rotation=45) + plt.ylim(0,40) + plt.subplot(212) + plt.bar(labels,timings) + plt.xticks(rotation=45) + plt.yscale('log') + plt.ylim((10**-3,10**2)) + plt.tight_layout() + plt.show() + +if __name__ == "__main__": + # Run all the tests! + tests = np.array([test_roots_1_1, + test_roots_1_2, + test_roots_1_3, + test_roots_1_4, + test_roots_1_5, + test_roots_2_1, + test_roots_2_2, + test_roots_2_3, + test_roots_2_4, + test_roots_2_5, + test_roots_3_1, + test_roots_3_2, + test_roots_4_1, + test_roots_4_2, + test_roots_5, + test_roots_6_1, + test_roots_6_2, + test_roots_6_3, + test_roots_7_1, + test_roots_7_2, + test_roots_7_3, + test_roots_7_4, + test_roots_8_1, + test_roots_8_2, + test_roots_9_1, + test_roots_9_2, + test_roots_10]) + res_passes = np.zeros_like(tests,dtype=bool) + norm_passes = np.zeros_like(tests,dtype=bool) + times = np.zeros_like(tests) + for i,test in enumerate(tests): + t, passes = test() + res_pass,norm_pass = passes + res_passes[i] = res_pass + norm_passes[i] = norm_pass + times[i] = t + print('\n\nSummary') + print(f'Residual Test: Passed {np.sum(res_passes)} of 27, {100*np.mean(res_passes)}%') + where_failed_res = np.where(~res_passes)[0] + failed_res_tests = tests[where_failed_res] + print(f'Failed Residual Test on \n{[t.__name__ for t in failed_res_tests]}') + print(f'Norm Test : Passed {np.sum(norm_passes)} of 27, {100*np.mean(norm_passes)}%') + where_failed_norm = np.where(~norm_passes)[0] + failed_norm_tests = tests[where_failed_norm] + print(f'Failed Norm Test on \n{[t.__name__ for t in failed_norm_tests]}') + plot_timings(tests,times) diff --git a/tests/devastating_example_test_scripts.py b/tests/devastating_example_test_scripts.py new file mode 100644 index 00000000..8a0a1c50 --- /dev/null +++ b/tests/devastating_example_test_scripts.py @@ -0,0 +1,278 @@ +# Collection of methods for studying the devastating example for Möller-Setter +# matrix methods +# Author: Hayden Ringer + +import yroots as yr +import numpy as np +from scipy.stats import ortho_group +from yroots.polynomial import MultiPower, MultiCheb +from yroots.Multiplication import ms_matrices, ms_matrices_cheb, ms_matrices_p, build_macaulay, multiplication +from yroots.MacaulayReduce import reduce_macaulay_svd, reduce_macaulay_qrt, reduce_macaulay_tvb, reduce_macaulay_p +from yroots.utils import ConditioningError +import scipy.linalg as la +import matplotlib.pyplot as plt +from yroots.subdivision import full_cheb_approximate, trim_coeffs + +def condeig(A,eig,v): + """Calculates the condition number of an eigenvalue of A""" + n = A.shape[0] + Q = hh(v) + B = ((Q.conj().T)@A@Q) + R = la.qr(B[1:,1:]-eig*np.eye(n-1))[1] + z = la.solve_triangular(R,-B[0,1:],trans=2) + return (1+la.norm(z))**.5 + +def hh(x): + u = x.copy().astype('complex') + u[0] += np.exp(1j*np.angle(x[0]))*la.norm(x) + u = u/la.norm(u) + return np.eye(len(u)) - 2*np.outer(u,u.conj()) + +def condeigs(A): + n = A.shape[0] + w,v = la.eig(A) + cond = np.zeros(n) + for i,eig in enumerate(w): + Q = hh(v[:,i]) + B = ((Q.conj().T)@A@Q) + R = la.qr(B[1:,1:]-eig*np.eye(n-1))[1] + z = la.solve_triangular(R,-B[0,1:],trans=2) + cond[i] = (1+la.norm(z))**.5 + return w,cond + +def polyqeps(Q,eps): + dim = Q.shape[0] + polys = [] + for i in range(dim): + coeff = np.zeros([3]*dim) + spot = [0]*dim + for j in range(dim): + spot[j] = 1 + coeff[tuple(spot)] = Q[i,j]*eps + spot[j] = 0 + spot[i] = 2 + coeff[tuple(spot)] = 1 + polys.append(MultiPower(coeff)) + return polys + +def chebpolyqeps(Q,eps): + dim = Q.shape[0] + polys = [] + for i in range(dim): + coeff = np.zeros([3]*dim) + spot = [0]*dim + coeff[tuple(spot)] = .5 + for j in range(dim): + spot[j] = 1 + coeff[tuple(spot)] = Q[i,j]*eps + spot[j] = 0 + spot[i] = 2 + coeff[tuple(spot)] = .5 + polys.append(MultiCheb(coeff)) + return polys + +def spolyqeps(Q,eps): + dim = Q.shape[0] + polys = [] + const = (np.eye(dim)-eps*Q).sum(axis=1) + for i in range(dim): + coeff = np.zeros([3]*dim) + spot = [0]*dim + coeff[tuple(spot)] = const[i] + for j in range(dim): + spot[j] = 1 + coeff[tuple(spot)] = Q[i,j]*eps + if i == j: + coeff[tuple(spot)] -= 2 + spot[j] = 0 + spot[i] = 2 + coeff[tuple(spot)] = 1 + polys.append(MultiPower(coeff)) + return polys + +def chebspolyqeps(Q,eps): + dim = Q.shape[0] + polys = [] + const = (1.5*np.eye(dim)-eps*Q).sum(axis=1) + for i in range(dim): + coeff = np.zeros([3]*dim) + spot = [0]*dim + coeff[tuple(spot)] = const[i] + for j in range(dim): + spot[j] = 1 + coeff[tuple(spot)] = Q[i,j]*eps + if i == j: + coeff[tuple(spot)] -= 2 + spot[j] = 0 + spot[i] = 2 + coeff[tuple(spot)] = .5 + polys.append(MultiCheb(coeff)) + return polys + +def macaulayqeps(Q,eps,kind): + if kind == 'power': func = polyqeps + elif kind == 'spower': func = spolyqeps + elif kind == 'cheb': func = chebpolyqeps + else: func = chebspolyqeps + polys = func(Q,eps) + return build_macaulay(polys) + +def macaulaypolys(polys): + return build_macaulay(polys) + +def redmacaulayqeps(Q,eps,kind,method,P=None): + matrix,matrix_terms,cut = macaulayqeps(Q,eps,kind) + if method == 'qrt': func = reduce_macaulay + elif method == 'tvb': func = reduce_macaulay_tvb + elif method == 'byu': func = reduce_macaulay_byu + elif method == 'p': + try: + E, Q2 = reduce_macaulay_p(matrix, cut, P, 1e5) + except ConditioningError as e: + raise e + return E,Q2,matrix_terms,cut + try: + E, Q2 = func(matrix, cut, 1e5) + except ConditioningError as e: + raise e + return E,Q2,matrix_terms,cut + +def redmacaulaypolys(polys,method,P=None): + matrix,matrix_terms,cut = macaulaypolys(polys) + if method == 'qrt': func = reduce_macaulay + elif method == 'tvb': func = reduce_macaulay_tvb + elif method == 'byu': func = reduce_macaulay_byu + elif method == 'p': + try: + E, Q2 = reduce_macaulay_p(matrix, cut, P, 1e5) + except ConditioningError as e: + raise e + return E,Q2,matrix_terms,cut + try: + E, Q2 = func(matrix, cut, 1e5) + except ConditioningError as e: + raise e + return E,Q2,matrix_terms,cut + +def msmatqeps(Q,eps,kind,method,P=None): + E,Q2,matrix_terms,cut = redmacaulayqeps(Q,eps,kind,method,P) + if method == 'qrt': + if kind in ['power','spower']: + return ms_matrices(E,Q2,matrix_terms,Q.shape[0]) + else: + return ms_matrices_cheb(E,Q2,matrix_terms,Q.shape[0]) + else: + return ms_matrices_p(E,Q2,matrix_terms,Q.shape[0],cut) + +def msmatpolys(polys,method,P=None): + E,Q2,matrix_terms,cut = redmacaulaypolys(polys,method,P) + if method == 'qrt': + if isinstance(polys[0],MultiPower): + return ms_matrices(E,Q2,matrix_terms,len(polys)) + else: + return ms_matrices_cheb(E,Q2,matrix_terms,len(polys)) + else: + return ms_matrices_p(E,Q2,matrix_terms,len(polys),cut) + +def mseigqeps(Q,eps,var,kind,method,P=None): + m = msmatqeps(Q,eps,kind,method,P)[...,var] + w,v = la.eig(m) + if kind in ['power','cheb']: + i = np.argmin(np.abs(w)) + return np.abs(w[i]), condeig(A,w[i],v[:,i]) + else: + i = np.argmin(np.abs(w-1)) + return np.abs(w[i]-1), condeig(A,w[i],v[:,i]) + +def mseigpolys(polys,var,kind,method,P=None): + m = msmatpolys(polys,method,P)[...,var] + w,vl,vr = la.eig(m,left=True) + if kind in ['power','cheb']: + i = np.argmin(np.abs(w)) + return np.abs(w[i]), 1/np.abs(vl[:,i]@vr[:,i]) + else: + i = np.argmin(np.abs(w-1)) + return np.abs(w[i]-1), 1/np.abs(vl[:,i]@vr[:,i]) + +def randq(dim): + return ortho_group.rvs(dim) + +def randpoly(dim,eps,kind): + """Returns MultiPower objects for a random devastating example of dimension + dim and parameter value of eps. + """ + Q = randq(dim) + if kind == 'power': func = polyqeps + elif kind == 'spower': func = spolyqeps + elif kind == 'cheb': func = chebpolyqeps + else: func = chebspolyqeps + return func(Q,eps) + +def randmacaulay(dim,eps,kind): + Q = randq(dim) + return macaulayqeps(Q,eps,kind) + +def randredmacaulay(dim,eps,kind,method): + Q = randq(dim) + return redmacaulayqeps(Q,eps,kind,method) + +def randmsmat(dim,eps,kind): + Q = randq(dim) + return msmatqeps(Q,eps,kind,method) + +def perturbpoly(dim,deg,basis,eps): + if basis == 'power': MultiX = MultiPower + else: MultiX = MultiCheb + coeff = eps*rand_coeffs(dim,deg,1)[0,0] + return MultiX(coeff) + +def perturb(polys,eps): + dim = polys[0].dim + if isinstance(polys[0],MultiPower): basis = 'power' + else: basis = MultiCheb + newpolys = [] + for poly in polys: + newpolys.append(poly + perturbpoly(dim,2,basis,eps)) + return newpolys + +def chebapprox(polys,a,b,deg,atol=1e-15,rtol=1e-15,ttol=1e-15): + chebcoeff = [] + inf_norms = [] + errors = [] + for poly in polys: + coeff,_,inf_norm,error = full_cheb_approximate(poly,a,b,deg,atol,rtol) + chebcoeff.append(coeff) + inf_norms.append(inf_norm) + errors.append(error) + chebcoeff = trim_coeffs(chebcoeff,atol,rtol,ttol,inf_norms,errors)[0] + chebpolys = [] + for coeff in chebcoeff: + chebpolys.append(MultiCheb(coeff)) + return chebpolys + +def mx2d(Q,eps): + M = np.zeros((4,4)) + M[[1,0],[3,2]] = 1 + M[[1,0],[1,0]] = -eps*Q[0,0] + M[2,1] = -eps*Q[0,1] + M[1,0] = (eps**2)*Q[0,1]*Q[1,0] + M[2,0] = (eps**2)*Q[0,1]*Q[1,1] + return M + +def smx2d(Q,eps): + A = eps*Q[0,0]-2 + B = eps*Q[0,1] + C = eps*Q[1,0] + D = eps*Q[1,1]-2 + E = eps*(Q[0,0]+Q[0,1])-1 + F = eps*(Q[1,0]+Q[1,1])-1 + M = np.array([[0,E,0,-B*F], + [1,-A,0,B*C], + [0,-B,0,B*D+E], + [0,0,1,-A]]) + + M = np.array([[-A,0,1,0], + [B*C,-A,0,1], + [B*D+E,-B,0,0], + [-B*F,E,0,0]]) + return M diff --git a/tests/gen_random_tests.py b/tests/gen_random_tests.py new file mode 100644 index 00000000..28abe09e --- /dev/null +++ b/tests/gen_random_tests.py @@ -0,0 +1,32 @@ +import numpy as np +from random_tests import save_tests +np.random.seed(2) + +degrees = {} +degrees[2] = np.arange(2,81) +degrees[3] = np.arange(2,21) +degrees[4] = np.arange(2,16) +degrees[5] = np.arange(2,11) +degrees[6] = [2,3,4,5] +degrees[7] = [2,3,4] +degrees[8] = [2,3] +degrees[9] = [2,3] +degrees[10] = [2,3] + +N = {} +N[2] = 300 +N[3] = 300 +N[4] = 300 +N[5] = 300 +N[6] = 200 +N[7] = 200 +N[8] = 200 +N[9] = 100 +N[10] = 100 + +if __name__ == "__main__": + from sys import argv + kind = argv[1] + + for dim in np.arange(2,11): + save_tests(dim,degrees[dim],N[dim],kind) diff --git a/tests/intervals.pdf b/tests/intervals.pdf new file mode 100644 index 00000000..22066f5b Binary files /dev/null and b/tests/intervals.pdf differ diff --git a/tests/maxdeg_testing.py b/tests/maxdeg_testing.py new file mode 100644 index 00000000..92644cc8 --- /dev/null +++ b/tests/maxdeg_testing.py @@ -0,0 +1,14 @@ +from random_tests import * +from yroots.polynomial import MultiPower, MultiCheb +from yroots.polyroots import solve +from timeit import default_timer as timer + +def test_maxdeg(dim,degrees,basis): + if basis == "power": MultiX = MultiPower + else: MultiX = MultiCheb + for deg in degrees: + coeffs = rand_coeffs(dim,deg,1)[0] + polys = [MultiX(coeff) for coeff in coeffs] + t = timer() + solve(polys) + print(f"dim {dim}/deg {deg}: time = {timer()-t}") diff --git a/tests/qrt_test_scripts.py b/tests/qrt_test_scripts.py new file mode 100644 index 00000000..41f6ab66 --- /dev/null +++ b/tests/qrt_test_scripts.py @@ -0,0 +1,155 @@ +from timeit import default_timer as timer +import numpy as np +import pandas as pd +from yroots.polyroots import solve +from yroots.utils import ConditioningError +from random_tests import load_tests +from yroots.polynomial import MultiPower, MultiCheb + +def run_test(polys): + try: + t = timer() + roots = solve(polys) + t = timer()-t + res = np.abs([poly(roots) for poly in polys]) + logres = np.log10(res,out=-16*np.ones_like(res),where=(res!=0)) + return np.array([logres.max(),logres.mean(),t,0],dtype='float64') + except ConditioningError: + return np.array([0,0,0,1],dtype='float64') + +infilefmt="random_tests/dim{dim}_deg{deg}.npy" +def run_tests(dim,degrees,basis,N=None,filefmt=infilefmt): + arr = np.zeros((len(degrees),6)) + for i,deg in enumerate(degrees): + arr[i,0] = deg + tests = load_tests(dim,deg,basis,N=N,filefmt=infilefmt) + for polys in tests: + arr[i,1:5] += run_test(polys) + arr[:,5] = len(tests) + arr[:,1:4] /= (arr[:,5]-arr[:,4])[:,np.newaxis] + arr[:,1:3] = (10**arr[:,1:3]) + return arr + +def run_tests_parallel(tests,MultiX): + arr = np.zeros(6,dtype='float64') + for test in tests: + polys = [MultiX(coeff) for coeff in test] + arr[1:5] += run_test(polys) + arr[5] = tests.shape[0] + return arr + +outfilefmt="test_qrt/{ver}/dim{dim}_{basis}.csv" +columns = ['deg','maxres','avgres','time','fails','N'] +intcols = ['deg','fails','N'] +def run_save(dim,degrees,basis,ver,N=None,infilefmt=infilefmt,outfilefmt=outfilefmt): + arr = run_tests(dim,degrees,basis,N=N,filefmt=infilefmt) + df = pd.DataFrame(arr,columns=columns) + df[intcols] = df[intcols].applymap(np.int64) + df = df.set_index('deg') + print(df) + df.to_csv(outfilefmt.format(dim=dim,basis=basis,ver=ver)) + +def run_save_parallel(COMM,RANK,SIZE,dim,degrees,basis,ver,N=None,infilefmt=infilefmt,outfilefmt=outfilefmt): + # set polynomial basis + if basis == "power": MultiX = MultiPower + else: MultiX = MultiCheb + + if RANK == 0: + arr = np.empty((len(degrees),6),dtype='float64') + + for i,deg in enumerate(degrees): + if RANK == 0: + # load test array + tests = np.ascontiguousarray(np.load(infilefmt.format(dim=dim,deg=deg))) + + # compute splitting into different processes + if N is None: + N = tests.shape[0] + n = N//SIZE + + # send number of tests to each process + for j in range(1,SIZE-1): + COMM.Isend(np.array(n,dtype='int'), dest=j) + COMM.Send(np.array(N-(SIZE-1)*n,dtype='int'), dest=SIZE-1) + + # send arrays to different processes + for j in range(1,SIZE-1): + COMM.Isend(tests[j*n:(j+1)*n],dest=j) + COMM.Isend(tests[(SIZE-1)*n:N],dest=SIZE-1) + + # run local tests + arr[i] = run_tests_parallel(tests[:n],MultiX) + + else: + n = np.empty(1,dtype='int') + COMM.Recv(n, source=0) + n = int(n) + tests = np.empty((n,dim,*(deg+1,)*dim),dtype='float') + COMM.Recv(tests, source=0) + buffer = run_tests_parallel(tests,MultiX) + + if RANK == 0: + # recieve results from other processes + buffer = np.empty((SIZE-1,6),dtype='float64') + for j in range(1,SIZE-1): + COMM.Irecv(buffer[j-1], source=j) + COMM.Irecv(buffer[-1], source=SIZE-1) + + else: + COMM.Send(buffer, dest=0) + + COMM.barrier() + + if RANK == 0: + # print("deg = ",deg,"\nbuffer =\n", buffer.sum(axis=0)) + # sum up results + arr[i] += buffer.sum(axis=0) + arr[i,0] = deg + + if RANK == 0: + arr[:,1:4] /= (arr[:,5]-arr[:,4])[:,np.newaxis] + arr[:,1:3] = (10**arr[:,1:3]) + df = pd.DataFrame(arr,columns=columns) + df[intcols] = df[intcols].applymap(np.int64) + df = df.set_index('deg') + print(df) + df.to_csv(outfilefmt.format(dim=dim,basis=basis,ver=ver)) + +degrees = {} +degrees[2] = np.arange(2,26) +degrees[3] = np.arange(2,11) +degrees[4] = np.arange(2,6) +degrees[5] = [2,3,4] +degrees[6] = [2,3] +degrees[7] = [2] +degrees[8] = [2] +degrees[9] = [2] +degrees[10] = [2] + +if __name__ == "__main__": + from sys import argv + from mpi4py import MPI + + N = int(argv[1]) + if N == 0: N = None + + basis = argv[2] + ver = argv[3] + + # run in parallel + COMM = MPI.COMM_WORLD + RANK = COMM.Get_rank() + SIZE = COMM.Get_size() + if SIZE > 1: + for arg in argv[4:]: + dim = int(arg) + if RANK == 0: print(f"{basis} dim {dim} running on {SIZE} processes") + COMM.barrier() + run_save_parallel(COMM,RANK,SIZE,dim,degrees[dim],basis,ver,N) + + # run on a single process + else: + for arg in argv[4:]: + dim = int(arg) + print(f"{basis} dim {dim} running on 1 process") + run_save(dim,degrees[dim],basis,ver,N) diff --git a/tests/random_tests.py b/tests/random_tests.py new file mode 100644 index 00000000..a0c9ca05 --- /dev/null +++ b/tests/random_tests.py @@ -0,0 +1,44 @@ +import numpy as np +from yroots.polynomial import MultiCheb, MultiPower + +filefmt="random_tests/coeffs/dim{dim}_deg{deg}_{kind}.npy" +def rand_coeffs(dim,deg,N,kind,maxint=10): + shape = (*(deg+1,)*dim,dim,N) + if kind == 'randint': + coeffs = np.random.randint(-maxint,maxint+1,size=shape) + elif kind == 'randn': + coeffs = np.random.randn(*shape) + # if pcnt_sparse is not None: + # #make sparse + # idx = np.random.choice(np.arange(coeffs.size),replace=False,size=int(coeffs.size * pcnt_sparse)) + # idx = np.unravel_index(idx,shape) + # coeffs[idx] = 0 + for idx in np.ndindex((deg+1,)*dim): + if np.sum(idx) > deg: + coeffs[idx] = 0 + return coeffs.T + +def gen_tests(dim,degrees,N,kind): + arrs = [] + for deg in degrees: + arrs.append(rand_coeffs(dim,deg,N,kind)) + return arrs + +def save_tests(dim,degrees,N,kind,filefmt=filefmt): + arrs = gen_tests(dim,degrees,N,kind) + for i,deg in enumerate(degrees): + np.save(filefmt.format(dim=dim,deg=deg,kind=kind),arrs[i]) + print(f"dim {dim}/deg {deg}: saved N={N} systems") + +def load_tests(dim,deg,basis,kind,N=None,filefmt=filefmt): + arr = np.load(filefmt.format(dim=dim,deg=deg,kind=kind)) + if basis == "power": MultiX = MultiPower + else: MultiX = MultiCheb + tests = [] + if N is None: N = arr.shape[0] + for test in arr[:N]: + polys = [] + for coeff in test: + polys.append(MultiX(coeff)) + tests.append(polys) + return tests diff --git a/tests/test_CHEBYSHEV/__init__.py b/tests/test_CHEBYSHEV/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/test_CHEBYSHEV/test_tvb.py b/tests/test_CHEBYSHEV/test_tvb.py deleted file mode 100644 index ad31e875..00000000 --- a/tests/test_CHEBYSHEV/test_tvb.py +++ /dev/null @@ -1,164 +0,0 @@ -import numpy as np -from CHEBYSHEV.TVB_Method.cheb_class import Polynomial, MultiCheb -from CHEBYSHEV.TVB_Method.TVB import find_degree, mon_combos, sorted_matrix_terms -from CHEBYSHEV.TVB_Method.root_finder import roots - -from itertools import product -import warnings - -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiCheb. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - return MultiCheb(ACoeff) - -def correctZeros(polys, checkNumber = True): - ''' - A helper function for test_TVB. Takes in polynomials, find their common zeros using TVB, and calculates - how many of the zeros are correct. - In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if - the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even - on bad random runs) - ''' - zeros = roots(polys) - assert(zeros != -1) - if checkNumber: - expectedNum = np.product([poly.degree for poly in polys]) - assert(len(zeros) == expectedNum) - correct = 0 - outOfRange = 0 - for zero in zeros: - good = True - for poly in polys: - if not np.isclose(0, poly(zero), atol = 1.e-3): - good = False - if (np.abs(zero) > 1).any(): - outOfRange += 1 - break - if good: - correct += 1 - assert(100*correct/(len(zeros)-outOfRange) > 95) - -def test_TVB_roots(): - ''' - The following tests will run TVB on relatively small random upper trianguler MultiCheb and MultiCheb polynomials. - The assert statements will be inside of the correctZeros helper function. - ''' - - #Case 1 - Two MultiCheb 2D degree 10 polynomials. - A = getPoly(10,2,False) - B = getPoly(10,2,False) - correctZeros([A,B]) - - #Case 2 - Three MultiCheb 3D degree 4 polynomials. - A = getPoly(4,3,False) - B = getPoly(4,3,False) - C = getPoly(4,3,False) - correctZeros([A,B,C]) - - #Case 3 - Four MultiCheb 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - correctZeros([A,B,C,D]) - - #Case 4 - Two MultiCheb 2D, one degree 5 and one degree 7 - A = getPoly(5,2,False) - B = getPoly(7,2,False) - correctZeros([A,B]) - - #Case 5 - Three MultiCheb 3D of degrees 3,4 and 5 - A = getPoly(3,3,False) - B = getPoly(4,3,False) - C = getPoly(5,3,False) - correctZeros([A,B,C]) - -def test_find_degree(): - #Test Case #1 - 2,3,4, and 5 2D Polynomials of degree 3 - - degree3Coeff = np.array([ - [1,1,1,1], - [1,1,1,0], - [1,1,0,0], - [1,0,0,0]]) - - A = MultiCheb(degree3Coeff) - B = MultiCheb(degree3Coeff) - C = MultiCheb(degree3Coeff) - D = MultiCheb(degree3Coeff) - E = MultiCheb(degree3Coeff) - assert(find_degree([A,B]) == 5) - assert(find_degree([A,B,C]) == 7) - assert(find_degree([A,B,C,D]) == 9) - assert(find_degree([A,B,C,D,E]) == 11) - - #Test Case #2 - A 2D polynomials of degree 3 and one of degree 5 - degree5Coeff = np.array([ - [1,1,1,1,1,1], - [1,1,1,1,1,0], - [1,1,1,1,0,0], - [1,1,1,0,0,0], - [1,1,0,0,0,0], - [1,0,0,0,0,0]]) - F = MultiCheb(degree5Coeff) - assert(find_degree([A,F]) == 7) - - #Test Case #3 - Two 3D polynomials of degree 15 - G = MultiCheb(np.random.rand(6,6,6)) - H = MultiCheb(np.random.rand(6,6,6)) - assert(find_degree([G,H]) == 29) - -def test_mon_combos(): - ''' - Tests the mon_combos function against the simpler itertools product. - ''' - #Test Case #1 - degree 5, dimension 2 - deg = 5 - dim = 2 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - - #Test Case #1 - degree 25, dimension 2 - deg = 25 - dim = 2 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - - #Test Case #1 - degree 5, dimension 3 - deg = 5 - dim = 3 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) - - #Test Case #1 - degree 5, dimension 5 - deg = 5 - dim = 5 - mons = mon_combos(np.zeros(dim, dtype = int),deg) - mons2 = list() - for i in product(np.arange(deg+1), repeat=dim): - if np.sum(i) <= deg: - mons2.append(i) - for i in range(len(mons)): - assert((mons[i] == mons2[i]).all()) diff --git a/tests/test_LinearProjection.py b/tests/test_LinearProjection.py new file mode 100644 index 00000000..884f990e --- /dev/null +++ b/tests/test_LinearProjection.py @@ -0,0 +1,104 @@ +import numpy as np +from yroots.LinearProjection import remove_linear, project_down, bounding_parallelepiped, proj_approximate_nd +from yroots.polynomial import Polynomial, MultiCheb, MultiPower, getPoly +from yroots.MacaulayReduce import find_degree, mon_combos +from yroots import polyroots as pr +from yroots.utils import InstabilityWarning, arrays +from yroots.Multiplication import create_matrix +from itertools import product +import unittest +import warnings +import yroots.subdivision as sbd + +def correctZeros(original_polys, new_polys, transform, MSmatrix): + ''' + A helper function for polyroots tests. Takes in polynomials, find their common zeros using polyroots, and calculates + how many of the zeros are correct. + In this function it asserts that the number of zeros is equal to the product of the degrees, which is only valid if + the polynomials are random and upper triangular, and that at least 95% of the zeros are correct (so it will pass even + on bad random runs) + ''' + zeros = transform(pr.solve(new_polys, MSmatrix = MSmatrix)) + correct = 0 + outOfRange = 0 + for zero in zeros: + good = True + for poly in original_polys: + if not np.isclose(0, poly(zero), atol = 1.e-3): + good = False + if (np.abs(zero) > 1).any(): + outOfRange += 1 + break + if good: + correct += 1 + assert(100*correct/(len(zeros)-outOfRange) > 95) + +def test_bounding_parallelepiped(): + num_test_cases = 10 + + np.random.seed(31) + A = getPoly(1, 2, True) + p0,edges = bounding_parallelepiped(A.coeff) + rand = np.random.rand(edges.shape[1], num_test_cases) + pts = np.dot(edges, rand).T + p0 + assert np.allclose(A(pts), 0) + + A = getPoly(1, 3, True) + p0,edges = bounding_parallelepiped(A.coeff) + rand = np.random.rand(edges.shape[1], num_test_cases) + pts = np.dot(edges, rand).T + p0 + assert np.allclose(A(pts), 0) + + A = getPoly(1, 6, True) + p0,edges = bounding_parallelepiped(A.coeff) + rand = np.random.rand(edges.shape[1], num_test_cases) + pts = np.dot(edges, rand).T + p0 + assert np.allclose(A(pts), 0) + +# def test_project_down(): +# num_test_cases = 10 + +# np.random.seed(821) +# linear = getPoly(1, 2, True) +# A = getPoly(3, 2, True) +# (A_prj,), T = project_down([A],linear.coeff, 1e-4, 1e-8) +# A_prj = MultiCheb(A_prj) +# pts = np.random.rand(num_test_cases, linear.dim-1) +# assert np.allclose(A(T(pts)), A_prj(pts)) + +# linear = getPoly(1, 3, False) +# A = getPoly(10, 3, False) +# B = getPoly(10, 3, False) +# (A_prj, B_prj), T = project_down([A,B],linear.coeff, 1e-4, 1e-8) +# A_prj, B_prj = map(MultiCheb, (A_prj, B_prj)) +# pts = np.random.rand(num_test_cases, linear.dim-1) +# assert np.allclose(A(T(pts)), A_prj(pts)) +# assert np.allclose(B(T(pts)), B_prj(pts)) + +# linear = getPoly(1, 5, True) +# A = getPoly(3, 5, True) +# B = getPoly(3, 5, True) +# C = getPoly(3, 5, True) +# D = getPoly(3, 5, True) +# (A_prj, B_prj, C_prj, D_prj), T = project_down([A,B,C,D],linear.coeff, 1e-4, 1e-8) +# A_prj, B_prj, C_prj, D_prj = map(MultiCheb, (A_prj, B_prj, C_prj, D_prj)) +# pts = np.random.rand(num_test_cases, linear.dim-1) +# assert np.allclose(A(T(pts)), A_prj(pts)) +# assert np.allclose(B(T(pts)), B_prj(pts)) +# assert np.allclose(C(T(pts)), C_prj(pts)) +# assert np.allclose(D(T(pts)), D_prj(pts)) + +# def test_remove_linear(): +# linear = getPoly(1, 3, False) +# A = getPoly(4, 3, False) +# B = getPoly(4, 3, False) +# (A_prj, B_prj), T, is_projected = remove_linear([A, B, linear], 1e-4, 1e-8) +# assert is_projected == True +# correctZeros([A, B], [A_prj, B_prj], T, 0) +# correctZeros([A, B], [A_prj, B_prj], T, -1) + + +if __name__ == "__main__": + # test_bounding_parallelepiped() + # test_project_down() + test_remove_linear() diff --git a/tests/test_OneDimension.py b/tests/test_OneDimension.py index df8cc919..3deac9b6 100644 --- a/tests/test_OneDimension.py +++ b/tests/test_OneDimension.py @@ -1,6 +1,6 @@ import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.OneDimension import solve +from yroots.polynomial import Polynomial, MultiCheb, MultiPower +from yroots.OneDimension import solve def getPoly(deg, power): ''' @@ -43,19 +43,16 @@ def test_OneD_power_eigenvalues(): #Case 1 - MultiPower degree 10 poly = getPoly(10,True) - correctZeros(poly, 1) correctZeros(poly, 0) correctZeros(poly, -1) #Case 2 - MultiPower degree 50 poly = getPoly(50,True) - correctZeros(poly, 1) correctZeros(poly, 0) correctZeros(poly, -1) #Case 3 - MultiPower degree 100 poly = getPoly(100,True) - correctZeros(poly, 1) correctZeros(poly, 0) correctZeros(poly, -1) @@ -69,19 +66,16 @@ def test_OneD_power_eigenvectors(): #Case 1 - MultiPower degree 10 poly = getPoly(10,True) - correctZeros(poly, 1, eigvals=False) correctZeros(poly, 0, eigvals=False) correctZeros(poly, -1, eigvals=False) #Case 2 - MultiPower degree 50 poly = getPoly(50,True) - correctZeros(poly, 1, eigvals=False) correctZeros(poly, 0, eigvals=False) correctZeros(poly, -1, eigvals=False) #Case 3 - MultiPower degree 100 poly = getPoly(100,True) - correctZeros(poly, 1, eigvals=False) correctZeros(poly, 0, eigvals=False) correctZeros(poly, -1, eigvals=False) @@ -95,19 +89,16 @@ def test_OneD_cheb_eigenvalues(): #Case 1 - MultiCheb degree 10 poly = getPoly(10,False) - correctZeros(poly, 1) correctZeros(poly, 0) correctZeros(poly, -1) #Case 2 - MultiCheb degree 50 poly = getPoly(50,False) - correctZeros(poly, 1) correctZeros(poly, 0) correctZeros(poly, -1) #Case 3 - MultiCheb degree 100 poly = getPoly(100,False) - correctZeros(poly, 1) correctZeros(poly, 0) correctZeros(poly, -1) @@ -121,18 +112,15 @@ def test_OneD_cheb_eigenvectors(): #Case 1 - MultiCheb degree 10 poly = getPoly(10,False) - correctZeros(poly, 1, eigvals=False) correctZeros(poly, 0, eigvals=False) correctZeros(poly, -1, eigvals=False) #Case 2 - MultiCheb degree 50 poly = getPoly(50,False) - correctZeros(poly, 1, eigvals=False) correctZeros(poly, 0, eigvals=False) correctZeros(poly, -1, eigvals=False) #Case 3 - MultiCheb degree 100 poly = getPoly(100,False) - correctZeros(poly, 1, eigvals=False) correctZeros(poly, 0, eigvals=False) correctZeros(poly, -1, eigvals=False) diff --git a/tests/test_ProjectiveSpace.py b/tests/test_ProjectiveSpace.py index bbb01a97..50cf5dd0 100644 --- a/tests/test_ProjectiveSpace.py +++ b/tests/test_ProjectiveSpace.py @@ -1,5 +1,5 @@ -from numalgsolve.ProjectiveSpace import * -from numalgsolve.polynomial import MultiCheb, MultiPower +from yroots.ProjectiveSpace import * +from yroots.polynomial import MultiCheb, MultiPower import unittest #@unittest.skip("Projective Space is still a work in progress") diff --git a/tests/test_cheb.py b/tests/test_cheb.py index 60ec4c51..42512487 100644 --- a/tests/test_cheb.py +++ b/tests/test_cheb.py @@ -1,5 +1,5 @@ import numpy as np -from numalgsolve.polynomial import MultiCheb, MultiPower, poly2cheb, cheb2poly +from yroots.polynomial import MultiCheb, MultiPower, poly2cheb, cheb2poly import pytest import pdb diff --git a/tests/test_convert.py b/tests/test_convert.py index 8d548125..87cf35f2 100644 --- a/tests/test_convert.py +++ b/tests/test_convert.py @@ -1,6 +1,6 @@ import numpy as np import os, sys -from numalgsolve.polynomial import MultiCheb, MultiPower, cheb2poly, poly2cheb +from yroots.polynomial import MultiCheb, MultiPower, cheb2poly, poly2cheb import pytest import pdb import time diff --git a/tests/test_polyroots.py b/tests/test_polyroots.py index 7cd90034..2bc782c9 100644 --- a/tests/test_polyroots.py +++ b/tests/test_polyroots.py @@ -1,12 +1,13 @@ import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve.MacaulayReduce import find_degree, mon_combos -from numalgsolve import polyroots as pr -from numalgsolve.utils import InstabilityWarning, arrays -from numalgsolve.Multiplication import create_matrix +from yroots.polynomial import Polynomial, MultiCheb, MultiPower, getPoly +from yroots.MacaulayReduce import find_degree, mon_combos +from yroots import polyroots as pr +from yroots.utils import InstabilityWarning, arrays +from yroots.Multiplication import create_matrix from itertools import product import unittest import warnings +import yroots.subdivision as sbd def test_paper_example(): @@ -76,23 +77,6 @@ def test_paper_example(): assert np.isclose(0, c1(root), atol = 1.e-8) assert np.isclose(0, c2(root), atol = 1.e-8) -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial - of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be - MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - if power: - return MultiPower(ACoeff) - else: - return MultiCheb(ACoeff) - def correctZeros(polys, MSmatrix): ''' A helper function for polyroots tests. Takes in polynomials, find their common zeros using polyroots, and calculates @@ -162,6 +146,13 @@ def test_power_roots_mult(): correctZeros([A,B,C], 2) correctZeros([A,B,C], 3) + # Case 6 - Two MultiPower 2D degree 1 polynomials. + A = getPoly(1, 2, True) + B = getPoly(1, 2, True) + correctZeros([A, B], 1) + correctZeros([A, B], 2) + correctZeros([A, B], 3) + def test_cheb_roots_mult(): ''' The following tests will run polyroots on relatively small random upper trianguler MultiCheb. @@ -207,81 +198,14 @@ def test_cheb_roots_mult(): correctZeros([A,B,C], 1) correctZeros([A,B,C], 2) correctZeros([A,B,C], 3) -""" -def test_power_roots_multR(): - ''' - The following tests will run polyroots on relatively small random upper trianguler MultiPower. - The assert statements will be inside of the correctZeros helper function. - ''' - - np.random.seed(423) - - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,True) - B = getPoly(10,2,True) - correctZeros([A,B], 'multR') - - #Case 2 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,True) - B = getPoly(4,3,True) - C = getPoly(4,3,True) - correctZeros([A,B,C], 'multR') - - #Case 3 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(2,4,True) - correctZeros([A,B,C,D], 'multR') - - #Case 4 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,True) - B = getPoly(7,2,True) - correctZeros([A,B], 'multR') - - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,True) - B = getPoly(4,3,True) - C = getPoly(5,3,True) - correctZeros([A,B,C], 'multR') - -def test_cheb_roots_multR(): - ''' - The following tests will run polyroots on relatively small random upper trianguler MultiCheb. - The assert statements will be inside of the correctZeros helper function. - ''' - - np.random.seed(59) - - #Case 1 - Two MultiCheb 2D degree 10 polynomials. - A = getPoly(10,2,False) - B = getPoly(10,2,False) - correctZeros([A,B], 'multR') - - #Case 2 - Three MultiCheb 3D degree 4 polynomials. - A = getPoly(4,3,False) - B = getPoly(4,3,False) - C = getPoly(4,3,False) - correctZeros([A,B,C], 'multR') - #Case 3 - Four MultiCheb 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - correctZeros([A,B,C,D], 'multR') + # Case 6 - Two MultiCheb 2D degree 1 polynomials. + A = getPoly(1, 2, False) + B = getPoly(1, 2, False) + correctZeros([A, B], 1) + correctZeros([A, B], 2) + correctZeros([A, B], 3) - #Case 4 - Two MultiCheb 2D, one degree 5 and one degree 7 - A = getPoly(5,2,False) - B = getPoly(7,2,False) - correctZeros([A,B], 'multR') - - #Case 5 - Three MultiCheb 3D of degrees 3,4 and 5 - A = getPoly(3,3,False) - B = getPoly(4,3,False) - C = getPoly(5,3,False) - correctZeros([A,B,C], 'multR') -""" def test_power_roots_multrand(): ''' The following tests will run polyroots on relatively small random upper trianguler MultiPower. @@ -325,7 +249,7 @@ def test_cheb_roots_multrand(): The assert statements will be inside of the correctZeros helper function. ''' - np.random.seed(59) + np.random.seed(590) #Case 1 - Two MultiCheb 2D degree 10 polynomials. A = getPoly(10,2,False) @@ -424,138 +348,5 @@ def test_div_cheb_roots(): C = getPoly(5,3,False) correctZeros([A,B,C], -1) -@unittest.skip("This is an unfinished test") -def test_qr(): - """Tests BYU-style qr reduction. Specifically, makes sure that QR reduction - doesn't accidentally eliminate information in some rows, i.e. if the bottom - left block isn't originally all zeros. - """ - - #Power/Mult - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,True) - B = getPoly(10,2,True) - d = find_degree([A,B]) - matrix, matrix_terms, cuts = create_matrix([A.coeff, B.coeff], d, 2) - if np.allclose(matrix[cuts[0]:,:cuts[0]], 0): - reduced_matrix, matrix_terms = rrqr_reduceMacaulay2(matrix, matrix_terms, cuts, number_of_roots, accuracy = accuracy) - else: - reduced_matrix, matrix_terms = rrqr_reduceMacaulay(matrix, matrix_terms, cuts, number_of_roots, accuracy = accuracy) - assert np.allclose(reduced_matrix[cuts[0]:,:cuts[0]], 0) - assert np.allclose(reduced_matrix[cuts[0]:,:cuts[0]], 0) - #assert reduced_matrix = - - """ - #Case 2 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,True) - B = getPoly(4,3,True) - C = getPoly(4,3,True) - - #Case 3 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(2,4,True) - MacaulayReduce.createMatrixFast([A,B,C,D], ) - - #Case 4 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,True) - B = getPoly(7,2,True) - MacaulayReduce.createMatrixFast([A,B], ) - - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,True) - B = getPoly(4,3,True) - C = getPoly(5,3,True) - MacaulayReduce.createMatrixFast([A,B,C], ) - - #Cheb/Mult - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,False) - B = getPoly(10,2,False) - MacaulayReduce.construction([A,B], ) - - #Case 2 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,False) - B = getPoly(4,3,False) - C = getPoly(4,3,False) - MacaulayReduce.construction([A,B,C], ) - - #Case 3 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - MacaulayReduce.construction([A,B,C,D], ) - - #Case 4 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,False) - B = getPoly(7,2,False) - MacaulayReduce.construction([A,B], ) - - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,False) - B = getPoly(4,3,False) - C = getPoly(5,3,False) - MacaulayReduce.construction([A,B,C], ) - - #Power/Div - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,True) - B = getPoly(10,2,True) - MacaulayReduce.createMatrixFast([A,B], ) - - #Case 2 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,True) - B = getPoly(4,3,True) - C = getPoly(4,3,True) - MacaulayReduce.createMatrixFast([A,B,C], ) - - #Case 3 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,True) - B = getPoly(2,4,True) - C = getPoly(2,4,True) - D = getPoly(2,4,True) - MacaulayReduce.createMatrixFast([A,B,C,D], ) - - #Case 4 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,True) - B = getPoly(7,2,True) - MacaulayReduce.createMatrixFast([A,B], ) - - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,True) - B = getPoly(4,3,True) - C = getPoly(5,3,True) - MacaulayReduce.createMatrixFast([A,B,C], ) - - #Cheb/Div - #Case 1 - Two MultiPower 2D degree 10 polynomials. - A = getPoly(10,2,False) - B = getPoly(10,2,False) - MacaulayReduce.createMatrixFast([A,B], ) - - #Case 2 - Three MultiPower 3D degree 4 polynomials. - A = getPoly(4,3,False) - B = getPoly(4,3,False) - C = getPoly(4,3,False) - MacaulayReduce.createMatrixFast([A,B,C], ) - - #Case 3 - Four MultiPower 4D degree 2 polynomials. - A = getPoly(2,4,False) - B = getPoly(2,4,False) - C = getPoly(2,4,False) - D = getPoly(2,4,False) - MacaulayReduce.createMatrixFast([A,B,C,D], ) - - #Case 4 - Two MultiPower 2D, one degree 5 and one degree 7 - A = getPoly(5,2,False) - B = getPoly(7,2,False) - MacaulayReduce.createMatrixFast([A,B], ) - - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - A = getPoly(3,3,False) - B = getPoly(4,3,False) - C = getPoly(5,3,False) - MacaulayReduce.createMatrixFast([A,B,C], ) - """ +if __name__ == "__main__": + test_div_power_roots() diff --git a/tests/test_power.py b/tests/test_power.py index 373b4429..66611772 100644 --- a/tests/test_power.py +++ b/tests/test_power.py @@ -1,8 +1,8 @@ import numpy as np import os,sys -from numalgsolve.polynomial import MultiPower +from yroots.polynomial import MultiPower import pytest -from numalgsolve import utils +from yroots import utils def test_add(): diff --git a/tests/test_subdivision.py b/tests/test_subdivision.py index a7c5f354..860a11ad 100644 --- a/tests/test_subdivision.py +++ b/tests/test_subdivision.py @@ -1,24 +1,9 @@ import unittest import numpy as np -from numalgsolve.polynomial import Polynomial, MultiCheb, MultiPower -from numalgsolve import subdivision as subdiv +from yroots.polynomial import Polynomial, MultiCheb, MultiPower, getPoly +from yroots import subdivision as subdiv from itertools import product -def getPoly(deg,dim,power): - ''' - A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. - power is a boolean indicating whether or not the polynomial should be MultiPower. - ''' - deg += 1 - ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) - for i,j in np.ndenumerate(ACoeff): - if np.sum(i) >= deg: - ACoeff[i] = 0 - if power: - return MultiPower(ACoeff) - else: - return MultiCheb(ACoeff) - def correctZeros(polys, a, b): ''' A helper function for test_subdivision_solve. Takes in polynomials, find their common zeros using subdivision, and calculates @@ -42,24 +27,23 @@ def correctZeros(polys, a, b): if len(zeros) == outOfRange: raise Exception("No zeros found") else: - assert(100*correct/(len(zeros)-outOfRange) > 95) + assert(100*correct/(len(zeros)-outOfRange) > 95),(zeros) def test_subdivision_solve_polys(): ''' The following tests will run subdivision.solve on relatively small random upper trianguler MultiPower. The assert statements will be inside of the correctZeros helper function. - The fit occurs on [-1,1]X[-1,1]X..., so no transform is needed. ''' #Case 1 - Two MultiPower 2D degree 10 polynomials. #choose a seed that has a zero like 1,3,7,8,12,20,21,22,22,27,38,41,42,43,46,51,54,55,57,60,65,67,68,69,73,74,78,80,81,84,86,90,95 - np.random.seed(1) + np.random.seed(3) a = -np.ones(2);b = np.ones(2) A = getPoly(10,2,True) B = getPoly(10,2,True) correctZeros([A,B], a, b) #Case 2 - Three MultiPower 3D degree 4 polynomials. - #choose a seed that has a zero like 1,23,27,29,39,43,44,46,51,53,54,68,71,72,93 + # #choose a seed that has a zero like 1,23,27,29,39,43,44,46,51,53,54,68,71,72,93 np.random.seed(1) a = -np.ones(3);b = np.ones(3) A = getPoly(4,3,True) @@ -68,8 +52,8 @@ def test_subdivision_solve_polys(): correctZeros([A,B,C], a, b) #Case 3 - Four MultiPower 4D degree 2 polynomials. - #choose a seed that has a zero like 21,43,65,72,83 - np.random.seed(21) + #choose a seed that has a zero like 2 + np.random.seed(2) a = -np.ones(4);b = np.ones(4) A = getPoly(2,4,True) B = getPoly(2,4,True) @@ -79,11 +63,11 @@ def test_subdivision_solve_polys(): #Case 4 - Two MultiPower 2D, one degree 20 and one degree 28 #choose a seed that has a zero like 0,1,2,3,4,5,6,7,8,9,10 - # np.random.seed(0) - # a = -np.ones(2);b = np.ones(2) - # A = getPoly(20,2,True) - # B = getPoly(28,2,True) - # correctZeros([A,B], a, b) + np.random.seed(0) + a = -np.ones(2);b = np.ones(2) + A = getPoly(20,2,True) + B = getPoly(28,2,True) + correctZeros([A,B], a, b) #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 #choose a seed that has a zero like 1,3,5,11,13,16,24,28,31,32,33,41,42 @@ -112,14 +96,13 @@ def test_subdivision_sine(): (3,0), (3,1), (3,2), (3,3), ''' - f = lambda x: np.sin(np.pi*x[:,1]) - g = lambda x: np.sin(np.pi*(x[:,0]+x[:,1])) + f = lambda x,y: np.sin(np.pi*y) + g = lambda x,y: np.sin(np.pi*(x+y)) a = -0.511*np.ones(2) b = 3.511*np.ones(2) zeros = subdiv.solve([f, g], a, b) zeros = np.array(sorted(list(zeros), key=lambda x: 10*x[0] + x[1])) - assert len(zeros) == 16 X,Y = np.meshgrid(range(4),range(4),indexing='ij') @@ -142,12 +125,12 @@ def test_subdivision_solve_with_transform(): #Case 2 - Three MultiPower 3D degree 4 polynomials. #choose a seed that has a zero like 1,23,27,29,39,43,44,46,51,53,54,68,71,72,93 - np.random.seed(1) - a = -2*np.ones(3);b = 2*np.ones(3) - A = getPoly(4,3,True) - B = getPoly(4,3,True) - C = getPoly(4,3,True) - correctZeros([A,B,C], a, b) + # np.random.seed(1) + # a = -2*np.ones(3);b = 2*np.ones(3) + # A = getPoly(4,3,True) + # B = getPoly(4,3,True) + # C = getPoly(4,3,True) + # correctZeros([A,B,C], a, b) #Case 3 - Four MultiPower 4D degree 2 polynomials. #choose a seed that has a zero like 21,43,65,72,83 @@ -161,27 +144,28 @@ def test_subdivision_solve_with_transform(): #Case 4 - Two MultiPower 2D, one degree 20 and one degree 28 #choose a seed that has a zero like 0,1,2,3,4,5,6,7,8,9,10 - np.random.seed(0) + # This test slows down with tighter tolerances. + np.random.seed(1) a = -2*np.ones(2);b = 2*np.ones(2) A = getPoly(20,2,True) B = getPoly(28,2,True) correctZeros([A,B], a, b) # This case works, but it's really slow - #Case 5 - Three MultiPower 3D of degrees 3,4 and 5 - #choose a seed that has a zero like 1,3,5,11,13,16,24,28,31,32,33,41,42 - # np.random.seed(1) - # a = -2*np.ones(3);b = 2*np.ones(3) - # A = getPoly(3,3,True) - # B = getPoly(4,3,True) - # C = getPoly(5,3,True) - # correctZeros([A,B,C], a, b) + # Case 5 - Three MultiPower 3D of degrees 3,4 and 5 + # choose a seed that has a zero like 1,3,5,11,13,16,24,28,31,32,33,41,42 + np.random.seed(1) + a = -2*np.ones(3);b = 2*np.ones(3) + A = getPoly(3,3,True) + B = getPoly(4,3,True) + C = getPoly(5,3,True) + correctZeros([A,B,C], a, b) def test_subdivision_solve_with_transform_1d(): - #Case 6 - One MultiPower 1D of degrees 10 + #Case 6 - One MultiCheb 1D of degree 10 #choose a seed that has a zero like ? - np.random.seed(2) - a = -2*np.ones(1);b = 2*np.ones(1) + np.random.seed(1) + a = -np.ones(1);b = np.ones(1) A = getPoly(20,1,False) correctZeros([A], a, b) @@ -190,6 +174,8 @@ def test_good_zeros_nd(): The good zeros function should remove zeros with imaginary part or outside the range [-1,1]X[-1,1]X... ''' + imag_tol = 1.e-5 + real_tol = 1.e-5 zeros = np.array([ [0.9+0j, 0.9+0j], #good @@ -197,8 +183,7 @@ def test_good_zeros_nd(): [-1.1+0j, 0.1+0j], #out of range [0.1+0.1j, 0.1+0j], #imaginary ]) - - assert np.all(subdiv.good_zeros_nd(zeros) == zeros[:2]) + assert np.all(subdiv.good_zeros_nd(zeros, imag_tol=imag_tol,real_tol=real_tol) == zeros[:2].real) zeros = np.array([ [0.9+0j, 0.9+0j, -0.1+0j], #good @@ -207,7 +192,7 @@ def test_good_zeros_nd(): [0.1+0.1j, 0.1+0j, 0.8-0.1j], #imaginary ]) - assert np.all(subdiv.good_zeros_nd(zeros) == zeros[:2]) + assert np.all(subdiv.good_zeros_nd(zeros,imag_tol=imag_tol,real_tol=real_tol) == zeros[:2].real) def test_copy_block(): np.random.seed(0) @@ -248,6 +233,3 @@ def test_copy_block(): idx2 = idx.copy() idx2[i] = slice(2*deg-1,deg,-1) assert np.all(values[tuple(idx1)] == values[tuple(idx2)]) - -if __name__ == "__main__": - test_subdivision_solve_with_transform() diff --git a/tests/test_utils.py b/tests/test_utils.py index 8632a863..473fb122 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,9 +1,9 @@ import pytest import numpy as np import sympy as sy -from numalgsolve import utils as ut -from numalgsolve.utils import * -from numalgsolve.polynomial import MultiCheb, MultiPower +from yroots import utils as ut +from yroots.utils import * +from yroots.polynomial import MultiCheb, MultiPower from scipy.linalg import qr, solve_triangular from itertools import product diff --git a/tests/test_zero_checks.py b/tests/test_zero_checks.py index b6347bb2..39eede5a 100644 --- a/tests/test_zero_checks.py +++ b/tests/test_zero_checks.py @@ -1,66 +1,183 @@ import numpy as np -from numalgsolve.subdivision import constant_term_check, full_quad_check, full_cubic_check, curvature_check, \ -linear_check, quadratic_check1, quadratic_check2, quadratic_check3 -from numalgsolve.polynomial import MultiCheb,MultiPower +from yroots.IntervalChecks import constant_term_check, quadratic_check, IntervalData +from yroots.old_code.OldIntervalChecks import full_quad_check, full_cubic_check, curvature_check, linear_check +from yroots.polynomial import MultiCheb,MultiPower +import itertools +from scipy import linalg as la + +macheps = 2.220446049250313e-16 + +def base_quadratic_check(test_coeff,tol): + """Slow nd-quadratic check to test against. """ + #get the dimension and make sure the coeff tensor has all the right + # quadratic coeff spots, set to zero if necessary + dim = test_coeff.ndim + interval_data = IntervalData(-np.ones(dim), np.ones(dim), []) + intervals = interval_data.get_subintervals(interval_data.a, interval_data.b, [], tol, False) + padding = [(0,max(0,3-i)) for i in test_coeff.shape] + test_coeff = np.pad(test_coeff.copy(), padding, mode='constant') + + #Possible extrema of qudaratic part are where D_xk = 0 for some subset of the variables xk + # with the other variables are fixed to a boundary value + #Dxk = c[0,...,0,1,0,...0] (k-spot is 1) + 4c[0,...,0,2,0,...0] xk (k-spot is 2) + # + \Sum_{j\neq k} xj c[0,...,0,1,0,...,0,1,0,...0] (k and j spot are 1) + #This gives a symmetric system of equations AX+B = 0 + #We will fix different columns of X each time, resulting in slightly different + #systems, but storing A and B now will be helpful later + + #pull out coefficients we care about + quad_coeff = np.zeros([3]*dim) + A = np.zeros([dim,dim]) + B = np.zeros(dim) + for spot in itertools.product(np.arange(3),repeat=dim): + if np.sum(spot) < 3: + spot_array = np.array(spot) + if np.sum(spot_array != 0) == 2: + #coef of cross terms + i,j = np.where(spot_array != 0)[0] + A[i,j] = test_coeff[spot].copy() + A[j,i] = test_coeff[spot].copy() + elif np.any(spot_array == 2): + #coef of pure quadratic terms + i = np.where(spot_array != 0)[0][0] + A[i,i] = 4*test_coeff[spot].copy() + elif np.any(spot_array == 1): + #coef of linear terms + i = np.where(spot_array != 0)[0][0] + B[i] = test_coeff[spot].copy() + quad_coeff[spot] = test_coeff[spot] + test_coeff[spot] = 0 + + #create a poly object for evaluations + quad_poly = MultiCheb(quad_coeff) + + #The sum of the absolute values of everything else + other_sum = np.sum(np.abs(test_coeff)) + + def powerset(iterable): + "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)" + s = list(iterable) + return itertools.chain.from_iterable(itertools.combinations(s, r)\ + for r in range(len(s)+1)) + mask = [] + for interval_num, interval in enumerate(intervals): + + extreme_points = [] + for fixed in powerset(np.arange(dim)): + fixed = np.array(fixed) + if len(fixed) == 0: + #fix no vars--> interior + if np.linalg.matrix_rank(A) < A.shape[0]: + #no interior critical point + continue + X = la.solve(A, -B, assume_a='sym') + #make sure it's in the domain + if np.all([interval[0][i] <= X[i] <= interval[1][i] for i in range(dim)]): + extreme_points.append(quad_poly(X)) + elif len(fixed) == dim: + #fix all variables--> corners + for corner in itertools.product([0,1],repeat=dim): + #j picks if upper/lower bound. i is which var + extreme_points.append(quad_poly([interval[j][i] for i,j in enumerate(corner)])) + else: + #fixed some variables --> "sides" + #we only care about the equations from the unfixed variables + unfixed = np.delete(np.arange(dim), fixed) + A_ = A[unfixed][:,unfixed] + if np.linalg.matrix_rank(A_) < A_.shape[0]: + #no solutions + continue + fixed_A = A[unfixed][:,fixed] + B_ = B[unfixed] + + for side in itertools.product([0,1],repeat=len(fixed)): + X0 = np.array([interval[j][i] for i,j in enumerate(side)]) + X_ = la.solve(A_, -B_-fixed_A@X0, assume_a='sym') + X = np.zeros(dim) + X[fixed] = X0 + X[unfixed] = X_ + if np.all([interval[0][i] <= X[i] <= interval[1][i] for i in range(dim)]): + extreme_points.append(quad_poly(X)) + + #No root if min(extreme_points) > (other_sum + tol) + # OR max(extreme_points) < -(other_sum+tol) + #Logical negation gives the boolean we want + mask.append(np.min(extreme_points) < (other_sum + tol) + and np.max(extreme_points) > -(other_sum+tol)) + + return mask def test_zero_check2D(): - interval_checks = [constant_term_check,full_quad_check, curvature_check] #full_cubic_check - subinterval_checks = [linear_check,quadratic_check1,quadratic_check2,quadratic_check3] + #curvature_check was causing import errors so it's not included... + interval_checks = [constant_term_check,full_quad_check, full_cubic_check] a = -np.ones(2) b = np.ones(2) - interval_checks.extend([lambda x: f(x, [(a,b)])[0] for f in subinterval_checks] ) + interval_data = IntervalData(a, b, []) + tol = 1.e-4 + interval_checks.extend([lambda x, tol: ~quadratic_check(x, interval_data.mask, tol, interval_data.RAND, np.array([(a, b)] * 4))[0][0]]) + + test_cases =[ + np.array([ + [ 100, 2, -2, -1], + [ 2, 2, 1, 0], + [-1, 1, 0, 0], + [ 1, 0, 0, 0] + ]), + np.array([ + [ 6.5, 2, -2, -1], + [ 2, 2, 1, 0], + [-1, 1, 0, 0], + [ 1, 0, 0, 0] + ]), + np.array([ + [1.019, .2, .5], + [0.2, .001, 0], + [0.5, 0, 0], + ]), + np.array([ + [-1.3, .2, 0.5], + [.2, .001, 0], + [.5, 0, 0], + ]), + np.array([ + [-1.6, .2, .5, .1], + [.2, .001, .1, 0], + [0.5, .1, 0, 0], + [0.1, 0, 0, 0] + ])] + correct_results = [False,True,True,True,True,True] for method in interval_checks: - # this function barely does not have a zero - # may return true or false - c = np.array([ - [ 7.5, 2, -2, -1], - [ 2, 2, 1, 0], - [-1, 1, 0, 0], - [ 1, 0, 0, 0] - ]) - # assert can_eliminate(c, a, b) is not True - - # this function obviously does not have a zero - # must return false - c = np.array([ - [ 100, 2, -2, -1], - [ 2, 2, 1, 0], - [-1, 1, 0, 0], - [ 1, 0, 0, 0] - ]) - assert method(c) == False - - # has zeros, must return true - c = np.array([ - [ 6.5, 2, -2, -1], - [ 2, 2, 1, 0], - [-1, 1, 0, 0], - [ 1, 0, 0, 0] - ]) - assert method(c) == True - - # has zeros, must return true - c = np.array([ - [1.019, .2, .5], - [0.2, .001, 0], - [0.5, 0, 0], - ]) - assert method(c) == True - - # has zeros, must return true - c = np.array([ - [-1.3, .2, 0.5], - [.2, .001, 0], - [.5, 0, 0], - ]) - assert method(c) == True - - # has zeros, must return true - c = np.array([ - [-1.6, .2, .5, .1], - [.2, .001, .1, 0], - [0.5, .1, 0, 0], - [0.1, 0, 0, 0] - ]) - assert method(c) == True + for res,c in zip(correct_results,test_cases): + assert res == method(c,tol) + +def test_quadratic_check(): + #keep this updated with the deg_dim used in subdivision solve + deg_dim = {1: 100, 2:20, 3:9, 4:9} + num_tests_per_dim = 100 + tests_per_batch = num_tests_per_dim//2 + tol = 1.e-4 + for dim in deg_dim.keys(): + print(dim) + deg = deg_dim[dim] + interval_data = IntervalData(-np.ones(dim), np.ones(dim), []) + subintervals = interval_data.get_subintervals(interval_data.a, interval_data.b, [], tol, False) + _quadratic_check = lambda c, tol: ~quadratic_check(c, interval_data.mask, tol, interval_data.RAND, subintervals) + np.random.seed(42) + rand_test_cases = np.random.rand(*[tests_per_batch]+[deg]*dim)*2-1 + randn_test_cases = np.random.randn(*[tests_per_batch]+[deg]*dim) + for c in rand_test_cases: + assert np.allclose(base_quadratic_check(c,tol), _quadratic_check(c,tol).flatten()) + for c in randn_test_cases: + assert np.allclose(base_quadratic_check(c,tol), _quadratic_check(c,tol).flatten()) + +def test_quadratic_check3D(): + #test 1 + a = np.array([-2.78150902e-05, -2.78150902e-05, -2.78150902e-05]) + b = np.array([4.19383023e-05, 4.19383023e-05, 4.19383023e-05]) + tol = macheps + + +if __name__ == "__main__": + test_zero_check2D() + test_quadratic_check() diff --git a/tox.ini b/tox.ini index 82cb5fb8..d240a9e1 100644 --- a/tox.ini +++ b/tox.ini @@ -21,7 +21,5 @@ deps = ; -r{toxinidir}/requirements.txt commands = pip install -U pip - py.test --cov numalgsolve --cov CHEBYSHEV --cov tests + py.test --cov yroots --cov tests ; codecov --token=a3abdaf4-6e48-4c09-af4c-818dd740ed31 - - diff --git a/yroots/ChebyshevSubdivisionSolver.py b/yroots/ChebyshevSubdivisionSolver.py new file mode 100644 index 00000000..956a7c48 --- /dev/null +++ b/yroots/ChebyshevSubdivisionSolver.py @@ -0,0 +1,1264 @@ +import numpy as np +from numba import njit, float64 +from numba.types import UniTuple +from itertools import product +from scipy.spatial import HalfspaceIntersection +from scipy.optimize import linprog + +# Code for testing. TODO: Set up unit tests and add this to it! +from mpmath import mp +from itertools import permutations, product + +def sortRoots(roots, seed = 12398): + if len(roots) == 0: + return roots + np.random.seed(seed) + dim = roots.shape[1] + r = np.array(np.random.rand(dim)) + order = np.argsort(roots@r) + return roots[order] + +def runSystem(degList): + #Each row of degList is the degrees of 1 polynomial + degList = np.array(degList) + dim = len(degList) + #Get the actual roots + mp.dps = 50 + actualRoots = [] + for i in permutations(range(dim)): + currDegs = np.array([degList[i[j],j] for j in range(dim)]) + currRootList = [] + for deg in currDegs: + d = int(deg) + theseRoots = [float(mp.cos(mp.pi*(mp.mpf(num)+0.5)/mp.mpf(d))) for num in mp.arange(d)] + currRootList.append(np.array(theseRoots)) + for root in product(*currRootList): + actualRoots.append(np.array(root)) + actualRoots = sortRoots(np.array(actualRoots)) + #Construct the problem + Ms = [] + for degs in degList: + M = np.zeros(degs+1) + M[tuple(degs)] = 1.0 + Ms.append(M) + errors = np.zeros(dim) + #Solve + foundRoots = solveChebyshevSubdivision(Ms, errors) + return sortRoots(np.array(foundRoots)), actualRoots + +def isGoodSystem(degList): + zeros = [[float(mp.cos(mp.pi*(num+0.5)/d)) for num in mp.arange(d)] for d in degList] + zeros = np.sort(np.hstack(zeros).ravel()) + return len(zeros) <= 1 or np.min(np.diff(zeros)) > 1e-12 + +def getTestSystems(dim, maxDeg): + systems = [] + for degrees in product(range(1, maxDeg+1), repeat=dim): + if isGoodSystem(degrees): + systems.append(degrees) + return systems + +def runChebMonomialsTests(dims, maxDegs, verboseLevel = 0, returnErrors = False): + allErrors = [] + for dim, maxDeg in zip(dims, maxDegs): + currErrors = [] + if verboseLevel > 0: + print(f"Running Cheb Monomial Test Dimension: {dim}, Max Degree: {maxDeg}") + testSytems = getTestSystems(dim, maxDeg) + numTests = len(testSytems)**dim + count = 0 + for degrees in product(testSytems, repeat = dim): + count += 1 + polyDegs = np.array(degrees).T + if verboseLevel > 1: + print(f"Running Cheb Monomial Test {count}/{numTests} Degrees: {polyDegs}") + errorString = "Test on degrees: " + str(polyDegs) + foundRoots, actualRoots = runSystem(polyDegs) + assert(len(foundRoots) == len(actualRoots)), "Wrong Number of Roots! " + errorString + maxError = np.max(np.abs(foundRoots - actualRoots)) + if returnErrors: + currErrors.append(np.linalg.norm(foundRoots - actualRoots, axis=1)) + assert(maxError < 1e-15), "Error Too Large! " + errorString + if returnErrors: + allErrors.append(np.hstack(currErrors)) + if returnErrors: + return allErrors + +#The actual Code + +@njit +def TransformChebInPlace1D(coeffs, alpha, beta): + """Applies the transformation alpha*x + beta to the chebyshev polynomial coeffs. + + Written for 1D, but also works in ND to transform dimension 0. + + Parameters + ---------- + coeffs : numpy array + The coefficient array + alpha : double + The scaler of the transformation + beta : double + The shifting of the transformation + Returns + ------- + coeffs : numpy array + The new coefficient array following the transformation + """ + transformedCoeffs = np.zeros_like(coeffs) + arr1 = np.zeros(len(coeffs)) + arr2 = np.zeros(len(coeffs)) + arr3 = np.zeros(len(coeffs)) + + #The first array + arr1[0] = 1. + transformedCoeffs[0] = coeffs[0] + #The second array + arr2[0] = beta + arr2[1] = alpha + transformedCoeffs[0] += beta * coeffs[1] + transformedCoeffs[1] += alpha * coeffs[1] + #Loop + maxRow = 2 + for col in range(2, len(coeffs)): + thisCoeff = coeffs[col] + #Get the next arr from arr1 and arr2 + + #The 0 spot + arr3[0] = -arr1[0] + alpha*arr2[1] + 2*beta*arr2[0] + transformedCoeffs[0] += thisCoeff * arr3[0] + + #The 1 spot + if maxRow > 2: + arr3[1] = -arr1[1] + alpha*(2*arr2[0] + arr2[2]) + 2*beta*arr2[1] + transformedCoeffs[1] += thisCoeff * arr3[1] + + #The middle spots + for i in range(2, maxRow - 1): + arr3[i] = -arr1[i] + alpha*(arr2[i-1] + arr2[i+1]) + 2*beta*arr2[i] + transformedCoeffs[i] += thisCoeff * arr3[i] + + #The second to last spot + i = maxRow - 1 + arr3[i] = -arr1[i] + (2 if i == 1 else 1)*alpha*(arr2[i-1]) + 2*beta*arr2[i] + transformedCoeffs[i] += thisCoeff * arr3[i] + + #The last spot + finalVal = alpha*arr2[i] + if abs(finalVal) > 1e-16: #TODO: Justify this val! + arr3[maxRow] = finalVal + transformedCoeffs[maxRow] += thisCoeff * finalVal + maxRow += 1 + + arr = arr1 + arr1 = arr2 + arr2 = arr3 + arr3 = arr + return transformedCoeffs[:maxRow] + +@njit +def TransformChebInPlace1DErrorFree(coeffs, alpha, beta): + """Applies the transformation alpha*x + beta to the chebyshev polynomial coeffs. + + Written for 1D, but also works in ND to transform dimension 0. + Uses Error Free Transformations to minimize error in the matrix construction + + Parameters + ---------- + coeffs : numpy array + The coefficient array + alpha : double + The scaler of the transformation + beta : double + The shifting of the transformation + Returns + ------- + coeffs : numpy array + The new coefficient array following the transformation + """ + if alpha == 0.5 and abs(beta) == 0.5: + return TransformChebInPlace1DErrorFreeSplit(coeffs, np.sign(beta)) + transformedCoeffs = np.zeros_like(coeffs) + arr1 = np.zeros(len(coeffs)) + arr2 = np.zeros(len(coeffs)) + arr3 = np.zeros(len(coeffs)) + arr1E = np.zeros(len(coeffs)) + arr2E = np.zeros(len(coeffs)) + arr3E = np.zeros(len(coeffs)) + + alpha1,alpha2 = Split(alpha) + beta1,beta2 = Split(beta) + + #The first array + arr1[0] = 1. + transformedCoeffs[0] = coeffs[0] + #The second array + arr2[0] = beta + arr2[1] = alpha + transformedCoeffs[0] += beta * coeffs[1] + transformedCoeffs[1] += alpha * coeffs[1] + #Loop + maxRow = 2 + for col in range(2, len(coeffs)): + thisCoeff = coeffs[col] + #Get the next arr from arr1 and arr2 + + #The 0 spot + #arr3[0] = -arr1[0] + alpha*arr2[1] + 2*beta*arr2[0] + V1, E1 = TwoProdWithSplit(beta, 2*arr2[0], beta1, beta2) + V2, E2 = TwoProdWithSplit(alpha, arr2[1], alpha1, alpha2) + V3, E3 = TwoSum(V1, V2) + V4, E4 = TwoSum(V3, -arr1[0]) + arr3[0] = V4 + arr3E[0] = -arr1E[0] + alpha*arr2E[1] + 2*beta*arr2E[0] + E1 + E2 + E3 + E4 + transformedCoeffs[0] += thisCoeff * (arr3[0] + arr3E[0]) + + #The 1 spot + if maxRow > 2: + #arr3[1] = -arr1[1] + alpha*(2*arr2[0] + arr2[2]) + 2*beta*arr2[1] + V1, E1 = TwoSum(2*arr2[0], arr2[2]) + V2, E2 = TwoProdWithSplit(beta, 2*arr2[1], beta1, beta2) + V3, E3 = TwoProdWithSplit(alpha, V1, alpha1, alpha2) + V4, E4 = TwoSum(V2, V3) + V5, E5 = TwoSum(V4, -arr1[1]) + arr3[1] = V5 + arr3E[1] = -arr1E[1] + alpha*(2*arr2E[0] + arr2E[2] + E1) + 2*beta*arr2E[1] + E2 + E3 + E4 + E5 + transformedCoeffs[1] += thisCoeff * (arr3[1] + arr3E[1]) + + #The middle spots + for i in range(2, maxRow - 1): + #arr3[i] = -arr1[i] + alpha*(arr2[i-1] + arr2[i+1]) + 2*beta*arr2[i] + V1, E1 = TwoSum(arr2[i-1], arr2[i+1]) + V2, E2 = TwoProdWithSplit(beta, 2*arr2[i], beta1, beta2) + V3, E3 = TwoProdWithSplit(alpha, V1, alpha1, alpha2) + V4, E4 = TwoSum(V2, V3) + V5, E5 = TwoSum(V4, -arr1[i]) + arr3[i] = V5 + arr3E[i] = -arr1E[i] + alpha*(arr2E[i-1] + arr2E[i+1] + E1) + 2*beta*arr2E[i] + E2 + E3 + E4 + E5 + transformedCoeffs[i] += thisCoeff * (arr3[i] + arr3E[i]) + + #The second to last spot + i = maxRow - 1 + C1 = (2 if i == 1 else 1) + #arr3[i] = -arr1[i] + C1*alpha*(arr2[i-1]) + 2*beta*arr2[i] + V1, E1 = TwoProdWithSplit(beta, 2*arr2[i], beta1, beta2) + V2, E2 = TwoProdWithSplit(alpha, C1*arr2[i-1], alpha1, alpha2) + V3, E3 = TwoSum(V1, V2) + V4, E4 = TwoSum(V3, -arr1[i]) + arr3[i] = V4 + arr3E[i] = -arr1E[i] + C1*alpha*arr2E[i-1] + 2*beta*arr2E[i] + E1 + E2 + E3 + E4 + transformedCoeffs[i] += thisCoeff * (arr3[i] + arr3E[i]) + + #The last spot + #finalVal = alpha*arr2[i] + finalVal, finalValE = TwoProdWithSplit(alpha, arr2[i], alpha1, alpha2) + arr3E[maxRow] = finalValE + alpha * arr2E[i] + arr3[maxRow] = finalVal + transformedCoeffs[maxRow] += thisCoeff * (arr3[maxRow] + arr3E[maxRow]) + if abs(arr3[maxRow] + arr3E[maxRow]) > 1e-32: #TODO: Justify this val! + maxRow += 1 + + #Rotate the vectors + arr = arr1 + arr1 = arr2 + arr2 = arr3 + arr3 = arr + arr = arr1E + arr1E = arr2E + arr2E = arr3E + arr3E = arr + return transformedCoeffs[:maxRow] + +@njit +def TransformChebInPlace1DErrorFreeSplit(coeffs, betaSign): + #alpha = 0.5 + #beta = 0.5 if betaSign == 1 else -0.5 (betaSign must be -1) + transformedCoeffs = np.zeros_like(coeffs) + arr1 = np.zeros(len(coeffs)) + arr2 = np.zeros(len(coeffs)) + arr3 = np.zeros(len(coeffs)) + arr1E = np.zeros(len(coeffs)) + arr2E = np.zeros(len(coeffs)) + arr3E = np.zeros(len(coeffs)) + + #The first array + arr1[0] = 1. + transformedCoeffs[0] = coeffs[0] + #The second array + arr2[0] = betaSign*0.5 + arr2[1] = 0.5 + transformedCoeffs[0] += betaSign*coeffs[1]/2 + transformedCoeffs[1] += coeffs[1]/2 + #Loop + maxRow = 2 + for col in range(2, len(coeffs)): + thisCoeff = coeffs[col] + #Get the next arr from arr1 and arr2 + + #The 0 spot + #arr3[0] = -arr1[0] + alpha*arr2[1] + 2*beta*arr2[0] + V1, E1 = TwoSum(arr2[1]/2, betaSign*arr2[0]) + V2, E2 = TwoSum(V1, -arr1[0]) + arr3[0] = V2 + arr3E[0] = -arr1E[0] + arr2E[1]/2 + betaSign*arr2E[0] + E1 + E2 + transformedCoeffs[0] += thisCoeff * (arr3[0] + arr3E[0]) + + #The 1 spot + if maxRow > 2: + #arr3[1] = -arr1[1] + alpha*(2*arr2[0] + arr2[2]) + 2*beta*arr2[1] + V1, E1 = TwoSum(arr2[0], arr2[2]/2) + V2, E2 = TwoSum(V1, betaSign*arr2[1]) + V3, E3 = TwoSum(V2, -arr1[1]) + arr3[1] = V3 + arr3E[1] = -arr1E[1] + arr2E[0] + arr2E[2]/2 + betaSign*arr2E[1] + E1 + E2 + E3 + transformedCoeffs[1] += thisCoeff * (arr3[1] + arr3E[1]) + + #The middle spots + for i in range(2, maxRow - 1): + #arr3[i] = -arr1[i] + alpha*(arr2[i-1] + arr2[i+1]) + 2*beta*arr2[i] + V1, E1 = TwoSum(arr2[i-1], arr2[i+1]) + V2, E2 = TwoSum(V1/2, betaSign*arr2[i]) + V3, E3 = TwoSum(V2, -arr1[i]) + arr3[i] = V3 + arr3E[i] = -arr1E[i] + (arr2E[i-1] + arr2E[i+1] + E1)/2 + betaSign*arr2E[i] + E2 + E3 + transformedCoeffs[i] += thisCoeff * (arr3[i] + arr3E[i]) + + #The second to last spot + i = maxRow - 1 + C1 = (1 if i == 1 else 0.5) + #arr3[i] = -arr1[i] + C1*alpha*(arr2[i-1]) + 2*beta*arr2[i] + V1, E1 = TwoSum(C1*arr2[i-1], betaSign*arr2[i]) + V2, E2 = TwoSum(V1, -arr1[i]) + arr3[i] = V2 + arr3E[i] = -arr1E[i] + C1*arr2E[i-1] + betaSign*arr2E[i] + E1 + E2 + transformedCoeffs[i] += thisCoeff * (arr3[i] + arr3E[i]) + + #The last spot + #finalVal = alpha*arr2[i] + arr3[maxRow] = arr2[i]/2 + arr3E[maxRow] = arr2E[i] / 2 + transformedCoeffs[maxRow] += thisCoeff * (arr3[maxRow] + arr3E[maxRow]) + if abs(arr3[maxRow] + arr3E[maxRow]) > 1e-32: #TODO: Justify this val! + maxRow += 1 + + #Rotate the vectors + arr = arr1 + arr1 = arr2 + arr2 = arr3 + arr3 = arr + arr = arr1E + arr1E = arr2E + arr2E = arr3E + arr3E = arr + return transformedCoeffs[:maxRow] + +def TransformChebInPlaceND(coeffs, dim, alpha, beta, exact): + #TODO: Could we calculate the allowed error beforehand and pass it in here? + #TODO: Make this work for the power basis polynomials + if alpha == 1.0 and beta == 0.0: + return coeffs + TransformFunc = TransformChebInPlace1DErrorFree if exact else TransformChebInPlace1D + if dim == 0: + return TransformFunc(coeffs, alpha, beta) + else: + order = np.array([dim] + [i for i in range(dim)] + [i for i in range(dim+1, coeffs.ndim)]) + backOrder = np.zeros(coeffs.ndim, dtype = int) + backOrder[order] = np.arange(coeffs.ndim) + return TransformFunc(coeffs.transpose(order), alpha, beta).transpose(backOrder) + + + +class TrackedInterval: + def __init__(self, interval): + self.topInterval = interval + self.interval = interval + self.transforms = [] + self.ndim = len(self.interval) + self.empty = False + + def addTransform(self, subInterval): + #This function assumes the interval has non zero size. + #Get the transformation in terms of alpha and beta + if np.any(subInterval[:,0] > subInterval[:,1]): + self.empty = True + return + a1,b1 = subInterval.T + a2,b2 = self.interval.T + alpha1, beta1 = (b1-a1)/2, (b1+a1)/2 + alpha2, beta2 = (b2-a2)/2, (b2+a2)/2 + self.transforms.append(np.array([alpha1, beta1])) + #Update the current interval + for dim in range(self.ndim): + for i in range(2): + x = subInterval[dim][i] + if x == -1.0 or x == 1.0: + continue #Don't change the current interval + self.interval[dim][i] = alpha2[dim]*x+beta2[dim] + + def getLastTransform(self): + return self.transforms[-1] + + def getFinalInterval(self): + #Use the transformations and the topInterval + #TODO: Make this a seperate function so it can use njit. + #Make these _NoNumba calls use floats so they call call the numba functions without a seperate compile + finalInterval = self.topInterval.T + finalIntervalError = np.zeros_like(finalInterval) + for alpha,beta in self.transforms[::-1]: + finalInterval, temp = TwoProd_NoNumba(finalInterval, alpha) + finalIntervalError = alpha * finalIntervalError + temp + finalInterval, temp = TwoSum_NoNumba(finalInterval,beta) + finalIntervalError += temp + finalInterval = finalInterval.T + finalIntervalError = finalIntervalError.T + self.finalInterval = finalInterval + finalIntervalError + self.finalAlpha, alphaError = TwoSum_NoNumba(-finalInterval[:,0]/2,finalInterval[:,1]/2) + self.finalAlpha += alphaError + (finalIntervalError[:,1] - finalIntervalError[:,0])/2 + self.finalBeta, betaError = TwoSum_NoNumba(finalInterval[:,0]/2,finalInterval[:,1]/2) + self.finalBeta += betaError + (finalIntervalError[:,1] + finalIntervalError[:,0])/2 + return self.finalInterval + + def size(self): + return np.product(self.interval[:,1] - self.interval[:,0]) + + def copy(self): + newone = TrackedInterval(self.topInterval) + newone.interval = self.interval.copy() + newone.transforms = self.transforms.copy() + return newone + + def __contains__(self, point): + return np.all(point >= self.interval[:,0]) and np.all(point <= self.interval[:,1]) + + def overlapsWith(self, otherInterval): + #Has to overlap in every dimension. + for (a1,b1),(a2,b2) in zip(self.interval, otherInterval.interval): + if a1 > b2 or a2 > b1: + return False + return True + + +def getLinearTerms(M): + """Helper Function, returns the linear terms of a matrix + + Uses the fact that the linear terms are indexed at + (0,0, ... ,0,1) + (0,0, ... ,1,0) + ... + (0,1, ... ,0,0) + (1,0, ... ,0,0) + which are indexes + 1, n, n^2, ... when looking at M.ravel(). + + Parameters + ---------- + M : numpy array + The coefficient array ot get the linear terms from + + Returns + ------- + A 1D numpy array with the linear terms of M + """ + spot = 1 + MArray = M.ravel() + A = [MArray[spot]] + for i in M.shape[1:][::-1]: + spot *= i + A.append(MArray[spot]) + return A[::-1] + + + +def find_vertices(A_ub, b_ub): + # This calcualtes the feasible point that is MOST inside the halfspace + #It then feeds that feasible point into a halfspace intersection solver and finds + #the intersection of the hyper-polygon and the hyper-cube. It returns these intersections, which when + #we take the min and max of, give the set of intervals that we should shrink down to. + #I am going to document exactly what the function does, but wanted to get it pushed because I will be + #working on finals and out of town for the next week. + + m, n = A_ub.shape + + arr = np.hstack([np.vstack([np.identity(n, dtype = float), -np.identity(n, dtype = float)]), -np.ones(2 * n, dtype = float).reshape(2 * n, 1)]) + + o = arr.shape[0] + + # Create the halfspaces array in the format the scipy solver needs it + halfspaces = np.zeros((m + o, n + 1), dtype = float) + halfspaces[:m, :n] = A_ub + halfspaces[:m, n:] = -b_ub + halfspaces[m:, :] = arr + + # Find a feasible point that is MOST inside the halfspace + norm_vector = np.reshape(np.linalg.norm(halfspaces[:, :-1], axis=1), (halfspaces.shape[0], 1)) + c = np.zeros((halfspaces.shape[1],), dtype = float) + c[-1] = -1 + A = np.hstack((halfspaces[:, :-1], norm_vector)) + b = - halfspaces[:, -1:] + + L = linprog(c, A, b, bounds = (-1,1)) + + #If L.status == 0, it means the linear programming proceeded as normal, so there is shrinkage that can occur in the interval + if L.status == 0: + feasible_point = L.x[:-1] + else: + #If L.status is not 0, then there is no feasible point, meaning the entire interval can be thrown out + return 1, None + + #If the last entry in the feasible point is negative, it also means there was not a suitable feasible point, + #so the entire interval can be throw out + if L.x[-1] < 0: + return 1, None + + #Try solving the halfspace problem + try: + intersects = HalfspaceIntersection(halfspaces, feasible_point).intersections + except: + #If the halfspaces failed, it means the coefficnets were really tiny. + #In this case it also means that we want to keep the entire interval because there is a root in this interval + return 2, np.vstack([np.ones(n),-np.ones(n)]) + + #If the problem can be solved, it means the interval was shrunk, so return the intersections + return 3, intersects + + +def linearCheck1(totalErrs, A, consts): + dim = len(A) + a = -np.ones(dim) + b = np.ones(dim) + for row in range(dim): + for col in range(dim): + if A[row,col] != 0: + v1 = totalErrs[row] / abs(A[row,col]) - 1 + v2 = 2 * consts[row] / A[row,col] + if v2 >= 0: + a_, b_ = -v1, v1-v2 + else: + a_, b_ = -v2-v1, v1 + a[col] = max(a[col], a_) + b[col] = min(b[col], b_) + return a, b + +def BoundingIntervalLinearSystem(Ms, errors): + """Finds a smaller region in which any root must be. + + Parameters + ---------- + Ms : list of numpy arrays + Each numpy array is the coefficient tensor of a chebyshev polynomials + errors : iterable of floats + The maximum error of chebyshev approximations + + Returns + ------- + newInterval : numpy array + The smaller interval where any root must be + changed : bool + Whether the interval has shrunk at all + """ + errorToAdd = 1e-10 + + #Get the matrix of the linear terms + A = np.array([getLinearTerms(M) for M in Ms]) + #Get the Vector of the constant terms + consts = np.array([M.ravel()[0] for M in Ms]) + dim = A.shape[0] + #Get the Error of everything else combined. + totalErrs = np.array([np.sum(np.abs(M)) + e for M,e in zip(Ms, errors)]) + linear_sums = np.sum(np.abs(A),axis=1) + err = np.array([tE-abs(c)-l for tE,c,l in zip(totalErrs,consts,linear_sums)]) + if dim <= 4: + #Use the other interval shrinking method + a, b = linearCheck1(totalErrs, A, consts) + #Now do the linear solve check + #We use the matrix inverse to find the width, so might as well use it both spots. Should be fine as dim is small. + if np.linalg.cond(A) < 1e10: #Make sure conditioning is ok. + Ainv = np.linalg.inv(A) + center = -Ainv@consts + + #Ainv transforms the hyperrectangle of side lengths err into a parallelogram with these as the principal direction + #So summing over them gets the farthest the parallelogram can reach in each dimension. + width = np.sum(np.abs(Ainv*err),axis=1) + #Bound with previous result + a = np.maximum(center - width, a) + b = np.minimum(center + width, b) + #Add error and bound + a -= errorToAdd + b += errorToAdd + a[a < -1] = -1 + b[b > 1] = 1 + changed = np.any(a > -1.) or np.any(b < 1.) + return np.vstack([a,b]).T, changed + + ##NEW CODE## I will document this much better, but wanted to get it pushed before finals/I leave town. + #Define the A_ub and b_ub matrices in the correct form to feed into the linear programming problem. + A_ub = np.vstack([A, -A]) + b_ub = np.hstack([err - consts, consts + err]).T.reshape(-1, 1) + + # Use the find_vertices function to return the vertices of the intersection of halfspaces + tell, P = find_vertices(A_ub, b_ub) + if tell == 1: + #No feasible Point, throw out the entire interval + return np.vstack([[1.0]*len(A),[-1.0]*len(A)]).T, True + elif tell == 2: + #No shrinkage possible, keep the entire interval + return np.vstack([[-1.0]*len(A),[1.0]*len(A)]).T, False + else: + #Return the reduced interval + a = P.min(axis=0) - errorToAdd + b = P.max(axis=0) + errorToAdd + a[a < -1.] = -1.0 + b[b > 1.] = 1.0 + changed = np.any(a > -1.) or np.any(b < 1.) + return np.vstack([a,b]).T, changed + + + +@njit +def isValidSpot(i,j): + """Helper for makeMatrix. + + Parameters + ---------- + i : number + The row of the matrix + j : number + The col of the matrix + + Returns + ------- + isValid : bool + True if this is a spot in the matrix that I should be updating to make the Chebyshev Transformation Matrix. + This means the index is on the upper diagonal of a matrix. + """ + return i >= 0 and j >= i + +@njit +def makeMatrix(n,a,b,subMatrix=None): + """Creates the Chebyshev transformation matrix. + + Parameters + ---------- + n : integer + The size of the matrix to create. Will be the degree + 1. Must be at least 2. + a : number + The lower bound of the interval we are transforming onto + b : number + The upper bound of the interval we are transforming onto + subMatrix : numpy array (optional) + The mxm Chebyshev Transformation matrix for the same interval where m < n. Used to speed up construction if known. + + Returns + ------- + M : numpy array + The Chebyshev Transformation matrix to transform a polynomial of degree n-1 from [-1,1] to [a,b]. + """ + polyType = "C" #C for Chebyshev, P for Power + #Matrix creation with njit + M = np.zeros(n*n) + M = M.reshape(n,n) + #Use the submatrix if exists + startValue = 2 + if subMatrix is not None: + M[:subMatrix.shape[0],:subMatrix.shape[1]] = subMatrix[:n,:n] + startValue = min(2, min(subMatrix.shape[0], subMatrix.shape[1])) + #Initial Values of the Matrix + M[0,0] = 1 + M[0,1] = b + M[1,1] = a + #Use the reccurence relation + #M[i,j] = 2*b*M[i,j-1] - M[i,j-2] + a*M[i-1,j-1] + a*M[i+1,j-1]*(2 if i==1 else 1) + maxRow = startValue - 1 + for j in range(startValue, n): #Loop over the columns starting at 2 + for i in range(j+1): #Loop over the rows on the upper diagonal + maxRow = max(maxRow, i) + val = 0 + aVal = 0 + bVal = 0 + if polyType == "C": + if isValidSpot(i,j-2): + val -= M[i,j-2] #Adds no error + if isValidSpot(i-1,j-1): + aVal += M[i-1,j-1] * (2 if i == 1 else 1) #Adds no error. Doubles magnitude of previous error. + if isValidSpot(i,j-1): + bVal += 2*M[i,j-1] #Adds no error. Doubles magnitude of previous error. + if isValidSpot(i+1,j-1): + aVal += M[i+1,j-1] #Could add machEps error + M[i,j] = val + a*aVal + b*bVal #Could add 4 machEps error. Total of 5 machEps error added at most. + elif polyType == "P": + M[i,j] = a*M[i-1,j-1] + b*M[i,j-1] + #TODO: Could we calculate the allowed error beforehand and pass it in here? + #Not sure how to make that work when we want to store the matrix. Maybe + #when we store the matrix we also store the max up to that row? That way we can easily grab a chunk of it. + #TODO: Fix this. We could have a random 0 or small number anywhere if i > maxRow? +# if i > 2 and abs(M[i,j]) < 1e-16: #Don't build the really small numbers into the matrix, assume it isn't worth it. +# break + return M[:maxRow+1] + +@njit(UniTuple(float64,2)(float64, float64)) +def TwoSum(a,b): + x = a+b + z = x-a + y = (a-(x-z)) + (b-z) + return x,y +def TwoSum_NoNumba(a,b): + x = a+b + z = x-a + y = (a-(x-z)) + (b-z) + return x,y + +@njit(UniTuple(float64,2)(float64)) +def Split(a): + c = (2**27 + 1) * a + x = c-(c-a) + y = a-x + return x,y +def Split_NoNumba(a): + c = (2**27 + 1) * a + x = c-(c-a) + y = a-x + return x,y + +@njit(UniTuple(float64,2)(float64, float64)) +def TwoProd(a,b): + x = a*b + a1,a2 = Split(a) + b1,b2 = Split(b) + y=a2*b2-(((x-a1*b1)-a2*b1)-a1*b2) + return x,y +def TwoProd_NoNumba(a,b): + x = a*b + a1,a2 = Split_NoNumba(a) + b1,b2 = Split_NoNumba(b) + y=a2*b2-(((x-a1*b1)-a2*b1)-a1*b2) + return x,y + +@njit(UniTuple(float64,2)(float64, float64, float64, float64)) +def TwoProdWithSplit(a,b,a1,a2): + x = a*b + b1,b2 = Split(b) + y=a2*b2-(((x-a1*b1)-a2*b1)-a1*b2) + return x,y + +@njit +def makeMatrixErrorFree(n,a,b,subMatrix=None): + polyType = "C" #C for Chebyshev, P for Power + a1,a2 = Split(a) + b1,b2 = Split(b) + #Matrix creation with njit + M = np.zeros(n*n) + M = M.reshape(n,n) + Me = np.zeros(n*n) + Me = Me.reshape(n,n) + #Use the submatrix if exists + startValue = 2 + if subMatrix is not None: + M[:subMatrix.shape[0],:subMatrix.shape[1]] = subMatrix[:n,:n] + startValue = min(2, min(subMatrix.shape[0], subMatrix.shape[1])) + #Initial Values of the Matrix + M[0,0] = 1 + M[0,1] = b + M[1,1] = a + #Use the reccurence relation + #M[i,j] = 2*b*M[i,j-1] - M[i,j-2] + a*M[i-1,j-1] + a*M[i+1,j-1]*(2 if i==1 else 1) + maxRow = startValue - 1 + for j in range(startValue, n): #Loop over the columns starting at 2 + for i in range(j+1): #Loop over the rows on the upper diagonal + maxRow = max(maxRow, i) + if polyType == "C": + #Sum the values for the As + if isValidSpot(i-1,j-1) and isValidSpot(i+1,j-1): + AVal, AValE = TwoSum(M[i-1,j-1] * (2 if i == 1 else 1), M[i+1,j-1]) + ErrorAVal = Me[i-1,j-1] * (2 if i == 1 else 1) + Me[i+1,j-1] + elif isValidSpot(i-1,j-1): + AVal, AValE = M[i-1,j-1] * (2 if i == 1 else 1), 0 + ErrorAVal = Me[i-1,j-1] * (2 if i == 1 else 1) + elif isValidSpot(i+1,j-1): + AVal, AValE = M[i+1,j-1], 0 + ErrorAVal = Me[i+1,j-1] + else: + AVal, AValE = 0, 0 + ErrorAVal = 0 + #Get the value for the b + BVal = 0 + ErrorBVal = 0 + if isValidSpot(i,j-1): + BVal = 2*M[i,j-1] + ErrorBVal = 2*Me[i,j-1] + #Get the final val + Val = 0 + ErrorVal = 0 + if isValidSpot(i,j-2): + Val = -M[i,j-2] + ErrorVal = -Me[i,j-2] + #TODO: Should I check for 0 values before these to speed it up? + #Do the 2 multiplications + P1, P1E = TwoProdWithSplit(a, AVal, a1, a2) + P2, P2E = TwoProdWithSplit(b, BVal, b1, b2) + #Do the final sum + S1, S1E = TwoSum(P1, P2) + S2, S2E = TwoSum(S1, Val) + M[i,j] = S2 + Me[i,j] = ErrorVal + a*(ErrorAVal+AValE) + b*ErrorBVal + P1E + P2E + S1E + S2E + if i > 2 and abs(M[i,j] + Me[i,j]) < 1e-32: #TODO: Justify This! + break + elif polyType == "P": + P1, P1E = TwoProdWithSplit(a, M[i-1,j-1], a1, a2) + P2, P2E = TwoProdWithSplit(b, M[i,j-1], b1, b2) + S1, S1E = TwoSum(P1, P2) + M[i,j] = S1 + Me[i,j] = a*Me[i-1,j-1] + b*Me[i,j-1] + P1E + P2E + S1E + #TODO: Figure out what this should be got Power Basis +# if i > 2 and abs(M[i,j] + Me[i,j]) < 1e-320: +# break + return (M + Me)[:maxRow+1] + +def getTransformPoints(newInterval): + """Given the new interval [a,b], gives c,d for reduction xHat = cx+d""" + a,b = newInterval + return (b-a)/2, (b+a)/2 + +def getTransformationError(M, dim): + """Returns a bound on the error of transforming a chebyshev approximation M""" + #The matrix is accurate to machEps, so the error is at most machEps * each number in the matrix + #time the number of rows in the transformation, which is M.shape[dim] + machEps = 2**-52 + error = M.shape[dim] * machEps * np.sum(np.abs(M)) + return error #TODO: Figure out a more rigurous bound! + +def transformCheb(M, As, Bs, error): + """Transforms the chebyshev coefficient matrix M to the new interval [As, Bs]. + + Parameters + ---------- + M : numpy array + The chebyshev coefficient matrix + As : iterable + The min values of the interval we are transforming to + Bs : iterable + The max values of the interval we are transforming to + error : float + A bound on the error of the chebyshev approximation + + Returns + ------- + M : numpy array + The coefficient matrix on the new interval + error : float + A bound on the error of the new chebyshev approximation + """ + #This just does the matrix multiplication on each dimension. Except it's by a tensor. + for dim,n,a,b in zip(range(M.ndim),M.shape,As,Bs): + error += getTransformationError(M, dim) + M = TransformChebInPlaceND(M,dim,a,b,True) + return M, error + +def transformChebToInterval(Ms, As, Bs, errors): + """Transforms chebyshev coefficient matrices to a new interval. + + Parameters + ---------- + Ms : list of numpy arrays + The chebyshev coefficient matrices + interval : numpy array + The new interval to transform to + originalInterval : numpy array + The current interval on which Ms are valid + errors : numpy array + A bound on the error of each chebyshev approximation + + Returns + ------- + newMs : list of numpy arrays + The chebyshev coefficient matrices on the new interval + newErrors : list of numpy arrays + The errors of the newMs. This just adds the errors of applying the Chebyshev Transformation Matrix. + """ + #Transform the chebyshev polynomials + newMs = [] + newErrors = [] + for M,e in zip(Ms, errors): + newM, newE = transformCheb(M, As, Bs, e) + newMs.append(newM) + newErrors.append(newE) + return newMs, np.array(newErrors) + +def zoomInOnIntervalIter(Ms, errors, trackedInterval): + """One iteration of the linear check and transforming to a new interval. + + Parameters + ---------- + Ms : list of numpy arrays + The chebyshev coefficient matrices + errors : numpy array + A bound on the error of each chebyshev approximation + trackedInterval : TrackedInterval + The current interval that the chebyshev approximations are valid for + + + Returns + ------- + Ms : list of numpy arrays + The chebyshev coefficient matrices on the new interval + errors : numpy array + The errors of the new Ms. This just adds the errors of applying the Chebyshev Transformation Matrix. + trackedInterval : TrackedInterval + The new interval that the chebyshev approximations are valid for + changed : bool + Whether the interval changed as a result of this step. + """ + dim = len(Ms) + #Zoom in on the current interval + interval, changed = BoundingIntervalLinearSystem(Ms, errors) + #Check if we can throw out the whole thing + if np.any(interval[:,0] > interval[:,1]): + trackedInterval.empty = True + return Ms, errors, trackedInterval, True + #Check if we are done interating + if not changed: + return Ms, errors, trackedInterval, changed + #Transform the chebyshev polynomials + trackedInterval.addTransform(interval) + Ms, errors = transformChebToInterval(Ms, *trackedInterval.getLastTransform(), errors) + return Ms, errors, trackedInterval, changed + +def getTransposeDims(dim,transformDim): + """Helper function for chebTransform1D""" + return [i for i in range(transformDim,dim)] + [i for i in range(transformDim)] + +def chebTransform1D(M, alpha, beta, transformDim): + """Transform a chebyshev polynomial in a single dimension""" + return TransformChebInPlaceND(M, transformDim, alpha, beta, exact), getTransformationError(M, transformDim) + +def getInverseOrder(order, dim): + """Helper function to make the subdivide order match the subdivideInterval order""" + order = 2**(len(order)-1 - order) + newOrder = np.array([i@order for i in product([0,1],repeat=dim)]) + invOrder = np.zeros_like(newOrder) + invOrder[newOrder] = np.arange(len(newOrder)) + return tuple(invOrder) + +class Subdivider(): + #TODO: It might be better to just always do the transformations in place, and then get rid of this class. + #Keep the functions but don't store anything. + #This class handles subdividing and stores the precomputed matrices to save time. + def __init__(self): + #TODO: Make this 0.5. DO subdivide exactly in half. Requires combining intervals to work. + self.RAND = 0.5#139303900908738 #Don't subdivide exactly in half. + self.precomputedArrayDeg = 1 #We don't compute the first 2 + self.subdivisionPoint = self.RAND * 2 - 1 + self.transformPoints1 = getTransformPoints([-1., self.subdivisionPoint]) #TODO: This isn't always [-1,1]!!! + self.transformPoints2 = getTransformPoints([self.subdivisionPoint, 1.]) + #Note that a transformation of a lower degree is just the submatrix of the higher degree transformation + #So we can just store the highest degree transformation we have to save on space. + #And we can compute the higher degree transformation starting at the degree we already have. + + def subdivideInterval(self, trackedInterval): + #Get the new interval that will correspond with the new polynomials + results = [trackedInterval] + for thisDim in range(len(trackedInterval.interval)): + newSubinterval = np.ones_like(trackedInterval.interval) #TODO: Make this outside for loop + newSubinterval[:,0] = -1. + newResults = [] + for oldInterval in results: + newInterval1 = oldInterval.copy() + newInterval2 = oldInterval.copy() + newSubinterval[thisDim] = [-1., self.subdivisionPoint] + newInterval1.addTransform(newSubinterval) + newSubinterval[thisDim] = [self.subdivisionPoint, 1.] + newInterval2.addTransform(newSubinterval) + newResults.append(newInterval1) + newResults.append(newInterval2) + results = newResults + return results + + def subdivide(self, M, error): + #Get the new Chebyshev approximations on the 2^n subintervals + dim = M.ndim + degs = M.shape + order = np.argsort(degs)[::-1] #We want to subdivide from highest degree to lowest. + #Iterate through the dimensions, highest degree first. + resultMs = [[M,error]] + for thisDim in order: + thisDeg = M.shape[thisDim] + newResults = [] + for T,E in resultMs: + #Transform the polys + P1, E1 = chebTransform1D(T, *self.transformPoints1, thisDim) + P2, E2 = chebTransform1D(T, *self.transformPoints2, thisDim) + newResults.append([P1, E1 + E]) + newResults.append([P2, E2 + E]) + resultMs = newResults + if dim == 1: + return resultMs #Already ordered because there's only 1. + else: + #Order the polynomials so they match the intervals in subdivideInterval + return [resultMs[i] for i in getInverseOrder(order, dim)] + +#The subdivider class. Stores the precomputed matrices. +mySubdivider = Subdivider() + +def trimMs(Ms, errors, absErrorIncrease, relErrorIncrease): + """Reduces the degree of chebyshev approximations and adds the resulting error to errors + + If the incoming error is E, will increase the error by at most max(relErrorIncrease * E, absErrorIncrease) + + Parameters + ---------- + Ms : list of numpy arrays + The chebyshev approximations of the functions + errors : numpy array + The max error of the chebyshev approximation from the function on the interval + absErrorIncrease : float + The largest increase in error allowed + relErrorIncrease : float + The largest relative increase in error allowed + + Returns + ------- + No return value, the Ms and errors and changed in place. + """ + dim = Ms[0].ndim + for polyNum in range(len(Ms)): #Loop through the polynomials + allowedErrorIncrease = max(relErrorIncrease * errors[polyNum], absErrorIncrease) + totalSum = np.sum(np.abs(Ms[polyNum])) + #Use these to look at a slice of the highest degree in the dimension we want to trim + slices = [slice(None) for i in range(dim)] + for currDim in range(dim): #Loop over the dimensions + slices[currDim] = -1 + lastSum = np.sum(np.abs(Ms[polyNum][tuple(slices)])) + #Check if the sum of the highest degree is of low error + #Keeps the degree at least 2 + while lastSum < allowedErrorIncrease and Ms[polyNum].shape[currDim] > 3: + allowedErrorIncrease -= lastSum #Update the error we are allowed + errors[polyNum] += lastSum #Update the error + slices[currDim] = slice(None,-1) + Ms[polyNum] = Ms[polyNum][tuple(slices)] #Trim the polynomial + slices[currDim] = -1 + lastSum = np.sum(np.abs(Ms[polyNum][tuple(slices)])) + slices[currDim] = slice(None) + +def shouldStopSubdivision(trackedInterval): + #TODO: WRITE THIS!!! + #In 1D a good check could be if the linear term is less than the error term (or close to it). + #Not sure how to extend that to ND yet. + #For now just checks if the interval is small. This won't work if there is a large error term. + return np.all(trackedInterval.interval[:,1]-trackedInterval.interval[:,0] < 1e-10) + +def isExteriorInterval(originalInterval, trackedInterval): + return np.any(trackedInterval.interval == originalInterval.interval) + + + +def solvePolyRecursive(Ms, trackedInterval, errors, trimErrorRelBound = 1e-16, trimErrorAbsBound = 1e-32, level = 0): + + """Recursively finds regions in which any common roots of functions must be using subdivision + + Parameters + ---------- + Ms : list of numpy arrays + The chebyshev approximations of the functions + trackedInterval : TrackedInterval + The information about the interval we are solving on. + errors : numpy array + The max error of the chebyshev approximation from the function on the interval + + Returns + ------- + boundingBoxesInterior : list of numpy arrays (optional) + Each element of the list is an interval in which there may be a root. The interval is on the interior of the current + interval + boundingBoxesExterior : list of numpy arrays (optional) + Each element of the list is an interval in which there may be a root. The interval is on the exterior of the current + interval + """ + #TODO: Check if trackedInterval.interval has width 0 in some dimension, in which case we should get rid of that dimension. + + #Constant term check, runs at the beginning of the solve and before each subdivision + #If the absolute value of the constant term for any of the chebyshev polynomials is greater than the sum of the + #absoulte values of any of the other terms, it will return that there are no zeros on that interval + consts = np.array([M.ravel()[0] for M in Ms]) + err = np.array([np.sum(np.abs(M))-abs(c)+e for M,e,c in zip(Ms,errors,consts)]) + if np.any(np.abs(consts) > err): + return [], [] + + #The random numbers used below. TODO: Choose these better + #Error For Trim trimMs +# trimErrorAbsBound = 1e-32 +# trimErrorRelBound = 1e-16 + #How long we are allowed to zoom before giving up + maxZoomCount1 = 10 + maxZoomCount2 = 50 + #When to stop, again, maybe this should just be 0??? + minIntervalSize = 1e-16 + #Assume that once we have shrunk this interval this much, we will be able to shrink it all the way. + #The is something to look into. + zoomRatioToZip = 0.01 + + #Trim + Ms = Ms.copy() + originalMs = Ms.copy() + trackedInterval = trackedInterval.copy() + errors = errors.copy() + trimMs(Ms, errors, trimErrorAbsBound, trimErrorRelBound) + + #Solve + dim = Ms[0].ndim + changed = True + zoomCount = 0 + originalInterval = trackedInterval.copy() + originalIntervalSize = trackedInterval.size() + #The choosing when to stop zooming logic is really ugly. Needs more analysis. + #Keep zooming while it's larger than minIntervalSize. + while changed and np.max(trackedInterval.interval[:,1] - trackedInterval.interval[:,0]) > minIntervalSize: + #If we've zoomed more than maxZoomCount1 and haven't shrunk the size by zoomRatioToZip, assume + #we aren't making progress and subdivide. Once we get to zoomRatioToZip, assume we will just converge + #quickly and zoom all the way by maxZoomCount2. + if zoomCount > maxZoomCount1: + newIntervalSize = trackedInterval.size() + zoomRatio = (newIntervalSize / originalIntervalSize) ** (1/len(Ms)) + if zoomRatio >= zoomRatioToZip: + break + elif zoomCount > maxZoomCount2: + break + #Zoom in until we stop changing or we hit machine epsilon + Ms, errors, trackedInterval, changed = zoomInOnIntervalIter(Ms, errors, trackedInterval) + if trackedInterval.empty: #Throw out the interval + return [], [] + zoomCount += 1 + secondaryInterval = trackedInterval.copy() #TODO: USE THIS WHERE NEEDED + if shouldStopSubdivision(trackedInterval): + #Return the interval. Maybe we should return the linear approximation of the root here as well as the interval? + #Might be better than just taking the midpoint later. + #Or zoom in assuming no error and take the result of that. + #If that throws it out, tag that root as being a possible root? It isn't a root of the approx but + #may be a root of the function. + if isExteriorInterval(originalInterval, trackedInterval): + return [], [trackedInterval] + else: + return [trackedInterval], [] + else: + #Otherwise, Subdivide + resultInterior, resultExterior = [], [] + #Get the new intervals and polynomials + newInts = mySubdivider.subdivideInterval(trackedInterval) + allMs = [mySubdivider.subdivide(M, e) for M,e in zip(Ms, errors)] + #Run each interval + for i in range(len(newInts)): + newInterior, newExterior = solvePolyRecursive([allM[i][0] for allM in allMs], newInts[i], [allM[i][1] for allM in allMs], trimErrorRelBound, trimErrorAbsBound, level=level+1) + resultInterior += newInterior + resultExterior += newExterior + #Rerun the touching intervals + idx1 = 0 + idx2 = 1 + #Combine any touching intervals and throw them at the end. Flip a bool saying rerun them + for tempInterval in resultExterior: + tempInterval.reRun = False + while idx1 < len(resultExterior): + while idx2 < len(resultExterior): + if resultExterior[idx1].overlapsWith(resultExterior[idx2]): + #Combine, throw at the back. Set reRun to true. + combinedInterval = originalInterval.copy() + newAs = np.min([resultExterior[idx1].interval[:,0], resultExterior[idx2].interval[:,0]], axis=0) + newBs = np.max([resultExterior[idx1].interval[:,1], resultExterior[idx2].interval[:,1]], axis=0) + final1 = resultExterior[idx1].getFinalInterval() + final2 = resultExterior[idx2].getFinalInterval() + newAsFinal = np.min([final1[:,0], final2[:,0]], axis=0) + newBsFinal = np.max([final1[:,1], final2[:,1]], axis=0) + oldAs = originalInterval.interval[:,0] + oldBs = originalInterval.interval[:,1] + oldAsFinal, oldBsFinal = originalInterval.getFinalInterval().T + #Find the final A and B values exactly. Then do the currSubinterval calculation exactly. + #Look at what was done on the example that's failing and see why. + currSubinterval = ((2*np.array([newAsFinal, newBsFinal]) - oldAsFinal - oldBsFinal)/(oldBsFinal - oldAsFinal)).T + #If the interval is exactly -1 or 1, make sure that shows up as exact. + currSubinterval[:,0][oldAs == newAs] = -1 + currSubinterval[:,1][oldBs == newBs] = 1 + #Update the current subinterval. Use the best transform we can get here, but use the exact combined + #interval for tracking + combinedInterval.addTransform(currSubinterval) + combinedInterval.interval = np.array([newAs, newBs]).T + combinedInterval.reRun = True + del resultExterior[idx2] + del resultExterior[idx1] + resultExterior.append(combinedInterval) + idx2 = idx1 + 1 + else: + idx2 += 1 + idx1 += 1 + idx2 = idx1 + 1 + #Rerun, check if still on exterior + newResultExterior = [] + for tempInterval in resultExterior: + if tempInterval.reRun: + if np.all(tempInterval.interval == originalInterval.interval): + newResultExterior.append(tempInterval) + else: + #Project the MS onto the interval, then recall the function. + #TODO: Instead of using the originalMs, use Ms, and then don't use the original interval, use the one + #we started subdivision with. + tempMs, tempErrors = transformChebToInterval(originalMs, *tempInterval.getLastTransform(), errors) + tempResultsInterior, tempResultsExterior = solvePolyRecursive(tempMs, tempInterval, tempErrors, level=level+1) + #We can assume that nothing in these has to be recombined + resultInterior += tempResultsInterior + newResultExterior += tempResultsExterior + elif isExteriorInterval(originalInterval, tempInterval): + newResultExterior.append(tempInterval) + else: + resultInterior.append(tempInterval) + return resultInterior, newResultExterior + +def solveChebyshevSubdivision(Ms, errors, returnBoundingBoxes = False, polish = False): + """Finds regions in which any common roots of functions must be + + Parameters + ---------- + Ms : list of numpy arrays + The chebyshev approximations of the functions on the interval [-1,1] + errors : numpy array + The max error of the chebyshev approximation from the function on the interval + returnBoundingBoxes : bool (Optional) + Defaults to False. If True, returns the bounding boxes around each root as well as the roots. + polish : bool (Optional) + Defaults to True. Whether or not to polish the roots at the end by zooming all they way back in. + + Returns + ------- + roots : list + The roots + boundingBoxes : list of numpy arrays (optional) + Each element of the list is an interval in which there may be a root. + """ + #Solve + originalInterval = TrackedInterval(np.array([[-1.,1.]]*Ms[0].ndim)) + + b1, b2 = solvePolyRecursive(Ms, originalInterval, errors, exact) + + boundingIntervals = b1 + b2 + + #Polish. Testing seems to show no benefit for this. If anything makes it worse. + if polish: + newIntervals = [] + for interval in boundingIntervals: + finalInterval = interval.getFinalInterval() + newInterval = interval.copy() + newInterval.interval = finalInterval + + tempMs, tempErrors = transformChebToInterval(Ms, interval.finalAlpha, interval.finalBeta, errors, exact) + b1, b2 = solvePolyRecursive(tempMs, newInterval, tempErrors, exact) + + newIntervals += b1 + b2 + boundingIntervals = newIntervals + + #TODO: Have an options to reRun all the bounding boxes with tight precision after a first run at lower precision. + #For Example: + #boundingBoxes = [solvePolyRecursive(transformedMs, box, errors, ) for box in boundingBoxes] + #TODO: Don't return the midpoint, return the point this matrix converges to if we don't include any error. + roots = [] + for interval in boundingIntervals: + finalInterval = interval.getFinalInterval() + roots.append((finalInterval[:,1] + finalInterval[:,0]) / 2) + if returnBoundingBoxes: + return roots, boundingIntervals + else: + return roots \ No newline at end of file diff --git a/yroots/Combined_Solve.py b/yroots/Combined_Solve.py new file mode 100644 index 00000000..daa89317 --- /dev/null +++ b/yroots/Combined_Solve.py @@ -0,0 +1,41 @@ +import numpy as np +from yroots import ChebyshevSubdivisionSolver, M_maker + +def solver(funcs,a,b,guess_degs,rescale=False,rel_approx_tol=1.e-15, abs_approx_tol=1.e-12): + """ + Finds the roots of the system of functions + + parameters + ---------- + funcs: list + list of the vectorized functions (R^n --> R) + a: ndarray + lower bound on the search interval + b: ndarray + upper bound on the search interval + guess_degs: list + guess of the best approximation degree for each function + rescale: bool + whether to rescale the approximation by inf_norm or not + rel_approx_tol: float + relative approximation tolerance + abs_approx_tol: float + absolute approximation tolerance + + returns + ------- + ndarray: + the yroots of hthe system of functions + """ + approximations = [] + errs = [] + for f,deg in zip(funcs,guess_degs): + approx = M_maker.M_maker(f,a,b,deg,rel_approx_tol,abs_approx_tol) + if rescale == True: + approximations.append(approx.M_rescaled) + else: + approximations.append(approx.M) + errs.append(approx.err) + errs = np.array(errs) + yroots = np.array(ChebyshevSubdivisionSolver.solveChebyshevSubdivision(approximations,np.array([a,b]).T,errs)) + return yroots \ No newline at end of file diff --git a/yroots/Combined_Solver.py b/yroots/Combined_Solver.py new file mode 100644 index 00000000..1f48af05 --- /dev/null +++ b/yroots/Combined_Solver.py @@ -0,0 +1,92 @@ +import numpy as np +import ChebyshevSubdivisionSolver, M_maker +from utils import transform +from polynomial import MultiCheb + +def solver(funcs,a,b,guess_degs,rescale=False,rel_approx_tol=1.e-15, abs_approx_tol=1.e-12): + """ + Finds the roots of the system of functions + + parameters + ---------- + funcs: list + list of the vectorized functions (R^n --> R) + a: ndarray + lower bound on the search interval + b: ndarray + upper bound on the search interval + guess_degs: list + guess of the best approximation degree for each function + rescale: bool + whether to rescale the approximation by inf_norm or not + rel_approx_tol: float + relative approximation tolerance + abs_approx_tol: float + absolute approximation tolerance + + returns + ------- + ndarray: + the yroots of the system of functions + """ + #TODO: allow for a,b to default to neg1_1, require input dim? it's tedious (-), it's a good sanity check (+) + #handle for when input deg is less than the approximation degree used to build that Multicheb object + #guess deg input default + #maybe the SHOULD know what degree to input + #handle for when the input deg is too high + #handle for when there is no input deg + if len(a) != len(b): + raise ValueError("Dimension mismatch in intervals.") + + if (b>=a).any(): + raise ValueError("At least one lower bound is >= an upper bound.") + + is_neg1_1 = True + arr_neg1 = np.array([-1]*len(a)) #what if a>b + arr_1 = np.ones(len(a)) + + if np.allclose(arr_neg1,a,rtol=1e-08) and np.allclose(arr_1,b,rtol=1e-08): + pass + else: + is_neg1_1 = False + + is_multi_cheb_arr = [] + + for func in funcs: #USE + if isinstance(func,MultiCheb): + is_multi_cheb_arr.append(True) + pass + else: + is_multi_cheb_arr.append(False) + + is_multi_cheb_arr = np.array(is_multi_cheb_arr) + funcs = np.array(funcs) + + MultiCheb_idxs = list(np.where(is_multi_cheb_arr==1)[0]) + non_MultiCheb_idxs = list(np.where(is_multi_cheb_arr==0)[0]) + + errs = np.array([0]*len(funcs)) + + for idx in non_MultiCheb_idxs: + approx = M_maker.M_maker(funcs[idx],arr_neg1,arr_1,guess_degs[idx],rel_approx_tol,abs_approx_tol) + if rescale: + funcs[idx] = MultiCheb(approx.M_rescaled) + else: + funcs[idx] = MultiCheb(approx.M) + errs[idx] = approx.err + + for idx in MultiCheb_idxs: + approx = M_maker.M_maker(funcs[idx],arr_neg1,arr_1,guess_degs[idx],rel_approx_tol,abs_approx_tol) + if rescale: + funcs[idx] = MultiCheb(approx.M_rescaled) + else: + funcs[idx] = MultiCheb(approx.M) + + funcs = [func.coeff for func in funcs] + yroots = np.array(ChebyshevSubdivisionSolver.solveChebyshevSubdivision(funcs,errs)) + + #transform doesn't work on empty arrays + if is_neg1_1 == False and len(yroots) > 0: + yroots = transform(yroots,a,b) + + return yroots \ No newline at end of file diff --git a/yroots/IntervalChecks.py b/yroots/IntervalChecks.py new file mode 100644 index 00000000..10eded21 --- /dev/null +++ b/yroots/IntervalChecks.py @@ -0,0 +1,1418 @@ +""" +The check functions are all functions that take in a coefficent matrix and run a quick check +to determine if there can ever be zeros on the unit box there. They are then put into the list +all_bound_check_functions in the order we want to run them (probably fastest first). These are +then all run to throw out intervals as possible. +""" +import numpy as np +from itertools import product +import itertools +from yroots.polynomial import MultiCheb +from matplotlib import pyplot as plt +from yroots.polynomial import MultiCheb, Polynomial +from matplotlib import patches +from scipy import linalg as la +from math import fabs # faster than np.abs for small arrays +from yroots.utils import memoize, transform, get_var_list, isNumber +from copy import copy + + +INTERVAL_REDUCTION_FUNCS = ["improveBound", "getBoundingParallelogram"] + +class IntervalData: + ''' + Class to handle all the things related to intervals. It holds and runs the interval checks + and also tracks what happened to each interval, and how much progress has been made. + + Attributes + ---------- + interval_checks: list + A list of functions. Each function accepts a coefficient matrix and a tolerance, + and returns whether the Chebyshev Polynomial represented by that matrix, and + accurate to within that tolerance, can ever be zero on the n dimensional interval [-1,1]. + subinterval_checks: list + A list of functions. Each function accepts a coefficient matrix, a list of subintervals, a list of + sign changes, and a tolerance. It then returns a list of booleans whether the Chebyshev Polynomial + represented by that matrix, and accurate to within that tolerance, can ever be zero on the given subintervals. + Before the checks can be run the subintervals must be rescaled to subintervals of [-1,1] + The list of sign changes represents if we already know the function changes sign on a given subinterval. + a: numpy array + The lower bounds of the overall interval to solve on. + b: numpy array + The upper bounds of the overall interval to solve on. + interval_results: dictionary + A dictionary of funciton names to lists of intervals that were solved by that function. + total_area: float + The total n dimensional volume of the overall interval being solved on. + current_area: float + How much of the n dimensional volume has been checked. + polishing: bool + If true this class is just being used as a shell to pass into the polish code. + polish_intervals: list + The intervals polishing will be run on + polish_num: int + The number of time polishing has been run + polish_interval_num: int + The current interval being polished + polish_a: numpy array + The lower bounds of the interval being polished + polish_b: numpy array + The upper bounds of the interval being polished + + tick: int + Keeps track of how many intervals have been solved. Every 100 it resets and prints the progress. + intervalReductionMethodsToUse: list + A list of indices to index into INTERVAL_REDUCTION_FUNCS_2D and INTERVAL_REDUCTION_FUNCS_ND + to run interval reduction methods on each subinterval. + + Methods + ------- + __init__ + Initializes everything. + get_subintervals + Returns the intervals needed for subdivision after running subinterval checks + check_intervals + Checks if a polynomial can be zero on an interval. + check_subintervals + Checks if a polynomial can be zero on an list of intervals. + track_interval + Tracks what happened to a given interval. + print_progress + Prints what percentage of the domain has been searched + print_results + Prints the results of how much each method contributed to the overall search + plot_results + Plots the results of subdivision solve + ''' + def __init__(self, a, b, intervalReductions): + self.interval_checks = [] + self.subinterval_checks = [quadratic_check] + self.a = a + self.b = b + self.interval_results = dict() + for check in self.interval_checks: + self.interval_results[check.__name__] = [] + for check in self.subinterval_checks: + self.interval_results[check.__name__] = [] + self.interval_results["Base Case"] = [] + self.interval_results["Macaulay"] = [] + self.interval_results["Too Deep"] = [] + self.interval_results["getBoundingInterval"] = [] + self.total_area = np.prod(self.b-self.a) + self.current_area = 0. + self.tick = 0 + + #For polishing code + self.polishing = False + self.polish_intervals = [] + self.polish_num = 0 + self.polish_interval_num = -1 + self.polish_a = np.array([]) + self.polish_b = np.array([]) + + #for keeping track of condition numbers + self.cond = 0 + self.backcond = 0 + + # Variables to store for Subintervals + if isNumber(a): + return + dim = len(a) + self.RAND = 0.5139303900908738 + self.mask = np.ones([2]*dim, dtype = bool) + self.throwOutMask = np.zeros([2]*dim, dtype = bool) + self.middleVal = 2*self.RAND - 1 + self.middleValChebSqrd = 2*self.middleVal**2 - 1 + self.middleValSqrd = self.middleVal**2 + self.subintervals = np.zeros([2]*dim + [2, dim]) + for spot in product([0,1], repeat=dim): + for i,val in enumerate(spot): + self.subintervals[spot][0][i] = -1 if val == 0 else self.middleVal + self.subintervals[spot][1][i] = self.middleVal if val == 0 else 1 + self.__intervalReductionMethodsToUse = [] + for methodName in intervalReductions: + if methodName in INTERVAL_REDUCTION_FUNCS: + self.__intervalReductionMethodsToUse.append(INTERVAL_REDUCTION_FUNCS.index(methodName)) + + def add_polish_intervals(self, polish_intervals): + ''' Add the intervals that polishing will be run on. + + Parameters + ---------- + polish_intervals : list + The intervals polishing will be run on. + ''' + self.polishing = True + self.polish_intervals = polish_intervals + self.polish_num += 1 + self.polish_interval_num = -1 + + def start_polish_interval(self): + '''Get the tracking ready to track the next polished interval + ''' + #self.tick = 99 #So it will print right away. + self.polish_interval_num += 1 + self.polish_a, self.polish_b = self.polish_intervals[self.polish_interval_num] + self.total_area = np.prod(self.polish_b-self.polish_a) + self.current_area = 0. + + def get_subintervals(self, a, b, polys, errors, runChecks): + """Gets the subintervals to divide a search interval into. + + Parameters + ---------- + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + dimensions : numpy array + The dimensions we want to cut in half. + polys : list + A list of MultiCheb polynomials representing the function approximations on the + interval to subdivide. Used in the subinterval checks. + errors: list of floats + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + subintervals : list + Each element of the list is a tuple containing an a and b, the lower and upper bounds of the interval. + """ + #Try to find a bounding interval + boundingSize = np.inf + if len(self.__intervalReductionMethodsToUse) != 0: + boundingInterval = getBoundingInterval(polys, errors, self.__intervalReductionMethodsToUse) + else: + boundingInterval = None + if boundingInterval is not None: + boundingSize = np.product(boundingInterval[1] - boundingInterval[0]) + boundingInterval = transform(boundingInterval, a, b) + #See we should use it + if boundingSize == 0: + self.track_interval_bounded(getBoundingInterval.__name__, [a,b], boundingInterval) + return [] + elif boundingSize < 0.5: #Something to think about + self.track_interval_bounded(getBoundingInterval.__name__, [a,b], boundingInterval) + return [boundingInterval] + + #Default to keeping everything + self.mask.fill(True) + + #For getting the subintervals + temp1 = b - a + temp2 = b + a + + #Create the new intervals based on the ones we are keeping + newIntervals = self.subintervals.copy() + newIntervals[:,:1,:] = (newIntervals[:,:1,:] * temp1 + temp2) / 2 + newIntervals[:,1:,:] = (newIntervals[:,1:,:] * temp1 + temp2) / 2 + + thrownOuts = [] + if runChecks: + #Run checks to set mask to False + for check in self.subinterval_checks: + for poly,error in zip(polys, errors): + #The function returns things we should throw out + throwOutMask = check(poly, self.mask, error, self.RAND, self.subintervals) + #Throw stuff out + thrownOutIntervals = newIntervals[throwOutMask] + for old_a,old_b in thrownOutIntervals: + thrownOuts.append([check.__name__, [old_a,old_b]]) + self.mask &= ~throwOutMask + + if boundingSize < np.sum(self.mask) and boundingSize < 3: #Something to think about + self.track_interval_bounded(getBoundingInterval.__name__, [a,b], boundingInterval) + return [boundingInterval] + + for params in thrownOuts: + self.track_interval(*params) + + return newIntervals[self.mask] + + def check_interval(self, coeff, error, a, b): + ''' Runs the interval checks on the interval [a,b] + + Parameters + ---------- + coeff : numpy array. + The coefficient matrix of the Chebyshev approximation to check. + error: float + The approximation error. + a: numpy array + The lower bounds of the interval to check. + b: numpy array + The upper bounds of the interval to check. + Returns + ------- + check_interval : bool + True if we can throw out the interval. Otherwise False. + ''' + for check in self.interval_checks: + if not check(coeff, error): + if not self.polishing: + self.track_interval(check.__name__, [a,b]) + return True + return False + + def track_interval(self, name, interval): + ''' Stores what happened to a given interval + + Parameters + ---------- + name : string + The name of the check or process (Macaulay, Base Case, Too Deep) that solved this interval + interval: list + [a,b] where a and b are the lower and upper bound of the interval to track. + ''' + if not self.polishing: + self.interval_results[name].append(interval) + self.current_area += np.prod(interval[1] - interval[0]) + + def track_interval_bounded(self, name, interval, bounding_interval): + ''' Stores what happened to a given interval when we use a new bounding interval inside it + Parameters + ---------- + name : string + The name of the check or process (Macaulay, Base Case, Too Deep) that solved this interval + interval: list + [a,b] where a and b are the lower and upper bound of the interval to track. + bounding_interval: list + [a,b] where a and b are the lower and upper bound of the bounding_interval to subdivide into. + ''' + if not self.polishing: + self.interval_results[name].append(interval) + self.current_area += np.prod(interval[1] - interval[0]) - np.prod(bounding_interval[1] - bounding_interval[0]) + + def print_progress(self): + ''' Prints the progress of subdivision solve. Only prints every 100th time this function is + called to save time. + ''' + self.tick += 1 + if self.tick >= 100: + self.tick = 0 + if not self.polishing: + print("\rPercent Finished: {}% ".format(round(100*self.current_area/self.total_area,2)), end='') + else: + print_string = '\rPolishing Round: {}'.format(self.polish_num) + print_string += ' Interval: {}/{}:'.format(self.polish_interval_num, len(self.polish_intervals)) + print_string += " Percent Finished: {}%{}".format(round(100*self.current_area/self.total_area,2), ' '*20) + print(print_string, end='') + + def print_results(self): + ''' Prints the results of subdivision solve, how many intervals there were and what percent were + solve by each check/method. + ''' + results_numbers = np.array([len(self.interval_results[name]) for name in self.interval_results]) + total_intervals = sum(results_numbers) + self.total_intervals = total_intervals + checkers = [name for name in self.interval_results] + print("Total intervals checked was {}".format(total_intervals)) + print("Methods used were {}".format(checkers)) + print("The percent solved by each was {}".format((100*results_numbers / total_intervals).round(4))) + + def plot_results(self, funcs, zeros, plot_intervals, print_plot=True): + ''' Prints the results of subdivision solve. Only works if the functions are two dimensional. + + Parameters + ---------- + funcs : list + A list of the functions the were solved + zeros: numpy array + Each row is a zero of the funcitons + plot_intervals: bool + If true, shows on the plot which areas were solved by which check/method. + ''' + #colors: use alpha = .5, dark green, black, orange roots. Change colors of check info plots + #3D plot with small alpha, matplotlib interactive, animation + #make logo + #make easier to input lower/upper bounds as a list + #plt.figure(dpi=300) + fig,ax = plt.subplots(1) + fig.set_size_inches(6.5, 6.5) + fig.set_dpi(300) + plt.xlim(self.a[0],self.b[0]) + plt.xlabel('$x$') + plt.ylim(self.a[1],self.b[1]) + plt.ylabel('$y$') + plt.title('Zero-Loci and Roots') + + dim = 2 + + #print the contours + contour_colors = ['#003cff','#50c878'] #royal blue and emerald green + x = np.linspace(self.a[0],self.b[0],1000) + y = np.linspace(self.a[1],self.b[1],1000) + X,Y = np.meshgrid(x,y) + for i in range(dim): + if isinstance(funcs[i], Polynomial): + Z = np.zeros_like(X) + for spot,num in np.ndenumerate(X): + Z[spot] = funcs[i]([X[spot],Y[spot]]) + plt.contour(X,Y,Z,levels=[0],colors=contour_colors[i],zorder=20) + else: + plt.contour(X,Y,funcs[i](X,Y),levels=[0],colors=contour_colors[i],zorder=20) + + colors = ['w', '#101010', '#b3b3b3','#707070', '#E8E8E8', '#D3D3D3','#202020','#303030'] + #colors = ['w','#c3c3c3', 'C8', '#708090', '#897A57', '#D6C7A4','#73e600','#ccff99'] + #colors = ['w','#d3d3d3', '#708090', '#c5af7d', '#897A57', '#D6C7A4','#73e600','#ccff99'] + + if plot_intervals: + plt.title('Interval Tracking') + #plt.title('What happened to the intervals') + #plot results + i = -1 + ordering = {i:i for i in range(len(self.interval_results))} + ordering[1] = 9 + ordering[4] = 0 + ordering[0] = 2 + for check in self.interval_results: + i += 1 + results = self.interval_results[check] + first = True + for data in results: + a0,b0 = data + if first: + first = False + rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1], + linewidth=.1, edgecolor='k', + facecolor=colors[i], + zorder=ordering[i], label=check) + else: + rect = patches.Rectangle((a0[0],a0[1]),b0[0]-a0[0],b0[1]-a0[1], + linewidth=.1, edgecolor='k', + facecolor=colors[i], zorder=ordering[i]) + ax.add_patch(rect) + plt.legend() + + #Plot the zeros + if len(zeros) > 0: + ax.plot(np.real(zeros[:,0]), np.real(zeros[:,1]),'o',color='#ff0000',markeredgecolor='#ff0000',markersize=3,alpha=0.5, + zorder=22) + + if print_plot: + plt.savefig('intervals.pdf', bbox_inches='tight') + plt.show() + +def getBoundingInterval(coeffs, errors, intervalReductionMethodsToUse): + numPolys = len(coeffs) + if numPolys == 0: + return None + dim = coeffs[0].ndim + if numPolys != dim: + return None + elif numPolys == 2: + return getBoundingInterval2D(coeffs, errors, intervalReductionMethodsToUse) + else: + return getBoundingIntervalND(coeffs, errors, intervalReductionMethodsToUse) + +def mergeIntervals(intervals): + if len(intervals) == 0: + return [-1, 1] + result = [max([interval[0] for interval in intervals]), min([interval[1] for interval in intervals])] + if result[0] > result[1]: + return [0,0] + return result + +def boundingIntervalWidthAndBoundCheck(interval): + MIN_WIDTH = .01 + a,b = interval + + #Bound a,b by [-1,1] + a = max(min(a,1),-1) + b = max(min(b,1),-1) + #If the interval is now empty, return + if a == b: + return [0,0] + + #Apply the minnimum width + width = (b-a) + if width < MIN_WIDTH: + center = (a+b)/2 + a = center - MIN_WIDTH/2 + b = center + MIN_WIDTH/2 + #Bound a,b by [-1,1] again it case it is now outside it + a = max(min(a,1),-1) + b = max(min(b,1),-1) + return [a,b] + +def improveBound2D(intervals, x_terms, y_terms, consts, errors): + """Get a basic bound on x from y being in [-1, 1], and on y from + x being in [-1, 1]. + + Parameters + ---------- + intervals : list + A list of bounds found for each variable x_i. + x_terms : list + A list of coefficients of the x terms of the polynomials in the 2D system. + y_terms : list + A list of coefficients of the y terms of the polynomials in the 2D system. + consts : list + An array of all the constant terms of the polynomials in our + system of equations. + errors : numpy array + The total error with the x, y and constant terms subtracted off. + + Returns + ------- + allIntervals : list + A list of bounds found for each variable x_i, with each new + bound found added. + """ + allIntervals = copy(intervals) + #Get a basic bound on X from y being in [-1, 1] + if x_terms[0] != 0: + width = (abs(errors[0]) + abs(y_terms[0])) / abs(x_terms[0]) + center = -consts[0]/x_terms[0] + allIntervals[0].append([center - width, center + width]) + if x_terms[1] != 0: + width = (abs(errors[1]) + abs(y_terms[1])) / abs(x_terms[1]) + center = -consts[1]/x_terms[1] + allIntervals[0].append([center - width, center + width]) + #Get a basic bound on Y from x being in [-1, 1] + if y_terms[0] != 0: + width = (abs(errors[0]) + abs(x_terms[0])) / abs(y_terms[0]) + center = -consts[0]/y_terms[0] + allIntervals[1].append([center - width, center + width]) + if y_terms[1] != 0: + width = (abs(errors[1]) + abs(x_terms[1])) / abs(y_terms[1]) + center = -consts[1]/y_terms[1] + allIntervals[1].append([center - width, center + width]) + + return allIntervals + +def improveBoundND(intervals, A, consts, errors): + """Get a basic bound on x_i from x_j, i != j, being + in [-1, 1]. + + Parameters + ---------- + intervals : numpy array + A list of bounds found for each variable x_i. + A : numpy array + An array of all linear terms for the polynomials + in our system of equations. + consts : numpy array + An array of all the constant terms of the polynomials in our + system of equations. + errors : numpy array + The total error with the sum of linear terms and constant terms subtracted off. + + Returns + ------- + allIntervals : numpy array + A list of bounds found for each variable x_i, with each new + bound found added. + """ + allIntervals = copy(intervals) + dim = len(allIntervals) + #Get a basic bound on each variable from the others being in [-1, 1] + for funcNum in range(dim): + totalError = sum([abs(num) for num in A[funcNum]]) + abs(errors[funcNum]) + for var in range(dim): + if abs(A[funcNum][var]) == 0: + continue + width = totalError / abs(A[funcNum][var]) - 1 + center = -consts[funcNum]/A[funcNum][var] + allIntervals[var].append([center - width, center + width]) + + return allIntervals + +def getBoundingParallelogram2D(intervals, x_terms, y_terms, consts, errors): + """Get the bounding parallelogram given the x, y and constant terms from + the 2D system of polynomials. + + Parameters + ---------- + intervals : list + A list of bounds found for each variable x_i. + x_terms : list + A list of coefficients of the x terms of the polynomials in the 2D system. + y_terms : list + A list of coefficients of the y terms of the polynomials in the 2D system. + consts : list + An array of all the constant terms of the polynomials in our + system of equations. + errors : numpy array + The total error with the x, y and constant terms subtracted off. + + Returns + ------- + allIntervals : list + A list of bounds found for each variable x_i, with each new + bound found added. + """ + allIntervals = copy(intervals) + #Get a bound from the parallelogram + denom = x_terms[0]*y_terms[1] - x_terms[1]*y_terms[0] + if denom != 0: + yCenter = (x_terms[1]*consts[0]-x_terms[0]*consts[1])/denom + xCenter = (y_terms[0]*consts[1]-y_terms[1]*consts[0])/denom + yWidth = (abs(x_terms[1]*errors[0]) + abs(x_terms[0]*errors[1]))/abs(denom) + xWidth = (abs(y_terms[1]*errors[0]) + abs(y_terms[0]*errors[1]))/abs(denom) + allIntervals[0].append([xCenter - xWidth, xCenter + xWidth]) + allIntervals[1].append([yCenter - yWidth, yCenter + yWidth]) + + return allIntervals + +def getBoundingParallelogramND(intervals, A, consts, errors): + """ + Get the bounding parallelogram given the coefficient arrays of the polynomials + in the system, the constant terms, and the required errors. + + Parameters + ---------- + intervals : numpy array + A list of bounds found for each variable x_i. + A : numpy array + An array of all linear terms for the polynomials + in our system of equations. + consts : numpy array + An array of all the constant terms of the polynomials in our + system of equations. + errors : numpy array + The total error with the sum of linear terms and constant terms subtracted off. + + Returns + ------- + allIntervals : numpy array + A list of bounds found for each variable x_i, with each new + bound found added. + """ + allIntervals = copy(intervals) + dim = len(allIntervals) + #right hand sides + B = np.array([-consts+np.array(err_comb) for err_comb in product(*[(e,-e) for e in errors])]).T + #solve for corners of parallelogram + #We should probably check to make sure A is full rank first? + X = la.solve(A,B) + #find the bounding interval + a = np.min(X,axis=1) + b = np.max(X,axis=1) + for i in range(dim): + allIntervals[i].append([a[i], b[i]]) + + return allIntervals + +INTERVAL_REDUCTION_FUNCS_2D = [improveBound2D, getBoundingParallelogram2D] +INTERVAL_REDUCTION_FUNCS_ND = [improveBoundND, getBoundingParallelogramND] + +def getBoundingInterval2D(coeffs, errors, intervalReductionMethodsToUse): + P1 = coeffs[0] + P2 = coeffs[1] + xIntervals = [] + yIntervals = [] + + #Get Variables for Calculations + a1 = P1[1,0] + b1 = P1[0,1] + c1 = P1[0,0] + e1 = np.sum(np.abs(P1)) - abs(a1) - abs(b1) - abs(c1) + errors[0] + a2 = P2[1,0] + b2 = P2[0,1] + c2 = P2[0,0] + e2 = np.sum(np.abs(P2)) - abs(a2) - abs(b2) - abs(c2) + errors[1] + + # Run through all of the interval reduction methods specified by the user. + for idx in intervalReductionMethodsToUse: + xIntervals, yIntervals = INTERVAL_REDUCTION_FUNCS_2D[idx]([xIntervals, yIntervals], [a1, a2], [b1, b2], [c1, c2], [e1, e2]) + + #Merge the intervals and check the bounds and min width + xInterval = boundingIntervalWidthAndBoundCheck(mergeIntervals(xIntervals)) + yInterval = boundingIntervalWidthAndBoundCheck(mergeIntervals(yIntervals)) + + return np.array([xInterval, yInterval]).T + +def getBoundingIntervalND(test_coeffs, tols, intervalReductionMethodsToUse): + dim = len(test_coeffs) + allIntervals = [[] for i in range(dim)] + + #Solve the bounding parallelogram + #create the linear system + dim = len(test_coeffs) + A = np.array([coeff[tuple(get_var_list(dim))] for coeff in test_coeffs]) + #compute the error terms + consts = np.array([coeff[tuple([0]*dim)] for coeff in test_coeffs]) + linear_sums = np.sum(np.abs(A),axis=1) + err = np.array([np.sum(np.abs(coeff))+tol - fabs(c) - l for coeff,tol,c,l in zip(test_coeffs,tols,consts,linear_sums)]) + + # Run through all the interval reduction methods specified by the user. + for idx in intervalReductionMethodsToUse: + allIntervals = INTERVAL_REDUCTION_FUNCS_ND[idx](allIntervals, A, consts, err) + + #Merge the intervals and check the bounds and min width + for i in range(dim): + allIntervals[i] = boundingIntervalWidthAndBoundCheck(mergeIntervals(allIntervals[i])) + + return np.array(allIntervals).T + +def constant_term_check(test_coeff, tol): + """One of interval_checks + + Checks if the constant term is bigger than all the other terms combined, using the fact that + each Chebyshev monomial is bounded by 1. + + Parameters + ---------- + test_coeff : numpy array + The coefficient matrix of the polynomial to check + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + constant_term_check : bool + False if the function is guarenteed to never be zero in the unit box, True otherwise + """ + test_sum = np.sum(np.abs(test_coeff)) + if abs(test_coeff[tuple([0]*test_coeff.ndim)]) * 2 > test_sum + tol: + return False + else: + return True + +def quadratic_check(test_coeff, mask, tol, RAND, subintervals): + """One of subinterval_checks + + Finds the min of the absolute value of the quadratic part, and compares to the sum of the + rest of the terms. quadratic_check_2D and quadratic_check_3D are faster so runs those if it can, + otherwise it runs the genereic n-dimensional version. + + Parameters + ---------- + test_coeff_in : numpy array + The coefficient matrix of the polynomial to check + intervals : list + A list of the intervals to check. + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + mask : list + A list of the results of each interval. False if the function is guarenteed to never be zero + in the unit box, True otherwise + """ + if test_coeff.ndim == 2: + return quadratic_check_2D(test_coeff, mask, tol, RAND, subintervals) + elif test_coeff.ndim == 3: + return quadratic_check_3D(test_coeff, mask, tol, RAND, subintervals) + else: + return quadratic_check_nd(test_coeff, mask, tol, RAND, subintervals) + +def quadratic_check_2D(test_coeff, mask, tol, RAND, subintervals): + """One of subinterval_checks + + Finds the min of the absolute value of the quadratic part, and compares to the sum of the + rest of the terms. There can't be a root if min(extreme_values) > other_sum or if + max(extreme_values) < -other_sum. We can short circuit and finish + faster as soon as we find one value that is < other_sum and one value that > -other_sum. + + Parameters + ---------- + test_coeff_in : numpy array + The coefficient matrix of the polynomial to check + intervals : list + A list of the intervals to check. + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + mask : list + A list of the results of each interval. False if the function is guarenteed to never be zero + in the unit box, True otherwise + """ + if test_coeff.ndim != 2: + return mask + + #Get the coefficients of the quadratic part + #Need to account for when certain coefs are zero. + #Padding is slow, so check the shape instead. + c = [0]*6 + shape = test_coeff.shape + c[0] = test_coeff[0,0] + if shape[0] > 1: + c[1] = test_coeff[1,0] + if shape[1] > 1: + c[2] = test_coeff[0,1] + if shape[0] > 2: + c[3] = test_coeff[2,0] + if shape[0] > 1 and shape[1] > 1: + c[4] = test_coeff[1,1] + if shape[1] > 2: + c[5] = test_coeff[0,2] + + # The sum of the absolute values of the other coefs + # Note: Overhead for instantiating a NumPy array is too costly for + # small arrays, so the second sum here is faster than using numpy + other_sum = np.sum(np.abs(test_coeff)) - sum([fabs(coeff) for coeff in c]) + tol + + # Function for evaluating c0 + c1 T_1(x) + c2 T_1(y) +c3 T_2(x) + c4 T_1(x)T_1(y) + c5 T_2(y) + # Use the Horner form because it is much faster, also do any repeated computatons in advance + k0 = c[0]-c[3]-c[5] + k3 = 2*c[3] + k5 = 2*c[5] + def eval_func(x,y): + return k0 + (c[1] + k3 * x + c[4] * y) * x + (c[2] + k5 * y) * y + + #The interior min + #Comes from solving dx, dy = 0 + #Dx: 4c3x + c4y = -c1 Matrix inverse is [4c5 -c4] + #Dy: c4x + 4c5y = -c2 [-c4 4c3] + # This computation is the same for all subintevals, so do it first + det = 16 * c[3] * c[5] - c[4]**2 + if det != 0: + int_x = (c[2] * c[4] - 4 * c[1] * c[5]) / det + int_y = (c[1] * c[4] - 4 * c[2] * c[3]) / det + else: # det is zero, + int_x = np.inf + int_y = np.inf + + throwOutMask = mask.copy().reshape(4) + for i, interval in enumerate(subintervals.reshape(4, 2, 2)): + if not throwOutMask[i]: + continue + throwOutMask[i] = False + min_satisfied, max_satisfied = False,False + #Check all the corners + eval = eval_func(interval[0][0], interval[0][1]) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + eval = eval_func(interval[1][0], interval[0][1]) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + eval = eval_func(interval[0][0], interval[1][1]) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + eval = eval_func(interval[1][0], interval[1][1]) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Check the x constant boundaries + #The partial with respect to y is zero + #Dy: c4x + 4c5y = -c2 => y = (-c2-c4x)/(4c5) + if c[5] != 0: + cc5 = 4 * c[5] + x = interval[0][0] + y = -(c[2] + c[4]*x)/cc5 + if interval[0][1] < y < interval[1][1]: + eval = eval_func(x,y) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + x = interval[1][0] + y = -(c[2] + c[4]*x)/cc5 + if interval[0][1] < y < interval[1][1]: + eval = eval_func(x,y) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Check the y constant boundaries + #The partial with respect to x is zero + #Dx: 4c3x + c4y = -c1 => x = (-c1-c4y)/(4c3) + if c[3] != 0: + cc3 = 4*c[3] + y = interval[0][1] + x = -(c[1] + c[4]*y)/cc3 + if interval[0][0] < x < interval[1][0]: + eval = eval_func(x,y) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + y = interval[1][1] + x = -(c[1] + c[4]*y)/cc3 + if interval[0][0] < x < interval[1][0]: + eval = eval_func(x,y) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Check the interior value + if interval[0][0] < int_x < interval[1][0] and interval[0][1] < int_y < interval[1][1]: + eval = eval_func(int_x,int_y) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + # No root possible + throwOutMask[i] = True + return throwOutMask.reshape(2, 2) + +def quadratic_check_3D(test_coeff, mask, tol, RAND, subintervals): + """One of subinterval_checks + + Finds the min of the absolute value of the quadratic part, and compares to the sum of the + rest of the terms. There can't be a root if min(extreme_values) > other_sum or if + max(extreme_values) < -other_sum. We can short circuit and finish + faster as soon as we find one value that is < other_sum and one value that > -other_sum. + + Parameters + ---------- + test_coeff_in : numpy array + The coefficient matrix of the polynomial to check + intervals : list + A list of the intervals to check. + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + mask : list + A list of the results of each interval. False if the function is guarenteed to never be zero + in the unit box, True otherwise + """ + if test_coeff.ndim != 3: + return mask + + #Padding is slow, so check the shape instead. + c = [0]*10 + shape = test_coeff.shape + c[0] = test_coeff[0,0,0] + if shape[0] > 1: + c[1] = test_coeff[1,0,0] + if shape[1] > 1: + c[2] = test_coeff[0,1,0] + if shape[2] > 1: + c[3] = test_coeff[0,0,1] + if shape[0] > 1 and shape[1] > 1: + c[4] = test_coeff[1,1,0] + if shape[0] > 1 and shape[2] > 1: + c[5] = test_coeff[1,0,1] + if shape[1] > 1 and shape[2] > 1: + c[6] = test_coeff[0,1,1] + if shape[0] > 2: + c[7] = test_coeff[2,0,0] + if shape[1] > 2: + c[8] = test_coeff[0,2,0] + if shape[2] > 2: + c[9] = test_coeff[0,0,2] + + #The sum of the absolute values of everything else + other_sum = np.sum(np.abs(test_coeff)) - sum([fabs(coeff) for coeff in c]) + tol + + #function for evaluating c0 + c1x + c2y +c3z + c4xy + c5xz + c6yz + c7T_2(x) + c8T_2(y) + c9T_2(z) + # Use the Horner form because it is much faster, also do any repeated computatons in advance + k0 = c[0]-c[7]-c[8]-c[9] + k7 = 2*c[7] + k8 = 2*c[8] + k9 = 2*c[9] + def eval_func(x,y,z): + return k0 + (c[1] + k7 * x + c[4] * y + c[5] * z) * x + \ + (c[2] + k8 * y + c[6] * z) * y + \ + (c[3] + k9 * z) * z + + #The interior min + #Comes from solving dx, dy, dz = 0 + #Dx: 4c7x + c4y + c5z = -c1 Matrix inverse is [(16c8c9-c6^2) -(4c4c9-c5c6) (c4c6-4c5c8)] + #Dy: c4x + 4c8y + c6z = -c2 [-(4c4c9-c5c6) (16c7c9-c5^2) -(4c6c7-c4c5)] + #Dz: c5x + c6y + 4c9z = -c3 [(c4c6-4c5c8) -(4c6c7-c4c5) (16c7c8-c4^2)] + #These computations are the same for all subintevals, so do them first + kk7 = 2*k7 #4c7 + kk8 = 2*k8 #4c8 + kk9 = 2*k9 #4c9 + fix_x_det = kk8*kk9-c[6]**2 + fix_y_det = kk7*kk9-c[5]**2 + fix_z_det = kk7*kk8-c[4]**2 + minor_1_2 = kk9*c[4]-c[5]*c[6] + minor_1_3 = c[4]*c[6]-kk8*c[5] + minor_2_3 = kk7*c[6]-c[4]*c[5] + det = 4*c[7]*fix_x_det - c[4]*minor_1_2 + c[5]*minor_1_3 + if det != 0: + int_x = (c[1]*-fix_x_det + c[2]*minor_1_2 + c[3]*-minor_1_3)/det + int_y = (c[1]*minor_1_2 + c[2]*-fix_y_det + c[3]*minor_2_3)/det + int_z = (c[1]*-minor_1_3 + c[2]*minor_2_3 + c[3]*-fix_z_det)/det + else: + int_x = np.inf + int_y = np.inf + int_z = np.inf + + throwOutMask = mask.copy().reshape(8) + for i, interval in enumerate(subintervals.reshape(8, 2, 3)): + if not throwOutMask[i]: + continue + throwOutMask[i] = False + #easier names for each value... + x0 = interval[0][0] + x1 = interval[1][0] + y0 = interval[0][1] + y1 = interval[1][1] + z0 = interval[0][2] + z1 = interval[1][2] + + min_satisfied, max_satisfied = False,False + #Check all the corners + eval = eval_func(x0, y0, z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x1, y0, z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x0, y1, z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x0, y0, z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x1, y1, z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x1, y0, z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x0, y1, z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + eval = eval_func(x1, y1, z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + #Adds the x and y constant boundaries + #The partial with respect to z is zero + #Dz: c5x + c6y + 4c9z = -c3 => z=(-c3-c5x-c6y)/(4c9) + if c[9] != 0: + c5x0_c3 = c[5]*x0 + c[3] + c6y0 = c[6]*y0 + z = -(c5x0_c3+c6y0)/kk9 + if z0 < z < z1: + eval = eval_func(x0,y0,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c6y1 = c[6]*y1 + z = -(c5x0_c3+c6y1)/kk9 + if z0 < z < z1: + eval = eval_func(x0,y1,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c5x1_c3 = c[5]*x1 + c[3] + z = -(c5x1_c3+c6y0)/kk9 + if z0 < z < z1: + eval = eval_func(x1,y0,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + z = -(c5x1_c3+c6y1)/kk9 + if z0 < z < z1: + eval = eval_func(x1,y1,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Adds the x and z constant boundaries + #The partial with respect to y is zero + #Dy: c4x + 4c8y + c6z = -c2 => y=(-c2-c4x-c6z)/(4c8) + if c[8] != 0: + c6z0 = c[6]*z0 + c2_c4x0 = c[2]+c[4]*x0 + y = -(c2_c4x0+c6z0)/kk8 + if y0 < y < y1: + eval = eval_func(x0,y,z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c6z1 = c[6]*z1 + y = -(c2_c4x0+c6z1)/kk8 + if y0 < y < y1: + eval = eval_func(x0,y,z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c2_c4x1 = c[2]+c[4]*x1 + y = -(c2_c4x1+c6z0)/kk8 + if y0 < y < y1: + eval = eval_func(x1,y,z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + y = -(c2_c4x1+c6z1)/kk8 + if y0 < y < y1: + eval = eval_func(x1,y,z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Adds the y and z constant boundaries + #The partial with respect to x is zero + #Dx: 4c7x + c4y + c5z = -c1 => x=(-c1-c4y-c5z)/(4c7) + if c[7] != 0: + c1_c4y0 = c[1]+c[4]*y0 + c5z0 = c[5]*z0 + x = -(c1_c4y0+c5z0)/kk7 + if x0 < x < x1: + eval = eval_func(x,y0,z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c5z1 = c[5]*z1 + x = -(c1_c4y0+c5z1)/kk7 + if x0 < x < x1: + eval = eval_func(x,y0,z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c1_c4y1 = c[1]+c[4]*y1 + x = -(c1_c4y1+c5z0)/kk7 + if x0 < x < x1: + eval = eval_func(x,y1,z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + x = -(c1_c4y1+c5z1)/kk7 + if x0 < x < x1: + eval = eval_func(x,y1,z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Add the x constant boundaries + #The partials with respect to y and z are zero + #Dy: 4c8y + c6z = -c2 - c4x Matrix inverse is [4c9 -c6] + #Dz: c6y + 4c9z = -c3 - c5x [-c6 4c8] + if fix_x_det != 0: + c2_c4x0 = c[2]+c[4]*x0 + c3_c5x0 = c[3]+c[5]*x0 + y = (-kk9*c2_c4x0 + c[6]*c3_c5x0)/fix_x_det + z = (c[6]*c2_c4x0 - kk8*c3_c5x0)/fix_x_det + if y0 < y < y1 and z0 < z < z1: + eval = eval_func(x0,y,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c2_c4x1 = c[2]+c[4]*x1 + c3_c5x1 = c[3]+c[5]*x1 + y = (-kk9*c2_c4x1 + c[6]*c3_c5x1)/fix_x_det + z = (c[6]*c2_c4x1 - kk8*c3_c5x1)/fix_x_det + if y0 < y < y1 and z0 < z < z1: + eval = eval_func(x1,y,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Add the y constant boundaries + #The partials with respect to x and z are zero + #Dx: 4c7x + c5z = -c1 - c40 Matrix inverse is [4c9 -c5] + #Dz: c5x + 4c9z = -c3 - c6y [-c5 4c7] + if fix_y_det != 0: + c1_c4y0 = c[1]+c[4]*y0 + c3_c6y0 = c[3]+c[6]*y0 + x = (-kk9*c1_c4y0 + c[5]*c3_c6y0)/fix_y_det + z = (c[5]*c1_c4y0 - kk7*c3_c6y0)/fix_y_det + if x0 < x < x1 and z0 < z < z1: + eval = eval_func(x,y0,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c1_c4y1 = c[1]+c[4]*y1 + c3_c6y1 = c[3]+c[6]*y1 + x = (-kk9*c1_c4y1 + c[5]*c3_c6y1)/fix_y_det + z = (c[5]*c1_c4y1 - kk7*c3_c6y1)/fix_y_det + if x0 < x < x1 and z0 < z < z1: + eval = eval_func(x,y1,z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Add the z constant boundaries + #The partials with respect to x and y are zero + #Dx: 4c7x + c4y = -c1 - c5z Matrix inverse is [4c8 -c4] + #Dy: c4x + 4c8y = -c2 - c6z [-c4 4c7] + if fix_z_det != 0: + c1_c5z0 = c[1]+c[5]*z0 + c2_c6z0 = c[2]+c[6]*z0 + x = (-kk8*c1_c5z0 + c[4]*c2_c6z0)/fix_z_det + y = (c[4]*c1_c5z0 - kk7*c2_c6z0)/fix_z_det + if x0 < x < x1 and y0 < y < y1: + eval = eval_func(x,y,z0) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + c1_c5z1 = c[1]+c[5]*z1 + c2_c6z1 = c[2]+c[6]*z1 + x = (-kk8*c1_c5z1 + c[4]*c2_c6z1)/fix_z_det + y = (c[4]*c1_c5z1 - kk7*c2_c6z1)/fix_z_det + if x0 < x < x1 and y0 < y < y1: + eval = eval_func(x,y,z1) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + #Add the interior value + if x0 < int_x < x1 and y0 < int_y < y1 and\ + z0 < int_z < z1: + eval = eval_func(int_x,int_y,int_z) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + continue + + # No root possible + throwOutMask[i] = True + return throwOutMask.reshape(2, 2, 2) + +@memoize +def get_fixed_vars(dim): + """Used in quadratic_check_nd to iterate through the boundaries of the domain. + + Parameters + ---------- + dim : int + The dimension of the domain/system. + + Returns + ------- + list of tuples + A list of tuples indicating which variables to fix in each iteration, + starting at fixing dim-1 of them and ending with fixing 1 of them. This + intentionally excludes combinations that correspond to the corners of the + domain and the interior extremum. + """ + return list(itertools.chain.from_iterable(itertools.combinations(range(dim), r)\ + for r in range(dim-1,0,-1))) + +def quadratic_check_nd(test_coeff, mask, tol, RAND, subintervals): + """One of subinterval_checks + + Finds the min of the absolute value of the quadratic part, and compares to the sum of the + rest of the terms. There can't be a root if min(extreme_values) > other_sum or if + max(extreme_values) < -other_sum. We can short circuit and finish + faster as soon as we find one value that is < other_sum and one value that > -other_sum. + + Parameters + ---------- + test_coeff_in : numpy array + The coefficient matrix of the polynomial to check + intervals : list + A list of the intervals to check. + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + mask : list + A list of the results of each interval. False if the function is guarenteed to never be zero + in the unit box, True otherwise + """ + #get the dimension and make sure the coeff tensor has all the right + # quadratic coeff spots, set to zero if necessary + dim = test_coeff.ndim + padding = [(0,max(0,3-i)) for i in test_coeff.shape] + test_coeff = np.pad(test_coeff.copy(), padding, mode='constant') + + #Possible extrema of qudaratic part are where D_xk = 0 for some subset of the variables xk + # with the other variables are fixed to a boundary value + #Dxk = c[0,...,0,1,0,...0] (k-spot is 1) + 4c[0,...,0,2,0,...0] xk (k-spot is 2) + # + \Sum_{j\neq k} xj c[0,...,0,1,0,...,0,1,0,...0] (k and j spot are 1) + #This gives a symmetric system of equations AX+B = 0 + #We will fix different columns of X each time, resulting in slightly different + #systems, but storing A and B now will be helpful later + + #pull out coefficients we care about + quad_coeff = np.zeros([3]*dim) + #A and B are arrays for slicing + A = np.zeros([dim,dim]) + B = np.zeros(dim) + pure_quad_coeff = [0]*dim + for spot in itertools.product(range(3),repeat=dim): + spot_deg = sum(spot) + if spot_deg == 1: + #coeff of linear terms + i = [idx for idx in range(dim) if spot[idx]!= 0][0] + B[i] = test_coeff[spot].copy() + quad_coeff[spot] = test_coeff[spot] + test_coeff[spot] = 0 + elif spot_deg == 0: + #constant term + const = test_coeff[spot].copy() + quad_coeff[spot] = const + test_coeff[spot] = 0 + elif spot_deg < 3: + where_nonzero = [idx for idx in range(dim) if spot[idx]!= 0] + if len(where_nonzero) == 2: + #coeff of cross terms + i,j = where_nonzero + #with symmetric matrices, we only need to store the lower part + A[j,i] = test_coeff[spot].copy() + A[i,j] = A[j,i] + #todo: see if we can store this in only one half of A + + else: + #coeff of pure quadratic terms + i = where_nonzero[0] + pure_quad_coeff[i] = test_coeff[spot].copy() + quad_coeff[spot] = test_coeff[spot] + test_coeff[spot] = 0 + pure_quad_coeff_doubled = [p*2 for p in pure_quad_coeff] + A[np.diag_indices(dim)] = [p*2 for p in pure_quad_coeff_doubled] + + #create a poly object for evals + k0 = const - sum(pure_quad_coeff) + def eval_func(point): + "fast evaluation of quadratic chebyshev polynomials using horner's algorithm" + _sum = k0 + for i,coord in enumerate(point): + _sum += (B[i] + pure_quad_coeff_doubled[i]*coord + \ + sum([A[i,j]*point[j] for j in range(i+1,dim)])) * coord + return _sum + + #The sum of the absolute values of everything else + other_sum = np.sum(np.abs(test_coeff)) + tol + + #iterator for sides + fixed_vars = get_fixed_vars(dim) + + throwOutMask = mask.copy().reshape(2**dim) + for k, interval in enumerate(subintervals.reshape(*[2**dim, 2, dim])): + if not throwOutMask[k]: + continue + throwOutMask[k] = False + + Done = False + min_satisfied, max_satisfied = False,False + #fix all variables--> corners + for corner in itertools.product([0,1],repeat=dim): + #j picks if upper/lower bound. i is which var + eval = eval_func([interval[j][i] for i,j in enumerate(corner)]) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + Done = True + break + #need to check sides/interior + if not Done: + X = np.zeros(dim) + for fixed in fixed_vars: + #fixed some variables --> "sides" + #we only care about the equations from the unfixed variables + fixed = np.array(fixed) + unfixed = np.delete(np.arange(dim), fixed) + A_ = A[unfixed][:,unfixed] + #if diagonal entries change sign, can't be definite + diag = np.diag(A_) + for i,c in enumerate(diag[:-1]): + #sign change? + if c*diag[i+1]<0: + break + #if no sign change, can find extrema + else: + #not full rank --> no soln + if np.linalg.matrix_rank(A_,hermitian=True) == A_.shape[0]: + fixed_A = A[unfixed][:,fixed] + B_ = B[unfixed] + for side in itertools.product([0,1],repeat=len(fixed)): + X0 = np.array([interval[j][i] for i,j in enumerate(side)]) + X_ = la.solve(A_, -B_-fixed_A@X0, assume_a='sym') + #make sure it's in the domain + for i,var in enumerate(unfixed): + if interval[0][var] <= X_[i] <= interval[1][var]: + continue + else: + break + else: + X[fixed] = X0 + X[unfixed] = X_ + eval = eval_func(X) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + Done = True + break + if Done: + break + else: + #fix no vars--> interior + #if diagonal entries change sign, can't be definite + for i,c in enumerate(pure_quad_coeff[:-1]): + #sign change? + if c*pure_quad_coeff[i+1]<0: + break + #if no sign change, can find extrema + else: + #not full rank --> no soln + if np.linalg.matrix_rank(A,hermitian=True) == A.shape[0]: + X = la.solve(A, -B, assume_a='sym') + #make sure it's in the domain + for i in range(dim): + if interval[0][i] <= X[i] <= interval[1][i]: + continue + else: + break + else: + eval = eval_func(X) + min_satisfied = min_satisfied or eval < other_sum + max_satisfied = max_satisfied or eval > -other_sum + if min_satisfied and max_satisfied: + Done = True + #no root + if not Done: + throwOutMask[k] = True + + return throwOutMask.reshape(*[2]*dim) + +def slices_max_min_check(test_coeff, intervals, tol): + dim = test_coeff.ndim + #at first just implement WRT x + mask = [True]*len(intervals) + #pull out the slices + # min_slice = + + for i, interval in enumerate(intervals): + Done = False + #check interval + + #no root + if not Done: + mask[i] = False + + return mask diff --git a/numalgsolve/LinearProjection.py b/yroots/LinearProjection.py similarity index 50% rename from numalgsolve/LinearProjection.py rename to yroots/LinearProjection.py index 091d8aab..09a88396 100644 --- a/numalgsolve/LinearProjection.py +++ b/yroots/LinearProjection.py @@ -1,14 +1,59 @@ from itertools import product import numpy as np -from numpy.fft.fftpack import fftn -from numalgsolve.utils import get_var_list -from numalgsolve.polynomial import MultiCheb, MultiPower, Polynomial -from numalgsolve.subdivision import interval_approximate_nd, trim_coeff,\ - chebyshev_block_copy +from scipy.fftpack import fftn +from yroots.utils import get_var_list +from yroots.polynomial import Polynomial, MultiCheb from scipy.linalg import qr -def project_down(polys, linear): +def remove_linear(polys, approx_tol, solve_tol, transform_in=None): + """This function recursively removes linear polynomials from a list by + applying the project_down function once for each linear polynomial. + This function assumes these polynomials had the zeros removed already, so that + it can rely on dimensions to detect linear polynomials. + + Parameters + ---------- + polys : list of polynomial objects + Polynomials to find the common roots of. + approx_tol: float + A tolerance to pass into the trim_coeff. + solve_tol : float + A tolerance to pass into the trim_coeff. + transform_in : function + only intended for use in recursion + + Returns + ------- + polys : list of polynomial objects + Polynomials to find the common roots of. + transform : function + A function mapping the roots of the output system to the roots of the + original system. + projected : bool + True is projection was performed, False if no projection + """ + assert len(polys) > 0 + transform_in = transform_in or (lambda x:x) + if len(polys) == 1: + if polys[0].shape[0] <= 2: + raise ValueError("All of the polynomials were linear.") + else: + return polys, transform_in + for poly in polys: + max_deg = max(poly.shape) - 1 + if max_deg < 2: + polys_copy = polys[:] + polys_copy.remove(poly) + new_polys, transform2 = project_down(polys_copy, poly.coeff, approx_tol, solve_tol) + new_polys = list(map(MultiCheb, new_polys)) + transform3 = lambda x: transform_in(transform2(x)) + ret_polys, transform4, _ = remove_linear(new_polys, approx_tol, solve_tol, transform3) + return ret_polys, lambda x: transform4(x), True + else: + return polys, transform_in, False + +def project_down(polys, linear, approx_tol, solve_tol): """This function reduces the dimension of a polynomial system when one of functions is linear. For polynomials in n variables, it uses an affine transformation that maps the (n-1) dimensional hyper-square to cover the @@ -50,10 +95,13 @@ def project_down(polys, linear): for p in polys: proj_poly_coeff.append(proj_approximate_nd(p, T)) - return proj_poly_coeff, T + if len(polys) > 1: + from yroots.subdivision import trim_coeffs + return trim_coeffs(proj_poly_coeff, approx_tol)[0], T + else: + return proj_poly_coeff, T - -def proj_approximate_nd(f,transform): +def proj_approximate_nd(f, transform): """Finds the chebyshev approximation of an n-dimensional function on the affine transformation of hypercube. @@ -69,19 +117,17 @@ def proj_approximate_nd(f,transform): coeffs : numpy array The coefficient of the chebyshev interpolating polynomial. """ + from yroots.subdivision import chebyshev_block_copy dim = f.dim proj_dim = dim-1 deg = f.degree degs = np.array([deg]*proj_dim) - # assert hasattr(f,"evaluate_grid") - # dang, we don't get to use evaluate_grid here - cheb_values = np.cos(np.arange(deg+1)*np.pi/deg) cheb_grids = np.meshgrid(*([cheb_values]*proj_dim), indexing='ij') flatten = lambda x: x.flatten() - cheb_points = transform(np.column_stack(map(flatten, cheb_grids))) + cheb_points = transform(np.column_stack(tuple(map(flatten, cheb_grids)))) values_block = f(cheb_points).reshape(*([deg+1]*proj_dim)) values = chebyshev_block_copy(values_block) coeffs = np.real(fftn(values/np.product(degs))) @@ -102,7 +148,7 @@ def proj_approximate_nd(f,transform): for i in range(proj_dim): slices.append(slice(0,degs[i]+1)) - return trim_coeff(coeffs[tuple(slices)]) + return coeffs[tuple(slices)] def bounding_parallelepiped(linear): """ @@ -116,7 +162,7 @@ def bounding_parallelepiped(linear): Second Note: This first attempt is very simple, and can be greatly improved by creating a parallelepiped that much more closely surrounds the points. - Currently, it just makes a rectangle. + Currently, it just makes an nd-rectangle. Parameters ---------- @@ -145,20 +191,19 @@ def bounding_parallelepiped(linear): upper = np.ones(dim) vert = [] for i in range(dim): - for pt in product(*coord): - val = -const - skipped = 0 - for j,c in enumerate(coeff): - if i==j: - skipped = 1 - continue - val -= c*pt[j-skipped] - one_point = list(pt) - if not np.isclose(coeff[i], 0): - one_point.insert(i,val/coeff[i]) - one_point = np.array(one_point) - if np.all(lower <= one_point) and np.all(one_point <= upper): - vert.append(one_point) + pts = np.array([(pt[:i]+ (0,) + pt[i:]) for pt in product(*coord)]) + val = -const + for j,c in enumerate(coeff): + if i==j: + continue + val = val - c*pts[:,j] + if not np.isclose(coeff[i], 0): + pts[:,i] = val/coeff[i] + else: + pts[:,i] = np.nan + mask = np.all(lower <= pts, axis=1) & np.all(pts <= upper, axis=1) + if np.any(mask): + vert.append(pts[mask]) # what to do if no intersections if len(vert) == 0: @@ -166,10 +211,9 @@ def bounding_parallelepiped(linear): Q, R = np.linalg.qr(np.column_stack([coeff, np.eye(dim)[:,:dim-1]])) edges = Q[:,1:] return p0, edges - # raise Exception("What do I do!?") # do the thing - vert = np.unique(np.array(vert), axis=0) + vert = np.unique(np.vstack(vert), axis=0) v0 = vert[0] vert_shift = vert - v0 Q, vert_flat, _ = qr(vert_shift.T, pivoting=True) @@ -180,3 +224,54 @@ def bounding_parallelepiped(linear): p0 = Q[:,:-1].dot(min_vals) + v0 edges = Q[:,:-1].dot(np.diag(max_vals-min_vals)) return p0, edges + +def nullspace(linear_polys): + """Builds a matrix to represent the system of linear polynomials. + Columns 1:-1 represent coefficients of linear terms, while Column -1 represents + the constant terms. + Parameters + ---------- + linear_polys : list + list of linear MultiCheb objects + Returns + ------- + A: ((n,n) ndarray) + The RREF of A. + Pc: ((n,) ndarray) + The column pivoting array. + """ + dim = linear_polys[0].dim + A = np.zeros((len(linear_polys),dim+1)) + for i,p in enumerate(linear_polys): + A[i,:-1] = p.coeff[tuple(get_var_list(dim))] + A[i,-1] = p.coeff[tuple([0]*dim)] + + return rref(A) + +def rref(A): + """Reduce the square matrix A to RREF with full pivoting. + Parameters: + A ((n,n) ndarray): The matrix to be reduced. + Returns: + ((n,n) ndarray): The RREF of A, with columns pivoted as the algorithm chooses + ((n,) ndarray): The column pivoting array. + """ + A = np.array(A, dtype=np.float, copy=True) + m,n = A.shape + Pr = np.arange(m) + Pc = np.arange(n) + for j in range(m): + row,col = np.where(np.abs(A[j:,j:-1])==np.abs(A[j:,j:-1]).max()) + k,l = row[0]+j,col[0]+j + A[[j,k]] = A[[k,j]] + Pr[j],Pr[k] = Pr[k],Pr[j] + A[:,np.array([j,l])] = A[:,np.array([l,j])] + Pc[j],Pc[l] = Pc[l],Pc[j] + if A[j,j] == 0: + #handle rank deficient case + return A[:j],Pc + for i in range(m): + if i != j: + A[i,j:] -= A[j,j:] * A[i,j] / A[j,j] + A[j] = A[j]/A[j,j] + return A,Pc diff --git a/yroots/M_maker.py b/yroots/M_maker.py new file mode 100644 index 00000000..3417b8d5 --- /dev/null +++ b/yroots/M_maker.py @@ -0,0 +1,358 @@ +import numpy as np +from yroots.utils import transform, slice_top +from scipy.fftpack import fftn +from itertools import product + +class M_maker: + def __init__(self,f,a,b,guess_deg,rel_approx_tol=1.e-15, abs_approx_tol=1.e-12): + """ + Used to find M, an array of Chebyshev coefficients. + + Attributes + ---------- + dim: int + the dimension of the space we approximate in + f: vectorized, callable function + the function from R^n --> R that we approximate + a: ndarray + the lower bounds on the region + b: ndarray + the upper bounds on the region + rel_approx_tol: float + relative approximation tolerance + abs_approx_tol: float + absolute approximation tolerance + memo_dict: dictionary + the evaluation of an approximating polynomial at each chebyshev point in the region + keys are degree, values are arrays of evaluations + deg: int + the degree of the approximation + values_block: ndarray + the evaluation of the approximating polynomial at the chebyshev critical points in the region + err: float + the error on the approximation + M: array + the coefficient tensor + M2: array + the coefficient tensor of double degree + M_rescaled: array + the coeffficient tensor divided by inf_norm + inf_norm: float + the max of the absolute values of the coefficients + + Parameters + ---------- + f: vectorized, callable function + the function from R^n --> R that we approximate + a: ndarray + the lower bounds on the region + b: ndarray + the upper bounds on the region + guess_deg: int + the user's guess on the degree of approximation + rescale: bool + whether to rescale by self.inf_norm or not + rel_approx_tol: float + relative approximation tolerance + abs_approx_tol: float + absolute approximation tolerance + + Methods + ------- + error_test: determines whether the approximation is sufficiently accurate + find_good_deg: uses error_test by doubling up to a good deg until error_test is passed + interval_approximate_nd: calculates the chebyshev coefficients + chebyshev_block_copy: preparatory step to Fast Fourier Transform, so as to save time complexity in getting the chebyshev coeffs + block_copy_slicers: slicers to make the block copy of the values block + interval_approx_slicers: slicers to make the whole approximation + """ + self.max_deg = {1: 100000, 2:1000, 3:9, 4:9, 5:2, 6:2, 7:2, 8:2, 9:2, 10:2} #need to experiment with this + + dim = len(a) + if dim != len(b): + raise ValueError("dimension mismatch") + self.dim = dim + self.f = f + self.a = a + self.b = b + self.rel_approx_tol = rel_approx_tol + self.abs_approx_tol = abs_approx_tol + self.memo_dict = {} + + self.values_block = None + self.M = None + self.M2 = None + self.err = None + self.inf_norm = None + + self.find_good_approx(f,guess_deg,dim,a,b) + self.M_rescaled = self.M / self.inf_norm + self.values_block = list(self.memo_dict.values())[-2] + + def get_err(self,M,M2): + """ + Calculates the error of the approximation + + Parameters + ---------- + M: array + The coefficient tensor + M2: array + The coefficient tensor for a double degree approximation + + Returns + ------- + (float) the error + """ + coeff2 = M2.copy() + coeff2[slice_top(M.shape)] -= M + return np.sum(np.abs(coeff2)) + + def error_test(self,error,abs_approx_tol,rel_approx_tol,inf_norm): + """ + Determines whether the approximation is within the error tolerance + + Parameters + ---------- + error: float + The absolute value of the difference of the sum of abs values of M and M2 + rel_approx_tol: float + abs_approx_tol: float + inf_norm: float + the sup norm on the approximation + + Returns + ------- + (bool) if the error test has been passed or not + """ + return error < abs_approx_tol+rel_approx_tol*inf_norm + + def find_good_approx(self,f,deg,dim,a,b): + """ + Finds the right degree with which to approximate on the interval. + + Parameters + ---------- + f : function from R^n -> R + The function to interpolate. + deg : numpy array + The degree of the interpolation in each dimension. + dim: int + Dimension + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + + Returns + ------- + deg: the correct approximation degree + """ + self.M, self.inf_norm = self.interval_approximate_nd(f, a, b, deg, return_inf_norm=True) + self.M2 = self.interval_approximate_nd(f,a,b,deg*2) + self.err = self.get_err(self.M,self.M2) + + if deg >= self.max_deg[dim]: + deg = self.max_deg[dim] + + while deg < self.max_deg[dim]: + if self.error_test(self.err,self.abs_approx_tol,self.rel_approx_tol,self.inf_norm): + break + elif 2*deg > self.max_deg[dim]: + deg = self.max_deg[dim] + + self.M, self.inf_norm = self.interval_approximate_nd(f, a, b, deg, return_inf_norm=True) + self.M2 = self.interval_approximate_nd(f,a,b,deg*2) + self.err = self.get_err(self.M,self.M2) + + break + else: + deg = 2*deg + + self.M, self.inf_norm = self.interval_approximate_nd(f, a, b, deg, return_inf_norm=True) + self.M2 = self.interval_approximate_nd(f,a,b,deg*2) + self.err = self.get_err(self.M,self.M2) + + self.deg = deg + + def interval_approximate_nd(self,f, a, b, deg, return_inf_norm=False, save_values_block=False): + """Finds the chebyshev approximation of an n-dimensional function on an + interval. + + Parameters + ---------- + f : function from R^n -> R + The function to interpolate. + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + deg : numpy array + The degree of the interpolation in each dimension. + return_inf_norm : bool + whether to return the inf norm of the function + save_values_block : bool + whether to save the values block as an attribute + + Returns + ------- + coeffs : numpy array + The coefficient of the chebyshev interpolating polynomial. + inf_norm : float + The inf_norm of the function + """ + half_deg = deg / 2 + + if hasattr(f,"evaluate_grid"): + cheb_values = np.cos(np.arange(deg+1)*np.pi/deg) + chepy_pts = np.column_stack([cheb_values]*self.dim) + #two dimensions + #odds = chepy_pts[:,1::2] + #xyz = np.column_stack((chepy_pts,odds)) + #p1 = f.evaluate_grid(xyz) + #xyz2 = np.columns_stack((odds,odds)) + #p2 = f.evaluate_grid(xyz2) + cheb_pts = transform(chepy_pts,a,b) + values_block = f.evaluate_grid(cheb_pts) #version 0 no memoization here + self.memo_dict[deg] = values_block + else: + cheb_vals = np.cos(np.arange(deg+1)*np.pi/deg) + cheb_grid = np.meshgrid(*([cheb_vals]*self.dim),indexing='ij') + flatten = lambda x: x.flatten() + cheby_pts = np.column_stack(tuple(map(flatten, cheb_grid))) + cheb_pts = transform(cheby_pts,a,b) + + if deg in self.memo_dict.keys(): + values_block = self.memo_dict[deg] + + elif half_deg in self.memo_dict.keys(): + half_deg_arr = self.memo_dict[half_deg].flatten() + slices = tuple([slice(0, deg+1,2)]*self.dim) + mask = np.ones([deg+1]*self.dim,dtype=bool) + mask[slices] = False + unknowns_mask = mask.flatten() #this mask will say where the unknown stuff is in the array + knowns_mask = ~unknowns_mask #this mask will say where the known stuff is + values_arr = np.empty((deg+1)**self.dim) + values_arr[knowns_mask] = half_deg_arr + values_arr[unknowns_mask] = f(*cheb_pts[unknowns_mask].T) + values_block = values_arr.reshape(*([deg+1]*self.dim)) + else: + values_block = f(*cheb_pts.T).reshape(*([deg+1]*self.dim)) + + if save_values_block == True: + self.values_block = values_block + + self.memo_dict[deg] = values_block + + values = self.chebyshev_block_copy(values_block) + + if return_inf_norm: + inf_norm = np.max(np.abs(values)) + + x0_slicer, deg_slicer, slices, rescale = self.interval_approx_slicers(self.dim,deg) + coeffs = fftn(values/rescale).real + + for x0sl, degsl in zip(x0_slicer, deg_slicer): + coeffs[x0sl] /= 2 + coeffs[degsl] /= 2 + + if return_inf_norm: + return coeffs[tuple(slices)], inf_norm + else: + return coeffs[tuple(slices)] + + def chebyshev_block_copy(self,values_block): + """This functions helps avoid double evaluation of functions at + interpolation points. It takes in a tensor of function evaluation values + and copies these values to a new tensor appropriately to prepare for + chebyshev interpolation. + + Parameters + ---------- + values_block : numpy array + block of values from function evaluation + Returns + ------- + values_cheb : numpy array + chebyshev interpolation values + """ + dim = values_block.ndim + deg = values_block.shape[0] - 1 + values_cheb = np.empty(tuple([2*deg])*dim, dtype=np.float64) + block_slicers, cheb_slicers, slicer = self.block_copy_slicers(dim, deg) + + for cheb_idx, block_idx in zip(cheb_slicers, block_slicers): + try: + values_cheb[cheb_idx] = values_block[block_idx] + except ValueError as e: + if str(e)[:42] == 'could not broadcast input array from shape': + values_cheb = np.empty(tuple([2*deg])*dim, dtype=np.float64) + values_cheb[cheb_idx] = values_block[block_idx] + else: + raise ValueError(e) + return values_cheb[slicer] + + def block_copy_slicers(self,dim, deg): + """Helper function for chebyshev_block_copy. + Builds slice objects to index into the evaluation array to copy + in preparation for the fft. + + Parameters + ---------- + dim : int + Dimension + dim : int + Degree of approximation + + Returns + ------- + block_slicers : list of tuples of slice objects + Slice objects used to index into the evaluations + cheb_slicers : list of tuples of slice objects + Slice objects used to index into the array we're copying evaluations to + slicer : tuple of slice objets + Used to index into the portion of that array we're using for the fft input + """ + block_slicers = [] + cheb_slicers = [] + full_arr_deg = 2*deg + for block in product([False, True], repeat=dim): + cheb_idx = [slice(0, deg+1)]*dim + block_idx = [slice(0, full_arr_deg)]*dim + for i, flip_dim in enumerate(block): + if flip_dim: + cheb_idx[i] = slice(deg+1, full_arr_deg) + block_idx[i] = slice(deg-1, 0, -1) + block_slicers.append(tuple(block_idx)) + cheb_slicers.append(tuple(cheb_idx)) + return block_slicers, cheb_slicers, tuple([slice(0, 2*deg)]*dim) + + def interval_approx_slicers(self,dim, deg): + """Helper function for interval_approximate_nd. Builds slice objects to index + into the output of the fft and divide some of the values by 2 and turn them into + coefficients of the approximation. + + Parameters + ---------- + dim : int + The interpolation dimension. + deg : int + The interpolation degree. #SEE WE TAKE THIS AS A SCALAR + + Returns + ------- + x0_slicer : list of tuples of slice objects + Slice objects used to index into the the degree 1 monomials + deg_slicer : list of tuples of slice objects + Slice objects used to index into the the degree d monomials + slices : tuple of slice objets + Used to index into the portion of the array that are coefficients + rescale : int + amount to rescale the evaluations by in order to feed them into the fft + """ + x0_slicer = [tuple([slice(None) if i != d else 0 for i in range(dim)]) + for d in range(dim)] + deg_slicer = [tuple([slice(None) if i != d else deg for i in range(dim)]) + for d in range(dim)] + slices = tuple([slice(0, deg+1)]*dim) + return x0_slicer, deg_slicer, slices, deg**dim \ No newline at end of file diff --git a/yroots/MacaulayReduce.py b/yroots/MacaulayReduce.py new file mode 100644 index 00000000..12d1ff7f --- /dev/null +++ b/yroots/MacaulayReduce.py @@ -0,0 +1,239 @@ +import numpy as np +import itertools +from scipy.linalg import qr, solve_triangular, qr_multiply, svd +from yroots.polynomial import Polynomial, MultiCheb, MultiPower +from yroots.utils import row_swap_matrix, MacaulayError, slice_top, mon_combos, \ + num_mons_full, memoized_all_permutations, mons_ordered, \ + all_permutations_cheb, ConditioningError, TooManyRoots +from matplotlib import pyplot as plt +from warnings import warn + +macheps = 2.220446049250313e-16 + +def plot_scree(s,tol): + plt.semilogy(s,marker='.') + plt.plot(np.ones(len(s))*tol) + plt.show() + +def add_polys(degree, poly, poly_coeff_list): + """Adds polynomials to a Macaulay Matrix. + + This function is called on one polynomial and adds all monomial multiples of + it to the matrix. + + Parameters + ---------- + degree : int + The degree of the Macaulay Matrix + poly : Polynomial + One of the polynomials used to make the matrix. + poly_coeff_list : list + A list of all the current polynomials in the matrix. + Returns + ------- + poly_coeff_list : list + The original list of polynomials in the matrix with the new monomial + multiplications of poly added. + """ + + poly_coeff_list.append(poly.coeff) + deg = degree - poly.degree + dim = poly.dim + + mons = mon_combos([0]*dim,deg) + + for mon in mons[1:]: #skips the first all 0 mon + poly_coeff_list.append(poly.mon_mult(mon, returnType = 'Matrix')) + return poly_coeff_list + +def find_degree(poly_list, verbose=False): + '''Finds the appropriate degree for the Macaulay Matrix. + + Parameters + -------- + poly_list: list + The polynomials used to construct the matrix. + verbose : bool + If True prints the degree + Returns + ----------- + find_degree : int + The degree of the Macaulay Matrix. + + ''' + if verbose: + print('Degree of Macaulay Matrix:', sum(poly.degree for poly in poly_list) - len(poly_list) + 1) + return sum(poly.degree for poly in poly_list) - len(poly_list) + 1 + +def reduce_macaulay_qrt(M, cut, bezout_bound, max_cond=1e6): + """Reduces the Macaulay matrix using the Transposed QR method. + + Parameters: + ----------- + matrix : 2d ndarray + The Macaulay matrix + cut : int + Number of columns of max degree + max_cond : int or float + Max condition number for the two condition number checks + + Returns: + -------- + E : 2d ndarray + The columns of the reduced Macaulay matrix corresponding to the quotient basis + Q2 : 2d ndarray + Matrix giving the quotient basis in terms of the monomial basis. Q2[:,i] + being the coefficients for the ith basis element + """ + # Compute numerical rank + s = svd(M, compute_uv=False) + tol = max(M.shape)*s[0]*macheps + rank = len(s[s>tol]) + # Check if numerical rank doesn't match bezout bound + bezout_rank = M.shape[1]-bezout_bound + if rank < bezout_rank: + warn("Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}. System potentially has infinitely many solutions.".format(bezout_rank,rank)) + elif rank > bezout_rank: + warn('Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}.'.format(bezout_rank,rank)) + + # Check condition number before first QR + cond_num = np.linalg.cond(M[:,:cut]) + if cond_num > max_cond: + return None, "Condition number of the Macaulay high-degree columns is {}".format(cond_num) + + # QR reduce the highest-degree columns + Q,M[:,:cut] = qr(M[:,:cut]) + M[:,cut:] = Q.T @ M[:,cut:] + Q = None + del Q + + # If the matrix is "tall", compute an orthogonal transformation of the remaining + # columns, generating a new polynomial basis + if cut < M.shape[0]: + Q = qr(M[cut:,cut:].T,pivoting=True)[0] + M[:cut,cut:] = M[:cut,cut:] @ Q # Apply column transform + + # Return the backsolved columns and coefficient matrix for the quotient basis + return solve_triangular(M[:cut,:cut],M[:cut,bezout_rank:]),Q[:,-bezout_bound:] + +def reduce_macaulay_svd(M, cut, bezout_bound, max_cond=1e6): + """Reduces the Macaulay matrix using the Transposed QR method. + + Parameters: + ----------- + matrix : 2d ndarray + The Macaulay matrix + cut : int + Number of columns of max degree + max_cond : int or float + Max condition number for the two condition number checks + + Returns: + -------- + E : 2d ndarray + The columns of the reduced Macaulay matrix corresponding to the quotient basis + Q2 : 2d ndarray + Matrix giving the quotient basis in terms of the monomial basis. Q2[:,i] + being the coefficients for the ith basis element + """ + # Compute numerical rank + s = svd(M, compute_uv=False) + tol = max(M.shape)*s[0]*macheps + rank = len(s[s>tol]) + # Check if numerical rank doesn't match bezout bound + bezout_rank = M.shape[1]-bezout_bound + if rank < bezout_rank: + warn("Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}. System potentially has infinitely many solutions.".format(bezout_rank,rank)) + elif rank > bezout_rank: + warn('Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}.'.format(bezout_rank,rank)) + + # Check condition number before first QR + cond_num = np.linalg.cond(M[:,:cut]) + if cond_num > max_cond: + return None, "Condition number of the Macaulay high-degree columns is {}".format(cond_num) + + # QR reduce the highest-degree columns + Q,M[:,:cut] = qr(M[:,:cut]) + M[:,cut:] = Q.T @ M[:,cut:] + Q = None + del Q + + # If the matrix is "tall", compute an orthogonal transformation of the remaining + # columns, generating a new polynomial basis + if cut < M.shape[0]: + Q = svd(M[cut:,cut:])[2].conj().T + M[:cut,cut:] = M[:cut,cut:] @ Q # Apply column transform + + # Return the backsolved columns and coefficient matrix for the quotient basis + return solve_triangular(M[:cut,:cut],M[:cut,bezout_rank:]),Q[:,-bezout_bound:] + +def reduce_macaulay_tvb(M, cut, bezout_bound, max_cond=1e6): + # Compute numerical rank + s = svd(M, compute_uv=False) + tol = max(M.shape)*s[0]*macheps + rank = len(s[s>tol]) + # Check if numerical rank doesn't match bezout bound + bezout_rank = M.shape[1]-bezout_bound + if rank < bezout_rank: + warn("Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}. System potentially has infinitely many solutions.".format(bezout_rank,rank)) + elif rank > bezout_rank: + warn('Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}.'.format(bezout_rank,rank)) + + # Check condition number before first QR + cond_num = np.linalg.cond(M[:,:cut]) + if cond_num > max_cond: + return None, "Condition number of the Macaulay high-degree columns is {}".format(cond_num) + + # QR reduce the highest-degree columns + Q,M[:,:cut] = qr(M[:,:cut]) + M[:,cut:] = Q.T @ M[:,cut:] + Q = None + del Q + + # If the matrix is "tall", compute an orthogonal transformation of the remaining + # columns, generating a new polynomial basis + if cut < M.shape[0]: + M[cut:,cut:],P = qr(M[cut:,cut:], mode='r', pivoting=True) + M[:cut,cut:] = M[:cut,cut:][:,P] # Permute columns + + # Check condition number before backsolve + cond_num_back = np.linalg.cond(M[:bezout_rank,:bezout_rank]) + if cond_num_back > max_cond: + return None, "Condition number of the Macaulay primary submatrix is {}".format(cond_num) + + return solve_triangular(M[:bezout_rank,:bezout_rank],M[:bezout_rank,bezout_rank:]),P + +def reduce_macaulay_p(M, cut, P, max_cond=1e6): + # Compute numerical rank + s = svd(M, compute_uv=False) + tol = max(M.shape)*s[0]*macheps + rank = len(s[s>tol]) + # Check if numerical rank doesn't match bezout bound + bezout_rank = M.shape[1]-bezout_bound + if rank < bezout_rank: + warn("Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}. System potentially has infinitely many solutions.".format(bezout_rank,rank)) + elif rank > bezout_rank: + warn('Rank of Macaulay Matrix does not match the Bezout bound. Expected rank {}, found rank {}.'.format(bezout_rank,rank)) + + # Check condition number before first QR + cond_num = np.linalg.cond(M[:,:cut]) + if cond_num > max_cond: + return None, "Condition number of the Macaulay high-degree columns is {}".format(cond_num) + + # QR reduce the highest-degree columns + Q,M[:,:cut] = qr(M[:,:cut]) + M[:,cut:] = (Q.T @ M[:,cut:])[:,P] + Q = None + del Q + + # If the matrix is "tall", compute an orthogonal transformation of the remaining + # columns, generating a new polynomial basis + if cut < M.shape[0]: + M[cut:,cut:] = qr(M[cut:,cut:])[1:] + + # Check condition number before backsolve + cond_num_back = np.linalg.cond(M[:,:cut]) + if cond_num_back > max_cond: + return None, "Condition number of the Macaulay primary submatrix is {}".format(cond_num) + + return solve_triangular(M[:bezout_rank,:bezout_rank],M[:bezout_rank,bezout_rank:]),P diff --git a/yroots/Multiplication.py b/yroots/Multiplication.py new file mode 100644 index 00000000..709d1fab --- /dev/null +++ b/yroots/Multiplication.py @@ -0,0 +1,663 @@ +import numpy as np +import itertools +from scipy.linalg import solve_triangular, eig, schur +from yroots.LinearProjection import nullspace +from yroots.polynomial import MultiCheb, MultiPower, is_power +from yroots.MacaulayReduce import reduce_macaulay_qrt, find_degree, \ + add_polys, reduce_macaulay_tvb, reduce_macaulay_svd +from yroots.utils import row_swap_matrix, MacaulayError, slice_top, get_var_list, \ + mon_combos, mon_combosHighest, sort_polys_by_degree, \ + deg_d_polys, all_permutations_cheb,\ + newton_polish, condeigs, solve_linear, memoize +import warnings +from scipy.stats import ortho_group + +def multiplication(polys, max_cond_num, verbose=False, return_all_roots=True,method='svd'): + ''' + Finds the roots of the given list of multidimensional polynomials using a multiplication matrix. + + Parameters + ---------- + polys : list of polynomial objects + Polynomials to find the common roots of. + max_cond_num : float + The maximum condition number of the Macaulay Matrix Reduction + verbose : bool + Prints information about how the roots are computed. + return_all_roots : bool + If True returns all the roots, otherwise just the ones in the unit box. + returns + ------- + roots : numpy array + The common roots of the polynomials. Each row is a root. + ''' + #We don't want to use Linear Projection right now +# polys, transform, is_projected = polys, lambda x:x, False + + if len(polys) == 1: + from yroots.OneDimension import solve + return transform(solve(polys[0], MSmatrix=0)) + poly_type = is_power(polys, return_string = True) + dim = polys[0].dim + + #By Bezout's Theorem. Useful for making sure that the reduced Macaulay Matrix is as we expect + bezout_bound = np.prod([poly.degree for poly in polys]) + + matrix, matrix_terms, cut = build_macaulay(polys, verbose) + + roots = np.array([]) + + # If cut is zero, then all the polynomials are linear and we solve + # using solve_linear. + if cut == 0: + roots, cond = solve_linear([p.coeff for p in polys]) + # Make sure roots is a 2D array. + roots = np.array([roots]) + else: + # Attempt to reduce the Macaulay matrix + if method == 'svd': + res = reduce_macaulay_svd(matrix,cut,bezout_bound,max_cond_num) + if res[0] is None: + return res + E,Q = res + elif method == 'qrt': + res = reduce_macaulay_qrt(matrix,cut,bezout_bound,max_cond_num) + if res[0] is None: + return res + E,Q = res + elif method == 'tvb': + res = reduce_macaulay_tvb(matrix,cut,bezout_bound,max_cond_num) + if res[0] is None: + return res + E,Q = res + else: + raise ValueError("Method must be one of 'svd','qrt' or 'tvb'") + + # Construct the Möller-Stetter matrices + # M is a 3d array containing the multiplication-by-x_i matrix in M[...,i] + if poly_type == "MultiCheb": + if method == 'qrt' or method == 'svd': + M = ms_matrices_cheb(E,Q,matrix_terms,dim) + elif method == 'tvb': + M = ms_matrices_p_cheb(E,Q,matrix_terms,dim,cut) + + else: + if method == 'qrt' or method == 'svd': + M = ms_matrices(E,Q,matrix_terms,dim) + elif method == 'tvb': + M = ms_matrices_p(E,Q,matrix_terms,dim,cut) + + # Compute the roots using eigenvalues of the Möller-Stetter matrices + roots = msroots(M) + + if return_all_roots: + return roots + else: + # only return roots in the unit complex hyperbox + return roots[[np.all(np.abs(root) <= 1) for root in roots]] + +def indexarray(matrix_terms,m,var): + """Compute the array mapping monomials under multiplication by x_var + + Parameters + ---------- + matrix_terms : 2d integer ndarray + Array containing the monomials in order. matrix_terms[i] is the array + containing the exponent for each variable in the ith multivariate + monomial + m : int + Number of monomials of highest degree, i.e. those that do not need to be + multiplied + var : int + Variable to multiply by: x_0,...,x_(dim-1) + + Returns + ------- + arr : 1d integer ndarray + Array containing the indices of the lower-degree monomials after multiplication + by x_var + """ + mults = matrix_terms[m:].copy() + mults[:,var] += 1 + return np.argmin(np.abs(mults[:,np.newaxis] - matrix_terms[np.newaxis]).sum(axis=-1),axis=1) + +def indexarray_cheb(matrix_terms,m,var): + """Compute the array mapping Chebyshev monomials under multiplication by x_var: + + T_1*T_0 = T_1 + T_1*T_n = .5(T_(n+1)+ T_(n-1)) + + Parameters + ---------- + matrix_terms : 2d integer ndarray + Array containing the monomials in order. matrix_terms[i] is the array + containing the degree for each univariate Chebyshev monomial in the ith + multivariate monomial + m : int + Number of monomials of highest degree, i.e. those that do not need to be + multiplied + var : int + Variable to multiply by: x_0,...,x_(dim-1) + + Returns + ------- + arr1 : 1d integer ndarray + Array containing the indices of T_(n+1) + arr2 : 1d + Array containing the indices of T_(n-1) + """ + up = matrix_terms[m:].copy() + up[:,var] += 1 + down = matrix_terms[m:].copy() + down[:,var] -= 1 + down[down[:,var]==-1,var] += 2 + arr1 = np.argmin(np.abs(up[:,np.newaxis] - matrix_terms[np.newaxis]).sum(axis=-1),axis=1) + arr2 = np.argmin(np.abs(down[:,np.newaxis] - matrix_terms[np.newaxis]).sum(axis=-1),axis=1) + return arr1,arr2 + +def ms_matrices(E,Q,matrix_terms,dim): + """Compute the Möller-Stetter matrices in the monomial basis + + Parameters + ---------- + E : (m,k) ndarray + Columns of the reduced Macaulay matrix corresponding to the quotient basis + Q : (l,n) 2d ndarray + Matrix whose columns give the quotient basis in terms of the monomial basis + matrix_terms : 2d ndarray + Array with ordered monomial basis + dim : int + Number of variables + + Returns + ------- + M : (n,n,dim) ndarray + Array containing the nxn Möller-Stetter matrices, where the matrix + corresponding to multiplication by x_i is M[...,i] + """ + n = Q.shape[1] + m = E.shape[0] + M = np.empty((n,n,dim)) + A = np.hstack((-E.T,Q.T)) + for i in range(dim): + arr = indexarray(matrix_terms,m,i) + M[...,i] = A[:,arr]@Q + return M + +def ms_matrices_cheb(E,Q,matrix_terms,dim): + """Compute the Möller-Stetter matrices in the Chebyshev basis + + Parameters + ---------- + E : (m,k) ndarray + Columns of the reduced Macaulay matrix corresponding to the quotient basis + Q : (l,n) 2d ndarray + Matrix whose columns give the quotient basis in terms of the Chebyshev basis + matrix_terms : 2d ndarray + Array with ordered Chebyshev basis + dim : int + Number of variables + + Returns + ------- + M : (n,n,dim) ndarray + Array containing the nxn Möller-Stetter matrices, where the matrix + corresponding to multiplication by x_i is M[...,i] + """ + n = Q.shape[1] + m = E.shape[0] + M = np.empty((n,n,dim)) + A = np.hstack((-E.T,Q.T)) + for i in range(dim): + arr1,arr2 = indexarray_cheb(matrix_terms,m,i) + M[...,i] = .5*(A[:,arr1]+A[:,arr2])@Q + return M + +def ms_matrices_p(E,P,matrix_terms,dim,cut): + r,n = E.shape + matrix_terms[cut:] = matrix_terms[cut:][P] + M = np.empty((n,n,dim)) + A = np.hstack((-E.T,np.eye(n))) + for i in range(dim): + arr = indexarray(matrix_terms,r,i) + M[...,i] = A[:,arr] + return M + +def ms_matrices_p_cheb(E,P,matrix_terms,dim,cut): + """ Compute the Möller-Stetter matrices in the Chebyshev basis in the + Telen-Van Barel method. + + Parameters + ---------- + E : (m,k) ndarray + Columns of the reduced Macaulay matrix corresponding to the quotient basis + P : (,l) ndarray + Array of pivots returned in QR with pivoting, used to permute the columns. + matrix_terms : 2d ndarray + Array with ordered Chebyshev basis + dim : int + Number of variables + + Returns + ------- + M : (n,n,dim) ndarray + Array containing the nxn Möller-Stetter matrices, where the matrix + corresponding to multiplication by x_i is M[...,i] + """ + r,n = E.shape + matrix_terms[cut:] = matrix_terms[cut:][P] + M = np.empty((n,n,dim)) + A = np.hstack((-E.T,np.eye(n))) + for i in range(dim): + arr1,arr2 = indexarray_cheb(matrix_terms,r,i) + M[...,i] = .5*(A[:,arr1]+A[:,arr2]) + return M + +def sort_eigs(eigs,diag): + """Sorts the eigs array to match the order on the diagonal + of the Schur factorization + + Parameters + ---------- + eigs : 1d ndarray + Array of unsorted eigenvalues + diag : 1d complex ndarray + Array containing the diagonal of the approximate Schur factorization + + Returns + ------- + w : 1d ndarray + Eigenvalues from eigs sorted to match the order in diag + """ + n = diag.shape[0] + lst = list(range(n)) + arr = [] + for eig in eigs: + i = lst[np.argmin(np.abs(diag[lst]-eig))] + arr.append(i) + lst.remove(i) + return np.argsort(arr) + +@memoize +def get_Q_c(dim): + """Generates a once-chosen random orthogonal matrix and a random linear combination + for use in the simultaneous eigenvalue compution. + + Parameters + ---------- + dim : int + Dimension of the system + + Returns + ------- + Q : (dim,dim) ndarray + Random orthogonal rotation + c : (dim,) ndarray + Random linear combination + """ + np.random.seed(103) + Q = ortho_group.rvs(dim) + c = np.random.randn(dim) + return Q,c + +def msroots(M): + """Computes the roots to a system via the eigenvalues of the Möller-Stetter + matrices. Implicitly performs a random rotation of the coordinate system + to avoid repeated eigenvalues arising from special structure in the underlying + polynomial system. Approximates the joint eigenvalue problem using a Schur + factorization of a linear combination of the matrices. + + Parameters + ---------- + M : (n,n,dim) ndarray + Array containing the nxn Möller-Stetter matrices, where the matrix + corresponding to multiplication by x_i is M[...,i] + + Returns + ------- + roots : (n,dim) ndarray + Array containing the approximate roots of the system, where each row + is a root. + """ + dim = M.shape[-1] + + # perform a random rotation with a random orthogonal Q + Q,c = get_Q_c(dim) + M = (Q@M[...,np.newaxis])[...,0] + + eigs = np.empty((dim,M.shape[0]),dtype='complex') + # Compute the matrix U that triangularizes a random linear combination + U = schur((M*c).sum(axis=-1),output='complex')[1] + + for i in range(0,dim): + T = (U.conj().T)@(M[...,i])@U + w = eig(M[...,i],right=False) + arr = sort_eigs(w,np.diag(T)) + eigs[i] = w[arr] + + # Rotate back before returning, transposing to match expected shape + return (Q.T@eigs).T + +def MSMultMatrix(polys, poly_type, max_cond_num, macaulay_zero_tol, verbose=False, MSmatrix=0): + ''' + Finds the multiplication matrix using the reduced Macaulay matrix. + + Parameters + ---------- + polys : array-like + The polynomials to find the common zeros of + poly_type : string + The type of the polynomials in polys + verbose : bool + Prints information about how the roots are computed. + MSmatrix : int + Controls which Moller-Stetter matrix is constructed. The options are: + 0 (default) -- The Moller-Stetter matrix of a random polynomial + Some positive integer i < dimension -- The Moller-Stetter matrix of x_i + max_cond_num : float + The maximum condition number of the Macaulay Matrix Reduction + macaulay_zero_tol : float + What is considered 0 in the macaulay matrix reduction. + Returns + ------- + multiplicationMatrix : 2D numpy array + The multiplication matrix for a random polynomial f + var_dict : dictionary + Maps each variable to its position in the vector space basis + basisDict : dict + A dictionary of terms not in the vector basis a matrixes of things in the vector basis that the term + can be reduced to. + VB : numpy array + The terms in the vector basis, each row being a term. + ''' + try: + basisDict, VB, varsRemoved = MacaulayReduction(polys, max_cond_num=max_cond_num, macaulay_zero_tol=macaulay_zero_tol, verbose=verbose) + except ConditioningError as e: + raise e + + dim = max(f.dim for f in polys) + + # Get the polynomial to make the MS matrix of + if MSmatrix==0: #random poly + f = _random_poly(poly_type, dim)[0] + else: #multiply by x_i where i is determined by MSmatrix + xi_ind = np.zeros(dim, dtype=int) + xi_ind[MSmatrix-1] = 1 + coef = np.zeros((2,)*dim) + coef[tuple(xi_ind)] = 1 + if poly_type == "MultiPower": + f = MultiPower(np.array(coef)) + elif poly_type == "MultiCheb": + f = MultiCheb(np.array(coef)) + else: + raise ValueError() + + if verbose: + print("\nCoefficients of polynomial whose Moller-Stetter matrix we construt\n", f.coeff) + + #Dictionary of terms in the vector basis their spots in the matrix. + VBdict = {} + spot = 0 + for row in VB: + VBdict[tuple(row)] = spot + spot+=1 + + # Build multiplication matrix m_f + mMatrix = np.zeros((len(VB), len(VB))) + for i in range(VB.shape[0]): + f_coeff = f.mon_mult(VB[i], returnType = 'Matrix') + for term in zip(*np.where(f_coeff != 0)): + if term in VBdict: + mMatrix[VBdict[term]][i] += f_coeff[term] + else: + mMatrix[:,i] -= f_coeff[term]*basisDict[term] + + # Construct var_dict + var_dict = {} + for i in range(len(VB)): + mon = VB[i] + if np.sum(mon) == 1 or np.sum(mon) == 0: + var_dict[tuple(mon)] = i + + return mMatrix, var_dict, basisDict, VB + +def build_macaulay(initial_poly_list, verbose=False): + """Constructs the unreduced Macaulay matrix. Removes linear polynomials by + substituting in for a number of variables equal to the number of linear + polynomials. + + Parameters + -------- + initial_poly_list: list + The polynomials in the system we are solving. + verbose : bool + Prints information about how the roots are computed. + Returns + ----------- + matrix : 2d ndarray + The Macaulay matrix + matrix_terms : 2d integer ndarray + Array containing the ordered basis, where the ith row contains the + exponent/degree of the ith basis monomial + cut : int + Where to cut the Macaulay matrix for the highest-degree monomials + varsToRemove : list + The variables removed with removing linear polynomials + A : 2d ndarray + A matrix giving the linear relations between the removed variables and + the remaining variables + Pc : 1d integer ndarray + Array containing the order of the variables as the appear in the columns + of A + """ + power = is_power(initial_poly_list) + dim = initial_poly_list[0].dim + poly_coeff_list = [] + degree = find_degree(initial_poly_list) + + # linear_polys = [poly for poly in initial_poly_list if poly.degree == 1] + # nonlinear_polys = [poly for poly in initial_poly_list if poly.degree != 1] + # #Choose which variables to remove if things are linear, and add linear polys to matrix + # if len(linear_polys) >= 1: #Linear polys involved + # #get the row rededuced linear coefficients + # A,Pc = nullspace(linear_polys) + # varsToRemove = Pc[:len(A)].copy() + # #add to macaulay matrix + # for row in A: + # #reconstruct a polynomial for each row + # coeff = np.zeros([2]*dim) + # coeff[tuple(get_var_list(dim))] = row[:-1] + # coeff[tuple([0]*dim)] = row[-1] + # if not power: + # poly = MultiCheb(coeff) + # else: + # poly = MultiPower(coeff) + # poly_coeff_list = add_polys(degree, poly, poly_coeff_list) + # else: #no linear + # A,Pc = None,None + # varsToRemove = [] + + #add nonlinear polys to poly_coeff_list + for poly in initial_poly_list:#nonlinear_polys: + poly_coeff_list = add_polys(degree, poly, poly_coeff_list) + + #Creates the matrix + # return (*create_matrix(poly_coeff_list, degree, dim, varsToRemove), A, Pc) + return create_matrix(poly_coeff_list, degree, dim)#, varsToRemove) + +def makeBasisDict(matrix, matrix_terms, VB, power): + '''Calculates and returns the basisDict. + + This is a dictionary of the terms on the diagonal of the reduced Macaulay matrix to the terms in the Vector Basis. + It is used to create the multiplication matrix in root_finder. + + Parameters + -------- + matrix: numpy array + The reduced Macaulay matrix. + matrix_terms : numpy array + The terms in the matrix. The i'th row is the term represented by the i'th column of the matrix. + VB : numpy array + Each row is a term in the vector basis. + power : bool + If True, the initial polynomials were MultiPower. If False, they were MultiCheb. + + Returns + ----------- + basisDict : dict + Maps terms on the diagonal of the reduced Macaulay matrix (tuples) to numpy arrays that represent the + terms reduction into the Vector Basis. + ''' + basisDict = {} + + VBSet = set() + for i in VB: + VBSet.add(tuple(i)) + + #We don't actually need most of the rows, so we only get the ones we need + if power: + neededSpots = set() + for term, mon in itertools.product(VB,get_var_list(VB.shape[1])): + if tuple(term+mon) not in VBSet: + neededSpots.add(tuple(term+mon)) + + for i in range(matrix.shape[0]): + term = tuple(matrix_terms[i]) + if power and term not in neededSpots: + continue + basisDict[term] = matrix[i][matrix.shape[0]:] + + return basisDict + +def create_matrix(poly_coeffs, degree, dim):#, varsToRemove): + ''' Builds a Macaulay matrix. + + Parameters + ---------- + poly_coeffs : list. + Contains numpy arrays that hold the coefficients of the polynomials to be put in the matrix. + degree : int + The degree of the Macaulay Matrix + dim : int + The dimension of the polynomials going into the matrix. + varsToRemove : list + The variables to remove from the basis because we have linear polysnomials + Returns + ------- + matrix : 2D numpy array + The Macaulay matrix. + matrix_terms : numpy array + The ith row is the term represented by the ith column of the matrix. + cut : int + Number of monomials of highest degree + ''' + bigShape = [degree+1]*dim + + matrix_terms, cut = sorted_matrix_terms(degree, dim)#, varsToRemove) + + #Get the slices needed to pull the matrix_terms from the coeff matrix. + matrix_term_indexes = list() + for row in matrix_terms.T: + matrix_term_indexes.append(row) + + #Adds the poly_coeffs to flat_polys, using added_zeros to make sure every term is in there. + added_zeros = np.zeros(bigShape) + flat_polys = list() + for coeff in poly_coeffs: + slices = slice_top(coeff.shape) + added_zeros[slices] = coeff + flat_polys.append(added_zeros[tuple(matrix_term_indexes)]) + added_zeros[slices] = np.zeros_like(coeff) + del poly_coeffs + + #Make the matrix. Reshape is faster than stacking. + matrix = np.reshape(flat_polys, (len(flat_polys),len(matrix_terms))) + + #Sorts the rows of the matrix so it is close to upper triangular. + matrix = row_swap_matrix(matrix) + return matrix, matrix_terms, cut + +def sorted_matrix_terms(degree, dim):#, varsToRemove): + '''Finds the matrix_terms sorted in the term order needed for Macaulay reduction. + So the highest terms come first,the x,y,z etc monomials last. + Parameters + ---------- + degree : int + The degree of the Macaulay Matrix + dim : int + The dimension of the polynomials going into the matrix. + varsToRemove : list + The variables to remove from the basis because we have linear polysnomials + Returns + ------- + sorted_matrix_terms : numpy array + The sorted matrix_terms. The ith row is the term represented by the ith column of the matrix. + cuts : int + Number of monomials of highest degree + ''' + highest_mons = mon_combosHighest([0]*dim,degree)[::-1] + + other_mons = list() + d = degree - 1 + while d > 1: + other_mons += mon_combosHighest([0]*dim,d)[::-1] + d -= 1 + + #extra-small monomials: 1,x,y, etc. + xs_mons = mon_combos([0]*dim,1)[::-1] + + #trivial case + if degree == 1: + matrix_terms = np.reshape(xs_mons, (len(xs_mons),dim)) + cuts = 0 + #normal case + else: + matrix_terms = np.reshape(highest_mons+other_mons+xs_mons, (len(highest_mons+other_mons+xs_mons),dim)) + cuts = len(highest_mons) + + # for var in varsToRemove: + # B = matrix_terms[cuts[0]:] + # mask = B[:,var] != 0 + # matrix_terms[cuts[0]:] = np.vstack([B[mask], B[~mask]]) + # cuts = tuple([cuts[0] + np.sum(mask), cuts[1]+1]) + + return matrix_terms, cuts + +def _random_poly(_type, dim): + ''' + Generates a random linear polynomial that has the form + c_1x_1 + c_2x_2 + ... + c_nx_n where n = dim and each c_i is a randomly + chosen integer between 0 and 1000. + + Parameters + ---------- + _type : string + Type of Polynomial to generate. "MultiCheb" or "MultiPower". + dim : int + Degree of polynomial to generate (?). + + Returns + ------- + Polynomial + Randomly generated Polynomial. + ''' + _vars = get_var_list(dim) + + random_poly_shape = [2 for i in range(dim)] + + # random_poly_coeff = np.zeros(tuple(random_poly_shape), dtype=int) + # for var in _vars: + # random_poly_coeff[var] = np.random.randint(1000) + + random_poly_coeff = np.zeros(tuple(random_poly_shape), dtype=float) + #np.random.seed(42) + + coeffs = np.random.rand(dim) + coeffs /= np.linalg.norm(coeffs) + for i,var in enumerate(_vars): + random_poly_coeff[var] = coeffs[i] + + if _type == 'MultiCheb': + return MultiCheb(random_poly_coeff), _vars + else: + return MultiPower(random_poly_coeff), _vars diff --git a/numalgsolve/OneDimension.py b/yroots/OneDimension.py similarity index 69% rename from numalgsolve/OneDimension.py rename to yroots/OneDimension.py index 6d83107d..9b70260e 100644 --- a/numalgsolve/OneDimension.py +++ b/yroots/OneDimension.py @@ -1,7 +1,7 @@ import numpy as np from scipy.linalg import eig, eigvals from numpy import linalg as la -from numalgsolve.polynomial import MultiCheb, MultiPower +from yroots.polynomial import MultiCheb, MultiPower def solve(poly, MSmatrix=0, eigvals=True, verbose=False): """Finds the zeros of a 1-D polynomial. @@ -14,8 +14,7 @@ def solve(poly, MSmatrix=0, eigvals=True, verbose=False): MSmatrix : int Controls which Moller-Stetter matrix is constructed For a univariate polynomial, the options are: - 0 (default) -- The companion or colleague matrix, rotated 180 degrees - 1 -- The unrotated companion or colleague matrix + 0 (default) -- The companion or colleague matrix -1 -- The inverse of the companion or colleague matrix Returns @@ -23,67 +22,25 @@ def solve(poly, MSmatrix=0, eigvals=True, verbose=False): one_dimensional_solve : numpy array An array of the zeros. """ - if MSmatrix not in [-1, 0, 1]: - raise ValueError('MSmatrix must be -1 (inverse companion), 0 (rotated companion), or 1 (standard companion)') + if MSmatrix not in [-1, 0]: + raise ValueError('MSmatrix must be -1 (inverse companion), or 0 (rotated companion)') if type(poly) == MultiPower: size = len(poly.coeff) coeff = np.trim_zeros(poly.coeff) zeros = np.zeros(size - len(coeff), dtype = 'complex') - if MSmatrix == 1: + if MSmatrix == 0: + #multPower is rotated 180 so it plays nice with hessenberg properties return np.hstack((zeros,multPower(coeff, eigvals, verbose=verbose))) - elif MSmatrix == 0: - return np.hstack((zeros,multPowerR(coeff, eigvals, verbose=verbose))) else: return np.hstack((zeros,divPower(coeff, eigvals, verbose=verbose))) else: - if MSmatrix == 1: + if MSmatrix == 0: return multCheb(poly.coeff, eigvals, verbose=verbose) - elif MSmatrix == 0: - return multChebR(poly.coeff, eigvals, verbose=verbose) else: return divCheb(poly.coeff, eigvals, verbose=verbose) def multPower(coeff, eigvals=True, verbose=False): - """Finds the zeros of a 1-D power polynomial using a multiplication matrix. - - Parameters - ---------- - coeff : numpy array - The coefficients of the polynomial. - - Returns - ------- - zero : numpy array - An array of the zeros. - """ - n = len(coeff) - 1 - - # linear/constant cases - if n < 1: - return np.array([], dtype=coeff.dtype) - if n == 1: - return np.array([-coeff[0]/coeff[1]]) - - matrix = np.zeros((n, n), dtype=coeff.dtype) - bot = matrix.reshape(-1)[n::n+1] - bot[...] = 1 - matrix[:, -1] -= coeff[:-1]/coeff[-1] - if verbose: - print('Companion Matrix\n', matrix) - if eigvals: - zeros = la.eigvals(matrix) - if verbose: - print('Eigenvalues\n',zeros) - return zeros - else: - vals,vecs = eig(matrix.T) - if verbose: - print('Eigenvalues\n',vals) - print('Left Eigenvectors\n',vecs) - return vecs[1,:]/vecs[0,:] - -def multPowerR(coeff, eigvals=True, verbose=False): """Finds the zeros of a 1-D power polynomial using a rotated multiplication matrix. Parameters @@ -109,6 +66,7 @@ def multPowerR(coeff, eigvals=True, verbose=False): bot[...] = 1 matrix[:, -1] -= coeff[:-1]/coeff[-1] matrix = np.rot90(matrix,2) + matrix = np.array(matrix, dtype=float) if verbose: print('180 Rotated Companion Matrix\n', matrix) if eigvals: @@ -205,51 +163,8 @@ def multCheb(coeff, eigvals=True, verbose=False): print('Left Eigenvectors\n',vecs) return vecs[1,:]/vecs[0,:] -def multChebR(coeff, eigvals=True, verbose=False): - """Finds the zeros of a 1-D chebyshev polynomial using a multiplication matrix. - - Parameters - ---------- - coeff : numpy array - The coefficients of the polynomial. - - Returns - ------- - zero : numpy array - An array of the zeros. - """ - n = len(coeff) - 1 - - # linear/constant cases - if n < 1: - return np.array([], dtype=coeff.dtype) - if n == 1: - return np.array([-coeff[0]/coeff[1]]) - - matrix = np.zeros((n,n), dtype=coeff.dtype) - matrix[1][0] = 1 - bot = matrix.reshape(-1)[1::n+1] - bot[...] = 1/2 - bot = matrix.reshape(-1)[2*n+1::n+1] - bot[...] = 1/2 - matrix[:,-1] -= .5*coeff[:-1]/coeff[-1] - matrix = np.rot90(matrix,2) - if verbose: - print('Rotated Colleague Matrix\n', matrix) - if eigvals: - zeros = la.eigvals(matrix) - if verbose: - print('Eigenvalues\n',zeros) - return zeros - else: - vals,vecs = eig(matrix, left=True, right=False) - if verbose: - print('Eigenvalues\n',vals) - print('Left Eigenvectors\n',vecs) - return np.conjugate(vecs[-2,:]/vecs[-1,:]) - - def getXinv(coeff): + """Helper function for division matrix""" n = len(coeff)-1 curr = coeff.copy() xinv = np.zeros(n, dtype=coeff.dtype) @@ -261,7 +176,6 @@ def getXinv(coeff): xinv[0]+=temp return xinv,curr[0] - def divCheb(coeff, eigvals=True, verbose=False): """Finds the zeros of a 1-D chebyshev polynomial using a division matrix. diff --git a/numalgsolve/ProjectiveSpace.py b/yroots/ProjectiveSpace.py similarity index 97% rename from numalgsolve/ProjectiveSpace.py rename to yroots/ProjectiveSpace.py index cd3a6ac0..ad86dda3 100644 --- a/numalgsolve/ProjectiveSpace.py +++ b/yroots/ProjectiveSpace.py @@ -3,8 +3,8 @@ August 17, 2018 """ import numpy as np -from numalgsolve.polynomial import MultiPower -from numalgsolve.OneDimension import solve +from yroots.polynomial import MultiPower +from yroots.OneDimension import solve def common_root_at_inf(polys, return_root=False): ''' diff --git a/yroots/RootTracker.py b/yroots/RootTracker.py new file mode 100644 index 00000000..f8769a86 --- /dev/null +++ b/yroots/RootTracker.py @@ -0,0 +1,199 @@ +import numpy as np + +def rootInBox(root, a, b): + """Checks to see if a root is in a box. + + Parameters + ---------- + root : numpy array + The root to check. + a : numpy array + The lower bound on the box. + b : numpy array + The upper bound on the box. + Returns + ------- + rootInBox : bool + Whether the root is in the box + """ + return np.all(root > a) and np.all(root < b) + +class RootTracker: + ''' + Class to track the roots that are found found using the subdivision solver. + + Attributes + ---------- + roots: numpy array + The roots of the system being solved + possible_duplicates : list + Roots that were outside their search interval so might be duplicates + potential_roots : numpy array + Places that may or may not have a root that we found. + intervals : list + The intervals that the roots were found in. + polish_intervals : list + The intervals to run polishing on. + methods : list + The methods used to find the roots. + + Methods + ------- + __init__ + Initializes everything. + add_roots + Adds roots the were found to the list, along with their information. + add_potential_roots + Adds roots that were found by the solver but are questionable. We will + want to double check that these roots aren't duplicated elsewhere or that + they give a fairly good answer. + get_polish_intervals + Gets the intervals to run the next round of polishing on. + ''' + def __init__(self): + self.roots = np.array([]) + self.possible_duplicates = [] + self.potential_roots = np.array([]) + self.intervals = [] + self.methods = [] + #for tracking condition numbers and gradients + self.conds = [] + self.grads = [] + + def add_roots(self, zeros, a, b, method): + ''' Store the roots that were found, along with the interval they were found in and the method used. + + Parameters + ---------- + zeros : numpy array. + The roots to store. + a: numpy array + The lower bounds of the interval the roots were found in. + b: numpy array + The upper bounds of the interval the roots were found in. + method : string + The method used to find the roots + ''' + for zero in zeros: + if rootInBox(zero, a, b): + self.add_root(zero, a, b, method) + else: + found = False + for a_,b_ in self.intervals: + if rootInBox(zero, a_, b_): + found = True + break + if not found: + self.possible_duplicates.append([zero, a, b, method]) + + temp = [] + for zero, a_, b_, method in self.possible_duplicates: + if rootInBox(zero, a, b): + pass + else: + temp.append([zero, a_, b_, method]) + self.possible_duplicates = temp + + +# if not isinstance(a, np.ndarray): +# dim = 1 +# else: +# dim = len(a) +# if len(self.roots) == 0: +# if dim == 1: +# self.roots = np.zeros([0]) +# else: +# self.roots = np.zeros([0,dim]) + +# if dim > 1: +# self.roots = np.vstack([self.roots, zeros]) +# else: +# self.roots = np.hstack([self.roots, zeros]) +# self.intervals += [(a,b)]*len(zeros) +# self.methods += [method]*len(zeros) + + def add_root(self, zero, a, b, method): + ''' Store the root that was found, along with the interval it was found in and the method used. + + Parameters + ---------- + zero : numpy array. + The root to store. + a: numpy array + The lower bounds of the interval the roots were found in. + b: numpy array + The upper bounds of the interval the roots were found in. + method : string + The method used to find the roots + ''' + if not isinstance(a, np.ndarray): + dim = 1 + else: + dim = len(a) + if len(self.roots) == 0: + if dim == 1: + self.roots = np.zeros([0]) + else: + self.roots = np.zeros([0,dim]) + if dim > 1: + self.roots = np.vstack([self.roots, zero]) + else: + self.roots = np.hstack([self.roots, zero]) + self.intervals += [(a,b)] + self.methods += [method] + + def add_potential_roots(self, potentials, a, b, method): + ''' Store the potential roots that were found, along with the interval + they were found in and the method used. + + Parameters + ---------- + potentials : numpy array. + The potential roots to store. + a: numpy array + The lower bounds of the interval the roots were found in. + b: numpy array + The upper bounds of the interval the roots were found in. + method : string + The method used to find the roots + ''' + if not isinstance(a, np.ndarray): + dim = 1 + else: + dim = len(a) + if len(self.potential_roots) == 0: + if dim == 1: + self.potential_roots = np.zeros([0]) + else: + self.potential_roots = np.zeros([0,dim]) + + if dim > 1: + self.potential_roots = np.vstack([self.potential_roots, potentials]) + else: + self.potential_roots = np.hstack([self.potential_roots, potentials]) + self.intervals += [(a,b)]*len(potentials) + self.methods += [method]*len(potentials) + + def get_polish_intervals(self): + ''' Find the intervals to run the polishing on. + + Deletes the rest of the info as subdivision will be rerun on these intervals. + + returns + ------- + polish_intervals : list + The intervals to rerun the search on. + ''' + polish_intervals = np.unique(self.intervals,axis=0) + self.intervals = [] + self.roots = [] + self.methods = [] + return polish_intervals + + def keep_possible_duplicates(self): + ''' Adds the possible duplicate roots to the roots + ''' + for zero, a, b, method in self.possible_duplicates: + # Pass in None for the condition number since we don't have it + self.add_root(zero, a, b, method) + self.possible_duplicates = [] diff --git a/yroots/Tester.py b/yroots/Tester.py new file mode 100644 index 00000000..59ea47f7 --- /dev/null +++ b/yroots/Tester.py @@ -0,0 +1,64 @@ +#imports +import time +import numpy as np +import yroots as yr +from ChebyshevSubdivisionSolver import solveChebyshevSubdivision + +np.random.seed(17) +def getMs(dim=2, deg=5, divisor = 3): + #This returns functions that look hopefully kind of lke Chebyshev approximations + Ms = [] + for i in range(dim): + M = np.random.rand(*[deg]*dim)*2-1 + M[tuple([0]*dim)] = (np.random.rand()*2-1)/10 + for spot, num in np.ndenumerate(M): + scaler = divisor**np.sum(spot) + M[spot] /= scaler + Ms.append(M) + return Ms + + +#Test one degree and dimension at a time +dim = 2 +deg = 5 +print("dim: ", end = '') +print(dim,end='') +print(" deg: ", end = '') +print(deg) + +intervals = np.vstack([-np.ones(dim),np.ones(dim)]).T +errs = np.zeros(dim) + +ms = getMs(dim, deg) +ms = [x*1000 for x in ms] + +start = time.time() +roots = solveChebyshevSubdivision(ms,errs, exact = True) +end = time.time() +print(roots) +print(end - start) + + +# # Test many different degrees at a time in any dimension +# dim = 4 + +# for j in range(4,6): +# deg = j +# print("dim: ", end = '') +# print(dim,end='') +# print(" deg: ", end = '') +# print(deg) + +# intervals = np.vstack([-np.ones(dim),np.ones(dim)]).T +# errs = np.zeros(dim) + +# ms = getMs(dim, deg) +# # ms = [x*1000 for x in ms] + +# start = time.time() +# roots = solveChebyshevSubdivision(ms, errs, exact = True) +# end = time.time() + + +# print(roots) +# print(end - start) \ No newline at end of file diff --git a/yroots/__init__.py b/yroots/__init__.py new file mode 100644 index 00000000..9d11ecdc --- /dev/null +++ b/yroots/__init__.py @@ -0,0 +1,7 @@ +# Do not delete this file. It tells python that groebner is a module you can import from. +#public facing functions should be imported here so they can be used directly +name = "yroots" +from .subdivision import solve +from .polyroots import solve as polysolve +from .polynomial import MultiPower +from .polynomial import MultiCheb diff --git a/numalgsolve/_stability.py b/yroots/_stability.py similarity index 93% rename from numalgsolve/_stability.py rename to yroots/_stability.py index 4510e66f..4acdee6b 100644 --- a/numalgsolve/_stability.py +++ b/yroots/_stability.py @@ -1,10 +1,10 @@ import numpy as np -from numalgsolve.polynomial import MultiCheb, MultiPower -from numalgsolve.OneDimension import multPowerR, multChebR, multPower, multCheb, divPower, divCheb -from numalgsolve.TVBMethod import solve as TVBsolve -from numalgsolve.polyroots import solve -from numalgsolve.Division import division -from numalgsolve.Multiplication import multiplication +from yroots.polynomial import MultiCheb, MultiPower +from yroots.OneDimension import multPower, multCheb, divPower, divCheb +#from yroots.TVBMethod import solve as TVBsolve +from yroots.polyroots import solve +from yroots.Division import division +from yroots.Multiplication import multiplication from numpy.polynomial.polynomial import polyfromroots, polyroots from numpy.polynomial.chebyshev import chebfromroots, chebroots from matplotlib import pyplot as plt @@ -36,12 +36,10 @@ def __call__(self, poly, *args): else: return self.solver(poly.coeff) -multPowerR_s = OneDSolver(multPowerR, "Rotated Mult Power", 'power', True) multPower_s = OneDSolver(multPower, "Mult Power", 'power', True) divPower_s = OneDSolver(divPower, "Div Power", 'power', True) numpy_s = OneDSolver(polyroots, "Numpy Power", 'power', False) -multChebR_s = OneDSolver(multChebR, "Rotated Mult Cheb", 'cheb', True) multCheb_s = OneDSolver(multCheb, "Mult Cheb", 'cheb', True) divCheb_s = OneDSolver(divCheb, "Div Cheb", 'cheb', True) numpyCheb_s = OneDSolver(chebroots, "Numpy Cheb", 'cheb', False) @@ -49,11 +47,10 @@ def __call__(self, poly, *args): multiplication_s = Solver(multiplication, "Multiplication", "both", True, defaults_kwargs={'MSmatrix':1}) multrand_s = Solver(multiplication, "Multiplication Random", "both", True, defaults_kwargs={'MSmatrix':0}) division_s = Solver(division, "Division", "both", True) -TVB_s = Solver(TVBsolve, "TVB", "power", False) -all_solvers = [multPowerR_s, multPower_s, divPower_s, numpy_s, - multChebR_s, multCheb_s, divCheb_s, numpyCheb_s, - multiplication_s, multrand_s, division_s, TVB_s] +all_solvers = [multPower_s, divPower_s, numpy_s, + multCheb_s, divCheb_s, numpyCheb_s, + multiplication_s, multrand_s, division_s] def create_roots_graph(args, results): nrows = len(results) @@ -204,7 +201,7 @@ def run_n_dimension(args, radius, eigvals): chebpolys = [] if by_coeffs: for i in range(dim): - from numalgsolve.polynomial import getPoly + from yroots.polynomial import getPoly powerpolys.append(getPoly(num_points, dim, power=True)) chebpolys.append(getPoly(num_points, dim, power=False)) else: diff --git a/numalgsolve/_timing.py b/yroots/_timing.py similarity index 86% rename from numalgsolve/_timing.py rename to yroots/_timing.py index dddd71cf..ef81c3ee 100644 --- a/numalgsolve/_timing.py +++ b/yroots/_timing.py @@ -1,9 +1,13 @@ import numpy as np -from numalgsolve.OneDimension import multPowerR, multChebR -from numalgsolve.polynomial import MultiCheb, MultiPower, getPoly -from numalgsolve.polyroots import solve as prsolve -from numalgsolve.subdivision import solve as subsolve -from numalgsolve.TVBMethod import solve as TVBsolve +from yroots.OneDimension import multPower, multCheb, divCheb, divPower +from yroots.polynomial import MultiCheb, MultiPower, getPoly +from yroots.polyroots import solve as prsolve +from yroots.subdivision import solve as subsolve +try: + from yroots.TVBMethod import solve as TVBsolve + TVB_avail = True +except: + TVB_avail = False import matplotlib.pyplot as plt import argparse import cProfile, pstats, io @@ -12,12 +16,6 @@ import pickle import warnings -def _multPowerR(poly): - multPowerR(poly[0].coeff) - -def _multChebR(poly): - multChebR(poly[0].coeff) - def _div(poly): prsolve(poly, MSmatrix=-1) @@ -30,8 +28,9 @@ def _nproots(poly): def _npcheb(poly): np.polynomial.chebyshev.chebroots(poly[0].coeff) -def _TVB(poly): - TVBsolve(poly) +if TVB_avail: + def _TVB(poly): + TVBsolve(poly) def bertini(polys): def mononmial_from_exp(exponents, var_chars): @@ -83,9 +82,8 @@ def coeff_to_str(coeff, var_chars): from subprocess import call # call(['./bertini/bertini.exe']) # call(['./bertini/bertini-serial']) - call(['./bertini/bertini-run-parallel']) - - # print(coeff_to_str(poly.coeff)) + # call(['./bertini/bertini-run-parallel']) + call(['./bertini/bertini-parallel']) # One Dimension def timer(solver, dim, power): @@ -106,25 +104,16 @@ def timer(solver, dim, power): list of average times for the solver based on degree """ times = [] - # max_degree = {1:250, 2:20, 3:7, 4:4, 5:3}[dim] #keys by dimensions - # interval = {1:30, 2:3, 3:1, 4:1, 5:1}[dim] - # min_degree = {1:10, 2:2, 3:2, 4:2, 5:2}[dim] - # if solver.__name__ == 'bertini': - # max_degree = {1:60,2:6,3:5,4:4,5:4}[dim] - # interval = {1:10,2:1,3:1,4:1,5:1}[dim] - # degrees = list(range(min_degree,max_degree+1,interval)) - degrees_dct = {2: [7,13,19,25,31,37,43,49],#,55,61], + degrees = {2: [7,13,19,25,31,37,43,49],#,55,61], 3: [3,5,7,9,11,13], 4: [2,3,4], 5: [2,3] - } - degrees = degrees_dct[dim] + }[dim] if solver.__name__ == 'bertini': degrees = [i for i in degrees if i < 19] for deg in degrees: np.random.seed(121*deg) tot_time = 0 - #print(deg) for _ in range(args.trials): polys = [getPoly(deg, dim=dim, power=power) for _ in range(dim)] start = time.time() @@ -159,15 +148,17 @@ def run_timer(args): results['Multiplication power'] = times print('Finished trials for multiplication power') - degrees, times = timer(_TVB, args.dim, power=True) - results['TVB power'] = times - print('Finished trials for TVB power') + if TVB_avail: + degrees, times = timer(_TVB, args.dim, power=True) + results['TVB power'] = times + print('Finished trials for TVB power') if args.bertini: degrees, times = timer(bertini, args.dim, power=True) results['bert_degrees'] = degrees results['bertini'] = times print('Finished trials for multiplication power') + degrees, times = timer(_div, args.dim, power=False) results['div cheb'] = times print('Finished trials for division chebyshev') @@ -176,19 +167,12 @@ def run_timer(args): results['mult cheb'] = times print('Finished trials for multiplication chebyshev') - degrees, times = timer(_TVB, args.dim, power=False) - results['TVB cheb'] = times - print('Finished trials for TVB cheb') + if TVB_avail: + degrees, times = timer(_TVB, args.dim, power=False) + results['TVB cheb'] = times + print('Finished trials for TVB cheb') if args.dim == 1: - degrees, times = timer(_multPowerR, args.dim, power=True) - results['MultiplicationRotate power'] = times - print('Finished trials for rotated multiplication power') - - degrees, times = timer(_multChebR, args.dim, power=True) - results['multR cheb'] = times - print('Finished trials for rotated multiplication chebyshev') - degrees, times = timer(_nproots, args.dim, power=True) results['numpy power'] = times print('Finished trials for numpy power') @@ -202,7 +186,6 @@ def run_timer(args): def create_graph(results, args): degrees = results['degrees'] xmax = int(1.05*max(degrees)) - ymax = 1.05*max([max(v) for k,v in results.items() if ('degrees' not in k)]) ymax = max(ymax, 0.1) plt.figure(figsize=(11,5)) diff --git a/yroots/cond_numbers.py b/yroots/cond_numbers.py new file mode 100644 index 00000000..33de0e02 --- /dev/null +++ b/yroots/cond_numbers.py @@ -0,0 +1,51 @@ +import numpy as np +from scipy import linalg +from matplotlib import pyplot as plt + + +a_space = np.linspace(0,1,11) +b_space = np.linspace(-1,1,11) + +for a in a_space: + C_1_cond = [] + C_2_cond = [] + C_3_cond = [] + for b in b_space: + + C_1 = np.array([[1,b], + [0,a]]) + + sing1 = linalg.svd(C_1)[1] + cond1 = sing1[0] / sing1[-1] + C_1_cond.append(cond1) + + C_2 = np.array([[1,b,2*b**2+a**2-1], + [0,a,4*a*b], + [0,0,a**2]]) + + sing2 = linalg.svd(C_2)[1] + cond2 = sing2[0] / sing2[-1] + C_2_cond.append(cond2) + + C_3 = np.array([[1,b,2*b**2+a**2-1,6*(a**2)*b+4*b**3-3*b], + [0,a,4*a*b,3*a**3+12*a*b**2-3*a], + [0,0,a**2,6*(a**2)*b], + [0,0,0,a**3]]) + + sing3 = linalg.svd(C_3)[1] + cond3 = sing3[0] / sing3[-1] + C_3_cond.append(cond3) + + plt.scatter(b_space,C_1_cond,label="C1 (2x2)") + plt.scatter(b_space,C_2_cond,label="C2 (3x3)") + plt.scatter(b_space,C_3_cond,label="C3 (4x4)") + #plt.set_yscale("log") + plt.title("a={}".format(a)) + plt.legend() + plt.ylabel("condition number") + plt.xlabel("beta") + plt.show() + + +#svd +#ratios \ No newline at end of file diff --git a/yroots/old_code/CPDSimultaneousDiag.py b/yroots/old_code/CPDSimultaneousDiag.py new file mode 100644 index 00000000..d2bd03b2 --- /dev/null +++ b/yroots/old_code/CPDSimultaneousDiag.py @@ -0,0 +1,77 @@ +""" +Methods for using the tensor CPD to comute the eigenvalues of commuting multiplication +matrices simultaneously. Based off matlab code written by Telen and Tensorlab, +and Hayden's implicit rotation code. These functions were never completed, but +could be useful to revist in the future to compare for speed and accuracy against +the current simultaneous diagonalization code. +Suzanna Parkinson, 6/12/2020 +""" +from scipy.linalg import solve_triangular, eig, schur, svd +import numpy as np + +def msroots_GEVD_rotated(M): + """Computes the roots to a system via the eigenvalues of the Möller-Stetter + matrices. Implicitly performs a random rotation of the coordinate system + to avoid repeated eigenvalues arising from special structure in the underlying + polynomial system. Solves using the cpd gevd algorithm. + + Parameters + ---------- + M : (n,n,dim) ndarray + Array containing the nxn Möller-Stetter matrices, where the matrix + corresponding to multiplication by x_i is M[...,i] + + Returns + ------- + roots : (n,dim) ndarray + Array containing the approximate roots of the system, where each row + is a root. + """ + num_roots,dim = M.shape[1:] + + # perform a random rotation with a random orthogonal Q + Q,c = get_Q_c(dim) #todo... don't need c really + M = (Q@M[...,np.newaxis])[...,0] + + #stack on a copy of the identity matrix + #todo there's definitely a more efficient way to do this + M_ = np.stack((*[M[...,i] for i in range(dim)],np.eye(num_roots)),axis=2) + C = specialized_cpd_gevd(M_,rank=num_roots) + #rescale result + C[:-1] /= C[-1] + # Rotate back before returning, transposing to match expected shape + return (Q.T@C[:-1]).T + +def specialized_cpd_gevd(T,rank): + num_roots,num_slices = T.shape[1:] + V,S,sv = mlsvd(T); + GenVecs, GenVals = la.eig(S[:,:,1].T,S[:,:,2].T) + T1 = T.reshape(num_roots,num_roots*num_slices) + X = T1.T@np.conj(V[1])*GenVecs + for r in range(rank): + u,s = mlsvd(X[:,r].reshape((num_roots,num_slices)),[1,1]) + C = np.concatenate(C,u[1],axis=1) + return C + +def mlsvd(T): + shape_tens = np.array(T.shape) + dim = T.ndim + U,S = [0]*dim,T + for n in range(dim): + print(n,'of',dim) + U[n],s,v = svd(tens2mat(S,mode_row=n),full_matrices=False) + print(v.shape) + print(shape_tens) + S = mat2tens(v,shape_tens,mode_col=n) + return U,S + +def mat2tens(M,shape_tens,mode_row=None,mode_col=None): + # if mode_col is None: + # # mode_col = np.delete + # TODO Fix + return M.reshape(shape_tens) + +def tens2mat(T,mode_row): + col_list = list(T.shape) + row = col_list.pop(mode_row) + return T.reshape((row,np.prod(col_list))) diff --git a/numalgsolve/Division.py b/yroots/old_code/Division.py similarity index 70% rename from numalgsolve/Division.py rename to yroots/old_code/Division.py index 38afb869..c8125fe5 100644 --- a/numalgsolve/Division.py +++ b/yroots/old_code/Division.py @@ -1,25 +1,66 @@ import numpy as np import itertools from scipy.linalg import solve_triangular, eig, qr -from numalgsolve.polynomial import MultiCheb, MultiPower, is_power -from numalgsolve.MacaulayReduce import add_polys, rrqr_reduceMacaulay, rrqr_reduceMacaulay2 -from numalgsolve.utils import get_var_list, slice_top, row_swap_matrix, \ +from yroots import LinearProjection +from yroots.polynomial import MultiCheb, MultiPower, is_power +from yroots.MacaulayReduce import add_polys, #rrqr_reduceMacaulay +from yroots.utils import get_var_list, slice_top, row_swap_matrix, \ mon_combos, newton_polish, MacaulayError -import warnings -def division(polys, get_divvar_coord_from_eigval = False, divisor_var = 0, tol = 1.e-12, verbose=False, polish = False): +import numpy as np +import scipy.linalg as la +def condeig(A): + """Calculates the condition numbers of the eigenvalues of A""" + n = A.shape[0] + w, vl, vr = la.eig(A,left=True) + vl, vr = vl/la.norm(vl,axis=0), vr/la.norm(vr,axis=0) + out = np.empty(n) + for i in range(n): + out[i] = 1/np.abs(np.dot(vl[:,i],vr[:,i])) + return out + +def condeigv(A): + """Calculates the condition numbers of the eigenvectors of A""" + n = A.shape[0] + w, vr = la.eig(A) + out = np.empty(n) + for i in range(n): + #compute Householder vector u + x = vr[:,i] + u = x + u[0] += np.exp(np.angle(x[0]))*la.norm(x) + + #form Householder matrix + Q = np.eye(n) - (2/la.norm(u)**2)*np.outer(u.conj(),u) + + #compute minimum singular value of B-w[i]I + s = la.svd((Q.T.conj()@A@Q)[1:,1:]-w[i]*np.eye(n-1),compute_uv=False)[-1] + if s == 0: + out[i] = np.inf + else: + out[i] = 1/s + return out + + +def division(polys, divisor_var=0, max_cond_num=1.e6, macaulay_zero_tol=1.e-12, verbose=False, polish=False, return_all_roots=True): '''Calculates the common zeros of polynomials using a division matrix. Parameters -------- - polys: MultiCheb Polynomials + polys: list of MultiCheb Polynomials The polynomials for which the common roots are found. divisor_var : int What variable is being divided by. 0 is x, 1 is y, etc. Defaults to x. - get_divvar_coord_from_eigval: bool - Whether the divisor_var-coordinate of the roots is calculated from the eigenvalue or eigenvector. - More stable to use the eigenvector. Defaults to false - + max_cond_num : float + The maximum condition number of the Macaulay Matrix Reduction + macaulay_zero_tol : float + What is considered 0 in the macaulay matrix reduction. + verbose : bool + If True prints information about the solve. + polish: bool + If True runs a newton polish on the zeros before returning. + return_all_roots : bool + If True returns all the roots, otherwise just the ones in the unit box. Returns ----------- zeros : numpy array @@ -27,20 +68,21 @@ def division(polys, get_divvar_coord_from_eigval = False, divisor_var = 0, tol = ''' #This first section creates the Macaulay Matrix with the monomials that don't have #the divisor variable in the first columns. + polys, transform, is_projected = polys, lambda x:x, False + if len(polys) == 1: + from yroots.OneDimension import solve + return transform(solve(polys[0], MSmatrix=0)) power = is_power(polys) dim = polys[0].dim - #By Bezout's Theorem. Useful for making sure that the reduced Macaulay Matrix is as we expect - degrees = [poly.degree for poly in polys] - max_number_of_roots = np.prod(degrees) - - matrix_degree = np.sum(poly.degree for poly in polys) - len(polys) + 1 + matrix_degree = np.sum([poly.degree for poly in polys]) - len(polys) + 1 poly_coeff_list = [] for poly in polys: poly_coeff_list = add_polys(matrix_degree, poly, poly_coeff_list) - matrix, matrix_terms, cuts = create_matrix(poly_coeff_list, matrix_degree, dim, divisor_var, get_divvar_coord_from_eigval) + matrix, matrix_terms, cuts = create_matrix(poly_coeff_list, matrix_degree, dim, divisor_var) + if verbose: np.set_printoptions(suppress=False, linewidth=200) print('\nStarting Macaulay Matrix\n', matrix) @@ -49,32 +91,28 @@ def division(polys, get_divvar_coord_from_eigval = False, divisor_var = 0, tol = #If bottom left is zero only does the first QR reduction on top part of matrix (for speed). Otherwise does it on the whole thing if np.allclose(matrix[cuts[0]:,:cuts[0]], 0): - matrix, matrix_terms = rrqr_reduceMacaulay2(matrix, matrix_terms, cuts, max_number_of_roots, tol) + matrix, matrix_terms = rrqr_reduceMacaulay(matrix, matrix_terms, cuts, max_cond_num=max_cond_num, macaulay_zero_tol=macaulay_zero_tol) else: - matrix, matrix_terms = rrqr_reduceMacaulay(matrix, matrix_terms, cuts, max_number_of_roots, tol) - - rows,columns = matrix.shape + matrix, matrix_terms = rrqr_reduceMacaulay(matrix, matrix_terms, cuts, max_cond_num=max_cond_num, macaulay_zero_tol=macaulay_zero_tol) - #Make there are enough rows in the reduced Macaulay matrix, i.e. didn't loose a row - #This should be a valid assert statement but the max_number_of_roots won't be the product of dimensions for non homogenous polynomials - #assert rows >= columns - max_number_of_roots + if isinstance(matrix, int): + return -1 VB = matrix_terms[matrix.shape[0]:] - matrix = np.hstack((np.eye(rows),solve_triangular(matrix[:,:rows],matrix[:,rows:]))) - + if verbose: np.set_printoptions(suppress=True, linewidth=200) print("\nFinal Macaulay Matrix\n", matrix) print("\nColumns in Macaulay Matrix\n", matrix_terms) - + #------------> chebyshev if not power: #Builds the inverse matrix. The terms are the vector basis as well as y^k/x terms for all k. Reducing #this matrix allows the y^k/x terms to be reduced back into the vector basis. - inverses = matrix_terms[np.where(matrix_terms[:,divisor_var] == 0)[0]] - inverses[:,divisor_var] = -np.ones(inverses.shape[0], dtype = 'int') - inv_matrix_terms = np.vstack((inverses, VB)) - inv_matrix = np.zeros([len(inverses),len(inv_matrix_terms)]) + x_pows_over_y = matrix_terms[np.where(matrix_terms[:,divisor_var] == 0)[0]] + x_pows_over_y[:,divisor_var] = -np.ones(x_pows_over_y.shape[0], dtype = 'int') + inv_matrix_terms = np.vstack((x_pows_over_y, VB)) + inv_matrix = np.zeros([len(x_pows_over_y),len(inv_matrix_terms)]) #A bunch of different dictionaries are used below for speed purposes and to prevent repeat calculations. @@ -91,12 +129,12 @@ def division(polys, get_divvar_coord_from_eigval = False, divisor_var = 0, tol = term = matrix_terms[i] diag_reduction_dict[tuple(term)] = matrix[i][-len(VB):] - #A dictionary of terms to the terms in their quotient when divided by x. + #A dictionary of terms to the terms in their quotient when divided by x. (symbolically) divisor_terms_dict = dict() for term in matrix_terms: divisor_terms_dict[tuple(term)] = get_divisor_terms(term, divisor_var) - #A dictionary of terms to their quotient when divided by x. + #A dictionary of terms to their quotient when divided by x. (in the vector basis) term_divide_dict = dict() for term in matrix_terms[-len(VB):]: term_divide_dict[tuple(term)] = divide_term(term, inv_matrix_terms, inv_spot_dict, diag_reduction_dict, @@ -112,6 +150,9 @@ def division(polys, get_divvar_coord_from_eigval = False, divisor_var = 0, tol = #Reduces the inv_matrix to solve for the y^k/x terms in the vector basis. Q,R = qr(inv_matrix) + if np.linalg.cond(R[:,:R.shape[0]]) > max_cond_num: + return -1 + inv_solutions = np.hstack((np.eye(R.shape[0]),solve_triangular(R[:,:R.shape[0]], R[:,R.shape[0]:]))) #A dictionary of term in the vector basis to their spot in the vector basis. @@ -151,59 +192,65 @@ def division(polys, get_divvar_coord_from_eigval = False, divisor_var = 0, tol = else: division_matrix[:,i] -= basisDict[term] #<----------end Power - - vals, vecs = eig(division_matrix.T) - if verbose: - print("\nDivision Matrix\n", np.round(division_matrix[::-1,::-1], 2)) - print("\nLeft Eigenvectors (as rows)\n", vecs.T) + + + vals, vecs = eig(division_matrix,left=True,right=False) + #conjugate because scipy gives the conjugate eigenvector + vecs = vecs.conj() + +# if len(vals) > len(np.unique(np.round(vals, 10))): +# return -1 + +# eigenvalue_cond = np.linalg.cond(vecs) +# if eigenvalue_cond*tol > 1: +# return -1 + +# if verbose: +# print("\nDivision Matrix\n", np.round(division_matrix[::-1,::-1], 2)) +# print("\nLeft Eigenvectors (as rows)\n", vecs.T) +# if not power: +# if np.max(np.abs(vals)) > 1.e6: +# return -1 + #Calculates the zeros, the x values from the eigenvalues and the y values from the eigenvectors. zeros = list() + for i in range(len(vals)): - if abs(vecs[-1][i]) < 1.e-3: - #This root has magnitude greater than 1, will possibly generate a false root due to instability - continue +# if power and abs(vecs[-1][i]) < 1.e-3: +# #This root has magnitude greater than 1, will possibly generate a false root due to instability +# continue +# if np.abs(vals[i]) < 1.e-5: +# continue root = np.zeros(dim, dtype=complex) - if get_divvar_coord_from_eigval: - for spot in range(0,divisor_var): - root[spot] = vecs[-(2+spot)][i]/vecs[-1][i] - for spot in range(divisor_var+1,dim): - root[spot] = vecs[-(1+spot)][i]/vecs[-1][i] - else: - for spot in range(0,divisor_var): - root[spot] = vecs[-(3+spot)][i]/vecs[-1][i] - for spot in range(divisor_var+1,dim): - root[spot] = vecs[-(2+spot)][i]/vecs[-1][i] - - if get_divvar_coord_from_eigval: #If get_divvar_coord_from_eigval, use the eigenval to calulate that coordinate - root[divisor_var] = 1/vals[i] - elif power: #If using the eigenvector and it's in power form, normal calculation of coordinate - root[divisor_var] = vecs[-(2)][i]/vecs[-1][i] - else: #If using the eigenvector and it's in cheb form, have to use quadratic formula to calculate coordinate. - vecval = vecs[-(2)][i]/vecs[-1][i] #vecval = cT2(x)/cT1(x) = c(2x^2-1)/cx = (2x^2-1)/x - root1 = root.copy() - root1[divisor_var] = (vecval + np.sqrt(vecval**2 + 8))/4 - root2 = root.copy() - root2[divisor_var] = (vecval - np.sqrt(vecval**2 + 8))/4 - - if sum(np.abs(poly(root1)) for poly in polys) < sum(np.abs(poly(root2)) for poly in polys): - root = root1 - else: - root = root2 + for spot in range(0,divisor_var): + root[spot] = vecs[-(2+spot)][i]/vecs[-1][i] + for spot in range(divisor_var+1,dim): + root[spot] = vecs[-(1+spot)][i]/vecs[-1][i] + + root[divisor_var] = 1/vals[i] + +# conditions = condeigv(division_matrix.T) +# if np.abs(vals[i]) > 1: +# print(root, conditions[i]) + if polish: - root = newton_polish(polys,root,tol = tol) - zeros.append(root) + root = newton_polish(polys,root) - zeros = np.array(zeros) + #throw out bad roots in cheb + if not power: + if np.any([abs(poly(root)) > 1.e-1 for poly in polys]): + continue - #Checks that the algorithm finds the correct number of roots with Bezout's Theorem - assert zeros.shape[0] <= max_number_of_roots,"Found too many roots" #Check if too many roots - #if zeros.shape[0] < max_number_of_roots: - # warnings.warn('Expected ' + str(max_number_of_roots) - # + " roots, Found " + str(zeros.shape[0]) , Warning) - # print("Number of Roots Lost:", max_number_of_roots - zeros.shape[0]) - return zeros + zeros.append(root) -def get_matrix_terms(poly_coeffs, dim, divisor_var, deg, include_divvar_squared=True): + if return_all_roots: + return transform(np.array(zeros)) + else: + # only return roots in the unit complex hyperbox + zeros = transform(np.array(zeros)) + return zeros[np.all(np.abs(zeros) <= 1,axis = 0)] + +def get_matrix_terms(poly_coeffs, dim, divisor_var): '''Finds the terms in the Macaulay matrix. Parameters @@ -214,10 +261,6 @@ def get_matrix_terms(poly_coeffs, dim, divisor_var, deg, include_divvar_squared= The dimension of the polynomials in the matrix. divisor_var : int What variable is being divided by. 0 is x, 1 is y, etc. - include_divvar_squared: bool - Whether the divisor_var^2 (or T_2(divisor_var) for cheb division) term is included in the vector basis. - Should be true if calculating divisor_var-coordinates from the eigenvector and not the eigenvalue - Defaults to True Returns ----------- @@ -227,16 +270,6 @@ def get_matrix_terms(poly_coeffs, dim, divisor_var, deg, include_divvar_squared= When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate where those cuts happen. ''' - """ - #The following code is just for testing, just two dimensional divide by x. - mDeg = deg - array = np.array(mon_combos([0]*dim,mDeg)) - perm = np.arange(mDeg+1) - perm = np.hstack((perm, np.arange(len(perm)+2,len(array)), np.arange(len(perm),len(perm)+2)[::-1])) - mons = array[perm] - cuts = tuple([mDeg+1, len(perm) - 2]) - return mons, cuts - """ matrix_term_set_y= set() matrix_term_set_other= set() for coeffs in poly_coeffs: @@ -251,21 +284,12 @@ def get_matrix_terms(poly_coeffs, dim, divisor_var, deg, include_divvar_squared= matrix_term_set_other.remove(tuple(base)) matrix_term_end = base.copy() except KeyError as e: - print(matrix_term_set_other) - print(poly_coeffs, dim, divisor_var, deg, include_divvar_squared) - raise e + raise MacaulayError('Key Error Thrown in get_matrix_terms; probably linear') #sorts the terms that do include the variable to be divided by into submatrices #matrix_term_end terms are always include in the basisDict #matrix_term_set_other are included only as needed - #if include_divvar_squared is set to True, then x^2 (or whatever variable we're dividing by) will be included in the basis - if include_divvar_squared: - divvar_squared_term = np.zeros(dim, dtype = 'int') - divvar_squared_term[divisor_var] = 2 - matrix_term_set_other.remove(tuple(divvar_squared_term)) - matrix_term_end = np.vstack((divvar_squared_term,matrix_term_end)) - try: for i in range(dim): if i != divisor_var: @@ -275,13 +299,7 @@ def get_matrix_terms(poly_coeffs, dim, divisor_var, deg, include_divvar_squared= matrix_term_end = np.vstack((term,matrix_term_end)) base[i] = 0 except KeyError as e: - print(term) - print(matrix_term_set_other) - print(poly_coeffs, dim, divisor_var, deg, include_divvar_squared) - raise e - - #for term in needed_terms: - # matrix_term_set_other.remove(term) + raise MacaulayError('Key Error Thrown in get_matrix_terms; probably linear') matrix_terms = np.vstack((np.vstack(matrix_term_set_y),np.vstack(matrix_term_set_other),matrix_term_end)) return matrix_terms, tuple([len(matrix_term_set_y), len(matrix_term_set_y)+len(matrix_term_set_other)]) @@ -325,7 +343,7 @@ def makeBasisDict(matrix, matrix_terms, VB): basisDict[term] = matrix[i][matrix.shape[0]:] return basisDict -def create_matrix(poly_coeffs, degree, dim, divisor_var, get_divvar_coord_from_eigval = False): +def create_matrix(poly_coeffs, degree, dim, divisor_var): ''' Builds a Macaulay matrix for reduction. Parameters @@ -338,9 +356,6 @@ def create_matrix(poly_coeffs, degree, dim, divisor_var, get_divvar_coord_from_e The dimension of the polynomials going into the matrix. divisor_var : int What variable is being divided by. 0 is x, 1 is y, etc. Defaults to x. - get_divvar_coord_from_eigval: bool - Whether the divisor_var-coordinate of the roots is calculated from the eigenvalue or eigenvector. - More stable to use the eigenvector. Defaults to false Returns ------- @@ -354,10 +369,7 @@ def create_matrix(poly_coeffs, degree, dim, divisor_var, get_divvar_coord_from_e ''' bigShape = [degree+1]*dim - #If you get_divvar_coord_from_eigval, then you should not include_divvar_squared in the basis, and vice versa - include_divvar_squared = not get_divvar_coord_from_eigval - - matrix_terms, cuts = get_matrix_terms(poly_coeffs, dim, divisor_var, degree, include_divvar_squared) + matrix_terms, cuts = get_matrix_terms(poly_coeffs, dim, divisor_var) #Get the slices needed to pull the matrix_terms from the coeff matrix. matrix_term_indexes = list() @@ -370,10 +382,9 @@ def create_matrix(poly_coeffs, degree, dim, divisor_var, get_divvar_coord_from_e for coeff in poly_coeffs: slices = slice_top(coeff) added_zeros[slices] = coeff - flat_polys.append(added_zeros[matrix_term_indexes]) + flat_polys.append(added_zeros[tuple(matrix_term_indexes)]) added_zeros[slices] = np.zeros_like(coeff) - coeff = 0 - poly_coeffs = 0 + del poly_coeffs #Make the matrix. Reshape is faster than stacking. matrix = np.reshape(flat_polys, (len(flat_polys),len(matrix_terms))) @@ -451,13 +462,13 @@ def get_divisor_terms(term, divisor_var): ---------- term: numpy array The term to divide. + divisor_var : int + What variable is being divided by. 0 is x, 1 is y, etc. Returns ------- terms : numpy array Each row is a term that will be in the quotient. - divisor_var : int - What variable is being divided by. 0 is x, 1 is y, etc. """ dim = len(term) diff = np.zeros(dim) @@ -489,10 +500,9 @@ def build_division_matrix(VB, VB_spot_dict, diag_reduction_dict, inv_reduction_d Returns ------- - row : numpy array - The row we get in the inverse_matrix by dividing the term by x. + div_matrix : numpy array + The division matrix. """ - div_matrix = np.zeros((len(VB), len(VB))) for i in range(len(VB)): term = VB[i] diff --git a/yroots/old_code/NewDivision.py b/yroots/old_code/NewDivision.py new file mode 100644 index 00000000..a8672c0b --- /dev/null +++ b/yroots/old_code/NewDivision.py @@ -0,0 +1,304 @@ +import numpy as np +import itertools +from scipy.linalg import solve_triangular, eig, qr +from yroots import LinearProjection +from yroots.polynomial import MultiCheb, MultiPower, is_power +from yroots.MacaulayReduce import add_polys, rrqr_reduceMacaulay +from yroots.utils import get_var_list, slice_top, row_swap_matrix, \ + mon_combos, newton_polish, MacaulayError +from yroots.Division import create_matrix + +def divisionNew(polys, divisor_var=0, tol=1.e-12, verbose=False, polish=False, return_all_roots=True): + '''Calculates the common zeros of polynomials using a division matrix. + + Parameters + -------- + polys: list of MultiCheb Polynomials + The polynomials for which the common roots are found. + divisor_var : int + What variable is being divided by. 0 is x, 1 is y, etc. Defaults to x. + tol : float + The tolerance parameter for the Macaulay Reduce. + verbose : bool + If True prints information about the solve. + polish: bool + If True runs a newton polish on the zeros before returning. + + Returns + ----------- + zeros : numpy array + The common roots of the polynomials. Each row is a root. + ''' + #This first section creates the Macaulay Matrix with the monomials that don't have + #the divisor variable in the first columns. + polys, transform, is_projected = LinearProjection.remove_linear(polys, 1e-4, 1e-8) + if len(polys) == 1: + from yroots.OneDimension import solve + return transform(solve(polys[0], MSmatrix=0)) + power = is_power(polys) + if power: + raise ValueError("This only works for Chebyshev polynomials") + + dim = polys[0].dim + matrix_degree = np.sum([poly.degree for poly in polys]) - len(polys) + 1 + poly_coeff_list = [] + for poly in polys: + poly_coeff_list = add_polys(matrix_degree, poly, poly_coeff_list) + + matrix, matrix_terms, cuts = create_matrix(poly_coeff_list, matrix_degree,\ + dim, divisor_var) + + x_pows_over_y = matrix_terms[:cuts[0]].copy() + x_pows_over_y[:,divisor_var] = -np.ones(cuts[0], dtype = 'int') + inv_matrix_terms = np.vstack((x_pows_over_y, matrix_terms)) + + num_rows = matrix.shape[0] + inv_matrix = np.zeros([num_rows + matrix.shape[0], len(inv_matrix_terms)]) + cuts = tuple([cuts[0], cuts[0]+cuts[1]]) + + #A dictionary of term in inv_matrix_terms to their spot in inv_matrix_terms. + inv_spot_dict = dict() + spot = 0 + for term in inv_matrix_terms: + inv_spot_dict[tuple(term)] = spot + spot+=1 + + #A dictionary of terms to the terms in their quotient when divided by x. (symbolically) + divisor_terms_dict = dict() + for term in matrix_terms: + divisor_terms_dict[tuple(term)] = get_divisor_terms(term, divisor_var) + + #A dictionary of terms to their quotient when divided by x. (in the vector basis) + term_divide_dict = dict() + for term in matrix_terms: + term_divide_dict[tuple(term)] = divide_term(term, inv_matrix_terms, inv_spot_dict, + divisor_terms_dict) + + #Builds the inv_matrix by dividing the rows of matrix by x. + for i in range(num_rows): + inv_matrix[i] = divide_row(matrix[i], matrix_terms, term_divide_dict, + len(inv_matrix_terms)) + + inv_matrix[num_rows:,cuts[0]:] = matrix + + matrix, matrix_terms, perm = rrqr_reduceMacaulay(inv_matrix, inv_matrix_terms, cuts, + accuracy=tol, return_perm=True) + if isinstance(matrix, int): + return -1 + + for term in term_divide_dict: + term_divide_dict[tuple(term)] = term_divide_dict[tuple(term)][perm] + + VB = matrix_terms[matrix.shape[0]:] + basisDict = makeBasisDict2(matrix, matrix_terms) + +# print(len(VB)) + + #Dictionary of terms in the vector basis their spots in the matrix. + VBdict = {} + spot = 0 + for row in VB: + VBdict[tuple(row)] = spot + spot+=1 + + #Builds the division matrix and finds the eigenvalues and eigenvectors. + division_matrix = build_division_matrix(VB, VBdict, basisDict, term_divide_dict, + matrix_terms) + + vals, vecs = eig(division_matrix,left=True,right=False) + #conjugate because scipy gives the conjugate eigenvector + vecs = vecs.conj() + + if len(vals) > len(np.unique(np.round(vals, 10))): + return -1 + + vals2, vecs2 = eig(vecs) + sorted_vals2 = np.sort(np.abs(vals2)) #Sorted smallest to biggest + if sorted_vals2[0] < sorted_vals2[-1]*tol: + return -1 + + if verbose: + print("\nDivision Matrix\n", np.round(division_matrix[::-1,::-1], 2)) + print("\nLeft Eigenvectors (as rows)\n", vecs.T) + + if np.max(np.abs(vals)) > 1.e6: + return -1 + + #Calculates the zeros, the x values from the eigenvalues and the y values from the eigenvectors. + zeros = list() + + for i in range(len(vals)): + if np.abs(vals[i]) < 1.e-5: + continue + root = np.zeros(dim, dtype=complex) + for spot in range(0,divisor_var): + root[spot] = vecs[-(2+spot)][i]/vecs[-1][i] + for spot in range(divisor_var+1,dim): + root[spot] = vecs[-(1+spot)][i]/vecs[-1][i] + + root[divisor_var] = 1/vals[i] + + if polish: + root = newton_polish(polys,root,tol = tol) + + #throw out bad roots in cheb + if np.any([abs(poly(root)) > 1.e-1 for poly in polys]): + continue + + zeros.append(root) + + if return_all_roots: + return transform(np.array(zeros)) + else: + # only return roots in the unit complex hyperbox + zeros = transform(np.array(zeros)) + return zeros[np.all(np.abs(zeros) <= 1,axis = 0)] + +def divide_row(coeffs, terms, term_divide_dict, length): + """Divides a row of the matrix by the divisor variable.. + + Parameters + ---------- + coeffs : numpy array. + The numerical values of the terms we want to divide by x. + terms: numpy array + The terms corresponding to the numerical values. + term_divide_dict: dictionary + Maps each term as a tuple to a numpy array representing that term divided by x. + length : int + The length of the rows in the inv_matrix. + Returns + ------- + new_row : numpy array + The row we get in the inverse_matrix by dividing the first row by x. + """ + new_row = np.zeros(length) + for i in range(len(coeffs)): + new_row+=coeffs[i]*term_divide_dict[tuple(terms[i])] + return new_row + +def divide_term(term, inv_matrix_terms, inv_spot_dict, divisor_terms_dict): + """Divides a term of the matrix by the divisor variable. + + Parameters + ---------- + term: numpy array + The term to divide. + inv_matrix_terms: numpy array + The terms in the inverse matrix. + inv_spot_dict : dictionary + A dictionary of term in inv_matrix_terms to their spot in inv_matrix_terms. + diag_reduction_dict : dictionary + A dictionary of terms on the diagonal to their reduction in the vector basis. + VB_size : int + The number of elements in the vector basis. + divisor_terms_dict : dictionary + A dictionary of terms to the terms in their dividend when divided by x. + + Returns + ------- + row : numpy array + The row we get in the inverse_matrix by dividing the term by x. + """ + row = np.zeros(len(inv_matrix_terms)) + divisor_terms = divisor_terms_dict[tuple(term)] + parity = 1 + for spot in divisor_terms[:-1]: + row[inv_spot_dict[tuple(spot)]] += parity*2 + parity*=-1 + spot = divisor_terms[-1] + row[inv_spot_dict[tuple(spot)]] += parity + return row + +def get_divisor_terms(term, divisor_var): + """Finds the terms that will be present when dividing a given term by x. + + Parameters + ---------- + term: numpy array + The term to divide. + divisor_var : int + What variable is being divided by. 0 is x, 1 is y, etc. + + Returns + ------- + terms : numpy array + Each row is a term that will be in the quotient. + """ + dim = len(term) + diff = np.zeros(dim) + diff[divisor_var] = 1 + initial = term - diff + height = term[divisor_var]//2+1 + terms = np.vstack([initial.copy() for i in range(height)]) + dec = 0 + for i in range(terms.shape[0]): + terms[i][divisor_var]-=2*dec + dec+=1 + return terms + +def build_division_matrix(VB, VBdict, basisDict, term_divide_dict, + matrix_terms): + """Builds the division matrix. + + Parameters + ---------- + VB: numpy array + The vector basis. + VBdict : dictionary + A dictionary of term in the vector basis to their spot in the vector basis. + diag_reduction_dict : dictionary + A dictionary of terms on the diagonal to their reduction in the vector basis. + inv_reduction_dict : dictionary + A dictionary of terms of type y^k/x to their reduction in the vector basis. + divisor_terms_dict : dictionary + A dictionary of terms to the terms in their dividend when divided by x. + + Returns + ------- + div_matrix : numpy array + The division matrix. + """ + div_matrix = np.zeros((len(VB), len(VB))) + for i in range(len(VB)): + term = VB[i] + row = term_divide_dict[tuple(term)] + for spot, val in enumerate(row): + if val != 0: + term = tuple(matrix_terms[spot]) + if term in VBdict: + div_matrix[VBdict[term]][i] += val + else: + div_matrix[:,i] -= val*basisDict[term] + + return div_matrix + +def makeBasisDict2(matrix, matrix_terms): + '''Calculates and returns the basisDict. + + This is a dictionary of the terms on the diagonal of the reduced Macaulay + matrix to the terms in the Vector Basis. + It is used to create the multiplication matrix in root_finder. + + Parameters + -------- + matrix: numpy array + The reduced Macaulay matrix. + matrix_terms : numpy array + The terms in the matrix. The i'th row is the term represented by the i'th + column of the matrix. + VB : numpy array + Each row is a term in the vector basis. + + Returns + ----------- + basisDict : dict + Maps terms on the diagonal of the reduced Macaulay matrix (tuples) to numpy + arrays of the shape remainder_shape + that represent the terms reduction into the Vector Basis. + ''' + basisDict = {} + for i in range(matrix.shape[0]): + term = tuple(matrix_terms[i]) + basisDict[term] = matrix[i][matrix.shape[0]:] + return basisDict diff --git a/yroots/old_code/OldIntervalChecks.py b/yroots/old_code/OldIntervalChecks.py new file mode 100644 index 00000000..fffbba52 --- /dev/null +++ b/yroots/old_code/OldIntervalChecks.py @@ -0,0 +1,504 @@ +""" +This file contains functions that used to be part of IntervalChecks.py but are +not being used as of May 2020. This functions may be revamped in the future, but +in their current implementations they are not fast enough to be useful. If these +functions were carefully optimized, it's possible they could become useful, and +the ideas behind the checks could be used to develop better checks in the future. +A general description of each check is provided. +""" +import numpy as np +from itertools import product +import itertools +from yroots.polynomial import MultiCheb +from matplotlib import pyplot as plt +from yroots.polynomial import MultiCheb, Polynomial +from matplotlib import patches + +""" +LINEAR CHECK +This is a fast and simple check. It compares the range of the linear terms on +the intervals to the sum of the absolute values of the other coefficients. +If min(linear-part) > other-coef-sum or max(linear-part) < -other-coef-sum, +there cannot be any roots. +Although this check is very efficient, we found that quadractic_check in IntervalChecks.py +removed most of the regions that linear_check removed plus some more. Experimentally, +it wasn't worth the time in 2D to run the linear check before the quadratic check because +quadratic check would throw out the region anyway. +**Because the nd-quadratic check is slow, it's quite possible that this check could be +useful in dimensions 3+ *** +Originally a subinterval check +""" +def linear_check(test_coeff, intervals, tol): + """One of subinterval_checks + + Checks the max of the linear part of the approximation and compares to the sum of the other terms. + + Parameters + ---------- + test_coeff : numpy array + The coefficient matrix of the polynomial to check + intervals : list + A list of the intervals to check. + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + mask : list + A list of the results of each interval. False if the function is guarenteed to never be zero + in the unit box, True otherwise + """ + dim = test_coeff.ndim + coeff_abs_sum = np.sum(np.abs(test_coeff)) + + #Get the linear and constant terms + idx = [0]*dim + const = test_coeff[tuple(idx)] + lin_coeff = np.zeros(dim) + for cur_dim in range(dim): + if test_coeff.shape[cur_dim] < 2: + continue + idx[cur_dim] = 1 + lin_coeff[cur_dim] = test_coeff[tuple(idx)] + idx[cur_dim] = 0 + + coeff_abs_sum -= np.sum(np.abs(lin_coeff)) + mask = [] + + for i, interval in enumerate(intervals): + + corner_vals = [] + for ints in product(interval, repeat = dim): + corner_vals.append(const + np.array([ints[i][i] for i in range(dim)])@lin_coeff) + corner_vals = np.array(corner_vals) + + # check if corners have mixed signs + if (corner_vals.min() < 0 < corner_vals.max()): + mask.append(True) + continue + + abs_smallest_corner = np.min(np.abs(corner_vals)) + if abs_smallest_corner > coeff_abs_sum + tol: + # case: corner is far enough from 0 + mask.append(False) + else: + mask.append(True) + + return mask +""" +QUAD CHECK +Although it's confusingly named, the quad_check is NOT the same +as the quadratic_check. This function splits up the polynomial into +smaller chunks--polynomials with fewer terms, each of which is quadratic in one variable. +There cannot be a root if the minimum of the absolute value of the first chunk is greater +than the sum of the maximums of the absolute values of the other chunks. +Warning: this has quite a few issues. This check needs to be more mathematically +rigorous to be functional. There are coefficients that are ignored right now, which +must be added in if this check is to be airtight. The mathematical basis of how +extreme_val_3 is used on monomial multiples of quadratics should be explored +before using this check. For now, print statements are included to give some intuition +on how the chunks are made. +Originally an interval check +""" +def extreme_val3(test_coeff, maxx = True): + ''' Finds the extreme value of test_coeff on -1 to 1, used by quad_check + + test_coeff is [a,b,c] and represents the funciton a + bx + c(2x^2 - 1). + Basic calculus can be used to find the extreme values. + + Parameters + ---------- + test_coeff : numpy array + Array representing [a,b,c] + maxx: bool + If true returns the max of the absolute value of the funciton, otherwise returns + the min of the absolute value of the function. + Returns + ------- + extreme_val3 : float + The extreme value (max or min) of the absolute value of a + bx + c(2x^2 - 1). + ''' + a,b,c = test_coeff + #CAREFUL: There's a hard coded tolerance here... + if np.abs(c) < 1.e-10: + if maxx: + return abs(a) + abs(b) + else: + if abs(b) > abs(a): + return 0 + else: + return abs(a) - abs(b) + else: + vals = [a - b + c, a + b + c] #at +-1 + if np.abs(b/c) < 4: + vals.append(a - b**2/(8*c) - c) #at -b/(4c) + if maxx: + return max(np.abs(vals)) + else: + vals = np.array(vals) + if np.any(vals > 0) and np.any(vals < 0): + return 0 + else: + return min(np.abs(vals)) + +def quad_check(test_coeff, tol): + """One of interval_checks + + Like the constant term check, but splits the coefficient matrix into a one dimensional + quadratics and uses the extreme values of those to get a better bound. + + Parameters + ---------- + test_coeff : numpy array + The coefficient matrix of the polynomial to check + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + quad_check : bool + False if the function is guarenteed to never be zero in the unit box, True otherwise + """ + #The check fails if the test_coeff isn't at least quadratic + if np.any(np.array(test_coeff.shape) < 3): + return True + dim = test_coeff.ndim + slices = [] + slices.append(slice(0,3)) + slice_direc = 0 + for i in range(dim-1): + slices.append(0) + + print('coef:',np.round(test_coeff,2),sep='\n') + print('start coef:',test_coeff[tuple(slices)],sep='\n') + #Get the min of the quadratic including the constant term + start = extreme_val3(test_coeff[tuple(slices)], maxx = False) + print('start coef min:',start,sep='\n') + rest = 0 + + #Get the max's of the other quadratics + shape = list(test_coeff.shape) + shape[slice_direc] = 1 + for spots in itertools.product(*[np.arange(i) for i in shape]): + if sum(spots) > 0: + for i in range(1, dim): + slices[i] = spots[i] + rest += extreme_val3(test_coeff[tuple(slices)]) + print('chunk coef:',test_coeff[tuple(slices)],sep='\n') + print('chunk coef min:',extreme_val3(test_coeff[tuple(slices)]),'rest:',rest) + + #Tries the one-dimensional slices in other directions + while slice_direc < dim - 1: + slice_direc += 1 + slices[slice_direc] = slice(0,3) + + shape = np.array(test_coeff.shape) + shape[slice_direc] = 1 + shape_diff = np.zeros_like(shape) + for i in range(slice_direc): + shape_diff[i] = 3 + shape -= shape_diff + for spots in itertools.product(*[np.arange(i) for i in shape]): + spots += shape_diff + for i in range(dim): + if i != slice_direc: + slices[i] = spots[i] + rest += extreme_val3(test_coeff[tuple(slices)]) + print('chunk coef:',test_coeff[tuple(slices)],sep='\n') + print('chunk coef min:',extreme_val3(test_coeff[tuple(slices)]),'rest:',rest) + + if start > rest + tol: + return False + else: + return True + +def full_quad_check(test_coeff, tol): + """One of interval_checks + + Runs the quad_check in each possible direction to get as much out of it as possible. + + Parameters + ---------- + test_coeff : numpy array + The coefficient matrix of the polynomial to check + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + full_quad_check : bool + False if the function is guarenteed to never be zero in the unit box, True otherwise + """ + for perm in itertools.permutations(np.arange(test_coeff.ndim)): + if not quad_check(test_coeff.transpose(perm), tol): + return False + return True + +""" +CUBIC CHECK +Although it's confusingly named, the cubic_check is more similar to quad_check than +to any of the quadratic_check functions. This function splits up the polynomial into +smaller chunks--polynomials with fewer terms, each of which is cubic in one variable. +There cannot be a root if the minimum of the absolute value of the first chunk is greater +than the sum of the maximums of the absolute values of the other chunks. +Warning: this has quite a few issues. This check needs to be more mathematically +rigorous to be functional. There are coefficients that are ignored right now, which +must be added in if this check is to be airtight. The mathematical basis of how +extreme_val_3 is used on monomial multiples of quadratics should be explored +before using this check. +Originally an interval check +""" +def extreme_val4(test_coeff, maxx = True): + ''' Finds the extreme value of test_coeff on -1 to 1, used by cubic_check + + test_coeff is [a,b,c,d] and represents the funciton a + bx + c(2x^2 - 1) + d*(4x^3 - 3x). + Basic calculus can be used to find the extreme values. + + Parameters + ---------- + test_coeff : numpy array + Array representing [a,b,c,d] + maxx: bool + If true returns the absolute value of the max of the funciton, otherwise returns + the absolute value of the min of the function. + Returns + ------- + extreme_val4 : float + The extreme value (max or min) of the absolute value of a + bx + c(2x^2 - 1) + d*(4x^3 - 3x). + ''' + a,b,c,d = test_coeff + if np.abs(d) < 1.e-10: + return extreme_val3([a,b,c], maxx = maxx) + else: + vals = [a - b + c - d, a + b + c + d] #at +-1 + + #The quadratic roots + if 16*c**2 >= 48*d*(b-3*d): + x1 = (-4*c + np.sqrt(16*c**2 - 48*d*(b-3*d))) / (24*d) + x2 = (-4*c - np.sqrt(16*c**2 - 48*d*(b-3*d))) / (24*d) + if np.abs(x1) < 1: + vals.append(a + b*x1 + c*(2*x1**2 - 1) + d*(4*x1**3 - 3*x1)) + if np.abs(x2) < 1: + vals.append(a + b*x2 + c*(2*x2**2 - 1) + d*(4*x2**3 - 3*x2)) + if maxx: + return max(np.abs(vals)) + else: + vals = np.array(vals) + if np.any(vals > 0) and np.any(vals < 0): + return 0 + else: + return min(np.abs(vals)) + +def cubic_check(test_coeff, tol): + """One of interval_checks + + Like the constant_term, but splits the coefficient matrix into a one dimensional + cubics and uses the extreme values of those to get a better bound. + + Parameters + ---------- + test_coeff : numpy array + The coefficient matrix of the polynomial to check + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + cubic_check : bool + False if the function is guarenteed to never be zero in the unit box, True otherwise + """ + #The check fails if the test_coeff isn't at least cubic + if np.any(np.array(test_coeff.shape) < 4): + return True + dim = test_coeff.ndim + slices = [] + slices.append(slice(0,4)) + slice_direc = 0 + for i in range(dim-1): + slices.append(0) + + #Get the min of the cubic including the constant term + start = extreme_val4(test_coeff[tuple(slices)], maxx = False) + rest = 0 + + #Get the max's of the other cubics + shape = list(test_coeff.shape) + shape[slice_direc] = 1 + for spots in itertools.product(*[np.arange(i) for i in shape]): + if sum(spots) > 0: + for i in range(1, dim): + slices[i] = spots[i] + rest += extreme_val4(test_coeff[tuple(slices)]) + + #Tries the one-dimensional slices in other directions + while slice_direc < dim - 1: + slice_direc += 1 + slices[slice_direc] = slice(0,4) + + shape = np.array(test_coeff.shape) + shape[slice_direc] = 1 + shape_diff = np.zeros_like(shape) + for i in range(slice_direc): + shape_diff[i] = 4 + shape -= shape_diff + for spots in itertools.product(*[np.arange(i) for i in shape]): + spots += shape_diff + for i in range(dim): + if i != slice_direc: + slices[i] = spots[i] + rest += extreme_val4(test_coeff[tuple(slices)]) + + if start > rest + tol: + return False + else: + return True + +def full_cubic_check(test_coeff, tol): + """One of interval_checks + + Runs the cubic_check in each possible direction to get as much out of it as possible. + + Parameters + ---------- + test_coeff : numpy array + The coefficient matrix of the polynomial to check + tol: float + The bound of the sup norm error of the chebyshev approximation. + + Returns + ------- + full_cubic_check : bool + False if the function is guarenteed to never be zero in the unit box, True otherwise + """ + for perm in itertools.permutations(np.arange(test_coeff.ndim)): + if not cubic_check(test_coeff.transpose(perm), tol): + return False + return True +""" +This check uses the curvature of the polynomial and interval arithmetic +to determine the range of the polynomial over the specified domain. +Like the other checks in this graveyard, it wasn't fast enough to use. +We've had issues with the first import statement in this check preventing +installation of yroots, so since this code is no longer used, the import +is currently commented out. If you decide to include this check, it's a +good idea to add it back into the unit test as well, since currently +it's not being tested due to this import error. +Originally an interval check +""" +# from mpmath import iv +from itertools import product +from copy import copy +def lambda_s(a): + return sum(iv.mpf([0,1])*max(ai.a**2,ai.b**2) for ai in a) + +def beta(a,b): + return iv.mpf([-1,1])*iv.sqrt(lambda_s(a)*lambda_s(b)) + +def lambda_t(a,b): + return beta(a,b) + np.dot(a,b) + +class TabularCompute: + def __init__(self,a,b,dim=False,index=None): + """Class for estimating the maximum curvature. + Parameters + ---------- + a (int) - the starting value of the interval + b (int) - the ending value of the interval + dim (bool or int) - False if this is not an interval for a dimension + integer indicating the number of dimensions + index (int) - defines which dimension this interval corresponds to + + """ + self.iv = iv.mpf([a,b]) + self.iv_lambda = iv.mpf([0,0]) + if dim: + assert isinstance(dim, int) + assert isinstance(index, int) and 0<=index0) or all(corners<0)): + return False + + min_corner = abs(min(corners)) + + x = [] + n = len(a) + for i,(ai,bi) in enumerate(zip(a,b)): + x.append(TabularCompute(ai,bi,dim=n,index=i)) + x = np.array(x) + + max_curve = abs(chebvalnd(x, poly).iv_lambda) + return min_corner > max_curve * n * h**2/8 + tol + +def curvature_check(coeff, tol): + poly = MultiCheb(coeff) + a = np.array([-1.]*poly.dim) + b = np.array([1.]*poly.dim) + return not can_eliminate(poly, a, b, tol) diff --git a/yroots/old_code/OldRRQRreduce.py b/yroots/old_code/OldRRQRreduce.py new file mode 100644 index 00000000..7baaada4 --- /dev/null +++ b/yroots/old_code/OldRRQRreduce.py @@ -0,0 +1,105 @@ +"""Old Macaulay RRQR reduction for eigenvectors""" + +def rrqr_reduceMacaulay(matrix, matrix_terms, cuts, max_cond_num, macaulay_zero_tol, return_perm=False): + ''' Reduces a Macaulay matrix, BYU style. + + The matrix is split into the shape + A B C + D E F + Where A is square and contains all the highest terms, and C contains all the x,y,z etc. terms. The lengths + are determined by the matrix_shape_stuff tuple. First A and D are reduced using rrqr without pivoting, and then the rest of + the matrix is multiplied by Q.T to change it accordingly. Then E is reduced by rrqr with pivoting, the rows of B are shifted + accordingly, and F is multipled by Q.T to change it accordingly. This is all done in place to save memory. + + Parameters + ---------- + matrix : numpy array. + The Macaulay matrix, sorted in BYU style. + matrix_terms: numpy array + Each row of the array contains a term in the matrix. The i'th row corresponds to + the i'th column in the matrix. + cuts : tuple + When the matrix is reduced it is split into 3 parts with restricted pivoting. These numbers indicate + where those cuts happen. + max_cond_num : float + Throws an error if the condition number of the backsolve is more than max_cond_num. + macaulay_zero_tol : float + What is considered to be 0 after the reduction. Specifically, rows where every element has + magnitude less that macaulay_zero_tol are removed. + return_perm : bool + If True, also returns the permutation done by the pivoting. + Returns + ------- + matrix : numpy array + The reduced matrix. + matrix_terms: numpy array + The resorted matrix_terms. + perm : numpy array + The permutation of the rows from the original. Returned only if return_perm is True. + Raises + ------ + ConditioningError if the conditioning number of the Macaulay matrix after + QR is greater than max_cond_num. + ''' + #controller variables for each part of the matrix + AD = matrix[:,:cuts[0]] + + BCEF = matrix[:,cuts[0]:] + # A = matrix[:cuts[0],:cuts[0]] + B = matrix[:cuts[0],cuts[0]:cuts[1]] + # C = matrix[:cuts[0],cuts[1]:] + # D = matrix[cuts[0]:,:cuts[0]] + E = matrix[cuts[0]:,cuts[0]:cuts[1]] + F = matrix[cuts[0]:,cuts[1]:] + + #RRQR reduces A and D without pivoting sticking the result in its place. + Q1,matrix[:,:cuts[0]] = qr(AD) + #Conditioning check + cond_num = np.linalg.cond(matrix[:,:cuts[0]]) + if cond_num > max_cond_num: + raise ConditioningError("Conditioning number of the Macaulay matrix "\ + + "after first QR is: " + str(cond_num)) + + #Multiplying BCEF by Q.T + BCEF[...] = Q1.T @ BCEF + del Q1 #Get rid of Q1 for memory purposes. + + #Check to see if E exists + if cuts[0] != cuts[1] and cuts[0] < matrix.shape[0]: + #RRQR reduces E sticking the result in it's place. + Q,E[...],P = qr(E, pivoting = True) + + #Multiplies F by Q.T. + F[...] = Q.T @ F + del Q #Get rid of Q for memory purposes. + + #Permute the columns of B + B[...] = B[:,P] + + #Resorts the matrix_terms. + matrix_terms[cuts[0]:cuts[1]] = matrix_terms[cuts[0]:cuts[1]][P] + + #use the numerical rank to determine how many rows to keep + matrix = row_swap_matrix(matrix)[:cuts[1]] + s = svd(matrix,compute_uv=False) + tol = max(matrix.shape)*s[0]*macheps + rank = len(s[s>tol]) + matrix = matrix[:rank] + + #find the condition number of the backsolve + s = svd(matrix[:,:rank],compute_uv=False) + cond_num = s[0]/s[-1] + if cond_num > max_cond_num: + raise ConditioningError("Conditioning number of backsolving the Macaulay is: " + str(cond_num)) + + #backsolve + height = matrix.shape[0] + matrix[:,height:] = solve_triangular(matrix[:,:height],matrix[:,height:]) + matrix[:,:height] = np.eye(height) + + if return_perm: + perm = np.arange(matrix.shape[1]) + perm[cuts[0]:cuts[1]] = perm[cuts[0]:cuts[1]][P] + return matrix, matrix_terms, perm + + return matrix, matrix_terms diff --git a/numalgsolve/polynomial.py b/yroots/polynomial.py similarity index 90% rename from numalgsolve/polynomial.py rename to yroots/polynomial.py index 164d74c5..3b923d1b 100644 --- a/numalgsolve/polynomial.py +++ b/yroots/polynomial.py @@ -3,31 +3,30 @@ from numpy.polynomial import chebyshev as cheb from numpy.polynomial import polynomial as poly from scipy.signal import fftconvolve, convolve -from numalgsolve.utils import Term, makePolyCoeffMatrix, match_size, slice_top, slice_bottom +from yroots.utils import Term, makePolyCoeffMatrix, match_size, slice_top, slice_bottom import time from numba import jit -@jit(cache=True) +# @jit(cache=True) def polyval(x, cc): #pragma: no cover c0 = cc[-1] for i in range(2, len(cc) + 1): c0 = cc[-i] + c0*x return c0 -@jit(cache=True) +# @jit(cache=True) def polyval2(x, cc): #pragma: no cover - cc = cc.reshape(cc.shape + (1,)*x.ndim) c0 = cc[-1] for i in range(2, len(cc) + 1): c0 = cc[-i] + c0*x return c0 -@jit(cache=True) +# @jit(cache=True) def chebval(x, cc): #pragma: no cover if len(cc) == 1: c0 = cc[0] - c1 = 0 + c1 = np.zeros_like(c0) elif len(cc) == 2: c0 = cc[0] c1 = cc[1] @@ -41,12 +40,11 @@ def chebval(x, cc): #pragma: no cover c1 = tmp + c1*x2 return c0 + c1*x -@jit(cache=True) +# @jit(cache=True) def chebval2(x, cc): #pragma: no cover - cc = cc.reshape(cc.shape + (1,)*x.ndim) if len(cc) == 1: c0 = cc[0] - c1 = 0 + c1 = np.zeros_like(c0) elif len(cc) == 2: c0 = cc[0] c1 = cc[1] @@ -60,7 +58,7 @@ def chebval2(x, cc): #pragma: no cover c1 = tmp + c1*x2 return c0 + c1*x -def getPoly(deg,dim,power): +def getPoly(deg,dim,power,pcnt_sparse=None,integer=False,maxint=10): ''' A helper function for testing. Returns a random upper triangular polynomial of the given dimension and degree. power is a boolean indicating whether or not the polynomial should be MultiPower. @@ -68,7 +66,14 @@ def getPoly(deg,dim,power): deg += 1 # ACoeff = np.random.random_sample(deg*np.ones(dim, dtype = int)) dimensions = (deg,)*dim - ACoeff = np.random.randn(*dimensions) + if integer: + ACoeff = np.random.randint(0,maxint,size=dimensions) + else: + ACoeff = np.random.randn(*dimensions) + if pcnt_sparse is not None: + idx = np.random.choice(np.arange(ACoeff.size),replace=False,size=int(ACoeff.size * pcnt_sparse)) + idx = np.unravel_index(idx,dimensions) + ACoeff[idx] = 0 for i,j in np.ndenumerate(ACoeff): if np.sum(i) >= deg: ACoeff[i] = 0 @@ -138,6 +143,11 @@ def __init__(self, coeff, order='degrevlex', lead_term=None, clean_zeros = True) ''' if isinstance(coeff,np.ndarray): self.coeff = coeff + # If coeff has integer coefficients, + # cast as numpy floats for jit compilation + if coeff.dtype == np.int32 or coeff.dtype == np.int64: + coeff = coeff.astype(np.float64) + elif isinstance(coeff,str): self.coeff = makePolyCoeffMatrix(coeff) elif isinstance(coeff, tuple): @@ -392,7 +402,7 @@ def _fold_in_i_dir(solution_matrix, dim, fdim, size_in_fdim, fold_idx): indexer2[fdim] = slice_1 #makes first slice in sol equal to the slice we fold around in solution_matrix - sol[indexer1] = solution_matrix[indexer2] + sol[tuple(indexer1)] = solution_matrix[tuple(indexer2)] #Loop adds the slices above and below the slice we rotate around and inserts solutions in sol. for n in range(size_in_fdim): @@ -410,12 +420,12 @@ def _fold_in_i_dir(solution_matrix, dim, fdim, size_in_fdim, fold_idx): if fold_idx+n+2 > size_in_fdim: break else: - sol[indexer1] = solution_matrix[indexer2] + sol[tuple(indexer1)] = solution_matrix[tuple(indexer2)] else: if fold_idx+n+2 > size_in_fdim: - sol[indexer1] = solution_matrix[indexer3] + sol[tuple(indexer1)] = solution_matrix[tuple(indexer3)] else: - sol[indexer1] = solution_matrix[indexer3] + solution_matrix[indexer2] + sol[tuple(indexer1)] = solution_matrix[tuple(indexer3)] + solution_matrix[tuple(indexer2)] return sol @@ -463,7 +473,7 @@ def _mon_mult1(initial_matrix, idx, dim_mult): idx = [i-j for i,j in zip(p1.shape,initial_matrix.shape)] result = np.zeros(np.array(initial_matrix.shape) + idx) - result[slice_top(initial_matrix)] = initial_matrix + result[slice_top(initial_matrix.shape)] = initial_matrix initial_matrix = result Pf = p1 + initial_matrix return .5*Pf @@ -514,7 +524,8 @@ def __call__(self, points): c = self.coeff n = c.ndim - c = chebval2(points[:,0],c) + cc = c.reshape(c.shape + (1,)*points.ndim) + c = chebval2(points[:,0],cc) for i in range(1,n): c = chebval(points[:,i],c) if len(c) == 1: @@ -542,9 +553,9 @@ def evaluate_grid(self, xyz): xyz = super(MultiCheb, self).__call__(xyz) c = self.coeff - n = c.ndim for i in range(xyz.shape[1]): - c = chebval2(xyz[:,i] ,c) + cc = c.reshape(c.shape + (1,)*xyz[:,i].ndim) + c = chebval2(xyz[:,i] ,cc) if np.product(c.shape)==1: return c[0] @@ -776,7 +787,8 @@ def __call__(self, points): c = self.coeff n = c.ndim - c = polyval2(points[:,0],c) + cc = c.reshape(c.shape + (1,)*points.ndim) + c = polyval2(points[:,0],cc) for i in range(1,n): c = polyval(points[:,i],c) if len(c) == 1: @@ -804,9 +816,9 @@ def evaluate_grid(self, xyz): xyz = super(MultiPower, self).__call__(xyz) c = self.coeff - n = c.ndim for i in range(xyz.shape[1]): - c = polyval2(xyz[:,i] ,c) + cc = c.reshape(c.shape + (1,)*xyz[:,i].ndim) + c = polyval2(xyz[:,i] ,cc) if np.product(c.shape)==1: return c[0] @@ -966,7 +978,7 @@ def is_power(poly_list, return_string = False): else: return 'MultiCheb' else: - print([type(p) == MultiPower for p in initial_poly_list]) + print([type(p) == MultiPower for p in poly_list]) raise ValueError('Bad polynomials in list') ############################################################################ @@ -1021,10 +1033,6 @@ def polyvalnd(x,c): ############################################################################ -#### CHEBYSHEV APPROXIMATOR ################################################ -def cheb_approx(data): - pass - #polynomial generator def solve(poly1, poly2): """ @@ -1058,71 +1066,3 @@ def solve(poly1, poly2): #print('\n', poly1, poly2, poly_coeffs[::-1]) return tuple(poly_coeffs[::-1]) - - -def solve_poly(mylist): - """give it the list of tuples to solve - - returns: - tuple of solved polynomial - """ - if len(mylist) == 1: - return mylist[0] - - tuples = [] - size = len(mylist) - if size % 2 == 0: #even number of roots - for i in range(size//2): - tuples.append(solve(mylist[i], mylist[-(i+1)])) - else: #odd number of roots - size -= 1 - extra = mylist[size//2] - for i in range(size//2): - tuples.append(solve(mylist[i], mylist[-(i+1)])) - tuples.append(extra) - return solve_poly(tuples) - - -def gen_poly(degree, variables=1): - """ - generate degree number of random numbers in [-1,1] - p=(x-n)(x-n)... for n in random numbers - return n for n in numbers (roots), and the polynomial p - - (x-2)(x-3)(x-4)(x-5) - - | 1 -2 | - | 1 1 -2 | = 1 -5 6 => x^2 -5x +6 - | -3 -3 6 | - - T_m*T_n=.5(T_(m+n)+T_(m-n)) - - returns roots - list of roots - solve_poly[::-1] - tuple with coefficients of resultant polynomial in ascending degree order - """ - #generate random numbers - deg = [] - for i in range(degree): - #append tuples of form (1,-x) where x is a root - deg.append((1,np.random.uniform(-1,1))) - - roots = [] - for i in range(degree): - roots.append(-1*list(deg[i])[1]) - return roots, np.array(solve_poly(deg))[::-1] - -def gen_poly2(rootList = [], variables=1): - """ - generate degree number of random numbers from a list of roots - - returns roots - list of roots - solve_poly - tuple with coefficients of resultant polynomial in ascending degree order - """ - if rootList is None: - return [], [] - - deg = np.zeros((len(rootList),2)) - for i,v in enumerate(rootList): - #append tuples of form (1,-x) where x is a root - deg[i] = (1,-1*v) - return rootList, np.array(solve_poly(deg))[::-1] diff --git a/numalgsolve/polyroots.py b/yroots/polyroots.py similarity index 70% rename from numalgsolve/polyroots.py rename to yroots/polyroots.py index 3b3e0abb..8cc99166 100644 --- a/numalgsolve/polyroots.py +++ b/yroots/polyroots.py @@ -1,12 +1,13 @@ import numpy as np import itertools -from numalgsolve import OneDimension as oneD -from numalgsolve.polynomial import MultiCheb, MultiPower, is_power -from numalgsolve.Division import division -from numalgsolve.Multiplication import multiplication -from numalgsolve.utils import Term, get_var_list, divides, MacaulayError, InstabilityWarning, match_size, match_poly_dimensions +from yroots import OneDimension as oneD +from yroots.polynomial import MultiCheb, MultiPower, is_power +from yroots.Multiplication import multiplication +from yroots.utils import Term, get_var_list, divides, MacaulayError, \ + InstabilityWarning, match_size, match_poly_dimensions, \ + ConditioningError -def solve(polys, MSmatrix=0, eigvals=True, verbose=False): +def solve(polys,MSmatrix=0, eigvals=True, verbose=False, return_all_roots=True, max_cond_num=1.e6, macaulay_zero_tol=1.e-12,method='svd'): ''' Finds the roots of the given list of polynomials. @@ -29,6 +30,12 @@ def solve(polys, MSmatrix=0, eigvals=True, verbose=False): Roots of multivariate polynomials are always comptued from eigenvectors verbose : bool Prints information about how the roots are computed. + return_all_roots : bool + If True returns all the roots, otherwise just the ones in the unit box. + max_cond_num : float + The maximum condition number of the Macaulay Matrix Reduction + macaulay_zero_tol : float + What is considered 0 in the macaulay matrix reduction. returns ------- @@ -59,7 +66,8 @@ def solve(polys, MSmatrix=0, eigvals=True, verbose=False): zeros = common return zeros else: - if MSmatrix < 0: - return division(polys, verbose=verbose, divisor_var=-MSmatrix-1) + res = multiplication(polys, max_cond_num=max_cond_num, verbose=verbose, return_all_roots=return_all_roots,method=method) + if res[0] is None: + raise ConditioningError(res[1]) else: - return multiplication(polys, verbose=verbose, MSmatrix=MSmatrix) + return res diff --git a/yroots/subdivision.py b/yroots/subdivision.py new file mode 100644 index 00000000..4407be4c --- /dev/null +++ b/yroots/subdivision.py @@ -0,0 +1,1086 @@ +""" +Subdivision provides a solve function that finds roots of a set of functions +by approximating the functions with Chebyshev polynomials. +When the approximation is performed on a sufficiently small interval, +the approximation degree is small enough to be solved efficiently. + +""" + +import numpy as np +from scipy.fftpack import fftn +from yroots.OneDimension import divCheb, divPower, multCheb, multPower +from yroots.Multiplication import multiplication +from yroots.utils import clean_zeros_from_matrix, slice_top, MacaulayError, \ + get_var_list, ConditioningError, TooManyRoots, \ + Tolerances, solve_linear, memoize, Memoize, transform +from yroots.polynomial import MultiCheb +from yroots.IntervalChecks import IntervalData +from yroots.RootTracker import RootTracker +from itertools import product +from matplotlib import pyplot as plt +from scipy.linalg import lu +import time +import warnings +from numba import jit +from math import log2, ceil + +macheps = 2.220446049250313e-16 + +def solve(funcs, a, b, rel_approx_tol=1.e-15, abs_approx_tol=1.e-12, + max_cond_num=1e5, good_zeros_factor=100, min_good_zeros_tol=1e-5, + check_eval_error=True, check_eval_freq=1, plot=False, + plot_intervals=False, deg=None, target_deg=1, + return_potentials=False, method='svd', target_tol=1.01*macheps, + trust_small_evals=False, intervalReductions=["improveBound", "getBoundingParallelogram"]): + """ + Finds the real roots of the given list of functions on a given interval. + + All of the tolerances can be passed in as numbers of iterable types. If + multiple are passed in as iterable types they must have the same length. + When the length is more than 1, they are used one after the other to polish + the roots. + + Parameters + ---------- + funcs : list of vectorized, callable functions + Functions to find the common roots of. + More efficient if functions have an 'evaluate_grid' method handle + function evaluation at an grid of points. + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + rel_approx_tol : float or list + The relative tolerance used in the approximation tolerance. The error is bouned by + error < abs_approx_tol + rel_approx_tol * inf_norm_of_approximation + abs_approx_tol : float or list + The absolute tolerance used in the approximation tolerance. The error is bouned by + error < abs_approx_tol + rel_approx_tol * inf_norm_of_approximation + max_cond_num : float or list + The maximum condition number of the Macaulay Matrix Reduction + macaulay_zero_tol : float or list + What is considered 0 in the macaulay matrix reduction. + good_zeros_factor : float or list + Multiplying this by the approximation error gives how far outside of [-1, 1] a root can + be and still be considered inside the interval. + min_good_zeros_tol : float or list + The smallest the good_zeros_tol can be, which is how far outside of [-1, 1] a root can + be and still be considered inside the interval. + check_eval_error : bool + Whether to compute the evaluation error on the fly and replace the approx tol with it. + check_eval_freq : int + The evaluation error will be computed on levels that are multiples of this. + plot : bool + If True plots the zeros-loci of the functions along with the computed roots + plot_intervals : bool + If True, plot is True, and the functions are 2 dimensional, plots what check/method solved + each part of the interval. + deg : int + The degree used for the approximation. If None, the following degrees + are used. + Degree 100 for 1D functions. + Degree 20 for 2D functions. + Degree 9 for 3D functions. + Degree 9 for 4D functions. + Degree 2 for 5D functions and above. + target_deg : int + The degree the approximation needs to be trimmed down to before the + Macaulay solver is called. If unspecified, it will either be 5 (for 2D + functions) or match the deg argument. + return_potentials : bool + If True, returns the potential roots. Else, it does not. + method : str (optional) + The method to use when reducing the Macaulay matrix. Valid options are + svd, tvb, and qrt. + target_tol : float + The final absolute approximation tolerance to use before using any sort + of solver (Macaulay, linear, etc). + trust_small_evals : bool + Whether or not to trust function evaluations that may give floats + smaller than machine epsilon. This should only be set to True if the + function evaluations are very accurate. + intervalReductions : list + A list specifying the types of interval reductions that should be performed + on each subinterval. The order of methods in the list determines the order + in which the interval reductions are performed. To stop any interval + reduction method from being run, pass in an empty list to this parameter. + + If finding roots of a univariate function, `funcs` does not need to be a list, + and `a` and `b` can be floats instead of arrays. + + Returns + ------- + zeros : numpy array + The common zeros of the polynomials. Each row is a root. + """ + # Detect the dimension + if isinstance(funcs, list): + dim = len(funcs) + elif callable(funcs): + dim = 1 + else: + raise ValueError('`funcs` must be a callable or list of callables.') + + + # make a and b the right type + a = np.float64(a) + b = np.float64(b) + + # Choose an appropriate max degree for the given dimension if none is specified. + if deg is None: + deg_dim = {1: 100, 2:20, 3:9, 4:9} + if dim > 4: + deg = 2 + else: + deg = deg_dim[dim] + + # Sets up the tolerances. + if isinstance(abs_approx_tol, list): + abs_approx_tol = [max(tol, 1.01*macheps) for tol in abs_approx_tol] + else: + abs_approx_tol = max(abs_approx_tol, 1.01*macheps) + tols = Tolerances(rel_approx_tol=rel_approx_tol, + abs_approx_tol=abs_approx_tol, + max_cond_num=max_cond_num, + good_zeros_factor=good_zeros_factor, + min_good_zeros_tol=min_good_zeros_tol, + check_eval_error=check_eval_error, + check_eval_freq=check_eval_freq, + target_tol=target_tol) + tols.nextTols() + + # Set up the interval data and root tracker classes and cheb blocky copy arr + interval_data = IntervalData(a, b, intervalReductions) + root_tracker = RootTracker() + values_arr.memo = {} + initialize_values_arr(dim, deg+3) + + if dim == 1: + # In one dimension, we don't use target_deg; it's the same as deg + target_deg = deg + solve_func = subdivision_solve_1d + if isinstance(funcs, list): + funcs = funcs[0] + else: + solve_func = subdivision_solve_nd + + # TODO : Set the maximum number of subdivisions so that + # intervals cannot possibly be smaller than 2^-51 + max_level = 52 + + + + # Initial Solve + solve_func(funcs, a, b, deg, target_deg, interval_data, + root_tracker, tols, max_level, method=method, + trust_small_evals=trust_small_evals) + root_tracker.keep_possible_duplicates() + + # Polishing + while tols.nextTols(): + polish_intervals = root_tracker.get_polish_intervals() + interval_data.add_polish_intervals(polish_intervals) + for new_a, new_b in polish_intervals: + interval_data.start_polish_interval() + solve_func(funcs, new_a, new_b, deg, target_deg, interval_data, root_tracker, tols, max_level, method=method) + root_tracker.keep_possible_duplicates(), + print("\rPercent Finished: 100%{}".format(' '*50)) + + # Print results + interval_data.print_results() + + # Plotting + if plot: + if dim == 1: + x = np.linspace(a, b, 1000) + plt.plot(x, funcs(x), color='k') + plt.plot(np.real(root_tracker.roots), np.zeros(len(root_tracker.roots)), 'o', color = 'none', markeredgecolor='r') + plt.show() + elif dim == 2: + interval_data.plot_results(funcs, root_tracker.roots, plot_intervals) + + if len(root_tracker.potential_roots) != 0: + warnings.warn("Some intervals subdivided too deep and some potential roots were found. To access these roots, rerun the solver with the keyword return_potentials=True") + + if return_potentials: + return root_tracker.roots, root_tracker.potential_roots + else: + return root_tracker.roots + +@Memoize +def initialize_values_arr(dim, deg): + """Helper function for chebyshev_block_copy. + Initializes an array to use throughout the whole solve function. + Builds one array corresponding to dim and deg that can be used for any + block copy of degree less than deg + + Parameters + ---------- + dim : int + Dimension + deg : int + Degree + + Returns + ------- + An empty numpy array that can be used to hold values for a chebyshev_block_copy + of dimension dim degree < deg. + """ + return np.empty(tuple([2*deg])*dim, dtype=np.float64) + +@Memoize +def values_arr(dim): + """Helper function for chebyshev_block_copy. + Finds the array initialized by initialize_values_arr for dimension dim. + Assumes the degree of the approximation is less than the degree used for + initialize_values_arr. + + Parameters + ---------- + dim : int + Dimension + + Returns + ------- + An empty numpy array that can be used to hold values for a chebyshev_block_copy + of dimension dim and degree less than the degree used for initialize_values_arr. + """ + keys = tuple(initialize_values_arr.memo.keys()) + for idx, k in enumerate(keys): + if k[0]==dim: + break + return initialize_values_arr.memo[keys[idx]] + +@memoize +def block_copy_slicers(dim, deg): + """Helper function for chebyshev_block_copy. + Builds slice objects to index into the evaluation array to copy + in preparation for the fft. + + Parameters + ---------- + dim : int + Dimension + dim : int + Degree of approximation + + Returns + ------- + block_slicers : list of tuples of slice objects + Slice objects used to index into the evaluations + cheb_slicers : list of tuples of slice objects + Slice objects used to index into the array we're copying evaluations to + slicer : tuple of slice objets + Used to index into the portion of that array we're using for the fft input + """ + block_slicers = [] + cheb_slicers = [] + full_arr_deg = 2*deg + for block in product([False, True], repeat=dim): + cheb_idx = [slice(0, deg+1)]*dim + block_idx = [slice(0, full_arr_deg)]*dim + for i, flip_dim in enumerate(block): + if flip_dim: + cheb_idx[i] = slice(deg+1, full_arr_deg) + block_idx[i] = slice(deg-1, 0, -1) + block_slicers.append(tuple(block_idx)) + cheb_slicers.append(tuple(cheb_idx)) + return block_slicers, cheb_slicers, tuple([slice(0, 2*deg)]*dim) + +def chebyshev_block_copy(values_block): + """This functions helps avoid double evaluation of functions at + interpolation points. It takes in a tensor of function evaluation values + and copies these values to a new tensor appropriately to prepare for + chebyshev interpolation. + + Parameters + ---------- + values_block : numpy array + block of values from function evaluation + Returns + ------- + values_cheb : numpy array + chebyshev interpolation values + """ + dim = values_block.ndim + deg = values_block.shape[0] - 1 + values_cheb = values_arr(dim) + block_slicers, cheb_slicers, slicer = block_copy_slicers(dim, deg) + + for cheb_idx, block_idx in zip(cheb_slicers, block_slicers): + try: + values_cheb[cheb_idx] = values_block[block_idx] + except ValueError as e: + if str(e)[:42] == 'could not broadcast input array from shape': + values_arr.memo[(dim, )] = np.empty(tuple([2*deg])*dim, dtype=np.float64) + values_cheb = values_arr(dim) + values_cheb[cheb_idx] = values_block[block_idx] + else: + raise ValueError(e) + return values_cheb[slicer] + +def interval_approximate_1d(f, a, b, deg, return_bools=False, return_inf_norm=False): + """Finds the chebyshev approximation of a one-dimensional function on an + interval. + + Parameters + ---------- + f : function from R -> R + The function to interpolate. + a : float + The lower bound on the interval. + b : float + The upper bound on the interval. + deg : int + The degree of the interpolation. + return_inf_norm : bool + Whether to return the inf norm of the function + Returns + ------- + coeffs : numpy array + The coefficient of the chebyshev interpolating polynomial. + inf_norm : float + The inf_norm of the function + """ + extrema = transform(np.cos((np.pi*np.arange(2*deg))/deg), a, b) + values = f(extrema) + + if return_inf_norm: + inf_norm = np.max(np.abs(values)) + + coeffs = np.real(np.fft.fft(values/deg)) + coeffs[0]/=2 + coeffs[deg]/=2 + + if return_bools: + # Check to see if the sign changes on the interval + is_positive = values > 0 + sign_change = any(is_positive) and any(~is_positive) + if return_inf_norm: return coeffs[:deg+1], sign_change, inf_norm + else: return coeffs[:deg+1], sign_change + else: + if return_inf_norm: return coeffs[:deg+1], inf_norm + else: return coeffs[:deg+1] + +@memoize +def get_cheb_grid(deg, dim, has_eval_grid): + """Helper function for interval_approximate_nd. + + Parameters + ---------- + deg : int + The interpolation degree. + dim : int + The interpolation dimension. + + Returns + ------- + get_cheb_grid : numpy array + The chebyshev grid used to evaluate the functions in + interval_approximate_nd + """ + if has_eval_grid: + cheb_values = np.cos(np.arange(deg+1)*np.pi/deg) + return np.column_stack([cheb_values]*dim) + else: + cheb_values = np.cos(np.arange(deg+1)*np.pi/deg) + cheb_grids = np.meshgrid(*([cheb_values]*dim), indexing='ij') + flatten = lambda x: x.flatten() + return np.column_stack(tuple(map(flatten, cheb_grids))) + +def interval_approximate_nd(f, a, b, deg, return_inf_norm=False): + """Finds the chebyshev approximation of an n-dimensional function on an + interval. + + Parameters + ---------- + f : function from R^n -> R + The function to interpolate. + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + deg : numpy array + The degree of the interpolation in each dimension. + return_inf_norm : bool + whether to return the inf norm of the function + + Returns + ------- + coeffs : numpy array + The coefficient of the chebyshev interpolating polynomial. + inf_norm : float + The inf_norm of the function + """ + dim = len(a) + if dim != len(b): + raise ValueError("Interval dimensions must be the same!") + + if hasattr(f, "evaluate_grid"): + cheb_points = transform(get_cheb_grid(deg, dim, True), a, b) + values_block = f.evaluate_grid(cheb_points) + else: + cheb_points = transform(get_cheb_grid(deg, dim, False), a, b) + values_block = f(*cheb_points.T).reshape(*([deg+1]*dim)) + + values = chebyshev_block_copy(values_block) + + if return_inf_norm: + inf_norm = np.max(np.abs(values_block)) + + x0_slicer, deg_slicer, slices, rescale = interval_approx_slicers(dim, deg) + coeffs = fftn(values/rescale).real + for x0sl, degsl in zip(x0_slicer, deg_slicer): + # halve the coefficients in each slice + coeffs[x0sl] /= 2 + coeffs[degsl] /= 2 + + if return_inf_norm: + return coeffs[tuple(slices)], inf_norm + else: + return coeffs[tuple(slices)] + +@memoize +def interval_approx_slicers(dim, deg): + """Helper function for interval_approximate_nd. Builds slice objects to index + into the output of the fft and divide some of the values by 2 and turn them into + coefficients of the approximation. + + Parameters + ---------- + dim : int + The interpolation dimension. + deg : int + The interpolation degree. + + Returns + ------- + x0_slicer : list of tuples of slice objects + Slice objects used to index into the the degree 1 monomials + deg_slicer : list of tuples of slice objects + Slice objects used to index into the the degree d monomials + slices : tuple of slice objets + Used to index into the portion of the array that are coefficients + rescale : int + amount to rescale the evaluations by in order to feed them into the fft + """ + x0_slicer = [tuple([slice(None) if i != d else 0 for i in range(dim)]) + for d in range(dim)] + deg_slicer = [tuple([slice(None) if i != d else deg for i in range(dim)]) + for d in range(dim)] + slices = tuple([slice(0, deg+1)]*dim) + return x0_slicer, deg_slicer, slices, deg**dim + +def full_cheb_approximate(f, a, b, deg, abs_approx_tol, rel_approx_tol, good_deg=None): + """Gives the full chebyshev approximation and checks if it's good enough. + + Parameters + ---------- + f : function + The function we approximate. + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + deg : int + The degree to approximate with. + rel_approx_tol : float or list + The relative tolerance used in the approximation tolerance. The error is bouned by + error < abs_approx_tol + rel_approx_tol * inf_norm_of_approximation + abs_approx_tol : float or list + The absolute tolerance used in the approximation tolerance. The error is bouned by + error < abs_approx_tol + rel_approx_tol * inf_norm_of_approximation + good_deg : numpy array + Interpoation degree that is guaranteed to give an approximation valid + to within approx_tol. + + Returns + ------- + coeff : numpy array + The coefficient array of the interpolation. If it can't get a good + approximation and needs to subdivide, returns None. + inf_norm : float + The inf norm of f on [a, b] + error : float + The approximation error + """ + # We don't know what degree we want + if good_deg is None: + good_deg = deg + # Try degree deg and see if it's good enough + coeff = interval_approximate_nd(f, a, b, good_deg) + coeff2, inf_norm = interval_approximate_nd(f, a, b, good_deg*2, return_inf_norm=True) + coeff2[slice_top(coeff.shape)] -= coeff + + error = np.sum(np.abs(coeff2)) + if error > abs_approx_tol+rel_approx_tol*inf_norm: + return None, inf_norm, error + else: + return coeff, inf_norm, error + + +def zeros_in_interval(zeros, a, b, dim, within_interval_tol=1e-9): + """Returns the zeros that are only in the interval [a, b]. + + Parameters + ---------- + zeros : numpy array + The zeros found using the solver. + a : numpy array + The lower bounds of the interval for each variable. + b : numpy array + The upper bounds of the interval for each variable. + dim : int + The dimension of the system. + + Returns + ------- + zeros : numpy array + The zeros that are in the interval [a, b] + """ + # Check along each axis to ensure roots are within the boundaries + for i in range(dim): + zeros = zeros[zeros[:, i] - a[i] >= -within_interval_tol] + zeros = zeros[zeros[:, i] - b[i] <= within_interval_tol] + + return zeros + + +def good_zeros_nd(zeros, imag_tol, real_tol): + """Get the real zeros in the -1 to 1 interval in each dimension. + + Parameters + ---------- + zeros : numpy array + The zeros to be checked. + imag_tol : float + How large the imaginary part can be to still have it be considered real. + real_tol : float + How far the real part can be outside the interval [-1, 1]^n and still be + considered valid. + + Returns + ------- + good_zeros : numpy array + The real zeros in [-1, 1]^n of the input zeros. + """ + # Take care of the case where we found only 1 root + if len(zeros.shape) == 1: + mask = np.all(np.abs(zeros.imag) <= imag_tol, axis = 0) + mask *= np.all(np.abs(zeros.real) <= 1 + real_tol, axis = 0) + else: + mask = np.all(np.abs(zeros.imag) <= imag_tol, axis = 1) + mask *= np.all(np.abs(zeros.real) <= 1 + real_tol, axis = 1) + return zeros[mask].real + +def get_abs_approx_tol(func, deg, a, b, dim): + """ Gets an absolute approximation tolerance based on the assumption that + on the interval of size linearization_size * 2, the function can be + perfectly approximated by a low degree Chebyshev polynomial. + + Parameters + ---------- + func : function + Function to approximate. + deg : int + The degree to use to approximate the function on the interval. + a : numpy array + The lower bounds of the interval on which to approximate. + b : numpy array + The upper bounds of the interval on which to approximate. + + Returns + ------- + abs_approx_tol : float + The calculated absolute approximation tolerance based on the + noise of the function on the small interval. + """ + # Half the width of the smaller interval + linearization_size = 1e-14 + + + # Get a random small interval from [-1, 1] and transform so it's + # within [a, b] + x = transform(random_point(dim), a, b) + a2 = np.array(x - linearization_size) + b2 = np.array(x + linearization_size) + + # Approximate with a low degree Chebyshev polynomial + coeff = interval_approximate_nd(func, a2, b2, 2*deg) + coeff[deg_slices(deg, dim)] = 0 + + # Sum up coeffieicents that are assumed to be just noise + abs_approx_tol = np.sum(np.abs(coeff)) + + # Divide by the number of spots that were summed up. + numSpots = (deg*2)**dim - (deg)**dim + + # Multiply by 10 to give a looser tolerance (speed-up) + # print(abs_approx_tol*10 / numSpots) + return abs_approx_tol*10 / numSpots + +@memoize +def deg_slices(deg, dim): + """Helper function for get_abs_approx_tol. Returns a slice object for + accessing all the terms of total degree less than deg in a coefficient + tensor. + + Parameters + ---------- + deg : int + The degree of the Chebsyhev interpolation. + dim : int + The dimension of the system. + + Returns + ------- + slice + The slice that accesses all the coefficients of degree less than + deg. + """ + return (slice(0, deg), )*dim + +@memoize +def random_point(dim): + """Gets a random point from [-1, 1]^dim that's used for get_abs_approx_tol. + Since this is memoized, subsequent calls will be a lot faster. + + Parameters + ---------- + dim : int + The dimension of the system/how many samples to take from [0, 1]. + + Returns + ------- + numpy array + The random point that haas dim entries. + """ + np.random.seed(0) + # Scale the points so that they're each within [-1, 1] + return np.random.rand(dim)*2 - 1 + +def subdivision_solve_nd(funcs, a, b, deg, target_deg, interval_data, + root_tracker, tols, max_level,good_degs=None, level=0, + method='svd', use_target_tol=False, + trust_small_evals=False): + """Finds the common zeros of the given functions. + + All the zeros will be stored in root_tracker. + + Parameters + ---------- + funcs : list + Each element of the list is a callable function. + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + deg : int + The degree to approximate with in the chebyshev approximation. + target_deg : int + The degree to subdivide down to before building the Macaulay matrix. + interval_data : IntervalData + A class to run the subinterval checks and keep track of the solve + progress + root_tracker : RootTracker + A class to keep track of the roots that are found. + tols : Tolerances + The tolerances to be used. + max_level : int + The maximum level for the recursion + good_degs : numpy array + Interpoation degrees that are guaranteed to give an approximation valid + to within approx_tol. + level : int + The current level of the recursion. + method : str (optional) + The method to use when reducing the Macaulay matrix. Valid options are + svd, tvb, and qrt. + use_target_tol : bool + Whether or not to use tols.target_tol when making approximations. This + is necessary to get a sufficiently accurate approximation from which to + build the Macaulay matrix and run the solver. + """ + + if level >= max_level: + # TODO Refine case where there may be a root and it goes too deep. + interval_data.track_interval("Too Deep", [a, b]) + # Return potential roots if the residuals are small + root_tracker.add_potential_roots((a + b)/2, a, b, "Too Deep.") + return + + dim = len(a) + + if tols.check_eval_error: + # Using the first abs_approx_tol + if not use_target_tol: + tols.abs_approx_tol = tols.abs_approx_tols[tols.currTol] + if level%tols.check_eval_freq == 0: + numSpots = (deg*2)**len(a) - (deg)**len(a) + for func in funcs: + tols.abs_approx_tol = max(tols.abs_approx_tol, numSpots * get_abs_approx_tol(func, 3, a, b, dim)) + # Using target_tol + else: + tols.target_tol = tols.target_tols[tols.currTol] + if level%tols.check_eval_freq == 0: + numSpots = (deg*2)**len(a) - (deg)**len(a) + for func in funcs: + tols.target_tol = max(tols.target_tol, numSpots * get_abs_approx_tol(func, 3, a, b, dim)) + + # Buffer the interval to solve on a larger interval to account for + # corners. Right now, it's set to be 5e-10 so that on [-1, 1], the + # buffer goes out 1e-9 around the initial search interval. + # DETERMINED BY EXPERIMENTATION + interval_buffer_size = (b - a) * 5e-10 + og_a = a.copy() + og_b = b.copy() + a -= interval_buffer_size + b += interval_buffer_size + + cheb_approx_list = [] + interval_data.print_progress() + if good_degs is None: + good_degs = [None]*len(funcs) + inf_norms = [] + approx_errors = [] + # Get the chebyshev approximations + num_funcs = len(funcs) + for func_num, (func, good_deg) in enumerate(zip(funcs, good_degs)): + if use_target_tol: + coeff, inf_norm, approx_error = full_cheb_approximate(func, a, b, deg, tols.target_tol, tols.rel_approx_tol, good_deg) + else: + coeff, inf_norm, approx_error = full_cheb_approximate(func, a, b, deg, tols.abs_approx_tol, tols.rel_approx_tol, good_deg) + inf_norms.append(inf_norm) + approx_errors.append(approx_error) + # Subdivides if a bad approximation + if coeff is None: + if not trust_small_evals: + approx_errors = [max(err,macheps) for err in approx_errors] + intervals = interval_data.get_subintervals(og_a, og_b, cheb_approx_list, approx_errors, False) + + #reorder funcs. TODO: fancier things like how likely it is to pass checks + funcs2 = funcs.copy() + if func_num + 1 < num_funcs: + del funcs2[func_num] + funcs2.append(func) + for new_a, new_b in intervals: + subdivision_solve_nd(funcs2,new_a,new_b,deg,target_deg,interval_data,root_tracker,tols,max_level,level=level+1, method=method, trust_small_evals=trust_small_evals) + return + else: + # Run checks to try and throw out the interval + if not trust_small_evals: + approx_error = max(approx_error, macheps) + if interval_data.check_interval(coeff, approx_error, og_a, og_b): + return + + cheb_approx_list.append(coeff) + + # Reduce the degree of the approximations while not introducing too much error + coeffs, good_approx, approx_errors = trim_coeffs(cheb_approx_list, tols.abs_approx_tol, tols.rel_approx_tol, inf_norms, approx_errors) + if not trust_small_evals: + approx_errors = [max(err, macheps) for err in approx_errors] + # Used if subdividing further. + # Only choose good_degs if the approximation after trim_coeffs is good. + if good_approx: + # good_degs are assumed to be 1 higher than the current approximation + # but no larger than the initial degree for more accurate performance. + good_degs = [min(coeff.shape[0], deg) for coeff in coeffs] + good_zeros_tol = max(tols.min_good_zeros_tol, sum(np.abs(approx_errors))*tols.good_zeros_factor) + + # Check if the degree is small enough or if trim_coeffs introduced too much error + if np.any(np.array([coeff.shape[0] for coeff in coeffs]) > target_deg + 1) or not good_approx: + intervals = interval_data.get_subintervals(og_a, og_b, cheb_approx_list, approx_errors, True) + for new_a, new_b in intervals: + subdivision_solve_nd(funcs, new_a, new_b, deg, target_deg, interval_data, root_tracker, tols, max_level, good_degs, level+1, method=method, trust_small_evals=trust_small_evals, use_target_tol=True) + + # Check if any approx error is greater than target_tol for Macaulay method + elif np.any(np.array(approx_errors) > np.array(tols.target_tol) + tols.rel_approx_tol*np.array(inf_norms)): + intervals = interval_data.get_subintervals(og_a, og_b, cheb_approx_list, approx_errors, True) + for new_a, new_b in intervals: + subdivision_solve_nd(funcs, new_a, new_b, deg, target_deg, interval_data, root_tracker, tols, max_level, good_degs, level+1, method=method, trust_small_evals=trust_small_evals, use_target_tol=True) + + # Check if everything is linear + elif np.all(np.array([coeff.shape[0] for coeff in coeffs]) == 2): + if deg != 2: + subdivision_solve_nd(funcs, a, b, 2, target_deg, interval_data, root_tracker, tols, max_level, good_degs, level, method=method, trust_small_evals=trust_small_evals, use_target_tol=True) + return + zero, cond = solve_linear(coeffs) + # Store the information and exit + zero = good_zeros_nd(zero, good_zeros_tol, good_zeros_tol) + zero = transform(zero, a, b) + zero = zeros_in_interval(zero, og_a, og_b, dim) + interval_data.track_interval("Base Case", [a, b]) + root_tracker.add_roots(zero, a, b, "Base Case") + + # Solve using spectral methods if stable. + else: + polys = [MultiCheb(coeff, lead_term = [coeff.shape[0]-1], clean_zeros = False) for coeff in coeffs] + res = multiplication(polys, max_cond_num=tols.max_cond_num, method=method) + #check for a conditioning error + if res[0] is None: + # Subdivide but run some checks on the intervals first + intervals = interval_data.get_subintervals(og_a, og_b, cheb_approx_list, approx_errors, True) + for new_a, new_b in intervals: + subdivision_solve_nd(funcs, new_a, new_b, deg, target_deg, interval_data, root_tracker, tols, max_level, good_degs, level+1, method=method, trust_small_evals=trust_small_evals, use_target_tol=True) + else: + zeros = res + zeros = good_zeros_nd(zeros, good_zeros_tol, good_zeros_tol) + zeros = transform(zeros, a, b) + zeros = zeros_in_interval(zeros, og_a, og_b, dim) + interval_data.track_interval("Macaulay", [a, b]) + root_tracker.add_roots(zeros, a, b, "Macaulay") + +def trim_coeffs(coeffs, abs_approx_tol, rel_approx_tol, inf_norms, errors): + """Trim the coefficient matrices to reduce the degree by zeroing out any + entries in the coefficient matrix above a certain degree. + + Parameters + ---------- + coeffs : list + The coefficient matrices of the Chebyshev polynomials we are solving. + rel_approx_tol : float or list + The relative tolerance used in the approximation tolerance. The error is bouned by + error < abs_approx_tol + rel_approx_tol * inf_norm_of_approximation + abs_approx_tol : float or list + The absolute tolerance used in the approximation tolerance. The error is bouned by + error < abs_approx_tol + rel_approx_tol * inf_norm_of_approximation + inf_norms : list + The inf norms of the functions + errors : list + The approximation errors of the functions + Returns + ------- + polys : list + The reduced degree Chebyshev polynomials + good_approx : bool + Whether all the approximations were good + """ + # Assume we start with good approximations + good_approx = True + for num, coeff in enumerate(coeffs): + # Get the error inherent in the approximation + error = errors[num] + + # Try to zero out everything below the lower-reverse-hyperdiagonal + # that's a fancy way of saying monomials that are more than the specified degree + dim = coeff.ndim + deg = np.sum(coeff.shape) - dim - 1 + initial_mons = [] + for deg0 in range(coeff.shape[0], deg+1): + initial_mons += mon_combos_limited_wrap(deg0, dim, coeff.shape) + mons = np.array(initial_mons).T + slices = tuple(mons[:dim]) + slice_error = np.sum(np.abs(coeff[slices])) + # increment error + error += slice_error + if error > abs_approx_tol+rel_approx_tol*inf_norms[num]: + # FREAK OUT if we can't zero out everything below the lower-reverse-hyperdiagonal + good_approx = False + else: + # try to increment the degree down + coeff[slices] = 0 + deg = coeff.shape[0]-1 + # stop when it gets linear... + while deg > 1: + # try to cut off another hyperdiagonal from the coefficient matrix + mons = mon_combos_limited_wrap(deg, dim, coeff.shape) + mons = np.array(mons).T + slices = tuple(mons[:dim]) + slice_error = np.sum(np.abs(coeff[slices])) + # if that introduces too much error, backtrack + if slice_error + error > abs_approx_tol+rel_approx_tol*inf_norms[num]: + if deg < coeff.shape[0]-1: + slices = tuple([slice(0, deg+1)]*dim) + coeff = coeff[slices] + break + # otherwise, increment the error + else: + error += slice_error + coeff[slices] = 0 + deg-=1 + if deg == 1: + slices = tuple([slice(0, 2)]*dim) + coeff = coeff[slices] + break + coeffs[num] = coeff + errors[num] = error + + return coeffs, good_approx, errors + +@memoize +def mon_combos_limited_wrap(deg, dim, shape): + """A wrapper for mon_combos_limited to memoize. + + Parameters + -------- + deg: int + Degree of the monomials desired. + dim : int + Dimension of the monomials desired. + shape : tuple + The limiting shape. The i'th index of the mon can't be bigger than the + i'th index of the shape. + + Returns + ----------- + mon_combo_limited_wrap : list + A list of all the monomials. + """ + return mon_combos_limited([0]*dim, deg, shape) + +def mon_combos_limited(mon, remaining_degrees, shape, cur_dim = 0): + """Finds all the monomials of a given degree that fits in a given shape and + returns them. Works recursively. + + Very similar to mon_combos, but only returns the monomials of the desired + degree. + + Parameters + -------- + mon: list + A list of zeros, the length of which is the dimension of the desired + monomials. Will change as the function searches recursively. + remaining_degrees : int + Initially the degree of the monomials desired. Will decrease as the + function searches recursively. + shape : tuple + The limiting shape. The i'th index of the mon can't be bigger than the + i'th index of the shape. + cur_dim : int + The current position in the list the function is iterating through. + Defaults to 0, but increases in each step of the recursion. + + Returns + ----------- + answers : list + A list of all the monomials. + """ + answers = [] + if len(mon) == cur_dim+1: # We are at the end of mon, no more recursion. + if remaining_degrees < shape[cur_dim]: + mon[cur_dim] = remaining_degrees + answers.append(mon.copy()) + return answers + if remaining_degrees == 0: # Nothing else can be added. + answers.append(mon.copy()) + return answers + temp = mon.copy() # Quicker than copying every time inside the loop. + for i in range(min(shape[cur_dim], remaining_degrees+1)): # Recursively add to mon further down. + temp[cur_dim] = i + answers.extend(mon_combos_limited(temp, remaining_degrees-i, shape, cur_dim+1)) + return answers + +def good_zeros_1d(zeros, imag_tol, real_tol): + """Get the real zeros in the -1 to 1 interval + + Parameters + ---------- + zeros : numpy array + The zeros to be checked. + imag_tol : float + How large the imaginary part can be to still have it be considered real. + real_tol : float + How far the real part can be outside the interval [-1, 1] and still be + considered valid. + + Returns + ------- + good_zeros : numpy array + The real zeros in [-1, 1] of the input zeros. + """ + zeros = zeros[np.where(np.abs(zeros) <= 1 + real_tol)] + zeros = zeros[np.where(np.abs(zeros.imag) < imag_tol)] + return zeros.real + +def subdivision_solve_1d(f, a, b, deg, target_deg, interval_data, root_tracker, + tols, max_level, level=0, method='svd', + trust_small_evals=False): + """Finds the roots of a one-dimensional function using subdivision and + chebyshev approximation. + + Parameters + ---------- + f : function from R -> R + The function to interpolate. + a : numpy array + The lower bound on the interval. + b : numpy array + The upper bound on the interval. + deg : int + The degree of the approximation. + target_deg : int + The degree to subdivide down to before building the Macauly matrix. + interval_data : IntervalData + A class to run the subinterval checks and keep track of the solve progress + root_tracker : RootTracker + A class to keep track of the roots that are found. + tols : Tolerances + The tolerances to be used. + max_level : int + The maximum level for the recursion + level : int + The current level of the recursion. + + Returns + ------- + coeffs : numpy array + The coefficient of the chebyshev interpolating polynomial. + """ + if level > max_level: + # TODO Refine case where there may be a root and it goes too deep. + interval_data.track_interval("Too Deep", [a, b]) + return + + + # Determine the point at which to subdivide the interval + RAND = 0.5139303900908738 + interval_data.print_progress() + + # Approximate the function using Chebyshev polynomials + coeff = interval_approximate_1d(f, a, b, deg) + coeff2, sign_change, inf_norm = interval_approximate_1d(f, a, b, deg*2, return_bools=True, return_inf_norm=True) + + coeff2[slice_top(coeff.shape)] -= coeff + + # Calculate the approximate error between the deg and 2*deg approximations + error = np.sum(np.abs(coeff2)) + allowed_error = tols.abs_approx_tol+tols.rel_approx_tol*inf_norm + + if error > allowed_error: + # Subdivide the interval and recursively call the function. + div_spot = a + (b-a)*RAND + good_deg = deg + subdivision_solve_1d(f, a, div_spot, good_deg, target_deg, interval_data, root_tracker, tols, max_level, level+1) + subdivision_solve_1d(f, div_spot, b, good_deg, target_deg, interval_data, root_tracker, tols, max_level, level+1) + else: + # Trim the coefficient array (reduce the degree) as much as we can. + # This identifies a 'good degree' with which to approximate the function + # if it is less than the given approx degree. + last_coeff_size = abs(coeff[-1]) + new_error = error + last_coeff_size + while new_error < allowed_error: + if len(coeff) == 1: + break + #maybe a list pop here? idk if worth it to switch away from arrays + coeff = coeff[:-1] + last_coeff_size = abs(coeff[-1]) + error = new_error + new_error = error + last_coeff_size + if not trust_small_evals: + error = max(error, macheps) + good_deg = max(len(coeff) - 1, 1) + + # Run interval checks to eliminate regions + if not sign_change: # Skip checks if there is a sign change + if interval_data.check_interval(coeff, error, a, b): + return + + try: + good_zeros_tol = max(tols.min_good_zeros_tol, error*tols.good_zeros_factor) + zeros = transform(good_zeros_1d(multCheb(coeff), good_zeros_tol, good_zeros_tol), a, b) + interval_data.track_interval("Macaulay", [a, b]) + root_tracker.add_roots(zeros, a, b, "Macaulay") + except (ConditioningError, TooManyRoots) as e: + div_spot = a + (b-a)*RAND + subdivision_solve_1d(f, a, div_spot, good_deg, target_deg, interval_data, root_tracker, tols, max_level, level+1) + subdivision_solve_1d(f, div_spot, b, good_deg, target_deg, interval_data, root_tracker, tols, max_level, level+1) diff --git a/yroots/test_Combined_Solver.py b/yroots/test_Combined_Solver.py new file mode 100644 index 00000000..21a56ebf --- /dev/null +++ b/yroots/test_Combined_Solver.py @@ -0,0 +1,166 @@ +""" +A solid 2 dimensional check before I hit the pull request. +""" +import numpy as np +import M_maker +import ChebyshevSubdivisionSolver +import pytest +from polynomial import MultiCheb +from utils import transform + +f = lambda x,y: (x-1)*(np.cos(x*y**2)+2) +g = lambda x,y: np.sin(8*np.pi*y)*(np.cos(x*y)+2) +f_deg,g_deg = 20,20 + +def solver(funcs,a,b,guess_degs,rescale=False,rel_approx_tol=1.e-15, abs_approx_tol=1.e-12): + """ + Finds the roots of the system of functions + + parameters + ---------- + funcs: list + list of the vectorized functions (R^n --> R) + a: ndarray + lower bound on the search interval + b: ndarray + upper bound on the search interval + guess_degs: list + guess of the best approximation degree for each function + rescale: bool + whether to rescale the approximation by inf_norm or not + rel_approx_tol: float + relative approximation tolerance + abs_approx_tol: float + absolute approximation tolerance + + returns + ------- + ndarray: + the yroots of the system of functions + """ + #TODO: allow for a,b to default to neg1_1, require input dim? it's tedious (-), it's a good sanity check (+) + #handle for when input deg is less than the approximation degree used to build that Multicheb object + #guess deg input default + #maybe the SHOULD know what degree to input + #handle for when the input deg is too high + #handle for when there is no input deg + if len(a) != len(b): + raise ValueError("Dimension mismatch in intervals.") + + if (a>=b).any(): + raise ValueError("At least one lower bound is >= an upper bound.") + + is_neg1_1 = True + arr_neg1 = np.array([-1]*len(a)) + arr_1 = np.ones(len(a)) + + if np.allclose(arr_neg1,a,rtol=1e-08) and np.allclose(arr_1,b,rtol=1e-08): + pass + else: + is_neg1_1 = False + + is_multi_cheb_arr = [] + + for func in funcs: #USE + if isinstance(func,MultiCheb): + is_multi_cheb_arr.append(True) + pass + else: + is_multi_cheb_arr.append(False) + + is_multi_cheb_arr = np.array(is_multi_cheb_arr) + funcs = np.array(funcs) + + MultiCheb_idxs = list(np.where(is_multi_cheb_arr==1)[0]) + non_MultiCheb_idxs = list(np.where(is_multi_cheb_arr==0)[0]) + + errs = np.array([0]*len(funcs)) + + for idx in non_MultiCheb_idxs: + approx = M_maker.M_maker(funcs[idx],arr_neg1,arr_1,guess_degs[idx],rel_approx_tol,abs_approx_tol) + if rescale: + funcs[idx] = MultiCheb(approx.M_rescaled) + else: + funcs[idx] = MultiCheb(approx.M) + errs[idx] = approx.err + + for idx in MultiCheb_idxs: + approx = M_maker.M_maker(funcs[idx],arr_neg1,arr_1,guess_degs[idx],rel_approx_tol,abs_approx_tol) + if rescale: + funcs[idx] = MultiCheb(approx.M_rescaled) + else: + funcs[idx] = MultiCheb(approx.M) + errs[idx] = approx.err + + funcs = [func.coeff for func in funcs] + yroots = np.array(ChebyshevSubdivisionSolver.solveChebyshevSubdivision(funcs,errs)) + + if is_neg1_1 == False and len(yroots) > 0: + yroots = transform(yroots,a,b) + + return yroots + +def solver_check(funcs,a,b): + """ + raw functions on [-1,1]^n + """ + + f,g = funcs + guess_degs = [f_deg,g_deg] + yroots_1 = solver(funcs,a,b,guess_degs) + + arr_neg1 = np.array([-1]*len(a)) #what if a>b + arr_1 = np.ones(len(a)) + + f_approx = M_maker.M_maker(f,arr_neg1,arr_1,f_deg) + g_approx = M_maker.M_maker(g,arr_neg1,arr_1,g_deg) + + yroots_2 = np.array(ChebyshevSubdivisionSolver.solveChebyshevSubdivision([f_approx.M,g_approx.M],np.array([f_approx.err,g_approx.err]))) + if len(yroots_2) > 0: #transform doesn't work on empty arrays + yroots_2 = transform(yroots_2,a,b) + + return np.allclose(yroots_1,yroots_2) + +def test_solver(): + a = -1*np.random.random(2) + b = np.random.random(2) + assert solver_check([f,g],a,b) == True + b = np.ones(2).astype(float) + a = -1*b + assert solver_check([f,g],a,b) == True + + a,b = np.array([-0.5,-0.75]), np.array([0.25,0.7]) + g_approx = M_maker.M_maker(g,a,b,g_deg) + h = MultiCheb(g_approx.M) + f_approx = M_maker.M_maker(f,a,b,g_deg) + k = MultiCheb(f_approx.M) + + assert solver_check([h,k],a,b) == True + + a,b = np.array([-0.9,-0.9]), np.array([0.9,0.9]) + assert solver_check([h,k],a,b) == True + + +def test_bad_intervals(): + a,b = np.array([1,-1]),np.array([1,1]) + funcs = [f,g] + with pytest.raises(ValueError) as excinfo: + solver([f,g],a,b,[f_deg,g_deg]) + print(excinfo) + assert excinfo.value.args[0] == "At least one lower bound is >= an upper bound." + + a = [a[0]] + with pytest.raises(ValueError) as excinfo: + solver([f,g],a,b,[f_deg,g_deg]) + print(excinfo) + assert excinfo.value.args[0] == "Dimension mismatch in intervals." + +#WHAT CAN WE TEST ABOUT THIS CODE +#WE CAN CHECK THAT IT PRESERVES WHAT ERIKs solver does when it is given the approximations + #CASES + #not neg1_1 and not all multicheb + #neg1_1 and not all multicheb + #not neg1_1 and all multicheb + #both neg1_1 and all multicheb + + #value error check for a and b \ No newline at end of file diff --git a/numalgsolve/utils.py b/yroots/utils.py similarity index 81% rename from numalgsolve/utils.py rename to yroots/utils.py index 08851fcb..a1a95cae 100644 --- a/numalgsolve/utils.py +++ b/yroots/utils.py @@ -1,9 +1,36 @@ # A collection of functions used in the F4 Macaulay and TVB solvers import numpy as np import itertools -from scipy.linalg import qr, solve_triangular -from scipy.misc import comb +from scipy.linalg import qr, solve_triangular, svd, norm, eig, lu +from scipy.special import comb import time +from numba import jit +import warnings +from numba import jit + +class Memoize: + """ + A Memoization class taken from Stack Overflow + https://stackoverflow.com/questions/1988804/what-is-memoization-and-how-can-i-use-it-in-python + """ + def __init__(self, f): + self.f = f + self.memo = {} + def __call__(self, *args): + if not args in self.memo: + self.memo[args] = self.f(*args) + return self.memo[args] + +def memoize(function): + cache = {} + def decorated_function(*args): + if args in cache: + return cache[args] + else: + val = function(*args) + cache[args] = val + return val + return decorated_function class InstabilityWarning(Warning): pass @@ -11,6 +38,32 @@ class InstabilityWarning(Warning): class MacaulayError(np.linalg.LinAlgError): pass +class ConditioningError(Exception): + """Raised when the conditioning number of a matrix is not + within the desired tolerance. + + Attributes + ---------- + message : str + A message describing the error that occurred. + """ + + def __init__(self, message): + self.message = message + +class TooManyRoots(Exception): + """Raised when the number of roots found by the Macaulay matrix exceeds the + Bezout bound. + + Attributes + ---------- + message : str + A message describing the error that occurred. + """ + + def __init__(self, message): + self.message = message + class Term(object): ''' Terms are just tuples of exponents with the grevlex ordering @@ -160,132 +213,6 @@ def quotient(a, b): ''' return np.subtract(a,b) -def rrqr_reduce(matrix, clean = False, global_accuracy = 1.e-10): - ''' - Reduces the matrix into row echelon form, so each row has a unique leading term. - - Parameters - ---------- - matrix : (2D numpy array) - The matrix of interest. - clean: bool - Defaults to False. If True then at certain points in the code all the points in the matrix - that are close to 0 are set to 0. - global_accuracy: float - Defaults to 1.e-10. What is determined to be zero when searching for the pivot columns or setting - things to zero. - - Returns - ------- - matrix : (2D numpy array) - The reduced matrix in row echelon form. It should look like this. - a - - - - - - - - 0 b - - - - - - - 0 0 0 c - - - - - 0 0 0 0 d - - - - 0 0 0 0 0 0 0 e - ''' - if matrix.shape[0]==0 or matrix.shape[1]==0: - return matrix - height = matrix.shape[0] - A = matrix[:height,:height] #Get the square submatrix - B = matrix[:,height:] #The rest of the matrix to the right - Q,R,P = qr(A, pivoting = True) #rrqr reduce it - PT = inverse_P(P) - diagonals = np.diagonal(R) #Go along the diagonals to find the rank - rank = np.sum(np.abs(diagonals)>global_accuracy) - if rank == height: #full rank, do qr on it - Q,R = qr(A) - A = R #qr reduce A - B = Q.T.dot(B) #Transform B the same way - else: #not full rank - A = R[:,PT] #Switch the columns back - if clean: - Q = np.clean_zeros_from_matrix(Q) - B = Q.T.dot(B) #Multiply B by Q transpose - if clean: - B = np.clean_zeros_from_matrix(B) - #sub1 is the top part of the matrix, we will recursively reduce this - #sub2 is the bottom part of A, we will set this all to 0 - #sub3 is the bottom part of B, we will recursively reduce this. - #All submatrices are then put back in the matrix and it is returned. - sub1 = np.hstack((A[:rank,],B[:rank,])) #Takes the top parts of A and B - result = rrqr_reduce(sub1) #Reduces it - A[:rank,] = result[:,:height] #Puts the A part back in A - B[:rank,] = result[:,height:] #And the B part back in B - - sub2 = A[rank:,] - zeros = np.zeros_like(sub2) - A[rank:,] = np.zeros_like(sub2) - - sub3 = B[rank:,] - B[rank:,] = rrqr_reduce(sub3) - - reduced_matrix = np.hstack((A,B)) - return reduced_matrix - -def rrqr_reduce2(matrix, clean = True, global_accuracy = 1.e-10): - ''' - Reduces the matrix into row echelon form, so each row has a unique leading term. - Note that it preforms the same function as rrqr_reduce, currently I'm not sure which is better. - - Parameters - ---------- - matrix : (2D numpy array) - The matrix of interest. - clean: bool - Defaults to True. If True then at certain points in the code all the points in the matrix - that are close to 0 are set to 0. - global_accuracy: float - Defaults to 1.e-10. What is determined to be zero when searching for the pivot columns or setting - things to zero. - - Returns - ------- - matrix : (2D numpy array) - The reduced matrix in row echelon form. It should look like this. - a - - - - - - - - 0 b - - - - - - - 0 0 0 c - - - - - 0 0 0 0 d - - - - 0 0 0 0 0 0 0 e - ''' - if matrix.shape[0] <= 1 or matrix.shape[0]==1 or matrix.shape[1]==0: - return matrix - height = matrix.shape[0] - A = matrix[:height,:height] #Get the square submatrix - B = matrix[:,height:] #The rest of the matrix to the right - independentRows, dependentRows, Q = row_linear_dependencies(A, accuracy = global_accuracy) - nullSpaceSize = len(dependentRows) - if nullSpaceSize == 0: #A is full rank - Q,R = qr(matrix) - return clean_zeros_from_matrix(R) - else: #A is not full rank - #sub1 is the independentRows of the matrix, we will recursively reduce this - #sub2 is the dependentRows of A, we will set this all to 0 - #sub3 is the dependentRows of Q.T@B, we will recursively reduce this. - #We then return sub1 stacked on top of sub2+sub3 - if clean: - Q = clean_zeros_from_matrix(Q) - bottom = matrix[dependentRows] - sub3 = bottom[:,height:] - sub3 = Q.T[-nullSpaceSize:]@B - if clean: - sub3 = clean_zeros_from_matrix(sub3) - sub3 = rrqr_reduce2(sub3) - - sub1 = matrix[independentRows] - sub1 = rrqr_reduce2(sub1) - - sub2 = bottom[:,:height] - sub2[:] = np.zeros_like(sub2) - - reduced_matrix = np.vstack((sub1,np.hstack((sub2,sub3)))) - if clean: - return clean_zeros_from_matrix(reduced_matrix) - else: - return reduced_matrix - def sorted_polys_coeff(polys): '''Sorts the polynomials by how much bigger the leading coefficient is than the rest of the coeff matrix. @@ -358,9 +285,9 @@ def row_swap_matrix(matrix): leading_mon_columns = list() for row in matrix: leading_mon_columns.append(np.where(row!=0)[0][0]) - #print(np.argsort(leading_mon_columns)) return matrix[np.argsort(leading_mon_columns)] +@memoize def get_var_list(dim): '''Returns a list of the variables [x_1, x_2, ..., x_n] as tuples.''' _vars = [] @@ -465,6 +392,53 @@ def triangular_solve(matrix): # The case where the matrix passed in is a square matrix return np.eye(m) +def solve_linear(coeffs): + """Finds the roots when the coeffs are **all** linear. + + Parameters + ---------- + coeffs : list + A list of the coefficient arrays. They should all be linear. + + Returns + ------- + solve_linear : numpy array + The root, if any. + """ + dim = len(coeffs[0].shape) + A = np.zeros([dim,dim]) + B = np.zeros(dim) + for row in range(dim): + coeff = coeffs[row] + spot = tuple([0]*dim) + B[row] = coeff[spot] + var_list = get_var_list(dim) + for col in range(dim): + if coeff.shape[0] == 1: + A[row,col] = 0 + else: + A[row,col] = coeff[var_list[col]] + #solve the system + try: + return np.linalg.solve(A,-B), np.nan + except np.linalg.LinAlgError as e: + if str(e) == 'Singular matrix': + #if the system is dependent, then there are infinitely many roots + #if the system is inconsistent, there are no roots + #TODO: this should be more airtight than raising a warning + + #if the rightmost column of U from LU decomposition + # is a pivot column, system is inconsistent + # otherwise, it's dependent + U = lu(np.hstack((A,(-B).reshape(-1,1))))[2] + pivot_columns = [np.flatnonzero(U[i, :])[0] for i in range(U.shape[0]) if np.flatnonzero(U[i, :]).shape[0]>0] + if not (U.shape[1]-1 in pivot_columns): + #independent + raise TooManyRoots('System has infinitely many roots.') + return np.zeros([0,dim]), np.zeros([0,dim]) + else: + raise e + def first_x(string): ''' Finds the first position of an 'x' in a string. If there is not x it returns the length @@ -561,20 +535,20 @@ def makePolyCoeffMatrix(inputString): matrix[tuple(matrixSpot)] = coefficient return matrix -def slice_top(matrix): +def slice_top(matrix_shape): ''' Gets the n-d slices needed to slice a matrix into the top corner of another. Parameters ---------- - coeff : numpy matrix. - The matrix of interest. + matrix_shape : tuple. + The matrix shape of interest. Returns ------- slices : list - Each value of the list is a slice of the matrix in some dimension. It is exactly the size of the matrix. + Each value of the list is a slice of the matrix in some dimension. It is exactly the size of matrix_shape. ''' slices = list() - for i in matrix.shape: + for i in matrix_shape: slices.append(slice(0,i)) return tuple(slices) @@ -636,9 +610,9 @@ def match_size(a,b): new_shape = np.maximum(a.shape, b.shape) a_new = np.zeros(new_shape) - a_new[slice_top(a)] = a + a_new[slice_top(a.shape)] = a b_new = np.zeros(new_shape) - b_new[slice_top(b)] = b + b_new[slice_top(b.shape)] = b return a_new, b_new def _fold_in_i_dir(solution_matrix, dim, fdim, size_in_fdim, fold_idx): @@ -752,7 +726,7 @@ def _mon_mult1(initial_matrix, idx, dim_mult): idx = [i-j for i,j in zip(p1.shape,initial_matrix.shape)] result = np.zeros(np.array(initial_matrix.shape) + idx) - result[slice_top(initial_matrix)] = initial_matrix + result[slice_top(initial_matrix.shape)] = initial_matrix initial_matrix = result Pf = p1 + initial_matrix return .5*Pf @@ -962,18 +936,8 @@ def arrays(deg,dim,mon): else: return memoized_arrays(deg-1,dim,mon)+memoized_arrays(deg,dim-1,mon) -def memoize(function): - cache = {} - def decorated_function(*args): - if args in cache: - return cache[args] - else: - val = function(*args) - cache[args] = val - return val - return decorated_function - memoized_arrays = memoize(arrays) +slice_top = memoize(slice_top) def permutation_array(deg,dim,mon): '''Finds the permutation array to multiply a row of a matrix by a certain monomial. @@ -1095,7 +1059,6 @@ def cheb_perturbation3(mult_mon, mons, mon_dict, var): -------- cheb_pertubation3 : list list of indexes for the 3rd case of cheb mon mult - """ perturb = [0]*len(mon_dict) #print(mons) @@ -1274,6 +1237,26 @@ def mons_1D(dim, deg, var): mons.append(mon) return np.array(mons) +@jit(nopython=True) +def transform(x, a, b): + """Transforms points from the interval [-1, 1] to the interval [a, b]. + Parameters + ---------- + x : numpy array + The points to be tranformed. + a : float or numpy array + The lower bound on the interval. Float if one-dimensional, numpy array + if multi-dimensional. + b : float or numpy array + The upper bound on the interval. Float if one-dimensional, numpy array + if multi-dimensional. + Returns + ------- + transform : numpy array + The transformed points. + """ + return ((b-a)*x+(b+a))/2 + def newton_polish(polys,root,niter=100,tol=1e-5): """ Perform Newton's method on a system of N polynomials in M variables. @@ -1324,3 +1307,159 @@ def Df(x): x0 = x1 i+=1 return x1 + +def isNumber(x): + """Determines if x is a number + + Parameters + ---------- + x : var + The variable to check. + + Returns + ------- + isNumber : bool + True if x is an number, otherwise False. + """ + return isinstance(x, (int, float, complex)) and not isinstance(x, bool) + +def isNumOrBool(x): + """Determines if x is a number or a bool + + Parameters + ---------- + x : var + The variable to check. + + Returns + ------- + isNumber : bool + True if x is an number or bool, otherwise False. + """ + return isinstance(x, (int, float, complex, bool)) + +class Tolerances: + ''' + Class to track the tolerances being used in the subdivision solver. + + Any number of tolerances may be passed in. + + A tolerance may be a float or an iterable type (ex. list, numpy array, etc). If an iterable + is used, all iterables passed in must be the same length. Any floats passed in will be + resized into lists of the same length as the iterables being used. + + If a tolerance of name "tol" is passed in, the object self.tols is created to store all + or the values to use for "tol". self.tol will be the current value for "tol". + + ---DEVELOPER WARNING--- + IF ANY OTHER ATRRIBUTE IS CREATED OF ITERABLE TYPE THIS CLASS MAY CRASH!!! + + Attributes + ---------- + numTols: int + The number of tolerances that are being used. + currTol : int + The current tolerances to check + **tols : list + The list of tolerances to be used. + *tol : int + The next tolerance to use. + + Methods + ------- + __init__ + Initializes everything. + nextTols + Sets up the next tolerances to be used + ''' + def __init__(self, **tolerances): + numTols = 1 + #Finds the number of the tolerances to be used. + for name in tolerances: + if not isNumOrBool(tolerances[name]): + numTols = len(tolerances[name]) + + for name in tolerances: + value = tolerances[name] + if isNumOrBool(value): #Turns the number into a list of the right name. Stores as attribute. + self.__setattr__(name+'s', [value]*numTols) + elif hasattr(value, '__iter__'): #Makes sure the list is the right length. Stores as attribute. + self.__setattr__(name+'s', value) + if len(value) != numTols: + raise ValueError("Length of tolerence lists must be the same!") + else: + raise TypeError("Tolerance value must be number or boolean type or iterable!") + + self.currTol = -1 + self.numTols = numTols + + def getTolDict(self): + tolDict = dict() + for name in self.__dict__: + if hasattr(self.__dict__[name], '__iter__'): + tolDict[name] = self.__dict__[name] + return tolDict + + def nextTols(self): + """Determines the next tolerances + + Returns + ------- + nextTols : bool + True if there are more tols to run, otherwise False. + """ + self.currTol += 1 + if self.currTol >= self.numTols: #Returns False if there are no more tols to be used + return False + else: + names = [] + vals = [] + for name in self.__dict__: + #Finds every iterable type being stored + if not hasattr(self.__dict__[name], '__iter__'): + continue + #Finds the next tol and stores it + names.append(name[:-1]) + vals.append(self.__dict__[name][self.currTol]) + #The storing is done outside the loop so the dictionary size doesn't change during iteration. + for name, val in zip(names, vals): + self.__setattr__(name, val) + return True + +### Eigenvalue/vector conditioning ### +def condeig(A,eig,x,condvec=False): + """Estimates the condition number of an eigenvalue of A. Optionally + estimates the condition number of the eigenvector. + """ + n = A.shape[0] + Q = householder(x) + B = ((Q.conj().T)@A@Q) + R = qr(B[1:,1:]-eig*np.eye(n-1),mode='r')[0] + v = solve_triangular(R,-B[0,1:].conj(),trans=2) + if condvec: + return (1+norm(v)**2)**.5,1/(svd(R,compute_uv=False)[-1]) + else: + return (1+norm(v)**2)**.5 + +def condeigs(A,w,v,condvec=False): + """Estimates the condition numbers of the eigenvalues of A. Optionally + estimates the condition numbers of the eigenvectors.""" + n = A.shape[0] + + if condvec: cond = np.empty((n,2)) + else: cond = np.empty(n) + + for i in range(n): + cond[i] = condeig(A,w[i],v[:,i],condvec) + + if condvec: return cond[:,0],cond[:,1] + else: return cond + +def householder(x): + """Given a vector x, computes a Householder reflector Q such that the first + column of (Q^H)AQ is a multiple of e_1, whenever x is an eigenvector of A. + """ + u = x.copy().astype('complex') + u[0] += np.exp(1j*np.angle(x[0]))*norm(x) + u = u/norm(u) + return np.eye(len(u)) - 2*np.outer(u,u.conj())